esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The study employed a combined SAR-based analysis and geological-geotechnical approach to investigate and quantify the effects of liquefaction, including ground subsidence, and demonstrate their correspondence with InSAR observations. Analysis of the SAR data unveiled postseismic ground surface subsidence between the villages of Sant'Agostino and Mirabello (nowadays, the Terre del Reno Municipality). The observed displacement time series indicated a minor initial uplift, followed by rapid subsidence that gradually lessened over approximately three to six months. This widespread ground displacement pattern was closely associated with the extensive liquefaction and post-liquefaction consolidation of deep sandy layers, spreading all over the study area.
## 3 Results
### Effects of CWP on Concrete Mechanical Properties
#### 3.2.1 Effects of Replacement Levels and Particle Sizes on Compressive Strength of CWP Concrete  
Several studies have observed that only low CWP replacement levels generally yield an increase in compressive strength, probably due to the limited pozzolanic ability of CWP [24; 26; 50; 52; 61; 67; 78; 79]. At 10% replacement of PC with CWP, studies by [PERSON] et al., [PERSON], and [PERSON] et al. reported an increase in compressive strength compared to conventional concrete, with a decline observed beyond this replacement level [41; 44; 67; 76]. [PERSON] et al. and [PERSON] et al. noted a similar trend at 15% replacement [57; 65]. [PERSON] et al. observed that CWP concrete exhibited compressive strength comparable to that of PC concrete for replacement levels below 30%; however, the strength decreased significantly beyond this point [57]. These findings indicate that CWP enhances compressive strength only within a limited replacement range, likely due to the low reactivity of its silica content.  
It can also be deduced from the studies that particle size plays a significant role in compressive strength outcomes. For example, [PERSON] et al. [41] noted that CWP with particle sizes below 75 \(\upmu\)m effectively contributes to concrete strength due to the increased surface area, which promotes the pozzolanic reaction. [PERSON] et al. [77] also observed that the compressive strength increased significantly with the addition of nano ceramic powder. Figure 11 shows that the maximum compressive strength was achieved with 6% nano CWP. The small CWP particles of CWP facilitate pozzolanic reactions, which enhance bonding within the cement matrix, leading to higher strength. However, when the particle size increases, the concrete's ability to achieve strength is hindered due to reduced reactivity, as seen in studies such as that by [PERSON] et al. 2021 [28], where decreased performance at higher replacements was observed due to increased particle size.
# Transmitter Diversity Gain Technique Aided Irregular Channel Coding for Mobile Video Transmission
## 7 Conclusions  
This article presents a novel Irregular Error Protection (IREP) approach using Multi-code IRCC. This research aims to observe the convergence behavior of the iterative IRCC and the URC coding paradigm and exploit the innate irregular error protection capability of the Multi-code IRCC channel codes in combination with Unity Rate Coder (URC) while considering SP modulation aided DSTS transmitter diversity gain MIMO technique. The results were obtained while transmitting the H.264/AVC coded \"KIYO\" video phone sequence using REP, IREP-1, and IREP-2 error protected schemes. From the EXIT chart analysis of the REP, IREP-1, and IREP-2 error-protected schemes, it was observed that the iterative Multi-code IRCC coding paradigm in combination with URC results in better convergence behavior when compared to the using Uni-code IRCC employed in the REP scheme. Furthermore, improvement is achieved in the proposed system model by utilizing multidimensional SP modulation aided DSTS MIMO scheme. Additionally, a performance improvement is gained by exploiting the innate irregular error protection capability of the employed Multi-code IRCC, while keeping the relative importance of the H.264/AVC coded data partitions in view. Owing to the better convergence behavior of the employed irregular error protection scheme using Multi-code IRCC, it results in better BER performance in comparison with the regular error protection scheme using Uni-code IRCC. More specifically, it is observed that the employed IREP-2 scheme achieved an \(E_{b}/N_{0}\), a gain of 1 dB with reference to the IREP-1 scheme, and an \(E_{b}/N_{0}\), a gain of 0.6 dB as compared to the REP scheme, over the PSNR degradation of 1 dB.  
Conceptualization, N.M. and K.U.; methodology, N.M.; software, N.M.; validation, N.M., K.U. and J.F.; formal analysis, K.U.; investigation, N.M.; resources, K.C.; data curation, M.A.J.; writing--original draft preparation, K.U.; writing--review and editing, N.M., J.F., M.A.J.; visualization, N.M.; supervision, N.M.; project administration, J.F.; funding acquisition, N.M., J.F. and K.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the institutional research of the Faculty of Operation and Economics of Transport and Communications--University of Zilina, no. 3/KKMHI/2020.  
The financial support of the National Center of Big data and Cloud Computer (NCBC), University of Engineering and Technology, Peshawar, under the auspices of Higher Education Commission, Pakistan is gratefully acknowledged.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work REported in this paper.
# Power-Efficient Random Access Design for Machine Type Communication
## 3 LTE-nMTC Random Access Design
### Power Consumption Analysis  
The power consumption is important to some MTC devices, especially the devices whose batteries are unchangeable or not easy to change. The typical battery life of MTC is expected to range from days (e.g., for smart watches) to years (e.g., for metering). Hence the focus of our random access channel design is the battery life. The battery consumption of a device in the random access process is related to the signal transmit and receive time, it can be written as  
\[C=t_{\mathrm{Tx}}I_{\mathrm{Tx}}+t_{\mathrm{Rx}}I_{\mathrm{Rx}} \tag{1}\]  
where \(t_{\mathrm{Tx}}\) is the transmit time of the device in the random access process, \(t_{\mathrm{Rx}}\) is the receive time, \(I_{\mathrm{Tx}}\) and \(I_{\mathrm{Rx}}\) are the current drawn caused by the signal processing during the transmission and reception. During the reception, the current drawn is similar to the baseband processing current drawn \(I_{0}\), that is  
\[I_{\mathrm{Rx}}\approx I_{0} \tag{2}\]  
A typical value of the baseband process current drawn is \(I_{0}=$150\,\mathrm{mA}$\). The current drawn of transmission is equal to the drawn of signal transmission plus the drawn of baseband processing, that is  
\[I_{\mathrm{Tx}}\approx\frac{p/\eta}{V}+I_{0} \tag{3}\]  
where \(p\) is the transmit power, \(\eta\) is the power amplifier efficiency ranging from 10% to 40%, \(V\) is the battery voltage (e.g., 3 V). When the transmit power is \(p=$20\,\mathrm{dBm}$\), \(\eta=$30\%$\), the transmission current drawn is about 260 mA. Apparently, the air time (transmit time and receive time) is very important to the battery life.  
In the following subsection, we propose a power-optimized random access signal design for MTC. Firstly, we simplify the random access procedure. Then, we optimize the random access channel resource configuration in the sense of minimal air time and maximal overall spectral efficiency.
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 4 Development of _Smart Metering_ Worldwide
### America
#### 4.2.2 Canada  
In contrast to other countries, whose _Smart Metering_ initiatives have been highly promoted by governments and laws, in Canada they have also been driven by necessity: there are vast distances and hostile terrains separating power resources from consumers. In fact, Canada's _SG_ technology is more advanced than that of most other nations [25]. As of June 2014 there were more than 6 million installed _SM_s and it is expected that two thirds of Canadian households will be equipped with _SM_s by 2016 [69]. Several provinces (Ontario, British Columbia, Saskatchewan, and Quebec) have already implemented or intend to implement a _SM_ rollout [46]. To date, the Ontario _Smart Metering_ initiative has been identified as one of the most successful deployments of Canada, with almost 4.5 million installed _SM_s. Initial _SM_ testing involved _GSM_ technology. Then, the utility decided to use radio mesh technology for most customers. The utility also deployed a _ZigBee HAN_ within the _SM_s to communicate consumption data to market daily; however, they do not have remote connection and disconnection capability [38]. Along with _SM_s, the government introduced mandatory time-of-use pricing, making Ontario the largest electricity market in the world with mandated time-of-use rates and pilot programs at the start of the implementation showing peak savings of around 5%-8% [70]. However, a recent audit outlines some weaknesses of the deployment such as: (i) lack of cost-benefit study to support the deployment (as done in other provinces and also in Germany, Australia, and the U.K.); (ii) up to 73 distributed companies were in charge of the deployment, with the subsequent difficulty of ensuring a cost-effective implementation of _Smart Metering_. This situation also led to varied electricity billing amounts; (iii) the time-of-use pricing model has not had the expected impact on reducing peak demand since targets set by the Ministry of Energy have not been met; and (iv) complaints regarding time-of-use rates and billing errors have been reported [71].  
Full rollouts of _SM_s are also taking place in British Columbia and Quebec. In 2011 BC Hydro initiated the _Smart Metering Program_ in British Columbia as a first step towards modernizing BC's electricity grid, with approximately 1.8 million _SM_s equipped with _RF_ technology installed in 2012. According to the utility, the _SM_ deployment will detect and reduce energy theft, with subsequent energy savings, and will also enhance power outage control and provide new customer applications such as lower rates and customer money-saving tools [72]. On the other hand, Hydro-Quebec hasalready installed more than 2.7 million _RF_-based _SM_s and continues the rollout throughout Quebec, with an expected total number of 3.8 million _SM_s by 2017 [73]. The Canadian experience provides a great benchmark from which to learn, as it has already implemented _SM_s and time-of-use rates for millions of customers [70].
## 2 Introduction to the Database
### Tunnel Element Service State Ratings  
The machine learning models developed in this study utilize the service state ratings of three distinct types of tunnel elements: structural, civil, and non-structural. The NTI dataset was utilized to thoroughly examine and evaluate the service state of each tunnel element, categorizing their in-service conditions into four levels. Condition 1 represents a good condition with no significant performance degradation. Condition 2 is a fair condition with one instance of failure or deterioration. Condition 3 is a poor condition with multiple deterioration conditions but no reduction in load-carrying capacity. Condition 4 indicates severe deterioration, requiring a structural safety review. The four serviceability conditions defined are in line with the SNTI [20] and give detailed indicators for the service condition assessment of each tunnel element. Here, \(i\) denotes the index of a particular tunnel (\(i\) = 1, 2, \(\ldots\), the total number of tunnels), and \(j\) denotes the index of a specific element of a tunnel (\(j\) = 1, 2, \(\ldots\), \(n_{i}\)), where \(n_{i}\) is the number of specific elements contained in a certain type of element in tunnel  
Figure 2: Distribution of tunnels’ geographic locations. The combined goodness of a specific element of a tunnel can be calculated according to Equation (1). \[x_{ij}=\frac{\text{number of element $j$ in condition 1}}{\text{total number of element $j$}} \tag{1}\]  
After obtaining the combined goodness of all specific elements, the overall goodness of a certain type of element of tunnel \(i\) can be obtained using Equation (2). \[\text{Overall goodness of a type }=\frac{\sum_{j=1}^{n_{i}}x_{ij}}{n_{i}} \tag{2}\]  
where \(x_{ij}\) is the combined goodness of element \(j\) of tunnel \(i\). According to the regulations of the American Society of Civil Engineers (ASCE) [21], the service state of infrastructure can be divided into four ratings. Table 2 displays the relationship between ratings and overall goodness. In this study, a similar concept was adopted to grade the service states of tunnel elements. It is worth noting that the dataset does not include tunnels classified as substandard F status as they are not in service. A total of 2092 data were collected over the four years from 2018 to 2021. After undergoing thorough data screening, 1203 complete observations were used for this study. The data-screening process was conducted in accordance with two rules: (1) The samples could have been given their in-service status gradings for all three types of elements, resulting in three labeled values per sample. (2) All of the samples' values had to be within the range of features considered in this paper.
# Natural Frequencies of a Tethered Satellite System
## 1 Introduction
[13] examined three-dimensional vibrations of tethered satellite systems, offering closed-form solutions to coupled longitudinal and transverse oscillations. [PERSON] et al. [14] constructed a scaled model of a tethered system with pendulum-type elastic metamaterials, experimentally demonstrating bandgap formation and its correlation with beam resonance. Despite the extensive research on conventional satellites and certain aspects of tethered satellite systems, a gap remains in systematically and simply calculating the natural frequencies of both rigid-body motion and deformation in tethered satellite systems. To address this gap, this study aims to provide an investigation into the natural frequencies associated with the rigid-body motion and tether deformation in a tethered satellite system. By employing a dumbbell model for rigid-body dynamics and a tensioned beam model for tether deformation, this work proposes systematic approaches to calculate these natural frequencies. In this research, the natural frequencies of tethered satellite systems were investigated through a two-pronged approach. First, a dumbbell model was developed to capture the rigid-body motion, with nonlinear equations of motion derived using [PERSON]'s equations and linearized via perturbation methods to formulate an eigenvalue problem. This analysis identified the natural frequencies associated with orbital and librational motions and evaluated the influence of system parameters such as tether length, orbital radius, and satellite masses. Second, tether deformation was analyzed using an absolute nodal coordinate formulation (ANCF) simulation, with frequency spectrum analysis applied to extract the dominant vibrational frequencies. To complement the computational analysis, an analytical model treated the tether as a tensioned beam with end masses, enabling efficient calculation of transverse and longitudinal natural frequencies. These methodologies collectively provide a characterization of the dynamic behavior of tethered satellite systems, ensuring that critical insights are derived for practical implementation.
# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
## 4 Result and Discussion
### Simulation Experiment Results on Flower Dataset  
We train with the flower dataset and test with two specified images \"\(\hat{J}_{\text{c}}\)\" and \"UCAS\". The loss function has two weight parameters to adjust the loss function of SAE and Unet. The weight of the SAE loss function and the weight of the Unet loss function are set to 0.3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline b & 0.1 & 0.2 & 0.3 & 0.4 & 0.5 \\ \hline PSNR & 19.8245 & 19.8544 & **19.9447** & 19.9263 & 19.9212 \\ \hline b & 0.6 & 0.7 & 0.8 & 0.9 & 1 \\ \hline PSNR & 19.8925 & 19.7244 & 19.6404 & 19.6163 & 19.7862 \\ \hline \end{tabular}
\end{table}
Table 1: Hyperparameter b changes and PSNR of the reconstructed image with MR = 0.05.  
\begin{table}
\begin{tabular}{c c c c c c} \hline a & 0.1 & 0.2 & 0.3 & 0.4 & 0.5 \\ \hline PSNR & 19.8425 & 20.0116 & **20.1404** & 20.1263 & 19.9712 \\ \hline a & 0.6 & 0.7 & 0.8 & 0.9 & 1 \\ \hline PSNR & 19.8225 & 19.7244 & 19.4404 & 19.4163 & 19.5712 \\ \hline \end{tabular}
\end{table}
Table 2: Hyperparameter a changes and PSNR of the reconstructed image with MR = 0.05.  
The reconstructed images were obtained at the measurement rates of 0.01, 0.05, 0.1 and 0.2, respectively. We compare the proposed method with traditional methods of TVAL3, one-norm prior and DDPM. The Chinese character \"\(\Downarrow\)\" reconstruction result is shown in Figure 5, and the letter \"UCAS\" reconstruction result is shown in Figure 6. It can be seen that our method is superior to TVAL3, one-norm prior and DDPM in terms of clarity and brightness of reconstructed images. The PSNR and SSIM of the reconstructed images by different methods are shown in Table 3. The PSNR of the proposed method's reconstructed image is better than those of TVAL3, one-norm prior and DDPM.
## 4 Hierarchical Cache Management (HCM) Policy
### SSD Cache Admission Filter
#### 4.2.3 Cache Load Tuning  
The data transfer speeds of SSD and DRAM differ significantly, and the write load on the SSD must be carefully controlled to avoid data loss. Although the number of replicas written to the SSD has been greatly reduced through the filtering process described above, there may still be some cache redundancy between nodes on different transmission paths, which could lead to decreased cache utilization efficiency. To address this, we introduce a load tuning function, \(\omega(t)\), based on the cache admission probability described above, with a value range of \((0,\,1)\). The purpose of \(\omega(t)\) is to further adjust the SSD replica admission probability to ensure that the SSD write load does not exceed its I/O bandwidth. Specifically, \(\omega(t)\) can be dynamically adjusted based on the write load at time \(t\) or set to a fixed value (e.g., 0.1).  
Based on the above, for any replica \(m\) replaced in the DRAM cache queue (\(Q_{d}\)), its SSD admission probability \(\mathcal{P}_{m}\) can be expressed by Equation (3), where \(p_{m}\) is given by Equation (2).  
\[\mathcal{P}_{m}=p_{m}\times\omega(t) \tag{3}\]  
Algorithm 1 summarizes the entire process of SSD cache filtering described above.  
```
0: SSD cache filter result: \(\mathit{res}\)
0:\(m\gets null\), \(\mathit{res}\leftarrow\mathit{False}\)
1:if DRAM cache replacement occurs then
2:\(m\leftarrow\) Get replaced replica based on LRU
3:endif
4:\(\mathcal{P}_{m}\leftarrow\) 1.0
5:if SSD cache space not full then
6:\(\omega(t)\leftarrow\) Get cache load tuning value
7:\(\mathcal{P}_{m}\leftarrow\)\(\mathcal{P}_{m}\times\omega(t)\)
8:else
9:\(pop_{m}\leftarrow\) Get replica popularity from local RIT
10:if\(pop_{m}\geq T_{pop}\)then
11:\(r_{m}\leftarrow\) Get replica redundancy from local RIT
12:\(\mathcal{P}_{m}\leftarrow\)\(\mathcal{P}_{m}\times 1/r_{m}\)
13:\(\omega(t)\leftarrow\) Get cache load tuning value
14:\(\mathcal{P}_{m}\leftarrow\)\(\mathcal{P}_{m}\times\omega(t)\)
15:else
16:\(\mathcal{P}_{m}\leftarrow\) 0.0
17:endif
18:endif
19:\(p\leftarrow\) Generate a random decimal between (0,1)
20:if\(p<\mathcal{P}_{m}\)then
21:\(\mathit{res}\leftarrow\) True
22:endif
```  
**Algorithm 1**SSD Cache Filtering
## 3 Results and Discussion
### The Best Values of Parameters  
Table 6 presents the total bed capacity and the total percentage of Ag\({}^{+}\) and Cu\({}^{2+}\) removed under different conditions. The maximum sorption capacity increases with the initial concentration of metal ions and decreases with the bed height and flow rate. The maximum total percentage of metal ions amounted to 60.4% and 68.7% for Ag\({}^{+}\) and Cu\({}^{2+}\), respectively.  
Figure 6 shows Pareto charts of standardized results. At an assumed significance level of 5.0%, all parameters in the case of silver ion sorption have a significant influence on the adsorption process, while in the case of copper ion adsorption, the initial concentration of the metal ion solution and the adsorbent height have a significant influence.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{\(Ag^{+}\)} & \mul  
The profile for predicted values and desirability options in the STATISTICA software is used for the optimization process. The CCD optimization design matrix (Figure 7) shows the maximum removal (60.43 and 68.76% for Ag\({}^{+}\) and Cu\({}^{2+}\), respectively) under the following conditions: 3 cm bed height, 5000 mg/cm\({}^{3}\) initial metal ions concentration, and 2 cm\({}^{3}\)/min and 3 cm\({}^{3}\)/min flow rate for Ag\({}^{+}\) and Cu\({}^{2+}\), respectively.