esa-sceva/satcom-chunk-collection huggingface dataset:
# Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network
## 5 Conclusions
#### Directions for Further Research  
At the current stage of the proposed research, the developed hybrid solar PV and fuel cell-focused macro cellular network without energy sharing mechanisms have much room for being improved and extended in several interesting directions. Future extensions of this research work are summarized below:  
* Developing a prototype system to ensure the effectiveness of the hybrid solar PV/H/ FC-based green mobile communication.
* Developing a generic algorithm and control system for sharing green energy across surrounding BSs and industry/power grid by maximizing the use of renewable energy in the heterogeneous cellular networks.
* Developing a cell zooming technique for running the heterogeneous cellular networks based on the amount of green energy generated and the rate of incoming traffic.
* For enhancing energy efficiency performance, the renewable energy cooperation of cloud radio access network (C-RAN) heterogeneous networks paradigm may be examined.
* For a better compromise between data rate and fairness, an efficient resource and energy scheduling technique can be implemented.  
Additionally, Australia can be included in this study in the future. Australia generated 29% of its total electricity from renewable sources in 2021. Over the past ten years, renewable electricity generation has more than doubled. Australia has a total of 171.2 PJ of biomass and 99.8 PJ of solar PV accessible as energy sources [73].  
Conceptualization, K.Z.I, M.S.H. and B.M.R.A.; methodology, B.M.R.A. and K.Z.I; software, K.Z.I.; validation, M.S.H. and F.S.; formal analysis, M.S.H.; investigation, G.M.S. and F.S.; resources, M.S.H.; data curation, B.M.R.A.; writing--original draft preparation, K.Z.I. and M.S.H.; writing--review and editing, K.Z.I., G.M.S. and F.S.; visualization, K.Z.I.; supervision, F.S. and G.M.S.; project administration, F.S. and G.M.S.; funding acquisition, F.S. and G.M.S. All authors have read and agreed to the published version of the manuscript.  
There is no external fund for this work.  
**Data Availability Statement:** Not Applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods  
### Experimental Materials  
The materials examined in this study were obtained from a debris flow event that occurred in Jiangjiagou, located in Kunming City, Yunnan Province of China, which is a typical debris flow gully in the Xiaojiang River Basin, a tributary of the Jinsha River. Approximately 1.5 tons of samples were collected from different locations in the same region and transported back to the laboratory for further research. The region experiences frequent and large-scale debris flow outbreaks of various types, making it known as the \"debris flow museum\" among domestic and foreign scholars [20]. In terms of topographic and geomorphologic conditions, the average elevation of the Jiangjiagou Basin is 2200 m, with a relative elevation difference of 2181 m, and many longitudinal slopes upstream are more than 35\({}^{\circ}\). The terrain rises like a ladder from west to east, and the landform has obvious stratification. The common feature of the landforms is that there are thick loose accumulations (including eluvium, residual slope accumulation and debris flow accumulation), which provide favorable topographic conditions for the development of debris flow. Figure 1 shows the sample collection locations. The samples were collected from an accumulation downstream of the debris flow. To prevent the fine particles from leaching out due to precipitation, a surficial deposit of 30 cm was removed using an iron shovel before sampling, to obtain well-preserved debris flow raw materials. Subsequently, these samples were dried and sieved to obtain raw materials with a maximum particle size of 2 mm. Additionally, particles below 0.075 mm were obtained through water sieving, and XRD (X-ray diffraction) tests were used to determine the mineral composition of the raw materials. As Table 1 shows, the main clay minerals in the Jiangjiagou debris flow were illite and chlorite.  
In accordance with the \"soil test method standards\" (GB/T50123-2019) [21] for grain size distribution tests, the grading curve of the raw samples in this study was determined. Particles above 0.075 mm were tested using sieving methods, while particles below 0.075 mm were analyzed using a lase r particle size analyzer. The obtained grading parameters are listed in Table 2, and based on these data, the grading curve was plotted, as shown
# Evaluation and Improvement of Lidar Performance Based on Temporal and Spatial Variance Calculation
## 4 Optimization of Data Acquisition Parameters and Calibration of Lidar Data
### Estimations of the Optimal Threshold Voltage of Photon-Counting System  
In a PC system, when the voltages of the pulse heights of the PMT outputs are higher than the threshold voltage, the discriminator will produce a series of electronic pulses with a standard shape and height and send them to the counter. Therefore, the setting principle of the threshold voltage of a PC system is to record the actual lidar return signals and suppress the noise signal maximally, which can be analyzed based on the distributions of the pulse heights of the PMT outputs.  
For the Licel transient recorder TR40-160, the threshold voltage can be preset between 0 mV and 25 mV using 64 discriminator levels, which means the step voltage is 0.40 mV. Figure 7a shows examples of the lidar profile of the PC data with different settings of threshold voltage at the same atmospheric conditions. The discriminator levels of 1, 8, and 15 correspond to the threshold voltages of 0.40 mV, 3.20 mV, and 6.00 mV, respectively. Not only is the lidar detection range related to the setting of the discriminator levels (Figure 7a), but the number statistics of non-zero data distributions are also related to the settings of the threshold voltage (Figure 7b).  
Figure 8 shows the number statistics of non-zero data distributions in the clear atmospheric conditions. It can be seen that the number of non-zero PC data points had its maximum value at the discriminator level of 3 (corresponding to the threshold voltage of 1.20 mV). When the preset threshold  
Figure 6: Estimations of the dead time of the photon-counting system for the correction of pile-up effects, which came to 3.488 ns. (**a**) Spatial variance distribution before and after dead time correction (DTC); (**b**) the probing profiles of photon-counting data before and after dead time correction.  
Figure 7: (**a**) Probing profiles of photon-counting data with different settings of threshold voltage at the same atmospheric conditions and (**b**) their corresponding statistics of the non-zero distributions.  
voltage was less than the threshold voltage of discriminator level 3, the PC system was susceptible to RF noise, combined with the pulse pile-up effect in near range, resulting in fewer non-zero points compared with that of discriminator level 3. When the preset threshold voltage was greater than the threshold voltage of discriminator level 3, the PC system was not powerful enough to discriminate the lidar echo signal from the far field. The number of non-zero data points in the probing profile decreased with the increased preset of the threshold voltage. Based on such analysis, it was concluded that the optimal discriminator level of Licel transient recorder TR40-160 in the ultraviolet elastic scanning lidar is 3, corresponding to the threshold voltage of 1.20 mV, which makes the maximum lidar detection range of the ultraviolet elastic scanning lidar.
## 4 VRIS Multidimensional Assessment
### Visual Quality Assessment  
#### 4.2.1 Evaluation Indicator Selection  
1. PSNR  
PSNR (Peak Signal-to-Noise Ratio) is an indicator used to evaluate image quality, especially in image steganography, where it measures the quality degradation of encrypted images compared to the originals. It is derived by calculating the Mean Squared Error (MSE) between the original image and the encrypted image and normalizing it to the maximum pixel value of the image. A higher PSNR value indicates a smaller difference in quality between the encrypted image and the original image, meaning that the embedding process has a minimal impact on image quality, and the secret information is effectively hidden. PSNR mainly focuses on the image pixel-level error, and in image steganography, the level of PSNR value directly reflects the extent to which the image quality is maintained after secret image embedding.  
2. SSIM  
SSIM is an image quality evaluation metric that simulates the human eye's visual system and evaluates the similarity of images by considering brightness, contrast, and structure. In image steganography, SSIM is used to quantify the similarity between the encrypted image and the original image and to evaluate the influence of embedded information on image quality. A higher SSIM value indicates that the image still maintains a better visual quality after embedding the secret information, and at the same time can effectively hide the secret information. Compared with PSNR, SSIM pays more attention to image structure and provides an assessment that is more in line with human perception.  
3. Noise factor  
In this study, the noise factor is used as a very important parameter to control the intensity of the random Gaussian noise by adjusting its value, which affects the performance of the image steganography model and produces encrypted images of different quality.
## 2 Methods
### Comprehensive Evaluation of Node Importance  
To comprehensively evaluate node importance, this study proposes an integrated average ranking algorithm by combining multiple parameters (e.g., CS, DNC, SNC, NE, and EPR). The algorithm categorizes nodes into three levels based on their overall rankings:  
* Highly Important (Level 1);
* Important (Level 2);
* Moderately Important (Level 3).  
Each category represents one-third of the total number of nodes. The algorithm's expression is as follows:  
\[Level_{i}=\left\{\begin{array}{l}1,\ |\ min+\frac{2}{3}(max-min)<SN_{i}\leq max \\ 2,\ |\ min+\frac{1}{3}(max-min)<SN_{i}\leq min+\frac{2}{3}(max-min)\\ 3,\ |\ min\leq SN_{i}\leq min+\frac{1}{3}(max-min)\end{array}\right., \tag{16}\]  
\[SN_{i}=\frac{1}{M}\sum_{i=1}^{M}SN_{i}^{(m)}, \tag{17}\]  
\[SN_{i}^{(m)}=\frac{1}{5}\Big{(}SN_{CS_{i}^{(m)}}+SN_{DNC_{i}^{(m)}}+SN_{SNC_{i} ^{(m)}}+SN_{NE_{i}^{(m)}}+SN_{EPR_{i}^{(m)}}\Big{)}, \tag{18}\]where  
* \(max=MAX(SN_{i})\), \(min=MIN(SN_{i})\): Represent the maximum and minimum comprehensive importance rankings among all nodes;
* \(SN_{i}\): Denotes the comprehensive importance ranking of node \(V_{i}\) across all networks, calculated using Equation (17);
* \(M\): Represents the number of network layers;
* \(SN_{i}^{(m)}\): Denotes the comprehensive importance ranking of node \(V_{i}\) in the \(m\)-th network layer, calculated using Equation (18);
* \(SN_{CS_{i}^{(m)}}\), \(SN_{DNC_{i}^{(m)}}\), \(SN_{SNC_{i}^{(m)}}\), \(SN_{NE_{i}^{(m)}}\), \(SN_{EPR_{i}^{(m)}}\): Represents the ranking of node \(V_{i}\) in the CS, DNC, NE, and EPR parameters, respectively, within the \(m\)-th network layer.
# Tourism Demand Forecasting Based on an LSTM Network and Its Variants
## 1 Introduction
The number of passengers is still the most popular TD measure over last decades. Since the time series model only requires historical observation of a variable, the cost of data collection and model estimation is low. Hence, this study adapts an LSTM network and its variants to forecast Taiwan's TD. In order to validate the model, a data set including the severe acute respiratory syndrome (SARS) outbreak threatening tourism demand from November 2002 to June 2003 was used to compare the prediction results of the models reported in the other papers. In view of the strong autoregressive pattern of the number of tourists [26], data from the SARS outbreak was used to train the network to predict the impact of the current COVID-19 epidemic on the number of tourists in Taiwan. The remainder of this paper is organized as follows. Section 2 describes the LSTM, Bi-LSTM, and GRU networks. Section 3 presents data description. Section 4 describes the results and discussion for TD forecasting before our conclusions are provided in Section 5.
# Analyzing Key Features of Open Source Software Survivability with Random Forest
## 3 Analyzing the Relationship Between OSS Survivability and Features
### Experimental Setup  
#### 3.1.1 Issue Retention Rate and Survivability  
The issue retention rate is a key metric for evaluating the survivability of OSS projects, serving as an important indicator of project management and community activity. OSS projects depend on voluntary contributions, making the speed of issue resolution and retention rate effective measures of their survivability. The issue retention rate refers to the probability that an issue remains unresolved over a specific period, reflecting the maintenance capacity and activity levels of the community.  
This study assumed that OSS survivability can be effectively assessed through issue activity, specifically the retention rate of unresolved issues. While this assumption was not directly tested, it underpins the analytical framework, with conclusions contingent on its validity.  
The significance of the issue retention rate is supported by prior studies. [PERSON] et al. [11] emphasized that unresolved issues lead to the accumulation of technical debt, undermining a project's long-term potential. Their research highlights that prolonged unresolved issues can erode trust between users and developers, increasing the likelihood of project abandonment. Similarly, [PERSON] et al. [12] analyzed collaborative processes on GitHub and found that faster issue resolution is strongly linked to effective communication and division of responsibilities among contributors. These factors collectively enhance project stability and survivability.  
[PERSON] et al. [18] further noted that higher issue retention rates are associated with decreased maintenance efficiency and reduced community activity, ultimately jeopardizing project sustainability. Their findings support the idea that unresolved issues are critical indicators of maintenance challenges. [PERSON] et al. [19] explored the transparency and collaborative structure of GitHub, demonstrating how issue activity can foster community engagement and facilitate problem-solving. These studies collectively highlight the utility of issue retention rates as a proxy for evaluating OSS project health and survivability.  
Building on these findings, this study focused on issue retention rates as a core metric for assessing OSS survivability. GitHub issues were employed as the primary data source, providing insights into a project's maintenance status and future development potential. Kaplan-Meier survival analysis was applied to capture temporal changes in retention rates, offering a dynamic and comprehensive evaluation of how unresolved issues affect project survivability.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 4 Discussion
### Case 1: CWRU Bearing Dataset
#### 4.2.4 Visualization and Analysis Under Noise Interference  
To compare the diagnostic performance of the models under noise interference for different fault types with a simulated SNR of 2 dB, this experiment visualized the confusion matrices of EfficientNetV2-S, RepViT, and MPNet, which performed better in the noise experiments, alongside HFDF-EffNetV2, as shown in Figure 10.  
All models exhibited varying levels of misclassification in diagnosing fault label 2, with the number of misclassified samples for EfficientNetV2-S, RepViT, and MPNet being 30, 29, and 47, respectively, while the proposed model misclassified only 17 samples. Additionally, RepViT and MPNet exhibited poor diagnostic performance (P) for fault label 9, achieving only 70% and 84%, respectively. Although the performance of the proposed model was not optimal for all fault types, its diagnostic P reached 100% for fault labels 0, 1, 3, 4, and 6, and exceeded 94% for fault labels 5, 7, 8, and 9. The results show that HFDF-EffNetV2 significantly improves diagnostic P for samples with confusable fault types.  
Figure 10: Confusion matrix visualization for different models at SNR (2 dB) in Case 1.
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
## 3 Material and Methodology
### Data Processing  
In this paper, multiple satellite passes were recorded. These recordings were scheduled in SDR Console, a software program used to record the incoming IQ data. The recording starts once a satellite reaches an elevation angle of 5\({}^{\circ}\) and continues as long as the satellite remains at an elevation angle above 5\({}^{\circ}\). This window of recording was calculated with the help of Two Line Element (TLE) files. These publicly available files precisely define the current orbit of satellites and can be used to predict the future trajectory [21]. In practice, this resulted in recording fragments of about 12-15 min. The sample rate of the SDR was set to 1 MHz and captured the full Orbcomm downlink spectrum from 137 MHz to 138 MHz. After recording the satellite passes, the data is processed offline. The processing flow is illustrated in Figure 2. Firstly, the recording gets split into 1-s fragments and fed into the processing algorithm where it is windowed to prevent spectral leakage [22]. After this, the conversion to the frequency domain is made by applying a Fast Fourier Transform (FFT). The zero frequency component is shifted to the center and the absolute value is calculated. Then, we make the conversion to the power spectrum [22]. To calculate \(P_{N}\), first, the average noise power per bin is calculated. This is done by sorting the bins and discarding the highest bins that match the active Orbcomm channels and DC bin. Subsequently, the noise density is calculated. To get an estimate of the noise power in the Orbcomm channel, the noise density is multiplied by the bandwidth. Following this, the conversion to dBW is made. To calculate \(P_{C}\), the peak bin within an Orbcomm channel is identified and the surrounding bins are summed. Afterwards, the conversion to dBW is made. Finally, to calculate the \(C/N\) and \(C/N_{0}\) the formulas of Section 3 are employed.
## 3 Lateral Stability Tracking Control of Designed Rollator
### Lateral Controller Development
#### 3.3.1 Trajectory Error Function  
According to the kinematic model in Equation (6) and tracking error model in Figure 8, we can obtain the relationship as:  
\[\left[\begin{array}{c}\dot{X}_{r}\\ \dot{Y}_{r}\\ \dot{\phi}\end{array}\right]=\left[\begin{array}{c}\cos\varphi\\ \sin\varphi\\ \tan\delta/L\end{array}\right]v_{r} \tag{12}\]  
where \((x,y)\) is the center coordinate, \(\varphi\) is yaw angle, and \(v\) is the velocity. Then the rollator control system can be seen as input variable \(u(v,\delta)\) and state variable \(\chi(x,y,\varphi)\):  
\[\dot{\mathcal{X}}=\mathcal{W}(\mathcal{X},u) \tag{13}\]To facilitate the MPC controller [36], we define the expected equation of motion trajectory as:  
\[\dot{\chi}_{d}=\mathcal{W}\left(\mathcal{X}_{d},u_{d}\right) \tag{14}\]  
where expected state variable is \(\chi_{d}=\left[x_{d},y_{d},\varphi_{d}\right]^{T}\) and the expected input variable is \(u_{d}=\left[v_{d},\delta_{d}\right]\). We transform the Equation (13) according to the [PERSON] formulation and ignore the high-order term, then:  
\[\dot{x}=w\left(x_{d},u_{d}\right)+\left.\frac{\partial\mathcal{W}(x,u)}{ \partial x}\right|_{\begin{subarray}{c}x=x_{d}\\ u=u_{d}\end{subarray}}\left(x-x_{d}\right)+\left.\frac{\partial w(x,u)}{ \partial u}\right|_{\begin{subarray}{c}x=x_{d}\\ u=u_{d}\end{subarray}}\left(u-u_{d}\right) \tag{15}\]  
Therefore, we can get the lateral error function according to Equations (14) and (15).  
\[\dot{x}_{e} =\left(\dot{x}-\dot{x}_{d}\right)=-v_{d}\sin\varphi_{d}\left(x-x_ {d}\right)+\cos\varphi_{d}\left(v-v_{d}\right) \tag{16}\] \[\dot{y}_{e} =\left(\dot{y}-\dot{y}_{d}\right)=v_{d}\cos\varphi_{d}\left(y-y_ {d}\right)+\sin\varphi_{d}\left(v-v_{d}\right)\] \[\dot{\varphi}_{e} =\left(\dot{\varphi}-\dot{\varphi}_{d}\right)=\frac{\tan\delta_ {d}}{L}\left(v-v_{d}\right)+\frac{v_{d}}{L\cos^{2}\delta_{d}}\left(\delta- \delta_{d}\right)\]  
Furthermore, to discretize the error function, we can obtain the model as follow  
\[\widetilde{\mathcal{X}}(k+1)=\mathcal{H}_{k,t}\widetilde{\mathcal{X}}(k)+ \mathcal{K}_{k,t}\bar{u}(k) \tag{17}\]  
Among them, \(\mathcal{H}_{k,t}=\left[\begin{array}{ccc}1&0&-v_{d}T\sin\varphi_{d}\\ 0&1&v_{d}T\cos\varphi_{d}\\ 0&0&1\end{array}\right],\mathcal{K}_{k,t}=\left[\begin{array}{ccc}T\cos \varphi_{d}&0\\ T\sin\varphi_{d}&0\\ \frac{\tan\delta_{d}}{L}T&\frac{v_{d}}{L\cos^{2}\delta_{d}}T\end{array}\right]\) and \(T\) is the sampling time.  
Figure 7: MPC controller structure.
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
## 4 Results
### Impact on Quality  
To measure the quality impact, the Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) are commonly used [32]. Although these measures are well understood, they do not model the subjective quality accurately. More recently, the Video Multimethod Assessment Fusion (VMAF) was designed, which offers a better prediction of human quality perception [33]. The VMAF score is a number between 0 and 100, where 100 means that the two videos are subjectively indistinguishable. It has been claimed that a 6-point difference in VMAF score is just noticeable, i.e., it is a just-noticeable difference (JND) [34]. In this paper, the VMAF metric is mainly used, although some PSNR and SSIM scores are additionally given for completeness.  
First, Section 4.3.1 analyzes the impact on the VMAF score. Then, Section 4.3.2 evaluates the number of frames before \(\text{KI}_{\text{NS}}\) is of a better quality than \(\text{KI}_{\text{src}}\). Then, the PSNR and SSIM results are discussed in Sections 4.3.3 and 4.3.4, respectively. Last, Section 4.3.5 discusses the worst-case VMAF scores.