esa-sceva/satcom-chunk-collection huggingface dataset:
# Risk Management for the Reliability of Robotic Assisted Treatment of Non-resectable Liver Tumors
## 2 Materials and Methods
### Definition of the Medical Task  
The first step in the risk management process (see Figure 1) is to define the intended use and safety characteristics, i.e., the medical task. The proposed therapeutic procedure aims to achieve an efficient and reliable treatment (which minimizes the risk) of non-resectable HCC tumors by taking into account all the existing medical and technical constraints, focusing on three targeted treatment options: HDR brachytherapy (using, e.g., a 1.6 mm gauge needle), intratumoral chemotherapy (using, e.g., a 1.6 mm gauge needle), and RFA (using, e.g., a 2 mm gauge needle). All these procedures are performed percutaneously by inserting a specific needle through the skin, on a linear trajectory, inside the tumor. In order to enable safe and accurate needle positioning, the authors propose the use of an intraoperative ultrasound probe that can monitor in real time the needle placement. Thus, the procedure has three main stages:  
1. Preoperative: the patient is investigated using non-invasive molecular imaging techniques that determine the tumor location and characteristics (size, density, proximity to blood vessels), the most efficient treatment option, and possible safe needle trajectories;
2. Intraoperative: the therapy is performed in the operating room, by surgeons, using a total of three medical instruments [13]: (1) an endoscopic camera, guided manually by a surgeon, that enables the fast transfer of the intraoperative ultrasound (I-US) probe in the targeted area of the liver and the continuous evaluation of the surgical field; (2) the I-US probe, guided by a robotic arm, used to locate the tumor and monitor in real time the needle insertion in the liver parenchyma; (3) the therapeutic needle(s), guided by a second robotic arm used to insert the needle on a linear safe trajectory from outside the body into the live tumor;
3. Postoperative: the patient evolution is monitored by assessing the procedure results and the patient evolution.  
The procedure takes place in an operating room (Figure 2) where the necessary equipment is provided: an operating table adjustable on three axes, a vital signals monitoring system (respiration, cardiac rhythm), and anesthesiology equipment. On the lateral side the laparoscopic tower is positioned, and in its proximity, the ultrasound tower.
## 2 Materials and Methods
### Electrophysiological Processing
#### 2.6.3 Artifact Rejection  
Although artifact minimization is ostensibly an important part of EEG and ERP preprocessing, there are surprisingly few BCI systems that use online tools to deal with transient artifacts such as EOG and EMG [40,41]. Nevertheless, to ensure that our results in this study were not contaminated by or attributable to gross artifact (e.g., blinking, EOG, EMG, poor electrode contact), offline across- and within-participant analyses made use of artifact rejection in EEG preprocessing. The only exceptions to this rule were that classifiers made use of the entire data set, in order to better match the current iteration of BiPy software, and align more realistically with real use cases, where artifact rejection is often absent. As applied, artifact rejection occurred after epoch segmentation for both the ERP and time-frequency analyses, but prior to separation of the stimuli into target and non-target classes. We applied semi-automatic review, triggered by any violations of pre-defined criteria: voltage slope \(>\) 50 \(\mu\)V/ms; change in voltage \(>\) 125 \(\mu\)V within a span of 50 ms; absolute voltage \(>\) 75 \(\mu\)V; and lack of activity, defined as absolute voltage \(<\) 0.5 \(\mu\)V sustained for at least 100 ms. Flagged violations were reviewed by experienced research staff and removed from the analysis if there was any apparent artifact (EOG, EMG, blinking, movement artifact, or other non-descript EEG noise) coincident with the ERP and alpha attenuation effect windows.  
Overall rejection rates were similar across the test conditions for both ERP and alpha analyses, according to two-tailed paired-samples \(t\)-tests (all \(p\) values \(\geq\) 0.40). Because of unequal target class representation in the RSVP paradigm, we also looked more specificallyat target rejection rates. Again, there were no significant differences in target rejection rates between the test conditions for the ERP or time-frequency analyses (all \(p\) values \(\geq\) 0.077). The majority of participants retained at least two-thirds of their target stimuli across the various calibration recordings in both the ERP and alpha analyses following artifact rejection. We included all participants in the primary analysis, regardless of target rejection rates. Further discussion is included in Section 3.7.1.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 3 System Model and Problem Description
### Problem Description  
In this study, the edge node deployment problem is formulated as an optimization task, where the objective is to identify the optimal locations from a set of candidate sites, subject to specific constraints. [24]. Given that node deployment is a well-known NP-hard problem, heuristic algorithms are commonly employed to address it. In practical scenarios, edge nodes generally have access to a stable power supply network, ensuring sufficient energy availability [25]. Furthermore, the distinctive nature of the mining environment magnifies its vulnerability to latency. Consequently, this study omits the consideration of energy consumption by edge nodes and focuses on the aspects of cost and delay.  
In this section, uniform mathematical notation is employed to enhance the clarity of problem formulations in various scenarios. The set of edge nodes is represented by E, while the set of tasks is denoted as T. To distinguish between fixed and mobile equipment, the prefixes 'S' and 'M' are used, respectively. For example, in the context of the fixed equipment scenario, E corresponds to SE, whereas in the mobile equipment scenario, E corresponds to ME.
# Forward Link Optimization for the Design of VHTS Satellite Networks
## 4 Proposed Methodology
### Capacity of the VHTS Satellite Network Using CCM/VCM  
In the first stage of the methodology, the number of beams and the value of the optimum user station \(G\)/\(T\) are obtained as well as the cost of Gbps in orbit for an ideal channel capacity (Shannon).  
Next, the procedure for dimensioning the VHTS satellite network using CCM or VCM is presented (see the flowchart in Figures 4 and 5, respectively).  
As defined in [14], we introduce the service area percentage (SAP) to evaluate the performance of CCM and is defined as the percentage of the service area that fulfils the _CINR_ requirement for a given ModCod.  
* For CCM: With the values obtained from the number of beams (B) and \(G\)/\(T\), the _CINR_ must be calculated for each geographic coordinate in the service area in order to select the ModCod that provides the highest channel capacity and that meets the SAP requirement for the selected link availability (\(P\)).
* For VCM: ModCod is modified for each geographic coordinate in the service area depending on the value of _CINR_ for the required link availability. VCM increases the total capacity of the satellite by using ModCods with the highest spectral efficiency according to the particular site link conditions.  
Figure 4: Flowchart for dimensioning using Constant Coding and Modulation (CCM).
## 7 Conclusions  
The MPwise system, as seen by the range of applications outlined in this work, shows a great potential for a variety of seismic mitigation activities, including earthquake early warning, post-event aftershock recording, structural monitoring under various circumstances, and site assessment. Most of the components are off-the-shelf, thus reducing the cost of these units, leading them to be much less expensive than standard seismometers and digitizers, and therefore allowing more extensive and/or dense networks to be established.  
One of the planned next steps is to expand the capacity of the system to include single-frequency GNSS receivers that are suitable for dense deployments because of their small size, low cost, and relatively low power consumption. Displacement monitoring systems using such instruments have been investigated in a number of studies. For example, [32] employed a low-cost L1 GNSS for static displacement measurements, in particular using the carrier phase signal. Another step will be including displacement measurements from the real-time analysis of acquired images, which will require moving from laboratory tests, as discussed in the previous section, to the establishment of networks that include camera sensors in actual structures.  
In conclusion, the MPwise is a system that offers a range of possibilities for seismic, and eventually, multi-, hazard and risk assessment. Due to its ease of use and intended modular, flexible nature it offers great potential for cost effective risk assessment, for both pre- and post-event cases.  
The work presented in this paper was partly supported by the EC-ECHO (European Commission--Humanitarian Aid and Civil Protection) project SIBYL (Selsmic monitoring and vulneraBilitY framework for civil. protection project, 1 January 2015 to 31 December 2016), grant agreement ECHO/SUB/2014/695550). [PERSON] and [PERSON] (Helmholtz-Zentrum Potsdam Deutsches GeoForschungszentrum, Germany) have considerably supported the engineering of the first prototypes.  
[PERSON] did the bulk of the software development, data analysis and testing as part of his doctoral studies. [PERSON] assisted in the camera testing and processing. [PERSON] helped develop the software and testing. [PERSON] was responsible for the overall supervision of the work. [PERSON] was involved in the hardware development and testing. [PERSON] assisted in the writing and supervision.  
The authors declare no conflict of interest.
## 3 Results
### Analysis of the Mean Hydrodynamic Characteristics of Different Codends
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Codends** & **Catch Weights** & \(\mathbf{F_{D}}\) & \(\mathbf{C_{d1}}\) & \(\mathbf{C_{d2}}\) & \(\mathbf{R_{e}}\)**as Function of \(\boldsymbol{d}\)** \\ \hline \multirow{4}{*}{Codend 1} & 0 kg & 26.41 \(\pm\) 10.59 & 2.96 \(\pm\) 0.196 & 0.12 \(\pm\) 0.0081 & 140,467.92 \(\pm\) 32,882.28 \\  & 7.4 kg & 33.88 \(\pm\) 14.02 & 2.86 \(\pm\) 0.156 & 0.15 \(\pm\) 0.0083 & 160,607.39 \(\pm\) 37,654.54 \\  & 14.8 kg & 39.64 \(\pm\) 16.45 & 2.33 \(\pm\) 0.141 & 0.18 \(\pm\) 0.011 & 193,508.74 \(\pm\) 46,289.02 \\  & 22.3 kg & 41.70 \(\pm\) 17.94 & 2.03 \(\pm\) 0.071 & 0.2 \(\pm\) 0.0066 & 211,988.54 \(\pm\) 50,384.62 \\ \hline \multirow{4}{*}{Codend 2} & 0 kg & 29.53 \(\pm\) 12.17 & 4.04 \(\pm\) 0.16 & 0.11 \(\pm\) 0.0047 & 126,906.05 \(\pm\) 29,338.69 \\  & 7.4 kg & 33.47 \(\pm\) 14.20 & 3.14 \(\pm\) 0.11 & 0.13 \(\pm\) 0.0044 & 152,806.04 \(\pm\) 36,011.46 \\  & 14.8 kg & 37.24 \(\pm\) 15.31 & 2.35 \(\pm\) 0.12 & 0.14 \(\pm\) 0.0068 & 186,769.1 \(\pm\) 43,151.27 \\  & 22.3 kg & 40.34 \(\pm\) 16.84 & 2.07 \(\pm\) 0.57 & 0.15 \(\pm\) 0.0043 & 206,588.87 \(\pm\) 47,033.56 \\ \hline \multirow{4}{*}{Codend 3} & 0 kg & 30.23 \(\pm\) 12.92 & 4.61 \(\pm\) 0.13 & 0.156 \(\pm\) 0.0047 & 119,711.53 \(\pm\) 27,563.24 \\  & 7.4 kg & 33.23 \(\pm\) 14.20 & 3.81 \(\pm\) 0.11 & 0.176 \(\pm\) 0.0049 & 138,395.59 \(\pm\) 31,510.67 \\ \cline{1-1}  & 14.8 kg & 37.11 \(\pm\) 15.32 & 2.75 \(\pm\) 0.81 & 0.195 \(\pm\) 0.0057 & 171,871.21 \(\pm\) 38,49
# Morphological Reconstruction for Variable Wing Leading Edge Based on the Node Curvature Vectors
## 4 Experimental Validations
### Results and Discussion
#### 4.2.2 Comparison of Morphology Reconstruction Errors between LI-CPM and NCV-CPM  
The actual form of the leading edge was evaluated using measurements from laser displacement sensors to assess the effectiveness of the method proposed in this study for morphological reconstruction. Taking a leading-edge deflection angle of \(12^{\circ}\) as an example, the reconstructed variable wing leading-edge cross-sectional form closely matched the actual form curve, as illustrated in Figure 16.  
As depicted in Figure 17a, when the leading-edge deflection angle was \(12^{\circ}\), the morphological reconstruction error of the wing leading edge using NCV-CPM was significantly lower than that of the conventional LI-CPM, with the average relative error of morphological reconstruction decreasing from \(17.05\%\) to \(4.92\%\). For deformation states with leading-edge deflection angles of \(3^{\circ},6^{\circ},\) and \(10^{\circ}\), the morphological reconstruction accuracy of the method proposed in this study was notably superior to that of the conventional curvature recursion method, as shown in Figure 17b.  
The experimental results demonstrate that the proposed method achieves an improvement in morphological reconstruction accuracy by reducing both the curvature calculation error at sensing points and the cumulative error in curvature reconstruction.  
Figure 16: Morphological reconstruction results of NCV-CPM for wing leading edge of deflection at angle of \(12^{\circ}\): (**a**) leading-edge deflection diagram; (**b**) reconstruction effect comparison.  
Figure 15: Comparison of reconstruction results in Path B: (**a**) curvature results; (**b**) strain results.
# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 4 Detection and Classification Method
### Overview of the Proposed Method  
To provide an overview of how the training and detection process was carried out, this section presents, through Figure 6, the main steps involved: selection and annotation of the dataset, image augmentation, actual training of the YOLOv12 model, performance evaluation, license plate detection, and the identification of the types of vehicles in which they are found.  
After detection, the license plates are cropped from the image and processed using the PaddleOCR library [22], which performs text recognition. For each identified text segment (e.g., a word or a sequence of characters), a global confidence score is computed. The text is retained only if this score exceeds the threshold of 0.6. Subsequently, the text is standardized by removing irrelevant symbols and converting it to uppercase. Its validity is then checked using regular expressions that test compliance with Romanian or foreign license plate formats (such as DE, IT, GR, etc.).  
In parallel, the same type of YOLOv12 model (pretrained for object detection) is used to identify the type of vehicle in the image. The position of each detected license plate is compared with the previously identified objects in order to determine the type of vehicle it belongs to (e.g., car, truck, motorcycle).  
For Romanian license plates, the county of origin is also displayed, while international plates are marked as foreign.  
Figure 6 provides a visual summary of the workflow.
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 2 RBF-Kriging for borehole Data Interpolation
### Efficient Samples  
As seen from Section 2.1, for the ordinary Kriging interpolation, each data point that is to be interpolated is calculated through all available samples. According to the first law of geography of \"All attribute values on a geographic surface are related to each other, but closer values are more strongly related than are more distant ones\", coal seam thickness at a given data point is more related to its neighborhood. In other words, the samples with the larger weighting coefficients are concentrated around the interpolated point. This property is described by the Gaussian model in the proposed method, as shown in the Equation (5),  
\[f(x)=\frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(x-x)^{2}}{2\sigma^{2}}} \tag{5}\]  
which is widely used as the variation function model in the ordinary Kriging, where \(\mu\) and \(\sigma\) are the mean and variance of the Gaussian distribution and \(x\) is the samples. For the Gaussian model, the probability of samples fall within \([-3\sigma,3\sigma]\) is 99.74%, i.e.,  
\[p\{\left|x-\mu\right|<3\sigma\}=2\phi(3)-1=0.9974.\]  
In order to ensure that the nearest point has a greater influence on the interpolation point in the Kriging method, the variation function, as shown in Equation (3), is only calculated through the efficient samples. In this paper, the efficient samples are restricted to the samples falling within \([-3\sigma,3\sigma]\) to the interpolated point by experience. Thus, the Kriging linear equations, by which the weight can be calculated, is derived as follows:  
\[\left\{\begin{array}{c}\sum_{i=1}^{n}{\lambda_{i}}=1\\ \sum_{i=1}^{n}{\lambda_{i}\gamma(x_{i},x_{j})}=\gamma(x_{i},x_{0})\\ d(x_{i},x_{0})\leq 3\sigma\end{array}\right. \tag{6}\]where, \(d(x_{i},x_{0})\) represents the distance between observation point and the interpolated point. Notice that the utilization of the efficient samples not only improves the interpolation performance, but also reduces the computational complexity.
## 1 Introduction
Several methods are proposed in the literature to design Ultra-Wideband (UWB) monopole antennas with various shapes, such as rectangular, elliptical, square, trapezoidal, circular, and other patches [22]. Alterations to these patches are achieved by etching out or through the addition of a conducting structure on the feed-plane or the ground planes so as to achieve the desired bandwidth/characteristics. The removal or addition of structures have been performed by adding stubs or by carving out slots or slits in the conducting surface, which causes perturbations in the uniform flow of the current. This can be used to enhance the bandwidth. There are various methods available in the literature using the above-mentioned techniques, which can be used to improve the bandwidth and/or reduce mutual coupling by, for example, introducing stubs in the ground/feed-plane of the L-shape [23], T-Shape [24], fork-shape [25], or stepped stub [26], ground isolating [27], etc. In this paper, a compact Ultra-Wideband four-port MIMO antenna radiating in the 24.25-29.5 GHz range and covering the n257/n258/n261 band is proposed. The antenna structure of the four-port MIMO is very small in size, and the overall dimensions are 16.2 mm \(\times\) 16.2 mm \(\times\) 0.254 mm. The proposed antenna structure uses the popular inset feeding technique to control and enhance the radiation characteristics of the antenna. The formation of the slit along both sides of the feedline provides junction capacitance that can modify the resonance frequency of the antenna. Also, the dimensions of the inset length and the inset width of the slit can be varied to influence the resonant frequency and other characteristics [28]. The proposed four-port MIMO antenna has very small dimensions. In spite of the four-port configuration and the very small size, the proposed MIMO antenna offers acceptable isolation of \(>\)23 dB. The presented antenna has a simple planar design; it avoids any complicated structures, such as vias or multiple layers, and it uses an ultra-thin Rogers substrate of 0.254 mm, which makes it easy to fabricate. The MIMO antenna delivers a wide bandwidth of about 5.25 GHz and a peak gain of about 6 dB. All of these benefits make the proposed structure suitable for application using an n257/n258/n261 band. In Section 2, we discuss the design aspect of the proposed antenna. In Section 3, we describe the details of how the step-wise development of the antenna design was undertaken. Section 4 deals with how the dimensional parameters of the antenna are altered to obtain optimized results.
## 4 Hardware Evaluation
### Development Framework  
The development framework incorporates the MicroSemi Libero SoC toolchain and a collection of our hardware description language (HDL) and software components. The FPGA fabric contains three main HDL components: the _AFE interface_, the baseband _application skeleton_ and the _MSS interface_. The _AFE interface_ utilizes the DDR capable I/O blocks of the FPGA to communicate with the AFE at sample rates up to 22 MHz, both in receive (ADC) and transmit (DAC) modes. The FPGA fabric part of the _application skeleton_ may host only parts of the PHY layer, shifting most of the computational burden onto the microcontroller, or it may incorporate the radio stack up to the MAC level, relieving the Cortex-M3 processor. In either case, the _MSS interface_ uses the AMBA APB bus to transfer data between the FPGA fabric and the ARM Cortex-M3, and serves as a gateway between the application-specific HDL and software components. The MSS software components primarily control the radio front-end and manage the higher-levels of the radio stack. The current framework provides firmware to initialize and control the RF transceiver chip via I/O pins and a MSS SPI peripheral and to communicate with the FPGA in the form of simple register read and write operations, interrupt requests and optional direct memory access transfers.
## 2 Mobility Models in FANETs  
The FANETs mobility model describes the motions of UAVs in a specific region over time, including changes in speed, direction, and acceleration. Due to their mobility, UAVs may be tailored to the specific needs of an application, leading to better performance and flexibility. The study of node motion can be carried out by simulation or mathematical  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference/** & **Routing** & **Comparison** & & & & & \\
**Year of** & **Routing** & **Analysis of** & **Routing** & **Challenges** & **Mobility** & **Analysis of** & **Communication** & **Open** \\
**Publication** & **Protocols** & **Routing** & **Challenges** & **Models** & **Mobility** & **Mobility** & **FANET** & **Issues** \\ \hline Ref. [27]/2014 & \(\surd\) & X & X & X & X & \(\surd\) & \(\surd\) \\ \hline Ref. [28] 2017 & \(\surd\) & \(\surd\) & X & X & X & \(\surd\) & \(\surd\) \\ \hline Ref. [29] 2019 & \(\surd\) & \(\sur  
modeling. The simulation method can realistically emulate the actions of UAVs to obtain as realistic results as possible just before actual deployment and provides a better solution for complex problems. The existing mobility models that can be used for UAVs can be categorized as random, group, time-dependent, and path-planned models. Figure 3 illustrates the taxonomy of the mobility model in FANETs.