esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Smooth Communication Index and Design of Communication Device Installation Plan  
In this study, a quantitative index to support the installation of communication devices in underground mines was proposed based on LOS analysis. Once a communication device is installed at a specific point within a mine, the location at which the next device should be installed must be determined. Regarding this, the first aspect is to confirm whether an LOS connection from the previously installed device exists; this can be confirmed via LOS analysis, as described above. The next consideration is whether a sufficiently wide communicable area can be obtained where a new device can be installed. As shown in Figure 3, assuming two candidate points within the communicable area of the first installed device (point O), the most appropriate location for installing the next device must be determined. In the figure, the communicable area of point A is indicated in red and the mine surface within the area is indicated by red dots, whereas the communicable area of point B is indicated in blue. To determine the more appropriate location for installing the next device, the overall installation direction of the devices and the target area for communication should also be considered. More importantly, to ensure that the installation point must have a wide communicable area, herein, an SCI that considers this was proposed. Although only two points were used as examples, the SCI must be analyzed for all points within the mine or within the communicable area of a pre-installed device. When the same number of rays are emitted from each point, the number of points intersected by the rays can be used. However, even if the communicable area is different, the number of points intersected by the rays may be the same. In fact, in the case of an underground mine, the 3D model is a closed curved surface, except for the mine adit; thus, any ray emitted from a specific point can contact the inner surface of the mine. Therefore, as an indicator of the location of installing the next device, the number of points intersected by a ray is not as appropriate as the SCI in an underground mine. In this study, the average distance between points intersected by rays was calculated and proposed as the SCI. In Figure 3, point B has a wider communicable area compared with point A, which is associated with a wider distribution of blue dots. In this method, points with a structurally narrow communicable area exhibit a low SCI value, whereas points with a wide communicable area exhibit a high value. Essentially, a point with a high SCI can cover a wider area and, therefore, is a suitable point to install a device.  
Figure 2: Schematic showing the need to set the ray in the direction of the normal vector of the inner surface. Point A is an example of inappropriate normal vector setting, and point B is an appropriate example.
## 3 System Performance under Both Weak Turbulence and Moderate-to-Strong Turbulence  
In this section, closed-form expressions are derived for the average BER and the average capacity of an FSO communication system under lognormal and Gamma-Gamma channel turbulence.  
The average BER of the FSO communication system can be expressed as  
\[P_{e}=\int_{0}^{\infty}P_{ber}p(I)dI, \tag{7}\]  
where \(P_{ber}\) is the BER of the FSO communication system with BPSK modulation and coherent detection, given by [11]  
\[P_{ber}=\int_{0}^{\infty}\frac{1}{\sqrt{\pi}\sigma}\exp\left[-\frac{(i_{d}+0. 5 RIA\xi)}{\sigma^{2}}\right]di_{d}=Q\left(\frac{RIA\xi}{\sqrt{2\sigma^{2}}} \right), \tag{8}\]  
where \(Q(\cdot)\) is the \(Q\)-function from [13] with \(Q(x)=\frac{1}{2\sqrt{\pi}}G_{1,2}^{2,0}\left[\frac{x^{2}}{2}\left|\begin{array} []{cc}1&\\ 0,\frac{1}{2}\end{array}\right.\right]\), where \(G_{p,d}^{m,n}\left(\left|\begin{array}{cc}\cdots\\ \cdots\end{array}\right.\right)\) is the Meijer's G function and the definition is seen in Appendix A. On the other hand, \(\sigma^{2}\) in Equation (8) is the total noise including background noise, thermal noise, and quantum noise. In addition, \(i_{d}\) is the baseband signal, and can be expressed as  
\[i_{d}(t)=\frac{d_{j}RIA\xi}{2}+n_{d}(t), \tag{9}\]where \(d_{j}\) is \(-1\) or \(1\) for the \(j\)-th data packet representing the symbol for \"0\" or \"1\" and \(n_{d}(t)\) is the Gaussian white noise, subject to a distribution of \(N(0,\sigma^{2})\).  
Another parameter that is crucial in characterizing the performance of an FSO communication system is the average channel capacity, which refers to the maximum information transmitted per unit time, and this is also evaluated here. According to [PERSON]'s theorem, the channel capacity under invariant optical intensity can be expressed as \(C=B\text{log}_{2}(1+SNR)\).  
For a FSO communication system under a specific turbulence condition, the average channel capacity under the corresponding channel model can be expressed as  
\[C=\int_{0}^{\infty}B\text{log}_{2}(1+SNR)p(I)dI. \tag{10}\]
# Synthesis and Biological Studies of New 2-Benzoxazolinone Derivatives as Antibacterial Agents
## 2 Materials and Methods
### General Procedure for the Preparation of Hydrazones 8-23
#### 2.4.2 Determination of the Minimum Inhibitory Concentration (MIC) by the Broth  
Mercolitation Method  
Serial two-fold dilutions (volumes of 50 uL) were prepared in Mueller-Hinton broth (Liofilchem, Teramo, Italy) in 96-well plates. The prepared concentrations of the compounds were 500, 250, 125, 62.5, 31.2, 15.6, 7.8, 3.9, 1.95, and 0.98 ug/mL. Then, these plates were inoculated with 0.05 mL of a bacterial suspension containing 5 \(\times\) 104 CFU and incubated at 37 \({}^{\circ}\)C for 18-24 h [47; 48]. The MIC values were evaluated by the lowest concentration of the compound at which bacterial growth is completely inhibited.
# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 2 Related Work on Authorship Attribution  
Over the years, various methodologies have been employed for authorship attribution. Traditional approaches often rely on statistical measures and handcrafted features, while newer methods leverage machine learning (ML)/deep learning (DL) models based on Transformer architectures. Feature-based models, stylometric analyses, and linguistic profiling are among the common methodologies employed. These techniques investigate aspects such as word frequency, sentence length, syntactic structures, and other linguistic attributes to create models capable of differentiating individual writing styles.
# Design of External Rotor Ferrite-Assisted Synchronous
## 3 Design of Fe-PMaSynRM for High Power Density
### The Designs of Flux Barrier
#### 3.1.2 Variation Developed Torque and Anti-Demagnetization Ability with PM-Shape  
The curvature (\(r_{arc}\)) degree will influence the motor's average torque. To show the effect of \(r_{arc}\) on the developed torque and anti-demagnetization ability, an FEA model of the proposed motor specified in Table 2 has been taken as the base model. Then \(r_{arc}\) is varied in a certain rage to record the average torque (\(T_{avg}\)) and maximum allowable q-axis current (\(i_{q,irr}\)). Figure 5a shows the variation of average torque with changes of \(r_{arc}\). With the decrease of the radius of the magnet surface (\(r_{arc}\)), the surface area of the magnet increases for the same magnet span (\(\theta_{m}\)) and magnet depth from the rotor surface (\(d_{m}\)), which will increase the average torque \(T_{avg}\). Simultaneously, the allowable maximum q-axis without irreversible demagnetization will be impacted due to a change in \(r_{arc}\). Figure 5b shows the changes in allowable maximum q-axis circuit current (\(I_{q,irr}\)) vs. degree of curvature of the PM, \(r_{arc}\). Overall, with the higher degree of curvature of the magnet (lower \(r_{arc}\)), average torque reduces, and maximum allowable q-axis current (\(I_{q,irr}\)) increases.
## 2 Problem Definition
### Multi-Timescale Spatio-Temporal Characteristic Modeling  
To fully explore the implied periodicity and trend information within passenger flow time series data, the data are divided into three distinct groups according to the timeline: near-term, mid-term, and distant-term. This division allows for the extraction of three temporal characteristics: closeness, period, and the trend of historical passenger flow data. Assuming that the daily total duration is evenly divided into \(T\) time steps, for a target predicting time slice \(t\), the time segments \(X_{closeness}^{(0)}\), \(X_{period}^{(0)}\), and \(X_{trend}^{(0)}\) are respectively extracted along the timeline with three timescales, and the number of time slices contained in each time segment is denoted by variables \(c\), \(d\), and \(w\). \(X_{closeness}^{(0)}\), \(X_{period}^{(0)}\), and \(X_{trend}^{(0)}\) represent the historical passenger flow tensor corresponding to each time segment, which will be used as inputs of the spatio-temporal module. The process of constructing the spatio-temporal passenger flow feature matrix is as follows.  
The tensor received by the closeness module is given by \(X_{closeness}^{(0)}\),  
\[X_{closeness}^{(0)}=\left(X_{t-c},X_{t-(c-1)},\cdots,X_{t-1}\right) \tag{2}\]where \(X^{(0)}_{closeness}\in\mathbb{R}^{2c\times I\times J}\). The closeness module uses the 2-channel passenger flow matrices of the closest c time slices to model the feature, and the position of the nearby time segment sequence \(P_{closeness}\) on the time axis is shown in Figure 1.
2. The tensor received by the period module can be expressed as \(X^{(0)}_{period}\), \[X^{(0)}_{period}=\left(X_{t-dT},X_{t-(d-1)T},\cdots,X_{t-T}\right)\] (3) where \(X^{(0)}_{period}\in\mathbb{R}^{2d\times I\times J}\). The period module input uses the same time slices from the previous d days adjacent to the time slice \(t\), and the position of the periodic time segment sequence \(P_{period}\) on the time axis is shown in Figure 2.
3. The tensor received by the trend module can be expressed as \(X^{(0)}_{trend}\), \[X^{(0)}_{trend}=\left(X_{t-7 wT},X_{t-7(w-1)T},\cdots,X_{t-7T}\right)\] (4) where \(X^{(0)}_{trend}\in R^{2w\times I\times J}\). Using the same time slices on the same day of the week in the \(w\) weeks prior to the time slice \(t\) as the trend module input, the position of the trending time segment sequence \(P_{trend}\) on the time axis is represented in Figure 3.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### How the Data Used to Train the Deep Learning Models Was Gathered/Generated?  
Deep learning models (both supervised and unsupervised) require datasets for their training and testing. However, acquiring a good dataset, in some cases, remains a considerable challenge.  
The works we reviewed either used different datasets or created their own data using different techniques, as shown in the Table 2.  
Most of works (more precisely 24 of them) used simulation to generate their dataset. This is often justified as the authors are unable to a suitable variety of available datasets focused on 5G, since this is a novel technology and is being slowly deployed since 2020. Nonetheless, as many as 18 works used actual datasets to train their models. Some works measured the data through experiments using their own platform, whereas other works used public datasets available across the Internet. Four papers generated synthetic datasets. They contained some parameters of the evaluated 5G environment that were randomly generated. Finally, 10 works did not describe the source of the data used to train the proposed models. This is a point of concern in our view, as it makes the reproducibility and verification of the results of these works very difficult if not impossible altogether.  
Unfortunately, none of the the works that created their own datasets (through simulation, measurements, or generated synthetically) made the data available. As a result, future works cannot use them to train new deep learning models or even use their results for comparison. Indeed, the availability of datasets for cellular networks is usually restricted to researchers subject to non-disclosure agreements (NDAs) and contracts with telecommunication operators and other private companies as also confirmed by [93].  
\begin{table}
\begin{tabular}{c c c} \hline
**Data Source** & **Number of Articles** & **References** \\ \hline Generated through simulation & 24 & [2; 15; 25; 34; 36; 47; 51; 58; 57; 59; 64; 68; 72; 73; 75; 77] \\ Real data (generated using prototypes or public dataset) & 18 & [5; 7; 10; 30; 38; 44; 43; 46; 52; 58; 66; 67; 71] \\ Synthetic (generated randomly) & 4 & [11; 19; 74; 76] \\ Not described (the work did not provide information about the dataset used) & 10 & [32; 21; 24; 26; 27; 29; 65; 70] \\ \hline \end{tabular}
\end{table}
Table 2: Data source.  
Therefore, in this section, we describe some of the few public datasets used in the works that we managed to verify during this systematic review. The idea is to provide a brief description of these datasets that may be used in new works based on deep learning and provide useful pointers to the reader on where to find these. Note that public 5G traces and datasets remain difficult to find and that most of the existing traces are relatively old and related to 4G technology.
## 1 Introduction
The literature to date shows consistently that accuracy increased with an increase in the number of GCPs and rapidly reached an asymptotic trend [25; 26; 27]. Conclusions differ among studies regarding the amount of GCPs required to produce a favorable outcome. In [28], 0.5-1 GCP per ha was the optimal GCP density, and GCPs were placed inside the area with stratified distribution to obtain the minimum total error. In [29], the optimal density was 1.8 GCPs per ha, uniformly distributed across the whole surface. [PERSON] et al. reported setting control points in the center and edge of the study area, which is of great significance to reduce the height vertical error in spatial concentration [30]. The purpose of this study was to evaluate the accuracy of UAV RTK/PPK direct georeferencing and to determine whether the method could be used as a solution for rapid mapping applications with data generated from images of natural environments, including buildings, low vegetation and so on. The aim was to understand the difference in survey effectiveness between using direct georeferencing and GCPs. In addition, the effect of GCP quantity and distribution on the quality of the results was also studied. The evaluation was performed by calculating the vertical and horizontal _RMSE_ of checkpoints on a digital surface model (DSM) and digital orthomosaic (DOM), respectively, and the Multiscale Model to Model Cloud Comparison (M3C2) distance based on point clouds [31]. The M3C2 method is the unique way to compute signed (and robust) distances directly between two point clouds.
## 2 Samples and Experimental Methods
### Experimental Methods  
Image processing is the most direct approach to studying the microstructure, genesis, and morphology of pores [45]. However, SEM imaging is not suitable to study the nanoscale PSDs of unconventional reservoirs. Argon board ion beam tools were firstly used to study shale nanoscale pores by providing a pseudogaussian surface with extremely high quality, thus making the observation of nanopores possible. Some research also used this method to investigate nanopores in coal and tight sandstone reservoirs [21; 33; 46]. Bulk samples were selected and polished by using a stand-alone argon beam machine (IB-09020 CP Cross-sectioning Polisher, JEOL) and prepared via gold spray treatment, then imaged with a S-4700 field emission scanning electron microscope. Energy-dispersive X-ray spectroscopy (EDS) was used to study the genesis and type of pores in the observation. Note that the borders between clay and brittle minerals and mineralogical edges were determined by combining these two imaging methods during FESEM image processing and data support from an energy dispersive spectrometer (EDS). The secondary electron image mainly reflects the morphological characteristics of the sample surface at about 10 nm, and backscattered electron imaging can not only analyze morphological features as an imaging signal but can also be used to display atomic number contrast and qualitatively analyze components. During image processing, we combined these two types of images to determine the spatial location and types of pores and verified them with EDS data.  
LP-N\({}_{2}\)GA was carried out to study pores with diameters ranging from 3.5 A to 5000 A using automated pore size and surface area analyzer (Tristar II 3020). Samples weighing 2 g with particle sizes between 60-80 mesh were dried for 2 h at 105\({}^{\circ}\)C and outgassed for 12 h. High-pressure mercury intrusion porosimetry (MIP) was conducted using an AutoPore IV 9500 V1.09 Mercury intrusion tester as a method for quantifying PSDs at the Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education. Selected bulk samples were sized to 0.1-1 cm\({}_{3}\) and prepared by drying for 24 h.  
The porosity and permeability values were determined using helium porosity measurement and the pressure pulse decay method (PDM), respectively. The testing of sample physical properties is mainly used to detect whether the sample belongs to typical tight sandstone and unconventional gas reservoirs and to support relevant discussions.  
Figure 3: Comparison of pore systems of three types of unconventional reservoirs in coal-bearing strata.
## 6 Performance Evaluation
### Energy Consumption Analysis  
Energy consumption in IoD devices (drones and ODs) is a critical performance metric, directly influenced by computational complexity and communication load (data transmission and reception). Lightweight cryptographic operations and fewer/smaller messages inherently lead to lower energy usage. While precise energy figures require hardware-specific measurements, we can make qualitative comparisons based on our computational and communication cost analyses. Given its extremely low total computational cost (**0.0411** ms) and minimal communication overhead (**672** bits), Bio-2 FA-IoD is expected to be very energy efficient. The computational energy on the OD (**0.029** ms) would typically be in the microjoule range, even with conservative power estimates for mobile CPUs during short cryptographic bursts. Transmission energy for a mere 84 bytes is significantly less than for protocols transferring several kilobytes. For instance, schemes like HCALA [2], using HECC, report an energy consumption for communication around \(3.38\times 10^{-4}\) J (338 uJ) for three messages, indicating a higher baseline due to larger cryptographic payloads typical of public-key schemes. The proposed protocol's reliance on fast symmetric cryptography and hashing for the online phase, with biometric processing localized to the OD, significantly reduces the energy drain, making it well suited for battery-dependent IoD components. The HOOPOE protocol [6] also provides detailed energy analysis, showing that RSA/AES operations are more energy intensive than the symmetric primitives predominantly used in Bio-2 FA-IoD. This makes Bio-2 FA-IoD highly advantageous for extending operational time and minimizing the power burden on IoD devices.
## 4 System Performance Discussion
### Parameter Discussion on System Performance
#### 4.2.2 Isolation  
Isolation refers to the ratio of the power of the initial signal to the power of the other ports, the general unit is dB. It is an important indicator of the performance of the MIMO system, which can be described as:  
\[k_{s}=10\lg\frac{P_{i}}{P_{s}} \tag{28}\]  
Pi and Ps represent the power of the input port and the isolation port, respectively. Low isolation will lead to mutual confusion of signal components between two signals, causing erred judgement  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & **Switch Time** & **Insertion Loss** & **Isolation** & **Clutter** & **Noise** & **Clutter Bandwidth** & **Noise Bandwidth** \\ \hline Value & 8 ns & 0.5 dB & 30 dB & 5 V & 1 V & 10 KHz & 6 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 4: Initial system parameter setup for single RF link MIMO radar.  
for the radar-receiving part. The ideal condition set in the simulation system is 200 dB. In actual engineering, the requirements depend on the specific circumstances. When 30 dB is used, the output signal of the isolated port is 0.1% of the output signal of the original port, which can meet engineering requirements in most cases.  
From (26) and (27), we can infer that the antenna that is not occupied will transmit signals outward due to isolation. Because of the existence of this signal, the signal energy in the working link will be reduced and the performance of the system will be affected. Therefore, for the single RF link MIMO radar system, because of the multiple SPDT switches, the isolation index plays a more important role than other parameters. Theoretically, the higher the isolation degree, the greater the energy employed when the other conditions are constant. For the switches selected in this study, the isolation is different at various frequencies, with higher frequency responding to lower isolation. Although the specific isolation value is not given in the case of 10 GHz, the isolation degree under 27 GHz has sufficiently low isolation, which can be used in X band single RF link MIMO radar applications.
## 3 Results
### Simulation Results
An adjacent interference assessment is shown in Figure 20. Therefore, the FSS-ES is not affected in the 5G-BS because the spectrum emission mask features are lower than the adjacent interference. Figure 17: Co-channel interference scenario for the INR CDF is evaluated for 5G-BS elevation angle based on the protection distance of 10 km. Figure 18: Co-channel interference scenario for the INR CDF is evaluated for 5G-BS elevation angle based on the protection distance of 20 km. Figure 19: Co-channel interference scenario for the INR CDF is evaluated for 5G-BS elevation angle based on the protection distance of 35 km. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Distance Protection—km**} & \multicolumn{3}{c}{**5G-BS Elevation Angles (\%)**} \\ \cline{2-4}  & **15\({}^{\circ}\)** & **20\({}^{\circ}\)** & **30\({}^{\circ}\)** \\ \hline
10 & 65.4 & 37.1 & 6.4 \\
20 & 41.7 & 26.8 & 0.88 \\
35 & 18.1 & 10.5 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Interference probability depending on protection distance and elevation angle of 5G-BS. Figure 20: Adjacent interference scenario for the INR CDF is evaluated on the basis of the protection distance. We focused on determining the out-of-band emission mask level causing interference in the FSS-ES by adjusting the protection distance of 5G-BS to 0.6 km [33]. Figure 21 depicts the INR CDF as a function of the adjacent channel's attenuation level by using the 5G-BS out-of-band emission mask. Therefore, the INR probability lower than \(-12.2\) dBm will be 100% if the attenuation is higher than \(-53\) dBc, indicating no interference. Interference occurred when the INR was less than \(-12.2\) dBm (\(-55\) dBc), and the attenuation was smaller than that. Therefore, attenuation of \(-53\) dBc had a 98.6% chance of satisfying an INR of less than \(-12.2\) dBm. Thus, attenuation of at least \(-53\) dBc was needed to achieve an interference probability of less than \(5\%\).