esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Mobile Sensing Networks in the Smart City
### Vehicle-Centric MSNs  
These networks comprise smart vehicles--equipped with on-board sensors--that can communicate with each other, and thus form a Vehicular Ad-Hoc Network (VANET), or with road side infrastructure, to enable a variety of road monitoring, driving safety, emergency response, and parking availability applications [50, 54, 55].  
One of the basic issues in vehicle-centric MSNs is achieving an efficient use of the wireless medium, which is prone to become congested in dense scenarios due to the transmission of data readings from vehicles. Solutions have been proposed mainly in two areas: (i) mechanisms for fair shairing of the available bandwidth; and (ii) reduction of the transmitted data by exploiting correlation in time and space of the observed physical magnitudes and/or events [51]. Other problems of vehicle-centric MSNs comprise high mobility of the network nodes, as well as network partitioning. Researchers have developed good performing solutions for these scenarios, using DTN-inspired concepts, whereby otherwise traditional sensor network protocols would fail [54, 56].
## 6 Challenges
### Process Complexity  
It is not uncommon to find that many academic learning facilities were built before the Industry 4 concept was defined, resulting in demands on transitioning existing systems into Industry 4-oriented frameworks [7; 41; 45; 88]. Pedagogical framework design must be in line with Industry 4 principles to ensure that students are able to acquire adequate skills and competences [45]. Teaching and training activities need to be carefully designed to cover the complex Industry 4 framework [32]. Also, the staff in existing facilities need to acquire adequate competence to push the transition [45]. Moreover, advanced IT infrastructure and cutting-edge technologies are needed to showcase the desired Industry 4 features [14]. Thus, [PERSON] et al. [104] believe that it is hard to develop a suitable project-based learning context by redesigning the current training and research facilities to demonstrate Industry 4 characteristics.  
Manufacturing processes can be highly complex, which makes it difficult to establish a pilot-scale learning context in academia that captures this aspect [32; 38; 87]. The well-established Industry 4 learning facilities can only focus on limited research topics in the Industry 4 context [25; 103], such as flexible assembly [40; 56; 93], quality management [56], lean production and energy efficiency [10], intralogistics [107; 158], metal forming [36], etc.  
Flexible assembly received the most contributions in the reviewed literature in such a hybrid educational and research environment [2; 3; 4; 26; 56; 88; 98; 123]. However, it is challenging in virtual training environments to create high-quality dimensional product replicas, which in turn causes limitations on collecting data from the product and simulating collaboration [31; 164]. As a result, participants must simplify the process complexity as a trade-off solution [26; 71]. Pick-and-place movement by robotic arms is the easiest manufacturing process to be digitised and simulated for discussing the flexibility of assembly processes [3; 13; 24].
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 1 Introduction
### Innovative Aspects and Comparison with Existing Methods
#### 1.2.1 Innovative Aspects  
1. Pedagogical Innovation: This paper introduces a novel method for teaching induction motor concepts through spreadsheet software, which is not commonly used in this context. This approach allows students to engage with the material in a hands-on manner, reinforcing theoretical knowledge through practical application.
2. Industrial Application: Beyond its educational value, the spreadsheet model offers practical benefits for industrial applications. It provides a straightforward and cost-effective tool for modeling and selecting induction motors, making it accessible to a wider audience.
## 2 Truck Platooning Speed Control Based on Multi-Objective Optimization  
In a CVIS environment, an intelligent roadside system can access real-time traffic states using roadside sensors and detectors. Accordingly, traffic flow models have been adopted to predict future traffic states. Through the collaborative planning of truck platooning speeds by means of an exchange of information between intelligent roadside systems and truck platooning, traffic efficiency can be improved. This study focuses on the on- and off-ramp weaving sections of highways. The vehicle speed of the truck platooning upstream at the highway mainline is adjusted in advance to relieve or dissipate traffic congestion. In this study, model predictive control (MPC) and a cell transmission model (CTM) were used to predict the traffic flow states of highways, and truck platooning speed was optimized using a non-dominated sorting genetic algorithm. The algorithm's underlying elite strategy considers the maximum bottleneck cell traffic flow, minimum traffic time of all vehicles on the road, and minimum fuel consumption of truck platooning as optimization objectives.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 3 Applications of FFD in Early Fault Diagnosis of Rotating Machinery  
### Adaptive Decomposition Methods  
#### 3.1.1 Emd  
EMD can self-adaptively decompose a complicated signal into a series of IMFs, which represent the natural oscillations embedded in the vibration signal. Analyzing the IMFs, the health state of rotating machinery can be reflected. [PERSON] et al. [60] used EMD to decompose the signal into IMFs, then spectral analysis was used to get the IMFs for bearing damage detection at an earlier stage. [PERSON] et al. [61] applied correlation coefficients to select sensitive IMFs. Then envelope analysis was used to demodulate the selected sensitive IMFs for the EFD of rolling bearings. [PERSON] et al. [62] applied statistical parameters of IMFs for early gearbox diagnostics. [PERSON] et al. [63] utilized bandwidth EMD to decompose the signal into IMFs, then adaptive multiscale morphological analysis was used to demodulate the IMFs for EFD of rolling bearings. [PERSON] et al. [64] used approximate entropy and EMD to quantitative the spall-like fault of the rolling element bearing in early stage. [PERSON] et al. [65] extended the EMD into MEMD to simultaneously analyze the multivariate signal during the period of early failure. [PERSON] et al. [66] applied EMD to decompose signal into IMFs, then variable cosine window was used for IMFs to minimize boundary distortion problem and statistical parameters of IMFs were used for gearbox diagnosis. Above mentioned method has been summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Authors** & **Methodologies** \\ \hline [PERSON] et al. [60] & EMD \\ [PERSON] et al. [61] & EMD + correlation coefficient \\ [PERSON] et al. [62] & EMD \\ [PERSON] et al. [63] & Bandwidth EMD + adaptive multiscale morphological analysis \\ [PERSON] et al. [64] & Approximate entropy + EMD \\ [PERSON] et al. [65] & Multivariate EMD \\ [PERSON] et al. [66] & EMD + variable cosine window \\ \hline \hline \end{tabular}
\end{table}
Table 1: Applications of empirical mode decomposition (EMD) method in early fault diagnosis of rotating machinery.  
Figure 7: The structure of a BP neural network.
## 4 Discussion
### Time to NMES following Stroke: Is Earlier Intervention More Effective?  
In general, the greatest gain in recovery tends to occur immediately after stroke, with slower gains over time [35]. A previous study suggested that critical time windows exist during which the brain is more responsive to training-dependent plasticity [36]. In particular, the first month after stroke onset offers the highest rate of recovery. Another study suggested that intervention training in the early phase of stroke can promote motor  
Figure 5: The preference of outcome measures used is shown. FMA: [PERSON] motor assessment scale for upper extremity; ARAT: action research arm test; BBT: box and block test; AMAT: arm motor ability test; WMFT: Wolf motor function test, FTHUE: functional test for hemiparetic upper extremity.  
Figure 6: The sensitivity of outcome measures to UE motor function. Comparisons of sensitivity were made among FMA, WMFT and BBT as (**a**) WMFT vs. FMA, (**b**) FAM vs. BBT, (**c**) WMFT vs. BBT. Note that FMA has almost the same sensitivity to severe UE motor function as WMFT, and that of FMA has a close association with WMFT, while BBT is less sensitive than FMA and WMFT.  
improvement rapidly, with a delayed enlargement of the motor map relative to behavioral changes [37]. With regard to the effectiveness of rTMS, there may be optimal time windows for motor recovery as well. A recent study suggested the timing-dependent effectiveness of rTMS applied pos-stroke in the following descending order: acute phase > subacute phase and >chronic phase [38]. The same may be true for other neuromodulators. Therefore, we anticipated the superiority of earlier intervention with NMES over later intervention in terms of functional recovery. However, there was only a trend toward the usefulness of early intervention of NMES, but no significance (Figure 2). Furthermore, there was no correlation between the time since stroke and PR. These results may, however, be mainly due to a lack of statistical power because of the small sample size.  
Interestingly, a recent report discovered the surprising role of reactive astrocytes as phagocytes in the clearance or synaptic remodeling of the penumbra area in the acute phase (from 7 days of onset) of cerebral infarct [39]. This supports the concept that early intervention may provide the advantage of motor recovery after a stroke. Accordingly, it is plausible that NMES is most effective when it is applied during the optimum therapeutic window.
# IFDA: Intermittent Fault Diagnosis Algorithm for Augmented Cubes Under the PMC Model
## 4 Intermittent Fault Diagnosability of Augmented Cubes  
The \(t_{p}\)-diagnosability of \(AQ_{n}\) has been proven to be \((2n-1)\) in previous studies [14]. It can be seen from Lemma 3 that for \(AQ_{n}\), \(\frac{2(2n-1)}{3}\leq m=t_{i}\leq 2n-1=t_{p}\), i.e., in \(AQ_{n}\), \(t_{i}\leq t_{p}=2n-1\). To further discuss the relationship between \(t_{i}\) and \(t_{p}\), we give the following theorem.  
**Theorem 1**.: _For an augmented cube \(AQ_{n}\) where \(n\geq 4\), \(t_{i}<2n-1\)._  
Proof of Theorem 1.: From the previous paragraph, we can obtain that \(t_{i}\leq 2n-1\). To prove \(t_{i}<2n-1\), it is only necessary to prove that \(t_{i}\
eq 2n-1\), and we use Lemma 2 to prove it. In \(AQ_{n}\), let \(v\in V\), \(S_{1}=\{v\}\), \(S_{2}=N_{AQ_{n}}\{v\}\). At this time, \(|S_{1}|\leq 2n-1\), \(|S_{2}|\leq 2n-1\), \(S_{1}\cap S_{2}=\emptyset\), and there is no test link from \(Z=V\) - \(S_{1}\cup S_{2}\) to \(S_{1}\). As seen from Lemma 2, the value \(t_{i}\) of the intermittent fault diagnosability of this system \(AQ_{n}\) is not equal to \(2n-1\). That is, in any \(n\)-dimensional augmented cube, the value of \(t_{i}\) will never be equal to the value of \(t_{p}=2n-1\). i.e., \(t_{i}<2n-1\) where \(n\geq 4\).  
To further investigate the intermittent fault diagnosability of \(AQ_{n}\), we give the following lemma:  
**Lemma 14**.: _In \(AQ_{n}\)\((n\geq 4)\), for any node subcollection \(M\), as long as \(1\leq|M|\leq 2n-2\) always has \(\left|N_{AQ_{n}}(M)\right|\geq 2n-1\) holds._  
Proof of Lemma 14.: The complete proof of Lemma 14 is presented in Appendix A.2.  
**Theorem 2**.: _For an augmented cube \(AQ_{n}\) where \(n\geq 4\), \(t_{i}=2n-2\)._  
Proof of Theorem 2.: For \(n\)-dimensional augmented cubes \(AQ_{n}\), given any two sets of units in the system, \(S_{1}\) and \(S_{2}\), \(|S_{1}|\), \(|S_{2}|\leq 2n-2\), \(S_{1}\cap S_{2}=\emptyset\), as seen from Lemma 14, \(\left|N_{AQ_{n}}(S_{1})\right|\geq 2n-1\). This also means that there must be a test edge from the remaining set \(R=V-S_{1}\cup S_{2}\) to \(S_{1}\). In the same way, there must be a test edge from the remaining set \(Z=V-S_{1}\cup S_{2}\) to \(S_{2}\). By Theorem 1, for an augmented cube \(AQ_{n}\) where \(n\geq 4\), \(t_{i}<2n-1\). It follows from Lemma 2 that the augmented cube \(AQ_{n}\) is \((2n-2)\)-\(t_{i}\)-fault diagnosable where \(n\geq 4\). In summary, we have completed the proof of Theorem 2.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## Appendix A Control Plane Path Allocation Procedure in Enhanced Mode Scenario  
Figure 11 shows the control plane procedure for the path allocation. Firstly, the MEO asks the SMF to trigger the SDN controller for SR Endpoint3 configuration. SR Endpoint3, being the last device in the SR domain, requires a rule to forward the packets after the SRH decapsulation. Then, the MEO triggers the AMF to configure the H.Encaps.Red rule on gNB1, which is the first device of the SRv6 domain, in charge of encapsulating the original packet.
# SPDepth: Enhancing Self-Supervised Indoor Monocular Depth Estimation via Self-Propagation
## 4 Experiments  
### Implementation Details  
Datasets: We trained and evaluated SPDepth on the NYU Depth V2 dataset [47]. NYU Depth V2 was recorded with the Microsoft Kinect. It contained 582 indoor scenes in total. The same training set as the previous works [1,4] was used. The training split was composed of 21,483 RGB images captured in 283 scenes. The training images were sampled at intervals of 10 frames. For each target frame \(I_{t}\), its source frames \(\{I_{t-1},I_{t+1}\}\) were used for training. We evaluated SPDepth on the official test dataset containing 654 RGB-D images.  
We performed zero-shot generalization studies on the 7-Scenes dataset [48] made up of 7 indoor scenes. We conducted experiments on the official test image sequences. The sequences were sampled at intervals of 10 frames. The final test dataset was composed of 1700 images.  
Experimental setup: The architecture of SPDepth was based on P\({}^{2}\)Net [1]. Images were left-right flipped and color-augmented randomly in training. The color-augment operation includes brightness, contrast, saturation, hue changes and left-right flipping. The size of the training images was 288 \(\times\) 384. The local attention window radius \(R\) was set to 1. Adam [49] was adopted as the optimizer. We trained SPDepth on a single NVIDIA GeForce RTX 3090 GPU for 37 epochs. The training lasted about 15 h. The batch size was 10. The learning rate was set to 1 \(\times\) 10\({}^{-4}\) in the first 27 epochs, and 1 \(\times\) 10\({}^{-5}\) in the last 10 epochs.  
Evaluation metrics: The median scaling strategy similar to previous methods [1,3,4] was adopted because of the scale ambiguity. The evaluation metrics for measuring performance were calculated according to  
\[Abs\ Rel=\frac{1}{N}\sum_{i\in N}\frac{\abs{D_{i}-\hat{D}_{i}}}{\hat{D}_{i}} \tag{14}\]  
\[RMS=\sqrt{\frac{1}{N}\sum_{i\in N}\abs{\abs{D_{i}-\hat{D}_{i}}}^{2}} \tag{15}\]  
\[Mean\ \log 10=\frac{1}{N}\sum_{i\in N}\abs{\log_{10}D_{i}-\log_{10}\hat{D}_{i}} \tag{16}\]\[Accuracies=\%\;of\;D_{i}\;s.t.\;\text{max}\bigg{(}\frac{D_{i}}{\hat{D}_{i}},\frac{ \hat{D}_{i}}{\hat{D}_{i}}\bigg{)}=\delta\;<\;thr \tag{17}\]  
where \(\hat{D}\) stands for ground truth, \(D\) denotes the predicted depth, \(N\) is the number of pixels and \(thr\) represents thresholds (1.25, 1.25\({}^{2}\), 1.25\({}^{3}\)) for calculating accuracies. The accuracies in Equation (17) stand for the percentage of predicted depths within a certain range of ground truth depths. And the thresholds limit the range.
# An ML-Based Solution in the Transformation towards a Sustainable Smart City
## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **RSsquared** & **Absolute Loss** & **Squared Loss** & **RMS Loss** \\ \hline LbfgsPoissonRegressionRegression & 0.6534 & 1153.40 & 2311041.62 & 1520.21 \\ LbfgsPoissonRegressionRegression & 0.6542 & 1139.34 & 2305941.48 & 1518.53 \\ FastTreeRegression & 0.6283 & 1221.52 & 2478228.27 & 1574,24 \\ LbfgsPoissonRegressionRegression & 0.6972 & 1111.92 & 2018649.36 & 1420.79 \\ FastTreeRegression & 0.7427 & 1016.11 & 1715846.96 & 1309.90 \\ LbfgsPoissonRegressionRegression & 0.6895 & 1083.60 & 2070330.99 & 1438.86 \\ FastTreeRegression & 0.6145 & 1247.94 & 2570239.36 & 1603.20 \\ LbfgsPoissonRegressionRegression & 0.7056 & 1099.95 & 1963138.93 & 1401.2 \\ FastTreeTreeRegression & 0.6013 & 1267.53 & 2658605.93 & 1630.52 \\ FastTreeRegression & 0.6049 & 1260.06 & 2634609.08 & 1623.15 \\ LbfgsPoissonRegressionRegression & 0.6112 & 1267.24 & 25295238.55 & 1610.14 \\ FastTreeRegression & 0.7604 & 974.84 & 1597501.02 & 1263.92 \\ LbfgsPoissonRegressionRegression & 0.6404 & 1219.63 & 2397823.37 & 1548.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of different energy consumption algorithms (48 algorithms tested, only best shown) to select the best algorithm for aggregation of data sets from the energy management area (Stage 2). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **RSsquared** & **Absolute Loss** & **Squared Loss** & **RMS Loss** \\ \hline FastForestRegression & 0.7759 & 7.05 & 102.82 & 10.14 \\ LightGbmRegression & 0.7677 & 7.12 & 106.56 & 10.32 \\ FastForestRegression & 0.7542 & 7.44 & 112.75 & 10.62 \\ LbfgsPoissonRegressionRegression & 0.6620 & 7.42 & 155.06 & 12.45 \\ FastTreeRegression & 0.8010 & 6.65 & 91.32 & 9.56 \\ LightGbmRegression & 0.7808 & 6.69 & 100.56 & 10.03 \\ FastTreeTreeRegression & 0.6788 & 7.82 & 147.36 & 12.14 \\ FastForestRegression & 0.7773 & 7.03 & 102.17 & 10.11 \\ LbfgsPoissonRegressionRegression & 0.7540 & 6.61 & 112.85 & 10.62 \\ FastTreeRegression & 0.8577 & 5.51 & 65.30 & 8.08 \\ FastForestRegression & 0.7801 & 7.00 & 100.86 & 10.04 \\ LbfgsPoissonRegressionRegression & 0.8173 & 6.28 & 83.83 & 9.16 \\ LightGbmRegression & 0.7845 & 6.89 & 98.86 & 9.94 \\ FastTreeRegression & 0.8442 & 5.74 & 71.48 & 8.45 \\ FastTreeTreeWeedieRegression & 0.8507 & 5.20 & 68.50 & 8.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of different movement and transport assessment algorithms (53 algorithms tested, only best shown) to select the best algorithm for aggregation of data sets from the health area (Stage 2). We have developed and tested a conceptual framework based on the data sets, but it is the resulting model that needs further refinement and development, especially based on real data. In the individual areas, the best deep learning results were for  
* Health data: 0.8322;
* Energy consumption data: 0.6513;
* Traffic data: 0.7523. * Mean aggregated value: 0.7453.
## 2 Materials and Methods
### System's Design and Construction
#### 2.1.2 Hardware: Motor, Controller, and Transmission  
The movement of the carriage was performed using one high-torque, bipolar stepper motor (12 V DC, 0.4 A/phase, 200 steps/revolution, size NEMA17) with a 1.8\({}^{\circ}\) full step angle [45]. The stepper motor was positioned beneath the carriage and oriented with the shaft pointing upwards in order to link it with the timing belt and create a linear rotor drive system (Figure 5a).  
A GT2 aluminum timing belt pulley with 12 teeth was attached to the shaft of the stepper motor (Figure 5a). Its small diameter retained the torque produced by the stepper motor. In addition, a 3.5 m long GT2-type timing belt (2 mm pitch, 6 mm width, made of neoprene and fiberglass materials) was attached to the two ends of the beam, which was used to transmit motion. As the axis of the stepper motor rotated, the timing pulley forced the carriage to move across the beam. The control of the stepper motor was achieved with a TMC2209 V2.0 [46] stepper motor driver connected to the Raspberry Pi and used a microstepping factor of 2 (e.g., half step), resulting in 400 steps per revolution of the motor shaft (Figure 5b). The calculated steps per mm for the linear motion produced by the belt was found to be \(\frac{steps}{mm}=\frac{S_{w}\cdot f_{m}}{p\cdot N_{t}}=16,667\) steps/mm. Here, \(S_{rev}\) is the number of steps per revolution of the motor, \(f_{m}\) is the microstepping factor, \(p\) is the pitch, and \(N_{t}\) is the number of teeth on the pulley attached to the motor shaft.
## 4 Experimental Design
### Evaluation Metrics  
Quantitative evaluation was performed using F1-score-based metrics for both localization and damage assessment [31], which is defined as  
\[F1_{b}=\frac{2 TP}{2 TP+FP+FN}\]  
where _TP_, _FP_, and _FN_ denote the number of true-positive, false-positive, and false-negative pixels of segmentation results, respectively.  
The quantitative performance of the proposed models is evaluated using metrics such as localization \(F1_{b}\), damage assessment \(F1_{d}\) in a similar manner with \(F1_{b}\), and overall \(F1_{s}\) scores. More specifically, the first one (\(F1_{b}\)) measures the accuracy of detecting road boundaries and positions, and the second one (\(F1_{d}\)) assesses the model's ability to capture detailed damage within detected roads. The last one (\(F1_{s}\)) is a weighted average defined as \(F1_{s}=0.3\times F1_{b}+0.7\times F1_{d}\), highlighting the importance of precise damage assessment in disaster response [22; 23; 40; 42]. This design enables fair comparison across models with respect to both spatial accuracy and semantic damage identification, allowing for a quantitative comparison of the proposed models with a particular focus on accurately identifying damage rather than merely locating roads.  
In addition to the \(F1\)-score, to provide a more detailed diagnostic of model performance, this study also reports on other standard metrics. These include precision, which measures the accuracy of positive predictions (i.e., what proportion of pixels predicted as a certain class were actually that class), and recall (or sensitivity), which measures the model's ability to find all relevant instances (i.e., what proportion of actual positive pixels were correctly identified). The analysis is further supplemented by intersection over union (IoU), which is a standard metric for segmentation tasks that evaluates the degree of overlap between the predicted and ground-truth regions.  
For generating the final segmentation maps, a class is assigned to each pixel by applying an argmax function to the model's output logits; therefore, an explicit segmentation threshold (e.g., 0.5) was not used. As a general strategy to ensure model generalization and mitigate potential overfitting to the majority classes, this study employed the standard cross-entropy loss function and selected the model checkpoint with the minimum validation loss for final evaluation.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
###### Abstract  
This paper proposes a semi-automatic green tide extraction method based on the _NDVI_ to extract Yellow Sea green tides from 2008 to 2022 using remote sensing (RS) images from multiple satellites: GF-1, Landsat 5 TM, Landsat 8 OLL_TIBS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, Recall, and F1-score, showed that our extraction method can be applied to the images of most satellites and different environments. We traced the source of the Yellow Sea green tide to Jiangsu Subei shoal and the southeastern Yellow Sea and earliest advanced the tracing time to early April. The Gompertz and Logistic growth curve models were selected to predict and monitor the extent and duration of the Yellow Sea green tide, and uncertainty for the predicted growth curve was estimated. The prediction for 2022 was that its start and dissipation dates were expected to be June 1 and August 15, respectively, and the accumulative cover area was expected to be approximately 1190.90-1191.21 km\({}^{2}\).  
Yellow Sea green tide; NDVI; multi-source RS; source tracing; growth curve; prediction