esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Factor Analysis of Changes in Crop Rotation Patterns
The introduction of advanced irrigation infrastructure in the FPEC has effectively expanded arable land areas, consequently altering spatial-temporal cropping distributions. Additionally, municipal initiatives positioning Zhangjiakou as China's potato capital, with an emphasis on potato-based staple foods, have encouraged local farmers to expand cultivation areas for potato and oats. Continuous cropping barriers constitute another critical consideration influencing planting decisions. The practice of continuous monoculture poses considerable threats to agricultural sustainability, such as nutrient depletion, increased susceptibility to drought conditions, soil degradation, and imbalanced soil microbial communities. In particular, continuous potato cropping has been associated with heightened disease prevalence in this region. To mitigate these negative impacts, farmers widely implement crop rotation to restore soil nutrients, control pests and diseases, and minimize production risks [64]. The high-resolution crop maps and rotation analyses produced in this study can directly support agricultural management. We recommend targeted rotations, such as oats-vegetables, maize-potato, oats-potato, and sesame-potato, in Zhangjiakou. Rotations combining deep-rooted and shallow-rooted crops improve soil moisture, nutrient cycling, and overall soil structure. By revealing precise crop distributions and rotation cycles, local governments can optimize water and fertilizer allocation and formulate informed rotation recommendations aligned with local climatic and soil conditions.
## 1 _Article_
### Recommendations for Overtopping Limits and Mitigation  
A breakwater constructed to protect a harbour area should satisfy rigorous conditions if we want to reduce wave overtopping and its impact. Several proposals in the literature define those conditions and propose recommendations to study wave overtopping. The most widely used manual regarding wave overtopping is the EurOtop manual [7], which guides designers and operators of breakwaters, reclamations, inland lakes, or reservoirs on analysing and predicting wave overtopping for flood defences attacked by wave action. The manual describes the main variables involved in an overtopping event and provides formulas and tools for predicting said events based on tests carried out in hydraulic laboratories. However, the manual notes that one must consider the specific characteristics of a port when using these resources due to the sensitivity of overtopping to changes in the geometry of the port, incident waves, and the local bathymetry.  
Although it is impossible to establish precise limits to tolerable overtopping for all conditions, this manual includes some guidance on tolerable mean discharges and maximum overtopping volumes for various circumstances or uses. The manual also emphasises that these limits may be adopted or modified depending on the site's circumstances and uses.  
Another recommendation for the conditions a breakwater should satisfy to protect a harbour area by reducing wave overtopping and its impact is the Spanish Ports System's Recommendations for Breakwater Construction Projects, ROM 1.1-18 [5]. ROM 1.1-18, for instance, indicates that the conditions a breakwater satisfies regarding wave energy transmission, either from the overtopping of the crown or propagation through the breakwater section, are the following: the breakwater's relative height (h + F\({}_{\text{C}}\))/H, which generally has dimensions on the order of 1, O(1), and relative width, B/L, on the order of 1/10, O(1/10), where h is the depth at the site, and H, L, F\({}_{\text{C}}\), B are, respectively, the characteristic wave height, characteristic wavelength, freeboard, and the representative magnitude of the breakwater width (see Figure 1).  
When a wave overtopping occurs in a commercial port environment, the best-case scenario will be the disruption of activities. Even this scenario has negative financial repercussions. A system that detects overtopping events would provide valuable information to port operators, allowing for the minimisation of the impact of overtopping, including the financial impact, property damage, or even physical harm to port workers. Wave overtopping has traditionally been studied from three possible approaches: small-scale physical modelling, numerical modelling, and in situ measurement.
## 4 The Algorithm
### Determining Beam Couple Switching Points and the Manipulator Path  
In the regions, one couple may have a critical region while another couple may not have. In such a situation, it is necessary to switch from one couple to another one. Note that this switching process is different from the switching process mentioned in Section 4.2 in such a way that previous switching process is implemented within only a couple while the switching process in this section is implemented between couples.  
Figure 11: Beam couples. (**a**) One couple. (**b**) Two couples. (**c**) Three couples. (**d**) Four couples.  
Figure 12: Critical regions and points for one beam couple.  
As seen in Figure 13, while the critical regions numbered as 1 and 2 belong to the first couple, the critical region numbered as 3 belongs to the second couple. The path points are shown with consecutive green points and the beam switching occurs at the point 9 which is the middle point of the end points numbered as 7 and 8 of successive critical regions.  
Once the manipulator path is determined in 3D as described above, it is an easy process to move the end points of the manipulator links on the robot path points. Therefore, the manipulator navigates successfully in a 3D environment cluttered with many obstacles having tight maneuvering corners.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 8 ML and Cyberattack Detection
### ML Challenges in WSN
#### 8.3.3 Challenges Related to the ML Framework  
Pre-processing, feature selection and extraction, and hyperparameter tuning are essential for the success of any ML model learning process; however, collecting labeled data is not always possible in WSNs, as certain attacks may only appear in very few nodes and with low frequency. Thus, selecting an algorithm that can use minimally labeled data in a way that is sufficient for the learning process is crucial. Data reduction is required to reduce the processing time of the learning proccess on large datasets, especially for large-scale WSNs. ML preprocessing includes the process of adjusting the raw data to a format that can be used to train an ML model, such as removing features, sample size reduction, class rebalancing, missing data imputation, data normalization, encoding labeled data, and changing the data type of certain features. The process of reducing redundancy and correlation by selecting the most informative features during feature extraction while dropping irrelevant or partially relevant features from the dataset can be classified as follows: filter-based, wrapper-based, or embedded-based. Filter-based methods filter out irrelevant features independently of the learning algorithm, making it much faster and computationally effective than other methods and more suitable for WSNs[90]. Stacked-based feature extraction has been used as well; it combines several feature selection algorithms ordered as a stack and executed one after another, then applied to the dataset[91].  
Hyperparameters are the set of parameters or arguments that are set manually before training and optimizing the ML model structure for better classification. These parameter value ranges are different for each ML algorithm. Hyperparameter selection significantly affects prediction results. Default parameters are the initial values that are pre-established when no values are explicitly provided. Optimized hyperparameters can be determined manually or automatically. Manual hyperparameter tuning is time-consuming, especially with a high number of possible combinations. Optimization algorithms can automate the process of finding the hyperparameters' this is called hyperparameter optimization. Different approaches include Bayesian optimization, grid search, random search, genetic algorithms, and particle swarm optimization. Several parameter combinations can be identified via search to determine the set of parameters that provide better detection results. Hyperparameter tuning is time-consuming when additional hyperparameters are added, as the number of parameter search combinations increases.
## 3 Materials and Methods  
Throughout this work, four machine learning modules were developed, aiming to assist the inspection of defects in ceramics tableware. These modules comprised a piece zone segmentor to identify the relevant regions of the ceramics pieces (Section 3.3), an anomaly detection module to select possible defective pieces (Section 3.4), a defect detector to detect defects in ceramic pieces (Section 3.5) and, finally, a classification module to categorize different types of defects (Section 3.6). Each module was first developed and evaluated independently, before being aggregated in a semi-automated pipeline (Figure 1) that is able to visually inspect ceramics tableware through the identification and characterization of defects.  
The initial step of the pipeline, Piece Zone Segmentation, involves the separation of distinct zones within the ceramic plates. Considering this information, the input image is cropped to include solely the plate area and it is then divided into patches of dimensions of \(512\times 512\), with a \(50\%\) overlap between adjacent patches.  
Figure 1: Overview of the pipeline developed in this work towards the automated visual inspection of tableware ceramics.  
Following the segmentation step, the pipeline offers an optional module, Anomaly Detection. This module analyzes each patch individually to determine the presence of any defects. Patches that are identified as containing defects are then passed on to the Defect Detection model, which generates bounding box predictions encompassing the defect areas. To refine the predictions, the pipeline applies a two-step process. First, a Non-Maximum Suppression (NMS) algorithm selects the most suitable bounding box among a collection of overlapping boxes within each patch, considering only predictions with confidence score higher than 90%. Subsequently, the coordinates of the selected boxes are transformed back to the original image coordinate space, followed by a second NMS operation to filter out redundant detections resulting from overlapping bounding boxes.  
The final component of the pipeline is the Defect Classification model, responsible for identifying the specific type of defect present. This classification is subsequently associated with the corresponding zone in which the defect occurs, leading to the final decision regarding the defect analysis.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 2 The Metaverse and Mixed Reality
### Areas of Application
#### 2.2.2 Smart Cities  
On another note, incorporating the Metaverse into smart cities can also provide significant advantages, particularly in traffic management, monitoring, planning, design, and transportation services. This integration may enhance the overall quality of life for residents.  
Traditionally, the physical presence of architects is necessary at construction sites to perform urban construction planning. However, digital twins can create a virtual replica of the space, allowing architects and administrators to access it through the Metaverse without needing a physical presence. This technological innovation has the potential to revolutionize current construction planning methods by simulating construction possibilities and providing real-time status updates to construction workers.  
Incorporating MR into smart cities can offer significant benefits across various sectors, particularly urban planning, construction, and infrastructure management. MR allows city  
Figure 5: Some of the areas of application of the Metaverse and MR adapted from [8].  
planners, architects, and engineers to overlay virtual models onto real-world environments, facilitating real-time visualization and interaction with future city layouts, transportation systems, and public spaces. For instance, MR can enhance traffic management by enabling virtual simulations of traffic flow and pedestrian behavior, helping authorities design better roads, manage congestion, and improve overall transportation efficiency.  
In construction, MR can complement digital twins by allowing architects and construction workers to interact with holographic models of buildings at the actual site. This technology enables real-time design adjustments, immediate feedback on construction progress, and more accurate visualizations of how new developments will integrate into the urban landscape. By combining virtual and physical perspectives, MR enhances decision-making and collaboration, reducing the need for physical presence while increasing the precision and efficiency of urban development projects. This innovation holds great potential for transforming urban infrastructure planning and management [5; 6].
# Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
## 5 Design and Validation of Lead Filter
### Estimation of the Lateral Dynamics Lag  
To design an effective lead filter, it is essential to initially characterize the original system's dynamics in terms of a transfer function. This characterization begins with a sensitivity analysis to identify the factors influencing the lag. Numerous simulations were conducted, maintaining a constant steering profile representative of a dual-lane-change (DLC) maneuver (a standardized test maneuver [43]). During these simulations, various parameters were modified, such as the payload mass and the desired velocity/speed for the entire maneuver. Figure 7 illustrates that, although the lag due to payload inertia is negligible, it is dependent on vehicle speed, with increased speed resulting in a greater lag.  
To characterize and reduce the lag, we use a second-order transfer function given using:  
\[G_{lag}(s)=\frac{\omega_{n}^{2}}{s^{2}+2\zeta\omega_{n}s+\omega_{n}^{2}} \tag{5}\]  
Here, \(\omega_{n}\) represents the natural frequency and \(\zeta\) represents the damping ration of the steering dynamics correspondingly. Both these parameters were fine-tuned using a comparative analysis with various simulation outcomes, as depicted in Figure 8.  
Figure 8 illustrates the comparison among three curvature profiles: the curvature predicted using the simple bicycle model (derived from the Ackermann steering angle at the front, given using: \(\textbf{tan}\mathbf{\phi}=\textbf{L}\textbf{/}\textbf{R}_{\textbf{1}}\), where \(\mathbf{\phi}\)--ackermann steer angle, **L**--wheelbase of the vehicle, and **R**--radius of curvature of the vehicle's rear axle center), the actual curvature computed from the rear axle coordinates of the vehicle, and the lagged curvature attained by filtering the kinematic model's output. As depicted, the proposed second-order transfer function effectively mirrors the inherent lag observed in TruckSim(r) simulation results, stemming from the vehicle's lateral/steer dynamics.  
Figure 7: Variation of curvature w.r.t, (**a**) _payload mass_, (**b**) _vehicle speed_ for the same steering profile input.
## 4 Results and Discussion
### Sensory Experience and HHP  
In the study conducted with the HHP method created, it was observed that this method provides, in addition to a realistic haptic-visual multimodal integration, a way of being able to choose the sensations that users need to feel when faced with a specific product. This makes it possible for the product to be more competitive in the market. This way of detecting the information that is communicated to the client through the product makes is possible to carry it out in the early stages of the conceptualization of the product with the rapidity of implementation and effectiveness that the I4.0 demands.  
Accordingly, this simple evaluation method, thanks to HHP, provides the creation of a new method that offers the evaluation of a sensory experience related to the finishes of a product. In this way other techniques related to haptic experience have been investigated, however, few works of literature have been found about devices that recreate the sense of touch. An example of this is the creation of a glove prototype that allows hand movements to be monitored and acted on [55]. Work is also being done on the use of general-purpose devices such as the Phantom Omni, a device that is also applied in the simulation of a tool in an operating room or controlling a robot arm during a virtual mechanical assembly [43]. However, with this device when evaluating a texture a bias is generated in the transmitted information. In this device, it is not the user who can directly touch the texture sample and, therefore, this complex sensory chain of electrical currents of the body is replaced by one based on vibrations. This is not the case with HHP, where the haptic experience is real and it must also be added that the visual experience in the case of AR allows an integration of the object in the real environment, which creates an integrated multimodal experience and not a disaggregated experience[PERSON] The simplicity of the use of HHPs also allows for a simpler and less costly implementation in monetary terms than in the case of the Phantom Omni.  
With regard to Product Semantics, it should be noted that there are some outstanding authors who have made important contributions in this field, such as [PERSON] [10; 20; 56], [PERSON] [57; 58] or [PERSON] [59; 51][PERSON] However, no contributions have been found on the evaluation of semantic content transmitted via haptics technology. However, as explained in previous research, an attempt is made to integrate haptic sense with visual sense but they do not provide an effective methodology for the selection of textures in the creation of new products. On the contrary, the HPP semantic evaluation method has managed to integrate both perceptions and, therefore, it is possible to verify the transmission and correct reception of the information in a multimodal way.
## 3 Calculation of Propagation Time and Attenuation Rate Using the Amiet Method
### Attenuation Rate
#### 3.3.2 Derivation of \(dA_{0}\)  
The distance \(R_{0}\) from the points \(d\) to \(b\) is given by the geometric relationship, as shown in Figure 3:  
\[R_{0}=R_{m}\cdot\frac{\sin\theta_{m}}{\sin\theta_{0}}. \tag{12}\]  
Equation (13) can be derived from the sine formula:  
\[\frac{l_{ae}}{\sin\theta_{0}}=\frac{\mathrm{d}}{\mathrm{d}\theta_{0}}(-\frac{ R_{t}}{\tan\theta}), \tag{13}\]  
where \(l_{ae}\) is the distance from the points \(a\) to \(e\) as shown in Figure 3. Equation (13) can be recast into Equation (14) by substituting Equation (2) into Equation (13), as follows:  
\[\frac{l_{ae}}{\sin\theta_{0}}=R_{t}\cdot\frac{1-M\cos\theta^{\prime}}{\sin^{2 }\theta^{\prime}}\cdot\frac{d\theta^{\prime}}{d\theta_{0}}. \tag{14}\]  
Equation (3) is transformed, as follows:  
\[\frac{\cos\theta^{\prime}}{\cos\theta_{0}}=1-M\cos\theta^{\prime}. \tag{15}\]  
Equation (16) is obtained by taking the derivative of both sides in Equation (3):  
\[\frac{\mathrm{d}\theta^{\prime}}{\mathrm{d}\theta_{0}}=\frac{\sin\theta_{0} \cos^{2}\theta^{\prime}}{\sin\theta^{\prime}\cos^{2}\theta_{0}}. \tag{16}\]  
The substitution of Equation (16) into Equation (15) leads to Equation (17):  
\[\frac{\mathrm{d}\theta^{\prime}}{\mathrm{d}\theta_{0}}=\frac{\sin\theta_{0}} {\sin\theta^{\prime}}\cdot(1-M\cos\theta^{\prime})^{2}. \tag{17}\]  
Equations (18) and (19) are obtained by substituting Equation (17) into Equation (14):  
\[l_{ae}= R_{t}(1-M\cos\theta^{\prime})^{3}\cdot\frac{\sin^{2}\theta_{0}}{\sin^ {3}\theta^{\prime}}, \tag{18}\] \[l_{ad}= l_{ae}-\frac{R_{t}}{\sin\theta_{0}}=\frac{R_{t}}{\sin\theta_{0}} \Big{\{}(1-M\cos\theta^{\prime})^{3}\cdot\frac{\sin^{3}\theta_{0}}{\sin^{3} \theta^{\prime}}-1\Big{\}}. \tag{19}\]  
Here, \(l_{ad}\) is the distance from the points \(a\) to \(d\). Subsequently, \(R_{a}\) is given by \(R_{a}=R_{0}+l_{ad}\), as follows.  
\[R_{a}=R_{0}+\frac{R_{t}}{\sin\theta_{0}}\Bigg{\{}(1-M\cos\theta^{\prime})^{3} \cdot\frac{\sin^{3}\theta_{0}}{\sin^{3}\theta^{\prime}}-1\Bigg{\}} \tag{20}\]  
\(\mathrm{d}A_{0}\) can be derived by considering Equations (12) and (20).  
\[\mathrm{d}A_{0}=2\pi\sin\theta_{0}R_{0}R_{a}\mathrm{d}\theta_{0} \tag{21}\]
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 2 Data and Methods  
### Satellite Data  
Daily Level 1B MODIS data acquired both from Terra and Aqua satellites from March 2000 to December 2016 over Nigeria in night-time conditions were used, for a total of 16.473 images (8.932 from Terra and 7.541 from Aqua), collected at monthly scale. These data were downloaded from the NOAA web site archive ([[https://www.class.ngdc.noaa.gov](https://www.class.ngdc.noaa.gov)]([https://www.class.ngdc.noaa.gov](https://www.class.ngdc.noaa.gov))). The middle and thermal infrared bands of the sensor, channels 20 (3.660-3.840 \(\upmu\)m), 31 (10.780-11.280 \(\upmu\)m) and 32 (11.770-12.270 \(\upmu\)m), respectively, were processed in terms of Brightness Temperature (BT), considering, as Region of Interest (ROI), an area of 600 pixel \(\times\) 400 lines including the seven states of the Niger Delta region (Figure 1), re-projected in Lat/Long (WGS-84).  
Figure 1: The Niger Delta region (red borders). Within the map, the seven states analyzed in this work are numbered and named. P1 and P2 are two locations used in the analyses (see text).  
The satellite dataset, used in all analyses, consists of clear (i.e., not cloudy) pixels, identified by applying the OCA approach [27; 28] based on MODIS channel 32 (11.770-12.270 \(\upmu\)m) and acquired with a satellite zenith angle (SatZA) lower than 40\({}^{\circ}\) and solar zenith angle (SolZA) less than 101\({}^{\circ}\) to assure nighttime conditions. Due to these filters, considering the double impact of clouds and sensor viewing geometry, for each year (from 2000 to 2016) only a low percentage (ranging from 12% to 30%, with a mean value of 22%) of images available for the ROI resulted useful. However, due to the huge amount of original MODIS imagery, all the considered yearly datasets still remain sufficiently and homogeneously populated to guarantee a statistical significance of the achieved results.