esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Field Test and Analysis
### The Prediction of Settlement  
Recently, many calculation methods have been used for the settlement of the soft foundation. In this study, the three-point method based on the hyperbola method [19] was selected. The schematic diagram of the three-point method and hyperbola method are shown in Figures 6 and 7. When \(t\rightarrow\infty\), the final settlement is calculated according to the following Formula:  
\[S_{\infty}=S_{1}+\frac{1}{\beta}=S_{1}+\frac{(S_{3}-S_{1})(S_{2}-S_{1})}{2(S_ {2}-S_{1})-(S_{3}-S_{1})}, \tag{1}\]  
Figure 5: Variation of settlement and embankment filling height of the K12 + 850 section in the field test.  
Figure 6: The schematic diagram of the three-point method.  
According to the test data, the three-point method based on the hyperbola method is used to predict late settlement. The late settlement prediction is shown in Table 2. It can be seen from Table 2 that the settlements of CJ1-1 in November 2012, December 2012, and January 2013 are 5.27 mm, 4.53 mm and 3.93 mm, respectively. For CJ1-1, in September, October, November and December 2012, the settlements are 5.36 mm, 4.69 mm, 4.13 mm, and 3.68 mm, respectively.  
According to the unloading conditions: (1) The settlement after construction is less than 30 cm and this condition has been met; (2) monthly settlement is less than 5 mm for 2 consecutive months, which means the unloading should be in early February 2013, about 13 months after surcharge preloading. Preparation of the foundation settlement in the FORTRAN computer program (Hohai University, Nanjing, China) can be predicted according to the consolidation coefficients method, and it can be calculated by the consolidation coefficients method that the settlement in November 2011 was nearly 5.61 mm, the settlement in December 2011 was nearly 5.12 mm, the settlement in January 2013 was nearly 4.68 mm, and the settlement in February 2013 was nearly 4.27 mm. Therefore, according to the unloading condition, the monthly settlement is less than 5 mm for 2 consecutive months and the unloading should be in early March 2013--14 months after surcharge preloading. In total, taking the field test as an example, the unloading is nearly in late February 2013 and early March so that the surcharge preloading is nearly 14 months earlier.
## 4 Discussion  
In this section, the results obtained in the experiments are shown and discussed. XGBoost and gbm models are the ones providing highest overall accuracy followed closely by rf. Friedman test was performed to determine whether the differences in accuracy were significant. For a significance level \(\alpha=0.05\) in the Friedman test (\(p\)-value = 0.01), evidence was found that the seven classifiers achieve the same accuracy and therefore H\({}_{0}\) was rejected. Box and Whisker Plots allow us to observe the spread of the estimated accuracies for different methods and how they are related to each other. The boxes are ordered from highest to lowest mean accuracy. The evaluation metrics are accuracy and kappa because they are easy to interpret. The algorithms were chosen for their diversity of representation and learning style (Figure 10).  
Cross-validation methods provide good error estimations for a model, although it is valuable to make sure that there is no overfitting. All models (Table 3) achieved more correct predictions in the  
Figure 10: Results of the model training. A comparison of the cross-validated Accuracy and Kappa estimated from XGBoost (eXtreme Gradient Boosting), rf (Random Forest), gbm (Stochastic Gradient Boosting), lmt (Logistic Model Trees), svm (Support Vector Machine), glm (Generalized Linear Model) and lda (Linear Discriminant Analysis) for meteorological dataset [2].  
Figure 9: The cross-validation profiles for a Stochastic Gradient Boosting model applied to the Tenerife meteorological data [2]. The final model was fit using the hyperparameters shown in Table 4.  
training set than in the test set, which is why the metrics obtained in training shall not be used as the only option to assess the models since these show an overly optimistic result.  
The XGBoost model is the one achieving best results both in the training set and in the testing set. Models based on rf, gbm, and lmt achieve very similar values in the test set. However, according to the contrast of non-parametric Friedman test and the validation results, the models based on lda and glm are inferior to the XGBoost model.  
If the priority were to maximize the prediction ability of the model, the XGBoost model would be the best option. However, if interpretability [66] of the model were more important, then the rf or lmt models would be the most suitable.  
The most relevant predictors in the XGBoost model are _GPH_, _Rha_, _Rhs_, which contrasts with gbm and rf models. It is worth highlighting that, in the models that provided the highest accuracy (models based on XGBoost, gbm and rf), the NAO predictor is not relevant, which contrasts with the results obtained in other studies carried out in the Canary Islands (Table 5).
## 3 Bioactivity of _Chlorella vulgaris_ Compounds
### Detoxification and Heavy Metal Chelation  
_C. vulgaris_ has garnered considerable attention for its ability to bind and eliminate heavy metals and toxins from the body, demonstrating strong detoxification and chelation capabilities. This detoxification potential is attributed primarily to its cell wall structure, which is rich in polysaccharides, proteins, and glycoproteins that can bind to heavy metals and other toxins. By doing so, _C. vulgaris_ can effectively reduce the bioavailability of harmful substances, promoting their excretion and mitigating their toxic effects.  
Studies have shown that _Chlorella_ can facilitate the removal of heavy metals such as mercury, lead, cadmium, and arsenic [111; 112; 113; 114]. For instance, research has demonstrated that _C. vulgaris_ supplementation significantly reduced mercury levels in animal models exposed to mercury contamination, thereby lowering the accumulation of this toxic metal in tissues such as the liver, kidneys, and brain [115; 113]. These findings suggest that _C. vulgaris_ can serve as a protective agent in individuals exposed to environmental mercury, such as those who consume large amounts of fish or live near industrial areas.  
Similarly, _C. vulgaris_ has been studied for its effectiveness in reducing lead accumulation. A study by Queiroz et al. [116] found that _C. vulgaris_ supplementation decreased lead levels in the blood and tissues of lead-exposed rats while mitigating oxidative stress caused by lead toxicity. These results indicate the potential of this microalgae in preventing lead poisoning, which remains a significant public health concern in many parts of the world.  
Cadmium is another heavy metal that poses severe health risks, including kidney damage and bone demineralization. _C. vulgaris_ has demonstrated the ability to chelate cadmium, reducing its absorption and promoting its excretion. A study by Farag et al. [117] found that cadmium-exposed rats treated with _C. vulgaris_ showed lower cadmium concentrations in their tissues and reduced oxidative stress markers. This chelating ability highlights _C. vulgaris_ as a potential intervention for populations exposed to cadmium through industrial pollutants or contaminated food and water.  
In addition to heavy metals, _C. vulgaris_ has been shown to aid in the detoxification of organic pollutants, including polychlorinated biphenyls (PCBs), dioxins, and pesticides. The complex structure of _Chlorella_'s cell walls allows for the adsorption and elimination of these harmful compounds from the body. Studies have shown that _C. vulgaris_ supplementation decreased levels of dioxins and PCBs in the blood of individuals exposed to these environmental toxins, highlighting its potential role in reducing the burden of persistent organic pollutants [118; 119].  
Table 1 summarizes key chemical compounds and their bioactivities of _C. vulgaris_, showcasing this microcalga's various benefits.
# A Modified Bearing Capacity Model for Inclined Shallow Anchor Cable with Experimental Verification
## 4 Discussion
### Parameter Sensitivity Analysis
#### 4.1.1 Geometrical Parameters of the Failure Surface  
Using the relationship between \(\beta_{1}\), \(\beta_{2}\), \(\theta\), and \(\alpha\) obtained from the experiments, the corresponding relationship with the bearing capacity \(F\) can be derived by substituting these parameters into the calculation model presented in this paper, as shown in Figure 9. From the figure, it is evident that the bearing capacity decreases as the failure angle \(\beta_{1}\), \(\beta_{2}\) increases, while it increases as the failure angle \(\theta\) grows. This is because, as \(\beta_{1}\) and \(\beta_{2}\) increase, a larger volume of soil is involved in resisting the pull-out force, thereby increasing the bearing capacity. Similarly, as \(\theta\) increases, more soil participates in resisting the pull-out, which also leads to an increase in bearing capacity.  
Figure 8: The theoretical values and measured values of the ultimate bearing capacity.
# Privately Generated Key Pairs for Post Quantum Cryptography in a Distributed Network
## 3 Privately Generated Key Pairs for Post-Quantum Cryptography  
This section describes secure challenge-response-pair (CRP) mechanisms that can enhance security in the current PKI systems that use public-private cryptographic keys. The root of trust mechanism of terminal devices can be the result of unique responses that are responding to known challenges.  
Examples of CRP-generating systems can be tokens containing Physically Unclonable Functions (PUFs) [24], biometric images that contain unique information about an individual, or a structured digital file (see Figure 3). The PUF can be based on SRAM, RRAM, or MRAM [25; 26; 27]. During an enrollment cycle, which is performed in a secure setup, the CRP generating system generates what is called a Crypto-Table, which is the lookup table containing a list of challenges and their corresponding responses.  
Some of the objectives of the scheme are to mitigate problems such as Man-in-the-middle attacks (MIM), impersonation attacks, compromised key generation, distribution and storage, and malicious HSMs. The unique Crypto-Table should be protected by anti-tampering measures, unique, and sufficiently complex, i.e., with high entropy. Other priorities include the enhancement of the one-wayness of the CRPs and the implementation of ternary logic [28; 29], which masks the fuzzy and unstable CRPs.
# Signal Detection for Ambient Backscatter Communication with OFDM Carriers
## 2 System Model and Problem Formulation
### Notations  
The following notations are used throughout the paper.  
* \(\mathbb{E}(\cdot)\) and \(\text{Var}(\cdot)\) denote the expectation and variance operators, respectively.
* \(\mathcal{N}(\mu,\sigma^{2})\) and \(\mathcal{CN}(\mu,\sigma^{2})\) denote the Gaussian and the circularly-symmetric Gaussian distributions with mean \(\mu\) and variance \(\sigma^{2}\), respectively.
* \(\text{Re}\{\cdot\}\) is the real part of a complex number.
* A random variable \(X\) that is gamma-distributed with shape \(k\) and rate \(\theta\) is denoted as \(X\sim\Gamma(k,\theta)\). The corresponding probability density function (PDF) in the shape-rate parametrization is \(f(x;k,\theta)=\frac{1}{\Theta\Gamma(k)}x^{k-1}\exp\{-x/\theta\}\), where \(\Gamma(k)=\int_{0}^{\infty}x^{k-1}e^{x}dx,k\in(0,\infty)\) is the gamma function evaluated at \(k\).
## 4 Multimodal Fusion for Infrastructure Inspection
### Applications in Practice
#### 3.4.1 Utility Mapping and Infrastructure Monitoring  
In urban environments, GPR is commonly used to detect buried cables, pipes, and ducts. AI models trained on annotated B-scans can automate this process with high accuracy. One particular case study, using a CNN-based model trained on synthetic and field data, achieved 94% precision in detecting PVC and metallic pipes in mixed soils, significantly outperforming human operators in cluttered scenarios. Another case study, involving bridge and tunnel inspection, benefited from AI-enhanced GPR, where segmentation models detected delamination, voids, and corrosion-related anomalies [59, 60].
# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 3 Image Dehazing Model
### Loss Function and Training  
The \(G\) and \(D\) of the image dehazing model were trained simultaneously, where \(G\) minimized the generative objective, and \(D\) maximized the discriminator objective. Therefore, the training process was formulated as a min-max of loss function. The generator objective \(L_{G}\) consisted of three weighted terms--standard objective \(L_{gan}\), \(L_{1}\) loss and perceptual loss \(L_{p}\).  
\[L_{G}=W_{gan}L_{gan}+W_{L1}L_{L1}+W_{p}L_{p} \tag{3}\]  
\[L_{gan}=-mean[\log(D(x,G(x)))] \tag{4}\]  
\[L_{L1}=E_{x,y}[\left\|y^{\prime}-G(x)\right\|_{1}] \tag{5}\]  
\[L_{p}=c\times mse[V\big{(}G(x),V\big{(}y^{\prime})\big{)}] \tag{6}\]  
where \(x\) is the hazy image, \(y^{\prime}\) is the clear image, \(V\) is the VGG-19 network and \(W_{gan}\), \(W_{L1}\) and \(W_{p}\) are the weights to be determined empirically. \(L_{p}\) is the mean squared error (\(mse\)) between the VGG-19 outputs of the dehazed image and clear image scaled by the constant \(c\). The discriminator objective is as follows:  
\[L_{D}=mean\big{[}\log\big{(}D\big{(}y^{\prime},y\big{)}\big{)}+log(1-D(x,G(x) ))\big{]} \tag{7}\]  
The model was trained on a computer with Intel Xeon Silver 4108 16-core CPU, Nvidia RTX 2080 Ti GPU, and 55 GB RAM. In our experimentations, the dataset was partitioned into around 90% training samples and 10% testing samples. To find the best set of hyperparameters, 20% of the training samples were selected as a validation set. The model was trained with a learning rate of 0.001. The training process stopped when there was no improvement in accuracy.  
Figure 4: The discriminator.
# Effect of IoT Capabilities and Energy Consumption behavior on Green Supply Chain Integration
###### Abstract  
The Internet of Things (IoT) is the next generation of internet-connected information communication technologies (ICT). IoT typically integrates supply chain activities to enhance green supply chain performance (GSCP). Since every organization has different IoT capabilities in comparison with other organizations, GSCP can enable supply chain integration activities for enhanced performance. The implementation of an IoT system can reduce the consumption of organizational resources like energy, electricity, and time and can increase the operational speed to gain better logistics and, ultimately, improved supply chain performance. This study has developed and empirically tested the relationship between IoT capabilities, energy consumption behavior (ECB), supply chain integration, green training (GT), and supply chain practices. Such a multidisciplinary relationship has not previously been established in the literature. The proposed study can fulfill the literature gap and opens new horizons for interdisciplinary research. Data used in this study are collected through offline and online survey methods. A total number of 250 out of 400 respondents participated in the survey. Data has been analyzed through partial least square--structure equation modeling (PLS--SEM) technique. The results of this study empirically test the developed model. IoT has a positive effect on supplier integration (SI), and customer integration (CI). Furthermore, SI and CI have a mediating role between IoT and GSCP, and GT has a positive impact on GSCP. It is concluded that the implementation of IoT can integrate CI and SI to increase GSCP. GT and ECB can ultimately improve GSCP. Additionally, the use of technology and GT can motivate employees to save energy and protect the environment to increase GSCP.  
Keywords:internet of things; energy consumption behavior; green training; green supply chain performance +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Flow Information Entropy Calculation Methods  
In our implementation we provided measures for characterising mean payload entropy in both flow directions (inbounds and outbound), as part of the feature engineering process. These features have been implemented in the GSYNX analysis suite, which will be made available at [GIT]. Since payload is typically fragmented over multiple packets, entropy may vary during the lifespan of the flow and will be summarised from multiple consecutive samples. For TCP, these content 'fragments' may represent encapsulated data and/or other protocol plus data. Our implementation was based on a modified Krichevsky-Trofimov (KT) estimator [27], which estimates the probability of each symbol in a particular symbol alphabet.  
* A KT class was implemented as a bi-directional in-memory cache (a hash table of symbol frequencies, of capacity 256 since we are dealing with a byte encoded stream; each symbol is 8 bits wide, corresponding to 0-255) and held symbolic frequency data for the two payloads.
* The flow tuple was used to index the cache. Each flow effectively had a single cache entry, with statistics and state tracked for both flow directions.
* Cache entries were updated when each new packet was encountered. The updates were assigned to the associated flow record in the cache, or a new flow record was created, and those updates were applied.
* Cumulative flow entropy values were recalibrated per packet, per direction, based on the current payload symbol frequencies and the running payload length.  
Once a flow was finalised, the final payload information entropy values were calculated using the total length of the payload against the cumulative symbol frequencies per direction.  
Even if a flow is not terminated correctly, a cumulative entropy value is maintained and exported during flow dataset creation. The final entropy value will be in the range of 0 to 8, for reasons described below.
## 7 Signal Processing:
### Two-Degree-of-Freedom Displacement Responses  
The system identification methods for 2 DOF require displacement responses. The raw responses were converted to displacement by first filtering out high-frequency noise and then integrating twice. The dominant frequencies for vertical and torsional responses were 3.01 Hz and 5.02 Hz, respectively, for Setup 1 and 3.202 Hz and 5.04 Hz, respectively, for Setup 2. A band-pass filter with lower and upper cut-off limits of 2 Hz and 6 Hz was used. Vertical displacement was obtained by averaging the two sensors, while torsional displacement was based on the sensor position. For Setup 1, the extracted vertical and torsional displacement responses from the vertical-dominant coupled response are presented in Figure 19, and the torsional-dominant responses are in Figure 20. Similar responses were generated for Setup 2.
## 3 Experiments and Analysis
### Comparisons on Mainstream Methods
#### 3.2.1 Results on S2 Looking Dataset  
Figure 5 visually compares CD results on images with varying style differences. Unlike other methods, RCCD demonstrates robustness to variations in radiation, weather, phenology, and refurbishment, exhibiting broader applicability and effectively mitigating visual style errors. Under consistent radiation (e.g., rows 1-3), RCCD shows fewer missed detections and more accurate change boundaries. In the presence of irrelevant changes like clouds and vegetation phenology (e.g., rows 4-8), RCCD generates fewer false positives (e.g., rows 5, 7, and 8). In the fifth set of images, the changes between cultivated land and bare land are not of interest in this dataset; however, the significant semantic and color differences have led most methods to misclassify these changes. In the ninth set of images, vegetation is detected as change due to varied visual characteristics across different images caused by phenological variations, whereas buildings and bare land, presenting similar  
visual appearances due to dust, result in omissions during detection. For uninteresting changes like road construction, our method demonstrates greater stability. These results confirm RCCD accurately extracts changes in interest under complex background interference, effectively suppressing visual style variations compared to mainstream methods.  
Table 2 compares the performance of different methods on the S2 Looking test set. Our RCCD method achieved the highest F1 and IoU scores across the full dataset and showed substantial improvement on the Large-VS subset. Compared to CDNeXt, RCCD achieved a 0.89% and 1.95% increase in F1-score on the full dataset and the Large-VS subset, respectively. Compared to the third-best method, these increases were 6.15% and 7.47%,  
Figure 5: Visualizations of different methods on S2 Looking.  
representing relative improvements of 119.1% and 21.5% on the Large-VS subset. Compared to the RCCD baseline, our method further improved F1-score by 1.81% (full dataset) and 2.57% (Large-VS subset), a relative increase of 41.9%. Consistent accuracy improvement was also observed on the Small-VS subset, indicating the LS and GS components do not negatively impact performance in other scenarios. This suggests our method effectively mitigates visual style discrepancies. Interestingly, feature differencing methods performed better on the Small-VS subset, while self- and cross-attention methods excelled on the Large-VS subset.
## 3 Results and Discussion
### Performance Based on Geometry Optimization Using Objective Functions  
#### 3.3.1 j- and f-Factor  
The values of the j-factor according to velocity for the optimized shapes based on the objective functions are presented in Figure 10a. It is evident that the Gv shape has the highest j-factor at the same velocity. Compared to the reference shape, the j-factor of the Gv shape improved by an average of about 4.48%. It is about 5% higher than the reference shape at low velocities, but the performance improvement tends to decrease as the velocity increases. The reduced-pumping-power shape showed a decrease in the j-factor compared to the reference shape, with an average reduction of about 2.1%.  
Figure 10: Comparison of heat transfer and friction coefficient vs. Reynolds number for optimized shapes and reference wavy-slit fin. (**a**) j-factor. (**b**) f-factor.  
The values of the f-factor according to velocity for the optimized shapes are shown in Figure 10b. At the same velocity, the reduced-pumping-power shape has the lowest f-factor, with an average reduction of about 1.8% compared to the reference shape. In contrast, the Gv shape showed an average increase of 8.1% compared to the reference shape.