esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Line Rating--An Effective Method to Increase the Safety of Power Lines
## 2 Problem Identification--Materials and Methods
### Details of the Power Line  
The field measurement and the numerical simulation were carried out on a 400 kV, single-circuit interconnection power line. Its maximal design temperature was 40 \({}^{\circ}\)C in the 1970s, but the line was upgraded to 60 \({}^{\circ}\)C after its strategic importance increased. The applied conductor is a double-bundle 500/65 ACSR (Aluminum Conductor Steel Reinforced) in each phase with a semi-horizontal configuration. Both the tension and suspension towers are lattice ones and glass and composite insulators are applied depending on the towers' function. National regulations require an 8 m safety distance for a 400 kV OHL for a usual, open space location, but this value can be higher for those line sections that cross residential buildings and roads, rivers, and other OHLs, etc.
## 3 Design Process of Single Suspension Unit
### Determine the Force Exerted by Compressed Spring  
Let \(F_{j}\) be the force exerted by the compressed spring on the bogie arm of the suspension units. Figure 11 illustrates the free-body diagram of the single suspension unit. Note that the reaction at connected point \(C\) is not shown because it is not necessary to take it into account. Let \(F_{r}\) be the reaction force exerted by the PV panel on the freewheel at the contact point, and \(\beta_{i}\) be the angle formed by the reaction \(F_{r}\) and the \(x\)-axis at the contact point \(G_{i}\)  
Figure 10: Position of connected point \(A\) on the track-wheeled.  
The equation to determine the force \(F_{j}\) is described as follows:  
\[F_{i}=\frac{F_{r}[\cos(\beta_{i})(l_{CD}\sin(\gamma_{2,j})+r)+\sin(\beta_{i})l_{ CD}\cos(\gamma_{2,j})]}{l_{BC}\cos(\phi_{i})} \tag{16}\]  
According to [PERSON]'s third law, assumptions (3) and (5), the reaction force \(N\) is determined as follows:  
\[F_{r}=\frac{mg\cos(\alpha)}{2\sum_{i=1}^{7}\sin(\beta_{i})} \tag{17}\]  
where \(g\) has a value of \(9.81\) m \(\cdot\) s\({}^{-2}\), which is the gravitational acceleration.  
By considering the dot product between the unit vector of the \(x\)-axis (1, 0) and the direction vector of the tangent line equation to \(\Delta\) at point \(G_{i}\) (\(f^{\prime}(x_{G_{i}}),-1\)), the angle \(\beta_{i}\) is calculated as follows:  
\[\beta_{i}=\arccos\left(\frac{1}{\sqrt{f^{\prime}(x_{G_{i}})^{2}+1}}\right) \tag{18}\]  
On the other hand, the coordinates of the connected point \(D_{j}\) are determined based on the coordinates of the contact point \(G_{i}\) as follows:  
\[\begin{cases}x_{D_{j}}=x_{G_{i}}\\ z_{D_{j}}=z_{G_{i}}+r\end{cases} \tag{19}\]  
Based on the orbital equation of point \(D_{j}\) (circle with center \(C_{j}\) and radius \(l_{CD}\)) combined with Equation (18), the equation to determine \(x_{G_{j}}\) is described as follows:  
\[(x_{G_{j}}-x_{C_{j}})^{2}+[f(x_{G_{j}})+r-z_{C_{j}}]^{2}=l_{CD}^{2} \tag{20}\]  
The horizontal coordinate of the contact point \(G_{i}\) is the real solution of Equation (20) and located on the right-hand side of the point \(C_{j}\) in the \(Oxz\) coordinate system. The vertical coordinates of the contact points \(z_{G_{i}}\) are determined by substituting \(x_{G_{j}}\) into Equation (1). Based on the coordinates of connected points \(D_{i}\) and \(C_{i}\) calculated at Equations (7) and (19), the angle \(\gamma_{2,j}\) is determined as follows:  
\[\gamma_{2,j}=\sin^{-1}\left(\frac{z_{D_{j}}-z_{C_{j}}}{l_{CD}}\right) \tag{21}\]  
By substituting Equation (20) into (18), the angle \(\beta_{i}\) can be obtained. Equation (18) is then substituted into Equation (17) to solve for the reaction \(F_{r}\). Finally, the results obtained from Equations (17), (18), and (21) are utilized to determine the force \(F_{i}\) of Equation (16).  
Figure 11: Free body diagram of the suspension.
## 2 Study Area and Materials
### Remote Sensing Data and Preprossing  
The NDVI can accurately capture and reflect the growth and nutrition information of the vegetation, and is used to characterize the growth conditions of wetland vegetation for detecting the vegetation dynamic changes in this research. For acquiring the NDVI time series of the last 20 years, all available standard Level 1 terrain corrected and orthorectified Landsat TM/ETM +/OLI Top of Atmosphere (TOA) reflectance images (Path/Row: 124/40, available pixels \(\geq\) 30%), and the MODIS 13Q1 NDVI products (MODIS tile number: h27, v06) covering the whole study area from 2000 to 2019 were downloaded from the USGS website ([[https://glovis.usgs.gov/](https://glovis.usgs.gov/)]([https://glovis.usgs.gov/](https://glovis.usgs.gov/))).  
The acquisition time of Landsat images is from 1 January 2000 to 1 January 2012 (Landsat 5 TM), 1 January 2000 to 31 December 2019 (Landsat 7 ETM +) and 1 April 2013 to 31 December 2019 (Landsat 8 OLI). A total of 370 Landsat images were acquired, including 93 Landsat 5 images, 20 Landsat 7 SLC-on images, 137 Landsat 7 SLC-off images and 60 Landsat 8 images. The acquisition time of these images is shown in Figure 2. The preprocessing steps of the Landsat TOA reflectance images include: (1) convert TOA reflectance data to surface reflectance via radiometric calibration and FLAASH atmospheric correction, (2) generate monthly Landsat images using simple synthesis algorithm and quality bands of the Landsat image acquisition [49], (3) detect and mask the remaining cloudy area in monthly Landsat images using the Fmask algorithm [50], (4) calculate NDVI using the B3 and B4 bands of Landsat TM/ETM + sensor and the B4 and B5 of Landsat OLI sensor, (5) clip images using the study area vector.  
A total of 460 geo-rectified MODIS NDVI images with the spatial resolution of 250 m and temporal resolution of 16 days were downloaded. Firstly, the maximum value composite method was used to generate the monthly MODIS NDVIs and reduce the cloud contaminated pixels [51]. The monthly MODIS NDVI data were re-projected into the WGS 84/UTM zone 49 N projection and resampled to 30 m by the nearest neighbor method to match the Landsat images. Then, a Landsat image was selected as the reference to register and clip the MODIS NDVIs. Moreover, the Savitzky-Golay filter with the window size of 4 was used to optimize the low-quality pixels in MODIS NDVI time series, which can keep the shape and width of the original signal while filter the noise [8; 9]. Therefore, the bad quality data in MODIS NDVI can be eliminated and the real dynamic disturbance can be preserved as much as possible. Finally, the high-quality, monthly MODIS NDVI time series was acquired for subsequent spatial-temporal fusion.  
Figure 2: The numbers and acquisition time of Landsat images.
## 2 Brief Review of VTL  
In 1980, the basic concept of VTL was described in [PERSON] paper [5]. It introduced a GPS/Inertial Navigation System (INS) composite filter utilizing the user's navigation state to estimate tracking control input. [PERSON] proposed a combined tracker-navigator construction and presented simulation results showing improved tracking performance of an attenuated signal [6]. The term Vector Tracking Loop was first used by [PERSON] [7]. He proposed a vector delay-lock loop (VDLL) algorithm, combining all the tracking channels and navigation function. Implementation of EKF-based VDLL, mathematical derivation of noise performance improvement and several potential VTL advantages were addressed. A recent performance analysis was well described by [PERSON] [8]. As software receivers developed, many studies of VTL were conducted [9]. Several VTL implementation methods in software receivers and their field test results showing improved tracking performance were reported in [10,11].  
GPS/INS ultatight integration (deep coupling) was thoroughly investigated as a VTL application [12,13]. Recent VTL implementations with a variety of filters were analyzed and compared by [PERSON] [14]. Expansion of VTL to vector-based phase lock loop (VPLL) is an ongoing research theme in this field [15,16]. The concept of VTL is easily explained by comparing a conventional tracking loop and VTL, as shown in Figures 1 and 2, respectively. Figure 1 shows a conventional tracking loop composed of several ITLs tracking each satellite signal. Channel tracking results are transferred to the navigation function. All the channels are independent, and tracking and navigation functions are also independent. Figure 2 shows the VTL concept. Comparing with Figure 1, the discriminator outputs are directly connected to the tracking control input, while all the input is estimated from the user navigation result. Because the navigation result is derived from all channel tracking results, all the channels and navigation function are combined. This structure can track temporarily attenuated or blocked satellite signals because the navigation result can be derived from other visible satellites.  
As a more concrete example, VTL and ITL construction in a GNSS receiver is shown in Figure 3. The top part of Figure 3 shows an ITL implementation.  
Figure 1: Independent tracking loop.  
Figure 3: Receiver implementation of ITL (top) and VTL (bottom).  
Figure 2: Vector tracking loop.  
Each channel's tracking measurements are transferred to the navigation module. The measurements are generated in each channel tracking loop independently and the navigation module works separately. VTL, however, does not contain channel-independent tracking loops. Instead, the navigation module collects all the channel tracking results and estimates each tracking control input. Thus, there is one big loop including tracking channels and navigation module.
## 3 Existing QL-Based GF RA Protocols
However, the problem is multi-agent since we have \(K\) devices, and thus we have \(K\) unidimensional Q-tables, which can be better organized in a single Q-table with \(K\) lines. It is worth noting that updating each line occurs without considering the values of the other lines since there is no coordination between the devices. Finally, no discount factor is implemented in the mMTC random access context as proposed in [28, 30], and thus we arrive at the Equation (4). A simple way to compute the rewards \(R(k,\ell)\) in (4) is proposed in [28] through an _independent_ QL approach for mMTC (**iQmMTC**), where \(R(k,\ell)=+1\) if the transmission succeeds, or \(R(k,\ell)=-1\), otherwise. However, [30] has shown that better results can be achieved through a _collaborative_ approach (**cQmMTC**), where the device is rewarded either with \(R(k,\ell)=+1\) for a successful transmission or with \(R(k,\ell)=-P_{c}(k)\) if the transmission fails, with the collaborative penalty function \(P_{c}(k)\) being computed as  
\[P_{c}(k)=\frac{1}{K_{a}}CL(k), \tag{5}\]  
where \(K_{a}\) is the number of active devices in the cell and the congestion level \(CL(k)=|\mathcal{S}_{c(k)}|\) is the _number of contenders_ for pilot \(c(k)\)[30], while \(\mathcal{S}_{c(k)}\) is the set of devices choosing the same pilot \(c(k)\). In this framework, each device keeps its own \(1\times\tau_{p}\) Q-table. Initially, this Q-table is filled with zeros, and all \(\tau_{p}\) RA pilots are equally likely to be chosen by it. Then, at each subsequent transmission attempt, the devices are rewarded with \(R(k,\ell)\), and their Q-tables are updated according to (4). Once an individual device has updated its Q-table, it will only choose pilots among the ones with the highest Q-value. The process is repeated until the \(L\) packets are transmitted. In the cQmMTC approach of [30], the congestion levels in case of collisions are computed as the number of contending devices, \(|\mathcal{S}_{c(k)}|\). However, nothing is discussed about the feasibility of making this information available on the device's side. At first glance, it would require conceiving an estimator to be employed at the BS and then feedback the result to devices, which would spend significant signaling overhead. For comparison purposes, herein, we assume perfectly known the congestion level at the device's side for this scheme, referring to it as _genie_ cQmMTC. Furthermore, in a practical network not all IoT devices are active at a given instant since they activate independently with certain probabilities in such a way that the actual number of active devices is not known by the BS. To avoid complex computations and excessive signaling overheads while simplifying the procedure, we propose a two-step QL-based GF RA protocol making use of a large number of BS antennas to allow a collaborative penalty function computation at the devices' side in case of collisions with minimal complexity and overhead, as described in the following.
# Aerostat-Based Observation of Space Objects in the Stratosphere
## 3 Aerostat-Based Observation Installation for Space Objects
### Observation Platform  
The high-altitude balloon has two kinds of zero-pressure balloons and over-pressure balloons, which can be selected and customized as an observation platform according to the application requirement. It can provide a load capacity of dozens of kilograms to more than one ton for observation payloads and can carry out a long-time flight of dozens of days [17]. The test high-altitude balloon can reach a long-time flight record of more than 100 days [18]. The aerostat-based observation installation can be moved onboard and released and recovered in the field, so it has the ability of mobile deployment and rapid response.  
According to the requirements of floating observation tasks, a balloon pod (also known as a payload service module) should be developed. It serves as an integrated installation platform for application payloads and support equipment, which supply the energy source and control, data storage and transmission, and measurement and flight control. The pod is connected to the high-altitude balloon through a special cable and separated from the balloon by a remote-control command after the observation task and parachuted for recovery. The pod can be customized according to the application task and has a small limit on the weight and size of the observation payloads. It has significant advantages over both satellite and aircraft platforms. In the Strategic Priority Research Program of the Chinese Academy of Sciences, \"Scientific Experiment System in Near Space\", special task pods for scientific experiments were designed and formed [19; 20], including the pod for optical detection. From 2018 to 2022, this program carried out more than twenty scientific experiments in Inner Mongolia, Qinghai, Gansu, Xinjiang, and other places in China, in which all pods completed the tasks and were recovered successfully. These experiments fully verify the environmental adaptability of the pod in the stratospheric airspace. Figure 7 is an example of an atmospheric detection pod.  
Figure 6: Composition of space object floating observation system (SOFOS).  
Compared with the ground observation telescope, the space object floating observation system is a mobile observation platform. The attitude control of the telescope fixed in the pod finds it difficult to detect dark space objects with a long integration time. Generally, at least two levels of control are required to achieve the tracking accuracy required for detection. The primary orientation control is provided through the pod, and the turntable provides the secondary orientation and pitch control, enabling the telescope to point to the object sky area and achieve stable object's observation.
# Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks
###### Abstract  
In data-centric wireless sensor networks (WSNs), sensing data have a high time-space correlation. Most queries are spatial and used to obtain data in a defined region. Geographic routing (GR) protocols are the optimal choice for routing spatial queries. However, several drawbacks still exist in GRs, and these the include premature death of nodes and communication latency, which result in reduced network life and query efficiency. A new clustering GR protocol called quadtree grid (QTGrid) was proposed in this study to save energy and improve spatial query efficiency. First, the monitoring area was logically divided into clusters by a quadtree structure, and each grid's location was encoded to reduce the memory overhead. Second, cluster head (CH) nodes were selected based on several metrics, such as distance from the candidate node to the grid center and adjacent CHs and residual energy. Third, the next-hop routing node was selected depending on the residual energy of the candidate node and its distance to the sink node. Lastly, a lossless data aggregation algorithm and a flexible spatial query algorithm were adopted to reduce the transmission of redundant data and meet the application requirements, respectively. Simulation results showed that compared with three related protocols, QTGrid has lower energy consumption and higher spatial query efficiency and is more suitable for large-scale WSN spatial query application scenarios.  
Keywords:wireless sensor network; geographic routing protocol; spatial query; energy balance +
Footnote †: journal:
# A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
## 5 Discussion
### Memory Consumption  
Similar to the \(\mathcal{O}\) notation, we use \(\mathcal{S}\) for space complexity.  
Convolution-based calculation produces the whole histogram array at once before it could be used for further calculation. This means \(\mathcal{S}(m\cdot n^{2})\) space complexity.  
Figure 10: Parameter sensitivity of the algorithm for (**a**) the chest CT, and (**b**) the head CT image.  
However, local histograms could be calculated directly using the pairwise weight calculation. This approach only needs to store one local histogram (\(\mathcal{S}(m)\)) the input image and the output image (\(\mathcal{S}(n^{2})\)).  
This means that there is a trade-off between space complexity and time complexity. The convolution-based algorithm has better asymptotic time complexity but an order of magnitude worse space complexity than the direct pairwise method.
## 2 Materials and Methods
### Sensitivity Analysis  
A sensitivity analysis was performed to assess the relative importance of input parameters. Sensitivity analysis involves the systematic variation of input parameters and the quantification of their impact, as shown in Table 7.  
Based on the sensitivity analysis, it was found that in the first scenario, the independent parameter \(y_{c}\)/\(H\) and in the second scenario, the independent parameter \(D\)/\(H\) had the greatest effect on predictions of the relative energy dissipation and relative pool depth. The SVM model has a high sensitivity to the \(y_{c}\)/\(H\) parameter for predicting the relative  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Independent Parameters**} & \multirow{2}{*}{**Eliminated Parameter**} & \multirow{2}{*}{**Training**} & \multicolumn{2}{c}{**Testing**} \\ \cline{3-5}  & & **RMSE \(\times\) 100** & & **R\({}^{2}\)** & **RMSE \(\times\) 100** & **R\({}^{2}\)** \\ \hline \multicolumn{5}{c}{First scenario: Relative energy dissipation} \\ \(\mathrm{Fr_{0},y_{c}/H,D/H}\) & — & 0.768 & 0.992 & 2.42 & 0.963 \\ \(\mathrm{Fr_{0},y_{c}/H}\) & D/H & 0.628 & 0.995 & 0.539 & 0.996 \\ \(\mathrm{Fr_{0},D/H}\) & \(\mathrm{y_{c}/H}\) & 0.7 & 0.993 & 2.18 & 0.69 \\ \(\mathrm{y_{c}/H,D/H}\) & \(\mathrm{Fr_{0}}\) & 0.721 & 0.986 & 1.7 & 0.967 \\ \multicolumn{5}{c}{Second scenario: Relative pool depth} \\ D/H, \(\mathrm{y_{c}/H}\) & — & 3.95 & 0.988 & 3.89 & 0.988 \\ D/H & \(\mathrm{y_{c}/H}\) & 9.45 & 0.648 & 8.36 & 0.733 \\ \(\mathrm{y_{c}/H}\) & D/H & 5.42 & 0.974 & 4.88 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Sensitivity Analysis.  
Figure 9: Variation of relative pool depth versus relative critical depth.  
energy dissipation and a high sensitivity to the \(D/H\) parameter for predicting the relative pool depth.
## 2 CMOS and MTJ Technologies Operating at Cryogenic Temperatures
### Magnetic Tunnel Junctions (MTJs) Operating at Cryogenic Temperatures  
Several papers have experimentally demonstrated the cryogenic operating temperature of MTJ nanopillars, mainly based on different stack layers. [PERSON] et al. evaluated the low-temperature performance of CoFeB/MgO-based perpendicular MTJs by characterizing their switching voltage, write error rate, and endurance at temperatures as low as 9 K [37]. [PERSON] et al. investigated the optimization of perpendicular MTJs for efficient STT switching at cryogenic temperatures [38]. Their study compared four storage layer structures: FeCoB/W, Mg/FeCoB/W, FeCoB/Ru/W, and FeCoB/W/Py/W (Py is permalloy Ni\({}_{80}\)Fe\({}_{20}\)). The inclusion of permalloy in the storage layer was shown to effectively reduce interface anisotropy, thereby lowering critical switching currents. [PERSON] et al. explored the development and performance of perpendicular MTJs utilizing molybdenum (Mo) as a key material [39]. The study focuses on achieving sub-nanosecond switching speed while operating at cryogenic temperatures. [PERSON] et al. evaluated the performance of nanoscale perpendicular MTJs when operating at low temperatures [40]. Results suggest that the MTJs that present a free layer with double MgO interfaces are promising candidates for applications in cryogenic environments. [PERSON] et al. analyzed the spin-transfer switching characteristics of perpendicular MTJ nanopillars (CoFeB/W/CoFeB) with diameters ranging from 40 to 60  nm, when operating at temperatures down to 4 K and switching on nanosecond timescales [41].  
In this work, we focus on STT-SMTJ and STT-DMTJ stacks based on CoFeB/MgO layers. These nanopillar structures are comprehensively evaluated as part of a LIM scheme operating at 77 K, as detailed in Section 3. The reliability benefits of nanopillar structures under cryogenic conditions are further discussed in Section 4.