esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Evaluations and Results
### Performance Comparison with Existing Methods
These statistics show that the proposed method performs the best, followed by the Rajaraman method, which achieves a good detection rate. The results presented in Table 4 indicate that the proposed deep malaria detector is an efficient and reliable tool to test microscopic blood smears for _Plasmodium_ parasite infection. The proposed method exploits the bilateral filtering to suppress the noise in the images and provide better quality images for model training. Moreover, the data augmentation techniques allow variations in the dataset that helps in improving the performance and also generalizes the model to avoid over-fitting. It is also worth noting that the computational cost of the proposed method is reduced by using a \(3\times 3\) convolution filter for all convolutional layers, which eventually reduces the number of input channels. The deep learning models discussed in Table 4 have at least 16 layers and a large number of training parameters. Contrastingly, the proposed CNN architecture has only 8 layers and requires fewer training parameters compared to other deep learning models.
## 5 Results and Discussion
### System Implementation Details
#### 5.4.2 Implementation and Configuration  
The system's implementation, configuration, and development are illustrated in the following sections.
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 4 System Model: Proposed Framework
### Scenario 1 (Fully Distributed)  
In this scenario, training the MLP model and the detection apply at the edge layer. The framework initialises to work using an offline MLP model that is already pre-trained using historical data. The model keeps updated and trained at the edge using the real-time data that come from the sensors. The edge servers here conduct all functions train the model and use the model for detection, as shown in Figure 5.
# Characteristics of 2.45 GHz Surface-Wave-Sustained Argon Discharge for Bio-Medical Applications
## 4 Discussion  
The results obtained show that the plasma characteristics of the surface-wave-sustained argon plasma torch operating at 2.45 GHz can be easily optimized to meet the requirements for bio-medical applications. For such applications, the operation regime is at a low wave power. With increasing the wave power, the length of the plasma torch increases. It is usually assumed for SWD that the increase in the wave power leads to the addition of a new part of the plasma column close to the wave exciter but without changing the plasma properties already produced by the lower power part near the plasma column end. This is correct for SWD at low pressure, but this investigation shows that at atmospheric pressure, the temperature at the same distance from the column end increases with the wave power. In order to keep the temperature below 45 \({}^{\circ}\)C, it is necessary to work at a low wave power (to about 30 W) regime of operation. It is good to keep the wave power as low as possible in order to avoid thermal damage to the sample.  
Another important parameter than can be varied for decreasing the temperature is the discharge tube size. The temperature is lower when the inner diameter of the tube is bigger. For our tubes with an outer diameter of 8 mm, the temperatures lower than 45 \({}^{\circ}\)C are obtained at inner diameters of 4 mm and 3 mm at the wave power that can be increased up to 30 W. The temperature at the same position of the plasma in the tube with an inner diameter of 2 mm can be below 45 \({}^{\circ}\)C only at a wave power lower than 10 W.  
The argon flow rate is another parameter that can be varied for decreasing the temperature. With increasing the gas flow rate, the temperature decreases. However, this decrease is not as significant as expected. Increasing the argon flow rate from 2 L/min to 15 L/min leads to a decrease in the temperature of about 10 \({}^{\circ}\)C at high wave power and even less at low wave power. Additional problems are the high gas consumption, affecting the soft surfaces during the treatment by the high gas flow. That is why the optimal operational regime is at a gas flow rate from 5 L/min to 8 L/min at the given discharge tubes of 8 mm/4 mm and 8 mm/3 mm.  
Figure 11: Microwave radiation distribution around the sample position at wave frequency 2.45 GHz, wave power 9 W, reflected power less than 1 W, discharge tube 8/3, argon flow 7 L/min: (**a**) side view; (**b**) top view.  
The measurements show that problem with microwave radiation on the treated sample is not significant under the low wave power operational conditions. At the sample position, the microwave 2.45 GHz radiation does not exceed 0.4 mW. The radiation up to 4 mW that appears at the connection of the coaxial cable with the surfatron antenna can be easily shielded without disturbing the sample treatment.
# Facilitating the Integrative Use of Security Knowledge Bases within a Modelling Environment
## 1 Introduction
report the integration of a subset of CAPEC attack patterns into a tool that remains unavailable [22]. [PERSON] et al. report that none of the existing tools cover the full range of ATT&CK techniques [20]. Furthermore, their own effort to extract information from ATT&CK was performed manually, and the authors explicitly suggest that future improvement can be in automating the extraction process. Interestingly, [PERSON] et al. report extracting 266 ATT&CK Enterprise techniques without mentioning the MITRE ATT&CK repository version from which these were derived. We were able to trace this number of ATT&CK techniques to ATT&CK v6.3, which was the version of the ATT&CK repository prior to [PERSON] et al. 's article submission ([[https://attack.mitre.org/versions/v6/techniques/enterprise/](https://attack.mitre.org/versions/v6/techniques/enterprise/)]([https://attack.mitre.org/versions/v6/techniques/enterprise/](https://attack.mitre.org/versions/v6/techniques/enterprise/)), accessed: 7 March 2024). At the time of their article submission, however, ATT&CK v7.2 had already been published, with complete reorganisation of the techniques into 156 main techniques and 272 sub-techniques ([[https://attack.mitre.org/versions/v7/techniques/enterprise/](https://attack.mitre.org/versions/v7/techniques/enterprise/)]([https://attack.mitre.org/versions/v7/techniques/enterprise/](https://attack.mitre.org/versions/v7/techniques/enterprise/)), accessed: 7 March 2024), and as of today (v14.1), there are 201 main techniques and 424 sub-techniques. The significant increase in techniques throughout the evolution of ATT&CK attests to the dynamic nature of the security body of knowledge and the need to accommodate it. [PERSON] et al. explicitly mention the need to further integrate additional information sources--including CAPEC--as well as the need to continuously update their language based on the expanding body of knowledge. [PERSON] et al. conclude their recent research article by explicitly acknowledging the need for a tool that \"_...can gather data from diverse taxonomies and link it..._\" [11]. To our knowledge, there is no security modelling environment that allows for the use of existing knowledge bases (e.g., CAPEC and NIST SP800-53) in an integrated form, let alone in an open manner that allows for their update (as the knowledge bases continuously evolve). In this work, we deliver security experts and researchers a security modelling environment that integrates information from existing knowledge bases. This supports the disciplined design and analysis of systems' security postures. Also, we provide for building structured security policies and guidance, integrating the knowledge bases and creating new insights.
# Biotin Auxotrophy and Biotin Enhanced Germ Tube Formation in _Candida albicans_
## 2 Materials and Methods  
### C. albicans Strains and Growth Conditions  
_C. albicans_ strain SC5314 was obtained from the American type culture collection, Rockville, MD and strain A72 was obtained from Prof. [PERSON] Sullivan, then at Otago Univ., Dunedin, New Zealand. The HLC52 (_efg1/efg1_), JCK19 (_cph1/cph1_), and HLC54 (_efg1 cph1/efg1 cph1_) mutants were from Prof. [PERSON] and CR216 (_cdc35/cdc35_) was from Prof. [PERSON] (Table 1). Cells were grown overnight in 250 mL flasks with 50 mL of either Glucose-Salts-Biotin (GSB) or Yeast Extract-Peptone-Devtrose (YPD) media at 30 \({}^{\circ}\)C with aeration and were harvested by centrifugation at 5000 rpm for 5 min and washed three times with 50 mL of wash buffer (3.5 g KH\({}_{2}\)PO\({}_{4}\), 4.1 g K\({}_{2}\)HPO\({}_{4}\) in 1 L H\({}_{2}\)O, pH 6.5); samples were stored at 4 \({}^{\circ}\)C overnight for further use [6]. YPD consists of 10 g of yeast extract, 20 g of peptone, and 20 g of glucose per L. GSB is a minimal defined glucose-salts-biotin medium [6] and GPP is the defined glucose-phosphate-proline medium of [PERSON] and [PERSON] [19]. We also used the defined glucose-ammonium-salts medium of [PERSON] [20].  
Cells were prepared for growth experiments by diluting the overnight culture in 50 mL of YPD, GPP, or GSB to an OD\({}_{600}\) of 0.1 (1 \(\times\) 10\({}^{6}\) cells per mL). All OD\({}_{600}\) values were recorded on a Spectra Max Plus microplate reader (Molecular Devices, Sunnyvale, CA, USA). Cells were tested in vitro with 2.5 mM \(N\)-acetylglucosamine at 37 \({}^{\circ}\)C [6] to be sure that their germ tube forming ability was close to 100%.  
Figure 1: Diagram of proposed biotin biosynthesis pathway in _Candida albicans_ based on a model available at the Candida Genome Database [18] and mitochondrial targeting predictions for _BIO32_, _BIO3_, _BIO4_, _BIO2_, and _BPL1_. This model assumes that biotin biosynthesis occurs in the cytoplasm except for the conversion of desthiobotin to biotin by _BIO2_, which occurs in the mitochondrion. Biotin, desthiobotin, and KAPA (8-amino-7-oxononanoate) could be taken up by two different transporters, which are _VHT1_ and _HNM3/BIO5_, respectively. Note that the mitochondrial transporters for biotin uptake, desthiobotin uptake, and biotin secretion may be different manifestations of the same transport system. This mitochondrion model is based on different roles of biotin and desthiobotin in growth and filamentation.
## 4 Discussion  
### Determination of Punching Diameter  
The TMA Grand-Master (3 DHISTECH Ltd., Budapest, Hungary) offers a choice of four different punching-diameters (0.6, 1.0, 1.5, 2.0 mm), which can be selected according to the aim of the study and the targeted tissue. Since it was unclear how accurate the ngTMA(r) method operates, the biggest diameter was used for abdominal aorta, femoral artery, nerve and vein bundle (A&V) and epithelium of the tongue. Homogeneous tissues such as the renal medulla, were punched with a 1.5 mm diameter, except for the carotid artery. Skin tissue was punched with different punching diameters of 2, 1.5 and 1 mm. (Table 1). For reasons of tissue preservation to enable further studies, for each tissue type individual blocks were performed.  
Independently of how the TMA design is ordered, it is essential that no symmetry exists [7]. If, by accident, a paraffin section lands upside down in the water bath and is, therefore, subsequently placed upside down on the microscope slide, identification and evaluation of each sample is not possible. A lack of symmetry was achieved in this study by omitting the first two punching positions on the TMA grid and subsequently inserting two positions with testicular control tissues. When using a distance of 0.7 mm between the punching cores, it is possible to insert up to 112 cores with a diameter of 1.5 mm and 66 cores with a diameter of 2 mm also according to the manufacturer's specifications. This grid design was chosen to achieve a high number of punches that can be inserted into a single recipient block without compromising the sectioning process by tight margins [10; 18; 31; 32; 33].
# Development of Potato Mass Estimation System Based on Deep Learning
## 3 Results and Discussion
### Mass Estimation Equation
#### 3.2.2 Volume Estimation Equation  
As a result of carrying out a linear regression analysis using the major axis length and the minor axis length as independent variables and the volume as a dependent variable, the root mean square errors (RMSEs) of Superior, Atlantic, and Jopung were found to be 22.11 cm\({}^{3}\), 17.15 cm\({}^{3}\), and 21.66 cm\({}^{3}\), respectively, and the determination coefficient (R\({}^{2}\)) values were found to be 0.88, 0.90, and 0.92, respectively. As a result of carrying out a regression analysis using the data obtained by summing up the values of the three varieties, RMSE was found to be 21.50 cm\({}^{3}\), whereas the determination coefficient was 0.90 (Table 3). Accordingly, since the major axis length and the minor axis length showed a high correlation in all the varieties examined (Figure 11), it was judged that the potato  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variety** & **Position** & \begin{tabular}{c} **Major Axis** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Minor Axis** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Volume** \\ **(cm\({}^{3}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Mass** \\ **(g)** \\ \end{tabular} &
\begin{tabular}{c} **Density** \\ **(kg m\({}^{-3}\))** \\ \end{tabular} \\ \hline Superior & Top & 81.6 \(\pm\) 12.9 & 71.2 \(\pm\) 11.6 & 154.5 \(\pm\) 63.9 & 164.3 \(\pm\) 60.4 & 1085.3 \(\pm\) 81.7 \\ Atlantic & Side & 82.2 \(\pm\) 13.1 & 58.9 \(\pm\) 9.1 & 142.6 \(\pm\) 56.3 & 136.3 \(\pm\) 61.5 & 1080.7 \(\pm\) 68.7 \\ Jopung & Top & 87.1 \(\pm\) 20.9 & 70.6 \(\pm\) 13.4 & 157.9 \(\pm\) 80.4 & 166.1 \(\pm\) 86.9 & 1043.9 \(\pm\) 72.5 \\ Side & 88.7 \(\pm\) 22.1 & 55.9 \(\pm\) 9.5 & 157.9 \(\pm\) 80.4 & 166.1 \(\pm\) 86.9 & 1043.9 \(\pm\) 72.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical properties of different potato varieties.  
Figure 10: Operation screen of the potato mass estimation system.  
volume could be estimated using the major axis and minor axis lengths. Further, since the regression model created by mixing all three varieties also showed a high correlation, such as the regression model by variety, it was judged that a result similar to the case of using a regression model of each variety could be obtained.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 3 How Technologies Can Fix the Current Healthcare System
### Big Data for Healthcare  
In the last few years, we are increasingly living in a digital world where devices like smartphones, EHRs, biomedical and wearable sensors produce a large volume of health data. Such data can be referred to as \"big data\" due to its high-velocity and wide variety; this holds great promise for evidence-based human developmental efforts [21]. The digitization of human beings allows the remote and continuous monitoring of each heartbeat, moment-to-moment blood pressure, oxygen concentration in blood, body temperature, glucose, human activities and emotions. This big health data and behaviour information can be used for analytics to gain deep insights into the various aspects of human life. For example, when the information about human activities, their geographical location, shopping habits, travel patterns and social circle is combined with health information (such as health records and genetic information), latent personalized and population-based health patterns can be discovered. Big data provide the necessary support for developing a patient-centric personalized healthcare system in which the right health intervention for a given person and the health problem can be identified in an evidence-based manner. While big data have a disruptive potential to herald the much-awaited dawn of personalized medicine, there are substantial technical, legal and socio-economic challenges that remain to be addressed.  
Rapid strides of big data analytics have been taken to disrupt almost every other sector in the global economy, but the healthcare industry has been relatively slow in its adoption of big data analytics [22]. This is largely due to the fact that the problems of the healthcare industry are complex and interconnected and have to be addressed by a myriad of stakeholders in academia, industry, public organizations, healthcare industry and government [23]. Perhaps the most pressing challenge is the lack of patients' trusts to share their personal data, as patients are wary of the abuse of their personal data when they are not in control of their health information. To overcome this barrier, patients must be put in control over their personal information by providing them full visibility and control in a big data ecosystem (see Figure 2). The new strategies must be formed to stimulate active,legal and performance-based models. This will encourage open information sharing and a vast variety of opportunities for the healthcare system.
## 3 Results and Discussion
### Monitoring Results
Figure 12: Elapsed time–displacement relationship depending on the position of the wall facing (monitoring for 4 months): (**a**) total station value [STA.80]; (**b**) total station value [STA.85]; (**c**) digital camera value [STA.80]; (**d**) digital camera value [STA.85]; (**e**) cellphone camera value [STA.80]; (**f**) cellphone camera value [STA.85]. Figure 13 shows the evaluation results of the digital image photogrammetry error based on each commercial imaging device against the traditional monitoring method using the measurement results. The results of digital camera-based digital image photogrammetry show that the measurement values for low-level displacements were less different from those of the traditional monitoring method. However, the errors between the two methods were shown to be large when displacements were large. In addition, the measurement errors of cellphone camera-based digital image photogrammetry were evaluated to be at a level that was not meaningful for analysis compared to the measurement results of other methods; thus, they were excluded from discussion in this study.
# Lightweight, Secure, Similar-Document Retrieval over Encrypted Data
## 4 Scheme Overview  
### Problem Definition  
Consider a data owner \(Bob\) to have a private document collection \(D=\{d_{1},d_{2},\ldots,d_{m}\}\) of size \(m\). \([PERSON]\) outsources the storage and computing of his collection to a remote server \(S\), such as a cloud server, to enjoy high-quality services at a reasonable and efficient cost. However, the server \(S\) is not trusted to see the contents of the stored collection, and as such, \([PERSON]\) has to encrypt his collection before outsourcing it.  
To enable efficient retrieval, \([PERSON]\) also builds a secure inverted index from the private document collection and uploads it together with the documents to the non-secure server \(S\), such as a cloud server. The smart device user, _[PERSON]_, has a document \(Q\) which she wants to test for similarity against all the \(m\) documents that \([PERSON]\) has stored in \(S\) without either revealing \(Q\) to \(S\) or revealing \(D\) to _[PERSON]_. In order to achieve this, _[PERSON]_ first extracts the fingerprint of her document, encrypts it, and then sends the result to the server \(S\).  
Upon receiving the secure fingerprint of the document \(Q\) from _[PERSON]_, the server \(S\) matches the provided fingerprint against its secure index and then responds to _[PERSON]_ by the scores which represent the matching score for all the stored \(m\) documents. Finally, _[PERSON]_ downloads the top-\(h\) documents. Figure 1 shows the structure of the proposed work.
## 5 Proposed Algorithm
### Novel Update Mechanism  
The second step in developing machine learning algorithms is to create an update mechanism to update solutions [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42]. The stepwise update function is a unique update mechanism of SSO [17; 18; 35; 36; 37; 38; 39; 40; 41; 42]. In the single-objective problem, there is only one _gBest_ for the traditional SSO. However, in the multi-objective problem, the number of temporary nondominated solutions is not always only one, and some temporary nondominated solutions may not be temporary nondominated solutions in the next generation [17]. Note that a nondominated solution is not dominated by any other solution, while a temporary nondominated solution is a solution nondominated by other found solutions that are temporary in the current generation, and it may be dominated by other updated solutions later [13; 17; 19].  
Hence, in the proposed tSSO, the role of _gBest_ is removed completely and the _pBest_ used in the tSSO is not the original definition in the SSO. The _pBest_ for each solution in the proposed tSSO is selected from temporary nondominated solutions, i.e., there is no need to follow its previous best predecessor. The stepwise update function used in the proposed tSSO is listed below for multi-objective problems for each solution \(X_{i}\), with \(i\) = 1, 2,..., N\({}_{\text{sol}}\):  
\[x_{i,j}=\begin{cases}x_{i}^{*}&\text{if }\rho_{[0,1]}\in[0,C_{p})\\ x_{i,j}&\text{if }\rho_{[0,1]}\in[C_{p},C_{w})\\ x&\text{otherwise}\end{cases}, \tag{8}\]  
where \(X^{*}=(x_{1}^{*},x_{2}^{*},\ldots,x_{k}^{*})\) is one of the temporary nondominated solutions selected randomly, \(\rho_{[0,1]}\) is a random number generated uniformly in [0, 1], and \(x\) is a random integer generated from 0, 1, 2,..., N\({}_{\text{var}}\).  
For example, let \(X_{6}\) = (1, 2, 3, 2, 4) and \(X^{*}\) = (2, 1, 4, 3, 3) be a temporary nondominated solution selected randomly. Let \(C_{p}\) = 0.50, \(C_{w}\) = 0.95, and \(\rho\) = (\(\rho_{1}\), \(\rho_{2}\), \(\rho_{3}\), \(\rho_{4}\), \(\rho_{5}\)) = (0.32, 0.75, 0.47, 0.99, 0.23). The procedure to update \(X_{6}\) based on the stepwise function provided in Equation (8) is demonstrated in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **1** & **2** & **3** & **4** & **5** \\ \hline \(X_{5}\) & 1 & **2** & 3 & 2 & 4 \\ \(X^{*}\) & **2** & 1 & **4** & 3 & **3** \\ \hline \(\rho\) & 0.32 & 0.75 & 0.47 & 0.99 & 0.23 \\ \hline New \(X_{5}\) & 2 & 2 & 4 & 4* & 3 \\ \hline \hline \end{tabular}  
* * indicates that the corresponding feasible value is generated randomly.  
\end{table}
Table 1: Example of the update process in the proposed tSSO.  
From the above example, the simplicity, convenience, and efficiency of the SSO can also be found in the update mechanism of the proposed tSSO.