esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards
###### Abstract
If correctly applied, the abovementioned sensor allocation study leads to better allocation of resources minimizing the costs, while maximizing the efficiency of the system's operation. Precision agriculture applications and technologies can be particularly handy in field topography mapping and management zones delineation, which can be used for better allocation of sensor nodes. These management zones should ideally be temporally stable. This can be achieved if these zones are delineated based on parameters that show low temporal variability, such as landscape and soil properties [12]. Soil electrical conductivity (EC) is a measurable soil parameter that is well corelated to soil properties such as texture, organic matter, soil moisture content and others. Furthermore, with the rise of the concept of precision agriculture, several commercial sensors were made available making EC a very popular candidate for delineating soil management zones [12; 13; 14]. Numerous machine learning algorithms are used in agriculture for different operations [15], with field classification being one of them. One of the most popular classification methods for delineation of management zones is fuzzy clustering. It is an explorative data analysis technique that has been successfully used for delineating management zones based on soil electrical conductivity (EC) and soil parameters, in the framework of precision agriculture [12; 13; 14]. Another basic constraint in the use of wireless sensing systems in agriculture is the fact that the end users of such systems, the farmers, are not particularly skilled with electronics, programming, and sensing systems [16]. Therefore, the systems need to be simple in installation and usage, offering a plug and play experience to the farmers. Based on the literature review, the main gaps presented in the field of wireless sensing systems that make them non-viable for agricultural use are related to cost, complexity, reliability, energy consumption and energy dependence. In this work, a low cost, low power consumption, and simple wireless sensing system dedicated for agricultural environments, is presented. The system architecture selected for this work was simple and straightforward to avoid complications in the operation, increasing the reliability of the network. The main goal was to develop a light wireless system architecture that can be applied to small-medium sized fields, located anywhere with cellular network coverage, even in isolated rural areas. The developed system was evaluated in laboratory and in field conditions in a commercial orchard where smart farming technologies were utilized for better allocation of sensor nodes.
## 3 Materials and Methods
### Identifying PVI Location  
A typical CV tag-based system to identify PVI location consists of tags, a database, a camera, a processing unit, and the use of audio and haptic feedback [41]. Identifying markers is the most crucial part of our system. They help find the destination successfully, as long as they are identified accurately. So, the authors put together the following three research questions.  
RQ1: Which tag gives better accuracy based on the chosen criteria?  
RQ2: Are there any difficulties or problems with identifying tags?  
RQ3: Is this solution suitable for real-time usage?  
To answer these questions, the authors have divided the implementation of this system into three steps:  
1. Comparing QR code with Aruco markers to select which tag is the best.
2. Solving the problem of detecting markers in challenging conditions using the convolutional neural network (CNN) model.
3. Simplifying the CNN model to minimize the response time.
# Comparison of Dual-Combustion Ramjet and Scramjet Performances Considering Combustion Efficiency
###### Abstract  
The performances of a dual-combustion ramjet (DCR) and a scramjet were compared via computational fluid dynamics numerical simulation to provide theoretical guidance for engine selection for a hypersonic vehicle. Kerosene, C\({}_{12}\)H\({}_{23}\), with an equivalence ratio of 0.8, was employed as the fuel, and the reactive flow was modeled using six-species and four-step chemistry. The results show that the DCR has a central combustion mode, which has a smaller temperature gradient and more uniform heat release, resulting in higher combustion efficiency, compared to the near-wall combustion mode of the scramjet. The total pressure recovery coefficient of scramjet is 0.9% lower than that of DCR under the Ma6 condition, but 5.6% higher than that of DCR under the Ma7 condition. The combustion efficiency of DCR is 35.6% and 25.4% higher than that of the scramjet under Ma6 and Ma7 conditions, respectively. The decrease in the combustion efficiency of the DCR is caused by the increase in the dissociation rate of CO\({}_{2}\) into CO with the increase in temperature. The performance of DCR is better than that of scramjet under both conditions. However, the performance advantage of DCR decreases as the Mach number increases. Specifically, under the conditions of Ma6 and Ma7, the specific impulse or specific thrust of DCR was 2.67 times and 1.51 times that of scramjet, respectively.  
Keywords:dual-combustion ramjet; scramjet; combustion mode; combustion efficiency; specific impulse +
Footnote †: journal: Applied Sciences
# Application of the Improved Entry and Exit Method in Slope Reliability Analysis
## 2 Random Field Modeling of Spatially Variable Soil Properties  
To conduct slope reliability analysis, it is essential to characterize soil spatial variability, often modeled applying random field theory. The autocorrelation function adopted in this research is as follows [6]:  
\[\rho(x,y)=\exp\left(-\left[\left(\frac{x-x^{\prime}}{l_{x}}\right)^{2}+\left( \frac{y-y^{\prime}}{l_{y}}\right)^{2}\right]\right) \tag{1}\]  
where (\(x\), \(y\)) and (\(x^{\prime}\), \(y^{\prime}\)) are two random points, and the autocorrelation distances are represented as \(l_{x}\) in the horizontal direction and \(l_{y}\) in the vertical direction.  
In this research, under Karhunen-Loeve (KL) expansion [9; 32; 33], the lognormal random field is shown as  
\[H(\mathbf{x},\theta)=\exp\left(\mu_{\text{In}}+\sum_{i=1}^{M}\sigma_{\text{In}} \sqrt{\lambda_{i}}\varphi_{i}(\mathbf{x})\chi_{i}(\theta)\right) \tag{2}\]  
where \(\mu_{\text{In}}=\ln\mu-(\sigma_{\text{In}})^{2}/2\) is the mean of \(\ln(H)\) and \(\sigma_{\text{In}}=(\ln(1+(\sigma/\mu)^{2}))^{0.5}\) is the standard deviation of \(\ln(H)\), \(\chi_{i}(\theta)\) is a set of orthogonal random coefficients (uncorrelated random variables with zero mean and unit variance), and \(\varphi_{i}(\mathbf{x})\) and \(\lambda_{i}\) are the characteristic values and characteristic functions of the autocorrelation function. The value of \(M\), which represents the number of truncated terms, is closely tied to the desired precision level. However, the level of accuracy is achieved through \(\varepsilon\) to quantify \(\varepsilon\), defined as  
\[\varepsilon=\sum_{i=1}^{M}\lambda_{i}\bigg{/}\sum_{i=1}^{\infty}\lambda_{i} \tag{3}\]  
In slope reliability analysis, cohesion (\(c\)) and friction angle (\(\varphi\)) are commonly regarded as uncertain geotechnical coefficients. The random fields (regarding \(c\) and \(\varphi\)) produced applying the KL expansion method can be represented as follows [6]:  
\[H_{c}(\mathbf{x},\theta)=\exp\left(\mu_{\text{In}\,c}+\sum_{i=1}^{M}\sigma_{\text{ In}\,c}\sqrt{\lambda_{i}}\varphi_{i}(x)\chi_{ci}(\theta)\right) \tag{4}\]  
\[H_{\varphi}(\mathbf{x},\theta)=\exp\left[\mu_{\text{In}\,\varphi}+\sum_{i=1}^{M} \sigma_{\text{In}\,\varphi}\sqrt{\lambda_{i}}\varphi_{i}(\mathbf{x})\left(\chi_{ ci}(\theta)\cdot\rho_{c,\varphi}+\chi_{\varphi i}(\theta)\cdot\sqrt{1-\rho^{2} _{c,\varphi}}\right)\right] \tag{5}\]  
where \(\chi_{ci}(\theta)\) and \(\chi_{\varphi i}(\theta)\) are two independent vectors, and \(\rho_{c,\varphi}\) means the cross-association parameter between \(c\) and \(\varphi\).
# Robust Template-Based Watermarking for DIBR 3D Images
## 2 Background
### DIBR Process  
The whole DIBR process is shown in Figure 2. DIBR consists of three steps: depth image preprocessing, pixel location warping and hole-filling.  
Depth image preprocessing, the first step, improves the quality of the rendered image by reducing the number of holes [32; 33; 34]. When the viewpoint is moved by the DIBR, an area where no pixel information exists is generated. These areas, referred to as holes, are the main cause of 3D image quality degradation. Holes occur mainly when the depth difference between two adjacent pixels is large. Hence, the image quality can be improved by reducing the number of holes through depth image smoothing.  
Figure 1: Watermark extraction scenario in a DIBR image with geometric attack. Extraction is possible only if the watermark survives both in the DIBR, which is a non-linear attack, and the geometric distortion, which is a linear attack.  
Pixel location warping, the second step, changes the position of pixels along the horizontal direction, allowing users to feel a 3D effect. The warping equation is as follows,  
\[x_{L}=x_{C}+\left(\frac{t_{x}}{2}\times\frac{f}{Z}\right),\quad x_{R}=x_{C}- \left(\frac{t_{x}}{2}\times\frac{f}{Z}\right) \tag{1}\]  
where \(x_{C}\) is the pixel position on the \(x\)-axis of the center image, \(x_{L}\) and \(x_{R}\) are the pixel positions on the \(x\)-axis of the left view and the right view, respectively, \(t_{x}\) is the baseline distance, which means the distance from the center axis to the left and right, \(f\) is the focal length and \(Z\) is the value of the depth image. During warping, two or more pixels can overlap in one position. In this situation, the highest \(Z\) value of a pixel has to be selected to prevent an unnatural image.  
The last step is hole-filling, which creates pixel values in holes caused by pixel location warping. There are several hole-filling techniques, such as interpolation and inpainting. This is a field that is constantly studied in pursuit of a more natural image [4; 35; 36].
## 2 Manufacturing Processes of Musical Instruments Made of Composites and Alternative Materials
### Metamaterials in Musical Instrument Manufacturing  
Metamaterials, designed to exhibit unique acoustic properties, not found in nature, often display distinctive transmission spectra influenced by their specific geometric and material configurations. Analysis of the recorded acoustic signal provides information about acoustic band gaps, resonance frequencies, and other key features contributing to the metamaterial's ability to manipulate and control sound propagation. In the literature a comprehensive definition of acoustic metamaterials hinges on the concept of utilizing purposefully designed internal structures, as opposed to inherent material properties, for the manipulation and control of acoustic fields and waves within fluids [6]. Mechanical metamaterials constitute another subset of architected materials, characterized by unparalleled mechanical properties or functionalities. Comparable to acoustic metamaterials, these distinctive attributes arise from the synergistic interaction between the deliberate design of their 2D or 3D (micro)architectures and the materials they are composed of [6].  
The incorporation of metamaterials in musical instruments offers the potential to significantly alter the instrument's sound. Modifying existing instrument geometries introduces additional band gaps in their spectrum, and the strategic utilization of multiple such band gaps allows for the intentional shaping of a designed sound. In the literature, metamaterial structures have been made by attaching a ring of masses on a drum membrane [55], leading to a cloaking behavior of vibrations from within the ring into the area outside the ring and vice versa. In a similar study [56], tubes and bars attached to a plain PLA plate resonated at discrete frequencies. The vibrations in the bars were damped due to internal damping, leading to bandgaps in the radiation spectrum of the entire plate occurring at these discrete bar frequencies. To generate metamaterial structures on the top plate of a guitar [57], the soundboard underwent modifications by incorporating a pattern of elliptical holes on the underside. Two types of holes were implemented based on their orientation. When the long side was parallel to the longitudinal axis of the soundboard's wood, they were designated as longitudinal holes, while alignment with the radial axis led to the designation of radial holes. The results indicated that these metamaterials effectively tuned the instrument's response without compromising its structural integrity.  
Vibro-Acoustic Analysis and Assessment of Musical Instruments Made of Composites and Alternative Materials  
The experimental and numerical methods used for vibration and acoustic analysis retrieved from the literature will be presented, as well as studies for vibro-acoustic analysis of composite musical instruments, 3D-printed musical instruments and metamaterials used in musical instruments.
## 8 Discussion and Conclusions
Fewer than 20% of reviewed papers report radiologist or neuropathologist agreement on synthetic images. Future research should incorporate double-blind reader studies where experts rate synthetic vs. real scans, and correlate GAN/diffusion outputs with biomarker levels or longitudinal outcomes. To bridge the remaining gaps, future work should prioritize the development of more interpretable and adaptable generative architectures. Which could make generative models even more effective and easier to use. Transfer learning is one promising strategy. By fine-tuning models that were trained on large datasets, researchers can adapt them to new patient groups or imaging modalities with relatively little data. Integrating explainable AI (XAI) methodologies, including Grad-CAM, SHAP or visualization tools, to help clinicians understand and trust what the model is doing. Emerging approaches like federated learning allow hospitals to train models collaboratively without sharing patient data, preserving privacy. Graph-based generative models (e.g., BG-GAN) also show promise in capturing the complex progression of Alzheimer's. Together, these advances could make generative tools more accurate, scalable, and clinically trustworthy. Through a comprehensive critical evaluation of the strengths and limitations of current generative methods, this review advances the state of the art by not merely summarizing existing work, but clearly outlining targeted recommendations and directions for future research. It offers a solid framework for developing next-generation diagnostic tools that are not only accurate and efficient but also interpretable, clinically practical, and ethically responsible. In doing so, it bridges technical innovation with the broader goal of enhancing early diagnosis and long-term care for individuals living with Alzheimer's disease. Conceptualization, M.M.A. and S.L. ; methodology, M.M.A. ; software, M.M.A. ; validation, M.M.A. and S.L. ; formal analysis, M.M.A. ; investigation M.M.A. ; resources, M.M.A. ; writing--original draft preparation, M.M.A. ; writing--review and editing, M.M.A. and S.L. ; visual ization, M.M.A. and S.L. ; supervision, S.L. ; project administration, S.L. ; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. No data was created, and no specific dataset was used. The pertinent information is properly cited throughout the manuscript. The authors declare no conflicts of interest.
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 3 Dynamic Model of the Transmission System
### Derivation of the Motion Differential Equations
#### 3.3.1 Motion Differential Equation for the Sun Gear, \(a\)  
The bearing support force acting on the sun gear, \(a\), is as follows:  
\[\left\{\begin{array}{l}F_{bxa}=c_{bxa}\dot{x}_{a}+k_{bxa}x_{a}\\ F_{bya}=c_{bya}\dot{y}_{a}+k_{bya}y_{a}\\ F_{bza}=c_{bza}\dot{z}_{a}+k_{bza}z_{a}\\ M_{bzaza}=c_{bza}\dot{\theta}_{za}+k_{bza}\theta_{za}\end{array}\right., \tag{8}\]  
where \(k_{bza}\), \(k_{bas}\), \(k_{za}\), and \(k_{bza}\) represent the support stiffnesses of the bearing in the respective directions, and \(c_{bza}\), \(c_{bza}\), \(c_{bza}\), and \(c_{bza}\) represent the support dampings of the bearing.  
The support force of the shaft acting on the sun gear, \(a\), is as follows:  
\[\left\{\begin{array}{l}F_{xas}=c_{xas}(\dot{x}_{a}-\dot{x}_{s})+k_{xas}(x_{a }-x_{s})\\ F_{yas}=c_{yas}(\dot{y}_{a}-\dot{y}_{s})+k_{yas}(y_{a}-y_{s})\\ F_{zas}=c_{zas}(\dot{z}_{a}-\dot{z}_{s})+k_{zas}(z_{a}-z_{s})\\ M_{bzaza}=c_{bzaza}(\dot{\theta}_{za}-\dot{\theta}_{zs})+k_{bza}(\theta_{za}- \theta_{zs})\end{array}\right., \tag{9}\]  
where \(k_{xas}\), \(k_{yas}\), \(k_{za}\), and \(k_{bza}\) represent the support stiffnesses of the shaft in the respective directions, and \(c_{zas}\), \(c_{yas}\), \(c_{zas}\), and \(c_{bza}\) represent the support dampings.  
The force acting on the sun gear, \(a\), as a result of its mesh with the planet gear, \(mi\), is as follows:  
\[\left\{\begin{array}{l}F_{amiL}=k_{amiL}\delta_{amiL}+c_{amiL}\dot{\delta}_{ amiL}\\ F_{amiR}=k_{amiR}\delta_{amiR}+c_{amiR}\dot{\delta}_{amiR}\end{array}\right., \tag{10}\]  
where \(k_{amiL}\) and \(k_{amiR}\) represent the mesh stiffnesses of the left and right gear pairs between the sun gears, \(a\), and the planet gears, \(m\), respectively; and \(c_{amiL}\) and \(c_{amiR}\) represent the mesh dampings.  
The motion differential equation for the sun gear, \(a\), is as follows:  
\[\left\{\begin{array}{l}m_{a}\ddot{x}_{a}+\sum\limits_{i=1}^{N}(F_{emiL}+F_{amiR}) \cos\beta_{b}\sin\psi_{ami}+F_{bxa}+F_{xus}=0\\ m_{a}\ddot{y}_{a}+\sum\limits_{i=1}^{N}(F_{amiL}+F_{amiR})\cos\beta_{b}\cos\psi_{ami }+F_{bya}+F_{yas}=0\\ m_{a}\ddot{z}_{a}-\sum\limits_{i=1}^{N}(F_{amiL}-F_{amiR})\sin\beta_{b}+F_{bza}+F _{zas}=0\\ J_{a}\ddot{\theta}_{za}+\sum\limits_{i=1}^{N}(F_{amiL}+F_{amiR})r_{ba}\cos\beta_ {b}+M_{b\thetaza}+M_{\theta zas}=T_{in}\end{array}\right., \tag{11}\]
# Evaluating the Impact of Optical Interconnects on a Multi-Chip Machine-Learning Architecture
## 2 Background and Motivation
### DaDianNao  
DaDianNao [7] is a custom multi-chip machine-learning architecture proposed to follow trends emphasizing neural networks, such as CNN, characterized by their large size. It achieves much higher performance and energy efficiency compared to existing systems (e.g., GPU) by maximizing parallelism and fully mapping total memory footprints from _neurons_ (nodes in a neural network)and _synapses_ (weights of edges between nodes) to on-chip embedded DRAMs (eDRAM) that require no main-memory access (a major performance/energy bottleneck). Therefore, the number of chips required is determined by the size of the neural network model and input data (e.g., multiple input images forming a batch and concurrently propagating through an image recognition CNN). In Figure 1, we sketched the accelerator chip and its cluster (multi-chip) architecture specified in [7]. A chip has 16 tiles, a central eDRAM (4 MB), fat-tree on-chip interconnect, router fabric, and four physical layers (PHYs) for electrically connecting to other chips. Each tile contains computational logic, a SRAM neuron buffer (16 KB), a local eDRAM (2 MB), and input/output interfaces to the central eDRAM. For PHYs, a commercially available HyperTransport (HT) 2.0 IP block is implemented. Each PHY has bidirectional ports and provides a bandwidth of 25.6 Gbps in each direction with 16 pairs of differential links. The cluster architecture (see the right hand side of Figure 1) is a set of chips arranged in 2D-mesh topology.
## 4 Experimental Validation of STST Based on TVCM-VPF
### Method and System of STST Based on TVC-VPF  
#### 4.2.1 Strategy of STST  
When conducting the STST, the TLD was chosen as the ES loaded with one shaking table, and the three-story frame structure was regarded as the NS. The two substructures interacted at the boundary in terms of the equilibrium of interface shear forces and the compatibility of interface deformations. The STST block diagram of the three-story frame structure with the TLD is depicted in Figure 5. The system mainly includes a real-time calculation system, controller, shaking table, TLD specimen, and data acquisition computer. The real-time calculation system conducts a step-by-step numerical calculation, that is, the proposed explicit CDM on the NS and the TVC feedforward to generate the displacement command for the controller of the shaking table system. This embedded controller generates and drives the shaking table to track the desired displacement. The data acquisition computer collects the displacement and acceleration responses of the shaking table for the three-variable controller and measures the shear force and acceleration from the specimen for a real-time calculation system.  
The block diagram of STST using the proposed explicit CDM for the three-story frame structure with TLD is depicted in Figures 5 and 6. The experiments in this study were conducted with a \(3\times 4\) m shaking table at the Structural and Seismic Testing Center, Harbin Institute of Technology. This old shaking table is mainly composed of a Schenck servo-hydraulic actuator, a hydraulic power unit, and an MTS controller. This table was originally designed in the displacement control mode with a traditional PID controller. In contrast to existing research on STST using electromagnetic shaking tables, this study was performed on a considerably large and old hydraulic shaking table, which implies that this study was extremely challenging to conduct; therefore, this method can be viewed to have realistic applications. The NS and the realization of the TVCM-VPF were programed and calculated by the Calculation Editor module of the MTS FlexTest GT controller software. The sampling and calculation frequency of the calculation editor module is 2048 Hz. The parameter values of the TVCM-VPF in STST are presented in Table 1.  
Figure 5: Block diagram of shaking table substructure testing (STST) for three-story frame structure with a tuned liquid damper (TLD).