esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experimental Evaluation
### Benchmarks
#### 7.1.1 Constellation and Requests Features  
We consider a low-Earth-orbit constellation (500 km altitude) composed of \(n_{p}\) regularly spaced orbital planes having a 40-degree inclination, with \(n_{p}\in\{2,4,8,16\}\) and two regularly spaced satellites over each orbital plane (Walker constellation). We randomly generate requests for four agents wishing to obtain orbit slot ownerships to implement some repetitive ground acquisitions of POIs belonging to the same area. POIs are randomly selected within an extracted subset from [25], around Grenoble, France. All the agents have the same template for each request \(r\), that is, communicating and getting observations every day at three requested times (RTs): 8:00 + \(\delta_{r}\), 12:00 + \(\delta_{r}\), and 16:00 + \(\delta_{r}\), where \(\delta_{r}\) is uniform random time shift in \([-2\text{h},2\text{h}]\). Note that \(\delta_{r}\) applies to all RTs of the same request. For each request \(r\) and each RT \(t\) for \(r\), the slots over which orbit ownership can be claimed for achieving \(r\) around time \(t\) are determined thanks to a space mechanics toolbox, based on the assumption that a satellite is relevant for a POI as soon as its elevation above the horizon is greater than 15 degrees. Depending on the number of satellites in the constellation, there might not be a satellite passing over a POI exactly at the RT. We consider a tolerance window \(\Delta\) equal to 1 h before and after each RT, meaning that an orbit slot is considered as valid for an RT \(t\) if the middle of its temporal window is less than an hour from that RT. Finally, we impose a minimum duration \(minD\) of 120 s for all requests and do not consider orbit slots whose duration is shorter than this duration.  
Note that these features were validated as realistic by a satellite constellation manager we work with. In fact, in the case of orbit slot allocation problems, the number of users that can afford to own orbit slots is quite low and so is their number of requests.
# Experimental Verification of Yam (_Dioscorea rotundata_) Drying with Solar Adsorption Drying
## 1 Introduction
It has been reported that drying methods effect the final moisture content, but not other components of yam flours [15]. Maintaining the original color of food samples is a priority of the processor and the demand of the consumer. Yam contains phenolic compounds, which are associated with oxidative browning by enzymes and oxidative browning [16]. Thus, any processing method that is consumer-acceptability driven by maintaining either the whitish or creamer color of yam meets great demand. Solar drying systems, where either the product is directly dried in a glass covered chamber or indirectly by channeling hot air into a drying chamber by first heating ambient air through a solar collector system, improves the rate of drying as well as the food quality [17]. In both systems, drying results in a reduced drying time and is an improvement over the open-air drying method. Moreover, contamination of the final product is reduced in the solar dryer drying systems [18]. Solar systems are not full proof since the sun's energy is limited to between six to eight hours a day in the tropics. During the night, rewetting of the products may occur. A solar dryer system with thermal storage was designed and constructed by [PERSON] et al. [19], which enabled and extended drying to continue after sunset. However, the intensity of the solar radiation is not always enough to sufficiently heat the thermal mass. Thus, with this system, the continuity of drying is not warranted. An improved method is Solar Adsorption Drying (SAD) (see Figure 1), which is a combination of solar collector and adsorption systems. In this system, night drying is made possible by utilizing dehumidified air as a result of passing ambient air through a chamber filled (packed bed) with adsorbents, while releasing adsorbent energy (latent heat) due to a phase change. The saturated adsorbent is regenerated during the day time by using a separate solar collector. The first aim of this work is to quantify the yam drying performance in a Solar Adsorption Drying system (SAD) compared to a common solar dryer (SD) and open sun drying (OSD) and to compare the effect on vitamin C retention in these three systems. The second aim is to quantify the drying performance, vitamin C retention, and the product color of different cultivars of yam for the three different drying methods. To this end, an experimental system was designed and realized according to the schematic diagram shown in Figure 1[20].
# Impact of Sound and Image Features in ASMR on Emotional and Physiological Responses
###### Abstract  
As media consumption through electronic devices increases, there is growing interest in ASMR videos, known for inducing relaxation and positive emotional states. However, the effectiveness of ASMR varies depending on each video's characteristics. This study identifies key sound and image features that evoke specific emotional responses. ASMR videos were categorized into two groups: high valence-low relaxation (HVLR) and low valence-high relaxation (LVHR). Subjective evaluations, along with physiological data such as electroencephalography (EEG) and heart rate variability (HRV), were collected from 31 participants to provide objective evidence of emotional and physiological responses. The results showed that both HVLR and LVHR videos can induce relaxation and positive emotions, but the intensity varies depending on the video's characteristics. LVHR videos have sound frequencies between 50 and 500 Hz, brightness levels of 20 to 30%, and a higher ratio of green to blue. These videos led to 45% greater delta wave activity in the frontal lobe and a tenfold increase in HF HRV, indicating stronger relaxation. HVLR videos feature sound frequencies ranging from 500 to 10,000 Hz, brightness levels of 60 to 70%, and a higher ratio of yellow to green. These videos resulted in 1.2 times higher beta wave activity in the frontal lobe and an increase in LF HRV, indicating greater cognitive engagement and positive arousal. Participants' subjective reports were consistent with these physiological responses, with LVHR videos evoking feelings of calmness and HVLR videos inducing more vibrant emotions. These findings provide a foundation for creating ASMR content with specific emotional outcomes and offer a framework for researchers to achieve consistent results. By defining sound and image characteristics along with emotional keywords, this study provides practical guidance for content creators and enhances user understanding of ASMR videos.  
Keywords:autonomous-sensory-meridian-response; heart-rate-variability; electroencephalography; sound; image; emotional-keywords
## 3 Results of Complete Soft Galloping (SG) Responses and Complete Hard Galloping (HG) Responses
### Soft Galloping (SG)
#### 3.1.2 Response Path 2: Decreasing Velocity and No Manual External Excitation  
When the flow velocity reduces from a high velocity with no manual external excitation acting on the prism at the beginning, response path 2 can be observed. This response path is was found for the first time in the present experiment and has since been proven by numerous tests. When \(U_{r}\) is in the range of \(12.25<U_{r}<14.125\), \(A^{*}\) is too small (only at 0.1-0.2) but \(f^{*}>>1\). Then, as \(U_{r}\) is reduced to 11, \(A^{*}\) is raised to 0.923, accompanied with a decrease of \(f^{*}\) from 1.814 to 1, indicating that the prism undergoes the VIV lower branch. The oscillation is induced by the vortex shedding with a bad synchronization. As \(U_{r}\) is continuously reduced to 11, \(A^{*}\) jumps up to 1.755, and \(f^{*}\) drops to 0.720, indicating that the prism is spontaneously initiated from VIV (lower branch) to galloping. The transition process is clearly observed in Figure 9c,d. As \(U_{r}\) further decreases from 11 to 0, the prism undergoes the VIV-galloping transition branch followed by the VIV initial branch. The corresponding responses of \(A^{*}\) and \(f^{*}\) are the same as those observed in response path 1.  
According to path 2, new conclusions can be drawn as follows: (a) In addition to the three branches mentioned in the previous studies [12; 13; 14; 28], the VIV lower branch could also be observed in the SG responses of the triangular prism when the velocity decreases and no external excitation acts on the prism; (b) When the flow velocity is reduced to a specific value (\(U_{r}=11\) in the present experiment), the prism can also be self-initiated from VIV to galloping.
# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## 4 Overview of Indoor Localization Accuracy Based on the RBF Method
### Localization Errors from the Wall GW Combinations  
The results indicate a clear impact of the number of gateways on the localization error. As the number of gateways increased, a reduction in localization error was observed across all body positions. Specifically, with two gateways, the error ranged from 1.36 m to 1.62 m, while with five gateways, the error ranged from 1.21 m to 1.72 m. This suggests that a higher number of gateways improves indoor localization accuracy. However, the effect of gateway count on error varies by body position. Figure 11 illustrates the average localization errors associated with different combinations of wall-mounted gateways.  
Figure 11: Indoor localization errors using all possible combinations of wall GWs.  
The results indicate that positioning significantly affects localization accuracy. The front pocket consistently shows higher errors compared to other positions, particularly with five gateways, likely due to increased signal blockage or attenuation, which impacts the performance of the BLE beacon system.  
When analyzing consistency across gateway combinations, certain positions, such as the wrist and top pocket, exhibit consistently low errors across all wall-mounted gateway configurations, indicating these positions are generally more favorable. The shoulder position also displays relatively consistent error rates across different gateway configurations, suggesting it may be less sensitive to variations in the number and position of receivers. Table 3 illustrates the best indoor localization errors at the five body positions, considering different combinations of wall-mounted receivers. The errors are reported for combinations of two, three, four, and five gateways. Figure 12 shows the best localization errors along with their respective wall gateway combinations, with the top figures corresponding to the shoulder position and the bottom figure corresponding to the wrist position.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 6 Contemporary Developments with UOC Technology
### AI in UOC
Intel's AI chip is known as \"Loihi\". Xilinx recently developed a ULSI-technology-based Field Programmable Gate Arrays (FPGA) core for AI applications. NVIDIA Jetson Nano lets us to bring incredible new capabilities to millions of small, power-efficient AI systems [445]. The hardware used for AI today mainly consists central processing units (CPU), graphics processing units (GPU), FPGA, and application-specific integrated circuits (ASIC). This type of integration of numerous subsystems and AI algorithms in a single chip will highly support FSO communication systems in achieving near-optimal performances.
## 3 Results
### Feedstock Properties
Regarding ash melting temperature, the values obtained in this study for CPHs are quite similar to those presented for other biomass feedstocks found in the bibliography, as can be observed in Table 3.  
In Table 3, TDI corresponds to the Initial Deformation Temperature, TR to the Reduction Temperature, TH to the Hemisphere Temperature, and TF to the Flow Temperature. \begin{table}
\begin{tabular}{c c c c c} \hline
**BIOMASS** & **IDT** & **TR** & **TH** & **TF** \\ \hline CPH & 888 & 1145 & 1340 & 1420 \\ (this study) & 1062 & 1121 & 1186 & 1237 \\ (Chestnut shells & 1425 & 1473 & 1497 & 1500 \\ Rice shells & 1438 & 1492 & 1500 & 1500 \\ Cocoa shells & 888 & 1145 & 1340 & 1420 \\ \hline \end{tabular}
\end{table} 
Table 3: Ash melting characteristic temperatures of feedstock and other biomass materials [40]. Cocoa pod shells show a considerably lower IDT, which is associated with their high potassium oxide (K\({}_{2}\)O) content. Potassium acts as a flux, lowering the ash fusion temperatures. This behavior was confirmed by [PERSON] et al. (2019), who found that biomass with higher potassium content tends to exhibit lower the IDT due to the interaction between K\({}_{2}\)O, silica, and alumina (2019). The chemical composition of cocoa pod shell ashes is dominated by potassium (59.79%), followed by calcium (6.28%) and magnesium (3.79%), with minor contributions from phosphorus (1.89%), silicon (1.62%), and sulfur (1.43%). The detailed composition is presented in Table 4:  
The high potassium content has a direct impact on the ash fusion properties. Potassium reduces the IDT and promotes the formation of slag and deposits, potentially leading to operational challenges in combustion systems. However, this property can also be leveraged for agricultural purposes, as potassium is a critical nutrient in fertilizers such as potassium sulfate (K\({}_{2}\)SO\({}_{4}\)) and potassium chloride (KCI) ([PERSON] et al., 2019). The moderate levels of calcium and magnesium indicate a composition that aligns with other woody biomass types, contributing to less corrosive ash deposits and improving ash handling. Trace elements such as rubidium, zinc, and chlorine are present in small amounts and are not expected to significantly influence the combustion behavior.
# TDLAS Monitoring of Carbon Dioxide with Temperature Compensation in Power Plant Exhausts
## 2 Absorption Spectroscopy  
### Direct Absorption Spectroscopy  
The fundamental theory that governs absorption spectroscopy for narrow-linewidth radiation sources is embodied in the Beer-Lambert law (Equation (1)). The ratio of the transmitted intensity \(I_{\mathrm{t}}\) and initial (reference) intensity \(I_{0}\) of laser radiation through an absorbing medium at a particular frequency \(v\) is exponentially related to the transition line intensity \(S(T)\) (cm\({}^{-2}\cdot\)atm\({}^{-1}\)), line-shape function \(\varphi(v)\) (cm), total pressure \(P\) (atm), volume concentration of the absorbing species \(X\), and the path length \(L\) (cm):  
\[\frac{I_{\mathrm{t}}}{I_{0}}=\exp[-PS(T)\phi(v)XL] \tag{1}\]If both sides of Equation (1) are computed logarithmically and integrated over the whole frequency domain, the volume concentration \(X\) is described by [34]:  
\[X=\frac{\int_{-\infty}^{+\infty}-\ln\frac{L}{t_{0}}dv}{PS(T)L}=\frac{A}{PS(T)L} \tag{2}\]  
If the minimum detectable integrated absorbance \(A_{\text{min}}\) is known, i.e., SNR (signal-to-noise ratio) = 1, the detection limit \(X_{\text{j,min}}\) of species j can be expressed as:  
\[X_{\text{j,min}}=\frac{A_{\text{min}}}{PS(T)L} \tag{3}\]  
The temperature-dependent line intensity for a particular transition is determined by its line intensity \(S\) at a reference temperature \(T_{0}\) with the following equation:  
\[S(T)=S(T_{0})\bigg{(}\frac{Q(T_{0})}{Q(T)}\bigg{)}_{T_{0}}\exp\left[-\frac{ hcE_{i}^{\prime\prime}}{k}\bigg{(}\frac{1}{T}-\frac{1}{T_{0}}\bigg{)}\right] \cdot\bigg{[}\frac{1-\exp(-hcv_{0,i}/kT)}{1-\exp(-hcv_{0,i}/kT_{0})}\bigg{]} \tag{4}\]  
where \(Q(T)\) is the partition function of the absorbing molecule (CO\({}_{2}\)), \(v_{0,i}\) is the frequency of the transition, \(E^{\prime\prime}\) is the lower-state energy of the transition, \(h\) is the [PERSON]'s constant (6.626 \(\times\) 10\({}^{-27}\) erg-s), \(k\) is the [PERSON]'s constant (1.38 \(\times\) 10\({}^{-16}\) erg/K), and \(c\) is the speed of light in vacuum [35].  
Temperature correction is required for TDLAS measurements in the environment with varying gas temperatures. According to Equation (2), in the case of the constant optical path and pressure, the ratio of integrated absorbance and line intensity, \(A/S(T)\) is theoretically proportional to gas concentration \(X\) in the same system. Therefore, the linear correction between \(A/S(T)\) and gas concentration can be applied to compensate the errors caused by temperature influence as shown in the Equation (5):  
\[y_{A/S(T)}=a+bx_{X} \tag{5}\]  
In Equation (5), \(a\) and \(b\) are fitting coefficients. \(a\) is the background signal without absorption, and \(b\) is the gradient of the linear correction.
# Impact Load Sparse Recognition Method Based on Mc Penalty Function
## 3 Impact Load Sparse Identification Model
### Verification of Load Identification Method Based on Simulation Signals  
In order to improve the validity of the simulation information, the noise control ratio (SR) was set to control the influence of noise on the impact load signal.  
\[SR=\frac{||\mathbf{f}_{1}||_{2}^{2}}{||\mathbf{n}||_{2}^{2}} \tag{37}\]  
where \(n\) represents noise. The simulation signal model of its impact load is as follows:  
\[y=e^{\frac{-2m\omega\beta\sin(2m\omega\cot t)}{\sqrt{1-\beta^{2}}}} \tag{38}\]  
where, \(\beta\) represents damping coefficient, \(\omega\) represents vibration frequency; for the other parameters, the sampling frequency is set as 2000 Hz, the delay time t_0 is 0.05 s, and the signal-to-noise ratio \(SR\) = 5.7724. After calculation, the fault characteristic frequency is 300 Hz. The time domain diagram of the simulation signal is shown in Figure 6. The time domain diagram of no-noise is shown in Figure 6a. The time domain diagram of noisy simulation signal is shown in Figure 6b.  
Figure 5: Accuracy changes under different input sample sizes.  
Figure 6: Time domain diagram of simulated signal. (**a**) time-domain diagram of no-noise, and (**b**) time domain diagram of noisy simulation signal.  
The MC penalty function sparse recognition model is used to compress the noise-added simulation signals, and then the impact load signals are identified by sparse decomposition. The sparse decomposition results are shown in Figure 7, and the iterative convergence is shown in Figure 8.  
It can be seen from Figure 7 that the measured impact load is a typical pulse signal in the time interval [0, 1] s, it is sparse in time domain. According to the simulation signal, the peak force of the sparse recognition method based on MC penalty function reaches the peak value at t = [0.0505 s, 0.2513 s, 0.4541 s, 0.6505 s, 0.8545 s]. It can be seen that it is highly consistent with the peak force t = [0.051 s, 0.251 s, 0.451 s, 0.651 s, 0.851 s]. On the contrary, the peak force identified by the Tikhonov method based on L1 norm is different from the peak force of simulation signal.  
Moreover, MC-based sparse decomposition results are less affected by noise, while L1-based regularization decomposition results are more affected by noise, and the high amplitude is weakened to a certain extent, which is mainly caused by the fast linear growth rate of L1 norm error. From the perspective of convergence rate, it can be seen in Figure 8 that, although the iterative processes based on L1 norm regularization and MC sparse decomposition tend to converge, there is an obvious gap in convergence rate. The L1 norm regularization tends to be stable after about 30 iterations
## 2 Materials and Methods
### Data and Preprocessing
#### 2.2.3 Validation Data  
To verify the accuracy of the downscaled SIF products, we chose two types of SIF data for comparison. The first dataset is the SIF data from the TanSat satellite, generated by [PERSON] et al. [43], and the TanSIF product produced by [PERSON] et al. [44] based on the satellite's data in the 758 nm band, with a temporal resolution of 4 days, a spatial resolution of 0.05\({}^{\circ}\), and a coverage period of 2017-2019, which is the dataset chosen for this experiment for the 2018-2019 range. The second dataset is the GOSIF product for the 2018-2020 period, which was derived by [PERSON] and [PERSON] based on the discrete OCO-2 SIF reconstruction with a resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\). The accuracy of the downscaled SIF product was validated by comparing it with both datasets. Furthermore, to assess the reliability of downscaled SIF based on the GPP-SIF mechanism relationship, we used the GOSIF GPP dataset estimated by [PERSON] and [PERSON] et al. from 2018 to 2020 as an additional validation [45].
# An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection
###### Abstract  
The challenging issues of computer networks and databases are not only the intrusion detection but also the reduction of false positives and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior, which are assumed to be potential intrusions or threats. Several techniques have already been successfully tried for this purpose. However, the normal and suspicious behaviors are hard to predict as there is no precise boundary differentiating one from another. Here, rough set theory and fuzzy set theory come into the picture. In this article, a hybrid approach consisting of rough set theory and intuitionistic fuzzy set theory is proposed for the detection of anomaly. The proposed approach is a classification approach which takes the advantages of both rough set and intuitionistic fuzzy set to deal with inherent uncertainty, vagueness, and indiscernibility in the dataset. The algorithm classifies the data instances in such a way that they can be expressed using natural language. A data instance can possibly or certainly belong to a class with degrees of membership and non-membership. The empirical study with a real-world and a synthetic dataset demonstrates that the proposed algorithm has normal true positive rates of 91.989% and 96.99% and attack true positive rates of 91.289% and 96.29%, respectively.  
intuitionistic fuzzy sets; fuzzy correlation; fuzzy relation; \(\alpha\)-cut of a fuzzy relation; similarity relation; fuzzy lower and upper approximation of sets +
Footnote †: journal: Applied Sciences  
1
# Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
###### Abstract  
Wireless sensor networks (WSNs) are structured for monitoring an area with distributed sensors and built-in batteries. However, most of their battery energy is consumed during the data transmission process. In recent years, several methodologies, like routing optimization, topology control, and sleep scheduling algorithms, have been introduced to improve the energy efficiency of WSNs. This study introduces a novel method based on a deep learning approach that utilizes variational autoencoders (VAEs) to improve the energy efficiency of WSNs by compressing transmission data. The VAE approach is customized in this work for compressing WSN data by retaining its important features. This is achieved by analyzing the statistical structure of the sensor data rather than providing a fixed-size latent representation. The performance of the proposed model is verified using a MATLAB simulation platform, integrating a pre-trained variational autoencoder model with openly available wireless sensor data. The performance of the proposed model is found to be satisfactory in comparison to traditional methods, like the compressed sensing technique, lightweight temporal compression, and the autoencoder, in terms of having an average compression rate of 1.5572. The WSN simulation also indicates that the VAE-incorporated architecture attains a maximum network lifetime of 1491 s and suggests that VAE could be used for compression-based transmission using WSNs, as its reconstruction rate is 0.9902, which is better than results from all the other techniques.  
data aggregation; energy optimization; data transmission; autoencoder; data compression Article