esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 2 Review Methodology
### Results  
The initial search resulted in a total of 2361 publications. A total of 609 duplicates, 36 non-English articles, one abstract, and two incomplete articles were removed before the screening. Based on the abstracts and titles, 1713 articles were set aside for screening. A total of 1178 articles were excluded through title skimming and 346 were excluded through abstract skimming. Four articles were not retrieved. As a result, 185 articles were included as eligible articles, from which 26 review articles, 2 dataset articles, 1 non-bleeding articles, 4 non-human articles, 1 poster, and 4 book chapters were excluded. Through the process, 156 articles were included in the systematic review after including 9 published review articles. Figure 1 shows the methodology and results of the systematic review.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Inclusion Criteria** & **Exclusion Criteria** \\ \hline Articles published in peer-reviewed venues. & Articles that do not involve bleeding, lesion, or hemorrhage. \\ \hline Articles published from 1 January 2001 to 24 July 2023. & Articles not written in English. \\ \hline Articles must address a set of keywords: (_Bleeding OR_ & \\ _Hemorrhage OR blood) AND (Detection OR Segmentation OR_ & Exclude articles on non-humans. \\ _Recognition OR Classification) AND (Capsule Endoscopy)._ & \\ \hline Articles that describe an automatic computer-aided bleeding & Exclude posters and book chapters. \\ detection system for capsule endoscopy. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Eligibility Criteria.  
Figure 1: Flowchart of the inclusion and exclusion processes of this systematic review.
## 3 Results and Discussion
### Limitations  
In this study, the PEF treatment of intact onions were studied at electric field strengths lower than 2 kV/cm. Future research could investigate the impact of other PEF parameters such as specific energy, frequency, and number of pulses. The changes in volatile compositions in onion cultivars may affect the perception of onion flavour in culinary applications. Further work would be required to understand the relationship between volatile concentrations and flavour perception.
### Network Architecture and Requirement of Uwsn
#### 1.3.3 3D-Uwsn Architecture  
The architecture of such a type consists of clusters that are deployed at different depths of the sea. The scenario goes beyond 2D-UWSN. Here, the nodes communicate with each other within the cluster, the cluster heads communicate with each other, and finally, the cluster heads relay the message to the surface [11].
# Workload Assessment of Operators: Correlation Between NASA-TLX and Pupillary Responses
## 4 Results
### Effect of Task Repetition on NASA-TLX Scores and Pupil Size Measurements  
To assess the third hypothesis on the effect of task repetition, we first aggregated data from both the experiments. The visual representation is shown in Figure 5. In the first trial, the participants reported a NASA-TLX score of 54.4 \(\pm\) 23.6; their original mean pupil size was 3.84 \(\pm\) 0.91 mm, and the adjusted pupil size was 0.19 \(\pm\) 0.36 mm. In contrast, during the second trial, the participants reported a NASA-TLX score of 53.0 \(\pm\) 22.8; their original mean pupil size was 3.79 \(\pm\) 0.94 mm, and the adjusted pupil size was 0.09 \(\pm\) 0.31 mm. Paired t-tests were conducted to examine the differences in the NASA-TLX scores and the pupil sizes between the first and second trials. Task repetition was found to have a significant impact on the NASA-TLX (t = 2.86, \(p\) = 0.005), on the adjusted pupil size (t = 6.34, \(p\) < 0.001), and on the original mean pupil (t = 5.50, \(p\) < 0.001). Notably, the workload, and mean and adjusted pupil size were significantly lower during the second trial when the task was repeated.  
Analyzing each experiment separately, we found that in the visual tracking task, repetition led to a significant reduction in the adjusted pupil size (t = 11.10, \(p\) < 0.001) and original mean pupil (t = 11.57, \(p\) < 0.001). Due to the brief nature of the task one sessions and the retrospective design of the NASA-TLX assessment, we did not collect NASA-TLX data between the first and second trials in the visual tracking task. The NASA-TLX was conducted only once for each difficulty level session. Therefore, data on the effect of repetition on the NASA-TLX scores was only available from the laparoscopic surgery task. The laparoscopic task also showed a significant difference in the adjusted pupil size (t = 2.33, \(p\) = 0.02), but no significant difference in the original mean pupil (t = 1.01, \(p\) = 0.31) after repetition.  
Figure 4: Bar plots across two task types. Bar plots with error bars comparing different task types for: (a) NASA-TLX scores, (b) original mean pupil size, and (c) adjusted pupil size.  
Figure 5: Bar plots across task trials. Bar plots with error bars depicting the effect of task repetition on: (a) NASA-TLX scores, (b) original mean pupil size, and (c) adjusted pupil size.
## 2 Sensing Method
### Ultrasonic Sensing  
The ultrasonic sensing principle of implantable sensor devices predominantly relies on the piezoelectric effect. Commonly employed piezoelectric materials include piezoelectric ceramics and piezoelectric polymers. When ultrasonic waves impinge upon piezoelectric materials, these materials generate electric charges as a result of mechanical stress, thereby efficiently converting ultrasonic signals into electrical signals. The converted electrical power can be harnessed to supply energy to the implantable system. Furthermore, by leveraging the reflection and scattering properties of ultrasonic waves, they can be utilized for wireless data transmission. Alternatively, through the analysis of the echo characteristics of implantable devices under diverse conditions using ultrasonic waves, the measured physical quantities can be derived. This distinctive feature renders it extensively applicable in the realm of biological signal detection. Implantable ultrasonic sensors possess distinctive advantages in medical monitoring. These include high sensitivity and non-contact measurement capabilities, enabling them to monitor in real-time the alterations in internal tissues [79]. Nevertheless, they are not without limitations. For instance, temperature fluctuations can undermine the measurement accuracy. Additionally, relatively large-sized ultrasonic transmitting and receiving devices are necessary, which poses challenges to the miniaturization of the system. Furthermore, the processing and reconstruction of ultrasonic signals remain highly challenging tasks.
###### Abstract
Comparing these approaches helps assess whether open-source models, despite their potentially lower out-of-the-box performance, can meet the dual requirements of compliance and efficiency for GDPR-specific tasks. This study uses the generation of data categories for processing activity records by LLMs as a test case to evaluate their suitability for supporting GDPR compliance documentation. Despite general limitations with respect to law-specific language and real-time updates, the creation of data categories seems to be feasible as it relies on core GDPR definitions rather than evolving legal terms. In this study, we explore partial automation of drafting records of processing activities. If the quality of the generated data categories proves to be sufficient, this approach could be extended to automate the generation of other required attributes in these records. This study evaluates whether open-source models, when combining them with context-specific techniques, can match or exceed the performance of GPT-4 while ensuring GDPR compliance. We develop experiments to assess the response quality of LLMs in generating  
Figure 1: Report of a processing activity as required by the GDPR. data categories. These experiments compare the out-of-the-box performance of the models with results we obtain using context-augmenting techniques. These techniques include few-shot prompting [5] and Retrieval Augmented Generation (RAG) [6], which augment the models with closed-domain data to improve factual accuracy. Furthermore, we test the understanding of the task with two benchmarks. The generation benchmark uses five different prompts asking the LLM to generate data categories for a given processing activity, while the multiple choice benchmark asks the LLM to select the correct data categories for a given processing activity. In particular, this research seeks to answer the following research questions:  
* **Research Question (RQ1)**: Can open-source LLMs generate data categories for GDPR compliance documentation as effectively as GPT-4? * **Research Question (RQ2)**: To what extent do context-augmenting techniques such as few-shot prompting and Retrieval Augmented Generation (RAG) enhance LLM performance in drafting documents for GDPR compliance? This study systematically compares open-source and closed-source LLMs on a GDPR-specific task and explores the impact of advanced prompting and context-augmenting techniques. It aims to establish a framework for applying LLMs in regulated, text-intensive domains such as GDPR compliance. The remainder of this paper is structured as follows: Section 2 reviews related work on LLMs in legal tasks and GDPR compliance. Section 3 details the methodology, including model selection, use case, and experimental setup. Section 4 presents the experimental results, followed by a discussion in Section 5. Finally, Section 6 concludes the study with key findings and implications.
## 3 Results and Discussion
### Satellite Images of Yellow Dust Transport
\({\rm b}_{\rm i}\) (i = 1 to 3) are intercepts in the equations of \(Y_{\rm i}\) as the error term, respectively. The final multiple regression equations are given in Figure 4:0  
Table 1 indicates that multiple correlation coefficients are present among PMs, and meteorological parameters (air temperature, T; wind speed, W; and relative humidity, RH) of Gangneung city (Korea) are associated with the PMs of Beijing city. Additionally, predictive regression equations of each PM concentration at Gangneung city before, during after the dust periods and all periods are shown. Multivariate statistical modeling for evaluating the predicted values of the PMs of Gangneung was performed in four classifications of period via multiple regression analyses, using IBM SPSS Statistics-25. Multiple correlation coefficients among PM\({}_{10}\)(G) PM\({}_{2.5}\)(G) and PM\({}_{1}\)(G) were in the range of 0.916 to 0.998. In particular, the correlation coefficients of PM\({}_{10}\)(G), PM\({}_{2.5}\)(G) and PM\({}_{1}\)(G) before, during and after the dust periods were 0.983 to 0.998, 0.916 to 0.998 and 0.941 to 0.998, respectively. However, for all periods, those coefficients were 0.954 to 0.998, showing perfect correlations. Except for the slightly lower coefficients (0.916, 0.941) of PM\({}_{10}\) during and after the dust periods, the correlation coefficient of any PM concentration was in the range of 0.954 to 0.998. Correlation analysis showed that PM\({}_{10}\)(G) PM\({}_{2.5}\)(G), PM\({}_{1}\)(G), T(G), W(G), RH(G), PM\({}_{10}\)(B), PM\({}_{2.5}\)(B) were significantly correlated at \(p<0.001\), though the correlation coefficients were different. Regardless of non-dust and dust periods, in particular, the correlation coefficients of both PM\({}_{2.5}\) and PM\({}_{1}\) were in the range of 0.997 to 0.998, showing perfect correlations.
## 2 Calculation Methods
### Solar Position Algorithms  
The complexity of the algorithms is an especially significant issue since the operations required by the algorithms must be performed by control systems. A reduced complexity allows the algorithms to be employed on simple and low-cost control systems. The complexity of an algorithm could be measured by counting the required number of products and divisions, and ignoring sums and subtractions (considered as negligible with respect to multiplications and divisions). [PERSON] has tested the calculation time, completed on a Sony Vaio laptop equipped with an Intel Core-Duo P8700 processor with 2.53 GHz clock, using the GNU C++compiler. He find out that sums and products are almost indistinguishable from the point of view of computational time; divisions and square roots are roughly 10 times slower; trigonometric functions, direct and inverse, have a computational time among 25 and 50 times a sum [9].  
We can find several SPAs with different levels of accuracy and complexity in the solar engineering literature. This paper evaluates several SPA algorithms. Among them, the SPA proposed by NREL is the one with the highest accuracy. Its accuracy is about two orders of magnitude lower than other algorithms, but its computation cost is also very high, more than 10,000. In this paper, we assume that the sun position calculated by SPA proposed by NREL is the real sun position, which is used to evaluate the error of the result calculated by other algorithms.  
Generally, SPA requires time and geographical location to calculate sun position. The simplest method is to first calculate the solar declination and hour angle by the empirical formula, and then calculate the sun's azimuth angle and altitude angle through the following formula:  
\[\sin z=\sin l\sin\delta+\cos l\cos\delta\cos\omega \tag{6}\]  
\[\gamma=\textit{Arctan2}(\frac{\sin\omega}{\cos\omega\sin l-\tan\delta\cos l}) \tag{7}\]  
where \(\delta\) is solar declination angle, \(\omega\) is hour angle, \(l\) is latitude.
# Application of Deep Learning to Construct Breast Cancer Diagnosis Model
## 1 Introduction  
According to the 2020 statistics globally sourced by the World Health Organization, countries have reported that breast cancer is considered a dominant disease ranked the highest cause of death in women [1]. Taking Taiwan as an example, according to the Ministry of health's 2019 death statistics of women, the leading cause of death among all diseases such as diabetes, chronic respiratory disease, hypertension, chronic liver diseases, etc., breast cancer mortality rate was ranked fourth. Its mortality rate significantly increased from 12.8% (1439) in 2006 to 22.2% (2633) in 2019 [2]. Compared to the United States of America (USA), the USA projected 41,760 deaths in 2019 at a rate of 19.9 per 100,000 women per year, and more than 3.8 million have a history of breast cancer [3, 4].  
Despite the presence of an upsurge in the annual mortality rate, early detection of breast cancer increases the survival rate of patients if the appropriate treatment is providedto avoid the need for a surgical procedure. [PERSON] and [PERSON] (2016) [5] mentioned that physicians and healthcare workers may make more informed decisions regarding a patient's condition if breast cancer survivability can be accurately predicted. In the last decade, numerous data mining tools have been used to determine the factors affecting the survival of patients with breast cancer [6; 7; 8; 9; 10; 11; 12]. Due to the advancement of technology, many machine learning tools were used to predict and diagnose patients with breast cancer [13]. These data mining tools have assisted doctors in making accurate diagnoses. However, one of the most used machine learning methods for detecting or diagnosing diseases are classifiers [14]. It was noted by [15; 16; 17] that ANN and SVM are among the most commonly used supervised learning methods in the medical field for breast cancer diagnosis. Despite, the fact that multiple classifiers were used to deal with medical-related classification problems. [PERSON] (2012) [18] suggested that when the training samples are imbalanced, balanced learning with an optimized decision is employed to improve the performance of both ANN and SVM. Although, some previous works have highlighted significant gains in the area of deep learning to identify lesion in breast cancer [19]. Despite the small number of images in this study, we developed a model architecture by exploiting ANN, SVM, AlexNet [20], ResNet 101 [21], and InceptionV3 [22] networks to improve the diagnostic accuracy and reduce the misdiagnoses rate of patients with breast cancer.
## 2 Current Status of Development of Geophysical Exploration Techniques
### Exploration Techniques Based on Apparent Resistivity Parameters  
Most earth and rock dams are binary structures with an impermeable clay cover and permeable sand, gravel, and fine sand layers. The main potential problem in dams is seepage [9]. Based on the physical characteristics of embankment hazards, the commonly used hazard detection methods are electrical-based exploration and electromagnetism-based exploration [10]. Electrical-based exploration is mainly based on the difference in the electrical conductivity between the embankment hazard and the surrounding media and the study of the resistivity change caused by the hazard [11]. It provides information on the nature, yield, and location of hidden hazards by analyzing the structural characteristics of the dike  
Figure 1: Deformation and damage around the corridor at the base of a clay core wall dam.  
and the geological data of the area where it is located. Therefore, the factors affecting the electrical conductivity of the dike soil are particularly important [12]. For these factors, [PERSON] et al. [13] conducted tests on the temporal variation in the resistivity in saturated and unsaturated areas. The results showed a response to the natural variability characteristics and processes on the subsurface, and the range of resistivity variation in different areas was determined. Through indoor geotechnical tests, [PERSON] et al. [14] concluded that the electrical conductivity of powdered clay in an embankment is exponentially correlated with the water content and linearly and positively correlated with the compactness. The dielectric properties of powdered clay were also linearly and positively correlated with the water content and compactness. The above research provides a basis for the development of electrical-based geophysical exploration technologies.
## Materials and Methods
### Quantification of Terpenoid Production in Engineered Strains  
Engineered strains were pre-cultured in 5 mL aliquots in a yeast minimal medium SC-Ura (1X yeast nitrogen base, 2% glucose, complete supplement mixture (CSM) without uracil) overnight and used to inoculate 10 mL fresh SC-Ura-mixed carbon source medium (1X yeast nitrogen base, 1.8% galactose, 0.2% glucose, complete supplement mixture (CSM) without uracil) in 50-mL Falcon tubes to achieve an initial OD\({}_{600}\) of 0.05. Yeast cultures were overlaid with 2 mL dodecane to reduce evaporation of the terpenoid compounds. The cultures were grown at 30 \({}^{\circ}\)C and 250 rpm in an orbital shaking incubator. Samples were taken at 72 h to determine the production of terpenoid compounds. The identification of terpenoid compounds dissolved in the dodecane layer was determined using Gas Chromatography-Mass Spectrometry (GC-MS, Agilent 7000 GC/MS Triple Quad, Agilent, Santa Clara, CA, USA). Briefly, 500 \(\mu\)L of dodecane from the cultures was analyzed on GC-MS using an HP-5 MS 5% Phenyl Methyl Silox column. Samples were run using a previously described method [20] with some differences. The GC program was as follows: an initial temperature of 140 \({}^{\circ}\)C, followed by ramping to 180 \({}^{\circ}\)C at a rate of 2 \({}^{\circ}\)C/min, then ramping to 300 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min, where the temperature was held for 5 min. The quantification of terpenoid compounds dissolved in the dodecane layer was determined using GC with flame-ionization detection (GC-FID, Shimadzu 2014 AFSC, Shimadzu, Kyoto, Japan) using a Restek Rxi-5 sil MS column with the same GC program as above. The external calibration curves of the standards \(\alpha\)-humulene and \(\beta\)-eudesmol were plotted to quantify the terpenoid components. One mg/mL stock solution of each standard was accurately prepared. Each stock solution was serially diluted by factors of 10, 50, 250, and 1250. The resulting solutions were detected by GC-FID to construct external calibration curves.
# Deep-Learning-Based Models for Pain Recognition: A Systematic Review
## 3 Review Papers
### Single-Model-Based Pain Recognition
#### 3.1.2 Speech Analysis  
Another study [5] recognized the pain level based on speech analysis and the use of long short-term memory (LSTM) NNs. First, they employed an unsupervised learning NN to extract the vocal features using the Chinese corpus. Then, they fine-tuned it based on NNs and an emergency triage database to output the specific acoustic representation at the sentence-level for each patient. Finally, they performed a pain-level classification to two or three classes using the SVM. As a result, their methods achieved weighted average recall (WAR) values of 72.3% in binary-class and 54.2% in three-class pain-intensity classification tasks. To the best of our knowledge, this work is the first study that used the speech only to detect the pain level.
# Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network
## 3 Methodology
### Tabu Search (TS) Algorithm  
A local heuristic search that can discover the solution space ahead of optimality is guided through the metaheuristic TS. A TS strategy is employed that goes beyond local search adapting N(\(x\)) as the search develops, by effective replacement through another neighborhood N*(\(x\)). The move is the operation through which a local or neighborhood search process is guided to attain the solution \(x^{\prime}\in\) N(\(x\)) from the solution \(x\in\) X iteratively and is associated with the neighborhood of solution \(x\). TS moves towards its closest neighbor at every iteration, even if the objection function deteriorates. This is different from the classical hill-climbing process as it is only permitted to move to the neighbor solution with improvement objective function [29; 30].  
Special memory structures are used, which is the prime feature of the TS approach that serves in determining N*(\(x\)), and it is organized in the manner where exploration of space is done. To avoid cycling, certain moves are prohibited by TS from being re-instantiated for a specific period by utilizing a special memory structure that is short-termed called recency-based memory implemented through the Tabu list. By assigning Tabu-active designation, recency-based memory is exploited to choose attributes that take place in Tabu-active designation. Tabu-tenure is the iteration span to which an attributed is maintained as Tabu. Additionally, aspiration criteria are used by TS as a device in overriding the Tabu status of a move, thus offering a flexible performance. TS protocol's pseudocode is shown below in Algorithm 1 [29; 30].  
```
1. Start
2. Select An initial \(x\in X\)
3. Let \(x^{*}:=x.\{x^{*}\text{denote the best soultion currently founded}\}\)
4. Set the iteration number \(k=0\).
5. \(T=\phi\). {\(T\) is Tabu List}
6. If \(S(x)-T\) is empty
7. go to step \(16\).
8. Else
9. \(k=k+1\)
10. Select \(x_{k}\in S(x)-T\) such that \(s_{k}(x)\in Optimum(s(x):s\in S(x)-T)\)
11. End it
12. \(x:=s_{k}(x)\)
13. If \(C(x)<C(x^{*})\)
14. \(x^{*}:=x\)
15. End if
16. If a chosen number of iterations has elapsed either in total or sin \(c\). \(x*\) was last improved, or if \(S(x)-T=\phi\)
17. Upon reaching this step directly from step \(6\).
18. Else
19. Update \(T\) (as subsequently identified)
20. Return to step \(6\).
21. End if.
22. End.
```  
**Algorithm 1** TS protocol's pseudocode