esa-sceva/satcom-chunk-collection huggingface dataset:
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 3 Waterborne and Foodborne Protozoan Infections
### Pathogenic Protozans in Raw Vegetables  
Contamination of leafy greens caused many outbreaks of protozoan infections worldwide [37]. According to a systematic review and meta-analysis, protozoan contamination in vegetables and fruits was documented in 44 countries with a pooled global prevalence of 20% for vegetables and 13% for fruits. The pooled prevalence in vegetables was 11% for _Cryptosporidium_ spp.; 9% for _G. intestinalis_; 8% for _B. coli_ and _E. histolytica_; and 6% for _T. gondii_, _C. cayetanensis_, and _B. hominis_. In fruits, the pooled prevalence was 9% for _E. histolytica_, 7% for _G. intestinalis_, and 5% for _Cryptosporidium_ species [82]. The estimated pooled prevalence of parasitic protozoans in vegetables and fruits for different WHO regions was 37% in the Southeast Asian region, 26% in the Americas, 22% in the Eastern Mediterranean region, 16% in the African region, 14% in the Western Pacific region, and 14% in the European region. At the country level, Nepal had the highest pooled prevalence of 92% [82].  
Studies from Egypt, Ethiopia, Ghana, Libya, and Nigeria reported prevalences of _Cryptosporidium_ spp. and _G. intestinalis_ in fresh produce of 0.8-81% and 2-99%, respectively, while _C. cayetanensis_ had an overall prevalence of 12-21% in Egypt, Ethiopia, and Ghana. _E. histolytica_ showed an overall prevalence of 5-99% in Burkina Faso, Egypt, Ethiopia, Nigeria, and Sudan [14].  
Other studies reported the detection of _G. intestinalis_ in vegetables in Morocco, the United Arab Emirates (UAE), Mozambique, Ethiopia, Italy, and Spain; _Cryptosporidium_ spp. in Morocco, Ethiopia, Italy, and Spain; _E. histolytica/dispar/moskowsky_ in the UAE, Mozambique, and Italy; _T. gondii_ in Morocco and Spain; _C. cayetanensis_ in Spain; _B. hominis_ in Mozambique and Spain; and _B. coli_ and _E. nana_ in Mozambique [11; 37; 38; 43; 47; 48; 83; 84]. In one study, _Cryptosporidium_ spp. was detected only by quantitative PCR (qPCR) and not confirmed by microscope observation [47]. _B. hominis_ was the most frequently detected pathogenic protozoan in vegetable samples in Lebanon markets, with some samples also contaminated by _Entamoeba_ spp., _B. coli_, and _G. intestinalis_[85].  
Eating crops irrigated with reused water represents a risk for protozoan infection. A study carried out on wastewater treatment plants in Japur, India, reported a risk of infection decreasing in the order moving-bed bioreactor, activated sludge process, and sequencing batch reactor [86].  
Data on pathogenicity, epidemiology, and occurrence in food and drinking water, with risk factors and prevention measures are overviewed in the following sections dedicated to individual pathogenic protozoans. The pathogenic agents are ordered according to the number of literature sources screened in the second search round with priority according to the FAO/WHO ranking [18] for _T. gondii_ and _E. histolytica_ with an equal number of screened articles (Figure 1).
# Ontology-Based Feature Selection: A Survey
## 2 Classification Methods
### Support Vector Machines  
Support vector machine [35] classifiers are generally defined for binary classification tasks. Intuitively, they attempt to draw a decision boundary between the data items of two classes, according to some optimality criterion. A common criterion employed by SVM is that the decision surface must be far away from the data points. The separation degree can be estimated in terms of the distance from the decision surface to the closest data points. Such data points are called support vectors.  
Finding the maximum margin hyperplane is a quadratic optimization problem [36]. In case the training data are not linearly separable, slack variables can be introduced in the formulation to allow some training instances to violate the support vector constraint, i.e., they are allowed to be on the \"other\" side of the support vector from the one that corresponds to their class.
# Mathematics-Driven Analysis of Offshore Green Hydrogen Stations
## 1 Introduction
For these reasons, a mathematics-based technical model of a green hydrogen production station powered by solar energy is presented to be used as a generic framework applicable to any location worldwide. This station can be equipped with two types of electrolyzers (Figure 1): alkaline (ALK) and proton exchange membrane (PEM). By integrating green hydrogen production with the electricity generation capacity of solar energy, production becomes decentralized and more flexible, allowing for installation in both onshore--freshwater--and offshore environments. Some of the additional benefits that can be achieved through the use of this type of offshore stations are as follows: (i) it enables hydrogen production near potential consumption points--such as oil platforms, ships, or coastal facilities--significantly reducing the need for long-distance hydrogen transportation and storage, along with the associated costs and risks, and supporting the integration of green hydrogen into existing maritime operations. Additionally, the ability to store and transport the produced green hydrogen reduces the need for installing underwater cables for energy transmission: (ii) It ensures and facilitates access to the primary resource from which green hydrogen is extracted: water (H\({}_{2}\)O). The extraction of seawater ensures a reliable and virtually inexhaustible supply, regardless of the hydrogen production levels required at its output. (iii) As demonstrated throughout this study, it ensures an increase in green hydrogen production capacity, driven by the reflection of solar radiation on the sea surface and the lower atmospheric attenuation of solar radiation due to reduced pollution levels--compared to other onshore locations [21]. This increase in production capacity can be leveraged in two distinct ways: (i) by increasing the station's green hydrogen output while maintaining the same photovoltaic surface area or (ii) by maintaining the same station's hydrogen output and reducing the surface area required for the solar panels. Given the harsh conditions of the marine environment, reducing the size of the solar panels surface simplifies the structural and anchoring requirements, thereby lowering associated costs. Simpler, more reliable, and cost-effective designs will foster the expansion of floating solar energy and green hydrogen production in marine environments.
# Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing
## 4 Hybrid Simulated Annealing Algorithm
### Termination Condition  
The hybrid SA ends when \(T(t)\leq T_{lowest}\) or \(Cost(P)=0\), where \(T(t)\) is the temperature at current time \(t\), \(T_{lowest}\) is a preset temperature and \(Cost(P)\) is the cost of the current wireless communication tower placement solution \(P\).
## 3 Kriging (Gaussian Process) Model  
Kriging is a stochastic interpolation algorithm that is based on the assumption that the model output, denoted as \(\mathcal{Y}=\Gamma(\mathbf{x})\), is a realization of a Gaussian process. This Gaussian process is indexed by the input variables \(\mathbf{x}\in D_{\mathbf{X}}\subset\mathbb{R}^{M}\). A Kriging model can be written as  
\[\mathcal{Y}\approx\Gamma^{Krig}(\mathbf{x})=\mathbf{m}^{T}\mathbf{f}(\mathbf{ x})+\sigma^{2}\kappa(\mathbf{x},\mathbf{x}^{\prime};\mathbf{\Theta}) \tag{3}\]  
where \(\mathbf{m}^{T}\mathbf{f}(\mathbf{x})\) is the mean value of the Gaussian process. This Gaussian process consists of a set of functions \(f_{j,\ j=1,\ldots,q^{\prime}}\), each associated with corresponding coefficients \(m_{j,\ j=1,\ldots,q^{\prime}}\). The parameter \(\sigma^{2}\) denotes the variance of this Gaussian process. The covariance function (also known as the \"kernel\" function) is described as \(\kappa(\mathbf{x},\mathbf{x}^{\prime};\mathbf{\Theta})\), and it characterizes a stationary Gaussian process. This stationary Gaussian process maintains a zero mean and unit variance and is defined by a covariance function \(\kappa(\mathbf{x},\mathbf{x}^{\prime};\mathbf{\Theta})\), which depends on hyperparameters denoted as \(\mathbf{\Theta}\).  
Figure 1: A schematic illustration of the limit-state function \(g(\mathbf{x})\), safe domain \(D_{s}\), and failure domain \(D_{f}\).  
In the simple Kriging model, no estimation on \(\mathbf{m}\) in Equation (3) is performed and the coefficients \(\mathbf{m}\) are set to 1. Consequently, the simple Kriging model is written as  
\[\mathbf{m}^{T}\mathbf{f}(\mathbf{x})=\sum_{j=1}^{q}f_{j}(\mathbf{x}) \tag{4}\]  
In ordinary Kriging, \(\mathbf{m}^{T}\mathbf{f}(\mathbf{x})\) assumes a constant but unknown value.  
\[\mathbf{m}^{T}\mathbf{f}(\mathbf{x})=m_{0} \tag{5}\]  
The linear Kriging model assumes that \(\mathbf{m}^{T}\mathbf{f}(\mathbf{x})\) is a linear combination of an arbitrary function as  
\[\mathbf{m}^{T}\mathbf{f}(\mathbf{x})=m_{0}+\sum_{j=1}^{M}m_{j}x_{j} \tag{6}\]  
where \(M\) denotes the number of input variables.
# Acceleration Techniques for Teeth Movements in Extractive Orthodontic Therapy
## 4 Discussion
### Vibration Therapy
An electrical engineer designed a device to generate a weak PEMF of 0.5 mT (Tesla), at 1 Hz. PEMF therapy was initiated on the same day as the application of the closed-coil spring, and the PEMF device consisted of an integrated circuit powered via a battery and embedded in a removable acrylic appliance. Patients were required to wear the appliance for 8 h at night, and the device was checked at each appointment. The results showed that the ExGr, exposed to the PEMF, experienced a 1.2-fold increase in the rate of OTM compared to the CGr, translating to a 41% increase. The average time for canine retraction in the ExGr was 4.5 months, while the CGr took 6-6.5 months. The study concludes that PEMF therapy can physiologically increase the rate of OTM, thereby reducing overall TT. When used in combination with closed-coil springs, 1 Hz PEMFs were successful in increasing OTM. The study suggests that PEMF therapy could be safely and routinely used during orthodontic treatment to shorten TT [32]. [PERSON] et al. [74] analysed two studies that compared the use of OrthoCcel and Tooth Masseuse devices with standard orthodontic mechanics during alignment and canine retraction stages, respectively. The trials evaluated tooth mobility objectively, but meta-analysis was hampered by varying outcome measures at various stages of treatment. Additionally assessed were discomfort, pain, and negative consequences. There needed to be a discussion of duration or how frequently to visit. Over ten weeks, the Tooth Masseuse improved lower incisor alignment with minimal pain variations. The maxillary canine movement was slightly faster with OrthoCcel, with no clinical significance. In summary, although vibration therapy offers a non-invasive and user-friendly approach, its effectiveness for orthodontic tooth movement remains inconclusive [75; 76; 77]. Despite a customised device's economic advantage, low-frequency (30 Hz) vibratory stimulation for 20 min daily did not significantly accelerate canine retraction. Pulsed electromagnetic field (PEMF) therapy increased the OTM rate by 1.2-fold, shortening treatment time. PEMF therapy, especially when combined with the other methods, offers the potential for accelerated orthodontic treatment.
# A Data-Driven Intermittent Online Coverage Path Planning Method for AUV-Based Bathymetric Mapping
## 4 Experimental Validation in Simulated Environments  
### Simulation Setup  
In order to validate the performance of the proposed CPP method, a simulator is built in MATLAB, which is composed of terrain model, sonar model, AUV model, guidance algorithm and path planner, as illustrated in Figure 5.  
The terrain model is applied to simulate the terrain of seafloor. It is generated by a fractal terrain generator based on diamond-square algorithm [27]. The sonar model generates sonar data points and swath width based on the information from the AUV model and the terrain model. The proposed method is realized by the path planner, producing waypoints intermittently based on the sonar data. Meanwhile, the guidance model computes the desire heading for tracking the waypoints, and the AUV model makes maneuvers.  
A ray-tracing sonar model [28] is implemented to simulate the sonar propagation and measurements. The sound propagation is simulated with rays, which are distributed every 1.8\({}^{\circ}\) in a 120\({}^{\circ}\) section. The first intersection point between a specific ray and the terrain is recorded as a detected point that can be represented into the earth frame using the transformation function (Equation (1)). These sonar data points are used to update the observation conditions for the corresponding cells and the swath widths are used for predicting future coverage in the path planning stage, as described in Algorithm 1.  
To track the waypoints, the Line-of-sight (LOS) guidance law is applied [29]. The desired yaw will be continuously calculated based on the current position and the coordinates of the previous waypoint and the current desired waypoint.  
The desired yaw is input to the AUV model, which is built in the Simulink environment. As the AUV is assumed to maneuver in a plane, and the velocity is set to a constant value, yaw angle is the only control object. The desired yaw and the current yaw are input to a PID controller to adjust the rudder angle, which is then imported into the 6 DOF REMUS hydrodynamic model [30] developed based on conventional marine vehicle model [31], and the position and pose of the AUV is updated.
# Improving Polar-Coded SCMA System by Information Coupling and Parity Check
## 4 Joint Iterative Detection and SCL Decoding Receiver
### Update Function Nodes of MPA Detector  
As described in Section 2, the iterative detection and decoding are performed after the whole TB's signal \(\left\{\mathbf{y}_{1},\ldots,\mathbf{y}_{l},\ldots,\mathbf{y}_{l}\right\}\) is obtained by the receiver. The function node \(g_{i}\) of the MPA detector updates its information and pass it to its neighboring variable nodes. The messages passing from the \(i\)-th function node \(g_{i}\) to the \(j\)-th variable node \(q_{j}\) can be calculated by  
\[M_{g_{i}\to q_{j}}\left(s_{v}^{l}\right)=\sum_{s_{v}^{l},t\in \left\{\mathcal{G}_{i}\backslash i\right\}}\left\{P\left(y_{1,j}\right]s_{v}^{ l},s_{t}^{l},t\in\left\{\mathcal{G}_{i}\backslash i\right\}\right\}\cdot \prod_{t\in\left\{\mathcal{G}_{i}\backslash i\right\}}M_{q_{t}\to g_{i}} \left(s_{t}^{l}\right)\right\}\cdot \tag{4}\]  
Figure 6: Partially information-coupled (PIC) PCCA-polar encoder.  
Then, the bits extrinsic message of the MPA detector can be expressed as  
\[P_{e,\text{MPA}}^{p,(l-1)Q+m}(x)=\sum_{s_{v,x}^{l}\in[\hat{x}_{v,y}^{l}] ^{m}_{v}=x]}\prod_{t\in\mathcal{Q}_{j}}M_{gt\to q_{j}}\big{(}s_{v,x}^{l}\big{)}, \qquad 1\leq m\leq Q. \tag{5}\]  
where \(\big{\{}s_{v}^{l}\big{|}q_{v}^{m}=x\big{\}}\) is the SCMA codewords set whose elements satisfy the mapping relationship \(\big{\{}g_{v}:\big{(}q_{v}^{1},\cdots,q_{v}^{m-1},x,q_{v}^{m+1},\cdots,q_{v}^{ Q}\big{)}\to s_{v}^{l}\big{\}}\).  
Thus, in the form of LLR, the extrinsic bit messages of the MPA detector can be expressed as  
\[L_{e,\text{MPA}}^{v,(l-1)Q+m}(x)=\ln\frac{P_{e,\text{MPA}}^{p,(l-1)Q+m}(x=0)}{ P_{e,\text{MPA}}^{v,(l-1)Q+m}(x=1)}. \tag{6}\]  
As the prior information of the coupled polar decoder, the extrinsic messages of the MPA detector \(\mathbf{L}_{e,\text{MPA}}^{v}=\big{\{}\mathbf{L}_{e,\text{MPA}}^{v,1},\ldots,\mathbf{L}_{e,\text{MPA}}^{v,k},\ldots,\mathbf{L}_{e,\text{MPA}}^{v,L_{c}}\big{\}}\) is de-interleaved to \(\mathbf{z}_{v}=\big{\{}\mathbf{z}_{v,1}\ldots,\mathbf{z}_{v,k},\ldots,\mathbf{z}_{v,l_{c}} \big{\}}\), where \(\mathbf{L}_{e,\text{MPA}}^{v,k}=\big{\{}L_{e,\text{MPA}}^{v,1},\cdots,L_{e,\text{ MPA}}^{v,N}\big{\}}\) and \(\mathbf{z}_{v,k}\) are expressed by  
\[\mathbf{z}_{v,k}=\Pi^{-1}\big{(}\mathbf{L}_{e,\text{MPA}}^{v,k}\big{)},\qquad 1\leq k \leq L_{c}. \tag{7}\]
## 3 Results and Discussion
A total of 27 SMAP grid cells overlapping with the study areas were extracted, and statistical comparison with ERA5 values was conducted. The results yielded a mean correlation coefficient of R = 0.71 \(\pm\) 0.06, a mean bias of \(-\)0.016 m\({}^{3}\)/m\({}^{3}\), and an RMSE of 0.039 m\({}^{3}\)/m\({}^{3}\). These values are consistent with those reported in prior studies (e.g., [27; 28]) and support using ERA5 data to capture relative soil moisture dynamics. Nevertheless, we acknowledge the need for ground-truth calibration, and a field campaign using TDR sensors is planned for the 2025 season. In addition, although the raw spectral bands from Sentinel-2 provide a comprehensive information base, we found that integrating derived spectral indices (e.g., NDVI, NDSI, BI, SI1-SI5) substantially improves model performance. These indices are physically meaningful transformations that enhance contrast between soil types (e.g., salty crusts, vegetation, and sand). Control experiments showed that using only the 13 original bands reduced the F1-score from 0.999 to 0.987, while including indices restored the original performance. Furthermore, normalized indices improve noise resistance, reducing feature variance by up to 20%, and are readily available or computable in real-time from platforms such as Google Earth Engine and Sentinel Hub. Thus, their use ensures higher accuracy and consistency between training and operational deployment of the model. **Limitations and Future Work**  
Despite the promising results, the proposed methodology has several limitations that should be acknowledged. First, the model exhibits sensitivity to atmospheric scattering under high aerosol concentrations, which may affect spectral index accuracy. Second, during periods of dense vegetation cover, the contrast of key indices (e.g., NDVI, NDWI) may decrease, reducing segmentation performance. Third, the spatial resolution of climate covariates from the ERA5-Land dataset remains relatively coarse, potentially limiting local-scale precision. Fourth, the model was primarily trained and validated in arid and semi-arid regions, so its transferability to humid or highland zones requires further testing. Fifth, ground-truth data on electrical conductivity are incomplete, and field calibration campaigns are planned to address this gap. To enhance the robustness and generalizability of the method, future work will focus on several directions:  
1. Integrating synthetic aperture radar (SAR) data to improve sensitivity to soil moisture;
2. Adopting deep segmentation architectures such as U-Net for finer pixel-level classification;
3. Incorporating season-aware mechanisms to adapt model performance across different phenological phases.
## 3 Modeling Method
### Support Vector Machine
#### 3.2.2 Construction of SVR Model  
As discussed above, the SVR model is constructed by optimizing the \(\varepsilon\)-insensitive loss function, in which the parameter \(\varepsilon\) and the regularized constant \(C\) are the important optimization factors. Furthermore, the selection of the kernel function is also closely related to the SVR model performance. The commonly used kernel functions in the regression include the linear kernel function, polynomial kernel function, radial basis function (RBF), and sigmoid kernel function. Considering the infinite dimensional feature space corresponding to the RBF, the following RBF is adopted in this study:  
\[K(\mathbf{x}_{i},\mathbf{x}_{j})=e^{-\frac{\|\mathbf{x}_{i}-\mathbf{x}_{j}\| ^{2}}{\sigma^{2}}}=e^{-g\|\mathbf{x}_{i}-\mathbf{x}_{j}\|^{2}} \tag{12}\]  
where \(\sigma\) indicates the smoothness of the derived function and \(g=1/\sigma^{2}\) is the key parameter of the RBF.  
To select the optimal values of the parameters \(\varepsilon\), \(C\), and \(g\) involved in the SVR model, a grid search algorithm was used in the present study. The basic idea of the grid search algorithm is to try every possible value of the parameters in a certain space with a specified step distance, and the parameters that optimize the performance of the SVR model with the best accuracy can be derived based on the cross-validation [46]. The process of the algorithm is illustrated through a flowchart, as shown in Figure 11. Similar to the construction of the neural network in Section 3.1.2, the collected experimental data of the moment redistribution coefficient were divided into two sets (85% for training and 15% for testing) for SVR. To increase the efficiency of the SVR training, the input and target data were normalized within the range of 0.1 to 0.9, in accordance with Equation (5), before the training of the input data. Based on the grid search algorithm and a six-fold cross-validation, the values of the parameters for the SVR were determined as follows: \(\varepsilon=0.01\), \(C=20\), and \(g=0.03\).
## 4 Array Method
### Allocation of Processors to Tasks
#### 4.2.2 Matrix of the Start Times of the Tasks  
**Definition 18**.: _Matrix of the Start Times of the Tasks, stores the results of the calculations of the start times of each sub-task of the PTGs._  
This matrix is constituted by the following columns.  
* Task ID
* Task arrival time: The time at which the task was queued in the waiting queue.
* Task start time: This parameter is calculated according to Definition 12.
* Task completion time: This time is calculated using the formula in Definition 13 and is recorded in the matrix when the execution on the processor is complete.  
The procedure for filling the the matrix of the start times of the task, four parameters are used:  
* Processor status. When the processor is unoccupied, the task can start its execution, and only the time of the task transfer is considered. When the processor is occupied, it considers the waiting time in the queue plus the time of the task transfer.
* The execution time of the parent tasks of \(n\). Any task that has one or more parent tasks (predecessor nodes) is considered the longest completion time for each of the parent tasks. The execution time of its predecessor nodes takes a value of 0 when the task has no parent node; when the task has predecessor nodes, the time is the sum of the execution times of its predecessor nodes; see Definition 9.
* The communication time of the data of the parent tasks of \(n\). This time is obtained from the time required for the data to reach the processor allocated to the task; this is considered the longest communication time. This parameter is obtained by calculating the communication times for all the parent tasks of the node. The time with the highest value is obtained from this operation, which means that the task must wait until all its parent nodes have finished their execution.
* The transmission time of the task to the processor is a value that depends on the network speed and it is calculated through two parameters: if the task is allocated in the same cluster, the internal speed of the cluster is considered, if the task is transmitted to an external cluster, then the internal speed of the cluster is considered and it is added to the speed of the external cluster. The transmission time of the task to the processor is considered as a fixed parameter of 5 nanoseconds for each distance unit.  
For the start sub-task that has no predecessors, the start time is limited by the release of the processor assigned to it.  
Table 9 shows the DAG task start times for Figure 2, and Table 10 shows the start times of Figure 3. For both cases, the transmission time of the task to the processor is 0 because the task is executed in the cluster where the arrival queue resides.  
With the table of task start times, whether the DAG is allocated to the selected resources or queued for execution is determined. A list of the main steps of the algorithm is provided in Appendix A.1 in Algorithm A2 for once the data structures that the algorithm uses have been exposed.