esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the Desalination of Waste Leachate Using the Combined Freezing Method
## 3 Results and Discussion
### The Desalination Effect of Different Desalination Methods on Waste Leachate
#### 3.2.2 Research on the Desalination Effect of the FCM on Waste Leachate
This also proves that the centrifugal speed has a significant impact on salt rejection and also proves that the increase in salt rejection requires a decrease in the ice production rate as a cost. In summary, when using the FCM to treat waste leachate, increasing the centrifugal time and speed has a positive effect on the removal rate. Increasing the centrifugal speed within a certain range can accelerate the removal of concentrated saline water. Figure 6: The FCM desalination parameters at different centrifugal times (speed 2000 rpm). Figure 7: The FCM desalination parameters under different centrifugal speeds (time 2 min). After polynomial fitting of the centrifugal time and centrifugal speed, it can be seen that the two factors have a good correlation with the salt rejection, with correlation coefficients (R\({}^{2}\)) of 0.95541 and 0.98476, respectively (Figure 8). At the same time, using analysis of variance (ANOVA), it can be concluded that the correlation between salt rejection and centrifugal time (\(p\) = 0.007, F = 45.292) and centrifugal speed (\(p\) = 0.026, F = 16.747) is high, and the \(p\)-values are all less than 0.05 (Table 2), indicating a significant correlation.
# Global Contextual Dependency Network for Object Detection
## 4 Experiments
### Ablation Studies
#### 4.3.2 Pyramid Scales  
Table 3 shows the experimental results of the different settings of pyramid scales. It can be observed that single-scale GCDN (1 st row) is inferior to multi-scale GCDN, indicating that multi-scale context representations are helpful to capture the visual features of objects with large size variations. Moreover, GCDN with larger pyramid scales is better at detecting large objects (\(AP_{l}\) increases from 1 st row to 4 th row).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Backbone** & **Method** & **GCDN** & **AP** & **AP\({}_{\mathbf{50}}\)** & **AP\({}_{\mathbf{75}}\)** & **AP\({}_{s}\)** & **AP\({}_{m}\)** & **AP\({}_{l}\)** \\ \hline ResNet-50-FPN & FPN & & 37.4 & 58.1 & 40.8 & 21.4 & 40.8 & 48.5 \\ ResNet-50-FPN & FPN & ✓ & **38.9** & **60.3** & **41.9** & **22.7** & **42.6** & **49.8** \\ \hline ResNet-50-FPN & Mask R-CNN & & 38.2 & 58.9 & 41.5 & 22.4 & 41.6 & 49.7 \\ ResNet-50-FPN & Mask R-CNN & ✓ & **39.4** & **60.5** & **42.8** & **23.0** & **43.5** & **50.6** \\ \hline ResNet-101-FPN & FPN & & 39.6 & 60.6 & 43.3 & 22.7 & 43.6 & 52.2 \\ ResNet-101-FPN & FPN & ✓ & **40.3** & **61.5** & **43.8** & **23.8** & **44.5** & **53.0** \\ \hline ResNet-101-FPN & Mask R-CNN & & 40.2 & 60.4 & 44.1 & 22.9 & 44.1 & 53.3 \\ ResNet-101-FPN & Mask R-CNN & ✓ & **41.0** & **62.1** & **44.6** & **24.0** & **45.1** & **53.7** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons with baselines on MS-COCO 2017 \(val\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **CRM** & **GAP** & **CDM** & **AP** & **AP\({}_{\mathbf{50}}\)** & **AP\({}_{\mathbf{75}}\)** & **AP\({}_{s}\)** & **AP\({}_{m}\)** & **AP\({}_{l}\)** \\ \hline FPN & & & & 37.4 & 58.1 & 40.8 & 21.4 & 40.8 & 48.5 \\ FPN & & ✓ & & 37.8 & 59.1 & 41.0 & 21.9 & 41.4 & 48.3 \\ FPN & & & ✓ & 38.5 & 59.9 & **42.0** & 22.6 & **42.6** & 49.4 \\ FPN & ✓ & & ✓ & **38.9** & **60.3** & 41.9 & **22.7** & **42.6** & **49.8** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects of different context operations on MS-COCO 2017 \(val\).
# Mail Inspection Based on Terahertz Time-Domain Spectroscopy
## 2 System Design
### System Setup  
The TDS system realized in the mail scanning device is driven by a femtosecond fiber laser working at telecommunication wavelengths of 1550 nm. The laser system with a repetition rate of 100 MHz provides optical pulses with a duration of about 80 fs and a pulse energy of about 0.3 nJ at each fiber output port illuminating the terahertz transmitter and detector antennas, respectively. The light guidance is completely done within polarization maintaining single-mode fibers, except for the voice-coil based delay-line (up to 40 Hz waveform acquisition frequency). The system is working in transmission only, why the material of the tray has to be highly transparent in the terahertz regime but mechanically stable at the same time. Therefore, we used a special plastic foil stretched within a metal frame. This frame is mounted on a XY-scanner to exactly place the sample to the single-pixel terahertz sensor (see also next section).  
The inner setup of the system is reminiscent of a sandwich: in the center plane, the XY-scanner with the tray is fixed in between two breadboard-like metal sheets with a distance of about 80 mm. The terahertz emitter and detector units are located on the facing sides each. Therefore, the detector unit is hanging upside down, which does not affect its performance. Fiber-coupled photoconductive switches (PCS) are used for terahertz generation and detection while the terahertz beam guiding is realized with plane gold-coated mirrors and parabolic mirrors for collimating and refocusing at the measurement points and the PCS, respectively.  
An early, exemplary version is shown in Figure 4, accepting DIN C5 envelopes (229 \(\times\) 162 mm\({}^{2}\)) to be placed on the automated scanner.  
Figure 3: Spectra of (**a**) drugs, (**b**) explosives, (**c**) mock-up substances (simulants), and (**d**) isomers of aminobenzoic acids acquired with the terahertz TDS system used in the mail inspection system. While each substance shows characteristic absorption features, the number of features and their amplitude differ significantly. The shown cocaine spectra are typical for the two classes of cocaine that can be found on the market. Spectra are vertically shifted for better readability.
## 3 Fully Intelligent Accident Liability Determination Method Based on Collision Detection
### Accident Identification Based on Collision Detection
#### 3.3.1 Calculation of the Amplitude Change in the Optical Flow Vector  
The HS algorithm was used to derive the energy functional of the optical flow vector as described in Equation (4). In certain image areas, particularly where gradient values are low, the optical flow constraints may not be valid, potentially resulting in unreliable optical flow calculations. Furthermore, the smoothness constraint does not apply in directions perpendicular to image boundaries, which can cause excessive smoothing and diminish critical image details [29]. Based on these observations, we propose an improved method that dynamically adjusts the smoothing parameters to accommodate changes in image content. A predefined threshold \(T\) sets the stage for defining the dynamic smoothing factor \(\lambda\) as follows:  
\[\lambda=\begin{cases}1&\text{if}{\left|{I_{x}u+I_{y}v+I_{t}}\right|>T}\\ &0.5&\text{otherwise}\end{cases} \tag{12}\]  
The resulting improved energy function is defined as:  
\[E(u,v)=\iint\big{(}I_{x}u+I_{y}v+I_{t}\big{)}^{2}+\alpha^{2}\lambda(x,y)\Big{(} {\left|{\
abla u}\right|^{2}+{\left|{\
abla v}\right|^{2}}}\Big{)}dxdy \tag{13}\]  
The optical flow vector \(\big{(}u_{x,y,t},v_{x,y,t}\big{)}\) is derived by solving the Euler-Lagrange equation. Abnormal flow, potentially indicative of a collision, is detected by analyzing the amplitude changes in the optical flow vector. The amplitude change in the optical flow vector is calculated using the formula:  
\[\Delta m_{x,y,t}=\sqrt{u_{x,y,t}^{2}+v_{x,y,t}^{2}}-\sqrt{u_{x,y,t-1}^{2}+v_ {x,y,t-1}^{2}} \tag{14}\]A threshold \(\beta\) is used to construct a binary significance index \(b_{x,y,t}\), which indicates significant amplitude changes:  
\[b_{x,y,t}=\begin{cases}1&\text{ if }\Delta m_{x,y,t}>\beta\\ 0&\text{ otherwise}\end{cases} \tag{15}\]  
Each frame generates a binary significance map \(b_{x,y,t}\), which reflects the significance of the change in the amplitude of the optical flow at each pixel.
# Non-Contact SpO2 Prediction System Based on a Digital Camera
## 1 Introduction
For example, the blue (B) and/or red (R) wavelengths have been used instead of IR [8; 34; 35; 36; 37]. Another study by [PERSON] et al. [5] used the green (G) and orange wavelength illuminations as an alternative to IR to extract SpO2. However, extracting SpO2 using visible light cameras presents additional issues compared to standard pulse oximetry. First, an unclear ROI resulting from movement may distort the reading. Second, using visible wavelengths means more interference with the outer tissues and superficial vasculature than using the longer wavelengths of red and IR. In addition, iPPG is prone to other artefacts, involving surface reflections, shadowing and skin tone [8]. The principal contributions of the proposed system are: (1) the possibility of predicting SpO2 based on RRs obtained from a visible-light camera using green wavelength instead of IR while keeping red wavelength as the saturation-sensitive wavelength, (2) the extraction of SpO2 directly from one camera using two channel components (R and G) and reducing skin tone limitations resulting from the B channel, thus improving the accuracy of SpO2 measurement, (3) the automatic selection of the forehead region based on a skin detection algorithm and the use of a signal decomposition technique based on complete EEMD [38] and ICA [39] techniques to decompose iPPG signals into different frequency signals and to remove noise artefacts embedded in the iPPG signal. This paper is structured as follows: Section 2 introduces the principle of SpO2 measurement; Section 3 presents the materials and methods, including research ethics considerations, participants and experimental setup and an overview of the proposed system; Section 4 reports and discusses the experimental results of SpO2 predictions with comparison to standard pulse oximetry readings, with statistical results; finally, Section 5 concludes the work presented in this paper.
# An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP
## 1 Introduction
In this paper, we describe the linear combination relationship between DCB and OSB by analyzing their time-varying characteristics, and, on this basis, we comprehensively analyze the effect of multichannel DCB on the positioning performance of SPP and PPP considering the new signal of BDS-3.
## 1 Introduction  
Corrugated steel plate shear walls (CSPSWs), as part of steel plate shear wall systems, effectively achieve \"energy dissipation damping\" due to their unique corrugated structure. Compared to other seismic-energy-dissipating components such as isolation bearings and dampers, CSPSWs offer lower costs while providing essential lateral stiffness to building structures, making their study critical for practical engineering. To align with the energy-efficient and sustainable development of prefabricated buildings, this study adopts bolted connections for assembled CSPSWs. This approach reduces resource consumption and carbon emissions throughout the building lifecycle compared to traditional welding while improving structural disassembly and recyclability.  
[PERSON] and [PERSON] [1] conducted quasi-static tests on diagonally arranged corrugated infill plates, demonstrating that corrugated steel plates enhance stiffness, ductility, and energy dissipation in shear walls. [PERSON] [2] found that CSPSWs exhibit superior energy dissipation compared to flat steel plate shear walls (FSPSWs). [PERSON] and [PERSON] [3] numerically compared FSPSWs and CSPSWs, while [PERSON] [4] explored the impact of connection methods, aspect ratios, and wave parameters on lateral resistance. [PERSON] [5] emphasized the influence of connection methods on system performance. [PERSON] et al. [6] compared seismic performance under varying corrugation angles, while [PERSON] and [PERSON] [7] evaluated trapezoidal and sinusoidal CSPSWs. [PERSON] et al. [8] and [PERSON] et al. [9] studied double-corrugated shear walls, and [PERSON] et al. [10] integrated dampers into CSPSWs. [PERSON] [11] and [PERSON] [12] utilized low-yield-point (LYP) steel to enhance hysteretic behavior. [PERSON] et al. [13] proposed steel-concrete composite CSPSWs, and [PERSON] and [PERSON] [14] developed lightweight CFRP-corrugated sandwich shear walls.  
This study conducts lateral resistance tests on assembled CSPSWs, compares them with FSPSWs, and analyzes the effects of plate thickness, width/height ratio, and wave height through parametric studies. This study provides theoretical support for the optimal design and engineering application of the assembled corrugated steel plate shear wall.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 3 JRCO Algorithm
### Generate the Optimization Objective Function  
So far, the \(i\)-th UAV was of the ability to build optimization objectives based on the information of the neighboring \(M_{i}\) UAVs (or its own stored information). Based on Equation (21), when \(\bar{\mathbf{r}}_{i}^{t}\), \(\bar{\mathbf{C}}_{\theta^{\prime},i}^{t}\), \(\bar{\mathbf{C}}_{\theta^{\prime},j}^{t}\), \(\hat{\mathbf{u}}\) were known, the CRLB of the target location estimation was also known.
## 4 Experimental Results and Analysis
### Loss Stage Experiment  
In the dynamic loss function, the loss function is divided into two stages. In the latter stage, a boundary refinement loss function is proposed to refine the ice segmentation output. This section of the experiment explores where to start the boundary refinement training. The experiment is divided into the first 25% of the training, 50% of the training, and 75% of the training stage. These percentage points represent different training stages and can observe the performance of the model at different levels of maturity. Thus, 25% represents the early training stage, 50% represents the mid stage, and 75% is close to training completion. We switch losses at different stages to explore the optimal timing for fine-grained segmentation output. The experimental results are shown in Figure 7.  
Figure 6: Dilation rate combination experiment results.  
From the analysis of Figure 7, we can see that when the next stage of refined training is started in the first 25% of the training, the mIoU value of the blue curve grows significantly faster, proving that the boundary refinement loss function can accelerate the convergence of the model. However, since the ice-covered image generated at this time is not accurate enough, the refinement effect is not good. In the 75% stage of training, its mIoU and F1-Score are not the best choices, and the benefit of the boundary refinement loss function is not great. Therefore, this paper chooses to change the loss function halfway through the training. As shown in Figure 7, when the training reaches 50%, the mIoU value of the purple curve has increased to a certain extent.
## 2 Materials and Methods
### Matrix Size and Equivalent Iterations Analysis  
The first round of reconstructions was performed on P1 and P2 images, varying the matrix size (128 \(\times\) 128, 200 \(\times\) 200, 256 \(\times\) 256 and 400 \(\times\) 400). The number of EIs was fixed to 21 (1 iteration, 21 subsets), using the clinical default setting of the scanner, and no Gaussian filter was selected. The quantitative data accuracy was evaluated by analyzing RC values, defined as:  
\[\text{RC (\%)}=\text{C\_voi/C\_true}\times 100, \tag{1}\]  
where C_voi is the VOI mean activity concentration (kBq/mL) extracted from the image, and C_true is the activity concentration in the VOI (kBq/mL) calculated from the ground truth measurements with the TDCR counter. Image noise was assessed with the Coefficient of Variation, defined as:  
\[\text{CoV(\%)}=\text{SD\_voi/C\_voi}\times 100, \tag{2}\]  
where [SD]_voi is the standard deviation of C_voi (kBq/mL) and C_voi is the VOI mean activity concentration (kBq/mL) extracted from the image. After choosing the matrix size, a verification of the impact of EI on the RC and CoV was performed, varying Equivalent Iterations from 21 to 105 without using any Gaussian Filter. The combination of EI was obtained only varying the number of iterations (1 to 5) without changing the number of subsets (fixed to 21 by manufacturer).
## 5 Algorithm Simulation Analysis
The simulated pulsar signal with a white Gaussian noise (SNR = 0 dB) is shown in Figure 21. By setting the decimation factor \(D\) = 8, the number of channels was set as \(K\) = 32, and the bin number \(K_{1}\) and the decimation factor \(D_{2}\) of the second-stage WOLA filter bank were 2048 and 8, respectively. The simulated pulsar signal was inputted to the proposed IWOLA + WOLA algorithm. This yielded the spectrum shown in Figure 22 with a frequency resolution of approximately 24.4 kHz. As can be seen from the figure that Before dedispersion, there was dispersion smearing at the observed frequencies within an observation duration of 35 ms. The pulsar was dispersed within a range of 1200-1600 MHz. After a direct dedispersion algorithm, the dispersed signal was aligned to the arrival time, which corresponded to 1200 MHz. The dedispersed signal is displayed in Figure 23. It was subjected to a succeeding overlap-adding processing, which yielded the dedispersed time series of the simulated pulsar, as shown in Figure 24. As can be observed from the figure, the SNR of the pulsar signal output from the IWOLA + WOLA algorithm was 24.07 dB. Figure 19: Comparison between the simulation analysis results (magnification of Point A). Figure 16:0 Coherent dispersed pulses with noise. Figure 16:1 Comparison of the simulation analysis results (magnification of Point B). The simulation results revealed that the proposed IWOLA + WOLA algorithm demonstrated a superior accuracy and performance to the direct FFT and the IWOLA + FFT  
Figure 16:2 Spectrogram of the simulated pulsar obtained by the proposed algorithm (after dedispersion). Figure 16:3 Spectrogram of the simulated pulsar obtained by the proposed algorithm (before dedispersion). algorithms. The proposed IWOLA + FFT and IWOLA + WOLA algorithms solved the overlapping interference problem of the adjacent subchannels caused by the first-stage channelisation that is realised by an IWOLA filter bank. By selecting the appropriate \(K\), \(D\), \(D_{2}\), and \(K_{1}\) values, in addition to the operating frequency for hardware such as FPGAs, a broadband spectral analysis system can be designed with a satisfactory performance that consumes less resources.
## 3 Results and Discussion
### Variations in ARGs and LRGs After Remediation via Multiple Methods
#### 3.1.1 ARGs Variations
Despite similar pH levels in BES-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\), BES-pH5-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\), and BES-pH9-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\), Pb concentrations in BES-pH5-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\) were lower [15]. This suggested that the lower initial pH may be responsible for the elevated ARGs abundance in BES-pH5-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\). Figure 3: Relative abundance of various ARG types (**a**), subtypes (The data was log10(relative abundance)) (**b**), antibiotic resistance mechanisms (**c**), and LRGs (**d**) in soils after different remediation methods.
## 2 Materials and Methods
### Data Pre-Processing and Feature Extraction  
Data pre-processing and feature extraction are closely related sequential steps in the data preparation pipeline for time series analysis. Data pre-processing focuses on cleaning, preparing, and transforming the raw data into a usable format, while feature extraction comes next to create additional features that are fed into the ML model to capture underlying patterns more effectively.
# Scale-BOSS-MR: Scalable Time Series Classification Using Multiple Symbolic Representations +
## 4 Evaluation
### Evaluation of SBMR-RF with Multiple Windows and Dilation Configuration  
Table 6 and Figure 4 report on configurations combing window and dilation configurations, with representations including bigrams, including trend information. The results show the following:  
1. SBMR-RF-W8-D6-trend-BG achieves an accuracy of 0.875 with 489 s of mean total execution time compared to the undilated 0.863 with 216 s of mean total execution time reported in Table 5;
2. SBMR-RF-W14-D6-trend-BG achieves a slightly worst accuracy of 0.863 with significantly reduced mean total execution time of 149 s versus 0.850 with 54 s for the undilated case reported in Table 5;
3. SBMR-RF-W11-D6-trend-BG achieves an accuracy of 0.857 with 116 s compared to 0.846 with 36 s for the undilated case reported in Table 5;
4. SBMR-RF-W10-D6-trend-BG achieves an accuracy of 0.865 with 217 s compared to 0.862 with 72 s for the undilated case reported in Table 5.  
From the above, we can conclude the following:  
1. Dilation combined with window configurations has a considerable impact on total execution time, as expected, but significantly increases the accuracy of predictions for any of the windowing configurations;
2. Dilation configuration D3 is slightly better in terms of accuracy compared to configuration D6, which is significantly faster than D3. This is due to the fact that D3 increases the time series representations when combined with window configurations, resulting in the worst total execution time compared to D6. This leads us to believe that D6 achieves a good balance between total execution time and accuracy when it is combined with suitable window configurations.  
Figure 3: Total execution time (in seconds) and accuracy for single window representation.
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 2 E-Nose System
### The Electronic Nose's Brain: Algorithms and Visualization
#### 2.4.3 Partial Least Squares Regression  
PLSR is a method that amalgamates the attributes of both PCA and multiple linear regression MLR. Its primary objective is the analysis or prediction of dependent variables based on independent variables or predictors. This predictive capability is harnessed by extracting orthogonal factors termed latent variables from the predictors [87]. These latent variables encapsulate the most potent predictive insights. PLSR is especially advantageous when it comes to forecasting dependent variables from an extensive array of independent variables. This technique not only captures underlying patterns and relationships but also handles situations where there might be multicollinearity among predictors. In essence, PLSR combines the strengths of PCA and linear regression to offer a comprehensive and robust approach to predictive modeling.