esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis  
### Cracks and Failure Modes of the Specimens  
The crack patterns and failure of specimens are shown in Figure 11. Initial cracks were observed at the midspan on all of the specimens, but as the load increased, the crack patterns differed in each specimen. As in typical reinforcement concrete beams, the flexural cracks in CRC occurred in the midspan, and were propagated into the compression zone. In the PCM-A and PCM-B which connected to the two PC modules, cracks occurred at the bottom of the connection while the flexural cracks were increasing, and they subsequently progressed along the shape of connection to the compression zone. In the beams with joints, cracks progressed along joint configuration [24; 25; 26]. Therefore, this result indicated the general behavior of precast concrete beams with joints. The structural performance of a beam with joints should be similar to a monolithic beam, and it can be confirmed by crack patterns. The PC modular beam system proposed here had initial crack patterns similar to those in typical RC beams, which indicates that the proposed system could be applied at actual construction sites. The PC modules were connected in the midspan where flexural cracks occurred in the beams. Thus, flexural cracks occurred in the connections between the modules, and the length of the connection affected the integrity of the structure. Comparing the cracks in the two different connection lengths, cracks began in the PCM-A (350 mm connection length) when the displacement was 30 mm and the load reached 84% of the maximum. In the PCM-B (700 mm connection length), the cracks began when the displacement was 20 mm and 90% of the maximum load was reached. This confirmed that the PCM-B with the longer connection was more brittle than the PCM-A. The failure of specimens is shown in Figure 12. As shown in Figure 12a, the CRC specimen had a large number of cracks in the midspan compared to the other test specimens because it was monolithic. Unlike the CRC, the PC module specimens had few flexural cracks in the center, as shown in Figure 12b,c, and also propagated along with the shape of the connection. However, it was confirmed that the PC module-to-PC module connections were not detached or partially destroyed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Instrument** & **Measure** & **Description** \\ \hline Strain gauge supplied by TML & Concrete strain & PLA-60-11: 60 mm gauge length, 2.12 \(\pm\) 1 gauge factor, 120 \(\pm\) 0.3 \(\Omega\) gauge resistance \\ (Tokyo Sokki Kenkyujo Co., & & \\ Ltd., Japan) & Rebar and steel plate strain & FLA-5-11-11: 5 mm gauge length, 2.13 \(\pm\) 1 gauge factor, 120.3 \(\pm\) 0.5 \(\Omega\) gauge resistance \\ \hline Linear variable & & \\ differential & & SDP-300D- 300 mm capacity, rated output 5 mV/V (10,000 displacement & \(\times\) 10\({}^{6}\) strain) \(\pm\) 0.3\% \\ transducers (LVDT) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Instruments installed on specimens.  
Figure 10: Position and naming of gauges.
# Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks
## 1 Introduction
### Related Work  
The resource allocation schemes involve user pairing and power allocation as an interactive approach to improving NOMA performance. The literature is reviewed to examine the available user pairing and power allocation approaches as tabulated in Table 1. The summary of the investigated methods is discussed based on the objectives and the proposed methods for resource allocation in the NOMA system. The authors in [5] proposed a closed-form solution for power allocation scheme under conditions of the Karush-Kuhn-Tucker (KKT) condition for single-input single-output (SISO) and multiple-input multiple-output (MIMO) in downlink NOMA. Similarly, the closed-form solution is derived for optimal power using KKT condition for sum-rate maximization in the NOMA system with imperfect channel state information (CSI) transmitter [6]. [PERSON]. et al. [7] proposed an optimal power allocation solution using a bisection search algorithm with high computational complexity for ergodic capacity maximization under statistical channel state information (CSI) for the MIMO NOMA system. The authors in [8] have proposed a user selection approach based on an exhaustive search and power allocation method using the minimum mean square error (MMSE) to maximize the weighted sum rate of a downlink NOMA system. Further, the optimal solution is found by employing the KKT condition for the power allocation problem. [PERSON] [9] proposed user and channel matching and iterative power allocation scheme to maximize the total sum-rate for orthogonal frequency-division multiple-access (OFDM) based NOMA. The optimization problem in [9] is divided into subproblems, where greedy users and channel matching are employed based on heuristic solutions. Then the difference of convex (DC) programming is used to obtain the suboptimal power for each subchannel and power ratio between the multiplexed users. The authors in [10] proposed subchannel matching for user and subchannel assignment and DC to determine the subchannel and user's power to improve the NOMA
# Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models
## 5 System Evaluation with Air Pollutant Prediction Models
### Review of Predictions by Feature and Network Selection  
We also train ConvLSTM with three features, including temperature, humidity, and PM\({}_{2.5}\), which are selected in the temporal predictive modeling in Section 5.2. The MAPE of ConvLSTM with the three features and 6 h of time lag is 21.9%. After reselecting the features, we can see that predictive performance is better. PM\({}_{2.5}\) and PM\({}_{10}\) are very similar features. As seen in Section 5.2, the spatial overlap may also reduce the prediction performance. Since the spatial information of each feature is different, the spatial correlation of the prediction result may also be different. Therefore, in the spatiotemporal prediction deep learning modeling process involving spatial factors, it is worth exploring how significantly the spatial information of a feature can affect the prediction.  
We attempted to interpret the prediction results for each case as we stepped through the changes of features, time lags, and deep learning models. The proposed system enables deep learning modeling with spatiotemporal data and supports interpreting the causes for the results. During the modeling process, we investigate the prediction results of deep learning models, improve our understanding of the data, and explore the deep learning models faster. In particular, during the process of analyzing the prediction results of the deep learning model with spatiotemporal data, efficient feature selection can be performed by comparing not only the correlations between variables, but also the spatial and temporal correlations.
## 3 Optimizing ADC Dynamic Range for BLE Protocol: A Comprehensive Analysis  
BLE receiver ADC specifications demand precise balancing to achieve optimal system performance. The ADC must deliver a dynamic range exceeding 60 dB to effectively process signals ranging from weak desired signals at \(-\)77 dBm to strong adjacent channel blockers at \(-\)50 dBm, as illustrated in Figure 3[17].  
Figure 2: Block diagram of (**a**) direct-conversion and (**b**) low-IF receivers.  
The system incorporates essential margin requirements for reliable operation. A 10 dB back-off margin prevents the ADC's inherent noise floor from degrading receiver sensitivity, while an additional 6 dB margin accommodates the stepped transitions of automatic gain control (AGC) implementations. These combined margins ensure stable system operation across varying signal conditions. The ADC must maintain a minimum SNR of 9 dB even under maximum blocker conditions, translating to approximately 4-bit resolution when operating at sampling frequencies between 10 and 14 MHz. This specification balances quantization noise requirements with processing bandwidth (BW) constraints.  
An SNDR target of 24 dB has proven sufficient for maintaining robust system performance. The ADC's performance integrates closely with the baseband filtering architecture, where digital complex bandpass filtering provides crucial antialiasing and image rejection functions. This filtering maintains a bit error rate (BER) of 0.1% or better by attenuating out-of-band blockers before they can saturate the ADC's input stage. Performance enhancement through oversampling techniques offers additional resolution improvements.  
Implementation requirements demand that the ADC handle the complete range of signal conditions specified in the BLE standard [18]. The sampling frequency must be carefully selected to capture the required signal BW while preventing interferer aliasing. These comprehensive specifications ensure effective BLE signal digitization with robust performance amid various interferers and blockers in the increasingly congested 2.4 GHz ISM band. The successful integration of resolution, sampling rate, and filtering requirements enables reliable wireless communication in this challenging environment.
# Characterizing Micro-Displacements on Active Faults in the Gobi Desert with Time-Series InSAR
## 3 Methodology
### Estimation of Vertical and Eastward Displacement  
The vertical, eastward, and northward displacements (D\({}_{\text{U}}\), D\({}_{\text{E}}\), D\({}_{\text{N}}\)) are three components that can be estimated from the measured line-of-sight (LoS) surface displacement. They can be derived from the decomposition of LoS displacements in both ascending and descending directions, as shown in Figure 4a. In theory, this can be expressed in Equation (4) [43; 44]:  
\[[\cos\theta-\sin\theta\cos\alpha\ \sin\theta\sin\alpha\ ]\left[\begin{array}{cc} \text{D}_{\text{U}}&\\ \text{D}_{\text{E}}&\\ \text{D}_{\text{N}}&\\ \end{array}\right]=\ \Delta R \tag{4}\]where \(\alpha\) is the azimuth of the satellite-heading vector, \(\theta\) is the incidence angle at the reflection point, and \(\Delta R\) is ground displacement in the LoS direction between the two acquisitions.  
The right-looking viewing geometry of Sentinel-1 SAR satellite imaging (and that of most near-polar-orbiting earth observation SAR sensors) makes it insensitive to north-south oriented motions. Thus, it is difficult to measure \(\rm D_{N}\) accurately and, out of necessity, the contribution of \(\rm D_{N}\) to total LoS displacement (\(\Delta R\)) is considered insignificant and negligible. Meanwhile, the large errors introduced by atmospheric phase delay and orbit error residuals may further reduce the accuracy of \(\rm D_{N}\) estimation [43]. These make the LoS displacements strongly sensitive to \(\rm D_{U}\), modestly sensitive to \(\rm D_{E}\), and almost insensitive to \(\rm D_{N}\)[44]. Therefore, to guarantee the interpretation is achieved based on accurate data and minimise the chance of an incorrect solution, this research excludes the \(\rm D_{N}\) data estimated from LoS displacements.  
The eastward displacement (\(\rm D_{E}\)) is estimated based on the combination of PS points in datasets from ascending and descending nodes. However, as these points are irregularly and differently distributed, the PS values at each dataset are therefore spatially interpolated to allow the decomposition at each gridded point. If we neglect northward displacement (\(\rm D_{N}\)), Equation (4) can be simplified to Equation (5), to estimate the vertical displacement (\(\rm D_{U}\)) and eastward displacement (\(\rm D_{E}\)). This can be expressed as [43; 44]:  
\[\left[\ \cos\theta-\sin\theta\cos\alpha\ \right]\!\!\left[\ \begin{array}{c} \rm D_{U}\\ \rm D_{E}\end{array}\right]=\Delta R \tag{5}\]
## 4 Results  
### Experiment in Gradual Change Area (Vegetation Phenology)  
From Figure 4, the predicted Landsat-like images by four methods can monitor the changes of vegetation growth, and the results of the four fusion methods are similar to the true image on the whole. However, the yellow elliptical circles in Figure 4 show the differences of the four methods, and we can get that STARFM blurs the boundaries of different objects, STRUM produces block effects, FSDAF cannot retain the spatial details, and EFSDAF is closest to the true image. Especially, EFSDAF can accurately retain the boundaries of objects from Figure 5, and it can capture more spatial details of the object than FSDAF.  
Figure 3: Test data with abrupt change (flood) in Gwydir. (**a**) Landsat image of 26 November 2004; (**b**) Landsat image of 12 December 2004; (**c**) and (**d**) MODIS images of the same periods to Landsat images. All images use NIR-red-green as R-G-B.  
From quantitative indices of four methods in Table 1, the fusion results by four methods can achieve high accuracy. For all six bands and the mean, it can be seen that the accuracy of EFSDAF is the highest by comparing it with the other three methods, which have lower AD and RMSE, as well as higher CC and SSIM. Taking the mean of all bands as an example, the CC values of the four methods are 0.807, 0.826, 0.858, and 0.883 from left to right, respectively. In addition, because the vegetation phenology change happened in dataset 1, the prediction accuracy of band 4 (NIR) can better illustrate the performance of the four methods. From the scatter plot of Figure 6, it can be confirmed that the synthetic image values by EFSDAF are closer to the true values than the other three methods.  
Figure 4: Visual comparison of the predicted Landsat-like images by STARFM, STRUM, FSDAF, and EFSDAF in dataset 1 (vegetation phenology). From left to right, green-red-NIR (standard false color) composites of (**a**) a true Landsat image of 6 August 2004; (**b–e**) predicted images of 6 August 2004 by STARFM, STRUM, FSDAF, and EFSDAF, respectively. (Figure 4 shows the predicted Landsat-like images of 6 August 2004 by four fusion methods in gradual change area, and the yellow elliptical circles represent the intuitive differences of the four fusion methods. The zoom-in black square areas marked in Figure 4 are used to visually evaluate the performance of the four methods for the vegetation phenology in heterogeneous regions (Figure 5)).  
Figure 5: Zoomed in scenes of the black square areas marked in Figure 4. From left to right, (**a**) true Landsat image of 5 July 2004(base date); (**b**) true Landsat image of 6 August 2004(prediction date); (**c–f**) predicted images of 6 August 2004 by STARFM, STRUM, FSDAF and EFSDAF, respectively.
# Multipath Error Fusion Modeling Methods for Multi-GNSS
## 3 Results  
A set of data collected on the top floor of the Teaching and Experimental Building of Wuhan University on DOVs 215-234 in 2018, with a total of 20 days, was selected for testing and analysis. Three stations A, B, and C were set up during the collection of this set of data. A metal baffle was mounted in the west of station A to simulate a strong multipath environment, and stations B and C were normal. The distances between the three stations were 2.7 m, 4.7 m, and 6.9 m, respectively. The location relationship and surrounding observation environment are shown in Figure 3.  
The positioning and navigation data analyst (PANDA) software developed by Wuhan University was used for the data processing [30; 31]. The specific strategies using in the data processing are shown in Table 2.  
In the process of modeling, the coordinates of the three stations were fixed to known values so as to obtain high-precision carrier phase residuals. Considering that orbit repeat periods of different satellites vary from 1 to 10 days, the observation data of DOY's 215-233 in 2018 were selected for the multipath error modeling. The observation data of DOY's 225-234 in 2018 were used for the validation analysis to evaluate the improvement of the observation residuals, positioning results, and other indicators after adopting different error correction strategies. For the MHGM, we set \(E_{0}\) = 5\({}^{\circ}\), \(E_{1}\) = 85\({}^{\circ}\), and \(c=d=2^{\circ}\) considering the usage of computational resources. The corresponding weights of (5), (6) and (7) were 5.0 mm, 4.8 cm, and 1.0 cm per degrees, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Model** \\ \hline Satellites & GPS+BDS+Galileo \\ Observations & Phase and code observations \\ Signal & GPS L1+BDS B1+Galileo E1 \\ Sampling rate & 1s \\ Observation weight & Satellite elevation \\ Cutoff elevation & 7 degree \\ Estimator & Least square method \\ Phase wind-up & Corrected \\ Phase center pattern & igs14.atx \\ Tropospheric delay & GPT2+Saastomonient+GMF \\ Satellite clock & Broadcast + Process \\ Receiver clock & Estimated+white noise \\ Station displacement & Solid earth tide+Pole tide+Ocean tide loading \\ Station coordinate & Fixed; Estimated for static positioning \\ Terrestrial frame & ITRF2014 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model and parameters for data processing.  
Figure 3: Location relationship and surrounding observation environment of the stations. A for station A which a metal baffle is mounted in the west, B, C for stations B and C which is normal.
## 3 Results and Discussion
### Process of Oxygen Into the Soil  
As shown in Figure 7a,b, when the holes were sealed, whether the bottom of the petri dish was covered by the film or not, the agar blocks within them remained colorless, indicating that no oxygen entered the petri dish. However, after tearing off the tape, color change occurred in the agar block in the petri dish covered by the film. A blue round plaque formed rapidly at the hole in the bottom of the dish, and gradually expanded and deepened in color, indicating that radial oxygen diffusion occurred in the bottom of the agar block. At the same time, no obvious color change occurred in the other agar block, where the petri dish was not covered by the film, except for a small blue plaque at the central hole, indicating oxygen diffusion was dependent on the film.  
Figure 7: Color change of agar block. The bottom surface of the petri dish on the right was covered by the ramie fiber/starch nonwoven film whereas the left was not, the diameter of the petri dish was 9 cm.  
In order to detect the actual effect of the film on the oxygen supply to soil, loam was used to cover the film. It is well known that this kind of soil has an enormous number of capillary pores, with an equivalent pore size range of 2-20 \(\upmu\)m, and thus has good water holding capacity [34]. However, the main pore size range of the ramie fiber/starch nonwoven film was 250-300 \(\upmu\)m. That was non- capillary porosity, which means that water in the pores of the film were inclined to be absorbed by the soil when in close contact. After the loss of moisture, the film returned to its initial porous state and air diffusion occurred. As the effective diffusion coefficient in the film was of the same in magnitude as that in air (\(10^{-5}\) to \(10^{-4}\) m\({}^{2}\)/s), the oxygen transfer rate in the porous film was much higher than that in water-saturated soil. As shown in Figure 8, the outside oxygen entered the film from the hole, spread over the film rapidly, then the diffusion of oxygen into the soil occurred across the entire film surface, rather than only at the hole (which occurred in the plate that was not covered by the film). Consequently, more oxygen diffused into the bottom soil and compensated for the oxygen consumption there.
## 2 The Elementary Principle and Design of the Method
### Quantification Method
#### 2.2.4 \(\mathcal{ESC}\) Algorithm  
Based on RL, we propose using the _esc_ algorithm to quantify scenarios through modeling and analysis. The algorithm's goal is the same as that of some popular RL algorithms, such as Q-learning and Sarsa, which work directly on the state-action value matrix \(Q(S,A)\) to solve variants of the reinforcement learning problem [49; 50]. The value of every unit \(Q(s,a)\) of the matrix represents the discounted cumulative reward starting at state s, taking action a. The \(\mathcal{ESC}\) algorithm, like Sarsa, is called on-policy learning, which selects actions at subsequent moments as the being same as the current moment. The updated law of the \(\mathcal{ESC}\) values can be expressed as follows:  
\[\mathcal{ESC}(s_{t},a_{t})\leftarrow\mathcal{ESC}(s_{t},a_{t})+\beta[( \mathcal{E}(s_{t},a_{t})\mathcal{S}(s_{t},a_{t})+\gamma\mathcal{C}(s_{t},a_{t} ))-\mathcal{ESC}(s_{t},a_{t})], \tag{11}\]  
where the meaning of \(\mathcal{ESC}\) value is the same as the meaning of Q value. \(\beta\) is learning rate, and \(\gamma\) is discount factor. The equation can be further interpreted as:  
\[\mathcal{ESC}(s_{t},a_{t})\leftarrow\mathcal{ESC}(s_{t},a_{t})+\beta[\left(P( a_{t}|s_{t})\mathcal{TTC}(s_{t},a_{t})+\gamma\frac{1}{m}\sum_{i=1}^{m}\mathcal{ ESC}[(s_{t},a_{t}),a_{i}]\right)-\mathcal{ESC}(s_{t},a_{t})]. \tag{12}\]  
The \(\mathcal{ESC}\) algorithm is summarized in Algorithm 1. We reduce the complexity by matrix operations. Based on [51], Algorithm 1 requires \(\mathbf{O}(|A||S|)\) memory and \(\mathbf{O}(|A|^{2}|S|)\) computation per iteration. Furthermore, according to the \(\mathcal{ESC}\) algorithm, the flow chart of our method is shown in Figure 3.  
```
Initialize \(P(A|S)\), \(\mathcal{TTC}(S,A)\), \(\mathcal{ESC}(S,A)\); \(\forall s\in S,a\in A\), \(\mathcal{ESC}(s,a)=P(a|s)\); \(error=Threshold+1\); whileerror \(>\) Threshold do  Initialize \(\mathcal{ESC}(s,a)\leftarrow\frac{1}{m}\sum_{i=1}^{m}\mathcal{ESC}[(s,a),a_{m}]\); \(D\gets P(A|S)\cdot[\mathcal{TTC}(S,A)+\gamma\mathcal{ESC}(S,A)]\)-\(\mathcal{ESC}(S,A)\); \(\mathcal{ESC}(S,A)\leftarrow\mathcal{ESC}(S,A)+\beta D\); \(error\leftarrow|D|\) ; endwhile return\(\mathcal{ESC}(S,A)\)
```  
**Algorithm 1**\(\mathcal{ESC}\)_Algorithm_.  
Figure 3: The flowchart of the proposed method.
## 3 Results and Discussion
### Effect of Temperature and Time on Sludge Dewatering  
The dewatering ratio of sludge and volume of dewatered sludge under various temperatures were investigated in this paper, and the results are shown in Figure 2a (the setting time was 60 min). As the temperature increased from 45 to 65 \({}^{\circ}\)C, the dewatering ratio of sludge increased from 70.5% to 93.1%, and the final sludge volume decreased from 33.5 to 10.5 mL. These improvements are primarily associated with the thermal treatment, which can promote the suspended particles bumping into larger particle reunion below 70 \({}^{\circ}\)C so that the free water and part of the bound water are released from the sludge. When the temperature was further increased from 70 to 80 \({}^{\circ}\)C, the dewatering ratio of sludge stayed in the range of 94.9-95.1%, while the final sludge volume remained around 8.1 mL, and sludge volume reduction was 91.9%. The explanation is that at the temperature of 70 \({}^{\circ}\)C, which may be reaching the softening point of acrylic resins, the polymer in the sludge softened and shrank quickly; as a result, the sludge dewatered and solidified automatically. In this study, we named the thermal solidification treatment in order to distinguish it from the traditional hydrothermal treatment. The suggested thermal solidification temperature should not be lower than 70 \({}^{\circ}\)C.  
Figure 2b shows that thermal treatment at 70 \({}^{\circ}\)C with increasing time resulted in a significant increase in the dewatering ratio of sludge from 89.6% to 95.1% and an obvious decrease in the final sludge volume from 13.8 to 8.1 mL. It indicated that the polymer chains need a certain period of time to accomplish the thermal shrink process after reaching their softening point. It can also be seen that the dewatering ratio of sludge and the final sludge volume were not further improved when the thermal setting time was extended from 30 min to 60 min, indicating that no more sludge volume reduction was seen after the polymer chains shrank to an extent. Therefore, the suggested setting time was no less than 30 min.  
Figure 1: Appearance of raw sludge (**a**), solidified sludge at 60 \({}^{\circ}\)C (**b**), and 80 \({}^{\circ}\)C (**c**) for 30 min.