esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Buck-Boost Topology
#### 5.3.3. Diode Stress (Buck-Boost Step-Up, CC Mode)  
Figure 13 shows the analytic and experimental diode power loss in the CC mode in the step-up section of the buck-boost topology. A positive power loss difference \(L_{Diff}\) implies that the diode loss of the proposed TSBB converter is large, whereas if it is negative, it implies a large diode loss of the conventional TSBB converter.  
To calculate the diode loss, information on several parameters was referenced from the datasheet and all data are typical values at 25 degrees. Parameters not provided in the datasheet were used to calculate the diode loss with some assumptions.  
* Forward voltage \(V_{F}\): obtained from the V\({}_{\text{F}}\)-I\({}_{\text{F}}\) characteristic curve.
* Equivalent resistance \(R_{D}\): obtained from the V\({}_{\text{F}}\)-I\({}_{\text{F}}\) characteristic curve by linear approximation under the current conditions used in the experiment.
* Reverse recovery time \(T_{rr}\) and peak reverse recovery current \(I_{rm}\): estimated and calculated using value 16 nsec at the forward current \(I_{F}\) = 1.0 A and the reverse current \(I_{R}\) = 0.5 A.
* Peak reverse recovery voltage \(V_{rm}\): assumed \(\frac{dI_{F}}{dR_{R}}=0.5\) at \(V_{R}\Big{(}1+\frac{dI_{F}}{dR_{R}}/\frac{dI_{F}}{dR_{R}}\Big{)}\) and corrected coefficient by temperature with V\({}_{\text{R}}\) characteristic curve.
* Ignored any other parasitic factors like internal inductance, capacitance, and so on.  
Due to several assumptions and uncertain parameter values, the two results are slightly different. This is expected to be due to the reverse recovery characteristics that  
Figure 12: Power efficiency comparison of the buck-boost topology (step-up): (**a**) CV mode; (**b**) CC mode.  
change exponentially with the increase in temperature and voltage stress, and also to be affected by parasitic components on the PCB and errors in measurement equipment.  
Despite these differences, both results show a similar trend in which diode losses rapidly increase with voltage stress over a certain region, thereby diode power loss of the  
Figure 13: Analytic and experimental diode power loss of the buck-boost topology step-up period in the CC mode: (**a**) Duty = 0.556, I\({}_{\text{O}}\) = 0.8 A; (**b**) Duty = 0.571, I\({}_{\text{O}}\) = 0.75 A; and (**c**) Duty = 0.600, I\({}_{\text{O}}\) = 0.667 A.  
proposed TSBB converter is bigger than the conventional converter over a certain input voltage. The increase in the D\({}_{1}\) loss of the proposed TSBB converter becomes larger than the power efficiency improvement by the removal of D\({}_{2}\). The power efficiency is reversed at the point where the difference in diode loss changes from negative to positive (+). For the proposed TSBB converter, the power efficiency increases at 60 W or lower at the duty ratio of 0.556; the power efficiency increases at 50 W or lower at the duty ratios of 0.571 and 0.600.
## 4 Discussion and Conclusions  
Simulative and experimental results can prove that the characterization of phase modulation of the SLM, based on the phase in the Fourier domain of the hologram, was effective. This means that, for holography, not only the reconstructed wave field from a hologram, but also the phase in the Fourier domain can be directly utilized in practice. Some processes, such as digital refocusing and/or phase compensation, are not needed in the new method and it will be faster than characterization based on the traditional treatment of a hologram [24]. In this method, all steps can be carried out automatically; it can be implemented more conveniently than the traditional method. The SLM characterized by the new method has also been applied for coherent imaging. The results show that, with characterization, the specimen was reconstructed successfully. This indicates that this characterizing method for phase modulation can be precise in practice.  
Although the information of the specimen can be successfully retrieved in the coherent imaging system with SLM, the robustness of the system is not very strong. The tolerance of the error of the phases added by the SLM is very small in this system. If the environment varies, the phase modulation may need to be recharacterized. Moreover, variations can also bring pixel misalignments into the experimental setup and lead to a failure in retrieving the information. If the SLM is used effectively in coherent imaging, unexpected relative lateral shifts between it and the monitor, as well as the specimen, must be avoided, and the environment should be kept stable.
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 5 Enhancements for M-Rep
### Manual Configuration of Router D  
Another way to select a router that performs the decapsulation of the M-REP multicast flow back to unicast is to manually configure a router as the decapsulating router (router D). In practice, the network administrator would manually select and configure a router to perform the decapsulation process (Figure 12).  
An example of the situation, in which router D has to be manually configured when the destination of the protected unicast flow is not in the domain where a failure occurred is presented in Figure 12 (Manual configuration of Router D). In this case, the administrator must manually configure the Provider Edge Output (PeO) for router function D.
## 2 Materials and Methods
### Derivatization of OAs and GC-MS Analysis  
After the \(\upmu\)SPead(r) procedure, the extract collected in the vial was evaporated and reconstituted with 100 \(\upmu\)L of CHCl\({}_{3}\) and 100 \(\upmu\)L of TFA at 60 \({}^{\circ}\)C for 20 min for derivatization [11]. Finally, the extract derivatized was evaporated and reconstituted in 20 \(\upmu\)L of MeOH for GC-MS analysis.  
GC-MS analysis was performed using an Agilent 6890 N (Palo Alto, CA, USA) gas chromatograph system coupled to an Agilent 5975 quadrupole Mass Selective Detector. The injection volume was 5 \(\upmu\)L, and the mode was splitless mode. The injector temperature was 280 \({}^{\circ}\)C, and helium was used as a gas carrier at 1.3 mL/min. For the chromatographic separation, an HP-5 capillary column ((5\(\%\)-phenyl)-methyl)polysiloxane nonpolar phase, 30 m \(\times\) 320 \(\upmu\)m i.d., 0.25 \(\upmu\)m film thickness; Hewlett Packard, Palo Alto, USA) was used and was thermostated at 220 \({}^{\circ}\)C for 1 min then raised up to 300 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min and held for 1 min, resulting in a method of 15.33 min. The ionization was achieved using an electron ionization source (EI) at 70 eV. The transfer line, ion source and quadrupole analyzer temperatures were maintained at 220, 180 and 200 \({}^{\circ}\)C, respectively, and a solvent delay of 4 min was selected.  
Initially, a full-scan (FS) MS acquisition mode with a mass range of fragment ions from 50 to 500 amu was employed for qualitative detection, to select the characteristic fragment ions for the target OAs. For this purpose, a standard solution of the analytes at a concentration of 50 \(\upmu\)g/mL, previously derivatized with the optimized protocol, was used. The selected ion monitoring (SIM) mode was used as a quantitative scan, whereby the molecular ion of the analyte was monitored in a narrow amu window. For each compound the most abundant fragment ions were used (Table 1). In this MS acquisition mode, only the selected fragment ions with m/z values of interest, rather than a wide range of m/z values, are collected, significantly improving the selectivity and consequently the detection limits of the technique.
# Analysis of Vertical Earth Pressure Acting on Box Culverts through Centrifuge Model Test
## 4 Measurement Results of Vertical Earth Pressure Acting on Box Culvert
### Measured Vertical Earth Pressure Acting on Box Culvert  
Figure 9 shows the vertical earth pressure measured in the centrifuge model test under the loading and unloading conditions. The centrifuge model test was performed twice with three sensors at each cover depth (4, 8, 12 cm), and a total of six data points were recorded at each gravitational acceleration (10, 20, 30, 40, and 50 g).  
The measured vertical earth pressure acting on the box culvert was greater in the unloading condition than in the loading condition. In addition, the loading condition exhibited a linear tendency with increasing gravitational acceleration. By contrast, the unloading condition exhibited a non-linear tendency with decreasing gravitational acceleration, as shown in Figure 9a-c. This is because the loading stage proceeded with the process of application of gravitational acceleration, unlike the unloading stage. That is, the vertical earth pressure of 50 g acted as the residual vertical earth pressure during the unloading process.  
Figure 8: Process of applying gravitational acceleration.  
For example, during the process of unloading from 50 g to 40 g, the vertical earth pressure can be calculated as the sum of the vertical earth pressure of 40 g and the partial residual vertical earth pressure in the sensor at 50 g. In the case of 30 g, the residual vertical earth pressures of 50 g and 40 g are mixed non-linearly. With the progress of the unloading process, the residual vertical earth pressure gradually increases, and the difference between the data of the unloading and loading processes increases.  
Figure 9d shows the average measured vertical earth pressures (Figure 9a-c) for various cover depths. As the cover depths set during the ground formation process increased, the difference in vertical earth pressure increased. This result was ascribed to the fact that the influence of vertical earth pressure on the inherent variability of the ground increases with increasing cover depths. Because a large quantity of soil is loaded on the top of the box culvert, uncertainty, including inherent variability, increases, and it affects the residual vertical earth pressure.
# Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
## Methodology
### Implementation-1  
In implementation 1, each of five base learners (BiGRU_1, BiLSTM_1, GRU_1, LSTM_1, and simple RNN_1) has one hidden layer and the sixth based learner (LG_1) has two hidden layers, followed by one output Dense layer. For all the base learners, the number of hidden layer units is determined based on the integer value obtained from \((seq\_len+2)/2\), where \(seq\_len\) is the length of the feature set (i.e., vector length of the time-series data) and constant 2 is used because number of classification outputs are two (rumor and non-rumor). We considered this approach by following one of rule-of-thumb methods, which states that the number of hidden layer neurons should be between the size of the input layer and the size of the output layer [34]. \(RandUniform\) kernel initializer is used for all the hidden layers with values \((-0.5,0.5)\). \(sigma\) activation is applied only to the RNN model's hidden layer, and Flatten layer is applied only to BiGRU and BiLSTM models to flatten the data before the final output Dense layer that is activated using \(softmax\) function. Adam optimizer is used with learning rate \(1\times 10^{-5}\) along with _categorical cross-entropy_ loss function. Batch input size is set to 32 and number of epochs is 300. We did not use the Dropout technique with these models since their architectures are simple, and using it may cause under-fitting issues. The variants of the proposed model follow the same neural network design except for the hyperparameter that is tuned, for example, batch input size and learning rate.
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 3 Techniques and Methods
### The QoS Parameters  
The proposed routing scheme would select the next node for packet forwarding to the destination node based on the following three parameters: channel quality, link quality, and residual energy.
## 3 Results and Discussion  
In order to demonstrate the effectiveness of the proposed mathematical model and solution method, several scheduling cases are examined. Case 1 and Case 2 are the cases that originated from the manufacturing company that motivated this research. Case 3 is examined to demonstrate the generalizability of the model and solution method, as the load factors for all products in both Case 1 and Case 2 are similar. In this instance, each product has its own load factor. In Case 4, tentative delivery schedules are considered. The results for various values of \(\alpha\) are presented at the end of this section.
# Attack Classification Schema for Smart City WSNs
## 5 Proof of Concept
### Analysis
#### 5.2.2 Enhanced Analysis with Attack Classification  
In this proof of concept, AT1 and AT2 alarms can be constantly triggered and, therefore, do not deserve administrators' attention until an AT4 alarm is received. Upon receiving an AT4 alarm (Step (a) in the schema shown in Figure 5), administrators can proceed with the other steps in the classification procedure:  
1. Administrators must retrieve other alarms from the same service, from the same area or from some network that shares important components with the network that triggered the alarm (Step (b)). Thus, in this scenario, alarms from the parking and sound service have to be retrieved and make up the base alarm set for this analysis.
2. The answer to the question, \"Multiple nodes affected?\", is \"Yes\" (Step (c)).
3. The answer to the question, \"Nodes geolocalized together?\" is \"No\" (Step (d)), since Figure 9 shows that the anomalies detected in the sound sensors and parking clusters are sparsely distributed.
4. The answer to the question, \"Multiple services affected?\" is \"Yes\" (Step (k)).
5. At this point, administrators could have already taken preventive countermeasures involving the transmission medium and the gateway. They should request the gateway provider to undertake a comprehensive analysis of the gateway (Step (m)) to confirm that the networks are under the attack Model 4. As can be seen in Section 4.2, this corresponds to a widely-dispersed attack, so administrators can conclude that the most likely attack candidates aim at the gateway and match the traces of a selective forwarding, a non-constant unfairness, a non-constant collision or a non-constant jamming. In this situation, following the proposed recommended contingency strategies, they should mark an exclusion area near the gateway, redirect WSN traffic to other gateways (if possible) and quarantine the data from the compromised gateway, the nearby services and other services that were using this gateway.  
As this example has shown, using the classification schema and taking into account the alarms from the smart city WSNs as a whole, smart city administrators not only detect anomalies in a more reliable way, but are also provided with a clearer picture of the attacks causing the incidents.
## 2 Methods
### Structure Dip-Oriented Smoothing Filter  
We constructed a dip-oriented smoothing filter, which is a transversal filter based on the scanning results of dip and azimuth. We performed horizontal seismic trace smoothing on the provided seismic data. The purpose was to apply filtering along the seismic  
Figure 2: 9 \(\times\) 9 horizontal time window dip angle and tilt azimuth angle search schematic diagram. The red solid circles are the analysis points, and the green boxes indicate different horizontal analysis windows. The dip angle and the azimuth angle of each analysis point are defined as the dip angle and the azimuth angle of the window containing the maximum coherence analysis point. Using this time domain and lateral slip analysis window method helps to include angular unconformities and other valuable geological features.  
horizon to eliminate random noise and enhance the lateral continuity of reflections through filtering. The key to constructing a guided smooth filter is to distinguish between the inclined azimuth and the noise superimposed of the reflecting layer. After estimating the dip and azimuth of the formation, a filter was used to enhance the signal along the reflector. The most commonly used filters are Mean filter and Median filter. The input data are the preprocessed seismic data, the apparent dip of the inline and the cross-line.  
The Mean filter was the simplest filter to suppress random noise. On the plane, the Mean filter was a low-pass filter, which was a typical running window mean filtering technique. The output data value is the average value of all samples falling in the central analysis window. The window size is usually an odd number, such as \(3\times 3\), \(5\times 5\), or \(9\times 9\), or rectangular or elliptical. The Mean filter of time \(t\) is defined as:  
\[u_{mean}(t)=\frac{1}{J}\sum_{j=1}^{J}u_{j}(t) \tag{4}\]  
In the formula, \(u_{j}(t)\) represents the \(j\)th channel in the \(J\) channel that falls into the analysis window.  
The Median filter is one of the most widely used nonlinear techniques in signal and image processing. The Median filter replaces each sample on the seismic trace of the window with the median value of all samples that fall into the analysis window. It rejects dissident samples (outliers) during the execution process. The window size is usually odd. One method in the calculation is to use the ordered index (\(k\)) to sort all \(J\) samples in the analysis window:  
\[u_{j(1)}(t)\leq u_{j(2)}(t)\leq\ldots\leq u_{j(k)}(t)\leq u_{j(k+1)}(t)\ldots \leq u_{j(J)}(t) \tag{5}\]  
Therefore, the median value can be denoted as:  
\[u_{median}(t)=u_{j(k=\frac{|j+1|}{2})}(t) \tag{6}\]
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 2 Related Work
### Comparison of the Three Detection Algorithms  
Both YOLO and SSD algorithms are single-stage algorithms that are significantly faster than the Faster R-CNN, so they should be preferred in warehouse scenarios that require real-time detection.  
The YOLO algorithm has progressed to its fifth version as of the time of writing. YOLOv5 has made further improvements on the previous version, with increased detection accuracy and speed. Among its variants, YOLOv5s maintains excellent detection capability within a compact size (7.3 MB), laying the foundation for prospective explorations into hardware platforms for cost-effective and capacity-efficient logistics warehouse object tracking [11].  
The DSSD513 algorithm, an improved iteration derived from the SSD algorithm, exhibits a marked improvement in detection accuracy but diminished detection speed. The three algorithms--YOLOv3-320, YOLOv5, and DSSD513--are compared using the COCO dataset to determine the most suitable one for the logistics warehouse object-tracking scenario. The outcome of the comparison is shown in Table 1.  
In Table 1, mAP [12] refers to the mean Average Precision (mAP), which measures the accuracy of the object detection. The delay time refers to the time taken by the algorithm to execute the detection process, signifying the processing speed of the algorithm. The delay time is a critical performance metric in real-time applications.  
As seen in Table 1, YOLOv5s combines both detection accuracy and speed compared to YOLOv3-320 and DSSD513. It has a compact volume size of only 7.3 MB, which ensures its applicability to real-world scenarios. Therefore, YOLOv5 is chosen as the algorithm for object detection in the logistics warehouse scenario.
## 3 Simulated and Measured Results  
The following simulation results were obtained using the Finite Element Method (FEM). More specifically, Ansys HFSS was employed for the simulation of the electromagnetic performances, while the Ansys Workbench was used for the electro-mechanical ones. The material properties considered in the following simulations are the ones of the gold used in the fabrication: a 19,300 kg/m\({}^{3}\) density, a 98.5 GPa Young's modulus, and a 0.22 Poisson's ratio.
## 4 Mathematical Simulations of Biofilm Evolution in Porous Media
### Interactions between Fluid Flow and Biofilm Development  
There are two ways in which flow fields in pore space affect the distribution of biofilm: mass transport and shear stress. The growth of biofilm increases the heterogeneity of flow velocity in porous media which, in turn, leads to the heterogeneity of both local mass transport and shear stress, resulting in the redistribution of biofilms in porous media.  
In [PERSON] et al.'s [86] study, biofilm was considered as a heterogeneous porous material, and the spatiotemporal evolution of biofilms in the pore space of a 2D granular media was simulated. Biofilm growth was described with a DbM approach. The formation of preferential flow paths within the granular porous media induced by the biofilm growth was also simulated. Since the stripped biofilm fragments were considered in the modeling process, it was found that even small fragments can affect the dynamic behavior of the entire system. [PERSON] et al. [106] also discussed the issues of preferential flow paths using a two-dimensional pore-scale numerical model. The system was represented by a rectangular domain, which was divided into three different domains: the grains, the fluid, and the biofilm. The porous media was assumed to be uniform. The biofilm was considered homogeneous and described by an extended DbM method. Based on different assumptions, they simulated flow path dynamics in biofilm-forming porous media under  
Figure 7: After 27 h development of biomass, velocity maps at slice z = 600 \(\upmu\)m for impermeable biofilms (X = \(\infty\)) and permeable biofilms (X = 80 and X = 3) are shown in (**a**) with biomass (green areas) and (**b**) biomass omitted, (reproduced with permission from [105], Copyright John Wiley and Sons, 2012).  
shear forces. The results showed that considering the biomass decay or not results in different flow paths, and the flow paths will change due to the substrate redistribution. We can conclude from these two simulations that whether the biofilm as well as solid grains are homogeneous or not does not affect the generation of preferential flow paths.  
During biofilm evolution in porous media, breakthrough channels recur due to shear stress and nutrient deprivation forming preferential flow paths, which are thus inferred as another cause of the existence of the lowest permeability discovered in early laboratory simulation studies [17; 37].
## 4 Results and Discussion
### Dataset  
For the proposed fine-grained classification approach, we employed the brain tumor classification (MRI) dataset [35] to test, train and validate the different TL-based approaches, with the intention of identifying the best DL classifier. The dataset is freely available as a standard Kaggle dataset. The dataset comprises two brain tumor MRI image collections, i.e., testing and training. Each collection contains four types of brain tumor MRI images, i.e., no tumor, meningioma tumors, pituitary tumors and glioma tumors. However, we only used the meningioma, pituitary and glioma tumor MRI images. The latest version of the research dataset contains 822 MRI images of meningioma, 827 MRI images of pituitary tumors and 826 MRI images of glioma brain tumors in the training folder. The samples from each brain tumor category are shown in Figure 3. Moreover, the testing folder contains 115 images of meningioma, 72 images of pituitary tumors and 100 images of glioma brain tumors. We combined images from both folders. Then, 80% of the data was used for training, and the remaining 20% was used for testing. The dataset comprises grayscale images of different resolutions. In the preprocessing stage, the MRI images of the dataset were resized by using the augmented image data store according to the image input size requirements of different DL models; for example, for mobilenetv2, MRI images were resized to 224 \(\times\) 224, and for darknet19, images were resized to 256 \(\times\) 256. The details of the research dataset adopted for brain image classification are shown in Table 1, which describes the number of images against each tumor type, image format and brain image type.