esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Future Perspectives and Practical Applications  
Future studies should explore the integration of biomechanical parameters alongside RPE to enhance model accuracy. Additionally, implementing hybrid models that combine ML techniques may provide a more comprehensive approach to monitoring athlete recovery. Measuring the player tracking data and psychophysiological features associated with the recovery and mental fatigue states of U15, U17, and U19 football players using an ML classifying model can optimize the matching wearable-tracking technology. As AI-based techniques continue to evolve, their application in sports will likely expand, providing deeper insights and more precise interventions. This progress holds the promise of elevating training standards and improving the overall health and performance of athletes in youth football and beyond. Also, we hope that this study can inform evidence-based interventions and enhance training and recovery protocols in youth football programs. With this, we can improve motor engagement times and the enjoyment of sports practice and reduce the risk of mental fatigue with consequences for retention in practice and mental health problems.  
While the findings provide valuable insights for sub-elite youth football players, the limited sample size and focus on Portuguese players may restrict generalizability. Future studies should consider including a broader and more diverse cohort to enhance the applicability of the proposed models across different populations. Despite moderate predictive accuracy, the ML models offer practical value by identifying trends in fatigue and recovery that can inform adjustments in training loads, allowing coaches to proactively manage athlete readiness and minimize injury risks. Coaches can leverage model predictions by decreasing training intensity or prioritizing active recovery protocols on days when predicted fatigue levels are elevated, ensuring a balanced workload that aligns with each player's recovery capacity.
# Communication Transport Protocol Strategies for Rail Applications
## 2 The Railway Context
### Railway Applications and Users
Based on government requests, this refers to providing Internet connectivity both to passengers for personal reasons (as if they are at home or in a coffee shop) as well as business passengers (thus improving their productivity). Examples are document access, video streaming and call conferences, and entertainment/gaming. Connectivity can be provided through access nodes within the train. The last service category has been included in the description although it is not a requirement for rail operations. Nevertheless, it has been receiving great interest both from passengers and governments for people's satisfaction, societal demands and as an economic driver. Table 1 reports the service categories, the functional requirements of communication for each of them in terms of required bit rate and some service examples or supporting applications.
# An FPGA-Based Performance Analysis of Hardware Caching Techniques for Blockchain Key-Value Database
## 4 FPGA Implementation Details of Cache Mapping
### 4-Way Set-Associative Mapping  
In this work, we design a 4-way set-associative cache system by using the LRU algorithm method. Figure 12 shows the cache layout of the 4-way set-associative cache system. Each set of 4-way set-associative cache contains 4 blocks of data. To test the 4-way cache system design, we create \(4.09\times 10^{3}\) sets, as shown in Figure 12. _Tag_ is shown in red color and _Value_ is shown in green color. A single set of 4-way cache systems containing _Tag_ and _Value_ uses up to 2048 bits of memory. 4-way set-associative cache has fewer data conflicts, so it has lower miss rates than direct mapping and 2-way cache mapping. The LRU replacement algorithm is used to replace memory blocks when it needs to replace an already occupied memory block [29].
# Detection and Localization of Failures in Hybrid Fiber-Coaxial Network Using Big Data Platform
## 5 Failure Detection and Localization Based on Big Data  
One common problem related to telecom service providers is network failure. Network failures are not rare and happen for a variety of reasons. Power failure, optical cable being cut, and equipment failure are only some of the problems that occur on a daily basis. There are two challenges that telecom service providers need to overcome: failure detection and failure localization. To overcome these challenges in the case of HFC networks, we propose the FDLBD mechanism. In this section, we first give an in-depth description of the FDLBD mechanism. Then, we provide more detailed descriptions of failure detection and failure localization parts of the FDLBD mechanism.  
An important requirement is that any failure needs to be detected as soon as possible. Failures are monitored on the CMTS level. CMTS is the centralized device for one part of the network, which is responsive for querying, and, therefore, the most suitable. We use the metric cdxCmtsCmRegistered [33] in FDLBD. Metric cdxCmtsCmRegistered shows the number of online and active modems per MAC domain. Since the number of active modems constantly changes, the failure threshold needs to be defined to avoid false detections. We define the following rule: a failure is detected on one CMTS and one MAC domain when the current collected number of active modems is 15% less than the average of twenty previous collections for that CMTS and MAC domain. This rule has been tested in practice on a real HFC network. The practice has shown excellent results for failure detection while minimizing false detections.  
After the failure is detected, the failure localization part of the FDLBD is performed. Failure localization helps the HFC network operator to efficiently solve the failure problems. First, the FDLBD mechanism tries to establish a connection with every CPE device that belongs to the problematic CMTS MAC domain. Information on whether the connection is established or not is correlated with network topology. FDLBD traverses from the bottom (CPE) to the top (ON) of the tree topology and seeks the top device in the network hierarchy under which all modems are unavailable. The found device is the point of failure.  
Figure 3 shows an example of failure localization. Red color shows that all CPEs under such devices are offline. Green color shows that all CPEs under such devices are online. Yellow color shows that there are both online and offline CPEs under such devices. Tree topology makes it easier to detect the root of the problem. It is the top device under which all CPEs are offline. In the given example, the amplifier AMP_ng2 is the point of failure. AMP_as218 is not the point of failure because there are online CPEs beneath it.
# Numerical Study on the Yaw Control for Two Wind Turbines under Different Spacings
## 2 Physical Models and Numerical Approach
### Actuator Line Model  
ALM is a full three-dimensional transient aerodynamic model first proposed by [PERSON] and [PERSON] [11]. In order to reproduce the effect of the turbines in a wind farm, the wind turbines are represented as body force fields. In ALM, the rotating blades are simplified into actuator lines, which several blade elements are distributed along. Once the aerodynamic forces at each blade element are calculated, the corresponding forces are projected onto the flow field to exert the effect of the turbine blades. For the local wind speed, the aerodynamic volume force [25] is solved by:  
\[\mathbf{f_{e}}=\frac{1}{2}\rho{U_{rel}}^{2}c(C_{L}\mathbf{e_{L}}+C_{D}\mathbf{e_{D}})^{T} \tag{2}\]  
where \(U_{rel}\) is the local wind velocity, \(c\) is the chord length of the airfoil, \(C_{L}\) and \(C_{D}\) are the lift and drag coefficients, and \(\mathbf{e_{L}}\), \(\mathbf{e_{D}}\) are unit vectors in the direction of lift and drag force, respectively.  
The tip/root loss correction was implemented by the Prandtl-Glauert rule, and the specific formula of correction factor \(F\)[26] was as follows:  
\[F=\frac{4}{\pi^{2}}\cdot\arccos\left[\exp(-\frac{B}{2}\frac{R_{tip}-r}{r\sin \phi})\right]\cdot\arccos\left[\exp(-\frac{B}{2}\frac{r-R_{root}}{r\sin\phi} )\right] \tag{3}\]  
where \(B\) is the number of the blades; \(R_{tip}\) and \(R_{root}\) are the radius of the blade tip and root; \(r\) is the spacing between the blade-element location and the root of the blade; and \(\phi\) is the angle between the local relative velocity and the chord.  
The actuator forces are projected to body forces through using a three-dimensional Gaussian function:\[f(a)=\frac{1}{\varepsilon^{3}\pi^{\frac{3}{2}}}\exp\left[-\left(\frac{a}{\varepsilon} \right)^{2}\right] \tag{4}\]  
where \(a\) is the spacing between the body force center and the projection point, and \(\varepsilon\) is the smoothing factor, which controls the numerical stability and physical sense of the projection method. [PERSON] et al. [17] pointed out that it is appropriate when \(\varepsilon\) is twice the grid length.
# Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs
## 1 Introduction
AI application in dentistry was assessed for three main purposes: (1) as a triage tool for optimizing the clinical workflow, (2) as a diagnostic tool for the separation of normal from abnormal conditions, and (3) as a treatment decision-making tool for the detection of specific diseases [7]. Studies in the literature about the value of AI from bitewing radiographs in assisting dentists in decision-making are plentiful. In general, these studies demonstrate the utility of AI algorithms in diagnosing dental caries, periodontal disease, and classifying restorations [13; 14; 15]. Most of these studies investigated the effectiveness of AI models from bitewings in diagnosing dental caries. These studies demonstrated that deep learning-based AI algorithms increased the accuracy of diagnosing proximal dental caries [13; 16; 17; 18]. A few studies investigated the utility of AI from bitewings in diagnosing periodontal disease. Overall, AI models for diagnosing periodontitis are still developing but might be useful adjunctive tools [16]. In one study, [PERSON] et al., 2021, demonstrated that deep learning-based CNNs from periapical and bitewing radiographs was a potentially beneficial technique in detecting and differentiating restorations [14]. However, there are no studies in the literature that investigate the development or utility of AI algorithms to classify bitewings based on their diagnostic value. Accordingly, the aim of this study is to develop and assess the effectiveness of machine learning-based AI algorithm to classify bitewing radiographs according to their diagnostic quality.
## 3 Experiments and Data Processing
### Data Processing  
Data processing is significant for model training, mainly with regard to data cleaning and data normalization. The purpose of data cleaning is to fill in missing values and remove duplicate values. In addition, data normalization is used to limit the raw data to a specific range, which can speed up model training and average the impact of different dimensional data on the results. The min-max normalization method is adopted to normalize the raw data to the interval [0,1]. The min-max normalization formula is as follows:  
\[x^{
## 3 Algorithms
### Parcel-Receiving Station and Public Route Planning Based on GA-MMAS
#### 3.2.4. Parcel-Receiving Station and Public Air-Route Planning Flow  
The pseudo-code and algorithm flow chart of the GA-MMAS is shown in Algorithm 1 and Figure 7. In the solution, the cost of the selected station count, the cost of the average picking distance, the cost of picking fairness, and the cost of public air-route operation are considered. If the number of parcel-receiving stations in the parcel-receiving station location scheme exceeds \((1+\chi)N_{S}\), it will not be transferred to the lower-layer model for the solution, making the cost of public air-route operation infinite. Otherwise, we use the max-min ant system to calculate the public air-route operating cost, and the individual fitness of the parcel-receiving station and public air-route planning scheme is calculated based on the cost sequence with Function (24). The upper-layer model fitness \(F_{i}^{\mathrm{U}}\) of scheme \(i\) includes the cost of the selected station count \(F_{i1}\), the cost of the average picking distance \(F_{i2}\), and the cost of picking fairness \(F_{i3}\), expressed as \(F_{i}^{\mathrm{U}}=\omega_{1}F_{i1}+\omega_{2}F_{i2}+\omega_{3}F_{i3}\), where \(\omega_{1}+\omega_{2}+\omega_{3}=1\). The total fitness \(F_{i}\) is the sum of the fitness of the parcel-receiving station location results and the public air-route planning results, expressed as \(F_{i}=\phi_{1}F_{i}^{\mathrm{U}}+\phi_{2}F_{i}^{\mathrm{L}}\), where \(\phi_{1}+\phi_{2}=1\).  
The max-min ant system pseudo-code of the public air-route planning model is shown in Algorithm 2. Based on the pheromone distribution and greedy algorithm, the optimal public air-route planning sequence, optimal public air-route configuration, and minimum public air-route operating cost were obtained.  
```
init antCount, alpha, beta, and other coef's 1 for j = (1:max_MMAS_iter){ for k(1:antCount)[lower_cost[j][k] = cal_route_cost(pheromone);] c_route[i] = min(lower_cost[j]); pheromone = update(min(lower_cost[j]));// updating pheromone matrix using the best ant] } Output(min(lower_cost[j]));
```  
**Algorithm 2** The max-min ant system pseudo-code.  
Figure 7: GA-MMAS algorithm flow chart.
## 4 Discussion: Comprehensive Analysis of the Deep-Learning-Oriented Framework
### Contribution to Seaport Operations' Efficiency and Sustainability
#### 4.1.1 Optimizing Traffic Flow and Operational Efficiency  
The implementation of advanced technologies within seaport operations has been the subject of extensive research, aiming to streamline the processes and enhance efficiency. Our proposed framework, deeply rooted in AI and CV technologies, plays a pivotal role in optimizing the traffic flow within seaports. By leveraging predictive algorithms [38] and real-time data analysis, our system dynamically redirects incoming trucks during the peak hours, effectively mitigating congestion and ensuring uninterrupted traffic flow.  
This dynamic optimization not only minimizes delays, but also significantly enhances the overall operational efficiency of seaports.
# SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems
## 3 Administration of IoT Deployments
### Agent Technologies & Middleware Solutions  
SPD-Safe utilizes JADE [64] as the top-layer multi-agent system. It adopts and implements standardized approaches to agent deployment, such as the ACL [41] by FIPA. The JADE-S add-on [65; 66] safeguards communication at the overlay and offers built-in security functionality for confidentiality, integrity, authentication, and authorization.  
Then, each agent is ported as a bundle in the middleware platform Open Service Gateway initiative (OSGI) [67]. Through it, an agent can monitor the underlying subsystem, enhancing real-time management. Network gateways also deploy a controlling bundle in the same platform, defining the offered functionality as a service in the DPWS standard [43]. The agent and the related network bundles interchange well-structured semantic data, as defined in the OASIS standardized Common Alerting Protocol (CAP) [68]. The OSGI platform also provides its own built-in security features for the inner-platform communication, limiting bundle functionality to pre-defined capabilities and protecting both the agent and controller bundles.  
Here, other than these built-in features that are provided by the deployed platforms at the overlay and middleware layers, SPD-Safe integrates an additional defense mechanism for the network and node layers, namely Secure Route (SecRoute) [61], a security protocol that protects the wireless ad hoc communication of the underlying embedded devices. This protocol counters several types of threats and attacks at the network link, protects the nodes' assets and their resource consumption, and acts as an intrusion detection module for the upper layers. When a security incident is recorded, the network gateway bundle will send related CAP messages to the responsible agent, which may take further action. SecRoute is detailed in the next section.  
Metrics that evaluate the various system aspects are now an integral feature of the development cycle. They offer a quantitative indication regarding the compliance with the targeted requirements of the application domain. An evaluation method for the estimation of the security, privacy, and dependability (SPD) properties for configurable embedded systems is presented by the authors in [PERSON] et al. [63]. For every configuration option, the metrics derive a triple vector of \(<\)Security, Privacy, Dependability\(>\), whereby the vector's factors are assigned a value from 0-100, representing no to full protection respectively. SPD-Safe adopts this methodology to enable a metric-driven SPD- and safety-aware administration, where the reasoning procedure triggers runtime system adaptations to reach specific SPD goals [69].
## 4 Proposal for Data Transformation to Resolve Incompatibilities between Medical Formats
### Data Transformation and Representation  
Due to the aforementioned problem of data heterogeneity, the data must be transformed in different ways to combat it. However, with all the different formats available out there, it is impossible to write individual converters for absolutely every possible type and format. The proposed solution, in this case, is instead of converting formats from one another, the system could convert different types of data to a single format with the usage of NLP, all while attempting to make the method accept as many types of data as possible, which is later on used as a direct input for diagnosis, disease prediction or treatment plan assignment.  
Due to a lot of machine learning algorithms dealing with vectors, the proposed schema is a vector representation of the data, with each sample of data presented as an N-dimensional vector, where \(N\) is the number of all accounted traits, which represent symptoms and patient characteristics ever stored in the system and can be either numerical or Boolean (represented as 1 and 0 in the implementation).  
When the system is running, the data are processed according to its type, transformed with the help of semantic analysis (if structured differently), normalized and saved in a format accepted by processing system. Newer traits can be added by analysing different sources, text and images at the processing stage. Old and new data are then merged and updated automatically, with the missing values being replaced by either \"0\" (if Boolean) or the mean of all entries in the training set (if numerical). There is a problem arising from it, though, as too much analysis can result in data with a high amount of dimensions.  
When a diagnosis algorithm of choice is used, distance can be evaluated between the samples with any possible distance score due to the vector representation of the data, making such a schema easy to use with most of the existing algorithms.
## 4 Discussion and Future Works
### Future Works  
The 3D model of San Cristobal de La Laguna which has been created will serve as a basis for creating cultural heritage applications. Two different products are currently in development.  
The first product is a VR-guided tour of San Cristobal de La Laguna, based on the map from 1588. Users will have the ability to explore various parts of the city and receive information from virtual characters about its history and important features. The tour will offer multiple modes of navigation, including free movement, teleportation, and guided options. The visit will be divided into different scenarios, with a subset of virtual characters in each scenario, depending on the context.  
The second product is an augmented reality mobile application that combines the 16 th-century city with the present one. The application will use location and orientation sensors on mobile devices to superimpose the 3D model over the current city, illustrating the changes over time. The application will have a guidance system that visually directs users to various points of interest based on their location. When users arrive at these points, the virtual characters present in the city at that time will narrate information related to them.
# A Federated Generalized Linear Model for Privacy-Preserving Analysis
## 2 Materials and Methods
### Linear Regression  
This model assumes a Gaussian distribution of the error and uses an identity link function. Notice that this particular case corresponds to that of a general linear model (not to be confused with a GLM; in other words, a general linear model is a specific case of a GLM). The target variable \(y\) was generated according to Equation (14):  
\[y=0.25x_{1}+0.5x_{2}+\zeta \tag{14}\]  
where  
\[x_{1}= \sim\mathcal{N}(1,\,1) \tag{15}\] \[x_{2}= \sim\mathcal{N}(2,\,1)\] (16) \[\zeta= \sim\mathcal{N}(0,\,1) \tag{17}\]Poisson Regression  
This model assumes a Poisson distribution of the error and uses a log link function. The target variable \(y\) was generated according to Equation (18):  
\[y=\text{round}(e^{0.25x_{1}+0.5x_{2}+\zeta}) \tag{18}\]  
where \(x_{1}\), \(x_{2}\), and \(\zeta\) were defined the same way as in the previous model (Equation (15), Equation (16) and Equation (17), respectively).  
Logistic Regression  
This model assumes a binomial distribution and uses a logit link function. In this case, the data were generated using scikit-learn v. 1.0.2 [27]. They consisted of normally distributed clusters of points (with a standard deviation of 1) around vertices of a 2D plane (since we chose to use two features, \(x_{1}\) and \(x_{2}\)) [28].  
In all cases validating the federated GLM's accuracy, the data comprised 3000 records, which were randomly split into three simulated parties with 1000 records each. The federated GLM was run using a mock server-client architecture. This was available from our previously developed open-source priVAcy preserviNg federaTed LeArninG infrastructureE for Secure Insight Exchange--vantage6 [29]. In short, it recreated a server and the nodes of all three parties locally (which is sufficient for verifying the mathematical implementation of the algorithm), making the development and testing very practical. We set the algorithm to stop its execution if it converged (i.e., if the difference between the coefficients across iterations was smaller than \(1\times 10^{-8}\)) or if it reached 50 iterations (the latter condition was never reached; the algorithm converged in all the presented cases).  
Afterwards, we compared the federated GLM output to their centralized counterpart, which can be considered the gold standard. Here, the data were all put together as if they were all available to a single party. Then, a (centralized) GLM was generated using R's glm function from the stats package [25]. Specifically, we compared the federated and centralized models' coefficients, standard errors, \(p\)-, and \(z\)-values. Tables were generated using the package stargazer v. 5.2.3 [30].