esa-sceva/satcom-chunk-collection huggingface dataset:
# Constant-Beamwidth LCMV Beamformer with Rectangular Arrays
## 3 Conventional Beamformers
### CB Beamformer  
Constant-beamwidth (CB) beamformers guarantee a certain beamwidth around the desired direction that is constant over frequency. This is important to ensure the correct receiving of the desired signal, even if \(\theta_{\mathrm{d}}\) is not precisely calibrated.  
We define \(\theta_{\mathrm{B}}\) as the first-null beamwidth (FNBW), such that \(|\mathcal{B}(\mathbf{h},\mathbf{d})|>0\) if \(|\theta-\theta_{\mathrm{d}}|<\theta_{\mathrm{B}}/2\). That is, \(\theta_{\mathrm{B}}/2\) is the first angle in which a null of the beampattern occurs. A constant-beamwidth beamformer can be achieved using a window-based design technique [22]. Here, the Kaiser window is used [30], which can be written as  
\[\left[\mathbf{w}\right]_{m}=\frac{J_{0}\bigg{(}\beta\sqrt{1-\big{[}\frac{2m}{ M-1}-1\big{]}^{2}}\bigg{)}}{J_{0}(\beta)}, \tag{13}\]  
where \(J_{0}(\cdot)\) is the zero-order modified Bessel function of the first kind, and \(\beta(\omega)\) ([22], Section 3.3.2) is frequency-dependent to maintain the \(\theta_{\mathrm{B}}\) constant. This technique requires that the desired source signal impinges on the array from the broadside direction [22]. To satisfy the distortionless constraint, we normalize \(\mathbf{w}\), obtaining  
\[\mathbf{h}_{\mathrm{CB}}=\frac{\mathbf{w}}{\sum_{m=0}^{M-1}[\mathbf{w}]_{m}}. \tag{14}\]
# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
## 3 Proposed Method  
As mentioned above, the singular point detection rates of the existing methods are still unsatisfactory, especially for the low-quality fingerprint. Aiming at higher singular point detection rate, we propose a new singular point detection method, which regards singular point detection as a semantic segmentation of the small singular point area completely and directly. In our proposal, the main contribution lies in that we design a new customized convolutional neural network called SinNet which is used to segment the fingerprint singular point area directly and precisely. With the output of SinNet, a simple and fast post-processing called SimpleBlobDetector [31] is used to locate the corresponding singular points quickly.  
Therefore, our detection method consists of a main part and an auxiliary part: the former includes the detailed design of SinNet and how to use the newly designed SinNet to extract the small singular area which has dual channel structure, one branch for the core area detection and the other branch for the delta; and the latter is the existing blob detector named SimpleBlobDetector for singular point location. In the following, we shall introduce our proposal in details.
# Gaussian Distribution Model for Detecting Dangerous Operating Conditions in Industrial Fish Farming
## 3 Results
### Data Investigation of the Pre-Fattening Phase of Senegalese Sole Development  
The raw data set constituted of several parameters from the monitoring of the Senegalese sole fish farm and has a distribution not defined according to Figure 2a. After applying the PCA on these raw data, a new set of variables with well-defined Gaussian distribution (except for the small side tails) is obtained, as exhibited in Figure 2b. Subsequently, this new set of variables was divided into three groups according to fish mortality. The definition of the mortality threshold that describes each of these operating conditions depends on each installation. A large fish farm will have a mortality threshold definition that is different from a small one. In our case, we consider that the mortality rate is low when it is less than 2%. It is moderate when it is between 2% and 10%. And it is high when it exceeds 10%. Figure 3 displays these three groups, each of which describes an operation condition of the fish farm production. From top to bottom is a normal condition with a low mortality rate, followed by a warning condition in which the mortality rises above usual, and, finally, a dangerous condition characterized by high fish mortality rates. These results demonstrate that as the mortality increases, the standard deviation of the Gaussian distribution that represents the data in each of these three groups also grows. Further investigation reveals that there is a well-defined linear relationship between these two quantities, as exhibited in Figure 4a. In this way, an image that indicates the current state of the fish hatchery was designed based on these statistical analyses of Senegalese sole production monitoring data as exposed in Figure 4b.
# Mechanistic Insights into Fish Spoilage and Integrated Preservation Technologies
## 2 Mechanisms of Spoilage
### Microbial Spoilage  
#### 2.3.1 Initial Colonization  
Freshly harvested fish are immediately exposed to micro-organisms present in their aquatic environment and on processing surfaces. The initial colonizers are typically psychotrophic, Gram-negative bacteria--including Pseudomonas, Shewanella, Vibrio, Serra-atia, and Aeromonas--whose prevalence depends on water microbiota, handling practices, and onboard hygiene conditions [9; 44].
## 2 MOFs
### Most Useful Types of MOF for Wastewater
#### 2.1.3 UiO  
UiOs are generally defined as a class of MOFs that are constructed from a zirconium atom and a carboxylic acid as ligands [56]. Specifically, the unit cell consists of an octahedral arrangement formed by a Zr\({}_{6}\)O\({}_{4}\)(OH)\({}_{4}\) cluster that is surrounded by the aforementioned ligands [56]. It should be noted that a structural characteristic of these MOFs is that the cluster is always the same and the only factor that differentiates each type of UiO is the carboxylic acid used as a ligand [57].  
The best quality of this type of MOF is that they are the most stable in water even in acidic and basic conditions [58]. In addition to this, they also have properties such as a relatively uniform and large pore size and a specific surface area. The development of modified UiO capable of adsorbing heavy ions is of heightened interest since it is believed to be a reliable and promising solid adsorbent [59]. However, they are also becoming relevant as catalysts for the degradation of pollutants such as dyes or pharmaceuticals [60].  
The commonly used UiO classes in scientific works are UiO-66, UiO-67, UiO-68 and UiO-69 [61]. The most widely used among them is UiO-66, either directly or with modifications (Figure 1) [62]. Table 3 below gives some examples of the removal of pharmaceuticals and polycyclic aromatic hydrocarbons from aqueous media. It should be noticed that few works have been found using the MOF type UiO-67, UiO-68 and UiO-69, but its application was only for dye removal [63; 64], so they are not included in the table.
## 5 Conclusions and Future Perspectives
### Future Directions and Research Opportunities  
Several avenues for future research emerge from this study, namely the following:  
* Refine small-scale hydrodynamic modelling;
* Explore hybrid systems (wind + wave + solar);
* Assess socio-economic feasibility of marine energy technologies.  
Future research should investigate the economic feasibility of different marine energy technologies, considering installation costs, maintenance requirements, and long-term operational benefits. Furthermore, policy frameworks and financial incentives tailored to promote renewable energy adoption in ports must be analysed to support the large-scale implementation of sustainable energy solutions. Regulations for consenting processes must be streamlined to improve efficiency.  
As an example of the direct application of the methodology described in this article, it is worth noting that it has been applied in the preliminary feasibility studies for the implementation of two pilot actions in the ports of Sagunto and Valencia. These pilot projects, funded by IDAE (Spanish Institute for Diversification and Energy Saving) under the Next Generation Programme (Renmarinas Demos Initiative), aim to demonstrate the feasibility of integrating marine renewable energy into port infrastructure.  
The first pilot project involves the deployment of a wave energy converter (WEC) in the Port of Sagunto. This device, with an estimated capacity of 270 kW, will harness the kinetic and potential energy of ocean waves to generate electricity. The WEC selected for this case study is designed to operate in nearshore conditions, with a focus on modularity, the ease of maintenance, and environmental integration. The system is particularly relevant for ports with moderate to high wave energy potential, and its performance will help inform the broader application of wave energy in port infrastructures.  
The second pilot project is a \(1\) MW floating photovoltaic platform (Floating PV-BOS) to be installed in the Port of Valencia. This system consists of photovoltaic panels mounted on buoyant structures designed to float on water surfaces within the port. The platform aims to make use of underutilized water areas in the port basin while avoiding land-use conflicts. In addition to clean energy generation, the floating system offers secondary benefits such as reduced water evaporation and improved panel efficiency due to the cooling effect of the water body. Its integration into port energy systems is expected to demonstrate a scalable, replicable model for other ports aiming to increase renewable energy uptake without significant land requirements.  
Together, these two pilot devices provide a concrete context for the future implementation of the methodology and demonstrate its ability to evaluate different renewable technologies under port-specific environmental and operational conditions.
# RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot
## 3 Results
### Comparison Results in a Corridor Environment  
Experiment 3 was performed in a corridor.  
Figure 10(a) shows the experimental environment. It was a relatively regular rectangle, where the length was much larger than the width. There were doors at the end and sides of the corridor, but they all had a higher floor than the corridor; therefore, they were not represented in the robot's map. Compared with Experiment 2, the environment of Experiment 3 had fewer obstacles and the map structure was simpler. However, the long and narrow shape made most of the grids in the map contain straight lines, which brought different challenges for straight-line extraction.  
Figure 10(b) shows the grid map of the mobile robot. The size of each grid was 20 cm \(\times\) 20 cm.  
Figure 10(c) shows the line segments obtained using RM-Line, and Figure 10(d) shows the line segments obtained using CannyLine.  
Table 3 shows the comparison of two algorithms in experiment 3.  
As shown in Figure 11, in a map containing long straight lines, RM-Line extracted accurate line segments, adequately reflecting the straight-line information in the map. CannyLine also obtained regular line segments, but they were distributed on both sides  
Figure 11: The experiment in a corridor: (**a**) the environment; (**b**) grid map; (**c**) line segments obtained using RM-Line; (**d**) line segments obtained using Canny-Line.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average Dis (cm)** & **Number of Lines** & **Time (ms)** \\ \hline CannyLine & 20.8 & 17 & 60 \\ RM-Line & 1.6 & 8 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of two algorithms in experiment 3.  
of the real contours, which were not conducive for further data processing by the robot. RM-Line extracted eight straight lines from the grid map, while CannyLine extracted 17 straight lines. When these lines were used for map compression, the proposed RM-Line saved 52.9% more storage space than CannyLine.
## 3 Performance Analysis of the MPS Beamformer
### Performance of MPS Beamformer for Rectangular Arrays  
As the MPS beamformer searches for the radiation peaks other than the mainlobe, it is expected to work with rectangular planar arrays also. Figure 9a,b display the configuration and optimized radiation pattern for a rectangular array of \(10\times 20\) elements using the first two major sidelobe planes (i.e., \(P=2\) (\(M+N-4\))) at \(\beta=0.1\) to achieve \(-45\) dB SLL. The MPS beamformer converges to the required SLL with elliptic beam cross section due to the rectangular array configuration. The impact of rounded corners on the same optimized rectangular array has minimal effects as shown in Figure 9c,d for \(\delta_{T}=0.8\)\(\delta_{MAX}\) where the reduced sidelobes have slightly spread over wider angular ranges.
# DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation
## 4 Performance Analysis
### Experimental Setting  
We selected the AIS data of two waterways in order to predict the waterway depths for Nantong and Fangcheng ports. Through the integration of depth data acquisition and the AIS data, the data of waterway depth and AIS data of the two waterways were obtained as the data sources for this experiment. The total number of data points was 195,742, of which there were 137,006 training data points and 58,718 testing data points. The dataset was divided into training and testing datasets (70% of the data as a training data set, and 30% of the data as a testing data set). These data are used to train the DDTree model to predict the depth of waterway.  
Before training, the data were labeled. The data in the waterway area were divided into 25 categories at two-meter intervals from 0 to 50 m depth, and the 26 th label was used for depths \(>\)50 m. We chose this interval because most of the waterway depth data are around 30 to 40 m, and waterway depth data of less than 30 m and more than 50 m meters are limited.  
The hyperparameters of the model (depth of decision tree, activation function, iterations, epochs) were adjusted, and the prediction of waterway depths under different hyperparameter values was recorded. After setting the parameters, the optimal parameters were selected, and the depth data were predicted by the DDTree model and compared with the actual waterway depth (ground truth). Using the model on the test data, the prediction accuracy and the error of the model were calculated.  
The input data were classified using a decision-tree classification algorithm. The input parameters were six ship attributes of the dynamic data portion of the AIS data, including speed over ground, ship width, course over ground, draft, and ship steering rate. The processing times of the actual prediction of depth in the Nantong Port and Fangcheng Port using the trained model were 279 s, and 468 s respectively. Table 2 presents the prediction performance to evaluate the decision-tree parameters of the DDTree combination model in relation to the ground truth waterway-depth data. The results we contained with the baseline model, which used a traditional method of data interpolation.  
Table 2 reveals that the DDTree model prediction accuracy was the highest when the value of k was six, the depth of the decision tree was five, the iteration step was 200, and the epoch was 600. After selecting the appropriate neural network parameters, the waterway depth information of the Nantong Port and Fangcheng Port was predicted and analyzed by the model.  
Figure 6: Density map of ship positions in the Nantong Port AIS dataset.  
We can see that the proposed model has accurate prediction, and there is an error in the depth data of individual shallows and other locations. The overall prediction accuracy is relatively high and can accurately reflect the distribution of waterway depth in Nantong Port.
# Determination of pH in Powdered Concrete Samples or in Suspension
## 2 Materials and Methods
### Interdigitated Electrodes-Based Platform  
An interdigitated electrodes-based platform was fabricated on a PVC foil substrate using xurographically engraved gold (Ag) electrodes in the form of planar interdigitated capacitors (IDC), with either 4 or 5 fingers, as illustrated in Figure 1(a). The width of these electrodes, including the gap between them, was 2 mm. The total dimensions of both IDC structures, with either 4 or 5 fingers, were 1.5 cm \(\times\) 2.5 cm. Figure 1(b) displays this manufactured multi-layered structure after lamination.  
The working principle of this structure is based on changing permittivity among the fingers of the interdigitated capacitive structure when the concrete sample in powder form is placed on the top of the device. The pH value of powdered concrete has influence on permittivity and this will change the capacitance which is measured at the terminals. The size and dimensions of this structure were determined to be robust enough from one side and from another side to ensure the appropriate range of the capacitance variation which results in adequate sensitivity.  
The reason for fabricating and using IDC-based structure is to cover both the possibility to analyse concrete samples in suspension (liquid form), using the already-presented microfluidics-based platform in Section 2.1, as well as to analyse concrete samples in powder form, which is more useful for applications on-site, where it is difficult to make suspensions, but it is easy to make powder from the concrete structure in some object.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 2 Related Work  
MEC and NFV are complementary frameworks, defined in many quite complex ETSI specifications. For the sake of clarity and completeness, this section begins by summarizing the most relevant MEC and NFV concepts and highlights their common aspects and significant differences.
## 2 Main Result
### Robustness Lyapunov Analysis for Error System
#### 2.5.1 Upper Bounding \(-\frac{1}{2}e_{x}^{\dagger}Q_{x}e_{x}\)  
In order to find the upper bound of \(-\frac{1}{2}e_{x}^{\dagger}Q_{x}e_{x}\), first consider bounding the Lyapunov function in Equation (12) using Sylvester's Inequality:  
\[\frac{\lambda_{\min}(P_{x})}{2}\|e_{x}\|^{2}\leq V_{e}=\frac{1}{2}e_{x}^{ \dagger}P_{x}e_{x}\leq\frac{\lambda_{\max}(P_{x})}{2}\|e_{x}\|^{2}, \tag{22}\]  
where \(\{\lambda_{\min}(P_{x}),\lambda_{\max}(P_{x})\}\) are the minimum and maximum eigenvalues of matrix \(P_{x}\), respectively, and \(\|e_{x}\|\) is the norm of the internal state error \((e_{x})\). Continuing, \(V_{e}\) can be upper-bounded via:  
\[V_{e}\leq\frac{\lambda_{\max}(P_{x})}{2}\|e_{x}\|^{2}. \tag{23}\]  
From Equation (23), \(\|e_{x}\|^{2}\) can be solved for:  
\[\frac{2V_{e}}{\lambda_{\max}(P_{x})}\leq\|e_{x}\|^{2}. \tag{24}\]  
Moving forward, consider using Sylvester's inequality to bound the \(-\frac{1}{2}e_{x}^{\dagger}Q_{x}e_{x}\) term from Equation (21):  
\[-\frac{\lambda_{\min}(Q_{x})}{2}\|e_{x}\|^{2}\geq-\frac{1}{2}e_{x}^{\dagger}Q _{x}e_{x}\geq-\frac{\lambda_{\max}(Q_{x})}{2}\|e_{x}\|^{2}, \tag{25}\]  
where \(\{\lambda_{\min}(Q_{x}),\lambda_{\max}(Q_{x})\}\) are the minimum and maximum eigenvalues of matrix \(Q_{x}\), respectively. Therefore, the upper-bound of Equation (25) follows:  
\[-\frac{1}{2}e_{x}^{\dagger}Q_{x}e_{x}\leq-\frac{\lambda_{\min}(Q_{x})}{2}\|e_ {x}\|^{2}. \tag{26}\]  
Substituting the \(\|e_{x}\|^{2}\) term from Equation (24) into Equation (26) results in the following:  
\[-\frac{1}{2}e_{x}^{\dagger}Q_{x}e_{x} \leq-\frac{\lambda_{\min}(Q_{x})}{2}\underbrace{\frac{2V_{e}}{ \lambda_{\max}(P_{x})}}_{=\|e_{x}\|^{2}} \tag{27}\] \[\leq-\underbrace{\frac{\lambda_{\min}(Q_{x})}{\lambda_{\max}(P_{ x})}}_{=2\mu>0}V_{e},\]  
where \(a\geq\frac{\lambda_{\min}(Q_{x})}{2\lambda_{\max}(P_{x})}>0\) and is a scalar. Substituting Equation (27) into Equation (21),  
\[\dot{V}_{eL}\leq-2 aV_{e}+(P_{x}e_{x},v)-a\operatorname{tr}(L\gamma_{y}^{-1} \Delta L^{\dagger}), \tag{28}\]  
results in an updated closed-loop energy-like time rate of change for the error system.
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 4 Key Techniques for Voxel-Based Navigation
### Voxel Storage and Management  
Voxel data structure plays a crucial role in 3D spatial representation, especially when dealing with complex scenes. However, due to the large number of voxels, especially in large-scale environments, storing and managing voxel data becomes challenging. To address these challenges, researchers have proposed various voxel data structures. We provide an overview of voxel structures based on the frequency of data structure updates, dividing them into static and dynamic grids.
## 4 Future Research Challenges
### Small Cell  
As the use of a high-frequency band in the current 5G network increases, the utilization of small cell deployment is a mandatory approach to serve a higher number of subscribers [256]. However, the existing spectrum allocation algorithms are insufficient to deliver optimum spectrum allocation efficiency in the small cell network [257]. Therefore, an efficient algorithm, such as K-Nearest Neighbor (KNN) learning algorithm, can be used to classify all the small cells according to their geographic locations and interference radius; thus, the spectrum allocation efficiency can be improved [258].  
Additionally, for a limited backhaul capacity network, some efficient spectrum allocation solution is required to provide significant performance improvement in throughput enhancement, delay reduction, and energy savings for small-cell networks [259]. The switching between small cells and Wi-Fi remains a challenging task that is being explored in the latest 3 GPP releases [260]. Furthermore, in small cell-based HetNet, which consists of multilayers with a shared spectrum, a dynamic spectrum and multicell logarithmic resource allocation algorithm are required [261]. The utilization of various new approaches, such as block-aware power allocation, efficient relay selection, and cooperative caching algorithms, must also be developed to deliver the optimum results for the current 5G network [262; 263]. The BS in HetNet is experiencing a seamless switching between different technologies, such as Wi-Fi and cellular. This continuous switching makes the network suffer from negative parameters, such as intercell interference, SNR, fading, and downstream power. Hence, an optimal solution for resource and power allocation using a feed-forward neural network approach can be implemented for the stability of the network [264]. Similarly, traffic offloading is a critical issue in multitier HetNet; therefore, an autonomous traffic offloading technique based on machine learning is required to reduce transmission delay [265]. Moreover, an efficient design for some new AI-based clustering approach can manage the resource framework while enhancing the efficiency and throughput of the small cell [266]. To improve coverage, AI-based optimization approaches are required, especially for software-defined networking controllers [267].
## 2 Design of the 3.7 kW WCS
### Design of the Input and Output Electrical Modules
#### 2.1.3 Design of Inverter  
The 425V DC voltage from PFC needs to be inverted to 85 kHz AC voltage. The single-phase full-bridge inverter is selected as shown in Figure 2, which contains four mosfets (S\({}_{2}\), S\({}_{3}\), S\({}_{4}\), S\({}_{5}\)). The full-bridge inverter owns higher efficiency than half-bridge inverter. In addition, these mosfets need extra reliable drive circuit to send reliable signal.