esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature
## 5 Experiments
### Autoencoder for Anomaly Detection  
In the experiments, a typical AE architecture given in Figure 4 was used for anomaly detection in DC motor temperature. The decoder and encoder parts of the AE are consecutive neural networks sharing one layer, which is the hidden layer. The sizes of these parts are determined by the number of neurons in the layers. The number of neurons in the input and output layers was chosen as the size of the sliding window and the number of neurons in the hidden layer was chosen as equal to code length.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Actual** & \\ \hline  & & Abnormal (Faulty) & Normal \\ \hline  & & Abnormal (Faulty) & TP (True Positive) & FP (False Positive) \\ \cline{2-4}  & & Normal & FN (False Negative) & TN (True Negative) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix.  
Both numbers of neurons were determined by using a well-known grid search algorithm. In the architecture, log-sigmoid (_logsig_) and linear transfer function (_purelin_) were used as the activation functions, which are given in Equations (8) and (9), for the hidden layer and output layer neurons, respectively.  
\[logsig(x)=1/\left(1+e^{-x}\right) \tag{8}\]  
\[purelin(x)=x \tag{9}\]  
In the training phase, the AE was trained for 1000 epochs and the network weights were updated by a scaled conjugate gradient backpropagation algorithm, the details of which can be found in [26]. The optimum values for the sliding window's size and the code length were selected using a grid search in the interval between 5 and 200. The experimental results show that the best performance was achieved when the size of the sliding window was 100 and the code length was 50.  
A visual interpretation of the AE operation is given in two figures: Figures 6 and 7 for normal and abnormal samples from the dataset, respectively. The figures show the samples in a sliding window, the generated codes, the reconstructed version of the samples, and the prediction errors.  
A normal test sample (NTS) is shown in Figure 6a. The code generated by the autoencoder for an NTS is given in Figure 6b. The reconstructed version of the NTS is represented in Figure 6c. The root mean square error between the NTS and the reconstructed NTS is given in Figure 6d.  
Figure 6: (**a**) NTS; (**b**) code for NTS; (**c**) reconstructed NTS; (**d**) error for NTS.  
An abnormal test sample (ATS) is shown in Figure 7a. The code generated by the autoencoder for an ATS is given in Figure 7b. The reconstructed version of the ATS is represented in Figure 7c. The root mean square error between the ATS and the reconstructed ATS is given in Figure 7d.  
The experimental results for the AE are summarized by using a confusion matrix, and the matrix is given in Table 2.
# Evaluation of Radio Communication Links of 4G Systems
## 3 Materials and Methods  
The function of the drive test is the evaluation of the level of fields transmitted from a transmitter at different positions within the coverage region. Data is collected for different clusters for further analysis. The drive test produces data files containing field-level measurements and GPS coordinates collected for each point.  
The equipment used for the drive test are:  
* The GENEX PROBE V3.5 software (installed on a laptop and which records the data received).
* The GPS gives each signal capture point a geographical location.
* The vehicle with all facilities.  
To obtain best measurements, the above conditions must be respected:  
* Conduct at moderate speed (less than 50 km/h).
* Stay in the same over floor class whenever possible.
* Cross route for one time and repeat the measurements in the case of errors.  
The TEMS Investigation is an excellent tool for verifying, optimizing, and maintaining wireless networks. TEMS Investigation is a complete solution for all of a carrier's wireless network optimization tasks. With TEMS surveys, operators can achieve better service performance, better accessibility, and improved voice quality. The TEMS is used for:  
* Tune and optimize networks.
* Detect faults and troubleshoot wireless networks.
* Verify actual endpoint performance with UE.  
Figure 5: Variation of attenuation vs. wavelength (\(\lambda\)).  
Check coverage cell, accessibility, capacity, and integrity.  
The location of the study area (Tiaret-Algeria) is 35\({}^{\circ}\)22\({}^{\prime}\)52\({}^{\prime\prime}\) N and 1\({}^{\circ}\)18\({}^{\prime}\)08\({}^{\prime\prime}\)  
E. Figure 6 presents an overview of the studied area.  
Planning a 4G LTE network is a critical step. Therefore, the 4G LTE network operators must focus on planning before any implementation of their networks to avoid additional costs. The planning and dimensioning is an essential task; indeed, these fundamental tasks are a result of the optimization task. Moreover, the optimization task is based on the network's deployment and maintenance. The first step is collecting the necessary information about the studied area. Based on the collected information, the 4G LTE network performances are tested to evaluate the quality of service (QoS) offered. If the QoS is acceptable, the studied area is approved. Otherwise, the collected information must be analyzed to improve the QoS. Based on these analyses, suggestions, and recommendations about the necessary modification and configurations that must be done are delivered. After realizing the recommended amendment, the new collection data and QoS test are required. The process is repeated until the QoS is acceptable.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 4 Integration of Artificial Intelligence in LPBF Defect Sensing
### Summary of Applicable Sensing Techniques for Detecting LPBF Defects  
The quality of LPBF parts can be characterized using various material characterization technologies and techniques that have been used for decades. These sensor-based techniques include optical, thermal, acoustic, ultrasonic, and other miscellaneous methods. Depending on the technology, part material, and conditions, sensing technologies allow for destructive and non-destructive analyses. Destructive techniques cause permanent sample damage and samples become unusable. On the other hand, non-destructive techniques allow for samples to remain accessible and to be used repeatedly. LPBF part analysis typically involves a combination of multiple sensing technologies to increase the information gathered on part quality and accurate defect detection. The role of sensing technologies in detecting LPBF part defects has been addressed in detail in a series of publications by the authors [3]. The previous article covered the capabilities of individual sensing technologies (destructive and non-destructive), listing their corresponding advantages and disadvantages in LPBF defect detection.  
Optical-based techniques are commonly used in LPBF final print quality evaluations and the detection of defects. Optical sensors probe the sample's structure internally or externally using electron trajectories. Sources generate optical signals that can be traced and detected within the part, including via electron and laser beams, X-rays, and more [3].  
Thermal techniques are based on temperature differences and reflected light to detect defects and determine the final part quality. Thermal imaging, infrared cameras, and pyrometers are commonly implemented to monitor the LPBF melt pool during part processing. After completing melt pool solidification, they also provide information regarding the defect's distribution, such as size and type. Additional thermal-based technology includes differential scanning calorimeters (DSCs), which monitor the thermal energy distribution during the part heating or cooling processes. The heat distribution enables monitoring transitions between different phases to identify potential defect formations.  
Acoustic (resonance) techniques are reliable in detecting vibrations. They are proven to detect internal and external defects in LPBF-printed parts. The acoustic signals emitted (AE) during the printing process are collected to develop the relation between the signals and the location, providing reliable information regarding the size of defects.  
Ultrasonic techniques use waves to estimate the surface quality of LPBF-printed products. The ultrasonic waves are artificially created within the sample by interference with an external wave emitter. Other techniques used to determine the LPBF metal print quality and quantitatively determine the defect's characteristics include electromagnetic induction, probes, and pyrometers. Figure 11 summarizes all LPBF defect detection sensing technologies.
# Theoretical Study on Geometries of Electrodes in Laboratory Electrical Resistivity Measurement
## 4 Discussion
### Parametric Study: Influence of the Electrode Geometry on Boundary Effect  
A parametric study was conducted to explore the effect of cylindrical electrode geometries and container size. The initial conditions of the electrodes and the medium were assumed as \(r\) = 0.001 m, \(l\) = 0.01 m, \(L\) = 0.1 m and \(W\) = 0.3 m. The electrical resistance (\(R\)) obtained by the given container size was normalized using the electrical resistance at the infinite boundary condition (\(R_{\infty}\)). The input parameters are tabulated in Table 2. Figure 8 shows the variation of the normalized electrical resistance. As the \(W\)/\(L\) increases, the normalized resistance decreases and the boundary effect becomes negligible in all cases. The slender (large \(l\)/\(r\)) and more spaced (large \(L\)/\(l\)) electrodes rapidly diminish the boundary effect. The theoretical equation can be used to design the container size for electrical resistivity measurements. For example, once the electrode geometries (\(l\)/\(r\)) are given, the minimum container size and maximum electrode distance can be simply determined.  
Figure 6: Experimental test results (4.78 \(\Omega\)m): (**a**) \(l\)/\(r\) = 1, (**b**) \(l\)/\(r\) = 2, and (**c**) \(l\)/\(r\) = 4. Continuous lines are obtained from Equation (9) and dashed lines are gained from Equation (15) by replacing \(r\) from Equation (16).  
Figure 8: Variation of normalized resistance with respect to electrode geometries and container size: (**a**) \(L/l=3\) and (**b**) \(L/l=10\). Lines are obtained from Equation (13).  
Figure 7: Effect of \(l/r\) and \(L/r\) on the electrical resistance between two equivalent electrodes.
## 2 System Model
Upon propagation through the atmospheric channel, the received signal can be written as follows [19]:  
\[S_{R}(t)=h\cdot S_{T}(t)+n(t) \tag{3}\]  
where \(h\) denotes the channel fading coefficient, and \(n(t)\) denotes the additive noise. The channel fading coefficients exhibit a Gamma-Gamma distribution and their probability density function can be mathematically formulated as follows [20]:  
\[\varphi_{h}=\frac{2(\alpha\beta)^{\frac{\alpha+\beta}{2}}}{\Gamma(\alpha) \Gamma(\beta)}\cdot h^{\frac{\alpha+\beta}{2}-1}\cdot N_{\alpha-\beta}\cdot(2 \sqrt{\alpha\beta h}) \tag{4}\]  
where \(N_{\alpha-\beta}(\cdot)\) denotes the second-class modified Bessel function of order \(\alpha-\beta\), \(\Gamma(\cdot)\) denotes the gamma function, \(\alpha\) denotes the large-scale scattering coefficient, and \(\beta\) denotes the small-scale scattering coefficient, which can be respectively expressed as follows:  
\[\alpha=\left\{\exp\left[\frac{0.49\sigma^{2}}{\left(1+1.11\sigma^{12/5}\right) ^{7/6}}\right]-1\right\}^{-1} \tag{5}\]  
\[\beta=\left\{\exp\left[\frac{0.51\sigma^{2}}{\left(1+0.69\sigma^{12/5}\right) ^{5/6}}\right]-1\right\}^{-1} \tag{6}\]  
where \(\sigma^{2}=0.5C_{n}^{2}k^{7/6}l^{11/6}\) denotes the Rytov variance, \(C_{n}^{2}\) denotes the atmospheric refractive index structure constant, \(k=2\pi/\lambda\), \(\lambda\) denotes the laser wavelength, and \(l\) denotes the laser transmission distance. At the receiver end, the signal received \(S_{R}(t)\) from multiple optical antennas is converted into an electrical signal by PDs. Subsequently, it passes through a matched filter and is sampled at intervals of \(\tau T\). The vector representation of the sampled signal can be expressed as follows:  
\[Y=[y_{1},y_{2},\ldots,y_{n}]=[y_{r\times p/2+1},y_{r\times p/2+1+p^{\prime}},\ldots,y_{r\times p/2+1+(n-1)\times p^{\prime}}] \tag{7}\]  
where \(y_{n}\) denotes the sampled symbol, \(Y=y_{1}+y_{2}+\ldots+y_{n}\) indicates that the signals from all the receiving antennas are combined with equal gain. Subsequently, the sampled signal \(Y\) is subjected to maximum likelihood sequence detection, followed by the demapping process for the restoration of user data.
# Deep Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis
###### Abstract  
In the area of traffic sign detection (TSD) methods, deep learning has been implemented and achieves outstanding performance. The detection of a traffic sign, as it has a dual function in monitoring and directing the driver, is a big concern for driver support systems. A core feature of autonomous vehicle systems is the identification of the traffic sign. This article focuses on the prohibitive sign. The objective is to detect in real-time and reduce processing time considerably. In this study, we implement the spatial pyramid pooling (SPP) principle to boost Yolo V3's backbone network for the extraction of functionality. Our work uses SPP for more comprehensive learning of multiscale object features. Then, perform a comparative investigation of Yolo V3 and Yolo V3 SPP across various scales to recognize the prohibitory sign. Comparisons with Yolo V3 SPP models reveal that their mean average precision (\(mAP\)) is higher than Yolo V3. Furthermore, the test accuracy findings indicate that the Yolo V3 SPP model performs better than Yolo V3 for different sizes.  
TSD; object detection; TSR; Yolo V3; SPP; scale analysis +
Footnote †: journal: Medical  
and Yolo [18; 19] already used convolutional neural network (CNN) that can be used in handheld devices and consumer goods. Yolo has been a successful CNN rival in real-time object detection [20]. Research [21; 22] reveals that high-speed detection efficiency has been closely tracked to identify smaller objects. Yolo's latest update, Yolo V3 [23] and Yolo V4 [24], have exhibited important development in that ability. Yolo V3 is selected as one of the main objectives in real-time recognition as the object identification system. This paper aims to eliminate the network fixed-size restriction, obtain the best features in max-pooling layers, and enhance Yolo V3 performance and the layer is established by the SPP layer [25; 26; 27; 28].  
This paper's major contributions are: (1) Follow the principle of SPP to strengthen the original structure of Yolo V3 for building features extraction and determining maximum information flow between layers in the system; (2) Use spatial pyramids to organize and aggregate local features at various levels in the same layer for more detailed training of multiple scale object functions; (3) A comparative study of Yolo V3 and Yolo V3 SPP on various scales for the identification of prohibited signs in Taiwan are carried out; (4) The result suggests that the detection time would be quicker if a big number of scales is used. Therefore, the precision declines relative to the original Yolo V3 scale. Similarly, in Yolo V3 SPP, the accuracy declines to take on various sizes.  
The following parts of this paper describe the proposed model. The related work of traffic sign identification systems explains in the Section 2. The approach methodology briefly discusses in Section 3. Furthermore, Section 4 includes a summary, the preparation, and system test outcomes. In Section 5, assumptions are established, and further study is introduced.
## 1 Introduction
It involves defining, designing, developing, and testing (verification and validation, commissioning, and support after launch) the software. Even with a lot of earlier developed software reuse and open-source software use, it is still a significant challenge for the development teams to use the software properly and to obtain solid flight-ready software promptly. This article will provide an alternative approach to using a modern software development lifecycle, i.e., agile-like, and proper technology for the implementation, deployment, and use of the software. After the definition of a new development approach and its technical background is given, the authors make a top-level design of a CubeSat software structure in a newly introduced paradigm. To ensure that the proposed method is sound, the authors carried out a performance check on the selected variety of classical computer science algorithms.
## 5 Case Studies, Including Impact on the Ecosystem
### Marine Ecosystems
(**D**) _Gymnothorax griseus_ inhabiting the artificial reef structure in the inner spaces (23 November 2020). (**E**) The artificial reefs inhabited by around _60 Pseudanthias squanipunnis,_ together with various benthic organisms, two years after deployment (21 October 2021). (**F**) Interspecies interaction _Botrylus eilatensis, Stylophora pistillata_, and _Xentiidae_ sp. covering the artificial reef (11 August 2022) [19]. research. Nowadays, the efficiency of artificial reefs is assessed mainly using comparisons to traditional methods [64]. Currently, there are no standardized methods for assessing biological invasions or \"ecological services\" (functioning of artificial reefs). There is a lack of effective methods for comparison and assessment that could take into account different phases of successional ecology [3]. It is necessary to develop such protocols in future studies. Artificial reefs, as a form of ecological engineering created by humans, have the potential to play a positive role in maintaining the stability and diversity of aquatic ecosystems over long time scales [90]. The condition, however, is their proper design, supported by comprehensive research and long-term monitoring. A similar problem is connected with monitoring the succession and biodiversity in artificial reefs, because of a lack of unified standards [9,91]. The most important thing in this area seems to be the long-term monitoring of the ecosystem-carrying capacity, which could potentially lead to unanticipated ecological consequences [9,91]. Artificial reefs can be also connected with some treatments for ecosystems. In the context of artificial reef planning, it is crucial to consider potential ecological threats. One important issue is the phenomenon known as \"ocean sprawl\", i.e., the excessive spread of artificial structures along natural environments, which can pose a threat to ecosystems. The increased connectivity of systems caused by the proliferation of artificial reefs has the potential to facilitate biotic homogenization or introduce species outside their native ranges [3]. However, some research confirmed the reverse mechanism as a promotion of the settlement of coralline algae and discouraging invasive species in subtidal zones [17]; the problems connected with potential \"ocean sprawl\" and the wide spreading of invasive species should be taken into consideration during planning the research.
## 8 Research on Diagrid Nodes  
A crucial element in the assessment of diagrid structures lies in the correct design and realization of the nodes, where a connection usually consists of four intersecting diagonal columns and several beams (Figure 3a). These elements are pivotal, as they are in charge of transferring the high axial stresses between the diagonal members. The failing of one single node induces a redistribution of the load path, and might compromise the overall stability and resistance of the diagrid, especially under cyclic and dynamic loading conditions. As a matter of fact, due to the seismic concept \"stronger connection, weaker component\", special attention needs to be conveyed to the mechanical behavior of diagrid joints [53].  
The joints are mainly divided into three types depending on the employed material, namely steel, reinforced concrete and concrete-filled steel tube (CFST) joints. Each of them is characterized by different mechanical behaviors, particularly under cyclic loading conditions and with reference to the hysteretic energy dissipation. [PERSON] et al. [54] investigate the bearing capacity of CFST joints (Figure 17a), where the influence of connection details, the intersecting angle between the diagonals and the loading type is analyzed with regards to the bearing performance of the node. Based on the experimental results, it is found that the diagonal angle plays a key role in the definition of the joint failure mode, whereas the loading type (symmetric or asymmetric) has little influence. The authors also propose a simple equation for the calculation of joint bearing capacity, which is verified against the experimental outcomes. [PERSON] et al. [55] perform an experimental campaign to analyze the cyclic behavior of the steel nodes from the Lotte Super Tower in Seoul (Figure 17b). Open- and box-section joints are realized, and their cyclic performance is investigated in terms of stiffness and strength. Attention is also paid by the researchers to different welding methods. Subsequently, [PERSON] et al. [56] study web-continuous steel connections for diagrid nodes under cyclic loads. Different welding methods and design details are taken into account, and they are not found to have a significant influence on the joint's initial stiffness and yielding strength. Conversely, they can significantly modify the joint failure modes, as well as the energy dissipation characteristics. Spatial concrete nodes are also studied by [PERSON] et al. [57], who investigate their failure mode and bearing capacity, focusing particular attention on the influence of the amount of transverse stirrups on the connection performance. It is shown that the volume ratio of transverse stirrups affects the bearing capacity of the joint, by effectively confining the concrete core under high compressive loads (Figure 17c).  
These studies are fundamental for a thorough evaluation of the mechanical behavior of diagrid nodes, which in turn strongly affects the overall structural response of the diagrid system.
## 5 Model Accuracy Results
Moreover, the Okumura-Hata model, which is initially thought to be for frequencies in between 150 MHz and 1500 MHz, shows partly comparable and superior results along route A and route B, respectively, in comparison with COST 231 and ECC-33 models, which are supposed to be more suitable PL prediction candidates due to the applied center frequency of our study, which is 1800 MHz.
# OGSM: A Parallel Implicit Assembly Algorithm and Library for Overlapping Grids
## 4 Implementation and Application of Overlapping-Grid-Sewing-Machine Library
### Data Ferry Technique
#### 4.1.2 Data Ferry  
In addition to using the _DataStream_s to solve the problem for large-scale data transmission, we further introduce the concept of a _data ferry_, which is integrated into our OGSM library. The top part of Figure 10(b) denotes the processes related to the source grids, whose ranks are represented with \(\{i_{0},i_{1},\cdots,i_{S-1}\}\). They send messages to the processes for the target grids \(TG\) (bottom), which are represented by ranks \(\{j_{0},j_{1},\cdots,j_{T-1}\}\). Concretely speaking, the work-flow for data ferry is as follows:  
1. Each process \(p_{i_{m}}(m\in[0,S-1])\) creates a _DataStream_ object and sends the collected data to the root process \(p_{i_{0}}\).
2. Process \(p_{i_{0}}\) receives the messages from \(S\)_DataStream_ objects and gathers them into a _collector_ of the pointer object.
3. The information in _collector_ is broadcasted to the processes \(p_{j_{n}}(n\in[0,T-1])\). When the sender \(p_{i_{m}}\) and the receiver \(p_{j_{n}}\) are the same process, a memory-copy operation is called instead to replace the MPI communication.