esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 5 Discussion
### Comparison with Existing Positioning Techniques
#### 5.2.3 Light-Pulse Distance Sensing  
This direct method uses a laser diode (sender and receiver) to detect transmission lines. The main applications are the mapping of conductors and pylons and detection of trees near the transmission grid. Its advantage lies in its accuracy, noise immunity and its robustness under variable lighting conditions. However, several point clouds have to be acquired for mapping small and narrow objects and the sensor technology can be highly expensive.
## 2 Materials and Methods
### Currency Trend Forecast Application: HFT Grid Trading System  
In the previous sections, the forecast pipeline of the medium-term trend of a currency has been described based on the analysis of the correlated currency dynamics through a financial technique known as triangular arbitrage (both through deep learning methodologies and through reinforcement learning techniques). Once the forecast robustness of the trend in the medium term has been established (we have defined without loss of generalization, an average trend defined by 100 quotations on 1M timeframe) it is now necessary to plan a trading strategy that based on such forecasts, can execute a series of financial transactions (trades) agree with the estimated trend. For this application, the author intends to use the HDT algorithm with the grid already effectively used and illustrated in a previous work published in this journal [2]. We make a brief reference to this type of trading system which in all respects can be counted among the HFT algorithms.  
Basically, a grid trading strategy is an algorithm that seeks to make profit on the market movements of the underlying financial instrument by positioning properly time-spaced buy and sell orders (grid distance). In our case, the trading system will work as follows.  
Using the prediction system of the trend just described in the previous sections, a medium-term trend will be generated, i.e., for the next N quotations (in our case N = 100 has been hypothesized without loss of generalization of the method). Based on this predicted trend, the grid trading system as described in paper [2] will open operations in accordance with the predicted trend (buy for a predicted LONG trend; sell (or short selling) for a predicted SHORT trend ) and appropriately spaced according to the adaptive grid methodology illustrated in detail in the algorithm implemented in [2] and which will basically avoid opening up close positions with each other if the overall drawdown of the financial portfolio is increasing (therefore mediating 'the loss' with operations of the same sign appropriately spaced) and vice versa will attempt to open closer positions (dynamically reducing the distance of the grid) if the drawdown is decreasing. In the event that the pipeline produced will provide a trading range forecast (no well-defined forecasted trend), the trading system will not execute any operation entering the hold status. As described in previous sections, we perform trading operations on EUR/USD cross. The next paragraphs report the results of the testing phase of the proposed approach (trend forecast with grid trading system) which will confirm the efficiency of the proposed method beyond any reasonable doubt.
# Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI
## 2 Methodology
### YOLOv8 and SAMURAI Architecture
#### 2.3.2 SAMURAI Architecture  
SAMURAI is an object tracking architecture that utilizes temporal relationships between consecutive frames to track object locations [62]. Unlike single-frame-based detection models, SAMURAI incorporates a memory-based approach to maintain object information over time [62]. This study applies SAMURAI to analyze worker movements in a construction environment and assess detection performance under different occlusion conditions.  
The SAMURAI architecture consists of key components, including an image encoder, memory attention, mask decoder, and additional modules for processing and evaluation. The image encoder processes input frames and extracts feature maps, converting visual data into structured representations. These extracted features are then used to detect objects and analyze spatial relationships. The memory attention module utilizes stored object information from previous frames to determine object locations in the current frame, facilitating tracking across sequential frames.  
The mask decoder reconstructs object locations using the memory-attended information. Additionally, the motion modeling module accounts for object movement characteristics such as displacement and velocity. The master encoder further refines detection results, and two auxiliary components, the affinity head and object head, assess the confidence of detected objects based on the consistency of their features across frames.  
SAMURAI includes a motion-aware memory selection mechanism, which prioritizes and utilizes relevant temporal features for each object across sequential frames. This  
Figure 3: Improved YOLOv8 architecture compared to YOLOv5.  
mechanism enables continuous object tracking by dynamically selecting memory features that reflect motion patterns, even under conditions involving occlusion or scene changes.  
In this study, SAMURAI is used to track construction workers performing brick transportation and bricklaying tasks. The collected data are processed through the memory module, allowing for the verification of detection consistency across frames. The detailed evaluation of detection performance will be discussed in subsequent sections.  
Figure 4 illustrates the SAMURAI architecture implemented in this study.
## 3 Measurement Setup
### Wind Turbine Gearbox  
Vibration data were recorded from an operational industrial wind turbine at full load using an INGESYS system manufactured by INGETEAM Service, Spain. The monitored wind turbine had a rated power of 850 kW with a 3-stage planetary gearbox and a ratio of approximately 1:62. The wind turbine was known to be due for maintenance and it had some gear teeth wear and bearing damage at the planetary stage.  
Figure 3: The test setup used to measure the vibration of the simple gearbox. On the left the system with 3 blades is visible and on the right a different setup with a single blade is shown.  
Figure 2: Test-rig used to measure the vibration of rolling elements. The figure on the left showing the complete setup and the image on the right is zoomed in on the rolling element under the test.  
The INGESYS monitoring system was installed in an industrial cabinet in the nacelle of the wind turbine. It consists of the power supply, communications and vibration acquisition system. During testing it was connected to the ground data recording unit using fibre optic.  
In order to measure vibrations in the drive-train, 8 piezoelectric accelerometers were used. Two of them were specifically selected to measure low vibration frequencies in the main shaft. The two types of the accelerometer sensors that were used are PRE-1010-MS and PRE-1030-MS by IMI Sensors capable of covering the range of 0.5 Hz to 10 kHz and 2 Hz to 10 kHz, respectively. These sensors were installed on both radial and axial directions on the main shaft, planetary stage, high speed shaft and the generator. Some of the accelerometers installed on the wind turbine gearbox are shown in Figure 4.
# Application of Internet of Things in a Kitchen Fire Prevention System
## 2 Literature Review
### Current Research on Kitchen Fire Prevention
#### 2.4.2 Sensor-Based Fire Detection Technologies  
The actuator provides a wide range of possibilities for human-machine interaction, including appropriate interventions for each detected risk situation, and an appropriate response based on user needs. Establishing a sensor-based safe cooking environment requires in-depth risk analysis [49]. The disaster risk of the kitchen consists of three factors: fire, burn and intoxication. The following is a summary of these three factors.  
The sensor-based fire detection technology is highly accurate, easy to install, inexpensive, and easy to deploy [51]. The vision-based visual fire detection algorithms are used for fire color modeling and motion detection, and the sensor network can be combined with this system. These network combinations seem attractive, but they increase system overhead and increase the complexity of system installation and deployment [64]. Compared with the above techniques and methods, safe from fire (SFF) has a simple strategy for detecting fires, and can inexpensively and efficiently deal with fraudulent fire scenarios. It is also effective for early detection of fires. In this study, Arduino sensing components were used as fire detectors for home kitchens. These detectors include a flame detector, a temperature detector, and a gas detector. Information about the program and control modules will be discussed in detail in Section 3.
# Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware
###### Abstract  
In this article, we investigate the design of reconfigurable intelligent surface (RIS)-aided transmission as a smart method to reflect signals received from access points to users and, hence, improving users' performance. To implement smart Internet of Things (IoT) networks, massive connectivity and low-cost deployment are essential in designing such systems. In particular, we consider two practical scenarios (dual-hop and single-hop transmissions). These scenarios highlight the potential of RIS in enhancing the system's outage probability performance. Furthermore, to characterize channel conditions in practice, we pay particular attention to two-channel distributions that are non-central chi-square (NCCS) distributions that approximate the channel distribution of the RIS-aided wireless system and the squared \(K_{G}\) distribution. In addition, the RIS-aided system may face imperfect hardware-related issues in practice. Therefore, we need to consider the degraded performance of practical RIS-aided systems by considering the detrimental impact of in-phase and quadrature-phase imbalance (IQI). To characterize the main system performance metric, we provide closed-form formulas of outage probability and ergodic capacity. We then evaluate system performance under the impacts of signal-to-noise ratio (SNR), the number of meta-surfaces, and channel parameters. All closed-form outage expressions are validated via Monte Carlo simulations. Simulation results indicate that the considered RIS scheme at dual-hop and single hop under the impact of IQI and RIS hardware impairment achieves significant improvements in terms of outage probability at high SNR and high meta-surface number N. Additionally, the simulation results demonstrate that the impact of IQI on the proposed system is limited. It is worth noting that, in terms of ergodic capacity, ergodic capacity faces an upper limit. Despite this limitation, the proposed system can still work well once some parameters are controlled well, such as the transmit SNR, levels of IQI, and the number of RIS components.  
reconfigurable intelligent surface (RIS); outage probability; in-phase and quadrature-phase imbalance (IQI) +
Footnote †: journal: Electronics Letters
# A Review on Decentralized Finance Ecosystems
## 4 The Taxonomy
### Advantages
#### 4.3.1 Benefits  
DeFi has the capacity to revolutionize business operations by optimizing processes, increasing efficiency, reducing costs, and enhancing overall performance. Integrating DeFi with Fintech presents considerable benefits, including heightened security and governance, thereby delivering substantial value. Below are a few examples of the benefits of using DeFi.  
Self-Custody  
Self-custody in DeFi allows individuals or businesses to hold and manage their digital assets, like cryptocurrencies, independently without relying on third-party custodians. Within the DeFi ecosystem, users maintain complete ownership and control of their assets,interacting with financial applications and protocols without requiring permission from intermediaries. This direct interaction often results in lower transaction costs [11].  
Self-custody offers individuals complete control over their assets, ensuring privacy and protecting identities from external interference. It enables constant access and use of assets from any location, which is considered beneficial for preserving crypto wealth over time. However, self-custody poses risks, such as potential loss or theft of private keys if not securely managed or backed up. Additionally, a lack of control over data in DeFi might expose users to fund theft or loss, along with limitations or risks associated with custodial wallets. Despite these risks, self-custody is an essential feature because it fosters trust in the assets that users hold. DeFi aims to empower individuals to engage in various financial interactions, such as lending, borrowing, asset speculation, diversification, earning returns, or obtaining insurance without intermediaries [31].
# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
## 4 Experimental Results
### Latency Assessment
These include using real-time operating systems (RTOS) to ensure deterministic task management and optimizing UART configurations to enhance buffer handling and transmission efficiency.
## 6 Results and Discussion
### Mean Delay Performance  
The mean delay is a summation of the MAC layer delay and the delay due to time spent in the queue before the actual packet transmission. Figure 9 represents the mean delay for the proposed FASBA protocol.  
The mean packet delays of FASBA in an ad hoc network for N = 60 nodes is shown in Figure 10. One can observe that FASBA outperforms MP-EDCA for N = 24 to 60 nodes. For example, FASBA's mean packet delay is about 30% lower than MP-EDCA for a network with N = 60 nodes.  
Both emergency traffic and time-sensitive applications are delay-sensitive. It is found that FASBA can support up to 32 emergency nodes, whereas MP-EDCA can support about 24 nodes. It is observed that FASBA's mean delay is lower than MP-EDCA in both ad hoc and infrastructure networks.  
The main conclusion that can be drawn from Figure 10 is that nodes using FASBA have substantially lower mean packet delays than the nodes using MP-EDCA, especially under medium- to high-traffic loads.  
Figure 8: Mean network throughput of the proposed FASBA (infrastructure network scenario).  
Figure 7: Mean network throughput of MP-EDCA and the proposed FASBA (ad hoc network scenario).
# The Fast Discrete Tchebichef Transform Algorithms for Short-Length Input Sequences
###### Abstract  
In this article, the fast algorithms for the discrete Tchebichef transform (DTT) are proposed for input sequences of lengths in the range from 3 to 8. At present, DTT is widely applied in signal processing, image compression, and video coding. The review of the articles related to fast DTT algorithms has shown that such algorithms are mainly developed for input signal lengths 4 and 8. However, several problems exist for which signal and image processing with different apertures is required. To avoid this shortcoming, the structural approach and a sparse matrix factorization are applied in this paper to develop fast real DTT algorithms for short-length input signals. According to the structural approach, the rows and columns of the transform matrix are rearranged, possibly by changing the signs of some rows or columns. Next, the matched submatrix templates are extracted from the matrix structure and decomposed into a matrix product to construct the factorization of an initial matrix. A sparse matrix factorization assumes that the butterfly architecture can be extracted from the transform matrix. Combining the structural approach with a sparse matrix factorization, we obtained the matrix representation with reduced computational complexity. Based on the obtained matrix representation, the fast algorithms were developed for the real DTT via the data flow graphs. The fast algorithms for integer DTT can be easily obtained using the constructed data flow graphs. To confirm the correctness of the designed algorithms, the MATLAB R2023b software was applied. The constructed factorizations of the real DTT matrices reduce the number of multiplication operations by 78% on average compared to the direct matrix-vector product at signal lengths in the range from 3 to 8. The number of additions decreased by 5% on average within the same signal length range.  
Keywords:discrete Tchebichef transform; sparse matrix factorization; data flow graphs; butterfly architecture; fast algorithms; computational complexity +
Footnote †: journal:
# Closed-Form Performance Analysis of the Inverse Power Lomax Fading Channel Model
## 4 Simulation and Results' Analysis  
To support the analytical derivations and verify the results, simulations were performed. The results evaluated using the obtained exact expressions (see Lemmas 1 and 2 and Theorems 1-5) and plotted with solid lines in Figures 1-8 were tested against those obtained via a Monte Carlo simulation with \(10^{8}\) samples (plotted with markers) and supplemented, where applicable, with high-SNR asymptotics (plotted with dashed blue curves).  
The channel parameters (\(\tilde{\gamma}\), \(\alpha\), and \(\beta\)) were chosen to cover all possible scenarios, including heavy and light fading. It must be pointed out that for all results, \(\alpha>0\), since no restrictions are imposed on it. Therefore, in most plots, its lowest value was chosen to be 0.1, except for the hyper-Rayleigh analysis, where the smallest value of \(\alpha\) was 0.01. Conversely, the lowest value of \(\beta\) was chosen differently depending on the analyzed characteristics: if its evaluation required the second-order moment, \(\beta\) was greater than two (e.g., the AoF, ABER, EC, and HRR); otherwise, \(\beta>1\) (e.g., the PDF, CDF, and MGF). As mentioned earlier, since the IPL model (as well as the baseline Lomax model) belongs to the heavy-tailed distributions and exhibits various HHRs, the simulation specifically focused on the lower values of the parameters.  
Figure 1: PDF comparison of numerical evaluation and numerical simulation for various channel parameters.  
Figure 2: CDF comparison for various channel parameters.  
Figure 4: MGF comparison of numerical evaluation and derived closed-form expression for various channel parameters.  
Figure 3: IPL positioning among various fading channel models.  
Figure 5: Empirical data from D2D communication experiment with fitted IPL model and \(\kappa-\mu/\kappa-\mu\) model: (**a**) LOS indoor environment (see [56] Figure 12a), (**b**) NLOS indoor environment (see [56] Figure 12b), (**c**) LOS outdoor environment (see [56] Figure 12c), and (**d**) NLOS outdoor environment (see [56] Figure 12d).  
Figure 6: Empirical data from D2D communication experiment [57] with fitted IPL model and \(\alpha-\mathcal{F}\) model (see [42]).
# Thermo-Mechanical Properties of PLA/Short Flax Fiber Biocomposites
## 3 Predictive Models of Mechanical Properties
### Elastic Modulus Prediction  
The composites analyzed are constituted by short flax fibers randomly oriented in the PLA matrix. The analytical models for short fibers were derived from the elasticity theory, starting from the models developed for long fiber composites. To better fit the experimental data and to take into account the differences between long and short fiber composites, a consistent numbers of geometrical, topological, and mechanical parameters [6; 34] must to be taken into account. Different efforts to fit analytical models with experimental data can be found [40].  
However, due to the low aspect ratio, it can be expected that the mechanical behavior and the elastic response of the produced composites is similar to the behavior of particulate filled composites. Consequently, some analytical models suitable for particulate filled composites [41; 42; 43] were applied to the PLA/short flax fibers composites. The analytical models adopted in this work to predict the Young's modulus of the composites are reported in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **E composite** \\ \hline [PERSON] [43] & \(E_{c}=E_{m}\Big{(}1+2.5V_{f}\Big{)}\) \\ [PERSON] [42] & \(E_{c}=E_{m}\Big{(}1+\frac{1.25\cdot V_{f}}{1-1.2\cdot V_{f}}\Big{)}\) \\ [PERSON] [40] & \(E_{c}=\Big{(}1-V_{f}\Big{)}E_{m}+V_{f}E_{f}\Big{(}1-\frac{\tan\ln(n\cdot q_{i}) }{(n\cdot q_{i})}\Big{)}\) \\ [PERSON] [44] & \(E_{c}=\frac{3}{8}E_{l}+\frac{5}{8}E_{t}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analytical models used for the prediction of the composite Young’s modulus.  
In the models of Table 2, \(E_{c}\), \(E_{m}\), and \(E_{f}\) represent the elastic modulus of the composite, matrix and fibers, respectively, \(V_{f}\) is the fiber volume fraction, \(a_{r}\) is the aspect ratio of the fibers. The adimensional parameter \(n\) of the Cox model is defined as (Equation (4)) [45]:  
\[n=\frac{2E_{m}}{E_{f}(1+v)ln\left(\frac{P}{V_{f}}\right)} \tag{4}\]  
where \(v\) is the Poisson ratio of the matrix (\(\approx\)0.4) and \(P\) is the packing factor of the fibers, considered equal to \(2\pi\)/\(\sqrt{3}\).  
Concerning the Halpin-Tsai model [44], the two terms \(E_{l}\) and \(E_{t}\) are, respectively, the longitudinal and the tangential modulus; these values can be calculated by the following equations:  
\[E_{l}=E_{m}.\frac{1+2\cdot a_{r}\left(\frac{\frac{E_{f}}{E_{m}}-1}{\frac{E_{ f}}{E_{m}}+2a_{r}}\right)V_{f}}{1-\left(\frac{\frac{E_{f}}{E_{m}}-1}{\frac{E_{ f}}{E_{m}}+2a_{r}}\right)V_{f}} \tag{5}\]  
\[E_{t}=E_{m}.\frac{1+2\cdot a_{r}\left(\frac{\frac{E_{f}}{E_{m}}-1}{\frac{E_{ m}}{E_{m}}+2}\right)V_{f}}{1-\left(\frac{\frac{E_{f}}{E_{m}}-1}{\frac{E_{ f}}{E_{m}}+2}\right)V_{f}} \tag{6}\]
# Towards a Decentralized Collaborative Framework for Scalable Edge AI
## 6 Experimental Evaluation
### Heterogeneity Impact  
As discussed previously, one of the main problems in decentralized IoT scenarios is the device, configurations, and environment heterogeneity [7; 11; 14]. We extensively run experiments in FL settings covering over one thousand five hundred different heterogeneity settings, FL configurations, and learning hyper-parameters. The average model accuracy is evaluated by invoking a test round after every 10 training rounds. During test rounds, the model accuracy is evaluated by every client using its own test dataset, and then these accuracy figures are averaged to produce the average test accuracy of the model. Figure 3 shows a bar plot for the achieved test accuracy from all the experiments (normalized to the maximum baseline accuracy for each of the five benchmarks) and contrasts the following cases: (i) U: uniform or homogeneous situations in which all clients have identical hardware and network settings and are constantly accessible. (ii) **BH**: a behavior heterogeneity case where the devices have variable availability patterns based on real-world trace; (iii) **DH**: a device heterogeneity case where each device is assigned a different compute and communication profile from real-world trace; and (iv) **H**: a mixed heterogeneity case with both device and behavior heterogeneity.  
As Figure 3 shows, all the heterogeneity cases (**BH**, **DH**, and **H**) significantly impact the test accuracy compared to the homogeneous case (**U**). The results show that the median accuracy (the light green line) is lowered by up to 12%, and the variance is severely high in the heterogeneous cases. What makes matters worse is that heterogeneity can prevent training from converging. This reinforces the motivation of this work, that heterogeneity is endemic to decentralized learning and to scale the training should cope with heterogeneity to produce high-quality models (c.f. [14] for more detailed results).