esa-sceva/satcom-chunk-collection huggingface dataset:
# The XTRA Option at the NEUTRA Facility--More Than
## 2 Data Interpretation and Data Fusion
### Combined analyses of separate data-sets  
Another approach to the data analyses, which was developed subsequently, involves correlated analyses of both data sets, as represented in bivariate histograms, and corresponding combined segmentation. Often, this colocation analysis requires a registration operation to align the two data sets. At NEUTRA, this is less important due to the use of the same beam geometry and thus the projections are already aligned through the setup. There are cases when the sample has been displaced between the acquisitions. In these cases, the registration is mostly done using commercial software such as VG Studio MAX. This enables, in many cases, the identification of specific material phases due to their complementary contrast behavior and specific combinations of neutron and X-ray attenuation (Figure 4) [12,13]. Specifically, in [12] the registration was done using a combination of commercially available and custom-developed Monte Carlo-based algorithms.  
Analyses of combined data sets  
The most advanced approach consists in the attempt to correlate data before or during reconstruction. One example is to use X-ray data with a high signal-to-noise ratio and higher resolution to improve the quality of the reconstructed neutron data. The concept of using structural priors provided by reconstructed X-ray data to improve the quality of the reconstructed neutron data was demonstrated by [14].  
Visualization of bi-modal tomographic data is another challenge not only for data presentation but also for inspection and understanding. For efficient visualization, different strategies are known and applied. These include but are not limited to the following:  
2. Visualization based on color coding using the measured intensity of the modalities directly based on different color models and opacity: 1. [label=()]
3. Let each contrast modality control a color channel in an RGB image [13] (Figure 4). The remaining color channel is usually the average of the two modalities; 2. Let one contrast modality index a color map while the brightness is controlled by the other modality; 3. Interpret the images as orthogonal vectors, and the computed length of the vector sum and angle between the sum and an image will indicate which is more intense compared to the HSV color model.
4. Visualization based on segmentation: 1. [label=()]
5. Attribute defined colors to regions segmented using the bivariate histogram of the two modalities (Figure 4e) [12]; 2. Utilize one modality to segment a region, while the content of it is visualized by the second modality (examples: Zuger sword, battery).
# SC-YOLOv8 Network with Soft-Pooling and Attention for Elevator Passenger Detection
## 3 Method  
### SC-YOLOv8 Network with Soft-Pooling and Attention Mechanism  
Building upon the strengths inherited from the previous YOLO series networks, the YOLOv8 network significantly improves the overall detection performance through further optimization of the network structure. It has gained widespread application in various object detection tasks due to its efficient real-time processing and network architecture. The overall structure of the YOLOv8 network can be divided into three parts: the backbone network, the neck network, and the detection head.  
The backbone network functions as the feature extraction component, aiming to perform preliminary feature extraction on the input image and generate three different scales of feature maps for subsequent use.  
The neck network is located between the backbone network and the detection head, and its role is to combine the feature information of the three feature maps extracted by the backbone network to realize the retention of features and extract more detailed information, and the neck network will output three new feature maps.  
The detection head employs the features extracted by the preceding networks to make predictions, producing the final output of the YOLOv8 network.  
This paper aims to optimize the feature extraction process of the YOLOv8 backbone network, and consequently, the enhancements to the YOLOv8 network are primarily focused on the backbone network. The modified YOLOv8 network structure proposed in this paper is depicted in Figure 1.  
First, we propose an SC-ConvModule, which reduces spatial and channel redundancy between features during convolution operations by replacing the standard convolution of the ConvModule in the YOLOv8 backbone network with SCConv. Simultaneously, improvements are proposed for the SPP-Fast module in the YOLOv8 network. In the original SPP-Fast module, the use of max-pooling operations may lead to partial loss of local information. To solve this problem, we introduce soft-pooling branches to build a dual-branch SPP-Fast module to fully extract feature information. To further enhance the network performance, we embedded a newly designed SPM-CBAM module in the YOLOv8 backbone network. We improve the CBAM (convolutional block attention mechanism) module by combining a multi-scale depth-separable convolutional kernel and soft-pooling techniques to more effectively regulate channel attention and spatial attention. This innovation aims to enhance the model's adaptability to different scales and features, thus improving the accuracy and robustness of target detection. In summary, this paper is  
Figure 1: Structure of SC-YOLOv8 network with soft-pooling and attention.  
dedicated to optimizing the feature extraction and attention mechanism of the YOLOv8 network through several module and technique improvements, aiming to achieve a higher level of performance.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 2 Theoretical Background of TD-NIRS
### Expansion of the RTE by Spherical Harmonics and the \(P_{n}\) Approximations  
One way to simplify the RTE is to expand \(I(\mathbf{r},\hat{s},t)\), \(q(\mathbf{r},\hat{s},t)\), and \(p(\hat{s},\hat{s}t)\) into a series of spherical harmonics, \(Y_{l}{}^{m}(\hat{s})\) (\(l=0\), \(1\), \(2\), \(\ldots\), \(m=-l\), \(-l+1\), \(\ldots\), \(l-1\), \(l\)), to separate the angular dependences of \(I(\mathbf{r},\hat{s},t)\) and \(q(\mathbf{r},\hat{s},t)\) on \(\hat{s}\) from the dependences on \(\mathbf{r}\) and \(t\)[5, 6] [7] (pp. 282-288). After some mathematical manipulations, the RTE is rewritten in terms of a series of spherical harmonics for the expansion coefficients \(i_{lm}(\mathbf{r},t)\) of \(I(\mathbf{r},\hat{s},t)\),  
\[\sum_{l=0}^{\infty}\sum_{m=-l}^{l}\bigg{\{}\bigg{[}\frac{1}{c}\frac{\partial} {\partial t}+\hat{s}\
abla+[\mu_{a}(r)+\mu_{s}(r)(1-p_{l})]\bigg{]}i_{lm}(r,t) -q_{lm}(r,t)\bigg{\}}Y_{l}^{m}(\hat{s})=0 \tag{2}\]  
where \(q_{lm}(\mathbf{r},t)\) and \(p_{l}\) are the expansion coefficients which are known. For a particular combination of (\(l\), \(m\)) = (\(L\), \(M\)), Equation (2) is transformed to an infinite number of coupled partial-differential equations for \(i_{LM}(\mathbf{r},t)\) with \(L\) ranging from \(0\) to \(\infty\) and \(M\) ranging from \(-L\) to \(+L\)[7] (pp. 282-288).  
By retaining the equations for \(L\) from \(0\) to \(N\) with \(M=-L\), \(-L+1\), \(\ldots\), \(L-1\), \(L\), the number of the retained coupled partial-differential equations is [summation of (\(2L+1\)) from \(L=0\) to \(N\)] = (\(N+1\))\({}^{2}\), the same as the number of unknown functions, \(i_{LM}(\mathbf{r},t)\). The system of these (\(N+1\))\({}^{2}\) equations for (\(N+1\))\({}^{2}\) unknowns is the \(P_{N}\) approximation. For example, for the \(P_{1}\) approximation, there are four unknowns of \(i_{LM}(\mathbf{r},t)\), and for the \(P_{3}\) approximation, there are 16 unknowns of \(i_{LM}(\mathbf{r},t)\). Even-order \(P_{N}\) approximations are not useful and only odd-order \(P_{N}\) approximations are considered.
## 2 Materials and Methods
### Hyperspectral Imaging
#### 2.1.2 Data Preprocessing: Calibration to Diffuse Reflectance  
Hyperspectral data analysis and tissue classification were performed using MATLAB 2018b (The MathWorks Inc., Natick, MA, USA). Prior to data analysis, raw hyperspectral data of the tissue sample was calibrated as described in reference [15]. In short, the captured photons counts were converted to diffuse reflectance relative to Spectralon (SRT-99-100, Labsphere Inc., Northern Sutton, NH, USA), which is known to diffusely reflect light with approximately 99%.
## 1 Introduction
For these reasons, in this work, two publicly available models, FaceNet [5] and OpenFace [6], were evaluated for SSPP verification scenarios together with two small-size proprietary models, which were specially designed for mobile devices applications and resources saving environments. This evaluation aimed not only to provide a fair comparison between the models but also to measure to what extent a progressive reduction of the model size influences the obtained results. The models were assessed in terms of accuracy and size with the aim of evaluating their applicability to scenarios with different environmental conditions and requirements, including a test where the image of the ID Card is compared against a usual facial image. Many varied databases, public and private, were used with the aim of covering as many scenarios as possible. A distance based classifier was used for matching purposes due to the SSPP nature of the scenarios and to guarantee the straightforward portability of the solution to mobile devices. The remainder of this article is organised as follows. First, the methods involved in the evaluation are presented in Section 2. Then, the evaluation protocol, the involved databases and the obtained results are presented in Section 3. Finally, conclusions are provided in Section 4.
## 3 Results
### Primary Part: Individual Patient-Level Data for Patients Initially Diagnosed with Stage I-III NSCLC
#### 3.1.2 Patient Characteristics  
Patient sociodemographic and clinical characteristics at initial ES-NSCLC diagnosis, overall and per stage are shown in Table 1. Initial diagnostic assessment involved imaging in 97.2% (310/319) of the overall population (Table 1). Disease staging was based on AJCC /UICC TNM 8 th Edition criteria in 99.0% (284/287) of evaluable patients. Overall, 94.0% (300/319) of patients underwent biomarker testing during the retrospective observation period, performed a median of 0.0 (0.0-3.5) days from diagnosis. For 51.7% (155/300) of the tested patients a commercially available panel was used, for 27.7% (83/300) specific biomarkers were examined, while 20.7% (62/300) were tested using both commercially available panel and specific biomarker testing. Testing methodology and findings for the most frequently tested biomarkers among patients who underwent specific biomarker testing are presented in Figure 1 and Table S1. The highest positivity rates were observed for PD-L1 (TPS \(\geq\) 1% via immunohistochemistry; 57.9%) (Figure 1A), KRAS (22.2%), and EGFR (18.2%) (Figure 1B). Slight variations were observed between subpopulations by stage, with Stage I having the lowest PD-L1 and highest EGFR positivity rates across stages, whereas the opposite was noted for Stage II.
# Underground Disposal of Coal Gauge Backfill in China
## 3 Gangue Backfilling Technologies
### Longwall-Roadway Cemented Backfilling  
Longwall-roadway cemented backfilling [72] (Figure 4f) involves solid waste (such as coal gangue and fly ash) as the filling materials. The solid wastes are added with a cementing agent at a specific ratio to prepare a high-concentration paste by mixing without the additional need for dehydration [73]. The paste is delivered underground by a pipeline pumping system to backfill the goat. In longwall-roadway cemented backfilling, the coal is broken using a bolter miner instead of a coal cutter via the longwall mining system. After the connection roadway has been excavated, the roadway is backfilled with cemented filling materials [74]. Another connection roadway is excavated simultaneously while the roadway is backfilled.  
Figure 4: The history of the gangue backfilling technology development.
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 6 Reliability Prediction of the Communication Module Under the Mission Profile
### Functional Failure Reliability Prediction of Key Components
Using the same method, the reliability of the SRAM solder joint \(R_{\text{SRAM-sj}}\) is calculated, and the reliability curve for SRAM solder joints is plotted, as shown in Figure 27. Due to the differences in stress, structure, and materials, the SRAM solder joint life is longer, and the decline in its reliability curve is less pronounced and lasts longer. During long-duration mission cycles, the fatigue life of the SRAM solder joints does not become the primary failure factor for the communication module. Figure 26: Reliability curve of FPGA solder joints. Figure 25: Lifetime histogram of FPGA solder joints.
# An Improved Jacobi-Based Detector for Massive MIMO Systems
## 2 System Model  
### System Model  
Consider an uplink large-scale MIMO system where the BS is equipped with \(N_{r}\) receiving antennas serving \(N_{t}\) single-antenna users and \(N_{r}\gg N_{t}\)[22]. The received signal vector, which is denoted by \(\mathbf{y}=[y_{1},y_{2},\cdots,y_{N_{r}}]^{T}\), at the BS is expressed as  
\[\mathbf{y}=\mathbf{H}\mathbf{x}+\mathbf{n}, \tag{1}\]  
where \(\mathbf{x}=[x_{1},x_{2},\cdots,x_{N_{t}}]^{T}\) denotes the transmitting signal set and \(x_{i}\in\Omega\) is the transmitted signal from the \(i\)th user with \(\Omega\) being a complex signal set obtained by modulating and mapping the source message \(\mathbf{s}\). \(\mathbf{H}\in\mathbf{C}^{N_{r}\times N_{t}}\) represents a flat-fading channel matrix which has a complex-valued independent and identically distributed (i.i.d.) Gaussian entries following \(CN\left(0,1\right)\), and \(\mathbf{n}=[n_{1},n_{2},\cdots,n_{N_{t}}]^{T}\) is the complex-valued additive white Gaussian noise vector with zero mean and \(\sigma^{2}\) variance, respectively.  
The purpose of MIMO signal detection is to estimate the transmitted vector \(\mathbf{x}\). The channel matrix \(\mathbf{H}\) and the received signal vector \(\mathbf{y}\) are utilized for the detection. The structure of the signal detection is illustrated in Figure 1.
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 3 Materials and Methods
### Edge Enhancement  
In this part, we will investigate different edge extraction algorithms used in the EESN of the generator network. Through experiments, better edge extraction algorithms are found by comparing their effects. The results are shown in Table 3. Furthermore, we adopt the Mask Branch in EEGAN [5] to suppress false edges and noise. Figure 5 shows the details of this operation. First, we use the intermediate SR image generated by the UDSN as an input (i.e., Base Image in Figure 5), and a Base Edge image is obtained through the edge extraction method, and then it is fed to two sub-networks (i.e., edge enhancement and mask branch) separately. The outputs of these two sub-networks eventually combine (via multiplication) to produce the image with sharper edges. Then, the sharper edges (Edge in Figure 5) will replace the noisy edges (Base Edge in Figure 5) in the intermediate SR image and output the final SR image. In the conducted experiments, we try the Sobel and Canny methods [33]. In consequence, we find that Canny method can generate the final SR image with better performance among the three compared algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Block Type** & **Topology** & **\# of Blocks** & **\# of Conv. Layers** & **PSNR** & **SSIM** & **FSIM** \\ \hline RDB & 1D & 7 & 3 & 31.401 & 0.845 & 0.985 \\ RDB & 2D & 6 & 4 & 32.560 & **0.901** & 0.990 \\ RRDB & 1D & 4 & 5 & 32.074 & 0.882 & 0.985 \\ RRDB & 2D & 3 & 5 & **32.774** & 0.899 & **0.991** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The best results of four combinations used to replace the original block in the UDSN, where the best results are shown in boldface.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Block Type** & **Topology** & **\# of Blocks** & **\# of Conv. Layers** & **PSNR** & **SSIM** & **FSIM** \\ \hline RDB & 1D & 3 & 3 & 32.755 & 0.903 & **0.990** \\ RDB & 2D & 3 & 2 & 32.676 & 0.890 & 0.986 \\ RRDB & 1D & 3 & 2 & 32.935 & 0.910 & 0.989 \\ RRDB & 2D & 2 & 3 & **32.947** & **0.913** & **0.990** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The best results of four combinations used to replace the original block in the EESN, where the best results are shown in boldface.  
Figure 4: The RDB in the RRDB connection has two-dimensional topological convolution layers.
## 4 Experimental Results
### Ablation Study  
To further evaluate the impact of each module on the network performance, we conduct an ablation analysis on the MSFO and AMBFF modules in the network. In this study, we replace the AMBFF module with a 1 \(\times\) 1 convolution fusion operation and the MSFO module with a basic residual block to form a comparative baseline network. Table 5 presents the results of the ablation experiments for an upsampling factor of 8. From the table, it can be observed that the baseline network achieves relatively higher RMSE values, indicating poorer performance. When the MSFO module is added to the network, the multi-scale expression ability of the network is enhanced. By extracting multi-scale features, it can better adapt to the changes of details at different scales and enhance the network performance. With the addition of the AMBFF module, the network is able to leverage the attention mechanism to suppress unnecessary texture information from the color image. This helps address the issue of texture replication caused by incorrect guidance from the color image. The module facilitates better fusion of features between the color and depth images, enhancing the overall performance of the network. The effectiveness of MSFO and AMBFF modules in depth map super-resolution is fully demonstrated by ablation experiments.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**2\(\times\)**} & \multicolumn{4}{c}{**4\(\times\)**} & \multicolumn{4}{c}{**8\(\times\)**} \\ \cline{2-13}  & **Cones** & **[PERSON]** & **Tsukuba** & **Venus** & **Cones** & **Teddy** & **Tsukuba** & **Venus** & **Cones** & **Teddy** & **Tsukuba** & **Venus** \\ \hline Bicubic & 4.81 & 4.51 & 7.07 & 4.25 & 5.60 & 5.02 & 9.53 & 4.52 & 6.88 & 5.70 & 13.06 & 4.91 \\ TGV [42] & 3.36 & 2.67 & 7.20 & 1.58 & 4.31 & 3.20 & 10.10 & 1.91 & 7.74 & 4.93 & 16.08 & 2.65 \\ SRCNN [9] & 2.32 & 2.10 & 4.49 & 1.28 & 4.10 & 3.32 & 7.88 & 2.69 & 6.35 & 5.32 & 12.13 & 3.94 \\ DGN [23] & 2.29 & 2.14 & 4.34 & 1.55 & 3.57 & 3.20 & 7.90 & 2.77 & 5.45 & 4.62 & 11.09 & 3.95 \\ MFR-SR [24] & 2.09 & 1.98 & 3.59 & 1.25 & 3.31 & 2.72 & 6.63 & 1.57 & 5.01 & 3.79 & 9.95 & 1.99 \\ RDN-GDE [38] & 2.02 & 1.97 & 3.70 & 1.11 & 3.13 & 2.82 & 6.56 & 1.59 & 4.84 & 3.93 & 9.78 & 2.46 \\ ours & **1.85** & **1.69** & **3.48** & **0.97** & **2.88** & **2.49** & **6.37** & **1.34** & **4.67** & **3.52** & **9.51** & **1.57** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative Analysis of Noisy DSR Results (RMSE) on Dataset A.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**MSFO**} & **AMBFF** & **RMSE** \\ \hline No & No & 2.57 \\ No & Yes & 2.24 \\ Yes & No & 2.32 \\ Yes & Yes & 2.15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: RMSE value of ablation study.
## 2 Materials and Methods
### Analysis of Dry Mass and Antioxidant Activity
#### 2.4.8 FRAP Method  
Methanol extract was diluted with 70% methanol to obtain the appropriate antioxidant concentration and mixed with the reaction mixture--acetic buffer, TPTZ solution, and iron chloride solution in a ratio of 10:1:1 [26]. After 10 min, the absorbance at 593 nm was measured by RayLeigh UV-1800 spectrophotometer (Beijing, China). The computation of antioxidant activity using the FRAP method was executed by employing the equation derived from the standard curve: y = 0.0098x + 0.0265, R\({}^{2}\) = 0.9910.
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 3 Methods
### Modeling  
In the modeling phase, a DBN is learnt from data. The algorithm for non-stationary networks is sketched in Algorithm 2 as proposed in [14]. Since we want to model the distribution underlying the MTS discretized data the log-likelihood (LL) score is used to measure the fitness of the transition networks to the data. The output is a tree-like DBN allowing for one parent in the current time slice (intra-slice network) and at most \(p\) parents from the preceding \(m\) time slices (inter-slice network).  
For each transition from time slices \(\{t-m,\ldots,t-1\}\) to time slice \(t\) a complete directed graph is built at time \(t\) (steps 3-4). Each edge \(X_{i}[t]\) in this graph is then weighted with a local LL score given by the optimal set of parents: up to \(p\) parents from the previous \(m\) time slices and the best parent from time slice \(t\) (step 5). Having the completed graph weighted, [PERSON]'s algorithm [35] is applied to obtain a maximum branching for the intra-slice network from which a transition network is easily extracted (step 6). In non-stationary DBNs, transition networks \(B_{t-m}^{t}\) are collected in each for-loop iteration (step 7). In the case of a stationary network only one transition network is retrieved.
```
1:Input: A set of input MTS discretized over \(w\) time slices; the Markov lag \(m\); the maximum number of parents \(p\) from preceding time slices. Output: A tree-augmented DBN structure.
2:procedureTree-augmented DBN(MTS,\(m\),\(p\))
3:for each transition \(\{t-m,\ldots,t-1\}\to t\)do
4: Build a complete directed graph in \(\mathbf{X}[t]\)
5: Calculate the weight of all edges and the optimal set of \(p+1\) parents
6: Apply a maximum branching algorithm
7: Extract transition \(t-m\to t\) network and the optimal set of parents
8: Collect transition networks \(B_{t-m}^{t}\) to obtain a tDBN structure
```
**Algorithm 2** Optimal Non-Stationary \(m\)-Order Markov tDBN Learning
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 3 Interest Flooding Attack: Concept and Taxonomy
### Types of IFAs  
IFAs in NDN exploit the network's unique architecture by overwhelming it with malicious interest packets, leading to resource exhaustion and degraded performance. These attacks can be categorized into several distinct types, each targeting specific vulnerabilities within the NDN framework, as shown in Figure 3.