esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Three-Dimensional Frame Generation  
Users can choose to generate either a 3D mesh or a recently emerged multi-plane image (MPI) for an in-depth investigation of the surgery. We discuss the advantages and disadvantages of those different types of 3D frames through a comparison on an autostereoscopic display (see Section 3).  
**Mesh Generation.** We create a 3D mesh from a selected multi-view frame. Given the intrinsic and extrinsic parameters from our calibration procedure, a 3D mesh is constructed from a 3D point cloud from COLMAP using a multi-view stereo library, OpenMVS [27].  
Figure 2: Our multi-frame multi-view feature matching strategy. We randomly select \(N\) (\(\leq\)M) frames from \(M\) consecutive frames with a fixed McSL location. We then extract feature points from the camera images in each frame and run matching between the McSL cameras. The resulting correspondences between all \(N\) sets of inter-camera features are used to determine the geometric relationship between the cameras.  
**MPI Generation and Disocclusion Rendering.** Given the same multi-camera data for the mesh generation, we can generate an MPI using local light field fusion (LLFF) [29]. LLFF is a 3D convolutional neural network that can infer multi-layer scene representation (aka. MPI) [34] or soft 3D representation [28] from calibrated multi-view images. MPI consists of multiple RGB+a layers spaced along the camera forward direction in inverse depth for efficiency. LLFF blends multi-view MPI depending on per-camera weights for quality:  
\[c=\frac{\sum_{i=1}^{K}w_{i}^{C}\alpha_{i}c_{i}}{\sum_{i=1}^{K}w_{i}^{C}\alpha_ {i}+t}, \tag{1}\]  
where \(w_{i}^{C}\) represents a blending weight (scalar) of \(i_{-th}\) rendered MPI (among MPIs of individual McSL cameras). \(K\) means the total number of cameras (i.e., \(K=5\) in our case of McSL. See Figure 1a). We calculate Euclid distances between the rendered view and the five McSL cameras so that we blend closer cameras with more weights and vice versa. \(c_{i}\) is a pixel color (RGB values) of the \(i_{th}\) rendered MPI and \(\alpha_{i}\) is the corresponding opacity. \(t\) is a small value to avoid zero division.  
We apply the LLFF inference to a selected McSL frame to render the frame in 3D. To avoid occlusion, users can control the viewpoint until the rendered view disocludes the obstacle. Furthermore, given a camera occlusion metric, we can down-weight cameras under occlusion:  
\[c=\frac{\sum_{i=1}^{K}w_{i}^{O}w_{i}^{C}\alpha_{i}c_{i}}{\sum_{i=1}^{K}w_{i}^{ O}w_{i}^{C}\alpha_{i}+t}, \tag{2}\]  
where \(w_{i}^{O}\) is a per-camera value that suggests disocclusion significance in the camera view (e.g., the camera score [1]).
# Multi-Classifier Pipeline for Olive Groves Detection
## 2 Materials and Methods
### Satellite Imagery Classification
#### 2.3.1 Baseline Pipeline  
The baseline pipeline using Random-Forest (RF) [17] is implemented within the IOTA2 framework. IOTA2 (infrastructure for land use by automatic processing using Orfeo toolbox application) is an open-source land cover mapping framework developed by the French Center for Spatial Studies of Biosphere (CESBIO) [18]. The framework implements a classification pipeline using several libraries such as Orfeo Toolbox and Scikit-Learn to perform a temporal supervised pixel-based classification. IOTA2 process, as described in Figure 2, produces a land cover map where each pixel is assigned to a class by processing the surface reflectance taken at different times (multi-temporal images). Only the valid pixels (selected based on the various Sentinel-2 masks and the SCL--Scene CLassification--band) are processed and the different features listed in the configuration file are extracted. As part of the default features extracted, various spectral indices, such as NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index), are calculated to support land cover type identification.  
The baseline pipeline classifies the input images into Urban, Forest, Nature, Grassland, and Olive classes. The reference data for the baseline pipeline was created by referencing the RPG dataset for Olive class and by photointerpretation and spectral indices analysis for other classes.
###### Abstract  
In this work, an ultra-wideband graphene-based micro-sized circular patch-shaped Yagi-like MIMO antenna was investigated over 1-30 THz of the frequency spectrum. The proposed antenna structure was designed over a polyimide substrate with 620 \(\times\) 800 \(\mathrm{\SIUnitSymbolMicro m}^{2}\). This antenna was radiated over three bands over 1-30 THz. The maximum bandwidth achieved 10.96 THz, with \(-\)26 dB of the return loss isolation. These three bands were also mathematically analyzed using various MIMO antenna parameters to match the MIMO antenna criteria. This antenna provided all the accepted results as per the limits set by these antenna parameters. The effect of the different port excitation on the change of directivity of the overall structure is also reported. The MIMO antenna parameters such as TARC (Total Active Reflection Coefficient), ECC (Envelope Correlation Coefficient), MEG (Mean Effective Gain), CCL (Channel Capacity Loss), and DG (Directivity Gain) were investigated for the proposed structure. These values were also identified to check the compatibility and challenges associated with short-distance communication. The suggested THz MIMO antenna provides the operating bands of 1-10 THz and 15-30 THz, with good isolation values. An average of 7 dB gain was observed in the 2 \(\times\) 2 antenna structure. The newly developed MIMO antenna in the THz frequency range may be used for high-speed short-distance and terahertz communications.  
t 2022111305. [[https://doi.org/10.3390/electronics11091305](https://doi.org/10.3390/electronics11091305)]([https://doi.org/10.3390/electronics11091305](https://doi.org/10.3390/electronics11091305))
# Could We Realize the Fully Flexible System by Real-Time Computing with Thin-Film Transistors? +
## 5 Simulations and Analysis  
### Verification of Functional Correctness  
To determine whether our design could perform convolution in the correct way, we choose two continuous scale coordinates in scale space: \(\sigma 1\) = 1.5450 and \(\sigma 2\) = 2 = 1.9466. Then, the corresponding Gaussian convolution units are constructed based on the Oxide TFT model. A 128 \(\times\) 128 test image is used as the simulated sensor output. The circuit simulation is done by Cadence; meanwhile, the comparison digital result is calculated by MATLAB. The images after Gaussian filtering are shown in Figure 8a [15]. It is a little hard to tell the difference between the circuit output and the digital results, so we use PSNR to measure the accuracy of the circuit computed result, taking the software results as precise data. Calculating formulas for PSNR, are shown as following:  
\[\text{PSNR }=\ 10\log_{10}\left(\frac{255}{\text{MSE}}\right) \tag{9}\]  
\[\text{MSE }=\ \frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\left|I(i,j)\ -\ S(i,j)\right|^{2} \tag{10}\]  
where \(I\) represents the circuit-output image, and \(S\) is the precise data. The PSNR of two scales is 54.1 dB and 50.7 dB respectively. The reason why the larger \(\sigma\) has the lower accuracy is that the corresponding mask size is enlarged when the \(\sigma\) increases. The larger mask size causes more information lost on the edge of the image. It is easy to see that the edge of circuit output images appears to be black, while the software outputs have more smooth edges because it is easy to compensate the edge pixel in the software algorithm. However, it is a relatively acceptable result for image filtering. This simulation demonstrated the functionality of the proposed Gaussian convolution unit design.  
As shown in Figure 8b, software and flexible circuit simulation results are compared for the capability of classifying input image, where the typical datasheet MNIST with the size of 28 \(\times\) 28 is used. The circuit simulation is done by HSPICE level 62 model for oxide TFT, and the software simulation is based on MATLAB. With the same network size, the error rates of software and circuit implementation both  
Figure 8: (**a**) Comparison of flexible computing circuit and software outputs of implementing convolution algorithm; (**b**) Classification error rate of MLP.  
converge to the same value of 7.01%. Flexibility is an important property of Oxide TFT. According to [18], the mobility of oxide TFT decreases by 15.7%, with the device being bend at R = 30 mm. In order to evaluate the system performance after the bending of oxide TFT, the accuracy simulation with the mobility decreasing by 20% is given as Figure 8b. The error rate with the bending oxide TFT converges to the value of 7.7%, which is only 0.69% lower than the circuit implementation at the same network size, and still has a fast convergent trend. That is, although the analog circuit and the bending of oxide TFT introduce variations to computation, the MLP eliminates the error by training.
## 2 Study Area and Methods  
### Study Area  
In this study, the Ganzi Tibetan Autonomous Prefecture was selected as the study area. Ganzi is located in the east of the Qinghai-Tibet Plateau; it covers about 150,000 km\({}^{2}\) (Figure 1). The main landform types of Ganzi include hilly plateau, mountain plain, and alpine valley. The average elevation is over 3.50 km, and the relative elevation difference between the highest elevation and the lowest elevation is 6.56 km. Ganzi is located in the upper reaches of the Yangtze River and is considered a global biodiversity hotspot region [22]. Ganzi contains a huge number of rare and indigenous species, consisting of more than 5223 higher plants, 652 terrestrial vertebrates, 436 birds, 31 reptiles, and 30 amphibians [23].  
Forest, shrub, grassland, farmland, and wetland are major landcover types in Ganzi. However, Ganzi's natural ecosystems are sensitive and vulnerable, and are presently suffering from land desertification [19] and soil erosion [24]. Furthermore, increased intensity of human activities [19; 25] has led to greater ecological risk. Furthermore, in 2015, 25.5% of Ganzi's GDP (i.e., gross domestic product) came from agriculture and 85% of Ganzi's population engaged in agriculture; thus, there exists a significant contradiction between agricultural development and ecosystem conservation.  
Since 2000, the Ganzi government has implemented a series of ecological projects, including a natural forest protection program, sloping cropland conversion program, desertification prevention program, comprehensive management of soil erosion program, natural grassland restoration and protection programs, and grazing land restoration program. The goals of these policies and ecological projects are to improve ecosystem services and control ecological risks.
# Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information
## 3 Proposed Method
### Protocol Details  
In this section, we describe the details of the proposed method, PSC-UL. This method is used to improve spatial reuse of uplink traffic. In other words, when a UL transmission is in place, a station in a different BSS can use the method to transmit its packet in parallel. It does not mean that downlink traffic cannot co-exist in the same channel. DL (Downlink) traffic may co-exist, but its cannot benefit from proximity information to start a concurrent transmission, unless APs maintain proximity information of stations in its own BSS as well as other neighboring BSSes.
## 6 Results
### Multimodal-Multi-Optima Problems  
The following compares the S-EPSO predictions for the multimodal functions of the Ref. [17] benchmark to the results published in [19; 20]. In keeping with the study main focus,the comparison includes functions up to 5D. Since [17] provides a complete description of the functions, Table 1 below lists their names and describes the landscapes, but does not repeat their formulations. The function sequence respects the order established in [17]. The evaluated version of S-EPSO includes no fine-tuning of any of the adjustable parameters. They were instead all set to values that were considered reasonable. For instance, parameter \(W\) and \(c_{2}\) in Equation (8), which were inherited from the original PSO algorithm, were set at the values recommended in the literature for PSO 0.729 and 1.495, respectively [31]. All other constants were set at the values already indicated in the text. For clarity, Table 2 presents the values set for all parameters. Among them, the medium haziness \(\gamma^{*}\) is probably the most controlling parameter. Thus, based on the results presented in Figure 2a, \(\gamma^{*}\) is set at \(10^{3}\) for the simpler landscapes, which are described by F\({}_{1}\) to F\({}_{5}\). These landscapes correspond to 1D functions or to 2D functions containing few optima. Functions F\({}_{6}\) to F\({}_{17}\) involve numerous optima, and for most of them, many local minima. Thus, to help the particles gather around the optima and preserve them during the iterations (once again inspired by Figure 2a), \(\gamma^{*}\) is set at \(10^{4}\). The segmentation required for the Initial weighted generation of the particle distributions remains the same for all simulations, and involves 5 segments along each component of the \(D^{*}\) dimensions. Thus, the maximum numbers of subdomains are 5, 25 and 125 for the 1D, 2D, and 3-5D domains, respectively. Moreover, all simulations involved the same number of particles during the domain segmentation procedure and optimization. Adding the function evaluations involved during these two gave the total number of function evaluations. The Maximum number of Function Evaluations _MFE_ imposed respects the values prescribed in [17], namely, \(5\times 10^{4}\) for functions F\({}_{1}\) to F\({}_{3}\), \(2\times 10^{5}\) for the 2D functions, and \(4\times 10^{5}\) for the 3 and 5D functions.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 2 Jellyfish Attack Overview
### Jellyfish Periodic Drop Attacks  
In this attack, the malicious node deliberately selects and discards a portion of the packets or all of the packets for a short period of time in the midst of the ongoing communication process, as illustrated in Figure 2. Moreover, the ACK packets should arrive at the source node before the retransmission timeout (RTO) value expires, but they are delayed due to a periodic dropping attack, leading the source to believe that the packet has been destroyed [23]. This timeout is interpreted by the sender as a sign of extreme congestion, and the slow start phase is initiated. The RTO value is compounded for each retransmission of unacknowledged segments until it reaches a threshold RTO value, at which point the connection is terminated [24]. As the frequency of packets discarded by the attacker node increases the network throughput decreases. A Jellyfish node may delete packets as soon as the sender exits its slow start phase to maximize the damage of attacks. As a result, the flow will constantly be in a precarious slow start state [25].  
\begin{table}
\begin{tabular}{c c c} \hline
**Jellyfish Attack Subcategory** & **Aim** & **Impact** \\ \hline Reordering attack & Scrambling the packet order & Decreases throughput \\  & & Increases network congestion \\ \hline Periodic drop attack & Discarding packets periodically & Data are lost in communication \\  & & Network throughput falls \\ \hline Delay variance attack & Introduces delay before transmitting & Increase delay \\  & & Significant network congestion \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of jellyfish attack categories.  
Figure 1: Jellyfish reordering attack.
## 3 Materials and Methods
### Problem Formulation  
With an image sequence of frames \"\(N\),\" MOT aims to decipher all the bounding boxes with identity labels for all the interesting objects in all frames, including their corresponding locations. For the input image sequence \(\{F_{t}\}_{i=1}^{N}\), here \(F_{t}\in\mathbb{R}^{c\times uv\times h}\) representing the \(t\)th frame, and the output would be all bounding boxes represented as \(\{B_{t}\}_{i=1}^{N}\), whereas labels are represented as \(\left\{L_{t}^{GT}\right\}_{i=1}^{N}\), for all the relevant objects present in a frame. Moreover, \(B_{t}\) is defined as a subset of \(\mathbb{R}^{4}\) indicating the ground truth bounding boxes of the number of targets \((K_{t})\) in the \(t\)th frame and \(L_{t}^{GT}\) is defined as the subset of \(\mathbb{Z}\), which indicates their identities/labels. The MOT is divided into multiple processes, namely object detection using feature extraction process with data association. However, the researchers revealed that association efficiency relies on detection performance [46], thereby indicating its importance among the three procedures. To better exploit their correlation, the proposed Super Chained-Tracker (SCT) uses a single network mechanism.
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 3 Human Pose Estimation Method by Progressive Feature Fusion
### Network Architecture
In the final Stage \(T\), the network outputs \(\mathbf{L}^{T}\) and \(\mathbf{S}^{T}\), marking the completion of the proposed RGB-D network's operation. Based on empirical findings from the OpenPose model, \(T\) is set to 6, while \(R\) is optimized to 3, ensuring the highest performance when color and depth information is fused up to Stage 3. The final outputs \(\mathbf{L}\) and \(\mathbf{S}\) from the proposed RGB-D network are used in the PAF grouping strategy of OpenPose to construct the skeletal structure for each individual. Figure 5 compares the confidence maps of the right shoulder at Stages 3 and 4 between the standard OpenPose and our proposed modification. The confidence maps extracted from Stage 3 and Stage 4 in both models are analyzed to determine the impact of incorporating depth information on joint detection accuracy. The traditional model struggles to distinguish the shoulder positions of two closely positioned individuals when significant occlusion occurs. In Stage 3, the output confidence map \(\mathbf{S}^{3}\) exhibits a merged peak, making it difficult to differentiate between the right shoulders of the two individuals. This problem worsens in Stage 4, where the peaks remain indistinguishable, making it even more challenging to separate the shoulder locations compared to Stage 3. In contrast, the proposed RGB-D network effectively leverages depth information from Stage 1, allowing it to incorporate both topological information and color features throughout the process. As a result, in Stage 3, the confidence map \(\mathbf{S}^{3}\) successfully separates the peaks for the leftmost person's shoulder and the more distant individual's shoulder, making them easily distinguishable. Furthermore, the output confidence map \(\mathbf{D}^{3}\) from the third branch is combined with \(\mathbf{S}^{3}\), refining the Stage 4 output \(\mathbf{S}^{4}\). This results in a more distinct separation of peaks, ultimately improving the accuracy of the right shoulder localization for each individual compared to previous stages. Figure 4: Structure of inference layer in Stage \(n\) (\(n\geq 2\)). Figure 5: Comparison of confidence maps of right shoulder at Stages 3 and 4: (**a**) OpenPose; (**b**) the proposed model.
# A Gesture-Based Teleoperation System for Compliant Robot Motion
## 2 Gesture-Based Human-Robot Interface
### Hand-Robot-Tool Mapping  
The typical teleoperation configuration is shown in Figure 3. An operator faces the robot with leap motion in front of him/her. There are several coordinate frames involved in this system, including the robot base frame \(\Sigma_{B}\), robot tool frame \(\Sigma_{T}\), and leap motion frame \(\Sigma_{L}\).  
The robot can be guided in different frames--a base/tool/leap motion frame. In this paper, it is assumed that all the robot is guided along the robot base frame. Although physically, \(\Sigma_{L}\) and \(\Sigma_{B}\) are located at different places, they can be treated as sharing the same origin point because what matters is the relative motion instead of absolute movement.  
The teleoperation is realized in an incremental way. Before each continuous movement, the teleoperation controller records the initial position of the robot tool \({}^{\Sigma_{B}}\mathbf{r}_{0}\) and hand \({}^{\Sigma_{L}}P_{0}\). At time \(t\), the new hand position \({}^{\Sigma_{L}}P_{t}\) can be mapped to a robot with  
Figure 2: Block diagram of the Gaussian mixture model (GMM) based hand gesture classification.  
\[\Sigma_{B}\mathbf{r}_{t}=\Sigma_{B}\mathbf{r}_{0}+\Sigma_{B}R_{\Sigma_{L}}\times \left(\Sigma_{L}P_{t}-\Sigma_{L}P_{0}\right), \tag{3}\]  
where \(\Sigma_{B}R_{\Sigma_{L}}\) is the rotation matrix between \(\Sigma_{B}\) and \(\Sigma_{L}\) and \(\Sigma_{B}R_{\Sigma_{L}}=\left[\begin{array}{ccc}0&0&1\\ 1&0&0\\ 0&1&0\\ \end{array}\right]\).  
It is noticeable that the initial positions can be updated during the teleoperation, so the hand is not bounded by the working range of the leap motion sensor. It can be reset by renewing the mapping origins.
# A Practical and Adaptive Approach to Predicting Indoor CO2
## 7 Conclusions  
In this paper, we have presented a practical approach for indoor CO\({}_{2}\) prediction. In particular, we have introduced a deep learning solution based on a dynamic mobile window that allows system deployment with no pre-training of the AI model, and an adaptive mechanism to keep the model up to date upon environmental changes. The proposed approach guarantees high performance in a short time frame after the initial deployment and automatically tunes the size of the mobile window until the best settings are reached. The predictions can be effectively used to regulate HVAC systems in advance in order to avoid high levels of CO\({}_{2}\), hence guaranteeing high levels of comfort on site.  
Evaluation results show that the proposed system can be effectively executed on edge devices, resulting in a potential zero-touch approach for indoor CO\({}_{2}\) prediction. It is worth noting that with our solution, each edge device relies only on its own collected data to update the model and to predict the future CO\({}_{2}\) level, making the system setup and its general operation more practical in a real-world scenario.  
Conceptualization, G.S., C.P. and D.S.; methodology, G.S., R.D.-C. and D.S.; software, G.S.; validation, G.S., R.D.-C. and D.S.; formal analysis, G.S., R.D.-C. and D.S.; investigation, G.S., R.D.-C. and D.S.; resources, G.S.; data curation, G.S., R.D.-C. and D.S.; writing--original draft preparation, G.S.; writing--review and editing, G.S., R.D.-C. and D.S.; visualization, G.S. and C.P.; supervision, C.P. and D.S.; project administration, T.G. and D.S.; funding acquisition, C.P. and T.G. All authors have read and agreed to the published version of the manuscript.  
The work in this paper has been partially funded by \"Programma operativo FESR 2014-2020\" of Provincia autonoma di Trento, with co-financing of European ERDP funds, through project \"GEM-Retail\" and by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 872548 (DIH4 CPS: Fostering DIHs for Embedding Interoperability in Cyber-Physical Systems of European SMEs).  
Not applicable.  
Figure 6: RMSE as a function of the forecast days.