esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Affected Genes and Their Functional Category  
Each H*R subpopulation showed one to three mutations compared to the parental population, with the exception of SS37 HRC3, which carried 10 mutations, and RUSA239 HRC8, for which we could not detect mutations in raw reads (Table 1).  
In LGA251 H*R derivatives, two loci were repeatedly affected: _lpt_ in LGA251 HRC2 and LGA251 HRC5, encoding a hypoxanthine phosphoribosyltransferase that has been suggested to be involved in guanine metabolism in several Firmicutes [28; 29; 30]; and tRNA_48 in LGA251 HRC4 and LGA251 HRC10, involved in protein biosynthesis. Curiously, LGA251 HRC4 and LGA251 HRC10 showed exactly the same mutation in tRNA_48, a single nucleotide polymorphism (SNP) at nucleotide 1934923 leading to the substitution of a guanine by an adenine at the 3' end of this tRNA. LGA251 HRC2 carries, besides the mutation in _lpt_, an additional mutation in a putative pathogenicity island. LGA251 HRC9 carries two SNP mutations, one in a putative membrane protein and one in _mutL_, coding for the DNA mismatch repair protein MutL [31].  
No common mutations have been found among RUSA239 H*R derivatives. The four derivatives each showed a single SNP: RUSA239 HRC3 and RUSA239 HRC6 in the same intergenic region; RUSA239 HRC2 in _pheS_, coding for the phenylalaninel-tRNA synthase involved in protein biosynthesis; and RUSA239 HRC4 in _relA2_, a GTP pyrophosphokinase involved in the synthesis of (p)ppGppp, the stringent stress response regulator molecule.  
_guaB_, which codes for the IMP dehydrogenase involved in guanine metabolism [32], was repeatedly affected in all SS37 H*R derivatives, except in SS37 HRC3. _guaB_ was the unique mutation found in SS37 HRC9, and in SS37 HRC8 an additional mutation was found in an intergenic region. SS37 HRC5 showed, besides the mutation in _guaB_, an SNP mutation in _fnA_, coding for ferritin, an iron storage protein [33]. SS37 HRC2 showed two additional mutations in two SNPs: one in _tagF_2, coding for a glycosyl glycerol phosphate transferase involved in teichoic acid biosynthesis [34], and the other in _qkC_, coding for a peptidoglycan of the NlpC/P60 family of proteins [35].  
Of all the isogenic derivative strains analyzed, SS37 HRC3 was the only one that presented more than three mutations affecting different functional categories (see Table 1 for details) that may be explained by the presence of an SNP mutation in the _mutL_ gene coding for a DNA mismatch repair protein [31].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**H*R Isolate**} & **Nucleotide** & **Amino Acid** & **Affected** & **Locus Tag(s) in** & **Product c** \\  & **Change (5′ \(\rightarrow\) 3′) a** & **Change** & **Genes** & **Reference Strains b** & **Product c** \\ \hline LGA251 HRC2 & 66 delG & Gly23 fs & _lpt_ & SARLGA251\_04440 &
\begin{tabular}{c} putative hypoxanthine \\ phosphoribosyltransferase \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mutated loci identified in high oxacillin resistant (H*R) isolates carrying different methicillin genetic determinants.
# Design Considerations for the Liquid Air Energy Storage System Integrated to Nuclear Steam Cycle
## 2 Analysis Methodology
### Round-trip Efficiency  
Round-trip efficiency is the most important parameter to evaluate the performance of energy storage systems. It is the ratio of energy released by the system and energy consumed by the system. In the case of LAES integrated NPP, the power from NPP is reduced during the energy release. This is because the system utilizes power from the NPP through steam bypass while operating in discharge mode. Moreover, the system is designed to have storage mode eight times longer than release mode. This is also considered in the calculation of round-trip efficiency.  
The total power from release mode in LAES system is a sum of power from air turbines and power consumption of cryogenic pump. This process is shown in the Figure 1, flow number 15 to 32.  
\[W_{ER}=\Sigma W_{air~{}TB}-W_{ cryogenic~{}pump} \tag{7}\]  
The power loss from NPP is calculated from the efficiency of NPP, steam flow rate, and enthalpy from inlet and outlet of NPP steam side. This is number 33 and 34 in Figure 1. The specific values of efficiency and boundary conditions for NPP are in Tables 1 and 2, respectively.  
\[W_{NPP}=\eta_{NPP}\cdot m_{steam}\cdot(h_{steam_{in}}-h_{steam_{out}}) \tag{8}\]  
The power consumption from storage mode in LAES system is sum of power consumption from compressors and power output from cryogenic turbine. This process is described in the Figure 1, flow number 1 to 14.  
\[W_{ES}=\Sigma W_{CP}-W_{ cryogenic~{}TB} \tag{9}\]  
The round-trip efficiency of LAES system is calculated by the total power from release mode, power loss from NPP, and power consumption from storage mode following Equation (9). The operation time for each mode is shown in Tables 1 and 2.  
\[\eta_{RTE}=\frac{(W_{ER}-W_{NPP})\cdot t_{ER}}{W_{ES}\cdot t_{ES}} \tag{10}\]
## 2 Results and Discussion
### Volatile Compounds
#### 2.2.3 Volatiles Derived from Carbohydrates  
A large variety of volatile flavours can also be produced through carbohydrate metabolism. It has been well established that plants obtain all of their energy directly from photosynthesis. The photosynthetic pathways involve conversion of CO\({}_{2}\) into sugars that are then metabolized into lipids and amino acids. Therefore, one may state that nearly all plant flavours come indirectly from carbohydrate metabolism. However, there are certain flavour constituents that come directly from carbohydrate metabolism. Terpenes, for example, derive both from carbohydrate and lipid metabolism. Terpenes are classified as monoterpenes containing two isoprene units (10 carbons), sesquiterpenes containing three isoprene units (15 carbons) and diterpenes containing four isoprene units (20 carbons). Of these groups, the monoterpenes, and more specifically, the oxygenated monoterpenes, are considered most important to the aroma of certain fruits, e.g., citrus products. Limonene, a monoterpene hydrocarbon possessing little odour, is the major terpene in most citrus oils accounting for up to 95% of some oils. The oxygenated terpen, often comprising less than 5% of the oil,generally provide the characteristic flavour of different citrus species. For example, citral is considered the flavour impact component of lemon oil [25]. All four cherry cultivars were rich in ethanol which most probably derives from glycolysis [21].  
It should be noted that besides volatile compounds, sweet cherry aroma is also dependent on non-volatiles _i.e._, alcohols, terpenoids, norisoterpenoids, organic acids, _etc._, bound in the form of glycosides [15]. These authors documented, however, that the latter are of the same nature as the first. They reported that regarding prediction of aroma, the free volatiles showed a fresh-green, citrus and floral aroma, whereas the bound volatiles were odourless.
# Infrared Thermography to Evaluate Thermal Comfort under Controlled Ambient Conditions
## 2 Framework
### Data Analysis  
#### 2.4.1 Phase I: IR Camera Comparison  
The comparison of the IR cameras included qualitative and quantitative approaches. Initially, a qualitative analysis of the images was performed, with the focus being the detection of variations in facial temperature in different ambient conditions. To achieve this, the thermal images captured with the two IR cameras for the same T\({}_{\text{SP}}\) and RH\({}_{\text{SP}}\) were compared.  
Afterwards, the quantitative analysis included (i) the impact of the ambient conditions on the superficial temperature, (ii) the detection of the location of the hottest point, and (iii) a statistical analysis. For each thermal image, the value and location of the hottest temperature measured on the face were selected for the subsequent data analysis (i.e., T\({}_{\text{max}}\)1--temperature of the hottest point measured by IR camera 1; T\({}_{\text{max}}\)2--temperature of the hottest point measured by IR camera 2). The ambient conditions used in the quantitative analysis were the ones measured using the portable sensor inside the climatic chamber (i.e., T\({}_{\text{amb}}\)--air temperature inside the climatic chamber; RH\({}_{\text{amb}}\)--relative humidity inside the climatic chamber). The temperatures measured with the two thermometers were differentiated by contact and non-contact (i.e., T\({}_{\text{contact}}\)--thermometer with contact; T\({}_{\text{non-contact}}\)--thermometer without contact).
## 2 IoH Architecture
### Persistence Layer  
All data is precious, and all data and actions should be stored. In IoH platform, the persistence layer is the layer responsible for storing all the data resulted from sensors, platform activity, and user actions. The persistence layer is responsible for storage mechanisms providing a storage class and storable interface to be used in objective oriented storable classes in the IoH platform.  
Figure 1: IoH platform architecture.  
The persistence layer is composed of multiple database drivers that enable the use of multiple databases technologies. The location of the database should be a combination of local and remote databases. The local database is for near present data and metadata, while the remote database stores the older historical data--also used for the backup and historical register.  
Data needed for building's management algorithms, such as forecasting, should be stored in the local database to improve the speed of data access. Storage time periods should be chosen according to each resource or device.
# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
## 2 Materials and Methods
### Evaluation of the CBM Sensor in Measuring Crop Height, Fresh Weight, and Dry Weight  
The CBM sensor-derived results were compared with manual field measurements of the crop height, FW, and DW within each plot. Simple linear regression was used to establish and test on the empirical relationships. Parameters such as coefficient of determination (\(R^{2}\)) coefficients, with their corresponding root mean square error (\(RMSE\)) and the mean absolute error (\(MAE\)) were used in the evaluation for best fit models. The \(R^{2}\), \(RMSE\), and \(MAE\) for each model were obtained according to Equations (8)-(10), respectively.  
\[R^{2}=\frac{\sum_{i}(\hat{y}_{i}-\overline{y})}{\sum_{i}(y_{i}-\overline{y})} \tag{8}\]  
\[RMSE=\sqrt{\frac{\sum_{i}(\hat{y}_{i}-y_{i})^{2}}{N}} \tag{9}\]\[MAE=\frac{\sum_{i}|\hat{y}_{i}-y_{i}|}{N} \tag{10}\]  
where \(y_{i}\) is the actual value for \(i\)th observation, \(\overline{y}\) is the mean of the actual value of observations, \(\hat{y}_{i}\) is the predicted value for \(i\)th observation, and \(N\) is the number of observations.  
Figure 7: Signal processing to extract BioVolume. The frontal views of the CropBioMass (CBM) sensor geometry for (**a**) medium-, (**b**) short-, and (**c**) tall-sized plots. The top views showing the detector footprint for (**d**), medium-, (**e**) short-, and (**f**) tall-sized plots. The footprint of the individual LiDAR segments vary with the crop height. Short-sized plots are farther from the CBM providing extended FoV coverage area, therefore, the segments on the edge are required to be removed. Tall-sized plots are closer to the CBM providing smaller FoV coverage areas; therefore, extrapolation is required to account for the missed plants on the edge.
## 7 Results and Discussion
### The Influence of Velocity Slip Parameter (\(\mathbf{\Lambda}\))
However, this conclusion has the consequence of expanding the system entropy. Figure 22 shows than an increase in the injection parameter increases the fluid velocity because the flow is injected or boosted up to an inner layer, resulting in an increase in the overall flow velocity. This result shows that MWCNT-CMC is much faster than SWCNT-CMC with respect to the injection effect, possibly because the MWCNT-CMC moves more easily when it is pushed, whereas SWCNT-CMC has more difficulties moving. A secondary consequence of the increased injection parameter is an increase in the flow temperature. Figure 23 clearly shows this outcome. The graphic shows how changing the injection parameter increases the flow temperature. However, the injection parameter does not have a noticeable effect on SWCNT-CMC, as this nanofluid is a better heat conductor than MWCNT-CMC. Despite the escalation in entropy, the system entropy decreases because the surface lacks porosity, making it easier for the system to regulate the flow of energy. Figure 21: Temperature variations versus \(S>0\). Figure 20: Velocity variations versus \(S>0\). Impact of Reynolds Number (\(\mathbf{R_{e}}\)) and Brinkman Number (\(\mathbf{B_{r}}\)) on Entropy Generation  
As shown in Figure 24, the entropy of a system reduces as the Reynold number increases due to increased inertial forces in the system. The particles in the hybrid nanofluid have high kinetic energy, which leads them to move more quickly and exhibit a high Reynold number. The fact that MWCNT-CMC nanofluid has a lower entropy value than SWCNT-CMC is surprising. MWCNT has a far greater affinity for basic fluids than SWCNT. As a result, entropy is unable to cause a rapid breakdown of the system. The impact of the Brinkman number is visually appealing because it depicts the ratio of heat produced by viscous dissipation to heat conveyed by molecular transference. As shown in Figure 25, the Brinkman number expends the system's entropy generation. The generated heat is mainly a result of viscous dissipation. Furthermore, MWCNT-CMC lower entropy than SWCNT-CMC nanofluid. MWCNT has a far greater affinity for aqueous solutions than SWCNT. As a result, entropy is unable to rapidly destabilize the system. Figure 23: Temperature variations versus \(S<0\). Figure 13:0 Velocity variations versus \(S<0\).
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 2 Mobile Authentication Method and Issues
### Securing Authentication for Mobile Networks
#### 2.7.2 Authentication and Privacy-Preserving Techniques Employ Countermeasures  
Some important countermeasures employed by the authentication and privacy-preserving techniques for 4G and 5G cellular networks are described in this subsection. These defenses fall into three categories: cryptographic techniques, human factors, and intrusion detection techniques.  
First, a physical unclonable function (PUF) is a gadget that takes advantage of intrinsic randomness produced during production to provide a unique 'fingerprint' or trust anchor for a physical entity [49]. These devices have a number of potential uses, ranging from anti-counterfeiting, identity, authentication, and key generation to advanced protocols such as oblivious transfer, key exchange, key renovation, and virtual proof of reality. Another possibility is to use PUFs, which are clone-proof, cost-effective, and resistant to a variety of physical attacks. A PUF takes advantage of the inherent random variations created by manufacturing processes to generate secret keys on the fly [49].  
Second, physical-layer authentication (PLA), which is based on the dynamic nature of physical layer properties, is gaining traction as a viable method for increasing wireless security [50]. PLA has recently attracted considerable academic interest due to its information-theory security and simplicity. However, numerous academics have concentrated on PLA and its potential for increasing wireless security [51].
## 4 Proposed Modulation Recognition Scheme
### Implementation Details of CDRNN Model  
The proposed CDRNN model concentrates on combining DenseNet and ResNet, with a focus on considering feature sharing and reuse to enhance the recognition accuracy. As shown in Figure 2b, the main schematic of the model adopts a dense connectivity pattern with four dense blocks. The dense block consists of several combinatorial residual joins, which we call CDRNN.  
The size of the input TIFs of the image-based CDRNN is \(1\times 224\times 224\). In the shallow layer of the CDRNN model, under a large sensory field, the overall feature information is extracted by using a convolutional layer with a convolutional kernel of size \(7\times 7\). Inthe deeper layer of the network, the size of the convolutional kernel is reduced to \(3\times 3\) for deeply extracting subtle features under localized regions in the TFIs. This endows this branch of the network with better non-linear mapping capability. The dense block is composed of a combination of several CRJs. By connecting the outputs of each layer with those of all the previous layers, it improves feature reuse and the efficiency of the network. Our final choice of dense block has 2, 3, 4, and 2 residual combinations, respectively. The transition layer mainly functions as a means to compress the model. Specifically, it reduces the size of the feature map and decreases the number of channels by means of convolution and pooling operations, which, in turn, controls the complexity of the model, prevents overfitting, and enhances computational efficiency. Moreover, the transition layer also serves as a transition between different dense blocks, thus making the network structure clearer and more efficient.  
Here, we consider using a \(1\times 1\) convolution kernel and residual structure for feature sharing and enhancement, as well as a \(3\times 3\) convolution kernel for feature extraction. Among them, the main function of the \(1\times 1\) convolution kernel is to lift the dimension to match the front and back channels.
# Hash Based DNA Computing Algorithm for Image Encryption
## 2 Related Work
### DNA Computing-Based Image Encryption  
DNA computing builds the encryption system based on modular arithmetic cryptography. Moreover, a chaotic strategy has been proved to be well matched with DNA ciphers, due to its characteristics of pseudo randomness, unpredictability and so on [16]. For example, [PERSON] et al. [17] realize a theoretically unbreakable data encryption algorithm, where they build a chaos strategy based on the difference between the original and optimized DNA-based encryption messages. Later, [PERSON] et al. [18] first use piecewise linear chaotic mapping (PWLCM) and logistic mapping to generate encryption parameters, and then use DNA computing for information encoding. Specifically, their PWLCM could make each small segment of the original data be paired with an element of the pseudo-random sequence, thereby increasing the complexity of the encryption algorithm. Afterwards, [PERSON] et al. [19] use three chaotic systems including PWLCM, Lorenz and 4D Lorenz to build a multi-level security system, which could largely increase the non-repeatability of the key.  
To reduce the computational cost of image encryption, [PERSON] et al. [20] use a tent map to select key streams generated by their proposed confusing and diffusing channels. Later, [PERSON] et al. [21] propose a DNA-based image encryption method to effectively reduce memory usage, which selects the most informativepart from the visual appearance analyzed by their proposed dependent chaotic system. Afterwards, [PERSON] et al. [22] propose a medical image encryption algorithm based on the combination of the integer wavelet transform (IWT), DNA and chaos system. Their strategy of utilizing the advantages of different encryption methods ensures the security of electronic health records during network transmission. Recently, [PERSON] et al. [23] proposed a multi-image encryption algorithm, which not only resists conventional attacks to protect image content, but also improves the transmission speed via Internet by reducing image size by encoding. Most recently, [PERSON] et al. [24] designed a random encoding, sequencing and diffusion strategy based on content-aware DNA computing, which greatly improves the difficulty of cracking even if attackers obtain partial or all information of the transmitted image. Most relevant to our method, [PERSON] et al. [25] propose an image encryption scheme that incorporates adaptive diffusion permutation and DNA random coding. We further improve their idea by involving the hash function and image content-based encoding, thus greatly improving the capability to resist different types of attacks.
# Video-Based Parking Occupancy Detection for Smart Control System
## 2 Materials and Methods
### Detection Model
#### 2.2.3 Vehicle Detection Based on MobileNet--YOLO Model  
In this study, we implemented a YOLO v3 detection model with MobileNet v2, and the model considerably reduced parameters and gained high accuracy within limited hardware conditions. The initial weight of MobileNet v2 was trained in ImageNet; from this, we could obtain a great effect and use fewer samples to train YOLO. MobileNet is a base network for extracting image features, and the characteristics extracted using MobileNet v2 and upsampling contain three scales of object detection and modify the filter numbers from 512, 256, and 128 to 160, 96, and 64, which are the settings for MobileNet v2. All the calculations of the convolution layer are changed to depthwise and pointwise convolution. Compared with the original YOLO-based on Darknet53, the version implemented with MobileNet reduces the number of required parameters and improves the prediction speed.  
MobileNet is only a feature extraction layer. In the implementation process, only the convolution layer in front of the pooling layer is used to extract features; therefore, multidimensional feature fusion and prediction branches are not reflected in the network.
## 1 Introduction
### Pressurization-Decompression Test under Manual Control  
The test results of the pressurization-decompression test of the APRV of the different structural configurations under manual control are illustrated in Figure 8.  
Though there is one more orifice in structural configurations II, III and IV than structural configuration I, all the configurations show no significant difference in the manual-controlled pressurization response, because the volume ratio between the air tank and the control cavity is rather big. The response time for structural configurations II, III and IV to reach 75% of the target pressure is 0.15 s, and response time for structural configuration I to reach that is 0.16 s.  
In the manual-controlled decompression test, the control cavity of the APRV exhausts the air to the outer environment, and the pressure in the cavity changes greatly. Limited by the number of orifices, the decompression response time of structural configurations II, III and IV is 0.54 s, and the decompression response time of structural configuration I is 0.38 s. Though the decompression speed of structural configurations II, III and IV is slower, it still meets the requirements of the commercial vehicle braking system.