esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Method  
#### 2.3.1 Compound Extreme Climate Index  
Copula theory provides a flexible and accurate framework for modeling the joint behavior of temperature and precipitation. This framework separates the marginal distributions from the dependence structure, thereby enabling the independent modeling of the marginal distributions of temperature and precipitation. The dependence between these variables is then captured using copula functions. This methodology involved calculating marginal distributions and determining their correlation using the Kolmogorov-Smirnov (K-S) test. A suitable copula function was then selected, parameterized, and validated through a goodness-of-fit test to construct a joint probability distribution model. Finally, CECIs were derived using the chosen copula function.  
The SPI was derived from an analysis of precipitation time series data recorded over a consecutive 38-year period. To circumvent the potential loss of nuance in the data due to excessive smoothing, a 3-month cycle and a 12-month cycle were employed for the calculation of the SPI and STI, respectively, at the seasonal and annual scales. The methodology assumes that the precipitation data follow a specific probability density distribution, i.e., the gamma distribution [32]. The corresponding cumulative probability function is then derived and transformed into a standard normal distribution format. The SPI is calculated to correspond to the exact location of the cumulative probability of precipitation for each period along the horizontal axis of the normal distribution curve [33].  
The calculation of the STI is analogous to that of the SPI, but with a crucial difference: the STI is determined through its direct fit to the standard normal distribution, bypassing the initial step of fitting the data with a gamma distribution [34].  
A cold-dry event is defined as one in which both temperature and precipitation are low during the same period. Specifically, the joint probability distribution of cold-drought can be expressed as follows:  
\[\mathrm{P}(\mathrm{U}<\mathrm{u},\mathrm{V}<\mathrm{v})=\mathrm{C}(\mathrm{U},\mathrm{V}). \tag{1}\]  
Similarly, the joint distribution of low temperatures and high rainfall can be expressed as  
\[\mathrm{P}(\mathrm{U}>\mathrm{u},\mathrm{V}<\mathrm{v})=\mathrm{U}-\mathrm{C} (\mathrm{U},\mathrm{V}), \tag{2}\]  
where the variable \(\mathrm{U}(\mathrm{V})\) is the uniform marginal distribution of precipitation (temperature) calculated from the SPI (STI) series using the cumulative distribution function (CDF), and \(\mathrm{C}(\mathrm{U},\mathrm{V})\) denotes the joint distribution based on Copula theory [19].  
The CDW and CDC can be expressed as exponential values. When the absolute value of the exponential value is below 0.8, it falls within the normal fluctuation range. Compound warm-dry climate conditions are represented by a CDW \(\geq\) 0.8, while compound cold-wet ones are represented by a CDW \(\leq-0.8\). Similarly, a CDC \(\geq\) 0.8 represents compound cold-dry climate conditions and a CDC \(\leq-0.8\) represents compound warm-wet ones [10].
## 3 Results and Discussion
### The MAS Model to Identify the Intensity of Connectivity
#### 3.2.1 Description of the 24 August 2015 Episode  
On 24 August 2015, the inhabitants of Le Glape were once again confronted with runoff from the plots upstream from Rue de la Mare Tina (Figure 9). The meteorological station of Poterie Cap d'Antifier located just near the pond records the following data: a cumulative rainfall of 95.7 mm following 19 August allowed the formation of a driving crust. The 18 mm rainfall that occurred on 24 August, preceded by a 12.5 mm rainfall the day before, finally triggered the runoff. Even though it was located less than 5 km away, the measurement point did not capture the epicentre of the storm. The testimonies collected from the residents mention a rainstorm bringing more than 40 mm of rain in two hours.  
The flood originated on the cultivated plots upstream from the pond called \"Tina Pond\" (Figure 9). The flow followed the street of Tina Pond before joining the street of Oursel farm (where it was reinforced by meeting the flow coming from the latter). As the topography becomes a little higher as it follows the road, the runoff left the road by branching off and passing through the dwellings (in particular, the B and B and \"Oursel farm\"). Thereafter, the runoff emerged beyond the dwellings to reach the road to Gonneville. This is where it was reinforced by the inflow from another plot and ended by completely flooding the road, preventing traffic.  
Figure 9: Simulation of water paths for the 24 August 2015 episode from the MAS model.
## 5 Case Study and Analysis
### Results  
#### 5.2.1 Simulation Examples  
The experiment is implemented in JAVA on an Intel Xeon i5-3337U 1.8 GHz computer. The effectiveness of the two-stage hybrid metaheuristic approach (TSHM) is verified by solving a practical problem. In this section, a hazardous chemicals road transportation enterprise was chosen as the example. The distribution center has a geographic coordinate of 118.64611 and 31.94249. Other customer information is shown in Table 1. Two kinds of special vehicles were used with capacity of 120 and 176 units, respectively. The former one consists of two vehicles and the latter one consists of three vehicles.  
The other parameters of the model are as follows: the fixed costs of two kinds of special vehicles are 70 yuan and 180 yuan; the variable costs are 5 yuan/km and 3 yuan/km; the unit carbon tax is 2 yuan/kg [30]; the emission factor is 2.61 Kg/L [33]; and the unit distance fuel consumption is 0.255L/km with a full load and 0.165L/km with an empty load.  
The manmade schedule (MM), basic genetic algorithm (GA), local search strategy (LS), and TSHM are applied to solve the example; the latter three are run 20 times. The results obtained are shown in Table 2, which reports the approximate optimal and average values. Our proposed method obtained the best solution with approximate optimal value and average value (4199.21 and 4282.32 yuan, respectively) which is lower than the other three solutions (5070.58 and 5336.54 yuan for GA, 4891.25 and 5019.92 yuan for LS, and 8550.61 yuan for MM) indicating that the TSHM outperforms the other three methods. The standard deviation obtained by TSHM is also smaller than that obtained by GA and LS, which indicates that TSHM is more stable than GA and LS in terms of stability.  
Table 3 shows the approximate optimal routes (only for MM, GA, and TSHM). It is observed that TSHM outperforms the GA in vehicle routing planning. The former not only avoids unnecessary return trips under the condition of meeting customer needs, but also reduces travel cost and carbon emission cost due to a shorter travel distance and profitable delivery visit sequence.  
We also solved the model using CPLEX 12.5 and obtained an optimal solution with a cost of 4134.67, consisting of a constant cost of 1080, a travel cost of 2373.3, and a carbon emission cost of 681.37 with a computational time of 29.76 s. The results show that three special vehicles with a capacity of 176 are used, and none with a capacity of 120 are used.  
The cost of our solution obtained using our proposed algorithm is 4199.21, and the gap is only 1.5%; however, our algorithm only spent about 1.92 s in solving this problem, which shows the effectiveness of our proposed algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Approximate Optimal Value (yuan)** & **Average Value (yuan)** & **Standard Deviation** \\ \hline MM & 8550.61 & 8550.61 & 0.00 \\ GA & 5070.58 & 5336.54 & 236.45 \\ TSHM & 4199.21 & 4262.32 & 42.63 \\ LS & 4891.25 & 5019.92 & 110.39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results obtained by manmade schedule (MM), GA, TSHM, and LS.
## 3 Methodology
### Detection of a Single Satellite
#### 3.1.2 Determination of the Valley Period  
The valley period is defined as the period between the falling moment and the rising moment and must be satisfied as follows:  
* The time of the rising moment is higher than the falling moment.
* The \(C/N_{0}\) at any time in this period is less than the smaller of that at the falling moment and that at the rising moment.  
The set of valley periods, which is denoted as \(P\), can be expressed as  
\[P=\left\{\left(a_{i},b_{j}\right)\right|\begin{array}{c}a_{i}<b_{j},\forall t \in\left(a_{i},b_{j}\right),\\ C/N_{0t}<\text{Min}\left(C/N_{0a_{i}},C/N_{0b_{j}}\right)\end{array}\right\}, \tag{5}\]  
where \(t\) is the moment within the period \((a_{i}\),\(b_{j})\). \(C/N_{0t}\), \(C/N_{0a_{i}}\) and \(C/N_{0b_{j}}\) represent the ratio of the carrier to noise at moments \(t\), \(a_{i}\) and \(b_{j}\), respectively. As an example, the valley periods in Figure 4 are marked in magenta. Additionally, we show two valley periods in Figure 4 to indicate that the proposed method can effectively distinguish various valley periods.  
Similarly, the valley period of each satellite over the same station at a selected frequency can be calculated and defined as \(P_{s}(1\leq s\leq l)\), where \(s\) and \(l\) represent the corresponding satellite and the number of observed satellites, respectively.
## 2 Phonation and Simulation Model
### Phonation Model  
The phonation model consists of two rectangular channels (subglottal and supraglottal) with a cross-section of 18 \(\times\) 15 mm\({}^{2}\), a length of 190 mm and the synthetic larynx in between (see Figure 2). The larynx includes the vocal folds (VF) and the rigid ventricular or false vocal folds (fVF), described in the experiments of [39; 40]. The geometry of the vocal folds replicates the M5 model introduced by [44; 45]. Instead of incorporating the fluid-structure interaction of the VFs, the glottal area waveform (GAW) was extracted from the flow-induced motion in the experimental model and transfered to the CFD model as moving boundary.  
For the acoustic simulation, following the hybrid aeroacoustic approach, a different grid is used than for the CFD simulation. The three dimensional computational acoustics (CA) grid as shown in a coronal plane of the larynx in Figure 2, is inherited from [18], which is referred to for a detailed explanation. For computational efficiency, the subglottal channel is truncated by an absorbing boundary condition (ABC) to prevent reflections. At the end of the vocal tract (supraglottal channel), a cuboid-shaped propagation domain is attached with a two element thick, perfectly matched layer (PML) [46] region on the boundary, to sufficiently ensure the open domain property [18]. All other boundaries, including the bottom plane of the propagation domain, are modeled as acoustic hard. The larynx region and vocal tract are meshed by approximately 127 500 tetrahedral elements with a maximum element size of 4.3 mm. In contrast, the propagation and PML regions are meshed with hexahedral elements.  
The flow field is interpolated to the CA grid using a conservative interpolation technique presented in [18]. Then, the finite element approach solves the PCWE that describes the acoustic wave propagation. This framework has been implemented in openCFS [47] to perform the aeroacoustic simulations presented in this work. The incompressible fluid density is set to \(\bar{p}=1.138\,\mathrm{kg/m^{3}}\) and the speed of sound is given by \(c=343.4\,\mathrm{m/s}\).
# Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network
## 3 Surface Defect Detection Model
### Surface Defect Discrimination Model  
This paper establishes a surface defect discrimination model based on the information difference of internal characteristics between perfect and defect images to realise the discrimination of the presence of image defects in step I. Since the collected original image is a gray image, the gray value of each internal pixel is \(\Omega\) (0-255) for an original image. Perfect and defect images are respectively expressed as \(p\) and \(d\). The distribution statistics of the gray values of all pixels in pending image \(f\) indicate that Equation (6) can determine whether \(f\) belongs to a perfect or defect image. Figure 4a shows the statistical results of the gray value distribution of a perfect image, and Figure 4b-i shows the statistical results of the gray value distribution of eight classifications of defect images. The figure reveals that the gray values of the perfect image are all clustered and distributed around the median value of 127.5, whilst a part of the gray values of defect image is distributed in the range of \(<\)100 and \(>\)200. Thus, the perfect and defect images can be quickly distinguished. Meanwhile, internal information amongst defect images from Figure 4b-i demonstrates strong similarity, which is difficult to classify using a simple model. Therefore, step II should be implemented to establish the surface defect recognition model.  
\[f=\left\{\begin{array}{ll}p&\varepsilon_{1}\leq\Omega\leq\varepsilon_{2}\\ d&\Omega<\varepsilon_{1},\Omega>\varepsilon_{2}\end{array}\right. \tag{6}\]  
where \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are discrimination coefficients, and their values are determined in accordance with the production site environment. The values in this paper are \(\varepsilon_{1}=100\) and \(\varepsilon_{2}=200\).  
Figure 4: Statistical results of gray value distribution of perfect and defect images: (**a**) perfect image, (**b**) LM, (**c**) BS, (**d**) CP, (**e**) EC, (**f**) Ho, (**g**) IS, (**h**) Pe, and (**i**) Sm.
# Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG
## 4 Results and Discussion
### Classification Results
On the other hand, less pre-processing (i.e., without the need of running ICA) was needed to classify the datasets by means of the CNN; and it is completely automatic. Even if two relatively small datasets were available, we could show that our CNN model can achieve classification accuracies in line with two well-established baseline models. Moreover, we obtained similar performance with a simpler pre-processing pipeline, reducing it to those steps (e.g., filtering and automatic trial rejection) that could be performed in an online modality. This may be explained by the fact that the CNN \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Subject** & **CNN** & **sLDA (0.6 s)** & **sLDA (0.8 s)** & **sLDA (1 s)** & **RF (0.6 s)** & **RF (0.8 s)** & **RF (1 s)** \\ \hline S000 & 0.62 & 0.60 & 0.64 & 0.64 & 0.58 & 0.58 & 0.58 \\ S001 & 0.66 & 0.50 & 0.52 & 0.60 & 0.54 & 0.56 & 0.69 \\ S004 & 0.74 & 0.75 & 0.73 & 0.75 & 0.69 & 0.73 & 0.69 \\ S006 & 0.84 & 0.76 & 0.72 & 0.70 & 0.86 & 0.74 & 0.82 \\ S007 & 0.68 & 0.76 & 0.80 & 0.76 & 0.78 & 0.73 & 0.80 \\ S008 & 0.85 & 0.78 & 0.78 & 0.83 & 0.88 & 0.80 & 0.93 \\ S009 & 0.61 & 0.62 & 0.67 & 0.50 & 0.62 & 0.58 & 0.62 \\ S010 & 0.59 & 0.52 & 0.54 & 0.57 & 0.52 & 0.50 & 0.46 \\ \hline
**MEAN** & **0.70** & 0.66 & 0.68 & 0.67 & 0.68 & 0.65 & **0.70** \\
**STD** & 0.10 & 0.11 & 0.10 & 0.11 & 0.14 & 0.11 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of classification performance (in terms of accuracy) in validation from _Dataset 1_. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Subject** & **CNN** & **sLDA (0.6 s)** & **sLDA (0.8 s)** & **sLDA (1 s)** & **RF (0.6 s)** & **RF (0.8 s)** & **RF (1 s)** \\ \hline G01 & 0.79 & 0.65 & 0.65 & 0.65 & 0.61 & 0.59 & 0.67 \\ G02 & 0.43 & 0.61 & 0.55 & 0.53 & 0.49 & 0.63 & 0.47 \\ G03 & 0.58 & 0.51 & 0.59 & 0.67 & 0.59 & 0.53 & 0.  
can behave as an automatic feature extraction method as well as an efficient classifier. As mentioned earlier, the CNN showed the drawback of a higher variability in the individual participant accuracies compared to the baseline models. Therefore, further studies could investigate the inter-individual differences of the CNN performance. Furthermore, CNN needs to have 5 s EEG images as an input, while sLDA and RF could work with shorter windows: thus, further studies are needed to identify the minimal input length for the CNN architecture in order to produce acceptable classification outcomes. This could pave the way for its application in an online scenario, i.e., the assisted living or a BCI system. Finally, CNN could take larger advantage by the spatial information in the EEG dataset, by applying a spatial convolution at its second layer. On the other hand, sLDA and RF did not use this kind of information to enhance their predictions.
# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 9 External Excitation and Resulting Vibrations  
This section introduces several studies that have applied deep learning to health monitoring in which the system was directly excited and its response was measured. [PERSON] et al. [140] introduced the modal macro strain (MMS), the sensing technology of a long-gauge distributed fiber Bragg grating (FBG) and deep learning. They modeled a basalt fiber-reinforced polymer (BFRP) composite pipeline with the finite element method. Random signals were loaded to the pipe in the direction perpendicular to the wall. The location of the excitation was 0.2 m away from the pipe support, as shown in Figure 10. The longitudinal and circumferential distributions of the MMS were measured to locate the damage, load excitation and support.  
Figure 10: Pipeline modeling from [140], Copyright 2018, MDPI.  
The schematic diagram of the applied CNN is shown in Figure 11. Conventional image-based damage identification shows only the external damage. The introduced method of using dynamic responses is suited for complicated structures and environments and can overcome this limitation. [PERSON] et al. [141] used the Qatar University (QU) grandstand simulator to develop a 1-D CNN for a horizontal girder made of steel. The bolt joints were loosened, and a modal shaker was used to vibrate the structure. A deep-learning-based damage detection system was used to localize the damage in real time. Raw vibration data were used to extract the optimal damage-sensitive features.  
Monitoring only the natural frequencies of a cantilever beam is not effective because the temperature or vibration amplitude can affect the variation in natural frequencies. [PERSON] [142] chose deep learning for noninvasive monitoring of the cantilever beam conditions. The vibration data were measured to obtain extended time-frequency signatures. The undamaged beam and two types of damaged beams with different temperatures were characterized. The results showed that the proposed method can eliminate the ambient effect (i.e., temperature) and determine if the beam is damaged or not. [PERSON] et al. [143] proposed using a deep CNN for a novel detection method of structural damage to a simply supported beam. In a simulation, a beam was subjected to a burst of random excitation, and different damage levels were simulated by a reduction in the bending rigidity. The results show that the CNN had high accuracy in noisy environment; it could identify multiple points of damage and learned hierarchical features layer by layer. [PERSON] et al. pointed out the importance of big data, which can implicitly consider all the factors. Thus, deep learning can be used for classification with excellent performance, reduced computation time and wide applicability. [PERSON] et al. [144] used an electromechanical impedance (EMI)-PZT to monitor structural damage in aluminum structures. This application of EMI-PZT to a CNN was the first trial in this field of research. The proposed method outperformed the currently developed structural health monitoring approach.
## 5 Experimental Setup  
This section describes the details of our simulation setup, metrics, and evaluation methodology. In our simulations, we compare the performance of EESEVBF with ESEVBF. We deploy a 3D volumetric region of _length (2 km) \(\times\) width (2 km) \(\times\) depth (4 km)_. The header size of the data packet is 11 bytes, the payload is 72 bytes, neighbor request and acknowledgment are 50 bits, and the container packet size is 70 bits. The data rate is 16 kbps. The propagation speed of sound underwater is 1500 m/s.  
Figure 10: Forwarder Node Selection Scenario.  
The number of nodes varies from 200 to 450 to demonstrate the performance in the sparse and dense networks, and the transmission range varies from 500 m to 900 m. The centralized station for receiving the data packets is static and at the water surface. The nodes can move with a constant speed of 2 m/s. The initial energy, sending energy, and receiving energy/idle energy of the node is set to 80J, 4.5J, and 0.017J, respectively. The background underwater wireless environment and the acoustic channel parameters (ambient noise, site-specific noise, central acoustic signal frequency) are set for simulation, as given in [27].
# Effective Modal Volume in Nanoscale Photonic and Plasmonic Near-Infrared Resonant Cavities
###### Abstract  
We survey expressions of the effective modal volume, \(V_{\mathit{eff}}\), commonly used in the literature for nanoscale photonic and plasmonic cavities. We apply different expressions of \(V_{\mathit{eff}}\) to several canonical cavities designed for nanoscale near-infrared light sources, including metallo-dielectric and coaxial geometries. We develop a metric for quantifying the robustness of different \(V_{\mathit{eff}}\) expressions to the different cavities and materials studied. We conclude that no single expression for \(V_{\mathit{eff}}\) is universally applicable. Several expressions yield nearly identical results for cavities with well-confined photonic-type modes. For cavities with poor confinement and a low quality factor, however, expressions using the proper normalization method need to be implemented to adequately describe the diverging behavior of their effective modal volume. The results serve as a practical guideline for mode analysis of nanoscale optical cavities, which show promise for future sensing, communication, and computing platforms.  
effective modal volume; nanocavities; laser resonators; near-infrared; theory and design Article
## 6 Verification of the Proposed Protocols
### Formal Analysis of DDACap Protocol  
The DDACap protocol was also simulated in ProVerif using insecure public channel and processes (!ProcessUEB()) for \(UE_{B}\) and (!ProcessUEC()) for \(UE_{C}\). There was no attack found, as the ProVerif results show in Figure 9. The desired security properties are similar to those of the DDSec protocol; however, the IDs (\(UB/UC\)) are not queried for secrecy as they are sent in clear text in DDACap protocol. ProVerif results indicate that the secrecy of rand_bc, rand_rc, skueb, skuec, d1, Acap holds and mutual authentication of \(UE_{B}\) to \(UE_{C}\) holds in the form of non-injective and injective agreements, hence the protocol has no flaw.  
The events that prove the correspondence and arguments taken, ID, \(PK(X)\) and nonce are the same as DDSec protocol. If the arguments match, \(UE_{B}\) sends the data to \(UE_{C}\), otherwise it sends authentication failure and terminates the communication. The correspondence proved in with the same methods used in Section 6.3. The direct proof of this correspondence in ProVerif holds because Message 3 is sent before Message 4. The correspondence is proved and concluded the desired correspondence by noticing that the event which has uc, d1, rand_rb,rand_rc, Ts_4 as argument cannot be executed before ub, d1, rand_rb has been sent, that is, before rand_rb request has been executed with rand_rb,rand_rc response as part of the mutual authentication exchange. Which holds in ProVerif.