esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design of the Wideband GGW Bandpass Filter
### Filter Design
(**a**) Implementation using stepped impedance transmission lines. (**b**) Implementation using impedance inverters and transmission lines with the same impedance and electrical length. Figure 6 depicts the EM and theoretical frequency responses of the fifth-order wideband GGW Chebyshev bandpass filter (Figures 1 and 5b). The structure of the practical GGW filter (Figure 1) was simulated (HFSS) with the dimensions given in Tables 2 and 4, while data included in Table 3 were used for the equivalent circuit model of Figure 5b. The conductors were considered perfect in the EM simulations. As can be seen in Figure 6, the EM simulations display wideband properties with a bandwidth \(BW=3.7\) GHz (\(FBW=36.4\%\)), which meets the design specifications initially defined in Table 1. This wideband bandpass response is due to the combination of the high-pass behavior of the GGW and the low-pass characteristics of the metallic pins and GGW sections of electrical length (\(\theta_{e}\)) that work as impedance inverters. In this way, the lower cut-off frequency \(f_{C,L}=8.3\) GHz of this filter is consequence of the high-pass behavior of the GGW, while the higher cut-off frequency \(f_{C,H}=12\) GHz is due to the low-pass characteristics of the metallic pins and GGW sections of electrical length (\(\theta_{e}\)). Return loss in the passband is less than \(RL=20\) dB, except at frequencies near to \(f_{C,L}\), where it is better than 10 dB. This deterioration is due to the high dispersion of the quasi-TE\({}_{10}\) mode close to the GGW cut-off frequency (\(f_{C,L}\)) and to the strong frequency dependence of the impedance inverters in this region [19; 47]. The passband shows five reflection zeros, while the rejection band has a deep spurious-free range up to 15 GHz with a maximum rejection level \(RLSB=77\) dB. Outside the frequency range (8 GHz-15 GHz), the two periodic structures of the GGW no longer behave as PMC sidewalls (Figure 2) and leakage waves occur in the transverse directions between the gap \(h\) (Figure 1). As a result, spurious bands appear below 8 GHz and above 15 GHz. A comparison between theoretical and EM \(|S_{21}|\) results (Figure 6) shows that the out-of-band frequency responses do not match. The out-of-band rejection of the theoretical results is worse than the practical GGW filter. This is because the six impedance inverters of the practical GGW filter (Figure 1) are realized by means of six metallic pins dependent of frequency, while the impedance inverters of the equivalent circuit (Figure 5b) are ideal and constant with the frequency. The six ideal inverters lead to a fifth-order Chebyshev response, while the metallic pins add six additional reactive elements in the frequency response of the practical GGW filter. As a results, the GGW structure shown in Figure 1 has a frequency response of an eleventh-order (\(5+6\)) filter, although this was determined from a fifth-order equivalent circuit [19; 47].
## 4 Discussion
### Differences in Soil Se Content Across Parent Rock Types and Land Use Categories
Given that Pearson and Spearman correlation analyses primarily capture linear or monotonic relationships and may fail to detect more complex nonlinear associations between variables, we further analyzed the mean soil Se content across different intervals of topographic factors (Figure 7). SOM, previously identified as a key influencing factor, was also included in this analysis. As illustrated in Figure 7, the relationship between total Se content and topographic factors is not strictly monotonic. Instead, a nonlinear pattern is observed, characterized by an initial decrease in Se content followed by a subsequent increase. This trend aligns with earlier findings, in which elevation and slope exhibited positive and negative correlations with soil Se content, respectively, as reflected in the patterns shown in Figure 7.  
Two widely used indices for assessing the influence of topography on surface runoff are the TWI and the SPI. Lower TWI values generally indicate drier soil conditions, whereas higher SPI values correspond to areas with greater runoff potential and, consequently, wetter soils. Both indices are commonly employed as proxies for soil moisture status. As shown in Figure 6, when the TWI exceeds 7, both total Se and SOM contents are relatively high. Similarly, the highest concentrations of Se and SOM are observed when the SPI is less than 1.5. However, this positive association does not persist across all SPI intervals. Notably, a marked decline in total Se content occurs when SPI values range between 1.5 and 2.5, despite SOM levels remaining relatively high. This discrepancy may be attributed to the effects of runoff-induced soil erosion, which likely outweigh the capacity of SOM to adsorb and retain Se. Under such conditions, the rate of Se loss due to erosion may surpass its retention by SOM, resulting in a net decrease in total Se content. Figure 7: Average content of soil total Se and SOM at different (**a**) elevations, (**b**) slopes, (**c**) TWI and (**d**) SPI values. The polyline represents the mean variation trends of total soil Se and SOM across different intervals of topographic factors, while the curve illustrates the continuous change trends of total Se and SOM with increasing values of the topographic variables.
###### Abstract
Third, in addition to representing users by their visited POIs, we think the POIs can also be depicted by those users who visit there. Rather than quantitatively considering POIs in a statistical manner, we jointly project users and locations into their common embedding space so that we can estimate and exploit their distances to better distinguish their affinity to the targeted POIs. Below summarizes the contributions of this paper. * We formulate two novel problems: Targeted Propagator Discovery (TPD) and Targeted Customer Discovery (TCD). To the best of our knowledge, we are the first study that proposes the problem of TPD. * We propose a network embedding model, LBSN2 vec, to jointly learn the feature representations of POIs and users based on the social network and user-POI check-in data in LBSN. A more effective variant, PLBSN2 vec, is also presented to further boost the predictability of the embedding space. * Experiments were conducted on a large-scale Instagram dataset to examine the performance of LBSN2 vec and PLBSN2 vec. The results exhibit that LBSN2 vec can outperform well-known network embedding methods in both TPD and TCD tasks. This paper is organized as below. We first present the problem formulation in Section 2. Then we describe the proposed LBSN2 vec and PLBSN2 vec in Section 3. Section 4 demonstrates the experimental setup and presents a discussion of the results. Section 5 concludes this work. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Simple Scoring Heuristics** & **Embedding Learning Approach** \\ \hline \multirow{3}{*}{Example Methods} & (a) Number of involved geodesics & (a) The proposed LBSN2 vec \\  & (b) Weighted neighborhood size & (b) Typical node embedding (e.g., node2 vec [18]) \\  & (c) Centrality-enhanced measures & (c) Transition embedding (e.g., POI2 vec [14]) \\ \hline \multirow{2}{*}{Predictability} & No. Just simple statistics. & Yes. Make prediction based on learned embeddings. \\ \cline{2-3}  & No. Do not characterize and distinguish & Yes. Learning the semantic distinguishable high-order \\ \cline{2-3}  & users and POIs from one another & interactions between users and POIs \\ \hline \multirow{2}{*}{Comparable} & No. User and POIs are different & Yes. Users and items are mapped into a common \\  & information types. & embedding, and thus comparable \\ \hline \multirow{2}{*}{Applicable Scenarios} & (a) Finding popular POIs and users. & (a) Targeted propagator discovery (TPD) \\  & (b) Mining frequent user-POI interactions. & (b) Targeted customer discovery (TCD) \\ \cline{1-1}  & (c) POI recommendation systems \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between simple scoring heuristics and embedding learning-based approach.
## 5 Experiments and Assumptions on Abnormal Behavior Detection and Noise Removal Performance
### Dataset and Environments  
This section describes the dataset used to experiment with and evaluate the accuracy improvement in network attack detection and classification through noise detection and data trend reflection. This experiment used the CIC-IDS-2017 Dataset [15], which collected periodic DDoS attack attempts to observe the data pattern change effectively. This dataset is about network packet data collected through simulations in a typical IT network environment for about a week. It includes 77 various statistical characteristics that can be observed in the network. Detailed information on the CIC-IDS-2017 dataset is shown in Table 1 below. In addition, to reflect the degree of change in extreme data patterns, data bias, and realistic attack distribution, a filtered dataset that reduces the number of attack data from the original dataset to 20% was used. This dataset was used in the experiment to explain the influence of the threshold described in Section 4.  
This experiment was performed in Ubuntu 20.04.3 LTS operating system environment with Intel i7-9700 KF (3.60 GHz) CPU, 64 GB of RAM, and GPU of Geforce RTX 2080 SUPER, with Jupyter Lab version 3.1.4.
## 3 Design
### Security  
With the scheme of secret sharing, the secret sent to the control center through the blockchain network can be privacy-preserved. However, there is one special case in which the node in charge of the job of splitting and distributing is not honest anymore, which may cause the transaction information to be garbled from the very beginning. As shown in Figure 1, they indicate a \((2,3)\)-threshold secret sharing system in which any two shares can reconstruct the secret, but there exists at least one security issue: the dealer is malicious so that the secret shares sent to three players are not incorrect. For example, the share sent to the third player is garbled, and it is possible that the reconstruction will fail. To solve this problem, we choose to integrate the Verifiable Secret Sharing instead of the basic secret sharing, such as [PERSON]'s Secret Sharing.
# A Review of the Role of Critical Parameters in the Design and Operation of Biogas Production Plants
## 3 Process Parameters Involved in a Biogas Production Plant
### Vfa
The relationship between H\({}_{2}\) partial pressure and the interaction between H\({}_{2}\)-producing and H\({}_{2}\)-consuming anaerobes towards influencing individual VFA component are complex and are not clearly understood. Nonetheless, the research on H\({}_{2}\) partial pressure manipulation by employing various external techniques is ongoing [249], and some promising results in this line are already achieved. As with the factors above, the organic loading rate (OLR) is another parameter strongly correlated with the VFA yield and its conversion. Independent of the feedstock type, a high OLR in general leads to VFA accumulation as a result of an imbalance between the growth rates of methanogenic archaea and VFA producing bacteria and speed of methane and VFA producing bacteria. Also, a high OLR can contribute to changes in VFA composition from low molecular weight ones to the high molecular weight ones, for example, from acetic acid to n-butyric acid [244], which eventually can promote methane inhibition. To achieve improved VFA utilization, a sophisticated OLR adjustment with reduced or withdrawn feeding were proved to be beneficial in previous research [252; 118; 244]. However, the range of optimal OLR values corresponding to the reactor and feedstock type, microbial adaptation pattern, and methane yield remain unexplored.
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 3 Method
### Pixel-Wise Guided Filter
When a single regularization value is applied globally in the filter, two situations could be encountered: either the value is big enough to blur out the details in these zones, or it is small enough to preserve the detailed information without efficiently reducing noise. To solve this issue, the application of a regularization map instead of a single value is proposed, as it can offer a more effective balance between noise reduction and texture preservation. Considering that the parameter map \(\mathcal{A}\) has smaller values in the dark regions and larger values in the bright areas, we utilize the parameter map \(\mathcal{A}\), produced directly from the network in this instance, rather than building a new mapping. When the regularization map is treated as a matrix, each pixel is assigned a unique regularization map. This way, the regularization map in the guided filtering, along with the low-light image as guidance, produces outputs that are satisfactory to users. Finally, the PGF can be summarized as follows:  
\[E(a,b)=\sum_{i\in w}\Bigl{(}\left(aI_{i}+b-N_{i}\right)^{2}+\mathcal{A}^{ \prime}a^{2}\Bigr{)}. \tag{4}\]  
where \(I\) represents the low-light image used as the guidance, \(N\) indicates the normal-light image containing high noise, and \(\mathcal{A}^{\prime}\) denotes the map after scaling by the parameter map \(\mathcal{A}\).
# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 3 Results and Discussion
### Dataset  
For performance evaluation we selected a diverse dataset comprising of soccer and cricket videos from YouTube as done by the comparative methods, i.e., [24; 25; 26]. The dataset includes 10 videos of 13 h from six major broadcasters, i.e., ESPN, Star Sports, Ten Sports, Sky Sports, Fox Sports, and Euro Sports. In addition, we included the sports videos of different genre and tournaments in our dataset. Cricket videos consist of 2014 One Day International (ODI) tournament between South Africa and New Zealand, 2006 ODI tournament between Australia and South Africa, 2014 test series and ODI series between Australia and Pakistan, 2014 ODI series between South Africa and New Zealand, 2014 Twenty20 cricket world-cup tournament, and 2015 ODI cricket world-cup tournament. For soccer videos, we considered the 2014 FIFA world-cup and 2016 Euro-cup videos. Soccer videos consist of Brazil vs. Germany semifinal match and Argentina vs. Germany final match of 2014 FIFA world-cup, Portugal vs. France final match and France vs. Germany semifinal match of 2016 Euro-cup.  
Each video has a frame resolution of 640 \(\times\) 480, frame rate of 30 fps and recorded in MPEG-1 format. The videos represent different illumination conditions (i.e., daylight, artificial lights). The dataset videos are comprised of different shot types, i.e., long, medium, close-up, and crowd shots, as shown in Figure 5. We used 70% frames of our dataset for training purpose and rest of the 30% for validation purpose. Our dataset videos can also be accessed at Reference [27] for research purposes.
## 1 Introduction
These methods have been proven to have good control performance, but the adaptive law is related to the control law needed to be designed together with the control law, which will lead to inconvenience in some cases. The dynamical neural network identifier, proposed by [PERSON], can not only approximate unknown nonlinear dynamic systems well, but also dynamically adjust weighted parameters independent of control law [27; 28]. And it has been applied in DC motors [27; 28], unmanned quadrotor formation flight [29], wastewater treatment bioprocess [30], etc. Inspired by this, a dynamic inverse tracking control design method for a FAHV based on DDN is proposed. The main contributions of this paper include the following:  
1. A DNN is used to identify the FAHV model. The weighted parameters of the neural network are updated by the adaptive law and compared with conventional system identification techniques, such as maximum likelihood estimation method [31; 32] and Kalman filtering method [33], this approach does not require the exact mathematical model of the object. 2. Compared with the widely used adaptive neural network control [21; 22; 23; 24], the DNN system identification method adopted in this paper is independent of the control law design, which is convenient for system identification and control law design. 3. A dynamic inverse controller is designed based on the identification model, which avoids complex model transformations; thus, the controller design process is simplified. The remainder of this paper is organized as follows. Section 2 presents the FAHV model and preliminaries. The neural network identification model and the dynamic inverse controller are developed in Sections 3 and 4, respectively. Simulation studies are made in Section 5 and the conclusions are presented in Section 6.
## 4 Feature Extraction Method for Sparse Trajectories
### Deep Feature Extraction: A Comparative Baseline
* Fine tuning and feature extraction Fine-tuning is performed by adding an output layer to the top of the pretrained DNN constructed by the SDA and minimizing the loss function \(\mathcal{L}_{\text{supv}}(\mathbf{y},\mathbf{z}_{\text{supv}})\) between the output \(\mathbf{z}_{\text{supv}}\) and the target labels \(\mathbf{y}\) (Figure 4b). In the fine-tuning process, the target labels are defined as binary vectors representing the classes. Finally, the fine-tuned IDNN takes the vector \(\mathbf{x}_{k}^{\text{img}}\) as input, and the feature vector \(\mathbf{f}_{k}^{\text{IDNN}}\) is extracted from the \(\lambda\)-th intermediate layer of the IDNN. The dimensionality of the extracted feature vector \(\mathbf{f}_{k}^{\text{IDNN}}\) is equal to the number of neurons in the \(\lambda\)-th middle layer. Through this series of procedures, the feature vectors \(\mathbf{f}_{k}^{\text{IDNN}},\forall k\in K\) are obtained. Notably, the IDNN proposed by [PERSON] et al. [16] utilizes not only the feature vectors derived from trajectories but also other semantic information as features. By contrast, as this study aims to classify trajectories without semantic information, the classification performance is evaluated using only the feature vectors \(\mathbf{f}_{k}^{\text{IDNN}}\) obtained through the IDNN.
# Fault Investigation of Circulation Pumps to Detect Impeller Clogging
## 3 Results and Analysis
### Motor Current Signature Analysis--Impeller Clogging
#### 3.3.2 Constant Power Consumption  
As can be seen in Table 1, the power consumption of the pump decreases with increasing clogging. For this reason, the third test run was performed for the healthy pump (see Section 2.3). Here the power is adjusted by adjusting the shut-off valve to match the fault cases. This ensures that the results analyzed were not obtained by decreasing the power consumption. In addition, the following results allow a better comparison of faulty and healthy variants. For better visualization, the current signal is cut to 1 s and for windowing, the hamming window is used. The focus was on the two fault frequencies \(f_{r,pump}\). In Figure 7 the MCSA for the faulty variants and the corresponding healthy variant with the same power consumption (see Table 1) of the pump are shown.  
If the MCSA of the healthy variant is considered first at a frequency of 125 Hz the amplitude first decreases with decreasing power consumption from \(-\)91.2 dB (a) to \(-\)110.2 dB (b) to \(-\)121.8 dB (c). If (c) is considered, no peak is noticeable at this point. However, from then the amplitude starts to increase again with increasing clogging. The amplitudes of the frequency 208.4 Hz have the same characteristic. These results show that the power consumption has a small influence on the peak characteristics at the fault frequencies \(f_{r,pump}\).  
Now the faulty configurations are compared with the corresponding healthy ones. As the results of the second test run have already shown, the faults with three and four clogged channels show the most obvious characteristics. In any case, the amplitude at \(f_{r,pump}\) is greater than the corresponding healthy variant. Table 2 gives an overview of how much the amplitude increases in comparison to the healthy variant. Additionally, the results for the speed of 1800 rpm and 1000 rpm entered. \(f_{r,pump}\) was determined by Equation (2). At a speed of 2500 rpm, the increase is highest with three clogged channels, followed by four and two clogged channels. The remaining fault variants are more difficult to detect  
Figure 7: MCSA of a pump with a different number of clogged impeller channels at a speed of 2500 rpm and corresponding healthy variant with the same power consumption: (**a**-**g**) 1-7 clogged channels.  
due to the lower increase. This trend can also be seen at a speed of 1800 rpm. Here the increase of the amplitudes is about the same at three and four clogged channels. At a rotational speed of 1000 rpm the results were no longer clear. At four clogged channels, even an amplitude decrease of \(-27.9\) dB can be observed.
## 3 Fast Channel Information Prediction Based Random Forest
### RB and Power Allocation Algorithm for Energy-Efficient Optimization
From (c), we can obtain \(\rho^{*}\geq\frac{\hat{R}(x)}{\hat{P}(x)},\forall x\in\mathbb{S}\), namely, \(\rho^{*}\) is the maximum energy efficiency; from (d),we can obtain \(\rho^{*}=\frac{\hat{R}(x^{*})}{\hat{P}(x^{*})}\), namely, \(x^{*}\) is the optimal RB and power allocation scheme. Therefore, Theorem 1 is proved. The proof is over. \(\square\)  
According to Theorem 1, the Dinkelbach method or dichotomy method can be used to obtain the optimal energy efficiency \(\rho^{*}\). Both the Dinkelbach method and the bisection method obtain the optimal parameter \(\rho\) by iterating and updating \(\rho\) until the convergence conditions are satisfied. The two methods have the same convergence speed, but the Dinkelbach method does not need to set the upper limit of \(\rho\) in advance, so we use the Dinkelbach method to find the optimal energy efficiency \(\rho^{*}\). See Algorithm 2 for the specific steps. Next, we are to assume that \(\rho\geq 0\) for solving the optimization problem \(P_{4}\). For the convenience of analysis, the \(P_{4}\) is rewritten as \(P_{4-1}\),  
\[P_{4-1}\;:\;\max_{b,\hat{p}}\left[\;\hat{R}(b,\hat{p})-\rho^{\rm i-1}\;\hat{P}(b, \hat{p})\right] \tag{16}\]  
s.t. Formulas (14a), (14b), (14c), (14d)  
Where \(\rho^{\rm i-1}\) is the energy efficiency of the previous iteration, assuming that \(\rho^{\rm i-1}\geq 0\) is a constant. \(\hat{R}(b,\hat{p})\) is a concave function, \(\hat{P}(b,\hat{p})\) is an affine function, and the constraints are all linear combinations. We can obtain that \(P_{4-1}\) is a convex optimization problem. Therefore, corollary 1 can be obtained. **Inference 1**. : _If the optimization problem \(P_{4-1}\) is feasible, then \(P_{4-1}\) is a convex optimization problem for feasible solutions of all the variables \(\{b,\hat{p}\}\)._  
Since \(P_{4-1}\) is a convex optimization problem, \(P_{4-1}\) can be solved using a Lagrangian dual method and the optimal solution can be obtained. For the optimization problem \(P_{4-1}\), the Lagrangian function we constructed is as follows:  
\[\mathcal{L}(b,\hat{p},\lambda,\mu)=\;\hat{R}(b,\hat{p})-\rho\;\hat{P}(b,\hat{p })+\lambda\Bigg{(}P_{\rm T}-\sum_{n=1}^{N}\sum_{m=1}^{M}\sum_{k\in\cup_{n}} \sum_{j\in Jm}S_{n,m}^{kj}\Bigg{)}+\mu\Bigg{(}1-\sum_{k\in\cup_{n}}\sum_{j\in J m }n_{n,m}^{kj}\Bigg{)} \tag{17}\]  
where \(\lambda\geq 0\) is the Lagrangian multiplier related to the constraint of \(S_{n,m}^{kj}\); \(\mu\geq 0\) is the Lagrangian multipliers related to the constraint conditions \(n_{n,m}^{kj}\) ; and \(\rho\) is a shorthand for \(\rho^{\rm i-1}\). See Algorithm 3 for the update \(\rho\).