esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Testing of the Solution
### Simulated AUV-CCS Performed Tests
#### 4.2.2 Laboratory Communications Tests for Underwater and Overwater Data Transfers  
The goal of these tests was to prove that data can be transferred efficiently from the middleware to an AUV and backwards when the AUV is either overwater or underwater. This means testing both for IP and acoustic channels communications that the middleware: (a) can send data to the vehicle and (b) can receive and understand data from the vehicle and store the information in the SWARMs ontology and database. On the other hand, they are also useful to determine if an AUV (a) can send data to the middleware and (b) can receive and understand requests from the middleware.  
The complete data transfer process involves the software components depicted in Figure 6. The components related to the middleware are displayed in green, whereas the components related to the communication system are displayed in blue.  
Two different communication channels have been tested:  
1. _IP channel_: a WiFi network was established to test the IP communications (displayed in a blue line in Figure 5) between the Middleware and Naiad when it is overwater.
2. _Acoustic channel_: an acoustic modem was used in order to simulate and test the acoustic communications (displayed in a doted blue line in Figure 5) between the Middleware and the autonomous vehicle when it is underwater.
3. _Results_: three succesful DDS communication tests were performed: * _Acoustic data transfer AUV-Middleware_: the ROS-DDS Proxy publishes the location in the topic \"local_data\". The DDS/Acoustic Converter subscribed to this topic reads the data, translates it to the modem acoustic format and transmits it through the acoustic modem. The DDS/Acoustic Converter connected to the middleware converts the data  
Figure 6: Data transfer architecture.  
from acoustic to DDS format and publishes the information in the topic \"Naiad_data\". The Publish/Subscription Manager subscribed to this topic reads it and sends it to the Data Access Manager so information can be stored in the ontology and transferred to the MMT.
* _IP data transfer Middleware-AUV_: the Publish/Subscription Manager publishes a task in the topic \"MW_tasks\". The ROS-DDS Proxy subscribed to this topic, reads the data, translates it to ROS and sends it to the AUV.
## 3 Non-Dimensionalization and Parameter Discussions  
### Dimensionless Parameters Design and Non-Dimensionalization  
The essence of dimension is expansion of the quantity coefficient according to basic quantity class. The different selection of basic quantity class, or even of families the unit system belongs to, may cause the expression form of dimensional equations to be changed. Non-dimensionalization means all the dimensions are constructed into one through appropriate variable substitutions. Thus, some or all units involved in the physical quantity equations are removed to ensure the form of the equations remains independent from unit system families. For convenient analysis, dimensionless parameters are designed by introducing the variables group listed as \(\Theta=\frac{8\kappa k\theta}{Ql}\gamma^{3/2}\): dimensionless surplus temperature;  
\(\xi=\frac{Ux}{2\kappa};\mu=\frac{Uy}{2\kappa};\zeta=\frac{Uz}{2\kappa}\): dimensionless coordinate;  
\(\gamma^{2}=\mu^{2}+\zeta^{2}\): dimensionless distance away from the \(x\) axis;  
\(\kappa=\frac{t}{t}\): dimensionless time;  
\(\beta=\frac{U^{2}T}{2\kappa}\): dimensionless velocity and travel parameter.  
Then, the non-dimensionalized forms of each parts can be expressed as  
\[\Theta_{1}^{N}=\frac{\mathrm{e}^{-[\xi+\beta(N-\kappa)]}}{\sqrt{[\xi+\beta(N- \kappa)]^{2}+\gamma^{2}}}\times\left[\begin{array}{c}\Gamma\left(\frac{1}{2}, \frac{[\xi+\beta(N-\kappa)]^{2}+\gamma^{2}}{2\beta(\kappa-N)};\frac{[\xi+\beta (N-\kappa)]^{2}+\gamma^{2}}{4}\right)\\ -\Gamma\left(\frac{1}{2},\frac{[\xi+\beta(N-\kappa)]^{2}+\gamma^{2}}{2\beta( \kappa-N-1/2)};\frac{[\xi+\beta(N-\kappa)]^{2}+\gamma^{2}}{4}\right)\end{array}\right] \tag{18}\]  
\[\begin{array}{rl}\Theta_{2}^{N}=&\frac{\mathrm{e}^{\xi+\beta(\kappa-N-1)}}{ \sqrt{[\xi+\beta(\kappa-N-1)]^{2}+\gamma^{2}}}\\ &\times\left[\begin{array}{c}\Gamma\left(\frac{1}{2},\frac{[\xi+\beta(N- \kappa-1)]^{2}+\gamma^{2}}{2\beta(\kappa-N-1/2)};\frac{[\xi+\beta(\kappa-N-1)] ^{2}+\gamma^{2}}{4}\right)\\ -\Gamma\left(\frac{1}{2},\frac{[\xi+\beta(N-\kappa-1)]^{2}+\gamma^{2}}{2\beta( \kappa-N-1)};\frac{[\xi+\beta(\kappa-N-1)]^{2}+\gamma^{2}}{4}\right)\end{array} \right]\\ \end{array} \tag{19}\]  
If \(\kappa-K\leq 1/2\),  
\[\begin{array}{rl}\Theta_{\mathrm{r}}=&\Theta_{1}^{K}+\frac{\mathrm{e}^{\xi+ \beta(\kappa-K-1)}}{\sqrt{[\xi+\beta(\kappa-K-1)]^{2}+\gamma^{2}}}\\ &\times\left[\Gamma\left(\frac{1}{2},\frac{[\xi+\beta(\kappa-K-1)]^{2}+\gamma ^{2}}{2\beta(\kappa-K-1/2)};\frac{[\xi+\beta(\kappa-K-1)]^{2}+\gamma^{2}}{4} \right)\right]\end{array} \tag{21}\]  
specifying when \(K=0\), \(\Theta=\Theta r\).
###### Abstract  
To increase the usage of renewable energy, it is vital to maximize local energy production by properly combining various renewable-energy sources by collecting their data and storing it on the cloud. The energy optimization utility, which is used for making decisions to optimize renewable-energy resources, is hosted on the cloud to benefit from cloud capabilities in data storage. Hosting such sensitive data and utilities on the cloud has created some cybersecurity challenges. This paper presents a new token-revocation access control (TR-AC) which revokes the authorization of malicious users before authorizing them to access cloud-hosted energy optimization utilities. TR-AC employs a set of multi-authorities to measure the authentic level for each authenticated user. Although the user is authenticated to access the online system, this authentication can be revoked to utilize the energy optimization utility based on the user's level of authentication. The cloud storage servers are not fully trusted and, therefore, have no control over access controls. Finally, the proposed TR-AC has been proven to be secure against any attacker that is not authentic according to Diffie-Hellman assumptions. In addition, performance analysis has proven that the time elapsed for both encryption and decryption in TR-AC is very small compared with previously introduced schemes. Therefore, it will not affect the performance of the cloud-hosted system.  
access control; energy optimization utility; environmental sustainability; cloud security; user revocation
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
## 7 Challenges of FAST  
### X-Handling  
During FAST, many unknown values will be present in the longer paths because the gates in the long path may not have finished their computation within that smaller clock period. Handling these X values before generating the signature for the built-in self-test (BIST) is very important. Because during the signature generation using MISR (Multiple Input Signature Registers), if the X values enter into MISR, it may generate some invalid signatures, leading to loss of test coverage. The major challenge in X handling during the FAST is that we cannot predict the distribution of X values in the long paths. This is because the distributions change with different test clock frequencies and patterns [44]. Various X handling techniques are available in the literature. The X masking technique [45] is one such thing in which the X values are masked before they enter into the compactor. The next type is the X tolerant compaction [46]. This method tolerates the X values to some extent. Another important type is X canceling, which filters out the unknown values after compaction and extracts only useful information from the signature. These techniques cannot be used as such for the FAST-based X handling because of the variable X distribution.  
A few works are available for X handling during FAST in the literature. In [47], a multiplexer-based method is used for the FAST-based X handling. But the disadvantage here is the considerable number of control signals and the neglect of many test responses because of multiplexer selection. As the X distributions in the long paths differ for different test clocks, the X canceling-based method is chosen for BIST-based FAST [48]. Here, the bits that have information, say D bits, are of focus. Suppose the compacted response from the scan slice does not contain any information bits. In that case, the intermediate signatures must be stored for further analysis, which consumes memory space. A greedy heuristic algorithm is used for the D-bit analysis and intermediate memory storage minimization. With this method, for larger MISR, the fault coverage increases, but with the drawback of the considerable overhead of memory storage. In [49], a hybrid stochastic compaction method is introduced where X masking is used first, and X canceling MISR is used next for handling the X values that come from the previous X masking step. The space compaction uses the probabilistic model for control-signal generation. The hybrid stochastic compaction uses a deterministic model too. The fault is masked for certain critical patterns due to compaction. Separate control signals are provided for this in a deterministic mode. This improves both fault-masking and X reduction ratios. But the hardware overhead is a disadvantage. Massive hardware is required to enhance the X reduction ratio and masking slightly. These works give the direction for researchers to further enhance the X handling for FAST by hybrid X handling approaches.
# Numerical Analysis of Fatigue Life of Wind Turbine Blades Reinforced with Graphene Platelets
## 3 Fatigue Life Assessment for Wind Turbine Blades
### Fatigue Cycle Estimation  
For simple loads with a single frequency, it is not necessary to additionally calculate the fatigue cycle. Actual structures, especially those that operate in complex dynamic environments (e.g., wind turbine blades), however, are exposed to complex load cycles that include various frequencies. In this case, stress and amplitude can be precisely defined by decomposing load cycles with frequencies. In this study, aerodynamic loads that correspond to the wind speed data generated earlier were calculated using BEMT, and they were applied to finite element analysis to extract fatigue loads in the time domain. The extracted fatigue load data were then converted into the frequency domain using a fast Fourier transform (FFT). Here, FFT is used for efficient calculation of the discrete Fouriertransform presented in Equation (7). Figure 5 shows an example of a time-domain stress spectrum and its transformation into the frequency domain.  
\[X(k)=\sum_{n=0}^{N-1}x_{n}e^{-i\frac{2\pi}{N}kn} \tag{7}\]  
where \(X(k)\) is the signal in the frequency domain, \(k\) is the frequency index, \(x_{n}\) is the discrete signal samples in the time domain, \(N\) is the number of samples, and \(n\) is the time index.
# Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
## 2 Methodology
### Hazard
#### 2.3.3 Structural Damage Based on Flood Depth  
In urban flood scenarios, the level of damage sustained by a building largely depends on its structural characteristics and the depth of inundation. A common method for evaluating flood-depth-related structural damage is to utilize depth damage functions, which compute damage rates according to flood depth and building type. Drawing on experimental data, these functions estimate how much structural harm a building might incur at varying flood depths. This study employed damage functions developed by the Korea Institute of Civil Engineering and Building Technology (KICT [25]) for different types of facilities. The findings not only detail the extent of structural damage but also assist in estimating financial losses, offering crucial insights into total damage across the affected region.
## 2 Preliminaries
### Element Perturbation  
To increase the robustness of watermarking and to enable the watermarking of movable elements, this paper employs element perturbation based on the work of [PERSON] [29]. The spatial coordinates are extended from two-dimensional to three-dimensional, allowing for the movement of elements in any direction of the \(X\), \(Y\), and \(Z\) axes. With the coordinate accuracy of elements in the BIM data reaching 13 decimal places, precise changes in the coordinates can be made to carry watermark information using the parity of coordinate values. A schematic diagram of element perturbation is shown in Figure 1.  
Figure 1: Schematic diagram of element perturbation.  
As shown in Figure 1, after the movable elements such as doors, windows, and floors are moved in the \(X\)-axis direction, the value of a position in the coordinates changes to correspond to the watermark information of the watermark bit. This approach has minimal impact on the elements and does not easily cause model errors.  
However, positioning the points of different elements in BIM data may not be synchronized with the movement of the element after model operations, such as translation and rotation. Therefore, to unify the spatial positioning of the elements and facilitate the accurate embedding and detection of the watermark information, we calculated the centroid of the enclosing box based on the element and used it as the coordinates of the spatial position of the elements. The center point of the enclosing box was calculated as follows:  
\[V_{i}=(\frac{Box_{i}\cdot x_{\max}-Box_{i}\cdot x_{\min}}{2},\frac{Box_{i} \cdot y_{\max}-Box_{i}\cdot y_{\min}}{2},\frac{Box_{i}\cdot z_{\max}-Box_{i} \cdot z_{\min}}{2}) \tag{1}\]  
where \(i(i=1,2,3\ldots)\) represents the element index. \(V_{i}\) is the coordinate of the element, \(Box_{i}\) represents a collection of the enclosing box coordinate points of the element, and \(()_{\max}\) and \(()_{\min}\) represent the maximum and minimum values, respectively.  
By establishing a unified method for locating the spatial positions of the elements, watermark information can be accurately embedded and detected using element perturbation.
## 2 Design and Fabrication  
### Design and Circuit Analysis  
The design, simulation, and verification of the proposed BPF were performed by making full use of advanced design software 2016 (ADS, Keysight Technologies Inc., Santa Rosa, CA, USA). The 3D view of the QFN-packaged BPF mounted on a PCB board is shown in Figure 1a, as well as an enlarged view of the proposed BPF, shown in Figure 1b. A dendritic capacitor was placed in the center of an approximate octagonal outer spiral inductor, and both are composed of three laminated conductor layers. Each metal layer is composed of Cu/Au at a ratio of 9/1. The metal layers are named bond, text, and leads layers from bottom to top. The detailed geometry of the three layers is depicted in Figure 1d.  
In general, the resonant frequency \(f_{0}\) of an LC circuit can be calculated as follows:  
\[f_{0}=\frac{1}{2\pi\sqrt{LC}}, \tag{1}\]  
where \(L\) is the total inductance and \(C\) is the total capacitance of the entire device, respectively. After considering the parasitic effect under high-frequency excitation, the lumped model consists of four parts, namely, the non-overlapping part of the metal line, the coupling capacitor between the metals, the induced parasitic effect produced by the SiN\({}_{x}\) passivation layer and GaAs substrate, and the center capacitor. To accurately evaluate the equivalent circuit, we developed an analytical model for performance evaluation of the proposed BPF using the segmentation method, the mutual inductance method, and the simulated scattering parameters (S-parameters) [18; 19].
# EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure
## 2 Theoretical Background
### Shannon Entropy (SE)  
The SE index (SEI), a nonlinear measurement in dynamic signals, provides a measurement of the average information contents associated with the data randomness encountered in a signal or event [61]. SEI can vary from 0 to the number of bits of the analog-digital converter (ADC) employed to measure the input time signal, e.g., an ADC of 8 bits allows SEI values from 0 to 8, where a low value indicates that a low level of randomness in the signal; on the other hand, a high SEI value indicates that a high level randomness in the analyzed signal is present. Hence, the SEI can be a useful tool to show changes in the measured signals associated with the structure condition.  
SEI of a time signal \(x(t)\) is calculated as follows:  
\[SEI=-\sum_{i=1}^{N}p(\alpha_{i})\log_{2}[p(\alpha_{i})] \tag{8}\]  
where \(\alpha_{i}\) denotes the values of a random signal \(x\) with \(N\) possible outcomes \(x_{0},x_{1},x_{2},\ldots,x_{N-1}\), can be taken, \(p(\alpha_{i})\) represents the probability of \(\alpha_{i}\) in the signal \(x\), and \(log_{2}(.)\) is the binary logarithm.
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 3 Communication
### Underwater Acoustic Communication
#### 3.2.3 Time Slot Size  
The size of communication time slots can be defined during the transition to a specific state of the time scheduling state machine. For selecting time slot size to ensure successful communication with prevention of collisions between transmissions, multiple factors should be taken into account. The most influential factors are the longest message transmission time \(T_{m}\), maximal signal propagation time based on the range of the acoustic modem \(T_{p}\), clock synchronisation offset \(T_{c}\), maximum allowed clock drift time \(T_{d}\), and guard time \(T_{g}\). Maximum allowed clock drift time \(T_{d}\) is determined by the RTC module specifications and rate of periodical clock synchronisation. Guard time is defined as a constant and serves to separate subsequent slots in the case of uncertainties in signal propagation time.  
Communication between agents can be divided into two categories: data transmission, which requires no response, and data exchange, which requires a response. The longest duration of a single transmission will belong to the second category. The time slot of the agent must therefore be capable of accommodating two longest message transmission times \(T_{m}\), a round-trip time based on the range of the modem \(2*T_{p}\), one worst case clock synchronisation offset equal to \(T_{p}\), and a guard time of \(T_{g}\). This leads to a minimal time slot size for elimination of collisions between transmissions given by:  
\[T_{s}=2*T_{m}+2*T_{p}+T_{c}+T_{g}=2*T_{m}+3*T_{p}+T_{g} \tag{1}\]  
Since the time slot is large enough for two data transmissions, if an agent does not require a response from the receiving end--such as an aMussel reporting on the status of an environmental variable--it can send twice the amount of data within its timeslot.  
Expected time necessary for completing the transmission can be calculated for each specific transmission or exchange, which is useful for determining when in the agent's time slot a message can be sent. If the time left in the current time slot is shorter than the expected completion time, the message needs to be sent queued up for sending in the next time slot.  
The longest single message that can be sent using the equipped acoustic modem is 7 bytes long. The transmission time of a message is defined by \(T_{m}=0.675+n*0.2[s]\) and is heavily dependent on message size. Minimal time slot size with a guard time of \(T_{g}=0.85\) s calculated using (1) is \(T_{s}=6\) s. Since minimal time slot size is calculated based on a worst case scenario, more consideration while choosing time slot size may improve acoustic channel utilisation. For example, the choice of time slot size may be approached statistically by determining the mean value of message size being sent using usual communication patterns while deployed. While time slot size cannot be shortened without making the longest messages cause transmission collision, it can be expanded to better accommodate multiple shorter messages.
# Design Approach for Composite Pavement Structure Incorporating Reflective Crack Considerations
## 5 Engineering Examples
### Calculation of Cracking at the Bottom of Cement Concrete Slabs  
The cumulative number of actions \(N_{e}\) of the design lane standard load can be calculated, as shown in Equation (30):  
\[N_{e}=\frac{N_{1}\times\left[\left(1+g_{r}\right)^{t}-1\right]\times 365}{g_{r}} \times\eta=1.33\times 10^{7} \tag{30}\]  
where \(N_{1}\) is the number of daily actions of the design axile load, taken as 5000 times; \(g_{r}\) is the annual average growth rate of traffic volume; \(t\) is the expected service life, taken as 20 years; \(\eta\) is the lateral distribution coefficient of the vehicle wheel track, taken as 0.22.  
According to the specifications [15] (the meaning of symbols can be found in reference specifications), two schemes for checking the cracking of cement concrete slab bottom were carried out, and the calculation results are demonstrated in Table 5 (See Supplementary Materials for details):  
According to Equations (1) and (2), the verification Scheme 1 is as follows:  
\[\gamma_{r}\big{(}\sigma_{pr}+\sigma_{tr}\big{)}=1.22\times\left(3.19+0.18 \right)=4.11\;\mathrm{MPa}\ \leq\ f_{r}=4.5\;\mathrm{MPa} \tag{31}\]  
\[\gamma_{r}\big{(}\sigma_{pm}+\sigma_{tm}\big{)}=1.22\times\left(2.39+0.80\right) =3.89\;\mathrm{MPa}\ \leq\ f_{r}=4.5\;\mathrm{MPa} \tag{32}\]  
According to Equations (1) and (2), the verification Scheme 2 is as follows:  
\[\gamma_{r}\big{(}\sigma_{pr}+\sigma_{tr}\big{)}=1.22\times\left(3.29+0.26\right) =4.33\;\mathrm{MPa}\ \leq\ f_{r}=4.5\;\mathrm{MPa} \tag{33}\]  
\[\gamma_{r}\big{(}\sigma_{pm}+\sigma_{tm}\big{)}=1.22\times\left(2.47+0.92\right) =4.14\;\mathrm{MPa}\ \leq\ f_{r}=4.5\;\mathrm{MPa} \tag{34}\]  
In summary, both Schemes 1 and 2 have successfully met the requirements in terms of the calculation of bending tensile stress at the bottom of the plate.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Symbol** & **Unit** & **Scheme 1** & **Scheme 2** \\ \hline \(D_{e}\) & MN-m & 54.27 & 54.27 \\ \(r_{g}\) & m & 0.99 & 0.99 \\ \(\sigma_{ps}\) & MPa & 1.41 & 1.41 \\ \(\sigma_{p}\) & MPa & 2.70 & 2.70 \\ \(\zeta_{a}\) & — & 1.45 & 1.45 \\ \(\sigma_{psa}\) & MPa & 1.25 & 1.29 \\ \(\sigma_{pma}\) & MPa & 2.39 & 2.47 \\ \(k_{f}\) & — & 2.55 & 2.55 \\ \(k_{r}\) & — & 0.87 & 0.87 \\ \(k_{c}\) & — & 1.15 & 1.15 \\ \(\sigma_{pr}\) & MPa & 3.19 & 3.29 \\ \(\sigma_{pm}\) & MPa & 2.39 & 2.47 \\ \(\sigma_{tr}\) & MPa & 0.18 & 0.26 \\ \(\sigma_{tm}\) & MPa & 0.80 & 0.92 \\ \(\gamma_{r}\) & — & 1.22 & 1.22 \\ \hline \end{tabular}
\end{table}
Table 5: Checking calculation of cracking at the bottom of cement concrete slabs.