esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methodology  
### Desalination Plant and Data Collection  
The data obtained to carry out this study were extracted from an experimental reverse osmosis desalination plant with a capacity of 80-100 m\({}^{3}\)/day (Figure 1) located in the facilities of the Instituto Tecnologico de Canarias (ITC), in Gran Canaria. Figure 1 shows a simplified diagram of the main elements that comprise the desalination plant. At the start of the process is the pretreatment system consisting of sand and cartridge filters (F.A. and F.C.). After that is the high-pressure pump (B.A.P. in Spanish), and finally the reverse osmosis membrane. This plant is part of the DESAL + LIVING LAB platform and is used for research purposes.  
As seen in Figure 2, the plant is located in a 20 ft container, which makes the plant very easy to transport and apply to different environments, such as a marine vessel in this case. Since this desalination plant is used for research purposes, it has been designed with several possible configurations, allowing the researchers to obtain different recovery and energy consumption ratios. Apart from this, another fundamental element is its plug-and-play technology, which allows different experiments to be carried out quickly and efficiently. The main characteristics of the plant are described in Table 1.  
The data used were recorded over a 24-h period, at one-minute intervals between each measurement. The variables recorded include the working speed of the high-pressure pump, pressures and flow rates at different points in the system, seawater pH and conductivity, conductivity of permeate water, general energy consumption (kWh), total specific energy consumption (kWh/m\({}^{3}\)), and finally, the conversion factor. A total of 19 variables were obtained, although only the ones shown in Table 2 were used in this study.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Unit Values/Characteristics** \\ \hline Production capacity & 80–100 m\({}^{3}\)/day \\ High-pressure pump (HPP) operating pressure & 20.8–57 Bar \\ HPP maximum pressure & 57 Bar \\ HPP nominal RPM & 600 \\ Feed flow rate & 8.01–10.15 m\({}^{3}\)/h \\ Permeate flow rate & 3.09–4.15 m\({}^{3}\)/h \\ General energy consumption & 3.8–9 kWh \\ Total specific energy consumption (Salino & 1.97–2.5 kWh/m\({}^{3}\) \\ pressure center) & Hydranautics SWC4 MAX (Spiral wound) \\ Permeate recovery rate & 34–43\% \\ \hline \end{tabular}
\end{table}
Table 1: Main parameters of the SWRO plant used in the study.  
Figure 1: Desalination plant diagram.  
Figure 2: Container housing the plant.
## 3 Methods and Fabrication
### Fabrication  
Figure 4 presents a schematic illustration showing the basic steps in the MEMS process used to fabricate the ZnO pyroelectric devices. As shown, the multilayer sensor was supported on a silicon wafer with both sides polished. Si\({}_{3}\)N\({}_{4}\) thin films with a thickness of 1 \(\upmu\)m were deposited on either side of the wafer using a low-pressure chemical vapor deposition (LPCVD) process to serve as electrical insulation layers (see Figure 4a). An Au/Cr bottom electrode was patterned on the substrate by electron beam evaporation and wet etching, as shown in Figure 4b-d. Note that the Cr layer (thickness 300 A) was deposited first as an intermediary layer to improve the adhesion between the gold layer (thickness 1000 A) and the substrate. The ZnO sensing layer was sputtered on the lower electrode/substrate using a ZnO target with a purity of 99.99% (see Figure 4e). Note that the ZnO target was pre-sputtered for 30 min prior to the deposition process in order to remove any surface impurities. In addition, the chamber was pumped to a base pressure of 5 \(\times\) 10\({}^{-6}\) Torr before sputtering. The chamber was then supplied with argon gas at a rate of 30 sccm to maintain a constant chamber pressure of 5 \(\times\) 10\({}^{-3}\) Torr during the deposition process. The sputtering process was performed with an RF power of 150 W for 2 h. The substrate was heated at a temperature of 220 \({}^{\circ}\)C throughout the deposition process in order to improve the microstructural quality of the ZnO film. Following the sputtering process, the ZnO sensing layer was patterned by photolithography and wet etching with an HNO\({}_{3}\):H\({}_{2}\)O (1:4) etchant (see Figure 4f,g).  
Figure 3: (**a**) Mesh, (**b**) location and (**c**) distribution of analyzed points in the simulation model of suspended sensing structure.  
A Au/Cr electrode was patterned on the ZnO sensing layer using a similar process to that used for the bottom electrode, with the exception of a lift-off patterning process (see Figure 4h-j). Finally, the sensing layer on the suspended structure was released by etching the underside Si\({}_{3}\)N\({}_{4}\) layer with CF\({}_{4}\) 25 sccm and O\({}_{2}\) 5 sccm at a power of 50 W for 8 min (see Figure 4k) and then wet etching the silicon substrate with KOH 30 wt. % at 80 \({}^{\circ}\)C at a rate of 1.5 \(\upmu\)m/min (see Figure 4l).
## 3 Proposed Method  
In this section, we present an enhanced dual RDH method, integrating the EMD technique with the LSB replacement strategy to boost performance. Our new method uses two images instead of one to hide data, which is different from the traditional EMD and LSB methods that use a single image. This two-image approach avoids the problem of the original image being permanently altered and un-recoverable. By employing a pair of cover images, our approach not only provides more effective data concealment but also assures the receiver can retrieve and reconstruct the original images, enhancing secure data management.  
This technique significantly boosts security and upholds data integrity by requiring two images to fully recover hidden data, effectively mitigating the risks associated with a single image compromise. It guarantees the original data cannot be recovered from just one image, thus reinforcing communication security. Additionally, it preserves the quality of the cover images throughout the data embedding process by discreetly altering a single pixel in each pixel pair, which keeps the changes invisible and maintains the images' natural look.  
Using two pixels for pair-wise data hiding, our method can conceal a quinary integer, equivalent to 2.32 bits of information, translating to a data hiding efficiency of 1.16 bits per pixel. This level of efficiency, validated by traditional EMD methods, is retained in our approach, providing a robust and aesthetically unaltered data hiding solution that fulfills complex data protection needs.  
Figure 3 illustrates the flow of the enhanced dual RDH process. The sender creates two cover images from the original image and utilizes the RDH technique to embed secret data into each, resulting in two stego images. The receiver employs these images to extract the hidden information and reconstruct the original image through a data extraction and image recovery process. This diagram effectively demonstrates how the proposed method enhances security and maintains data integrity while securely transmitting information.  
Sections 3.1 and 3.2 provide detailed explanations of the data hiding process and the data extraction and original image restoration process, respectively.  
Figure 3: Schematic diagram for the proposed model.
# Power Control Techniques for Interference Management-
## 6 Limitations  
The study performed cannot be without limitations. The range of years from 2018 to 2023 is a limitation. The range could have been extended to 2018, when 5G technology was in the process of conceptualization. However, we understand that the rigorous procedure of our systematic literature review reduced the likelihood that the omitted research would have contained information that would significantly affect our results. Nevertheless, our future review will take this into account. Although three significant databases were used for searching relevant studies, many un-indexed journals and publications from such journals might have been missed. We recommend considering other pertinent databases such as ScienceDirect and Google Scholar. No search string can be 100% perfect; therefore, false positive and negative results are always possible. Even though other expert contributions are related to selecting search keywords from information specialists (i.e., librarians), more academics in the field will be involved for quality results. Only English language papers were considered for this study, which may have biassed results toward English-speaking countries to some extent. Collaboration with other non-English speaking authors will be sought (in future) to obtain a more comprehensive analysis.
## 2 Methodology
### Material  
This study focuses on Tritan, a copolyester polymer widely used in filament form for 3D printing via FDM (Fused Deposition Modeling) [19]. With a diameter of (1.75 \(\pm\) 0.05) mm, the filament ensures consistency and is compatible with most standard 3D printing systems. Tritan is well-known for its impressive mechanical and thermal properties, such as high tensile strength, impact resistance, and rigidity, which make it an ideal choice for producing durable, functional parts like prosthetic components.  
One of the notable characteristics of Tritan is its ability to remain thermally stable, with a high glass transition temperature (Tg) of approximately 100-110 \({}^{\circ}\)C, allowing the material to withstand high temperatures without losing its structural integrity. This is crucial for applications where the material may be exposed to varying thermal conditions. Additionally, Tritan's melting temperature typically ranges between 230 and 250 \({}^{\circ}\)C, providing reliable fusion during the FDM printing process.  
Unlike other materials, Tritan is not brittle, which significantly improves its durability, making it suitable for designs that require flexibility and strength. Its high resistance to chemical agents, such as oils, solvents, and common cleaning solutions, further contributes to its suitability in environments where exposure to such substances is frequent. This characteristic is particularly important in the context of prosthetic applications, where hygiene and long-term wear resistance are key factors.  
Tritan also exhibits transparency, which can be an advantage when designing prosthetic devices that seek a balance between functionality and aesthetics. In orthopedic applications, Tritan's ability to combine strength, flexibility, and chemical resistance makes it viable for the design of parts with complex geometries, often required in customized prosthetic designs, offering both form and function. Furthermore, the material's ability to be printed with high precision in standard FDM 3D printers supports its viability for manufacturing custom prosthetic parts with dimensional accuracy and efficiency.  
Due to these combined properties, Tritan has shown great orthopedic potential, particularly for functional designs, such as hand prostheses, where the balance between durability, comfort, and adaptability is essential for patient satisfaction and device longevity.
# Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text
###### Abstract
In [11], the authors created a dataset to annotate the Saudi dialect sentiment analysis and built a lexicon for this dialect to develop a weighted lexicon-based algorithm. The latter will help detect and classify Arabic sentences as polar or non-polar, taking into consideration some additional linguistic features such as negation. In a previous work [12], we showed that the presence of a pattern (part of the post consisting of one or two words) could help determine the polarity of the post as positive, negative, dual neutral, or spam. However, results showed that about 17% of errors were due to negation. Therefore, exploiting patterns in a lexicon-based classifier and disregarding that their sentiment may be affected by other words, such as inverters, may lead to incorrect classification. On the other hand, these inverters have more variability in their influence. In other words, they do not always flip the polarity of sentiment in a post. As a consequence, the treatment of such words is not trivial. Besides, the complex nature and richness of the Arabic language complicate their analysis. For example, the exact words that can flip the sentiment of what comes after them may hold a meaning not related to negation, such as, which means \"how beautiful\" despite the use of the word, which can act as an inverter in another context. Moreover, the exact words can appear as substrings of another word. When considering Dialectal Arabic (DA) with the absence of grammar and informal spelling, this problem becomes even more challenging. This paper presents an approach to treat the negation issue and its effect on sentiment analysis. We modify the classification used in previous work [12] to consider negation. Our results show that treating negation has the potential to lead to an increase in classification performance. This performance increase relies on the negated positive frequency and the negative targets. The rest of the paper is divided as follows: In Section 2, some essential background information is added. Section 3 illustrates the materials and methods used in this paper. Section 4 presents the results of the presented work, followed by a discussion in Section 5. Finally, Section 6 concludes the paperwork and includes possible work extensions.
## 4 Experiment and Discussion
### Gain Estimation  
#### 4.5.1 Results  
The target gains of five frequencies (250 Hz, 500 Hz, 1 kHz, 2 kHz and 4 kHz) are compared in the experiment. Results are shown in Figure 10. From the figure, the optimal gain is computed based on the initial setting through the professional fitting software [35], and then is corrected by the audiologist for about two or three times, which will lasts several weeks. The first seven figures are the estimated gains of seven subjects, respectively, and the average gain is shown in Figure 10h.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method Comparisons**} & \multicolumn{8}{c}{**Subjects ID**} \\ \cline{2-7}  & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** \\ \hline I vs. G & * & \(\Delta\) & ** & \(\Delta\) & \(\Delta\) & ** & \(\Delta\) \\ I vs. P & *** & \(\Delta\) & *** & *** & * & * & *** \\ G vs.P & \(\Delta\) & * & \(\Delta\) & *** & ** & ** & ** \\ \hline \hline \end{tabular} Note: Statistical significance is shown by asterisk symbols: \(\Delta\)\(p>0.05\); * \(0.01<p<0.05\); * \(0.001<p<0.01\); *** \(p<0.001\).  
\end{table}
Table 3: Statistical analysis of the recognition rate for environmental sounds.  
Figure 9: Recognition rate for environmental sound.
## 2 The IDIVS Concept and Application Domains
### Application Areas
#### 2.4.3 Smart Health  
The IDIVS can be used to estimate and output the different activities humans perform, e.g., sitting still, resting, walking, biking, etc., which can be used in different health applications. Self-reflection on personal data has been shown to have a positive impact on the overall quality of a user's health. Whether the purpose is to monitor one's own health and well-being or to change habits for a healthier lifestyle, the IDIVS can potentially enhance the capabilities of personal sensors, such as smartphones, smart watches, and wearable technology, as well as make use of stationary sensors, e.g., motion sensors, to estimate richer and more elaborated states. It can also be applied, for instance, to work ergonomics by tracking a user's activities. In the examples above, the user feedback makes the system more interactive and accurate. Furthermore, the active engagement of the user may make them reflect on their activities during the day. For recent examples of the IoT in the healthcare domain, see the review by [PERSON] et al. [19].
# Interpreted Petri Nets Applied to Autonomous Components within Electric Power Systems
## 4 Case Study
### Analysis of Specifications and Required Modifications  
The formal specifications, in the form of an interpreted Petri net, may then be analyzed and verified to check its basic properties using analytical or verification methods. Some properties may be checked by application of available Petri net tools. The most important properties include:  
1. [label=()]
2. [label=()]
3. liveness, i.e., whether or not the model gets stuck in a deadlock state;
4. safety, i.e., whether all places contain at most one token;
5. reversibility, i.e., whether it is possible to return to the initial state;
6. determinism, i.e., whether the net is deterministic (weakly or strongly).  
Thus, the analysis of the interpreted Petri net from Figure 6 reveals that it is live, safe, and reversible. The initial marking is reachable from any other marking in the net and the algorithm does not get stuck.  
However, the Petri net is not deterministic. For example, when place \(p3\) is active, two transitions may be realized--either \(t3\) with guard \"\(x2\)\" or transition \(t17\) with guard \"\(x1\)\" (in the case when \(x1\) is false and \(x2\) is true). If only one of these conditions is fulfilled, the net behaves in a deterministic way. Otherwise--if two of them are fulfilled (\(SoC\leq 0.6\)\(SoC_{MAX}\) and a request to quit the algorithm is sent)--we cannot be sure which of them will be realized. This is obviously not a desired property, so we have to adjust the guards of the transitions so that they are mutually exclusive. The highest priority of the system's operation is always the signal to disable the provision of the power stabilization service (in our case it is signal \(x2\)).To do so, the priorities must be given to transitions (according to the proposed Algorithm 2), which result in more complex guards of transitions. For example, conflicting transitions \(t3\) and \(t17\) are then resolved by changing the guard of transition \(t17\) to \"\(x1\) & \(t2\)\" and giving a higher priority for a request to quit the algorithm. Moreover, the charging and discharging of various parameters is dependent on the value of \(\Delta P_{\mathit{nf}}/S_{kA}\), and therefore the guards of transitions outgoing from those places are also extended to be strongly deterministic and to show the priorities. It should be noted that not all transitions are really in conflict, although when looking at the structure of the Petri net, it seems that this may be the case. Transition \(t2\) and \(t21\) are not in conflict, despite the fact that they are assigned just simple input signals. Here, it is not possible that input signals \(x1\) (guard of \(t2\)) and input signal \(x8\) (guard of \(t21\)) can be active at the same time, because either SOC \(\leq 0.6\) SOCMAX or SOC \(\geq 0.65\) SOCMAX (see also Table 2). The revised deterministic interpreted Petri net is presented in Figure 7.
### UAV Imagery  
In this study, the two UAVs used were a Da-Jiang Innovations (DJI) (DJI, Shenzhen, China) Matrice 100 and a DJI Phantom 4 RTK [38]. The DJI Phantom 4 was flown at 30 m altitude as per the manufacturer's recommendations regarding the visual sensor system's optimal performance and connection to the RTK GNSS base station. The RGB image resolution was 0.9 \(\times\) 0.9 cm and capture was set to 80% side and 80% front image overlap. A fine resolution, typically \(<\) 10 cm, and high image overlap ensure a greater chance of successful image mosaicking as crop canopies densify during a growing season [27].  
The DJI Matrice 100 carried a MicaSense RedEdge (MicaSense Inc., Seattle, WA, USA) narrowband multispectral camera including the following bands ordered by increasing wavelength: (1) blue, (2) green, (3) red, (4) red-edge, and (5) near-infrared (NIR) (Table 2) [39]. Image acquisition was conducted on the same dates as ground fieldwork before biomass collection. For the fieldwork conducted during the week of 27 May, the weather conditions were characterized by strong winds and sudden rain showers, so the UAV was flown on different dates between 26 May and 29 May for each field whenever the weather was suitable.  
The flight plans for each field were made in the Pix4D software suite \"Pix4 Dcapture\" app (Pix4D S.A., Prilly, Switzerland) to cover entire fields in a zigzag pattern [40]. Pix4 Dcapture includes the function of adding custom UAV and sensor properties to calculate the flight plan's estimated total time, battery usage, and image resolution at the plan's designated altitude. The flight altitude for each field's plan was set at 60 m, as \(>\)60 m wind and gust conditions were usually greater than the UAV manufacturer recommendations. Based on the MicaSense camera's specifications, the resulting image resolution was 4 cm \(\times\) 4 cm with capture set to 80% side and 80% front overlap. Unfortunately, images for field W3 on 5 May and field W1 on 27 May were corrupted and unusable for further processing.
## 2 Materials and Methods
### Retrieval Algorithms
#### 2.2.4 Cloud Detection and Flagging
Paths with black arrows indicate the primary classification results: Only one primary classification result can be attributed to a given elevation sequence. Paths with blue arrows indicate secondary classification results, which complement the primary classification results. Figure taken from [43]. Within the FRM4 DOAS project, it was therefore decided to use this cloud classification scheme. However, since this algorithm has so far only been applied to a few MAX-DOAS measurements, further tests and adjustments are still necessary and are currently ongoing. Once tested and verified, the resulting cloud classification algorithm will be implemented in the FRM4 DOAS processor. The cloud classification algorithm can in principle be applied in all climatic zones worldwide. However, there are two restrictions which are mainly important for measurements at high latitudes. First, especially in winter, the solar zenith angles at such locations will be rather high and might not cover the SZA ranges needed for the calibration procedures. Second, for measurements above high surface albedo due to snow or ice, the threshold values, especially for O\({}_{4}\), would have to be adapted (for more details see [45]).