esa-sceva/satcom-chunk-collection huggingface dataset:
# HORSIC+: An Efficient Post-Quantum Few-Time Signature Scheme
## 3 The HORSIC+ Signature Scheme
A cryptographic hash function \(H\) is used to map each message \(M\) to a \(k\)-element ordered subset \((i_{1},i_{2},\ldots,i_{k})\) of a \(t\)-element set \(\{1,2,\ldots,t\}\). A counter \(ctr\) is used to ensure that all \(i_{j}\) are distinct. A cryptographic hash function \(G\) and a bijective function \(\mathbb{C}_{k,z}\) are used to map each message \(M\) to a \(k\)-part integer composition \((a_{1},a_{2},\ldots,a_{k})\) of \(z\). Each \(sig_{j}\) is generated by applying the chaining function \(w-a_{j}\) times on \(x_{i_{j}}\). Algorithm 7 represents the verification of HORSIC+. Each \(sig_{j}\) is verified by applying the chaining function \(a_{j}\) times and comparing it with the verification key. Figure 1: Signing of HORSIC+. ```
```
System Parameters: Parameters \(n\), \(t\), \(k\), \(z\), and \(w\) Input: Signature key \(X\), message \(M\), function key \(\kappa\), and bitmasks \(\vec{r}\) Output: Signature \(\sigma\)
```
1: Compute \(g=G(M)\)
2: Compute \((a_{1},a_{2},\ldots,a_{k})=C_{k,z}(g)\)
3: Set \(ctr=0\)
4: Compute \(h=H(M\mid ctr)\)
5: Split \(h\) into \(k\) pieces \((h_{1},h_{2},\ldots,h_{k})\) of length \(\log_{2}t\) bits each
6: Interpret each \(h_{j}\) as an integer \(i_{j}\) for all \(j\in\{1,2,\ldots,k\}\)
7:if there exist \(p\) and \(q\) with \(p,q\in\{1,2,\ldots,k\}\) such that \(i_{p}=i_{q}\) and \(p\
eq q\)then
8:\(ctr=ctr+1\) and go to Step 4
9: Compute \(sig_{j}=c^{w-a_{j}}(x_{ij},\vec{r})\) for all \(j\in\{1,2,\ldots,k\}\)
10:return\(\sigma=(ctr,sig_{1},sig_{2},\ldots,sig_{k})\) ```
**Algorithm 6**Signing of HORSIC+ (\(Sign_{HORSIC+}(X,M,\kappa,\vec{r})\))
``` System Parameters: Parameters \(n\), \(t\), \(k\), \(z\), and \(w\) Input: Verification key \(Y\), message \(M\), and signature \(\sigma\) Output: \"accept\" or \"reject\"
1: Compute \(g=G(M)\)
2: Compute \((a_{1},a_{2},\ldots,a_{k})=C_{k,z}(g)\)
3: Compute \(h=H(M\mid ctr)\)
4: Split \(h\) into \(k\) pieces \((h_{1},h_{2},\ldots,h_{k})\) of length \(\log_{2}t\) bits each
5: Interpret each \(h_{j}\) as an integer \(i_{j}\) for all \(j\in\{1,2,\ldots,k\}\)
6:if there exist \(p\) and \(q\) with \(p,q\in\{1,2,\ldots,k\}\) such that \(i_{p}=i_{q}\) and \(p\
eq q\)then
7:return \"reject\"
8:if there exist \(j\in\{1,2,\ldots,k\}\) such that \(c^{d_{j}}(sig_{j},\vec{r}_{w-a_{j}+1,w})\
eq y_{i_{j}}\)then
9:return \"reject\"
10:return \"accept\" ```
**Algorithm 7**Verification of HORSIC+ (\(Vf_{HORSIC+}(Y,M,\sigma)\))
## 2 Methodology
### Dataset Assessment
#### 2.1.2 Feature Selection  
Identifying correlated features during the data preprocessing process is essential since they indicate a strong relationship between two dependents. This study used a correlation technique, Pearson's Correlation [24; 25], to predict how well the variables are correlated. This technique calculates a score that quantifies the strength of a linear relationship between \(x\) and \(y\). A positive score represents a positive linear correlation, while a negative score indicates a negative correlation. This coefficient, \(P\), is calculated using the following:  
\[P=\frac{\sum_{i=1}^{n}(x_{i}\ -\overline{x})(y_{i}\ -\overline{y})}{\sqrt{\sum_{i=1}^{ n}\left(x_{i}\ -\overline{x}\ \right)^{2}\ (y_{i}\ -\overline{y})^{2}}} \tag{1}\]  
where \(P\) is the correlation coefficient, \(x_{i}\) is the value of the \(x\)-variable for sample \(i\), \(\overline{x}\) is the mean of the values of the \(x\)-variable for sample \(i\), \(y_{i}\) is the value of the \(y\)-variable for sample \(i\), \(\overline{y}\) is the mean of the values of the \(y\)-variable for sample \(i\), \(n\) is the number of samples in the dataset, and \(i\) is the index of a sample. If \(P\) is less than 0.39, it is considered a weak correlation between the two given variables; however, it is a moderate correlation if \(P\) is between 0.40 and 0.89. If \(P\) is greater than 0.9, then the those two variables are highly correlated.  
A feature importance approach, the Chi-Squared Test [25], was used to estimate each feature significance. This technique is widely used to test the independence of two variables: \(O\) and \(E\). It computes how the expected and observed variables deviate from each other. The Chi-Squared Test score is given by:  
\[X_{\varepsilon}^{2}\ =\sum\frac{(O_{i}-E_{i})^{2}}{E_{i}} \tag{2}\]  
where \(c\) is the degree of freedom, \(O_{i}\) denotes the observed value, and \(E_{i}\) is the expected value. The degree of freedom is a statistical measure that indicates the number of samples, or the number of control points, that can be used in the computation. This value can guarantee the test's validity when comparing the observed and expected values to determine whether a particular hypothesis is correct. Higher parameter values indicate a more reliable classification decision, which also strongly affects the related ACC.
## 2 Related Work
### Broadcast Routing Protocols
[20] present a probabilistic and opportunistic flooding algorithm that controls rebroadcasts and retransmissions opportunistically. The sender controls the number of retransmissions opportunistically according to error rate at its neighbors. Another interesting extension of opportunistic flooding is presented in [21]. They propose the contribution-level-based opportunistic flooding to achieve outstanding transmission efficiency and reliability to handle the failure case of the broadcast packet at the relay node. [PERSON] et al. [22] address the network lifetime issue of the opportunistic flooding algorithm. Their approach called Energy Saving via Opportunistic Routing (ENS_OR) ensures minimum power cost during data relay and protects the nodes with relatively low residual energy. Moreover [PERSON] et al. [23] present a performance evaluation framework for the opportunistic flooding algorithm. They focus on two mechanisms based on acknowledgments and probabilistic retransmissions and analyze the behavior of the two mechanisms in different environments. Finally, [PERSON] et al. [24] present a comparative analysis for opportunistic routing in wireless sensor networks in terms of power usage, data aggregation, scalability and the data delivery model. These opportunistic broadcast protocols also do not consider real-time communication. Furthermore, opportunistic broadcast protocols tend to show longer delay than geographic broadcast protocols since they use the back-off delay plan for neighbor contention. In wireless sensor networks, many communication protocols and applications rely on flooding for various networking purposes. Prior efforts focus on how to design efficient flooding algorithms; that is, they seek to achieve full reliability while reducing the number of redundant broadcasting across the network. To achieve efficient flooding, most of the existing protocols try to reduce the number of transmissions, which is decided without considering any online transmission result. In this paper, we propose a probabilistic and opportunistic flooding algorithm that controls rebroadcasts and retransmissions opportunistically. It seeks to achieve a target reliability required by an application. For this purpose, it makes a given node select only the subset of its one-hop neighbors to rebroadcast the same message. It considers node relations such as link error rates among nodes in selecting eligible neighbors to rebroadcast. The sender controls the number of retransmissions opportunistically by tracking the current status of message reception at its neighbors. Simulation is carried out to reveal that our proposed scheme achieves the given target reliability with less overhead than other flooding algorithms in most cases, thus prolonging the network lifetime.
## 2 Materials and Methods
### The Design of Experimentation  
A central composite design (CCD), a prominent design within the framework of the response surface methodology (RSM), was utilized to facilitate the estimation of both linear and quadratic effects, as well as the interactions of influential factors, enhanced by the inclusion of axial points. This design was specifically selected for this study to explore the optimal extraction conditions for quantifying the total phenolic content. The investigation focused on two critical variables, namely the concentration of ethanol (X\({}_{1}\)) and the extraction time (X\({}_{2}\)).  
The coding for each variable was assigned at three levels as follows: \(-1\), \(0\), and \(+1\). The experiment was performed once. The choice of variables and their corresponding levels was determined through preliminary experiments, a review of the literature, and the constraints of the analytical instrumentation available. Both the coded and actual values of these variables are presented in Table 1.  
The central composite design was employed to perform a regression analysis and second-order polynomial multiple regression as in Equation (1).  
\[\mathrm{Y}=\beta_{0}+\beta_{1}\mathrm{X}_{1}+\beta_{2}\mathrm{X}_{2}+\beta_{1 1}\mathrm{X}_{12}+\beta_{22}\mathrm{X}_{22}+\beta_{12}\mathrm{X}_{1}\mathrm{X} _{2}+\epsilon \tag{1}\]  
Equation (1) delineates the correlation between the response variable, symbolized as Y, and the quantifications of total phenolic content (TPC), measured in mg GAE/g. These values stem from evaluations of the total phenolic levels. The independent variables influencing the dependent variable, Y, are X\({}_{1}\) and X\({}_{2}\), as illustrated in Table 1. The model incorporates regression coefficients \(\beta_{0}\), \(\beta_{1}\), \(\beta_{2}\), \(\beta_{11}\), \(\beta_{22}\), and \(\beta_{12}\), which correspond to the intercept, linear effects, quadratic effects, and interaction terms, respectively. The model also includes \(\epsilon\), which represents the random error component.  
The analysis of variance (quadratic regression analysis ANOVA) was employed to assess the adequacy of the quadratic approximation within the central composite design for the response surface. F-statistics and the corresponding \(p\)-values were used to determine the statistical significance of the different term groups, including linear terms, two-factor interactions, and quadratic terms, along with the regression coefficients obtained from the model. The terms were considered statistically significant if they displayed \(p\)-values below 0.05, indicating a 95% confidence level.
# SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
## 5 Experimental Evaluation
### Datasets  
To evaluate the proposed motif detection technique, we rely on three datasets--the key characteristics of these datasets are outlined in Table 1:  
* The _motion capture (MoCap) dataset_[10] consists of movement records from sensors placed on subjects' bodies as they perform 8 types of tasks. We use the Acclaim-Skeleton-File/Acclaim Motion Capture (ASF/AMC) data format, where the original coordinate readings are converted into 62 joint angles' data. We treat the readings for each joint angle as a different uni-variate time series. The hierarchical spatial distribution (e.g., left foot, right foot, left leg, etc.) of the joint angles on the body is used to create the underlying correlation matrix used as contextual metadata.
* The _building energy dataset_ consists of energy simulations generated through the EnergyPlus simulation software [36]. In this case, the variate context is defined based on the topology of the building being simulated.
* The _BirdSong dataset_[37] consists of Mel-frequency cepstral coefficient (MFCC) features for different bird calls. Each MFCC coefficient captures the short-term power spectrum of a sound for a given frequency band. The original dataset contains 13 MFCC coefficients (i.e., variates) for 154 bird calls of 8 classes, with the average time length of 397 time stamps. For our experiments, we convert these into the frequency domain and produce a multi-variate time-series composed of 52 variates. The context is defined in terms of the proximity of the constituent frequencies; in particular, we consider them to be equally spaced on the Mel scale.  
Note that, as reported in Table 1, the contextual metadata are more strongly defined for the motion capture and building energy datasets, where the spatial proximity and connections among body and building components are used to capture the context. The contextual metadata is less strongly defined for the birdsong dataset.  
Samples of these time series are visualized in Figure 8--note that the repeated patterns are also discernible to the naked eye in these samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **\# Variates** & **\# Time Series** & **\# Classes** & **Contextual Strength** \\ \hline MoCap [10] & 62 & 184 & 8 & high \\ Building energy [36] & 27 & 100 & n/a & high \\ Birdsong [37] & 52 & 154 & 8 & low \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the datasets.
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 7 Related Works  
### Analyzing Coverage Metrics  
[PERSON] et al. introduced the concept of _sensitivity_ in coverage-guided fuzzing, which can be utilized to compare different coverage metrics in fuzzing [22]. This previous research systematically examined the impact of various coverage metrics on fuzzing performance. Using a formally defined concept of sensitivity, their research evaluated a wide range of metrics, including basic branch coverage, context-sensitive branch coverage, and memory-access-aware branch coverage, with the AFL tool on extensive datasets. The evaluation results indicate that more sensitive coverage metrics generally lead to quicker and more frequent bug discoveries. Additionally, different metrics tend to find distinct sets of bugs, and the optimal metric may vary at different stages of the fuzzing process. The main difference between such previous works and ours is that we intend to investigate the cost versus gain of utilizing coverage feedback in fuzzing in terms of closed-source systems, which impose significant engineering challenges for developing coverage-guided fuzzers.
# The Automatic Algorithm of Optimizing the Position of Structured Light Sensors
## 4 Conclusions  
For the problem of specular reflection in three-dimensional measurement, this paper proposes a sensor optimization algorithm. The algorithm optimizes the number and pose of sensors in the measurement system. The goal of optimization is to minimize the number of sensors as much as possible while avoiding the impact of specular reflection. It can improve the efficiency of determining sensor coverage areas and reduce subsequent measurement costs. First, the specular reflection problem is viewed as a multi-sensor position search problem. Then, an optimization algorithm is used to find the optimal number and bit positions of sensors to avoid specular reflection. Experiments show that the best multi-sensor system obtained by the algorithm improves the model coverage rate and solves the specular reflection problem.  
This article optimizes the sensor for measuring turbine blades. The result of algorithm optimization is that three sets of sensors can cover the overall measurement area of the blade while solving the problem of specular reflection. This algorithm minimizes the number of sensors as much as possible while ensuring that the sensor's field of view covers the measurement area. This algorithm achieves automated matching of multiple sensor coverage areas, highlight areas, and object measurement areas, saving time and improving efficiency.  
We believe that future work should include the following aspects: (1) optimizing the structure of algorithms and improving their operational efficiency; (2) considering large objects as measurement objects and optimizing the sensors; and (3) writing an optimization software based on algorithms.  
Figure 10: Location of sensors. (**a**) Location of sensors in the algorithm. (**b**) Location of sensors used by experts. The black and yellow objects are the projector sensors, while the blue object is the camera sensor. The solid blue line connecting the sensor and the object indicates the direction of the sensor’s field of view.  
**Author Contributions:** Conceptualization, Q.X.; methodology, Q.X. and X.S.; software, Z.Z.; validation, Z.Z. and Q.X.; formal analysis, Z.Z.; investigation, Q.X.; resources, Q.X.; data curation, Z.Z. and X.Y.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.Z. and Q.X.; visualization, Q.X.; supervision, Q.X.; project administration, Q.X.; funding acquisition, Q.X. and X.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Henan Provincial Science and Technology Research Project (No. 222102210073) and the National Natural Science Foundation of China (No. 61705198).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data can be obtained from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Point-Kernel Code Development for Gamma-Ray Shielding Applications
## 6 Discussion  
The development of the PK program for gamma shielding analysis using several basic source distributions and a slab shield has been demonstrated through theoretical and numerical study. The verification of PK results for selected test cases has been carried out by MAVRIC shielding sequence of SCALE6.1.3 code. A generally good agreement of obtained results was noticed for reasonable shield thicknesses and obtained numerical \(B\)-factors showed excellent agreement with theoretical values. Noticeable PK deviations from the referent MC solution, which simulates real physical interactions, demonstrate inherent limitations of this fast method, which are generally compensated by a high CPU time speedup.  
The PK-to-MC difference will always occur when the PK method is used outside of its \"design basis assumptions\", and in our experience, this happens for the following: irregular geometry configurations, composite or multilayered shields with ducts, voids or mazes, complicated sources (in geometry, gamma spectrum, anisotropic emission), non-symmetric shielding and off-line point detectors. In such cases, significant PK underprediction in gamma fluxes is possible relative to the MC solution. The PK method cannot handle gamma-ray scattering (so a corrective \(B\)-factor is needed), streaming radiation-augmenting angle-dependent radiation peaks with localized radiological significance, real-life sources with energy-spatial gradients, radiation backscattering from surrounding structures, albedos, and similar boundary conditions. For such specific purposes, the MC should be used as a standard simulation tool for coupled neutron-photon transport, but this burdens computational hardware and requires user experience in transport theory.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
## 2 Materials and Methods
### Quality Measures
#### 2.1.4 Mean Drop Rate  
The mean drop rate, denoted as \(\delta\), allows for the gauging of the reliability and efficiency of a network. For applications like file transfers, occasional packet drops do not impact the user experience much since there is time enough time for retransmissions. However, for real-time applications, like video streaming or VoIP, the packet loss may lead to significant quality degradation. The lost video frames or audio snippets can lead to jittery playback or call drops.  
The mean drop rate is calculated as  
\[\delta=\frac{D}{S}\times 100\% \tag{12}\]  
where:  
* \(D\)--total number of dropped packets;
* \(S\)--total number of sent packets.
## 3 Results and Discussion
### Advanced Energy Management Systems and Control Architectures
#### 3.1.4 Trends Toward Decentralized Intelligence and Interoperability Standards  
Recent advancements in microgrid management emphasize the adoption of decentralized intelligence architectures, where energy resources operate autonomously and coordinate through local optimization algorithms [49; 78]. Reinforcement learning and adaptive control strategies support continuous performance improvements in microgrid operations [12; 13]. In parallel, growing interest exists in the development of open platforms and interoperability standards to enable secure and efficient communication among devices from different manufacturers [14; 79]. The establishment of such standards is critical to facilitate microgrid participation in local energy markets and peer-to-peer energy trading schemes [15; 80; 81].  
Future research should prioritize the implementation of explainable AI (XAI) models to improve transparency in AI-based control systems, and the exploration of blockchain technologies to support secure and decentralized energy transaction management [30; 58; 67]. Furthermore, the creation of open data repositories is essential to enhance the training of advanced models while preserving user privacy and ensuring data security [22; 55; 61].
## 5 Discussion  
### Influence of seawater Temperature on System Performance  
The temperature of the water changes as the ship travels in different seas and seasons. The BORC and FORC systems use the outboard sea water as the heat sink, and the seawater temperature change will influence the system's performance. Figure 3a illustrates the impact of seawater temperature on the condensation temperature and expansion ratio of the ORC system, assuming constant heat source temperature and flow rate. As the seawater temperature rises, the condensation temperature of the ORC system experiences an increase, while the expansion ratio undergoes a decrease. When the seawater temperature is increased from 293.15 K to 303.15 K, the condensation temperature increases from 332.9 K to 342.8 K in the BORC system, and the expansion ratio decreases from 10.5 to 8.0. In the FORC system, the condensing temperature increases from 334 K to 343.3 K, and the expansion ratio drops from 10.9 to 8.1. The impact of variations in seawater temperature on both the condensation temperature and expansion ratio remains consistent across the BORC and FORC systems.  
The influence of seawater temperature on the net output power and thermal efficiency of the ORC system is shown in Figure 3b. With the increase in seawater temperature, the net output power of the BORC and FORC systems decreases. As the seawater temperature rises, the net output power of both the BORC and FORC systems experiences a decline. When the seawater temperature rises from 293.15 K to 303.15 K, the net output power of the BORC system undergoes a reduction from 445.3 kW to 405.5 kW, representing a decrease of 8.94%. The net output power of the FORC system is reduced from 491.3 kW to 426 kW by 13.29%. The net output power of the FORC system is always greater than  
Figure 3: Variations in performance with seawater temperature on two systems (**a**) condensation temperature and expansion ratio; (**b**) net output power and thermal efficiency.  
that of the BORC system. The addition of the preheater yields a notable enhancement in the net output power of the ORC system, resulting in an improvement ranging from 5% to 10.3%. The significance of this increase becomes more pronounced under lower seawater temperatures. Hence, in scenarios where the ship predominantly operates in sea areas characterized by lower temperatures, it is advisable to prioritize the PORC system for efficient recovery of unused heat from the main engine. In relation to the thermal efficiency of the system, it is observed that as the seawater temperature rises, the net output power of the system decreases. Consequently, a corresponding decrease in the system's thermal efficiency occurs. The thermal efficiency of the BORC system depth drops from 12.84% to 11.70% as the seawater temperature rises from 293.15 K to 303.15 K. The thermal efficiency of the PORC system fell from 12.71% to 11.54%. As a result, the thermal efficiency of the PORC system is slightly lower than that of the BORC system.