esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluations
### Performance Evaluation
#### 5.2.3 Performance versus Network Scale  
In this experiment, we compare the schedule results and the execution time of the four techniques for different network scales. In total, 100 test cases are handled with a number of bridges and links (4, 32), (4, 36), (13, 60), and (15, 100). The results are shown in Figure 13. It can be noticed that SPRF-EtoED and LBF-EtoED not only showed a massive number of timeouts but also showed a rapid increase with larger networks, whereas better network  
Figure 11: Percentage of solved, timeout, and infeasible schedules and execution time of the SPRF-EtoED, LBF-EtoED, MPFRS-FC, and APCRS-FC for 100 test cases with 20 flows, tested in MM network. (**a**) Technique 1: SPRF-EtoED. (**b**) Technique 2: LBF-EtoED. (**c**) Technique 3: MPFRS-FC. (**d**) Technique 4: APCRS-FC.  
Figure 12: The sum of end-to-end delays of the SPRF-EtoED, LBF-EtoED, MPFRS-FC, and APCRS-FC for 100 test cases with 20 flows, tested in MM network.  
connectivities can slow down the execution time to some extent. Thus, we adjusted the timeout limit to 180 s in the \(G(27,100)\) scenario, which would otherwise have more timeout results in two baseline algorithms. In contrast, the MPFRS-FC and APCRS-FC can solve all problems within 60 s. In general, the MPFRS-FC appears to have fewer timeouts, and it is more applicable in complex networks.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 4 Protection Techniques
### Wireless Key Generation
For instance [66] presents a multi-bit key-agreement scheme for LoRaWAN, which is optimized for low correlation channel conditions and is thus suitable for mobile and static scenarios. According to extensive experimental results evaluated for static and mobile wireless scenarios, even for correlation coefficient values between 20-60% tolerable KDR values between 10-20% can be achieved. Further, in [67], the authors investigate simple tolerance- and difference-based quantization algorithms and their impact on LoRaWAN key generation. The main result indicated that key refreshment is realizable for LoRaWAN communication in 30 min, given the error-correction capacity of the reconciliation step. Finally, in [68], the authors proposed RSSI precorrection schemes based, firstly, on a discrete cosine transform and, secondly, on principal component analysis. With a blockwise preprocessing, the authors were able to suppress KDR down to a few percent, which indicates the efficiency of such methods in LoRaWAN scenarios where RSSI sets contain noise or nonreciprocity.
## 6 Conclusions
In addition, the comparison results also indicate that there are obvious seasonally dependent differences in the GNSS-derived PWV when compared to the PWV computed from ERA5. The PPP-B2b service represents a significant advancement in GNSS-based atmospheric sensing by enabling real-time orbit and clock corrections without the need for internet access. This capability is particularly beneficial for remote and low-income regions where reliable internet connectivity may be limited, thereby democratizing access to high-accuracy weather and climate data. However, despite these advantages, the current performance of the PPP-B2b service exhibits considerable spatial variability, with diminished accuracy and reliability in regions distant from China. This limitation highlights the need for further enhancements to bring PPP-B2b to the level of robustness and precision found in internet-based real-time services like CNES, which currently outperform PPP-B2b in ZTD and PWV accuracy. Nonetheless, the innovative approach of PPP-B2b provides a strong foundation for expanding real-time GNSS applications, particularly in underserved regions. Conceptualization, X.W. ; methodology, X.W. ; software, K.Z. and J.Z. ; validation, Q.H., Y.C. and C.Q. ; formal analysis, X.W. ; investigation, X.W. and H.L. ; resources, X.W. ; data curation, X.W. ; writing--original draft preparation, X.W. ; writing--review and editing, X.W. ; visualization, X.W. ; supervision, X.W. ; project administration, X.W. ; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript. This research was supported by National Natural Science Foundation of China (42474015) and the funding program from the Aerospace Information Research Institute. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. The authors would like to thank the GFZ for providing high-accuracy orbit and clock products, DLR and CAS for providing DCB products, ECMWF for providing ERA5 data, and IGS for providing GNSS observations. The authors declare no conflicts of interest.
## 6 Summary and Concluding Remarks  
A new formulation of the model for computing E-fields both ahead and behind of the downward moving leader tip was developed. The electric field waveform on the axis of vertically descending leader is a sharp pulse with its peak corresponding to the leader tip passing through the observation point.  
Dependence of the E-field peak on model input parameters was examined. The E-field peak increases with increasing the prospective return-stroke peak current (a proxy for leader tip potential) and with decreasing the leader speed. This is an interesting finding, as generally, higher peak-current strokes tend to have faster leaders; thus, high peak-current strokes with slow leaders may be uncommon, but they are expected to be prolific X-ray/gamma-ray producers.  
For a free electron to run away, the Coulomb force acting on that electron must be greater than the friction force (rate of electron energy loss per unit distance), the latter being a function of electron energy. In this study, we computed subsequent-leader electric fields to see if their associated Coulomb forces can overcome the warm-air friction force acting on ambient electrons with energies less than 30 eV or so. We found that for realistic model input parameters, the E-field peak can exceed the value (-4 MV/m) required for ambient electrons to run away in warm channels that are traversed by subsequent lightning leaders.  
[PERSON] et al. (2012) [2] discovered that subsequent strokes can be more prolific X-ray/gamma-ray producers than first strokes in the same flash. They hypothesized that their unexpected observation could be explained by the fact that subsequent leaders traversed still warm (3000 K) remnants of previously created but decayed channels, for which the friction curve is considerably lower than for cold air. In this paper, we confirmed [PERSON] et al.'s hypothesis by showing that subsequent-leader electric fields can be high enough to overcome the warm-air friction curve, but not enough to overcome the cold-air friction curve, the latter one being applicable to first-stroke leaders.  
Conceptualization, I.K. and V.R.; methodology, V.R. and I.K.; mathematical model implementation, L.C., D.M., A.M. and V.C.; validation, Z.D.; writing--original draft preparation, I.K.; writing--review and editing, V.R., L.C. and I.K.; visualization, L.C. and A.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by NSF grant AGS-2114471.  
Not applicable.  
Figure 10: Electric field waveform corresponding to the “worst case” combination of the model input parameters: \(z_{p}=150\) m, \(I_{p}=\)55 kA, \(H=7.5\) km, and \(v=0.5\times 10^{7}\) m/s. Full time scale is 5 \(\mu\)s.
# Optimization Design of the Spaceborne Connecting Structure for a Lightweight Space Camera
## 4 Engineering Analysis and Performance Comparison
### Performance Comparison  
In order to further evaluate the effect of the optimization method, a traditional bipod support structure (as shown in Figure 11) is selected to contrast with the structures designed in this paper. The camera is mounted using the traditional structure in the same assembling method. Positions A, B, and C adopt the same configuration but with different flexible sizes, which are optimized by using size parametric optimization. \(t_{1}\) and \(t_{2}\) are set as the design variables. The objective and constraint functions are the same as those functions in Equation (5). After the convergence of optimization iteration, the optimized \(t_{1}\) and \(t_{2}\) are 12.5 mm and 9.8 mm, respectively. The optimization results of all responses are summarized in Table 7.  
The optimization results of the traditional structure show that after installing the traditional support structure, the first natural frequency meets the performance requirements, but the relative displacements between mirrors exceed the design requirements. In contrast, \(R_{1}\) and \(R_{5}\) for the optimized spaceborne connecting structure optimized in this paper are 35% and 42% smaller compared with the traditional structure under the action of 1 g of gravity. They are 43% and 29% smaller under the action of a temperature rise of 4 \({}^{\circ}\)C. Furthermore, the optimized connecting structures in this paper have a higher lightweight ratio. These demonstrate the effectiveness of the design method presented in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Direction** & **X** & **Y** & **Z** \\ \hline The results of random vibration analysis/Hz & 22.02 & 20.73 & 20.18 \\ Amplification & 4.71 & 4.43 & 4.32 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of random response analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Random Vibration** & **Input Conditions (X, Y, Z)** & & \\ \hline Frequency range/Hz & 20 & 40 & 500 & 700 & 1200 & 2000 \\ Power spectral density (g\({}^{2}\)/Hz) & 0.001 & 0.01 & 0.01 & 0.02 & 0.02 & 0.004 \\ Root mean square (g) & & & 4.67 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Random vibration test conditions.
## 5 Core Structure of Ses Transmission
### Line Coder Equation of Physical Waveform  
The line encoder follows the specific pattern of value for \(\mathbf{a_{k}}\). It is the function of digital input bits or an ADC output. Equation (7) is used for the encoder output waveform.  
\[x(t)=\sum_{k=-\infty}^{\infty}a_{k}p(t-kT_{b}) \tag{7}\]  
\(\mathbf{p(t)}\): The waveform input data bits according to the line code scheme.  
\(\mathbf{T_{b}}\): The position for calculating the digital bit period.  
\(R_{b}\): \(\frac{1}{T_{b}}=nfs\), for ith bit iteration.  
The line coder takes as input \(\mathbf{a_{k}}\) digital bits and converts them into a physical waveform, according to the modulation of the adapted line coding scheme. Figure 7 shows how the line coder system transfer digital bits into a physical waveform [29; 30].
# Sustainability Assessment on an Urban Scale: Context, Challenges, and Most Relevant Indicators
## 3 Results and Discussion Regarding the Selected Indicators within the Potential Categories
### Context and Vulnerabilities  
This category focuses on analysing the issues related to context and vulnerabilities and is promoted through six indicators (Figure 12). Climate-related disasters have escalated in the previous three decades, revealing a new and alarming degree of damage and devastation due to current global climate change [75]. These failures have led to casualties, property destruction, and vast economic loss. Many studies have acknowledged the importance of identifying the various vulnerabilities of communities and analysing the efficiency and effectiveness of the relevant policies in urban areas to take the right step toward reducing disaster risk. In this context, SDG 13 is positioned for taking urgent action to combat climate change and its impacts. In addition to these, goal 11 has allocated an indicator for evaluating local disaster risk reduction strategies. Flood risk assessment is an indicator addressed by all the sustainability methods that this study addresses. Moreover, ISO 37120 emphasizes emergency response services and considers the assessment of natural-hazard-related deaths. Consequently, the study combined several relevant indicators, as shown in Figure 12, to make a comprehensive indicator, which is described below:  
Adapting to climate change: The objective of this indicator is to ensure a resilience to known and predicted impacts of climate change. The concept for this indicator comprises the assessment of flood risks, windstorms, earthquake events, and other natural and technological risks of the area. These issues have been promoted through separated indicators, by SBToolPT_Urban (2018), SNTool (2020), BREEAM-C (2012), and LEED-ND (2018). However, SNTool (2020) and BREEAM-C (2012) have a mixed format for this indicator, emphasizing flooding events. Therefore, the study proposed a composite indicator format to make it comprehensive in all of the mentioned aspects due to the close relationship between the criteria. In this regard, evidence regarding the known and predicted impacts of climate change on the project area should be provided by the local authority and statutory bodies to demonstrate how the risks will be managed, minimizing the risk of localized natural disasters and technological hazards (e.g., increased temperatures (including the heat island effect), flood risk, increased weather volatility, impacts on water resources, changes in ground conditions, etc.).
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 7 Experimental Results
### Hardware Implementation  
For the testing phase, a series of simulations were conducted using our Simulink model. Adjustments were made to the PID controller parameters to fine-tune the performance, yielding the desired outcomes. Subsequently, the model was deployed and loaded onto the PX4 board. Before initiating any flight, it was essential to calibrate the quadcopter meticulously. This process ensured that all the sensors and components were accurately aligned for optimal performance. Furthermore, a thorough safety check was performed to verify the quadcopter's readiness. This entailed inspecting critical aspects, such as the secure mounting of all the propellers and other essential components. The custom-built UAV, as shown in Figure 9, was meticulously configured, and primed for flight, encompassing all the required components described in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Weight** & **Characteristics** \\ \hline Frame & 282 g & Dimension: 450 mm \\  & & Voltage distributor: Yes \\ \hline  & & KV (Rpm/V): 2200 \\ Motor & 52 g & Battery operating: 2–3 Lipo \\  & & Working amps: 14–22 A \\  & & Thrust at 3S with 1045 propeller: 1200 g \\ \hline \hline \end{tabular}
\end{table}
Table 3: Components of the custom-built UAV.  
Figure 9: Final UAV build.
## 1 Introduction
### Paper Contribution  
To the best of our knowledge, this work is one of the first studies investigating the effect of high percentage of missing values (i.e., 30%, 50% and 70%) on HRV analysis. In previous studies, the inconsistency of RR-intervals was due to a small number of ectopic beats, while wrist-worn wearable devices introduce motion and mechanical artifacts that produce a huge quantity of abnormal heart beats.  
Moreover, to the best of our knowledge, this is the first study to analyse the effect on HRV features of interpolation on time versus interpolation on duration. We show the difference among interpolation methods (i.e., no-interpolation, nearest neighbor, linear, quadratic and cubic spline) on both time and duration timeseries in order to detect which interpolation method yields lower error in HRV features estimations. This analysis permits to provide insight about how the interpolation methods work in quantifying the noise introduced into the timeseries.  
We conclude by showing that interpolation on time is the best choice for preprocessing RR timeseries with missing values, contradicting the approach traditionally followed, based on durations timeseries.
## 4 Discussion
Secondly, although MC simulation is highly accurate, the LUT is not imaging system-specific. When applying this LUT to quantify optical properties of the sample imaged with a specific SFDI system, additional calibration and scaling of the LUT are likely needed to match the characteristics of the imaging system and to achieve a high quantification accuracy. Thirdly, the sample's 3D surface shape affects the diffuse reflectance, and thus the quantification, which was not accounted for in our method. Multiple techniques have been reported to correct for surface height, and these methods could be adopted to improve quantification accuracy [13; 28]. Finally, the two-stage LUT was based on two wavelengths that are sensitive to oxygenated and deoxygenated hemoglobin. If the tissue to be measured presents additional major chromophores, such as melanin, this method may not yield a high quantification accuracy. Higher-dimensional (e.g., three-dimensional) LUTs could be generated to account for additional chromophores.
## 5 Experimental Results
### Velocity Estimation Results  
For validating the proposed velocity estimator, the desired helix trajectory function in Equations (62) and (64) is implemented in the system program. Based on the desired trajectory and convergence criterion information in [44; 49], we can choose the suitable parameters of the estimator to ensure the error convergence condition and stability of the differentiator. Figure 11 presents the angular velocities estimation using BD and the proposed observer. In particular, the angular velocity estimation obtained by the BD technique shows the large spikes and discontinuous values around the desired velocity. In contrast, the SOSM with LPF estimator does not only give stable fitting values but also reacts with the spike velocities of the BD technique.  
Figure 11: Desired and estimated velocities using BD and SOSM + LPF techniques.  
Figure 10: The measuring system diagram (**a**) and experiment setup (**b**).
## 3 Statistical Properties
### Quantile Function and Stochastic Ordering
#### 3.5.2 Stochastic Ordering  
To define the stochastic ordering, a random variable \(X\) is smaller than a random variable \(Y\) in the following:  
(1) Mean residual life order \((X\underset{MRLO}{\leq}Y)\), if \(m_{X}(t)\leq m_{Y}(t)\) for all \(t\). (2) Likelihood ratio order \((X\underset{LRO}{\leq}Y)\), if \(\frac{f_{X}(t)}{f_{Y}(t)}\) decreases for all \(t\). (3) Hazard rate order \((X\underset{HRO}{\leq}Y)\), if \(h_{X}(t)\geq h_{Y}(t)\) for all \(t\). (4) Stochastic order \((X\underset{SO}{\leq}Y)\), if \(F_{X}(t)\leq F_{Y}(t)\) for all \(t\). **Remark 2**.: \(X\underset{LRO}{\leq}Y\Rightarrow X\underset{HRO}{\leq}Y\Rightarrow X \underset{MRLO}{\leq}Y\)_._  
**Theorem 1**.: _Let \(X_{PZD}\sim f_{XPZD}(t;\beta,\theta)\) and \(Y_{PZD}\sim f_{YPZD}(t;\alpha,v)\) be two random variables. If \((\beta>\alpha,\,\forall\theta,v)\), or \((\beta=\alpha\) with \(\theta>v)\), then \(X\underset{LRO}{\leq}Y\), and thus \(X\underset{HRO}{\leq}Y\), \(X\underset{MRLO}{\leq}Y\) and \(X\underset{SO}{\leq}Y\)._  
Proof.: _Let \(X_{PZD}\sim f_{XPZD}(x;\beta,\theta)\) and \(Y_{PZD}\sim f_{YPZD}(x;\alpha,v)\). Therefore,_  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(p\) & \(\theta=2\), \(\beta=4\) & \(\theta=3\), \(\beta=3\) & \(\theta=7\), \(\beta=5\) & \(\theta=0.5\), \(\beta=0.8\) \\ \hline
0.1 & 0.77493 & 0.60731 & 0.61184 & 2.14411 \\
0.2 & 0.86098 & 0.70059 & 0.66758 & 3.43875 \\
0.25 & 0.89311 & 0.73638 & 0.68827 & 4.05443 \\  
\[\frac{f_{XPZD}(t;\beta,\theta)}{f_{YPZD}(t;\alpha,v)}=\frac{\frac{\beta\theta^{3} }{2+\vartheta}t^{2\beta-1}\big{(}1+t^{\beta}\big{)}\mathrm{e}^{-\vartheta t^{ \beta}}}{\frac{\alpha v^{3}}{2+v}t^{2\alpha-1}(1+t^{\alpha})\mathrm{e}^{- \vartheta t^{\alpha}}}=\frac{\beta\,\theta^{3}\,(2+v)}{\alpha\,v^{3}\,(2+ \theta)}\frac{t^{2\beta-1}\big{(}1+t^{\beta}\big{)}}{t^{2\alpha-1}(1+t^{\alpha })}e^{-(\vartheta t^{\beta}-vt^{\alpha})}.\]  
For simplification, we have  
\[\ln\frac{f_{XPZD}(t;\beta,\theta)}{f_{YPZD}(t;\alpha,v)}=\ln\left(\frac{\beta \,\theta^{3}\,(2+v)}{\alpha\,v^{3}\,(2+\theta)}\right)+\ln\left(t^{2\beta-2 \alpha}\right)+\ln\left(\frac{1+t^{\beta}}{1+t^{\alpha}}\right)-(\theta t^{ \beta}-vt^{\alpha})\]  
Thus,  
\[\frac{\partial}{\partial t}\ln\frac{f_{XPZD}(t;\beta,\theta)}{f_{YPZD}(t; \alpha,v)}=\frac{\beta\Big{(}3-\frac{1}{1+\beta}-t^{\beta}\theta\Big{)}+ \alpha\Big{(}-3+\frac{1}{1+t^{\alpha}}+t^{\alpha}v\Big{)}}{t}\]  
To this end, if \((\beta>\alpha,\,\forall\theta,v)\), or \((\beta=\alpha\) with \(\theta>v)\), then \(\frac{\partial}{\partial t}\ln\left(\frac{f_{XPZD}(t;\beta,\theta)}{f_{YPZD}( t;\alpha,v)}\right)<0\). This shows that \(X\underset{LRO}{\leq}Y\), and according to Remark 2, the theorem is proved. \(\square\)
# Dual Roles of Water Availability in Forest Vigor:
## 2 Materials and Methods
### Materials
#### 2.2.3 Meteorological Drought Index  
Meteorological drought indexes, such as the Palmer Drought Severity Index (_PDSI_) [30], Standardized Precipitation Index (_SPI_) [31], and _SPEI_[14], are commonly used indicators for drought monitoring and assessment because they simplify complex interrelationships between many climate and climate-related parameters. The _SPEI_ is a widely used and multiscalar drought index that can be effective for characterizing the cumulative effect of water availability on a pixel scale provided that a suitable time-scale is selected [11; 12; 21]. The _SPEI_ is a standardized drought index that reflects the relative change of the water condition. The main steps employed to calculate _SPEI_ were as follows: (1) The water balance represented as the difference (\(D\)) between precipitation (\(P\)) and potential evapotranspiration (\(PET\) obtained by Thornthwaite or Penman--Monteith methods) was calculated; (2) the \(D\) values were aggregated at various time-scales Equations (2) and (3) the series of \(D\) was fitted by a three-parameter log-logistic probability density function and converted by the standard normal distribution [14; 18].  
\[D_{j}^{i}=\sum_{n=0}^{j-1}\left(P_{i-n}-PET_{i-n}\right),i\geq j \tag{2}\]  
where \(i\) represents the calendar month, and \(j\) (months) is the timescale of the water balance aggregation.  
Given that the Climatic Research Unit (CRU, University of East Anglia, UK) _SPEI_ was calculated based on global monthly observed meteorological datasets that were interpolated to 0.5\({}^{\circ}\) and the _PET_ was estimated using the well-known formula of FAO-56 Penman--Monteith [32; 33], the CRU _SPEI_ could characterize the actual water conditions more precisely on a regional scale [34; 35]. In addition, the selected values of time-scales employed for capturing the cumulative effect were diverse in different research studies, with the longest time-scales of _SPEI_ reaching 48 months [20]. Therefore, in this study, the monthly _SPEI_ values with a 1-48 month time-scale derived from the latest CRU dataset (Version 2.5) were used ([[http://digital.csic.es/handle/10261/153475](http://digital.csic.es/handle/10261/153475)]([http://digital.csic.es/handle/10261/153475](http://digital.csic.es/handle/10261/153475)), accessed 10 March 2020). To match with other data, such as remote sensing _NDVI_ and the forest map, only the _SPEI_datasets spanning 2001-2015 were selected and resampled to a spatial resolution of 0.01\({}^{\circ}\) using the nearest neighbor method of ArcGIS.