esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed System Model  
The system model being proposed comprises an N-node UWSN, where information is sent to a central sink node through acoustic communication. The network model consists of one sink node (Rx), one courier node, and multiple transmitter (Tx) nodes communicating through time-slotted channels. The Rx node manages the wake-up signals and communication windows to ensure optimal data flow. Each frame is split into \(S\) time slots, and each transmitter sends exactly one packet per frame, using a distinct time slot to avoid interference with other transmitters. To avoid collisions in the receiver, nodes are selected to transmit in the available slots using a receiver initiated wake-up mechanism along with power control techniques. When a packet is received, the Rx node broadcasts an acknowledgment (ACK) indicating successful delivery. Critical data is prioritized to ensure that time-sensitive packets are transmitted promptly, while non-critical data is managed based on network capacity.The proposed DAWPC-MAC protocol address collision challenges, enhance energy efficiency, and maximize channel utilization using reinforcement learning. Under this framework, Rx nodes function as learning agents, and their transmission strategies are updated in response to feedback from the sink node regarding previous actions. Decision-making is optimized by DQN through the updating of Q-values to improve long-term communication efficiency. Among these features, power adaptation for extending the lifetime of battery devices, avoidance of idle listening, monitoring of peer requests, and scaling according to the requested volume of messages under the specific scale of the deadline network are its key features. By incorporating DQN, robust performance is achieved in delivering critical data while maintaining high energy efficiency and scalability for diverse underwater applications.
## 3 Toward the Generation of Isolated Attosecond Hard X-ray Pulse
### Optimization of Current Profile by the Selection of Laser Wavelengths (Single Peak Current Profile)  
The effect of laser wavelength on the current profile has been studied for the generation of an attosecond hard X-ray pulse, using the parameters of the electron bunch of PAL-XFEL [4]: A beam energy of 10 GeV, a total electron bunch charge of 0.2 nC, a beam normalized emittance of 0.5 \(\upmu\)m-rad and an electron bunch average-current of 3 kA or higher. A CEP-stabilized few-cycle (800 nm, \(\sim\)0.13 mJ) laser modulates the electron beam energy. The beam waist at focus is 250 \(\upmu\)m. The  
Figure 5: (Color online). ESASE scheme for attosecond pulse generation.  
wiggler magnet is a two-period magnet with B\({}_{0}\) = 1.1459 Tesla and \(\lambda_{\mathrm{w}}\) = 55 cm. ELEGANT [41], the six-dimensional particle tracking code, is used to calculate electron bunch properties.  
First, an electron bunch with a total length of \(\sim\)12 \(\upmu\)m (\(\sim\)40 fs) and an average current of 6 kA is considered. The longitudinal energy distribution of this electron bunch before wiggler and after chicane, respectively, is shown in Figure 6a,b. The simulations for different laser wavelengths and pulse durations have been done. The laser wavelength is scanned from 800 nm (5 fs FWHM) to 2400 nm (14 fs FWHM), maintaining the number of laser cycles around 1.85 It is found that the current peaks vary in number and magnitude, depending on the laser wavelength and pulse duration. It is found that a 1200 nm, 7.5 fs FWHM laser produces the maximum main-peak current in the current profile of the electron bunch after chicane with the highest contrast ratio (the contrast ratio is defined by the ratio of the current of the strongest peak to that of the second strongest peak), as shown in Figure 6c. Figure 6d shows the variation of the contrast ratio with respect to laser wavelengths, 800 nm to 2400 nm. A 1200 nm and 7.5 fs laser gives the best contrast ratio with a maximum current of the main peak.
# A Review on Deep-Learning-Based Cyberbullying Detection
## 9 Challenges, Open Issues, and Future Trends
### Issues in DL  
* Require a large amount of dataset: Large volumes of labeled data are required for DL. For example, the creation of self-driving cars involves millions of photos and hundreds of hours of video [198]. It is commonly known that data preparation consumes 80-90% of the time spent on ML development. Furthermore, even the strongest DL algorithms will struggle to function without good data and present weak performance to handle biased and unclean data during model training [199].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  
* High computational power: DL takes a lot of computational power. The parallel design of high-performance GPUs is ideal for DL. When used in conjunction with clusters or cloud computing, this allows development teams to cut DL network time for training from weeks to hours or less [198].
* Reasoning of prediction unexplainable: DL result prediction follows the Black-Box testing approach. Thus, it is not capable of making any explainable predictions. Since DL's hidden weight and activation are non-interpretable, its predictions are considered as non-explainable [200].
* Security issue: Preventing the DL models from security attacks is the biggest challenge nowadays. Based on the occurring time, there are two types of security attacks. One is _poisoning_ attack, which occurs during the training period, and another one is _evasion_ attack, which occurs during interference (after training). By corrupting the data with malicious examples, poisoning attacks compromise the training process. On the other hand, evasion attacks use adversarial examples to confuse the entire classification process [201].
* Models are not adaptive: In the present world, data are very dynamic. Data are changing due to various factors, which may be constantly changing, such as location, time, and many other factors. However, DL models are built using a defined set, which is called the training dataset. Later, the performance of the model is measured by the data, which also comes from the same distribution of the training data, and eventually, the model performs well. Later, the same model may start performing poorly due to the changing the characteristics of the data, which are not entirely different, but have some variations from the training data. This is difficult to manage in DL to retrain the old models.
## 2 Description of the Analytical Model and Details of the Investigated Building  
In this study, we meticulously assess Building Number 7, located within the premises of the Vaszary Kolos Hospital Facility, which was established in the year 1975. Our analysis is founded on the original design plans, with a strong emphasis on its alignment with Eurocode standards [16]. The methodology of Knowledge Level 2 [14] has been adopted, reflecting a comprehensive understanding of the overall design and specific dimensions. This comprehension has been derived primarily from preliminary construction sketches utilized during both the initial construction phase and subsequent adjustments. Our grasp of structural intricacies has been further bolstered via meticulous on-site inspections focusing on key components to ensure congruence with actual conditions. The mechanical attributes of the materials have been gleaned from initial design specifications, supplemented via on-site tests to validate their properties. This knowledge level allows us to use any method of analysis with the confidence level determined at 1 and 2.  
To assess the structural performance of Building Number 7, we have undertaken an evaluation using both the Equivalent Linear Static and Nonlinear Static analysis methods. These methods enable a thorough examination of the building's behavior under seismic loads, providing insights into its response characteristics and vulnerabilities.  
1. General Overview and Imagery  
The building in question falls under the category of a four-story reinforced concrete movement-resisting frame construction. It is characterized by the presence of concrete beams and columns designed to withstand both lateral and vertical loads. This classification aligns with the FEMA P-154 categorization, specifically denoted as a C1 type building [17]. The building's dimensions are detailed as 17.4 m in the Y direction and 23.7 m in the X direction. The heights of the individual stories are as follows: the ground floor stands at 2.53 m, while the subsequent floors and the roof maintain a uniform height of 3.1 m. The plan of the building is in the form of a T-shape.  
For a comprehensive visual representation, please refer to the accompanying imagery and diagrams--Figure 1 for the satellite view, Figure 2 for the site plan, Figure 3 for the floor plan, Figure 4 for the  
2 Principal Structural Components  
The structural components of the building are outlined in Table 1, highlighting their respective details, reinforcement sizes, and additional relevant information. These components encompass slabs, beams, columns, shear walls, and masonry infill walls. Detailed specifications of the dimensions, reinforcement sizes, and arrangements of these elements are provided for a comprehensive understanding.
# Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
## 2 Theoretical Background and Proposed Method
### Convolutional Deep Brief Network and Its Improvement
#### 2.2.2 CDBN and Its Improvement  
Proposed by [PERSON] in 2009, a CDBN is a network model that consists of a CRBM. Multiple CRBMs are connected to form a CDBN. The outcome of the previous CRBM layer is regarded as the input of its subsequent layer. The model fitting ability is further improved by multi-layer linking.  
The objective function of a CDBN is to maximize the log-likelihood function \(\theta\) (\(\cdot\)) in order to get the optimal parameters, and reconstruction error is used to evaluate the model. The reconstruction error refers to the difference between the original data after [PERSON] sampling with the training sample as the initial state and the distribution of the RBM model. A smaller reconstruction error indicates better training. Reconstruction error is given by Equation (12), with \(\hat{X}_{i}\) and \(X_{i}\) indicating the real output and ideal output:  
\[Error=\ \frac{1}{N}\sum\
olimits_{i=1}^{N}\left(\hat{X}_{i}\left(W^{k},b^{k} \right)-X_{i}\right)^{2} \tag{12}\]  
Based on the standard CDBN model, an improved structure is proposed. The standard CDBN uses only single-layer output features but ignores the comprehensive utilization of the features of each layer. As a consequence, the classification results are not representative. An improvement of the model connection mode is proposed in this study. The outputs of a two-layer CRBM are combined into a vector and input to the softmax classifier by multi-level feature fusion to utilize features, further improving classification accuracy.  
Model training can be summarized according to the following steps:  
1. Forward propagation: 1. Use CD algorithm to pre-train \(W\) and \(b\) and determine the opening and closing of the corresponding hidden element; 2. Propagate upward layer by layer, calculate the excitation value of each hidden element, and use the sigmoid function to complete the standardization;
2. Backpropagation: 1. Use the minimum mean square error criterion for the backward error propagation algorithm and update the parameters of the network. 2. Update the weight and bias of the network with Adam optimizer.
# Joint Extraction of Multiple Relations and Entities from Building Code Clauses
## 2 Methodology
### Joint Extraction Model  
Currently, a number of models have been developed for the joint extraction of entities and relations [27; 28; 29]. However, few models excel at solving the overlapping relation problem. For example, in Figure 1, eight entities exist in the clause, and five different relations refer to the common entity \"(i.e. \"#1 (apartment)\". To account for situations of this kind, some models assume that potential relations exist between each pair of identified entities [30]. In general, \(N\) entities can produce approximately \(N^{2}\) candidate pairs; however, most of these are meaningless, resulting in low relation extraction performance, especially when entities are densely present in the sentences of building codes. For instance, fifty clauses from Chinese building codes were randomly selected and labeled using the predefined entity and relation types presented in Section 2.1 to investigate the existence of overlapping relations in each sample clause. On average, 6.1 entities and 4.9 relations were identified per code clause. These results show that compared with documents from other domains (such as the dataset of New York Times articles, which contains, on average, 3.3 entities and 1.7 relations per sentence), entities appear more densely in building codes, and the relations between these entities are richer. Moreover, the overlapping relation problem is exacerbated by the fact that although each relation has only one subject entity, a single subject entity may be involved in several relations.  
To address the aforementioned overlapping relation problem, a joint extraction model is developed based on the application of a decomposition strategy. In the proposed model, all subject entities are first identified using a hybrid deep learning algorithm, and subsequently, for each identified subject entity, its associated object entities and predicate relations are simultaneously extracted. In this way, nonrelated pairs can be excluded through the judicious recognition of subject entities, and the overlapping relation problem can be naturally addressed.
## 3 Results and Discussion  
### Performance of BDE 209 Degradation  
To investigate the impact of co-milling agents, BDE 209 was decomposed via the MC process employing LiCoO\({}_{2}\) Fe powder separately, a mixture of LiCoO\({}_{2}\) and Fe, and a control without additives (Figure 1). The results revealed that the degradation rate of BDE 209 was only 13.5% within 300 min without a grinding reagent. When LiCoO\({}_{2}\) or Fe was added individually, there was a modest increase, with removal rates of 34.6% and 59.3% for BDE 209 within 300 min, respectively. However, the simultaneous addition of Fe and LiCoO\({}_{2}\) significantly enhanced the decomposition of BDE 209, achieving a remarkable removal rate of 99.8% after 180 min. This indicates a synergistic effect in the degradation of BDE209 in the MC treatment when Fe and LiCoO\({}_{2}\) are used in combination. Therefore, subsequent experiments were performed to explore the influence of milling conditions on the decomposition and debromination of BDE 209, with both Fe and LiCoO\({}_{2}\) participating in the MC process within a milling time of 240 min.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 4 Methodology
### PLM-ICD-C
#### 4.7.1 PLM-ICD-C Rules  
Three rules were defined for PLM-ICD-C. \(S_{P}\) stands for similarity of PLM-ICD, \(S_{C}\) stands for similarity of the improved Cosine algorithm. \(PC\) is a code determined as probable candidate to positive, and \(NC\) stands for candidate to negative. \(T_{1}\), \(T_{2}\) and \(T_{3}\) are similarity thresholds to be defined on each experiment. \(T_{1}\) and \(T_{2}\) are thresholds used for the PLM-ICD similarity levels and \(T_{1}>T_{2}\). \(T_{3}\) is a threshold used for the ICS similarity level. The rules are as follows.  
1. If \(S_{P}\geq T_{1}\longrightarrow PC\)
2. If \(T_{2}\leq S_{P}<T_{1}\) AND \(S_{C}\geq T_{3}\longrightarrow PC\)
3. If \(S_{P}<T_{2}\longrightarrow NC\)  
Rule number 1 states that when PLM-ICD finds a code with high probability of being a positive, it is accepted as a positive candidate without further screening by the improved Cosine method.  
Rule number 2 brings ICS into play when PLM-ICD retrieves a code with just average confidence. That happens when the similarity is normally about 0.5, which means there is almost the same chance of a code being positive as it is of being negative. Hence, when \(S_{P}\) is between \(T_{2}\) and \(T_{1}\), then \(S_{C}\) is calculated for that code and if it is above \(T_{3}\) then the code is proposed as \(PC\), otherwise it is proposed as \(NC\).  
Rule number 3 states that when PLM-ICD assigns very low probability to a code, it is also not screened by ICS, because chances of it being a true positive are very low, so it is immediately accepted as a negative candidate.
## 1 Introduction  
The most abundant natural polymer on earth, viz. cellulose, is continuously scoring points in all domains of research particularly on the development of multifunctional materials [1; 2; 3]. In fact, the versatility of cellulose is even fostered when its nanoscale forms are considered, namely cellulose nanof fibrils (CNFs), cellulose nanocrystals (CNCs) and bacterial cellulose (BC), whose interest for the design of innovative and all-purpose nanomaterials has skyrocketed during the last couple of years, including in the nanocomposites domain [4; 5; 6; 7; 8; 9]. Within the nanoscale forms of cellulose, BC has a huge advantage in terms of mechanical performance and dimensional stability, apart from its inherent biodegradability, biocompatibility and high water-holding capacity [10; 11]. The most salient feature of BC is associated with its moldability during biosynthesis and shape retention, as well as with the recently achieved degree of control over the three-dimensional morphology of BC films across several length scales [12] that enables the design of materials with engineerable size and form for myriad applications.  
The synergetic combination of the ultrafine nanofibrous network of BC with a proton or anion conducting phase has proven its viability for application as partially bio-based platform in the fabrication of ion conducting membranes for polymer electrolyte fuel cells (PEFCs) [13; 14; 25; 24]. Practical applicable proton- or anion-exchange membranes derived from BC can satisfy the strict requirements for application as separators in PEFCs, namely conductivity, suitable mechanical performance, gas tightness, dimensional and chemical stability under oxidative environment, and variable temperature and humidity conditions [26; 27]. The fabrication of these BC-based membrane separators can be performed following different strategies like for example the in-situ deposition of metallic nanoparticles on BC membranes [13; 14], the diffusion of an ion conductor onto the BC membrane network [18; 22], blending of BC dispersed nanofibers with a polyelectrolyte or ionomer followed by casting [25], modification of BC via grafting polymerization with an electrolyte [19], the in-situ polymerization of electrolyte monomers within the pure BC three-dimensional network [20; 21; 24], and by chemical modification of the BC template [15; 16].  
Following our ongoing interest in bio-based ion separator materials for application in PEFCs [20; 21; 22; 23; 24], this study reports the fabrication of polyelectrolyte membranes through the exploitation of a mono-acidic phosphate bearing polymer containing two polymerizable methacryloyl moieties, i.e., poly(_bis[2-(methacryloyloxy)ethyl]_ phosphate) [P(_bis_MEP)], and BC. The key aspect differentiating these membranes from polymer electrolyte membranes (PEMs) reported in literature is the use of a bifunctional monomer that obviates the need of using a cross-linker to retain the polyelectrolyte inside the BC network, and modulate the properties of the ensuing proton separator material.
# Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review
## 6 Case Studies of Smart Roads and Intersections
The core members are involved with their own C-ITS pilot deployments, either in place or in preparation. In addition, many associated members closely follow the C-Roads Platform and the pilot deployments of C-ITS services [166]. In light of some intentional experiences, it is believed that, in the future, cities will have to improve their adaptive governance modalities, along with transparent AI decision-making and accessible mobility planning, so smart intersections are part of equal--and resilient--urban transport systems. Key Performance Indicators (KPIs) such as the average vehicle delay, queue length, and total travel time can be the most important indicators in evaluating the performance of smart intersections. As mentioned earlier, one of the successful examples of smart intersection implementations is London's SCOOT system, which has significantly reduced intersection delays through adaptive signal optimization, while Toronto's AI-based intersections have effectively reduced queue lengths during peak periods. In Amsterdam, cooperative vehicle infrastructure systems have significantly improved overall travel efficiency. From a safety perspective, indicators such as the reduction in crashes and near misses have also been considered, which were positively demonstrated in an operational study in Pyeongtaek (South Korea) using edge AI and real-time safety monitoring. In the Barcelona Superblock model, prioritizing pedestrian zones reduced vehicle-pedestrian conflicts. On the other hand, environmental impact indicators, which are essential sustainability factors, such as reduced vehicle emissions and fuel economy, have been improved through intersection optimization strategies in Toronto and Amsterdam. In addition, according to public surveys in cities such as Toronto and Amsterdam, people's perceptions of safety, congestion management, and the commuting experience have improved. The consistent use of these KPIs will provide planners and policymakers with the tools they need to deploy smart intersections in different urban environments.