esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mineral Composition of TPW, TPW-PEEL, and TPW-SEED  
The mineral composition of TPW is presented in Table 3. It is evident that different fractions of the material have distinct mineral profiles. TPW-PEEL had the highest contents of Fe, Mn, and K. The peel was especially rich in Fe and Mn, while TPW-SEED contained lower amounts of Fe, Mn, as well as Br, Rb, Se, and K. Previous research reports variable mineral composition of TPW [56; 59], which is expected, as it depends on genetic variation, fertilization, irrigation, cultivation method, and ripening stage among other factors [60; 61; 62; 63; 64]. To the authors knowledge there is no other research concerning the mineral content of indigenous Croatian tomato varieties.  
Since TPW is relatively rich in minerals, there have been efforts to use it in enrichment of food products. For example, [PERSON] and co-workers [65] incorporated TPW in the production of muffins and bread which resulted in higher content of K, Mg, Ca, and Fe. They also observed lower sodium content, which might prove as a valuable outcome considering the excessive consumption of sodium (table salt) in many parts of the world. The products also had a softer texture and improved shelf life compared to those without the addition of TPW. [PERSON] and [PERSON] [66] prepared crackers containing tomato pomace that had significantly higher content of Mg, Ca, K, P, Mn, Zn, and Fe compared to controls. The respondents reported certain bitterness in the taste; however, they did not report significant decrease in sensory attractiveness. An interesting study was performed by [PERSON] and [PERSON] [67], who made tarhana (fermented wheat/yogurt food product) comprising seed from tomato by-products to improve its characteristics. They reported the increase in the content of all the analyzed minerals in the study--Mg, CA, K, P, Mn, Zn, Fe, and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element (mg/kg)** & **TPW-70** & **TPW-LIO** & **TPW-PEEL** & **TPW-SEED** \\ \hline Cr & 1.24 \(\pm\) 0.65 a & 1.06 \(\pm\) 0.08 a & 1.17 \(\pm\) 0.75 a & 0.54 \(\pm\) 0.01 a \\ \hline Mn & 15.3 \(\pm\) 2.06 b & 18.17 \(\pm\) 1.38 b & 25.97 \(\pm\) 4.01 c & 1.0 \(\pm\) 0.0 a \\ \hline Ni & 2.03 \(\pm\) 1.01 a & 1.66 \(\pm\) 0.22 a & 1.42 \(\pm\) 0.39 a & 2.15 \(\pm\) 0.23 a \\ \hline Br & 3.07 \(\pm\) 0.27 b & 3.23 \(\pm\) 0.21 b & 5.09 \(\pm\) 0.35 c & 2.11 \(\pm\) 0.18 a \\ \hline Rb & 3.73 \(\pm\) 0.09 b & 3.56 \(\pm\) 0.09 b & 8.98 \(\pm\) 0.83 c & 0.24 \(\pm\) 0.01 a \\ \hline Sr & 2.29 \(\pm\) 0.09 b & 2.85 \(\pm\) 0.32 b & 4.85 \(\pm\) 0.92 c & 0.0 \(\pm\) 0.0 a \\ \hline Fe & 117.5 \(\pm\) 34.65 b & 131.36 \(\pm\) 0.68 b & 202.91 \(\pm\) 12.57 c & 21.6 \(\pm\) 0.7 a \\ \hline K (g/kg) & 10.79 \(\pm\) 0.73 b & 11.77 \(\pm\) 3.36 b & 19.21 \(\pm\) 1.28 c & 0.92 \(\pm\) 0.6 a \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mineral composition of TPW-70, TPW-LIO, TPW-PEEL, and TPW-SEED.  
Cu. Tarhana containing 15% tomato seeds also received the highest like scores by the respondents in sensory analysis. There are also examples of adding TPW to animal feed to improve the mineral content of meat [68].
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 3 Proposed Hybrid Graph Embedding and Adaptive Convolution Networks
### Adaptive Convolutional Network  
The Adaptive Convolutional Network is essential for handling feature representation fluctuations across scales and temporal contexts. Parts of Dynamic Kernel Generation and multi-scale sensor data fusion are discussed below:
# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
## 5 Application to Predictive Maintenance of LNG Tank Refilling by Means of Trucks  
The problem formulated in Section 3 is solved in two phases: the first deals with data processing, and the second phase builds a prognostic model for determining the remaining time for refilling in the LNG tank. The second phase is solved by using the two methodologies explained below. The FIRmethodology uses annual mean temperature to predict the tank-emptying time, while the Brown method uses tank-level evolution.
## 3 Model Formulation
### Covariate Bands Estimation
#### 3.5.2 Band Estimation Process  
The following steps depict the process of obtaining the state bands:  
1. _Selecting the optimal number of clusters_: Use Sturges' rules if necessary. Subsequently, analyze the AIC and BIC scores. Then, the optimal cluster quantity identified from these techniques is chosen.
2. _Definition of band ranges_: After model training and acquiring cluster centroids, the probabilities for each data point belonging to a cluster from GMM are obtained and used to define the ranges for each state. Values on the \(f(\gamma,z)\) axis are selected in ascending order to complete the number of clusters obtained in step 1 for the range selection. The results of these band ranges serve as the operational band states within the PHM.
3. _State classification:_ Each data point is classified according to its state as determined in the previous step. Additionally, if the probability of belonging to a cluster is less than 1%, a random choice process is used for data classification.
4. _Obtaining the transition and probability matrices_: After classifying each data point, the total time spent in each state is calculated to derive the transition state rate matrix in Equation (10) and the transition state probability matrix in Equation (11).
## 2 Literature Review
### Fractal Manufacturing System (FrMS)  
As mentioned before, FrMS is based on the concept of autonomously cooperating multi-agents, referred to as fractals. FrMS exhibits several characteristics, including self-similarity, self-organization, and goal-orientation, and so on [15; 16]. Furthermore, the definitions of a fractal and FrMS are as follows.  
* A fractal is a set of self-similar agents whose goal can be realized through cooperation, coordination, and negotiation with others. It can reorganize the configuration of the fractal system to realize a more efficient and effective configuration;  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Architecture** & **Description** \\ \hline Industry 4.0 by IBM & Reference architecture comprising platform/hybrid cloud layer and equipment/device layer \\ \hline Service-oriented SMS by NIST & Functional architecture utilizing a service bus to connect various types of services in the system \\ \hline RAMI 4.0 by Platform Industrie 4.0 & Reference architecture comprising six layers with different functions, perspectives, and levels of controls. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Three representative architectures for SMSs.  
* FrMS is a flexible and fault-tolerant system developed and operated with a fractal architecture.  
The FrMS is an agent-based system that continuously reorganizes the system configuration to remain in an optimal environment. The agent-based system can autonomously improve its structure, such as upgrading servers, moving services, and performing load balancing interposed without interruptions or revisions to the network and clients. Figure 1 illustrates the concept of FrMS. The architecture of FrMS comprises a hierarchical structure developed using the elements of BFU, which is a fundamental component of the FrMS. As shown in Figure 2, The five functional modules in the BFU are the analyzer, organizer, observer, resolver, and reporter. The observer module gathers information or messages from other fractal agents and the environment. Subsequently, it delivers this information to the analyzer module and resolver module. The analyzer module obtains this information from the observer module and analyzes information, such as scheduling, simulation, and changes in the fractal's objective. The resolver module then makes alternative decisions for current fractals, such as new objectives or negotiations with another fractal, based on the analyzed information and message derived from the analyzer and observer modules. The organizer module checks the current status of the fractal, and it can alter the fractal structure. The organizer module then transmits the data related to the status and altered structure to the analyzer and resolver modules. Lastly, the reporter module reports all types of data and information to the other fractals or sensors.
## 2 Experimental
### Cell Assembly and Test  
The performances of electrode samples were tested in single dynamic cells. The 'TGF' before or after modification with Bi was used as the negative electrodes, and the 'TGF' was used as the positive electrodes. Both the negative and positive electrodes possess an active area of 3.0 cm \(\times\) 3.0 cm. A perfluorinated ion-exchange membrane Nafion(r) 115 (DuPont, Wilmington, DE, USA) was employed to separate positive and negative electrolytes. Graphite plates were employed for charge collection and transfer. The above-mentioned three components were sealed with silicon rubber gaskets. The positive electrolyte was composed of 50 mL 1.0 M V\({}^{4+}\) + 3.0 M H\({}_{2}\)SO\({}_{4}\), and the negative electrolyte was composed of 50 mL 1.0 M V\({}^{3+}\) + 3.0 M H\({}_{2}\)SO\({}_{4}\). The electrolyte was stored in external tanks and cyclically pumped into the corresponding compartments during operation. The electrolyte flows through a peristaltic pump (MP-10R, Shanghai Zhiwo Pump Valve Co., Ltd., Shanghai, China) at a flow rate of 100 mL/min. The cells were monitored by a charge-discharge test system (CT2001C-5V/2A, Wuhan Land Co., Ltd., Wuhan, China) at the current density of 20-80 mA/cm\({}^{2}\) and the voltage window between 0.8 V and 1.65 V. The coulombic efficiency (CE), voltage efficiency (VE) and energy efficiency (EE) of single cells were estimated by the following formulas [47]:  
\[\mathrm{CE}=\frac{Q_{\mathrm{discharge}}}{Q_{\mathrm{charge}}}\times 100\% \tag{1}\]  
\[\mathrm{EE}=\frac{E_{\mathrm{discharge}}}{E_{\mathrm{charge}}}\times 100\% \tag{2}\]  
\[\mathrm{VE}=\frac{\mathrm{EE}}{\mathrm{CE}} \tag{3}\]  
where Q\({}_{\mathrm{charge}}\) and Q\({}_{\mathrm{discharge}}\) represent the charge and discharge capacity, respectively; E\({}_{\mathrm{charge}}\) and E\({}_{\mathrm{discharge}}\) stand for the charge and discharge energy, respectively.
# Analysis of AMI Communication Methods in Various Field Environments
## 4 Area Types of Field Environments
Because no distance information between measurement points is provided from the graphical information of the NDIS map, the distance information for both wired and wireless communications can be obtained by using a distance measurement function provided by Naver Map for the same location as shown in Figure 19b. In order to organize the measurement data for the analysis process after measurement, the pole numbers and the customer smart meters taken in the field survey process are displayed on the aerial images as in Figure 19b. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Population** & \multicolumn{3}{c}{**Distances**} \\
**Site** & **Density** & **MPs** & **(Meters)** & **Characteristics** \\ \hline
1-1 & Middle & 5 & 32, 76, 118, 99, 60 & Small houses from 1 to 3 stories \\ \hline
1-2 & Middle & 4 & 11, 30, 57, 101 & \\
1-3 & Middle & 3 & 17, 31, 35 & Small 1-story houses \\
1-4 & Low & 3 & 35, 32, 84 & \\
1-5 & Middle & 3 & 52, 60, 96 & \\ \hline
2-1 & High & 2 & 6, 61 &
\begin{tabular}{c} Medium-sized commercial buildings below 10 stories \\ Noise from neon signs for PLC \\ \end{tabular} \\ \hline
2-2 & High & 3 & 16, 55, 72 & Medium-sized business hotels below 10 stories \\ \hline
2-3 & High & 2 & 5, 44 & Small-sized commercial buildings less than 3 stories \\
2-4 & Middle & 3 & 3, 44, 45 & \\ \hline
3-1 & Low & 3 & 64, 98, 147 &
\begin{tabular}{c} Small 1-story houses \\ PV panels around the low-voltage distribution lines \\ Switching noise for PLC during solar power generation \\ \end{tabular} \\ \hline
3-2 & Low & 2 & 140, 179 &
\begin{tabular}{c} Few houses \\ Long distances from the transformers \\ \end{tabular} \\ \hline
3-3 & Low & 3 & 6, 55, 90 &
\begin{tabular}{c} Small and medium-sized houses \\ Roof-type solar power plants with PV panels of \(\approx 3\)kW \\ \end{tabular} \\ \hline
4-1 & Low & 2 & 10, 22 & Small factories below 2 stories \\
4-2 & Low & 1 & 49 & Short distances between customers and transformers \\
4-3 & & 2 & 10, 10 & High-frequency noise for PLC from motors \\ \hline
5-1 & Middle & 1 & 21 &
\begin{tabular}{c} Multi-family houses with 3 to 4 stories \\ From the transformer to the 1 st pole; overhead lines \\ \end{tabular} \\ \hline
5-2 & High & 3 & 72, 138, 214 &
\begin{tabular}{c} 5-story multi-family houses and commercial buildings \\ Long-range underground low-voltage distribution lines \\ \end{tabular} \\ \hline
5-3 & High & 3 & \
\begin{tabular}{c} 1 st, 8 th, 17 th floors \\ from the 1 st floor \\ \end{tabular} &
\begin{tabular}{c} Apartment complex of 8 buildings below 20 stories \\ Measured in the 18-story building (144 households) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the 18 Sites with 48 measurement points (MPs) in Figures 14-18.
## 7 Security Considerations and Proposed Zero-Trust Security Model
### Security Considerations to Support Zero-Trust Model  
#### 7.1.1 Identity and Access Management for Operational Technology  
The concept of Identity and Access Management (IAM) works in a similar way for the OT system as it does in the IT environment. The OT application owner creates Role-Based Access Control (RBAC) in the IAM, and the application users request access from the self-service tool which is verified and granted by the owner. It is critical to maintain separate accounts for IT and OT environments managed in different directories and set a user policy to keep a distinct username and password for them. Figure 9 below demonstrates the steps involved in implementing the access control process for OT applications.  
The OT application owner creates a role-based access control policy for the particular application on the IAM admin portal. The OT application user first requests an identity from the IAM self-service portal and then requests access to the OT application for that newly created identity. The site owner authorizes and grants the least privileged access to the user based on their role in that application. This ensures that the right person has the right level of access to the OT applications and is also in compliance with termination and periodic access review processes in the event of user role change. User IDs for accessing OT applications are kept separate from the ones used for IT applications and also stored in different credential stores to impede malware propagation.  
Figure 8: High-level steps involved in designing a zero-trust model for the manufacturing environment.  
Figure 9: Identity & Access Management for OT applications.
## 2 CoAP Overview
### Blockwise Transfer  
In many cases the payloads that CoAP needs to carry are very small (e.g., just a few bytes for temperature sensor, door lock status or toggling a light switch). In these cases the basic CoAP message provides very efficient means of communication and works very well. However in some cases CoAP needs to handle larger payloads (e.g., images or firmware update). Since CoAP is based on datagram transports such as UDP or DTLS, data fragmentation and reassembly is not offered by these transport protocols. Relying on IP fragmentation is also not very helpful, because IP fragmentation can handle only payloads up to 64 KB. Thus, providing a mechanism at the application layer that is able of transferring large amounts of data in smaller pieces becomes a necessity. This will not just help avoiding the 64 KB UDP datagram limit, but will also help avoiding both IP fragmentation (MTU of 1280 for IPv6) and also 6 LoWPAN adaptation layer fragmentation in LLNs (60-80 bytes).  
To overcome the payload size limitation, draft-ietf-core-block defines two CoAP options: Block1 and Block2 [13]. By using this pair of options CoAP becomes capable of transferring a large payload in multiple smaller CoAP messages. Both Block1 and Block2 options can be present both in request and response messages. In either case, the Block1 Option pertains to the request payload, and the Block2 Option pertains to the response payload. Block sizes are represented inside the Block1 and  
Figure 3: An example resource discovery by using a Resource Directory.  
Block2 Options as a three-bit unsigned integer called \(SZX\) indicating the size of a block to the power of two. Thus:  
\[blocksize=2^{SZX+4} \tag{1}\]  
The allowed values of \(SZX\) are 0 to 6 and thus the resulting allowed block sizes are: 16, 32, 64, 128, 256, 512 and 1024 bytes.  
6 LoWPAN might start using fragmentation/reassembly for datagrams as soon as the payload size gets larger than 60 bytes. This fragmentation/reassembly process burdens the lower layers with conversation state and is sometimes not implemented to conserve resources at the constrained devices. To avoid such fragmentation and reassembly, blockwise transfer with Block1 and Block2 sizes of 16 or 32 should be used whenever the payload exceeds 60 bytes.  
An important aspect of the blockwise transfer mechanism is that often the server can handle block transfers in a stateless fashion. It does not require connection setup and the server does not need to track each transfer separately and thus conserves memory.
## 4 Internet of Things (IoT) in Healthcare  
### Overview of IoT and Its Applications in Healthcare  
The Internet of Things (IoT) is a group of interrelated, internet-connected elements that collect, record, exchange and act on data in real-time. IoT technology supports end-users in visualizing, determining trends, managing and acting on data through real-time reporting or proactive alerts on smart devices. An initial implementation of the IoT in healthcare can begin with a single department in a nursing home or hospital, health clinics or personal health monitoring and expand into systems to monitor many devices worn by patients. In the health sector, the equipment used for the IoT is commonplace, such as sensors attached to patients, wearable devices, computing hardware and smart devices [44]. Health IoT also provides a platform for various services such as smart medical devices, stress management, nurse call systems and reminders for medication. The integration of the IoT into healthcare will have significant effects on diagnoses, treatments and care.  
The application of the IoT platform can use machine learning and data analysis techniques. The IoT health platform can identify and filter huge datasets collected through medical sensing devices and smart devices into events that require monitoring or actuation. Short messages and alerts can be delivered in real-time to organizations and health professionals managing patient care or health-related tasks [45]. During the processing of health data, IoT technology can predict patient outcomes based on various models derived from historical health data. IoT can significantly promote the quality, accessibility and affordability of healthcare. It serves as a platform for collecting data using connected sensors and devices to leverage services based on their ability to deliver decision-making access to crucial real-time information [46, 47, 48, 49]. The healthcare sector can effectively use IoT for medical monitoring, smart wearable devices, healthcare sensors and medication adherence to improve outcomes and reduce costs [50, 51, 52].  
IoT enables remote monitoring of individuals and chronic disease patients using health sensors, leading to early detection and unanticipated chronic disease prevention. It also facilitates the development of wearable smart devices to generate comprehensive data from various biological and nonbiological sensors, allowing for better decision-making regarding health, safety and personalized treatment [53]. Connected smart utensils and pillboxes integrated with IoT can unobtrusively track eating habits and medication adherence. Additionally, telemedicine and mobile health create remote interconnectedness and low-cost wearables for continuous monitoring of patients during and after hospital discharge [54, 55].
## 4 Experiment Setup and Results
### Anonymity and Unlinkability  
The Fabric enables advanced cryptographic algorithms and includes privacy features such as anonymity, unlinkability, and small disclosure of attributes. The Fabric uses Idemix, which is a cryptographic protocol that ensures strong features of privacy preservation and authentication. It allows users to prove their authentication without disclosing their real identities. It also enables users to send multiple transactions that are unlinkable. In addition, it is also not possible to identify multiple transactions that are sent by a single user. The actors who are involved in this protocol suite are the user, issuer, and verifier. Figure 5 shows the performance of the enhanced Fabric transactions with different block sizes.
## 3 Results
### Osmotic Dehydration of PEF Pre-Treated Tomatoes
#### 3.4.2 Water Activity, pH and Titractable Acidity of PEF Treated Tomatoes during OD  
The evolution of water activity of untreated and HP-treated samples during OD is presented in Figure 6a. It was observed that increasing treatment pressure led to a decrease in water activity during OD. A treatment at 600 MPa leads to a drastic decrease in water activity from 0.93 down to 0.76 after 150 min of OD. At 600 MPa, water activity drops below 0.90 after only 40 min of OD, whereas the same value is achieved for the untreated sample  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{\(D_{\mathit{eff}}\)} & \multicolumn{2}{c}{\(D_{\mathit{eff}}\)} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**\(D\)EI**} \\  & **Water Loss** & & & **Solids Gain** & \\  & **(m\({}^{2}\)/s \(\cdot\) 10\({}^{-10}\))** & & & **(m\({}^{2}\)/s \(\cdot\) 10\({}^{-10}\))** & & \\ \hline Control & 5.17 \(\pm\) 0.69 \({}^{\text{a}}\) & 0.955 & 3.82 \(\pm\) 0.42 \({}^{\text{ab}}\) & 0.920 & 1.35 \(\pm\) 0.03 \({}^{\text{a}}\) \\
100 MPa/5 min & 5.74 \(\pm\) 0.67 \({}^{\text{a}}\) & 0.975 & 3.50 \(\pm\) 0.70 \({}^{\text{a}}\) & 0.855 & 1.64 \(\pm\) 0.14 \({}^{\text{b}}\) \\
200 MPa/5 min & 6.15 \(\pm\) 0.52 \({}^{\text{a}}\) & 0.966 & 4.61 \(\pm\) 0.79 \({}^{\text{b}}\) & 0.896 & 1.33 \(\pm\) 0.12 \({}^{\text{a}}\) \\
600 MPa/5 min & 7.40 \(\pm\) 0.54 \({}^{\text{b}}\) & 0.966 & 5.07 \(\pm\) 0.85 \({}^{\text{b}}\) & 0.874 & 1.46 \(\pm\) 0.14 \({}^{\text{ab}}\) \\ \hline \hline \end{tabular} Different superscript letters indicate significant differences between means \(D_{\mathit{eff}}\) \(\pm\) standard deviation and _DEI_\(\pm\) standard deviation as calculated by [PERSON]’s multiple range test for a significance level of \(p=0.05\).  
\end{table}
Table 2: Effective moisture diffusion (\(D_{\mathit{eff}}\) for Water Loss) and solids diffusion (\(D_{\mathit{eff}}\) for Solids Gain) coefficients, and dehydration efficiency index (_DEI_) for different HP treatment conditions as calculated from fitting [PERSON]’s law to the experimental data.  
Figure 5: Evolution of (**a**) water loss (_WL_) and (**b**) solids gain (_SG_) during osmotic dehydration of untreated and HP treated samples, treated at 100, 200, and 600 MPa for 5 min (average values \(\pm\) standard deviations). Solid lines represent the fitting of [PERSON]’s law to the experimental data.  
at OD times exceeding 3 h. The evolution of pH values for untreated and HP pretreated tomatoes during OD are presented in Figure 6b. Increasing treatment pressure led to a decrease in pH values over the course of OD.