esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Response Analysis of Overpass Ramp Based on Grey System Theory Model
## 4 Predictive Analysis
### Prediction Result Analysis  
The grey prediction model was used to predict the maximum dynamic deflection, maximum dynamic strain, and vibration acceleration of the measuring point when the vehicle speed was 20-60 km/h. The prediction results are shown in Table 4.  
Figure 13 shows the dynamic deflection of a vehicle simulated to pass through the mid-span measurement point at a constant speed ranging from 30 to 60 km/h in the finite element software MIDAS CIVIL NX.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Monitoring Item Speed (km/h)** & **20** & **30** & **40** & **50** & **60** \\ \hline
**Maximum dynamic strain (\(ue\))** & 6.40 & 7.36 & 8.19 & 9.12 & 10.16 \\
**Maximum dynamic deflection (mm)** & 0.825 & 1.023 & 1.164 & 1.324 & 1.506 \\
**Vibration acceleration (mm/s\({}^{2}\))** & 3.017 & 3.250 & 3.287 & 3.326 & 3.365 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Monitoring project projections.  
Figure 13: _Cont._  
Similarly, the maximum dynamic strain and vibration acceleration at speeds ranging from 30 to 60 km/h were simulated in the finite element software, and the simulated values were then compared with the predicted values, as shown in Figure 14.  
Figure 14: _Cont._  
Figure 13: Simulation of dynamic deflection of measuring points at different speeds (unit: mm): (a) 30 km/h; (b) 40 km/h; (c) 50 km/h; and (d) 60 km/h.  
Figure 14 above shows a slight difference between the values predicted by the grey prediction model and those simulated by the finite element method. This indicates that the simulation results are compelling. The model can simulate vehicle operating conditions at different speeds and can be used when field test conditions are limited.
# Enhanced Ant Colony Algorithm Based on Islands for Mobile Robot Path Planning
## 3 Brief Introduction of ACO
### Adaptive Pheromone Update Rule  
Once all ants finish an iterative cycle, the pheromones on the original path volatilize at a certain speed, while the new pheromones are deposited by ants traversing through the path. After multiple iterations, the optimal route is identified as the one with highest pheromone concentration. In this study, an improved pheromone update rule is introduced to accelerate pheromone accumulation speed on the optimal route, reduce the pheromone accumulation rate on the worst route, and incorporate a dynamic adaptive volatilization coefficient. The specific calculations are presented as follows:  
\[\tau_{ij}(t+1)=(1-\rho)\tau_{ij}(t)+\sum_{k=1}^{m}\Delta_{ij}^{k}+\rho\Delta \tau_{ij}^{*}. \tag{23}\]  
\[\Delta\tau_{ij}^{*}=\begin{cases}\frac{\kappa*Q*d_{SE}}{L_{best}^{2}},&\text{ if $edge(i,j)$ belongs to the optimum path},\\ -\frac{\kappa*Q*d_{SE}}{L_{worst}*L_{best}},&\text{ otherwise $i$ $edge(i,j)$ belongs to the worst path},\\ 0,&\text{ otherwise.}\end{cases} \tag{24}\]  
\[\rho=\kappa*\bigg{(}\frac{d_{SE}}{L_{best}}\bigg{)}. \tag{25}\]  
When all ants complete an iteration, the pheromones of the entire map will be updated by Equation (23), where \((1-\rho)\tau_{ij}(t)\) represents the volatilization term, indicating the volatilization of the original pheromone; \(\sum_{k=1}^{m}\Delta_{ij}^{k}\) implies the item of pheromones addition, signifying the addition of new pheromones to the path the ants have passed; and \(\Delta\tau_{ij}^{*}\) refers to the term of reward or punishment, which can be calculated according to Equation (24). This observation involves enhancing the optimal path by adding extra pheromones to reinforce its guidance for subsequent iterations while reducing pheromones on the worst path to minimize its misleading effect. \(L_{best}\) represents the optimum route length; \(L_{worst}\) implies the worst route length in current iteration; and \(\rho\) denotes the adaptive volatilization rate, which can be calculated using Equation (25), where \(\kappa\) implies the adjustment coefficient ranging from 0 to 1. From this formula, we can conclude that, at the beginning of the algorithm, a large \(L_{best}\) leads to a small \(\rho\) and minimal differences in the pheromone values across paths, allowing the ant colony to maintain a strong global path planning capability. As \(L_{best}\) decreases, the value of \(\rho\) increases gradually, leading to greater differences in pheromone values across paths. Consequently, more ants are guided toward the optimal path, allowing the algorithm to achieve faster convergence in later stages.
# Antimicrobial Effect of Titanium Hydroxapatite in Denture Base Resin
## 2 Materials and Methods
### Evaluation of the Antimicrobial Effect of TiHA against Planktonic Bacteria  
To evaluate the antimicrobial effect of TiHA against planktonic bacteria, an aliquot (100 \(\mu\)L) of microorganism suspension, with OD\({}_{595}\) approximately 1.0, was transferred to the surface of each specimen that had been placed on a wet filter paper in a petri dish to maintain moisture. After 2 or 4 h irradiation with UVA (352 nm) from a distance of 20 cm, the microorganism suspension was diluted and then serially plated to determine the CFU value. Bacteria from specimens placed in a shade for 2 or 4 h (i.e., without irradiation) were used as negative controls.
## 4 Optimization of the Energy Management Strategy  
### Selection of Optimization Method  
At present, energy management strategies based on optimization methods can be divided into two categories. One type is based on static data tables or historical data, a global optimal energy management method for energy optimization control under specific operating conditions. The other type is based on the real-time status of the vehicle or the current parameters of the recurrence control, which can usually guarantee a local or instantaneous optimal energy allocation strategy [25; 26].  
Since the instantaneous optimal energy allocation strategy is generally aimed at automotive operating conditions, and depending on the operating characteristics of the tram, its operating conditions can be determined. Therefore, the global optimal energy management method is selected here. Based on the energy management strategy of the global optimal, the most representative are the dynamic programming control method [27], Pontryagin minimum method [28], genetic algorithm [29], and other intelligent control methods. Among them, the genetic algorithm pays no attention to the system model. It just needs to determine how the evaluation function can be evolved through optimization. It is also advantageous in providing more convenience in changing the energy allocation strategy in the original tram and it is easy to implement in the project. So, the genetic algorithm was adopted in this report to optimize the energy allocation strategy in the original tramcar [30].
## 4 The Smart Shipping Use Case
### Student Participation  
The main users involve the backend employees (e.g., office or administrative personnel, security experts, CSO, etc.), as well as, the captain and the crew of a smart vessel, who must be in position to face cyber threats even in the case where the communication with the backend systems/experts is not feasible. In general, the captain is a valuable actuator and he is the person in charge with the responsibility to take decisions for a potential ongoing cyber security incident in the vessel. Although he/she is not a security expert, he/she ought to possess sufficient knowledge in order to take the correct actions. On the other hand, the crew is ordinarily considered as users with low security awareness.  
Shipping Company's staff have a key role in protecting Information Technology (IT) and Operational Technology (OT) systems. Training and awareness should be tailored to the appropriate levels for:  
* On-board personnel including the master, officers and crew.
* Shore-side personnel, who support the management and operation of the ship.  
Applicable personnel should be able to identify the signals when a system has been compromised. The objective is to increase the security awareness in shipping ICT systems' operators, and security attacks and help towards identifying new threats which jeopardize the operations of ICT systems in the Shipping Management industry.  
A secure network depends on the IT/OT set up onboard the ship, and the effectiveness of the company policy based on the outcome of the risk assessment.  
Special attention should be given when there has been no control over who has access to the on-board systems. This could, for example, happen during dry-docking, layups or when taking over a new or existing ship.  
Cyber Security protection measures may be technical and focused on ensuring that on-board systems are designed and configured to be resilient to Cyber Attacks. Protection measures may also be procedural and should be covered by company policies, safety management procedures, security procedures and access controls.  
Implementation of Cyber Security controls should be prioritized, focusing first on those measures, or combinations of measures, which offer the greatest benefit.  
The guidelines for preventing deliberate attacks on ships and port facilities is defined in the International Ship and Facility Security Code ISPS adopted by the International Maritime Organization (IMO) in 2002 [56]. DNAOS is also following the guidelines of the Center of Internet security (CIS) [57] to apply critical security controls to equipment and data onboard vessels.
# Predicting Human Location Using Correlated Movements
## 2 Related Work
### Human Mobility Prediction
Therefore, to address the limitation of [8; 9], our work attempts to design a more flexible and applicable model that does not require a dataset of physical locations. Moreover, our model considers behavioral similarity between people as well as friendship. Note that behavioral similarity may not be revealed based on direct interactions or encounters between people. In regard to datasets of symbolic locations, among the models considering social correlation features to predict person movements, a few approaches worked with datasets of non-geographic locations [10; 18]. In [18], a location prediction model was considered based on two factors: periodic movements and social relationships. Specifically, a Markov-based model was constructed to captureperiodic movements while colocation frequency was used to measure the closeness between people. In order to reflect the impacts of both factors on human mobility, the location prediction model was built where a different weight was assigned to each factor. Meanwhile, [PERSON] et al. [10] proposed an algorithm called NextCell to predict the future locations of people. A boosting technique was used to combine two predictors that are based on periodic behaviors and social interplay. The periodic behavior predictor considers a probability distribution over locations. Meanwhile, in the social interplay predictor, the probability that two people co-locate at a given time was estimated as a function of phone call features. Although the above studies [10; 18] consider the datasets of symbolic coordinations, those models do not take into account the impact of time features on human mobility which are believed to be an important factor for human mobility prediction [2; 8; 29]. Meanwhile, our work aims at considering both temporal and spatial information of social friends to design the movement prediction model. Moreover, for predicting the current or future location of a person, the proposed framework also takes into account other human mobility characteristics, e.g., encounter frequency, community interactions, and behavioral similarity.
# FPGA Implementation of a BPSK 1D-CNN Demodulator
## 3 FPGA Implementation
### Pipeline and Parallel Structure  
The neural network runs in a pipelined mode between layers. The data are not cached from input to output. Layers are called every clock period. Each clock period, a set of data is fed into the 1D-CNN, and a set of results is output, which is delayed for several clock periods compared with the corresponding input. The pipelined mode maximizes the utilization of resources.  
The pipelined mode between layers requires a parallel computing architecture inside each layer. The speed of the data stream is fixed in each layer, with no multiplexing of hardware resources between each step. In the convolution layer, two convolution kernels are routed respectively. Calculations of each element of the two output vectors are obtained synchronously; in the hidden layer, calculations of 20 parallel neurons are routed respectively. This parallel structure avoids the problem of routing across the clock domain in the implementation of the FPGA, and also avoids the problem of timing tension caused by frequent calls of the critical paths. The designed pipeline and parallel structure improves the stability of the circuit, and also provides the possibility to accelerate the speed of operations.
## 2 Materials and Methods
### Preparation of the Receptor and Docking-Based Screening  
The 3D structure (PDB ID: 4R3I, X-ray crystal structure, resolution of 1.80 A [27]) was modified to generate the structure of the YTH domain of YTHDC1 in the complex with N6-methyladenosine-5\({}^{\prime}\)-monophosphate (6 MZ), where the RNA other than the m6A residue was removed and the missing atoms for 6 MZ was then added using Chimera.  
Figure 1: Flow chart for a comprehensive structure-based virtual screening method using molecular docking approach, MD simulations, MM/PBSA analysis, and ADMET profiling.  
Another protein structure (PDB ID: 2 YUD) shows the apo-state YTHDC1, which was used to compare the holo-state YTHDC1 in the complex with 6 MZ(4R3I). The 3D structures of 4R3I and 2 YUD were obtained from the Protein Data Bank (PDB) website (rcsb.org). For calibration, the X-ray structures of protein-ligand complexes for these 11 ligands were downloaded from the RCSB Protein Data Bank (PDB Code: 6T10, 6T11, 6T12, 6T01, 6T06, 6T0A, 6T0C, 6T0D, 6 SZT, 6 SZT, 6 SZX). MD simulations were then performed to provide dynamic and energetic insights for the interactions between YTHDC1 proteins and ligands in the aforementioned nine complexes. In the protein preparation step, we only used chain A of the structure 4R3I for study and removed all other chains and water molecules. Atom types were assigned to protein atoms. Hydrogen atoms and AMBER charges were added. The docking of 6 MZ was performed with Surflex-Dock [55] in both the screen mode and the Geom X mode, with docking scores of 8.1 and 9.1, respectively. With the threshold set to 0.5 and the bloat settings used as the default, a ligand-based protocol was produced using 6 MZ. The docking procedure was completed utilizing the default settings. The screening and docking of the 820 shortlisted compounds were performed using Geom X mode. When compared to the screen mode, which has a spin density of 3, this mode's spin density for search was higher (set to 9). As a result, as the search was denser, the accuracy of the hit ranking was enhanced based on the docking score. In Surflex, flexible ligands were docked into binding sites using the Hammerhead procedure. In this method, fragments of ligands were produced and aligned to recognized probes, and the ligands' remaining fragments were docked to them. The weighted sum of the atomic van der Waals surface distances between the protein and ligand was used to determine the scores. The scoring function included the terms entropic, hydrophobic, solvation, polar, crash, and repulsive. The results indicated binding affinity expressed in \(-\)log 10 units (Kd). Surflex docking resulted in 92 compounds with docking scores higher than that of the 6 MZ, which was 9.1, and they were then inspected visually and shortlisted into 15 compounds with high binding affinity and strong interactions with pocket regions. In UCSF Chimera [56], high-affinity small molecules were visualized and analyzed. Docking results were visualized using BIOVIA Discovery Studio version 22.1 [57] to confirm the binding positions of the small molecules.
## 4 Results and Analysis
### Evaluation of System Performance
While the actual energy consumption with emulation process cannot be observed and requires an entirely different approach [70], the power/energy consumption during emulation can be monitored across devices based on their respective process IDs and resource allocation [71]. Observations and calculations were systematically recorded for each message transmission, ensuring precise tracking of resource usage. The summarized results of these observations are presented in Table 8.  
Figure 24: Network utilization during emulation process carried out in Mininet with POX controller, highlighting peak activities in the network during the encryption process. The study of power and energy in the SDN environment highlights stable power uses, with an average of 10.004 W, and minimal variation, on a processor with a per-core-TDP of 5.42 W. Figure 23, Table 8 and per-core-TDP indicate an imbalanced load distribution but show a consistent pattern of resource utilization [72]. The imbalance in load distribution is more visible with energy consumption, where variability better reflects the need for some specific optimization. Although the finding implies stable power levels and predictable performance for the current SDN environment, conclusive results can only be obtained after power and energy analysis is conducted per node with different experiments performed on a large network, which is not within the scope of this study.
# Transient Time Reduction in Time-Varying Digital Filters via Second-Order Section Optimization
## 2 Related Work
### Gap Identification  
A key challenge in the current state-of-the-art design of time-varying filters is the flexibility of transient time. In this paper, we shift the focus to another practical aspect of these techniques: the memory consumption required to store time-varying parameters. Previous studies have proposed selecting coefficient sets in a way that allows parameter values to be approximated using simple curve-fitting techniques, reducing the need for extensive storage.  
This paper introduces an alternative approach to selecting coefficients, aiming to achieve efficient transient time reduction while minimizing memory usage. Specifically, the method focuses on optimizing a single section within a decomposed higher-order digital filter, balancing transient performance with reduced storage requirements.
# Impact Analysis of Satellite Geometry Variation on ARAIM Integrity Risk over Exposure Interval
###### Abstract
Finally, we present the resulting integrity risk performance of the ARAIM under various simulation conditions, discuss the influence of satellite geometry variation on integrity performance, and concludes with remark on the future work.
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 6 Digital Transmission
### Radio Networks  
Trunked Radio Networks [68] provide a two radio system that multiplexes user conversations into a few dedicated frequencies (Table 6). Radio Networks enable talk groups and fleet maps to be used for external communications and mission critical applications within the Smart Infrastructure such as Police, Fire Brigade and Rail. Due its low Frequency, Radio Networks provide high levels of geographic coverage such as city scale at reduced infrastructure costs. In addition, Radio Handsets can communicate directly without the need of a radio infrastructure and be used as tracking device for user management.
###### Abstract  
Indoor human detection based on artificial intelligence helps to monitor the safety status and abnormal activities of the human body at any time. However, the complex indoor environment and background pose challenges to the detection task. The YOLOv8 algorithm is a cutting-edge technology in the field of object detection, but it is still affected by indoor low-light environments and large changes in human scale. To address these issues, this article proposes a novel method based on YOLOv8 called CIHD-YOLO, which is specifically designed for indoor human detection. The method proposed in this article combines the spatial pyramid pooling of the backbone with an efficient partial self-attention, enabling the network to effectively capture long-range dependencies and establish global correlations between features, obtaining feature information at different scales. At the same time, the GSEAM module and GSCConv were introduced into the neck network to compensate for the loss caused by differences in lighting levels by combining depth-wise separable convolution and residual connections, enabling it to extract effective features from visual data with poor illumination levels. A dataset specifically designed for indoor human detection, the HCIE dataset, was constructed and used to evaluate the model proposed in this paper. The research results show that compared with the original YOLOv8s framework, the detection accuracy has been improved by 2.67%, and the required floating-point operations have been reduced. The comprehensive case analysis and comparative evaluation highlight the superiority and effectiveness of this method in complex indoor human detection tasks.  
human detection; CNN; indoor scene; YOLOv8 +
Footnote †: journal: Applied sciences
# Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF
## 4 Experiment and Analysis
### Time Complexity of the Hybrid Algorithm UMAP-RF  
The main factors that determine the time complexity of the dimensionality reduction algorithm UMAP include: the number of data points in the high-dimensional space is \(n\), the original dimension of the high dimension is \(D\), and the target dimension of the low dimension is \(d\). The time complexity of the computational process of mapping the data points in the high-dimensional space to the low-dimensional space is \(O(nD)\); therefore, the time complexity of the UMAP algorithm is \(O(nD)\).  
The main factors that determine the time complexity of the random forest algorithm include: the size of sample is \(N\), the number of features is \(M\), and the depth of the tree is \(F\). When the cart grows, all the values within the feature are taken as split candidates, and an evaluation index is calculated for them, so the time complexity of each layer is \(O(NM)\), and the time complexity of the tree at layer \(F\) is \(O(NMF)\); therefore, the time complexity of the random forest algorithm is \(O(NMF)\).  
As shown above, the time complexities of the UMAP algorithm and the random forest algorithm are of the same order of magnitude, so the time complexity of the hybrid algorithm UMAP-RF is \(O(nD+NMF)\).