esa-sceva/satcom-chunk-collection huggingface dataset:
# Re-Engineered Word Embeddings for Improved Document-Level Sentiment Analysis
## 6 Discussion  
A novel mechanism for re-engineering the word embeddings has been proposed in this paper, and its effectiveness and robustness are demonstrated using four public benchmarking datasets. Analyses for two-way, as well as five-way, semantic classifications were conducted, and substantial improvements were observed compared to the published results in the literature.  
In this section, a conceptual comparison between the proposed mechanism and mainstream neural network (NN) methods is conducted. The re-engineering mechanism as a generic framework is further explored to highlight its characteristics which differentiate it from NN-related methods. Main components of the proposed system and conventional NN systems are illustrated in Figure 7.  
The core recognition module of typical neural nets is the loss function of the model; as the iterative training of the model progresses, the measured loss is minimized. Such  
Figure 7: System flowchart diagram of the comparison between the neural nets and the proposed re-engineering framework for embedding feature generation. The system adaptively re-engineers the word embeddings for each given query sample to provide the best possible match, as opposed to fitting different query samples with a static model.  
algorithms usually have three major drawbacks: (1) the training process can be quite time consuming, especially for challenging classification tasks when the model must be trained using a large database. (2) These methods are based on model parameter optimization, which is inherently subject to overfitting if there are insufficient training data. (3) For some application scenarios, the robustness of the model across different databases may suffer great degradation without careful fine-tuning. Indeed, to tackle these problems, some mechanisms based on pretrained models, such as transfer learning and reinforcement learning, were developed, hoping to better adapt specific implementation scenarios with minimal parameter adjustment (retraining). These approaches, however, may not reduce the overall training time; in addition, as the pretrained model was not specifically designed to fit for the problem at hand, retraining may not guarantee a high level of performance.  
The proposed feature re-engineering mechanism, benefiting from its instance-based data-driven design, is able to adaptively optimize the between-class separation of the training set, while achieving the best possible match between the training embeddings and the test samples. Compared to the general methods based on deep nets, this recreation of the word embeddings results in a good performance using only a small amount of data with much faster training.  
To better clarify and highlight the novelty of the proposed method, it is worth pointing out three distinctive characteristics of this re-engineering mechanism, i.e., component-wise vector reconstruction, transformation irreversibility, and the analysis efficiency.
## 4 Experiments
### Ablation Study  
To confirm the efficacy and superiority of the proposed MSWT-SSDN network structure and loss function, ablation tests were carried out to evaluate the effects of different factors such as the network branches, modules, and loss functions.
## 4 Active Distribution Network Scheduling Model
### Objective Functions
#### 4.1.3 Maximize User Satisfaction  
The level of satisfaction can affect users' enthusiasm for participating in DR. From the users' point of view, satisfaction can be defined as the percentage of actual running time after the electrical equipment participates in the response and the initial total demand time [26]. Usually, the control of interruptible electrical equipment will directly affect users' habits. Therefore, reducing interruptible electrical equipment is considered to describe user satisfaction after participating in DR in this paper [39; 40] as Equation (15):  
\[\text{max}f_{3}=1-\sum\limits_{n=1}^{N_{ll}}\frac{T_{il,n}}{T_{ll,n}} \tag{15}\]
# Comparative Analysis of Target Displacements in RC Buildings for 2023 Turkiye Earthquakes
###### Abstract  
The Kahramanmaras (Turkiye) earthquake on 6 February 2023, one of the largest earthquakes of the century, caused the collapse or severe damage of thousands of structures. This catastrophic disaster resulted in over 53,000 fatalities and rendered many structures unusable. This study addresses the observed damage in reinforced concrete (RC) structures, which constituted the majority of the existing urban building stock. In this study, firstly, information about the destructive Kahramanmaras earthquakes was given. The predicted PGAs in the last two earthquake hazard maps used in Turkiye were compared with the measured PGAs from actual earthquakes to determine whether the earthquake hazard is adequately represented for eleven affected provinces in the earthquake region. The damages in RC structures were evaluated within the scope of civil and earthquake engineering. Structural analyses for the model created to represent mid-rise RC buildings in the region were carried out separately for each province using predicted and measured PGAs. Additionally, target displacements that were used in performance-based earthquake engineering for damage prediction, were examined comparatively for all provinces. While the predicted earthquake hazard and targeted displacements were exceeded in some provinces, there was no exceedance in the other provinces. The realistic representation of earthquake hazards will allow the predicted displacements for various performance levels of structures to be determined in a much more realistic way. Consequently, the performance levels predicted for the structures will be assessed with greater accuracy. The study highlights the importance of accurately presenting earthquake hazards to predict building performance effectively.  
Keywords:Kahramanmaras; earthquake; target displacement; PGA; reinforced-concrete licensees/by/4.0 _Applied sciences_
# Functional Properties of Fruits of Common Medlar (_Mespilus germanica_ L.) Extract
## 3 Results and Discussion
### Polyphenic Compounds--Phenolic Acids  
In the profile of the phenolic compounds present in common medlar extracts, phenolic acids account for the largest proportion (Table 4). Moreover, the concentration of medlar fruit extract (Amberlite XAD-16) resulted in a doubling of phenolic compounds (MF and WF). The interest in phenolic acids stems from their potential protective role against oxidative damage. Furthermore, the antioxidant effect of plant products is mainly attributed to phenolic compounds such as phenolic acids [49]. Moreover, phenolic acids have been shown to increase glucose uptake and glycogen synthesis, improve glucose and lipid profiles in certain diseases (obesity, cardiovascular disease, melitius, and reduce its complications) [50; 51; 52]. Fruit tissues are able to synthesize phenolic compounds. Phenolic acids are widely found in daily foods such as fruits, vegetables, cereals, legumes, and wine [14]. Through the diet, we provide our body with beneficial phenolic acids through the consumption of fruit and vegetables. We can increase our intake of phenolic acids by making concentrated fruit extracts. The consumption of polyphenol-enriched foods is associated with a reduced risk of several diseases such as atherosclerosis [53; 54], dyslipidemia [55], and diabetes [56]. From Table 4, it can be concluded that gallic acid has the highest proportion of the methanolic fraction (MF) and water fraction (WF) of common medlar extract (36.8 and 43.8 mg/g of extract, respectively). It is known for its antioxidant [57], anti-inflammatory [58], and antimicrobial [59] properties. The second most abundant compound is protocatechuic acid: 11.66 and 7.00 mg/g of dry extract in MF and WF, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Percentage of Inhibition**} \\ \cline{2-5}
**Material** & \multicolumn{4}{c}{**Concentration [mg/mL DMSO]**} \\ \cline{2-5}  & **1** & **5** & **10** & **20** \\ \hline Fruit extract (MFE) & 14.98 \(\pm\) 0.07 c & 47.22 \(\pm\) 0.07 b & 61.23 \(\pm\) 0.19 b & 100 \(\pm\) 0.00 a \\ Methanolic fraction (MF) & 25.79 \(\pm\) 0.07 b & 27.14 \(\pm\) 0.10 c & 30.74 \(\pm\) 0.08 c & 100 \(\pm\) 0.00 a \\ Water fraction (WF) & 34.96 \(\pm\) 0.08 a & 52.11 \(\pm\) 0.05 a & 79.29 \(\pm\) 0.22 a & 100 \(\pm\) 0.00 a \\ \hline \hline \end{tabular} Mean values with different letters (a–c) within the same column were statistically different (\(p\) < 0.05), the same letters form one homogeneous group. Values expressed as mean \(\pm\) standard deviation.  
\end{table}
Table 3: Antidiabetic activity of the tested extracts expressed in % inhibition of \(\alpha\)-amylase.
# Structured Data REST Protocol for End to End Data Mashup
## 2 Related Work and Challenges
### Searching the Right MSP/DSP  
Data services as well as mashup services, are increasing day by day and their users too. Due to an exponential growth of the services, searching the right MSP/DSP is becoming the challenging task for users. Each MSP/DSP publishes one or more services and thus searching the appropriate service of that MSP/DSP again is tedious and time consuming. Reference [3] designed and implemented lightweight services mashup platform for service creations by ordinary user without expecting specific computing knowledge. Reference [30] recommended the framework for data service mashup, based on several mashup patterns and the corresponding recommendation method. Reference [31] proposed a novel framework for service discovery which exploits social media information and different methods to measure social factors with weight learning algorithm to learn an optimal combination of the measured social factors. Reference [32] proposed a novel category-aware service clustering and distributed recommending method for automatic mashup creation and developed a category-aware distributed service recommendation model, which is based on a distributed machine learning framework.  
Reference [33] explored API recommendation for mashups in the reusable composition context with the goal of helping developers to identify the most appropriate APIs for their composition tasks and proposed a probabilistic matrix factorization approach to solve the recommendation problem and enhance the recommendation diversity. Reference [24] suggested the MapReduce in skyline query processing for optimizing composite web services in large scale cloud mashup applications and proposed a block-elimination-based partitioning approach to shorten the process. Reference [34] proposed a service-oriented approach to generate and manage mashups and also developed the mashup services system to support users to create, use, and manage mashups with little or no programming effort. All these approaches were developed for service discovery for creating the mashup but cover only one aspect of the whole life cycle of the mashup development and suitable for MSP/EUD but not for ordinary users because of some learning expectation and the complexity. Searching the mashup or data services can be automatic, manual or semi-automatic. The method for searching of the MSP/DSP and then selecting appropriate service is assumed to be manual in the current work. There is no need of searching the DSP when a user would like to mashup his own private data because its source is already known to him but it still requires selection of the right data service among all the services available at DSP. In case of public data mashup, users are required to search the right MSP/DSP and select the appropriate service thereafter.
# Size and Shape Optimization of a Guyed Mast Structure under Wind, Ice and Seismic Loading
## 2 Case Study  
The considered structure is a guyed radio mast. It is a thin, slender, vertical structure sustained by tension cables fixed to the ground and typically arranged at 120\({}^{\circ}\) between each other.  
The main body is a single central column made of tube profiles or truss systems when a high elevation must be reached, see Figure 1. More than one set of cables is placed at different elevations to prevent instability phenomena. Guyed towers are usually built for meteorological purposes or to support radio antennas, such as the one considered in this research. In particular, this structure can be used for a limited time during an event or maintenance of primary transmission towers. Therefore, it is also called a temporary base transceiver station (BTS), typically adopted to supply the immediate service. Sporting events, concerts, motor racing, military camps, and emergency events are typical examples of temporary BTS applications. The BTS is usually mounted on a moveable platform called the shelter.  
The considered structure is located in Bassano Del Grappa, in the north of Italy, at a 129 m elevation from the sea level. The surrounding area is low-urbanized, with no relevant obstacles to the wind loads. The total height of the mast is 30.00 m. It is sustained by a central pole where 21 cables are fixed, see Figure 2. Other structural elements with rectangular cross-sections are used to create truss systems connecting cables and the central pole.  
Figure 1: (**a**) Render model realized using Tekla Structures. (**b**) Technical drawing of the structure investigated with dimensions in mm.  
The central pole consists of five circular hollow steel profiles with flanged joints and 6 m in length. All connections are bolted, as well as those connecting the cables to the pole. The shelter is a steel box devoted to partially sustaining the structure and hosting electronic equipment. It is usually mounted on a moveable platform.
# Using Imaging Techniques to Analyze the Microstructure of
## 4 X-ray Computed Tomography (CT Scan)
### Discussion  
The non-destructive X-ray CT scanning technique can capture high-resolution images of the internal microstructure of AC, which can be used to perform numerical simulations to investigate the mechanical properties of AC. The use of X-ray CT scanning can significantly reduce the time and cost required for analyzing the performance of AC mixtures, compared to destructive testing methods.  
The X-ray CT scan systems can generate 2D and 3D images for the microstructure of AC mixtures, see Figure 7, in a short time without cutting samples. However, this technique uses X-rays that produce dangerous ionizing radiation and requires a radiology technologist. This technique needs access to special institutions due to the expensive instruments and safety precautions involved. CT scan image processing has some challenges; one of them is contact between coarse aggregate particles, where they appear as a large single particle in the image. Another challenge is noise and/or poor contrast in the images due to having  
Figure 6: Air void content changes with increasing F–T cycles for CM, SMA, and OGFC mixtures [22].  
individual components in the AC mixture with similar density values. Special filters were used in the literature to overcome these limitations.  
Several studies used the CT scanning technique to investigate internal damage to AC mixtures due to traffic loading and explained the changes to the microstructure of the mixture. However, fewer studies use the CT scanning technique to investigate internal structure evolution and changes due to F-T cycling. CT scanning can provide information about the performance of AC mixtures under environmental loading in cold regions and help in selecting better materials for sustainable pavement structures. Table 3 summarizes the features of the CT scanning technique to analyze AC microstructure.
# Intelligent Ship Scheduling and Path Planning Method for Maritime Emergency Rescue
## 4 Experiment and Analysis
### Discussion of Analysis  
The core problem of route planning is how to minimize the distance and time from the departure location to the mission location under the influence of external conditions such as weather and obstacles so as to maximize the utilization of resources. According to the location of the incident area and the constraints of each stage of the mission planning, the path is reasonably planned to help to comprehensively reduced consumption and achieve the greatest benefit.  
The experimental results show that the performance of the improved A* algorithm presented in this paper is better than that of the traditional algorithm in terms of path length, number of turns, and average turn Angle, indicating that the algorithm is more efficient, economical, safe, and flexible in planning the navigation route and is more suitable for the actual needs of maritime navigation. Compared with the differential evolution algorithm, the full coverage path planning algorithm has a shorter planned path and greater regional coverage rate within the specified time, which provides obvious advantages in terms of path planning efficiency.
## 1 Introduction
The accuracy of the error model and the feasibility of the compensation strategy are verified by error compensation experiments.
# Multi-Feature Extension via Semi-Autoencoder for Personalized Recommendation
## 2 Related Work  
### Collaborative Filtering  
CF is one of the most critical methods in RSs. CF-based methods find user preferences by mining historical interaction information between users and items and recommend potentially interesting items to users. In general, CF methods are divided into memory-based methods and model-based methods [10]. Memory-based CF aims to compute the similarity between users and items. This method is widely used due to its efficiency and ease of implementation. However, as the recommendation system size and data sparsity increase, the computation of similarity becomes more difficult. To address the above issues, various model-based CF methods have been proposed, which aim to build recommendation models based on extracting hidden features between users and items, such as latent semantic models [11], clustering models [12], MF models [13], etc.  
Among various CF methods, MF is commonly used, which maps users and items to vectors of the same dimension to represent the latent features of the user or item. The classical MF methods include Singular Value Decomposition (SVD) [14] and Probabilistic Matrix Factorization (PMF) [15]. Compared with MF, [PERSON] et al. introduced the feature domain and proposed the field-aware factorization machine model to further enhance the feature crossover ability [16]. Although the MF-based methods achieves excellent performance in RS, it still suffers from the severe cold-start problem, that is, what suggestions should be given when a new user or item enters the system [17].  
Recently, incorporating side information in CF is effective in dealing with the issues with cold starts and data sparsity [18]. For example, [PERSON] and [PERSON] proposed a multidimensional MF model using CF to predict course ratings based on external resource information and make them available for users to reference [19]. [PERSON] and [PERSON] et al. proposed a Graph features-based Recommendation metohd (GraphRec) to extract generic graph-based attribute features for both users and items to improve recommendation performance [20]. [PERSON] et al. proposed a method for collaborative knowledge-aware attentive networks that use the KGs to acquire side feature information and analyze potential semantic relationships [21]. To some extent, the aforementioned methods can ease the existing difficulty, however, how to learn more effective feature representations has influenced the further development of these methods.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 5 Discussions  
### LEO and Its Potential in Future Internet of Things Services  
In this paper, INMARSAT GEO was used. Note that using GEO-based service is only one possible approach to implement the proposed satellite access point. There are LEO satellite constellations that are currently being deployed or designed to provide global IoT connectivity, which are likely to achieve lower costs and higher throughput than that of our system. For example, SpaceX, a private American aerospace manufacturer and space transportation services company, has recently submitted filings to the International Telecommunication Union to arrange a spectrum for 30,000 additional Starlink satellites (LEO-based) [20]. SpaceX expects its constellation to have 23-35 ms of latency and up to a gigabyte of throughput. As predicted by the CEO of Telesat, a Canadian satellite communications company, the deployment of these new satellite systems will significantly reduce the price of IoT services via satellite over the next few years [21].
## 3 Results and Discussion
### Infrared Spectral Split Peak Fitting of Extracts
#### 3.2.2 Extracts 1000-1800 cm\({}^{-1}\) Interval Peak Splitting Fit Results  
Figure 3 shows the IR peak fitting curves of the three solvent extracts in the 1000-1800 cm\({}^{-1}\) interval. Combining the fitted parameters of the spectra shows that the acetone extracts are  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Peak \#**} & \multirow{2}{*}{**Position \(\sigma\) (cm\({}^{-1}\))**} & \multirow{2}{*}{**Assignment**} & \multicolumn{3}{c}{**Area Percentage (\%)**} \\ \cline{5-6}  & & & **ACE** & **CS\({}_{2}\)** & **ACE/CS\({}_{2}\)** \\ \hline
1 & 900\(-\)860 & Five adjacent H deformation & 9.78 & 30.27 & 38.49 \\
2 & 860\(-\)810 & Four adjacent H deformations & 29.47 & 23.30 & 14.82 \\
3 & 810\(-\)750 & Three adjacent H deformations & 31.28 & 16.30 & 27.05 \\
4 & 750\(-\)720 & Two adjacent H deformations & 27.37 & 30.13 & 19.64 \\
5 & 710 & One adjacent H deformations & 2.10 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Infrared spectra of three solvent extracts 700–900 cm\({}^{-1}\) split peak fitting ratio of each absorption peak area.  
Figure 2: Infrared spectra of extracts in the 700–900 cm\({}^{-1}\) range. ((**a**) is the carbon disulphide fit, (**b**) is the acetone fit and (**c**) is the mixed solvent fit. The red line is the fitted curve and the blue line is the sub peak).  
dominated by asymmetric vibrational absorption peaks in the C-H plane and absorption peaks associated with the C-O-C band, which is probably the ether group of dibenzofuran. The CS\({}_{2}\) extracts are dominated by asymmetric vibrational absorption peaks in the C-H plane and asymmetric =C-O-C absorption peaks in aromatic ethers. The mixed extracts are dominated by the C-O stretching vibrations of the phenols. From the results of the fitted data in Table 3, it is clear that the acetone/CS\({}_{2}\) solvent mixture extracts more phenolics.