esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
* The proposed network with low-cost MR glasses can attain the optimal fusion of low-resolution low-quality (LRLQ) depth maps and RGB images to achieve accurate high-precision depth maps, where the LRLQ depth map originally captured by the ToF depth camera in the MR glasses is very blurry. * A novel adaptive dynamic-range bin estimator is proposed for promptly estimating the depth distribution of the captured scene, making the finally resulted depth maps be considerably appropriate for specified employments. * The proposed networks with two decoding variants can be successfully implemented on Jorjin MR glasses [19] for hand-gesture MR and augmented reality (AR) applications.
# Approximate Sensory Data Collection: A Survey
## 3 Compressive Sensing Based Approximate Data Collection Algorithms
### Hybrid Algorithms
Firstly, the network is divided into clusters. Then, each cluster-head collected the sensory values within its cluster and generate Compressive Sensing (CS) measurements. Finally, such CS measurements were forwarded to the base station for recovery. Two routing strategies were also proposed in [37]. Such algorithms are efficient to reduce the scale of sensory data in a distributed manner. However, it still has the following problems. First, the block diagonal matrix may not be appropriate to describe the relationship among sensory data since the sensory values belonging to different clusters may be correlated with each other. Second, the authors decided the compression rate of each cluster based on the number of sensors in it; however, it may not be a good choice and the distribution of the sensory data should be also considered. Third, there also exists some redundant information since the correlations of the sensory values belonging in different clusters are not considered. Finally, the overhead of each cluster-head is quite high, so that it would lead to an imbalance of energy consumption in the whole network and shorten the lifetime of the WSN. The JSM-2 model is introduced by [38] for data compression in sensor networks, where \"\(JSM\)\" is short for Joint Sparsity Model. According to the distributed compressive sensing theory, there are three different joint sparsity model (JSM), which are JSM-1, JSM-2 and JSM-3. The JSM-2 model is known as the Common Sparse Support model and requires that all the sensing signals are constructed from the same sparse set of basis vectors. Hence, the original sensory signals can be recovered using the same bases via Simultaneous Orthogonal Matching Pursuit algorithm. Due to the common sparsity structure of different sensing signals, the transmission cost can be largely reduced. However, the assumption in [38] also seems a little strong. Since the sensing data distribution of two sensor nodes may be quite different when the monitoring area is large, the common sparsity cannot be achieved and is impractical for this case. According to the above discussion, most of these compressive sensing algorithms assume that the sparsity of the sensory data is constant and known. In practice, such an assumption seems too ideal since the sensory values continuously vary. Due to such a reason, the work in [39] proposed an adaptive data gathering schema which combined compressive techniques and network coding methods. Such schema allow the sink to query those interesting nodes adaptively for acquiring an appropriate number of measurements. Finally, in order to minimize the overall transmission cost, the authors also defined an NP-complete problem and proposed a greedy approximation algorithm to solve it. Such work is flexible and efficient for data collection; however, it only considers the scalar data and is not suitable to process multi-modal sensory data.
## 3 System Model  
As shown in Figure 1, we consider a highway vehicular networks scenario that includes an MBS, several RSUs, and vehicle users (VUs). The MBS connects to the core network (CN) through the backhaul link. Denote \(\mathcal{M}=\{1,\ldots,M\}\) and \(\mathcal{U}=\{1,\ldots,U\}\) be the RSUs set and vehicle users set, respectively. Each RSU is equipped with an MECS of size \(\varphi_{\textit{MEC}}\) to store a number of popular video replicas, which helps reduce the delay of content delivery and improve QoE of VUs. Vehicular users can access nearby RSU or MBS and download videos from the MECS or CN. We assume that each vehicular user request one of the interesting videos once the vehicle enters the service coverage areas of RSU but experiences a higher download rate if the requested one is pre-cached in MECS. Set the vehicle user arrival probability to a Poisson distribution of \(B\) different parameters that obey the Markov process, then the parameter of vehicle user arrival probability at time slot \(\tau\) is recorded as \(\lambda(\tau)\in\{\lambda_{1},\lambda_{2},\ldots,\lambda_{B}\}\). Then, at time slot \(\tau\), the probability that the number of vehicle users \(U(\tau)\) in the service coverage is expressed as  
\[P(U(\tau))=\frac{\left(\lambda(\tau)\right)^{U(\tau)}}{U(\tau)!}e^{-\lambda( \tau)}. \tag{1}\]  
We define the state transition probability of the parameter of arrival probability \(\lambda(\tau)=\lambda_{i}\), \(i\in\{1,\ldots,B\}\) at time slot \(\tau\) transfers to \(\lambda(\tau+1)=\lambda_{j},j\in\{1,\ldots,B\}\) at time slot \(\tau+1\) as \(\theta_{i,j}\), the transition matrix \(\Gamma\) of parameter \(\lambda\) is expressed as  
\[\Gamma=\begin{pmatrix}\theta_{1,1}&\ldots&\theta_{1,B}\\ \vdots&\ddots&\vdots\\ \theta_{B,1}&\ldots&\theta_{B,B}\\ \end{pmatrix}. \tag{2}\]
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 3 Experimentation
### Training Dataset  
We conducted related experiments on the autonomous driving outdoor dataset, Cityscapes, which is large in scale and covers 50 cities in Germany and nearby countries, including street scenes in the spring, summer and autumn, and is widely used in RGB-D semantic segmentation research. It contains 5000 densely annotated images, which are officially divided into 2975 training sets, 500 validation sets and 1525 testing sets. The resolution of the images is 2048 \(\times\) 1024. The finely annotated objects in this dataset can be divided into 19 categories, including road, sidewalk, building, wall, fence, pole, traffic light, traffic sign, vegetation, terrain, sky, pedestrian, rider, car, truck, bus, train, motorbike and bicycles. This dataset does not provide the ground truth segmentation images of the test set, so we use the validation set as the test set to test the experimental effect of the network model.
# Two-Factor Authentication Scheme for Mobile Money: A Review of Threat Models and Countermeasures
## 2 Related Work
### Threat Models
#### 2.7.2 Attacks against Authentication
* Trojan horse attacks: A Trojan horse is malicious software that, once installed on a phone, either steals sensitive information and sends it to the attacker or creates a backdoor for the attacker to have access to the phone. It uses a Trojan horse program employed by hackers and adversaries to compromise the authentication system. Mobile money users and agents are tricked by some form of social engineering into loading and executing Trojans on their phones. Once activated, Trojans can enable hackers to spy on mobile money users and agents, steal their mobile money PINs, and gain backdoor access to their mobile money accounts [19; 26; 48]. Moreover, adversaries can install malware that gives them the exclusive right to redirect users to their network [14].
## 4 Experiments and Results
### Auto-Label-Based Object Detection Experiment  
Next, we trained and evaluated object detection using the auto-labels obtained through Grounding DINO. The results can be found in Table 7. While there were instances where the performance was lower compared to object detection using manual labels, it was observed that the performance was higher for group 1, trained on the entire dataset. Although determining overall superiority is challenging, the performance was generally satisfactory.
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 1 Introduction
Consequently, in this paper, we propose a new platform of a UAV or drone, drone-as-a-gateway (DaaG), which can connect the sensor and the actuator networks on demand. In contrast with other research works, we have focused on 'which sensor needs to be focused on in terms of service', 'which actuator has to be firstly visited by the drone to reduce the main service delay' and 'which data transmission method is to be used in the given environment', namely improvement of the interoperability of the drones to connect to a separate network. We provide DaaG with the intelligence that dynamically determines the flying path for drones and to generate messages to the actuators to improve the interoperability of the DaaG. In the previous firefighting example, when DaaG is deployed in the same situation, it can directly collect sensor data, which can be used to efficiently conduct search-and-rescue and firefighting missions, and send command messages to actuators. We have implemented the proposed system with multi-rotor-based drones and then showed via simulations and experiments that DaaG can connect independent network systems and devices as a gateway and also can enable large-scale on-demand deployment of sensing and actuation infrastructure for a WSAN or IoT network as a mobile sink. The rest of the paper is organized as follows. In Section 2, we explain the structural and functional design of DaaG. Section 3 shows the simulation results by which our system can reduce the main service delay. We present an empirical experiment and evaluate this result to verify that DaaG works well in Section 4, and finally, we conclude our paper with Section 5.
## 2 Materials and Methods
### FE Modelling from the Geometrical Model
#### 2.3.3 Finite Element Analysis
The authors evaluated the prediction of dynamic effects, convergence problems, accuracy and stability of the calculations, the difference in computational time, and the influence of mass-scaling in the explicit method. Their result showed that the implicit method is more reliable for dynamic analysis due to its iterative approach, but the user is forced to accept simplification to obtain convergence. Comparing implicit and explicit dynamics, the implicit method seems to be more appropriate at lower speeds considering calculation time and accuracy, while explicit analysis is suitable to simulate dynamic loading of the knee joint, but the authors suggest not to use mass scaling and that, even if it can decrease the mean computational time, it is not recommended for high speeds activities where inertial forces are important. All the model components were meshed with linear TET10 elements. Since the manufacturer and the interviewed athletes reported perfect brace adhesion with the limb, all the connections between it and the limb were numerically simulated as a bonded contact. A frictionless contact was set only between the tibia and the connection to evaluate the knee distortion. In the condition of the extension of the limb, the brace reaches its fixed configuration and, according to the manufacturer, no relative movements are allowed between the components. Thus, a bounded contact was set between the components of the bracket in the connection point where the crews are located; these are not visible in the exploded view of Figure 3c. A constant damping factor of 0.55 was set at the limb component [14]. The mechanical properties of the components were obtained by the manufacturer. The mechanical properties of the bracket components and the limb are reported in Table 1. Figure 3 illustrates the geometrical models of the limb with and without the knee brace.
## 2 EMS Problem Formulation
### Constraints of the Objective Function  
To simulate the real response of a microgrid against the decisions of the EMS, it is essential to put many constraints that are mandatory for smooth operation of the microgrid.  
The first constraint is that all active power generated by all DERs and the grid must equal the load active power as per Equation (2).  
\[\sum_{i=1}^{NG}P_{Gi}^{t}+P_{Grid}^{t}=\sum_{D=1}^{ND}[P_{L_{D}}^{t}] \tag{2}\]  
where \(P_{L_{D}}^{t}\) is the value of the load active power at time \(t\), \(ND\) is total number of load levels.  
Additionally, it is important to include the generation limits for each DER and the utility grid as illustrated in Equations (3) and (4). These are mandatory to guarantee stable operation of the microgrid.  
\[P_{Gi,min}^{t}\leq P_{Gi}^{t}\leq P_{Gi,max}^{t} \tag{3}\]  
\[P_{Grid,min}^{t}\leq P_{Grid}^{t}\leq P_{Grid,max}^{t} \tag{4}\]  
where \(P_{Gi,min}^{t}\) and \(P_{Gi,max}^{t}\) are the generating limits of the generator \(i\) at time \(t\) while \(P_{Grid,min}^{t}\) and \(P_{Grid,max}^{t}\) are the generating limits of the utility grid at time \(t\).  
To keep the system running with high reliability, it is essential to include a portion of the microgrid generation capacity as spinning reserve, especially if most of the DERs are from renewable resources that may suffer from output instability. Equation (5) describes how the criteria of spinning reserves are met.  
\[\sum_{i=1}^{NG}P_{Gi,max}^{t}+P_{Grid,max}^{t}\geq\sum_{D=1}^{ND}[P_{L_{D}}^{t} +R^{t}] \tag{5}\]  
where \(R^{t}\) is the scheduled spinning reserve at time \(t\).  
One of the important constraints for energy storage elements is the limitation of charge and discharge cycles, especially the time interval needed for each cycle. The amount of energy stored in the battery is calculated based on the following rule:  
\[W_{ess,t}=W_{ess,t-1}+\eta_{charge}P_{charge}\Delta t-\frac{1}{\eta_{discharge}}P_{discharge}\Delta t \tag{6}\]  
\[\left\{\begin{array}{c}W_{ess,min}\leq W_{ess,t}\leq W_{ess,max}\\ P_{charge,t}\leq P_{charge,max};\ P_{discharge,t}\leq P_{discharge,max} \end{array}\right. \tag{7}\]  
where \(W_{ess}\) is the amount of energy stored in the battery. \(P_{charge}\) is the allowed rate of charge while \(P_{discharge}\) is the allowed rate of discharge. \(\Delta t\) is a predefined time period in which the rate of charge or discharge is allowed.
# A Two-Step Surface Reconstruction Method Using Signed Marching Cubes
## 2 Method
### Two-Step Surface Reconstruction Method
The method filters the invalid voxels that do not intersect with the target isosurface by using the fast multipole method with a lower expansion order \(p^{\prime}\) to evaluate the cube points in the process of traversing all the voxels, and the rest potential voxels that intersect with the target isosurface can be obtained. Then, the points of the rest voxels are evaluated to estimate the intersection positions corresponding to the target isosurface by using the fast multipole method with a higher expansion order \(p\). Finally, similar to the surface following method, the voxels are triangulated one by one to extract the approximated isosurface based on the estimated intersection positions. Figure 3 shows the basic principle of the two-step surface reconstruction method. The detailed process of the two-step surface reconstruction algorithm is described as follows:  
Step 1: Specify the reconstruction parameters, including the voxel size \(S_{c}\) used to define the reconstruction accuracy of the mesh, the lower expansion order \(p^{\prime}\), and the higher expansion order \(p\). Step 2: Based on the specified expansion orders, precompute the expansions of the radial basis kernel function using the black-box fast multipole method to accelerate the speed of evaluation. Step 3: Construct the interpolation constraints from the sampling data using the implicit modeling method [1,2]. Solve the interpolation equation constructed by the interpolation constraints and obtain the interpolated implicit function \(f(x)\). Step 4: Determine the region of surface reconstruction according to the minimum bounding box for the sampling data. Divide the region into voxels \(C\) to extract the target isosurface. Step 5: Traverse the voxels \(C\), and judge the type of each cube by estimating the sign of the cube vertexes. The sign is determined by the estimating function \(s_{p^{\prime}}(x)\) using the fast multipole method with a lower expansion order \(p^{\prime}\). Then, obtain the possible boundary voxels \(C_{bw}\) by eliminating the interior voxels \(C_{int}\) and the exterior voxels \(C_{ext}\). Step 6: Traverse the possible boundary voxels \(C_{bw}\), compute the accurate function values by the estimating function \(s_{p}(x)\) using the fast multipole method with a higher expansion order \(p\). Step 7: Similar to the surface following method, triangulate each boundary cube based on the lookup table defining the basic intersection topologies between the target isosurface and the cube. Step 8: Unite the triangular facets and obtain a complete triangular mesh. Figure 3: The evaluation process using the signed marching cubes method. The evaluation points are divided into two categories, including the points evaluated with a higher expansion order and the points evaluated with a lower expansion order.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 3 Preliminaries
### System Model  
There are usually three entities in VANETs: Certification Authority (CA), roadside unity (RSU), and vehicle, as shown in Figure 1.  
**CA:** CA can perform well in computing and storing. It is responsible for managing the entire VANETs and tracing and revoking the real identity of any misbehaving vehicle. Usually, vehicles and RSUs need to be registered in the CA. Then CA stores RSU's and vehicle's vital information such as RSU's secret key of authentication. Therefore, it can help the mutual authentication between vehicles and RSUs. Finally, CA is assumed to be fully trusted.  
**RSU:** RSU is deployed at the roadsides and is embedded with a PUF chip. The communication method between the vehicle and RSU is via wireless channels, while a stable wired channel is between RSU and CA. In particular, RSU generates its authentication key only when authenticating with the vehicle. RSU is honest but curious.  
**Vehicle:** OBU and PUF chips are usually equipped on the vehicle. The OBU is responsible for facilitating communication with other OBUs, RSUs, and CA. Conversely, the primary role of the PUF chips is to generate CRPs. Ultimately, the vehicle is characterized as honest yet curious in its behavior.  
**Adversary:** Anyone who intends to change, manipulate, hide the data, or gain physical access to vehicles and RSUs for secret keys is regarded as the active adversary. The adversary may inject new packets, store old messages, initiate a session, or pretend to be a valid device.
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
## 4 Transmission Error Spectrum Experiment
### Data Cleaning  
(1) Excluding start and stop fluctuations During TE testing, the initial few seconds after recording begins and just before it ends may exhibit minor fluctuations in the input speed and transmission ratio. Although these fluctuations are minor, they should be discarded to ensure accuracy in data analysis. Since the testing involves recording a 370\({}^{\circ}\) rotation of the flange, after excluding the aforementioned data, the remaining data should still cover at least 360\({}^{\circ}\) of flange rotation.  
(2) Removing duplicate data The test data include some duplicate entries with the same timestamp and result. Including duplicate data in a Fourier analysis can lead to frequency resolution errors, thereby affecting the accuracy of the final results. Therefore, entries with identical timestamps and TE results should be identified, and only one instance retained, while the rest should be deleted to free up sampling points.  
(3) Interpolation for missing data A Fourier analysis necessitates data with a consistent sampling interval. Missing data at specific timestamps can disrupt the frequency resolution, thereby impacting spectral analysis results. Therefore, a time series should be created with standard sampling intervals, and missing TE values should be filled in through extrapolation and interpolation. After testing, the spectral resolution was found to be more accurate post-interpolation, with different interpolation methods (e.g., linear interpolation and spline interpolation) showing minimal impact on the spectral analysis results.  
After data cleaning, only records with a stable input speed of 30 rpm and a stable real-time transmission ratio of 121 were retained for analysis. The real-time TE \(X(j)\) is calculated as \(X(j)=r_{1}/121\)-\(r_{2}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency** & **190 BX-121-RVE** & **65 BX-121-RVE** \\ \hline _CF_1 & 0.5 & 0.5 \\ _CF_2 & 0.9917 & 1.4876 \\ _CF_3 & 0.1612 & 0.1612 \\ _CF_4 & 0.3223 & 0.3223 \\ _CF_5 & 0.004132 & 0.004132 \\ _CF_6 & 0.1653 & 0.1653 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Theoretical frequency components of kinematic error (Hz).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **190 BX-121-RVE** & **65 BX-121-RVE** \\ \hline \(z_{\text{sun}}\) & 12 & 12 \\ \(z_{\text{pla}}\) & 36 & 36 \\ \(z_{\text{cyc}}\) & 39 & 39 \\ \(z_{\text{pin}}\) & 40 & 40 \\ \(n_{\text{pla}}\) & 2 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Main parameters of experimental prototypes.