esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comprehensive Analysis of the Orientation Criterion under Three Dominant Stress Fields
### \(\sigma_{\rm Z}\)-Dominant Stress Field Orientation Criterion  
In the \(\sigma_{\rm Z}\)-dominant stress field, when \(\alpha\) is 0\({}^{\circ}\), the roadway is in a high-deviatoric-stress environment, which creates conditions for the formation of a butterfly plastic zone. With the increase in the axial angle \(\alpha\) of the roadway, the confining pressure ratio gradually increases (close to 1), and the butterfly risk gradually decreases. When \(\alpha\) is 90\({}^{\circ}\), although the confining pressure ratio of the roadway is closest to 1, due to the influence of the axial stress of the roadway, the size of the plastic zone of roadway surrounding rock is slightly larger than 50\({}^{\circ}\) but the shape is slightly affected by axial stress, which is lateral ellipse and easy to support. When \(\alpha\) is about 50\({}^{\circ}\), the influence of the confining pressure ratio begins to increase, and the butterfly risk increases sharply with an increase in the angle.  
Therefore, in the \(\sigma_{\rm Z}\)-dominant stress field, the roadway is a butterfly-free stable area within the 50-90\({}^{\circ}\) range, and the requirements for the surrounding rock support of the roadway are not high. In the 0-40\({}^{\circ}\) range, it is a butterfly-shaped risk area, where it is difficult to provide support and there is a high risk of roof fall.  
Figure 12: Variation in the depth of the plastic zone with axial angle \(\alpha\).
# Nanoformulation of Peptides for Pharmaceutical Applications: In Vitro and In Vivo Perspectives
## 1 Introduction  
Peptides are essential for the general functioning and health of higher living organisms [1]. They play major roles in living organisms (especially in humans) as peptide hormones, such as vasopressin and defensins [2]. Peptide-based drugs have been widely used in pharmaceutical applications for the treatment of various diseases [3]. Further, peptides are easily synthesized with good scalability compared to other biomolecules [4]. They offer several benefits, such as drug delivery agents, due to their high cellular circulation, accumulation/penetration for cell internalization, and cytosolic drug release for improved disease treatment [5]. There are increasing applications of peptides in drug designs and deliveries that explore the in vitro and in vivo properties of peptides [6]. However, naked peptide-based drugs are reported to have challenges, such as premature disintegration before reaching the target site, low stability, and poor membrane permeability [7].  
The advancements in the field of nanotechnology have led to several novel nanoparticles and nanomaterials that are able to overcome the limitations of naked peptides in disease treatment [8]. Nanostructured materials have allowed for the controlled release of drugs for drug delivery, i.e., toward target cells for efficient disease therapy [9]. Further, the encapsulation of peptides in nanoparticles has the potential to address challenges associated with peptide stability during circulation, providing better membrane permeability,and promoting tumor targeting effects with minimal adverse impacts [10]. Peptides can also be modified and self-assembled into nanosized particles, such as nanomedicine, to improve their pharmacokinetic profiles [11]. This article provides an overview of peptides, recent trends in their synthesis, purification, and characterization, as well as their nanoformulation mechanisms. In addition, recent developments in the in vitro and in vivo analyses of nanoformulated peptides for pharmaceutical applications are discussed.
## 2 The Classical Estimators of the Parameters and Reliability
### Maximum-Likelihood Estimation  
Let \(\underline{x}=(x_{1},x_{2},\cdots,x_{n})^{\prime}\) denote a sample of size \(n\) from the Burr XII distribution; therefore, the likelihood function is computed as  
\[L(\alpha,\beta;\underline{x})=\prod_{i=1}^{n}f(x_{i};\alpha,\beta)=\alpha^{n} \beta^{n}\prod_{i=1}^{n}x_{i}^{\alpha-1}(1+x_{i}^{\alpha})^{-\beta-1}, \tag{5}\]  
and the log-likelihood function is  
\[\ln L(\alpha,\beta;\underline{x})=n\ln\alpha+n\ln\beta+(\alpha-1)\sum_{i=1}^ {n}\ln x_{i}-(\beta+1)\sum_{i=1}^{n}(1+x_{i}^{\alpha}). \tag{6}\]  
Consequently, the derivative of Function (6) concerning \(\beta\) is  
\[\frac{\partial\ln L}{\partial\beta}=\frac{n}{\beta}-\sum_{i=1}^{n}\ln(1+x_{i} ^{\alpha}). \tag{7}\]  
The second-order derivative of Function (6) is expressed as  
\[\frac{\partial^{2}\ln L}{\partial\beta^{2}}=-\frac{n}{\beta^{2}}<0, \tag{8}\]  
which means that \(\hat{\beta}_{MLE}\) exists and is unique.  
We set Function (7) to zero in order to find the value of \(\beta\) that maximizes Function (6), which is also the MLE of \(\beta\). It is obtained as follows:  
\[\hat{\beta}_{MLE}=\frac{n}{\sum_{i=1}^{n}\ln(1+x_{i}^{\alpha})}, \tag{9}\]  
where \(\alpha\) is supposed to be known.  
According to the unique invariance property of the MLE, \(\hat{R}_{MLE}(t)\) is naturally computed as  
\[\hat{R}_{MLE}(t)=(1+t^{\alpha})^{-\hat{\beta}_{MLE}}. \tag{10}\]
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
## 5 Testbed  
To implement our model, we base our work on the diagram of a LPWAN defined in [1], which considers four blocks: end device, radio gateway, network server, and application server, as shown in Figure 6.  
For the end device, we used a LoPy4 node. This node is a development board that supports four types of networks (LoRa, SigFox, WiFi, Bluetooth). The Firmware was developed in the built-in MicroPython intepreter, an efficient version of the Python3 programming language that includes a small subset of the Python standard library and is optimized to run on microcontrollers with limited resources, such as the ESP32 microcontroller.  
The specifications of this development board are the following:  
* core 32
- bit LX6 microprocessor (s), up to 600 DMIPS;
* Memory: RAM: 520 KB + 4 MB and External flash: 8 MB;
* WiFi: 802.11b/g/n 16 mbps;
* RTC: Running at 150 kHz;
* Security: SSL/TLS and WPA Enterprise security support;
* Hash/encryption: SHA, MD5, DES, AES.  
For the radio gateway, we used a LoRaWAN(tm)gateway model RAK7240 from RAK Wireless [14]. The gateway is designed for outdoor use and comes with backhaul connectivity based on Wi-Fi, LTE, and Ethernet technologies, enabling multiple connectivity options to fall back on in the event of a network outage. The management and configuration firmware is based on OpenWRT. This gateway has dual LoRa concentrators for up to 16 channels, supports version 1.0.3 of the LoRaWAN(tm)stack, and is designed for the AU915 band ranging from 915 MHz to 928 MHz.  
The network server we used is the The Things Network (TTN). The Things Network is a global network of gateways that deliver LoRaWAN coverage worldwide. The gateways are connected to the network servers of TTN from which the information sent by each sensor or end device can be obtained. The TTN's backend handles message duplication, download message orchestration, and the management of platform integration.  
A Linux server with a version of Ubuntu was used as the application server. This server has the Flask framework installed, which allows Python code PySCHC to be executed from an HTTP request. It should be noted that PySCHC is a Python implementation of the SCHC standard based on RFC8724 (SCHC: Generic Framework for Static Context Header Compression and Fragmentation) and RFC9011 (Static Context Header Compression and Fragmentation (SCHC) over LoRaWAN) and is implemented on both the end device side and the application server. The source code of the SCHC ACK-on-Error fragmentation mode is fully available in ([[https://github.com/niclabs/PySCHC](https://github.com/niclabs/PySCHC)]([https://github.com/niclabs/PySCHC](https://github.com/niclabs/PySCHC))) (accessed on: 26 December 2021).
## 1 Introduction  
Piezoelectric motors have been seen as successors of the electromagnetic ones in areas such as robot joints, high precision machines, micro robots and MEMS. The commercial application in a camera auto-focus system has verified its unique advantage. Furthermore, it has been successfully used in lunar rover Yutu of China, which also shows its advantage for driving aerospace mechanism as the merits of simple structure without coils, quick response at millisecond, high precision and resolution at nanometer, low speed without reducer, self-locking in the power-off state and absence of electromagnetic radiation [1; 2; 3].  
According to the output motion modes, piezoelectric motors can currently be classified into: rotary [4; 5; 6; 7; 8], linear [9; 10; 11; 12; 13] and multi-degree-of-freedom motors [14; 15; 16; 17; 18]. The first type has attracted more attention because of its higher applicability in the above system. Most piezoelectric motors obtain rotary actuating by generating a traveling wave in a metal ring or cylinder [19; 20]. The ring-shaped traveling wave piezoelectric motor is the most classic and successful one for rotary driving; but it has a complicated operating principle as it needs to generate two bending standing waves with the same frequency, the same amplitude, a temporal shift of 90\({}^{\circ}\) and a spatial distance of one quarter wavelength. These requirements make it difficult to be fabricated and controlled. On the other side, the axial and circumferential displacements of the driving teeth are produced by the traveling wave in the stator: one is used to overcome the preload while the other one push the rotor. However, these two displacements are coupled, since they are from one traveling wave, which limits its flexibility on mechanical output performance adjustment.  
In this study, we report a novel rotary piezoelectric motor using first bending hybrid transducers. Three transducers are distributed uniformly along the circumferential direction to push a disk-shaped rotor into rotation, and the elliptical movement of the driving tip is produced by the superimposing of two orthogonal first bending modes. The proposed motor has obvious advantages compared with the classic ring-shaped traveling wave piezoelectric motor as the horizontal and vertical vibrations of the driving tip are generated independently by the horizontal and vertical bending modes, which gives the proposed motor good flexibility on mechanical output adjustment. Furthermore, the dimensions of the bending transducer can be adjusted freely to satisfy different requirements for space and weight, and more transducers can be set into the motor to increase its output torque.
## 5 Data Analyses and Results
### DIC Analyses
#### 5.2.2 Analyses of the Temporal Resolution Effect  
The regions of the processed imagery affected by the temporal resolution were similar to those affected by a low SNR. This matter was investigated through an analysis of the ascending CSK SAR absolute amplitude dataset. In Figure 8, the gradual intensification of the spatial extent of the decorrelated signal is shown. An analysis and comparison of the available images among the datasets showed that the random speckle pattern, a feature of the SAR absolute amplitude images, was always affected by different gray levels and tonal degrees. In fact, because of the different data collection times, the areas surrounding the Montescalioso slope were characterized by important gray level, texture and tonal changes unrelated to ground movement.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & & **Approach \#1** & **Approach \#2** & **\(\Delta\) (Approach** \\  & **Sensor** & **Image** & **Noise-Mean** & **Noise-Mean** & **\#1-Approach \#2** \\ \cline{2-6}  & & & **Pixel** & **Pixel** & **Pixel** \\ \hline COSMO-SayMed (CSK) ASC & SAR & SAR absolute amplitude & -0.14 & -0.47 & 0.33 \\ \hline COSMO-SayMed (CSK) DESC & SAR & SAR absolute amplitude & -0.12 & -0.35 & 0.23 \\ \hline COSMO-SayMed (CSK) ASC & SAR & SAR temporal average amplitude & -0.12 & -0.10 & 0.02 \\ \hline COSMO-SayMed (CSK) DESC & SAR & SAR temporal average amplitude & -0.15 & -0.05 & 0.1 \\ \hline LANDSAT8 (CL-T18 & Multi- spectral & Panchromatic & -0.10 & -0.05 & 0.05 \\ \hline Digital Terrain Model (DTM) & LiDAR & Shaded relief & -0.81 & -0.80 & 0.01 \\ \hline Orthophoto & Optical & Optical high-resolution & -0.21 & -0.20 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Background noise retrieved by the region of interest (ROI) method: Approaches #1 and #2.  
Figure 8: DIC displacement maps retrieved from CSK SAR ascending absolute amplitude images. Here, an increase in the areas affected by the decorrelated signal from (A) to (D) (highlighted in yellow polygons) in the surrounding sectors of the Montescalioso landslide area (red polygons) is clearly visible. The time span between the pre-event (3 December 2013) and post-event images increases from the top left to bottom right: (**A**) 18 December 2013 (15 days); (**B**) 3 January 2014 (31 days); (**C**) 4 February 2014 (63 days); and (**D**) 8 March 2014 (95 days).  
Specifically, by using the ascending CSK SAR absolute amplitude dataset and analyzing the same ROIs previously used in the background noise estimation (specifically in approach #1) (Figure 6), a strategy has been adopted. With the aim of evaluating the percentage increase of the sectors affected by a decorrelated signal during the period, the DIC analyses performed between the last pre-failure image (3 December 2013, as the master image) and a number of post-failure images (from 18 December 2013 to 28 June 2014, as slave images) were considered (Figure 9, Table 3).
# An Efficient Routing Protocol for Quantum Key Distribution Networks
## 3 Simulation Results and Analysis
### Simulation Results of QOLSR and Analysis
(**d**) Communication level = 0.8.  
from transmitting to receiving is referred to as OWD. Although routing cost fluctuates during communication, the routing cost of QOLSR is always minimal, as shown in Figure 11. The routing cost of QOLSR is substantially lower than that of original OLSR and multi-SPF, as seen in Figure 12. As a result of the increased link-state awareness and path optimization suggested in this research, the quantum secure keys spent by routing packets is lowered. This cuts down on the waste of quantum secure keys. Herein, it can be seen from Figure 13 that the delay of QOLSR during communication is similar to that of multi-SPF, and it has been kept at a low level. The OWD of QOLSR is much lower than that of original OLSR, as illustrated in Figure 14, which further demonstrates that the quality of service in QKD networks with QOLSR has improved. Figure 11: Comparison of OLSR, multi-SPF, and QOLSR in real-time routing cost. (**a**) Communication level = 0.2. (**b**) Communication level = 0.4. (**c**) Communication level = 0.6. (**d**) Communication level = 0.8. Figure 12: Comparison of OLSR, multi-SPF, and QOLSR in routing cost. In summary, the simulation results show that QOLSR not only has better quantum secure key utilization, it also performs well in routing cost and OWD. This is statistically significant because in QKD networks, quantum secure key resources are very precious. A key reason for this is that the efficient link-state awareness mechanism of the routing protocol is based on the amount of remaining key resources, which reserves time for path switching and reduces packet loss. Moreover, efficient path optimization is based on key recovery capability, which reduces the frequency of path switching. As a result, the efficiency of the proposed QOLSR routing protocol is verified. Figure 14: Comparison of OLSR, multi-SPF, and QOLSR in one-way delay (OWD). Figure 13: Comparison of OLSR, multi-SPF, and QOLSR in real-time one-way delay (OWD). (**a**) Communication level = 0.2. (**b**) Communication level = 0.4. (**c**) Communication level = 0.6. (**d**) Communication level = 0.8.
## 3 Experimental Results  
### Dynamic Characteristics  
Dynamic characteristics were tested through free vibrations excited by the artificial pulse method. Table 4 provides the natural periods and the associated vibration modes. The first fundamental period was 0.319 s and the corresponding vibration mode was torsion, as shown in Figure 8. This was due to the special structural pattern, resulting in the small torsional stiffness. For this kind of hybrid structure, although the plan layouts are rather regular and symmetric, as shown in Figure 2, the stiffness and mass mainly distribute on the upper part of the structure in the vertical direction, easily generate the torsional effect. In addition, it was found that the torsion-translation period ratio (the ratio for the period of first torsion mode to that of first translation mode) is about 1.003, which was larger than 0.85. According to the Chinese seismic design code [16], the period ratio between the first torsional mode and the first translation mode should not be larger than 0.85 so as to prevent excessive structural torsion. This indicated that the torsion effect should not be neglected in the design of this kind of hybrid structure.  
Figure 7: Cyclic loading protocol of quasi-static tests (QST).
# Dynamic Prediction Model for Progressive Surface Subsidence Based on MMF Time Function
## 3 Case Study
### Dynamic Prediction Results  
Based on the progressive surface subsidence parameters calculation method and dynamic prediction model above, surface subsidence was predicted at 52, 84, 114 and 178 d after the first observation for the working panel 22101. Considering that the surface subsidence had basically stabilized at 178 days after the first observation, the measured data at 178 d were interpolated from the data at 164d and the data at 192 d.  
The parameters of the probability integral method (PIM) were obtained based on the final subsidence curve (the subsidence curve of 325 days after the first observation) [16], as shown in Table 2.  
During the process of dynamic prediction for four observation subsidence curves, each unit length along the strike main section direction can be 0.1\(H_{0}\) = 22 m [44]. The number of units for the prediction of four subsidence curves (A \(\rightarrow\) B (52 days, advance 100 m), A \(\rightarrow\) D (84 days, advance 150 m), A \(\rightarrow\) E (114 days, advance 190 m), A \(\rightarrow\) G (178 days, advance 410 m)) can be \(n_{1}\) = 5, \(n_{2}\) = 7, \(n_{3}\) = 9, \(n_{4}\) = 19, respectively. Equation (25) was applied for predicting the dynamic subsidence for the selected four subsidence. The prediction results are shown in Figure 14.  
According to Figures 12 and 14, with the advance of the working panel, the range of subsidence gradually increases, and eventually a flat bottom appears. During this process, the surface deformation is also changing, so the dynamic prediction is of great value for grasping the surface deformation in real time, enhancing the under  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(q\) & tan \(\beta\) & \(\Theta_{0}\)\({}^{c}\) & s\({}_{1}\)/m & s\({}_{2}\)/m & s\({}_{3}\)/m & s\({}_{4}\)/m \\ \hline
0.59 & 2.19 & 83.19 & \(-\)2.43 & \(-\)2.43 & 7.57 & 7.57 \\ \hline \multicolumn{8}{c}{The number of exploitation times:} & \multicolumn{6}{c}{First exploitation} \\ \hline \end{tabular}
\end{table}
Table 2: Prediction parameters of the PIM and number of exploitation times for working panel 22101.  
Figure 14: Comparison of measured data and prediction results based on MMF time function.  
movement and deformation, reasonably making the mining plan and protecting the surface buildings and structures.
## 2 Controller Design  
The traditional PID controller equation is defined as follows:  
\[u(t)=K_{p}e(t)+K_{i}\int_{0}^{t}e(t)dt+K_{d}\frac{de(t)}{dt} \tag{1}\]  
where \(u(t)\) is the output of PID controller; \(e(t)\) is the system error; and \(K_{p}\), \(K_{i}\), and \(K_{d}\) denote the coefficients for the proportional, integral, and derivative terms respectively.  
The PID controller is mainly suitable for systems whose basic characteristics are linear. Hence, when the traditional PID controller is applied to CNC systems with non-linear factors, the control performance will be worsened. The nonlinear transformation of control parameters in the NLPID controller ensures that the control parameters can be changed in real-time according to the error of the system. Then, the disturbance of nonlinear factors in the system can be handled well.  
The system error \(e(t)\) in the CNC system can represent the control effect in real-time so the control parameters in NLPID controller are converted to nonlinear functions of \(e(t)\), the equation is as follows:  
\[u(t)=K_{p}(e(t))\cdot e(t)+K_{i}(e(t))\cdot\int_{0}^{t}e(t)dt+K_{d}(e(t)) \cdot\frac{de(t)}{dt} \tag{2}\]The nonlinear transformations of \(K_{p}\), \(K_{i}\), and \(K_{d}\) can be determined with Equations (3)-(5) [6], as follows:  
\[K_{p}(e(t))=a_{p}+b_{p}\big{(}1-\text{sech}\big{(}c_{p}e(t)\big{)}\big{)} \tag{3}\]  
\[K_{i}(e(t))=a_{i}\cdot\text{sech}(b_{i}e(t)) \tag{4}\]  
\[K_{d}(e(t))=a_{d}+b_{d}\Big{/}\Big{(}1+c_{d}\cdot\text{e}^{d_{d}e(t)}\Big{)} \tag{5}\]  
where \(a_{p}\), \(b_{p}\), \(c_{p}\), \(a_{i}\), \(b_{i}\), \(a_{d}\), \(b_{d}\), \(c_{d}\), and \(d_{d}\) are the nine parameters of the NLPID controller to be optimized.  
Typically, \(K_{p}\) can reduce overshoot and increase the response speed of the system. If \(e(t)\) is large, \(K_{p}\) should be large, and \(K_{p}\) should be reduced when \(e(t)\) decreases to 0. \(K_{i}\) is primarily used to reduce steady-state error in the system. If \(e(t)\) is large, \(K_{i}\) should be small. If \(e(t)\) is small, the system is approaching or has reached a steady state, and \(K_{i}\) must be increased to eliminate steady-state error. \(K_{d}\) can reduce settling time and improve system stability. If \(e(t)\) is small, \(K_{d}\) should be large. Otherwise, \(K_{d}\) should be reduced to improve the sensitivity of the system.
## 2 Extended Pick Failure Probability Assessment Approach
### Change of the Pick Failure Probability over Time  
The failure probability prediction models given in the above sections show the relationship between the pick failure probability and drum revolutions. It may be desired to understand the change of the pick failure probability over time during mining production. This can be done by linking the revolution to time in the above models. As all revolutions are equally segmented, the relationship between the pick failure probability and time can be expressed using the following piece-wise function:  
\[F(t)\,=\,\left\{\begin{array}{c}0\,\ t=0\\ F_{s1},\ 0<t\leq T_{s}\\ \vdots\\ F_{sk},\ (k-1)T_{s}<t\leq kT_{s}\end{array}\right. \tag{28}\]  
In Equation (28), \(F(t)\) is the pick failure probability over time \(t,F_{s1}\) is the pick failure probability for cutting the first segment, and \(F_{sk}\) is the pick failure probability for sequentially cutting \(k\) segments starting from the first. This calculation needs to consider the cutting stages and the variation of rock thickness and can be done by using the models developed in the previous sections. Parameter \(T_{s}\) is the time required for a pick to cut one segment (sec) which is given by  
\[T_{s}=\ \frac{a_{s}}{6n}, \tag{29}\]  
where \(a_{s}\) is the central angle of a segment (in degrees).