esa-sceva/satcom-chunk-collection huggingface dataset:
# Trends in LPWAN Technologies for LEO Satellite Constellations in the NewSpace Context
## 3 Architectures and Protocols for IoT Communications via Satellites
### Architecture
Terrestrial IoT gateways are part of the gateway layer as they transmit and receive data from the physical layer. * Satellites: Functioning as in-orbit gateways, these satellites collect messages from IoT nodes or terrestrial IoT gateways and retransmit them to the nearest ground station. Satellites are part of the gateway layer as they transmit and receive data from another layer, in this case, from the IoT network layer. * Ground stations: These ground-based systems receive satellite data and forward them to the satellite operator's cloud for processing and storage. Certain stations are responsible for monitoring the health of satellites and controlling their functions. Ground stations are elements of the IoT network layer as they bridge the satellite and terrestrial networks. * Operator cloud: This is where satellite data are processed, stored, and forwarded to external applications. The operator cloud belongs to the middleware layer in the IoT satellite communications stack model, and the satellite operating company typically owns it. * Vertical or business applications: These applications utilize satellite data through the operator's cloud. They analyze and process the data for specific purposes, such as visualization. These applications are typically used in business contexts and are part of the business application layer. When designing a satellite constellation, several key factors must be considered. These include global coverage, specific applications, individual satellite costs, and the overall constellation configuration. These considerations involve evaluating key parameters, like orbit eccentricity, altitude, and inclination, typically associated with low Earth orbits. Additionally, constellations can be designed with or without inter-satellite links (ISLs). Each satellite functions as a transparent relay in non-ISL constellations, channeling traffic from IoT nodes and ground stations to Earth. Conversely, satellites act as network switches in ISL constellations, enabling communication with neighboring satellites [28]. There are two primary approaches to the IoT network architecture in satellite constellations (illustrated in Figure 3): centralized and direct. In the centralized approach (Figure 3a), called the indirect-to-satellite IoT (Its-IoT), ground-based communication gateways serve as intermediaries between the IoT node and the satellite. The terrestrial gateway relays data from the IoT device to the satellite. In the direct approach (Figure 3b), known as the direct-to-satellite IoT (DtS-IoT), the IoT device sends the data directly to the satellite [30]. Figure 2: The physical architecture of IoT satellite communications. In both cases, the collected data are transmitted to the ground station and delivered to a data repository or database for further processing by computer applications. Table 3 highlights the primary differences between these two methods (DtS-IoT and ItS-IoT), clearly comparing their respective features and applications.
# Object-Level Semantic Map Construction for Dynamic Scenes
## 3 Methodology
### Accurate Camera Pose Tracking  
Accurate camera pose tracking play a very important role in map construction. In this step, using the culling dynamic part \(C_{i}^{dynamic}\) in the current frame, which was acquired in the previous step, the accurate camera pose \(T_{cur}\) can be obtained by performing alignment  
Figure 4: Each stage processing results of geometric segmentation on current frame depth data (**a**) Depth data processed by bilateral filtering; (**b**) raw geometric segmentation results; (**c**) dilation and erode segmentation results; (**d**) final statistical results of geometric components (each color represents a component), showing that the potted plant is over-segmented in multiple components.  
Figure 5: Segmentation results are associated with the object model database to find the dynamic part in the current frame, create a new object, and transfer the semantic information.  
between the current frame data without the dynamic part and rendering the map data from previous frame \(t-1\) by minimizing the geometric and photometric error functions. The geometric and photometric error functions are respectively defined as follows:  
\[E_{icp}^{static}=\begin{cases}\sum\limits_{i}\bigl{(}\bigl{(}v^{i}-T_{cur}v_{t}^ {i}\bigr{)}\cdot n^{i}\bigr{)}^{2}&\text{if }i\
otin C_{i}^{dynamic}\\ 0&\text{if }i\in C_{i}^{dynamic}\end{cases} \tag{11}\]  
The geometric error function (11) represents that after the current frame depth data accurately removes the dynamic part, the remaining vertex data \(v_{t}^{i}\) and the vertex data \(v^{i}\) that come from the predicted depth map of the static model (including the background model and static objects) from the previous frame \(t-1\) are used to calculate the precise current frame camera pose \(T_{cur}\) based on the point-surface ICP iterative algorithm.  
\[E_{rgb}^{static}=\begin{cases}\sum\limits_{u}\bigl{(}I_{t}(u)-I_{t-1}(\pi \bigl{(}\operatorname{KT}_{cur}\pi^{-1}(u,D_{t}))\bigr{)}\bigr{)}^{2}&\text{if }u\
otin C_{i}^{dynamic}\\ 0&\text{if }u\in C_{i}^{dynamic}\end{cases} \tag{12}\]  
In photometric error function (12), \(I_{t}(u)\) is the current frame gray intensity map after removing the dynamic part, \(I_{t-1}(\cdot)\) provides the color attached to a vertex on the model in frame \(t-1\), and \(C_{i}^{dynamic}\) is the dynamic object part in the current frame. After acquiring the accurate camera pose \(T_{cur}\), each object model pose is redefined in the database for object model updating and subsequent fusion. The new object pose is set as 4D identity matrix and \(T_{obs}\)=\(T_{cur}\):  
\[T_{m}{}^{dynamic}=T_{m}\ T_{m}^{\Delta} \tag{13}\]  
\[T_{m}{}^{static}=T_{obs}{}^{-1}T_{cur} \tag{14}\]  
\[T_{m}{}^{new} \tag{15}\]  
where \(T_{m}^{\Delta}\) is the amount of change in the object pose relative to the camera movement and \(T_{m}{}^{new}\) is newly created object model pose.
## 2 System Model
### RT Mode  
In the RT mode, the relay will help the source forward data to the destination. In this mode, the destination can always receive data packet, so it will be difficult to distinguish the presence of jamming. From the received signals at the destination, we can realize whether the jammer actually attacks the channel or not. That is because the signal strength from received data packet when the jammer attacks will become stronger than a normal received data packet (i.e., without jamming). Therefore, the destination can recognize whether the original signal contains the jamming signal or not, in terms of the predefined jamming threshold \(\chi_{jam}\) such as  
\[J=\left\{\begin{array}{ll}\text{Presence,}&\text{if }P_{D}\geq\chi_{jam};\\ \text{Absence,}&\text{otherwise.}\end{array}\right. \tag{23}\]  
where \(P_{D}\) is the received signal energy at the destination.  
_Observation 1_ (\(\Phi_{1}\)): The sensing result indicates the presence of the jammer, the source transmits data packet via the relay and the jammer actually attacks the channel. In this case, jammer attack is well detected, and corresponding achieved throughput is given by  
\[R\left(e_{t}^{re},p_{t}^{b}\left|\Phi_{1}\right.\right)=\frac{T-\tau_{s}-\tau_ {SR}}{T}C_{1}. \tag{24}\]  
The probability that case \(\Phi_{1}\) happens can be calculated as  
\[\Pr\left(\Phi_{1}\right)=\frac{p_{t}^{b}P_{d}}{p_{t}^{  
Observation 2 (\(\Phi_{2}\)): In this case, the sensing result indicates the presence of the jammer, the source transmits data via the relay, and the jammer actually does not attack the channel. Hence, we recognize the false alarm happens in this case. The achieved throughput is given as  
\[R\left(e_{t}^{re},p_{t}^{b}\left|\Phi_{2}\right.\right)=\frac{T-\tau_{\text{s}}- \tau_{SR}}{T}C_{2}. \tag{29}\]  
The updated belief for the next time slot is given as  
\[p_{t+1}^{b}=P_{\bar{J}\bar{J}}. \tag{30}\]  
The remaining energy for the next time slot can be updated as  
\[e_{t+1}^{re}=\min\{e_{t}^{re}+E_{h}\left(t\right)-E_{tr},E_{ca}\}. \tag{31}\]  
The probability that case \(\Phi_{2}\) happens is  
\[\Pr\left(\Phi_{2}\right)=\frac{(1-p_{t}^{b})P_{f}}{p_{t}^{b}P_{d}+(1-p_{t}^{b })P_{f}}. \tag{32}\]  
The transition probability if the case \(\Phi_{2}\) happens is  
\[\Pr\left(e_{t}^{re}\to e_{t+1}^{re}\left|\[PERSON]_{2}\right.\right)=\Pr\left[E_{h }\left(t\right)=e_{k}^{h}\right]\Pr\left(\Phi_{2}\right). \tag{33}\]
## 3 Analysis of Kinematics
### Forward Position Kinematics  
For a set of the input actuated joint variables, \(q_{i}(i=1,2,3)\), the position of the moving platform, \({}^{\mathcal{O}}P_{P}=\left[x\,yz\right]^{\mathrm{T}}\), is solved using the forward position kinematics. In Equation (8), combining the second equation with the first equation yields:  
\[-q_{1}^{2}+q_{2}^{2}+2q_{1}r-2q_{2}r+2q_{1}x+q_{2}x-3 rx-\sqrt{3}q_{2}y+\sqrt{ 3}ry=0 \tag{10}\]  
Additionally, combining the third equation with the first equation yields:  
\[-q_{1}^{2}+q_{3}^{2}+2q_{1}r-2q_{3}r+2q_{1}x+q_{3}x-3 rx+\sqrt{3}q_{3}y-\sqrt{ 3}ry=0 \tag{11}\]  
For \(q_{i}(i=1,2,3)\), the solution from Equations (10) and (11) is:  
\[x=\frac{(q_{2}+q_{3}-2r)\Big{(}q_{1}^{2}-2 rq_{1}-q_{2}q_{3}+rq_{2}+rq_{3}\Big{)} }{2(q_{1}q_{2}+q_{1}q_{3}+q_{2}q_{3}-2 rq_{1}-2 rq_{2}-2 rq_{3}+3r^{2})} \tag{12}\]  
Additionally,  
\[y=\frac{(q_{2}-q_{3})\Big{(}2q_{1}q_{2}+2q_{1}q_{3}+q_{2}q_{3}-6 rq_{1}-3 rq_{2} -3 rq_{3}+q_{1}^{2}+6r^{2}\Big{)}}{2\sqrt{3}(q_{1}q_{2}+q_{1}q_{3}+q_{2}q_{3}-2 rq_{1}-2 rq_{2}-2 rq_{3}+3r^{2})} \tag{13}\]  
Substituting Equations (12) and (13) into the first equation in Equation (9) yields:  
\[z=\pm\sqrt{l^{2}-y^{2}-\left(q_{1}-r-x\right)^{2}} \tag{14}\]  
Equation (14) gives two solutions for \(z\). \(z\) must be negative, because the moving platform must move under the fixed base, so the solution of \(z\) is:  
\[z=-\sqrt{l^{2}-y^{2}-\left(q_{1}-r-x\right)^{2}} \tag{15}\]The unique solution of the forward position kinematic for the PH-LDR is defined in Equations (12), (13), and (15).
## 4 Results and Discussion
### Dataset
#### 4.2.1 Randomness Tests  
All recorded audios were evaluated with the four above randomness tests. The null hypothesis of randomness is tested against nonrandomness, and a \(p\)-value is calculated which is used in the context of null hypothesis testing in order to quantify the idea of statistical significance of evidence, that is,  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Environmental Sound** & **Duration (s)** & **Number of Records** \\ \hline S1—Traffic jam in a city & 49 & 3 \\ S2—People on a street without traffic & 34 & 3 \\ S3—Very strong traffic & 70 & 5 \\ S4—City park with children & 33 & 3 \\ S5—Pedestrian zone of a city with traffic & 32 & 3 \\ S6—Inside of a noisy room by traffic & 60 & 4 \\ S7—Ambulance passing with the siren & 29 & 4 \\ S8—Drilling machine in a city & 18 & 3 \\ S9—Police car passing with the siren & 28 & 3 \\ S10—Ambulance siren. Doppler effect & 24 & 3 \\ S11—Dense traffic in a city & 72 & 5 \\ S12—Indoor dam & 23 & 3 \\ S13—Indoor gun shots & 98 & 3 \\ S14—Slicing vegetables in a kitchen & 40 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Audio recordings dataset.  
Figure 6: Acoustic anechoic chamber at IDeTIC.  
the probability of finding the observed results when the null hypothesis is true. In the tests, if the \(p\)-value is less than 0.05, the null hypothesis is rejected because a significant difference exists.  
Table 2 shows the \(p\)-values of four randomness tests. As can be seen, all \(p\)-values for [PERSON], Mann-Kendall and Wald-Wofowitz tests are less than 0.05. For Cox Stuart test, most results return a \(p\)-value less than 0.05, and only eight tests are slightly greater than this value. It is not significant, and hence the null hypothesis can be rejected. Thus, it can be considered that the recorded audio files by wireless acoustic sensor are not significantly random, and therefore, have a recognizable pattern.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Record** & **[PERSON]** & **Cox Stuart** & **Mann-Kendall** & **[PERSON]** \\ \hline S1-R1 & 0 & 0.06 & \(\approx\)0 & 0 \\ S1-R2 & 0 & 0.01 & \(\approx\)0 & 0 \\ S1-R3 & 0 & 0.09 & \(\approx\)0 & 0 \\ S2-R1 & 0 & \(\approx\)0
# Review of Advanced Medical Telerobots
## 2 Medical Telerobotic Topologies
### Single-Leader Single-Follower Topology (SL/SF)
#### 2.1.2 Motor Augmentation through SL/SF Telebotic Surgery
This technology is usually used under training environments (not during actual surgeries) when novice trainee surgeons practice surgery with robots on highly structured and clean surgical training phantoms mimicking an actual surgical site and allowing the implementation of guiding virtual fixtures for helping trainees with learning and developing a motor task [136; 163; 206; 208; 218].
# Recurrent Deep Learning for Beam Pattern Synthesis in Optimized Antenna Arrays
## 2 Problem Formulation  
### Antenna Array Optimization  
Let us consider a linear array configuration comprising \(N\) omnidirectional antenna elements within the azimuthal plane (\(xy\) plane). Its array factor (\(AF(\theta)\)) for a collection of arbitrary inputs \([\mathbf{I}]\) is given by [24]:  
\[AF(\theta,\mathbf{I})=\sum_{n=1}^{N}I_{n}\exp[jkd_{n}\cos\theta-\alpha_{n}], \tag{1}\]  
where \(I_{n}\) denotes the complex weights (\(I_{n}=A_{n}e^{\beta_{n}}\), where \(\xi_{n}\in[0,180]\)) of the \(n\)-th radiator of the array, \(k\) is the wavenumber, \(d_{n}\) is the position of the radiator \(n\), and \(\alpha_{n}=kd_{n}\cos\theta_{0}\) is a phase delay related to the peak directivity attained (\(\theta_{0}\)).  
In this work, the beam pattern synthesis problem consists of generating a beam pattern with minimum sidelobe level (SLL) and maximum directivity (D) in a pre-defined direction of interest. Then, the beam pattern obtained from the optimization process is taken as input by the DNNs under study. To attain the aforementioned pre-determined design characteristics, the associated synthesis issue can be mathematically formulated as a constrained optimization problem in the following manner [24]:  
\[\begin{split}\min_{\mathbf{I}}&\left\{\frac{|AF(\phi_{ SL},\mathbf{I})|}{|AF(\phi_{\text{max}},\mathbf{I})|}\right\}+\left\{\frac{1}{D(\phi,\mathbf{I})} \right\}\\ \text{s.t.}&|AF(\phi_{\text{max}},\mathbf{I})|=1,\\ &\mathbf{I}=\{A_{n}e^{\beta_{n}}\mid\mathbf{I}\in\mathbb{I}\},\;\forall n \in N.\end{split} \tag{2}\]  
In (2), \(\phi_{SL}\) denotes the angle where the maximum sidelobe is achieved on either side of the main lobe, \(\phi_{max}\) represents the main beam direction, and \(\mathbb{I}\) is the amplitude and phase domain. Moreover, given the degrees of freedom and the intricate nature of antenna synthesis optimization, a classic metaheuristic approach based on evolutionary computation is employed, and the setup is detailed in the following.
# Ischemic Preconditioning (IPC) Enhances the Accuracy and Stability of Proprioception
## 4 Discussion
### Effect of IPC on Proprioception  
IPC optimizes proprioceptive acuity and precision. Previous research [7; 9; 10; 11; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29] has robustly established the ergogenic benefits of IPC for maximal strength, muscular endurance, anaerobic power output, and endurance performance. During incremental exercise tests, IPC has been shown to prolong time to exhaustion and reduce ratings of perceived exertion (RPE) [7] while concurrently enhancing maximal oxygen uptake (VO\({}_{2}\)max), attenuating fatigue sensitivity, and improving exercise efficiency [17]. These enhancements in aerobic exercise capacity are attributed to accelerated blood flow velocity and augmented oxygen delivery [18]. Furthermore, IPC enhances maximal voluntary isometric contraction (MVIC) torque output [30] and endurance capacity [19], with peak improvements in anaerobic power typically observed between 45 and 50 min post-application [20]. However, contradictory findings regarding the impact of IPC on anaerobic capacity have generated controversy within the literature [21; 22]. The findings in the present study provide stronger support for the perspective that IPC can acutely enhance anaerobic capacity. We posit that enhanced proprioceptive accuracy optimizes movement economy during anaerobic exercise execution, thereby mediating the observed acute improvement in anaerobic capacity. IPC exerts regulatory effects on target organs by stimulating autonomic and neuralpathways [27]. This process involves IPC enhancing afferent neuronal excitability [28]. Additional research suggests IPC may induce the production of humoral factors such as nitric oxide (NO) and bradykinin to potentiate afferent signaling [29]. Collectively, these studies share the commonality of supporting IPC's capacity to augment afferent neural function, offering compelling neurohumoral explanations for the improvement in proprioceptive accuracy and stability. The current study aligns with this consensus perspective. However, the primary focus of these prior investigations has been on acute performance enhancement, largely overlooking the potential contribution of altered proprioception to these acute performance gains. While the current study cannot substantiate this perspective, it proposes a novel hypothesis: IPC may achieve acute performance enhancement through improved proprioception. Subsequent investigations should prioritize exploring this proposed mechanistic pathway.
## 3 Finite Element Analysis
### Mesh Size  
In the FE model, shell and solid elements were used in the case of CHS XP-joints to select the appropriate element. Table 4 shows the mesh size of both elements, and the FE model was divided into two parts: the outside part of the joint and the joint part.  
The solid element could accurately represent the weld, but the numbers of FE elements are much greater than those of the shell element, and the analysis time becomes excessive. On the other hand, the shell element has fewer elements than the solid, but there is an assumption that the welds become the shell elements, which could lead to inaccurate results. Both the shell and solid elements are modeled as shown in Figure 3. Because the modeling of the weld affects the joint strength, [PERSON] and [PERSON] [32] proposed the size and composition of the weld modeling. The welds were modeled as shell elements, and a weld size of 6 mm was input.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element Type** & **Outside Part of Joint (O.J.)** & **Joint and Weld Part (J.W.)** \\ \hline \multirow{3}{*}{Shell (S4R) and Solid (C3D8R) element} & 30 mm & 15 mm \\  & 20 mm & 10 mm \\ \cline{1-1}  & 10 mm & 5 mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Plan of mesh convergence study.  
Figure 3: Mesh composition of the shell and solid element (LH-N700-0.0): (**a**) shell; (**b**) solid element.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test** & & \(F_{y}\) (MPa) & \(F_{u}\) (MPa) & **Yield Ratio** & **Elongation (\%)** \\ \hline \multirow{2}{*}{Tensile} & SS400 & 356 & 497 & 0.72 & 38.2 \\  & HSB600 & 478 & 630 & 0.76 & 34.8 \\ \hline \multirow{2}{*}{Stub-column} & SS400 & 440 & 487 & 0.90 & - \\  & HSB600 & 485 & 606 & 0.80 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of material properties.  
All the mesh sizes (30/15, 20/10, and 10/5 mm) and the element types (shell and solid) were analyzed. Table 5 and Figure 4 show the FE results that best agree with test results based on mesh size and element type and the comparison with the results. The joint strength is determined when an indentation of the chord wall at the connection face reaches 3% of the chord diameter (3%\(D\)) as suggested by [PERSON] et al. [33]. As shown in Table 5, the shell and solid elements show almost the same joint strength, which are determined by 3%\(D\), compared to the test. The joint strengths of shell and solid elements show a 1.01 ratio compared to the test result. The comparison shows that the effect of element type on the CHS XP-joint is insignificant. Thus, the shell element (S4R), with 20 mm and 10 mm of the outside and the joint part, was used as the model for FE analysis.
## 8 Navigation
### Path Planning  
The rovers use advanced algorithms to move autonomously and efficiently in the fields while avoiding obstacles and ensuring comprehensive coverage. These algorithms must consider various factors, such as terrain topography, the presence of obstacles, the need to optimize routes to maximize the efficiency of agricultural operations [94; 95; 96], and path traversability in relation to UGV characteristics [97]. Path planning can be achieved at global or local levels, depending on the available environment information, and there are different types of environment modeling and different path evaluation methods. As reported by [98], it is very common to execute a global planning in order to identify the main targets and then execute a local planning for better smoothness and trajectory definition. Path planning is often supported by artificial intelligence to allow for it to dynamically adapt to changing conditions. As reported by [99] the most popular path planning algorithms are as follows:* A* Algorithm, a well-known popular pathfinding algorithm that searches for the shortest path from a start point to an endpoint by considering both the cost to reach a point and the estimated cost to the destination. It is widely used in agricultural UGVs for its efficiency and accuracy in path planning [100; 101].
* [PERSON]'s Algorithm, which calculates the shortest paths from a single source node to all other nodes in a weighted graph. Its application in UGVs involves determining the optimal routes for tasks such as soil sampling and weed management.
* Rapidly exploring Random Trees (RRT) algorithm, designed for exploring high-dimensional and cluttered environments, is often used to find feasible paths in real-time scenarios [102]. This algorithm incrementally builds a tree of random samples from the UGV's starting position towards the goal, avoiding obstacles while randomly exploring the space. Its speed and low computational intensity means that it is adopted in many real-world scenarios. Several variations have been developed and adopted, such as RRT*, which ensures asymptotic convergence to the optimal path, a feature not guaranteed in the original algorithm [103].  
Even though there are many works related to navigation and path-planning, most of them are based on static environments without moving objects (obstacles and targets) [101]: future studies will focus on dynamic environments where swarms of heterogeneous robots and humans operate in the same environment. In summary, the combination of these technologies allows agricultural rovers to operate autonomously, improving the efficiency and effectiveness of agricultural operations. With the advancement of sensor technology and AI, navigation, localization, and mapping are expected to become increasingly accurate and reliable, opening up new possibilities for modern agriculture.