esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 3 Data and Methodology
### Data
#### 3.1.2 Production of Data Sets  
Firstly, employing 0.80 m high-resolution, non-offset Google remote sensing imagery supplied by the project team, the data were uniformly projected onto the WGS_1984 coordinate system. The debris flow vector data were then overlaid onto the high-resolution remote sensing imagery to compile the sample set, as illustrated in Figure 1. The sample set was subsequently processed by executing a cutting script, uniformly cropping and normalizing the images into 512 \(\times\) 512 pixel blocks, yielding a total of 1268 geological disaster (debris flow) samples in JPG format. Following a preliminary screening, 898 valid geological disaster (debris flow) samples were acquired, as illustrated in Figure 2.  
To prevent overfitting and effectively enhance the sample diversity during the training process, thereby ensuring the model's robustness across various environments, data augmentation techniques were employed. Following data augmentation processes including image cropping, contrast, brightness, and noise adjustments, the count of geological disaster (debris flow) samples increased to 4585. The augmented geological disaster (debris flow) samples, in \(JPG\) format, were imported into the _Labeling_1.8.6. Utilizing vector debris flow location information, all samples were annotated in accordance with the _Pascal VOC_ dataset format, resulting in the generation of.xml type annotation files, as illustrated in Figure 2. Lastly, to maintain the dataset's independence, it was partitioned into training, validation, and test sets in an 8:1:1 ratio.  
Figure 1: (**a**) Study area extent map; (**b**) remote sensing image and debris flow vector data overlay.
## 2 Dynamic Centrifuge Tests
### Input Ground Motions and Test Procedures  
Three different earthquakes (E1, E2, and E3) defined in the seismic hazard assessment report for TBPP were selected as the input ground motions. These natural earthquake records have been adjusted to reflect the geological characteristics of the engineering site. The amplitude of acceleration for each input ground motion was linearly scaled to three levels of 0.1 g, 0.2 g, and 0.3 g, and the seismic loadings were input from the bottom of the model box along the lateral direction by a specified sequence as shown in Table 4. The white noise is applied to obtain any changes on the site's frequency spectrum characteristics in the course of being subject to seismic loadings with increasing peak acceleration amplitudes. An adequately long interval between every single seismic loading was needed for a full dissipation of the excess pore pressure, for which the subsequent seismic waves were not input until approximately 60 s (model time) after precedent excitation. The M1 and M2 models were applied with the same loading condition.  
Figure 4: Schematic of instrumentations layout.  
As shown in Figure 6, the seismic responses of pore pressure for M1 and M2 models behave quite differently. When subjected to different earthquakes, the maximum values of EXPP at different depths for both M1 and M2 models rise with the increase of the peak acceleration, and the increments get smaller in the deeper soils. The maximum values of EXPP resulted from earthquakes E2 and E3 are significantly higher than that of E1, and there is trivial difference between responses of E2 and E3. Moreover, compared with M1, the maximum values of EXPP for M2 are smaller at every depth.  
Similar phenomena can be more explicitly observed in the maximum values of EXPPR along the depth. As shown in Figure 7, on the excitation of E2 and E3 with a peak acceleration of 0.3 g (E2\(-\)3 and E3\(-\)3), the maximum values of EXPPR at the depths of 5 m and 10 m both reached up to around 1.0 for M1, indicating that the soils have completely liquefied; the E1 with the same peak acceleration (E1\(-\)3) can only lead to peak values of 0.8 and 0.6 for EXPPR at the same depth of soils in M1; and the peak value of EXPPR at the depth of 20 m in M1 is only about 0.4 in the cases of E2\(-\)3 and E3\(-\)3. Generally, for different input motions, E2 and E3 cause a higher maximum value of pore pressure with the same peak acceleration; and when subjected to the same seismic loading, the EXPPR in the shallower soils present higher maximum values. The stronger liquefaction resistance of deeper soils might be relevant to the higher confining pressure, which brings about higher dynamic strength of soils and more significant contraction and densification behavior.  
Moreover, the maximum values of EXPPR at the depth of 5 m and 10 m are just about 0.9 and 0.6, respectively, for M2 in the loading cases of E2\(-\)3 and E3\(-\)3. The lower EXPPR implies soils with a stronger liquefaction resistance than that in M1 and could be explained by the higher relative density of M2.
## 2 Wireless Communication for Maritime Applications
### Communication between Sensors and Relays  
In order to establish communication links between sensors in a subnetwork to coordinator devices or even to base stations located within a short range of the sensors, several low-power communication protocols can be implemented. Some of them are discussed in the following paragraphs, followed by a highlight and comparison of their key parameters in Figure 1 and Table 1.
## 3 Results
### Machine Learning-Based Processing
The experiment results can be seen in Table 4 for the two models employed, namely, MLP and CNN. According to the workflow sketched in Figure 3, the models have been validated by using a 10-fold cross-validation technique. Precisely, for the second experiment proposing unbalanced class samples (the \"Event\" class is smaller than the others), a stratified k-fold algorithm from Python Scikit-learn library was carried out to enforce the class distribution in each split of the data to match the distribution in the complete training dataset. The table shows the mean and the standard deviation of accuracy results for the training data and loss, as well as for testing data. The mean and the standard deviation are also provided for P, R, and F1. As reported, the performance of the results of the two ML models are quite satisfactory: with the MLP model, Experiment 1 provides better performance than Experiment 2, which involves not only the background signals but also additional transient signals. The latter are different from the background signals of the predefined classes, used in some sense to \"dirty\" the dataset to evaluate the ability of the model in discriminating extraneous transient events from the background signals to be classified. Similar behavior appears for the CNN model, whose performance in the case of Experiment 1 is very promising since the values of both accuracy and F1 are close to 100%. This result emphasizes how CNN is able to correctly discriminate between balanced classes of noise signals. In the case of Experiment 2, instead, a certain unbalance in the class distribution reflects in the results returned by the model, with a significant standard deviation value for the accuracy. The F1 score, although assuming a similar value to accuracy, is more stable (i.e., the standard deviation is low), according to its characteristic to work better with unbalanced classes. \begin{table}
\begin{tabular}{c c c c} \hline
**P** & **R** & **F1** & **Acc** \\ \hline P = \(\frac{tp}{tp+fp}\) & R = \(\frac{tp}{tp+fn}\) & F1 = \(2*\frac{P*R}{P+R}\) & Acc = \(\frac{tp+tn}{tp+fn+fn}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Precision, recall, F1 score, and accuracy definitions. The concatenated confusion matrices computed by considering the predictions for test data in each fold by cross-validation are also reported in Table 5, according to the model used and for each experiment. The confusion matrices confirm the observations reported above, highlighting the satisfying performance of the CNN model in both experiments.
## 1 Introduction  
The demand of new devices is increasing, and in several cases, the graphene was used as one component (or layer) in nanostructures, for example graphene-based biosensors [1] and gas sensors [2; 3], battery technology [4; 5], graphene field-effect transistors [6] and graphene based memristor devices [7; 8]. It has been noticed that a combination of conductive materials as graphene and relatively better insulating materials (typically metal-oxides, high-k), if deposited alternately, can provide decrement in the forming voltage to the level of switching voltages, also mechanical flexibility and transparency. The new generation nanodevices are very thin and the mechanical properties of graphene should be taken into consideration. Especially, if these devices must be flexible, then [PERSON]'s modulus of graphene and the materials surrounded should be addressed.  
High-k dielectrics layers on graphene are commonly used for building semiconductor nanodevices (containing graphene), because they are electrically and thermally insulating and can be easily up for wafer-scale fabrication. There are many methods of depositing dielectrics onto graphene, such as sputtering [9], a pulsed laser deposition [3] and atomic layer deposition (ALD) [7; 8; 10; 11; 12], but there are not many works for learning the mechanical properties of those devices. There are several theoretical works where graphene nanoindentation has been studied [13; 14] and molecular dynamics used for modelling [PERSON]'s modulus [15; 16; 17], but the maximum load has been small and graphene has not been located inside the nanostructure. Graphene-metal nanolaminates have been investigated using molecular dynamics [18]. It was deduced that graphene inclusions could increase modulus and strength approximately 90-100%. Other theoretical calculations have indicated that during nanoindentation the generated stresses are the highest at the tip of the indenter and the amplitude of the stress reduces outwards the site along the graphene structure [19]. Obviously, the graphene layer inside a nanodevice affects the nanostructure strength and the elastic modulus, but it has not been widely studied in literature up to now.  
In this study, we are measuring the effect of graphene to Young's modulus and observing how the atomic layer deposited (ALD) thin film affects the graphene quality and Young's modulus thereof. The graphene transferred onto a Si/SiO\({}_{2}\) substrate (as a typical substrate for nanodevices) and a thin layer film was made with ALD in Al(CH\({}_{3}\))\({}_{3}\) and H\({}_{2}\)O process (also well-known and widely used). We show that even the few cycles will improve the elastic properties of graphene, even though the deposition of thin film on graphene slightly decreases the quality of graphene.
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 3 Antenna Configurations
### Transparent Antenna  
The transparent antenna designed using the honeycomb-shaped mesh structure can be integrated into an OLED light source structure with minimal impact on the luminous efficiency. The proposed antenna was designed to cover the WLAN band, but it can be operated on other bands by optimizing the mesh parameters. Figure 7 shows the geometry of the mesh antenna with the dimensions of the honeycomb mesh. For the ease of comparison, the non-transparent and mesh antennas have the same dimensions
# Ins and Outs of the Ankle Syndesmosis from a 2D to 3D CT Perspective
## 3 Results
### Novel 3D Measurement Methods
#### 3.4.3 The 3D Volume Measurements  
Several authors have investigated the use of volumetric measurements of the distal tibiofibrular articulation. In this technique, the total interosseous volume is calculated, extending from the level of the tibial plafond up to a height of 1, 3, 5, or 10 cm proximally (Figure 7). These 3D volume measurements were introduced by [PERSON] et al. in their cadaveric experiment, showing a 43% (441 mm\({}^{3}\)) increase in syndesmotic volume after 1 mm diastasis and an additional 20% increase for each extra 1 mm [43]. Ten years later, [PERSON] et al. described the use of volume measurements to compare the post-operative syndesmotic reduction between screw fixation and suture-button techniques, unveiling a significant increase of 8% (118.5 mm\({}^{3}\)) in suture-button fixation [44]. Additionally, they found an intra-observer reliability of 0.882 and an interobserver reliability of 0.861 for their measurement technique. [PERSON] et al. and [PERSON] et al. recently popularized these 3D volume measurements, showing high sensitivity (95.8%) and specificity (83.3%) for the detection of syndesmotic instability [8,9]. Moreover, they stated a cut-off value of 11.6 cm\({}^{3}\) (or 25.4% increase in volume) at the level of 5 cm above the tibial plafond, which reported an excellent ICC of 0.93.
# Cyber Physical Systems Dependability Using CPS-IOT Monitoring
## 5 Performance Evaluation
### The Impact of INMP on the CPS Dependability
#### 5.5.1 Impact of Clustering on Energy Consumption  
A first set of experiments was conducted to evaluate the energy consumption of a single IoT network compared to a partitioned network using clustering. The key performance parameter considered was the energy consumed by the sink node. Being the collection point of all data collected by other nodes, the sink node is one of the most energy-consuming nodes in an IoT network. The results in Table 3 revealed that the maximum energies consumed by the sink nodes when the network was partitioned into 2, 3, 4, and 5 clusters were respectively 5.86%, 6.0%, 5.71%, and 5.84% of the total energy consumed by the entire IoT network while on the average, it consumed 5.07%, 4.38%, 3.92%, and 4.23% of the total IoT network's energy. A higher sink energy consumption of 6.60% was recorded when the IoT network was designed with a single sink. This revealed the relevance of using network partitioning as an efficient network reconfiguration strategy to lower the energy consumption of the CPS-IoT subsystem.  
Figure 11: Epidemic profile: Interference and path length.
## 3 Aircraft Low Altitude Operation Scene
### Design Requirements of Avionics System for Low-Altitude Operation Mode of Aircraft  
Commercial aircraft avionics systems now typically include flight management systems, modular integrated processing systems, integrated display systems, communication systems, navigation systems, air traffic control systems, air data systems, information systems, cabin systems, engine indication and cockpit warning systems, traffic collision avoidance systems, ground proximity warning systems, and data recording systems [8]. Avionics systems cover a very wide range of aircraft functions and basically cover the main electronic systems on the aircraft; therefore, avionics systems perform most of the core functions of aircraft control, such as communication, navigation, display, alarm, and so on. The development of an avionics system based on these functions generally follows the civil aircraft development process of ARINC ARP 4754A to carry out system design (see Figure 1) [9]. System design requirements are the basis of system development but also the key to system design, which should be focused on.  
When a commercial aircraft operates in the air, most of the functions of the aircraft's avionics system need to be carried out, regardless of whether it is a high-altitude or low-altitude or manned and unmanned aircraft. When performing the functions of the avionics system, there are still some differences between high-altitude and low-altitude and manned and unmanned aircraft. Commercial aircraft operated at low altitudes need some design requirements to achieve avionics function.  
When a commercial aircraft operates at low altitude, the aircraft is always in the troposphere, the electromagnetic environment is complex, and the density of the low-altitude aircraft is high. As the main aircraft function system, avionics systems need to focus on the system's electromagnetic environment requirements, system network security of aircraft, accurate navigation, real-time data sharing between air and ground, and so on.
# n = sample size  
alpha_check <- function(n, numberOfTests) {  
bigger <- c(0); i <- 0 empir_quantumupper <- empir_quantumlower <- varXY <- avgX_minus_avgY <- numeric(numberOfTests)  
for (k1 in 1:10) { #the first-scenario parameter combinations for (k2 in 1:10) {  for (theta1 in 1:30) { theta2 <- k1 * theta1/k2 #calculation of theta2, so that Ho holds for (j in 1: numberOfTests) { #many tests for the given k1,k2, theta 1,2 X <- rgamma(n, shape = k1, scale = theta1) Y <- rgamma(n, shape = k2, scale = theta2) avgX_minus_avgY[j] <- mean(X) - mean(Y) varXY[j] <- (1/n) * ((n - 1)/(n + 1)) * (var(X) + var(Y)) #the min MSE estimate of var (\(\overline{X}-\overline{Y}\)) empir_quantumlower[j] <- -1.96 * sqrt(varXY[j]) #the lower percentile for testing Ho empir_quantumupper[j] <- 1.96 * sqrt(varXY[j]) #the upper percentile for testing Ho  
i <- i + 1 #saving in\"bigger\" the relative frequency of cases when Ho is rejected bigger[i] <- (sum( avgX_minus_avgY > empir_quantumper )/numberOfTests) + (sum( avgX_minus_avgY < empir_quantumlower )/numberOfTests)  
return(bigger )  
#\"bigger\" is a vector whose i-th component gives an estimate of the type I error #probability corresponding to the i-th parameter set-up within the scenario  
R function estimating the test power (here, for the 1 st parameter scenario set-ups) # n=sample size, const = the constant in H\({}_{1}\) (see the top of page 5), const > 0
the higher the \"number\" of tests, the more precise the test power estimation power_check <- function(n, const, number) { test_power <- c(0); i <- 0 for (k1 in 1:10) { #the first-scenario parameter combinations for (k2 in 1:10) {  for (theta1 in 1:30) { theta2 <- (k1*theta1 + const)/k2 # the set-up of a H1 case numberOfSUCCESS <- 0 for (test_run in 1:number) { #test power for given k1, k2, theta 1, 2, n, const X <- rgamma(n,shape = k1, scale = theta1) Y <- rgamma(n,shape = k2, scale = theta2) percentile <- 1.96 * sqrt((1/n) * (n - 1)/(n + 1) * (var(X) + var(Y))) #test percentile, # here for the 1 st scenario  
if (abs(mean(X)-mean(Y)) >= percentile) { numberOfSUCCESS <- numberOfSUCCESS + 1}
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 6 Performance Study of the UWOC Link
### Impact of QAM Constellation Size
The LBI and \(Z_{\rm{max}}\) corresponding to a target BER of \(10^{-3}\) are summarized in Table 6, which are larger than those for the case of a single LED (compare with Tables 3 and 5). Nevertheless, the minimum operational distance (before SiPM saturation) is also increased, as expected [9]. For instance, for \(R_{b}=20\,\)Mbps with \(\eta\approx 1\,\)bps\(/\,\)Hz, the LBI is about 93.7, 91.2, and 89.3\(\,\)m for DCO, ACO, and LACO schemes, respectively. In practice, if the channel parameters (including the link range) are known at the Tx, the number of actually activated LEDs can be adjusted so as to allow working within the LBI adaptively and, hence, to optimize the link performance. Figure 12: BER performance as a function of distance using 100 LEDs with \(P_{\rm{Tx,e}}=50\,\)mW for each; \(N=1024\). (a) \(\eta\approx 1\,\)bps\(/\,\)Hz (e.g., DCO-OFDM with 4-QAM) with \(N_{\rm{CP}}=9\), 20, and 28, for \(R_{b}=20\), 50, and 100 Mbps, respectively; (b) \(\eta\approx 2\,\)bps\(/\,\)Hz (e.g., DCO-OFDM with 16-QAM) with \(N_{\rm{CP}}=5\), 11, and 20, for \(R_{b}=20\), 50, and 100 Mbps, respectively.
# Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-Physical Collectives +
## 5 Evaluation
### Case Study: Post-Disaster Area Exploration  
We consider a post-disaster area exploration application as a case study, inspired by the Italian seismic sequence events mentioned in Section 3.1. The system consists of a team of \(N_{r}\) explorers that can sense their own position, the aggregate system considers _logically_ connected devices within a range \(R\), and spawns a new virtual device situated at its current position whenever one of the following conditions hold:  
* no other device is logically reachable (isolation); or all the reachable devices are at a distance higher than a parameter \(R_{\text{p}}\).  
The idea is to exploit logical connectivity to create a live physical reachability map quickly, at the expense of some virtualisation overhead: low values of \(R\) and \(R_{\text{p}}\) are expected to raise the system precision, as it is increasingly likely that devices separated by a short space are directly reachable in physical space.
## 2 Materials and Methods
### NIRS Calibration  
The spectrum data were first pre-treated using standard normal variate and detrending (SNVD) (D = 2, G = 5, S1 = 2, S2 = 1) to correct for external interferences on the spectrum data. D indicates the derivative order number (0 indicates no derivation, 1 means the first derivative, and so on), G indicates the gap (the number of data points over which derivation is computed), S1 indicates the number of data points in the first smoothing (1 means no smoothing), and S2 indicates the number of data points in the second smoothing (1 means no smoothing) [15; 16].  
Calibration equations were developed in the R statistical platform using the PLS and RF models from the caret package. The datasets were first screened for outliers using the Mahalanobis distance method [28; 29], and the retained set (\(n\) = 177) was divided into calibration and validation sets in a ratio of 3:1, respectively. The repeated cross-validation method was used in developing equations for TCC using iCheck and Chroma Meter as well as for DMC. The performance of the cross-validated (10-fold cross-validation repeated 25\(\times\)) equations was used in predicting the values of the validation set. The entire process from the data division into calibration and validation sets to the prediction of validation set values was repeated five times, and the reported statistics (the average of five by twenty-five cross-validation replications) include the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP), and the coefficient of correlation (r) in both the calibration and validations sets.  
The root mean square error of cross-validation/prediction (RMSECV/RMSEP) was calculated as follows:  
\[\textbf{RMSECV}\text{(RMSEP)}=\sqrt{\frac{\sum_{i=1}^{N}(\hat{y_{i}}-y_{j})^{ 2}}{N}}\]where \(\hat{y}_{t}\) is the predicted value of each sample in the calibration or validation sets, \(\mathbf{y}_{i}\) is the measured/actual value of each sample, and \(\mathbf{N}\) is the sample number in the calibration and prediction sets.
# Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
## 2 Materials and Methods
### Triple Collocation Analysis Methodology
For the multiplicative model, \(CC\) is the same equation and also unitless, but the root mean square error (\(RMSE\)) can be manipulated using the Taylor-series expansion of the logarithmic operator, as in [51], to become:  
\[RMSE=\mu_{X}\sqrt{C_{X,X}-\frac{C_{X,Y}C_{X,Z}}{C_{Y,Z}}} \tag{6}\]  
where \(\mu\) is the mean of \(X\) the selected CYGNSS product. This transforms the \(RMSE\) to be in the same units as the additive model and easier to interpret. Bootstrapping (n = 1000) created 99% confidence intervals of the values, as statistical significance tests are not recommended in TCA [28]. Histogram density plots of matchups between pairs of datasets (CYGNSS-SAR, CYGNSS-SMAP, SMAP-SAR) are generated based on using the same values in TCA to help interpret the relationship between each dataset. These plots use RMSD instead of RMSE, as they are a comparison between two datasets.