esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in IoMT for Healthcare Systems
### Security and Privacy of Helthcare Data  
IoMT services must address the bring-your-own-device (BYOD) phenomenon, where patients and doctors use their personal devices to access medical data from the cloud. On the one hand, the use of BYOD policies makes IoMT services (such as e-visits, telemedicine, and remote patient monitoring) crowdsourced and cost-effective. On the other hand, it also brings major challenges to the security and protection of medical data. Therefore, we need to robustly deploy security and privacy controls (e.g., NIST SP800-30, ISO 27001, ISO/IEC Standard 27018 [5; 6]) to protect medical data prevalent on the Internet from becoming prey to hackers. In this context, many research topics have received attention, including, but not limited to, the following: risk assessment, device security, session security, cloud security, data protection, and authentication.
## 2 Basic Theory of the Non-Ordinary State-Based Peridynamics Model  
### Thermomechanical PD Equations  
In the PD theory, a continuum solid is discretized into a set of individual material points \(\mathbf{x}_{i},i=1,2,3,\ldots N\). Figure 1 illustrates the undeformed and deformed PD states related to \(\mathbf{x}_{i}\). Here, \(\mathbf{y}_{i}=\mathbf{x}_{i}+\mathbf{u}_{i}\) is the position in the deformed configuration, and \(\mathbf{u}_{i}\) is the displacement vector of point \(\mathbf{x}_{i}\). Furthermore, \(\Omega_{\mathbf{x}_{i}}\) is the neighborhood horizon of \(\mathbf{x}_{i}\) with a given radius \(\delta\), and \(\mathbf{x}_{j}\) is a neighborhood point. For mechanical problems, \(\underline{\mathbf{x}}\langle\mathbf{\xi}_{ij}\rangle=\mathbf{\xi}_{ij}= \mathbf{x}_{j}-\mathbf{x}_{i}\) is defined as the bond between two interacting points, and the corresponding deformation vector state is defined as \(\underline{\mathbf{Y}}\langle\mathbf{\xi}_{iij}\rangle=\mathbf{y}_{j}-\mathbf{y }_{i}\). For thermal problems, the heat temperature scalar state is defined as \(\underline{Q}\langle\mathbf{\xi}_{ij}\rangle=T_{j}-T_{i}\), where \(T_{i}\) and \(T_{j}\) represent temperatures related to points \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\).  
Figure 1: Schematic diagram of states and the volume of material point in the PD theory.  
According to pioneering works, the governing PD equations for thermomechanical problems at time \(t\) for material point \(\mathbf{x}_{i}\) are given by [48]:  
\[\rho\ddot{\mathbf{u}}(\mathbf{x}_{i},t)=\int_{\Omega_{\mathbf{x}_{i}}}\big{(} \mathbf{\dot{t}}[\mathbf{x}_{i},t]\big{\langle}\boldsymbol{\xi}_{ij}\big{\rangle} -\mathbf{\dot{t}}[\mathbf{x}_{j},t]\big{\langle}\boldsymbol{\xi}_{ji}\big{\rangle} \big{)}\mathrm{d}V_{\mathbf{x}_{j}}+\mathbf{b}(\mathbf{x}_{i},t), \tag{1}\]  
\[\rho c\dot{T}(\mathbf{x}_{i},t)=\int_{\Omega_{\mathbf{x}_{i}}}\big{(}\underline {\tau}[\mathbf{x}_{i},t]\big{\langle}\boldsymbol{\xi}_{ij}\big{\rangle}- \underline{\tau}[\mathbf{x}_{i},t]\big{\langle}\boldsymbol{\xi}_{ji}\big{\rangle} \big{)}\mathrm{d}V_{\mathbf{x}_{j}}+s_{\mathrm{b}}(\mathbf{x}_{i},t), \tag{2}\]  
where \(\rho\) is the density, \(\ddot{\mathbf{u}}\) is the acceleration vector, \(\dot{T}\) is the derivative of the temperature, \(\mathrm{d}V_{\mathbf{x}_{j}}\) is the volume associated with point \(\mathbf{x}_{j}\), \(\mathbf{b}\) is the external body force, \(c\) is the specific heat capacity, \(s_{\mathrm{b}}\) is the volumetric heat generation per unit mass, and \(\underline{\tau}\) and \(\underline{\tau}\) are the force vector state and heat flow scalar state related to the bond.
## 2 Hybrid Model Updating Method
### Latin Hypercube Sampling with Bounded Parameters  
Reasonable computer experimental design is necessary for establishing explicit relationship between selected parameters and interested responses of FE model. The simplest method to generate the input data sample is random sampling, but a significant drawback of the method is the lack of guarantee that the chosen data covers the entire design space uniformly. To overcome this shortage, Latin hypercube sampling (LHS), instead of methods of orthogonal design, uniform design, etc., has simpler operation, smaller calculation, higher accuracy, and can rapidly generate samples with interested number of parameters and dimensions [1; 27].  
For uncertain but bounded parameters, supposing \(\mathbf{X}\) is a variable vector of \(d\) parameters \([x_{1},x_{2},\cdot\cdot\cdot,x_{d}]\), \([\{l_{1},\mu_{1}\}[l_{2},\mu_{2}]\),... \([l_{d},\mu_{d}])\) is the lower and upper boundary value of each parameter, \(N\) is the interested number of samples. The steps of the LHS method are as follows. Firstly, each parameter is divided into \(N\) equal parts according to its boundary, namely \([l_{i},l_{i}+\frac{u_{i}-l_{i}}{N}]=P_{1i}<P_{2i}<\ldots<P_{Ni}=[l_{i}+\frac{u _{i}-l_{i}}{N}(N-1),u_{i}]\), \(i=1,2,\ldots,d\); Secondly, a \(N\times d\) matrix \(\mathbf{S_{1}}\)  
\[\mathbf{S_{1}}=\left[\begin{array}{cccc}P_{11}&P_{12}&\ldots&P_{1d}\\ P_{21}&P_{22}&\ldots&P_{2d}\\ \vdots&\vdots&\ddots&\vdots\\ P_{N1}&P_{N2}&\ldots&P_{Nd}\end{array}\right] \tag{4}\]  
can be obtained in which every element is a small boundary; Thirdly, a matrix \(\mathbf{S_{2}}\) is transformed from matrix \(\mathbf{S_{1}}\) with elements at each column, namely \(P_{1i}\), \(P_{2i}\),..., \(P_{Ni}\), arranging to be in random order; Then, a matrix \(\mathbf{S}\) is transformed from matrix \(\mathbf{S_{2}}\) with elements chosen randomly in correlation small boundaries. In matrix \(\mathbf{S}\), each row represents one sample vector of \(d\) parameters.  
Figure 1: Gaussian mutation particle swarm optimization (GMPSO) algorithm flowchart.
## 2 Method  
### Data  
The dataset used in this study consists of four sources. First, the following three large-scale open-source datasets were used: ChEMBL [31; 32], ZINC [33], and QM9 [34]. We performed several preprocessing steps on these datasets, including the removal of duplicates and null values; constraining molecular length to within 369 characters; retaining only molecules containing the elements H, B, C, N, O, F, Si, P, S, Cl, Se, Br, and I; and excluding charged molecules. After processing, we obtained a subset of 1.05 million molecules from ChEMBL, 1 million molecules from ZINC, and approximately 130,000 molecules from QM9.  
In the pre-training datasets ChEMBL, ZINC, and QM9, there are 131, 1, and 130 molecules, respectively, that overlap with PROTAC-DB. These overlapping moleculesare structurally simple and broadly applicable, and together, they account for only \(5.97\times 10^{-3}\%\), \(5.16\times 10^{-5}\%\), and \(9.71\times 10^{-2}\%\) of each pre-training dataset, respectively. Given their extremely low proportion relative to the total dataset size, their influence on the validity of generated molecules is considered negligible. To maintain the coverage of common chemical motifs and avoid selective bias, these molecules were retained in the pre-training sets.  
The linker dataset used in this study was derived from the PROTAC-DB database introduced by [PERSON] et al. We extracted 1501 linker molecules from this source for use in subsequent transfer learning. All molecular properties were calculated using RDKit (version: 2020.09.1.0). An introduction to several of these properties is provided below:  
**Molecular weight.** Molecular weight is the sum of the atomic weights of all atoms in a molecule, reflecting its overall mass.  
**The Logarithm of the Partition coefficient (LogP).** LogP quantifies a compound's lipophilicity by measuring its distribution between octanol and water phases, serving as a key indicator of membrane permeability and drug-likeness [35].  
**Synthetic Accessibility Score (SAS).** The SAS evaluates the ease of synthesizing a molecule based on its structural complexity and fragment contributions [36].  
**Quantitative Estimate of Drug-likeness (QED).** The QED integrates multiple molecular properties into a single score to assess a compound's overall drug-like potential [37].
## 2 Methodology
### Dynamic Cell Optimization Method
This distinction highlights areas where the speed variance is greater, necessitating further analysis or action to address the uneven speed patterns in certain sections. \[\overline{u_{s}}=\overline{u_{t}}-\frac{\sigma^{2}}{\overline{u_{t}}} \tag{2}\]4 A cell optimization model. \[\overline{u_{s}}=\overline{u_{t}}-\frac{\sigma^{2}}{\overline{u_{t}}} \tag{2}\]5 (**a**) The TMS/SMS values for Highway 1; (**b**) examples of the speed/frequency distribution. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**TMS** & **SMS** & **TMS** & **SMS** & **TMS** & **SMS** & **TMS** & **SMS** & **TMS** & **SMS** & **TMS** & **SMS** \\ \hline \multicolumn{2}{c}{Segment 1} & \multicolumn{2}{c}{Segment 2} & \multicolumn{2}{c}{Segment 3} & \multicolumn{2}{c}{Segment 4} & \multicolumn{2}{c}{Segment 5} & \multicolumn{2}{c}{Segment 6} \\ \hline
85.87129 & 84.24125 & 85.87931 & 84.1697 & 85.66216 & 83.9289 & 92.67708 & 91.58171 & 92.42945 & 91.71342 & 88.46188 & 87.52088 \\ \hline \multicolumn{2}{c}{Segment 7} & \multicolumn{2}{c}{Segment 8} & \multicolumn{2}{c}{Segment 9} & \multicolumn{2}{c}{Segment 10} & \multicolumn{2}{c}{Segment 11} & \multicolumn{2}{c}{Segment 12} \\ \hline
87.355 & 86.46803 & 85.56085 & 84.24371 & 87 & 85.83241 & 88.76761 & 86.71955 & 89.925 & 86.75775 & 92.55172 & 89.1639 \\ \hline \multicolumn{2}{c}{Segment 13} & \multicolumn{2}{c}{Segment 14} & \multicolumn{2}{c}{Segment 15} & \multicolumn{2}{c}{Segment 16} & \multicolumn{2}{c}{Segment 17} & \multicolumn{2}{c}{Segment 18} \\ \hline
86.90164 & 85.07607 & 87.18852 & 85.0415 & 87.73438 & 85.77782 & 89.42735 & 87.33165 & 90.65625 & 88.72391 & 85.41905 & 83.04821 \\ \hline \multicolumn{2}{c}{Segment 19} & \multicolumn{2}{c}{Segment 20} & \multicolumn{2}{c}{Segment 21} & \multicolumn{2}{c}{Segment 22} & \multicolumn{2}{c}{Segment 23} & \multicolumn{2}{c}{Segment 24} \\ \hline
88.44615 & 86.00723 & 60.96491 & 49.8142 & 76.53991 & 73.34736 & 75.6037 & 70.04409 & 42.47281 & 21.45532 & 58.92891 & 29.19867 \\ \hline \multicolumn{2}{c}{Segment 25} & \multicolumn{2}{c}{Segment 26} & \multicolumn{2}{c}{Segment 27} & \multicolumn{2}{c}{Segment 28} & \multicolumn{2}{c}{Segment 29} & \multicolumn{2}{c}{Segment 30} \\ \hline
46.03828 & 33.76407 & 42.55709 & 35.86897 & 53.57416 & 46.1577 & 61.86298 & 48.05713 & 59.51131 & 33.29078 & 82.06452 & 80.42212 \\ \hline \multicolumn{2}{c}{Segment 31} & \multicolumn{2}{c}{Segment 32} & \multicolumn{2}{c}{Segment 33} & \multicolumn{2}{c}{Segment 34} & \multicolumn{2}{c}{Segment 35} \\ \hline
85.34188 & 79.97025 & 91.30159 & 90.39961 & 90.94828 & 89.72717 & 92.01676 & 91.11377 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: TMS/SMS of 34 segments on Highway 1. Figure 9: A cell optimization model.
# Exploring the Benefits of Photovoltaic Non-Optimal Orientations in Buildings
## 2 Materials and Methods
### Case Study #1: Residential PVSCS
#### 2.2.5 Life Cycle Analysis  
Photovoltaic technology does not produce CO2 emissions during its operation, but this technology presents several environmental impacts, mainly during manufacturing and disposal of the components. The life cycle analysis of the PVSCS was evaluated for the PV modules and the balance-of-system (inverter, cabling, structures, etc.). The net saving of emissions is calculated from Equation (14).  
\[Carbon\ Balance=E_{PVy}\frac{N}{1}(1-0.008)^{n}.LCE_{grid}-LCE_{PVSC} \tag{14}\]  
In addition, the greenhouse gas and energy payback time are calculated from Equations (15) and (16).  
\[GPBT=\frac{LCE_{PVSC}}{E_{PVy}.LCE_{grid}} \tag{15}\]  
\[EPBT=\frac{E_{PV}+E_{BoS}}{E_{PVy}} \tag{16}\]  
where \(E_{PV},\ E_{BoS}\) are the energy needed for manufacturing the \(PV\) modules and the balance - of - system.
## 3 Results and Discussion
### NF Characterization  
NFs were characterized for particle size and zeta potential at different temperatures and in various brines. The NFs were prepared in DIW, LSW and SSW. Particle-size measurements at different temperatures and the zeta potential measurements at 25 \({}^{\circ}\)C are presented in Figure 2. Figure 2a shows the average hydrodynamic diameter of the NP, which was diluted with DIW, LSW and SSW to obtain 1 g/L NP concentration. NFs prepared in DIW and LSW showed almost similar particle size (the average size for the three temperatures, 25, 50 and 80 \({}^{\circ}\)C was about 38.4 \(\pm\) 0.6 nm). In SSW, NPs displayed higher particle size of around 57 nm (25 and 50 \({}^{\circ}\)C) and about 88 nm at 80 \({}^{\circ}\)C. For higher salinity (SSW), the average size at all temperatures was about 67 \(\pm\) 0.3 nm, which is approximately 43% higher than the average particle size for all tested temperatures with DIW and LSW. One possible reason for the difference in the measured zeta potential may be the compression of the double layer at higher salinity. The measured surface zeta potential at 25 \({}^{\circ}\)C for the NFs prepared in SSW was about \(-\)6.4 mV compared with \(-\)30.73 and \(-\)12.13 mV for DIW and LSW, respectively. [PERSON], et al. [48] stated a similar observation for DP9711 NFs. They observed that increasing the brine salinity did not immediately increase particle size but that, after a certain point in time, a sudden rise in particle size was seen. To address particle size and stability, DLS measurements were repeated after three months from the preparation time. These tests showed that all measurements were close to the initial measured values (within 5 nm). In addition, the NFs remained visually clear with no sign of sedimentation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Core Id** & \begin{tabular}{c} **Porosity** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Permeability** \\ **(mD)** \\ \end{tabular} & \begin{tabular}{c} **Length** \\ **(cm)** \\ \end{tabular} & \begin{tabular}{c} **Dia (cm)** \\ \end{tabular} & \
\begin{tabular}{c} **Swi** \\ **Fluid** \\ \end{tabular} &
\begin{tabular}{c} **Primary** \\ **Fluid** \\ \end{tabular} \\ \hline SK5 & 50.7 & 3.9 & 8.83 & 3.785 & 0.13 & SSW & DP (1 g/L) in SSW \\ SK6 & 50 & 3.9 & 5.96 & 3.785 & 0.28 & LSW & DP (1 g/L) in LSW \\ SK7 & 50.24 & 3.9 & 4.658 & 3.785 & 0.275 & SSW & DP (1 g/L) in LSW \\ \hline \end{tabular}
\end{table}
Table 4: List of nanofluid experiments with hydrocarbons.
## 3 Proposed Method  
In this study, clustering and optimal route selection are applied with a primary emphasis on the distribution of emergency messages. The key elements of this work are a 5G base station, an Edge-RSU (Edge-RSU), a fog server, and the cloud. To decrease communication overhead and boost dependability, 5G technology was used. Fog nodes and cloud computing were used to reduce storage requirements and enable flexible processing. Additionally, clustering was carried out in the fog nodes, offering high-speed data processing while using less energy. To spread the emergency message, the best route was detected as well. This section briefly discusses the four main processes, as follows:  
* Two-dimensional (2D) with 3D grid-based network construction.
* Energy-saving-based super-clustering.
* Hybrid protocol-based path selection.
* DRL-based emergency message dissemination.
## 2 Methodology
### Model Validation  
A statistical method called cross-validation is used to evaluate how well machine-learning models generalize to new data, hence determining model performance. By offering a more reliable assessment of the model's actual performance, it helps reduce overfitting, a prevalent issue in machine learning where a model learns to memorize the training data instead of learning the underlying patterns. The many cross-validation methods, such as leave-n-out cross-validation, k-fold cross-validation, and hold-out cross-validation, each have their benefits and applicability based on the size, structure, and modeling goals of the dataset. For this study, the hold-out cross-validation method was applied. In holdout cross-validation, the dataset is divided into a training set and a testing set, which are two distinct sets. Part of the data is set aside for testing the model's performance and another portion is utilized for training the model. This entails understanding the underlying patterns in the training data, fitting the model to it, and then optimizing its parameters to minimize the selected objective function (e.g., maximizing accuracy in classification or minimizing loss in regression). The testing set which comprises unseen data not used in training, is used to assess the model's performance after it has been trained. This evaluation gives an estimate of the model's generalization ability to new, unseen instances. One disadvantage of this approach is the fact that the findings could be affected by the specific random split of the data.
# A VHF Circularly Polarized Turnstile Antenna with Balun for Meteor Radar
## 4 Experimental Results and Improved Design
### Analysis and Improved Design  
Deformed pattern is often caused by reverse current. Therefore, it is possible that some reverse currents exist on the surface of the antenna. In order to solve this problem, a study was done as following:  
Firstly, such pattern defects never appear in the simulated radiation pattern results of the designed turnstile antenna. Secondly, as a critical component, the balun plays a role in realizing balanced feeding for the dipole antenna. The balun used in the fabricated antenna may be insufficient to choke all the inverse currents, and its magnitude may not be equal to that of a differential port of the dipole antenna.  
Generally, a balun is a three-port power splitter, similar to a Wilkinson or resistive power divider, and its two outputs will have equal magnitudes and opposite phases (a 180\({}^{\circ}\) phase shift).  
To solve this problem, 1:1 Ferrite transmission line transformers are used in the antenna feeding network. Figure 10 shows the transform. The ferrite is ring-like with a size of 13 \(\times\) 7 \(\times\) 5 mm, and the initial permeability of the material is 100.  
\begin{table}
\begin{tabular}{c c c} \hline
**Time** & **Meteor Radar with ATRAD** & **Meteor Radar with Proposed Antennas** \\ \hline
0–24 & 10,231 & 10,033 \\
24–48 & 10,746 & 9635 \\
48–72 & 8630 & 9498 \\
72–96 & 13,860 & 12,993 \\
96–120 & 13,665 & 11,415 \\ \hline \end{tabular}
\end{table}
Table 3: Statics table of meteor quantity.  
Figure 10: _Cont._
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 4 RSTS Protocol
### Multi-Hop Network Synchronization  
RSTS focuses primarily on layered network topology. Although the global synchronization methods in RBS and FTSP are available, RSTS works in fundamentally different ways, which results in significant notes in constructing a multi-hop synchronization path.  
For example, Figure 4a shows an example of a multi-hop chain topology in FTSP, and Figure 4b shows the corresponding synchronization logical graphs of RSTS, where the black arrow represents the communication relationship between two nodes and the green arrow is the synchronization path. Node 0 is the root, also known as the time source, and other ordinary nodes synchronize with node 0 hop-by-hop. It can be observed that the black arrows are unidirectional and bidirectional in Figure 4a,b, respectively, which indicates that the transmission direction of time information is not the same in FTSP and RSTS. Each ordinary node can work as a master or a slave in both FTSP and RSTS. Moreover, RSTS's node has an additional role, the reference node, which serves as a shared neighbor node. For each node, it can work as a master after achieving synchronization with the upper node. To ensure the composition of the global network time synchronization path, at least one triangle sub-topology must exist in the network, as shown in Figure 4b, 0\(\leftrightarrow\)1\(\leftrightarrow\)10\(\leftrightarrow\)0.  
In the comparison of the synchronization path, we can see that RSTS has a potential advantage in shortening the longest synchronization path of the network, i.e., 10 in FTSP and 5 in RSTS as shown in Figure 4, and the closer the triangle sub-topology is to the root node, the better the effect. Meanwhile, in layered topology, the node broadcasts synchronization messages to other nodes in the lower level only after entering the sync state. The synchronization accuracy will decrease as the synchronization path increases and the distant nodes may wait a long time to synchronize. Hence, RSTS shows a better performance than FTSP in improving the network synchronization accuracy and convergence speed.  
Figure 4: Multi-hop chain topology. (**a**) FTSP; (**b**) RSTS.
## 4 Discussion
[21] proposed the idea of ASI, which considered heat accumulated in silage over time even if they started heating at a similar time [21]. With infrared camera technology, we were able to record the temperature of the silage working face on a daily basis for determining the ASI. Bunker silo results showed that applying DPI reduced heat accumulation in silage (Table 4), and the inoculant effects were dependent on the silage layer. Silage density is a function of height, and the density near the top of the silo is the lowest [33], indicating that top silage may be more susceptible to aerobic deterioration [5]. As a result, we can infer that DPI inoculation is likely to improve the aerobic stability in silages that are the most vulnerable to spoil. In the baled silage trial, responses to DPI inoculation were not obvious, which may be attributed to the high forage DM. Forage DM affects the density and porosity of silage [5]. The more porous the silage structure is, the more quickly it may spoil when exposed to air [29]. In high DM forage (>40-45% DM), low water availability limits LAB growth and slows fermentation [34]. A prolonged fermentation period means fewer antimicrobials to suppress the activity of yeasts and molds [29], which may explain why the inoculant effects were unremarkable in the silo bale trial, where the forage DM was approximately 40%. Speaking of the on-farm losses over the ensiling process, management practices are far more important than inoculation. Inferior practices such as undesirable forage conditions, no coverings, and a slow feed-out rate may increase the DM losses by 41% during the feed-out phase [5]. Therefore, it can be said that the bacterial inoculant is an 'extra bonus' to reduce the risks from uncertain factors (e.g., weather), but only when the silages are well managed would the inoculants work. In this study, the effectiveness of DPI was successfully demonstrated in both laboratory and field trials; however, the bunker size, which was smaller than the usual commercial size, might be the limitation of this research. On-farm management practices might be the main obstacle when conducting field trials in local farms. First, it is challenging to leave half of the forage untreated in a large silage bunker where compression and inoculation occurred simultaneously. If two silage bunkers were used, it was less likely to open two bunker silos at one time owing to on-farm management practices. Therefore, further studies are required to validate the results with larger commercial bunker silos to ensure that inoculant efficacy is maintained under such circumstances.