esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Point Cloud Reconstruction  
### Reconstruction Process  
To convert from a laser signal to a real point in the WGS-84 system, the target point is determined using several coordinate transitions. The process of point cloud reconstruction can be abstracted as coordinate transformations of a vector. According to Figure 1, the streak signal undergoes a reflection as it travels from the target to the streak tube detector. When data such as stripe signals and POS are synthesized, the rotation of coordinates is inevitably involved due to placement errors.  
A vector should be multiplied by a rotation matrix while being transformed between different Cartesian coordinates. The three-dimensional rotation matrix can be expressed by Euler angles as \(R(A,B,C)\), whose matrix form is shown as  
\[R(A,B,C)=\begin{bmatrix}\cos A\cos B&-\sin A\cos C+\cos A\sin B\sin C&\sin A \sin C+\cos A\sin B\cos C\\ \sin A\cos B&\cos A\cos C+\sin A\sin B\sin C&-\cos A\sin C+\sin A\sin B\cos C \\ -\sin A&\cos A\sin B&\cos B\cos C\end{bmatrix}. \tag{1}\]  
A vector should be multiplied by a reflection matrix while reflected by a mirror. The three-dimensional reflection matrix can be calculated by the mirror norm vector \(\mathbf{u}\). We define \(M\) as the mirror reflection matrix, which can be expressed by  
\[M=I-2\mathbf{u}^{T}\mathbf{u}. \tag{2}\]  
We define the normalized vector of incident light as \(L_{S}\), the photon travel distance as d, and the whole transformation from this vector to the point in world frame as  
\[\mathbf{P}=R_{W}^{P}\times\Big{(}d\cdot R_{P}^{L}\times M\times R_{L}^{S}\times L _{S}+\mathbf{P}_{P}\Big{)}. \tag{3}\]  
This equation contains one reflection and three rotations: \(R_{L}^{S}\) is the rotation matrix between the sensor and laser footprint, \(R_{P}^{L}\) is the rotation matrix from the POS to the footprint, and the matrix \(R_{W}^{P}\) expresses the rotation from the POS frame to the real world.  
Figure 3: Diagram of the PSO algorithm.  
The parameter \(P_{p}\) is the POS position calculated from the GPS and IMU. The specific form of the position is  
\[\mathbf{P}=\begin{bmatrix}x_{p}&y_{p}&z_{p}\end{bmatrix}^{T}. \tag{4}\]
# Finding Optimal Manipulator Arm Shapes to Avoid Collisions in a Static Environment
## 3 Simulation Model
### Workplace and Trajectory  
A trajectory is a 3D curve (path) that defines the required robot TCP movement. The trajectory is given by the actual task and is considered immutable in this research. The trajectory is defined by key points, where each point has the position and orientation (\(3+3\) values), the velocity of the TCP, and the type of segment that connects this point with the previous one (linear or arc). This type of trajectory definition follows the common principle used in industrial robot teaching and programming.  
For the simulation, the trajectory definition in the form of key points is replaced by a set of points in a constant time distance (0.005 s), where each point has only position and orientation (transformation matrix of the TCP). During the simulation, the robot TCP is placed into each of these points and inverse kinematics are calculated to get the joint angles of the robot (see Section 3.2).  
In addition to the trajectory, the workplace is mainly defined by the objects that act as obstacles for the robot. Obstacles are used for collision checking and can be defined by various methods, for example, as bounding boxes, convex hulls, general meshes, or point clouds.  
For simplicity (while also maintaining high flexibility), this research uses oriented bounding boxes to model the obstacles, which is mostly suitable for simple obstacles, forbidden areas, and borders like walls, ceilings, and floors.
## 4 EDETA Protocol Description
### Normal Operation Phase  
When the tree is built, the normal operation phase begins. The network structure is complete and every node must send its data to the CH at the scheduled time, and during the remaining time the nodes enter the sleep state. When a CH has received the data from all its nodes, it aggregates it and sends it to its father at the established time.  
As mentioned above, the father of a cluster informs on the time its sons will have to send their data. Sons will send their data only when they receive a POLL message. This allows the father to decide exactly when the data will be sent. This will require some fault tolerant mechanisms, as discussed in later sections, without inquiring collision of messages. Moreover, this polling mechanism allows timing between all the CHs in the tree to be synchronized.  
This phase is performed during several rounds, with duration TIME_SUPERFRAME, as defined by the parameter MAX_INTRAROUNDS and planned in the calendar for each node. After that, the network structure is considered obsolete and every node restarts from the beginning at the initialization phase set out in the calendar.
# A Joint Strategy for Fair and Efficient Energy Usage in WLANs in the Presence of Capture Effect
## 2 Related Works
While this mechanism increases overall throughput, it is worth mentioning that it does not address the issue of unfair energy consumption among the groups of nodes present in the network. In [25; 26] authors proposed transmission power control mechanisms to increase energy efficiency in multi-hop wireless networks. Their propsed mechanism is based on link scheduling and routing. This work, however, does not address the issue of unfair energy consumption among the nodes that may have been caused by the use of transmission power control. Finally, in [27] authors proposed a three dimensional Markov chain that takes into account the capture effect as well as various transmission power levels with which a node may transmit. This work, too, like many previous works focuses on the overall saturation throughput. It does not address the issue of unfair energy consumption among the various groups of nodes in the network. In contrast to the above studies, our work presents a comprehensive energy consumption analysis of IEEE 802.11-based wireless networks in the presence of PHY capture effect. In this research work:  
* We show that although capture effect improves overall energy efficiency of the wireless network, it causes unfairness; a group of nodes located farther away from the AP consumes more energy as compared to the group of nodes located closer to the AP. * For the fair energy consumption among the groups of nodes, we propose a mechanism that is based on transmission power control and contention window adjustment technique. * We show that the proposed solution lets the nodes spend their energy resources fairly without sacrificing the overall energy efficiency of the network. * Unlike the notion of fairness used in the literature being 'equal energy consumption', our work uses [PERSON]'s Fairness Index [28].
## 3 Results and Discussion
### Trend of Cd Pollution Exceeding the Standard  
Before assessing the exceedance rates of Cd pollution in Hainan Island's agricultural soils, we conducted a comparative analysis of evaluation standards referenced in the literature before and after the implementation of the GB15618-2018. Notably, prior to this standard, studies often employed a more stringent benchmark of 0.3 mg/kg as the exceedance threshold for soil Cd using the single-factor index method. To ensure consistency and rigor, this study also uses 0.3 mg/kg as the baseline for assessing Cd exceedance across different periods.  
Cd exceedance rates have exhibited a notable rise over the past two decades, with levels recorded from 2017 to 2023 surpassing those from 2005 to 2014 by over sevenfold, underscoring an escalating pollution issue (Figure 9). However, the trends within the two periods differ significantly. Specifically, from 2005 to 2014, Cd exceedance rate remained relatively stable at approximately 1%, with peak of 1.40% in 2009 and a low of 0.96% in 2005. Conversely, from 2017 to 2023, the exceedance rate exhibited a sharp decline, decreasing from 20.93% in 2017 to 10.50% in 2019, and further down to 7.78% in 2023. Despite signs of improvement, Cd pollution levels in soils remain considerably higher than those recorded two decades ago. The reduction in Cd exceedance rates during 2019-2021 may partly reflect decreased human activity during the COVID-19 pandemic, which reduced anthropogenic Cd inputs. Future research should compare soil Cd concentration and exceedance rates before and after 2019.  
The observed pattern, characterized by rising exceedance rates between periods (2005-2017) and declining rates within periods (2017-2023), can be attributed to two main factors. One key factor for this trend is the intensification of agricultural activities, industrialization and urbanization, which has led to soil degradation and increased Cd accumulation, as evidenced by the significantly higher exceedance rates in 2017-2023 relative to 2005-2014. Another factor is the implementation of policy measures, including the enforcement of stricter environmental pollution standards, which have helped in raising awareness and curbing further Cd accumulation, as reflected in the declining exceedance rates during 2017-2023. These results highlight the pressing need for sustained monitoring and strategies management efforts to combat Cd contamination and preserve the integrity of Hainan's agricultural land.
# Enhancing Soft Tissue Differentiation with Different Dual-Energy CT Systems: A Phantom Study
###### Abstract  
To quantitatively evaluate the possible advantages of quantifying and differentiating various soft tissues using virtual monochromatic images (VMI) derived from different dual-energy computed tomography (DECT) technologies. This study involved four DECT scanners with different technologies. CIRS phantom images were acquired in single-energy (SECT) and DECT modes with each scanner. The analysis focused on five equivalent soft-tissue inserts: adipose, breast, liver, muscle, and bone (200 mg). The signal-to-noise ratio (SNR) was calculated for each equivalent soft-tissue insert. Finally, the contrasts of tissue pairs between DECT and SECT images were compared using Wilcoxon signed-rank tests adjusted for multiple comparisons. Average CT numbers and noise showed a significant difference pattern between DECT with respect to SECT for each CT scanner. Generally, energy levels of 70 keV or higher led to improved SNR in VMI for most of the equivalent soft-tissue inserts. However, energy levels of 40-50 keV showed significantly higher contrasts in most of the equivalent soft-tissue insert pairs. DECT images at low energies, especially at 40-50 keV, outperform SECT images in discriminating soft tissues across all four DECT technologies. The combined use of DECT images reconstructed at different energy levels provides a more comprehensive set of information for diagnostic and/or radiotherapy evaluation compared to SECT. Some differences between scanners are evident, depending on the DECT acquisition technique and reconstruction method.  
dual-energy CT; soft tissue; SNR; virtual monochromatic imaging +
Footnote †: journal: _Applied Sciences_
## 3 Nanosized-Lipid-Based Drug Delivery Systems in Wound Healing
### Vesicular Systems
#### 3.1.1 Conventional Liposomes in Wound Healing
Researchers evaluated the potential effects on wound healing of MA liposomes prepared by a double emulsion method. They revealed a formulation with a small particle size, remarkable long-term stability and high EE%. Moreover, in vivo experiments showed that MA double emulsion liposomes improved transdermal penetration and wound healing [49]. Another study on MA liposomes showed that the storage temperature is essential to its stability. A lower temperature (4 \({}^{\circ}\)C) was optimal for the storage of MA liposomes with a higher EE% [105]. In the section above, we found studies that incorporated vaso drugs, genes, plant extracts, intracellular molecules or growth factors in liposomes. Even though liposomes are nontoxic, biodegradable and biocompatible with skin [106], these molecules face significant obstacles to their expansion in clinical use: low stability, low reproducibility and rapid drug leakage [107].
## 4 Conclusions  
In this article, a small-sized crescent monopole UWB MIMO antenna is presented, which demonstrates triple notching characteristics at the C-band, WLAN band, and satellite band frequencies of 4.5 GHz, 5.5 GHz, and 8.8 GHz, respectively. The implementation of Complementary Split-Ring Resonators (CSRRs) in the feeding line of the patch for C-band notching and on the ground plane for WLAN band notching, along with a curved C-shaped structure on the top of the patch for satellite network notching, successfully achieves the desired notching properties. The orthogonal positioning of the MIMO antenna system enables high isolation, pattern diversity, and polarization diversity. The diversity parameters, including ECC < 0.5, DG \(\geq\) 10, TARC < 0, and CCl < 0.4 bits/s/Hz, are achieved, indicating improved performance. The incorporation of CSRRs in this study effectively achieves notching and high isolation for the system. The simulated and measured results are in good agreement, validating the proposed monopole crescent-shaped UWB-MIMO antenna. Overall, the presented antenna design, with its small size and desirable characteristics, is well suited for wireless communication, portable system devices, and WLAN systems.  
Conceptualization, U.U.R.Q., S.B., F.S., F.N., H.H. and M.A.K.; Methodology, U.U.R.Q., S.B., F.S. and M.A.K.; Software, U.U.R.Q., S.B., F.S. and M.A.K.; Validation, U.U.R.Q., S.B., J.A., F.S. and M.A.K.; Formal Analysis, U.U.R.Q., S.B., M.S., F.S., Y.Y.Z. and M.A.K.; Case Studies, U.U.R.Q., S.B., F.S., K.O., S.A.H.M., M.A.H.M. and M.A.K.; Resources, U.U.R.Q., S.B., F.N., H.H., F.S. and M.A.K.; Data Curation, U.U.R.Q., S.B., F.S., Y.Y.G., H.H., F.N. and M.A.K.; Writing--Original Draft Preparation, U.U.R.Q., S.B., F.S., F.N., Y.Y.G., H.H., M.A.H.M. and M.A.K.; Writing--Review and Editing, U.U.R.Q., S.B., F.S., S.A.H.M. and M.A.K.;Visualization, U.U.R.Q., S.A.H.M., S.B., F.S., J.L. and M.S.; Supervision, M.A.K. All authors have read and agreed to the published version of the manuscript.  
This article received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 2 LoRa Overview
### LoRa Network Architecture  
A typical LoRa network is \"a star-of-stars topology\", which includes three different types of devices, as shown in Figure 1.  
The basic architecture of a LoRaWAN network is as follows: end-devices communicate with gateways using LoRa with LoRaWAN. Gateways forward raw LoRaWAN frames from devices to a network server over a backhaul interface with a higher throughput, typically Ethernet or 3G. Consequently, gateways are only bidirectional relays, or protocol converters, with the network server being responsible for decoding the packets sent by the devices and generating the packets that should be sent back to the devices. There are three classes of LoRa end-devices, which differ only with regards to the downlink scheduling.
# Arithmetic Optimization AOMDV Routing Protocol for FANETs
## 4 Performance Evaluation
### Simulation Results and Discussion  
In this section, the simulation results of the AO-AOMDV algorithm were analyzed and compared. Figure 2 illustrates the spatial distribution of the UAV nodes, with 100 UAV nodes uniformly distributed within the specified area. In our simulation, the UAV nodes dynamically and randomly vary within a fixed range of 3D space at a certain rate of change.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 2 Related Work
### Predictive Modeling of Shot Accuracy in Badminton  
Building on the success of advanced analytics in various sports, recent studies aim to expand these techniques to predict performance using Quiet Eye (QE) metrics. Research has shown that longer QE durations, especially in high-stakes situations, lead to better outcomes. While the study by [PERSON] et al. [4] focused on golf, its findings could potentially be applied to a precision sport such as badminton.  
[PERSON] et al. [3] found that competitive elite players demonstrated longer QE online durations compared to semi-elites, especially in high-pressure situations. This suggests that QE metrics could be valuable predictors of performance across various sports, including badminton.  
Although previous studies have combined QE metrics with biomechanical variables to provide real-time predictions during competitive play [14], there is limited research on integrating QE metrics with neural network models to predict shot accuracy in real-time badminton matches. To address this gap, our work integrates both QE and biomechanical data into a predictive model designed for real-time feedback during training and competitions.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 4 Results
### Results Obtained from the IoT-23 Dataset Using a Unsupervised Machine Learning Approach
#### 4.2.1 Research Analysis According to [46]  
Research [46] has investigated anomaly detection in IoT networks, utilizing the IoT-23 dataset. The methodology encompasses several key steps.  
Step 1: Data Preparation and Processing  
The dataset underwent preliminary processing to eliminate null, infinite, and other erroneous values. The \"Cat\" class label, encompassing five categories (Mirai, Scan, DoS, Normal Records, and MITM-ARP), was employed. All 83 features, along with the \"Cat\" label, were directly fed into the model, without feature extraction or removal. The dataset was partitioned into 80% for training and 20% for testing.  
Step 2: Model utilization  
An Autoencoder, comprising an encoder and a decoder, was employed. The encoder compressed the input data while the decoder reconstructed the input from the compressed representation. Sequential layers were used to construct both the encoder and decoder. The \"softmax\" activation function was utilized to differentiate between the original signal and five artificially created class states, with \"categorical_cross_entropy\" loss being specified. The model was trained until convergence, aiming to approximate the identity function of the input.  
Step 3: Model evaluation  
The model achieved a loss of 0.033698 and an accuracy of 0.992697. The low loss and high accuracy indicated the model's effectiveness in anomaly detection within the data.  
Step 4: Result verification  
When compared to other deep learning models, the Autoencoder achieved the second-highest accuracy, following the CNN model. However, the Autoencoder's execution time was significantly shorter than that of the CNN model. This suggests that the Autoencoder is both efficient and effective for detecting anomalies in IoT data.
# Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science
## 4 Multidimensional Projection Approaches and Domains
### Multidimensional Scaling  
One of the first global multidimensional projection techniques is Multidimensional Scaling (MDS), a classic algorithm that originated from psychophysics, becoming famous after the [PERSON] [50] paper. Nowadays, MDS comprises a family of nonlinear methods that seek to define an injective mapping of data objects from a multidimensional space to a lower one, maintaining data distance relations [9]. The way that preserves distances throughout the transformation process determines the differences among approaches based on MDS.  
_Classical Scaling_ is the best-known MDS method. This technique builds a transformed space by applying spectral decomposition of a symmetric matrix generated from distancesof multidimensional objects [6; 27]. More specifically, it seeks to satisfy \(\delta(x_{i},x_{j})=d(y_{i},y_{j})\), in Euclidean space, with \(\delta(x_{i},x_{j})\) being the dissimilarity between objects \(x_{i}\) and \(x_{j}\), and \(d(y_{i},y_{j})\) the dissimilarity between projected objects. The step of spectral decomposition is similar to the step demonstrated in PCA (Section 4.1).  
The outcomes attained by Classical Scaling are considered satisfactory and precise concerning global distance preservation. However, its computational complexity of \(O(n^{3})\) bounds the approach application. Thus, applying Classical Scaling in large datasets (more than millions of items) is not viable. To mitigate this problem, some alternatives were developed, such as Landmark MDS (LMDS) [51] and Pivot MDS [52].  
Instead of decomposing the complete dissimilarity matrix, LMDS selects an initial subset from the original dataset containing \(s\) reference instances named _landmarks_. These reference instances can be selected randomly or by an algorithm that maximizes the distances among them. The approach performs Classical Scaling in the landmarks subset, projecting these reference points on the \(\mathbb{R}^{p}\). Finally, the remaining data instances are mapped on the new space through distance-based triangulation.  
LMDS preserves MDS characteristics and is more efficient with its complexity of roughly \(O(s^{3}+sn)\). Nonetheless, it should be selected at least \(p+1\) landmarks to generate a good projection.
## 3 Results
### MLR Method
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Input Parameters** & \(\mathbf{R^{2}}\) & \(\mathbf{RMSE}\) & \(\mathbf{MAE}\) \\ \hline Dataset 1 & \(V_{p}\) and \(PIGT\) & 0.95 & 0.000986 & 0.000893 \\ Dataset 2 & \(V_{p}\), \(PIGT\), and \(CAL\) & 0.96 & 0.000976 & 0.000899 \\ Dataset 3 & \(V_{p}\), \(PIGT\), \(CAL\), and \(PR\) & 0.96 & 0.000993 & 0.000954 \\ Dataset 4 & \(V_{p}\), \(PIGT\), \(CAL\), \(PR\), and \(RT\) & 0.96 & 0.000969 & 0.000891 \\ Dataset 5 & \(V_{p}\), \(PIGT\), \(CAL\), \(PR\), \(RT\), and \(GR\) & 0.96 & 0.000969 & 0.000910 \\ Dataset 6 & \(V_{p}\), \(PIGT\), \(CAL\), \(PR\), \(RT\), \(GR\), and \(RHOB\) & 0.96 & 0.000310 & 0.000252 \\ Dataset 7 & \(V_{p}\), \(PIGT\), \(CAL\), \(PR\), \(RT\), \(GR\), \(RHOB\), and \(TEMP\) & 0.96 & 0.000881 & 0.000764 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The calculated statistical indicators (\(R^{2}\), \(RMSE\), and \(MAE\)) values corresponding to the different MLR models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Unit** & **Coefficient** & **Range** \\ \hline \(V_{p}\) & (ft/ us) & 0.456290 & 0.0127–0.0205 \\ \(PIGT\) & - & \(-\)0.000726 & 0.0036–0.2340 \\ \(CAL\) & in & 0.000141 & 5.6562–10.5145 \\ \(PR\) & - & \(-\)0.003617 & 0.2121–0.3745 \\ \(RT\) & \(\Omega\cdot\)m & 0.000001 & 0.5456–7200 \\ \(GR\) & GAPI & \(-\)0.000005 & 9.0195–65.0151 \\ \(RHOB\) & g/cm\({}^{3}\) & 0.119900 & 2.3955–2.3400 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Coefficients and ranges of parameters in Equation (10).