esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Action-Rule Discovery Based on Attribute Correlation and Vertical Data Partitioning
## 4 Materials and Methods
### Additional RSES-Based Method for Action Rule Discovery  
Again, our main experiments utilized the Python package developed by [PERSON] and [PERSON] (see [37]) for action rule discovery, adopting a rule-based approach to action rule mining.  
Figure 2: Illustration of the Random Vertical Partitioning Method. This method randomly groups features for action rule discovery, serving as a baseline for comparison with our correlation-based approach.  
We then utilized a secondary RSES-based action rule discovery method for comparison purposes. Initially, classification rules are generated using the Rough Sets Exploration System (RSES) tool. These rules are extracted from a dataset and typically represent patterns or associations between various attributes. Next, these classification rules outputted into a text file, are parsed and transformed into a structured format suitable for further analysis. This involved developing a parser that reads the text file, identifies the format of the rules, and converts them into a programmatically accessible format.  
The classification rules are then categorized based on their decision attributes. In our case, they are grouped into \"passive\", \"promoter\", and \"detractor\" bins. For generating action rules, pairs of classification rules are compared, particularly focusing on rules that transition between different decision attribute values. Since our experiments focused on generating action rules reclassifying passive to the promoter, those are the bins we focused on.  
Next, pairs of classification rules are compared (e.g., from 'passive' to 'promoter') to identify potential action rules. The comparison focuses on two types of attributes: stable attributes and flexible attributes. The method ensures that for any pair of classification rules being compared, their stable attributes are compatible (i.e., they have the same values or are absent in one of the rules). Flexible attributes are subject to change and are the focus of the action rules. This method looks for differences in flexible attributes between the pairs of rules to suggest possible actions.  
Based on the comparison of classification rules, action rules are generated, suggesting changes in one or more flexible attributes that could lead to a transition from one decision class to another. The method takes into account various scenarios for each flexible attribute in the ruling pair, such as when an attribute is the same in both rules, when it differs, or when it's present in one rule but not in the other. The action rules are formulated to recommend changes in attribute values that are hypothesized to result in the desired change in the decision attribute.
## 2 System Model
### Differential Game Model
#### 2.4.2 Optimal Control  
The optimal control for each node is derived by solving the Hamilton-Jacobi-Bellman (HJB) equation, which provides the value function \((u_{k})\) representing the minimum cost-to-go from any given state [48]. The HJB is expressed as in (11) and as the Hamiltonian in (12) [34]:  
\[-\delta_{t}u_{k}(t,s_{k}(t))=\min_{E_{k}(t)}\left[c_{k}(t,s_{k}(t),E_{k}(t))+ \
abla u_{k}(t,s_{k}(t))\cdot\delta_{t}s_{k}(t)\right] \tag{11}\]  
where the term \(-\delta_{t}u_{k}(t,s_{k}(t))\) indicates how the value function changes with a small increment in time, \(c_{k}(t,s_{k}(t),E_{k}(t))\) is the intermediate cost which quantifies the instantaneous cost incurred due to the chosen action in the given state at that time, \(\
abla u_{k}(t,s_{k}(t))\) represents the value function's gradient with respect to \(s_{k}(t)\), which points in the direction of the steepest increase in the value function and \(\delta_{t}s_{k}(t)\) denotes the change in \(s_{k}(t)\) with respect to \(t\) and it captures how the state of the system evolves over time. To find \(E_{k}\), it is necessary to solve \(K\) HJB equations concurrently. In densely populated WSNs, this approach becomes impractical because the number of simultaneous partial differential equations (PDEs) increases with the number of nodes. As a result, the complexity of optimizing energy consumption and maintaining network performance grows proportionally with the network. To address this challenge, we leverage the MFG framework, which offers a robust method for analyzing and optimizing large-scale systems.  
\[\mathbf{H}(s_{k}(t),E_{k}(t),\
abla u_{k}(t,s_{k}(t))=\min_{E_{k}(t)}[c_{k}(s_ {k}(t),E_{k}(t))+u_{k}(t,s_{k}(t))\cdot\delta_{t}s_{k}(t)] \tag{12}\]
## 2 Materials and Methods
### Data Processing  
#### 2.5.1 CO\({}_{2}\) Flux Correction and Gap Filling  
The raw data obtained from flux observations are based on ideal conditions (e.g., flat and homogeneous underlying surfaces) and therefore require further processing, including double coordinate rotation and Webb-Pearman-Leuning (WPL) correction. All of these procedures were performed using EddyPro software (v7.0.6, LI-Cor., LI-COR., Lincoln, NE, USA). Outlier detection was conducted separately for daytime and nighttime data within a 13-day moving window. Nighttime periods were identified using a global solar radiation (\(R_{\mathrm{g}}\)) threshold of 20 W/m\({}^{2}\), and day/night segmentation was determined based on \(R_{\mathrm{g}}\).  
The R package REddyProc (v1.3.3) [30] was employed to perform \(u^{*}\) filtering with 200 bootstrapped resamplings (default setting). The gap-filling procedure was applied to data that had already undergone outlier removal, and the Marginal Distribution Sampling (MDS) method was used for flux data interpolation.
# Design of Cloud-Based Real-Time Eye-Tracking Monitoring and Storage System
## 2 Materials and Methods
### Hardware Implementation  
In the system, three Docker virtual environments were used to perform experiments, stream real-time eye movements, and store data. In order to carry out online experiments, two separate physical AMD Central Processing Units (CPUs), 1 GB of Random Access Memory (RAM), and 25 GB of Solid State Disk (SSD) hardware were used for the front-end where the eye-tracking model runs and for live feed eye-tracking data. Furthermore, to store the data and manage the back-end, 2 physical Intel CPUs, 2 GB of RAM, and 25 GB of SSD hardware were used. The locations of the servers where the Dockers are used are located in Frankfurt, Germany for online experiments. The local experiments were conducted with Intel i5 8600k CPUs and 16 GB of RAM. Lastly, in both systems, eye-tracking data are collected in X and Y coordinates, while the data for time in seconds are stored in Year:Month:Day:Hour:Minute:Second:Millisecond.
## 2 Developments
### SEFISoC: an Embedded Microprocessor to Implement SEFI  
We have used reconfigurable hardware technology in order to design and implement the SEFI digital architecture. Reconfiguration of circuitry at runtime to suit the application at hand has created a promising paradigm of computing that blurs traditional frontiers between software and hardware. This powerful computing paradigm, named reconfigurable computing [18], is based on the use of programmable logic devices, mainly field programmable gate arrays (FPGAs) [19] incorporated in board-level systems. FPGAs have the benefits of the hardware speed and the software flexibility; also, they have a price/performance ratio much more favorable than ASICs (Application-Specific Integrated Circuits). For these reasons, FPGAs are a good alternative for many real applications in image and signal processing, robotics, telecommunications, networking and computation in general. Furthermore, as the reconfigurable computing is becoming an increasingly important computing paradigm, many techniques are appearing in order to facilitate the FPGA design using embedded processors. In this line, embedded processors have been developed to bring custom processing solutions easy to program [20]. The advantages of using an embedded processor to suit the SEFI algorithm are the ones previously pointed out: good performance in an acceptable time, low power, low cost and small area, as well as the reconfigurability needed in some cases, for example firmware updates.  
We have used the ISE v13.3 technology and the FPGA devices provided by Xilinx [21]. The design basically consists of an embedded microprocessor named Microblaze, with floating point unit, processor local bus and standalone operating system. Microblaze is a soft processor, that is, a processor fully customizable by the user and implementable on FPGA. The main characteristics of the implementations built in this work are listed in Table 1. Two implementations have been done: S3 (based on a Spartan3E FPGA) and V5 (based on a Virtex5 FPGA), using commercial prototyping boards (the FPGA devices used do not need neither fan nor heat skin, and external devices or peripherals are not necessary, making so possible a circuitry very reduced, necessary for mobile terminals). The first one is oriented to a very small, low power and economic device, and the second one represents a high-performance solution. The S3 implementation uses a 50 MHz clock and offers a very low on-chip power consumption of 0.01 W (estimated from the XPower analyzer tool), but it requires using an external memory because the FPGA internal resources are not able to host the SEFI code too (the external RAM chip can be easily included in the mobile sensor device). The occupied area indicates that the microprocessor can be implemented on an economic xc3s250e device (from $20). On the other hand, the V5 implementation uses a faster clock and the internal resources are enough to host both the microprocessor and the SEFI code, doing so a high-performance solution (but with an increased power consumption) interesting for some types of situations.
## 4 Discussion
These studies also achieved higher methodological scores for PRP production and quality, with scores of 85 and 94, respectively, further supporting their reliability. These findings highlight the importance of a rigorous study design and standardized reporting in PRP research to ensure the validity and reproducibility of the results. The seven randomized experimental studies that evaluated the effects of PRP versus saline on equine models of tendinitis and desmistis [48; 49; 50; 51; 52; 53; 54] had an overall low risk of bias. However, their major limitations included short follow-up periods (ranging from 16 days to 24 weeks) and the absence of subjective or objective lameness assessments during the trials. In contrast, the two randomized experimental studies evaluating PRP combined with MSCs [55] and the isolated effects of BM-MSCs, AD-MSCs, PRP, and [PERSON]'s solution [56] had an overall risk of bias rated as \"some concerns\". These concerns stemmed from problems in the randomization process, missing animals, and a lack of blinding of operators, which may have affected the reliability of their results. Future experimental studies should include longer follow-up periods and include both subjective (e.g., lameness scores) and objective (e.g., biomechanical testing) outcome measures to provide a more comprehensive evaluation of PRP efficacy. This systematic review has several important limitations. The high heterogeneity in PRP preparation methods (e.g., centrifugation protocols, leukocyte concentrations, and activation techniques), study designs, and outcome measures precluded a meaningful meta-analysis or subgroup analyses [67]. Without standardized protocols or consistent methodologies, a quantitative synthesis would likely yield unreliable results, highlighting the need for more uniform research approaches in this field. The predominance of small-scale studies (median of n = 9 horses) represents another significant constraint. Only three trials [40; 46; 47] included more than 50 subjects, increasing the risk of overestimated treatment effects and underreported adverse events. The neutral outcomes reported by some studies [54; 56] may reflect insufficient statistical power rather than true therapeutic equivalence. Additional limitations include (1) the high risk of bias in case series, particularly for long-term outcomes; and (2) the absence of cost-effectiveness data comparing PRP to conventional treatments. Future research should prioritize large, randomized trials with standardized protocols and incorporate economic analyses to address these gaps.
# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
###### Abstract  
Multi-target tracking, a high-level vision job in computer vision, is crucial to understanding autonomous driving surroundings. Numerous top-notch multi-object tracking algorithms have evolved in recent years as a result of deep learning's outstanding performance in the field of visual object tracking. There have been a number of evaluations on individual sub-problems, but none that cover the challenges, datasets, and algorithms associated with visual multi-object tracking in autonomous driving scenarios. In this research, we present an exhaustive study of algorithms in the field of visual multi-object tracking over the last ten years, based on a systematic review approach. The algorithm is broken down into three groups based on its structure: methods for tracking by detection (TBD), joint detection and tracking (JDT), and Transformer-based tracking. The research reveals that the TBD algorithm has a straightforward structure, however the correlation between its individual sub-modules is not very strong. To track multiple objects, the JDT technique combines multi-module joint learning with a deep network framework. Transformer-based algorithms have been explored over the past two years, and they have benefits in numerous assessment indicators, as well as tremendous research potential in the area of multi-object tracking. Theoretical support for algorithmic research in adjacent disciplines is provided by this paper. Additionally, the approach we discuss, which uses merely monocular cameras rather than sophisticated sensor fusion, is anticipated to pave the way for the quick creation of safe and affordable autonomous driving systems.  
autonomous driving; deep learning; visual multi-object tracking; transformer
# Comparison of Three Approaches to Assess the Flavour Characteristics of Scotch Whisky Spirit
###### Abstract  
This study compared the use of three sensory and analytical techniques: Quantitative Descriptive Analysis (QDA), Napping, and Gas Chromatography-Mass Spectrometry (GC-MS) for the assessment of flavour in nine unmatured whisky spirits produced using different yeasts. Hierarchical Multiple Factor Analysis (HMFA) showed a similar pattern of sample discrimination (RV scores: 0.895-0.927) across the techniques: spirits were mostly separated by their Alcohol by Volume (ABV). Low ABV spirits tended to have heavier flavour characteristics (_fentity, cereal, sour, only, sulphury_) than high ABV spirits, which were lighter in character (_fruity, sweet, floral, solventy, soapy_). QDA differentiated best between low ABV spirits and GC-MS between high ABV spirits, with Napping having the lowest resolution. QDA was time-consuming but provided quantitative flavour profiles of each spirit that could be readily compared. Napping, although quicker, gave an overview of the flavour differences of the spirits, while GC-MS provided semi-quantitative ratios of 96 flavour compounds for differentiating between spirits. Ester, arenes and certain alcohols were found in higher concentrations in high ABV spirits and other alcohols and aldehydes in low ABV spirits. The most comprehensive insights on spirit flavour differences produced by different yeast strains are obtained through the application of a combination of approaches.  
QDA; GC-MS; mapping; sensory; volatile compounds; whisky +
Footnote †: journal: Applied Sciences
# Security Enhancement for Data Migration in the Cloud
## 2 Background Preliminaries  
### Cryptographic Algorithms  
This is the study of techniques for ensuring the secrecy and/or authenticity of information [5]. The three main cryptographic algorithms are: Symmetric-Key Algorithm, Asymmetric-Key Algorithm and Hashing Algorithms.
# Heart Rate Variability in Basketball: The Golden Nugget of Holistic Adaptation?
## 5 Practical Applications
### Predicting Performance and Injury Risk  
A key determinant for successful teams in basketball is limiting the number of injuries sustained by players and the subsequent sideline time [85]. Basketball performance should be assessed from a holistic approach and consider more than just isolated variables to better understand the players' performance during basketball competition [86].  
Monitoring HRV can be an efficient and cost-effective way to gather valuable information about an athlete's physical readiness for training and competition. Compared to other methods of assessing an athlete's physiological state, such as laboratory testing, monitoring HRV requires minimal resources in terms of time and money. With the use of wearable technology and smartphone applications (e.g., HRV4 training or Elite HRV), HRV monitoring can be completed quickly and easily, making it a practical solution for basketball practitioners. Despite its simplicity, HRV has been shown to be a valuable tool for assessing the athlete's physiological state and predicting their readiness to perform [53; 55]. HRV monitoring can help coaches and trainers identify athletes who are not coping with the stress they are being exposed to and, consequently, athletes who are at risk of underperforming or present a higher risk of injury, all while investing minimal resources. Some key findings regarding HRV should be considered as follows:  
* A multivariable approach incorporating subjective and objective internal, as well as external, load measures, appears advisable for interpreting HRV data in basketball players.
* Biofeedback emerges as a viable tool for regulating HRV and associated performance markers in basketball players.
* The addition of psychological insights into HRV interpretation can offer valuable context to the data. Personalizing the interpretation of HRV data is recommended, due to the diverse responses athletes may exhibit.  
* \(\text{TL}_{\text{HRV}}\) could serve as a useful tool for monitoring the training load and adaptation in basketball players.
* HRV analysis can aid in assessing the holistic adaptation of athletes to training programs or their recovery from the effects of the program.
* Protocols for HRV measurement are sensitive to changes in the measurement process, which can compromise data reliability.
* Consistency in the measurement process and protocol is crucial for validating the collected data.
* Considering the context in which the measurements are taken, and tailoring the interpretation and use of data accordingly, is crucial.
# Sulfate Resistance in Cements Bearing Bottom Ash from Biomass-Fired Electric Power Plants
## Materials and Methods
### Method  
The OPC, OPC + 10 BA, and OPC + 20 BA pastes were mixed with deionized water at a water/cement ratio of 0.5 to prepare \(1\times 1\times 6\) cm\({}^{3}\) prismatic specimens (further to the Koch-Steinegger method), 12 each per mix, medium (sulfates or water), and exposure time. They were demolded after 24 h and subsequently cured for 21 days at 100% relative humidity and a temperature of \(20\pm 1\)\({}^{\circ}\)C (consistent with the aforementioned Koch-Steinegger procedure). Groups of 12 specimens were then soaked in an aggressive 0.3 M sodium sulfate solution (4.4 wt% Na\({}_{2}\)SO\({}_{4}\) at a liquid/solid volume ratio of 22) or deionized water as the reference at 20 \({}^{\circ}\)C for 14 days, 56 days, 90 days or 180 days. Known as the Koch-Steinegger method [28; 29], this procedure is deemed optimal for assessing blended cement resistance to this aggressive medium because it simultaneously monitors the pozzolanic reaction and assesses most of its benefits [30].  
At each test age and prior to characterization, the specimens were washed three times in deionized water to eliminate any excess salts and dried to a constant weight in a laboratory kiln at 40 \({}^{\circ}\)C.  
Specimen flexural strength and variation in weight and length were also determined at each exposure time, and pore size distribution was analyzed in the 56 days and 180 days specimens. These microstructural studies were supplemented with XRD and SEM/EDX identification of the new components formed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Property** & & \multicolumn{3}{c}{**Blended Cement**} & **EN 197-1 Requirement \({}^{\star}\)** \\ \cline{3-6}  & & **OPC** & **OPC + 10 BA** & **OPC + 20 BA** & \\ \hline \multirow{2}{*}{Physical} & Initial setting time (min) & 135 & 204 & 264 & \(\geq\)60 \\  & Expansion (mm) & 0 & 1 & 1 & \(\leq\)10 \\ \multirow{2}{*}{Mechanical} & Compressive strength (MPa) & 2 days & 40.71 & 38.71 & 31.88 & \(\geq\)20.00 \\  & & 28 days & 68.22 & 67.84 & 58.17 & \(\geq\)42.50 \\ \multirow{2}{*}{Chemical} & Sulfate oxide content (\%wt.) & 3.14 & 3.06 & 2.80 & \(\leq\)40.0 \\  & Chloride content (ppm) & 0.01 & 0.02 & 0.04 & \(\leq\)0.10 \\ \multirow{2}{*}{C} & Pozzolanicity \({}^{\star}\) & - & - & Positive & Positive \\ \hline \hline \end{tabular}  
* This property is not required for standard.  
\end{table}
Table 1: New cement physical, mechanical and chemical properties [19].
# Adaptive Piecewise Poly-Sinc Methods for Ordinary Differential Equations
## 3 Piecewise Collocation Method
### Initial Value Problem  
In this section, we provide examples for first-order and second-order IVPs.  
Relaxation Problem  
We discuss the piecewise collocation method for a first-order IVP in integral form. Consider the following relaxation or decay Equation [46] on the interval \([a,b]\)  
\[\frac{\mathrm{d}y(x)}{\mathrm{d}x}=-\alpha y(x),\quad y(a)=y_{a}, \tag{11}\]  
where \(\alpha>0\) is the relaxation parameter. The exact solution is \(y(x)=y_{a}\exp(-\alpha(x-a))\). We transform the IVP (11) into an integral form  
\[y(x)=y_{a}-\alpha\int_{a}^{x}y(t)\;\mathrm{d}t\,.\]  
\[R_{\mathrm{I}}(x)=y_{\mathrm{c}}(x)-y_{a}+\alpha\int_{a}^{x}y(t)\;\mathrm{d}t\,.\]We approximate the indefinite integral as discussed in Section 2.4.1 and the approximate residual becomes  
\[\tilde{R}_{\mathrm{I}}(x,a,y_{a},y_{\mathrm{c}}(x))=y_{\mathrm{c}}(x)-y_{a}+ \alpha(\mathcal{J}_{m}^{+}y_{\mathrm{c}})(x).\]  
The domain \([a,b]\) is partitioned as discussed in Section 3. For the \(k-\)th partition, \(k=1,2,\ldots,K\), we replace \(a\) with \(x^{k-1}\) and \(y_{a}\) with \(y_{\mathrm{c},k-1}(x^{k-1})\). The approximate residual becomes  
\[\tilde{R}_{\mathrm{I},k}(x,x^{k-1},y_{\mathrm{c},k-1}(x^{k-1}),y_{\mathrm{c}, k}(x))=y_{\mathrm{c},k}(x)-y_{\mathrm{c},k-1}(x^{k-1})+\alpha(\mathcal{J}_{m}^{+ }y_{\mathrm{c}})(x),\qquad x\in I_{k}.\]  
We remove the equation corresponding to the leftmost Sinc point in each partition, and replace them with the conditions  
\[y_{\mathrm{c}}(x^{0}) =y_{a}, \tag{12a}\] \[y_{\mathrm{c},k}(x^{k-1}) =y_{\mathrm{c},k-1}(x^{k-1}),\quad k=2,3,\ldots,K, \tag{12b}\]  
and the set of equations  
\[\tilde{R}_{\mathrm{I},k}(x_{j,k},x^{k-1},y_{\mathrm{c},k-1}(x^{k-1}),y_{ \mathrm{c},k}(x_{j,k}))=0,\ j=2,3,\ldots,m_{k},k=1,2,\ldots,K. \tag{13}\]  
The set of equations (12b) is known as the continuity equations at the interior boundaries [47]. The collocation algorithm for the IVP (11) is outlined in Algorithm 1.  
```
input :\(K\) : number of partitions \(m_{k}\) : number of Sinc points in the \(k-\)th partition output :\(y_{\mathrm{c}}(x)\): approximate solution  Replace \(y(x)\) with the global approximate solution (10).  Solve for the \(m_{k}\,K\) unknowns \(\{c_{j,k}\}_{j=1,k=1}^{m_{k},K}\) using the initial condition (12a),  continuity Equation (12b), and the set of equations for the residual (13).
```  
**Algorithm 1** Piecewise Poly-Sinc Algorithm (IVP (11)).
# Bilateral Teleoperation System with Integrated Position/Force Impedance Control for Assembly Tasks
## 5 Experimental Validation
### Experimental Results  
In our experiments, we evaluated the three different control methods designed in Section 4. The results are shown below.