esa-sceva/satcom-chunk-collection huggingface dataset:
# Projection-Based Augmented Reality Assistance for Manual Electronic Component Assembly Processes
## 5 Discussion  
We propose to use direct projection in the workspace for improving user satisfaction and at the same time reducing assembly errors. The previous section shows that operators actually find the system more usable, feel more secure with it and require less time to do their tasks. A further advantage is that operators requires less training time, as the system gives assistance throughout the assembly. Moreover, this system allows the production managers to have traceability of the most complex components or PCBs to be assembled. This enables them to take further measures for ensuring operator satisfaction while also optimizing production because of the reduction of potential errors.  
To guarantee that the projection-based solution is effective, the illumination conditions of the workspace have to be considered. The ambient light cannot be strong, so that the light emitted by the projector is predominant and the projected content is shown with contrast and sharpness. A balance must be achieved between a valid ambient light for object detection (electronic components in our case) and light that does not defeat the visibility of the projector. Similarly, it is preferable to work on non-specular surfaces, so that no brightness is generated that hinders the visibility of the projection. In our scenario, we had to deal with this difficulty, since PCBs are specular, and therefore, we had to use more sophisticated visualization techniques to capture the operator's attention (flickering).  
In the use case presented in this paper (assembly small electronic components in a PCB) we have not had problems with hidden areas of projection. These areas appear when an object that is in the workspace and in front of the projector has large dimensions and occludes the area behind it. Thus, the rays emitted by the projector cannot reach this area, and therefore, it is not possible to project content in this zone. To solve this limitation, a multiprojector configuration should be used.  
Figure 8: Mounting times of each group. Blue and yellow bars represent the original and proposed system, respectively. Group 1 started with the original, Group 2 started with the proposed and Group 3 already had experience.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Informal Security Analysis
#### 5.3.5 DC Impersonation Attack Resistance  
In order to impersonate a legitimate data concentrator, adversary \(A\) must compute a valid \(\{M_{5},M_{7},M_{8}\}\). \(M_{7}=(PID_{i}^{new}||E_{i}^{new})\bigoplus H_{0}(M_{6}||E_{i})\) and \(M_{8}=H(ID_{i}||PID_{i}||E_{i}||M_{2}||M_{5}||SK_{jk})\) are calculated by \(\{ID_{i},PID_{i},PID_{i}^{new},E_{i},E_{i}^{new}\}\) and computed by the hash function. Therefore, \(A\) cannot directly obtain these parameters and cannot guess the values successfully in polynomial time. In order to calculate the above parameters, \(A\) must obtain \(\{ID_{i},s,r_{i}\}\), but it is impossible to obtain the private values \(\{s,r_{i}\}\) in polynomial time, and because of the anonymity and un-traceability of the scheme, \(A\) cannot obtain \(ID_{i}\). Therefore, \(A\) cannot calculate a valid response message, so the scheme can effectively resist data concentrator impersonation attack.
## 3 Enabling the Enhancements of Language Features and Domain Knowledge  
### EMGSCC: Enhanced Model for Generating System Configuration Code  
The scenario selected in this article for generating dependency code is to generate the expected accompanying dependency libraries (ADL) based on the dependency libraries already employed in the system. According to the enhancement path we have formulated, the enhanced model proposed in this paper focuses on introducing the language characteristic of the Language Feature-based Model and capturing the usage pattern of the Domain Knowledge Awareness Model. To achieve this purpose, considering the research status quo, the deep sequence-to-sequence-based models are the top options, in conjunction with putting forward targeted improvements that rely on the motivating factors of system configuration code. Hence, we have started with the most feasible and accessible direction of improvements, namely dealing with the concise and unordered list of dependency libraries, and tentatively obtaining usage patterns from the domain information.  
On this basis, we propose a set-to-set based enhanced model for generating system configuration code (EMGSCC), which employs recurrent neural networks to learn the underlying characteristics from relevant dependency corpus. EMGSCC obeys the token-based constraints [8] and concentrates on generating the critical slots of dependency code; that is, the dependency library coordinates. EMGSCC uses an augmented attention mechanism to process the unordered set of input dependency libraries and a multi-label classification layer to output the unordered set of output dependency libraries. Furthermore, EMGSCC seeks practical ways to integrate domain knowledge to learn usage patterns of dependency code. Domain tags and descriptions of dependency libraries from the Maven Central Repository are reliable sources of domain information, enabling EMGSCC to learn more about the relations between dependency libraries.  
Figure 2 explains how EMGSCC works. For the selected \(n_{d}\) dependency libraries in the system, we collect the domain tags and descriptions of each library and obtain representations with integrated domain information through the LibRepLayer (see Figure 3 for the detailed structure of LibRepLayer). Then, each library representation in the system naturally grounds to a time step in the LSTM. Meanwhile, we adopt an attention mecha  
nism for the set [20], through which we obtain the weighted representation vector of all dependency libraries. These mechanisms enable sequential-based models to deal with the \"order does not matter\" problem. Then, EMGSCC passes through several fully connected layers using ReLU as the activation function. Finally, to cope with the output dependency library set, under the idea of binary relevance [21], we decompose the multi-label learning problem into independent binary classification problems. Therefore, EMGSCC outputs the probabilities \(prob_{n_{D}}\) of \(n_{D}\) dependency libraries accompanied by \(n_{d}\) libraries as input.
# Line of Sight and Image Motion Compensation for Step and Stare Imaging System
## 3 LOS and Motion Compensation Kinematics and Algorithm
### Image Motion Compensation
Then the compensation speeds of M1 and M2 mirrors are:  
\[\dot{\delta}=\frac{\dot{\beta}+\omega_{\rm bx}\cos\alpha+\omega_{\rm z}\sin \alpha}{2} \tag{21}\]  
\[\dot{\lambda}=\frac{\cos 2\delta\big{[}\cos\beta\big{(}\dot{\alpha}+\omega_{ \rm yy}\big{)}+\sin\beta(\omega_{\rm bra}\cos\alpha+\omega_{\rm x}\sin\alpha) \big{]}-\sin 2\delta\big{[}\sin\beta\big{(}\dot{\alpha}+\omega_{\rm yy}\big{)}- \cos\beta(\omega_{\rm bra}\cos\alpha+\omega_{\rm x}\sin\alpha)\big{]}}{2} \tag{22}\]  
From the above equations, it can be known the angular rate that the M1 mirror required to rotate is consistent with the value sensed by the gyro of the inner gimbal. So it can be obtained directly by collecting the value of the inner gimbal gyro. However, the angular rate of the M2 mirror is related to several parameters, including the current angle of the M1 mirror, the angles and angular rates of the inner and outer gimbals, and the angular rates of the aircraft. If a gyro can be installed on the z-axis of the M1 mirror, the rate sensed by this gyro can be used as the compensation parameter of the M2 mirror. However, because of limiting installation space, the axis cannot be installed with a gyro. Considering the parameters required by the M2 mirror are only used as the inputs of the system, and its control feedback uses a special position sensor, so the requirements for the acquisition bandwidth of the above parameters are not high. And therefore, the sensors installed on the gimbals, M1 mirror and the aircraft can be used, then the compensated parameters can be obtained by calculation. The block diagram of the system is shown in Figure 6.  
Figure 6: Block diagram of image motion compensation.
# Double-Borehole Superimposed Effect of a New Non-Explosive Directional Rock-Breaking Method
## 2 Methodology  
### Structure and Working Principle of IESF  
The IESF is a directional rock-breaking technique that utilizes coal-based solid waste materials as the primary raw material, instantaneously inducing two-dimensional fracture planes in rock masses. The main structure of IESF (Figure 1) consists of four parts: a slit-oriented tube, coal-based solid waste expansive agent, coupling medium, and electrical initiating device.  
The slit-oriented tube is equipped with two rows of energy-gathering holes symmetrically positioned at diametrically opposite ends of its circular cross-section. Each row of holes is arranged equally spaced along the axial direction of the tube. The primary components of the coal-based solid waste expansive agent include: oxidizer A: 30-40%, combustible/reducing agent B: 30-65%, additive C: 5-30%, catalyst D: 0.5-3%, and binder E: 1.25%. The coupling medium serves to transmit rock-breaking energy and can use air, water, or sand-gravel mixtures as materials. The electric initiating device comprises a fuse, wire, and a current emitter, where the current emitter activates the coal-based solid waste expansive agent through the fuse.  
When employing IESF, the expansion agent is electrically initiated, undergoing rapid phase transition from solid particles to high-pressure gases within 0.05-0.5 s. A mass of 1 g of the expansion agent generates approximately 300 mL of gases, which is used to fracture the rock mass through high gas pressure. These high-pressure gases form \"point-like\" energy-gathered jets through the energy-gathering holes on the slit-oriented tube in each borehole, resulting in uniform compressive stress in non-slotted directions and tensile stress along the slotted orientation.  
This mechanism drives directional tensile fracture initiation along the slotted direction on the borehole wall. Following fracture initiation, the fractures are instantaneously filled with high-pressure gases, inducing stress-superimposed effects between the double boreholes. Under the tensile action of these gases, the fractures propagate directionally until connecting adjacent slotted fracture planes generated from neighboring boreholes, forming interconnected fracture surfaces that achieve directional fracture planes (Figure 2). Thus, IESF precisely controls the direction of crack propagation by means of a slit-oriented tube.  
Figure 1: Components of IESF: A—slit-oriented tube, B—energy-gathering hole, C—coupling medium, D—coal-based solid waste expansive agent, E—current emitter, F—wire, and G—fuse.
# IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities
## 6 IoT-Based Wireless Gas Sensors
### Sensing Requirements for IoT-Based Gas Sensors
In terms of energy consumption, IoT-ready sensors based on batteries should be able to operate for long periods, reaching the mark of years, mainly in outdoor environments. For gas sensors, it would be desirable to maintain the operation until the transducer lifetime expires. It would be possible to combine energy sources, including solar panels to recharge the batteries. Some gas sensing technologies consume less energy, as the transducers do not need a temperature compensation and can be turned off during the measurement intervals, as the acoustic and optic methods for detecting gases. Other technologies, such as electrochemical, MOS, and catalytics require a pre-heating time, and thus, turning off these transducers would influence negatively on the performance and capacity of delivering correct measurement values. For indoor environments, the proposed sensors could rely on the power grid energy, mitigating the necessity of batteries. The stability of the transducers must be sufficient to provide accurate measurements, which in some technologies, such as MOS, electrochemical, and catalytic can be reached through periodic calibration. These calibrations must be performed according to the characteristics of the transducers; smart sensors can also analyze collected data to predict the necessity of calibration, as two discrepant measurements occur in sequence, the calibration must be performed, granting better accuracy to the measurements.
## 4 Construction of the Crack Kriging Surrogate Model
By setting the derivatives of Equation (8) with respect to \(\textbf{Y}^{1}\) and \(\sigma^{2}\) to zero, one can obtain the most likely values of \(\hat{\mu}\) and \(\hat{\sigma}\):  
\[\hat{\mu}=\frac{1^{\mathrm{T}}\mathrm{R}^{-1}\mathrm{Y}^{1}}{1^{\mathrm{T}} \mathrm{R}^{-1}1} \tag{10}\]  
\[\hat{\sigma}^{2}=\frac{\left(\textbf{Y}^{1}-\textbf{1}\hat{\mu}\right)^{ \mathrm{T}}\mathrm{R}^{-1}\left(\textbf{Y}^{1}-\textbf{1}\hat{\mu}\right)}{n} \tag{11}\]  
Then the concentrated likelihood function can be obtained by substituting \(\hat{\mu}\) and \(\hat{\sigma}\) into Equation (8) and removing the constant term of \(-\frac{n}{2}\ln(2\pi)\):  
\[\ln(L)\approx-\frac{n}{2}\ln\left(\hat{\sigma}^{2}\right)-\frac{1}{2}\ln| \textbf{R}| \tag{12}\]  
The value of this function depends on the unknown parameters \(\theta\) and \(\textbf{p}\) corresponding to the parameters of the Kriging function in Equation (6), called sensitivity vector of crack parameters and smoothness coefficient vector of correlation function respectively, therefore the problem of constructing the surrogate model is transformed into finding the \(\theta\) and \(\textbf{p}\), which maximize the likelihood. The global search method, such as GA, can be used. Once \(\hat{\mu}\), \(\hat{\sigma}\), \(\theta\) and \(\textbf{p}\) are obtained, the \(2\times\) amplitude from measurement point A at an untried point \(\textbf{x}^{*}\) can be predicted based on,  
\[\hat{g}(\textbf{x}^{*})=\hat{\mu}+\textbf{r}^{\mathrm{T}}\mathrm{R}^{-1} \left(\textbf{Y}^{1}-\textbf{1}\hat{\mu}\right) \tag{13}\]  
where **r** is the correlation vector between the untried point \(\textbf{x}^{*}\) and the observed data \(\textbf{x}\):  
\[\textbf{r}=\left\{ \begin{array}{c}R[\epsilon(\textbf{x}_{1}),\epsilon( \textbf{x}^{*})]\\ \vdots\\ R[\epsilon(\textbf{x}_{n}),\epsilon(\textbf{x}^{*})]\end{array} \right\} \tag{14}\]  
Better performance of a Kriging surrogate model requires the fewer prediction error in the entire design space, and the prediction error can be described by [52],  
\[\text{MSE}(\textbf{x}^{*})=\hat{\sigma}^{2}\left[1-\textbf{r}^{\mathrm{T}} \mathrm{R}^{-1}\textbf{r}+\frac{\left(1-\textbf{1}^{\mathrm{T}}\mathrm{R}^{-1 }\textbf{r}\right)^{2}}{\textbf{1}^{\mathrm{T}}\mathrm{R}^{-1}\textbf{r}}\right] \tag{15}\]  
To evaluate the prediction error more reasonable, the relative MSE defined by the ratio between MSE and the corresponding amplitude of \(2\times\) component is adopted:  
\[\text{RMSE}(\textbf{x}^{*})=\text{MSE}(\textbf{x}^{*})/\textbf{Y}(\textbf{x} ^{*}) \tag{16}\]  
Here, the Kriging model for measurement point A is finished, and the same process is for B.
# Framework for Developing Bio-Inspired Morphologies for Walking Robots
## 4 Phase: Design
### Components and Materials  
The motors were selected according to torque output, speed, mass, equipped sensors and price. Here, the Dynamixel motor series XM430-W350-R from Robotis was an optimal choice, due to their high torque output of 4.8 Nm, low weight of 0.082 kg, good torque density of 58 Nm/kg, fair speed of 57 RPM, current sensors for impedance control, drivers, price, and Robotis's production capability. Furthermore, the threaded flanges on all sides simplified the robot design, and enabled the motors to also function as the mechanical coupling points for the framewhere higher stiffness, intricate details and threads were needed, such as the joint couplings 12 (13) and pushrods 17 (18) for the HT, TA, FT and CTF joints. Carbon fibre tubes were ideal for the tibia links, as they lower distal mass and inertia, as well as allow easy attachment of a greater variety of foot mechanisms and sensors.  
High battery energy density (Watt-hour/kg) was crucial to extending the operational time at a specific weight. Two LiPO 1850 6S 22.2v battery packs from MaxAmps were found to have the highest specific energy on the market at the current time (\(S_{\text{E}}\)):  
\[S_{\text{E}}=\frac{1850\,\text{mA}\,\text{h}\cdot 22.2\,\text{V}}{0.235\, \text{kg}}=174.76\,\frac{\text{Wh}}{\text{kg}} \tag{1}\]
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 4 Integration of Co-Simulation Components
### Design Challenges and Solutions of CARLA--Simulator System Hardware Integration  
The integration of CARLA with external simulation hardware presents multiple challenges, particularly in establishing seamless real-time communication between different simulator components while ensuring high-fidelity synchronization. CARLA, as an open-source driving simulation platform, is primarily designed for software-based vehicle control and visualization, lacking built-in support for physical driving, bicycle, and pedestrian simulators. This limitation necessitates the development of custom modules to enable connectivity between CARLA and various hardware components.  
A key challenge in this integration is achieving low-latency data transmission between CARLA and external simulators. The system requires precise synchronization between the driving simulator's steering wheel, pedals, and motion platform, the bicycle simulator's smart trainer and steering block, and the pedestrian simulator's VR headset and treadmill.  
Figure 4: Traffic signal synchronization module.  
Any delay or inconsistency in data transmission could disrupt the immersive experience and impact the validity of behavioral studies. To address this, middleware solutions are implemented to facilitate real-time data exchange, ensuring that user inputs and feedback mechanisms remain responsive.  
CARLA also lacks native support for haptic feedback systems, which are essential for enhancing realism in driving simulations. To provide a more immersive experience, a haptic feedback module was developed using the D-BOX motion platform, enabling the simulation of vehicle dynamics, including acceleration, braking, and road surface conditions. This integration requires the continuous extraction of motion parameters such as speed, yaw, and roll from CARLA, which are then processed and transmitted to the motion platform in real-time.  
Additionally, CARLA does not include built-in support for physiological sensor integration, which is crucial for understanding human responses in transportation studies. To capture real-time biometric data such as heart rate variability (HRV), electrodermal activity (EDA), and cognitive load measurements, a robust synchronization mechanism is necessary. The Lab Streaming Layer (LSL) framework was employed to ensure that physiological sensor data are time-aligned with simulation events, allowing for comprehensive analysis of human stress, cognitive load, and emotional responses under different traffic conditions.  
This section details the technical solutions developed to address these challenges, including modifications to CARLA's API for hardware integration, real-time motion synchronization strategies, and physiological data collection methods. The subsequent subsections discuss the specific implementations for driving, bicycle, and pedestrian simulator integration, as well as the developed haptic feedback and physiological sensor synchronization modules.
## 4 Methodology
### Data Extraction and Analysis  
Data extraction was performed on the selected papers, focusing on implementation details, the application domain (vulnerability analysis, detection of malicious binaries, or smart contracts), sample size, and the performance of the methods. The papers used different metrics for evaluating the performance of their methods, so we converted their results into a standardized set of metrics to have a basis for comparison.  
For evaluating the performance of the analysis techniques, we opted to use precision, recall, and F1 scores. Precision measures the proportion of retrieved items that are relevant, while recall measures the proportion of relevant items that are retrieved. A high number of false positives will decrease the precision, while a high number of false negatives will decrease the recall. The F1 score is the harmonic mean of precision and recall and provides a way to combine these two metrics into a single value.  
These metrics are mathematically defined as:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\] \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (2) \[\text{F}_{1}\text{ score}=2\times\frac{\text{Precision}\times \text{Recall}}{\text{Precision}+\text{Recall}} \tag{3}\]These metrics are used instead of accuracy because they are better suited for evaluating the performance of analysis techniques in the presence of imbalanced datasets, which have been common in the literature. In addition to these metrics, the performance of static-based methods has been evaluated using the detection time, while the performance of dynamic-based methods has been evaluated using the runtime overhead. These metrics provide a way to compare the different analysis techniques and assess their relative strengths and weaknesses.
# A Comparative Study of the Application of Different Commercial Software for Topology Optimization
## 7 Discussion
It seems that the discretization of the CAD models was conducted in different ways. That could be a reason for the different optimized designs. Regardless of the fact that all software platforms use the same TO method, their TO algorithms and implementation are different, as it was shown both from the different derived solutions and their optimization times. In addition, each software applies different smoothing tools to fix sharp edges and overhangs and to eliminate stress singularities of the faceted geometries. In spite of this, these tools changed both the shape and the weight of the optimized designs; in the case of SolidWorks and ABAQUS, they increased their weight, while in ANSYS, they decreased them. CAD designers should take into account these parameters at the beginning of the TO implementation. It is known that the TO results are sensitive to designers' inputs; however, in some cases, the optimization can lead to unintended designs, such as the design solution of the pillow bracket by ANSYS, which comprised two parts instead of one, while the other software resulted in one optimized part. An additional design constraint could easily avoid the separation of the component. For example, adding the faces between the holes of the pillow bracket to the 'frozen areas' could preserve the removal of material from them and thus prevent the part split. Despite the fact that all these issues can be avoided using proper design and manufacturing constraints it seems that the commercial software can sometimes still be a 'black box' for CAD designers. Moreover, all these additional constraints demand manual input by the CAD designers and decrease the available design space for optimization, and thus, algorithms' flexibility.