esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In thermal environments, suspended cables experience unexpected changes in their natural frequencies and mode shapes, resulting in shifted crossover points between in-plane and out-of-plane mode frequencies. Non-linear and effective interaction coefficients also increase or decrease with changes in temperature. When external excitation is applied in the in-plane direction, decreasing temperature leads to significant changes in resonant responses by reducing response amplitudes. However, when external force is applied in the out-of-plane direction, the out-of-plane response amplitude decreases with decreasing temperature, while the in-plane response amplitude increases with decreasing temperature at large excitation amplitudes. Additionally, smaller excitation amplitudes and frequencies can excite the saddle-node, pitch-fork, and Hopf bifurcations when the temperature increases, and periodic motions may transition to chaotic ones. Perturbation and numerical solutions agree well in the non-internal resonant region, but the perturbation solutions slightly overestimate or underestimate the response amplitude in direct or internal excited modes when the one-to-one internal resonance is excited.  
Conceptualization, W.N. and Y.Z.; methodology, Z.G.and H.L.; software, Z.G. and H.L.; validation, Z.G. and H.L.; formal analysis, Z.G. and H.L.; investigation, Z.G. and H.L.;  
Figure 9: Frequency-response amplitude curves with thermal effects when \(f_{w}=0.002\) and \(\sigma_{2}=0\): (**a**) in-plane; (**b**) out-of-plane.  
writing--original draft preparation, Z.G., H.L. and Y.Z.; writing--review and editing, H.L., W.N. and Y.Z.; visualization, W.N. and Y.Z.; supervision, W.N. and Y.Z.; funding acquisition, W.N. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
The research described in this paper was financially supported by National Natural Science Foundation of China (No. 12272139), Natural Science Foundation of Fujian Province (No. 2022J01290) and Collaborative Innovation Platform Project of Fuzhou-Xiamen-Quanzhou National Self-Innovation Zone (3502 ZCQXT2022002).  
Not applicable.  
The relevant data are all included in the paper.  
The authors declare no conflict of interest in preparing this article.
# LaRoAwan CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin
## 4 Methodology
### Experimental Setup
#### 4.2.2 Experiment Performed  
The signal is attenuated as per the slant range and path loss values shown in Table 3 to test the payload and end nodes modules. Various attenuators can be used to evaluate the output and practically determine incoming signal intensity. Here, fixed attenuators were used with a difference of 30 dB apart. The experiment was arranged in an enclosed, shielded box with an end node, and attenuators were introduced before the antenna to reduce the transmit signal strength. The received signal strength was compared as per path loss calculations. The experiment setups are shown in Figure 8.  
At first, the end node was enclosed in a box without a shield. It was observed that the signal was not lowering the level as per the attenuator. After troubleshooting, electromagnetic energy radiating from the antenna connector was detected. The box was then shielded with aluminum foil to create a Faraday cage-like environment. The attenuators were also covered with the same to prevent radiation escape. Figure 9 shows the schematic diagram representing this experimental setup. As per the slant range calculation, the maximum distance of 2705.3 km had a path loss of 148.8 dB at an angle of 0\({}^{\circ}\) elevation. Three attenuators were cascaded to achieve this distance. Each attenuator had 10-20-30 dB capabilities. They were connected in series to make one module with a maximum power of 60 dB, shown in Figure 9. The maximum value of 150 dB was achieved by combining the three modules.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bandwidth (kHz)** & **Spreading Factor** & **Sensitivity (dBm)** \\ \hline
125 & 12 & \(-\)139 \\
125 & 7 & \(-\)126 \\
250 & 12 & \(-\)136 \\
250 & 7 & \(-\)123 \\
500 & 12 & \(-\)134 \\
500 & 7 & \(-\)120 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Receive sensitivity threshold of the LoRaWAN Gateway used in an experiment.  
Figure 7: The hardware of end nodes setup with RFM 98 LoRa module and ATMEGA A328P Arduino Uno microcontroller board [28; 29].
# Detachment Energy Evaluation in Nano-Particle Cleaning Using Lateral Force Microscopy
## 5 Discussion  
The dominant energy required to detach the particle from the surface is the energy of the hydrogen bond (Si-OH bonding) represented as \(\mathbf{E_{\text{H-bond(total)}}}\)between the silica particle and the oxide film. This indicates that the particle detachment involves breaking the hydrogen bond between the nano-particle and the film. The possible dominance (importance) of hydrogen bonding had already been pointed out by estimation by measuring the vertical force interaction [25]; however, our experimental results measured the lateral force in the same direction as observed in the cleaning phenomenon, and a theoretical comparison **determined** it clearly. Another reason for the dominance of hydrogen bonding is due to the formation of silanol groups on the surface and enabling hydrogen bonds to occur because the surfaces of the silica nano-particles and oxide film are hydrated in the alkaline solution  
\begin{table}
\begin{tabular}{c|c c c} \hline
**Diameter [nm]** & \(\mathbf{W_{\text{Iffm}}}\) **[f]** & \(\mathbf{E_{\text{s-bond(total)}}}\) **[f]** & \(\mathbf{E_{\text{H-bond(total)}}}\) **[f]** & \(\mathbf{E_{\text{VdW}}}\) **[f]** \\ \hline
50 & 0.10 & 0.84 & 0.04 & \(6.81\times 10^{-4}\) \\
80 & 0.13 & 1.47 & 0.07 & \(1.11\times 10^{-3}\) \\
300 & 0.37 & 7.30 & 0.34 & \(4.36\times 10^{-3}\) \\ \hline \end{tabular}
\end{table}
Table 4: Measured results of work to detach nano-particle and calculated potential energy.  
\begin{table}
\begin{tabular}{c|c c} \hline
**Diameter [nm]** & \(\mathbf{E_{\text{VdW}}}\) **[f]** & \(\mathbf{a_{\text{def}}}\) **[nm]** \\ \hline
10 & 1.32 \(\times\)\(10^{-4}\) & 0.5 \\
100 & 1.39 \(\times\)\(10^{-3}\) & 2.1 \\
1000 & 1.56 \(\times\)\(10^{-2}\) & 9.9 \\ \hline \end{tabular}
\end{table}
Table 3: Typical calculated results of energy of van der Waals interaction.  
Figure 7: The potential energy of bonding and interaction between the silica nano-particle and the oxide film surface in the atmosphere. (**a**) Each calculated theoretical potential energy; (**b**) dominant total interaction energy.  
(pH 10.5). In the case of the siloxane bond, it was significantly higher, suggesting that it required a higher temperature in the atmosphere, unrelated to the water reaction [27].  
In addition, the detachment energy density was considered to evaluate the particle's size-dependent detachability as shown in Figure 8. It was implied that the smaller particle size is difficult to detach from the surface due to higher energy density (energy per contact area).
# Decimal Multiplication in FPGA with a Novel Decimal Adder/Subtractor
## 4 Decimal Multiplier
### Partial Product Reduction  
Partial product reduction adds the N partial products \(\{PP_{0},PP_{1},\ldots,PP_{N-1}\}\) with \(PP_{i}\) left shifted by \(i\) decimal places. Formally the addition of partial products is calculated according to Equation (17).  
\[\sum_{i=0}^{N-1}PP_{i}\times 10^{i} \tag{17}\]  
The multioperand addition is designed using an adder tree, similar to [44]. The tree has \(L=log_{2}N\) levels of adders. Each level \(i\) has \(\frac{N}{2^{i+1}}\) adders, where level 0 is the first set of adders.  
The complete partial product reduction tree for N operands of size N+1 uses \(\frac{N}{2}\times log_{2}(N)+N^{2}-N\) BCD/excess-6 single digit adders (_digAdder_). The critical path of the adder tree with N partials is given by \(log_{2}N\) digit adders plus \(4\times 2N\) carry chain bits.
# Evaluation of Machinery Readiness Using Semi-Markov Processes
###### Abstract  
This article uses Markov and semi-Markov models as some of the most popular tools to estimate readiness and reliability. They allow to evaluate of both individual elements as well as entire systems--including production systems--as multi-state structures. To be able to distinguish states with varying degrees of technical readiness in complicated and complex objects (systems) allows to determine their individual impact on the tasks performed, as well as on the total reliability. The application of the Markov process requires, for the process dwell times in the individual states, to be random variables of exponential distribution and the fulfilling Markov's property of the independence of these states. Omitting these assumptions may lead to erroneous results, which was the authors' intention to show. The article presents a comparison of the results of the examination of the process of non-parametric distribution with an analysis in which its exponential form was (groundlessly) assumed. Significantly different results were obtained. The aim was to draw attention to the inconsistencies obtained and to the importance of a preliminary assessment of the data collected for examination. The diagnostics of the machine readiness operating in the studied production company was additionally performed. This allowed to evaluate its operational potential, especially in the context of solving process optimization problems.  
semi-Markov model; Markov model; empirical data distribution; readiness; production machines +
Footnote †: journal: Applied Sciences
# JorGPT: Instructor-Aided Grading of Programming Assignments with Large Language Models (LLMs)
## 2 Related Work
### AI in Academic Assessment  
The application of Artificial Intelligence (AI) to academic assessment has gained growing interest in recent years. Traditional evaluation methods depend heavily on human graders, making them time-consuming and susceptible to subjectivity. AI-driven approaches seek to automate and improve assessment processes, aiming to enhance both efficiency and consistency. This section reviews previous research on AI-based evaluation systems, with particular emphasis on machine learning (ML) techniques and large language models (LLMs).  
Early AI-based assessment systems were designed for objective grading tasks, such as scoring multiple-choice questions and detecting plagiarism [19]. Over time, these approaches were extended to more complex domains, including automated essay evaluation [20] and the assessment of programming assignments [20]. The incorporation of deep learning techniques has significantly enhanced performance in these areas, allowing for improved understanding of both textual and structured inputs.  
Machine learning techniques have been widely applied to the evaluation of student submissions, particularly in areas such as Natural Language Processing (NLP) and code analysis. Traditional models such as Support Vector Machines (SVMs) and Random Forests have been used for automated essay grading [21], while deep learning architectures--such as Recurrent Neural Networks (RNNs) and Transformers--have demonstrated superior performance in interpreting complex responses [22]. In the context of programming assessment, methods including code similarity analysis [23] and execution-based grading [24] have been proposed to enhance the effectiveness of student solution evaluation.  
Recent advances in large language models (LLMs), such as OpenAI's GPT-4 [25] and Google's PaLM [26], have opened new avenues for automated assessment. These models have been utilized to deliver student feedback [27] and to evaluate open-ended responses [28]. While LLMs provide enhanced contextual understanding, challenges persist in ensuring fairness, reducing bias, and maintaining pedagogical rigor [29].  
A recent contribution to this area is AI-PAT [30], an LLM-based system designed for exam evaluation and the management of student appeals within computer science education. An analysis of over 850 assessments and 185 appeals revealed correlations between models such as ChatGPT and Gemini, although prompt design significantly influenced result variability. The study uncovered disagreements among human graders and a high rate of grade changes (74%) after appeals, underscoring the subjectivity inherent in manual grading and the necessity to enhance automated systems. While students valued the speed and detail of AI-generated feedback, concerns regarding trust and fairness were also expressed. The authors highlight that AI-PAT is scalable but requires human oversight, well-defined rubrics, and effective appeal procedures to ensure fair outcomes.
## 2 RTD-A Control Scheme
### Stability Analysis
#### 2.1.2 Polynomial Form  
The characteristic polynomial of the RTD-A controller in (18) can be written as in [14]:  
\[P(z^{-1})=(1-\theta_{R}z^{-1})(1-az^{-1})\left(\sum\limits_{i=1}^{N}\eta_{i}^{ 2}(1-az^{-1})+(\sum\limits_{i=1}^{N}a^{i}\eta_{i})z^{-1}\right)+\]  
\[\sum\limits_{i=1}^{N}\eta_{i}(1+\beta_{1i}(1-z^{-1}))(1-\theta_{R})((1-az^{-1} )z^{-(m_{0}+1)}-(1-az^{-1})z^{-(m+1)})\, \tag{18}\]  
where  
\[\beta_{1i}=\frac{a}{1-a}(1-a^{m+i});i=1,2,\ldots,N. \tag{19}\]  
The stability can be analyzed on the basis of the Jury's test. A necessary condition for all the roots of the \(n^{\text{th}}\) order polynomial \(\sum\limits_{i=0}^{n}c_{0}z^{i}\) to lie inside the unit circle is that:  
\[|c_{0}|<c_{n}, \tag{20}\]  
where the coefficient \(c_{0}\) and \(c_{n}\) can be calculated using (21) and (22).  
\[c_{0}=-a(1-\theta_{R})\sum\limits_{i=1}^{N}\eta_{i}\beta_{i}, \tag{21}\]  
\[c_{n}=\sum\limits_{i=1}^{N}\eta_{i}^{2}. \tag{22}\]  
The system will be stable if (23) and (24) is satisfied.  
\[P(z)\big{|}_{z=1}>0, \tag{23}\]  
\[P(z)\big{|}_{z=-1}>0\text{ for even power and }\left.P(z)\right|_{z=-1}<0\text{ for odd power}. \tag{24}\]  
The closed loop stability in terms of the robustness tuning parameter \(\theta_{R}\) can also be determined. The necessary condition is given in (25). The right-side term in (25) is defined as \(\theta_{\textit{ref}}\) for simplicity.  
\[\theta_{R}\geq 1-\frac{\sum\limits_{i=1}^{N}\eta_{i}^{2}}{a\sum\limits_{i=1}^{N }\eta_{i}\beta_{1i}}. \tag{25}\]  
The discussed necessary conditions in this section are used to check the stability of the liquid level control and pressure control in the evaluation section.
###### Abstract  
Large reservoir-induced landslides pose a persistent threat to the safety of the Three Gorges Project and the Yangtze River shipping channel. A comprehensive multi-field monitoring system has been established to observe potential landslide areas within the Three Gorges Reservoir Area. The tasks of effectively utilizing these extensive datasets and exploring the underlying correlation among various monitoring objects have become critical for understanding landslide movement patterns, assessing stability, and informing disaster prevention measures. This study focuses on the No. 1 riverside sliding mass of the Huangtupo landslide, a representative large-scale landslide in the Three Gorges Area. We specifically analyze the deformation characteristics at multiple monitoring points on the landslide surface and within underground tunnels. The analysis reveals a progressive increase in deformation rates from the rear to the front and from west to east. Representative monitoring points were selected from the front, middle, and rear sections of the landslide, along with four hydrological factors, including two reservoir water factors and two rainfall factors. These datasets were classified using the K-means clustering algorithm, while the FP-Growth algorithm was employed to uncover correlations between landslide deformation and hydrological factors. The results indicate significant spatial variability in the impacts of reservoir water levels and rainfall on the sliding mass. Specifically, reservoir water levels influence the overall deformation of the landslide, with medium-to-low water levels (146.32 to 163.23 m) or drawdowns (\(-\)18.70 to \(-\)2.16 m/month) accelerating deformation, whereas high water levels (165.37 to 175.10 m) or rising water levels (4.45 to 17.33 m/month) tend to mitigate it. In contrast, rainfall has minimal effects on the front of the landslide but significantly impacts the middle and rear areas. Given that landslide deformation is primarily driven by periodic fluctuations in reservoir water levels at the front, the movement pattern of the landslide is identified as retrogressive. The association rules derived from this study were validated using field monitoring data, demonstrating that the data mining method, in contrast to traditional statistical methods, enables the faster and more intuitive identification of reservoir-induced landslide deformation patterns and underlying mechanisms within extensive datasets.  
data mining; association rules; Huangtupo landslide; deformation monitoring; Three Gorges Reservoir
## 6 Methods for Obtaining Selenium-Containing Polysaccharides
### Encapsulation of Nanoselenium Particles into Polysaccharides
For example, several filamentous fungi species [374], yeast _Saccharomyces cerevisiae_[375], _Aspergillus terreus_[376], _Phanerochaete chrysosporium_[377], and even higher fungi such as _Lentinula edodes_[378] were already used. The list of species used for the production of SeNPs is much longer, and the species presented above should be treated as examples. The nanoselenium particles in fungal and microbial cultures may be present inside the cell or secreted into the culture medium. Depending on the location, they interact with either the components of the culture medium or the cytosol, leading to natural stabilization of SeNPs through the interactions with proteins and polysaccharides. Thus, SeNPs stabilized with polysaccharides can be obtained after extraction from the cultures of mushrooms or microbes enriched with high concentrations of selenium, as previously described for _L. edodes_ mycelia [13]. When the selenium content in the _L. edodes_ Se-polysaccharides was equal to 1045 ug/g, the X-ray absorption fine structure (XAFS) spectra analysis in the near edge region (XANES) confirmed that selenium in the Se-polysaccharides structure is zerovalent (at 0 oxidation state). The disadvantage of synthesis in vivo by macro- or microorganisms are the changes of the particle morphology and shape caused by the diversity of the reducing enzymes in organisms [289].
# An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC
## 4 Dataset & Simulation Experiments
### Binary-Class Room Classification Experiments Using a Matrix Code  
The best two models in phase 1 were VGG19 and VGG16 with all layers fine-tuned. Although this work was carried out through one of Compute-Canada Servers, i.e., Graham, there are many works in robotic applications that can be processed using local machines. Therefore, considering the time of training is an important factor for this phase, which has multiple binary classifiers for training. For this reason, the selected model to be trained in this phase was the VGG16 with 0 freezing layers, which has an accuracy of 93.29%, which is quite similar to the best one. The binary classifiers can be designed through grouping classes based on an exhausted matrix code, as explained in Reference [9]. The following 5 \(\times\) 15 matrix is the best for this experiment, as it does not have any repeated and complimented columns.  
\[M_{\text{5X15}}=\left[\begin{array}{cccccccccccccccc}1&1&1&1&1&1&1&1&1&1&1&1& 1&1\\ 0&0&0&0&0&0&0&1&1&1&1&1&1&1&1\\ 0&0&0&0&1&1&1&1&0&0&0&0&1&1&1\\ 0&0&1&1&0&0&1&1&0&0&1&1&0&0&1\\ 0&1&0&1&0&1&0&1&0&1&0&1&0&1&0\end{array}\right]\Rightarrow\left[\begin{array}{ c}bathroom\\ bedroom\\ dining room\\ kitchen\\ living room\end{array}\right] \tag{4}\]  
Let us take an example of the classifier in column 3 of matrix \(M\), which has [1 0 0 1 0] values. The first group of this classifier would be the classes with zero value, i.e., bedrooms, dining rooms, and living rooms. The second group is the classes with ones, i.e., bathrooms & kitchens. Table 3 shows the validation accuracies of all 15 binary fine-tuned VGG16 classifiers. The main advantage of the binary classifier is high accuracy depending on classification, as it reached 98.5% for this project, and the average of all 15 classifiers was 95.37%, which is still higher than the multi-classification approach.
## 3 Results
The rest parameters take the same values as in Figure 2. If we comparatively study Figures 3a and 7a, we note that, for low values of the inner radius, the modification of the dielectric constant of the core subtly affects the spectral characteristics of \(\mathrm{Im}\big{[}\chi_{\mathrm{SQD}}\big{]}\). On the other hand, the profile of the SQD absorption spectrum presents a strong dependence on the dielectric constant of the core. The same holds with regard to the spectral profile of \(\mathrm{Im}[\chi_{\mathrm{MNS}}]\). When we increase the dielectric constant of the core, the magnitude of the gain dip that arises on the MNS absorption spectrum is suppressed, for any possible value of the inner radius \(r_{1}\). Above a critical value of \(\varepsilon_{\mathrm{c}}\), which varies according to the value of \(r_{1}\), the gain is eliminated, as illustrated in Figure 7b. For \(\varepsilon_{\mathrm{c}}=7.5\), the spectral profile of \(\mathrm{Im}[\chi_{\mathrm{MNS}}]\) has a Lorentzian-like line shape, as opposed to the case with \(\varepsilon_{\mathrm{c}}=1\) shown in Figure 4b, where the spectral profile of the absorption coefficient \(\mathrm{Im}[\chi_{\mathrm{MNS}}]\) exhibits a Fano-type line shape. In the limiting case of a quite thick nanoshell (\(r_{1}\leq 1\) nm), the characteristic width of the MNS absorption peak is given by the approximate coefficient \(\mathrm{FWHM}_{\mathrm{MNS}}^{r_{1}\to 7.5\mathrm{nm}}=2G_{\mathrm{I}} \sqrt{\frac{3}{r_{2}G_{\mathrm{I}}}+2}\). Moreover, the gain dip does not practically depend on the dielectric constant of the core. The previously reported effects emerge due to the increase of \(\varepsilon_{\mathrm{c}}\), which leads to a substantial decrease of the factor \(\widetilde{\mathrm{Z}}_{\mathrm{I}}\) that appears in the R-dependent term of Equation (10).