esa-sceva/satcom-chunk-collection huggingface dataset:
# Local Differential Privacy-Based Federated Learning under Personalized Settings
## 5 Experiments
### Datasets  
All experiments are carried out on the MNIST ([[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)), accessed on 25 November 2022), Fashion-MNIST ([[https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)]([https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)), accessed on 25 November 2022), and forest cover-types ([[https://archive.ics.uci.edu/ml/datasets/Covertype](https://archive.ics.uci.edu/ml/datasets/Covertype)]([https://archive.ics.uci.edu/ml/datasets/Covertype](https://archive.ics.uci.edu/ml/datasets/Covertype)), accessed on 25 November 2022) datasets. These three datasets are commonly used for classification tasks in federated learning [19, 26].  
The MNIST hand-written digits dataset, is composed of 0 to 9 handwritten digit pictures and digit labels. It consists of 60,000 training samples and 10,000 test samples, each of which is a 28 \(\times\) 28 pixel grayscale handwritten digit picture. We use the digits dataset as well, which is the built-in dataset of scikit-learn. It is a copy of the test set of the MNIST hand-written digits dataset, which consists of 1797 samples with 64 attributes. Each sample is an 8 \(\times\) 8 size image of integer pixels, in the range 0 to 16.  
The Fashion-MNIST dataset consists of 60,000 training examples and 10,000 testing examples. Each example is a 28 \(\times\) 28 size gray-level image, depicting an item from one of ten different fashion classes.  
The forest cover-types dataset consists of 581,012 samples with 54 features. Samples correspond to 30 \(\times\) 30 m\({}^{2}\) patches of forest in the US, collected for the task of predicting each patch's cover type, i.e., the dominant species of tree. There are seven cover types: spruce/fir, Lodgepole pine, Ponderosa pine, cottonwood/willow, aspen, Douglas-fir and Krummholz.The original dataset does not differentiate between training data and testing data. In our experiments, we randomly selected 500,000 samples as the training data, and 30,000 samples were used as testing data. The training data was evenly distributed to 10 clients.
###### Abstract
They first extracted the features from the received signals and then trained an LSTM network to detect whether the period is rainy or not. Artificial neural networks (ANN) have also been used to recognize the rainy and non-rainy periods by [10; 19] and convolutional neural networks (CNN) by [20]. The classification of rainy and non-rainy periods has been performed using Markov switching models [21] and by analyzing the signal in the frequency domain by applying Fourier transformations on a rolling window [22]. Deciding whether a given period is rainy or not can be defined as a binary classification problem. Different classifiers have been used in previous works for the classification of rainy conditions from microwave links [10; 13; 18; 23], such as SVM and LSTM classifiers. In this work, four shallow machine learning algorithms have been proposed and compared to find out the most suitable one for classifying rainy and non-rainy observations from satellite down-links signals. The goal is to identify an effective classifier being also energy efficient to be deployed in a low-power and low-cost embedded device supplied by a battery. The four algorithms are an ANN with one hidden layer, a kernel-support vector machine (K-SVM), a decision tree (DT), and a random forest (RF). The performances of the classifiers have been compared by evaluating three metrics: specificity indicating the capability of classifying non-rainy samples, recall measuring the correctness in predicting the rainy samples, and F1-score averaging the precision (ratio between true rainy and classified as rainy samples) and the recall. The remainder of the paper is organized as follows: Section 2 presents the dataset and its processing, Section 3 describes the algorithms and the metrics computed to evaluate the performance, Section 4 reports the results and discussion, and conclusions are drawn in Section 5.
# Research on Data Transmission of Laser Sensors for Reading Ruler
## 4 Sensor Technology and Code Reading Error Analysis  
The purpose of laser positioning technology, as a form of high-precision distance measurement, is to use a precise timing system to measure the time difference of laser transmission so as to accurately determine the distance between a moving subject and the measurement target. In practical applications, laser positioning technology is usually applied to one end of the track on mobile rail locomotives with a fixed laser rangefinder installed.  
Laser reflectors that can move with the locomotive can be installed to ensure a stable horizontal linear relationship between the two. Collisional laser sensors and laser ranging sensors are commonly used sensors in code readers.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline Encoding sequence & 0 & 0 & 0 & 1 & 1 & 0 & 1 & 1 & 0 & 1 & 0 & 1 & 0 & 0 \\ \hline \multirow{4}{*}{Coding ruler} & 1 & 1 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 1 \\  & 1 & 1 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 1 \\  & 1 & 1 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 1 \\  & 1 & 1 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Characterization of Encoder.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 6 Resource Utilization Analysis  
The ESP32-CAM was selected as the target hardware platform due to its integrated Wi-Fi, dual-core processor, embedded camera, and compatibility with lightweight ML frameworks. Deploying both training and inference of an AE model, along with Wi-Fi-based communication for FL, required careful resource management within the device's constraints.  
The ESP32-CAM features the Xtensa dual-core 32-bit LX6 processor running at up to 240 MHz. It includes 520 KB of internal SRAM, divided into 200 KB of IRAM (Instruction RAM) for storing executable code and 320 KB of DRAM (Data RAM) used for dynamic data. The module also includes external PSRAM (4 MB in this implementation) and 4 MB of Flash memory. These resource constraints significantly influence how models are stored, how training is executed, and how data is managed.  
Training the AE directly on the ESP32-CAM was enabled by designing compact network architectures with reduced depth and neuron count. Integer arithmetic was employed wherever possible to reduce memory consumption and increase execution speed, while model weights were kept in floating-point format to maintain accuracy comparable to standard TensorFlow implementations.
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 1 Introduction
Therefore, RSPT can help the DR efficiently establish downlink multicast paths in the case of asymmetric routing. In addition, the RSPT-based MJN selection method is distributed, low-latency, and has the potential for practical engineering deployment. The main contributions of this paper are:  
1. This article proposes an efficient load detection mechanism for downlink multicast paths. By constructing RSPT, this mechanism enables DR to evaluate the load status of downlink multicast paths efficiently. This mechanism is fully applicable to situations where routing is asymmetric;
2. We have designed a real-time, distributed MJN selection algorithm that can immediately provide service for new group members, ensuring minimal delay in multicast service. Additionally, this algorithm does not require a central management node to participate in the calculation of multicast paths, which helps improve the scalability of multicast;
3. Through a large number of experiments, the proposed method has been shown to have a low cost, low latency, and the ability to effectively balance the load state in the network, even the routing is asymmetric. The structure of this article is organized as follows. Section 2 presents a review of the related work. Section 2 introduces the proposed MJN selection method, which consists of two main parts: the construction of RSPT and the implementation of the MJN selection method. Section 4 describes the experiments conducted to evaluate the proposed method. Finally, Section 5 concludes the paper. The notations and definitions used in this paper are summarized in Table 1.
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 2 Artificial Intelligence for Wind Speed-Power Forecasting
These models may fail to capture localized weather phenomena, leading to inaccuracies in short-term predictions. On the other hand, machine learning can handle high-resolution data and learn complex patterns from historical information, which is heavily dependent on the quality and quantity of the input data. When these approaches are combined, there is a risk that the inaccuracies or biases present in the NWP data can be transferred to the ML models, potentially degrading prediction performance [57; 58]. Also, hybrid systems are computationally expensive due to the need to run both high-resolution NWP simulations and complex ML algorithms [57; 58]. Another limitation arises from the dynamic nature of atmospheric conditions, which can change rapidly, particularly in regions with high variability in wind patterns. While NWP models provide general trends based on physical laws, ML models struggle to adapt to unseen or rapidly changing weather conditions that deviate from historical trends. This makes it difficult for the combined models to generalize effectively across different time horizons or geographical regions [38; 59]. In addition, the integration of NWP and ML requires sophisticated tuning and coordination between both models to avoid overfitting or underfitting [60; 61].
## 4 Signal Processing
### Heartbeat Signal Analysis  
Recently, it has been shown that radar systems are able to perform contactless measurement of heart sounds by choosing the right cutoff frequencies [29]. This can be achieved by filtering the raw distance signal between 16 Hz and 80 Hz. Furthermore, [PERSON] et al. stated that the analysis of heart sounds results in higher accuracies compared to pulse waves or rather sphygmograms [42] when analyzing the heart rate. Therefore, this paper will compare the performance of both methods when compared to a reference ECG. As a first step, the so called interbeat intervalls (IBIs) are calculated using both the sphygmograms and the heart sounds. To do so, two state-of-the-art algorithms are used: the hidden semi-Markov model (HSMM) segmentation algorithm of [43] in case of heart sounds and the template matching algorithm of [44] in case of sphygmograms. Both algorithms predict points in time where single heartbeats are detected. The IBIs constitute the distances between successive heartbeats which in case of the ECG are calculated as the distance between the R-peaks. By comparing the predicted and reference IBI values \(I\) and \(I_{ECG}\), the RMSE can be determined:  
\[\mathrm{RMSE}=\sqrt{\frac{\sum_{n=1}^{N}(I_{n,ECG}-I_{n})^{2}}{N}}. \tag{8}\]  
The smaller the RMSE value, the closer the IBI values are to the reference which in turn indicates a high accuracy. If confirmed that the segmentation of the heart sounds performs better than the analysis of the sphygmograms, additional scores are calculated. Therefore, a threshold needs to be determined for which heartbeats are labeled as detected correctly. In case a single heart sound is identified in a range of 75 ms around a reference ECG R-peak, it is marked as true positive (TP). This value is chosen well within the limits of inaccuracy permitted in certified medical products (150 ms [23]). The even narrower margin of half the tolerance reflects the accuracy of radar-based heart sound detection. If no heart sound is detected in this area, a false negative (FN) is counted. If no reference peak is around a predicted heart sound, it is labeled as false positive (FP). Using the sum of these values, precision and recall can be calculated:  
\[\mathrm{Precision}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}, \tag{9}\] \[\mathrm{Recall}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}. \tag{10}\]  
Using those values, the F1 score is determined which constitutes the harmonic mean of these both parameters:  
\[\mathrm{F1}=2\cdot\frac{\mathrm{Precision}\cdot\mathrm{Recall}}{\mathrm{ Precision}+\mathrm{Recall}}. \tag{11}\]Figure 9 shows an example of heart sound segmentation using the HSMM algorithm. The algorithm predicts four phases: S1, systole, S2 and diastole. If the start of S1 is detected in the tolerance range around the R-peak, a TP is counted.
# An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates
## 4 Experiment and Analysis of Surface Defects Detection Based on MB-LBP
### Experimental Design
#### 4.1.5 Parameter Selection of MB-LBP  
There is a certain fluctuation in the recognition accuracy, as shown in Figure 15. When the block size increased from \(2\times 2\) to \(4\times 4\), the accuracy increased, and when it increased to \(8\times 8\), it decreased. This is because the block enlargement was equivalent to image compression, and too large a block size lost too much defect feature information, so the accuracy decreased. Through analysis, this experiment selected \(4\times 4\) block size for calculation.
# SLoPCloud: An Efficient Solution for Locality Problem in Peer-to-Peer Cloud Systems
###### Abstract  
Peer-to-Peer (P2P) cloud systems are becoming more popular due to the high computational capability, scalability, reliability, and efficient data sharing. However, sending and receiving a massive amount of data causes huge network traffic leading to significant communication delays. In P2P systems, a considerable amount of the mentioned traffic and delay is owing to the mismatch between the physical layer and the overlay layer, which is referred to as locality problem. To achieve higher performance and consequently resilience to failures, each peer has to make connections to geographically closer peers. To the best of our knowledge, locality problem is not considered in any well known P2P cloud system. However, considering this problem could enhance the overall network performance by shortening the response time and decreasing the overall network traffic. In this paper, we propose a novel, efficient, and general solution for locality problem in P2P cloud systems considering the round-trip-time (RTT). Furthermore, we suggest a flexible topology as the overlay graph to address the locality problem more effectively. Comprehensive simulation experiments are conducted to demonstrate the applicability of the proposed algorithm in most of the well-known P2P overlay networks while not introducing any serious overhead.  
P2P cloud systems; overlay networks; locality problem; generalized hypercube connected cycle +
Footnote †: journal:
## 7 Routing Protocols proposed by Spanish Universities
### QoS Routing Protocols based on Artificial Intelligence  
In [30] a routing protocol that guarantees some QoS requirements by means of an artificial intelligence technique is presented. Neural networks are then introduced into the sensor nodes and a self-organized map is used. The simulation results show its ability to reduce the end-to-end delay and the network overhead compared to the Directed Diffusion protocol.
## 3 The Finite Element Model of the Tire Established in ABAQUS  
### Establishment of the Tire Finite Element Model  
To investigate the acceleration, strain, and displacement response characteristics of a rolling tire under different loading conditions, a finite element model of the 205/55 R16 radial tire with a complex tread pattern was developed. The modeling process was divided into two stages: the construction of the tire carcass structure and the tread pattern. In the modeling process, the tire was divided into the tread pattern and the carcass structure (including all components except the tread) [24]. The 2D cross-sectional profile of the tire, created in AUTOCAD, was imported into HYPERMESH for mesh generation, resulting in a 2D finite element mesh model of the tire carcass structure. The rubber-cord structure of the tire was described using the Rebar model, with rubber element types defined as CGAX3H and CGAX4H and the cord element type defined as SFGAX1. After importing the 2D tire carcass mesh model into ABAQUS, the *Revolve command was used to rotate a 3D tire carcass mesh model with a single pitch. To improve the computational accuracy in the contact area, local mesh refinement was applied. The rim and road surface were both defined as analytical rigid bodies, and their interactions with the tire were described using contact constraints with a Coulomb friction model. The rubber material exhibits significant nonlinear mechanical properties, and its mechanical properties vary across different tire components. To accurately describe these properties, our research team conducted uniaxial tensile tests on the tire rubber material, and the experimental data were fitted using ABAQUS. The detailed testing process is provided in reference [25]. Based on the analysis results, the Yeoh model was chosen to describe the mechanical properties of the rubber material, and its strain energy function is defined as follows:  
\[U=C_{1}(I_{1}-3)+C_{2}(I_{1}-3)^{2}+C_{3}(I_{1}-3)^{3} \tag{7}\]  
In the equation, \(I_{1}=\lambda_{1}^{2}+\lambda_{2}^{2}+\lambda_{y}^{2}\), \(I_{1}\) represents the first invariant of the strain tensor. \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) represent the principal elongation ratios. \(C_{1}\), \(C_{2}\), and \(C_{3}\) are the material parameters that characterize the mechanical properties under different strain levels.  
During the tread pattern modeling process, a solid 3D tread pattern with a single pitch was first constructed using CATIA and then imported into HYPERMESH for 3D mesh generation. Considering the incompressibility of the rubber material, hybrid elements were chosen as the mesh type. The *Tie command in ABAQUS was subsequently used to bond the 3D tread pattern mesh with the 3D tire carcass mesh, forming a sector-shaped tire model with a single pitch. The *Symmetric model generation command was subsequently applied to rotate and generate the complete tire model. The process of establishing the tire finite element model is shown in Figure 1.  
Figure 1: Finite element modeling process of a tire with a complex tread pattern.
# Multi-Access Channel Based on Quantum Detection in Wireless Optical Communication
## 1 Introduction
A similar attempt to do so was made in [31] but proved to be unsuccessful [32]. Thanks to the development of quantum information, unlike the OMA and NOMA techniques, this paper attempts to provide a solution for multi-access in WOC from the perspective of quantum measurement. Unlike the conventional approach, we consider using symmetric coherent-state signals in the complex plane as carriers for multi-user WOCs. By using quantum detection, multi-access channel can be adapted to communication situations with lower optical signal power. It is well-suited for free-space optical communication, especially in deep space optical communication where the received signal power is highly restricted [33]. We let each user correspond to a pair of coherent-state signals, thus constructing a single-bit multi-access channel. This will be detailed in Section 2.2, and by applying MED and USD, we can obtain the optimal quantum detection and error-free detection of the coherent-state signals, respectively. These are placed in Section 2.3. Section 2.4 gives a typical example of two-user channel to facilitate our calculation of the channel capacity values. Further, we study the case of USD with thermal noise in Section 2.5 to evaluate the impact on error-free detection. Specific numerical experimental results are given in Section 3 to demonstrate the effectiveness of our approach.
## 2 Material and Methods
### HS-SPME Procedure  
The HS-SPME technique (Figure 1), based on previous publications by our research group [14; 46; 48], was used for the simultaneous determination and quantification of safrole in cowpea beans. Briefly, the assays were performed using a manual SPME holder (Supelco, Bellefon, PA, USA) equipped with a divinylbenzene-polydimethylsiloxane-50/30 \(\upmu\)m fiber (DVB/CAR/PDMS-50/30 \(\upmu\)m) (Supelco, Bellefont, USA). Samples (5.0 g) were weighed on an analytical balance (Sartorius BP 221 S, Goettingen, Alemanha) in 44 mL amber flasks fitted with a Teflon cap and silicone/PTFE septum. After the fortification with safrole, the samples were homogenized in a vortex (Vortex Mixer CE, Vixar, Korea) for 1 min, and the flasks were allowed to stand for 30 min for solvent evaporation and interaction of the analyte with the matrix. The vials containing the samples were then closed and transferred to a water bath equipped with a magnetic stirrer and set at 60 \({}^{\circ}\)C. The fiber (which had been daily conditioned according to the manufacturer's recommendations) was exposed to the headspace gas phase during the warm-up period (10 min). Following extraction, the fiber was removed from the flask and immediately inserted into a gas chromatography injector, where it remained exposed for 3 min. The analyte was thermally desorbed from the fiber under the carrier gas flow and then led to the chromatographic column.
# W-Band 4 th Order Waveguide Filter Based on Double Layer SU8 Microfabrication
## 4 Measurement Results and Discussion
### Analysis of Measurement Results  
The S-parameter measurement and simulation results under the two different press blocks are shown in Figure 12. The measured results of clamping the SU-8 chip with the pressure blockIshow that the center frequency is 92 GHz, the equal ripple bandwidth is 5.5 GHz, the minimum insertion loss is 0.8 dB, and the return loss is better than 12.4 dB. The measured results of clamping the SU-8 chip with the pressure block Ilshow that the center frequency is 92.25 GHz, the equal ripple bandwidth is 5.5 GHz, the minimum insertion loss is 0.9 dB at this center frequency, and the return losses are better than 11.7 dB across the whole passband. The reason for the higher center frequency of the passband and the narrower FBW is that manufacturing imperfections are not considered in the design. The measured insertion loss of the pressure block I is 0.1 dB smaller than that of the pressure block II. This is due to the fact that the SU-8 chip with pressure block II has a large gap between the layers which introduces additional losses. The measured insertion loss using the two kinds of pressure block structures are higher than the theoretical insertion loss value of 0.27 dB using the conductivity of silver. The reasons for this small difference in insertion loss may be attributed to the following aspects: (i) Small calibration errors. (ii) Imperfect contact between adjacent layers makes the return loss worse, thereby reducing the insertion loss. (iii) The imperfect surface roughness can degrade the effective conductivity to 2/3 and cause another loss. (iv) The type and thickness of surface metallization of the waveguide's sidewall have an effect on the insertion loss [18].  
Figure 11: Photograph of the measurement setup.  
Figure 12: Simulation and measurement results of the designed filter over the whole \(W\)-band with two kinds of press block.