esa-sceva/satcom-chunk-collection huggingface dataset:
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 9 Discussion and Comparison among Experiments
### On the Auditory Component Effect
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Condition** & **st. dev.** & \(t\) & **df** & \(\rho\)**-Value** & **95\% CI** \\ \hline
**A-TC0** & **0.528** & \(-\)**4.988** & **67** & **0.000** & \(\{-\)0.889, \(-\)0.381** \\
**A-TC1** & **0.629** & \(-\)2.056** & **65** & **0.044** & \(\{-\)0.624, \(-\)0.009** \\
**A-TC2** & - & - & - & - & - \\
**A-TC3** & 0.804 & 0.353 & 142 & 0.725 & \([-\)0.249, 0.357] \\
**A-TC4** & - & - & - & - & - \\
**A-TC0** & **0.491** & \(-\)**2.373** & **68** & **0.020** & \(\{-\)0.513, \(-\)0.044** \\
**A-TC5** & - & - & - & - & - \\
**A-TC6** & **0.555** & **2.247** & **68** & **0.028** & \(\{\)0.033, 0.563\(\}\) \\
**A-TC7** & - & - & - & - & - \\
**A-TC8** & **0.487** & **6.001** & **69** & **0.000** & \(\{\)0.463, **0.925** \\
**A-TC0** & **0.556** & \(-\)**3.518** & **67** & **0.001** & \(\{-\)0.738, \(-\)0.204\(\}\) \\ A-TC9 & 0.786 & \(-\)1.358 & 105 & 0.178 & \([-\)0.538, 0.101] \\ A-TC10 & - & - & - & - & - \\ A-TC11 & 0.631 & 1.041 & 143 & 0.300 & \([-\)0.112, 0.360] \\ A-TC12 & - & - & - & - & - \\
**A-TC0** & **0.557** & \(-\)**3.664** & **70** & **0.000** & \(\{-\)0.7430, \(-\)0.219\(\}\) \\ A-TC13 & - & - & - & - & - \\
**A-TC14** & **0.794** & **2.365** & **136** & **0.019** & \(\{\)0.060, **0.6750** \\ A-TC15 & - & - & - & - & - \\ A-TC16 & 0.673 & 1.199 & 109 & 0.233 & \([-\)0.107, 0.434] \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Two-sample \(t\)-test analysis between equivalent conditions from Experiment 2 and Experiment 3 (st. dev: Standard deviation, \(t\): Test statistic, \(\text{df}\): Degrees of freedom). Conditions with statistically significant differences are highlighted. Figure 13: Average MQS values for different audio test conditions in Experiments 2 and 3. As for the correlation of responses between Experiment 2 and Experiment 3, Figure 12b shows that there is a small positive correlation between quality scores of Experiments 2 and 3. For most test conditions, markers appear above the red line, which indicated that responses for Experiment 2 were higher. Yet, some test conditions (A-TC 9 and A-TC1) of Experiment 2 presented lower quality scores than their equivalent test conditions in Experiment 3. All in all, results showed that some types of audio distortions have a greater impact on quality. More specifically, background noise and clipping distortions presented a similar impact on the overall audio-visual quality when compared to their equivalent test conditions with visual distortions.
## 4 Software Architecture
### Core Functional Module Design
#### 4.2.2 Node Management Module  
The TS must maintain and manage the nodes, including node access, status monitoring, information changes, and connectivity testing.
* Node Access  
Node access is the most complex and crucial function of this module. The initial access process is illustrated in Figure 4. It is important to note that the node information stored in the database determines whether a node is accessing the area for the first time.  
Upon powering on, a node will initiate an application that is customized to the OBU manufacturer to connect to the logical controller and upload its MAC address. After the logic controller obtains the node access request and queries it through the database, it will notify the user of the presence of a new online node in the GUI and request the user's configuration and the authorization data for the node. Subsequently, the controller generates a unique identifier for the node based on its MAC address, notifies the node of its successful access, stores the node information in the database, and updates its access status on the GUI.  
Nodes without configuration will be automatically shut down after a certain period to free up logical controller resources. Configured nodes will become available resources, entering the node resource pool and being uniformly managed by the logical controller.
* Status Monitoring  
This function includes the ability to view the working status and statistics of the nodes. A node status can be idle, busy, faulty, or disabled. A node fault is critical and needs to be checked by management personnel in a timely manner. Statistical information includes historical working status, network traffic, package quantity, and other data.
* Information Change  
This function can change and control two aspects of the information. The first is the configuration information, such as the bound physical scenario, node coordinates, and node name. The second is the node status, which turns the node on and off according to the requirements.
* Connectivity Testing  
This function confirms the connectivity between the node devices and eliminates hardware faults. The platform supports the development of C-V2X interoperability testing between nodes. If the nodes fail the testing process, their status information becomes faulty.  
Figure 4: Node first access process.
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method Category** & **Core Techniques** & **Latency** & **Cost** & **Scalability** \\ \hline \multirow{2}{*}{Traditional methods} & Signature-based IDS [21]; & \multirow{2}{*}{Medium–High} & \multirow{2}{*}{Low} & \multirow{2}{*}{Poor} \\  & A threshold-based IDS [19; 20] & & & \\ \hline \multirow{4}{*}{ML-based methods} & PCA/EFS + SMOTE + & \multirow{4}{*}{Medium} & \multirow{4}{*}{Medium} & \multirow{4}{*}{Medium} & \multirow{4}{*}{Moderate} \\  & AdaBoost [10]; & & & \\ \cline{1-1} \cline{3-5}  & RF + GAO-LightGBM [22]; & & & \\ \cline{1-1} \cline{3-5}  & KNN [24]; RF [25]; & & & \\ \cline{1-1} \cline{3-5}  & AE + XGBoost [12]; & & & \\ \cline{1-1} \cline{3-5}  & Ensembles [23] & & & \\ \hline \multirow{4}{*}{DL-based methods} & Autoencoders [5]; & \multirow{4}{*}{Medium–Low} & \multirow{4}{*}{High} & \multirow{4}{*}{Strong} \\  & CNN [7; 30]; & & & \\ \cline{1-1} \cline{3-5}  & Hybrid DNNs [26; 27; 28]; & & & \\ \cline{1-1} \cline{3-5}  & BiLSTM-GMM [29]; & & & \\ \cline{1-1} \cline{3-5}  & CNN-LSTM-Attention [8] & & & \\ \hline Ours & ROSE-BOX & Low & Medium–Low & Strong \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Condensed comparison of intrusion detection methods. low computational demand of ROSE-BOX makes it ideally suited for deployment across a wide array of devices, from industrial controllers to edge computing nodes and embedded systems. This scalability, coupled with a reduction in required computing resources, allows for broad deployment without overburdening system resources or incurring significant operational costs. In summary, these design choices position ROSE-BOX to meet the stringent real-time and resource constraints of modern IIoT deployments while providing comprehensive, multi-class intrusion detection, as detailed in the following sections.
# Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
## 3 Materials and Methods
### Accuracy Assessment  
Both the conventional model and the proposed model produce numerical outputs as their spacecraft poses (\(Poses\)), which is given by seven-column datasets and is expressed as follows:  
\[Poses=[x,y,z,q_{1},q_{2},q_{3},q_{0}] \tag{9}\]  
This vector is separated into two parts, namely, a position part (\([x,y,z]\)) and an attitude or orientation part (\([q_{0},q_{1},q_{2},q_{3}]\)) that is expressed as a quaternion. The magnitudes of the X-, Y-, and Z-axes represent \(x,y,z\), respectively. Regarding the orientation, \(q_{0}\) is the real part of the quaternion, and \(q_{1},q_{2},q_{3}\) are the components contained in the vector part.  
The error computation is also separated into position error and orientation error components, which are calculated by the Euclidean distance measure, as described in the following questions.  
\[Error_{position}=\left|x_{i}-x_{gt}\right| \tag{10}\]  
\[Error_{orientation}=\left|q_{i}-q_{gt}\right| \tag{11}\]  
where \(x_{i}\) represents the spacecraft pose (\(Poses\)) in the position part and \(x_{gt}\) is the ground truth. This value corresponds with the formula used to calculate the orientation error, where \(q_{i}\) is the output predicted from the PE model and \(q_{gt}\) is the ground truth for the orientation part. The combination of \(Error_{position}\) and \(Error_{orientation}\) is the total error (\(Error_{total}\)), which is represented in the following equation.  
\[Error_{total}=Error_{position}+Error_{orientation} \tag{12}\]  
These three kinds of errors are used for training the PE model using both the conventional and the proposed methods. Moreover, the position error is calculated in meters, whereas the orientation error transforms the quaternions in the aerospace sequence to Euler angles for model performance representation purposes.
## 3 Optimization of the Boundary of Energy Supply Area Based on Relative-Load-Distance
### The Criterion, Relative-Load-Distance  
We mentioned that the premise of optimizing the energy supply area of an energy station is to find out the load points that need to be adjusted, but this does not mean that any point can be adjusted. To solve this problem, this paper puts forward the criterion, \"relative-load-distance\", using the relative positions of the energy stations and the load points to adjust the ownership of part of the load point, and carry out the network planning, so as to obtain the most economical scheme.  
First, calculate the difference between the distance from a load point to energy station 1 and the distance from the point to energy station 2, as shown in the following equation:  
\[\Delta l=|\Delta l_{i-1}-\Delta l_{i-2}| \tag{13}\]  
where \(\Delta l_{i-1}\) is the distance from the node i to energy station 1 and \(\Delta l_{i-2}\) is the distance from the node \(i\) to energy station 2.  
Then, the relative-load-distance criterion of a certain point is as follows:  
\[\frac{\Delta l}{Q_{i}}\leq k\frac{R}{Q_{all}} \tag{14}\]  
In the formula, \(Q_{i}\) represents the load value of node \(i\), \(R\) represents the energy supply radius of the planning area, \(Q_{all}\) represents the total value in the area, and \(k\) is the coefficient.  
If the standard formula is established, it is necessary to adjust the point to another energy station; otherwise, there is no need to adjust it.
# VENuS-Derived NDVI and REIP at Different View Azimuth Angles
## 2 Materials and Methods
### Study Area  
The study area was located in the overlapping zone of the west strip's tile W08 and the south strip's tile S01 (Figure 1b). This 188.31 km\({}^{2}\) area contained different types of land-use and land-cover categories: urban, vegetation, soil, vineyards, orchards, and water, amongst others. Table 2 shows the acquisition times in UTC and the BRDF components of the W08 and S01 tiles on 18 April 2018. It can be seen that tile S01 was acquired 1 min and 56 s after tile W08, and the difference in the view azimuth is notable, i.e., more than 150 degrees. Furthermore, image W08 was acquired in a forward direction, while S01 was in a backward direction. The current study focused on a representative section in the northern part of the overlap zone, including three polygons (Figure 1c). Two polygons in the center comprise irrigated fields in a cultivated area, representing agricultural soil and vegetation surfaces (hereafter named CircleField-N and CircleField-S). A third polygon included an urban area (hereafter named Urban) and is an example of a heterogeneous surface. To fulfill the study goal, three clear-sky days were used in different seasons of the year (i.e., 18 April, 27 June, and 11 September 2018). The CircleField-N area had bare soil on the first date, developed corn plants on the second date, and bare soil on the third. The CircleField-S area had developed chickpea plants on the first date, ready-to-harvest dry chickpea plants on the second date, and bare soil on the third.
## 1 Introduction
CASNet [26] performs adaptive sampling and high-quality reconstruction. However, there is a robustness problem in different types of images. [PERSON] et al. [27] developed a dual-path attention network for compressive sensing image reconstruction, which enhances texture recovery effect, but increases the complexity of the model. [PERSON] et al. [28] proposed OCTUF, which uses a cross-attention transformer to improve reconstruction quality, but its efficiency decreases when dealing large-scale data. [PERSON] et al. [29] introduced HFIST-Net to combine traditional CS with deep learning for MR image reconstruction, but its reliance on domain-specific data restricts its wider applicability. CSCNet [30] proposed by [PERSON] et al., uses local structure sampling to enhance measurement correlations, which is difficult to capture global information in complex scenes. DMFNet [31] integrates detailed texture and global structure to achieve better reconstruction, although its high computational requirements pose challenges, especially for high-resolution images. Despite the significant advance in these deep learning-based methods, challenges remain in balancing reconstruction quality, computational efficiency, and generalization across different image types. To address these limitations, this study proposes a new deep network model, CBAM_ISTA-Net\({}^{+}\), which integrates a dual-attention mechanism and compressive sensing mechanism to improve compressive reconstruction of bird images and audio spectrograms. The model is designed to enhance the peak signal-to-noise ratio (PSNR). The main contributions of this paper are as follows:  
(1) Introduce the attention module CBAM to the ISTA-Net\({}^{+}\) framework to enhance the quality of compressed reconstruction. (2) Apply the CBAM_ISTA-Net\({}^{+}\) model for compressive reconstruction of bird images and audio data (Mel spectrograms and wavelet transform spectrograms), which significantly improve the peak signal-to-noise ratio (PSNR) under different compression ratios. (3) The classification results of reconstructed bird images verify the effectiveness of this method in maintaining the integrity of image features and demonstrate the potential application in practical ecological monitoring.
## 4 High-Resolution Images in Camera-Based WMSNs
### Image Stitching Technique
#### 4.1.3 Blending  
The purpose of blending step is to make overlapped image edges disappear and it is necessary because even after a perfect image registration, that is practically impossible, some image edges are still visible due to some effects such as vignetting (intensity decreases towards the edge of the image), parallax effects due to unwanted motion of the optical center, radial distortion, etc.  
Although there are good methods like the multi-band blending method presented by [PERSON] and [PERSON] [38] which results in a very smooth and seamless image, the method used to blend the images in WMSNs should be simple. Whereas one of the stitching purposes in this application is to reduce the amount of transaction with Sink, stitching procedure should be performed in cluster heads which have resource limitations. In addition, it is not possible to send [PERSON] and [PERSON] [38] multi-band maps to cluster heads because the stitching subset change by each query and new maps are needed. Besides, using different maps for each image in each query requires memory space in upper-tier nodes.  
Blending result is generated by overlapping images which have gradient transparency near the edges. So an opacity matrix is used for images as a map to determine opacity of pixels. Due to the fact that the nodes in lower-tier are homogeneous and the sizes of all images are equal, one predefined opacity map which is restored in upper-tier nodes is enough for all of the images. Figure 4 depicts this map.  
Using a general map for all images has merits and demerits. As mentioned before, storing only one map in each cluster head memory is adequate to blend all the images. Also, it is possible to perform image stitching completely in cluster heads and avoid sending all images to Sink. Stitching images in cluster head reduces the size of data transmission and consequently decreases network traffic between upper-tier and Sink and prolongs nodes lifetime in upper-tier (see subsection 5.3). Nevertheless, the result of this method is not as uniform and smooth as the result image of other ones.  
Figure 4: Predefined opacity map for blending.  
All in all, in the response to Sink queries, except the first one, cluster head gathers the low-resolution images of a proper subset of its camera nodes, applies the opacity map to them and transforms them by transformation matrices which have been sent to it after the first query by Sink. The result is a high-resolution image which is sent to Sink. How this proper subset of camera nodes is selected is described in the following.
## 5 Solving Strategy Based on NSGA3
### Probabilistic Power Flow Based on Monte Carlo Sampling  
The power flow calculation is the basis for the optimization analysis of the ADN in this paper; due to the probability property of renewable energy and the load response, the power flow is uncertain. To deal with the problem, probabilistic power flow based on Monte Carlo sampling is used.  
Use Monte Carlo sampling to generate lots of deterministic scenarios based on the probability distribution characteristics and the limits of renewable distributed power generation and controllable load response. Assume the number of sampling times is set to \(k\) times. The random vectors of each controllable load and renewable power supply are obtained as Equation (34):  
\[\left\{\begin{array}{l}\mathbf{\lambda}_{\text{RL}}=[\lambda_{i}^{1},\lambda_{ i}^{2},\cdots,\lambda_{i}^{k}]\\ \mathbf{P}_{\text{WT}}=[P_{wt,i}^{1},P_{wt,i}^{2},\cdots,P_{wt,i}^{k}]\\ \mathbf{P}_{\text{PV}}=[P_{pv,i}^{1},P_{pv,i}^{2},\cdots,P_{pv,i}^{k}]\\ \end{array}\right. \tag{34}\]  
A large number of random sampling samples are obtained under certain constraints, and then deterministic power flow calculation is carried out to obtain the probability characteristics of the node voltage and branch power flow.  
The estimation probability is expressed by the large number theorem; that is, the chance constraint condition, such as (17) and (18), holds if and only if the probability condition is satisfied.  
Assume the number that satisfies the chance constraint condition is \(k^{\prime}\). The more Monte Carlo simulation scenarios are generated, the closer the estimated probability \(k^{\prime}/k\) is to the probability that the actual chance constraints are satisfied.  
A flowchart of multiobjective optimal scheduling of the ADN based on the NSGA3 algorithm is shown in Figure 6.
## 4 Model Studies
### Geometric Model Introduction  
The Tanjiahe landslide was located on the right bank of the Yangtze River, and it was 56 km from the Three Gorges Dam. Located in the zone impacted by a subtropical monsoon climate, precipitation in this area was abundant with an average annual rainfall of almost 1027.5 mm, which can be the major factor that leads to landslide deformation. In this research, rainfall is characterized by uniform rainfall (5 mm/h). In addition, the main slip surface was selected as the computed section, and a two-dimensional geometric model was built with a length of 820 m and a height of 320 m (Figure 4). The pressure and matric suction of three monitoring points (H, I, J) and the pore gas pressure at the vertical section D\({}^{\prime}\)-D were analyzed in this research.  
The sliding mass was divided into vertical slices with a width of 5 m, which was equal to 5% of the slope length. As the properties of the soil were different for various water contents in the horizontal direction, the sliding mass was divided according to the different soil moisture levels.
## 1 Introduction  
Over two million bone graft operations are performed worldwide every year, and this number is increasing dramatically due to an aging population [1]. Autogenous bone is the gold-standard for bone repair. However, autogenous bone is available in limited amounts, and is usually harvested from the iliac crest, which is accompanied by a second operation and associated surgical risks in the donor site [2]. Therefore, there is an urgent need for alternative strategies, such as tissue engineering. Tissue engineering involves the development of scaffolds to mimic the extracellular matrix to aid new bone formation [3]. The scaffold should be biocompatible, biodegradable and porous [2]. Various types of scaffolds have been developed, including artificial polymers, natural polymers, calcium phosphate-based ceramics, and self-assembling peptides [4]. However, they have limited osteoinduction and bioactivity [4]. The introduction of growth factors in the scaffolds increases the bioactivity; however, it is associated with protein diffusion and high cost drawbacks [5; 6]. In addition, the cell-based scaffolds are associated with complex challenges such as cell survival and cell diffusion [7].  
Calcium phosphate biomaterials have been used for bone and tooth repairs; however, none of these materials possesses bioactivity comparable to autogenous bone and teeth [1]. Therefore, there is an urgent need for better scaffold materials. This review focuses on demineralized dentin matrix (DDM), originating from extracted human teeth. Teeth and bone have similar histogenesis, both originating from the neural crest during embryonic development [8]. Dentin and bone also have similar components, both composing of an organic phase (90% type I collagen) and an intrafibrillar mineral phase (mostly apatite crystallites) [9]. The ratios of organic/inorganic components in human dentin and bone are both at approximately 2:1 [8]. Dentin was reported to have a bone-inducing function in 1967, and DDM was found to have better regenerative properties than undemineralized dentin matrix (UDM). Since then, several other studies were published on the use of DDM in tissue engineering [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. However, to date, a literature search has revealed no review article on the application of DDM for teeth and bone regenerations.  
Therefore, the objective of this article was to review the recent findings and cutting-edge research on the effects of DDM in promoting hard tissue repair and regeneration. First, we discussed the 3D hierarchical structures and components of DDM. Then, we described the preparation of DDM via various methods. Next, we summarized the investigations and major results in using DDM for bone and tooth regeneration. Finally, we offered insights into the application of DDM in the biomaterials development field, including the key challenges and potential solutions.