esa-sceva/satcom-chunk-collection huggingface dataset:
# Normalized Weighting Schemes for Image Interpolation Algorithms
## 3 Results and Discussions
### Objective Image Interpolation Quality Assessment
Among all image interpolation algorithm cases, TC achieved the best PSNR scores. However, among non-traditional image interpolation algorithms, when the scaling ratio was 2, the HR-based algorithm achieved better PSNR scores than AC, AT, and MD-based image interpolation algorithms. Moreover, when the scaling ratio was 4, the AC-based algorithm achieved better PSNR scores than MD, AT, and HR-based image interpolation algorithms. In addition, here, it is important to remember that, in theory, the higher PSNR score (closest to 89) means the better image interpolation quality. To simplify the interpretation of results presented in Figures Figures 10-12, a simplified representation or interpretation scheme of results (from both non-traditional algorithms  
Figure 11: Average SSIM: traditional nearest neighbor (TN), traditional bicubic (TC), traditional bilinear (TB), normalized minimum side-based diameter-based (MD), normalized hypotenuse radius-based (HR), normalized virtual pixel length-based height for the area of the triangle-based (AT), and normalized virtual pixel length for hypotenuse-based radius for the area of the circle (AC) interpolation algorithms. Figure 12: Average PSNR: traditional nearest neighbor (TN), traditional bicubic (TC), traditional bilinear (TB), normalized minimum side-based diameter-based (MD), normalized hypotenuse radius-based (HR), normalized virtual pixel length-based height for the area of the triangle-based (AT), and normalized virtual pixel length for hypotenuse-based radius for the area of the circle (AC) interpolation algorithms. or new schemes-based algorithms and traditional algorithms) was given or presented in Tables 1 and 2. In these two tables, the maximum number of times each image interpolation algorithm was expected to score highest was equal to three because there were three IQA metrics, namely: MSE, SSIM, and PSNR. Here, a method that outperformed all others, in terms of the above three metrics scores, would score the highest score at 100%. For instance, if in each case of MSE, SSIM, and PSNR metrics, the TN achieved the highest scores, that means that TN achieved the highest score 3 times. In other words, TN achieved the highest at 100% because 3 times was the maximum number of times each image interpolation algorithm was expected to achieve the highest scores. Now, referring to the above example, it can be seen that, when the scaling ratio was equal to 2 and 4, TC achieved the highest at 100% among all image interpolation algorithms mentioned. However, among non-traditional image interpolation algorithms alone, when the scaling ratio was 2, the HR-based image interpolation algorithm achieved the highest at 66.6%. However, when the scaling ratio was equal to 4, the AC-based image interpolation algorithm achieved the highest 66.6% among all non-traditional image interpolation algorithms.
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods
### Data Augmentation Methods
#### 2.3.2 Pitch Shifting (PS)  
The pitch of an audio sample can be shifted up or down by several semitones without altering the playback speed and amplitude, as shown in Equation (5). These semitone shift factors, denoted as \(\tau\), are constrained within the range of [-10, 10] to ensure a uniform distribution of transformation effects.  
\[f_{r}=f_{r}\times 2^{\frac{\tau}{2\tau}} \tag{5}\]  
This study introduced five sets of shift factors for deformation. These five sets of shift factors are (\(-1.0\), \(-0.5\), \(0.0\), \(0.5\), \(1.0\)), (\(-3.0\), \(-1.5\), \(0.0\), \(1.5\), \(3.0\)), (\(-5\), \(-2.5\), \(0.0\), \(2.5\), \(5\)), (\(-8\), \(-4\), \(0.0\), \(4\), \(8\)), and (\(-10\), \(-5\), \(0.0\), \(5\), \(10\)). Through this transformation, the frame rate of the original audio is altered while preserving the output characteristics of the original sound. Similar scale ranges have also been applied in the previous literature [34].
## 2 Materials and Methods
### Activation Energy  
The changes in effective mass diffusion coefficient (D\({}_{\text{eff}}\)) and convective mass transfer coefficient (h\({}_{\text{m}}\)) by drying temperature according to Equations (12) and (13), which is known as the Arrhenius equation, are used to obtain the activation energy for diffusion mass transfer and convective mass transfer.  
\[\text{D}_{\text{eff}}=\text{D}_{\text{e}}\,\exp\biggl{(}-\frac{\text{E}_{ \text{d}}}{\text{R}(\text{T}+273.15)}\biggr{)}, \tag{12}\]  
\[\text{h}_{\text{m}}=\text{h}_{\text{m}}\,\exp\biggl{(}-\frac{\text{E}_{\text{ c}}}{\text{R}(\text{T}+273.15)}\biggr{)}, \tag{13}\]  
By converting the above two equations into logarithmic form, the following equation is obtained:  
\[\text{lnD}_{\text{eff}}=-\frac{\text{E}_{\text{d}}}{\text{R}(\text{T}+273.15 )}\text{lnD}_{\text{eff}}, \tag{14}\]  
\[\text{ln}\,\,\,\text{h}_{\text{m}}=-\frac{\text{E}_{\text{c}}}{\text{R}( \text{T}+273.15)}\text{lnh}_{\text{m}}, \tag{15}\]  
Therefore, by drawing the plot of In D\({}_{\text{e}}\) against 1/T and the ln h\({}_{\text{m}}\) against 1/T, the amount of activation energy for diffusive (E\({}_{\text{d}}\)) and convective mass transfer (E\({}_{\text{c}}\)) can be calculated [22; 33].
# Does Intensive Land Use Contribute to Energy Efficiency?--Evidence Based on a Spatial Durbin Model
## 3 Data and Methods
### Methods: Regression Model
#### 3.2.3 Spatial Threshold Regression  
Additionally, threshold regression is to test whether the parameters of the sample group divided according to the threshold value are significantly different, and it is often used to study the heterogeneity of the interaction between variables. The threshold regression model developed by [PERSON] (1999) [39] can endogenously divide the data interval according to the characteristics of the data itself, avoiding the randomness of artificially dividing the sample interval. The relationship between each city level and innovation performance may be nonlinear. Traditional linear regression cannot explain the relationship between the two. The threshold model regression is closer to reality. Therefore, this paper adopts the threshold regression model of [PERSON] (1999), which is different from the traditional threshold model. The spatial threshold model adopted in this paper adds the spatial lag of explanatory variables and explained variables. First, the following single threshold regression model is set:  
\[y_{it} =\lambda_{0}+\lambda_{1}w\ \times\ D_{it}\cdot I\ (\mathit{thre}_{it}\leq\ r_{1})+ \lambda_{2}w\ \times\ D_{it}\cdot I\ (\ \mathit{thre}_{it}\!\!>\ r_{1})+\lambda_{3}X_{it}+ \gamma\cdot t+\epsilon_{it} \tag{9}\]  
Among them, \(I\ (\cdot)\) represents an indicative function, and the value is 1 when the expression in the brackets is true and 0 when it is false. \(D_{it}\) is the core explanatory variable, where \(w\times D_{it}\) refers to the spatial lag term of the explained variable, \(\mathit{Urbs}_{it}\) is the threshold variable, \(X_{it}\) represents the control variable, and \(\epsilon_{it}\) is the random disturbance term. When \(\mathit{Urbs}_{it}\leq r_{1}\), the core explanatory variable \(D_{it}\) coefficient is \(\lambda_{1}\); when \(\mathit{Urbs}_{it}>r_{1}\), the core explanatory variable \(D_{it}\) coefficient is \(\lambda_{2}\). \(t\) is the time effect, \(\lambda_{0}\) is a constant term and \(\epsilon_{it}\)-(0, \(\sigma\)) is a random interference term. Additionally, Equation (9) only assumes that there is one threshold, but there may be two or more thresholds. Due to space limitations, the double and more threshold tests will not be repeated.  
To estimate these variables we mentioned above, we used software named STATA 15. This software is a general-purpose statistical software package developed by StataCorp for data manipulation, visualization, statistics and automated reporting.
# AxP: A HW-SW Co-Design Pipeline for Energy-Efficient Approximated ConvNets via Associative Matching
## 3 Co-Design Pipeline: Concept and Methodology
### Approximation Pipeline (AxP)  
The approximation pipeline (AxP) is built to increase data repetitiveness across the ConvNet model. The rationale is that reducing the number of different parameters and activations minimizes the arithmetic unit utilization since most of the operations can be pre-computed and reused during the inference by exploiting the associative-based processing element. To achieve this, an iterative clustering and approximation procedure is implemented as described by Algorithm 1.  
Figure 4: Co-Design tool overview.  
The main inputs are (i) the pre-trained _model_, (ii) the validation set (_valid_set_), (iii) a user-defined accuracy drop target (\(\epsilon\)), and (iv) a stopping variable (_max_iteration_) to ensure convergence of the iterative loop. The algorithm delivers as main outcome the set of configurations (\(N_{\textit{crnv}},N_{\textit{fc}},\textit{Abit}\)) which are fed as input to the latter stage of the framework, namely, the simulation engine. Going deeper into details, the AxP is composed of two sub-stages run sequentially: clustering and _Approximate Pattern Matching Analysis_ (apma). The clustering step explores different solutions in a discrete space \(S_{\textit{sv}}\in\{2^{1},\dots,2^{n}\},n\) defined empirically, seeking the optimal number of weight clusters for both the convolutional and the fully-connected layers (the parameters \(N_{\textit{crnv}}\) and \(N_{\textit{fc}}\) respectively). The optimal number of bits used for the approximation pattern matching (the parameter _Abit_) is evaluated during the apma sub-stage, which explores the viable configurations in the interval \(S_{\textit{ab}}=[\frac{\textit{bw}}{2},\textit{bw}]\), \(\textit{bw}\) as the parallelism of the current hardware data-path (32- or 16-bit in our study). During the iterative loop, shown in Algorithm 1, all the tuples (\(N_{\textit{crnv}}\), \(N_{\textit{fc}}\), _Abit_) that satisfy the accuracy constraint are kept as possible optimal solutions. Here the tool employs the solution found in the clustering stage, characterized by \(N_{\textit{crnv}}\) and \(N_{\textit{fc}}\) parameters, and iteratively performs the forwarding pass on the validation set. In each iteration, a certain partial matching configuration (while \(N_{\textit{crnv}}\) and \(N_{\textit{fc}}\) are fixed) is evaluated on the validation set and all the solutions that meet the user constraint \(\epsilon\) are collected. This additional approximation phase introduces a new degree of freedom in the design phase and a finer grain control in the accuracy vs. energy efficiency space.  
From here follows the detailed description of clustering and apma sub-stages and the knob involved.
# SpaceSheep: Satellite Communications for Ovine Smart Grazing
###### Abstract
The temporal comparison of the activity of the herd elements showed a lag in the activity of different herd members, suggesting that there may be a collective behavior of rotational herd guarding. To optimize the shepherd's pastoral activity and allow supervision to be conducted remotely, an event monitoring application, which kept track of the animals and the equipment was developed and integrated with the communication gateway [11]. The monitoring logic monitored the collar and beacon communications [12] and emitted an alarm whenever human supervision was required. An alarm system depends on fast communications to operate effectively and trigger the necessary corrective measures. Such capability is not taken for granted in rural areas, where available technologies, such as a wireless local area network (WLAN) and even mobile networks [13], provide low coverage. Currently, several technologies are available that target greater coverage in IoT scenarios [14], namely NB-IoT [15], Sigfox [16], and others [17; 18]. Nonetheless, these technologies are mainly only available in or around urban centers, with rural areas still left with an extensive coverage hiatus or limited access to all the technologies' capabilities, which would be more useful in cases where IoT is used [12]. This paper documents an extension that was performed over the SheepII system, where a satellite communications interface was added to address the difficulties in accessing the Internet, ensuring sufficient performance and availability of the alarm mechanisms. The remainder of this paper is organized as follows: In Section 2, the proposed system is described. Section 3 assesses the communication latency and bandwidth being used, while the results are discussed in Section 4. Finally, Section 5 concludes the paper and discusses future work.
## 2 Materials and Methods
### Statistical Analysis  
#### 2.2.1 Standardized Anomalies (SA)  
Using the original monthly data (environment variables and carbon fluxes) during each of spring (March, April, and May; MAM), summer (June, July, and August; JJA), and autumn (September, October, and November; SON), we computed the seasonal means for temperature, VPD, and the sums of precipitation, SW, NEP, GPP, and RE. The annual temperature, precipitation, SW, and VPD were denoted as TA\({}_{\text{year}}\), P\({}_{\text{year}}\), SW\({}_{\text{year}}\), and VPD\({}_{\text{year}}\), with corresponding terms used for spring (TA\({}_{\text{MAM}}\), P\({}_{\text{MAM}}\), SW\({}_{\text{MAM}}\), and VPD\({}_{\text{MAM}}\)), summer (TA\({}_{\text{JJA}}\), P\({}_{\text{JJA}}\), SW\({}_{\text{JJA}}\), and VPD\({}_{\text{JJA}}\)), and autumn (TA\({}_{\text{SON}}\), P\({}_{\text{SON}}\), SW\({}_{\text{SON}}\), and VPD\({}_{\text{SON}}\)). Additionally, we calculated these parameters' standardized anomalies (SA) for each site as follows:  
\[SA_{tn}=\frac{x_{tn}-\mu_{n}(x)}{\sigma_{n}(x)} \tag{1}\]  
where \(x_{tn}\) is the annual or the seasonal value (spring, summer, or autumn) of a variable and \(t\) is the year in the flux datasets, \(\mu_{n}\) is the mean value of the variable in season \(n\), and \(\sigma_{n}\) is the standard deviation of the variable in season \(n\) in our study period. Z-scores were created from the standardized original values to enable subsequent comparisons.
## 1 Introduction
The steel strip images are generally grayscale images captured by CCD cameras, and those grayscale images contain less information than color images. Furthermore, the defect positive samples are far less than the background parts. The attention mechanism in neural network models has the potential to solve this problem. The idea is to make CNN-imitated human functions to focus on the information of interest but ignore the irrelevant information when processing an image. Lots of research studies have been conducted and good results were achieved in this area. For example, SE-Net [14], CBAM [15], Non-local [16]. Figure 1: Complex defects on the steel strip surface, boxes of the same color represent the same defect type. (**a**) multiple defects; (**b**) multiclass defects; (**c**) overlapping defects. Inspired by the above-mentioned excellent work, a DPN module is presented in this paper to refine the feature maps of different scales generated by the backbone and fuse features of different scales. The problem of missing minor defect features as the network deepens and the detection difficulty caused by the diversity of defect scales will be resolved. Since in the proposed DPN module, the receptive field is expanded and the multi-scale information of the feature maps is enriched. Finally, an object detection network for surface defect detection of steel strips is proposed. The main contributions of this paper are as follows:  
1. A novel residual atrous spatial pyramid pooling (RASPP) module is developed by exploiting multiple scale dilated convolutions to capture feature information and introduce residual connection. This module can enrich the multi-scale information of feature maps and expand the receptive field, while speeding up information dissemination. 2. A double pyramid network (DPN) is proposed to speed up the transfer of rich location information in the low-level features by embedding the RASPP module into top-down and bottom-up feature pyramid networks. DPN improves the robustness of the model to defect scales by integrating feature information of different scales multiple times and expanding the receptive field of the feature maps. 3. A steel strip surface defect detection network, DPN-Detector, is proposed, which is formed by embedding DPN into Faster R-CNN and replacing the original detection head with a designed double head. The double head contains a fully connected detection head biased towards classification and a convolution detection head focused on bounding box regression. Its embedded non-local attention blocks can enhance the defective features and provide high-quality localization information. DPN-Detector significantly improves the detection capability of multi-scale defects and can achieve the high-precision detection of steel strips. The rest of this paper is organized as follows: Section 2 outlines related work. Section 3 details the proposed method. Section 4 presents extensive experiments and results analysis. Finally, the conclusion and prospect of this paper are given in Section 5.
## 2 Materials and Methods  
Applications of numerical and analytical methods such as FEM and LEM to estimate the slope stabilities are the most common procedures used by various scholars. In the meantime, different approaches and various commercial software have been developed based on the principles of limit and numerical balance. LEM methods are one of the oldest and most basic analytical approaches for slope stability analyses that are widely used in slope stability studies because of their simplicity, low complexity of formulation, and shorter analysis times. LEM based on massive analysis or slices investigates a possible slippery mass at the top of the assumed slip surface and the polyhedral force vector closure or incurring moments in the equilibrium state, which are capable of being utilized in static and dynamic conditions for two-dimensional and three-dimensional spaces [30]. The LEM method provides a closed-form calculation of the safety factor (F.S) by considering the probable sliding surface implemented in both massive and slice procedures [31]. The method provides the static/dynamic formulation of the sliding mass in a slope body driven by mobilize/resist-force and moment equilibrium [32]. The possibilities of calculating the F.S\({}_{\text{min}}\) for the sliding mass, presenting the probable slip surface along with the lowest F.S coefficient, considering the unique F.S for the entire active mass, and involving cohesive and frictional components in slope mass strength characteristics, are some of the benefits of using limit equilibrium methods. The LEM methods have been developed extensively by various researchers since 1936 [33]. Scholars attempted to conduct complicated formulations based on LEM principles [30]. Also, LEM-based commercial software such as SLOPE/W is used worldwide. On the other hand, the application of the FEM approach provides near approximations to calculate the stress distributions, stress-strain relationships, displacements, and plastic points on slope mass based on finite-element equations;however, these parameters must be assumed in the LEM method [33]. Thus, using both methodologies (FEM-LEM) can help to provide realistic and more accurate results for slope stability assessments. The presented study used the two-dimensional FEM models (by SEEP/W and PLAXIS software) to estimate the rapid drawdown conditions in the Alavian earth-fill dam. The study led to an evaluation of the stability charts used for the rapid drawdown-based instability analysis. In the seepage analysis, the various stages of rapid drawdown conditions are provided and all variables are considered in calculating the flow conditions. These conditions are obtained from SEEP/W, and PLAXIS is used for slope stability modeling and extracting the graphical charts for the demonstration of the stability charts in earth-fill dams.
# Long-Distance Measurements Using a Chromatic Confocal Sensor
## 4 Methods  
The methodology entails leveraging the chromatic aberration exhibited by a lens to establish a working range, given by the focal lengths, corresponding to different wavelengths. The illumination of the primary lens with a collimated white-light beam generates this working range.  
Figure 8 shows two spectra that were captured at two separate positions of the sample, separated by 800 \(\upmu\)m. Even though the spectrum has changed, the maximum value lies in the same wavelength, thus demonstrating that the detection of the peak wavelength in a raw spectrum signal is not enough even to resolve a large distance. To mitigate this issue, two distinct methods were employed: low-pass filtering and Gaussian fitting.
## 1 Introduction
### Research Goals  
Our previous research also identified new areas where important questions remained. Firstly, all our measurements were performed in the Netherlands at 52.7\({}^{\circ}\)N, with a magnetic dip angle of 69\({}^{\circ}\), on a north-to-south path. Nearly perfectly circular polarization was observed in 2 different NVIS experiments spaced seven months apart [25; 28]. To test the generality of these observations and of the isolation between the characteristic waves, measurements over multiple azimuths are needed, preferably repeated at a second location with a different magnetic dip angle. Furthermore, pronounced fading was observed in NVIS experiments in which the transmitted and received polarization were matched to one of the characteristic waves, which was unexpected [28].  
Our current research therefore has three main objectives: (i) Verification of the isolation between both characteristic waves for different azimuths, distances and magnetic dip angles; (ii) Simultaneous measurement of the polarization dynamics on multiple radio wave paths with different azimuths and distances; (iii) Analysis of signal fading and its possible origins, by means of comparing the radio wave propagation on all paths.  
The first goal is the focus of the project. It will validate the previous measurements conducted in the Netherlands [25] and confirm the hypothesis that the achievable data rate can be doubled using characteristic wave propagation. The second goal is relevant for (possibly dynamic) optimization of antenna polarization, which will improve the isolation between the ordinary and the extraordinary waves. The third goal is perhaps the most ambitious: although signal fading is common in HF propagation, the proposed study will expand the knowledge on its mechanisms and drivers, and enable future mitigation techniques.