esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Control Parameters  
During DF assays, pH, temperature, redox potential, and conductivity were monitored as control parameters. Table 3 shows the average values of pH, redox potential, and conductivity during tests with different HRTs.  
Figure 3: Global overview of the operational parameters in the HRT4, HRT5, and HRT6 assays.  
The temperature in the bioreactor was kept constant at 37 \(\pm\) 1 \({}^{\circ}\)C during all the assays. The maintenance of this parameter is crucial since the temperature at which DF occurs has a direct impact on the microbial metabolism and conversion efficiency of the substrate into \(\text{bioH}_{2}\).  
The pH provides information on the stability of the bioreactor as its variation depends on its buffer capacity. The variations in pH are related to the variations in the chemical species involved in the DF process, such as the accumulation of VFAs, ammonia, and CO\({}_{2}\). During the DF assays, the pH was adjusted to a value of 5 (if necessary, with the addition of 1 M HCl), to inhibit the activity of methanogenic hydrogenotrophic Archaea. It was observed that the pH remained relatively stable in all the assays at a pH value of approximately 5. The DF is considered stable for pH values between 4.5 and 5.5 since this is the pH interval in which the activity of hydrolytic and acidogenic bacteria is favored [24].  
The redox potential is a significant parameter during DF as it causes interference in bacterial physiology and in processes as important as maintaining the pH of the interior of cells and the transmembrane potential [25]. Both the HRT4 and HRT5 assays showed higher values of \(-47\pm 38\) and \(-56\pm 26\) mV, respectively, compared to the value of \(-110\pm 12\) mV in the HRT6 assay.  
There are a limited number of bibliographic references in the literature on adequate redox potential for \(\text{bioH}_{2}\) production. Some of them even express redox potential in terms of pH values [26], which makes the redox conditions more difficult to interpret. In any case, it is known that acidogenic bacteria participating in DF can produce \(\text{bioH}_{2}\) under the same redox conditions as methanogens (between \(-200\) and \(-350\) mV) [27]. As shown in Table 3, only relatively low reduction conditions have been achieved in the DF assays as compared to [27], but \(\text{bioH}_{2}\) was produced as will subsequently be shown in this work.  
Concerning the electrical conductivity, relatively stable values of 25 \(\pm\) 2, 25 \(\pm\) 1, and 22 \(\pm\) 2 \(\text{mS.cm}^{-1}\) were registered for the HRT4, HRT5, and HRT6 assays, respectively, which do not indicate an increase in the salt content inside the bioreactor.
## 2 Background Literature Review
### Traditional Approach
#### 2.1.2 Readers Exam  
This type utilizes human readers to assist BVI students during assessments, facilitating access to written content that may otherwise be inaccessible. This approach involves trained readers who verbally present exam questions and materials, allowing students to focus on comprehension and critical thinking rather than the mechanical aspects of reading [2; 15; 18; 23].  
One significant advantage of using human readers is the personalized support they provide. This interaction can help students process information more effectively, as the reader can clarify questions, modulate their tone for emphasis, and adjust the pace of delivery based on the student's needs [2; 15; 16; 18]. Additionally, the dynamic nature of a human reader allows for immediate feedback and clarification, which can enhance comprehension and retention of material [2; 16]. Research has shown that this mode of assessment can lead to higher engagement levels and greater satisfaction among BVI students, as they feel more supported during the examination process [2; 15; 23]. However, there are notable disadvantages to the Readers exam format. One primary concern is the potential for variability in reader effectiveness. Differences in the reader's skills, familiarity with the subject matter, and communication style can lead to inconsistency in how information is presented [2; 15; 18; 23]. Such variability may affect the student's performance, as reliance on a human reader introduces an element of subjectivity that can complicate assessment outcomes [2; 18]. Additionally, there may be challenges related to privacy and independence, as some students may feel uncomfortable or hindered by the presence of a reader during assessments, impacting their overall experience and performance [2; 18; 23].  
Furthermore, logistical issues can arise, such as the need for trained readers and the resources required to ensure their availability during exams. This can lead to increased costs and complexity in exam administration [2; 15; 18; 23].  
In summary, while the Readers exam offers significant benefits in terms of personalized support and improved comprehension for BVI students, it also presents challenges that necessitate careful consideration. As educational practices evolve, it is important to explore blended assessment methods that combine human support with technology to create a fairer and more effective assessment process for all students.  
Educators and assessment designers are increasingly advocating for more flexible assessment methods that accommodate different learning styles and cognitive processes to mitigate these challenges. This may include using assistive technologies, such as audio assessments or computer-based testing, which can provide alternative avenues for students to demonstrate their knowledge and skills [2; 15; 18; 20; 23]. By expanding assessment formats, educational systems can promote a more inclusive and equitable approach, ensuring that BVI students have the opportunity to succeed academically.
## 5 Experimental Outcomes, Comparative Analysis, and Discussion
### Experiment 1: Marine Vessel Classification
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **Best** & **Worst** & **Mean** & **Median** & **Std** & **Var** \\ \hline XG-BPSO & **0.995561** & **0.993814** & **0.994878** & **0.994970** & **0.000531** & **2.82 \(\times\) 10\({}^{-7}\)** \\ XG-PSO & 0.994379 & 0.990388 & 0.993022 & 0.993561 & 0.001328 & \(1.76\times 10^{-6}\) \\ XG-GA & 0.994098 & 0.982394 & 0.990003 & 0.990381 & 0.003349 & \(1.12\times 10^{-5}\) \\ XG-ABC & 0.992582 & 0.983179 & 0.989497 & 0.990521 & 0.002917 & \(8.51\times 10^{-6}\) \\ XG-BA & 0.994660 & 0.990496 & 0.993149 & 0.993396 & 0.001194 & \(1.42\times 10^{-6}\) \\ XG-WOA & 0.994660 & 0.989709 & 0.993029 & 0.993369 & 0.001442 & \(2.08\times 10^{-6}\) \\ XG-HHO & 0.994715 & 0.991679 & 0.993612 & 0.993901 & 0.000850 & \(7.23\times 10^{-7}\) \\ XG-ChOA & 0.994379 & 0.988417 & 0.992685 & 0.993227 & 0.001796 & \(3.23\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overall objective function (Cohen’s kappa) results—Cohen’s kappa maximization experiment. Figure 5: Vessel classification (kappa maximization plots) result visualization. (**a**) Objective box plot; (**b**) Objective convergence; (**c**) Precision recall curve; (**d**) Receiver operating characteristic curve; (**e**) Data diversity. Figure 6: Vessel classification (Cohen’s kappa) confusion matrix of the proposed XG-BPSO approach.
# Stability of Feature Selection in Multi-Omics Data Analysis
## 2 Materials and Methods
### Datasets  
In our study, we utilized the same datasets as those described in the benchmark study of feature selection by [PERSON] et al. [24], as we aimed to enhance their results by measuring feature-selection stability. Specifically, we selected the same 15 cancer datasets from The Cancer Genome Atlas (TCGA, [[http://cancergenome.nih.gov](http://cancergenome.nih.gov)]([http://cancergenome.nih.gov](http://cancergenome.nih.gov)), accessed on 30 October 2024) [8]. The selection criteria stipulated that datasets must contain at least 100 samples, have no missing observations, include the outcome variable for TP53 mutations, and not have more than 90% of observations in a single class. From these datasets, we included four omics layers: _cnv_ (CNV, copy number variation), _minra_ (miRNA, micro ribonucleicic acid), _mutation_, and _rna_ (RNA, ribonucleic acid). The _methylation_ block was excluded due to its substantial size, which would have resulted in excessive computational times.  
These datasets were also previously studied by [PERSON] et al. [48], who selected 18 datasets and considered the outcome variable to be the (censored) survival time. Various prediction methods from both machine-learning and statistical approaches demonstrated the limited utility of multi-omics data for this task.  
An overview of the 15 included datasets is provided in Table 1. Following [PERSON] et al. [24], we predict the presence of TP53 mutations, which have been associated with poor clinical outcomes in cancer patients [49].
# A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
## 4 Comparing EDZL and EDF\({}^{(k)}\)  
This section compares the schedulability of EDZL and EDF\({}^{(k)}\). EDZL and EDF\({}^{(k)}\) are not optimal, but they are practical with respect to implementation cost, scheduling overhead, and high processor utilization. This section investigates the dominance between their schedulability tests and compares the scheduling performance through simulations.
###### Abstract  
The use of surface electromyography (sEMG) in equine locomotion research has increased significantly due to the essential role of balanced, symmetrical, and efficient movement in riding. However, variations in sEMG signal processing for forelimb extensor muscles across studies have made cross-study comparisons challenging. This study aims to compare the sEMG signal characteristics from carpal extensor muscles under different filtering methods: raw signal, low-pass filtering (10 Hz cut-off), and bandpass filtering (40-450 Hz cut-off and 7-200 Hz cut-off). sEMG signals were collected from four muscles of three horses during walking and trotting. The raw signals were normalized and filtered separately using a 4 th-order Butterworth filter: low-pass 10 Hz, bandpass 40-450 Hz, or bandpass 7-200 Hz. For each filtered signal variant, eight activity bursts were annotated, and amplitude, root mean square (RMS), median frequency (MF), and signal-to-noise ratio (SNR) were extracted. Signal loss and residual signal were calculated to assess noise reduction and data retention. For m. extensor digitorum lateralis and m. extensor carpi ulnaris, bandpass filtering at 40-450 Hz resulted in the lowest signal loss and the highest amplitude, RMS, MF, and SNR after filtering. However, variations were observed for the other two carpal extensors. These findings support the hypotheses that the characteristics of myoelectric activity in equine carpal extensors vary depending on the filtering method applied and differ among individual muscles, thereby guiding future research on sEMG signal processing and, consequently, equine biomechanics. Since both noise and its reduction alter raw sEMG signals, potentially affecting data analysis, this study provides valuable insights for improving the reliability and reproducibility of equine biomechanics research across different sEMG studies.  
horse; extensor muscle; myoelectric activity; electromyography; signal processing 2025  
154737
# Thermal Control of CubeSat Electronics Using Thermoelectrics
## 2 Methodology  
### A CubeSat Thermoelectric Cooling System  
For this assessment, a simple thermoelectric cooling architecture, which could be applied to a CubeSat payload, is considered. In the CubeSat, the thermoelectric module would be placed in contact with the electronics to be cooled. This might consist of a printed circuit board (PCB), containing a central processing unit (CPU), in direct contact with the module. During operation, the CPU would generate heat that is removed by the thermoelectric module, which would absorb heat at its cold (bottom) side. The heat rejected from the hot (top) side of the module would then be conducted to a radiator (for example, via copper thermal strap) to be rejected to deep space. In order that a broad assessment of feasibility could be carried out, the dimensions of the system were able to be discounted. Likewise for simplicity, the overall thermal resistance between the thermoelectric module and the radiator (i.e., the thermal strap and radiator) was combined into a single variable. As a baseline scenario, the performance of the system without the thermoelectric module was used for comparison.
# 5G Physical Layer-Based Procedure to Support Time-Sensitive Networking
###### Abstract  
Deploying 5G in new and diverse use cases, such as Industry 4.0 and factory automation, requires 5G systems to harmonize with the communication technologies used in these industries. To this end, 3 GPP Release 16 and Release 17 have made significant progress in integrating 5G systems with the IEEE 802.1 working group specifications on Time-Sensitive Networking (TSN). This paper explains a method and architecture for supporting TSN over a wireless channel in a 5G network that adds the TSN synchronization function in a Small Cell gNB implementing the TSN Translator function (SC-TT). This TSN-capable small cell provides TSN over the wireless network to synchronize UEs with the Grand Master (GM) using the physical layer signal of the 5G radio frame and the transmission of the GM reference time to provide high-precision synchronization. This paper explores the pivotal role of a Slot Indicator Signal in enhancing synchronization precision, ensuring that the UE-GM synchronization occurs within an exceptionally narrow timeframe as small as 10 ns.  
5G; TSN; IEEE 802.1; synchronization; small cell; TSN translator Article
## 2 Related Works
### Online Learning Pattern Analysis  
An important aspect of analyzing student learning patterns is identifying the most influential features that affect academic performance. To quantify the impact of these features, researchers can employ techniques such as SHapley Additive exPlanations (SHAP) or examine the model's parameters. For example, [PERSON] et al. [19] and [PERSON] et al. [20] utilized SHAP to reveal that engagement frequency and submission times, and interaction timing and quiz scores, respectively, are the most significant features driving academic performance. Similarly, studies analyzing the parameters of models like Random Forest and Logistic Regression have shown that second-semester grades and exam registration counts are important in predicting performance [24].  
In addition to feature importance, clustering techniques have been used to categorize students into distinct interaction profiles. [PERSON] et al. [18] applied agglomerative clustering to identify various student groups, from high performers to minimally engaged learners. Similarly, [PERSON] et al. [25] showed that clustering based on student engagement with course materials could highlight different performance levels across groups.  
Other research has demonstrated strong correlations between clustered learning profiles and student performance. [PERSON] et al. [26] identified three distinct clusters where students with higher levels of interaction achieved better academic outcomes. Similarly, other research has reinforced this relationship, showing that students who fall into specific clusters, such as those with frequent early submissions, tend to outperform others [20].  
While many studies highlight influential features affecting academic performance, it is necessary to address the process of selecting these features beyond explanations. For example, although [PERSON] et al. [19] and [PERSON] et al. [20] utilize SHAP to identify significant attributes, they do not retrain their models to validate these findings. In contrast, [PERSON] et al. [18] effectively reduce features through agglomerative clustering, strengthening the reliability of their student interaction profiles. This gap highlights the need for future research to combine feature selection with model retraining for a more robust analysis of student learning patterns.  
In summary, while prior research has made significant advances in predicting student performance and analyzing learning behaviors, several gaps remain. First, there is no standardized approach to feature selection or validation across studies, limiting the generalizability of findings. Second, most methodologies assume a linear course structure, making them less applicable to non-linear, self-paced environments. Finally, the literature often treats performance prediction and pattern analysis as separate areas, overlooking potential synergies between these perspectives. Addressing these shortcomings is crucial for developing more robust and generalizable models of online student behavior.
###### Abstract  
This study explores the potential in substituting ordinary Portland cement (OPC) with industrial waste silica fume (SF) as a mineral filler in asphalt mixtures (AM) for flexible road pavements. The Marshall and indirect tensile strength tests were used to evaluate the mechanical resistance and durability of the AMs for different SF and OPC ratios. To develop predictive models of the key mechanical and volumetric parameters, the experimental data were analyzed using artificial neural networks (ANN) with three different activation functions and leave-one-out cross-validation as a resampling method. The addition of SF resulted in a performance comparable to, or slightly better than, OPC-based mixtures, with a maximum indirect tensile strength of 1044.45 kPa at 5% bitumen content. The ANN modeling was highly successful, partly due to an interpolation-based data augmentation strategy, with a correlation coefficient \(\text{R}_{\text{CV}}\) of 0.9988.  
recycling; silica fume; cement; asphalt mixtures; artificial neural network; data augmentation
## 1 Introduction
* Building and testing a multifractional drone equipped with state-of-art 5G MIMO antennae as well as a thermal camera and sensors for search-and-rescue operations. * Validating the results against other well-known modified empirical models in view of further development. The rest of this paper is organized as follows. Section 2 presents related work, and then highlights this paper's motivation. This is followed by Section 3, which describes the proposed model. Section 4 presents the implementation setup and discusses the results. Section 5 concludes the study.
## 4 MMPD Performance Analysis
### MMPD Phase-Detection Gain Analysis
The deviation between the calculated gain and the measured gain is mainly caused by the insufficient precision of the approximate substitution [8,16]. When \(\sigma=0.09\) UI and \(\varphi_{ref}=0.09\) UI, the output average voltage is input to \(\mu\), as shown in Figure 11:5  
Figure 11:6 Average output voltage of PD with various variance. Combining the numerical analysis and images, select (7) as an approximate expression. \[ \begin{split}\mu&\approx\frac{1}{\sqrt{\pi}}\int_{ \frac{\varphi_{ref}\varphi^{2}\varphi}{\sqrt{2\pi}}}^{\frac{\varphi_{ref} \varphi}{\sqrt{2\pi}}}1-y^{2}+\frac{1}{2}y^{4}dy\\ &=\frac{\varphi_{0}}{\sqrt{2\pi}\sigma}\Bigg{(}2-\frac{1}{3} \times\frac{3\varphi_{ref}{}^{2}+\varphi_{0}{}^{2}}{\sigma^{2}}+\frac{1}{10} \times\frac{5\varphi_{ref}{}^{4}+\varphi_{0}{}^{4}+10\varphi^{2}_{ref}\varphi _{0}{}^{2}}{2\sigma^{4}}\Bigg{)}\end{split}  \tag{12}\]  
The gain of the PD is the slope of the output versus input phase difference, when the data source is an 11-level pseudo-random binary sequence (PRBS11), the phase-detection density of DB PAM-4 is 21/32, which is higher than 1/2 phase-detection density 30%, the phase-detection gain is 30% higher. \(K_{\text{MM}}\):  
\[K_{\text{MM}}=\frac{21}{32}\frac{\partial\mu}{\partial\varphi_{0}} \tag{13}\]  
Input Gaussian jitter:  
\[ \begin{split} K_{\text{MM-Gauss}}=\frac{21}{32}\frac{\partial \mu}{\partial\varphi_{0}}\hskip-2.845276 pt=&\frac{21}{32\sqrt{2 \pi}\sigma}\Bigg{(}2-\frac{1}{3}\times\frac{3\varphi_{ref}{}^{2}+3\varphi_{0} {}^{2}}{\sigma^{2}}+\frac{1}{10}\times\frac{5\varphi_{ref}{}^{4}+5\varphi_{0}{ }^{4}+30\varphi^{2}_{ref}\varphi_{0}{}^{2}}{2\sigma^{4}}\Bigg{)}\end{split}  \tag{14}\]  
At \(\sigma=0.09\) UI, \(\varphi_{ref}=0.09\) UI, the measured gain at \(\varphi_{0}=0\) is 3.5287, and the calculated gain is 3.6362, which basically matches the gain error and proves that the gain calculation is correct. Input uniform jitter and sinusoidal jitter [16]:  
\[ \begin{split} K_{\text{MM-Uniform}}=\frac{21}{32\sqrt{3}\sigma} \approx 4.21\end{split}  \tag{15}\]  
Input sinusoidal jitter:  
\[ \begin{split} K_{\text{MM-Sin}}=\frac{21}{32\sqrt{2}\pi\sigma} \approx 1.64\end{split}  \tag{16}\]
## 2 The Challenges of Image Classification
### Training Sample Selection
This obvious statement is difficult to implement in reality, where the reference data may originate from: (1) other classification products featuring their own uncertainty; (2) in situ observations altered by instrumental malfunction or typographical errors; (3) crowdsourcing data featuring many subjective observations; and (4) poorly delineated training polygons. Furthermore, in the case of low-resolution imagery covering a heterogeneous landscape, pixels are usually composed of mixed classes, which ultimately have to be classified into a single class. Alternatively, some sub-pixel classification techniques [38; 39; 40] can be applied to infer the fractional proportion of classes within such pixels. Nevertheless, from the perspective of an image classifier, the mislabeled pixels introduce noise, which has to be dissected from the true spectral signature associated with a certain class. A limited number of studies have elaborated on the sensitivity of the image classifiers to mislabeled input data. In [34], it is found that the random forest algorithm is less sensitive to class mislabeling than support vector machines (SVMs) thanks to the bootstrap and random split operations incorporated into the random forest method. The robust multi-class AdaBoost (Rob_MulAda) classifier [41] was proposed to overcome the class overfitting problem of the standard AdaBoost algorithm, where there are mislabeled noisy training datasets. In [42], the Naive Bayes classifier was found to be less sensitive to class mislabeling as compared to the decision tree classifier [43], IBk instance-based learner [44] and the Sequential Minimal Optimization (SMO) support vector machine classifier [45]. Recently, in [46], a novel CMR-NLD method has been proposed to filter out the mislabeled samples by the thresholding of a covariance matrix calculated between sample dimensions. Ultimately, it has to be emphasized that a reliable training dataset is a prerequisite for the high accuracy of supervised image classification. Thus, it is advisable to perform data screening prior to classification instead of relying on a classifier to cope with noise in the input data.