esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental
### Electrochemical Testing  
To test the electrochemical performance of the printed MWCNTs/AC/BNNTs composite electrode material in a three-electrode system, a stainless-steel sheet (printed with MWCNTs/AC/BNNTs) with dimensions of 1 \(\times\) 1 cm\({}^{2}\) was used directly as a working electrode, with Pt foil as a counter electrode and Hg/HgO as a reference electrode. The electrochemical performance of the printed MWCNTs/AC/BNNTs composite electrode material was tested at an electrochemical work station (SP-150 model, Bio-Logic, France, driven by EC-lab 11.25 software) in a 6 M KOH electrolyte solution. Furthermore, the kinetics of the electrical conductivity of the MWCNTs/AC/BNNTs composite electrode material were studied by electrochemical impedance spectroscopy (EIS) in the frequency range of 100 kHz to 0.01 Hz at the same electrochemical station.  
The specific capacitance of the working electrode was computed by Equation (1) [19; 20].  
\[\mathrm{C_{m}}=\frac{2\mathrm{1 SVdt}}{\mathrm{m}\mathrm{\Delta V}^{2}} \tag{1}\]  
where, \(\mathrm{\Delta V}\), I, m and \(\mathrm{\int}\)Vdt represent the working potential window (V) of the MWCNTs/AC/BNNTs composite electrode material, electron flow of the charging-discharging cycle (A), mass of the deposited MWCNTs/AC/BNNTs composite material (g) and integral area under the discharge curve of the galvanostatic charge/discharge (GCD) cycle, respectively.
# Position Accuracy Improvement by Implementing the DGNSS-CP Algorithm in Smartphones
## 3 Approach and Implementation  
### Target Smartphone Specification  
The target devices used in this study were a Galaxy S5 (Samsung Electronics, Suwon, Korea) and LG V10 (LG Electronics, Seoul, Korea), which were released in April 2014 and October 2015, respectively. The Galaxy S5 includes a Qualcomm MSM8974 AC Snapdragon 801 Chipset running Android OS v4.4.2 (KitKat). The Snapdragon 801 processor obtains location information from IZat Gen8B [20] using a tri-band location platform that supports GPS, GLONASS, and BeiDou. The LG V10 has a Qualcomm Snapdragon 808 MSM8992 running Android 5.1.1 (Lollipop), and contains an IZat Gen8C [21]. This platform also supports GPS, GLONASS, and Beidou.  
Figure 2 shows the GNSS Satellite in View (GSV) message captured from National Marine Electronics Association (NMEA) data obtained by the Galaxy S5. The LG V10 captures similar logged messages. The GPGSV, GLGSV, and BDGSV data printed in the message represent the satellite IDs for GPS, GLONASS, and BeiDou, respectively. Thus, these three GNSS constellations are used to calculate the device's position, and DGNSS corrections for GPS, GLONASS, and Beidou should be used together to improve the position accuracy.  
The use of all three systems provides users with the benefits of observing more satellites in view and acquiring more reliable location information, but the imprecision of the time offset means that the position is not as precise as when the time systems are perfectly synchronized [22]. There are two approaches to mitigate this effect: use the time offset correction broadcast in the navigation messages, or introduce an additional unknown into the positioning solution [23]. Because the positioning algorithm is proprietary, and therefore not open to the public, we must guess which algorithm is used in the target device and check that this is correct in the field test experiment.
# JorGPT: Instructor-Aided Grading of Programming Assignments with Large Language Models (LLMs)
## 3 Materials and Methods  
### General Approach  
To address the challenges of consistency, transparency, and feedback quality outlined in the previous section, the primary objective of this research was to design and validate an automated evaluation system for programming assignments in an undergraduate \"Introduction to Programming\" course, leveraging large language models (LLMs). The primary goal was to replicate the assessment standards typically applied by human instructors, thereby reducing manual grading workload without compromising evaluation quality. The system was developed to integrate seamlessly with the Canvas learning management system, facilitating a more efficient grading pipeline.  
An overview of the system's operation is illustrated in Figure 1. The assessment workflow begins with the export of student submissions from Canvas in CSV format. This dataset is processed through a graphical user interface built with PyQT5, where instructors can select the desired LLM for evaluation and optionally edit the grading rubric. Once configured, the system automatically analyzes each submission using the selected model, generating both grades and formative feedback. The results are stored in an Excel file for instructor review and, if needed, possible revision before final feedback is delivered to students via Canvas, as shown in Figure 1. This approach enables scalable and consistent evaluation across large groups of students, while preserving instructor oversight.
# Adaptive Method for Packet Loss Types in IoT: An Naive Bayes Distinguisher
## 6 Conclusions  
In this paper, we developed a new congestion control mechanism for IoT with a classification model which called NB-TCP to distinguish packet loss types and enhance network performance. The algorithm used Naive Bayesian model to classify congestion loss and wireless random loss effectively. We explained the value extraction of features and described the implementation process of NB-TCP in detail. The result proved that the improvement method has higher accuracy and more enhanced performance than the comparison methods. Our method performs better when the wireless random loss is high and congestion loss is not heavy. With different WER and transmission data rates in IoT, NB-TCP have almost improved throughput effectively. The improved method also has good friendliness to other methods and the fairness of resource allocation.  
**Author Contributions:** Conceptualization, Y.C. and L.L.; methodology, Y.C. and L.L.; software, Y.C.; validation, Y.C.; formal analysis, Y.C.; investigation, Y.C., L.L., X.Y. and X.L.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C., L.L., X.Y. and X.L.; supervision, L.L.; project administration, L.L.; funding acquisition, L.L.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (Grant No. 61771002) and the Fundamental Research Funds for the Central Universities (2017 JBM025).  
**Conflicts of Interest:** The authors declare no conflict of interest.
### Deep Learning for Object Detection  
Deep learning object detection models were initially developed for natural images, but have been successfully adapted for identifying and localizing different lesions in medical images [19; 20].  
These models comprise two main components: the backbone and the detector. The backbone, typically a convolutional network, such as Residual Network (ResNet) or VGG (the model from Visual Geometry Group), is responsible for extracting features from the input images. The detector, also known as the head, analyzes these features to classify and localize the objects within the defined classes. The object detectors can be categorized according to their anchor dependence or the number of stages in their architecture. Single-stage detectors, such as Fully Convolutional One-Stage Object Detection (FCOS) and RetinaNet use dense sampling to analyze the images in a single-shot fashion. In contrast, two-stage detectors, such as Region-based Convolutional Neural Networks (R-CNNs), have an initial stage where region proposals are computed that will then be classified in the second stage. Single-stage detectors are more efficient and the two-stage detectors tend to be more accurate. Regarding anchor dependence, anchor-based models, like RetinaNet or R-CNN, rely on predefined bounding boxes (anchors) for object detection. Meanwhile, anchor-free models, like FCOS, eliminate this dependency, simplifying the detection process. The performance of the object detection model is mainly affected by three challenges: intra-class variation, a high number of classes, and efficiency [21].
# Adaptive Neural Network for a Stabilizing Shunt Active Power Filter in Distorted Weak Grids
## 2 System Configuration
### Robust Synchronizer Filter (RSF)  
Synchronization of the SAPF reference signal with the grid voltage is necessary for grid connection. RSF can separate the harmonic component from the main component of the voltage signal in unbalanced conditions and apply a without-harmonic signal to the PLL. First, by using the Clarke transform matrix, the voltage \(v_{s}\) in the \(abc\) domain is the transformed \(\alpha\beta\) domain according to the following equations.  
\[\begin{bmatrix}v_{\alpha}\\ v_{\beta}\end{bmatrix}=\sqrt{\frac{2}{3}}\begin{bmatrix}1&-\frac{1}{2}&-\frac {1}{2}\\ 0&\frac{\sqrt{3}}{2}&-\frac{\sqrt{3}}{2}\end{bmatrix}\begin{bmatrix}v_{\alpha} \\ v_{b}\\ v_{c}\end{bmatrix} \tag{14}\]  
In the \(\alpha\beta\) domain, the following equation holds for the transformed source voltage signal \(v_{\alpha\beta}\) due to harmonics distortion:  
\[\begin{bmatrix}v_{\alpha}\\ v_{\beta}\end{bmatrix}=\begin{bmatrix}v_{\alpha,fund}+v_{\alpha,har}\\ v_{\beta,fund}+v_{\beta,har}\end{bmatrix} \tag{15}\]  
where \(v_{\alpha,fund}\) and \(v_{\beta,fund}\) are the fundamental and \(v_{\alpha,har}\) and \(v_{\beta,har}\) are harmonic components in the \(\alpha\beta\) domain. In this step, RSF is used to remove the harmonic parts and separate fundamental components from harmonic components according to the following equations.  
\[v_{\alpha,fund}=\frac{K}{s}\Big{(}v_{\alpha}-v_{\alpha,fund}\Big{)}+\frac{2 \pi f_{c}}{s}\Big{(}-v_{\beta,fund}\Big{)} \tag{16}\]  
\[v_{\beta,fund}=\frac{K}{s}\Big{(}v_{\beta}-v_{\beta,fund}\Big{)}+\frac{2\pi f _{c}}{s}\Big{(}v_{\alpha,fund}\Big{)} \tag{17}\]  
where \(K\) is a constant gain parameter and \(f_{c}\) is the cut-off frequency. Then, by using the inverse Clarke transform matrix, the fundamental component in the \(\alpha\beta\) domain is transformed to the \(abc\) domain according to the following equation.  
\[\begin{bmatrix}v_{\alpha,fund}\\ v_{b,fund}\\ v_{c,fund}\end{bmatrix}=\sqrt{\frac{2}{3}}\begin{bmatrix}1&0\\ -\frac{1}{2}&\frac{\sqrt{3}}{2}\\ -\frac{1}{2}&-\frac{\sqrt{3}}{2}\end{bmatrix}\begin{bmatrix}v_{\alpha,fund} \\ v_{\beta,fund}\end{bmatrix} \tag{18}\]  
By using (18), the obtained \(v_{\alpha\text{,}fund}\) is converted into its unity form, which serves as the synchronization signal \(sin(\omega t+\theta)\).  
\[sin(\omega t+\theta)=\frac{v_{\alpha,fund}}{\sqrt{v_{\alpha,fund}{}^{2}+v_{ \beta,fund}{}^{2}}} \tag{19}\]
## 5 Discussions
### Extension of the U-WSN Use-Case to NTN-Aided IoT  
The current state of the art shows the need for 3D modeling and simulation of NTN, including moving in space connected objects. Problems studied in the case of UWSN could be extended to general non-terrestrial network use cases. Opportunistic routing under energetic constraints is challenging, particularly in autonomous crewless vehicles and nano-satellites. Even though recent works on energy harvesting show the opportunity to optimize the overall system either through flying ad-hoc networks deployment [84] and drones trajectory control [85] or by using efficient data collection [86] combined with fast wireless power transfer [87]. The system control could be via centralized, distributed, or multi-tier architectures. Designed for WiFi devices, crowd-based schemes [88] allow autonomous adaptive placement without exploiting the device's location. The applicability of those distributed schemes raises energy optimization needs when it is a matter of things with low-power. The self-organization of the NTN is particularly challenging, to efficiently collect and send data via the network on intermittent connection to infrastructure to the cloud. Autonomy and placement to maintain permanent connectivity [89] dictate revisiting communications from UAVs to the cloud, which are investigated in the context of 5G and beyond [90].  
Furthermore, in addition to the issues mentioned in this paper, there are wide open challenges in U-WSN applications. For example, the charging challenges, collision avoidance and swarming, etc.  
The UAV missions necessitate effective energy management for battery-powered UAVs. Reliable, continuous, and intelligent management can help UAVs achieve their missions and prevent loss and damage. The UAV's battery capacity is a crucial factor  
Figure 8: The number of sensors (\(N_{node}\)) that participate the communications. The \(T_{sync,i}\) (\(i=1,2,3,4,5\)) are the synchronization intervals.  
in enabling sustainable missions. However, as the battery capacity increases, its weight increases, which causes the UAV to consume more energy for a given task. The main directions in the literature to mitigate the limitations in UAV batteries are UAV battery management [91], wireless charging for UAVs [92], and solar powered UAVs [93].  
UAVs can collide with obstacles by moving or stationary objects in indoor or outdoor environments. Therefore, during UAV flights, it is vital to avoid accidents with these obstacles. Thus, the development of UAV collision avoidance techniques has gained research interest.
# ErbB4 Is a Potential Key Regulator of the Pathways Activated by NTRK-Fusions in Thyroid Cancer
## 3 Results
### Regulators for the Genes Upregulated in Samples with NTRK1 and NTRK3 Gene Fusions  
To determine which proteins were engaged in activating the gene set observed, we performed an upstream search for the regulators of these genes, using the GeneWays database of the human gene regulatory network, employing the key-node search algorithm \"regulator search\" of the geneXplain platform (Supplementary Table S1). Among the top 10 regulators found, we identified: SRC, STAT5A, LCK, VAV1, STAT3, STAT1, MYO15A, EGF, CBL, and FYN. In addition, the log2 FoldChange values for all discovered regulatory genes showed that three of them were equally upregulated in the samples with _NTRK1_ and _NTRK3_ gene fusions, but not in the no-NTRK samples: _ERBB4_, _BMX_, and _BMPRIB_ (Figure 3).  
GSEA for all regulators using the \"reactome pathways\" database identified two small pathways, \"signaling by EGFR in cancer\" (false discovery rate, FDR = 0.016) and \"signaling by ERBB4\" (FDR = 0.038) (Supplementary Table S2). We compared the normalized expression value distributions for the genes that belong to these two pathways of signaling by EGFR and ERBB4 in normal thyroid gland tissues, in thyroid cancers without an NTRK gene fusion, and in thyroid cancers with _NTRK1_- and _NTRK3_-fusions (Figure 4). The median normalized values were higher for all genes for samples with _NTRK1_- or _NTRK3_-fusions, with the highest difference for _ERBB4_, _BMX_, and _EPOR_.  
Figure 2: Tree map of biological processes found by GSEA of the upregulated genes in the _NTRK1_ and _NTRK3_ tumors.
## 2 System Description  
### Parabolic Trough Collector Field  
The parabolic trough collector (PTC) is able to track the sun and concentrate the solar radiation into a focal line. Each device is supported by a rigid structure, over which the aluminum parabolic profiles were assembled. The reflector is an aluminum plate, the parabolic shape is obtained because the plate rest over the profiles. The absorber is a black copper tube. The rays of the sun are reflected at the focal point where the absorber is located. A fraction of the solar radiation is transferred to a fluid, which flows inside the absorber pipe [15]. The PTC design was based on the works published by [PERSON] et al. [39] and [PERSON] et al. [40]. The material and manufacture process considered the [PERSON]'s design and were installed at the CICAp. Figure 1 shows the PTC field. The collectors' array consists of 15 parabolic trough collectors of 38.43 m\({}^{2}\) area and divided into three rows. The tracking system was one-axis North-South direction.  
Table 1 shows the design parameters of the experimental PTC with 90\({}^{\circ}\) rim angle (see Figure 1). Experimental correlation for \(\eta_{I}\) (at volumetric flow of 4 L/min) according to the ASRAHE 93-1986 (RA 91) standard is shown in Equation (1). The collector thermal performance was reported by [PERSON] et al. [40].  
\[\eta_{I}=0.6128-2.3025\bigg{(}\frac{\Delta T}{G_{b}}\bigg{)} \tag{1}\]  
The collectors' array was connected in series and installed on the building roof, the delivery pump takes the HTF (water-ethylene 30%) at the inlet temperature and feeds every row, the outlet  
Figure 1: Photograph of parabolic trough collector during the test.  
temperature of the collectors increases according to the solar radiation. The HTF is stored inside two storage tanks with a capacity of 300 L in each one, the temperature increases gradually during the day. The operating conditions in the collectors' array, such as temperature, solar radiation, and mass flow rates were monitored using the instrumentation described in the next section.  
Table 2 shows the optical parameters measured and calculated for the PTC designed.  
Instrumentation of PTC  
The temperature was measured by a set of thermocouples type \"T\"; to register the data of solar irradiance it was utilized a couple of _Licor_ pyranometers series PY101122 and PY106225. The mass flow rate was registered using a propeller flowmeter. An HP data acquisition system using the software Agilent VEE Pro 9.2 was utilized in order to process the outlet signals from the different sensors.
## 4 Experiment
### Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Rental Volume**} & \multicolumn{2}{c}{**Return Volume**} & \multicolumn{2}{c}{**Rental Volume**} & \multicolumn{2}{c}{**Rental Volume**} \\ \cline{2-9}  & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline \multicolumn{9}{c}{15 min} & \multicolumn{5}{c}{45 min} \\ \hline HA & 1.2370 & 0.7269 & 1.2204 & 0.7194 & 2.5817 & 1.4848 & 2.5270 & 1.4594 \\ ANN & 0.8637 & 0.4951 & 0.8660 & 0.4976 & 0.9865 & 0.6190 & 1.1461 & 0.7451 \\ XGBoost & 0.8737 & 0.4953 & 0.8754 & 0.4986 & 1.0827 & 0.6738 & 1.0857 & 0.6803 \\ SVR & 0.9649 & 0.5635 & 0.9706 & 0.5663 & 1.1277 & 0.7307 & 0.9800 & 0.6106 \\ LSTM & 0.7139 & 0.3843 & 0.7397 & 0.4034 & 0.8513 & 0.5140 & 0.8509 & 0.5084 \\ GCN & 0.7600 & 0.4196 & 0.6815 & 0.3585 & 0.8477 & 0.5066 & 0.8475 & 0.5109 \\ GC-LSTM & 0.6415 & 0.3314 & 0.6735 & 0.3551 & 0.8410 & 0.5040 & 0.8452 & 0.5071 \\ STGA-LSTM & 0.6127 & 0.3101 & 0.6107 & 0.3088 & 0.7912 & 0.4663 & 0.7930 & 0.4690 \\ \hline \multicolumn{9}{c}{30 min} & \multicolumn{5}{c}{60 min} \\ \hline HA & 1.9530 & 1.1399 & 1.9087 & 1.1155 & 3.2052 & 1.8228 & 3.1310 & 1.7844 \\ ANN & 0.9450 & 0.5740 & 1.0903 & 0.6852 & 1.0845 & 0.6910 & 1.2304 & 0.8218 \\ XGBoost & 0.9848 & 0.5999 & 0.9917 & 0.6035 & 1.1825 & 0.7602 & 1.1700 & 0.7547 \\ SVR & 1.1041 & 0.6949 & 0.9454 & 0.5747 & 1.2613 & 0.8383 & 1.0775 & 0.6886 \\ LSTM & 0.7912 & 0.4573 & 0.8001 & 0.4651 & 0.9484 & 0.5839 & 0.9972 & 0.6218 \\ GCN & 0.8014 & 0.4651 & 0.7784 & 0.4480 & 1.0230 & 0.6414 & 0.9406 & 0.5834 \\ GC-LSTM & 0.7266 & 0.4078 & 0.7332 & 0.4134 & 0.9126 & 0.5527 & 0.9137 & 0.5586 \\ STGA-LSTM & 0.7098 & 0.3952 & 0.7118 & 0.3978 & 0.8979 & 0.5418 & 0.8998 & 0.5440 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Predictive performance of different models on the test dataset. of demand. In summary, the proposed STGA-LSTM model, as demonstrated in Figure 4, is able to produce reliable predictions on rental and return demand.
## 1 Introduction
Some of them has focused on the interfered digital modulation signals, and the maximum bit error probability of the radio receiver was derived based on the APD of arbitrary interference [18; 19; 20], without consideration of the forward error correction (FEC) coding gain. As the coding gain is closely related to the signal-to-interference-plus-noise ratio (SINR) of a code block, which is determined by both the amplitude and duration of a burst, the impact on RSTT with FEC is much more complex than digital modulation signals. Since APD cannot provide information on the time patterns of a disturbance, the evaluation is usually limited to particular circumstances. For example, a method that maps the coding gain to the impulsiveness correction factor (ICF) was proposed to evaluate the performance degradation [21; 22; 23]. However, this method is only effective in the case of impulsive noise and requires prior simulation related to the statistics of EMI. As for the scenario of HSR environment where transient and permanent disturbances both exist, the acquisition of ICF would take lots of time-consuming simulation work and may cause a large error due to the various statistics of disturbances, so it can not be used to give a real-time evaluation of RSTT performance. In this paper, a novel approach is proposed to evaluate the impact on RSTT of the complex interferences based on the time-domain statistics. Since the disturbances are generally regarded as pulses, the pulse duration and amplitude are both major factors that influence the performance of radio services. Therefore, we use the pulse duration distribution (PDD) and APD jointly to characterize EMI, in order to analyze the system performance in a pulsed noise environment. Furthermore, a dynamic effective SINR mapping model is proposed to establish the relation between the block error performance of on-board radio and the joint statistical characteristics of disturbances with a mutual information (MI)--based metric. This paper is organized as follows. In Section 2, we propose the methodology to supervise the interference effect of RSTT, and a general dynamic MI-effective SINR mapping (MI-ESM) model is proposed based on the statistical characteristics of pulsed disturbances. Then, in Section 3, a MI-based metric is analyzed to indicate the interference impact on encoded transmission under time-varying disturbances. Furthermore, the impact on RSTT by different disturbances is analyzed based on the APD and PDD in Section 4. The evaluation result is verified by the simulation in Section 5. Section 6 concludes the paper.