esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### POD of GOCE Satellite Based on the RD_STP Method  
The parameter settings for the RD_STP POD of GOCE are listed in Table 2. For comparison, the parameters used by [PERSON] et al. [11] are also listed in the table. Moreover, because the released kinematic solutions of GOCE are provided at a sampling rate of 1 Hz, it is reasonable to choose \(\Delta t\ =\ 1\)s in the RD_STP POD. Thus, the RMS error of the GOCE's 1 s STP pseudo-observation is set as \(\sigma_{\Delta\Delta t_{L}}\ =\ 10^{-5}\) m according to Equation (3), when \(\sigma_{\overset{\rightarrow}{r}_{M}}\ =\ 10^{-5}\) ms\({}^{-2}\). POD Results and Analysis  
Using the preprocessed data, the precise orbits are determined via the RD_STP method based on the self-developed low-orbit satellite orbit determination software. For the evaluation of the 1s RD_STP solutions, here, we use the RD solutions as the \"true\" values of the satellite orbit. The errors of the 1s RD_STP solutions in the radial (\(R\)), along-track (\(T\)), and normal component (\(N\)) directions are shown in Figure 7. The errors of the KIN solutions and the latitudes of the satellite are also shown in Figure 7. According to the figure, the 1s RD_STP solutions are smoother than the KIN solutions because the dynamic constraint is applied and the solutions are closer to the RD solutions. Because the assumption of \(\sigma_{\overset{\rightarrow}{r}_{M}}\ =\ 10^{-5}\) ms\({}^{-2}\) is much larger than the true errors (shown in Section 3.1.3), the dynamic constraints from  
Figure 6: Three-dimensional root mean square (3D RMS) of the GOCE’s integrated STP errors corresponding to two different models (EIGEN5S and EGM2008) with different maximum degrees and orders (30, 40, 50, 60, 70, 80, 90, 120, and 150) and different sampling intervals (30, 60, 90, 120, 180, 240, and 300 s). The RD POD solutions are used as the true orbit. the STPs are not as strong as those in the traditional RD method. This outcome means that the RD_STP solutions are not intended to approach the RD solutions. In addition, the systematic differences between the RD_STP and RD solutions are possibly caused by the differences of the a priori dynamic models used here, and the orbit determination software platforms (e.g., the cycle slip detection method). The daily means and RMSs of the differences between the 1s RD_STP solutions and the RD solutions for the selected 15 days are shown in Figure 8. According to Figure 8, all 15 24-h RMSs are less than 2 cm with nearly zero means in the three directions (R, T, N), and their mean 3D RMS is 1.93 cm. This outcome indicates that the RD_STP method performs very well, and the accuracy of its solutions is consistent with that of the RD solutions. To further analyze the influences of the a priori gravity field model and the statistical model on the RD_STP solutions, Figure 9 shows the differences of the RD_STP solutions with the different dynamic constraints corresponding to the EGM2008 and EIGEN5S models at a specific time interval of 3 h at DOY 330, 2009. The maximum degree and order
## 3 Results
### CoP Measurements  
Significant changes were observed in all measured CoP variables during stair de-scent among EG participants following the 10-week intervention (Table 4). In the AP direction, the EG exhibited significant increases in AP range, excursion, and velocity, accompanied by a marked reduction in AP RMS. Additionally, significant decreases were observed in ML range, excursion, velocity, and RMS. The EG also demonstrated significant increases in total excursion and total velocity.  
Furthermore, significant differences between the EG and CG were identified in AP excursion, AP velocity, AP RMS, ML range, ML excursion, ML velocity, ML RMS, and total velocity. In contrast, no significant changes were observed within the CG.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Group**} \\ \hline
**Variables** & **Training** & **EG (N = 15)** & **CG (N = 15)** \\ \hline AP Range (cm) & pre & 29.73 \(\pm\) 1.68 & 31.10 \(\pm\) 12.38 \\ \cline{2-4}  & post & 37.85 \(\pm\) 12.24 * & 30.64 \(\pm\) 16.56 \\ \hline AP Excursion (cm) & pre & 38.34 \(\pm\) 3.53 & 40.46 \(\pm\) 15.57 \\ \cline{2-4}  & post & 58.15 \(\pm\) 12.85 ***, \(\dagger\) & 42.40 \(\pm\) 17.34 \\ \hline AP Velocity (cm/s) & pre & 6.53 \(\pm\) 0.73 & 6.89 \(\pm\) 2.35 \\ \cline{2-4}  & post & 9.74 \(\pm\) 1.88 ***, \(\dagger\) & 7.12 \(\pm\) 2.34 \\ \hline AP RMS (cm) & pre & 6.68 \(\pm\) 0.67 & 6.36 \(\pm\) 1.29 \\ \cline{2-4}  & post & 1.48 \(\pm\) 0.81 ***, \(\dagger\) & 6.42 \(\pm\) 1.42 \\ \hline ML Range (cm) & pre & 24.78 \(\pm\) 1.95 & 19.59 \(\pm\) 9.57 \\ \cline{2-4}  & post & 12.54 \(\pm\) 7.06 ***, \(\dagger\) & 19.51 \(\pm\) 7.87 \\ \hline ML Excursion (cm) & pre & 28.56 \(\pm\) 2.11 & 26.89 \(\pm\) 10.16 \\ \cline{2-4}  & post & 19.38 \(\pm\) 7.15 ***, \(\dagger\) & 28.12 \(\pm\) 10.43 \\ \hline ML Velocity (cm/s) & pre & 4.11 \(\pm\) 0.85 & 3.65 \(\pm\) 1.44 \\ \cline{2-4}  & post & 2.88 \(\pm\) 0.99 **, \(\dagger\) & 3.80 \(\pm\) 1.40 \\ \hline ML RMS (cm) & pre & 7.44 \(\pm\) 0.69 & 7.06 \(\pm\) 1.38 \\ \cline{2-4}  & post & 4.51 \(\pm\) 0.78 ***, \(\dagger\) & 6.62 \(\pm\) 0.97 \\ \hline Total Excursion (cm) & pre & 46.84 \(\pm\) 3.32 & 52.95 \(\pm\) 10.86 \\ \cline{2-4}  & post & 54.86 \(\pm\) 13.57 * & 58.47 \(\pm\) 14.58 \\ \hline Total Velocity (cm/s) & pre & 6.98 \(\pm\) 1.09 & 7.60 \(\pm\) 2.09 \\ \cline{2-4}  & post & 9.54 \(\pm\) 1.60 ***, \(\dagger\) & 7.89 \(\pm\) 1.96 \\ \hline \hline \end{tabular}  
* AP, antero-posterior; ML, mediolateral; RMS, root mean square.
* \(p<0.05\), *
* \(p<0.01\), **
* \(p<0.001\): pre- vs. post-training, \(\dagger\)\(p<0.05\), EG vs. CG groups. All parameters differed significantly.  
\end{table}
Table 4: CoP parameters during stair descent (mean \(\pm\) SD).
## 3 Results
### Research Aims and Maturity Assessment  
Studies in telemedically advanced specialties such as cardiology and rehabilitation have emphasized outcome measurement, integration into care pathways, and the implementation of AI. In contrast, studies on less mature specialties have focused on feasibility, user perception, and platform usability. These differences reflect the varying stages of telemedicine and eHealth maturity across different specialties.
## 3 Modeling of the System and Optimization  
### Energy Model  
The basic energy balance equation of the SWH can be written as utilized energy \(Q_{u}\) equals absorbed energy \(Q_{ab}\) minus the losses \(Q_{l}\):  
\[Q_{u}=Q_{ab}-Q_{l} \tag{1}\]  
\(Q_{ab}\) represents the energy absorbed by the FPC from the insolation after passing through the glazing. Hence, it can be written as [28]:  
\[Q_{ab}=A_{a}S=A_{a}(\tau\alpha)G_{T} \tag{2}\]  
\[As,S=(\tau\alpha)G_{T} \tag{3}\]  
Various types of heat loss occur from the FPC to the surrounding through conduction, convection and radiation. Considering all the losses, the total heat loss in terms of overall heat loss coefficient \(U_{l}\) can be written as [29]:  
\[Q_{l}=U_{l}A_{a}(T_{p}-T_{a}) \tag{4}\]  
Hence, Equation (1) can be written for steady state as [28]:  
\[Q_{u}=A_{a}S-U_{l}A_{a}(T_{p}-T_{a}) \tag{5}\]  
However, it is difficult to predict \(U_{l}\) and \(T_{p}\) simultaneously. A more convenient approach is used to write the utilized energy in terms of fluid inlet temperature to the collector by Hottel-Whillier equation [28] as:  
\[Q_{u}=A_{a}F_{R}[S-U_{l}(T_{in}-T_{a})] \tag{6}\]  
where  
\[F_{R}=\frac{\dot{m}C_{p}}{U_{l}A_{a}}[1-e^{\{-\frac{P^{\prime}U_{l}A_{a}}{mC_ {p}}\}}] \tag{7}\]  
Figure 3: Schematic diagram of the total setup of the solar water heating system [1].  
The utilized energy in terms of fluid inlet and outlet temperature to and from the collector can be also written as [28]:  
\[Q_{u}=\dot{m}C_{p}(T_{out}-T_{in}) \tag{8}\]  
The thermal efficiency of the system can be written as [28]:  
\[\eta_{en}=\frac{Q_{u}}{G_{T}A_{a}} \tag{9}\]  
It is not easy to measure the mass flow rate because it is a passive (thermosyphon) system. Fluid flow occurs due to density variation, which is due to temperature variation between inlet and outlet of the absorber and also in the water tank. The mass flow rate is calculated from the energy balance equation considering absorber plate and serpentine flow path as the control volume (CV). Energy equation of the CV at steady state with zero work output is [30]:  
\[\sum\dot{m}(h+\frac{c^{2}}{2}+gz)_{in}-\sum\dot{m}(h+\frac{c^{2}}{2}+gz)_{out} +Q=0 \tag{10}\]  
The mass flow rate and the velocity of flow depend on each other. It is difficult to calculate both the parameters simultaneously, so it has been calculated by iterative process.
# Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image +
## 2 PV Module Imaging System Using Drone Equipped with Dual Camera
### GCS Equipped with Dual Camera  
In this work, the GCS was developed as shown in Figure 5. The GCS is designed to control and implement the image processing for both thermal cameras with a resolution of \(640\times 512\) and a visible camera with a resolution of \(1280\times 720\). The output from the GCS is the control value for tilt motor and roll  
Figure 4: Applied gimbal system.  
Figure 3: Comparison between using and not using a gimbal during a drone flight.  
motor. With this controller, firstly, image processing is done from the image of the visible camera to find the target ROI for the thermal camera, and it controls the gimbal on a real-time basis. It is finally possible to obtain an excellent quality of the image from a thermal camera, which requires positioning the target area of the object in the center of the ROI.
## 7 Numerical Results and Discussion  
In this section, we evaluate the BER and PER performances of some BSDCD schemes. In all simulations, the payload length of the physical layer protocol data unit (PPDU) is 20 octets [1], and the detection procedure is repeated until enough error packets are collected. The carrier frequency was selected as 924 MHz, i.e., the maximum values required in a 915 MHz band [1]. All of these parameters are described in detail in Table 2. Note that the bits in the PHR, which indicates the number of octets of the PPDU, are also considered in all simulations. All experiments in this section were developed on a personal computer (3.2 GHz, 8 GB RAM) in a MATLAB (R2017a) (MathWorks, Beijing, China) platform.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multicolumn{3}{c}{**Absolute Error \(|e|\)**} \\ \cline{2-4}  & **Estimator in [8]** & **Estimator in [4]** & **Estimator in this work** \\ \hline \([-\pi,-3\pi/4)\) & \(\varphi+\pi\) & \((-\pi+\tan\varphi)-\varphi\) & \(\varphi-(-\pi-\sin\varphi)\) \\ \([-3\pi/4,-\pi/2)\) & \(-\varphi-\pi/2\) & \(\varphi-(-\pi/2+\cot\varphi)\) & \(\varphi-(-\pi-\sin\varphi)\) \\ \([-\pi/2,-\pi/4)\) & \(\varphi+\pi/2\) & \((-\pi/2-\cot\varphi)-\varphi\) & \(\sin\varphi-\varphi\) \\ \([-\pi/4,0)\) & \(-\varphi\) & \(\varphi-\tan\varphi\) & \(\sin\varphi-\varphi\) \\ \([0,\pi/4)\) & \(\varphi\) & \(\tan\varphi-\varphi\) & \(\varphi-\sin\varphi\) \\ \([\pi/4,\pi/2)\) & \(-\varphi+\pi/2\) & \(\varphi-(\pi/2-\cot\varphi)\) & \(\varphi-\sin\varphi\) \\ \([\pi/2,3\pi/4)\) & \(\varphi-\pi/2\) & \((\pi/2-\cot\varphi)-\varphi\) & \((\pi-\sin\varphi)-\varphi\) \\ \([3\pi/4,\pi)\) & \(-\varphi+\pi\) & \(\varphi-(\pi+\tan\varphi)\) & \((\pi-\sin\varphi)-\varphi\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the absolute error.  
Figure 5: The absolute error \(|e|\) of three different estimation methods.
# Heterogeneity of Ex Vivo and In Vivo Properties along the Length of the Abdominal Aortic Aneurysm
## 2 Materials and Methods
### In Vivo Analysis
#### 2.3.2 Maximum Principal Strain  
Three-dimensional principal strain analysis was performed in vivo on dynamic CT images by means of proprietary software ViTAA (Virtual Touch Aortic Aneurysm--patent WO-2018/068153-A1) [31; 32]. The strain analysis relied on an optical flow-based algorithm and used a triangular surface mesh of the outer aortic wall as a 3D tracking model in order to measure the displacement of each mesh node over the cardiac cycle and derive the deformation gradient. Finally, the in vivo principal strain was computed from the Green-Lagrange tensor [31; 32].
# Application of a Novel High-Order WENO Scheme in LES Simulations
## 3 Application Cases  
The shockwave-turbulent boundary layer interaction is a prominent topic in LES research. This study investigates this issue using the WENO-ZQ scheme, with a comparison of the results with experimental data [50] and direct numerical simulation (DNS) [51]. The focus is on the formation of supersonic turbulent boundary layers, which is central to the analysis, including a comparative evaluation of the computational grid resolution required in this context. Finally, the WENO-ZQ5 scheme is applied to investigate the separation transition phenomenon in a Pak B turbine cascade. The test cases were executed on an H3C UniServer R4700 G3 high-performance rack server, manufactured by H3C Technologies Co., Ltd., headquartered in Hangzhou, China. It is equipped with 768 cores, featuring EPYC 7742 CPUs and 64 GB of 3200 MHz memory.
# Tempo and Metrical Analysis by Tracking Multiple Metrical Levels Using Autocorrelation
## 3 Proposed Method
### Acetuation Curve  
The proposed method for the inference of the accentuation curve follows the same general principle of the model introduced in [9], detecting and tracking the apparition of partials locally in the spectrogram, as explained in Section 2.1. In our case, the spectrogram is computed for the frequency range below 5000 Hz and the energy is represented in the logarithmic scale in decibel.  
We use different parameters for the specification of the temporal scope and the frequency width of the contextual background. In [9], the frequency width is 43 Hz and the temporal depth 23 ms. After testing on a range of musical styles, we chose a frequency width of 17 Hz and a temporal depth of 40 ms (Figure 3). (The parameters indicated in our previous paper [4] were inaccurate.) By enlarging the temporal horizon of the contextual background, this enables filtration of _tremolo_ effects and to focus on more prominent increase of energy.  
In the proposed model, the second condition for onset detection specified in [9] --namely, that the energy on the frame succeeding the current one should be higher than the contextual background--is withdrawn, for the sake of simplicity. That constraint seems aimed at filtering out bursts of energy that are just one frame long, but bursts that are two frames long would not be filtered out. We might hypothesize that short bursts of energy might still be perceived as events.  
Finally, the degree of onset is different from the one proposed in [9]. Instead of conditioning the degree of onset to the increase of energy with respect to the contextual background, we propose to condition it to the absolute level of energy:  
\[do(t,f)=p(t,f)\]This is because a burst of energy of a given level \(p(t,f)\) might be perceived as strong, and could contribute therefore to the detection of a note onset, even if there was a relatively loud sound in the frequency and temporal vicinity. This modification globally improved the results in our tests.  
In our proposed method, the accentuation curve shows more note onsets than in [9]. This leads to a more detailed analysis of periodicity and a richer metrical analysis. This allows sometimes the discovery of the underlying metrical structure that was hidden under a complex surface and was not detected using [9].
# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## Appendix C Commands and Codes Used in the Study  
Here we list the commands used for the analysis along with the program version numbers and their availability. **time** version: GNU time 1.7  
Code Available at: [[https://ftp.gnu.org/gnu/time/](https://ftp.gnu.org/gnu/time/)]([https://ftp.gnu.org/gnu/time/](https://ftp.gnu.org/gnu/time/))  
/usr/bin/time -v <command to measure> 2> <stderr file> 1> <stdout file>  
**FastTree** version: FastTree Version 2.1.10 Double precision (No SSE3), OpenMP  
Code Available at: [[http://www.microbesonline.org/FastTree](http://www.microbesonline.org/FastTree)]([http://www.microbesonline.org/FastTree](http://www.microbesonline.org/FastTree))  
FastTreeMP -nt -gtr -gamma <input alignment>  
**IQ-TREE** version: IQ-TREE multicore version 1.6.12  
Code Available at: [[http://www.iqtree.org/](http://www.iqtree.org/)]([http://www.iqtree.org/](http://www.iqtree.org/))  
iqtree -s <input alignment> -m GTR+F+G4 -pre <output prefix>  
**IQ-TREE** version: IQ-TREE multicore version 2.0.6  
Code Available at: [[http://www.iqtree.org/](http://www.iqtree.org/)]([http://www.iqtree.org/](http://www.iqtree.org/))  
iqtree2 -s <input alignment> -nt AUTO -ntmax <num threads> -seed 10101 \ -m GTR+G -pre <output prefix>  
**RAxML-NG** version: RAxML-NG v. 1.0.1  
Code Available at: [[https://github.com/amkozlov/RAxML-NG](https://github.com/amkozlov/RAxML-NG)]([https://github.com/amkozlov/RAxML-NG](https://github.com/amkozlov/RAxML-NG))  
raxmlng --msa <input alignment> --threads <num threads> --seed 10101 \ --model GTR+G --prefix <output prefix>  
**Constrained-INC** commit id: 0b387d31a3874c60c284f6b619562a271 bead0b2  
Code Available at: [[https://github.com/steven-le-thien/INC](https://github.com/steven-le-thien/INC)]([https://github.com/steven-le-thien/INC](https://github.com/steven-le-thien/INC))  
constraint_inc -i <input matrix> -o <output prefix> -q subtree \ -g <input tree> -t <space separated list of constraint trees>  
**GTM** commit id: 98d76 bd2a553 af0b8 fa0872 ba0 fedffa60c7b73  
Code Available at: [[https://github.com/vlasmirnov/GTM](https://github.com/vlasmirnov/GTM)]([https://github.com/vlasmirnov/GTM](https://github.com/vlasmirnov/GTM))  
python gtm.py -s <input tree> -t \ <space separated list of constraint trees> -o <output prefix>  
**TreeMerge(PAUP*)** commit id: deelab9 da49d36e1a83 ebc4 dc29b800f3574 fe68  
The version of PAUP* is 4.0.
## 4 Results
### Machine Learning-Based IDS Implementation on IoT Device
3.2 ML-Based IDS Implementation on Smart Thermostat Using Connection and Statistical Activity Features  
Considering the limited access to the lwIP library and the nature of our application, only connection activity and statistical activity network features are considered. To avoid overfitting, we excluded timestamp, source and destination IP addresses. The input features that can be extracted on the Esp32-based smart thermostat are source port, destination port, proto, service, duration, source bytes, destination bytes, connection state, missed bytes, source packets, source IP header bytes, destination packets, and destination IP header bytes. Based on these 13 features, the XGBoost, RF, DT, and ANN models are trained for binary classification and the results are shown in Table 8. The results show that XGBoost with the depth of 8 and 100 estimators outperformed DT, RF, and ANN in terms of accuracy, FPR, and FNR, while DT with a depth of 5 is the fastest in terms of inference time. The best results of XGBoost, DT, RF, and ANN are summarized in Figure 3. XGBoost (100 estimators, depth = 8), DT depth = 5), RF (150 estimators, depth = 8), and ANN are compared in terms of accuracy, program memory, inference time, FPR, and FNR. The F1-score, recall, precision, and accuracy are summarized in the subfigure titled Accuracy(%). Although the inference time of DT is 3.2 \(\upmu\)s, the FPR and FNR of DT are 0.60% and 0.72%, respectively, compared to XGBoost, which has an FPR and FNR of 0.30% and 0.06%, respectively. Since the required program memory is available in the microcontroller of the smart thermostat, XGBoost is the preferred choice for implementing an IDS with 13 features due to its superior performance. Figure 3: Comparison of XGBoost-, RF-, DT-, and ANN-based IDS implementation on smart thermostat using Ton_IoT dataset.
## 1 Introduction
[13] proposed the so called \"uneven offset\" method, but the method is very specific and focuses on the elimination of materials remaining left on the corners. [PERSON] and [PERSON]'s [14] and [PERSON] and [PERSON]'s [15] works are dedicated to the geometrical definition of the different offsets; however, they do not provide efficient and robust algorithms. Variable offset can be constructed based on sampling, using rendering-based methods [16]; however, computational efficiency and accuracy in extracting the actual offset from the rendered images are still open issues. Recently, [PERSON] et al. [17; 18] computed a variable offset exploiting the new variable-radius Voronoi diagrams. Although interesting, their works require several steps, increased effort in the implementation and are specific, since they do not apply to external offset. This limitation is common to most of the proposed approaches for non-uniform offset that are designed on specific cases and they lack generality. This work presents a general algorithm for non-uniform offset of polyline curves, inspired by the uniform offset algorithm proposed by [PERSON] [8]. The method, based on 2D polygons and Boolean union operation, is robust for complexity of both the polyline and variable offset magnitude along that line. Indeed, the main contribution of the proposed approach is the generality: the algorithm can be applied to both external and internal offset and it is effective for any type of contour and variable offset desired, not returning ambiguities or intersections in the resulting contour. In addition, another novel aspect characterizing the proposed approach is its implementation: the method is not based on specific complex algorithms (e.g., rendering-based, straight skeleton or Voronoi diagrams), which generally not easy to implement, but only on simple basic steps, exploiting traditional operations (i.e., projections, Boolean operations). Such operations are already adopted in CAM software, but in the proposed approach they are adapted to be used to compute non-uniform offset and achieve a desired variable machining allowance on the part. In Section 2, the scope of the algorithm and the working principle is proposed, then in Section 3 results on several examples and applications to thin-wall milling cases are shown. Finally, in Section 4 some conclusions are drawn.
# Experimental Study on the Straw Flexible Space Assembly Flood Control System
## 3 Proposal and Validation of the Straw Flexible Space Theory  
### Straw Flexible Space Theory  
The flood control system is not rigid. It has a flexible connection between the oncoming and backing panels. The oncoming and backing boards are connected by connecting rods and are designed with space for movement. When the water board is subjected to wave load impact, the straw-filled water baffles will be compressed. The elastic-plastic deformation of the straw plays a buffering role; it does not directly affect the water board and the column. After the impact force disappears, the deformed straw rapidly returns to its original state. The water board provides support to the initial position, so that the impact of the wave and the straw after the water absorption and expansion of the water board generates a space. This is called the straw flexible space, which is formed by the combined effect of the wave impact and the supporting force of the straw on the headboard.  
Therefore, the straw flexible space theory is proposed, according to which, in the process of the flood control system, the static water in the straw flexible space becomes dynamic water owing to the gap between the water board and the two sides of the column. Thus, part of the wave impact kinetic energy is converted into water kinetic energy, and the straw deformation buffer in the flexible space drains off part of the impact kinetic energy. Thus, the straw flexible space enables the backing board and column to withstand the impact force. It reduces the impact force on the backsplash and columns, thus improving the stability and impact resistance of the flood control system.