esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 4 Integration of Quantum Money with Quantum Networks  
Integrating quantum money with emerging quantum networks--whether terrestrial or space-based--raises both technical challenges and promising opportunities, particularly in the domains of security, efficiency, and decentralization of financial transactions. One of the most viable approaches for enabling the transfer of quantum money is quantum teleportation, a protocol that combines quantum entanglement with classical communication to transmit quantum states without physical transport of the underlying particles [31]. In thissection, we examine the principles of quantum teleportation and outline the infrastructure required to support secure, large-scale quantum money transfers within future quantum communication networks.
## 4 Simulation and Dataset Generation
### Spacecraft Description
The estimator, which is one of the many versions of an Extended Kalman Filter (EKF), takes the gyroscope's filtered output as a very first estimate of the angular rates, associated with their own uncertainty, then uses the other two measures available from the sensors to correct the attitude prediction made. The MEKF then outputs estimates of the attitude expressed as the four component quaternions, and the three-axis angular rates. The spacecraft model can follow one of three possible attitude modes. As a first basic option, actuators can be completely deactivated, leaving the spacecraft in a \"No Control\" mode, where initial angular rates are random and the only torques acting on the vehicle are perturbations and the ones due to initial angular accelerations. Another customized control action sets the spacecraft in an Earth-pointing mode, with a very slow angular rate around a precise axis, to follow throughout the simulation. Finally, to test an even more generic scenario, the simulation can reproduce a typical slew maneuver to mimic real operations of a satellite, where the attitude algorithm is instructed to start a rotation around one axis and return to stillness in a desired time length: during the creation of a large dataset, the 3D rotation axis is generated randomly at the beginning of every simulation, and the angle is randomized as well, reaching up to 60 deg in 100 s of maneuver. In this final iteration of the model, actuators models are substituted by a simulation block which simply takes the ideal target torque to impose to the satellite and feeds it back into the dynamics and kinematics of the model, with some noise applied. This is carried out in order to avoid unnecessary layers of complexity: by avoiding to refer to particular hardware types, the generality of the observations made is preserved.
# Sparse Representation and SVM Diagnosis Method for Inter-Turn Short-Circuit Fault in PMSM
## 4 Experimental Results and Analysis
### Feature Extraction Based on Sparse Representation
After the above-mentioned OMP algorithm is used to obtain the sparse coefficients of 40 sets current signals and the sparse coefficients of 40 sets vibration signals, select the largest and second largest sparse coefficients in them as features. The first and second features extracted from the 20 sets of normal current signals and 20 sets of fault current signals are shown in Figure 3.4  
The first and second features extracted from the 20 sets of normal vibration signals and 20 sets of fault vibration signals are shown in Figure 3.5  
Figure 3.6 Features extracted from the current signals (**a**) First feature of current; (**b**) Second feature of current. Figure 3.7 Sparse coefficients of a set of normal current signal. As can be seen from the above figures, the feature values of signals of different fault types are also different. All the first features of fault vibration are higher than 0.4, while the normal features are lower. Besides, the second features of fault vibration are higher than 0.25, while the normal features are lower. Similar phenomena can also be found in the current signals, although not so obvious. The boundary value between the first features of the normal and fault current is about 50, and the boundary value of the second features is about 33. Therefore, combining the different features into one feature vector is suggested so that no information source will be ignored. Next, the SVM will be trained and tested with the feature vectors.
## 6 Security and Performance Analysis
### Performance Analysis  
As shown in Table 1, our scheme is compared with prominent multi-authority schemes, single-authority privacy-preserving schemes, and some privacy-preserving multi-authority schemes. The salient features of our scheme are also highlighted in Sections 1 and 2. Table 2 shows the comparison of storage overhead and encryption and decryption computation costs. Table 3 presents a summary of the notations that are used in the comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{3}{c}{**Storage Overhead**} & \multicolumn{3}{c}{**Computation Cost**} \\ \cline{2-6}  & **Public Key** & **User’s Secret Key** & **Ciphertext** & **Encryption** & **Decryption** \\ \hline
[28] & \(|{\rm|G_{T}|+(n+4)|G|}\) & \((|{\rm A_{C}}|+2)|G|\) & \((4|{\rm A_{C}}|+2)|G|+2|G_{T}|\) & \((7\mid{\rm A_{C}}|+4)\) E & \((2|{\rm I}|+4){\rm P}+(4|{\rm I}|)\)E \\ \hline
[38] & \(|{\rm|G_{T}|+4|G|}\) & \((|{\rm A_{U}}|+2)|G|\) & \((3|{\rm A_{C}}|+2)|G|+2|G_{T}|\) & \((7\mid{\rm A_{C}}|+4)\) E & \((2|{\rm I}|+3){\rm P}+(3|{\rm I}|)\)E \\ \hline
[16] & \({\rm n|G_{T}|+n|G|}\) & \((|{\rm A_{U}}|)|G|\) & \((2|{\rm A_{C}}|)|G|+({\rm I_{C}}|+1)|G_{T}|\) & \((5\mid{\rm A_{C}}|+1)\) E & \((2|{\rm I}|){\rm P}+(|{\rm I}|)\)E \\ \hline
[23] & \(\begin{array}{c}{\rm n_{a}}|G_{T}|+\\ ({\rm n+n_{a}}+1)|G|\end{array}\) & \(({\rm n_{a}}+|{\rm A_{U}}|+2)|G|\) & \((2|{\rm A_{C}}|+1)|G|+|G_{T}|\) & \((3|{\rm A_{C}}|+2)\)E & \((2|{\rm I}|+1){\rm P}+(|{\rm I}|+1)\)E \\ \hline
[26] & \({\rm n|G_{T}|+(n+1)|G|}\) & \((|{\rm A_{U}}|+1)|G|\) & \((2|{\rm A_{C}}|)|G|+({\rm I_{C}}|+1)|G_{T}|\) & \((5\mid{\rm A_{C}}|+1)\) E & \((2|{\rm I}|){\rm P}+(3|{\rm I}|)\)E \\ \hline
[35] & \({\rm n|G_{T}|+n|G|}\) & \((2|{\rm A_{U}}|)|G|\) & \((2|{\rm A_{C}}|+1)|G|+({\rm I_{C}}|+1)\) & \((5\mid{\rm A_{C}}|+1)\) E & \((2|{\rm I}|+1){\rm P}+|{\rm I}|\)E \\ \hline
[39] & \(\begin{array}{c}{\rm n_{a}}\mid G_{T}|+\\ ({\rm n+n_{v}})|G|\end{array}\) & \(({\rm n_{a}}+|{\rm A_{U}}|)|G|\) & \((2|{\rm A_{C}}|+1)|G|+|G_{T}|\) & \((5\mid{\rm A_{C}}|+1)\)E & \((3|{\rm I}|){\rm P}+|{\rm I}|\)E \\ \hline
[**Ours**] & \(\begin{array}{c}{\rm n_{a}}\mid G_{T}|+\\ ({\rm n+n_{v}}+1)|G|\end{array}\) & \(({\rm n_{a}}+|{\rm A_{U}}|+2)|G|\) & \((2|{\rm A_{C}}|+1)|G|+|G_{T}|\) & \((3|{\rm A_{C}}|+2)\)E & \((2|{\rm I}|+1){\rm P}+|{\rm I}|\)E \\ \hline \hline \end{tabular}
\end{table}
Table 2: Storage and computation cost comparison.  
From the above numerical performance analysis, it can be observed that the key generation time of our scheme and schemes [23; 39] are comparable, while other schemes have shorter key generation times. The reason for this is that either they are single-authority or not provide privacy preservation features. The numerical decryption time of our scheme is less than that of the others, as the number of exponentiation operations performed is less.
# Scalable Resource Provisioning Framework for Fog Computing Using LLM-Guided Q-Learning Approach
## 7 Conclusions  
This paper presents a novel LLM-guided Q-learning framework for resource provisioning in fog computing. The uncertainty in fog computing is modeled using an LLM model. The heuristic value of the LLM model is used to provide guidance to the Q-learning agent. Over and under provisioning of resources is prevented through proper exploration of the state space using the maximum entropy solution. The expected value analysis of the framework under finite and infinite fog computing scenarios is performed. The experimental results obtained by the iFogSim 3.3. simulator are good with respect to processing delay, load balancing, and SLA violations. As future work, detailed analytical modeling and analysis of the framework will be carried out in terms of resource migration and placement. Extension of the framework will be conducted pertaining to fault tolerance, auto recovery, and cost optimization.  
**Author Contributions:** Conceptualization, B.K. and S.G.S.; methodology, B.K.; software, S.G.S.; validation, B.K. and S.G.S.; formal analysis, B.K.; investigation, S.G.S.; resources, B.K.; data curation, B.K.; writing--original draft preparation, B.K.; writing--review and editing, S.G.S.; visualization, B.K.; supervision, S.G.S.; project administration, B.K.; funding acquisition, S.G.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to corresponding authors.  
Figure 5: Time (ms) versus SLA violation (%).
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
###### Abstract  
Due to the limited quantity and high cost of high-quality three-dimensional annotations, generalized zero-shot point cloud segmentation aims to transfer the knowledge of seen to unseen classes by leveraging semantic correlations to achieve generalization purposes. Existing generative point cloud semantic segmentation approaches rely on generators trained on seen classes to synthesize visual features for unseen classes in order to help the segmentation model gain the ability of generalization, but this often leads to a bias toward seen classes. To address this issue, we propose a semantic-guided adaptive bias calibration approach with a dual-branch network architecture. This network consists of a novel visual-semantic fusion branch alongside the primary segmentation branch to suppress the bias toward seen classes. Specifically, the visual-semantic branch exploits the visual-semantic relevance of the synthetic features of unseen classes to provide auxiliary predictions. Furthermore, we introduce an adaptive bias calibration module that dynamically integrates the predictions from both the main and auxiliary branches to achieve unbiased segmentation results. Extensive experiments conducted on standard benchmarks demonstrate that our approach significantly outperforms state-of-the-art methods on both seen and unseen classes, thereby validating the effectiveness of our approach.  
adaptive bias calibration; visual-semantic contrastive learning; generalized zero-shot semantic segmentation Article
# Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing
Another important issue in this field is the path-following problem, which concerns the ability to drive a mobile robot as autonomously as possible on a predefined reference path [21]. The path-following problem can be solved by different control methods, such as a PID controller [22; 23], model predictive control [24; 25; 26], and the fuzzy logic approach [27]. In multi-robot systems, these processes become even more complex. It is naturally necessary for each robot in a multi-robot system to navigate independently while cooperating with other robots for efficient performance, which depends on each robot's conditions, such as its structure, location, and speed [28]. One frequently preferred trajectory-tracking approach in multi-robot systems is the leader-follower approach [29]. In this approach, a leader robot follows a specified path, and a follower robot follows the leader robot at a specified distance and angle [29]. In this study, the formation control of autonomous mobile robots is achieved utilizing the Turkish natural language. Turkish commands received are subjected to parsing employing Turkish natural language processing techniques. Subsequently, the mobile robotsexecute the assigned tasks based on these parsed commands. Various scenarios associated with these tasks are formulated for both obstacle-laden and obstacle-free environments. It is very difficult to parse Turkish because of its agglutinative structure, not only for robots but also for people learning the language. To give an example, in Turkish, the single word \"Gorusmyeceklemmis\" translates into English as \"I heard that they are not going to be able to see each other\". This example shows how difficult it is to analyze language. The Turkish language has an agglutinative structure, and it is very difficult to decode its sentence structure in a way that robots can understand. Parsing of the given command, path planning, path tracking, and formation control were carried out. According to our review of the literature, this study is the first study in the literature to use Turkish NLP for multi-robot formation control. The Zemberek library used in this study is used for the first time in robot control. This study is a pioneering study in the application of Turkish natural language processing within the domain of multiple-mobile-robot systems. This study tested natural commands from a user using Turkish NLP on a multi-mobile-robot system. After parsing the commands received from the user, the robots were enabled to move from a starting point to a target point while maintaining their formation. The A* algorithm was used for path planning, and a PID controller was used for path tracking. The leader-follower approach and platoon-type formations were used. In this study, ROS (Robotic Operating System), Noetic version, and Gazebo were used to create a simulation environment, and the Turtlebot3 Burger model was chosen as the mobile robot.
# Construction of Nonblocking Wavelength/Space Switches with AWGs and WSSes
## 5 Conclusions  
In this paper, we showed a new way of designing an AWG-based nonblocking wavelength/space switch. The new method is a generalization of that given in [11], but simpler and more flexible in terms of the construction of different switch sizes. It is based on a decomposition carried out in the transformed space-domain only topology. Not only is the new decomposition simpler, it is also easy to see how different (space, wavelength) combinations can be achieved with this decomposition technique. We also studied another class of wavelength/space switches which are based on WSSes (wavelength selective switches) and compared the two approaches in terms of tradeoffs between the number of WCs and the amount of optical space switching required. The new decomposition technique, we believe, can be applied to other AWG-based switching systems in the future.  
**Acknowledgments:** [PERSON]'s research is supported by the Ministry of Science and Technology, Taiwan, under Contract MOST 105-2221-E-024-002. [PERSON]'s research is supported by Hong Kong RGC Grant 16206015.  
**Author Contributions:** Architecture design was done by both [PERSON] and [PERSON]. Data analysis was mostly done by [PERSON]. Both [PERSON] and [PERSON] participated in the writing of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Removal of Volatile Phenols From Wine Using Crosslinked Cyclodextrin Polymers
## 3 Materials and Methods
### Binding Experiments Comparing CDs and CD Polymers  
The two functional CDs, \(\beta\)-CD and \(\gamma\)-CD (in their monomer forms), were also evaluated in the current study to enable a comparison to be made with the performance of the CD polymers. CDs (20 g/L) were added to red wine samples spiked with 1 mg/L of volatile phenols. Samples were then incubated at 35 \({}^{\circ}\)C with 120 rpm agitation for 20 min, prior to analysis using the four-phase HS-SPME GC-MS method [35]. Controls (without any CD addition) were also prepared and analyzed (in triplicate).
## 3 Results
### BET and BJH Analysis of Modified Mt  
The results of nitrogen the adsorption-desorption analysis are stipulated in Table 1. It was observed that the BET surface area and BJH pore volume of Mt obviously increased after acid activation, which was due to the ability of the acid activation to dissolve impurities and open the Mt edges. Similar results were also observed in the comparison of the P-Mt and H-P-Mt, and Si-Mt and H-Si-Mt samples. For the H-P-Mt, it was noticed that the BET surface area and BJH pore volume decreased relative to the H-Mt, which could be explained by the intercalation and filling of the TTPC surfactants. However, an increase in the BET surface area was found in the H-Si-Mt, compared with the H-Mt, due to the grafting of \(\gamma\)-MPS molecules onto the external surfaces and the broken edges. This result once again indicated the successful grafting of \(\gamma\)-MPS molecules onto the external surfaces, rather than into the interlayer spacing.  
Figure 5: TEM images obtained for (**A**) H-P-Mt, (**B**) H-Si-Mt, (**C**) P-Si-Mt, (**D**) Si-P-Mt, (**E**) H-P-Si-Mt and (**F**) H-Si-P-Mt samples.  
Compared with the Si-P-Mt, the increase in the BET surface area and BJH pore volume was also observed in the H-Si-P-Mt, due to the acid activation. The decrease in the BET surface area of the H-Si-Mt after modification with the surfactants suggested that the TTPC molecules had been intercalated and filled into the interlayer spacing. On the contrary, compared with the H-P-Mt (80.09 m\({}^{2}\)/g), a larger BET surface area was observed in the H-P-Si-Mt (95.25 m\({}^{2}\)/g). This can be responsible for the hindrance effect of the TTPC surfactants for the \(\gamma\)-MPS molecules, to penetrate into the interlayer spacing. Similar conclusions can also be obtained from the XRD and FTIR results.
###### Abstract  
Aim: The objective of this study was to compare the efficacy of supportive periodontal therapy (i.e., scaling and root planning, SRP) alone versus ALADENT medical device used in association with SRP in the treatment of chronic periodontitis in adult patients. Materials and Methods: A total of 20 patients with a diagnosis of chronic periodontitis (40 localized chronic periodontitis sites) aged between 35 and 55 were selected. None of these patients previously received any surgical or non-surgical periodontal therapy, and they presented radiographic evidence of moderate bone loss. Two non-adjacent sites in different quadrants were identified and observed in each patient, analyzing treatment effectiveness (split-mouth design). Clinical pocket depth, clinical attachment loss, and bleeding on probing were evaluated at time 0 and after 6 months, while microbial analysis (MA) was conducted at baseline and after 15 days. Significant differences were calculated using SPSS program and paired simple statistic \(t\)-test. Results: Total bacteria loadings had a statistically significant reduction before and after treatment with SRP (left site) (total average decrease of 27%). The sites treated with SRP plus ALADENT (right) showed a significantly reduced total bacterial loading compared to the untreated sites (right) (total average decrease of 75%). Mean values of CAL/PD and percentages data of BOP, recorded after SRP + ALADENT therapy, showed a higher reduction (CAL = 2.42, FD = 2.87 mm, 90% of sites with no bleeding) than those obtained after SRP treatment (CAL = 4.08 mm, PD = 4.73 mm, 70% of sites with no bleeding). Conclusion: The treatment of moderate and severe chronic periodontitis should include, beside SRP, the use of ALADENT medical device, which has been proved to be a useful adjuvant therapy.  
p 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/](https://creativecommons.org/licenses/py/)]([https://creativecommons.org/licenses/py/](https://creativecommons.org/licenses/py/)) 4.0/).  
**Featureated Application: This paper highlights the advantages of photodynamic therapy for treatment of periodontal disease.**  
**Acticle**  
**Photodynamic Therapy Using 5-Aminolevinic Acid (Ala) for the Treatment of Chronic Periodontitis: A Prospective Case Series.**_Appl. Sci._**  
**2022**, 12, 3102. [[https://doi.org/10.3390/app12063102](https://doi.org/10.3390/app12063102)]([https://doi.org/10.3390/app12063102](https://doi.org/10.3390/app12063102))  
Academic Editor: [PERSON]  
Received: 28 February 2022  
Accepted: 16 March 2022  
Published: 18 March 2022  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Keywords:** periodontal disease; chronic periodontitis; moderate and severe periodontitis; adjuvant periodontal treatment; photodynamic therapy; 5-aminolevulinic acid; bactericidal activity; deactivation and detoxification of periodontal pathogens; scaling and root planning
# Predicting Location of Tweets Using Machine Learning Approaches
## 1 Introduction  
Twitter is a microblogging service with over 368 million active users worldwide as of December 2022 [1]. Users share their thoughts, opinions, and ideas via short messages, or \"tweets,\" on various topics and events. One unique aspect of Twitter is that users can attach location information to their posts in the form of GPS coordinates [2].  
The ability to attach location information to tweets has attracted attention from various research communities for its potential applications, such as real-time event detection [3; 4; 5], intelligent transportation systems [6], location-based recommendations [7; 8], monitoring public health [9], and emergency and disaster analysis [10; 11; 12].  
However, despite the usefulness of location data, only 1% of tweets in the Twitter stream have geographical information [13; 14]. This limits the sample size for analysis, and the users who release their geographical information are not representative of the entire Twitter population [15]. Researchers have proposed new methods, such as named entity recognition [16; 17; 18; 19] and analysis of Twitter networks [20] and user home locations from profiles [21; 22], to analyze tweet content. However, these methods remain insufficient for applications that require precise geo-located data.  
This challenge is even greater when considering tweets in the Arabic language. Only 0.5% of the 50 million Arabic tweets collected were geo-tagged, and only 15% of those tweets were tagged to a reasonable location [13; 14]. This leaves only 0.08% of the collected 50 million Arabic tweets that can be used to build a learning model.  
In this paper, we present several approaches for predicting the geographical location of tweets using Saudi Twitter content. Our research aims to determine the percentage of the most and least tagged tweets in comparison to the general locations in Saudi Arabia, taking into account various events and circumstances in the region. We extract text contentfrom tweets and identify locations based on the geographical names mentioned. Our methods include multiple approaches for categorizing and evaluating the tweets, with the detection corresponding to the nature of the tweets and their references. The geographical data gathered have the potential for various applications and are effective for general time evaluation.