esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Adversary Model  
Before discussing the details of the system, we want to introduce the adversary model and attack strategies considered for our system. We assume a powerful adversary, \(\mathbf{A}\), who has managed to find a way to get hold of ImgR, OtaM, or EcuX to modify the code of a software cluster image to control the ECU in the vehicle or even the vehicle itself, perform reverse engineering of the software image, or read the contents of the ECU software to discover confidential or intellectual property information. The adversary also knows the details of the software update algorithm and how the software is organized within an ECU, so it aims to modify one software entity or revert to an old software version. Due to the storage of the keys in secure protected memory, the attacker can only alter the software image itself or part of it but cannot modify any signature values generated by OemR. Our threat model encompasses attacks not only on data integrity and authentication but also on the compatibility of the complete software. Such attacks are mainly categorized into spoofing, splicing, and replay attacks. Spoofing attacks correspond to \(\mathbf{A}\) being able to replace an existing software image with a malicious code. Splicing attacks or mix-and-match attacks correspond to \(\mathbf{A}\) being able to swap software between different ECUs or  
Figure 3: Software updates using a digital signature scheme: The signature generation process is used by the repositories to generate the signature on the software images, and the signature verification process is used by the ECUs in the vehicle to verify the received images.  
vehicles. Replay attacks correspond to \(A\) being able to revert a software image to an older image.  
We assume that adversary \(A\) can obtain direct access to the ECU or the image repository and can repeat the process of modifying the image as many times as it wants until it can create an image that can evade the detection of software update verification. Any compromised applications can directly influence other applications in the ECU as well as other ECUs in the vehicle. Thus, the weakest image repository and ECU that can be attacked by \(A\) determines the security level of all applications running in the ECU and vehicle. Any of the actors having access to the image software can potentially be taken up by malicious actors in the supply chain and are prone to man-in-the middle attacks. On the one hand, adversary \(A\) can intercept and modify traffic between the vehicle and image software repositories or inside the vehicle between the ECUs or directly accessing and compromising an ECU. In contrast, \(A\) can compromise and control the image repository.
## 4 Conclusions  
Considering the results obtained from a multi-scenario evaluation of treatments for organic fraction of municipal solid waste (OFMSW) with aerobic digestion (Scenario 1), anaerobic digestion for renewable electricity generation (Scenario 2), and anaerobic digestion for biomethane injection into natural gas networks (Scenario 3) using Monte Carlo simulation, the following conclusions were drawn:  
1. Monte Carlo simulation is a useful tool for quantitative analysis and decision making in OFMSW management.  
Figure 2: \(NPV_{s}\) density and distribution function. (**a**) Scenario 2, renewable electricity production; (**b**) Scenario 3, biomethane production.  
2. Although the initial costs for Scenarios 2 and 3 are consistently higher than the initial costs reported for Scenario 1, the revenues that rise with an increase in the final quality of the product are higher and they present a social benefit.
3. The lowest average fee that neutralizes the \(NPV_{f}\) is 6.38 \(\upxi\)/tonne treated in Scenario 3, and the highest is estimated in Scenario 2 (22.09 \(\upxi\)/tonne), therefore, Scenario 3 is the most financially viable scenario.
4. The average \(NPV_{s}\) calculated in Scenario 3 (16.60 \(\upxi\)/tonne) is higher than that obtained in Scenario 2 (13.59 \(\upxi\)/tonne), therefore, Scenario 3 is the most socially viable scenario.  
Considering the above, anaerobic digestion versus the traditional aerobic digestion process for composting must be considered as a viable solution for the treatment of OFMSW. Moreover, anaerobic digestion for biomethane injection into natural gas networks could be more interesting than anaerobic digestion for renewable electricity generation in relation to the social viability.  
The authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors appreciate the support of the research group TEP-968 (Technologies for Circular Economy) of the University of Granada.  
The authors declare no conflict of interest.
## 3 Results
### Specific Findings
#### 3.2.1 Driver Emotion and Behavior
[PERSON] [42] emphasized the importance of emotional intelligence in driving across different cultural settings, highlighting the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Driver Emotion(s)** & **Driver Behavior(s)** & **Road Accident Factor** & **Cultural Factor** & **Human Factor** \\ \hline \multirow{3}{*}{[43]} & \multirow{3}{*}{anger} & high speed; short & \multirow{3}{*}{risk-taking driving \(\uparrow\)} & \multirow{3}{*}{SN} & \multirow{3}{*}{—} \\  & & following distance and & & & \\  & & aggressive behavior & & & \\ \hline \multirow{3}{*}{[44]} & negative emotion, & \multirow{3}{*}{aggressive driving} & speeding \(\uparrow\) & \multirow{3}{*}{—} & \multirow{3}{*}{—} \\  & contempt & & & & \\ \hline \multirow{3}{*}{[45]} & negative emotion & \multirow{3}{*}{deceleration increase} & conservative driving & \multirow{3}{*}{SN} & \multirow{3}{*}{—} \\  & negative emotion & & & & \\ \hline \multirow{3}{*}{[18]} & negative emotion & increasing speed & distracted attention & — & — \\ \hline \multirow{3}{*}{[46]} & anger and frustration & \multirow{3}{*}{aggressive driving} & deliberate & \multirow{3}{*}{—} & \multirow{3}{*}{—} \\  & & & & & \\ \hline \multirow{3}{*}{[47]} & — & driving attention & risky driving attitude & SN & EI \\ \hline \multirow{3}{*}{[48]} & negative emotion & \multirow{3}{*}{weaving in and out of lanes} & \multirow{3}{*}{reckless behavior} & \multirow{3}{*}{SN} & \multirow{3}{*}{—} \\  & & driving attention & risky driving & SN & \\ \hline \hline \end{tabular} Note: EI: emotional intelligence; SN: social norm; PC: personal characteristic; \(\downarrow\): decrease; \(\uparrow\): increase.  
\end{table} 
Table 1: _Cont._association between anger, aggressiveness, and risky driving behaviors. Anxiety can reduce driving performance and promote caution, while sadness and depression, especially when accompanied by rumination, can diminish driving performance due to self-focus. A number of studies (\(n=6\), 42.85%) [39; 43; 45; 47; 48; 49] indicate that cultural background, especially social norms, is a significant factor influencing driver behaviors. Although [45], who conducted their study in China, do not explicitly state how culture influences driver behaviors, they suggest that cultural factors impact driver behaviors, and particularly, describe how emotional responses can vary and affect driving safety. One study in Bangkok, Thailand, by [43], posits that culture influences driver behaviors, such as attitudes toward traffic safety and social norms, which might impact driver behaviors. Since the emotional state of drivers has been identified as a factor contributing to driving risks, it is essential to modulate drivers' emotions. At present, some sensors have been developed for intelligent vehicles to identify drivers' emotions through affective computing. Using a human-centered approach to effectively modulate drivers' emotions, driving safety can be improved [50].
## 3 Conceptual Model: Procurement Order Quantities Regulation for Shortage Scenarios
### Target System  
This subsection aims to define the requirements of the model to be built, that is, to identify the system's capabilities. To achieve this, the target parameter system is developed and consists of the following six blocks shown in Figure 2. The first block refers to the final customer demand that must be monitored to evaluate its changes, as well as its implications in the needs of the products on other levels obtained by exploiting the bill of materials. Second, another parameter to control is the stock level for the different types of flows in relation to an industrial organization, so that the value of available material is known. Third, the service level that is being provided to the client must be known in terms of quantity and date. In addition, the fourth block seeks to analyze the procurement costs.  
The fifth block of parameters deals with the supply orders, how many, of what quantity, when, and how long it takes to physically arrive and be ready so that the model always knows at any time the capacities and supply options to subsequently determine which are optimal for the target service level and thus optimize the stock levels and associated costs. Sixth, the block from which the deviations versus the initial schedules are analyzed. In this sense, the objective is to know and trace the supply deviations and their implications over time.  
In addition, the parameter system works by comparing the target and actual values with a predefined frequency; so that, in each period, it is determined whether the evolution is positive or negative, converging or diverging from the objective of the logistics system. If evolution presents explanatory cause-effect relationships, an attempt is made to prevent the causes that originate from the divergences and to promote or enhance the causes or effective measures that enable the system's convergence to the logistical objective. In the absence of explanatory cause-effect relationships, new measures must be analyzed and defined to help determine the behavior and causes of the results obtained.
## 3 Measurement Results  
### Magnetization Curve Measurement Results  
By comparing the magnetic properties of different silicon magnetic materials under different frequencies of sinusoidal magnetization, suggestions regarding different types of magnetic materials to be used under different operating conditions can be given, which provide empirical support for the design of power electronic equipment [19].  
Figure 4 shows the magnetization curves of the 0.10 mm UTGO silicon steel and FBA alloy. It can be seen from the figure that the magnetization curve of the 0.10 mm UTGO silicon steel tends to become saturated when the magnetic induction intensity is 1.6 T, and the magnetic field intensity is about 80 A/m. The magnetization curve of the FBA alloy tends to become saturated when the magnetic field intensity is about 28.5 A/m, and the magnetic induction intensity is 1.4 T. Thus, the 0.10 mm UTGO silicon steel has a high-saturation magnetic induction strength and a high permeability, whereas the FBA alloy has a low-saturation magnetic induction strength and a low permeability. With the increase in frequency, the permeability of the 0.10 mm UTGO silicon steel and FBA alloy decreases.
## 3 The Proposed Method  
### Architecture Overview  
Our proposed architecture for light-field occlusion removal integrates CSPDarknet53 with the BiFPN and the HINet blocks, as detailed in Figure 2. The system comprises three key components: multi-scale feature extraction, multi-scale feature fusion, and image refinement sub-network. Reliable and multi-scale feature extraction is provided by the backbone, CSPDarknet53. This is supplemented by BiFPN, which improves feature integration via sophisticated multi-scale fusion and a swish attention mechanism. To further refine the occluded areas, the HINet block is utilized, focusing on both local details and global structures. The network's efficiency is maintained through separable convolutional blocks, ensuring high performance without compromising resources. We show significant improvements over current approaches, offering efficient occlusion removal across various scenarios. Because of this multi-perspective method, our network handles occlusions well and provides a reliable solution for accurate occlusion removal. The complete process can be summarized as  
\[I_{\text{out}}=\text{tanh}(\text{HINet}(\text{Conv}_{3\times 3}(\text{ BiFPN}(\text{CSPDarknet53}(L_{0}))_{X6}))) \tag{1}\]
# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
## 2 Materials and Methods
### Normalization  
As various parameters vary greatly in their maximum and minimum values, normalization is applied so that parameters can be compared within a certain size range. P-Norm is applied in this research, which is defined by MATHWORKS (2017) as Equations (23) and (24):  
\[\|v\|_{p}=\left[\sum_{l=1}^{N}\lvert v_{l}\rvert^{q}\right]^{1/q} \tag{23}\]  
\[v_{n}=\frac{v_{l}}{\|v\|_{p}} \tag{24}\]  
where \(\|v\|_{p}\) is P-Norm; \(v_{l}\) is the vector before normalization; \(l\) is the serial number of \(v_{l}\); \(N\) is the total number of vectors; \(q\) can be any positive real value, which is selected to be 2 here namely 2-Norm because it is the standard norm and most widely applied in P-Norm; \(v_{n}\) is the normalized vector.
## 3 Results and Discussion
### Microbiological Analyses  
The microbiological loads of the samples collected during the cheese-making trials are reported in Table 2. The microbiological analyses of GPP did not evidence the presence of any of the microbial groups object of investigation. The absence of microorganisms in GPP is undoubtedly due to the oven-drying stabilization [34]. Raw ewes' milk hosted levels of TMM of 6.51 Log CFU/mL, higher than the limit (<10\({}^{5}\) CFU/mL) established in Europe for  
Figure 1: Acidification kinetics of cards. Full symbols: Control cards. Empty symbols: Experimental cards. Results indicate mean values \(\pm\) SD of four determinations (carried out in duplicate for two independent productions). In comparison with the corresponding result of grape pomace powder (GPP)-enriched curd, values are significant with *** \(p\) < 0.0001. Abbreviations: CC36, CC94, CC169, and CC190, control curd with _L. lactis_ MISE36, MISE94, MISE169, and MISE190, respectively; EC36, EC94, EC169, and EC190, experimental curd with 1% of GPP and _L. lactis_ MISE36, MISE94, MISE169, and MISE190, respectively. Bars represent standard deviation of the mean.  
Figure 2: Stretching step of the acidified curds. (**a**) Control cheese production; (**b**) GPP-enriched cheese production.  
raw ewes' milk for cheese production, with mesophilic rod and coccus LAB at \(10^{6}\) CFU/mL and thermophilic rod and coccus LAB at \(10^{4}\) CFU/mL. The high levels probably depend on the conservation conditions [35]. After pasteurization, TMM, thermophilic rod, and coccus LAB were recorded at \(10^{3}\) CFU/mL, while mesophilic rod and coccus LAB were lower than \(10^{2}\) CFU/mL, showing the ability of the indigenous thermoduric LAB to survive during the thermal treatment [36]. However, the effect of pasteurization depends on the initial cell densities of microorganisms in the raw milk [37] and the temperatures applied [38]. According to [PERSON]'s test, statistically significant differences were found only for the levels of TMM in curd samples. The levels of TMM were almost superimposable to those of mesophilic coccus LAB in all samples analyzed. After inoculation with each NMSC, all mills showed approximately 7 Log cycles of TMM and almost the same levels of mesophilic coccus LAB, confirming that the inoculums occurred at \(10^{7}\) CFU/mL and the starter LAB cocci dominated the microbial community of cheees. After coagulation, an increase of about 1 Log cycle was registered for the LAB levels in all control and experimental cards as a consequence of whey draining [39]. The levels of TMM and mesophilic coccus LAB reached values of about 9 log CFU/g in all acidified curds and these levels remained almost constant in control and experimental cheees soon after production as well as after 15 d of refrigerated storage as previously observed by [PERSON] et al. [40,41] during the production of Vastededa della valle del Belice PDO cheees. The results highlighted that the addition of 1% (\(w/w\)) of GPP did not influence the fermentation process carried out by the four _L. lactis_ (Mise36, Mise94, Mise169, and Mise190) strains used individually.
## 5 Discussion  
In traditional robust model estimation algorithms, each point is treated with equal importance. This approach often results in noisy points being included in the model estimation, thereby reducing its accuracy. In deep learning-based methods, many researchers typically use the distance from data points to the model (epipolar distance in our paper) as the direct optimization objective. However, through experiments, we observe that the epipolar distance is not the sole factor affecting model accuracy. To address the shortcomings of the two methods, we propose a dual-branch neural network that simultaneously focuses on both the confidence information and spatial location information of the data points. Experiments show that our approach achieves optimal performance in most scenarios. Additionally, we conduct ablation experiments to evaluate the performance improvement contributed by each module. We find that all of our modules complement each other, with the MP module effectively solving the randomness issue caused by farthest point sampling and clustering operations. Furthermore, we experiment with the number of layers in the MP module and find that \(L=4\) is optimal. Too many layers may lead to insufficient sampling at each layer, while too few layers may expose the model to random interference, affecting the experimental results. Although our work achieves relatively good performance, in order to avoid the spatial randomness introduced by farthest point sampling and clustering operations, we introduce a multi-layer parallel neural network to eliminate this randomness. This, however, significantly increases the number of parameters in the network and sacrifices efficiency in terms of the training and inference time.
## 4 Results and Discussion
### Comparison with Contemporary Methods
Using RML 2016.10A, an edge intelligence algorithm of a convolutional neural network (CNN) based on an attention mechanism to carry out modulation recognition (MR) was developed, in which a similar misclassification in QAM modulations was reported [60]. They suggested that the misclassification of QAM modulations was due to a lack of information and similar symbolic structures when short-term observations are made in the presence of a large amount of noise. Likewise, in [61], RML 2016.10A was utilized to propose a framework that integrated one-dimensional (1D) convolution, two-dimensional (2D) convolution, and long-short-term-memory (LSTM) layers for feature extraction. The results presented in their study also indicated a similar misclassification of QAM modulations. Several other works that utilized the said dataset, including [51; 62; 63; 64], have reported a similar misclassification of QAM modulations. The similar problem reflected in several of the recent works suggests that the misclassification must have been caused by the ineffective curation of the QAM dataset. However, we ought to improve the classification performance of the proposed model by introducing a fusion of various feature extraction techniques, including tSNE, PCA, etc., along with the convolutional features for various communication datasets in the future. Considering the superior performance of the proposed model for other modulation schemes, it is anticipated that the model would exhibit adequate performance to classify higher-order QAMs.
# Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
## 2 Bus-Based Energy Management System  
### Structure of the System  
An energy management system is considered, as shown in Figure 1. This system consists of an energy management controller (Jianghuai Auto., Hefei, China), an intelligent battery sensor (IBS, Hella, Lippstadt, Germany), and an intelligent alternator (Bosch, Stuttgart, Germany), which communicate with each other through the LIN bus. The body controller module (BCM), engine management system (EMS), and other control units, such as the controller for the air conditioning, are located in the controller area network (CAN). Various control signals between the LIN and CAN buses are transferred by the gateway BCM.  
The intelligent alternator is the main power source, and the battery is the auxiliary power source, in vehicles. The IBS collects the voltage and current, and calculates the state of charge (SoC) of the battery in real-time. The BCM sends the on-off state of electrical loads, while the EMS provides the vehicle and engine speed, and engine torque. When the energy management controller receives the aforementioned information, it will optimize the distribution of the alternator output power and battery output power to maintain the best fuel consumption.  
Figure 1: Structure of energy management system.
## 3 Results and Discussion  
### Number and Mass Concentration  
Figure 4 is a graph showing the ratio of the total number of particles by averaging measurement results of residual particles in the epitaxial growth chamber after the epitaxial process. First, looking at the number concentration measurement results in (a), about 86% of particles were measured in the first stage and 11% of particles were measured in the second stage. The total amount of particles measured in these two stages was 97%, confirming that most particles had a size of less than 20 nm. Looking at the mass concentration measurement results in (b), the percentage of detected particles was found to be 79% at the 15 th stage and 14% at the 14 th stage. Thus, a total of 93% of particles were detected at these two stages. Contrary to the number concentration measurement results, the number of detected particles was mainly measured at top stages, where micrometer-sized particles were mainly collected. The reason is that the cascade impactor uses an aerodynamic diameter, which detects a virtual spherical particle having the same sedimentation rate and unit density when separating each particle size and the volume of the sphere has a cubic value of the radius. Thus, the difference according to particle size significantly changed. This is a natural phenomenon. It is a limitation of mass-based particle measurement techniques. Therefore, for the accurate analysis of nano-sized particles, techniques for measuring number concentration are required.  
Figure 5 shows the particle number concentration graph by time. The y-axis is the total number concentration of all stages divided by the initial total concentration. The valleys that occur regularly in the graph correspond to the purge interval. It was confirmed that the number of measured particles gradually decreased with the passage of time for about 22 min, during which the measurement was possible. At the end of the measurement, the particle number concentration decreased to 36.9% of the initial measurement value. The measurement results confirmed that the number of residual particles was reduced well by the purging operation in the chamber. The removal from the chamber of particles that can directly adversely affect subsequent process results is essential. In order to confirm that the particle amount was reduced to an appropriate amount and to find an appropriate purge time for cost and time efficiency, such real-time monitoring technique was required. When using an EPLI to measure particle concentration during the purging of the chamber after the process and to confirm that the particle number falls to a certain level before proceeding to the next process, it is important to prevent the effect that residual particles may have on the process.