esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of APD to Other Sites
### Onsay Soft Clay Site  
The NGTS soft clay site at Onsoy, established in 2016, has been the focus of an extensive laboratory and field testing program. In situ tests included piezoconc tests (CPTu), seismic cone penetration tests (SCPT), seismic flat dilatometer tests (SDMT), and self-boring pressuremeter tests (SBPMT). Laboratory investigations involved determining in situ water content, unit weight, and Afterberg limits, as well as conducting constant rate of strain oedometer (CRS) tests, triaxial tests, and direct simple shear (DSS) tests. Both block and various types of tube samplers were used for sample recovery [52].  
The in situ tests selected as input for APD, along with the corresponding methods and additional details about the Onsey soft clay site, are presented in [3]. A comparison between the APD-generated results and the reference values interpreted for this site is shown in Figure 4.  
Figure 4a presents the estimated total unit weight. The output of the CPT-based workflow shows that certain methods--particularly those reflected by the upper limit of the blue-shaded area--closely match the reference data. Nonetheless, as most CPT methods tend to yield lower values, the overall CPT average also underestimates the measured unit weight. In contrast, the DMT- and \(V_{s}\)-based approaches produce average values that closely align with the reference measurements. Figure 4b shows the overconsolidation ratio (OCR) estimates. The mean results from all three methods indicate a slightly overconsolidated condition, consistent with the reference interpretation.  
Figure 4c indicates that the CPT-based workflow provides estimates of \(E_{ved}\) that are generally consistent with the reference values, except in the final layer where it underestimates stiffness. The DMT-based workflow also yields reliable \(E_{ped}\) estimates, while the \(V_{s}\)-based workflow tends to overestimate the reference values. Regarding the small-strain shear modulus \(G_{0}\) (Figure 4d), the \(V_{s}\)-based workflow results in the most reliable predictions,  
which is expected since it directly utilises measured \(V_{s}\) data. In contrast, the DMT-based workflow significantly underestimates \(G_{0}\), and the CPT-based workflow provides a wide range of values. Nonetheless, in this case study, the average of the CPT-based range aligns well with the reference values. For undrained shear strength \(s_{u}\) (Figure 4e), the DMT-based workflow shows overall good agreement with the reference values. The \(V_{s}\)-based workflow overestimates \(s_{u}\) in the upper layer but provides reasonable predictions in the lower layer. On average, the CPT-based workflow tends to overestimate the reference \(s_{u}\) values.  
Figure 4: Comparison between APD and interpreted values at Onsoy soft clay test site (the blue, green, and red shaded areas represent the range of values obtained from the CPT, DMT, and \(V_{s}\) workflows, respectively): (**a**) unit weight, (**b**) OCR, (**c**) constrained modulus, (**d**) small-strain stiffness, (**e**) undrained shear strength (modified from [3]).
# Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion
## 3 Algorithm Description
### Noise Perturbation
#### 3.4.3 Impact of System Parameters on Localization Accuracy  
First of all, with the increase of number of samples at each calibrated location, the RSS variation can be described as:  
\[\lim\limits_{u\rightarrow\infty}(\frac{\sum_{i=1}^{n}\left(r_{ij}-m_{j1} \right)^{2}}{n}-\frac{\sum_{i=1}^{n+u}\left(r_{ij}-m_{j2}\right)^{2}}{n+u})>0 \tag{29}\]  
where \(r_{ij}\) is the RSS from the \(j\)-th AP in the \(i\)-th RSS sample; \(n\) is the number of samples; and \(m_{j1}\) and \(m_{j2}\) are the mean of \(n\) and \(n+u\) RSSs from the \(j\)-th AP, respectively. Based on (29), we can easily obtain that as the sample number increases, the value \(\Delta\) will decrease, and consequently, higher localization accuracy is achieved.  
Second, as the number of calibrated locations increases, the values \(\mathbf{L}_{x}\) and \(\mathbf{L}_{y}\) decrease, which will result in the decrease of the values \(\mathbf{H}(\mathbf{I}+2\mathbf{L}_{x}-\lambda\mathbf{I})\) and \(\mathbf{G}(\mathbf{I}+2\mathbf{L}_{y}-\lambda\mathbf{I})\) accordingly. Then, to make (23) equal to zero, the value \(\mathbf{P}\) should be increased, which indicates that an increase of the number of calibrated locations results in a higher localization accuracy.  
Third, with the increase of augmentation dimension from \(m\) to \(2m\), \(3m\), etc., the values \(\mathbf{L}_{x}\) and \(\mathbf{L}_{y}\) increase, which will result in the increase of the values \(\mathbf{H}(\mathbf{I}+2\mathbf{L}_{x}-\lambda\mathbf{I})\) and \(\mathbf{G}(\mathbf{I}+2\mathbf{L}_{y}-\lambda\mathbf{I})\) accordingly. In this case, to make (23) equal to zero, the value \(\mathbf{P}\) should be decreased, which indicates that the increase of augmentation dimension results in the lower localization accuracy.
# Unsupervised Image Segmentation on 2D Echocardiogram
## 2 Related Work
### Challenges and Limitations  
Despite significant advances, several challenges remain in applying deep learning to echocardiogram analysis and cardiac image segmentation. Deep learning models typically require large amounts of labeled data for training, which is scarce in medical domains. Techniques such as data augmentation and methods that can learn from smaller datasets are crucial to overcoming this limitation ([PERSON] et al., 2016). In addition, although deep learning models can achieve high accuracy, their decision-making process is often difficult to interpret, posing a barrier to clinical acceptance. Researchers are exploring ways to develop more interpretable deep learning models ([PERSON] et al., 2016; [PERSON] et al., 2016). Furthermore, models trained on one dataset may not generalize well to data from different scanners, acquisition protocols, or patient populations. Ensuring robustness and generalizability is essential for real-world clinical deployment.  
In summary, deep learning is transforming how we analyze echocardiograms and segment cardiac images, with applications ranging from automated view classification to disease detection and quantitative cardiac function assessment. However, challenges related to data availability, model interpretability, and generalization must be addressed to fully realize the potential of deep learning in clinical practice. This work directly addresses these challenges by leveraging an unsupervised approach to segmentation, enhancing model generalization, and reducing reliance on large labeled datasets, thus contributing to the broader applicability of deep learning in cardiovascular diagnosis, treatment, and patient management.
# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 2 Related Work
Although most of existing multi-view subspace clustering methods have achieved good clustering performance, they still have some limitations. First, the subspace representation generated by the self-expression reconstruction model usually ignores the local structure of the data set. Second, most multi-view subspace clustering methodsusually divide the subspace representation learning process and the subsequent clustering task into two separate processes, ignoring the interactions between them. To address these issues, in this paper we propose an LGOMSC method that considers adding graph learning to explore local information adaptively for obtaining subspace representations. Moreover, LGOMSC performs multi-view information fusion directly on the subspace representation and introduces rank constraints on the Laplacian matrix of the common subspace representation matrix, which helps to naturally partition the data points into the desired number of clusters. Our approach integrates similarity learning, multi-view information fusion and clustering as a unified framework to achieve multi-view clustering in an end-to-end manner. [PERSON] et al. [45] propose a multi-view subspace clustering (MVSCLG) that also utilizes the local and global information to achieve the end-to-end clustering. The main differences between MVSCLG and LGOMSC include: (1) to explore the consistency between different views, MVSCLG adopts the spectral matrix fusion, but LGOMSC adopts the graph matrix fusion; (2) to achieve end-to-end clustering, MVSCLG adopts a rotation matrix to map the common spectral matrix to the final cluster label matrix, but LGOMSC adopts a rank constraint on the common Laplacian matrix to directly achieve clustering. Moreover, compared with MVSCLG, LGOMSC has some advantages. Firstly, MVSCLG involves many singular value decomposition procedures and contains many variables, which leads to longer running times and more memory usage than LGOMSC. Secondly, LGOMSC contains fewer hyperparameters than MVSCLG, which is more suitable for practical applications.
## 7 Conclusions  
All MAC protocols proposed for wireless LANs can be categorized into contention-based or polling-based protocols and single or multi-hop protocols. In Wi-Fi sensor networks, where many sensors exist, contention-based protocols have a significant disadvantage because of the immense interference among sensors. Wi-Fi sensor networks may suffer from poor MAC performance. However, because of the inherent nature of polling-based protocols, they can avoid interference among sensors through efficient polling schemes. Single-hop protocols have become more attractive for Wi-Fi sensor networks because of recent sensor-technology advancements through which a great extension of the transmission range of sensors can be achieved. This study uses a polling-based single-hop MAC protocol based on a connectivity-based multipolling mechanism.  
Since adopting the polling-based MAC protocol for supporting real-time traffic in wireless LANs, the connectivity-based multipolling MAC protocol has been researched to optimize the number of polling frame transmissions using the connectivity among Wi-Fi nodes. In particular, when Wi-Fi sensor networks connect a large number of Wi-Fi sensors to the Internet through wireless LANs, the optimization problem for minimizing the number of polling frame transmissions is challenging, and a real-time implementable algorithm for a minimal number of polling sequences is required for the successful implementation of the connectivity-based multipolling MAC protocol.  
This paper proposes a real-time implementable, efficient node insertion algorithm for deriving a near-optimal number of polling sequences for the connectivity-based multipolling MAC protocol. The simulation results show that the number of polling sequences derived by the proposed node insertion algorithm converges to only one as the number of Wi-Fi sensor nodes increases in Wi-Fi sensor networks.  
According to the proposed node insertion algorithm, each Wi-Fi sensor is granted an equal transmission opportunity to transmit its uplink data. However, some sensors may have urgent data, which should be transmitted with high priority. Future research should be conducted to modify the connected multipolling sequences to simultaneously satisfy the different service requirements of sensors.  
This work was supported by the Dong-A University research fund.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The author declares no conflict of interest.
# Stabilization of Road Embarkments on Peat Soils Using Oil Shale Ash and Pozzolanic Additives
## Materials
### Oil Shale Ash  
EestiEnergia (an Estonian national energy company) generates millions of tons of oil shale ash every year, and approximately 5% of this is reused as a raw material; the majority of the ash is deposited in landfills [5].  
The organic component of oil shale usually does not exceed 40% of the total mass (usually less)1. The composition of the mineral part, i.e., oil shale ash, which remains after burning is quite variable depending on the type of layer the oil shale used for heating derives from, and even more so on how many intermediate layers are left in the oil shale during sorting. The main part of the intermediate layers is made up of calcium carbonate or limestone. During firing, it decomposes into quicklime CaO and carbon dioxide.  
In power plants, finely ground oil shale is burned floating in air (pulverized firing PF, older energy blocks) or in circulating fluidized bed boilers (CFB, newer energy blocks).  
PF fly ash, consisting of the mineral part of oil shale, is separated from combustion gases using cyclones and electric filters. Fly ash is suitably fine for use as a binder and has the best hardening properties. It can have different mineral and elemental compositions depending on where it is obtained (cyclone or electrostatic precipitator).  
Fluidized bed burning takes place at a lower temperature, and thus some of the calcium carbonate present in oil shale remains intact. At the same time, some toxic components of burning gas (phenoles, sulfur oxides) are trapped in the same ash, forming calcium sulfate and other substances.  
The third type of oil shale ash is DeSO\({}_{\mathrm{x}}\) ash, obtained from the desulfurization system of power plants. CaO from boilers is used in semidry DeSO\({}_{\mathrm{x}}\) systems to remove sulfur oxides from emission gases. This ash has a high sulfate content.  
The compositions of ashes used in the experiments are described in Table 2.
## 3 Results and Discussion  
### Visual Analysis of Orthogonal Experiment Results  
Table 6 presents the average fluidity values of the SSCFC alongside the average compressive strengths at 7, 14, and 28 days. Table 7 presents the sample size for each test group, as well as the standard deviations of fluidity and compressive strength. As shown in Table 6, the compressive strength of Group 8 was lower than that of the control group. Excluding Group 8, all other eight groups demonstrated higher compressive strengths than the control, with Groups 1, 3, and 5 exceeding 15 MPa. Group 1 (W/C ratio = 0.4, 10% steel slag, 20% ceramsite) achieved the peak compressive strength of 17.2 MPa. Field flowability tests indicated optimal workability when the flow spread ranged between 170 and 200 mm. Therefore, the preliminary analysis indicates that SSCFC achieves its peak comprehensive compressive performance at a W/C ratio of 0.4 (A1), with 10% SS replacement (B1) and 20% ceramsite replacement (C1). However, due to the inherent limitations of factor-level combinations within the orthogonal design, mathematical optimization methods are required to further identify the ideal parameter configuration.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Number** & **A (-)** & **B (\%)** & **C (\%)** & **Fluidity (mm)** & \begin{tabular}{c} **7d Compressive** \\ **Strength (Mpa)** \\ \end{tabular} & \
\begin{tabular}{c} **14d Compressive** \\ **Strength (Mpa)** \\ \end{tabular} &
\begin{tabular}{c} **28d Compressive** \\ **Strength (Mpa)** \\ \end{tabular} \\ \hline
1 & 0.4 & 10 & 20 & 183 & 14.9 & 16.5 & 17.2 \\
2 & 0.4 & 15 & 30 & 171 & 13.0 & 13.9 & 14.3 \\
3 & 0.4 & 20 & 25 & 140 & 12.8 & 14.9 & 15.6 \\
4 & 0.45 & 10 & 30 & 224 &
## 3 Engineering Applications  
### Central Yunnan Water Diversion Project  
#### 3.1.1 Central Yunnan Water Diversion Project Profile  
The central Yunnan water diversion project is the largest and most invested water resources allocation project in Southwest China. The total length of the main water diversion channel is about 661 km, and there are 63 main tunnels with a total length of about 610 km. The underground tunnels account for more than 90% of the total length of the total route length [25]. The water diversion route comprises the following sections: Dali I, Dali II, Chuxiong, Kunming, Yuxi and Honghe. The location and water delivery route of the central Yunnan water diversion project are shown in Figure 6. It passes through some active faults of Holocene and Late-Pleistocene, and the impact of these active faults on the diversion route during the construction--especially the groundwater environment--cannot be ignored [26]. Among them, the Xianglushan tunnel is the control project of the central Yunnan water diversion project, located at the head of Dali I. It is the longest deep-burried tunnel of the project, with a total length of about 63 km and a maximum buried depth of 1512 m. Meanwhile, it is a large-slope inclined shaft tunnel with the most difficult construction and complex hydrogeological conditions along the whole line. The tunnel construction has problems such as water inrush and mud inrush, a high external water pressure, crossing active faults, a large buried depth, a difficult construction and groundwater treatment [27,28]. After the completion of the project, water can be diverted from the main stream of Jinsha River, with relatively abundant water, to central Yunnan, which will solve severe water shortages in the central Yunnan region, improve the ecological and environmental conditions of rivers and lakes in the region and effectively promote the sustainable economic and social development of Yunnan [29,30].
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 3 Proposal Architecture
### Design of the Packet Structure
#### 3.5.1 Types of Packet Payload Content
* **Hops:** the number of jumps from the data's origin. * **Packet counter:** an incremental packet counter of the data source. * **Data:** the information that will be sent through the network. * **Latitude:** the 12-byte latitude given by the GPS. * **Longitude:** the 12-byte longitude given by the GPS.
## 3 Experimental Design and Implementation
### Experimental Procedure  
Data Import and Initial Processing  
The first step of the experiment is data import and format conversion. Raw data is provided in text file format and needs to be parsed and converted into structured data format. In the Python environment, data is read using pandas' read_csv() function, followed by the following initial processing:  
1. Data type conversion: Ensuring correct data types for each column, such as converting timestamps to integer type.
2. Index setting: Establishing appropriate indices to improve query efficiency.
3. Basic statistics: Computing basic statistical information to understand overall data characteristics.  
This stage also includes creating necessary directory structures such as data/, figures/, results/, etc., preparing for subsequent processing.
# Blockchain for Future Wireless Networks: A Decade Survey
## 4 Blockchain: A Solution for Security and Privacy in WNs
### Application Layer  
This layer comprises various smart applications, such as smart healthcare, smart cities, smart industries, etc. The smart application components are linked via a wired or wireless connection. In the case of wireless connection, communication happens between two  
Figure 8: The proposed architecture: enabling blockchain technology for wireless networks.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Year** & **Author** & **Applications** & **Security Issues** & **Approach** & **Pros** & **Cons** \\ \hline
2021 & [PERSON] et al. [178] & Healthcare & Private information protection of medical system & Elliptic curve Diffie-Hellman key exchange approach and file authorization contracts & Enhanced reliability and bandwidth utilization of data transmission & Did not discuss security attacks on the existing approach \\ \hline
2022 & [PERSON] et al. [179] & Healthcare & Monetary and resource loss & Temper register blockchain bad framework with IPFS storage for the healthcare & They used symmetric and asymmetric key encryption, digital signature and hash-ing algorithm and IPFS to deal with scalability issue & Did not discuss security and privacy attacks in the system \\ \hline
2022 & [PERSON] et al. [180] & Healthcare & Data tampering & Blockchain-based cloud technology for healthcare & They presented state of the art on blockchain enabled healthcare system & Did not consider security issues of the existing solution \\ \hline
2022 & [PERSON] et al. [181] & Crowd-sensing systems & Data confidentiality, integrity, and system availability & Proposed crowd-sensing systems using smart contracts and blockchain & They discussed security and privacy issue with blockchain based solution in crowd-sensing systems & Did not discuss security and privacy problem to add blockchain and smart contract in the system \\ \hline
2022 & [PERSON] et al. [182] & IoT healthcare Industry 4.0 systems & User authentication, access control, and privacy protection & Analyzed MCDM problem in blockchain enabled IoT healthcare industry 4.0 systems & Proposed spherical FWZIC for security and privacy, GRA-TOPSIS, and BES approach for optimization & Did not discuss security and privacy problem to add blockchain and smart contract in the system \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._users using a mobile terminal. For example, the energy bill is generated through smart meters in the smart home. The energy bill is shared with the consumer (who is consuming energy) and the smart grid administrator via WNs (e.g., 4G or 5G). During data transfer, communication is established from a user device, such as a computer or smartphone to the nearest access point of the WN layer. Then it is transferred further to the intended destination. In the proposed architecture, communication between the application and WN layers through the blockchain layer is discussed in detail in the next section.