esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Risk AI Systems--Lie Detection Application
## 3 Current AI Technologies Used for Lie Detection
### Multimodal Approaches  
In recent times, advancements in artificial intelligence technologies have been progressively embracing multimodal approaches that effectively integrate a variety of different data sources. These may include but are not limited to facial expressions, vocal cues, and behavioral biometrics, which all contribute to creating a more robust and highly accurate system of deception detection. Such sophisticated systems are able to cross-validate information gathered by bringing together and synthesizing data from these various modalities. This significantly enhances the accuracy of the detection and decreases the overall likelihood of encountering false positives or negatives in the assessment process. For instance, a far-reaching multimodal AI system can establish a complicated process of analyzing the micro-expression of a subject and their vocal pattern simultaneously, during an interrogation. That process of simultaneous analysis helps to support the normally critical process of cross-checking any inconsistencies that may come up between what is said verbally and the non-verbal gestures accompanying such verbal communication.  
Moreover, several research studies, such as the 2022 study titled Multimodal Deception Detection: A Survey [36], highlighted significant benefits derived from integrating different types of modalities. They indicated that such integration could meaningfully raise the effectiveness and reliability of lie detection systems implemented in various settings.
# Enhancing Freeway Traffic Capacity: The Impact of Autonomous Vehicle Platooning Intensity
## 2 Conceptual Framework
### Platooning Intensity with Market Penetration Rate  
Due to the presence of heterogeneous traffic flow, different car-following behaviors can occur. To simplify this problem, we consider two specific types of vehicles: CAVs and HVs. In this scenario, there are four types of potential car-following behaviors corresponding to four types of time headways, \(h_{CC}\), \(h_{CH}\), \(h_{HC}\), and \(h_{HH}\), as illustrated in Figure 1, where \(h_{CC}\) represents a CAV following another CAV; \(h_{CH}\) represents a CAV following an HV; \(h_{HC}\) represents an HV following a CAV, and \(h_{HH}\) represents an HV following another HV.  
Here, \(N\) represents the total number of vehicles on the road, \(N_{C}\) represents the total number of CAVs on the road, and \(N_{H}\) represents the total number of HVs on the road. The MPR is represented by Equation (6),  
\[P_{C}=\frac{N_{C}}{N} \tag{6}\]  
Another crucial indicator, platooning intensity, \(P_{CC}\), is proposed to represent the willingness of CAVs to form platoons [36], where \(N_{CC}\) represents the number of CAVs following another CAV.  
\[P_{CC}=\frac{N_{CC}}{N_{C}} \tag{7}\]  
From all the possibilities of car-following behaviors, we derive three weights: \(P_{C}P_{CC}\), \(P_{C}(1-P_{CC})\), and \((1-P_{C})\). These weights represent the probability of a CAV following another CAV, a CAV following another HV, and HVs following another vehicle regardless of the type, respectively. These weights can be combined into Equation (8).  
\[\sum P=P_{C}+(1-P_{C})=P_{C}P_{CC}+P_{C}(1-P_{CC})+(1-P_{C})=1 \tag{8}\]  
The term \(N(N_{CC})\) is proposed to reflect the number of all possible scenarios for each \(N_{CC}\) that may occur, and is formulated in Equation (9). It is mainly obtained through the permutation and combination method. It should be mentioned that all the scenarios that may occur are assumed to show equal probability.  
\[N(N_{CC})=C_{N_{H}+1}^{N_{C}-N_{CC}}\times C_{N_{C}-1}^{N_{CC}} \tag{9}\]  
Given a certain MPR and \(n\), we derive \(N_{CC}(N_{CC}\in[0,N_{C}-1],\ N_{CC}\in N)\), which occurs with possibility \(P(N_{CC})\), as formulated in Equation (10), where \(N_{CC}\geq N_{C}-N_{H}\) due to the inherent relationship between \(P_{CC}\) and \(P_{C}\). Here, \(n\) is the number of vehicles in the fleet.  
\[P(N_{CC})=F(P_{c},n)=\frac{N(N_{CC})}{\sum N} \tag{10}\]  
The weighted average of platoon intensity \(\overline{P_{CC}}\) can be formulated by the sum of each \(P_{CC}\) times its possibility \(P(N_{CC})\).  
Figure 1: Vehicle arrangement and time headway diagram.  
When \(N_{C}\leq N_{H}\),  
\[\overline{P_{CC}}=\sum_{0}^{N_{C}-1}P_{CC}\times P(N_{CC}) \tag{11}\]  
\[\overline{1-P_{CC}}=\sum_{0}^{N_{C}-1}\left(1-P_{CC}\right)\times P(N_{CC}) \tag{12}\]  
when \(N_{C}>N_{H}\),  
\[\overline{P_{CC}}=\sum_{N_{C}-N_{h}}^{N_{C}-1}P_{CC}\times P(N_{CC}) \tag{13}\]  
\[\overline{1-P_{CC}}=\sum_{N_{C}-N_{h}}^{N_{C}-1}\left(1-P_{CC}\right)\times P(N _{CC}) \tag{14}\]
# Data Imputation Based on Retrieval-Augmented Generation
## 4 Experimental Verification
### Evaluation of the Overall Experimental Effect of Data Imputation
#### 4.1.2 Baseline Model  
**Reasoner Baseline**: We use the following baselines for end-to-end data imputation:  
* GPT-3.5/GPT-4.0: We directly use GPT-3.5 and GPT-4.0 as the reasoners for data imputation without the retrieval module. Additionally, to guide the model in recognizing the format of missing values, we add an extra complete tuple to the table that requires imputation.
* BM25-based GPT-3.5/GPT-4.0: To study the impact of different retrieval methods in our retrieval-enhanced imputation framework, we use BM25 [102] as the retriever module. This setup allows us to evaluate the influence of various retrieval strategies on the accuracy of data imputation.
## 2 Methods
### Data Description  
Two types of data have been used. On the one hand, the Fisher Spanish database made up with 163 h of telephone speech from 136 native Caribbean and non-Caribbean Spanish speakers. It contains around 4 h reserved for the test set and another 4 h for development, keeping the rest of the corpus for training.  
To augment the data for training with simulated distortions (packet losses and/or speech coding), a Fisher Spanish training data set has been replicated by artificially adding the following distortions. First, one of the three possible speech codecs considered (MP3 with 8 or 16 Kbit/s and GSM-FR) has been applied randomly to each recording. Then, a packet loss simulation system is applied that randomly applies a 0%, 5%, 10%, 15% or 20% packet loss percentage randomly selecting among three different methods (no bursts, only bursts or mixed).  
For evaluation, we have used both simulated and real data. For the simulated data, the test and development data sets reserved in Fisher Spanish have been replicated several times by adding the same types of distortions explained above. However, in this case, the same type of codec or the same type and amount of packet losses are applied to all the files in a single copy, thus obtaining three copies including the different types of codecs and eight copies including different types and amounts of packet losses. This allows us to analyze the results and robustness against each particular type of distortion.  
Finally, real data gathered from actual recordings of three real call centers with different types of data and different topics have been used to validate the use of data augmentation to compensate the effects of these distortions in real data. The data have been chosen in this way to have more variability and provide stronger conclusions about the techniques used.
# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
## 2 Low-Resolution Sequence Target Detection Algorithm  
In low-resolution remote sensing data, the target mainly presents unclear texture and blurred edges, and the gray distribution tends to be consistent. In addition, there are problems in sequence image frames, such as significant frame shaking and complex registration of sea surface scenes. At the same time, there is a considerable amount of data in the wide-field view, which is a great challenge to the real-time performance of data processing [21]. Therefore, the detection of low-resolution wide-area remote sensing targets mainly relies on feature recognition and motion correlation. This section gives a multilevel discriminant detection algorithm to improve accuracy and reduce the false alarm rate. First, the basic algorithm framework is introduced in Section 2.1. Then, Section 2.2 discusses adaptive segmentation preprocessing and SVM false alarm elimination with simplified HOG features. Finally, Section 2.3 discusses the target association of sequence images and the false alarm identification of sequence data based on S-YOLO.
# No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
## 3 Materials and Methods  
### Applied Datasets  
For our experiment, we used three different stereoscopic sequences (3D_02, 3D_car and 3D_03) from the RMIT3 DV [37] and EPFL [38] video datasets summarized in Table 1. All had a 3-s playback duration, full HD resolution of \(1920\times 1080\) and 25 frames per second, and consisted of different pictorial contents, such as low or high contrast and object movements, and textures. The two sequences, 3D_02 and 3D_car, which were from the EPFL dataset, showed a person bicycle-riding on a road and a car taking a turn while moving, respectively. The other sequence, 3D_03 which was from the RMIT3 DV dataset, was called Flag Waving in the state library which consists of a very fast motion. These videos consisted of different low to high level of 3D depth perception and various motions.
## 3 Results and Discussion  
### Simulation of Runoff Infiltration and Storage Process  
During the process of rainwater collection, retention, and discharge in the concave herbaceous field, the runoff generated by rainwater first gathers from the road surface on one side of the green space and flows into the green space aquifer after a short drop in the curb height under the action of gravity. The runoff then permeates vertically through the planting layer and collects at the end of the green space along the slope contained in the planting soil (slope from high to low inclined overflow wells). The excess rainwater beyond the permeability of the layer is collected through the overflow wells at the end. The lateral seepage pipe is arranged at the bottom side of the overflow well, which can discharge the rainwater collected from the overflow well through the drainage pipe. At the same time, the seepage effect on the side of the pipe and the rainwater from the planting soil will further percolate into the base soil to enhance the runoff absorption effect. In addition, the end of the seepage drainage pipe can be connected to a water storage structure, such as a reservoir, in order to make full use of the collected rainwater resources, to achieve the purpose of combining the drainage and storage functions of the sponge city. Moreover, in order to compare the changes of runoff characteristics (including peak runoff and peak generation time) generated by rainwater before and after the implementation of the concave herbaceous field, a \"hardened structure\" of the same size is set for each concave herbaceous field scheme as the \"hardened surface\" control group of LID facilities. The difference in stormwater runoff transport between traditional urban development mode and low impact development mode is more directly compared.
## 2 Calculation Model and Numerical Method
### Control Equations and Discretization Schemes  
The train was assumed to operate at a speed of V = 110 m/s and a temperature of T = 300 K (27 \({}^{\circ}\)C). At this time, the Mach number of the incoming flow relative to the train was about M = 0.32, which belonged to the subsonic range of aerodynamics. The compressibility of the air needed to be considered, so the complete gas model was adopted. If the length of the device in the x direction was taken as the reference length of the Reynolds number L = 0.5 m, then Re = 3.54 \(\times\) 10\({}^{6}\), which belongs to the turbulent flow.  
Because the Mach number of the incoming flow was M = 0.32, which belongs to the subsonic category, the air followed the complete gas model, and the Reynolds number corresponding to the flow was about Re = 3.54 \(\times\) 10\({}^{6}\). Therefore, the control equation of the problem was the compressible Reynolds average Navier-Stokes equation (RANS equation), in which the shear stress transfer (SST) turbulence model was used to treat the turbulence. The three-dimensional conservative RANS equations follow the computational coordinate system. For the specific meaning of each item, please refer to books on computational fluid mechanics.  
\[\frac{\partial Q}{\partial t}+\frac{\partial E}{\partial x}+\frac{\partial F}{ \partial y}+\frac{\partial G}{\partial z}=\frac{\partial E_{v}}{\partial x}+ \frac{\partial F_{v}}{\partial y}+\frac{\partial G_{v}}{\partial z} \tag{1}\]  
The finite volume method was used to discretize the N-S equation [16; 17; 18], a practical and widely used method in engineering. The bounded central differencing (BCD) scheme was used to discretize the flux terms; with this scheme, we adopted a blended coefficient (\(\varphi\)) to adjust the numerical accuracy and stability. For the present simulation, the coefficient (\(\varphi\)) was set to 0.15 to reduce the numerical dissipation considerably. This calculation is unsteady, and we finally obtained steady results with a high convergence rate. The implicit scheme with dual-time steps was adopted, the physical time step was 0.00005 s, and 10,000-time steps were iteratively calculated.
# Beam Scanning and Capture of Micro Laser Communication Terminal Based on MEMS Microm mirrors
###### Abstract  
With the development of space laser communication and the planned deployment of satellite Internet constellations, there is a growing demand for micromiature laser communication terminals. To meet the requirements of size, weight and power (SWaP), miniaturized terminals require smaller drive components to complete on-orbit scanning and capture, which must be fast and efficient to enable satellite laser communication networks. These miniaturized laser communication terminals are highly susceptible to the impact of the initial pointing accuracy of the laser beam and microvibrations of the satellite platform. Therefore, this paper proposes a laser scanning-capture model based on a Micro-electromechanical Systems (MEMS) micromirror that can provide a fast, large-scale scanning analysis. A scanning overlap factor is introduced to improve the capture probability under the influence of microvibrations. Finally, experimental analysis was carried out to verify the effectiveness of the proposed model, which can establish a theoretical basis for future ultra-long-distance microspace laser communication.  
free-space optical communication; MEMS micromirror; laser scanning and capture +
Footnote †: journal:  
0203 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# 4D Printing of Origami Structures for Minimally Invasive Surgeries Using Functional Scaffold
## 2 Design and Experimental Setup
### Origami Design  
Origami tessellations are origami-folding patterns that repeat themselves and therefore can be scaled up and down depending on application, with the potential of the fold being infinitely repeated. Different tessellations are enabled for acute management of pre- and post-shape recovery as well as how the polymer would origami-move between the states [41; 42]. The design of the scaffolds was split into microstructure and macrostructure design. The microstructure design was concerned with how the scaffold's pores are shaped, sized, and arranged to promote cell differentiation, migration, and proliferation, whereas the macrostructure was concerned with the design of a recovered scaffold. The primary consideration of the macrostructure was to replicate bone morphology while simultaneously employing origami techniques to exploit the SME of PLA.  
Origami herringbone and waterbomb tessellation designs were considered in this study, as shown in Figure 1a,b. The tessellations were curved around on themselves to create a tube. As recovery occurs, the cavity in the middle grows and so accurately represents the medullary cavity found in many long bones. By far the most significant benefit of this design is the substantial difference in volume between recovered and collapsed forms Figure 1c shows how compressing what is already very similar in structure to real cancellous bone will close the gaps between replicated trabeculae, allowing for a smaller geometry before recovery. Almost all bones have a section of compact bone with a cancellous bone interior. Therefore, this kind of scaffold would be highly transferable between different bones within the body. There are two potential problems with this design. First, the size difference between deformed and recovered form is not as significant as in other designs. Secondly, and most importantly, this design does not offer a great solution to reducing the size of the scaffold that needs to substitute for compact bone. The combination of an origami design for the outer edges of the scaffold, which would substitute for compact bone, and the porous replica of cancellous bone to fill the centre, is shown in Figure 1d. This merger of designs eliminates the weaknesses of the individual designs to create a solution that will meet the needs of the patient and surgeon.  
Figure 1: CAD drawings of (**a**) herringbone tessellation origami, (**b**) waterbomb tessellation origami, (**c**) natural cancellous bone, and (**d**) combined tessellation origami and natural cancellous bone design.
## 4 Analysis, Results, and Discussion
### Stage 2--Data Analysis Using WEKA
CSVs were used in this research as they can be extracted from Excel spreadsheets directly so that the results can be visualized, as shown in Figure 14. In addition, the visual qualifications of WEKA make machine learning easy for non-programmers. In addition to the explorer, the workbench was used to combine the analysis of all the other applications under one perspective [23]. Cluster analysis is mostly used to estimate the accuracy of the resulting predictive model and to visualize erroneous predictions via regression algorithms, either through logistic regression or linear regression (they can be trained incrementally) (see Figure 15). Figure 14: Visualization of results for the main entry associations. Figure 13: Attribute entry interface in WEKA. From the application part, the resulting processed data from the literature review analyzed via machine learning, and prioritized and focused using the following output, are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Key Hazards** & **Agent** & **Impact** & **Adaptation of Key Actions** \\ \hline \multicolumn{5}{c}{Changing storm conditions (magnitude and frequency)} \\ \hline Sea-level rise & Water & \(\bullet\) & Material erosion & \(\bullet\) & Map, communicate, and mitigate the risk through temporary engineering solutions. \\  & Water & \(\bullet\) & Salty saturation of materials & \(\bullet\) & Identify heritage solutions. \\ \hline Coastal change (erosion patterns and change) & Water and wind & \(\bullet\) & Coastal heritage risk from \(\bullet\) & Identify areas susceptible to rapid coastal change and the assets that are vulnerable or will be lost; then, choose biological or engineering solutions. \\ \hline \multicolumn{5}{c}{Changing groundwater conditions} \\ \hline Building subsidence & Water & \(\bullet\) & Physical deterioration & \(\bullet\) & Identify items of greatest risk. \\ \hline Identify which building types are most at risk and define the conditions that lead to failure. & Water & \(\bullet\) & The unified approach to dealing with heritage sites causes significant deterioration to the physical conditions of buildings & Identify building types most at risk and define engineering solutions. \\ \hline Identify potential long-term impacts & Water & \(\bullet\) & Side effects & \(\bullet\) & Identify long-term treatments. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prioritized key hazards and adaptation actions that formulate the model [24]. Figure 15: Data mining visualizing prediction boundaries.
## 2 Materials and Methods
### Decision-Making Model
#### 2.3.3 Decision-Making Model  
As seen in Figure 5, the gravitational force that the target point exerts on the AV serves as its driving force. The acceleration brought on by the pseudo-gravitational force will be constrained by the expected speed with the limited speed in order to prevent unrestricted acceleration in special circumstances. It is capable of defining the driving force as:  
\[G_{i}=m_{i}g_{e}, \tag{22}\]  
where \(m_{i}\) denotes the mass of vehicle \(i\), and \(g_{e}\) denotes the acceleration due to class gravity. We define \(g_{e}\) as  
\[g_{e}=k_{g}\frac{v_{exp}}{v_{lim}}g, \tag{23}\]  
where \(k_{g}\) is a constant (here we take \(k_{g}=0.2\)), \(v_{exp}\) is the driver's desired velocity, \(v_{lim}\) is the limiting velocity, and \(g\) is denoted as the gravitational acceleration.  
In addition, traffic rules also exert a force on the driver to keep the speed within a reasonable range: the driving force will drive the vehicle to accelerate when the self vehicle speed is less than the required minimum speed of the current road, and the binding force will constrain the vehicle to slow down when the self vehicle speed is greater than the required maximum speed of the current road. We define the traffic rule binding force as.  
\[F_{c}=m_{i}g_{e}\left(\frac{v_{i}}{v_{lim}}\right)^{\tau}, \tag{24}\]  
Also, to ensure that AVs do not change lanes unnecessarily and maintain their current lane at intersections or other non-lane change scenarios, we define lane repulsion as follows.  
\[F_{l}=m_{i}l_{t}\left(\frac{D}{2}-r_{li}\right)^{k_{2}}, \tag{25}\]  
Figure 5: Schematic diagram of the effect of basic forces.  
where \(l_{t}\) denotes the road type (where we take dot line for \(l_{t}=1\) and solid line for \(l_{t}=2\) with intersection lane line \(l_{t}=3\)), \(r_{li}\) denotes the distance between the lane line and the vehicle center point on both sides and \(k_{2}\) is a constant (take \(k_{2}=1.2\)).  
Although UDORF has simplified the in-junction scenario significantly, further extraction of key information is still needed to shorten the evaluation process. Figure 5 illustrates that natural driving scenarios can be obtained from the following equations.  
\[L=\frac{1}{2}m_{i}v_{i}^{2}-\int_{t_{0}}^{t_{f}}{(R_{i}-G_{i})v_{i}dt} \tag{26}\]  
where \(G_{i}\) denotes the attractive force acting on vehicle \(i\) and \(R_{i}\) denotes the hindering force, which includes the traffic rule binding force \(F_{ci}\), the lane repulsion force \(F_{li}\) and the electric field-like force \(F_{ji}\).  
Therefore, the driving action of the vehicle can be obtained by the following equation.  
\[S=\int_{t_{0}}^{t_{f}}{\left(\frac{1}{2}m_{i}v_{i}^{2}-\int_{t_{0}}^{t_{f}}{(R _{i}-G_{i})v_{i}dt}\right)dt}, \tag{27}\]  
The same is true for the cost function of the multiple objective decision model. The cost function takes into account safety and efficiency to optimize the driving decision process. In addition, we will illustrate this cost function in the next section using specific examples.