esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 3 Methodology
### Proposed Protocol Design
#### 3.2.6 Security Analysis  
The proposed protocol exhibits comprehensive security features designed to effectively mitigate significant cyber threats within smart grid environments. It addresses man-in-the-middle (MITM) attacks through the implementation of mutual certificate verification and shared secret derivation, which collectively renders it highly challenging for adversaries to impersonate either party involved in communication. Furthermore, the incorporation of timestamps and random nonce generation enhances protection against replay attacks, ensuring that each communication session is distinct and not vulnerable to reuse by malicious entities.  
To reduce the likelihood of Denial-of-Service (DoS) attacks, the protocol incorporates an early verification mechanism that suspends authentication when certificate validation fails, thereby preventing the unnecessary consumption of resources. In terms of session security, the protocol generates unique symmetric keys for each session, effectively mitigating risks associated with session key attacks. It also ensures the confidentiality of sensitive identifiers through secure encryption, while robust mutual authentication measures--anchored in secure private key usage and HMAC verification--provide deterrence against impersonation attempts.  
Lastly, mutual authentication is established through a bidirectional exchange and validation of certificates, ensuring that only authorized participants can engage in secure communications. These security features collectively highlight the protocol's resilience and suitability for protecting critical smart grid infrastructures.
# Discrete Integral Optimal Controller for Quadrotor Attitude Stabilization: Experimental Results
## 5 Conclusions  
Satisfactory experimental results using integral control are obtained. So, can be stated that the Optimal Integral Controller (OIC) improves the LQR controller behavior, because it includes an integral term. Moreover, the tuning of integral action can be optimally conducted by solving the discrete Riccati Algebraic Equation associated to LQR problem, allowing to penalize the energy consumption and the convergence rate of the state. According to optimal control theory, the exact linearization applied to the altitude and yaw subsystems allows for stability of the closed loop, and then it is guaranteed. For the subsystems \(y\)-\(\phi\) and \(x\)-\(\theta\), although the optimal control obtained was synthesized using a linearized model, the experimental tests show the robustness of the controller, and the real-time results show an important energy savings rate. So using this strategy, it is possible to achieve proper UAV stabilization for both attitude and position. As future work, it is intended to apply this control strategy in external environments to verify the robustness and efficiency of the algorithm in outdoor flights. This will be carried out on a UAV with the same configuration as proposed in this investigation. Other future work includes experiments in a wind tunnel to evaluate the discharge time of the battery in order to compare the performance of the controllers more accurately.  
[PERSON] and [PERSON] conceived the design of the control algorithm. [PERSON] and [PERSON] coded the control and navigation algorithm in the controller, built the prototype and carried out the experimental tests. [PERSON], [PERSON] and [PERSON] performed data validation, statistical analysis and writing of the article. [PERSON] and [PERSON] conducted a critical review of the results. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The first autor is grateful to CONACyT for granting the scholarship to the CVU number: 552521.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Controller** & **Total Energy (k)** & **Saved Energy \%** \\ \hline Proportional Integral & 3.3718 & 0 \\ Derivative & 1.7888 & 53.0514 \\ Optimal Integral Controller & 1.7888 & 53.0514 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Energy saving.  
Figure 18: Total energy consumption comparison using a PID controller and the OIC.
## 3 Problem Solution
\begin{array}{ll}\text{minimize}&z,\ \ f=1,2,\ldots,F\\ \text{subject to}&w_{1}\max\{\mathbf{s}_{f}(k)\}+w_{2}\max\{\mathbf{s}_{f}(k)- \mathbf{b}_{f}(k)\}-z\leq 0\\ &\mathbf{x}_{f}(k)\succeq 0\\ &\mathbf{1}^{\mathrm{T}}\mathbf{x}_{f}(k)=1\\ &\mathbf{s}_{f+1}(k)=\mathbf{s}_{f}(k)-\mathbf{b}_{f}(k)\circ\mathbf{x}_{f}(k).\end{array}  \tag{5}\]  
It is worth mentioning that an LP is the most simple form among all convex optimization problems and that algorithms for its solution have matured to the point of providing extremely accurate results in a very short processing time. In the present case, even large values of \(N\) will not represent a limiting factor for accurate and fast solution of problem (4) or (5). Real-time convex optimization [39] approaches could be applied and embedded in digital signal processors if frame time requirements of the specific application become stringent. Nevertheless, the optimization problem (4) or (5) is meant to be solved for each \(k\) in a central node with more processing power than an ordinary sensor node. The optimization variables obtained from the \(k\)-th optimization event are the activity level matrix \(\mathbf{X}(k)=[\mathbf{x}_{1}(k),\mathbf{x}_{2}(k),\ldots,\mathbf{x}_{F}(k)]\) to be assigned to the sensor nodes and the predicted (not necessarily actual) residual energy matrix \(\mathbf{S}(k)=[\mathbf{s}_{1}(k),\mathbf{s}_{2}(k),\ldots,\mathbf{s}_{F+1}(k )]\), \(k=1,2,\ldots,K\). The input parameters are the number of sensor nodes, \(N\); the optimization span defined by \(F\); the initial energy levels of the batteries, \(\mathbf{s}_{1}(1)\); and the estimated maximum consumed energies in the first \(F\) frames, \(\mathbf{B}(1)=[\mathbf{b}_{1}(1),\mathbf{b}_{2}(1),\ldots,\mathbf{b}_{F}(1)]\). The maximum consumption matrix that will be used as input to the event \(k+1\) is updated using Equation (2), from the assigned activity levels and actual residual and recharge energies associated with the \(k\)-th block of \(F\) frames. There are two options for updating this matrix: each of its rows is computed by each sensor node and reported to the central node or the entire consumption matrix is computed at the central node from the residual and recharge information reported by the sensor nodes. Algorithm 1 synthesizes the steps for optimizing the activity levels of the sensors nodes with the purpose of increasing the network lifetime. The last step of the algorithm is meant to indicate that the activity levels must be converted into a fraction of slots in a frame, this fraction being subsequently assigned to the sensor nodes by means of a proper scheduling or equivalent resource allocation algorithm. Figure 5 is meant to expose the time line of Algorithm 1, as well as the communication events between the sensor nodes and the sink nodes.
###### Abstract  
This study examines the effects of two flyometric training interventions over an eight-week preparatory period on straight punch impact force, cardiovascular fitness, and muscle strength in national-level boxers. Twenty male professional boxers participated voluntarily, with an average age of 22.64 \(\pm\) 3.12 years and an average training experience of 5.11 \(\pm\) 0.88 years. Their mean body weight and height were 70.20 \(\pm\) 10.13 kg and 184.28 \(\pm\) 5.38 cm, respectively. The participants were randomly assigned to two groups. Group 1, the Plyometric Stair Jump group, consisted of ten male boxers, while Group 2, the Plyometric Reaction Box Jump group, also included ten male boxers. To assess maximum punching velocity (PVmax), an accelerometer was embedded within the boxing glove, capturing data during three maximal-speed jabs with each arm to evaluate both rear-arm (RA) and lead-arm (LA) punches. Upper-body strength was assessed using a one-repetition maximum (1 RM) bench press (BP) test, while maximum velocity at various percentages of 1 RM was recorded via a linear encoder. Significant correlations were observed between the right arm punch velocity maximum (RA PVmax) and the bench press velocity at all submaximal intensities in both intervention groups (\(p\) < 0.05). However, no correlation was found between left arm punch velocity maximum (LA PVmax) and bench press velocity at any intensity within the Plyometric Reaction Jump (PRJ) group. Conversely, in the Plyometric Stair Jump (PSJ) group, a velocity at 80% of 1 RM was the sole significant predictor of RA PVmax at submaximal bench press intensities. Adjusting for the strength-to-weight ratio significantly influenced the predictive values in intergroup comparisons (\(p\) < 0.005). These findings suggest that high-load bench press exercises (e.g., at 80% 1 RM) may serve as reliable predictors of performance in specific boxing movements. However, since no significant relationship was observed with LA PVmax in this study, further research is warranted to identify exercises and intensities that may explain left arm punch velocity.  
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## 3 Results and Discussion
### Chemical Analysis of Pozzolanicity (CAP)  
Figure 5a-d presents the results of the chemical analysis of pozzolanicity, both at 8 and 15 days. As can be observed, the samples of unaltered pyroxenic andesites UPA-01 and UPA-02 do not show any pozzolanic behavior at 8 days of testing (Figure 5a); however, even though after 15 days they tend to move imperceptibly towards the isothermal solubility curve (Figure 5b), they do not come into contact with it. For this reason, it is concluded that  
Figure 4: Diffractograms obtained from the study of the phases present in the PAAZ-01 and PAAZ-04 samples.  
samples of unaltered pyroxenic andesites lack pozzolanic activity in their natural condition without being ignited. The reason why these samples do not have pozzolanic reactivity could be mainly due to their mineral composition, meaning that the SiO\({}_{2}\) present in the structure of pyroxenes and amphiboles is strongly combined in the silicate structural group, forming stable crystalline structures, which prevents their reaction with cement. However, [PERSON] et al. [34] have achieved hydraulic reactions of materials of volcanic origin through a very fine grinding process which causes an increase in the surface area of the grains and consequently their pozzolanic reactivity.  
A very different situation has been found in the analysis of samples of pyroxenic andesites altered to zeolites. In Figure 5c, after just 8 days of testing, the pozzolanic nature of all samples can already be established, specifically PAAZ-01 and PAAZ-02; also noteworthy are PAAZ-04 and PAAZ-03. At 15 days (Figure 5d), the pozzolanic behavior of the samples seems to be visibly reinforced, with PAAZ-01 and PAAZ-02 being the most reactive. The mineral constitution of these samples, characterized by an increase in the contents of mordenite and smectite, the albitization and sericitization of plagioclase with formation of smectites, the presence of amorphous products, as well as the chemical composition with a high presence of reactive SiO\({}_{2}\)[28], seem to be the fundamental causes behind the pozzolanic behavior of these materials.
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
## 5 Performance Evaluation
### Experimental Results
#### 5.2.1 Time Overhead  
The time overhead is tested in term of key generation, index building, trapdoor generation and testing. The experiment results are shown in Figure 2.  
1. Key generation. Because of adopting the technique called dual pairing vector space (DPVS)[8], the time complexity of key generation in OT15 is \(O(n^{2})\). The time cost of key generation in other three schemes are all linear with \(n\).
2. Index building. The time cost of index building in ZZ11, OT15 and ours are all linearly with \(n\). The time cost of index building in our scheme is still much less than that in ZZ11 and OT15. Furthermore, as \(n\) grows, for example, \(n>10\), the time cost of index building in ZLW19 is more than that in our scheme since it is linear with \(n^{2}\).
3. Trapdoor generation. Although the time cost of trapdoor generation in ZZ11, OT15 and ours are all linear with \(n\), our scheme needs less time cost than ZZ11 and OT15 due to needing less exponentiation computation operations. The time cost of trapdoor generation in ZLW19 is linear with \(m\). Because \(m\) is less than \(n\), the time cost of trapdoor generation in our scheme is slightly more than that in ZLW19.
4. Testing. The time cost of test in these four schemes are all linear with \(n\). Compared with ZZ11 and ZLW19, our scheme needs less pairing operations. Compared with OT15, our scheme needs less exponentiation operations on group elements. Since the time cost of exponentiation operation is only one fourth of that of pairing operation, our solution requires less test time.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 5 System Performance Evaluation
### Localization Accuracy  
The accuracy of the proposed system is tested in lab environment for small distances (between 1 and 5 m) where the ground-truth is determined with a mm-accuracy laser meter. For longer distances (between 50 and 75 m), the accuracy was tested on a empty parking lot where a roll-meter and reference lines on the ground were used for determining the ground truth. The distribution of these samples is given in Figure 7, together with a cumulative distribution function of the error of the measurements. The mean reported distances are 49.99 and 74.95 m, deviating only 1 and 5 cm from the real distance.
# Secure Communication for Uplink Cellular Networks Assisted with Full-Duplex Device-to-Device User
## 5 Conclusions  
We propose a secure communication scheme suitable for FD D2D user-assisted cellular uplink transmission. In this scheme, the cooperative D2D communication model is built for realizing long-distance cellular uplink transmission and reliable cooperative transmission. In particular, the D2D user as a relay is operated in FD mode to improve the spectrum efficiency of the system, and the combination of the confidential signal and the interference signal is transmitted by the CU so that the untrusted relay cannot successfully decode the information to be forwarded to the BS. We formulate our problem in order to maximize the D2D link rate while achieving a secrecy rate for the cellular uplink. The closed-form expressions of the power allocation factors at the D2D user and the CU are derived. The simulation results show that the D2D link rate of the proposed FD scheme is significantly improved compared with the conventional HD scheme.  
Conceptualization, L.X.; data curation, L.X.; formal analysis, L.X.; funding acquisition, X.S.; methodology, L.X.; project administration, X.S.; software, L.X.; supervision, X.S.; validation, L.X. and S.X.; visualization, L.X.; writing--original draft, L.X.; writing--review and editing, X.S., L.X., S.X. and Y.W. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Nature Science Foundation of China under Grant 61473066 and Grant 61601109, in part by the Fundamental Research Funds for the Central Universities under Grant No. N152305001, and in part by the Natural Science Foundation of Hebei Province under Grant F2017501039. The authors thank the anonymous reviewers for their insightful comments that helped improve the quality of this study. The authors declare no conflict of interest.
# Privacy-Preserving Federated Singular Value Decomposition
## 1 Introduction
### Related Work  
The concept of privacy-preserving federated SVD has been studied in several works, which are briefly summarized below.  
In the literature, many anonymization techniques have been proposed to enable privacy protection in federated machine learning and other tasks. [PERSON]. [20] proposed substitute vectors and length-based frequent pattern tree (LFP-tree) to achieve the data anonymization. It focuses on what data can be published and how they can be published without associating subjects or identities. With the concept of data anonymization in mind, [PERSON]. [21] proposed a strategy by decreasing the correlation between data and the identities. However, the utility of the data will be affected. And, [PERSON]. [22] focused on high-dimensional dataset, which is divided into different subsets; then, each subset is generalized with a novel heuristic method based on local re-coding. While these works contain interesting techniques, they do not directly offer a solution for privacy-preserving federated SVD. A more detailed analysis can be found in [1].  
Technically speaking, the algorithms utilized to compute SVD are mostly iterative, such as the power iteration method [23]. Recently, these algorithms were adopted to a distributed setting to solve large-scale problems [24; 25]. While these works tackle important issues and advance the field, they all disregard privacy issues: we are only aware of two federated SVD solutions in the literature explicitly providing a privacy analysis [18; 19]. [PERSON] et al. [19] proposed a federated SVD algorithm with a star-like architecture for high-dimensional data such that the aggregator cannot access the complete eigenvector matrix of SVD results. Instead, each node device has access, but only to its shared part of the eigenvector matrix. In addition to the lack of a rigorous privacy analysis, its aim is different from most other federated SVD solutions where the aim is to jointly compute a global feature space. In contrast, [PERSON] et al. [18] proposed a federated SVD algorithm based  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protects** & **The Individual Updates** & **The Aggregate** \\ \hline Secure Aggregation & ✓ & ✗ \\ Central DP & ✗ & ✓ \\ Local DP & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparing secure aggregation with local and central differential privacy.  
on the distributed power method, where both the server and all the participants learn the entire eigenvector matrix. Their solution incorporated additional privacy-preserving features, such as participant and aggregator server noise injection, but without a rigorous privacy analysis. We improve upon this solution by pointing out an error in its privacy analysis and by providing a tighter privacy protection with less utilized noise. Overall, these existing literature works do not provide a privacy-preserving federated SVD solution with a rigorous analysis in our setting.
# Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising
###### Abstract
Fuzzy logic has added excellent noise removal capabilities against different types of noise due to uncertainty features [19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. A fuzzy logic based filtering mechanism is proposed in [19] for additive noise, which is mainly, based on fuzzy image derivatives in eight directions. This fuzzy filter gives good results but edges and sharp details of the image are not well preserved. [PERSON] et al. in [20] proposed a fuzzy logic based nonlinear filtering mechanism to address the problem in above said filter. [PERSON] et al. in [22] proposed a directional mechanism in four directions based on fuzzy reasoning. The main drawback was in non-adaptive behavior of the threshold parameters. [PERSON] et al. in [23] proposed cluster-based approach for better classification of impulse noise but was unsuccessful at low and very high noise densities. [PERSON] et al. in [24; 25] proposed fuzzy reasoning based clustering criteria of pixels to address the problems in above filters. Similar fuzzy logic based other filters [26; 27; 28; 32] address neighboring issues and hybrid approaches for effective impulse noise removal. In this paper, a new fuzzy inference system based impulse noise removal technique is proposed that uses a convolved feature vector layer to effectively restore the corrupted image details. The proposed technique first extracts a small layer of pixels from input image, convolves it to form a convolved feature vector layer and forward this layer to fuzzy inference system to determine the noise type in that particular patch. Fuzzy membership degrees play a vital role in filtering phase to remove the corrupted pixels. Noise-free pixels remain unaffected throughout this process contributing maximum detail preservation. The paper is organized as follows: Noise model and reviews are presented in Sections 2 and 3, respectively. Methodology of the proposed filter is described in Section 4. Experimental setup, results and discussions are briefly explained in Section 5 whereas conclusions are drawn in Section 6.
## 3 Materials and Methods
### Apparatus and Materials  
This study used the BEATS system to collect 32-channel EEG data [32], and utilized PsychoPy (version 2022.5) for designing a continuous text reading task, capturing both stimulus timing and participant responses [33; 34].  
The experimental materials for this study were selected from the Fractuality Evaluation Benchmark (FRANK) dataset [4]. This dataset comprises 2250 summaries generated by 9 models on 2 datasets (CNN/DM and XSum) and manual annotations. Each summary within this dataset has been meticulously annotated for factual errors, utilizing a detailed factual error classification system.  
The criteria for material selection in this study included: (a) Presence of Factual Errors: Materials were required to have annotations indicating factual errors in model outputs; (b) Participant Fatigue Prevention: Each article was limited to a maximum of 200 words, with no more than 7 articles used per experiment, keeping sessions under 1 h to minimize participant fatigue; (c) Simplicity in Language: The chosen texts minimized the use of infrequent vocabulary and proper nouns, ensuring clarity in language processing tasks. The details of the selected materials are presented in Table 1. A total of 7 English short texts were chosen for the study. After merging all texts and tokenizing using the Python spaCy, 1220 tokens were obtained.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Article Hash** & **Count of Sentences** & **Count of Words** & **Count of Tokens** \\ \hline
32143053 & 10 & 154 & 180 \\
38329319 & 11 & 200 & 242 \\
32457391 & 7 & 152 & 176 \\
33652722 & 6 & 135 & 148 \\
31566848 & 6 & 128 & 143 \\
31920236 & 7 & 139 & 160 \\
38595401 & 6 & 138 & 171 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selection of experimental materials.  
In subsequent sections, unless expressly stated otherwise, any mention of the term \"word\" refers to a \"token.\"