esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The worldwide urbanization process represents a formidable challenge and attracts attention toward cities. In particular, in the near future, the quality of life of billions of people will depend on capability of cities of saving energy, reducing harmful emissions, improving the living conditions and increasing citizens security. These challenges need to be addressed through the implementation of Information and Communication Technology (ICT) intelligent solutions in the urban ecosystem. Indeed, the smart city concept is based on functional integration of software systems, network infrastructures, heterogeneous user devices, and collaboration technologies. Motivated by the fact that a reliable, robust, secure, and scalable communication architecture plays a crucial role for the successful operation of a smart environment, this paper provides an overview of the communication infrastructure of a smart city and the related enabling technologies. First, a review of main characteristics, challenges,and proposed solutions is provided. Then, the focus of the paper is moved on cognitive radio (CR) technology, which is considered an efficient methodology to address communication needs and challenges in many smart city contexts. In particular, in the final part of the paper, CR technology is investigated as a viable solution for mitigating the spectrum shortage that can arise from the use of two key technologies--Heterogeneous Networks (HetNets) and Machine-to-Machine (M2M) communications--that nowadays are envisaged as two promising communication paradigms for future smart cities, specifically characteristics, challenges, and literature solutions are discussed.
## 3 Results and Discussion  
### Proximate Composition and Total Dietary Fiber  
Table 3 shows the results for the proximate composition and dietary fiber of the sausage. Regarding the proximate composition, moisture, ash, fat, and total dietary fiber contents showed significant differences (\(p<0.05\)) between the different levels of seaweed added in the sausages. The addition of seaweed (KA, SP, and CL) reduced moisture and fat content (\(p<0.05\)) and raised the dietary fiber content and ash content (\(p<0.05\)) in sausages. However, despite the inclusion of various levels of seaweed to the formulation, no significant difference in protein content was identified.  
water holding capacity; therefore, the type and amount of their polysaccharides affect the gelation process [29].  
The good WHC contributed by seaweeds resulted in a lower cooking loss in the sausages. Cooking loss was calculated from differences in the weights of uncooked and cooked samples, expressed as the percentage of initial weight. When the sausages were formulated with seaweeds, there were significant improvements in the cooking loss compared to sausages without seaweed (\(p<0.05\)). The control sample showed the highest cooking loss as seaweed fiber lowered the cooking loss due to its capacity to hold water and fat. Seaweed decreases the loss of fat and moisture during cooking and stabilizing emulsions [30].  
Colour is one of the key parameters that determines the consumer acceptance of a product. Table 4 shows the lightness (L*), redness/greenness (a*), and yelowness/blueness (b*) of the sausage formulations. The results indicated that the inclusion of seaweed had altered the colour characteristics of chicken sausage. The chicken sausages darkened as more seaweed was added. The L* values of sausages with added KA seaweed decreased slightly compared to the SP- and CL-treated sausages. The SP6 sausage had the lowest L* value due to the brownish colour of the seaweed. The redness value (a*) of the chicken sausages with seaweed also increased. The CL-treated sausages had the lowest a* value, due to the green hue of the CL seaweed. In terms of yellowness (b*) values, there was no significant difference between the control sausage and the KA- treated sausages at all levels of inclusion (\(p<0.05\)). However, the b* decreased significantly (\(p<0.05\)) in SP- and CL-treated sausages. The higher the percentage of seaweed, the darker the colour of the chicken sausages. The phycoerythrin and phycocyanin compounds in seaweeds caused the colour changes in the chicken sausages (KA). These pigmentations overlap with other pigments such as chlorophyll and caroten beta [31]. Meanwhile, the reduction of lightness in the chicken sausages with SP was due to SP's brown colour, brought upon by yellow pigments such as chlorophyll, phycopine, and xanthophyll [32]. Meanwhile, C. _lentilifira_ (CL) contained starch, cellulose, xylan, and ionic polysaccharide consisting of sulphate group and uronic acid [28].
# WAMS-Based Online Disturbance Estimation in Interconnected Power Systems Using Disturbance Observer
## 5 Simulation Results and Discussions
### Comparison with the Existing Methods  
In order to show the superiority of the disturbance observer over the swing equation, we compare three different disturbance scenarios. The swing equation is the commonly applied technique to estimate the magnitude of the power system disturbance. The equation is given as  
\[\frac{2H_{i}df_{i}}{f_{n}dt}=P_{mi}-P_{ei}=\Delta P_{i} \tag{34}\]  
where \(H_{i}\), \(f_{i}\), \(P_{mi}\), \(P_{ei}\), \(\Delta P_{i}\) and \(f_{n}\) are the generator inertia, generator frequency, input mechanical power, output electrical power and the nominal system frequency respectively.  
For comparison purposes, four disturbance scenarios are investigated as in Tables 3 and 4. In the event of a disturbance, the frequency gradient is negative, therefore, using the swing equation we would expect the disturbance magnitude to be the most negative value measured. However, in Figures 12-15, we inverted the swing curve for comparison purposes. Table 3 shows that the greater the disturbance the greater the estimation error when using the swing equation. In scenario 1, a disturbance of 0.01 p.u magnitude occurred and the estimation error was 1.22%. For a large disturbance 0.2 p.u in scenario 4, the estimation error was 2%. Table shows the estimation results when using the disturbance observer. In all the simulated scenarios, the disturbance estimation error was 0%. In as far as disturbance estimation is concerned, the disturbance observer is superior to the swing equation.  
The swing equation based method can be improved for better disturbance estimation considering the following suggestions: (i) associating the swing equation-based method with accurate online inertia estimation method, (ii) online updating the swing equation-based method with the online and accurately estimated damping coefficient, (iii) using her sapling methods and curve fitting techniques for estimating the missing data in the frequency response curve, and (iv) developing a fast method for calculating the center-of-inertia online, which can improve the disturbance estimation using the swing equation-based method.
## 4 Experiment and Results
### Optimization Results
#### 4.4.2 Adjusted Route Solutions  
Adopting the strategy of the dynamic adjustment, the three initial routes are adjusted based on the real-time crowding flow indicator (\(F_{i}\)) updated hourly on 4 October. In the adjustment stage, scale factor \(\varphi\) of the crowd threshold are set to be 0.8. According to the ratio of the longest distance to the average distance of the past tourist trips, the distance scale factor \(\lambda_{d}\) is set to be 1.4. Among them, the five-spot route is adjusted due to the sudden increase in crowds, shown in Figure 10. One point to note is that although the route adjustment can reduce the crowding in the actual trip, it will unavoidably affect other objective values, and we attempt to minimize the three objectives.  
In the adjusted route, #67 Dalian Forest Zoo is replaced by #54 Yinggeshi Botanical Garden. If the route is executed as the planned initial route, the value of the real-time crowding objective function would be 2.137, while after the adjustment, the value is 1.667, with a decrease of 21.99%, which indicates that the dynamic adjustment strategy can successfully provide alternatives to avoid sudden increases in crowds. Hence, the value objective function obtains a 28.01% increase after adjustment, from \(-\)8.303 to \(-\)5.977, due to the loss of the over-crowded 4A attraction #67 Dalian Forest Zoo, and the total distance increased by 17.769 km. This is the result of considering all three objectives simultaneously, which reduces the crowding at the expense of increases in the other objectives. Overall, dynamic route adjustment can be an excellent solution to sudden increases in crowds, providing tourists with a less-crowded route during the actual tour while minimizing the impact on other objectives.  
Figure 9: Initial routes designed for the tourist and the historical crowd flow indicator (\(\overline{F}_{i}\)) of the involved attractions.
## 5 Conclusions  
_G. lucidum_ is an excellent Se carrier, not only rich in natural bioactive metabolites, but also could transform inorganic Se into organic Se with the advantage of efficacy. The growth and bioactive metabolites of _G. lucidum_ were greatly altered by the application of Na\({}_{2}\)SeO\({}_{3}\) in the liquid culture process. Specifically, 50 ppm Na\({}_{2}\)SeO\({}_{3}\) did not influence growth, but 200 ppm Na\({}_{2}\)SeO\({}_{3}\) could impose toxic effects on mycelia growth and bioactive metabolites, especially on the 4 th-6 th days of mycelia development. (i) Mycelia growth and ultrastructure, odor, and color were basically unchanged with 50 ppm Na\({}_{2}\)SeO\({}_{3}\), but decreased biomass, degraded mycelial cells, a red color, and an unpleasant odor were the main characteristics of _G. lucidum_ with 200 ppm Na\({}_{2}\)SeO\({}_{3}\). (ii) Ten volatile Se compounds were identified in the culture broth of Se-enriched _G. lucidum_, which led to an odor change. (iii) The total Se accumulated in _G. lucidum_ was enhanced with the increased levels of Na\({}_{2}\)SeO\({}_{3}\), and SeMet was the major selenoamino acid in _G. lucidum_ with 50 ppm Na\({}_{2}\)SeO\({}_{3}\), but SeMet and SeCys\({}_{2}\) content gradually declined and MeSeCys increased in this process in the 200 ppm Na\({}_{2}\)SeO\({}_{3}\) group. Further, polysaccharide yields were promoted and inhibited with 50 and 200 ppm Na\({}_{2}\)SeO\({}_{3}\), respectively. This study provides further insights into our understanding of the growth and bioactive metabolite alternations of _G. lucidum_ caused by Se addition in the liquid culture process and is useful for the production of functional Se-enriched mushrooms.  
The following are available online at [[https://www.mdpic.com/article/10.3390/foods10081860/s1](https://www.mdpic.com/article/10.3390/foods10081860/s1)]([https://www.mdpic.com/article/10.3390/foods10081860/s1](https://www.mdpic.com/article/10.3390/foods10081860/s1)), Figure S1: GC-MS total ion map and structures of the identified volatile Se-compounds, Figure S2: HPLC-ICP-MS map of the selenoamino acids. (a) standards; (b-d) _G. lucidum_ cultured with 50 ppm Na\({}_{2}\)SeO\({}_{3}\) from the 4 th to 6 th day; (e-g) _G. lucidum_ cultured with 200 ppm Na\({}_{2}\)SeO\({}_{3}\) from the 4 th to 6 th day.  
Conceptualization, M.X. and Z.D.; methodology, M.X., Z.W. and S.Z.; software, M.X.; validation, M.X. and L.Z.; formal analysis, M.X.; investigation, M.X.; resources, Z.D.; data curation, M.X.; writing--original draft preparation, M.X.; writing--review and editing, Z.D. and S.Z.; visualization, L.W.; supervision, G.S., Z.D. and S.Z.; project administration, Z.D.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the national first-class discipline program of Light Industry Technology and Engineering (LITE2018-22).  
The authors declare no conflict of interest.
# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 1 Introduction
### Related Work
[PERSON] and [PERSON], [PERSON] selected both the transmit and receive antennas for SM using deep learning (DL) architectures [26]. We used DL-based algorithms to execute TAS for FGSM in [3] to meet the SE requirements and improve classification accuracy. The following is a list of research gaps identified:  
* Most of these data-driven approaches are proposed and implemented for conventional MIMO and basic SM architectures. * These data-driven strategies are neither proposed nor implemented for high-rate variants of SM. * The TAS dataset is not available for FGSM in any of the repositories. Listed below are the key contributions of this work:
* Through the repeated application of the FD-TAS algorithm, 4 different datasets (with a total of 10,000 entries) are produced. The dataset contains channel information for a variety of MIMO setups and antenna counts, as well as essential performance measures, like FD. * For FGSM, four distinct supervised ML classification methods are used and tested for TAS: SVM, NB, KNN, and DT. The dataset size, the value of \(k\), which is used in \(k\)-fold cross-validation, and parameters, such as the kernel type and hyperparameter \(K\), are optimized to obtain the best results. * The ABER and computational complexity of ML-based TAS approaches are contrasted to those of FGSM-NTAS and FD-TAS. * An analysis of the proposed work is carried out in terms of SNR gain and classification accuracy. The following is how the manuscript is structured: The principle of operation of FGSM is discussed elaborately in Section 2. The various ML algorithms used for TAS in the FGSM system are covered in Section 3. The proposed ML-based TAS practices and the conventional FD-TAS practice are subjected to a computational complexity study in Section 4. The ABER performance of ML-based algorithms is contrasted to the FD-TAS scheme in Section 5. Section 6 concludes the work by laying out future research directions.
# A Blockchain-Based Protocol for Fair Delivery for Receips
## 4 Proposal Overview  
The scenario on which our solution is based is shown in Figure 2, which summarizes the involved participants and the relationships among them. The parties that use the protocol are described as follows:  
* **Sender (\(A\))**. The sender is the party who wishes to deliver a document (or any other digital asset) to another party in exchange for a receipt as evidence of delivery.
* **Recipient (\(B\))**. The recipient is the party intended to receive the document and has the option to accept or decline delivery.
* **Smart contract (\(SC\))**. A smart contract is self-executing digital code deployed on a blockchain that is designed to manage the delivery process and settle any disputes that may arise.  
We assume that a sender (\(A\)) wants to deliver digital content (\(M\)) to a recipient (\(B\)), who then acknowledges the receipt. The digital content to be delivered may include any material that can be delivered in a digital format. The protocol has three steps and essentially orchestrates communication between the sender and recipient, with the collaboration of a smart contract (\(SC\)). The protocol begins with an off-chain message in which \(A\) provides  
Figure 2: Fair delivery for recepts scenario.  
with content encrypted via a symmetric key algorithm, along with a nonrefutable signature. Upon receiving the message, \(B\) has the option to accept or decline the message. If they opt to accept, they send an acceptance acknowledgment to the blockchain, which serves as nonrepudiation evidence that \(B\) is willing to receive the content. If \(B\) chooses to decline (e.g., if they suspect that it is a fraudulent request), they simply disregard the message without detriment to either party's fairness. Once \(A\) is aware of the acceptance (by means of an event emitted by the blockchain), they publish the encryption key with an on-chain transaction, protecting it to ensure exclusive access by \(B\). Finally, once \(B\) sees that \(A\) has published the key (again, through a blockchain event), they can retrieve, decrypt, and use the key to access the content delivered in the initial step. Notably, although there are three steps in the protocol, only Steps 2 (acceptance) and 3 (key publication) involve write operations to the blockchain, whereas the initial step involves only an off-chain message from \(A\) to \(B\) (transmitted through a secure connection such as a TLS connection) that has no blockchain cost.  
Table 1 provides a summary of the notation used in the protocol description.
## 5 ML Intersection with 6G Enabling Technologies
### ML and Blockchain Integration for Future 6G Networks
The authors investigated PoL's effectiveness in overcoming the huge computing power waste during training that is associated with the traditional proof-of-work (PoW) protocol. The proposed AI-Chain approach yielded a significant reduction in computing power wastage and better learning ability compared to the existing approach. Table 7 gives a summary of ML integration with BC for optimal future 6G networks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** &
\begin{tabular}{c} **ML** \\ **Algorithm** \\ \end{tabular} & **Application** & \multicolumn{1}{c}{**Outcome**} \\ \hline
[162] & FL &
\begin{tabular}{c} Secure data exchanges \\ and verification \\ \end{tabular} & Improved latency and minimized average learning completion rate. \\ \hline
[163] & FL & \
\begin{tabular}{c} Data privacy and \\ security \\ \end{tabular} &
\begin{tabular}{c} The proposed approach resulted in higher efficiency and accuracy while \\ ensuring secure data privacy and security. \\ \end{tabular} \\ \hline
[164] & FL, DRL & Security and reliability & The convergence achieved reduced latency and provided. \\ \hline
[165] & DRL & Secure distribution &
\begin{tabular}{c} The developed approach enhanced the performance of the system in terms of \\ user profit ratio, throughput, secure distribution, security, and automation of \\ the network. \\ \end{tabular} \\ \hline
[167] & FL & Task offloading &
\begin{tabular}{c} The proposed BRACE approach significantly lowered the total queuing delay \\ and block creation delay, thereby proving to optimize the overall system \\ performance. \\ \end{tabular} \\ \hline
[166] & DRL & \
\begin{tabular}{c} Optimized content \\ caching \\ \end{tabular} &
\begin{tabular}{c} The novel approach achieved better convergence in all learning rates, while \\ the learning rate of \(10^{-3}\) outperformed all the other learning rates. \\ \end{tabular} \\ \hline
[113] & DRL & \
\begin{tabular}{c} Joint resource \\ allocation \\ \end{tabular} &
\begin{tabular}{c} The proposed methods achieved a significant reduction in computing power \\ usage and a better learning rate. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of blockchain integration with ML for optimized 6G communication network. Figure 5: The moving average of the profit ratio vs. the training epoch. Adapted from [165].
# A Model Based Framework for IoT-Aware Business Process Management
## 4 IoT-Aware BPM Framework
### IoT4 SysML: An IoT-Aware UML Profile for SysML  
The proposed IoT-aware profile for annotating SysML models providing the _structural view_ of sensor networks is shown in Figure 4.  
The profile includes the following stereotypes, which have been introduced according to relevant concepts/relationships specified in the IoT Metamodel:  
* **System**, which extends both the Package and the Class metaclass and identifies the _Sensors Network System_;
* **Platform**: which extends the Package metaclass and denotes a SysML Block element representing a _Platform_. It provides the position property for allowing the specification of the Platform's location and orientation;
* **Sensor**, which identifies SysML Blocks representing _Sensors_ within the addressed sensor network. It provides the following attributes:
* serviceURI, that specifies an URI endpoint for inquiring the sensor and retrieving the collected measures;
* position, that describes the sensor's position in terms of its location and orientation;
* **FeatureOfInterest**, that denotes a physical object observed by a sensor. It includes the definition attribute for referring to an external resource (i.e., a dictionary or an ontology) which formally defines the feature;
* **ObservableProperty**, which denotes a SyML Block Property representing an _observable property_. The formal definition of the property can be provided by use of the definition attribute;* **Observes**, which extends the Association metaclass. It allows the proper specification of the relationships among sensors and observed entities;
* **SensorData**, which extends the Property metaclass and identifies a SysML Block attribute representing data measured by the sensor. As SensorData represents physical and measurable quantities, the uom attribute allows the specification of the related _unit of measure_;
* Dictionary, which specifies a reference to an external resource;
- ItemList, which includes the list of allowed values;
* **SensorDataRecord**, which extends the DataType metaclass and identifies a _DataRecord_ type;
* **SensorDataArray**, which extends the DataType metaclass and identifies an _Array_ type;
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Routing Decision Process  
We start this section with a simplified description of the routing process. As stated, every node has a global pheromone map and location table containing the tentative position of every UAV. Thus, a sender UAV knows its position, can easily learn the destination UAV position, and its one- and two-hop neighbor UAV positions. With the help of its path-planning mechanism, a sender can also estimate the next possible cell ID of the destination and its own. Concurrently, the sender considers its own and the one- and two-hop neighbor \(\varphi\) (i.e., average buffer occupancy). From this position information and \(\varphi\), we propose that the sender selects a UAV from itself and its one-hop neighbors as a custodian. The custodian should have enough space to accommodate the packets in its buffer and is expected to be in the closest position with the destination in the near future. When a custodian UAV receives the packet, it follows the same procedures as the sender.
# Design and Modelling of a Novel Integrated Photonic Device for Nano-Scale Magnetic Memory Reading
## 1 Introduction
Such claddings with a perpendicular magnetic orientation are expected to display larger amplitude magneto-optical effects, yet still small quantitatively. This calls for developing novel approaches to amplify the magneto-optical effects while showing the importance of on-chip analytical tools to explore the fundamental mode conversion properties of photonic waveguides with PMA claddings. To assess the feasibility of using MOKE for on-chip all-optical magnetic memory reading functionality, as well as using it as an analytical tool to quantitatively measure magnetization-induced mode conversion, we investigated specially designed photonic devices whose waveguides are cladded with ultra-thin (12 nm), nano-scale (50 \(\times\) 400 nm) PMA magnetic memory bits, of the composition mentioned before. By using mathematical models of the designed photonic devices, whose building block performance parameters are chosen according to the InP Membrane on Silicon (IMOS) platform [21], the accuracy of the memory-bit read-out, optical loss and tolerance to noise are tested. It has been shown that the device is able to read a nanoscale memory bit (400 \(\times\) 50 \(\times\) 12 nm) by using a Kerr rotation as small as 0.2\({}^{\circ}\), in the presence of a \(\sim\)10 dB noise in terms of signal-to-noise ratio (SNR). This paper is structured in the following way. In Section 2 materials and methods are given. Device designs, magneto-optic simulation, mathematical modelling and data analysis topics are covered. In Section 3 the results obtained via the mathematical model are presented for devices with varying degrees of performance parameters. A data analysis technique using Fourier transformation is presented. Lastly, in Section 4, the conclusions are given.