esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### Exhaustive Search  
As shown in Figures 4-7 are the experimental results for each cipher. The X-axis is blocks per grid, the Y-axis is gigabits per second, the legend is threads per block. In general, the larger the blocks per grid, the higher the throughput. However, the threads per block are not high, the higher the throughput. According to blocks per grid, there are threads per block with the highest throughput. There are threads per block showing the highest throughput per block per grid. For example, in Figure 5, block 4096 per grid achieves the highest throughput with 256 threads per block. However, for blocks 8192 per grid, the highest throughput is achieved with 128 threads per block. All four ciphers implemented show similar characteristics. The achieved throughput of the proposed method is shown in Table 2. The peak throughput of PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128 were 553.932 Gbps, 529.952 Gbps, 583.859 Gbps, and 214.284 Gbps, respectively. PRESENT-80 implemented by the proposed method as shown in Table 3 showed 4.39 times higher performance than 1.8852 Gblocks/s in RTX 3070 environment of [10], even in a GPU environment with lower performance (i.e., RTX 3060).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cipher** & **Blocks per Girl** & **Threads per Block** & **Throughput** \\ \hline PRESENT-80 & 131,072 & 192 & 553.932 \\ PRESENT-128 & 131,072 & 128 & 529.952 \\ GIFT-64 & 131,072 & 64 & 583.859 \\ GIFT-128 & 16,384 & 160 & 214.284 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The highest throughput results of the exhaustive search version of the proposed technique. The unit of throughput is Gigabit per second (Gbps).  
Figure 4: PRESENT-80 exhaustive key search: Comparison of the number of key-search per second.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cipher** & **Ref.** & **Model** & **Throughput** \\ \hline PRESENT-80 ECB & [20] & intel i7-9750H & 0.001 \\ PRESENT-80 ECB & [12] & Tesla V100 & 14.15 \\ PRESENT-80 ECB & [12] & Tesla V100 & 24.525 \\   using multiple stream & [12] & Tesla V100 & 24.525 \\   PRESENT-80 ECB & this work & RTX 3060 & 24.264 \\ PRESENT-80 CTR & [10] & RTX 3070 & 115.73 \\ PRESENT-128 ECB & [12] & Tesla V100 & 14.15 \\ PRESENT-128 ECB & [12] & Tesla V100 & 24.525 \\   using multiple stream & [12] & Tesla V100 & 24.525 \\ PRESENT-128 ECB & this work & RTX 3060 & 24.522 \\ GIFT-64 ECB & [21] & intel i7-9750H & 0.003 \\ GIFT-64 ECB & this work & RTX 3060 & 85.283 \\ GIFT-128 ECB & [21] & intel i7-9750H & 0.014 \\   GIFT-128 ECB & this work & RTX 3060 & 10.723 \\ PRESENT-80 & [10] & RTX 3070 & 120.64 \\ EXHAUSTIVE & & & \\ PRESENT-80 & & & \\ EXHAUSTIVE & this work & RTX 3060 & 553.932 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of implementation results for CPU and GPU.
# Clinical Applications of Nano-Hydroxapatite in Dentistry
## 3 Results
### Study Characteristics
#### 3.2.5 Periodontology and Implantology  
[PERSON] et al. [37] applied nano-HAp paste to the treatment of intrabony defects associated with papilla preservation flap surgical procedure. At 6 months follow-up, the probing bone gain was 4.3 \(\pm\) 1.4 mm in the nano-HAp group and 2.6 \(\pm\) 1.4 mm after access flap surgery alone. Probing Pocket Depth (PPD) significantly decreased from 8.3 \(\pm\) 1.2 mm to 4.0 \(\pm\) 1.1 mm in the nano-HAp group compared with the control group (from 7.9 \(\pm\) 1.2 mm to 5.0 \(\pm\) 1.2 mm).  
[PERSON] et al. [38] compared intrabony defects filling with nano-HAp and \(\beta\)-tricalcium phosphate in terms of PPD reduction, Clinical Attachment Level (CAL) gain, gingival recession and radiographic defect depth. Statistically significant reduction in PPD was observed in the nano-HAp group at 3 months, while at 6 months no difference was observed. CAL gain improved in both groups at 3 and 6 months, in the absence of significant difference between groups at the two timepoints. No difference was found in gingival recession, which increased in both groups. Higher radiographic bone fill was observed in the nano-HAp group compared to \(\beta\)-tricalcium phosphate group at 3 months, while no statistically significant difference was seen at 6 months.  
[PERSON] & [PERSON] [39] evaluated the degree of nano-HAp particles retention on root surfaces in periodontal defects following (i) application of 10-100 nm nano-HAp; (ii) application of 10-100 nm nano-HAp following 24% EDTA gel treatment; (iii) composite graft consisting of equal volumes of 10-100 nm nano-HAp and 63-150 nm \(\beta\)-tricalcium phosphate; (iv) composite graft consisting of equal volumes of 10-100 nm nano-HAp and 63-150 nm \(\beta\)-tricalcium phosphate following 24% EDTA gel treatment. The authors found a higher nano-HAp retention in the presence of EDTA surface treatment and association with \(\beta\)-tricalcium phosphate.  
[PERSON] et al. [40] compared titanium mini-implants and nano-HAp-coated mini-implants in the rehabilitation of partial edentulism. The authors found a similar immunological response to the different implant surfaces when evaluating IL-6, TGF-82, MMP-8, CCL-3, IL-8 and IL-1\(\beta\). No statistically significant difference was found at histomorphometric evaluation with regards to number of inflammatory cells around the two mini-implant surfaces.  
Results are summarized in Table 5.
## 2 Materials and Methods
### Storm Surge Impacts on Carbon Exchange  
Storm surges that could affect the PYR and HK sites were screened (Figure 1) during 2020\(-\)2022 and 2016\(-\)2018, respectively. According to [PERSON] et al. [19] and the official database of the Zhejiang Provincial Typhoon and Flood Information Center ([[https://typhoon.slt.zj.gov.cn/slt.htm](https://typhoon.slt.zj.gov.cn/slt.htm)]([https://typhoon.slt.zj.gov.cn/slt.htm](https://typhoon.slt.zj.gov.cn/slt.htm)), accessed on 9 March 2025), only storm events whose tracks passed within 350 km of either study site were retained for analysis. This distance threshold was chosen to ensure that each selected event had the potential to generate significant hydrological or ecological impacts on the local mangrove ecosystems. For each event, we extracted key characteristics, including the international storm number, name, time of closest approach, duration, maximum wind speed, and minimum distance to the flux tower. These parameters were used to assess storm intensity and proximity and are summarized in Table 2. The storm surge information was meticulously extracted from the official bulletins of marine disasters published by Zhejiang Province ([[https://rzytz.zj.gov.cn/col/col12899](https://rzytz.zj.gov.cn/col/col12899)]([https://rzytz.zj.gov.cn/col/col12899](https://rzytz.zj.gov.cn/col/col12899)) 33/index.html, accessed on 9 March 2025) and Guangdong Province ([[https://nr.gd.gov.cn/zwgknew/sifb/tjsj](https://nr.gd.gov.cn/zwgknew/sifb/tjsj)]([https://nr.gd.gov.cn/zwgknew/sifb/tjsj](https://nr.gd.gov.cn/zwgknew/sifb/tjsj)), accessed on 9 March 2025) (Figure 2). To assess the influence of storm surges on carbon flux processes, the mean values of key environmental and flux variables were calculated, including air temperature, soil temperature, photosynthetic photon flux density (PPFD), relative humidity, wind speed, vapor pressure deficit (VPD), as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviations** & **Parameters Description** & **Units** \\ \hline NEE & Net ecosystem CO\({}_{2}\) exchange & \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) \\ GPP & Gross primary productivity & \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) \\ Reco & Ecosystem respiration & \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) \\ CH\({}_{4}\) & Flux of methane & \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) \\ VPD & Vapor pressure deficit & hPa \\ RH & Relative humidity & \% \\ TA & Air temperature & \({}^{\circ}\)C \\ WS & Wind speed & m/s \\ PPFD & Photosynthetic photon flux density & \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) \\ TS & Soil temperature & \({}^{\circ}\)C \\ WTD & Water table depth & m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Abbreviations and descriptions of fluxes and biophysical variables at two sites.  
well as carbon flux components such as NEE, gross primary productivity (GPP), ecosystem respiration (Reco), and CH\({}_{4}\) flux [20].
# Quadcopter Adaptive Trajectory Tracking Control:
## 3 Trajectory Tracking Control: Adaptive Backstepping Design and Stability Analysis
### Horizontal Position Controller
Equation (27) shows the relationship between the movement in the \(xy\)-plane and attitude angles to prove the stability of the whole system in Theorem 1, which will definitely eliminate the finite escape time phenomenon._  
The third Lyapunov candidate function (29) and its time-derivative along solution of models (20), (23), and (26):  
\[V_{3}= V_{2}+\frac{1}{2}\left(\vartheta-r_{2}\right)^{T}\left(\vartheta-r_{ 2}\right) \tag{29}\] \[\dot{V}_{3}= -\xi^{T}\mathbf{K}_{1}\xi-\left(V-r_{1}\right)^{T}\mathbf{K}_{2} \left(V-r_{1}\right)\] \[+\left(\vartheta-r_{2}\right)^{T}\left(\mathbf{B}(\phi,\theta) \dot{\vartheta}-\dot{r}_{2}+\mathbf{A}(U_{1},\psi)^{T}\left(V-r_{1}\right)\right) \tag{30}\]Because the matrix \(\mathbf{B}(\phi,\theta)\) is invertible, the virtual control input for (30) with positive definite matrix \(\mathbf{K}_{3}\in\mathbb{R}^{2\times 2}\) can be selected as:  
\[r_{3}=\mathbf{B}(\phi,\theta)^{-1}\left[\dot{r}_{2}-\mathbf{A}(U_{1},\psi)^{T} \left(V-r_{1}\right)-\mathbf{K}_{3}\left(\vartheta-r_{2}\right)\right] \tag{31}\]  
In order to obtain the horizontal tracking, two first Euler angle models in (9) are investigated to find the intermediate control input \(U=[U_{2},U_{3}]^{T}\). \[\ddot{q}=\mathbf{E}^{-1}\left[\mathbf{EC}\left(\dot{\phi},\dot{ \theta},\dot{\psi},\mu_{4},\mu_{5}\right)+U\right] \tag{32}\] \[\mathbf{EC}\left(\dot{\phi},\dot{\theta},\dot{\psi},\mu_{4},\mu_ {5}\right)=\mathbf{C}_{1}\left(\dot{\phi},\dot{\theta},\dot{\psi}\right)\Gamma \tag{33}\]  
where  
\[\mathbf{E}^{-1}= \begin{bmatrix}b_{1}&0\\ 0&b_{2}\end{bmatrix} ,C\left(\dot{\phi},\dot{\theta},\dot{\psi},\mu_{4},\mu_{5 }\right)= \begin{bmatrix}\dot{\phi}\dot{\psi}a_{1}-\dot{\phi}\mu_{4}\\ \dot{\phi}\dot{\psi}a_{3}-\dot{\theta}\mu_{5}\end{bmatrix}  \tag{34}\]  
\[\mathbf{C}_{1}\left(\dot{\phi},\dot{\theta},\dot{\psi}\right)= \begin{bmatrix} \dot{\phi}\dot{\psi}&-\dot{\phi}&0&0\\ 0&0&\dot{\phi}\dot{\psi}&-\dot{\theta}\end{bmatrix} ,\Gamma= \begin{bmatrix}a_{ 1}/b_{1}\\ a_{4}/b_{1}\\ a_{3}/b_{2}\\ \mu_{5}/b_{2}\end{bmatrix}  \tag{35}\]  
**Assumption 3**. : _It is assumed that \(\mathbf{E},\Gamma\) are model uncertainties because they absolutely depend on the environment, material, and arrangement of devices in a real model. Additionally, \(\mathbf{E}\) and \(\Gamma\) satisfy the following boundary conditions:_  
\[\Gamma_{i-min}\leq\Gamma_{i}\leq\Gamma_{i-max} \tag{36}\] \[b_{i-min}\leq b_{i}\leq b_{i-max} \tag{37}\]  
**Remark 2**. : _It can be seen that the Equation (32) is transformed to separate matched uncertainty \(\Gamma\) and multiplied control input parameter \(E\).
## 2 Background and Related Work
### ML in Embedded Systems  
The emergence of lightweight ML enables the deployment of sophisticated ML algorithms on devices with severely resource restricted resources through model compression through quantization and pruning techniques reducing the memory footprint by 10-100\(\times\), hardware acceleration using specialized processors optimized for ML inference, and real-time frameworks providing deterministic execution guarantees for safety-critical applications [20; 21; 22].  
The deployment of ML in motor control contexts presents unique challenges including sub 10 \(\upmu\)s execution requirements with consistent execution time for real-time guarantees, resource limitations under 100 KB memory and 150 mW power budgets, and safety assurance that requires provable behavior under all operational conditions.
# A Distributed Mix-Context-Based Method for Location Privacy in Road Networks
## 9 Analysis and Discussion
### Privacy and Traffic Optimization  
The majority of the research conducted for location privacy protection in VANETs is based on the pseudonyms' changing process. The design of the privacy scheme may have an impact on the performance of other fields of the VANETs. One consideration is the impact on communication protocols, high frequency of changing pseudonym improves privacy but creates complications for the routing protocols. It degrades the performance of communication protocols that may impact the packet delivery ratio. The impact of the pseudonym changing process discussed in [67] on the packet delivery ratio is as follows: if vehicles (nodes) change their pseudonyms after every five seconds, the packet delivery ratio is about 60% to the destination. If pseudonyms are changed after every  
Figure 21: Average computation cost with a various number of vehicles.  
Figure 20: CMC computation overhead for the pseudonyms’ changing process.  
10 s, the successful packet delivery ratio is about above 75%, while increasing duration to 30 s successful delivery ratio becomes above 85%. However, the increasing duration for pseudonym changes will reduce the level of privacy. Thus, there should be a reasonable balance between privacy protection and traffic optimization.
# AutoFL: Towards AutoML in a Federated Learning Context
## 5 Evaluation
### Discussion  
The previous experiments highlighted the impact of different kinds of classical ML models and neural networks, the frameworks used to implement these ML models, the way the training data is distributed over the clients (i.e., i.i.d. or non-i.i.d. ), the resource availability of the clients, and the application of privacy-enhancing techniques. In these experiments, we constrained the hyperparameter search space to limit the time required while also enabling a fair comparison between different configurations. For example, we fixed the number of federated averaging rounds to 20 and the local training rounds on each client per federated averaging round to 1. These hyperparameters and several others can also be optimized, possibly in terms of other error metrics, with or without data pre-processing. Additionally, there are opportunities to (a) experiment with other datasets, (b) identify more trade-offs with mixed hardware-accelerated federated learning, (c) try different variations of imbalanced datasets, (d) compare adaptive client sampling, and (e) sophisticated strategies for federated averaging, (f) support more types of classical ML models as well as neural network architectures, (g) analyze the impact of adversarial training, etc. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & **Accuracy** & **Memory (MB)** & **CPU Time (s)** & **Pareto-Optimal Configuration** \\ \hline
**0002** & 0.9800 & 647.282 & 64.329 & \\
0010 & 0.9806 & 914.833 & 113.37 & \\
**0016** & 0.9811 & 604.106 & 65.076 & \\
**0020** & 0.9826 & 662.110 & 86.150 & \\
**0021** & 0.9834 & 652.775 & 86.362 & \\
**0022** & 0.9821 & 652.513 & 86.751 & \\
0043 & 0.9823 & 675.852 & 124.59 & 0020, 0021, 0096 \\
0073 & 0.9804 & 651.759 & 107.83 & 0016, 0096, 0100 \\
0089 & 0.9822 & 660.127 & 106.97 & 0021 \\
**0096** & 0.9830 & 651.382 & 107.03 & \\
**0100** & 0.9806 & 650.543 & 85.550 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Secure aggregation configurations of MNIST i.i.d. with an accuracy \(>=\) 0.98, with Pareto-optimal ones marked in bold. The search space of hyperparameters--those of the ML models as well as those for federated learning--is so large that a Bayesian optimization process limited to 100 configuration evaluations will leave further room to identify ML models that may perform better in terms of either accuracy or resource usage or both. Indeed, due to the limited exploration time, the model configurations selected through Bayesian optimization and trained on the i.i.d. MNIST training dataset on 10 clients only achieved an accuracy of up to 0.983. However, the manually crafted neural network depicted in Listing 4 achieves an accuracy of 0.9945 on the test set. We would need to grant the Bayesian optimization process more time to obtain similar results.
## 3 Results and Discussion
### MMBGSA Studies  
Molecular mechanics/generalized Born surface area (MMGBSA) studies provide a comprehensive scoring function for examining the binding interactions between a ligand-receptor complex over the course of the entire simulation, taking into account the binding poses and the free energy involved for various interactions [109]. As seen in Table 5, TauRP1-2 and TauRP1-14 showed higher overall \(\Delta\)G binding energy at \(-\)75.71 kcal/mol and \(-\)70.7 kcal/mol, respectively, indicating that these two peptides had relatively stronger binding interactions with tau. The lowest \(\Delta\)G binding energy was observed for TauRP1-8 at \(-\)42.05 kcal/mol. Overall, electrostatic interactions appeared to be a significant contributor for binding in each case in addition to van der Waals interactions. As expected, the most electrostatic interactions were seen for TauRP (1-14) due to the higher number of  
Figure 7: Comparison of RMSFs of the PHF Tau backbone upon binding with (**a**) TauRP1-2; (**b**) TauRP1-4; (**c**) TauRP1-8; (**d**) TauRP1-14. (Vertical lines show regions of interactions with PHF Tau.) (**e**) Comparison of radius of gyration of the selected four TauRP peptides upon binding with PHF Tau over 100 ns simulation. (**f**) Comparison of SASA values of the selected four TauRP peptides upon binding with PHF Tau over 100 ns simulation.  
interactions with the lysine side chains, as explained earlier. Furthermore, compared to the other peptides, TauRP1-14 contains three glutamic acid residues and two aspartic acid residues, while all others have higher numbers of D and only one E residue. This may tend to increase electrostatic interactions with the positively charged groups such as K and R present in the tau protein and lead to higher electrostatic interactions due to the longer side chain of E that may allow it to delve deeper into the binding pocket of the tau protein.  
Based on the results obtained from docking and molecular dynamics, as a proof of concept, we conducted laboratory experiments to validate the results obtained and to determine if there was an effect on LRP1-mediated internalization of Tau into LRP1-expressed cells in the presence of the Tau RP1-14 sequence. Thus, HEK293 cells were transfected with LRP1 protein and interactions with fibrillary Tau was examined. Furthermore, we also carried out CD and SPR analysis to confirm interactions with fibrillar Tau.
## 1 Introduction
The second structure has an undoped GaAs core surrounded by a doped InAs shell, and is also placed in hexane (u-GaAs/d-InAs/Hexane). Hexane is one of the standard solvents used in the preparation of colloidal quantum dots [37; 38; 39], which allows for long-distance propagation of terahertz waves. The core radius \(R_{I}=R_{core}=100\) nm coincides with the inner shell radius, and \(R_{2}=R_{shell}=120\) nm is the outer shell radius. The typical intrinsic concentration of charge carriers in InAs is \(10^{15}\) cm\({}^{-3}\) at room temperature [40] with a corresponding bulk plasmon energy \(\sim\)0.7 meV. The intrinsic charge carrier concentration in GaAs is negligibly small (\(10^{6}\) cm\({}^{-3}\)). In both cases, the plasmon energy is much smaller than the interface phonon energies (25 to 30 meV), and, therefore, the phonon and plasmon systems are decoupled. From now on, we use the following terminology: \"undoped\" denotes a medium without the phonon-plasmon interaction and \(N_{e}<10^{15}\) cm\({}^{-3}\), while \"doped\" refers to a medium with the phonon-plasmon interaction and \(N_{e}\)\(>10^{17}\) cm\({}^{-3}\). The material parameters of InAs, GaAs, and hexane are taken from [41; 42; 43]. The electron doping of the semiconductor core or shell affects the vibrational spectrum of the nanostructure and enhances the response of the system to electromagnetic radiation. The nanostructures analyzed here are particularly interesting because of their potential application in photovoltaics, midinfrared and infrared photodetectors, and in optoelectronic devices [44; 45; 46; 47]. Figure 1: A scheme of the spherical core/shell/outer medium nanostructure (**a**). Full curves show the interface phonon–plasmon energies versus the electron concentration in the d-InAs/u-GaAs/Hexane nanostructure and (**b**) u-GaAs/d-InAs/Hexane nanostructure (**c**). Dashed and dash-dotted curves represent the limiting cases, as discussed in the text. Full circles, labeled by numbers, indicate energies corresponding to the absorption peaks shown in Figure 2. The radii of the circles represent the calculation error. The pink-shaded areas indicate the regions of strong interface phonon-plasmon hybridization.
# Insights into the Issue of Deploying a Private LoRaWAN
## 4 Experimental and Analytical Results
### Deployment Ease Evaluation
#### 4.3.1 Deployment Costs Evaluation
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Parameter** & **Description** \\ \hline \multirow{6}{*}{\(C_{\text{prop}}\)} & From the end-user perspective, in the case of public network, the propriety costs are mainly composed of the costs of the end-devices. However, the private networks must also include other costs, i.e., gateways, racks, cables, antennas, feeders, software customized solutions, and others. Moreover, the costs for network optimization must include covering the places with a higher noise level (for an estimation of a precise simulation model needs to be made). If a larger private network is considered, it might also be necessary to include core infrastructure building costs if needed. \\ \hline \multirow{2}{*}{\(C_{\text{sus}}\)} & Sustainability needs to be included if the horizon of the considered application is beyond the device’s lifetime (i.e., devices with a 15-year lifetime will be used in applications with a horizon of 30 years). \\ \hline \multirow{6}{*}{\(C_{\text{bld}}\)} & If a large-scale private network is considered, there might also be additive costs for renting roofs or buildings for gateways. However, this item considers only the buying price for the buildings, where the fees (if any) are included in the similar item for the OPEX costs. \\ \hline \multirow{2}{*}{\(C_{\text{res}}\)} & The LoRaWAN technology is still quite new on the market, and most of the end-devices are of basic character. More specific applications could require research of the end-devices, which will also impact the final CAPEX costs. \\ \hline \multirow{2}{*}{\(C_{\text{cap-oth}}\)} & There might be other additional costs not mentioned above, i.e., high-level design (HLD), low-level design (LLD), detailed-level design (DLD), installation and deployment costs, device configuration, supplies or network optimization costs (work). \\ \hline \multirow{2}{*}{\(C_{\text{dis}}\)} & There will be a certain discount on the price of the devices (item \(C_{\text{prop}}\)) based on the seller and amount of devices. \\ \hline \multirow{2}{*}{\(C_{\text{sell}}\)} & If the time difference between the application horizon and the device lifetime is \textgreater{}0, there is a possibility of selling the network equipment, which slightly lowers the CAPEX costs (i.e., the devices with a 15-year lifetime will be used in an application with a horizon of 5 years). \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The list of components of CAPEX costs. The whole costs of the application might be computed as follows:  
\[\sum C=C_{\text{capex}}+C_{\text{opex}}. \tag{4}\]