esa-sceva/satcom-chunk-collection huggingface dataset:
# Highly Imbalanced Classification of Gout Using Data Resampling and Ensemble Method
## 3 Methods
### Machine Learning Classifiers
#### 3.3.4 Gradient Boosting  
Gradient boosting (GB) is a popular machine learning technique used mainly for classification problems [42]. It combines multiple weak learners to create a powerful learning model. The technique works by training a weak learner, such as a decision tree, on the data and calculating the residuals or errors. It then fits a subsequent weak learner to the residuals, with the aim of reducing the overall loss. This process is repeated iteratively, with each additional weak learner used to correct the errors made by the previous learners. The final model is a more accurate and powerful predictive model. Gradient boosting has the advantage of being able to effectively handle missing data values, making it robust and versatile for working with real-world datasets that often contain missing or incomplete data. To summarize, gradient boosting is a widely used technique for classification tasks. It involves combining a set of weak learners to create a powerful and precise model.
## 7 Evolutionary Methods
### Process Optimization and Energy Efficiency  
The food industry is characterized by energy-intensive processes such as drying, freezing, and pasteurization. Evolutionary algorithms provide a robust framework for optimizing these processes, reducing energy consumption, and enhancing overall efficiency.  
For example, PSO has been used to optimize drying conditions for fruits and vegetables, achieving a 15% reduction in energy usage while preserving nutritional quality. Similarly, GA has been applied to optimize freezing protocols, reducing energy consumption by 12% and minimizing texture degradation. These advancements directly support SDG 13 (Climate Action) by reducing the carbon footprint of food production [65].  
Quantitative data underscore the impact of evolutionary methods in process optimization. A study on thermal processing optimization using GA reported a 10% improvement in energy efficiency, translating to annual savings of over $1 million for a mid-sized food manufacturing plant. These savings not only improve economic viability but also enhance the industry's sustainability credentials [65].
# A Gamified AI-Driven System for Depression Monitoring and Management
## 3 Materials and Methods: A Gamified AI-Driven System
### System Architecture
#### 3.1.1 Multi-Platform Application  
The application, developed with React Native and Expo, captures comprehensive behavioral data using therapeutic micro-games, mood self-assessments, and adaptive surveys. Location-based services, including weather data, capture environmental context to support affective state modeling. All user interactions, timestamps, game metrics, and emotional feedback are securely transmitted via AWS API Gateway.
## 2 Project Overview
### Monitoring Scheme  
The project focused on brine temperature monitoring, freeze zone temperature monitoring, and ground displacement monitoring, with the following monitoring program:  
(1) Brine temperature monitoring: Valves and temperature measurement points were provided on the brine piping and cooling water circulation piping. Every 3 or 4 freeze tubes in a series were a group, and each group of the series circuit was set as a temperature measurement point. Brine temperature monitoring data are shown in Figure 3.  
Figure 1: Project overview: (**a**) construction layout location plan; (**b**) geological map.  
Figure 2: Freeze-method construction: (**a**) frozen soil wall layout diagram; (**b**) freeze tube layout diagram.  
(2) Freezing zone temperature monitoring: Temperature measurement hole C1 for the outer ring monitoring was located outside the outer ring freezing tube. Temperature measurement hole C2 for the middle ring temperature monitoring was located between the outer ring freezing tube and the middle ring freezing tube. Temperature measurement hole C3 for the inner ring temperature monitoring was located between the inner ring freezing tube and the middle ring freezing tube. Each temperature measurement hole was arranged in 2 to 3 measurement points; the specific layout of the temperature measurement holes is shown in Figure 2.  
(3) Ground surface displacement monitoring: The original reference data for ground surface vertical displacement monitoring were established before the construction of horizontal holes. Monitoring began on the first day when the horizontal holes were constructed and continued until the frozen soil wall melts after the completion of the frozen construction. Monitoring displacement twice a day during drilling and once a day during freezing reinforcement can effectively reduce errors in monitoring ground surface frost heave displacement due to uncertainties.
# Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory
## 2 Non-Gaussian and Nonlinear Environment (NGNLE) with Finite Memory  
As mentioned before, a NGNLE means that most of the observables (outputs) from the measurement are not linear functions of unknown NGNLE inputs. Also, the observables do not have a Gaussian distribution. In most NGNLE applications, we can measure the observables as the output. However, since the current situation of NGNLE is unknown, we need to decide between \(M\) discrete known situations.  
Suppose \(\mathbf{X}^{n}=[X_{1}^{n},X_{2}^{n},\ldots,X_{S}^{n}]\) is a vector of variables as a function of discrete time \(n\) corresponding to \(S\) known discrete states or situations (See Figure 2). For example, in communication systems that uses QAM-4, there are four possible symbols, \(X_{1}^{n},X_{2}^{n},X_{3}^{n}\)_and_\(X_{4}^{n}\) (00, 01,10, 11) which vary as a function of time. The receiver may select \(X_{2}\) out of four possible symbols as the symbol that was transmitted at time \(n\). The relation between observables and a NGNLE situation can be defined as follows:  
\[\mathbf{Y}_{n}^{m}=g_{n}(X_{1}^{n},X_{2}^{n},\ldots,X_{S}^{n},w_{n}), \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Technique Implemented** & **Q-Factor** & **Data Rate** \\  & & **Improvement** & **Enhancement** \\ \hline
[20] & Simple CDS & 2.7 dB & 13\% \\ \hline
[21; 23] & CDS with action outcome prediction (Virtual actions) & 3.5 dB & 23\% \\ \hline This work & New CDS using different focus levels & & \\  & adaptively and Virtual actions & 7 dB & 43\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between proposed work and related work published in literature.  
\[\mathbf{Y}_{n}^{m}=[Y_{n-m},Y_{n-m+1},\ldots,Y_{n}]. \tag{2}\]  
Here, \(\mathbf{Y}_{n}^{m}\) is set of observables that is extracted from measured signals from the sensors (e.g., [26; 27]) from discrete time \((n-m)\) to \(n\). Also, \(m\) is the current focus level that can be used for the reasoning with acceptable complexity. As mentioned before, \(X_{s}^{n}\) corresponds to the current NGNLE condition or situation, which we want to estimate. In (1), \(\mathbf{Y}_{n}^{m}\) is known by measurements or another information gathering technique, but, \(X_{s}^{n}\) is unknown. Also, in (1), \(w_{n}\) represents noise with an arbitrary probability density function (PDF), which could be non-Gaussian. The \(g_{n}(\cdot)\) is a nonlinear mapping of \(\mathbf{X}^{n}\) and \(w_{n}\) to \(\mathbf{Y}_{n}^{m}\). One example of such a system can be seen in a long-haul orthogonal frequency division multiplexing fiber optic link. In the next section, we briefly describe the OFDM fiber optic link.
# CRCSI: A Generic Block Interleaver for the Next Generation Terrestrial Broadcast Systems
## 4 Simulation Results
### Link-Level Simulation Results
Moreover, when TIL equals 39, the performance of the proposed block interleaver outperforms the other two types of interleaver by 0.6 dB and 0.3 dB\(@BLER=10^{-3}\). It is found that CRCSI performs better when the time selective fading is significant, which can be attributed to the more time diversity gain it obtains in the interleaving operation. In addition, the simulation results are consistent with the theoretical analysis of the minimum interleaving distance in Table 3.  
Figure 7 compares the performance of the interleavers at three moving speeds, ranging from handheld terminals to the highway scenario. It can be seen that the system performance improves by at least 0.2 dB at the speed of 30 km/h, 0.35 dB at 60 km/h and up to 0.5 dB at 120 km/h @\(BLER=10^{-3}\). Thus result can be explained as follows. As the moving speed increases, the coherence time caused by the Doppler frequency shift gradually decreases. The coherence time at 120 km/h is \(T_{c}=12.9\) ms, which is less than the duration of one frame of 40 ms. At this time, the data on the time-frequency resource block is more susceptible to time-selective fading than in the low-speed case. From the above performance evaluations we can conclude that, compared to other classical block interleavers, the proposed CRCSI is able to effectively alleviate the impact of deep fading, providing superior system performance under MPMT and LPLT scenarios. The proposed CRCSI can meet the 5G terrestrial broadcasting system's requirements for wider coverage and faster transmission rates.
## 4 Experiments
### Hyperparameter Tuning
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**0.05**} & \multicolumn{2}{c}{**0.01**} & \multicolumn{2}{c}{**0.005**} & \multicolumn{2}{c}{**0.001**} \\ \cline{2-10}  & **Top-1** & **Top-3** & **Top-1** & **Top-3** & **Top-1** & **Top-3** & **Top-1** & **Top-3** \\ \hline (2,21) AB & 98.689 & 99.827 & 98.689 & 99.793 & 98.999 & 99.896 & 98.758 & 99.827 \\ (3,32) ABC & 97.917 & 99.774 & 97.962 & 99.592 & 98.392 & 99.660 & 98.053 & 99.457 \\ (4,42) ABCD & 97.912 & 99.465 & 98.378 & 99.569 & 98.447 & 99.500 & 98.223 & 99.362 \\ (5,53) ABCDE & 96.874 & 98.742 & 97.635 & 99.166 & 98.113 & 99.439 & 97.949 & 99.262 \\ (6,63) ABCDEF & 97.239 & 99.275 & 98.022 & 99.137 & 98.263 & 99.218 & 98.079 & 99.160 \\ (7,74) ABCDEFG & 97.131 & 99.158 & 98.149 & 99.266 & 98.374 & 99.256 & 98.306 & 99.275 \\ (8,84) ABCDEFGH & 97.041 & 99.120 & 98.223 & 99.224 & 98.275 & 99.249 & 98.352 & 99.180 \\ \hline Average & 97.543 & 99.337 & 98.151 & 99.392 & 98.409 & 99.460 & 98.246 & 99.360 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy (%) of the ENN model according to the learning rate. Table 10 shows the prediction accuracy of the ENN model with varying depth configuration for the FC residual block. We fixed all other hyperparameters at the best values we observed. The ENN model yielded the best accuracy with a depth of six. Overall, the best ENN model we obtained through hyperparameter optimization achieved the Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 2 Problem Statement
### Moving in Formation  
When talking about formation control, it is a fundamental problem to enable a group of agents to move along a predefined trajectory while maintaining a rigid geometric relationship. The virtual structure approach is an intuitive idea to solve this problem by considering the formation as a single rigid body, enabling the movement of the virtual structure. In contrast to a virtual structure, the geometric relationship is maintained by controlling virtual joints at predefined constant values in the virtual linkage approach (see Figure 2).  
Another way to illustrate the formation ability of the virtual linkage exists in the relationship between the virtual linkage and virtual structure. In mechanical engineering, a linkage designed to be stationary is called a structure and can be regarded as a single rigid body. Similarly, a virtual linkage also degenerates to a virtual structure when it is designed to be stationary by controlling the virtual joint at a fixed value. In this way, the formation problem of using a virtual linkage can be solved by controlling the virtual joints at fixed angles and enabling the movement of the virtual  
where the formation pattern should be reconfigured in order to perform a complex task. Figure 4 shows an example of formation reconfiguration. The three agents initially move in a line formation in order to have a broad view, and the formation pattern needs to be reconfigured as an arrow shape in order to go through obstacles.  
In the virtual structure approach, each virtual structure corresponds to a unique rigid body. Therefore, it should be redesigned if different formation patterns are required. A solution to this problem is to model the group of agents as a virtual linkage and set the desired virtual joint angle \(\theta_{d}\) to different values. For example, in Figure 4, the three agents are in line formation when \(\theta_{d}=0\), and can be reconfigured into a regular triangle shape when \(\theta_{id}\) is set to \(120^{\circ}\).
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## 3 Weather Data
### Data from Visibility Sensor  
Visibility data were collected by PWD20 [29] installed in Boseong, South Korea (see Table 5). PWD20 manufactured by Vaisala is a device that is used to observe the MOR (measurement range) and current weather condition. As its observation range is 10-20,000 m, and vertical resolution is 1 m, it allows a determination of long-range visibility. PWD20 can be fixed to various types of towers because the device is short, compact, and lightweight. The mechanical properties of PWD20 are outlined in Table 6.  
fog were 1000 m or lower visibility for 20 mins, 90% or higher relative humidity, and no precipitation. The AWS data were used for precipitation sensing and relative humidity. As the ceilometer backscatter data, AWS and visibility sensor data have different observation intervals, the data of the nearest time slot were matched and used based on the ceilometer backscatter data.
# Auditory Coding of Reaching Space
## 2 Materials and Methods
### Experimental Conditions  
The subjects, parallelized for sex, were randomized into two groups. While executing identical reaching tasks to invisible targets, 12 subjects were blindfolded and 12 subjects had their eyes open during the experiment. The experimental task was to hit the instructed target positions as exactly as possible. All participants received auditory instruction and feedback. There were three different conditions:  
1. Continuous condition: * Instruction: Sonification of the model's reaching movement towards the target positions followed by the sonification of the model's final position for 1 s. * Feedback: Real-time sonification of the participant's reaching movement followed by the sonification of the participant's final position for 1 s.
2. Discrete short condition: * Instruction: Sonification of the model's final position for 1 s. * Feedback: Real-time sonification of the participant's final position for 1 s.
3. Discrete long condition: * Instruction: Sonification of the model's final position for the duration of the model's reaching movement and one more second (in total 2.0-2.65 s). * Feedback: Real-time sonification of the participant's final position for 1 s.
## 2 GNSS Stations and Data Processing  
### General Description of the Emplacements  
The GNSS stations of the ROA have been installed at the different sites for more than 20 years, some through completed collaborative projects and others on the own initiative of the staff of the geophysics section. The main characteristics of the equipment and current status of these stations are summarized in Table 1; they are listed below in alphabetical order. For the stations belonging to IGS, the considered period ranges from the installation of the station to 16 June 2024. The station belonging to Topo-Iberia has observations from 2008 to 2 February 2014, the end of the research project [11]. The stations belonging to the ROA, along with those inherited from Topo-Iberia, do not have the same period of data since they were installed at different times, and some have had technical problems. In any case, all stations except ALHU have more than five years of data. Most have more than 10 years of data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
As can be observed, several of these continuous GNSS station sites exhibit a series of constraints that render them challenging to exploit. Power supply, vandalism, or weather conditions add to the difficulty in accessing them in some cases, making the maintenance and monitoring of these sites a daunting task. Therefore, to keep this type of station up to date, it is necessary to have the necessary infrastructure and good associated logistics. This means that the stations are exposed to periods of maintenance or failures that make it difficult for them to operate continuously. Communications also play a key role as real-time service is required. In stations where there is no internet network connection, satellite communications or multi-operator modems have been installed so that it is possible to access the equipment remotely. Due to the circumstances described above, all installed equipment must be robust and energy-efficient so as to ensure the reliability of the station over a long period of time. Some locations can be seen in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
Figure 3: Some of the continuous stations belonging to the ROA. (**a**) CEUD (Desnarigado Castle in Ceuta), (**b**) CHAF (Isabel II Island Lighthouse in the Chafarinas Archipelago), (**c**) ROTA (Rota Naval Base Dock), and (**d**) PVLZ (Vélez de la Gomera Rock Lighthouse).
## 2 Materials and Methods
### Preparation of Chiral Selectors
#### 2.1.2 Synthesis of Eremosaminyl Aglycon of Eremomycin and Eremomycin Aglycon
The obtained results for E-CSP, Chloro-E-CSP, Amide-E-CSP, O-CSP, \(\mathrm{DEE}\)-CSP, and \(\mathrm{E}\)Ag-CSP including retention factors, \(\mathrm{eantioselectivity}\), and \(\mathrm{eantiomer}\) peaks are summarised in Table 3. The corresponding \(\mathrm{chromatograms}\) for selected -blockers are shown in Figure 3.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Chiral Stationary**} & \multicolumn{3}{c}{**E-CSP**} & \multicolumn{3}{c}{**Chloro-E-CSP**} & \multicolumn{3}{c}{**Amide-E-CSP**} & \multicolumn{3}{c}{**O-CSP**} \\ \multicolumn{1}{c}{Phases} & & & & & & & & & & & & \\ \hline Beta-Blocker & \(k^{\prime}_{\mathbf{1}}\) & & & & & & & & & & & \\ \hline Propranolol & 0.71 & 1.15 & 0.61 & 0.40 & 1.00 & 0.00 & 0.27 & 1.00 & 0.00 & 0.40 & 1.00 & 0.00 \\ Pindolol & 0.76 & 1.16 & 0.71 & 1.43 & 1.00 & 0.00 & 0.32 & 1.00 & 0.00 & 0.38 & 1.11 & 0.27 \\ Alprenolol & 0.46 & 1.17 & 0.64 & 0.14 & 1.00 & 0.00 & 0.32 & 1.00 & 0.00 & 0.23 & 1.00 & 0.00 \\ Metoprolol & 0.49 & 1.23 & 0.83 & 0.15 & 1.00 & 0.00 & 0.29 & 1.00 & 0.00 & 0.24 & 1.20 & 0.37 \\ Atenol & 1.66 & 1.12 & 0.70 & 1.45 & 1.00 & 0.00 & 0.26 & 1.00 & 0.00 & 0.89 & 1.00 & 0.00 \\ Oxprenalol & 0.42 & 1.14 & 0.52 & 0.03 & 1.00 & 0.00 & 0.28 & 1.00 & 0.00 & 0.18 & 1.00 & 0.00 \\ Sotalol & 0.94 & 1.16 & 0.68 & 0.54 & 1.00 & 0.00 & 0.26 & 1.00 & 0.00 & 0.19 & 1.00 & 0.00 \\ Pronenthal & 0.70 & 1.12 & 0.57 & 0.36 & 1.00 & 0.00 & 0.30 & 1.00 & 0.00 & 0.40 & 1.00 & 0.00 \\ Clenbuterol & 0.76 & 1.24 & 0.72 & 0.27 & 1.30 & 0.37 & 0.34 & 1.00 & 0.00 & 1.00 & 1.00 & 0.00 \\ Salbutamol & 1.24 1 Figure 3: _Cont._  
It should be noted that labetalol has two chiral centres in the molecule (see Table 1), so there are two racemates containing four stereoisomers that are present in equal amounts. The separation of all four enantiomers of labetalol has been reported with Chirobitic V.  
Figure 3: Separation of -blockers enantiomers on CSPs with immobilised eremomycin (E-CSP) and eremomycin aglycons (EAg-CSP, DEE-CSP, and EEA-CSP) chiral columns (250 \(\times\) 4.0 mm I.D. ); eluents: 100% methanol with 0.01% TEAA (EEA-CSP) and 50% methanol–50% acetonitrile containing 0.1% TEAA; flow rate 0.7 mL/min for other columns, temperature 25 \({}^{\circ}\)C: injection volume 10 L, UV detection at 210 nm. Analytics: metoprolol (**A**), pindolol (**B**), alprenolol (**C**), atenol (**D**), propranolol (**E**), and oxprenalol (**F**). The numbers indicate peak areas.
## 4 Evaluation  
MIRA provides efficient and reliable healthcare services to the users. To ensure productivity, we evaluated MIRA based on performance measures, task completion, security, and user experience. For this purpose, we circulated a call for participants on the university's mailing list and social media. A total of 33 participants belonging to seven countries registered, including 20 males and 13 females within the age group of 18 to 43 years as shown in Figure 6. The participants were affiliated with different departments such as Healthcare Subject Matter Experts (5), Medical Practitioners (4), and students belonging to Medical (7), Computer Science (9), Bioinformatics (3), Life Science (3), and International Relations (2) disciplines. Each participant was given a set of procedural documents, which contained a checklist of tasks, consent form, hints for acting as a particular patient type, and a user experience questionnaire. The consent form clearly describes the data collection procedure, including audio and video recording of interactions with MIRA, data storage, data usage, and disposal details. Moreover, participants were shortly briefed about the goal of the activity, and we instruct them to sign the consent form after reading it carefully. Upon agreement, the voice sample along with demographic information (name, address, gender, age, and medical history) was collected to complete the MIRA registration process.  
Figure 5: MIRA smartphone application screenshot on android pie.  
As per the scope of this study, MIRA predicts glaucoma and diabetes based on the trained model. The remaining diseases including normal conditions are out of scope and are considered under the other label. Therefore, MIRA analyzed user interactions, identified chief complaints, and categorized these as glaucoma, diabetes, or other. Among the 33 registered participants, 17 did not belong to the medical profession. For this reason, we provided a list of chief complaints as described in Table 3, which guide the participants to act as a patient for three health conditions. In the case of other label, we selected cardiovascular and orthopedic chief complaints that are similar to glaucoma and diabetes. If MIRA did not generate a final recommendation for some reason, then it politely responded 'I am sorry, I am not able to diagnose your disease based on the provided knowledge. Do you want me to assist you further?'. Moreover, the participants were allowed to use synonyms, ask questions in a random sequence, and interact in a natural way of communication.