esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Latency QC-LDPC Encoder Design for 5G NR
## 3 Design of 5G QC-LDPC Encoder  
### QC-LDPC High-Parallel Encoding Algorithm  
In [19], an LDPC encoding algorithm for 5G has been proposed. Based on it, this paper optimizes the calculation flow of the parity codeword and arranges the operations in parallel to improve the parallelism of the overall encoding and reduce the latency of the encoder. According to the 3 GPP standard, the code block \(C\) is divided into information code blocks \(S\), the first group of parity \(P_{1}\), and the second group of parity \(P_{2}\), whose lengths are \(K_{b}\times Z\), \(4\times Z\), \((M_{b}-4)\times Z\) corresponding to \([A\quad C]^{T}\), \([B\quad D]^{T}\), \([O\quad I]^{T}\)of \(H\) matrix respectively. \(K_{b}\) is the number of columns of the kernel matrix and \(M_{b}\) is the number of rows of the \(H\) matrix, so that \(C^{T}\) can be expressed as  
\[c^{T}=\left[s|p_{1}|p_{2}\right]^{T}=\left[s_{0},s_{1},\cdots,s_{k_{b}-1}|p_{1,1},p_{1,2},p_{1,3},p_{1,4}|p_{2,1},p_{2,2},\cdots,p_{2,m_{b}-4}\right]^{T} \tag{1}\]  
According to the structure of the parity matrix \(H\) shown in Figure 3, the check equation for encoding is represented as  
\[HC^{T}=\left[\begin{array}{cc}A&B&0\\ C&D&I\end{array}\right]\left[\begin{array}{c}s^{T}\\ p_{1}^{T}\\ p_{2}^{T}\end{array}\right]=0^{T} \tag{2}\]  
The expansion of (2) denoted as  
\[As^{T}+Bp_{1}^{T}+0p_{2}^{T}=0^{T} \tag{3}\]  
\[Cs^{T}+Dp_{1}^{T}+lp_{2}^{T}=0^{T} \tag{4}\]  
Simplify and obtain \(P_{1}\) and \(P_{2}\) denoted as  
\[p_{1}^{T}=B^{-1}As^{T} \tag{5}\]  
\[p_{2}^{T}=Cs^{T}+Dp_{1}^{T} \tag{6}\]  
Figure 4: Parity matrix structure and parameters, punching and shortening of 5G LDPC Codes.  
When \(P_{1}\) is obtained, the \(CS^{T}\) offers hardware reuse possibility to complete the \(DP_{1}^{T}\) operation, and the second group of parity \(P_{2}\) is calculated according to (16), where \(c_{ij}\) is the element of each row of \(C\) matrix, \(d_{i,K_{b}+j}\) is the element of each row of \(D\) matrix, and \(s_{j}\) is the code word of each segment \(Z\) in the information \(S\).  
\[P_{2i}=\sum_{j=1}^{k_{2}}c_{ij}s_{j}+\sum_{j=1}^{4}d_{i,k_{b}+j}P_{ij},i=1,2, \ldots,m_{b}-4 \tag{16}\]
## 1 Introduction
However, the perceived usefulness and perceptive ease of use in the TAM model do not fully explain the user's intent to use [8]. Previous researchers revealed that TAM mainly explores the effects of attitude by using the real benefits of technology products, the theoretical perception of ease of use, and perceptual usefulness are both external motivations and the lack of intrinsic motivation discussed [9]. Researchers later integrated perceived enjoyment in the model as an intrinsic motivation and defined perceived usefulness as an extrinsic motivation [10]. [PERSON] and [PERSON] extended TAM by adding perceived playfulness as an intrinsic motivation factor. They found that perceived playfulness was a significant factor in user acceptance of websites [8]. The diffusion of innovation theory could be considered as one of the most popular theories that can be used to explain factors that affect an individual to adopt an innovation or new technology [11]. [PERSON] identified five attributes or characteristics of innovation, namely relative advantage, compatibility, complexity, trialability, and observability, which are viewed to determine an individual's rate of innovation adoption during the adoption process [11]. The Diffusion of Innovations (DOI) and Technology Acceptance Model (TAM) are considered to have the most powerful theoretical emphasis in innovation adaptation literature. TAM and DOI share similar premises, which assess innovation adoption on the perception of their characteristics. For example, relative advantage and perceived usefulness can be viewed as value-oriented features; complexity and perceived ease of use can be viewed as effort-oriented features. [PERSON] and [PERSON] developed an instrument to measure an individual's perceptions concerning the attributes of an innovation [12]. They renamed [PERSON] complexity construct as ease of use, which is consistent with [PERSON] [9]. According to prior research in the meta-analysis of 75 diffusion articles, only the relative advantage, compatibility, and complexity were consistently related to the adoption of technical innovations [13]. In addition, compatibility has repeatedly been observed as a major factor manipulating the adoption of inventions [14]. Therefore, compatibility is separated as an observable independent factor. Based on this background, this study adds perceived playfulness as an intrinsic motivation to explore the user's motivation for using Nintendo Switch. In addition, considering the characteristics of Switch, this study also adds the compatibility from Diffusion of Innovations Theory (DOI) to the model for a more complete exploration of the user's acceptance of Nintendo Switch. Such research will help gaming designers and vendors to be better understand Switch users' behavioral patterns. The objective of this study is described as follows: (1) To explore the influence of compatibility on perceived ease of use and perceived playfulness. (2) To explore the influence of perceived usefulness and perceived ease of use on the user's attitude. (3) To explore how attitude influences user's behavioral intention.
###### Abstract  
This article presents a study of the use of ferrocement and expanded metal in the columns, beams, and walls of a reinforced concrete building structure to increase its seismic resistance. This study focused on a concrete building with three stories, located in Thailand. In the design of the reinforcement structure, a calculation and analysis of seismic resistance were conducted in a comparison of the original concrete building structure with the reinforced concrete building structure using the nonlinear static force method (pushover analysis method). The results showed that the seismic resistance of the reinforced concrete building structure was higher than the design value (DPT), with different seismic resistance values for the reinforced frames, columns, and walls of approximately 14%, 81%, and 19%. In particular, it had increased stiffness values for the frames, columns, and walls and increased ductility values for the walls. Moreover, the seismic resistance values originating from the reinforcement were significantly higher than those of the concrete building structure. Therefore, reinforcement should be applied to all concrete building structures with the implementation of the damage index, ensuring that it is reduced to the allowable level.  
nonlinear static force method; pushover analysis method; strengthening; ferrocement; expanded metal +
Footnote †: journal: Applied Sciences  
using an efficiency multiplier of the steel grating to predict the shear strength value of the reinforced column [6].  
In a study of the seismic-resistant behavior of a six-story-high building structure that was reinforced with the cover steel column cladding method, it was found that the reinforced structure had higher lateral strength and a higher toughness value. However, it was also found that structural irregularity can reduce a building's seismic resistance [7]. In a study of the seismic-resistant behavior of a three-story building structure that was reinforced by the column cladding method with steel mesh and expanded metal, the results showed that the stiffness value and lateral force resistance value of the reinforced structure were higher. Otherwise, a structural model based on flexibility provided less improvement in terms of its effects than a structural model based on fixity [8].  
In this study, we conducted a seismic resistance analysis of a three-story concrete building structure with columns, beams, and walls reinforced using ferrocement and the expanded metal technique. We used the nonlinear static analysis method (pushover analysis) to conduct comparisons of the results, comparing the seismic resistance strength of the reinforced columns, beams, and walls with that of the original building structure.
## 2 Materials and Methods
### Survey Experimental Design  
The data for this experiment were acquired on the afternoon of July 25, 2017. Considering the reduced operation of the Galileo system, finding a place that could guarantee adequate visibility for a kinematic survey was not a trivial task. The location and the acquisition time were chosen to guarantee constant visibility of at least four Galileo satellites with a cut-off of 10\({}^{\circ}\) and six Galileo satellites for the major part of the survey. A peak of 11 GPS and 9 GLONASS satellites were respectively available during the survey (Figure 3).  
Figure 3 shows the number of satellites and the geometric dilution of precision (GDOP--calculated by using the RTKPLOT tool) for the whole surveying session and clearly depicts the difference between the statuses of operability of the three positioning systems. The spikes in the GDOP plot are generally due to cycle slips or to a temporary loss of tracking of one or more satellites. From this point of view, the GPS constellation features the best configuration. It is worth mentioning that Galileo satellites occupied only the southern part of the Skyplot.  
The full acquisition, made with both the GS14 receiver and the MMS, took almost one hour at a sampling rate of 1 Hz. Because of firmware restrictions, the GS14 was able to acquire only the E1 signal from the Galileo constellation. For this reason, the analysis was always referred to the E1, L1, and G1 signals acquired, respectively, from the Galileo, the GPS, and the GLONASS constellations. From the whole set of acquired epochs, only 1376 were used to perform the inter-constellations comparisons. This epoch selection was carried out accurately thanks to the MMS' odometer with which only the kinematic part of the survey was considered. The vehicle, equipped with both the MMS and the GS14, completed the whole route four times. Before the beginning of each round on the path, a static session of few epochs was executed only to initialize the fixing of the phase ambiguities for the reference trajectory before the beginning of the next trip. This was necessary also in order to fulfill the aim of testing all the possible satellite acquisition conditions during the survey (further details can be found in the next section). The data from the Leica Smartnet ItalPoS network [44; 45] were used to compute the differential post-processed trajectory related to the GS14 receiver.
## 2 Materials and Methods
### Characterization of the Electrodes  
The morphology of the RVC and the RVC/coated was analyzed by Scanning Electron Microscopy (SEM) in a Hitachi S-3000N.  
Phase identification and crystallographic structure of the magnetic materials deposited on RVC were analyzed by X-Ray diffraction (XRD) performed in a Bruker D8 powder diffractometer equipped with a primary monochromator and an ultrafast Lynxeye XE-T multichannel detector with CuK\({}_{\alpha}\) radiation. To carry out the measurements, the RVC electrode was dispersed in an agate mortar, taking the amount necessary for its analysis. The patterns were collected within 10\({}^{\circ}\) and 80\({}^{\circ}\) in 29, with an angular increment of 0.04\({}^{\circ}\) and increment time of 4 s. The diffractograms were analyzed using the PANalytical X'Pert HighScore program, and their profile fitting was carried out to obtain more reliable peak parameters.  
To determine the ratio Fe\({}_{3}\)O\({}_{4}\)/Fe\({}_{2}\)O\({}_{3}\), Mossbauer spectroscopy was used applying the called \"center of gravity\" method, which allows determining the ratio magnetite/maghemite nanoparticles from the area weighted mean isomer shift [24]. The spectra were measured at room temperature in triangular mode, using an emission spectrometer with a \({}^{57}\)Co/Rh source. Spectral analyses were performed by nonlinear fit, using the NORMOS [25] program, and energy calibrations were accomplished with \(\alpha\)-Fe (6 \(\upmu\)m) foil.
## 3 Results and Discussion
### Fourier-Transform Infrared Spectroscopy (FTIR)  
The FTIR spectra were recorded to study the change in the functional entities in the CH modified materials, i.e., CH-gly and CH-ANP. The chemical modification of CH with 1,4-bis (3-aminopropyl) piperazine (CH-ANP) was further substantiated by comparing its FTIR spectrum with CH and CH-gly. A typical FTIR spectrum of chitosan showed characteristic bands at (i) 3405 cm\({}^{-1}\) because of the OH and NH\({}_{2}\) stretching vibrations, while the (ii) bands at 2916 and 2877 cm\({}^{-1}\) were due to the C-H  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample** & **C/\%** & **N/\%** & **C/mmol g\({}^{-1}\)** & **N/mmol g\({}^{-1}\)** & **C/N** \\ \hline CH & 40.63 & 7.39 & 33.86 & 5.27 & 6.42 \\ \hline CH-gly & 44.04 & 5.89 & 36.7 & 4.20 & 8.73 \\ \hline CH-ANP & 45.65 & 5.38 & 38.04 & 5.20 & 7.31 \\ \hline \end{tabular}
\end{table}
Table 1: Elemental analysis profile showing the available carbon (C), and nitrogen (N) in percent, number of moles, and the corresponding C/N molar ratio of pristine CH and modified CH materials, i.e., CH-gly and CH-ANP.  
stretching vibrations. (iii) Amide 1 showed a band at 1655 cm\({}^{-1}\), while (iv) the band for the N-H bond and primary OH alcohol bond appeared at 1592 cm\({}^{-1}\) and 1426 cm\({}^{-1}\), respectively. (vi) The CH\({}_{3}\) symmetrical deformation mode appeared at 1379 and 1419 cm\({}^{-1}\). (vii) The band for the C-O stretching vibration and \(\beta\)-glycosidic bond between carbon 1 and 4 was presented at 1076 cm\({}^{-1}\). The spectra of chemically modified chitosan CH-gly and CH-ANP were different from the pristine chitosan, as can be seen in Figure 2. The CH-gly showed a band at 1719 cm\({}^{-1}\) that represents the carbonyl of glycidyl methacrylate and a stretching vibration carbon double bond at 1635 cm\({}^{-1}\). In the second step of the modification, the reaction of CH-gly with 1,4-bis (3-aminopropyl)piperazine results in the disappearance of the double bond in the spectrum of CH-ANP, which corroborates the successful attachement of 1,4-bis (3-aminopropyl)piperazine to CH-gly [34].
## 3 Results  
This section presents the findings of the systematic review, examining 94 selected studies across six core themes that emerged from the eligibility and synthesis processes. Each subsection addresses a critical aspect of DES and P2P energy management. First, Optimization and Modeling in Energy Systems investigates robust and hybrid techniques for handling uncertainties in DES, including variable renewable generation and market fluctuations. Next, Multi-Agent Systems and Distributed Control explore decentralized frameworks and reinforcement learning methods that enhance scalability and resilience in complex energy networks. Simulations, Case Studies, and Real-World Applications highlight how theoretical models and frameworks translate into practical solutions, providing evidence of cost savings and environmental benefits under diverse conditions. Blockchain, Smart Contracts, and Emerging Technologies discuss the use of secure, transparent platforms to automate P2P transactions, integrate AI-driven consensus protocols, and address scalability challenges. Frameworks for Robust and Decentralized Energy Management focus on hierarchical and multi-layered approaches that handle fault tolerance, market disruptions, and evolving resource configurations. Finally, Electric Machines and their role in DES underscores the design and operational enhancements of electric machines--especially their integration into V2G and decentralized microgrids--to bolster flexibility, efficiency, and renewable energy penetration.
## 2 Methodology
### Spatiotemporal Adaptive Prediction Network
#### 2.2.4 Model Performance Evaluation Metrics  
To comprehensively evaluate the performance of the proposed model, three metrics are adopted: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R\({}^{2}\)). Smaller RMSE and MAE values indicate lower prediction errors, while an R\({}^{2}\) value closer to 1 signifies higher prediction accuracy and better model fit. The specific formulas for these metrics are as follows:  
\[\text{RMSE}\ =\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{12}\]  
Figure 4: CNN structural framework.  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{13}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i }-\bar{y})^{2}} \tag{14}\]  
where \(n\) is the total number of data points, \(y_{i}\) is the observed value for the \(i\)-th data point, \(\hat{y}_{i}\) is the predicted value for the \(i\)-th data point, and \(\bar{y}\) is the mean of the observed values.
# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 2 Literature Review
### Deep-Learning Based Techniques  
#### 2.2.1 PPG-Based Recognition Method  
The authors of [23] proposed a two-level fusion PCANet (Principal Component Analysis Network) deep recognition network. It achieved an over 95% recognition rate.  
There are many papers that use deep learning methods to analyze ECG signals. The authors of [24] proposed a PPG-based end-to-end architecture using Convolutional Networks. The approach was tested on PulseID and Troika databases and achieved an AUC of 78.2% and 83.2%, respectively. Another study [25] suggested a method that used a deep learning model consisting of a CNN and an LSTM for the person verification process. The proposed system was evaluated on Biose1, Biose2, and PRRB datasets. The authors of [26] proposed a framework for human identification using PPG signals. The model consists of two CNN layers and two LSTM layers, followed by a dense output layer. The model was evaluated on the TROIKA database, achieving an average accuracy of 96%. The study [27] also used the TROIKA dataset to create a human identification system. Each PPG source is grouped into different groups and classified using Deep Belief Networks (DBN). The approach achieves an accuracy of 96.1%. The authors of [28] proposed a Personalized Verification System, PPSNet, based on PPG signals by building a network using CNN and LSTM. The network was evaluated on the BioSec and achieved an average accuracy of 96% in a single session and 72.7% in two sessions.
## 1 Introduction
Social robots are a new concept in Tanzania, especially in the education sector, with most of such interventions being performed in the Global North, formerly called the developed countries [11,15,16]. The nucleus of the strength of LLMs lies in the availability of training data, from which they learn patterns and generate new content. Interestingly, Swahili--Tanzania's national language and medium of instruction in public primary schools--only contributes to about 0.009% of the of the Common Crawl dataset, a key source in training OpenAI's GPT models [17]. This poses an additional research question as to whether, despite that, OpenAI's GPT can still generate relevant and contextualised Swahili-based content that can be used in teaching in primary schools and achieve anticipated results. To address the knowledge gap, as well as assess the technology's cross-cultural feasibility, this study seeks to answer the following research question: what are the perceptions of primary school mathematics teachers in Tanzania regarding the use of social robots in schools? Our exploration focuses on the general idea of using social robots in teaching as a way to alleviate heavy workloads, while also probing teachers' insights on content generated by GPT-3.5 for the task at hand. Our focus on teachers is motivated by influential technology adoption models, such as the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Robotic Process Automation (RPA) User Acceptance Model, which emphasise the pivotal role of users' attitudes towards the technology in its adoption [18; 19; 20]. Similar studies appreciating the role of teachers have been conducted elsewhere [13; 21]. However, the novelty of this intervention in Tanzania, the critical needs of its education sector, the status of Swahili as a low-resource language, and recent advancements in LLMs together warrant an in-depth, context-specific study. The remainder of this paper is structured as follows. Section 2 presents the methodology, including the design of the contextualised social robot-based intelligent tutoring systems and the study setup. Section 3 reports the results, which are discussed in Section 4. Finally, Section 5 presents our conclusions, recommendations, and limitations.
## 4 Layered Models
### Sensory Layer  
For the collaboration of vehicular networks, it is crucial to collect heterogeneous data describing the internal state of a vehicle and environmental data about the vehicle's surroundings. Furthermore, if the network is aimed to employ cognition to improve performance, those data should also encompass the state of the network [48]. The sensory  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Layers** & **Applications** \\ \hline
[44] & Data, Virtualization, Control, Application & See-Through, Collision Warning \\ \hline
[45] & Sensing, Communication, Cognition, Control, Application & Safety, Transportation Management \\ \hline
[46] & Perception, Communication, Application & Vehicle Collaboration \\ \hline
[47] & User, Data Acquisition, Filtering, Communication, Control, Processing & Traffic Efficiency, Safety \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of current IoV architectures.  
Figure 1: IoV layered model without the cognition layer. Adapted from [43].  
layer, as the lowest one, encompasses all sensors present in the network, and its principal objective is to collect and potentially preprocess data collected from multiple sources, including the surroundings of a vehicle from both onboard and offboard sensors, data for movement control, and data regarding a network state, that is, traffic or resource utilization, among others. Vehicles can realize this functionality by integrating onboard sensors and units, which can collect and classify various types of data and transmit them to upper layers [49].  
In certain designs, like the one depicted in Figure 2, this particular layer might not be explicitly delineated, as has been observed in prior work [44]. However, in architectures specifically detailing sensory components [45], its role involves aggregating diverse data from various origins, such as physical space, network traffic, and resource distribution within the network. Consequently, the collection of data extends to internal vehicle sensors, navigation systems [50], intervehicle communication, traffic lights, or other environmental devices [47].