esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Security Analysis
### Threat Model  
In the context of a network entirely under adversarial control, we analyze our protocol. Our aim is to demonstrate the high-level characteristics of secrecy and trusted key authentication. Authentication, in this context, is implicit, meaning only the intended party can compute the key, rather than providing explicit evidence. The desired outcomes include the maintenance of secrecy for derived session keys in various compromise scenarios. This includes situations where a long-term secret is compromised but a medium-term or ephemeral secret remains secure, achieving forward secrecy. Additionally, for scenarios where a state is compromised, an uncompromised asymmetric stage follows, resulting in future or post-compromise secrecy. Identity keys and medium-term keys are considered out-of-band verifiable.  
1. If the long-term key is compromised and the ephemeral key remains secure, the attacker will be unable to decrypt the secret key \(T_{key}\), preventing access to the information.
2. If the ephemeral keys are compromised and the long-term key remains secure, using the long-term key does not retrieve the secret key \(T_{key}\).
3. If both the long-term key and the ephemeral keys are compromised, the attacker will obtain the secret \(T_{key}\) used for encrypting that message session. However, they will not be able to retrieve the next or the previous session keys.
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 3 System Model
### Computation Model
#### 3.4.2 Helper Execution  
The task execution process by a helper UH\({}_{i}\) consists of two steps. First, the UE transfers task \(k\) to the helper UH\({}_{i}\) via D2D communication on the up-link, and then task \(k\) is executed by UH\({}_{i}\). As illustrated in Figure 3, the total execution delay is  
\[T_{k}^{h_{i}}=\frac{b_{k}}{r_{k}^{\text{UH}_{i}}}+\frac{b_{k}c_{k}}{f_{k}^{ \text{UH}_{i}}}, \tag{6}\]  
whereby \(r_{k}^{\text{UH}_{i}}\) is the transmission rate from the UE toUH\({}_{i}\), and \(f_{k}^{\text{UH}_{i}}\) is the CPU cycle frequency of UH\({}_{i}\) allocated for the execution of task \(k\). We neglect the delay for sending the result back from helper node UH\({}_{i}\) to the UE in our model due to the typically small number of output bits.  
Following Equation (6), the battery energy consumption of the helper execution mode consists of (a) the transmission from the UE to the helper UH\({}_{i}\), (b) the helper energy consumption for task execution, and (c) the energy that the UE consumes to wait for receiving the result back from the helper node:  
Figure 3: Timing diagram for the execution of task \(k\) by an external computing resource (helper UH\({}_{i}\)): The total task execution delay \(T_{k}^{h_{i}}\) consists of the transmission delay \(t_{k}^{\text{tr}}=b_{k}/r_{k}^{\text{UH}_{i}}\), the execution delay \(t_{k}^{\text{exe}}=b_{k}c_{k}/f_{k}^{\text{UH}_{i}}\), and the transmission delay for returning the computation result (which is neglected in our model).  
\[E_{k}^{h_{i}}=P_{k}^{tr}\left(\frac{b_{k}}{r_{k}^{\mathrm{UH}_{i}}}\right)+e\left( f_{k}^{\mathrm{UH}_{i}}\right)^{2}b_{k}c_{k}+P_{k}^{\mathrm{wait}}\left(\frac{b_{k}c_{k}}{f^{ \mathrm{UH}_{i}}}\right), \tag{7}\]  
where \(P_{k}^{tr}\) is the UE transmission power, and \(P_{k}^{\mathrm{wait}}\) is the UE idle circuit power while the UE is waiting to receive the result back.  
As described in Section 3.1, there are \(I\) number of helpers available for the UE in the area. Let \(h_{k}^{i}=1,h_{k}^{i}\in\mathcal{H}\), indicate that the UE chose to offload task \(k\) to \(\mathrm{UH}_{i}\), whereby the set \(\mathcal{H}=\{h_{k}^{i}|k\in\mathcal{K},i\in\mathcal{I}\}\) contains all helper node selection variables. Since task \(k\) can only be offloaded to at most one helper at the same time, an offloading selection algorithm should follow the constraint  
\[\sum_{i\in\mathcal{H}}h_{k}^{i}\leq 1\ \ \forall k\in\mathcal{K}. \tag{8}\]
## 6 Simulation and Analysis
### Analysis of Simulation Results
#### 6.2.2 High Fault Resistance Test  
ABg-phase-phase-to-ground faults are set at different positions of the feeder B5B6, and the ability to withstand the fault resistance is tested. The fault resistances are 25 \(\Omega\), 50 \(\Omega\), 100 \(\Omega\), and 150 \(\Omega\), respectively. The simulation results are shown in Table 5. When high resistance grounding faults occur in feeder B5B6, the amplitude ratio of the PSCFC on both sides of feeder B5B6 is always greater than 1.5, and the phase of the PSCFC on the IBDG side is compensated. The compensated ACDP can trip correctly and has high sensitivity. As  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline \hline  & 10 kV & Capacity of DG4 & 4 MW \\   Capacity of grid & 50 MW & Capacity of L7 & (1.2 + 0.6) MVA \\   Capacity of DG1 & 2 MW & Capacity of other loads & (0.6 + 0).3) MVA \\   Capacity of DG2, DG3 & 2.5 MW & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the system model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline \hline  & & & \\ positive-sequence impedance & (0.13 + j0.402) \(\Omega\)/km & Length of feeder section B5B6 & 5 km \\   Zero-sequence impedance & (0.26 + j0.87) \(\Omega\)/km & Length of the other feeder sections & 4 km \\   Length of feeder sections B1B4 and B4B5 & 6 km & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of feeders.  
the fault resistance of the fault point increases, the sensitivity of TCDP gradually decreases. When the fault resistance is greater than 50 \(\Omega\), TCDP may refuse to trip. For example, when the fault occurs at the end of feeder B\({}_{5}\)B\({}_{6}\), the fault resistance is 100 \(\Omega\), or when the fault occurs in the middle of feeder B\({}_{5}\)B\({}_{6}\), the fault resistance is 150 \(\Omega\), and TCDP will refuse to trip. The TCDP may not trip correctly in the case of high resistance grounding since the sensitivity of TCDP is mainly around 1 in the case of high resistance and the uncertainty of external factors such as transformer transmission error, protection measurement error, and data processing error. The ACDP proposed in this paper has superior operational performance compared with the TCDP.
## 5 Discussion
In all 19 cases, at least one cross-mountain transect of horizontal wind derived from WCR dual Doppler synthesis was available. (In the 2008, 2009, and 2013 flights there were one or two per flight; in the 2006 flights, there were more.) In all flights the cross-mountain track was within 20\({}^{\circ}\) of east-west, and aligned to be normal to the low-level wind, which was close toesterly. We computed the average WCR along-track wind speed within 500 m AGL and within 10 km horizontal distance from the MBM crest, to obtain a mean upwind and mean leeside wind speed. We compute upwind stability as for Table 2, and upwind wind profile parameters are computed along the direction of the flight track. Stronger winds in the lee (than the upwind side) are common, as predicted even by linear wave theory: they were observed in all but three cases (84%), but in most of these, the cross-crest acceleration was small. Significant acceleration across the crest (40% or higher increase in wind speed) occurred in just seven cases (37%). The wind increase in the 11-12 January 2013 case is rather larger (60% increase, on average for the three transects in Figure 12 with enough WCR data). There is no clear relationship between the cross-crest acceleration and different upstream conditions believed to be conducive to downslope windstorms (based on in [PERSON] and [PERSON] [10], see Section 3.2). Conditions (a) (mountain asymmetry, vertically propagating waves) and (c) (wind direction) were always satisfied, and conditions (e) and (g) are neither relevant nor assessable from an upwind sounding alone. The three remaining conditions mentioned in Section 3.2 are condition (b) (wind speed near mountaintop level), condition (d) (high stability at low levels and/or near mountaintop level, or decrease of stability from around mountaintop level to ~1 km higher), and condition (f) (a critical level aloft within the troposphere or reverse wind shear across mountaintop level). Scatterplots of mountaintop wind speed, stability (or change of stability with height), and wind shear (over various depths) against the leeside wind speed and against cross-crest acceleration ratio for these 19 cases (not shown) reveal that these sounding parameters offer little predictive value for downslope windstorms. These findings should not be interpreted as implying that upstream conditions used by forecasters and stated in textbooks such as [10] are questionable. Rather, a single sounding at a fixed upstream location may not capture the relevant upstream conditions at the right time and place for specific mountain ranges. Given the non-linear nature of downslope windstorms, rotors, etc., upstream conditions at one time are not sufficient to predict them deterministically, as shown in [49; 50]. Coarse-resolution operational weather prediction model output can be used to indicate general probabilities, but a more accurate (albeit computationally still prohibitively expensive) approach is to run an ensemble of simulations of sufficient resolution to resolve these phenomena. A 1-km resolution appears sufficient, at least in the case examined here.
# Prediction of Intraday Electricity Supply Curves
## 3 Methodology
### Models and Predictions
#### 3.4.7 CNN Model  
Convolutional neural network (CNN) [30] models are primarily used for visual data analysis (for example, image classification or object detection). For an application of a CNN in energy demand forecasting, see [31]. The main features of these networks are convolutional layers (where convolutional filters are applied, producing feature maps), pooling layers (which reduce the spatial dimensions of the feature maps), and fully connected layers (used to make final predictions). The considered hyperparameters were the following:  
* epoch. The number of complete passes of the training dataset (epochs). The assessed values are 50 and 100;
* n_dense. The number of fully connected layers besides the output layer. We studied one, two, and three layers;
* n_conv. The number of convolutional and pooling layers (we always introduce a pooling layer after each convolutional layer). We considered one, two, and three layers;
* neuron. The number of neurons in each fully connected layer. The tested values are 5, 10, 20, 50, and 100;
* fil. The number of filters in each convolutional layer. We considered eight and 16 filter cases;
* kernel. The dimensions of the kernel matrix in each convolutional layer. The values studied are \(3\times 3\), \(5\times 5\), and \(7\times 7\).  
The selected hyperparameters for the CNN model are available in Table A2 in Appendix A.
## 2 Materials and Methods
### Reference/Ground Truth Data Collection
#### 2.6.2 Pulse/Echo Ultrasonic Testing  
In order to compare against leading NDE methods, the part was scanned with pulse/echo UT. The UT system was setup using an in-house immersion system presented in previous work (see, e.g., [4; 19]). Here, an Olympus Focus PX with a 100 MHz sampling frequency was connected to a 10 MHz spherical focused immersion transducer with a nominal focal length of 1.5 inches operating in the pulse/echo configuration. This transducer was mounted on a 3-axis motorized gantry system for 3D translation throughout the immersion tank. Raw a-scans were taken over a 50 \(\times\) 50 mm area on the surface of the part  
Figure 7: Ground truth using X-ray CT and the Radon Transform (**a**) top-down mean of CT scan for laminae 1-5 and (**b**) reference fiber orientation extracted from CT data.  
at a spatial resolution of 0.1 mm, and the results are shown in Figure 8. These results are from the same UT dataset presented in [13].  
Detection of in-plane waviness in a unidirectional CFRP is difficult for pulse/echo UT, and currently, the methods for such a defect with pulse/echo UT are very limited, if existent at all. One reason for the ineffectiveness of pulse/echo UT at characterizing in-plane waviness is that the defect is orthogonal to the acoustic waveform transmission path. Out-of-plane waviness can be effectively characterized with UT (see, e.g., [19]) for the opposite reason. In order to detect in-plane waviness using pulse/echo UT, the scan resolution would have to be small enough to enable accurate image processing on the waviness region. However, the width of the narrow region of the ultrasonic beam is a limiting factor to the effective resolution from UT scanning. For the scan shown in Figure 8, the resolution of 0.1 mm provides that the general location of the in-plane wave can be observed, but we have been unsuccessful in the development of automated algorithms for consistent quantification of the local orientation, nor were we successful in finding any in the literature. This is a path for future research in ultrasound development.
## 3 Results  
### Classification Accuracy on Intact-Limb Subjects  
As described above, the source model was trained using signals from 20 intact-limb subjects. We obtained a \(91.6\%\pm 3.4\%\) classification accuracy and \(93.4\%\pm 3.2\%\) weighted F1-score. The classification accuracy was improved by \(6.1\%\) (\(91.6\%\) vs. \(85.5\%\)) compared to a similar experiment [19] with the same features, gestures, and subjects.  
### Classification Accuracy on Intact-Limb Subjects  
The average classification accuracy and weighted F1-score of the LECNN-TLs were significantly higher than LECNN-Onlys (78.1 \(\pm\) 5.6% and 79.6% \(\pm\) 7.2% vs. 73.5% \(\pm\) 5.2% and 76.2% \(\pm\) 6.7%, respectively, \(p\)-value = 0.0005 \(<\) 0.05, and [PERSON]'s d = 0.24 \(>\) 0.2). Figure 7 shows the comparison of each LECNN-Only and LECNN-TL model pair of the classification accuracy and weighted F1-score. As shown in Figure 7, the classification accuracies after transfer were all higher than their non-transfer counterparts, and the improvement on s3, s7, and s9 exceeded 5%.
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
## 4 Applying Corrections to AD/NS Simulation  
### Application of Glauert Tip Loss Factor \(F\)  
Equations (32) and (33) where the Glauert tip loss factor \(F\) is applied are not employed in the AD/NS method because the flow is simulated by the NS solver instead of the momentum theory. As a result, an alternative way has to be employed correctly to apply the tip loss factor \(F\). Nevertheless, among the literature studies, little literature mentions the detail of how the tip loss factor is applied to the AD/NS simulations. After an extensive literature review, we have found three representative documents in which [PERSON] et al. [41], [PERSON] [42], and [PERSON] et al. [43] described their implementations. In order to facilitate the distinction, the implementations adopted by the three studies are denoted as Glauert-A, Glauert-B and Glauert-C in the present paper, respectively.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 9 Meeting the IoT-BCN Challenges: Tools, Techniques and Strategies
### IoT Security Controls and Policies  
As explained in Section 6, the attack surface of an IoT network is as large as the Internet. Applying the tools and techniques to provide the desired security assurances for an IoT system is insufficient. Additionally, it is crucial to establish and apply standard security controls to effectively mitigate the threats to an IoT system and thwart foreseeable attacks on the system. In this section, we adapt the CIS Controls (The Center for Internet Security (CIS) is an \"independent, nonprofit organization with a mission to create confidence in the connected world\" ([[https://www.cisecurity.org/controls](https://www.cisecurity.org/controls)]([https://www.cisecurity.org/controls](https://www.cisecurity.org/controls)) (accessed 22 June 2022); Alternative to CIS's controls are the framework from National Institute of Science and Technology (NIST), ISO 27001, and PCI-DSS among others [95].)) to strengthen the protection and defend an IoT system.  
* Implement IoT devices and firmware version inventory management: This control encourages network owners to create an effective inventory of authorized network and sensor devices. This inventory assists in establishing authorized network devices and detecting unauthorized devices.
* Implement IoT application services and version management: This control provides a mechanism to create an inventory of software or firmware packages used by IoT devices. Such inventory is valuable during firmware/software vulnerability fixing and patch management.
* Implement IoT network or device access control management: Access control mechanism is one of the crucial techniques to provide operational and other permissions over IoT devices. It also assists in monitoring IoT device access by different users. Proper authentication mechanisms should also be implemented to avoid leaking or reusing user credentials such as usernames and passwords.
* Implement IoT network and application isolation: It is crucial to establish a distinction between an IoT network and an application (or analysis) layer. While an IoT network is a network of devices contributing to harvesting sensor data, the application layer consists of services used by end users to support organization decisions. A stringent isolating boundary should be created to mitigate risks of vulnerabilities affecting each other.
* Implement IoT network and access Log management: This control encourages network owners to collect, alert, review and retain events logs that could assist in detecting, comprehending, and recovering from any attack on an IoT system.  
* Data protection and recovery management: Critical asset produced by an IoT is the vast set of application-specific data. Implementing adequate data backup procedures is crucial to avoid data loss during an attack or a disaster. User access and data encryption should also be properly managed to prevent information leaks.
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 4 Parallel Edge Deblocking Filter Architecture (PEDBF)
### Filter Module
#### 4.3.5 Filter Modules  
The architecture includes filter modules that make use of both strong and weak/normal filters. Any of these filters are activated according to the filtering decisions determined by the filter decision unit. Therefore, any one of the weak filter or the strong filter is activated to execute the filtering process. The filtering process is omitted if the filtering decision unit determines for no filtering. The weak or strong filtering is executed according to the filtering equations specified in [41]. The similarities in these equations allow for the creation and implementation of a resource-sharing architecture for the filter module, which optimizes the area. The data from the horizontally filtered pixel block are then saved in the external memory, while the data from the vertically filtered pixel block are stored in the internal memory.
## 4 A Fixed-Parameter Algorithm for \(\ell\)-LCDP\({}_{p}\)
The other conditions, as shown in the proof of Corollary 7, test the existence of an \(L^{\prime\prime}\)-labelled \(g\)-colored \(st\)-path \(\pi\) for some color \(g\in C\). Thus, if there exists a bi-partition which satisfies all the conditions, then there exits an \(L\)-labelled set \(\mathcal{S}=\mathcal{S}^{\prime}\uplus\{\pi\}\) of \(k+1\) uni-color \(st\)-paths. Moreover, since \(L^{\prime}\) and \(L^{\prime\prime}\) are disjoint, by Property 9, path \(\pi\) and any path of \(\mathcal{S}\) are disjoint. Furthermore, since \(|L^{\prime\prime}|=\ell-1\), by Property 10, the length of \(\pi\) is, at most, \(\ell\) (in particular, \(\ell-1\) from \(s\) to vertex \(v\), plus \(1\) from \(v\) to \(t\)). As a consequence, \(\mathcal{S}\) is an \(\mathcal{L}\)-labelled set of \(p=k+1\) disjoint uni-color \(st\)-paths of length, at most, \(\ell\). Now, we prove that if there exists an \(\mathcal{L}\)-labelled set \(\mathcal{S}\) of \((k+1)\) disjoint uni-color \(st\)-paths, then \(P[\mathcal{L}]\) is true. For each path \(\pi\in\mathcal{S}\), let \(L_{i}\) be the set of labels labelling \(\pi_{i}\). Since the paths in \(\mathcal{S}\) are disjoint, also the sets \(L_{1},\ldots,L_{k+1}\) are disjoint. Moreover, since the length of each path is at most \(\ell\), we have that \(|L_{i}|\leq(\ell-1)\). Notice that \(|\mathcal{L}|\) is \((\ell-1)(k+1)\), thus it is possible to find a partition of \(\mathcal{L}\) in \(p\) sets \(L^{\prime}_{1},\ldots L^{\prime}_{k+1}\) of cardinality \(\ell-1\) such that each \(L_{i}\) is a subset of \(L^{\prime}_{i}\). Let us consider a generic path \(\pi_{i}\). Since \(\pi_{i}\) is a uni-color \(st\)-path (of length at most \(\ell\)), then there exists a vertex \(v\) and a color \(g\) such that \(M[L^{\prime}_{i},v,g]\) is true and \(\{v,t\}\in E_{g}\) (Corollary 7). Finally, since \(\bar{L}^{\prime}_{i}=L\setminus L^{\prime}_{i}\) is a set of labels of cardinality \((\ell-1)k\) and \(\mathcal{S}\setminus\{\pi_{i}\}\) is an \(\bar{L}^{\prime}_{i}\)-labelled set of \(k\) disjoint uni-color \(st\)-paths of length at most \(\ell\), by induction hypothesis we have that \(P[\bar{L}^{\prime}_{i}]\) is true. Therefore, the bi-partition \(\{L^{\prime}_{i},\bar{L}^{\prime}_{i}\}\) satisfies all the condition of Equation 4.2 and \(P[\mathcal{L}]\) is true. An immediate consequence is that the \(\mathcal{L}\)-labelled \(\ell\)-LCDP\({}_{p}\) problem can be solved in polynomial time when \(\ell\) and \(p\) are parameters. **Corollary 11**. : _The \(\mathcal{L}\)-labelled \(\ell\)-LCDP\({}_{p}\) problem can be solved in time \(O(2^{2\ell p}m)\), where \(m=\sum_{g\in C}|E_{g}|\)._  
Proof. : The evaluation of \(M\) needs \(O(2^{|\mathcal{L}|}m)\) time. For a fixed \(L\subseteq\mathcal{L}\), the evaluation of \(P[L]\) requires \(O(2^{|L|}m)\) and, since there are \(2^{|\mathcal{L}|}\) possible subsets of \(\mathcal{L}\), the time needed to evaluate \(P\) is \(O(2^{|\mathcal{L}
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 2 Architecture Design
### Overview of Architecture Design  
The perception layer, network layer, and application layer are the three layers that make up the architecture. The perception layer is mainly responsible for collecting data. The network layer, also as the core layer, mainly provides up/down data transmission services. In the satellite network system, the ground node network, satellite backbone network, and satellite access network constitute the satellite network layer. The satellite networks relay sensing data to the core network. For the sake of inter-operation and integration of satellite networks and IoT, it is very significant to establish the terrestrial base stations. In addition, when normal terrestrial terminals are not linked to satellites, we assume that the terrestrial base stations could send the information to the satellites to complete communication between satellites and ground.  
Figure 3 shows the schematic diagram of this project. First of all, sensors constitute the main part of perception layer of SIoT, which is responsible for collecting weather data; the IoT network on the Earth's surface and satellite network make up the network layer in SIoT. They mainly provide the up/down data transmission. Satellite routing design belongs to the application layer of SIoT.  
The process of the project is as follows: sensors input the collected weather data, such as temperature and humidity, into the extended ELM model. Then, blockage caused by bad weather is derived through classification fitting analysis, applying the extended ELM model. Consequently, the satellite communication routing design can avoid bad weather and improve communication efficiency by adding an extended ELM algorithm to the SAGIN system.  
Figure 2: Satellite-based IoT Network.