esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Works
#### 1.1.2 YOLO (You Only Look Once)  
YOLO [30] is one of the deep learning algorithms for detecting objects in images. YOLO divides the image into grids, and for each grid cell, it predicts the class and BB information of the object it contains. YOLO uses relative position information within grid cells to estimate BB. This means that the coordinates of the BB are expressed as a value relative to the upper left corner of the grid cell. Because of this, YOLO predicts an offset, which is a value representing relative position information. For example, if BB is predicted to be (0.2, 0.3, 0.4, 0.5) within one grid cell, this is the location information relative to the upper left corner of that grid cell. Therefore, in order to calculate the coordinates of the bounding box within the actual image, we need to calculate the absolute coordinate values using the location information of the corresponding grid cells and the predicted offset values. By using this relative position information, YOLO is a technique for predicting the position of objects in an image. In YOLO, the Anchor Box is a box that predefines the shape and size of the BB predicted in each grid cell. Anchor boxes are used to detect objects with different sizes and proportions. The size and shape of the anchor boxes are predefined in YOLO, and these values are set differently depending on the dataset. Each  
Figure 1: Comparison of IoU and GIoU [28]**(a)** is the case where the 2-Norms of the two BBs are the same, and (**b**) is the case where the 1-Norms are the same.  
grid cell can have multiple anchor boxes, which allows to detect objects of different sizes and shapes. By calculating the difference between the predicted BB and the anchor box, YOLO accurately predicts the position and size of the object. Direct location prediction in YOLOv2 [31] predicts the BB as a 5-dimensional vector in a cell according to the anchor box, constraining the BB to not deviate from the grid cell, and if the original YOLO predicted the center of the grid, v2 predicts how far it moves from the top left vertex.  
In this paper, the Wi-Fi data points represented by BBs are represented by one grid cell containing all of them, and the BBs are divided into grid cells containing the minimum number of BBs to represent the clusters of BBs and calculate the correlation of each grid cell. This grid cell is called a cell in this paper.
# An Observer-Based Current Sensor-Less Control Scheme for Grid-Following Converters
## 2 Modeling of Grid-Following Inverter with L-Type Filter
\begin{array}{ccc}1/L_{f}&0&0\\ 0&1/L_{f}&0\\ 0&0\end{array} \right]\quad C=\left[ \begin{array}{ccc}0&0&1\end{array} \right] \tag{10}\]  
In the linearized model, the nonlinear terms are linearized at the operating point (\(P_{dc},Q^{ref}\)) as follows:  
\[\frac{d}{dt}W_{c}\approx P_{dc}-\frac{3}{2}v_{gd}i_{d}-\frac{3}{2}L_{f}\,i_{d} ^{ss}\frac{di_{d}}{dt}-\frac{3}{2}L_{f}\,i_{q}^{ss}\frac{di_{q}}{dt} \tag{11}\]  
According to (7) the operating point values of \(i_{d}\) and \(i_{q}\) are:  
\[ \begin{array}{l}i_{d}^{ss}=P_{dc}/\left(\frac{3}{2}v_{gd}\right)\\ i_{q}^{ss}=Q^{ref}/\left(\frac{3}{2}v_{gd}\right)\end{array}  \tag{12}\]  
Substituting (3) and (12) into (11) leads to the following linearized model of DC-link voltage dynamic:  
\[\frac{d}{dt}W_{c}\approx P_{dc}-\frac{3}{2}v_{gd}i_{d}-\frac{3}{2}L_{f}i_{d}^ {ss}\left(-\omega.i_{q}-\frac{R_{f}}{L_{f}}i_{d}+\frac{(v_{td}-v_{gd})}{L_{f}} \right)-\frac{3}{2}L_{f}i_{q}^{ss}\left(\omega.i_{d}-\frac{R_{f}}{L_{f}}i_{q}+ \frac{(v_{td}-v_{gq})}{L_{f}}\right) \tag{13}\]  
By defining \(a=-\frac{3}{2}L_{f}i_{d}^{ss}\), \(b=-\frac{3}{2}L_{f}i_{q}^{ss}+\frac{3}{2}\frac{R_{f}}{w}i_{d}^{ss}\) and simplifying (13), the linearized model of DC-link voltage is represented by (13):  
\[\frac{dW_{c}}{dt}=P_{dc}+\left(-\frac{3}{2}v_{gd}+bw\right)i_{d}-awi_{q}+a \frac{(v_{td}-v_{gd})}{L_{f}}+b\frac{(v_{td}-v_{gq})}{L_{f}} \tag{14}\]Despite the higher accuracy, the system model of (14) is dependent on the operating point, and is more complicated compared to (8). Based on (14), the closed form of the state-space equations is as follows:  
\[ \begin{bmatrix}\frac{d_{i_{d}}}{dt}\\ \frac{d}{dt}\\ \frac{d}{dt}\end{bmatrix} =A_{2} \begin{bmatrix}i_{d}\\ i_{q}\\ i_{q}\\ W_{c}\end{bmatrix} +B_{u-2} \begin{bmatrix}\frac{1}{L_{f}}&0\\ 0&\frac{1}{L_{f}}\\ \frac{a}{L_{f}}&\frac{b}{L_{f}}\end{bmatrix}\begin{bmatrix}v_{td}\\ v_{td}\end{bmatrix} +B_{d-2} \begin{bmatrix}v_{gd}\\ v_{gq}\\ P_{dc}\end{bmatrix} \\ y=C \begin{bmatrix}i_{d}\\ i_{q}\\ W_{c}\end{bmatrix}  \tag{15}\]  
where,  
\[ \begin{array}{c}A_{2}=\begin{bmatrix}0&-w&0\\ w&0&0\\ -\frac{3}{2}v_{gd}+bw&-aw&0\end{bmatrix}&B_{u-2}=\begin{bmatrix}1/L_{f}&0\\ 0&1/L_{f}\\ 0&0\end{bmatrix}\\ B_{d-2}=\begin{bmatrix}1/L_{f}&0&0\\ 0&1/L_{f}&0\\ \frac{-a}{L_{f}}&\frac{-b}{L_{f}}&1\end{bmatrix}&C=[0\;0\;1]\end{array}  \tag{16}\]  
It is worth mentioning that the model of (9) is the same as the model of (15), but in a special operating point, i.e., (\(P_{dc}=0,\;Q^{ref}=0\)).
# Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence
## 1 Introduction
The effectiveness of the proposed method was also validated with projection data obtained from a physical X-ray CT scanner. The findings demonstrate the advantage of efficiently determining parameters in the preprocessing stage of reconstruction, allowing for high-quality images in iterative reconstruction using the PDEM method.
## 2 Research Objects and Numerical Methods
### Meshing and Independence Verification  
In order to guarantee the mesh quality of the blade body, the first stage nozzle inlet extension and the third stage impeller outlet extension are meshed by H-type grid, the stator and rotor of each stage are meshed by O-type grid. Grid encryption is made near each boundary layer to ensure the accuracy of the calculation. Meanwhile, taking into consideration the calculation accuracy and efficiency, a grid-independence test for a three-stage axial single-channel model is completed. Taking the total efficiency of the whole machine as the verification standard, the calculation models of different grid densities of 500,000, 1.5 million, 2.2 million and 2.8 million grids are numerically calculated. The verification results are shown in Table 4. The total efficiency difference calculated by the number of 2.8 million and 2.2 million grids is less than 1%, so the number of grids is finally selected as about 2.2 million. Figure 4 shows the diagram of grid generation for a single passage of the three-stage axial turbine.  
The total-total efficiency in Table 4 is the performance evaluation standard for the S-CO\({}_{2}\) axial turbine. The specific calculation method is as follows.  
\[\eta_{\rm T-T}=\frac{h_{\rm Tout}-h_{\rm T_{in}}}{h_{\rm T_{is,out}}-h_{\rm T _{in}}} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**NO.** & **Number of Grids/\(\times\)10\({}^{4}\)** & **Total-Total Efficiency/\%** & **Relative Error/\%** \\ \hline
1 & 50 & 92.12 & - \\
2 & 100 & 91.89 & 0.25 \\
3 & 150 & 91.74 & 0.16 \\
4 & 220 & 91.60 & 0.15 \\
5 & 280 & 91.55 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Independence verification.  
Figure 4: Grid schematic.  
where \(h\) represents the enthalpy of the working fluid, subscript T represents the total parameter, subscript in represents the inlet of the first stage nozzle, and subscript out represents the outlet of the third stage impeller.
# Silicon Integrated Nanophotonic Devices for On-Chip Multi-Mode Interconnets
## 1 Introduction
Figure 1: **(a)** The calculated effective indices for the higher-order modes in a silicon-on-insulator (SOI) waveguide with varied waveguide width (\(\mathrm{w_{wg}}\)). The inset shows the cross-section of a multi-mode SOI waveguide with some key parameters labeled. **(b)** The calculated field profiles for the first four transverse electric (TE) and transverse magnetic (TM) modes. **(c)** The configuration for a typical four-channel mode-division multiplexing (MDM) system. In this paper, we review the recent progress on silicon integrated nanophotonic devices for on-chip multi-mode interconnects. In Section 2, we begin by reviewing silicon multi-dimensional (de)multiplexers, including silicon mode multiplexers in Section 2.1, silicon hybrid multiplexers in Section 2.2, and densely packed waveguide arrays in Section 2.3. In Section 3, we give a review on silicon multi-mode routing devices, including silicon multi-mode bends in Section 3.1, silicon multi-mode crossings in Section 3.2, and silicon multi-mode splitters in Section 3.3. In Section 4, we turn to the discussion of inverse-designed multi-mode devices. In Section 5, we give a review on reconfigurable MDM devices. In Section 6, we offer our outlook of the development prospects for silicon multi-mode photonics. Figure 2 illustrates the main topics that will be discussed in the following sections.
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
## 3 Technical Overview
### MAC Sublayer  
The MAC sub-layer includes basic wireless communication features such as beacon management, channel access, GTS management, frame validation, acknowledged frame delivery, and association procedures, in a similar fashion to the IEEE 802.15.4. As communications use the visible light spectrum, additional functionalities are implemented such as:  
1. Color function: Providing information such as device status and channel quality to the human eye via color.
2. Visibility: Sending an idle pattern during idle time or the receive operation to provide continuous illumination.
3. Color stabilization: A control loop for the stabilization of the color emitted by CSK transmitters.
4. Dimming support: Reducing the radiant power of a transmitting light.  
Interestingly, PHY I, II and III support over-the-air MAC and PHY configuration for increased flexibility and on-the-fly re-configurability. Additionally to these MAC data services, the protocol also supports mechanisms to mitigate the impact of dimming or flickering.
## 1 Introduction
Some other researchers have studied city obstacles, weather conditions, and other vehicles on the ground, which are taken as possible threats [28; 29]. In this paper, a beetle swarm optimization (BSO)-based path-planning algorithm for UAVs in urban environments is proposed. The ground risk distribution is considered as an input for this optimization model. First, a quantitative mechanism was designed to evaluate the effects of ground risk on a generated UAV path. Next, the turning angles and path length were incorporated as constraint conditions for the proposed BSO model. Subsequently, an adaptive step-size mechanism for each beetle and a random proportionality coefficient were designed to improve the deficiencies of the slow convergence and local optimum, which usually appear in standard BASs. Finally, a global optimum attraction operator was established for all the beetles to share social information in the whole beetle swarm in order to lead each individual towards the global best position in the search space. In comparison with PSO, GA, FA, and standard BAS, the proposed BSO method worked well under all the scenarios with different swarm sizes, individual dimensions, and iterations. Simulation results confirmed the suitability of the proposed approach. The remainder of this paper is organized as follows: The description of the problem is provided in Section 2. A detailed model and the methodology are provided in Section 3. Section 4 describes the proposed BSO-based path-planning algorithm for UAVs in urban areas. A case study and algorithm validations can be found in Section 5. Finally, the paper is concluded in Section 6.
# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 2 Related Work  
### Traditional Background Subtraction  
Traditional motion target detection can be divided into four categories: temporal difference, optical flow, background subtraction, and deep-learning-based methods [15]. The temporal difference method is a simple low-volume algorithm; however, it cannot adapt to complex environments or overcome the void phenomenon. Optical flow methods are computationally complex and are sensitive to light. Deep-learning-based methods are computationally complex and data sensitive. In contrast, background subtraction is rich in variety, and it is more effective [16]. [PERSON] and [PERSON] proposed modeling the change in a particular pixel over time using multiple Gaussian models for the background [17]. This method helps restore the target profile to its original shape, but the detection results are not excellent. The visual background extractor (ViBE) algorithm proposed by [PERSON] and [PERSON] builds a background model by aggregating the previous observations at each pixel location [18]. However, this method suffers from ghosting, incomplete target extraction, and a shadowed foreground. [PERSON] and [PERSON] proposed an improved ViBe algorithm. In order to reduce the repetition rate of the pixel value in the background model, the proposed method changes the neighborhood selection method to improve the accuracy of the initialization of the background model [19]. A detection framework called E-GMMTFD was proposed by [PERSON] et al. [20]. The E-GMMTFD framework utilizes the adaptability of the GMM to complex environmental changes and the strength of the TFD method to eliminate background movement, thus reducing the false alarms caused by illumination change and background movement. However, traditional background subtraction methods do not adequately consider the characteristics of satellite videos and cannot effectively distinguish between noise interference, pseudo-motion targets, and moving vehicles.
# Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
## 4 Experimental Results
### Evaluation of the Impact of Noise Injection on the Robustness of Classifiers  
The noise injection mechanism, as an effective defense strategy, makes the model robust in the face of adversarial attacks. We tested the performance of the noise injection mechanism in defense strategies on the CIFAR10 dataset and analyzed its effectiveness and applicability through experiments. Throughout all testing scenarios, the CNN served as the image classification model, while the Adam optimizer was employed, utilizing a consistent learning rate of 0.01. In order to improve the performance of the classifier model under noise interference, we added different intensities of noise to the training set. Figure 6 shows the detection results of the classifier model after adding noise to the training set. The black dashed line represents defense methods that have not adopted noise injection mechanisms. Through comparison, it can be found that the defense method using noise injection mechanism has significantly improved performance under noise interference. This indicates that injecting noise during the training process can significantly improve the robustness of the classifier model. To further analyze the impact of noise injection mechanisms with different intensities on defense effectiveness, we tested the defense effectiveness of noise intensity sigma from 1 to 10. The experimental results show that as the noise intensity increases, the performance of the defense model is further improved. This indicates that within a certain range, an increase in noise intensity can improve defense effectiveness. However, when the noise intensity exceeds a certain level, the defense performance will actually decrease. This is because excessive noise intensity can interfere with the
The chemical composition of shoots (taken as a hardwood) and shells are as follows, respectively: 40-55% and 25-30% cellulose, 25-40% and 25-30% hemicellulose and 15-25% and 30-40% lignin, representing an attractive source of organic compounds of interest [31; 32]. Indeed, these lignocellulosic residues can be valorized through the extraction of organic fractions, such as polyphenols and other secondary metabolites, and converted into high value-added compounds, following the circular economy concept [33; 34; 35]. Cellulose and lignin can be extracted too and further converted in their nanoscale form, exhibiting many more exploitable features with respect to their bulk counterpart [36]. Cellulose nanocrystals (CNC) have some different interesting properties: high stiffness, low density (approximately 1.57 g cm\({}^{-3}\)), very low thermal expansion coefficient, high elastic modulus around 150 GPa; moreover, in the last few years, attention has been paid to the surface modifications of CNC, specifically to the abundant hydroxyl groups present on their surfaces. Chemical functionalizations have been investigated in order to use CNC in the biomedicine, pharmaceutical, and agri-food sectors for the release of specific drugs and active substances, especially since their application seems to do not show cytotoxicity or any other harmful effects on human health [37; 38; 39; 40; 41]. Very recently, lignin-based polymers have also attracted the interest of the research community: indeed, lignin can be used as a low-cost, eco-friendly reinforcement to prepare high-performance nanocomposites [42; 43; 44]. Different nanoscale lignin compounds can be synthesized by different chemical/physical approaches [45; 46; 47]. Lignin nanoparticles (LNP), thanks to their antioxidant activity and other characteristics, can find applications both as active ingredients and as a nanocarri structure [48; 49]. Lignin and LNP, as well as for cellulose and CNC, can be isolated and extracted from agricultural waste, adding value to the whole supply-chain [50; 51; 52]. In this context, the proposed research gives an original and innovative perspective on the valorization of the agricultural biomasses as starting matrices for organic nanomaterials synthesis, investigating at the same time their potential role as nanopesticides. Indeed, the possibility of using hazelnut lignocellulosic waste as a novel source for synthetizing CNC and LNP was investigated for the first time in this study. Obtained nanomaterials were chemically and morphologically characterized and assayed for their antimicrobial properties respect to Xac. As far as we know, the results represent the first report of CNC antimicrobial activity on a plant pathogenic bacterium in the literature. These studies highlight the potential of hazelnut CNC and LNP as active nanomaterials and plant-compatible nanocarriers, perfectly meeting the concept of circular economy and sustainable plant-protection strategies.
# Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM
## 3 Experiment  
### Disk Laser Welding  
The experiment was performed by using a high-power disk laser TruDisk 10003 (TRUMPF, Ditzingen, Germany), and high-speed camera system Memrecam fx RX6 (NAC, Tokyo, Japan). The beam diameter of the disk laser focus was 480 \(\upmu\)m, while the laser wavelength was 1030 nm and the laser power was 10 kW. Argon was used as a shielding gas, and the nozzle angle was 45\({}^{\circ}\). Type 304 stainless steel with dimensions of 119 \(\times\) 51 \(\times\) 20 mm was employed as the specimen. Three different welding speeds, 3, 4.5 and 6 m/min, were taken into consideration in this experiment (see Table 1). The high-speed camera was setup at a position perpendicular to the welding direction to capture the images of plume and spatters. The frame rate was 2000 f/s, and the image resolution was 512 \(\times\) 512 pixels. An optical filter was placed in front of the RX6 to reduce the effect of multiple reflections.
# Genomic Approaches for Monogenic Kidney Diseases:
## 2 Genetic Testing: The Current Framework  
A review, including original studies and reviews reporting the diagnostic yield of the genomic approach for kidney diseases, was performed. A Pubmed advanced search was conducted, which identified a total of 102 studies published from 2000 to July 2023. The MESH keywords used in the search were kidney disease, diagnostic yield and genetic test. Seven articles were additionally identified and included.  
Screening criteria specified the exclusion of studies not reporting the diagnostic yield of the genomic test (30 studies), including <20 patients (20 studies), focused on population screening (3 studies) or renal cancer (8 studies) or reporting on patients already included in previous studies (1 study). Selection based on inclusion and exclusion criteria was performed using the platform Rayyan.  
A total of 47 studies were considered and analyzed. Each study's chosen technology, patient cohort characteristics and diagnostic yield were recorded in a dedicated spreadsheet (Table 1). According to the patient cohort age, studies were grouped as pediatric, pediatric/adult or adult only, and as specific or non-specific CKD clinical presentations. A subset of three studies, which included direct comparisons between two different sequencing approaches, was created. [PERSON]'s exact test was used to evaluate the incremental rate of the diagnostic yield for each technology.  
The most frequently discussed specific clinical presentations were cystic diseases, focal segmental glomerulosclerosis and SRNS, and tubulopathies. Technologies identified in this review included Sanger sequencing (2 studies), targeted gene panel testing (20 studies), WES (16 studies) and WGS (1 study). A total of 8 retrospective studies included assessments performed using a targeted gene panel and WES but did not provide separate outcomes.  
Figure 1 shows the heterogeneity of the diagnostic yield distribution for the different technologies. No formal statistical analysis could be performed, due to the wide differences and variety of the studies collected.
# Seasonal Variability of Volatilome from _Dictyota dichotoma_
## 2 Results and Discussion
### Variations of Headspace Volatilome of Fresh D. dichotomous  
The identified HS-VOCs of fresh _D. dichotomous_ (HS-FrDd) within all collection months can be classified into aliphatic compounds (including C\({}_{11}\)-hydrocarbons (dictyoplerenes)), terpenes (mainly sesquiterpenes) and C\({}_{13}\)-norisoprenoids. Great variability can be noted in May in comparison to other months (Tables 1 and 2), particularly with respect to the higher abundance of sesquiterpenes. It should be noted that the sea temperature in May was the lowest (20.1 \({}^{\circ}\)C) in comparison with other months. Such a low temperature could affect the biosynthesis of secondary metabolites. The major compound of HS-FrDd for all months was pentadecane, with the highest abundance in July and August (up to 23.41% (I) and up to 11.51% (II)) and the lowest in September (4.92% (I); 2.14% (II)). Pentadecane was reported for the first time as a metabolite of _D. dichotomous_ using nuclear magnetic resonance (NMR) and electron ionization mass (EIMS) spectra [2]. Its unsaturated derivative, pentadec-1-ene, was the most abundant in May (3.98% (I); 3.69% (II)). These two compounds were not present in our previous research of fresh _D. dichotomous_ headspace [14], but the sample was from a different single-point collection location. On the other hand, pentadecane was found in the headspace of _D. dichotomous_ from the Yellow Sea of China [15]. Another present higher alkane was heptadecane (up to 3.53% (I) or 2.35% (II)), which was not present in the research of [PERSON] et al. [15]. Tridecanal abundance increased from May to September up to 23.24% (I) or 13.89% (II), as well as (_E_)-non-2-enal up to 1.97% (I) or 1.80% (II). (_E_)-Non-2-enal was present in _D. dichotomous_ from the Yellow Sea of China [15] and tridecanal was a minor compound; several unsaturated aliphatic compounds (not present in current research) were found (e.g., heptadec-8-ene, or (\(Z\))-pentadec-11-enal). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.