esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 1 Introduction
By adaptively adjusting the overlapping area between two sectors, the charging efficiency can be improved to shorten the charging time. Our second algorithm based on DE further minimizes battery overflow while keeping the charging time short. The results of the experiment show that, compared with the baseline algorithms, the proposed greedy algorithm can shorten the charging time by up to 55% and the DE algorithm can reduce wasted energy by up to 70%. Furthermore, the DE algorithm can improve the data rate of the devices by more than 160%. The main contributions of this work are summarized as follows. 1. We proposed two algorithms, greedy and DE, for different purposes. The greedy algorithm is designed to minimize the charging time and the DE algorithm can achieve a better performance for energy overflow minimization. The greedy algorithm attempts to charge devices as early as possible by rotating the direction of the directional antenna's beam. The DE algorithm jointly considers the issue of battery overflow to reduce the number of fully charged devices at the same time. 2. To gradually increase the number of fully charged devices, the problem of minimizing both energy overflow and charging time is formulated as a joint optimization problem. Therefore, the algorithm proposed for the optimization problem can pursue a charging schedule that may reduce the number of fully charged devices at the same time. 3. We conduct a comprehensive performance evaluation to demonstrate the performance of our scheme. The experimental results show that both greedy and DE algorithms can achieve a short charging time and the proposed DE algorithm can further reduce the amount of energy wasted by charged devices. We also show that, by reducing the number of fully charged devices at the same time, the performance of data transmission can be improved. The remainder of this paper is organized as follows. In Section 2, we discuss some previous literature. In Section 3, we introduce our system model and formulate the research problem. In Section 4, we describe the proposed greedy and DE algorithms. Section 5 discusses the simulation results and Section 6 concludes this work.
## 2 Related Research
### Rule-Based Approach  
Early methods of relation extraction mainly used rules formulated by experts in the relevant professional fields as the basis for extracting relations in texts, that is, a rule-based relation extraction method. However, this approach often requires many specialized domain personnel to spend a lot of time developing rules. In recent years, experts and scholars have also made some improvements in the generation of rules to address this deficiency. [PERSON] et al. [14] used a dictionary-based matching algorithm to add medical terminology terms from the National Center for Biotechnology Information disease corpus and the Unified Medical Language System to the lexicon for matching, achieving an F1 value of 63.7%. Rule-based relation extraction has high accuracy when the template design is accurate, but portability and scalability are poor. The rule-based approach has low robustness, requires manual construction for each relation, and does not yet recognize entity-pair relations beyond the template.
# Forward/Backward Decomposition for Dispersive Wave Propagation Measurements
## 2 Theory
\tag{8}\]  
A unique solution to this linear system exists if the matrix is nonsingular, which requires the locations \(x_{i}\) and wavenumbers \(k_{1},k_{2}\) to be distinct. This system must be solved at every frequency \(\omega_{n}\) corresponding to the \(n^{th}\) Fourier coefficient \(\hat{u}_{n}(x_{i})\) in each measurement \(u(x_{i},t)\). If the measurement locations \(x_{i}\) are too close together (relative to the wavelengths/wavenumbers of interest), the matrix will be ill conditioned and close to singular; in practice, it is therefore necessary to space out the sensors appropriately such that the spatial resolution is sufficient to capture the largest wavelength or lowest frequency of interest. The solution to the linear system given by Equation (8) consists of the unknown amplitude spectra \(\mathbf{x}= \begin{bmatrix}\hat{A}&\hat{B}&\hat{C}&\hat{D}\end{bmatrix} ^{T}\). These can be inserted back into the general solution in Equation (6) to reconstruct the full system response. However, the motivation of this work is to separate reflections in the measured signals. Thus, instead of reconstructing the full response, we can reconstruct the individual wave mode responses by simply omitting the others. In particular, the forward-propagating component which contains the incident wave is given by  
\[u(x_{i},t)=\hat{A}e^{-ik_{1}x_{i}}e^{i\omega t}, \tag{9}\]  
which is in practice obtained by performing an inverse Fourier transform on the quantity \(\hat{A}e^{-ik_{1}x_{i}}\).
# Efficient Modulation Scheme for Intermediate Relay-Aided IoT Networks
## 3 Proposed Solution
### A Heuristic Modulation Diversity Scheme  
For Rician fading channels, there is a strong LOS component in \(h_{1}\) and \(h_{2}\). Our quadratic assignment problem's solution benefits from two different gains. Firstly, by allowing \(\lambda_{1}\
eq\lambda_{0}\) and \(\lambda_{2}\
eq\lambda_{0}\), we achieve the signal space diversity gain just like existing modulation diversity schemes. Secondly, by jointly designing \(\lambda_{1}\) and \(\lambda_{2}\) and allowing \(\lambda_{1}\
eq\lambda_{2}\), we achieve the cooperative gain between the source and the relay. When the channels are independently Rayleigh, however, there is little cooperative gain to exploit and we can greatly simplify the modulation diversity design problem by forcing equal HARQ mapping; i.e., \(\lambda_{1}=\lambda_{2}\). Though we can solve the resulting quadratic assignment problem rigorously, from (5), we can take a simple heuristic approach noticing that the two indices mapped to two symbols close to each other in Phase 1 should be mapped to two symbols far apart in Phase 2. Based on such heuristic, \(\lambda_{1}=\lambda_{2}\) can be designed for 16-QAM and 64-QAM. For 32-QAM constellation, a similar heuristic modulation diversity design may be extended by remapping the three most significant bits (MSBs) and the two least significant bits (LSBs) separately. We will show in the next section that, when channels experience deep fading, such a heuristic remapping method offers a comparable performance gain to that of our quadratic assignment problem-based modulation diversity.
## 6 Conclusions  
A lightweight detection network YOLO-Ship has been presented, integrating the novel TSA mechanism to address ship component recognition challenges in intelligent manufacturing. The main contributions include the development of the triplet spatial reconstruction attention mechanism and its successful integration with YOLOv8n to create an effective detection network optimized for industrial scenarios.  
Experimental validation demonstrates substantial performance improvements: mAP reaches 88.7% (3.5% increase), precision achieves 84.2% (2.2% increase), and recall attains 87.1% (3.8% increase), while maintaining computational efficiency with only 2.6 M parameters and 7.5 GFLOPs. These results validate the practical value of TSA in addressing small-sample learning and component occlusion challenges in industrial environments.  
Limitations include dataset scale constraints and domain-specific validation that may affect broader applicability. Future research should prioritize cross-domain validation across diverse manufacturing sectors including automotive and heavy machinery, to demonstrate TSA's broader industrial applicability, focus on developing adaptive threshold mechanisms for TSA, and investigate the method's general effectiveness across different intelligent manufacturing systems.  
Conceptualization, B.F. and Z.Y.; methodology, B.F.; software, B.F.; validation, B.F., Z.Y. and C.F.; formal analysis, B.F.; investigation, B.F. and C.F.; resources, Z.Y.; data curation, B.F.; writing--original draft preparation, B.F.; writing--review and editing, B.F., Z.Y. and C.F.; visualization, B.F.; supervision, Z.Y.; project administration, Z.Y.; funding acquisition, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The ship component dataset constructed in this study, including all 792 images with corresponding annotations, will be made publicly available through GitHub repository upon publication. The dataset includes detailed geometric classification labels for all five component categories in standard annotation format. The implementation was developed using Python 3.8.10, PyTorch 1.8.1+c111, and CUDA 11.1 for GPU acceleration. The source code and detailed implementation of the proposed TSA mechanism and YOLO-Ship network are not publicly released due to ongoing coherent research; however, researchers may obtain the relevant code and technical details by submitting reasonable academic requests to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
YOLO & You Only Look Once  
QR & Quick Response  
ResNet & Residual Network  
DenseNet & Densely Connected Convolutional Network  
SENet & Squeeze-and-Excitation Network  
CBAM & Convolutional Block Attention Module  
EMA & Efficient Multi-scale Attention
## 4 Value-Based Methods
This architecture helps the network learn which states are valuable independent of the specific actions taken, improving performance in complex environments. The pseudocode shown in Algorithm 2 presents how the D3 QN method works in a step-by-step fashion. ```
Input: Learning rate \(\alpha\), discount factor \(\gamma\), batch size \(B\), replay buffer size \(N\), target network update frequency \(\tau\) Output: Trained dueling Q-network \(Q(s,a;\phi)\)  Initialize dueling Q-network \(Q(s,a;\phi)\) with random weights;  Initialize target network \(Q(s,a;\phi^{-})\) with \(\phi^{-}\leftarrow\phi\);  Initialize empty replay buffer \(\mathcal{D}\) of size \(N\); forach episodedo  Initialize environment and observe initial state \(s_{0}\); forach time step \(t\)do  Select action \(a_{t}\) using \(\epsilon\)-greedy policy: \[a_{t}=\left\{ \begin{aligned} &\text{random action, with probability }\epsilon,\\ &\arg\max_{a\in\mathcal{A}}Q(s_{t},a;\phi),\text{otherwise.}\end{aligned} \right.\] (33)  Execute \(a_{t}\), observe reward \(r(s_{t},a_{t})\) and next state \(s_{t+1}\);  Store transition \((s_{t},a_{t},r(s_{t},a_{t}),s_{t+1})\) in \(\mathcal{D}\); if replay buffer \(\mathcal{D}\) has at least \(B\) samplesthen  Sample a mini-batch of \(B\) transitions \((s,a,r,s^{\prime})\) from \(\mathcal{D}\);  Compute the target for each transition: \[y=r+\gamma Q(s^{\prime},\arg\max_{a^{\prime}\in\mathcal{A}}Q(s^{\prime},a^{ \prime};\phi);\phi^{-})\] (34)  Decompose the Q-network into state-value \(V(s;\phi)\) and advantage \(A(s,a;\xi)\): \[Q(s,a;\phi,\xi)=V(s;\phi)+A(s,a;\xi)\] (35)  Perform gradient descent step on the loss: \[L(\phi,\xi)=\frac{1}{B}\sum(Q(s,a;\phi,\xi)-y)^{2}\] (36) if\(t\mod\tau=0\)then  Update target network: \(\phi^{-}\leftarrow\phi\);
```  
**Algorithm 2**Dueling Deep Q-Network (D3 QN) Algorithm
# LoS Theoretical and Experimental MIMO Study from 1-40 GHz in Indoor Environments
## 3 Path Loss and K-Factor
All values correspond to FI model values in LoS conditions of the indicated references. Figure 5: Measured path loss (PL) using all bandwidth, line-of-sight (LoS) PL, and Friis model. The antenna effect has been subtracted from the measured values. Figure 3:0 PL for the bands and their 1-slope models. The 1-slope PL model has been also evaluated for the bands 3.3-4.99 GHz, 24.25-27.5 GHz and 37-40 GHz. Figure 7 presents the PL as a function of these three bands and the respective 1-slope models (PL index shown in Table 5). It is noteworthy that only 3 GHz of bandwidth was available for the 37-43.5 GHz band due to the maximum 40 GHz frequency measured in this contribution. The results indicate that even if both slope values are close, the PL index for the 37-40 GHz band is larger than that of the first band. Also, the PL for the second band is larger as expected with a 3.8 dB difference on average agreeing rather well with the 3.6 dB Free Space path loss. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(f_{0}\) (GHz) & **Reference** & \(n\) & \(L_{0}\) (dB) & \(\sigma_{X}\) (dB) & **Type of Environment** \\ \hline \multirow{2}{*}{26} & [24] & 1.00 & 70.50 & 3.82 & Corridor \\  & mmMAGIC [33] & 1.38 & 62.32 & 1.18 & Office \\ \hline \multirow{4}{*}{28} & [25] & 1.20 & 60.40 & 1.80 & Large office \\  & [26] & 1.78 & - & - & Corridor \\  & [27] & 1.40 & 61.90 & 1.70 & Office \\  & [28] & 2.27 & 27.29 & 1.62 & Industrial \\ \hline
39 & [24] & 1.29 & 67.52 & 4.04 & Corridor \\ \hline \hline \end{tabular}
\end{table} 
Table 4: 1-slope model of the PL for several bands extracted from works of the literature. The standard deviation and R\({}^{2}\) parameters are also shown. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Band** & \(n\) & \(L_{0}\) (dB) & \(\sigma_{X}\) (dB) & \(\mathbf{R^{2}}\) \\ \hline
3.3–4.99 GHz & 1.51 & 34.00 & 0.47 & 0.97 \\
24.25–27.5 GHz & 1.28 & 62.59 & 0.84 & 0.90 \\
37–40 GHz & 1.43 & 65.95 & 1.16 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: 1-slope model for selected World Radio Conference (WRC) bands. Figure 7: PL for the World Radio Conference (WRC) bands and their 1-slope models. The K-factor has been computed as the quotient between the LoS component and the remaining energy; the K factor is shown in Figure 8.Negative values are measured, even in LoS situation due to the large number of contributions received (see PDP in Figure 4). This has also been observed in [19].
###### Abstract  
This research aimed to develop a system for classifying horizontal road signs as correct or with poor visibility. In Poland, road markings are applied by using a specialized white, reflective paint and require periodic repainting. Our developed system is designed to assist in the decision-making process regarding the need for repainting. It operates by analyzing images captured by a standard car camera or driving recorder. The image data undergo initial segmentation and classification processes, facilitated by the utilization of the YOLOv4-Tiny neural network model. The input data to the network consist of frames extracted from the video stream. To train the model, we established our proprietary database, which comprises 6250 annotated images and video frames captured during driving. The annotations provide detailed information about object types, their locations within the image, and their sizes. The trained neural network model effectively identifies and classifies objects within our dataset. Subsequently, based on the classification results, the identified image fragments are subjected to further analysis. The analysis relies on assessing pixel-level contrasts within the images. Notably, the road surface is intentionally designed to be dark, while road signs exhibit relatively lighter colors. In conclusion, the developed system serves the purpose of determining the correctness or visibility quality of horizontal road signs. It achieves this by leveraging computer vision techniques, deep learning with YOLOv4-Tiny, and a meticulously curated database. Ultimately, the system provides valuable information regarding the condition of specific horizontal road signs, aiding in the decision-making process regarding potential repainting needs.  
Keywords:road sign classification; YOLOv4-Tiny; image analysis; deep learning +
Footnote †: journal: Applied sciences
## 3 Non-Enzymatic Defensive Mechanisms
### Phenolic Compounds  
Flavonoids, anthocyanins, tannins, hydroxycinamic acid esters, and lignins are phenolic compounds that belong to the secondary metabolites arising from the phenylpropanolid pathway in plant tissues [267]. The enzyme phenylalanine ammonia-lyase (PAL) is the tropical initiator of phenylpropanolid, which converts L-phenylalanine to trans-cinamic acid by deamination. This pathway is necessary for the biosynthesis of secondary metabolites in living cells [268]. Phenolic compounds are naturally synthesised in the cell under optimal conditions, but when there is biotic or abiotic stress, the concentration of these products are significantly affected [152]. Also, any alteration in the activity of biosynthesis or degrading enzymes may influence the amount of these compounds in plant cells.  
Flavonoids are a large group of secondary metabolites that are widely distributed among plants and have multiple roles, including contributing to colouring of flowers, seeds and pollen grains, helping in pollination, germination and pollen tube growth, and auxin transport [26], as well as inducing protection against photosynthetic damages caused by excess excitation energy [269]. The latter function is presumably associated with its ROS scavenging capacity [152]. Thanks to the presence of flavonoids, living cells are capable of alleviating the damage of \({}^{1}\)O\({}_{2}\) on the outer envelope of the chloroplastic membrane [269; 270]. The main enzymes involved in the biosynthesis of flavonoids are PAL and chalcone synthase (CHS). Until now, about 12 groups of flavonoids have been identified, the three most important of which are flavonoids, flavonoids and anthocyanins [271]. Aside from the roles mentioned above, phenolic compounds also have an antioxidant property in the cell.  
Considering the fundamental role of phenolic compounds in reducing or inhibiting lipid auto-oxidation, eliminating oxygen free radicals, quenching singlet oxygen or decomposing peroxides, they have also been known as essential antioxidants responsible for protection against proliferation and advancement of the oxidation chain and defence against reactive oxygen species [271]. The antioxidant properties of phenolic compounds are tied to their chemical structure that may act as an electron or H\({}^{+}\) donor. Polyphenols have been shown to chelate intermediate metals such as iron, thus preventing the Fenton and/or Haber-Weiss reaction [272]. Numerous studies in plants demonstrated the impact of potentially toxic heavy metals on the amounts of phenolic compounds, glutathione, phytochelatins, ascorbate, carotenoids, anthocyanins and the activity of PAL enzyme. For instance, the accumulation of these compounds in the presence of zinc oxide nanoparticles was reported [273].
## 2 MANTiS-32 Wireless Measurement System Design
### Design and Operation of the MPU-6050 Sensor Module  
As depicted in Figure 2, the MPU-6050 sensors affixed to the structure are designed to continuously capture real-time vibration data. These signals are acquired via an I2C interface by the onboard PIC16F15344 microcontroller (Microchip Technology Inc., Chandler, AZ, USA), which subsequently encodes the data using the RS485 communication protocol for robust transmission to the MANTIS-32 hub. The transmitted data are then relayed wirelessly to the PBM system through Wi-Fi communication. To ensure high-precision temporal alignment across all sensor nodes, the system integrates GPS-based time synchronization. This synchronization capability significantly enhances the accuracy of data correlation, thereby enabling precise identification of structural dynamic characteristics and supporting high-fidelity, real-time SHM of the target infrastructure.  
The MPU-6050 sensor's measurement range was configured to \(\pm\)2 g, optimized for capturing ambient vibrations typically exhibited by in-service bridge structures. This range selection was based on empirical observations of operational vibration amplitudes, ensuring sufficient resolution and sensitivity for structural health monitoring under service-level conditions. Furthermore, the sensor's integrated FIFO buffer, with a maximum capacity of 1024 bytes, enables efficient block-based data acquisition and transmission. This design facilitates precise temporal synchronization across multiple sensor nodes, thereby improving data integrity and the overall performance of the wireless sensing network.
# Body Area Networks in Healthcare: A Brief State of the Art
## 6 Cross-Layer Properties
### Common Security Solutions
* A key aspect of physiological signals is that they are time-variant and unpredictable. This prevents external sources from knowing future values if the current value is known. This aspect also presents a challenge, as the communicating devices must be synchronized to perform a secure exchange of data. The processing of physiological signals can be performed in two ways. In the first one, time-domain-analysis signal values are transformed into binary streams and concatenated. The problem with this approach is that devices must be synchronized. The second one is frequency-domain analysis, which produces similar values even if devices are not synchronized. However, this method requires mathematical transformations that can be expensive, especially in resource-constrained devices. An example of security applied to medical data transmission is a recent proposal [60], where the authors designed a framework for healthcare able to improve the security and privacy through the use of homomorphic encryption. In addition, some researchers have proposed a four-tier architecture for health monitoring systems [61]. They identify the security requirements in each layer and review the literature on improving WBANs security and privacy. Moreover, apart from security aspects, there are studies related to the mixture of protocols using cross-layer bridges to address congestion issues in sensor networks [62].
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
## 2 Modal Parameters Identification  
The aim of this project is the development of an adaptive control system which can automatically modify the control policy. In this way, the stand-alone devices can be installed on whatever structure and actively control vibrations without any a priori knowledge on its dynamics.  
In the first step, the natural frequencies of the structure must be detected. The superimposition of coupled modes may affect the results of the modal identification, although such phenomena usually occur in the high frequency range. In this work, the controlled modes are sufficiently unrelated to be clearly identified. So, each actuator is used as a disturbance source, in order to excite the structure in a given range of frequencies. During the sweep excitation of an actuator, the collocated sensor acquires the acceleration signal in order to get the spectrum and then find out the eigenfrequencies. One by one, each stand-alone device perform this task, since the position of the device on the structure may affect the results. Once these operations are over, the information collected by the stand-alone devices are shared with the Central Control Unit (CCU). Such operations have been already implemented and described in [10].  
The second phase is related to the evaluation of the non-dimensional damping ratio \(\xi_{i,j}\) and the structural gain \(R_{i,j}\). Each actuator performs its own evaluations of \(\xi_{i,j}\) and \(R_{i,j}\), for the-th vibration mode and the \(j\)-th actuator. The two parameters play a central role for the setting of the band-pass filter and of the control gains, as reported in Section 4.