esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, we deeply analyzed how the integration of the edge computing and a 5G-enabled industrial network can provide an end-to-end solution that fulfills the requirements of the Industry 4.0 use cases.  
One main finding is that the 3 GPP NPN and edge computing deployment options can be combined in numerous ways, enabling different types and levels of interactions between the domains. However, the benefits of edge computing--especially in the context of specific industry use case requirements--can only be leveraged if the domains are deployed and managed in a coordinated way. A general rule-of-thumb is that the stricter use case requirements require more properly integrated NPN and edge domains; however, this should not only cover the purely technical aspects but the different business ownership options and the various edge computing deployment and service models (e.g., self-managed deployment, IaaS, and PaaS) must also be considered.  
Another finding is that the strict industry use case requirements can only be fulfilled by a deployment where there is tight interworking between the edge computing and the industry network domains. It means that some networking functionalities (e.g., TSN FRER) should be virtualized and deployed in the edge domain, as well as the edge infrastructure must be capable of providing specific network and application support features (e.g., workload placement to SmartNICs, resource isolation, and seamless L2 networking support).  
Summing up, the different technology domains (i.e., 5G-TSN support, 5G NPN, and cloud computing) are evolving in a loosely connected way, but Industry 4.0 support requires a converged end-to-end solution where it is not enough just to optimize the characteristics of the domains independently. Instead, the active interworking and harmonization between the operation of the domains is a must.  
Conceptualization, J.H. and M.M.; methodology, J.H. and M.M.; investigation, J.H. and M.M.; writing--original draft, J.H. and M.M.; writing--review and editing, M.M.; supervision, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was carried out within the 5G-SMART project (5G for Smart Manufacturing, [https://5 gsmart.eu/](https://5 gsmart.eu/)) that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 857008.  
This study did not report any data.  
The authors would like to acknowledge the contributions of their colleagues from 5G-SMART, Budapest University of Technology and Economics, and Ericsson.  
The authors declare no conflict of interest.
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 2 Preliminaries
### Incremental Learning Algorithm Based on 'Clone-and-Branch'
#### 2.1.2 Step 2: Utilizing Similarity Score  
To get the sharing capacity of the base network for each new-task, a similarity score is utilized. For generating a similarity score, few random samples of each class in a new task are passed through the pre-trained base network. From the classification results, they assume that the number of repeating classes is regarded as a similarity score, which is approximately quantified the similarity between the classes of the base network and the new task. This is because if same classification results repeatedly appear in classes of a new task as in some specific classes of the base network, it can be considered that feature representation of new classes is similar to that of specific classes in the base network. However, utilizing the similarity score is not accurate or ideal. Because it can not be robust on few randomly sampled data, the similarity score essentially has approximation errors on accuracy degradation in incremental learning.
## 5 Results and Discussion
### Statistical Analysis Results  
#### 5.4.1 Spearman Correlation Coefficient (\(\rho\))  
The application of Spearman correlation coefficients (Table 2) revealed a positive correlation between TOC, S\({}_{1}\), S\({}_{2}\), and GP with a strong negative correlation with OI (Figure 6), indicating the influence of TOC on S\({}_{1}\) and S\({}_{2}\). Furthermore, the lack of a correlation between TOC and both Tmax and PI suggested that the maturity of the source rock was independent of the amount of organic matter [13]. S\({}_{2}\) had a strong positive correlation with both S\({}_{1}\) and HI (Figure 6). This correlation demonstrated that the highest HI occurred at specific maturity levels rather than at lower or higher maturity levels. Tmax and OI were also found to have a negative correlation (Figure 6).
# A Novel Adaptive Gain of Optimal Sliding Mode Controller for Linear Time-Varying Systems
###### Abstract  
In this study, a new optimal control law associated with the sliding mode control is developed for the linear time-varying system based on the Bolza-Meyer criterion. The salient characteristic of the controller proposed in this work is to have adjustable gains in which the gain values can be larger than 1. This leads to the enhancement of control performances with the given cost function. It is noted here that conventional optimal control laws have a constant gain of 1 or less than 1, and hence, control performances such as the convergence speed are not satisfactory. After formulating the proposed optimal control law for linear time-varying systems, several illustrative examples are adopted and control performances were evaluated to show some benefits of the proposed controller. In particular, three crucial index values of control gain index, main input control index and the state index were investigated. Among illustrative examples, one is related to vibration control problem of the vehicle seat suspension system with magnetorheological (MR) damper. This example is specially treated to evaluate the practical applicability of the proposed optimal controller by considering the measured road profiles; two different random road excitations.  
optimal control law; sliding mode control; Bolza-Meyer criterion; faster convergence; cost function; Hamiltonian function; vibration control +
Footnote †: journal: Applied Soft. 2019, 9, 5050; doi:10.3390/app9235050
# Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
###### Abstract  
Operations and maintenance of Offshore Wind Turbines (OWTs) are challenging, with manual operators constantly exposed to hazardous environments. Due to the high task complexity associated with the OWT, the transition to unmanned solutions remains stagnant. Efforts toward unmanned operations have been observed using Unmanned Aerial Vehicles (UAVs) and Unmanned Underwater Vehicles (UAVs) but are limited mostly to visual inspections only. Collaboration strategies between unmanned vehicles have introduced several opportunities that would enable unmanned operations for the OWT maintenance and repair activities. There have been many papers and reviews on collaborative UVs. However, most of the past papers reviewed collaborative UVs for surveillance purposes, search and rescue missions, and agricultural activities. This review aims to present the current capabilities of Unmanned Vehicles (UVs) used in OWT for Inspection, Maintenance, and Repair (IMR) operations. Strategies to implement collaborative UVs for complex tasks and their associated challenges are discussed together with the strategies to solve localization and navigation issues, prolong operation time, and establish effective communication within the OWT IMR operations. This paper also briefly discusses the potential failure modes for collaborative approaches and possible redundancy strategies to manage them. The collaborative strategies discussed herein will be of use to researchers and technology providers in identifying significant gaps that have hindered the implementation of full unmanned systems which have significant impacts towards the net zero strategy.  
collaborative; unmanned vehicles; UAV; USV; UUV; offshore wind turbine; unmanned operations; localization +
Footnote †: journal: Advances in Neural Information Processing Systems
## 3 Implementation
### Data Preprocessing
Figure 7a depicts the gray distribution histogram of the FODI image captured on 7 September. Noticeably, pixels with values close to 0 constitute  
Figure 6: The coefficient adjustment flowchart: Choose an image as the standard, unify the brightness and contrast of other Braille markers, calculate the adjustment coefficient, and use it to adjust the entire images. a relatively high proportion and are densely distributed, suggesting that the image is generally dark and exhibits low contrast. Subsequently, we equalized the histogram of the image, resulting in the histogram displayed in Figure 7b. Upon examination, it is evident that the gray distribution after processing is more uniform, signifying an enhancement in image contrast. To prevent issues such as abnormal damage area calculation arising from significant contrast differences, it is crucial to ensure that the contrast values between each image are similar. The contrast is computed using Formula (2) as follows:  
\[ \begin{split} c=&\sum r(i,j)\cdot r(i,j)\cdot p(i,j)\\ & r(i,j)=|i-j|\end{split}  \tag{2}\]  
The contrast value \(c\) is determined by the upper formula, where \(r\)(i,j) represents the gray difference between adjacent pixels, and \(p(i,j)\) denotes the pixel distribution probability of \(r\) for the gray difference between adjacent pixels. The contrast values before and after the aforementioned image processing are \(c_{\text{ori}}=117.99\) and \(c_{\text{7 adj}}=214.63\), respectively. A comparison between the processed image and the previous one is depicted in Figure 8. It is obvious that the progression of the damages becomes normal compared with Figure 4.  
Each Braille mark can be conceptualized as a unique combination of a group of small circular points. Therefore, we employed a feature extraction technique for detecting circles, namely the Circle Hough Transform (CHT) [32]. This method was combined with the standard Braille pattern corresponding to the last four digits of its component number to identify and locate Braille marks. Figure 9 illustrates the Braille target pixel area detected before and after adjusting the brightness and contrast of the eight pictures. It is evident that the adjusted Braille target area values are similar, indicating that the eight pictures essentially achieve normalization in terms of brightness and contrast. To establish the mapping between online and offline images, we utilized Braille positioning to standardize their coordinate systems. Upon obtaining the coordinates of the four corner Braille marks, the original image was cropped to extract training set samples, as depicted in Figure 10.  
Figure 7: Pixel grayscale histogram: (**a**) before adjustment; (**b**) after adjustment. Figure 8: The effect of revision: (**a**) the contrast of the adjusted graph with its pre-date graph; (**b**) the development of the damage area after adjustment. Figure 9: Braille pattern detection area before and after adjustment.
## 1 Introduction
Machine learning (ML) approaches have also been applied for LSM in the last few decades. Among them, the Support Vector Machine (SVM) [24; 25], Artificial Neural Network (ANN) [19; 21; 23] and Random Forest (RF) [26] are increasingly used. The fundamental source for LSM with any of abovementioned methods is the landslide inventory map, which presents the location and extent of landslides. Generally, when modeling terrain's susceptibility to land sliding, 70% of existing landslides are used for modeling and 30% are used for model validation [2; 3; 13; 18; 20; 22; 24; 27], but some authors have even applied 80% of existing landslides for modeling purposes [19; 28]. The landslides for modelingare selected randomly across the entire study area, so they can capture a variety of conditions. This is a huge advantage in LSM. Unfortunately, there are still areas for which landslide inventory maps are simply not generated due to financial or reachability issues; they are not available in some hardly accessible areas. The authors of [29] presented a summary of the completeness of landslide inventories for various countries in Europe in 2012, which confirms that there are still areas for which landslide maps have not been generated. For example, landslide inventory in Poland has been generated step by step (commune area after commune area), and the generation of such an inventory sometimes proved to be very time consuming when the geological conditions were complex, resulting in a lack of landslide inventory in such regions. From another point of view in such a region, there is still the need for land-use planning, decision making, and the continuation of building and investment. Considering this led to the following research question: could we assess landslide susceptibility in an area for which landslide inventory is not available but where it is available for the surrounding places? To answer this research question, we performed LSM based on so-called cross-modeling. More specifically, we divided our study area into two separate regions (\"Lososina\" and \"Grodek\") and performed modeling for the entire study area based on the \"Lososina\" and \"Grodek\" areas in the first and the second modeling scenarios, respectively. For the LSM, we utilized various topographical, geological and environmental LCFs together with the widely used Frequency Ratio method. LSM was performed in the study area greatly affected by landslide activity in the Polish Carpathians, in the area of the Roznow Lake.
# A Wideband Circularly Polarized Filtering Dipole Antenna
###### Abstract  
This paper presents a circularly polarized (CP) antenna based on crossed dipoles with bandpass-type filtering radiation response. The antenna employs a pair of crossed dipole arms as radiators, which are printed on the upper and lower planes of the substrate. To achieve bandpass filtering effects, radiation nulls are introduced on both sides of the passband. By vertically extending the ends of the four dipole arms, a ring-shaped current is formed between adjacent dipoles, generating the upper-band radiation null. Additionally, four parasitic patches are introduced parallel to the ends of the crossed dipole arms, creating another upper-band radiation null, further enhancing the frequency selectivity at the band edges and broadening the axial ratio (AR) bandwidth. Moreover, a square-ring slot is etched on the ground plane to introduce a lower-band radiation null, ultimately achieving a good bandpass filtering response. The proposed wideband CP filtering dipole antenna is implemented and tested. The antenna has a compact size of 0.49\(\lambda_{0}\)\(\times\) 0.49\(\lambda_{0}\)\(\times\) 0.16\(\lambda_{0}\) (where \(\lambda_{0}\) denotes the wavelength corresponding to the lowest operating frequency). The measured results show that the proposed antenna has an impedance bandwidth of 75% (1.65-3.66 GHz) and an overlapping AR bandwidth of 46.9% (2.25-3.63 GHz). Without additional filtering circuits, the antenna exhibits a stable gain of approximately 7 dB and three radiation nulls, with suppression levels of 20 dB in both the lower and upper stopbands, achieving good bandpass filtering performance.  
circular polarization; cross dipole; filtering antenna; wide axial ratio bandwidth Article
# Fit for What Purpose? NER Certification of Automatic Captions in English and Spanish
## 3 Materials and Methodology: Assessment of AI-Media's Automatic Captions in English and Spanish
### Method: The NER Model  
The automatic captions produced by Lexi were assessed with the NER model [19]. Based on the WER model [18], which was traditionally used to assess the accuracy of speech recognition, the NER model has gradually become established as the most widely used method to assess live captioning quality around the world. It is currently used by broad-casters, media companies and universities, and it is included in the official accessibility guidelines in countries such as Canada, the UK and Spain [24; 25].  
In the NER model (see Figure 1), the N stands for the number of words in the subtitles, the E stands for edition errors, those stemming from strategies applied by the subtitler (omissions, for example) and the R stands for recognition errors, that is, misrecognitions. These edition and recognition errors are classified as serious (when they introduce misleading but credible information), standard (when they cause confusion and loss of information) or minor (when they do not impact on comprehension), scoring \(-1\), \(-0.5\) and \(-0.25\), respectively. The NER model also identifies correct editions (CEs), that is, instances in which the subtitler's editing has not led to a loss of information. In this model, a 98% accuracy rate (AR) is the minimum quality threshold required, as originally established following user tests in the DTV4 ALL project [26] and as confirmed in more recent reception studies in Canada and Poland [27; 28]. Table 1 shows a recalculation of ARs in NER to a 10-point scale.  
\[Accuracy=\frac{N-E-R}{N}\times 100\]  
CE (correct editions):  
Assessment:  
Figure 1: Formula used by the NER model to calculate accuracy.  
In general, and for the purpose of this article, ARs between 98% and 98.5% are considered average, 98.5% to 99% is considered good, 99% to 99.5% is considered very good and 99.5% to 100% is regarded as excellent. The evaluation of [PERSON]'s captions for the above-described clips was conducted twice (thus involving a first NER evaluator and a reviewer).  
The next section includes the results of the analysis performed on [PERSON]'s automatic captions in English and Spanish.
## 3 Results and Discussion
### IEC and Water Uptake  
The ion-exchange capacity is yields the ionic conductivity of the membranes, while the water uptake can affect the transport behavior of ions across the membrane. The IEC and the water uptake values as a function of ZIF-8 content for all of the prepared membranes are presented in Figure 5. Changes of the IEC and water uptake values can be observed after surface modification of the MPD/TMC layer from 1.34 mmol/g to 1.42 mmol/g and 33.4% to 30.6%, respectively. Since MPD/TMC composites are negatively charged [32], the IEC was increased after introducing more negative functional groups. The dense structure of the MPD/TMC layer is more hydrophobic, which indicates the reduced water molecular accessibility to the surface matrix. Therefore, an increased IEC and  
Figure 4: Atomic force microscopy (AFM) topography of the membrane with and without modification.  
Figure 3: Contact angle of the modified ion exchange membranes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Membrane Type** & **M-0** & **M-1** & **M-2** & **M-3** & **M-4** & **M-5** \\ \hline Root mean square height (Rsq) & 1.0 nm & 36.3 nm & 43.4 nm & 51.5 nm & 65.2 nm & 70.8 nm \\ Maximum height (Rsq) & 4.4 nm & 210 nm & 230 nm & 274 nm & 347 nm & 335 nm \\ \hline \hline \end{tabular}
\end{table}
Table 3: AFM surface roughness parameters of the pristine and modified membranes.  
reduced water uptake for the MPD/TMC modified membrane was obtained. Furthermore, all of the membranes exhibited an increase in IEC and water uptake with increasing ZIF-8 content. Besides, a large cavity of ZIF-8 that can accommodate water molecules, which can be another factor that contributed to the higher water uptake. Consequently, the prepared PA/ZIF-8 membrane surface has an enhanced water uptake, and a more negative charge density with increased ZIF-8 content.
## 5 Conclusions  
Most existing technological approaches for promoting the proper wearing of safety helmets involve attaching a sensor to the chinstrap of the helmet to determine whether or not it is being worn properly using data pertaining to whether or not the chinstrap is fastened, as well as additional sensing data. However, it is not possible to determine whether a safety helmet is being worn improperly solely by using a sensor attached to the chinstrap.  
To resolve this problem, a system in which a three-axis accelerometer sensor is attached to the safety helmet was developed in this study. This system can be used to identify not only when the helmet is being properly worn or not worn, but also when it is improperly worn by employing the sensing data generated during work according to the activity of the worker at the construction site. The developed system was verified by performing tests using the RandomTree algorithm from WEKA. The results confirmed that the system can classify when a safety helmet is being worn properly, being worn improperly, and not being worn with mean accuracies of 96.89%, 96.90%, and 98.26%, respectively.  
This study is expected to contribute to the prevention of construction accidents by monitoring whether or not construction workers properly wear safety helmets. It is also expected to serve as a first step toward identifying worker activities and whether or not work is occurring by using three-axis accelerometer sensing.  
\begin{table}
\begin{tabular}{c c c} \hline  & **No. of Data** & **Accuracy (\%)** \\ \hline Normally Wearing Helmet & 140,800 & 96.89 \\ Abnormally Wearing Helmet & 140,800 & 96.90 \\ Not Wearing Helmet & 12,800 & 98.26 \\ \hline \end{tabular}
\end{table}
Table 5: Results of analysis of the recognition accuracy and number of data for properly wearing, not wearing, and improperly wearing the safety helmet for each worker activity.  
Because a high recognition rate can be achieved when classifying worker activities using a three-axis accelerometer sensor, it is believed that the applications of this method will be expanded in the future through studies on construction work process improvement and productivity management.  
[PERSON] and [PERSON] conceived and performed the conceptualization, the experiment, the formal data analysis, the validation and the composition of the research paper. [PERSON] and [PERSON] developed the hardware part of the sensing helmet. Finally all the authors have read, revised and approved the final manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NO. 2017R1A2B1011776).  
The authors declare no conflict of interest.