esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Centralized Algorithm  
Since the problem in Equation (7) is NP-hard, it is unpractical to achieve the optimal solution. An alternative method is to consider the problem as a combination of a scheduling problem under the consumption of equal power allocation and a power allocation problem with a given scheduling result. In this way, an approximated solution of the problem can be obtained in polynomial time.  
In the rest of this section, we propose a heuristic algorithm based on CE for RB scheduling with equal transmit power at the first place. Then, a Karush-Kuhn-Tucker-method to solve the power allocation problem is presented. The algorithm involving both RB scheduling and power allocation proposed in this section is centralized, which means that the resource allocation is operated at the CU with global CSI. The centralized algorithm is capable of achieving a favorable system performance, but it requires significant computational effort of the CU. In the next section, we also propose a decentralized algorithm of resource allocation with lower complexity and delay of computation, which leads to decreased performance unavoidably.
## 3 Applications of the CDS for the Fiber Optic Link
### Main Feedback, Internal Feedback, and Feedforward Channels  
As shown in Figure 7, the main feedback and the internal feedback channels were used to exchange the information between perceptor and executive. The perceptor sends the BER for calculating the internal reward to the executive through the main feedback channel. In addition, the perceptor sends the probabilistic model (\(P(\hat{\gamma}_{n}^{k}|\hat{X}_{n}^{k})\)), evidence (\(P(\hat{\gamma}_{n}^{k})\)) and current perceptor mode to the executive through the internal feedback channel. The executive sends information to the perceptor through feedforward channel such as OH (overhead percentage of forward error correction (FEC)) and new constellation geometry (if geometric shaping is used), fiber launch power \(P_{tx}\), data rate, and \(P(X_{n}^{k})\) (if probabilistic shaping is used).
## 3 Results and Discussion
### Application of the Model
#### 3.1.2 Presentation of the CE  
The analysis results before implementation are displayed in Figure 2: 47,681 units were analyzed, of which 13,209 (28%) were non-compliant due to their attributes. The Pareto principle indicates that 20% of causes cause 80% of defects. This study found that four vital categories (27%) of the 15 analyzed represented 79.3% of the total non-conformities.  
Table 1 shows the percentage of economic loss due to adjustments and discrepancies in implementing new products in the enterprise. Waste costs can rise to 20%, depending on the product stage. If a product has a profit of 25%, and there are returns for non-compliance in the quality stage, the enterprise only earns 5% of the estimate.  
CE implementation's awareness phase did not have the resistance that some authors [36; 37] have stated. The leader obtained the management's support without difficulty because in the last implementation of new products, there was a loss equivalent to USD 7000. Customer complaints about non-compliance with specifications, presented as poor-quality costs, was an essential aspect in making the implementation decision. Its relevance lies in the fact that each product's quality directly affects the customer and affects their trust and loyalty [38].
# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
###### Abstract  
The High Efficiency Video Coding Standard (HEVC) is one of the most advanced coding schemes at present, and its excellent coding performance is highly suitable for application in the navigation field with limited bandwidth. In recent years, the development of emerging technologies such as screen sharing and remote control has promoted the process of realizing the virtual driving of unmanned ships. In order to improve the transmission and coding efficiency during screen sharing, HEVC proposes a new extension scheme for screen content coding (HEVC-SCC), which is based on the original coding framework. SCC has improved the performance of compressing computer graphics content and video by adding new coding tools, but the complexity of the algorithm has also increased. At present, there is no delay in the compression optimization method designed for radar digital video in the field of navigation. Therefore, our paper starts from the perspective of increasing the speed of encoded radar video, and takes reducing the computational complexity of the rate distortion cost (RD-cost) as the goal of optimization. By analyzing the characteristics of shipborne radar digital video, a fast encoding algorithm for shipborne radar digital video based on deep learning is proposed. Firstly, a coding tree unit (CTU) division depth interval dataset of shipborne radar images was established. Secondly, in order to avoid erroneously skipping of the intra block copy (IBC)/palette mode (PLT) in the coding unit (CU) division search process, we designed a method to divide the depth interval by predicting the CTU in advance and limiting the CU rate distortion cost to be outside the traversal calculation depth interval, which effectively reduced the compression time. The effect of radar transmission and display shows that, within the acceptable range of Bjontegaard Delta Bit Rate (BD-BR) and Bjontegaard Delta Peak Signal to Noise Rate (BD-PSNR) attenuation, the algorithm proposed in this paper reduces the coding time by about 39.84%, on average, compared to SCM8.7.  
shipborne radar digital video; unmanned ship; HEVC; SCC; deep learning; delay of compression Article
# Affect Analysis in Arabic Text: Further Pre-Training Language Models for Sentiment and Emotion
## 3 Methodology
### Task-Adaptation Approach
#### 3.1.4 Within-Task Adaptation Approach  
The main phase of the approach presented in Figure 1 is to continue pre-training QARiB with sentiment and emotion datasets that we collected and preprocessed in the previous sections. The proposed approach (i.e., within-task adaptation) can be explained as: The QARiB language model is further pre-trained using training data for a target task. The model is trained on an unlabelled task-specific dataset (e.g., sentiment dataset) and evaluated by performing fine-tuning on the labelled dataset from the same task (e.g., sentiment dataset). This can be expressed as:  
\[QARiB_{T}\to PretrainingDataset_{T}\to FinetuningDataset_{T} \tag{1}\]  
\"T\" refers to the target task, which might be either sentiment or emotion. The overall process of the within-task adaptation approach of the model's development is illustrated in Figure 1.  
The adaption of our models followed the settings for training the QARiB language model since this model was trained using only the masked language modelling objective. In this experiment, we further pre-trained five versions of the QARiB language model from the BERT-base-QaRiB checkpoint. Three of them were trained with sentiment-specific datasets including STD, SRD, and STRD. The objective was to investigate the impact of continuous pre-training on sentiment task performance. Furthermore, two models were trained using ETD, which is an emotion-specific dataset. The goal is to determine whether further pre-training may improve the results of emotion classification. The four datasets were divided into train and test sets at a ratio of 80% and 20%, respectively. We utilised the training script run_mlm.py provided by huggingface ([[https://github.com/huggingface/transformers.git](https://github.com/huggingface/transformers.git)]([https://github.com/huggingface/transformers.git](https://github.com/huggingface/transformers.git)), accessed on 1 February 2022), and we ran it on a single GPU provided by Google Colab Pro+. Every 5000 steps, a model checkpoint was saved. The models were trained using the QARiB model's default hyperparameters, as stated below:  
* A batch size of 64;
* A maximum sequence length of 64 words;
* A learning rate of 8 \(\times\) 10\({}^{-5}\).  
The next subsections offer a detailed explanation of each model we developed for each task (sentiment and emotion).
## 3 Results and Discussion  
### Principal Component Analysis (PCA)  
Raman spectral data were the application of the array of 1005 variables which were cross-sensitive toward the different biochemical composition in the human cells, so they contained overlapped information which brought some difficulty to the study. Multivariate data analysis could be applied to solve the problem. It is possible that PCA can extract the main information from the Raman spectra, and eliminate some of the overlapped information [7,24,25]. In the work, PCA based on spectra pre-processing method was the first attempted to visualize and extract the useful information from the multivariate spectral data to examine the qualitative differences among all types of cell samples.  
Figure 4 shows score cluster plot of the eight different human cells with PC1, PC2, and PC3, which were labelled according to their types. Eight human cell groups appeared in cluster trends along the top three PCs axes. PC1 interprets 95.52% variances, PC2 3.17% variances, and PC3 0.66% variances. The cumulative contribution rate of the top three PCs was 99.35%. The 3-dimensional space represented by the top three PCs score indicated 99.35% information from the original spectral data, which covered most of the main information of them.  
It could be observed from Figure 4 that there are some inherent component and structure differences among human cell samples even though they actually belong to the same types. The cell samples could not be classified directly using PCA. The separation of the eight different types of samples was not distinct, and especially, some overlapped samples could be examined from the groups of HeLa, HO-8910 and A549. It can be assumed that the biochemical composition of the samples, such as protein, nucleic acid, glycolipid, are similar among the three groups of human cells.  
Figure 3: The average Raman spectra of the eight different human cells subtracted background obtained from baseline calibration. Error bars are standard deviation of the mean.  
Based on the PCA score plots, geometrical exploration gives the clear clusters trend in the 3D space, instrumental in discriminating types of samples but it cannot be used as a classification tool. Therefore, some discrimination models were used to classify the samples. Supervised pattern recognition approaches refer to some techniques with which a priori knowledge about the category membership of calibration samples are used for distinguishing purposes. The classification model is calibrated on training samples set with different categories [26; 27]. The performance of the calibration model is evaluated using the prediction or test set. Two discrimination models were attempted comparatively.
###### Abstract  
A pilot anaerobic bioreactor requires near-daily monitoring and frequent maintenance. This study aimed to upgrade a pilot bioreactor into a low-cost IoT device via ESP32 microcontrollers. The methodology was based on remote data acquisition and online monitoring of various parameters towards assessing the anaerobic digestion performance. A semi-continuous tank bioreactor with a 60 L total volume was initially inoculated mainly with livestock manure and fed daily with a mixture of glucose, gelatin, and oleic acid, supplemented with a basic anaerobic medium. Under steady-state conditions, the organic loading rate was 2 g VS L\({}_{\text{R}}\)\({}^{-1}\) d\({}^{-1}\). Sensors for pH, temperature, RECDX potential, and ammonium concentration, along with devices measuring biogas volume and methane content, were integrated and validated against analytical methods. Biogas production was recorded accurately, enabling the early detection of production declines through ex-situ data analysis. Methane concentration variance was less than 6% compared to gas chromatography, while temperature and pH deviations were 0.15% and 1.67%, respectively. Ammonia ion measurements required frequent recalibration due to larger fluctuations. This IoT-enhanced system effectively demonstrated real-time monitoring of critical bioreactor parameters, with ESP32 enabling advanced control and monitoring capabilities.  
biogas; IoT bioreactor; anaerobic digestion; methane; ESP32 2020
## 1 Introduction  
Measuring the knowledge connections between science and technology is crucial for revealing their interaction mechanisms and providing strategic decision-making for managers [1]. With the rapid development of human science and technology, science and technology are becoming increasingly tightly integrated. The two interact, spread, and diffuse continuously, giving birth to important technologies driven by scientific principles or generated through cross-integration [2]. The scientific literature presents the groundbreaking cognition of basic research at the theoretical level, whereas patents represent the practical trajectory of technology transformation at the application level. As the two leading forces driving innovation and progress, the combination of science and technology promotes the vigorous development of related fields [3].  
With the acceleration of the digitization process, cyberspace has been deeply integrated into the social operation system and has become an important component of critical infrastructure. Meanwhile, the demand for cybersecurity also shows a continuous growth trend. According to the report \"2025-2031 Global and China Cybersecurity Market Status and Forecast\" released by the research team of QYResearch, the Compound Annual Growth Rate (CAGR) of the global cybersecurity market is expected to reach 9.7% from 2025 to 2031. As a non-traditional security element, cybersecurity not only concerns individual safety but also has strategic value for national sovereignty, economic operation, and social stability.  
Against this background, there is a high degree of interaction between basic research and technological applications in the field of cybersecurity. Exploring this field from the perspective of science-technology associations is of great significance. Through quantitative analysis of their association and evolution rules, it can not only provide an empirical basis for the knowledge flow mechanism of disciplines and lay the foundation for R&D decision-making, but also offer an important reference for the study of knowledge association in other applied disciplines. Specifically, this study introduces an LDA2 vec literature topic evolution analysis method that integrates the LDA and Word2 vec models [4]. Firstly, based on the Web of Science and Derwent Innovations Index databases, we selected relevant literature data in the field of cybersecurity from 2005 to 2024 and divided them into ten stages. Subsequently, we employed the LDA model for topic mining, and then utilized the Word2 vec model to generate topic vectors to compute similarity, eventually constructing the science-technology topic association evolutionary pathway.
One of the most efficient technologies that is already applied as an established process by the meat industry globally is high pressure (HP) technology [6; 7]. At the end of 2018, 385 HP units (representing a global volume of about 95,000 L) were in production, and one third of the global volume capacity of high pressure equipment was processing meat [8; 9]. High pressure (HP) processing is a preservation technology that in combination with mild or low temperatures can adequately reduce pathogenic and spoilage microorganisms [10; 11] and detrimental food enzyme activity [12]. HP processing of meat products is an active topic of research, because of its potential to extend shelf-life and produce safer meat products [11; 13; 14; 15]. Application of HP at pressures as high as 600 MPa has demonstrated the potential of most pathogenic and spoilage bacteria inactivation, thus resulting in shelf life extension of RTE meat products (i.e., low-fat pastrami, Strasburg beef, export sausage, Cajun beef, cooked ham, dry cured ham, and marinated beef loin) [13; 16]. Shelf-life of sliced ham treated at 400 MPa for 15 min was extended from 19 (control samples) to 85 days [14]. [PERSON] et al. [17] efficiently investigated the effect of HP treatment at 600 MPa for 10 min to extend the shelf life of Spanish blood sausages (Morcilla de Burgos). [PERSON] et al. [18] reported that HP maintains the sensory acceptability of products such as reduced sodium naturally cured wieners, successfully extending the shelf life up to 12 weeks. [PERSON] et al. [19] reported microorganisms inactivation on cooked ham applying HP at 600 MPa, resulting in 6 log CFU/g lower lactic acid bacteria (LAB) count in HP cooked ham compared to untreated samples after 60 days of storage. [PERSON] et al. [20] reported that treatment at 414 MPa and 50 \({}^{\circ}\)C for 2 min is sufficient for the production of fresh pork sausages safe from _Listeria monocytogenes_.  
HP technology has established efficiency as an in-pack cold pasteurization technique, as an additional final processing step, offering prolonged preservation and increased safety of meat products. Due to the development of highly efficient HP machines, processing costs have been reduced to acceptable levels [21], and this has led to wider application of this technology in the food industry [8]. As an environmentally friendly and waste-free technology, HP has a high acceptance among industries, and it is expected to be more widely applied in the near future.  
In this work, HP processing at ambient temperature was applied to several vacuum packed meat products with short shelf life. The objective was to investigate the effect of HP on spoilage microbial population and quality characteristics of sliced cooked ham, sliced cooked turkey, cooked ham cubes, and Bratwurst sausages. The benefit of HP treatment on the shelf life extension of these products was modeled and validated through simulation scenarios based on real cold chain data. It is demonstrated that cold chain management and spoiled meat products waste were significantly improved, by increased remaining shelf life for HP treated products along the whole cold chain.
## 2 Existing Works on Power-Balancing Countermeasures by Employing Dual-Rail-Based Encoding Schemes  
PB methods are associated with hiding countermeasures that apply DPL styles of hardware to software. In hardware, DPL operates in pairs of data bits and complement bits. This circuit consumes the same amount of power, irrespective of the amount of data. Because hardware can be flexibly employed in circuit design, it is easy to implement operations of the DPL styles. On the other hand, software operated on 8/16/32-bit microcontrollers exhibit difficulties when DPL styles are applied. PB methods define operations that satisfy constant HW/HD using operators supported by microcontrollers. These methods are divided into \((x,y)\)-code-based schemes and dual-rail-based schemes according to an encoding rule of constant HW/HD.  
\((x,y)\)-code-based schemes encode data using a codeword in which the HW is denoted by \(x\) and the size of the bits is denoted by \(y\). For instance, if we encode 4-bit nibble data into a \((3,6)\)-code, we only use \({}_{6}C_{3}\) codewords, which are of 6-bit sizes with HW\(=3\). In 2014, [PERSON] el al. proposed constant HW codes using LUTs [14]. [PERSON] et al. developed a tool to apply \((x,y)\)-code-based countermeasures to arbitrary cryptographic algorithms [15]. [PERSON] et al. proposed a methodology for selecting optimal codewords on \((x,y)\)-code [16]. This method was improved by [PERSON] et al. [17]. [PERSON] et al. applied the \((x,y)\)-code-based countermeasure to AES and embedded it on an ARM Cortex-M3 [18]. One advantage of \((x,y)\)-code-based schemes is that memories may be constructed compactly, but these schemes can only define operations using LUTs.  
Dual-rail-based schemes operate on codewords that double or quadruple the size of the data. For instance, data \(a_{1}a_{0}(a_{i}\in F_{2})\) is encoded into a codeword \(\overline{a_{1}}a_{1}\overline{a_{0}}a_{0}\). In 2011, [PERSON] et al. first tried to bring dual-rail-based schemes of hardware into software [7]. [PERSON] et al. proposed a constant XOR operation that does not require LUTs based on their encoding rule [10]. [PERSON] et al. applied [PERSON]'s encoding rule with constant HW/HD to the PRINCE block cipher [11]. [PERSON] et al. conducted further experiments with [PERSON]'s method [12; 13]. The most recent balancing countermeasure based on a Han-like encoding rule is the method proposed by [PERSON] et al. [19]. They first proposed a constant AND with only a small number of LUTs and applied these operations to the SIMON block cipher. One disadvantage of dual-rail-based schemes is that they have large codewords, but it is easy to define the operations because the encoding rules exhibit regularity.  
In this section, we describe a 1-to-4 encoding rule, which encodes 8-bit data into a 32-bit codeword based on the method proposed by [PERSON] et al. We denote constant XOR, NOT, and AND operations based on 1-to-4 encoding [19]. At the end of this section, we discuss the drawbacks of existing research and how to overcome it.
###### Abstract  
This novel study was triggered by a lack in the international literature of the simultaneous use of ground recycled concrete (GRC) as a cement replacement and mixed recycled aggregate as part of the granular skeleton in recycled concrete. It explores the thermal behaviour of concrete mixes bearing 10 wt% or 25 wt% GRC as a cement replacement and 25 wt% or 50 wt% mixed recycled aggregate (MRA) sourced from construction and demolition waste (CDW). The experimental programme conducted assessed concrete's dry density, open porosity, electrical and thermal conductivity and specific heat capacity. The findings showed that the use of 10% and 25% GRC, in conjunction with 50% MRA, reduced thermal conductivity by 7.9% to 11.8% and raised specific heat capacity by 6.0% to 9.1% relative to concrete with 100% natural aggregate (NA). A cross-property analysis revealed that improved thermal performance was linearly related to lower density and higher porosity. The results also support the conclusion that these new recycled aggregate concrete mixes are more energy-efficient construction materials than conventional concrete.  
recycled aggregate; ground recycled concrete; recycled materials concrete; construction and demolition waste; thermal properties +
Footnote †: journal: Applied Sciences