esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 How Normal Anatomy of Pleural Plane is Represented in Ultrasound Images  
The visceral pleura, firmly covering the lungs and the interlobular fissures, together with the parietal pleural layer, lining the thoracic cage and the virtual space between them, act like a single surface of discontinuity. In sonographic images the visceral and parietal pleura appear as an echogenic  
Figure 1: Normal pleural line. (**Left**): Healthy subject, B Mode image. The pleural line is regular, below the pleural line, the image does not represent the anatomy of the lung. (**Right**): Below the echogenic pleural surface the replica effects of the pleural line can be seen. In the box C, the replica of the contents of box A and the mirror effect of the contents of the box B are added together. (Images obtained with a commercially available machine, Toshiba Aplio XV, equipped with a linear probe, 9 MHz, without harmonic imaging).  
Figure 2: Vertical artifacts at the base of the right lung. Early pulmonary edema. Note the presence of a uniform white artifact along with artefact showing clear bands overlap. Early pulmonary edema. (Image obtained with a commercially available machine, Toshiba Aplio XV, equipped with a convex probe, 6 MHz, without harmonic imaging).  
regular pleural-line [26]. Pleural membranes are covered by a single layer of flattened mesothelial cells (1-4 \(\upmu\)m thick). Underneath the mesothelial cells, there is a layer of connective tissue (10-15 \(\upmu\)m thick). In adult lungs, the aerated lobules are placed below the visceral pleura and are separated from each other by the interlobular septa, incompletely demarcating polyhedral units of lung parenchyma.  
Each lobule measures from 1 to 2.5 cm and contains three to five acini, composed by alveolar spaces [27]. Interlobular septa are made of connective tissue, pulmonary veins and lymphatics. Their thickness is no more than 100 \(\upmu\)m and they represent the main acoustic discontinuities along the normal pleura.  
Considering this kind of cortical lung porosity, the ultrasound pulse that hits a smooth visceral pleura, is reflected almost completely. Once the reflected echo has reached the probe, it can have enough energy to be partially back-reflected and reach the pleural-line again. In this case, the probe receives two echoes coming from the pleural-line. Consequently, with the transformation of the signal from the time domain to the space domain, the obtained spatial function shows two pleural-lines: the first is the real one and the second is an artifactual line, localized at a double distance (Figure 1).  
This process can also generate more than two pleural-lines (replica effects) [28]. These lines, in the case of a healthy pleura, are regular, bright, and relatively thin. In literature, these horizontal artifacts are named A-Lines. We prefer to identify these artifacts, together with every sub-pleural mirror and replica effects of the chest wall tissues, as specular artifacts. In general, they indicate a healthy pleural surface [4].
# Security Assessment of Agriculture IoT (AIoT) Applications
## 2 Materials and Methods
### Process Step: SuC Identification
#### 2.4.1 SuC Identification Key Questions
Farm segment:  
Hardware/software assets:  
* WLAN base station
* Gateways (collects the data from the field sensors)
* Base Transceiver Station (BTS), supported by the telephone provider
* Business computer (not shown in Figure 1)
* Internet router (not shown in Figure 1)  
Communication protocols:  
* HTTPS/SSL/TLS/MQTTS  
Semantic Middleware:  
Hardware/software assets:  
* DDS manager service
* Decision support system (DSS) manager service
* Internet service provider (ISP) access point service
* Data manager service
* MQTT Broker service
* REST service manager service
* Data storage service  
Communication protocols:  
* HTTPS/SSL/TLS/MQTTS  
Farm Management:  
Hardware/software assets:  
* Mission management service
* Configuration management service
* Data storage repository service  
Communication protocols:  
* HTTPS/SSL/TLS/MQTTS
* Apache THRIFT [10] binary communication protocol  
The hardware and software assets of the \"Semantic Middleware\" and the \"Farm Management\" segments are denoted as \"services\". This means that the services are executed on one or on different computing units by the appropriate service provider. In the architecture segments Levels 1 and 2, the asset owner has full control over the installed security measures to harden the system against cybersecurity attacks. For Levels 3 and 4, the asset owner can only perform a very accurate provider selection and must trust that the defined security specifications are fulfilled. The asset owner has little or no influence upon the security conditions of these services. He/she can only request security certificates and must trust the service provider on compliance with the duty of caution. Unfortunately, from time to time, one hears about cyberattacks against service providers in which private customer data was stolen. Further points of discussion in security and privacy are the questions of where the data are stored by active service providers operating worldwide with remotely located cloud-based data repositories and service farms. Continuous security monitoring is the only activity the asset owner can perform to increase confidence. This is a requirement which may be performed by medium and large farm companies. Small farm companies need support and consultation from their professional associations to reach a level of security and privacy such that the company can work trouble-free.
# A Critical Review on Obesity: Herbal Approach, Bioactive Compounds, and Their Mechanism
## 4 Herbs That Control Obesity
### Caralluma fimbriata  
This herb belongs to the Asclepiadaceae family; it is a native herb of the countries India, Pakistan, and Afghanistan [96]. The major phytochemical compounds of the herb are pregnane glycosides, megastigmane glycosides, flavone glycosides, and saponins. These phytochemicals contribute to its biological activities such as antimicrobial, anti-inflammatory, anti-obesity, anti-diabetic, antinociceptive, antipyretic, antioxidant, anti-helminthic, etc. [97]. The active component, pregnane glycosides, is known for its appetite-suppressing effects in the hypothalamus. It suppresses appetite by inhibiting ghrelin production in the stomach and neuropeptide Y (NPY) release in the hypothalamus [96].  
It increases the fat-burning capacity and inhibits the synthesis of fat inside the body [98]. The pregnancy glycosides are suggested to reduce fat accumulation by inhibiting citrate lyase. It also inhibits malonyl coenzyme A, further preventing fat synthesis in the metabolic pathway. By acting on malonyl coenzyme, it prevents the formation of new fat cells [99].
# Effect of Plasma Treatment of Titanium Surface on Biocompatibility
## 4 Discussion
To the best of our knowledge, the present study is to compare RBM cell proliferation on plasma-treated titanium surfaces with that on unprocessed controls. Plasma treated titanium surface enhances the adhesion of cells such as osteoblasts and fibroblasts. Thus, it was proposed that altered material surface energy may promote tissue growth because it increases the adsorption of certain proteins relative to materials with microscale features [41]. The adsorption of certain proteins may, in turn, induce cell adhesion on the implant surface and perform other functions. Numerous studies demonstrated that the stimulation of cell adhesion and proliferation on modified surfaces could be beneficial for various therapeutic bladder, bone, vasculature, and nervous system applications [16; 17; 18]. The RBM cells on plasma-treated titanium surfaces presented with greater ALP activity, calcium deposition, and bone formation-related gene expression than those on the untreated control surfaces. ALP activity is a marker of bone differentiation at the early stages, bone formation, and osteoblast activity [42]. There were significant differences between plasma-treated and untreated titanium implant surfaces in terms of the expression levels of osteoblast-specific markers. Furthermore, plasma-treated surfaces upregulated _ALP, Bglap, BMP-2_ and _Runx2_ (important transcription factors mediating osteoblast differentiation) in RBM cells. Plasma treatment maintains the viability of adherent stromal cells and promotes their differentiation into osteoblasts. According to the experimental results, low-temperature, atmospheric-pressure plasma treatment removes contaminants from pure titanium metal surfaces, increases its hydrophilicity, enhances the initial adhesion of protein and rat bone marrow cells, and induces hard tissue differentiation. The piezobrush plasma device used in the present study is invaluable to clinicians because it is smaller and easier to operate than conventional plasma devices. Moreover, the findings of the in vitro efficacy assays performed in the present study suggest that this novel technology could be highly useful in dental practice.
## 3 Methods
### System Architecture and Related Models
#### 3.1.1 System Architecture  
The moving bottleneck, as one of the common phenomena in the uncertainties, frequently occurs in the traffic network and may likely be caused by the chain effects of different characteristics and behaviors of each vehicle, including vehicle acceleration/deceleration, lane change, and so on. The moving bottleneck results in an average of three vehicles per road segment, which is lower than in the upstream and downstream segments. The cause of this phenomenon is instantaneous but the phenomenon can persist for long periods and distances. The moving bottlenecks will lead to the duration and scope of traffic congestion being longer, increasing the cost of traffic and the total waiting time. At the same time, bottlenecks will also increase vehicle emissions and pollute the environment. Considering the special traffic characteristics of the long segment, which only has one entrance and one exit, the bottleneck can only be mitigated by adjusting the speed of vehicles located on the bottleneck upstream.  
Therefore, to address bottlenecks caused by emergency incidents on roads, we propose an MVSPC method, specifically designed for long stretches such as extended-span bridges, enabling rapid response and swift mitigation of the adverse effects of moving bottlenecks. The framework of this strategy, as shown in Figure 1, consists of three core modules:  
Information module: This module integrates real-time road information, including vehicle data, road conditions, and traffic flow information, providing comprehensive situational awareness for the system.  
Monitoring module: Leveraging data from the information module, this component continuously monitors road conditions. Upon detecting an abnormal event or vehicle, such as an accident or construction, the system immediately triggers the control module.  
Control module: In this module, we developed an extended macroscopic traffic flow model based on the classic MetaNet model. This model not only retains the core strengths of the MetaNet model but also expands its applicability and predictive capabilities. Building on this, we introduce advanced model predictive control (MPC) technology, utilizing road speed and density as key control variables. By precisely adjusting these critical parameters, our strategy effectively mitigates traffic congestion and optimizes flow distribution, thereby enhancing road efficiency and significantly reducing the risk of secondary accidents.  
This innovative strategy exemplifies our in-depth research in traffic flow modeling and control, providing robust technical support for achieving safer and more efficient traffic systems. Through this integrated approach, we are better equipped to manage and alleviate traffic bottlenecks, ensuring road user safety while enhancing overall traffic efficiency.  
The related parameters of this paper are covered in Table 1.  
Figure 1: The framework of the proposed methods.
## 3 Proposed Protocol Architecture
### Addressing Scheme
#### 3.5.1 Intra-SMAG Communication  
An IP-WSN node will be uniquely identified by a 16 bits short address for local communication. This means communication within the SMAG domain. Here we assume that total number of nodes within an SMAG domain will not exceed 216. Figure 4 depicts addressing format for inter-SMAG communication.  
This type of communication is required when transmission of information is carried out within RFD IP-sensor nodes. It just sends the message to the neighboring node. Finally it reaches the SMAG node. This reduces the energy consumption and communication overhead. In Figure 5, sensor nodes move from one link to another link within the same SMAG domain. This is identified by the re-association procedure with another node and finally handled by the corresponding SMAG.  
Figure 4: Addressing format to communicate within a SMAG.
# Improved Homotopy Perturbation Method for Geometrically Nonlinear Analysis of Space Trusses
## 4 Results and Computational Examples
### Example 2: 42-Member Space Truss  
The second space truss example is taken from [PERSON] and [PERSON] studies [9] which is schematically shown in Figure 4. The considered space truss consists of 42 members and 19 nodes. The pinned supports are considered as the boundary conditions. The identical cross-sectional properties are assumed for all the members and the material constitutive model has a module of elasticity of 211.67 kN/mm\({}^{2}\). The section area is 16 mm\({}^{2}\) and the concentrated force applied to the system is P = 0.135 N with \(\Delta\)P = 0.03 N.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Decrease Percentage** & \multicolumn{2}{c}{**Present Method**} & \multicolumn{2}{c}{**Newton Method**} \\ \hline
**Iteration no.** & **Time (s)** & **Iteration No.** & **Time (s)** & **Iteration No.** & **Time (s)** \\ \hline
27 & 10.93 & 79 & 0.145 & 108 & 0.1628 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the computational cost and iteration number  
Figure 3: Space truss with 24–members, all dimensions are in mm.  
Figure 2: Space truss with 24–members, all dimensions are in mm [28].  
The load-displacement behavior of this model is shown in Figure 5. Table 2 shows the comparison of the classic Newton-Raphson with the perturbation method. It is concluded that the PM method is able to precisely evaluate the results, and reduce the computational cost and iteration number by 12.89% and 29.1%, respectively.
## 2 Results and Discussion
### Analysis of Real Samples  
To elucidate the functionality and applicability of the DI- and EE-DI-SPME-GC-FID procedures for real samples, they were employed for the direct analysis of nicotine in urine and plasma samples. The results (summarized in Table 2) demonstrated the reliability and applicability of the developed strategies, especially EE-DI-SPME-GC-FID, as a novel solvent-free and green analytical method, for the extraction and quantitation of nicotine in biological samples.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Sample** & **LDR (ug mL\({}^{-1}\))** & **LOD (ng mL\({}^{-1}\))** & **RSD (\%)** & **Ref.** \\ \hline LE-PTC-GC-MS & Urine, saliva and hair & 0.01–3 & 0.60 & 3–20 & [30] \\ MISPE-UV & Spiked urine & 0.17–9.7 & NR & 10 & [31] \\ UE-GC-FID & Pharmaceutical formulations & 1–500 & 250 & 2 & [32] \\ LC-UVDAD & Human milk & NR & 13 & 5.8 & [33] \\ GC-FID & Aqueous extract of baculum leaves & 5–60 & 500 & 3.2 & [34] \\ IT-SPME-LC-MS & Human urine and saliva & 0.0005–0.02 & 0.015–0.04 & 4.7–11.3 & [35] \\ DI-SPME-GC-FID & Urine and plasma & 0.1–10 & 10 & 6.1 & This work \\ EE-DI-SPME-GC-FID & Urine and plasma & 0.001–10 & 0.3 & 4.6 & This work \\ \hline \hline \end{tabular}  
* LE-PTC-GC-MS: liquid extraction into dichloromethane through a phase-transfer catalyst (for derivatization) followed by GC-MS determination. MISPE-UV: molecularly-imprinted solid-phase extraction method coupled to spectrophotometric determination. UE-GC-FID: ultrasonic extraction with heptase followed by GC-FID separation and quantitation. LC-UVDAD: liquid chromatography-diode array UV detection. IT-SPME-LC-MS: in-tube SPME coupled with liquid chromatography-mass spectrometry. NR: not reported. LOD: limit of detection.  
\end{table}
Table 1: Comparison of the developed DI- and EE-DI-SPME-GC-FID methods with similar reported procedures for the quantification of nicotine in liquid samples. LDR, linear dynamic range.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Added (\(\mu_{\text{g}}\) mL\({}^{-1}\))**} & \multicolumn{2}{c}{**DI-SPME-GC-FID**} & \multicolumn{2}{c}{**EE-SPME-GC-FID**} \\ \cline{3-6}  & & \(\mu_{\text{g}}\) mL\({}^{-1}\) & **R (\%)** & \(\mu_{\text{g}}\) mL\({}^{-1}\) & **R (\%)** \\ \hline Urine 1 (Male, 2 years) & 0 & NF & — & NF & — \\  & 2 & 1.82 (8.6) & 90 & 2.20 (5.1) & 110 \\ \hline Urine 2 (Female, non-smoker, 23 years) & 0 & NF & — & NF & — \\  & 2 & 1.88 (7.2) & 94 & 2.06 (4.9) & 103 \\ \hline Plasma 1 (Male, smoker, 47 years) & 0 & 1.43 (9.5) & — & 1.95 (7.4) & — \\  & 2 & 3.57 (6.3) & 105 & 3.61 (3.8) & 91 \\ \hline Plasma 2 (Male, smoker, 38 years) & 0 & 2.1 (5.8) & — & 2.47 (4.5) & — \\  & 2 & 3.03 (4.7) & 75 & 4.4 (3.6) & 98 \\ \hline \hline \end{tabular}  
* R: recovery. NF: not found.  
\end{table}
Table 2: Application of the developed DI- and EE-DI-SPME-GC-FID procedures for the extraction and measurement of nicotine in biological fluids of the human body.
# Prediction of Short-Shot Defects in Injection Molding by Transfer Learning
## 2 Background Knowledge
### Back Propagation Neuron Network (BPNN)  
Back Propagation Neural Network (BPNN), also known as Multilayer Perceptron, is a type of feedforward neural network belonging to supervised learning. It operates on the concept of gradient descent and adjusts the weights and biases of each node through backward propagation until the output values closely match the desired values, as illustrated in Figure 2. The structure of the Back Propagation Neural Network primarily consists of three layers, as described below:  
* Input Layer The input data are received through this layer and serve as input to the neural network. The nodes in this layer do not perform computations; typically, the received data are preprocessed here before being forwarded to the next layer.
* Hidden Layer A neural network can have more than one hidden layer, and the number of hidden layers and the number of neurons in each layer are set based on the specific problem requirements. When there are more hidden layers, the network's computational complexity and time consumption increase. There is no one-size-fits-all standard for the number of hidden layers and neurons, and typically, a trial-and-error process is used to adjust and find the optimal configuration, often guided by learning and validation curves.
* Output Layer This layer receives the output computed by the hidden layers, and the form of the output in the output layer depends on the model's task. In binary classification problems, the output layer typically contains one neuron, while in multi-class classification problems, the output layer usually contains several neurons, with each neuron corresponding to a category. The output in the output layer is often processed using an activation function to obtain the final prediction of the model. Common activation functions include Softmax, ReLU, Tanh, and Sigmoid functions.
## 6 Thermal Cracking Simulation in FGMs
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Thermal Physical Parameters** & **Ceramic** & **Metal** \\ \hline Elastic modulus \(E\) (GPa) & 117 & 66 \\ Poisson’s ratio \(v\) & 0.333 & 0.321 \\ Thermal expansion coefficient \(\alpha\) (K\({}^{-1}\)) & \(7.11\times 10^{-6}\) & \(10.3\times 10^{-6}\) \\ Heat conduction coefficient \(k\) (W/(m\(\cdot\)K)) & 2.036 & 18.1 \\ Heat capacity \(C_{p}\) (J/(kg\(\cdot\)K)) & 615.6 & 808.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermophysical parameters of FGMs. Figure 19: Boundary conditions for double cracks. Figure 20: Effect of crack spacing and inclination angle on crack propagation. as an example, when d increases from 0.04 to 0.16, the crack propagation angle \(\phi\) decreases by about 27%. As can be seen, a larger crack spacing leads to a smaller crack propagation angle when the crack inclination angle remains constant. In addition, adjacent cracks have little effect on the crack propagation angle when the crack spacing is large. Next, take \(d=a\) and \(\beta=80^{\circ}\) as an example to study the crack growth and the evolution process of temperature with time. Figure 19 shows the crack propagation over time under thermal load. It can be seen that the cracks extend to the bottom of the model under temperature load. When t = \(10^{-5}\) s, the temperature gradient at the crack tip is the largest, and the right crack is affected by the left crack and starts to deflect to the right. At this time, the left crack has not yet begun to deflect along the initial crack direction. When t = \(2\times 10^{-5}\) s, the right crack starts to deflect to the right due to the influence of the stress field distribution of the left crack. Until t = \(3\times 10^{-5}\) s, the left crack begins to extend to the lower left. At t = \(8\times 10^{-5}\) s, the propagation direction of the two cracks has deviated significantly, and the deflection angle of the right crack is \(\varphi\) = \(43.9^{\circ}\). In summary, the propagation paths of these two cracks are affected by each other under thermal load. In this example, the hindering effect of cracks on heat conduction is considered to match the actual situation. In Figure 23, the temperature distribution on both sides of the cracks is continuous and drops faster on both outsides because cracks hinder heat conduction. The simulation results of crack propagation are consistent with the theoretical research [58]. Figure 23: Evolution of temperature and crack with time under thermal load. Figure 22: Effect of crack spacing on crack propagation angle.
## 4 Results: Proof of Concept
### Identity Management Implementation
#### 4.1.1 Registration and DID Management  
The Raspberry Pi's identity was registered using a DID document (DDo) published on the IOTA network. Key fields in the DDo include  
* id: Unique identifier of the Raspberry Pi's identity;
* capabilityInvocation: Cryptographic permissions for updating the DDo using Ed25519 keys;
* meta: Creation and update timestamps;
* proof: Digital signature ensuring document authenticity.  
The DDo was serialized in JSON-LD format, ensuring semantic interoperability and integration across heterogeneous systems as shown in Listing 1.  
Figure 1: Proof of concept Diagram—RPi Registration and Identity Management.  
```
1 DDorpi:{
3\"doc\":{
4\"id\":\"did:iota4 VeSj35 Ag5 WeiGwnu77 YKs2d74 SkawhHGiGSFsp5H\",
5\"capabilityInvocation\":[
6{
7\"id\":\"did:iota4 VeSj35 Ag5 WeiGwnu77 YKs2d74 SkawhHBiGSFsp5 iH#sign-0\",
8\"controller\":\"did:iota4 VeSj35 Ag5 VeiGwnu7 YKs2d74 SkawhHGiGSFsp5 iH\",
9\"type\":\"Ed25519 VerificationKey2018\",
10\"publicKeyMultibase\":\"z9 BpD7g16 hgLqrwMGMan3 ndKt5 ofMyujelCSKaDCEU182\"
11}
12]
13},
14\"meta\":{
15\"created\":\"2023-03-31T11:14:11Z\",
16\"updated\":\"2023-03-31T11:14:11Z\",
17},
18\"proof\":{
19\"type\":\"JsE2d25519 Signature2020\",
20\"verificationMethod\":\"#sign-0\",
21\"signatureValue\":
22\"2 FV78 iAhCrjrq2 YKbp96e2 ZUTUGRaJspbSpaLsuXJiG73w6 ceJFL23 WTEXYhnQyfkt8
23 qBGU2 UTM92 Mxsi3 Qg\"
24} }
```  
**Listing 1** Example DID Document (DDo) for the Raspberry Pi.