esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Interfering Mechanism and Major RFI Sources
### Terrestrial RFI Sources  
Most of the RFI sources are associated with human activity over land. Terrestrial commercial or industrial radio devices are considered as major RFI sources for SAR, including but not limited to radiolocation radars, wind profiler, telecommunication devices, television networks, amateur radios, etc. Figure 6a illustrates possible terrestrial radio emitters. In recent years, many terrestrial RFI cases have been observed in airborne SAR systems such as AIRSAR [18; 19], E-SAR [20], UAVSAR [5], and space-borne SAR systems like PALSAR [21; 22] and Sentinel-1 [23; 24; 25].  
Figure 6b illustrates the interfering mechanism between the terrestrial RFI and the SAR system. The detailed derivation for estimating the received RFI power could be found in [26]. Because the terrestrial interference only undergoes a single-way propagation, the received strong RFI signal will  
Figure 5: Illustration of allocated radio services in the vicinity of (a) P-band: 432–438 MHz and (b) L-band: 1215–1300 MHz.  
Figure 6: Illustration of the terrestrial RFI case. (a) Possible terrestrial radio emitters. (b) Interfering geometry of terrestrial RFI. (c) SAR image contaminated by RFI and (d) without presence of RFI.  
raise the noise floor and degrade the SINR significantly. Figure 6c,d provide a comparison of the SAR image with and without the contamination of RFI. This is an airborne SAR dataset acquired in Xi'an, China, with a resolution of 1 m \(\times\) 1 m. This illuminated scene is a suburban area consisting of apartment buildings and bare lands. The vertical and horizontal directions denote the range and azimuth, respectively. It is shown that the linear patterns with strong energy are very obvious, in which some strong scatterers are also buried under the artifacts. This really hinders further image interpretation process like target detection, land cover classification, etc.
## 6 Conclusions
### Result Overview
Although the voting classifier stood out as the best-performing model in several scenarios, the other models are generally preferred in cases where they achieve a similar performance level. By applying SHAP to these models, we were able to clarify the decision process and justify the final classification. In contrast, the voting classifier lacked clear reasoning, making its decisions challenging to explain. In the best-performing scenarios, for both the selected offer and the outcome prediction, we presented a pruned decision tree, which visualized the decision process (Figures 6 and 10), making the model classification interpretable. These results have shown that the selected offer depended more on the contestant's profile rather than their cash builder result or particular offers. In contrast, the game's outcome was more dependent on the game-specific features, like the number of chasers, the cash builder result, and the amount of time the chasers received in the second phase of the game, rather than the contestant's profile. These results can help both the show's producers and the contestants. The show's producers can use the information about a contestant for personalized offer tailoring, making the show more suspenseful. The contestants can benefit from this research by predicting their performance in the second phase of the game, which would help them in selecting the optimal offer. Although this study focuses exclusively on the Croatian version of Beat the Chasers, the question of generalizability to other versions of the show, or even other TV quiz shows, arises naturally. We tend to be of the opinion that cultural and socioeconomic factors may influence contestants' behaviors. For instance, one might hypothesize that contestantsfrom less affluent regions are more motivated by potential financial gain, while those from wealthier areas may primarily participate for fun or experience. However, such interpretations are purely speculative and cannot be confirmed or refuted within the scope of this study. In this study, we do not specifically address cultural factors, and although we acknowledge that contestants' behaviors may, indeed, be culturally specific, our current data and methodology do not allow for definitive conclusions. The features and methods used in our models are appropriate for the Croatian context but may not transfer directly to other countries without model retraining and feature reselection, as different cultural or structural dynamics could render other variables as more relevant.
## 3 Field-Circuit Coupling Analysis Method
### Nonlinear Modeling of Arresters  
According to the insulation coordination design of the MMC-HVDC transmission system, it is necessary to install surge arresters in the converter stations to suppress over-voltage levels. In the offshore wind power MMC-HVDC transmission system shown in Figure 1, zinc oxide surge arresters were installed on the valve side of the bridge arm reactors and the valve side of the transformers.  
The characteristics of the metal oxide varistor (MOV) model for the surge arresters are defined as follows:  
\[\begin{cases}U={\rm C}I^{\alpha}\\ r(i)=\frac{dU}{dI}={\rm Ca}I^{\alpha-1}\end{cases} \tag{8}\]  
In Equation (8), \(U\) and \(I\) are the voltage and current of the surge arrester, \({\rm C}\) is the material constant, and \(\alpha\) is the nonlinearity coefficient. Typically, the value range of \(\alpha\) is \(0.02<\alpha<0.1\). Therefore, by transforming Equation (8), the resistive characteristic of the surge arrester can be expressed as a function of the current flowing through it.  
The resistive characteristic of the surge arrester is defined as follows:  
\[r_{\rm MOV}(i)=C\frac{\alpha}{i^{1-\alpha}} \tag{9}\]From Equation (9), when the current through the surge arrester is small, the nonlinear resistance of the surge arrester exhibits a high-resistance state. In this case, the resistance \(r_{\text{MOV}}\) is large and can be considered negligible. When the voltage at the end of the surge arrester exceeds the reference voltage, the current \(i\) begins to increase, leading to a decrease in \(r_{\text{MOV}}\). This, in turn, causes a further increase in the current through the surge arrester. During the energy absorption process, the surge arrester exhibits an instantaneous low-resistance characteristic.  
The nonlinear impedance models of surge arresters at all locations within the system are calculated and incorporated into the CF model, as shown in Figure 6.  
Since faults within the valve hall are generally permanent faults, the converter station can only be shut down and locked. During the fault process, the voltage and current of the MMC valve exhibit transient impulse characteristics. Therefore, it is necessary to consider the field-circuit coupling characteristics of the MMC valve.
## 8 Results and Discussion
### Scenario 3 (30% DR)  
The introduction of a 30% DR program causes notable changes in the household's energy consumption patterns, leading to significant cost savings and better use of backup systems. This scenario evaluates the effects of the DR program on hourly energy load, production from PV and wind, the use of backup systems like batteries, FCs, and EVs, and the total operating costs and LPSP. The degree of participation from both primary and backup systems in satisfying the load demand in Scenario 3 is shown in Figure 14.  
The 30% DR program markedly affects the hourly load distribution, especially during peak hours. In the early morning, the load starts at 1093.81 W at 1:00 a.m., dropping to 897.97 W by 2:00 a.m. and 800.06 W by 3:00 a.m. This decrease continues as household activities remain low during off-peak hours. PV generation begins at 6:00 a.m. with 304.7 W and steadily rises, peaking at 910.00 W by 2:00 p.m. Wind generation is more variable, hitting its highest point at 880.1 W at 4:00 p.m. The highest load demand occurs in the evening, reaching 1504.24 W at 9:00 p.m., with PV generation stopping after sunset and wind generation gradually decreasing.  
Backup systems are essential for maintaining energy balance, particularly when PV and wind generation are insufficient. Battery storage discharges heavily in the early morning, peaking at 718.37 W at 2:00 a.m. In the afternoon, battery discharge reaches up to 903.24 W at 8:00 p.m. FCs provide intermittent support, peaking at 383.59 W at 3:00 a.m. and 312.26 W at 9:00 p.m. EVs also play a role through both charging and discharging cycles, with notable discharges of 350.53 W at 1:00 a.m. and charging phases at \(-\)156.00 W at various times like 2:00 a.m., 3:00 a.m., and 4:00 a.m.  
The operating costs rise significantly during periods of high load. For example, at 1:00 a.m., the total expense is $16.86, increasing to $31.80 by 2:00 a.m. By the end of the study period, the total cumulative operating expense reaches $749.09. The LPSP stays within the satisfactory range of 0 to 0.05 throughout the day as represented in Figure 15. The highest LPSP value is 0.0010 at 2:00 a.m., indicating a reliable system capable of meeting energy demands with minimal risk of power shortages.  
The 30% DR program shows how further reductions in peak load demand can result in significant reductions in energy costs and better use of backup systems. The household adjusts its energy consumption patterns to capitalize on off-peak periods, resulting in a more balanced and efficient energy system. The cumulative operating cost for the day is $749.09, lower than both the \"No DR\" and \"20% DR\" scenarios, demonstrating the economic benefits of more aggressive DR programs.  
Figure 14: The degree of participation from both primary and backup system in satisfying the load demand in Scenario 3.
## 1 Introduction
These features together with simple linear classifier can achieve best performance in different type of video analysis tasks. To automatically extract temporal features and improve the recognition rate, we present a 3 dimensional convolutional neural network (3D CNN) structure in this research, which can extract the spatio-temporal features of facial expressions. Though 3D CNN was proposed before [20; 22], to our knowledge, this study is one of the first work to explore the 3D CNN in NIR FER to achieve best recognition rate. Besides spatial and temporal features, facial expression representation can also be divided into global and local features. Researchers generally focused on the separated global or local feature extraction methods of facial expressions alone [11; 23]. Few studies tried global and local feature fusion methods in NIR FER. To make full use of the facial expression characteristics, we extract global features from the whole face and local features from the partial faces (upper and lower regions) in temporal and spatial dimensions and then fuse the features for FER by using three-stream 3D CNN. One of the problems of CNN is that over-fitting would happen if the size of the network and the size of the database were not well matched. [PERSON] et al. [24] found that a medium-sized network could achieve higher accuracy than what a large-sized network can achieve under the circumstance of middle size dataset. In NIR FER, the database (Oulu-CASIA NIR facial expression database) is not too large, too many layers of network are not suitable for the recognition neither. We therefore design a medium-sized network for NIR FER in this research in order to avoid over-fitting problems. As described above, the main contributions of this paper are three-fold. First, we present a 3D CNN that can automatically extract spatio-temporal features from NIR video for FER. Secondly, we design a three-stream 3D CNN for extracting global features and local features for further improving the recognition rate. Thirdly, we design the network with a medium-size specifically for Oulu-CASIA NIR facial expression database, which may prevent over-fitting to some extent. Experiment results show that our proposed methods for FER can achieve 78.42% recognition accuracy, which is higher than other recognition methods, such as Histogram of Oriented Gradient to 3D (HOG 3D) (60%), Local binary patterns from three orthogonal planes (LBP-TOP) (72.33%), deep temporal appearance-geometry network (DTAGN) (66.67%), and adapt 3D Convolutional Neural Networks (3D CNN DAP) (72.12%).
## 4 Numerical Simulations  
A simulation platform is developed based on Figure 1 to verify the efficacy of the proposed adaptive compound controller in regulating lateral stability and energy savings. The platform comprises a 7-DOF vehicle dynamics model, motor model, battery model, driver model, and adaptive compound controller, all implemented in MATLAB/Simulink 2023b. Outputs from the vehicle dynamics model, such as the vehicle speed, side slip angle, yaw rate, and SOC from the battery model, are provided as feedback to the adaptive compound controller. The torque distribution results from the adaptive compound controller are then used as inputs to the vehicle dynamics model, forming a closed-loop control system.  
This section evaluates the performance of the adaptive stability and energy-saving controller (SEC) in comparison to two limiting cases: the energy-saving controller (EC) and the stability controller (SC). In the EC, no external yaw moment control is applied, which corresponds to \(M_{z}^{\prime}=0\) (Type I) in the SEC, whereas in the SC, no external yaw moment modification is added, which corresponds to \(M_{z}^{\prime}=M_{z}\) (Type III) in the SEC.  
The vehicle performs a lane-changing maneuver on a flat road surface (\(\mu=0.85\)) at a constant speed of 72 km/h, with the remaining vehicle parameters shown in Table 4. Various steering angles (0.5 rad, 1.0 rad, and 1.5 rad) and initial SOC levels (0.2 and 0.8) are considered, with the vehicle speed maintained at 72 km/h while following the desired path under PI control. A larger steering angle generally indicates a higher likelihood of vehicle instability. Note that \(\text{SOC}=0.2\) represents a typical low battery charge, while \(\text{SOC}=0.8\) represents a high battery charge. The steering angle varies sinusoidally with an amplitude of 0.5 rad and a frequency of \(\pi/2\), ranging between 1 s and 5 s, as illustrated in Figure 8.
# Elliptic-Curve Crypto Processor for RFID Applications
## 2 Preliminaries
### Architecture of RFID Tag Chip with ECC  
An RFID tag chip embedded with ECC consists of four parts: (i) analog frond-end, (ii) random number generator, (iii) electrically erasable programmable read-only memory (EEPROM), and (iv) a digital baseband controller, as shown in Figure 1. The description of these parts is given as follows:  
* Analog Front-End reads an input analogue signal through an embedded antenna and converts it into a digital format. The converted signal is fed as an input to the baseband controller as shown in Figure 1. It also performs carrier signal modulation (or) demodulation. Finally, it generates the clock and reset signals for the baseband controller.
* Random Numbers Generator (RNG) is required to introduce randomness in each authentication process so that the secret information/data are unpredictable.
* EEPROM is used to keep a pair of keys (i.e., private and public) and ECC parameters including the \(x\) and \(y\) coordinates of a base point and other required ECC constants.  
Baseband Controller consists of several units, i.e., (i) pre-processing circuit, (ii) system controller, (iii) read access memory (RAM), (iv) memory interface, and (v) Elliptic-curve crypto processor (ECP). Moreover, as shown in Figure 1, a communication bus is used to integrate the aforementioned units. A pre-processing circuit is responsible to extract the relevant information from the incoming frame. If the input frame is available, then a system controller generates the corresponding signal to store the frame data into a RAM unit. Subsequently, the system controller generates a relevant signal to enable ECP for further computations on the stored frame data. Once all the required parameters of ECC and the frame data are present, the controller waits for the feedback signal from the ECP unit.
## 3 Results
### The Cause of Erythroderma
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & Number of patients & 47/49 & 27/27 & 26/27 & 22/22 & 21/21 & 37/39 \\ \cline{2-9}  & CRP & Median & & & & & & \\  & mg/L & 28.6 (7.5–48.5) & 8.4 (2.7–20) & 6.05 (1.6–11.6) & 28.7 (5.7–88.9) & 11.4 (4.7–42) & 21 (5–34) \\ \cline{2-9}  & & Number of patients & 11/49 & 2/27 & 1/27 & 8/22 & 1/21 & 5/39 \\ \cline{2-9}  & & Median & & & & & \\  & mg/L & 0.06 (0.03–0.08) & 0.05 (0.04–0.05) & 0.03 & 0.115 (0.05–0.31) & 0.15 (0.15–0.15) & 0.08 (0.01–0.09) \\ \cline{2-9}  & & Number of patients & 6/49 & 23/27 & 11/27 & 10/22 & 14/21 & 21/39 \\ \cline{2-9}  & IgE total & Median & & & & & \\  & U/mL & 90.6 (32.9–542) & 19.100 & 1870 (183–4240) & 45.1 (11–111) & 200 (147–1090) & 943 (330–2160) \\ \cline{2-9}  & & (Q1–Q3) & & & & & \\ \cline{2-9}  & & Number of patients & 29/49 & 14/27 & 20/27 & 17/22 & 13/21 & 21/39 \\ \cline{2-9}  & Albumin & Median & & & & & \\  & mg/mL & 3.7 (3.5-3.9) & 4 (3.6-4.3) & 3.85 (3.5-4) & 3.3 (3.1-3.9) & 3.6 (3.3-3.9) & 3.4 (2.8–4) \\ \cline{2-9}  & & Number of patients & 14/49 & 12/27 & 21/27 & 15/22 & 16/21 & 21/39 \\ \cline{2-9}  & LDH & Median & & & & & \\  & U/L & 202.5 (174–222) & 303.5 (242–372) & 282 (222–279) & 254 (199–413) & 245 (204.5–313.5) & 291 (2
# A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes
## 3 The Proposed HCD Algorithm
### Protocol Performance
#### 3.2.1 Cluster Initialization
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## 8 Discussion and Conclusions  
In this article, we have presented the developments around the SensorNC framework since its inception in [4]. We have presented the basic mathematical methodology, as well as some of the most significant advancements such as the accommodation of in-network processing and self-modeling. From a practical point of view, we also emphasized the issue of tool support, which is by now well catered for by the DiscoDNC software. Applications of the SensorNC are abundant; a few have been discussed in some detail; several others have been mentioned.  
As the SensorNC calculates performance bounds, an immediate question arises with respect to the accuracy of the bounds. This has been investigated via simulations and practical experiments, and the results speak in favor of SensorNC's precision [36; 45]. Clearly, this also depends on many details of the respective WSN settings.  
The work on the SensorNC should be continued. Clearly, some important challenges are still open. In particular, the wireless characteristics could be better reflected in a stochastic system model. There has been a huge amount of work along the lines of a general stochastic network calculus (see, e.g., [46] for a recent paper) and more specifically to model wireless channels, e.g., [47]. However, how to integrate these results into the SensorNC specifics is still open mainly due to the important issue of dealing with stochastic dependencies in larger topologies. Furthermore, the issue of retransmissions due to loss has been addressed in a single-node stochastic network calculus setting [48]. Furthermore, these results should be transferred suitably to the more challenging setting in SensorNC. Again, dealing with stochastic dependencies becomes a major mathematical challenge. Yet another advanced issue results from feedback control loops as, e.g., typical in cyber-physical systems [1]. Here, progress has been made recently in the stochastic setting, which could open up new opportunities for applications of the SensorNC [49; 50; 51].  
At last, combinations of SensorNC with other formal verification techniques as in [52] could bring up new fruitful capabilities in the quest for WSNs with predictable performance.  
**Acknowledgments:** [PERSON] is supported by a Carl Zeiss Foundation grant.  
**Author Contributions:** The original SensorNC framework was chiefly conceived by [PERSON] as well as the advanced aspects of in-network processing; [PERSON] is the main contributor towards the self-modeling and tool aspects; [PERSON] contributed substantially to the SensorNC applications. [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.