esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Determination of Total Viable Count  
The total viable count, also known as the standard plate count or the aerobic plate count, was investigated to determine the microbiological quality (in terms of number of growing/viable bacterial cells) of the samples withdrawn from the biodgester [31]. In this study, the spread plate method was employed to evaluate the number of viable bacterial cells of the samples collected from the biodgester that were increasingly serially diluted. This was performed as per the procedure reported by [PERSON] [32]. Selective microbiological media were used to restrict the growth of nontarget bacteria, including _Salmonella_/_Shigella_ agar (SSA), _E. coli_ Chromogenic agar, Blood-free _Campylobacter_ agar base (CCDA), _Listeria_-selective agar base (LSAB), and _Yersinia_-selective agar base (YSA) for the growth of strains of _Salmonella_, _E. coli_, _Campylobacter_, _Listeria monocytogenes_, and _Yersinia enterocolitica_, respectively. For the growth of the last three bacteria, the microbiological media were made selective by introducing CCDA supplements, Oxford _Listeria_-selective supplements, and _Yersinia_ (C.IN) supplements, respectively. All the microbiological media were purchased from CONDA, Madrid, Spain and the selective supplements were from Oxoid, UK. Into a sterile Falcon tube, 10 mL of the slurry was extracted from the digester, which was 10-fold serially diluted in bacteria-free normal physiological saline as previously described. Concisely, 1 g of the sample was dissolved in 9 mL of 0.9% physiological saline contained in sterile test tubes.  
Each medium plate was inoculated with 100 uL of each serially diluted sample in triplicates. The inoculated plates were incubated at different temperatures and time intervals according to the specific medium and the bacterial pathogen. In relation to the growth of _Salmonella_ and _Escherichia coli_, the inoculated plates were incubated at 37 \({}^{\circ}\)C for a period between 18 and 24 h and for 24-48 h for the growth and isolation of _Listeria_. Colonies with dark blue or crystal violet on the _E. coli_ Chromogenic agar were counted as _E. coli_[33], pink colonies with a black centre produced on SSA were considered as _Salmonella_ sp. [34], and grey colonies surrounded by black halos (esculin hydrolysis) were enumerated as _L. monocytogenes_[35]. In addition, incubation was performed at 30 \({}^{\circ}\)C for 24-48 h for the growth and isolation of _Yersinia_, while _Campylobacter_ species were grown at 42 \({}^{\circ}\)C under microaerophilic conditions (CampyGenTM, Oxoid, Basingstoke, Hants) in an anaerobic jar for 24-48 h. Colourless colonies with a red centre or which appeared with red bull's eyes were counted as _Y. enterocoltica_[36], while tiny, smooth, convex, and colourless translucent-to-grey-coloured colonies were quantified as _Campylobacter_ species [37].  
After incubation, the mean of the developed colonies obtained from the triplicate inoculation was determined and recorded on respective tables as colony-forming units per gram in 1 mL of initial material.
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
###### Abstract  
Machine learning (ML) and artificial intelligence (AI) are widely applied in many disciplines including medicine. Pattern recognition or automatization has been successfully implemented in various field studies. Similarly, multiple efforts have been made in medicine to implement AI/ML technology to solve medical problems, for example, for automating osteoporosis detection. In general, the success of AI/ML technology is highly dependent on the amount of available data, especially during the training stage. Feature generation is a common technique that allows the manipulation of available data for the training stages. This paper aims to study the feasibility of adopting signal-processing techniques for feature generation in medical image processing. Signal attributes from signal processing workflow were adopted and applied to image processing of CT and DEXA scanning data to differentiate between normal and osteopororotic bone. Five attributes, namely amplitude, frequency, instantaneous phase, roughness, and first derivative or contrast attributes, have been tested. An attribute index number is formulated to indicate the attribute's strength at the selected region of interest (ROI). A case study applying these attributes to the CNN model is presented. More than five hundred CT scan images of normal and osteoporosis bone were used during the training stage to test classification performance with and without developed attributes as an input. From the ten selected CT scan images used to test the CNN model, 90% were well predicted in the scenario only utilizing the grayscale as input. However, when including the developed attributes, the CNN can predict all the images well (100% were well predicted). In conclusion, the technique adopted from the signal-processing technique has the potential to enhance feature generation in image processing, whereby the results can be used for the early application of AI/ML in osteoporosis identification. Further research testing this proposed method in different image modalities needs to be conducted to verify the robustness of the proposed method.  
attribute; image processing; density; osteoporosis; CT-scan
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 5 Material Platforms  
Integrated OPAs demonstrated on photonics platforms enable low-cost chip-scale applications. In addition to conventional Si platforms, OPAs have been implemented on other photonic integrated platforms. The major platforms of integrated OPAs, which include Si photonics platforms, III/V platforms, and III-V/Si hybrid platforms, will be discussed in this section.
# Quantum Computing for Healthcare: A Review
## 5 Quantum Computing Architectures for Healthcare
### Secure Quantum Computing for Healthcare  
[PERSON] et al. [118] proposed quantum block-based scrambling and encryption for telehealth systems (image processing application); their proposed approach has two levels of security that work by selecting an initial seed value for encryption. The proposed system provides higher security against statistical and differential attacks. However, the proposed system produces immense overhead during complex computations of quantum cryptography. [PERSON] et al. [119] proposed a quantum digital signature for the access control of critical data in the Big Data paradigm that involves signing parties, including the signer, the arbitrator, and the receiver. The authors did not propose a new quantum computer; rather, they implemented a quantum protocol that does not put more overhead on the network. However, this scheme does not consider sensitive data transferred from the source to the destination during the proposed quantum computing implementation. [PERSON] et al. [120] proposed a quantum walk-based cryptography application, which is composed of substitution and permutations.  
In a recent study [121], a hybrid framework based on blockchain and quantum computing was proposed for an electronic health record protection system, where blockchain is used to assign roles to authorize entities in the network to access data securely. However, the performance of the proposed system suffers, as the quantum computing and blockchain infrastructure pose immense network overhead. Therefore, the performance of the proposed system should be assessed intuitively before its actual deployment. [PERSON] et al. [122] proposed two novel quantum information hiding techniques, i.e., a steganography approach and a quantum image watermarking approach. The quantum steganography methodology hides a quantum secret image into a cover image using a controlled-NOT gate to secure embedded data, and the quantum watermarking approach hides a quantum watermarking gray image into a carrier image. [PERSON] et al. [123] propose a quantum key management scheme with negligible overhead. However, this scheme lacks a comparison with the available approaches to demonstrate its efficacy.
## 2 Results and Discussion
### Analysis of \"in Vivo\" VOCs
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Control**} & \multicolumn{3}{c}{_O. sphegodes subsp. sphegodes_} & \multicolumn{3}{c}{_O. bertoloni subsp. benacensis_} & \multicolumn{3}{c}{_N. tridentatus_} \\ \hline
**Compounds** & **RT \({}^{*}\)** & **Mean (\(n=3\)) \({}^{\bf{s}}\)** & **SD \({}^{*}\)** & **Mean (\(n=3\)) \({}^{\bf{d}}\)** & **SD \({}^{*}\)** & **Mean (\(n=3\)) \({}^{\bf{d}}\)** & **SD \({}^{*}\)** & **Mean (\(n=3\)) \({}^{\bf{d}}\)** & **SD \({}^{*}\)** \\ \hline decanal & 23.63 & 0.72 & 0.64 & 0.86 & 0.02 & 3.48\({}^{\rm{s}}\) & 0.07 & 7.42\({}^{\rm{d}}\) & 0.78 \\ \hline
2-manual & 24.13 & ND & - & 1.08 & 0.72 & 0.58 & 0.41 & ND & - \\ \hline undecanal & 25.86 & 0.05 & 0.30 & ND & - & 0.29 & 0.67 & 0.64 & 0.03 \\ benzene accateldehyde & 26.43 & ND & - & ND & - & 0.05 & 0.04 & 0.06 & 0.01 \\ dodecanal & 28.00 & 0.40 & 1.23 & 0.46 & 0.30 & 4.90\({}^{\rm{s}}\) & 0.79 & ND & - \\
2-ethylbenzaldehyde & 28.33 & 0.26 & 0.21 & ND & - & 0.11 & 0.00 & ND & - \\ \hline
**Total** & & **5.59** & & **4.92** & & **17.63** & & **18.47** & \\ \hline _Esters_ & & & & & & & & & \\ ethyl acetate & 3.01 & 0.96 & 0.04 & 0.09 & 0.00 & ND & - & 0.69 & 0.01 \\
2-methylbutanoic acid methyl ester & 5.85 & ND & - & ND & - & ND & - & 12.36\({}^{\rm{s}}\) & 0.21 \\
3-hexen-1-ol-acetate & 19.00 & 0.48 & 0.12 & 1.64 & 0.24 & 1.69 & 0.21 & 0.34 & 0.02 \\
**germaly acetate** & 30.64 & 0.33 & 0.00 & ND & - & 0.11 & 0.07 & 0.55 & 0.06 \\ \hline
**Total** & & **1.77** & & **1.73** & & **1.8** & & **13.93** & \\ \hline _Kotones_ & & & & & & & & & \\
3-penten-2-one & 5.11 & ND & - & ND & - & 0.54 & 0.54 & 2.04 & 1.12 \\
2-nonen-4-one & 15.93 & 1.17 & 0.10 & ND & - & 2.32 & 1.88 & 1.84 & 0.67 \\
2-methyl-6-heptanone & 16.21 & 0.70 & 0.00 & ND & - & 0.12 & 0.08 & ND & - \\
3-octanone & 16.78 & ND & - & 0.18 & 0.18 & 0.26 & 0.15 & 0.12 & 0.00 \\ ac action & 17.6 & 2.42 & 1.58 & ND & - & 0.52 & 0.67 & ND & - \\
2-octanone & 17.86 & 0.32 & 0.09 & 0.22 & 0.24 & 0.81 & 0.49 & 0.20 & 0.03 \\
1-octen-3-one & 18.39 & 0.37 & 0.00 & ND & - & 0.64 & 0.47 & ND & - \\ pentadecanone & 23.83 & ND & - & 0.33 & 0.00 & 1.26 & 1.78 & 0.06 & 0.00 \\
3.5-octadiene-2-one & 23.92 & ND & - & 0.3 & 0.12 & 9.85\({}^{\rm{s}}\) & 1.50 & 0.71 & 0.11 \\ pentoic lactone & 24.39 & ND & - & ND & - & 0.56 & 0.31 & ND & - \\
2-pentadecanone & 32.8 & ND & - & ND & - & 0.44 & 0.62 & 0.25 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 2 Related Work
### Augmentation of Neural Machine Translation
#### 2.2.3 Pivot Language Technique  
Besides, some research employs rich resource languages that are different from the source and the target (called auxiliary languages [30]). For example, [PERSON] et al. [31] directly use English as the pivot between German and French by training a German-English model and an English-French model. [PERSON] et al. [32] train a predictor to estimate the potential translation accuracy according to the choice of pivot language. The critical point in using an auxiliary language is to select an appropriate language. To better exploit the information from auxiliary languages, researchers may choose languages that are in the same or similar family. The auxiliary language may have the same writing system or similar grammar as the source or target language. In addition, if we choose languages whose speakers communicate frequently with speakers of source or target languages, the auxiliary language and source or target language may share expressions or words (called loan words), which helps build a shared hidden representation space for the translation model [82]. [PERSON] et al. [83] define \"similar languages\" by clustering languages based on their embedding instead of prior domain knowledge. They train a multi-lingual translator, whose input is a sentence and a tag indicating its language. Then they extract the hidden representation of the tag (a vector) in the trained model as the embedding. [PERSON] et al. [84] consider using auxiliary language models for transfer learning and propose LANGRANK, which selects the optimal language by ranking the candidates according to the data size, typological information, and so on. [PERSON] et al. [85] proposes target-conditioned sampling. In this algorithm, the model first samples a sentence in the target language (low-resource), then samples its corresponding source sentence, where the source language is not fixed. The joint probability distribution \(Q(sentence\_X,sentence\_Y)\) is constructed to minimize the expected loss function of the translation task: \(\mathbb{E}_{(x,y)\sim\mathbb{Q}}[loss(x,y|\theta)]\). Then the sampling is according to the conditional probability distribution: \(P(y|x)=Q(x,y)/Q(x)\). If we consider introducing auxiliary languages, we should balance the size of training data in different languages. Otherwise, the model cannot gain much knowledge about the low-resource language because of its finite capacity. [PERSON] et al. [86] train a scorer that automatically decides the size of the data given the languages.
# Simulation of Wind Speeds with Spatio-Temporal Correlation
## 2 Methodology
### Modeling
#### 2.1.3 Obtaining the Weibull Parameters  
After the filtering process, using Equation (2) involves the consideration that wind speed follows a Weibull distribution in all the locations, widely used in wind energy studies [10]. This equation describes the probability distribution function of such a distribution:  
\[f(v;c,k)\ =\ \left\{\begin{array}{c}0,\ v<0\\ \frac{k}{c}\left(\frac{v}{c}\right)^{k-1}e^{-\left(v/c\right)^{k}},v\geq 0 \end{array}\right. \tag{2}\]  
where \(v\) is the wind speed, \(c\) is the scale parameter, and \(k\) is the shape parameter.  
Both parameters are obtained by using the method of maximum-likelihood estimation, i.e., the scale parameter is estimated according to Equation (3), and the shape parameter according to Equation (4):  
\[c=\left(\frac{1}{n}\sum_{i=1}^{n}v_{i}^{k}\right)^{1/k} \tag{3}\]  
\[k=\left(\frac{\sum_{i=1}^{n}v_{i}^{k}ln(v_{i})}{\sum_{i=1}^{n}v_{i}^{k}}-\frac {\sum_{i=n}^{n}ln(v_{i})}{n}\right)^{-1} \tag{4}\]  
where \(c>0\), \(k>0\), \(n\) is the length of the series, \(v_{i}\) is the \(i\)-th wind speed value.
## 4 Materials and Methods  
### Reagents  
The following antibodies were used in our study: anti-ERK1/2 (Cat# 61-7400; Thermo Fisher Scientific, Waltham, MA, USA), anti-phospho-ERK1/2 (9101; Cell Signaling Technology, Danvers, MA, USA), anti-phospho-p70 S6 kinase (Cat# 9206; Cell Signaling Technology), anti-phospho (Ser/Thr) AKT substrate (Cat# 9611; Cell Signaling Technology), anti-phospho-p85 (Cat# 9206; Cell Signaling Technology), anti-Smad4 (Cat# sc-7966; Santa Cruz Biotechnology, Santa Cruz, CA, USA), anti-E-cadherin (Cat# 610181; BD Transduction Laboratories, BD Biosciences, San Jose, CA, USA), anti-N-cadherin (Cat# 610921; BD Transduction Laboratories), anti-Vimentin (Cat# sc-32322; Santa Cruz), anti-Snail1 (Cat# C15D3; Cell Signaling Technology), anti-Endolase1 (Cat# 3810; Cell Signaling Technology), anti-GAPDH (Cat# sc-47724; Santa Cruz), anti-ALDOA (Cat# 11217-1-AP; Proteintech, Proteintech Europe Ltd, Manchester, UK), anti-Pyruvate kinase (Cat# 3106; Cell Signaling Technology), anti-Lamin A/C (Cat# sc-376248; Santa Cruz), anti-g-Catenin (Cat# A0963; ABclonal, Woburn, MA, USA), anti-Plakophilin (Cat# sc-33636; Santa Cruz), anti-\(\beta\)-Actin (Cat# A1978; Sigma-Aldrich, St Louis, MO, USA). GSK690693, U0126 and SB-203580 were purchased from Selleck Chemicals (Houston, TX, USA). TGF-\(\beta\) was purchased from PeproTech (London, UK).
## 2 PV Cell and PSC Modeling
### MPPT and the Effect of PSC  
The power-voltage characteristic of a PV cell is illustrated in Figure 2, with an irradiance of 1000 W/m\({}^{2}\) and a temperature of 25 \({}^{\circ}\)C. The black point in the curve indicates the MPP of the PV cell in standard condition. At this point, the cell voltage and current are the highest. The performance of a PV cell is proportional to environmental conditions such as PSCs. The PV cell is often partially or completely shaded by the passing clouds of adjacent buildings as well as towers and clouds. Under the PSC, the PV characteristic becomes very complex and has several peaks [9; 10]. Among multiple peaks, one peak is named global MPP (GMPP), and the other peaks are called local MPP (LMPP). On the other hand, the maximum power of a PV cell occurs at the GMPP.  
Figure 1: PV Model as single-diode.
## 4 Proposed Key Management Infrastructure Design for IACS  
The proposed design aims at supporting all the common key management operations, viz. key generation, device registration, key establishment, key storage, device addition, key revocation, key update, key recovery, key archival, and key de-registration and destruction. The design considers the following communication scenarios: (i) among field level devices; (ii) among control level devices; (iii) among plant level devices; (iv) across field level and control level devices; and (v) across control level and plant level devices. The key management phases are explained as follows:  
_Assumptions:_ CKMI server is physically protected. Each device in the network has a unique \(ID\) and Join key. The Join keys are preloaded in the devices by the device manufacturing unit during the device manufacturing phase. The CKMI server has the Join keys of all the devices. It is assumed that trust is employed between the device manufacturing plant and industrial plant.
## Data and Methods
### Data
#### 3.1.5 TMPA  
The TRMM was launched in 1997 by JAXA and NASA for monitoring tropical and sub-tropical precipitation conditions. TRMM operated in low earth orbit, with an inclination of 35\({}^{\circ}\) and altitude of 400 km and orbits the Earth 16 times per day. TRMM satellite was equipped with a number of instruments, including the Precipitation Radar (PR), Microwave Imager (TMI), Visible Infrared Scanner (VIRS), Cloud and Earth Radiant Energy System (CERES) and Lightning Imaging Sensor. TRMM Multi-satellite Precipitation Analysis (TMPA) uses Passive Microwave (PM) precipitation estimates from low orbiting satellites and in case PM observations are not available, then PM calibrated Infrared observations are used for gap filling [19]. In the next step, the monthly gauge-based observations are used to produce TMPA from TMPA real time precipitation estimates. The input data used for TMPA is derived from the Special Sensor Microwave Imager (SSM/I) and Sounder (SSMI/S), Microwave Imager (TMI), the Advanced Microwave Sounding Unit (AMSU), the Microwave Humidity Sounder (MHS) and Advanced Microwave Scanning Radiometer for Earth Observing system (AMSR-E). In this study, daily TMPA version 7 with a grid size resolution of 0.25 degrees was used for comparison with station data. Further details are provided in References [1; 46].
## 4 Overcurrent Protection Coordination Results, Analysis and Discussion
### Protection Coordination Results in Scenario 3  
Scenario 3 is a distribution network topology of the second FISR plan after the 3 ph-G fault at Bus 2 is cleared. As referred to the dashed line (d) in Figure 10, the REC Hao-Vo recloser is opened to isolate the fault at Bus 2. Then the LBS Can-Thanh 97 is closed to restore the power of interrupted customers while the REC Can-Thanh-Hao-Vo recloser is opened and a CB GEN circuit breaker of DG sources is closed. The optimal protection coordination results of the OCRs in Scenario 3 which are calculated by the GA, GSA and hybrid PSO-GSA are indicated in Table 6. In this table, \(I_{f}\) is the tripping-current threshold selected for the 50P and 50G functions of the OCRs including the CB GEN OCR and the REC Can-Thanh 163 OCR. \(t_{trip}^{defined}\) is the pre-defined tripping time of the 50P and 50G functions of these OCRs. Moreover, the TDS multiplier and \(A\), \(B\), and \(C\) coefficients of the 51P and 51G OC functions of the above OCRs are shown in the table. The CTI is still selected to 0.30-0.35 s. It is worth noting that the parameter settings of the REC Can-Thanh 163 OCR must be simultaneously updated to be adaptable to Scenario 3 instead of Scenario 1 or Scenario 2. To validate the optimal setting parameters obtained by the ASPC system for the above OCRs, a 3 ph-G fault is given at Bus 3. The REC Can-Thanh 163 OCR operates as the primary protection to detect and clear this fault while the CB GEN OCR operates as the backup protection. Their tripping time \(t_{trip}\) to clear the fault at Bus 3 are also indicated in Table 6. The achieved results have demonstrated that the CTI constraints from Equations (3)-(5) are completely satisfied.  
Figure 12: _Cont_.  
Figure 12: Optimal protection characteristic curves of the OC relays in Scenario 2 of the 22 kV distribution system.
# A Through-Transmission Ultrasonic Method for the Detection of Ferrite Tile Defects
## 4 Design and Experimentation of the Ultrasonic Transmission System
### Experimental Results
#### 4.3.1 Transmission Test of Aluminum Alloy Sample
Comparing the transmission signal energy of extremely small defect samples with the simulation results, a strong linear relationship between the energy and defect size is observed. The transmitted signal energy measured from the aluminum alloy sample is linearly fitted, and the residual difference between the measured attenuation slope and the simulated attenuation line is obtained by comparing the measured and simulated fitting lines. The calculated results show that the experimental data align well with the theoretical simulation model, with the maximum difference between the measured attenuation slope and the residual of the simulated attenuation line being 0.05. These findings validate the capability of ultrasonic transmissive method in detecting and determining defect sizes within the blind zone of the reflection method.