esa-sceva/satcom-chunk-collection huggingface dataset:
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 5 Performance Evaluation
### Parameter Variation Comparison  
In this simulation set, we analyze in detail our protocol by tweaking several of its configuration parameters. The first parameter is the number of allowed packet copies, ranging from 1 to 3. The second parameter taken into consideration is the permission to keep or delete the packet copy in the sending node when it is forwarded. Considering the choices, a total of 6 configurations emerge. The parameter combination used in the general comparison (previous section) is that of one copy with deletion of the packet in the sending node whenever the packet is forwarded.  
As illustrated in Figure 7, the delivery ratio for all the combinations is very high, with a slight decline in the case of a single packet approach. In fact, even when considering the values of the confidence intervals, the delivery ratio does not provide us with enough information about the parameter combination differences.  
Figure 8 shows the overhead, which increases with the number of nodes. The one copy with packet deletion configuration performs better than the other ones, slightly exceeding an overhead of 2 in the scenarios of 15 and 20 nodes. We notice a faster overhead increase in the case of \"no deletion\" with respect to \"deletion\"; this is due to the absence of packet deletion after the forwarding, resulting in a rapid increase of packet copies in the network when increasing network density.  
Finally, the packet delivery latency is shown in Figure 9. As expected, the copy with deletion combination exhibits the worst performance, while all the other combinations achieve good values, less than 150 s in the case of 20 nodes. In an operational environment, one can chose the number of copies based on the requirements of the specific application, as a tradeoff between redundancy and packet delivery delay. Another strategy worth exploiting could be that of dynamically adapting the protocol behavior whereby the number of replicas is computed dynamically based on some time to destination criteria.  
Figure 7: Average delivery ratio of GeoSaW with different parameters combinations when varying the number of nodes.
## 4 Discussion  
### Definition of the Shoreline on Aerial Photographs and GPR  
Defining the shoreline on aerial photographs of Dungeness is difficult due to the limited difference in texture and tone between the foreshore and the backshore, both of which are composed of gravel, as well as the large semi-diurnal tidal range and tidal cycles which change position and elevation daily. In the field, there is a sharp break of slope at the base of the gravel beach where the relatively steep gravel beach meets the lower angle intertidal sand flat (Figure 2), which is a feature of composite gravel beaches [10]. This contact between the gravel foreshore and tidal sand flat, over which the gravel is prograding, can be identified on black and white as well as color aerial photographs and satellite images due to the distinct change in tone between the pale gravel and the darker sand.  
The gravel-sand contact used to define the shoreline is visible on the aerial images and can also be identified on GPR profiles due to the change in slope between the relatively steep gravel beach and the lower angle sand flat, where reflections often terminate. As a consequence, we were able to correlate the reflections on the GPR with the position of the shoreline when aerial photographs were taken. This contact is neither the high tide line nor the low tide line, but because the change in composition from gravel to sand is marked by a break in slope as well as a change in tone, we are able to pick the change in tone from aerial photographs and correlate this with a break in slope on the GPR profiles, which occurs around 1 m OD. This enabled us to reconstruct the form and position of the beach, since the earliest available aerial photographs were taken in 1940, and to estimate the volumes of shingle that have accumulated along the studied section of coast since 1940.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Cross-Section of Beach from** \\ **GPR (m2)** \\ \end{tabular} & \
\begin{tabular}{c} **Increased Width of Beach from** \\ **Historic Images (m) (1940–2019)** \\ \end{tabular} &
\begin{tabular}{c} **Average Thickness of Shingle at** \\ **Each Profile Derived by Dividing** \\ **the Cross-Section Area by the** \\ **Change in Beach Width (m)** \\ \end{tabular} \\ \hline Beach 1 & 758 & 146 & 5.19 \\ \hline Beach 2 & 584 & 120 & 4.85 \\ \hline Beach 3 & 476 & 89 & 5.35 \\ \hline Beach 4 & 402 & 81 & 4.96 \\ \hline Beach 5 & 393 & 84 & 4.68 \\ \hline Beach 6 & 405 & 90 & 4.5 \\ \hline Beach 7 & 278 & 68 & 4.09 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-section area of shingle beach deposits between 1940 and 2019 derived from GPR profile, the width of the beach accretion between 1940 and 2019, and the average thickness of shingle derived by dividing the cross-sectional area by the width of accretion.  
Errors in image registration are typically between 2.5 and 3 m, which is around 3% of the measured change in beach width between 1940 and 2019.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
###### Abstract  
Lithology classification is a crucial step in the prospecting process, and polarimetric synthetic aperture radar (Pol-SAR) imagery has been extensively used for it. However, despite significant improvements in both information content of Pol-SAR imagery and advanced classification approaches, lithology classification using Pol-SAR data may not provide satisfactory classification accuracy due to high similarity of certain classes. In this paper, a novel Pol-SAR lithology classification method based on a stacked sparse autoencoder (SSAE) is proposed. By using superpixel segmentation, new features can be extracted from dual-frequency Pol-SAR data, which can increase the class separability of the input data. Then, these features and the coherency matrices are incorporated into SSAE to classify the lithology. The classification performance is evaluated on an SIR-C dataset acquired over Xinjiang, China. The experimental result shows that this method is effective for lithology classification and can improve the overall accuracy up to 98.90%.  
polarimetric synthetic aperture radar; dual-frequency Pol-SAR data; stacked sparse autoencoder; lithology classification +
Footnote †: journal: Applied Sciences
## 2 Methods  
### K-Means Algorithm  
The main idea of the K-means algorithm is to divide the data set into different categories through the iterative process, so that the criterion function for evaluating the clustering performance is optimized and each generated cluster is compact and independent. It was first proposed by [PERSON] in his doctoral dissertation. The steps are as follows [20,21].  
For a rock mass of tunnel section data set \(X=\{X_{1}\), \(X_{2}\), or \(X_{n}\}\), it can be divided into \(c\) classes, expressed as \(C=(C_{1}\), \(C_{2}\), \(\ldots\), \(C_{c})\), and the division matrix \(U(X)\) is obtained, expressed as \(U(X)=[u_{ij}]_{c\times n}\) (\(I=1,\ldots,c,j=1,\ldots,n\)), where \(u_{ij}\) is the membership degree of the sample \(x_{j}\) to the \(C_{i}\) class. The division result needs to meet the following conditions:  
\[\left\{\begin{aligned} & U_{i=1}^{c}C_{i}=X\\ & C_{i}\cap C_{j}=\varnothing,i,j=1,\cdots,c;i\
eq j\\ & C_{i}\
eq\varnothing\hskip 28.452756 pti=1,\cdots,c\end{aligned}\right. \tag{1}\]  
Using this method to divide the number of rock mass quality classification can be achieved by the following steps:  
1. Initialize the basic parameters of Fuzzy C-means clustering algorithm to determine the number of cluster centers \(c\) (\(2\leq c\leq\)\(n\)).
2. Calculate the membership \(u_{ij}\) and the initial clustering center \(y_{i}\) of each cluster according to the results of the initial division. \[u_{ij}=1/\sum_{l=1}^{c}\left[d\left(x_{j},y_{i}\right)/d\left(x_{j},y_{l} \right)\right]^{\frac{1}{n-1}}\] (2) \[y_{i}=\sum_{j=1}^{n}\left(u_{ij}\right)^{m}x_{j}/\sum_{j=1}^{n} \left(u_{ij}\right)^{m}\] (3) where \(u_{ij}\) is the membership degree of the \(j\)th rock mass belonging to the \(i\)th subset \(C_{i}\), \(m\) is the fuzzy weighted index, and the general value is \(2\); \(d\left(x_{j}\),\(y_{i}\right)\) represents the distance from the \(j\)th rock mass to the cluster center of the \(i\)th subset.
3. Compute the distance from each discontinuity to each cluster center, and then reclassify this according to the new distance. That is, each discontinuity is divided with the nearest group.
4. Solve and update the membership \(u_{ij}\) and clustering center \(y_{i}\) using Equations (2) and (3).
5. Solve the objective function by: \[J(U,Y)=\sum_{i=1}^{c}\sum_{j=1}^{n}\left(u_{ij}\right)^{m}d\left(x_{j},y_{i}\right)\] (4)
6. Repeat Steps (3), (4), and (5) to minimize the objective function.  
When it cannot be reassigned to different clusters, the iteration process ends.
# Numerical Quantification of Controllability in the Null Space for Redundant Manipulators
## 2 Method
### The Null Space Projector  
When using the null space projection for control, the joints of the manipulator move simultaneously in compensation to prevent interference with the high priority task. This compensation acts as a constraint for the secondary task. Thus, finding the controllable joint motion is important. Since the controllable joint motion is the basis vector of the joint space, it is equivalent to the eigenvector of the null space projector. Thus, the eigenvalue decomposition method can be used to acquire an eigenvector. The null space projector consists of the \(I\) and \(J^{T}J^{T+}\). Because \(J^{T}J^{T+}\) is a hermitian matrix with real numbers, and by the definition of the pseudo inverse, the null space projector \(N^{T}\) is always a symmetric matrix. Therefore we can derive the eigenvalues and eigenvectors of the null space projector expressed in the form below:  
\[N^{T}=PDP^{-1}\qquad(\textit{where}\quad P=[P_{1}\cdots P_{k}]) \tag{4}\]  
The eigenvector \(P_{i}\) is the \(n\times 1\) vector and the number of eigenvalue and eigenvector are \(k=n-m_{P}\). \(m_{P}\) is the DoF of primary task. By using these eigenvectors, we can notice the joint constraints and the relationship between each joint while executing the primary task at the end-effector. Furthermore, by projecting the input torque to this eigenvector, we can control the extra joint DoF independently.  
\[\Gamma=J_{P}^{T}F_{P}+N_{P}^{T}\Gamma_{S} \tag{5}\]  
\[\Gamma_{S}=K_{P}P_{1}\frac{P_{i}\cdot(q_{d}-q)}{|P_{i}\cdot(q_{d}-q)|}|q_{d}- q|-K_{v}q \tag{6}\]  
Equation (5) shows torque input of a manipulator within the task space, while controlling the joints with null space projector. \(\Gamma_{S}\) is the projected torque vector within the eigenvector \(P_{i}\). The vector \(q\) and \(q\) are the vectors representing joint angle and joint angular velocity, respectively. \(q_{d}\) represents the desired joint angle. \(K_{p}\) and \(K_{v}\) are the diagonal matrices containing the proportional and derivative gains, respectively. It is sufficient to perform several tasks like posture control and singularity avoidance by joint space control within the null space, but the tasks like obstacle avoidance, should be performed by task space control even in the null space. We have dealt with this problem in the next section by proposing a new index named null space quality and designating appropriate control direction in the task space.
## 5 Results and Discussion  
### Accuracy of Map of Artificial Objects Using HSR Imagery  
We validated the results of the classification of artificial areas for 2009 and 1996-1997. The overall accuracy for 2009 was 90.57%. This is a very good classification rate for an automatic classification method. Errors were the result of the method's inability to distinguish between non-cultivated agricultural/rocky areas and artificial areas.  
Concerning the 1996-1997 classification, the IRS images had a lower spatial resolution, which reduced the detection of isolated buildings. We used the 2009 results as a mask to reduce the error rate. The overall accuracy obtained was 88.33%.  
The user accuracy and producer accuracy values obtained are listed in Table 3. They confirm the high quality of the results.  
\begin{table}
\begin{tabular}{|c|l|c|c|c|} \cline{3-5} \multicolumn{1}{c}{} & \multicolumn{1}{c|}{**User Accuracy**} & \multicolumn{1}{c|}{**Producer Accuracy**} & \multicolumn{1}{c|}{**Overall Accuracy**} \\ \hline \multirow{3}{*}{**2009**} & **Artificial Areas** & 86\% & 80\% & \multirow{3}{*}{90\%} \\ \cline{2-2} \cline{4-5}  & **No Artificial Areas** & 92\% & 95\% & \\ \cline{2-2} \cline{4-5}  & **Artificial Areas** & 82\% & 93\% & \\ \cline{2-2} \cline{4-5}  & **No Artificial Areas** & 94\% & 85\% & \\ \hline \end{tabular}
\end{table}
Table 3: User and producer accuracy for artificial areas in 1996–1997 and 2009.
# Review of 3D Imaging by Coded Aperture Correlation Holography (COACH) +
## 3 System Applications
### D Imaging  
One of the foremost characteristics related to the term holography is 3D imaging. The 3D imaging capability of holography has been the main feature, which has made the area of research attractive at the time of its invention. In general, many types of holograms contain 3D information of the observed object such that 3D image can be reconstructed from these holograms. The entire holograms presented in the previous section have 3D imaging capabilities. COACH-based methods have the same axial resolution as direct imaging, but the use of incoherent interferometers makes these methods less attractive. Therefore, the newly introduced incoherent digital holography techniques without two-wave interference, namely, I-COACH, seem the best candidates for 3D imaging. In this section, the imaging by I-COACH of real 3D objects distributed on multiple transverse planes is discussed.
# Evaluation of the Mid-Latitude Ionospheric trough Using GRACE Data
## 3 Results and Discussion  
As mentioned in the previous section, approximately 45,000 Ne profiles were detected with MIT signatures. Thus, we obtained a database of MIT parameters, such as trough minimum, trough width, and trough depth. The occurrences of MIT parameters are statistically analyzed in this section. The parameters were analyzed as functions of different space weather indices, for instance, solar radio flux index F10.7; solar wind (SW) plasma speed; flow pressure; interplanetary magnetic field components Bx, Bz, and By; SYM-H; and Hp30. Additionally, the dependency of MIT parameters on different geophysical conditions, such as geographic/magnetic location, magnetic local time, and seasons, were analyzed.
## 3 Results
### End-to-End Communication System Emulation in Interfering Model
#### 3.5.2 Test 2 Results (Interference Increasing)  
To re-evaluate the analyses, we increase the interfering signal level:  
\[I_{(SPECT)}=-36.8\ \mathrm{dBm}@P_{T_{(SMJ)}}=-11.0\ \mathrm{dBm}.\]  
Tuning the SMJ generator to give:  
\[\frac{E_{b}}{N_{0}+I_{0}}=10.5\ \mathrm{dB},\]  
we have:  
\[P_{T_{(SMJ)}}=-17.5\ \mathrm{dBm}(\mathrm{checked})\.\]  
\[INR\prime=INR-(-11.0+17.5)dB,\therefore INR\prime=4.5-6.5=-2\ \mathrm{dB}. \tag{8}\]  
Since,  
\[1+INR\prime=10^{\frac{12.7-10.5}{10}}\Longrightarrow INR\prime=1.65\because \ INR\prime\approx-1.9\ \mathrm{dB}. \tag{9}\]  
The value (9) slightly differs from that previously calculated in (8). Based on the consistency of our communication-interfering system test results, we can extend the computations on the system with MODCOD schemes, which aim to mitigate interference levels, optimizing the required \(E_{b}/N_{0}\) according to the variation of INR.
###### Abstract  
In the context of railway safety, it is crucial to know the positions of all trains moving along the infrastructure. In this contribution, we present an algorithm that extracts the positions of moving trains for a given point in time from Distributed Acoustic Sensing (DAS) signals. These signals are obtained by injecting light pulses into an optical fiber close to the railway tracks and measuring the Rayleigh backscatter. We show that the vibrations of moving objects can be identified and tracked in real-time yielding train positions every second. To speed up the algorithm, we describe how the calculations can partly be based on graphical processing units. The tracking quality is assessed by counting the inaccurate and lost train tracks for two different types of cable installations.  
DAS; fiber optic sensing; train tracking; pattern recognition Article
###### Abstract  
The verticals of 5G, such as the automotive, smart grid, and smart cities sectors, will bring new sensors and IoT devices requiring Internet connectivity. Most of these machine-type terminals will be sparsely distributed, covering a very large geographical area and, from time to time, will have to update their software, firmware, and/or other relevant data. Given this situation, one viable solution to implement the \"Over-the-Air\" update of these IoT terminals can be done with the aid of GEO satellite systems. However, due to the ultra-dense radio frequency reuse factor that contemporary High-Throughput Satellite (HTS) systems implement in the access link to serve the IoT terminals, the use of a time-packed Free Space Optical (FSO) link represents a practical solution to avoid the bottleneck that the satellite gateway experiences in the feeder link. The performance of both Detect-and-Forward and Decode-and-Forward relaying strategies are studied, assuming that the single-carrier \(M\)-PAM symbols that are transmitted on the optical feeder link are mapped into \(M\)-QAM symbols that modulate the multiple sub-carriers of the OFDM-based radio access link. In addition, the benefits of encapsulating the NB-IoT frames into DVB-S2(O) satellite frames is also analyzed in detail. The effects of the impairments introduced in both the optical feeder and radio access links are characterized in detail, and the end-to-end error correction capabilities of the Modulation and Coding Schemes (MCS) defined in the contemporary releases of the NB-IoT and DVB-S2(X) standards are studied for different working regimes.  
NB-IoT; DVB-S2(X); high-throughput satellite; optical feeder link; over-the-air updates; time-packing; decode-and-forward; scintillation; beam-wander; convolutional coding 2021213952[[https://doi.org/10.3390/s21123952](https://doi.org/10.3390/s21123952)]([https://doi.org/10.3390/s21123952](https://doi.org/10.3390/s21123952))
# Assessing Blockchain Consensus and Security Mechanisms against the 51% Attack
## 6 51% Attack Mitigation Techniques
### ChainLocks  
ChainLocks is a security technique developed to secure DASH. It results from the implementation of long living masternode quorums (LLMQs) to mitigate the 51% attack [50]. ChainLocks executes a network-wide vote process which comprises a \"first-seen\" policy. For each particular block, an LLMQ of a large number of master nodes is approved. It requires every participant to sign the noticed block so that the active chain can be extended. The majority of the participants, 60% or more, verify the distinct block and generate a P2P message (CLSIG) to notify every other node in the network about the event. The (CLSIG) message cannot be generated unless enough members comply with it. The message involves a valid signature for authenticity and verifiable by all the nodes within the network. In this security protection technique, the transaction gets confirmed after the first confirmation. Once confirmed, it cannot be reversed back as the signed block cannot be acknowledged at a later time. This security feature lifts the six confirmation aspects and enhances a secure transaction after just one confirmation. In addition to the 51% attack, ChainLocks also helps to mitigate other security issues, such as selfish mining described in Section 5.
## 2 Methods and Data
### Data Source and Pre-Processing
#### 2.2.3 Satellite-Based SIF Dataset  
This work used two types of SIF datasets: GOSAT SIF and CSIF. The GOSAT SIF dataset was generated by integrating OCO-2, MODIS, and reanalysis data with a high spatial resolution of 0.05\({}^{\circ}\) and a monthly interval, and the data have been verified to have a high correlation with GPP from 91 FLUXNET sites (R\({}^{2}\) = 0.73, \(p\) < 0.001). The CSIF dataset has a higher temporal interval of daily ranging from 2000 to 2018. The two SIF products were obtained from the Global Ecology group website ([[https://globaecology.unth.edu/data/GOSIF.html](https://globaecology.unth.edu/data/GOSIF.html)]([https://globaecology.unth.edu/data/GOSIF.html](https://globaecology.unth.edu/data/GOSIF.html)), (accessed on 25 August 2023)).
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 3 System Model and Problem Statement
### P-SFCs and BVNFs Deployment
Next, we calculate the end-to-end delay when these instances in a subset process a user request simultaneously with all VNF instances of other \(VNF^{l}\ \in SFC_{i}\). If the delay satisfies the delay requirement, we define the subset of these instances as a feasible set and delete any remaining subset that contains all instances of the feasible set. Then, we calculate the reliability of all feasible sets. The operation example of calculating the reliability \(r_{VNF^{l}}\) of \(VNF^{l}\) on \(SFC_{i}\) is shown in Figure 2. To avoid calculating reliability repeatedly, it is necessary to obtain an ordered permutation of all feasible sets and then find the intersection of feasible sets. For example, the intersection of two feasible sets, i.e., [_V4, V5, V6_] and [_V4, V5, V7_], is [_V4, V5_]. The reliability of these two sets is equal to the reliability of [_V4, V5_] times the reliability of [_V6, V7_]. Since both _V4_ and _V5_ must remain active, only one of _V6_ and _V7_ remain active, and the reliability of [_V6, V7_] is equal to 0.96 (i.e., \(1\ -0.2^{2}\)). The reliability of [_V4, V5_] is equal to 0.00512 (i.e., \(0.2^{*}\times\) 0.2 \(\times\) 0.2 \(\times\) 0.8 \(\times\) 0.8). The value of \(0.2^{3}\) is to ensure that _V1_, _V2_, and _V3_ fail and avoid the intersection between the reliability of these two sets, and the feasible set contains _V1_, _V2_, and _V3_. The reliability of P-SFCs is the product of all active instances of P-SFCs. The service reliability of P-SFCs for \(SFC_{i}\) is denoted as \(R_{P-SFC_{i}}\). The accumulative reliability of \(R_{P-SFC_{i}}\) is as follows. \[R_{P-SFC_{i}}=\sum_{p=1}^{L_{i}}r_{VNF^{p}} \tag{4}\]When there are \(\left|N_{a,l,i}\right|\) BVNFs for \(VNF^{l}\) in \(SFC_{i}\), let \(r_{i,l,1},r_{i,l,2},\ldots,r_{i,l,N_{a,l,i}}\) be their reliability, respectively. The accumulative reliability \(R_{SFC_{i}}\) of user request \(r_{i}\) is, therefore:  
\[R_{SFC_{i}}=\sum_{p=1}^{L_{i}}\left(1-\left(1-r_{VNF^{p,i}}\ \right)\sum_{j=1}^{N_{a, l,j}}\left(1-r_{i,l,j}\ \right)\ \right) \tag{5}\]  
Here, we consider two kinds of delay, namely, the transmission delay along multiple P-SFCs between the source node and the destination node and the processing delay in each edge server. The overall processing delay of sub-flow k of traffic \(r_{i}\) at all edge servers is denoted as \(d_{pro}(f_{i,k})\). Let \(P(s_{i},d_{i},\ k)\) be the routing path for sub-flow k of user request \(r_{i}\) between the source node and the destination node. The transmission delay along the path \(P(s_{i},d_{i},\ k)\) is \(d(P(s_{i},d_{i},k))=\sum_{E_{j,k}\in P(s_{i},d_{i},k)}d\left(B_{j,h}\right)\), where \(d(E_{j,h})\) is the transmission delay of sub-flow k on link \(E_{j,h}\in E\). The accumulative delay of user request \(r_{i}\) is, therefore:  
\[D_{SFC_{i}}=max\big{\{}d(P(s_{i},d_{i},k))+d_{pro}(f_{i,k})\big{\}} \tag{6}\]