esa-sceva/satcom-chunk-collection huggingface dataset:
# Industrial IoT Monitoring: Technologies and Architecture Proposal
## 4 Proposed Architecture  
Current WSN diagnostic tools have several drawbacks, as already pointed in our previous work [31]. In our opinion, some of these drawbacks are hampering the use of WSNs in industry because, nowadays, to the best of our knowledge, there aren't multi-network, standard-compliant monitoring tools that support the IWSN technologies addressed in this proposal (ZigBee, ISA100.11a, WirelessHART, WIA-PA). In order to make this clear, in Sections 2 and 3, a review of the state-of-the-art in what concerns the main Industrial IoT standards, network management, and diagnostic tools was made. As a result, some questions arose: how can multiple networks, possibly comprising equipment compliant with different standards, be monitored in an integrated way? How can management functionality be added at the gateway level? How can firmware and hardware be monitored without extra costs in hardware, firmware, and network?  
In what concerns network monitoring, three important aspects were identified in Sections 2 and 3: the set of techniques used by current diagnostic tools to monitor the network; the available metrics, provided by almost all industrial standards, that can be collected and shared at gateway level; and the current management standards. In what concerns hardware and firmware monitoring, several approaches and technologies were also presented, as well as log techniques that can be used to convey hardware and firmware metrics inside the network. Thus, the review made in these sections identified several crucial points and research directions that led us to the proposal of this architecture and related properties. Implementations of the proposed architecture will lead to diagnostic tools that are totally compatible with industrial standards. In this respect, a proof-of-concept implementation will be presented in Section 5, which also discusses evaluation results.  
It should be highlighted that the current architectural proposal goes well beyond identifying components and respective abstract relations. More than defining the requirements, interactions, and roles to be performed by each architecture component, this section defines technologies, approaches, and protocols to be used in realistic, practical industrial scenarios. For instance, this section addresses the techniques and technologies for collecting hardware, firmware, and network metrics; defines the services used in each standard for conveying the monitoring information; and identifies the management protocols and technologies for dealing with the monitoring information.  
The remainder of this section is organized as follows: initially, the proposed monitoring architecture is presented, providing the reader with a global view of its components and their main roles; subsequently, each component is described in detail, by specifying their role and requirements. Moreover, the technologies applicable to each component will be identified.
## 4 Experimental Results
### Augmented Results  
Figure 7a-c show examples of WBMs with a 'donut' defect pattern generated through each augmentation model. Figure 7a shows the original WBM used to train the augmentation model, and Figure 7b,c show the output results of each augmentation model trained using the original WBM. As shown in Figure 7b, the CAE-based model produced almost the same results as the original WBM. However, as shown in Figure 7c, the DCGAN-based model created more diverse types of WBMs while maintaining the characteristics of the defect patterns of the original WBM. This applies not only to the 'Donut' defect pattern, but to all defect patterns learned by the DCGAN-based model. Figure 7d displays defect patterns and their labels randomly generated from DCGAN-based models. These results demonstrate that the DCGAN-based model can generate more diverse WBM defect patterns than the CAE-based model, not only for the 'Donut' pattern but also for several other defect patterns. Randomly generated WBM defect patterns from the DCGAN-based model can create WBMs with meaningful variations while maintaining the characteristics of the defect patterns for all WBMs learned during training. Similar to WBMs generated in DCGAN-based models, WBMs generated to have more diverse shapes while maintaining the characteristics of defect patterns alleviate the overfitting of classifiers to specific defect patterns. This can be seen in the experimental results of Section 4.3.1 and Section 4.3.2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Part** & **Layer** & **Kernel Size, Stride** & **No. of Parameters** & **Output Shape** \\ \hline \multirow{6}{*}{Encoder} & Input & - & - & (64, 64, 3) \\  & Convolutional & \(3\times 3\times 64,1\) & 1792 & (64, 64, 64) \\  & Max-Pooling & \(2\times 2,2\) & - & (32, 32, 64) \\  & Convolutional & \(3\times 3\times 128,1\) & 73,856 & (32, 32, 128) \\  & Max-Pooling & \(2\times 2,2\) & - & (16, 16, 128) \\  & Convolutional & \(3\times 3\times 556,1\) & 295,168 & (16, 16, 256) \\  & Max-Pooling & \(2\times 2,2\) & - & (8, 8, 256) \\ \hline \multirow{6}{*}{Decoder} & Conv. Transpose & \(3\times 3\times 256,1\) & 590,080 & (8, 8, 256) \\  & Up-Sampling & - & - & (16, 16, 256) \\ \cline{1-1}  & Conv. Transpose & \(3\times 3\times 128,1\) & 295,040 & (16, 16, 128) \\ \cline{1-1}  & Up-Sampling & - & - & (32, 32, 128) \\ \cline{1-1}  & Conv. Transpose & \(3\times 3\times 64,1\) & 73,792 & (32, 32, 64) \\ \cline{1-1}  & Up-Sampling & & & (64, 64, 64) \\ \cline{1-1}  & Conv. Transpose & \(3\times 3\times 3,1\) & 1731 & (64, 64, 3) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Convolutional autoencoder architecture and hyperparameters.
## 2 Materials and Methods
### Architecture of IndoorPathNet  
As illustrated in Figure 1, IndoorPathNet represents an end-to-end semantic segmentation network tailored for extracting drivable paths within indoor warehouse environments. Its architecture comprises the following:  
1. U-shaped Encoder-Decoder Structure: The original image is processed through encoding and decoding stages, facilitating the automatic segmentation and extraction of drivable paths from indoor BEV images;  
2. Encoding Phase: In the encoding component, IndoorPathNet adopts a contraction path akin to U-Net, employing dilated convolution kernels with dilation factors of 2 and 5 in lieu of standard 3 \(\times\) 3 convolution kernels; this integration, coupled with spatial self-attention mechanisms, serves to preserve critical spatial information while downsizing the feature map dimensions;  
3. Decoding Phase: The decoding segment follows an expansion path reminiscent of U-Net, utilizing upsampling and feature enrichment based on attention mechanisms; it harnesses an enhanced spatial self-attention mechanism to prioritize the processing and reconstruction of regions deemed more crucial for prediction.  
IndoorPathNet draws its design inspiration from U-Net, where the encoder's initial layer utilizes small convolution kernels (typically 3 \(\times\) 3) to capture fundamental image features like edges and textures. This approach reduces model parameters while maintaining sensitivity to details. As the encoder progresses through subsequent layers, it transitions to extracting more abstract features such as image structures and shapes. This hierarchical feature extraction ensures the network's ability to interpret image content across multiple scales, providing the semantic information necessary for precise segmentation. At the deepest level of U-Net, the fourth layer, along with its bottom bridge connections, performs the highest-level semantic feature extraction.  
Expanding on this theoretical foundation, we conducted structural optimization based on the U-Net architecture, retaining only the upper three layers of the encoder-decoder structure for training. However, we encountered challenges as the model gradually began to overfit and struggled to differentiate between textures resembling walls and floors. Consequently, we reintroduced a bottleneck layer to introduce deeper features. This was coupled with spatial self-attention mechanisms and dilated convolutions, resulting in the development of the complete IndoorPathNet model.
## 3 Numerical Examples
### Characterization of Longitudinal Thermoelastic Guided Wave  
Longitudinal thermoelastic guided wave propagation characteristics in orthotropic anisotropic hollow cylinders are investigated using hollow cylinders of silicon nitride as an example. Based on the theoretical method in the first part, a theoretical model of the longitudinal thermoelastic behavior of silicon nitride is established. It should be pointed out that the cutoff term in the calculation is taken to be M = 10, and the inner radius, outer radius, and temperature of the temperature field in which the hollow cylinder is located are 293 K. Based on the above model, the longitudinal thermoelastic guided wave phase velocity dispersion curves of the hollow cylinder of silicon nitride, as well as the phase velocity dispersion curves when there is no coupling of the heat transfer equation, are calculated numerically, compared, and analyzed, as shown in Figure 3.  
Figure 3: Longitudinal guided wave dispersion curve: (**a**) no temperature term; (**b**) with temperature term; (**c**) localized zoom in region a; (**d**) localized zoom in region b.  
As can be seen from the above Figure 3, the dispersion curves of the longitudinal guided waves in the thermoelastic hollow cylinder are very similar to the dispersion curves in the elastic structure when there is no coupled temperature effect. And, due to the coupling of the temperature field, new modes, namely thermal modes, have appeared in the figure. These have been marked in the above figure, from which it can be seen that these thermal modes are very similar to the elastic modes and are very close to the torsional modes. Moreover, all these thermal modes have a cutoff frequency and dispersion behavior, and their phase velocity decreases with the increase in the frequency and finally reaches a steady state.
## 4 Numerical Validation
### Aerodynamic Coefficients  
The full body drag coefficient of the [PERSON] body is computed and compared to the experimental work of [48]. The drag coefficient \(C_{D}\) is defined as follows:  
\[C_{D}=\frac{F_{D}}{0.5\rho AU_{\infty}^{2}} \tag{10}\]  
where \(F_{D}\), \(\rho\), \(U_{\infty}\), and \(A\) stand for the drag force (force in the positive \(x\) or stream direction), fluid density, reference velocity, and characteristic area (normally the frontal area of the vehicle), respectively. In this study, we use \(A=0.115\) m\({}^{2}\) and \(U_{\infty}=40\) m/s. Fluid properties are evaluated in a standard atmosphere. With the vehicle height, \(H\), used as the reference length, these correspond to a Reynolds number, \(Re_{H}=7.7\times 10^{5}\).  
Full body and base drag, as well as pressure drag at the rear fascia, obtained by the numerical model are 0.299 and 0.216; this shows good agreement with the reported value of \(C_{D}=0.315\) in experimental work [48]. The relative error is approximately 5% and it has about a 170 count difference. It is worth mentioning that the reported global drag in the literature for square-back [PERSON] body varies within the range of 0.250-0.364 and there is significant disagreement in the literature about the [PERSON] body reported drag [49; 50; 51; 52]. The computed drag coefficient lies within the reported range in the literature.  
To better understand the contributions of different body sections to the drag force components, the geometry was divided into three parts: (a) the nose (frontal surface), (b) the side, upper, and lower surfaces, and (c) the rear fascia. The drag coefficient for each section was then calculated separately, as shown in Figure 1.  
As shown in Table 1, the rear fascia pressure drag (\(C_{D,p}\)), also known as base drag, makes the largest contribution, accounting for nearly 70%. Following this, the friction drag (\(C_{D,f}\)) on the side body contributes around 11%. The remaining contributions come from the pressure drags on the body and nose. Notably, the pressure drag on the body is entirely due to the slits; otherwise, the pressure drag on the rest of the assigned body area is zero, as the pressure is perpendicular to the flow direction.
## 3 Results and Discussion
### Extraction Yield of Anthocyanins  
Various solvents such as acidified water, alcohols and acetone have been used for the UAE of different bioactive compounds from plant materials [15]. In particular, ethanol, with a different percentage of water, possesses the highest affinity for phenols, and thus, \begin{table}
\begin{tabular}{l c} \hline Moisture content (\%) & 57.3 \(\pm\) 0.4 \\ Water activity (aw) & 0.977 \\ Protein content (\% in f.w.) & 5.2 \(\pm\) 0.3 \\ L* & 42.26 \(\pm\) 0.08 \\ C* & 14.36 \(\pm\) 0.28 \\ h\({}^{\circ}\) & 5.49 \(\pm\) 0.13 \\ Total anthocyanin content (mg/100 g f.w.) & 853.2 \(\pm\) 48.7 \\ Content of individual anthocyanins (mg/100 g f.w.): & 200.2 \(\pm\) 10.5 \\ delphinidin-3-O-rutinoside & 387.9 \(\pm\) 23.3 \\ cyanidin-3-O-glucose & 59.4 \(\pm\) 5.2 \\ cyanidin-3-O-rutinoside & 205.7 \(\pm\) 9.7 \\ \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the studied blackcurrant pomace. it is usually the first choice for the extraction of phenolic compounds. In addition, water acidification is one of the common methods for efficient UAE of bioactive compounds [33], especially while targeting anthocyanins from berries and their pomace [16; 20]. Moreover, aqueous solvents are desirable by the food or pharmaceutical industry because they are environmentally friendly and low in cost to obtain. Our study revealed that the choice of solvent is important for the extraction efficiency of anthocyanins from blackcurrant pomace (Table 2). Regardless of the extraction parameters selected, the highest yield was always obtained with the water-ethanol solvent acidified with HCl. For this solvent, changing the material/solvent ratio from 1:20 to 1:7 did not adversely affect the extraction yield, indicating that a ratio of 1:20 is too high and, consequently, not advantageous for such extraction. In contrast, the opposite was observed for the water-citric acid solvent. Significantly higher yields were obtained with a ratio of 1:20 instead of 1:7. Probably, the citric acid partially saturated the solvent and reduced its extraction capacity. According to the literature, solvent pH is an important factor affecting the UAE yield and characteristics of many bioactive compounds [15]. Generally, the optimal pH for the extraction of phenolic compounds is in the range of 1-3 [20; 22]. Therefore, to maximize anthocyanin recoveries from pomace in our experiment, we used acidified solvents with a pH of 1.5. The results of the experiments showed that extending the anthocyanin extraction time to 60 min had no positive effect. The extraction time of 60 min showed the same or lower yield than that observed for 30 min, which was mostly attributable to the degradation of anthocyanins due to mechanical or chemical processes [16]. The optimized sonication time for the extraction of different phenolic compounds from plant by-products has been reported to be in the range of 10-90 min [15].
# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 2 Design of Docking Logic and Kinematic Analysis for LNG Loading Arm
### Kinematic Modeling and Docking Posture Control of LNG Loading Arms
#### 2.2.1 Kinematic Modeling of LNG Loading Arm
Since the solution to inverse kinematics is often not unique, this paper adopts a geometric approach to solve for each joint angle. Figure 6 shows the motion posture of the robotic arm. The coordinates of the arm's end-effector are \(\left(x,y,\text{ and }z\right)\), and the joint angles are calculated using the following formulas:  
\[\theta_{1}=\text{atan2}\left(\left(d_{2}+d_{3}\right),\sqrt{x^{2}+y^{2}-\left( d_{2}+d_{3}\right)^{2}}\right)+\text{atan2}y,x \tag{7}\]  
\[\theta_{2}=\text{tan2}\left(\left(z-d_{1}\right),\sqrt{\left(a_{2}+a_{3}*\cos (\theta_{3})\right)^{2}+\left(z-d_{1}\right)^{2}}\right)-\text{atan2}(a_{3}* \sin(\theta_{3}),a_{2}*\cos(\theta_{3})) \tag{8}\]  
\[\theta_{3}=-\text{acos}\left(\frac{x^{2}+y^{2}+\left(z-d_{1}\right)^{2}-{a_{2 }}^{2}-{a_{3}}^{2}-{d_{2}}^{2}}{2*a_{2}*a_{3}}\right) \tag{9}\]  
Figure 6: Relationship between end-effector position and joint angles. In the above calculations, the joint-angle limits are defined based on the actual working range of the LNG loading arm:  
* \(\theta_{1}\): Range \([-45^{\circ},45^{\circ}]\);
* \(\theta_{2}\): Range \([0^{\circ},90^{\circ}]\);
* \(\theta_{3}\): Range \([-90^{\circ},-180^{\circ}]\). Using these formulas, the required joint angles can be determined for the LNG loading arm to reach the target position, providing theoretical support for precise docking operations.
## 3 Results  
### Formulation and Characterization of Liposomes  
Liposomal formulations were prepared initially to encapsulate edavirenz alone (without GSH) using different lipids/ lipid mixtures along with cholesterol (Table 1) and water as hydration media. As depicted in Table 1, we observed that liposomal suspension prepared using a single lipid led to coagulation within 24 h of storage, whereas formulations prepared using lipid mixture of DMPC/DMPG-Na along with cholesterol, hydrated with phosphate buffer pH 6.8 were stable. Hence, this composition was selected to prepare EFA liposomes with and without GSH, which is illustrated in Table 2.  
On the other hand, GSH, an endogenous tripeptide, is a water-soluble molecule with a molecular weight of 307.32 Da that was to be encapsulated within the aqueous phase. Liposomes are able to encapsulate: (a) hydrophobic molecules such as edavirenz in the bilayer membrane and (b) hydrophilic compounds such as glutathione in the aqueous internal cavity.  
Hence in this present work, lipid mixtures (DMPC and DMPG) were used to encapsulate hydrophobic EFA, which led to % EE greater than 90% facilitating its inclusion into the lipid bilayers and hydrophilic GSH (% EE of 43%) within the aqueous core during the formation of the liposomes.  
Particle size reported as the \(Z\)-average diameter was about 120 nm to 205 nm without and with GSH, respectively, whereas polydispersity index value <0.3 indicated that liposomal vesicles were unimodal, revealing that sonication led to the formation of a homogeneous population.  
Negative surface charge on the liposomes as indicated by the zeta potential values were greater than \(-50\) mV leading to stable suspensions with reduced aggregation. The negative surface charge could be due to the polar head group of phospholipid and hydroxyl groups of cholesterol [30].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Formula Code**} & \multirow{2}{*}{**EFA: GSH (mM)**} & \multirow{2}{*}{**Size (nm)**} & \multirow{2}{*}{**Zeta**} & \multicolumn{2}{c}{**Encapsulation Efficiency (\%)**} \\ \cline{5-6}  & & & & **Potential (mV)** & **EFA** & **GSH** \\ \hline F1 & 3.17: 0.0 & 127.7 \(\pm\) 12.98 & \(-93.8\)\(\pm\) 3.56 & 95.63 \(\pm\) 3.91 & - \\ \hline F2 & 3.17: 0.95 & 205.03 \(\pm\) 11.59 & \(-89.2\)\(\pm\) 2.58 & 96.23 \(\pm\) 3.45 & 43.5 \(\pm\) 4.87 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Formulation and characterization data of edavirenz liposomes loaded with and without glutathione.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### The Watermarking Performance Metrics
#### 2.3.3 Signal to Noise Ratio (SNR)  
This metric calculates the signal's strength, which is based on the background noise. Additionally, it calculates an object's sensitivity [30]. The formula of calculating the _SNR_ can be shown in (7) as follows:  
\[SNR=10 log_{10}\left(\frac{P_{noise}}{P_{signal}}\right) \tag{7}\]
# Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection
## 4 Experiment
### Results and Analysis
#### 4.2.2 OTB-2015 Benchmark  
In Figure 10, we present results on the OTB-2015 dataset. It is observed that our tracker achieves a prominent improvement over the baseline (Staple) with a success rate gain of 12.9% and a precision gain of 12.4%. Our proposed approach provides the best performance with a success rate of 80.3% and a precision of 87.1%.
# Single-Shot Object Detection with Split and Combine Blocks
## 5 Conclusions  
In this paper, a novel feature fusion block is designed to improve the quality of feature representation, referred to as an SC block. It evenly splits the input into several parts, and processes each part with different sampling branches, and then concatenates the output of each branch one by one hierarchically. This structure instructs the network to rank information based on its importance to the task, and can consequently improve the model efficiency. A backbone network and an FPN employing this unit are built to construct our one-stage detector SCNet. Enhanced with the latest training tricks and modules, SCNet exhibits good performance in both speed and accuracy on the COCO test-dev split, achieving an AP of 38.9% with 40 FPS. Experimental results illustrate that the proposed block can generate strong discriminative features and improve the detection performance of the network. The SC block is scalable, simple, and effective. In addition, this unit may potentially provide a new idea of network construction for object detection and other visual tasks.  
Figure 10: FPS vs. Average Precision on COCO test-dev. SCNet lags behind ATSS and CenterNet in terms of accuracy but is superior in speed. The proposed SCNet exhibits good speed-accuracy trade-off compared with other algorithms.  
**Author Contributions:** Conceptualization, H.W. and D.L.; methodology, H.W. and D.L.; software, Y.S. and Q.G.; validation, H.W., D.L., and Y.S.; formal analysis, H.W. and Y.S.; investigation, H.W.; resources, D.L.; data curation, Y.S. and Q.G.; writing--original draft preparation, H.W. and Y.S.; writing--review and editing, Y.S. and Z.W.; visualization, Z.W.; supervision, Z.W. and C.L.; project administration, Z.W. and C.L.; funding acquisition, Q.G. and C.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Fundamental Research on Advanced Technology and Engineering Application Team, Tianjin, China, under Grant No. 20160524.  
**Conflicts of Interest:** The authors declare no conflict of interest.