esa-sceva/satcom-chunk-collection huggingface dataset:
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 4 Analysis of Constellation Collision Maneuver
### Starlink Constellation Collision Avoidance Maneuver  
Based on the analysis of the collision avoidance maneuvers of the other three orbital layers mentioned in Section 3.4, Table 9 shows the average collision probability and average object flux of a single satellite in each orbital layer during the mission. When the value of ACPL is \(10^{-4}\), the total avoidance maneuver times of the satellite in each stage and in the whole mission under different orbit parameters are shown in Figure 12. The flux  
Figure 11: The relationship between risk and the mean number of avoidance manoeuvres during Phase 3.  
Figure 10: The relationship between the mean number of avoidance manoeuvres and ACPL during Phase 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \(ACP_{d}\) & \(ACP_{w}\) & \(Flux_{d}\) **(/m\({}^{2}\)/a)** & \(Flux_{w}\) **(/m\({}^{2}\)/a)** \\ \hline \(7.76\times 10^{-5}\) & \(7.78\times 10^{-5}\) & \(2.20\times 10^{-6}\) & \(2.20\times 10^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 8: Single satellite annual collision probability (ACP) and space object flux during Phase 3.  
of space objects in the orbit with high inclination is larger, and more collision avoidance maneuvers are needed in the climbing phase to ensure the safe and orderly follow-up work of the satellite.  
When the orbit height is similar, the number of avoiding maneuvers in the low inclination orbital layer is about 1500 times during the mission cycle, and the total avoidance times of the high inclination orbital layer satellites is about 500. The satellites on the low inclination orbital layer need to pay more attention to their operation status and replace damaged or failed satellites in time.
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 5 Experimental Design and Results
### Discussion of the Issues  
While the experimental results indicate that LIF-M exhibits superior performance, it is worth noting that in environments resembling terrain irregularities, such as the oscillations observed in the red trajectory in Figure 5a, noise in the z-axis can introduce fluctuations in the system. This represents a specific error in the three-dimensional manifold space; however, due to the uncertainties arising from the system's left-invariant and right-invariant properties, this oscillation can quickly converge to stability.
## 4 Design and Electrical Performance Characterization
### Configuration Set-Up, and Identification of Materials and Their Electrical Performance  
This section presents the materials used along with their electrical properties, as well as the experimental setup for characterizing resonator behavior via design charts.
## 2 Materials
### Airborne Digital Imagery and Preprocess  
The airborne digital imager was equipped with a 50 mm focal length lens and a \(11,608\times 8708\)-pixel (4.6 \(\upmu\)m physical pixel size) sensor, resulting in a field of view of 56.2\({}^{\circ}\)\(\times\) 43.7\({}^{\circ}\). The imager achieved a ground resolution of 0.165 m relative to the flight altitude of 1800 m. The forward overlap and side overlap of the imager are 63% and 20%, respectively.  
After airborne digital imagery acquisition, the position information and orientation information of each image were extracted based on the onboard GPS and IMU data. The red-green-blue (RGB) images were processed by a photogrammetry technique, so-called digital aerial photogrammetry (DAP). The overlapped RGB images with the corresponding geographic coordinates of waypoints were imported into Agisoft Metashape Pro (Agisoft LLC, St. Petersburg, Russia) to produce a dense point cloud and a georeferenced DSM. Our processing steps are identical to the recommended photogrammetry processing pipeline, as can be found in the Metashape user manual. The resulting point density of DAP was around 50 points/m\({}^{2}\) while, in contrast, the point density of ALS was about 10 points/m\({}^{2}\).  
The DAP and LiDAR point clouds of the entire study area were firstly co-registered through a coarse adjustment by hand then a fine iterative closest point (ICP) algorithm. After the co-registration, the DAP and LiDAR point clouds were processed according to the boundary of forest plots. For each plot, the co-registration between DAP and LiDAR point clouds was fine-tuned using the ICP algorithm to achieve the optimal co-registration in local extent. The point clouds of each plot were denoised to remove isolated outlier points using a manual trimming by visual interpretation and automatic noise removal by a median filter in CloudCompare version 2.11.0 software ([[https://www.danielgm.net/cc/](https://www.danielgm.net/cc/)]([https://www.danielgm.net/cc/](https://www.danielgm.net/cc/)), accessed on 10 August 2022). The DAP-based digital surface model (DSM\({}_{\text{DAP}}\)) with grid size (i.e., spatial resolution) of 1 m was then created using all points with the k-nearestneighbor with inverse distance weighting interpolation algorithm. Next, the normalized canopy height model (CHM\({}_{\text{DAP}}\)) was generated by subtracting DTM\({}_{\text{ALS}}\) from DSM\({}_{\text{DAP}}\). It is noted that no DTM\({}_{\text{DAP}}\) was created from DAP point clouds. This is because DAP points of the ground surface were usually scarce due to crown occlusion, especially in dense forest regions, thus failing to obtain an effective DTM. Finally, with the reported 30 m resolution land cover product, we extracted the forest regions from the generated CHM\({}_{\text{DAP}}\).
## 2 Materials and Methods
### Evaluation Tools
#### 2.3.1 Pre- and Post-Intervention Assessment  
Assessments were carried out before the start of the intervention program and at its completion, and they were performed in both groups. The following values were assessed:  
* For the sociodemographic and pharmacological characteristics of the sample, an ad hoc questionnaire was conducted where the following variables were collected: age, sex, years since [PERSON]'s diagnosis, first symptom of the disease, as well as any antiparkinsonian pharmacological treatment being received.
* Regarding functional aspects, the following variables were analyzed: 1. For dynamic balance, we used the Timed Up and Go (TUG) test [34]. This test evaluates basic functional mobility of the subject, in addition to dynamic and static balance and the risk of falling [35]. As well as the basic version of the test, it was also performed with the addition of a simultaneous double task. (The participants performed mathematical operations.)2. Lower extremity strength with Five Sit-to-Stand Test (FSTST) [36; 37].
3. The gait and balance were measured using the Tinetti Mobility Test [38; 39]. This test also helps with identifying people at risk of falling.
* For quality of life, the Parkinson's Disease Questionnaire (PDQ-39) [40; 41] was used to assess this aspect. The PDQ-39 scale assesses 8 different domains: mobility (10 items), activities of daily living (6 items), emotional well-being (6 items), stigma (4 items), social support (3 items), cognitive status (4 items), communication (3 items), and bodily discomfort (3 items).
* With regard to symptomatology and follow-ups, the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) [42] was used for this purpose. It is made up of four parts: Part I (non-motor experiences of daily living), Part II (motor experiences of daily living), Part III (motor exploration), and Part IV (motor complications).
# Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images
## 5 Overview on Semi-fragile Watermarking Algorithms
### Unrecoverable Semi-Fragile Watermarking Algorithms  
The unrecoverable semi-fragile watermarking algorithms can only be used to detect and locate tampered areas. In this paper, we will review them from two aspects: spatial domain and transform domain.
# Promising Cr-Doped ZnO Nanorods for Photocatalytic Degradation Facing Pollution
## 4 Photocatalytic Properties
### MO Concentration Dependency of the Photodegradation  
The removal efficiency is significantly affected by the initial concentration of the pollutant [25]. In this respect, we compared the photodegradation levels for different initial MO concentrations ranging from 5 ppm to 30 ppm with a constant ZnO-Cr\({}_{1}\) sample concentration of 20 mg/L (Figure 11). The photocatalytic activity decreased with increasing initial concentrations of MO. The recorded removal percentages of MO after 30 min were 100%, 77%, 37.7%, and 23.9% for the initial concentrations of 5, 10, 20 and 30 ppm, respectively.
# Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion
## 2 Literature Review
### Analysis of PM\({}_{2.5}\) Concentration in Taiwan
In addition to locally produced pollutions, long-range transported pollutions from other countries significantly affect Taiwan's air quality during different seasons and weather conditions. For instance, every autumn pollutants are carried into Taiwan by the northeast monsoon [13], and in spring the air quality of Taiwan is usually spoiled by north Asian dust storms [14] and the ambient particles transported from northern China [15].
## 4 Results
### Neural Network Evaluation
Finally, the F1-score, which combines precision and recall, was 77.15%, highlighting a reasonable balance between these two metrics. To quantify the improvement achieved by the hybrid model over the pure neural network, we define the accuracy difference as:  
\[\Delta Acc=Acc_{hybrid}-Acc_{NN}\]  
where \(Acc_{hybrid}\) represents the accuracy of the model incorporating the causal approach, and \(Acc_{NN}\) denotes the accuracy of the pure neural network without causal integration. In our experiments, after 10 independent runs, the average accuracy values were:  
\[Acc_{hybrid}=0.865,\quad Acc_{NN}=0.854\]  
Thus, the accuracy improvement is calculated as:  
\[\Delta Acc=0.865-0.854=0.011\]  
which translates to a 1.1% increase in prediction accuracy. To verify the statistical significance of this improvement, we conducted a paired-sample \(t\)-test comparing the accuracy values of both models. The results yielded a \(p\)-value lower than 0.05, confirming that the observed improvement is statistically significant and unlikely to be due to random variation. Additionally, a 95% confidence interval was computed for the accuracy difference, yielding a range of [0.007, 0.015]. This new evidence shows that the hybrid model consistently does a better job of predicting the future than the pure neural network in most of the situations that were tested. This enhancement improves strategic decision-making in the game, facilitating anticipation and response to opponent plans. The evidence indicates that adding causal reasoning to learning models can enhance the efficacy and interpretability of decision-making processes. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Metric** & **Equation** & **Precision** \\ \hline Precision (P) & TPs & 71.01\% \\  & TPs + FPs & 84.48\% \\  & TPs + FNs & \\ Accuracy (A) & TPs + TNs & 71.00\% \\  & TPs + TNs + FPs + FNs & \\ F1-Score & \(2\cdot\frac{\text{P}\cdot\text{R}}{\text{P}+\text{R}}\) & 77.15\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics for the hybrid model.
# Human-Centered Dynamic Service Scheduling Approach in Multi-Agent Environments
## 3 Human-Centered Dynamic Service Scheduling Approach  
### Proposed Scheduling Framework Overview  
Humans can receive services from one or more agents (service providers) of different types and functions in their daily lives. There are two types of service providers. An active agent can perform a variety of tasks through their own processes with distinct mechanisms, whereas a passive agent is limited to specific processes, such as turning a smart lamp on and off in a home management system. In this study, both are referred to as agents and can share and collaborate on a given task according to their capabilities. These capabilities are assumed to be predetermined and stored in a database so that the scheduler can distribute tasks accordingly.  
When a user requests a service, it is set as a single job. The job is then divided into an ordered set of operations, where each operation is characterized by its numbered sequence and the agents that can execute it. In this study, we define an operation as an essential action that is completed independently by a single agent. Thus, a job can be performed by multiple agents one after another completing sequential operations.  
A user may have various service requirements, and each job may have additional constraints on when it should be provided to the user. A user may request that a specific service be provided before or after a different service, or only when a specific condition is met. For example, a user can ask for a meal and coffee to be delivered simultaneously or for coffee to be delivered first. Therefore, services must be provided in a manner that satisfies these requirements.  
To provide the requested services, an appropriate agent must perform the task while satisfying the constraints requested by the user. That is, the job is decomposed into operations that each agent can perform, and these operations must be performed by the appropriate agent based on the agent's capability and duration while satisfying the constraints requested by the user.  
In this study, a scheduling framework is proposed to allow multiple agents to properly perform the services requested by the user while satisfying dynamic requests (Figure 1). The proposed scheduling framework comprises three main components.  
1. Job Decomposer: It decomposes the job requested by a user into a list of operations that an agent can perform.
2. Operation Manager: It sequences and organizes the operations using operation queues.
3. Operation Allocator: It allocates an operation to an appropriate agent.
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 3 Research Methodology
### Selection of Non-Landslide Points Using the Information Value Method  
The quality of landslide sample selection greatly influences the accuracy of landslide susceptibility assessments. To mitigate the uncertainty caused by the random selection of non-landslide points in previous methods, this study computes the information content for different landslide categories within each feature factor using the IV model, based on the feature factor data and the spatial distribution of 657 historical landslide points,  
Figure 3: Flowchart of the proposed method. to guide the selection of non-landslide points [27]. The number of categories for each feature factor is determined based on real-world conditions (e.g., aspect, lithology) or by maximising the information content difference (e.g., elevation, slope). The strategy of maximising the information content difference determines the largest possible interval length threshold between adjacent intervals and applies this threshold for categorisation. Adjacent intervals with identical information values are merged. Table 2 presents the final categorisation results for each feature factor. The IV model compares the frequency of landslide occurrences in each feature factor within evaluation units to the regional frequency of landslide occurrences. Table 2 depicts the classification criteria for each feature factor. It quantifies the contribution of each feature factor in a data-driven manner [28], enhancing the interpretability of the assessment results. The calculation is presented as follows:  
\[I_{i}=\ln\frac{N_{i}/N}{S_{i}/S} \tag{2}\]  
where \(I_{i}\) is the IV of the feature in interval \(i\) for geological hazard occurrence, \(N_{i}\) and \(N\) represent the number of landslide points in interval \(i\) and the total number of landslide points, respectively, \(S_{i}\) is the area of the grid for the feature in interval \(I\), and \(S\) is the total grid area. According to the IV model findings, the natural breaks classification method was used to categorise landslide susceptibility into four levels: non-susceptibility, low susceptibility, moderate susceptibility, and high susceptibility [29]. Based on historical landslide points and slope data, areas fulfilling criteria such as non-susceptibility, slope less than 10\({}^{\circ}\), and distance over 500 metres from historical landslide points were selected. Next, 657 non-landslide points were randomly chosen as negative samples, maintaining a minimum distance of 2 km between points to create a landslide sample set with a 1:1 ratio to historical landslide points. Furthermore, a high spatial resolution of Google Earth imagery is used
## 2 Fault Detection Method
### Optimization Algorithm  
The Adam optimizer is usually used to train network parameters [35]. It combines the advantages of AdaGrad and RMSProp and has the advantages of easy parameter adjustment and fast convergence. The process from Equations (1)-(8) can be expressed as:  
\[L=J(\mathbf{x},\mathbf{y},\mathbf{\theta}) \tag{9}\]  
where \(\mathbf{x}\) is the input signal, and \(\mathbf{\theta}\) is all trainable hyperparameters of fault detection model. The update rules for trainable parameters are as follows:  
\[g=\
abla_{\theta}J(\mathbf{\theta}) \tag{10}\]  
\[m=\beta_{1}m+(1-\beta_{1})g \tag{11}\]  
\[t=\beta_{2}t+(1-\beta_{2})g^{2} \tag{12}\]  
\[\hat{m}=m/(1-\beta_{1}) \tag{13}\]  
\[\hat{t}=t/(1-\beta_{2}) \tag{14}\]  
\[\mathbf{\theta}=\mathbf{\theta}-\alpha*\hat{m}/\left(\sqrt{\hat{t}}+\varepsilon\right) \tag{15}\]where \(\beta_{1}\), \(\beta_{2}\) are the exponential decay rate, \(\varepsilon\) is an almost zero decimal, and \(\alpha\) is the learning rate. The learning rate is calculated as follows [36]:  
\[\alpha_{i}=\alpha_{0}/(1+10e_{i}/epoch)^{0.75} \tag{16}\]  
where _epoch_ is the total iteration steps, and \(e_{i}\) is the current iteration steps.  
The proposed fault detection network is shown in Figure 7, and the corresponding model parameters are shown in Table 1. Where, CK1 to CK3 represent the kernel sizes of convolution in the residual layer in Figure 7, and CK1 to CK3 represent Stride, respectively. The model contains four residual layers. The length of the time-domain signal for a single sample is 2048, and the channel is 3. The vibration displacement signal is collected by three sensors. Firstly, the original signal is input into a down-sampling layer (i.e., a convolution layer) and becomes data with a width of 256 and a depth of 32. Then, after 4 residual layers, it becomes data with a width of 4 and a depth of 256. Finally, through a pooling operation, a flatten operation and a fully connected layer, the classification probability is output to further complete the detection of crack location. Before training the fault detection model, the total sample is divided into a training and a test set, as shown in Figure 6. Without special instructions, we use the first 70% of the total sample as the training set and the remaining 20% as the test set, and we remove the middle 10%. In the training process, no noise is added to the training set; only Gaussian noise is added to the test set.
## 4 Results and Discussion  
### Potential Motivations for the Occurrence of CBR  
Based on questionnaires and user interviews, the motivations for CBR occurrence were identified. The dramatic changes in rural life scenes are an important reason for the development of CBR. The traditional HTSTO is more suitable for agriculture-oriented families. The flat roof is suitable for drying food, while the open courtyard can be used to grow plants and raise animals. However, with the adjustment of the rural economic structure, many farmers choose to work in factories, and families are becoming less and less dependent on agriculture. The popularity and development of television, mobile phones, and mobile Internet have made farmers more willing to stay indoors rather than outdoors.  
In addition, the occurrence of CBR is also driven by the following factors. Open courtyards mean high-intensity interaction between indoor and outdoor environments, providing a way for energy exchange, sand intrusion, mosquito nuisance, etc., making it difficult to meet modern farmers' living needs for health, comfort, and hygiene. In recent years, with the rapid development of local industries, changes in climatic conditions, such as severe smog, are challenging traditional vernacular architecture. Existing residential buildings cannot meet farmers' needs for a healthy and comfortable living environment.  
The thermal performance of a brick-concrete HTSTO is weak and cannot match the current energy metering system, which indirectly increases the energy and economic burden on farmers. An HTSTO's existing envelope structure is basically brick walls and concrete  
Figure 5: The equipment used in the study and its parameters.  
roofs without insulation layers, and its thermal performance is very poor. However, a comprehensive upgrade of the HTSTO envelope means a huge economic investment, which many farmers cannot afford. Therefore, CBR can effectively improve the overall thermal performance of the building with less investment, and has become a popular building renovation measure among local people.  
CBR expands the original functions of the vernacular buildings. CBR enlarged the interior space of the HTSTO to expand and extend its functionality. The enhanced sealing can prevent the intrusion of external dust and fallen leaves, especially in the windy and sandy spring, and can provide a clean and tidy environment that is easier to maintain. Moreover, CBR also avoids the interference of rain and snow on the use of courtyard space, and can also ensure washing, drying, and other activities in the courtyard in cold weather.
# Residual Echo Suppression Considering Harmonic Distortion and Temporal Correlation
## 5 Experiments  
To demonstrate the performance of the proposed RES, we compared the echo return loss enhancements (ERLEs) in the far-end single-talk periods and the perceptual evaluation of speech quality (PESQ) scores in double-talk periods for the linear AEC output without RES, that with the power filter-based RES (PFRES) [27], that with the HDRES [28], and that with the proposed RES. As for the linear AEC, we adopted the frequency-domain NLMS-based echo canceller proposed in [8]. The DTD module used in the experiments was the one proposed in [30]. The ERLE is defined by:  
\[ERLE(t)=10\log_{10}\left[\frac{E[y(t)^{2}]}{E[\hat{s}(t)^{2}]}\right]. \tag{29}\]  
The parameter values for the proposed RES were \(M=257\), \(S=3\), \(H=5\), \(K=1\), \(T=1\), \(\mu=0.01\), \(\rho=0.9\), \(\alpha=0.7\), and \(\beta=2\). The parameters \(H\), \(T\), and \(K\) are related to the nonlinearity of the acoustic echo, so the values for those parameter would depend on the device configurations. The smoothing factors \(\rho\) and \(\alpha\) were set to be rather high values, as the echo signal is highly nonstationary. The step size parameter \(\mu\) was set to control the trade-off between the convergence rate and misalignment. We performed experiments for both simulated and real-recorded data with various noise types, echo-to-noise ratios (ENRs), near-end signal-to-noise ratios (SNRs), and near-end signal-to-echo ratios (SERs).