esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results Analysis
### Comparison of Advanced Positioning Techniques  
We compare the IPSOGWO-MSVR algorithm with FIFS, C-map, and LCAF. This is because they all solely use CSI amplitude as the fingerprint for localization. The detailed processing procedures of the comparison methods can be found in [19; 47; 48]. Table 4 presents the error statistics for the four localization methods in two experimental scenarios.  
Table 4 shows that in Scenario 1, the average localization error of the algorithm proposed in this paper is 0.59 m. Compared with FIFS, C-map, and LCAF algorithms, the average localization accuracy is improved by 60.7%, 42.7%, and 52.4%, respectively. In Scenario 2, the average localization error of the algorithm proposed in this paper is 1.12 m, with accuracy improvements of 45.4%, 29.1%, and 16.4% compared to the other  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multirow{2}{*}{**Method**} & **Maximum** & **Minimum** & **Mean** \\  & & **Error (m)** & **Error (m)** & **Error (m)** \\ \hline \multirow{4}{*}{Scenario 1} & FIFS & 2.61 & 0.47 & 1.50 \\  & C-map & 2.07 & 0.18 & 1.03 \\ \cline{1-1}  & LCAF & 2.21 & 0.36 & 1.24 \\ \cline{1-1}  & PSOGWO-MSVR & 1.35 & 0.11 & 0.59 \\ \hline \multirow{4}{*}{Scenario 2} & FIFS & 3.27 & 0.42 & 2.20 \\ \cline{1-1}  & C-map & 2.44 & 0.33 & 1.34 \\ \cline{1-1}  & LCAF & 2.56 & 0.32 & 1.58 \\ \cline{1-1}  & PSOGWO-MSVR & 1.92 & 0.41 & 1.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of root-mean-square errors across four methods.  
Figure 12: Root-mean-square error for the four algorithms in Scenario 2.  
three algorithms, respectively. We also note that the overall localization accuracy in Scenario 2 is lower than in Scenario 1, with the error rates of various algorithms respectively increasing by 46.7%, 30%, 27.4%, and 89.8%.  
Figure 13 shows the localization cumulative probability distribution function (CDF) of the four methods in two scenarios. In Scenario 1, with an error threshold of 1 m, the CDF value for IPSOGWO-MSVR is 80.3%. In comparison, FIFS, LCAF, and C-map have values of 17.3%, 26.8%, and 47%, respectively. This indicates that the majority of our algorithm's localization errors are less than 1 m. In Scenario 2, with a threshold of 1.5 m, FIFS, LCAF, and C-map report CDF values of 29.5%, 42%, and 62.9%, respectively. Meanwhile, IPSOGWO-MSVR achieves 79.8%. From the error statistics and CDF comparison, our proposed IPSOGWO-MSVR algorithm outperforms the other three methods.
# Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation
## 3 Proposed Low-Complexity Iterative Decoding for LDPC-Based Product Codes
### Performance of Product Codes Using Iterative Decoding  
This part presents the iterative decoding performance of product codes consisting of LDPC codes and Hamming codes with different code lengths. For the LDPC code, we choose the LDPC code with the code length of 576 bits and the code rate of 0.5 in IEEE 802.16e as an example. The BP decoding algorithm is used for LDPC codes and the maximum number of iterations is set to 30, for it is enough for the convergence of the decoding algorithm. The simulation is performed under the AWGN channel with BPSK constellation. It should be noted that, in order to verify the performance of the product codes, an ideal decoding algorithm is used for outer decoding. During the decoding process, the number of errors occurring among the outer codewords can be determined. If the number of errors occurring is within the error correction capability of the outer code, the decoder will correct all the errors; otherwise, the decoder will not perform any operation on this codeword. In this way, some
# Groundwater Detection Using the Pseudo-3D Resistivity Method: A History of Case Studies
## 3 Case Studies
### Groundwater Exploration Cases
#### 3.2.1 Groundwater Survey in Zixing City with Geophysical Methods
From the perspective of the deep aquifers, it satisfies the conditions needed to form hot water. There is a fault with good water content near the No. 400 measuring point of profile K8 and the No. 350 measuring point of profile K9 in the Zhoumensi survey area.
# Influence of Metro Track Irregularities on Pantograph Vibration and Its Interaction with Catenary
## 4 Result and Analysis
### Comparison with the Measured Irregularity of Beijing Metro  
The influences of the measured irregularity on Metro Line 1 in Z City were analyzed in Sections 4.1 and 4.2, and two broadband peaks in the irregularity spectrum are preliminarily associated with the serious wear of the pantograph strip. In this part, to more accurately determine the relationship between the large vibration and rapid wear of the pantograph strip of Z City Metro Line 1 and its track irregularity, the measured irregularity of the Beijing Metro is compared. The comparison of the track irregularity spectrum of the two lines is shown in Figure 11 and the PC contact normal force and the friction work are shown in Figure 12.  
Due to the short operation time of Z City Metro Line 1, its long-wave irregularity (with a wavelength longer than 20 m) is significantly lower than that of the Beijing Metro. The amplitude of the level irregularity is higher in the wavelengths of 10-20 m and 3-4 m. Dynamic calculation results show that below 0.8 Hz, the PC contact force of the Z  
Figure 11: Comparison of the measured irregularity in Z City Metro and Beijing Metro: (**a**) vertical irregularity; (**b**) lateral irregularity.  
Figure 12: Comparison of the PC contact: (**a**) normal force; (**b**) frictional work.  
City Metro is smaller, but at the peak around 1 Hz, it is larger than that of the Beijing Metro. In terms of friction work, the PC friction work of Z City Metro Line 1 is much larger than that of the Beijing Metro in the whole frequency band.  
These results and comparisons further confirm that there is an inevitable relationship between the large vibration and rapid wear of the pantograph strip in the Z City Metro and the excessive amplitude of the track irregularity with the two wavelengths.
## 2 Materials and Methods
### Sft/hrt  
A high-rate reactor such as an ESGB reactor can decouple HRT and SRT, thereby increasing the residence time of a biomass element within the reactor [15; 34; 35]. One of the main selection criteria for a reactor is a high SRT/HRT ratio, which prevents the washout of slow-growing methanogens [15].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Input** & **Inside the Reactor** & **Output** \\ \hline Temperature (\({}^{\circ}\)C) & & x & \\ Dry matter (\%) & x & x & x \\ Organic dry matter (\%) & x & x & x \\ C/N (\%) & x & & \\ Chemical oxygen demand (mg\({}_{CO}\)/L) & x & & x \\ Biochemical oxygen demand at 5 th day (mg\({}_{CO}\)/L) & x & & x \\ Loading rate per unit volume (kg\({}_{COD}\)/m\({}^{3}\cdot\)d) & x & & \\ Hydraulic residence time (d) & x & & \\ pH value (-) & x & & \\ Gas composition in volume fractions (\%, ppm) & & & x \\ Ratio of volatile organic acids to total inorganic carbon (-) & & x & \\ \hline \hline \end{tabular}  
\({}^{\text{x}}\): measured in the corresponded position.  
\end{table}
Table 2: Registered variables in the monitoring of reactor operations.  
The sludge age (SRT) in d is given by Equation (2).  
\[\text{SRT}=\frac{\text{Mass of sludge in reactor}}{\text{Mass of sludge wasted per day}}\;(\text{d}) \tag{2}\]  
If a steady-state condition was assumed, Equation (2) can be written as Equation (3).  
\[\text{SRT}=\frac{\text{x}_{\text{i}}\cdot\text{V}_{\text{R}}}{\text{Q}_{\text {eff}}\cdot\text{X}_{\text{eff}}}\;(\text{d}) \tag{3}\]  
where \(\text{x}_{\text{i}}\), \(\text{V}_{\text{R}}\), \(\text{Q}_{\text{eff}}\), and \(\text{X}_{\text{eff}}\) are the viable biomass concentration inside the reactor (\(\text{kg}_{\text{VS}}\)/ \(\text{kg}_{\text{FM}}\)), volume of the bioreactor (m\({}^{3}\)), effluent flow out (L/d) of the reactor, and viable biomass concentration in the effluent (\(\text{kg}_{\text{VS}}\)/\(\text{kg}_{\text{FM}}\)), respectively. Since the input and output flows were equal (steady-state condition), Equation (3) was transformed to (4).  
\[\frac{\text{SRT}}{\text{HRT}}=\frac{\text{x}_{\text{i}}}{\text{X}_{\text{eff} }}\;(\text{d}) \tag{4}\]  
The ratio SRT/HRT values were calculated for all three reactors using a biomass, which was sampled from the top of each reactor where biomass concentration was lowest. The ideal SRT/HRT ratio should be >3 [15; 17; 19].
## 2 Related Work  
### Methods of Constant False Alarm Rate (CFAR)  
Constant false alarm rate (CFAR) is commonly used for traditional ship detection. [PERSON] et al. [11] introduced a model to recognize single-polarization SAR images. They use a clutter CFAR-based model with K-distribution. [PERSON] et al. [12] proposed a combination technique of CFAR and wavelet transform for SAR image ship target detection. [PERSON] et al. [13] divided each candidate target's context slice into grids after identifying prospective targets using the maximum stable extreme region (MSER) approach. Traditional techniques are effective, but feature classifiers must be manually developed to extract image features. For instance, a CFAR algorithm's effectiveness depends on ocean disturbance modeling. Additionally, training in complicated settings is not optimal, and the generalization is poor since the structure cannot be modified in accordance with the objective properties. The techniques are also not an entire training model.
# MCIDN: Deblurring Network for Metal Corrosion Images
## 3 Method
### Architecture  
Our network employs an encoder-decoder architecture, enabling the generation of feature maps at different scales, as illustrated in Figure 1. The encoder-decoder block in the network is comprised of large residual dual-attention group networks, with downsampling modules utilized between encoders and upsampling modules between decoders. The encoders adjust the channel size through convolution operation, allowing cascading with the corresponding decoder for output.  
The process begins with the input of a blurred image, from which shallow features are extracted using a \(3\times 3\) convolutional layer. These features are further processed through successive encoder blocks. Upon reaching the deepest features via the final encoder, the decoders are employed to progressively restore high-resolution features. During this restoration process, feature fusion between corresponding encoders and decoders facilitates enhanced image reconstruction.  
Figure 1: Overall architecture of the proposed MCIDN.  
Given a blurry image of dimensions \(H\times W\times 3\), where \(H\times W\) represents the height and width, and 3 denotes the RGB channels, an initial \(3\times 3\) convolutional layer extracts shallow features of size \(H_{\downarrow}\times W_{\downarrow}\times C_{\uparrow}\). These features are progressively enriched with deep features through multiple encoder subnetworks. Subsequent downsampling modules following each encoder expand the channel dimensions while reducing the spatial resolution. The deep features produced by the final encoder are input into the decoders, where subsequent upsampling modules compress the channels and increase the resolution, gradually restoring the features to the original image size and producing sharp features. The encoder and corresponding decoder are combined through skip connections to aid in the reconstruction task, where \(3\times 3\) convolutions halve the feature channel dimensions. Following the final decoder, a \(3\times 3\) convolution and a skip connection with the input image features are employed to generate the sharp image.  
The internal structure of both the encoder and decoder is identical, each comprising two residual attention networks. In the first residual network, the input first passes through a Layer Normalization (LN) layer, followed by the spatial frequency network unit (SFNU), producing the output, which is then connected residually to the input. The SFNU includes the SCAM and FCAM, which will be elaborated upon later. The output of the first residual network is the input of the second residual network. In the second residual network, the input similarly passes first through the LN layer, then through the feedforward channel network unit (FCNU), producing the output, which is residually connected to the input.
## 4 Verification of Energy-Based Impact Formula
### Perforation Limit Assessment  
The perforation limit thickness formula is newly suggested as Equation (38), and is equal to the sum of penetration depth (\(x_{pe}\)), tunneling depth (\(x_{t}\)), and scabbing depth (\(x_{sc}\)). To verify this formula, only the experimental results obtained by [PERSON] [29] are likely used in the assessment of the scabbing limit formula. The modified NDRC, Hughes, and ACE formulae are used for comparison.  
As shown in Figure 10, the specimen with perforation failure is assumed to lie below 1 [30]. Table 3 classifies the specimens into perforated and non-perforated, and shows the accuracy of impact formulae. Sixty-three specimens are perforated in the experiments, of which the proposed formula obtains 41, with 63% accuracy. In addition, in 37% of the specimens, perforated failure is not predicted to occur but does occur. By contrast, the proposed formula predicts that 180 out of 182 specimens does not incur perforated failure, which is 99% accurate. The ACE formula predicts that 57 of the 63 perforated specimens would perforate, which is 88% accurate. Six specimens are predicted to not perforate, but are perforated in practice. The ACE formula predicts that 166 out of 182 unperforated specimens would not perforate, which is 91% accurate. Thus, the ACE formula exhibits a good accuracy compared to the other formulae. Considering that the perforation limit thickness is calculated to prevent perforation, the proposed formula predicts, with an accuracy rate of 99%, that the thickness calculated from the proposed formula can prevent perforation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Formula**} & **Scabbing Failure** & **No Scabbing Failure** \\  & **(Predicted/Tested)** & **(Predicted/Tested)** \\ \hline Proposed & 100\% & 81\% \\  & (75 of 75 specimens) & (139 of 172 specimens) \\ \hline Modified NDRC & 100\% & 66\% \\  & (75 of 75 specimens) & (113 of 172 specimens) \\ \hline Hughes & 100\% & 43\% \\  & (75 of 75 specimens) & (74 of 172 specimens) \\ \hline ACE & 100\% & 66\% \\  & (75 of 75 specimens) & (113 of 172 specimens) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of scabbing limit thickness formula.
# The 2022 Seismic Sequence in the Northern Adriatic Sea and Its Long-Term Simulation
## 3 Earthquake Simulation Method
The simulation algorithm allows ruptures to jump from one fault segment to another (even if they belong to two different faults) if their distance is shorter than a limit assigned by the user. This feature is essential to accurately represent the interaction of seismic events and the way in which they can trigger each other. 10. The rupture stops when the stress of no neighbouring cell exceeds the computed strength threshold. This feature is important to ensure that the simulation accurately represents the natural behaviour of seismic events, which eventually reach a point where no further energy can be released. The reason for choosing a trapezoidal shape for the seismic sources is to achieve a more precise representation of curved seismic structures. For instance, this helps to reduce gaps that may exist between adjacent rectangular segments when they do not have the same strike. It should be noted that using fault segments of rectangular or trapezoidal shapes to model seismic sources constitutes a simplification of the algorithm adopted in our physics-based simulation code. It is not a constraint that implies a rupture stops at the edges of every segment. The simulator algorithm has two free parameters that control how a rupture nucleates, propagates, and stops. The first of these parameters is \"Strength Reduction\" (S-R), which reduces the strength at the edges of growing ruptures through a sort of weakening mechanism. Increasing this parameter favors the growth of ruptures and, consequently, it has the effect of decreasing the b-value of the frequency-magnitude distribution. The second parameter is \"Aspect Ratio\" (A-R), which is introduced to limit the effect of the S-R parameter when a rupture grows beyond certain specified limits. This parameter assumes a significant role in the frequency-magnitude distribution in the range of large magnitudes, as shown in Figure 5 of [PERSON] et al. [8]. It is important to note that neither of the free parameters mentioned above influences the long-term annual seismic moment rate of the simulated seismicity, which depends only on the slip rate assigned to the simulator within the kinematic fault model. In this study, we analysed the simulations computed by [PERSON] et al. [6] with the free parameters S-R = 0.2 and A-R = 5, obtaining a synthetic catalogue characterised by a b-value of the Gutenberg-Richter distribution equal to 0.95 and a realistic production of multiple events.
# Fault Controls on Hydrocarbon Migration--An Example from the Southwestern Pearl River Mouth Basin
## 3 Data and Methods
### Methods  
Ten faults identified as potentially controlling fluid migration from the basin center to the southern horst were analyzed in terms of their geometry, activity history, and sealing capacity. The geometry of these faults was examined to understand their orientation, dip, and scale, which provides insights into the structural framework and connectivity between different reservoir units. The fault activity history was investigated to determine the timing of the fault movement and any associated tectonic events that may have influenced fluid migration patterns. Furthermore, the sealing capacity of these faults was evaluated. This involved assessing their ability to inhibit or prevent fluid migration across fault planes, as an understanding of the sealing capacity of these faults is crucial for assessing the potential for hydrocarbon accumulation and the effectiveness of fault barriers in controlling fluid migration pathways.
# Monitoring of [PERSON]'s Disease Based on Wireless Sensing Technology
## 3 Principle of Wireless Sensing Technology
### Indoor Propagation Model of Wireless Signal  
When the wireless signal propagates in the indoor environment, it is influenced by the obstacles in the propagation path, which cause reflection and diffraction. After the propagation of different paths, each component reaches the receiving end with different strengths and phases, resulting in a multipath effect [16]. The indoor propagation model of the wireless signal is shown in Figure 1.  
According to the Fries transfer formula [17], the receiving power of the receiving antenna can be expressed as:  
\[P_{r}=\frac{P_{t}G_{t}G_{r}\lambda^{2}}{\left(4\pi R\right)^{2}} \tag{1}\]  
where \(P_{t}\) and \(P_{r}\) are the power of transmitting antenna and receiving antenna, respectively; \(G_{t}\) and \(G_{r}\) are the gain of transmitting antenna and receiving antenna, respectively; and the distance between transmitting antenna and receiving antenna is \(R\). \(\lambda\) is the wavelength of electromagnetic wave.  
Assuming that the propagation path length of the electromagnetic wave through the interference of static objects is \(D\), and the propagation path length through the interference of the human body is \(X\), the Fries transfer formula can be rewritten as follows.  
\[P_{r}=\frac{P_{t}G_{t}G_{r}\lambda^{2}}{16\pi^{2}(R^{2}+D^{2}+X^{2})} \tag{2}\]  
In Formula (2), we can see that both \(R\) and \(D\) do not change. When people are indoors, \(X\) will change, resulting in a change in the power of the receiving antenna. At the same time, because the signal phase is a linear function of the distance of the propagation path, the change of the propagation path will also lead to the change of the signal phase [18]. Human behavior changes the strength and phase of the signal, and the CSI describes the loss and fading on the transmission path. When there is a moving target between the transmitting and receiving devices, the wireless signal reflected by the moving target increases the dynamic component of the channel. The fluctuation of the channel corresponds to the motion information of the target one by one. We can get CSI data from the signals collected by the receiving devices. By analyzing the CSI data, we can sense the changes of the external environment. In the next section, we explain the essence of CSI and its mathematical expression [19].
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 3 System Model
### Computation Model
#### 3.4.3 Server Execution
##### 3.4.3.1 Direct Offloading from UE to MEC Server  
The time delay for executing task \(k\) can be calculated as  
\[T_{k}^{s_{m}}=\frac{b_{k}}{r_{k}^{S_{m}}}+\frac{b_{k}c_{k}}{f_{k}^{S_{m}}}, \tag{9}\]  
where \(r_{k}^{S_{m}}\) is the transmission rate from the UE to server \(S_{m}\), and \(f_{k}^{S_{m}}\) denotes the CPU computation cycle frequency of server \(S_{m}\) for the execution of task \(k\). The corresponding UE battery energy consumption is  
\[E_{k}^{s_{m}}=P_{k}^{tr}\left(\frac{b_{k}}{r_{k}^{S_{m}}}\right)+P_{k}^{\mathrm{ wait}}\left(\frac{b_{k}c_{k}}{f_{k}^{S_{m}}}\right), \tag{10}\]  
In Equation (10), the MEC server energy consumption for executing task \(k\) is not included, since MEC servers do not typically rely on battery power. Throughout this study, the focus is on saving battery energy consumption. Incorporating the saving of energy consumption in the MEC servers, which are powered from the wired grid, is an interesting direction for future research.  
Since there are \(M\) servers available for the UE in the area, the UE can choose to offload task \(k\) to \(S_{m}\), and in this case, \(s_{k}^{m}=1\), whereby the set \(s_{k}^{m}\in\mathcal{S},\ \mathcal{S}=\{s_{k}^{m}|k\in\mathcal{K},m\in \mathcal{M}\}\) contains all MEC server node selection variables. Since a given task \(k\) can only be offloaded to one MEC server (and not split among multiple MEC servers), the offloading selection algorithm should follow the constraint  
\[\sum_{m\in\mathcal{M}}s_{k}^{m}\leq 1\ \ \forall k\in\mathcal{K}. \tag{11}\]
## 1 Introduction  
\(\alpha\),\(\omega\)-Dialkoxyfluoropolyethers (DA-FPEs) are partially fluorinated polyethers not containing chlorine atoms. Hence, they do not contribute to ozone depletion [1].  
\[\mathrm{R_{H}O(CF_{2}CF_{2}O)_{n}(CF_{2}O)_{m}R_{H}}\qquad\qquad\mathrm{R_{H}=CH_{3 },C_{2}H_{5},\,CH_{2}=CHCH_{3}} \tag{1}\]  
The incorporation of two alkoxy-groups provides reactive sites, which limit their atmospheric lifetime [1; 2; 3]; therefore, when compared with perfluoropolyethers (PFPEs), they have a lower environmental impact in terms of global warming potential (GWP) [1]. Furthermore, perfluorinated ether units from polymeric backbones also makes them well characterized by typical properties of PFPEs, like high thermal and chemical stability, no acute toxicity and excellent heat exchange properties [4]. Lastly, due to the presence of the above-mentioned alkoxy-groups as ending groups, they show good solvent properties with several organic liquids, such as ketones and alcohols [5]. All these appealing properties of DA-FPEs make them excellent candidates as CFC, perfluorons and halons substitutes in a number of applications, like foaming and fire extinguishing agents, cleaning agents for sophisticated electronic devices and heat transfer fluids [6; 7; 8].