esa-sceva/satcom-chunk-collection huggingface dataset:
# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
## 5 Result and Discussion
It is recommended to use the CNN model, which, with a prediction time of 4.95 s, remains within an acceptable timeframe while offering significantly higher prediction accuracy, up to 0.9431. 4. In some common dredging construction areas, if a pressure sensor failure causes the mud pump water sealing system to fail to work properly, experienced operators can still increase the sealing water pressure as much as possible to minimize the wear of the mud pump. In such cases, the calculation time requirement is not strict, but it is crucial to accurately sense the seal water pressure required by the mud pump and understand its current working conditions. Therefore, it is recommended to utilize the MCNN transformer model. To further illustrate the experimental comparison results of the seven proposed models, Figure 15 shows the prediction curves of all models on the test set, Figure 16 shows the prediction curve of the MCNN transformer model, and Figure 17 shows the prediction curve of the remaining six models. By observing the partially enlarged picture, it can be clearly seen that the MCNN transformer model proposed in this study can fit the test data better. The parameters in the figure generally fluctuate within a certain range, indicating that the construction status of the mud pump is good and the soil properties are stable. A certain section is selected in the construction interval, select the parameters transmitted back by 25 dredger sensors are selected 700 data points from each parameter are used as the training set, and the remaining 300 data points are used as the testing set. Based on the above observations, it is shown that the integration strategy of using MCNN to convolve the input data and then input it into the Transformer is effective and can significantly improve the performance of a single Transformer model. Figure 16: MCNN-Transformer prediction result. Figure 15: Overall comparison of model prediction results.
# Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
## 3 Methodology
### Theory
Although the simple structure for logical response (prediction) involves adding a single layer to the conventional FWNN architecture, multiple layers can be included with additional features added to the fifth layer from the input layer during the training process to increase the effectiveness of the model in the demodulation process. The parameters of the fuzzy wavelet neural network need to be automatically updated and adjusted during the network training process. The logical FWNN weights have two different effects on the network output. The first is the direct effect because a change in weight causes an immediate change in the output at the current time step (this first effect can be computed using standard backpropagation (BP)). The second is an indirect effect because some of the inputs to the layer are also functions of the weights. To account for this indirect effect, you must use dynamic BP to compute the gradients, which is more computationally intensive and expected to take more training time [24; 25; 26; 27; 28]. Here, the gradient descent method is used to adjust the network parameters with a major focus given to initialization. To update the parameters \(p_{ij}^{r}\), \(t_{ij}\), \(d_{ij}\), \(w_{j}\) and \(w_{nk}\), in order to obtain a good prediction effect, the optimal value of these parameters will be selected as the parameter value of the prediction part.
## 2 Background
### Windows PE Files  
The PE file is the standard binary format of DLLs and Executables in the Microsoft Windows family [24]. Its layout contains a Header area, a Section area and the Unmapped Information area. As shown in Figure 1, the Header area contains the DOS Header, DOS Stub, PE Header, PE Optional Header and Section Table. Both the DOS Header and DOS Stub are used for compatibility with the MS-DOS system. The PE Header contains global information about the PE file. For example, it contains the number of sections and the creation time. The PE Optional Header contains information to describe the alignment of the binary data. Notice that the binary data must be recorded in the executable code by satisfying the constraints that each section of the PE file must start at an offset multiple to that field, and the size of each header must be a multiple of the file alignment. The PE Optional Header contains the offset pointers to find the functions in the Import Table or the Export Table. The Section Table provides global information about Sections such as names, offsets and sizes. The Section area contains the consecutive sections with the executable code (.text) and required data (.data), which include global and static variables. The number of sections in the Section area is not fixed a priori. Additional sections can be used to report the information regarding the address and size of the import table (.idata) and export table (.edata), resource data such as icons and menus (.rsrc), uninitialised data (.bss), extended text on additional linking (.textbss) or relocation information (.reloc). The Unmapped Data Section contains all data that are recorded in the PE file, although these data are never loaded into memory during the program execution. Examples of unmapped data include uninitialised data and debug information.  
As reported in [8], _\"a Windows PE malware is a PE file with a malicious intention, e.g., an executable accessing the system without user permissions, stealing private or confidential information, requiring a ransom\"_. Several machine learning methods have been studied for Windows PE malware detection in the last decade. They are commonly developed from a sufficient amount of labelled Windows PE files for which an engineering phase is performed to produce the numerical input expected for the machine learning methods. In particular, the developed detection models differ in the type of input features (e.g., byte sequences, strings, grayscale images, API calls and control flow graphs), the feature extraction approaches (e.g., the static analysis of the binary files without running them, the dynamic analysis of the run-time behaviour of executable files run in an isolated environment or hybrid analysis) and the learning methods (e.g., machine learning methods such as Naive Bayes, LGBM, SVM, Random Forest or deep neural networks such as CNNs, Graph Neural Networks or Long Short-Term Networks (LSTM). Extensive surveys of the relevant literature in the field are described in [8; 25].
## 3 Compact Omni Wheel Robot Design
### Compact Omni Wheel Robot Kinematics  
To properly control an omni wheel robot, it is necessary to find the relationship between the velocity and angular speed of the robot's center of mass and its wheels' rotational speed. That is, the robot's kinematic and inverse kinematic equations.  
For the robot's stability, a four-wheel robot is selected. Figure 2 shows the diagram of such a four-wheel robot.  
Figure 2: Pattern diagram of the four-wheel robot: The parameters of each wheel of a four-wheel robot are shown when a wheel of radius \(r\) is placed at position (\(x\), \(y\)) and direction \(\theta\). \(V_{x}\) and \(V_{y}\) are the frontal and leftward velocities of the robot, respectively, and \(\omega\) and \(V\) are the rotational and ground speeds of the wheel.  
Extending the kinematics equation proposed by [PERSON] et al. [13] for a three-wheel robot to a four-wheel robot, we have that  
\[\begin{pmatrix}\omega_{1}\\ \omega_{2}\\ \omega_{3}\\ \omega_{4}\end{pmatrix} =T\begin{pmatrix}V_{x}\\ V_{y}\\ \Omega\end{pmatrix} \tag{4}\] \[=-\frac{1}{r}\begin{pmatrix}\cos(\theta_{1})&\sin(\theta_{1})&l_{1 }\sin(\theta_{1}-\beta_{1})\\ \cos(\theta_{2})&\sin(\theta_{2})&l_{2}\sin(\theta_{2}-\beta_{2})\\ \cos(\theta_{3})&\sin(\theta_{3})&l_{3}\sin(\theta_{3}-\beta_{3})\\ \cos(\theta_{4})&\sin(\theta_{4})&l_{4}\sin(\theta_{4}-\beta_{4})\end{pmatrix} \begin{pmatrix}V_{x}\\ V_{y}\\ \Omega\end{pmatrix}\]  
where \(r\) and \(\omega\) are the radius and angular velocity of the omni wheel. \(\theta\) is the angle at which it is mounted. \(l=\sqrt{x^{2}+y^{2}}\) and \(\beta=\arctan(\frac{y}{x})\) are the distance and direction of the omni wheel with respect the robot's center.  
As shown in Equation (4), in the case of a four-wheel robot, the kinematics matrix is not square, so the inverse kinematics cannot be obtained by inverting it. Instead, the pseudoinverse matrix is used to find the inverse kinematics as proposed in [15; 16; 23].  
Following the aforementioned method, the inverse kinematics equation is found to be  
\[\begin{pmatrix}V_{x}\\ V_{y}\\ \Omega\end{pmatrix}=T^{+}\begin{pmatrix}\omega_{1}\\ \omega_{2}\\ \omega_{3}\\ \omega_{4}\end{pmatrix}=(T^{\top}\cdot T)^{-1}T^{\top}\begin{pmatrix}\omega_{ 1}\\ \omega_{2}\\ \omega_{3}\\ \omega_{4}\end{pmatrix} \tag{5}\]  
Each row of the Jacobi matrix \(T\) must be linearly independent if the matrix has the pseudoinverse matrix \(T^{+}\). Therefore, each omni wheel direction must be different. In other words, to realize a compact four omni wheel robot, wheels must be along the periphery, and adjacent wheels must be orthogonal to each other.
# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 4 Experimental Results  
The proposed tracker with MBD-based descriptor was implemented in C++ on an Intel Core i7-6700K (Intel Corporation, Santa Clara, CA, USA) 3.40 GHz CPU with 8 GB RAM and run at 4.7 FPS (frames per second). We evaluated our tracker on the OTB-100 (object tracking benchmark) dataset [17] and TColor-128 (Temple Color) dataset [49]. The experimental results demonstrate the effectiveness of our algorithm through qualitative analysis and quantitative analysis. For a fair comparison, the parameter \(\gamma\) was empirically set as 0.395, and \(a=1.052\), \(M=33\), \(\theta=0.3\), \(k=3\) for all the sequences in the experiment.
## 3 Modeling Approaches
### Geometry and Meshes  
According to the actual structure of CMCs in Figure 2, a representative three-dimensional model is established with COMSOL Multiphysics. Figure 4 demonstrates positive isometric drawing and mesh generation of the CMCs model, respectively. In order to guarantee, as much as possible, a precision calculation, a model size of 150 \(\upmu\)m (length) \(\times\) 100 \(\upmu\)m (width) \(\times\) 40\(\upmu\)m (height) is adopted. As shown in Figure 4, curving SiC warp yarn and horizontal SiC weft yarn with 1 \(\upmu\)m PyC coating are woven orthogonally. The randomly distributed SiC fibers with 8-15 \(\upmu\)m diameters are inlaid into the SiC base. Furthermore, the incidence direction of LWJ is arranged right above the CMCs substrate with a jet diameter of 50 \(\upmu\)m due to the contraction effect [22]. Figure 4 exhibits the mesh generation of the CMCs model with a total grid number of 640,000. The free tetrahedral grid method is utilized for the SiC base with a maximum mesh size of 5 \(\upmu\)m. The SiC fibers and PyC coating have a relatively small dimension, and the local grid refinement and swept mesh method are adopted with a maximum mesh size of 0.5 \(\upmu\)m.
###### Abstract  
The importance of body area sensor networks (BASNs) is increasing day by day because of their increasing use in Internet of things (IoT)-enabled healthcare application services. They help humans in improving their quality of life by continuously monitoring various vital signs through biosensors strategically placed on the human body. However, BASNs face serious challenges, in terms of the short life span of their batteries and unreliable data transmission, because of the highly unstable and unpredictable channel conditions of tiny biosensors located on the human body. These factors may result in poor data gathering quality in BASNs. Therefore, a more reliable data transmission mechanism is greatly needed in order to gather quality data in BASN-based healthcare applications. Therefore, this study proposes a novel, multiobjective, lion mating optimization inspired routing protocol, called self-organizing multiobjective routing protocol (SARP), for BASN-based IoT healthcare applications. The proposed routing scheme significantly reduces local search problems and finds the best dynamic cluster-based routing solutions between the source and destination in BASNs. Thus, it significantly improves the overall packet delivery rate, residual energy, and throughput with reduced latency and packet error rates in BASNs. Extensive simulation results validate the performance of our proposed SARP scheme against the existing routing protocols in terms of the packet delivery ratio, latency, packet error rate, throughput, and energy efficiency for BASN-based health monitoring applications.  
Keywords:Internet of things; body area network; body area sensor network; biomedical sensors; healthcare; routing protocol +
Footnote †: journal:  
_Article_  
A Multiobjective, Lion Mating Optimization Inspired Routing Protocol for Wireless Body Area Sensor Network Based Healthcare Applications
## Materials and Methods  
### Sampling Activities  
The experimental campaign took place in the viticulture area of the province of Mantua at \"Azienda Agricola Ricchi\" (Monzanbano, Lombardy, Italy; 45\({}^{\circ}\)23\({}^{\prime}\)22.848\({}^{\prime\prime}\) N 10\({}^{\circ}\)41\({}^{\prime}\)36.427\({}^{\prime\prime}\) E) during the ripening period at the end of July and the end of August 2020 and 2021. Sampling was performed on Chardonnay grapes (_Vitis vinifera_ L.) in different plots distributed in the vineyard. Chardonnay is a white grape strong variety with moderate grape production; its berry clusters are tiny, cylindrical, and winged, and can range in size from well-filled to compact [11].  
All the samples were collected from 8 plots and in each plot, 3 plants were selected and marked in order to take the samples as representative as possible of the entire vineyard. In 2020, a total of 80 samples were collected and analyzed in 4 different sampling dates (8 samples for time 0, 24 samples for each of the other 3 sampling dates). In 2021, a total of 96 samples (24 samples for each sampling date) were collected with the same procedure.
## 3 Results  
### Primary Search Results  
Seventeen articles were selected after a meticulous screening procedure, and Figure 1 provides the selection procedure of the systematic review of the articles. A total of 1384 articles were identified based on the search keyword and research article criteria, with inclusion restricted to those written in the English language throughout the entire process. Following the process, 97 of these articles were identified as duplicates and eliminated. The titles and abstracts of the articles were reviewed to evaluate the relevancy of the article studies, and 983 articles were then eliminated. An additional screening was performed by reading the full text of the articles to ascertain the goals of the studies based on the eligibility criteria that were assessed, and 304 articles were eliminated. A total of 283 articles were rejected because the studies did not provide clear details on the methodology or the protocol or design of their study, while 49 articles were discarded because they were unable to satisfy the inclusion and exclusion criteria established. Some of the articles merely presented their methodological studies in a generic and ambiguous manner, lacking information pertaining to the whole diagnostic process and unable to provide clear objectives or results of their studies. There were 17 articles retrieved for further review that related to and met the criteria. The final articles were scientific research articles pertaining to the evaluation of scoliosis deformity that were deemed suitable for inclusion in this investigation. In this study, the researchers conducted empirical inquiries for their scholarly exploration, wherein they formulated and designed their inquiries that encompassed regulated variables and deliberate data. Certain researchers utilized randomized controlled studies in their studies, wherein they compared their approach with the existing method. Conversely, others adopted a single-case design, focusing solely on the development of their novel approach, providing their own control for comparison, and demonstrating the efficacy of their method instead of evaluating disparities with the current approach.  
Figure 1: PRISMA flowchart of study selection procedure from reviewed articles.
## 3 Results and Discussion
### CNS Microbiota Composition
succinus_ was identified only in Belgian products as a minor component of the CNS population [24]. Therefore, in Ventricina del Vastese, this species is exceptionally predominating, possibly as a consequence of the manufacturing method that creates high salt concentrations on the surface of the meat cubes constituting the sausage. Indeed, this species can be isolated from fermented products with high salt content, such as doenjang, a traditional fermented soybean Korean food [25]. Strains of _S. succinus_ are able to colonize the surfaces of the manufacturing plants, as found in a study aimed at the characterization of the CNS population in a small establishment producing French traditional dry fermented sausages [26]. The _S. succinus_ strains from fermented soybean products did not show hazardous traits, as they were susceptible to all of the antibiotics tested, did not form biogenic amines and were not hemolytic [25]. Among them, the strain 14 BME20 was selected as a starter candidate, and its genomic analysis confirmed that it is devoid of virulence factor-encoding genes and possesses genes for lipid degradation that can lead to the formation of volatile compounds [27]. In addition, in this study, the _S. succinus_ isolates had a low frequency of AR determinants and did not bear genes for the production of the most dangerous BAs, histamine and tyramine [13,14]. The only BA formed by _S. succinus_ is cadaverine, as a lysine decarboxylase-encoding gene was reported to be present in the isolates producing low levels of this compound [25]. In the _S. succinus_ 14 BME20 genome (Acc. N. NZ_CP018199.1), a nitric oxide synthase (NOS) gene is found, indicating the possible production of nitric oxide (NO) by this bacterial species from L-arginine in the presence of oxygen and NADPH [28], which could contribute to the meat's red color, though _S. succinus_ does not reduce nitrate with the exception of the subspecies _S. succinus_ subsp. _casei_[29]. As the addition of nitrate is not allowed in Ventricina del Vastese, the NOS activity of _S. succinus_ could compensate for the red color maintenance. The _S. succinus_ isolates exhibited a lower diversity compared to the other CNS species in Ventricina del Vastese, with some clusters joined at a similarity above 90% that comprised isolates from different manufacturers. This bacterial group could, therefore, represent a typical component of the CNS population in the production area to be characterized for its influence on the quality and distinctness of the product. Indeed, _S. succinus_ was reported to produce species-specific volatile compounds when used as a starter culture [30].
## 3 Results and Discussion
### Effects of Loading Frequency and Testing Temperature on the Dynamic Modulus
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**G-S**} & \multirow{2}{*}{**M-P**} & \multirow{2}{*}{**S-P**} & \multicolumn{8}{c}{**Loading Frequency (Hz)**} \\ \cline{3-11}  & & & **25** & **20** & **10** & **5** & **2** & **1** & **0.5** & **0.2** & **0.1** \\ \hline \multirow{8}{*}{1} & \multirow{4}{*}{(MPa)} & \(|\overline{E^{\ast}}|\) & 1253 & 1137 & 868 & 655 & 446 & 353 & 281 & 213 & 171 \\  & & \(\sigma\) & 47.6 & 47.5 & 66.3 & 25.2 & 24.1 & 29.2 & 10.9 & 8.0 & 8.8 \\  & & \(C_{\rm p}\) & 3.8 & 4.2 & 7.6 & 3.8 & 5.4 & 8.3 & 3.9 & 3.8 & 5.1 \\  & & \(\overline{\delta}\) & 37.2 & 36.2 & 34.8 & 33.2 & 31.5 & 29.6 & 27.9 & 26.0 & 24.8 \\  & & \(\sigma\) & 1.9 & 1.5 & 0.9 & 1.0 & 1.7 & 1.2 & 1.7 & 1.1 & 1.3 \\  & & \(C_{\rm p}\) & 5.2 & 4.1 & 2.5 & 3.1 & 5.4 & 4.2 & 6.2 & 4.2 & 5.4 \\ \hline \multirow{8}{*}{2} & \multirow{4}{*}{(MPa)} & \(|\overline{E^{\ast}}|\) & 1037 & 873 & 643 & 472 & 317 & 249 & 197 & 149 & 123 \\  & & \(\sigma\) & 68.7 & 39.3 & 37.8 & 17.4 & 14.1 & 11.3 & 14.3 & 9.8 & 6.5 \\  & & \(\overline{C}_{\rm p}\) & 6.6 & 4.5 & 5.9 & 3.7 & 4.5 & 4.6 & 7.2 & 6.6 & 5.3 \\  & & \(\overline{\delta}\) & 38.4 & 37.2 & 36.0 & 34.5 & 32.5 & 30.6 & 28.7 & 26.6 & 25.0 \\  & & \(\sigma\) & 1.6 & 1.2 & 2.2 & 1.1 & 1.5 & 0.8 & 1.0 & 1.9 & 1.4 \\  & & \(C_{\rm p}\) & 4.0 & 3.2 & 6.1 & 3.2 & 4.5 & 2.6 & 3.5 & 7.1 & 5.7 \\ \hline \multirow{8}{*}{3} & \multirow{4}{*}{(MPa)} & \(|\overline{E^{\ast}}|\) & 916 & 807 & 616 & 468 & 324 & 259 & 208 & 160 & 131 \\  & & \(\sigma\) & 38.3 & 42.4 & 20.3 & 18.1 & 13.4 & 15.8 & 14.9 & 8.8 & 6.7 \\ \cline{1-1}  & & \(C_{\rm p}\) & 4.2 & 5.3 & 3.3 & 3.9 & 4.1 & 6.1 & 7.2 & 5.5 & 5.1 \\ \cline{1-1}  & & \(\overline{\delta}\) & 37.5 & 36.3 & 34.7 & 33.2 & 31.4 & 30.0 & 28.7 & 27.1 & 25.9 \\ \cline{1-1}  & & \(\sigma\) & 1.5 & 1.6 & 1.1 & 2.3 & 0.9 & 1.5 & 1.2 & 1.5 & 1.1 \\ \cline{1-1}  & & \(C_{\rm p}\) & 4.1 & 4.5 & 3.2 & 7.1 & 2.8 & 5.1 & 4.2 & 5.5 & 4.2 \\ \hline \hline \end{tabular}
\end{table}
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
## 4 Application of Modulation Spectroscopy for Methane Detection
### Wavelength Modulation Spectroscopy Applied for Methane Detection
#### 4.1.4 Multi-Mode Diode Laser Applied for Methane Detection Based on WMS  
In the above research, almost every light source falls into a single-mode diode laser. The multi-mode diode laser is also used as a light source in some systems. A tunable multi-mode diode laser with a central wavelength of 1318 nm was applied for remote detection of methane by [PERSON] et al. [94]. In the experiment, the multi-mode laser modes merely depended on the input current and temperature, and the tuning range was continual. The reliability of the data analysis process was certificated. Later, [PERSON] et al. [95] reported what was probably the first application of measuring CH\({}_{4}\) and CO by using a multi-mode ECDL emitting around 2.33 \(\upmu\)m, as shown in Figure 3. Correlation spectroscopy was used for signals identification and quantitative analysis. Although it is easier to obtain the signal within the tuning range with a multi-mode diode laser than with a single-mode diode laser due to its larger covering area, the mode-jump and mode competition, an intrinsic property of the multi-mode laser, becomes an obstacle to further applications in trace gas detection.