esa-sceva/satcom-chunk-collection huggingface dataset:
# Geometric Similarity Measurement Method for Micro Scene Generalization
## 2 Methodology  
A micro scene contains line groups and polygon groups; shape transformation, direction transformation, and position transformation should be considered in the similarity calculation of micro scenes (Figure 2). In this study, we constructed a method of evaluating the geometric similarity of micro scenes based on hybrid feature description, a direction Voronoi diagram, and a position graph. Shape similarity was calculated by combining the Included Angle Chain and the Hausdorff distance; the directional Voronoi diagram was used to calculate the direction similarity; the Delaunay triangulation network was constructed to calculate the position similarity.
# Synchronizing Many Filesystems in Near Linear Time
## 4 Algorithms
### Checking and Ordering Canonical Sets  
This algorithm checks whether the command set \(A\) is canonical, assuming that it is sorted lexicographically according to the nodes of the commands. It checks the first two conditions of Definition 3, directly. Instead of the third condition (\(A\) is \(\ll\)-connected), the following clearly equivalent conditions are verified:  
1. If \(A\) contains a command on the node \(n\) and also on an ancestor of \(n\), then it contains a command on the parent of \(n\);
2. If \(\sigma,\tau\in A\) are on parent-child nodes, then either \(\sigma\ll\tau\) or \(\tau\ll\sigma\).  
**Algorithm 2** (Determining if \(A\) is canonical, Code 2).: _Start with all commands in \(A\) arranged in a doubly linked list according to a lexicographic order of the command nodes. Loop through the commands and check that there is one command on each node, and none of the commands is a null-command. Run Algorithm 1 to define the up pointers. Loop through the commands. If the up pointer is not \(\bot\), then it must point to the parent node; moreover, the current command and the command at the parent must be \(\ll\)-related._  
```
1-sort sequence lexicographically by the nodes of the commands
2-add up pointers using Algorithm 1
3 foreach command in sequence do
4 ifpreviousCommand.node = command.node then
5 returnfalse \(not\) canonical, as multiple commands are on the same node
6
7 end if
8 ifcommand.up \(\
eq\bot\)and not( command.up, command) is a constructor pair or command, command.up) is a destructor pair ) then
9 returnfalse \(not\) canonical because the closest command on an ancestor is not on the parent, or they do not form a valid pair
10
11 end if
12 returntrue
```  
**Code 2.** Check whether a set of commands is canonical.  
A canonical set \(A\) can always be ordered to honor \(\ll\). Perhaps the simplest way to obtain such an ordering is to make two passes through the lexicographically sorted set \(A\), as done by Algorithm 3.  
**Algorithm 3** (Ordering a canonical set, Code 3).: _Sort commands in a canonical set lexicographically. First, scan the commands forward (top-down), extracting the constructor commands, and place them at the beginning of the output sequence. Second, place the remaining commands on the output sequence in reverse lexicographical order (bottom-up). This includes destructors and edit commands matching \((\bullet,\mathbb{F},\mathbb{F})\). It is clear that this sequence order honors the \(\ll\) relation._  
```
1 sequence \(\leftarrow\) commandSet in some order
2-sort sequence lexicographically by the nodes of the commands
3 foreach command in sequence do
4 ifcommand is a constructor then
5 push command on output
6
7 end if
8 forach command in sequence backwards do
9 ifcommand is not a constructor then
10 push command on output
11
12 end if
13
14 returnoutput
```  
**Code 3.** Order a canonical command set and return a canonical sequence.  
**Algorithm 4** (Ordering a canonical command set and return a canonical sequence.  
Both Algorithm 2 and Algorithm 3 of this section clearly run in near linear time.
###### Abstract  
This study employs density functional theory (DFT) to evaluate the optoelectronic features of five natural dyes (cyanidin, delphinidin, pelargonidin, peonidin, and petunidin) in gas and ethanol phases for potential dye-sensitized solar cell (DSSC) applications. Calculations cover HOMO and LUMO energy levels, charge transfer potential gaps, and light absorption properties correlated with oscillator strengths. Photovoltaic aspects, including light-harvesting efficiency (LHE), electron injection efficiency (\(\Delta G^{injact}\)), regeneration efficiency (\(\Delta G^{projan}\)), open-circuit voltage (\(V_{OC}\)), excited-state lifetime (\(\tau\)), and the electronic coupling constant (\(1\,V_{RP}\,1\)), were computed to assess DSSC suitability. DFT analysis reveals that cyanidin, delphinidin, and petunidin exhibit favorable LUMOs for efficient electron injection into the semiconductor's conduction band. Cyanidin demonstrates a high quantum yield for light absorption. Delphinidin and petunidin act as effective light absorbers with high excitation energies and oscillator strengths, while petunidin and delphinidin display strong LHE, indicating excellent electron-donating capabilities. Penonidin shows promising \(\Delta G^{injact}\) despite needing more energy for injection. Pelargonidin excels in \(\Delta G^{projan}\) and \(|\,V_{RP}\,|\), enhancing DSSC performance. Petunidin and delphinidin exhibit a high V\({}_{OC}\). Petunidin efficiently transmits energy through a large \(\tau\), while pelargonidin's \(|\,V_{RP}\,|\) confirms its potential as a favorable sensitizer. In summary, each dye possesses unique properties, and understanding them aids in selecting the most suitable dye for enhanced DSSC performance.  
annocyanin; density functional theory; dye-sensitized solar cell; optoelectronic properties; natural dyes +
Footnote †: journal: Applied Sciences
###### Abstract  
As the field of high-precision applications based on carriers continues to expand, the development of low-cost, small, modular receivers and their application in diverse scenarios and situations with complex data quality has increased the requirements of carrier-phase data preprocessing. A new geometry-based cycle slip detection and repair method based on Global Position System (GPS) + BeiDou Navigation Satellite System (BDS) is proposed. The method uses a Time-differenced Carrier Phase (TDCP) model, which eliminates the Inner-System Bias (ISB) between GPS and BDS, and it is conducive to the effective combination of GPS and BDS. It avoids the interference of the noise of the pseudo-range with cycle slip detection, while the cycle slips are preserved as integers. This method does not limit the receiver frequency number, and it is applicable to single-frequency data. The process is divided into two steps to detect and repair cycle slip. The first step is cycle slip detection, using the Improved Local Analysis Method (ILAM) to find satellites that have cycle slips; The second step is to repair the cycle slips, including estimating the float solution of changes in ambiguities at the satellites that have cycle slips with the least squares method and the integer solution of the cycle slips by rounding. In the process of rounding, in addition to the success probability, a decimal test is carried out to validate the result. Finally, experiments with filed test data are carried out to prove the effectiveness of this method. The results show that the detectable cycle slips number with GPS + BDS is much greater than that with GPS. The method can also detect the non-integer outliers while fixing the cycle slip. The maximum decimal bias in repair is less than that with GPS. It implies that this method takes full advantages of multi-system.  
GPS + BDS; TDCP; single frequency; cycle slip; ILAM; outlier +
Footnote †: journal: Sensors
## 1 Introduction
Therefore, we propose preprocessing-free RDH-ED based on the improved TCSIS (VTSIS), which uses a random substitution matrix as the hiding key to implement embedding in the polynomial coefficients, which leads to the existence of only a single pixel value or an extra secret in each row and column of the polynomial coefficient matrix. The experiments demonstrate that the scheme in this paper achieves better security and ER without preprocessing. The main contributions of this paper are as follows:  
1. A VRIE RDHED scheme applicable to a distributed variable security environment is proposed, which extends the application scenarios. 2. Improving the security of existing TCSIS and using the redundancy space to embed extra data in the encryption process results in better security and embedding performance. 3. Using the random substitution matrix as the hidden key, only the seed key needs to be transmitted without transmitting the hidden key to the extractor, which effectively reduces communication overhead and improves security. 4. Theoretical analyses and experimental results show that the proposed scheme has high security, a large embedding rate, complete reversibility, and low computational complexity. In addition, it does not require preprocessing and solves the problem of pixel overflow. The remainder of this paper is as follows: In Section 1, the relevant work is introduced, which mainly focuses on the security analysis and experimental effect demonstration of the TCSIS scheme. Section 2 describes the algorithm in detail and provides a specific example for elucidation. In Section 3, the superior performance of the scheme is proven by many theoretical analyses and experiments. Finally, this paper concludes with a summary.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 16 Hierarchical Clustering in a Heterogeneous Network
### Problem Formulation  
A mixed binary linear program was formulated to select the intermediate nodes and assign to them other nodes to relay their sensed data such that the maximum energy consumption across all the nodes in the cluster is minimized. The formulation details are as follows:  
* Decision variables: the two sets of decision variables are, 1. \(A_{u,w}\) are binary decision variables that determine whether a node \(w\) is used as an intermediate node to relay data from a general node \(u\).
2. \(E_{max}\) is a continuous non-negative variable that represents the maximum consumed energy among all nodes in the cluster.
* Objective function: is a continuous single variable linear function that minimizes the maximum energy variable \(E_{max}\).
* Constraint sets: the interpretation of the different constraint sets is given below, 1. A linear constraint set in the binary variables \(A_{u,w}\) that guarantees that the number of packets relayed by an intermediate packet do not exceed the maximum that it can receive from general nodes, i.e., \(\sum_{u\in\mathcal{N}_{u}}A_{u,w}\leq k_{w}\), where \(\mathcal{N}_{u}\) is the set of general nodes.
2. A linear constraint set in the binary variables \(A_{u,w}\) that ensures that one general sensor node can forward its packets to only one intermediate sensor nodes on a direct link, i.e., \(\sum_{w\in\mathcal{N}_{w}}A_{u,w}\leq 1\), where \(\mathcal{N}_{w}\) is the set of intermediate nodes.
3. A constraint that contains both the continuous maximum energy variable \(E_{max}\) and the binary assignment variables \(A_{u,w}\). This constraint enforces an upper bound of the energies of all nodes and was given in [11] as \(E_{max}\geq E_{i}\left(\mathbf{A}\right)\). The energies of all nodes were shown in [11] to be linear in the assignment vector of the variables \(A_{u,w}\).
# Reducing Defense Vulnerabilities in Federated Learning:
## 4 Attack Scenarios and Results
### Performance Analysis
#### 4.3.3 Data Poisoning Scenario and Results (Non-IID)
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **\%0** & **\%10** & **\%20** \\ \hline Krum [14] & 74.15 & 71.62 & 68.85 \\ Median [16] & 74.51 & 73.85 & 73.73 \\ TMeans [16] & 74.66 & 74.14 & 72.87 \\ FLDefender [29] & 75.07 & 74.44 & 73.92 \\ Ours OC-SVM & **75.11** & **74.48** & **74.27** \\ Ours Agg-C & 74.56 & 74.36 & 72.27 \\ Ours k-NN & 73.82 & 73.31 & 67.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: OAcc performance against label-flipping attack for non-IID CIFAR. Another experiment was carried out on the GTSR dataset. In the simulation, the data among the participants are set to non-IID. The comparative results presented in Table 11 demonstrate the performance of FedAvg, Krum [14], LFGuard [22], and FoolsGold [33]. Through evaluations conducted with three variants of the proposed method (OC-SVM, Agg-C, and k-NN), it has been observed that the OC-SVM-based approach exhibits superior performance even under high malicious ratios (up to 50%). Notably, at a 30% adversarial ratio, the OC-SVM variant achieved an accuracy rate of 96.03%, surpassing its nearest competitor, LFGuard (95.75%). These findings suggest a marked improvement in robustness against malicious participants whilst maintaining model accuracy under increasingly challenging scenarios.
## 4 Safety Indices and Predictive Safety Evaluation
### Time to Collision (TTC)  
TTC is computed using the current swing acceleration and maximum swing velocity. The following equations were used to calculate \(TTC\).  
\[||r_{obs}||=\sqrt{{x_{obj}}^{2}+{y_{obj}}^{2}}\,. \tag{28}\]  
\[\theta_{ATC}=tan^{-1}\left(\frac{y_{obj}}{x_{obj}}\right)-\varphi_{sw}. \tag{29}\]  
\[c=||r_{obs}||\theta_{ATC}-B_{c}. \tag{30}\]  
If \(\theta_{ATC}<0,\;TTC^{2 nd}\leq t_{m}\),  
\[TTC=\frac{\theta_{ATC}}{\dot{\theta_{sw,k}}-\theta_{obj,k}}. \tag{31}\]  
If \(\theta_{ATC}\geq 0,\;t_{m}\leq TTC^{2 nd}\),  
\[TTC=\frac{\theta_{ATC}+\pi}{\theta_{sw,k}-\theta_{obj,k}}. \tag{32}\]  
where \(r_{obj}\) in Figure 7 denotes the object's distance to the coordinate origin, the object's estimated coordinates are denoted by \(x_{obj}\) and \(y_{obj}\), \(\varphi_{sw}\) is the swing angle of the excavator, \(\theta_{ATC}\) shows the angle to a collision, \(B_{c}\) is the bucket clearance, \(t_{m}\) denotes the predicted time to reach maximum velocity, \(\dot{\theta_{sw,k}}\) is the swing velocity, \(\theta_{obj,k}\) presents the angular velocity of a detected object, \(TTC^{2 nd}\) is the second-order \(TTC\) that represents \(TTC\) while considering the current swing acceleration.  
Figure 7: Illustration of \(r_{obj}\), \(c\), and \(\theta_{ATC}\).
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Supervised Learning
#### 4.1.3 Channel Estimation/Allocation  
Estimation of future radio communication channels is rather challenging, due to their growing complexity [16]. In [49], data-driven supervised DNN estimators are used to predict channels, with results showing that using this approach the authors can predict more accurate channels compared to conventional channel estimation algorithms. The authors in [50] propose a supervised deep neural network (DNN) approach for adaptive bit allocation with imperfect Channel State Information (CSI) in heterogeneous networks. The accurate CSI estimation in heterogeneous networks can greatly impact the system's performance. Furthermore, the reduction of feedback overhead is an important challenge in heterogeneous networks. Even though many different quantization techniques have been used to address this issue, the system's performance cannot increase linearly with the number of bits increasing exponentially. The bits need to be distributed to the cells and then they are further allocated to each channel optimally. This conventional approach is time-consuming and so in order to enable direct allocation for the entire network, the proposed method is used. Using the supervised DNN the optimized number of bits can be directly obtained for a different number of bits and scenarios, leading to complexity reduction. Simulations show that the proposed method achieves a closer to optimal performance than the conventional approaches.
# Estimating the Volume of the Solution Space of SMT(LIA) Constraints by a Flat Histogram Method
## 4 The Proposed Flat Histogram Method
### The Sampling Algorithm
It can also be seen that the volume estimations of MCMC-Flat\({}^{1/\text{t-pv}}\) on instances _ran2_, _ran4_, _ran7_, and _ran10_ are close to those of MCMC-Flat\({}^{1/2}\). On all the random instances tested, the running times of MCMC-Flat\({}^{1/\text{t-pv}}\) varies from about ten seconds to a few hundreds of seconds, whereas those of MCMC-Flat\({}^{1/2}\) varies from 7.59 to 3266.07 s. Therefore, MCMC-Flat\({}^{1/\text{t-pv}}\) is more scalable than MCMC-Flat\({}^{1/2}\). Next, let us look at the results of the Exact Method. The Exact Method runs out of memory on _ran2_, _ran4_, _ran7_, _ran10_ and _ran11_, which is of dimension 15, 10, 8, 8, and 9 respectively (please refer to Table 1). Seeing that the Exact Method succeeds on other instances with less than 8 dimensions, we can conclude that the Exact Method does not scale well. Overall, we see that MCMC-Flat\({}^{1/\text{t-pv}}\) has a good scalability and outperforms MCMC-Flat\({}^{1/2}\) and the Exact Method. Another note is that both MCMC-Flat\({}^{1/2}\) and MCMC-Flat\({}^{1/\text{t-pv}}\) return greater than zero estimates on all the random instances, especially on those with 8 or more dimensions. The results indicate the ability of our MCMC-Flat algorithms in finding at least one solution. In contrast, the direct Monte Carlo method implemented in [24] failed to find any solution for instances with dimensions greater than 7, which results in a relative error of 100% (please refer to Figure 10b in [24]). We argue that this good ability of our MCMC-Flat algorithms is due to both the transition probability in Equation (2) and a mapping from the valuation space (with possibly exponentially number of states) of a SMT(LIA) formula to our defined energy space that is linear in the number of clauses. ### More Results on Convex Bodies

In the second part of experiments, we focus on a restricted form of STM(LIA), the convex body. Specifically, using the volume computation problem of convex bodies, we compare our MCMC-flat methods with some state-of-the-art model counters. Instances of convex bodies were obtained from the benchmarks ([[http://code.google.com/p/rvc/](http://code.google.com/p/rvc/)]([http://code.google.com/p/rvc/](http://code.google.com/p/rvc/))) used in [24]. All the problems are extracted from the sub-folder named \"Exp3_1\". A problem with name _m-n-i_ involves \(m\) variables, and \(n\) + 1 constraints where \(i\) is the index of a variant. Each variable has a value bound [\(-\)15, 15]. Every constraint is an inequality, and coefficients of variables are rational numbers represented by fractions. For example, the first inequality of the problem \"7-5-1\" is:

\[-1/2\cdot x_{1}-2/5\cdot x_{2}-7/5\cdot x_{3}+7/10\cdot x_{4}+3/8\cdot x_{5} \leq-3/5. \tag{7}\]

A brief summary on properties of the selected instances are presented in Table 4. \"#Variable\", \"#Constraint\", and \"Space\", in Table 4, stand for the number of variables, the number of LIA constraints,
## 4 Experimental Results and Analysis
### Experimental Result  
#### 4.2.1 Algorithm Gene Replacement Times and Convergence Analysis  
To find the best times of gene replacement, this paper carried out several experiments on the road network datasets of Xi'an and Wuhan,. The results are shown in Figure 6. In the figure, when the number of gene substitutions is equal to 8, the chromosomal scenic score reaches the maximum and will not change with an increase in the number of gene substitutions. The reason for this phenomenon is that the selection conditions of replacement genes are relatively strict, and the chromosomes are not very long after the search area has been limited. Therefore, when replacement times reach a certain value, it is difficult to find a replacement edge that meets the conditions. Therefore, this paper considers that 8 is the best number of gene replacements. When the number of gene replacements is greater than 8, gene replacement will not be performed.  
In addition, the convergence of several algorithms is experimentally analysed. The results are shown in Figure 7. The figure shows that the fastest algorithm converges first. When the number of iterations reaches 10, the improved GA converges. The MA and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**City** & **Number of Users** & **Number of POIs** & **Number of POI Types** \\ \hline Xi’an & 1161 & 1587 & 10 \\ Wuhan & 1583 & 2039 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Introduction to other data.  
Figure 6: Number of gene replacements.  
traditional GA reach the maximum scenic score after 15 iterations, and no change occurs in subsequent iterations.
# Multi-Source Satellite Imagery and Machine Learning
## 4 Materials and Methods
### Classification Algorithms  
Classification algorithms are essential artificial intelligence tools that allow data to be categorized by assigning specific labels, thus facilitating automated decision-making. The choice of algorithm is necessary and depends on the particular characteristics of the data to be analyzed. We selected three particularly efficient algorithms for our experiments: Gradient Boosting, XGBoost, and Random Forest [18].
## 3 Data Modeling Methodology
### Differential Evolution (DE)  
DE is an algorithm based on biological population differences and cross-evolution, which is proposed on the basis of genetic algorithms, and is commonly used to solve the overall optimal solution in a multi-dimensional space. With its powerful global search ability, DE has been widely used in the fields of constrained optimization, clustering optimization, nonlinear optimization, and neural network optimization, etc. The DE algorithm is to randomly initialize the population and take the fitness value of each individual in the population as the evaluation criterion, and the main process consists of three parts: variation, crossover, and selection. The basic idea of the algorithm is to apply the differences of individuals in the current population to reorganize to get the intermediate population, and then apply the offspring population to compete with the parent individuals to get the new generation population [34]. The advantage is that in the early stage of the algorithm, the individual differences of the populations are large, and the variation operation will make the algorithm have a strong global search ability, while in the late stage of the algorithm the individual differences of the populations are small, and the mutation operation will make the algorithm have a strong local search ability.