esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Method  
### Raw Materials  
#### 4.1.1 Soil Samples and Water Environment  
In engineering practice, the behavior of the slurry penetration is greatly dependent on the soil properties, water environment, and slurry property at the same operating conditions. For more concise discussion, only the soil gradation, water environment, slurry penetration mass, and CMC additive contents were considered in detail. In this study, China ISO (International Organization for Standardization) standard sand was used to prepare the soil samples. Two types of soil gradation, 0.5-2 mm and 0.075-2 mm, were used in the tests. The grain size distribution of both types of sand is shown in Figure 3. The water environment of the saturated sand is widely acknowledged as a vital factor for the slurry penetration and soil resistivity. Three types of water environment with different seawater content (0%, 25%, and 100%) were considered in those tests. The water environment had a 0% seawater content equal to the deionized water while the 100% seawater content corresponded to the synthetic seawater prepared according to ASTM D1141-98 [31]. The proportions of each component in the substitute ocean water are shown in Table 1. The total salt concentration was 3.58% and NaCl accounted for 68.4% of the total salt mass.  
Figure 2: Experimental setup of soil electrical resistivity. (**a**) Schematic. (**b**) Photograph.
## 2 Mathematical Model of PEM and Numerical Method
### Computational Grid  
The governing equations were discretized and solved using a finite element scheme and solved using a multi-physics computational fluid dynamics (CFD) code. The solutions were subjected to rigorous numerical analyses to guarantee that they were grid independent. It was assumed that when the proportional error was less than \(10^{-6}\) within each domain between two consecutive iterations, the solution was deemed to be convergent, and the related set of equations was determined iteratively.
## 2 Data Sources and Methods  
### Data Sources  
#### 2.1.1 Study Region  
The whole study region is Shandong province, China. Shandong is located along the eastern coast of China (in the lower stream of the Yellow river), within 34\({}^{\circ}\)22\({}^{\prime}\) N-38\({}^{\circ}\)24\({}^{\prime}\) N and 114\({}^{\circ}\)47.5\({}^{\prime}\) E-122\({}^{\circ}\)42\({}^{\prime}\) E. It measures 721.03 km from east to west, and 437.28 km from north to south. The land area of the province is 155,800 km\({}^{2}\), of which 14.59% is mountainous, 5.56% is water (such as lakes), 15.98% is forest, and 53.82% is cultivated land. The annual total planting area of crops in the province is approximately 162 million mu. The main food crops of this region are wheat and maize. In 2016, the wheat planting area was 57.45405 million mu, and in 2017 it was 57.6435 million mu [69].  
In this paper, we used the ground data and remote sensing data of Feicheng county, Ningyang county and Zhangqiu county, Shandong province. The three counties are similar in topography, all relatively flat, which can eliminate the influence of topographic fluctuations on the experimental results.
## Appendix A Simulation Environments
### Environment Base Model  
We consider three model classes: (1) a zero-inflated Poisson, (2) a hurdle model with a square root transform, and (3) a hurdle model with a log transform, and choose one out of these three model classes for each user. We define these three model classes below. Additionally, below \(g(S)\) is the baseline feature vector of the current state defined in Appendix A.1, \(w_{i,b},w_{i,p},w_{i,\mu}\) are user-specific weight vectors, \(\sigma_{i,\mu}^{2}\) is the user-specific variance for the normal component, and \(\text{sigmoid}(x)=\frac{1}{1+e^{-x}}\) is the sigmoid function.  
(1) Zero-Inflated Poisson Model for Brushing Duration  
\[Z\sim\text{Bernoulli}\Big{(}1-\text{sigmoid}(g(S)^{T}w_{i,b}) \Big{)}\] \[Y\sim\text{Poisson}\Big{(}\exp\Big{(}g(S)^{T}w_{i,p}\Big{)} \Big{)}\] Brushing Duration in \(\text{Seconds}:D=ZY\)  
(2) Hurdle Model with Square Root Transform for Brushing Duration  
\[Z\sim\text{Bernoulli}\Big{(}1-\text{sigmoid}\Big{(}g(S)^{T}w_{i,b}\Big{)} \Big{)}\] \[Y\sim\mathcal{N}\Big{(}g(S)^{T}w_{i,\mu},\sigma_{i,\mu}^{2} \Big{)}\] Brushing Duration in \(\text{Seconds}:D=ZY^{2}\)  
Note that the nonzero component of this model, \(Y^{2}\), can also be represented as a constant times a noncentral chi-squared, where the noncentrality parameter is the square of the mean of the normal distribution.  
(3) Hurdle Model with Log Transform for Brushing Duration  
\[Z\sim\text{Bernoulli}\Big{(}1-\sigma\Big{(}g(S)^{T}w_{i,b}\Big{)} \Big{)}\] \[Y\sim\text{Lognormal}\Big{(}g(S)^{T}w_{i,\mu},\sigma_{i,\mu}^{2} \Big{)}\] Brushing Duration in \(\text{Seconds}:D=ZY\)  
Since we want to simulate brushing duration in seconds, we also round outputs of the hurdle models to the nearest whole integer. Notice that the zero-inflated Poisson model is a mixture model with a latent state. The Bernoulli draw \(Z\) is latent and represents the user's intention to brush, and the Poisson models the user's brushing duration when they intend to brush (this is because the brush time can still be zero when the user intends to brush). On the other hand, the hurdle model provides a model for brushing duration conditional on whether the user brushed or not. The Bernoulli draw \(Z\) in the hurdle model is observed.  
Note that the hurdle model is used for the simulation environment only and _not_ the RL algorithm. The hurdle model conditions on a collider (e.g., whether the person brushes their teeth), thus potentially leading to causal bias [44; 45]. For example, consider an unobserved cause \(U\), intervention \(A\), whether the user brushed or not \(Z\), brushing duration \(D\), and a directed acyclic graph with \(A\to Z,U\to Z,U\to D\), and \(Z\to D\). Then conditioning on collider \(Z\) of treatment opens a pathway from \(A\) to \(D\) through \(U\)[46]. Suppose in reality \(A\) only impacts whether the user brushes their teeth but not the duration. Then, if we condition on \(Z\) to evaluate the impact of \(A\) on \(D\), we may erroneously learn that \(A\) impacts the duration of brushing. This makes the hurdle unsuitable as a model for an RL algorithm that aims to learn causal effects.
# Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
## 1 Introduction
### Image Binarization  
Several articles have explored the complexities of image binarization, especially in the context of historical documents. The evolution of methodologies has highlighted the incorporation of machine learning to enhance preservation efforts [31]. One review underscores the challenges faced in preserving and digitizing historical documents, which often suffer from degradation, variable text quality, and background noise. This work is pivotal in guiding future research towards developing more robust and adaptive binarization techniques. The AprilTag 2 system has been developed and optimized for better efficiency and accuracy to present significant improvements in the detection of fiducial markers, which are essential for robotics and augmented reality applications [32]. The AprilTag 2 system improves the detection of fiducial markers. These markers serve as reference points in physical space for various technological applications, including navigation, object tracking, and interaction in augmented reality environments. The presented advancements signify a substantial improvement in the operational capabilities of systems that rely on fiducial markers. This showcases the potential for more seamless integration of virtual and physical elements in technological applications. For instance, in the agricultural sector, researchers have developed an image recognition system for cow identification. This system uses YOLO for cow head detection and CNNs for ear tag recognition, which supports improved herd management in precision dairy farming [33]. By employing advanced image recognition techniques, the study demonstrates the applicability and benefits of such technologies in the agricultural sector, particularly in enhancing the management and welfare of livestock through improved identification and tracking capabilities. These studies demonstrate the increasing range and ongoing enhancement of image analysis technologies, such as binarization and other techniques, in diverse fields.
## Methods  
### Study Site  
The study area is a managed Mediterranean TGE or \"dehesa\" located in Las Majadas del Tietar, in Caceres, Spain, located at 259 m a.s.l. (39\({}^{\circ}\)56\({}^{\prime}\)29\({}^{\prime}\)/N, 5\({}^{\circ}\)46\({}^{\prime}\)24\({}^{\prime}\)/W) (Figure 1). Climate is Continental Mediterranean with an annual mean temperature of 16.7 \({}^{\circ}\)C and annual mean precipitation \(\sim\)700 mm [32]. In summer, temperature goes over 40 \({}^{\circ}\)C and only the 6% of the annual rainfall is accumulated [33]. The tree layer is mostly composed by scattered plants of _Quercus__i_e_l subsp. _ballota_ L., an evergreen Mediterranean species; these are separated by 18.8 m (standard deviation, \(\sigma\) = 5.0 m) so that the fractional cover is approximately 20%. Mean tree height is 7.9 m (\(\sigma\) = 0.9 m), mean crown horizontal radius is 4.18 m (\(\sigma\) = 0.9 m) and mean vertical radius is 2.7 m (\(\sigma\) = 0.5 m) [34]. The grass layer is rich in species such as _Runex acetosella_ L., _Ergium campestre_ L., _Erodium cicaturium_ L. or _Erodium botrys_ ([PERSON]) with a variable spatial and temporal distribution. Grasses show a strong phenology, they usually peak in spring, senesce by summer, regrow in autumn immediately after the first rainfall and go dormant in winter. Cow grazing induces spatial variability and usually keeps the grass layer shorter than 30 cm.  
Figure 1: Location of the flux towers in Majadas del Tiétar. Each square corresponds to a synthetic 1 km pixel centered on each one of the towers.  
Three EC flux towers operate in the site. They are included in the FLUXNET research network ([[http://fluxnet.ornl.gov/site/440](http://fluxnet.ornl.gov/site/440)]([http://fluxnet.ornl.gov/site/440](http://fluxnet.ornl.gov/site/440)), last accessed on 16 May 2017) and monitor a large scale fertilization experiment (MaNiP) [35]. The first tower (CTRL) is operative since 2003; moreover, two additional towers were installed in April 2014. The former one (N-ADD) is located 435 m northwest of CTRL, and the latter (NP-ADD) 645 m on the south of CTRL. The location of the new towers was based on the CTRL tower footprint estimation based on ten years of existing measurements. The main wind directions at the site are east-northeast and west-southwest; therefore, the footprints display along this line and show, especially under daytime conditions, no sampling outside of the fertilized area of interest. Analyses carried out by [PERSON] et al. [36] show no overlap between the 80% iso-lines of the footprint climatology for the three towers. Between December 2014 and March 2015, a fertilization experiment was carried out in the surroundings of the N-ADD and NP-ADD towers. An area of about 20 ha was fertilized with nitrogen and phosphorous + nitrogen in the N-ADD and NP-ADD towers, respectively [37].
## 3 Proposed Joint Iterative Semi-Blind Scheme Version
```
Inputs :\(\mathbf{y},\mathbf{x}^{\prime}_{SI},t_{max},\mathbf{x}_{pilot},\beta,\)\(K,N,M\); Outputs :\(\mathbf{\hat{h}}^{(i_{max})}_{AA},\mathbf{\hat{h}}^{(i_{max})}_{BA}\); Initialisation:\(\mathbf{\hat{y}}^{(0)}_{BA}=0,\mathbf{\hat{h}}^{(0)}_{AA}=0\), \(\mathbf{\hat{h}}^{(0)}_{BA}=0\);
1 for\(i=1\) to \(i_{max}\)do
2 if\(i=1\)then /* Perform Step 1 and Step 2 for all of the \(E^{\prime}=(1+\beta)E\) symbols and \(E=N/log_{2}(M)\) symbols, where \(\beta\) is the pilot symbol coefficient, \(N\) is the codeword length, and \(M\) is the modulation order */ for\(n=1\) to \(E^{\prime}\)do
3 Step 1: SI channel estimation and DSIC process
4 Estimate: \(\mathbf{\hat{h}}^{(i)}_{AA}\);
5 Calculate: \[\tilde{g}^{(i)}[n]=y[n]-\tilde{g}^{(i)}_{SI}[n]=y[n]-(\mathbf{\hat{h}}^{(i)}_{ AA}*\mathbf{x}^{\prime}_{SI})[n];\]
6 Step 2: Intended channel estimation Estimate: \(\mathbf{\hat{h}}^{(i)}_{BA^{\prime}}\);
7 end for
8
9 else /* Perform Step 1 and Step 2 for only \(\beta E=\beta N/(log_{2}(M))\) symbols, where \(\beta\) is the pilot symbol coefficient, \(N\) is the codeword length, and \(M\) is the modulation order */ for\(n=1\) to \(\beta E\)do
10 Step 1: SI channel estimation and DSIC process
11 Estimate: \(\mathbf{\hat{h}}^{(i)}_{AA}[n]\);
12 Calculate: \[\hat{y}^{(i)}[n]=y[n]-(\mathbf{\hat{h}}^{(i)}_{AA}*\mathbf{x}^{\prime}_{SI})[ n];\]
13
14 Step 2: Intended channel estimation Estimate: \(\mathbf{\hat{h}}^{(i)}_{BA^{\prime}}\);
15 end for
16
17 end for
18
19 if\(i<i_{max}\)then /* Perform Step 3 for only \(\beta E\) symbols, where \(\beta\) is the pilot symbol coefficient */ for\(n=1\) to \(\beta E\)do
20 Step 3: Feedback loop
21 Using \(\mathbf{x}_{pilot}\) to form feedback loop to obtain \(\hat{y}^{(i)}_{BA}\);
22 Calculate: \(\hat{y}^{(i)}_{BA}[n]=(\mathbf{\hat{h}}^{(i)}_{BA}*\mathbf{x}^{(i)}_{pilot})[ n];\)
23 Update: \(y^{(i+1)}_{DSIC}[n]=y[n]-\hat{y}^{(i)}_{BA}[n];\)
24 end if
25
26 else
27 Go to return
28
29 end for
30
31 end for return\(\mathbf{\hat{h}}^{(i_{max})}_{AA},\mathbf{\hat{h}}^{(i_{max})}_{BA}\). ```  
**Algorithm 1**Iterative part of the proposed joint iterative semi-blind scheme.
## 2 Description of the Mathematical Model
### Design of the FSB-ASMC with \(H_{\infty}\) Tracking Performance
Therefore, the \(H_{\infty}\) tracking performance for the overall system must satisfy: (see (37))_  
\[\tfrac{1}{2}\!\int_{0}^{T}\mathbf{e}^{T}(\tau)\mathbf{Q}_{\mathbf{I}}\mathbf{e }(\tau)d\tau\leq\tfrac{1}{2}\!s^{2}(0)+\tfrac{1}{2}\mathbf{e}^{T}(0)\mathbf{A} \mathbf{e}(0)+\tfrac{1}{2}\widetilde{\mathbf{W}}_{\overline{G}}^{T}(0) \mathbf{\Gamma}_{1}^{-1}\widetilde{\mathbf{W}}_{\overline{F}}(0)+\tfrac{1}{2 }\widetilde{\mathbf{W}}_{\overline{G}}^{T}(0)\mathbf{\Gamma}_{2}^{-1} \widetilde{\mathbf{W}}_{\overline{G}}(0)+\tfrac{1}{2}\sigma^{2}\!\int_{0}^{T} w^{2}(\tau)d\tau \tag{37}\]  
_where \(\widetilde{\mathbf{W}}_{\overline{F}}=\mathbf{W}_{\overline{F}}-\hat{\mathbf{ W}}_{\overline{F}}\) and \(\widetilde{\mathbf{W}}_{\overline{G}}=\mathbf{W}_{\overline{G}}-\hat{\mathbf{ W}}_{\overline{G}}\)._  
**Proof:** See Appendix B. \(\Box\)  
**Remark 4**. : _For a specified set of initial conditions, \(e(0)=0,s(0)=0\), \(\hat{\mathbf{W}}_{\overline{F}}(0)=\hat{\mathbf{W}}_{\overline{F}}^{\star}(0)\) and \(\hat{\mathbf{W}}_{\overline{G}}(0)=\hat{\mathbf{W}}_{\overline{G}}^{\star}(0)\), and if the matrix \(\mathbf{Q}_{I}\) is set to an identity matrix, the control performance satisfies:_  
\[\frac{\|\mathbf{e}\|_{2}}{\|w_{t}\|_{2}}\leq\sigma \tag{38}\]  
_where \(\|w_{t}\|_{2}^{2}=\int_{0}^{T}w_{t}{}^{2}(\tau)d\tau\) and \(\|\mathbf{e}\|_{2}^{2}=\int_{0}^{T}\mathbf{e}^{T}(\tau)\mathbf{e}(\tau)d\tau\). In other words, an arbitrary attenuation level is achieved if \(\sigma\) is properly chosen. Equation (38) also shows that \(\|\mathbf{e}\|_{2}\leq\sigma\|w_{t}\|_{2}\), thus, the bearing of the system (21) on the lumped uncertainty is stable. According to **Assumption 2**, the system (13) is stable._
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 3 Synthesis of Molecularly Imprinted Polymers
### Bulk Polymerization  
This method is the most commonly used approach, as it has shown to be very easy, simple, and effective; the produced polymer can be grained and sieved according to the particle size formats desired in the target application. This method involves first the formation of a pre-polymerization complex between the template and functional monomer with suitable functional groups to form non-covalent interactions followed by the addition of a cross-linker and the initiator in a suitable solvent with certain ratios that can be optimized according to the resulting rebinding properties of the synthesized polymer. Using an insufficient amount of a crosslinker decreases the structural stability of the polymer, leading to the shedding of functional monomers, while an excessive amount of cross-linking agent reduces the number of recognition sites of MIP, and reduces its binding efficiency. After deoxygenating, degassing, and applying thermal or photoinitiation, the polymer is formed. The resulting polymer can then be ground and sieved to obtain the desired average particles size according to the concerned application.  
The main drawback of such approach is the irregularity in shape of resulting particles due to the grinding step that may reduce the recognition capacity and affects the applicability of polymers produced via such a polymerization reaction in chromatographic stationary phases, even though there are many reported applications; however, it has shown to be very efficient as modifiers for different types of electrochemical sensors [9]. The wasting process is time consuming and requires high amounts of washing solvents [10; 11]. One of the major drawbacks is the comparably high amounts of template required for the synthesis, which is economically inefficient in case-expensive templates [12].
## 2 Dense Feature Extraction and Matching Theory and Mismatch Cause Analysis
### Coarse Mismatch Removal Using RANSAC  
The RANSAC algorithm [26] is a method based on the principles of probability theory to remove outlier data. The best model to describe the data was achieved through iterative  
Figure 3: Mismatches generated using SIFT features. (**a**) Meaningless mismatch. (**b**) Ambiguity in mismatch.  
correction, and it can retain the corresponding correct data and minimize the influence of noise and outliers. The number of iterations \(n\) in RANSAC is calculated as follows:  
\[n=\frac{\log(1-p)}{\log(1-\varepsilon^{s})} \tag{5}\]  
where \(\varepsilon\) is the proportion of outliers in the data, \(p\) is the expected probability, and \(s\) is the minimum number of samples required to estimate the model. RANSAC randomly selects \(s\) points from all data to estimate the model and calculates the points that fit the current estimated model. When the number of points exceeds the threshold set or the iteration number reaches the upper limit, the correcting process stops, and the output information is contained in the best model.  
In structural motion detection, the RANSAC algorithm can remove a large number of feature point pairs whose displacement is clearly different from that of correct matches, achieving the elimination most of the mismatches after SIFT feature extraction and FLANN matching. However, it is a statistical mismatch removal method, and the iterative model process is random, the outlier data cannot be completely removed, and further removal should be introduced.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 10 Splicing and Termination of Hollow-Core Fibers
### Common Issues
Arc fusion splicing is applicable to fused silica fibers only. Filament splicing can also be used for joining soft glass fibers. For fibers made of polymers like PMMA, hot-melt gluing or mechanical splicing can be used.
## 2 Materials and Methods
### Fault Classification and Model Comparison  
To evaluate the feasibility of fault localization using acoustic features, six machine learning models were tested using scikit-learn (1.5.2), XGBoost (3.0.0), and LightGBM (4.6.0) library: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost, LightGBM, and Multi-Layer Perceptron (MLP).  
The models were trained using labeled frequency-domain data obtained from FFT analysis of the recorded signals. Each sample was annotated based on the robot's structural condition during recording (e.g., normal, head removed, right arm removed, etc.).  
Figure 5: Visual illustration of the robot under various structural conditions. (**a**) Fully assembled robot (baseline condition); (**b**) fully assembled robot from side (**c**) robot with head removed; (**d**) robot with right arm removed; (**e**) robot with left arm removed; (**f**) robot with right hand removed; (**g**) robot with left hand removed; (**h**) robot with leg removed.  
Hyperparameter tuning was conducted using the training dataset, based on 5-fold cross-validation. For SVM, KNN, and RF, hyperparameters were optimized using randomized search, while for XGBoost, LightGBM, and MLP, a grid search approach was applied. This tuning procedure aimed to identify model configurations that generalized well across the internal folds of the training set, without reference to the test data.  
Once optimal hyperparameters were determined, 5-fold cross-validation was again applied within the training data to assess classification performance under consistent conditions.  
Subsequently, the tuned models were applied to a completely independent test dataset to compare the performance of all six classifiers under real-world conditions.  
This two-phase process, consisting of a systematic cross-validation-based tuning and independent testing, was designed to support the development of a robust diagnostic system for fault localization using acoustic sensing.
## 3 Alternative Paths for Message Streams in Cluster-Tree Wireless Sensor Networks  
In this paper, we propose a new communication scheme for cluster-tree wireless sensor networks, called _Alternative-Route Definition_ (ARounD). The underlying idea is to define alternative communication paths avoiding congested areas of the network. The ARounD communication paths avoid interferences with the previously defined cluster-tree paths, by means of a careful selection of the timing instants when messages their are transferred.  
The proposed ARounD communication scheme is intended for wide-scale applications requiring the support of source-to-destination message streams. It improves several performance metrics of wide-scale cluster-tree networks, considering that: (1) by defining alternative communication paths, it decreases the network congestion around the PAN coordinator; (2) by selecting shorter inter-cluster paths, it can potentially decrease end-to-end communication delays of message streams; and (3) it is able to guarantee a higher level of Quality-of-Service (QoS) for source-to-destination message streams, as the involved nodes will access the medium without contention.  
In order to establish the new source-to-destination paths, the ARounD communication scheme adds new functionalities for specific cluster-tree nodes and defines two main algorithms: _Alternative-Path Definition_ (ARounD-Def) and the _Alternative-Path Activation_ (ARounD-Act). In the next subsections, we describe both the network model and the ARounD communication algorithms.