esa-sceva/satcom-chunk-collection huggingface dataset:
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 4 Results
### Evaluation of Snow Characteristics
Figure 4: Distribution of the Probability of Snow Cover Presence Index (PSCPI) per elevation over the main mountain ranges of the Iberian Peninsula: (**a**) Sierra Nevada, (**b**) Pyrenees, (**c**) Cantabrian System, (**d**) Iberian Range and (**e**) Central System. The red color scatterplots indicate the average PSCPI per elevation. The shaded area represents PSCPI values within the confidence interval between 5% and 95%. and snowline elevation as reflected in the high values of the determination coefficient (R\({}^{2}\geq\) 0.84) under the examined thresholds (Figure 6b). This underscores the significant influence of latitude on snow distribution patterns across the diverse montane regions of the Iberian Peninsula. Figure 5: Daily distribution of the Probability of Snow Cover Presence Index (PSCPI) over the main Iberian mountains taking into account different elevation ranges. The right numbers indicate the percentage of area relative to the total area corresponding to each elevation band.
# A Piecewise Linear Regression Model Ensemble for Large-Scale Curve Fitting
## Appendix A Learning Algorithm of the LHM  
The main goal of this learning algorithm is to estimate the number and horizontal positions of the knots of the linear B-splines, before applying OLS or WLS to determine the vertical coordinate of each knot. This learning algorithm combines a greedy divide-and-conquer strategy with computationally efficient pruning and special updating formulas. It consists of four main consecutive stages: smoothing, growing, pruning and refitting. The first three steps are used to automatically identify the complexity of the model, whereas the final refitting improves the accuracy of the estimation of the position of the basis functions.  
The first step consists of scatter-plot smoothing, removing possible noise by applying the Supersmoother [25]. This smoother makes use of simple smoothers based on local linear regression with different span lengths, using cross-validation to automatically estimate the optimal span for each abscissa value.  
After filtering the noise with the Supersmoother, the growing algorithm builds a large and accurate piecewise linear model, possibly over-fitted (with a high number of knots). It uses a top-down partitioning algorithm where the splitting process is repeated recursively until all points of the smoothed scatterplot are sufficiently well approximated by the resulting model. This step of the algorithm is based on the well known 'iterative end point fit' or Ramer-Douglas-Peucker algorithm (see [26]), with some subtle differences: for example, its stopping criterion is a preset value of admissible MSE, instead of using a threshold distance. This growing algorithm explores many candidate splits to select the best ones until the termination criteria are reached.  
Then, the large initial model is pruned by removing the irrelevant knots. The pruning procedure aims at selecting a subset of knots in order to reduce model complexity while preserving accuracy with respect to the original data. The pruning algorithm first generates a pruning sequence of models of decreasing complexity, then selects the best one of the sequence by choosing the one which achieves the best compromise between simplicity and accuracy. The criterion applied in this model selection step is the one-standard error rule, proposed in [27].  
Once the proper complexity of the resulting model is determined, the final step of the learning algorithm is refitting the pruned model. During this stage, both coordinates of the knots are optimised by means of an iterative process where each knot is considered one by one, moving its position in order to minimise the error in the training set.  
Further details about the LHM can be found in [9; 28]. Practical applications of the LHM in a broad range of disciplines, such as engineering and medicine can be seen in [29; 30; 31; 32; 33; 34; 35; 36].
## 6 Experimental Results
### Virtuino App for Logging and Visualizing Sensor Values
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pin Configuration in Arduino Mega 2560** & **Connection** \\ \hline Pin 16: Tx2 & Transmitter 2 for serial communications \\ Pin 17: Rx2 & Receiver 2 for serial communication \\ Pin 18: Tx1 & Transmitter1 for serial communication \\ Pin 19: Rx1 & Receiver 1 for serial communication \\ Pin 11: OC1A/PCINT5 & Digital pin 11 \\ Pin 12: PB6 (OC1B/PCINT6) & Digital pin 12 \\ Pin 13: PB7 OC0A/OC1C/PCINT7 & Digital pin 13 \\ PF0 (ADC0) & Analog pin 0 \\ \hline \hline
**Pin configuration in Wi-Fi modem** & **Connection** \\ \hline Vcc & Power supply for Wi-Fi (3.3 V) \\ Rx & Receiver pin connecting to RX1 pin of Arduino \\  & Uno \\ Tx & Transmitter pin connecting to TX1 pin of \\  & Arduino Uno \\ GND & Common ground \\ \hline \hline
**Pin configuration in Flow sensor** & **Connection** \\ \hline Vcc & Power supply for RF modem (3.3 V) \\ Rx & Receiver pin connecting to RX 2 pin, i.e., 16 th \\  & pin of Arduino Uno \\ Tx & Transmitter pin connecting to TX 2 pin i.e., 17 th \\  & pin of Arduino Uno \\ GND & Common ground \\ \hline \hline
**Pin configuration in Flow sensor** & **Connection** \\ \hline Vcc & Power supply for flow sensor (+5 V) \\ Po & Pulse output connect to A\({}_{0}\) pin of Arduino Uno \\ Sig OUT & Signal connected to 11 pin of Arduino Uno \\ GND & Common ground \\ \hline \hline
**Pin configuration in Ultrasonic sensor** & **Connection** \\ \hline +5V & Power supply to sensor \\ Trigger & Trigger the ultrasonic sound pulses and it is \\  & connected to pin 13 of Arduino Uno \\  & Pin produces a pulse when the reflected signal \\ Echo & is received, and it is connected to pin 12 of \\  & Arduino Uno \\ GND & Common ground \\ \hline \hline
**Pin configuration in LCD** & **Connection** \\ \hline Pin 4: RS & Pin 7 of Arduino Uno \\ Pin 5: RW & Gnd to the Arduino Uno \\ Pin 6: E & Pin 6 of Arduino Uno \\ Pin 11: D 4 & Pin 5 of Arduino Uno \\ Pin 12: D 5 & Pin 4 of Arduino Uno \\ Pin 13: D 6 & Pin 3 of Arduino Uno \\ Pin 14: D 7 & Pin 2 of Arduino Uno \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Nomenclature of pin configuration.
# Privately Generated Key Pairs for Post Quantum Cryptography in a Distributed Network
## 3 Privately Generated Key Pairs for Post-Quantum Cryptography
### PKI for Lattice-Based Digital Signature Algorithm (LWE)
Clients without a valid root of trust cannot generate a recognizable response \(a_{i}\); thus, the method allows the CA to authenticate client \(i\). 5. Client \(i\) generates the secret key \(S_{k(i):}\) (\(S_{(1)i}\); \(S_{(2)i}\)) by using its own random number generator. Figure 5: Public key distribution and verification for DSA with LWE cryptography. Here, we exploit the \"one-wayness\" of the lattice protocol. If the CA can successfully verify message M with the public key \(P_{k(i)}\), the following conclusions can be deducted:  
* Message M is valid. * Public key \(P_{k(i)}\) is valid. * The CA knows that client \"\(i\)\" generated the same matrix \(\mathrm{A_{i}}\) from the root of trust. * The CA knows that client \"\(i\)\" computed a valid private key from its own random number generator that was also used to compute the vector \(t_{i}=P_{k2(i)}\).
# Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory
## 5 Conclusions  
In this paper, a new wildlife monitoring strategy was proposed using a team of UAVs based on the optimal transport theory. The proposed works can incorporate complementary information such as GPS trackers into the plan, to increase the wildlife detection rate. Through the OT-based wildlife monitoring scheme, the UAV trajectories were generated enabling UAVs to collaboratively monitor the wildlife with a given priority. Moreover, the spatio-temporal evolution of animals' locations was combined with the proposed monitoring scheme, leading to an increase in the wildlife detection rate. Numerous simulations were conducted with variation in the number of UAVs and exploration time while randomly generating the animal locations to validate the proposed method. The statistical data for numerously different scenarios demonstrated that the proposed wildlife monitoring scheme can result in high performance in terms of detection rate.  
Although the satellite-based communication technology can be adopted for the global coverage, and hence centralized communication between UAVs, it is known that this type of communication device is heavy and bulky with high energy-consumption, reducing the capability of the multi-UAV system. Therefore, decentralized communication is more desirable because it is unnecessary for UAVs to have all-time communications between all of them during the monitoring mission. As future works, we will thus focus on the decentralized communication scheme to extend the capability of the proposed multi-UAV wildlife monitoring method.  
Figure 9: Statistical data for the performance comparison among OT (TV-Gauss), LM (TI-Uni) and OT (TI-Uni) with 600 s delay and 3 agents: boxplots for target detection rates for (**a**) exploration time = 900 s; (**b**) exploration time = 1800 s; and (**c**) exploration time = 3600 s. Mean values for the boxplots are indicated by asterisk symbols.  
**Author Contributions:** Conceptualization, K.L.; methodology, K.L.; software, R.H.K. and K.L.; validation, R.H.K. and K.L.; formal analysis, R.H.K. and K.L.; investigation, R.H.K. and K.L.; resources, R.H.K. and K.L.; data curation, R.H.K. and K.L.; writing--original draft preparation, K.L.; writing--review and editing, R.H.K. and K.L.; visualization, R.H.K. and K.L.; supervision, K.L.; project administration, K.L.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by New Mexico NASA EPSCoR Research Infrastructure Development (RID) Program with Cooperative Agreement 80 NSSC19M0181.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Design of Seltape++
### Immediate Packet Verification  
To achieve the desired compatibility with SMR, similar to Seluge, we reserve some bytes of each packet for assigning hash values. The length of these values differs depending on the hash algorithm used. In Seluge++, to provide the highest security, we use 160-bit SHA-1, which will need 20 bytes per  
Figure 5: Wormhole attack in a wireless sensor network (WSN) [6].  
Figure 6: Seluge++ code partitioning.  
packet. We will also consider 72 bytes of update data to be stored in each packet; therefore, a sum of 92 bytes will be used as the default payload, which is less than the maximum of 102 bytes defined in IEEE 802.15.4. Like the original Seluge, the hash value of each packet in last page P is inserted to its corresponding packet in the previous page (P-1). This procedure takes place until all the hash values of the packets from the last page through the first page are hashed and placed in their relative packet. For example, assume there are 10 pages to be transmitted; therefore, \(P=10\). The base station will start from the first packet in the tenth page \((Pkt_{10,1})\) and will store its hash value into \(Pkt_{9,1}\), continuing this process with storing the hash value of \(Pkt_{10,2}\) in \(Pkt_{9,2}\), and so on. The hash values of page 1, which do not have any corresponding packet for storage, will then be called page 0. For the sake of simplicity, this packet preparation process hereafter will be named the Data Verification Method (DV Method), which is already illustrated in Figure 4.  
The DV method will only be effective on the verification of data from page 1 through P, but packets in page 0 must also be disseminated in a way that they can be immediately verified. In Section 3, we have shown that using the Merkle hash tree does not comply with our assumption of a mission-critical network; therefore, unlike Seluge, we introduce a new method for the verification of packets in page 0. This method benefits from the use of a message authentication code. Since using MAC needs to have a symmetric encryption, therefore Seluge++ will use both asymmetric cryptography for authentication preservation of the entire image data and symmetric cryptography for both integrity preservation and packet verification (using MAC) of hash packets in page 0. The detailed process of this verification will be discussed in the following. Hereafter, for the sake of simplicity, we will recall this verification of packets in page 0 as the _Hash Verification Method_ (HV Method).  
Seluge++ uses symmetric key similar to \(\mu\)PKI, but better in performance, since the receiver nodes do not need to sign data using public cryptography, whereas in \(\mu\)PKI, they do need to do this. This approach also removes the overhead of the Merkle hash tree.  
Therefore, using a combination of the Hash Dependency Method (the DV Method) and message authentication code (the HV Method), the authenticity of updates and also their integrity against any possible modification by attackers is guaranteed.
## 2 Preliminaries  
### The MD Algorithm  
Let \(X\), \(Y\), and \(M\) be three integers, where the greatest common divisor \(GCD(Y,M)=1\). The MD problem aims to find an integer \(R\) satisfying \(RY\equiv X(\text{mod}\ M)\). [PERSON] and [PERSON] [31] optimized the extended binary GCD algorithm for the MD over a prime field, which requires less iteration than the other existing algorithms. Its equivalent description is shown as Algorithm 1. Figure 1: The concurrent error detection scheme for the modular division (MD) in [40]. ```
1:\(X,Y,M(M\) is a prime), \(0\leq Y\leq M\), \(0\leq X\leq M\)
2:Output:\(R(RY\equiv X\ mod\ M)\)
```  
```
1:Initialization
2:\(i=0,\varnothing^{0}=1,\ \delta^{0}=1\)
3:\(\left\{A^{0},B^{0},R^{0},S^{0}\right\}\leftarrow\left\{Y,M,X,0\right\}\)
4:// Modular Division Computation
5:While\(A^{0}\
eq\mp 1\)
6:i = i + 1
7:// Control Element
8:\(\alpha^{i}=\left\{ \begin{array}{ll}0&\text{if}\ \ A^{i-1}\ mod\ 2=0\\ 1&\text{else if}\ \left(A^{i-1}+B^{i-1}\right)\!\!\mod\ 4=0\\ -1&\text{else if}\ \left(A^{i-1}-B^{i-1}\right)\!\!\mod\ 4=0\\ 0&\text{if}\ \left(R^{i-1}+\alpha^{i}S^{i-1}\right)\!\!\mod\ 2=0\\ \varnothing^{i-1}&\text{otherwise}\end{array} \right.\)
10:\(



As shown in Figure 2, the proposed secure architecture contains two modules, namely, the MFM (marked by green line) and the EDM (marked by red line). The former is employed to compute the MD and the latter to detect the error in MFM. The computing process of the secure architecture is summarized as Algorithm 2. In this algorithm, Lines 4, 18-20 describe the error detection function, which is mapped into EDM of Figure 2, and the left lines describe the word-oriented computing process of MD, which is mapped to MFM. MFM architecture of Figure 2 is similar with that in [40]. In this architecture, the \(n\)-bit operands A, B, R, S are split into \(e\)\(w\)-bit words, each of which is processed by a PE. A total of \(e\) PEs are combined into a systolic array to processes them word by word where \(e=\lceil n/w\rceil\). Specifically, the control element (CE) executes the operations in Lines 9-15, producing control variables \((\alpha,\beta,\phi,\lambda,\delta)\), initial carry-in signals (1-bit CA\({}_{\cdot 1}\) and 2-bit CR\({}_{\cdot 1}\)), and the \(f\) signal that controls the initialization in Lines 2-4; the PEk, the physical mapping of the \(k\)-th step in Line 17 in for-loop, targets the \(k\)-th word of A, B, R, and S. From right to left, a total of \(e\) PEs are connected to complete the computation of one iteration (\(e=4\) in Figure 2). In addition, in MFM, the testing element (TE) module is used to execute the condition judgment operation in Line 6. Finally, the output control element (OCE) executes the operations in Lines 23-24, ensuring that the MD problem outputs parallel results. This paper implements the secure architecture for four types of MFM structures, namely, Type-8, Type-16, Type-32, and Type-64. The number in the name of each type equals \(w\), the word size of PE. Note that, in this paper, we do not show the implementation of MFM; the implementation details of MFM can be found in [40].
## 4 Development of CLI Models
### Regression Analysis of Data
#### 4.1.2 Linear and Non-Linear Multi-Variable Regression Analysis  
The prediction of CLI based on other rock properties can be carried out either using a linear or non-linear model. However, it is known that one of the common CLI estimation models from CAI [5] is polynomial a function. In this study, both linear and non-linear multivariable regression analyses were performed. While running the SPPS program, data were divided into two sets including: a training set (70%) and a testing set (30%). Table 6 is the summary of bi-linear multiple regression together with input variables of each model and statistical indices (#).  
Figure 8: Relations between the BTS and CLI.  
Figure 9: Relations between the BI and CLI.  
The results of the analysis for both linear and non-linear multi-variable regression show that models with three inputs can offer a reliable estimate of CLI with a coefficient of correlation of 0.82. This is very close to the correlation coefficient obtained from model 1 for training and testing data, respectively (Figures 12 and 13).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Non-L** & **1** & **2** & **4** & **4** & **5** & **6** & **7** \\ \hline R & 0.85 & 0.83 & 0.79 & 0.76 & 0.69 & 0.81 & 0.79 \\ R\({}^{2}\) & **0.72** & 0.69 & 0.62 & 0.58 & 0.47 & 0.66 & 0.62 \\ MSE & 124.88 & 138.22 & 154.67 & 167.51 & 235.07 & 159.15 & 154.71 \\ RMSE & 11.18 & 11.76 & 12.44 & 12.94 & 15.33 & 12.62 & 12.44 \\ VAF & 72.33 & 68.86 & 62.39 & 57.58 & 47.30 & 66.01 & 62.07 \\ \hline \end{tabular}
\end{table}
Table 8: Summary of non-linear multi-variable statistical analysis of CLI.  
Figure 11: Relations between the measured CLI and predicted CLI for testing (model 1-MLRM).  
\begin{table}
\begin{tabular}{c c c} \hline  & **Inputs** & **Equations for CLI** \\ \hline
1 & D, UCS, BTS, BI & \(-\)4.099 \(\times\) D +9.771\(\times\) Ln(UCS) \(-\)30.94 \(\times\) Ln(BTS) \(-\)0.006 \(\times\) BI\({}^{2}\) \\  & \(-\)0.361 \(\times\) BI +166.102 \\
2 & D, UCS, BTS & \(-\)3.938 \(\times\) Ln(UCS) \(-\)32.179 \(\times\) ln(BTS) \(+\)185.433 \\
3 & UCS, BTS & \(-\)3.38 \(\times\) ln(UCS) \(-\)40.63 \(\times\) ln(BTS) \(+\)128.817 \\
4 & D, UCS & \(-\)5.741 \(\times\) D \(-\)10.418 \(\times\) ln(UCS) \(+\)225.352 \\
5 & UCS, BI & \(-\)13.933 \(\times\) ln(UCS) \(+\)0.025 \(\times\) BI\({}^{2}\) \(-\)2.258 \(\times\) BI +136.543 \\
6 & BTS, BI & \(-\)35.366 \(\times\) ln(BTS) \(+\)0.018 \(\times\) BI\({}^{2}\) \(-\)1.604 \(\times\) BI +131.049 \\
7 & D, BI & \(-\)6.344 \(\times\) D \(-\)0.018 \(\times\) BI\({}^{2}\) \(+\)0.256 \(\times\) BI +199.683 \\ \hline \end{tabular}
\end{table}
Table 9: Results of multi-variable non-linear regression of CLI as function of other rock properties.  
It should be noted that the linear regression analysis approach has clear advantages over the black box models provided by AI systems, simply by offering equations for use by everyone.
## 2 Materials and Methods
### Loading and Boundary Conditions  
For model predictions and validation for clinically relevant loading scenarios, three different loading conditions related to the loads applied in the experimental research were applied to this FE study. Identically simulated loading protocols were used in the experiment for validation of the model. Under the first loading condition, 150 N was applied to the tibia with 30\({}^{\circ}\) and 90\({}^{\circ}\) flexion in the knee joint to evaluate the anterior and posterior tibial translations [23]. Under the second loading condition, an axial load of 1150 N was applied on the FE model to obtain the contact stresses and to compare them with those reported in a published FE study on the knee joint [27]. Under the third loading condition, UKA model validation was conducted with four flexion angles (0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\)) using passive flexion simulation. Furthermore, anterior and posterior drawer loads of 130 N were applied separately to the tibia at the center of the knee, equivalent to the conditions in experimental research [43]. As a fourth loading condition, gait cycle loading was applied to compare the biomechanical effects of the three different tibial bearing materials (Figure 2) [44]. A computational analysis was performed using force controls of the anterior-posterior force in regard to the compressive load applied to the hip [45; 46]. A proportional-integral-derivative controller was integrated into the computational model to allow for the control of the quadriceps in a manner equivalent to that in the experiment [47]. Internal-external and varus-valgus torques were applied to the tibia [45; 46]. The contact stresses on the lateral meniscus  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Material** & **Modulus of Elasticity** & **Poisson’s Ratio** \\ \hline Femoral part & Cobalt chromium alloy (CoCr) & 195 GPa & 0.3 \\ \hline Tibia part & Titanium alloy (Ti6 Al4V) & 110 GPa & 0.3 \\ \hline \multirow{3}{*}{Tibia bearing} & UHMWPE & 685 MPa & 0.4 \\  & PEEK & 3500 MPa & 0.3 \\  & CFR-PEEK & 18,000 MPa & 0.4 \\ \hline Bone cement & PMMA & 1940 MPa & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties applied in FE analysis.  
and tibial cartilage were analyzed in the other compartments, as well as tibial bone stress, influencing residual pain. Five regions of interest (ROIs) were defined on the proximal tibia (Figure 3). ROIs 1 and 4 were defined at the resection corner between the sagittal and transverse tibia bone cuts. The other three ROIs were located on the trabecular bone surface beneath the tibial baseplate, with ROIs 2 and 3 being medial and lateral to the keel slot, respectively. ROI 5 was defined to investigate the cause of residual pain and was positioned on the proximal anteromedial surface of cortical bone for all models.  
Figure 3: Five regions of interest (ROIs) of the tibia bone.  
Figure 2: Input function of the gait cycle applied in the study: (**a**) flexion angle; (**b**) axial load, (**c**) anterior-posterior (AP) load; (**d**) internal-external (IE) torque.