esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 8 Conclusions  
This research looks at the fundamentals of WSN routing algorithms, including their features and how they work. We also go over the pros and cons of several methods that can be employed to enhance the efficiency of routing algorithms in WSNs. In recent years, WSNs have made great strides due to the application of ML methods. This study provides a comprehensive analysis of ML techniques. Lastly, this paper highlights the difficulties of using ML to create routing algorithms in WSNs and suggests areas that could benefit from further investigation using ML in the future. Anybody interested in ML and WSNs can benefit from this conversation. The following could be part of future studies: Machine learning methods are not suitable for large-scale deployment in low-power sensors because of the computational bottleneck and energy consumption limitations of WSNs. Distributed learning approaches, on the other hand, do not have to take the whole network's data into account; therefore, they use less power, have smaller memory footprints, and demand less processing capacity than centralized learning algorithms. Distributed cooperative learning is ideal for WSNs because it eliminates the mathematical bottleneck and enables ML-based green routing while reducing energy usage.  
Conceptualization, A.R.G. and A.D.P.; methodology, A.R.G. and A.D.P.; software, A.R.G. and A.D.P.; validation, A.R.G. and A.D.P.; formal analysis, A.R.G. and A.D.P.; investigation, A.R.G. and A.D.P.; resources, A.R.G. and A.D.P.; data curation, A.R.G. and A.D.P.; writing--original draft preparation, A.R.G. and A.D.P.; writing--review and editing, A.R.G. and A.D.P.; supervision, A.R.G. and A.D.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All the data used are made available in the present work.  
The authors declare no conflicts of interest.
# A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector
## 3 Software Architecture
### Path Planning  
The path planning task is in charge of generating a feasible trajectory that the vehicle can follow to reach a goal location, such as in [17; 18]. To handle the complexity of generating that trajectory, path planning is usually divided into global planning and local planning.  
* Global planning considers all the fixed obstacles, such as the features of the roads (number of lanes, width, connections between different roads, etc.), to find the shortest path between the vehicle's current position and the goal destination.This task is done automatically by obtaining all necessary information from OpenStreetMaps (OSM) and generating the shortest path using [PERSON]'s algorithm. The global route also includes information about the maximum speed for each road or the lane in which the vehicle should be [10]. Figure 7 shows a global path (red line) between the current position (blue dot) and the goal destination (green dot), including all the OSM nodes (red dots).
* On the other hand, local planning tries to follow the global plan, except that it considers local obstacles that are not on the global map but are detected with the perception algorithms described above (Figure 8). Besides, the trajectories generated by this module must satisfy some constraints that will make the path followable by the vehicle. All this information is transformed into the frenet frame in order to determine the optimal trajectory, and that trajectory is then transformed back into real-world coordinates so that the control module is able to follow it [19]. Thus, the vehicle is able to overtake slower vehicles if possible or to accelerate or brake accordingly, always maintaining the reference global path and the target speed as goals.
# Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS)
###### Abstract  
Box compression strength (BCS) is a critical parameter for assessing the performance of shipping containers during transportation. Traditionally, BCS evaluation relies heavily on physical testing, which is both time-consuming and costly. These limitations have prompted industry to seek more efficient and cost-effective alternatives. This study explores the application of artificial neural networks (ANNs) to estimate BCS at an industry-applicable level. A real-world dataset--covering approximately 90% of the box dimensions commonly used in the industry--was utilized to train a generalized ANN model for BCS prediction. The model achieved a prediction error of approximately 10%. When validated against experimentally measured data from laboratory testing, with single-wall B-flute as a representative, the prediction error was at a much lower level, further demonstrating the model's reliability. This study offers a novel approach to BCS prediction, providing a cost-effective and scalable alternative to traditional physical testing methods in the packaging industry.  
corrugated packaging; box compression strength (BCS); artificial neural network (ANN) Article
## 2 Materials and Methods
### Dataset and Preprocessing
#### 2.2.4 Evaluation Measures  
The performance of the CNN models was evaluated using several metrics: accuracy, precision, recall, F1-score, and confusion matrix [35]. Accuracy measures the proportion of correctly classified predictions to the total number of predictions considering both positive and negative samples [6; 36]:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}, \tag{1}\]  
where \(TP\) indicates the number of true positives, \(TN\) indicates the number of true negatives, \(FP\) indicates the number of false positives, and \(FN\) indicates the number of false negatives.  
Precision is the proportion of true positive predictions out of all samples predicted as positive:  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
The true positivity rate, or the percentage of all actual positives correctly classified as positive, is commonly referred to as precision. When combined with accuracy, if considered as a binary classification problem, precision reflects the accuracy for each individual category.  
Recall measures the proportion of true positive predictions out of all actual positive samples:  
\[Recall=\frac{TP}{P}, \tag{3}\]  
where \(P\) is the total number of actual positive samples.  
The F1-score is a weighted harmonic average of precision and recall based on parameter \(\alpha\):  
\[F=\frac{\left(\alpha^{2}+1\right)\ \times\ Precision\ \times\ Recall}{\alpha^{2}\ \times\ Precision\ \times\ Recall} \tag{4}\]  
The receiver-operating characteristic (ROC) curves are visual tools used to assess the performance of a classifier across different decision thresholds. The horizontal axis represents the false positive rate (or 1-specificity), while the vertical axis shows the true positive rate (or sensitivity). Additionally, the precision-recall (PR) curve is an evaluation tool that calculates precision and recall at different thresholds and plots them, providing insights into the classifier ability to distinguish classes.
## 2 Methodology
### Dynamic Allocation of Satellite Resources for Emergency Scheduling  
The purpose of emergency observation mission scheduling is to determine the execution order of the missions and reasonably allocate satellite resources and execution time for each mission. As some new emergency missions are added, the complexity of the problem is further increased. Because there are many constraints in the multi-satellite scheduling problem, and the arrival of new missions will affect the mission set, satellite resource set, time window and constraints in the initial model, thus influencing many scheduled missions. Therefore, this paper proposes a dynamic scheduling algorithm based on mission priority to solve the scheduling problem of newly arrived missions. In this model, the initial schedule is obtained through a hybrid genetic tabu search algorithm. When new missions arrive, based on the initial scheduling results, the satellite that matches the new mission request is searched in the satellite resource set, the operations including insertion, reallocation, replacement and deletion are performed to execute the new missions with high priority. We design this method to ensure that newly arrived missions can be quickly scheduled under emergency conditions and minimize the change of initial scheduling plan.
## 4 Experimental Dataset and Simulation Results
### Dataset Description  
To evaluate the proposed model, a dataset was constructed, and it is available online at this URL: [[https://www.u-aizu.ac.jp/labs/is-pp/pplab/swr/sign_word_dataset.zip](https://www.u-aizu.ac.jp/labs/is-pp/pplab/swr/sign_word_dataset.zip)]([https://www.u-aizu.ac.jp/labs/is-pp/pplab/swr/sign_word_dataset.zip](https://www.u-aizu.ac.jp/labs/is-pp/pplab/swr/sign_word_dataset.zip)). There were twenty isolated hand gestures (11 single-hand gestures and nine double hand gestures). The images of the dataset were collected with a pixel resolution of 200 \(\times\) 200. Figure 8 depicts the example of the dataset images. To collect dataset images, three volunteers (mean age of 25) were requested to perform the gesture of the sign word. We collected three hundred images for each gesture sign from each individual. There was a total of 18,000 images of 20 isolated hand gestures. While capturing the images, we considered the complex background, different lighting illumination, different directions, and skin-like backgrounds.  
Figure 7: One-versus-rest multiclass SVM structure for classification.
# Thermal Effusivity Tester (TET)--A New Device to Determine Thermal Effusivity of Textiles
## 5 Conclusions  
Thermal effusivity measurements with TET were performed on different textile fabrics and the results show accordance with other commercially available measurement devices. A good correlation was achieved with Q\({}_{\text{max}}\) from KES-f Thermo Labo. TET can therefore be considered reliable for assessing the thermal effusivity of textile fabrics and offers a flexible measurement approach that considers textile structure. Differences among the three devices occurred on rather fluffy constructions, which are more sensitive to the applied pressure in terms of compression behavior, which leads to different fabric densities C\({}_{\text{p}}\). The results obtained by TET and Alambeta devices on single-layered samples show that the minimal sample thickness indicated by ASTM D7948 should not be mandatory if heat break-through can be avoided. Less accordance among the test devices was observed in the thermal conductivity, where especially higher conductivity was measured with TCi due to the higher applied weight. The effect of fabric compression in fluffy/resilient fabric constructions and under different compressions relevant to wear situations is to be a subject of a more detailed study.  
Conceptualization, M.A.-R., S.-P., R.K.S., J.-A.I. and J.L.; Methodology, M.A.-R. and J.L.; Software, R.K.S. and J.L.; Validation, R.K.S. and W.M.; Formal analysis, J.L. and W.M.; Resources, S.S.-P.; Data curation, M.A.-R.; Writing--original draft, M.A.-R.; Writing--review & editing, M.A.-R., S.S.-P., R.K.S., J.-A.I. and J.L.; Supervision, J.L.; Project administration, S.S.-P.; Funding acquisition, J.-A.I. All authors have read and agreed to the published version of the manuscript.  
A part of the project was funded by the Austrian Research Promotion (FFG)--grant number 863083.  
Not applicable, not relevant.  
Not applicable.  
Raw measurement data can be provided by corresponding author upon request.  
Figure 7: TET and Kawabata KES-f thermal effusivity results on woven (W) and knitted (K) fabrics.  
**Acknowledgments:** The authors wish to thank [PERSON] and his team for the design, development and realization of the device, [PERSON] and [PERSON] (Product Development Labs at Lenzing AG) for performing the Alambeta and TET measurements, [PERSON] for the valuable contributions to the discussions on the device design and the trial setting, C-Therm Inc. (Canada) for the measurements on fabrics 1-4 and IFTH (France) for the Kawabata Thermo Labo measurements.  
**Conflicts of Interest:** The authors declare no conflict of interest.
### Frequency Spectrum of Pulse
Moreover, in the modulation scheme used by 5G NR (orthogonal frequency-division multiplexing or OFDM), signals are transmitted over many subcarriers, each having a very narrow bandwidth (15 kHz), that are distributed over the channel bandwidth. [PERSON] and [PERSON] state Brillouin precursors \"cover [] the full spectrum of frequencies from \(-\infty\) to \(\infty\)\". That is only true for the idealized waveform (Equation (1)), which has discontinuities in the slope of the modulating function at each end of the pulse. They state \"the likelihood of producing Brillouin precursors increases with extra transmission speed, increasing the pulse rate into Gbps (billion bits per second), and with a GHz bandwidth of more than 500 MHz\". In fact, 5G NR (as well as other communications signals) are strictly band-limited, both by the spectrum available to the carrier and (for cellular communications) by the slice of spectrum (the channel) that the carrier has assigned to the user. For the case of 5G NR, higher data transmission speeds are accomplished through use of wider channels as provided by the 5G NR standard, taking care to ensure that the signal remains within the frequency band assigned to the carrier. Moreover, waveforms of 5G NR and other digitally modulated signals are more complicated than a series of square-wave-modulated pulses as in Equation (1). To suppress intersymbol interference and other undesirable effects from sidebands, designers of 5G equipment include sophisticated filtering algorithms [9]. In addition, a signal radiated from any personal cellular communication device or other transmitter is limited in bandwidth by the electronic circuits generating the RF signals, the transmission lines feeding the antennas, and the antennas themselves. Thus, the square-wave-modulated RF-EMF pulse in Equation (1) with infinite bandwidth is simply irrelevant to cellular communications signals.
# Analysis of Cascading Failures and Recovery in Freeway Network Under the Impact of Incidents
## 3 Methodology
### Recovery Strategies
The matrix \(X\) is then converted into a standardized matrix \(R\) by using the normalization formula \(r_{ij}=\frac{x_{ij}-min\left(x_{j}\right)}{max\left(x_{j}\right)-min\left(x_{ j}\right)}\) to eliminate the dimensional effects of different indicators, as shown in Equation (10). \[X= \begin{bmatrix}x_{11}&x_{12}&x_{13}\\ x_{21}&x_{22}&x_{23}\\ \vdots&\vdots&\vdots\\ x_{n1}&x_{n2}&x_{n3}\end{bmatrix} \rightarrow\ R= \begin{bmatrix}r_{11}&r_{12}&r_{13} \\ r_{21}&r_{22}&r_{23}\\ \vdots&\vdots&\vdots\\ r_{n1}&r_{n2}&r_{n3}\end{bmatrix}  \tag{10}\]  
The entropy weight method is used to determine the weight of each indicator. According to the weight formula \(p_{ij}=\frac{r_{ij}}{\sum_{j=1}^{n}r_{ij}}\), the weight matrix \(P=\left(p_{ij}\right)_{n\times 3}\) is obtained. Subsequently, information entropy is calculated according to Equation (11), where \(k=\frac{1}{ln\left(m\right)}\). The weight is calculated according to Equation (12), where \(w_{j}\) is the weight of the \(j^{th}\) indicator, and n is the total number of indicators. Then the weighted standardized decision matrix \(V=\left(v_{ij}\right)_{n\times 3}\) is obtained according to \(v_{ij}=w_{j}\times r_{ij}\). \[e_{j}=-k{\sum_{i=1}^{m}p_{ij}}{\ln\left(p_{ij}\right)} \tag{11}\]  
\[w_{j}=\frac{1-e_{j}}{\sum_{j=1}^{n}\left(1-e_{j}\right)} \tag{12}\]  
Figure 3: Cascading failure and recovery process. For each indicator, determine the maximum and minimum values, which respectively form positive ideal solution \(A^{+}=\left\{max\left(v_{ij}\right)\left|j\in I_{1}\right\},\left\{min\left(v_{ ij}\right)\left|j\in I_{2}\right.\right\}\right.\) and the negative ideal solution \(A^{-}=\left\{min\left(v_{ij}\right)\left|j\in I_{1}\right\},\left\{max\left(v_ {ij}\right)\left|j\in I_{2}\right.\right\}\), where \(J_{1}\) is the set of benefit-type criteria, and \(J_{2}\) is the set of cost-type criteria. The Euclidean distance is used to measure the distance of each decision option from the positive ideal solution \(D_{i}^{+}=\sqrt{\sum_{j=1}^{m}\left(v_{ij}-v_{j}^{+}\right)^{2}}\), and to form the negative ideal solution \(D_{i}^{-}=\sqrt{\sum_{j=1}^{m}\left(v_{ij}-v_{j}^{-}\right)^{2}}\). The similarity score \(C_{i}=D^{-}/(D^{+}+D^{-})\) is calculated; the higher the proximity score, the higher the priority during the recovery process. Figure 4 illustrates the overall structure of the proposed method. Once the road network \(G\) is obtained, it is utilized along with the traffic feature data as inputs to the evaluation matrix. The weight of each evaluation criterion is calculated based on the entropy weight method, followed by the use of the TOPSIS algorithm to obtain the importance ranking of road segments. Finally, the ranking results of the critical road segments are output.
# A Variable-Order Dynamic Constitutive Model for Clay Based on the Fractional Calculus
## 4 Establishment of Dynamic Constitutive Model
### The Strain Response of the Crep Constitutive Model  
Assuming long-term stress is equal to critical dynamic stress, i.e., \(\sigma_{s}=\sigma_{cr}\).  
1. when \(\sigma_{1}\leq\sigma_{cr}\), an expression of strain response based on the creep constitutive model in Figure 11 can be obtained, if the load duration is in the range of \(0\) to \(t_{1}\), i.e., \(0<t\leq t_{1}\).  
For the initial stage, we find  
\[J_{11}=\frac{1}{E_{0}}+\frac{\cos(\alpha_{1}\pi/2)}{\eta_{1}w^{\alpha_{1}}} \tag{37}\]  
\[J_{22}=\frac{\sin(\alpha_{1}\pi/2)}{\eta_{1}w^{\alpha_{1}}} \tag{38}\]  
Substituting Equations (37) and (38) into Equations (29) and (30) to reflect the viscous effects of clay under long-term alternating load, the strain response constitutive equation can be expressed as  
\[\varepsilon_{a}(t)=\sigma_{1}\sqrt{J_{11}^{2}+J_{12}^{2}}\sin\biggl{(}wt+ \arctan\biggl{(}\frac{J_{22}}{J_{11}}\biggr{)}\biggr{)} \tag{39}\]  
For the steady stage, we find  
\[J_{s1}=\frac{\cos(\alpha_{2}\pi/2)}{\eta_{2}w^{\alpha_{2}}} \tag{40}\]  
\[J_{s2}=\frac{\sin(\alpha_{2}\pi/2)}{\eta_{2}w^{\alpha_{2}}} \tag{41}\]  
where \(t_{1}<t\leq t_{2}\), substituting Equations (40) and (41) into Equations (29) and (30) to reflect the viscous effect, a strain response constitutive equation is given as follows:  
\[\varepsilon_{a}(t)=\sigma_{1}\sqrt{J_{s1}^{2}+J_{s2}^{2}}\sin\biggl{(}wt+ \arctan\biggl{(}\frac{J_{s2}}{J_{s1}}\biggr{)}\biggr{)} \tag{42}\]  
(2) When \(\sigma_{1}>\sigma_{cr}\),  
for the accelerating stage, we find  
\[J_{a1}=\frac{e^{\beta t}}{\eta} \tag{43}\]  
\[J_{a2}=0 \tag{44}\]  
Substituting Equations (43) and (44) into Equations (29) and (30) to reflect the viscous effect, the strain response constitutive equation is given as follows:  
\[\varepsilon_{a}(t)=\sigma_{1}\sqrt{J_{a1}^{2}+J_{a2}^{2}}\sin\biggl{(}wt+ \arctan\biggl{(}\frac{J_{a2}}{J_{a1}}\biggr{)}\biggr{)} \tag{45}\]  
where \(t>t_{2}\). As for \(0<t<t_{1}\) and for \(t_{1}<t<t_{2}\), the strain response constitutive equations for the initial and steady stages are identical with Equations (39) and (42), respectively.  
**5. The Dynamic Constitutive Model for Clay**  
By substituting Equations (26a), (26b), (26c), (39), (42) and (45) into Equation (8), the dynamic constitutive model based on variable-order fractional Abel dashpot, expressing viscoplastic body and strain response is given by  
\[\varepsilon_{d}(t)=\frac{\sigma_{0}}{E_{0}}+\frac{\sigma_{0}}{\eta_{1}}\frac{ (t-0)^{\alpha_{1}}}{\Gamma(\alpha_{1}+1)}+\sigma_{1}\sqrt{J_{11}^{2}+J_{2}^{2 }}\sin(wt+\arctan(J_{2}/J_{11}))\ (0<t\leq t_{1}) \tag{46}\]  
\[\varepsilon_{d}(t)=\frac{\sigma_{0}}{\eta_{2}}\frac{(t-t_{1})^{\alpha_{2}}}{ \Gamma(\alpha_{2}+1)}+\sigma_{1}\sqrt{J_{s1}^{2}+J_{s2}^{2}}\sin(wt+\arctan( J_{s2}/J_{s1}))\ (t_{1}<t\leq t_{2}) \tag{47}\]  
\[\varepsilon_{d}(t)=\frac{H(\sigma_{0}-\sigma_{s})}{\eta e^{-\beta t}}+\sigma_{ 1}\sqrt{J_{a1}^{2}+J_{a2}^{2}}\sin(wt)\ \ (t>t_{2}) \tag{48}\]
## 3 Results
### HPLC-DAD Separation, Identification and Quantitation of Carotenoids
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Mean Values** & **LUT** & **ZEA** & **\(\alpha\)-CAR** & **\(\beta\)-CAR** & **\(\beta\)-CAR-** & **\(\beta\)-CAR-** & **\(\beta\)-CRP** & **ARO** & **NEO** & **VLX** & **LUX** \\ \(\pm\)**SD** & & & & & & & **Diepoxide** & **Epoxide** & & & & \\ \hline SFO1-3 & 0.038 & 0.036 & 0 & 0.018 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ SD & 0.001 & 0.001 & 0 & 0.001 & & & & & & & \\ aSFO4 & 0.039 & 0.038 & 0 & 0.019 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline PO1-3 & 0.156 & 0.042 & 0 & 0.027 & 0.023 & 0.022 & 0 & 0.032 & 0 & 0 & 0 \\ SD & 0.053 & 0.001 & & 0.003 & 0.002 & 0.002 & & 0.005 & & & \\ aPO4 & 0.134 & 0.043 & 0 & 0.026 & 0.022 & 0.027 & 0 & 0.02 & 0 & 0 & 0 \\ \hline LSO1-2 & 0.245 & 0.055 & 0.021 & 0.024 & 0.055 & 0.020 & 0 & 0.069 & 0.059 & 0.038 & 0 \\ SD & 0.239 & 0.021 & 0.001 & 0.005 & 0.022 & 0.002 & 0 & 0.044 & 0.030 & 0.002 & 0 \\ aLSO3 & 0.065 & 0.040 & 0 & 0.020 & 0.033 & 0.018 & 0 & 0.036 & 0.034 & 0.035 & 0 \\ \hline HO1-3 & 0.196 & 0.040 & 0.020 & 0.041 & 0.019 & 0 & 0 & 0 & 0 & 0 & 0.098 \\ SD & 0.080 & 0.001 & 0.002 & 0.001 & 0.001 & & & & & & \\ aHO4 & 0.168 & 0.031 & 0.025 & 0063 & 0.028 & 0 & 0 & 0 & 0 & 0.0077 \\ \hline SO2-3 & 0.798 & 0.064 & 0.010 & 0.028 & 0.076 & 0.021 & 0.016 & 0 & 0 & 0 & 0 \\ SD & 0.358 & 0.016 & 0.014 & 0.006 & 0.018 & 0.001 & 0.022 & & & & \\ aSO1 & 0.396 & 0.043 & 0.000 & 0.022 & 0.057 & 0.019 & 0.000 & 0 & 0 & 0 & 0 \\ \hline WO1-3 & 0.046 & 0.045 & 0.018 & 0.022 & 0 & 0 & 0.034 & 0 & 0 & 0 & 0 \\ SD & 0.002 & 0.008 & 0.001 & 0.001 & & & 0.001 & & & & \\ aWO4 & 0.044 & 0.037 & 0.017 & 0.022 & 0 & 0 & 0.035 & 0 & 0 & 0 & 0 \\ \hline SBO2-4 & 16.000 & 94.640 & 0 & 5.077 & 0 & 0 & 10.218 & 0 & 3.816 & 0 & 0 \\ SD & 5.411 & 58.011 & & 1.354 & & 4.984 & & 0.987 & & \\ aSBO1 & 10.903 & 25.422 & 0 & 7.037 & 0 & 0 & 3.431 & 0 & 2.677 & 0 \\ \hline EVOO2,4 & 0.093 & 0.041 & 0.011 & 7.037 & 0.044 & 0.019 & 0.031 & 0.038 & 0.037 & 0.041 & 0.085 \\ SD & 0.082 & 0.009 & 0.015 & 0.020 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline EVOO1,3 & 0.130 & 0.042 & 0.019 & 0.027 & 0.040 & 0.018 & 0.030 & 0.036 & 0.054 & 0.038 & 0.076 \\ SD & 0.009 & 0.001 & 0.001 & 0.001 & 0 & 0 & 0 & 0 & 0.027 & 0 & 0.002 \\ \hline \end{tabular} ZEA, zeaxanthin; BC, \(\beta\)-carotene; \(\beta\)-CRP, \(\beta\)-cryptoxanthin; ARO, auroxanthin; NEO, neoxanthin; LUX, luteoxanthine; CAR, total carotenoids; TOC, totaloberols.  
\end{table} 
Table 3: Mean values (mg/100 g) of individual carotenoids separated and identified in commercial and authentic oils, respectively. For oil abbreviations, see Table 1.