esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 3 Review of Adaptive Data Rate Algorithms  
Having an exceptional ADR scheme gives vendors a competitive edge, as such they maintain their applications confidential. Thankfully, there is an open source network manager that made its ADR algorithm publicly available--The Things Network [27]. This algorithm is based on Semtech's recommended algorithm for rate adaptation. The LoRaWAN ADR is a scheme used to optimise the network's data rates, ToA and energy consumption. To maximize battery life of the end nodes and global system capacity, the LoRaWAN NS controls the data rate and TP for all the end nodes independently through an ADR scheme. Several data rate adaptation schemes have been propositioned in literature that attempt to enhance communication performance. In literature ADR scheme has been modified and implemented to meet different objectives in the LoRaWAN targeting network performance metrics. We review the body of work that employs ADR to improve metrics such as scalability, throughput, channel access, received signal strength (RSS) and energy efficiency.
# Efficacy of Fire Protection Techniques on Impact Resistance of Self-Compacting Concrete
## 3 Results and Discussion
### Residual Compressive Strength and Mass Loss of SCC after Exposure
#### 3.1.2 Mass Loss  
The initial reduction in mass observed in SCC can be attributed to the evaporation of free and physically bound water, which converts into vapor under high-temperature conditions [35]. This phenomenon typically occurs during the rapid heating phase, which spans up to 60 min of temperature exposure. A comparison of mass loss between specimens of FA and GGBFS without CPP (FA/EPA, FA, GGBFS/EPA & GGBFS) and CPP-coated FA and GGBFS (FA/EPA/CPP, GGBFS/EPA/CPP & GGBFS/CPP) reveals a range of 5.70% to 4.60% and 3.70% to 4.10%, respectively. The minimal variation in mass loss observed among the SCC specimens can be attributed to the influence of internal EPA content. As the level and duration of temperature increase beyond 60 to 120 min, there is a proportional escalation in mass loss, likely due to the degradation of the gel structure (CSH) and portlandite (CH), leading to a more significant loss in strength [36].  
Figure 6: Interaction graph showing variation between residual compressive strength (represented by the line) and mass loss (represented by histograms) of SCC specimens for different duration of heating.
## 3 Results and Discussion
### Reusability of the Sorbent  
To reuse the zeolite H-Beta as DSPE material in the cleanup procedure, the regeneration of the used zeolite H-Beta was developed. In our study, 0.5% (\(v/v\)) NH\({}_{4}\)OH in ACN (5 mL) was employed to eliminate the residual pollution on the zeolite H-Beta from the previous sample preparation during regeneration. The procedure for cleaning included vortexing at 2000 r/min for 5 min and ultrasonication for 5 min, followed by centrifugation at 3200\(\times\)\(g\) for 5 min, discarding the supernatant, repeating the procedure once, and air-drying overnight in a fume hood after the last centrifugation. To examine the regeneration effect, the last supernatant was used for UHPLC-MS/MS analysis. When the recoveries for all the targets were below 1%, zeolite H-Beta was considered successfully regenerated. As shown in Figure 5, the recoveries remained nearly stable between 75.6% and 90.1% for 10 cycles of the reuse procedure, indicating the continuous stability and excellent reusability of zeolite H-Beta as a sorbent. Therefore, the established DSPE method is promising as an environment friendly and cost-effective method with sufficient accuracy.
## 3 Results and Discussion
### Dry-Hopped Cider Analysis
Terpenes are in black 1–8 (1: \(\beta\)-myrcene; 2: D-limonene; 3: ocimee; 4: linalool; 5: geraniol; 6: cyclotene; 7: carophyllene; 8: humulene), esters are denoted in red 9–16 (9: 3-methylbutyl-2-methylpropanoate, 10: methyl hepatomaate, 11: methyl nonanoate, 12: 2-ethylhexyl pentanoate, 13: 2-methylbutyl-2-methylbutanoate, 14: methyl 4-decanoate, 15: methyl geraoate, 16: 2-methylpropanoate), ketones are purple 17–20 (17: 2-dodecanone, 18: 2-dridecanone, 19: 2-decanone, 20: 2-undecanone) and propanoic acid is light blue (21). perception threshold in Mosaic hopped samples only. Lastly, propanoic acid may add a spicy, soy fragrance in ciders hopped with Citra and Galaxy. Each hop variety generated a unique set of aroma-contributing VOCs when added to identical cider samples: Citra-hopped cider was citrus and spicy, Galaxy-hopped cider was fruity, and Mosaic-hopped cider had both citrus and floral VOCs based on these perception threshold analyses.
## 3 Results
### Normal Population  
The NHANES database was utilized to extract commonly used continuous variables, including age (years), weight (kg), height (cm), body mass index (BMI), upper leg length, upper arm length, arm circumference, waist circumference, hip circumference, systolic blood pressure, diastolic blood pressure, pulse, high-density lipoprotein (HDL) (mg/dL), total cholesterol (mg/dL), white blood cell count (1000 cells/\(\mu\)L), hemoglobin (g/dL), and high-sensitivity C-reactive protein (mg/L). After excluding individuals with missing data, the final analytic cohort consisted of 5866 participants. This cleaned dataset was used as input for the synthetic cohort generator. A comparison of summary statistics between the real cohort and all simulated scenarios is presented in Appendix E, Table A1.  
Figure 2 presents scatter plots comparing simulated versus real correlation values across five scenarios (A-E). Scenario A (Figure 2A) shows the strongest agreement between simulated and real correlations, with a coefficient of determination R2 = 99.6%, sum of squared errors (SSE) = 0.039, and mean absolute error (MAE) = 0.011. Scenario B (Figure 2B) displays moderate concordance, with R2 = 47.5%, SSE = 5.600, and MAE = 0.158. Scenario E (Figure 2E) exhibits poor agreement, showing an R2 = 0.0%, SSE = 14.001, and MAE = 0.219, indicating the greatest deviation from the real-world correlation structure.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Population** & **Scenario** & **MAE** & **MSE** & **RMSE** & **R2** & **SSE** & **Ranking** \\ \hline \multirow{8}{*}{**Normal**} & Scenario A & 0.011 & 0.000 & 0.017 & 0.996 & 0.039 & 1 \\ \cline{2-7}  & Scenario B & 0.158 & 0.041 & 0.203 & 0.475 & 5.600 & 2 \\ \cline{2-7}  & Scenario C & 0.182 & 0.076 & 0.276 & 0.026 & 10.395 & 4 \\ \cline{2-7}  & Scenario D & 0.161 & 0.046 & 0.214 & 0.417 & 6.216 & 3 \\ \cline{2-7}  & Scenario E & 0.219 & 0.103 & 0.321 & 0.000 & 14.001 & 5 \\ \hline \multirow{8}{*}{**Cardiac**} & Scenario A & 0.035 & 0.003 & 0.058 & 0.868 & 0.709 & 1 \\ \cline{2-7}  & Scenario B & 0.106 & 0.020 & 0.141 & 0.219 & 4.183 & 2 \\ \cline{2-7}  & Scenario C & 0.138 & 0.037 & 0.191 & 0.000 & 7.672 & 5 \\ \cline{2-7}  & Scenario D & 0.136 & 0.036 & 0.190 & 0.000 & 7.612 & 4 \\ \cline{2-7}  & Scenario E & 0.096 & 0.024 & 0.156 & 0.040 & 5.141 & 3 \\ \hline \multirow{8}{*}{**Low Dimension**} & Scenario A & 0.013 & 0.000 & 0.015 & 0.994 & 0.001 & 1 \\ \cline{2-7}  & Scenario B & 0.067 & 0.006 & 0.080 & 0.807 & 0.038 & 2 \\ \cline{1-1} \cline{2-7}  & Scenario C & 0.154 & 0.038 & 0.194 & 0.000 & 0.226 & 4 \\ \cline{1-1} \cline{2-7}  & Scenario D & 0.074 & 0.007 & 0.083 & 0.787 & 0.042 & 3 \\ \cline{1-1} \cline{2-7}  & Scenario E & 0.145 & 0.041 & 0.202 & 0.000 & 0.244 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of correlation fit metrics across synthetic data-generation scenarios and real populations.
## 3 Target Degradation Strategy (TDOS)
### Payload Reduction Method  
When the minimum orbit-injection requirement cannot be achieved by taking the energy reduction approach or when the orbit energy reduction will seriously affect the payload system function, the launch vehicle will be forced to abandon part of the payload under power failure.  
Usually, multiple payloads carried on the same mission are different, and for the simplification of the problem, the mass reduction at each payload drop is assumed to be \(m_{p}\), and the payload mass of the launch vehicle after \(n\) load drops can be described as:  
\[M_{\text{load}}=M_{\text{int}}-nm_{p},\ n=1,2,\ \ldots\,N\left(Nm_{p}\leq M_{ \text{int}}\right) \tag{7}\]  
where \(M_{\text{load}}\) denotes the total mass of the payload after degradation; \(M_{\text{int}}\) denotes the initial mass of the payload; \(N\) denotes the number of times the maximum payload is degraded.  
Considering the altitude degradation gradient in the energy reduction method and the mass degradation gradient in the payload reduction method, the capacity generated by the mission decision making will be no less than the actual optimal capacity.
## 5 Experimental Results  
To assess the performance of the 4 \(\times\) 4 fast optical switch PIC, the experiment setup shown in Figure 5 is employed. Four WDM optical channels with packetized NRZ-OOK (PRBS 211-1) payloads at data rates of 10 Gb/s, 20 Gb/s, and 40 Gb/s are generated at 1525.0 nm, 1528.9 nm, 1532.9 nm, and 1532.5 nm. The 1532.5 nm and 1532.5 nm are generated at 1525.0 nm, 1532  
1536.8 nm. The packet has the duration of 540 ns and 60 ns guard time. The four WDM channels are de-correlated, amplified, and injected into the photonic-integrated WDM optical switch PIC via a polarization controller. Module 1, as one of the four identical modules, has been selected for the switching performance assessment. The total power launched into the input Port 1 is 2 dBm (corresponding to \(-4\) dBm per channel). The input SOA, acting as a booster amplifier, is continuously biased with 100 mA of current. The shorter SOAs in the WSS acting as optical gates on the different channels are controlled by an FPGA-based switch controller.
## 4 Experimental Results  
### Experimental Procedure  
In order to verify the recognition effect of internal wire breakage in steel wire ropes using residual networks, this paper conducts comparative experiments and compares the residual network with three other neural networks. The basic framework is shown in Figure 9. Firstly, this article uses continuous wavelet transform technology to convert one-dimensional MFL signals into two-dimensional time-frequency maps, preserving the time-domain and frequency-domain information of the signals, and constructs a Residual Network 18 (ResNet18) model. The preprocessed time-frequency characteristics are used as inputs to ResNet18. Secondly, as a comparison, a convolutional neural network is constructed, and the two-dimensional time-frequency map transformed by continuous wavelet transform is used as the input of the convolutional neural network. Feature vectors formed by the peak, pulse width, and inter-peak features are manually extracted from the signal, and the backpropagation (BP) neural network and the support vector machine (SVM) are constructed, with the manually extracted signal features being used as inputs for quantitative recognition in the latter two networks. Finally, the article will compare the accuracy of internal and external disconnection for the four models and select the best recognition model.  
Figure 8: Time–frequency diagram of internal and external wire breakage of 20 mm diameter steel wire rope.
## 4 Stress Field Simulation of Variable Density Micro-Textured Ball-End Milling Cutter  
### Establishing the Tool Model  
Due to the limitation of conditions, it is impossible to measure the instantaneous stress field of the cutter in real time by an experimental method. Therefore, in this paper, the finite element simulation method was used to study the distribution of the stress field of the cutter during the titanium alloy cutting process. The instantaneous change of the stress field at any point on the cutting tool was obtained by finite element simulation, which provided basic data for further optimizing the parameter design of the micro-textures. First, the cutter was modeled by SolidWorks, a micro-textured cutter with a texture density combination of 0.09-0.09-0.09 was taken as an example, and the model of the cutter is shown in Figure 5. The tool diameter is 20 mm and the micro-texture is placed in the cutter-chip compact contact area. The shape of the micro-texture is micro-pits with a diameter of 50 \(\upmu\)m and a depth of 35 \(\upmu\)m, and the distance from the cutting edge is 120 \(\upmu\)m. The micro-textures are uniformly distributed, and the center distance between adjacent textures is 150 \(\upmu\)m. The material parameters of the cutter are shown in Table 6.  
ANSYS Workbench 16.0 software (ANSYS company, Canonsburg, PA, USA) was used to simulate and analyze the instantaneous stress field of the cutter. A force distribution simulation of the micro-textured ball-end milling cutter was carried out, following the steps of inputting model, defining material attributes, partitioning meshes, defining boundary conditions, solving the problem, and analyzing images. Meshing is very important, and the quality of meshing directly determines the accuracy of simulation results. Therefore, it is necessary to refine the grids of the cutter-chip contact area. Mesh optimization was performed using the ICEM CFD module in the ANSYS Workbench. Tetrahedral mesh is suitable for fast and efficient meshing of complex models, which is realized through automatic mesh generation. Therefore, tetrahedral mesh was used in the mesh model. There are 931,230 nodes in total and the minimum edge length is \(2.5~{}\times~{}10^{-8}\) m. The meshing of the cutting tool is shown in Figure 6. The fewer the optimized mesh nodes, the accurate and faster the calculation. It turns out that the force distribution for the simulated
## 4 Experimental Results
### Qualitative Assessment  
Figures 3 and 4 present some examples of the CD results from single-task learning and MTL, corresponding to Tables 3 and 4, respectively. These figures can be used to compare the false negatives and false positives of the different approaches.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F1**} \\ \hline \multirow{2}{*}{ChangeStar} & BE & 90.00 & 97.14 & 92.44 & 94.73 \\  & CD & 78.33 & 89.03 & 86.70 & 87.85 \\ \hline \multirow{2}{*}{Ours} & BE & 91.38 & 97.21 & 93.85 & 95.50 \\  & CD & 81.86 & 91.27 & 88.81 & 90.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative MTL results from ChangeStar and ours.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **IoU** & **Precision** & **Recall** & **F1** \\ \hline UNet & 89.89 & 96.42 & 93.00 & 94.68 \\ FarSeg & 90.52 & 97.02 & 93.12 & 95.03 \\ Ours & **91.65** & **97.19** & **94.15** & **95.65** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Building extraction (BE) results of three different networks. The bold values represent the best results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Pretrain** & **IoU** & **Precision** & **Recall** & **F1** \\ \hline Siam-UNet & ImageNet & 59.77 & 78.80 & 71.23 & 74.82 \\ Siam-UNet++ & ImageNet & 62.07 & 71.67 & **82.25** & 76.59 \\ ChangeStar & ImageNet & 62.20 & 73.59 & 80.08 & 76.70 \\ Ours & ImageNet & 64.15 & **84.89** & 72.10 & 78.16 \\ ChangeStar & WHU-CD & **70.46** & 78.84 & **86.88** & **82.67** \\ Ours & WHU-CD & **72.44** & **86.74** & 81.47 & **84.02** \\ \hline \hline \end{tabular}
\end{table}
Table 3: CD results of different models using pre-training choices. The bold values represent the best two results.  
Figure 4: Comparative BE and CD results of two models on the WHU-CD testing set. (**a**) \(t_{1}\) image patch. (**b**) \(t_{2}\) image patch. (**c**) CD GT patch. (**d**) CD results of ChangeStar. (**e**) Our CD results. (**f**) Our BE results from \(t_{1}\) Image. (**g**) Our BE results from \(t_{2}\) Image. Red indicates false positives, and green indicates false negatives.  
Figure 3: Comparative CD results of different models and pre-training choices on the Wuhan University building CD dataset (WHU-CD) testing set. (**a**) \(t_{1}\) image patch. (**b**) \(t_{2}\) image patch. (**c**) ground truth (GT) patch. (**d**) Results of Siamese-UNet pre-trained on the ImageNet dataset. (**e**) Results of Siamese-UNet++ pre-trained on the ImageNet dataset. (**f**) Results of FarSeg pre-trained on the ImageNet dataset. (**g**) Ours pre-trained on the ImageNet dataset. (**h**) Results of FarSeg pre-trained on the WHU-CD dataset. (**i**) Ours pre-trained on the WHU-CD dataset. Red indicates false positives, and green indicates false negatives.
# Non-Parametric Model-Based Estimation of the Effective Reproduction Number for SARS-CoV-2 +
## 2 Materials and Methods
### Simulation Setting  
To show the performance of the presented AKS method for the estimation of time-dependent parameters in nonlinear ODE models, we first applied the algorithm to simulated data sets from various scenarios. In each simulation scenario, a different combination of model structure and values of the time-constant parameters or time courses of the time-dependent parameters were chosen. The ODE system was then solved for the given initial conditions by using the R package deSolve[67]. In order to generate realistic incidence data for the respective quantities, the fluxes \(v_{I}\), \(v_{R}\) and \(v_{D}\) of the ODE system were discretized, and relative Gaussian noise with a zero mean and given standard deviation was added. In the initial examples, different parameter time courses and noise levels were chosen in an SIRD model for data generation, followed by scenarios that consider data sets generated by non-SIRD models. Ultimately, the method was applied to officially reported incidence data for SARS-CoV-2 in Germany.  
Note that all the noisy incidence data sets were analyzed by the presented SIRD-AKS method, i.e., based on the reparameterized SIRD model from Equation (21), regardless of the chosen model structure for data generation. Further, no information about the true values, time dependency or function class of the model parameters were provided to the SIRD-AKS algorithm, but only the respective incidence data sets. Then, the resulting estimates of the SIRD-AKS algorithm for model states, fluxes and parameter time courses were compared to the respective true quantities derived from the solutions of the data-generating ODE models. In particular, the accordance of the estimated time-dependent effective reproduction number \(\mathcal{R}_{t}\) and its true time course was analyzed.
## 3 TBM (Tunnel Boring Machine) Data Processing
### Data Preprocessing
#### 3.2.1 Removal of Invalid Data  
Due to the operational characteristics of TBMs, the actual excavation work time constituted only a small portion of the total construction time. As a result, the stored data contained large volumes of non-operational data, such as those related to surrounding rock support, equipment maintenance, and downtime. Since these periods were outside the scope of this study, such data were removed. Moreover, the complete excavation cycle was divided into four phases based on TBM excavation data characteristics: idle pushing, ramp-up, steady-state, and ramp-down. Data from the idle pushing and ramp-down phases were classified as invalid rock-breaking data and subsequently removed. As illustrated in Figure 2, gaps between dataset intervals correspond to non-operational segments, which were excluded during preprocessing to focus solely on effective excavation data.  
When any operational parameter, such as advance speed, cutterhead rotation speed, or total load, is zero or near zero, it is typically considered as \"idle pushing\" data or non-operational data. Such data points are removed through direct numerical filtering in this study. The following table shows the statistical values of the processed data.  
Table 2 shows the statistical summary of the dataset, partitioned by disc rotational speed. It includes the frequency, maximum, mean, and standard deviation of both rotational speed and torque. The disc speed is mostly concentrated in the 6-8 range, representing the stable operation zone. In contrast, the torque shows greater fluctuations, better reflecting the data variability. Therefore, torque is selected as the key metric for evaluating processing effectiveness.
# Enhancement of Subcooled Flow Boiling Heat Transfer with High Porosity Sintered Fiber Metal
## 4 Conclusions  
The present research reports on the heat transfer enhancement under the subcooling flow boiling using a high-porosity sintered fiber attached to the surface. Two different porous surface thicknesses (1 and 0.5 mm) and one bare surface were tested. The same experimental conditions for the three cases and the same flow channel were used for all the cases. After our analysis of the experimental results, our conclusions are presented.  
* The effect of the porous surface on the heat flux was almost marginal on the single-phase region; however, after the onset nucleate boiling (ONB), the influence of the porous surface is important. At the same wall superheat (40 K), the heat flux on the bare surface was around 1600 kW\(\cdot\)m\({}^{-2}\); on the other hand, using the porous surface, the heat flux was around 2500 kW\(\cdot\)m\({}^{-2}\), which represents an enhancement of around 56%. At the same wall superheat (2000 kW\(\cdot\)m\({}^{-2}\)), the wall superheat was around 48 K and around 36 K for the bare surface and the porous surface, respectively; in other words, a reduction of around 12 K on the wall superheat.
* At a higher mass flux and higher porous thickness, the heat flux was higher, and the wall superheat degree was lower. Thus, the ONB shifted to a higher wall heat flux. At the higher porous thickness, the bubble formation rate and departure rate increased.
* Using high sintered fiber as a passive method to promote bubble formation and departure increases the heat flux in comparison with the bare surface. The wall temperature degree variation was more stable using the porous surface. In addition, the wall superheat degree was reduced, avoiding burnout on the surface.  
Data curation, Y.O., E.S.G. and K.E.; Funding acquisition, K.E.; Investigation, Y.O., E.S.G. and K.E.; Project administration, K.E.; Visualization, Y.O. and E.S.G.; Writing--original draft, Y.O.; Writing--review & editing, K.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData openly available in a public repository that issues datasets with DOIs.  
We would also like to recognize the following researchers: [PERSON], [PERSON], [PERSON], and [PERSON], who are affiliated with the Mitsubishi Materials Corporation.  
The authors declare no conflict of interest.
## 4 Experimental Results and Discussion
### Impact of Sudden Variations on the Performance of the WT System
#### 4.2.1 Power Variation  
The change of power represents a frequent situation in LPPT mode. Figure 17a,b depict two cases of a sudden variation of power, i.e., the sudden increase and decrease in power. The selected \(P_{\text{lim}}1\) and \(P_{\text{lim}}2\) correspond to 50 W and 82 W, respectively. For both cases, the wind speed is maintained constant at 6.5 m/s. It can be clearly observed in the case of a sudden increase and decrease in power that all power control strategies operate successfully with a difference in terms of oscillation as described previously. The required time to make a transition to the new operating point is almost the same for all power control strategies. However, the decrease in power requires more time than the increase in power. It means that the increase in power from \(P_{\text{lim}}1\) to \(P_{\text{lim}}2\) needs 13 s, while it takes place within 20 s in the opposite case. It is important to also note that the FL technique stabilizes quickly compared to the fixed step size and [PERSON]'s methods in the case of an increase and decrease in power.