esa-sceva/satcom-chunk-collection huggingface dataset:
# A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems
## 3 Proposed MP-AIC Scheme of Clipping Noise
### The Procedure of the MP-AIC Method
Message from \(x_{a}\) to \(\tilde{d}_{b}\): The pmf vector \(p_{ab}^{(i+1)}\,_{(k)}\) can be updated as: \[p_{ab}^{(i+1)}\,_{(k)}=\Delta p_{ab}^{(i)}\,(a_{j})+(1-\Delta)\Delta p_{ab}^{( i-1)}\,_{(k)}(a_{j}),\] (34) where: \[p_{ab}^{(i)}\,_{(k)}\propto\prod_{c\in S,c\
eq b}\Pr(\tilde{d}_{c}|d_{a}=a_{j },H_{eq}),\] (35)and:  
\[\Pr(\hat{d}|d_{a}=a_{j},H_{eq})\propto\exp\left(\frac{-\left|\hat{d}_{c}-\mu_{ca} ^{(i)}-H_{ca}a_{j}\right|^{2}}{\sigma_{c,a}^{2(i)}}\right). \tag{36}\]  
Return to step 4 until \(\max\limits_{a,b,a_{j}}\left|p_{ab}^{(i+1)}(a_{j})-p_{ab}^{(i)}(a_{j})\right|\leq\varepsilon\) or \(i\geq\tau_{\max}-1\). Return to step 3 until \(k\geq N_{iter}-1\). Output the mp-decoded symbol as:  
\[\hat{d}_{a}=\operatorname*{arg\,max}_{a_{j}\in\Lambda}p_{a}(a_{j}),a\in[0,L-1]. \tag{37}\]  
In conclusion, the pseudo-code of MP-AIC method for clipping noise cancellation can be expressed by Algorithm 1. ```
0:\(\beta,k_{p},l_{p},h_{p}\), \(N_{iter}\), \(\tau_{\max}\), \(\sigma_{n}^{2}\), \(\Delta=0.6\), \(\varepsilon=0.01\). 0:\(\hat{d}_{a}=\operatorname*{arg\,max}_{a_{j}\in\Lambda}p_{a}(a_{j})\) and \(p_{ab}^{(\tau_{\max}-1)}\). Initialize \(k=0\), \(j=0\), \(\sigma_{All(0)}^{2}=\sigma_{All^{\prime}}^{2}\), \(p_{ab}^{(0)}=1/Q\), \(\hat{\mathbf{d}}_{(0)}=\hat{\mathbf{d}}\), \(\sigma_{\delta(0)}^{2}=\sigma_{\delta}^{2}\). while\(k\leq N_{iter}-1\)do \(\sigma_{All(k)}^{2}=\beta^{-2}(\sigma_{\delta(k)}^{2}+\sigma_{n}^{2})\) (Equations (20), (24) and (28)). \(\hat{\mathbf{d}}_{(k)}=\beta^{-1}\hat{\mathbf{s}}_{(k)}\) (Equations (25) and (27)). while\(\max\limits_{a,b,a_{j}}\left|p_{ab}^{(i+1)}(a_{j})-p_{ab}^{(i)}(a_{j})\right|>\varepsilon\) or \(i\leq\tau_{\max}-1\)do for\(a=1\) to \(S\)do for\(b=1\) to \(S\)do \(\mu_{ba}^{(i)}=\sum\limits_{c\in S,c\
eq a}\sum\limits_{j=1}^{Q}p_{ab}^{(i)}(a_{ j})a_{j}H_{b,c}\) \((\sigma_{ba}^{(i)})^{2}=\sum\limits_{c\in S,c\
eq a}\sum\limits_{j=1}^{Q}p_{ab \ \left(k\right)}^{(i)}\left|a_{j}\right|^{2}\left|H_{b,a}\right|^{2}-\sum\limits _{c\in S,c\
eq a}\left|\sum\limits_{j=1}^{Q}p_{cb\ \left(k\right)}^{(i)}(a_{j})a_{j}H_{b,c}\right|^{2}+\sigma_{all(k)}^{2}\) endfor endfor for\(a=1\) to \(S\)do for\(b=1\) to \(S\)do  Message from \(x_{a}\) to \(\tilde{d}_{b}\): \(p_{ab\ \left(k\right)}^{(i)}\propto\prod\limits_{c\in S,c\
eq b}\Pr(\tilde{d}_{c}|d _{a}=a_{j},H_{eq})\) \(p_{ab\ \left(k\right)}^{(i+1)}=\Delta p_{ab\ \left(k\right)}^{(i)}(a_{j})+(1-\Delta) \Delta p_{ab\ \left(k\right)}^{(i-1)}(a_{j})\) endfor endfor \(i=i+1\) endwhile \(k=k+1\) endwhile
```  
**Algorithm 1** The procedure of the MP-AIC method.
# DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients
###### Abstract
Recent studies have shown that a DNN model is capable of estimating the joint torque in the presence of high noise in biosignals [24; 25; 26; 27]. [PERSON] et al. demonstrated that estimation accuracy can be improved by using biosignal data from agonist and antagonist muscles, rather than from agonists alone [28]. Among many DNN models, the convolutional neural network (CNN) appears to be a promising model for many applications such as estimation models for biosignals, medical image segmentation, and medical diagnosis [29; 30; 31; 32]. Specifically, it performs well in extracting features from time-series data, such as EMG [32]. To predict the required joint torques for the motion of the genetic limb using a neural network model, a natural choice for the input to the network would be EMG signals from the unaffected limb [33; 34; 35]. [PERSON] et al. developed a robotic rehabilitation system with a muscle-to-muscle interface for post-stroke hemiplegic patients [35]. In the rehabilitation system, the FES intensity on the target muscles at the paretic side was controlled by an online comparison of the EMG signals from the paretic and unaffected sides. Therefore, we proposed a machine learning technique to control gait rehabilitation systems for hemiplegic patients. The FES input to the paretic side is generated based on EMG data collected from normal, natural gaits to provide a reference walking pattern for the paretic side. The envelopes of the EMG signals were functionalized for various walking speeds. Using the functionalized EMG data as the input, the neural network model predicted the desired joint torque, and the amplitude of the FES input to the paretic leg was determined using the linear relationship between the FES and joint torque. The effectiveness of the proposed technique was tested in healthy human subjects through isometric contraction and treadmill walking experiments. Its performance was compared with that of a trapezoidal envelope of FES intensity, which was conventionally used in clinical trials. The experimental results showed that leg motion could be improved by relieving muscle fatigue and increasing the range of joint motion with the proposed control system. The remainder of this paper is organized as follows: Section 2 describes the proposed FES control algorithm. Section 3 explains the experiments conducted to test the FES control system. Sections 4 and 5 summarize and discuss the experimental results, respectively. Section 6 concludes and proposes future works.
# Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms
## 3 Methodology and Problem Definition
### Genetic Algorithm  
Natural selection and evolution are fundamental to GAs [33; 34], which are effective tools for solving complex optimization problems. The process begins with the creation of an initial population of individuals (chromosomes), each representing a potential solution to the problem. GAs offer several advantages in optimization, particularly their ability to explore a large solution space efficiently. They are robust and adaptable, capable of finding near-optimal solutions even in complex and non-linear problem landscapes [35].  
These individuals are evaluated based on a fitness function, which quantifies their suitability as solutions. Subsequent generations are created by applying genetic operators, such as crossover and mutation, detailed in [23]. Crossover combines traits from selected parent chromosomes to produce offspring, while mutation introduces small random changes to ensure genetic diversity and prevent premature convergence. The underlying principle is that fitter parents produce offspring with improved characteristics, increasing their likelihood of survival and contributing to the refinement of the solution. This iterative process continues over multiple generations, with the population evolving toward better solutions. The algorithm terminates when a stopping criterion is met, typically when the population converges to the best individual or a predefined number of iterations is reached. The final solution represents the optimal or near-optimal answer to the given optimization problem, making GAs a robust and adaptive approach for tackling routing challenges and other complex optimization tasks [36].  
In this work, each individual in the GA corresponds to a single chromosome, which is represented as a sequence of positive integers. Each integer corresponds to the unique ID of a charging station included in the route. The length of the chromosome is adaptable, ranging from a single node to the total number of nodes in the route [37]. This adaptable-length representation allows the algorithm to dynamically explore a wide range of routing paths, from simple, direct routes to more complex alternatives that traverse multiple  
Figure 2: K-means clustering of charging stations along the route.  
charging stations. By incorporating this flexibility, the GA can effectively navigate the solution space and adjust to the dynamic requirements of the problem. Figure 3 illustrates the chromosome representation.
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 4 Experimental Simulation
### Experimental Design
#### 4.1.2 Signal Test  
In the signal reconstruction experiment, four unidimensional sparse signals are selected, respectively, as shown in Table 1.  
Figure 2: The flow chart of adaptive bat algorithm optimal observation matrix.
# An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services +
## 4 Operational Semantics
### Transition Rules
#### 4.2.2 Search Function  
For convenience, we define a function _search_ which is used by the directory service to determine the opportunity to serve a new service request \(\rho\) by selecting one existing service matching the requirements of \(\rho\), or a set of contributing services which could collectively contribute to serving \(\rho\). The _search_ function takes as parameters a service request \(\rho\) and the name of the client service \(s\) requesting \(\rho\), and returns one of three pairs: (1) \((\{s_{m}\},c)\), a pair of the name of an existing service \(s_{m}\) which matches \(\rho\)'s requirements and a new contract \(c\) created between \(s_{m}\) and \(s\); (2) \((S_{\rho},\mathcal{O})\), a pair of a set of potential contributing services \(S_{\rho}\) which could collectively contribute to serving \(\rho\) based on their capabilities and an empty set \(\mathcal{O}\) indicating that no contract is created at this point; or (3) \((\mathcal{O},\mathcal{O})\) to indicate that there is no way of serving \(\rho\). The _search_ function is defined as follows:  
\[\begin{array}{l}search(\rho,s)=(\{s_{m}\},c)\mid(S_{\rho},\mathcal{O})\mid( \mathcal{O},\mathcal{O})\end{array} \tag{6}\]
# Evaluation of the Urban Weather Generator on the City of Toulouse (France)
## 2 Materials and Methods
### UWG: Urban Weather Generator Model  
#### 2.2.1 General Description  
Urban Weather Generator (UWG) is a physics-based simulation model dedicated to urban environments. Given surface parameters associated with relevant meteorological parameters measured at a reference rural weather station, UWG calculates, among other variables, hourly values of air temperature and humidity inside the urban canyon (with a resolution of hundreds of meters). UWG is composed of four coupled modules [45] as we can observe in Figure 2:  
* The **Rural Station Model (RSM)**: this corresponds to a rural canopy model that takes hourly values of weather data in a rural reference station (_outside urbanized area_) and computes sensible heat fluxes that will be used as inputs;
* The **Vertical Diffusion Model (VDM)**: this computes vertical profiles of air temperature _above the rural weather station_ from temperatures, velocities, and sensible heat fluxes calculated issued from the RSM;
* **The Urban Boundary Layer (UBL) Model**: this module calculates air temperatures _above the urban canopy layer_ from temperatures at different heights provided by the VDM associated with sensible heat fluxes provided by the RSM and the UC-BEM.
* The **Urban Canopy and Building Energy Model (UC-BEM)**: this final module gives air temperature and humidity _inside the urban canyon_ from the RSM module (radiation, precipitation data, air velocity, and humidity) and information above the urban canopy issued from UBL module.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Station** & **RMSE (\({}^{\circ}\)C)** & **MBE (\({}^{\circ}\)C)** & **LCZ [43]** \\ \hline Compans-Cafarelli & 2.35 & \(-\)0.51 & 11-Dense trees \\ Busca & 2.1 & 0.37 & 3-Compact low-rise \\ Carmes & 1.73 & 0.02 & 2-Compact mid-rise \\ \hline \hline \end{tabular}
\end{table}
Table 2: **Performance indicators** of the 3 chosen stations between simulated and measured air temperatures.  
Figure 2: Structuration of UWG in 4 main blocks (illustration mainly inspired by [46]). Icons of this figure are designed by Freepik ([[https://fr.freepik.com/](https://fr.freepik.com/)]([https://fr.freepik.com/](https://fr.freepik.com/))).
## 5 Applications of Phase Space Representations in Digital Holography  
### Exploring an Arbitrary Hologram  
Now, let us consider some typical questions posed for a hologram. Imagine being presented with a hologram, in its complex-valued representation, without any additional information aside from pixel pitch and its wavelength.  
Figure 7: Geometric interpretation in phase space of the Hadamard product of an object wave \(O\) and distinct reference waves \(E_{R}\), see Equation (1), yielding a hologram. \(O\) used in this figure is the complex-conjugated wave of Figure 6, after up-sampling it by a factor of 4 in order to avoid aliasing.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 3 Methods
### Machine Learning Models
#### 3.1.1 Rf  
The RF model is a widely used integrated learning method for regression tasks proposed by [PERSON] in 2001. The method combines the prediction results from multiple decision trees and improves the accuracy and robustness of the model by introducing Bootstrap sampling and random feature selection [33]. The model can handle high-dimensional data and assess the importance of features. It is robust to missing and noisy data.  
1. Decision tree construction  
For each decision tree \(T_{\text{b}}\): randomly select samples with put-back from the training dataset \(\{(\text{x}_{i},\text{y}_{i})\}_{i=1}^{\text{N}}\) to form a Bootstrap sample \(\{\big{(}\text{x}_{i}^{*},\text{y}_{i}^{*}\big{)}\}_{i=1}^{\text{N}}\); node splitting--at each node, randomly select a subset of features and choose the optimal splitting point to maximize the purity of data splitting (e.g., Gini impurity or information gain).  
2. Prediction  
Each tree \(T_{\text{b}}\) gives a prediction \(\hat{\text{y}}_{\text{b}}(\text{x})\) for the input sample x. The final prediction of the random forest is the average of the predicted values of all trees, denoted as:  
\[\hat{\text{y}}_{RF}(x)=\frac{1}{B}{\sum_{b=1}^{B}\hat{\text{y}}_{b}(x)} \tag{13}\]  
where \(\hat{\text{y}}_{RF}(x)\) is the predicted value of the input sample \(x\) from the random forest, \(B\) is the number of decision trees, and \(\hat{\text{y}}_{b}(x)\) is the predicted value of the input sample \(x\) from the \(b\)th decision tree.
## Appendix A Training Process for DVS Anomaly Detection Model  
The unsupervised anomaly detection model for the dynamic vision sensor (DVS), based on the EfficientAD architecture from the _anomalib_ library [44], was trained exclusively on benign data collected from normal driving scenarios within the CARLA simulator. The training objective is for the model to learn a compact, normative representation of event-based data streams under non-attack conditions. Deviations from this learned representation are then flagged as anomalies.  
The initial model was trained for 40 epochs, as illustrated in Figure 11. A key observation from the training process is the behavior of the loss function over time. The training loss rapidly decreases during the initial epochs, indicating that the model is effectively learning the underlying patterns of normal DVS event data. However, after approximately 30 epochs, the loss begins to plateau and then slightly increase. This trend suggests that the model has reached its optimal understanding of the training data and continuing the training process further could lead to overfitting, where the model starts to memorize the specific training examples rather than generalizing the patterns of normal behavior.  
This observation is critical for tuning the model effectively. To prevent overfitting and ensure the model remains generalized for detecting a wide range of unforeseen anomalies,training was concluded after this optimal point was identified. This ensures that the resulting model is both sensitive to genuine attacks and robust against the inherent variability of normal DVS data streams, thereby minimizing false positives while maintaining high detection accuracy.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## 4 HAP Beam Pointing for Extended Coverage
### Beam-Pointing Algorithm
Note that the proposed algorithm, described in Algorithm 1, is applicable for other antenna beam patterns as long as \(h\), \(\rho\), \(\epsilon\), and \(\beta\) at the SPPC can be defined. The algorithm considers the resulting beam footprints on the ground and not the antenna producing the beams. While the proposed algorithm enables extended HAP coverage, it is important to understand the limits of coverage extension based on the capacity and spectrum performance of the system especially at the edge of coverage. The following section provides a capacity analysis of the extended HAP coverage. Interestingly, Algorithm 1 has much less asymptotic time complexity of \(\mathcal{O}(c)\) in comparison with that of the state-of-the-art in [14], which is \(\mathcal{O}(cik)\), where \(c\), \(i\), and \(k\) denote the numbers of cells or \(K\)-means centroids declared in [14], users, and \(K\)-means iteration respectively.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 6 Fiber Optic SPR Sensor Based on MITs
### Developments on FO-SPR-MIP-Based Sensors
#### 6.3.8 Atrazine  
Atrazine is the member of the triazine class (herbicides), which controls broadleaf weeds in crops, roadway grasses and forestry products, etc. [178]. It belongs to the'restricted use pesticide' (RUP) category, which means that only registered professionals are eligible to use this chemical and its application is strictly restricted for the public. Usually it is found in wastewater samples and due to its toxic nature, it can affect the ecosystem and the human health by several diseases like cancer, reproductive abnormalities, etc., which make its sensing important. A detection method for atrazine sensing was proposed by [PERSON] et al. using MIT and the SPR phenomenon over an optical fiber substrate [47]. The sensing probe was fabricated by the deposition of 40 nm thick Ag layer over 1 cm long unclad core of the optical fiber. Further, the MIP layer was coated over the Ag layer. The MIP layer was prepared using two functional monomers MAA and HEMA, while atrazine was used as the template molecule. The operating range of the sensor was kept within the atrazine concentration range of 10\({}^{-12}\) to 10\({}^{-7}\) M. The morphological image of polymer coated region over the Ag/core of the fiber is shown in Figure 16a. The sensor possessed the highest sensitivity of 17.34 nm/log M near atrazine concentration of 10\({}^{-12}\) M. Its limit of detection and limit of quantification reported are 1.92 \(\times\) 10\({}^{-14}\) M and 7.61 \(\times\) 10\({}^{-14}\) M, respectively. Figure 16b shows the SPR response of the sensor as a function of atrazine concentration. The sensitivity of the sensor was also improved by including  
Figure 15: (**a**) The step by step fabrication of FO-SPR-MIP based sensor probe; (**b**) calibration curve and (**c**) sensitivity variation for profenofos concentration in the range 10\({}^{-4}\)–10\({}^{-1}\)\(\upmu\)g/L. Reprinted with permission from [144]. Copyright 2016 Elsevier.  
a 10 nm thick Al layer between Ag and MIP which resulted in 27% enhancement from the Ag/MIP probe. The highest sensitivity was reported as 22 nm/log M for 10\({}^{-12}\) atrazine concentration.
# Gesture Recognition of Sign Language Alphabet Using a Magnetic Positioning System
## 2 Materials and Methods
### Machine Learning Algorithm
#### 2.3.2 Description of Tools Used  
Our workflow started with the selection of a high-quality dataset for the ASL alphabet letters from Kaggle. Kaggle is a subsidiary of Google LLC that provides a platform as a service for data science purposes. It allows users to write, store and execute code written in Python and R. Due to its extensive collection of machine learning libraries and packages, we decided to use Python as the programming language. We used the implementation of SVM provided by the Scikit-learn library and, instead of approaching the classification problem as a computer vision problem, we used the ready-to-use and efficient Python solutions offered by MediaPipe as prebuilt Python packages.  
MediaPipe is an open source framework designed by Google, which allows building pipelines to perform inference over arbitrary sensory data [50]. It has a selection of high accuracy machine learning models for human body parts detection and tracking, able to track key points on these body parts, which are called landmarks. In particular, landmarks are three-dimensional coordinates points normalized within a \([0,1]\) interval. Google trained these custom-built models on its largest and most diversified datasets. Developers can use these models by modifying the pipelines of information flow or simply by implementing a system on top of the custom-built models.  
In particular, MediaPipe comes with a hand tracking solution [51] named MediaPipe Hands that, by accessing a 2D camera, detects a hand and tracks its movement inside the field of view of the camera. It utilizes a pipeline that consists of two models:  
* A palm detection model that takes as input a 2D image and returns an oriented bounding box that contains the detected hand;
* A hand landmark model that takes as input the output box from the previous model and returns a prediction of the hand skeleton with landmark points, a number indicating the probability of hand presence in the cropped image and a binary classification of handedness, i.e., if the detected hand is a left or a right hand.  
The pipeline is built in a way that palm detection is performed only when the hand landmark model is no longer able to detect hand presence. The resulting model is lightweight enough to run in real-time also on mobile devices [52].  
An example of the hand model reconstructed by MediaPipe Hands is shown in Figure 10, which also lists the 21 hand landmarks for the detected hand. Each of these landmarks represents a three-dimensional coordinate: \(x\) and \(y\) assume values in the \([0,1]\) range and they correspond to the position of the pixel in the image, normalized with respect to image width and height, while \(z\) represents the distance from the palm. Smaller values of \(z\) are linked to closer points to the camera. The scaling factor chosen for the \(z\) coordinate is roughly the same as the one used for the \(x\) coordinate.