esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
This research addressed a critical gap in the automated analysis of complex patent data by developing a new metric to quantify the alignment between subject matter expert (SME) classifications and NLP-based topic assignments. However, this research has a few limitations, which set the stage for future work. Although the author's expertise and experience in the domain of CV is substantial, relying on a single SME to identify dominant themes can result in subjective bias. Nevertheless, the novel metric developed in this work establishes a foundation for future work to investigate the nature of a distribution in the misalignment between SME and NLP topic identification. To do so, future work will select multiple SMEs with different technical backgrounds to identify dominant themes from the same corpus and evaluate the distribution of misalignment with NLP topic classification.  
Another limitation is the focus on U.S. patents, which may miss broader international themes. However, foreign patents are not consistently available in the English language, making a mixture analysis more challenging. The results of this work highlight that the gross misalignment of ML-assigned topics with SME classification questions the generalizability of NLP in patent analysis, including across other domains. Therefore, future work will evaluate the same approach on patents focused on related technologies such as autonomous vehicles, advanced air mobility, battery technology, and cybersecurity.  
This study established a benchmark in a snapshot of time for future work to compare with by using the metric proposed for objective and quantitative comparison. The evolution from free open-source NLP models, such as the ones evaluated in this research, to LLMs, including more powerful multimodal versions currently under development by companies such as OpenAI, Google, X Corp. (formerly Twitter), Meta, and Microsoft, could also help to identify topics. Hence, future research will include various LLMs in the SME mix to evaluate their misalignment with traditional NLP topic models.  
Figure 9: UMAP of topic model probabilities, colored by SME topic assignments.  
Overall, this work delivers valuable insights to various stakeholders involved in technology development and innovation. Stakeholders include patent analysts and researchers who often face challenges in manually sifting through vast amounts of patent data to identify trends and key areas of innovation. The unsupervised methods can automatically categorize patents into coherent topics, significantly aiding in the quick identification of technological trends and focus areas. Additionally, policy makers and strategic planners can benefit by gaining a macro-level view of technological advancements in the CV domain to inform decision making and strategic planning, especially in technology forecasting and policy formulation. Furthermore, insights gained from topic classification can help innovators and entrepreneurs identify under-explored areas and potential opportunities for innovation, thereby guiding further research and development efforts.
## 3 Results and Discussion
### Free Amino Acid Content  
Using an amino acid analyzer, we identified 19 FAAs in the samples, as presented in Table 4. Both chicken breed and freshness state influenced the total FAA concentration owing to distinct differences in each individual FAA. These FAAs in chicken meat affect taste perception differently: Glu and Asp are umami FAAs; Thr, Ser, Gly, and Ala are sweet FAAs; Val, Met, Ile, Leu, Phe, Tyr, His, Arg, and Lys are bitter FAAs; and Asn, Trp, and Cys do not impart a specific taste perception [40]. In this study, under fresh conditions, the total FAA content was higher in CB and WRMD 1 than in WRMD2 and HH3 (\(p<0.05\)).  
Under fresh conditions, the content of glutamic acid, the main umami FAA, did not differ between CB, WRMD1, and WRMD 2, but was lower (\(p<0.05\)) in HH3. However, the concentration of Asp, the secondary umami FAA, was higher (\(p<0.05\)) in CB than in all KNC breeds. In addition, under fresh conditions, the concentration of total sweet FAAs  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**FAA** & **Insider** & **Hb3** & **WRMD1** & **WRMD2** & & & \\ \hline
**(mg/100 g)**  
in CB and WRMD 1 meats was higher than that in WRMD 2 and HH3 meats. Among Frozen-Thawed meats, the concentration of total sweet FAAs was higher in CB meat than in HH3, WRMD 1, and WRMD 2 meats. Similarly, under fresh conditions, CB, WRMD 1, and WRMD 2 meats had a higher (\(p<0.05\)) concentration of total bitter FAAs than HH3 meat. However, under Frozen-Thawed conditions, total bitter FAA concentration was higher (\(p<0.05\)) in CB than in all KNC breeds. The abundant content of FAA inside the bone marrow were reported to shift into the meat over two weeks of frozen storage. The water-soluble compounds, including FAAs, move steadily with the influence of structural alteration due to the formation of ice crystals, creating routes for substance transmission, and causing different osmotic pressure [13]. In addition, unlike the minerals that declined over the storage period, the FAA concentration was intensified by enzymatic hydrolysis. Therefore, this study suggested that the differences in the enzymatic mechanism among chickens might create differences in FAA profiles after frozen.  
As explained by [PERSON] et al. [41], the proportion of FAAs is highly influenced by the chicken breed and slaughtering age. Slaughtering age has a positive correlation with the formation of umami-related non-volatile compounds, including Glu, due to increased protein percentage, whereas chicken breed determines the FAA composition of different muscle fiber types. Interestingly, in this study, we found that the concentration of each individual FAA, except for taurine, tryptophan, and carnosine, was higher in Frozen-Thawed meat than in fresh meat, irrespective of the chicken breed. Two main factors may be responsible for the increased concentration of FAA in chicken meat following freezing and thawing: (1) the structural damage of muscle tissue due to a formation of ice crystals during freezing, which promotes the migration of FAA from the bone marrow into muscle tissue; and (2) hydrolysis of protease [13].
## 3 Electrothermal and Lifetime Model Explanation for LiCs
### Lifetime Model  
The lifetime of all types of the energy storage technologies is affected by two phenomena:  
1. calendar effect; and
2. cycling effect.  
The calendar effect occurs while the cell is placed in the storage room for a long time or when it is in the rest period, for example, when the car is not in use for a certain time, the battery system is considered in the calendar mode. Many parameters take part in this phenomenon, like the storage temperature, and the storage SoC. Cycling, on the other hand, is the application in which the cells are used to deliver energy to the load. In these usages, they are charged a discharged fully or partially with different current rates and at different ambient temperatures. Therefore, the lifetime of the cell is affected by different parameters, like the cell temperature, the current rate, the depth of discharge, and the frequency level. In order to study this effect, many experiments were performed in our facilities in the battery innovation center (BIC), MOBI group, VUB University. The lifetime models for LiB and LiC have been explained in [6; 13], respectively. Figures 7 and 8 show the capacity and internal resistance trends for the 2300 F LiC cells. As it is seen in Figure 7, the capacity decreases as the number of cycle increases. Moreover, the ambient temperature plays a role in the degradation. The lower the temperature, the lower the capacity degradation. Figure 8 shows the cycling effect on the internal resistance variation. As it is seen, for the cell at 45 \({}^{\circ}\)C, after 150,000 cycles, the internal
# Research on Semi-Active Vibration Control of Pipeline Based on Magneto-Rheological Damper
###### Abstract  
This paper proposes a scheme to control the low-frequency vibration of pipelines by using magneto-rheological (MR) vibration reduction technology. The state equation and the transfer function of the pipeline system are established, and its stability and the sinusoidal excitation response are analyzed. The prototype of MR damper is developed. The dynamic characteristics of MR damper are tested and the double-sigmoid model of MR damper is established. The two-state control, Proportion Integration Differentiation (PID) control and sliding-mode-variable-structure (SMVS) control methods of pipeline vibration using MR damper are analyzed comparatively, and the vibration control laws are deduced. The simulation analyses are carried out to predict the control effect of different pipeline vibration control algorithms. The verification tests through a semi-active measurement and control platform are carried out, and the feasibility and applicability of different pipeline vibration control strategies are analyzed. The test results show that the three kinds of pipeline vibration control methods based on MR damper can effectively control the pipeline vibration. Especially, SMVS control has the best vibration control effect, the pipeline amplitude drop and the acceleration drop can reach 22.31 dB and 16.34 dB respectively, while the amplitude attenuation rate and the acceleration attenuation rate can reach 92.34% and 84.77%, respectively.  
pipeline vibration; MR damper; semi-active control; PID; SMVS
# Laser Welding on Cerclage Wire Joining in Femur Surgery
## 4 Experimented Results and Discussion
### Optimal Laser Parameters  
Adjusting the laser parameters and the welding length, the experimental results, considering the weld appearance and ultimate load at failure, can be analyzed to find the optimal laser parameters for welding on stainless steel 316L. In this study, the criteria for selecting the optimal laser parameters was designed to give a better ultimate load at failure or acceptable ultimate load at failure. The results are shown in Figure 17. The heat input was set at the lowest possible degree in the criteria. Figure 17 shows the laser parameters' process-window which indicates an adequate area of laser parameters.  
In the experimental work, the example of weld appearance from the adequate area have a good weld appearance or nice ripples, free oxidation, and good joint strength. The increment of charge voltage and pulse frequency results in a higher heat input and ultimate load at failure. Furthermore, the increment of weld length brings about a higher ultimate load at failure as well. For example, the 5 mm welding length has the minimum ultimate load at failure but it is still within the acceptable range, as shown in Figure 14. At the welding length of 5, 10, and 15 mm in an adequate area of laser parameters, the experimental results show that all examples have ultimate load at failure and are within an acceptable range. In addition, all examples have the ultimate load at failure more than the traditional cerclage joint.  
In summary, the adequate area of optimal laser parameters is chosen with the following conditions: The charge voltage should be at the range of 80-110 V, the pulse frequency should be at the range of 6-9 Hz, while the pulse width was kept at constant of 3 ms. In this area, the heat input is low and it is free of oxidation.  
Figure 16: Effect of laser parameters on ultimate load at failure \(\pm\)SD (length of welding 15 mm).
Among the ML techniques, Artificial Neural Networks (ANN) are particularly noteworthy as they act as \"black boxes\" that establish mathematical relationships between the inputs and the output data without prior knowledge of the specific relationship (linear or nonlinear) existing between them [30]. Numerous researchers have used ANNs to calculate UVER, using GHI as main input [31; 32; 33; 34; 35; 36] regardless of the characteristics of the other variables used. Table 1 shows an overview of the variables used in different studies to estimate UVER and the ratio UVER/GHI from ANN, LR, and multilinear regressions (MLR). Notably, GHI and TOC [31; 34; 35; 36] have been used as the most used inputs in conjunction with other variables in all cases. The main objective of this work is to develop mathematical models using different strategies for UVER from meteorological parameters measured in Burgos (Spain) during an extensive experimental campaign run from September 2020 to June 2022. After selecting the variables based on the Pearson correlation coefficient, both MLR and ANN techniques were employed to model GHUVE under different sky conditions, including all skies, overcast, intermediate, and clear skies, classified according to ISO/CIE standard sky classification [41]. It is important to highlight that the variables used in the models developed in this study were experimental variables typically recorded in terrestrial facilities that underwent the strictest quality controls. The use of variables from satellite observations or additional databases was discarded due to their different sampling frequency and to guarantee, as far as possible, the applicability of locally obtained models in other emplacements using only ground meteorological data. The work is structured as follows: in Section 2, the experimental data used for modeling are described and analyzed, along with the criteria ensuring their quality. Section 3 presents the feature selection process based on the Pearson criterion. A complete discussion of the results is shown in Section 4. Finally, Section 5 presents the key findings and main conclusions obtained from this study.
## 3 Public Transport-Assisted Data-Dissemination System
### Multi-Attribute Decision Making  
MADM is being used for network selection among all the available networks. The network-selection procedure ultimately aims for the best network that can support the required service(s) and avoid excessive switching among different networks to minimize service interruptions and energy consumption. Therefore, we introduce the MADM method used by the controller in response to suitable network selection. This model helps to make forwarding decisions fairly. MADM is an important tool that assists in the solution of complex decision-making problems and analyzes network-selection problems in a heterogeneous network. There are a few characteristics of MADM given below:  
1. [label=()]
2. Alternatives: Alternatives are defined as several different options to prioritize or select. These can be called candidates, users, or networks, etc.;
3. Decision Matrix: Any MADM problem can be mathematically defined by using a decision matrix, \(L(M\times N)\): \[L=\begin{pmatrix}c_{1}&C_{2}&\dots&C_{j}&\dots&C_{N}\\ x_{1,1}&x_{1,2}&\dots&x_{1,j}&\dots&x_{1,N}&A_{1}\\ x_{2,1}&x_{2,2}&\dots&x_{2,j}&\dots&x_{2,N}&A_{2}\\ \vdots&\vdots&\ddots&\vdots&\ddots&\vdots\\ x_{i,1}&x_{i,2}&\dots&x_{i,j}&\dots&x_{i,N}\\ \vdots&\vdots&\ddots&\vdots&\ddots&\vdots\\ x_{M,1}&x_{M,2}&\dots&x_{M,j}&\dots&x_{M,N}\end{pmatrix},\] (1) where \[A_{1},A_{2},A_{3},\dots A_{i},\dots A_{M}\] denotes all the alternatives/parameters to consider for decision making. \[C_{1},C_{2},C_{3},\dots C_{j},\dots,C_{N}\] represents all \[N\] criteria, which is being calculated as per different alternatives and denotes its performance. For example, \[x_{i,j}\] is the performance ranking of the \[ith\] alternative w.r.t. to the \[jth\] alternative. The main aim of the decision matrix is to select the best alternative from the given alternatives with respect to others;
4. Attribute Weight: Attribute weight is the value obtained by the decision-maker as per each attribute of the network. This weight depends upon the value assigned to the attribute. This weight is calculated by the pairwise comparison matrix;  
Figure 2: Flowchart of the proposed data-dissemination system.  
* Normalization: The attribute used for network selection has different measurement units. Therefore, normalization is a necessary step for this calculation.  
MADM algorithms have high accuracy and low difficulty. They capture different parameters (e.g., QoS, bandwidth, delay, data volume, cost, etc.) and select the most suitable network. There are many possible solutions for MADM problems. The whole process of network selection is shown below, in Figure 3.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 3 GAN-Enhanced CNN for Voiceprint Recognition
### GAN Model Construction
#### 3.5.3 Discriminator Design  
The discriminator evaluates the authenticity of audio samples. Its structure mirrors the generator but operates in reverse, as follows:  
1. Input dimension: takes a flattened input of 4096 dimensions (or reshaped as (64, 64, 1)).
2. Layer reduction: successively reduces the feature dimensions from 4096 to 256.
3. Activation functions:  
* LeakyReLU maintains gradient flow for efficient learning.
* Sigmoid outputs a probability indicating whether a sample is real (close to 1) or synthetic (close to 0).  
The discriminator and generator jointly refine their performance during adversarial training.  
The generator's goal is to trick the discriminator into judging the generated audio samples as real. Therefore, the loss of the generator is the cross-entropy between the output of the discriminator on the generated sample and the target label 1 (true). For the transformation process, the generator in the GAN model performs the following transformations. First, the initial noise vector (latent_dim = 600) serves as a random input to the generator. The generator applies multiple fully connected and convolutional layers to increase the dimensionality of the data, producing a flattened vector of size 4096. This output vector is reshaped into a matrix of dimensions (64, 64, 1), simulating a single-channel feature map. The GAN produces a flattened vector of size 4096. This output vector is reshaped into a matrix of dimensions (64, 64, 1) to match the input requirements of the CNN model, as Figure 2 shows.  
Figure 2: CNN architecture. The output layer was configured with 5 nodes to classify the 5 speaker classes used in this study. **(Left)** Illustration of the CNN architecture used in the study. The input dimension (64, 64, 1) represents the reshaped MFCC feature matrix derived from audio samples, while the output value of 7 corresponds to the number of speaker classes in the classification task. **(Right)** GAN model training process for voiceprint recognition.  
This transformation process bridges the gap between the generator's latent space and the input requirements of the CNN, enabling the end-to-end integration of GAN-augmented data into the classification pipeline.
# A Review on Deep-Learning-Based Cyberbullying Detection
## 4 Data Representation Techniques
### Image Data Representation
#### 4.2.6 Optical Character Recognition (OCR)  
Optical character recognition (OCR) [84] enables computers to read printed or handwritten text and to turn it into digital text that can be edited, searched, and analyzed. OCR can be used to analyze text-based content on social media sites, online forums, and messaging services in order to detect cyberbullying.  
Studies [85; 86] proposed a multimodel cyberbullying detection framework where they applied OCR to detect cyberbullying from image data. In addition to that, they employed another method named Image Similarity to classify cyberbullying from image data. [PERSON] et al. [87] utilized OCR to extract text from the images to classify cyberbullying in image data. For instance, Instagram uses OCR to find bullying in pictures and captions. The program looks for offensive language in the captions and images, and if it finds any, it notifies the user that their post may be offensive [88]. Similarly, Facebook employs OCR technology to find offensive material such as cyberbullying, hate speech, and graphic images [89]. [PERSON] et al. [90] proposed a novel method for identifying cyberbullying on Chinese social media platforms. The system extracts text from images using a combination of OCR and image processing techniques and then uses deep learning algorithms to categorize the text as either normal or abusive. [PERSON] [91] identifies cyberbullying on Indian social media platforms. OCR was used by the system to extract text from images, and machine learning algorithms were then used to determine whether any of the text was threatening or offensive.  
OCR technology can instantly analyze text-based content, spotting behavioral patterns and identifying offensive language. Message tonality analysis and detecting sarcasm and other subtle forms of bullying can also be performed using technology. These systems can detect threatening or offensive language that might otherwise go unnoticed by conventional text-based analysis techniques by extracting text from images using OCR. These systems can also learn and adapt over time with the help of machine learning algorithms, which enhance their precision and efficiency. Social media platforms and online forums can promote a safer and more positive online environment by utilizing OCR technology for cyberbullying detection.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods
### Multispectral Data
#### 3.2.2 Medium-High Resolution Multispectral Data  
For our analysis, we used Landsat-8 and Sentinel-2 data.  
Landsat-8, launched in 2012, acquires images with 11 spectral bands; it has a spatial resolution ranging from 15 to 100 m and a revisit time of 16 days. In particular, we used six bands (visible, NIR and SWIR) with 30 m spatial resolution. Level-1 data were downloaded from the Earthexplorer portal of USGS [72] and used for the Arahal and Ebro case studies. For the Po and Tanaro flooded areas, no cloud-free images are available close to the flood event, and we did not use Landsat-7 images because they are affected by a no-data strip.  
For the Arahal and Po basin areas, we used the Sentinel-2 constellation composed of two satellites, S-2A and S-2B, launched in June 2015 and on March 2017, respectively. These satellites are characterized by a revisit time of 5 days in Europe and 5 to 10 days in the rest of the world, and they acquire data in 12 bands with spatial resolution ranging from 10 to 60 m. For our study, we focused on the RGB, NIR and SWIR bands (10 to 20 m spatial resolution; data were downloaded from the Sentinel scientific data hub [62]).
## 4 Case Study  
The AEMR is small but has both electromagnetic and mechanical mechanisms, so its overall structure is complex, which also determines its failure mechanism. Therefore, the storage degradation test, using multiple stresses, can best reflect the actual degradation state of the AEMR. However, conducting a multi-stress storage degradation test will increase the difficulty of the test. At the same time, when multiple degradation mechanisms are coupled, the degradation trend of the collected data may not be obvious, resulting in the loss of value of the data and even some other unexpected consequences. In fact, the environment of AEMR is relatively stable during storage. Only the temperature, as continuous stress, is affecting the storage state of AEMR and accelerating the process of physical and chemical changes [17].  
Therefore, in this section, the temperature was chosen as the accelerating stress for certain types of AEMR, and it carried out the constant stress accelerated storage degradation test. It is used to obtain degradation data to verify the validity of the storage reliability assessment method proposed in this paper. A total of 10 AEMRs, with a coil voltage of 28V, were used for the test. The specific procedure of the test is, first, to place the AEMRs with no load in the incubator, as shown in Figure 4 and set the temperature to 170 degrees Celsius for an accelerated storage degradation test. Then, take them out every 20 h, place them back to room temperature, and use the developed time parameter test system to measure them. When the measurement frequency reaches 200, they will not be measured but placed in the constant incubator until the AEMRs fail. The degradation data obtained from the test are shown in Figure 5.  
Figure 4: Temperature chamber.