esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Experimental Results of Execution Time  
This experiment compares the relationship between the execution time of different algorithms and the number of sensors. Figure 8 shows the average execution time. As the RPRO algorithm selects the position and charging direction of the charger randomly and does not have too many calculations, it requires little execution time. However, the number of chargers required by the RPRO algorithm is very large. The PBAD algorithm randomly selects the charger position and repeats the operation multiple times to obtain the best results. Although it can obtain good results, the iterative operations are performed multiple times. As a result, the average execution time is more than that of SMRU and RPRO, and the execution time will increase as the number of recalculations increases. When SMRU selects the position that covers the largest sensor, it decides whether to compare multiple positions. Only when the number of sensors (candidate positions) around the position is more than three will it be executed. This is because when the number of candidate positions is less than three, regardless of the position selected, it does not affect the coverage results, and thus can reduce the execution time of many repeated calculations. Additionally, the SMRU algorithm only needs to be executed once to obtain the results, and it is not necessary to repeatedly take the best value like PBAD. Therefore, the execution time performance is much better than that of PBAD.
# Shielding Grounding Optimization Method for Spaceborne Multi-Cable
## 4 Results and Analysis
### Analysis of Experimental Test Results  
The measured results are shown in Figure 8; the state number order 1-64 corresponds to \(R_{AN}R_{AF}R_{MN}R_{MF}R_{FN}R_{FF}=000000\), \(000001,\dots\), \(111111\), where \"0\" means no grounding and \"1\" means grounding. According to the test results, the angle accuracy of the scanner can also be divided into three levels, which were analyzed as follows:  
(1) Accuracy level 1 (in green) contains the state numbers 1, 2, 3, and 4, corresponding to \"000000, 000001, 000010, 000011\", respectively. This means that the shield of both the angle measurement and motor drive cable is not grounded. Regardless of how the shield of the field excitation cable is grounded, the scanning mirror has the smallest angle accuracy and the best shielding effect, which is consistent with the meaning of \(Y_{1}\) in Equation (2). (2) Accuracy level 2 (in blue) contains the state numbers 5-16, corresponding to \"000100-001111\". This shows that, when the angle measurement cable shield is not grounded and at least one end of the motor drive cable shield is grounded, the angle accuracy of the scanning mirror is second to accuracy level 1, which is consistent with the meaning of \(Y_{2}\) in Equation (2). However, states 10 and 14 are two special points,  
Figure 7: CST simulation model of the cable of the scanning mirror drive control system.  
which do not conform to the above rule and were analyzed for errors, as described in Section 4.3. (3) Accuracy level 3 (in red) contains the state numbers 17-64, corresponding to \"010000-111111\". This summarizes the law of the angle measurement cable shield, which states that, as long as one end is grounded, the scanner cannot complete the pointing command, indicating that the system is subject to the greatest interference, and the shielding effect is at a minimum, which is consistent with the meaning of \(Y_{3}\) in Equation (2).  
Among all the states in which the scanner can complete the pointing command, state 15 of accuracy level 2 corresponds to an angle accuracy of 7.3 arcseconds, and state 4 of accuracy level 1 corresponds to an angle accuracy of 4.2 arcseconds, which shows that the optimal shield grounding state can improve the angle accuracy of the scanner by 42.5%.  
In summary, the overall trend of the experimental and simulation results is consistent. Except for the error of two states, the rest of the states are consistent with the results of the optimization method proposed in this paper, proving that the method is valid and accurate. In addition, when the number of cables continues to increase, the grounding state of the shield will exponentially increase, and it is difficult to use the experimental method to test each state one by one. This method can quickly establish the mapping relationship between the grounding state and shielding effect of a multi-cable shield, determining the optimal grounding mode and eliminating the time cost of many experimental tests, so the method is more rapid and convenient.
# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 4 Cervical Cancer
### Nodal Involvement, Lymph Vascular Space Invasion, and Parametrial Invasion  
With regard to the detection of metastatic nodes, multiple original radiomics studies are available so far [76; 77; 78; 79]; these studies included a number of patients ranging from almost 100 to more than 700 and only four of these studies were multicentric [86; 87; 88; 89] (Table 2). In addition, two metanalyses investigating the accuracy of radiomics for the assessment of nodes status have been published in 2022 by [PERSON] et al. [90] and [PERSON] et al. [91]. Based on the metanalysis by [PERSON] et al. [92], the pooled diagnostic odds ratio, sensitivity, specificity, and AUC of radiomics in detecting nodes metastasis were 12, 80%, 76%, and 0.83, respectively; in addition, the assessment of multiple sequences and radiomics combined with clinical factors seemed to improve the diagnostic odds ratio compared with the human reading. However, this metanalysis also highlighted the high heterogeneity among the included studies [90]. The metanalysis by [PERSON] et al. [91] concluded that compared with the radiomics analysis, ADC values are more clinically promising because they are more easily accessible and widely applied, with similar pooled diagnostic values. It is worth saying that some additional studies investigated MRI-based radiomics in the prediction of the LVSI preoperatively [92; 93; 94; 95; 96], but only the study by [PERSON] et al. [96] was multicentric. In this multicentric study with 168 patients, the authors developed an MRI-based nomogram by combining contrast-enhanced T1 WI and T2 WI and obtained a moderately high AUC (0.830) for predicting LVSI in the test cohort [96]. To the best of our knowledge, only one study investigated the role of radiomics for predicting parametrial invasion in a retrospective single-centre study with 137 patients; in this study, radiomics signatures obtained with T2 WI and joint T2 WI and DWI yielded an AUC of 0.797 vs. 0.946 and 0.780 vs. 0.921, respectively, in the primary and validation cohorts [97].
# EBR: Routing Protocol to Detect Blackhole Attacks in Mobile Ad Hoc Networks
## 3 Proposed Protocol
### Methodology
This only can be accepted if other paths are suspicious as they go through one of the blackhole attackers;
8. When \(RTT_{i}>RTT_{a}\) and TTL value is large: Location of the blackhole attacker is somewhere away from the source node. \(RTT_{i}\) is large because of havingseveral congested nodes or due to the large value of \(n\). Therefore, the whole path should be avoided. 6. Data Communications: As shown in Algorithm 3, EBR is a combination of a reactive routing protocol and our TR algorithm. Selected routes should consider the confidence values calculated using our TR method during the testing phase. When the source node wants to communicate, it will only consider the routes which have positive confidence levels and, also, are less congested. Zero confidence indicates that a node has a blackhole attacker next to it. Negative confidence indicates that this is a blackhole node.
# Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset
## 3 Materials and Methods
### Train and Test Setup  
The dataset consisting of synthetic images was used to train deep learning-based classification networks--specifically AlexNet, SqueezeNet, and VGG16--in the setups for experiments 1 and 2 (see Figure 1). All combinations of AlexNet, SqueezeNet, VGG16 DL networks, and adaptive moment estimation (Adam) [40], adaptive learning rate method (AdaDelta) [41], stochastic gradient descent (SGD) [42], and root mean square error probability (RMSprop) [43] optimizers were applied for these two experiments. Networks and optimizers were trained with combinations of the different hyperparameters listed in Table 1.  
Figure 5: Some examples of real bird and RW-UAV images. (**a–c**) Bird images; (**d–f**) RW-UAV images.  
In experiment 2, unlike experiment 1, a CDNTS layer was included. By comparing the results of experiments 1 and 2, the success of the CDNTS layer in the training of the network was measured and reported. The synthetic dataset created in the green box was divided into train, validation, and test sets, as shown in Figure 4. The synthetic test data background was combined with modern and old city views. Training, validation, synthetic testing, and real test data were trained, tested, and reported for four different DL networks. Deep learning-based classification networks should be selected considering their speed and accuracy [44]; therefore, in this study, we tested four different networks and compared the speeds and accuracy of the networks to present a comprehensive comparison. All training and testing stages were carried out by using the GPU computing system (GeForce GTX 1080 ti, Nvidia Corp., Santa Clara, CA, ABD), 128 gigabyte(GB) random access memory (RAM), I7 processors and the TensorFlow-based [19] Keras [20] library. Experiment 2 consists of a CDNTS layer combined with deep learning-based classification methods in train and test phases. As shown in Figure 1, the synthetic training dataset was introduced to the DL networks after passing through the CDNTS layer. The F-score value and AUC metric were used to verify the classification accuracy performance of the trained AI model. The F-score (Equation (2)) was calculated alongside the precision (Equation (3)) and recall (Equation (4)).  
\[F_{score}=2\cdot\frac{Precision\cdot Recall}{Precision+Recall}\, \tag{2}\]  
\[Precision=\frac{TP}{TP+FP}\, \tag{3}\]  
\[Recall=\frac{TP}{TP+FN}\, \tag{4}\]  
where _TP,TN_, _FP_, and _FN_ are true positives, true negatives, false positives, and false negatives, respectively.
## 2 UAV Video Image Collection Program
### UAV Vison-Based Monitoring Plan and Reference Target Layout
It is noted that the targets installed on the top of the rocking walls (within the yellow dash rectangles in Figure 2) are not considered since there was an elevation difference between the roof panel and the top of the rocking wall; thus, the pixel resolutions and monitored motions of these targets were different from the targets installed on the roof panel. For the installation of targets, either high-strength Velcro tape or high-tack adhesive was applied between each target and its attachment to ensure a reliable installation. Targets were securely attached to the building specimen, ensuring movement of the targets within the plane of the attachment. Notably, advancing prior work [13], a wide range of reference target patterns were investigated to evaluate the effectiveness of the proposed target detection and tracking algorithm on different patterns and the robustness of different target patterns. Specifically, three patterns were adopted for stationary targets, while seven patterns were adopted for moving targets. A summary of the reference targets is presented in Table 4, with their patterns and dimensions described in Figure 3. The seven patterns adopted for the moving targets on the roof encompass various image features for detection and tracking, considering (a) the number of tiles (two, four, and five), (b) color (black-and-white and red-and-white), (c) shape (square and concentric circles), and (d) dimension. A statistical analysis is conducted to investigate the optimal patterns (features) for structural displacement monitoring under earthquakes by comparing the error characteristics of the displacements extracted from the proposed methodology for each of the moving target patterns. A detailed discussion of this statistical analysis is presented in Section 6. Figure 2: Example camera view of the plan-view videos (the first video frame from MID 15 test video) and zoom-in view (3\(\times\)) for reference targets with various patterns.
###### Abstract  
The frequency of ground motions during earthquakes is typically in the order of a few hertz. As the earthquake-induced liquefaction of soils is widely assessed by performing laboratory tests, it is necessary to consider various loading frequencies generated by real earthquakes. The effect of loading frequency has been studied by cyclic triaxial tests; however, it has rarely been investigated by cyclic direct simple shear tests, which are more similar to the cyclic loading conditions associated with earthquakes. In this study, a series of cyclic direct simple shear tests were performed on clean sand with a relative density (D\({}_{\rm r}\)) of 40% (loose sand) and 80% (dense sand), obtained from Nakdong River. The parameters considered are the initial vertical effective stresses (\(\sigma_{\rm{v0}}\)\({}^{\prime}\) = 50, 100, and 200 kPa) and the loading frequencies (\(f\) = 0.05, 0.1, 0.5, and 1 Hz) to evaluate the effect of the loading frequency on the liquefaction prediction of clean sand. The results showed that the liquefaction resistance of the sand increases with the increase in the loading frequency, regardless of the initial vertical effective stress and relative density. When the loading frequency increased from 0.1 to 0.5 or 1 Hz, the maximum increase in the cyclic resistances were 15%, and 19% for loose and dense sand, respectively. For a given loading frequency, the liquefaction resistance of the sand decreased when the initial vertical effective stress increased.  
s and liquefaction; loading frequency; cyclic direct simple shear tests +
Footnote †: journal: Applied Physics Letters  
_Article_  
Effect of Cyclic Loading Frequency on Liquefaction Prediction of Sand
## 3 Materials and Methods  
In this research, we investigated the influence of local government expenditures on environmental safety by lowering CO\({}_{2}\) emissions at the municipal level from 1993 to 2020. Local government capital and recurrent investments were employed as explanatory variables in this study. The data for these predictor factors were from the Central Bank of Nigeria Statistical Bulletin, whereas the dependent variable (CO\({}_{2}\) emissions) is from the World Bank Development Indicators, measured in millions of tons. The entire dataset was converted to natural log for convenience of use. In the study, summary statistics were used to first examine the nature of the dataset and its normality status. The unit root test, homoscedasticity or same variance test, stability test, and test of explanatory variable interdependence, as well as some other diagnostics, were performed. An effect study was performed using the multiple regression technique to validate the effects of local government spending on carbon dioxide reduction. Finally, a correlational analysis was conducted out to assess the relationship between the variables.  
The model specified for this study is as follows:  
\[\text{Y}=\beta_{0}+\beta X_{1}+\beta X_{2}+\mu_{it}\]  
where:  
Y = carbon pollutants (CO\({}_{2}\));  
\(X_{1}\) = local government recurrent spending;  
\(X_{2}\) = local government capital investments;  
\(\beta_{0}\) = _coefficient of the parameter estimate_;  
\(\mu_{it}\) = error term;The above model can be specifically applied to this study as:  
\[LnCO_{2}=\beta_{0}+\beta_{1}LnLRX_{1}+\beta_{2}LnLCX_{2}+\mu_{it}\]  
where:  
\(Ln=\) _natural log of variables;_  
\(CO_{2}=\) _carbon dioxide emissions;_  
\(LRX=\) _local government recurrent overhead;_  
\(LCX=\) _local government capital investments;_  
\(\beta_{0}=\) _coefficient of the parameter estimate;_  
\(\beta_{1}-\beta_{2}=\) _intercept;_  
\(\mu_{it}=\) _error term._  
A priori, _we anticipate_ \(\beta_{1}>0\)_,_ \(\beta_{2}>0\)_._
## 4 Performance Evaluation
### Analysis of End-to-End Delay
#### 4.2.3 **a. Comparing Three Broadcast Methods**  
With scenarios S1, S2, and S3, we compared PDRs for the three broadcast methods, CBT, SCBT, and SSCBT. From the analysis in Section 3.4.2, \(N\), the number of BSs, was set to 4 for S2 and 5 for S3.  
Figure 9 shows that the PDRs of the three approaches depended on how well they could handle competing nodes. The SSCBT achieved almost 100% PDR regardless of the size of CW, while the others showed decreasing PDRs as CW decreased. It was obvious that the PDR of SCBT approached that of SSCBT as CW increased. We can see that SSCBT was not sensitive to the type of scenario or the CW size since it used a BS scheduling. According to Equations (8) and (10), the smallest possible size of CW has to be selected to reduce _len(BS)_. From this study, CW was set to three or less for the rest of the simulation.
###### Abstract  
Boron and oxygen-doped Cr-Al-Ti-N coatings were deposited by closed field unbalanced magnetron sputtering (CFUBMS) of TiB target manufactured by self-propagating high-temperature synthesis, and Ti, Cr, and Al targets. To evaluate the influence of doping elements, as-deposited coatings were studied by glow discharge optical emission spectroscopy (GDOES), SEM, XRD, and optical profilometry. Mechanical properties were measured by nanoindentation and tribological, abrasive and electrochemical testing. The introduction of boron suppresses columnar growth and leads to structural refinement and a decrease of coating's surface roughness. The addition of 2.3 at.% boron results in the highest mechanical properties: hardness \(H\) = 15 GPa, stable friction coefficient \(f\) = 0.65, and specific wear \(Vw=7.5\times 10^{-6}\) mm\({}^{3}\)N\({}^{-1}\)m\({}^{-1}\). To make the coating more visually appealing, oxygen was introduced in the chamber near the end of the deposition cycle. Upper Cr-Al-Ti-B-O-N layers were studied in terms of their composition and coloration, and the developed two-layer decorative coatings were deposited on cast metallic art pieces.  
Cr-Al-Ti-B-O-N coatings; TiB target; unbalanced magnetron sputtering; CFUBMS; composition and structure; hardness and elastic modulus; wear; abrasive and corrosion resistance; friction coefficient; decorative properties +
Footnote †: journal: Applied Physics Letters
## 4 Engineering Example Verification
### Comparison of Engineering Measured Data and Calculation Results
According to the engineering experience, an error of 15.44% is within the acceptable range. When solving the hydraulic head distribution function, the series in the function is an infinite series. The mathematical characteristics of the hydraulic head distribution function are analyzed, and it can be seen that as the number of stages \(p\) increases, the growth rate of head \(h\) tends to decrease with smaller and smaller increments. After a trial calculation, when \(p\) takes 10, the hydraulic head increment is not in an order of magnitude with the hydraulic head value \(h\); therefore, the calculation takes \(p=50\) to meet the accuracy requirements. When substituting the parameters in Table 2 into Equation (11), and drawing the equal hydraulic head line inside the foundation pit according to the calculation results, as shown in Figure 4(a), the \(x\) and \(z\) axes are consistent with Figure 2.  
To verify the rationality of the hydraulic head distribution results under conditions of non-extreme aquifer thickness, the thickness of the flow cross-section \(M\) is set to 12 m, and, accordingly, the water level in the pit is adjusted to 21.62 m, the thickness of the aquifer is adjusted to 30 m, and the other parameters remain unchanged when calculating the hydraulic head distribution. The results are illustrated in Figure 4(b). It can be observed that the change trend in the medium hydraulic head line in Figure 4(b) is similar to that reported by [PERSON] [25] and others, which verifies the correctness of the calculation results from the side. Based on the hydraulic head distribution results of the calculation model established using the foundation pit of Yangwan Station, it is noted that due to the significant volume of groundwater, the impact of groundwater seepage on the overall seepage field is minimal, leading to a small range of variation in the equal hydraulic head line.
## 5 Conclusions
Although the proposed model has achieved initial optimization in inference speed and structural compactness through the integration of the sliding window attention mechanism and multi-scale feature extraction strategy, it is primarily tailored for typical radial distortion scenarios. Its adaptability to more complex distortion patterns--such as extreme wide-angle or non-uniform distortions--remains limited. Future work may explore the incorporation of physics-based modeling constraints or multimodal information fusion strategies to enhance the model's generalization ability and robustness across diverse nonlinear distortion conditions. System-level integration and multi-task expansion: Building upon the current image correction framework, future developments may incorporate modules for object detection, behavior recognition, and other perception tasks. This will facilitate the creation of a more comprehensive edge-intelligent system tailored for practical applications in smart surveillance, industrial vision, and other real-world scenarios, thereby enhancing the system's versatility and environmental adaptability. In summary, this study presents a feasible technical pathway for wide-angle image correction modeling, system integration, and embedded deployment, providing both theoretical support and a practical foundation for the engineering application of visual perception technologies in resource-constrained environments. Conceptualization, M.C. and S.L. ; methodology, S.L. and Z.C. ; software, W.Z. ; validation, Z.C., S.L. and W.Z. ; formal analysis, S.L. ; investigation, Z.C. ; writing--original draft, S.L. ; writing--review and editing, S.L., Z.C., W.Z. and M.C. ; visualization, S.L. ; supervision, M.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. All data generated or analyzed during this study are included in this article. The raw data are available from the corresponding author upon reasonable request. The authors thank all the participants in this study for their time and willingness to share their experiences and feelings. The authors declare no conflicts of interest concerning the research, authorship, and publication of this article.
## 4 Discussion
### Lactic Acid Adsorption Isotherms  
Among the isotherms investigated, the Langmuir isotherm was found to be more suitable in explaining the adsorption mechanism compared to the Freundlich and Temkin isotherms. The Dubinin-Radushkevich isotherm and intraparticle diffusion kinetic model were investigatedin this study for lactic acid adsorption, but none of these two models were able to explain the experimental results of lactic acid adsorption (data not shown). The understanding of the lactic acid adsorption mechanism can be improved by applying the Redlich-Peterson isotherm (a hybrid isotherm featuring both Langmuir and Freundlich) and a multilayer adsorbent-adsorbate interface Brunauer-Emmett-Teller (BET) isotherm model to fixed bed column experiments [29].  
In a similar study, adsorption of formic acid onto Amberlite(r) IRA-67 followed the Langmuir isotherm under similar operating conditions [32]. In contrast, [PERSON] et al. [25] showed that the lactic acid adsorption onto Amberlite(r) IRA-67 was fitted by the Freundlich isotherm model. The plot (Figure 3a) from the Langmuir isotherm shows that the lactic acid was adsorbed as a monolayer and homogeneously distributed over the surface of the adsorbent with restricted interaction between the adsorbate (i.e., lactic acid) molecules (Table 4). In addition, the Langmuir isotherm also suggests that adsorption of lactic acid onto the GAC, RLX425 and AMB400 is favorable (\(0<R_{L}<1\)).