esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### The Performance of Detecting Cut Operations
Also, cut operations longer than 1 s are always detected for recordings longer than 10 min, with perfect detection of cut zones in more than 95% of the cases, while cuts shorter than 1 s are perfectly detected if the watermark is perfectly extracted, which happens if the presented power ratios between the participating signals are achieved.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Hierarchical Classification Trees Construction by Decision Tree Algorithms
#### 3.6.3 Conceptual Hierarchical Structure  
Identifying the main land cover characteristics in each mapping unit and establishing a proper conceptual hierarchical structure are essential for the construction of hierarchical classification trees. Because the spectral signatures and the distinguishing capabilities of land cover class varied with the acquisition time of satellite images, the conceptual hierarchical structure needs to vary with mapping units. Figure 5 shows a representative conceptual hierarchical structure used for classification in a randomly selected mapping unit. It is noted that the structure in Figure 5 will not always be suitable for other mapping units.  
For each hierarchical structure in Figure 5, the classification errors were hard to avoid in the classification results, which could be introduced into the following hierarchical levels. Therefore, these errors in the following hierarchical level needed to be revised. For example, some non-vegetation might be confused with vegetation, so the misclassified non-vegetation needed to be extracted in the vegetation division stage as well as following stages (Dish line in Figure 5). Owing to spatial limitations, Figure 5 only shows the handling of non-vegetation and vegetation, and the handling of other land cover classes is the same.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types** & **Features** & **Calculation Method** \\ \hline \multirow{6}{*}{Spectral features} & The mean value of each band & \\  & The standard deviation of each band & \\  & Brightness & Brightness = \((b1+b2+b3+b4)/4\)[44] \\  & NDVI & NDVI = \((b4-b3)/(b4+b3)\)[45] \\  & MNDWI & MNDWI = \((b2-b5)/(b2+b5)\)[42] \\  & NDBI & NDBI = \((b5-b4)/(b5+b4)\)[43] \\ \hline \multirow{2}{*}{Topographic features} & Elevation & \\  & Slope & \\  & Aspect & \\ \hline \multirow{6}{*}{Texture features} & GLCM-mean & \\  & GLCM-standard deviation & Calculated in the eCognition 8.7 \\  & GLCM-entropy & platform [44] \\  & GLCM-contrast & \\ \hline \multirow{2}{*}{Shape features} & Shape index (SI) & \(SI=b_{v}/p_{v}\)[44] \\  & Length/Width & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The multi-type features used in land cover classification. The \(b1\), \(b2\), \(b3\), \(b4\) and \(b5\) denote respectively the blue, green, red, near infrared and mid-infrared band of satellite images. The GLCM is gray level co-occurrence matrix, which has a good ability to distinguish texture features of natural objects. The \(b_{v}\) is the border length of image object, and the \(p_{v}\) is the area of image object.  
Figure 5: A representative conceptual hierarchical structure of the land cover classes used for generating the CLC-SW2010 product.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 3 Methods  
The following section describes the dust detection methods applied in this study and the evaluation metrics of the dust detection methods.
## 3 Methodology and Proposed Model
### Data Pre-Processing
#### 3.2.5 Data Normalization  
Real-world datasets often exhibit variations in range, units, and magnitude, which can lead to suboptimal classification outcomes. Features with wider ranges may dominate the model's learning process, overshadowing other important features [67]. For example, the \"Daily Rate\" feature in the IBM dataset spans from 234 to 2877, and this significant difference in values could impair model performance. To address this, it is essential to rescale the feature values to fall within a consistent range. A commonly used technique for rescaling is normalization. In this study, feature values have been rescaled to a range between 0 and 1, as shown in Equation (1). This normalization serves as a practical pre-processing step that enhances the model's performance, reduces bias, and improves model interpretability.  
\[\hat{x}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
where the values of the specified feature are denoted by \(x_{min}\) for the minimum and \(x_{max}\) for the maximum, respectively.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 2 Network Slicing in 5G
### Network Instance Layer  
In network function layer, a blueprint is used to initiate the network slice instance and it presents the network attributes which are essential for it. This can also be shared over multiple services instances as it may consist of more than one sub-instances [13]. A network slice instance can be logically separated from the rest of network slice instances. Sub-network instance consists of logical or physical resources and set of network functions, defined by sub-network blueprint [13]. A sub-network instance can split into more than one slices.
# Optimization of Protection Level of GBAS with Gaussian Mixture Model
## 4 Optimizing the GMM Protection Level
The specific method is as follows:  
\(S\) can be expressed as [21]:  
\[S=S_{0}+\beta\Delta s \tag{28}\]  
where \(S_{0}\) is the least square mapping matrix; \(\Delta s\) represents the adjustment vector of the mapping matrix, \(\Delta s=u_{j}Q\), \(Q\) represents the parity check matrix, \(\mathbf{Q}{H}=\mathbf{0}_{n-m,n}\) and \(QQ^{T}=I_{n-m}\), \(u_{j}\) represent the direction of the unit causing \(S_{0}{H}{{(H^{T}Q^{T}Q{H)}}^{-1/2}}\) deviation in parity space. Then, the optimized \(S\) as \(S_{NLS}\) can be obtained by searching \(\beta\) using the one-dimensional search method. 1. Dynamic allocation of integrity risk  
As \(S_{NLS}\) and the solution set are determined, \(VPL\) is only determined by the allocation of the integrity risk, and the objective function is simplified as:  
\[ \begin{array}{l}\min(\max(VPL_{set}^{l}))\\ st\sum\limits_{i=1}^{K}P_{set}^{l}=P_{\text{int}}\end{array}  \tag{29}\]  
\[VPL^{l_{j}}=K_{ffmd\_set}^{l}\sqrt{\sum\limits_{i=1}^{M}S_{NLS,3,i}{2\sigma_{ i}^{l}}^{2}} \tag{30}\]  
\[K_{ffmd\_set}^{l}=\Phi^{-1}(1-P_{set}^{l}) \tag{31}\]  
Dynamic allocation of integrity risk mainly solves the protection level imbalance caused by the unbalanced allocation of integrity risk in each solution set, and finally realizes the protection level equalization of solutions under different assumptions. The optimal allocation of integrity risk can be achieved through one-dimensional search [20].
# Two-Dimensional Digital Beam Steering Based on Liquid Crystal Phase Gratings
## 3 Results and Discussion
(**b**) Simulated \(72\times 72\) power diffraction efficiency map (%) as a function of the diffraction angle.
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.3 Data Segmentation and Selection  
In this study, a movement cycle refers to participant's action of picking up a cube from the right compartment of the box, placing it in the left compartment, and returning to pick up the next cube from the right compartment in both the RBBT and VBBT. Movement cycles were automatically segmented with manual review and visual correction applied when necessary: each movement cycle begins and ends when the hand grasps a cube from the container, which corresponds to the lowest hand height, i.e., the highest positive Z-coordinate value in the global coordinate system (Figure 1).  
All kinematic and sEMG data were segmented by movement cycle, with their durations rescaled from 0 to 100. Data were resampled to 100 values, except for sEMG waveform parameter, which were not resampled. For sEMG waveform parameter computation, time labels were adjusted (not resampled): They were scaled by dividing by the number of frames recorded to facilitate comparison and then multiplied by 100. The last five movement cycles per participant were selected for the subsequent analyses to ensure consistency despite variations in the number of recorded cycles, since all participants completed at least five cycles. Two types of parameters were analysed for both kinematic and sEMG data: cycle-averaged parameters, representing single-value metrics extracted from each cycle (e.g., median, range), and frame-by-frame temporal parameters, capturing the evolution throughout each cycle.
# Estimating Underwater Light Regime under Spatially Heterogeneous Sea Ice in the Arctic
###### Abstract  
The vertical diffuse attenuation coefficient for downward plane irradiance (\(K_{d}\)) is an apparent optical property commonly used in primary production models to propagate incident solar radiation in the water column. In open water, estimating \(K_{d}\) is relatively straightforward when a vertical profile of measurements of downward irradiance, \(E_{d}\), is available. In the Arctic, the ice pack is characterized by a complex mosaic composed of sea ice with snow, ridges, melt ponds, and leads. Due to the resulting spatially heterogeneous light field in the top meters of the water column, it is difficult to measure at single-point locations meaningful \(K_{d}\) values that allow predicting average irradiance at any depth. The main objective of this work is to propose a new method to estimate average irradiance over large spatially heterogeneous area as it would be seen by drifting phytoplankton. Using both in situ data and 3D Monte Carlo numerical simulations of radiative transfer, we show that (1) the large-area average vertical profile of downward irradiance, \(\overline{E_{d}}(z)\), under heterogeneous sea ice cover can be represented by a single-term exponential function and (2) the vertical attenuation coefficient for upward radiance (\(K_{Lu}\)), which is up to two times less influenced by a heterogeneous incident light field than \(K_{d}\) in the vicinity of a melt pond, can be used as a proxy to estimate \(\overline{E_{d}}(z)\) in the water column.  
apparent optical properties; 3D Monte Carlo numerical simulations; downward irradiance; upward radiance; sea ice heterogeneity; vertical attenuation coefficient; melt ponds
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 4 Differential Privacy
### Methods to Implement Differential Privacy
#### 4.2.1 Local Differential Privacy  
In Local Differential Privacy (LDP), the data contributor is responsible for adding noise to their data before sharing them with a central aggregator and data collector, so it does not require any trusted party [41]. In LDP, noise is introduced to individual data points. Suppose each user has a sensitive bit of information (\(b_{i}\in\{0,1\}\)). Each user perturbs their data locally using a randomized response mechanism with a probability of \(\frac{1+\epsilon}{2}\) to report \(b_{i}\) or with a probability of \(\frac{1-\epsilon}{2}\) to report \(1-b_{i}\).  
This ensures the privacy of each individual's preserved before aggregation or analysis occurs. In LDP, noise addition occurs at the individual level. The main advantage of local DP it does not require trust in the data aggregator, as it is unaware of the real values. But problem is that every user will have to introduce noise in personal information, which will increase the total amount of added noise. But this problem can be mitigated by using high values of epsilon \(\epsilon\).
## 3 System Methodology
### Constraints in the ILP formulation  
1. Supply and Demand Constraints \[\sum_{j}T_{ij}^{mm}\leq C_{i}^{m}\] (25) \[\sum_{j}T_{ij}^{mm}\geq D_{j}^{n}\] (26)  
These constraints ensure that the total amount of goods transported from each origin city does not exceed the capacity of the transportation mode and meets the demand for each shipment.
2. Non-negativity Constraints \[T_{ij}^{mm},X_{ij}^{mm},Y_{j},d_{ij}\geq 0\] (27)  
These constraints ensure that decision variables representing quantities, selections, and delays are non-negative.
3. Mode Selection Constraints \[\sum_{m}X_{ij}^{mm}=1\] (28)  
This constraint ensures that for each shipment from origin \(i\) to destination \(j\), only one transportation mode is selected.
4. DEA Efficiency Constraints \[\rho_{ij}=\frac{\sum_{k=1}^{n}\lambda_{k}\cdot x_{kj}}{\sum_{k=1}^{n}u_{k} \cdot c_{kj}}\] (29)  
This constraint ensures that the DEA efficiency scores are calculated based on the historical efficiency and predicted CO\({}_{2}\) emissions.
5. QoS Constraints for Multicast Routing \[\delta_{ij}\leq q_{ij}\cdot x_{ij}\] (30)  
These constraints ensure that the QoS parameters for each arc in multicast routing meet specified parameters.  
The incorporation of these constraints reflects a contemporary optimization model that considers historical efficiency, predictive accuracy, and QoS parameters, enabling a robust and adaptable decision-making framework. The inclusion of Markovian decision-making using DEA and HMM enhances the model's ability to capture dynamic changes in the transportation network, ensuring optimal and reliable solutions in the context of the Iraq Belt and Road Initiative project.
6. Risk Threshold Constraint\[\sum\
olimits_{ij}\delta_{ij}x_{ij}\leq R_{T} \tag{31}\]  
This constraint controls the ratio of the product of the QoS parameter and risk value to the demand for final goods.
## 3 Results
### Quantitative Analysis
#### 3.4.2. Heterogeneity  
A high level of heterogeneity was observed in the meta-analysis, with an I\({}^{2}\) statistic of 73% and a Chi\({}^{2}\) value of 11.00 (df = 3, \(p\) = 0.01). This substantial heterogeneity reflects considerable variability across the included studies, which can be attributed to several factors:  
* Differences in Study Design: Variations in sample recruitment, selection methods, and study methodologies contributed to inconsistencies in outcomes.
* Diagnostic Criteria for TMDs: The included studies employed a range of diagnostic tools, such as clinical examinations, self-reported questionnaires, and imaging techniques, with only one study using standardized Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD). This lack of standardization likely influenced TMDs prevalence reporting.
* Sample Characteristics: Differences in participant age, malocclusion types, and orthodontic interventions further contributed to the variability in outcomes.  
Although the issue of overlapping populations was addressed by including only one study per population group in the final meta-analysis, high heterogeneity persisted due to methodological and diagnostic differences among the included studies. Future research should consider subgroup analyses and sensitivity testing to account for these variations and enhance the reliability of pooled estimates. Additionally, adopting standardized diagnostic criteria, such as RDC/TMD or DC/TMD, would improve the comparability of findings across studies.
## 2 Configuration of the WalkON Suit  
The WalkON Suit has various unique features. For example, it utilizes a bi-articular actuation mechanism, which actuates both the hip and knee joints simultaneously. The bi-articular mechanism improves the overall energy efficiency significantly [30]. The brace of the WalkON Suit is manually fabricated by medical orthotists and customized for each user, which is to guarantee the best fitness between the human body and the robot.  
The WalkON Suit was first introduced to the public through the Powered Exoskeleton Race of the Cybathlon 2016 [29], where the pilot of the WalkON Suit, Mr. [PERSON], won the bronze medal. The control of the WalkON Suit, however, was not simple enough for a paralyzed user, because the user had to check the operation state continuously through a small LCD display, like the other powered exoskeletons.  
An improved version of the WalkON Suit was introduced in the torch relay of the Pyeongchang Paralympics 2018. Both robots for Cybathlon and Pyeongchang Paralympics share the same actuation mechanism, basic UI system, crutches, and so on, as shown in Figure 2. Four synchronized 100-W BLDC motors are utilized to actuate each joint in both systems. The hip joints are actuated directly by the motors, and the knee joints are bi-articularly actuated through a linkage mechanism [29]. The bi-articular mechanism is effective for controlling the position of an end-effector (i.e., the foot of the robotic leg), because it is similar to the human musculoskeletal system. In addition, all the hip and knee joint motors are mounted in the backpack placed at the trunk; therefore, the knee joint motor does not impose any unnecessary burden unlike the conventional mono-articular mechanism [31].  
The Cybathlon version of the WalkON Suit introduced in 2016 used the CompactRIO of National Instruments as the primary control processor, while the second prototype introduced in this paper uses a customized printed circuit board and the system on module (SoM) manufactured by the same company. By this replacement, the weight of the processor has been reduced from 2250 g to 77 g.  
In addition, the UI system of the WalkON Suit has been improved compared to that in 2016. The crutches of the WalkON Suit are specially designed as a primary user interface; a 1.3-inch OLED display and two tact switches are installed on the handle of the crutch. Moreover, the information mandatory for the paralyzed human to control the WalkON Suit is informed through see-through display (STD) glasses specially developed for the WalkON Suit. The details of the proposed UI are described in the following section.  
Figure 1: A complete paraplegic wearing the WalkON Suit for the torch relay of the Pyeongchang Paralympics 2018.
# Graphene-Based Functional Hybrid Membranes for Antimicrobial Applications: A Review
## 5 Antimicrobial Applications
### Antifungal Applications  
In the study of [PERSON] et al. [119], cellulose nanocrystals (CNC) and GR were combined in two different ratios and added to polylactic acid (PLA) using the melt-blending technique. The obtained PLA-CNC/GR nanocomposites were hot-pressed to prepare films, their antifungal properties against aspergillus niger were evaluated, and it was found that the CNC/GR hybrid films had better antifungal activity. [PERSON] et al. aimed to introduce GO to oral PE membranes for the delivery of antifungal drugs and investigated the effect of GO on membrane properties, drug release properties, and antifungal activity [120]. Using chitosan and alginate as binders, a PE membrane was formed with the antifungal drug clotrimazole. The excipients were mixed with clotrimazole, and the oral formulation was prepared by probe sonication, followed by film casting and drying. The results showed that the electrostatic hydrophobic interaction between GO and clotrimazole also affected the antifungal effect of clotrimazole. [PERSON] et al. prepared several graphene/polyvinylidene (PVDF/G) films with different graphene loadings (0-10 wt%) by a phase-inversion method [121]. The antifungal activity test of PVDF/G film showed that the inhibition rate of PVDF/G film against _curvularia_ strain was increased. However, the antifungal surface properties were found to be the result of a synergistic effect of graphene toxicity and surface topography. The GO-AgNPs nanocomposites synthesized by [PERSON] et al. were first utilized to investigate _graminearum_. Additionally, for the first time, the antibacterial activity of GO-AgNPs against wheat head blight was studied in vitro and in vivo. Damage to spores and hyphae, possibly due to the remarkable synergy of GO-AgNPs, created an antibacterial mechanism, resulting in physical damage and the production of chemical reactive oxygen species. More importantly, the fungal spores-mediated chemical reduction in GO may be responsible for its high antibacterial activity. The in vitro leaf experiments confirmed that the nanocomposite can significantly control _graminearum_ and protect plants from pathogen infection (Figure 8) [122]. Figure 7: Mechanism of action of Cu-Gr coating on antiviral surfaces. (Printed image with permission from Ref. [118], copyright 2020, American Chemistry Society). The above introduction on antimicrobial applications of graphene-based membranes is summarized in Table 1.
# 2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network
## 3 Experimental System and Results
### Gaze Estimation Accuracy Comparison of Different Methods  
As shown in Figure 9, gaze estimation accuracy is expressed as intersection angle \(\theta\) between actual gaze direction (\(A\) as gaze point) and estimated gaze direction (\(A^{\prime}\) as gaze point).  
Figure 8: Gaze point estimation with/without considering error compensation: (**a**) gaze point estimation utilizing a four-marker calibration model; (**b**) gaze point estimation utilizing a six-marker calibration model; (**c**) gaze point estimation utilizing a nine-marker calibration model; (**d**) gaze point estimation utilizing a 16-marker calibration model.  
Figure 9: Definition of gaze estimation accuracy.  
Angle \(\theta\) can be calculated through Equation (12), where \(L\) is the distance between the human eye and the monitor screen:  
\[\theta=\arctan\left(\frac{\sqrt{\left(x_{A}-x_{A^{\prime}}\right)^{2}+\left(y_{A }-y_{A^{\prime}}\right)^{2}}}{L}\right). \tag{12}\]  
The standard deviation of gaze estimation accuracy \(\theta\) is defined as Equation (13), where \(\overline{\theta}\) represents the mean value of \(\theta_{\text{j}}\left(j=1,2,\cdots,K\right)\) and \(K\) is the total number of gaze points estimated:  
\[\Delta_{std}=\sqrt{\frac{1}{K}\sum_{j=1}^{K}\left(\theta_{j}-\overline{\theta} \right)^{2}}. \tag{13}\]
# A Noise-Tolerant Carrier Phase Recovery Method for Inter-Satellite Coherent Optical Communications
## 4 Simulation Results and Analysis
### Computational Complexity  
Table 4 outlines the total computational load associated with the noise-tolerant method in comparison to the Diff-4 th methods for a data segment of length \(K\). In this context, \(N_{1}\) denotes the block length for the first-stage FO estimation in the Diff-4 th methods, \(N_{2}\) represents the block length for the second-stage fourth-phase estimation, and \(N\) corresponds to the block length for the fine phase estimation stage in the noise-tolerant method. Notably, the number of required multiplications is only contingent upon \(K\), with the noise-tolerant method requiring only 36% of the multiplications needed by the Diff-4 th methods. For the simulations conducted in this study, the parameters \(N_{1}=8192\), \(N_{2}=32\), and \(N=64\) were utilized. The noise-tolerant method demands \(67K\) additions, whereas the Diff-4 th methods require \(8223K\) additions, which corresponds to a mere 0.8% of the computational load of the latter.