esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Simulation Experiment
#### 2.2.1 Footprint Calculation with LPDM  
We used the Lagrangian particle dispersion models (LPDM) FLEXPART [44, 45] to estimate the footprint of Fluxnet stations. In Lagrangian models, individual particle trajectories (representing air parcel transport) are calculated following a predetermined atmospheric wind field and other meteorological variables. LPDMs can run forward (source to a receptor) or backward (receptor to source) modes.  
We used the FLEXPART version 8.2, which utilizes National Center for Environmental Prediction Final (NCEP FNL) operational global meteorological data on a regular latitude-longitude horizontal grid with 0.25-1.0 degree spatial resolution on hybrid sigma-pressure vertical levels and temporal resolution of 3-6 h. The model output grid has a spatial resolution on a latitude-longitude grid with 0.002 degrees (about 150-200 m), and the smoother result, the model synchronization time step, was 15 s.  
We ran the model in a backward mode to obtain the weighted footprint for a specific station, setting station parameters such as location coordinates and above-ground height as receptor position. The duration of trajectories was selected corresponding to the original EC system time scale, which is 30 min for which the residence time is calculated. Residence time is a regular output parameter of FLEXPART model in the backward mode. This model setup was run six times a day (i.e., every 4 h) for every station for every date where satellite images were available to obtain a daily footprint.  
One of the regular output parameters of the FLEXPART model in backward mode is residence time, which is measured in seconds. That gives an understanding of how long one particle (effective air parcels), or group of particles, has \"lived\" in a particular grid cell, which allows us to evaluate the receptor's sensitivity to the surface grid cells. Thus, the resulting residence time map can be interpreted as a weighting function (see Figure 3 right panels), which we need to rely on when processing the remote sensing data.  
Figure 2: General flowchart of regression model training process.
# Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System
## 2 System Model and Problem Statement
### System Model
The total delay of the system is divided into two parts, one is the delay of transmission from the user offloading task to the UAV on the S2A link and the computation delay on the UAV, the other part is the transmission delay of the user offloading task to the BS on the S2G link and the computation delay on the BS. The transmission delay of the user on S2A and S2G links can be, respectively, expressed as  
\[t_{\rm S2A}=\frac{\alpha L}{R_{\rm UAV}}, \tag{9}\]  
\[t_{\rm S2G}=\frac{(1-\alpha)L}{R_{\rm BS}}. \tag{10}\]  
The computation delay for the user processing tasks on the UAV and the BS can be, respectively, expressed as  
\[t_{\rm UAV}=\frac{\alpha L}{f_{\rm UAV}}, \tag{11}\]  
\[t_{\rm BS}=\frac{(1-\alpha)LC}{f_{\rm BS}}, \tag{12}\]  
where \(C\) represents the number of cycles required to compute one bit task, \(f_{\rm UAV}\) is the CPU frequency allocated for computation on the UAV, and \(f_{\rm BS}\) is the CPU frequency allocated for computation on the BS. The total system delay can be described as  
\[T=\max\left\{t_{\rm S2A}+t_{\rm UAV},t_{\rm S2G}+t_{\rm BS}\right\}. \tag{13}\]
# A LiDAR-Based Backfill Monitoring System
## 2 Materials  
### Mechanism of Fundamental Devices  
The proposed system workflow relies on the point cloud data obtained from LiDAR scanners. Equation (1), typically used by LiDAR to calculate the distance of each point, is based on the principle of measuring the round-trip time of light [28]. On account of the distance of each point calculated using Equation (2), the coordinate of one of the points scanned by LiDAR is illustrated in Figure 1.  
\[d=\frac{c\cdot t}{2} \tag{1}\]  
\[\begin{cases}x=d\cdot\cos(\phi)\cdot\sin(\theta)\\ y=d\cdot\cos(\phi)\cdot\cos(\theta)\\ z=d\cdot\sin(\phi)\end{cases} \tag{2}\]  
where \(d\) represents the distance from the point to the LiDAR, \(\theta\) is the horizontal scanning angle, \(\phi\) is the vertical scanning angle, and \(x,y,z\) are the coordinates of the point in three-dimensional space.  
A point cloud is a collection of points scanned by LiDAR in three-dimensional space. Given the need for rapid processing and computation of complex point cloud data, designing a device that integrates LiDAR technology with an NUC (next unit of computing, a minicomputer developed by Intel) mainboard is crucial for achieving a semi-autonomous monitoring process. Unlike open-pit scenes, underground mine tunnels present unique challenges with their winding paths, limited visibility, and simple geometric features. Additionally, the working conditions within underground mines are characterized by high temperatures and pressures [29]. From the perspective of security and efficiency, a explosion-proof, multi-functional backfill monitor based on LiDAR technology was designed and assembled.
## 2 Materials and Methods
### Determination of Antioxidant Activity  
The antioxidant capacity of SEWS was evaluated in water (SEWS-W) and 70% ethanol (SEWS-E) by three methods differing by their principle. The study was conducted with SEWS in different concentrations (varying from 0.005 to 0.1%).  
The scavenging effect of SEWS on 2,2-diphenyl-1-picrylhydrazyl (DPPPH) radical was estimated by reduced absorbance of the reagent after SEWS addition. Experimental details are described by [PERSON] et al. [12]. The absorbance (A\({}_{\text{sample}}\)) of the DPPH-SEWS mixture was measured at 517 nm with a spectrophotometer (Spekol 11; Carl Zeiss Jena, Jena, Germany) after 30 min in darkness. The antiradical activity (AA) was calculated by the formula AA = (A\({}_{\text{control}}\) - A\({}_{\text{sample}}\))/A\({}_{\text{control}}\)) and multiplied by 100 to express in percentage. The control was treated as the corresponding sample but without the addition of SEWS. The synthetic antioxidant butylhydroxytoluene (BHT) was used as a positive control.  
Ferric reducing antioxidant power (FRAP) assay was performed as described by [PERSON] et al. [24]. Briefly, a 3 mL freshly prepared FRAP reagent [12] was mixed with 0.1 mL 0.1% SEWS prepared either in water or 70% aqueous ethanol solution. The mixture was maintained in dark for 10 min and the absorbance was measured at 593 nm against blank prepared with water or 70% ethanol. A standard curve was built with FeSO\({}_{4}\).7H\({}_{2}\)O. The results of the FRAP analysis were expressed as \(\upmu\)mol Fe\({}^{2+}\) equivalents per gram dry weight SEWS [25].  
Hydroxyl radical scavenging activity of SEWS was evaluated only in water (SEWS-W) due to interference of ethanol with the assay. The experiments were performed as before [12]. Mannitol, a classical OH\(\bullet\) scavenger, was used as a positive control [26; 27; 28].
## 3 Methodology
### A3: Video Distribution Using WebRTC  
**Step 7: Designing Web Screen Transitions**  
In Step 7, we divide the web screen of the secure streaming system into three parts: (U1) the home page, (U2) the video streaming page, and (U3) the video viewing page. Figure 7 shows an overview of the Web screen transitions in the proposed method. The proposed system consists of the following five points. (P1) The hospital side starts a new video stream from U1, transitions to U2, and generates the only URL with a random ID. (P2) When U2 is opened, the video streaming starts automatically, and a web link for viewing the U2 video is generated in the distribution list of U1. (P3) The classroom moves to U3 by opening a Web link selected from U1's distribution list. (P4) The video stream to be distributed by U2 is routed to the PeerJS server, which then distributes the video stream to the corresponding U3. (P5) Allows us to send a text stream to either U3 or U2 via a WebSocket server.  
**Step 8: Introduction of WebSocket Technology**  
In Step 8, WebSocket technology is introduced in the P2 and P5 sections described in Step 7. Specifically, for P2, the ID of the URL corresponding to the new transmission is prepared on the U2 client-side, and the client-side of U1 receives the ID and assigns it to the URL for viewing via a WebSocket server. On the other hand, for P5, set the methods to send and receive new messages on the client-side of U2. Meanwhile, it sends them to the client-side of U1 via the WebSocket server. By going through the WebSocket server, the  
Figure 6: Algorithms for retrieving key point coordinates while the head is tilted: (**a**) Eye line mosaic refer to Figure 4. (**b**) Face mosaic refer to Figure 5.  
Figure 7: Overview of Web screen transitions in the proposed method.  
delivery content with the specified ID and the content of the receiving and sending can be shared on the viewing page.  
**Step 9: Introduction of PeerJS technology**  
In Step 9, PeerJS technology is introduced in the P4 section described in Step 7. In conventional PeerJS technology, bidirectional communication is expected. The proposed system requires unidirectional communication to distribute the video stream from the hospital to the classroom. Hence, the communication process of PeerJS technology must be devised. Our key idea is to apply image processing technology to the hospital-side video stream, convert it into a canvas stream, and distribute it to the classroom side. However, we do not distribute the video stream from the classroom side to the hospital side but instead create an empty video stream by ourselves, thereby eliminating this process. This way makes P4 a unidirectional communication, and communication from the classroom side to the hospital side can be achieved only by receiving and sending texts using WebSocket.
## 3 Results and Discussion
### Canopy Assemblage BRDF Effects
Typically reflectance decreased slightly as the view angle moved from nadir, although the pattern was stronger for denser canopies, such as D74 (Figure 9), and even included a slight increase in reflectance close to horizontal, being a pattern also seen in models of dense seagrass canopies [21]. Figure 8: Examples from BRDF generation of canopy using reflectances of deep corals at 74% coral coverage (D74). Shown as nadir-view (\(\theta_{\text{e}}=0^{\circ}\)) orthogonal projected rendering above each canopy, corresponding to incident radiance from: (**a**) \(\theta_{\text{i}}=10^{\circ}\); (**b**) \(\theta_{\text{i}}=30^{\circ}\) and; (**c**) \(\theta_{\text{i}}=50^{\circ}\), respectively, azimuth \(\varphi\)s = 0\({}^{\circ}\). RGB images are created from hyperspectral data using the tristimulus functions and represent an area of 30 cm \(\times\) 30 cm.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 4 Micro-Supercapacitor Electrolytes
### Quasi-Solid-State Electrolytes  
Quasi-solid-state gel polymer electrolytes are flexible and elastic, and are a preferred material for wearable electronics [125]. Gel polymer electrolytes consist of polymer, electrolytic salt, and a solvent, which are either mixed together by melting or solution processes, for example, by in-situ polymerization [126], casting [127] or phase inversion methods [125; 128; 129]. Nevertheless, this type of electrolyte still faces some challenges regarding evaporation of the solvent and decrease in conductivity, leading to a poorer performance of the micro-supercapacitor over time [50]. Ionogel is a specific class of (quasi-)solid-state electrolytes (Figure 6), with fair ionic conductivity and excellent stability, and it is synthesized by mixing fumed silica nanopowder with an ionic liquid, such as 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [130].  
A quasi-solid-state supercapacitor, produced with polyvinyl alcohol (PVA)\(/\)H\({}_{3}\)PO\({}_{4}\) electrolyte and 3D porous cellular graphene films, exhibited an energy density of 0.22 \(\upmu\)Wh/cm\({}^{2}\) and a power density of 0.37 mW/cm\({}^{2}\)[83]. Similarly, PVA/H\({}_{3}\)PO\({}_{4}\) gel electrolyte was used for a micro-supercapacitor with multi-walled carbon nanotube electrodes, fabricated using mask-free micro-plasma-jet etching at ambient conditions to achieve interdigitated electrode patterns. Such micro-capacitors with 12 interdigitated electrodes had a stack capacitance of 2.0 F/cm\({}^{3}\) at a scan rate of 10 mV/s [131]. The performance of flexible micro-supercapacitors with graphene/PEDOT composite were evaluated with three different gel electrolytes, namely PVA/H\({}_{2}\)SO\({}_{4}\), PVA/LiClO\({}_{4}\), and PVA/H\({}_{3}\)PO\({}_{4}\). The results showed that PVA/H\({}_{2}\)SO\({}_{4}\) gel electrolyte achieved the highest performance for the graphene/PEDOT micro-supercapacitor with maximum operating potential window of 1.2 V, specific capacitance of 37 mF/cm\({}^{2}\), energy density of 6.4 mWh/cm\({}^{2}\), and capacitance retention of 89% after 2500 cycles [132]. In another study, PVA/H\({}_{2}\)SO\({}_{4}\) gel electrolyte was used in a micro-supercapacitor with 3D hierarchical porous lignin-derived carbon/WO\({}_{3}\), prepared by carbonization and a solvothermal process, which had a capacity of 20 mF/cm\({}^{2}\)[133].
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 2 Preliminaries
### POB Number System  
The POB number system was proposed by [PERSON] and [PERSON] (2018). The system is denoted POB (\(n\), \(r\)), where \(n\geq r\) and both n and r are nonnegative integers. In this context, \(r\) stands for the number of 1s in a string, and \(n\) stands for the number of bits. In the POB number system, the range of P(A) is \(0,\ 1,\cdots\begin{pmatrix}n\\ r\end{pmatrix}-1\), where \(A=a_{j-1}a_{j-2}\cdots a_{0}\).  
The following can be used to obtain P(A):  
\[P(A)=\sum_{j=0}^{n-1}a_{j}(\begin{array}{l}j\\ v_{j}\end{array}) \tag{4}\]  
where \(v_{j}=\sum_{j=0}^{n-1}a_{j}\).  
The POB number in binary form can be calculated by the above equation to obtain the matching POB value and vice versa.  
For example, the 10-bit binary string 10110010, denoted as POB (10, 5), has a POB value that can be calculated by Equation (4).  
\[\begin{array}{l}IP(A)=a_{0}\times\begin{pmatrix}0\\ 0\end{pmatrix}+a_{1}\times\begin{pmatrix}1\\ 1\end{pmatrix}+\cdots\ldots+a_{8}\times\begin{pmatrix}8\\ 4\end{pmatrix}+a_{9}\times\begin{pmatrix}9\\ 5\end{pmatrix}\\ =0+1+0+\begin{pmatrix}3\\ 2\end{pmatrix}+0+0+\begin{pmatrix}6\\ 3\end{pmatrix}+\begin{pmatrix}7\\ 4\end{pmatrix}+0+\begin{pmatrix}9\\ 5\end{pmatrix}\\ =185\end{array}\]  
The obtained POB value of 185 can be converted to the corresponding 8-bit binary, that is, 10111001. This result has a crucial impact on some research based on data hiding. It can simultaneously perform lossless compression and re-encryption in the process of data hiding.  
Similarly, 010111011 is denoted as POB (9, 6), and its corresponding POB value is 10, whereas a POB value of 28 would be 010101101 for POB (9, 5). Since the images used in this paper are all 8-bit images, and both secret information and authentication bits are embedded with two bits, the 10-bit POB (10, \(r\)) string in each operation can be converted into an equivalent 8-bit decimal POB value, which ranges from 0 to 251.
# Power System Reliability Evaluation Based on Chronological Booth-Balieriaux Method
## 1 Introduction
This paper proposes a deterministic reliability evaluation method, incorporating chronological patterns, i.e., the characteristics of renewable resources and maintenance schedules. This paper proposes a deterministic reliability evaluation method for power systems. Deterministic methods assess reliability by estimating system availability based on probabilistic models of system components. Therefore, the proposed method includes an availability estimation approach and component modeling. As most probabilistic reliability indices necessitate a calculation of the loss of load probability (LOLP), this study focuses primarily on enhancing the LOLP calculation method. The proposed method is based on the load duration curve (LDC) convolution approach known as the Booth-Baleriaux method [11; 12]. This proposed method enables an hourly reliability evaluation by modeling hourly demand distribution and suggests a generator model that considers time-variant generation characteristics. Furthermore, the proposed model enables efficient convolution, reducing computational time. The contributions of this paper are as follows. (1) In the proposed method, the hourly demand distributions reflect the characteristics of time-dependent generators in a reliability evaluation. Moreover, the resolution of demand distribution is not limited to the \"hour\" and is extendable to arbitrary resolutions, which can accommodate time-varying generation resources with various frequencies. (2) The proposed method is deterministic and has consistent indices. Policymakers may prefer accurate and consistent results, and the method can be used to validate reliability evaluation algorithms based on stochastic approaches. (3) The proposed method was validated by applying it to the IEEE Reliability Test System 2020. Furthermore, a reliability evaluation was performed considering various chronological information and analyzing how chronological information affects reliability. This paper is organized as follows: Section 2 describes the mathematical formulation of the existing deterministic and proposed method. The reliability of the IEEE Reliability Test System 2020 is evaluated in Section 3 to verify the proposed method, followed by an analysis of the effect of various chronological patterns on reliability. Section 4 applies the proposed method to a reliability evaluation of the Korean power system to assess resource adequacy. Finally, Section 5 presents the conclusions.
# Recursive Optimal Finite Impulse Response Filter and Its Application to Adaptive Estimation
## 3 Simulation Results and Discussion
\begin{table}
\begin{tabular}{l c c c c} \hline
**Time Interval** & **[50~{}550]** & **[50~{}200]** & **[201~{}400]** & **[401~{}550]** \\ \hline Kalman filter & 0.7567 & 0.2157 & 2.6138 & 0.1789 \\ SHAK filter & 0.4107 & 0.4852 & 1.3432 & 0.7997 \\ LMAK filter & 0.3760 & 0.4394 & 1.2185 & 0.7809 \\ ROFIR filter & 0.1038 & 0.2469 & 0.3141 & 0.1965 \\ AFIR filter & 0.0872 & 0.2927 & 0.1546 & 0.2476 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the MREE for the first state (\(e_{1,r}\)). \begin{table}
\begin{tabular}{l c c c c} \hline
**Time Interval** & **[50~{}550]** & **[50~{}200]** & **[201~{}400]** & **[401~{}550]** \\ \hline Kalman filter & 0.2709 & 0.1933 & 0.4839 & 0.1579 \\ SHAK filter & 0.0642 & 0.2431 & 0.0998 & 0.5697 \\ LMAK filter & 0.0558 & 0.2387 & 0.0894 & 0.4248 \\ ROFIR filter & 0.0552 & 0.2314 & 0.0947 & 0.1779 \\ AFIR filter & 0.0216 & 0.2490 & 0.0240 & 0.2011 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of the MREE for the second state (\(e_{2,r}\)). Figure 5: Estimation error for the third state \(x_{3,k}\) (\(e_{3,k}=x_{3,k}-\xi_{3,k}\)). \begin{table}
\begin{tabular}{l c c c c} \hline
**Time Interval** & **[50~{}550]** & **[50~{}200]** & **[201~{}400]** & **[401~{}550]** \\ \hline Kalman filter & 0.2709 & 0.1933 & 0.4839 & 0.1579 \\ SHAK filter & 0.0642 & 0.2431 & 0.0998 & 0.5697 \\ LMAK filter & 0.0558 & 0.2387 & 0.0894 & 0.4248 \\ ROFIR filter & 0.0552 & 0.2314 & 0.0947 & 0.1779 \\ AFIR filter & 0.0216 & 0.2490 & 0.0240 & 0.2011 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of the MREE for the third state (\(e_{3,r}\)). than those of LMAK filter, even though the horizon length of the proposed AFIR filter is larger than that of LMAK filter. In addition, the estimation errors of the proposed AFIR filter rapidly converge to zero after temporary model uncertainty disappears, whereas those of adaptive Kalman filters do not. Moreover, the estimation errors of adaptive Kalman filters fluctuate and oscillate during time interval [401 550], which are caused by accumulation of estimation errors. From these results, it can be assumed that the modified sequential noise statistics estimation method and its combination with recursive FIR filtering make for more adaptive and faster convergence performance than the sequential noise statistics estimation with Kalman filtering.
## 2 Method and Material
### Evaluation of Remotely Sensed Phenological Metrics
In order to derive the phenological metrics, we first applied an algorithm based on the Savitzky-Golay Filter with band quality files and state flags to smooth the VI time series [41]. Then, the reconstructed VI time series were fitted using logistic functions for LOGISTIC1 and LOGISTIC2 method. Before applying the other methods, the smoothed VI time series were interpolated to daily values using a linear model. Dates of onset and offset for 2000-2010 were derived using each method for the five MODIS pixels that are encompassed in the phenology-observation area. The phenological dates averaged across the five pixels were compared with the metrics based on ground observations selected in Section 2.2. The performance of each metric was evaluated using the root mean square deviation (RMSD, Equation (3)) and [PERSON]'s rank correlation coefficient (\(\rho\)). RMSD describes how close remotely sensed phenological metrics are to ground observations, while the correlation coefficient describes how well the remotely sensed phenological metrics capture the inter-annual variability. \[RMSD=\sqrt{\frac{1}{N}\sum(DOY_{eva}-DOY_{ref})^{2}} \tag{3}\]  
where \(DOY_{eva}\) is the phenological date to be evaluated, \(DOY_{ref}\) is the reference phenological metric, and \(N\) is the sample size.
## 2 Materials and Methods
### Test Apparatus  
#### 2.2.1 In Situ Direct Shear Test  
The in situ direct shear test was conducted in accordance with the _\"Code for In-situ Direct Shear Test of Geotechnical Materials\"_ (HG/T 20693-2006) [24], with the experimental setup illustrated in Figure 2. An excavator base provided a rigid reaction frame, while vertical hydraulic lacks applied normal stress and horizontal hydraulic lacks delivered shear force and measured shear strength.  
Figure 1: (**a**) Location map of Jilangtan; (**b**) photograph of the Jilangtan platform; (**c**) photograph of the sampling site.  
Specimen preparation began with excavating a 1 m \(\times\) 1 m \(\times\) 0.5 m test pit at the summit of the Jilangtan platform. After clearing the surface loess layer, a square soil column (0.5 m \(\times\) 0.5 m \(\times\) 0.3 m) was sculpted at the pit's center, meeting the code's requirement that the height be at least four times \(d_{\rm max}\). The column's surface was then stabilized with poured concrete, leaving a 2 cm gap at the base to mimic the shear plane. To explore the shear strength of in situ CGS under varying moisture conditions, the test was split into two groups of four specimens each: one group retained its natural state, while the other was saturated by full submersion in water for 48 h.