esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
combined visible-near-infrared (VIS-NIR), shortwave infrared (SWIR), and fluorescence spectroscopy in a handheld device to achieve 95% accuracy [19]. [PERSON] and colleagues predicted freshness indicators such as pH, TVB-N, and K values in mackerel using VIS-NIR hyperspectral imaging and multivariate regression techniques (VIP-PLSR, SVR) and obtained R\({}^{2}\) values of up to 91% [20]. A common feature of these studies is their reliance on data fusion obtained from multiband systems. Unlike the studies mentioned above, this study proposes a hybrid approach to determining fish freshness by combining surface characteristics obtained from image data with optical reflection data obtained using a single-band laser reflectance module operating at a wavelength of 940 nm. The laser sensor used is low-cost and consumer-grade, operating on a principle similar to the IR-based facial recognition systems used in today's smartphones. This similarity offers significant potential for the proposed method to be transformed into a practical freshness assessment solution that can be integrated into mobile devices in the future. Therefore, it innovatively distinguishes itself from the image and multiband spectroscopic systems in the literature in terms of optical properties, portability, cost, and tissue-focused analysis.
# A Ka-Band Cylindrical Paneled Reflectarray Antenna
###### Abstract  
Cylindrical parabolic reflectors have been widely used in those applications requiring high gain antennas. Their design is dictated by the geometric relation of the parabola, which relate the feed location, \(f\), to the radiating aperture, \(D\). In this work, the use of reflectarrays is proposed to increase \(D\) without changing the feed location. In the proposed approach, the reflecting surface is loaded with dielectric panels where the phase of the reflected field is controlled using continuous metal strips of variable widths. This solution is enabled by the cylindrical symmetry and, with respect to rectangular patches or to other discrete antennas, it provides increased gain. The proposed concept has been evaluated by designing a Ka-band antenna operating in the Rx SatCom band (19-21 GHz). A prototype has been designed and the results compared with the ones of a parabolic cylindrical reflector using the same feed architecture. Simulated results have shown how this type of antenna can provide higher gain in comparison to the parabolic counterpart, reaching a radiation efficiency of 65%.  
reflectarray; Ka-band; reflector antennas +
Footnote †: journal: Electronics Letters
## 4 Development and Validation of the Platform
### Application Performance  
The evaluation of application performance for the Digital Twin prototype in immersive remote driving focuses on validating whether the platform can meet stringent operational requirements under real-world conditions. As teleoperation and feedback demand low-latency, high-throughput communication, a robust assessment is essential for determining the platform's viability in practical settings. This section introduces the core metrics and goals necessary for analyzing application performance, covering both the QoS and QoE parameters critical for remote driving applications.  
The QoS metrics aim to quantify the application's technical capabilities in terms of bitrate and latency. By setting specific measurement targets for each metric, we aim to simulate realistic usage scenarios and identify potential bottlenecks that could impact the user experience.  
In addition, QoE metrics capture user-centered performance indicators, emphasizing that the QoS offered by the platform can support real-time Digital Twin applications in terms of presence, engagement, control, sensory integration, and cognitive load. User  
Figure 6: Edge Site 1 network architecture for an immersive remote driving application, which includes two teleoperated vehicles leveraging 5G components and Edge servers for real-time control and feedback. The connectivity is provided through outdoor and indoor 5G bands (n40 and n78, respectively). The setup includes a Cloud Robotics Platform for processing the Digital Twin information and AI-driven applications for video-based environment detection, interfacing with Edge servers for lower-latency processing.  
feedback from real-world trials is integral to this analysis, as it highlights how well the platform aligns with user expectations and needs.
# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 3 Description of Our Method
### Feature Extraction
#### 3.2.4 Velocity Vector Regression Module  
The velocity vector regression module consists of two fully connected layers used to integrate abstract features extracted from the vibration signal matrix and output continuous velocity information. To mitigate the risk of overfitting, a dropout function is introduced in the fully connected layers, enhancing the model's generalization capability by randomly dropping neurons. Here, the dropout parameter [25] is set to 0.5.  
In the velocity vector regression process, the feature representation \(D_{9}\) is first flattened and used as input to the fully connected layers. The entire feature extraction module acts as function \(f_{\theta}\), optimizing network parameters through end-to-end training process, mapping the segmented matrix \(\mathbf{U}_{j}^{r}\) of the vibration signal to the velocity vector \((\vec{\mathbf{\sigma}}_{x}^{r},\vec{\mathbf{\sigma}}_{y}^{r})\) nonlinearly, as shown in Equation (10):  
\[\mathbf{U}_{j}^{r}\overset{f_{\theta}}{\rightarrow}(\vec{\mathbf{\sigma}}_{x }^{r},\vec{\mathbf{\sigma}}_{y}^{r}) \tag{10}\]
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 3 Reflector Antenna Configurations in Spaceborne SAR Systems
### Deployable Antenna
#### 3.1.1 Framework Deployable Antenna  
A framework deployable reflector antenna employs a spatial framework composed of interconnected rods and nodes, forming a grid-like structure. This configuration adopts a modular design approach [34], using elements such as tetrahedrons, quadrangular pyramids, hexagonal prisms, and hexagonal frusta. By adjusting the module quantity and size, apertures of various dimensions can be realized. Deployment is typically enabled through  
Figure 6: Deployable antenna of HJ-1-C [30]: (**a**) on-orbit folded state; (**b**) on-orbit deployed state.  
hinged trusses with spring-loaded mechanisms [35], offering a high stowage ratio (typically around 1:10), high surface precision, and stable deployment performance.  
Framework antennas provide high structural rigidity, lightweight design, and wideband capability [36]. Compared to other large deployable antenna types, they demonstrate superior deployment stiffness, excellent thermal stability, and scalability for on-orbit assembly [37].  
Key advantages of this configuration include simplified structural design, reliable deployment mechanisms, and accurate surface control. However, challenges remain, notably the complex assembly and calibration due to numerous rods and joints. Additionally, a non-uniform weight distribution can impact antenna pointing accuracy and satellite attitude stability.  
Despite these limitations, framework deployable antennas are often used to meet the requirements of large apertures and broadband. Representative examples include China's HJ-1-C SAR satellite (launched in 2012 under the \"2+1\" disaster monitoring constellation, as illustrated in Figure 7a), the Ku-band Qilu-1 SAR satellite (launched in 2021), and Russia's Kondor-FKA SAR satellite (illustrated in Figure 7b), all of which utilize framework deployable reflectors.
## 5 Topologies in NCSs
### Decentralized Topology  
This topology is shown in Figure 7. Each controller node depends only on the local information that the controller possesses for making its local decision. In this topology, the controller nodes do not share their information with the corresponding neighboring nodes. These types of topologies are helpful in reducing the time taken to analyze and synthesize the NCSs. Here, the controllers are decentralized controllers.  
Figure 6: Centralized topology.  
Figure 7: Decentralized topology.
## 2 Fingerprint Presentation Attack Detection and Interoperability  
### Fpad  
The threat of fingerprint spoofs has been known since 1998, the year of publication of the first paper that demonstrated the vulnerability of fingerprint sensors to artificial replicas [4]. A few years later, the first hardware and software countermeasures were proposed [5; 6]. The PAD software systems focus on anatomical, physiological, and textural properties and other features that can be extracted and used for matching purposes. As in all fields of pattern recognition, the feature extraction and image classification phases for the detection of presentation attacks have passed from hand-crafted approaches and shallow classifiers to the deep-learning era. Among the hand-crafted extraction methods are local descriptors such as SIFS, BSIF, LBP, and LPQ [7; 8]. These approaches use a binary code to characterize each pixel's neighborhood, which is derived by computing the image's convolution with a collection of linear filters and then binarizing the filter responses. The resulting feature vectors are often inputs to shallow classifiers such as SVM [9], but can be input to complex neural networks. Other deep learning techniques are often based on convolutional neural networks (CNN) [10]. They provide extremely high accuracy, but require many training data and a significant amount of time and resources. In recent years, the two types of techniques, hand crafted and deep learning-based, have been increasingly combined to overcome limitations such as the difficulty of modern PADs to generalize and the need for extensive computational resources. One example is Fingerprint SpoofBuster [11], which uses locally centered and aligned patches using fingerprint minutiae to train a MobileNet-v1 model.
## 3 Results and Discussion
### Gas Flow Rate  
The discharge plasma power does not depend only on the applied peak-to-peak voltage, but also on the gas flow rate. The applied peak-to-peak voltage is limited to 11.2 kV for our experiment. Figure 3a shows the influence of the flow rate on the discharge plasma power for the emerging jet of pure argon discharge: the flow rate increases from 0.2 to 3 slm, and the argon discharge plasma power increases from 1.95 to 2.4 W. By increasing the argon flow rate further to 4 slm, the discharge plasma power begins to decrease, from 2.31 to  
Figure 2: (a) Voltage waveform and current waveforms for (b) 1 slm argon gas, (c) admixture of argon 1 slm/0.25% oxygen, and (d) admixture of argon 1 slm/1.5% oxygen.  
1.7 W [26]. Figure 3b shows that for oxygen/argon admixtures (see Table 2), the discharge plasma power of the emerging jet decreases from 1.75 to 1.65 W, for oxygen admixture ratios increasing from 0.25% to 1.5% [27]. As the oxygen percentages increases, ions, atoms, and free radicals are produced due to inelastic collisions, leading to losses in the discharge process and lower power than in the case of argon discharges. This is useful in sterilization and inactivation because of the longer interaction between the jet and the sample.
## 3 Results
### Qualitative Validation
#### 3.2.2 Results  
The registration results of the two key stages in the calibration process are shown in Figure 15. The results of the initial offset matrix estimation using DOT matching are shown in the first column, and the results of the global parameter optimization of the offset matrix are shown in the second column. The results reveal that the optimization framework obviously improves the calibration accuracy compared with the DOT matching process. The proposed method has eliminated the trouble of double-image phenomenon in AR registration of texture-less and specular industrial parts by utilizing the proposed global gradient descriptor.
## 2 Hybrid Numerical Model
### Gas Dynamic Model  
In the present work, the compressible unsteady flow in the pipeline systems is simulated via a 1D gas dynamic numerical model. In fact, the flow in the pipe is thoroughly governed by compressible Navier-Stokes equations. However, they could be simplified to Euler equations since viscosity has been considered to have a minor influence on wave motion. In addition, due to the length of pipes being paramount compared to diameter, the propagation of pressure pulsation waves can be essentially assumed to be 1D. Furthermore, it is more realistic to involve the non-homentropic phenomena such as the variation of cross-sectional area and friction and heat transfer processes between gas and walls. To this end, a non-homogeneous hyperbolic system of the continuity, momentum and energy equations with a strong conservative arrangement is naturally given by the following equations [40]:  
\[\frac{\partial\mathbf{U}(z,t)}{\partial t}+\frac{\partial\mathbf{F}(\mathbf{U} )}{\partial z}+\mathbf{B}_{1}(\mathbf{U})+\mathbf{B}_{2}(\mathbf{U})=0 \tag{22}\]\[\mathbf{U}(z,t) =\left[\begin{array}{c}\rho A\\ \rho uA\\ \rho e_{0}A\end{array}\right],\ \ \ \mathbf{F}(\mathbf{U})=\left[\begin{array}{c}\rho uA\\ \left(\rho u^{2}+p\right)A\\ \rho u_{0}A\end{array}\right] \tag{23}\] \[\mathbf{B}_{1}(\mathbf{U}) =\left[\begin{array}{c}0\\ p\frac{dA}{dz}\\ 0\end{array}\right],\ \ \ \ \ \ \mathbf{B}_{2}(\mathbf{U})=\left[\begin{array}{c}0\\ \rho GA\\ -\rho qA\end{array}\right]\]  
where \(z\) is the spatial dimension; \(\mathbf{U}\) and \(\mathbf{F}\) are, respectively, the solution vector and flux vector; \(\mathbf{B}_{1}\) and \(\mathbf{B}_{2}\) are the source term vectors representing the respective contribution of area variation and that of heat transfer processes and friction; \(G\) and \(q\) denotes the respective friction term and heat transfer term; subscript \(0\) represents the stagnation state. The closure of the conversation system is performed by the equation of gas properties. Since the hyperbolic system cannot be solved analytically, it could be only computed by using numerical techniques.
# Integrating a LiDAR Sensor in a UAV Platform to Obtain a Georeferenced Point Cloud
## 2 Materials and Methods
### Data Collection and Processing Procedure Development
#### 2.2.5 Post-Processing  
Once the point cloud is obtained, the noise filter tool of the CloudCompare software is used. CloudCompare is an open-source project with many advanced techniques for point cloud registration and resampling. Python scripts are employed for georeferencing the point cloud and creating files in *.las format. The process consists in using the Clean tool and the Noise filter option. In the window tool, the Radius option is selected with a value of 0.03 for Neighbors, and for Max error, the relative option is chosen with a 1.00 value.  
Figure 14: Conversion of vectors to the UTM reference system.  
Figure 13: Point cloud georeferenced in the UAV reference system (x\({}^{\prime\prime},y^{\prime\prime},z^{\prime\prime}\)).  
The Remove isolated points option is also selected [34]. This process uses the tool to clean out the outliers, using an algorithm that adjusts planes locally (around each point in the cloud) and then eliminates the point if it is too far from the adjusted plane. To record and align the clouds generated based on the different trajectories, the alignment tool was used by selecting pair sets of points, through which two entities may be aligned by selecting at least three matching point pairs in both entities. Finally, the fusion tool generated a point cloud from all the strips. The filtering process allowed a more defined point cloud of the area, eliminating around 25% of points not calibrated with the point cloud, as shown in Figure 15. The georeferencing proposal is constrained by the quality of the measurements of the GNSS/INS sensors. The unique semi-automatic part of the georeferencing process is the location of each parallel path's start and end times. The extrapolation of the method in other study cases is associated with using correctly georeferenced control points.
## 5 Implementation Details Using MATLAB  
The MMTO algorithm discussed in this work is implemented using MATLAB 2023b, and the software code is freely accessible online (see Code Availability Statement). Specifically, the code is organized as follows:  
* main_stat.m is the main file of the project, containing all user-defined parameters of MMTO algorithm and case study. Once their are properly setup, main_stat.m calls three further scripts, namely initialize_mma.m, which contains the parameters of the MMA algorithm, start_mma.m, which solves the MMTO problem using the MMA algorithm, and plot_densities.m, which takes care of producing the figures displayed in this paper.
* The casestudy folder contains the code required to define the setup of the case study, including the definition of loads (defineLoads.m), the definition of supports (defineSupports.m), and the computation of the dynamic system matrices \(B\) and \(C\) (prepareDynamicSystemSS_BandC.m).
* The costandconstraints folder contains the code required to define the objective function and the constraints of the MMTO problem. Specifically, the function compliance_stat_mma.m calls two dedicated subroutines to evaluate the objective function and its derivatives (computeCostWithSensitivities.m), and evaluate constraint violations and their derivatives (computeConstraintsWithSensitivities.m) at the current guess for the MMTO problem.
* The filteringprojectioninterpolation folder contains the code implementing the filtering, projection and interpolation functions for Multi-Materials, as discussed in Section 2.
* The matrixnorms folder contains the code required to carry out the computation of tensor norms (pnormmat.m and pnormmatmenouno.m) and their derivatives (pnormmatsens.m), as discussed in Section 3.
* The stiffnesscomputations folder contains the code required to compute the stiffness matrix of the system, based on the desired finite element discretization, relying on the subroutines prepareStiffnessMatIdxs.m, prepareFEM.m and computeStiffnessMatrix.m.
* The MMA folder contains the code of the MMA algorithm, as made available by [23].
* The utils folder contains the computeDependentParams.m function, which computes additional parameters of the MMTO algorithm, based on the user-defined parameters set in main_stat.m.  
Thanks to its modularity, the proposed code structure allows the new user to successfully run the code by focusing on the main_stat.m file only. If required, a number of variations of the case studies discussed in this work can be straightforwardly obtained by implementing minor modifications to the subroutines contained in the casestudy folder. Furthermore, the code can be straightforwardly adjusted for use with any MATLAB optimization algorithm, by defining suitable objective and constraint functions based on computeCostWithSensitivities.m, and computeConstraintsWithSensitivities.m.
# Collaborative Filtering Model of Graph Neural Network Based on Random Walk
## 1 Introduction  
How users' int determine preferences are perceived and how much they engage with items usually determines the effectiveness of a recommender system (RS). Collaborative filtering is one of the most popular and well-known approaches in RS (CF) [1]. The model and memory-dependent approach has achieved some success. However, some problems still need to be solved: (1) In memory-based recommendations, the matrix sparsity problem arises when there are few user-item interactions. The method's performance decreases as the number of users and items increases. Users are less likely to be recommended if they have never interacted with an item. (2) In model-based recommendations, such as logistic regression (LR) [2], not only is feature crossover and feature filtering not possible, but the performance of the model representation could be better. Later, the researchers improved the model. While performance improved to some extent, it made the data sparser, making the model difficult to converge and increasing the feature weights and training cost. Later, factor decomposers (FM) [3] also faced problems such as combinatorial explosion and difficulty fusing higher-order features. The domain-aware factorization model (FFM) [4] further increased the training cost of the model, complicating parallel training and increasing training time. A recommendation method is proposed to solve these problems, called the graph convolutional collaborative filtering recommendation algorithm based on random wandering (RW-GCF). This method performs spectral convolution between spectral domains. It optimizes the adjacency matrix by random walking to find the hidden information connecting users and items in the graph. In addition, the new approach is to reduce the complexity of the model by optimizing the convolutional kernel, constructing a deep feedforward neural network, and using finite-order polynomials to adjust the size of each spectral domain dynamically, to discover user and item relevance and achieve mitigation of the user cold start problem in collaborative filtering. In addition, the randomwalk approach has also played an important role in other fields; for example, in the medical field, [PERSON] et al. [5] combined the unbalanced double random walk approach, constructed a virus-drug association (VDA) identification framework (VDA-rwIrls), and implemented unbalanced double random walks on the viral network and drug network, respectively, to find clues for the treatment of COVID-19, and made good progress. The authors of [6] applied a two-dimensional random walk Monte Carlo to perform calculations to better understand the spread of COVID-19, and the model designed can predict second and third-wave infections with reasonable accuracy, which helps in planning mitigation strategies during the current pandemic.
# Performance Analysis of Ambient Backscatter NOMA Systems
## 6 Conclusions  
This paper studied the impacts of RHIs and ipSIC on the performance of AmBC-NOMA systems in detail. The OP and asymptotic OP expressions of the users and the BD with ipSIC/pSIC were derived. The diversity orders of the users and the BD were obtained, utilizing asymptotic analysis, which equaled 0 for the users as well as the BD. It can be interpreted that the self-interference generated by ipSIC and backscatter links had become the main reason for limiting the further improvement of system performance in the high SNR interval. More particularly, the reliability of the user decreased as \(\beta\) increased. Simulation results demonstrated that the AmBC-NOMA systems exhibited superior outage behavior than the AmBC-OMA systems. The fairness of the NOMA system was also reflected in the lower OP that could be achieved by the far user. Finally, we discussed the AmBC-NOMA system's throughput and EE in delay-limited transmission mode. We observe that the AmBC-NOMA systems outperformed the AmBC-OMA system in terms of throughput and energy efficiency.  
Conceptualization, C.Z., X.Y., Y.Y., and X.L.; methodology, C.Z.; software, C.Z. and X.Y.; validation, C.Z.; formal analysis, C.Z. and Y.Y.; data curation, C.Z. and X.L.; writing--original draft preparation, C.Z., X.Y., Y.Y., and X.L.; writing--review and editing, C.Z. and X.Y.; visualization, C.Z.; supervision, X.H,L.; project administration, X.Y. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China: Grant 62071052; Project of Cultivation for young top-motch Talents of Beijing Municipal Institutions: Grant BPHR202203228; Beijing Natural Science Foundation: Grant L22004.  
Figure 8: EE at different hardware damage coefficients versus the transmitting SNR with \(P_{S}=5\) dBw and \(P_{UE,d}=10\) dBm.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.