esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 3D DIC in Blast and Shock Loading
was able to use the 3D DIC to capture longitudinally travelling axisymmetric vibrations in the cylindrical shell, which are excited by the local radial compression [60]. [PERSON] et al. [61] examined the response of mild steel and armour steel plates to air blast explosive loading and used 3D DIC to compare experimental results to computational models. The plates had a nominal thickness of 4.6 mm. The air blast was generated by circular disks of PE4 plastic explosive, with a mass ranging from 24 g to 70 g and a standoff distance of 25 mm, 38 mm and 50 mm. The 3D DIC imagery was taken with a pair of IDT Vision NR4 S3 high-speed cameras (IDT Vision, Pasadena, CA, USA) running at 30,000 FPS. The authors used the Dantec Dynamics Istra 4D DIC software package (Dantec Dynamics, Skovlunde, Denmark) to process the imagery. The authors obtained the deflection, strain and strain rate and compared it to the computational models. The authors show that the models varied from between 4.97% and 33.41% for the final deflection of the mild steel plates from the 3D DIC data. The difference between the experimental and computational varied from 2.95% to 30.27% for the ARMOX370T and 7.46% to 26.63% for the ARMOX440T. Their results show that the ARMOX steel panels exhibited lower maximum and residual plastic deformation, due to the higher ability of energy dissipation from the blast, which subsequently increases as the material yield strength increases [61]. [PERSON] et al. [62] studied the response of copper plates subjected to underwater impulsive loading. The plates were made from C11000 copper and had thicknesses of 1 mm, 2 mm and 3 mm. The authors also examined the response when cross and ring pre-notches were cut into the plates. The depth of the notches was 0.5 mm. The pressure pulse was generated by a modified gas gun apparatus. The authors used a pair of Photron Fastcam SA5 cameras (Photron USA, Inc., San Diego, CA, USA) to obtain the high-speed imagery and used the VIC-3D software package (Correlated Solutions, Inc., Irmo, SC, USA) to generate the 3D DIC data. The authors show that the relative out-of-plane error measurements are between 0.84% and 6.83%. For the specimens with no pre-notch, their results show a linear relationship between normalized final deflection and dimensionless impulse. Without a pre-notch, the primary failure mode was large ductile deformation, while the pre-notched specimens exhibited four other typical failure modes. These other typical failure modes are large ductile deformation with local necking, splitting, splitting and tearing and fragmentation [62].
## 5 Results and Discussions
### Artificial Intelligence Analysis  
Artificial Intelligence was used to identify the experimental findings. In machine learning processing, 21 experimental data records were used. Table 2 shows the correlation coefficient between the input and output variables. The correlation coefficient in Table 2 was used to examine the degree of correlation between input and output variables. An absolute value of the coefficient greater than or equal to 0.8 represents high correlation; whereas a value smaller than 0.8 but greater than or equal to 0.7 shows medium-high correlation. Therefore, the maximum temperature with high or medium-high correlation to \(\rm V_{s}/V_{P}\), stiffness, pre-peak toughness, and post-peak toughness was obtained.  
An Artificial Neural Network (ANN) was built using WEKA. The inputs of the ANN include the aforementioned variables, and the output is the maximum temperature. For prediction, the mean absolute error is 99.2042, and the root-mean-square error is 130.3246. A high correlation coefficient of 0.8646 between the real maximum temperature and the one predicted by the ANN model is obtained. High correlation means high covariance between the two temperatures, and high covariance means that the model describes how the predicted maximum temperature varies or changes with the real maximum temperature well. Therefore, the maximum temperature can be predicted using the established ANN model. These results show that the fire-damage temperature could be simply  
Figure 9: Correlation of wave velocity ratio (\(\rm V_{S}/V_{P}\)) to reduced ratio of (**a**) maximum temperature; (**b**) stiffness; (**c**) strength; (**d**) and pre-peak toughness, respectively.  
detected by \(\rm{V_{s}}/\rm{V_{p}}\). Note that the rate of heating is only related to the strength with a coefficient of 0.19. However, more data records are needed to confirm this finding. Table 3 shows the correlation coefficient between the output variables. The \(\rm{V_{s}}/\rm{V_{p}}\) has a high correlation with stiffness, strength, and pre-peak toughness, as shown in Table 3. These analysis results show a good agreement with those from the experiment.
# Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
## 4 Results  
As mentioned in Section 1, a Proof-of-Concept (PoC) prototype CTD (see Figure 9) was set up with the purpose of achieving the following primary objectives associated with this applied research project:  
1. Verify the instantiation of NB-IoT communication to the OneM2M-compliant platform;
2. Verify the interfacing of the sensors with the MCU;  
Figure 8: Flowchart of the algorithm instantiated for CTD’s communications.  
Verify the provisioning of a BLE Mesh network and sensor data exchange with the gateway node;
* Verify the size of the battery pack by measuring the power consumption.  
The setups shown in Figure 9 have been implemented for testing purposes in different scenarios, consisting of five Huzzah ESP32 feather boards with ancillary elements as needed for the tests.  
Ideally, all the ESP32 boards should have had a Quectel BG95 shield mounted on top of them, so that in the final CTD's implementation, any one of them can send the data to the cloud if the cellular connectivity is available, but since the test goals are to verify the BLE Mesh capability, only one BG95 shield was used. It was only installed on the provisioner/gateway node (which in Figure 9 is called \"client\" so that once the mesh is provisioned, the BLE Mesh \"client\" node fetches the data from the BLE server nodes) that can send data to the cloud. Furthermore, each ESP32 board should be connected to BME680 and ADXL 345 sensors to measure environmental data and shock detection, respectively, of each individual container. Nevertheless, for the PoC implementation, only one BME 680 and only two ADXL 345 were available; therefore, only one of the BLE Mesh nodes (called servers in Figure 9) has BME 680 and ADXL 345, and one of the BLE Mesh nodes has just ADXL 345. Furthermore, in the PoC setup, the battery has not been used. The boards are powered using the USB ports of the laptop. Since the door sensor was not available, it has not been used in the PoC setup.  
The results of each test are presented in the following subsections.
# Antioxidant Production in _Dunaliella_
## 5 Culturing Systems  
Commercially, large-scale microalgae biomass is produced in open ponds, raceway ponds, enclosed photobioreactors (PBRs), or a combination of hybrid systems [157]. The cultivation of _Dunaliella_ also employs these various production systems [40, 106, 158, 159, 160, 161] depending on the location and the desired end-products.  
The PBR is considered a suitable production method for microalgae (_Dunaliella_) cultivation to enhance antioxidant production with biomass productivity in a'monoculture' and to maintain culturing reproducibility. This method is flexible, controllable, and able to provide sustainable biomass production [105, 162]. In open pond systems, the cell concentration is lower compared to that obtained during PBR cultivation as external factors (light irradiation, temperature, evaporation, contamination, aeration) are difficult to control. However, PBRs are not yet outperforming open pond systems in production volume and costs [40]. To the best of knowledge, no studies so far have reported the use of specific PBRs to culture _Dunaliella_ strains to enhance enzymatic antioxidants and non-enzymaticantioxidants. The effect of the production system on antioxidants needs to be studied further.
## 2 Bit-Patterned Media Recording (BPMR) Channel Model  
To record user data into a medium, an analytical 2D Gaussian pulse response is used for BPMR channel modeling. For modeling simplicity, the 2D Gaussian pulse response without considering media noise and write errors is expressed as follows [11]:  
\[P(z,x)=A\cdot exp\left\{-\frac{1}{2c^{2}}\left[\left(\frac{z}{PW_{z}}\right)^{ 2}+\left(\frac{x}{PW_{x}}\right)^{2}\right]\right\} \tag{1}\]  
where \(A\) is the normalized peak amplitude; \(c\) represents the relationship between the PW50, which is a parameter of the pulse width at half of the peak amplitude and the standard deviation of the Gaussian function; \(PW_{z}\) and \(PW_{x}\) are the PW50 of the cross- and down-track pulses, respectively. In this study, we set \(A=1\), \(c=1/2.3548\), \(PW_{z}=24.8\) nm, and \(PW_{x}=19.4\) nm. The 2D channel response \(h_{m,n}\) can be obtained by sampling the isolated pulse response as follows:  
\[h_{m,n}=P(m\cdot T_{z}+\Delta_{TMR},n\cdot T_{x}) \tag{2}\]  
where \(T_{z}\) and \(T_{x}\) are the track pitch and bit period, respectively; and \(\Delta_{TMR}\), which is expressed as \(\Delta_{TMR}=(TMR_{z}\times T_{z})/100\), is the read head offset. In this study, we set \(T_{z}=T_{x}=14.5\) nm to achieve an areal density of 3 Tb/in\({}^{2}\). The discrete BPMR readback signal, which is obtained by sampling \(T_{z}\) and \(T_{x}\), can be written as follows:  
\[r_{j,k}=\sum_{m=-M_{c}}^{M_{c}}\sum_{n=-N_{c}}^{N_{c}}a_{j-m,k-n}h_{m,n}+n_{j,k} \tag{3}\]  
where \(M_{c}\) and \(N_{c}\) are the lengths of the interference from neighboring islands in the cross- and down-track directions, respectively; \(a_{j,k}\in\{-1,\,1\}\) is the \(k\)-th recorded user data bit of the \(j\)-th track, and \(n_{j,k}\) is the electronic noise modeled as additive white Gaussian noise with zero mean and variance \(\sigma^{2}\). In this study, we set \(M_{c}=N_{c}=1\) for simplicity.
# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
## 6 Simulation Results  
In this section, we implement the proposed buffer- and segment-aware fuzzy-based adaptive video streaming to evaluate its performance. We implemented the experiments by utilizing the ns-3 simulation software. We modified code available from [35] to perform our experiments. The efficiency of the proposed DASH scheme was evaluated against other adaptation algorithms, namely, FDASH [25], DBT [11], QLSA [16], and DASH-Google [18]. The topology implemented in this paper is shown in Figure 6. Clientsdownloaded segments over an LTE network. Configuration details for the underlying LTE cellular network are in Table 2. The clients moved at vehicular speed within the cell. A grid-based road topology was used to simulate mobility. The clients' arrival times were uniformly distributed within the first 30 s.  
The HAS server stores two test sequences: Years of Steel (Dataset 1) and Big Buck Bunny (Dataset 2). The available video segments were produced using trace files of several DASH streams from [36]. Table 3 presents the client/server settings used to evaluate the algorithms. The experiment was repeated 10 times for each setting, and the average of the results is presented in this section. The videos were streamed for 10 min to evaluate the algorithms. As the experiments detailed in Table 3 were repeated, the performance of each algorithm was analyzed for 500 min. In the following results, we calculated QoE using Equation (4), and bandwidth inefficiency at time \(t\) was calculated with \(\frac{\left|\sum_{i}R_{i}^{(t)}-W\right|}{W}\)[7], where \(W\) is the total bandwidth available at the base station.
# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 2 Materials and Methods
### Machine Learning-Based Filter Bank (MLFB) Algorithm
#### 2.3.3 Evaluation of the Performance of MLFB Algorithm  
The signal reconstructed by rPPGMLFB was then subjected to band-pass filtering (BPF), which filtered the spectral components in the frequency band 0.8 to 4.2 Hz, containing a large proportion of rPPG signals, to remove frequency noise outside the bpm. The filtered signal over the entire time period of the dataset was calculated using a fast Fourier transform in the form of bpm by multiplying the frequency corresponding to the most significant energy peak by a sampling of 60 Hz. The mean absolute error (MAE) compared with the HR obtained from cPPG was used (as a reference) for validation. The HR was computed at concatenated intervals of 20, 60, and 100 s. After a 0.8 to 4.2 Hz BPF of the cPPG signal, the frequency at which the dominant power was determined was set to the HR at each interval. Here, MAE denotes the difference between the predicted HR\({}_{\text{rPPG}}\) and reference HR\({}_{\text{cPPG}}\) and was computed as  
\[\text{MAE}=\frac{1}{N}{\sum\limits_{i=1}^{N}}|HR_{rPPG}(i)-HR_{cPPG}(i)| \tag{4}\]  
where \(N\) denotes the number of concatenated intervals. To evaluate the level of signal quality, the SNR was evaluated using the average power density approach, as follows:  
\[\text{SNR}_{density}=10\log_{10}\frac{\sum_{f=0.8}^{4.2}U(f)\hat{S}(f)}{\sum \
olimits_{f=0.8}^{4.2}\left(1-U(f)\right)\hat{S}(f)}\times\frac{\sum_{f=0.8} ^{4.2}1-U(f)}{\sum\
olimits_{f=0.8}^{4.2}U(f)} \tag{5}\]  
where \(f\) is the frequency ranging from 0.8 to 4.2 Hz, \(\hat{S}(f)\) is the spectral power of rPPG, \(U(f)\) is a binary window that contains the signal frequency component, and \(1-U(f)\) is a binary window that contains the noise frequency component [12]. A binary window for the signal was centered based on the frequency analysis of cPPG, with the bandwidth set to 0.08 Hz.  
The performance of our approach was compared with four different noise reduction algorithms: no pre-processing (none), BPF, ASF, and the MLFB algorithm. \"None\" refers to the application of the POS algorithm without noise reduction preprocessing. We applied these noise-reduction techniques to six different pulse extraction algorithms for rPPG signals: G [4], G-R [28], PCA [15], ICA [14], CHROM [12], and POS [13].
# Demand Forecasting Model for Airline Flights Based on Historical Passenger Flow Data
## 2 Materials and Methods
### Algorithm for Medium-Term Flight Demand Forecasting
The diagram below introduces the following designations:  
Historical_Demand_Rate is the historical passenger demand; Capacity is the capacity of the aircraft operating the flight; Adjusted_Demand_Rate is the forecast demand; Macroeconomic_Factor is the additive function of the influence of macroeconomic factors; A_D_Population is the weighting factor of the influence of the population values of the departure and arrival points, respectively; A_D_GRP is the weight coefficient of influence of the gross regional product values of the departure and arrival points, respectively; and A_D_AMI is the weight coefficient of influence of the median average per capita income values of the departure and arrival points, respectively. Based on the modeling results, the forecast of demand distribution should be determined with discreteness for each hour of the forecast period from 2025 to 2029. To ensure the accuracy of this approach, statistical data on the demand for turnaround flight direction will be employed for validation purposes. Therefore, it is recommended that data be collected on the distribution of historical passenger demand and flight departure times for a single airline destination from 2019 to the first half of 2024. Subsequently, the findings on the forecast of demand distribution up to 2029 and an evaluation of the resulting forecast are presented.
# Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression
## 4 Experimental Results
### Performance Analysis of End-to-End Rate Control  
The Vimeo-90k [34] and BVI-DVC [35] datasets are used to train the two-branch residual-based network and two-branch regression network. The iterations are set as \(2\times 10^{5}\), and the initial learning rate is set as \(1\times 10^{-4}\). After \(8\times 10^{4}\) iterations, the learning rate decreases to \(1\times 10^{-5}\) via exponential decay. With respect to the proposed end-to-end rate control, [PERSON] et al. [22] used the novel R-D-\(\lambda\) model to derive an allocated bit for learned rate control, and [PERSON] et al. [9] used Lagrange multiplier \(\lambda\) to control bit rates, which are used for comparisons. In total, 100 frames were used to encode every test sequence. DCVC is used as an anchor, and four rate distortion (RD) points are selected, which are \(\lambda\) = 256, 512, 1024, and 2048. The bit rate accuracy is defined as  
\[\text{M}=\frac{\left|R-\hat{R}\right|}{R} \tag{18}\]  
where \(R\) is the target bit rate, and \(\hat{R}\) is the actual bit rate. The bit rate accuracy results are shown in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline Operating System & Windows 11 \\ CPU & i7-14700 kf \\ GPU & GeForce RTX 4080 super \\ RAM & 32 GB \\ Deep Learning Software & Pytorch \\ CUDA Version & 12.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental platform configuration. Figure 7: Upsampling network. It is observed from Table 2 that the average bit rate accuracy results are 2.62%, 3.89%, 5.93%, and 2.25%, respectively. The control accuracy of the proposed algorithm is better than the others. Since control of the bit rate is a highly challenging task for end-to-end coding, the accuracies of the four algorithms remain high. The coding quality comparisons are shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**DecVC**} & **[PERSON] et al. [22]** & **[PERSON] et al. [9]** & **Proposed** \\ \cline{2-5}  & & **M\%** & **M\%** & **M\%** & **M\%** \\ \hline Class A1 & 4.21 & 5.41 & 7.60 & 2.13 \\ \hline Class A2 & 4.18 & 5.38 & 7.56 & 2.12 \\ \hline Class B & 1.43 & 3.24 & 6.27 & 3.36 \\ \hline Class C & 1.31 & 2.75 & 4.71 & 2.80 \\ \hline Class D & 2.65 & 3.24 & 3.83 & 1.76 \\ \hline Class E & 1.95 & 3.30 & 5.60 & 1.35 \\ \hline
**Average** & **2.62** & **3.89** & **5.93** & **2.25** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Bit rate accuracy comparisons of DCVC: [PERSON] et al. [22], [PERSON] et al. [9], and the end-to-end rate control.
# Experimental Investigation of Mechanical Properties of Clay-Cement Slurry Containing Graphene Oxide
## 3 Results
### Anti-Permeability Performance of Grouting Reinforcement
#### 3.2.2 Variance Analysis of Permeability Coefficient Test Results  
To further evaluate and verify the effects of OPC content (Factor A), GO content (Factor B), and gradation (Factor C) on the permeability coefficient of the grout-strengthened body, a variance analysis was conducted on the results obtained from the nine sets of experiments. The results are shown in Table 7.  
The F-value is the ratio of the two mean square deviations. The error sums of squares and degrees of freedom are 2.605 and 2, respectively. By consulting the F-distribution table for a degree of freedom of (2,2) for significance levels of 0.1, 0.05, and 0.01, we can determine that F0.1(3,3) = 9, F0.05(3,3) = 19, and F0.01(3,3) = 99. From Table 7, it can be seen that Factor A satisfies both F0.1(2,2) and F0.05(2,2), while none of the factors satisfy F0.01(3,3). Comparing the F-values, it can be that the influence of GO content (Factor B) is greater than the influence of gradation concluded (Factor C). Based on a comprehensive analysis, it can be concluded that the OPC content (Factor A) has a significant influence on the permeability  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{3}{c}{**K/m\(\cdot\)s\({}^{-1}\)**} \\ \cline{2-4}  & **OPC/\%** & **GO/wt.\%** & **n** \\ \hline \(Q_{1}\) & 6.12 & 12.14 & 14.70 \\ \(Q_{1}\) & 10.46 & 14.78 & 13.77 \\ \(Q_{1}\) & 24.50 & 14.16 & 12.61 \\ \(R\) & 6.13 & 0.88 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Range analysis of permeability coefficient of grouting reinforcement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **\(\Delta\sigma\)/MPa** & **\(\Delta\)/s** & **Q/m\({}^{3}\)** & **Permeability Coefficient/ 10\({}^{-7}\)m\(\cdot\)s\({}^{-1}\)** \\ \hline
1-1 & 0.173 & 13 & 5,00 \(\times\) 10\({}^{-9}\) & 1.11 \\
1-2 & 0.153 & 10 & 8.21 \(\times\) 10\({}^{-9}\) & 2.68 \\
1-3 & 0.137 & 12 & 7.66 \(\times\) 10\({}^{-9}\)  
coefficient of the grout-strengthened body. The GO content (Factor B) also demonstrates a relatively significant impact. In contrast, the gradation of materials (Factor C) shows no significant influence on the permeability coefficient of the grout-strengthened body.
# Evaluating Uncertainties in Marine Biogeochemical Models: Benchmarking Aerosol Precursors
## 2 Methods
The reliability of results generated in a model-data benchmarking exercise is directly connected to the quality of the benchmarking dataset. Therefore, it is worth mentioning that there could be the possibility of inherent uncertainty in the Lana DMS observational data either through sampling and analytical complications or extrapolation procedures employed to calculate the climatology from the scarce data points. This uncertainty could be partly responsible for DMS ocean concentration bias, especially in the polar seas. We computed a referenced sea-air DMS flux dataset using the Schmidt equation described in [PERSON] et al. [67] and sea-air transfer coefficient in [PERSON] et al. [68]. The dataset is available at the IOMB-Data page and also available from the authors on request. This benchmark DMS flux dataset was used to evaluate sea-air sulfur emission results directly from the ESMs (DMS Model Flux-DMF). In addition, we also used the generated dataset to evaluate estimated sulfur emission (DMS Computed \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Temporal Coverage** & **Spatial Coverage** & **Variables** & **References** \\ \hline WOA & Monthly Climatology & Globally Gridded and Layered & no3, p04, si, o2, sos, tos & [61; 62] \\ SeaWIFS & Monthly Climatology & Globally Gridded & chl & [63] \\ NCEP Reanalysis & Monthly Climatology & Globally Gridded & stcWind & [64] \\ Boyer Montegut & Monthly Climatology & Globally Gridded & omlmax & [65] \\ OSU & Monthly Climatology & Globally Gridded & intpp & [66] \\ Lana & Monthly Climatology & Globally Gridded & dms & [14] \\ Ogunro & Monthly Climatology & Globally Gridded & fgdms & This work \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Observational dataset incorporated into International Ocean Model Benchmarking (IOMB) to benchmark various model outputs. All of the ocean data were encoded in the four-dimensional climate and forecast (CF) compliant format. All of the variable naming and abbreviation follows CMIP5 convention [60]: Dissolved nitrate (no3), Dissolved phosphate (po4), Dissolved silicate (si), Dissolved oxygen (o2), Sea surface salinity (sos), Sea surface temperature (tos), Chlorophyll (chl), Near-surface wind speed (stcWind), Maximum ocean mixed layer thickness (omlmax), Primary organic carbon production (intpp), Dimethylsulfide (dms), Ocean surface upward dms flux (fgdms). Flux-DCF), which we computed offline using similar flux equation with respective model variables of sea surface temperature and wind speed. Model outputs from the CMIP5 class and Department of Energy models were assessed for this period (Table 2). Emphasis is placed upon the trace nutrients (and implicitly their sources), since they are the primary drivers of overall primary production. However, aerosol precursor generation may have phytoplankton class dependence, and this is particularly true for the sulfur compounds. Our analyses focus on only four ESMs that contain prognostic marine and atmospheric DMS concentrations. Two of these models participated in the CMIP5 simulations.