esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Artificial Intelligence Algorithms
### Recurrent Neural Network
#### 3.2.2. Gated Recurrent Unit  
The GRU has been used in many applications, but has seen more significant results in music and speech modeling, as well as natural language processing. This method was developed with the intent of allowing each recurrent unit to alter the sequence of dependencies in an adaptive manner over time. To accomplish this, the unit was built with the following four steps, where \(x_{t}\) is the current input, \(h_{t-1}\) is the output of the previous step, \(W\) and \(U\) are parameter matrices for the weights, and \(\sigma\) is the sigmoid function [29; 32]:  
1. The first step is an update gate, as shown in Figure 4 (circled in green color), which is used to determine how much information will be updated in the unit. This gate is denoted as \(z_{t}^{j}\) and calculated by: \[z_{t}^{j}=\sigma(W_{z}x_{t}+U_{z}h_{t-1})^{j}.\]
2. The next step is a reset gate, seen in Figure 4, circled in purple, denoted as \(r_{t}^{j}\) and calculated by: \[r_{t}^{j}=\sigma(W_{r}x_{t}+U_{r}h_{t-1})^{j}.\] This gate is used to determine how much information needs to be forgotten. When the value of this gate is close to zero, it determines that the \(j\)-th information should be forgotten in the current memory. Any value close to one means that the data will be preserved.
3. The third step is to determine the current memory. This gate is denoted as \(h_{t}^{j}\) in the following expression and, in Figure 4, seen as \(h_{t}\). \[h_{t}^{\prime}=\tanh(W_{h}x_{t}+r_{t}\cdot U_{h}h_{t-1}))^{j}.\] This gate also uses the Hadamard product to decide how much of the hidden state of the content should be forgotten.
4. Finally, the last step is to determine the information to be stored in the hidden layer at the current iteration that will be passed onto the next cell. This step is denoted as \(h_{t}\) in the following equation and, in Figure 4, is denoted as \(h_{t-1}\): \[h_{t}=(1-z_{t}^{j})h_{t}^{j}+z_{t}^{j}\widetilde{h}_{t}^{j}.\] These steps can all be easily visualized in Figure 4.  
Figure 3: The architecture of an LSTM cell with the input cell circled in green, the forget cell in yellow, the cell state in red, and finally, the output gate in purple.
## 2 Materials and Methods
### EMG Data Collection and Process  
Four pairs of surface EMG sensors (Blue Sensor M-00-S/25, Ambu, Ballerup, Denmark), each with a 20 mm inter-electrode distance and a 10 mm diameter, were positioned on the vastus lateralis, vastus medialis, biceps femoris long head, and semitendinosus muscles (Figure 2). Before electrode placement, the skin was shaved, abraded with sandpaper, and cleaned with alcohol to reduce skin impedance. The EMG activity was recorded using a TeleMyo telemetric hardware system (Noraxon U.S. Inc., Scottsdale, AZ, USA) at a 1000 Hz sampling frequency. The electrode placement adhered to SENIAM guidelines [25], and ultrasonography was used to ensure proper electrode alignment with the fascicle orientation while minimizing crosstalk from adjacent muscles. A reference electrode was placed on the ipsilateral patella, and sensor cables were securely taped to the skin to prevent motion-related signal artifacts. The raw EMG amplitude data were exported to MATLAB (v. 2019b), where a custom script was used for processing. The data were filtered with a 4 th order zero-lag Butterworth filter, band-passed from 20 to 450 Hz, to remove movement artifacts and noise. The EMG signals were then smoothed using a root mean square method with a 50 ms window. Each contraction was divided into eccentric (knee flexion) and concentric (knee extension) phases based on the knee joint angle, and the average EMG activity during each phase was calculated for further analysis. Synchronization of the EMG and dynamometer data (torque and angle) was achieved by feeding the dynamometer output signals directly into the EMG system.
# Membrane Engineering for Sustainable Development: A Perspective
## 2 Membrane Operations in Sustainable Development: Case Scenario
### Raw Material Production  
Membrane crystallization (MCr) has been used in several investigations to assess its potential for raw materials recovery from brine and wastewater steams [20]. The process fundamentally exploits the potential of MD to concentrate the solutions to their saturation level. The concept was introduced in 2002 by [PERSON] et al. [21]; the process has been successfully applied at lab-scale for the recovery of several salts and other compounds from wastewater streams [22; 23; 24]. In addition to the salts recovery, the process increases the freshwater recovery from the brine and offers an elegant way to achieve the inspiring objective of zero liquid discharge in desalination [25]. From economical perspective, it has been calculated that the water production cost can become negative if the sale of the recovered salt is also considered [26]. Recovery of salts through MCr has been mostly carried out by using direct contact configuration, however, a current study [27] suggests that this configuration is not suitable when crystallizing salts, such lithium chloride, which have very high solubility. Highly soluble salts at high solution concentrations possess high osmotic pressure which can overcome the vapor pressure induced by the thermal energy provided to the solution and thus the system exhibits a net negative flux as shown in Figure 4. In order to overcome this limitation, an alternative configuration i.e. vacuum MCr (VMCr) has been proven suitable [27]. In VMCr, the condensing media (cold water in case of direct contact MCr) is replaced with the vacuum, thus the suction of water from the other side of the membrane is eliminated. Air gap MCr may also be suitable for such scenario, however, no specific study has been carried out on these lines so far by using air gap MCr. The further area of research on MCr should focus on developing anti-scalant membranes and techniques, better system for recovery of crystals from the magma and efficient techniques/strategies to separate the co-precipitating crystals of different salts. Improved understanding of membrane fouling and scaling is needed. The appropriate membrane features including membrane thickness, conductivity, surface roughness and appropriate module lengths need to be specified.  
Figure 3: The concept of simultaneous production of freshwater, energy and raw materials through MD. Reproduced with permission from [19]. Copyright Elsevier, 2018.
## 6 Limitations and Future Work  
The current study has some limitations that should be acknowledged. Firstly, the diversity of the datasets used in terms of their language, cultural background, and educational context could be further expanded. Secondly, the performance of the SER system in real-world distance learning environments may differ from its performance on benchmark datasets. Thirdly, this study focuses on recognizing a limited set of discrete emotions, which may not fully capture the complexity of human emotions. Fourthly, individual differences in emotion expression and perception are not extensively explored. Lastly, the SER system relies primarily on acoustic features without incorporating additional modalities such as facial expressions and body language.  
Future research should focus on addressing these limitations by incorporating more diverse datasets, conducting field studies in real distance learning environments, exploring fine-grained emotion recognition, personalizing the SER system to individual users, and integrating multimodal information. Additionally, future work should investigate methods to enhance the explainability and interpretability of the SER system, explore ethical considerations, and study its potential impact on student-teacher relationships. Deploying the SER system on cloud platforms like Amazon Web Services (AWS) can facilitate scalability, high availability, and efficient resource management for real-world applications. Furthermore, exploring federated learning techniques can enable privacy-preserving training and personalization of the SER model across multiple institutions without directly sharing sensitive speech data.  
Despite these limitations, the current study provides a foundation for further research and development of SER systems in distance education. By addressing the identified challenges and opportunities, we can work towards creating more robust, reliable, and user-centered SER systems that effectively support and enhance the distance learning experience.
## 3 Different Aspects of Colour-Based Method
### Motion Artefacts
[82] enhanced the POS method to measure continuous HR considering different fitness applications. However, this technique cannot suppress motion artefacts when the motion and the pulse have the same frequency. Moreover, this method is susceptible to degradation with skin tone and illumination variation. [PERSON] et al. introduced a time-frequency analysis method using continuous wavelet transform (CWT) [83] and a motion resistant spectral peak tracking (MRSPT) method [84] to measure HR via webcam, considering seven motion circumstances when subjects were driving, running and engaged in fitness training. Nevertheless, these methods considered very short ranges of around 1.5 m. For monitoring precise HR using a video camera, [PERSON] et al. [85] presented another method using singular spectrum analysis (SSA) where the motion signal measured from signal and SSA based on singular value decomposition (SVD) was employed to rectify motion artefacts during treadmill exercise. However, they only considered treadmill exercise and a small number of subjects. Moreover, only the first three leading decomposed components of the motion signal were removed from BVP, which may affect the performance of the proposed method. [PERSON] et al. [86] designed a computationally efficient method based on linear transformation exploiting parameters attained from tissue-like models of the skin to extract HR and HRV accurately in the presence of various head motions using a colour camera. However, the apparatus needed to be calibrated via a known colour grid and only systematic head motions were considered. [PERSON] et al. [87] introduced a signal quality index (SQI) and verified the capability of SQI as a tool in order to enhance the consistency of heart rate measuring applications considering videos of a moving object. However, this SQI on videos needs to be tested with participants carrying out smoother movements, in time-varying illumination environments. Motion artefacts are not only caused by camera or subject movement but also caused by cardiac-related, i.e., ballistocardiographic (BCG) artefacts. Using a CCD camera, [PERSON] et al. [88] proposed a motion robust PPG-imaging through colour channel-mapping based on chrominance (CHROM) and the blood-volume pulse (BVP) signature methods to overcome BCG artefacts. They extracted a reference PPG signal from the palm and obtained PPG-images as the normalised inner-product between this reference and the streams from the skin sensor array. In the proposed method, first they pre-process the videos. After frame preprocessing, remote-PPG signals are acquired in each sensor-element, mapped according to CHROM or PBV algorithms and correlated with a reference PPG signal from the palm. PPG amplitude and phase images are obtained using channel mapping algorithms. The result showed that the proposed method reduced BCG artefacts to less than 10% of the reference PPG signal strength at the palm.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 2 Related Work
### Statistical Heterogeneity and Drift Metrics
#### 2.1.2 Recent Extensions  
Canonical FL baselines serve as yardsticks for assessing new federated-learning methods. We compare our approach to eight widely used algorithms: FedAvg [1], FedProx [3], SCAFFOLD [4], FedNova [8], FedCurv [6], FedDyn [5], the adaptive-server optimisers FedAdam and FedYogi [9], and the personalised scheme Ditto [20]. These baselines differ mainly in how they mitigate client heterogeneity and communication costs, yet all share the goal of learning a global model without centralising data. Concise descriptions of each method are provided in Section 4.  
Although these approaches report notable gains, each relies on auxiliary metadata or the per-client state--distribution fingerprints, link-quality statistics, or task-specific adapters--that must be stored or exchanged alongside model parameters. Such side information grows with the client population and can become unreliable under churn. By contrast, ERFO introduces a single-scalar entropy regularizer that is computed locally and aggregated exactly like the weights, adding no extra communication, storage, or bookkeeping beyond standard federated updates while still providing provable bounds on non-IID divergence.
# A Robust Wind Turbine Component Health Status Indicator
## 2 Materials and Methods
### The Gaussian Mixture Copula Model
#### 2.5.3 Failure Index Generation  
The next step consists of transforming the log-likelihood obtained from the GMCM output into a handy and understandable index to detect failures.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**S2_GBX_F1**} \\ \hline k & \multicolumn{6}{c}{\(\Sigma\)} \\ \hline
3 & \multicolumn{6}{c}{comp1} \\ \hline  & & 1.000 & \(-\)0.045 & \(-\)0.014 & \(-\)0.106 & 0  
In general, log-likelihood provides information about the probability of belonging to the fitted model in the learning stage. In this sense, an observation for which the log-likelihood is high means that it would fit the model's behavior and, therefore, have a higher probability of belonging to it. In the case of this study, it would mean that this observation corresponds to the healthy behavior of the component. If the observation does not fulfill the model, it does not correspond to healthy behavior, and the log-likelihood value will be lower.  
In order to generate a smart and comparable index to define an empirical probability of belonging to unhealthy behavior, a transformation is conducted. The transformation applied to GMCM output (log-likelihood) is based on the inverse of the cumulative empirical function [44]. This resulting parameter intends to determine the risk of failure of the component or the probability of working in an unhealthy mode. The Failure Index (_FI_) is defined in Equation (7).  
\[FI(\%)=(1-\frac{(\sum_{i=1}^{i=n}ecdf(P_{i}))}{n})*100 \tag{7}\]  
where,  
* \(P_{i}\) is the log-likelihood estimated with the copula model.
* \(ecdf\) function is the empirical cumulative density function fitted in the learning period to the log-likelihood output variable. This function is used to standardize values (between 0 and 1).
* \(n\) is the number of observations  
Finally, an _FI_ value is generated for each observation where higher _FI_ values will indicate a higher probability of operation under unhealthy conditions. Later, the GMCM coefficients are used with the test data to evaluate the performance of the _FI_.
## 2 Method and Approach
### Enriching Paths with Local Clearance Width  
The passage width can be estimated from the map geometry in combination with the corresponding paths. The skeleton itself has the advantage that it is located in the center of the geometry (see Figure 10). Thus, only the shortest distance from the skeleton to the respective boundary has to be calculated for the approximation of the passage width. This can be achieved with the so-called Euclidean distance transformation. For each pixel, the distance to the boundary is calculated. Then the distance values of the respective path \(p_{i}\in J_{k}\) can be taken based on the pixels, where the smallest value is the passage width \(d_{k}\) with  
\[d_{k}=\min(\{EDT(p_{i}),\ EDT(p_{i+1}),\ldots,EDT(p_{n})\}). \tag{4}\]  
Figure 4 shows the result of the distance transformation as numerical values, a colored representation of the distance and the intersection of the distance transformation with the individual paths. In the simple example from Figure 4 a minimum width of 1 pixel applies to each path, while in the example from Section 3.2 a variety of passage widths result.  
An edge of the graph can be stored as a tuple with the respective polyline as well as the passage width as \((P_{k},d_{k})\). If a robot or its load is too large for passing through, an alternative route can be planned directly. Similarly, a path can be traversed by multiple robots, on which the number can be limited by \(d_{k}\). Based on this graph from 2.3 in combination with the clearance width, a digital twin can now be derived, which is covered in the following section.  
Figure 4: Clearance width estimation: map with Euclidian distance values, colored representation, and estimated clearance width of each path.  
Figure 3: Polyline path estimation: topological segmentation of the skeleton, path segmentation, collapse metric for path simplification, simplified path as a polyline.
## 2 Materials and Methods
### Data Acquisition  
A total of 106 sampling points were systematically established using a 1 \(\times\) 1 km regular grid (Figure 1b), and soil samples were collected from 15 July to 31 July 2015. At each point, three soil samples were taken within a 5 m radius using a soil auger and then combined to form a composite sample from the topsoil layer (0-20 cm). The samples were air-dried, gently crushed, and sieved through a 2 mm mesh for analysis of edaphic chemical and physical indicators. Soil organic matter (SOM) was measured using the dichromate oxidation method, while soil available nitrogen (SAN) was determined by isotope analysis. Soil available phosphorus (SAP) was extracted using alkaline sodium bicarbonate in a 20:1 ratio, and soil available potassium (SAK) was extracted using the ammonium acetate extraction method and analyzed with a flame photometer.  
Sand, silt, and clay content were determined by the pipette method in the lab of agricultural resources and environment. Undisturbed soil samples were collected using a metallic core cylinder with a volume of 100 cm\({}^{3}\) (5 cm in height and 5 cm in diameter) from the 0-20 cm depth. Soil bulk density (SBD) was measured by the oven-dry method, and soil porosity (SP) was calculated as follows:  
\[\text{SP }(\%)=\left(1-\frac{\rho_{b}}{\rho_{d}}\right)\times 100 \tag{1}\]  
where \(\rho_{b}\) is the SBD and \(\rho_{d}\) is particle density of 2.65 g/cm\({}^{3}\).  
The gravimetric SWC was determined by weighing samples before and after oven-drying, expressed as a percentage of SWC in dry soil weight. Volumetric SWC at depths of 10, 30, and 50 cm was obtained from a data center ([[https://data.tpdc.ac.cn/home](https://data.tpdc.ac.cn/home)]([https://data.tpdc.ac.cn/home](https://data.tpdc.ac.cn/home)), (accessed on 5 January 2025)) and validated using the method of Universal Triangle [12]. The digital elevation model (DEM) data with 30 m resolution was used to extract elevation information. Slope and aspect were calculated, and the cosine of the azimuth angle of aspect was used to determine the aspect factor. Additionally, secondary topographic factors related to hydrological processes, such as convergence index (CGI), general curvature (GCVR), topographic wetness index (TWI), and valley depth (VAD), were calculated. The main null hypothesis for these factors is that topographic features have no significant effect on the spatial distribution of SWC or hydrological behavior. By analyzing these topographic factors, we aim to investigate how variations in terrain morphology may contribute to the differences in SWC in the coal and non-coal mining areas, thus addressing the research questions regarding the influence of topography on SWC dynamics.
## 2 An Overview of the Experimental Environment  
An image of the experimental environment can be seen in Figure 1 which includes the hTetro robot and five obstacles. The outline of hTetro robot is shown in Figure 2, where all the four square-blocks are linked together with hinges. These blocks are driven by actuating the hinges with the smart servo motors mounted on it. The dimension of each hTetro block has a length, width, and height of 140 mm, 140 mm, and 55 mm, respectively. Owing to the smaller and compact design structure of hTetro, each box is assigned to perform a specific function: Block 1 manages the locomotion of the robot, while control and power modules are located on block 2. Meanwhile, all the blocks are equipped with cleaning functions, but while the robot is performing reconfigurations, the cleaning function is turned off in order to devote the actuation energy fully to the servo motors controlling hinges. The aggregate weight of hTetro, including all its peripheral devices is approximately 3 kg. For the mobility and transformation, hTetro is equipped with a set of six geared DC motors and three actuator servo motors, respectively. The three actuated servo motors, placed at hinges, are controlled to switch the robot between the seven possible configurations illustrated in Figure 3. For the actuation of hinge points during reconfiguration, Herkulex DRS-0101-7 V smart servos are utilized with a maximum rotation angle of 270\({}^{\circ}\). The microcontroller used in hTetro is an Arduino Atmega 2560 16-Bit which performs three major functions:  
* Receiving the command from the user by means of a smartphone which is connected wirelessly through a Bluetooth serial communication protocol. Arduino uses UART (Universal Asynchronous Receiver-Transmitter) for this communication.  
* Generating control signals to the motor driver which controls multiple DC motors; in addition, using PWM (Pulse Width Modulation), it controls the speed of the motor by providing the required current for the motor to run.
* Establishing full duplex serial communication with the servomotors where Arduino sends the control signals to the servo motors and simultaneously receives feedback from them.  
For a thorough explanation of the mechanical design of hTetro and a detailed report on the content and functionality of each block, the reader is referred to [11; 12]. The detailed system architecture of electrical conventions is given in Figure 4.  
Figure 1: The experimental environment.  
Figure 3: Seven one-sided tetrominoes.  
Figure 2: The hTetro architecture.