esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Robot Exploration Employing Harmonic Map Transformations
## 1 Introduction
### Contributions  
This paper presents a novel method for both single-robot and multi-robot exploration, leveraging a HMT for frontier navigation and obstacle avoidance. This modified version will hereby be referred to as HMT for Exploration. In the proposed approach, a map of the perceived space is obtained and the HMT for Exploration is computed. Employing a greedy frontier allocation strategy, the robot is able to choose its goal and navigate to it safely using the HMT. In the multi-robot case, the exploration process is optimized by sharing the local maps of the robot and constructing a merged map. The key contributions of this work include the following:  
* The extension of the concept of HMT to incorporate frontier points on the boundary of the perceived workspace, enabling its use for exploration tasks.
* The design of a comprehensive exploration method that integrates the HMT for effective navigation and mapping in unknown environments.
* The extension of the proposed method to facilitate multi-robot exploration, enabling collaborative exploration efforts with improved efficiency and coverage.  
To the best of our knowledge, this is the first time that the HMT has been extended for exploration in a multi-robot framework. This paper is organized as follows: In Section 2, the problem of multi-robot exploration is formally introduced. In Section 3, the proposed methodology is presented. The validity of the proposed methodology is evaluated through both high-fidelity simulations and real-world experiments, the results of which are detailed in Section 4. Finally, conclusions regarding the presented method and future work are shown in Section 5.
# Experimental Research on Unit Operation for Fruit Crops' Bones Calibration
## 1 Introduction  
One of the ways to reduce the prime cost of fruit crops seedlings (cherries, sweet cherries, cherry plums, apricots and almonds) is to introduce technology into their production to make using the site for growing rootstocks unnecessary [1, 2, 3]. The above technology eliminates the necessity of operations of digging rootstocks and transplanting them to thearea of seedling cultivation. That is, the cultivation of rootstocks takes place directly on the site for seedlings growing with their subsequent grafting of the cultivar.  
It is possible to reduce labor costs for sown rootstock care on the site of seedlings where the distance in a row between the seeds when sowing bones should be between 15 to 17 cm. Thus, the process involves the use of a precision seeder with sowing apparatus designed to sow calibrated bones. To form the appropriate batches of bones for sowing, their total mass should be divided into homogeneous in size fractions [4,5].  
Following such a practice enables obtaining a batch of bones, which will be aligned in size with a deviation by weight of up to 10%. This quality can be achieved by ensuring that sieves are at least 80% free from stuck bones during calibration.  
Analysis in recent studies indicates that scientific data on the development of sorting or calibration machines for agricultural seeds [6, 7, 8, 9, 10] are not sufficient for fruit bones.  
Therefore, the working out of the unit for fruit bones calibration is a prerequisite for the application of the above technology [11, 12, 13, 14].  
The scientific novelty of the obtained results consists of the following: a mathematical model for the process of calibration of the bones of fruit crops has been obtained, which establishes dependence between the quality of separation of bones into fractions and the factors influencing the process of calibration. This allows scientific substantiation of the technology of seed preparation for sowing. The practical value of the obtained results is in determining the optimal values of factors influencing the process of seed separation.
## 2 Methodology
### Sandia National Laboratories Methodology  
The Sandia methodology is widely present in the literature and turns out to be one of the most common methods for calculating a TMY [8; 17; 18; 19; 20]. The TMY is obtained from multiannual historical series, for instance, 30 years (climate cycle), of different meteorological parameters: among others, temperature (mean, maximum, minimum, and range) and solar irradiance (global horizontal irradiance). At first, these parameters were data measured at the study site (26 SOLMET stations) for 23 years beginning in 1953 and extending through 1975 [8]. From the available daily time series, the Sandia methodology selected 12 Typical Meteorological Months (TMM) to establish information on the annual variability of the parameters studied. Using the FS statistic, a TMM is chosen for each of the 12 calendar months of all the years available in the time series. This was done by assigning a weighting factor (wf) to the meteorological parameters considered, resulting in a reduction in the amount of data, losing the least amount of information as possible [21; 22]. The wf can vary, depending on the importance of the variable [23]. The dataset achieved represents a typical year of reliable data in the simulation of energy of renewable energy technologies [20].  
In addition to using FS statistics for generating a TMY, some studies introduced other approaches, such as the principal component analysis or genetic algorithms [24; 25]. There are other methodologies, different from those listed above, based on the availability of meteorological data and the application of the generated sequence. Among them are the Test Reference Year (TRY) [26; 27], the Design Reference Year (DRY) [28], and the Short Reference Years (SRY) [29]. To date, these methodologies have had remarkable results compared to average long-term weather data from meteorological stations [19; 21; 30; 31].
The advantages of pursuing a microservice-based design are that (1) each individual data processing service can be deployed close to the system, application or user being monitored and that (2) it can be scaled in terms of the amount of information it needs to process; and this irrespective of the other microservices. Additionally, (3) the decentralized design spreads the risk of data breaches and reduces the ability of a data broker to mine for sensitive information, as a single microservice only provides access to a subset of the data. Indeed, monolithic applications prefer a single database for persistent data, whereas microservices manage their own database on top of possibly different database technologies. A consequence of the decentralized governance and data management, however, is that enforcing access control on the data endpoints to prevent information leakage or preserve one's privacy becomes far more challenging. Indeed, on the one hand, authorization policies should restrict access to the data endpoints of the microservices itself, but on the other hand, they rely on the same data endpoints to evaluate their access control decisions, as we will later demonstrate. The contribution of this paper is a workflow-oriented authorization framework that mutually authorizes microservices to grant access to each others' data endpoints based on the state of the data processing workflow; and this while protecting against illegitimate access from unauthorized subjects and microservices. Our framework also enables the evaluation of authorization policies in a decentralized manner where the delegated policy evaluation leverages feature toggles that are managed at runtime by software circuit breakers to decouple the execution of the data processing workflow from the policy-based authorization logic. The remained of this paper is structured as follows. Section 2 provides background information on the microservice building blocks that are used in our framework. Section 3 presents a motivating use case in a healthcare context. The core and implementation details of our workflow-based authorization framework are presented in Section 4 and evaluated in Section 5. We end the paper by summarizing the main contributions of our work and highlighting opportunities for future research in Section 6.
## 5 Results and Discussion
### Performance Comparison between the Proposed and Other Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Performance Indices**} & \multirow{2}{*}{**Hyperparameters**} \\ \cline{2-2} \cline{4-5}  & **R\({}^{2}\)** & & **RMSE** & **MAE** & **WI** \\ \hline ANN & 0.8683 & 15.1724 & 10.8323 & 0.9619 & \(N_{h}=2\); \(N_{c}=4\),\(A\) \\ SVR & 0.8592 & 15.6918 & 11.7625 & 0.9591 & \(C=128\); \(R_{k}=0.25\) \\ ELM & 0.8795 & 14.5124 & 10.2086 & 0.9665 & \(N_{cs}=65\) \\ KELM & 0.8987 & 13.3074 & 8.4755 & 0.9716 & \(R_{c}=128\); \(R_{k}=1.0\) \\ GRNN & 0.9010 & 13.1593 & 8.2674 & 0.9717 & \(S_{f}=0.3\) \\ MQE & 0.8318 & 17.1476 & 13.5849 & 0.9497 & Equation (7) \\ \hline \hline \end{tabular} Note: \(N_{h}\)—the hidden layers number; \(N_{c}\)—the number of neurons in the corresponding hidden layer; \(C\)—penalty parameter; \(R_{k}\)—RBF kernel parameter; \(N_{os}\)—the number of neurons in a single hidden layer; \(R_{c}\)—regularization coefficient; \(S_{f}\)—smoothing factor.  
\end{table} 
Table 5: Performance results of five common ML models using test set. among the six other models by means of the best performance indices. It obtains the highest rank score value and the best regression distribution. Figure 10: Regression diagrams of the other six models for predicting the rock EM. Figure 9: Ranking scores of the six comparison models using the test set. Thus, the GRNN model is used to compare the performance with the PRO-RF model for predicting the rock EM. The bar chart tool is utilized to make an intuitive comparison between the measured EM and the EM predicted by the GRNN model and PRO-RF model, as shown in Figure 11. In Figure 11a, the predicted EM values of the No. 1, No. 7, No. 11, No. 13 and No. 15 samples by the PRO-RF model showed deviations. On the other hand, the GRNN model also does not accurately predict the EM values of the No. 2, No. 3, No. 4 and No. 19 samples, in addition to the above samples. The error analysis results of the PRO-RF model and the GRNN model for predicting the rock EM are shown in Table 6. It can be intuitively observed that the error in the statistical indices based on the PRO-RF model is better than the GRNN model ones, such as the error sum of the PRO-RF model (145.014) being lower than the GRNN model (198.417). The relative deviation (RD) distribution is also an effective tool to evaluate the model performances [64]. The definition of RD is the ratio of the error between the predicted value and the measured value to the measured value. This also means that the models with a better performance have lower RD values. As illustrated in Figure 12a, the maximum RD of the PRO-RF model is 44.77%, and most of the RD values are lower than 40%. Especially, there are 11 RD values lower than 10%. It should be noted that the maximum RD of the GRNN model is higher than 70%, and only 9 RD values are lower than 10%. Therefore, the PRO-RF model has a better predictive performance than the GRNN model for calculating the rock EM.
# Automatic Selective Encryption of DICOM Images
## 2 The DICOM Image Encryption: Motivations and Limitations
### Proposed Extension to the DICOM Standard and Other Frameworks  
The primary goal of this paper is to reduce the required encryption time of DICOM images. Most of the presented work in the literature developed codes in MATLAB. Hence, the encryption speed was limited. Nevertheless, this paper focuses on finding efficient libraries that have compiled C code implementation. Additionally, all the developed methods in this paper were implemented using Python and then compiled into C code using Cython Library. Hence, the presented work can be implemented in real platforms to support and extend DICOM applications.  
It is worthwhile to mention that the work presented in this paper does not aim to provide security requirements (confidentiality, authenticity, and integrity). This paper provides an effective encryption mechanism to reduce the encryption time, as it is the bottleneck to generating a secure DICOM object [24]. This mechanism can be used within a security framework to achieve these security requirements, such as authenticity and integrity in the presented framework by [PERSON] et al. [24] as shown in Figure 1. In this framework, a segmentation map is generated based on the proposed approach, will be stored in the header, and then used to encrypt pixel data as suggested in the Kobayashi framework. Interested readers can find out more about this framework in [24].  
In addition to the Kobayashi framework, the proposed approach can be used to achieve confidentiality goals, by extending the Kobayashi framework as shown in Figure 2. As the DICOM standard recommended certificate-based encryption [1], the encryption of the header data (including the segmentation map) can be achieved using RSA. This kind of secret data exchange is supported by Transport Layer Security (TLS) which is also recommended by the DICOM standard in Part 15, Annex B. In this extended framework, the private key on the sender side and the public key on the receiver side are embedded in a Digital Certificate (DC). The DC is generated by an independent Certification Authority (CA) which identifies network users and their authorities. In this framework, the proposed approach can efficiently contribute to reducing the required encryption time and achieving authenticity, integrity, and confidentiality.  
Figure 1: Encryption flow chart: Kobayashi framework, the proposed approach can contribute to reducing the computational overhead of the encryption process (red box).
## 4 Discussion  
This study investigated the relationship between the eccentric utilization ratio (EUR) and performance variables including sprint, change of direction (COD), change of direction deficit (COD\(\%\); defined as the difference between sprint and COD times), and vertical jump (VJ). Considering the limited number of studies focusing specifically on female athletes, this research may be regarded as one of the few that explores EUR-performance relationships in women. Overall findings indicated that EUR did not exhibit statistically significant correlations with these performance variables across the entire sample (\(p\) > 0.05). This result aligns with the views of [PERSON] and [PERSON] (2017), who argued that EUR has limited predictive value for performance [9]. Similarly, the findings of [PERSON] et al. (2021) also support the limited usefulness of EUR in predicting sport-specific performance parameters [19]. In contrast, absolute and normalized strength-power parameters such as CONMP, ECCMP, ECCPP, and PP demonstrated strong associations with key athletic performance outcomes including sprint velocity, VJ, COD, and COD\(\%\) (\(p\) < 0.05). Rather than merely noting statistical associations, it is more meaningful to interpret concentric mean power (CONMP) as a shared neuromuscular foundation for both linear sprinting and multidirectional movements. This suggests that athletes with superior concentric power can apply force more rapidly and effectively during explosive tasks, particularly during initial acceleration and re-acceleration phases. In these phases, the ability to generate concentric force in a brief time window is a key determinant of movement efficiency and performance. This becomes particularly relevant when considering the role of eccentric force in optimizing change of direction performance. The predictive power of ECCMP and ECCPP for sprinting, jumping, and change of direction performance likely stems from the crucial role of eccentric strength in neuromuscular control during high-intensity movement transitions. In particular, effective eccentric force production during deceleration phases may enhance mechanical stability and readiness for subsequent re-acceleration, which is essential for sharp directional changes. These findings highlight the significance of eccentric capacity not only in energy storage, but also in regulating movement quality and performance efficiency in multidirectional sports tasks. The relationship between COD\(\%\) and eccentric force capacity appears particularly relevant during the transition from braking to re-acceleration, potentially minimizing angular velocity losses and enhancing COD efficiency [28; 29; 30; 31]. This
## 6 Experiment
### Practicality  
To evaluate the practicality of FLuaScan, we adopted a combined approach of quantitative and qualitative evaluation. In quantitative terms, we separately recorded the runtime \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Brand**} & \multirow{2}{*}{**Device**} & \multicolumn{5}{c}{**FLuaScan**} & \multicolumn{5}{c}{**TscanCode**} \\ \cline{3-10}  & & & **Alerts** & **TP** & **FP** & **Handlers** & **Alerts** & **TP** & **FP** \\ \hline
1 & xiaomi & r3a & 16 & 5 & 11 & 448 & 149 & 0 & 149 \\ \hline
2 & xiaomi & r3600 & 17 & 6 & 11 & 488 & 179 & 0 & 179 \\ \hline
3 & xiaomi & r4c & 10 & 5 & 5 & 399 & 166 & 0 & 166 \\ \hline
4 & tplink &
\begin{tabular}{c} TL- \\ WR1043N \\ \end{tabular} & 3 & 1 & 1 & 347 & 141 & 0 & 141 \\ \hline
5 & tplink &
\begin{tabular}{c} Archer- \\ C1900 \\ \end{tabular} & 2 & 1 & 1 & 321 & 138 & 0 & 138 \\ \hline
6 & tplink &
\begin{tabular}{c} Archer- \\ AXE75 \\ \end{tabular} & 2 & 1 & 1 & 820 & 297 & 0 & 297 \\ \hline
7 & zyzel & NBG6716 & 3 & 2 & 1 & 92 & 90 & 0 & 90 \\ \hline
8 & zyzel & NBG6604 & 3 & 2 & 1 & 65 & 118 & 0 & 118 \\ \hline
9 & linksys & EA6350V2 & 0 & 0 & 0 & 0 & 134 & 0 & 134 \\ \hline
10 & ttolink & A6000R & 7 & 3 & 4 & 113 & 152 & 0 & 152 \\ \hline
11 & ttolink & X5000R & 7 & 3 & 4 & 91 & 163 & 0 & 163 \\ \hline
12 & trendent &
\begin{tabular}{c} TEW- \\ 821 DAP \\ \end{tabular} & 2 & 1 & 1 & 37 & 122 & 0 & 122 \\ \hline
13 & netgear & EX8000 & 8 & 4 & 4 & 43 & 96 & 0 & 96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detailed result of the test set. of FLuaScan and Tscancode to detect the same image. In Figure 2, we present these results in the form of bar and line charts. The brown and green bars represent the runtime of FLuaScan and Tscancode, respectively, while the blue line represents the number of Lua files. As shown in Figure 4, within the same brand, as the number of Lua files increases, the runtime of both Tscancode and FLuaScan also increases. However, regarding the same image, FLuaScan always takes more time than Tscancode. FLuaScan has an average runtime of 25.16 s, while Tscancode has an average runtime of 1.51 s. The primary factor contributing to the higher time overhead of FLuaScan is its greater analytical depth in comparison to Tscancode, in addition to the extra operations involving database writing and analysis. Meanwhile, on average, FLuaScan can complete vulnerability detection for a real-world device image in less than half a minute. Considering the higher accuracy of FLuaScan, we believe that this time overhead level is acceptable. Since security analysts typically need to manually verify the results provided by automated tools, it is essential for the tool to be able to collaborate with manual code auditing.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 4 Machine Learning Classifiers
### Extreme Gradient Boosting  
Extreme gradient boosting [41] is an advanced ensemble learning algorithm that builds gradient descent decision trees sequentially. For each iteration, it corrects shortcomings from previous models until reaching a final strong learner. In addition, it leverages parallel processing to accelerate computations, making it significantly faster. Furthermore, XGBoost integrates both L1 and L2 regularization to enhance generalization and mitigate overfitting. On the other hand, it may be memory-expensive when dealing with very large datasets or a high number of boosting cycles.  
We construct an XGBoost model employing the softmax objective function to address the multiclass classification task. The number of classes is set to four. The model is configured with a default learning rate of 0.3 and 100 trees within the boosting process. In addition, the maximum depth is left unconstrained.  
Table 8 summarizes the hyperparameters of the three models used in our experiments. It provides information on the configurations that led to the respective performances.
###### Abstract
The same study [24] found that the G3 TCG method is more efficient than \(\beta_{k}^{HZ}\) in some cases and competitive in some other cases. The convergence rate for the CG method is linear unless the iterative procedure is occasionally restarted at least every \(n\) iterations [25]. [PERSON] [26] suggested the use of the two-term CG method instead of the steepest descent (SD) method (\(d_{k}=-g_{k},\ \forall k\geq 1\)) as the restart search direction. He also extended this method to a three-term method as a non-restart direction. Furthermore, [PERSON] [25] suggested restarting the search direction using [PERSON]'s method if  
\[f(x_{k}+\alpha_{k}d_{k})-f(x_{k})\leq\delta\alpha_{k}g_{k}^{T}d_{k},\]2  
holds for every \(n\) iteration, whichever first occurs. [PERSON] and [PERSON] [27] extended the [PERSON] restart criteria (7) to the following form  
\[f(x_{k}+\alpha_{k}d_{k})-f(x_{k})\leq\delta\alpha_{k}g_{k}^{T}d_{k},\]3  
Besides, [PERSON] et al. [28] presented the following simple formula  
\[\beta_{k}^{AZPRP}=\left\{ \begin{array}{ll}\frac{\|g_{k}\|^{2}-\mu_{k}|g_{k}^ {T}g_{k-1}|}{\|g_{k-1}\|^{2}},&\text{if}\ \|g_{k}\|^{2}>\mu_{k}|g_{k}^{T}g_{k-1}|,\\ 0,&\text{otherwise},\end{array} \right. \tag{15}\]  
where \(\|\cdot\|\) represents the Euclidean norm and \(\mu_{k}\) is defined as follows:  
\[\mu_{k}=\frac{\|x_{k}-x_{k-1}\|}{\|y_{k}\|}\]  
Lemma 3.1 in [28] shows that if Assumption 1 holds and the Beale-Powell restart condition is violated for non-restart search direction, then \(\|g_{k}\|^{2}>\mu_{k}|g_{k}^{T}g_{k-1}|\) holds. Moreover, [PERSON] et al. [29] proposed a non-negative CG formula with convergence properties, and compared the new formula with \(\beta_{k}^{AZPRP}\), where the proposed method is given as follows:  
\[\beta_{k}^{PKT}=\left\{ \begin{array}{ll}\frac{\|g_{k}\|^{2}-g_{k}^{T}g_{k-1 }}{\max\{d_{k-1}^{T}g_{k-1}-g_{k-1}^{T}g_{k-1}^{T}d_{k-1}\}},&\text{If}\ 0<g_{k}^{T}g_{k-1}<\|g_{k}\|^{2}\\ \frac{\|g_{k}\|^{2}}{\max\{d_{k-1}^{T}g_{k-1}-g_{k-1}^{T}g_{k-1}^{T}d_{k-1}\}},&\text{otherwise}.\end{array} \right.\]  
To solve non-linear monotone equations with convex constraints, [30] proposed using the Powell symmetrical technique to the Liu-Storey conjugate gradient method. The reader can refer to the references for more about non-linear monotone equations with convex constraints and their application [31; 32; 33; 34].
# Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
## 5 Model Application
### Effect of Anchorage Segment Length  
Figure 17 presents the ultimate pullout capacity, lateral resistance, and end resistance variation curves according to an increase in the anchorage segment length. As can be seen, the anchorage segment length had a great influence on the lateral resistance, while it had nearly no influence on the end resistance. To be specific, when the anchorage segment length increased from 6 m to 14 m, i.e., the anchorage segment length increased by 133%, the lateral resistance increased from 336.7 kN to 922.6 kN by about 174%. This was because the increase in the anchorage segment length brought about a sharp increase in the lateral surface and the average buried depth of the anchorage segment. This led to a sharp increase in the normal soil pressure acting on the anchorage segment, but did not have any influence on the three-dimensional velocity discontinuity surfaces in the logarithmic spiral region. Therefore, the change in the anchorage segment length only affected the lateral resistance of the expanded anchor cable.  
Figure 16: The ultimate force variation curves according to changes in the anchorage segment diameter.