esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Attribute Collaborative Optimization for Multimodal Transportation Based on User Preferences
## 4 Methodology
### Genetic Algorithm
#### 4.1.1 Encoding and Decoding  
A node connectivity database is established based on the network topology to characterize the interrelationships between nodes.  
Subsequently, for each arc segment in the network, we randomly assign a unique real-value priority to the three transportation modes: highway, railway, and waterway.  
Figure 3: Hybrid algorithm flowchart.  
These priority values are sequentially stored in the chromosome vector, thereby completing the construction of a single chromosome. By repeating this process for the predefined number of chromosomes, we can construct the entire population of chromosomes.  
Specifically, under this encoding scheme, each chromosome is represented as a vector of length \(N\times 3\), where \(N\) represents the total number of arc segments in the network. The structure of this vector is as follows: for each arc segment (there are \(N\) in total), we sequentially store the priority values for the highway transportation mode, the railway transportation mode, and the waterway transportation mode. Consequently, the overall length of the vector is the number of arc segments \(N\) multiplied by the number of transportation modes 3, which equals \(N\times 3\).  
This encoding method ensures that each chromosome is of equal length and contains the priority information for all arc segments and all transportation modes within the network, thereby providing a solid foundation for subsequent genetic operations, such as selection, crossover, and mutation. The encoding scheme is illustrated in Figure 4, where H, R, and W denote highway, railway, and waterway transportation modes, respectively.  
The decoding process is conducted through the following steps:  
Step 1: Starting from the origin node, identify the set of reachable nodes as the candidate node set.  
Step 2: Select the transportation mode with the highest priority among the candidate nodes corresponding to the current node. Record the selected node and the adopted transportation mode.  
Step 3: Repeat the above operations until the destination node is reached, and output the combined path and transportation mode scheme.
## 3 New Generation Sensor Web Enablement
### Revised Identifier Handling  
In the first generation of SWE service specifications, the way that identifiers (of sensors, observed properties, features _etc._) were used was not harmonized. The SWE Service Model therefore established guidelines for identifier modeling and encoding. In the conceptual model, properties with the purpose of identifying certain objects are modeled as the type of the according object. For example, a _DescribeSensor_ request has a _procedure_ property of type _OM_Process_ which identifies the sensor/process whose description shall be retrieved. From an object oriented programming perspective, this approach is like using object pointers to identify a given object. In the XML implementation of the conceptual model, such object identifying properties are represented as Unified Resource Identifier (URI) so that they are able to store the identifier value. Consequently each resource--such as a sensor, feature or offering--that needs to be identifiable in SWE service models gets a URI. Going a step further, it is recommended to use Unified Resource Locators (URLs), a subtype of URI, to identify such resources. Following this approach, resource identifiers can be easily de-referenced into the actual resource representation. This facilitates the integration of SWE 2.0 concepts into the Linked Open Data Cloud [68] and allows the definition of REST APIs to sensors, features _etc._, which realizes the idea of a Web of Things (Section 2.4) in a standardized way.
# 2-D Cross-Plot Model Analysis Using Integrated Geophysical Methods for Landslides Assessment
## 2 Methods
### Volumetric Calculation of Data  
The volume of mass movement was estimated based on the integration of 2-D resistivity and seismic refraction methods. In this part, the location of the potential slip surface identified in 2-D cross-plot models was digitized for further analysis of volumetric calculation. The mass movement after the landslide was calculated based on three methods; Trapezoidal Rule, Simpson's Rule, and Simpson's 3/8 Rule, in determining the volume in Surfer8 as follows in Equation (1). Mathematically, the volume under the function f (x,y) is defined as;  
\[V=\int\limits_{Xmin}^{Xmax}\int\limits_{Ymin}^{Ymax}f(x,y)dxdy \tag{1}\]  
In Surfer, the software computed the values by first integrating the X (column) to produce an area under the individual rows with following the integrating of Y (rows) for the final volume. The software uses three classical numerical integration algorithms, which is Trapezoidal Rule, Simpson's Rule, and Simpson's 3/8 Rule [72]. The estimated volume of the mass movement was calculated with Equation (2) based on the calculation of the volume of total area subtracted with the volume under the failure plane. Figure 6 shows the graphical illustration of volumetric mass movement calculations.  
\[\mathrm{V_{T}-V_{S}=V_{MM}} \tag{2}\]  
\(\mathrm{V_{T}=volume\ of\ total\ area}\)  
\(\mathrm{V_{S}=volume\ below\ the\ failure\ plane/sliding\ plane}\)  
\(\mathrm{V_{MM}=volume\ of\ mass\ movement}\)
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## 2 Theory and Method
### Network Architecture  
#### 2.2.1 Encoder  
The full waveform \(\mathbf{u}\in\mathbf{R}^{1\times T}\) of the encoder can be divided into overlapping segments of length L, represented by \(\mathbf{s}_{k}\in\mathbf{R}^{1\times L}\), where \(k=1,\dots,\hat{T}\) represents the segment index, \(\hat{T}\) represents the total number of segments to be split and the overlap rate between fragments is 50% (see Figure 3a). The encoder converts \(\mathbf{s}_{k}\) to a one-dimensional vector of length N, \(\mathbf{w}\in\mathbf{R}^{1\times N}\). This process can be represented by matrix multiplication (see Figure 3b), where \(\mathbf{x}\) stands for \(\mathbf{s}_{k}\),  
\[\mathbf{w}=\mathbf{x}\mathbf{U}^{\top}, \tag{2}\]  
where \(\mathbf{U}\in\mathbf{R}^{N\times L}\) is a matrix containing N vectors (encoder basis functions), with each vector having a length of L. In MC-ConvTasNet, the encoder is implemented by a 1-D convolution layer. One-dimensional convolution is different from convolution in mathematics. According to the convention of deep neural networks, performing segmentation and mapping on 1-D input data (Figure 3) is called 1-D convolution. The convolution kernel of 1-D convolution is learned during the training process. The convolution kernel size is 16, the step size is 8 and the number of convolution kernels is 512.  
Figure 3: The schematic diagram for segmentation and mapping of the encoder and the schematic diagram for mapping and overlapping addition of the decoder. (**a**) shows that the full wave is divided into overlapping segments. (**b**) shows that the encoder converts a full wave segment \(\mathbf{x}\) to an encoded representation \(\mathbf{w}\). (**c**) shows that the decoder converts an encoded feature representation of a direct wave or a reflected wave \(\mathbf{d}_{i}\) to a time-domain segment \(\hat{\mathbf{x}}_{i}\). (**d**) shows that, by performing overlapping addition on segments in a block, the output of the decoder is converted back to a sequential output.  
Figure 2: MC-ConvTasNet model flow chart. The process of the MC-ConvTasNet model is introduced using channel 1 as the currently processed channel. \(\oplus\) represents concatenation of the output features of the encoder and Conv2d. \(\odot\) represents calculation of the Hadamard product of the outputs of the encoder and separator. \(\mathbf{w}\) represents an encoded representation of a full wave. \(\mathbf{m}_{i},i=1,2\) represents a mask of a direct wave or a reflected wave. \(\mathbf{d}_{i},i=1,2\) represents an encoded feature representation of a direct wave or a reflected wave.
# Prospects for the Development of High Energy Density Dielectric Capacitors
## 2 A Review of the Literature on Energy Storage Using Dielectric Materials
### Nanocomposites Using Carbon and Graphene Particles and Nano-Sheets  
There have been many studies [37; 38; 39; 40; 41] of the application of nano-carbon and graphene in the study of polymer nanocomposites. The carbons in different forms act as conducting elements embedded in the host polymer and thus can act as charge collectors in the micro-capacitors formed throughout the host polymer. The size (\(\mu\) or nm) of the carbon elements is critical in determining the dielectric constant resulting from their use. Unfortunately, the dimensions of the carbon elements and films used to test the composites are often not given in papers.  
The use of graphene nano-sheets to increase the dielectric constant in composite polymers is of particular interest. The research seems to have been successful in significantly increasing the dielectric constants of composite polymers. It does not seem difficult to produce composites with graphene nano-sheets of a few nm in thickness oriented parallel to a surface. The research in [39; 41] is directly related to the approaches analyzed in Section 3 of this report. Ref. [37] dealt with graphene nano-sheets in PVDF. Figure 3 shows 2-6 nm nano-sheets in PVDF as the host polymer at a volume fraction of 0.0177. This volume fraction is low, but the film prepared is thick. From Figure 3, the spacing of graphene sheets is relatively uniform. The polymer composite was hot pressed into disks 9 mm in diameter and 5 mm in thickness. This is a very thick film and is clearly not suitable for a film capacitor. The Excel spreadsheet model in Section 3 predicted a very high effective dielectric constant (over \(10^{5}\)) for a 5 mm thickness and 0.018 loading. The measured characteristics of the 5 mm thick disk are shown in Figure 4 taken from [39]. The maximum dielectric constant measured was about \(10^{4}\). The dielectric constant of the host polymer is about 8. Even at a volume fraction loading of 0.0177, both the conductivity and dielectric loss of the film were high. This research [39] was not done to meet energy storage objectives, but it does seem to indicate that the proposed approach of embedding nm carbon elements in a host polymer can make sense as a way to develop high energy density, high voltage film capacitors.  
Figure 3: SEM micrographs of the graphene/PVDF composite containing graphene volume fractions of 0.0177 (**a,b**) [38].
## 3 Results and Discussions
### Adsorption Isotherm  
The distribution of the molecule at the equilibrium time between liquid and solid phases is indicated by the adsorption isotherms. The affinities of adsorbent material and insights about surface properties and the understanding of adsorption mechanisms were achieved using both adsorption isotherms. The relation between the adsorption ability of the adsorbent and adsorbate concentration can be completely investigated through adsorption isotherms. The first type of adsorption isotherm is Freundlich isotherm [53] that supposes heterogeneous adsorption and the second is Langmuir isotherm [54] supposes monolayer adsorption with energetically similar active sites. Freundlich isotherm introduces the opinion of heterogeneous surface and multilayer adsorption depending on the fact that non-ideal sorption can be modeled using an exponentially decaying adsorption site energy distribution. The linearized form of the Freundlich model can be written according to the following equation:  
\[\ln q_{e}=\ln K_{F}+(1/n\ln C_{e}), \tag{3}\]  
where \(n\) and \(K_{F}\) denotes Freundlich constants. \(C_{e}\) and \(q_{e}\) denote equilibrium concentration and adsorption capacity, respectively. The linear fit of \(\ln C_{e}\) against \(\ln q_{e}\) used for these parameters\({}^{\prime}\) calculation. More system heterogeneity is indicated by a higher \(n\) value. Monolayer chemisorption is represented by the Langmuir isotherm that can be applied according to linearized form according to the following equation:  
\[C_{e}/q_{e}=(1/\ K_{L}\ q_{m})+C_{e}/q_{m}, \tag{4}\]  
where \(K_{L}\), \(C_{e}\), \(q_{m}\), and \(q_{e}\) denote Langmuir constant, equilibrium concentration, maximum adsorption capability, and equilibrium adsorption capacity, respectively. The linear fit between \(C_{e}\) and \(C_{e}/q_{e}\) used for these parameters' calculations. Freundlich and Langmuir adsorption isotherm data are shown in Figure 8 and different parameters were tabulated in Table 1.  
Figure 7: The effect of contact time on Pb\({}^{2+}\) ions (**a**) and NR dye (**b**) adsorption over MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite.  
According to the results in Table 1, Langmuir isotherm is better fitted to the experimental results for Pb\({}^{2+}\) ions and NR dye adsorption than Freundlich isotherm. This is indicated from the R\({}^{2}\) value equal to 0.998 and 0.996 (Langmuir fit), 0.986, and 0.859 (Freundlich fit) for Pb\({}^{2+}\) and NR dye adsorption, respectively.  
These results indicate the homogenous energy distribution for active adsorption sites with a monolayer the adsorption process over the adsorbent surface. Langmuir isotherm indicated that Pb\({}^{2+}\) and NR dye's maximum adsorption capacity are 636.94 and 46.08 mg/g, respectively. Future studies must be including the thermodynamic parameters [55; 56; 57] of the adsorption process to get a full image of the removal process.
## 3 Data Sets and Methods
### GPS Data Set and Processing  
In this study, the datasets from two GPS continuous observation stations (Figure 1a) were employed for the verification of InSAR displacements. The WOLS station, operated by the National  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mean Height** & **Standard Deviation** & **Root Mean Square** & **Root Mean Square** \\  & **Difference (m)** & **of Height** & **of Height** & **Deviation of Height** \\  & **Difference (m)** & **Difference (m)** & **Difference (m)** \\ \hline Survey & & & & \\ height-ALOS & \(-\)1.302 & 1.610 & 1.771 & 0.962 \\ PRISM DEM & & & & \\ \hline Survey & & & & \\ height-SRTM DEM & \(-\)1.803 & 3.651 & 3.029 & 2.555 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Intercomparison between survey control points and Shuttle Radar Topography Mission (SRTM) and Advanced Land Observing Satellite (ALOS) Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) DEM. Eleven survey control points were employed for the assessment.  
Geographic Information Institute (NGII), is located on stable ground, whereas the ULSN station is located on the roof of a building, which makes data less reliable for validation of deformation data owing to the thermal expansion of the building roof. The GPS processing period covered 2.5 years, from 1 January 2016 to 30 June 2018, and was conducted using relative positioning (RP) employing Bernese 5.2 S/W [49] for the extraction of locations and time series analysis of the station referencing. For precise position estimation, the International Terrestrial Reference Frame (ITRF) 2014 was applied and the GPS satellite trajectory was then postprocessed using ephemeris provided by the Center for Orbit Determination in Europe (CODE). It should be noted that the ITRF 2014 coordinates of stations were extracted from nine International GNSS service (IGS) Stations in China, Japan, Russia, and Mongolia. Then, the coordinates were again adjusted using the minimum constraint method, considering Helmert conversion elements and the stability of reference stations by the RP module of Bernese S/W. The parameters and models used to remove various GPS errors are given in Table 3. To segregate the background and seasonal migration of GPS observations, we conducted additional operations on GPS migrations. Find Outliers and Discontinuities In Time Series (FODITS) module [50] in the Bernese GPS Software was employed to eradicate outliers, discontinuities, and one or more linear velocities from GPS observations. For comparison, the seasonal components were interpolated using sinusoidal approximation [51], which can be expressed as follows:  
\[\mathrm{Y}=A\cos(\omega t+\phi) \tag{3}\]  
where Y is coordinate displacement, \(A\) is amplitude, \(\omega\) is frequency, \(t\) is observation time, and \(\phi\) is phase. As the outcomes from both methods fitted each other well, the seasonal components in the GPS signal were considered to be effectively removed.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 4 Experimental Example
### Comparison of the Experimental Results Between the Proposed Method and the Traditional Method
#### 4.3.2 Results with Different Loading Conditions Before and After Damage  
The data obtained in Part I, Part IV and Part V are applied here to evaluate the performance of the proposed method and the traditional approach in consideration of the differences in the loading conditions before and after damage (all displacement ILs are shown in Figure 15a). With the traditional method, as shown in Figure 15b, there is an enormous difference in the displacement ILs between Part I and Part IV (sensor #10), that is, the reference and damage states of the bridge; however, it is still necessary to obtain an obvious difference in the displacement ILs between Part I and Part V and therefore, the incorrect determination of damage is drawn. Consequently, the traditional method is invalid for situations involving different loading conditions before and after damage. Using the proposed method, the results drawn in Figure 15c show that damage can be detected even though the loading conditions of the reference state (Part I) and the damaged state (Part IV) are different; moreover, an erroneous determination of damage is avoided for both the reference state (Part I) and the healthy state (Part V). The above discussion demonstrates that the proposed method is more effective and robust than the traditional approach at detecting damage using quasi-static displacement ILs.  
Figure 14: Comparison of the damage detection performance between the traditional method and the proposed method under the same loading conditions before and after damage: (**a**) measured displacement ILs; (**b**) difference between the measured displacement ILs by the traditional method; (**c**) metric of the damage feature by the proposed method.
## 2 Road Traffic Situation Assessment Model
### Urban Road Monitoring Network
#### 2.1.1 Construction of the Urban Road Monitoring Network  
The urban road network maps the relationship between roads, and the monitoring data of road sensors in the network can be used to objectively analyze road traffic. Therefore, building a reasonable network is a sufficient condition for road traffic situation assessment. With the server as the central node and the urban road sensor as the child node, the urban road monitoring network is constructed through the geographical location relationship between roads.  
Since the main object of the urban road monitoring network diagram constructed includes the server of the application layer and the equipment sensor of the physical layer, and the intersections in the urban road traffic network are the most common intersections, this paper determines the traffic links and communication links in the network according to the road geographical location factors. It constructs the urban road monitoring network with the intersections as the main object.  
The network node reflects the crossing of each urban road, and the node connectivity reflects the linkage between the roads; thus, the geographic location information of urban roads is actually indicated by the node connectivity. If there is connectivity between road \(i\) and road \(j\), a wire connection will be made between node \(i\) and node \(j\) in the network. Similarly, it can be seen that there would not be a wire connection between node \(i\) and node \(j\) if road \(i\) and road \(j\) are not directly connected. Since our research focuses on the impact of emergencies on the roads in urban road traffic, and the possibility of multiple roads' linkage for emergency monitoring should be considered, we choose the undirected graph method in graph theory to build the monitoring network. It can remove the information that is irrelevant to this research and show the relationship between urban roads more clearly. When one of the road's monitors an emergency event, the geographic information of this road is used to identify the other roads that can engage in cluster monitoring of this emergency event and to form a collaborative monitoring area in the network for tracking the subsequent development of the emergency event on urban road traffic.
## 2 Overview of Modern Technologies and Frameworks in Smart Healthcare Systems
### Wearable Sensing Technology for Healthcare  
Wearable sensing technologies have a huge impact on people quality of life, with the advancement of innovative technologies such as Artificial Intelligence (AI), big data analytics (BDA), and blockchain. Wearable sensing technologies such as smart watch, smart shoes, tracking sensors, smart eye-wear, help to track and manage the patient's health data remotely by utilizing either the wireless sensor network or the conventional network [51]. These wearable sensing technologies have proves to be beneficial for the treatment of patients suffering from life-threatening diseases. Basically, these sensing devices can be attached to the patient's body or their clothing to gather and process data that can indicate the patient's health status. Moreover, these wearable technologies are made to be versatile and comfortable to wear, especially for elderly people. Thus, wearable sensing devices have completely transformed the working environment of hospitals because some patients can now be treated remotely. Moreover, the remote monitoring of patient's health overcomes the high cost and efficient treatment challenges associated with traditional patient care. This has motivated people to opt for wearable sensing technology, accounting for the increasing use of these technologies over the years. Considering the outlook and usage of wearable sensor technologies for healthcare, many researchers have considered the advantages of these wearable technologies for benefit of the healthcare system. For instance, [PERSON] et al. [29] developed a vision-based hand gesture recognition system for controlling the robotic hand. They have improved the performance of gesture recognition system to implement it in real-time scenarios. Further, by means of an implantable fluorescence image sensor the authors of [52] performed the real-time monitoring of immune response in cancer therapy. They have designed a prototype to improve the response time for 50% of cancer patients who were undertaking immunotherapy. However, the aforementioned authors did not consider the scenario where elderly people are being treated with the help of wearable sensor technologies. Thus, [PERSON] et al. [53] applied an AI and Internet of Things (IoT)-based disease diagnosis model for smart healthcare systems. They have considered the Cascaded Long Short Term Memory model for an efficient heart and diabetes diagnosis, achieving the improved accuracy.
# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 4 Analysis and Results
### Analysis 2: Code versus Carrier Phase Positioning Performance
This again indicates that the precision of carrier phase-based positioning will primarily degrade when there are not enough satellites available to determine a position. The performance of code positioning, on the other hand, will be noticeably degraded during jamming incidents._