esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Saprolite Thickness Needed to Remove _E. coli_ from Wastewater
###### Abstract  
Saprolite, weathered bedrock, is being used to dispose of domestic sewage through septic system drainfields, but the thickness of saprolite needed to remove biological contaminants is unknown for most saprolites. This study developed and tested a simple method for estimating the thickness of saprolite needed below septic drainlines to filter _E. coli_ from wastewater using estimates of the volume of pores that are smaller than the length of the coliform (\(\leq\)10 \(\upmu\)m). Particle size distribution (texture) and water retention data were obtained for 12 different saprolites from the Piedmont and Mountain regions of North Carolina (N.C.). Saprolite textures ranged from clay loam to coarse sand. The volume of pores with diameters \(\leq\)10 \(\upmu\)m were determined by water retention measurements for each saprolite. The data were used in an equation to estimate the saprolite thickness needed to filter _E. coli_. The estimated saprolite thicknesses ranged from 36 cm in the clay loam to 113 cm for the coarse sand. The average thickness across all samples was 58 cm. Saprolite thickness estimates increased as silt percentage decreased and as sand percentage and in situ saturated hydraulic conductivity increased. Silt percentage may be most useful for estimating appropriate saprolite thicknesses in the field.  
colorings; piedmont soils; mountain soils; septic systems; weathered bedrock 23 February 2021 26 February 2021 26 February 2021
## 2 Materials and Methods
### Proposed Algorithm
Such processing is utilized in the case of THz imaging for noise cancelation and spatial resolution improvement [24; 31]. The basic deconvolution process is very sensitive to noise and can result in considerable errors in the case of experimental results (if noise is present). In this work an improved, least squares deconvolution method was utilized [40], according to a following equation:  
\[g_{m}=\left(\mathbf{H}^{\mathrm{T}}\mathbf{H}+\lambda\mathbf{I}\right)^{-1} \mathbf{H}^{\mathrm{T}}s_{m} \tag{1}\]  
where: \(\mathbf{H}\) is the convolution matrix, \(\mathbf{I}\) is the identity matrix, \(\lambda\) is the regularization parameter, \(s_{m}\) is the measured signal, and \(g_{m}\) is the deconvolved signal--reflectivity profile of material. In the next step, weighting functions \(w_{\mathrm{CHI}}\) and \(w_{\mathrm{CH2}}\) were calculated as an envelope based on the signal obtained using the analytical model (presented in Section 2.3). This step is shown in Figure 7. The utilized model enabled the calculation of the waveform reflected from a given multi-layered structure if an incident wave is in the form of a wideband Gaussian pulse. In order to obtain analytical form of weighting function the following approximation was utilized:  
\[w_{\mathrm{CHK}}(t)=\sum_{i=1}^{L}p_{i}e^{q_{i}t} \tag{2}\]  
where \(w_{\mathrm{CHK}}(t)\) is the weighting function of given channel \(k\) = 1 or 2, and \(p_{i}\), \(q_{i}\) are the approximation coefficients. Figure 5.0 Signals acquired in the case of the inspection of the layered composite material (glass fiber-reinforced) consisting of 30 layers: (a) A-scan; and (b) B-scan. Figure 5.1 An algorithm of double-sided terahertz inspection of a layered structures. The sum of two exponential functions (\(I\) = 2) is sufficient to obtain acceptable level of approximation. In the next step both signals are combined using weighted sum:  
Figure 5.2  
where \(g_{cmb}(t)\) is the combined signal, \(w_{nCH1}(t)\) and \(w_{nCH2}(t)\) are the weighting functions normalized to the \(\langle 0;1\rangle\) range, associated with channel one and two, respectively, and \(g_{mCH1}(t)\) and \(g_{mCH2}(t)\) are the deconvolved signal of channel one and two, respectively. The reconstructed signal \(g_{cmb}(t)\) in the vicinity of the FSR pulse is calculated mainly based on \(g_{mCH1}(t)\), and close to the BSR pulse based on \(g_{mCH2}(t)\). The signal from channel two was multiplied by \(-1\) and time-reversed (\(-g_{mCH2}(-t)\)). This operation enabled obtaining the same polarization of FSR, BSR, and defect-caused pulses, thus, constructive interference is possible. The combined signal in case of measurements will be presented in Section 3. Finally, the obtained signal contains high amplitude pulses caused by material-air interfaces (FSR and BSR). The signal caused by layers reflections is symmetrical--the highest values are close to FSR/BSR. Even in the center of whole material response--where the signal is weakest--its amplitude is noticeably higher than in the case of the raw signal (obtained for single-side inspection).
# Efficiency Comparison of AC and DC Distribution Networks for Modern Residential Localities
## 1 Introduction
However, load variation was not considered for this study. The authors of [21] discussed the comparison of AC and DC systems with the inclusion of solar PV. They concluded that the DC system utilized solar energy was better than the AC system. However, the loads under consideration were LED lights with single power electronic converter as a driver. The author of [22] concluded from the comparison of AC and DC that if local generation is provided, only DC is a suitable option. The work presented in [23] was another efficiency comparison of the two paradigms. The study revealed that DC-DC converter efficiency as the _defining factor_ for AC and DC systems' efficiencies. The loads were divided on a 50-50 basis, i.e., half loads as AC and half DC, and the effect of power electronic inter-conversions (AC/DC, DC/AC) was not included. Across the globe, DC power is assumed to have not gained significant fame as compared to AC power in the residential sector [3; 24; 25]. This paper aims to present the viability of DC at the residential level, by presenting a comparative analysis of AC and DC distribution networks. Several factors influence the comparison; however, this study frames the factor of \"time\". This reflects how the power demands of loads installed within the home change through a day. Power electronic  
Figure 1: DC in the Power System. converters form the backbone of DC distribution systems. With changing load demand, the efficiencies of the installed power electronic converter change. Thus, a time-based study considering daily load variation and efficiency variation of converters with changing loads is important to perform a comparative analysis. In this study, average load data for a standard US home is used for the purpose of analysis. A mathematical model of the systems is formulated at an hourly basis. MATLAB is used for mathematical model-based calculations and analysis. The focus of this study is to keep the comparison fair; therefore, the conditions for both the systems are kept similar so that a real picture is obtained that portrays actual comparison in the time frame.
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 6 Experimental Results and Comparison
### Previous Work Comparison and Evaluation
#### 6.3.2 Anomaly Detection Accuracy  
As far as the prediction results are concerned, the results of either model are not satisfactory. This reflects the challenge of residential power consumption prediction. Therefore, because the prediction results are the main basis for detection with the results of [PERSON] et al. and [PERSON] et al., the results are not accurate enough. However, it should be pointed out that the main purpose of the [PERSON] et al. method is to detect the change in the trend of users' usage, not nontechnical loss.  
In terms of classification-based methods, the work of [PERSON] et al. exhibits a lower F1 score than our method, which verifies the necessarily of introducing the pattern matching-based detection part and the sampling efficiency of the proposed method. Introducing an independent anomaly detection design into a general regression model-based load management framework not only improves the accuracy of detection that future increases the security of the system but also enhances the interpretability of this method. More importantly, for the same training set size, the accuracy of the proposed pattern matching-based anomaly detector is higher than that of the general supervised learning method, which proves that this combination of FOA-SVM and the cross-entropy loss  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & \(R^{2}\) & RMSD & \(F_{1}\) \\ \hline This work & 0.6857 & 203.4326 & 0.9396 \\ [PERSON] et al. [28] & 0.5362 & 218.2022 & 0.7113 \\ [PERSON] et al. [12] & 0.6271 & 216.7611 & 0.9034 \\ [PERSON] et al. [13] & N/A & N/A & 0.8278 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the results between the proposed method and previously reported methods.  
function provides a novel detection approach that can effectively reduce the training cost and improve the detection accuracy. Finally, we conclude that the proposed method in this study yields the best prediction and anomaly detection performance among all the anomaly detectors.
# Anisotropic Shear Strength Behavior of Soil-Geogrid Interfaces
## 3 Experimental Results
### Influence of Shear Direction
The percentages of shear strength variation for all the soil-geogrid interfaces under different normal stresses are summarized in Table 2. With the increase in normal stress, the percentage of shear strength variation generally decreases, indicating less anisotropy. This is because greater normal stress could provide stronger confinement, and thus generate a more stable interlocking structure between the soil particles and the geogrid aperture, which would be less sensitive to the direction of loading. In general, the percentages of shear strength variation \(A_{s}\) for soil-biaxial geogrid interfaces are greater than those for soil-triaxial geogrid interfaces under the same normal stress, which indicates stronger anisotropy for soil-biaxial geogrid interfaces than soil-triaxial geogrid interfaces. This is because the triangular aperture of the triaxial geogrid is more stable than the rectangular aperture of the biaxial geogrid, which makes the soil-triaxial geogrid interaction less sensitive to shear direction. This is consistent with the data provided in Table 1, which demonstrates that the biaxial geogrid shows stronger anisotropy than the triaxial geogrid in terms of tensile strength. The stable aperture shape of the triaxial geogrid could improve the interlocking effect between the soil particles and geogrid apertures, thus showing less anisotropic shear strength behavior for the soil-triaxial geogrid interface.
## 4 Smart Grid Architecture
### Transmission Domain  
This domain is responsible for the large-scale transmission of electricity from the power plant to the distribution grid via multiple substations. The transmission domain includes actors such as RTUs, safety relays, phasor measurement units (PMUs), power quality control, substation meters, substation user interfaces, and so on. DERs, such as electrical energy storage or peaking generation systems, are often used in the transmission domain. The majority of transmission domain activities take place in substations. Transmission systems are typically monitored and controlled by a SCADA system, which consists of communication networks, monitoring equipment, and control equipment. The primary role of a regional transmission operator (RTO) or independent system operator (ISO) operating a transmission system is to maintain system stability by balancing generation (supply) with load (demand) on the transmission system. Energy and related services are purchased through the business domain, planned and controlled by the operations domain, and then distributed through the transmission domain to the distribution system and eventually to the consumer. The market domain participants and their functions are summarized in Table 5.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Participants** & \multicolumn{2}{c}{**Functions**} \\ \hline  & \(\bullet\) & Sell powers to end consumers \\ Retailing & \(\bullet\) & Connect to a trading organization \\ \hline DER aggregation & \multicolumn{2}{c}{Aggregators combine smaller participants} \\ \hline Trading & \(\bullet\) & Purchasing and selling of energy \\  & \(\bullet\) & Participants in the market \\ \hline \multirow{3}{*}{Market operations} & \multicolumn{2}{c}{Make the market functions smoothly} \\  & Clearing of financial and products exchanged, price quote sources, audit, and balancing \\ \hline \multirow{3}{*}{Market management} & \multicolumn{2}{c}{Markets managers include independent system operators} \\  & \multicolumn{2}{c}{(ISOs) for whole markets} \\ \hline Ancillary operations & \multicolumn{2}{c}{To provide support such as frequency, voltage, and spinning} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Participants of market domains and their functions.  
Figure 6: Markets domain.
## 4 Differential Evolution Algorithm for the ATO over ERTMS Eco-Driving Problem
### Optimization Method Flowchart  
The optimization method proposed follows the steps described below and is represented in a flowchart in Figure 1.  
1. The process starts generating a number of random individuals equals to the population size. Each individual is defined by a \(C_{m}\) containing random values.
2. These individuals are evaluated using the simulation model. The model receives the driving commands contained in the \(C_{m}\) of the individual to evaluate and returns the energy consumption of the journey, the passing times at the intermediate target points, and the stopping time at the arrival. Using these results, the fitness of each individual is calculated.  
Figure 1: Optimization method flowchart.  
The population is sorted in increasing value of fitness to find the best individual in the population.
4. The mutation process is applied to all the individuals in the population creating a donor vector for each individual in the population.
5. Crossover is applied to the donor vectors to create a trial vector for each individual in the population.
6. Donor vectors are evaluated using the simulation model, and the fitness value of each donor vector is calculated.
7. The population is subjected to the selection process to create the population result of the iteration. Each individual is compared with its corresponding trial vector. If the individual presents lower fitness value, the individual survives in the new population and the trial vector is suppressed. Otherwise, the trial vector substitutes the individual in the new population.
8. The new population is sorted in increasing value of fitness to find the best individual.
9. If the iteration number is equal to the maximum number of iterations of the algorithm, the process finishes providing the best solution found. Otherwise, the number of iterations increases by one, and the process is repeated from step 4.
# An Enhanced Feature Pyramid Object Detection Network for Autonomous Driving
## 2 Proposed Method
### Enhanced Feature Extraction Subnet
#### 2.1.1 Feature Channel Weight Module (FCWM)  
FCWM focuses on enhancing features along channel of each pyramid level. FCWM first explicitly models the dependency of features along channel and learns a channel specific descriptor through the squeeze-and-excitation method [18]. Then, it emphasizes the useful channels for more efficient global information expression of feature maps in each pyramid level.  
Suppose the feature maps in \(n\)th pyramid level is \(P_{n}\), which is generated by FPN. \(H_{n}\) and \(W_{n}\) are the spatial height and width of \(P_{n}\), respectively. The \(i\)th channel feature is \(P_{n}^{i}\).  
At the beginning, we do global average pooling on \(P_{n}^{i}\) to get the global distribution response \(Z_{n}^{i}\):  
\[Z_{n}^{i}=\frac{1}{H_{n}\times W_{n}}\sum_{p}^{H_{n}}\sum_{q}^{W_{n}}P_{n}^{i}( p,q). \tag{1}\]  
We use two fully connected layers to map the non-linear correlation between all global distribution responses \(Z_{n}\) and obtain the feature importance vectors \(\hat{Z}_{n}\):  
\[\hat{Z}_{n}=\mathrm{W}_{n}^{1}(\delta(\mathrm{W}_{n}^{2}Z_{n})), \tag{2}\]  
where \(\mathrm{W}_{n}^{1}\) is the weight of the first fully connected layer. \(W_{n}^{2}\) is the weight of the second fully connected layer. \(\delta\) represents the ReLU function.  
Then, we normalize \(\hat{Z}_{n}\) to \([0,1]\) as a weight vector:  
\[r_{n}=\sigma(\hat{Z}_{n}), \tag{3}\]  
where \(\sigma\) represents Sigmoid function.  
Figure 2: The structure of enhanced feature extraction subnet.  
Finally, we assign the weight \(r_{n}\) to the original feature \(P_{n}\) and get the new pyramid feature \(Pcr_{n}\) after channel-wised recalibration:  
\[Pcr_{n}=P_{n}r_{n}=[P_{n}^{1}r_{n}^{1},P_{n}^{2}r_{n}^{2},...,P_{n}^{n}r_{n}^{n }]. \tag{4}\]
## 3 Method
### Image Patch Feature Learning  
Our main idea is to find corresponding patches after MKD extraction of keypoints from input images. To achieve high accuracy of patch matching, the similarity of patch pairs is learned from the labeled training examples of matching and non-matching optical and SAR image patches. Suppose that the input images are denoted as \(I_{a}\) and \(I_{b}\), respectively. If MKD detects \(m\) keypoints on \(I_{a}\), the set of its image patches is \(P^{a}=\{p_{1}^{a},p_{2}^{a},\ldots,p_{n}^{a}\}\). Similarly, another set of image patches is \(P^{b}=\{p_{1}^{b},p_{2}^{b},\ldots,p_{m}^{b}\}\). Then we obtain the corresponding image patch pairs \(patch-pairs=\{p_{i}^{a},p_{j}^{b}\}\) by combining the patches in images \(I_{a}\) and \(I_{b}\) based on MKD keypoints, where \(i=1,2,\ldots,n\), \(j=1,2,\ldots,m\). Based on significant advantages of joint processing of image patches in two-channel network [8], we perform joint processing by channel fusion. Specifically, the input image patch pair is processed by channel fusion t first, then it is used as the input of the matching network. The matching labels of the image patch pair are obtained after feature learning. If the network predicts the label \(y_{i}^{\prime}=1\), i.e., the image patch pair represents a correct matching pair, its center point is used as matching point. In this way, we convert the image patch matching problem to a binary classification problem that predicts the relationship between input image patches and matching labels.  
Due to the imaging mechanism of optical and SAR images and the size of image patches, some similar image patches may be found in images with weak texture. This one-to-many matching situation may affect matching accuracy. Based on work in [19], we apply a global constrain method, i.e., RANSAC to remove the false matching points.
## 4 Evaluation and Performance Results
### Results Discussion  
To assess the impact and resilience of the algorithm in vehicle tracking under occlusion scenarios, YOLOv5s_DSC is utilized as a detector in conjunction with Deep SORT. Figure 4 illustrates the tracking boxes of various vehicle types, each represented by distinct colors. Each tracking box not only includes category and category confidence information, but also incorporates a tracking ID. In the hardware environment outlined in Table 3, the YOLOv5s_DSC + Deep SORT algorithm achieves a processing speed of 58 FPS.  
To further evaluate the algorithm's robustness, occlusion scenes are considered. Two occlusion situations are examined: (1) the target is obscured by foreign objects; and (2) the targets are obscured by each other. Initially, the algorithm's robustness is verified when the target is concealed by external objects. The algorithm's ability to recognize and retrack targets after they reappear is tested using a traffic video obstructed by a pillar. Figure 5 displays four consecutive images wherein the dark car with a tracking ID of 3 re-emerges after being blocked by a pillar, demonstrating the algorithm's strong resilience and accuracy in occluded scenes.  
The algorithm's performance is further evaluated in scenarios where targets are occluded by other targets. Specifically, the algorithm's capability to track targets that are partially concealed by other targets is assessed. A video sequence is selected in which a bus partially obscures a car that is being tracked. As depicted in Figure 6, despite the occlusion, the tracking ID for the car (ID 4) remains unchanged. This demonstrates that the proposed algorithm adeptly handles partial occlusions between targets, showcasing its effectiveness in practical applications of target tracking.
# Real-Time, Deep Learning Based Wrong Direction Detection
## 2 Background
### Vehicle Detection and Segmentation
#### 2.1.2 Optical Flow  
One of the machine learning methods for locating a car is an optical flow which is the pattern learning of a moving object. The most widely used optical flow method in computer vision is called the Lucas-Kanade method [4]. The basic idea of optical flow is to assume that the color or intensity of a pixel is constant under displacement from one frame to the next. This optical flow estimation algorithm also assumes that an object's frame-to-frame displacement distance is small and local. In addition, the objective measurement of the local level of reliability of motion information is provided. [PERSON] and [PERSON] [5] increased tracking performance by modifying the \"good feature\" selection criterion of reliability in order to evaluate the texture features of areas, which later led to the creation of the Kanade-Lucas-Tomasi (KLT) tracker [6] (see Figure 3). However, according to [PERSON] et al. [7], optical flow computation techniques are very sensitive to noise and object illumination. Generally, optical flow can be unreliable in cases where the brightness intensity changes, and object occlusions happen. Since our task in focus requires a minimal error rate, usage of optical flow as a detector could be unacceptable due to the changing lighting and weather conditions.  
[PERSON] et al. [8] described optical flow-based wrong-way driver detection. Their proposed system was separated into three main stages: learning, detection, and validation. For the learning stage, which is the car detection phase, they combined the Lucas-Kanade optical flow method with Gaussian mixture models [9]. They mentioned that their system could detect vehicles moving in the wrong direction from a \(320\times 240\) pixel image at 33 frames per second (fps). Accordingly, the experiments conducted on many scenes demonstrated that their proposed system has the following properties: wrong-way vehicle detection, speed, and robustness to illumination and weather conditions. However,  
Figure 2: Comparison between two background subtraction methods. KNN: k-nearest neighbor; MOG2: Gaussian mixture-based background/foreground segmentation.  
the simulation results and experiments on system accuracy were insufficient to prove the applicability of their method.