esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Over the last decades, the study of the plant metabolites of both grapes and wines has been the subject of many investigations. The importance of the approach used is emphasized since the regional or varietal origin of grapes and wines is needed to be identified. An approach that would allow an easy and reliable chemical identification of wines is still the object of interest. Our study was oriented on chemical composition of wines based on determination of alcohol content, main carbohydrates, organic acids and total phenolics and antioxidant activity. The impact of grape varieties and wine-growing regional conditions on selected chemical characteristics of white wine was studied. Correlations between the parameters was examined, we observed high positive correlation between total acids and tartaric acid content, alcohol and total polyphenols, between TPC and TAA was found a weak positive correlation. Negative correlation was high between tartaric acid and alcohol content and between malic and lactic acid. The results showed differences among the regions based on the above-mentioned chemical components. From the parameters that were used in our evaluation, organic acids, content of alcohol and glucose were found to be the most relevant in the classification of the wines. Statistical evaluation of the relationship among chemical parameters, wine variety and wine-growing region showed significant differences, especially in wines from two regions (the Stredoslovenska and Nitrianska wine regions). The white wines are considered to have lower antioxidant activity and content of polyphenols, but in the wines from four Slovak wine regions a high antioxidant activity was confirmed along with the high content of polyphenols. The content of the plant metabolites depends on several factors--grapes variety, region, weather, processing--thus evaluation based on chemical parameters is a complex procedure.  
Conceptualization, S.J. and V.I.-P.; data curation, S.J., Z.A., A.M., J.K. and M.S.; investigation, S.J., I.J. and M.F.; methodology, S.J., A.M. and M.S.; writing--original draft, S.J. and V.I.-P.; visualization, I.J.; writing--review and editing, M.F. and V.I.-P. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Slovak Research and Development Agency (APVV-19-0180), and by the Ministry of Education, Science, Research and Sport of the Slovak Republic grants (KEGA 017/SPU-4/2019 and VEGA 1/0239/21).  
Figure 3: Estimated marginal means of scaled data with 95% CI.
# An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors
## 4 Experimental Setup and Node Architecture
### Deployment (I)  
The custom made platform used in deployment (I) (Figure 3) is capable of supporting a variety of digital and analog sensors intended for structural health monitoring including the previously mentioned ultrasonic wind speed and direction (_WMT52_) sensor. The node uses the Texas Instruments MSP430F5437A microcontroller and the CC2520 as radio transceiver. The selected microcontroller has a 16 bit RISC CPU and offers various sleep modes with low power consumption. It also has sufficient SRAM and flash memory to accommodate the requirements of the application. The CC2520 exhibits fast wake-up times, low power consumption (two different sleep modes), and compliance with the updated IEEE802.15.4-2006 Standard.  
Figure 3: Custom made platform for deployment (I).  
Figure 2: Generic block diagram of a WSN platform with an energy harvesting power unit and power hungry sensor.  
The selected wind sensor is an ultrasonic Vaisala WMT52 sensor that has an array of three equally spaced ultrasonic transducers on a horizontal plane. The wind speed and the wind direction are determined by measuring the time it takes the ultrasound to travel from each transducer to the other two. The sensor can be interfaced using the RS-485 protocol, which requires an additional differential transceiver (level translator) in order to be interfaced to the microcontroller. A step up DC-DC converter from Analog Devices ADP2371 (3.3 V to 12 V) was used to provide the supply voltage to the wind sensor. To completely turn off the supply voltage for the sensor when not in use, a simple transistor based switch controlled by the MCU was implemented. This ultimately reduced the sleep current of the platform to 15 \(\mu\)A. A harvester power unit was used to harvest the wind energy with a scaled wind turbine. The harvester is a high efficiency power unit that can recharge a battery with harvested energy from the wind [31]. An off the shelf small size scaled wind turbine was used with radius of the blades of 0.17 m. This wind turbine provides a rectified (DC) output voltage that is regulated (stepped up or down) by the harvesting unit and fed to a rechargeable battery. To directly supply the node, the voltage from the battery is then further regulated to 3.3 V.  
The experiment for collecting the wind speed data was carried out over a period of 35 days. The node and the wind sensor were deployed on a roof-top of a 20 m tall building located in Cork, Ireland. The node was duty cycled, sending the collected wind data every 20 s (0.05 Hz) to a base station that was connected to a laptop storing the messages in a file. The wind sensor was configured to sample with a fixed sampling frequency of 1 Hz over a period of 10 s and sending the minimum, maximum and the average value of the speed and direction within those 10 s. The 35-day data set of continuous wind sensor monitoring was used to evaluate the proposed approach in simulation.
# Prototype of Augmented Reality Technology for Orthodontic Bracket Positioning: An In Vivo Study
## 2 Materials and Methods
### Method for Applying the AR-Assisted Bracket Navigation System  
The AR-assisted bracket navigation system comprised two technical modules, namely the facial axis of the clinical crown (FACC) detection module and the bracket bonding navigation module. Collecting real-time images captured by the intraoral camera, the FACC module segmented the image of each tooth through computer-vision-based image analysis, thereby generating dental features that included the complete contour and FACC of each tooth. The bracket bonding navigation module overlapped the real-time image with the bracket bonding positions determined during preoperational planning, thereby achieving preoperational planning visualization.
# HBIM: Background, Current Trends, and Future Prospects
## 5 Applications and Data Management
### HBIM for Structural Analysis
* Difficulties in 3D representation and visualizing real-time data updates. * Integrating the cultural and historical significance of heritage sites into their HBIM models. * Determining the amount of real-time data to be included in the development of object models. There are only a few examples of integration between HBIM and continuous SHM. [PERSON] et al. [62] developed a parametric multi-dimensional HBIM model of a timber cultural heritage building using Revit, incorporating four stages: construction, repair, monitoring, and FEA. In the FEA stage, a simplified structural geometry was exported to the finite element software ABAQUS. An SHM system with fiber grating strain sensors was installed, with safety thresholds set to trigger early warnings if anomalies were detected. While the finite element model could be updated with measured data, there is no indication of an automatic link between SHM readings and the structural model. Another example of SHM-HBIM integration was presented by [PERSON] et al. [109], who proposed a methodology to monitor both the structural behavior and human-centric environmental comfort of CH buildings within an HBIM environment. This study developed a Python-based software application that integrated a Revit model with data from operational modal analysis and continuous SHM. Another advanced example of HBIM and continuous monitoring integration was demonstrated by [PERSON] et al. [99], who developed a cyclic procedure starting with the creation of a BIM model of a steel frame bridge. From this model, a structural model was derived, which was then calibrated by adjusting cross-section dimensions and elastic properties in a modal analysis using SHM data, employing the modal assurance criterion (MAC). Once calibrated, the architectural model was updated with the new properties. This process was performed directly on the finite element model (built in ANSYS) without the need for a surrogate model due to the limited number of iterations and the relatively simple structural configuration (beam model). However, despite recent advances, further research is still required to achieve full integration between continuous monitoring systems, HBIM models, and structural analysis software. This complete integration would enable the automatic processing of SHM data, its incorporation into the structural (surrogate) model for analysis, performing real-time defect detection that activates an early alert system, and the periodic updating of the HBIM model with this information. A simplified flowchart illustrating this process is provided in Figure 9.  
Figure 9: Idealized flowchart for the integration of the structural condition assessment of a building within a HBIM environment.
# ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks
## 4 Important Security Issue in NSA-Based C-IoT Architecture  
In the C-IoT architecture, a large number of devices are connected to a base station. Providing secure communication over a shared channel is a challenging task for network service providers, specifically in systems that include a huge number of devices. Because of the huge number of large-scale systems, the C-IoT network needs a real-time and secure communication platform in order to entertain all the connected devices with high throughput and data rates. After ensuring the security and privacy issues, the device authentication process is required to increase the quality of service (QoS) parameter [19]. If any unauthorized user gained access to the identity of any objects or devices, then the attacker could execute both impersonation attacks and man-in-the-middle attacks. Further, as the NSA architecture uses both the 4G and 5G architecture, there is a possibility that the object's identity or keys could be revealed over the LTE core network.  
The heterogeneous traffic generated from the various types of networks must preserve the security and privacy of the data during the communication process. In the current scenario, the IoT services utilize the existing infrastructure and they use traditional encryption and the existing encryption processes to provide secrecy, which offers insufficient resistance against all the possible attacks over the different communication networks. In this research, we propose a security protocol that consists of cryptographic modules for confidentiality, integrity, authentication, and authorization for secure D2D communication [20]. As communication devices and IoT devices possess computational resources, they require lightweight authentication and authorization techniques to enable automated D2D communication by preserving traffic privacy and information security. Hence, the key generation and automated user authentication of UE/DE will be substantially more reliable [21].  
The other important issue in a cellular-based IoT communication system is fake identification. Factors that cause fake identification in an IoT network include the following:  
* The use of distinct unique IDs for different IoT devices or objects by user equipment can lead to confusion for mobile equipment and other IoT devices in terms of identification and authentication.
* The lack of availability of efficient algorithms for the IoT objects or device identification leads to security failures in the IoT system.
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
## 4 Evaluation
### Overall Performance  
#### 4.2.1 Evaluation of Detection of Head Movement during Sleep  
The detection results of Wi-Senser on human head motion are shown in the green bar chart in Figure 10. As shown in the figure, the characters 'G', 'B', 'M', and 'W' represent girl, boy, man, and woman, respectively. Specifically, the average detection accuracies of Wi-Senser for the ten volunteers' head movements are 96.5%, 93%, 98%, 95%, 100%, 99%, 100%, 100%, 100%, and 98%, respectively. In general, Wi-Senser achieves an average detection accuracy of over 97.95% for all volunteers' head movements. It should be noted that the average accuracy of monitoring children's head movement is lower than 95.7%, which degrades sensing performance by about 4% compared with the adults. The main reason for this phenomenon is that the children's head movements have smaller amplitude and weaker swing force, resulting in less fluctuation in the received CSI amplitude values and causing Wi-Senser to miss some slight head movements of the children. In addition, the average detection accuracy of male (i.e., two boys and three men) and female (i.e., two girls and three women) subjects is 98.4% and 97.5%, respectively. Therefore, the performance of our Wi-Senser is not sensitive to human gender. Overall, although the accuracy of Wi-Senser degrades a bit when applied to monitoring children's head movement, it can still achieve satisfactory performance (i.e., accuracy higher than 93%).
## 4 Reliability, Availability and Fault Tree Analysis
### Component Importance  
After computing the top event probability (or any other relevant metric, such as the reliability or availability), the user is able to foresee the system behavior from a dependability viewpoint. However, this does not highlight what is the contribution of each component to the final result. Such information is relevant because it allows the system designer to make decisions concerning the system structure, which can be used to optimize dependability metrics (e.g., availability), or other performance measures.  
In this section we will review some importance measures that can be used to rank components in order of importance. We assume a system composed of \(n\) independent components, where each component \(i\) is characterized by a reliability function \(R_{i}(t)\).
## 4 Results and Discussion
### Impact of Risk Factors to Value Creation
#### 4.4.5 Management Risks  
There is ample evidence in the literature to suggest that it is essential to have a skills and integration project team coupled with effective management processes to unlock value during the early stage of value planning as well as during the development operation of green facilities. The endeavor of the project should be geared towards identifying risks and opportunities to maximize value and minimize waste at every stage of the construction and procurement process, from the minute that the need for a building is identified to when it is ready for operation [33,61]. Figure 8 illustrates the respondents' perceptions about the management risks that might have an impact on value created by green building development if not taken into consideration during the value engineering analysis process. Out of the 17 managerial risk factors identified in the literature, only half of them were found to be having a negative impact on value creation attributes of green buildings, i.e., R28, R49, R50, R51, R53, R54, R57, and R64.  
\begin{table}
\begin{tabular}{l l} \hline
**Research Question** & **Environmental Risks:** \\ \hline Hypothesis & H\({}_{0}\): \(p\) \(>\) 0.05; H\({}_{1}\): \(p\) \(<\) 0.05 \\ \hline Results & The (ANOVA) results indicated that: \\  & There were significant differences between the survey participants regarding Risk factors: \\  & R38: Failure to consider obsolescence of equipment impact \\ \hline Researcher’s observation & It appears that architects in KSA are not acquainted with the fact that whole-life performance of green buildings is highly linked to the obsolescence of equipment and components. \\ \hline Conclusion & The null hypothesis was rejected for R38 risk. \\  & The null hypothesis (H\({}_{0}\): \(p\) \(>\) 0.05) was retained for other risks. \\ \hline \end{tabular}
\end{table}
Table 6: Environmental risks and hypothesis test.  
Figure 7: Ranking the environmental risk factors impacting on the project value.  
All respondents agree on considering R28 and R49 risks during the appraisal of green buildings. However, engineers viewed R51 and R50 risks different to other participants. This might suggest that engineers in KSA are not often involved in the very early stages of the design process. However, the findings here are consistent with [PERSON] and [PERSON] [51] who listed the factors that might influence the success of using value analysis in construction projects.
# Magnetic Solid-Phase Extraction of Organic Compounds Based on Graphene Oxide Nanocomposites
## 2 Preparation and Applications of GO for the MSPE of Organic Compounds
### Nanocomposites of Reduced GO with Fe\({}_{3}\)O\({}_{4}\) Nanoparticles
For this purpose, iron (III) chloride hexahydrate, sodium acetate, GO and CNTs were added in a Teflon-liner and the suspension was heated at 200 \({}^{\circ}\)C for 10 h. It was found that the Fe\({}_{3}\)O\({}_{4}\) nanoparticles were covered with RGO and CNTs, while CNTs were inserted between the RGO sheets in order to prevent the aggregation of the RGO sheets. The novel sorbent was used for the MSPEe of sulfonamides from milk prior to their determination with HPLC-UV [73]. [PERSON] and [PERSON] prepared a hierarchical porous polypyrrole-coated nanostructured composite of RGO and Fe\({}_{3}\)O\({}_{4}\) nanoparticles. After incorporation into alginate hydrogel microspheres, the developed sorbent was successfully applied for the MSPEe of phthalates from bottled drinks [74]. In order to increase the selectivity of the conventional RGO/Fe\({}_{3}\)O\({}_{4}\) sorbent, [PERSON] and [PERSON] reported the one-step green synthesis of \(\beta\)-cyclodextrintrin/iron oxide-RGO nanocomposite. The sorbent was used for the MSPE organochlorine pesticides residue from honey samples prior to their determination by gas chromatography-electron capture detection (GC-ECD). In this case, functionalization with \(\beta\)-cyclodextrintrin was chosen, due to the high potential supramolecular recognition capability of \(\beta\)-cyclodextrintrin [75]. [PERSON] et al. synthesized zero valent Fe-RGO quantum dots and used them for the MSPEe of organophosphorus pesticides from real water and fruit juice samples prior to their determination with GC-MS. The quantum dots were prepared with a hydrothermal cutting method and sodium borohydride was used for the reduction. The novel sorbent provided short extraction time, simple operation as well as high enhancement factor [76]. Figure 3: Scanning electron micrographs of reduced graphene oxide (RGO)/Fe\({}_{3}\)O\({}_{4}\) prepared by solvothermal (**a**), hydrothermal (**b**) and co-precipitation (**c**) methods. Reproduced with permission from [64]. Copyright Elsevier, 2015.
## 3 Results and Discussion
### Spectra
In lower gain images, the intensity of the measured response for soil areas generally exceeded response for the spinach leaf and for the manure. It would be impossible to reliably detect manure under these conditions. For the 100 cm drape condition, results were more positive. At the lowest gain, the intensity of the spectral response at 525 nm was just greater than the response of the spinach leaf and, by using a ratio calculation, the response differential could be marginally accentuated. For the zero drape condition, calculated spectra supported the ability to detect the manure. On a practical basis, detection of manure during field surveys under full daylight will require a more complex shading scheme or the use of a laser-based system that was designed to function in full daylight. Figure 5: Gain 20 (**left**) and gain 32 (**right**) images at 525 nm of manure on a spinach leaf. Images were acquired at dusk with the drape raised 250 cm. Figure 6: Spectra at gain 20 (**left**) and gain 32 (**right**) of soil, spinach, dairy manure, and stem created using the images in Figure 5; note the ROI (region of interest) for soil was selected for a bright area and a ROI was added for the stem as the stem appeared brighter than the leaf. Image was acquired at dusk with the drape raised 250 cm.
## 5 Experimentation, Results, and Analysis
### Experimental Setup
#### 5.1.2 Baselines  
To offer a comprehensive and rigorous assessment of the DCAT model, we selected six baselines that represent a diverse range of methodologies and have proven their effectiveness in the realm of image captioning. These baselines were carefully selected to encompass early foundational models, attention-based mechanisms, and recent state-of-the-art approaches.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Hyperparameter** & **Value** & **Rationale** \\ \hline Learning Rate (\(\eta\)) & \(1\times 10^{-4}\) & Optimal convergence \\ Batch Size & 64 & Hardware utilization \\ Epochs & 50 & Model convergence \\ \(\alpha\) (Custom Loss) & 0.7 & Linguistic accuracy \\ \(\beta\) (Custom Loss) & 0.3 & Contextual richness \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameter settings for proposed DCAT.  
* **Neural Image Captioning (NIC) [32]**: One of the pioneering models for image captioning that employs a Convolutional Neural Network (CNN) for image feature extraction and a Long Short-Term Memory (LSTM) network for caption generation.
* **Sequence to Sequence/Video to Text (S2 VT) [33]**: Originally developed for video captioning, it uses an encoder-decoder framework and has been adapted for image captioning tasks.
* **Up-Down Attention [4]**: A model that significantly improved the attention mechanism by implementing both bottom-up and top-down attention layers, allowing for more nuanced attention allocation.
* **Image Transformer [34]**: This model adapts the transformer architecture, originally designed for machine translation, to the task of image captioning, allowing for parallel computation and handling long-range dependencies effectively.
* **Object-Semantics Aligned Pre-training (Oscar) [35]**: Incorporates object-level semantic features into the image representation. It utilizes pre-trained object tags alongside the image features for a richer representation.
* **Attention on Attention Network (AoANet) [36]**: An extension of the attention mechanism that adds an additional layer of attention computation, improving the model's focus on relevant image features.  
Each of these baselines offers a unique angle of comparison, allowing us to evaluate the DCAT model's performance in different aspects, from basic architecture and attention mechanisms to semantic alignment and advanced attention layers.