esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Building Blocks and Recognition  
### Block Creation Algorithm  
In this subsection we present a short algorithm for creating a partition of \(V(G)\) using an independent set \(I\) and then checking if this partition is a block decomposition using Algorithm 1 from Section \(2\).  
```
\(U:=V(G)\); \(t:=0\); \(f:=\emptyset\) for\(i\in I\)do for\(v\in N^{+}(i)\cap U\)do \(f(v):=t\) endfor \(U:=U\setminus N^{+}(i)\) \(t:=t+1\) endfor for\(v\in U\)do \(f(v):=t\) endfor returnverify(\(G,f\))
```  
**Algorithm 5**\(\mathtt{test}(G,I)\)  
**Lemma 4.1**.: _Suppose that \(I\subseteq V(G)\) is an independent set with \(|I|\geq s(G)-1\) and \(V_{0}\cap I=\emptyset\) for some unipolar representation \(R=(V_{0},V_{1})\) of \(G\). Then \(\mathtt{test}(G,I)\) returns \(True\)._  
Proof.: On each step of the main loop a vertex from \(i\in I\) is selected. Since \(V_{0}\cap I=\emptyset\), the vertex \(i\) is a part of some side clique, say \(C\). Now \(C\cap N^{+}(j)=\emptyset\) for each \(j\in I\setminus\{i\}\), so \(C\subseteq U\). Also \(C\subseteq N^{+}(i)\), and hence \(C\subseteq N^{+}(i)\cap U\). Vertex \(i\) does not see vertices from other side cliques, so \(N^{+}(i)\cap U\) is correctly marked as a separate block.  
Since \(|I|\geq s(G)-1\), at most one side clique is not represented in \(I\). If there is an unrepresented side clique, say \(C\), then none of the previously created blocks can claim any vertex from it, and hence \(C\subseteq U\). We have shown that when the main loop ends, either \(U\cap V_{1}=\emptyset\) or \(U\cap V_{1}\) is a side clique; so \(U\) is correctly marked as a separate block. The set \(U\) also contains all remaining vertices, so \(f\) is partition of \(V\) into blocks, and hence Algorithm 1 returns \(True\).
# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
## 3 PRNet Framework
### Feature Extractors for Point Cloud and Image  
In this paper, the study selects a point cloud range of [\(-\)40, 40] \(\times\) [0, 70] meters, aligning with the camera's field of view. Subsequently, the study normalizes the number of points to 4096. While some networks transform point cloud data into formats like voxels or bird's eye view (BEV), the study opts to utilize raw point clouds as input to retain the most authentic point cloud information. For point cloud processing, PointNet++ is chosen as the backbone of this paper. PointNet++ is a neural network architecture predicated on point set processing, adept at capturing both global and local information within point clouds through set operations. The network's backbone is structured with 4 Set Abstraction (SA) modules and 4 Feature Propagation (FP) modules. The SA modules are tasked with down-sampling to extract global features, whereas the FP modules facilitate level-by-level feature propagation via interpolation and concatenation. This design strategy allows the network to more effectively manage issues of uneven point cloud density and sparsity, thereby enhancing the robustness of object detection. Concerning image data, this study employs a straightforward convolutional neural network as the backbone. This network generates four feature maps of varying sizes, with each map encompassing contextual information across different scales.  
Figure 1: The overall frame of PRNet structure.
# Computational Load Analysis of a Galileo OSNMA-Read Receiver for ARM-Based Embedded Platforms +
## 3 Development Work
### The Platforms Used for the Performance Analysis  
In this paper, the profiling analysis of the OSNMA ready receiver is described for three ARM-based boards, and for reference, a standard Intel-based desktop PC.  
The platform originally used for the implementation of eNGene was an ODROID-X2 [33], which entered the market in 2012 and was discontinued in 2015. The ODROID-X2 was a powerful, low cost and pocket-sized board, featuring a 1.7 GHz Quad Core ARM Cortex-A9, 2 GB RAM memory and a number of peripherals, such as a high-definition multimedia interface (HDMI) monitor connector and six USB ports, which can be used for keyboard, mouse and FE. The board hosts an Ubuntu Linaro OS distribution, booting from an embedded Multi Media Card (eMMC), so that the developer can work directly on the target platform using Eclipse IDE and GNU Compiler Collection (GCC) compiler.  
The performance obtained with ODROID-X2 has been compared with those gotten from two more recent boards, namely, the Raspberry Pi 4 [34] and ODROID-C4 [35]: while their general architecture is similar to that of ODROID-X2, they exhibit newer ARM processors: indeed, Raspberry Pi 4 uses an ARM Cortex A72 and ODROID-C4 uses an ARM-Cortex A55. Both processors implement the ARMv8 64-bit instruction set, whereas the ARM Cortex A9 on ODROID-X2 supports the ARMv7 32-bit instruction set. The factory configuration of the Raspberry Pi 4 uses the ARM processor as a 32-bit processor, so the eNGene developed for ODROID-X2 is fully compatible with the new board. On the contrary, ODROID-C4 required a rewriting of the assembly parts to allow the execution on the 64-bit processor, due to the different assembly syntax of the 64-bit instruction set with respect to the 32-bit one.  
Figure 4 reports a picture of the used platforms, and Table 2 summarizes their main hardware features. In particular, the first column reports the reference desktop PC characteristics, whereas the other three columns show the features of the three ARM-based platforms.  
Figure 4: The ARM-based platforms used in the tests, from the left: ODROID-X2, Raspberry Pi 4, ODROID-C4.
## 3 Results and Discussion
### Visible-Light Absorption
position of the signal is the same as that mentioned in our previous study [26], we conclude that the photonic center responsible for the visible-light absorption in this study is the same as those defects related to the carbon doping effect for the TiO\({}_{2}\) nanopowder mentioned in the previous study. The weak EPR signal and visible-light absorption for the sample with C\({}_{2}\)H\({}_{2}\) 600 sccm, C\({}_{2}\)H\({}_{2}\)/O\({}_{2}\) = 1:3 suggests that the visible-light-absorbing photonic centers are annihilated with the formation of rutile.
# Design and Implementation of Attitude Stabilization System for Marine Satellite Tracking Antenna
## 2 Introduction of Attitude Stabilization System
### Attitude Sensor Board  
Attitude sensor is a key part in the attitude stabilization system, whose main function is to output the current rotation velocity and angle of the antenna dish. It is mounted together with the antenna dish and mainly consists of one Advanced RISC Machine (ARM) chip, and one Inertial Measurement Unit (IMU). The photo of the attitude sensor board is shown in Figure 3. The ARM chip (Cortex-M7 core) with the CPU frequency of 216 MHz reads sensor data from the IMU module through Serial Peripheral Interface (SPI) bus port. Inside the IMU module, there is one tri-axis gyroscope, one tri-axis accelerometer and one temperature sensor. The core parameters of the IMU module are listed in Table 1. The sensor fusion algorithm executed in the ARM chip fuses data from the IMU module and output the current Euler angles of the antenna dish. The popular attitude sensor fusion algorithm can be found in [16; 17; 18; 19] and the definition of Euler is the same as that in [20]. The running results of the sensor fusion algorithm together with filtered gyroscope measurements are sent out through Universal Asynchronous Receiver/Transmitter (UART) communication port to the motor control and drive board with 1 Mbps baud rate.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Sensor** & **Parameter** & **Value** & **Unit** \\ \hline \multirow{4}{*}{Gyroscope} & In-Run Bias Stability & 8 & deg/h \\ \cline{2-4}  & Angle Random Walk & 0.12 & deg/h \\ \cline{2-4}  & Dynamic Range & \(\pm 100\) & deg/s \\ \cline{2-4}  & \(-3\) dB Bandwidth & 350 & Hz \\ \hline \multirow{4}{*}{Accelerometer} & Nonlinearity & \(\pm 0.1\) & \% of FS \\ \cline{2-4}  & Dynamic Range & \(\pm 5\) & g \\ \cline{2-4}  & \(-3\) dB Bandwidth & 350 & Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Core parameters of the IMU.  
Figure 3: Attitude sensor board: (**a**) 3D view of the board in PCB design software; and (**b**) photo of the real board.
## 1 Introduction  
The less-resourced languages face various issues from the language technology perspective. The lack of resources often is one of the main obstacles in the forefront of the resolution of those challenges. The Optical Character Recognition (OCR) systems play a crucial role in resource preparation and language processing. However, it is not feasible to develop or adapt an appropriate OCR for those languages if the required data does not exist. Kurdish is considered a less-resourced language that is facing several issues [1] including lack of OCR systems that are accurate and widely available. Tesseract Long-Short-Term Memory (LSTM) is a popular OCR engine that has been adapted for various languages, but LSTM-based methods require large amounts of data for their training. In this research, we suggest a resolution for that issue by using a training dataset for a language with a similar script and then applying a considerably small amount of data from the target language. Our target language is Kurdish, and we choose Sorani, one of its dialects that is mostly written in Persian-Arabic script. Therefore, we use an Arabic dataset as the base of the training, and we use a Kurdish dataset based on a series of court case documents in the Kurdistan Region of Iraq. We also investigate the fine-tuning phase of Tesseract using 10 Unikurd fonts and evaluate the approach by Lstmeval and Ocreval.  
The LSTM-based methods are not only used in image recognition systems, but they are also applicable to address and solve a wide range of problems. For example, they are used in time-series forecasting [2], energy consumption forecasting [3], commercial vacancy prediction [4], and various other areas. Although this paper focuses on suggesting a solution for the requirement of large amounts of data in one aspect of the applications of LSTM-based approaches, the suggestion could be extended to similar cases that have similarities of some sort in their training data.  
The rest of this paper is organized as follows. Section 2 provides a background about the Kurdish language focusing on its alphabets and script similarities with Arabic, and then briefly introduces the OCR history focusing on Tesseract. Section 3 reviews the related work and literature. In Section 4, we discuss our approach. Section 5 presents and discusses the results. Finally, Section 6 concludes the paper.
## 4 Evaluations
### Interpretations
#### 4.3.4 Segmentation without Semantic Classification  
As our first experiment has shown, the segmentation step can be considerably more time consuming than OCR, even when aiming for very low CERs. However, the required manual work to segment a book can be severely cut down when the aspirations regarding semantic classification are less strict. Therefore, we conducted another experiment where the single goal of the segmentation was to provide the subsequent OCR with the means sufficient to produce the required output. Apart from a clean text/non-text separation, this also includes ensuring the correct reading order. Figure 11 shows the desired results.  
We selected three books due to their widely differing layout properties: P1484, C1541, and N1506. For our experiment, the users 1C and 2B both segmented 20 representative pages of each book twice, first using the basic approach and then the complex approach from the first experiment.  
As expected, the basic segmentation approach required considerably less time than the exact one, leading to an average savings of 38% for the experienced and 45% for the inexperienced user. Regarding the comparison of two users with a different degree of experience, the results in general showed the expected tendency, namely a much faster processing by the more experienced user. On average, it took the novice user 2.65 times longer to perform the basic segmentation and 2.95 longer when an exact segmentation was required. Naturally, the achievable speed-up correlates heavily with the layout complexity.  
It is worth mentioning that applying the basic segmentation does not influence the quality of the generated GT in a negative way, since we focus on training a generic model for the task of text/non-text separation. In our experience, a fine-grained semantic distinction of layout elements does not seem feasible due to the high variations between books.
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 4 System Description
### LoRa Node  
The LoRa end device is in charge of gathering data from the environment through the different used sensors. The fire risk index estimation for an area that uses our system is based on the 30-30-30 rule. To do that, the node must gather information on temperature, relative humidity, wind speed and, rain of the last 30 days. The compliance of this rule and consequently, the existence of fire risk in an area, implies having a temperature higher than 30 \({}^{\circ}\)C, a relative humidity lower than 30%, wind speed higher than 30 km/h and absence of precipitation in the last 30 days.  
According to [38], processing only two variables and checking the compliance of the limit values is enough to establish the warning of a fire in an area with a success rate of 70%. According to this study, for the 2007-2016 period, the compliance of the three variables specifically correlated with 40% of large fires. Since the rain is not used in this study, the CO\({}_{2}\) concentration was added as a method for verifying the presence of a fire. Finally, it is important to keep in mind that the occurrence of forest fires, sometimes, depends on the area where it is located and, its topography and therefore, parameters such as rain may not be important.  
Figure 7 shows the block diagram of our nodes. As can be seen, the system is comprised of three sensors that measure four different parameters. Additionally, it will be powered by a solar panel and a battery.  
Figure 6: Network architecture of our proposal.  
The Arduino UNO Rev3 development board incorporates an ATmega328P microchip microcontroller [39]. To connect the Arduino node to the LoRa network, a Dragino LoRa Shield board [40] compatible with our Arduino development board is used. The Dragino LoRa Shield board incorporates the RFM95W transceiver [40] with the SX1276 chip (manufactured by Semtech) which allows using the LoRa modulation with the three working frequencies specified on the regulation, i.e., 915 MHz, 868 MHz, and 433 MHz [31]. Our model is pre-configured to work in the 868 MHz frequency band. Figure 8 shows the Dragino LoRa Shield coupled with the Arduino UNO module.
## 1 Introduction  
Cell-penetrating peptides (CPPs), also referred to as protein transduction domains (PTDs) or Trojan peptides, form a structurally diverse group of molecules with varied physicochemical properties. However, they usually have at least two features in common. Firstly, they can penetrate the cell membrane barrier even after binding with macromolecules. Secondly, they are mostly short peptides with a length of up to 30 amino acids [1]. Moreover, in vitro and in vivo studies have indicated their low cytotoxicity and immunogenicity together with high efficiency.  
CPPs may enter the cell due to several mechanisms, including clathrin-dependent and independent endocytosis and direct translocation. Additionally, this characteristic is not affected by the attachment of varied cargo molecules to the CPP [1].  
Two main approaches are currently applied for combining transported molecules with CPPs. The first is the introduction of a covalent bond, while the second is based on the creation of non-covalent interactions. Although covalent bonding is commonly used, it has several disadvantages. An example is the probability of undesirable changes in the attached molecule during a chemical reaction [2]. Moreover, it cannot be used for the transport of plasmids. To overcome these problems, non-covalent bonds can be applied. In such a case, the cargo and CPP are combined with electrostatic and hydrophobic interactions. This limits the possibility of changes in biological activity resulting from binding. Furthermore,it enables the transport of plasmids. Another advantage is the simplicity of the method since it only requires incubation of the compounds. Moreover, this strategy is similar, in terms of effectiveness and safety, to the introduction of covalent bonds [3, 4].  
The effectiveness of the non-covalent approach strongly depends on the charge ratio (CR), which is the ratio of the number of moles of positively charged amino acid groups in the CPP to the number of moles of negatively charged groups in the binding molecule [5]. Too high of a CR results in the formation of large complexes of molecules with low cellular penetration capacity, while too low is not effective due to insufficient internalization.  
The study aimed to assess the usefulness of selected CPPs (TAT, Pep-1, KALA) and nuclear localization sequences (NLS [47; 48; 49; 50; 51; 52; 53; 54; 55] and NLS [47; 48; 49; 50; 51; 52; 53; 54; 55; 56]) for introducing pmaxGFP plasmids to the human primary coronary artery endothelial cells. Both the effectiveness and potential cytotoxic effect were assessed. The results are presented in the form of extensive photographic documentation, which shows significant differences in the efficiency of the transfection process between electroporation and the use of CPPs.
## 5 Experiments  
### Experimental Setup  
The setup of the UAV-mounted UWB radar system is shown in Figure 1. The Z410 quad-rotor UAV carries the X4m200 UWB radar for human vital signal detection and life state discrimination. In the 30-s detection process, the subject lies supine in the detection area and remains stationary all the time to simulate the injured human target, and the detection system hovers directly above the subject and remains at an altitude of 2 m. Meanwhile, an ErgoLAB wireless respiration belt is connected to the subject to collect the reference respiratory signal.  
In this study, two typical scenarios (shown in Figure 5) were considered separately to evaluate the reliability and the noise tolerance of the proposed method for respiration detection. The first experiment was conducted in an outdoor scenario of relatively smooth ground as shown in Figure 5a with one female (subject 1: 168 cm, 55 kg) and two male (subject 2: 180 cm, 60 kg and subject 3: 175 cm, 70 kg) subjects. In scenario 1, the interference was mainly from UAV platform motion. The second experiment was conducted in an outdoor scenario of grassland as shown in Figure 5b with the same three subjects. Compared with scenario 1, scenario 2 adds the interference of grass movement, which is more complex and closer to the real SAR environment.
## 1 Introduction
The results were compared depending on the combination of input parameters (deflections, layer thickness and coordinates of the corresponding homogeneous section) and the combination of activation functions. [PERSON] and [PERSON] [23] created a model using the ANN method for predicting the elasticity modulus of asphalt layers and their thickness, using the input parameters of deflections measured by the FWD device. [PERSON] [24] predicted the elasticity modulus of the asphalt layer and the lower load-bearing layers using the SVM method. The input parameters for the asphalt layer moduli included layer thickness and measured deflections, whereas the lower load-bearing layer input parameters included the obtained asphalt layer moduli, layer thicknesses and measured deflections. The results were compared with the models created by the ANN method. Subsequently, a conclusion was drawn that the SVM method can represent an alternative to the ANN method, keeping in mind that the required database is often limiting and unrepresentative. [PERSON] [25] wrote the first research paper based on ML, in which models were designed with multiple methods, including ANNs, SVMs and BRTs. He predicted the modulus of elasticity and Poisson's coefficient with the input parameters of the measured deflections. In this work, the models were created within the aforementioned ML methods. Moreover, the impact of the input data on the output values of the \(\mathrm{E_{AC}}\) model was analyzed. Owing to possible problems while collecting data for input parameters, one of the main aims of this research is the automation of backcalculation procedures while reducing the number of input parameters needed for calculations, so as to obtain reliable and sufficiently accurate E\({}_{\text{AC}}\) values in a more rapid way. The research aims to find the best forecasting model, which would be much more practical to apply.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Inter-Cell Interference Mitigation Techniques
#### 4.3.4 ICI in Dynamic Time Division Duplexing and Satellite-Enabled Deployments  
The emergence of dynamic TDD and NTNs in B5G introduces new dimensions to ICI, particularly due to the increased variability and asymmetry of traffic. In dynamic TDD systems, the UL and DOL transmission patterns are no longer fixed, enabling flexible slot allocation based on RT traffic demand. However, this flexibility can cause severe asymmetric ICI, especially when neighboring cells independently schedule UL and DOL transmissions. Such mismatches lead to cross-link interference, where a cell's DOL transmission interferes with another cell's UL reception [189]  
To mitigate this, 3 GPP has proposed enhancements involving adaptive guard periods, inter-cell slot alignment, and UL/DOL configuration exchange mechanisms to coordinate dynamic TDD scheduling across BSs [190]. These mechanisms are particularly critical in dense deployments and in scenarios with high mobility, such as high-speed rail or UAV-supported cells.  
In satellite-enabled deployments (e.g., low earth orbit constellations), ICI challenges stem from beam overlap, non-uniform footprint distribution, and link desynchronization between satellites and terrestrial elements. The long propagation delays and Doppler shifts further complicate the synchronization of time and frequency resources [191]. To address these, 3 GPP Release 17 and ongoing Release 18 study items explore techniques such as beam nulling, adaptive inter-beam scheduling, and predictive Doppler compensation to reduce the impact of ICI in integrated satellite-terrestrial networks [179; 192]  
These advances reflect the need for coordinated and predictive IMI strategies that operate effectively under non-ideal, high-dynamic conditions, particularly where traditional synchronous RA is infeasible.