esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Aquaculture, or fish farming, refers to the cultivation of fresh- or saltwater organisms and is a well-developed economic activity that is accelerating rapidly throughout the world [1, 2]. Fish farming can contribute to the domestic output of fishery products, provide an important source of income for many smallholders, other landowners and their local communities, through their direct involvement in the value chain (production, processing, and sale), as well as offering employment opportunities [3, 4]. Aquaculture and fisheries are the fastest-growing food production systems around the world and their importance for food security and global nutrition in the twenty-first century is recognized increasingly by authorities such as [5, 6, 7].  
The installations required for the production of fish--either ponds or net enclosures--represent the principal investment in any aquaculture operation. However, the costs of the construction of ponds or the implantation of other structures may vary considerably according to factors such as the characteristics of the site (climate, topography, type of soil, vegetation cover, and drainage requirements), the configuration of the infrastructure, and the methods employed to install the operation. These costs can, nevertheless, be minimized through the adequate planning of the different stages of the installation process [8].  
Before making any investments or making decisions on the installation of fish farming infrastructure, it is extremely important to carefully evaluate the site earmarked for development. In particular, the technical conditions of the site should be verified, in addition to the most adequate procedures to ensure the integrity of the local environment, in line with the existing legislation. Many sites are totally inadequate for fish farming due to factors that range from the high permeability of the soil, the risk of floods and other environmental impacts, and an inadequate supply of water to a lack of access roads or access to a power grid [9].  
The selection of an appropriate site is fundamentally important here and adequate site selection will reduce the possible environmental impacts of the operation and the potential conflicts of this activity with the use of other resources and the sustainable development of aquaculture activities [6; 7; 10]. This will also guarantee an increase in economic returns [11; 12; 13].
## 3 Results  
### SEM  
In the SEM (Figure 1) images, it can be seen that the surface of substrate A is covered with large-sized, inhomogeneous, and chaotically arranged silver and gold structures. The particle average sizes are in the range from 100 to 300 nm due to some of the particles being agglomerated. Substrate A shows a fractal structure with high amounts of irregular details seen even at 150,000\(\times\) magnification, and, as such, the interstructural distances are small. In contrast, substrate B shows a less finely detailed, more ordered gold nanostructure with an, on average, larger effective inter-structural distance. Here, the average particle size is 97 nm. Substrate C consists of especially tiny silver nanoparticles (average size 18 nm), spread out very evenly across the surface, with distances being roughly of the same magnitude as the average particle size.  
These differences in the morphology of the substrates affect the efficiency of the Raman enhancement in various ways, although the complete relationships between geometric parameters and the SERS enhancement factor are not fully known. However, certain relationships can be noted. For one, the distances between nanostructures and the EF are strongly correlated, with smaller distances yielding a higher EF. At the same time, a reverse relationship can be noted for the sizes of the nanoparticles themselves where a larger nanoparticle shows increased scattering efficiency [14; 15]. A certain degree of fractality is also seemingly conducive to a higher Raman signal amplification, a hypothesis previously put forth and researched in some publications [16; 17; 18]. This influence of fractality, though not directly analysed in this paper, is tentatively characterised in Section 3.3. concerning FEM modelling.  
Figure 1: SEM images of the substrates: (**a**) substrate A; (**b**) substrate B; (**c**) substrate C.
# Coordinated Control after Grasping the Space Targets Using Controllable Damping Mechanism
## 2 Modeling of Spacecraft-Manipulator System with FDCDM
### Equivalent Kinematics Equation
Then, we have the angular velocity of the base in the inertial frame \(\mathbf{\omega}_{1}^{0}\) as  
\[\mathbf{\omega}_{1}^{0}= \begin{bmatrix}\hat{\beta}_{1}\\ 0\\ 0\end{bmatrix} +\mathrm{Rot}(X,\beta_{1})\cdot \begin{bmatrix}0\\ \hat{\beta}_{2}\\ 0\end{bmatrix} +\mathrm{Rot}(X,\beta_{1})\cdot\mathrm{Rot}(Y,\beta_{2})\cdot \begin{bmatrix}0\\ 0\\ \hat{\beta}_{3}\end{bmatrix}  \tag{4}\]  
Figure 3: Equivalent schematic of SMS-FDCDM. Then we have the kinematic equation of the model  
\[\dot{\mathbf{\beta}}=\mathbf{D}\cdot\mathbf{\alpha} \tag{5}\]  
where  
\[\mathbf{D}= \begin{bmatrix}\mathbf{H}_{3\times 3}&\mathbf{O}_{3\times 9}\\ \mathbf{O}_{9\times 3}&\mathbf{I}_{9\times 9}\end{bmatrix} \in\Re^{12\times 12},\;\mathbf{H}_{3 \times 3}=\frac{1}{c\beta_{2}} \begin{bmatrix}c\beta_{2}&s\beta_{1}\cdot s\beta_{2}&- c\beta_{1}\cdot s\beta_{2}\\ 0&c\beta_{1}\cdot c\beta_{2}&s\beta_{1}\cdot c\beta_{2}\\ 0&-s\beta_{1}&c\beta_{1}\end{bmatrix} \]  
where \(c\beta\) denotes \(\cos\beta\) and \(s\beta\) denotes \(\sin\beta\). Let \(x\in\Re^{12\times 12}\) replace \(\mathbf{\beta}\), and \(\mathbf{y}\in\Re^{12\times 12}\) replace \(\mathbf{\alpha}\); we have the kinematic equation of the system as  
\[\dot{\mathbf{x}}=\mathbf{D}\cdot\mathbf{y} \tag{6}\]
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 6 Performance Evaluation
### Simulation Parameters  
Initially, a Walker constellation [47] with six orbits and 11 satellites per orbit, as well as an altitude of 780 km, is built by _Systems Tool Kit_ (_STK_) [48], _SGP4_ [49], and _Networkx_ [50]. Then, satellite network \(G(V,E)\) can be established when the fifth satellite on the fourth orbit for the Walker constellation is viewed as the central satellite and the number of network hops is indicated as \(h=2\), where the initial storage resources for each satellite are 1000 Mbits, the initial bandwidth resources for each ISL are 1000 Mbps, and the maximum number of users provided with content services for each satellite is 30. Furthermore, cloud center \(dc\) is in the coverage of the fifth satellite on the sixth orbit, the number of paths between cloud center \(dc\) and access satellite \(v_{dc,a}\) is indicated as 4. In addition, the number of hops for each neighboring sub-network is viewed as \(h_{sub}=1\).  
Furthermore, it is assumed that users are randomly located within the coverage of satellites. For the wireless communication channel between a user and the access satellite, the bandwidth resources are [2; 4] MHz, and the transmission power, the channel gain, and the noise power are 3 W, \(-\)200 dB, and \(-\)174 dbm, respectively. Additionally, the velocity of data transmission is \(3\times 10^{8}\) m/s. For each content, the data size and the content popularity can be generated from [100, 500] Mbits and [1; 10], respectively. It is also assumed that the service delay of retrieving the content for each user can be less than the maximum service delay, and the cost factors of storage resource usage and network bandwidth consumption are indicated as 0.4 and 0.6.  
Table 2 provides the main simulation parameters, which are viewed as the default parameters if not specified. The simulation platform is built by a commodity computer with i5-1135G7 CPU, 16 GB, Windows 10, and Python language. The average results are provided after the experiments run for 100 times.
# Electronic and Magnetic Properties of Bulk and Monolayer CrSi\({}_{2}\): A First-Principle Study
## 2 Material and Methods  
Our calculations were performed using spin-polarized density functional theory (DFT) in the generalized gradient approximation (GGA) [35; 36], with the Pedew-Burke-Ernzerhof (PBE) function for exchange-correlation potential, which were implemented in the Cambridge Sequential Total Energy Package (CASTEP) [37]. Projector augmented-wave (PAW) potentials [38] were employed to illustrate electron-ion interactions. The convergence criterion of total energy was set to be 10\({}^{-6}\) eV, and energy cutoff of 310 eV was adopted for the expansion of plane waves after our test. The Monkhorst-Pack [39] k-point grids of 6 \(\times\) 6 \(\times\) 6, 6 \(\times\) 6 \(\times\) 1 were applied for the Brillouin-zone (BZ) integration in bulk, and monolayer CrSi\({}_{2}\) computation, respectively. For monolayer CrSi\({}_{2}\), vacuum-slabs of 15A were used to avoid interactions between adjacent atom layers. CrSi\({}_{2}\) has a hexagonal structure (C40) with nonsymmorphic space group D\({}_{6}^{4}\)-P6\({}_{2}\)22 [24; 25], containing no primitive translations which interchange individual CrSi\({}_{2}\) layers. The lattice constants were a = 4.431 and c = 6.364 A [24; 25]. The lattice constants and atomic positions were fully relaxed until the force on each atom was less than 0.03 eV/A. Monolayer CrSi\({}_{2}\) has a graphene-like honeycomb structure, which can be formed by a micromechanical cleavage technique due to weak van der Waals (vdW) forces between those layers and strong covalent bonding intralayer [40]. Top and side views of monolayer CrSi\({}_{2}\) after geometry optimization are depicted in Figure 1. According to chemical formula, per unit cell is constructed by one Cr atom and two Si atoms because in every intralayer, one Cr atom is in the center of each hexagonal hole of silicene lattice, leading to a 1:2 ratio between Cr and Si.
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 3 Results and Discussion
### Simulation Experiments
Figure 9: The instantaneous frequency measurement (IFM) results of the correct Channel 2 and 3: (a) the original IFM result of Channel 2; (b) the calibrated IFM result of Channel 2; (c) the original IFM result of Channel 3; (d) the calibrated IFM result of Channel 3. Figure 10: The IFM results of the correct Channel 5, 6, and 7: (**a**) the original IFM result of Channel 5; (**b**) the calibrated IFM result of Channel 5; (**c**) the original IFM result of Channel 6; (**d**) the calibrated IFM result of Channel 6; (**e**) the original IFM result of Channel 7; (**f**) the calibrated IFM result of Channel 7. In order to facilitate the display of simulation results, the bandwidth of the chirp signal used in the simulation is insignificant. The instantaneous bandwidth that the receiver architecture proposed in this paper can adapt to is much larger than that adopted by the simulation. The system sampling rate determines the instantaneous bandwidth. The higher the system sampling rate, the larger the instantaneous bandwidth the receiver can handle. The band-pass sampling theorem limits the specific relationship between the instantaneous bandwidth and the system sampling rate.
## 5 Results
### Comparison with Existing Approaches
Our experiments encompassed both types of upscaled images, and the results were systematically compared in order to highlight the effectiveness of our super-resolution approach. Anomaly recognition was executed through a two-stream architecture wherein a pre-trained CNN model extracted features from RGB images in the spatial stream, and Stacked Grayscale 3-channel Images (SG3I) were used in the temporal stream, substantially reducing the computational load of optical flow computation while capturing short-term temporal characteristics. The features from both streams were concatenated and passed through a Gated Recurrent Unit (GRU) layer to capture long-term temporal characteristics. The output of the GRU layer was then processed through dense and softmax layers before reaching the final classification layer. Our proposed methodology, coupled with the encoder-bank-decoder super-resolution model, yielded remarkable results, achieving a classification accuracy of 92.28%, an F1-score of 69.29%, and a false positive rate of just 4.41%.
# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 3 Campaign Test and Data Description
### Dataset Pre-Processing and Feature Selection  
Once the initial raw data of traces are obtained, we pass to the data cleaning step that implements the following two operations named _\"feature selection\"_ and _\"data preparation\"_. Concerning the feature selection operation, we have applied a correlation study between the main mobile physical parameters and the service status. Figure 4 shows the correlation results for the video streaming service. We notice that RSRP and RSSNR are the two most correlated with the service status (i.e., the bitrate) with 0.23 and 0.15, respectively. The RSRQ, LTE_asu and LTE_dbm come after with 0.13. We have selected RSRQ as the third important feature as it is consistent with what network operators do in general for radio provisioning [19].  
Concerning the \"data preparation\", we have decided to simply discard entire rows and/or columns containing missing values when the features are selected as we have enough measures and do not want to consider any modified data.  
After that, and to analyze the mobile experience from an end user's perspective, we calculate the user's mean opinion scores (MOS) for each considered service using an appropriate QoE model for each service from the literature, and we annotate the dataset with this new feature. In particular, we drive MOS score (i) from the bandwidth rate as in [1,20] for the downloading service, (ii) from the bitrate for HD video streaming service as in [21,22], and (iii) from the application buffer information as in [23,24] for the web browsing service. After calculating the user's MOS score values, we represent the MOS scale in 5-levels (\(mos\)) and 3-levels (\(quality\)) as done in the literature [25]. Table 1 reports the final clean dataset with the considered features that is ready for the in-depth analysis. A detailed description and script Python files for reproducing the analysis can be found in our Gitlab repository [9].  
After filtering incomplete entries, 164,426 trace measurements remain. Each trace is composed of 22 parameters corresponding to one of the four data types named categorical, numeric, temporal and spatial as presented in Table 1. Out of these features, we find the meta information like measurement instant (\(id\_QSCycle\), \(timestamp\)), geo-coordinates (\(latitude\), \(longitude\)), and location (\(code\_zone\), \(name\_zone\) ); the dataset also includes device  
Figure 4: Correlation matrix between radio indicators and service status.  
information (such as device model). In addition, key physical parameters are included like \(LTE\) Reference Signal Received Quality (\(RSRQ\)), \(LTE\) Reference Signal Received Power (\(RSRP\)), and the bitrate. Finally, details about some application parameters such as the initial buffering time, rebuffering duration, and rebuffering frequency are collected.
## 2 Materials and Methods  
### Wind and Wave Field Characteristic Parameters  
#### 2.1.1 Non-Operational Sea State  
The wave parameters of one in fifty years were selected for the self-storage sea state. According to the long-term wave statistics in the South China Sea, the wave parameters of one in fifty years were calculated [18; 19].  
The wave statistics were analyzed using the Weibull method according to DNV-RP-C205 \"Environmental Conditions and Environmental Loads\" to derive the design wave heights. The Weibull distribution is shown in Equation (1).  
\[\mathrm{F}=1-e^{-\frac{H^{m}}{P}} \tag{1}\]  
Since the design wave considers a return period of one in fifty years, the reliability of wave height emergence R = 0.02 and the unreliability of wave height emergence F = 1 - R = 0.98 according to the linear fitting formula shown in Figure 1.  
\[\mathrm{lnln}(1/(1-\mathrm{F}))=5.926\ \mathrm{lnH}-14.44 \tag{2}\]  
The final design wave height for the fifty-year event is H50 = 14.4 m.  
According to DNV-RP-C205 \"Environmental Conditions and Environmental Loads\", the design wave period and the design wave height satisfy the following Equation:  
\[\mathrm{T}_{\mathrm{H}_{\mathrm{max}}}=\mathrm{a}\cdot\mathrm{H}_{\mathrm{ max}}^{\mathrm{b}} \tag{3}\]  
DNV is given the reference values a = 2.94 and b = 0.5. According to the design wave height of one in fifty years, H50 = 14.4 m, the design wave period is obtained by Equation (3) T H50 = 11.2 s.  
According to the linear wave theory, the wavelength of the design wave can be determined by the following Equation (4):  
\[\mathrm{L}=\frac{\mathrm{gT}^{2}}{2\pi} \tag{4}\]  
By the 50-year design wave cycle T H50 = 11.2 s, we calculated the wavelength of the design wave L H50 = 196 m.  
Figure 1: Wave height linear fit.
# A Cloud Based Disaster Management System
## 6 Optimized Rescue Plan Development
With respect to the disaster management application, three objective functions came under consideration: total travel distance (TTD), maximum tour (MT), and deviation rates for the tour lengths (DR). Certainly, in systems such as fire disasters, the most critical issue is mission time, which is directly proportional to MT. Furthermore, minimizing the TTD and DR permits the minimization of total energy consumption and balancing of vehicle workloads. We then considered the following comparison matrix:  
\[A_{i,j}=\left( \begin{array}{cccc}&\text{TTD}&\text{MT}&\text{DR}\\ \text{TTD}&1&1/2&1/3\\ \text{MT}&2&1&1/2\\ \text{DR}&3&2&1\end{array} \right) \tag{11}\]  
This matrix shows that MT has double the priority of the TTD, and the DR has triple the priority of the TTD and double the priority of the MT. The proposed AHP-MTSP algorithm was designed to select the best result from three approaches namely the market-based approach, the RTMA approach and the balanced. However, for lack of space, we present here only the market-based approach and we refer the reader to paper [67, 68, 69] for more details. The market-based approach can leverage the use of the global knowledge of the system in the cloud server to dynamically adapt the allocation of tasks to robots based on the dynamic changes of an emergency situation in real-time through dynamic auction-bidding transactions for every new event when it occurs. In fact, in market-based, the coordinator of the cloud server will receive events in real-time from sensors spread in the environment and dynamically generates new auctions for the rescuer team (robots), which will bid on these tasks using the AHP-MTSP algorithm, and accordingly adapts the rescue plan in real-time. Following the market-based approach, existing robots compete to reach currently available targets. Therefore, each robot selects the best target, which provides the least local cost. The weighted sum of the OF costs is defined as local cost for a specific robot. After the target selection, the robots relay their bid to a central machine, with each bid containing the specific chosen target and corresponding costs for each OF. Once it receives the different bids, the central machine calculates global costs in each bid,  
Figure 6: Flowchart showing the proposed AHP-MTSP approach. then allocates the most appropriate target to the corresponding robot. The target with the least global cost is the best target. To illustrate the market-based approach, we considered a scenario with two robots, six targets, and two OFs (TTD and MT), with the weight vector W = [0.66, 0.33] (TTD has a priority two times greater than that of MT; Figure 7). More details about the market-based approach are described in our previous paper [67]. First, R1 chooses T1 and R2 chooses T5. Since global cost when the central machine assigns T1 to R1 is lower than when it assigns T5 to R2, the allocation goes to R1. This process goes on until the central machine allocates all robots their targets, as evident in Figure 7, and the TTD cost is promoted more by the central machine than the MT.
# Modeling Network-Controlled Device-to-Device Communications in SimuLTE
## 4 Overview of the SimuLTE Architecture  
As already stated, SimuLTE is a simulation library for system-level simulation of LTE networks. It is based on the OMNeT++ framework [12], which is built around the concept of module, a basic modeling unit which communicates via message exchanges and that can be hierarchically organized in compound modules. Each module is characterized by a structure, defined via Network Definition (NED) files, and a behavior, implemented via C++ classes. A general knowledge of the OMNeT++ framework is assumed henceforth. The interested reader can find more details on OMNeT++ in [39]. It goes without saying that SimuLTE modules can be instantiated in arbitrarily complex scenarios, involving any OMNeT++-compatible module: for instance, SimuLTE UEs can be vehicles moving about a floorplan according to Veins vehicular mobility model [34], communicating with fixed hosts provided by the INET library.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Focus** & **D2D Capabilities** \\ \hline
[22] & Link level & No \\
[8] & Link level & Yes \\
[23] & Link level & No \\
[24] & Link level & Cooperation service among vehicles only \\
[25] & Link level & D2D Discovery only \\
[26] & Link level & Yes \\
[15] & System level & No \\
[14] & System level & No \\
[16] & System level & Network-unassisted D2D \\ \hline \hline \end{tabular}
\end{table}
Table 1: Previous work describing existing LTE-A simulators and their D2D capabilities.  
We now describe the architecture of SimuLTE. In doing so, we also describe and justify its core architectural choices--related to the modeling of a classical infrastructure-based cellular communication. These choices, in fact, guide the modeling of D2D functionalities, which is described in the next section.