esa-sceva/satcom-chunk-collection huggingface dataset:
# Journey Planning Algorithms for Massive Delay-Prone Transit Networks +
## 3 Basic Public Transit Labeling
### Multi-Criteria Query Algorithm  
It is known that the above described wred-te graph model, combined with a 2 hc-sp labeling, can be used to answer efficiently to both earliest arrival and profile queries on a timetable (see Reference [13] for more details). In particular, the routine for answering a multi-criteria query mc-ea\((s_{i},s_{j},\tau)\) is as follows. First, a vertex \(c\in\textsc{dv}[i]\) satisfying the following conditions is computed:  
1. \(time(c)\geq\tau\);
2. there is no arc \((c^{\prime},c)\) in \(G\) such that \(c^{\prime}\in\textsc{dv}[i]\) and \(time(c^{\prime})\geq\tau\), meaning that vertex \(c\) is associated with the smallest departure time larger than \(\tau\).  
Then, the algorithm computes \(d(c,v)\), by querying the shortest path labeling L, for all \(v\in\textsc{dv}[j]\) such that \(time(c)<time(v)\), and selects the arrival vertex \(v\) having minimum \(d(c,v)\). Finally, the time associated to such vertex is the earliest arrival time while \(d(c,v)\) is the associated number of transfers. Hence, the corresponding journey is the one having the smallest number of transfers among those exhibiting the earliest arrival time: the structure of the journey again can be retrieved by applying a recursive query procedure [13; 21]. Note that, to achieve the fastest possible query times, ptl employs some pruning mechanisms [13]. Notice also that, differently from basic ptl, no compact version of shortest path labelings is known, that is there is no analog of stop labelings (see Section 3.3) for multi-criteria queries.
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Alternative to Chlorine Disinfectants
#### 4.7.4 Electrochlorination  
Out of various disinfection methods, only chlorination provides long-term water disinfection within distribution networks and equipment. In situ electrochlorination offers a user-friendly approach that tackles issues associated with transporting and storing hazardous chemicals. This method has several benefits, including easy process automation and utilizing renewable energy sources, making it well-suited for decentralized water disinfection. Incorporating electrochlorination into water reuse systems is a safety measure that ensures water disinfection and facilitates safe water recycling, thereby decreasing the need for freshwater resources. Nevertheless, in situ electrochlorination poses a similar risk of generating organic DBPs as traditional chlorination. Electrochemical water treatment systems have attracted considerable attention for their ability to effectively eliminate persistent pollutants and pathogens and their safety and operational convenience. Microorganism deactivation is accomplished by producing disinfecting agents during the electrochemical treatment process. This approach shows potential for decentralized treatment in rural regions, as it can operate autonomously without reliance on water and electrical grids. In these systems, chloride anions (Cl\({}^{-}\)) are oxidized on the anode surface, producing electrogenerated chlorine (Cl\({}_{2}\)(aq)), which then reacts with water to form hypochlorous acid (HOCI). In situ electrochlorination has the potential to reduce resource consumption and reduce the formation of organic byproducts, as lower doses of electrogenerated chlorine can achieve similar levels of inactivation compared to chemical treatments [41].
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 3 Dangampo Site: Equipment Testing
### Results and Discussion
#### 3.2.3 Scanning Sonar  
The images acquired by the SS did not show any significant errors or distortions, even without post-correction, and accurately displayed the topographic features of the SRA. This accuracy was attributed to the data acquisition method because the fixed position of the transducer on the seafloor prevents its exposure to various factors that would adversely affect the image quality, with the exception of tidal currents. Although the 5 m \(\times\) 5 m squares were not very evident, the initial grid of 10 m \(\times\) 10 m squares was clearly distinguishable. Moreover, the ridge-furrow surface profile caused by the sediment removal survey could be confirmed on the survey line along which the sediment removal survey had been conducted. The only disadvantage of the SS observations in this study was the diminishing resolution with increasing distance from the transducer.
# Intelligent Environment Monitoring System for University Laboratories
## 4 Real Experiments
### Hardware Testing Method  
Testing the power design was the first step in meeting the hardware testing requirements of our proposed system. Then, with power supply for the development board, we tested whether the power could successfully start the GPRS module. Furthermore, we tested whether the various sensors were able to collect environmental data and return such data to the display terminal. After the normal data collection test, data transmission of laboratory humidity and temperature or indoor light intensity was tested, as well as danger tips which are sent out under abnormal conditions (e.g., high requirements about temperature and humidity). In addition to this, we needed to test whether there was a message responding to a dangerous situation when a person walked around in the induction range of the human body infrared module, whether the buzzer alarm worked normally and whether the SMS alarm function was achieved under the condition of simulated fire. Finally, we integrated the proposed  
Figure 3: System programming flow chart.  
system and selected several laboratories for practical testing. In a word, under the operation of the system, we monitored the stability of all modules, analyzed the logical features of combined modules, and confirmed that the system can execute effective environmental monitoring and alarms.
###### Abstract  
This study focuses on establishing a conceptual design for a multirotor unmanned aerial vehicle (UAV). The objectives of this octocopter are to reduce the number of flight cancelations and car accidents owing to low-visibility issues and to improve abnormal environmental conditions due to the presence of smoke. The proposed octocopter contains a convergent-divergent [CD] duct-based storage tank, which provides a platform to store saltwater and allows it to fly in foggy zones. Fine saltwater is sprayed from the octocopter and dispersed into the low clouds, thereby altering the vapor's microphysical processes to break it up and improve visibility. The nature of the seawater and its enhanced fluid properties, due to the involvement of octocopter, creates the fluid flow mixing between atmospheric fluids and spraying particles, which increases the settling of foggy and smokey content groundwater. For deployment, the conceptual design of the octocopter was initially constructed through analytical approaches. Additionally, three unique historical relationships were created. The standard engineering approaches involved in this work were stability analysis through MATLAB and fluid-property analysis through computational fluid dynamics (CFD) cum multiple reference frame (MRF) tools. The systematic model of this octocopter was developed by CATIA, and thereafter CFD and fluid-structure-interaction (FSI) analyses were computed, in ANSYS Workbench, on the octocopter for various environmental conditions. The aerodynamic forces on the drone, the enhancement of dynamic pressure by the presence of high amounts of rotors and nozzle sprayer, suitable material to resist aerodynamic loadings, and tests on the efficiency of the controller and its electronic components were investigated in detail. Finally, the proposed octocopter-based dynamic system was conceptually constructed.  
CFD; environmental drone; FSI; ocean water; stability analysis; UAV +
Footnote †: journal: Applied Sciences
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 5 Evaluation
### Learning Performance  
The goal of each learning user is to choose the best serving TRxP among the many TRxPs from which it gets an RAO advertisement. Selecting the best serving TRxP allows the user to perform the RA procedure quickly, and transition into the phase of radio resource scheduling. Intuitively, this will be the least congested TRxP. Figure 5a shows the performance of the learning algorithm. It is observed that the performance increases and the reward becomes stable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Maximum number of retrials & 50 \\ Number of available preambles & 64 \\ Number of access points & 5 \\ Tolerable delay & 100 slots \\ Scheduler & Proportional Fair \\ Channel frequency & 28 GHz \\ Total number of users & 900 \\ Number of TRxPs & 5 \\ Mobility pattern & ConstantVelocityMobilityModel \\ Position allocator & Random uniform distribution allocator \\ Scheduler & Proportional Fair \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter (Description)** & **Value** \\ \hline \(m\) (Replay memory size) & 500,000 \\ \(M\) (Mini-batch size) & 32 \\ \(\gamma\) (Discount factor) & 0.95 \\ \(\epsilon\) & (0.01, 1.0) \\ \(\epsilon_{decay}\) & 0.0001 \\ \(\alpha\) (Learning rate) & 0.0001 \\ \(\tau\) (Copy rate) & 0.05 \\ Optimizer & Adam \\ Activation & 0.05 \\ Episodes & 1000 \\ Steps & 500 \\ Connected layers & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters used for DQN.  
We averaged the reward values for every other episode. The algorithm quickly reaches its peak reward value around the 20 th episode. Subsequently, it tries to maintain this value despite the randomness of the environment. The algorithm's performance does not dip any lower than the initial reward value afterwards. The effect of the learning rate on the DQN algorithm is shown in Figure 4(b). A lower learning rate eventually allows for better performance improvement despite the initial penalty. Initially, it is desirable that the algorithm relies on learning instead of exploiting its inadequate experience, and thus it shows a low performance. After a while, the learning rate should be smaller, and the users should be able to exploit their experience for better performance.
# Expectation vs. Reality: Attitudes Towards a Socially Assistive Robot in Cardiac Rehabilitation
## 3 Materials and Methods
### Patients of the Study
#### 3.2.2 Control Group  
As aforementioned, the interviews were applied to patients who are in an early stage of the phase II or phase III of the CR program. Similarly, they must have no previous experience with the above-described robotic system. According to these conditions, participants are briefly contextualized about SAR systems, the benefits that they can provide, and the variables that are measured in this application, followed by the presentation of a video where the real cardiac scenario is displayed and the robot with its functionality can be appreciated. Later, the questionnaire (Table A1) is introduced by the experimenter in order to specify the purpose of the questionnaire and the correct way to complete it. In addition to the questions based on Likert scale, two open questions were also included, regarding the questions of the users' opinions, which are stated below. As it was also mentioned in the intervention group, by the fact that the questions were formulated only in a positive manner, these questions are subject to bias.  
Question 1:  
Would you use the robot during the therapy? Why?  
Question 2:  
What expectations do you have about the therapy assisted by a robot?
# Post-synthesis Tellurium Doping Induced Mirror Twin Boundaries in Monolayer Molybdenum Disulfide
## 3 Results and Discussion
Due to the different doping levels, a statistical histogram about the number of X2 sites at MTBs in our experiment is shown in Figure 4, and the mean value was calculated as 8.2, which was very close to the expected value, indicating that the strain due to the large lattice mismatch was the driving force for the formation of MTBs. Herein, for the formation of an MTB, unlike previous reports [13], chalcogen vacancy might not be the unique dominant factor. Such a Te-substitution-induced MTB formation was mainly attributed to the energetic request to reduce the strain and interfacial energy after the substitution of sulfur by tellurium. Moreover, due to the higher doping temperature and the supported graphene film, we also could not observe atomic chains--the Te atoms located in between two neighboring Mo atoms, as reported in Reference [35]. Figure 4: The histogram of the number of X2 (S2, S+Te, or Te2) sites inside the MTBs. The derived mean value is 8.2, which is very close to the expected value of 8. Figure 3: The atomic models of possible mechanisms forming an inversion domain and MTB. Mo, S, and Te are denoted by the salmon, yellow, and blue spheres, respectively. (**a**) The substitution of some S atoms by Te. The green arrows represent the compressive strain. The gilding of the Mo atom plane followed by the loss of S atoms to form S divacancy lines. (**b**) The loss of some Mo and more S atoms to further relax the strain. (**c**) More S atoms were substituted by Te and the formation of an inversion domain and MTB.
## 4 Result Analysis and Discussion
### Feature Extraction Using Improved RBSWT
and internal faults, and CT saturation effects allows for a comprehensive understanding of transformer performance and enhances the accuracy of the proposed framework. It is noteworthy that the settings for differential protection were set up to handle all kinds of external faults, particularly those that arise when CT saturation is present. A wavelet analysis of the current signal was carried out with a ground fault resistance of 0.1 \(\Omega\) in the event of an external fault involving three-phase and ground faults (ABC-G) outside the protected zone of the power transformer (such as three-phase faults occurring on the load side). In order for the protection mechanisms to be able to respond to and lessen the effects of such faults, it was necessary to precisely identify and characterize the fault conditions.
## 4 Methodological Approach  
To achieve this study's objectives and close the aforementioned gaps in the literature, five key steps were undertaken: Step 1: identification of challenges; Step 2: correlation analysis of challenges; Step 3: network centrality analysis; Step 4: visualization of relationships; and Step 5: classification of challenges. Steps 2 and 3 were conducted using social network analysis (SNA).  
Originally, SNA was introduced in the literature as a statistical and graphical tool to examine relationships between social entities [82]. However, SNA has recently seen growing applications across various fields as a method for modeling relationships between non-human entities, as presented in research like that of [PERSON] et al. [83], [PERSON] et al. [84], [PERSON] et al. [85], and [PERSON] and [PERSON] [86]. A significant feature of SNA is its ability to illustrate the relationships among entities--such as individuals, organizations, components, or barriers--by creating a network of nodes interconnected by directed or undirected linkages. Beyond visualizing these connections, SNA also facilitates the structural analysis of the network using various metrics at both the links and nodes levels.  
This study utilized SNA as its analytical method due to its ability to effectively examine the interrelationships and patterns--both direct and indirect--among the challenges within a network [87]. Furthermore, SNA can be implemented using correlation coefficients, a feature not applicable to other comparable methods such as Interpretive Structural Modeling [88], Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) [89], and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) [90].
# Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
## 3 Related Work for the Kurdish Language
### Variational Autoencoder  
To effectively pre-train a VAE for waveform reconstruction, several key components and formulas are essential. The VAE framework aims to learn a probabilistic mapping from an input waveform \(x\) to a latent space representation \(z\) and subsequently reconstructs the input as \(\hat{x}\). This process involves two primary objectives: maximizing the likelihood of generating the input data given the latent variables and enforcing the learned latent space to follow a prior distribution.  
The wave encoder \(q_{\phi}(z|x)\), an approximate posterior distribution that is parameterized by \(\phi\), maps the input waveform \(x\) to a latent space representation \(z\), where \(z\sim\mathcal{N}(\mu_{\phi},\,\sigma_{\phi}^{2})\), with \(\mu_{\phi}\) and \(\sigma_{\phi}^{2}\) representing the mean and variance of the latent space distribution, respectively. Subsequently, the wave decoder \(p_{\psi}(\hat{x}|z)\) parameterized by \(\psi\) generates the reconstructed output \(\hat{x}\) conditioned on the sampled latent variable \(z\).  
Figure 1: (**A**) Initially, a VAE is pre-trained using speech-to-speech data. During this phase, the VAE focuses on reconstructing the input speech waveform. (**B**) Training Procedure: This is the alignment phase where the pre-trained wave encoder of the VAE is utilized to ensure that the text encoder produces a distribution of latent variables identical to that generated by the wave encoder. (**C**) Inference Procedure: In this phase, the text encoder is trained to generate distributions that the wave decoder of the VAE can interpret and convert into speech waveforms.  
For training our VAE, the total loss \(\mathcal{L}(\psi,\phi)\) is the sum of two terms: the reconstruction loss \(\mathcal{L}_{rec}\) and Kullback-Leibler \((KL)\) divergence.  
\[\begin{split}\mathcal{L}(\psi,\phi)=-&\ \mathbb{E}_{z\sim q_{\phi}(z|x)}[\log p_{\psi}(x|z)]\\ &+\mathcal{D}_{KL}[q_{\phi}(z|x)||p_{\psi}(z)]\end{split} \tag{2}\]  
\(\mathcal{L}_{rec}\) is expressed by the negative log-likelihood \(-\log p_{\psi}(x|z)\) capturing the probabilistic aspect of reconstruction, ensuring that the generated output closely resembles the input data distribution. For \(\mathcal{L}_{rec}\), we simply use the mean square error (MSE) between the input \(x\) and the reconstructed output \(\hat{x}\). Second, the \(KL\) divergence term enforces a regularization constraint, guiding the latent space towards a predefined prior distribution \(p_{\psi}(z)\), which is a Gaussian distribution of \(\mathcal{N}(0,1)\). Now, the target is to find the optimal \(\psi\) and \(\phi\) such that  
\[\psi^{*},\phi^{*}=\arg\max_{\psi\
eq\phi}\mathcal{L}(\psi,\phi) \tag{3}\]
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Software-Defined Network (SDN)
#### 3.1.3 Advantages of SDN  
SDN provides a flexible and programmable method for network management, enabling administrators to adjust and manage network resources dynamically in response to changing requirements. This adaptability allows for efficient resource allocation, enhanced network performance, and swift adjustments to changing network conditions. [37]. SDN enables the logical centralisation of network control, allowing for the deployment of distributed controllers throughout the network. This approach improves security and service quality by placing control logic nearer to the network's edges, which helps reduce latency and enhances responsiveness to network activities [38]. The architecture of SDN distinctly separates the control plane--responsible for determining how traffic is directed--from the data plane, which manages the actual transmission of data packets. This division allows for centralised control, where network policies and configurations can be defined and enforced uniformly across the network [39]. Additionally, SDN enhances traffic management by offering a comprehensive view of network traffic and topology, enabling administrators to apply mechanisms like traffic shaping, load balancing, and Quality of Service (QoS) prioritisation to optimise network efficiency and resource utilisation [39].
## 3 Remote-Sensing System Framework and Problem Defined
### The Onboard Computer: Nvidia Jetson NANO  
An Nvidia Jetson NANO was used as the onboard computer for process computing and image processing. Because the SWIR image is a high-resolution image, it needs a small-sized computing platform with a powerful graphic computing capacity and low energy consumption. Thus, the NANO module was mounted on a carrier board which had a 4-core ARM A57 @ 1.43 GHz processor, 128-core Maxwell 472 GFLOPs GPU, 4 GB RAM, 4 USB 3.0 interfaces, and a USB 2.0 Micro-B interface. The power usage in performance was around 5 W. It was little in size, low power consumption, and lightweight, making it suitable for drone application. However, we downgraded the four USB 3.0 interfaces to USB 2.0 cables to avoid any adverse effect on the GPS devices. The operating system was Ubuntu 18.04, and the flying motion command was sent to the drone using the DroneKit library via a serial communication connector.
###### Abstract
3. We further propose an ACDM-based VLPC scheme that simultaneously offers VLP and VLC capabilities. To mitigate the impact of MAI on decoding, we introduce an SICD technique that progressively eliminates interfering signals, optimizes decoding accuracy, and enhances VLC and VLP performance. 4. Extensive simulations are conducted to compare the performance of VLPC systems based on SICD-OPRC, OPRC, and OZCZ. Specifically, in asynchronous transmission scenarios, the OZCZ-based scheme achieves an average bit-error rate (BER) of 4.0 \(\times\) 10\({}^{-2}\) and an average positioning error (PE) of 32.5 cm. The OPRC-based scheme improves these metrics to an average BER of 2.3 \(\times\) 10\({}^{-2}\) and an average PE of 19.9 cm. Most notably, the SICD-OPRC scheme further reduces the average BER to 4.3 \(\times\) 10\({}^{-4}\) and the average PE to 2.7 cm, nearly matching the BER and PE levels observed in synchronized VLPC systems. The rest of this paper is organized as follows. Section 2 introduces the generation process of OPRC and provides a detailed analysis of its correlation characteristics. Section 3 focuses on the operation mechanism of the OPRC-VLP system, detailing the simulation and experimental setup and offering a comprehensive interpretation of the collected data to validate the system's performance. Section 4 introduces the VLPC network, emphasizing the framework of the SICD-OPRC-VLPC system, the construction of its simulation model, and simulations in both synchronous and asynchronous environments. Finally, Section 5 concludes the paper.