esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Formulation and Brief System Architecture  
A cloud-based healthcare environment enables the access to store information in a continuous time domain and further analysis through fast computing capabilities. The HPC collects the healthcare raw data and manages the data for processing to explore the most valuable information. In traditional healthcare systems, hospitals are the main source of generating healthcare data. We consider a hospital  
Figure 1: Three phases of interactive healthcare: Primary layer, Operating layer, and Application layer.  
along with individual patients as they are the major source of the proposed interactive healthcare environment. Consider an interactive healthcare system where patients are in set \(P=\{P_{1},P_{2},.......\}\) where the total number of patient is \(m=|P|\). There are some patients who consult with a doctor but do not need to stay overnight in the hospital. Those patients are continuously monitored through many biosensor devices. Some patient consult with a doctor but need to stay overnight in the hospital. We consider all patient in the set \(P\). The major source of generating biomedical big data is the hospital. We consider \(k\) number of hospitals are in a set \(H=\{H_{1},H_{2}.....\}\) where \(k=|H|\). It is assumed that \(n\) number of departments are associated with each hospital in a set \(Dp=\{Dp_{1},Dp_{2}.......\}\) where \(n=|Dp|\). Suppose there are \(l\) number of doctors who are working on a department in a hospital. We consider the set \(D_{ij}^{k}\), such that \(j=\{1,2,3.......\}\) and \(l=|j|\). The relationship illustrates the \(k^{th}\) hospital within \(i^{th}\) department, where \(\forall i\in Dp\) and \(\forall k\in H\). For example, \(D_{14}^{5}\) refers to the \(4^{th}\) doctor in the \(1^{st}\) department of \(5^{th}\) hospital. As we analyze all the big data in the common HPC server, the patient and doctor set would be \(P_{ij}^{k}=\{P_{1j}^{k}\cup P_{2j}^{k}\cup P_{3j}^{k}......\}\) and \(D_{ij}^{k}=\{D_{1j}^{k}\cup D_{2j}^{k}\cup D_{3j}^{k}......\}\) respectively.  
In the proposed model, it is assumed that data are gathered in a continuous time domain. The HPC will do a linear regression over the data considered in the Y-axis and time in X-axis for the primary prediction analysis. Then the analyzed result is sent to the cloud data center. HPC also sends the biomedical data to the cloud in a time interval \(t\), where \(t\) refers the change of biological data over time. All data centers \(DC=\{DC_{1},DC_{2}.....\}\) where \(\mu=|DC|\) relate to data warehouses, \(WH=\{WH_{1},WH_{2}......\}\) where \(q=|WH|\). The future prediction analysis will be done in the cloud server.
## 3 DC Bus Voltage Control Strategy
### Improved Control Strategy Combined with the Variable Step-Size Algorithm
#### 3.3.2 The Improved Variable Step-Size Algorithm for the S-Function  
In contrast to the instantaneous error, the S-function adaptive filter integrated with LADRC (SVA-LADRC) incorporates a nonlinear mapping mechanism, which dynamically transforms the error into a smooth and constrained step-size. This significantly enhances the robustness and convergence performance of the algorithm. By controlling the steepness of the S-function, the sensitivity of step-size variation can be flexibly adjusted, effectively avoiding oscillations and instability caused by noise or sharp fluctuations in error, which are typical issues in the instantaneous error algorithm. Additionally, it suppresses excessive step-size adjustment under extreme error conditions. This approach is well-suited for nonlinear, dynamic environments, or optimization scenarios with high noise levels, exhibiting stronger adaptability and stability. The mathematical expression is as follows:  
\[\mu(n)=b(\frac{1}{1+e^{-a|e(n)|}}-0.5) \tag{20}\]  
In the S-function variable step-size algorithm, the parameters \(a\) and \(b\) control the sensitivity of step-size adjustment and the central position, respectively. Parameter \(a\) determines the steepness of the S-function; the steeper the function, the more sensitive the step-size is to error changes. However, if \(a\) is too large, it may lead to oscillations, while if it is too small, the adjustment may become sluggish. Parameter \(b\) determines the horizontal shift of the S-function, affecting the threshold range for step-size adjustment. When \(b\) is large, it is suitable for scenarios with larger errors, and when \(b\) is small, it is better for precise adjustment in cases of small errors. In this paper, \(a=-0.015\) and \(b=0.001\).
# A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces
## 4 Result and Discussion  
### Single-Value Missing Data Completion  
Take the measuring point PL5-3 in Figure 6 as an example, the strongest correlation reference sequence with the deformation sequence of the measuring point is searched. The correlation between the sequence of target measurement points and the sequence of other measurement points is shown in Table 1.  
It can be seen from the calculation results in the above table that the deformation sequence of measuring point PL5-4 has the strongest correlation with the target sequence,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Adjacent Points** & **PL5** & **PL5-1** & **PL5-2** & **PL5-4** & **PL5-5** \\ \hline
**Target Points** & & & & & \\ \hline PL5-3 & 0.7952 & 0.9231 & 0.9563 & 0.9895 & 0.9668 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation results of deformation sequences of target measurement points and adjacent measurement points.  
Figure 6: Arrangement of measuring points on inverted vertical line of dam shuice section.  
Figure 7: Horizontal displacement process line of positive vertical line of dam shuice section.  
and the deformation law is the most similar. Therefore, the reference measuring point is PL5-4.  
For the deformation sequence from 31 August 2014 to 18 September 2014, there are 19 deformation data point, as shown in Table 2.  
Suppose that the deformation data of 10 September 2014 is missing, use the interpolation method based on non-local means proposed in this paper and the traditional method to estimate the missing value, respectively. The results are shown in Table 3.  
It can be seen from the calculation results of each interpolation method that the estimation result of the proposed single-value missing completion method based on non-local means is close to the original monitoring values. At the same time, it can be found that when the missing value is not within the range of two values before and after the missing value, the traditional interpolation method is difficult to estimate such deformation value effectively. However, the NLM interpolation method overcomes this limitation by using self-similarity of deformation sequences and introducing reference sequences, which increases the accuracy of missing value estimation.
## 3 Algorithm Description and Process
### Algorithm Description
#### 3.2.2. Constructing a Connected Path by the Hybrid Radius Path-Selection Method  
When every cluster head node \(f_{m}\) does not receive the reply of non-cluster head nodes over a period of time, \(f_{m}\) will construct a connected path to the sink node. \(f_{m}\) first compares \(d(f_{m},\mathit{Sink})\) to the radius of the hot-spot area \(d_{hot}\). If \(d(f_{m},\mathit{Sink})<d_{hot},f_{m}\) connects with the sink node directly; otherwise, \(f_{i}\) will find its next-hop node from the set of its neighbor cluster head nodes \(N_{CH}(f_{m})\). The detailed process is expressed as follows:  
1. \(f_{m}\) selects the cluster head nodes in the forward direction (_i.e._, the direction close to the sink node) from the nodes in \(N_{CH}(f_{m})\) by Equation (11). These nodes are denoted by the cluster head nodes of the forward direction of \(f_{m}\)\(B_{CH}(f_{m})\): \[B_{CH}(f_{m})=\left\{f_{j}|d(f_{j},\mathit{Sink})<d(f_{m},\mathit{Sink}),f_{j} \in N_{CH}(f_{m})\right\}\] (11)
2. \(f_{m}\) selects the \(k\) nodes nearest to itself from the nodes in \(B_{CH}(f_{m})\) as the set of alternative next-hop nodes \(F_{CH}(f_{m})\). If the number of the nodes in \(B_{CH}(f_{m})\) is smaller than \(k\), \(F_{CH}(f_{m})=B_{CH}(f_{m})\). If the node number is zero, Step (3) is performed; otherwise, Step (4) is performed.
3. \(f_{m}\) selects \(k\) non-cluster head nodes whose cluster head nodes are not \(f_{m}\) in the forward direction within its \(R_{c-}\mathit{Max}\) as the set of alternative next-hop nodes \(F_{CH}(f_{m})\). This process guarantees that the communication radius of \(f_{m}\) is greater than the communication radius of its next-hop node.
4. \(f_{m}\) selects a node \(s_{j}\) from the nodes in \(F_{CH}(f_{m})\) by minimizing the sum of the distance to \(f_{m}\) and the distance to the sink node as its next-hop node \(\mathit{next}(f_{m})\): \[next(f_{m})=\left\{s_{j}|\min\left(d(f_{m},s_{j})+d(s_{j},\mathit{Sink})\right),s_{j}\in F_{CH}\left(f_{m}\right)\right\}\] (12)
5. If \(\mathit{next}(f_{m})\) is a non-cluster head node, the node becomes a cluster head node and finds its next-hop node according to the preceding process. Otherwise, the entire process ends.  
Figure 3. Flow chart of Section 3.2.1.
# Propagation Attenuation Maps Based on Parabolic Equation Method +
## 3 Propagation Attenuation Map  
PAMs show the path loss values relative to the defined point, usually located in the center. This part presents the proposed algorithm for PAM creation. It includes the general idea, algorithm view, and exemplary results.
## 6 Security Analysis  
**Threat model.** We consider both internal and external attackers. Internal attackers may be compromised nodes of the HMBAC system or compromised users. Compromised nodes may attempt to illegally modify the access policies or the domain's stakeholders' set. Compromised users may attempt to bypass access control policies and gain unauthorized access. External attackers may attempt to gain unauthorized access to the system.  
**Assumptions.** We shall assume in our analysis that the underlying software components such as the orchestration engine (Kubernetes) and the isolation mechanisms (Pods and Hashicorp Vault) are trusted. Instead of requiring a fully trusted authority, we relax our trust assumptions to a majority of trusted stakeholders for each domain. We assume  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \# of Concur. Requests & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**10**} & \multicolumn{2}{c}{**20**} & \multicolumn{2}{c}{**40**} & \multicolumn{2}{c}{**60**} & \multicolumn{2}{c}{**80**} & \multicolumn{2}{c}{**100**} & \multicolumn{2}{c}{**200**} & \multicolumn{2}{c}{**300**} \\ \hline
**H/W Setups** & **S1** & **S2** & **S1** & **S2** & **S1** & **S2** & **S1** & **S2** & **S1** & **S2** & **S1** & **S2** & **S1** & **S2** & **S1** & **S2** & **S1** & **S2** \\ \hline
**Ticketing** & 0.004 & 0.002 & 0.003 & 0.002 & 0.002 & 0.002 & 0.003 & 0.003 & 0.005 & 0.025 & 0.008 & 0.018 & 0.015 & 0.015 & 0.024 & 0.032 & 0.02 \\
**Endorse** & 0.08 & 0.07 & 0.25 & 0.12 & 0.24 & 0.17 & 0.16 & 0.23 & 0.22 & 0.15 & 0.48 & 0.55 & 0.44 & 0.18 & 0.55 & 0.3 & 0.82 & 1.44 \\
**Commit** & 0.006 & 0.007 & 0.006 & 0.004 & 0.005 & 0.005 & 0.005 & 0.005 & 0.008 & 0.008 & 0.008 & 0.008 & 0.025 & 0.010 & 0.014 & 0.020 \\
**BC_RTT** & 2.19 & 2.2 & 2.41 & 2.22 & 2.47 & 2.07 & 2.95 & 2.49 & 3.19 & 2.76 & 3.33 & 2.74 & 4.22 & 3.76 & 6.6 & 4.63 & 8.09 & 5.62 \\
**Average** & 2.27 & 2.27 & 2.67 & 2.35 & 2.72 & 2.25 & 3.12 & 2.73 & 3.43 & 2.92 & 3.85 & 3.32 & 4.69 & 3.98 & 6.58 & 4.97 & 8.96 & 7.12 \\
**Min** & 2.26 & 2.27 & 2.58 & 2.28 & 1.88 & 2.1 & 2.02 & 2.92 & 1.62 & 1.61 & 1.88 & 1.98 & 2.04 & 2.45 & 1.74 & 1.54 & 1.65 & 2.33 \\
**Max** & 2.29 & 2.27 & 2.72 & 2.39 & 2.91 & 2.88 & 3.88 & 4.05 & 4.86 & 3.66 & 5.14 & 5.31 & 5.71 & 5.22 & 9.2 & 7.84 & 12.77 & 10.39 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detailed performance evaluation for various scenarios of concurrent requests and h/w setups (time in s).  
Figure 7: System Efficiency.  
that the majority of the participants in the consensus and voting protocols behave in a trusted way. We assume that the encryption and authentication mechanisms used (AES and MA-ABE) are secure (cannot be compromised by a probabilistic polynomial-time Turing machine). Finally, we assume that the user credentials are securely managed on the user side. As the main goal of HMBAC is to provide access control, we will first examine security against unauthorized access attacks and then other security characteristics of the proposed system.
## 3 Results and Discussion  
### Geotechnical Profile  
From the results of the boreholes, in situ test and cross-hole seismic tomography tests conducted, the stratigraphy of the area under study (Figure 6) may be divided into four main units: (i) the filling of the embankment, which consists of gravels with sands and a variable brown silty clay matrix, although up to a depth between 1.8 m and 3.0 m compacted gravels prevail; (ii) alluvial cover, composed of gravels with sands and fines, alternating with brown silts and clays, often with traces of organic matter, towards the Ebro River, and with a chaotic appearance due to being affected by the karstic processes; (iii) a transition ground, consisting of brown silty clays that incorporate fragments of evaporitic rocks (generally gypsum) and (iv) tertiary rock substratum. Deep boreholes indicate that this substratum can be divided into four lithost stratigraphic units, in ascending order [30; 31; 32]:  
Figure 6: Geotechnical investigation results with geological interpretation: (**a**) Correlation between boreholes S-1 and S1-C1 (March 2008), S-1 and S2 (July 2015) and S-1 and S-2 (March 2018), including the seismic tomography between them and the DPSH tests performed; (**b**) Proposed geotechnical profile following section A-A’ (see Figure 3).  
(1) marl and anhydrite basal unit; (2) halite unit; (3) glauberite-halite unit; and (4) anhydrite unit. The Alcala sinkhole, according to its elevation (221 m.a.s.l.), is directly underlain either by the lower part of the anhydrite unit or the upper part of the glauberite-halite unit [28].  
This last unit is altered in its upper part and is affected by the variations of the water table. Consequently, internal dissolution processes are generated, facilitating the formation of sinkholes. In all the boreholes conducted, between a depth of 16 m and 22 m (measured from the top of the embankment, about 2 m less from the street), several holes were recorded, with minimum resistance to penetration (drilling maneuver falls almost by its own weight), only recovering about 50 cm of brown silty clay soil with gravel pebbles. This means that the origin of the sinkhole problems is located at a level of approximately 6 m of thickness, about 14 m below the street, formed by very soluble rocks (gypsum in the form of nodules, glauberite, halite, thehandites and epsomites [28; 31; 32]). Materials (gravists with sands) coming from the detrital levels located above or from anthropic ones are found here, filling the holes left by karstic dissolution processes.  
This dissolution process results in subsidence rising towards the surface, which produces sinkholes. The phenomenon is related to the variations in the water table: sinkholes normally appear between June and October, being that period when the water level at the river is the lowest [33] and the contributions from the headwaters of the catchment basin is low.
## 9 Future Research Directions and Challenges
### Vertical Localisation Accuracy  
As discussed earlier, the localisation accuracy in the z-direction is always less than in x-y. Notably, the multi-path effect and the small offset of anchors in the z-direction yield poor vertical accuracy, relying on conventional methods [125]. MIMO technology and wide-band mmWave system of 5G provide promising solutions to improve the 3D positioning, such as taking full advantage of CFR in all frequencies or analysing antenna radiation pattern [126].
# An Optimization Design of Bi-log Hybrid Antenna with Taguchi's Method for EMI Measurements
## 3 Antenna Optimization using Taguchi's Method
### Feed Structure and Balun  
The design of the feed structure and Balun are essential for achieving the best performance of the proposed antenna. The optimized antenna not only considers the antenna structure and the elements but also needs to determine the characteristic impedance of the transmission line of feeds. The proposed structure of feed includes a wideband Balun for balancing improvement, an L-shape feeding structure for uniform current distribution, and a tapered offset spacing over transmission lines for each element placement. A more reliable and complete antenna configuration has been considered within the proposed Balun and feeding structure design using the CST simulation.  
The L-shape feed structure with two variable parameters of X (5.5 mm) and H (2 mm), as depicted in Figure 6a on the tip of the antenna, also presented the tapered configuration of each spacing over the booms. To attain this balance symmetry capability, quasi-air coil Balun with a bound of five as a 1:1 turn is utilized at the antenna's feed point. It offers the essential wideband matching and proper current phase for the elements, effectively covering the frequency range of 30 MHz to 200 MHz. In Figure 6b, the proposed Balun is an indispensable device for antenna-feed networks. It connects a balanced two-conductor port to an unbalanced port through a semi-rigid coaxial cable, where the radius a is 15 mm, spacing c is 4.6 mm, circumference L is 94.2 mm, and the total height is 60 mm. Figure 7 shows the simulated result of the real part (solid red line) and imaginary part (dot blue line) of Balun impedance. The Balun operates effectively across a wide frequency range and optimizes antenna performance. The proposed Balun not only reduces the unbalanced current on the surface of the feeding cable but also ensures accurate radiation characteristics of the bowtie part of the antenna in wideband. The structure can be easily configured on this antenna through the feeding cable directly without adding any extra components such as a matching circuit or transformer. The results confirm that the proposed Balun is suitable for the Bi-log hybrid antenna. Its feeding technique for this antenna has low design complexity and is easier to implement than traditional antennas.  
Figure 6: Antenna feeding structure (**a**) on the front end (side view) and (**b**) Balun geometry.
## 3 Results and Discussion
### Distribution Characteristics of the Subgrade Dynamic Displacement
Comparing the amplitude curves and attenuation coefficients, it was evident that the attenuation of the vertical vibration acceleration amplitude curve was more pronounced at the same positions. Considering that the vertical dynamic displacement decreased to below 0.02 mm at positions farther from the toe of the slope, this monitoring location might experience significant errors due to environmental changes, making accurate monitoring difficult. Therefore, the position 15 m from the toe of the slope was not included as a monitoring point for analysis. Figure 11: The distribution characteristics of dynamic displacement amplitude in the subgrade of a high-speed railway in a karst area. Figure 10: Displacement cloud diagram of the subgrade (unit: mm). In summary, the vertical dynamic displacement within a range of 0 m to 10 m from the toe of the subgrade slope can serve as an indicator of potential defects such as holes within the subgrade. If monitoring reveals a significant decrease in the dynamic displacement amplitude at or a certain distance from the toe of the subgrade, with an attenuation rate of approximately 30-50%, it can be inferred that there may be defects like soil holes in that section of the subgrade. The closer the monitoring point is to the defect, the greater the attenuation rate. By identifying the location with the maximum attenuation rate, the position of the defect can be determined, thereby assessing the hazardous state of the karst subgrade.
## 4 Experimental Verification
### Damage Identification Results
The damage condition DC17-DC24 simulates the damage of two hangers simultaneously, and the identification result is shown in Figure 13. It can be seen from Figure 14a that the change of cable force at the damaged hanger is also much larger than that at the undamaged place. Through this feature, it is easy to locate the damaged hanger, S4 and S5. Another observation that can be made is when the damage degree increases, the identification result also increases. This method can also be relatively quantitative when two hangers are damaged simultaneously, but the quantitative damage is more complicated. It needs further study of the mutual influence among the simultaneously damaged hangers. From Figure 14b, the recognition results show that the cable force of hangers S4 and S6 decreases, and the reduced value is obviously larger than that of other hangers. It can be inferred that the damaged hangers should be S4 and S6 which corresponds to the preset damage. In addition, it is found that when the damaged hanger is in a symmetrical position, the damage results are not completely symmetrical, which should be due to the fact that it is impossible to ensure that the initial state of each damage condition is completely consistent during the test. It can be seen from the test results that regardless of the location of the damage and the number of the damaged hangers, the proposed method can always reliably locate the damaged hangers. Experimental Verification of Hanger Damage Identification Considering Creep and Bridge Stiffness Degradation  
As analyzed above, both creep and overall stiffness degradation can be performed by reducing the modulus of elasticity in the numerical simulation. The preset damage cable for single cable damage conditions was S3, and the damage degree was 12.5%, 25%, 37.5, and 50%, respectively; the preset damage cable for double cable damage conditions was S3 and S7, and the damage degree was also set to 12.5%, 25%, 37.5 and 50%, and the recognition results are shown in Figure 15.  
Figure 14: Damage identification results for EDC17–EDC24: (**a**) the preset damage hangers are S4 and S5; (**b**) the preset damage hangers are S4 and S6. It can be seen from Figure 15 that the method proposed in this paper can accurately locate the damaged hanger when considering creep and overall stiffness degradation. Another observation drawn from this figure is that the reduction ratio of tension of the damaged cable has exceeded one. This is mainly because the cable damage occurs after the creep and stiffness degradation of the structure, and the corresponding deflection difference of the tie-beam is bound to be larger than that when only cable damage occurs, so the identification results will also become larger. The results show that the method is robust to creep and stiffness degradation.