esa-sceva/satcom-chunk-collection huggingface dataset:
# Elevation and Mass Changes of the Southern Patagonia Icefield Derived from TanDEM-X and SRTM Data
## 4 Discussion
### Elevation Changes: Southern SPI on Multiple Timesteps  
Glaciers of the southernmost end of the SPI show a positive elevation change rate for the years 2011/12-2015/16. In Section 3.3 we emphasized the role of the accumulation area, with its overly proportional size and a positive change rate. The positive change rates of \(<\)1 m\(\cdot\)a\({}^{-1}\) relate to \(\sim\)4 m of actual surface rise over the 3 years. Already the dh/dt measurements of autumn 2014 indicate more positive accumulation rates. We assume that heavy snowfall events in winters 2014 and 2015 have caused this increase. We cannot confirm such anomalous high precipitation events by observations, since there are no measurements available. However, it is known that the westerlies have become more intense in southern Patagonia in recent years, resulting in enhanced precipitation, driven by the orographic effect across the Andes [1]. This is a possible explanation of the measured positive elevation changes up to about 2000 m. Another aspect to explain the differences in the accumulation area (\(>\)2000 m) is differences in radar signal penetration. [PERSON] and [PERSON] [68] showed that the signal penetration into snow/fern covers depends on the grain size and surface wetness. Liquid water limits the signal penetration to the first centimeters. The depth of the scattering phase centers of a radar wave in a frozen, stratified fern cover formed during wet snow metamorphosis on the SPI may differ from season to season. Hence, the pronounced drop of the 2011/12-2015/16 mean dh/dt line (Figure 3c) in regions \(>\)2000 m might also be partially attributed to this influence. The winter 2016 data also shows higher negative elevation changes rates in elevations \(>\)1500 m, which can also be attributed to stronger signal penetration during winter conditions. The effect and visibility are partly reduced since the differencing was done over a 16-year period. This clearly shows the relevance to consider appropriate image acquisitions for radar elevation change measurements. In the case of SPI, summer conditions prove most suitable since wet snow conditions probably prevailed over most of the glaciated area.
## 4 Experiments
### Vision-Based Topological Navigation  
In this section, we adopt the topological map in Figure 11 to perform the final navigation experiment for the mobile robot. For robot navigation, it is challenging because the indoor experimental environment contains many repetitive structures and similar layouts. The topological navigation is performed by means of traversing a sequence of way-point keyframes in topological nodes, which is constructed by graph-based path planning. During the path planning, the robot continually matches the current image with the keyframes in the next target node. After reaching each target node, the robot re-estimates its pose and makes adjustments until reaching the final goal location. In order to provide the robot with the capability to cope with the incorrect estimation of relative orientation robustly, we limit the maximum directional change of the mobile robot to 20\({}^{\circ}\) and the maximum turning angle to 15\({}^{\circ}\), respectively. In this way, the target nodes is always in the view of camera even if the localization estimation is incorrect, which is conductive to restore the pose for the robot. In the topological navigation experiment, the mobile robot is initialized and then navigates from the starting position P0 to the doorways of different rooms which corresponds to nodes labeled with P1 to P5, as shown in Figure 11. In most cases, the robot pose successfully converges to the target node and reaches the goal location. To quantitatively evaluate the performance of navigation, we compare the proposed topological navigation method with a well-known metric-based navigation method [46]. The result of the two methods in navigation efficiency is shown in Figure 12. Each trial is performed 5 times. Compared with traditional metric-based navigation, our proposed method is slightly better in time consumption. For the path of P0 to P4, the topological navigation method takes more time. This is mainly because there is a warehouse with uneven illumination and lack of obvious visual features in the path of P0 to P4, which makes it non-trivial to resample the particles via image retrieval. In this case, the robot will rely more on the odometry to update the particles. Additionally, during the navigation experiment, ERF-IMCS only requires us to input the label attribute of the navigation goal, such as doorway of conference room, since the labels of local location are stored in each corresponding topological node, in contrast to other systems that adopt sequences of metric poses. In this way, the mobile robot can perform human-like navigation behavior. Overall, ERF-IMCS is completely sufficient to meet the requirements for indoor robot navigation.  
Figure 10: (**a**) A typical evolution of number of samples in global localization. (**b**) CDF of localization accuracy.
## 2 Experiment and Methods
### Data Collection and Indicator Setting  
According to the analysis of the causes of low oxygen in Section 2.2, changes in surface atmospheric pressure and temperature, and the periodic pressure step distance of the roof will affect the abnormal oxygen concentration in the corner of return air. Therefore, the air multi-physical parameter measuring instrument is arranged on the ground to record the temperature and atmospheric pressure of the air on the ground in real time. Combined with the parameters of the return air corner sensor and the periodic pressure data of the working face, the real-time data of the linkage between the upper and lower parts of the mine are measured in different stages of the advancing of the working face. The data collection layout is shown in Figure 8.  
The input indexes of the prediction model are determined as time, hourly temperature difference, hourly pressure difference, daily temperature difference, daily pressure difference and working face cycle pressure step distance. The output indexes are oxygen concentration in the corner of return air. In order to ensure the robustness of the model and avoid excessive fitting, the number of samples collected should be guaranteed, divided  
Figure 8: Layout of linkage data collection.  
into training samples and test samples, and the test samples should not be less than a quarter of the total number. Nine hundred twenty-nine groups of model index data were statistically obtained through the mine up-down linkage measurement. Hourly oxygen concentration, surface atmospheric pressure and temperature data samples are shown in Figure 9. Indicator data are obtained after pretreatment.
# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 2 Materials and Methods
### Methods
#### 2.2.2 Forest Object Generation  
Image segmentation is a crucial and essential part of OBIA for creating objects from the connected pixels with similar features [45]. As for the segmentation algorithms, superpixel segmentation is an automatic technique that divides images into non-overlapping superpixels, performing well in remotely sensed images over a large scale because of few parameters and low memory requirements [46; 47]. The SNIC method was adopted to yield homogenous superpixels by clustering contiguous pixels with similar NDVI trajectory patterns on the GEE. SNIC segmentation is an improved version of the widely used simple linear iterative clustering (SLIC) algorithm [48], which is non-iterative, requires less memory, and has a lower time complexity [49]. Segmentation quality has a significant effect on classification accuracy in OBIA, which is largely dependent on the parameter settings. Here, three parameters--size, compactness, and connectivity--are involved in the SNIC. As a scale parameter, size represents the average size of the forest objects, which is the most critical parameter for the algorithms. To ensure the homogeneity of the objects, 11 candidate sizes were, therefore, chosen for the sensitivity analysis. The compactness, with the capacity to constrain the object shape, was set to a relatively low value of 5, considering the irregular distribution and high landscape heterogeneity of the forested lands in the study area. We discovered that connectivity (4 or 8) had little effect on the segmentation results through testing; therefore, we set it to the slightly better performance value of eight neighbors. Effective superpixels should have characteristics of a minimum intra-segment variance and maximum inter-segment difference. The two metrics, the area-weighted local variance (ALV; Formula (1)) and Global Moran's I [50] of the layers (MIL; Formula (2)) were utilized to evaluate the intra- and inter-segment homogeneity, respectively [51]. Finally, the weighted score (WS; Formula. (3)) of the ALV and MIL was employed as a comprehensive indicator to clarify the performance of the SNIC algorithm under different scale values:  
\[\text{ALV}=\frac{\sum_{i=1}^{1}\left(\left(\sum_{i=1}^{n}\text{var}_{i}*_{q_{ i}}\right)/\sum_{i=1}^{n}\text{a}_{q_{i}}\right)}{1} \tag{1}\]
# Worldwide Connectivity for the Internet of Things Through LoRaWAN
## 4 How an End Device Joins a LoRaWAN(tm) Network  
In this section, we will examine some key procedures (e.g., how an end node is activated) and, by means of flow charts, we will be able to give a short overview also of the security aspects of the LoRaWAN(tm) architecture.  
Understanding how a node enters a LoRaWAN(tm) network is the best example to get a quick and clear understanding of the LoRaWAN(tm) architecture and its key principles:  
* security without SIM card;
* separate security and confidentiality between control/network-data and user-data. Indeed, the user data are encrypted with keys which are not known to the network operator, while the control commands (e.g., the MAC commands) are encrypted with keys which have nothing to do with the keys used to encrypt user data. According to the LoRaWAN(tm) specification, a node can enter the network in two ways:
* activation by personalization (ABP);
* over the air activation (OTAA).  
At a very high level, ABP is such that all the security-related items are installed in the end device at the time of fabrication/commissioning, and cannot be changed afterwards. On the other hand, with OTAA, some root keys are installed at the commissioning stage, but the actual security keys used in the communication (both of network commands and user data) are derived in a secure way at the time the end device enters the network. Those keys are session-specific, and they are renovated from time to time (e.g., on a daily basis) to increase the security.  
In this paper, we only consider the OTAA procedure, since it is the one recommended by the LoRa alliance, and it is the most secure. According to the OTAA procedure, an end device enters a network by sending a specific MAC command, i.e., a join-request command, which triggers the whole procedure of entering the network. Other MAC commands can be used from the network side, according to the specifications, in the process of an end device joining a network. In particular, rejoin-request type 1 and join-accept are MAC commands used in the joining process. The \"rejoin\" is used--in a broad sense--to force the renewal of the session keys. As a matter of fact, although the whole join process is believed to be secure, renewing the session keys is believed to make it even more secure in case the session keys get compromised.  
In the following, we will review the security items of a LoRaWAN(tm) end device before and after joining the network.
## 2 Model Formulation
### Rough Set and Improved TOPSIS Construction Sequence Sub-Model
\begin{array}{cccccc}x_{1,1}&x_{1,2}&\ldots&x_{1,j}&\ldots&x_{ 1,n}\\ x_{2,1}&x_{2,2}&\ldots&x_{2,j}&\ldots&x_{2,n}\\ \vdots&\vdots&\ldots&\vdots&\vdots\\ x_{i,1}&x_{i,2}&\ldots&x_{i,j}&\ldots&x_{i,n}\\ \vdots&\vdots&\ldots&\vdots&\ldots&\vdots\\ x_{m,1}&x_{m,2}&\ldots&x_{m,j}&\ldots&x_{m,n}\\ \end{array} \right]\ \left[ \begin{array}{cccccc}\omega_{1}&0&0&\ldots&0&\ldots&0\\ 0&\omega_{2}&0&\ldots&0&\ldots&0\\ \vdots&\vdots&\ldots&\vdots&\ldots&\vdots\\ 0&\ldots&\omega_{i}&\ldots&0\\ \vdots&\vdots&\ldots&\vdots&\ldots&\vdots\\ 0&0&\ldots&0&\ldots&\omega_{m}\\ \end{array} \right] \tag{6}\]  
\[=\left[ \begin{array}{cccccc}y_{1,1}&y_{1,2}&\ldots&y_{1,j}&\ldots&y_{1,n}\\ y_{2,1}&y_{2,2}&\ldots&y_{2,j}&\ldots&y_{2,n}\\ \vdots&\vdots&\ldots&\vdots&\ldots&\vdots\\ y_{i,1}&y_{i,2}&\ldots&y_{i,j}&\ldots&y_{i,n}\\ \vdots&\vdots&\ldots&\vdots&\ldots&\vdots\\ y_{m,1}&y_{m,2}&\ldots&y_{m,j}&\ldots&y_{m,n}\\ \end{array} \right]\]  
Step 4: Determine the PIS and NIS. Let \(U^{\prime}=\{u^{\prime}_{1},u^{\prime}_{2},\cdots u^{\prime}_{m}\}\) be the ideal solution set, let \(s_{i,j}\) be the sort value of \(c_{j}\), where \(s_{i,j}=sorted(y_{1,j},y_{2,j},\cdots,y_{m,j})[i]\) (\(sorted()\) represents a function that sorts the values from large to small), \(u^{\prime}_{i}=(s_{i,1},s_{i,2},\cdots s_{i,n})\). Then, the PIS (\(u^{\prime}_{1}\)) is as shown in Equation (7), and the NIS (\(u^{\prime}_{m}\)) is as shown in Equation (8):  
\[u^{\prime}_{1}=(1,1,1,\ldots,1) \tag{7}\]  
\[u^{\prime}_{m}=(0,0,0,\ldots,0) \tag{8}\]  
Step 5: Calculate the Euclidean distances. Let \(d_{i,h}\) be the Euclidean distance from \(u_{i}=(y_{i,1},y_{i,2},\cdots,y_{i,n})\) to \(u^{\prime}_{h}=(s_{h,1},s_{h,2},\cdots s_{h,n})\). It is calculated by Equation (9). \[d_{i,h}=\sqrt{\sum_{j=1}^{n}\Bigl{(}s_{h,j}-y_{i,j}\Bigr{)}^{2}},\forall i\in[ 1,m],h\in[1,m] \tag{9}\]  
Given that \(q_{a}=\min\bigl{\{}d_{a,h}\bigr{\}},(h=1,2,\cdots,m)\), \(q_{b}=\min\bigl{\{}d_{b,h}\bigr{\}},(h=1,2,\cdots,m)\). If \(q_{a}>q_{b}\), line \(a\) is after line \(b\) in the construction sequence; if \(q_{a}<q_{b}\), line \(a\) is before line \(b\) in the construction sequence; if \(q_{a}=q_{b}\), the closeness of \(\gamma_{a}\) and \(\gamma_{b}\) are defined according to Equations (10) and (11). \[\gamma_{a}=d_{a,m}/(d_{a,1}+d_{a,m}) \tag{10}\]  
\[\gamma_{b}=d_{b,m}/(d_{b,1}+d_{b,m}) \tag{11}\]  
If \(\gamma_{a}>\gamma_{b}\), line \(a\) is after line \(b\) in the construction sequence; if \(\gamma_{a}<\gamma_{b}\), line \(a\) is before line \(b\) in the construction sequence. Step 6: Determine the construction sequence. The optimized construction sequence is obtained as shown in Equation (12):  
\[U_{o}=\{u_{01},u_{02},\cdots,u_{om}\} \tag{12}\]
# Sensorless Control for DC-DC Boost Converter via Generalized Parameter Estimation-Based Observer
## 3 Sensorless Controller Design  
In this section, a sensorless control law is designed for the DC-DC boost converter. The design procedure can be divided into three steps.  
* It is supposed that the state \(i\) is measured. The full-information PI-PBC is designed.
* A GPEBO is devised to estimate the current with FTC.
* By combining the PI-PBC and GPEBO, a sensorless control scheme is achieved. The system in Equation (1) can be rewritten in the port-controlled Hamiltonian form \[\dot{x}=(\mathcal{J}u-\mathcal{R})Qx+\zeta,\] (5)  
Figure 2: The circuit topology of DC–DC boost converter.  
where \(u=1-d\),  
\[x=\left[\begin{array}{c}Li\\ Cv\end{array}\right],\mathcal{J}=\left[\begin{array}{cc}0&-1\\ 1&0\end{array}\right],Q=\left[\begin{array}{cc}\frac{1}{L}&0\\ 0&\frac{1}{\mathcal{C}}\end{array}\right],\mathcal{R}=\left[\begin{array}{ cc}0&0\\ 0&\frac{1}{\mathcal{R}}\end{array}\right],\zeta=\left[\begin{array}{c}E\\ 0\end{array}\right].\]  
Moreover, the desired dynamics for the system (Equation (5)) are given by  
\[\dot{x}_{\star}=(\mathcal{J}u_{\star}-\mathcal{R})Qx_{\star}+\zeta, \tag{6}\]  
where \(x_{\star}\in\mathcal{E}\).
# On Unmanned Aerial Vehicles Light Show Systems: Algorithms, Software and Hardware
## 4 Hardware Setup
### High Accuracy Positioning System  
In general, quadrotors make use of GPS and inertial measurement unit (IMU) system for positioning [20; 21]. However, GPS and IMU are not suitable for high-accuracy applications for two reasons: (1) GPS positioning accuracy is sensitive to atmospheric (ionosphere and troposphere) delay, ephemeris error, clock bias, and multi-path effects; (2) The choice of IMU is limited by the weight and size of UAVs. In this paper, the real-time kinematic GPS (RTK-GPS) system is employed. The RTK-GPS uses carrier phase as correction data to eliminate atmospheric delay of GPS which is the main source of the time error, resulting in centimeter-level positioning accuracy [22].  
The RTK-GPS has three parts, i.e., rover station, base station, and GCS connected to a wireless router. The RTK-GPS base station consists of a GPS antenna, a GPS receiver, and a stream server. The GPS receiver outputs raw positioning observation data, including satellite ephemeris, pseudorange, integrated carrier phase, and Doppler shift. The stream server converts the raw observation data into correction data in RTCM format. Then, the correction data are sent from the stream server to GCS. The GCS, connected to a wireless router, broadcasts the RTCM correction data to UAVs. The RTK-GPS rover unit in the quadrotor is composed of an GPS antenna, a GPS receiver, and a position solver board. The position solver board obtains the correction data of the base station through wireless communication with GCS and the observation data from the on-board GPS receiver, and produces the high accuracy real-time position of the rover antenna by the RTK-GPS library (RTKLIB) positioning algorithm [23].
# Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
## 1 Introduction
For IOOs, it emphasizes the need for infrastructure upgrades, including better road markings, signage, connectivity, and maintenance strategies to support safe AV deployment. This helps prioritize investments for seamless AV integration and regional mobility. For the automotive industry, it highlights the importance of adapting AVs to rural conditions, ensuring vehicle durability, precise navigation, and user-friendly interfaces tailored to rural demographics, enhancing safety and market opportunities.
# Subtitling 3D VR Content with Limited 6 DoF: Presentation Modes and Guiding Methods
## 2 Related Work
### Traditional 2D Video Subtitling
First, it was shown that _Always-visible_ subtitles provide the best user experience, but that subtitles _attached-to-the-speaker_ are also well received and have potential, if appropriately implemented. Second, it was shown that arrows are the simplest and most intuitive method for visually guiding the user toward the target speaker.
## 4 Proposed Energy-Efficient Relay Mechanism
### SR-Based Packet Relaying
In such way, intermediate nodes do not need to know the energy status of other nodes, avoiding the transmission of feedback messages. As intermediate \"hub\" nodes (which belong to more than one active path) in braided paths are crucial for network transmissions flows [36], the proposed relaying approach should be implemented only by those nodes. Such decision is motivated by the fact that the entire network may become inoperative if hub nodes run out of energy. Therefore, employing the proposed optimization approach in only critical intermediate nodes turns open-loop processing a proper option. As a final comment, there might be different approaches to establish the energy thresholds \(e_{I}\), \(e_{2}\) and \(e_{3}\). Besides configuration before deployment, the same SR assignment protocol presented in [13] could be employed to define the energy thresholds to be adopted by intermediate nodes, which would read this information while forwarding control packets between source nodes and the sink.
# Reinforcement Learning of Bipedal Walking Using a Simple Reference Motion
## 3 Proposed Method
### Inverse Kinematics  
As described in Section 3.1, at each time \(t\), the positions of the left and right sides with respect to the torso are specified by the reference motion. In our reference motion, the soles are not rotated; they remain in the rotation of the initial posture. Thus, at each time \(t\), the pose (i.e., both position and rotation) of each sole is specified.  
For each of the left and right legs, we find the joint angles that place the sole in the specified pose (Equation (4)). This is an IK problem, and there are various types of methods to solve it. Here, we briefly explain the solution method that we used for the left leg. The solution for the right leg was obtained in the same way.  
Let \(p\) be the specified pose of the left sole and \(q\) be the vector of the joint angles to be controlled, which are the angles from \(\varphi_{2}\) to \(\varphi_{6}\) shown in Figure 2 and Table 1. Given the DH parameters in Table 1, the forward kinematics function \(f\) that maps \(q\) to \(p\) is determined.  
To find \(q\) for the given \(p\), we numerically solved the following minimization problem  
\[\min E(q), \tag{10}\]  
where \(E(q)\) is the error function defined as  
\[E(q)=\frac{1}{2}e^{\text{T}}W_{e}\,e,\]  
in which \(e=p-f(q)\).  
We used the Gauss-Newton method to solve Equation (10). Specifically, we calculated  
\[q_{k+1} = q_{k}+\beta\,(J(q_{k})^{\text{T}}\,W_{e}\,J(q_{k}))^{-1}g_{k},\] \[g_{k} = J(q_{k})^{\text{T}}\,W_{e}\,e_{k}\]  
iteratively with \(k=0,1,2,\ldots\). Here, \(J(q_{k})\) is the Jacobi matrix, \(q_{0}\) is the initial joint angle that we set to the zero vector, and \(\beta\) is a step length that we set to 0.5. The iteration continues until the value for the error function \(E(q_{k})\) becomes less than a small constant \(\epsilon\) or until \(k\) becomes 30. We set \(\epsilon=10^{-6}\).
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 4 Reeb Graph Sequential Algorithm on Triangulated Surfaces
### Outline of the Sequential Algorithm
#### 4.2.2 Construction of the Ascending Paths  
The ascending paths from a critical point \(v_{0}\) are specified, as follows:  
1. If \(v_{0}\) is a merge saddle or a minimum, then we initiate a single ascending path \(apath(v_{0})\) specified, as follows. Let \(v_{1}\) be a vertex in \(Lk^{+}(v_{0})\) such that \(f(v_{1})>f(v_{0})\). At the \(k\)th iteration, \(apath(v_{0})\) consists of \(\{[v_{0},v_{1}],...,[v_{k-1},v_{k}]\}\) with \(f(v_{i+1})>f(v_{i})\) for \(0\leq i\leq k-1\).
2. If \(v_{0}\) is a split saddle, then we start two ascending paths \(P_{1}\) and \(P_{2}\) originating from the point \(p\) specified as follows. Divide the set \(Lk^{+}(p)\) into two disconnected components \(A\) and \(B\). Choose the vertex \(v_{A}\) in \(A\), such that \(f(v_{A})>f(v)\) for all \(v\in A\), and choose the vertex \(v_{B}\) in a similar manner. At the \(k\)th iteration, \(P_{1}\) consists of \(\{[v_{0},v_{A}],...,[v_{k-1},v_{k}]\}\) with \(f(v_{i})>f(v_{i-1})\) for \(0\leq i\leq k-1\) (here we assume \(v_{1}=v_{A}\)). The path \(P_{2}\) is similarly constructed.