esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimization Strategy to Position CHIEF Points in Boundary-Element Acoustic Problems
## 3 Numerical Experiments
### Case 1--Radiating Circular Cavity
Importantly, the optimization process requires estimation of derivatives with respect to each variable at each iterative step until optimality is reached, so its computational effort is heavily dependent on the number of variables involved. Indeed, since only the \(x\) and \(y\) position of the additional CHIEF point are involved at each iteration of ALG2, the required computations are significantly reduced, and the algorithm reaches the optimal position of each additional point much quicker. Figure 8: Results computed using the proposed algorithms: (**a**,**c**) show the relative error and smallest singular value and (**b**,**d**) show the number of CHIEF points used at each frequency. (**a**,**b**) refer to algorithm ALG1, and (**c**,**d**) to algorithm ALG2. Figure 9 illustrates results from both algorithms when different target values are set for the smallest SV, namely 0.05, 0.10, 0.20 and 0.30, considering a maximum of 10 CHIEF points, in terms of relative errors (Figure 9a1,b1) and of the smallest SV (Figure 9a2,b2). It is clear from these plots that limiting the value of the smallest SV has the effect of better controlling the registered error peaks, which tend to decrease when this limit is set to higher values. This occurs for both algorithms and gives a good indication about the interest of this approach in tackling the non-uniqueness problem occurring when the BEM is used for external problems. A sample point distribution for both algorithms and for a frequency of 470 Hz is shown in Figure 10, with six points being used in ALG1 to attain min(SV) \(>\) 0.3 and 10 points being required in ALG2 for the same purpose. Notably, in the case of ALG1, the locations of all point positions are optimized simultaneously, and so a more efficient control of the smallest SV is attained with far fewer points although at the expense of a higher computational cost than for ALG2. Figure 9: Relative error (**a1,b1**) and smallest singular value (**a2,b2**) computed for the two algorithms. ALG1 (**a1,a2**) and ALG2 (**b1,b2**), considering different acceptable values for min(SV) of 0.05, 0.1, 0.2, and 0.3. The same color pattern is used for all figures.
## 5 Conclusions  
This paper has presented vector control implementation on an IM for EVs operating at 40, 60, and 80 km/h. The reference speed was achieved in different driving scenarios within the minimum time intervals. The FM-PI controller-based EV model's speed control performance and error percentage are less than the existing state-of-the-art loaded models, which validates its applicability. The prediction of output speed using multiple regression for the input parameters of voltage, phase current, and torque showed 98% accuracy. The efficient prediction of speed from voltage, current, and torque, in turn, reduces the losses from the Li-ion DC batteries used in EVs. The simulation results illustrate the finding that the FM-PI controller's speed response was more effective and faster than the FOPID, KW-WOA-PID, and SVR-PI speed controllers. Therefore, the controller is reliable for achieving optimum control along with efficient handling of the machine. In the future, advanced machine learning techniques can be used to perform predictive analysis and reduce the response time to reach the desired speed.  
Conceptualization, H.S. and K.I.; methodology, H.S., K.I. and M.U.C.; software, H.S., V.B. and A.H.; validation, H.S., M.M.I. and A.F.; formal analysis, H.S., V.P. and M.J.; investigation, A.F., M.M.I. and V.B.; resources, K.I., M.U.C. and M.M.I.; data curation, K.I. and A.H.; writing--original draft preparation, H.S., M.U.C. and M.M.I.; writing--review and editing, K.I., V.B. and H.S.; visualization, A.F. and V.F.; supervision, K.I., M.U.C. and V.B.; project administration, M.U.C.; funding acquisition, V.B, V.P. and M.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by RF state assignment No. 075-03-2021-019/5 (Development of a set of technological solutions for creating a universal domestic platform for an electric vehicle with a high energy density battery, an energy-efficient drive with high torque and electric energy regeneration function).  
The authors declare no conflict of interest.
## 4 Types of FL
### Federated Transfer Learning  
Federated transfer learning (FTL) combines transfer learning and federated learning to adapt models trained in one context to a related task efficiently. It addresses differences in data distributions, advancing beyond traditional models that handle limited data [61; 62]. FTL leverages transfer learning to apply knowledge across domains and federated learning to fine-tune models on diverse datasets without direct data sharing. This approach enhances the privacy and reduces the computational demands, marking a significant shift from traditional methods that centralize data or operate within fixed boundaries [63].  
To clarify the potential of FTL, consider the domain of medical diagnostics, an area where data sensitivity and specificity are paramount. Let us assume a pre-existing machine learning model that is adept in diagnosing skin diseases based on a European demographic dataset. Historically, to adapt this model to an Asian demographic, a complete retraining process would ensue, often necessitating the transfer of high-volume, sensitive data across locations. However, with FTL, the model can be efficiently recalibrated using data from Asian hospitals, without the actual datasets ever leaving their respective institutions. This not only upholds data privacy but also capitalizes on the foundational knowledge embedded within the initial model, as shown in [64]. As with any advanced methodology, federated transfer learning (FTL) faces its own set of challenges.  
A major issue in federated transfer learning (FTL) is negative transfer, where knowledge from the source domain harms the model's performance in the target domain. Thisrequires careful validation and refinement to ensure that the transferred knowledge is beneficial. Traditional machine learning models often struggle with tasks outside their specific training data, showing poor performance with minor changes in the data distribution or objectives. FTL, however, offers dynamic adaptability, allowing models to use previous knowledge and adjust to new contexts without sharing raw data. This makes FTL more resilient and versatile compared to traditional models.
## 3 Results and Discussion
### Effect of Chloride Ions with Time  
Figure 9 shows the variation of determinant coefficient as a result of the linear regression analysis of the electrical resistivity ratio vs. the chloride concentration over time. Though the accuracy was not high, the correlation between the chloride concentration and the ratio of electrical resistivity ratio exceeded 0.70, which means the obvious effect of chloride ion on electrical resistivity. At the early stage of concrete curing, the determinant coefficient was low and then increased over time; however, the effect with the _w/c_ ratio was not clear. A low determinant coefficient seemed to be attributable to the irregular variation in electrical resistivity of concrete with chloride concentration when the hydration of the concrete was not activated in earnest during the early time.  
Figure 10 shows the incline of the linear regression analysis equation of the electrical resistivity ratio and chloride concentration with the concrete without chloride concentration. The gradient of the linear regression analysis equation was low during the early time; however, it was constant without significant variation from 50 days onwards. That is, a decrease in the electrical resistivity depending on chloride concentration was rather low till 50 days and then continued to rise. After 50 days, the trend could not be examined. The electrical resistivity of concrete vs. chloride concentration was insignificant after 50 days and this was attributable to two reasons. First, the C-S-H phase and AFm phase, which can react with water-soluble chloride to form bound chloride, are not well developed [26]. At the early stage, chloride adsorption is not active because of immature development of the two hydrates. In addition, it is known that chloride can reduce the electrical resistivity of water. Secondly, chloride can be a cause of the change of the micro-structural characteristics of cementitious materials. In general, sodium chloride increases the pH of the pore solution in concrete and increase in alkali content, which causes a heterogeneous C-S-H phase [27]. Therefore, chloride leads to capillary porosity with a large pore space and this can be a cause of the low electrical resistivity of concrete. The first reason of chloride ion absorption and the second reason of micro-structure on the electrical resistivity of concrete are not consistent. The micro-structure of hardened cementitious material has a greater influence on electrical resistivity than chloride absorption; however, consider the low gradient at the early stage and its increase with time.  
Figure 9: Coefficient of determination in the linear regression deriving from the resistivity ratio vs. chloride concentration.
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
## 1 Introduction
### Related Works
Compared with these papers, the novelty of this paper lies in the use of transmitted information control instead of chaotic sequence control of the frequency-hopping patterns, which on the one hand enhances the unpredictability of frequency-hopping patterns and further improves the security of information transmission; on the other hand, it increases the dimension of the transmitted information and enhances the transmission capacity of the anti-jamming system.
## 3 Results  
To assess the performance of our proposed FPGA-based image classification model, in this Section, we have provided experimental results and analyzed them from a critical parameters perspective. Table 1 summarizes the measured results of FPGA and CPU-based implementation of models. The measured results highlight the potential of FPGA-based acceleration for deploying CNNs in real-time, resource-constrained applications. Through the implementation of VGG16 and VGG19 models on FPGA for CIFAR-10 classification, significant improvements were observed in inference latency, model size, and resource efficiency compared to CPU-based counterparts, without substantial loss in accuracy. These results underscore the efficiency of FPGA-specific optimizations such as quantization and pruning, enabling scalable DL deployments on edge devices. The observed accuracy levels, paired with drastic reductions in computational demand, establish FPGA as a viable solution for real-time AI tasks in domains like IoT, autonomous systems, and mobile devices.  
Quantization, which reduces model precision to 8-bit integers, significantly lowers resource utilization and inference latency by reducing the computational complexity and memory footprint of the models. However, this comes at the cost of minor accuracy degradation, as observed in the slight difference between Top-1 accuracies of VGG16 on FPGA (89.54%) and CPU (89.69%). Similarly, compression techniques reduce the size of the model files--VGG16's file size on FPGA is 60.1 MB compared to 180 MB on CPU--while maintaining competitive accuracy. These results highlight the trade-offs between accuracy and resource efficiency, emphasizing that the optimizations effectively enhance performance without significant compromise on precision.
## 3 Results  
### Statistical Evaluation of CTH and CWP Retrievals  
It is essential to evaluate the accuracy of the CTH and CWP retrievals derived from AHI infrared observations before the estimate of CBH. An example where clouds of various types is first presented in Figure 2. The first row in Figure 2 shows the retrieved AHI CTH and the corresponding MODIS CTH, while the second row presents the retrieved AHI CWPand the corresponding MODIS CWP. The results illustrate that the CNN-based AHI CTH and CWP retrievals have similar spatial distribution with the MODIS products. Although the spatial resolution of AHI (i.e., 5 km) is lower than that of MODIS (i.e., 1 km), our results still capture the high- and low-value regions of CTH and CWP well.  
Moreover, we conduct a statistical evaluation using an independent data set consisting of the AHI-MODIS matchups from 2017. As illustrated in Figure 3a, the CTH retrievals derived from AHI infrared observations agree well with those derived from the MODIS MOD06 products. The correlation coefficient (R), mean deviation (BIAS), and root mean square error (RMSE) are 0.93, \(-\)0.10 km, and 1.92 km, respectively. Also, Figure 3c,d show that the CWP retrievals derived from AHI infrared observations are in good agreement with the MODIS data, with a BIAS of 0.003 kg/m\({}^{2}\), an RMSE of 0.16 kg/m\({}^{2}\), and an R of 0.79. Due to the limited penetration ability of infrared radiance, the AHI CWP retrievals tend to be slightly underestimated for clouds with CWP larger than 1 kg/m\({}^{2}\). However, according to Figure S2, which presents the probability density functions of the retrieved AHI CTH and CWP with MODIS cloud products, clouds with CWP larger than 1 kg/m\({}^{2}\) only account for less than 1% of the total samples. In addition, to evaluate the retrieval performance across different geopotential regions, we present the bias distribution for the retrieved CTH and CWP across various latitude bands in Figures S3 and S4. The results reveal a consistently negligible bias centered near zero for both CTH and CWP across all examined latitude zones. Consequently, the CNN-based model is capable of effectively retrieving CTH and CWP from AHI infrared observations in the majority of cases, which is crucial for the estimation of CBH.  
Figure 2: Comparison of the retrieved AHI CTH and CWP with Terra MODIS Collection 6.1 MOD06 cloud products on 12 October 2017, at 01:00 UTC. Panels (**a**,**c**) represent the CNN-based CTH and CWP retrievals, while panels (**b**,**d**) correspond to the MODIS-derived CTH and CWP labels.
# Sustainable Additives for the Production of Hydrogen via Sodium Borohydride Hydrolysis
## 2 Materials and Methods
### Hydolysis Test  
Hydrogen gas can be generated through the hydrolysis of NaBH\({}_{4}\) in the presence of a catalyst at moderate temperatures, as described by Equation (1). To increase the yield of H\({}_{2}\), an excess of water (denoted as x) is required, which helps to prevent the formation of intermediate boranes that can decrease the yield in hydrogen production. In this study, a value of x = 15 was chosen, as this provides sufficient water excess to achieve the complete hydrolysis of NaBH\({}_{4}\) while avoiding excessive dilution of the reaction mixture. Using lower values of x can result in incomplete hydrolysis of NaBH\({}_{4}\), leading to lower yields of H\({}_{2}\) and the formation of byproducts such as intermediate boranes. Additionally, inadequate alkalinity may reduce the reaction rate, leading to longer reaction times. Conversely, choosing a value of x higher than 15 can lead to excessive dilution of the reaction mixture, reducing the efficiency of the hydrolysis reaction, and resulting in lower H\({}_{2}\) yields and longer reaction times.  
\[NaBH_{4}+(2+x)\ H_{2}O\to NaBO_{2}\cdot xH_{2}O+4\ H_{2} \tag{1}\]  
Aqueous solutions of sodium borohydride (Sigma Aldrich, powder \(\geq\) 98.0%) at 10 wt% were prepared to perform the experiments. Moreover, sodium hydroxide (NaOH) (PanReac, Barcelona, Spain, pellets, 98.0%) at 7 wt% was added to ensure the reaction stability. The solutions were prepared by first mixing NaBH\({}_{4}\) and NaOH in deionized water and afterward they were fed to the reactor. Once both components were thoroughly mixed, the additives were added. All experiments were conducted at room temperature (26 \(\pm\) 1 \({}^{\circ}\)C) in a stainless-steel batch reactor with a free internal volume of 229 cm\({}^{3}\). Once in the reactor, the tests were performed without stirring and in duplicate, under the same conditions, to study the reproducibility of the process [26,34].
## 3 Results and Discussion
### Microstructure Evolution
As the CBD temperature increases, the thickness of the coating increases linearly and exhibits the highest growth rate at the temperatures above 70 \({}^{\circ}\)C. The CS particles are formed at 40 \({}^{\circ}\)C and grow rapidly with increasing temperature in 40-70 \({}^{\circ}\)C. The reason for this change can be attributed to the following two points: First, as the temperature increases, the ions in the sulfide solution are more active, resulting in an increase in reaction efficiency; and second, because of an increase in temperature, the water in the bath begins to evaporate and the concentration of the solution increases, which is more favorable for the reaction. Figure 5 illustrates the microstructure evolution of the FeS coating with increasing temperature. At low temperatures, a very thin coating with many microcracks is formed on the surface of the substrate (Figure 5a,A). As the temperature increases, the number of the microcracks increases greatly. Moreover, the CS particles begin to nucleate and grow up on the coating surface (Figure 5b). As the temperature increases further, the coating is completely covered by CS particles (Figure 5c). At high temperature, the CS particles disappear and are replaced by disordered thick petals (Figure 5d). However, since the moisture in the bath solution evaporates rapidly at a high temperature, the coating peels off in a partial region. Figure 4: Coating thickness and chrysanthemum-shaped (CS) particle size plotted as a function of temperature. Figure 5: Schematic diagrams illustrating microstructure evolution during heating: (**a,A**) 30 \({}^{\circ}\)C; (**b,B**) 40 \({}^{\circ}\)C; (**c,C**) 50–70 \({}^{\circ}\)C; (**d,D**) 90 \({}^{\circ}\)C.
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Baseline** & **1 Month** & \(p\) \\ \hline OSDI overall, abnormal, \(n\) (\%) & 39 (76.5) & 35 (68.6) & 0.206 [1] \\ OSDI overall, mean (SD) & 29.45 (20.36) & 27.94 (2.79) & 0.651 [1] \\ Ocular discomfort, mean (SD) & 19.51 (14.64) & 32.52 (21.60) & \textless{}0.001 [2] \\ Visual function, mean (SD) & 47.35 (30.94) & 14.19 (16.01) & \textless{}0.001 [2] \\ Environmental triggers, mean (SD) & 30.56 (29.80) & 30.06 (29.35) & 0.770 [2] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Change in OSDI after FLACS. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **MMP-9 (+)** & **MMP-9 (-)** & \multirow{2}{*}{\(p\)} \\  & **(_n = 46_)** & **(_n = 5_)** & \\ \hline MMP-9 & & & \\ Abnormal, \(n\) (\%) & & & \\
1 day & 46 (100.0) & 5 (100.0) & \\
1 week & 42 (91.3) & 4 (80.0) & 0.416 3 \\
1 month & 27 (58.7) & 2 (40.0) & 0.641 3 \\ Average, mean (SD) & & & \\ Baseline & 2.15 (0.76) & 0.00 (0.00) & \textless{}0.001 2 \\
1 day & 3.63 (0.61) & 2.80 (1.30) & 0.164 2 \\
1 week & 1.80 (1.15) & 1.00 (0.71) & 0.154 2 \\
1 month & 0.85 (0.82) & 0.60 (0.89) & 0.548 2 \\ Osmolarity & & & \\ Abnormal, \(n\) (\%) & & & \\ Baseline & 24 (52.2) & 3 (60.0) & \textgreater{}0.999 3 \\
1 day & 29 (63.0) & 2 (40.0) & \textgreater{}0.369 3 \\
1 week & 18 (39.1) & 2 (40.0) & \textgreater{}0.999 3 \\
1 month & 21 (45.7) & 42(40.0) & \textgreater{}0.999 3 \\ Average, mean (SD) & & & \\ Baseline & 310.35 (16.53) & 316.20 (17.30) & 0.458 1 \\
1 day & 310.11 (15.54) & 306.40 (20.98) & 0.626 1 \\
1 week & 303.48 (15.95) & 306.60 (17.90) & 0.724 2 \\
1 month & 305.30 (13.42) & 299.00 (27.07) & 0.633 1 \\ OSDI & & & \\ Abnormal, \(n\) (\%) & & & \\ Baseline & 34 (73.9) & 5 (100.0) & 0.323 3 \\
1 month & 30 (65.2) & 5 (100.0) & 0.167 3 \\ Average, mean (SD) & & & \\ Baseline & 27.96 (20.66) & 41.11 (13.58) & 0.091 2 \\
1 month & 27.04 (20.25) & 36.20 (16.23) & 0.218 2 \\ Ocular discomfort, mean (SD) & & & \\ Baseline & 18.70 (14.81) & 27.00 (11.51) & 0.164 2 \\
1 month & 32.16 (22.56) & 35.83 (9.59) & 0.395 2 \\ Visual function, mean (SD) & & & \\ Baseline & 44.74 (32.24) & 62.08 (12.27) & 0.096 2 \\
1 month & 13.78 (16.73) & 15.00 (17.13) & 0.264 2 \\ Environmental triggers, mean (SD) & & & \\ Baseline & 27.56 (30.48) & 38.33 (23.27) & 0.278 2 \\
1 month & 27.36 (28.01) & 48.33 (37.91) & 0.159 2 \\ \hline \hline \end{tabular} 1
[FOOTNOTE:1]Footnote 1: Independent _t_-test, 2
[FOOTNOTE:2]Footnote 2: Mann-Whitney test, 3  
\end{table} 
Table 5: Subgroup analysis according to the preoperative MMP-9 result.
# Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis
## 2 Data and ANN Model  
In data processing, Vis changes related to meteorological parameters are studied first (Section 1), and then relationships are set up using ANN. After finding out which meteorological factors affect Vis, the factors are elected to be used in the training of ANN to infer Vis. During this training, we eliminated insignificant meteorological parameters by making its weight influencing Vis small. Note that Vis here represents not only that for fog and precipitation, but also that for aerosols. Then, the ANN model is built on the selected factors, and the ANN model is trained with measured Vis and met parameters data, as well as with AOD. During analysis, Vis representing fog and precipitation was inferred using reanalysis gridded data from ECMWF and AOD from the MODIS (Moderate Resolution Imaging Spectrometer) satellite. Vis for both precipitation and fog was inferred using only reanalysis gridded data from ECMWF if satellite-based AOD was unavailable when the clouds were thick enough above the boundary layer. In the final step, inferred Vis was then corrected using measurements. The technical flowchart to infer gridded Vis is given in Figure 1.
## Appendix B Contiki-NG Modifications in Detail
Moreover, the neighbor cache is not maintained through NUD. Instead of building its candidate neighbor set (Figure 1) from the neighbor cache, RPL-Classic considers all rpl_parents members, regardless of reachability. Following our modifications, the rpl_parents members forming the parent set are marked eligible, but that is not governed by a reachability indication. RPL-Classic _does_ select the best eligible parent (partially) based on link freshness, but freshness is not an expression of how recently we have had a successful exchange with a neighbor. More specifically, since freshness tracking was shifted to interface list entries, freshness expresses how recently we have tried to transmit over a physical link. To keep all physical links fresh, RPL-Classic periodically selects a probing target and unicasts a DIO or DIS to it (over all its known interfaces). Finally, we updated the probing target selection and the parent selection algorithm to factor in interface freshness when deciding which neighbor to probe/select as the preferred parent. Note that Contiki-NG freshness probing is just an example of how to keep inferred metrics updated in the absence of IPv6 ND (see Section 4.1 and Appendix A).