esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Testbed Development
### Testbed Architecture
#### 4.2.4 Non-Real-Time Implementation of RFSimulator  
The implementation of software tools to simulate radio interfaces poses serious challenges, such as those related to timing aspects. RFSimulator declares itself as non-real-time, meaning that somehow its temporality does not coincide with that of the real world. The consequence is a different perception of latencies between RFSimulator and reality.  
To illustrate the idea, consider the following example (with hypothetical values): we observe the expiration of a timer, configured to 400 ms in the OAI code, after 50 ms from  
Figure 8: Boxplots of the bitrate achieved with WireGuard over Starlink in (**a**) uplink and (**b**) downlink.  
its start. Despite the fact that, from our perspective, the timer should not have expired, the \"internal clock\" of RFS simulator has consumed the equivalent number of ticks to those 400 ms. Although we do not perceive the same course of time as RFS simulator, the timing associated with 3 GPP procedures is respected, as both UE and gNB maintain the same temporality. They are synchronously aligned with the \"internal clock\" of RFS simulator.  
The speed of RFS simulator depends on the CPU [54]. If there are sufficient CPU resources, RFS simulator will run faster than real time. This occurred in the present work, and as RFS simulator was always utilized in the testbed, we had to take it into consideration in further results. In order to quantify the impact, we deployed a baseline architecture which isolated RFS simulator as much as possible, as explained in Section 5. Specifically, we measured the performance of a conventional Terrestrial Network (TN) architecture that was deployed in our controlled laboratory LAN. This enabled the collection of comparable results to those obtained from the proposed NTN architectures, leading to an enriching understanding of the proportional performance degradation.
## 3 Capacitive Sensing with Electronic Detection
### Capacitive Sensor Design and Fabrication
#### 3.2.2 Surface Functionalization by Silane Sensing Monolayers  
In our experiments, we selected a surface modification using 3-aminopropyltrimethoxysilane to recognise -NO\({}_{2}\) containing explosives trough the hydrogen bond interactions between the NH\({}_{2}\) and -NO\({}_{2}\) functional groups [22], as shown in Figure 9. Moreover, the partial charged complex pairs are not excluded, resulting in enhanced sensitivities in the sensing of TNT molecules [23]. Furthermore, the modified surface also provides a unique stereo selectivity to the meta-nitroamatics.  
The surface, used for molecular sensing, is a SiO\({}_{2}\) surface covered with layer of coxyalkylamino and trialcoxyarylamino silanes. Generally, the modification of the surface was carried out by dipping the sensors into a diluted solution of the corresponding silane in the organic solvent for an appropriate  
Figure 8: Scanning electron microscope (SEM) micrographs of a comb differential sensor. A pair of capacitors, forming the differential sensor is shown on the left and the detail of one comb capacitor is shown on the right.  
Figure 9: Schematic diagram, representing the surface modified with 3-aminopropyltrimethoxysilane and its sensitive binding mechanism to TNT molecules.  
period of time at room temperature. It was found that different procedures had to be applied for successful modification, with regards to the silane used (Figure 10). Depending on the silane, solvents with different polarity were used to assure its solubility. For the modification using APS organosilane (3-(trimethoxysilyl-propan-1-amine)), a 10 mL of 3 mM dimethylformamide solution of the corresponding silane was prepared in dry flat-bottomed glassware under argon atmosphere. The SiO\({}_{2}\) surface of the substrates was first treated with oxygen plasma, and then the substrates were immersed in the corresponding solution. The modification process was carried out for a period of 6 h at room temperature. Upon completion, the modified SiO\({}_{2}\) COMB sensors were removed from the solution and rinsed several times with dimethyl-formamide and methanol to remove any organic residue. Finally, the sensors were thoroughly dried under an argon stream. The surface modification of the sensors with APhS (trimethoxyphenylsilane) and UPS organosilanes (1-3-triethoxysilylpropylurea) was identical; the only difference was the use of dry toluene as a solvent (Figure 10). The XPS measurements confirmed the presence of an amino group on the SiO\({}_{2}\) COMB sensor surfaces for those conforming to successful modification with the listed organosilanes (see Figure 11), which shows XPS results for a freshly prepared sample, a one month-old sample, and a three month-old sample. The analysis results prove the stability of our modification process.
# Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link
## 5 Distributed Orbit Determination  
For distributed orbit determination algorithms based on ISL, data processing is assigned to each satellite. In this process, each satellite collects the ISL measurement data with respect to its visible satellite and estimates the self-related state vectors.
## 3 Concept, Classification, and Data Sources of Digital Service Trade
### Analysis of Small-World Characteristics
#### 4.2.2 Analysis of Network Average Path Length  
In a network, the shorter the average path length, the shorter the paths through which nodes in the network connect to each other. This short path length helps with processes such as information propagation and transmission.  
Based on the analysis of clustering coefficients (Figure 4), from 2006 to 2021, the average path length of the overall digital services trade network reached the shortest, with a value of 1.19. However, its clustering coefficient showed the opposite trend, increasing to the maximum, with a value of 0.90. Insurance and pension services and other commercial services followed, with average path lengths of 1.27 and 1.28 and clustering coefficients of 0.89 and 0.90, respectively. Finally, financial service, personal entertainment services, and communication, computer, and information technology (ICT) services also showed similar changes. The above analysis shows that RCEP member countries have obvious small-world characteristics in the digital service trade industry, that is, in any industry, on average, two countries can establish trade links through another intermediary country. This efficient trade link is conducive to the stable and positive development of digital service trade among member countries.
## 2 Cooperative MIMO Communication
### Cooperative Communication  
The physical phenomena monitored by sensor networks, e.g., forest temperature, water contamination, usually yield sensed data that are strongly correlated. Data aggregation is the tool by which the correlated data size can be significantly reduced depending on the correlation factor. Figure 2 explains the cooperative communication where the sensors at cluster 1 send the information data to the cluster head os cluster 2. At the first step, the sensors at cluster 1 send the data to their cluster head. The cluster head then aggregates the data in the second step. After the aggregation, the cluster head send the aggregated data back to all the sensors in that cluster. This is the step three in cooperative communication. At this stage, all the sensors at cluster 1 have the same information data. At the fourth step, the sensors transmit the aggregated data to the cluster 2. After receiving the data at the receiving cluster, sensors at cluster 2 transmit the received data to their cluster head locally and complete the cooperative communication.  
Figure 2: Cooperative communication.
## 4 Experiment Results and Discussion
### Distributed Vibration Sensing
The RMSE can reveal the error between observed values and true values, while the STD is better suited to show the dispersion of a dataset, given by the following expressions:  
\[X_{RMSE}=\sqrt{\sum_{i=1}^{k}\frac{(x_{m,i}-x_{true})^{2}}{k}}, \tag{10}\]  
\[X_{STD}=\sqrt{\sum_{i=1}^{k}\frac{(x_{m,i}-x_{mean})^{2}}{k}}, \tag{11}\]  
where \(k\) is the number of measurements, i.e., \(k=100\), \(x_{true}\) is the true position of the vibration, and \(x_{mean}\) is the mean of the measured position \(x_{m}\). The RMSE calculated from the above expression is 1.15 km, while the STD is 0.874 km. The peak of the Gaussian fitting curve is located at 100.837 km, which is in close agreement with the actual vibration position (100.51 km) obtained via OTDR, yielding an alternative positioning error of 327 m. The fiber length was verified using a commercial optical time-domain reflectometer (Anritsu, 20085, Atsugi, Japan) and cross-checked with the manufacturer's datasheet. Note that the absolute difference is not used as the positioning accuracy since it can be calibrated based on the effective index of the fiber.
# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 3 Methodology
### Weighted Radial Basis Function for Coordinate Transformation  
Radial Basis Function (RBF) neural networks have garnered widespread attention for their excellent capability in approximating nonlinear functions, theoretically able to approximate any nonlinear function [32]. The core of RBF neural networks lies in the linear combination of radial basis functions within the network layer, making them a powerful tool for handling nonlinear problems. Leveraging this characteristic of RBF neural networks, this paper proposes a weighted radial basis function to describe and handle the coordinate transformation problem under local nonlinear scale distortions in line-scan images of train carriages. The horizontal coordinate transformation equation from the scene point set to the target point set to is defined as shown in Equation (1).  
\[T(x)=x+\sum_{i=1}^{u}w_{i}\cdot RBF(x,c_{i},\delta) \tag{1}\]  
In the equation above, \(w_{i}\) denotes the weighting value of each radial basis function, a parameter to be determined. These weights determine the influence of their corresponding radial basis functions on the overall coordinate transformation. \(RBF(\cdot)\) represents a kernel function, with the Gaussian radial basis function selected, as detailed in Equation (2). Here, \(x\) designates the horizontal coordinate of a point in the scene point set, \(c\) is the center of the radial basis function which corresponds to the horizontal coordinate of a point in the target point set, and \(\delta\) denotes the standard deviation of the radial basis function. The value of s can either be predefined based on the problem's specifics or adjusted for optimal results.  
\[RBF(x,c,s)=\mathrm{e}^{-\frac{(x-c)^{2}}{2\delta^{2}}} \tag{2}\]  
By employing the weighted radial basis function concept, the strengths of RBF neural networks in approximating nonlinear functions are harnessed, providing a robust mathematical model to address the nonlinear distortions in carriage linear array images.
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 4 Combined Model Construction and Case Analysis Based on CEEMDAN and Machine Learning
### Short-Term Traffic Flow Combination Model Based on CEEMDAN
#### 4.1.1 Construction of BGWO-LSTM Models  
High-frequency component sequences have strong randomness, volatility, and nonlinearity. Specifically, these high-frequency component sequence data have many large fluctuations in a very short time. Because long short-term memory (LSTM) has a strong nonlinear mapping ability and can fit complex data, LSTM was selected to predict the high-frequency components. The accuracy of the LSTM model is greatly related to four discrete parameters, including the historical data step (look back, hereafter referred to as lb), the number of hidden network layers (lstm nets, hereafter referred to as ls), training sessions (epochs, hereafter referred to as ep), and dropout (dp). The binary gray wolf algorithm (BGWO) was selected to optimize these four parameters to construct a high-frequency component prediction model of BGWO-LSTM. The high-frequency component prediction process based on BGWO-LSTM is shown in Figure 1, which is composed of four modules, including data preprocessing, discrete parameter optimization, neural network training, and data prediction.  
Figure 1: High-frequency component prediction flowchart based on BGWO-LSTM.
# Internet of Things and Long-Range-Based Smart Lampposts for Illuminating Smart Cities
###### Abstract  
Intelligent and resilient infrastructure is necessary for smart cities for contributing flexible and smart amenities to the citizens. Concerning the United Nations (UN) estimation, the global population residing in urban cities will reach 68% by 2050. Additionally, the Sustainable Energy Action Plans (SEAP) report suggests implementing energy efficiency technologies in smart cities to meet the rising urban population requirement. Internet of Things (IoT) technology empowers to achieve the goal of energy efficiency by integrating sensors, wireless technology, and renewable energy sources in the lighting system. At present, the IoT-based lighting system in urban cities is implemented with streetlamps and lampposts. In this study, we are focusing on lampposts, as it has the flexibility of establishing and implementing a multitude of applications on a single system. Due to technological advancement, the lamppost is embedded with multiple sensors, communication protocols, and energy distribution infrastructure for delivering smart and affordable amenities to the citizens residing in the smart cities. This motivates us to implement a smart lamppost that provides a multitude of applications such as smart light, digital signs, environmental monitoring conditions, electric vehicle (EV) charging port, wireless fidelity (Wi-Fi) hotspot, etc., on a single lamppost. This study proposed the IoT-assisted fog and edge-based smart lamppost for the smart cities to realize the smart infrastructure. Further, this smart lamppost is integrated with low power and long-range communication, i.e., Long Range (LoRa), enabling the smart lamppost to communicate the sensory data to a long-range. Additionally, LoRa is integrated with a Wi-Fi module for establishing the interconnection between the smart lamppost and IoT server. Generally, the proposed architecture is broad perspective; however, we have developed and implemented the hardware models of three components including lighting system, environmental parameters and image sensing in real time. Lighting system and environmental parameter monitoring are integrated on same hardware model for sensing and logging the real-time values of temperature, humidity, CO and light intensity on the IoT server. The developed image sensing prototype based on ESP 32 controller is also evaluated in real-time scenarios, and the performance of the prototype is efficient. The proposed system delivers reliable performance in terms of sensing and communicating environmental parameters and images to the IoT server. Moreover, in future, we will complete the development of other components of the smart lamppost for enhancing the smarter infrastructure in smart cities.  
smart lamppost; IoT; ESP 32; LoRa; IoT server +
Footnote †: journal: _Sustainability_
# Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks
## 4 Discussion
### Comparative Evaluation with Classical and Transformer-Based Models  
The classical classifiers examined in this study--Logistic Regression, the Linear SVM, and Multilayer Perceptron (MLP)--achieved relatively higher scores in overall accuracy and precision when compared to those of the TF-IDF-based HQDNN model. Among them, MLP stood out, with an accuracy of 63.38% and precision of 66.45%, outperforming the HQDNN (TF-IDF) in these metrics. However, all the classical models exhibited a limited recall performance, ranging from 70.73% to 74.09%. For instance, although Logistic Regression yielded an accuracy of 61.96%, its recall score remained at 74.09%, significantly lower than the HQDNN's 92.58% with TF-IDF and 94.40% with DistilBERT.  
This trend reflects a crucial distinction in classification behavior: classical models tend to construct conservative decision boundaries that favor the negative class (i.e., true news), thereby minimizing the number of false positives, but increasing the number of false negatives. In contrast, the HQDNN adopts a recall-oriented learning strategy, favoring the positive class (fake news). This leads to more false positives, but substantially fewer false negatives, which is a desirable trade-off in domains where failing to detect fake content carries higher risk than occasional false alarms. This advantage becomes more apparent when contrasted with lightweight transformer-based architectures such as SetFit, which--despite its parameter efficiency--did not match the HQDNN's recall levels, reaffirming the strength of hybrid quantum designs for high-sensitivity tasks.  
From a cost-sensitive learning perspective, classical models treat all errors equally, which may not suit tasks like misinformation detection. The HQDNN, through its hybrid structure and quantum layer, demonstrates a more expressive capacity to represent minority class patterns. This is further supported by the ablation experiment, where removing the quantum layer reduced recall from 94.40% to 47.38%, making its behavior more aligned with the classical models.  
Furthermore, when compared to the transformer-based DistilBERT baseline, the HQDNN remains competitive. Although DistilBERT achieved the highest F1 score (71.76%) and recall (94.40%) among all the models, the HQDNN's DistilBERT-enhanced variant came very close in performance, while retaining the benefits of a hybrid design with significantly fewer parameters than the full-scale transformer models.  
In summary, the HQDNN stands out for its ability to prioritize recall and handle imbalanced classification tasks effectively. Its recall-driven strategy, combined with the benefits of quantum-assisted computation, reinforces the potential of hybrid quantum-classical models as viable and efficient alternatives to both shallow and deep classical architectures, especially when the cost of false negatives is high.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 3 Results
### Experimental Datasets
#### 3.1.2 Industrial Dataset  
A dataset from a meat processing plant consisting of detailed electrical load profiles captured at an hourly resolution, reflecting the operational energy consumption of an industrial facility, is used as the second benchmark dataset. This type of dataset is characterized by distinct load signatures, including high base loads during production hours, sharp demand fluctuations corresponding to equipment cycles (e.g., refrigeration, cutting, packaging), and relatively stable overnight or non-operational periods. The data provide valuable insights into industrial energy behavior, where load profiles are driven less by occupant activity and more by production schedules, machinery usage, and environmental controls. As a benchmark dataset, it offers a contrasting profile to residential data, enabling the evaluation of generative models on structured, high-load, and less behaviorally diverse time series. Its inclusion supports testing model robustness across different consumption domains, particularly in capturing steady-state loads, operational peaks, and equipment-induced load variability. The dataset includes 2 years of data and has approximately 700 samples, and can be classified as a medium-scale dataset. Figure 6 shows a heatmap of the Industrial dataset in the first week of March 2023. What is notable is the clear pattern  
Figure 5: Heatmap of the ERGAN dataset. The columns correspond to the hour of the day (0 to 23) and the rows to the energy consumption before any data preprocessing.  
of starting operations in the morning, high load on the weekdays, highest load in the first hours of the workday, etc.
# Multimodal Model for Automated Pain Assessment: Leveraging Video and fNIRS
## 4 Experiment and Results
### Test Accuracy Against Competitors
LOOCV refers to Leave-One-Out Cross-Validation. GSR denotes the galvanic skin response modality, while G + V represents the fusion of GSR and video inputs. The left subtable reports LOOCV accuracy (**a**), and the right subtable presents 10-fold cross-validation accuracy (**b**). Our proposed method, MMAPA, consistently outperforms PainAttNet across different modalities and evaluation settings. We performed benchmark on \(T_{0}\) vs. \(T_{4}\) scenario (baseline vs. highest pain in BioVid dataset). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods (Modality)** & **Val Acc w/oAF** & **Val Accuracy** & **Val F1 Score** & **Test Accuracy** \\ \hline MMAPA-Ours (fNIRS)\_B-1 & 57.00\% & 58.67\% & 39.59\% & - \\ MMAPA-Ours (Video)\_B-1 & - & 61.67\% & 41.79\% & - \\ MMAPA-Ours (Manual)\_B-1 & - & 59.00\% & 40.13\% & - \\
**MMAPA-Ours (Video + INIRS)\_B-1** & 60.00\% & **62.67\%** & 41.76\% & **51.33\%** \\ MMAPA-Ours (Video + Manual)\_B-1 & - & **62.67\%** & **42.63\%** & - \\ MMAPA-Ours (fNIRS + Manual)\_B-1 & 59.00\% & 59.67\% & 40.66\% & - \\ MMAPA-Ours (Video + INIRS + Manual)\_B-1 & **61.00\%** & 62.33\% & 42.08\% & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._the integration of GSR and video through our attention-based fusion mechanism allows the model to capture complementary patterns across modalities. Overall, our model exhibits not only superior accuracy but also enhanced robustness and generalizability across different validation schemes.