esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events
## 1 Introduction
(iii) A wind power model that considers the ramping rate is proposed and incorporated into a composite power reliability evaluation. The remainder of this paper is organized as follows. Section 2 introduces the typical communication architecture and risk of cybersecurity in the wind farms. In Section 3, the stochastic models for a state sampling of cyber-attacks and system defenses are proposed. The scenarios, including a worst-case attack and an imperfect attack (IPA), on WTs are illustrated in Section 4. In Section 5, the flow of reliability evaluation is constructed, and case studies are presented in Section 6. Finally, conclusions as well as research discussion are summarized.
## 4 Proposed Bio-2 FA-IoD Protocol
### Registration Phase  
This phase (illustrated in Figure 2) is performed in a secure environment and involves the operator (\(U_{O}\)), their operator's device (OD), and the trusted authority (TA).  
1. **Operator** enrollment (\(U_{O}\to TA\)): The operator \(U_{O}\) initiates the registration process by submitting their chosen identity \(ID_{O}\) to the TA and enrolls their biometric trait \(\beta_{O}\) using a trusted sensor interfaced with the TA's system.
2. **TA** operations (credential generation and storage): * The TA uses a fuzzy extractor to generate a stable biometric key \(K_{\beta O}\) and public helper data \(HD_{O}\) from the enrolled biometric \(\beta_{O}\): \((K_{\beta O},HD_{O})=Gen(\beta_{O})\)[12].
* TA generates a unique, high-entropy random password \(RP_{ID_{O}}\) specifically for this operator, and a random salt \(Salt_{1}\).
* TA computes a hashed version of this random password: \(RPMD_{ID_{O}}=H(RP_{ID_{O}}\|Salt_{1})\).
* TA securely stores the tuple \((ID_{O},RPMD_{ID_{O}},Salt_{1},LLT_{O}=0)\) in its database, where \(LLT_{O}\) is the operator's last login time, initialized to zero [8].
3. **OD** provisioning (\(TA\to OD\)): The TA securely transmits the generated random password \(RP_{ID_{O}}\) and the helper data \(HD_{O}\) to the operator's designated OD. This transfer must occur over a secure channel or through a secure physical provisioning process.
4. **OD** final setup (with \(U_{O}\) assistance): * The operator \(U_{O}\) is prompted to create a local password or PIN, denoted as \(P_{ID_{O}}\), on their OD.
* The OD derives a symmetric key \(K_{O}=KDF(P_{ID_{O}},Salt_{2})\), where \(Salt_{2}\) is another random salt.  
The OD encrypts the received random password \(RP_{ID_{O}}\) using \(K_{O}\): \(C_{RP}=E_{K_{O}}(RP_{ID_{O}})\). The OD securely stores \(C_{RP}\), \(HD_{O}\), and \(Salt_{2}\). It is important to note that the raw biometric template (\(\beta_{O}\)) is not stored; only the helper data (\(HD_{O}\)) is maintained on the OD, aligned with privacy-preserving practices.  
Drone \(D_{i}\) is also registered with TA/GCS and provisioned with its own credentials.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 5 NS-3 Simulation Tool  
Due to the lack of simulation tools for PLC, a framework for PLC simulations in NS-3 environment (hereinafter referred to as NS-3 Simulator) was developed [48].  
Like the above-mentioned simulators, the NS-3 Simulator focuses on the behaviour of power line transfer channel and it is possible to simulate both narrowband and broadband communication. The simulator enables definition of a variety of topologies and includes time- and frequency-variable behaviour of the PLC channel in the simulations.  
Based on this simulator, the G.hn (ITU standard for a PLC) Network Simulator was designed [49]. In a previous study [50], this NS-3 Simulator is used for medium access control scheduler simulation in a realistic in-home scenario.  
In comparison to MATLAB, the advantage of NS-3 is that libraries for a large number of communication technologies are implemented in NS-3. This allows the user to combine multiple technologies and to simulate applications for the Smart Grid and heterogeneous/hybrid networks.
## 6 Conclusions and Future Development Paths  
The productivity of the production lines can be improved with the introduction of fractional tasks in the design model, while the redundancy between two consecutive stations can reduce the impact of unforeseen events such as short failures or processing time fluctuations. This research proposes an allocation model based on the fuzzy logic of the fractional tasks considering the redundancy possibility between two consecutive stations. Then, it responds to the first research question asked: \"_Can a fuzzy policy support the dynamic allocation of the shared task in a fractional and redundancy assembly line optimizing the main performance measures?_\"  
The numerical results of the simulation show how the fuzzy model can improve the performance of the production line with more stability when the conditions of the production lines change. The production lines with redundancy control have a relevant coefficient of variation of the stations' use; the fuzzy model allows to drastically reduce this variation with more uniform use of the stations.  
The second answer to the research question asks: \"_What are the operating conditions of the assembly line in which the dynamic allocation of the shared tasks leads to better results?_\"  
The main effect on the fuzzy model proposed is the inter-arrival of the raw items in the production line. The fuzzy model works better when the fluctuation of the inter-arrival raw items is lower; the numerical results are better in the cases of raw items always available or following a normal distribution. When the inter-arrival follows an exponential distribution characterized by a high coefficient of variation (CV = 1), the improvements are lower.  
Some limitations of the proposed research to investigate in future developments are the following. The costs to support the introduction of redundancy in equipment will be studied to evaluate the costs of the improvements obtained. In the case of assembly lines with operators, the learning and forgetting effects can be investigated.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
## 6 Simulation Results and Analysis  
To verify the performance of AEBDC in terms of energy consumption balance, network lifetime and recharging efficiency, relevant experiments were carried out with the help of Java (JDK1.8) and Matlab R2014a. These simulation results were compared with EBCAG [16] and EBCH [17], which are two cluster-based data collection strategies in WSNs. Values of the experimental parameters are shown in Table 4.
## Materials and Methods
### Data Analysis  
The total ion chromatograms and the extracted ion profiles were obtained for each sample using the XCalibur software. The selected fragment ions for each class compounds were as follows: \(m/z=57.0\), 71.0, 85.0, and 99.0, for extracted alkane profile; \(m/z=55.0\), 69.0, 83.0, 97.0 for extracted cycloalkanes and alkenes profile; \(m/z=91.0\), 105.0, 119.0, and 133.0, for extracted aromatics (alkylbenzenes) profile; \(m/z=128.0\), 142.0, 156.0, for extracted polynuclear aromatics; and \(m/z=117.0\), 118.0, 131.0, 132.0, 145.0, 159.0 for extracted indane profile [15]. The target compounds and pyrolysis products were identified by a comparison of the fragmentation pattern of the mass spectrum of the unknown with the reference mass spectra in the NIST library. The mass spectrum matching score is reported as Reverse Match Factor (RSI).
###### Abstract  
Insertion guides are becoming popular for orthodontic mini-implant positioning. The aim of this study was to evaluate and compare the accuracy of two different mini-implant insertion guides, with or without pre-drilling, in a human cadaveric model. Maximilary casts of six fresh frozen specimens were digitized to create insertion guides. Sixty mini-implants were randomly inserted with full-arch or skeletonized guides, either with or without predrilling. Pre- and post-treatment CBCTs were superimposed using rigid registration. Transformation matrices of the planned and real positions were obtained, and distances at the mini-implant neck and apex, as well as the angular deviation, were calculated. The Kruskal-Wallis test was performed, followed by a post hoc test when indicated. Out of 60 inserted mini-implants, 46 could be evaluated. Of these, 10 initially assigned to no pre-drilling required this procedure due to very high bone density. Therefore, 32 implants were inserted with pre-drilling (\(n=15\) full-arch; \(n=17\) skeletonized) and 14 without (\(n=7\) full-arch; \(n=7\) skeletonized). The lowest mean deviation at the neck was 1.22 \(\pm\) 0.6 mm, registered in the full-arch/pre-drilling group. The skeletonized/no pre-drilling group presented the lowest mean values at the apex, i.e., 1.72 \(\pm\) 1.22 mm, as well as the lowest mean angular deviation, i.e., 8.23 \(\pm\) 4.24\({}^{\circ}\). Significant differences among groups were observed only at the neck, with higher mean deviation in the skeletonized/pre-drilling group than in the full-arch/pre-drilling one (\(p=0.014\)). In conclusion, within the limitations of the study, rather high deviations between planned and real mini-implant positions were found. Further studies are needed on how to improve the accuracy within in vivo settings.  
mini-implants; insertion template; CAD-CAM; additive manufacturing; CBCT +
Footnote †: journal: Applied Sciences
## 4 Data Analysis in Maritime IoT Applications
### Case Studies of Successful Data Analysis in Maritime IoT  
Several successful implementations highlight the power of data analysis in maritime IoT applications (Table 2).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Case** & **Application** & **Results/Benefits** \\ \hline Maersk & Predictive Maintenance & Reduced downtime and operational costs [76] \\ \hline Port of Rotterdam & Efficient Port Operations & Improved efficiency and reduced waiting times for vessels [77] \\ \hline Rolls-Royce Marine & Navigation and Safety & Enhanced safety through informed navigation decisions [78] \\ \hline Carnival Corporation & Environmental Compliance & Ensured compliance with environmental regulations, optimized fuel consumption [79] \\ \hline [PERSON]’s research & Ship Allocation Model & Balanced supply and demand, identified effective vessel specifications, gauged impact of vessel efficiency on demand [80] \\ \hline [PERSON] et al. & Optimization in Shipping and Port Industries Through Big Data Analytics & Improved maritime transport activities and port operations through effective Big Data management [81] \\ \hline \end{tabular}
\end{table}
Table 2: Case Studies of Successful Data Analysis in Maritime IoT.
## 4 The Performance of the Optimized SVR Model on Driving Decision-Making  
### The Performance of the Weighted Hybrid Kernel Function  
In the parameter optimization process of the optimized SVR model, 75% of the driving decision samples are randomly selected for training, and the remaining 25% samples are used for model validation. In order to evaluate the performance of the weighted hybrid kernel function, a SVR model  
Figure 6: The analysis process of driving decision samples using Principal Component Analysis (PCA).  
Figure 7: The results of principal component analysis for \(11\) reference indexes.  
with RBF kernel function is input with the same 75% samples to get its corresponding iteration results. We set to 200 the maximum number of training iterations.  
With the PSO algorithm, we can obtain the weighted hybrid kernel function of the optimized SVR model, as shown in formula (16).  
\[K(x,x_{i})=3\times 10^{-3}K_{\text{Poly}}(x,x_{i})+4.21\times 10^{-1}K_{\text{ Rbf}}(x,x_{i})+5.76\times 10^{-1}[K_{\text{Sig}}(x,x_{i})]^{3.13\times 10^{-1}} \tag{16}\]  
The optimal parameters of each basic kernel function incorporated in the weighted hybrid kernel function are shown in the following Table 3. The best penalty factor \(C=5.4142\). In the SVR model with RBF kernel function, the optimal parameters are \(\sigma=1.4142\), \(C=6.0524\). The iterative comparison results of fitted values can be seen from Figure 8.  
It can be seen that the fitted accuracy of SVR model with weighted hybrid kernel function and RBF kernel function, respectively, are 92.3% after 31 generations and 89.7% after 43 generations. So, when compared with RBF kernel function, the weighted hybrid kernel function shows better performance on driving decision-making.
# Comparison of NeQuick G and Klobuchar Model Performances at Single-Frequency User Level +
## 3 Results
### TEC Availability  
This section describes the monthly mean TEC availability results for the high/middle north/middle south/low latitudes and for the NeQuick G and Klobuchar model for the GRC network of stations as described above. Numerical results are presented in Figure 4. As explained in Section 2.3, the Galileo SF ionospheric algorithm specification serves as a benchmark for both models. Being the specification not designed to fit the Klobuchar model, its worst performances with respect to the NeQuick G one are evident. However, the patterns of the Klobuchar follow as seeing for the VTEC accuracy a seasonal variation, showing the worst results during equinoctial months. On the contrary, NeQuick G performs better in the middle south, middle north, and high-latitude regions. In both models, this behaviour is more visible in low latitudes, in agreement with the disturbed ionospheric conditions at equatorial regions. In addition, during the last period analysed it is observed that the performances for both models are worst in the low-latitude regions. As described in Section 3.1, this time series is not long enough to conclude that it is driven by the 11-year solar cycle.
## 4 Methodology
### Problem Definition  
The core task of this research is to perform nowcasting of the short-term directional change in the benchmark interest rate following the release of official central bank policy communication texts (such as FOMC statements and ECB speeches), based on the text itself and the historical benchmark interest rate trend preceding the release.  
Formally, given a policy communication text document \(x\) (with token sequence length \(L_{x}\) potentially reaching 10,000 or higher) and a historical benchmark interest rate time series \(\{r_{t}\}_{t=t_{0}-T}^{t_{0}-1}\) aligned with the document's release date \(t_{0}\) (where \(T\) is the lookback period, set to \(T=48\) months in this study, with sequence length \(L_{r}=48\)), our objective is to train a model \(g_{\theta}\) to predict the category \(y\) to which the future short-term interest rate change \(\Delta r\) belongs. The prediction category \(y\) is defined as a three-class classification:  
\[y=\begin{cases}\text{Up}&\text{if }\Delta r>\epsilon\\ \text{Down}&\text{if }\Delta r<-\epsilon\\ \text{Stable}&\text{otherwise}\end{cases} \tag{1}\]  
where \(\epsilon\) is a predefined threshold (set to 5 basis points, 5 bp, in this study).  
The model \(g_{\theta}\) takes the text \(x\) and the rate sequence \(r\) as input. To enable the model to handle these two different modalities and leverage their sequential properties, we first apply positional encoding (PE) to both separately, then concatenate or otherwise fuse them before feeding them into the model. The mathematical representation of the model can be simplified as:  
\[\hat{y}=\text{softmax}(\text{Head}(g_{\theta}(\text{PE}(x),\text{PE}(r)))) \tag{2}\]  
Figure 4: Comprehensive overview of our integrated framework showing the interaction between Hyena Hierarchy backbone, Delta-LoRA fine-tuning, domain-adversarial neural networks (DANN), and multi-task learning (MTL) components.  
where \(\hat{y}\) is the predicted class probability distribution, \(\text{PE}(\cdot)\) denotes applying positional encoding to the input sequences, \(g_{\theta}\) represents the core network structure composed of modules like Hyena Hierarchy, Delta-LoRA, DANN, etc., (with parameters \(\theta\)) detailed in subsequent sections, and Head\((\cdot)\) is the final classification head (typically one or more fully connected layers).  
The training objective is to minimize the difference between the model prediction \(\hat{y}\) and the true label \(y\), typically using a weighted cross-entropy loss function \(\mathcal{L}_{\text{now}}\). Additionally, to achieve domain adaptation and utilize auxiliary information, the total optimization objective will include extra loss terms, such as the domain-adversarial loss \(\mathcal{L}_{\text{dann}}\) and the auxiliary task loss \(\mathcal{L}_{\text{hawk}}\), as described in Sections 4.4 and 4.5.
## 1 Introduction
This is a reason why security provision should likely be implemented with reconfigurable hardware (such as FPGAs) in IoT rather than with software patches. The FPGAs (field programmable gate arrays) platform is very suitable for the implementation of cryptographic algorithms, because of its re-configurability and high performance features [9]. Initially, the IoT was specifically linked with resource-constrained environments but. today, the IoT has become a popular term, which is largely accepted to include all mobile and fixed devices (including the FPGA; i.e., modern reconfigurable hardware platforms) with Internet connectivity and computing capability [10; 11]. Although this research is conducted by utilizing marine robot application, the methods and results presented here can be beneficial for any remote control systems with video feedback. The encryption method is designed as a portable solution, which can be easily applied to various remote control systems. The analysis of latency (both video and network) is of great importance for systems with real-time constraints. The impact of latency on control systems is highlighted in [12; 13]. Here, the technical, experimental and procedure details involved to develop the system are explained in detail to make reproducibility of the work easy for researchers in the field. Main contributions of this work are as follow:  
* An improved version of our previously published work [12] in terms of having support for encrypted video on demand, while all previous functionalities are preserved. * To the best of author(s) knowledge, this is the first work which provides secure and unsecure video latency measurement detail analysis and comparison. * The implemented encryption does not have noticeable effect on video quality. * This is the first work which presents the video latency measurement results using two scenarios based on same hardware and different hardware on each end of the video communication. This shows the adoptability of the proposed work for different platforms. The remainder of the paper is organized as follow: Section 2 provides problem definition, Section 3 provides related literature review, Section 4 covers the details of the proposed video latency measurement technique, Section 5 describes the test configurations, Section 6 provides performance results, Section 7 contains discussion and comparison of results and Section 8 concludes.
## 4 Results and Discussion  
### pH of Stabilized Soils  
Figure 1 shows the pH of the stabilized soils with different FeSO4 and CaS5 dosages. The results indicated that CaS5 addition can increase the pH of the stabilized soil, which was contrary to the effect of FeSO4 addition. For illustration, the pH of the FeSO4 stabilized soil decreased from 7.86 to 2.62 when the reductant dosage increased from 0% to 5%. However, the pH of the CaS5 stabilized soil increased from 7.86 to 9.57. The changes in the FeSO4 and CaS5 tread soil were attributed to the different stabilization mechanisms in the Cr(VI)-contaminated soils. For the FeSO4 stabilized soil, Cr(VI) was reducted by Fe(II) and formed Fe(III) hydroxide precipitation and released a considerably greater amount of H+ [33]. The alkalinity of the CaS5 stabilized soils can be attributed to the fact that CaS5 is an alkaline material [13]. This result indicates that CaS5 was more advantageous than FeSO4 in which stabilized soil pH can reach values of 9.57 or higher, which increases the acid-buffering capacity.
## 4 Experimental Results and Discussion
### Experimental Setup  
The tests in this research were performed using the Google Colab Pro+ platform. The Tesla V100-SXM2-16 GB graphics processing unit (GPU) unit was operated to accelerate the training of the deep learning models. The suggested SEResNet-BiGRU model and other DL models were developed by Python using the TensorFlow backend (version 3.9.1) and CUDA (version 8.0.6). The GPU was utilized to accelerate the DL prototype training and testing.  
We performed studies to demonstrate recognition interpretation using four deep residual models: ResNet, ResNetSE, ResNeXt, and the proposed SEResNet-BiGRU model. The ResNetSE model combines a ResNet-based model with an SE module in the shortcut connection. ResNeXt is a deep residual network that needs fewer hyperparameters than ordinary ResNet. This is achieved via their cardinality usage, a dimension added to the width and depth of ResNet [55].
# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 3 Method
### Components  
DGGAN is mainly composed of the noise generator, reconstruction generator, and discriminator.
# Finding Optimal Stations Using Euclidean Distance and Adjustable Surrounding Sphere
## 2 Problem Formulation  
The studied problem is based on finding the next stations to achieve maximum spatial coverage. We apply the concept of Euclidean Distance and the k-nearest neighbor algorithm (k-NN) to calculate the distance between station neighbors. The objectives are set up a network and improve an existing network. In the preparation process, we divide map of study areas into a square of a grid and identify latitude longitude pairs at the centroid of all grids. Such geographic coordinates are used to calculate the distance. Given only a study area map and a specified number of stations, how can one calculate and recommend the stations to achieve maximum spatial coverage? The objective is to find the stations which meet the following constraints:  
1. [label=()]
2. Achieve maximum spatial coverage while maintaining proper overlapped area between the nearest-neighbors' stations
3. Propose methods without using historical pollution and city characteristics data.  
The proposed method was tested and demonstrated in three real cities and compared with existing network coverage. This study can provide recommendations for environmental authorities or city planners to select the stations for increasing spatial coverage of air quality monitoring.
## 2 Neural Interfaces Specifications and Requirements
### Device Sensitivity and Selectivity  
Sensitivity and selectivity are two important figures of merit to assess the quality of a biosensor. In general, the sensitivity is the capability to discriminate small differences in concentration of the test analyte, whereas the selectivity is the capability of the sensor to discriminate the analyte from interfering components [43].  
For a neural interface, the analyte corresponds to the membrane potential of the cell under analysis, that can be either the intracellular potential or the extracellular potential. Hence, the device sensitivity becomes the capability to discriminate small perturbation in the membrane potentials, whereas the selectivity becomes the capability to discriminate and sense a single cell from a bunch of neurons or a neural network.  
It is clear that, to achieve high SNRs, in a bio-sensing device, both selectivity and sensitivity should be maximized. However, this is not always possible and a good SNR is obtained only by a trade-off between sensitivity and selectivity [42]. For instance, using a planar micro-electrode array (MEA) technology, lowering the electrodes size increase the sensors selectivity but it lowers the electrodes capacitance and consequently the sensitivity. Conversely, increasing the electrode size improves the sensitivity but leads to a lower selectivity.