esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Discussion
### The Effect of Consolidation of Soil on the Earth Pressure at Rest  
The proposed solution stands for any Mohr-Coulomb, \(c^{\prime}-\varphi^{\prime}\) material having not been subjected in the past to stresses greater than the present ones. In relation to soils, it refers to normally consolidated ones. According to the literature, the normally consolidated soils have small cohesion intercept values (in terms of effective stresses in Mohr's diagram) or close to zero (e.g., [72]). The full truth is that both cohesion and friction angle highly depend on the imposed overburden stress. This is clear from the Mohr's failure envelope which, as known, is a curved and not a straight line and thus, the shear strength parameters derive from the proper tangent (Mohr-Coulomb failure criterion) to this curve in relation to the working stresses. Thus, the effect of cohesion in earth pressures is not only significant in the case of over Consolidated soils but also in the effective design of deep structures (such as, deep braced excavations, piles, \"cut and cover\" tunnels, and underground stations) in normally consolidated soils.  
Regarding over Consolidated soils, given that these soils exert greater lateral pressure due to the stresses \"trapped\" in the plastically deformed soil mass, the earth pressure at rest could be expressed as follows:  
\[\sigma_{\circ,OC} =K_{\infty}^{c-\varphi}(1-k_{v})(\gamma z+\Delta q)=K_{\infty}^{c -\varphi}(1-k_{v})\sigma_{c} \tag{62}\] \[=K_{\infty}^{c-\varphi}(1-k_{v})\gamma z\cdot OCR\]  
Equation (62) represents the earth pressure at rest at depth \(z\) for overconsolidated soils without considering soil volume rebound. As partial volume rebound, and thus, stress relief, always take place in soil after removing surcharge weight, the actual earth pressure at rest of the overconsolidatedmaterial will be smaller than the one described above. Adopting [PERSON]'s [73] correction, Equation (62) becomes  
\[\sigma_{\circ,OC}=K_{oe}^{c-\varphi}(1-k_{v})\gamma z\cdot OCR^{a}, \tag{63}\]  
where \(a=\sin\varphi^{\prime}<1\) is a reduction factor (since \(OCR>1\)).  
The generalized coefficient of earth pressure at rest is still given by Equation (16). However, for computing the mobilized shear strength, the term \(K_{oe}^{c-\varphi}(1-k_{v})\gamma z\) appearing twice in Equation (35) must be replaced by Equation (63); the term \((1-k_{v})\gamma z\) in Equation (35) also remains as it is.  
All shear strength parameters in this section refer to the overconsolidated material.
# Research on Optimization of Multi-Objective Regional Public Transportation Scheduling
## 6 Conclusions  
A bi-objective optimization model for regional public transport scheduling is established considering the total cost of passengers and the efficiency of public transport operation. Furthermore, a two-step algorithm is designed to solve this problem. In the first step, the NSGA-II algorithm with adaptive adjusted model for crossover and mutation probability is used to obtain the Pareto solutions. In the second step, the entropy weight-TOPSIS method is implemented to find a satisfactory scheme. At last, a regional public transportation network is collected to test the dispatch approach and algorithm. The results show that the scheduling scheme obtained in this paper is obviously better than the schemes with fixed departure interval, and the obtained solution set by ANSGA is superior to that obtained by FNSGA-II.  
In this paper, we assumed that the running time between stations is known. In reality, the running time may be uncertain due to delay. Therefore, future research can consider the followings:  
1. Considering the uncertainty of the running time to design the regional public transport scheduling model and algorithm.
2. Considering other factors, such as multiple types of buses, energy consumption, depots capacities, to the regional bus scheduling problem is studied, to research on the regional public transportation scheduling.  
Conceptualization, X.Y.; methodology, X.Y. and Y.Q.; software, X.Y.; validation, Y.Q.; formal analysis, X.Y.; investigation, Y.Q.; data curation, Y.Q.; writing--original draft preparation, Y.Q.; writing--review and editing, X.Y.; supervision, X.Y.; project administration, X.Y.; funding acquisition, X.Y. Both authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 71761024.  
Data Availability StatementData sharing is not applicable to this article.  
The authors declare no conflict of interest.  
Figure 8: The variation trend of non-dominated individuals.
## 2 Related Technologies  
### Spatio-Temporal Big Data Technology  
The term spatio-temporal big data refers to massive data characterized by time and space information, and are generally collected by various physical and virtual sensors, covering multi-dimensional information such as time, geographical location, and environmental parameters. These data are multivariate, heterogeneous, high-dimensional, high-resolution, and achieve fast updates, with vital values and broad application prospects. Spatio-temporal big data are widely used in fields such as urban planning, traffic management, emergency rescue, environmental protection, weather forecasting, and natural resource management. Simultaneously, spatio-temporal big data raise issues of privacy and security, bringing about higher requirements regarding data collection, storage, analysis, and sharing [19]. Spatiotemporal big data are a kind of data that mark time information and geographic location and have been widely applied in various fields. However, there are always security issues with spatiotemporal big data, especially in data collection and authentication. Traditional authentication protocols are less efficient in the face of ultra-large-scale IoT device verification, and the threat of single-point failure is relatively large. Given these complications, a group authentication scheme is proposed in this paper with blockchain spatiotemporal big data.
## 1 Introduction  
For ultra-high capacity point-to-point communications, the 71-76 and 81-86 GHz bands (mostly known as the \"E-band\") are worldwide licensed, and are capable of up to 5-GHz full-duplex bidirectional transmission. E-band wireless systems offer full-duplex Gigabit Ethernet connection at 1 Gbps and above rates in economic radio architectures with carrier-class availability within mile distances and beyond [1]. The role of radio channel propagation conditions is very important to meet the high availability requirements in wireless back-haul networks. The rain attenuation for millimeter-wave, especially E-band, is one of the most important factors to consider for achieving quality requirements in terms of throughput and outage. The ITU-R models [2; 3; 4; 5] for rain attenuation have been used to ensure the planned radio links meet the quality requirements. These models for conventional microwave bands are well examined in different temporal and tropical regions around the world [6; 7; 8; 9], however, there are less empirical data for the E-band.  
At the E-band, only a few studies have investigated the rain attenuation [10; 11; 12; 13; 14; 15; 16; 17; 18]. Based on a one-year measurement data in United Kingdom (UK) at 25.84 and 77.52 GHz [10] and over a short measurement period at 77 and 300 GHz [11], the rainfall rate statistics and rain attenuation were examined. In [13], based on ten months measurements at the 73.5 GHz band, the impact of rain on signal attenuation was analyzed and modeled along a 1-km terrestrial link in Molndal, Sweden. In Madrid, Spain, the measurements at 75 and 85 GHz bands were conducted over two years along an 840-m link to analyze and model the rain impact on signal attenuation [14]. The accuracy of the ITU-R P. 530-16 model [5] was examined based on the three years of experimental data at 75 GHz in South Korea along a 3.2-km link [15]. In Malaysia, the rainfall rate statistics and rain attenuation were investigated based on a one-year measurement data at 21.8 and 73.5 GHz in [17,18]. Most studies for rain attenuation at E-band were presented and compared in [17].  
Among the previous studies at E-band, none of them were investigated the rain effects on the performance of a full-duplex link using 70/80 GHz bands. This work is the only measurement campaign conducted in tropical region, to study the precipitation effects on the performance of full-duplex link using 73.5 GHz and 83.5 GHz E-band along 1.8 km and 300 m line-of-sight (LOS) wireless back-haul links. In this paper, the statistical analysis results of one year of rain attenuation at 73.5 GHz and 83.5 GHz E-band in Malaysia are presented. The performance of full-duplex E-band wireless back-haul link along 1.8-km and 300-m distances is investigated based on the one-year outage probability under rain effects.  
The rest of the paper is organized as follows. The measurement setup is presented in Section 2. The rain attenuation is investigated in Section 3. Section 4 shows the performance analysis of the full-duplex E-band link. The conclusion is drawn in Section 5.
# Text-Guided Refinement for Referring Image Segmentation
## 4 Experiment
### Evaluation Metrics  
In line with previous studies [1; 12; 34], we evaluated referring segmentation performance using the region-based metrics, Overall Intersection over Union (IoU), and Prec@X. Overall IoU is computed as the total intersection area divided by the total union area across all test samples. The Prec@X metric represents the percentage of test samples where the IoU between the predicted and ground-truth masks exceeds the threshold X, with X values chosen from \(\{0.5,0.6,0.7,0.8,0.9\}\).
## 5 Cryptosystems Choice
### Cryptosystem for Product Encryption
#### 5.3.1 Ecies  
The Elliptic Curve Integrated Encryption Scheme (ECIES) [23] is an elliptic curve-based public key encryption scheme whose security holds on the assumed intractability of the _Elliptic Curve Discrete Logarithm Problem_ (ECDLP).  
Such a cryptosystem is set by choosing an elliptic curve \(E\) represented as an expression of the form shown in Equation (5):  
\[Y^{2}=X^{3}+AX+B, \tag{5}\]  
with \(A,B\) being elements of a finite field \(\mathbb{F}\) such that its set of points \(E(\mathbb{F})\) has a cardinality divisible by a large prime \(q\). An order-\(q\) point \(P\) of \(E(\mathbb{F})\) is also chosen. Throughout this section, we assume \(q\) is prime.  
An ECIES private key is generated by choosing a random \(v\in\{0,\ldots,q-1\}\). The corresponding public key is the point of \(E(\mathbb{F})\) computed as \(Q=vP\).  
The probability that a random point \((x,y)\in\mathbb{F}\times\mathbb{F}\) belongs to \(E(\mathbb{F})\) is negligible since its components should satisfy Equation (5). Nevertheless, this drawback can be addressed by representing elliptic curve points in compressed form. A point \((x,y)\in E(\mathbb{F})\) can be represented as \((x,b)\) with \(b\) being a Boolean indicating whether \(y>-y\). In this way, a randomly generated compressed point \((x,b)\) belongs to \(E(\mathbb{F})\) (and hence it is a public key), if its \(x\)-component satisfies that \(x^{3}+Ax+B\) is a quadratic residue in \(\mathbb{F}\). This happens with a close to \(1/2\) probability [17].
## 4 Evaluation of Experimental Results
### Analysis of the Kraljic Matrices
The fitted trendlines show similar tendencies for both parameters, while the reliability level is somewhat lower only for the blue trendline, but not as low as in the 2D case (59.79-34.49% for 2D and 74.66-68.51% for 3D). The most favorable parameter combination remains the one marked in red, with low feed and low burnishing speed. Increasing feed rate worsened surface roughness at the same burnishing speed (gray trendline), while increasing burnishing speed increased power demand at the same feed rate (blue trendline). The simultaneous increase of both technological parameters resulted in even worse surface roughness. The minimum points of these parabolas can also be calculated here. Kraljic matrices were also created for the improvement factors introduced in the previous chapter. Figure 7 shows the 2D Kraljic matrices for the improvement factors of average roughness (left) and root mean square roughness (right). These values indicate the percentage improvement achieved in the surface roughness of the longitudinally turned  
Figure 6: Kraljic matrices of 3D surface roughness parameters during diamond burnishing. surface due to diamond burnishing. The structure of the figures, the trendlines, the R-squared values, and the color codes are similar to the previous figures, with the difference that here the upper-left cell corresponds to optimal energy efficiency. The greatest improvement was achieved with the parameter combination of high feed rate and low burnishing speed (gray trendline), which was also optimal regarding power demand, placing it mainly in cell I of the matrix. The red trendline (corresponding to lower feed rates at the same burnishing speed compared to the previous figure) showed smaller improvements with the same power demand. However, these two trendlines rise steeply, with their maxima shifted along the power axis, even if these maxima fall within cell I. Burnishing with higher speed (yellow and blue trendlines) produced less favorable results  
Figure 8: Kraljic matrices of improvement factors for 3D surface roughness parameters. Figure 7: Kraljic matrices of improvement factors for 2D surface roughness parameters. both in terms of power and improvement factors. It may happen that the maxima of these trendlines are lower than those of the red and gray ones due to their steepness. Figure 8 shows the last two Kraljic matrices of our study: the 3D arithmetic mean improvement factor (left) and the 3D root mean square improvement factor (right). The findings are like the 2D case, with the difference that the red and gray trendlines are much less steep, potentially yielding more favorable values in later maximum point calculations. All other conclusions remain valid in this dimension as well: at constant burnishing speed, increasing feed rate resulted in higher improvement factors, while increasing burnishing speed decreased the improvement values.
## 4 Experimental Results
### Number of Bits  
The proposed JD scheme is developed with the idea of benefiting low power real-time applications (VSNs) in terms of efficiency, data transmission and power utilization. This subsection observed the  
Figure 7: PSNR comparison of proposed SQ-ADPCM-JD with conventional codecs i.e., DISCOVER, H.264 (intra, (I-P-P)) and H.263 (intra, (I-P-P)) at various bitrate for different video sequences.  
bit rate savings among the independent BCS-TV-AL3 and the proposed JD-TV for different video sequences at the various reconstruction qualities (PSNR).  
The results presented in Table 8 shows the reconstruction quality at different numbers of bits for different video sequences. The results obtained can also be calculated as given in [30]. It can be noted that the number of bits essential for conventional BCS and the proposed JD schemes to achieve the same PSNR varies. The proposed scheme utilizes minimum number of bits as compared to the independent BCS scheme to achieve the same PSNR i.e., for higher motion content video sequence the average saving rate is ~42%, while for lower content video sequence the average saving rate is ~65% for all videos. This is due to the fact that the proposed JD offers improved reconstruction quality at lower bit rates. As the proposed scheme provides better reconstruction at lower bits this helps to reduce the computational burden from the encoder as the encoder will be transmitting small amounts of data. On average, the number of bits saved by the proposed scheme contrary to the independent scheme is ~50% at different reconstruction qualities.
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 3 Our Method
### Loss Function  
In the proposed framework, the loss function mainly contains three parts: reconstruction loss, self-expressive loss, and regularization terms. The reconstruction loss by the autoencoder is applied to encourage the network to learn meaningful representations of the input data. Suppose \(Q^{(v)}\) represents the input images and \(\hat{Q}^{(v)}\) donates the reconstructed samples from the latent features. Then the reconstruction loss is defined as follows:  
\[\mathcal{L}_{rec}=\left\|Q^{(v)}-\hat{Q}^{(v)}\right\|_{F}^{2} \tag{4}\]  
The self-expressive loss and the regularization terms are applied in the reconstruction module. The self-expressive loss is used to promote the preservation of the underlying subspace structures in the data, which encourages samples from the same subspace to have similar coefficient patterns. The loss is defined as:  
\[\mathcal{L}_{se}=\left\|Z^{(v)}-SE(Z^{(v)};\Theta_{s}^{(v)})Z^{(v)}\right\|_{2} ^{2} \tag{5}\]  
We introduce a combination of \(l_{1}\) regularization and \(l_{2}\) regularization with a balancing weighted parameter \(\lambda\) as the regularization term of our proposed network. This term can be defined as:  
\[\mathcal{L}_{reg}=\lambda\|SE(Z^{(v)};\Theta_{s}^{(v)})\|_{1}+\frac{1-\lambda }{2}\|SE(Z^{(v)};\Theta_{s}^{(v)})\|_{2}^{2} \tag{6}\]  
The overall loss function can be expressed as:  
\[\mathcal{L}=\mathcal{L}_{rec}+\mathcal{L}_{se}+\mathcal{L}_{reg} \tag{7}\]
## 4 Active Antiresonance Assignment with No Additional Masses
### State-Feedback Approaches
#### 4.2.3 The Rank-One Acceptance Method: The Original Formulation  
A milestone in the field of active control of LTI vibrating systems is the work proposed by [PERSON] and [PERSON] in [50], where the assignment of poles and zeros is interpreted as a rank-1 modification to the dynamic stiffness matrix of the system, leading to a receptance-based formulation. This method has been firstly developed to perform the pole placement through full-state feedback control of symmetric LTI systems with rank-one control. Then, a mathematical frame has been developed with reference to the adjoint system to assign a set of zeros, as well as to concurrently assign a set of poles.  
A meaningful result provided by [50] shows that the computation of the gain vector \(\mathbf{k}=\left\{\begin{array}{c}\mathbf{f}\\ \mathbf{g}\end{array}\right\}\in\mathbb{R}^{2N}\) for the assignment of \(n_{z}\) zeros through full-state feedback can be written as a linear system:  
\[\left[\begin{array}{ccc}z_{1}\mathbf{t}_{1}^{T}&\mathbf{t}_{1}^{T}\\ z_{2}\mathbf{t}_{2}^{T}&\mathbf{t}_{2}^{T}\\ \vdots&\vdots\\ z_{n_{z}}\mathbf{t}_{n_{z}}^{T}&\mathbf{t}_{n_{z}}^{T}\end{array}\right] \left\{\begin{array}{c}\mathbf{f}\\ \mathbf{g}\end{array}\right\}=\left\{\begin{array}{c}-h_{pq}(z_{1})\\ -h_{pq}(z_{2})\\ \vdots\\ -h_{pq}(z_{n_{z}})\end{array}\right\}, \tag{20}\]  
where the \(i\)-th vector \(\mathbf{t}_{i}\in\mathbb{C}^{N}\) is defined as follows:  
\[\mathbf{t}_{i}=h_{pq}(z_{i})\mathbf{H}(z_{i})\mathbf{b_{C}}-\left[\mathbf{e}_ {\mathbf{p}}^{T}\mathbf{H}(z_{i})\mathbf{b_{C}}\right]\mathbf{H}(z_{i}) \mathbf{e_{q}}. \tag{21}\]  
The use of receptance-based formulation has the advantage that the method just relies on the measured receptance, and hence it does not require the knowledge of the \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{K}\) matrices. Another great advantage of receptance-based formulation, for pole placement too, is the good numerical conditioning. On the other hand, receptances are sometimes difficult to measure for rotational coordinates [2]. Additionally, the practice of the implementation of state feedback control usually replaces the measured state with the estimated one by implementing state observers, which usually require first-order, state-space formulations.  
The receptance-based approach formulated in [50] has been subsequently exploited for some particular applications by adapting the formulation to the specific feature of the model under investigation. For example, in [59] it is applied to an active control for pole and zero assignment in viscoelastic systems.
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 2 Mission Concept
### Payload Design
These reservation commands can be decided based on satellite time and TLE, and a maximum of 10 reservation commands can be stored in the satellite. * **24-h Mode:** The mission payload can be turned on for 24 h continuously, and the receivers are always in the active listening mode. The operation and performance of the integrated flight model (FM) were tested in various emulated space environment conditions in order to demonstrate the satellite is able to operate properly in LEO. Fault mode analysis of each subsystem and SloT mission were also performed during the satellite development process to identify possible causes of failures and contingency plans were prepared to make the system tolerable to these faults. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Dimensions & 90 mm \(\times\) 86 mm \\ Operating Voltage & 3.5 V \\ Current consumption & 240 mA \\  & 399.900 MHz-401.948 MHz \\  & 433.000 MHz-435.048 MHz with a resolution of 1 kHz \\ Measured Sensitivities & \(-116\) dBm\(-135\) dBm \\  & 46 bps-488 bps \\ Reconfigurable SF & 6–12 \\ Reconfigurable BW & 7.8 kHz, 10.4 kHz, 156 kHz, 20.8 kHz, 31.25 kHz, 41.7 kHz, \\  & 62.5 kHz, 125 kHz \\ Reconfigurable Coding Rate & 4/5–4/8 \\   No. of receivers & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specifications of SloT receiver payload board.
## 7 Datasets
### Business and Finance  
In the business and finance sector, time series datasets are crucial for identifying and preventing fraud, market anomalies, credit risks, and other financial issues [3]. The NAB-realAdExchange dataset [87] is part of the Numenta Anomaly Benchmark (NAB). This dataset records online advertisement click-through rates, with the metric being cost per click (CPC). It contains one file with no anomalies and another file with anomalies, designed to evaluate the performance of anomaly detection algorithms on real-time streaming data. The Credit Card dataset [88] contains records of credit card transactions, primarily used for identifying and detecting anomalous transactions, that is, potential fraudulent activities.  
When dealing with financial time series involving various market risk factors, a major issue is the presence of anomalies. These anomalies can lead to calibration errors in models used for quantifying and managing risk, resulting in potential misestimations of risk. [PERSON] et al. [115] proposed an anomaly detection method for financial time series that combines Principal Component Analysis (PCA) and neural networks. By reducing dimensionality with PCA, this approach effectively extracts key features from high-dimensional financial time series data, simplifying the subsequent anomaly detection process. Neural networks are then applied to identify nonlinear relationships and complex patterns. To detect anomalies in more practical business scenarios in the real world, contextual information is needed for accurate prediction. Time-CAD [116] is a context-aware deep time series decomposition framework that uses deep learning techniques to decompose time series data and incorporates contextual information to improve the accuracy of anomaly detection. This method is capable of effectively detecting anomalies in business processes.