esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Optimal PI controller Tuning Based on Integrating Plus Time Delay Models
## 3 Tuning for Maximum Time Delay Error
### Using a \((2,1)\) Pade Approximation  
Consider the disturbance response with PI control,  
\[\begin{array}{rcl}\frac{y}{v}(s)&=&\frac{H_{p}}{1+H_{c}H_{p}}=\frac{k^{- \tau_{\text{s}}}}{1+K_{p}\frac{1+H_{p}^{2}}{1+H_{p}^{2}}k^{-\tau_{\text{s}}}} \\ &=&\frac{k\text{s}e^{-\tau_{\text{s}}}}{s^{2}+\frac{K_{p}^{2}}{1+H_{p}^{2}}(1+ \tau_{\text{s}})e^{-\tau_{\text{s}}}}.\end{array} \tag{30}\]  
Consider a \((2,1)\) Pade approximation, \(e^{x}=\frac{6+4x+x^{2}}{6-2x}\), i.e., with a second order numerator polynomial and a first order denominator polynomial, i.e., an approximation,  
\[e^{-\tau_{\text{s}}}\approx\frac{1-b_{1}s+b_{2}s^{2}}{1+a_{1}s}, \tag{31}\]  
where \(a_{1}=\frac{\tau}{3}\), \(b_{1}=\frac{2\tau}{3}\) and \(b_{2}=\frac{\tau^{2}}{6}\).  
Using the same procedure as in Section 5.2 in [1], and with unit relative damping, we find a third order polynomial for the closed loop response,  
\[\frac{y}{v}(s)=\frac{T_{i}s}{K_{p}}\frac{b_{2}s^{2}-b_{1}s+1}{(\frac{a_{1}T_{i}}{ KK_{p}}+b_{2}T_{i})s^{3}+(b_{2}-b_{1}T_{i}+\frac{T_{i}}{KK_{p}})s^{2}+(T_{i}-b_{1 })s+1}. \tag{32}\]  
We prescribe a third order polynomial  
\[\Pi(s) = (1-\tau_{0}s)(\tau_{0}^{2}s^{2}+2\tau_{0}s+1)=(1+\tau_{0}s)^{3}\] \[= \tau_{0}^{3}s^{3}+3\tau_{0}^{2}s^{2}+3\tau_{0}s+1. \tag{33}\]  
When comparing Equations (32) and (33), we find that  
\[\tau_{0}^{3} = T_{i}(\frac{a_{1}}{KK_{p}}+b_{2}), \tag{34}\] \[3\tau_{0}^{2} = b_{2}-T_{i}(b_{1}-\frac{1}{KK_{p}}),\] (35) \[3\tau_{0} = T_{i}-b_{1}. \tag{36}\]  
By inserting Equations (34) and (36) into Equation (35), it can be shown that  
\[\left(\frac{\tau_{0}}{\tau}\right)^{3}-\left(\frac{\tau_{0}}{\tau}\right)^{2} -\frac{7}{6}\bigg{(}\frac{\tau_{0}}{\tau}\bigg{)}-\frac{11}{54}=0. \tag{37}\]  
We solve the third order polynomial in Equation (37) with respect to \(\frac{\tau_{0}}{\tau}\), and find a real positive solution, \(\frac{\tau_{0}}{\tau}\approx 1.7385\).  
Furthermore, we find that the PI controller parameters  
\[T_{i}=3\tau_{0}+b_{1},\;\;K_{p}=\frac{a_{1}T_{i}}{k(\tau_{0}^{3}-b_{2}T_{i})}, \tag{38}\]  
where \(\tau_{0}\) may be seen as a tuning parameter.  
When assuming that the response time constant \(\tau_{0}=c\tau\), then we may express the PI controller parameters \(K_{p}=\frac{\alpha}{k\tau}\) and \(T_{i}=\beta\tau\) with  
\[\beta = \frac{9\,c+2}{3}=3\,c+\frac{2}{3}, \tag{39}\] \[\alpha = \frac{2(9\,c+2)}{18\,c^{3}-9\,c-2}=\frac{c+\frac{2}{9}}{c^{3}- \frac{1}{2}c-\frac{1}{9}}, \tag{40}\]  
where the product \(\bar{c}=\alpha\beta\) is a nonlinear function of the tuning parameter \(c\). We find that it makes sense to choose \(c\) in the interval, \(1.4\leq c\leq 2.5\).  
From the PI controller setting in Equation (38) with \(\tau_{0}=1.7385\), we find the MP parameter \(\bar{c}=\alpha\beta=K_{p}kT_{i}\approx 2.6985\).  
For reducing the complexity of the problem, the (1, 2) Pade approximation was used; e.g., a (2, 2) Pade would result in a fourth order polynomial. Notice that a (1, 1) Pade approximation was used in the earlier work of [1] in Section 5.2.
## 3 Results and Discussion  
### Evaluation Metrics  
To fully evaluate the model named NAMRTNet, we used four parameters: accuracy (\(ACC\)), Cohen Kappa (\(\kappa\)) [37], F1 score (\(F1\)), and \(MF1\):  
\[ACC=\frac{\sum\limits_{i=1}^{N}e_{ii}}{\sum\limits_{i=1}^{N}\sum\limits_{j=1}^ {N}e_{ij}} \tag{15}\]  
\[\kappa=\frac{ACC-p_{e}}{1-p_{e}} \tag{16}\]  
\[p_{e}=\frac{\sum\limits_{N=1}^{N}a_{N}\times b_{N}}{K\times K} \tag{17}\]  
\[F1=\frac{2}{\frac{1}{PR}+\frac{1}{RE}} \tag{18}\]  
\[PR=\frac{e_{ii}}{\sum\limits_{j=1}^{N}eij} \tag{19}\]  
\[RE=\frac{e_{ii}}{\sum\limits_{j=1}^{N}eji} \tag{20}\]  
\[MF1=\frac{\sum\limits_{N=1}^{N}F1}{N} \tag{21}\]  
where \(e_{ij}\) represents the elements of the ith row and jth column, \(N\) is the number of categories, \(K\) is the total number of epochs, \(PR\) represents the accuracy of distinguishing sleep stages from other stages, \(RE\) represents the accuracy of predicting sleep stages,\(a_{N}\) represents the number of epochs of the Nth category, and \(b_{N}\) represents the number of epochs predicted to be the Nth category.
# Estimating Road Segments Using Kernelized Averaging of GPS Trajectories
## 2 From Dynamic Time Warping to Time Elastic Kernels Averaging
### Kernelized Time Elastic Averaging of a Set of Time Series
This algorithm was used, among other state-of-the-art averaging algorithms such as Soft-DTW, in Steps 2 and 4 of the processing pipeline depicted in Figure 1.  
```
1: Let \(K\) be a time elastic kernel for time series satisfying a probabilistic interpretation Equation (9)
2: Let \(\mathbf{X}\) be a set of time series of \(d\) dimensional samples
3: Let \(C_{0}\) be an initial centroid estimate (e.g., the medoid of \(\mathbf{X}\)) of length \(n\)
4: Let \(\mathcal{T}\) and \(\mathcal{T}_{0}\) be two sequences of time stamps of length \(n\) initialized with zero values
5: Let \(MeanK_{0}=0\) and \(MeanK\) be two double values;
6:repeat
7:\(C=C_{0}\), \(\mathcal{T}=\mathcal{T}_{0}\), \(MeanK=MeanK_{0}\);
8: Evaluate \(C_{0}\) and \(\mathcal{T}_{0}\) according to Equation (11) //Average similarity between \(C_{0}\) and elements of \(\mathbf{X}\)
9:\(MeanK_{0}\)=\(\frac{1}{|\mathbf{X}|}\sum_{\mathbf{x}\in\mathbf{X}}K(C_{0},\mathbf{x})\)
10:until\(MeanK\leq MeanK_{0}\)
11:(\(C\), \(\mathcal{T}\)) is the centroid estimation
12: Finally, uniformly re-sample \(C\) using the time stamps \(\mathcal{T}\)
```  
**Algorithm 1** Iterative Time Elastic Kernel Averaging (iTEKA) of a set of time series. An early version of iTEKA was first published on Arxiv site in 2015 [23].
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 5 LoRa and LoRaWAN Technologies  
In this section, we discuss several LoRa/LoRaWAN characteristics. We start by introducing the overview of these technologies. Then, the different LoRa/LoRaWAN basics are presented.
## 2 Background  
There are many standards and guides, as there is an increasing drive to incorporate related information technologies into existing asset management processes. There is a need to standardise processes and produce guidelines to implement them effectively [6]. Since then, several standards have been produced on a national level (e.g., UK government standards, National BIM Standard (NBIMS) [7] and ISO 19650 [8]), or at organizational levels (e.g., the United States Airforce [9]). They generally tend to provide guidelines within which information management processes and technologies are expected to be implemented. However, there are certain ambiguities in the standards that eventually lead to the ineffective implementation of projects. For example, due to the varying nature of projects (a building with a small footprint compared to a road network), the standards do not precisely define what an information model may require for a specific type of asset.  
Studies such as those carried out by the National Building Specification (NBS) show a growing awareness, and the level of adoption has generally increased over the past few years in the UK. The National Building Specification (NBS) has carried out surveys on the adoption of and views of adopting information management in the UK since 2011. The level of adoption amongst participants grew yearly to 31% [10] (2011), 41% [11], 43% [12], and 54%, according to the results of the surveys. Following a dip in 2015, the level of adoption has grown to 73% amongst participants in the National Building Specification as of 2020 [13]. However, there is concern among experts that organizations face challenges that hinder adoption, which may slow down this growth.  
It has been observed that there is resistance to adopting VDC/information management technology and processes even though the value of adoption has been recognised [14]. Other industry reports also show that experts still believe that their adoption rate given the benefits remains low [15]. These reports state that there is resistance to change and that there are human factors involved. However, they have also observed that there are minimal tangible benefits to clients, which leads to a reluctance for them to move towards adopting information management technology.
## 1 Introduction  
Chronic kidney disease (CKD) is widely recognized as a global public health problem [1], with an estimated global prevalence of 13.4%, which is expected to increase as the population ages [2]. In addition to decreased renal function and increased vascular calcification [3], CKDin CKD [8]. However, how skeletal muscle atrophy changes over time in CKD and how factors related to muscle catabolism and anabolism and mitochondrial activity change over time are unclear.  
Several preclinical models of CKD have been used to study skeletal muscle atrophy in CKD, including nephrectomy [9], Cy/+ rats with polycystic kidney disease [7; 10], diabetic kidney disease [11; 12], and adenine-induced CKD [13; 14]. Of these models, the adenine-induced CKD model has many advantages for use as a preclinical CKD model: it is minimally invasive, cost-effective, does not require surgery or postoperative care, and has a low mortality rate [15]. In the adenine-induced CKD model, atrophy of the extensor digitorum longus, soleus, and thigh muscles [13; 15], decreased grip strength [16], and impaired physical function [17] have been reported. However, how skeletal muscle atrophy occurs over time in the adenine-induced CKD rat model, how the expressions of muscle anabolic and catabolic genes are altered, and how mitochondrial activity is changed are not clear. The elucidation of the temporal changes in these factors in adenine-induced CKD rat models, which are useful as CKD model rats, will be of great importance for future studies of the prevention and treatment of muscle atrophy in CKD. Therefore, the purpose of this study is to evaluate changes over time in skeletal muscle atrophy, expressions of skeletal muscle anabolic and catabolic genes, and mitochondrial activity according to skeletal muscle type in an adenine-induced CKD model.
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 1 Introduction
### Random-Oracle Model and Standard Model  
Provable security theory, which employs formal language to describe the security of cryptographic protocols, has played a critical role in designing and analyzing AKA protocols. Most early cryptographic schemes for provable security were inefficient. Practically oriented provably secure cryptographic schemes were proposed only after the famous Random-Oracle model was introduced by [PERSON] and [PERSON] (2016). In the Random-Oracle model, the hash function is treated as a completely randomized machine called \(R\), and the adversary has no access to its information. A random oracle is a theoretical model that takes deterministic inputs and produces random outputs. Finding a genuinely random function to replace the random oracle \(R\) in the Random-Oracle model is impossible. Many scholars have suggested avoiding using hash functions as random oracles in favor of designing cryptographic protocols directly under realistic conditions ([PERSON], 2016; [PERSON], 2017; [PERSON], 2018). This approach, called the standard model, avoids using idealized models such as hash functions. In general, cryptographic schemes that are provably secure under the standard model can provide more robust security than those that are provably secure under the randomized predicate model.
# Multi-Party Controlled Semi-Quantum Dialogue Protocol Based on Hyperentangled Bell States
## 3 Security Analysis
### Attacks by the Dishonest Controller
#### 3.4.2 Operation Tampering  
A malicious controller [PERSON]\({}_{i}\) could deliberately broadcast erroneous unitary operations \(R_{1}\) to disrupt the communication process. If [PERSON] does not recover the correct initial state due to this misinformation, it will result in discrepancies during subsequent measurements, preventing her from correctly comparing the outcomes with the initial system state. If [PERSON]\({}_{i}\) publishes an erroneous unitary operation \(R_{1}\), [PERSON] will be unable to accurately restore the initial hyperentangled state prepared by [PERSON]\({}_{1}\). During the verification phase, when [PERSON] and [PERSON]\({}_{n}\) compare their measurement results, the QBER will be higher than the pre-defined threshold due to the mismatch caused by the incorrect operation. This high QBER indicates potential tampering or errors in the protocol, leading to the termination of the protocol. Consequently, [PERSON]\({}_{i}\) will not be able to obtain any useful secret messages from [PERSON] or [PERSON].
# A New Concept of Instantaneous Whirling Speed for Cracked Rotor's Axis Orbit
## 4 The Qualitative Mechanism Explanation
However, the maximum value of the line speed in the X-direction is less than that in the Y-direction. Therefore, at position 2, the RWS of the synthetic motion is less than that at the reference position 1, thus the first minimum value appears in Figure 4b. Figure 8: Line speed curves: (**a**) normal system, (**b**) cracked system. From position 2 to position 3 (process II), it can be known from Figure 4a, the potential energy is turned into the kinetic energy in Y-direction, and the line speed \(LS_{y}\) reaches the negative maximum value. In X-direction, the kinetic energy is turned into the potential energy, and the line speed \(LS_{x}\) is close to 0 in the end, which has been shown in Figure 8b. However, the maximum value of the line speed in Y-direction is larger than that in the X-direction. Therefore, at position 3, the second maximum value of RWS of the synthetic motion appears in Figure 4b. From position 3 to position 4 (process III), the line speed in Y-direction tends to be 0 and the line speed in X-direction tends to be the positive maximum value. However, as it is shown in Figure 9b, at this time the additional stiffness excitations do negative work in the X-direction, which prevents the line speed from following the normal change rule, and the line speed \(LS_{x}\) increases at the beginning and then decreases, as it is shown in Figure 8b. On the other hand, the additional stiffness excitations do positive work in Y-direction, and the total power is less than that in X-direction. Therefore, the distortion of line speed \(LS_{y}\) is not significant. Eventually, this leads to the RWS of the synthesis motion to reach the minimum value in position 4, as shown in Figure 4b. From position 4 to position 1 (process IV), it should be that the line speed in Y-direction tends to be the positive maximum value and the line speed in X-direction tends to be 0. However, as shown in Figure 9b, the additional stiffness excitation do positive work in X-direction at this time. This causes the line speed \(LS_{x}\) to increase at the beginning and then decrease, as shown in Figure 8b. On the other hand, the additional stiffness excitations do negative work in Y-direction, and the total power is less than that in the X-direction. Therefore, the distortion of line speed \(LS_{y}\) is not particularly significant except the slow increasing speed of \(LS_{y}\), as shown in Figure 8b. The above qualitative analysis shows that the work of the additional stiffness excitations affects the normal transformation rule between the kinetic energy and the potential energy of the rotor system, resulting in the distortion of the line speed and finally making the relative whirling speed which is synthetized by the line speed vary transiently. The explanation of transient characteristics can deepen the understanding of the dynamic characteristics of the cracked rotors.
## 3 Results and Discussion
### GNSS Localization
diverge from the straight line compared with the green line, representing the internal GNSS receiver. Moreover, one segment of the blue line--between 604,561 m and 604,557 m--overlaps with the straight line. However, there is no overlap between the straight line and the GNSS data. The results from Figure 8 are quantified in Table 2. The columns in Table 2 represent two receivers and correspond to the blue and green lines in Figure 8. Table 2 shows the average deviation from the straight line calculated from the set of 22 samples. Table 2 complements Figure 8 and confirms that the blue line, representing the DGNSS receiver, is much closer to the ideal line (with a 21 cm mean deviation) than the green line, representing the GNSS receiver, which has a 119 cm deviation. Interestingly, the green line has a slope and direction that are similar to the ideal line but shifted by approximately 2 m.
## 2 Methods and Tools
### Satellite Images Time Series Analysis  
Monitoring the Dead Sea's environmental dynamics relies on employing diverse methodologies and tools, with Geographic Information Systems (GIS) serving as a cornerstone for diachronic analysis. GIS integrates multi-temporal data, enabling researchers to track surface changes over decades and analyze complex interactions between natural and anthropogenic processes. Historical imagery from the Corona program (1960s-1970s), with resolutions of 1.8-7.5 m, offers critical benchmarks for assessing early hydrological and geological changes. GIS facilitates the alignment of these datasets with more recent sources, such as
## 3 Numerical Micromodulus
### One-Dimensional Numerical Micromodulus  
Consider a bar subjected to tension load, the spacing between material points is \(\Delta x\). The volume of the material point is \(\left(\Delta x\right)^{3}\). In the PMB model, bond force is considered as spring force. When \(\delta=2\Delta x\), a set of bonds can pass through or end in the cross-section \(A_{i}\) of point \(X_{i}\) from a positive direction, as illustrated in Figure 3. The total bond force per unit volume acting through \(A_{i}\) is written as Equation (9),  
\[f_{V}^{L}=\sum_{j=1}^{k}f(\eta,\xi)V_{j}=2 fV_{j}=2c_{1}sV_{j} \tag{9}\]  
in which \(k\) is the number of bonds and \(c_{1}\) is the numerical micromodulus of the 1D model.  
Thus, the resultant force is  
\[F=2c_{1}sV_{j}V_{i} \tag{10}\]  
Then, the PD stress \(\sigma_{x}\) at \(X_{i}\) is  
\[\sigma_{x}=F/A_{i}=2c_{1}s\Delta x^{4} \tag{11}\]  
Figure 3: Bonds acting at the cross-section \(A_{i}\) of point \(X_{i}\) (\(\delta=2\Delta x\)).  
According to [PERSON]'s law, the peridynamic elasticity modulus \(E_{PD}\) can be given as  
\[E_{PD}=\frac{\sigma_{x}}{s}=2c_{1}\Delta x^{4} \tag{12}\]  
Since the elastic modulus is an inherent property of the material, \(E_{PD}\) equals to the elasticity modulus \(E\) in isotropic material; the \(c_{1}\) in \(\delta=2\Delta x\) is obtained as  
\[c_{1}=\frac{E}{2\Delta x^{4}} \tag{13}\]  
Similarly, \(c_{1}\) for different horizons are listed in Table 1.
# Experimental Study of Wave-Induced Response of Piles in Seabed with Various Permeability
## 2 Testing Arrangements and Programs
### Data Acquisition  
The miniature pressure sensors used (provided by Peneson; 6 mm in outer diameter and 20 mm in length) had a measurement range of 20 kPa with full-scale accuracy of \(\pm\)0.1% and were installed along a vertical line at a distance of 1.5 \(D\) away from the pile axis and at depths of 0.05 m, 0.15 m and 0.25 m below the mudline. Pore-pressure signals were sampled and amplified through a signal-processing device (USB-2533) and DAQ-pro data acquisition software. The laser displacement sensor (optoNCDT1402-20; manufactured by Micro-Epsilon) used in the experiments had a measurement range of 20 mm, with a resolution of 0.002 mm and a maximum sampling rate of 1.5 kHz. The sampling locations for pile-head displacement were at the pile front and 20 mm below the pile top. Wave-height gauges, with a measurement range of 0.60 m and a measurement precision of 0.1 mm, were located along the central axis of the wave flume and in front of the piles. A remote computer was connected to the servo system and acquisition system to synchronously record the signals from the pore-pressure sensors, displacement sensors and wave-height gauges, using a typical sampling frequency of 50 Hz. All the sensors employed in the experiments were capable of providing adequate measured data to investigate pile-seabed interactions under wave loading.
## 2 Materials and Methods
### Model Construction
The variables, along with their definitions and abbreviations, are summarized in Table 2. The model expression is as follows:  
\[MR_{ijt} =\beta_{0}+\beta_{1}S\_{rpopu}_{it}+\beta_{2}D\_{rpopu}_{it}+ \beta_{3}S\_{edut}_{it}+\beta_{4}D\_{edut}_{it}\] \[\quad+\beta_{5}S\_{pgd}_{it}+\beta_{6}D\_{pgd}_{it}+\beta_{7}S\_ {health}_{it}+\beta_{8}D\_{health}_{it}+\beta_{9}S\_{internet}_{it}\] \[\quad+\beta_{10}D\_{internet}_{it}+\beta_{11}S\_{ disaster}_{it}+\beta_{12}D\_{ disaster}_{it}+\beta_{13}S\_{topo}_{it}\] \[\quad+\beta_{14}D\_{topo}_{it}+\beta_{15}distance_{ij}+(\sum id )+\epsilon_{ijt}\]  
where \(MR_{ijt}\) denotes the total rural population migrating from province \(i\) to \(j\) at time \(t\); \(S\_{rpopu}_{it}\), \(S\_{edut}_{it}\), \(S\_{pgd}_{it}\), \(S\_{health}_{it}\), \(S\_{internet}_{it}\), \(S\_{ disaster}_{it}\), and \(S\_{topo}_{it}\) respectively represent the rural hukou population, educational attainment of the rural population, per capita GDP, number of beds in health facilities per 1000 people, internet penetration rate, natural disasters, and topographic relief of the emigration province \(i\) at time \(t\); \(D\_{rpopu}_{it}\), \(D\_{eudut}_{it}\), \(D\_{pgd}_{it}\), \(D\_{health}_{it}\), \(D\_{internet}_{it}\), \(D\_{ disaster}_{it}\), and \(D\_{topo}_{it}\) respectively refer to the rural hukou population, educational attainment of the rural population, per capita GDP, number of beds in health facilities per 1000 people, internet penetration rate, natural disasters, and topographic relief of the immigrating province \(j\) at time \(t\); \(distance_{ij}\) is the straight-line distance between emigration province \(i\) and immigrating province \(j\); and \(id\) and \(\epsilon\) are dummy variables and a random error term, respectively. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dimension** & **Variable** & **Definition** & **Abbreviation** \\ \hline Regional environment & Rural hukou population & Population according to the household registration system (Person) & \(S\_{rpopu}\)/\(D\_{rpopu}\) \\ \hline Individual characteristics & Educational attainment of rural population & Years of schooling per capita according to resident population (Year) & \(S\_{edut}\)/\(D\_{edut}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Preselected factors influencing the interprovincial migration of China’s rural population. Figure 1: Population migration model.