esa-sceva/satcom-chunk-collection huggingface dataset:
# REGNet: Ray-Based Enhancement Grouping for 3D Object Detection Based on Point Cloud
## 3 Our Approach
### Voting-Aware Module  
The vote scheme in the VoteNet does not consider relationships between point patches; however, this information is very important to the location and classification of the final prediction. MLCVNet proposes patch-patch context (PPC) to capture the relationships between point patches. It adopts the compact generalized non-local network (CGNL) [51] to explicitly build rich correlations between any pair of point patches. The detail is as follows:  
\[Re=sim(\theta(f),\phi(f))g(f) \tag{4}\]  
where \(\theta(\cdot)\), \(\phi(\cdot)\), \(g(\cdot)\) denote three transform functions, and \(sim(a,b)\) calculates the similarity between the two positions of \(a\) and \(b\). However, CGNL only has one head and one layer to build the relationship among the vote centers. Moreover, CGNL can only model the vote center and cannot consider the relationship between the vote center and the clusters. Additionally, in VoteNet, vote centers and cluster centers are generated through sampling and grouping from seed points. However, this method lacks consideration for the appearance information of objects. For [34], a ray-based feature grouping method is employed, which can learn a better feature representation of the surface geometry of foreground objects. However, the relationship between the vote center and the cluster has yet to be considered. With the development of transformer technology, more and more researchers are trying to use a transformer scheme to model the interconnections between different modules.  
In this paper, we adopt the ray-based feature grouping method proposed in [34] to generate vote centers and clusters, and the stacked multi-head self-attention and multi-head cross-attention modules are leveraged to establish the relationship between the voting center and the cluster. The multi-head self-attention module is used to simulate the relationship between votes, and the multi-head cross-attention module is used to simulate the interaction between votes and clusters. After stacking the attention modules, a feed-forward network (FFN) is used to obtain more reasonable transformation features for each object. The structural description of this module is shown in Figure 2. Denote the point features of the voting center as \(f_{v_{i}=1}^{M}\) and the cluster feature as \(f_{c_{i}=1}^{K}\). The self-attention module build the relationship between votes, formulated as follows:  
\[self-att(\theta(f_{v}),\phi(f_{v}))g(f_{v}) \tag{5}\]  
and the cross-attention module adopts point features to compute object features, formulated as follows:  
\[cross-att(f_{v_{i}},f_{c_{i}}) \tag{6}\]  
where \(f_{v_{i}}\in f_{v_{i}=1}^{M}\) and \(f_{c_{i}}\in f_{c_{i}=1}^{K}\).
# Privacy Preserving Classification of EEG Data Using Machine Learning and Homomorphic Encryption
## 2 Methods
### Use Case 2--Predisposition to Alcoholism  
Another use case where EEG signals provide valuable information is the analysis of the predisposition to alcoholism, based on the differences between the frequency bands of alcoholic and non-alcoholic individuals [37]. We used a dataset created by [PERSON] (Neurodynamics Laboratory, State University of New York Health Center, Brooklyn), made available at [35]. The data were collected during a study evaluating the correlation between EEG signals and genetic predisposition to alcoholism. The signals were measured by 64 electrodes scanned at 256 Hz (3.9 ms epoch) for 1 s. The sensors were placed on the subjects' scalps according to the International 10-20 standard. The subjects were exposed to one stimulus (S\({}_{1}\)) or two stimuli (S\({}_{1}\) and S\({}_{2}\)) represented by pictures from the [PERSON] and Vanderwart picture set [38]. The two stimuli were shown in a matched condition (S\({}_{1}\) was identical to S\({}_{2}\)) or in a non-matched condition (S\({}_{1}\) differed from S\({}_{2}\)).  
The original dataset is split into two folders--train and test--each of them containing 10 runs for 10 alcoholic and 10 nonalcoholic subjects. The folders consist of trails files  
Figure 1: NCC values for different versions of downsampled EEG signals.  
Figure 2: Comparison between the downsampled and the original EEG signals.  
with the following columns: trial number, sensor position, sample number, sensor value, subject identifier, matching, channel number, name, and time. In a single trial run, a patient provides 256 samples for each of the 64 electrodes. The pre-processed training dataset contains 153,600 samples of 64 features corresponding to the 64 electrodes; the corresponding ground truth labels are 0 if the subject is non-alcoholic and 1 if the subject is alcoholic. The pre-processed testing dataset also contains 153,600 samples of 64 features and the ground truth label. The input data are normalized in the range \([-1,1]\).
## Appendix B Addressing Data Conflicts and Rationale for Data Discarding  
In our study, we carefully considered the potential conflicts within the data and the criteria for discarding data to ensure the integrity and applicability of our results. The primary sources of conflict identified during the data collection process were as follows:  
Some participants provided incomplete responses to key questions, which rendered their data unusable for analysis. We excluded responses where more than 20% of the questions within the questionnaire were left unanswered.  
We noticed patterns of responses that suggested participants might not have answered the questions thoughtfully. For example, when a respondent answered \"Strongly Agree\" for all items across multiple scales without meaningful variation, this was considered a sign of non-engagement. Such responses were excluded from the analysis.  
We also conducted statistical checks for outliers or extreme values within the dataset, particularly for responses related to key constructs such as \"perceived sociability\" and \"behavioral intention.\" Outliers were identified using standard deviation thresholds, and responses that significantly deviated from the mean (beyond three standard deviations) were excluded.  
Data from participants who completed the survey in less than four minutes were discarded. We considered this duration insufficient to provide thoughtful and valid responses to the 33 items on the Likert scale, which may suggest a lack of attention to the questionnaire.  
In cases where responses indicated internal inconsistency across variables (e.g., high ratings on \"performance expectancy\" but low ratings on \"behavioral intention\"), we carefully reviewed the patterns. In cases where participants gave conflicting responseswithin the same variable or across related variables (such as between \"effort expectancy\" and \"behavioral intention\"), their data were excluded from the analysis to ensure internal consistency and the validity of the findings.
## 8 Conclusions  
This study successfully demonstrated the implementation of a novel CNN-based decision unit strategy in an optical receiver of UWOC systems. The proposed CNN models are found to predict SNR effectively with high performance, with the train and validation losses and RMSE demonstrating convergence towards smaller values. The results show an inverse strong correlation between the number of parameters in the model and the cost function, suggesting that increasing the CNN model's size enhances its performance. Even in diverse water types with fluctuating noise levels and environment variability, employing a CNN model as a decision unit in an optical receiver enables efficient decision-making with a low-cost function.  
Our innovative CNN tool's architecture and supporting mathematical formulations made it agnostic to the UWOC model and transmission modulation format. Hence, if any or all channel models in Table 1 are proven not to partially or fully satisfy the linear time-invariant system (LTIS) condition requirements, replacing any or all of these models with ones that comply will not impact the CNN tool computational software algorithm. Still, it might require altering the hyperparameters of the CNN model platform structure shown in Table 4 to ensure optimum model accuracy fitting, but from a hardware perspective, we do not expect any necessary change to the hosting math processor of the DU. It is worth mentioning that LTIS requirements are translated in terms of channel path loss, mean delay, Root Mean-Square delay spread, and the constancy of the frequency bandwidth with the model temporal profile broadening with linkspans.
_applied sciences_  
_Article_  
**Prediction of Residual NPK Levels in Crop Fruits by**  
**Electronic-Nose VOC Analysis following Application of**  
**Multiple Fertilizer Rates**  
[PERSON] 1, [PERSON] 1,\({}^{\star}\)\({}^{\copyright}\), [PERSON] 1, [PERSON] 2 and [PERSON] 3,\({}^{\star}\)\({}^{\copyright}\)  
\({}^{1}\) Department of Mechanical Engineering of Biosystems, Razi University, Kermanshah 67156-85421, Iran  
\({}^{2}\) Department of Biosystems Engineering, University of Mohaghegh Ardabil, Ardabil 56199-11367, Iran  
\({}^{3}\) Southern Hardwoods Laboratory, Center for Forest Health & Disturbance, Forest Genetics & Ecosystems Biology, Pathology Department, Southern Research Station, USDA Forest Service, 432 Stoneville Road, Stonveille, MS 38776-0227, USA  
\({}^{4}\) Correspondence: [EMAIL_ADDRESS] (E.M.-G.); [EMAIL_ADDRESS] (A.D.W.); Tel.: +98-833-833-1662 (E.M.-G.); +1-662-336-4809 (A.D.W.)  
**Abstract: The excessive application of nitrogen in cucumber cultivation may lead to nitrate accumulation in fruits with potential toxicity to humans. Harvested fruits of agricultural crops should be evaluated for residual nitrogen, phosphorus, and potassium (NPK) nutrient levels. This is necessary to avoid nutrient toxicity from the consumption of fresh produce with excessive nutrient levels. Electronic noses are instruments well-suited for the nondestructive detection of fruit and vegetable quality based on volatile organic compound (VOC) emissions. This proof-of-concept study was designed to test the efficacy of using an electronic nose with statistical regression models to indirectly predict excessive fertilizer application based on VOC emissions from cucumber fruits grown under controlled greenhouse conditions to simulate field conditions but eliminate most environmental variables affecting plant volatile emissions. To identify excess nitrogen in cucumber plants, five different levels of urea fertilizer application rates were tested on cucumbers (control without fertilizer, 100, 200, 300, and 400 kg/ha). Chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models to predict nitrogen (N), phosphorus (P), and potassium (K) levels in cucumber fruits following application of different fertilizer rates to greenhouse soils. The correlation coefficients for the MLR model (based on the optimal parameters of PCR and PLSR) were 0.905 and 0.905 for the calibration sets and 0.900 and 0.900 for the validation sets, respectively. The nitrogen prediction model for fruit nitrates was more accurate than other nutrient models. The proposed method could potentially be used to indirectly detect excessive use of fertilizers in cucumber field crops.**  
**Keywords: chemical fertilizers; odors; modeling; prediction**
# A Two-Stage Local Path Planning Algorithm Based on Sampling and Optimization Methods
## 3 Proposed Method
### Exploration Stage  
#### 3.2.1 Coordinate Transformation  
The core idea of the PSF algorithm is to minimize the deviation between the local path and the global path, meaning that the distance between corresponding points on the global and local path should be as small as possible. Therefore, the distance between the local path and the global path is a crucial parameter in the PSF algorithm. To better represent the relationship between any point in space and the global path, its position will be described based on its lateral distance and longitudinal distance from the global path, as illustrated in Figure 3.  
Figure 3: Transforming the reference coordinate system to the planning coordinate system.  
Figure 2: System overview of PSF.  
Taking the starting point \((x_{0},y_{0})\) of the global path as the origin, a planning coordinate system is established. The horizontal axis aligns with the direction of the global path, while the vertical axis aligns with its normal direction. Therefore, the coordinates of any position in the planning coordinate system represent the lateral and longitudinal distances from the point to the global path.  
First, the reference coordinate system is transformed into the planning coordinate system:  
\[\theta=arctan\frac{y_{1}-y_{0}}{x_{1}-x_{0}} \tag{1}\]  
\[\begin{bmatrix}cos\theta&sin\theta\\ -sin\theta&cos\theta\end{bmatrix}\cdot\begin{bmatrix}x_{i}-x_{0}\\ y_{i}-y_{0}\end{bmatrix}=\begin{bmatrix}s_{i}\\ d_{i}\end{bmatrix} \tag{2}\]  
where \(\theta\) in (1) is the rotation angle, representing the angle between the global path and the \(x\)-axis of the reference coordinate system. \((x_{i},y_{i})\) in (2) denotes the coordinates to be transformed, while \((s_{i},d_{i})\) represents the transformed coordinates. When \((x_{i},y_{i})\) lies on the global path, \(d_{i}\) equals 0.  
Therefore, the coordinates of any point in the reference coordinate system can be represented by its lateral and longitudinal distances from the global path, denoted as \((s,d)\). The objects scanned by the LiDAR are also transformed into this coordinate system, with the set of LiDAR point positions represented as \((Ls_{i},Ld_{i})\).
## 3 Proposed Methodology
### Automatic Test Generation Algorithm  
In this section, we provide a description of our designed ATG algorithm at both high and low levels. In the high abstraction level, the inputs of the algorithm are as follows.  
1. Model of the SoS formally capturing: (i) the SoS architecture (CSs, interfaces, data exchanged with their types, services exposed/required and related events, etc.); (ii) the behavior of each CS in terms of state machines; (iii) the events identifying external accidents or modeled faults for each CS; (iv) the ATG predicate over the formal elements of the model (events exchanged, states entered in state machines, etc.) that models the target of the ATG; and (v) additional assumptions on the CS or on the occurrences of the fault/accident events.
2. Automatic test generation algorithm's parameter settings: (i) test case maximum length bound--the maximum admissible length for the test case; and (ii) test cases maximum number--the maximum number of test cases produced by the ATG. The outputs of the ATG algorithms are:  
1. A test report summarizing the results of the ATG process (number of generated test cases and for each test case, its length, and the number of faults injected).
2. A test suite collecting the test cases as an XML file reporting for each case the sequence of occurrences of the accidental and fault events leading to the satisfaction of the hazard predicate. The detailed design of the low-abstraction-level algorithm is shown in Figure 2. The time complexity of this algorithm is O(n). The details of this algorithm can be summarized as the following steps:  
Figure 2: Flowchart of ATG algorithm supporting the test case generation.  
Step 1: During the initialization phase, the input model is processed, and auxiliary variables and constraints are synthesized to enable the successive test generation phase.  
Loop: It iterates through step 2 to step 5 until the exit condition is met.  
Step 2: Add permanently to the search problem the constraints imposed by the model for the current bound.  
Step 3: Check for the satisfaction of the hazard predicate considering the input model and the additional constraints.  
Step 4: In case the search has been successful, extract the found test cases and store them.  
Step 5: If the maximum number of desired test cases is not reached, compute additional constraints to efficiently exclude the generated test cases for the successive searches.  
Exit: If the maximum number of desired test cases is reached, exit.  
At the end of the ATG execution, if the hazardous condition can be verified within the maximum length bound, a test suite is generated that takes into account the constraints of the model.
## 3 Related Work on Protection against Sybil Attacks
\begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Method** & **Advantages** & **Limitations** \\ \hline [PERSON] et al. [37] & MDS & Resistant to massive attacks in which the attacker gains numerical advantages. & Privacy and security problems. \\ \hline [PERSON] et al. [40] & Pluck Incorrect Information & Effective in detecting false information. & Requires fast processing to avoid time-barring events. \\ \hline [PERSON] et al. [50] & GPS analysis & Does not require the use of additional hardware. & GPS inaccuracy might violate the results. \\ \hline [PERSON] et al. [45] & Motion Trajectories Similarity & Allows to detect each Sybil node separately. & Requires the approved infrastructure. \\ \hline [PERSON] et al. [46] & RSUs cooperation & Basis of a trilateration provides reliable results. & Require at least three RSUs at a particular crossing. \\ \hline [PERSON] et al. [47] & Lightweight Protection Regime & Hard to omit the rules by the attacker. & Can be problematic in case of signal power loss. \\ \hline [PERSON] et al. [49] & DMV & Does not require cars to reveal their information on the infrastructure. & It does not encourage a misbehaving node to behaving in a proper way and does not punish malicious nodes. \\ \hline [PERSON] et al. [43] & Rationalizing Positioning Of Vehicles & In case it is confirmed that data delivered do not provide false positives. & GPS inaccuracy might violate the results. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of current Sybil prevention methods. Bogus & Sybil Trust Level & Timestamp (B&STL&T) Algorithm versus Bogus & Sybil Enhanced Behavior Processing & Footprint (B&SEBP&F)  
The Bogus & Sybil Trust Level & Timestamp (B&STL&T) algorithm covered in Bogus & Sybil Trust Level & Timestamp (B&STL&T) algorithm covered in Bogus & Sybil Trust Level & Timestamp (B&STL&T) algorithm covered in Bogus & Sybil Trust Level & Timestamp (B&STL&T) algorithm covered in Bogus & Sybil Trust Level & Timestamp (B&SEBP&F) algorithm, multiple modifications were applied: an improved system for monitoring drivers behavior in case of an accident, heavy traffic, and police controls. Moreover, the footprint algorithm was implemented in order to prevent Sybil attacks.
## Materials and Methods
### Data Preprocessing  
April to October was selected as the vegetation growing season [53; 54] for the calculation of the average GPP and ET throughout the growing season, and the mean value of the WUE was obtained. The mean precipitation, temperature and solar radiation were also calculated from April to October. To maintain the accuracy of the land cover change estimation, nearest neighbor resampling was applied to the remaining datasets to match the 1 km spatial resolution of the land cover data.  
The WUE values of the croplands, forests and grasslands were mainly considered in this study since the TNR is dominated by these land cover types. The land cover data from 1990 to 2015 were processed, and those pixels that consistently remained one type of land cover or only changed land cover type once (e.g., from croplands to forests, from cropland to grasslands, etc.) were selected as the research object, which was called land cover changes (LCC). Since it is difficult to describe the relationship between WUE and multiple changes of land cover, we simplified the calculation by omitting the pixels with more than one change in land cover.  
To verify the effectiveness of the WUE from the GLASS products for the TNR, the WUE results derived from the GLASS products and MODIS products were compared for different land cover types in different regions. However, the relationship between the GLASS-WUE and the MODIS-WUE under different land cover types could not be verified yearly due to the limitation of land cover data with a temporal resolution of 5 years. For the pixels with consistent land cover types over the 5 years, we assumed that the land cover type of these pixels did not change during the 5 years. Therefore, the pixels of croplands, forests and grasslands with consistent land cover types from 1990 to 2015 were selected [51] as the target of the comparison, and the WUE values of the four regions were compared for each year.
## 3 Security Threats
### Layer-Based Attack Classification
This comprehensive, layer-centric approach enhances clarity, facilitates reproducibility, and supports the deployment of precise and effective blockchain security measures. From Table 3, we can infer that while our taxonomy is designed around traditional chain-based blockchains (e.g., Bitcoin, Ethereum), it can be extended to accommodate other architectures such as DAG-based systems (e.g., IOTA [38], Hedera), hybrid blockchains (e.g., Hyperledger Fabric [39]), sharded blockchains (e.g., Ethereum 2.0 [40]), and BFT-based platforms (e.g., Cosmos, Tendermint [41]). \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Attack Name** & **Layer** & **Impact** & **Counter Measure** \\ \hline Transaction Malleability & Data & Network reliability and Integrity. & Hashing of the transactions, Time commitment scheme. \\ \hline Quantum & Data & Cryptographic algorithm. & Quantum resistant key Scheme. \\ \hline Race & Network & Double spending, System integrity. & Enforcing multiple transaction confirmation. \\ \hline Finney & Network & Double spending, Integrity, Trust and reliability. & Increased the required number of Block confirmations for transactions. \\ \hline Replay & Network & Transaction duplication, Double spending, Network congestion. & Transaction sequencing techniques, Digital signatures. \\ \hline Eclipse & Network & Network partitioning, Isolation, Forking and reorganization. & Detection through monitoring, Introducing peer discovery protocols. \\ \hline DDoS & Network & Service disruption, unavailability, Network congestion, and performance degradation. & Traffic filtering, Rate limiting, IP blacklisting. \\ \hline BGP Hijacking & Network & Routing manipulation, Network isolation, Dropped transactions. & BGP monitoring, Relay network. \\ \hline Routing & Network & Network partitioning, Delay in transactions. & Audit, Verification, Cryptography. \\ \hline DNS & Network & Service disruption and unavailability, Phishing, and Information leakage. & Improved authentication Scheme, Monitoring DNS traffic. \\ \hline
51\% & Consensus & Double spending, Network disruption. & Diversify mining pool, Increased hash rate, improved consensus algorithms. \\ \hline Time-jacking & Consensus & Consensus disruption, Undermined integrity, Trust, and Reliability. & Enhanced timestamp validation. \\ \hline Sybil & Network & Identity, Integrity, Consensus, Double spending. & Robust identity verification such as PoW, PoS. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of different attacks on blockchain.