esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Validation  
The VPM model was run for the same two years to produce the corresponding GPP data. The observed and modeled GPP results for both 2008 and 2009 were compared to check VPM's applicability for the Zoige alpine wetland. To analyze the correspondence between the simulated and observed results, we adopted three statistical criteria for the validation tests: the coefficient of determination (\(R^{2}\)), the root of mean square error (RMSE), and the relative mean deviation (RMD). Each criterion investigates a specific aspect of the correlation.  
Seasonal patterns of GPP from this ecosystem for the observed and VPM modeled outputs were validated by [PERSON] _et al._[PERSON] _et al._ (2018). Comparisons between the simulated and observed eight-day GPP fluxes indicated that the VPM model captured the seasonal variations of observed GPP for the simulated two years reasonably well. The model creditably captured the U pattern of the plant growth driven by the temperature variation. In addition, the linear regression analysis of the modeled and observed GPP showed a general clustering of the data along an approximate 1:1 line with a strong correlation (\(R^{2}\) = 0.77, \(p\) < 0.0001). The RMSE and RMD values were 9.74% and \(-\)10.67%, respectively. In general, by comparing the modeling results with the observations, we gained confidence in the applicability of the VPM model for this alpine wetland ecosystem. We then upscaled the VPM model to examine the long-term variability and changes in climate, vegetation phenology, and gross primary production for the Zoige alpine wetland over the 2000-2011 period. By running the VPM model for this 12-year baseline climate scenario, we obtained seasonal and annual EVI, LSWI, GPP, and land surface phenology dynamics for each year.
## 3 Methods
### Evaluation Metrics  
The trained models were evaluated using the following performance and efficiency metrics to assess their effectiveness in digit recognition tasks:  
* Accuracy: Measures the overall proportion of correctly predicted digits out of all predictions, providing a general sense of model performance.
* Precision: Quantifies the proportion of true positive predictions among all predicted positives, indicating the model's ability to avoid false positives.
* Recall: Also known as sensitivity, which evaluates the proportion of true positives captured out of all actual positive cases, reflecting the model's ability to detect all relevant digits.
* F1-score: The harmonic mean of precision and recall, offering a balanced metric that is especially useful when dealing with class imbalance.
* Model size: Used to assess the memory footprint of each trained model, which is crucial for deployment on resource-constrained devices.  
* Inference time: Refers to the amount of time the model takes to process an input and produce an output, highlighting the model's efficiency and suitability for real-time applications. Accuracy, precision, recall, and F1-score are formulated below. _TP_, _TN_, _FP_, and _FN_ refer to true positive, true negative, false positive, and false negative, respectively. Accuracy \[=\frac{TP+TN}{TP+TN+FP+FN}\] (1) Precision \[=\frac{TP}{TP+FP}\] (2) Recall \[=\frac{TP}{TP+FN}\] (3) F1-score \[=\frac{2\cdot\text{Precision}\cdot\text{Recall}}{\text{Precision}+ \text{Recall}}\] (4)
# Bridge Crack Detection Based on SSENets
## 2 Materials and Methods
### Skip-Squeeze-and-Excitation Module  
To alleviate the vanishing gradient problem with the increase of the depth of the model, we design the embedded SSE module based on the skip-connection strategy, the structure of which is shown in Figure 3. F\({}_{tr}\) refers to any matrix transformation in the network. The feature map FM\({}_{i}\in\mathbb{R}^{H_{i}\times\text{W}_{j}\times C_{i}}\) can be obtained by F\({}_{tr}\), where \(i\leq n\), \(n\) is the total number of convolutional layers in the network. F\({}_{sq}\) represents the squeeze operator in the SSE module. The input of the Squeeze operation is the feature map FM\({}_{i}\), and its spatial dimensions of each channel will be aggregated to get the channel-wise descriptor d \(\in\mathbb{R}^{C_{i}}\). F\({}_{ex}\) represents the excitation operator in the SSE module. The excitation operator maps the input channel-wise descriptor d to a set of channel weights d\({}^{\prime}\in\mathbb{R}^{C_{j}}\), the channel number of which is the same as the output feature map FM\({}_{j}\in\mathbb{R}^{H_{j}\times\text{W}_{j}\times C_{j}}\). Then select the feature map FM\({}_{j}\in\mathbb{R}^{H_{j}\times\text{W}_{j}\times C_{j}}\) obtained by the j-th convolutional layer, multiply with channel weights d\({}^{\prime}\). During the training process, the channel weight d\({}^{\prime}\) is adjusted continuously, and each channel of FM\({}_{j}\) is recalibrated, so as to enhance the learning capability of the module.
## 3 Chidroid Architecture
### Configuration Files (Conf.Toml, Rules.Toml, Certs, and Manifest Files)  
There are two main Chidroid configuration files included in the APK: (i) \"config.toml\" and (ii) \"rules.toml\". The first (config.toml) is used to set up sources and sinks (outputs), while the second (rules.toml) is used to define the privacy rules and to set up the needed transformations to parse the sources to the required format. Tom's Obvious, Minimal Language (TOML ([[https://toml.io/en/v1.0.0](https://toml.io/en/v1.0.0)]([https://toml.io/en/v1.0.0](https://toml.io/en/v1.0.0))), accessed 20 February 2023) is a file format used to represent configuration data in a human-readable and easily editable format.  
TOML files are commonly used in software development to store configuration data for applications and other tools. The files use a simple and consistent syntax, with key-value pairs separated by an equal sign (=) and sections indicated by square brackets ([l and l]). A generic example is provided in Listing 2 and contains two stanzas (i.e., block of text within a configuration file). In this example, the server section includes a single option, \"port\", which specifies the port number on which the server will listen for incoming connections.  
```
1[server]
2 port=8080
3
4[database]
5 host=\"localhost\"
6 user=\"admin'
7 password=\"secret\"
```  
Listing 2: Example structure of TOML files.
# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
## 5 Results and Discussion
### Performance Metrics  
The classification between the drivable area and non-drivable area using the TLFC-RD method was analyzed by using different metrics: accuracy, sensitivity or recall, specificity, precision, F1-measure and max F measure. The performance metrics are described as follows.  
a. Accuracy  
Accuracy represents the classification capacity between the different classes, and this accuracy (ACC) is expressed in Equation (22).  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN} \tag{22}\]  
b. Sensitivity or recall  
Sensitivity or recall was used to calculate the capacity to search for the positive class, and the recall is expressed in Equation (23).  
\[Recall=\frac{TP}{TP+FN} \tag{23}\]  
c. SpecificitySpecificity represents the proportion of the negative class discovered during road detection, and this specificity is expressed in Equation (24).  
\[Specificity=\frac{TN}{TN+FP} \tag{24}\]  
Precision (PRE) was used to calculate the capacity of the model for categorizing the positive class, and this precision is expressed in Equation (25).  
\[PRE=\frac{TP}{TP+FP} \tag{25}\]  
F1-measure and max F value  
The harmonic mean between the recall and precision is defined as the F1-measure, which is expressed in Equation (26). The selection of the classification threshold \((\beta)\) was used to compute the max F, which is expressed in Equation (27).  
\[F1-measure=\frac{2\times PRE\times Recall}{PRE+Recall} \tag{26}\]  
\[MaxF=\underset{\beta}{argmaxF1-measure} \tag{27}\]
# Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions
## 1 Introduction
Based on the critical literature review, it appears that very few studies exist for the spherical shells that consider both external pressure and collision [42; 43; 44]. Therefore, this present paper is the first to reveal the dynamic buckling behavior for spherical shell structures experiencing the hydrostatic pressure and the concentrated dynamic force induced by subsea collision. This study employs ANSYS18.0 and LS-DYNA to establish the static and dynamic models, respectively. Parametric analysis has been carried out to determine and validate an explicit formulation for static critical buckling load. To obtain the relationship between dynamic buckling force and the static buckling force, the max and mean dynamic force over the duration of the collision are analyzed. The effects of the material and geometrical properties, external pressure, and added mass of the spherical shells are then investigated and discussed. Figure 1: The collision of exploration under water. (**a**) Exploration vehicle with ship; (**b**) Exploration vehicle under the sea.
## 3 System Architecture
Consequently, the data rate becomes (512/1024) \(\times\) 40 = 20 Gbps. To prevent intersymbol interference (ISI), a cyclic prefix of 100 samples is added after the Inverse FFT (IFFT) process. The proposed system supports \(N=32\) wavelengths over a WDM FSO link (32 \(\times\) 20 Gbps), covering frequencies between 193.1 THz and 194.65 THz (i.e., 1552.52 nm to 1540.16 nm). This yields a total capacity of 640 Gbps. Prior to transmission, pulse shaping is performed with cosine roll-off filters to further minimize ISI. The generated QAM-OFDM electrical signal then drives a Mach-Zehnder Modulator (MZM) that impresses it onto a laser beam for each WDM channel. All 32 wavelength channels (each 20 Gb/s) are optically combined, boosted by a gain-flattened EDFA (optical amplifier) to about 10 dBm launch power, and sent into the FSO channel. The FSO transmitter uses a collimating telescope or lens (\(\approx\) 5 cm aperture) to direct the multi-wavelength optical beam toward the remote receiver through the air. At the receiver end, a collecting telescope (\(\approx\) 20 cm aperture) gathers the incoming optical beam. The combined WDM signal is then demultiplexed by a DeMUX/AWG, where each wavelength is routed to its corresponding receiver. At the receiver, a PIN photodiode converts the received optical signal into an electrical RF signal, which is amplified by a low-noise amplifier to boost the weak photocurrent. In each channel's receiver, the electrical signal is fed to an OFDM demodulator, which performs FFT to recover the subcarriers and then de-maps the QAM symbols. These symbols are mapped back to bits, and finally, the stream passes through an LDPC decoder (using iterative belief-propagation) that corrects errors caused by noise or fading. Key hardware components include the optical transmit/receive lenses (antennas) for beam launching and collection, the EDFA, which counteracts propagation loss by amplifying the optical signal before free-space transmission, and the electrical amplifiers at the receiver to strengthen the detected signal. Each component interconnects in a pipeline: the LDPC-coded, OFDM-modulated signal is carried on an optical carrier (via MZM and laser) over the FSO link, and the reverse process (photodetection, OFDM, and LDPC decoding) retrieves the original data at the far end. This design leverages the enormous WDM-PON capacity and extends it wirelessly via FSO, while LDPC and OFDM ensure the link meets the high reliability and throughput needed for C-RAN fronthaul.
## 4 Results and Discussion
### Compression Results
#### 4.3.1 Longitudinal Modulus  
Elastic modulus was determined for each of the specimens by fitting a line to the initial linear portion of the stress-strain response. Figure 11 shows a comparison between experimental data and predicted values. On this plot, it is clear that, while there is some variation from specimen to specimen, the experimental data agrees well with that predicted. Note that due to the variation in results, these data were normalized against a reference material properties drawn from the PLA filament manufacturer's published modulus and density data (rather than against measured values from the 100% infill test-specimens).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nominal** & **Wall Thicknesses** & **Avg Relative** & **Londitudinal** & **Poisson’s** \\
**Relative Density** & **Wall Thicknesses** & **Density (\%)** & **Modulus (MPa)** & **Ratio** \\ \hline
100\% & - & 94 & 2300 & 0.25 \\
75\% & Single-bead & 59 & 639 & 0.18 \\
75\% & Double-bead & 75 & 1420 & 0.28 \\ \hline
50\% & Single-bead & 47 & 490 & 0.24 \\
50\% & Double-bead & 51 & 520 & 0.30 \\
25\% & Single-bead & 22 & 145 & 0.19 \\
25\% & Double-bead & 25 & 191 & 0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of experimental results.  
Figure 10: Longitudinal stress–strain curves from the compression experiments.  
It is also worth noting that for both modulus and Poisson's ratio, the \(z\)-orientation (default) specimens vary only slightly from their \(x\)- and \(y\)-orientation counterparts at 50% relative density.
## 3 Reduced Mass Flow Fitting
### Dual-Volute Turbine  
In dual-volute turbine, the flow path is designed in a way that each entry feeds separate sections of the rotor, due to the different volute lengths. Based on this different flow paths, the stator and rotor geometrical areas are estimated in different ways (see Figure 10). Doing this, it is appropriate to determine the mass flow parameter through the long and short volutes.  
As the flow is expanding at different sections of the rotor (Figure 10), the stator area is estimated by blade height (\(Bh\)) and half the wheel length (\((\pi/2)\cdot D_{3}\)) for each volute, as shown in Equation (16). Similarly, the rotor outlet area of each volute is also determined by considering half the value of entire area, which is estimated using Equations (A4) and (17). It is due to the flow coming from each volute is not expanding through the entire circumference of the rotor. The rotor outlet diameter (\(D^{DV}_{4m,i}\)) is estimated in the same way as in vaneless turbines, as shown in Equation (11).  
\[A^{DV}_{3,i}\;{}^{geom}=\pi\cdot\left(\frac{D_{3}}{2}\right)\cdot Bh^{DV} \tag{16}\]  
\[A^{DV}_{4,i}\;{}^{geom}=\frac{A^{\text{geom}}_{4}}{2} \tag{17}\]  
Once the effective equivalent nozzle area (Equation (8)) of each turbine entry of the turbocharger T#ITE and T#2 DV is known, the reduced mass flow parameter in each branch can be calculated using the expression of the flow through an orifice with an isentropic expansion, as shown in Equation (18).  
\[\dot{m}^{j}_{red,i}=A^{j}_{eff,i}\cdot\sqrt{\frac{\gamma}{R}}\cdot\left(\frac{ 1}{\Pi^{j}_{i,(0,4)}}\right)^{\frac{1}{\gamma}}\cdot\sqrt{\frac{2}{\gamma-1} \cdot\left[1-\left(\frac{1}{\Pi^{j}_{i,(0,4)}}\right)^{\frac{2-1}{\gamma}} \right]} \tag{18}\]
# Simultaneous Electrochemical Removal of Selenium and Strontium from Aqueous Solution  
[PERSON]  
1 Faculty of Civil Engineering, University of Zagreb, Fra [PERSON] 26, 10000 Zagreb, Croatia; [EMAIL_ADDRESS] (H.P. ); [EMAIL_ADDRESS] (D.N.) 1 Faculty of Civil Engineering, University of Zagreb, Fra Andrije Kacica-Miosica 26, 10000 Zagreb, Croatia; [EMAIL_ADDRESS] (H.H. ); [EMAIL_ADDRESS] (H.P. ); domagoj.nakic@grad. disadvantages such as incomplete removal, sensitivity to competing ions, fouling, slow operation, large spatial requirements, high costs, complex maintenance, and substantial secondary waste production [4; 15]. These limitations have spurred interest in alternative treatment methods. Electrochemical methods, particularly electrocoagulation (EC) and electrochemical reduction, offer promising alternatives due to their efficiency, cost-effectiveness, and environmental compatibility [16; 17]. EC uses an electric current to dissolve sacrificial electrodes that generate coagulants in situ, and these coagulants facilitate the precipitation of various contaminants from water [18; 19; 20; 21]. This process is also effective for strontium removal, where it can precipitate strontium in forms that are easily separable from water [15]. Furthermore, EC was shown to effectively remove selenium, transforming it from its dissolved forms to less soluble ones [3]. EC offers additional advantages, including operational simplicity, chemical-free treatment, reduced secondary waste, and potential for automation [22]. However, its efficiency is influenced by factors such as electrode material, current density, and water chemistry [4; 15]. Table 1 provides a review of studies that used EC to treat various wastewaters that contained Se and Sr. While high removal efficiencies are frequently reported, these studies were typically conducted on small volumes (lab-scale) and at low initial Se and Sr concentrations. The pH of the water significantly impacts the chemical forms, solubility, and environmental behavior of selenium and strontium. Se exists in various forms depending on the pH: selenate (SeO\({}_{4}^{2-}\)), selenite (SeO\({}_{3}^{2-}\)), elemental selenium (Se\({}^{0}\)), and selenide (Se\({}^{2-}\)) [3; 28; 29]. Selenate remains soluble across a wide pH range, while selenite's solubility decreases with lower pH, making it less mobile in acidic conditions [3]. Strontium behaves similarly to calcium, forming carbonates and sulfates. Its compounds are more soluble in acidic environments, leading to increased dissolution, whereas alkaline conditions promote precipitation as strontium carbonates, thereby limiting mobility [15; 30]. Adjusting the pH is an effective strategy to control the mobility of these elements in aquatic systems and enhance their removal from water and wastewater. To avoid the unnecessary addition of chemicals, the pH value of the treated water can be adjusted through electrochemical pH control [31; 32; 33]. The main principle of controlling the pH through water electrolysis involves the electrochemical decomposition of water (H\({}_{2}\)O) into its constituent elements--hydrogen (H\({}_{2}\)) and oxygen (O\({}_{2}\))--by passing
## 3 Results
### Low-Salinity Shock Resistance  
After a salinity shock challenge consisting of abrupt transfer and maintenance in freshwater for 24 h (Figure 3, by Kaplan-Meier analysis), no statistical differences were found between treatments. The average cumulative survival during the 24 h following the challenge was 42.50 \(\pm\) 5.0% for STD, 50.0 \(\pm\) 14.14% for STD+1, and 45.0 \(\pm\) 26.46% for STD+2 (one-way ANOVA, \(p\) = 0.8206).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Treatments** & **THC (10\({}^{6}\))** & \begin{tabular}{c} **Total Protein** \\ **Concentration (mg mL\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **PO Activity (U min\({}^{-1}\) mg\({}^{-1}\)** \\ **of Protein)** \\ \end{tabular} &
\begin{tabular}{c} **Agglutination** \\ **Titer (Log2)** \\ \end{tabular} \\ \hline \multicolumn{5}{c}{**Time 0 h**} \\ STD & 12.06 \(\pm\) 6.96 \({}^{\rm b}\) & 197.95 \(\pm\) 14.34 & 55.18 \(\pm\) 12.07 & 11.50 \(\pm\) 0.58 \\ STD+1 & 29.97 \(\pm\) 5.64 \({}^{\rm a}\) & 186.88 \(\pm\) 17.05 & 39.19 \(\pm\) 15.48 & 11.00 \(\pm\) 0.82 \\ STD+2 & 15.54 \(\pm\) 6.67 \({}^{\rm b}\) & 205.66 \(\pm\) 10.62 & 62.19 \(\pm\) 42.13 & 11.50 \(\pm\) 1.00 \\ \multicolumn{5}{c}{**Post 24 h Low-Salinity Shock**} \\ STD & 6.32 \(\pm\) 1.16 \({}^{\rm b}\) & 10.68 \(\pm\) 1.65 & 84.17 \(\pm\) 32.44 & 10.75 \(\pm\) 1.26 \\ STD+1 & 6.49 \(\pm\) 2.05 \({}^{\rm b}\) & 14.03 \(\pm\) 3.91 & 56.59 \(\pm\) 17.51 & 9.00 \(\pm\) 2.71 \\ STD+2 & 8.84 \(\pm\) 6.08 \({}^{\rm b}\) & 10.89 \(\pm\) 1.93 & 80.30 \(\pm\) 15.10 & 10.00 \(\pm\) 1.15 \\ \hline \multicolumn{5}{c}{ANOVA Factorial} \\ Time & *** & *** & * & ns \\ Treatment & ** & ns & ns & ns \\ Time \(\times\) Treatment & ** & ns & ns & ns \\ \hline \hline \end{tabular}  
* Mean \(\pm\) SD. Data were analyzed with a two-way ANOVA followed by Tukey’s test, both at the 5% significance level. Different superscript letters denote statistical significance between treatments at 0 h and 24 h time points (\(p\) < 0.05). Abbreviations: ns = not significant.
* = \(p\) < 0.05; *
* = \(p\) < 0.01; **
* = \(p\) < 0.001.  
\end{table}
Table 4: The hemato-immunological parameters of _P. vannamei_ reared in clear water at a suboptimal temperature (22 \({}^{\circ}\)C) and fed diets containing 0 (STD), 1 (STD+1), and 2 g kg\({}^{-1}\) (STD+2) of a functional palatability enhancer (FFE) over twelve-weeks, immediately before (0 h) and 24 h after a low-salinity shock.  
Figure 2: The total heterotrophic bacteria count (\(p\) < 0.01) and _Vibrio_ spp. (\(p\) < 0.01) in the midgut of _P. vannamei_ reared in clear water at a suboptimal temperature (22 \({}^{\circ}\)C) and fed diets containing 0 (STD), 1 (STD+1), and 2 g kg\({}^{-1}\) (STD+2) of a functional palatability enhancer (FPE) for twelve weeks. Data were analyzed using one-way ANOVA followed by Tukey’s test, both at a 5% significance level.
# Multifunctional Integrated Underwater Sound Absorption Materials: A Review
## 4 Summary of Research on USAMs with Hydrostatic Pressure Resistance and Corrosion Resistance  
### Preparing Multifunctional USAMs with Hydrostatic Pressure Resistance and Corrosion Resistance  
In recent years, multifunctional USAMs with hydrostatic pressure resistance and corrosion resistance have attracted increasing attention and experienced rapid development. These materials have various applications in underwater acoustics, such as in underwater communication systems and oceanographic research. Figure 8 illustrates the preparation process of these materials, which requires interdisciplinary research in materials science, acoustics, physics, electricity, mechanics, and other fields. The preparation of multifunctional USAMs with hydrostatic pressure resistance and corrosion resistance typically involves a multi-step process that may vary depending on the specific material being developed. The general steps involved in the preparation of such materials are as follows: firstly, a selection of suitable intrinsic acoustic materials, which should be able to resist corrosion and the filler materials, should provide the necessary strength and stiffness to resist hydrostatic pressure. Secondly, material characterization must be considered. Before the actual preparation of multifunctional USAMs, the intrinsic materials should be characterized to determine their properties, such as mechanical strength, acoustic properties, and corrosion resistance. This will help to determine the ideal composition and processing conditions for the material. Thirdly, the fabrication process should be optimized to ensure that the final product has the desired properties and is free from defects. Fourthly, the final USAMs must then be subjected to a series of tests to evaluate their performance, including hydrostatic pressure resistance, corrosion resistance, and sound absorption properties. This may involve subjecting the material to simulated underwater conditions and measuring its acoustic properties under various conditions. Finally, based on the test results, the composition and processing conditions of the material may be optimized to improve its performance. The material can then be scaled up for production and further testing in real-world applications.