esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analyses  
The goal of the study is to investigate the effect of the MR-subspace-enhanced imitation-based HRI module on the operator's ability to interact with objects in the remote environment. To evaluate the efficacy of the robotic manipulation system presented, human operator performance was assessed under three HRI modes denoted: B, MRD and MRS. B denotes the Baseline, using only a 6-DOF SpaceMouse and a monocular RGB display. MRD (MRnoVCMM/MR SpaceMouse) represents the MR direct control module using SpaceMouse and an MR-enhanced 3D point cloud visual method without deploying VCMM. MRD is a limited version of the proposed MR-HRI system. MRS (MRwithVCMM) provides the user with the proposed MR subspace module using the VCMM approach and a Vive controller as well as an MR-assisted 3D point cloud display.  
Task performance and work efficiency were evaluated by measuring the time for each task and the total time for both tasks. Participant perception of the different HRI modalities was measured using a questionnaire based on a previous study assessing user preferences and system usability, including the NASA Task Load Index (NASA-TLX) and Technology Acceptance Model (TAM) for evaluating the user's acceptance [54; 55; 56]. User performance was measured by time for each manipulation task and aggregated total time for both tasks. User effort and workload during teleoperation experiments were evaluated by the NASA-TLX score, where from 0 to 100 (most difficult), participants rated qualitative experiences of mental demands, physical demands, time demands, performance, effort, and frustration at the end of each experimental case. User acceptance and perception, including usefulness and ease-of-use, was assessed by questionnaires based on the TAM. This survey uses a 0 to 7 (best) scale to measure the acceptance and ease-of-use of different HRI modules. A one-way within-subjects ANOVA with repeated measures was used to analyse data from all measures. Greenhouse-Geisser correction was applied to assess the difference in the survey responses with B, MRD, and MRS HRI modules as within-subjects variables.
## 5 Modeling Results
### Influence of Variable Section Thickness of Sleeve Arch on Reinforcement Performance
#### 5.2.2 Reinforcement Performance of the Severely Cracked Lining  
As shown in Figure 17, the stress in thin sleeve arches of varying thicknesses at Point A is directly proportional to the load and inversely proportional to the thickness. Despite initial similarities, as the load increases, the stress gradually escalates. Thin sleeve arches at Point A, with thicknesses of 50 mm, 60 mm, 75 mm, 85 mm, and 100 mm, exhibit compressive stresses of \(-\)10.713 N/mm\({}^{2}\), \(-\)10.450 N/mm\({}^{2}\), \(-\)7.832 N/mm\({}^{2}\), \(-\)7.625 N/mm\({}^{2}\), and \(-\)7.356 N/mm\({}^{2}\), respectively, under a load of 1000 KN.  
As depicted in Figure 18a, during the initial stages of loading, the thin arch at Point B is primarily subjected to compression. Upon reaching a specific load, the compressive stress decreases, and the arch, along with the original lining, begins to share the load. As the load increases, the concrete at Point B predominantly experiences tension, and the tensile stress gradually escalates. At a load of 1000 KN, the thin arches at Point B with thicknesses of 50 mm, 60 mm, 75 mm, 85 mm, and 100 mm exhibited tensile stresses of 0.753 N/mm\({}^{2}\), 0.715 N/mm\({}^{2}\), 0.653 N/mm\({}^{2}\), 0.611 N/mm\({}^{2}\), and 0.471 N/mm\({}^{2}\), respectively.  
Figure 17: Relationship curve between load and stress at Point A.  
Figure 18: Relationship curve between load and stress: **(a)** Point B; **(b)** Point D.  
As depicted in Figure 18b, during the initial stages of loading, the concrete at the composite surface of the sleeve arch and lining is predominantly compressed. Upon reaching a specified load, the compressive stress diminishes. As the load increases, the concrete at the composite surface primarily experiences tension. At a load of 1000 KN, the thin arches at Point D with thicknesses of 50 mm, 60 mm, 75 mm, 85 mm, and 100 mm exhibited tensile stresses of 0.187 N/mm\({}^{2}\), 0.171 N/mm\({}^{2}\), 0.158 N/mm\({}^{2}\), 0.150 N/mm\({}^{2}\), and 0.138 N/mm\({}^{2}\), respectively.
## 3 Results and Discussion
### Differential Scanning Calorimetry  
Differential Scanning Calorimetry was used to characterize all products and raw materials. The results of the first heating runs are shown in Figure 8. PLA pellets show an endothermic peak at 60.8 \({}^{\circ}\)C that corresponds to the glass transition temperature (T\({}_{\text{g}}\)), an exothermic cold crystallization peak (T\({}_{\text{c}}\)) at 106.6 \({}^{\circ}\)C, while the endothermic peak at 173.4 \({}^{\circ}\)C corresponds to the melting point (T\({}_{\text{m}}\)). The melting point peak shows an asymmetrical shape due to overlapping peaks. In the case of PLA, this can be attributed to two different lamellae structures with different thicknesses. The thinner lamellae melt at a lower temperature than the wider counterpart. Another explanation is based on the melting-recrystallization mechanism in which the lower melting point is due to the initial amorphous phase, which is transformed, resulting in crystalline lamellae with a higher melting point [58]. [PERSON] et al. reported the thermal transition temperatures of 3D printed objects manufactured using the FDM technique. Using commercial PLA filament, the glass transition, cold crystallization, and melting temperatures were measured at 57.8 \({}^{\circ}\)C,  
Figure 7: ATR-FTIR spectra of neat polymers (PLA and PCL) and polymer blends (**a**), inorganic fillers (HAp and BT) (**b**), composite filaments (**c**), and 3D printed scaffolds (**d**).  
127.8 \({}^{\circ}\)C, and 156.8 \({}^{\circ}\)C, respectively. These differences in comparison with the values measured in the present manuscript can be attributed to the differences in molecular weight or the presence of additives used in commercial filaments [59].  
The thermal behavior of PCL pellets in the range between 25 to 200 \({}^{\circ}\)C presents a strong endothermic peak at 60.8 \({}^{\circ}\)C that corresponds to the melting point. The melting point of PCL is a crucial parameter to set the printing bed temperature. In our case, the bed temperature was set at 40 \({}^{\circ}\)C to eliminate PCL deformation of the printed parts. The DSC thermographs of PLA/PCL filaments and printed specimens show a decrease in T\({}_{\text{g}}\) temperature of PLA in comparison with the neat PLA pellets. This shift can be attributed to the plasticizing effect of PCL [60].  
Comparing the DSC graphs of filaments (Figure 8a) and printing objects (Figure 8b), the data obtained from the melting point peak show no significant changes. However, it is interesting to note that the cold crystallization temperature (T\({}_{\text{c}}\)) of PLA is decreasing in PLA/PCL blends (sample 0-0) as well as in the presence of fillers, indicating that both act as nucleators and therefore the nucleation occurs at lower temperatures. The enhancement of the nucleation process in PLA in the presence of PCL has been reported previously [61].
## 4 Results and Discussion
### Influence of Fluid-Structure Interaction on Aerodynamic Performance of Wind Turbines
#### 4.1.2 Influence of Bidirectional Coupling on Flow Field Characteristics  
Figure 14 shows the velocity distribution of CFD and 2D fluid-solid coupling flow field at 10 m/s wind speed. From the figure, it can be seen that the turbine flow field is divided into two regions regardless of whether the fluid-structure coupling technique is used or not: the normal region downstream of the wind turbine and the low-speed region upstream, and the effect of the turbine on the downstream spreads to about 1.5D when the time of calculation is 20 s. However, due to the large deformation of the blade tip, which is about 5 m, the fluid-structure coupling method considering the elastic deformation has a little loss of the blade tip velocity.  
The pressure distribution on each blade section at 10%, 50%, 70% and 90% of the length from the blade root was extracted, and the maximum pressure difference between the positive pressure zone on the windward side and the negative pressure zone on the leeward side was analyzed. The results are shown in Figure 15. It can be seen from the figure that the pressure difference between the windward and leeward sides of the blade increases nonlinearly with the increase in the span, and the longer the span, the faster the increase. Considering the deformation of the blade, the fluid-structure coupling method is adopted. The flapping motion of the blade makes the pressure change on both sides of the blade more obvious, and the surface pressure of the blade is greater, which has a greater test on the pressure resistance of the blade surface. The red dashed line indicates that the effect of bidirectional fluid-structure interaction becomes more significant as the wind speed increases. For higher wind speeds of 10 m/s, the pressure difference between 50% of the blade length and the blade tip increases rapidly, and the blade deformation is most severe. The higher the wind speed, the greater the effect of deformation on the pressure difference between the two sides of the blade, which again proves the necessity of aeroelastic analysis in the design of wind turbine blades.  
Figure 14: Comparison of flow field distribution under different calculation methods at 10 m/s. (**a**) Flow field distribution is calculated by CFD. (**b**) Flow field distribution is calculated by two-way coupling.
## 3 Results and Discussion
### CT Tomography Assessment
#### 3.1.4 Assessment of Test Specimens' Dimensions  
The production accuracy is assessed by the external dimensions of specimens and by comparing them with reference CAD models. The results show the gray specimens having the smallest deviation, followed by red specimens, while the black specimens feature the highest deviation. This result is in contrast with the visual assessment of specimens, where the red ones were of the worst quality (Table 6).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen Designation** & **A [mm]** & **B [mm]** & **C [mm]** & **Area A \(\times\) B [mm\({}^{2}\)]** & **Percentage Change in Area A \(\times\) B [\%]** \\ \hline CAD model & 35.000 & 35.000 & 7.000 & 1225.000 & - \\ \hline Gray lattice & 34.783 & 34.858 & 6.850 & 1212.660 & \(-1.023\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Assessment of external dimensions of specimens.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen Designation** & **Created Volume Share** & **Theoretical Specim** & **Actual Specimen** & **Percentage Change in** \\  & **[\%]** & **Volume \(V_{T}\) [mm\({}^{3}\)]** & **Volume \(V_{R}\) [mm\({}^{3}\)]** & **Volume [\%]** \\ \hline Gray lattice & 25.170 & 2158.370 & 1971.000 & \(-8.681\) \\ \hline Red lattice & 25.170 & 2158.370 & 1801.000 & \(-16.557\) \\ \hline Black lattice & 25.170 & 2158.370 & 1816.930 & \(-15.819\) \\ \hline Gray gyroid & 25.080 & 2150.770 & 2064.590 & \(-4.007\) \\ \hline Red gyroid & 25.080 & 2150.770 & 1995.000 & \(-7.243\) \\ \hline Black gyroid & 25.080 & 2150.770 & 1975.830 & \(-8.134\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Volume and percentage volume change in individual specimens.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen Designation** & **Theoretical Specim** & **Actual Specimen Surface \(A_{R}\)** & **Percentage Change of** \\  & **Surface \(A_{T}\) [mm\({}^{2}\)]** & **[mm\({}^{2}\)]** & **Surface [\%]** \\ \hline Gray lattice & 5525.038 & 8889.730 & 60.899 \\ \hline Red lattice & 5525.038 & 6646.072 & 20.290 \\ \hline Black lattice & 5525.038 & 6055.280 & 9.597 \\ \hline Gray gyroid & 8090.083 & 13,476.740 & 66.583 \\ \hline Red gyroid & 8090.083 &
# Analysis of Weighted Factors Influencing Submarine Cable Laying Depth Using Random Forest Method
## 2 Materials and Methods
### Method
#### 2.3.3 Isoa-bp  
Based on the principle of the BP neural network, the network's architecture is composed of a number of layers and neurons. Different architectures yield different performances. To find the optimal network structure and improve the predictive performance of the model, an improved seagull optimization algorithm (ISOA) is used to optimize the parameters of the number of layers (\(l\)) and neurons (\(n\)). The ISOA simulates the foraging behavior of seagulls, an approach that aligns well with the explorative nature of architectural optimization in neural networks. The process is illustrated in Figure 2.  
More details of the ISOA-BP are as follows:  
Initialization: In the context of the ISOA, each seagull in the population represents a potential neural network architecture. An initial population of seagulls is generated randomly, where each individual seagull's position corresponds to a specific combination of layers and neurons. For example, one seagull might represent a network with three layers and ten neurons in each layer.  
Fitness Evaluation: The fitness of each seagull is evaluated based on the mean squared error (MSE) between the predicted outputs of the neural network and the actual data. A lower MSE indicates a better-performing network architecture, hence a fitter seagull.  
Position Update: The position of each seagull is updated according to the fitness landscape. Two primary behaviors are simulated: Starving Search and Satisfied Search; there are detailed descriptions in step 3 of Section 2.3.1.  
Convergence Check: The algorithm iterates, updating positions and evaluating fitness until one of the termination criteria is met. The process terminates when either a seagull's fitness drops below a predefined threshold, which here is 0.01, or there are iterations up to 100.  
Figure 2: Overall framework of ISOA-BP.  
Model Training and Prediction: Utilizing the optimized architecture parameters--layers and neurons--the BPNN is trained on the dataset. The network undergoes forward and backward propagation to adjust the weights and biases, minimizing the prediction error. Subsequently, the trained model is used to make predictions on new data.  
The experiments were conducted on a Windows machine equipped with an AMD Ryzen 9 processor (16 cores @ 3.3 GHz, Advanced Micro Devices, Inc., Santa Clara, CA, USA) and 16 GB of RAM, using Python (version 3.9.12), scikit-learn (version 1.4.1), and TensorFlow (version 2.6.0). The results may be influenced by the specific experimental conditions, so it is recommended to conduct repeated tests under varying conditions to validate the model's stability.
## 5 Experimental Results and Analysis
### Strategy Discussion  
This article mainly proposes a method to adjust the encryption level based on the current environment. By scientifically detecting the gateway environment and quantifying the security at this time, the purpose of dynamically adjusting the encryption level is achieved. When the environment of the gateway changes and the importance of tasks varies, the encryption level should also be correspondingly increased. In secure environments, fast encryption methods should be used, and in high-risk environments, more secure encryption methods should be used to improve time efficiency. The environmental security we are considering has better universality compared to the MG-MTD strategy. We only need to establish traffic monitoring at the gateway, and our defense strategy is not complex to implement, achieved by designing the number of encryption rounds for the key. The DDR-MTD strategy can have good defense effects by quantifying attackers and mobile target defenders, but it poses a challenge to edge devices with scarce computing resources. Our solution incorporates lightweight encryption methods for computing resources, effectively reducing encryption latency. In this experiment, there are still some shortcomings, as too few encryption methods were used; mainly AES and RC5 with different encryption rounds were used, without considering the impact of key updates. In subsequent work, different encryption methods can be considered based on the characteristics of different data formats to study the impact of different encryption algorithms on encryption efficiency during data fusion.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 2 Wi-Fi Basics
### PHY in Wi-Fi
#### 2.1.1 Orthogonal Frequency Division Multiplexing (OFDM)  
OFDM is a multicarrier modulation scheme for data transmission in a channel. This means that the channel is subdivided into multiple subcarriers or tones, and data are transmitted in parallel over the subcarriers. The distance between the subcarrier frequencies may be low, e.g., 312.5 kHz in Wi-Fi 5. To minimize interference among the subcarriers, the frequencies must be chosen carefully to be orthogonal to each other. Then, each subcarrier is modulated independently.  
OFDM typically spreads the information of a high-bitrate stream over \(2^{n}\) subcarriers. To that end, \(m\)-bit groups are assigned to each subcarrier and converted to a constellation point using QAM. The entirety of all constellation points forms an OFDM symbol. Conversion to electromagnetic waves turns this OFDM symbol into an OFDM signal. An OFDM signal transmits one or multiple concatenated OFDM symbols.  
To avoid interference in time, a Guard Interval (GI) is added after every symbol. It is a short time interval between two consecutive symbols that protects against imperfect synchronization as successive transmissions must never overlap, particularly in the case of clock drifts or multipath delay spread. Symbol duration and GI are visualized in Figure 2(a). In Wi-Fi 5, the symbol duration is 3.2 \(\mu\)s long and the GI takes 0.4 \(\mu\)s or 0.8 \(\mu\)s.  
OFDM in Wi-Fi 2-5 is used for data transmission. A high-bitrate data stream is divided into multiple lower-bitrate data streams. Thus, each subcarrier has a \(\frac{1}{N}\) data rate of the total data rate. As data is transmitted in parallel at a \(\frac{1}{N}\) data rate, the symbol time increases by \(N\) compared to the single-tone transmission. Therefore, the OFDM symbol duration has a reverse relationship with the subcarrier spacing. Figure 3 illustrates the relationship between subcarrier spacing and symbol duration in OFDM: smaller subcarrier spacing requires a longer symbol duration.
## 4 Truck-UAV Collaborative Delivery Planning Algorithm Based on Improved Evolutionary Algorithm
### Problem
#### 4.1.1 Design of Solutions  
The resolution to the air-ground collaborative delivery problem is represented as a sequence that encompasses vehicle routes and drone task assignments. Each solution comprises two components, with each component stored in the form of a vector.  
The vehicle route is defined as a sequential arrangement that encompasses all demand points designated for delivery.  
The drone route is defined as a sequential set that includes both the departure and return locations of the drone in relation to the vehicle, as well as all designated demand points for drone delivery.
# Peptides That Block RAS-p21 Protein-Induced Cell Transformation
## 2 Anti-Onogenic RAS-p21 Peptides from RAS-p21 Itself
### Alternate Targets of RAF  
Several of these putative targets of activated wild-type RAS-p21-RAF complexes not utilized by oncogenic RAS-p21 proteins were subsequently identified, using differential gene expression libraries for oocytes that were induced to mature either with oncogenic (Val 12)-ras p21 or insulin. In the wild-type RAS-p21-matured oocytes but not in the oocytes induced to mature with Val 12-RAS-p21, two dual specificity kinases, TOPK (T-Cell Origin Protein Kinase) and Dyrk1A (Dual Specificity Tyrosine Phosphorylation Regulated Kinase 1A), were expressed [22]. The former is in the MEK family and is known to interact with RAF. siRNA against the expression of each of these proteins were injected into oocytes that were induced to mature either with Val 12-p21 or with insulin and were found to block maturation only in the _insulin-treated_ oocytes [15]. These findings suggested that both proteins participate on the signal transduction pathway of the activated wild-type protein but not the oncogenic protein.  
Molecular dynamics calculations on the structures of wild-type- and G12V-RAS-p21 bound to the ras-binding domain (RBD) of RAF (residues 25-131) show that there are major shifts in domain positions in the RBD, especially in a surface loop involving residues 97-110 of the RBD [15], as shown in Figure 3. This peptide has been synthesized and found to block G12V-RAS-p21-induced oocyte maturation with no effect on insulin-activated wild-type RAS-p21-induced maturation [15], suggesting that the interactions between the two ras proteins and RAF differ.
# Seismic Response Analysis of a Curved Bridge under Near-Fault and Far-Field Ground Motions
## 3 Design of Shaking Table Tests  
### Sensor Arrangement  
This test is carried out using a shaking table array system, and the test instruments mainly include acceleration (measuring range, \(\pm\)5 g; accuracy, 0.01 g), displacement (measuring range, \(\pm\)200 mm, accuracy, 0.4 mm), and pull-wire displacement (measuring range, 0-500 mm, accuracy, 0.01 mm) sensors. The acceleration and displacement sensors are arranged at the fulcrum and mid-span of the main girder, as well as the roof and bottom of the pier, to record the acceleration and displacement at the crucial positions of the main girder and piers. The bridge model is equipped with 16 acceleration and 16 displacement sensors, and 6 pull-wire displacement meters are arranged at the unidirectional sliding pot bearings to record the deformation of the bearings. The signals of all sensors are collected using an IMC dynamic system with a sampling frequency of 1000 Hz.
## 2 Problem Statement  
The present work aims to review the current literature in three different topics and to link them together, for extracting potential future lines of work in risk prevention in online social environments. The three topics are user state detection, risk prevention, and recommendation.  
* User state detection: refers to the automatic detection of an aspect of the user state by the system. Has many variations, but since in this work we aim to address risk prevention we focus on the detection of the sentiment and stress levels of users, which are sentiment analysis and stress analysis. Those two techniques address the problem of detecting automatically the sentiment and stress level of users by employing different techniques (e.g., machine learning, natural language processing (NLP)) on different data sources (e.g., text, audio, images), either using one data modality or multiple.
* Risk prevention: addresses the prevention of risks that users on a system can suffer. In our case, we focus on the prevention of risks that users can suffer while navigating online social environments, such as SNSs. It can be performed by employing user state detection and applying feedback to users when necessary or giving recommendations to them, which is the focus of the present survey, but it can also be addressed by performing analysis of relations between users and warning users about dangerous people, as an example.  
* Recommendation: encompasses the techniques used by a system to give recommendations about different matters to users (e.g., what to buy, when to invest, whom to trust). User state detection can be used by the system to perform recommendations, and recommendations can in turn be used to prevent risks that users could be exposed to in a system.  
In the following two sections, and to be able to later draw conclusions on how the existing state-of-art technologies can help risk prevention and user guidance in SNSs, and elaborate potential new lines of work, we will review works related to the user guiding process. Firstly, we start reviewing works on user automatic sentiment polarity and stress level detection. We also review works in Case-Based Reasoning (CBR) based sentiment analysis. Later we review works in risk prevention, and MAS-based recommendation and guiding systems.