esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Chemical Composition
#### 3.2.2 Chemical Composition of NSDD  
Using the methodology described in Section 2.4, the chemical composition of non-soluble deposit density (NSDD) was determined for the eighth campaign. As explained in Section 1, NSDD is mainly made up of dust, sand, clay, and water-insoluble organic material.  
Figure 7 shows the total composition (SDD+NSDD) for macroregions 1, 2, and 3. For MR1, the water-soluble fraction represented 46.9 \(\pm\) 22.0%, while 16.6% \(\pm\) 12.6% is represented by the carbonaceous fraction (WINSOM+BC) and the remaining 36.5 \(\pm\) 27.3% is represented by an unknown fraction (UF_NSDD). For MR2, the water-soluble fraction represented 57.6 \(\pm\) 11.0%, while 13.7% \(\pm\) 4.4% is represented by the carbonaceous fraction and 28.7 \(\pm\) 15.3% is represented by the UF_NSDD. MR3 is represented 67.2 \(\pm\) 14.0% by the water-soluble fraction, 17.7 \(\pm\) 5.1% by the carbonaceous fraction, and 15.1 \(\pm\) 8.5% by the unknown fraction.  
Previous research has already focused on studying the chemical composition of insoluble aerosol compounds. For example, [PERSON] et al. [39] studied two monitoring points, both next to a highway in South China; they found a value of NSDD of 1.79 \(\times\) 10\({}^{-2}\) and 9.70 \(\times\) 10\({}^{-2}\) for the two sampling points. The insoluble contamination of both monitoring points is mainly made up of SiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\), CuO, Al\({}_{2}\)O\({}_{3}\), Pb, CaSO\({}_{4}\), and CaO. These results are in agreement with the composition of insoluble contamination measured by [PERSON] et al. [40] in China. They found a great amount of SiO\({}_{2}\), followed by Al\({}_{2}\)O\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\), CaSO\({}_{4}\), and CaO. According to the aforementioned literature, it is reasonable to assume that the unknown fraction (UF_NSDD) mainly consists of crustal and metallic elements. Hence, to investigate this possibility, the samples were analyzed via energy dispersive X-ray micro-fluorescence analysis (ED-u-XRF) performed on the smaller 12 mm areas, which underwent TCA analysis, as described in Section 2.5. The main elements that resulted from XRF analysis were Al, Fe, K, Ca, and Mg with a relative proportion among them of 41.5 \(\pm\) 2.9%, 16.6 \(\pm\) 1.6%, 14.6 \(\pm\) 1.3% and 9.7 \(\pm\) 2.0%, and 6.5 \(\pm\) 1.6%, respectively. For the rest (percentage less than 13% in total), the contents were Ti \(>\) Zn \(>\) Pb \(>\) P \(>\) Mn \(>\) Cr \(>\) Cu \(>\) S (Supplementary Material, Table S6), The investigation of the unknown fraction (UF_NSDD) will be a subject of a more in-depth examination in future research endeavors. In the context of this study, the aim is to highlight that the obtained results are consistent with prior literature studies, indicating a crustal origin for the non-carbonaceous fraction of NSDD. This underscores the alignment of the obtained findings with established scientific knowledge.  
Figure 7: Total chemical composition (SDD and NSDD) for macroregions 1, 2, and 3 in the 8 th sampling campaign.
# Electrospun Porous PDLLA Fiber Membrane Coated with nHA
## 3 Results and Discussion
### Features of nHA/PDLLA Composite Fibrous Membranes with Different nHA Content  
#### 3.4.1 Morphologies of PDLLA/nHA Composite Fibrous Membranes  
Figure 6 illustrates SEM micrographs showing the morphologies of different PDLLA/nHA composite fibrous mats by immersing and shrinking the porous PDLLA and non-porous PDLLA membrane in the nHA particles dispersions with different nHA content (10 mg/mL, 5 mg/mL, 2.5 mg/mL, 1 mg/mL and 0.5 mg/mL). The mean diameters of composite fibers are around 1.2 \(\upmu\)m, except sample A (more than 2 \(\upmu\)m in diameter). The literature shows that hydroxyapatite does not show any degradation up to 1000 \({}^{\circ}\)C [34]. Pyrolysis of PLA was almost completed at 450 \({}^{\circ}\)C under nitrogen condition [35; 36]. Therefore, the nHA content of the different PDLLA/nHA composite fibrous membrane samples was studied with a TGA in an inert atmosphere, the results of which are shown in Table 1. Figure 6A shows that the fibers' surface of sample A was completely covered by nHA particles, with significant nHA agglomeration between fibers. The TGA indicates that the nHA content of sample A is 11.2 wt % (Table 1, sample A). The fibers' surface of sample B, which embedded 10.6 wt % nHA in the composite fibrous mats, was also completely covered with nHA particles. Tiny nHA agglomeration in the mats was still observed between fibers. From Figure 6C-E, parts of the fibers' surface were not completely covered by nHA, because of further reduction in concentration of nHA particles' dispersion. There was not enough nHA particles for adsorbing on the surface of porous PDLLA fibers in a certain amount of time.  
The nHA content of sample C was significantly increased compared with the control group (sample F in Figure 6 and Table 1), which was prepared by immersing non-porous PDLLA fibers in an nHA dispersion (2.5 mg/mL). And the sample C was produced by the same nHA dispersion (2.5 mg/mL). SEM indicates that there were few nHA particles adsorbed on the fibers' surface of non-porous PDLLA fibrous mats (Control group), and the nHA content of the composite mats was  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline nHA (wt \%) & 11.2 & 10.6 & 8.8 & 5.5 & 4.1 & 1.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: nHA content of PDLLA/nHA composite fibrous membranes.  
Figure 6: SEM of PDLLA/nHA composite fibrous membranes with different nHA content. Scale bars represent 1 \(\upmu\)m.  
only 1.9 wt %. (The nHA content of sample C is 8.8 wt %). The pore on the PDLLA fiber surface is the other key factor that influence the nHA adsorption. It is therefore demonstrated that the nHA particles' distribution and the content of PDLLA/nHA composite fibrous mats were both influenced by the pores on the PDLLA fibers and the concentration of nHA dispersion [37]. Nano-HA particles coated on the composite fibers surface will directly affect the surface wettability and mechanical property of PDLLA/nHA composite fibrous scaffold.
## 3 Methodology
### Decoding Stage  
The decoder building blocks highly follows concepts described in Section 3.2, i.e., it consists of FM-Pre-ResNet units. Every FM-Pre-ResNet unit in decoder doubles the spatial dimension while reduces the feature numbers by a factor of 2. Each decoder level is concatenated with the corresponding encoder output. The final layer of the decoder provides whole heart segmentation and has the same number of features and spatial size as the original input image.
## 4 Experiment and Analysis  
### Dataset and Experimental Environment  
In this study, we chose the FLIR and KAIST datasets for the exploitation of thermal infrared target detecting and the evaluation of algorithm performance.  
**FLIR dataset**: The FLIR dataset mainly contains thermal infrared images, which are images captured by thermal infrared cameras to capture the heat distribution of objects in the scene, making it able to provide unique information at night and in low-light conditions. The acquisition of infrared data was completed on streets and highways in California during both day and night, covering a range of scenes, involving but not confined to outdoor environments, day and night changes, buildings, and traffic scenes. We selected 10,228 frames of infrared images with annotated resolution \(640\times 512\) in the dataset, which included the categories person, bicycle, and car. To increase the diversity of the dataset and balance the sample distribution, we repartitioned the training dataset and the testing dataset, where the quantity of samples was 8862 and 1366, respectively. In addition, negative samples were introduced in the training phase to help the network study the background information of the object more accurately, increase the model's generalization capacity, and make the detecting model better-adapted to a variety of different scenarios in the testing phase.  
Figure 5: TAHNet structure rendering.  
Figure 6: Visual comparison between TAHNet and original detection head heat map.  
**KAIST dataset**[36]: The KAIST dataset contains 95,328 aligned thermal infrared and visible images, each with a resolution of 640 \(\times\) 480. With the intent of improving the training efficiency and reducing the risk of overfitting, the original dataset was cleaned to remove redundant or unnecessary samples. When training a model, a cleaner dataset can speed up the convergence process and reduce the overfitting of the model to noisy and abnormal conditions. The cleaned version of the dataset contained 9858 thermal infrared images with a 9:1 training and test set partition ratio, and we labeled the category \"pedestrian\". We experimentally verified that cleaning data increased the overall quality of the data, ensured that the dataset met the requirements of the task, and helped with more accurately evaluating model performance.  
Table 1 shows the configuration of the specific hardware devices used for model training and the evaluation framework of the software platform that provided environmental support.  
Hyperparameter initialization holds a key position in object detecting tasks, and setting the appropriate intensity can make the model learn more efficiently. The hyperparameter configuration in this study is shown in Table 2. The input sample size of the network was set to 640 \(\times\) 640; the learning rate and momentum were 0.01 and 0.9; the selection of regularization technology weight attenuation and batch size was 0.005 and 16, respectively; and the training cycle length of the model was 100 epochs.
# Carbazoles: Role and Functions in Fighting Diabetes
## 2 Carbazole Derivatives in the Pathogenesis of Diabetes
### Tetralhydrocarbazole Derivatives  
[PERSON] et al., in 2018 [37], developed a new series of tetrahydroocarbazoles and tested them in an in vitro assay on human hepatoma cell lines (HepG2) to assess their hypoglycemic activity. Several of the compounds tested exhibited significant activity, with carboxyl (9), 6-(benzyloxy)-9-(chlorobenzoyl)-2,3,4,9-tetrahydro-1\(H\)-carbazole-3-carboxylic acid (Figure 8) being the most promising. This compound was thus selected for further in vivo experiments. The results obtained confirmed that compound 9 has comparable hypoglycemic properties to that of pioglitazone and causes less weight gain compared to the drug in clinical use.  
A recent study [42], reported by the same above research group, led to the identification of an azatetrahydrocarbazole derivative, namely ethyl 8-(benzyloxy)-5-(4-chlorobenzoyl)-7-fluoro-3,4-dihydro-1\(H\)-pyrido[4,3-b]indole-2(5\(H\))-carboxylate (10) (Figure 8). This derivative showed hypoglycemic activity, in cell assays, approximately 1.2 times higher than that of metformin, used as a positive control. Studies aimed at elucidating the mechanism of action responsible for the hypoglycemic activity of these compounds have suggested  
Figure 8: Structures of: 6-(benzyloxy)-9-(chlorobenzoyl)-2,3,4,9-tetrahydro-1\(H\)-carbazole-3-carboxylic acid (9) and ethyl 8-(benzyloxy)-5-(4-chlorobenzoyl)-7-fluoro-3,4-dihydro-1\(H\)-pyridylo[4,3-b]indole-2(5\(H\))-carboxylate (10).  
Figure 7: Structures of: 2-(4-((9\(H\)-carbazol-9-yl)methyl)-1\(H\)-1,2,3-triazol-1-yl)-1-(3-bromo-4-hydroxyphenyl) ethanone (7) and 9-((1-(pyridin-3-yl-methyl)-1\(H\)-1,2,3-triazol-4-yl) methyl)-9\(H\)-carbazole (8).  
involvement of the AMP-activated protein kinase (AMPK). The AMPK-mediated pathway plays a key role in controlling the energy and metabolic homeostasis of cells, acting as a sensor for the cellular energy status [70,71].  
Compound **10** also showed appreciable chemical stability in gastrointestinal fluids and plasma in in vivo studies, which also confirmed a good tolerance of the compound after oral administration [38]. The promising hypoglycemic, pharmacokinetic, and pharmacodynamic properties of compound **10** validate the potential of carbazole as a versatile scaffold for the preparation of derivatives with improved bioactivities that can be useful in a variety of therapeutic applications.
###### Abstract  
Wearable sensor data can be integrated and interpreted to improve the treatment of chronic conditions, such as diabetes, by enabling adjustments in treatment decisions based on physical activity and psychological stress assessments. The challenges in using biological analytes to frequently detect physical activity (PA) and acute psychological stress (APS) in daily life necessitate the use of data from noninvasive sensors in wearable devices, such as wristbands. We developed a recurrent multi-task deep neural network (NN) with long-short-term-memory architecture to integrate data from multiple sensors (blood volume pulse, skin temperature, galvanic skin response, three-axis accelerometers) and simultaneously detect and classify the type of PA, namely, sedentary state, treadmill run, stationary bike, and APS, such as non-stress, emotional anxiety stress, mental stress, and estimate the energy expenditure (EE). The objective was to assess the feasibility of using the multi-task recurrent NN (RNN) rather than independent RNNs for detection and classification of AP and APS. The multi-task RNN achieves comparable performance to independent RNNs, with the multi-task RNN having F1 scores of 98.00% for PA and 98.97% for APS, and a root mean square error (RMSE) of 0.728 \(\frac{\text{cell}}{\text{exp}\text{kg}}\) for EE estimation for testing data. The independent RNNs have F1 scores of 99.64% for PA and 98.83% for APS, and an RMSE of 0.666 \(\frac{\text{cell}}{\text{exp}\text{kg}}\) for EE estimation. The results indicate that a multi-task RNN can effectively interpret the signals from wearable sensors. Additionally, we developed individual and multi-task extreme gradient boosting (XGBoost) for separate and simultaneous classification of PA types and APS types. Multi-task XGBoost achieved F1 scores of 99.89% and 98.31% for the classification of PA types and APS types, respectively, while the independent XGBoost achieved F1 scores of 99.68% and 96.77%, respectively. The results indicate that both multi-task RNN and XGBoost can be used for the detection and classification of PA and APS without loss of performance with respect to individual separate classification systems. People with diabetes can achieve better outcomes and quality of life by including physical activity and psychological stress assessments in treatment decision-making.  
multi-task learning; recurrent neural network; long short-term memory; acute psychological stress _classification;_ physical activity classification; energy expenditure estimation; wearable devices; diabetes +
Footnote †: journal: _Acticle_  
0023
## 4 Results and Discussion  
In the following paragraphs, the results of the offline estimation of the secondary paths and the results of the ANC application for each of the considered signals are illustrated. All the tests have been carried out with filters of a length of 1024 elements both for secondary paths and adaptive coefficients. The step-size parameter is kept the same for all the algorithms that require a fixed value (FF_FXLMS, FB_FXLMS, FF_FURLMS), while it changes with respect to the signal type and the location of the reference microphone. The choice of the fixed step-size parameters represents the maximum value, which guarantees the convergence for all the algorithms. The step-size parameter values are reported in Table 1; it is worth pointing out that the choice of the rate does not represent the best choice in an absolute sense for each algorithm. Moreover, Table 2 shows the values of the parameters used in the FF_FXNLMS algorithm (\(\mu_{max}\), \(\mu_{min}\), \(\mu_{in}\) and \(\beta\)) and the step size for the feedback coefficients updating (\(\mu_{b}\)) in the FF_FURLMS algorithm, that are maintained the same in all the tests.
# Sensitivity Analysis for Gaussian-Associated Features
## 4 Sensitivity Matrix Associated with a Least-Squares Circle Fit to Data in a Plane
Continuing in this way, let  
\[H_{\alpha}=\left[ \begin{array}{ccc}1/2+(\sin 2\alpha)/4\alpha&0&(\sin\alpha )/\alpha\\ 0&1/2-(\sin 2\alpha)/4\alpha&0\\ (\sin\alpha)/\alpha&0&1\end{array} \right], \tag{16}\]  
so that \(V_{\alpha}=H_{\alpha}^{-1}\) is given by  
\[V_{\alpha}=\frac{1}{D_{13}}\left[ \begin{array}{ccc}1&0&-(\sin\alpha)/ \alpha\\ 0&D_{13}/(1/2-(\sin 2\alpha)/4\alpha)&0\\ -(\sin\alpha)/\alpha&0&(1/2+(\sin 2\alpha)/4\alpha)\end{array} \right],\]  
where  
\[D_{13}=1/2+(\sin 2\alpha)/4\alpha-((\sin\alpha)/\alpha)^{2},\]  
is the determinant of the \(2\times 2\) submatrix of \(H_{\alpha}\) constructed from its first and third rows and columns. Then  
\[(J^{\top}J)^{-1}\approx\frac{1}{m}V_{\alpha}.\]  
If the variance matrix associated \(\mathbf{x}_{1:m}\) can be approximated by \(\sigma_{R}^{2}I\), then the variance matrix \(V_{A}\) associated with the fitted circle parameters is approximated by  
\[V_{A}\approx\frac{\sigma_{R}^{2}}{m}V_{\alpha},\]  
and the standard uncertainties associated with \(\mathbf{a}\) are given by \(\sigma_{R}\sqrt{\
u_{jj}/m}\), where \(v_{jj}\) is the \(j\)th diagonal element of \(V_{\alpha}\). The quantities \(s(a_{j})=\sqrt{\
u_{jj}}\) for selected values of \(\alpha\) are given in Table 1. For \(\alpha\) less than \(10\pi/180\), i.e., less than \(10\) degrees, then \(D_{13}\) is approximated by \(\alpha^{4}/45\) and the diagonal elements of \(V_{\alpha}\) are approximated by \(45/\alpha^{4},3/\alpha^{2}\)and \(45(1-\alpha^{2}/3)/\alpha^{4}\); see also Table 1. The uncertainty associated with the \(y\)-coordinate of the circle center scales with \(1/\alpha\) while the uncertainties associated with the \(x\)-coordinate and radius scale with \(1/\alpha^{2}\), for small \(\alpha\). The estimate of the \(x\)-coordinate of circle center is almost perfectly negatively correlated with the estimate of the radius.
# CNT-Coated Quartz Woven Fabric Electrodes for Robust Lithium-ion Structural Batteries
## 3 Results and Discussion
### Material Synthesis
It obtained the reversible capacity of 380 mAh g\({}^{-1}\), which is close to the theoretical capacity of graphite (372 mAh g\({}^{-1}\)). This remarkable energy density is expected to be caused by a large surface area of entangled CNTs, facilitating the reaction site to store lithium ion and providing the electron path between the nonconductive substrate and external circuit without any current collector. The rate performances of the C-QWF electrode are 660, 380, 298, 192, and 99 mAh g\({}^{-1}\) at different current densities from 0.05 to 0.1, 0.2, 0.5, and 1.0 C-rate, respectively, as shown in Figure 5a. When the current was decreased to 0.1 C-rate, it still exhibited a reversible capacity of 369 mAh g\({}^{-1}\). This means that the C-QWF anode can tolerate high-rate cycling even at 1 C-rate, although the main composition of the electrodes is amorphous silicon oxide, which cannot provide electron paths. The voltage profiles in Figure 5b demonstrate the excellent electrochemical reversibility with a coulombic efficiency of 99.5%, except the first cycle. The plateau located at 0.7 V in the first lithiation process is generally known to originate from the formation of the SEI layer, relating to the decomposition of the organic electrolyte and irreversible reactions. Usually, ohmic resistance (R\({}_{\text{e}}\)) in the battery is equal to the intercept at the Z\({}_{\text{real}}\) axis. The high-frequency semicircle is attributed to the resistance of the surface film (R\({}_{\text{s}}\)), the medium-frequency semicircle reflects the charge-transfer resistance (R\({}_{\text{ct}}\)), and the low-frequency tail could be considered as Warburg impedance, which is associated with Li-ion diffusion in the bulk of the electrode. As shown in Figure 6, the Nyquist plots of the QWF electrodes show a decrease of electrical conductivity according to the intercept at the Z\({}_{\text{real}}\) axis, similar to the electrochemical performance of C-QWF electrodes. The resistance was measured by fitting the impedance spectrum with the equivalent model combining the diameters of the semicircles. The smaller intercept at the Z\({}_{\text{real}}\) axis of the C-QWF (42.8 \(\Omega\) cm\({}^{-2}\)) than that of P-QWF (60.3 \(\Omega\) cm\({}^{-2}\)) indicates lower charge resistance. In view of these  
Figure 4: Cycle performances of QWF electrodes at a rate of 0.05 C-rate (red: C-QWF, black: P-QWF). results, the QWF electrode in which CNTs are grown can operate as an effective lithium storage structure and also serve as a support for an electrode material requiring structural stability. By establishing a high reversible capacity through C-QWF, it provides an effective and new perspective on the breakthrough of the limitations for conventional electrodes of LIBs.
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 4 Connectivity Properties of Ship MANET
### Boundary Conditions of Node Connectivity  
There will be losses when the signal propagates through the air. Assuming that an ideal isotropic antenna is lossless, according to the Frills Free Space Propagation Model, when a certain transmission and reception distance is \(d\), the average power of the receiver is Formula (6):  
\[P_{r}(d)=\frac{P_{t}G_{t}G_{r}\lambda^{2}}{\left(4\pi\right)^{2}d^{2}L} \tag{6}\]where, \(P_{t}\) is the transmit power of the signal at the transmission end, \(G_{t}\) is the gain of the transmission antenna, \(G_{r}\) is the gain of the receiving antenna, \(L\) is the system fugitive coefficient, which refers to losses unrelated to propagation (such as transmission line attenuation, filter loss, antenna loss, etc.), and \(\lambda\) is the wavelength of the electromagnetic wave.  
If we rewrite the above formula as the non-functional form of \(P_{r}(d)\), the loss formula in the ideal state is as Formula (7):  
\[P_{L}=32.44+20\lg(d)+20\lg(f) \tag{7}\]  
where \(d\) is the distance between the sending and receiving terminal, the unit is KM, the carrier frequency is \(f\), and the unit is MHz.  
We do a simulation example to visualize this problem, as shown in Figure 4.  
Therefore, within the network, suppose there are two nodes \(i\) and \(j\), separated by \(d\). According to the above model and formula, the signal \(P_{ij}\) of the message received by the receiver \(j\) is Formula (8):  
\[P_{ij}=\frac{P_{i}G_{i}G_{j}\lambda^{2}}{(4\pi)^{2}d^{2}L} \tag{8}\]  
Generally, the position of the ship's antenna is at the top of the bridge and the bridge is generally not in the center of the ship. The coverage of the antenna will not have the same coverage area in the forward and backward directions. However, for the convenience of research, we first assume that the coverage of the ship's antenna at the transmission end is a circle with a radius of \(R\). Then the minimum signal strength of the receiver is \(P_{threshold}\), as shown in Formula (9):  
\[P_{threshold}=\frac{P_{t}G_{t}G_{r}\lambda^{2}}{(4\pi)^{2}R^{2}L} \tag{9}\]  
The calculation of this threshold allows us to make it clear that even with the networking trigger conditions described above, networking can only be achieved if the receiver satisfies this condition.  
Figure 4: Path loss in the Free Space Propagation Model.