esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Joint Motion Planning Based on Modified Hermite Interpolation
### Cubic Hermite Interpolation  
Cubic Hermite interpolation is a commonly used interpolation method. There is \(n\) points to be interpolated, \((x_{0},\,y_{0})\), \((x_{1},\,y_{2})\),..., \((x_{n-1},\,y_{n-1})\). Cubic Hermite interpolation function of the \(i\)-th curve in the interval \([x_{i-1},\,x_{i}]\), whose interval is normalized, can be expressed as the following formula (17) in the normalized interval [21].  
\[y_{i}(u)=\left[1\ u\ u^{2}\ u^{3}\right]\left[\begin{array}{cccc}1&0&0&0\\ 0&0&1&0\\ -3&3&-2&-1\\ 2&-2&1&1\end{array}\right]\left[\begin{array}{c}y_{i-1}\\ y_{i}\\ \Delta_{i}\dot{y}_{i-1}\\ \Delta_{i}y_{i}\end{array}\right],u\in[0,1]\ (i=1,2,\cdots,n-1) \tag{17}\]  
where \(n\) is the number of interpolated points. \(u\) is the variable derived from variable \(x\), and the interval of \(u\) is [0,1] obtained by normalization of the interval \([x_{i-1},\,x_{i}]\). \(y_{i-1}\) and \(y_{i}\) are the two interpolated data points of the \(i\)-th interpolation curve. \(\dot{y}_{i-1}\) and \(\dot{y}_{i}\) are the two first-order derivatives of \(y\) with respect to \(x\) at the points \(x=x_{i-1}\) and \(x=x_{i}\). \(\Delta_{i}\) is the interval span without normalization, \(\Delta_{i}=x_{i}-x_{i-1}\). According to formula (15), \(\Delta_{i}\dot{y}_{i-1}\) and \(\Delta_{i}\dot{y}_{i}\) are the two first-order derivatives of \(y\) with respect to \(u\) at the points \(u=0\) and \(u=1\).  
According to Equations (13) and (14), formula (17) can be rewritten as the following formula (18), and the variable interval is changed from [0,1] to \([x_{i-1},\,x_{i}]\). The piecewise cubic Hermite interpolation curve is shown in Figure 6.  
\[y_{i}(x)=\left[1\ \frac{x-x_{i-1}}{x_{i}-x_{i-1}}\ (\frac{x-x_{i-1}}{x_{i}-x_{i-1}} )^{2}\ (\frac{x-x_{i-1}}{x_{i}-x_{i-1}})^{3}\right]\left[\begin{array}{cccc}1&0&0 &0\\ 0&0&1&0\\ -3&3&-2&-1\\ 2&-2&1&1\end{array}\right]\left[\begin{array}{c}y_{i-1}\\ y_{i}\\ (x_{i}-x_{i-1})\dot{y}_{i-1}\\ (x_{i}-x_{i-1})\dot{y}_{i}\end{array}\right],x\in[x_{i-1},x_{i}]\ (i=1,2, \cdots,n-1) \tag{18}\]  
It can be seen from formula (17) or formula (18), C\({}^{0}\) and C\({}^{1}\) continuity can be guaranteed as long as positions and first-order derivatives at the connection point between every two adjacent piecewise curves are specified equal. Whatever the values of \(y_{0},\,y_{1},\,\ldots,\,y_{n-1}\) and \(\dot{y}_{0},\dot{y}_{1},\,\ldots,\,\dot{y}_{n-1}\) are assigned to, the continuity of \(y(x)\) and \(\dot{y}(x)\) is guaranteed.  
However, arbitrarily specified \(\dot{y}_{0},\,\dot{y}_{1},\,\ldots,\,\dot{y}_{n-1}\) cannot guarantee the continuity of the second-order derivative \(\ddot{y}(x)\). Therefore, in order to guarantee C\({}^{2}\) continuity of cubic Hermite curves at each knot, \(\dot{y}_{0},\dot{y}_{1},\,\ldots,\,\dot{y}_{n-1}\) must satisfy certain conditions.
## 5 Discussion
### Results and Efficiency Improvement  
The application of the proposed method in the Xiong'an New District project, especially the Xiong'an Park Main Venue project, has demonstrated notable improvements in efficiency. In the initial situation, the lack of standardized information management protocols led to inefficiencies such as data format incompatibility, difficulty in extracting and sharing useful information, and poor coordination among different units. This often resulted in project delays, increased costs, and compromised quality.  
With the implementation of our multi-scale information collaborative management method, significant changes have occurred. The format conversion and standard unification at the micro-meso level have enabled seamless data exchange between contractor units and construction units. Contractors can now provide construction units with more accurate and standardized data in a timely manner, facilitating better decision-making and project control. For instance, the unified coding system allows construction units to quickly access and analyze key data such as room areas and building heights, enhancing their ability to monitor and manage the construction process effectively.  
At the meso-macro level, the integration of BIM and GIS models and data have provided government units with a more comprehensive and accurate understanding of the regional planning situation. The government can now make more informed decisions regarding land use, infrastructure planning, and overall regional development. The institutional guarantee and contract constraint mechanisms have also enhanced the compliance and cooperation of all parties involved, further improving the overall efficiency of the project.  
Although comparative figures are not available in all aspects, the qualitative improvements are evident. The elimination of information barriers and the enhanced communication and collaboration among different units have significantly reduced the time and effort required for information transfer and coordination. This has led to a smoother project execution process, with fewer errors and rework, ultimately contributing to cost savings and improved project quality.
## 2 Materials and Methods  
### Sample Fabrication  
Glass-ceramic powder was obtained from a commercial glass-ceramic, Ceran Suprema(r), manufactured by Schott. Next, 1 wt% and 4 wt% of Er\({}_{2}\)O\({}_{3}\), ultra-pure 99.99% (Sigma-Aldrich, St. Louis, MO, USA) were mixed and isostatically pressed at 200 MPa for 3 min and sintered at 1200 \({}^{\circ}\)C for 12 h to obtain the precursor rods. Glass samples were obtained departing from these erbium-doped glass-ceramic precursors by means of the laser floating zone (LFZ) technique. This manufacturing technique has been described elsewhere [29; 30; 31]. Glass samples were obtained at a growth rate of 300 mm/h, which provided both high axial and radial cooling gradients to manufacture glass samples. In addition, the manufacturing process was carried out in a semi-sealed chamber, which allowed working in different atmospheres such as oxygen, nitrogen, and air. Specifically, Er-doped glasses were fabricated in oxygen atmosphere to obtain samples with high optical transmission. Taking into account the theoretical weight percentage the samples were doped with, from now on they will be named as Er1 and Er4.
# Enhanced IoT-Based End-To-End Emergency and Disaster Relief System
## 4 Crow\({}^{2}\) System Implementation
### Body-To-Body Communication
#### 4.2.1 Android Mobile Devices  
These devices are designed based on the ORACE-Net Android application, which is a mobile app coded in Java and deployed on Android v4.2.2 CyanogenMod 10.0 distribution. This mobile app is dedicated to route data through the emergency network based on the ORACE-Net routing protocol. The ORACE-Net Android application is implemented at the user level as depicted in Figure 6a. It exploits the features of the Linux operating system at the kernel layer through the Dalvik Virtual Machine. Figure 6b depicts the ORACE-Net mobile application components, which are: (1) events listener, (2) broadcast receivers, (3) services, (4) content providers and (5) display activities. The relevant component in the architecture is the events listener, which triggers the rest of the tasks. An events listener is used to catch events (e.g., unicasted, multicasted or broadcasted packets, clicked button, typed text, etc.). In the ORACE-Net Android application, the events listener is implemented as a socket with a multi-cast IP address/Port: 224.0.0.1/10000. A similar socket is implemented with the C-language on Linux for the tactical deployed devices. Received packets through the events listener  
Figure 4: **(a)** A screen-shot from the Labeeb-IoT Shimmer sensing mobile app, which collects data from Shimmer [31] sensors and pushes them to the Internet of Things platform (Labeeb-IoT). **(b)** Testbed: a photo of the ORACE-Net mobile devices displaying the real-time events (received “Hello” and Advertisement (“ADV”) packets) and the current route. **(c)** The Labeeb-IoT [32] interface shows the variation of the sensed data from the Shimmer sensor connected to the mobile node.  
are handled by the broadcast receivers component to be hulled. Particularly, the content provider allows the application to share the application output with other servers or platforms. Figure 4b is a screenshot of the ORACE-Net mobile app showing the received/transmitted Hello and Advertisement (ADV)packets, the next-hop and the hop count to the CC node.  
Figure 5: Experimentation scenario and data flow from deployed nodes to the Labeeb-IoT platform. The Command Center (CC node) is placed at the Back Gate (BG); ORACE-Net Mobile Devices (OMD) are mobile devices carried by the rescuers to which Shimmer sensors are connected via Bluetooth. The tactical ORACE-Net network is established through ORACE-Net Linux Tactical Devices (OTD). All collected data go through the CC node to the Labeeb-IoT platform. A real-time dynamic topology website instantly displays the network topology.  
Figure 6: (**a**) ORACE-Net system-oriented stack over Linux and Android. (**b**) ORACE-Net Android application architecture.
## 3 Reliability Models of Electric Distribution System Components
### Information and Communication Devices
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**ICT** & **Protocols** & **Data Type** & **Function** & **DOD** & **Layers** & **OSI** \\
**Devices** & HTTP, FIT, Telnet, SMB, SMTP, SNMF, DNS, DNS, DNC, NPS & User Data & Provides services directly to the user applications; identities communication participant, dedicates quality of service, verify user authentication and privacy and addressing adequate nowadays availability & & & 7 \\ \hline Gateway & HTTP, FFE, Telnet, SMF, AFIP, TD1 & Encoded User Data & Provides data translutations with encoding, formatting, compression and encryption services; provides a common interface for user applications & & 6 \\ \hline Gateway & TCP, UDP; SPX, NetBBUI & Seasons & Establles, maintains and remnants connections between applications enable two approaches to communicate over a network by opening a session and synchronizing the participating hosts & & 5 \\ \hline Gateway & IP, IPX, NNLink, NetBBUI & Datagram/Segments & Provides reliable data transfer between hosts including flow control, segmentation, multiplexing, error control, transmission performance, maintaining quality of service functions & & 4 \\ \hline Gateway & IP, IPX, NNLink, NetBBUI & Datagram/Pockets & Establles, controls and participants network connectives; stratums and manages multi-node networks including traffic control, addressing, networking, networking, forwarding, forwarding, forwarding, and packet supervincing, translates logical network Address through physical machine address & & 3 \\ \hline Gateway & IEEE802, 2, LC, Ethernet, PDOL, Token Ring, IFEN02, 2, ILC, Ethernet, PDOL, Token Ring, IFEN02,11, (WLAN, WFIF), PTPC, DLC & Frames & Provides data transfer between nodes connected by a physical layer; divided into two subversive Media Access Control (MAC) layer provides data access and transmission, specified Link Control (LC) layer controls frames synchronization and error checking & 2 \\ \hline Gateway & IEEE802, IEEE802, 2, ISO 2110, Ethernet, FDOL Token Ring & Bits & Defines physical specifications of connection, communication medium, transmission mode, connection protocol control transmission and reception of data bit streams over the physical medium & 1 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of ICT equipment in CSI model [(67; 68; 69)]. Communication network equipment can fail causing interruptions in data transfer, information exchange and other corresponding services. Both hardware and software can be affected for various reasons, impacting the reliability of the communication network. The reliability parameters like mean time between failure (MTBF) and mean time to repair (MTTR) for chosen devices: phasor measurement unit (PMU), remote terminal unit (RTU), programmable logic controller (PLC), gateway and router are given in Table 8. These values are calculated based on literature research on simulation models, laboratory tests and vendors' data presented in [70; 71; 72].
# Effect of Diagonal Pattern Training on Trunk Function, Balance, and Gait in Stroke Patients
## 3 Result  
The general characteristics of the participants are shown in Table 3. In the control group, both the experimental and the control groups showed a significant increase in the TIS score, except for the coordination (Table 4). In addition, the experimental group displayed a significant increase in the static, dynamic, coordination, and total score of TIS compared to the control group (Table 4).  
Both the experimental and the control groups displayed a significant increase in the BBS and 10 MWT (Table 5). In addition, the experimental group showed a significant increase in the BBS and 10 MWT when compared to the control group (Table 5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **Experimental Group (n = 21)** & **Control Group (n = 21)** & **F** & \(p\) \\ \hline TIS (score) & Before & After & Before & After & \\ Static (7) & 6.19 \(\pm\) 0.68 & 6.76 \(\pm\) 0.44 [1, 2] & 6.29 \(\pm\) 0.64 & 6.52 \(\pm\) 0.60 [1] & 25.130 & 0.01 *.a \\ Dynamic (10) & 3.24 \(\pm\) 0.54 & 3.81 \(\pm\) 0.68 [1, 2] & 3.62 \(\pm\) 0.74 & 3.81 \(\pm\) 0.75 [1] & 29.091 & 0.01 *.a \\ Coordination (6) & 1.52 \(\pm\) 0.51 & 2.05 \(\pm\) 0.81 [1, 2] & 1.76 \(\pm\) 0.44 & 1.90 \(\pm\) 0.54 & 15.806 & 0.01 *.a \\ Total (23) & 10.95 \(\pm\) 1.40 & 12.62 \(\pm\) 1.28 [1, 2] & 11.67 \(\pm\) 1.39 & 12.24 \(\pm\) 1.22 [1] & 70.575 & 0.01 *.a \\ \hline \hline \end{tabular}  
* \(p<0.05\), 1 There was a significant difference between before test and after test (\(p<0.05\)), 2 The experimental group improved more than the control group (interaction), 3: Analyzed by two-way repeated measures ANOVA, TIS: trunk impairment scale, Experimental group: diagonal pattern training group, Control group: single plane training group.  
\end{table}
Table 4: Comparison of TIS in before and after.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Experimental Group (n = 21)** & **Control Group (n = 21)** & \(p\) \\ \hline Gender (male/female) & 16/5 & 14/7 & 0.495 \\ \hline Affected side (left/right) & 8/13 & 10/11 & 0.533 \\ \hline stroke type (infarction/hemorrhage) & 12/9 & 11/10 & 0.757 \\ \hline Onset (month) & 13.00 \(\pm\) 2.68 & 13.48 \(\pm\) 2.82 & 0.578 \\ \hline K-MMSE (point) & 26.14 \(\pm\) 1.62 & 26.33 \(\pm\) 1.71 & 0.713 \\ \hline Age (years) & 67.43 \(\pm\) 4.74 & 67.57 \(\pm\) 3.28 & 0.910 \\ \hline Height (cm) & 165.05 \(\pm\) 6.10 & 163.76 \(\pm\) 5.44 & 0.475 \\ \hline Weight (kg) & 69.86 \(\pm\) 6.60 & 70.43 \(\pm\) 6.22 & 0.774 \\ \hline \hline \end{tabular}  
* K-MMSE, Korean version of Mini Mental State Examination.  
\end{table}
Table 3: General characteristics.  
There was a significant increase in the cadence, speed, and stride length in both the experimental and control groups (Table 6). In addition, the experimental group had significantly increased the cadence, speed, and stride length than the control group (Table 6).
## 3 Results and Discussion
### Surface Modification for Antifouling Properties  
The objective of this study was to minimise the fouling of the microfiltration membrane, as shown in Figure 8. Fouling tends to reduce the permeate flux and thus increases the transmembrane pressure (TMP) and operational costs [34; 35]. Surface modification is the most effective technique in this regard. Thus, experiments were conducted to investigate the influence of inorganic surface modification on the antifouling properties of multichannel microfiltration membranes.  
The microfiltration membrane was surface-modified by a sol-gel technique using TEOS to form a SiO\({}_{2}\) coating layer on the surface of the alumina particles.  
The normalised time-dependent flux patterns of the pristine and silica-coated MF membranes at each step of the process are presented in Figure 9. In step 1, all microfiltration membranes maintained a stable flux. In step 2, during the supply of foulant (HA), the flux rapidly decreased for each microfiltration membrane, followed by a slower decline with time. The flux decline ratios were then estimated. After backwashing in step 3, the membranes were again permeated using DI water in step 4, and the flux recovery ratios were calculated. The flux decline ratio of the pristine membrane was 63%, which was higher than that of the surface-modified membrane (52%). However, the flux recovery ratio was 94% for the pristine and SiO\({}_{2}\)-modified membranes owing to the backwashing operation.  
The isoelectric point (IEP) of \(\alpha\)-alumina is 7.0-8.0, and that of HA is 4.7. In the neutral pH range (6.5-7.0) of water, strong attractive forces were generated between the alumina surface and HA, which significantly facilitated the membrane fouling. In contrast, the isoelectric point (IEP) of SiO\({}_{2}\) is 1.0-2.0. Therefore, after the surface modification, the surface charge of the alumina membrane was altered, and strong repulsive forces were generated between the SiO\({}_{2}\)-modified membrane and HA, inhibiting the deposition of foulants. This proves the importance of surface modification.  
Figure 8: Cross-section of a multichannel cylindrical tube after pure water permeability test. (**a**) As-prepared support and (**b**) microfiltration membrane layer.
# AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems
## 3 Proposed SDR-Based LTE-LAA Architecture and Its Resource Management Scheme
### Traffic Model  
In this work, it is assumed that each user's traffic reaches the BS \(i\) in line with a Poisson process, with flow lengths of an average value \(l_{j}\) and an arrival rate \(r_{j}\). Thus, a user \(j\) generates an average traffic load density in a BS \(i\) according to the following:  
\[\varepsilon_{ij}=\frac{r_{j}\cdot l_{j}\cdot(x_{ij}+y_{ij})}{D_{ij}^{max}} \tag{8}\]  
where \(x_{ij}\) is a binary decision variable which shows if the \(j\)th user is connected to the \(i\)th BS or not. Hence, we have  
\[x_{ij}=\begin{cases}1&\text{if user $j$ is connected to BS $i$}\\ 0&\text{otherwise}\end{cases} \tag{9}\]  
In addition, \(y_{ij}\) takes on the value of 1 if at least one subcarrier is available at BS \(i\) and is 0 otherwise. Accordingly, \(y_{ij}\) is expressed as follows:  
\[y_{ij}=\begin{cases}1&\text{if $j$th user is connected with BS $i$ using unlicensed carrier}\\ 0&\text{otherwise}\end{cases} \tag{10}\]  
The average traffic load density for a BS \(j\) can be calculated by aggregating the average load densities for all users connected to this BS as follows:  
\[\varphi_{i}=\sum_{j\in I}\varepsilon_{ij} \tag{11}\]  
While the users' traffic arrival rate \(r_{j}\) is in line with the Poisson process (PP), the time of service for each user's load follows the general distribution. Thus, a BS \(i\) realizes a sharing queue of the M/G/1 process type. The BS's user is expected to generate a data load \(l_{j}\) with a data rate \(D_{ij}\). Additionally, \(\varphi_{i}\) needs to be less than 1 to have a stable queue. In this queue, the average time of service (in seconds) for a user \(j\) is calculated as follows:  
\[T_{ij}=\frac{l_{j}}{D_{ij}^{max}\cdot(1-\varphi_{i})} \tag{12}\]  
The effective data rate (i.e., throughput in bps) for each user can be expressed as follows: [52; 53]:  
\[D_{ij}^{eff}=D_{ij}^{max}\cdot(1-\varphi_{i}) \tag{13}\]  
where \(D_{ij}^{eff}\) is the effective data rate for each user \(j\) connected to a BS \(i\). Thus, the total throughput of a BS \(i\) is expressed (in bps) as follows:  
\[\eta_{i}=\sum_{j\in I}D_{ij}^{eff} \tag{14}\]
# How Long Does It Take to Stop? Are Children Able to Stop on Demand?
## 3 Method
### Data Analysis  
Figure 3 illustrates the theoretical speed-distance characteristic. The children begin at the starting point (SP) and accelerate to the target speed. It is assumed that they maintain a constant steady-state speed until they reach the third light beam (LB3). Following the third light beam, an acoustic signal is given, and the deceleration phase commences.  
Formula (1) was used to calculate the speed at steady state, taking into account the distance between the second (LB2) and third light beam (LB3) (\(s_{3}-s_{2}\)) and the time difference captured at the light beams LB2 and LB3 (\(t_{3}-t_{2}\)) being taken into account. The distance traversed by the children from the initial location to their final position was meticulously measured. The deceleration from the acoustic signal to the rest position was calculated based on the speed and the distance moved (Formula (2)). Deceleration time was not measured separately from the reaction time to the signal.  
\[v\ =\ \frac{s_{3}-s_{2}}{t_{3}-t_{2}} \tag{1}\]  
\[a\ =\ \frac{v^{2}}{2(s_{EP}-s_{AS})} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **6 Years** & **7 Years** & **8 Years** & **9–10 Years** \\ \hline Number of children & 32 & 17 & 23 & 21 \\ Females & 11 & 8 & 11 & 9 \\ Males & 21 & 9 & 12 & 12 \\ Weight [kg] & 24.4 (SD = 4.0) & 26.9 (SD = 6.2) & 32.4 (SD = 7.0) & 36.9 (SD = 8.0) \\ Height [cm] & 121.3 (SD = 5.0) & 127.3 (SD = 6.6) & 134.6 (SD = 6.6) & 142.3 (SD = 8.1) \\ Leg length [cm] & 71.3 (SD = 3.8) & 76.1 (SD = 4.3) & 81.3 (SD = 5.1) & 86.7 (SD = 5.3) \\ \hline \end{tabular}
\end{table}
Table 1: Physiological parameters including the standard deviation of the different age groups (Separate tables for girls and boys can be found in the Appendices A and B).
# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 2 Methods
### Machine Learning for Aquatic Vegetation Classification
#### 2.2.6 Extracting GPS Coordinates from Images Post-Classification  
In conjunction with image classification, precise location of identified plant species is necessary for efficient treatment and recordkeeping. To create the required location database, the GPS coordinates superimposed on each hydroacoustic image (Figure 12) were extracted using the optical character recognition (OCR) functionality of MATLAB. Image preprocessing techniques, namely cropping, resizing, gray scaling, and binarizing, enabled extraction of accurate GPS coordinates from the classified images (Figure 15).
# Higher-Order Belief Propagation Correction Decoder for Polar Codes
## 2 Preliminaries  
### Polar Codes  
Polar codes are linear block codes based on the channel polarization phenomenon. After channel combination and splitting, \(N\) channel copies are converted to \(N\) polarized sub-channels, where the capacity of each sub-channel approaches 1 or 0 as \(N\) increases. The polar codes select \(K\) reliable sub-channels to transmit information bits, and the remaining sub-channels transmit frozen bits. The sets of information indices and frozen indices are denoted by \(\mathcal{I}\) and its complement \(\mathcal{I}^{C}\), respectively. For shorthand, we use \(a_{i}^{j}\) to denote the vector \((a_{i},a_{i+1},...,a_{j})\). The codeword \(x_{1}^{N}\) can be mapped by the generator matrix \(G_{N}\):  
\[x_{1}^{N}=u_{1}^{N}G_{N}, \tag{1}\]  
where \(N=2^{n}\), \(G_{N}=R_{N}F^{\otimes n}\). The \(F^{\otimes n}\) is the \(n\)-th Kronecker power of \(F=\left(\begin{array}{cc}1&0\\ 1&1\end{array}\right)\) and \(R_{N}\) is a \(N\times N\) bit-reversal permutation matrix. The source vector \(u_{1}^{N}\in\{0,1\}^{N}\) includes both information and frozen blocks.