esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Propagation Model
### Model Requirements
#### 3.1.3 Photon Reception  
The photon propagation cycle, i.e., path length \(\rightarrow\) photon scattered \(\rightarrow\) weight drops \(\rightarrow\) coordinates update, repeats until it reaches the following instances:  
1. Photon's remaining weight/power is negligibly small, in which case the weight is rouletted [25]. \[w_{n+1}=\left\{\begin{array}{ll}w_{n}\tau&\quad\text{if $R\leq\frac{1}{\tau}$}\\ 0&\quad\text{if $R>\frac{1}{\tau}$}\end{array}\right..\] (15) where \(w_{n+1}\) is the weight after scattering, and \(w_{n}\) is the old weight. \(R\) is the uniform random variable in the interval [0, 1], and \(\tau\) is the roulette threshold. If \(R\) is less than the fraction \(1/\tau\), then the old weight is scaled roulette threshold times, and if it is greater than the fraction, the weight tends to zero which means the photon is completely absorbed. This method limits the number of computations and also preserves the total probability.
2. If the photon hits a boundary other than the air-water interface (in such case the photon continues to propagate), it is terminated.  
We recommend the readers to refer to our previous work [10] for further details of the propagation model.
## 1 Introduction
### Wearable Health Monitoring Devices  
In recent years, wearable health monitoring devices, such as smartwatches and fitness trackers, have gained popularity as tools for personal health management. These devices are designed to track various physiological metrics and encourage healthier lifestyles. The widespread adoption of these technologies is driven by advances in wireless sensors and digital technology, which have enabled the creation of non-invasive, autonomous devices that monitor a broad range of health indicators, including physical activity, heart rate, and sleep patterns [13; 14; 15].  
Wearable devices are popular among older adults, allowing them to monitor their health remotely. Initially, mobile phones were the most common health monitoring tools for this age group, but since 2014, there has been a shift towards wearables [16]. These devices support self-monitoring and provide healthcare professionals with data to improve treatment plans and patient care [17; 18].  
Beyond fitness tracking, wearables have evolved into health management tools. Modern devices analyze biometric signals to assess stress levels, sleep quality, and overall wellness [19; 20]. This development reflects a growing trend of wearables becoming personal health management systems, delivering real-time health data and alerts to users and healthcare providers [21; 22].  
Advancements in sensor technology and the Internet of Things (IoT) have resulted in the creation of microcontroller health monitoring bracelets [23]. These devices track health metrics, including body temperature, blood pressure, and heart rate. They provide real-time data to healthcare professionals and users. The i-Health Monitoring Watch is a notable device that uses bioimpedance technology to measure heart rate, hydration levels, and blood glucose through silver electrodes. This prototype integrates various sensors into wearables for health tracking. Similarly, the advanced care and alert portable telemedical monitor (AMON) device features miniaturized sensors that monitor blood pressure, skin temperature, blood oxygen levels, and ECG, demonstrating the versatility of wearable health technology. The comparison of data collected from the proposed device against the reference device shows an impressive error less than 1% for body temperature [24].  
The ESP32 microcontroller is used for such wearable devices due to its built-in Wi-Fi and Bluetooth features, enabling seamless data transmission and connectivity [25; 26]. For instance, an intelligent bracelet developed with the ESP32 tracks heart rate and body temperature, displaying data on an Android app [27]. This mobile integration allows users to receive immediate feedback on health metrics. The ESP32's compatibility with various sensors makes it ideal for health monitoring solutions [28].
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Computational Tools and Summary  
This study implemented models using Python and R Software packages. The ANN trained with Bayesian regularised algorithm and the uncertainty metrics were implemented using Python while the benchmark models along with the forecasts combination were implemented in R. Both Software packages were used for data exploration and analysis. These mathematical formulae herein presented fits into our study because these formulae are either implemented in the computational tools employed or they are explicitly programmed in order to measure the accuracy and the uncertainties in predicting wind speed. The results from these metrics are reported in Section 3. A summary of these models, description and motivations for their use is presented in Table 1.  
This section presents the methodology employed for the study. Various theoretical underpinnings of our models are herein presented and summarised in Table 1. This work entails the use of two machine learning methods (ANN and SGB) along with GAM, a statistical learning method for point forecasting of wind speed. Lasso was used to select predictor variables from the dataset. The point forecasts from these models were combined using LQRA and AQRA, statistical methods for forecasts combination and evaluated using MAE, RMSE, and MAPE. However, AQRA was retained for PI construction in order to quantify the uncertainties in the forecasts from the models. Interval forecasting metrics such as PIW, PICP, PINAW, PINAD and PICAW were used to quantify the uncertainties in the forecasts. Finally, prediction interval limits were combined using the simple average and median PI combination methods.
## 3 Simulation and Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Simulation UTC begin time, \(t_{\text{b}}\) & 1 May 2030 04:00:00 \\ Simulation UTC end time, \(t_{\text{e}}\) & 1 May 2030 04:06:40 \\ Time step, \(\Delta t\) & 1 s \\ Earth equatorial radius, \(R_{\text{e}}\) & 6378 km \\ Boltzmann constant, \(\kappa\) & \(1.38\times 10^{-23}\) W/(Hz-K) \\ Downlink carrier frequency, \(f\) & 20 GHz \\ Bandwidth of each beam, \(BW\) & 25 MHz \\
**GSO satellite** & \\ Latitude of GSO center beam, \(\varphi\) & \(0^{\circ}\) \\ Longitude of GSO center beam, \(\Lambda\) & 21.26\({}^{\circ}\) \\ Transmit power of GSO satellite, \(P_{\text{tG}}\) & 18.57 dBW \\ GSO maximum transmit gain, \(G_{\text{mG}}\) & 45.39 dBi \\ GSO 3 dB beamwidth for 7 and 19 beams & 1.259\({}^{\circ}\) \\ GSO 3 dB beamwidth for 37 beams & 0.6295\({}^{\circ}\) \\ Radio of major axis to minor axis, \(z\) & 1.5 \\ near-in-side-lobe level, \(L_{N}\) & \(-20\) dB \\ Number of GSO beams & 7, 19, 37 \\
**NGSO satellite** & \\ Transmit power of NGSO satellite, \(P_{\text{tL}}\) & 12.55 dBW \\ NGSO maximum transmit gain, \(G_{\text{mL}}\) & 37.65 dBi \\ NGSO 3 dB beamwidth for 7 and 19 beams & 11.58\({}^{\circ}\) \\ NGSO 3 dB beamwidth for 37 beams & 5.79\({}^{\circ}\) \\ Number of NGSO beams & 7, 19, 37 \\
**GSO ground station** & \\ Noise temperature of receive antenna, \(T_{\text{r}}\) & 210 K \\ Antenna diameter, \(D\) & 1.2 m \\ Maximum receive gain, \(G_{\text{mr}}\) & 45.76 dBi \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 14: Change of total capacity with and without channel allocation and variation of rotation angle for the 37-beam scenario. (**a**) Change of total capacity with and without channel allocation for the 37-beam scenario; (**b**) Variation of rotation angle for the 37-beam scenario.
# Graphene Oxide Adsorption Enhanced by Attapulgite to Remove Pb (II) from Aqueous Solution
## 3 Results and Discussion
### Effect of pH  
The pH affects not only the charge and state of functional groups on the surface of the as-prepared GO, but also the ionic state of Pb in aqueous solution [36]. The optimal pH is different for the adsorption of different heavy metals. The pH in the adsorption of Pb\({}^{2+}\) on the as-prepared GO varied from 3 to 9; the results are shown in Figure 6. The contact time was 2 h to ensure that adsorption reached equilibrium. Figure 6 indicates that at a low pH value of 3, the oxygen-containing functional groups on the as-prepared GO surface were protonated. Consequently, because of electrostatic repulsion, the positively charged surface of the adsorbent did not complex well with Pb\({}^{2+}\), thus resulting in a low removal efficiency. When the pH was increased to 5, the degree of protonation of the oxygen-containing functional groups was weak, directly resulting in an increase in removal efficiency to 90%. When the pH was increased to 6, a precipitation reaction was initiated, thus maintaining the removal efficiency at approximately 99%. In fact, the main states of lead in aqueous solution include Pb\({}^{2+}\), Pb(OH)\({}^{+}\), Pb(OH)\({}_{2}\), Pb(OH)\({}_{3}\)\({}^{-}\), and Pb(OH)\({}_{4}\)\({}^{2-}\)[37]. When pH < 6, Pb\({}^{2+}\) is the primary form. Therefore, a pH value of 5 was chosen for the subsequent adsorption experiments of Pb\({}^{2+}\).  
Figure 5: Removal efficiencies of GO, ATP, and as-prepared GO.  
Figure 6: Effect of the solution pH on the adsorption of Pb\({}^{2+}\) by the as-prepared GO, \(C_{0}\) = 150 mg/L, m/v = 0.3 g/L, \(t\) = 2 h, and T = 298 K.
# An Overview of Open Source Deep Learning-Based Libraries for Neuroscience
## 1 Introduction  
In the last decade, deep learning has taken over most classic approaches in machine learning, computer vision, and Natural Language Processing (NLP) research, showing unprecedented versatility and matching or surpassing the performances of human experts in narrow tasks. The recent growth of deep learning applications in several domains, including neuroscience, consequently offers numerous open-source software opportunities for researchers. Mapping available resources can allow for faster and more precise exploitation. Neuroscience is a diversified field on its own, as much for the objects and scales it focuses on as for the types of data it relies on. The discipline is also historically tied to developments in electrical, electronic, and information technology. Modern neuroscience relies on computerization in many aspects of data generation, acquisition, and analysis. Statistical and machine learning techniques already empower many software packages that have become de facto standards in several subfields of neuroscience, such as Principal and Independent Component Analysis (PCA, ICA) in electroencephalography and neuroimaging, to name a few. Concurrently, the rich and rapidly evolving taxonomy of Deep NeuralNetworks (DNNs) is becoming both an opportunity and a hindrance. On the one hand, currently, open-source deep learning libraries allow an increasing number of applications and studies in neuroscience. On the other hand, the adoption of available methods is slowed down by a lack of standards, reference frameworks, and established workflows. Scientific communities whose primary focus or background is not in machine learning engineering may be left partially aside from the ongoing Artificial Intelligence (AI) gold rush. For such reasons, it is fundamental to provide an overview of open-source libraries and toolkits. Framing a panorama could help researchers in selecting ready-made tools and solutions when convenient and aid them in pointing out problems and filling in the blanks with new applications. This work would contribute to advancing the community's possibilities, reducing the workload for researchers to exploit deep learning, and allowing neuroscience to benefit from its most recent advancements.  
The rest of the paper is organized as follows: first in the Section 2, a historical perspective of the rise of deep learning in the last decade, then, a general presentation of the vast field of neuroscience followed by a definition of neuroinformatics and the role open source culture and deep learning would serve in it; subsequently, in the Section 3, the methodology to collect and present the libraries collection, and the most prominent features are discussed; the Section 4 shows the tables with libraries information; lastly, discussions and final remarks are offered to the readers (Sections 5 and 6).
## 4 Experiment and Results
### Ablation Study  
This section presents ablation experiments conducted to analyze and verify the effectiveness of each component in our model. The components used in the experiments include ESEM, PA, and FEM. The experiments are performed on the CelebA [33] training set, and the Helen test set. All parameters are kept consistent with the original network.  
A total of eight sets of experiments were designed by combining the three modules. Model 1 does not incorporate any module, models 2 to 7 incorporate only one or two modules, and model 8 incorporates all modules. The final results are compared quantitatively to evaluate the impact of each component on the performance.  
After training the networks used for ablation separately, the objective evaluation results are presented in Table 1. Comparing model 1, which does not have any module added, with the other models, it is evident that the addition of modules positively impacts the network performance. Among the added modules, the inclusion of ESEM yields the most significant improvement. The models incorporating two modules demonstrate better reconstruction results compared to those with only one module, with model 5, incorporating both ESEM and PA, exhibiting particularly notable effects. The PSNR value of Model 8, after incorporating all three modules, reaches 27.744 dB, which is 0.355 dB higher than that of Model 1. This confirms the effectiveness of the proposed method. It can be concluded that the addition of ESEM enhances the interaction ability of internal features across channels at each network level and improves the network's capability to extract structural information from the image, resulting in clearer contours and details in the reconstructed face. The incorporation of PA enhances the network's capability to mine the intrinsic correlations within multi-scale feature maps. Furthermore, the inclusion of the FEM enables the reconstruction process to retain rich spatial and contextual information across different scale features, thereby enhancing the quality of the SR image.  
To assess the impact of each module on the model's convergence, we present the comparison results of the training curves in Figure 6. The horizontal axis represents the number of iterations, while the vertical axis indicates the PSNR values. Notably, the training process of the model exhibits a smooth trajectory, characterized by minimal oscillation. Upon reaching approximately 30K iterations, the model essentially converges. Furthermore, it is evident that the model incorporating all three modules outperforms the other configurations, as indicated by significantly higher PSNR values.
# A Solar-Thermal-Assisted Adiabatic Compressed Air Energy Storage System and Its Efficiency Analysis
## 4 Results and Discussion
### Parameter Analysis
#### 4.2.1 Ambient Temperature  
Figure 5 shows the effect of ambient temperature on the \(W_{COM}\), \(W_{TUR}\), and pressure inside the SPT (\(P_{\text{SPT}}\)). The ambient temperature slightly affects \(W_{COM}\) and \(P_{\text{SPT}}\), with \(W_{TUR}\) kept constant. With the increasing ambient temperature, the inlet temperature of the compressor also increased, which augmented the \(W_{COM}\) calculated by Equation (3). Thus, when the ambient temperature increases from 15 \({}^{\circ}\)C to 35 \({}^{\circ}\)C, the energy consumption of \(W_{COM}\) increases by 8 kWh. Meanwhile, based on the ideal gas equation and constant volume of SPT, \(P_{\text{SPT}}\) increases by 0.15 MPa.  
Figure 6 shows that the ESE, RTE, and \(\eta_{\text{ex}}\) all decline with increasing ambient temperature. As illustrated in Figure 5, \(W_{COM}\) increases but \(W_{TUR}\) stays constant. Furthermore, the supplied heat load and heat energy provided by the STC remain constant. Therefore, when the ambient temperature increases from 15 \({}^{\circ}\)C to 35 \({}^{\circ}\)C, ESE, RTE, and \(\eta_{\text{ex}}\) all decrease by 1.1, 4, and 2, respectively. In general, a lower ambient temperature is more beneficial to system performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Unit** & **ST-CAES** & **Hybrid A-CAES** \\ \hline Compressor power & kWh & 1361.5 & 152.1 \\ consumption & h & 4 & 6.59 \\ Charging time & h & 4 & 5.13 \\ With/without TES & / & Yes & no \\ Expansion train working period & & load peak hours & Irradiation peak hours \\ Inlet temperature of air turbine & \({}^{\circ}\)C & 280 & 900 \\ Air turbine electricity generation & kWh & 949.1 & 228.54 \\ Round trip efficiency & \% & 61 & 76.5 \\ Exergy efficiency & \% & 50 & 53.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The comparison of performance of ST-CAES to hybrid A-CAES.  
Figure 5: Effect of ambient temperature on \(W_{COM}\), \(W_{TUR}\), and \(P_{\text{SPT}}\).  
Figure 6: Effect of ambient temperature on ESE, RTE, and \(\eta_{\text{ex}}\).
# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
###### Abstract  
Object detection is a pivotal task in the realm of autonomous driving, where reliance on single-modality information often proves inadequate for high-precision detection tasks. In current research, object detection networks based on point clouds effectively identify objects with dense point clouds. However, these networks face challenges when dealing with detection tasks involving objects with higher point cloud sparsity. To address this issue, this paper proposes a 3D object detection network based on point cloud and image fusion. This network employs a fusion module named PRF (Point-Region Fusion), utilizing the K-Nearest Neighbors (KNN) algorithm to find the nearest K points corresponding to point cloud features. It then collects regional features from image feature maps and fuses them with the point cloud features after aggregation. The designed Image Feature Fusion module (IF-Fusion) fuses image feature maps of varying sizes in a pairwise manner. This fusion process is key in preserving features of small objects and subsequently enriching the point cloud's features. In evaluations conducted on the KITTI benchmark, the method presented here surpasses prior fusion networks in detection accuracy, achieving rates of 91.95%, 81.10%, and 78.08%.  
3D object detection; Point-Region Fusion; K-Nearest Neighbors; KITTI
# Acquisition of Weak BDS Signal in the Lunar Environment
## 2 Materials and Methods
### Signal Acquisition Algorithms
Coherent differential integration can be expressed as:  
\[T(\hat{\tau},\hat{f}_{d})=|\sum_{m=2}^{M}Z_{m}(\hat{\tau},\hat{f}_{d})Z_{m-1}^{*} (\hat{\tau},\hat{f}_{d})|^{2}, \tag{16}\]  
where \(Z_{m-1}^{*}\) represents the complex conjugate of \(Z_{m-1}\). Furthermore, to prevent the effect of navigation data bit sign transitions, the alternate half-bit method is introduced. The BDS B1I signal undergoes a data bit sign transition every 20 ms. Therefore, if the bit transition occurs in the first 10 ms of a continuous 20 ms signal, it cannot occur in the second 10 ms. The alternate half-bit method divides the data into odd and even blocks at 10 ms intervals. As shown in Figure 6, the entire dataset is divided into \(2n\) blocks. Then, the effect of signal bit sign transitions can be avoided by performing integration operations on odd and even blocks, respectively, and then comparing the output of both. The semi-bit coherent differential integration (SBCODI) method [37] combines the advantages of the above methods and can efficiently acquire signals with a lower SNR. The SBCODI method performs differential integration of adjacent odd or even blocks in Figure 6. The larger of the two differential results is then taken as the detected value; thus, the method can be expressed as:  
\[T(\hat{\tau},\hat{f}_{d})=max(T^{odd}(\hat{\tau},\hat{f}_{d}),T^{even}(\hat{ \tau},\hat{f}_{d})), \tag{17}\]  
where  
\[T(\hat{\tau},\hat{f}_{d})=|\sum_{m=1}^{M}Z_{m}(\hat{\tau},\hat{f}_{d})|^{2}, \tag{13}\]0  
\[T(\hat{\tau},\hat{f}_{d})=|\sum_{m=1}^{M}Z_{m}(\hat{\tau},\hat{f}_{d})|^{2}, \tag{13}\]1  
and \(T^{odd}(\hat{\tau},\hat{f}_{d})\) and \(T^{even}(\hat{\tau},\hat{f}_{d})\) represent the results of differential integration for odd and even blocks, respectively. In fact, the detected value, \(Z\), of each processing block is the coherently integrated result for 10 ms before differential operation.
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 2 Methodology
### Experiment Equipment  
As illustrated in Figure 4, the hardware used in this research consists of a Raspberry Pi model B+, a Pi camera from Raspberry Pi, an FC from Pixhawk, a Huawei E3372 LTE modem, and a GPS module. The function of Pixhawk FC is to control the drone's movement, assisted with a GPS module to obtain the drone's location. CC data from the ground station is handed to FC over the Raspberry Pi, which is equipped with an active internet plan from a local network provider. To complement the standard drone operation requirement, a camera was equipped at the Raspberry Pi to stream live video footage to the ground station.  
Furthermore, the FC is connected to the Electronic Speed Controller (ESC), which adjoins the drone's brushless motor. Each of the drone components is interconnected, powered by a lithium-ion polymer (Li-Po) battery. Figure 5 shows the developed LTE-powered drone prototype.  
Figure 3: Configuration between drone and ground station with server domain.  
Figure 2: Concept of wireless communication considered.
## 2 Performance Indicators of System Response
### Step Response Indicators  
The shape of the system response cannot be simply determined based on the value of basic indicators, e.g., ISE. In addition, such quality indicators are highly dependent on measurement noises (in the case of online automatic tuning). For this reason, the step indicators (SIs) are commonly used to determine the required response of the system. The commonly used step indicators are as follows:  
* Percentage Overshot (PO): \[\text{SI}_{\text{PO}}=\frac{\max y(t)-y(\infty)}{y(\infty)-y(0)}\] (7)  
Figure 1: Impact of damping factor (\(\zeta\)) in second-order system on basic quality indicators.  
* Rising Time (RT): \[\text{SI}_{\text{RT}}=t_{90\%}-t_{10\%}\] (8) with \[t_{90\%}=\min\{t\in\langle 0,\infty\rangle\;\mid\;y(t)=90\%\cdot y(\infty)\,\}\] \[t_{10\%}=\min\{t\in\langle 0,\infty\rangle\;\mid\;y(t)=10\%\cdot y( \infty)\,\}\]
* Settling Time (ST): \[\text{SI}_{\text{ST}}=t_{2\%}\] (9) with \[t_{2\%}=\min\{t\in\langle 0,\infty\rangle\;\mid\;\forall_{\tau\geq t}\; \mid y(\tau)\mid\leq 2\%\cdot\mid y(\infty)\,\}\]
* Steady-State Error (SSE): \[\text{SI}_{\text{SSE}}=y_{\text{reference}}-y(\infty)\] (10)  
As mentioned earlier, integral value indicators cannot simply be determined for the required response of the system. The user can define the required response using the step indicators, e.g., required rise time or maximum percentage overshoot.  
The short summary of the considered integral quality indicators in terms of obtained step response indicators is presented in Table 1. The table can be used to determine which integral quality indicator should be selected for a particular application.