esa-sceva/satcom-chunk-collection huggingface dataset:
# Named Entity Recognition for Sensitive Data Discovery in Portuguese
## 3 NER Component
### Preprocessing Module  
The Preprocessing Module is the first element in the chain. It is responsible for preprocessing and treating the input data, performing a set of preprocessing tasks so that the text can serve as input to the next module. Preprocessing is one of the most important tasks of Natural Language Processing (NLP) and Information Retrieval (IR) studies [19]. Applying this set of techniques to the text means giving it another format so that it can be analyzed, and digested by an algorithm. In this case, the preprocessing module is divided into three parts that are invariably and consecutively executed.  
* **Segmentation:** So that each sentence is processed individually without depending on the context of the previous one. We start by dividing the entire text into sentences by the end of sentence punctuation marks: Period (.), question mark (?), exclamation (!), and suspension points (...).
* **Tokenization:** Is performed second on the module chain. This component divides the text in n-grams, words, or sets of words. The number of n-grams can also be parameterized, and this tokenization consists of representing the text as a vector of individual or sets of words. Regarding our approach, some decisions have been made in terms of punctuation, which consist of separating all the nonalphanumeric characters from the words. All punctuation marks except the hyphen (-), the at (@), and the slash (/) are separated by a blank space from all alphanumeric characters in the text. By default, the parameterization used in the processing chain consists of the division into unigrams.
* **Morphosyntactic Analysis:** After tokenization, we perform the morphosyntactic analysis of all separate text in unigrams. The text is analyzed and classified with Part-of-Speech Tagging using different techniques and tools. The task of Part-of-Speech (POS) Tagging consists of analyzing and tagging all the words in the text at the syntactic level. After studying the state-of-the-art, three different implementations were tested and analyzed to select the best to integrate into the NER Component. In the first and second experiments, we used the POS tagging model of the NLTK library. While in the first experiment the model was being applied directly, in the second one we retrained the model with Floresta Sintactica Corpus [20]. In the third experiment, we used the SpaCy library POS model. After testing, we concluded that the model with the best behavior was the last one, which consists of an implementation based on statistical models and the use of multitask CNN [21]. It achieved an accuracy of 86.4% for the transformed Floresta Sintactica corpus. Therefore, this is the default model used in the NER Component.
# Performance Analysis of Ground Target Detection Utilizing Beidou Satellite Reflected Signals
## 4 Experimental Results
### Results of the Second Experiment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Time (s)** & **PRN2 (s)** & **PRN3 (s)** & **PRN8 (s)** & **PRN23 (s)** & **PRN28 (s)** \\ \hline
1 & 15.05 & 13 & 13 & 13 & 11 & 13 \\
2 & 28.08 & 24 & 26 & 26 & 26 & 25 \\
3 & 40.09 & 38 & 38 & 38 & 36 & 38 \\
4 & 53.05 & 49 & 50 & 51 & 50 & 50 \\
5 & 64.05 & 62 & 62 & 62 & 60 & 62 \\
6 & 77.04 & 73 & 74 & 75 & 75 & 74 \\
7 & 89.02 & 87 & 86 & 87 & 85 & 86 \\
8 & 107.07 & 97 & 98 & 99 & 99 & 98 \\
9 & 113.04 & 111 & 110 & 111 & 109 & 111 \\
10 & 125.09 & 121 & 123 & 123 & 123 & 122 \\
11 & 138.02 & 135 & 135 & 135 & 133 & 135 \\
12 & 150.00 & 145 & 147 & 147 & 147 & 146 \\
13 & 162.00 & 159 & 159 & 159 & 157 & 159 \\
14 & 174.07 & 170 & 171 & 172 & 172 & 171 \\ \hline Detection parameter & Null & A & A/C & A/B/C & A & A/B \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Target detection time results from the BeiDou satellite reflected signals: (A) I branch component data; (B) phase difference information; (C) carrier-to-noise ratio. According to the geometric relationship shown in Figure 23, the processing results of the reflected signals of five BeiDou satellites can be explained. When the target moved to different positions, the reflected signal of the target changed greatly. When the target was in the first Fresnel diffraction region of the satellite, the reflection signal of the target decreased, and the reduction time was consistent with the time when the target passed directly below the antenna, as shown in the CNR information of the PRN8 satellite. When the target was not in the first Fresnel diffraction region, but still in the second or third Fresnel diffraction region, the reflected signals from other satellites were effectively received by the antenna, but the intensity of the reflected signals changed significantly and did not appear to show the same situation as the PRN8 satellite, as shown by the information from PRN23, PRN2, PRN27, and PRN3. From Table 5, each satellite detects the target at different times. Combining with the position relationship in Figure 23, it can be found that this was closely related to the position of the specular point. According to the information shown in Table 5, when the target moved from east to west, the satellites detecting the target were PRN23, PRN8 (PRN3), PRN27 and PRN2 in turn, and the location of the mirror points is the same from east to west. It should be mentioned that PRN23 and PRN2 were about 2 s behind PRN8 satellite when they detected the target, which shows that the effective detection range of the antenna was about 1 m below the antenna (the average velocity of the target motion is 0.5 m/s). It should be mentioned that the scope was based on the span of the target in the east and west.
## 3 Results
### Hardness  
Vickers and Rockwell hardness tests were performed to investigate the variation of the effect of WIP with scale as shown in Figure 10. The microscopic-scale Vickers hardness showed no significant change due to WIP but the deviation of the hardness along the build direction (at the S plane) was larger than the deviation of the results obtained from the L plane. At the macroscopic scale, the measurement results in all cases were different. In the L plane, the difference between the cases with and without WIP was larger than that along the building direction. This means that the Rockwell hardness in the L plane was more affected by the WIP process. [PERSON] et al. reported that an increase in porosity leads to a decrease in material hardness [39]. This is due to the collapse of the pores within the material under indenter load. In specimens with relatively large pores, the hardness sensitivity increases with a larger indenter. Hence, in the Rockwell hardness test, the hardness changed. These results indicate that the Rockwell hardness test is more effective in the study of the effect of pores on hardness.  
Figure 10: Measurement of (**a**) Vickers (microscopic) and (**b**) Rockwell (macroscopic) hardness with and without WIP.  
Figure 9: Results of tensile test with and without warm isostatic press (WIP): (**a**) tensile strength; (**b**) strain at ultimate tensile strength; (**c**) strain-stress curve.
## 3 Proposed Method
### Overall Integrated System  
Vehicle and license plate detection, parallel ensemble processing, corner detection, homography, and character recognition are integrated into a sequential pipeline structure. The detailed structure of the license plate recognition system can be found in Figure 5. Since the license plate is located within the vehicle area, a YOLOv8-based vehicle detector is used to detect large objects corresponding to vehicles in the input image. By first detecting vehicles as large objects in complex backgrounds, the search area of the license plate detector is restricted, enabling efficient object detection.  
Subsequently, a YOLOv8-based license plate detector is used to detect license plates within the detected vehicle area. In this process, detection is performed in a space limited to the vehicle area, reducing false detections due to background regions and improving accuracy.
# Experimental Study on Two PCM Macro-Encapsulation Designs in a Thermal Energy Storage Tank
## 3 Results and Discussion
### Temperature Evolution during the Discharging Process  
Figure 8 shows the discharging temperature profile of all sensors placed at the surface of the slabs at two different mass flow rates. Analyzing the influence of the PCM encapsulation design on both flow rates, the tank with ThinICE slabs finished the discharging process 30% faster than with FlatICE slabs. This can be explained by the higher heat transfer surface and the lower amount of PCM (30% less according to Table 3) when using ThinICE. Moreover, when analyzing the influence of the flow rate, Figure 8 shows that for both slab types at 4 L/min the experiments were completed 50% faster than at 2 L/min.  
When comparing temperature distribution inside the tank (Figure 8), a similar temperature profile between the top, middle, and bottom slabs was observed in all the experiments performed with ThinICE slabs. Moreover, this behavior changed with the use of the FlatICE, where the stratification and the profile temperature inside the tank depends on the mass flow rate.  
Figure 8: Discharge process PCM slab temperature profile for different slabs and HTF flow rates: (**a**) ThinICE and 2 L/min, (**b**) FlatICE and 2 L/min, (**c**) ThinICE and 4 L/min, and (**d**) FlatICE and 4 L/min. Note: The red line denotes the end of the charging experiment (T_out = 25 \({}^{\circ}\)C). The time axis is not presented on the same scale in all the figures.  
Figure 7: Total energy delivered to the PCM storage tank during the charging processes of the four study cases presented in this study.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 9 Practical Applications and Use Cases
### Tokenization of Cultural Heritage and Other Valuable Digital Assets
#### 9.1.3 Management of Intellectual Property Rights  
Tokenization can streamline the management of intellectual property (IP) rights, such as copyrights, patents, and trademarks. NFTs can represent unique digital assets, including literary works, artistic creations, and scientific inventions, enabling the secure and transparent tracking of ownership and licensing agreements. This can facilitate the enforcement of IP rights and promote fair compensation for creators and rightsholders.
## 3 Results and Discussion
### Identification and Quantification of MP Particles in Biota Samples Using Raman Spectroscopy
Additionally, MPs extracted from Sardina (epipelagic species) included polyvinyl-butyl ranging from 1 to 10 particles/fiber with a size range of 10-200 \(\upmu\)m analyzed with micro-Raman spectroscopy [67]. The size of MP particles detected was >5 \(\upmu\)m, and four fibers of <100 \(\upmu\)m were found in samples of _M. galloprovicialis_, comparable to the findings of [PERSON] et al. (2022) [69]. MPs (<3 \(\upmu\)m) from the south coast of the Mediterranean Sea were detected through an innovative extraction method coupled to the SEM-EDS technology, and diameter ranges from 1 to 3 \(\upmu\)m in _Mytilus galloprovicialis_ and 1.8-2.6 \(\upmu\)m in _Sparus aurata_. Finally, a comprehensive study in intertidal water of South Australia and mussel (_Mytilus galloprovicialis_) showed MP abundance within the range reported globally (mean = 3.58 \(\pm\) 8.18 particles individual\({}^{-1}\)) and the identification of three polymer types, including polyamide (nylon, 32%), acrylic resin (6%) and PP (44%) [6]. It should also be noted that the MP number of PVA in _Sparus aurata_ was not reported elsewhere but only in a few studies reporting the determination of synthetic polymers in Mediterranean surface waters (frequency of 2%) [70] and in canned tuna, salmon, and Sardine fishes from Taif markets (Taif, Saudi Arabia) [71]. Figure 4: (**a**) Identified particles of mussel sample on silicon filters; (**b**) mean relative contribution of each polymer type; (**c**,**d**) size (diameter and perimeter, respectively) of MPs extracted from mussels.
# SSCL-TransMD: Semi-Supervised Continual Learning Transformer for Malicious Software Detection
## 4 Experiment
### Experimental Analysis
#### 4.2.2 Analysis of Ablation Experiments  
Compared with the Transformer model, the SSCL-TransMD detection model proposed in Section 4 of this paper has three main differences:  
1. The addition of parameter \(\lambda\) to memorize and replay unlabeled input data and labeled historical data; 2. The adoption of the LLGC algorithm to provide pseudo-labels before training the information encoding component; 3. The usage of the MLP algorithm to decode the Encoder and directly output the classification detection results.  
The comparison models used in the ablation experiments are as follows:  
1. SSCL-Transformer model: The SSCL-Transformer model adds the MLP algorithm to the Transformer's encoder to decode and output the classification results. It continuously inputs semi-supervised malicious software data during the ablation experiment training process; 2. SSCL-TransMD model: The SSCL-TransMD model adds the LLGC algorithm to provide pseudo-labels for the Encoder layer in the SSCL-Transformer model, assisting in the computation of the information encoding layer. Other settings are the same as the SS-Transformer model.  
This subsection primarily discusses the improvement of the similarity calculation component on the SSCL-TransMD model and proves its effectiveness in the binary classification scenario.  
The visualization results for the CICMalDroid 2020 dataset are shown in Figure 4.  
Based on the ablation experiment results in Figure 5 and Table 11, the analysis of the similarity calculation component is as follows.  
As a pre-training component of the model, the processing of unlabeled data by the similarity calculation component has a significant impact on the classification accuracy of the model. With the input of unlabeled malware in the CICMalDroid 2020 dataset, it can be seen that the F1-score of the SSCL-Transformer model shows a significant downward trend. After the fourth input, which includes a mixture of historical labeled samples and new input unlabeled samples, the F1-score is, on average, decreased by 20.59% compared to the initially trained F1-score in the three datasets. The F1-score of the SSCL-TransMD model, which calculates similarity scores through the similarity calculation component, decreases by an average of 2.02%. Therefore, intuitively speaking, the similarity calculation component avoids catastrophic forgetting issues.  
Figure 4: Binary and multi-classification results on CICMalDroid2020. (**a**) Binary classification on CICMalDroid2020 shows the F1-score of binary classification during the continuous learning process. (**b**) Multi-classification on CICMalDroid2020 shows the Micro-F1 score of multi-classification during continuous learning.
## 5 Performance Evaluation
### Implementation Technical Details
#### 5.2.5 Evaluation Metrics  
We evaluate the performance of different algorithms via Accuracy, Precision, Recall and F1 Score metrics. We also measured execution time of every algorithm (i.e., including both training and testing times).
## 2 Materials and Methods
### Long Short-Term Memory (LSTM) Network  
LSTM networks can improve the fusion of temporal features from the state of different parts. This study combines our model with LSTM to extract temporal features from WT data. The LSTM contains an input gate, an output gate, and a forget gate. Its structure is shown in Figure 4.  
The information to be forgotten is controlled by forget gate \(f_{t}\), defined as:  
\[f_{t}=\sigma(W_{f}x_{t}+W_{h}h_{t-1}+b_{f}) \tag{2}\]  
where \(\sigma\) is the sigmoid function, and \(W_{f}\) and \(b_{f}\) represent the weight matrix and the bias of the forget gate, respectively. In the next step, input gate \(i_{t}\) receives \(h_{t-1}\) and \(x_{t}\) to determine the new data that should be stored in the cell state. At the same time, a vector of candidate values \(\tilde{C}_{t}\) is created by a hyperbolic tangent layer (tanh), which returns a value between \(-1\) and \(1\). The previous cell state \(C_{t-1}\) is then updated to the new cell state \(C_{t}\), as described in Equations (3)-(5) [40].  
\[i_{t}=\sigma(W_{i}x_{t}+W_{h}h_{t-1}+b_{i}) \tag{3}\]  
Figure 4: Schematic diagram of the LSTM architecture.  
\[\tilde{C}_{t}=\tanh(W_{c}x_{t}+W_{h}h_{t-1}+b_{c}) \tag{4}\]  
\[C_{t}=f_{t}\odot C_{t-1}+i_{t}\odot\tilde{C}_{t} \tag{5}\]  
Finally, output gate \(o_{t}\) determines the hidden state \(h_{t}\) as follows:  
\[o_{t}=\sigma(W_{o}x_{t}+W_{h}h_{t-1}+b_{o}) \tag{6}\]  
\[h_{t}=o_{t}\odot\tanh(C_{t}) \tag{7}\]  
where \(W_{o}\) and \(b_{o}\) are the weight matrix and the output gate bias, respectively. Output \(h_{t}\) is obtained according to the updated state of cell \(C_{t}\).
## 3 Results
### Carbonation Behavior of RRAC after Freeze-Thaw Cycles
#### 3.2.3 Third Generation  
For the third-generation RAC, its carbonation resistance deteriorated sharply. With the increase of replacement rate, the carbonation depths of RAC-3 increased significantly (Figure 8a). In other words, the influence of the replacement rate on the carbonation resistance of RAC-3 was particularly severe. When the replacement rate reached 100%, the carbonation rate of RAC-3 showed a substantial increase compared with that at a low replacement rate. The carbonation depth of RAC-3 with 100% replacement rate at a 28-d carbonation age was 3.65 mm, which was 1.85 times higher than that of NAC. This was caused by the poor quality of RCA-3 and the cumulative effect of carbonation [21]. In the preparation process of every generation RCA, the matrix concrete was carbonated under the 30% of ultimate tensile stress for 17 d to simulate the real service life, thus leading to the partially attached mortar being carbonated. The greater the generation, the more obvious the accumulation effect of carbonation.  
In Figure 8b, the carbonation resistance of RAC-3 falls off further with the increase of replacement rate and number of freeze-thaw cycles. After 400 times of freeze-thaw cycles, the carbonation depths of RAC-3-25, RAC-3-75, and RAC-3-100 were 3.12, 6.53, and 9.16 mm, respectively. Compared with the NAC one, the carbonation depth increased by 84.6%, 286%, and 442%, respectively, and the maximum carbonation depth of RAC-3 was 2.5 times higher than that in the non-freeze-thaw condition. It was found that the effect of freeze-thaw cycles on the carbonation resistance of RAC-3 was more detrimental. In the micro-frozen regions, the freeze-thaw cycle could be a significant factor to the deterioration of pore structure of concrete [45]. Under different pore structures, the difference of CO2 diffusion coefficient resulted in the changes of carbonation resistance of RAC [46]. In addition, the poor properties of RCA-3 also played a prominent role in the deterioration of carbonation resistance of RAC-3 [47]. The new mortar on the RAC-3 surface fell off easily after freeze-thaw cycles due to the weaker ITZs caused by the RCA-3, and CO2 was much easier to penetrate in the concrete, increasing the CO2 diffusion coefficient. Therefore, freeze-thaw damage and the quality of RCA were the critical factors to affect the carbonation resistance of RAC.
## 2 System Model
### Computation Model  
In the proposed system architecture, two sorts of delays are rigorously considered: computation and transmission delays. The processing of UE's tasks \(k\) in several tiers, such as the SUE, the E-AP, and the cloud computing zone, causes the computation delay. On the other hand, the fronthaul-based uploading of jobs \(M_{k}\) from the UE to the cloud or from the UE to the edge results in transmission latency. For downloading the computed data, the system also produces a delay. The following sub-sections offer a comprehensive breakdown of the delays incurred.
## 2 Endocannabinoid System (ECS)
### Endocannabinoids  
#### 2.2.1 Anandamide  
Anandamide was the first endocannabinoid ever discovered (in 1992 by [PERSON] [27]) and its name comes from 'ananda', the Sanskrit word for 'bliss'. It is an ethanolamide derivative of arachidonic acid. Anandamide features a lengthy lipid chain composed of 20 carbon atoms. Additionally, at one end, it possesses an amino group through which it interacts with cannabinoid receptors. It also has an ethanolamine group that connects the lipid portion with the amino group, acting as a bridge between them [28]. Anandamide is a high-affinity, partial agonist of cannabinoid receptors. Furthermore, it is capable of stimulation of TRPV1, transient receptor potential cation channel subfamily V member 1 (vanilloid receptor 1) [12]. Subsequent studies indicate that anandamide triggers CB1 receptors within the central nervous system as well as localized peripherally, CB2 receptors in peripheral tissues, which encompass immune cells, and TRPV1 receptors throughout various tissues [25]. It serves as the predominant transmitter in the ECS, and all biological functions of this system are contingent upon its activity. Interestingly, anandamide has also been found in certain species of plants and as an alcaloid, and there is a small amount of it in chocolate [29].
## 4 Simulation Results and Discussion  
The simulation model employs a decentralized control approach, where individual PID controllers are assigned to power plants in each area to regulate and stabilize the system frequency. In the simulation scenarios, each PID controller is responsible for managing  
Figure 1: Flowchart for the PSO algorithm.  
and adjusting the power output of its respective plant, ensuring that load variations and disturbances do not cause significant frequency fluctuations. The approach allows each plant to operate independently while maintaining overall system coordination.  
This section explores the performance of frequency control strategies in multi-area power systems through the following three main scenarios:  
* Scenario 1: A two-area power system integrating hydropower plants.
* Scenario 2: A three-area power system combining hydropower, reheat thermal, and non-reheat thermal power plants, with continuous load variations.
* Scenario 3: A three-area power system combining hydropower, reheat thermal, and non-reheat thermal power plants, with the integration of wind power.  
These simulations aim to highlight how effective control strategies can ensure stable frequencies and efficient operations under varying load changes.