esa-sceva/satcom-chunk-collection huggingface dataset:
# Rao and Wald Tests for Moving Target Detection in Forward Scatter Radar
## 5 Numerical Evaluation  
Monte Carlo simulations were conducted to assess the detection performance of the proposed Rao-SD-FSR, Wald-SD-FSR, Rao-FSR, and Wald-FSR detectors in this section. For comparison, the simulation results of the GLRT-SD-FSR, the GLRT-FSR, the CVD [21], and the ideal optimum detector [22] derived by assuming that both the direct signal and the noise parameters are known are also given. All the simulations are carried out using the Matlab (R2020b).  
When the far-field parameter \(f_{f}=2(\max\{L_{h},L_{\sigma}\})^{2}/\lambda(\min\{R_{0},R-R_{0}\})\) is smaller than 1, the target is in the far field of both the transmitter and receiver, and vice versa, the target is in the near field. Two cases are considered: the target is in the far field of the receiver and the transmitter (\(R=4000\)), and the target approaches the near field but is still in the far field (\(R=600\)). The direct signal-to-noise spectral density power ratio (DNSR) is [21]: DNSR \(=\frac{\left|\mathbf{a}\right|^{2}}{N_{0}}\), where \(N_{0}=BT_{e}F\) denotes a one-sided noise spectral density, \(F\) denotes the noise figure, \(T_{e}\) denotes the standard temperature, and \(B\) denotes Boltzmann's constant. The target parameters and radar system parameters of the two cases are given in Table 1.
# Grid-Connected PV Systems Controlled by Sliding via Wireless Communication
## 3 The DC/DC Converter and DC/AC Converter Controllers
### DC/DC Converter Sliding Controller
\[D_{eq}=\frac{-k_{1}Lip_{PV}+C_{1}x_{3}-k_{2}C_{1}Lx_{4}}{-k_{1}Lx_{2}+C_{1}(x_{1 }+x_{3})} \tag{16}\]  
This must fulfill Equations (17)-(19). \[\frac{\partial S}{\partial x}Bx=-\frac{k_{1}x_{2}}{C_{1}}+\frac{x_{1}+x_{3}}{ L}\
eq 0 \tag{17}\]  
\[-\left(\frac{\partial S}{\partial x}Bx\right)^{-1}=\frac{-C_{1}L}{-k_{1}Lx_{2 }+C_{1}(x_{1}+x_{3})} \tag{18}\]  
\[ \begin{array}{c}\frac{\partial S}{\partial x}(Ax+\delta)=(k_{1},1,0,k_{2}) \left(\begin{array}{c}\frac{i_{PV}}{\zeta_{1}}\\ -\frac{x_{3}}{\zeta}\\ \frac{x_{2}}{\zeta}-\frac{x_{3}}{\zeta\zeta}\\ x_{4}\end{array}\right)\\ =\frac{Lk_{1}ip_{V}-C_{1}x_{3}+C_{1}Lk_{2}x_{4}}{C_{1}L}\end{array}  \tag{19}\]  
The inductor current (\(x_{2}\)) is always positive because the DC/DC converter is CCM. In addition, if \(k_{1}\) and \(k_{2}\) are positive, the output voltage, \(x_{3}\), is always negative. A null vale for \(S\) means a globally asymptotically stable system. Thus, the control is achieved by means of Equations (12), (13) and (15), per Equation (20). \[D=\frac{-k_{1}Lip_{PV}+C_{1}x_{3}-k_{2}C_{1}Lx_{4}}{-k_{1}Lx_{2}+C_{1}(x_{1}+x _{3})}-k_{c}sgn(S) \tag{20}\]  
To make the systems fix to the sliding surface and stay on it, a Lyapunov function is used, Equation (21). \[V=\frac{1}{2}S^{2} \tag{21}\]  
The value of \(D\) has to make negative definite the time derivative of the Lyapunov function, which has to fulfill Equation (22). \[\dot{V}=S\dot{S}<0 \tag{22}\] \[\dot{V}=S\Big{(}\frac{3\mathcal{S}}{\partial x}(Ax+\delta)+\frac{ \partial S}{\partial x}B(x)D\Big{)}\]  
The sliding surface time derivative can be calculated applying Equation (23). \[\dot{S}(x)=k_{1}\Big{(}\dot{x}_{1}-\dot{v}_{PV}^{r}\Big{)}+\dot{x}_{2}+k_{2} \dot{x}_{4} \tag{23}\]  
The time derivative of the Lyapunov function yields Equation (24). \[\dot{V}(x)=S\dot{S}=S\Big{[}k_{1}\Big{(}\dot{x}_{1}-\dot{v}_{PV}^{r}\Big{)}+ \dot{x}_{2}+k_{2}\dot{x}_{4}\Big{]} \tag{24}\]  
The expression between brackets has to be solved, Equation (25). \[-k_{1}\dot{v}_{PV}^{r}-\frac{(x_{1}+x_{3})C_{1}-x_{2}Lk_{1}}{C_{1}L}k_{c}\frac {|S|}{S} \tag{25}\]  
The first term is considered null and Equation (24) converts to Equation (26). \[ \begin{array}{c}\dot{V}=S\Big{(}-\frac{x_{1}C_{1}+x_{3}C_{1}-x_{2}Lk_{1}}{C_ {1}L}k_{c}\frac{|S|}{S}\Big{)}\\ <-\frac{x_{1}C_{1}+x_{3}C_{1}-x_{2}Lk_{1}}{C_{1}L}k_{c}|S|<0\end{array}  \tag{26}\]The last term is negative. So, Equations (27) and (28) are obtained. \[(x_{1}C_{1}+x_{3}C_{1}-x_{2}Lk_{1})k_{c}<0 \tag{27}\]  
\[k_{1}<\frac{(x_{1}+x_{3})C_{1}}{-x_{2}L} \tag{28}\]  
These two equations close the sliding control.
## 2 Materials and Methods
### Spectra Analysis  
Each of the two different sets of spectra, related to healthy FHC cells and cancerous CaCo-2 cells, was separated into a calibration set, containing 70% spectra from each cell type, and a test set, including the remaining 30% of the spectra. Therefore, the calibration set included spectra of 35 healthy cells and 42 cancerous cells, whereas the test set comprised spectra of 15 healthy cells and 18 cancerous cells. The spectra of the calibration set were randomly selected by a random number generator; thus, the samples included in the calibration and test sets for the LWR and HWR corresponded to the same FTIR spectra.  
The machine learning training analysis was performed for the calibration sets using six classification models included in Orange software 3.35.0. In particular, the following algorithms were considered: CN2-RI, LR, CT, SVM, kNN, and NN. For each algorithm, the different parameter values that were used to control the learning process were tuned until the accuracy of the model was optimized. Full cross-validation was used to validate the results obtained via the investigated machine learning models with the spectra of the calibration set.
###### Abstract  
Performance evaluation tools for wireless cellular systems are very important for the establishment and testing of future internet applications. As the complexity of wireless networks keeps growing, wireless connectivity becomes the most critical requirement in a variety of applications (considered also complex and unfavorable from propagation point of view environments and paradigms). Nowadays, with the upcoming 5G cellular networks the development of realistic and more accurate channel model frameworks has become more important since new frequency bands are used and new architectures are employed. Large scale fading known also as shadowing, refers to the variations of the received signal mainly caused by obstructions that significantly affect the available signal power at a receiver's position. Although the variability of shadowing is considered mostly spatial for a given propagation environment, moving obstructions may significantly impact the received signal's strength, especially in dense environments, inducing thus a temporal variability even for the fixed users. In this paper, we present the case of lognormal shadowing, a novel engineering model based on stochastic differential equations that models not only the spatial correlation structure of shadowing but also its temporal dynamics. Based on the proposed spatio-temporal shadowing field we present a computationally efficient model for the dynamics of shadowing experienced by stationary or mobile users. We also present new analytical results for the average outage duration and hand-offs based on multi-dimensional level crossings. Numerical results are also presented for the validation of the model and some important conclusions are drawn.  
wireless channel; lognormal shadowing; stochastic differential equation; dynamics; 5G cellular networks +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 106; doi:10.3390/f11050106
# Optimization Design of the Spaceborne Connecting Structure for a Lightweight Space Camera
## 3 Optimization Design of the Spaceborne Connecting Structure
### Parametric Optimization Design  
There are some unreasonable local features in the structure obtained by topology optimization due to numerical errors. So, it is necessary to combine with the actual manufacturing technology for processing. On the premise of ensuring the overall shape and sizes of the topology and in combination with the designer's experience, the initial structure is trimmed to a regular shape that is easy to be manufactured. Some light-colored elements (the relative density is not 0 or 1) will inevitably exist in the topology optimization results, and these elements will be deleted by process treatment. Therefore, the performances of the structure after post-processing are not the same as the optimal result after topology. It is necessary to optimize its size parameters in detail. The geometric parametric model is shown in Figure 6. The structure of position A was parameterized, as shown in Figure 6b. \(L_{3}\) is the thinnest thickness of the structure, which provides great compliance for the structure. The structural parameters of position B and C were the same, as shown in Figure 6d,e. The outriggers of different thickness provide compliance for the structures, and on the premise of ensuring configurations, the thicknesses of each domain were parameterized as variables (\(L_{7}\)-\(L_{10}\), \(L_{12}\)-\(L_{14}\)) that can be modified.  
Figure 5: Results of the topology optimization. The relative density of red elements is 1, which represents the elements that should be retained. The relative density of the other color elements is less than 1, which represents the elements that can be removed.  
Figure 6: Conceptual configuration of connecting structures. (**a**,**c**) Geometry model. (**b**,**d**,**e**) Parametric model.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 3 Core Technologies in Vision-Based Collaborative Robotics  
### Visual Perception and Object Detection  
Visual perception forms the foundation of collaborative robotic systems [35], enabling machines to interpret their environments and interact meaningfully with objects and humans [34; 35]. Object detection--locating and classifying objects within an image or 3D space--is a critical competency, achieved through advanced deep learning models such as YOLO (You Only Look Once), Faster R-CNN (Region-based Convolutional Neural Networks), and transformer-based detectors [36; 37; 38; 39].  
In cobot applications, perception systems must operate under strict real-time constraints [40], offering high precision under variable lighting, occlusion, and dynamic backgrounds [41]. Recent advancements leverage multimodal data (e.g., RGB-D and thermal imaging) to enhance robustness, enabling cobots to identify workpieces, tools, and obstacles with human-like perceptual fluency [20; 42].  
Table 1 quantifies the trade-off between detector speed and accuracy in cobot-relevant use cases.  
Single-stage detectors--including state-of-the-art YOLOv6/v7--deliver real-time performance (hundreds to over 1000 FPS) with moderate accuracy (35-57% mAP), which is ideal for fast-paced tasks like collision avoidance and bin picking. Faster R-CNN, on the other hand, achieves a higher accuracy (~55% mAP) but is limited to \(\approx\)5 FPS, making it less suited for real-time edge deployment.  
Domain-specific variants demonstrate the impact of customization: AT-LI-YOLO reaches ~34 FPS and improves small-object detection by ~3% over YOLOv3, while Parallel YOLO-GG achieves ~94% accuracy and a 14% higher speed vs. YOLOv3 in grasping scenarios. Additionally, YOLOv4 has been validated for robotic arms, showing speed and accuracy gains over YOLOv3.  
In cobot systems, the real-time processing benchmark is typically \(\geq\)15-30 FPS on edge devices. Thus, YOLOv6/N quantized and YOLOv7 are highly suitable, especially when combined with hardware accelerators. For tasks requiring precise localization or small-object handling, customized light-weight versions (AT-LI-YOLO, YOLO-GG) offer compelling alternatives. Two-stage detectors like Faster R-CNN may still be used in contexts where model latency is acceptable or where high precision outweighs speed.
## 2 Materials and Methods
### Methods
#### 2.2.3 Preliminary Screening
However, it could not guarantee the removal of all FPs because the nodules might have attached to the adjacent pulmonary structures, particularly the vessels, in some cases. This type of nodule is called a juxta-vascular nodule, and it may be excluded, together with the tubular structures during preliminary screening. To prevent this, we set very high thresholds (0.9) for \(Ecc\) and \(VRR\). Nonetheless, it was not a perfect way to detect all the vessel-attached nodules. A more effective way would be to cut the nodules from the attached vessels. Fortunately, the nodules were in blob shapes in a three-dimensional structure, and hence, a Hessian-based blob enhancement filter could help to discriminate them from the vessels. Let \(V\) be the volumetric image, and \(H\) be its Hessian matrix, which can be calculated by the second-order partial derivatives of the voxels in \(V\). \[H\left(V\right)=\left[ \begin{array}{cc}\frac{\partial^{2}V}{\partial x^{2}} \frac{\partial^{2}V}{\partial x\partial y}\frac{\partial^{2}V}{\partial x \partial z}\\ \frac{\partial^{2}V}{\partial y\partial x}\frac{\partial^{2}V}{\partial y^{2}} \frac{\partial^{2}V}{\partial y\partial z}\\ \frac{\partial^{2}V}{\partial z\partial x}\frac{\partial^{2}V}{\partial z \partial y}\frac{\partial^{2}V}{\partial z^{2}}\end{array} \right. \tag{12}\]  
\[\
abla\,\left(H-I*\lambda\right)=0, \tag{13}\]  
where \(I\) is the identity matrix and \(\lambda=\left\{\lambda_{1}\,\,,\,\lambda_{2},\,\,\lambda_{3}\right\}\) is the associated eigenvalues of \(H\). These three eigenvalues can provide information about the shape of the objects as follows:  
\[V=Blob\,\,\,if\,\,\,\lambda_{1}<0,\,\,\,\lambda_{2}<0,\,\,\,\lambda_{3}<0 \tag{14}\]  
\[V=Tube\,\,if\,\,\,\,\lambda_{2}<0\,\,,\,\,\lambda_{3}<0 \tag{15}\]  
\[V=Plate\,\,if\,\,\,\lambda_{3}<0. \tag{16}\]  
Depending on the shape that we want to enhance, different mathematical computations can be done using these three eigenvalues. In our proposed method, we intend to detect pulmonary nodules. Hence, we enhance the blob-like structures and suppress tube-like objects using the following equations [37]. \[Blob={\lambda_{1}}^{2}{\lambda_{p}}\left[\frac{3}{2{\lambda_{1}}+{\lambda_{p} }}\right]^{3} \tag{17}\]
## 3 Proposed Hardware Architecture  
The proposed BEC hardware architecture is shown in Figure 1, illustrating its major components. The description of the major components is organized as follows: Section 3.1 explains the role of the memory unit within the system by illustrating efficient data storage and retrieval mechanisms. Section 3.2 describes multiplexers employed for routing purposes. The description includes a detailed analysis of their function in optimizing data flow and ensuring seamless communication between different parts of the system. Section 3.3 provides an in-depth explanation of the Arithmetic Logic Unit (ALU). It is the core component responsible for executing a diverse range of arithmetic operations essential for cryptographic computations. Section 3.4 introduces the controller, which takes the form of a Finite State Machine (FSM). It optimizes the overall functionality of the BEC architecture. Finally, the clock cycle information is provided in Section 3.5. The architectural design process was conducted using parameters stipulated by the National Institute of Standards and Technology (NIST).
## 3 Results and Discussion
During the ion sputtering of a binary compound, the selenium atoms quickly leave the working area because of their high volatility, and do not participate in the re-deposition process at the surface. In addition, the film temperature increases during ion sputtering. It is well known that the additional heating of the sample can also lead to the reduction of the chalcogen content in copper selenide films and contribute to the transformation from Cu\({}_{2.3}\)Se to Cu\({}_{2}\)Se [32; 33; 34]. The X-ray diffractograms of the samples after plasma treatment at ion energies of 25 and 200 eV for durations of 60 and 90 s are presented in Figure 5. The results of the X-ray diffractometry indicate a change in the phase composition during ion sputtering. Figure 4: SEM images of the film surface after plasma sputtering at ion energy of 200 eV and processing times of 30 s (**a**,**b**), 60 s (**c**,**d**), and 90 s (**e**,**f**). The sample tilts during imaging were 0\({}^{\circ}\) (**a**) and 70\({}^{\circ}\) (**b**,**d**,**f**). (**c**,**e**) show cross-sections of the film. The dashed line in (**c**,**e**) shows the upper border of the lower layer in Figure 1c. In the initial state, the films were single-phase cubic crystal lattice Cu\({}_{1.75}\)Se. After plasma treatment with 25 eV argon ions for 60 and 90 s, three phases appeared: cubic phase berzelianite Cu\({}_{1.78}\)Se (ICDD card 01-072-7490), monoclinic phase Cu\({}_{2}\)Se (ICDD card 00-058-0228), and cubic phase berzelianite Cu\({}_{2}\)Se (ICDD card 01-088-2043). After plasma treatment with 200 eV ions, two phases appeared in the diffractograms: monoclinic Cu\({}_{2}\)Se phase (ICDD card 00-058-0228) and cubic Cu\({}_{2}\)Se phase (ICDD card 01-088-2043). The coexistence of the cubic and monoclinic phases in Cu\({}_{2\times}\)Se is expected and has been reported previously, for example, in bulk samples synthesized via conventional solid-state sintering technology in [35]. Thus, the results of X-ray diffractometry of Cu\({}_{2\times}\)Se films, after plasma sputtering, are in good agreement with the EDX data, and confirm the transformation of Cu\({}_{1.75}\)Se to Cu\({}_{2}\)Se during the Ar plasma treatment.
# Niching Global Optimisation: Systematic Literature Review
## 1 Introduction  
Niching optimisation is a set of optimisation schemes which aims to enhance general-purpose metaheuristics and help them identify multiple local optima simultaneously [1, 2, 3]. Niching was introduced to maintain diversity within metaheuristic algorithms, which are often unimodal and tend to converge to a single promising region. These algorithms can be broadly classified into three families: sequential or temporal niching, parallel or spatial niching, and hybrid niching methods. These algorithms have been used to boost the exploration capacity in major metaheuristics, from genetic algorithms [4], particle swarm optimisation [5], differential evolution [6], and ant colony optimisation [7] to more modern optimisation methods, such as the bat algorithm [8], chimp optimisation [9], whale optimisation [10], and harmony search optimisation [11].  
Parallel niching methods divide the population into independent subpopulations, each searching for different solutions simultaneously [12]. Sequential niching methods identify multiple optimum points one at a time [4]. Hybrid niching methods combine more than one niching paradigm to locate multiple optima. Niching techniques can be considered near-deterministic stochastic optimisation methods which can efficiently approximate the performance of rigorous global optimisation methods, which often suffer from extremelyslow convergence speeds and intractability in complex problems [13; 14; 15]. Our research survey identified 10-11 niching subparadigms, namely sequential niching [4], fitness sharing [16; 17], crowding [18], restricted tournament selection, clearing [19], speciation [5; 20], clustering [12], multiobjectivisation [21], embedded hybrid methods [22], ensemble hybrid methods [2], and other hybrids [23] which are discussed in this study, including major trends, challenges, and potential future research directions.
## 2 Materials and Methods
### Physical Characterization of CMSC  
#### 2.5.1 Determination of Degree of Substitution (DS)  
Absolute values of the DS of CMSC products were determined by the standard method (ASTM D1439, West Conshohocken, PA, USA). In this method, 4 g of the dry powder CMSC was stirred in 75 mL of 95% ethanol for 5 min. Then 5 mL of 2 M nitric acid was added and the solution was boiled to convert NaCMSC to its acid form, H-CMSC. Next, the solution was removed from the heat and further stirred for 10 min. The liquid phase was removed, and the solid phase was washed with 20 mL of 80% (\(v/v\)) aqueous ethanol solution at 60 \({}^{\circ}\)C. The ethanol washing was repeated for four times. The precipitate was then washed with a small quantity of anhydrous methanol and filtered. Finally, the precipitate was dried at 100 \({}^{\circ}\)C for 3 h and cooled in a desiccator for half an hour.  
About 0.5 g of dry acid CMSC was weighed, and 100 mL of distilled water was added and stirred. Then 25 mL of 0.3 M NaOH was added and the mixture was heated to boil for 15 min. After complete solubilization of CMSC, the solution was titrated with 0.3 M HCl using phenolphthalein as indicator. The titration was repeated thrice for each sample, and the average volume of HCl used was recorded. The degree of substitution was then calculated using Equations (2) and (3) as follows:  
\[\mathit{Carboxymethyl\ content}\ \left(\%CM\right)=\left[\left(Vo-Vn\right)N \times 0.058\times 100\right]/M \tag{2}\]  
\[\mathit{Degree\ of\ substitution}=162\times\%CM/\left[5800-\left(57\times\%CM\right)\right] \tag{3}\]  
where \(V_{o}\) = mL of HCl used to titrate blank, \(V_{n}\) = mL HCl used to titrate sample, \(N\) = normality of HCl used, \(M\) = sample amount (g), and 58 = molecular weight of carboxymethyl group [28].
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 5 The Proposed Architecture
### Connector Layer  
The connector layer mainly involves different types of EV charging associated with the specific charging standards developed by the manufacturers or EV owners. Manufacturers focus on developing charging standards for EVs with a configuration designed based on different countries. However, charging standards equipped in the EVs should be compatible with the charging port available at the CS. The main principle of the EV charging connector layer highlights 2W, 3W, and 4W EVs associated with several charging standards. For example, we considered 2W and 3W EVs that support the SAEJ1772 charging standard. However, other charging standards are CHADEMO, CCS Combo 1, and CCS Combo 2 compatible with the 4W EV. Moreover, charging standards such as Bharat AC-001, Bharat DC-001, and Type 2-AC are supported by all types of EVs. The charging standards can be used for EV conductive charging. However, these charging standards differ in terms of the configuration, the number of pins, and the communication protocol required to facilitate charging between the electric grid and EV. Therefore, these charging standards need to be standardized to ensure their adaptability for various types of EVs. For example, if a 2W EV associated with the SAEJ1772 charging standard arrives at a CS, but the available charging connector of the CS does not support the configuration of SAEJ1772, EVs arriving at the CS cannot utilize community infrastructure for charging and it can further delay their travel plans. Moreover, different countries work on different charging standards, which can cause a difficult situation for efficient and adaptable EV charging. So, we considered a standardized layer to assimilate the different charging standards into a standardized charging standard considering the common number of pins and the communication protocols of several charging standards, i.e., SAEJ1772, CHADEMO, CCS combo 1/combo 2, Type 2-AC, Bharat AC-001, and Bharat DC-001, for efficient community charging.