esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of New Spectral Amplitude Coding OCDMA Code by Using Polarization Encoding Technique
###### Abstract  
OCDMA is an optical access technology that has a lot of potential because it can be asynchronously accessed and provides a higher level of security. The authors presented a new DW family code, a flexible double weight (FDW) code, and a novel polarization encoding approach in this paper. The new code is applicable to both odd- and even-weighted codes. The novel polarization encoding approach may be used for numerous wavelengths that overlap. Based on analytic principles, a comparison of two widely used spectrum amplitude-coding SAC-based OCDMA codes, notably modified frequency hopping (MFH), Hadamard, and the double weight (DW) code family. The comparison was based on observing the bit error rate (BER) in each situation. The DW code has a fixed weight of two. The FDW code was introduced to reduce phase-induced intensity noise and multiple access interference (MAI) in transmission networks. FDW codes are versions of the DW code family with weights larger than two. The FDW code outperforms the Hadamard, MFH, DW, modified double weight (MDW), and enhanced double weight (EDW) algorithms. FDW has the capacity to support up to 220 concurrent users. With the new polarization encoding technology, the FDW code can travel up to 60 km at a bit rate of 2.5 Gb/s and 40 km for a 10 Gb/s bit rate.  
spectral amplitude coding; optical code division multiple access; flexible double weight; modified double weight; enhanced double weight; multiple access interference; bit error rate Article Development of New Spectral Amplitude Coding OCDMA Code by Using Polarization Encoding Technique. _Applid. Sci._**2023**, _13_, 2829. [[https://doi.org/10.3390/app13052829](https://doi.org/10.3390/app13052829)]([https://doi.org/10.3390/app13052829](https://doi.org/10.3390/app13052829))
# An Improved Deviation Coupling Control Method for Speed Synchronization of Multi-Motor Systems
## 2 Research Methods
### Controller Based on Fuzzy Control Algorithm  
To uphold the comprehensive performance of multi-axis synchronization, beyond the adoption of an appropriate multi-axis synchronization control strategy, integration with suitable synchronization algorithms becomes imperative to ensure the precision of individual motor control. The controllers depicted in Figure 2 typically employ PID control algorithms. Nevertheless, conventional PID algorithms are limited to linear systems and may fail to accomplish dynamic regulation while exhibiting synchronization deficiencies. Among various algorithms, fuzzy control, devoid of reliance on exact mathematical models of the controlled entity, exhibits superior robustness and adaptability, rendering it highly suitable for deployment within the vector control system of permanent magnet synchronous motors (PMSMs). Consequently, this study amalgamates the fuzzy control algorithm with a conventional PID controller to formulate a fuzzy PID controller capable of autonomously adjusting parameters in response to external condition variations. This integration effectively enhances system stability and dynamic response performance.  
Figure 2: Improved deviation coupling control structure.  
The configuration of the fuzzy PID controller is illustrated in Figure 3. Initially, the system deviation \(e\) between the reference input and feedback, along with the rate of change \(ec\) of the deviation, are fed as inputs into the fuzzy controller, the appropriate quantization factors \(\lambda e\), \(\lambda e\), \(\lambda_{p}\), \(\lambda_{I}\), and fuzzy theory domain are selected for fuzzification. Subsequently, fuzzy rules are formulated, and the fuzzified quantity is determined utilizing the fuzzy inference table. Finally, the centroid method is employed for defuzzification, yielding an output of precisely \(\Delta K_{P}\), \(\Delta K_{I}\), and \(\Delta K_{D}\) to the PID controller. This process enables online adaptive adjustment of the PID parameters through the fuzzy controller [26].  
The traditional PID control formula is as follows:  
\[U_{PID}=K_{P}(e(t)+\frac{1}{T_{I}}\int\limits_{0}^{t}e(t)dt+\frac{T_{D}de(t)}{ dt})=K_{P}e(t)+K_{I}e(t)+K_{D}e(t) \tag{8}\]  
where \(e(t)\) represents the variance between the set speed and the actual speed, \(T_{I}\) signifies the integral time constant, and \(T_{D}\) represents the differential time constant.  
Leveraging fuzzy control theory, the output of the fuzzy PID control depicted in Figure 3 is derived as follows:  
\[U_{PID}=K_{P}^{\prime}e(t)+K_{I}^{\prime}e(t)+K_{D}^{\prime}e(t) \tag{9}\]  
where \(K_{P}^{\prime}=K_{P}+\Delta K_{P}\), \(K_{I}^{\prime}=K_{I}+\Delta K_{I}\), and \(K_{D}^{\prime}=K_{D}+\Delta K_{D}\) correspond to the values of the PID controller parameters set by fuzzy control, respectively.
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 1 Introduction
K-means clustering is applied to nodes based on nearby communication needs to optimize packet routes, minimize packet loss and latency, and ensure reliable data communication by division based on nodes' distance in a grid area. This method also prevents slot collisions and maintains efficient cell placement by ensuring that cells from the same communication node do not occupy the same slot frame. The proposed system allocates additional slots in the next available frame if the available slots are insufficient. The contribution of this study can be summarized as follows:  
* We improve cell allocation in the 6 TiSCH network, optimizing resource utilization, reducing packet loss, and ensuring reliable communication. * We utilize K-means clustering to group nodes based on nearby communication needs, which minimizes packet loss and latency, improves delivery ratio and throughput, and prevents slot collisions by maintaining efficient cell placement. * We enhance network performance and scalability for IIoT applications by providing insights into node distribution, communication patterns, and resource allocation, enabling better management of network resources and avoiding slot clashes through dynamic cell allocation. The structure of this paper is as follows: Section 2 provides an overview of the simulator and tools, and discusses the problem of clustering techniques; Section 3 reviews related works for comparison; Section 4 outlines the proposed density clustering mechanism for node-centric cell allocation in the 6 TiSCH network; Section 5 presents the performance evaluation, covering metrics such as packet delivery ratio, latency, time allocation for every node to join the network, and throughput; finally, Section 6 concludes the paper with a summary of the findings.
# An Improved Vicsek Model of Swarms Based on a New Neighbor Strategy Considering View and Distance
## 3 The Framework of the Proposed VM
### The Improved SPVM  
Different from the original VM, in SPVM, the way a particle selects its neighbors and updates its direction is changed, as illustrated in Figure 2. Firstly, the vision field of each particle is a sector symmetric about its current velocity direction. The view angle \(\omega\) determines the size of the vision field, which is consistent for all particles in SPVM. Mathematically, particle \(j\) is inside the vision field of particle \(i\) at step \(k\) if and only if \(\left\|\overrightarrow{X}_{i}(k)-\overrightarrow{X}_{j}(k)\right\|\leq R\) and \(angle(\overrightarrow{v}_{i}(k),\overrightarrow{ij}(k))\leq\omega/2\) are satisfied. \(\overrightarrow{v}_{i}(k)\) denotes the velocity vector of particle \(i\); \(\overrightarrow{ij}(k)\) denotes the vector which starts at the position of particle \(i\) and ends at the position of particle \(j\); \(angle(\overrightarrow{a},\overrightarrow{b})\) denotes the angle between vector \(\overrightarrow{a}\) and vector \(\overrightarrow{b}\). Then, \(N_{i,\omega}\) particles that fall into the vision field of particle \(i\) are sorted according to the Euclidean distance between particles \(N_{i,\omega}\) and particle \(i\). To further reduce the number of neighbors, we assume that only the farthest \(n_{i}(k)\) particles could be chosen as neighbors of particle \(i\) at time step \(k\). Moreover, we design that \(n_{i}(k)=p\times N_{i,\omega}(k)\), where \(p\) is the selection proportion and \(p\in[0,1]\). The neighbor set of particle \(i\) at time step \(k\) is \(\Gamma_{i}(k)\). Thus, the average direction of the neighbors of particle \(i\) is calculated by Equation (3):  
\[\langle\theta_{i}(k)\rangle=arctan(\frac{\sum_{j=1}^{n_{i}(k)}sin\theta_{j}( k)}{\sum_{j=1}^{n_{i}(k)}cos\theta_{j}(k)}),\quad j\in\Gamma_{i}(k) \tag{3}\]
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 4 Limitations and Challenges
### Dependence on Reference Databases  
One of the primary limitations of MALDI-TOF MS is its reliance on comprehensive and high-quality spectral reference databases. The identification of an unknown microorganism is contingent on the presence of its reference profile within the database. Many commercially available databases are heavily biased toward clinically relevant bacteria and may lack sufficient coverage of foodborne pathogens, spoilage organisms, or environmental isolates commonly encountered in food production. Consequently, rare, novel, or region-specific strains may remain unidentified or be misclassified [81; 136; 137].  
To address this, laboratories are encouraged to develop custom in-house libraries, tailored to the specific microbial profiles encountered in their operational settings. However, creating and validating these databases requires considerable time, resources, and expertise, and standardization across laboratories remains limited.
## 3 Materials and Methods
### Model Classification and Tuning  
We used three ML algorithms, DT, SVM, and RF, with hyper-parameter tuning to optimize the ML model parameters to achieve the best performance. The GridSearchCV technique was used for hyperparameter optimization; the hyperparameter is the parameter that is used to configure the algorithm or to determine the loss function minimization [28]. It picks up the parameters with the highest performance by searching for given values of the hyperparameters for the training algorithm that guarantee a significant improvement and a performance better than one selected randomly [29]. In addition, the GridSearchCV performs cross-validation during the training process. The data are divided into training data and testing data; the cross-validation divides the training data into k sets. Each time through the iteration, one partition is kept for testing and the other k-1 partitions are used to train the model. In the next iteration, the next partition is used as testing data and the last k-1 is used as training data, and so on. At each step, the model's performance is recorded. Finally, the average of all the results is given. In order to determine the predictive model's capacity to generalize and to avoid overfitting, cross-validation is one of the most popular data resampling techniques. The complete learning set is often subjected to the learning function to construct the final model for the forecasting of real future cases. It is not possible to cross-validate the final model. The goal of cross-validation during model construction is to predict how well the final model will perform when applied to fresh data [30].
## 5 Conclusions
### Limitations and Future Scope  
This research has some limitations that can lead to future lines of research:  
* In this research, the time variable was not considered. The dynamics and duration of risk events constitute an interesting challenge to be incorporated in the proposed mathematical model. This analysis can be conducted in terms of the severity of the impacts and the learning of the system.
* The quantification of ARS by process can be incorporated into the framework. It is important to emphasize that the relationships between all risk factors are not simply hierarchical and we must consider the interaction of risk agents. This analysis will allow for precise identification of redundant elements in the aggregation.
* Only one case study was used in this paper. Additional case studies can extend our model to other industries, and comparisons can also be made to determine how our model performs differently in diverse contexts. This would further enrich theoretical and managerial contributions.
* Additionally, although the mathematical model can be easily implemented in practice with a simple spreadsheet, input values require important data collection (different sources and nature) and brainstorming within the organization (cascade effect). Therefore, in the context of the forth industrial revolution (i.e., Industry 4.0), the integration with advanced technology tools and artificial intelligence techniques can be utilized to improve the decision-making and system learning.  
[PERSON] and [PERSON] conceived of the presented idea and designed the work, [PERSON] carried out data collection and literature review. [PERSON] proposed the initial structure of the article and [PERSON] and [PERSON] planned the methodology. [PERSON] carried out the presentation of the results after their interpretation, carried out in conjunction with [PERSON], [PERSON] and [PERSON]. The final critical review was conducted by [PERSON] and [PERSON].  
This research received no external funding.  
The authors declare no conflicts of interest.
## 3 Path Loss Models
### Alpha-Beta-Gamma (ABG) Model [23]  
This model includes frequency-dependent terms such as the \(\gamma\), \(\alpha\), and \(\beta\) to describe the path loss at various frequencies. The ABG model is presented in (16) as follows [23]:  
\[P_{L}^{ABG}(f,d)[dB]=10\alpha\text{log}_{10}\left(\frac{d}{d_{0}}\right)+\beta+ 10\gamma\text{log}_{10}\left(\frac{f}{f_{ref}}\right)+W_{\sigma}^{ABG}, \tag{16}\]  
where \(\alpha\) and \(\gamma\), \(\beta\), and \(P_{L}^{ABG}(f,d)\) are coefficients illustrating the dependence of path loss on distance and frequency, respectively; the optimal offset value for path loss in dB; and the path loss in dB over frequency and distance, respectively. The 3D transmitter-receiver (T-R) separation distance is denoted as \(d\) in meters, \(f\) is the carrier frequency in GHz, and \(W_{\sigma}^{ABG}\) is the SF standard deviation representing large-scale signal fluctuations regarding the mean path loss over distance [46].
# Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification
## 2 Related Work  
Feature extraction based on sparse representations has been widely introduced for several tasks [26; 27; 28; 29]. For example, [PERSON] et al. [26] designed sparse coding based on dictionary learning with orthogonal matching pursuit (OMP) to extract sparse features for fault classification and recognition. [PERSON] et al. [27] presented a joint sparse coding learning method for early fault feature extraction in rotating machinery with the aim of preserving weak fault features, promoting sparsity, and removing noise, thereby enhancing predictive maintenance. [PERSON] et al. [28] introduced a novel speech recognition feature extraction technique using sparse coding. Speech spectro-temporal patterns are represented as sparse linear combinations of an overcomplete set of dictionaries. The proposed technique outperform conventional features in both clean and noisy conditions. [PERSON] et al. [29] presented a novel two-factor authentication mechanism for IoT devices, addressing the challenge of heterogeneity and security concerns. The proposed method utilizes sparse coding for feature extraction for remote user biometric authentication. By employing hash operations and an overcomplete dictionary, it efficiently stores and retrieves biometric data. Furthermore, many recent works have shown that sparse coding is well-applied to biometrics recognition and classification tasks [15; 30; 31], such as [PERSON] et al. [30] presenting a method for classifying heart sounds using sparse coding. Preprocessed audio data are decomposed into a dictionary matrix representing key features and a sparse coefficient matrix mapping these features to each segment. [PERSON] et al. [31] investigated the impact of sparse coding in face recognition by using PCA and sparse representation classification (SRC) with a public image dataset. The proposed method enhances face recognition with significant accuracy. In our previous work [15], we proposed a feature extraction method for hand gesture classification using sparse coding based on a Gaussian random dictionary to reduce the number of features. The optimizer in the previous work was the Frobenius norm, which was used to solve the optimization problem and obtain sparse coefficients. Additionally, the evaluation was performed on a dataset consisting of only five objects (two-Euro coin, credit card, salt shaker, screw, and marker). The results showed that the sparse-coding-based approach improved feature reduction performance. However, the classification accuracy was not satisfactory. Several related works have shown that sparse-representation-based approaches have achieved satisfactory performance and motivated various works in the feature extraction tasks [15; 26; 27; 28; 29; 30; 31].  
Inspired by the success of the sparse-coding-based approach, this work proposes a sparse coding method based on a dictionary learning algorithm. This method utilizes the L1-norm as the optimizer to find the sparsest coefficients (the lowest number of nonzeros) for hand-grasp-type classification.
## 2 Developed Methodology
### Instance-Level Feature Importance Analysis for DSA-DL Model
#### 2.2.1 DSA Database Establishment  
A database of OCs for DSA mainly consists of historical data, real-time data, and planning data. The diversity of OCs can be enhanced by a stochastic variation of load demands and generation schedules. Upon each OC, the pre-defined credible contingencies are simulated by TDS. The post-disturbance dynamic security is evaluated using specific security criteria, e.g., transient stability, voltage stability, and frequency stability. Some variables that can reflect the system dynamics are chosen to construct the input features of DSA, including the active and reactive power of generators (\(G\)), AC line branches (\(AC\)) and loads (\(L\)), as well as the magnitude and phase of bus voltage (\(P_{G}\), \(Q_{G}\), \(PAC\), \(Q_{AC}\), \(P_{L}\), \(Q_{L}\), \(Vm_{Bus}\), \(Va_{Bus}\)). With the variables and the DSA results of OCs to be the input features and labels of models, samples are saved to form the DSA database.
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 4 Proposed Method
### Mobility Pattern  
This work considers two mobility patterns: a random waypoint (RWP) model [62] and a reference point group mobility (RPGM) model [63]. In RWP, mobile nodes (vehicles) move freely and randomly without restrictions. That is, the direction, speed, and destination are all chosen randomly, independent of other nodes. Its simplicity and wide availability make it one of the most popular mobility models. Its movement includes pause times between changes in destination and speed. A mobile node begins by staying in a location for a fixed number of seconds (pause time). Upon timeout, the node chooses a random destination in a simulation area and a random speed between _min_speed_ and _max_speed_, where min_speed is not zero. It then travels to the destination at this speed. Upon arrival, the node stays again for a fixed period before choosing another random destination and speed. It repeats this process until the end of the simulation. This work adopts the RWP model to simulate events that do not occur frequently but are important for training, such as traffic accidents [64]. In the model, a rare-event generation mechanism is used for sensing training data input. With respect to differential privacy, a noise multiplier is applied when updating model parameter values for adaptive clipping.  
In RPGM, mobile nodes are divided into groups, and group members move together. Each group has a logical center that travels through a random path following the RWP model. The motion of the center characterizes the movement of member nodes, including direction and speed. Each member node randomly moves around its own predefined reference point that is in the vicinity of the center and whose location is continuously updated according to the center's mobility. The node's random motion within the group also follows the RWP model. This way, the node follows the center's mobility closely, with some deviation. Figure 4 illustrates the movement of three mobile nodes with the RPGM model. At time \(t\), three black dots represent reference points, \(RP(t)\), for the three mobile nodes (denoted as big white circles). The motion of the logical center is represented by a group motion vector, \(\overline{GM}\), that is used to calculate new locations of the reference points, \(RP(t+1)\), at time \(t+1\). Then, the new position of a node is calculated by summing the new reference point with a random motion vector, \(\overline{RM}\). The direction of \(\overline{RM}\) is uniformly distributed between 0 and 2\(\pi\), and its length is also uniformly distributed within a predefined radius centered at \(RP(t+1)\). The RPGM model is one of the most popular group mobility models to represent vehicle platooning, and thus it is adopted in this work to represent vehicles' mobility in non-accident situations [65]. The model uses a grid-based observation mechanism as a training input data sensing method in a federated learning setting and an adaptive clipping method for differential privacy.  
Figure 4: Movement of three mobile nodes in the RPGM model.
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 4 Future Directions
### High Stability under Heavy and Changing Loads  
It is well known that drastic changes in load can challenge the robustness of the control algorithm. In the experiments of some literature, it is easy to observe that the estimation accuracy will be lower than the light load when the load is abruptly changed. How to effectively improve the accuracy of rotor position estimation is a worthy research direction under various working conditions.  
The principles of various types of sensorless methods have been introduced in detail, and their development has been teased out. The direction of the entire sensorless development is elucidated based on the current development direction. It is important to see what changes can be made in the future for each type of method. Table 3 presents future applications and future developments of the various methods summarized. From a practical application point of view, hybrid and observer-based methods enable a decoupling of speed/torque control strategies and position estimation in the full speed range.