esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 2 Interference Alignment
### Principles
\begin{split} y_{1}&=H_{11}x_{1}+H_{12}x_{2}+\ldots+H _{1K}x_{K},\\ y_{2}&=H_{21}x_{1}+H_{22}x_{2}+\ldots+H_{2K}x_{K}\, \\ \vdots\\ y_{B}&=H_{B1}x_{1}+H_{B2}x_{2}+\ldots+H_{BK}x_{K}, \end{split}  \tag{1}\]  
where there are \(B\) observations \(y_{1},y_{2},\ldots,y_{B}\), each in the form of a linear combination of \(K\) information symbols \(x_{1},x_{2},\ldots,x_{K}\) with coefficients \(H_{ij}\). \(K\) is the number of transmitters, each trying to send an information symbol, for a total of \(K\) independent information symbols \(x_{1},x_{2},\ldots,x_{K}\). The coefficients \(H_{ij}\) represents the effective channel coefficients. \(B\) is the number of signaling dimensions accessible at a receiver through a linear channel. Each signaling dimension produces a linear combination of the transmitted information symbols due to the channel being linear. Consequently, a receiver has access to \(B\) signaling dimensions. Assuming effective channel coefficients are taken from a continuous distribution, all the information symbols can be recovered, provided that there are at least as many observations as unknowns. Thus, if the receiver desires all symbols, at least \(K\) signaling dimensions at required. In IA networks just a subset of the symbols are sought after by the receiver and the remaining symbols carrying information for other receivers are undesired at the receiver, they solitary underwrite interference. For instance, suppose the receiver is only interested in symbol \(x_{2}\), which carries information desired by this receiver while all other symbols carry only interference. Overall, \(K\) signaling dimensions will be required to determine the one symbol wanted by this receiver. Assuming there are \(K\) receivers, each interested in a different symbol, and individually they have access to a different set of \(K\) linear equations dictated by its linear channel to the transmitters, each receiver will be able to solve the system of equations and  
Figure 1: Illustration of the interference alignment (IA) principle. recover its desired symbol. Therefore, a total of \(K\) signaling dimensions are used so that each receiver is able to resolve its desired one dimensional signal.
# A Hybrid Hidden Markov Model for Pipeline Leakage Detection
## 3 Pipeline Leakage Detection
### Leakage Detection with Three States  
In our previews work [14], three different leakage locations were chosen as three states for the GMM-HMM method. To compare the performance of DNN-HMM with GMM-HMM, the same state setting and initial parameters were applied in the two models. The leakage signals were collected by sensor \(P_{1}\). As shown in Figure 7, leakage at Section 1 (from \(P_{1}\) to \(L_{1}\) in Figure 5) of the pipeline as State 1, leakage at Section 2 (from \(L_{1}\) to \(L_{2}\)) of  
Figure 6: Performance of DBN-DNN.  
the pipeline as State 2, leakage at Section 3 (from \(L_{2}\) to \(L_{3}\)) of the pipeline as State 3 are chosen as states in ergodic HMM model, which allowed transitions from any state to any other state.  
100 groups, with 200,000 samples per group, of leakage signals were collected. In this study, 70 groups are used for training and the rest are used for testing. To reduce the computational volume, data for this experiment are cropped from the original data by 90,000 data points to each state. Two damage indices are obtained by using Equations (7) and (8) with 45 data points for each state in HMM.  
The micro \(F_{1}\) score is calculated to measure the performance. The micro \(F_{1}\) score of GMM-HMM is 0.94 and the micro \(F_{1}\) score of DNN-HMM approaches 0.95. Several tests are made to compare the performance of DNN-HMM and GMM-HMM, where the performance of DNN-HMM is little better than GMM-HMM in almost every trial. One of the experiment results is shown in Figure 8.
# Feasibility Study on Grouting Compactness Detection in Sleeves Using Piezoelectric Transducers
## 4 Test Results and Discussion
### Grouting Compactness Detection Result and Discussion  
The wavelet packet total energy values (WPTEVs) and Hilbert energy peak values (HEPVs) for the 15 specimens are presented in Table 3. It can be seen from Table 3 that the WPTEV and HEPV both showed a rising trend with the decrease of grouting compactness. It is indicated that the energy of the stress wave was decreased when the grouting gradually became full. However, the 2 nd group of A1S1 in 30% and 100% grouting compactness both showed as the abnormal values. Possible reasons were the unavoidable human or external conditions such as uncontrolled grouting and the different thicknesses of the epoxy.  
Figure 6: Sine sweep signals of PZT sensors for different grouting compactness values: (**a**) 1 st group of A1S1; (**b**) 2 nd group of A1S1; (**c**) 3 rd group of A1S1; (**d**) 1 st group of A2S2; (**e**) 2 nd group of A2S2; (**f**) 3 rd group of A2S2.  
Figure 7: The process diagram of the Hilbert–Huang conversion process.  
The relationship between the values of the grouting compactness and the proposed indices was established, as shown in Figure 8. The WPTEV and HEPV measured via the WPE and HHT methods, respectively, clearly exhibited relationships with the grouting compactness. With the increase of grout compactness, the compactness index signatures decrease, indicating that both signal-processing methods can be used for detecting the sleeve grouting compactness.  
From the comparison of two methods, it was found that with the increasing of grouting compactness, both WPTEV and HEPV declined. However, the decreasing ratio of HEPV is slightly large than that of WPTEV. It was indicated that both methods were able to detect grouting compactness, compared with the WPE method, the HHT method better reflected the relationship between the density and the piezoelectric signal index. Moreover, compared with the WPE method, the HHT method did not consider basis functions or the level number of decompositions. As a result, the authors recommend using the HHT method to detect grouting compactness.
## 4 Experimental Results
### Parameter Settings of DRKMSVM, DNMSVM, CNN and SVM  
Before applying DRKMSVM for short text classification, corresponding parameters need to be set. For DRKMSVM, the bidirectional structure is employed. The number of hidden nodes, learning rate, the size of mini-batch, dropout and penalty factors are shown in Table 3. Among them, the hyper-parameters in the experiments are selected by means of cross-validation on MR dataset, whose referential evaluation index is accuracy. The candidate set of hidden node number is \(\left\{i\times 100\left|i=1,2,3,4,5\right.\right\}\), the candidate set of learning rate is \(\left\{10^{i}\left|i=-4,-3,-2,-1\right.\right\}\), dropout is 0.5 in accordance with relevant papers, the candidate set of the size of mini-batch is 32, 64, 128, and the candidate set of penalty factor \(C\) is \(\left\{2^{i}\left|i=-3,-2,\cdots,8\right.\right\}\).  
For DNMSVM, the number of hidden layers is set as 2 for testing. Therefore, the structure of DNMSVM can be expressed as \(x-h_{1}-h_{2}-o\). Note that in our experiments, we rename DNMSVM with two hidden layers using the unified name DMKMSVM-2. Other parameters, such as the number of hidden nodes, learning rate, the size of mini-batch, dropout, and penalty factors are set in accordance with relevant papers. Among them, the hyper-parameters are selected by means of cross-validation on MR dataset, whose referential evaluation index is accuracy. The candidate set of hidden node number is \(\left\{i{*}100\left|i=1,....,5\right.\right\}\), the candidate set of learning rate is \(\left\{10^{i}\left|i=-4,\ldots,-1\right.\right\}\), the candidate set of the size of mini-batch is \(\left\{32,64,128\right.\), the candidate set of penalty factor \(C\) is \(\left\{2^{i}\left|i=-3,-2,\ldots,8\right.\right\}\), and the dropout is 0.5. The parameters of DMKMSVM-2 used in the experiments are illustrated in Table 4.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Name** & **Abbreviation** & **TSN** & **CN** & **ASL** & **DL** & **NVSS** \\ \hline Movie Review & MR & 9596 & 2 & 111 & 18,765 & 1000 \\ Custom Review & CR & 3397 & 2 & 93 & 5046 & 378 \\ Subject & Subj & 9000 & 2 & 125 & 17,913 & 1000 \\ Opinion polarity dataset & MPQA & 9546 & 2 & 18 & 5046 & 1060 \\ TREC QA & TREC & 5452 & 6 & 60 & 6083 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information of datasets.  
With regard to CNN, word vector is adopted, and its parameter settings are shown in Table 5. Notably, for RBF-SVM, the two parameters \(C\) and \(\gamma\) need to be determined by 10-fold cross-validation, where the candidate set of \(C\) is \(\left\{2^{i}\left|i=-3,-2,\ldots,8\right.\right\}\) and the candidate set of \(\gamma\) is \(\left\{2^{i}\left|-4,-3,\ldots,5,6,7\right.\right\}\). The final selection is shown in Table 6.
# Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
## 3 Proposed Method
### Data Set  
The focus of this work is an investigation for a method that can suit touch-based impressions acquired from a hand using a common key-invariant feature representation. It needs to tolerate geometrical variations in compared images and eliminate any pre-alignment process. To obtain many sub-graphs, we need partial prints with sufficient minutiae, and they should show enough geometrical variations. The prints are selected based on the number of minutiae each holds. If a print holds a lower number of minutiae that is not sufficient to form a minimal graph, that print is discarded. The algorithm should be validated in each of the above-mentioned specific experimental criteria.  
We have used fingerprints and palm prints for the experiments. The fingerprints were acquired from publicly available FVC2002 DB1 [52] data set. which consists of sufficient minutiae to form multiple sub-graphs. The data set is further split into two sets:  
1. Fingerprints without geometrical variations (without rotational changes). Fingerprints from 35 users -> 2-4 images from each users were used.
2. Fingerprints with geometrical variations (with rotation and scale changes). Fingerprint images from 67 users were used for this evaluation.  
We have used palm print image segments from the publicly available THUPALM-LAB database [53], which consists of palm prints acquired from 80 users captured from commercial scanner with a range of quality variations. They are high-resolution images with 2040 \(\times\) 2040 pixels. This data set was evaluated in several contact-based palm print applications in the existing literature [26; 54]. We have selected the hypothenar region of the palm. The work in [26] experimentally proved that this region comparatively performed better than the other regions. The segments show sufficient geometrical variations for the evaluation, as shown in Figure 1 (eight segments of prints from a single individual). It is noted that the data set with extremely-low-quality prints generated a large number of spurious minutiae. To categorize the images into different categories, such as partial-quality and low quality, we experimentally counted the number of minutiae each print holds. If the count is exceptionally large and shows a great deviation from the average number of the minutiae range each print holds, then the image is considered as extremely degraded and cluttered with a large number of false minutiae. To confirm this process, we manually checked each print. The data set was split into two categories as follows:  
1. Partially degraded images acquired from 35 users (Figure 1).
2. Extremely low-quality impressions acquired from 40 users (Figure 2).  
The evaluations were processed using partial-quality images with geometrical variations and extremely-low-quality impressions with geometrical variations.
## 3 Results and Discussion
### Wine Sensory Attributes of Taste and Mouthfel
Previous studies with descriptive sensory analysis have demonstrated that RG-II significantly decreased the intensity of astringency in terms of multiple descriptors, including roughness, chalkiness, dryness, puckering, coarseness, and fullness. These studies also explained that potential mechanisms behind this reduction in astringency are either that RG-II may have co-aggregated with astringency-triggering compounds, i.e., condensed tannin, or that it provided additional lubrication in the mouth to alleviate the perception of dryness [38; 39; 40]. Despite measuring up to 22% higher tannin concentrations in ACE with macerating enzymes treated wines as previously reported [22], panelists did not perceive increased astringency. This could be due to the release of RG-II, which may offset the astringency from higher tannins, and the naturally low tannin levels in Marquette grapes, making it challenging to detect treatment effects on astringency. Despite these insightful findings, several limitations must be acknowledged. The sensory evaluation was conducted with a relatively small panel of eight trained panelists which may limit the generalizability of the results. Additionally, the experimental conditions were highly controlled, which might not fully represent the variability encountered in commercial winemaking settings. Future research should aim to include a larger and more diverse sensory panel and conduct experiments under scaled-up winemaking conditions to enhance the robustness and external validity of the outcomes. Furthermore, exploring the long-term stability of the aromatic and sensory profiles during wine aging (over one year) could provide a more comprehensive understanding of the lasting impacts of ACE and maceration enzymes.
# LBFT: An Asynchronous Committee-Based Blockchain Storage Strategy on Zero Trust Model
## 6 Experimental Evaluation  
### Experimental Parameter Setting  
We implement LBFT Partition Chain and BFT-Store to comprehensively evaluate their performance by comparing their latency and throughput. We use the zfec library to implement RS coding and adopt LevelDB for data persistence. In all experiments, each node is equipped with 16 CPU cores with 2.10 GHz, 96 GB RAM, and 3 TB disk space; the network bandwidth is 1 Gbps.  
The security parameters and system initial state are the same as [3]. We select the case that the faulty factor of the system \(r=3\) and the number of replicas of each block equals 1 in BFT-Store. In LBFT, since the number of nodes in the experiments is much less than in practical application scenarios, we set the number of members of each committee \(c=\lfloor\frac{n}{10}\rfloor\).
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Network Level
#### 3.4.2 Network Segmentation  
Network segmentation is an effective means of improving network security [59]. Users may apply virtual local area networks (VLANs) [60], IP address ranges, and combinations to divide the network into smaller local networks. This strategy allows users to forge multiple security zones and represent different segments controlled by firewalls. Network segmentation reduces overall system risks, and facilitates rapid response and recovery by assuring that the data and resources in each segment are visible and accessible only to authorized users in the current segment.
## 2 Experimental Section
To be able to compare the OBS-ONE-XL with another system having an acquisition frequency of 10 Hz (see Section 3), a laboratory-grade QCL-IR (MEXA-ONE-QL-NX) was also used during two dedicated tests performed in the VELA7. The MEXA-ONE-QL-NX measures four nitrogen compounds (NO, NO\({}_{2}\), N\({}_{2}\)O, NH\({}_{3}\)) simultaneously by using a QCL light source and Infrared Absorption Spectroscopy (IR spectroscopy) as the measuring principle. The analyzer utilizes a high resolution spectrum and a high vacuum optical cell in order to minimize the interference offered by the co-existing gases. Moreover, the ammonia response has been improved by using a vacuum sample transfer line maintained at a temperature of 113 \({}^{\circ}\)C. The system consists of three main components--a Main Control Unit, an analyzer unit and a heated filter (F-01 HN). The F-01 HN contains a quartz filter element specifically designed to minimize the adsorption of the ammonia molecules present in the exhaust gas. The heated filter is connected to the analyzer unit via a heated line. The sampling of the exhaust is conducted using an insulated stainless steel sampling probe placed in front of the system's pre-filter, and a 10 m heated line connecting the pre-filter and the QCL. For the chassis dyno tests performed in this study, the concentrations of N\({}_{2}\)O and NH\({}_{3}\) measured by the OBS-ONE-XL and the PEMS-LAB were compared to those measured by the laboratory-grade FTIR. The laboratory-grade FTIR (and the QCL-IR in two dedicated tests) was selected as the reference method because FTIR and QCL-IR are two of the techniques prescribed in the EU Regulation 582/2011 [26] and UNECE GTR-15 [27] to measure NH\({}_{3}\) emissions from heavy-duty exhaust and light-duty exhaust, respectively. Furthermore, the UNECE GTR-15 also indicates that N\({}_{2}\)O emissions can be measured from light-duty vehicle exhaust using FTIR and QCL-IR, among other techniques (e.g., GC-ECD and NDIR). The regression lines presented in the study were fitted to zero.
# Optimizing Assembly in Wiring Boxes Using API Technology for Digital Twin
## 4 Proof of Concept for Optimizing Assembly in Wiring Boxes
### Testing and Calibrating the System Using Computer Vision and Digital Twin  
The integration of computer vision and digital twin technologies in the assembly process of fuses in cable boxes significantly improves testing and calibration. This approach enhances accuracy, reduces human errors, and increases efficiency, leading to a more reliable and streamlined production line.  
Figure 14 represents the case study focused on the automated assembly of fuses in wiring boxes, utilizing computer vision and digital twin technologies. This approach aims to enhance the accuracy, efficiency, and reliability of the manufacturing process.  
The image shows a top-down view of an assembly station for fuses in wiring boxes. The setup includes a structured arrangement of fuses on either side of a central black housing component, likely a wiring box.  
Components and arrangement:  
Fuses: The fuses are organized in a grid-like pattern, with red and green indicators. The red color indicates the absence of a fuse, while the green color indicates the presence of a fuse.  
Wiring box: The black component in the middle is likely the main housing for the cable connections, serving as the central point for the fuse assembly.  
Figure 14: Computer vision identification: (**a**) 26 parts, (**b**) 17 parts.  
* Workspace: The assembly takes place on a white surface, providing a clear and contrasting background for image processing.  
Computer vision techniques are used to identify, analyze, and count the fuses (as shown in Figure 14). The system detects fuses based on their color, shape, and position. The numbers \"Total: 26\" and \"Total: 17\" at the top left corner suggest the total count of detected fuses.  
The digital twin technology is likely used to create a virtual replica of the physical assembly process. This virtual model allows for real-time monitoring, simulation, and optimization.  
* Real-time monitoring: the digital twin mirrors the real-world setup, providing continuous feedback and ensuring the assembly process is on track.
* Simulation and optimization: by simulating the assembly process, potential issues can be predicted and addressed before they occur in the real world.  
The images exemplify the application of advanced technologies in the assembly of fuses in cable boxes. By leveraging computer vision for accurate detection and counting and digital twin for real-time monitoring and optimization, this project aimed to achieve a highly efficient and reliable manufacturing process. This innovative approach not only enhances productivity but also ensures high standards of quality and precision in the assembly of cable boxes.
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 1 Introduction
The rest of this paper is organized as follows. Section 2 presents the details of our proposed ship detection algorithm. Section 3 introduces the construction of micro-nano satellite computing platform. Section 4 introduces the ground demonstration and verification system and experimental results are shown in Section 5. Section 6 discusses the practical application. Finally, Section 6 concludes this paper.
# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## 5 Experimental Results
### Analysis of Monitoring System  
This section presents an analysis of our proposed system. In this system, users can easily observe and analyze the monitored room in different views, such as a camera view or top view. Additionally, we can quickly determine the hot areas in the room, as shown in Figure 14. This figure shows the reading room at different times, and the yellow points in Figure 14b,d denote people's positions.  
Figure 12: Root mean squared error of four schemes.  
Figure 13: The detection results of Mask R-CNN + Tiles scheme on five datasets.  
Figure 14a,b show room images from the camera view and the room's heat map at 8:00, respectively. At this time, the density of people is low, and users can easily view the room. When the density of people increases, room monitoring becomes difficult from the camera view, as shown in Figure 14c. However, it is more straightforward from the 2D top-view image and heat map of the room, as shown in Figure 14d.  
As mentioned above, our proposed Mask R-CNN + Tiles scheme achieved high accuracy. Figure 15 shows the predicted number of people in the reading room during opening hours from 8:00 to 22:00. In this figure, the gray polygon represents a 10% relative error tolerance region. Figure 15 shows that the highest predicted people count is inside the gray polygon. In other words, this scheme consistently achieved high accuracy. Additionally, we analyzed some information regarding room use. The average number of people in the room is 33.3 per hour. The density of people often reaches a peak from 14:00 to 16:00.  
Along with the accuracy, the processing rate is another important factor that affects the performance of the system. The trade-off between accuracy and processing rate is considered when designing and evaluating the system. In the proposed system, density of people was shown on a heat map and refreshed every 10 s owing to the lower frequency and speed of movement in the room. Therefore, our proposed Mask R-CNN + Tiles scheme has an acceptable processing rate of 0.53 frames per second (fps), as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Schemes** & **MobileNet-SSD** & **YOLO v3** & **Mask R-CNN** & **Mask R-CNN + Tiles** \\ \hline Processing rate & & & & \\ (fps) & 14.08 & 3.97 & 2.05 & 0.53 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Processing rate of four schemes.  
Figure 14: Heat map of reading room.