esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite Element Analysis of Geogrid-Incorporated Flexible Pavement with Soft Subgrade
## 3 Results
### Vertical Stress and Displacement at the Top of the Subgrade  
Table 2 presents the vertical stress at the top portion of the subgrade in both unreinforced and geogrid-reinforced pavements across various base thicknesses. Notably, the stresses in reinforced sections are significantly lower compared to unreinforced pavements. However, the reduction in stresses varies, being more pronounced with lower base thicknesses. As the base thickness increases, the disparity in stress between unreinforced and reinforced layers diminishes. With higher base thicknesses, a larger portion of the stresses induced by traffic loads is distributed, resulting in the geogrid bearing a smaller proportion of stress compared to lower base thicknesses. For instance, at a base thickness of 150 mm, the geogrid-reinforced model exhibits approximately 12-13% lower stress than the unreinforced model, whereas this reduction decreases to only about 6.5-7% at a base thickness of 250 mm. The vertical stresses observed from FE images for both reinforced and unreinforced subgrade sections are also mentioned in Figure 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Vertical Stress (kPa) at Subgrade’s Top**} \\ \hline
**Base Course (BC) Thickness** & **Unreinforced** & **Reinforced** \\ \hline
150 & 267.5 & 233.7 \\
180 & 229.3 & 204.4 \\
220 & 197 & 181.9 \\
250 & 175.4 & 163.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vertical stress at the top portion of the subgrade.  
Figure 12: Shear strain developed in the geogrid-reinforced subgrade.  
Figure 13: Vertical stresses at the subgrade’s top.  
Figure 14 displays profiles of vertical displacement along the normalized path of the pavement model for both unreinforced and reinforced sections, considering a 1% CBR value and a base thickness of 150 mm. Vertical displacement predominantly occurs beneath the tire imprint, diminishing to nearly zero at the pavement model's edge. The material beneath the tire imprint experiences higher stress and greater deflection than other parts of the model. Nevertheless, geogrid reinforcement effectively mitigates vertical displacement of the subgrade soil, particularly beneath the wheels. This is primarily due to the geogrid layer sustaining the transferred load from the base/sub-base layer, thus preventing excessive deformation of the subgrade. Overall, this study reveals that reinforced pavements exhibit approximately 31-39% less vertical displacement compared to unreinforced pavement models. The displacement of the full pavement model for reinforced and unreinforced layers is also shown in Figure 15, where it is clearly visible that the reinforced section offers lesser deformation compared to the normal pavement.
## 1 Introduction
Their investigation explored physicochemical properties of selected dyes based on intended geometries, resulting in favorable electrical and light absorption characteristics. The study achieved stabilization of the lowest unoccupied molecular orbital (LUMO) energy level and a lower energy gap through experiments that introduced electron-withdrawing groups to the bridge. This induced a bathochromic effect, shifting the maximum absorption bands to a lower energy, enabling the modulation of dye absorption properties to convert UV-Vis and near-infrared light into current [12]. The HOMO-LUMO energy gap plays a pivotal role, providing valuable insights into the dyes' ability to facilitate charge transfer, a fundamental process in the functionality of DSSCs. Consequently, the positioning of the LUMO energy level emerges as a crucial parameter, ensuring that electron transfer is both kinetically fast and thermodynamically feasible. This efficient electron injection from the dye molecules into the semiconductor's conduction band serves as a strategic approach for achieving high-performance DSSCs. In addition to energy levels, the absorption properties of these dyes are analyzed, with a specific focus on oscillator strengths. These strengths serve as indicators of a dye's effectiveness in absorbing light [13]. Strong oscillator strengths suggest the capability of these dyes to absorb light efficiently, leading to the generation of excitons, essential intermediates in the charge separation process within DSSCs. The comprehensive assessment of the photovoltaic properties of natural dyes is of significant importance, and parameters such as light-harvesting efficiency (LHE), electron injection efficiency (\(\Delta G^{inject}\)), electron regeneration energy (\(\Delta G^{region}\)), open-circuit voltage (\(V_{OC}\)), excited-state lifetime (\(\tau\)), and the electronic coupling constant ( \(\mid V_{RP}\mid\)) can be computed utilizing data obtained from DFT analysis [14]. These parameters collectively determine the overall performance and efficiency of DSSCs, rendering them indispensable in the selection of suitable sensitizers. The inspiration for this study stems from the motivation to discern the capability of natural dyes in augmenting the efficiency of DSSCs. By exploring the optoelectronic and photovoltaic properties of cyanidin, delphinidin, pelargonidin, penoidin, and petunidin in both gas (vacuum) and ethanol phases, the aim is to contribute to the broader understanding of how these dyes can be applied in DSSCs [15]. The DFT study for the selected dyes in gas (vacuum) and ethanol phases allowed the calculation of parameters such as highest occupied molecular orbital (HOMO), LUMO, LHE, \(\Delta G^{inject}\), \(\Delta G^{region}\), \(V_{OC}\), \(\tau\), and \(\mid V_{RP}\mid\). The results have opened new avenues for improving optoelectronic and photovoltaic properties, ensuring the creation of efficient and adaptable DSSCs. These advancements hold practical applications in the realm of renewable energy, aligning with the global commitment to harness solar power for a sustainable and environmentally responsible future.
## 3 Control Algorithm Description
### Linear-Quadratic Regulator  
In this work, the linear-quadratic regulator (LQR) is used for reference trajectory tracking problems. LQR is a well-known control algorithm, which can applied to the standard linear-quadratic problem in the case of two satellites' relative motion control. However, in the case of multiple satellites in formation, several different control vectors provided by LQR cannot be implemented simultaneously. Thus, the control implementation strategy to deal with this problem is developed in this paper.  
Figure 3: Relative trajectories of the satellites in a cross of two lines during the motion along circular trajectories in projection onto the \(Oxy\) plane.  
Consider the standard formulation of LQR. The equations of motion (4) can be represented in the following linear form:  
\[\dot{\mathbf{x}}_{ij}=\mathbf{A}\mathbf{x}_{ij}+\mathbf{B}\mathbf{w}^{ij}\]  
where \(\mathbf{x}_{ij}\) is the relative state vector consisting of curvilinear coordinates and their derivatives:  
\[\mathbf{x}_{ij}=\begin{bmatrix}\varphi_{ij}&\theta_{ij}&\rho_{ij}&\dot{\varphi }_{ij}&\dot{\theta}_{ij}&\dot{\rho}_{ij}\end{bmatrix}^{\mathrm{T}}, \tag{15}\]  
\(\mathbf{A}\) is a system matrix, and \(\mathbf{B}\) is the control matrix of the following form:  
\[\mathbf{A}=\begin{pmatrix}\mathbf{O}_{3\times 3}&\mathbf{E}_{3\times 3}\\ \mathbf{C}&\mathbf{D}\end{pmatrix},\quad\mathbf{B}=\begin{pmatrix}\mathbf{O}_ {3\times 3}\\ \mathbf{E}_{3\times 3}\end{pmatrix},\]  
where  
\[\mathbf{C}=\begin{pmatrix}0&0&0\\ 0&-\omega^{2}&0\\ 0&0&3\omega^{2}\end{pmatrix},\ \mathbf{D}=\begin{pmatrix}0&0&-2\omega/a_{0}\\ 0&0&0\\ 2\omega a_{0}&0&0\end{pmatrix},\]  
\(\mathbf{O}_{3\times 3}\) is the zero matrix, and \(\mathbf{E}_{3\times 3}\) is the identity matrix.  
Let us designate the trajectory deviation from the reference trajectory \(\mathbf{x}_{ij}^{\mathrm{des}}\) by \(\mathbf{e}_{ij}=\mathbf{x}_{ij}-\mathbf{x}_{ij}^{\mathrm{des}}\). The linear-quadratic regulator is the feedback control [29], minimizing the following function:  
\[J=\int\limits_{0}^{+\infty}(\mathbf{e}_{ij}^{\mathrm{T}}\mathbf{Q}\mathbf{e}_ {ij}+\mathbf{w}_{ij}^{\mathrm{T}}\mathbf{R}\mathbf{w}_{ij})dt,\]  
where \(\mathbf{Q}\) and \(\mathbf{R}\) are positive-definite matrices which are assumed to be diagonal in this paper. The control vector is calculated as follows:  
\[\mathbf{w}_{ij}=-\mathbf{R}^{-1}\mathbf{B}^{\mathrm{T}}\mathbf{P}\mathbf{e}_ {ij}, \tag{16}\]  
where matrix \(\mathbf{P}\) is the solution to the algebraic Riccati equation:  
\[\mathbf{A}^{\mathrm{T}}\mathbf{P}+\mathbf{PA}-\mathbf{P}\mathbf{B}\mathbf{R}^ {-1}\mathbf{B}^{\mathrm{T}}\mathbf{P}+\mathbf{Q}=0\]
## 2 Literature Review
### Market Efficiency  
To familiarize ourselves with relevant research, we examine the relevant literature related to market efficiency and inefficiency, herding behaviors and momentum strategies, price overreaction, and contrarian strategies, as well as technical analysis and technical trading rules. Similarly, [PERSON] [50]) contended that anomalies appear inconsistent with the efficient market hypothesis (EMH), implying that market inefficiency persists in the underlying asset-pricing model [51]. However, in terms of market efficiency, share prices appear difficult to forecast because all available information, based on the efficient market hypothesis (EMH), is fully reflected [1; 2]. As such, based on EMH, we may infer whether investors buy financial instruments as buy signals are emitted and then sell financial instruments as sell signals are emitted following BBTS strategies that might not generate profit by proposing H1.  
**H1:**_According to the market efficiency hypothesis, investors who utilize BBTS would be unable to achieve advantageous outcomes._
# MedChain: Efficient Healthcare Data Sharing via Blockchain
## 3 MedChain Model
### Directory Service
For directory lookup, MedChain employs Chord [40] to map a directory ID (i.e., _InvID_ or _SID_) to a server ID, which can also be replaced with any other P2P routing protocol implementing the distributed hash table (DHT) function [41]. Search over Blockchain  
In MedChain, a healthcare record or data stream is identified by its unique identity (_DID_). On the blockchain, however, _DID_ is encrypted together with patient identity (i.e., \(PK_{pat}\)) in the _Content_ section for a privacy purpose (see Def. (1)). Given a _DID_, the entire blockchain has to be downloaded, traversed, and decrypted to find the record as in some existing works. The directory service can be treated as the index of the events on the blockchain, providing an efficient way for search over blockchain. Like the breadcrumbs mechanism in References [33; 34], each patient inventory contains a reference (either _Ref_ or _Ref_\({}_{L}\)) to the corresponding record on the blockchain (see Def. (3)). A reference consists of a block hash (_BH_) and an event hash (_EH_). On a blockchain server, the events are stored on a database indexed first by block hash and then by event hash. Thus, an efficient search algorithm (e.g., binary search) can be applied for event lookup. If the binary search is applied, the search complexity is O(\(log(p)\cdot log(q)\)) where \(p\) is the number of blocks and \(q\) is the number of events in a block, which is much lower than (\(p\cdot q\)) if the entire blockchain is traversed (as in References [31; 32]). The same search process can be applied to event lookup for a session, since inventory and session share the same data structure in healthcare data description (Def. (3) and Def. (4)). The directory service maps a data stream to the last chunk generation event on the blockchain with the last chunk block hash (\(BH_{L}\)) and event hash (\(EH_{L}\)) (see Def. (3)). On the blockchain, each chunk, except the first one, links to the previous chunk of the same stream by referring to its block hash and event hash (see Def. (1)). Thus, to download all the chunks of a data stream on the blockchain, a client first locates the position of the last chunk (by referring to the \(Ref_{L}\) in Def. (3)) and then recursively search the previous chunk (by referring to the \(Ref_{L}\) in Def. (1)) until the first one. A more fine-grained and user-friendly chunk search approach can be designed by adding the chunk start time and end time into each chunk generation events on the blockchain to support chunk-level data search, which will be implemented in our future work.
# Advanced Noncoherent Detection in Massive MIMO Systems via Digital Beamspace Preprocessing
## 2 System Model
### Noncoherent Detection
#### 2.4.2 Sub-Array  
All above mentioned beamforming schemes for a full-array architecture can be applied analogously to the sub-array architecture. Instead of \(N_{\text{rx}}\) beams according to the number of receive antennas/branches, the number of beams is limited to the per-sub-array elements \(N_{\text{rc,sub}}=N_{\text{rc}}/N_{\text{sub}}\) and, when applied, the eigenspace-based post-processing is performed separately at each sub-array. The beam directions can be chosen from a different set and/or angular sector since each sub-array can process the incoming signals individually. However, the condition in (16) might be not fulfilled in this case. Therefore, the same set of beams is used per sub-array selected from a predefined FoV \(\Phi\). The corresponding weight matrix of the \(l\)-th sub-array is  
\[\mathbf{W}_{\text{BFsub},l}=[\mathbf{w}_{1},\mathbf{w}_{2},\dots,\mathbf{w}_{N_{\text{rx,sub} }}] \tag{28}\]and the total beamforming matrix is constructed via  
\[\mathbf{W}_{\text{BF}}=\text{\bf diag}\left(\mathbf{W}_{\text{BF}_{\text{sub}},1},\ldots, \mathbf{W}_{\text{BF}_{\text{sub}},N_{\text{sub}}}\right)\,. \tag{29}\]
## 3 Results and Discussion
### Effects of Initial Moisture Content  
To investigate the coupled effects of soil initial moisture content and the temperature gradient (see Table 2), the initial VWC was set within a range extending from field capacity (the VWC corresponding to a matric suction of 33 kPa) to the wilting point (the VWC associated with a matric suction of 1500 kPa). As depicted in Figure 4a, the evaporation rate at the surface of the cover demonstrates significant fluctuations in response to changes in the initial soil moisture content. As the initial moisture content decreases from the field capacity to the wilting point, the evaporation rate on the 30 th day declines from 1.8 mm/d to 0.4 mm/d. Overall, the evaporation rate of the cover shows a positive correlation with the initial water content. Specifically, lower soil moisture content corresponds to reduced vapor pressure and, therefore, decreased evaporation intensity [35]. After 30 days of evaporation, the water storage in Cases MS33, MS200, and MS500 decreases by 26.7 mm, 17.0 mm, and 5.3 mm, respectively, while Case MS1500 shows an increase of 1.4 mm (Figure 4b). The results demonstrate that under wet conditions, the evaporation intensity at the surface is relatively high, causing a continuous decline in water storage. Conversely, under dry conditions, the inflow of water vapor at the base of the cover is relatively high, leading to an increase in water storage [48].  
Figure 5a shows the distribution of O2 concentration on the 30 th day. The results demonstrate a negative correlation between O2 concentration and the initial moisture content. This may be attributed to the decrease in moisture content, which enlarges the gas migration pores within the soil [49]. Consequently, the increased pore size enhances the flux of air diffusion into the cover. The initial moisture content of the soil significantly influences the CH4 oxidation rate of the overburden (Figure 5b). When soil moisture is adequately sufficient, an increase in O2 concentration enhances the CH4 oxidation rate of the cover [10]. As a result, the CH4 oxidation rate in Case MS200 is significantly higher than in Case MS33 during the early stages. However, as continuous water evaporation renders soil moisture a limiting factor for MAMO [14], the CH4 oxidation rate in Case MS200 becomes lower than that of Case MS33 after day 28. Due to the lower initial moisture content, the CH4 oxidation rates in Cases MS500 and MS1500 are relatively low. Especially in the case of MS1500, the excessively low initial moisture content results in a CH4 oxidation rate of zero. As water vapor gradually flows in and supplements some of the moisture (see Figure 4b), it begins to exhibit CH4 oxidation performance, albeit at a low level.  
Figure 4: Effects of initial moisture content on (**a**) evaporation at surface and (**b**) water storage of the cover.
# Burnt-Area Quick Mapping Method with Synthetic Aperture Radar Data
## 2 Materials and Methods  
### Location and Data  
The location of this research is Kalimantan island, with the focused area of interest (AoI) marked with the red square on the regencies of Kapuas, Pulang Pisau, East Barito, South Barito, Barito Kuala, Hulu Sungai Utara, Hulu Sungai Tengah, Hulu Sungai Selatan, and Tapin, as shown in Figure 1.  
The radar image is obtained from reflected microwave emissions originating from the radar platform. As the radar sensor moves along a trajectory, the area highlighted by the radar also moves along with the platform, known as the footprint, to form an image. The digital radar image consists of pixel dots representing the backscatter of a surface. The radar system generally uses wavelengths that are undisturbed by the interference from particles and vapor in the air (i.e., clouds and rain), and since the system is independent of the illumination (irradiation) from the sun or other sources, it can function in all weather conditions. The synthetic aperture radar (SAR) works by detecting the phase-change of the reflected signals caused by the movement of the platform, to obtain the surface image with a good resolution (i.e., visually discernible) [22,32]. Example of the difference between optical and SAR sensor is shown in Figure 2.  
The data used in this study is Sentinel-1a (SAR) data, which has the advantage of not depending on weather conditions. This data processing uses cloud computing data processing, making it possible to process large amounts of data. The processing process, which consists of importing data, and preprocessing to classification, is executed in cloud computing, simultaneously, with commands using the JavaScript programming language and Python. The advantage of using this cloud computing method, compared to processing using a desktop, besides being faster in processing, the processor does not need high software specifications. When using desktop applications, raw data and output files from the processing results often fill the hard disk capacity, while using cloud computing, all files are stored in the cloud and can be downloaded, if needed. The cloud computing application that will be used in this research is the Google Earth Engine [20]. The data used in this research are the Sentinel 2A radar and Hotspot data, which comes from the SIPONGI system, used by the Ministry of Environment and Forestry [33]. The Sentinel data is used to detect the area affected, while the hotspot data is used to verify the burnt land. The data processing in this research uses cloud computing technology, by utilizing the Earth Engine. The processing is carried out by inputting a script in JavaScript language, to give commands to the server. The initial stage of the process is to form a composite image, as the basis for the classification process.  
Figure 1: Areas of interest/sampling areas on Kalimantan island.
## 4 Results and Discussion
### Case Study for DMSA in Dynamic Scheduling
#### 4.2.2 Algorithm Parameters Initializing  
For the initialization of ATSA, the length of the tabu list is dynamically determined by the maximum number of unscheduled tasks in the current solution (larger number leads to longer length), and the neighborhood scale is set to 5. The TS global maximum iteration number is set to 150, and the local maximum iteration number is set adaptively according to times of calling different TS (later iteration leads to smaller local maximum iteration number). The global early termination indication is that the task completion rate (the proportion of scheduled task in all tasks that can be observed) reaches at least 98%.
# Smart IoMT Framework for Supporting UAV Systems with AI
## 5 Discussion  
### Machine Learning Experimentation and Result Analysis  
The system generates the reports of the patients in real time through live streaming of the data. The data are captured in the UAV and, once it returns to the base station, it is stored in a database which is developed by the authors through the web service that is also implemented originally. The system is trained with 452 instances of the live stream data from the designed web page's server. These instances train the system with Logistic Regression, Decision Tree, Random Tree, and Random Forest algorithms. The test cases are run with a 66-34% split up of the training and the test data. The system takes 154 instances to test the performance of the system with various algorithms, and results are tabulated in Table 2. The decision tree provides an accuracy of 96.7532%, the Logistic Regression provides an accuracy of only 87.6623%, Random Tree provides an accuracy of 98.7013%, and the Random Forest algorithm provides an accuracy of 98.7013% as shown in Table 3:  
\[W(t)=\begin{bmatrix}1&if\ 50<w(t)<120\\ 0&else\ if\ w(t)>120\ |\ w(t)<50\end{bmatrix} \tag{1}\]  
\[X(t)=\begin{bmatrix}1&if\ 95<x(t)\\ 0&else\ if\ x(t)<95\end{bmatrix} \tag{2}\]  
\[Y(t)=\begin{bmatrix}1&if\ 36.00<y(t)<37.30\\ 0&else\ if\ y(t)>37.30\ |\ y(t)<36.00\end{bmatrix} \tag{3}\]  
\[Z(t)=\begin{bmatrix}1&if\ z(t)=1\\ 0&else\ if\ z(t)=0\end{bmatrix} \tag{4}\]  
\[K(t)=W(t).X(t).Y(t).Z(t) \tag{5}\]  
where \(w(t),x(t),y(t),andz(t)\) are the values of the heart rate, SpO\({}_{2}\), temperature, and myoware signals, respectively, and \(W(t),X(t),Y(t),\) and \(Z(t)\) are the detection functions, respectively. \(K(t)\) is an AND gate function that obtains inputs from different parameters in parallel. Whenever any parameter drops within these set limits, a trigger is set to the website, and the onboard buzzer as the following low-trigger function  
\[T(t)=\begin{bmatrix}1\ (no\ trigger)&if\ K(t)=1\\ 0\ (trigger)&else\ if\ K(t)=0\end{bmatrix} \tag{6}\]  
The performance of these systems is compared with the various machine learning algorithms and tabulated in Table 3 The random forest algorithm predicts with 98.7013% accuracy on the test data evaluation, which is on par with the random tree results. Thus, this system provides the best accuracy even in the situation of the real-time data analysis of the live streamed data on the web service. Thus, the proposed system provides adequate performance with the desired accuracy of producing the results of the vitals. The results are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Abbreviations** \\ \hline Correctly Classified Instances & P1 \\ Incorrectly Classified Instances & P2 \\ Kappa statistic & P3 \\ Mean absolute error & P4 \\ Root mean squared error & P5 \\ Relative absolute error & P6 \\ Root relative squared error & P7 \\ Total Number of Instances & P8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different types of the parameters used.