esa-sceva/satcom-chunk-collection huggingface dataset:
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 4 Experimental Results
### Performance Evaluation  
To evaluate the performance of the algorithm, we examined parking space detection and space occupancy classification separately using recall and precision rates, which are defined as follows:  
\[\textit{Recall}=\frac{\textit{No. of correctly detected slots}}{\textit{No. of existing slots}}, \tag{6}\]  
\[\textit{Precision}=\frac{\textit{No. of correctly detected slots}}{\textit{No. of detected slots}}. \tag{7}\]  
There are four relationships between classification results and actual conditions, defined as follows:  
1. True positive (_TP_): Classified as occupied, and is actually occupied.
2. False positive (_FP_): Classified as occupied, but is actually vacant.
3. True negative (_TN_): Classified as vacant, and is actually vacant.
4. False negative (_FN_): Classified as vacant, but is actually occupied.  
We chose the error rate to evaluate classification, as defined in Equations (8)-(10):  
\[\textit{FN Rate}=\frac{\textit{No. of occupied slots being detected as vacant}}{\textit{No. of occupied slots}}, \tag{8}\]  
\[\textit{FP Rate}=\frac{\textit{No. of vacant slots being detected as occupied}}{\textit{No. of vacant slots}}, \tag{9}\]  
\[\textit{Error Rate}=\frac{\textit{No. of incorrect detection}}{\textit{No. of correctly detected slots}}. \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Header** & **Growing Ratio** & **Edges (Pixels)** & **Status** \\ \hline First slot & 0.25 & 2036 & Occupied \\ \hline Second slot & 0.928 & 867 & Vacant \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parking space features for urban roadside parking setting.  
Figure 25: Experimental results for urban roadside parking spaces. (**a**) Experimental setting; (**b**) Detection process; (**c**) Detection results; (**d**) Detection process; (**e**) Detection results.
# Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m
## 4 Proposed Method
### Focal-EloU Loss Function  
To address the imbalance between high- and low-quality samples (the intersection size of different regions on the union of predicted-truth boxes and ground-truth boxes), we add a Focal-EloU loss function. The Focal-EloU loss function enhances the optimization contributions for high-quality samples with lower quantities when regressing the predicted bounding boxes towards the ground-truth boxes, while simultaneously diminishing the optimization contributions for low-quality samples with higher quantities in this regression process. The Focal-EloU loss function can effectively address the imbalance between high- and low-quality samples, expediting model convergence and augmenting detection precision.  
The EloU loss function, shown in Figure 8, is represented by Equation (3). The EloU is a metric for evaluating the dissimilarity between predicted boxes and ground-truth boxes. The Focal-EloU loss function utilizes this metric to guide the training process, emphasizing high-quality samples for more effective model learning and convergence.  
\[L_{EloU}=L_{IoU}+L_{dis}+L_{asp}=1-IoU(A,B)+\frac{\rho^{2}(b,b^{st})}{(l \epsilon)^{2}+(w^{c})^{2}}+\frac{\rho^{2}(h,h^{st})}{h^{c}}+\frac{\rho^{2}(w, w^{st})}{w^{c}} \tag{3}\]  
Figure 6: Spatial-attention submodule diagram.  
Figure 7: SPPCSPC Model diagram.  
As illustrated in Equation (3) and Figure 8 above, where \(1-IoUI(A,B)\) represents the loss function for the predicted box and the ground-truth box, \(\rho(\cdot)\) signifies the Euclidean distance, \(b\) and \(b^{gt}\) denote the centers of the predicted and ground-truth boxes, respectively, \(h^{c}\) and \(w^{c}\) denote the height and width of the bounding box, \(h\) and \(h^{gt}\) denote the height of the predicted and ground-truth boxes, and \(w\) and \(w^{gt}\) denote the widths of the predicted and ground-truth boxes, respectively.  
The EIoU loss function consists of three components: the Intersection over Union (IoU) loss between the predicted and ground-truth boxes, the center-distance loss between the predicted and ground-truth boxes, and the edge-length loss between the predicted and ground-truth boxes. The EIoU loss function directly minimizes the differences in width and height between the predicted and ground-truth bounding boxes, expediting model convergence and enhancing localization accuracy.  
After adding Focal to EIoU, EIoU focuses more on high-quality samples (large Intersection over Union between predicted and ground-truth boxes). The comprehensive formula for Focal-EIoU is given by Equation (4).  
\[L_{Focal-EIoU}=IoU^{\gamma}L_{EIoU} \tag{4}\]  
where \(IoU\) represents the Intersection over Union for predicted box A and ground-truth box B (\(IoU\)\(=(\)A\(\cap\)B\()/(\)A\(\cup\)B\()\)), \(\gamma\) is a parameter controlling the degree of suppression for low-quality samples (small Intersection over Union between predicted and ground-truth boxes), and \(L_{EIoU}\) represents the EIoU loss function.
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 1 Introduction
Therefore, how to effectively represent the coupling correlation between the spatial and spectral dimensions of hyperspectral images is a key issue in improving reconstruction quality. In order to cope with the above-mentioned issues, we propose a dual-branch CNN-Transformer complementary module (DualCT). As shown in Figure 1, the CNN branch of DualCT focuses more on obtaining the spatial dimension information of hyperspectral images, while the Transformer branch of DualCT captures the global correlation between spectral bands. At the same time, we also introduce bidirectional cross-branch interactions to fuse the complementary features from the spectral-domain Transformer and spatial-domain convolution. Therefore, DualCT can effectively represent the spatial-spectral correlation in hyperspectral images. Meanwhile, considering the physical mechanism of the CASSI imaging mode, we introduce residual mask attention into the DualCT module. It uses the aperture mask to guide the network to focus on regions with high-fidelity spectral representation, thereby enhancing the spatial-spectral structure of the reconstructed hyperspectral images. Based on the DualCT module, we further build a multi-scale encoding and decoding reconstruction network (DualCT-Net) to learn the end-to-end reconstruction mapping from snapshot measurements to the original hyperspectral image. We conduct comparative experiments and ablation studies to verify the performance of the proposed network. The experimental results show that the proposed network can effectively improve reconstruction performance, and the ablation studies also verify the effectiveness of our network design. The main contributions of this paper include:  
(1) The proposed DualCT module parallelizes the spectral Transformer branch and spatial CNN branch, and promotes complementary fusion of spatial and spectral features through bidirectional cross-branch interactions between the two branches. Therefore, the DualCT module can effectively integrate the advantages of CNN and Transformer to capture the spatial-spectral features of hyperspectral images. (2) Taking into account the physical mechanism of compressive snapshot imaging, we designed a residual mask attention mechanism to enhance the DualCT module. It can guide the DualCT module to focus on spatial regions with high-fidelity spectral representation, thereby enhancing the structural details of the reconstructed hyperspectral images. (3) By using DualCT as the basic building block, we further construct a multi-scale encoding and decoding model to learn the inverse reconstruction mapping. The experimental results verify the effectiveness of the proposed reconstruction network.
## 3 Analytical Model  
### User Type Probability  
We assume that the nearest distances from a typical UE to a PBS and a MBS are denoted by \(r_{p}\) and \(r_{m}\), respectively. Generally speaking, the user type of this typical UE can be defined according to the relationship between the biased received signal strength from its nearest MBS and PBS, respectively, as Equation (2) below:  
\[l=\left\{\begin{array}{l}m_{u},\text{when}\ P_{m}hr_{m}^{-\alpha}>B_{p}P_{p} hr_{m}^{-\alpha},\\ p_{c},\text{when}\ P_{p}hr_{m}^{-\alpha}>P_{m}hr_{m}^{-\alpha},\\ p_{e},\text{when}\ P_{p}hr_{m}^{-\alpha}<P_{m}hr_{m}^{-\alpha}<B_{p}P_{p}hr_{p}^ {-\alpha}.\end{array}\right. \tag{2}\]  
**Lemma 1**.: _The probability of this typical UE belonging to the user type \(l\) can be defined as \(A_{l}=\mathrm{Prob}\left[l\in L\right]\), which is given as below:_  
\[A_{l}=\left\{\begin{array}{l}\frac{\lambda_{m}}{\lambda_{m}+\left(B_{p}\hat {P}_{p}\right)^{2/\alpha}\lambda_{p}},\text{when}\ l=m_{u},\\ \frac{\lambda_{p}}{\lambda_{p}+\left(\hat{P}_{m}\right)^{2/\alpha}\lambda_{m }},\text{when}\ l=p_{c},\\ \frac{\lambda_{p}}{\lambda_{p}+\left(B_{p}^{-1}\hat{P}_{m}\right)^{2/\alpha} \lambda_{m}}-\frac{\lambda_{p}}{\lambda_{p}+\left(\hat{P}_{m}\right)^{2/\alpha }\lambda_{m}},\text{when}\ l=p_{e},\end{array}\right. \tag{3}\]  
_where \(\hat{P}_{p}=P_{p}/P_{m}\), \(\hat{P}_{m}=P_{m}/P_{p}\)._  
Proof: See Appendix A.
## 3 Modeling of the Rotational Speed System of the Propeller
### Comparative Analysis of the Learning Performance of the Proposed Adaptive Laws  
In literatures, most existing adaptive laws for the similar system as that shown in Equation (7) do not contain the optional parameter \(\mu\), which is usually disadvantageous to the further improvement of the adaptive learning performance. It is important to note that the optional parameter \(\mu\) in the proposed adaptive laws can significantly speed up the convergence rates of adjustable parameters. For example, as for the identical Lyapunov function candidate shown in Equation (13), we have  
\[\dot{V}=\left\{\begin{array}{ll}-a_{m0}e^{2},&\mu=0\\ -a_{m0}e^{2}-\lambda\left(a_{m1}\dot{e}+a_{m0}e\right)^{2k+2},&\mu=\lambda \left(a_{m1}\dot{e}+a_{m0}e\right)^{2k+1},\,k=0,\pm 1,\cdots\\ -a_{m0}e^{2}-\lambda\text{sgn}\left(a_{m1}\dot{e}+a_{m0}e\right)\left(a_{m1} \dot{e}+a_{m0}e\right),&\mu=\lambda\text{sgn}\left(a_{m1}\dot{e}+a_{m0}e \right)\end{array}\right., \tag{19}\]  
where \(\lambda>0\). From Equation (19), the introduced terms in \(\dot{V}\) for the case with \(\mu\), such as \(\dot{e}\), can greatly contribute to the convergence rates of adjustable parameters. We can therefore conclude that the learning performance of the proposed algorithm with \(\mu\) is superior to that without \(\mu\), such as most existing adaptive laws.  
However, due to the unpredictability of the input signal, large amounts of experimental test results suggest that \(\dot{k}_{a_{1}},\dot{k}_{a_{0}},k_{b_{0}}\) and \(\dot{k}_{b}\) should be restricted in a bounded range by means of soft limiting strategies in order to avoid the over-learning problem of adjustable parameters and suppress the oscillation of adjustable parameters, as shown in Section 5. Here, \(\dot{k}_{a_{1}},\dot{k}_{a_{0}},\dot{k}_{b_{0}}\) and \(\dot{k}_{b}\) are restricted as follows:  
\[\left\{\begin{array}{l}-\delta_{a_{1}}\leqslant\dot{k}_{a_{1}}\leqslant \delta_{a_{1}},\\ -\delta_{a_{0}}\leqslant\dot{k}_{a_{0}}\leqslant\delta_{a_{0}},\\ -\delta_{b_{0}}\leqslant\dot{k}_{b_{0}}\leqslant\delta_{b_{0}},\\ -\delta_{b}\leqslant\dot{k}_{b}\leqslant\delta_{b}.\end{array}\right. \tag{20}\]  
where \(\delta_{a_{1}},\delta_{a_{0}},\delta_{b_{0}}\) and \(\delta_{b}\) are threshold values.  
From an application perspective, the optional parameter \(\mu\), together with soft limiting strategies, can not only speed up the convergence rates of adjustable parameters, but can also ensure the overall stability of adjustable parameters, which is especially crucial for the flight safety of the MAV. It is finally important to note that, in fact, the determination of the optional parameter \(\mu\) and the threshold values of soft limiting strategies according to the constraint condition in Equation (16) are dependent on some _a priori_ information of the controlled system, such as the range of the input signal.
## 6 Impact Assessment of the System  
This section describes how to assess the utility of the system. This assessment will be conducted from two different points of view: that of the public transit user with special needs and that of the transit company.  
From the point of view of the transit user, system utility will be assessed according to two parameters. The first is the number and type of users using the system. Users must register for the client application and specify what kind of impairment they have (mobility, sight, hearing or cognitive). This information will give an indication of how many users intend to use the system, the number of potential users and what kind of impairment they have. The second parameter is how many times each user has used the system, what type of service and on what route. These parameters will enable us to objectively assess whether the system facilitates greater use of public transport for these people, as well as discern the type of group that uses it and what type of travel it is used for.  
From the point of view of the transit company, the system's utility is measured by whether it leads to an increase in the number of travelers with special needs using public transportation, and above all, how this use affects planned operations. Indeed, a drawback of people with special needs using public highway transit is that it impacts on the time required to service planned routes. The time required to board and disembark from the vehicle and make a payment is longer than it is for a traveler without special needs. Consequently, vehicles take longer to complete the route. To quantify this increase and whether the proposed system makes it more acceptable, the boarding and disembarking times of passengers at stops will be obtained. These times correspond to the periods during which vehicles are stationary at stops (speed zero indicated by the vehicle's GPS system). Moreover, as the ACli client application is associated to the type of user that uses it, we will be able to ascertain how this time is affected by user type.
## 2 Materials and Methods
### Research Modeling and Hybrid Neural Network Development
#### 2.6.3 Hybrid CNN-LSTM-ANN Architectural Design  
The triple hybrid model was designed for comparing the efficiency of the model at extracting the necessary features of the data by complimenting each other to learn both short and long-term dependencies. As seen in Figure 5, for this hybrid model, a recurrent neural network that runs in cycles is included, making it highly adept for analyzing sequence data. In comparison to the CNN-ANN model, the LSTM integrated as such, the constituting gates of the LSTM help it to retain necessary information from previous hidden states. The input data is fed to the 2-hidden layer 1D CNN with [32; 16] neurons, after which it is forwarded to the LSTM network with 32 hidden states and finally to the densely connected network which produces the general model predictions. The ANN model for this hybrid has 3 hidden layers with [25; 50; 25] neurons. Both the CNN and ANN architecture is identical to the hybrid CNN-ANN design discussed in the preceding section.  
Figure 5: CNN-LSTM-ANN hybrid model architecture.  
The summary of the neurons in the hidden layers of the hybrid model is [32; 16; 32; 25; 50; 25]. This model has basic calculations embedded in it which is described in four subsequent separate steps [46].  
**1 st Step:** According to the hidden state h\({}_{t-1}\) and the new input q\({}_{t}\) from Equation (4), the LSTM model will determine the information that will be thrown away from the \"forget gate\" \(f_{t}\) as seen in Equation (7).  
\[f_{t}=\sigma\Big{(}W_{f}\times[h_{t-1},q_{t}]+b_{f}\Big{)} \tag{7}\]  
where \(W_{f}\) is the weight of the matrices, \(\sigma(\dots)\) is the logistic sigmoid function, and \(b_{f}\) is the bias function.  
**2 nd Step:** In this step, the information that will be stored in the cell state will be decided. A new candidate cell (\(\widetilde{C}_{t}\)) is also generated and it is scaled by an \"input gate\" \(i_{t}\).  
\[\widetilde{C}_{t}=tanh(W_{C}\times[h_{t-1},q_{t}]+b_{C}) \tag{8}\]  
\[i_{t}=\sigma(W_{i}\times[h_{t-1},q_{t}]+b_{i}) \tag{9}\]  
_tanh_ (...) in Equation (8) is the hyperbolic tangent function.  
**3 rd Step:** The new cell \(C_{t}\) is updated with the combination of a previous cell state \(C_{t-1}\) and \(\widetilde{C}_{t}\). The former cell is affected by \(f_{t}\) and also scaled by \(i_{t}\).  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{10}\]  
**4 th Step:** In the final step, the output process is divided into two steps and an \"output gate\" \(o_{t}\) is built to decide the cell state that is outputted. \(C_{t}\) activated by _tanh_ function is filtered by the multiplication of \(o_{t}\). The result of the multiplication is the desired output \(h_{t}\)  
\[o_{t}=\sigma(W_{o}\times[h_{t-1},q_{t}]+b_{o}) \tag{11}\]  
\[h_{t}=o_{t}\times tanh(C_{t}) \tag{12}\]  
For this hybrid model, the flatten layer converts the matrix (Equation (12)) to a singular vector.  
\[Z=f(h_{t}) \tag{13}\]  
The output of the flatten layer (Z) will serve as the input for the ANN model (Equation (6)).
## 3 Methodology  
### The NextGEM Project Methodological Approach  
The NextGEM framework can efficiently integrate all technical enablers of the project and encompass real-time monitoring and historical and experimental data.  
* **Sensing and data source:** Data for RF-EMF exposure assessment will be collected from the literature and/or experimentally measured in different real-life scenariosthrough the case studies conceived and developed during the project. As regards the new generation communication networks, novel measurement paradigms aim to be developed together with wearable sensing devices suitable for the 5G mm wave band.
* **Analytics and experimentation:** The combination of multiple approaches from real-life case scenarios, exposure assessment, and umbrella reviews of epidemiological data in combination with in vitro, ex vivo, and in vivo experiments elucidates possible biological and health effects of RF-EMF interactions and their underlying mechanisms.
* **Applications, tools, and services:** NextGEM will provide novel exposure assessment devices and protocols as well as risk assessment tools, integrated into the _NIKH_, representing the main key exploitable outcomes.
###### Abstract  
We applied the Small Baseline Subset multi-temporal InSAR technique (SBAS) to two SAR datasets acquired from 2003 up to 2013 by Envisat (ESA, European Space Agency) and COSMO-SkyMed (ASI, Italian Space Agency) satellites to investigate spatial and temporal patterns of land subsidence in the Sibari Plain (Southern Italy). Subsidence processes (up to ~20 mm/yr) were investigated comparing geological, hydrogeological, and land use information with interferometric results. We suppose a correlation between subsidence and thickness of the Plio-Quaternary succession suggesting an active role of the isostatic compensation. Furthermore, the active back thrusting in the Corigliano Gulf could trigger a flexural subsidence mechanism even if fault activity and earthquakes do not seem play a role in the present subsidence. In this context, the compaction of Holocene deposits contributes to ground deformation. Despite the rapid urbanization of the area in the last 50 years, we do not consider the intensive groundwater pumping and related water table drop as the main triggering cause of subsidence phenomena, in disagreement with some previous publications. Our interpretation for the deformation fields related to natural and anthropogenic factors would be a comprehensive and exhaustive justification to the complexity of subsidence processes in the Sibari Plain.  
SBAS-InSAR; Sibari Plain; subsidence; geology; anthropic processes Article
## 2 Background
### Protection Levels and Permission Flags
#### 2.2.2 Signature Protection Level  
This protection level compares the certificate of declaring and requesting application to determine if it can be signed by the same certificate as that of the device manufacturer. Permission is granted only if the certification condition is satisfied; that is to say, signature permissions can only be granted if the same certification signs both parties. Signature permissions are only granted if the requesting application is signed by the same developer that defined the permission.
# An Eight-Channel C-Band Demux Based on Multicore Photonic Crystal Fiber
###### Abstract  
A novel eight-channel demux device based on multicore photonic crystal fiber (PCF) structures that operate in the C-band range (1530-1565 nm) has been demonstrated. The PCF demux design is based on replacing some air-hole areas with lithium niobate and silicon nitride materials over the PCF axis alongside with the appropriate optimizations of the PCF structure. The beam propagation method (BPM) combined with Matlab codes was used to model the demux device and optimize the geometrical parameters of the PCF structure. The simulation results showed that the eight-channel demux can be demultiplexing after light propagation of 5 cm with a large bandwidth (4.03-4.69 nm) and cross-talk (\(-\)16.88 to \(-\)15.93 dB). Thus, the proposed device has great potential to be integrated into dense wavelength division multiplexing (DWDM) technology for increasing performances in networking systems.  
photonic crystal fiber; demultiplexer; dense wavelength division multiplexing +
Footnote †: journal: Nanomaterials
# Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target
## 4 Experimental Validation  
The camera was calibrated using [PERSON]'s method [23] to obtain its intrinsic and extrinsic parameters. To validate the derived formulations, the world dimensions of the stick with respect to its angular displacements are transformed to the camera, image plane, and sensor coordinates in that order, through the characteristic pinhole-camera-based projection matrix in homogeneous coordinates, after which the resulting values are substituted into the formulated equations in Section 2.1 to find the corresponding angular displacements in degrees (\({}^{\circ}\)). Finally, the system is applied in a stationary high-speed train equipment cabin to measure wind speed.  
Figure 14: The improved fitting formulation between wind speed and yaw angle.