esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results--Systematic Literature Review
### Cross-Analyses
#### 3.3.2 Research Method vs. Macro Theme  
Figure 7 shows the cross-sectorial analysis of research method and macro theme to investigate the type of method used as a function of the macro theme studied. Several studies (\(\approx\)8.9%) dealing with the macro theme of resilience have used an empirical approach. This means that the researchers have observed and analyzed real-world data and field observations to better understand this topic in practice. Related papers have analyzed how the use of concrete data and observations in the field has made it possible to better understand the impact of the pandemic on SCs and to identify strategies to address emerging challenges. For example, [PERSON] et al. [87] highlighted how alternative networks for  
the seafood trade were able to adapt to disruptions to traditional SCs during the pandemic, underlining the importance of an empirical approach to understanding resilience dynamics in real-world settings. Similarly, [PERSON] and [PERSON]. [55] analyzed the challenges faced by the transport and logistics sector in Poland during the pandemic, using data from online surveys to assess the impact of disruptions on business operations and to identify key areas of vulnerability. These empirical findings have helped inform business decisions and public policies aimed at improving the SC resilience. In addition, a third study proposed a model of resilient SCs based on changes in product design, demonstrating how empirical analysis can be used to develop practical solutions to address emerging challenges [100]. In conclusion, the empirical analysis of SC resilience provided insights into the strategies adopted by companies to adapt to disruptions, identifying the most pressing challenges and the most effective solutions. This has allowed organizations to learn from past mistakes and develop more effective strategies to address future challenges.  
Statistical analyses, based mainly on the data collected through questionnaires and surveys, made it possible to investigate the point of view of the SCs before and after the COVID-19 pandemic and to identify significant patterns, trends and correlations. The statistical approach is also chosen in other contexts, including sustainability, economics and performance.  
It is interesting to note that the studies using decision-making approaches mostly deal with the topic of resilience and, obviously, planning. In particular, research about planning is based exclusively on this approach, which allows for the making of informed and strategically oriented decisions to achieve the objectives set.  
An interesting aspect to note is that the largest group of papers (12%) focuses on the topic of sustainability, coupled with statistical approaches. This suggests a growing  
Figure 7: Method vs. macro theme.  
interest in understanding and addressing sustainability challenges through quantitative data analysis, as highlighted previously.
## 4 Results and Discussion
### Results Comparison and Method Validation
Figure 12 illustrates the comparison of the lateral displacements under seismic excitation obtained by using the added mass method and the proposed ALE based fluid-structure interaction method at sections IS6 and RS6 (see Figure 7). It is seen that the variation law of the displacement shows good agreement and the displacement values are also very close to each other. While, at some points the displacement values from these two methods cannot fit perfectly and discrepancy exists. This is because the proposed method considers the sloshing of the fluid which is neglected by the added mass method. Besides, during the derivation of the added mass method the structure is assumed to be the rigid body without flexibility [40]; thus, it is considered that the results obtained from the proposed ALE based fluid-structure interaction method are closer to realistic conditions. Other sections show similar results. Thus, the correctness and reliability of the proposed ALE based fluid-structure interaction method for dealing with the coupling between the tunnel lining and inner water are verified. Figure 12: Contrast of the displacement responses using the proposed method and the added mass method at sections: (**a**) IS6; (**b**) RS6. IS6 and RS6 mean the chosen section at the island part and the cross-river part, respectively.
## 3 Results  
### Seasonal Patterns of CO\({}_{2}\) Exchange, Above-Ground Biomass, and NDVI Measurements  
At both sites, NDVI values obtained from the three different methods all showed similar patterns over the summer growing season, reaching peaks within the first half of July, while green biomass showed maximum values later in the month (Figure 5). An exception was a single MODIS Terra NDVI value in each year (8 July 2012 and 11 July 2013) that yielded one anomalously high NDVI value for each site and date (Table 1, and Figure 5). In most cases (particularly in 2012), the E3 site showed slightly higher productivity than the E5 site, demonstrated by the generally higher NDVI values for E3 (Table 1). In most cases, higher values of NDVIs occurred in 2013 as compared with 2012 (Figure 5, Table 1). A particularly low peak proxy NDVI value in E5 during 2012 (asterisk, Table 1) was the result of a mid-season data gap (spanning from 26 June to 27 August 2012). In general, MODIS Terra values were higher than the corresponding Aqua values, and MODIS values were higher than the ground-based NDVI values, in agreement with previous studies (e.g., [51]).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Peak NDVI Values**} & \multicolumn{4}{c}{**2012**} & \multicolumn{4}{c}{**2013**} \\ \cline{2-9}  & **E3** & \(\sigma\) & **E5** & \(\sigma\) & **E3** & \(\sigma\) & **E5** & \(\sigma\) \\ \hline NDVI\({}_{680,800}\) & 0.54 & 0.09 & 0.51 & 0.06 & 0.60 & 0.09 & 0.59 & 0.06 \\ Proxy NDVI & 0.56 & 0.01 & 0.47 * & 0.03 & 0.58 & 0.02 & 0.59 & 0.02 \\ MODIS NDVI-Terra & 0.69 & - & 0.60 & - & 0.68 & - & 0.66 & - \\ MODIS NDVI-Aqua & 0.61 & - & 0.53 & - & 0.58 & - & 0.59 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Peak NDVI values and standard deviation (\(\sigma\)) in 2012 and 2013 for both sites (E3 and E5), obtained with NDVI\({}_{680,600}\) (grid sampling method), proxy NDVI (phenology station), and MODIS satellite (AQUA and TERRA sensors). The asterisk (*) indicates an anomalously low peak value due to missing mid-season data (spanning 26 June–27 August 2012).
## 4 Results
#### Sensor Data Fusion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **AUC (\%)** & **CA (\%)** & **F1 (\%)** & **Precision (\%)** & **Recall (\%)** & **LogLoss (\%)** & **Specificity (\%)** \\ \hline KNN & 97.8 & 88.3 & 88.3 & 88.5 & 88.3 & 1.3 & 98.7 \\ SVM & 99.8 & 94.3 & 94.4 & 94.4 & 94.3 & 0.2 & 99.4 \\ SGD & 96.9 & 94.4 & 94.4 & 94.4 & 2.0 & 99.4 \\ Neural & 98.5 & 95.2 & 95.2 & 95.2 & 0.2 & 99.5 \\ Average & 98.3 & 93.1 & 93.1 & 93.1 & 0.9 & 99.3 \\ \hline \hline \end{tabular} Legend: KNN = K-Nearest Neighbours, NN = Neural Network, SGD = Stochastic Gradient Descent, SVM = Support Vector Machine, CA = Classification Accuracy, and AUC = Area Under the Curve.  
\end{table} 
Table 4: Evaluation results for Ceiling Sensor (T2) data using K-Means Clustering Method (KMCM). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **AUC (\%)** & **CA (\%)** & **F1 (\%)** & **Precision (\%)** & **Recall (\%)** & **LogLoss (\%)** & **Specificity (\%)** \\ \hline KNN & 99.5 & 95.4 & 95.4 & 95.5 & 95.4 & 0.4 & 99.5 \\ SVM & 100.0 & 97.7 & 97.7 & 97.8 & 97.7 & 0.1 & 99.7 \\ SCD & 98.7 & 97.6 & 97.6 & 97.6 & 97.6 & 0.8 & 99.7 \\ Neural & 100.0 & 98.1 & 98.1 & 98.1 & 0.1 & 99.8 \\ Network & 99.6 & 97.2 & 97.2 & 97.2 & 97.2 & 0.4 & 99.7 \\ \hline \hline \end{tabular} Legend: KNN = K-Nearest Neighbours, NN = Neural Network, SGD = Stochastic Gradient Descent, SVM = Support Vector Machine, CA = Classification Accuracy, and AUC = Area Under the Curve.  
\end{table} 
Table 3: Evaluation results for Lateral Sensor (T1) data using the K-Means Clustering Method (KMCM). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Lateral \%** & **Ceiling \%** & **Fusion \%** \\ \hline KNN & 96.8 & 91.7 & 94.3 \\ SVM & 98.4 & 96.1 & 96.6 \\ SGD & 98.1 & 95.7 & 97.8 \\ Neural Network & 98.7 & 96.5 & 97.3 \\ Mean Accuracy & 98.0 & 95.0 & 96.5 \\ \hline \hline \end{tabular} Legend: KNN = K-Nearest Neighbours, NN = Neural Network, SGD = Stochastic Gradient Descent, SVM = Support Vector Machine, CA = Classification Accuracy, and AUC = Area Under the Curve.  
\end{table} 
Table 5: Evaluation results for Lateral (T1), Ceiling (T2) and fused (F1) datasets using K-Means Clustering Method (KMCM). The pooled SD indicating the weighted average of the SDs for the three groups yielded a lower value of 1.5. In addition, the pooled Mean accuracy of all the models and parameters was obtained as 96.5%. Detailed analyses of the Mean values are presented in Table 6.
_applied sciences_  
_Article_  
Clayey Soil Improvement with Polyethylene Terephthalate (PET) Waste  
**[PERSON] 1**, [PERSON] 2,*(r), [PERSON] 1,*(r) and [PERSON] 2  
1 Department of Environmental Engineering and Sustainable Development Entrepreneurship, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj-Napoca, Romania; [EMAIL_ADDRESS]  
2 Department of Structures, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj-Napoca, Romania; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] (N.-ML); [EMAIL_ADDRESS] (O.N.); Tel.: +40(-02)-6440-1528 (N.-M.I.); +40(-02)-6420-2323 (O.N)  
**Abstract:** Population expansion and the development of technology have led to an increase in construction activities. In many cases, foundation grounds do not have a high enough bearing capacity and are not capable of ensuring the safe exploitation of the construction. A soil with poor mechanical characteristics must be improved using various methods, such as adding hydraulic binders (line and cement), natural fibres, or more recently, plastic waste materials. This work aims to study the behaviour of plastic waste materials made from polyethylene terephthalate (PET) in soil improvement. Thus, the mechanical characteristics of a clay improved with shredded PET were studied. PET was added in relation to the dry mass of the clay, in percentages of 2%, 4% and 6%. The studied clay was collected from a construction site around Cluj-Napoca, Romania, from a depth of 1 \(\div\) 10 m. PET was provided by a local plastic waste repository. It comes from recycled water, beer and soda bottles and was cleaned using specific methods for cleaning and recycling plastic waste. PET was shredded into irregular shapes with sizes ranging from 3 mm to 12 mm and was randomly distributed in the test specimens. Compression and direct shear tests were carried out to study the compressibility and shear parameters of the improved soil (internal friction angle and cohesion). The experimental results showed an improvement in the mechanical characteristics of the clay even at a low PET addition of 2% and 4%. This method can contribute to solving two current problems of the modern world: reducing pollution by recycling plastic waste materials and using them to improve the mechanical characteristics of soil.  
**Keywords:** clay; waste recycling; polyethylene terephthalate (PET); soil improvement; geotechnical parameters; cohesion; friction angle; soil deformation
## 2 Materials and Methods
### Data Analysis  
#### 2.4.1 Principal Component Analysis (PCA) and Outlier Detection  
Various factors, including technical issues, data collection errors, and incorrect sampling, can lead to the formation of improper samples, or outliers, during testing. To address this, principal component analysis (PCA) was initially employed to reduce data dimensionality. Subsequently, [PERSON]'s T\({}^{2}\) test was applied to identify outliers within the PCA-derived multidimensional space.
###### Abstract  
In this paper, a state estimation problem is considered for a target tracking scheme in wireless network environments. Firstly, a unified algorithm of finite memory structure (FMS) filtering and smoothing is proposed for a discrete-time state-space model. As shown in the terminology _unified_, both FMS filter and smoother are derived by solving one optimization problem directly with incorporation of the unbiasedness constraint. Hence, the unified algorithm provides simultaneously the current state estimate as well as the lagged state estimate using only finite measurements and inputs on the most recent window. The proposed unified algorithm of FMS filtering and smoothing shows that there are some unique properties such as unbiasedness, deadbeat, time-invariance and intrinsic robustness, which cannot be obtained by the recursive infinite memory structure (IMS) filtering such as Kalman filter. The on-line computational complexity of the proposed unified algorithm is discussed. Secondly, as an application of the proposed unified algorithm, a target tracking scheme in wireless network environments is considered via computer simulations for moving target's accelerations of various shapes. The proposed unified algorithm-based target tracking scheme provides estimates for position as well as acceleration of moving target in real time, while eliminating unwanted noise effects and maintaining desired moving positions. Due to intrinsic robustness and deadbeat properties, the proposed unified algorithm-based scheme can outperform the existing IMS filtering-based scheme when acceleration suddenly changes.  
Keywords:filter; finite memory structure; infinite memory structure; smoother; target tracking +
Footnote †: journal: Applied
# CAA-RF: An Anomaly Detection Algorithm for Computing Power Blockchain Networks
## 1 Introduction
### Related Work
Variational Autoencoders (VAEs) can learn the latent representations of data, mapping complex high-dimensional features to a lower-dimensional space, which can then be used for classification tasks. This approach is suitable for scenarios that require feature extraction and dimensionality reduction. The discriminator in Generative Adversarial Networks (GANs) can be regarded as a powerful classifier, and its effectiveness has been demonstrated in many tasks. With appropriate adjustments, the discriminator of a GAN can be used for classification with labeled data. Below are relevant literature references and our explanations. Studies on VAEs for classification tasks: In 2023, [PERSON] et al. proposed a VAE-based anomaly detection method for e-commerce data [26]. This method extracts features by learning the latent distribution of the data and, combined with a lightweight classification network, classifies anomalous data, demonstrating the effectiveness of VAEs in classification tasks. In 2023, [PERSON] et al. proposed a VAE-based anomaly detection method for supercomputing center indicators in power grids [29]. By preprocessing and extracting features from indicator data using VAEs and combining with a classifier, they achieved efficient anomaly detection, ultimately obtaining a high F1 score of 0.9091. In 2023, [PERSON] et al. proposed a multi-channel Variational Autoencoder (MCVAE) for anomaly detection and classification tasks on multimodal data, further validating the applicability of VAEs in classification problems [30]. Studies on GANs for classification tasks: In 2023, [PERSON] et al. proposed a GAN-based network intrusion detection method for SCADA systems [31]. This method uses the GAN discriminator to classify normal and abnormal traffic, demonstrating its potential in classification tasks. In 2023, [PERSON] et al. proposed a deep convolutional GAN (DCGAN)-based method for bearing anomaly detection and fault diagnosis [32]. This study extracted features from bearing data using GANs and combined them with the discriminator to achieve anomaly detection and classification tasks, proving the effectiveness of GANs in industrial fault diagnosis. In 2023, [PERSON] et al. proposed a GAN-based anomaly detection method for time series data [28]. By generating synthetic data with the generator and classifying normal and abnormal data with the discriminator, their method achieved excellent performance in F1 score, AUC, and other metrics.
## 4 Regulation of Virulence Factors
### ppGpp  
In a recent study, the role of the bacterial alarmone ppGpp in activating the T3 SS and virulence of _E. amylovora_ was investigated using ppGpp mutants generated by Red recombinase cloning. The virulence of a ppGpp-deficient mutant (ppGpp(0)), as well as a _dksA_ mutant of _E. amylovora_, wascompletely impaired, and bacterial growth was significantly reduced, suggesting that ppGpp is required for full virulence of _E. amylovora_. Expression of T3 SS genes was greatly downregulated in the ppGpp(0) and _dksA_ mutants. Western blotting showed that accumulations of the HrpA protein in the ppGpp(0) and dksA mutants were about 10% and 4%, respectively, of that in the wild-type strain. Furthermore, higher levels of ppGpp resulted in a reduced cell size of _E. amylovora_. Moreover, serine hydroxamate and \(\alpha\)-methylglucoside, which induce amino acid and carbon starvation, respectively, activated hrpA and hrpL promoter activities in hrp-inducing minimal medium. These results demonstrated that ppGpp and DksA play central roles in _E. amylovora_ virulence and indicated that _E. amylovora_ utilizes ppGpp as an internal messenger to sense environmental/nutritional stimuli for regulation of the T3 SS and virulence [75].
## 2 Materials and Methods
### Estimation of the Working Distance
Let \(\mathrm{\mu_{ix}}\) and \(\mathrm{\mu_{iy}}\) denote the average values of \(\mathrm{P_{i}}\) over the first and the second row, respectively. Now, we can center the data and construct the matrix \(\mathrm{Q_{i}}\). Equations (11)-(13) show these definitions. \(\mathbf{1_{n}}\) is a column vector containing 1 in all of its \(\mathrm{n}\) rows. \[\mathrm{\mu_{ix}}=\frac{\sum_{j=1}^{n}\mathrm{x_{ij}}}{\mathrm{n}} \tag{11}\]  
\[\mathrm{\mu_{iy}}=\frac{\sum_{j=1}^{n}\mathrm{y_{ij}}}{\mathrm{n}} \tag{12}\]  
\[\mathrm{Q_{i}}=\mathrm{P_{i}}-\left[ \begin{array}{c}\mathrm{\mu_{ix}}\\ \mathrm{\mu_{iy}}\end{array} \right]\cdot\mathbf{1_{n}^{T}} \tag{13}\]  
Now the direction capturing most of the variance of the data (\(\mathbf{u_{i}}\)) can be computed as,  
\[\mathbf{u_{i}}=\underset{[\mathbf{u}]=1}{\mathrm{arg\ max}}\left(\mathbf{u^{ T}}\cdot\mathrm{Q_{i}}\mathrm{Q_{i}^{T}}\cdot\mathbf{u}\right) \tag{14}\]  
Figure 7: (**A**) indexing of the laser points, (**B**) mean absolute error (MAE) of original and the proposed PCA method for different values of standard angle. The first principal component (\(\mathbf{v}_{\mathrm{i}}\)) would be the projection of the data points on the direction \(\mathbf{u}_{\mathrm{i}}\) and is computed as,  
\[\mathbf{v}_{\mathrm{i}}=\mathbf{u}_{\mathrm{i}}^{\mathrm{T}}\cdot\mathrm{Q}_{ \mathrm{i}} \tag{15}\]  
Now, the first principal component may be used to train the vertical calibration model. Let \(\mathbf{v}_{\mathrm{ij}}\) denotes the \(\mathrm{j}\) component of the vector \(\mathbf{v}_{\mathrm{i}}\) (i.e., projection of the point \(\mathrm{p}_{\mathrm{ij}}\) in direction \(\mathbf{u}_{\mathrm{i}}\)). Equations (16) and (17) are repeated for each laser point i.  
\[\mathrm{w}_{\mathrm{j}}=\mathcal{G}_{\mathrm{i}}\big{(}\mathrm{v}_{\mathrm{ ij}}\big{)} \tag{16}\]  
\[\mathcal{G}_{\mathrm{i}}\big{(}\mathrm{v}_{\mathrm{ij}}\big{)}=\beta_{\mathrm{ i1}}\mathrm{e}^{\beta_{\mathrm{i2}}\mathrm{v}_{\mathrm{i}}}+\beta_{\mathrm{i3}} \mathrm{e}^{\beta_{\mathrm{i4}}\mathrm{v}_{\mathrm{ij}}} \tag{17}\]  
Ten-fold cross-validation over Set 2 was used to evaluate the effect of different standard angles on the accuracy of the improved model. The standard angle was varied between \(0^{\circ}\) and \(180^{\circ}\) in \(5^{\circ}\) increments and for each value. The training set was used to estimate \(\mu_{\mathrm{ix}}\), \(\mu_{\mathrm{iy}}\), \(\mathbf{u}_{\mathrm{i}}\), and parameters of the model (\(\beta_{\mathrm{i1}}\), \(\beta_{\mathrm{i2}}\), \(\beta_{\mathrm{i3}}\), \(\beta_{\mathrm{i4}}\)). The trained model was then applied to the testing set and measurement errors were computed. Figure 7B shows the computed MAE of the proposed method over all 10 folds. This figure shows the robustness of the improved method to variations in standard angle. Experiment 1 in the next section presents the performance of the proposed improved method in more detail.
###### Abstract  
Under the influence of extreme climatic conditions, landslide disasters occur frequently in the Loess Plateau due to complex geological structures, loose soil, and frequent intense rainfall. These events are often concealed, posing significant challenges for disaster prevention. High-resolution optical remote sensing combined with field surveys can improve identification accuracy; however, concerns persist regarding issues such as omission and misidentification during hazard identification and monitoring processes. To address these challenges, this study proposes an integrated remote-sensing identification approach, focusing specifically on the central region of Tianshui, a typical landslide-prone area within the Loess Plateau. Utilizing Sentinel-1 and JLLF01A remote-sensing imagery collected from 2022 to 2023, we conducted ground deformation monitoring through the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique. By integrating deformation results with optical imagery features indicative of potential landslide sites, a comprehensive identification method was developed to precisely detect potential landslide hazards. Verification of the identified sites was subsequently performed using the Google Earth platform, resulting in the establishment of a final dataset of potential landslide hazards within the study area. This outcome clearly demonstrates the high applicability and accuracy of the integrated remote-sensing identification method in the context of landslide hazard assessment. Furthermore, this research provides a solid scientific foundation for geological hazard identification efforts and plays a critical guiding role in disaster prevention and mitigation in Tianshui City, thereby enhancing the region's capacity to withstand disaster risks and effectively safeguarding local lives and property.  
loess landslide hazards; comprehensive approach; SBAS-InSAR
## 5 Experiments
### Parameter Settings  
For our proposed MPHCL model, we utilize the Adam optimizer with a learning rate of \(\eta=10^{-3}\), a weight decay of \(5\times 10^{-4}\), and a hyperedge dropout rate of \(d_{p}=0.3\). The embedding dimension for both users and locations is searched from the range \(d=\{32,64,128,256\}\). To explore model depth, the number of layers in the hypergraph convolutional network is chosen from the candidate set \(L=\{1,2,3,4\}\), allowing us to examine the influence of multi-hop message aggregation. With respect to spatial filtering, we empirically set the geographical distance threshold from the range \(\Delta_{d}\)= 1.0 km, 2.0 km, 2.5 km, 3 km, considering urban scale and user mobility. In our cross-view contrastive learning module, the temperature parameter is selected from the range \(\tau=\{0.1,0.3,0.5,0.8,1.0\}\) to control the smoothness of the similarity distribution. The optimal parameter selection will be presented in Section 5.7.