esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Deformation and Fault Mechanisms in Bedding-Locked Rock Slope
###### Abstract  
In geotechnical engineering, a bedding slope often leads to geological disasters, particularly in the case of bedding-locked rock slopes. This study focuses on a typical bedding-locked rock slope in western Henan Province, China. The deformation and fault characteristics were examined through the development of laboratory analogue experiments which allowed for the analysis of the physical and mechanical properties of the rock slope when subjected to a load. The deformation and faulting mechanisms of the slope were studied through the application of different strength loads to the slope model, as well as through the combination of its physical properties. The deformation and fault process were then simulated through the application of numerical modeling. The results reveal that the upper part of the slope features a linear slip surface, while the lower part features a circular arc slip surface, resulting in \"linear + circular arc\" damage geometry and a relatively flat shear surface. Maximum and minimum lateral displacement occur around the top and the foot of the slope, respectively, leaving the intermediate values at the middle. The shear strength is greater at the locking section, with the highest value found at the foot of the slope, followed by the middle and the top. The evolution of slope deformation can be divided into three stages: the loose deformation stage at the slope top and back; the stage of crack development, extension, and interlayer misalignment; and the stage of complete through crack formation and complete instability damage of the slope. Thus, the process of the deformation and damage of a rocky slope under loading conditions can be described as follows: load-induced loosening and deformation at the top and back of the slope, crack development, expansion, and interlayer misalignment, as well as tension crack expansion at the foot of the slope. The latter can expand and intensify interlayer misalignment, leading to the destruction of the locking section and ultimately causing the destabilization of the entire slope.  
locked rock slope; deformation characteristics; physicochemical properties; numerical analysis +
Footnote †: journal: Applied Sciences
# MS Location Estimation Based on the Artificial Bee Colony Algorithm
###### Abstract  
With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm's foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased.  
Keywords:time of arrival (TOA); non-line-of-sight (NLOS); artificial bee colony (ABC); mobile station (MS); base station (BS) +
Footnote †: journal: Sensors
## 4 Experiments, Results, and Discussion  
The efficiency of the proposed new scheduling methodology was assessed using examples of four technological processes. Production processes from various industries and types of production were selected. Manufacturing processes from different industries and different types of production processes were selected to evaluate the methodology.  
Laboratory experiments were carried out on the basis of the virtual model of the production plant described in Section 3. The data needed to build virtual models of individual plants were obtained from archival data available in companies, as well as from technologists and production managers. Missing information was obtained from measurements made on site.  
Two separate experiments were carried out for each production process. The first test concerned the degree of improvement in production efficiency by applying the developed methodology. The measurement was carried out on the basis of the criterion of maximum completion time. The second experiment concerned the possibility of reducing the cost of production.  
Experiments were carried out for a different number of production orders, the specific numbers of which were individually selected for the specificity of individual technological processes. In order to facilitate the presentation and comparison of the results, it has been assumed that each production order involved one piece of the final product of a given technological process.  
The optimization of the schedule for all variants was performed based on the algorithm presented in Section 2.4. As explained in the section, the feature of this algorithm is its non-parametric nature. The only parameter that had to be set was the stop condition. Its relative value was determined according to the procedure described in Section 4.1. To isolate experiments on the minimization of execution time and cost criteria, the weights of the respective criteria were set to opposite values of 0.0 and 1.0.
## 2 Related Work
the central control functionality, FlowCover can select the target switches based on the view of all active flows instead of on per-flow basis. Although FlowCover can reduce the communication overhead to some extent, it does not innovate on the ways of flow statistics collection from switches. With finer and finer granularity monitoring requirements, the work of the monitoring mechanisms provided by OpenFlow is very limited. Generally, different application scenarios often have different characteristics in terms of the structure and the communication among each other. Therefore, each application scenario may be faced with different challenges on the design of monitoring framework. In Internet of Things for smart urban sensing, an appropriate IoT architecture is badly needed to avoid problems such as the embedded control intelligence, vendor-dependent interfaces, highly personalized applications, etc. [PERSON] et al. [18] proposed a software-defined IoT architecture for smart urban sensing, which successfully extends the spirit of the software-defined approach from network devices to sensor platforms and the cloud. However, they focused on the improved control feature and only introduced the basic functionalities of SDN. In data center, adopting the centralized model may result in much computing overhead on the central monitoring collector and much communication overhead between the controller and the switches. An appropriate management framework is badly needed to reasonably distribute the computing overhead on different collectors and the communication overhead on different links. [PERSON] et al. [19] proposed a management model, in which regional networks on lower layers are aggregated and viewed as single switches to upper layers. Another interesting application scenario is the RFID (radio frequency identification)-based smart parking management system. [PERSON] et al. [20] proposed a smart parking system architecture and fully uses the functionality of passive RFID tags, which realizes not only the power saving but also the constant monitoring of the parked cars. Given the above, the most important factor of a system or framework is reducing the related overhead as much as possible while maintaining the convenience of the core functionality. In terms of the research of the monitoring framework, the core motivation is reducing the overhead of the whole monitoring process as much as possible while carrying out the monitoring tasks normally. The monitoring mechanisms in SDN need to be further developed and studied because they are the key to the efficient work of the monitoring framework.
## 4 The Proposed TOCA-IoT Framework
### Threshold Optimization  
The threshold optimization technique is employed to handle the class imbalance in the dataset. This involved systematically evaluating different decision thresholds to determine the optimal balance between sensitivity and specificity, thereby maximizing the overall predictive performance of the models. The random forest model outputs probability scores, indicating the likelihood of an instance being an anomaly. To determine a suitable classification threshold, the threshold optimization strategy is used. This involved analyzing the precision-recall curve to identify the threshold that maximizes the F1 score, balancing the trade-off between recall and precision. This process ensures that the model achieves a balance between minimizing false positives (BENIGN traffic identified as DDoS) and reducing false negatives (DDoS traffic classified as BENIGN).  
Algorithm 1 shows the main steps of the threshold optimization technique.  
```
1:Input:
2: Model predicted probabilities \(p_{i}\) for each sample \(i\).
3: Validation set with true labels \(y_{i}\).
4: Range of thresholds \(t\in[0.1,0.9]\).
5:Output:
6: Optimal threshold \(t^{*}\).
7:Step 1: Define the Objective
8: Objective: Maximize the macro F1 score, which balances precision and recall.
9:Step 2: Evaluate Thresholds Across a Range
10:for each threshold \(t\) in the range \([0.1,0.9]\)do
11: Compute predicted labels \(\hat{y_{i}}\) for all samples: \[\hat{y_{i}}=\begin{cases}1&\text{if $p_{i}\geq t$,}\\ 0&\text{otherwise.}\end{cases}\] (8)
12: Calculate precision, recall, and F1 for each class \(c\): \[\text{Precision}_{c}=\frac{\text{True Positives}_{c}}{\text{True Positives}_{c}+\text{False Positives}_{c}},\] (9) \[\text{Recall}_{c}=\frac{\text{True Positives}_{c}}{\text{True Positives}_{c}+\text{False Negatives}_{c}},\] (10) \[\text{F1}_{c}=2\cdot\frac{\text{Precision}_{c}\cdot\text{Recall}_{c}}{ \text{Precision}_{c}+\text{Recall}_{c}}.\] (11)
13: Compute the macro F1 score: \[\text{Macro F1}(t)=\frac{1}{C}\sum_{c=1}^{C}\text{F1}_{c},\] (12) where \(C\) is the number of classes.
14:endfor
15:Step 3: Identify the Optimal Threshold Range
16: Select the optimal threshold \(t^{*}\) that maximizes the macro F1 score: \[t^{*}=\arg\max_{t}\text{Macro F1}(t).\]
17:Return: Optimal threshold \(t^{*}\).
```  
**Algorithm 1** Threshold optimization for maximizing macro F1 score
# Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication
## 4 Results and Discussion
### Discussion  
The results prove the superiority of the fusion anti-interference scheme qualitatively, which can eliminate the multifactorial interference problems faced by mining 5G communication systems without requiring complex control, affecting the signal bandwidth, or introducing significant delay. In the actual verification process, the algorithm parameters were not adjusted too carefully according to the different scenarios. There are two reasons for this: one is that the system itself had certain adaptive characteristics, and the other is that the architecture had certain space for optimization. Architecture evolution will be the content of future work, which includes transforming the function of interference monitoring from basic threshold judgment to global optimization, further reducing the learning cost of the system.
## 4 Flight Experiment Results
Figures 15 and 16 show SNR graphs created after the analysis of the received signal power variations during the hovering, as described in Figure 14. RF1 and RF2 in the Figure 15 denote the SNR from the first and second RF antennas used for the diversity combining process. Figure 16 presents the SNRs from a single antenna, as there was a cable connection problem at a specific RF band during the flight. As shown in the two figures, at the initial stage of hovering, the SNR values from most of the channels fluctuated severely due to the rotation of the heading. A few epochs in the RF1 of the master station and dozens of epochs from the slave Stations 2 and 3 dropped below a threshold and affect the geometry matrix for the positioning computation. Except for the rotation time (-about 40 s), the signal powers maintain relatively stable values. Figure 14: 2D trajectory: (**a**) Full trajectory; (**b**) Zoom-in figure of the upper right corner of (**a**). From Figures 14-16, we can conclude that the horizontal navigation performance and the received signal power showed stable results, except at the low-elevation area with a very high DOP (e.g., \(<\) several thousand PDOP) and during rotation while hovering. There were no position jumps or lock losses, which can stem from a false lock. An additional and more direct measure to check if there were false locks is to analyze the estimated velocity values, as a false lock represents the Doppler frequency bias. The velocity errors are plotted in Figure 17, for which the time scope corresponds to a cruise mode with a nearly constant altitude. Outliers caused by the rotation of the heading are visible, caused by the hovering time, as explained in Figures 15 and 16. Except for the velocity outliers, the figure shows that the estimated unsmoothed velocities are stable while in cruise mode. Figure 16: Received signal power transmitted from the slave stations while hovering. Figure 15: Received signal power transmitted from the master station while hovering. During flight trials, we had conducted three sorties, and performance analysis results are plotted in Figure 18. Solutions for which horizontal DOP is less than 2.0 are included for the analysis. Due to the lever arm, each trial has a slightly different RMS error, as can be seen in Figure 18a. Mean horizontal RSS (root sum square) error was approximately 2.4 m for all flight trials. Clock synchronization error, residual tropospheric delay, tracking jitter and the lever arm can be considered as dominant error sources. In the case of velocity performance, horizontal velocity RSS error was approximately 0.14 m, and all trials show similar performances. In this paper, vertical performance was not considered for the analysis, because vertical DOP is not good in tested altitudes, as explained previously. Figure 17: Velocity error while cruising, horizontal components only. Figure 11:0 Performance analysis. (**a**) Horizontal error; (**b**) Horizontal velocity error.
## 3 Results and Discussion
In the spectral region 4000-1200 cm\({}^{-1}\) on amosite, appearing under OM practically recovered by round dark particles, Figure 2a, bands ascribing to carbonaceous materials have been almost always detected, Figure 4a. In fact, bands at 1332, 1582, and 2663 cm\({}^{-1}\) have been observed and they can be ascribed respectively to D (diamond like), G (graphite like) and G' (second order Raman scattering) vibrations of CMs [31]. Also on crocidolite, showing often under OM dark particles on the crystal surface, Figure 2b, bands ascribed to CMs were identified when the laser beam was directed onto the grains, Figure 4b. On the spectra, the bands at 1333, 1600, 2660, and 2914 cm\({}^{-1}\) can be ascribed respectively to D, G, G', and C-H vibrations. The fact that on crocidolite the band at 1333 cm\({}^{-1}\) showed higher intensity with respect to the band near 1600 cm\({}^{-1}\), which appears large, proves a higher disorder in the CM particles lying on the crystal structure. On the other hand, applying fitting program to the 1600 cm\({}^{-1}\) band, two underlying bands at 1591 (G vibrations) and 1621 (D' vibrations) cm\({}^{-1}\) were identified (box in Figure 4b), confirming carbonaceous grains rich in defects. On the other amphibe phases, especially on actinolite and anthophyllite, bands ascribed to CMs were very rarely observed, confirming the data from OM observations, Figure 2c-e. On tremolite, at wavenumbers higher than 1200 cm\({}^{-1}\), an intense and large hump produced by fluorescence effects was always detected. This fact avoided detection of Raman bands in this spectral range. On chrysotile, in the 4000-1100 cm\({}^{-1}\) spectral range, different bands ascribing to carbonaceous materials were detected, Figure 4f. In particular the band at 1118 cm\({}^{-1}\) may be ascribed to symmetric stretching of C-O-C bonds, the band at 1273 cm\({}^{-1}\) to disordered C-H, the band at 1340 cm\({}^{-1}\) produced by D vibrations, the band at 1613 cm\({}^{-1}\) by G + D' vibrations, and the band at 1690 cm\({}^{-1}\) due to C=O bonds [31; 32]. With the laser wavelength used, 632.8 nm, the OH- vibrations were detected only on amosite, bands at 3618 and 3637 cm\({}^{-1}\), anthophyllite at 3666 and 3671 cm\({}^{-1}\), actinolite at 3643 and 3660 cm\({}^{-1}\), and chrysotile, bands at 3688 and 3699 cm\({}^{-1}\). The position of the obtained Raman bands are summarized in Tables 1 and 2. Figure 4: Raman spectra of the 4000–100 cm\({}^{-1}\) range obtained on the six regulated asbestos minerals: (a) amosite; (b) crocidolite; (c) tremolite; (d) anthophyllite; (e) actinolite; (f) chrysotile.
## 4 Method and Discussion
### Data Preparation and Calibration  
Before and after using Equation (4), we employed several corrections and data editions and outlier identification as follows:* GPS transmitter bias: GPS transmit powers are approximate estimates with some biases which should be considered. The main sources of these biases could be unknown transmitting powers of GPS satellites and the biases in \(P_{\mathbb{R}}^{i}\) associated with GPS pseudorandom noise (PRN) codes [16; 44]. We used empirical calibration developed by [PERSON] et al. (2018) for CYGNSS products. Table 3 shows the magnitude of the biases which should be corrected during the estimation of \(SNR_{c\ dB}\)[15].
* Incidence angle: This parameter also affects a coherent reflection when the incidence angles are above 40 degrees or 50 degrees and was negligible for our purpose [34], but we deleted data with an incidence angle of more than 65 degrees.
* Quality Control Flags: The Level 1A data product used in this study was refined by applying a set of quality control flags designed and included in the data to indicate potential problems [27; 45]. The specific flags we used were 2, 4, 5, 8, 16, and 17, which were related to S-band transmitter powered up, spacecraft attitude error, black body DDM, DDM is a test pattern, the direct signal in DDM, and low confidence in the GPS EIR estimate, respectively. Based on the work by [PERSON] et al. (2018) on soil moisture, we removed data with those quality flags in this study.
* Additional correction and removal: We removed data with \(SNR_{dB}\) less than 2 dB and CYGNSS antenna gain of less than 0 dB or more than 13 dB. These corrections were empirical and are not standardized, but have been shown to be beneficial [16].  
Figure 7 shows the statistical information for corrected SNR using three days of CYGNSS data during the flood time. Figure 7A shows the calculated surface reflectivity SNR of CYGNSS tracks before (left side) and after (right side) the data preparation. As can be seen in the middle part of the figure, some of the measurements that may be misleading were removed. Figure 7B,C show the distribution of the measurements with respect to the incidence angle and antenna gain. Despite the fact that the data rectification procedure discarded about 48% of the observations, CYGNSS still provided enough data to detect the flood. The flooding period continued until 17 January 2020. We analyzed a dataset consisting of three days of CYGNSS observations to reduce the effect of losing a significant portion of the data.
# An Arch-Bridge Topology-Based Expressway Network Structure and Automatic Generation
## 4 Experiment and Results Analysis
### Efficiency  
To evaluate the efficiency of the proposed method, we conducted experiments with datasets of varying sizes, which were respectively 1000, 10,000, 100,000, 1000,000 (1 M), and 10,000,000 (10 M). We repeated the experiment 10 times on each order of magnitude and calculated the mean of the running time.  
As shown in Figure 9, the runtime increased slowly with the increase in data volume, showing a nearly linear relationship that tended to be smooth. Taking 1 M data volume as an example, 2950 topologies were generated, and the runtime was only 5.76 s. In other words, the average time required to generate one topology was less than 2 ms. At the same time, the running time was fitted linearly and the linear fitting equation was given. These experimental results demonstrate that our proposed method has a low time complexity, which meets the efficient demand for the automatic generation of topology in an expressway network.
## 3 Proposed Method
### User Responsibility for Transaction Independence  
To address the difficulty of system-level dependency analysis, the proposed method assigns the responsibility for verifying transaction independence to users during transaction creation. Although this may initially appear burdensome, in practice, users typically base new transactions on data that have already be confirmed and finalized.  
Specifically, users are advised to reference only those transactions that are fully registered. A transaction is considered fully registered when its hash is included in a platform chain segment that has been finalized by anchoring to the reference chain \(\mathbb{R}\mathbb{C}\). For instance, if a user wishes to register transaction \(tx\) in block \(b^{[i,l]}\) of the segment \(b^{[i,l]}\) to \(b^{[i,N]}\), then other transactions in the same segment are not yet finalized at the time of \(tx\)'s creation. Finalization occurs only when the terminal block \(B^{[n_{i+1}]}\) is recorded on \(\mathbb{R}\mathbb{C}\). See Section 3.5 for more details.  
Failing to follow this guideline is risky. For example, suppose user \(w\) submits a transaction to buy asset \(D_{K}\) from user \(v\), assuming that transaction \(tx_{1}\), in which \(u\) sells \(D_{K}\) to \(v\), will be successfully executed. If \(tx_{1}\) is not finalized or fails execution, \(w\)'s transaction becomes invalid. In this framework, such risk is borne entirely by the user. By enforcing this design principle, the system preserves transactional integrity, while maintaining decentralized control over dependency validation.