esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
### Initial State Selection  
Although the initial states were not forced to a fixed point during the experimental verification, the three types of the IRW's dynamic characteristics, derailment, hunting, and offset running, are still observable and recognizable. During about half a year of conducting simulations and experiments, we have not encountered a situation where initial state selections can change the type of the dynamic characteristics. For example, the dynamic characteristic is always derailment if the IRW runs at low speeds and without yaw suspensions, no matter where the initial states are. In contrast, the dynamic characteristic is always offset running if the IRW runs at low speeds but with yaw suspensions, no matter where the initial states are. We found that for an already designed IRW system, only velocity, yaw stiffness, and yaw damping could significantly influence the type of the dynamic characteristics in one specific experiment. We speculate these three parameters prominently affect the system poles of the IRW's root locus diagrams and are now working on theoretical proof.  
In view of the simulation results in Figure 20 and the analysis in Section 4.2, if proper yaw suspensions are deployed, no matter where the initial states are, the IRW will experience two stages: a damped dynamic stage and a steady convergent stage. Firstly, they all converge to the straight line described by Equation (47), as shown in Figure 20. Then, they will move slowly along this straight line to the origin (\(y=0,\psi=0,\Delta\omega=0\)). This is a reflection of the simulation results on the explanation and speculation above.  
Additionally, since the sensors are high-precision and sensitive, especially the roller continuously rotating during the whole experiment, it is difficult to ensure that we can put the scaled IRW precisely on one fixed-state point every time we begin to record results.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 2 Frequency-Reconfigurable Antennas
### Mechanically Frequency-Reconfigurable Antennas
#### 2.2.3 Kirigami and Origami-Based Antennas
The table presents the antenna types, reconfigurability method, number of states, radiation performance and the materials that were used in these antennas to give a general overview on this category.
# Algorithm for Generating 3D Geometric Representation Based on Indoor Point Cloud Data
###### Abstract  
This study proposes a new method to generate a three-dimensional (3D) geometric representation of an indoor environment by refining and processing an indoor point cloud data (PCD) captured through backpack laser scanners. The proposed algorithm comprises two parts to generate the 3D geometric representation: data refinement and data processing. In the refinement section, the inputted indoor PCD are roughly segmented by applying random sample consensus (RANSAC) to raw data based on an estimated normal vector. Next, the 3D geometric representation is generated by calculating and separating tangent points on segmented PCD. This study proposes a robust algorithm that utilizes the topological feature of the indoor PCD created by a hierarchical data process. The algorithm minimizes the size and the uncertainty of raw PCD caused by the absence of a global navigation satellite system and equipment errors. The result of this study shows that the indoor environment can be converted into 3D geometric representation by applying the proposed algorithm to the indoor PCD.  
Keywords: indoor point cloud data; 3D geometric representation; automatic algorithm; random sample consensus; normal estimation +
Footnote †: journal: Applied Sciences
# Long-Term Thermo-Hydraulic Numerical Assessment of Thermo-Active Piles--A Case of Tropical Soils
## 2 Numerical, Modeling of the Long-Term Performance of Thermo-Active Piles
### Energy Demand Assessment
#### 2.2.1 Climatic Conditions in Brazil  
Brazil is the eighth-largest energy consumer throughout the world, with its vast tropical areas, the total primary energy consumption in Brazil has increased by 28% in the past decades due to economic growth [29]. The implementation of an efficient GSHP system would substantially reduce its primary energy consumption and carbon emissions. Several exploitations of the thermo-active geostatures were conducted in this region, but its application is still limited due to the scarcity of reliable thermal design approaches and poor understanding of its long-term thermo-hydraulic behavior in tropical regions. Anyhow, the Return On Investment (ROI) has to meet expectations in order to answer economic questions. Therefore, there is a strong interest in the assessment of the sustainability and efficiency of this technology in these regions [20].  
Although most of Brazil lies in the tropics, the climatic conditions are various in cities due to its large country size. While in the northern cities of the Amazonas state (Manaus), the climate can get extremely hot all year. Cities such as Sao Paulo, Brasilia andBelo Horizonte have mild climates. The southern cities of Porto Alegre and Curitiba have mild winters. Therefore, three cities representing different corresponding regional climate characteristics are selected as the source of different thermal solicitations applied in this study: Brasilia, Porto Alegre, and Manaus (Figure 2).  
The variation of ambient temperature in Brasilia can be described by a continuous sinusoidal curve:  
\[T(t)=\overline{T}_{year}+A_{T}(\sin(\omega t)+\varphi) \tag{12}\]  
where \(T(t)\) is the instant temperature at the time \(t\) (\({}^{\circ}\)C), \(\overline{T}_{year}\) is the annual average temperature which equals 20.7 \({}^{\circ}\)C, \(A_{T}=1.79\) is the maximum annual amplitude, \(\omega=2.03\times 10^{-7}\) is the radial frequency of the annual signal phase, and \(\varphi=2.11\) specifies where in its cycle the oscillation is at \(t=0\).  
Considering the same sinusoidal form as in Brasilia, the real annual temperature variation in Porto Alegre can be converted into a more complicated curve:  
\[T(t)=5.16\sin\Bigl{(}2.05\ \times\ 10^{-7}.t+1.39\Bigr{)}+0.04\cos(0.5t+80)+19.54 \tag{13}\]  
The temperature is relatively constant in Manaus with an average of 27.4 \({}^{\circ}\)C.
## 2 Materials and Methods
### Experimental Setup and Testing Procedure  
Experiments were conducted in a controlled indoor environment to evaluate the performance of the integrated system, focusing on gesture recognition, obstacle avoidance, and 3D mapping accuracy. The testing procedure consisted of three primary evaluations:  
* **Hand Gesture Control Test:** Users performed predefined hand gestures in front of a webcam to verify accurate classification and execution of drone commands. Each gesture was tested multiple times to assess recognition accuracy and command responsiveness.
* **Obstacle Avoidance Test:** The drone was commanded to move forward while obstacles were placed in randomized positions along its path. The system's ability to detect and avoid obstacles within the safety threshold was analyzed, recording the reliability of avoidance maneuvers and any unintended collisions.
* **3D Mapping Validation:** To assess mapping accuracy, the recorded flight path and obstacle locations were compared to real-world measurements. Errors in obstacle placement and discrepancies in flight path representation were quantified. Each test was repeated multiple times to ensure consistency.
# Role of Forster Resonance Energy Transfer in Graphene-Based Nanomaterials for Sensing
## 3 Application of Graphene-Based Nanomaterials in Sensing
### Explosive Sensor  
In particular, explosives are handled in military and aerospace fields, and their residues in the environment cause adverse effects to human beings, such as abnormal liver function and anemia [59]. Based on these aspects, [PERSON] et al. [60] fabricated a fluorescent probe **G6** through the combination of GQDs and amino groups of APTES via the solvothermal process. **G6** was treated against 2,4,6-trinitrotoluene (TNT) for 2 h in an ethanol solution, and later their complexation ability was observed using fluorescence with an excitation wavelength of 360 nm (Figure 8). **G6** containing amino groups could adsorb a large number of TNT via pores and could be involved in the accumulation process. During these processes, Meisenheimer complexation was observed between TNT and amino groups, which produced the sensing layer mediated FRET process. In this manner, compared with other nitro groups, TNT had a high binding affinity with **G6**, and the LOD was calculated to be 0.6 nM. Furthermore, the FRET mechanism was confirmed through the decay curves in the presence and absence of TNT with **G6**. Notably, the **G6** detection approach was successfully applied in TNT-spiked water samples.
# Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey
## 4 Game Theory Defense Strategies Against WSN Attacks
### WSN Attack Types vs. Corresponding Layer  
WSNs are suffering from several attack types that target different layers. In [33; 34; 76; 78; 79], WSN attacks are classified based on the layer attacked and the countermeasures which can be summarized as follows. The physical layer (L1) is prone to jamming and tampering attacks in which the attacks target L1 features. The data link layer (L2) attacks (e.g., sniffing, collision, exhaustion, unfairness, stealthy, etc.) aim for deteriorating the layer facilities such as media access techniques. The network layer (L3) can be affected by certain attacks such as spoofed altered relayed information, Sybil, wormhole, etc. More specifically, these attacks aim to corrupt the network layer routing protocols. Finally, the transport layer (L4) would be infected by some attacks (e.g., desynchronization, flooding, etc.) that aim at disrupting the layer functions such as the end-to-end protocols.
## 2 Network Architecture and Signal Model
### Signal Model
\[\mathbf{y}=(\mathbf{T}\otimes\mathbf{1}_{K})\mathbf{R}\mathbf{s}+\mathbf{n} \tag{4}\]
## 4 Discussion
Interestingly, even though growth adaptation was observed, the propagated colonies only showed a 2-4-fold increase in MIC (Supplementary Table S4), which falls below the commonly accepted 4-fold increase threshold for reliably identifying resistant strains [27]. We can only speculate why it is difficult to generate mutations that cause significantly increased MIC values above 4-fold. To try to understand intrinsic resistance towards the antimicrobial substance(s) in _S. glaucescens_, we searched for genes, where gene knock-out would increase susceptibility to the crude extract. One gene, the _hemB_ gene, stood out as particularly important, as the _hemB_ mutant showed a 64-fold decrease in MIC compared to the wild type-strain JE2 of the NTML library screened. _hemB_ encodes for a heme biosynthesis enzyme crucial for oxygen transport and energy production in _S. aureus_[28]. In accordance with previous observations, the _hemB_ mutants formed small colonies attributed to a lack of hemin biosynthesis, which, in turn, interferes with the functioning of the respiratory chain and subsequently impacts ATP synthesis [28; 29]. Previous research has shown that the gene significantly influences the susceptibility of _S. aureus_ towards tomatoidine and _N,N\({}^{\prime}\)_-dicyclohexylcarbodiimide (DCCD) with MICs of 0.12 ug/mL and 2-8 ug/mL for the mutant in contrast to an MIC of \(>\)128 ug/mL in the wild type [30; 31; 32]. It was reported that tomatoidine and _N,N\({}^{\prime}\)_- dicyclohexylcarbodiimide (DCCD) target ATP synthase in SCV with a defective electron transport chain (ETC) [32]. This may indicate that epifriedelanol also targets ATP synthesis and/or that natural resistance towards the active substance in the crude extract is energy-dependent. Further studies are indicated to investigate this, and such testing should include both crude extract, fractions and purified compound(s). _S. aureus_ has a propensity to generate transient variations, such as switching from the normal to SCV phenotype and then back to the normal phenotype. This flipping is an essential component of the pathophysiology of _S. aureus_ infections when causing chronic infections [33]. SCVs of _S. aureus_ are naturally resistant to gentamicin, but combining gentamicin with tomatoidine or _N,N\({}^{\prime}\)_-dicyclohexylcarbodiimide (DCCD) restores their sensitivity to the antibiotic [30, 31, 32]. In the current study, we have not combined the plant extract with other antimicrobials, although previous studies from other scholars have shown and there may be an avenue for combination treatment between plant medicine and traditional antibiotics [34]. It would be interesting to combine epifriedelanol with gentamicin to see if there is synergy between the two compounds. Other genes identified in our screen of susceptibility genes are associated with the formation of SCVs (_aroD_ and _menD_) [35, 36]. However, the changes in MIC caused by mutation in these genes were small, and they were deemed less interesting.
# Forward Link Optimization for the Design of VHTS Satellite Networks
###### Abstract  
The concept of geostationary VHTS (Very High Throughput Satellites) is based on multibeam coverage with intensive frequency and polarization reuse, in addition to the use of larger bandwidths in the feeder links, in order to provide high capacity satellite links at a reduced cost per Gbps in orbit. The dimensioning and design of satellite networks based on VHTS imposes the analysis of multiple trade-offs to achieve an optimal solution in terms of cost, capacity, and the figure of merit of the user terminal. In this paper, we propose a new method for sizing VHTS satellite networks based on an analytical expression of the forward link _CINR_ (Carrier-to-Interference-plus-Noise Ratio) that is used to evaluate the trade-off of different combinations of system parameters. The proposed method considers both technical and commercial requirements as inputs, including the constraints to achieve the optimum solution in terms of the user \(G\)/\(T\), the number of beams, and the system cost. The cost model includes both satellite and ground segments. Exemplary results are presented with feeder links using Q/V bands, DVB-S2X and transmission methods based on CCM and VCM (Constant and Variable Coding and Modulation, respectively) in two scenarios with different service areas.  
CCM; CINR; cost per Gbps in orbit; multibeam satellite communications; system optimization; VCM; VHTS Article
###### Abstract  
Wrist kinematics can provide insight into the development of repetitive strain injuries, which is important particularly in workplace environments. The emergence of markerless motion capture is beginning to revolutionize kinematic assessment such that it can be conducted outside of the laboratory. The purpose of this work was to apply open-source software (OSS) and machine learning (ML) by using DeepLabCut (OSS) to determine anatomical landmark locations and a variety of regression algorithms and neural networks to predict wrist angles. Sixteen participants completed a series of flexion-extension (FE) and radial-ulnar (RUD) range-of-motion (ROM) trials that were captured using a 13-camera VICON optical motion capture system (i.e., the gold standard), as well as 4 GoPro video cameras. DeepLabCut (version 2.3.3) was used to generate a 2D dataset of anatomical landmark coordinates from video obtained from one obliquely oriented GoPro video camera. Anipose (version 1.0.1) was used to generate a 3D dataset from video obtained from four GoPro cameras. Anipose and various ML algorithms were used to determine RUD and FE wrist angles. The algorithms were trained and tested using a 75%:25% data split with four folds for the 2D and 3D datasets. Of the seven ML techniques applied, deep neural networks resulted in the highest prediction accuracy (5.5\({}^{\circ}\)) for both the 2D and 3D datasets. This was substantially higher than the wrist angle prediction accuracy provided by Anipose (\(FE-99\); \(RID-25.2^{\circ}\)). We found that, excluding cubic regression, all other studied algorithms exhibited reasonable performance that was similar to that reported by previous authors, showing that it is indeed possible to predict wrist kinematics using a low-cost motion capture system. In agreement with past research, the increased MAE for FE is thought to be due to a larger ROM.  
machine learning; motion capture; DeepLabCut; Anipose; deep neural networks +
Footnote †: journal: Applied sciences
## 3 Results  
### Descriptive Analysis  
To examine the regional distribution of brain signals processed through AFNI and the AAL atlas, descriptive statistics were conducted for the 116 regions defined by the atlas. The mean signal intensities for each region are visualized in Figure 1.  
Subsequently, independent \(t\)-tests were performed to identify regions with significant differences in signal intensities between the trauma-exposed and control groups. The regions showing significant differences were as follows: the middle temporal gyrus (MTG) (Control: M = 1742.616, SD = 737.373; Trauma: M = 1101.460, SD = 609.891; t = 2.688, \(p\) = 0.012), the left inferior temporal gyrus (LITG) (Control: M = 2966.947, SD = 345.417; Trauma: M = 2665.265, SD = 354.601; t = 2.468, \(p\) = 0.019), the right thalamus (Control: M = 3587.764, SD = 449.178; Trauma: M = 3236.774, SD = 351.921; t = 2.462, \(p\) = 0.021), the left fusiform gyrus (Control: M = 3073.378, SD = 424.227; Trauma: M = 2764.026, SD = 295.638; t = 2.383, \(p\) = 0.025), and the right amygdala (Control: M = 3057.784, SD = 525.829; Trauma:M = 2632.298, SD = 597.392; t = 2.175, \(p\) = 0.037). These differences are summarized in Table 1 and visualized in Figure 2.
# An Improved Multiobjective Particle Swarm Optimization Based on Culture Algorithms
## 2 Preliminary
### Multiobjective Optimization Problems  
Multiobjective problems (MOPs) are problems whose goal is to optimize multiple objective functions simultaneously. A MOP global minimum problem with n objectives is defined as:  
\[\begin{array}{c}\min\text{f}(x)=[\text{f}_{1}(x),\,\text{f}_{2}(x),\,\ldots,\, \text{f}_{n}(x)]\\ \text{s.t.g}(x)\leq 0,\,\text{h}(x)=0\end{array} \tag{1}\]  
where g(\(x\)) \(\leq\) 0 and h(\(x\)) = 0 represent the constraints.  
**Definition 1**.: _(Pareto dominance) [26]: x is said to dominate y (denoted as x \(<\) y) if \(\forall i,f_{i}(x)\leq f_{i}(y),\,\exists i,f_{i}(x)<f_{i}(y)\)._  
**Definition 2**.: _(Pareto optimal set) [26]: P* = {x}\(\
eg\exists y\in\Omega,\,f(y)\,\prec\,f(x)\)._  
**Definition 3**.: _(Pareto front) [26]: PF* = {f(x) = [\(f_{1}(x),f_{2}(x),\,\ldots,\,f_{n}(x)]\,\,\text{l}\,\text{x}\in\)P*}._  
**Definition 4**.: _(\(\epsilon\)-Pareto dominance) [17]: x is said to \(\epsilon\)-dominate y, denoted as x \(\prec_{\epsilon}\)y, if f(x) \(\cdot\) (1 + \(\epsilon\)) \(\prec\) f(y)._
## 5 Simulations
### Simulation Results  
Algorithm 1 is made to work in the simulated RSS environment (Algorithm 2), and the performance is evaluated using the following parameters:  
**Theoretical optimum:**: The position \((x_{O},y_{O})\) at which the RSS values of both the server and the client nodes are maximum, as well as equal (\(\max RSS_{c}=\max RSS_{s}\)) without including any multipath effects (\(PL^{mp}=0\)). In simulations, the objective function \(f\) at each point in space can be calculated (because the RSS environment is fully known), and hence, the theoretical optimum is the global optimum (where \(f\) is maximum), as well. On the other hand, in field experiments, the theoretical optimum is calculated using Equation (1) (but without \(\mathcal{X}_{\sigma}\)) by substituting the empirical values of \(PL_{d_{0}}\), \(\eta_{c}\) and \(\eta_{s}\) observed in that environment.
**Threshold zone (\(\mathrm{m}^{2}\)):**: A circular area with the center as the theoretical optimum \((x_{O},y_{O})\) and the radius (\(r_{t}\)) equal to 10% of the total distance between the initial relay node position (when the algorithm started) and the theoretical optimum position (\(r_{t}=0.1\sqrt{(x_{r}^{0}-x_{O})^{2}+(y_{r}^{0}-y_{O})^{2}}\)), where \((x_{r}^{0},y_{r}^{0})\) is the initial relay node position).
**MAE (\(\mathrm{m}\)):**: The mean absolute distance error in reaching the theoretical optimum position.
**RMSE (\(\mathrm{m}\)):**: The root mean squared error in reaching the theoretical optimum position.
**Success rate (%):**: The percentage of success in reaching the threshold zone at the end of the algorithm. When the relay is at its final position \((x_{r}^{f},y_{r}^{f})\), success = 100 if \({x_{r}^{f}}^{2}+{y_{r}^{f}}^{2}\leq r_{t}^{2}\), and success = 0, otherwise. The success rate is the average value of the success scores over all of the simulation trials in each case.
**Distance cost (\(\mathrm{m}\)):**: The total (Euclidean) distance traveled from the initial position to the theoretical optimum position.
**Time cost (\(\mathrm{\#iter}\)):**: Time taken in reaching the optimum, measured indirectly using the number of iterations.
**Speed of convergence (\(\frac{m}{\mathrm{\#iter}}\)):**: The speed at which the algorithm reaches the optimum position, measured as \(\frac{\mathrm{Distance cost}}{\mathrm{Time cost}}\).  
Figure 7: Simulation of the radio environment in 2D. The server node is placed at (30,0), and the client node is placed at (\(-\)30,0). (**a**) Simulations without Gaussian noise; (**b**) Simulations with Gaussian noise \(\mathcal{N}(0,2)\).  
One hundred trial runs are executed in each simulation case. The mean and standard deviation values of the above-mentioned parameters are considered in the performance analysis. The results of each simulation case are presented below.
## 2 Clays and Organoclays
### Properties of the Modified Clay
#### 2.5.3 Hydrophobicity/Hydrophilicity of the Organoclay  
Different arrangements of organoclays are observed after modification, where organic modifiers can lie flat as a monolayer, bilayer, or multilayer. The clay colloid is now formed on the basis of the layer. When the organic modifier is firmly attached to the surfaces and blocks the interlayer, the water adsorption capacity decreases and the resulting organoclay behaves like hydrophobic clay. On the other hand, long molecules increase the d-spacing of the clay, leading to an increase in d-spacing and allowing more water to penetrate the organoclay, resulting in a hydrophilic nature [26]. The hydrophilic and hydrophobic nature of clay has been characterized by contact angle and wettability experiments. Organic modifiers with large structures and higher stearic activity are inherently hydrophobic while long-chain surfactants increase the hydrophilicity of clay molecules.