esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conventional Uplink Reception Strategies  
In this section, we describe the uplink reception without inter-cluster cooperation [5; 6] or with a conventional inter-cluster cooperation strategy [7]. Each \(\mathrm{RU}\ (i,r)\) needs to send the information of the uplink received signal \(y_{i,r}\) to the serving BBU \(i\). Due to the fronthaul capacity limitation, \(\mathrm{RU}\ (i,r)\) quantizes the signal and sends the resulting bit stream on the fronthaul link. Following the results from standard rate distortion theory [13], the quantized signal represented by the bit stream can be modeled as  
\[\hat{y}_{i,r}=y_{i,r}+q_{i,r}, \tag{2}\]  
Figure 1: Illustration of the uplink of a two-cluster C-RAN system, in which two neighboring clusters interfere with each other on the uplink channel and the BBUs cooperate via backhaul links.  
where the quantization distortion \(q_{i,r}\) is independent of \(y_{i,r}\) and distributed as \(q_{i,r}\sim\mathcal{CN}(0,\omega_{i,r})\) under the Gaussian test channel [5; 7]. Here, \(\omega_{i,r}\) stands for the quantization noise power with \(\omega_{i,r}>0\).  
In this work, we focus on a point-to-point compression strategy [7; 14; 15], in which BBU \(i\) decompresses the quantized signals \(\hat{y}_{i,1},\hat{y}_{i,2},\ldots,\hat{y}_{i,M}\) separately (the analysis and design with more sophisticated BBU operations, such as successive decompression [4] or joint decompression and decoding [5], is left as a future work). Under this assumption, the distorted signal \(\hat{y}_{i,r}\) after quantization can be recovered by BBU \(i\) based on the bit stream received on the fronthaul link, if the following condition is satisfied [13].  
\[I\left(y_{i,r};\hat{y}_{i,r}\right)=\log_{2}\left(1+\frac{\sigma_{y,i,r}^{2}}{ \omega_{i,r}}\right)\leq C_{F}, \tag{3}\]  
where the power \(\sigma_{y,i,r}^{2}\) of \(y_{i,r}\) is given as  
\[\sigma_{y,i,r}^{2}=\sum_{k\in\mathcal{K}}P|h_{i,r,k}|^{2}+\sum_{k\in\mathcal{ K}}P|g_{i,r,k}|^{2}+\sigma_{z}^{2}. \tag{4}\]
# Can Biochar Improve the Sustainability of Animal Production?
## 2 Summary of Biochar-Production Techniques
### Properties of Biochar
#### 2.3.1 Surface Area and Porosity  
The specific surface area of biochar in conjunction with its porosity is perhaps the most important property that affects the performance of biochars. Biochars with high surface areas are naturally equipped with large numbers of active sites. However, the relation between the specific surface area and the efficacy of biochar is not always linear [59]. Biochar surface areas can range from 0 to 520 m\({}^{2}\) g\({}^{-1}\) depending on the feedstock. Typically the surface area is highest for straw and wood biochars compared to manure-based biochars [60]. The total specific surface area of biochar is a result of the porosity that consists of a combination of the micro (<2 nm), meso (2-50 nm), and macro (>50 nm) pores.  
When biochars are used as adsorbents, catalysts, soil conditioners, or energy storage devices, the efficacy of biochar is a function of the pore size and the chemical species being adsorbed [61]. When the size of the adsorbate species is larger than the pore size, the biochar may not perform at its optimum capacity. However, the process conditions during carbonization may be tuned to synthesize biochars equipped with an optimal proportion of micro, meso, and macropores to maximize the biochar's efficacy. This largesurface area and distribution of pore size are likewise desirable for a variety of animal production applications.
# Multimodal Classification of Anxiety Based on Physiological Signals
## 3 Materials and Methods
### Feature Selection  
Feature selection is crucial before processing any high-dimensional data for clustering or classification. It can be supervised or unsupervised, depending on whether it considers the target variable. The main objective is to reduce the data's dimensionality by removing redundant or non-discriminatory features, which helps in the efficient execution of the classification step. Notwithstanding, not all features are relevant, leading to the need to determine the ones that best represent the analyzed condition--the two anxiety classes.  
The feature selection applied consisted of three steps: (a) missing values and variance analysis; (b) unsupervised correlation threshold evaluation; and (c) supervised wrapper method. After this step, all features were normalized using the Min-Max scaling.  
Concerning unsupervised methods for feature selection, the correlation between features was analyzed. This step aims to remove redundant variables that can cause bias in classification. Redundancy or collinearity may indicate very correlated processes or different ways of representing the same thing. The normality of the data was tested using a Shapiro-Wilk (SW) test. With a 95% interval confidence, the features LnHF and Corr SCR showed to follow a normal distribution. Thus, since not all variables followed a normal distribution, we used the Spearman correlation coefficient and checked the Pearson coefficient for the normal ones. The correlation coefficients that were shown to be significant (with a 0.01 or 0.05 significance level) were analyzed. The correlation coefficient threshold method--with a threshold of 0.90--was used, in which, between two highly correlated features, the one with the higher variance was selected.  
Supervised feature selection selects features based on the target variable and is divided into three types of methods: filter, wrapper, and embedded methods. In this case, a wrapper method was used, Recursive Feature Elimination with Cross-Validation (RFECV), from the package scikit-learn [25]. With this method, a subset of features is used to train a model, and then features are added or removed based on the inferences drawn from the model until a final subset is determined. RFECV derives the best feature subset using the same process as RFE, but unlike RFE, the number of features can be unspecified, and cross-validation is possible [33].  
A minimum of 10 features and a step equal to 1 (determining that one feature is eliminated in each computation) were the parameters for the RFECV method. The F1 was used as the scoring metric to choose the best set of features.
## 3 Results and Discussion
### Model Development and Statistical Analysis  
#### 3.2.1 Model Development  
The quadratic model was considered the best fitted and non-aliased model between the control factors and response by applying the regression analysis on the design matrix and the response. The regression model for the hydraulic conductivity (**HC**) is described in Equations (9) and (10) in terms of actual and coded factors, respectively.  
\[\begin{array}{llll}\mathbf{H}\mathbf{C}=1.81452&\times 10^{-9}-4.4344 7\times 10^{-11}\times\mbox{\it olivinecontent}-6.97041\times 10^{-11}\times\mbox{\it carbonationtime}\\ &-6.95504\times 10^{-12}\times\mbox{\it carbonationpressure}\\ &+7.35429\times 10^{-13}\times\mbox{\it olivinecontent}\times\mbox{\it carbonationtime} \\ &+2.16500\times 10^{-14}\times\mbox{\it olivinecontent}\times\mbox{\it carbonationpressure} \\ &+3.12571\times 10^{-14}\times\mbox{\it carbonationtime}\times\mbox{\it carbonationpressure} \\ &+5.18583\times 10^{-13}\times\mbox{\it olivinecontent}\ ^{2}+1.13578\times 10^{-12}\times \mbox{\it carbonationtime}^{2}\\ &+1.90533\times 10^{-14}\times\mbox{\it carbonationpressure}^{2} \\ \mathbf{H}\mathbf{C}=9.053&\times 10^{-11}-9.506\times 10^{-11} \times A-1.470\times 10^{-10}\times B-1.725\times 10^{-11}\times C+6.435 \times 10^{-11}\times AB\\ &+1.082\times 10^{-11}\times AC+1.368\times 10^{-11}\times BC+5.186\times 10^{-11} \times A^{2}+8.696\times 10^{-11}\times B^{2}\\ &+4.763\times 10^{-11}\times C^{2}\end{array} \tag{9}\]  
where \(A\), \(B\), and \(C\) are the coded terms of the independent variables, olivine content, carbonation time, and carbonation pressure, respectively. In terms of coded factors, the above model is useful for identifying the relative impact of the factors by comparing the factor coefficients. Likewise, in terms of actual factors, the model helps make predictions about the response for each factor given level.
# Effects of Sand-Cement Columns on Primary Consolidation Settlement
## 2 Materials and Methods
### Sand-Cement Mixture  
The sand used in this study was sourced from United States Silica Company to ensure a consistent and high-quality experimental material. Before creating the sand-cement mixture, the sand's properties were thoroughly analyzed following ASTM procedures. The results from these tests, including the Specific Gravity, Permeability [24], and Bulk Density, are summarized in Table 2. Portland cement was added at a ratio of 5% to the dry weight of the sand, a proportion selected based on its effectiveness in improving soil strength and workability, as highlighted in earlier studies by [PERSON] et al. (2018) [25] and [PERSON] et al. (2001) [12]. To create the sand-cement mixture, 5% cement by the dry weight of sand was carefully measured and mixed with the sand to ensure a uniform blend. The dry components were placed into a mortar mixer and thoroughly mixed for five minutes to achieve homogeneity. Afterward, water, equivalent to 25% of the sand's volume, was gradually added to the mixture, transforming it into a mortar paste.
# Numerical Study of the Flow and Blockage Ratio of Cylindrical Pier Local Scour
## 4 Numerical Results
### Clear-Water Case  
This section shows the capability of the proposed numerical model for reproducing the hydrodynamics and morphodynamics behavior around the cylinder in clear-water. The flow results are presented in Figures 4-7, and detailed in the section \"Hydrodynamic validation\", and the morphological results are presented in Figures 8-14, and detailed in the subsection \"Morphodynamic validation\". The numerical simulations are performed in two stages to avoid inaccuracies produced by the initial state of the flow. We only compute the hydrodynamic model in the first stage until the flow stabilizes. In the second stage, we calculate both the flow and sediment evolution. In the beginning, the initial condition of the pressure is zero, and the inflow Swamee boundary condition is imposed throughout the domain.  
Figure 6 displays the three-dimensional instantaneous flow at the non-dimensional time \(t/T=100\), just before the beginning of the second stage. The cylinder is represented by the gray surface and the flow by the black streamlines. Note the complex patterns of the streamlines close to the cylinder. The adverse pressure gradients induced by the cylinder obstruction create downflows in front of the cylinder and generate a horseshoe vortex (HV) near the bottom and wake region behind the cylinder. Figure 6a shows the main coherent structure in an instantaneous flow associated with an HV using the Q criterion. We clearly observed a clockwise vortex close to the bottom in Figure 6b, which also presents 3D streamlines of the wake region behind the cylinder. Figure 6c presents 3D streamlines of the wake region behind the cylinder. We note fluid particles situated close to the bed could be first entrained into the core of this region, and then, from there, toward the surface by an upwelling anti-clockwise vortex. This counter-clockwise rotating vortex would be responsible for the scour mechanisms downstream of the cylinder.  
Figure 4: Time analysis of **(a)** the velocity at the first vertical cell above the bed, and **(b)** the friction velocity for a flow with \(\text{Re}_{D}\approx 2000\) at \((x,y)=(-5,0)\).  
Figure 5: Comparison between the numerical vertical velocity profile at \((x,y)=(-5,0)\) and the PIV measurements in Lachausée, 2018 [18] for the flow with \(\text{Re}_{D}\approx 2000\) and \(U=0.1923\) m/s.  
Finally, Figure 7 shows the horizontal profile of the friction velocity amplification over the line \(y=0\) at different time stages. Here, \(u_{\textit{r}crit}=1.27\times 10^{-2}\) m/s as defined in Equation (16). In this case, the sediment transport starts at \(t/T=100\). Before a dune is formed, the friction velocity slightly surpasses the critical value. It is worth noting that the distance between the inflow boundary and the cylinder is enough to stabilize the flow. Thus, the friction velocity reaches an equilibrium value of \(8\pm 1\) mm/s, remaining unperturbed as long as it is far enough from the cylinder. This value agrees with the measurements given by [PERSON], 2018 [18].
## 2 Materials and Methods  
### Databases  
To evaluate the method based on 1D-CNN proposed in this paper, three databases were used: (1) the standard reference provided by CWRU, (2) the bearing failure dataset provided by [PERSON] and (3) the dataset generated by the NSF for IMS. Experimental setup and test conditions for each of the three databases are described below. In Table 2, the main characteristics of the three databases are outlined.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Database** & **Load** & **Rotation Speed** & **Sampling Frequency** & **Bearing Failures** & **Machinery** & **Failure Level** \\ \hline \multirow{4}{*}{CWRU} & \multirow{4}{*}{0-3 HP} & \multirow{4}{*}{1720–1797} & \multirow{4}{*}{12 and 48} & Electro-erosion & \multirow{2}{*}{IR} & Moderate \\  & & & & process & Severe \\ \cline{4-5}  & & & & & & \\ \cline{4-5}  & & & & & & \\ \cline{4-5}  & & & & & & \\ \cline{4-5}  & & & & & & \\ \hline \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{300–750} & \multirow{4}{*}{6400} & \multirow{4}{*}{OR} & Moderate \\  & & & & & & Severe \\ \cline{4-5}  & & & & & & \\ \cline{4-5}  & & & & & & \\ \cline{4-5}  & & & & & & \\ \hline \multirow{4}{*}{NSF-IMS} & \multirow{4}{*}{6000 lbs} & \multirow{4}{*}{200} & \multirow{4}{*}{20} & Failure arising & \multirow{4}{*}{OR} & Moderate \\  & & & & & & Severe \\ \cline{4-5}  & & & & & & Severe \\ \cline{4-5}  & & & & & & Severe \\ \cline{4-5}  & & & & & & Severe \\ \hline \hline \end{tabular}
\end{table}
Table 2: Databases to evaluate the proposed method based on 1D-CNN.
## 5 Results
### LQC--RL Comparison  
#### 5.2.1 Driving Cycle Results  
For the sake of visibility, in Figure 14, the comparison of the two controllers over the first follower vehicle is represented. As clearly shown by the figure, the controllers achieve almost identical speed profiles. The small differences in vehicle accelerations cause a clearer difference in time headway, especially during braking.  
With reference to the highlighted area where the platoon is braking, it can be observed that, while LQC's actual time headway is lower than the target one, RL has the opposite trend, i.e., the actual value is greater. This difference can be justified by the asymmetry of the RL control due to the penalty introduced in the reward function to avoid collisions during braking.  
Consequently, also from an energetic standpoint, the results are comparable since the state of charge profiles of the two simulations overlap (see the SOC trends reported in Figure 14).  
Figure 15 shows the results of the performance indices introduced to evaluate the drivability (RMS jerk and longitudinal acceleration), the energy consumption, and the controller performance (RMSE of the time headway).  
Figure 14: Results of the comparison of the two controllers during a FPT75 driving cycle: solid red RL results, dotted blue LQC results. The plot refers to the Follower 1 vehicle.  
Figure 15: Comparison of LQC and RL indices: RMS jerk, longitudinal acceleration, energy consumption, and error on time headway.  
The RMSE of time headway reduces from the first to the second follower for both the controllers, but the reduction is stronger for the LQC case, where the error passes from 6 to 2% and RL from 5.5% to 3%.  
As for the energy consumption of the first follower, the difference between the two controllers is less than 3%, respectively, 3.93 kWh/km (RL) and 3.83 kWh/km (LQC). From the comparison in terms of comfort, RL shows a lower RMS of acceleration but a higher jerk. Moreover, the jerk along the string increases for the RL, while it decreases for the LQC. However, the increase in the RMS of jerk results in comfortable driving because the values remain within the limit previously defined.
## 3 Review of Previous Works
### Elastic WLAN System  
The _elastic WLAN system_ has been designed and implemented to dynamically optimize the network topology and the configuration in accordance with the network conditions. Figure 4 depicts an example topology of the system.  
The elastic WLAN system implementation adopts the _management server_ to manage and control the hosts and the APs by running the _active AP configuration algorithm_[22]. This server has not only administrative access to all devices in the network but also controls the overall network using the following three procedures:  
1. It explores all network devices and also collects the information that are required for the active AP configuration algorithm.
2. After that, it executes the active AP configuration algorithm through the derived inputs from the previous step. The output of the algorithm contains the list of the active APs, the host associations, and the assigned channels of each active AP.
3. Finally, it applies the algorithm output into the network by changing the host associations, activating or deactivating the specified APs, and allocating the channels to the APs.  
Figure 3: Experimental field #2.  
Figure 2: Experimental field #1.
# Minimum Distance Optimization with Chord Edge Growth for High Girth Non-Binary LDPC Codes
## 5 Conclusions  
A low-complexity method that is based on progressive chord edge growth is developed for the configuration of non-zero entries, which can construct NB-LDPC codes with a low error floor. All of the candidate configurations of non-zero entries are traversed for each chord edge in turn, and the configurations that make the cycles in the current subgraph meet the FRC are retained. For a chord edge with a large number of eligible configurations, the limitation on the number of these configurations effectively reduces the memory usage. In addition, a slightly modified version of the proposed method is used to construct the NB-LDPC code with an efficient encoding structure. The analysis results show that the proposed method requires fewer FRC checks than the traditional cycle cancellation method, thus achieving lower complexity. Several design examples prove that our method can construct NB-LDPC codes with a large number of cancelled short cycles, thus effectively lowering the error floor. For future investigation, the idea of the proposed progressive chord edge growth may be applied to the optimization of other structures in NB-LDPC codes.  
Conceptualization, C.H., H.L. and W.C.; methodology, C.H. and W.C.; software, H.L.; formal analysis, C.H., H.L. and W.C.; investigation, C.H., H.L. and W.C.; writing--original draft preparation, H.L.; writing--review and editing, C.H. and W.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Natural Science Foundation of China (61671324), and the Director's Funding from Pilot National Laboratory for Marine Science and Technology (Qingdao) (QNLM201712). The authors declare no conflict of interest.
## 4 Conclusions  
Based on the field measurement of the initial defects of the steel pipe and the finite element simulation frame, the analysis and correction method is used to calculate the stability coefficient of the vertical bar under the actual situation. Through analysis and calculation, the following conclusions are drawn:  
1. Through the field measurement of the initial defects of steel pipes in a construction site, it is found that the initial bending values of most steel pipes are in the range of L/500-L/300, and the initial bending values of the most used 3 m and 6 m steel pipes are in L/400.
2. Therefore, the initial bending value of the bar L/400 that is closest to the actual construction condition is determined, and the stability coefficient table of the vertical bar when the initial bending value is L/400 is given, which provides a reference for the calculation of fastener-type steel pipe support under actual construction conditions.
3. The stability coefficient table is obtained by taking the initial bending of L/400 as the initial defect, which is 43.15% smaller than the stability coefficient given by the specification. It is suggested that the corrected stability coefficient should be applied to the actual project.
# Preprocessing for Unintended Conducted Emissions Classification with ResNet
## 3 Results
### FFT Window Length  
Another preprocessing parameter that was evaluated is the length of the computation of the FFT window. Increasing the length of the FFT results in a resolution increase in the frequency domain. The authors found that increased spectral resolution had a positive effect on classification performance, but the return diminished as spectral resolution increased as shown in Figure 5. The gain of the enhancement of the spectral resolution, which is calculated as given in Equation (5) given the main lobe width of \(0.1\times 10^{-4}\) rad  
Figure 4: Stationarity experimentation using FFT with a 10\(\times\) noise augmentation and switching training set from the second collection to the first one at epoch 200.  
and sample rate of \(2\times 10^{6}\) Hz, ceased to be of as much value relative to the loss in time resolution. Assuming the window length to be within the stationary period of the augmented data, which is likely given prior testing and results, the combination of Figure 5 and Equation (5) indicates that the features of interest to our deep classifier are not any closer than 3 Hz to one another, and not much narrower than 3 Hz in bandwidth.  
\[\frac{(0.1\times 10^{-4}\;\text{rad})(2\times 10^{6}\;\text{Hz})}{2\pi\;\text{ rad}}=3\;\text{Hz} \tag{5}\]  
An alternative and possibly complimentary explanation is that the increasing of the window length, which has the additional effect of lowering the noise floor, reaches a point at which it has uncovered all the easy-to-uncover spectral attributes from the noise floor and the remainder of distinguishable features are buried further down into the noise floor. In future work we plan to determine with more specificity to what degree each of the two factors (spectral resolution increase explanation vs. noise floor decrease explanation) was to credit for the diminishing improvements by running additional experiments with additional levels of noise augmentation. This would then inform us more specifically regarding the features used by the deep classifier.
# Microwave SINIS Detectors
## 7 Applications of SINIS Detectors  
Sub-terahertz detectors are intended for applications in various fields of science [44; 45; 46], astronomy [47; 48], technology, commercial applications, telecommunications [49; 50; 51], mili  
Figure 12: Schematic view of experimental setup (**left**). Photos from [43], **top—**He3 radiation shield with blackbody source; **bottom—**sample holders with sapphire lenses.  
tary applications [52; 53; 54], security systems, and life sciences. Astronomy sets the highest requirements for detector sensitivity and wide dynamic range, as primarily this is fundamentally important for ground-based observatories. In telecommunications, there is an urgent need to improve the performance of telecommunications channels, which can be solved by moving from the current satellite communications and mobile communications from centimeter waves to shorter waves. The growing demand for high-speed wireless channels has indicated a trend towards an increase in the signal frequencies towards 24-100 GHz for 5G, 140 GHz, 220 GHz, and 340 GHz for 6G, and 700 GHz suggested for 7G [55]. The optical range is poorly suited for creating cellular data transmission networks due to the suppression of transmission in rain, snow, and fog. Moreover, sub-THz telecommunications can be used to create secure communications for military applications. In radar systems, two areas can be distinguished: military applications (aircraft detection and the detection of anti-personnel mines), and a relatively new \"problem\"--the detection of space threats (such as asteroid-comet hazards, space debris, and space weather).