esa-sceva/satcom-chunk-collection huggingface dataset:
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 3 Generating Density Maps from Raw AIS Data
### Computing Traffic Density  
After cleaning the raw AIS data and creating the grid, we proceed with the computation of shipping density for the area over a specific time frame. It is worth noting that several different metrics can be used for calculating the shipping density within a given cell, according to the definitions previously mentioned (e.g., number of crossings, time spent etc.). For the purposes of this study, the density is extracted by calculating the cumulative interval that the vessels in question spent within each cell. In order to do so, trajectory segments are created from the cleaned AIS data; a segment is created for each pair of consecutive messages of the same vessel. During these segments, the vessel is considered to follow a linear path. As already mentioned, this approach may be considered accurate for relatively small time intervals and for small distances, but cannot be used universally. Thus, to avoid inaccurate measurements, segments that originate from AIS intervals larger than 6 h or have a length larger than 30 km are discarded. Also, similarly to the cleaning process, segments that indicate a vessel speed larger than 50 knots are also removed. Afterwards, each segment is split according to the grid, and the respective time for each cell is computed (see Figure 5).  
Figure 3: Grids for the area of the United Kingdom, for cell lengths of 500 km, 200 km, 100 km and 10 km (left to right).  
In this example, for the first segment (\(p_{1}\Rightarrow p_{2}\)) in Figure 5, the full time-interval is assigned to a single cell, since the vessel stays within its borders. For the second segment (\(p_{2}\Rightarrow p_{3}\)), the line is split so that the corresponding sub-segment intervals are assigned to four cells in total. Lastly, the third segment (\(p_{3}\Rightarrow p_{4}\)) does not provide any additional measurements, since the distance between its confines is greater than the threshold.  
After the density calculation, each cell is matched to the appropriate color, according to its value. More precisely, a series of density thresholds is provided for the map generation, along with the corresponding colors; cells with values between these predefined thresholds are assigned colors using a logarithmic scale (Figure 6). The resolution of the produced maps depends solely on the needs of the user; examples for four different resolutions for the whole area of Europe can be found in Figure 7.  
Figure 4: Grids only with cells that include water areas for the EU seas, using the EPSG:3035 projection system, for cells lengths of 500 km (orange), 200 km (green), 100 km (pink) and 10 km (purple).  
Figure 5: The time-at-cells density calculated from a vessel’s positions (p1 \(\Rightarrow\) p2 \(\Rightarrow\) p3 \(\Rightarrow\) p4).
# Research on the Compactness of Lunar Soil Simulant Based on Static Cone Penetration Test
## 2 Experimental Design
### Test Device
#### 2.2.2 Static Cone Penetration Equipment  
The static cone penetration equipment consists of a test bench, a loading control box, a penetration system, a measurement system, and other components. Taking into account the size effect of lunar soil simulant particles [18], a static cone penetration probe with a conical bottom cross-sectional area of 10 cm\({}^{2}\) was selected. An electrical measurement method was adopted in the data acquisition system. Specifically, the sensors inside the static cone penetration probe convert the penetration resistance into electrical signals, which are transmitted via a cable to the computer. This computer then displays the penetration resistance values collected at different depths. A schematic diagram of the static cone penetration testing equipment and static cone penetration lunar soil simulant sample is shown in Figure 2.
# A Driving Behavior Planning and Trajectory Generation Method for Autonomous Electric Bus
## 2 Framework  
Path planning provides safe and reliable maneuvers under various driving environment, and it is the basis for autonomous vehicle to drive without human intervention. Figure 1 shows the framework of the path planning system. An integrated navigation system (GPS/IMU) provides vehicle speed, heading angle, and global position. The perception layer provides environmental information including static or dynamic obstacles and road information for path planning. Global planning is employed to find the optimal path from the current position to the destination in the high-precision GIS map form. Local planning plans appropriate driving behaviors and generates a smooth trajectory for the autonomous bus to follow the route.  
In this paper, global planning is based on ArcGIS. Firstly, a high-precision GIS map is built according to original GPS data of path, which represents the road geometry. Then, the network analysis tool of ArcGIS is utilized to search for the optimal route in the high-precision map based on the current location which can be obtained by GPS/IMU.  
Local planning contains two parts: driving behavior planning and optimal trajectory generation. Driving behavior planning imitates an experienced driver to give reasonable driving behaviors (e.g., lane changing, overtaking or following the vehicle ahead) based on perception information and global waypoints. Double-layer FSM (finite state machine) is proposed to execute a rule-based decision process under different driving scenarios. The trajectory generation module generates a safe and feasible path for autonomous bus. Here, a cubic polynomial is used to express optimal trajectory smoothly, which fully considers the kinetics and safety of the bus.
# Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression
## 4 Case Study
### Evaluation Metrics
#### 4.2.2 Probabilistic Evaluation Metrics  
Prediction interval coverage probability (PICP) and prediction interval normalized average width (PINAW) are significant indicators to evaluate the performance of interval predictions, which have been successfully applied to probabilistic wind power forecasting and electrical load forecasting [30; 40]. The expressions are as follows:  
\[PICP_{\alpha}=\frac{1}{N}\sum_{i=1}^{N}\delta(y_{i}),\ \delta(y_{i})=\left\{ \begin{array}{l}1,\ y_{i}\in\left[L_{i},\ U_{i}\right]\\ 0,\ y_{i}\
otin\left[L_{i},\ U_{i}\right]\end{array}\right. \tag{17}\]  
\[PINAW_{\alpha}=\frac{1}{N}\sum_{i=1}^{N}\frac{U_{i}-L_{i}}{y_{i}} \tag{18}\]  
where \(PICP_{\alpha}\) and \(PINAW_{\alpha}\) are PICP and PINAW at confidence level \(\alpha\); \(N\) is the size of test datasets; \(y_{i}\) is the observed power output; and \(L_{i}\) and \(U_{i}\) are the lower and upper boundaries of the \(i\)th predicted CI. According to [40; 41], a good CI prediction should have  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attributes** & **WF1** & **WF2** & **WF3** \\ \hline Hub height & 80 m & 84 m & 90 m \\ \hline Rotor diameter & 108 m & 111 m & 171 m \\ \hline Rated power & 2 MW & 2 MW & 5 MW \\ \hline Rated wind speed & 9.5 m/s & 12 m/s & 10.9 m/s \\ \hline Cut-in wind speed & 3 m/s & 3 m/s & 3 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed information of wind turbines in each wind farm.  
both high PICP and low PINAW. Therefore, we use the ratio of \(PICP_{\alpha}\) and \(PINAW_{\alpha}\) for relative comparisons with several state-of-art probabilistic WTPC methods, expressed as:  
\[NC_{\alpha}=PINAW_{\alpha}/PICP_{\alpha} \tag{19}\]  
Although there is no specific index to evaluate the fitting effect, according to [41], the smaller the \(NC_{\alpha}\), the more appropriate the predicted CI.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 3 Case Study: Zhongma Landslide
### Feature Factors Selection for Landslide Data  
The effectiveness of a deep learning model is closely related to the input features. Therefore, it is necessary to select the features which have impacts on landslide deformation.  
Landslide deformation is usually related to rainfall, so rainfall intensity and duration are often used for predicting landslides [5]. Therefore, we consider the rainfall data in the model. The cumulative rainfall of the first 1-7 days \(P^{u}_{\text{cumulative}}(n=1,2,\ldots,7)\) and the maximum continuous effective rainfall of the 7 days \(P^{u}_{\text{valid}}\) (the effective rainfall factors  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Status** & **Speed (mm/d)** \\ \hline Low Speed State & 1.2685 \\ Medium Speed State & 12.7481 \\ High Speed State & 53.7578 \\ Super High Speed State & 113.9813 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clustering centroids of the Zhongma landslide.  
Figure 6: Elbow method results.  
Figure 7: Clustering results.  
are 1.0, 0.8, 0.6, 0.4, respectively) are selected as the influencing factors for landslide displacement. Among them, the maximum continuous rainfall is calculated as follows.  
\[P_{\rm valid}^{\alpha}=P_{0}+\alpha P_{1}+\alpha^{2}P_{2}+\ldots+\alpha^{n}P_{n} \tag{11}\]  
where \(P_{\rm valid}^{\alpha}\) is the maximum continuous rainfall, \(P_{i}\) is the amount of rainfall before the \(i\)-th day, \(i=0,1,\ldots,n\), \(\alpha\) is the effective rainfall coefficient and \(n\) is the maximum continuous rainy days.  
The landslide displacement change affects the landslide deformation state. So, the first 18 relative changes in the landslide displacement subseries \(D_{relative}^{n}(n=1,2,\ldots,18)\) were selected as supplements to the rainfall factor.  
The correlation between these factors and landslide deformation was calculated by gray correlation analysis, using the discrimination coefficient of 0.5, and the mean normalization method. Factors with the gray correlation greater than 0.6 can be used as input features for the model. In Figure 8, all factors have a gray correlation greater than 0.9, so they can be used as input features of the classification model. A total of 29 parameters were selected to construct a dataset for classification.
## 5 Proposed Reliable, Energy Optimized and Void Aware Routing Protocol
### Underwater Network Model Assumptions  
The underwater network is assumed to be a 3D model. The networking elements are based on surface sink nodes (SSNs) and multiple acoustic sensor nodes (ASNs). The SSNs are outfitted with both RF modems and acoustic modems for onshore and underwater communication. However, the ASNs are only capable of acoustic communication with each other and the SSNs. The ASNs have been installed with a buoyancy control system that enables each ASN to maintain a specific depth within each layer [32]. The ASNs at each layer can move with the flow of water at a velocity ranging from 1 to 3 m per second. Vertically, an ASN may have a slight difference in depth, which is typically considered insignificant [44].  
The battery life of SSNs is infinite but the battery life of every ASN is finite. Once depleted, ASNs are unable to sense, process, transmit, or receive. To conserve ASN and cluster head's energy, the algorithm runs on sink nodes, leveraging their superior computational capacity and resources. SSNs use their global view of the network topology to make optimal routing decisions, effectively resolving void issues and enhancing data delivery reliability. The sink nodes disseminate routing decisions through control messages, which are received by cluster heads and shared with member nodes. Periodic beaconing ensures network synchronization while minimizing unnecessary overhead.
## 3 Materials and Methods
### Proposed Methodology  
The implementation of optimized CNN, ANN, and LSTM classifiers is explained in this section. To obtain the training and testing datasets, the preprocessed data is divided in an 80:20 ratio. The training dataset is used to fit the classifier models, and the testing dataset is used to collect predictions. The optimized CNN classifier is implemented with a kernel regularization parameter of C = 1.0 and the activation function is ReLu. For the greatest known performance, the LSTM classifier is learned at a rate of 1.0 and with 400 estimators. The predicted performance of these classifiers will be used to justify the proposed classifier's performance.  
Table 2 lists the parameters that were used in our model. Here, we can see the parameters (P), activation layer (AL), optimizer (OP), learning rates (LR), epochs (E), and compilation time (CT). In our study, we use sigmoid and ReLu as an activation layer, and Adam and Adamax as optimizer, to acquire good accuracy. The using activation layer, optimizer, and learning rates fit our study, and our model produced good accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **P** & **E** & **AL** & **OP** & **LR** & **CT** \\ \hline Optimized CNN & 6497 & 8 & ReLu, sigmoid & Adam & 0.1 & 0.00968 \\ Optimized ANN & 381 & 8 & ReLu, sigmoid & Adam, Adamax & 0.01 & 0.00447 \\ Optimized LSTM & - & 8 & ReLu & Adam & - & 0.00527 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different algorithms’ parameters.  
Figure 2: Highly correlated features of CKD dataset.
## 3 Results and Discussion
### Characterization and Properties of Hybrid Foams ss-CRMFoams@PCMs
#### 3.2.2 EMI Shielding Effectiveness  
The shielding performance of the developed samples was examined in terms of EMI shielding effectiveness (SE) in the 3.2-9.0 GHz frequency range. SE\({}_{\text{T}}\) can be expressed as the sum of reflection from the surface (SE\({}_{\text{R}}\)), absorption (SE\({}_{\text{A}}\)) and multiple-reflection (SE\({}_{\text{M}}\)) as follows:  
\[\text{SE}_{\text{T}}(\text{dB})=\text{SE}_{\text{R}}(\text{dB})+\text{SE}_{ \text{A}}(\text{dB})+\text{SE}_{\text{M}}(\text{dB}) \tag{8}\]  
Multiple-reflection at internal interfaces inside the material can be excluded if SE > 10 dB [70] and thus:  
\[\text{SE}=\text{SE}_{\text{T}}\triangleq 10\log_{10}\biggl{(}\frac{\text{P}_{ \text{inc}}}{\text{P}_{\text{tr}}}\biggr{)}=10\log_{10}\biggl{(}\frac{1}{\text {T}}\biggr{)}=\text{SE}_{\text{R}}+\text{SE}_{\text{A}} \tag{9}\]  
where  
\[\text{SE}_{\text{R}}=10\log_{10}\biggl{(}\frac{1}{1-\text{R}}\biggr{)} \tag{10}\]  
\[\text{SE}_{\text{A}}=10\log_{10}\biggl{(}\frac{1-\text{R}}{\text{T}}\biggr{)} \tag{11}\]  
\(\text{SE}_{\text{R}},\text{SE}_{\text{A}}\) refer to the reflection and absorption SE, respectively.  
Figure 10: DSC curves of CRMFoam-2.5 and ss-CRMFoam-2.5@OD, ss-CRMFoam-2.5@RT18 hybrids.  
The higher the SE the better the shielding. The SE (also denoted as SE\({}_{\rm T}\), with A, T, R indicating the absorption, transmission, and reflection, respectively) is usually quantified in terms of the logarithm of the incident power P\({}_{\rm inc}\) over the transmitted power P\({}_{\rm trm}\)[71, 72, 73, 74, 75] and thus expressed in decibels (dB).  
Figure 11 shows the reflection (Figure 11a) and the absorption (Figure 11b) spectra of the CRMFoams, in the frequency range 3.5-9.0 GHz. Figure 11a clearly illustrates that the reflection of the CRMFoams is almost negligible indicating that the dominant shielding mechanism is absorption giving a total SE: SE\({}_{\rm T}\) = SE\({}_{\rm A}\) (see Equation (9)). Moreover, as one can notice from Figure 11d, the absorption levels increase as the porosity increases (sample CRMFoam-2.5 to CRMFoam-5) indicating that indeed enhanced porosity improves the microwave absorption [76, 77, 78, 79].  
It is worth noticing that the encapsulation of the paraffins into the foams increased the EMI efficiency to almost 20 bB reaching the entry-level value required for commercial applications [80] following the literature's findings that the existence of phase change Materials (PCMs) enhances the electromagnetic interference shielding effectiveness [81].
## 2 Design of the FSS  
### Design Concept  
When FSS is irradiated by the incident wave, a unit cell of FSS can be regarded as a resonant circuit. An \(L\)-\(C\) parallel resonance is the basic principle of bandpass FSS, where \(L\) and \(C\) represent the equivalent inductance and capacitance of the unit cell, respectively. When designing conductive patterns, both space efficiency and convolution principles must be considered. The former determines the total length of the stripes, and the latter can provide an appropriate balance between the capacitive part and the inductive part.  
For bandpass FSS, the capacitors distributed in the FSS unit can be divided into intercapacitor (\(C_{\text{inter}}\)) and intracapacitor (\(C_{\text{intra}}\)), where \(C_{\text{inter}}\) is the capacitance inside the element and \(C_{\text{intra}}\) is the capacitance between the elements. \(C_{\text{intra}}\) will introduce a new high-frequency resonance at high frequencies, while \(C_{\text{inter}}\) will increase the equivalent inductance (\(L_{\text{E}}\)) at low frequencies. The main resonance frequency (\(F\)) is determined by the following formulas.  
When \(0<\omega\leq\sqrt{1/LC_{\text{intra}}}\)  
\[\frac{1/(\text{j}\omega C_{\text{intra}})\times\text{j}\omega L}{1/(\text{j} \omega C_{\text{intra}})+\text{j}\omega L}=\text{j}\omega L_{\text{E}} \tag{1}\]  
\[L_{\text{E}}=L/(1-\omega^{2}LC_{\text{intra}}) \tag{2}\]  
\[F=1/2\pi\sqrt{L_{\text{E}}C_{\text{inter}}} \tag{3}\]  
Therefore, reducing the resonance frequency is equivalent to increasing the inductance or capacitance of the unit cell. For a square slot unit cell, resonance will occur when the perimeter of the square slot is approximately an integer multiple of the incident wavelength. Therefore, we can increase its physical size in a limited area by bending the square slot. In other word, we can reduce the resonance frequency without increasing the area of the unit cell and achieve the characteristics of miniaturization.
# Real-Time Location-Positioning Technologies for Managing Cart Operations at a Distribution Facility
## 3 An RFID-Enabled Real-Time Cart-Locating Platform for Mail Facilities
### Enhanced Operational Efficiency  
Our proposed platform could result in enhanced efficiency and throughput of the facility. In a vast facility which has a large number of carts circulating, it is very time consuming for the staff to locate the carts of flights which are soon to depart. The capability of locating the carts in real time can save staff members a significant amount of time from searching for the necessary carts. The mobile application developed on a portable device can be used to retrieve the real-time information from the main system and to display the locations of carts of the selected flights on the floor map of the facility. Signal lights are installed on the carts and used to help the staff identify the carts of selected flights more efficiently. An alert can be issued in the system for notifying the staff members of the situation that mail bags have not been moved to the ULD loading area by the planned time. This capability can also eliminate the cases of mail bags being late for loading or not being loaded to their intended flights. Consequently, this system can reduce the total delivery time of items from senders to receivers.
# Subjective Distance Estimates and Sense of Agency in Robotic Wheelchair Control
## 1 Introduction  
Over the course of life, individuals regularly make a distinction between their own actions and all other external events. We draw this distinction because we have a sense of agency (SoA) [1], a specific awareness of our own actions. SoA is closely related to the sense of control [2]--the sense that the future is largely determined by our momentary decisions. In order to successfully control one's body and employ external instruments, one must feel and comprehend that one has control over the world around oneself [3]. The lack of SoA results in frustration, distraction from current activities and insecurity in one's actions [4]. SoA studies are a field devoted to investigating the conditions under which an individual perceives themselves as a source of various events or, on the contrary, rejects them.  
This field of research can yield considerable practical value. In today's world, individuals have to interact with a variety of technical devices. Some of them, such as robotic grasping arms (e.g., [5]), functional prostheses (e.g., [6]) and robotic wheelchairs (e.g., [7]), can restore patients' ability to move around and act independently. Recently, the development of robotic wheelchairs has become an urgent task. In the last 30 years, the global prevalence of spinal cord injuries has severely increased [8]. What should such devices as robotic wheelchairs be like so that a person feels comfortable working with them and sees the instrument as an extension of their own body and will? According to Rule 7 of the The Eight Golden Rules of Interface Design [9], an interface has to support the internal locus of control, which is equivalent to maintaining SoA. SoA studies can aid in the development of various devices and interfaces and improve their efficiency. For example, there is ongoing research into explicit ratings of SoA and its EEG correlates during assisted driving [10,11]; in this setting, it is important that the operator maintains a sense of control over the movement of the vehicle.
## 2 Materials and Methods
### Data Extraction and Analysis  
Data extraction was performed independently by two reviewers ([PERSON] and [PERSON]) using the selected articles. The relevant data of the included studies were extracted in detail, using an Excel spreadsheet (Microsoft, Redmond, WA, USA, Version 2007). The extracted data included: year of publication, type of study, author, country, age group, defect characteristics, no. of patients included, no. of sites included, surgical procedure used in both test and control group, biomaterials used in both test and control groups,trade name of the biomaterials, parameters or outcomes which were assessed after the surgical procedure. The random effects models were applied during meta-analysis to account for methodological differences among studies. Forest plots were produced to graphically represent the confidence interval (CI) for the primary outcomes. Heterogeneity was assessed by using the Tau2 test, Chi-square test and I2 test, which ranged from 0 to 100% (lower values represent less heterogeneity). The reporting of these meta-analyses adhered to the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) statement.