esa-sceva/satcom-chunk-collection huggingface dataset:
# A Location Routing Problem with Time Windows Consideration: A Metaheuristics Approach
## 3 Results
### Benchmark Instances  
Since benchmarks for LRPTW are not available, we derived LRPTW instances from the three most notable LRP instances designed by [PERSON] [41], [PERSON] et al. [40], and [PERSON] [56]. We generated the time windows and defined the service time based on the best solution SALRP* from [PERSON] et al. [3], for both the customers and depots. The service time for each customer is set to be fixed for all instances. In generating the LRPTW instances, we add a tolerance time for the earliest and latest time for each customer. Due to its fixed tolerance value, the instances generated have different kinds of time window characteristic. Considering that the datasets are produced based on the best solution of LRP, the best-known solution of this problem will be the same as the LRP problem.
# A Spaceborne Mounting Method for Fixing a Cubic Fabry-Perot Cavity in Ultra-Stable Lasers
## 3 Experimental Results and Discussion
The wavelength of the laser was around 698.447 nm (429.227 THz in the frequency domain). As shown in Figure 5, the frequency of laser was tuned over an FSR 25 times. Because the tuning range was bigger than 1 FSR, two voltages could be recorded by the PD3 during each FSR tuning. Thus, 50 voltage data sets for the PD3 were recorded. It should be noted that the voltage of the PD3 in Figure 5 was not always the same; this was caused by the sampling and responding rate of the PD3 and the oscilloscope. A total of 10 sets of voltage data for PD3, which was acquired at the same time as the data shown in Figure 5, before and after the vibrations were recorded. Based on these data, the values of the average, standard deviation, and variance of each set of voltage data were calculated, which are shown Table 2.  
From Table 2, it can be noted that the values of the average, standard deviation, and variance are basically the same before and after the variation tests. The differences are due to the sampling and responding rates of the PD3 and the oscilloscope. Additionally, only the fundamental mode (TEM00) resonated in the cavity, which could be observed using the charge-coupled device (CCD) camera. Therefore, there is no obvious offset of the cavity that could influence the mode resonating in the cavity. Monitoring the voltage of the PD3 during the vibration tests can also indicate whether the FP cavity has a slight offset or not. If the voltage of the PD3 drops obviously or disappears, the FP cavity is definitely offset. Third, we monitored the transmission and error signals of the FP cavity while the frequency-tuning laser was locked to the FP cavity. Although the first and second criteria can help one to judge whether the FP cavity is offset or not during the vibration tests, this  
Figure 5: Voltage of PD3 during laser frequency tuning. is limited to the sampling and responding rate of the PD3 and the oscilloscope. So, these judgment criteria are still not clearly quantified. To solve this problem, a Pound-Drever-Hall (PDH)-technique-based ultra-stable laser system was applied. After modulation using an electro-optic modulator (EOM), the frequency tuning laser was locked onto the FP cavity with a high-speed PID electronic control system. Before and after the vibration tests, the transmission and error signals of the FP cavity were recorded to judge whether the FP cavity had an offset or not. The transmission signal can be directly obtained from the PD3, whereas the error signal can be obtained by mixing the alternating current (AC) signal of the PD2 with local oscillating signal of the EOM. Figure 6 shows the transmission and error signals before and after  
From the above-mentioned results of the three criteria, the proposed mounting method successfully fixed a 100 \(\times\) 100 \(\times\) 100 mm cubic FP cavity during aerospace vibration tests. The performance levels of the USLs were not influenced by the vibration tests, indicating that this method could be a good way to fix cubic cavities to space-based USLs.
## 3 Influence of NFTBT Structure's Size Parameters
### Influence of Distance between Adjacent Fasteners  
Fasteners are important components that connect rails and sleepers or other under-rail foundations, whose function is to maintain the correct position of the rails on the under-rail foundations and the reliable connection between the rails and sleepers. Fasteners can also prevent longitudinal and lateral movement of the rails and provide some elasticity for the NFTBT's structure. Therefore, in the analysis of the influence of the distance between the adjacent fasteners of the NFTBT, the displacement and stress of the NFTBT's structure at the rail seat at the longitudinal centerline of the track slab are mainly used as optimization design indicators to explore the mechanical characteristics of the track structure under the uneven settlement conditions of the tram load and the subgrade.  
After the greening and paving are completed, in the tram running stage, when the NFTBT's structure has the combined effect of the tram load, its self-weight, and the paving layer weight, the track structure displacement curve under the different distances between the adjacent fasteners conditions is shown in Figure 26 and the track slab and the stress change curve at the unfavorable position of the adjustment layer are shown in Figure 27.  
1. As the distance between the adjacent fasteners increases, the displacement of the NFTBT rail and rail platform first increases and then decreases, indicating that when the distance between the adjacent fasteners changes within a certain range, the rail is more affected by the uneven settlement of the subgrade, which is due to the larger track irregularity.
2. As the distance between the adjacent fasteners increases, the rail stress presents an upward trend: the greater the distance between the adjacent fasteners, the more intense the rising trend of rail stress. The stress at the bottom of the track slab first decreases and then increases, indicating that the distance between the adjacent fasteners is at a certain level. When the range is changed, the peak stress at the bottom of the NFTBT can be reduced.  
Figure 25: Influence of the distance between the centers of adjacent grouting holes on temperature stress of adjustment layer (In legend identification, “+” means positive temperature gradient; “\(-\)” means negative temperature gradient).  
3. Since the fasteners should have sufficient strength, buckle pressure, proper gauge and level adjustment, good elasticity, and insulation, they should also be standardized as much as possible, simple in structure, and easy to lay and repair. Therefore, the impact of the distance between the adjacent fasteners is related to the overall stiffness of the NFTBT and the ease of construction. Based on the comparative analysis of the displacement and stress changes of the track structure, the recommended distance between the adjacent fasteners is 567 mm.
# Using ChatGPT in Software Requirements Engineering: A Comprehensive Review
## 6 Limitations and Risks of Using ChatGPT
### Lack of Explainability and Transparency  
The results of LLMs are more difficult to understand because they often require additional explanation, making it even more difficult to penetrate their inner meaning and trace their genesis and fidelity.  
Explainability is one of the main limitations of using LLMs due to their immense number of parameters. For example, systems such as GPT-3 have 175 billion parameters, creating a highly complex network of interconnected nodes required for their operation. This complexity poses a significant problem for humans in understanding and interpreting the decision-making mechanisms implemented by the machine. In addition, training large language models requires collecting large amounts of data from multiple sources.  
The patterns and correlations learned from the data lead to implicit biases and associations that may not be immediately obvious or interpretable. Accordingly, when a large language model makes a decision, it is difficult to isolate what caused that decision because it has many interrelated parts. This difficulty also explains why the results generated by the model cannot be articulated easily.  
The main reason for the lack of explicability is that it may hinder the trust and full adoption of large language models (LLMs) in security applications. The inability to understand and decipher the decision-making mechanisms of LLMs may raise doubts about their trustworthiness, fairness, and potential biases. As a result, stakeholders such as policymakers, businesses, and users may require a longer period of time before they can rely on LLMs for essential contexts, prioritizing transparency and accountability [8].  
ChatGPT works by using large neural networks that have been trained on large amounts of data and refined to perform specific natural language processing (NLP) tasks. Although the software requirements generated by ChatGPT can be similar in quality to those generated by humans, it can be difficult for users to understand the mechanisms that led ChatGPT to produce the result [3].  
The need for explainability and transparency in LLM models such as ChatGPT is driving research to make GenAI more interpretable and reliable, which is essential for its adoption in critical software specification and other software engineering processes.
## 2 Materials and Methods
### Constellation, and Launch Considerations  
The MISTiC instrument payload has been specifically designed to be compatible with the ESPA-class microsatellites that have emerging over the last few years. The target payload mass of \(<\)15 kg, and target payload operating-power demand of \(<\)50 W are consistent with these spacecraft, even for configurations in which solar panels are not allowed to be in the view of one (the anti-sun) instrument face. Instrument physical dimensions, shown in Figure 3, together with host spacecraft are consistent with the size and configuration requirements of ESPA-class craft. The mass, power, and size accommodation needs have been verified through detailed physical layout, opto-mechanical design, and electronics design studies under the NASA Instrument Incubator Program.  
Relative to most micro-satellite payloads, MISTiC Winds would provide a very rich set of environmental observations, but these bring a higher data rate requirement as well. In continuous operation, the instrument generates about 5 Mbits/sec, following lossless onboard data compression. One option for relaying the data to the ground would employ X-band RF transmission to two ground stations, one in each polar region, with a maximum data latency of \(\sim\)50 min. A lower-latency approach  
Figure 9: Detailed (1000-node) thermal model analysis of passively cooled spectrometer temperature stability for worst-case sun-synchronous orbit shows substantial margin against requirements for stable IR sounder spectral calibration.  
would employ an S-band up-link to a GEO communications satellite such as NASA TDRS, followed by downlink. In light of the substantial commercial investment in small satellite LEO networks being developed by several companies, communication options for MISTiC Winds are expected to increase substantially in the next few years.
# Influence of Milling Techniques on the Performance of Wheat Straw Ash in Cement Composites
###### Abstract  
The worldwide production of cement is growing every year due to its increased use in the construction. Cement production is affiliated with an environmental concern as it contributes to the CO\({}_{2}\) emissions. It is imperative to reduce the cement production by incorporating supplementary cementitious materials in the cement composites. In this research study, wheat straw ash (WSA) was used as an alternate of ordinary Portland cement. The ash was ground separately with a ball mill and a disintegrator mill as well as with a combination of both to enhance its pozzolanic efficiency. Mortar and paste specimens were made by substituting cement with WSA (20% by weight). Ash specimens were examined in terms of particle size distribution, X-ray diffraction, and X-ray fluorescence analyses. The performance of the ash specimens in cement composites was examined via compressive and flexural strengths, and ultrasonic pulse velocity (UPV) tests. Isothermal calorimetric, thermogravimetric analyses (TGA), mercury intrusion porosimetry (MIP), and scanning electron microscopy (SEM) were also employed on the specimens. The results revealed that the particle size of the wheat straw ash specimens significantly reduced and specific surface area enhanced when ground with a combination of both milling techniques. Cement composites made with this type of ash demonstrated improved mechanical and physical properties, accelerated hydration reaction at the early ages, reduce calcium hydroxide content at the later ages, and densified microstructure.  
CO\({}_{2}\) emissions; wheat straw ash; milling; isothermal calorimetry; Thermogravimetric analysis Article Influence of Milling Techniques on the Performance of Wheat Straw Ash in Cement Composites
## 3 Results  
Figure 1 shows a representative distribution of the temporal error for a given metronome beat by age and gait conditions.  
Table 1 summarizes the repeated measures ANOVA results. The within-group difference according to the simultaneous cognitive task was found to be statistically significant (F (1.27) = 50.761, \(p\) < 0.05, \(\eta^{2}\) = 0.653), and the interaction between the age and the simultaneous cognitive task was significant (F (1.27) = 10.841, \(p\) < 0.05, \(\eta^{2}\) = 0.287). The effect between groups according to age was also statistically significant (F (1.27) = 13.109, \(p\) < 0.05, \(\eta^{2}\) = 0.327).  
Figure 1: Examples of the temporal errors between the metronome beat and the heel contact for each gait condition by age (Young subject: subject #1, Elderly subject: subject #8).  
Figure 2 shows the mean, standard deviation, and CV of the temporal errors for each participant. In all cases, temporal errors from the EG group were statistically greater than those of YG. In both age groups, the mean and standard deviation of the temporal errors were significantly increased by a cognitive task (i.e., backward counting). While there was no statistical difference in YG in terms of CV, EG exhibited significantly increased CV with a cognitive task.  
Figure 3 shows the cumulative distribution of all temporal errors by age and gait conditions. Overall, it can be seen that the older the participants, the wider the distribution of the temporal error becomes with the execution of additional cognitive tasks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Type III Sum of Squares**} & \multirow{2}{*}{**df**} & \multicolumn{2}{c}{**Mean**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**P Level**} & \multirow{2}{*}{**Partial Eta Squared (\(\eta^{2}\))**} \\  & & & & **Square** & & \\ \hline Gait conditions & 255,034.757 & 1 & 255,034.757 & 50.761 & * 0.000 & 0.653 \\ \hline Age & 115,845.156 & 1 & 115,845.156 & 13.109 & * 0.001 & 0.327 \\ \hline Gait conditions * Age & 54,470.768 & 1 & 54,470.768 & 10.841 & * 0.003 & 0.287 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical results for within-subject and between-subjects effects (*: Statistical difference according to gait conditions, age, and interaction between gait conditions and age).  
Figure 2: Mean, standard deviation (SD), and coefficient of variance (CV) of temporal errors between the metronome beat and the heel contact for each gait condition by age (*: Statistical difference between age groups by variable, a, b, c, d, e: Statistical difference by gait condition in each group; MET: walking with the metronome, MET + BC: walking with the metronome while counting backwards).
# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 3 Ore Rock Fragmentation Calculation Method  
We propose an ore rock fragmentation calculation method (ORFCM) by integrating the point cloud and image modalities. The ORFCM involves several steps, including image preprocessing, the construction of the Standard Deviation Map of the Local Gradient Differences (SDM) [23], multiscale adaptive edge detection based on SDM, the establishment of the edge-enhanced SDM, rock image segmentation using the watershed algorithm, and rock fragmentation calculation based on point cloud and image calibration mapping.
## 3 Theoretical Analysis and Proof
### Convergence Analysis  
This subsection proves the convergence of EAEND model (10) in a noise-free environment.  
**Theorem 2**.: _The steady-state computing error \(\lim_{k\to\infty}\|\varepsilon_{k}\|_{2}\) of EAEND model (10) is \(O(\sigma^{3})\)._  
Proof.: Considering \(\varepsilon_{k}^{\theta}\) as the \(\theta\)th subsystem of \(\varepsilon_{k}\), the \(\theta\)th subsystem of (14) is written as  
\[\varepsilon_{k+1}^{\theta}=(1.5-\mathrm{Y})\varepsilon_{k}^{\theta}-\varepsilon _{k-1}^{\theta}+0.5\varepsilon_{k-2}^{\theta}-\Gamma\sum_{i=0}^{k}\varepsilon _{i}^{\theta}+O(\sigma^{3}). \tag{15}\]  
Then, the \(\theta\)th subsystem of term \(\varepsilon_{k-1}\) is  
\[\varepsilon_{k}^{\theta}=(1.5-\mathrm{Y})\varepsilon_{k-1}^{\theta}- \varepsilon_{k-2}^{\theta}+0.5\varepsilon_{k-3}^{\theta}-\Gamma\sum_{i=0}^{k -1}\varepsilon_{i}^{\theta}+O(\sigma^{3}). \tag{16}\]  
Subtracting (16) from (15) yields\[\begin{split}\epsilon_{k+1}^{\theta}=&(2.5-\mathrm{Y}- \Gamma)\epsilon_{k}^{\theta}-(2.5-\mathrm{Y})\epsilon_{k-1}^{\theta}+1.5 \epsilon_{k-2}^{\theta}-\\ & 0.5\epsilon_{k-3}^{\theta}+O(\sigma^{3}).\end{split} \tag{17}\]  
Define \(\Phi_{k+1}^{\theta}\!=\![\epsilon_{k+1}^{\theta},\epsilon_{k}^{\theta}, \epsilon_{k-1}^{\theta},\epsilon_{k-2}^{\theta}]^{\mathrm{T}}\). Then, (17) can be rearranged as  
\[\Phi_{k+1}^{\theta}=P\Phi_{k}^{\theta}+O(\sigma^{3}), \tag{18}\]  
with  
\[P=\left[\begin{array}{cccc}2.5-\mathrm{Y}-\Gamma&-(2.5-\mathrm{Y})&1.5&-0.5 \\ 1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\\ \end{array}\right].\]  
Then, (18) can be generalized as  
\[\begin{split}\|\Phi_{k+1}^{\theta}\|_{2}&\leq\|P\Phi_{k}^{ \theta}\|_{2}\!+\!\|O(\sigma^{3})\|_{2}\\ &=\|P\[PERSON]_{k}^{\theta}\|_{2}\!+\!O(\sigma^{3})\\ &\leq\|P\|_{2}\|\[PERSON]_{k}^{\theta}\|_{2}\!+\!\|P\|_{2}O(\sigma^{3}) \\ &\leq\|P\|_{2}\|P\[PERSON]_{k-1}^{\theta}\|_{2}\!+\!\|O(\sigma^{3}) \\ &=\|P\|_{2}\|P\[PERSON]_{k-1}^{\theta}\|_{2}\!+\!O(\sigma^{3})\\ &\leq\|P\|_{2}^{2}\|\[PERSON]_{k-1}^{\theta}\|_{2}\!+\!O(\sigma^{3}) \\ &\qquad\vdots\\ &\leq\|P\|_{2}^{k}\|\[PERSON]_{1}^{\theta}\|_{2}\!+\!O(\sigma^{3}).\end{split}\]  
The four real parts of the eigenvalues of matrix \(P\) are in the range \([-1,1]\), it can be inferred that \(\lim_{k\to\infty}\|P\|_{2}^{k}\!=0\)[51]. Therefore, it has  
\[\lim_{k\to\infty}\|\[PERSON]_{k+1}^{\theta}\|_{2}\!\leq\lim_{k\to\infty}\|P\|_{2}^{ k}\|\Phi_{1}^{\theta}\|_{2}\!+\!O(\sigma^{3})=O(\sigma^{3}).\]  
According to the definitions of \(\Phi_{k+1}^{\theta}\) and \(\epsilon_{k}^{\theta}\), it is easy to conclude that the steady-state computing error \(\lim_{k\to\infty}\|\epsilon_{k}\|_{2}\) is \(O(\sigma^{3})\). The proof is complete.
# Malware Classification Based on Shallow Neural Network
###### Abstract  
The emergence of a large number of new malicious code poses a serious threat to network security, and most of them are derivative versions of existing malicious code. The classification of malicious code is helpful to analyze the evolutionary trend of malicious code families and trace the source of cybercrime. The existing methods of malware classification emphasize the depth of the neural network, which has the problems of a long training time and large computational cost. In this work, we propose the shallow neural network-based malware classifier (SNNMAC), a malware classification model based on shallow neural networks and static analysis. Our approach bridges the gap between precise but slow methods and fast but less precise methods in existing works. For each sample, we first generate n-grams from their opcode sequences of the binary file with a decompiler. An improved n-gram algorithm based on control transfer instructions is designed to reduce the n-gram dataset. Then, the SNNMAC exploits a shallow neural network, replacing the full connection layer and softmax with the average pooling layer and hierarchical softmax, to learn from the dataset and perform classification. We perform experiments on the Microsoft malware dataset. The evaluation result shows that the SNNMAC outperforms most of the related works with 99.21% classification precision and reduces the training time by more than half when compared with the methods using DNN (Deep Neural Networks).  
malware; neural network; n-gram; classification; static analysis +
Footnote †: journal:  
_Future Internet_
## Materials and Methods
### FGI AIRS Processing Chain and Calibration  
The FGI AIRS real-time processing is built on two software modules running on Raspberry Pi. The real-time module is programmed in C-language and handles the time-critical, low-level hardware communications and RTKLib processing. The high-level module is programmed in Python (version 3.6), and it handles processing and storing of the data and communications to the cloud and serial clients. The two modules transfer data between each other over an internal TCP socket.  
The AIRS installation geometry, the definition of attached camera systems, and types of real-time outputs are configured using an INI-file placed on the USB flash drive. Each output message is triggered by a pulse received on one of the input GPIO pins. Once the real-time processing code receives the trigger pulse, it will start calculating a dataset for the trigger event (Figure 2). First, the exact time of the exposure is calculated by converting the internal CPU time of the trigger pulse to GPS time and applying the pulse-to-exposure delay parameter. Second, the tilt corrected irradiance spectra are calculated by using the spectrometer, photodiode, and INS data (Equation (1)). Then, irradiance on each configured camera band is calculated by interpolating the irradiance spectrum at the time of the exposure and resampling it to match the spectral sensitivities of the camera bands. Third, the camera view direction is calculated by interpolating the INS data to the time of the exposure and by applying boresight parameters of the camera installation. Fourth, the camera GNSS position is calculated by interpolating the RTK data to the time of the exposure and by applying the camera lever arm offsets. This data is then combined to a single data message, which is stored to the USB drive and, optionally, sent to the cloud over WiFi/mobile data modem and to the local image processing unit over a serial cable.  
If AIRS data is needed after the mission, a post-processing algorithm can be used to reprocess the data. The post processing allows a higher quality of positioning by using two-directional PPK processing, usage of kinematic GNSS ground station data instead of RTK, and averaging irradiance readings in longer time-windows, but otherwise follows the same processing method as the real-time algorithm.