esa-sceva/satcom-chunk-collection huggingface dataset:
# VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification
## 3 VB-Net
### Training of BLS  
The features gained by extractor \(F\) in Section 3.1 are mapped to the feature layer \(Z\) using Equation (1) where \(W_{i}\) and \(\beta_{i}\) consist of random numbers. To generate nodes in the enhancement layer, Equation (2) is employed where \(W^{\prime}_{i}\) is a predefined matrix filled with random numbers following the standard normal distribution; \(\beta^{\prime}_{j}\) is a randomly generalized bias vector in this paper.  
\[z_{i}=\sigma(FW_{i}+\beta_{i}) \tag{1}\]  
\[h_{j}=\varphi(ZW^{\prime}_{j}+\beta^{\prime}_{j}) \tag{2}\]  
Nodes in output layer \(o_{i}\), \(o_{i}\in O\), \(I\in[1,k]\) are calculated using the following equation in [11] where matrix \(W^{*}\) is the training target of the training process:  
\[O=[Z\left|H\right|W^{*} \tag{3}\]  
Figure 1: The network structure of VB-Net.  
Figure 2: Feature extractor architecture for pretraining.  
This paper defines a new matrix \(M\) as \(M=[Z\mid H]\); then, the output matrix \(O\) is calculated using \(MW^{*}\). Therefore, our goal is to narrow the gap between the results of \(MW^{*}\) and values in output matrix \(O\). The ridge regression approximation algorithm is applied for adjusting the values in matrix \(W^{*}\). The \(l_{2}\) norm method is adopted for improving the generalization performance of the BLS as shown in Equation (4).  
\[W^{*}_{0}=\underset{W^{*}}{\operatorname{argmin}}\|MW^{*}-O\|_{2}^{2}+\lambda \|W^{*}\|_{2}^{2} \tag{4}\]  
The optimal solution \(W^{*}_{0}\) is calculated using \(W^{*}_{0}=M^{+}O\), where \(M^{+}\) is the pseudo inverse matrix of matrix \(M\). Considering that it is hard to compute \(M^{+}\) directly, an approximation method is adopted here as shown in Equation (5), where matrix \(I\) is an identity matrix, variable \(\lambda\in\mathds{R}\).  
\[W^{*}=M^{+}O=\left(\lambda I+MM^{T}\right)^{-1}M^{T}O \tag{5}\]  
As the output matrix \(O\) was already computed in Equation (3), only the matrix \(M^{+}\) needs calculation. As mentioned in [11], the solution \(M^{+}_{0}\) is close to the original pseudo inverse matrix as variable \(\lambda\) tends to 0. Thus, Equation (6) is employed to calculate matrix \(M^{+}\).  
\[M^{+}_{0}=\lim_{\lambda\to 0}\left(\lambda I+MM^{T}\right)^{-1}M^{T} \tag{6}\]  
The training process of the proposed BLS with the feature extractor is done. If classification accuracy does not meet the required number on the test set, we can increase or decrease the number of nodes in the enhancement layer and/or feature layer to improve the classification accuracy of our model.
# Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
## 3 Briefs on Convolutional Neural Network
### Evaluation Indicators of CNN Model  
To quantify the performance of different methods, four evaluation indicators were employed: accuracy, precision, recall, and \(f_{1}\)-score. The calculation of these indicators is shown in [4]. In engineering applications, the condition-monitoring system ideally triggers an alarm for real faults, and avoids false or missed triggers. Therefore, a good classification method that maximizes all evaluation indicators is required. More details about CNN setting parameters and configurations can be found in [4; 5; 8].
# Gateway-Assisted Retransmission for Lightweight and Reliable IoT Communications
## 5 Results
Each device saves at least 466.941 ms-mA (951 ms-mA \(\times 0.491\)) power for a packet. Indeed, compared to calculating the RTO by the device itself, sending two extra bytes for the RTT and receiving the RTO result in more power consumption. Compared to this consumption, the power savings of calculating the RTO is ignorable. According to [14], sending a packet over the air costs 31.5 ms-mA (see event 8). In our design, sending a packet that contains the RTT information occupies two extra bytes, which means that the device has to spend an additional 0.0645 ms (2 bytes/31 bytes). Therefore, the additional power consumption is 2.0318 ms-mA (0.0645 ms \(\times\) 31.5 mA). On the other hand, the device updates the RTO in two ways. One is receiving its ACK with two extra bytes to carry the RTO. The other is receiving the ACK to get the RTO by overhearing. In [14], the required current for receiving MAC ACK or application ACK is 26.5 mA (see event 16). With the first way, the device  
Figure 8: The average number \(\delta\) of retransmissions under different numbers of devices. Figure 7: The average latency \(\lambda\) under different \(\mu\). has to receive two extra bytes, which costs an additional 1.70925 ms\(\cdot\)mA (0.0645 ms \(\times\)26.5 mA). Using the second method, the device receives a whole 14 byte application ACK, which costs an additional 11.9677 ms\(\cdot\)mA (0.4516 ms \(\times\)26.5 mA). Note that performing CSMA/CA observation also consumes energy, but it is common for all mechanisms (performing one observation consumes 1.325 ms\(\cdot\)mA). To put it briefly, a device adopting GaCoAP spends at most 13.677 ms\(\cdot\)mA (per packet) to send the RTT and receive the RTO. However, this device can save at least 466.941 ms\(\cdot\)mA per packet from reducing retransmissions. The power consumption mentioned above can be converted to other units for easy understanding. For example, a normal AAA battery (with capacity 1200 mAh) can be used to transmit \(3,654,822\) packets (\(1200\times 3,600,000/1182\)) if no retransmission occurs. If the average number of retransmission is 0.1, then only \(3,382,664\) packets (\(1200\times 3,600,000/1277.1\)) can be transmitted. In Figure 5, when MLR = 10% and \(\mu=10\), the energy required for a packet in GaCoAP is \(1182+951\times 0.507+13.677=1677.834\) ms\(\cdot\)mA, while the energy required for a packet in MQTT-SN is \(1182+951\times 1.902=2990.802\) ms\(\cdot\)mA. In this case, 2,574,748 packets can be transmitted in GaCoAP, while only 1,444,428 packets can be transmitted using a single AAA battery. As a final remark, the network environment is simple in our experiment (a gateway connects to 30 devices), but it is more complex in the real sensor networks. From our simulation results, the GaCoAP mechanism outperforms the others, especially in worse conditions (i.e., large latency and high message loss rate).
## 4 Practical Experiments  
The apparent power measurements of a commercial power analyzer (Fluke 437 Series II) and our prototype power analyzer referred to as SIMPELEC [30] were tested in a laboratory and on real-world electrical networks using the expressions of the apparent and neutral-displacement powers in Section 3. The Fluke 437 power analyzer measures the apparent powers using the Buchholz formulation, while our SIMPELEC analyzer measures the same apparent powers, as well as the neutral-displacement powers in Section 3.  
The laboratory measurements were performed on a Chroma programmable three-phase source model 61,700 [32] under three different conditions: (1) sinusoidal balanced voltages, (2) sinusoidal unbalanced voltages, and (3) non-sinusoidal balanced voltages. The real-world network measurements were performed on the electrical network of a utility service using only the SIMPELEC analyzer, which is described in the Spanish Granted Patent [30], whose main features are described below.  
The hardware constitutes the following:  
* Processor board: PCM-9581 On-board Intel Pentium (Advantech) [33].
* Data acquisition: PCI-6220 of National Instruments, 16 bits, 16 AI, 250 KS/s [34].
* Transducers: Voltage Hall Effect LV 25-P (LEM) with accuracy 0.9% [35], AC Current Clamps Fluke 15 sPQ3, 5 A, accuracy 1% [36].  
The firmware of the SIMPELEC analyzer is based on LabVIEW platform to measure the complex RMS values (module and angle) of the voltages and currents, according to the following steps:  
Step 1. Measurement of the line-to-neutral voltages and the line and neutral currents, using Hall sensors.  
Step 2. Acquisition of samples of the line-to-neutral voltages and line and neutral currents from the previous measurements, using a 40.96-KS/s-per-channel acquisition card.  
Step 3. Calculation of the complex RMS (CRMS) values (module and angle) applying fast Fourier transform (FFT) to the above samples.  
Step 4. Calculation of apparent powers.  
It is noted in Table 1 that the difference between the apparent powers measured by the Fluke 437 and SIMPELEC analyzers is due to the accuracy of the voltage transducers used by these analyzers. As it may be observed in Table 1, Fluke analyzer is an A-Class instrument and, therefore, more accurate than the SIMPELEC. However, if we analyze the apparent powers measured in the following examples, the maximum difference between these powers measured by both analyzers is less than 0.15%. Thus, this accuracy may be negligible in determining the neutral-displacement powers, which are much smaller than the apparent powers.
# Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
## 4 Experimental Set Up
### Specimen of Model Test and Artificial AE Measurement Flow  
A used aluminum plate and installed sensors are shown in Figure 5. The aluminum plate is formed square-shaped, which is a size of 1000 mm and a thickness of 5.0 mm. Furthermore, the defect located at 730 mm from the upper of the specimen is a height of 40 mm and width of 500 mm. Furthermore, the notations of the sensors are S1-S12. If an AE is generated in the specimen, the diffraction wave is propagated around the defect. Moreover, a large number of diffraction waves are acquired at S3 and S4 since the defects are located in bottom right. Although huge localization errors are expected to appear in the source localizations considering the homogeneous model, the classified waves are expected to consist of direct propagation, improving the accuracy of localization in the specimen.  
These measurement conditions refer to the previous study [29] in which a PLB test was conducted in an aluminum plate. A flow of artificial AE measurement is illustrated in Figure 6. In the model test, artificial AEs generated by PLBs are acquired by the resonant sensors, wherein the resonant frequency is 55 kHz. Moreover, acquired signals are amplified 40 dB in the preamp and amplified signals are measured with the sampling rate 2 MHz in the used measurement board. It should be noted that the installed sensors, preamps, and measurement board are produced by PAC [30]. In the system produced by PAC, the waveform is acquired when an amplitude exceeds the measurement trigger, and the arrival time used in the source localizations is detected from the waveform. In this study, AR-AIC [18] is conducted in the waveforms measured by the measurement trigger 55 dB. An example of the detection is shown in Figure 7. In the AR-AIC algorithm, the autoregressive model is used to evaluate the start of the signals by the minimum AIC value. In addition, AR-AIC has been applied to a source localization considered homogeneous [6], and it is supposed that the detected arrival times are sufficiently accurate for the source localizations if only the straight propagation waves are used.  
Figure 5: The aluminum plate used as the specimen.  
Figure 6: Artificial AE measurement flow.
# Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty
## 4 Implementation of the Timed-SAS Approach
### Definition on Time Behaviors
#### 4.1.2 Uncertainty Delay Time Constraints  
The uncertainty delay time constraints mainly exist in the Plan process (i.e., \(Delay_{\text{Plan}}\)), the Execute process (i.e., \(Delay_{\text{Execute}}\)), and the application logic, and they come from a variety of sources, including the message processing, the network congestion of distributed systems, the algorithm running, the resource scheduling of large-scale systems, etc.  
Despite the variety of causes, the uncertainty delay time can be represented as probability distribution functions. According to the Timed-SAS approach, the uncertainty delay time constraints are mainly represented as the Uniform Distribution function and the Normal Distribution function, as shown in Figure 6.  
As for the SUIS example in Section 2.1, the uncertainty delay time constraints exist in the following aspects. Firstly, the running time of the self-adaptation strategies in the Plan process obeys the uniform distribution; secondly, the server scheduling time in the Execute process complies with the normal distribution; and finally, the networking delay time in the application logic obeys the uniform distribution. In the SUIS example, the uncertainty delay time constraints are set as follows, \(Delay_{\text{Plan}}\)\(\sim\)_U_ (1, 3), \(Delay_{\text{Execute}}\)\(\sim\)_N_ (5, 0.1) and \(Delay_{\text{App}}\)\(\sim\)_U_ (1, 3).
## 3 Results
### Performance of CW Treating P. Radiata Leachate
Radiata_ **Leachate**} \\ \cline{3-7}  & & & **Planted CW** & **Unplanted CW** & **Planted CW** & **Unplanted CW** \\ \hline \multirow{3}{*}{pH} & - & I & 7.34 \(\pm\) 0.06 & 7.52 \(\pm\) 0.12 & 7.40 \(\pm\) 0.05 & 7.40 \(\pm\) 0.06 \\  & - & II & 7.43 \(\pm\) 0.19 & 7.30 \(\pm\) 0.36 & 7.43 \(\pm\) 0.14 & 7.36 \(\pm\) 0.25 \\  & - & III & 7.05 \(\pm\) 0.51 & 7.04 \(\pm\) 0.54 & 7.32 \(\pm\) 0.34 & 7.37 \(\pm\) 0.46 \\ \hline \multirow{3}{*}{BOD\({}_{3}\)} & \multirow{3}{*}{mg/L} & II & 78.00 \(\pm\) 0.00 & 60.00 \(\pm\) 0.00 & 16.10 \(\pm\) 0.00 & 22.50 \(\pm\) 0.00 \\  & & III & 15.50 \(\pm\) 0.00 & 40.00 \(\pm\) 0.00 & 8.25 \(\pm\) 0.00 & 15.60 \(\pm\) 0.00 \\ \hline \multirow{3}{*}{COD} & \multirow{3}{*}{mg/L} & I & 79.28 \(\pm\) 22.97 & 118.38 \(\pm\) 23.98 & 49.41 \(\pm\) 24.10 & 74.97 \(\pm\) 15.49 \\  & & II & 137.58 \(\pm\) 6.155 & 209.20 \(\pm\) 0.26 & 82.53 \(\pm\) 13.04 & 78.36 \(\pm\) 30.06 \\  & & III & 417.73 \(\pm\) 77.44 & 330.50 \(\pm\) 63.25 & 121.64 \(\pm\) 46.68 & 91.98 \(\pm\) 28.68 \\ \hline \multirow{3}{*}{Aromatic compounds (UV\({}_{250 nm}\))} & \multirow{3}{*}{Abs} & I & 1.49 \(\pm\) 0.14 & 1.66 \(\pm\) 0.19 & 0.26 \(\pm\) 0.05 & 0.31 \(\pm\) 0.06 \\  & & II & 2.74 \(\pm\) 0.82 & 3.67 \(\pm\) 1.47 & 0.32 \(\pm\) 0.14 & 0.43 \(\pm\) 0.15 \\  & & III & 7.80 \(\pm\) 0.37 & 5.74 \(\pm\) 0.67 & 0. Figure 3a shows the COD variations during the CW operation under OLR from 10 to 20 g COD/m\({}^{2}\)/day. In the planted CW, COD removal was 70% at the OLR of 13 g COD/m\({}^{2}\)/day and 65-70% at the OLR of 19 g COD/m\({}^{2}\)/day. Effluent concentrations were 100 and 120 mg/L in the planted and the unplanted CW, respectively. On the other hand, COD removal in the unplanted CW was 80% at the OLR of 13 g COD/m\({}^{2}\)/day and 70% at the OLR of 19 g COD/m\({}^{2}\)/day, with effluent COD concentration of 120 mg/L. Figure 3b shows the total phenolic compounds variations. The planted CW at the OLR of 13 g COD/m\({}^{2}\)/day showed 60% removal and an effluent concentration of 200 mg/L, while at the OLR of 19 g COD/m\({}^{2}\)/day, the removal dropped to 50% with effluent concentration of 1000 mg/L. Moreover, the performance of the unplanted CW at the OLR of 13 g COD/m\({}^{2}\)/day was 70% with effluent concentration up to 550 mg/L and at the OLR of 19 g COD/m\({}^{2}\)/day the removal efficiency was 60% with effluent total phenolic compounds concentration up to 700 mg/L. Figure 2: Removal efficiency of (**a**) aromatic compounds; (**b**) lignin-derived compounds; and (**c**) lignocellulosic acids contained in _P. radiata_ leachate treated in the planted and unplanted CW.
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 6 Experimental Results
### Dataset  
We evaluated our framework on two standard image-classification benchmarks (Figure 3):  
* GTSRB [39] provides 26,640 RGB traffic sign images for training and 12,630 for testing, across 43 classes. Sample images are shown in Figure 3a.
* MNIST [40] contains 60,000 grayscale training images and 10,000 test images of handwritten digits (28 \(\times\) 28 px, 10 classes). Sample images are shown in Figure 3b.  
For each dataset, we first resized all images to 32 \(\times\) 32 px, then split the original training set into 80% training and 20% validation. This split is stratified by class label: the proportion of examples from each digit or sign category is maintained in the training and validation pools. As a result, every class remains well represented in the validation data that each server uses to assess incoming peer models. This balanced, stratified division guards against evaluation bias--where rare classes might otherwise be under or over-sampled--and provides an accurate measure of each model's generalization before it contributes to the global aggregation.
## 4 Discussion
### Numerical Simulation
#### 4.1.4 Combination of Phase Aberration Methods  
For the transition to in-vivo applications of the multi-focus method, the combination of all three PAC methods is necessary, because all the investigated deviations from an ideal flat homogenous plate are present in real cortical bone. Overall, the combination of the three PAC showed a strong improvement of precision and accuracy values for cortical thickness and speed of sound estimations than when compared to the values without PAC.
# An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework
## 3 Proposed Framework  
The framework is shown in Figure 1, where \(\mathbf{I}_{1}\) to \(\mathbf{I}_{k}\) represent the input source image \(1\) to k.  
\(\mathbf{I}_{h1}\) to \(\mathbf{I}_{hk}\) and \(\mathbf{I}_{l1}\) to \(\mathbf{I}_{lk}\) are high-frequency and low-frequency components of image \(1\) to k, respectively. \(\mathbf{z}_{h1}\) to \(\mathbf{z}_{hk}\) are the sparse coefficients of \(\mathbf{I}_{h1}\) to \(\mathbf{I}_{hk}\). The sparse coefficients \(\mathbf{z}_{h1}\) to \(\mathbf{z}_{hk}\) are from \(\mathbf{I}_{h1}\) to \(\mathbf{I}_{hk}\) by sparse coding with a constructed dictionary \(\mathbf{D}\). \(\mathbf{I}_{h}^{\prime}\) and \(\mathbf{I}_{l}^{\prime}\) are the fused high-frequency and low-frequency components respectively. As shown in Figure 1, the framework consists of three steps. In the first step, input source images are smoothed by the Gaussian filter. These Gaussian smoothed source images are recognized as low-frequency components \(\mathbf{I}_{ln}(n\in 1,2...k)\) of input source images. Then the high-frequency components \(\mathbf{I}_{ln}(n\in 1,2...k)\), can be got by subtracting the low-frequency components \(\mathbf{I}_{ln}(n\in 1,2...k)\) of original input source image \(\mathbf{I}_{n}(n\in 1,2...k)\). In the second step, the low-frequency components are fused by an L2-norm based weighted average method. For the high-frequency components, it uses a K-SVD algorithm to build the dictionary. Before the dictionary learning process, an information-entropy based approach is conducted for selecting the informative image blocks. These informative image blocks are used for dictionary learning, that can make the trained dictionary more informative. With the constructed dictionary, high-frequency components are sparse coded to a few sparse coefficients by orthogonal matching pursuit (OMP) algorithm. The sparse coefficients fused by \"Max-L1\" coefficient fusion rule. In the last step, the fused low-frequency and high-frequency components merged to the fused image \(\mathbf{I}^{\prime}\).  
If the high-low image components are not decomposed, \"Max-L1\" used for fusing sparse coefficients may select the wrong coefficients and cause an error in the fused image [24]. The decomposition of high-low image components was used by [PERSON] in image fusion. Source images were decomposed into high-low frequency image components, when he did sparse-representation based image fusion [27].
## 2 Materials and Methods
### Scenario  
Benchmarks for dynamic versions of the job shop scheduling problem can be roughly characterized into the following two categories:  
1. Established academic benchmark instances for _static_ scheduling problems that have been ad hoc \"dynamicized\" (e.g., [PERSON] et al. (2018) or [PERSON] and [PERSON] (2018));
2. Full-blown libraries that essentially have to include the full simulation code and are often intrinsically combined with optimization problems (e.g., [PERSON] et al. (2019) or [PERSON] et al. (2020))  
The former variation appears to be suitable for comparing the performance of different optimizers because at least the original states equate to the static scheduling problem instances that are known to be challenging. To the best of the authors' knowledge, most work focuses on scenarios where processing times are known a priori and arrival rates of new jobs follow very simple distributions and are independent of the simulations' current state. Such a setup is often quite reasonable as it challenges the optimizer and disentangle its performance from the performance of any machine learning models used to predict system properties (setup times, process times, breakdowns). However, this assumes very well-established and verified knowledge about the process that gives rise to the dynamic scheduling problem, which is not always given in industry 4.0 settings. The second variation is often driven by an existing real-world use case and is so complex that the full application code, including proprietary business logic is needed to accurately re-perform the numerical experiments.  
In this work, we opt for a simplified variation of the second approach, allowing us to introduce several different system behaviors that may influence the simulation-modeling-optimization composition. At the same time, we deliberately choose a smaller scenario in order to retain the explainability of the results.  
Figure 2 displays a small production system comprising upstream and downstream processes, a disassembly, an assembly stage, a processing stage and two intermediary buffers for the materials used in the individual steps. Table 1 summarizes the parameters and behavior of the machines, including three disassembly machines with different task times, three intermediary processing machines and two assemblers. Two machines (21 and 32) require setup times under certain conditions, which should be avoided by the optimizer, while the task time of machine 23 depends on specific material properties (\(x_{1,m}\cdot x_{2,m}\) is  
Figure 1: Asynchronous components of the experimental setup.  
presented to the learning model as a precalculated feature to fit the linear model). Both assembly and disassembly actions are predefined in the orders and target random materials in the generated task batch (without repetition). Therefore, neither task type introduces additional decisions that the optimizer has to make. The main impact these actions have is the increased demands on the buffer elements and the precedence constraints implicit with an assembly task (i.e., all sub-materials have to be ready).
## 5 Verification and Application
### Application on Capability Demand Satisfaction Evaluation of SCS
The discrepancies in the indicator values account for the variations in satisfaction levels among scheme 1, scheme 3, and scheme 4. Notably, scheme 3 yields the highest level of satisfaction. However, despite a notable reduction in packet loss ratio compared to scheme 1, its impact on overall improvement is minimal.