esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Multi-Constellation PPP Model
### Kalman Filter Model
In general, pure random walks are suitable for simulating the three-dimensional coordinates and ambiguities, where the tropospheric delay and the receiver can be modelled in two forms. For static PPP without speed and acceleration, the state parameter vector is:  
\[L_{i}=A_{i}X_{i}+e_{i}\ P_{i} \tag{6}\]3  
The corresponding state transition matrix, \(\Phi_{k+1,k}\), and the dynamic noise variance matrix of the three-dimensional position coordinate are:  
\[Q_{pos}=\left[ \begin{array}{ccc}\frac{q_{\varphi}\Delta t}{\left(\mathrm{R} _{m}+\mathrm{h}\right)^{2}}&0&0\\ 0&\frac{q_{\lambda}\Delta t}{\left(\mathrm{R}_{n}+\mathrm{h}\right)^{2}\cos^{ 2}\varphi}&0\\ 0&0&q_{h}\Delta t\end{array} \right] \tag{19}\]  
where:  
\(q_{\varphi}\): latitudinal spectral density;  
\(q_{\lambda}\): longitudinal spectral density;  
\(q_{h}\): elevation spectral density;  
\(R_{m}\): meridian radius of curvature;  
\(R_{n}\): radius of curvature on the dome circle; and  
\(h\): the height of the station. The dynamic noise variance matrix of the receiver is as follows:  
If it is a pure random walk:  
\[Q_{clk}=\left[q_{dt}\Delta t\right] \tag{20}\]  
If it is pure white noise:  
\[Q_{clk}=\left[\frac{q_{dt}}{\beta_{dt}}\right] \tag{21}\]  
where \(q_{dt}\) is the receiver clock density, and \(\beta_{dt}\) is the corresponding damping coefficient. In general, the tropospheric zenith delay is expressed as a pure random walk, and thus, its dynamic noise variance is:  
\[Q_{trop}=\left[q_{trop}\;\Delta t\right] \tag{22}\]  
where \(q_{trop}\) is the spectral density of the tropospheric zenith wet delay. The ambiguity parameter can be regarded as a constant: \(Q_{N}=0\). For dynamic PPP, the state parameters should include the speed and acceleration parameters, and the corresponding matrix should change. Thus, the state vector parameters should be:  
\[X_{k}=\left[\mathrm{X},\mathrm{Y},Z,\mathrm{V}_{x},\mathrm{V}_{y},\mathrm{V} _{z},a_{x},a_{y},a_{z},\mathrm{clk},\mathrm{trop},\mathrm{N}_{1},\cdots, \mathrm{N}_{n}\right]^{T} \tag{23}\]The corresponding state transition matrix is:  
\[\Phi_{k+1,k}=\left[ \begin{array}{cccc}\Phi_{pos}&0&0&0\\ 0&\Phi_{clk}&0&0\\ 0&0&\Phi_{trop}&0\\ 0&0&0&\Phi_{N}\end{array} \right]_{(11+n)\times(11+n)} \tag{24}\]  
where:  
\[\Phi_{pos}=\left[ \begin{array}{cccccccc}1&0&0&\Delta t&0&0&\frac{\Delta t^{2 }}{2}&0&0\\ 0&1&0&0&\Delta t&0&0&\frac{\Delta t^{2}}{2}&0\\ 0&0&1&0&0&\Delta t&0&0&\frac{\Delta t^{2}}{2}\\ 0&0&0&1&0&0&\Delta t&0&0\\ 0&0&0&0&1&0&0&\Delta t&0\\ 0&0&0&0&0&1&0&0&\Delta t\\ 0&0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&1\end{array} \right]_{9\times 9} \tag{25}\]  
\[\Phi_{clk}=1 \tag{26}\]  
\[\Phi_{trop}=1 \tag{27}\]  
\[\Phi_{N}=\left[ \begin{array}{ccc}1&\cdots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&1\end{array} \right]_{n\times n} \tag{28}\]  
The state noise variance is:  
\[Q=\left[ \begin{array}{cccc}Q_{pos}&0&0&0\\ 0&Q_{clk}&0&0\\ 0&0&Q_{trop}&0\\ 0&0&0&Q_{N}\end{array} \right] \tag{29}\]
# Mechanical Analysis of DS in Horizontal Directional Drilling
## 4 Analysis and Application of Engineering Case
### The Construction Stage of the Reaming  
The construction stage of the reaming is divided into two forms, the first is the reaming method of the backhaul DS and the second is the reaming method (similar to pilot hole) by drilling in the middle at both ends. The second method is more suitable for hard formation reaming.  
In the first stage of reaming, the spatial displacement distribution of DS is shown in Figure 14a. At this point, the thrust force is 30T. It can be seen from the displacement diagram that obvious spiral buckling occurred on the left side of the 7-5/8 in drill pipe and on the right side of the 6-5/8 in the drill pipe. In the secondary reaming, the spatial displacement distribution of the DS is shown in Figure 14b, and the thrust is 40T. Due to the increase of bore size, the spiral buckling of some DS in the hole is no longer complete (the DS is not attached to the borehole wall). The DS with complete spiral buckling is concentrated near the entrance of the DS of various sizes. In the third-stage reaming, the spatial displacement distribution of the DS is shown in Figure 14c, and the thrust is still 40T. At this point, 7-5/8 in DS no longer produces spiral buckling, but sinusoidal buckling near the entrance. The 6-5/8 in DS is still marked by spiral buckling near the entrance.  
Figure 13: DS contact force and torque distribution. (**a**) 20T. (**b**) 30T. (**c**) 40T.  
In the reaming operation, the contact force distribution and the torque distribution of DS are shown in Figure 15. As can be seen from the figure, as the bore size increases, the contact force between the DS and the borehole wall gradually decreases. In the third-stage reaming, the contact force of the back part of the DS becomes stable, because there is no buckling deformation of the DS.  
Figure 14: Displacement distribution of DS. (**a**) The first stage reaming. (**b**) The second stage reaming. (**c**) The third stage reaming.
# A Variable-Order Dynamic Constitutive Model for Clay Based on the Fractional Calculus
## 2 Theory of Clay Deformation under Cyclic Loading
### Deformation Characteristics of Clay under Different Loads
#### 2.2.2 Deformation Characteristics of Clay under Alternating Load  
The accumulative plastic strain-time curves of geotechnical materials under long-term alternating load can be categorized into three types [40]: (1) steady curve; (2) critical curve; (3) failure curve, as shown in Figure 4a. The dynamic stress that makes an accumulative strain-time curve evolve into a critical curve is called critical dynamic stress \(\sigma_{cr}\), and accumulative strain-time curves evolve into steady or failure curves, depending on whether the dynamic stress is lower or higher than \(\sigma_{cr}\). The rate of a steady curve is faster in the initial stage before settling into a constant level, the rate of a critical curve is characterized by fluctuation, while the rate of accumulative plastic strain increases with loading duration for the failure curve. However, it is laborious to obtain ideal experimental data as depicted in Figure 4a. More typically, accumulative plastic strain-time data such as we obtained resembles Figure 4b, where failure and steady curves are primarily distributed in the upper left and lower right regions of the figure, while critical curves are distributed in the grey region between them. Experimental tests can only obtain a range of critical dynamic stress, so it is hard to obtain the precise value of critical dynamic stress by laboratory testing [41]. For this reason, the critical dynamic stress \(\sigma_{cr}\) is taken to be the median of the range in this paper.  
Figure 3: Representative creep strain-time curve of clay.
## 2 Design Scheme of Aerial Remote Sensing Platform Based on Real-Time Satellite Communication
Based on the spatial stability of the shooting of camera, multiple frames of images can be effectively concatenated to obtain ground remote sensing information for larger areas. The designed technical indicators of the image stabilization platform (Table 1) include external dimensions, working temperatures, total weight, effective load (camera and lens), operation range in both pitch and roll states, maximum angular velocity and acceleration in the search state, and stability accuracy within the swing range. The wireless communication system is composed of airborne mobile communication equipment. Through the mobile communication equipment, mobile carriers such as vehicles, ships, and airplanes can track satellites in real time during when they are moving, continuously transmitting multimedia information such as voice, data, and images, which can meet the needs of emergency communication for military and civilian under various mobile conditions. During the transmission process, the mobile communication equipment autonomously tracks satellites, fully utilizing the characteristics of large coverage area, strong anti-interference ability, and stable line of satellite communication. It can achieve communication functions of point-to-point, point-to- multipoint, and point-to-the main station of mobile satellites. The technical indicators of the mobile communication equipment in this design scheme are shown in Table 2. \begin{table}
\begin{tabular}{l l c} \hline \hline
**Technical Indicators** & **States** & **Indicator Values** \\ \hline Operation range & Pitch & \(-10^{\circ}\)\(\leftarrow\)\(10^{\circ}\) \\  & Roll & \(-10^{\circ}\)\(\leftarrow\)\(10^{\circ}\) \\ Maximum angular velocity & Pitch & \(20^{\circ}\)/s \\  & Roll & \(20^{\circ}\)/s \\ Maximum angular acceleration & Pitch & \(20^{\circ}\)/s\({}^{2}\) \\  & Roll & \(20^{\circ}\)/s\({}^{2}\) \\ Stability accuracy & & \(\leq\)0.05\({}^{\circ}\) \\ Effective load (camera and lens) & & \(\leq\)8 kg \\ Maximum external dimensions & & 600 mm \(\times\) 300 mm \(\times\) 800 mm \\ Total platform weight (including load) & & \(\leq\)20 kg \(\pm\) 20\% \\ Working temperatures & & \(-20\)\({}^{\circ}\)C\(\leftarrow\)55 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technical indicators of the orthographic stabilization platform.
# Mini-Review: Potential of Diatom-Derived Silica for Biomedical Applications
## 5 Silica for Biomedical Applications: Advantages
### Availability of Biosilica Feedstocks  
In contrast with other synthetic materials, diatom biosilica is already available in huge amounts as diatomic. Moreover, diatom-derived silica feedstock could be easily obtained by culturing these microalgae in open ponds or enclosed systems, and separating them from the organic matter after culture dewatering.
## 2 Data  
### Data Description  
The historical DHK 200 Turkey map used in this study covers a large area of around 150,000 square km in northwest Turkey, including the regions of Ankara and Bursa (Figure 2). These two regions are the core focus areas of our ongoing research project. To our knowledge, the vectorized DHK 200 Turkey map has not been used in any other study except that by [16]. The map contains approximately 65,000 \(\times\) 18,000 pixels with a dpi of 300, and it has a raster size of 17 \(\times\) 17 m with a radiometric resolution of an 8-bit depth in the RGB color space. An excerpt from the map legend with different road types in the DHK 200 Turkey map is shown in Figure 3. The DHK 200 Turkey map legends are organized bilingually in accordance with the rest of the World War II German military maps: first in the language of the country of the map and secondly in German.  
Figure 2: The 27 DHK 200 Turkey images or sheets used in the study.  
Supervised classification approaches require reference data to be used in the training stage, which is also called ground truth masking, for the corresponding input images. In this study, the annotation and labeling step was conducted based on the manual digitization of different roads from the DHK 200 Turkey map and by entering different road types as attributes to each related road segment in the GIS environment. Among the road types present in the legend, five road types that help to obtain substantial insights on the transportation infrastructure are selected. These road types are as follows:  
* Stabilized or macadam roads (Turkish: _gose_, German: _Fahrstrasse_): partially expanded and suitable for automobiles in the dry season;
* Railways (Turkish: _demirylou_, German: _Eisenbalm_): normal gauge;
* Footpaths (Turkish: _patila_, German: _Fuswey_);
* Cart roads (Turkish: _araba yolu_, German: _Fahrweg/Karawanentweg_): not expanded, automobile suitability depends on the season, natural road with few improvements;
* Inferior roads (Turkish: _adi yol_, German: _Saumweg_).
## 5 The Proposed Solution
### The Extension Method for Arbitrary Polynomial Functions
#### 5.4.2 Multivariate Polynomials  
Let \(\mathcal{F}\) be the set of all arbitrary multivariate polynomials over a finite field \(\mathbb{Z}_{q}\). For any \(f(x_{1},\ldots,x_{m})=\sum_{i_{1},\ldots,i_{m}=1}^{d}f_{i_{1},\ldots,i_{m}} \cdot x_{1}^{i_{1}}\cdots x_{m}^{i_{m}}\in\mathcal{F}\), \(m\geq 2\), \(l=\lfloor m/2\rfloor\), parse the parameters as a \(d^{l}\times d^{m-l}\) matrix  
\[\mathbf{F}=\left(F_{(i_{1},\ldots,i_{l}),(i_{l+1},\ldots,i_{m})}\right) \tag{13}\]  
where \(F_{(i_{1},\ldots,i_{l}),(i_{l+1},\ldots,i_{m})}=f_{i_{1},\ldots,i_{m}}\). Set \(\mathbf{y}=\left(x_{1}^{i_{1}}\cdots x_{l}^{i_{l}}\right)\in\mathbb{Z}_{q}^{d}\) and \(\mathbf{x}=\left(x_{l+1}^{i_{l+1}}\cdots x_{m}^{i_{m}}\right)^{\top}\in\mathbb{Z}_ {q}^{d^{m-1}}\); then, we have \(f(x_{1},\ldots,x_{m})=\mathbf{y}\cdot(\mathbf{F}\cdot\mathbf{x})\).  
This is a two-phase computation: (i) the computation of \(\mathbf{u}=\mathbf{F}\mathbf{x}\) and (ii) the computation of \(f(x_{1},\ldots,x_{m})=\mathbf{y}\cdot\mathbf{u}\). The first phase is a matrix-vector multiplication that can be delegated to a cloud server using our verifiable computation scheme. It requires \(O(d^{m})\) arithmetic operations. The second phase is a substantially fast computation for the user that requires \(O(d^{\lceil m/2\rceil})\) arithmetic operations locally.
## 2 Mathematical Preliminaries
### Model Reduction  
The aim of model reduction is the algorithmic computation of surrogate reduced order models with lower computational complexity or memory footprint than the original full order model. For the sake of brevity, only combined state and parameter reduction is summarized here, which includes state-space reduction, parametric state-space reduction and parameter-space reduction as special cases; for an elaborate layout see [39].  
Given the general, possibly nonlinear, input-output system (1), a combined state and parameter reduced order model:  
\[\dot{x}_{r}(t) =f_{r}(t,x_{r}(t),u(t),\theta_{r}),\] \[\ddot{y}(t) =g_{r}(t,x_{r}(t),u(t),\theta_{r}),\] \[x_{r}(0) =x_{r,0},\]  
with a reduced state \(x_{r}:\mathbb{R}_{>0}\rightarrow\mathbb{R}^{n}\), \(n\ll N\), and a reduced parameter \(\theta_{r}\in\mathbb{R}^{p}\), \(p\ll P\), is sought. Accordingly, a reduced vector-field \(f_{r}:\mathbb{R}_{>0}\times\mathbb{R}^{n}\times\mathbb{R}^{M}\times\mathbb{R}^ {p}\rightarrow\mathbb{R}^{n}\) and a reduced output functional \(g_{r}:\mathbb{R}_{>0}\times\mathbb{R}^{n}\times\mathbb{R}^{M}\times\mathbb{R}^ {p}\rightarrow\mathbb{R}^{Q}\) describe the reduced system, for which the reduced system's outputs \(\ddot{y}:\mathbb{R}_{>0}\rightarrow\mathbb{R}^{Q}\) should exhibit a small error compared to the full order model, yet preserving the parameter dependency:  
\[\|y(\theta)-\ddot{y}(\theta_{r})\|\ll 1.\]  
A class of methods to obtain such a reduced order model with the associated requirements is described next.
# Balloon: An Elastic Data Management Strategy for Interlaced Magnetic Recording
## 5 Performance Evaluation
### Number of Extra I/Os  
Extra I/O refers to track-level I/O caused by RMW operations or data shuffling apart from the user request. Therefore, the number of extra I/Os is a critical factor affecting write performance. Figure 11a,b shows the number of extra I/Os introduced by each strategy at medium and high utilization, respectively.  
Seagate generates the highest number of extra I/Os in all scenarios because it only places incoming data on allocated tracks without using data shuffling. Conversely, other strategies significantly reduce the number of extra I/Os by periodically moving hot data to free-write regions, demonstrating the effectiveness of data shuffling. STC partially reduces track rewrites by placing hot tracks in the persistent buffer. However, the track-level data  
Figure 10: I/O performance of Balloon vs. competitors at 99.99% space utilization, where the X-axis represents different workloads and the Y-axis represents write latency, read latency, and average latency normalized to CMR.  
organization approach does not fully utilize the limited capacity of the persistent buffer, resulting in limited gains from data shuffling. On the other hand, DTM allows arbitrary hot and cold tracks to be swapped within zones and moved more bottom tracks to free-write regions, resulting in significantly fewer extra I/Os than STC. MOM uses hotness-aware data movement and performs well at moderate utilization. However, at high utilization, its extra I/Os increases dramatically because its data shuffling is limited to the top region, leaving hot data in the bottom track unmoved to the top region. Once the free tracks are exhausted, the expensive RMW operation is reactivated, resulting in a significant amount of extra I/O. Both Balloon-MS and MOM opportunistically use free top tracks to reduce track rewrite overhead. However, Balloon-MS goes a step further with its track merge/swap operation, which enables cost-free data shuffling using buffer tracks, effectively suppressing the introduction of extra I/O. Equipped with a block-level persistent buffer, MAGIC introduces significantly fewer extra I/Os than any other strategy except Balloon. In particular, Balloon has the lowest number of extra I/Os in all scenarios, which contributes significantly to its remarkable improvement in write performance.
## 2 Description of the Measurement System  
An axlebox accelerometer measurement system is installed on a trailer bogie of a commercial vehicle running on a subway network. It is made up of six accelerometers, an encoder and a gyroscope mounted on the carbody. Four accelerometers measuring the acceleration along the vertical direction are mounted on the axleboxes of the instrumented bogie, while two others measure the lateral acceleration of the wheelsets. As an example, the accelerometers mounted on the axleboxes of one of the instrumented wheelsets are shown in Figure 1. The absolute position of the vehicle along the line and the presence of curves are estimated by combining the speed signal from the encoder with gyroscope and lateral acceleration signals. The estimation of the position is facilitated by the stops of the vehicle in correspondence with the stations. The error in the estimation of the vehicle position is below a meter.  
The estimation of rail corrugation from axlebox acceleration measurements, which is performed in the frequency domain, requires the determination of the transfer function of the measurement system [7]. It has the form:  
\[H(f)=\frac{z_{irr}(f)}{\bar{z}_{ax}(f)}, \tag{1}\]  
where \(f\) represents the frequency, \(\bar{z}_{ax}\) is the measured acceleration and \(z_{irr}\) is the rail irregularity. Being mounted on the axlebox, the measurement system is dynamically coupled with the vehicle. Thus, the transfer function must be determined by studying the dynamics of the wheel-rail interaction subjected to rail irregularity.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 5 Discussion
This allows the possibility of deploying most of the elements that make up a realistic road, such as traffic lights, stop, multilane roads, and roundabouts. It is also possible to include generating the movement pattern of vehicles in a different typology to be used by the simulator [64]. An urban zone also enables variation analysis of vehicle speed, density, and distance [65]. In the case of highways, routing protocols are not evaluated so deeply because velocities are mostly constants, and the environment normally represents long, sparse, and clear paths [66; 67]; many routing protocols use the carry-and-forward method in these cases. Some evaluation in VANETs in highways focuses on propagation modeling, such as in [68]. Related to the research questions in Section 3.1, we concluded that position, direction, and density are the most used metrics, because these are the ones that consider the main characteristics of vehicle scenarios as movement, and consider the presence of other neighbors to make hop-by-hop communication. About the combination, a good proposal of metrics is one such as that proposed in [45], where the authors used position, direction, and density, obtaining good performance in terms of packet-delivery ratio and delay. These metrics are very frequently used and repeated in other proposals, but this special combination has good results in urban scenarios (that are more restricted by the presence of obstacles) regardless of vehicle density of the scenario due to the adaptability provided in the path selection. This helps for a fast-forwarding process for reaching the destination. On the other side, if the goal is to meet an offered QoS over forwarding speed, link quality and stability are some of the metrics that should be included in path selection. In this analysis, in GPSR results, as the protocol with the higher number of comparatives, more than half of the protocols presented were compared with GPSR because GPSR is the base of geographic protocols that are the angular piece in vehicular routing. Finally, in relation to the used simulator, the most used software for analysis is NS-2. Deploying and testing VANETs imply high cost and intensive labor. For this reason, simulations are a helpful alternative preceding actual implementation [69; 70]. Many researchers select NS-2 because it is an open-source simulator, has already had many implementations, and it is easy to edit to their needs. NS-2 also allows the use of several mobility generators, such as VanetMobiSim, MOVE, or SUMO, which improve the scenario design, making evaluations more realistic [71; 72]. However, several recent investigations have shown a tendency to use Veins and OMNET [73]. As gaps in this literature review, it is important to mention the importance of evaluating new proposals in the same environments, which can include the same simulator and same area (urban or highway) in order to make a fair comparison.
## 2 Structure of the Photovoltaic System
### Photovoltaic Panel Emulator  
For the design and implementation of the photovoltaic panel emulator (PVPE), the PVPs Keysight E4360A emulator was used as a reference. This device is widely used in research to substitute real PVPs presenting behaviors similar to real PVPs [49; 50; 51; 52; 53; 54]. It operates in three different modes: the first, it generates fixed voltages and currents; in the second, the device generates voltage and current signals of various magnitudes from a reference table; in the third mode, the emulator reproduces the panel's characteristic current-voltage curve, based on an exponential model defined by four parameters: open-circuit voltage \(V_{oc}\), voltage in the maximum power point \(V_{mpp}\), short-circuit current \(I_{sc}\) and current in the maximum power point \(I_{mpp}\)[55] and two variables \(R_{s}\) and \(N\), which in turn depend on these four parameters. The third mode was used for the design and implementation of the PVPE, the Equation (1) defines the relationship between the panel voltage \(V\) and its current \(I\).  
\[V=\frac{\frac{V_{oc}ln\left[2-\left(\frac{L}{I_{sc}}\right)^{N}\right]}{ln(2)} -R_{s}(I-I_{sc})}{1+\frac{R_{s}I_{sc}}{V_{oc}}} \tag{1}\]  
Equations (2)-(4) are used to calculate the model variables \(R_{s}\) and \(N\) based on the input parameters (\(V_{oc}\), \(V_{mpp}\), \(I_{sc}\) and \(I_{mpp}\)) and the variable a defined in Equation (3).  
\[R_{s}=\frac{V_{oc}-V_{mpp}}{I_{mpp}} \tag{2}\]  
\[a=\frac{V_{mpp}\left(1+\frac{R_{s}I_{sc}}{V_{oc}}\right)+R_{s}\left(I_{mpp}-I_{ sc}\right)}{V_{oc}} \tag{3}\]  
\[N=\frac{ln(2-2^{a})}{ln\left(\frac{I_{mpp}}{I_{sc}}\right)} \tag{4}\]