esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Framework of the Proposed TCM System  
In the cutting process, tool wear increases nonlinearly with cutting time. According to the characteristics of the tool wear curve, the tool wear process could be divided into three stages including initial wear, middle wear and severe wear [27], as shown in Figure 1. The tool wear increases rapidly in the initial wear stage as the surface of the new tool is rough and uneven and the blade is sharp. It can be seen that the tool wear increases slowly and uniformly in the middle wear stage, which is attributable to the polish of the tool surface at the cutting edge and the reduction of cutting temperature. When the cutting edge becomes rough in the severe wear stage, the cutting temperature increases rapidly, accelerating the wear speed.  
In recent research, TCM is mainly based on constructing a model such as an analytical model, neural network model and nonlinear classifier between tool wear and feature values of cutting signals. However, the feature values of cutting signals are prone to being affected by matters other than tool wear, including cutting parameters and lubrication. In this study, an important statistical correlation between tool wear and the distribution of cutting force features is revealed to find robust information indicating tool wear conditions. To achieve TCM under different cutting conditions, an unsupervised method based on cluster density of CAGS is proposed, as shown in Figure 2. First, force signals are collected during the cutting process under different tool wear conditions. Then, time-domain statistical features, which are commonly used in the machining process, are extracted from the force signals to form the feature space containing tool wear information. Using CAGS, the samples are divided into several clusters with different densities and thus the tool wear information is clarified. To accomplish TCM, the cluster density factor is proposed to transfer the density of a cluster to a value corresponding to tool wear.
# Attitudes towards the Potential Use of Aversive Geofencing Devices to Manage Wild Elephant Movement
###### Abstract  
Aversive geofencing devices (AGDs) or animal-borne satellite-linked shock collars might become a useful tool to mitigate human-elephant conflict (HEC). AGDs have the potential to condition problem elephants to avoid human-dominated landscapes by associating mild electric shocks with preceding audio warnings given as they approach virtual boundaries. We assessed the opinions of different stakeholders (experts, farmers, and others who have and have not experienced HEC; n = 611) on the potential use of AGDs on Asian elephants. Most respondents expressed positive opinions on the potential effectiveness of AGDs in managing elephant movement (62.2%). About 62.8% respondents also provided positive responses for the acceptability of AGDs if pilot studies with captive elephants have been successful in managing their movements. Some respondents perceived AGDs to be unacceptable because they are unethical or harmful and would be unsuccessful given wild elephants may respond differently to AGDs than captive elephants. Respondents identified several potential challenges for implementing AGDs as an elephant management tool. These issues need attention when developing AGDs to increase support from stakeholders and to effectively reduce HEC incidents in the future.  
**Keywords:** conservation; electric shock collars; _Elephas maximus_; human-elephant conflict; public opinion; virtual fencing; wildlife management
# Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Trend Seasonality** & **None** & **Additive** & **Multip.** & **None** & **Additive** & **Multip.** \\  & & **Normal** & & & **AR(1) Adjusted** & \\ \hline None & NNL & NAL & NML & NNC & NAC & NMC \\ Additive & ANL & AAL & AML & ANC & AAC & AMC \\ Damped additive & dNL & dAL & dML & dNC & dAC & dMC \\ Multiplicative & MNL & MAL & DML & MNC & MAC & MMC \\ Damped multiplicative & DNL & DML & DML & DMC & DAC & DMC \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Multiple seasonal Holt-Winters models’ nomenclature according to trend and seasonal method. The first letter determines the trend, N: none, A: additive, d: damped additive, M: multiplicative and D: damped multiplicative. The second letter is used for Seasonality, with only N (none), A (additive) and M (multiplicative) option. The third letter is used for the AR(1) adjustment: L (no adjustment) and C (adjusted). The article is organized as follows: Section 2 reviews the existing literature related to the smoothing parameters of Holt-Winters models. Section 3 presents the methodology followed in order to predict the Spanish electricity demand time series and to analyze the variability of the parameters; in Section 4 the results obtained for a given double and triple seasonal model are shown and in Section 5 the results are discussed. Finally, the conclusions reached in this article are shown.
## 1 An Overview of the Topic and Its Ramifications
### Waveguides Built of Artificial Materials
The use of such media opens numerous possibilities to implement diverse optical effects, including nonlinear ones [91] and guided-wave propagation, in forms that were not known previously (for instance, in the form of the surface waveguiding in photonic topological insulators, which is immune to scattering on defects because the scattering is suppressed by the topology of the guiding system), and are unified under the name of _metaoptics_[92; 93]. Another unifying concept is _nanophotonics_, the name originating from the fact that many of these materials are assembled of elements with sizes measured on the nanometer scale (which is deeply subwavelength, in terms of optics). One of the fundamentally interesting subjects of nanophotonics is trapping and transmitting light in _nanowires_. Nanowires are optical filaments (usually made of silicon) whose diameter--measured in nanometers--is much smaller than the wavelength of light, while a typical length may be a few millimeters; one of their important applications is in solar photovoltaic elements [94].
## 3 Results
### Testing Data  
#### 3.2.1 Left Bundle Branch Block Simulations  
Table 3 summarizes the accuracy obtained with the meshless SPH-based in the testing dataset. The initial impulses were fixed at 0 ms in the endocardial RV layer and at 9 ms in the septal area for Pig 4, one of the non-infarcted testing cases. The SPH-based simulation correctly replicated the conduction velocities of the LBBB EAM at different layers showing a low error of 5.1 ms, and specifically the RV epicardium with a 4 ms regional error (Table A2). Nevertheless, the anterior part of the LV endocardium showed a greater number of variations, corroborated by a regional error above the mean (6.4 ms in Table A2). The LV epicardial sequence was also similar (5.5 ms regional LAT RMSE in Table A2) in measurements and simulations, with the same latest activation point.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**SPH-Based**} & \multicolumn{4}{c}{**FEM-Based**} \\ \hline  & RV endo & RV epi & LV endo & LV epi & Scar & Average heart tissue & PK & Average heart tissue & PK \\ \hline Ischemic & 1.53 & 1.40 & 1.36 & 1.62 & 0.49 & 1.30 & 1.69 & 1.78 & 1.30 \\ \hline Non-ischemic & 0.83 & 0.65 & 0.63 & 0.51 & - & 0.65 & 2.40 & 0.50 & 2.60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Conduction velocity values (\(m/s\)) estimated by the SPH- and FEM-based solvers [29] for an ischemic and non-ischemic training cases at LBBB scenario. SPH: Smoothed particle hydrodynamics meshless method. FEM: Finite element method. RV epi: Right ventricle epicardium. RV endo: Right ventricle endocardium. LV: Left ventricle. PK: Purkinje system.  
The EAM of the infarcted testing case (Fig 6) had an initial impulse at the RV endocardi lateral wall, inducing a rapid RV activation, while the LV one was much more gradual. The scar in Pig 6 was located in over the whole septum, with 57% of transmurality. In the SPH-based simulations, the initial stimulus were placed at similar regions of the non-infarcted case but at 2.6 ms and 14 ms for the RV endocardial layer and septal area, respectively. To faithfully represent the ischemic region in the simulations, a reduction of over 87% in the conduction velocity with respect to the PK system was determined by the SLSQP optimization algorithm. The epicardial layer depicted the highest LAT regional error (see Table 10), specifically in the posterior part for the RV and in the LV anterior part. With a LAT-RMSE of 5.9 ms for Pig 6 (Table 3), the LV endocardial layer showed the best fitting (5.7 ms regional LAT-RMSE) for a delayed basal activation of the LV (Table 10).
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 8 Modulation Techniques
### Related Works
#### 8.4.2 Performance Evaluation  
[PERSON] et al. [184] compared the throughput of Wi-Fi 6 and Wi-Fi 5 over MCS 0-11 with different MPDU sizes. They revealed that over small MCS, different MPDU sizes do not affect the throughput. As the MCS index increases, the larger MPDU sizes provide higher throughput. Moreover, Wi-Fi 6 outperforms Wi-Fi 5 by up to two times in throughput. [PERSON] et al. [185] deployed an IEEE 802.11g Wi-Fi network that comprised one station and one access point. They compared these experimental measurements with simulations using the default Wi-Fi 6 package available in NS-3 to quantify the effectiveness of the novel MCS. More precisely, they emulated the wireless link from the station to the access point using the delay measured in the real deployment. However, this paper did not explain the implementation in detail, namely, thePHY model.  
The authors of [186] evaluated the performance at the application layer in a testbed. They revealed that Wi-Fi 6 provides better channel utilization and higher throughput than Wi-Fi 5. This channel utilization was achieved thanks to the higher-order modulation rate of 1024-QAM.  
The authors of [187] proposed a simulation model to measure the efficiency of all modulation schemes. Afterward, they assessed different combinations of all GIs and channel bandwidths to find the best values. They concluded that higher-order modulations perform better on narrower RUs. Moreover, GIs do not significantly affect performance.  
Some papers evaluated the throughput improvement. The authors of [21] compared the novel MCS-11 with MCS-9 (Wi-Fi 5). The simulation highlighted that MCS-11 outperforms MCS-9 by 14-18%. [PERSON] et al. [188] also compared the same MCS. In particular, they focused on the impact of the number of associated clients. They concluded that the response time is similar. However, MCS-11 outperforms MCS-9 in overall throughput as the network size increases. [PERSON] et al. [106] evaluated 1024-QAM modulation in an experiment using two different vendors. They measured that the new modulation improved throughput by 25%.
## 4 Factors to Study in VANETs
### Radio Propagation Model  
We observe that the most widely used simulators such as ns-2, Glemosim, QualNet and OPNET do not include a Radio Propagation Model (RPM) that offers enough accuracy for vehicular environments [39]. In particular, the physical obstacles present in urban environments (mostly buildings) are not taken into account, which is overly optimistic. For example, the commonly used Two Ray Ground (TRG) radio propagation model ignores effects such as Radio Frequency (RF) attenuation due to buildings and other obstacles, meaning that an alternative model must be introduced. However, for 802.11p-based VANETs, the received signal will largely depend on both the distance between the sender and the receiver, and the presence of obstacles.  
In the \(2^{k}\) factorial analysis, we use both the well-known deterministic TRG and the probabilistic Real Attenuation and Visibility Model (RAV) [4], a realistic RPM specifically designed for IEEE 802.11p-based VANETs that increases the level of realism of phenomena occurring at the physical layer, thereby allowing researchers to obtain more accurate and meaningful results [39].  
Figure 1 shows an example of the visibility scheme used in RAV, where vehicle (A) is trying to disseminate a message. In that case, and assuming that any vehicle receiving a message will rebroadcast it the first time, the result will be that some vehicles (B, C, D, F, G, and I) receive the message, while the others (E, H, and J) will never be reached by such message.
## 9 Mobility Environment  
The experiments were conducted utilizing the network simulation 2 (NS2) and (CRCN) patch installed on a Linux operating system. The network topology is a grid of 1000 m \(\times\) 1000 m, and the simulation time is 100 simulation seconds. Figure 4 shows the initial state of mobile nodes and their configurations. To generate initial nodes, the study utilized the NSG2 graphic user-interface system. The setup environment and the configuration connections between the source node and the destination node are represented in Table 2.  
Figure 4: The initial state of the mobile nodes consisting of sending nodes and receiving nodes.
# Inverse Design of Distributed Bragg Reflectors Using Deep Learning
###### Abstract  
Distributed Bragg Reflectors are optical structures capable of manipulating light behaviour, which are formed by stacking layers of thin-film materials. The inverse design of such structures is desirable, but not straightforward using conventional numerical methods. This study explores the application of Deep Learning to the design of a six-layer system, through the implementation of a Tandem Neural Network. The challenge is split into three sections: the generation of training data using the Transfer Matrix method, the design of a Simulation Neural Network (SNN) which maps structural geometry to spectral output, and finally an Inverse Design Neural Network (IDNN) which predicts the geometry required to produce target spectra. The latter enables the designer to develop custom multilayer systems with desired reflection properties. The SNN achieved an average accuracy of 97% across the dataset, with the IDNN achieving 94%. By using this inverse design method, custom-made reflectors can be manufactured in milliseconds, significantly reducing the cost of generating photonic devices and thin-film optics.  
Machine Learning; inverse design; tandem network; chromaticity; Distributed Bragg Reflector +
Footnote †: journal: Applied sciences
## 1 Introduction
Researchers have conducted a considerable number of studies on image feature extraction and description and proposed several classic feature description and extraction methods, such as scale invariant feature transform (SIFT), speeded up robust features (SURF), features from accelerated segment test (FAST), and binary robust independent elementary feature (BRIEF) [16; 17; 18; 19]. These methods obtain image feature points and their descriptors by finding the local extremum in the image and describing the features using the luminance information of their neighborhood. Feature matching is obtained by calculating the distances between feature points in different images. For example, the SIFT feature descriptor uses the Euclidean distance as the judgment standard between descriptors, whereas the BRIEF descriptor [20] is a type of binary descriptor that uses the Hamming distance as the judgment standard to describe the correspondence between two feature points [21; 22]. As described above, existing air sonar applied to hand gesture recognition by time-domain reflected signal by decoding its signal level or amplitude, the likely environmental noise and the distance between the users and speaker/microphone could decrease the accuracy rate of hand gesture recognition. Moreover, most of the image recognition techniques deals with the images directly obtained via camera. The large amount data points of images accompanying with the frame change not only requires the higher cost hardware but more computational effort compared to our proposed air sonar approach. In this study, we constructed an air sonar with a pair of ultrasonic emitters and receiver to investigate the hand gesture recognition. Through the cost-effective circuity, the acquired Doppler signals were processed for the study of hand gesture recognition. Two algorithms were developed with the one to judge the starting time of hand gestures by continuous short-time Fourier transform and the other to recognize the hand gesture by image processing with spectrogram. The Superior recognition results were obtained using the proposed scheme. Further details are described below.
# Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels +
## 2 System Model
### Setting and Channel Model
The hop frequencies are measured relative to the carrier frequency of the receiver. The spatial mode is given by \(a(\theta_{i,l}^{(m)})=e^{j2\pi(d/\lambda)\sin(\theta_{i,l}^{(m)})}\), where \(d/\lambda\) is the spacing between antennae expressed in units of wavelength \(\lambda\), and \(\theta_{i,l}^{(m)}\) is the DOA of the \(m\)th multipath of the \(l\)th cluster of the \(i\)th source. The DOA \(\theta_{i,l}^{(m)}\) can be decomposed as \(\theta_{i,l}^{(m)}=\theta_{i,l}+\theta_{i,l}^{(m)}\), where \(\theta_{i,l}\) is the mean DOA of the \(l\)th cluster of the \(i\)th source, and \(\theta_{i,l}^{(m)}\) is the deviation from the mean DOA, which is modeled as an i.i.d. Gaussian random variable with zero mean and variance \(\sigma_{\theta}^{2}\). For the single-cluster propagation environment, \(L_{i}=1\) and \(\theta_{i,l}^{(m)}\
eq 0\), whereas for the LOS propagation environment, \(L_{i}=1\) and \(\theta_{i,l}^{(m)}=0\). For hop \(k\) and the corresponding time interval, \(t_{k}\leq t<t_{k+1}\), observations \(y(j,t)\) are collected in a \(J\times(t_{k+1}-t_{k})\) matrix \(\mathbf{Y}_{k}\). Combining observations across all hops, matrices \(\mathbf{Y}_{k}\) are concatenated to form the \(J\times T\) observation matrix \(\mathbf{Y}\).
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 2 Materials and Methods
### Inference
#### 2.5.1 Filtering Stage  
Optionally, we can filter these detections with a water/land mask generated from OpenStreetMap coastline vector data. When enabled, we perform filtering by multiplying the image binary prediction mask with the water mask, before extracting connected components. In this way, ships that are moored will be detected without spillover to peers or land masses. A variation of this filtering involves a water mask than removes littoral regions (in a 600 m range), in order to perform detection only in open sea. This second open-sea mask is obtained by thresholding a distance-transform of the water/land mask. Our pipeline, thus, has an optional filtering stage with two variations: coastline **(CO)** or open-sea **(OS)**.
# Effects of Velocity and Permeability on Tracer Dispersion in Porous Media
## 3 Experimental Results and Discussion
### Discussion  
#### 3.3.1 Equations of Dispersion Coefficient in Cores  
The dispersion coefficient values in Tables 3 and 4 demonstrate that the coefficient is positively correlated with superficial velocity, while it is negatively correlated with core permeability. Based on these results, the dispersion coefficient as functions of injection velocity for the natural and sand-filled cores are plotted in Figures 7 and 8, respectively. The linear relations between the dispersion coefficient, the velocity, and the permeability are obtained through calculation, as follows:  
\[D_{N}=-1.2844\times 10^{-5}K+0.1040u+0.0675 \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Core No.** & **Porosity (\%)** & **Length (cm)** & **Permeability (mD)** & **Flow Rate (cm\({}^{3}\)/s)** & **Superficial Velocity (cm/s)** & **Dispersion Coefficient (cm\({}^{2}\)/s)** \\ \hline
4 & 0.372 & 25 & 621 & 0.02 & 0.011 & 0.004 \\ \hline
5 & 0.366 & 25 & 650 & 0.03 & 0.017 & 0.007 \\ \hline
6 & 0  
\[D_{A}=-3.0807\times 10^{-6}K+0.5514u-0.0041 \tag{2}\]  
where \(D_{N}\) and \(D_{A}\) are the dispersion coefficients of the natural and sand-filled cores, respectively (cm\({}^{2}\)/s); \(K\) is the core permeability (mD); and \(u\) is the tracer superficial velocity. These figures confirm the trends observed in Tables 3 and 4.  
The results of this work reveal a linear relationship between the dispersion coefficient and fluid velocity as shown in Equations (1) and (2), inconsistent with the previous findings of the nonlinear relationship [35; 36; 37; 38]. The cause of this difference can be attributed to the grain shape and fluid velocity. As [PERSON] and [PERSON] [36] demonstrated, the closer a particle is to a spherical shape, the more significant the nonlinear relationship between the dispersion coefficient and the velocity of water flow. As the grain diameters in core samples are in the scale of micrometers and the grain shape is close to spherical, it is reasonable to approximate the grains as spheres and obtain a linear relationship.
###### Abstract  
Visible Light Communication (VLC) systems use light-emitting diode (LED) technology to provide high-capacity optical links. The advantages they offer, such as the high data rate and the low installation and operational cost, have identified them as a significant solution for modern networks. However, such systems are vulnerable to various exogenous factors, with the background sunlight noise having the greatest impact. In order to reduce the negative influence of the background noise effect, optical filters can be used. In this work, for the first time, a low-cost optical vanadium dioxide (VO\({}_{2}\)) optical filter has been designed and experimentally implemented based on the requirements of typical and realistic VLC systems in order to significantly increase their performance by reducing the transmittance of background noise. The functionality of the specific filter is investigated by means of its bit error rate (BER) performance estimation, taking into account its experimentally measured characteristics. Numerous results are provided in order to prove the significant performance enhancement of the VLC systems which, as it is shown, reaches almost six orders of magnitude in some cases, using the specific experimental optical filter.  
bit error rate; VLC; experimental optical filter; eSSK; VO\({}_{2}\) filter +
Footnote †: journal: Computer Physics Communications