esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 The Proposed Architecture
Finally, the processing of data, information and knowledge requires the following components: (a) Natural to Formal Language conversion (NL2 FL), (b) Formal to Natural Language conversion (FL2 NL), (c) Knowledge Index Lookup (KI-LU), (d) Formal Language Question Execution (FL-Q-Exec). In Figure 3 the example presents museum visitors interacting through a chatbot dialog manager with two KG-based CBs and one MB. In this scenario the MB is acting as the host of the discussion. An example flow of the scenario is the following:  
1. Visitors interact with the host chatbot (MB) (via Text, Voice, AR, or VR) to reach the CollectionBot of their interest (CB1 or CB2). Figure 3: An example of a multi-chatbot graph-based architecture. [PERSON] searches the KI-stores using KI-LU, discovers the stored knowledge about the thematic collections and selects which CB can provide the appropriate answer. 3. If the KI-store recognizes the intent of a visitors NL-Q, initiates the chosen CB knowledge extraction process. CB, with its NL2 FL component, translates the NL-Q of the visitors to formal syntax and sends it to the FL-Q-Exec. At the FL-Q-Exec, FL-Q are turned into SPARQL-q in order to be looked-up to KG-store and retrieve the right answer. KGs are stored at the KG-store (as RDF triples). If an answer is not found in the matched KG-store, an additional search is conducted to the other KGs of the museum or to the connected LOD cloud sources. 4. When the matched RDF triples are retrieved, the FL-Q-Exec sends the FL-A to the FL2 NL component. 5. In the FL2 NL component, NLG techniques are used for the transformation of the FL-A to NL-A. 6. Finally, visitors receive the NL-As through their device user interface (Text, Voice, AR, VR). At any point of the conversation visitors might place a NL-Q about knowledge related to the museum, e.g., the location of the museum's shop. A look-up task identifies the intent and sends the NL-Q to the museum chatbot and provides the proper NL-A. Summarizing, the proposed KG-based, distributed, and collaborative multi-chatbot conversational AI system architecture is focusing in providing answers to the main challenges that chatbot applications face:  
1. The integration of KGs contribution in rich structured machine-understandable knowledge to the museum chatbots, solving the problem of limited and unstructured knowledge sources. 2. The distributed and collaborative approach contribution in the capability of answering more (if not all) questions, and at the same time delivers a system that can be easily updated and expanded (with new collaborative chatbots).
## 4 Discussions
### Influence of Thickness on Tensile Strength  
The tensile strength retention vs. specimen thickness under different ageing durations was plotted in Figure 11. It can be observed that the specimens with thinner thickness had smaller retention than that of specimens with thicker thickness at given ageing time. For example, the tensile strength retention of specimen B\({}_{1}\) was 5%-20% lower than that of specimen B\({}_{4}\). Combining the test results of tensile test and water absorption, it can be concluded that at the beginning of ageing (around 30 days), the specimen with a thinner thickness had a faster deterioration rate than that of the specimen with a thicker thickness. After that, the deterioration rate was not sensitive to the thickness, while the ageing degree of the specimen with a thinner thickness was more severe than that of the specimen  
Figure 10: Variation of water absorption vs. specimen thickness soaked in (**a**) deionized water and (**b**) alkaline solution in late stage.  
Figure 9: Variation of water absorption vs. specimen thickness soaked in (**a,c**) deionized water and (**b,d**) alkaline solution in early stage.  
with a thicker thickness during a long period of ageing. Figure 12 shows the ageing of the specimens with different thicknesses through the cross section. It should be noted that the aged areas shown in Figure 12 only represent the aged areas of the specimens at a certain ageing time and the aged areas will gradually increase with the increasing ageing time. Additionally, note that the proportion of each part of the schematic is not drawn strictly according to the actual size, but just to better explain what needs to be expressed. Since the thickness of the specimen is much smaller than its length and width, it can be assumed that the deterioration of the specimen is carried out along the direction of the thickness. The aged area gradually expands to the interior of the specimen with the increase of ageing time. Although the thicknesses of B\({}_{1}\), B\({}_{2}\), and B\({}_{4}\) are different, the diffusion coefficient of the immersion solution is identical due to the same fabrication materials [49; 50]. Therefore, the aged areas of B\({}_{1}\), B\({}_{2}\) and B\({}_{4}\) are the same after a same certain ageing time. It can be seen clearly that the thicker specimen has a larger unaged area, i.e., \(S_{1}<S_{2}<S_{4}\). As the ageing time increases, the relationship between the unaged area \(S\) of BFRP laminates with different thicknesses was still established, i.e., \(S_{1}<S_{2}<S_{4}\). Therefore, a thinner specimen is inferior to a thicker one on long-term hygrothermal properties due to a faster degradation rate.
## 2 Results
### Direct Functionalization of PLA Backbone: Towards PLA-Based Graft Copolymers
#### 2.2.3 PLA-\(g\)-Acrylic Acid (PLA-\(g\)-AA)  
Another functionalization of the PLA chain is used in the field of the compatibilization of PLA-based blends, namely the grafting of acrylic acid (AA) to give a PLA-\(g\)-AA graft copolymer. Typically, AA grafting is performed under free radical conditions, by adding a mixture of AA and BPO to molten PLA in a mixer at 95 \({}^{\circ}\)C for a period of 6 h [82]. A PLA-\(g\)-PAA copolymer is formed, which can then react with alcohol functions of the cellulosic derivatives by esterifying the alcohol functions of the cellulosic compound (Figure 16).  
Figure 16: Reaction scheme for the grafting of acrylic acid on PLA and reaction with starch (from [PERSON], [82], copyright Wiley-VCH GmbH. Reproduced with permission).  
Figure 15: Mechanism of the reaction between PLA-\(g\)-GPA and PBAT (from [PERSON] et al. [78], copyright Elsevier, reproduced with permission).  
As with PLA-\(g\)-MA, PLA-\(g\)-AA can compatibilize blends with natural cellulosic compounds, such as sisal fiber [31], wood flour [83], corn starch [84], rice husk [85], and hyaluronic acid [86]. In all cases, improvements in mechanical properties and/or biodegradation are obtained in the compatibilized mixtures. The compatibilization effect is shown by the size of the corn starch (CS) phase in PLA/CS and PLA/CS/PLA-\(g\)-corn starch. In a PLA/CS blend (50/50 \(w/w\)), the CS phase size decreased from 17.5 \(\upmu\)m to 7.3 \(\upmu\)m when PLA-\(g\)-corn starch was added in the blend [84].  
Acrylic acid can also be graft-polymerized onto PLA chains in a solution using a photoinitiator, typically benzophenone, under UV irradiation at 254 nm [87]. Finally, the grafting of PAA onto the PLA backbone was also obtained by a free-radical reaction of BPO onto a solution of PLA in chloroform, followed by a reaction and polymerization of AA at 100 \({}^{\circ}\)C for 10 min under pressure. A drastic decrease in toughness and an increase in tensile modulus were observed in PLA-\(g\)-PAA as compared to PLA [88].  
Inorganic-organic hybrid composites, based on mixtures of PLA and SiO\({}_{2}\)[89] and TiO\({}_{2}\)[90] generated via a sol-gel process, also showed improved mechanical and thermal properties when PLA was replaced by PLA-\(g\)-AA. This was attributed to stronger interfacial forces between carboxylic acid groups of PLA-\(g\)-AA and the residual Si-OH and Ti-OH groups [89].
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 4 Functions of the Fog Server in Details
### Authentication  
This section illustrates the authentication process of the proposed fog server (see Figure 6). As mentioned before, strong authentication has to be performed when the client connects to the fog server for the first time. The authentication information of this client will be maintained in the AuthTracker to allow performing weak authentication later.  
1. A client sends a request to the fog server that will be authenticated first by the AuthTracker (quick authentication).
2. As there is no authentication information stored for that client yet, the fog server sends the \"need_auth\" back to client.
3. The authentication flow will be started in this step. The client sends the challenge that will be generated by the client to the fog server.
4. After receiving the challenge, the device module of fog server will check the registration of the device first.
5. If the registration information exists, the corresponding response will be calculated and sent back to the accessing module.
6. While the accessing module receives the response, the device module will generate a ticket to be stored in the AuthTracker simultaneously.
7. The response will be sent back to client.
8. After the client receives the response, it will send the same request to fog server again and this request should be authenticated by AuthTracker as well. The quick authentication will be passed as AuthTracker already has stored the authentication information of that client.  
Figure 6: Authentication data flow.
## 3 Results and Discussion
### Assessment for Antifungal and Antibacterial Activity in the Contactless Test  
In order to develop an application method able to prove the remote effect of EOs on microorganisms, avoiding direct contact with the pigments of paintings, a preliminary in vitro contactless test was designed in this work. Since the oregano EO gave the most promising results with the contact test, it was chosen for the contactless test. In this test microorganisms, grown on agar plates, come in contact solely with the volatile components of the EO. In detail, once the microorganisms were plated on the growth medium, a drop of the essence was placed on the lid of the plate, and the plate was turned over and incubated under sterile conditions. The result of the test is shown in Table 2.  
In the test, a total inhibition effect was exhibited by oregano EO on bacterial clusters II (Xanthomonadaceae), IX (_Cellulosinicrobium cellulans_), and XII (_Paeinobacillus_ sp.). Moreover, if compared with the inhibition halo obtained with the contact test, oregano showed a higher effect in contactless tests both on fungi and bacteria (Tables 1 and 2). Even if a comparison between the two tests cannot be done because of the use of different EOs' quantities (10 \(\upmu\)L in the contact test and 100 \(\upmu\)L in the contactless test), the results obtained provide clues for the sole utilization of the volatile components in control practices. Previous work confirmed the antimicrobial efficacy of EOs' vapor phase for disinfection of textiles, reporting no changes in terms of structural parameters of the object [12; 16]. However, although these data provided support to the plethora of promising uses and properties exhibited by EOs, they did not avoid the procedure of direct contact of the oils with the surface of the object under study.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.2 Blood Pressure Monitors  
Blood pressure monitors [18] are sensors used to measure the force of blood against the walls of arteries as the heart pumps blood throughout the body. These sensors come in various forms, including wearable devices and home blood pressure cuffs. Monitoring blood pressure levels is crucial for managing hypertension, a significant risk factor for cardiovascular diseases such as stroke, heart attack, and kidney disease. Wearable sensors that continuously monitor blood pressure levels can provide valuable data for optimizing hypertension treatment and reducing the risk of associated complications. Additionally, a study has shown that 77% of patients with IS have a systolic blood pressure >139 mmHg, while 15% have a systolic blood pressure >185 mmHg [19]. Commercial devices include the Omron HeartGuide Wearable Blood Pressure Monitor, QardioArm Wireless Blood Pressure Monitor, iHealth Clear Wireless Blood Pressure Monitor, and several smartwatches.
## 2 Geological Setting  
### Regional Geological Context  
In the studied region, the following two geological units can be distinguished: the Palaeozoic basement and the sedimentary cover (Figure 1). The first unit is basically composed of intensely folded phyllites intruded by granite in the last stages of the Hercynian Orogeny [31; 32]. Post-Hercynian sedimentary cover is deposited unconformably on this basement, arranged sub-horizontally. This cover is composed of Triassic, Miocene and Quaternary materials. The basal Triassic deposits are characterized by the presence of levels of fluvial conglomerates and sandstones that are wedged laterally, with abundant quartzite and sandstone clasts, that do not exceed 20 m in thickness (T1 in Figure 1b), according to the information obtained in the geological research carried out in the nearby outcrops, as well as the data provided in previous works [31]. Above these facies, there is a set of red lutites with some sandstone intercalations whose thickness ranges between 60 and 120 m, depending on the sector (T2 in Figure 1b) [31]. Above this set and separated by a stratigraphic discontinuity is the Late Miocene (Messinian) unit, with thicknesses of approximately 100-150 m in the sector but sometimes reaching 200-300 m towards the centre and south of the basin. At the base, calcarenitic breccias or polygenic conglomerates (M1 in Figure 1b) appear, which change laterally and vertically to marl and limestone marl with fine calcarenitic intercalations (M2 in Figure 1b) [31]. The Quaternary deposits are associated only with the filling of river channels and are characterized by the presence of silt, sand and gravel [31; 32; 33].  
There are two subparallel normal faults (the Banos fault and Guarroman fault, Figure 1a), oriented NE-SW, that delimit a horst/graben structure. The differential erosion of these structures results in the sedimentary cover being conserved almost exclusively in the depressed sector (graben structure), which is known as the \"Bailen Basin\".  
The Bailen Basin is characterized by the presence of two superimposed aquifer units, which coincide with the basal Triassic materials (T1 in Figure 1b) and basal Late Miocene (M1 in Figure 1b). The lower aquifer offers low pumping flow rates (between 1-2 L/s), and the upper aquifer offers somewhat higher pumping rates (between 1 and 5 L/s). Laterally, these aquifers are limited by the two faults mentioned above. The movement of these faults was complex, with different pulses occurring over time [4]. Therefore, the subsidence and sedimentary filling of the basin were different, with lateral changes in facies and thicknesses, which in turn influenced both the hydrogeological characteristics of the units described and the discharge rates of the exploitation boreholes [33; 34].
# Detecting Jamming in Smart Grid Communications via Deep Learning
## 4 Background
### Convolutional Neural Networks  
A category of Deep Learning (DL) models known as CNN recently brought a paradigm shift in the domains of computer vision and image processing. Neural networks possess the ability to learn and extrapolate data, making them indispensable assets in an extensive spectrum of applications, including, but not limited to, object detection [45; 46], medical imaging [47; 48], as well as face detection and recognition [49; 50; 51; 52].  
CNNs consist of neurons that refine their performance through the learning process. Each neuron receives an input and executes operations, such as a scalar product, followed by a nonlinear function. Typically, a CNN architecture is defined as a sequence of convolutional layers, pooling layers, and fully connected layers primarily dedicated to pattern recognition in images. A typical representation of a CNN is provided in Figure 4, where the input layer serves as the model's input, and the output layer provides class predictions.  
The primary role of the convolutional layer is to extract features. In contrast, the pooling layer conducts down-sampling to enhance computational efficiency and broaden the receptive field. Fully connected layers are commonly used in the later stages of a CNN,  
Figure 3: BPSK modulation. The receiver assumes the positions of the symbols at red crosses under ideal conditions [\(I\)=\(-1\), \(Q=0\)] and [\(I=1\), \(Q=0\)], but signals are displaced from their actual positions due to noise and manufacturing imperfections.  
Figure 2: BPSK modulation and demodulation. The binary stream is translated into in-phase (I) and quadrature (Q) components. \(I\)–\(Q\) components are then modulated by a given carrier. During the demodulation phase, the I and Q components are extracted from the received signal to recover the transmitted stream.  
following convolutional and pooling layers, to perform advanced feature extraction and ultimately generate predictions. A convolutional layer has a kernel, also called a filter, that is convoluted with the input to the layer to generate a new feature map.  
Activation functions are used to introduce nonlinearity to CNNs; some common activation functions are the Rectified Linear Unit (ReLU) [53], sigmoid, and tanh [54], to name a few. The activation value of a feature map \(z^{l}_{i,j,k}\) of the layer \(l\) at location (_i,j_) is passed to obtain the corresponding feature map's activation value. Finally, the pooling layers are introduced to reduce the dimensionality of the data while simultaneously facilitating the extraction of critical features from the local neighborhood. These operations serve to diminish the susceptibility of the model to overfitting and provide the desirable property of shift invariance.
## 3 Results and Discussion
### Evaluation of HS Sampling and Identification of Pollutants in HS Gas
An important effect on the headspace sampling method efficiency has the sampling time because headspace sampling is an equilibrium-based technique. The influence of the headspace sampling time on the peak area was studied by adjusting the time in the range of 5-30 min. By using a higher sampling time, the transfer of the analytes into the gaseous phase is increased. The equilibrium was observed after 20 min. After this time, no significant changes were noticed in the extraction efficiency for the target analytes (Figure 2). The selected time was enough for the distribution of the analyte between the headspace and water media. However, the small decrease in the peak areas for VOCs after 20 min can be considered not relevant, but it was shown in the literature that there is a degeneration in the method precision for longer sampling times [31; 33; 34]. Therefore, the sampling time was fixed at 20 min. Figure 1: The effect of the headspace sampling temperature on the efficiency of the sampling of toluene, ethylbenzene, chlorobenzene, and total xylene transfer to the gas phase. The dependence of the peak areas on the sampling temperature. The mass transfer of the volatile analytes should be influenced by another important parameter, which is the stirring rate. This parameter has an effect on the time-enrichment profile. It was shown that mixing of the sample solution improves the mass transfers in the aqueous phase and induces convection in the headspace, and consequently, the equilibrium between the aqueous and vapor phases can be more rapidly attained [28]. Stirring rate was evaluated between 0 rpm and 1200 rpm. The increase in the efficiency of volatile compounds' transfer into the headspace reaching a stirring rate of 1000 rpm was confirmed, which was then sequentially decreased at higher stirring rates. At a higher speed, the stirring bar started to vibrate, and the mixing of the sample was not consistent. This means that higher agitation tends to be uncontrollable, which might cause a change in the equilibrium time and poor measurement precision [28]. Therefore, to reach the highest efficiency of the mass transfer, the stirring rate of 1000 rpm was selected for further experiments. It was shown that the best HS parameters for the isolation of toluene, ethylbenzene, xylene, and chlorobenzene from the paint-industrial water effluent were the following: 20 min sampling time, 70 \({}^{\circ}\)C sampling temperature, 1000 rpm stirring rate, and the volume of the gas phase at 40 \(\mu\)L.
## 1 Introduction
Meanwhile, for subsea jumpers with complex geometric structures, a few studies have been carried out using numerical simulations, but the research conditions have been singular, and the corresponding data support and experimental verification are lacking. Therefore, in the present study, to obtain a good understanding of the development of the slug flow inside subsea jumpers, a combination of a numerical simulation and an experimental study was used to investigate the slug characteristics, such as the slug length, slug frequency, and slug velocity, inside a subsea jumper and the slug flow-induced vibration characteristics of the jumper. The corresponding experimental data can verify the employed numerical model, and they also provide a certain reference for subsea jumpers' design, safe operation, and vibration control. In the present study, an experimental circuit was established. A visualization of the two-phase flow was conducted using a high-speed camera to analyze the flow characteristics and slug characteristics of the internal gas-liquid slug flow. Pressure sensors and accelerometers recorded the pressure and vibration data to investigate the relationship between the slug characteristics and the pressure and the induced vibration. The VOF model and the unidirectional fluid-structure coupling method were used in the numerical simulations to investigate the interaction between the slug flow and the jumper at various mixing gas-liquid velocities. The organization of the article's structure is as follows: The multiphase flow experimental device used in this work is briefly introduced in Section 2. The multiphase flow model for the numerical simulations and the mesh convergence studies are introduced in Section 3. The analysis of the slug characteristics and the induced flow vibration characteristics under the experimental conditions, the analysis of the numerical simulation results, and a comparison with the experimental results are given in Section 4. Finally, the conclusion is provided in Section 5.