esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficiency of D2D Multi-User Cooperation
## 3 Analysis of System Energy Efficiency
### Link Outage Probability  
The link is supposed to interrupt when delay \(\tau_{\min}\) is bigger than threshold \(\tau_{th}\) or data rate \(R\) is higher than the maximum data rate \(R_{m}\), which is defined by the Shannon Equation. Hence, the outage probability can be expressed as \(P_{out}=1-P\{R\leq R_{m},\tau_{\min}\leq\tau_{th}\}\), where \(R_{m}=B\log_{2}\left(1+Pow|h|^{2}/N_{0}B\right)\), \(\tau_{\min}=\tau_{\min}+\tau_{\min}+\tau_{p}\). \(N_{0}\) is single side band noise power spectral density; \(h\) is channel gain and \(|h|^{2}\) follows independent exponential distribution whose variance is \(1/\lambda^{2}\)[17]. \(B\) is bandwidth and _Pow_ is transmission power. \(\tau_{\min}=\tau_{\min}+\tau_{\min}+\tau_{p}\). \(\tau_{\min}\), \(\tau_{\min}\) and \(\tau_{p}\) are minimum transmission delay, reception delay and propagation delay, respectively. We ignore the propagation delay because of the short distance and suppose that the transmission delay is equal to the reception delay. \(\tau_{\min}=\tau_{\min}=D/R_{m}\). \(D\) is the length of the packet. The outage probability \(P_{out}\) can be expressed as:  
\[P_{out}=\left\{\begin{array}{c}1-\exp\left(-\frac{2\frac{B}{\tau_{th}}-1}{ \tau}\cdot\lambda\right),R\geq\frac{2D}{\tau_{th}}\\ 1-\exp\left(-\frac{2\frac{2D}{\tau_{th}}-1}{\tau}\cdot\lambda\right),R\leq \frac{2D}{\tau_{th}}\\ \overline{r}=\frac{Pow}{N_{0}B}\end{array}\right. \tag{9}\]  
The energy consumption \(E\) of sharing a data packet between two D2D users includes transmission energy \(E_{t}\), reception energy \(E_{r}\) and procession energy \(E_{p}\). We suppose that \(E_{tr}=E_{t}+E_{r}=2E_{t}=2E_{r}\), so \(E=E_{tr}+E_{p}\) because \(E_{t}=D\cdot E_{bit}/(1-P_{out})\), and the total energy consumption of each link can be expressed as \(E=2D\cdot E_{bit}/P_{pass}+E_{p}\), where \(E_{bit}\) is the consumed energy for 1 bit.
###### Abstract  
This study explored the effectiveness of VR-based social skills training for students with autism and typically developing students with social difficulties. Six autistic students and five typically developing students from upper elementary grades participated in the study. Participants were recruited based on their willingness to participate, ability to follow instructions, and absence of other significant learning or behavioral disorders. Five VR modules were developed, covering scenarios like classrooms, ticket booths, exhibitions, restaurants, and parks. These modules incorporated foundational social settings and more complex scenarios to enhance emotional regulation and adaptive responses, aligned with the 12-year Basic Education Curriculum Guidelines. The intervention took place from May to July 2023, with participants attending six 30-40 min VR sessions once or twice a week. Various assessment tools measured the impact, focusing on social responses, emotion recognition, and reactions to unexpected situations. Results indicated consistent improvements in conversation speed, expression effectiveness, and environmental adaptation. Social Skills Behavior Checklist scores showed significant differences between pre- and post-tests, while EEG data revealed enhanced empathetic responses among autistic students. Typically, developing students shifted from independent problem-solving to seeking social support. This study highlights the potential of VR as an effective tool for social skills development in both groups.  
high-functioning autism; social difficulties; virtual reality (VR); social skills; EEG
## 3 EV Charging Station Management Strategy
### Interaction with the EV Drivers  
The DC microgrid's sources provide the available power \(p_{EVs\_lim}\), namely power limitation for the EV charging station. The EV charging station outputs the power demand \(p_{EVs\_D}\) to the DC microgrid, which is a closed loop. EV charging depends on the charger priority and, in this paper, the EVs are assigned on a first-come-first-serve method.  
As shown in Figure 6, when a new EV arrives, the driver chooses a charging mode. If the power demanded by the chosen charging mode is less than the available power of the EV charging station, the EV is connected to the charger. If the power demanded by the chosen charging mode is greater than the available power of the EV charging station, the driver can choose waiting or departure. If the driver's choice is waiting, when the available power is greater than the demanded power, the EV starts to be charged. During the charging process, the driver can disconnect the EV at any time and leave the EV charging station. Therefore, this strategy provides the driver with the possibility to choose the charging mode.  
The EV charging station power limitation \(p_{EVs\_lim}\) is provided by \(p_{PV\_MPPT}\), \(P_{G\_LIM}\), and \(P_{S\_MAX}\), as expressed by Equation (11):  
\[p_{EVs\_lim}=p_{PV\_MPPT}+P_{G\_LIM}+P_{S\_MAX} \tag{11}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Sign** & **Question** \\ \hline Q1 & Which charging mode? The fast mode, the average mode, or the slow mode. \\ Q2 & The power is insufficient for the fast mode. Which do you choose among \\  & the average mode, waiting and departure? \\ Q3 & The power is insufficient for average mode. Which do you want to choose \\  & among the slow mode, waiting and departure? \\ Q4 & The power is insufficient for any recharging mode. Which do you want to \\  & choose between waiting and departure? \\ Q5 & The EV is being charged. Do you want to stop charging and leave? \\ \hline \end{tabular}
\end{table}
Table 2: Question lists.  
Figure 5: Interaction diagram between sources and EV charging station.  
\begin{table}
\begin{tabular}{c c} \hline
**Condition** & **Option** \\ \hline \(p_{EVs\_lim}>P_{FAST\_MAX}\) & The fast mode, the average mode, or the slow mode. \\ \(p_{EVs\_lim}<P_{FAST\_MAX}\) and \(p_{EVs\_lim}>P_{AVER\_MAX}\) & The average mode, waiting or departure. \\ \(p_{EVs\_lim}<P_{AVER\_MAX}\) and \(p_{EVs\_lim}>P_{SLOW\_MAX}\) & The slow mode, waiting or departure. \\ \(p_{EVs\_lim}<P_{SLOW\_MAX}\) & Waiting or departure. \\ \hline \end{tabular}
\end{table}
Table 3: Available power test and options.
###### Abstract
* Beamforming scheme: In the beamforming architectures of the massive MIMO technology, although the digital beamforming design can significantly improve the SE, it would bring high hardware complexity and high power consumption. Although the hardware overhead of hybrid beamforming architecture based on full connection is slightly higher than that of the partial connection architecture, it can balance the hardware complexity and system performance, and has higher cost performance. Therefore, in this paper, we selected the hybrid beamforming technology based on the full connection structure [10].
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 7 Related Works
### Previous Fuzzing Works  
Recent advancements in fuzzing have significantly improved the efficiency and effectiveness of finding bugs. This section summarizes related works regarding various fuzzing approaches, highlighting key contributions and their methodologies.  
Coverage-based fuzzing is a standard approach where the fuzzer aims to maximize the coverage of code paths during execution. The key idea in coverage fuzzing is using genetic algorithms to selectively preserve mutated inputs based on their contribution to expanding code coverage, thus significantly increasing the likelihood of discovering new code paths. To this end, various researchers have suggested novel approaches. [PERSON] et al. [9] discuss the reliability of coverage-based fuzzer benchmarking, emphasizing the need for consistent and reliable benchmarks in evaluating fuzzers. Similarly, ref. [28] introduced coverage-based greybox fuzzing as a Markov chain, laying the groundwork for other subsequent fuzzing techniques.  
Application-aware fuzzing techniques, such as VIuzzer [5], adapt fuzzing strategies based on application-specific knowledge, leading to a more effective identification of vulnerabilities. CollAFL [29] and FairFuzz [30] propose path-sensitive and targeted mutation strategies, respectively, to enhance coverage and effectiveness. Hawkeye [31] focuses on specific program paths or vulnerabilities, directing the fuzzer's efforts toward areas of interest. JITfuzz [32] specifically targets Just-in-Time compilers in JVMs, highlighting the applicability of fuzzing in diverse software environments.  
Integrating fuzzing with other analysis techniques has also shown promise. SAFL [33] combines symbolic execution with guided fuzzing to improve testing coverage, while Zeror [34] accelerates fuzzing with coverage-sensitive tracing and scheduling. Hardware-assisted fuzzing, as demonstrated by kAFL [35], leverages hardware features to improve the feedback mechanism and efficiency of fuzzing kernels and operating systems. Similarly, though based on software-only techniques, full-speed fuzzing [21] aims to reduce overhead through coverage-guided tracing, enhancing the speed and efficiency of fuzzing processes based on the debug interrupt feature.  
Network protocol-specific fuzzing has also gained attention with tools like AFLNET [36], which adapts greybox fuzzing for network protocols, and Fw-fuzz [37], focusing on firmware network protocols. These tools address the unique challenges of fuzzing network interactions and embedded systems. For IoT devices and firmwares, FIRM-COV [38] introduces a high-coverage greybox fuzzing framework optimized for IoT firmware through process emulation. This paper explores the effectiveness of coverage feedback in fuzzing considering their expensive porting cost in IoT and embedded firmware systems.
# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 2 Materials and Methods
### Satellite Data Processing  
We used optical images from the S2 satellite and SAR images from the S1 satellite. S2 optical images represent the reflectance of light from the Earth's surface as a function of the wavelength of the light. For crops, this spectral reflectance is determined by the plant's biophysical and biochemical properties, such as the leaf area, biomass, chlorophyll content, water content, and canopy structure, as well as external factors such as background soil [35]. The S2 optical image processing was carried out in Google Earth Engine (GEE) [36]. For Rwanda, going back to December 2018, GEE provides access to S2 images that are corrected to represent signal values at the bottom of the atmosphere. Values of reflectance in the four bands B2 (490 nm), B3 (560 nm), B4 (665 nm), and B8 (842 nm) [29] were extracted from the identified imagery after cloud masking was applied using a built-in GEE algorithm and visually verifying the masked results. The S2 multispectral instrument (MSI) offers 10-m ground resolution in these four selected bands [29].  
S1 SAR image processing was also carried out in GEE. SAR images represent the reflectance of electromagnetic radiation in the microwave range and are therefore not affected by cloud cover. For crops, this reflectance is primarily a function of the canopy architecture such as the size, shape, and orientation of the canopy components; the dielectric properties of the crop canopy; and the cropping characteristics such as plant density and row direction [37]. GEE provides access to S1 images that are preprocessed with thermal noise removal, radiometric calibration, and terrain correction. For this analysis, we selected images acquired using the interferometric wide (IW) swath mode and the vertical transmit/vertical receive (VV) polarization. The S1 SAR instrument offers a 10-m ground resolution in this band [38].
## 4 Results and Discussions
### Energy Dissipation Capacity  
The energy dissipation capacity of the specimens is expressed by the relation curves between the hysteretic loop area and the horizontal drift angle, as shown in Figure 15. The values of energy dissipation in the main stages are presented in Table 8.  
(1) The energy of the specimens of infilled cast-in-place RACSWs are dissipated mainly by the flexible deformation of the steel frames and the coarse aggregate friction and bite of the cracked surface of the RACSWs. The energy dissipation is 3.25 times higher in SPE2 infilled cast-in-place RACSWs than in the pure steel frame when \(\theta\) = 0.005 rad, and 2.6 times higher in SPE2 than in the pure steel frame when \(\theta\) = 0.02 rad.  
(2) The energy dissipation of SPE2 infilled RACSWs is 41% that of SPE1 infilled ordinary concrete wall when \(\theta\) = 0.005 rad, and 56% that of SPE1 when \(\theta\) = 0.02 rad. The energy dissipation is approximately 13% higher in SPE3 than in SPE2 when \(\theta\) = 0.005 rad, and approximately 28% higher in SPE3 than in SPE2 when \(\theta\) = 0.02 rad, thereby indicating that the end-plate joints are fully deformed and characterized by excellent energy dissipation during loading.  
(3) The energy of the specimens of infilled prefabricated RACSWs is dissipated mainly by the flexible deformation of the steel frames and the coarse aggregate friction and bite of the wall cracks and friction slip among the connectors. In the early stage of loading, the cracks on the wall of SPE5 occur early and the concrete cracking load is low; the energy dissipation is slightly higher in SPE5 than in SPE4. With the increase in displacement, the energy dissipation of the two specimens becomes the same. The energy dissipation capacity is approximately two times higher in the specimens of infilled prefabricated RACSWs than in the pure steel frame.  
(4) Compared with the pure steel frame, infilled RACSWs can greatly improve the stiffness and energy dissipation capacity of SFIRACSWs while reducing the ductility of the structure, thereby indicating that infilled RACSWs strongly influence the hysteretic behavior of SFIRACSWs.  
Figure 15: Energy dissipation curves.
## 3 Results
### Biologicals in Asthma and Allergic Rhinitis
#### 3.3.1 Omalizumab (Anti-IgE)  
It was the first biological approved for the treatment of asthma by the Food and Drug Administration (FDA) in 2003 [206]. Nowadays, it is used in patients aged 6 years and older with moderate-severe asthma, and it is recommended its subcutaneous administration every 2 or 4 weeks depending on IgE serum levels and body weight [15].  
Several studies have demonstrated the efficacy of omalizumab in the treatment of severe asthma [207]. The EXTRA study (NCT00314574) enrolled 850 patients with uncontrolled severe allergic asthma [208]. Asthma biomarkers: FeNO, blood eosinophil, and serum periostin showed a normalization in the patients that received omalizumab in comparison to the placebo group. The PROSPERO study (NCT01922037) demonstrated an improvement in asthma symptoms in 87% of the patients after receiving omalizumab for 48 weeks [209].  
Recently, two meta-analyses have been performed, evaluating several trials testing the use of omalizumab in AR [176,177]. In one meta-analysis, [PERSON] et al. included 16 randomized  
Figure 4: Biologicals used in asthma and AR. Mechanisms of action of different biologics are schemaatized (omalizumab, mepolizumab, reslizumab, benralizumab, tezepelumab, and dupilumab). Biologicals usually target inflammatory molecules, cytokines, or their receptors, blocking downstream inflammatory pathways or triggering-induced cell death. ADCC: antibody-dependent cell-mediated cytotoxicity; mAb: monoclonal antibody.  
clinical trials, while [PERSON] et al. developed a systematic review of 12 trials. Both studies reached almost the same conclusions: treatment with omalizumab significantly improves both ocular and nasal symptoms, reduces the use of rescue medication, improves the quality of life, and, in addition, no increase in adverse effects was observed compared to the placebo group.
## 3 Materials and Methods
### Architecture
#### 3.2.2 Partial Autocorrelation Function  
The partial autocorrelation function (PACF) is a statistical tool used to analyze time series data. It quantifies the correlation between a time series and its lagged versions while excluding the influence of other lagged versions. The PACF provides a more accurate measure of the correlation between lagged orders and current observed values, which is crucial  
Figure 2: Feature fusion.  
for time series analysis and modeling. By observing the truncation pattern of the PACF, suitable lagged orders can be determined, leading to the construction of accurate models. Therefore, the PACF is widely applied in time series forecasting [30]. In this model, the PACF was utilized to assess the correlation between temporal features, enabling the Transformer model to recognize and understand the temporal dependencies among different features. The usage of the PACF in this model is illustrated by the following formula:  
\[\text{PACF}=\text{Corr}(Y_{t},Y_{t-k}\mid Y_{t-1},Y_{t-2},\dots,Y_{t-k+1}) \tag{5}\]  
In this formula, \(Y_{t}\) denotes the time series observation at the current time \(t\) and \(Y_{t-k}\) denotes the observation at the lag \(k\). The partial autocorrelation function is calculated based on the correlation between \(Y_{t}\) and \(Y_{t-k}\) under a given condition, conditional on the observed values \(Y_{t-1},Y_{t-2},...,Y_{t-k+1}\)  
In this context, the PACF represents the correlation of individual temporal relationships obtained through the corr function. By averaging the PACF values among multiple features within the time series, we derive a feature vector of length \(k\), where \(k\) denotes the lag. This vector is concated with the original features to form a comprehensive vector encapsulating the temporal relationships among features. This process is expressed by the following formula:  
\[X_{\text{input}}=\text{cat}(F,\text{mean}(\text{PACF})) \tag{6}\]  
In this formula, \(X_{\text{input}}\) denotes the feature matrix to be fed into the Transformer model. \(F\) represents the original set of features. \(\text{mean}(\text{PACF})\) represents the feature vector of length \(k\) obtained by averaging the partial autocorrelation function (PACF) values between multiple features in the time series. For the partial autocorrelation function (PACF), traditional time series modeling methods primarily rely on the PACF for selecting model orders. However, these tools are not typically used directly in feature engineering. By introducing the mean (PACF) vector as an additional feature, our method innovatively utilizes the PACF to represent the global interactions between multiple features in time series data. Unlike traditional approaches that use the PACF solely for lag selection, our method transforms PACF values into a fixed-length feature vector, providing a statistical representation of multifeature dependencies. This not only enriches the feature matrix with global interaction information but also enhances the model's ability to capture temporal relationships, leading to improved predictive performance and robustness.
# Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid
## 3 The Overgrid Concept
### The Load Control Function  
Finally, in order to satisfy the DSO request (assumed to arrive every 15 minutes), the combined action of all the nodes in the network is needed. Let \(P_{DSO}\leq P\) be the maximum power demand enforced by the DSO into the Overgrid. Each node belonging to the Overgrid has to reduce its power demand in order to meet such a constraint. To achieve this goal, a distributed logic is carefully defined, avoiding all nodes reacting at the same time to the request (causing possible instabilities) and that the requested power reduction is balanced among different peers (avoiding fairness problems). For the first issue, we implemented a random delay mechanism that postpones the power reduction of the nodes for a certain amount of time (_back-off_) to de-synchronize the nodes among them. For the second issue, we assumed that each load can be fine-tuned, in order to implement a power reduction proportional to \(P_{DSO}/P\) for each specific load. This approach can be generalized in case of non-tunable loads, by switching off each specific appliance with probability \(1-P_{DSO}/P\). In more complex scenarios, other fairness measures could also be employed, for example tacking into account different class of nodes (e.g., industrial vs residential), different priorities (e.g., public safety), or different pricing levels (\"premium\" users might be less affected compared to \"basic\" consumers).
## 3 Results and Discussion
### CP/MAS \({}^{13}\)C NMR Spectroscopy  
Figure 4 shows the CP/MAS \({}^{13}\)C NMR spectra of the PdAS(x)-MA catalysts, where the main signals were found to correspond with the DVB component [55; 56]. However, the absence of signals attributable to the PdAS coordination compound does not mean that it is not present, and indeed it is likely that these signals are masked by the aromatic DVB profile and the aliphatic backbone of the polymer. As shown, characteristic bands of the DVB ethyl benzene -CH\({}_{2}\)- and -CH\({}_{3}\) moieties were observed at 33 and 18 ppm respectively, while a broad -CH\({}_{2}\) signal originating from the backbone of the polymeric matrix was centered at 50 ppm [53], while those at 121-153 ppm were attributed to the aromatic carbon atoms of the DVB cross-linker. Only two signals originating from MA were detected, the first as a shoulder at 58 ppm (-OCH\({}_{3}\)), and the second at 184 ppm (C=O). In line with the DRS UV-vis analysis results (see below), the decrease of both signals can be associated with an increase  
Figure 3: N\({}_{2}\) adsorption-desorption isotherm at \(-\)196 \({}^{\circ}\)C for the PdAS(x)-MA catalysts. (**a**) Pd-AS(1)-MA, (**b**) Pd-AS(2)-MA, (**c**) Pd-AS(5)-MA, and (**d**) Pd-AS(10)-MA.  
in the PdAS content of PdAS(x)-MA. These results therefore confirm the successful incorporation of PdAS to replace MA in the synthesized PdAS(x)-MA polymers.
# Turbo Decoder Design based on an LUT-Normalized Log-MAP Algorithm
## 2 Turbo Decoding Principles and Log-MAP Algorithm  
### Turbo Decoding Principles  
The decoding process of turbo codes involves the exchange of soft information between two SISO decoders [10]. In turbo encoding and decoding processes, we use the same interleaver, where de-interleaving is the inverse process of interleaving. The SISO decoder consists of three input ports, which are system information \(\lambda_{c}(u_{k})\), parity information \(\lambda_{c}(p_{k})\), and a priori information \(\lambda_{a}(u_{k})\) which is computed by another SISO decoder. Two output ports of the SISO decoder generate external information \(\lambda_{e}(u_{k})\) and posteriori information \(\lambda_{o}(u_{k})\). The different superscripts represent information corresponding to different SISO decoders, and subscript \(k\) denotes \(k\)-th bit information of the current variable. \(\Pi\) and \(\Pi^{-1}\) denote interleaving and de-interleaving, respectively. When the iteration stopping criterion is satisfied, decoding results can be obtained from \(\lambda_{e}(u_{k})\) after the de-interleaved and hard-decision operations [11; 13]. The turbo decoding procedure is shown in Figure 1.  
Figure 1: Traditional turbo decoding procedure using a SISO decoder.
## 1 Introduction
Meanwhile, the GF(5) based LDPC coding is implemented for 5-QAM symbol sequences, and GF(32) based LDPC coding is implemented for 9-QAM symbol sequences [30; 31]. Unlike the classical OAM multiplexed FSO links, where all spatial mode channels are centrally aligned, binary FEC coding is used for error correction. Furthermore, uniformly distributed M-QAM formats are used for data modulation, e.g., QPSK and 8-QAM. The proposed two-stage OAM mode crosstalk mitigation solution can largely enhance the communication reliability in atmospheric FSO links.