esa-sceva/satcom-chunk-collection huggingface dataset:
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
## 2 Materials and Methods
### Calibration  
The SN2881 chip employed in the LoRa receiver includes a register for reading out the SNR of the last successfully received packet, returning a value between \(-128\) and \(127\) dB, according to the documentation. However, measurements have shown that only a small detection range is available. Stronger signals quickly saturate the detector, whereas packets are no longer detected below a given SNR, and hence an SNR value is not available.  
Additionally, a calibration is necessary to obtain accurate received powers, expressed in dBm. This calibration is performed by connecting the LoRa transmitter, which transmits packets at a power of \(+14\) dBm, via switched attenuators to the receiver. For attenuations in the 100 to 130 dB range, a fairly linear response is obtained in the 868 MHz band. In the case of an attenuation less than 100 dB, the detector saturates. This means that for strong received signals the detector is saturated, resulting in an incorrect SNR measurement, while the packet is received successfully. The calibration results are  
Figure 6: Spectrogram of a LoRa packet. Note the 10 up sweeps, the 2.25 down sweeps of the preamble, and the discontinuities that represent the symbols.  
presented in Figure 7. The calibration procedure yields the following empirical formula to calculate the received power in the \(-130\) to \(-100\) dBm range:  
\[P_{\textrm{dBm}}=\frac{\textrm{SNR}_{\textrm{dB}}-88.759}{0.8215} \tag{1}\]  
As the 434 MHz band is received by the same chip and the SNR is measured by the same detector, a similar response as for the 868 MHz band is assumed.
# Analysis of Electroencephalograms Based on the Phase Plane Method
###### Abstract  
Ensuring noise immunity is one of the main tasks of radio engineering and telecommunication. The main task of signal receiving comes down to the best recovery of useful information from a signal that is destructed during propagation and received together with interference. Currently, the interference and noise control comes to the fore. Modern elements and methods of processing, related to intelligent systems, strengthen the role of the verification and recognition of targets. This makes noise control particularly relevant. The most-important quantitative indicator that characterizes the quality of the useful signal is the signal-to-noise ratio. Therefore, determining the noise parameters is very important. In the present paper, a signal model is used in the form of an additive mixture of useful signals and Gaussian noise. It is an ordinary model of a received signal in radio engineering and communications. It is shown that the phase portrait of this signal has the shape of an ellipse at the low noise level. For the first time, an expression of the width of the ellipse line is obtained, which is determined by the noise dispersion. Currently, in electroencephalography, diagnosis is based on the Fourier transform. But, many brain diseases are not detected by this method. Therefore, the search and use of other methods of signal processing is relevant.  
phase portrait; signal-to-noise ratio; electroencephalogram; wavelet transform Article
# TFBN: A Cost Effective High Performance Hierarchical Interconnection Network
## 5 Static Cost Effectiveness Analysis of a TFBN
### Wiring Complexity  
The wiring complexity is represented as the number of communication links required for interconnecting entire nodes of an MPC system. It is a static cost estimation for the communication links cost. The communication link cost depends on the length of the links and their underlying interconnection ranging from the VLSI chip level to the board level, cabinet-level, and system level. The chip level designed is ignored here because we have considered our design using node. Therefore, our consideration is from the board level to the system level. In static wiring complexity evaluation,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Cost** & **Wiring Complexity** & **Cost Performance Trade-off Factor** \\ \hline  & & & 256 Node \\ \hline
2D-Mesh & 120 & 480 & 0.25 \\
2D-Torus & 64 & 512 & 0.50 \\ TESH & 84 & 416 & 0.31 \\ TTN & 90 & 544 & 0.85 \\ TFBN & 80 & 800 & 2.50 \\ \hline  & & 4096 Node & \\ \hline
2D-Mesh & 504 & 8064 & 0.0625 \\
2D-Torus & 256 & 8192 & 0.1250 \\ TESH & 128 & 6680 & 0.2039 \\ TTN & 144 & 8736 & 0.5332 \\ TFBN & 152 & 12832 & 1.3191 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of static cost, wiring complexity, and cost performance trade-off factor for various interconnection networks.  
we have considered the number of communication links and not the length of the links. Thus, both the long length link and short length link are considered as an equal cost.  
The proposed TFBN consists of numerous basic or primary modules whereby this primary module is a 2D-torus network. The wraps links in these basic modules result in a bit high number of communication links, which is, as mentioned before, the wiring complexity. The wiring complexity of a TFBN is \(\left[\#\text{ of links in a BM}\times k^{2(L-1)}+\sum_{x=2}^{L}2(2^{q})\times k^{2(L-1)}\right]\), where \(L\) is the level number. As tabulated in Table 2, the wiring complexity of the proposed TFBN is more than that of mesh, torus, TESH, and TTN networks. These extra-short length wrap-around links in the basic module result in short diameter and low average distance.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 5 Micromegas for Muon Tomography  
An example for the use of a Micromegas as a tracking detector away from classical HEP experiments is the ScanPyramids project [59]. This project consisted on a scan to study the archaeological heritage of the great pyramids in Egypt using cosmic muons. Cosmic muons are highly energetic particles that have \(\sim\)200-times the mass of an electron at rest, resulting in a long radiation length in dense material like concrete.  
Muon tomography uses cosmic muons to make non-invasive images from the interior of larger structures, such as buildings, mountains, or pyramids. A part of the muons passing through thick material, such as several meters of concrete, interacts causing a reduction of the particle flux after the object. As structures of different density affect the flux differently, a muon detector can map the density distribution of an object. A three-dimensional image can be produced measuring the same object from different angles.  
\begin{table}
\begin{tabular}{c c} \hline Strips per PCB & 1022 \\ Strip width & 300 \(\upmu\)m \\ Strip pitch & 450 \(\upmu\)m \\ Pillar pitch & 7 mm \\ Pillar height & 128 \(\upmu\)m \\ \hline \end{tabular}
\end{table}
Table 1: Readout board parameters.  
Figure 12: Structure of the RO boards.  
A portable telescope consisting of four resistive bulk Micromegas with an active area of \(50\times 50\,\mathrm{cm}^{2}\) each and an XY-strip readout is used for the ScanPyramids project (Figure 13). The readout strips are multiplexed to reduce the number of electronic channels and to build a more compact detector unit [60]. A compact and lightweight detector with the given active area was built with Micromegas.  
Bulk Micromegas are robust, and they can operate autonomously for long time periods, even months, as long as a small gas flow maintains the gas quality [61]. For the ScanPyramids project, the Micromegas detectors took data continuously for approximately 100 days to accumulate \(\sim\)10 million recorded tracks. A previously unknown void inside of Khufu's Pyramid was detected by the ScanPyramids project [59]. The muon tomography of the pyramids in the ScanPyramids collaboration additionally used a scintillator hodoscope and nuclear emulsion plates.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Chemical Properties and Their Characterization of MNPs
#### 3.2.5 Mass Spectrometry (MSp)  
MSp methods can provide valuable information regarding nanoparticles structure, composition, and surface chemistry. MSp coupled with other techniques is useful for various analyses concerning the NPs' properties [110].  
MSp coupled with liquid chromatography is suitable for studying ligands' nature and distribution. MSp combined with matrix-assisted laser desorption can be used to analyze the ligands or the capping agents attached at the surface of MNPs and to identify MNPs' size distribution [111].  
Inductively coupled plasma MSp can support the elemental composition in order to understand their chemistry and to detect the presence of certain impurities [112].  
MSp can ensure both quantitative and qualitative assays and can provide insights into structural characterization. However, it is important to use other complementary techniques for a better understanding of the NPs' properties [113].
## 6 Stability Analysis of Surrounding Rock Based on Refined Modeling
### Analysis of Deformation Evolution  
The analysis of ore body deformation evolution is shown in Figure 19a,c,e. Under the combined mining conditions of the two sublevels, failure to set isolation pillars will result in significant deformation in the fill bodies and ore body at the junctions of the ore body with faults F2-1 and F18, particularly in the red dashed area between the 180 m and 260 m sublevels, which is detrimental to the safe conduct of mining activities. However, as the thickness of the isolation pillars increases, the displacement and deformation in the ore body and fill bodies in the mentioned areas gradually improve. Additionally, the local large deformation areas at the junctions of faults F2-1 and F18 with the ore body also improve. When the isolation pillar thickness is 20 m, the stability of the surrounding rock is relatively good. However, even with a 20 m isolation pillar, mining through faults still requires careful attention.  
The analysis of fault plastic zone evolution is shown in Figure 19b,d,f. Under the combined mining conditions of the two sublevels, without isolation pillars, mining activity impacts faults F2-1, F18, and the near-ore fault F10. This results in large deformation areas at the junctions of fault F2-1 with the ore body and adjacent locations and small deformation areas at the junctions of faults F18 and F10 with the ore body and adjacent locations. When the isolation pillar thickness is 10 m, it can somewhat improve the displacement deformation of critical faults in these areas, but the small deformation areas remain large. With a 20 m isolation pillar, the displacement deformation of critical faults in these areas is further improved, significantly reducing the small deformation areas. This more effectively controls the displacement deformation of the near-ore regions of the three critical faults, improving the displacement deformation at the junctions of the faults with the ore body.  
Figure 19: Analysis of deformation evolution of ore body and faults: (a) 0 m isolation pillar (frontal view of ore body); (b) 0 m isolation pillar (top view of fault); (c) 10 m isolation pillar (frontal view of ore body); (d) 10 m isolation pillar (top view of fault); (e) 20 m isolation pillar (frontal view of ore body); (f) 20 m isolation pillar (top view of fault).
###### Abstract  
State-of-the-art forest models are often complex, analytically intractable, and computationally expensive, due to the explicit representation of detailed biogeochemical and ecological processes. Different models often produce distinct results while predictions from the same model vary with parameter values. In this project, we developed a rigorous quantitative approach for conducting model intercomparisons and assessing model performance. We have applied our original methodology to compare two forest biogeochemistry models, the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) and Landscape Disturbance and Succession with Net Ecosystem Carbon and Nitrogen (LANDIS-II NECN). We simulated past-decade conditions at flux tower sites located within Harvard Forest, MA, USA (HF-EMS) and Jones Ecological Research Center, GA, USA (JERC-RD). We mined field data available from both sites to perform model parameterization, validation, and intercomparison. We assessed model performance using the following time-series metrics: Net ecosystem exchange, aboveground net primary production, aboveground biomass, C, and N, belowground biomass, C, and N, soil respiration, and species total biomass and relative abundance. We also assessed static observations of soil organic C and N, and concluded with an assessment of general model usability, performance, and transferability. Despite substantial differences in design, both models achieved good accuracy across the range of pool metrics. While LANDIS-II NECN showed better fidelity to interannual NEE fluxes, PPA-SiBGC indicated better overall performance for both sites across the 11 temporal and two static metrics tested (HF-EMS \(\overline{R^{2}}=0.73\), \(+0.07\), \(\overline{RMSE}=4.68\), \(-9.96\); JERC-RD \(\overline{R^{2}}=0.73\), \(+0.01\), \(\overline{RMSE}=2.18\), \(-1.64\)). To facilitate further testing of forest models at the two sites, we provide pre-processed datasets and original software written in the R language of statistical computing. In addition to model intercomparisons, our approach may be employed to test modifications to forest models and their sensitivity to different parameterizations.  
P rerfect Plasticity Approximation; SORTIE-PPA; LANDIS-II; forest ecosystem simulation; forest biogeochemistry model; forest landscape model; model intercomparison; Harvard Forest; Jones Ecological Research Center [1]10.3390/10020180
## 3 Results
### Evaluation of DSR View
#### 3.2.2 Objective Verification of the Accuracy of DSR View
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model No.** & **Position** & **Actual Model** & **DSR View** & **Difference** \\ \hline \multirow{3}{*}{1} & a & 31.51 & 31.51 & 0.00 \\ \cline{2-5}  & b & 41.65 & 41.65 & 0.00 \\ \hline \multirow{3}{*}{2} & a & 5.10 & 5.10 & 0.00 \\ \cline{2-5}  & b & 34.45 & 34.45 & 0.00 \\ \hline \multirow{3}{*}{3} & a & 18.27 & 18.27 & 0.00 \\ \cline{2-5}  & b & 41.97 & 41.97 & 0.00 \\ \hline \multirow{3}{*}{4} & a & 40.29 & 40.29 & 0.00 \\ \cline{2-5}  & b & 44.92 & 44.92 & 0.00 \\ \hline \multirow{3}{*}{5} & a & 18.38 & 18.38 & 0.00 \\ \cline{2-5}  & b & 41.64 & 41.64 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measurement results in the horizontal direction.
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 2 Theory and Method
### Improved YOLOv8 for Target Detection  
#### 2.2.1 Structure of the Improved YOLOv8 Model  
YOLOv8 is a YOLO serial algorithm developed by Ultralytics after YOLOv5 [16]. The YOLOv8 model mainly consists of four parts: input module, backbone network, neck module, and output module. The input module preprocesses images through mosaic data augmentation, adaptive image scaling, and grayscale padding. In the backbone network, the Conv, C2f, and SPPF modules extract image features through convolution and pooling. The neck module is designed based on path aggregation network structure and fuses feature maps of different scaling scales through up-sampling, down-sampling, and feature concatenation. The output module uses a decoupled head structure to decouple classification and regression processes. The YOLOv8 network uses the task-aligned assign [17] method to weight the classification score and regression score and matches positive samples based on the weighted results. The loss computation includes classification loss using binary cross-entropy, regression loss using distribution focal loss [18], and complete intersection over union (CIoU) loss function [19].  
The structure of the improved YOLOv8 model is shown in Figure 5.  
The proposed detection model is based on YOLOv8n, which has the smallest size and the fastest detection speed. To improve the model performance on the side-scan sonar dataset, the following improvement measures are taken.  
1. A global attention mechanism (GAM) [20] is added to the backbone and neck modules of the model. Considering the interaction between channels and space, the GAM fuses channel and spatial attention together to preserve more feature information and improve model performance.
2. Side-scan sonar target images have characteristics such as unclear edges and incomplete targets, and ordinary convolutional layers have poor feature extraction capabilities for such targets. A deformable convolution network (DCN) v3 [21] is combined with the C2f module in the backbone to construct the C2f-DCNv3 module, which can effectively improve the detection accuracy of the model.  
Figure 4: Structure of discriminators in CycleGAN.  
Figure 3: Structure of generators in CycleGAN.  
To achieve better optimization during model training, CIoU loss is replaced with efficient intersection over union (EIoU) loss [22].
## 2 Principle of Operation and Materials  
### Model Structure and Motion Analysis  
In order to optimize the structural parameters of the rotary blade, parametric modeling was carried out in SOLDWORKS. In this simulation test, the rotary blade of the 1 GQK-125 rotary component was used as the reference for modeling, and the schematic diagram of its rotary blade is shown in Figure 1.  
In order to realize dynamic simulation of the rotary blade in a discrete element simulation, a three-dimensional model of the rotary blade was established. Its three-dimensional assembly is shown in Figure 2, which consists of left (right) rotary blade, knife holder, and roller shaft parts.  
The analysis of the rotary tiller motion process provides a reference for setting boundary conditions for subsequent dynamic simulations of soil cutting. The rotary cutter soil-cutting movement process mainly includes two parts: (1) with the rotary tiller forward direction of motion, that is, the rotary blade forward horizontal speed; (2) the rotational movement around the rotary cutter axis (after the pendulum line), that is, the angular  
Figure 1: Schematic diagram of rotary blade structure. 1. positive cutting edge; 2. front cutting part; 3. bending line; 4. side cutting edge; 5. side cutting part; 6. mounting hole; 7. tool handle; 8. rotary center of knife roller; 9. curve of back edge.  
Figure 2: Structure of rotary tiller roller 3D model. 1. roller; 2. knife seat; 3. rotary tillage single knife.  
velocity of the rotary cutter, according to the definition of the soil cutting motion coordinate system, as shown in Figure 3[18]. The trajectory of the rotary tillage elbow can be expressed as:  
\[\left\{\begin{array}{l}x=R\cos(\omega t)+V_{m}t\\ y=R\sin(\omega t)\end{array}\right. \tag{1}\]  
where \(\omega\) is angular velocity of rotary blade rotation (rad/s); \(t\) is soil cutting time (s); \(V_{m}\) is the speed of rotary blade at a certain time (m/s).
# Malware Variants Detection Model Based on MFF-HDBA
## 5 Experimental Results and Discussion
### Validation with DataCon Data Set  
In this section, we validated the proposed method with the DataCon data set of 7896 mining samples and 15759 non-mining samples. The DataCon dataset is a challenging malware data set. The samples in the data set were captured from a live network in 2020, which consisted of numerous adversarial samples by malware development toolkits, such us packing, instruction virtualization, and metamorphism. We evaluated the Datacon data  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Year** & **Description** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline Ref [16] & 2011 & GIST + KNN & 97.18 & - & - & - \\ Ref [20] & 2016 & SPAM & 97.40 & - & - & - \\ Ref [36] & 2018 & DRBA + CNN & 94.50 & 96.60 & 88.40 & - \\ Ref [19] & 2019 & LGMP + KNN & 98.40 & - & 98.20 & 97.1 \\ Ref [26] & 2019 & NSGAII + CNN & 97.60 & 97.60 & 88.40 & - \\ Ref [52] & 2019 & CNN + LSTM & 96.30 & 96.30 & 96.20 & 96.20 \\ Ref [25] & 2019 & CNN + BiGRU & 96.30 & 91.80 & 91.50 & 91.60 \\ Ref [53] & 2019 & CSGM + KNN & 98.40 & - & 98.20 & 97.10 \\ Ref [54] & 2020 & MX + GLCM & 98.58 & 98.04 & 98.06 & 98.05 \\ Ref [21] & 2020 & Sliding Window & 98.65 & 98.86 & 98.63 & 98.74 \\ Ref [46] & 2020 & DCNN & 98.79 & 98.79 & 98.47 & 98.46 \\ Ref [28] & 2020 & IMCFN & 98.82 & 98.85 & 98.81 & 98.75 \\ Ref [45] & 2021 & DenseNet201 & 98.97 & - & - & 98.88 \\ Ref [33] & 2021 & DEAM + Densenet & 98.50 & 96.90 & 96.60 & 96.70 \\ Ref [55] & 2021 & Two-level ANN & 99.13 & - & - & - \\ Ref [56] & 2021 & MCFT-CNN & 99.19 & 97.72 & 97.76 & 97.68 \\ Ref [39] & 2022 & DTMIC & 98.93 & 99.00 & 99.00 & 99.00 \\  & & Multi-scale Feature & & & & \\ Ours & 2022 & Fusion + Channel & **99.36** & **99.39** & **99.36** & **99.36** \\  & & Attention + HDBA & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of a comparative assessment with recent state-of-the-art solutions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline No optimization & 98.82 & 98.86 & 98.82 & 98.84 \\ Manual optimization & 99.04 & 99.18 & 99.04 & 99.05 \\ HDBA & 99.36 & 99.39 & 99.36 & 99.36 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of the hyperparameter optimization experiment.  
set when no HDBA was applied. Then, we evaluated the Datacon data set with our model using HDBA. The experimental results are shown in Table 11. We achieved an overall accuracy of 96.64% for our model while 95.46% for no HDBA. The results further illustrated excellent recognition capability of our model, even for malware samples in live networks. Moreover, our model made adaptive adjustments according to different malware data sets.
# Designed Circularly Polarized Two-Port Microstrip MIMO Antenna for WLAN Applications
## 4 Parametric Study
### Changing the Distance (d) between the Patches  
The length and width of the patch corner cut are fixed at \(ls=3.1\) mm, and changes the edge-to-edge distance (\(d\)) between the two elements. The distance (\(d\)) varied from 10.2 to 13.2 mm between the patches. The antenna-simulated results are presented in Figure 8. The simulated results show that, when increasing the distance from 12.2 mm, the resonance frequency shift from 5.58 GHz to a high frequency 6.1 GHz and poor impedance matching between S11 and S21 also decrease the axial ratio. When the distance is reduced from 12.2 mm, the reflection coefficient S11 decreases, as does the isolation, as shown in Figure 8a. While, an increased axial ratio is presented in Figure 8b. The S11 and S21 frequency bands for various distances of \(d\) are shown in Figure 8a. It is clear that, when \(d\) was 12.2 mm, the S11 frequency band ranged from 5.23 to 6.42 GHz, resonating at 5.6 GHz. However, the remaining plots were also in the range of <10 dB. The distance (\(d\)) was fixed to 1.2 mm to achieve high isolation and improve the remaining results of the MIMO antenna within the frequency band. The performance was considerably better when the edge-to-edge distance (\(d\)) between the two elements and the length and width of the square-cut _ls_ were fixed at \(ls=3.1\) and \(d=12.2\) for better performance. Furthermore, the isolation improves; S11 and S21 are matched at the center frequency.  
Figure 7: (**a**) S-parameters of different plots for patches, corner cut length and width _ls_. (**b**) Axial ratio of different plots for patches, corner cut length and width _ls_.