esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations of the Study  
Our study focused on three STEC O157:H7 strains from recent outbreaks linked to Romaine lettuce to determine the impact of cold storage on physiological changes among these strains. Including additional strains as an outgroup for comparison could determine if these findings differ for strains not associated with leafy greens. We evaluated stress tolerance and virulence responses for STEC O157:H7 only on lettuce, so we cannot determine if the effects observed were simply due to changes in temperature, or a combination of association with the lettuce and changes in temperature. To discern inoculated strains from background microbes, we utilized rifampicin-resistant isolates. We did not compare the virulence or stress tolerance of the rifampicin-resistant strains to the parental strains that were rifampicin-susceptible, but research shows that rifampicin-resistant strains acted similarly compared to rifampicin-susceptible strains when presented with stress [35; 84; 85]. Studies that have determined exposure levels of O157:H7 have reported them to be usually quite low (<100 cells) [86], and the levels that were used here were designed to facilitate enumeration of cells in dormant states, which is similar to previous research [35; 87]. While allowing measurement of persister and VBNC cells, this does not allow us to account for any density dependent effects on stress tolerance or virulence.
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 4 Proposed Framework
### Load Disaggregation Algorithm (LDA)
#### 4.3.4 Challenges  
There are many challenges in NILM. One is the Type-3 appliances. Due to their varying, and at times random, energy consumption it is hard to fit them to a behavioral model. This is made further harder by the low sample rate. Many great features of the appliances is hidden due to the low sample rate most data is collected with [42]. Another problem is the top consumer's problem. In a household there are some appliances that require a lot of energy when they are on, such as stoves, air conditioners and refrigerators. These are referred to as the top consumers. Then there are the appliances that consume very little energy like laptops, DVD players and routers. The top consumer's problem is that the top consumers are so dominant that they make it hard to track the power signature of small consumers. This is why many researchers only focus on the top 10 consumers when during NILM applications [11].
## 3 Results  
### Study Selection Results  
The PRISMA flowchart in Figure 1 shows the results of the literature scan. Initial electronic and manual searches yielded 1343 results. After reading the titles and excluding duplicates, 1000 of the 1343 documents originally found were eliminated. Since reviewing the abstracts, 310 of the remaining 343 experiments were also eliminated. Based on the qualifying criterion (Table 2), just 13 of the remaining 33 articles were included in the qualitative study.
## 7 Results and Discussion
Hence, a considerable amount of initial construction cost could be reduced by eliminating the unnecessary longitudinal continuous steel bars in between the crack inducers (saw-cuts) without compromising the pavement performance. The longitudinal reinforcing steel bars in the CRCP and ARCP segments just ahead of saw-cut tips are selected for evaluating the maximum steel stress distribution. Figure 23 demonstrates the maximum steel stress distribution in the continuous steel bar of CRCP and partial steel bars of ARCP-1 and ARCP-2. It can clearly be observed that the steel stress peaks occur exactly over the saw-cut tips. In between the saw-cuts, the stress variations converge to zero, which indicates the effectiveness of the active crack control method to induce cracks at the desired locations. It may translate that the continuous steel bars between the crack inducers (saw-cuts) across the whole width of the concrete slab are unnecessary when no passive cracks are formed. As demonstrated in Figure 23, the continuous steel bar and partial steel bar experience the same stress peaks only over the saw-cut tips. This means that steel bars are only required at crack locations to hold the actively induced  
Figure 21: The predicted transverse crack pattern of ARCP-2. Figure 22: Comparison of crack strain over the saw-cut tips between CRCP and ARCP. cracks tightly and a lesser amount of the steel bars is required for any randomly occurring cracks between the crack inducers. Although a considerable amount of continuous steel bars, up to about 45% and 63% in ARCP-1 and ARCP-2 in relative comparison to that of CRCP, is reduced, the steel stress distributions in ARCP are almost similar to those in CRCP, as shown in Figure 23. Therefore, it may be stated that the behavior of ARCP even with a relatively lesser amount of continuous reinforcement is basically the same as the behavior of CRCP under environmental loading. As the overall configuration of the CRCP structure being used in Belgium is not quite different from that used in the United States and other parts of the world, the proposed ARCP structure can be applied in place of CRCP along with a relatively lower initial construction cost, subject to the extensive field trials.
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
## 4 Physical Model and Test Environment
### Test Platform and Experimental Environment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Values** & **Units** \\ \hline Total weight of GWEC & m\({}_{\mathrm{g}}\) & 50 & kg \\ GWEC volume & V & \(\phi 400\times 450\) & mm\({}^{3}\) \\ Weight of flywheel & m & 30 & kg \\ Moment of inertia of rotor in x axis & J\({}_{\mathrm{x}}\) & 0.211 & kgm\({}^{2}\) \\ Moment of inertia of rotor in y axis & J\({}_{\mathrm{y}}\) & 0.211 & kgm\({}^{2}\) \\ Moment of inertia of rotor in z axis & J\({}_{\mathrm{z}}\) & 0.372 & kgm\({}^{2}\) \\ Moment of inertia of platform in x axis & J\({}_{\mathrm{P}\mathrm{x}}\) & 1.40 & kgm\({}^{2}\) \\ Moment of inertia of platform in y axis & J\({}_{\mathrm{P}\mathrm{y}}\) & 0.93 & kgm\({}^{2}\) \\ Moment of inertia of platform in z axis & J\({}_{\mathrm{P}\mathrm{z}}\) & 1.40 & kgm\({}^{2}\) \\ Rated wave period & T & 7 & s \\ Rated pitch amplitude & A & 10 & degree (\({}^{\circ}\)) \\ Flywheel rated speed & \(\dot{\varphi}\) & 10,000 & rpm \\ Damping coefficient & c\({}_{\mathrm{y}}\) & 35 & Nm s/rad \\ Generator rated voltage & U & 24 & v \\ Generator rated power & P & 100 & w \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specification of the prototype system. Figure 6: Operation of the gyroscope wave power generation system. Due to \(\delta=\delta_{0}\sin(\omega t)\), and when neglecting the power to maintain the flywheel in vacuum and other energy losses, then the energy conversion efficiency is:  
\[\eta=\frac{P}{P_{in}}=\frac{2J_{z}\dot{\varphi}\varepsilon_{0}}{2J_{z}\dot{ \varphi}\varepsilon_{0}+\left|J_{pz}\delta_{0}\omega\sin(2\omega t)\right|}= \frac{2\left(J_{z}\dot{\varphi}\right)^{2}}{2\left(J_{z}\dot{\varphi}\right)^{2 }+\left|J_{pz}\varepsilon_{0}\omega\sin(2\omega t)\right|} \tag{16}\]  
From Equation (16), it is obvious that the conversion efficiency decreases as the damping coefficient \(c_{y}\) increases when other conditions remain unchanged. However, the damping coefficient will also change under different wave conditions and flywheel speeds. Therefore, given the strong coupling of the gyroscope system, it is impossible to directly obtain the relationship between energy conversion efficiency and other coefficients in the above formula.
## 2 Materials and Methods
### MODIS Datasets  
The MODIS data used in this study include MODIS GPP data (MOD17A2H products), FPAR data (MOD15A2H products), and Surface Reflectance data (MOD09A1 products) with Collection 6 at 500 m spatial resolution were downloaded directly from the Oak Ridge National Laboratory Distributed Active Center (ORNL DAAC) website. FPAR is the fraction of photosynthetically active radiation (400-700 nm) absorbed by green vegetation, which is a critical component of the MODIS GPP algorithm. To correct inferior values caused by the effects of clouds and aerosols, we reconstructed the MODIS FPAR time series data with Savizky-Golay filter algorithm [47]. Meanwhile, to validate the performance of the MODIS FPAR data in the study area, we observed the actual FPAR data of cropland and desert grassland sites in HRB using AccuPAR (METER Group, Inc., Pullman, USA) during the growing seasons of vegetation in 2012 [43], and then compared the observations with the MOD15A2H FPAR data at corresponding sites. In addition, we also used the MODIS surface reflectance data to derive vegetation indices, such as the normalized difference vegetation index (NDVI). For the site of NTZ, due to the growing season of cropland (i.e., cantaloupe) is short, the desert or low vegetation land cover was identified in the MOD15A2H product, and thus we calculated the FPAR from NDVI data following the empirical formula of: \(\text{FPAR}=1.24\times\text{NDVI}-0.168\)[48].
## 2 Materials and Methods
### Characterization Methods
#### 2.3.5 Fourier Transform Infrared Spectroscopy (FTIR)  
Fourier transform infrared (FTIR) spectroscopy was applied to the dried biomass of the cultures, as collected on Days 0 and 7 of cultivation, to qualitatively monitor the microalga functional group changes induced by the metal interaction. The FTIR spectra were recorded with a Cary 670 spectroscope (Agilent Technologies Inc., Palo Alto, CA, USA) using a diamond attenuated total reflectance (ATR) accessory (GladiATR, PIKE Technologies, Madison, WI, USA). The spectra were collected in the mid IR area (4000-400 cm\({}^{-1}\)), with 32 scans and a resolution of 4 cm\({}^{-1}\).  
Micro-ATR-FTIR measurements using a focal plane array (FPA) detector were applied to the Control sample on Day 0 to inspect the spatial distribution of its components. This method was applied over standard ATR-FTIR measurements to provide high resolution chemical mapping at the microscale of single _A. platensis_ filaments. For this reason, a single drop of the culture was placed on a glass slide and left to dry in room conditions (\(\sim\)25 \({}^{\circ}\)C). The measurements were performed with a Cary 620 FTIR microscope (Agilent Technologies Inc., Palo Alto, CA, USA)--coupled to the aforementioned FTIR spectroscopy--equipped with a germanium micro-ATR accessory attached to a 15\(\times\) objective visible/IR lens, using a 64 \(\times\) 64 focal plane array (FPA) detector. Each FPA image consists of 4096 data points/individual spectra, each one of them collected from a 1.1 \(\times\) 1.1 \(\upmu\)m\({}^{2}\) independent observed area. The measurements were performed in the range of 3950-700 cm\({}^{-1}\), with a resolution of 4 cm\({}^{-1}\) and 64 co-added scans.
## 2 Materials and Methods  
### Materials  
This study evaluates the effect of partial cement replacement with FA and QZ on the properties of cement-based materials. It uses Ordinary Portland Cement (OPC), sand, water, FA, and QZ as raw materials to prepare mortar specimens and paste samples. OPC was purchased from Jiangsu Helin Cement Co., Ltd., Jiangsu, China. The specific surface area of cement was 3870 cm\({}^{2}\)/g. The FA is first-grade FA with a specific surface area of 4150 cm\({}^{2}\)/g. QZ comes from Lingshou County, China. It uses the experimental standard sand produced by Xiamen ISO Company, China, and the tap water from the laboratory.  
Table 1 shows the OPC, QZ, and FA oxide contents measured by an X-ray fluorescence spectrometer (XRF). Figure 1 shows the particle size distribution (PSD) curve of the raw materials. The median particle sizes of OPC, FA, and QZ are 13, 5.8, and 7.64 \(\upmu\)m, respectively. The OPC, QZ, and FA densities measured according to ASTM C188-17 [27] are 3.15, 2.65, and 2.55 g/cm\({}^{3}\), respectively. Figure 2 shows the XRD test results of OPC, QZ, and FA. The main components of FA are silica and mullite. According to the ASTM C618 [28] standard, the calcium oxide content of FA is less than 10%, which can be classified as class F fly ash.
## 2 Model Description and Experimental Design
### Experimental Design  
With increasing aerosol concentration, the concentration of CCN and hence, cloud droplets, also increase dramatically [64]. Therefore, the concentration of cloud particles can reflect the concentration of aerosol. Given a fixed content of cloud water, the more cloud droplets there are, the smaller the size of droplets could be. For simplicity, cloud-aerosol interactions are not explicitly included in the two-moment bulk scheme described by [PERSON] et al. [63]; instead, different cloud droplet concentrations are prescribed to imitate the effects of different aerosol concentrations in clean and polluted conditions following some scholars [62; 65; 66]. For instance, [PERSON] [61] set the initial cloud droplet concentrations to 250 (750) cm\({}^{-3}\) to represent the moderately (highly) polluted conditions in understanding the aerosol effect on deep convections. Similarly, this study establishes nine experiments with the initial cloud droplet number concentrations (\(N_{c}\)) set to 50, 100, 250, 500, 1000, 2500, 3500, and 5000 (cm\({}^{-3}\)) to reveal the effect of aerosols on Mei-Yu system clouds and precipitation. Note that the highest \(N_{c}\) is set to 5000 cm\({}^{-3}\) to represent the extreme continental aerosol conditions.  
Figure 1: (**a**) Average initial measured (Dotted Line) and ideal model (Solid Line) potential temperature fields plotted together with the basic-state relative humidity field (thin lines are Potential temperature, thick lines are relative humidity) for the region [110\({}^{\circ}\)–125\({}^{\circ}\) E, 28\({}^{\circ}\)–32\({}^{\circ}\) N] on 12 July 2010 at 00 UTC. (**b**) Vertical profiles of the vertical shear of the meridional geostrophic wind (\(\partial V_{g}/\partial z\), solid line, Units: 10\({}^{-3}\) s\({}^{-1}\)) and the large-scale zonal gradient of the potential temperature (\(\partial\theta_{g}/\partial x\), dotted line, Units: K/100 km). (**c**) Velocity profile along the frontal boundary (\(u_{0}\), isolines, 2.0 m s\({}^{-1}\) intervals) and (**d**) potential temperature profile (\(\theta_{0}\), isolines, 5 K intervals) plotted with water vapor mixing ratio (\(q_{\mathrm{r}0}\), shaded, 2 g kg\({}^{-1}\) intervals).
# A Review of Health Assessment Techniques for Distribution Transformers in Smart Distribution Grids
## 3 Transformer Health Assessment Techniques
### Hybrid Artificial Intelligence Approaches
This represents the relationship bound tightly between transformer loading parameters, transformer's temperature and aging to the distribution transformer health. To the best of our knowledge, there is no limit to include the parameters into the studied model. However, in the studies that were implemented to assess distribution transformer health, transformer loading and transformer temperature (winding temperature, top oil temperature, etc.) are usually selected as the most important parameters determining the transformer health [73]. The transformer aging index is also an important parameter, but this parameter can only be calculated through other parameters [74]. The estimation of transformer aging parameters is complex and non-deterministic because the heat transfer process is distributed over different surfaces in the winding and insulation structures and there may be measurement errors. It requires a high-quality sensor to provide high accuracy data. Transformer aging is normally included in the evaluation models that use Health index calculation or Fuzzy logic [38] methodologies. Artificial intelligence approaches are often applied based on actual measurement parameters to enhance accuracy. Further research will be investigated in the future to evaluate the importance of the indicators on the distribution transformer, thereby improving the accuracy and reliability of the assessment models.
## 2 Materials and Methods  
To fabricate the CEB with a small volume, a two-layer resist technology was used to implement the two-angle evaporation method using electron beam lithography. Gold was used as the primary electrode material, with chromium deposited before gold to improve the adhesion of gold to the SiO\({}_{2}\) substrate. Palladium was also deposited on top of the gold layer as a buffer between gold and aluminum, preventing the formation of a gold and aluminum alloy to increase the contact resistance. The standard two-layer resist technology, using a 0.2-m-thick PMMA layer on top of a 0.8-m-thick Copolymer, was used to create the device's masks. The device consists of a superconducting electrode, the small-volume normal metal absorber, and the superconductor-insulator-normal metal (SIN) tunnel junction. The two-layer technique makes it possible to create all three device components in just one vacuum cycle.  
To fabricate the bolometer device, an aluminum electrode was used as the superconducting electrode, and chromium was used as the normal metal absorber. Chromium adheres better to the substrate and provides good impedance matching between the absorber and the antennae. To implement the two-angle evaporation method, the superconducting aluminum was first thermally evaporated at an angle, followed by the oxidation of the aluminum film, and then finally with the deposition at an angle of \(0^{\circ}\) of the normal metal chromium and copper. It was found that the optimal angle of evaporation for aluminum was \(55^{\circ}\) with respect to the normal of the film surface. The aluminum thickness was about 60 nm. Aluminum was oxidized for 2 min at a pressure of \(5\times 10^{-2}\) mbar. The normal metal absorber was composed of 30 nm-thick chromium film and 30 nm-thick copper film. After the vacuum deposition process, the e-beam resist was removed by lift-off in acetone.  
To simulate the theoretical prediction [15] and compare it with the experimental results, we used the heat balance equation given by  
\[P_{e-ph}+P_{R_{S}}+P_{0}=P_{cool} \tag{3}\]  
where \(P_{e-ph}=\Sigma v\left(T_{e}^{5}-T_{ph}^{5}\right)\) is the heating due to the electron-phonon coupling, \(P_{R_{S}}=2V^{2}/R_{S}\) is the Joule heating from resistance in the subgap region, \(R_{S}\), \(P_{0}\) is the absorbed heating power, and \(P_{cool}\) is the cooling power of the SIN tunnel junctions. The parameter, earlier defined in Equation (2) as a coupling parameter, has a value of \(\Sigma=1.3\) nW K\({}^{-5}\)mm\({}^{-3}\) obtained from fitting against the measured responsivity and sensitivity. The volume of the absorber was with a resistance of 63 \(\Omega\).
## 1 Introduction
The goal is to reduce model complexity, lower processing loads, and improve performance by eliminating redundant features. The goal is to assess how effectively these methods improve classification performance. The study emphasizes embedded FSM, which directly integrates the feature selection process with the model training. This approach is considered to be more efficient and robust. The study demonstrates that embedded methods can reduce model complexity and improve diagnostic accuracy. The main contributions of this study are listed below:  
1. Comparison of multiple feature selection methods: The study employs a diverse set of FSMs, including FS, MI, SFS, RFE, and RFI. This multi-method approach allows for effective noise reduction, dimensionality optimization, and improved classification accuracy;
2. Comparison of ML and DL algorithms: The classification performances of SVMs, one of the classical ML methods, and LSTM, one of the DL methods, on time series data, were compared;Comprehensive model validation: The performance of the proposed fault diagnostic model is rigorously validated using well-established benchmark datasets, namely the CWRU bearing dataset and the NASA PCoE lithium-ion battery dataset. This ensures the reliability of the model across multiple domains;
4. Emphasis on embedded feature selection methods: The study highlights the advantages of embedded FSMs, such as decision tree-based techniques, which are integrated directly with model training. The structure of the paper is organized as follows: Section 2 reviews the related work, while Section 3 outlines the materials and methods. Section 4 discusses the model evaluation, and Section 5 provides a discussion of the proposed model. Finally, Section 6 presents the conclusion of the paper.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 4 Intrusion Detection and Prevention Systems
### An Anomaly-Based Intrusion Detection System (AIDS)  
Recent research [31] has considered an anomaly-based intrusion detection system (AIDS), which is an approach to detect intentional attacks into network systems. This technique is used in intrusion detection problems--it can recognize unknown and known  
Figure 1: SIDS Model.  
attacks, making detection more efficient, and representing an improvement over the limitations of signature-based intrusion detection systems (SIDS). The idea behind AIDS is that it is used to protect the network by finding anomalous behaviors, i.e., actions related to deviations from standard user behavior are classified as intrusions. Based on the training method, this system can be separated into three groups: statistical, knowledge, and machine learning models. The most important improvement of AIDS is the capability to recognize the zero-day attacks as their peculiar network behavior distinguishes them from the signature database. An AIDS activates a warning signal when the scanned traffic diverges significantly from the common behavior. An additional benefit is that they can detect internal malicious actions, and if an intruder attempts to penetrate the system, an alarm is raised, and it informs the system manager to take action.