esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multichannel, High-Bandwidth Wirelane Receiver for D2D Interconnects
## 3 Receiver Circuits
### Mini-Pll  
#### 3.3.1 Design  
LC oscillators use passive components, such as inductors and capacitors, which are large in area, complicated in process, and difficult to redesign. Packaging and EMI issues require further consideration. Ring oscillators achieve a high level of integration and good coordination without the need for additional processes or packaging. In addition, ring oscillators can provide polymorphic clocks.  
The RX-PLL in Figure 10 adopts a divide-by-four structure. The output of the ring oscillator (number of ring oscillations N = 4) comprises eight phases to improve the rotation accuracy of the PI. Clock and data alignment (CDA) was used to detect eight-bit edge information, and after thermometer coding, three phase codes and 32 thermometer codes were generated. When the clock generated by the phase-locked loop is sampled to the best position of the data, it is locked and the output clock jitter is 2.4 ps.
## 3 Results and Discussion
### Evaluation of Antifungal Activity of Allicin
#### 3.1.3 Gas Phase Assay  
To investigate the antifungal activity of allicin via the gas phase, the 20 \(\upmu\)L of allicin (80 mM) was placed at the center of a Petri dish lid and PDA containing fungal culture in the petri dish base was inverted over the lid. Thus, there was diffusion of allicin in the petri dish, without any contact between  
Figure 3: Plate inhibition zone assay showing the antifungal activity of allicin against model fungi. Fungal cultures were incorporated into warm agar (50 \({}^{\circ}\)C) and poured into a Petri dish. (**a**) Representative pictures for fungi (_V. dahliae_, _V. longisporum_, _A. brassicicola_ and _B. cinerea_). The lower well contained 20 \(\upmu\)L of sterile water (Control). (**b**) The diameter of inhibition zone was measured for the hole containing 20 \(\upmu\)L of 2, 4 and 8 mM solution of allicin against _A. brassicicola_. Error bars show standard deviation about the mean, \(n=9\). *** \(p<0.001\).  
the allicin and the medium. Allicin produced an inhibition zone in the agar medium above the droplet in the Petri plate lid for all fungi tested. Interestingly, there was a complete growth inhibition for _V. dahliae_ and _B. cinerea_. Moreover, in case of the other two fungi, _V. longisporum_ and _A. brassicicola_, there was a clear zone of inhibition (greater zone of inhibition in case of _V. longisporum_ (Figure 4). In this experiment, surprisingly, there are often clear zones of inhibition with fairly sharp borders. This can be interpreted as the concentration gradient of allicin diffusing away from the central drop of allicin into the air in the closed Petri dish, and a tight concentration threshold for the inhibition of fungi in the agar medium.  
The potential of allicin as a grain fumigant against three stored product pests has previously been investigated. The efficacy of allicin as a botanical fumigant was already reported [51]. Similarly, garlic juice containing allicin has also been evaluated against dipteran pests. The study has reported the insecticidal activity of garlic juice against the pests tested [52]. In another study, the components and oils from garlic were reported to exhibit remarkable insecticidal activity through fumigation [53; 54]. The excellent antifungal activity of allicin via gas phase confirms the utilization of allicin as potential biofumgiant. Moreover, garlic juice may be extracted directly from garlic and sprayed into fields to control soil-borne pathogens and pests.
## 2 Materials and Methods
### Remote Sensing Dataset  
For windthrow and landslide detection, pre and posttyphoon PlanetScope Analytic Product images were acquired [25]. PlanetScope is a satellite constellation consisting of approximately 120 microsatellites delivering a near-daily temporal resolution. The images have a high spatial resolution of 3 m per pixel, which is suitable according to a field survey performed prior to the study, in four different spectral bands: blue (455-515 nm), green (500-590 nm), red (590-670 nm), and near-infrared (780-860 nm).  
The PlanetScope Analytic Product offers orthorectified images from which distortions caused by terrain and perspective effects on the ground have been removed, thereby restoring the geometry of a vertical shot, and data that have been radiometrically calibrated, which produces a surface reflectance product.  
For both test sites, we used pretyphoon images acquired on 27 August 2016, 3 days before the Lionrock typhoon hit, and posttyphoon images acquired on 21 September 2016, 21 days after the typhoons hit. The images from each date were mosaicked, and the urban areas and agricultural fields were masked using forest data provided by the Japanese Ministry of Land Infrastructure Transport and Tourism [26] that were adjusted to match the PlanetScope imagery. For cloud masking, we considered the unusable data mask (udm) file included alongside the PlanetScope Analytic Product, which provides information on unusable data, such as those affected by clouds. The mask did not cover all clouds nor cloud shadows, and with the study area being relatively small, we additionally manually masked the clouds and cloud shadows through visual interpretation of the data.  
Figure 1: _Setana North_ and _Setana South_ study area with PlanetScope data from 21 September, 2016.  
The input for the SAM and SVM classification methods was a composite image created by layer stacking the pre and posttyphoon mosaics. We layer-stacked the eight bands of the image in the following order: 1. blue (posttyphoon), 2. green (posttyphoon), 3. red (posttyphoon), 4. near-infrared (posttyphoon), 5. blue (prettyphoon), 6. green (prettyphoon), 7. red (prettyphoon), and 8. near-infrared (prettyphoon). This step was essential for identifying only the changes that occurred between the two dates by constituting a land cover change spectral signature. For the NDVI filtering method, the input data were the NDVI values of the pre- and posttyphoon mosaics.  
Additionally, RGB WorldView2 images from 20 August 2017, which were the closest images available from Digital Globe after the typhoons hit, were used to support the digitalization of training samples for the SAM and SVM classification methods. We visually defined the classes of each point for accuracy assessment, as we explain later. This dataset was selected due to its very high spatial resolution of 0.46 m/px, enabling the visual interpretation of the area [27].
# A Systematic Literature Review on Military Software Defined Networks
## 6 Statistical Analysis and Metrics
### Categorization of Software Defined Network Applications  
This section summarizes the categories of SDN that have been discussed through this systematic literature review. Section 5 presented additional SDN research contributions that discussed related SDN technologies such as Network Function Virtualization (NFV), Wireless sensor networks (WSN), Satellite Networks (SAT), Underwater Acoustic Networks (UAN), Unmanned Aerial Vehicles (UAV) and Software Defined Radio (SDR). These are mostly related to the application domains of Software Defined Network (SDN) applications and controls. Table 11 reflects the focus of research trends, where the significant research contributions for military SDN are not focused on the related SDN technologies, but mostly on SDN control plane applications.
## 4 Proposed PMFAN-GDSO Approach
### Traffic Flow Prediction Based on PMFAN
#### 4.2.2 Correlation Extraction with Multi-Flow Assistance  
The accuracy of the prediction model is crucial for the overall effectiveness of our algorithm as it can effectively reduce the computational latency of the orchestration algorithm. Therefore, we incorporate inter-service correlation information to push the prediction performance of the model to its limits. We aggregate multiple client flows into a three-dimensional matrix and use a CNN to extract the correlations between the business flows. DenseNet is chosen as our model for extracting business correlations due to its excellent performance in both feature reuse and alleviating gradient vanishing.  
The principle of DenseNet is to establish dense connections between different layers, which not only avoids overfitting but also further improves the prediction accuracy of the model, making the network training more stable and reliable. The position of DenseNet in the prediction model is shown in Figure 3. The main component of DenseNet is the densely connected BasicConv layer, where each layer is directly connected to all subsequent layers. The structure of BasicConv is also depicted in Figure 3, consisting of batch normalization, followed by ReLU activation and a convolutional layer with a 3 \(\times\) 3 kernel. Assuming the input to the network is represented as X0, the inputs to all layers can be represented as follows:  
\[X^{l}=f_{l}\Big{(}X^{0}\oplus X^{1}\oplus\cdots\oplus\ X^{l-1}\Big{)} \tag{11}\]  
where \(\oplus\) refers to the concatenation of the feature maps from all preceding layers. The introduction of batch normalization in the non-linear transformation aims to enhance the stability of the network during the parameter propagation process, accelerate the model training, and potentially improve the model prediction accuracy.  
The convolution operation in DenseNet can be represented in the following form:  
\[X^{(\alpha+1)}=f\Big{(}W^{(\alpha)}\odot X^{(\alpha)}+b^{(\alpha)}\Big{)} \tag{12}\]  
where \(\odot\) refers to the convolutional function, \(W\) represents the weights, and \(b\) represents the bias. Here, we chose the ReLU function as the activation function \(f\), which is defined as \(f(a)=max(0,a)\).
# Incremental Formula-Based Fix Localization
## 5 Results of the Experiments
### Result of RQ3: Efficiency and Scalability  
To answer RQ3, we compare AgxFaults with PF, FS, and FI techniques based on their computational expensive. We use the CPU time as a metric for measure the computational complexity. Specifically, we measure and report the CPU time spent on the solver to solve formulas and the total running time of the techniques for each run.  
Figures 8 and 9 show the formula solving time--the CPU time spent by the solver to solve formulas--and the total running time of PF, Agx, FS, and FI formula techniques for the 41 buggy versions of the TCAS program. Both the formula-solving time and total running time of Agx were significantly smaller than those of PF for most of the versions. On average, the Agx approaches was 28% faster than PF, but three time slower than FS and 51 time slower than FI at formula solving. The Agx approaches was 45% faster than PF, but 9.4 time slower than FS and 9.4 time slower than FI at total running.  
Because the computational complexity of AgxFaults and PF approaches are proportional to the loop unwinding bound that limits the maximum number of nested iterations for each loop in the target program, indeed, the computational complexity increased when the loops unwinding bound increased. Thus, we used the set of programs containing loops in [PERSON]'s benchmark to further evaluate the scalability of fault localization approaches with respect to loop unwinding bound. These programs included various variations of the SquareRoot, Sum, and BSearch programs. SquareRoot is a program that finds the integer part of the square root of an integer number. Sum is a program that computes the sum of all natural numbers from one to a given input value. BSearch is a program that implements the binary search algorithm to search in an increased array of integers. We run a fault localization technique multiple times for each program and each  
Figure 8: Formula solving time of different methods on the TCAS programs.  
failing test case. We varied the maximum number of loop unwinding for the program execution trace from 10 to 100.  
The fault localization result returned by both PF and AgxFaults are identical for each program. Specifically, both method return 6 MFCs for SquareRoot, 8 MFCs for Sum, and 8 MFCs for BSearch programs. Figure 10 shows the average formula solving time of Agx and PF approaches when applied to programs with increasing loop unwinding bounds. As shown in this graph, for a small loop unwinding bound, the formula solving time of PF and Agx are similar. However, when the number of loop unwinding bounds was increased, the time it took the PF approach to solve the problem increased exponentially, while that of the Agx approach increased at a significantly slower rate.
## 4 Breakdown and Modifications of MOST under Stable Conditions
### Data Processing  
The heat flux \(\overline{w^{\prime}\theta^{\prime}}\), the Obukhov length \(L\), and the friction speed \(u_{*}\) are evaluated based on measurements of the sonic anemometers [2]. In reference to Hogstrom [37], we enforce the criteria (1) \(u_{*}\geq 0.1\) m/s for the \(\phi_{m}-\)analysis and (2) \(|H_{S}|\geq 10\) W-m\({}^{-2}\) and \(u_{*}\geq 0.1\) m/s for the \(\phi_{h}-\)analysis, where \(H_{S}=\rho c_{p}\overline{w^{\prime}\theta^{\prime}}\) is the flux of sensible heat and \(c_{p}=1004\) J-(K-kg)\({}^{-1}\) is the constant pressure specific heat of air. The local gradients could be derived using the profile-fitting methods of Nieuwstadt [38] or Hogstrom [37] with more than four levels available to fit profiles of velocity and temperature. However, wind speeds and temperatures are only measured at three and two levels, respectively. To avoid overfitting profiles, the velocity and temperature gradients are approximated at the geometric mean height of levels \(z_{1}=30\) m and \(z_{2}=70\) m by the finite difference form [39, 40]  
\[\frac{\partial\Phi}{\partial z}\approx\frac{\Phi(z_{2})-\Phi(z_{1})}{\sqrt{z _{1}z_{2}}\ln(z_{2}/z_{1})} \tag{34}\]  
where \(\Phi\in\{U,\Theta\}\). It is easy to prove that this expression is rigorous for logarithmic profiles and has an error within \(\pm 3\%\) for log-linear profiles predicted by MOST.  
Figure 4: Comparison of the wind speeds from sonic anemometers (\(U_{sonic}\)) and cup anemometers (\(U_{cup}\)) for the wind direction in \(0^{\circ}\pm 10^{\circ}\): measurements in symbols and fit curves in solid lines. \(r_{xy}\) is the Pearson correlation coefficient.
## 4 Multi-Objective Scheduling Optimization Model
### Multi-Objective Scheduling Model
#### 4.1.2 Constraint Conditions
\begin{array}{l}Q_{t}-g_{ESS,t}^{dis}\left(1+\rho_{ESS,t}^{ dis}\right)\text{, \emph{during discharge}}\\ Q_{t}+g_{ESS,t}^{ch}\left(1-\rho_{ES,t}^{ch}\right)\text{, \emph{during charge}}\end{array} \right. \tag{28}\]  
where \(\rho_{ESS,t}^{dis}\) and \(\rho_{ESS,t}^{ch}\) are the power loss rates of the ESD during discharge and charge, respectively, at time \(t\). The main objectives of LLC are to optimize the ESD's discharge-charge cycles, improve the ESD's operation life, and simultaneously increase the system's peak shaving capacity. That is,\[\mathcal{S}_{ESD,t}^{ch}\to\mathcal{S}_{ESD}^{ch,\text{R}} \tag{29}\] \[\mathcal{S}_{ESD,t}^{dis}\to\mathcal{S}_{ESD}^{dis,\text{R}}\] (30) \[\mathcal{S}_{ESD,t}^{ch}\cdot\mathcal{S}_{ESD,t}^{dis}=0 \tag{31}\]  
where \(\mathcal{S}_{ESS,t}^{chr,\text{R}}\) and \(\mathcal{S}_{ESS,t}^{dis,\text{R}}\) are the rated power levels of the ESD charge and discharge, respectively. (4) System reserve constraints:  
\[\left(\mathcal{S}_{TPP,t}^{\max}-\mathcal{S}_{TPP,t}\right)+\left( \mathcal{S}_{HS,t}^{\max}-\mathcal{S}_{HS,t}\right)\geq r_{1}\cdot L_{t}+r_{2} \cdot g_{WPP,t}+r_{3}\cdot g_{PV,t} \tag{32}\] \[\left(\mathcal{S}_{TPP,t}-\mathcal{S}_{TPP,t}^{\min}\right)+\left( \mathcal{S}_{HS,t}-\mathcal{S}_{HS,t}^{\min}\right)\geq r_{4}\cdot g_{WPP,t}+r_ {5}\cdot g_{PV,t} \tag{33}\]  
where \(\mathcal{S}_{TPP,t}^{\max}\) and \(\mathcal{S}_{HS,t}^{\max}\) are the maximum outputs of the TPP and the HS at time \(t\), respectively. \(\mathcal{S}_{TPP,t}^{\min}\) and \(\mathcal{S}_{HS,t}^{\min}\) are the minimum outputs of the TPP and HS at time \(t\), respectively. \(r_{i}(i=1,2,3,4,5)\) represents the up reserve coefficients of the load, WPP, and PV and the down reserve coefficients of the WPP and PV, respectively. (5) Other power source constraints  
In the MECS, the other power sources are the TPP, the WPP, and the PV. The operation constraints of the WPP and the PV should satisfy the maximum output constraints, namely, the outputs of the WPP and the PV should not be greater than the system's allowable output, which has already been studied in Reference [21]. The constraints of the TPP operation comprise the maximum/minimum output constraints, the upwards/downwards climbing constraints, and start-shutdown time constraints, which have already been studied in Reference [28].
# A Review of Optical Neural Networks
## 3 Development Processes and Categories
### Deep Diffraction Neural Networks  
Optical neural networks have been studied in Fourier optics for decades, and the forward physical structure of multilayer coherent neural networks is thought to be promising [18; 52; 53]. Deep diffraction neural networks (D\({}^{2}\)NNs) have been proposed for various classification tasks and can approach high-dimensional information capacity via optical processing, with millions of neurons and hundreds of billions of connections [54; 55; 56; 57; 58; 59; 60; 61]. D\({}^{2}\)NNs also enable image saliency detection in Fourier space [53; 62; 63; 64; 65]. Diffraction is a prevalent physical phenomenon of incoherent light propagation, as seen in Figure 2. Any point in the plane perpendicular to the direction of propagation can be interpreted as a coherent superposition of complex amplitudes of integral points in the reference plane with a certain diffraction distance [66]. The superposition of diffraction meets the basic requirements of deep complex neural networks. Therefore, as an optical mechanism, coherent diffraction can provide an alternative method to fully connect multi-valued neurons. The training input of the underlying D\({}^{2}\)NN model is operated via spatial features. The input domain is filtered in the object or Fourier space by introducing a series of ordered passive filters prior to the diffraction network. This allows for the parallel processing of optical information.  
D\({}^{2}\)NNs do not have nonlinear capabilities. In a D\({}^{2}\)NN made of linear materials, nonlinear optical processes, including surface nonlinearity, are negligible. The only form of nonlinearity in the forward optical model occurs in the photoelectron detector plane [53; 67]. Deep neural networks using this scheme are capable of inference only for fixed tasks. Nonlinear operations are an indispensable and vital part of neuromorphic computing. Training, one of the key steps in the vast majority of neural network algorithms, has still not been implemented for such networks [68]. Optical neural networks are a promising energy-efficient method for implementing matrix multiplication. However, in practice, the neural network needs to be trained to be applied and cannot be implemented on hardware in which information can only flow in the forward direction.
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 8 Performance Evaluation and Simulation
### Residual Energy  
Residual energy is defined as the remaining energy of any SN in the deployed sensor network. The energy of the SNs is consumed by different operation such as sensing data, storing it, receiving and forwarding, processing etc. The energy consumption is burning parameters for any network having battery operated devices such as in WBANs. This presented scheme shown good performance in terms of energy usages which is portrayed in Figure 9 and in Table 6. EHCRP is leading this branch compared with other schemes. The energy depletion process of EHCRP and E-HARP during simulation was almost equal in ratio. The proposed protocol energy lasted almost till 20000 th round. The energy of EERP depleted very quickly and it completely drained before 7500 th round, which shows bad performance. Also, PCRP didn't execute well with respect to residual energy depletion as its energy lasted only to 10000 th round. The main reason behind the better performance of EHRCP in terms of less energy consumption is its optimized working mechanism.  
Figure 8: Breakdown of Live Nodes vs. Rounds.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Protocol**} & \multicolumn{6}{c}{**No. of Alive Nodes at Different No. of Rounds**} \\ \cline{2-11}
**Name** & **3000** & **5000** & **7000** & **9000** & **11,000** & **13,000** & **15,000** & **17,000** & **19,000** \\ \hline EHCRP (Proposed) & 10 & 10 & 10 & 9 & 8 & 6 & 3 & 1 & 0 \\ E-HARP & 14 & 14 & 14 & 13 & 11 & 9 & 4 & 0 & - \\ PCRP & 4 & 4 & 3 & 3 & 2 & 0 & - & - & - \\ EERP & 8 & 7 & 2 & 0 & - & - & - & - & - \\ EB-MADM & 8 & 8 & 6 & 2 & 0 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Live nodes vs. rounds.
# Metabolic Syndrome and Its Components among Taxi Drivers in the City of Tshwane, South Africa
## 2 Materials and Methods
### Data Collection and Tools  
Taxi drivers were recruited from the selected taxi ranks, which are Bloed, Marabastad, and Bosman. The researcher was accompanied by a South African National Taxi Council (SANTACO) representative from the selected taxi ranks for formal introduction to the taxi line marshals in the morning. The marshals were asked for permission to conduct the study and to recruit the drivers. They were also asked for permission to distribute posters and to help to gather the participants to be included in the study. The recruitment was undertaken with the assistance of researcher assistants.  
A self-administered questionnaire was used to collect data under the supervision of the main researcher ([PERSON]) and research assistants. Demographic factors, occupational information (experience and sleeping hours), and anthropometric data, including height and weight, lifestyle (smoking, alcohol use, and physical activity), and MetS components (obesity, hypertension, and diabetes) were recorded using a validated structured questionnaire (see Supplementary Material) informed by several studies [19; 40; 41]. Validity and reliability were ensured through content and face validity, backward and forward translation using a translator conversant with local language and English, a pilot study, and training of research assistants.  
Weight and height were measured twice for each participant and a third measurement was taken if the difference between two measurements was larger than 1 cm for height or 1 kg for weight. The average of two close measurements in height and weight was used in our study. Blood pressure was measured on the left upper arm, using an Elite 1219 Blood Pressure Monitor (Shanghai International Holding Corp. GmbH (Hamburg, Germany), following a five-minute resting period in a sitting position, on the right hand repeated two times with at least a five-minute interval. Capillary blood was taken using disposable lancets to determine glucose with an Accutrend(r) GCT (Roche Diagnostics, Basel, Switzerland). Participants were requested to fast overnight prior to data collection the following day in the morning. Two consecutive measurements were taken, and the mean value was used for the study. All the instruments were constantly calibrated during data collection for accuracy and consistency. In the present study, we used a modified version of the National Cholesterol Education Program definition of the MetS [44]. Because waist circumference measurements were missing for almost half of the participants, the National Cholesterol Education Program definition was modified by use of a BMI cut point instead of the National Cholesterol Education Program waist circumference criterion (>102 cm), similar to the approach of other researchers [45]. Therefore, MetS was defined by the participant having any three conditions, high BMI (>25 kg/m\({}^{2}\)), hypertension >135/85 mmHg, and Fasting Plasma Glucose (FPG) of >6.1 mmol/L (for 10-12 h).