esa-sceva/satcom-chunk-collection huggingface dataset:
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
###### Abstract  
Vision-based weld seam extraction poses a significant challenge for weldments with complex spatial structures in automated welding. Existing research primarily focuses on identifying welds from weldments with given positions and postures, while practical weld path planning requires multiple weld seams identified within arbitrarily placed weldments. This paper proposes a methodology that identifies welds from arbitrarily placed spatial planar weldments in a single run. First, by introducing a turntable calibrated with respect to a 3D camera, we perform 3D reconstruction on an arbitrarily placed spatial planar weldment. Second, an improved RANSAC algorithm based on Euclidean clustering is proposed to carry out plane segmentation, focusing on segmentation accuracy. Finally, we present a novel weld seam extraction algorithm leveraging the half-edge data structure to efficiently compute weld seams from the segmented planes. The experiments conducted in this study demonstrate that the average segmentation errors (as an indirect indicator of weld seam extraction error) are reduced by 90.3% to 99.8% over conventional segmentation methods, and the standard deviations are reduced by 64.8% to 97.0%.  
weld seam extraction; point cloud registration; plane segmentation; Euclidean clustering
## 2 Materials and Methods
### Satellite Data and Processing
#### 2.3.4 Accuracy Assessment and Validation  
We used accuracy assessment functions embedded in GEE for those parameters. The final ground-truthing is based on the VHR orthomosaic derived from UAV image acquisitions in November 2019. We used the confusion error matrix with these parameters: Overall Accuracy (OA), Kappa Coefficient, Producer Accuracy (PA), and Consumer Accuracy (CA) [87,90]. The OA is calculated by summing the number of correctly classified values and divided by the total number of values. PA is determined by comparing the number of correctly classified values of a particular class and number of reference pixels of the same class, while CA can be calculated by dividing the number of correctly classified values of a particular class and number of classified pixels in the class [98]. The OA, PA, and CA are expressed as percentage, with 100% accuracy representing a perfect classification. Kappa measures the difference between the actual agreement in the error matrix and the chance agreement that is indicated by the row and column total [93]. A kappa value of 0 represents no agreement, and a value of 1 indicates perfect agreement.
###### Abstract  
Myocardial infarction (MI) occurs due to the decrease in the blood flow into one part of the heart, and it further causes damage to the heart muscle. The 12-channel electrocardiogram (ECG) has been widely used to detect and localize MI pathology in clinical studies. The vectorcardiogram (VCG) is a 3-channel recording system used to measure the heart's electrical activity in sagittal, transverse, and frontal planes. The VCG signals have advantages over the 12-channel ECG to localize posterior MI pathology. Detection and localization of MI using VCG signals are vital in clinical practice. This paper proposes a multi-channel multi-scale two-stage deep-learning-based approach to detect and localize MI using VCG signals. In the first stage, the multivariate variational mode decomposition (MVMD) decomposes the three-channel-based VCG signal beat into five components along each channel. The multi-channel multi-scale VCG tensor is formulated using the modes of each channel of VCG data, and it is used as the input to the deep convolutional neural network (CNN) to classify MI and normal sinus rhythm (NSR) classes. In the second stage, the multi-class deep CNN is used for the categorization of anterior MI (AMI), anterior-lateral MI (ALMI), anterior-septal MI (ASMI), inferior MI (IMI), inferior-lateral MI (ILMI), inferior-posterior-lateral (IPLMI) classes using MI detected multi-channel multi-scale VCG instances from the first stage. The proposed approach is developed using the VCG data obtained from a public database. The results reveal that the approach has obtained the accuracy, sensitivity, and specificity values of 99.58%, 99.18%, and 99.87%, respectively, for MI detection. Moreover, for MI localization, we have obtained the overall accuracy value of 99.86% in the second stage for our proposed network. The proposed approach has demonstrated superior classification performance compared to the existing VCG signal-based MI detection and localization techniques.  
myocardial infarction; vectorcardiogram; multivariate VMD; deep CNN; accuracy
# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 3 Methodology
### Customer Satisfaction Rate  
To build the datasets that we will use for measuring the customer satisfaction rate, we used Python to interact with Twitter's search application programming interface (API) [83] to fetch Arabic tweets based on certain search keys, as follows. Firstly, the hashtags used in the search were the ones that indicated different Saudi telecom companies, such as STC,  
Figure 4: Time window of the prediction period.  
Mobily and Zain. Then we grabbed the top hashtags mentioning these telecom companies, which were: #STC, #Mobily, #Zain*\(\frac{11}{10}\),* (Mobily), and *\(\frac{32}{10}\),* (Saudi Zain).  
The aim was to monitor the telecom customers' sentiments continuously. This process was ongoing from January 2017 until June 2017, to generate the largest possible dataset, because this would subsequently shrink after spam and retweets are eliminated. This raw dataset comprised 3.5 million Arabic tweets. After filtering and cleaning, it shrank to 795,500 Saudi tweets. Then, we chose the sample of Saudi tweets randomly from the dataset to construct our corpus, AraCust [84], Table 4.
## 2 Description of the Proposed Approach
### Evaluation of Link Quality Between Nodes
That is to say, by integrating \(IQR\) and \(EWMA\_SPRR\), our approach effectively balances robustness against transient fluctuations and adaptability to long-term link quality trends, enhancing the reliability of our routing decisions. Assuming we have obtained the \(PRR\) values at the current time point \(t\) and \(n\) previous time points, we will take the obtained 10 \(PRR\) values (assuming they are already arranged from small to large) as an example to introduce the calculation process of the \(IQR\). First, arrange the data from small to large, and then find the positions of the 25 th and 75 th percentiles of the data, namely the lower quartile \(Q1\) and upper quartile \(Q3\):  
\[Q_{1}=PRR_{2}+0.5\times(PRR_{3}-PRR_{2}) \tag{18}\] \[Q_{3}=PRR_{7}+0.5\times(PRR_{8}-PRR_{7}). \tag{19}\]  
The final \(IQR\) is:  
\[IQR\text{=}Q_{3}-Q_{1}. \tag{20}\]  
Additionally, calculate \(EWMA\_SPRR\) using the following recursive formula:  
\[EWMA\_SPR_{t}=\lambda\times EWMA\_SPR_{t-1}+(1-\lambda)PRR_{t} \tag{21}\]  
where \(\lambda\) controls the smoothness of the weighted moving average. Furthermore, although the current evaluation environment assumes relatively stable underwater acoustic conditions, the proposed fuzzy-logic-based routing protocol exhibits strong adaptability to dynamic environmental factors such as temperature gradients, water currents, and multipath propagation effects. The modular structure of the fuzzy inference system allows for seamless integration of additional environment-aware parameters, such as signal delay spread or Doppler shift estimates, which can further enhance link quality assessment accuracy. This design flexibility ensures that the protocol remains robust and scalable even in complex and time-varying underwater acoustic environments, laying a solid foundation for future improvements in highly dynamic application scenarios. Figure 5 illustrates the process of obtaining the \(LQI\). The detailed application of the fuzzy logic reasoning system is further addressed in the subsequent design of the outer  
layer fuzzy reasoning system, which is used to prioritize candidate sets in the opportunistic routing and forwarding area.
## 3 Results
### Qualitative Results
The coding of the responses shows that 8% of the students did not perceive a direct relationship between the use of the strategy and motivation with the virtual platform. Comments such as the following confirm this: \"I didn't log on to the platform more times. I \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Competitive Group** & **Individual Group** & **Total** & **Example of a Student’s Comment** \\ \hline
**Task motivation** & **7** & **4** & **11 (15\%)** & \\ Innovation & 2 & 1 & _”The use of activities that we had never seen before.”_ \\ Visual design & 1 & 1 & _”I liked the activities, such as the videos with embedded games.”_ \\ Interactivity & 4 & 2 & _”Activities such as the videogame I found very enjoyable.”_ \\
**Motivation to learn** & **18** & **23** & **41 (53\%)** \\ Intrinsic motivation & 16 & 17 & _”When I was playing, I felt like I was learning simultaneously, which gave me confidence._ \\ Extrinsic motivation & 2 & 6 & _”I was motivated to get first place on the leaderboard.”_ \\
**Flow** & **19** & **5** & **24 (32\%)** \\ Fun & 14 & 5 & _”I was distracted by playing video games.”_ \\ Adequacy of the level & 5 & 0 & _”Some activities have been too difficult._ \\
**Total** & & **76 (100\%)** & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Question 1. Comparison between competitive, individual gamifications and comment examples. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Competitive Group** & **Individual Group** & **Total** & **Example of a Student’s Comment** \\ \hline
**Task motivation** & **6** & **6** & **12 (12\%)** & \\ Innovation & 1 & 0 & _”More variety in the types of video games.”_ \\ Visual design & 1 & 2 & _”Being able to see the activities in full screen.”_ \\ Interactivity & 4 & 4 & _”New questions each time you restart the video game.”_ \\
**Motivation to learn** & **18** & **23** & **52 (59\%)** & \\ Intrinsic motivation & 7 & 5 & _More story-based video games as I find them very useful for learning”_. \\ Extrinsic motivation & 11 & 18 & _”Competing against my teammates in Tvs1 duels to get the first position.”_ \\
**Flow** & **19** & **5** & **25 (29\%)** \\ Fun & 6 & 4 & _”Create some story related to the university.”_ \\ Adequacy of the level & 13 & 1 & _”Progressively increase the difficulty of video games and questions.”_ \\
**Total** & & **89 (100\%)** & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Question 2. Comparison between competitive, individual gamifications and comment examples. just tried to complete the games with the knowledge obtained in class\" (student 17). On the other hand, 92% of the students say that the gamified digital games have motivated them to access the platform. For example, \"Positively. Increasing my motivation to keep trying to get the first place, and for that reason, I reviewed the contents we had on the virtual platform\" (student 28). Table 6 shows the responses coded and compared by groups.
# DAB Signal Preprocessing for Passive Coherent Location
## 3 Results  
The performance of the proposed methods of the DAB signal preprocessing has been compared with the help of the PCL signal processing algorithm described in [12], which consists of the clutter removal filter and the cross-ambiguity function calculator. The signal processing algorithms described in the previous section are implemented in the pre-processing block that is placed before the clutter removal filter (see Figure 13). The signal processing algorithms (implemented in Matlab environment) get the raw baseband signals acquired in the system described in [12] (with time delays and phase shifts corrected using the method based on pilot signals [32,36]) and return the cross-ambiguity function plots as output. The integration time was set to 5 s in all the following experiments. This allowed us to extend velocity resolution and thus to investigate the sea clutter structure (during the storm and calm seas) and distinguish slow-moving targets from the clutter, which, in general, is a challenging problem [21].  
Figure 11: DAB frame structure with Null symbol replaced by MSC symbol.  
Figure 12: Autocorrelation of DAB signal with one MSC symbol copied in place of Null symbol (**a**) and its close-up (**b**).  
Figure 13: Signal processing pipeline used for performance evaluation.
## 5 Experimental and Analysis
### Results and Discussion  
#### 5.2.1 Sea State Degree: 0  
At a sea state degree of 0, the surface of the sea is calm with no wind or waves. The only external noise present is caused by underwater currents, and it is assumed that this noise follows a Gaussian distribution. Process noise \(w_{k}\sim N(0,Q)\), measurement noise \(v_{k}\sim N(0,R)\), \(R=diag[0.3,0.2,0.3,0.05]\), \(Q=diag[0,0,0,0,3,4,5,0.02,2,2,0.5]\).  
Figures 5 and 6 present the errors in the estimated displacement and velocity of the ROV using the proposed algorithm, as well as three other algorithms: CPF, CKF, and PF. It is evident that the PF algorithm suffers from particle degradation over time, which leads to a decrease in estimation accuracy and filter divergence. On the other hand, the estimation accuracy of CKF in the presence of Gaussian noise is consistent with expectations, and CPF effectively addresses the particle degradation issue by using CKF for importance density function calculation. Consequently, the proposed algorithm outperforms the other algorithms at a sea state of 0.
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 3 The Proposed Approach
### Hhs
#### 3.2.6 Application Loop Delay  
The application loop delay can be expressed by the CloudSim clock and the tuple's end time. \(T1\) is the tuple start time, \(T2\) is the tuple type to average CPU time, \(CC\) is the CloudSim clock, \((CC-T1)\) is the execution time, and \(C1\) is the number of executed tuple types. The tuple's end time [11] can be expressed by \(CC-T1\) when \(T2\) is calculated, or \(\frac{T1*C1+(CC-T1)}{C1+1}\) when \(T2\) is not calculated. The application loop delay is calculated by \(CC-ET\). \(CC\) is the CloudSim clock, and \(ET\) is the emitting time of a tuple. \(ET\) is calculated by sending the time of a module to another module. The tuple receipt time is calculated by \(\frac{T1*C2+(CC-ET)}{C1+1}\). \(C2\) is the number of received tuple types.  
The proposed method consists of two phases: training and testing. The pseudocode of the proposed approach is Algorithm 1.  
```
Input: number of areas, number of cameras, scheduling methods.
1: Initialization of number of areas, number of cameras, scheduling method.
2:for\(area_{i}=1\) to \(A\)do
3:for\(camera_{i}=1\) to \(C\)do
4: Task scheduling by GA, PSO, ACO, and SA, respectively.
5: Save the results in a dataset file.
6:endfor
7:endfor
8: Read new workflow.
9:for\(i=1\) to \(M\)do
10:\(D_{i}\) = Euclidean distance of new sample with dataset row \(i\).
11:endfor
12: The best scheduling algorithm = MinDistance(\(D\)).
13: Execute the application.
14: Calculate energy consumption, network usage, execution time, and total cost using Sections 3.2.3, 3.2.4, 3.2.5, and 3.2.6.
```  
**Algorithm 1** HHS.  
* **Training phase:** Initially, 64 different workflows enter the system. The proposed algorithm includes GA [22], PSO [30], ACO [26], and SA [53] and is implemented to allocate PEs to modules in all workflows and for the intelligent monitoring system that comes along with the modules. The energy consumption, network usage, and total execution cost of each algorithm are achieved for each workflow. Then, the results are stored in the database, and for each workflow, the best algorithm is selected.
* **Testing phase:** A new workflow enters the system. Then, the Euclidean distance between the new workflow and examples inside the database is obtained. The best algorithm is chosen. Then, the energy consumption, network usage, and total execution cost of the new workflow are calculated. Finally, the results are returned.
# SnoreLab Application in the Assessment of Obstructive Sleep Apnea Syndrome: A Pilot Study
## 5 Conclusions  
The efficacy of SnoreLab in reliably detecting snoring has yet to be demonstrated, and its capability to detect OSAS remains unsubstantiated. While SnoreLab records snoring, it is not designed for screening sleep apnea, as explicitly stated on their website. However, this distinction must be clear to users, as snoring and OSAS are often confounded and the multisystemic effects of OSAS still ignored in most of the population. Building on the pioneering work of [PERSON] et al. [7], our study represents a second attempt to investigate the utility of the SnoreLab app in assessing OSA. We examined correlations between SnoreLab metrics and PSG-derived parameters, specifically the AHI and T69. The absence of a significant correlation observed between SnoreLab indices and PSG-measured AHI emphasizes the need for caution when using the app for OSA screening. Therefore, our data do not support the efficacy of SnoreLab in detecting obstructive events. Nonetheless, due to its simplicity and user-friendly interface, SnoreLab holds potential as a \"sleep diary\" to enhance patient awareness and treatment compliance. Our findings contribute to the growing body of evidence suggesting that while SnoreLab may not replace PSG for diagnosis, it could serve as a complementary tool for monitoring snoring, and to improve the interaction between patients and clinicians when integrated into a suitable clinical assessment [18]. Further research, including multicenter international studies, is crucial to fully elucidate the app's clinical utility. Moreover, integrating clearer explanations within the app and associated scientific material may help users better understand the distinction between snoring and OSA, facilitating timely diagnosis and intervention, especially in rural underserved areas.  
Conceptualization, E.M.C.T. and A.L.; methodology, E.M.C.T. and M.C.C.; software, V.C.A.C.; validation, D.C., L.C. and M.C.; formal analysis, V.C.A.C.; investigation, A.L.; resources, D.C. and M.C.; data curation, S.P.; writing--original draft preparation, E.M.C.T. and A.L.; writing--review and editing, E.M.C.T. and S.P.; visualization, D.C.; supervision, M.C.; project administration, M.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Department of Otorhinolaryngology--University Hospital of Foggia (n. 0010 of 31 January 2020).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data are available upon request to the corresponding author (E.M.C.T.).  
The authors declare no conflicts of interest.
## 5 Performance Analysis
### Data Rate  
The evaluation of the data rate is carried out with Equation (1). The input parameters of the data rate are BW, SF, and CR. Here we have evaluated the data rate of LoRa individually for SF 7 to SF 12 by varying BW from 41.7 kHz to 500 kHz and by CR from 1 to 4. As shown in Figure 16, for all of the spreading factors at a particular bandwidth, as CR increases the data rate decreases exponentially.  
Figure 15: Receiver sensitivity of LoRa.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **BW 41.7** & **BW 62.5** & **BW 125** & **BW 250** & **BW 500** \\ \hline SF 7 & \(-142.79\) & \(-141.04\) & \(-138.03\) & \(-135.02\) & \(-132.01\) \\ SF 8 & \(-147.79\) & \(-146.04\) & \(-143.03\) & \(-140.02\) & \(-137.01\) \\ SF 9 & \(-152.79\) & \(-151.04\) & \(-148.03\) & \(-145.02\) & \(-142.01\) \\ SF 10 & \(-157.79\) & \(-156.04\) & \(-153.03\) & \(-150.02\) & \(-147.01\) \\ SF 11 & \(-162.79\) & \(-161.04\) & \(-158.03\) & \(-155.02\) & \(-152.01\) \\ SF 12 & \(-167.79\) & \(-166.04\) & \(-163.03\) & \(-160.02\) & \(-157.01\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Receiver sensitivity of LoRa.  
For example, for SF 7, the data rate calculated for BW 41.7 at CR 1 has a maximum of 1824 bps and when the CR value increases to 4, the data rate decreases to 1140 bps. In the overall calculations, the maximum value of the data rate, 21,875 bps, is observed at BW 500 at CR 1 for SF 7, and the minimum value of the data rate, 61 bps, is observed at BW 41.7 at CR 4 for SF 12. Table 10 represents the data rate at different spreading factors, and the plots are illustrated in Figure 16.  
Figure 16: Data rate.
## 2 German Traffic Space Analysis
### Test Vehicle
#### 2.1.2 Object Recognition  
The data were recorded locally in the vehicle and sent for offline evaluation to the TU Darmstadt's server at a later time. Here, object recognition software was used to identify the following objects:  
* Pedestrians;
* Cars;
* Buses;
* Trucks;
* Cyclists;
* Bicycles;
* Traffic signs.  
As a baseline, the software was trained with the Cityscapes Dataset [23]. To train the software on the recorded dataset, an additional training with 250 hand-labeled images recorded during these nighttime drives was used. The detailed process is not explained here but can be found in [24; 25; 26; 27; 28].  
The evaluation of the data showed that the object recognition performance led to around 70% confidence when identifying vehicles, which was the most important class for this paper. All other classes had a lower confidence performance, and especially for pedestrians, which were rather rare in our recordings, only a confidence of 40% was achieved. However, training the data with more images from our recordings did not lead to any improvements and PkULZC showed that these results were to be expected [29].
## 3 Case Study Results  
### Historical Climate, Sea-Level Change, and Hydomorphology  
#### 3.1.1 Climate  
The climate changes relatively quickly, affects the energy of the environment and life on Earth, and shapes the natural features of the Earth's surface through physical and chemical weathering. No relevant local data exist on climate change on the eastern coast of the Adriatic region. Therefore, historical climate change was considered based on the situation in Europe and the Mediterranean [19; 20; 21].  
Until modern times, climate change in Europe was caused by natural processes, but since 1850, humans have become increasingly significant generators of climate and environmental change. In prehistory (3200-1100 BC), the climate was warm and dry (subboreal climate) and favorable to nature and people (Holocene climatic optimum). After 2500 BC, the \"Subtlantic\" was created, which is the current climatic age of the Holocene epoch. Throughout history, owing to volcanic activity, solar radiation has dramatically decreased, and there has been a global drop in temperature, a change in the precipitation regime, and problems with food production and nutrition, which have caused socioeconomic problems [19; 20]. Thus, in the last 4,500 years, there have been 45 major decreases in temperature and cold periods of varying duration, and 2 decreases of shorter duration have occurred since 1970, indicating that volcanic activity continues to affect air temperature and precipitation regimes.  
In ancient times, the air temperature in the Mediterranean rose, reaching its height in around 50 BC (the Roman Warm Period; Figure 2). Between 27 BC and AD 547, the climate changed significantly and became colder, which negatively affected the environment and humanity [19]. From AD 750 to 1050, warming and a decrease in climate variability occurred. This was the period of the medieval climatic optimum. From 1050, the variability returned. After the eruption of the Samalas volcano in 1257, the eruptions of more than 30 volcanoes, and the expansion of Arctic ice, a small ice age occurred that lasted until 1800. Since 1860, the air temperature has been rising, creating a modern climate optimum that continues to this day. At the beginning of the 20 th century, under the influence of humans (greenhouse gases), temperature and weather variability significantly increased again.