esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Imbalance-Inducing Postprocessing  
We have now an ordering of the vertices that presumably places similar vertices close to each other. Instead of cutting the sequence at equidistant positions to obtain a fully balanced partitioning, we describe in this section several postprocessing techniques that allow us to take advantage of the permissible imbalance and produce better cuts.  
First we sketch how the problem can be solved optimally if the number of vertices is not too large. This approach is based on dynamic programming and requires the entire graph to live in memory, which is a barrier for using this technique even if the running time were almost linear. In fact, the procedure proposed here runs in time \(O(n^{3}\log k)\) but the running time can be improved to almost linear. Despite the memory requirement, this algorithm is usable with the caveat that one first needs to group together blocks of contiguous vertices and contract them into supernodes, such that the graph of supernodes fits in memory and is small enough for the algorithm to handle. The fact that nearby vertices are similar to each other implies that this sort of contraction should not hurt the subsequent optimization significantly. Indeed trying two different block sizes (resulting in 1000 and 5000 blocks) produced similar cuts in our experiments.  
The other two approaches work on the concept of \"windows\" and perform only local optimizations within each. Let \(\pi\) denote the permutation of vertices at the start of the local optimization and let \(\pi_{i}\) be the \(i\)-th vertex in the permutation for \(1\leq i\leq n\). Given that we want to get \(k\) parts of (almost) the same size, we consider the ideal split points \(q_{j}=\lfloor\frac{jn}{k}\rfloor\) for \(1\leq j<k\). Though there are only \(k-1\) real split points, we define two dummy split points at either end to make the notation cleaner: \(q_{0}=0\) and \(q_{k}=n\). Then, the ideal partitions, in terms of size, will be formed of \(\pi(q_{j}+1\to q_{j+1})\) for different \(0\leq j<k\). Given permissible imbalance \(\alpha>0\), a window is defined around each (real) split point allowing for \(\alpha/2\) imbalance on either side. More precisely, \(W_{j}=\pi(\pi_{q_{j}-\alpha^{\prime}}\rightarrow\pi_{q_{j}+\alpha^{\prime}})\) for \(\alpha^{\prime}=\lceil\frac{\alpha n}{2k}\rceil\). Essentially we are free to shift the boundaries of partitions within each window without violating the balance requirements. We propose two methods to take advantage of this opportunity. One finds the optimal boundaries within windows, while the other also permutes the vertices in each window arbitrarily to find a better solution.
## 3 Hardware Architecture
### Mobile Receiver Unit  
The mobile receiver unit consists of an underwater antenna, a modified DVB-T USB dongle with the capability to compute a power spectrum density and a single board computer (SBC). The major design criteria for the module are size and cost, as it has to fit into space-constrained \(\mu\)AUVs such as the HippoCampus in Figure 1.  
The mobile receiver unit carries out two main tasks:  
1. it calculates real-time RSS values based on the URSM;
2. and it computes its position from the RSS values.  
The DVB-T USB dongle is used to digitize a segment of the EM spectrum as in-phase/quadrature (I/Q) samples. In order to drive the DVB-T USB dongle and to perform the RSS localization tasks, the SBC requests I/Q samples from the DVB-T USB dongle and computes the power density spectrum. The RSS values of the transmitter channels can be extracted from the spectrum and used for the range estimation. The functionality of the localization module is depicted in Figure 11.  
Figure 10: Submerged rack with four beacons. Each beacon contains a signal generating unit and continuously emits an EM signal at a unique frequency.  
Figure 9: Circuit board with the RadiometrixTM USX2 module for generating EM carrier waves at 433 MHz.
# The Ionospheric Scintillation Effects on the BeiDou Signal Receiver
## 5 Effect on Tracking Process for BeiDou Receiver
### Determining DLL Variance for BeiDou B1I Signal
In the presence of scintillation, \(t^{(s)}(t)\) could be figured out (for the BeiDou signal) by:  
\[t^{(s)}=TOW+(30w+b)\times t_{bit}+(d+\frac{CP+\Delta CP}{N})\times t_{chip} \tag{54}\]  
In Equation (54), \(TOW\) is the time of week, \(w\) and \(b\) are the number of words and bits of navigation message the receiver has received by the current sampling moment, \(t_{bit}\) and \(t_{chip}\) are the time periods of one single bit and chip, \(\Delta CP\) is the code phase jitter caused by ionospheric scintillation, \(N\) is the number of chips in one period of PN code, which is 2046 for the BeiDou signal. Then we could obtain an expression of \(\rho(t)\) as a function of \(\Delta CP\). Figure 13 shows the deviation of pseudo-range in 12 s, which is the period of two sub-frames of the BeiDou signal. From the figure we can see that under moderate scintillation, the deviation could be within \(\pm 2\) m; when the intensity becomes higher, the deviation of pseudo-range caused by ionospheric scintillation could be more than 4 m under high scintillation intensity. The pseudo-range error could directly affect the receiver positioning accuracy. Considering that this paper mainly focuses on some low-latitude areas, we choose Sanya as an example. Sanya (N18\({}^{\circ}\)E109\({}^{\circ}\)) is a city located in Southern China and the receiver is assumed to be in the center of the city. In this case, only the impact of ionospheric scintillation is taken into consideration. The 14 BeiDou satellites are built in Satellite Tool Kit (STK) as shown in Figure 14, where the red point is a MEO satellite; the green point is a GEO satellite and the blue one is an IGSO satellite. The simulation period is from 07:00 a.m. to 10:00 a.m. The positioning accuracy could be evaluated by the deviation between the actual value (N18\({}^{\circ}\)E109\({}^{\circ}\)) and the calculation results. More details of the computational setup are shown in \[\sigma_{\hat{T}}=W_{B1I}\sigma_{T} \tag{49}\]0  
\[\sigma_{\hat{T}}=W_{B1I}\sigma_{T} \tag{49}\]1 Deviation of Pseudo-Range (**a**) \(S_{4}=0.46\); (**b**) \(S_{4}=0.7\).
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 3 SAR-Based Studies and Methods to Detect Snow
### Employed Methods to Monitor Snow Cover with SAR Data
#### 3.3.3 Wet and Dry SCE Detection
Due to the various information (referring to Appendix A) that can be retrieved from PolSAR parameters, more details about the characteristics of snow cover can be obtained when compared to backscattering- or InSAR-based approaches. For instance, [PERSON] et al. [166] revealed that backscattering of wet snow is primarily caused by surface scattering; [PERSON] and [PERSON] [92] found that snow wetness is proportional to surface scattering and inversely proportional to volume scattering. In addition, the morphology of snow also influences the scattering, such that an older snowpack would have larger grain size and thus would lead to increased volume scattering [30]. [PERSON] et al. [30] found that snow-covered regions show lower entropy, H(1-A) as well as higher polarimetric anisotropy; thus they proposed a threshold method to detect snow cover, which resulted in an accuracy comparable to a supervised Wishart classification. [PERSON] et al. [167] observed that dry snow is characterized by higher values in the Pauli surface parameter and lower value in the double-bounce parameter. Therefore, a combination of the two parameters enables to calculate the difference and then to map dry snow cover. Based on the observation that dry snow shows lower H and \(\overline{\alpha}\) values, they also derived the ratio of H and \(\overline{\alpha}\) to detect dry snow. Similar techniques were employed by [96; 118; 159; 168; 169], calculating the difference of available parameters or relying on machine learning (ML) classifications to detect snow cover. [PERSON] et al. [170; 171] even proposed a Radar Snow Index (RSI) to estimate the total SCE based on polarization fraction parameters. To deal with the influence of underlying land cover types, [PERSON] et al. [172] suggested an advanced supervised polarimetric contrast variation enhancement (PCVE) to enlarge the contrast of dry snow. Based on the research from recent years it became obvious that PolSAR-based detection of snow cover is developing quickly and that there is still potential to improve and complement existing approaches. Figure 8 presents an overview of the three different mainstreams of PolSAR-based wet and dry snow cover detection algorithms.
# KeyNet: An Asymmetric Key-Style Framework for Watermarking Deep Learning Models
## 4 The _KeyNet_ Framework
** The owner can extract the WM information from a suspicious remote DL by taking a random sample from her WM carrier set, putting her signature on one of the predefined positions, and querying the remote model. After that, he/she passes the remote model's predictions to the private model. If the private model decodes the WM information and provides the position of the signature with high accuracy, then the owner can claim her ownership. Figure 1: _KeyNet_ global workflow. To verify the ownership of a remote black-box DL model, the owner first delivers the WM carrier set and her signature to the authority. He/she also tells the authority about the methodology used to sign the WM samples along with the predefined positions where the WM may be placed. The authority (i.e., the _verifier_) randomly chooses a sample from the carrier set, puts the signature in a random position, queries the remote DL model, and sends the model's predictions to the owner. The owner (i.e., the _prover_) takes the predictions, passes them to her private model, and tells the authority the position of her signature on the image. The authority repeats the proof as many times as he/she desires. After that, the owner's answer accuracy is evaluated according to a minimum threshold. If the owner surpasses the threshold, his/her ownership is regarded as proven by the authority. More details about this phase are given in Section 4.4. The following subsections describe each phase in detail. First, we formalize the problem. Then, we describe the methodology for signing and labeling the WM carrier set using the hashed value of the owner's information. After that, we describe the WM embedding phase by training the original model and the private model on the original and the watermarking tasks jointly. Finally, we explain the WM extraction and verification phase.
# Application of Dynamic Time Warping to Determine the Shear Wave Velocity from the Down-Hole Test
## 3 Data Preparation
The informative value range \(\mathrm{[L,R]}\) is determined as follows: \[\mathrm{L}=\big{\{}\mathrm{min(i):q_{0.95}(i)>\mathrm{\overline{s}_{+}(i) \wedge q_{0.05}(i)<\mathrm{\overline{s}_{-}(i)}}\big{\}},\] (6)  
\[\mathrm{R}=\big{\{}\mathrm{max(i):q_{0.95}(i)>\mathrm{\overline{s}_{+}(i) \wedge q_{0.05}(i)<\mathrm{\overline{s}_{-}(i)}}\big{\}}. \tag{7}\]  
The most informative part starts with the appearance of the greatest amplitude and ends with the vanishing of the signal. The starting point is the first index, where the upper envelope is greater than the moving average from positive signal values, and the lower envelope is lower than the moving average from negative signal values. 1. After determining the informative interval for the signal registered using the first geophone \(\mathrm{[L_{1},R_{1}]}\) and the second one \(\mathrm{[L_{2},R_{2}]}\), both signals are limited to one interval being their sum: \[\mathrm{[L_{1},R_{1}]\cup[L_{2},R_{2}]}.\] (8)  
The period of time used in the simple moving average method and the size of the window used to calculate the signal envelope were experimentally chosen.
# Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses
## 5 Discussion
### Application in the Context of the Rural Farmers  
The deployed LoRaWAN-based solution can improve the agriculture by monitoring the field. It can save the efforts and time of the farmers and reduce the excessive usage of resources. This solution is best suited for the rural farmers because of its low cost and ease of deployment. For the deployment of this setup, we first check the LoRaWAN coverage and accordingly decide the requirement of gateway. Both the gateway and sensor devices are made to be plug and play and auto-configurable to the back-end system. We have tested the current system for three tomato growth cycles, and it stood to be stable. We have been sending sensor readings every five minutes to the gateway and plan to minimize the number of transmissions using ML. We plan to extend this setup to other greenhouses and enclosed chambers for monitoring crops like chicory and strawberries.
# Implicit A-Stable Peer Triplets for ODE Constrained Optimal Control Problems
## 2 Materials and Methods
### Construction of Peer Triplets
#### 2.4.2 \(A(\alpha)\)-Stable Four-Stage Methods  
Although our focus is on A-stable methods, we also shortly consider \(A(\alpha)\)-stable methods. We like to consider BDF4 (backward differentiation formulas) as a benchmark, since triplets based on BDF3 were the most efficient ones in [13]. In order to distinguish the different methods, we denote them in the form \(\texttt{APso}q_{1}q_{2}\texttt{aaaa}\), where AP stands for Adjoint Peer method followed by the stage number and the smallest forward and adjoint orders \(q_{1}\) and \(q_{2}\) in the triplet. The trailing letters are related to properties of the method like diagonal or singly diagonal implicitness.  
The Peer triplet AP4o43 bdf based on BDF4 has equi-spaced nodes \(c_{i}=i/4,\,i=1,\ldots,4\), yielding \(w=e_{4}\). The coefficients of the full triplet are given in Appendix A.1. Obviously the method is singly-implicit and its well-known stability angle is \(\alpha=73.35^{\circ}\). We also monitor the norm of the zero-stability matrix \(\|A^{-1}B\|_{\infty}\), which may be a measure for the propagation of rounding errors. Its value is \(\|A^{-1}B\|_{\infty}\leq 5.80\). Since BDF4 has full global order 4, the error constant from (56) is \(err_{4}=0\). Still, the end methods were constructed with the local orders \((4,3)\) according to Table 1. The matrices of the corresponding starting method have a leading \(3\times 3\) block and a separated last stage. We abbreviate this as block structure \((3+1)\). All characteristics of the boundary method are given in Table 2.  
Motivated by the beneficial properties of Peer methods with symmetric nodes seen in [13; 19], the nodes of the next triplet with the diagonally-implicit standard method were chosen symmetric to a common center, i.e., \(c_{1}+c_{4}=c_{2}+c_{3}\). Unfortunately, searches for large stability angles with such nodes in the interval \([0,1]\) did not find A-stable methods, but the following method AP4o43 dif with flip symmetric nodes and \(\alpha=84.00^{\circ}\), which is an improvement of 10 degrees over BDF4. Its coefficients are given in Appendix A.2. Although there exist A-stable methods with other nodes in \([0,1]\), this method is of its own interest since its error constant \(err_{4}\doteq 2.5\times 10^{-3}\) is surprisingly small. The node vector of AP4o43 dif includes \(c_{4}=1\), leading again to \(w=e_{4}\). Further properties of the standard method \((A,B,K)\) are \(\|A^{-1}B\|_{\infty}\leq 2.01\) and the damping factor \(|\lambda_{2}|=0.26\). See Table 2 for the boundary methods.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 3 Interleaver
### Proposed Interleaver
#### 3.7.3 Circular Minimum Spread Constraint Criterion  
The concept of propagation in the field of turbo codes was defined by [PERSON] in [39]. The circular minimum spread constraint (CMSC) criterion was proposed in [22] and is defined through the following equations:  
For an interleaver \(\pi\), with length \(L\), it will have a circular extension of \(S\) if and only if:  
\[d_{L}(i_{1},i_{2})<S\Rightarrow d_{L}(\pi(i_{1}),\pi(i_{2}))\geq S \tag{21}\]  
\(\forall\ i_{1},\ i_{2}\in\{0,1,2,\ldots,L-1\}\) and \(d_{L}(i_{1},i_{2})\) is the Lee distance of the two indices \(i_{1},\ i_{2}\), is defined as  
\[d_{L}(i_{1},i_{2})=\min(|i_{1}-i_{2}|,L-|i_{1}-i_{2}|) \tag{22}\]  
\begin{table}
\begin{tabular}{c c c} \hline \(L\) & \(f_{1}\) & \(f_{2}\) \\ \hline
64 & 7 & 16 \\ \hline
128 & 15 & 32 \\ \hline
256 & 15 & 32 \\ \hline
512 & 31 & 64 \\ \hline
4352 & 477 & 408 \\ \hline
5632 & 45 & 176 \\ \hline
6144 & 263 & 480 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters for QPP interleaver [36].  
For the \(S\) that satisfies Equation (23), an interleaver obtained ensures a minimum circular _Spread_\(S_{min}\):  
\[S\geq S_{min} \tag{23}\]  
In this way, the minimum circular dispersion constraint is satisfied.  
Where \(S_{min}\) is given by [33]:  
\[S_{min}=\sqrt{L/2} \tag{24}\]
## 3 Results and Discussion
### Seasonal Variation of XPD at the Selected Frequencies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **Season** & **t-Statistics** & \(\mathbf{p}\)**-Value** & **Significance** \\ \hline \multirow{4}{*}{11.7} & Autumn & –4.055 & 0.0062 & Significant \\ \cline{2-5}  & Winter & 2.347 & 0.0561 & Not Significant \\ \cline{2-5}  & Spring & –4.402 & 0.0046 & Significant \\ \cline{2-5}  & Summer & –5.673 & 0.0014 & Significant \\ \hline \multirow{4}{*}{15} & Autumn & –4.250 & 0.0060 & Significant \\ \cline{2-5}  & Winter & 2.890 & 0.0322 & Significant \\ \cline{2-5}  & Spring & –5.140 & 0.0024 & Significant \\ \cline{2-5}  & Summer & –6.370 & 0.0012 & Significant \\ \hline \multirow{4}{*}{20} & Autumn & –4.120 & 0.0058 & Significant \\ \cline{2-5}  & Winter & 2.650 & 0.0397 & Significant \\ \cline{2-5}  & Spring & –5.080 & 0.0021 & Significant \\ \cline{2-5}  & Summer & –6.430 & 0.0008 & Significant \\ \hline \multirow{4}{*}{25} & Autumn & –5.120 & 0.0023 & Significant \\ \cline{2-5}  & Winter & 3.020 & 0.0231 & Significant \\ \cline{2-5}  & Spring & –6.540 & 0.0009 & Significant \\ \cline{2-5}  & Summer & –7.320 & 0.0004 & Significant \\ \hline \multirow{4}{*}{35} & Autumn & –4.310 & 0.0051 & Significant \\ \cline{2-5}  & Winter & 2.890 & 0.0325 & Significant \\ \cline{2-5}  & Spring & –5.210 & 0.0027 & Significant \\ \cline{2-5}  & Summer & –6.120 & 0.0011 & Significant \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Metrics of statistical comparison between seasonal-Hr based XPDs and ITU Hr-based XPD.
## AE Characteristics
### Effect of Normal Stiffness on AE Characteristics of the Sheared Joints
In the III stage, the AE signal is lower than 27.8%, which is significantly lower than that in the II stage, because the main source of the AE signal is the friction of the structural plane, and there is no large number of gnawing failure of the rough body. Similarly, as the normal stiffness increases, the AE signal ratio also increases. Figure 12: Difference of AE hit rate and cumulative AE hit of joints under diverse normal stiffness status. (**a**) For \(k_{\rm n}=0\); (**b**) For \(k_{\rm n}=1\) MPa/mm; (**c**) For \(k_{\rm n}=3\) MPa/mm; (**d**) For \(k_{\rm n}=5\) MPa/mm; (**e**) For \(k_{\rm n}=7\) MPa/mm. rate is thicker and thinner. This is because the shear rate is different and the shear time is different, resulting in different amounts of AE data monitored. When the shear rate is high (10-20 mm/min), the loudest emission energy appears after the shear stress peak, and the AE energy exceeds 103 more times, which is mainly because the rough structure of the structural plane is broken in large pieces and more local cracks occur at the high shear rate. The AE hit rate and energy rate trends rise with a rise in shear rate, which indicates a larger energy release of the joint when the shear rate is large. This conclusion shows agreement with the observation of [PERSON] et al. [12]. Figure 14: Difference between AE hit rate and cumulative AE hit of joints with diverse shear rates under different CNS conditions. (**a**) Shear rate = 0.5 mm/min; (**b**) Shear rate = 2.5 mm/min; (**c**) Shear rate = 5 mm/min; (**d**) Shear rate = 10 mm/min; (**e**) Shear rate = 20 mm/min. The values of the peak AE hit/energy rate, the total number of AE hits/energy, and the number of hits/energy at three stages of joints under different shear rates are shown in Table 3. It is worth noting that less than 12.2% of the total AE signals are detected in stage I. The peak AE hit rate and energy rate tend to rise with a rise in shear rate, which indicates that more damage occurs for joint sheared under a higher shear rate at the moments of peak shear stress. Similar to the results of joints sheared under different normal stiffness conditions, more than 60% of the total number of AE signals was measured at stage II, and less than 30% of the total number of AE signals was generated at stage III. Overall, with the  
Figure 15: Difference between AE energy rate and cumulative AE energy of joints with diverse shear rates under diverse CNS conditions. (**a**) Shear rate = 0.5 mm/min; (**b**) Shear rate = 2.5 mm/min; (**c**) Shear rate = 5 mm/min; (**d**) Shear rate = 10 mm/min; (**e**) Shear rate = 20 mm/min. enhancement of the shear rate, the additive AE impact number declines. The additive AE energy rises with the rise of the shear rate at 0.5-5 mm/min but decreases with the rise of the shear rate at 10-20 mm/min. This is primarily since when the shear rate is higher than a specific worth, the surface roughness of the structure is nibbled instantaneously, and the shear duration is short.
# Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout
## 2 Materials and Methods
### Visual Interpretation
Three observers, experienced in airborne walrus reconnaissance (and the authors of the data release [31]), independently interpreted each satellite image. Each observer digitized the periphery of apparent walnvs aggregations when comprised of at least 6 contiguous pixels. Walnvs aggregations were digitized while using a desktop GIS to display the imagery in a UTM Zone-3 projection at a 1:10,000 scale for imagery with pixel sizes \(\geq\)10 m, at 1:5000 scale for imagery with pixel sizes 3-9 m, and at a 1:2000 scale for imagery with resolutions <3 m.