esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Model of Geolocation and Tracking Scenarios
### Conventional Geolocation Measurements
#### 3.1.3 Hybrid TDOA/FDOA  
The TDOA and FDOA measurements can be arranged in one vector \(\mathbf{h}_{i,1}(\mathbf{x}_{k})\) as  
\[\mathbf{h}_{i,1}(\mathbf{x}_{k})=\left(\begin{array}{c}\mathbf{\tau}_{k}\\ \mathbf{f}_{k}\end{array}\right)=\left(\begin{array}{c}\tau_{i,k}-\tau_{1,k}\\ f_{i,k}-f_{1,k}\end{array}\right), \tag{14}\]\[\mathbf{H}_{k}(\mathbf{x}_{k})=\left[\mathbf{h}_{2,1}(\mathbf{x}_{k}),\mathbf{h}_{3,1}(\mathbf{x}_{k}),\cdots\mathbf{h}_{M,1}(\mathbf{x}_{k})\right]^{\prime}\,, \tag{15}\]  
The corresponding noise vector is  
\[\mathbf{\hat{n}}_{k}=\left[\mathbf{n}_{k}^{\prime}\quad\mathbf{\hat{n}}_{k}^{ \prime}\right]^{\prime}, \tag{16}\]  
which assumes zero-mean Gaussian with covariance matrix \(\mathbf{R}_{k}\) of hybrid (TDOA /FDOA) [45].  
\[\begin{cases}\mathrm{E}[\mathbf{\hat{n}}_{k}]=0\,\\ \mathrm{E}[\mathbf{\hat{n}}_{k}\mathbf{\hat{n}}_{k}^{\prime}]=\mathbf{R}_{k} \,\end{cases} \tag{17}\]  
where  
\[\mathbf{R}_{k}=\left[\begin{matrix}\mathbf{R}_{n_{k}}&\\ &\mathbf{R}_{i_{k}}\end{matrix}\right]=\left[\begin{matrix}\underbrace{\sigma _{\tau}^{2}\cdots\sigma_{\tau}^{2}}_{(M-1)(K+1)}&\\ &\underbrace{\sigma_{\hat{f}}^{2}\cdots\sigma_{\hat{f}}^{2}}_{(M-1)(K+1)} \end{matrix}\right]\, \tag{18}\]
# Gelatin-Siloxane Hybrid Monoliths as Novel Heavy Metal Adsorbents
## 3 Results and Discussion
### Resistance of Gelatin Beads to Shaking in Water Solutions
#### 3.6.3 Adsorption Kinetics of Metal Ions on Gelatin-Siloxane Monoliths  
The adsorption kinetics of studied metal ions was analyzed by the use of pseudo-first-order kinetic models and pseudo-second-order kinetic models [19; 29; 35]. The pseudo-first-order adsorption model can be expressed as:  
\[log(q_{e}-q)=logq_{e}-k_{1}\cdot t\]  
where \(q_{e}\) (mg/L) denotes the amount of metal ions adsorbed at equilibrium, \(q\) (mg/L) the amount of metal ions adsorbed (mg/L) at time \(t\) (min) and \(k_{1}\) denotes the rate constant (1/min). The pseudo second-order adsorption model can be expressed as:  
\[\frac{t}{q_{t}}=\frac{1}{k_{2}q_{e}^{2}}+\frac{1}{q_{e}}\cdot t\]  
where \(q_{e}\) and \(q_{t}\) are the amount of metal ions adsorbed per unit mass of the adsorbent (mg/g) at equilibrium time and time \(t\), respectively, and \(k_{1}\) is the rate constant for the second-order kinetics. After the linearization of above equations rate constants (\(k_{1}\), \(k_{2}\)), maximum adsorption capacity (\(q_{e}\)) and correlation coefficients (R\({}^{2}\)) for pseudo-first order and the pseudo-second order equations values were calculated (Table 3).  
The values in Table 3 show that both the first- and the second-order pseudo kinetic models can fit the concentration profiles, however correlation coefficients are lower for second-order model (R\({}^{2}\) > 0.92).
# Approaching Electroencephalographic Pathological Spikes in Terms of Solitons
## 2 Nonlinear Media and Solitons  
Solitons are naturally robust to perturbations as they travel with little dissipation over long timeframes [22] ([PERSON] et al., 2022). The peculiar coherence of these highly localized wave packets can be attributed to the fact that the tendency for a wave pulse to disperse is exactly balanced by nonlinear effects occurring in the propagation medium [5]. While the linear and nonlinear effects acting alone would cause the wave to decay, in the right combination, they lead to waves that persist stably and compactly as they propagate, periodically returning to the initial state.  
Numerous natural and artificial phenomena are characterized by the slight nonlinear dynamics required to generate solitons. Looking for a model of soliton-like EEG oscillations, we must first look for the possible presence of nonlinear dynamics in the central nervous system.
## 6 Material and Method
### Building the Cost Matrix  
Once the optimal path connecting each pair of DPs have been computed, the next step is to build the cost matrix that is used by the GA at the next step. An example of a cost matrix for 10 DPs is shown at Table 1. It contains the cost of going from every DP to every other DPs. When optimizing for distance, filling the cost matrix is quite simple. The weight of the final node for each instance of the SSSP algorithm is simply taken. The resulting matrix is symmetrical. When optimizing for time or power consumption, the process is more complicated and requires that each path be rebuilt to accurately compute the time and energy required to travel the path. Although this operation is done at this step for every pair of DPs, the description of the path reconstruction is left to Section 6.6. The time and energy used to hover at the DP are also added to the cost of the path.
# GraphSAGE-Based Multi-Path Reliable Routing Algorithm for Wireless Mesh Networks
## 2 System Model
### Network Model  
The structure of wireless mesh networks is shown in Figure 1. which are made up of radio nodes that communicate with each other by WiFi6. As a new generation of wireless network technology, WiFi6 has the advantages of high bandwidth, low delay, and low power consumption, and is widely used in wireless mesh networks. The radio nodes include mesh clients, mesh routers, and gateways [16]. The clients communicate directly with mesh routers. And mesh routers are connected through wireless links in a self-organizing manner to form a backbone mesh network. Here, clients, mesh routers, and gateways are collectively called routing nodes. Traffic is forwarded to the gateway connected to the Internet via multiple nodes in a backbone mesh network.  
In this paper, the network topology is constructed as an undirected graph \(G(V,E)\). The total node set \(V=\{v|v=1,2,\cdots,N\}\) represents all routing nodes, and \(N\) is the total number of nodes. The link set \(E=\{e_{ij}|i_{j}\in V\}\) represents all links in the network. The link \(e_{ij}\in E\) exists only if \(d_{ij}\leq R\), where \(d_{ij}\) denotes the Euclidean distance between nodes \(i\) and \(j\), \(R\) represents the maximum radio transmission range.  
Figure 1: Structure of wireless mesh networks.
## 2 Results and Discussion
### Antitumor Activity
#### Structural Activity Relationship SAR  
Examination of the SAR led to the following conclusions:  
The activity of the synthesized compounds **4** and **6** against hepatocellular carcinoma depends on the structural skeleton and electronic environment of the molecules. For example, the activity of the tested compounds **4a,b,e** and **6a,b,e** were found to be highly related to their structures since replacement of electron-donating groups in the two aryl groups in compounds **4b** and **6b** with electron-withdrawing groups in compounds **4e** and **6e** dramatically decreases their cytotoxicity against HEPG2-1. On the other hand, the cytotoxicity of compounds **4a** and **6a** whose structures contain two phenyl groups (no substituent), is intermediate between the highly-potent and the weakly-potent compounds (See Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound No.** & **X** & **Y** & **Z** & **IC\({}_{50}\) (nM)** & **General Structure** \\ \hline
**Doxorubicin** & - & - & - & 0.08 \(\pm\) 0.07 & \\
**4a** & H & CN & NH\({}_{2}\) & 9.7 \(\pm\) 0.85 & \\
**4b** & Me & CN & NH\({}_{2}\) & 1.9 \(\pm\) 0.16 & \\
**4e** & Cl & CN & NH\({}_{2}\) & 17.2 \(\pm\) 0.83 & \\
**6a** & H & MeCO & OH & 12.3 \(\pm\) 0.37 & \\
**6b** & Me & MeCO & OH & 2.4 \(\pm\) 0.29 & \\
**6e** & Cl & MeCO & OH & 22.3 \(\pm\) 0.36 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: IC\({}_{50}\) values of tested compounds **4** and **6**\(\pm\) standard deviation against HEPG2-1.  
Figure 1: Cytotoxic activities of tested compounds against HEPG2-1.
## 4 Proposed Secure MP-FrFT-OFDM Cryptosystem
### Authenticated Decryption  
The deciphering technique's steps can be summed up as follows:  
1. Receive the authenticated encrypted image data.
2. Remove the cyclic prefix (CP) from the received secure MP-FrFT.
3. The first step in the decryption is the de-confusion by applying the inverse MP-FrFT-based OFDM on the encrypted image based on the inverse two secret fractional parameter vectors \(-a\) and \(-b\) as follows: \[-a=-(0.9+\frac{1}{100}\sum_{i=1}^{i=256}a_{i})\] (23) \[-b=-(0.9+\frac{1}{100}\sum_{i=1}^{i=256}b_{i})\] (24)
4. Apply QPSK de-mapping.
5. Apply convolutional de-coding for the diffusion 256 \(\times\) 256 \(\times\) 8 bits.  
Figure 5: The proposed secure MP-FrFT-OFDM based on MCC-MF sine map and DCBS generator.  
Convert the authenticated encrypted image into binary format.
* The second step in the decryption step is the de-diffusion by Xoring of the authenticated encrypted binary image data of 256 \(\times\) 256 \(\times\) 8 bits with the select random iterated chaotic sequence of 256 \(\times\) 256 \(\times\) 8 bits.
* Apply the required analysis.  
Examining the impact of noise, information entropy, visual inspection, histograms, assaults, differential, and encryption quality metrics, the effectiveness and security of the proposed system are examined. The recommended picture encryption approach maintains a good security quality, according to all numerical results.
## 3 Numerical Results
### Bayesian Update
#### 3.4.1 ES and gPCE-ES
\(\mu\) and \(\sigma\) are the mean and standard deviation of the updated (up) ensembles. Based on these outcomes, we consider now only the gPCE-ES framework. Figure 7a shows the gPCE-ES convergence with \(n_{MC}\). We recall that the computational cost is roughly constant, \(\eta_{g}=16\). The performance index \(AE^{u}\) decreases with \(n_{MC}\) for both parameters and stabilizes after \(\sim\)100 \(n_{MC}\). If a different \"true\" parameter set is used, for example, (\(\lambda^{*}=0.005,R=1.5\)), we obtain the behaviour shown in Figure 7b. In this case, the mean converges to the \"true\" value at a much faster rate for \(\lambda^{*}\), while \(R\) remains poorly constrained. The \"true\" parameter sets are chosen such that they provide very different \"true\" reference simulations, as shown in Figure 2b. Figure 5: State ensembles for increasing number of Monte Carlo realizations \(n_{\mathrm{MC}}\). In this case, the forecast ensemble (the grey one) has been obtained through the geomechanical model. The update ensemble (in red) is directly the output of the ES technique, that is, it does not derive from the geomechanical model run with the update ensembles of the parameters.
Evaluation of the Predictability and Accuracy of Orthognathic Surgery in the Era of Virtual Surgical Planning  
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models and articulators, face bow, etc.) and were interpreted before being able to develop a treatment plan [5]. This cast model-based surgery is complex and arduous, and difficulties may be encountered when correcting occlusal cant and facial asymmetries.  
The development of computer-aided surgical simulation represented a paradigm shift in surgical planning for patients with cranio-maxillofacial deformities. When using 3D planning, all the necessary information is provided in images that can be manipulated on a computer. Three-dimensional imaging-based planning systems enable the surgeon to establish necessary osteotomy planes preoperatively and assess different surgical scenarios [1]. Since this is done virtually, surgery is performed in a more predictable way.  
The surgical occlusion set-up is a vital step in virtual planning for orthognathic surgery [2] using intraoral scanning and a full digital set-up. With 3D printing, this information is transferred to the operating room. The clinical applications of 3D printers in orthognathic surgery include the production of occlusal splints, osteotomy/cutting guides, repositioning guides, spacers, fixation plates/implants, and 3D printed models [3]. Most studies have agreed that CAD/CAM occlusal splints with no modifications compared to traditionally manufactured occlusal splints provided a reliable substitute in orthognathic surgery [3]  
There are many commercially available software programs for virtual surgical planning and simulation [6]. However, surgical planning may not necessarily reflect the actual surgical outcomes, and its accuracy and predictability must be established.  
Most of the methods that have been proposed to assess the accuracy of the postoperative outcome versus the 3D surgical planning in the literature are based on the identification of cephalometric landmarks and computation of the differences between the planning model and the actual result [7].  
The purpose of this study was to evaluate predictability in orthognathic surgery and the accuracy of surgical outcomes using virtual surgical planning. The postoperative results of bimaxillary orthognathic patients were compared to the preoperative virtual surgical plan using different commercially available software.
## 4 Combining ground data and VIs to scale biophysical processes over large areas
### Examples of Combining VIs and Flux Tower Data to Estimate Carbon Fluxes
[PERSON] _et al._[71] used NDVI to estimate fPAR, and found that a simple LUE model moderated by temperature and soil moisture could predict daily gross primary productivity from 28 EC flux towers over forests, grasslands and savannas with coefficients of determination (r\({}^{2}\)) of 0.77 - 0.85. Further work is needed on how best to integrate satellite data into ecosystem production efficiency models. Attempts to improve upon the satellite EVI and tower GPP relationships through the use of PAR and fPAR variables have been generally unsuccessful [72,73] in part due to the weaker correlation of fPAR with satellite NDVI compared to EVI. There is a need to better separate canopy absorbed PAR into the photosynthetically-active (chlorophyll) and non-active (senesced vegetation, woody material) components and better understand the seasonal hysteresis observed in satellite greenness relationships with tower measured fPAR data [73]. [PERSON] _et al._[72] distinguished between the photosynthetically-active and non-photosynthetically active components of canopy absorbed radiation, and reported the EVI to be more closely related to the chlorophyll component of fPAR, while NDVI was more closely correlated with total canopy fPAR. Unlike NDVI, EVI incorporates both Blue and Red bands in its formulation, and chlorophyll absorbs strongly in both the Blue and Red bands. [PERSON] et al. [74] proposed combining VIs with remotely-sensed chlorophyll florescence, an indicator of plant stress, to derive LUE from remote sensing data alone.
## 2 Materials and Methods
### Experiment Management  
The lysimeters received artificial illumination (PAR: 500 umol s\({}^{-1}\)m\({}^{-2}\), 16 h day\({}^{-1}\)) and were kept at room temperature. They were initially saturated with water, and after 15 days of equilibration, seeds of \(L\). _sativium_ L. were sowed at the amount of 0.021 g of seed for each lysimeter. After two days, the germination was complete, and after 5 days from sowing, the drip irrigation started. Two sources of irrigation per lysimeter provided 1000 mL of deionized water once a week for 7 weeks. The leached water was collected at the bottom of the lysimeters after 3 days of irrigation. After the determination of the leachate volume, 50 mL of leachate were filtered with a cellulose nitrate membrane (pore size = 0.45 um), and the electric conductivity (EC) and pH (slurry ratio for biochar characterization: biocharwater = 1:20; slurry ratio for soil characterization: soil:water = 1:4) were measured. Then, the leachate samples were acidified in test tubes and stored in a refrigerator until the elemental characterization using an ICP-OES (Varian Inc., Palo Alto, CA, USA, Vista MPX).  
At the end of the experiment, the aboveground biomass was harvested from each lysimeter. The total number of plants and the number of plants at the flowering stage were counted. The height of the tallest plant was also measured, as well as the total dry biomass (70 \({}^{\circ}\)C, 24 h). The dry biomass was acid-mineralized with a microwave unit (MarsXpress, CEM Corporation, Charlotte, NC, USA) following the USEPA protocol [28] and analyzed with the ICP-OES to verify the Ce uptake. The substrates in the columns were also sampled at three depths (15, 25 and 35 cm); 2 mm-sieved; oven-dried (70 \({}^{\circ}\)C, 24 h) and acid-digested using the USEPA protocol [29] and analyzed with the ICP-OES.  
The total organic C (TOC) and total N (TN) in the soil leachates were measured by an automated elemental analyzer (Shimadzu TOC-VCPN, Kyoto, Japan).
## 2 Datasets and Methods
### Datasets  
#### 2.2.1 Global Monthly GPP and NPP Datasets  
This study used the same monthly GPP and NPP datasets as in ref. [19]. The GPP and NPP datasets were simulated by the BEPS model, which is driven by remote sensing LAI, clumping index (indicates the foliage's spatial distribution pattern), and meteorological and soil data. The datasets were validated with an annual global residual land sink (RLS), and the result indicated that the quality of the dataset is pretty good and could be used to analyze the global carbon budget [19].
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 2 Methods of On-Board Ship Detection
### Sea-Land Segmentation  
During ship detection, land area is not of interest, but the SAR images that are used commonly include both land and sea. The purpose of sea-land segmentation is to mask or remove the land area from the SAR images, so that the detection algorithm only processes the sea area and ignores the land [23]. This is particularly important, because many land objects appear in SAR images as strong scatterers that are similar to ships at sea. These land objects will produce false alarms and waste processing power that should be focused on the detection of ships. Therefore, effective sea-land segmentation can limit the detection range to sea areas, thereby improving the detection efficiency and accuracy.  
This paper first adopted a method in which geographic prior information (coastline vector data) is superimposed on SAR images using a vector layer, and the polygon vector elements in the vector layer are then used to determine whether the image is of the sea, land, or sea-land junction. The region growing method is then used for sea-land segmentation.
# Efficient Multi-Source Anonymity for Aggregated Internet of Vehicles Datasets
## 3 Models
### Motivation
: _A set of records that contains all the same \(QI\) values, constituting an equivalence class._  
Figure 3: Example of anonymized aggregation dataset. Figure 2: Example of publishing aggregated datasets. In addition to identity disclosures and attribute disclosures [41], a new form of privacy disclosure emerges; it is specifically based on linkability [42]. This form of disclosure may exist in an aggregated dataset. Linkability, in this context, refers to a data analyzer's ability to successfully discern whether two items of interest (IOIs) are linked, allowing the data analyzer to gain new information through the linkage. The definition of linkability is rooted in the literature by [PERSON] and [PERSON] [43]. Linkability can potentially lead to inference. When a data analyzer links two IOIs, it may be possible to infer the actual identity from their connection. In the IoV scenario, when data are multi-source, like related datasets or different sensors, the data from another source are valuable if the attacker owns all source data. For example, an attacker can access the database of the vehicle administration office. Thus, he knows a vehicle's registration date, license plate, and color. By observing the values in the anonymized aggregation dataset, the attacker obtains an accurate postcode value of 100200 in Figure 3, which is from another data source. This creates new privacy disclosures in multi-source scenarios. However, current techniques do not address this issue. This situation infers sensitive information, such as the postcode of the vehicle owner. K-anonymity may generalize only a few \(QI\) attributes. K-anonymity entails only the number of records in the \(QI\) group. Hence, some values remain unchanged. This privacy disclosure differs from identity and attribute disclosure because it leaks more information about the attack objective, which is harmful. We propose a new multi-source linkability attack based on the properties of the aggregated dataset that we described. **Definition 3** (**Multi-source Linkability Attack**). : _A multi-source linkability attack occurs when QI data from a source are linked with some extra knowledge. These data are then linked to other QIs. Attackers can obtain accurate information from other QIs._  
The core problem is that an attacker can identify an individual based on a data source. The inference is limited to the same record; thus, the extra data belong to the same person. Any extra single piece of knowledge can expand the information about the objective.