esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Automatic Berthing Support Program for Autonomous Ships
## 3 Results
### Automatic Berthing Support Program
#### 3.1.2 Determining Input Elements  
The main input elements required for the calculations to run the automatic berthing support program are classified in Tables 1-3. Table 1 includes essential specifications of the ship, such as length overall, waterline length, beam, and draft, along with conditions of the berthing environment. The frontal and lateral wind-exposed areas can be calculated based on the width and height of the superstructure above the waterline. To reflect the actual berthing environment, wind speed, relative wind direction, current speed, incident angle, and berthing speed should be input.  
Figure 4: Calculation procedure.  
Table 2 lists values determined through intermediate calculations based on the inputs in Table 1, and Table 3 contains constant values that are always applied. Thus, Tables 2 and 3 provide necessary items for calculations that do not require direct user input in the program.  
As the input elements in Table 1 consist of information that may be challenging to obtain quickly from the ship, correlations between these elements and the ship's type were analyzed using data from 105 ships. This analysis, presented in Table 4, simplifies input, making the program easier for captains and pilots to use.  
\begin{table}
\begin{tabular}{c c} \hline
**Input Data** & **Unit** \\ \hline LOA & m \\ \hline LBP & m \\ \hline Breadth (B) & m \\ \hline Draft & m \\ \hline Block coefficient (\(\mathrm{C_{b}}\)) & - \\ \hline Transverse projected area (\(\mathrm{A_{T}}\)) & m\({}^{2}\) \\ \hline Lateral projected area (\(\mathrm{A_{L}}\)) & m\({}^{2}\) \\ \hline Water depth & m \\ \hline Wind speed & Knot \\ \hline Relative wind angle & Degree \\ \hline Current speed & Knot \\ \hline Relative current angle & Degree \\ \hline Berthing speed & Knot \\ \hline \end{tabular}
\end{table}
Table 1: Input data.  
\begin{table}
\begin{tabular}{c c} \hline
**Input Data** & **Unit** \\ \hline Resultant wind pressure coefficient & - \\ \hline Lateral current force coefficient & - \\ \hline Hydraulic moment coefficient & - \\ \hline Wetted surface area & m\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 2: Fixing data during calculation.  
\begin{table}
\begin{tabular}{c c} \hline
**Input Data** & **Value** & **Unit** \\ \hline Air density & 0.125 & kg\(\cdot\)s\({}^{2}\)/m\({}^{4}\) \\ \hline Water density & 104.6 & kg\(\cdot\)s\({}^{2}\)/m\({}^{4}\) \\ \hline Frictional resistance coefficient & 0.002 & - \\ \hline \end{tabular}
\end{table}
Table 3: Fixed data.
## 3 Feature extraction and dimension reduction  
The development of novel instruments has produced sensors that generate data with extremely high dimensions, which provides an opportunity to carry out an extensive system investigation. However, analyzing these copious amounts of data depends on sophisticated techniques to extract pertinent features from original data. Generally, feature extraction and dimension reduction aim to create a map from a high dimensional space to a low dimensional space \(f:R^{m}\to R^{n}\)\(\left(n<<m\right).\) This step not only unloads heavy computational burdens from the subsequent system modeling step but also excludes irrelevant and noise signals from analysis.
## 4 Experimental Scenario for CO2 Emission Prediction Through the GreenNav Application  
In order to demonstrate the practical utility and accuracy of our hybrid CNN-LSTM model within a real-world context, we integrated the prediction system into our GreenNav application. This integration allows users to visualize and compare CO2 emissions for specific streets in Paris under various conditions. We present two distinct experimental scenarios to illustrate the model's capabilities.
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
## 2 Related Works  
This section of the study examines prior research from the standpoint of network architecture, as well as the many performance indicators that support the concept of extending network life.  
[PERSON] et al. [9] investigate strategies to optimize the number of packets sent in order to extend the life of UWSNs. This is done in order to determine how to make the most of UWSNs and maximize their potential benefits. To guarantee that a network lasts as long as feasible, it is critical to consider not only the time it takes for a packet to transmit, but also the overall size of the packet. Furthermore, in order to reduce the amount of power consumed by the network, we accurately simulate the connection layer.  
[PERSON] et al. [10] investigate a grid-based routing technique for UWSN as a means of addressing mobility concerns. The latency, energy usage, network density, and packet delivery ratio are all topics of discussion.  
[PERSON] et al. [11] utilize a universal wireless sensor network with a grid topology. In this study, both the efficiency of various network configurations and the utilization of various energy modalities are investigated.  
[PERSON] et al. [12] look at EAVARP, which is an energy-conscious and void-avoiding routing algorithm for wireless sensor networks. Transmission, vacancy, and flooding cycles have no effect on UWSN because it is immune to them.  
[PERSON] et al. [13] investigate interference-free localization routing for ultra-wideband sensor networks, with the intention of reducing the energy hole. The study details the overall amount of energy used, the total number of dropped packets, the total number of dead nodes, and a packet that was received at the sink.  
These studies, on the other hand, are more general inquiries into WSN energy use. The UWSN environment, on the other hand, is unique, and the underwater acoustic communication paradigm differs from that of typical WSNs. Furthermore, the aforementioned models are largely concerned with tackling general routing concerns in WSNs while ignoring clustering issues. This paper explores the optimization of energy consumption in clustered, routing-based underwater wireless sensor networks (UWSNs) and presents a CB-UWSN system for energy balancing to successfully balance the energy consumption of underwater wireless sensor nodes. This paper also tackles the optimization of energy consumption in UWSNs via clustered routing.
# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 6 Future Challenges and Research Scopes
### Limitations of Swarm Techniques in VANET
* Swarm behavior utilizing bio-inspired methodologies could impact the cooperation and interaction mechanisms of VANETs [47]. The methods employed can effectively address various challenges encountered in VANETs, such as route finding, scalability, resource demands, and signal diversity.
* Particle Swarm Optimization encounters security concerns in VANETs because of their unmonitored structure and the requirement for integrating different heuristics [194].
* Ant Colony Optimization necessitates significant computational power and extensive time computation to produce different results [195].
* Artificial Swarm Intelligence (ASI) leverages real-time communication and AI approaches to generate an integrated cognitive mechanism comprising human beings, forming a \"hive mind\" [196].
* The utilization of AI methods on vehicular networks encounters the obstacle of adjusting to novel V2X communication protocols.
* AI tools can improve the emerging V2X communication architecture by including highway and weather factors in planning data transmission, predictive methods, and assigning resources. Nevertheless, AI utilization must be cautiously approached at this stage with high standards.
* Ultimately, the research mentions energy-efficient AI/ML algorithms for future requirements. Recently, the proliferation of linked devices has significantly expanded, leading to applications where AI/ML operates on embedded gadgets. Such gadgets have minimal electrical consumption, yet AI/ML can generate computationally demanding tasks inappropriate for those gadget settings. Benchmarking AI/ML algorithms for energy efficiency is required when finding an appropriate solution for embedded devices. Also, a hybrid AI technique is needed to improve the adaptability and robustness of vehicular networks by minimizing the memory and computational overhead. It is also essential to convert the centralized system into a distributed one in order to share the ML workload over multiple computers since the demand for training data processing has outpaced the increase in computing devices' computational capacity. A cohesive model's construction and efficient training process parallelization are required for distributed systems.
# An Assessment of the Impact of Gypsum Deposit Development on Changes in the Radiation Environment
###### Abstract  
The aim of the conducted research was to assess the impact of gypsum deposit development on changes in the radiation levels of the abiotic components of the environment. For this purpose, a study of the radioactivity of water, bottom sediment, soil, gypsum and loam samples was performed. Ground-based studies of the distribution of the values of the ambient dose equivalent rate of gamma radiation and radon flux density were also carried out. It was shown that due to the high solubility of gypsum, the degree of karstification of the territory increases under the influence of meteoric waters, and as a result of the intensification of anthropogenic impact, the degree of chemical weathering of rocks increases. This leads to a coordinated change in not only the chemical but also the radiation conditions. In particular, radioactive contamination of quarry waters and areas of increased radon flux density in soil air were established. In bottom sediments, the significant correlations of \({}^{137}\)Cs, \({}^{238}\)U and \({}^{234}\)U activity concentrations with carbonates, organic matter and soluble salts contents, as well as Fe, Zn, Cu, Cr, Pb, Ni, Mo, Cd, Co, Ti and V, indicate a significant role of the anthropogenic factor in the accumulation in bottom sediments. This factor is associated with both regional atmospheric transport (\({}^{137}\)Cs) and the activity of the mining enterprise in the study area (\({}^{238}\)U and \({}^{234}\)U).  
mining; water; bottom sediments; soil; gypsum; radionuclides Article
## 3 Proposed Methodology
### Bert Model Structure
#### 3.2.1 Input Layer  
The input layer of the text classification model based on Bert and the robust Bert mechanism play a crucial role in processing raw text data. Its responsibilities go beyond  
Figure 1: Sample sentences from the SST-2 dataset for analysis and understanding.  
Figure 2: Architecture and components of Bert model structure for text classification.  
simply receiving the text, and it performs several key tasks as follows to prepare the input for further processing:  
* Text Preprocessing: The input layer begins by receiving the raw text data, which may include unstructured text from various sources such as documents, social media posts, or reviews. It performs initial preprocessing tasks such as removing punctuation, handling capitalization, and tokenizing the text into individual words or sub-word units.
* Tokenization: Once the text has been preprocessed, the input layer applies tokenization, which breaks the text into discrete units called tokens. These tokens typically correspond to words, subwords, or characters, depending on the tokenization strategy chosen. In the case of Bert, WordPiece Tokenization is commonly used, where the text is segmented into subword units based on a predefined vocabulary.
* Token Representation: After tokenization, each token is mapped to a unique numeric representation using an embedding lookup table. This process converts the textual tokens into dense vector representations, often called word embeddings or token embeddings. These embeddings capture semantic information about the tokens and facilitate the model's understanding of the input text.
* Sequence padding: To ensure uniform input dimensions, the input layer can apply sequence padding to the tokenized text. This involves adding padding tokens (usually zeros) to shorter sequences to ensure that all input sequences have the same length. Padding allows for efficient batch processing and matrix computation within the neural network architecture.  
In essence, the input layer serves as the gateway for raw text data into the neural network, orchestrating essential preprocessing steps to transform the text into a format suitable for further processing by subsequent layers. Its meticulous handling of text data sets the stage for effective learning and feature extraction by the subsequent layers of the model.
# Influence of Manufacturing Process Modularity on Lead Time Performances and Complexity
## 7 Case Study
### Analysis of Results  
All the five process structures were assessed using the above introduced optimal modularity indicator \(M(G)\) and operational complexity indicator \(PCI\), and also tested through simulation software to obtain MLT values. Obtained results for all the alternatives are depicted in Figure 13.  
Figure 9: Process design alternative B.  
When comparing process modularity values and MLT values, it is possible to state that there is the following relationship. If the modularity increases then MLT decreases, and vice versa (see Figure 13a). When comparing process modularity in relation to operational complexity, it can be seen that Alt. \"E\" has the lowest complexity, while its process modularity is the highest (see Figure 13b). The rest of process models are subject to the same tendency, and it can be said that OM positively impacts operational complexity of manufacturing systems.  
To confirm or deny the previous statements (from Section 6) to the RQs, the Spearman correlation analysis and the sensitivity analysis were applied.  
The Spearman correlation analysis confirmed very strong negative relationships (\(\rho=-0.82\)) between \(M(G)\) and MLT values, and between \(M(G)\) and \(PCI\) values as \(\rho=-0.87\).  
The sensitivity index between \(M(G)\) and MLT enumerated using Equation (10) equals 47.17 and between \(M(G)\) and \(PCI\) values is \(-6.13\). Based on the obtained results, it can be said that there is very high sensitivity between these two relationships.  
Figure 10: Process design alternatives C.  
Figure 11: Process design alternatives D.  
Figure 12: Process design alternatives E.
# Non-Plaque Induced Diffuse Gingival Overgrowth: An Overview
## 4 Gingival Enlargement Induced by Hormonal Changes  
### Pubertal and Gravidic Gingivitis  
Pregnancy has far-reaching systemic effects that extend beyond the reproductive organs. These effects are the end results of complex hormonal, immunological, dietary and behavioral changes that can exacerbate in the oral cavity causing gingival enlargement [2; 30].  
Excess gingival growth during pregnancy is a fairly common condition that affects 30% to 75% of all pregnant women [23; 31].  
Gingival enlargement in pregnant patients can be present both in localized and diffuse form and is characterized by mild or intense symptoms [2; 11; 23; 24]. In this condition gums appear erythematous, edematous and hyperplastic with hemorrhagic diathesis tendency, and with spontaneous bleeding at the slightest stress [23].  
Scientific evidence shows how an increase in circulating progesterone and estrogen alters the composition of the sub-gingival microflora, favoring more aggressive and destructive species [24].  
In addition, the immunological alterations occurring during pregnancy make women more susceptible to pathogens such as _P. gingivalis, P. intermedia, A. actinomycetemcomitans_ that overcome the host's defenses and become locally invasive [11].  
Sex hormones act on neutrophils, reducing the effectiveness of phagocytosis and bactericidal mechanisms [11]. They also stimulate pro-inflammatory mediators such as E2 prostaglandandins and endotoxins [11]. Finally, they cause an increase in vascular permeability and a reduced keratinization of the gingiva, which becomes more susceptible to stimuli [11].  
The hormonal changes that occur during puberty can cause gingival overgrowth too [11]. These hormonal fluctuations generally affect adolescents of 12-14 years old, usually causing more localized and mild gingival enlargement than in pregnancy [11; 32].  
The gums of these patients will appear hyperemic, hyperplastic and edematous, especially in correspondence to the papillae, prone to bleeding and at risk of bacterial infections [2; 11; 33].  
The pathogenetic mechanisms of pubertal and pregnancy gingival overgrowth are very similar, in fact in both an altered level of sex hormones is associated with an increase in gingival inflammation, with alteration of the gingival microflora and an increase in serum antibodies [11].
## 4 The Proposed DRLEA Approach
### Conventional Methods
#### 4.2.1 \(\mathcal{Q}\)-Learning  
\(\mathcal{Q}\)-learning is a kind of classical RL algorithm [38]. To search the optimal \(\mathcal{Q}\) function, \(\mathcal{Q}\)-learning builds a \(\mathcal{Q}\) table to record the sum of existing \(\mathcal{Q}\) value \(\mathcal{Q}(s,A)\) of the action \(A\) for the current state \(s\). The \(\mathcal{Q}\) value is obtained by Equation (9).  
\[\mathcal{Q}(s,A)\leftarrow\mathrm{Q}(s,A)+\beta\bigg{[}\mathcal{R}(s,A)+ \alpha\max_{j}\mathcal{Q}\big{(}s^{\prime},A_{j}\big{)}-\mathcal{Q}(s,A)\bigg{]}. \tag{9}\]  
Here, \(\beta\) denotes the learning rate and \(\max_{j}\mathcal{Q}(s^{\prime},A_{j})\) denotes performing the action \(A_{j}\) that can obtain the maximum \(\mathcal{Q}\) value under the state \(s^{\prime}\). The details of the \(\mathcal{Q}\)-learning algorithm is shown in Algorithm 1.  
```
1:\(s_{t},\alpha,e,\mathcal{A}\).
2:\(\mathcal{Q}(s_{t},A_{1}),\mathcal{Q}(s_{t},A_{2}),\dots,\mathcal{Q}(s_{t},A_{ J})\).
3:Build a \(\mathcal{Q}\) table \(QT\).
4:for epoch = 0 to E do
5: Initialize the antenna parameters.
6:for step \(t=0\) to \(T\)do
7: Obtain the antenna parameters as state \(s_{t}\).
8: Randomly generates \(r\) in the range of (0,1).
9:if\(r>\epsilon\)then
10: Randomly select an action \(A\in\mathcal{A}\)
11:else
12:\(A=\operatorname*{arg\,max}_{j}\mathcal{QT}(s_{t},A_{j})\)
13:endif
14: Perform the action \(A\).
15: Get reward \(\mathcal{R}(s_{t},A)\) by Equation (5).
16: Update \(s_{t}\) to \(s_{t+1}\).
17: Update the \(\mathcal{Q}\) table \(QT\):
18:\(QT(s_{t},A)=QT(s_{t},A)+\mathcal{Q}(s_{t},A)\)
19:endfor
20:endfor
```  
**Algorithm 1**\(\mathcal{Q}-learning\) method for HAPS antenna control.
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
## 2 Preliminaries
### Secure Multi-Party Computation Model  
(a) Semi-honest model: All participants will strictly abide by the agreement and will not actively change the agreement or data. However, intermediate calculation results may be retained and used to calculate the private data of other participants.  
(b) Semi-malicious model: The adversary can decide whether to faithfully execute the original agreement based on the input and a certain degree of randomness.  
(c) Malicious model: All computing participants can tamper with or leak the agreement and data at will, and even prevent the normal execution of the agreement.
# Numerical Estimation of Bending in Holographic Volume Gratings by Means of RCWA and Deep Learning
## 3 Results
### Deep Learning-Based Bending Estimator
A total of 8000 epoch trainings took approximately 1 h and 32 min for the FNN model and 2 h and 21 min for the hybrid one--due to its convolutional layers being more computationally costly. In Table 2 are shown the results for the Mean Absolute Errors for both models and for \(a_{1}\), \(a_{2}\), and \(a_{3}\) for the optical depth and index modulation. While both methods achieved good results for the prediction of the aforementioned parameters, slight differences in the overall error were observed between the proposed methods, although the hybrid model achieved slightly better accuracy for the overall bending coefficient parameters, while the FNN achieved better optical depth and index modulation prediction performance. A further standard deviation analysis of the difference between the model and target values was made, shown in Table 3, showcasing that the  
When we compare the bending phase polynomials that the FNN (BO) model predicted and that the hybrid (BO) model predicted with the base true data in the test subset, an excellent concordance can be observed, as shown in Figure 9a,b. When we did the proper RCWA-SM reconstruction of the diffraction patterns out of the aforementioned predicted and base bending parameters, we observe that the results are consistently closely to being identical, as shown in Figure 9c,d, proving the excellent efficiency of our bending prediction despite the non-negligible (0.04) bending coefficient losses. It can be noted that, unlike variations in optical depth and index modulation, variations of this magnitude (0.04) in bending did not significantly impact the diffraction patterns. This points out that the limit of accuracy in our prediction models are primarily constrained by such variations not translating into noticeable changes in optical properties.
# A Comprehensive Digital Model Approach for Adaptive Manufacturing Systems
## 1 Introduction
### Research Questions  
There are some research question that need to be asked:  
* How can a digital model be effectively utilized to enhance the adaptability of manufacturing systems?This question addresses the core premise of the paper, exploring the role of digital models in enabling adaptive manufacturing. The methodology aims to demonstrate how digital models can simulate, analyze, and optimize production processes to achieve adaptability.
* What insights and benefits can manufacturers derive from utilizing the digital model created through the proposed methodology?  
This question focuses on the practical outcomes of implementing the methodology. By evaluating the insights and benefits gained from the digital model, the paper aims to highlight the real-world impact of the methodology in enhancing manufacturing processes' adaptability, efficiency, and overall performance.
* How can the proposed methodology contribute to the realization of Industry 4.0 principles in the realm of adaptive manufacturing?  
This research question situates the paper within the context of Industry 4.0, aiming to explore how the methodology aligns with the principles of intelligent, interconnected manufacturing. It investigates how the digital model's capabilities resonate with the vision of Industry 4.0 and pave the way for smarter, more responsive manufacturing systems.
* How can the proposed methodology be adapted and applied across various adaptive manufacturing contexts?  
This question delves into the applicability of the methodology beyond competency islands. It explores the potential extension of the methodology to other adaptive manufacturing systems, such as reconfigurable systems, and examines how the structured approach can be tailored to suit different production scenarios.
## 5 Energy Efficiency and Sustainability  
Integrating RISs with HAPs introduces a groundbreaking approach that enhances communication capabilities and addresses some critical energy efficiency and sustainability concerns. The demand for wireless communication has surged, driving the quest for improved communication system performance, especially in remote areas. RIS-enhanced HAPs is among the technologies that offer promising energy efficiency and sustainability solutions in wireless networks.