esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 3 Results
### Landsat and Sentinel Burn Severity Correspondence with Field Data

Table 5: Paired scene method model fit (\(R^{2}\)) and normalized root-mean-square error (\(RMSE\)) for non-linear least squares regression between composite burn index (CBI) plots and remotely sensed burn severity estimates using either Landsat or Sentinel imagery. Results from Sentinel imagery are presented both for atmospheric and terrain corrected Bottom of Atmosphere (BOA) and uncorrected Top of Atmosphere (TOA) scenes. Models were fit to the combined dataset of 912 CBI plots from all 26 fires. hybrid composites, respectively), largely due to the low \(R^{2}\) of the paired scene RdNBR of 0.421. Notably, the \(R^{2}\) of RdNDVI from paired scene Sentinel BOA imagery was improved compared to compositing by 0.171 and 0.082 (extended and hybrid composites, respectively), although the opposite was true for Landsat, where the \(R^{2}\) of the paired scene RdNDVI was reduced by 0.073 and 0.081 (extended and hybrid composites, respectively).
## 3 Experimental Verification of Chatter Suppression Effect with TMDs
### Effect of Various Equivalent Mass Ratios of TMD on Chatter Suppression
In addition, the chatter suppression effect was examined in case the natural frequency of the TMD deviated by approximately 20% from the chatter frequency. A comparison of the chatter suppression effect is presented in Figure 14 with varying natural frequencies of the TMD. The workpiece vibration waveforms measured by the eddy current sensor of number 2 measuring point with the natural frequencies of all three TMDs tuned to \(f_{\rm d1}=f_{\rm d2}=f_{\rm d3}=2490\) Hz, \(f_{\rm d1}=f_{\rm d2}=f_{\rm d3}=1990\) Hz, and \(f_{\rm d1}=f_{\rm d2}=f_{\rm d3}=\)1600 Hz are presented in Figure 14a,c,e, respectively. The frequency analysis results of the time waveforms for the 0.05 s period in the initial 20 s after cutting (vertical  
Figure 13: Workpiece vibration displacement at number 2 measurement point during cutting without and with masses. Comparison of (**a**) vibration waveforms with and without masses (mounting arrangements: \(\theta_{1}=110^{\circ}\), \(\theta_{2}=220^{\circ}\), \(l_{\rm d}=50\) mm), (**b**) frequency analysis with and without masses (mounting arrangements: \(\theta_{1}=110^{\circ}\), \(\theta_{2}=220^{\circ}\), \(l_{\rm d}=50\) mm), (**c**) vibration waveforms with and without masses (mounting arrangements: \(\theta_{1}=110^{\circ}\), \(\theta_{2}=220^{\circ}\), \(l_{\rm d}=70\) mm), and (**d**) frequency analysis with and without masses (mounting arrangements: \(\theta_{1}=110^{\circ}\), \(\theta_{2}=220^{\circ}\), \(l_{\rm d}=70\) mm). dotted line in Figure 14a,c,e) are presented in Figure 14b,d,f, respectively. As observed from Figure 14, the chatter was suppressed only for the TMDs with the natural frequencies of \(f_{\rm d1}=f_{\rm d2}=f_{\rm d3}=1990\) Hz. Thus, the chatter could be controlled even for an extremely small equivalent mass ratio of the TMDs with the natural frequency approximately equal to the chatter frequency. Figure 14: Workpiece vibration displacement at number 2 measurement point during cutting without and with TMDs (mounting arrangements: \(\theta_{1}=110^{\circ}\), \(\theta_{2}=220^{\circ}\), \(I_{\rm d}=70\) mm). Comparison of (**a**) vibration waveforms without and with TMDs (\(f_{\rm d1}=f_{\rm d2}=f_{\rm d3}=\)2490 Hz), (**b**) frequency analysis without and with TMDs (\(f_{\rm d1}=f_{\rm d2}=f_{\rm d3}=\) 2490 Hz), (**c**) vibration waveforms without and with TMDs (\(f_{\rm d1}=f_{\rm d2}=f_{\rm d3}=\) 1990 Hz), (**d**) frequency analysis without and with TMDs (\(f_{\rm d1}=f_{\rm d2}=f_{\rm d3}=\) 1990 Hz), (**e**) vibration waveforms without and with TMDs (\(f_{\rm d1}=f_{\rm d2}=f_{\rm d3}=\) 1600 Hz), and (**f**) frequency analysis without and with TMDs (\(f_{\rm d1}=f_{\rm d2}=f_{\rm d3}=\) 1600 Hz).
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 5 Conclusions  
In this paper, we showcase several novel localization algorithms designed to accurately determine the locations of unknown sensor nodes. We proceed to evaluate and analyze these new algorithms across four distinct network topologies, focusing on metrics such as localization error and accuracy.  
More specifically, we enhance the basic DV-Hop algorithm by incorporating additional steps to enhance the precision of position estimation for unknown nodes. Through extensive simulations, we thoroughly assess the performance of these new algorithms within four different random network topologies in static WSNs. These simulations involve a number of variations in parameters, including the number of nodes, percentage of anchor nodes, communication range, and maximum number of iterations. Our results highlight the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Time Complexity** & **Space Complexity** \\ \hline DV-Hop & \(O(k^{2})\) & \(O(1)\) \\ PSODV-Hop & \(O(MaxG1*NP1*(n-k))\) & \(O(NP1)\) \\ MDV-Hop & \(O(MaxG1*NP1*(n-k))\) & \(O(NP1)\) \\ DV-HopCSO & \(O(MaxG2*NP2*(n-k))\) & \(O(NP2)\) \\ W-DV-Hop & \(O(k^{2})\) & \(O(1)\) \\ W-DV-HopCSO & \(O(MaxG2*NP2*(n-k))\) & \(O(NP2)\) \\ HW-DV-Hop & \(O(k^{2})\) & \(O(1)\) \\ HW-DV-HopCSO & \(O(MaxG2*NP2*(n-k))\) & \(O(NP2)\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time complexity analysis.  
exceptional performance of the HW-DV-HopCSO algorithm as compared to the standard DV-Hop and other competing algorithms in all the considered network experiments. Both the HW-DV-HopCSO and W-DV-HopCSO algorithms exhibit lower localization errors compared to the standard DV-Hop and other algorithms in the tested random square, H-shaped, O-shaped, and W-shaped network topologies. Furthermore, the simulation outcomes demonstrate that the HW-DV-HopCSO and W-DV-HopCSO algorithms consistently outperform the standard DV-Hop and other state-of-the-art approaches. In our ongoing research work, we intend to conduct future simulations to further evaluate the proposed algorithms in mobile wireless sensor networks. Additionally, the presented algorithms can be extended to more accurately calculate the locations of sensor nodes in three dimensions.  
Conceptualization, A.H.; methodology, A.H. and N.K.; software, A.H.; validation, A.H. and N.K.; formal analysis, A.H., M.-A.E.H. and N.K.; investigation, A.H.; resources, A.H.; data curation, A.H., M.-A.E.H. and J.E.K.; writing--original draft preparation, A.H. and N.K.; writing--review and editing, A.H., M.-A.E.H., J.E.K. and N.K.; visualization, A.H.; supervision, N.K.; project administration, A.H., N.K., M.-A.E.H. and J.E.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Evaluation of Nitrogen and Phosphorus Removal
## Materials and Methods
### Storm Event Sampling  
Collection of flow-based water samples occurred at four locations throughout the system (Figure 2) using automated water samplers (Teledyne ISCO 6712, Lincoln, NE, USA). Autosamplers were equipped with water-level measurement modules, which were set to record water level on a one-minute interval. Water-level measurements were used to calculate flow, as described below. To prevent drift, modules were calibrated at the zero-level point at the start of and throughout the sampling season.  
Inflow samples were taken from the inlet pipe to Tank 1 (System Inflow, Figure 2), where a compound weir (Thel-Mar, Boone, NC, USA) and a water level sensor (Teledyne ISCO 730 Bubbler Flow Module, Lincoln, NE, USA) measured stage. Flow rates were determined using stage-discharge values provided by the weir manufacturer. A second autosampler took samples from the effluent of Tank 3 (Tank Outflow, Figure 2). This autosampler was triggered via a communications cable to take samples concurrently with the first autosampler.  
Bioreactor outflow samples were collected individually from both water level control structures (WB and EB, Figure 2). As the outflow from each bioreactor filled the sums, effluent exited via a 15.2-cm diameter spillway equipped with an orifice plate containing a 7.6-cm diameter orifice, the top of which was the reference zero point for the water level sensor. Flow rate (q) was calculated from level readings from the attached water level sensor using a table of values developed from plotting H (autosampler level readings) vs. q; under weir (Equation (1)) and orifice (Equation (2)) conditions.  
\[\text{q}=3.33\text{LH}^{1.5}, \tag{1}\]  
where, q = flow rate (L s\({}^{-1}\)), L = length of weir (m), H = water height relative to weir (m).  
\[\text{q}=0.6\text{A}(2\text{g}\text{H})^{0.5}, \tag{2}\]  
where, q = flow rate (L s\({}^{-1}\)), A = area of orifice (m\({}^{2}\)), g = gravity constant, H = water height relative to weir (m).  
Each autosampler collected water samples on a predetermined flow-based interval. When compositing multiple samples across a storm hydrograph, flow-based sampling is preferred to limit sample processing [29]. Samples of 100 mL were taken after a specific volume of runoff passed through the sampling location. Each autosampler had four 3.7 L sampling bottles and a sampling interval was chosen to accommodate up to the anticipated runoff quantity for this drainage area for a 10-year24-h storm in South Burlington, VT calculated using the Soil Conservation Service Curve Number equation [30]. Within 24 h of a storm event, samples were collected, transported to the laboratory, and composited in the laboratory to prepare for analysis.
## 2 Sampled Apodized Grating Waveguide Structure and Design
### Lateral Shift Apodized Waveguide Grating Design
When \(B\) is larger than 10, the change in \(\kappa\) is smaller, and the minimum value is stable at approximately 15.0 mm\({}^{-1}\) at the edge sides of grating. Figure 5c displays the reflection spectrum of the proposed apodized grating according to the combination of Equations (10) and (12) (\(B\) = 6, 8, 10 and 12). It can be found that when \(B\) increases, \(\Delta\lambda_{b}\) has a mild decrease, and SLSR has an obvious improvement. When \(B\) is chosen as 10, \(\Delta\lambda_{b}\) is calculated as 5.0 nm. The SLSR has a value of 23.5 dB on both sides of the spectrum and shows a nearly ideal filter shape. Compared to the conventional amplitude modulated apodized grating, our proposed apodized grating has the advantages of both side-lobes suppression and simpler fabrication process. This can be explained as in the traditional amplitude apodized grating, the ridge heights of both sides are almost zero and the height gets a maximum value at the center. As the ridge height shows a Gaussian shape, the refractive index of grating also shows in the form of a Gaussian function. The changed value is estimated as 0.005 from the edge to the center of grating. According to the resonance condition of Bragg grating,  
Figure 4: (a) Relationship between \(\Delta h\) and \(k_{0}\); (b) Relationship between _Duty_ and \(k_{0}\); (c) Reflection spectrum at different grating lengths (\(P\) = 100, 200, 300, 400 and 500 \(\upmu\)m). Figure 5: (a) Reflection spectrum of the traditional apodized grating; (b) Relationship of \(k\) and \(B\) in our proposed grating; (c) Reflection spectrum of the proposed apodized grating (\(B\) = 6, 8, 10 and 12). the local refractive index in the center of the grating is larger than those of both sides and the resonant wavelength at the center of the Bragg grating is longer than those of both sides. Therefore, both sides of the Bragg grating form a Fabry-Perot cavity and the amplitude apodized grating has a large side lobe in the short wavelength region. As for the proposed lateral shift apodized grating, every grating period has a grating pitch of the same size, which means the effective refractive index is almost the same. The principle of the device is that the coupling coefficient can be changed by the adjusting ridge position. Both side-lobes are suppressed well with this method due to the coupling coefficients on both sides being much smaller than the coupling coefficient at the center.
## 4 Forecasting Results and Discussion  
This section aims to present the results obtained from forecasting the electricity demand data, as previously outlined in Section 3.1. The implemented approach is to evaluate the performance of single and multistage models to see which is best to be used to accurately forecast the data. This section therefore opens with Section 4.1, which will show the results of the single models, that is, the LASSO, FBP, LSTM, and SVR. Section 4.2 will then highlight the results obtained from running the multistage models with similar algorithms, that is, SVR-SVR and FBP-FBP, and Section 4.3 will present the results obtained from the multistage models with hybrid algorithms, that is, FBP-SVR, FBP-FBP, FBP-LSTM, and SVR-LSTM. Finally, Section 4.4 will present the results of the comparative study by comparing the computed metrics, that is, \(R^{2}\), MAE, and RMSE, of all the single and multistage models. The forecasting for each single/multistage model is performed in the presence and absence of the temperature data as a predicting feature, details of which were presented earlier in Section 2.2.
## 3 Results
### Ablation Result  
In order to evaluate the impact of each network component on registration performance, we conducted an effectiveness experiment on the structures of the registration subnetwork DSNet and the cascaded structure of the dual subnetwork on the Mindboggle101 dataset. This experiment tested the registration capability of DSNet and performed an ablation study on it. Table 2 lists three variants of DSNet, where \"DC\" represents dilated convolution combinations with different dilation rates and \"AG\" represents attention gate components. In \"DSNet (w/o DC)\", the dilated convolution combinations were removed and replaced with ordinary 3D convolutions, which can reflect the impact of a large receptive field on registration performance. In \"DSNet (w/o AG)\", the four attention gate components of different scales were replaced by skip connections. The \"DSNet (w/o DC + AG)\" variant removed both the dilated convolution combinations and the attention mechanism, retaining only the basic network structure, to verify the compatibility of dilated convolution combinations and attention gate components. The experimental data in Table 2 shows that the designs added to the encoder-decoder in this paper are fully effective.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Dice Score** & **SDlogl** & \(|J_{\mathbf{g}}(\mathbf{u})\leq\mathbf{0}\) & \%\(|J_{\mathbf{g}}(\mathbf{u})|\leq\mathbf{0}\) & **Time (s)** \\ \hline MCANet & \(0.649\pm 0.016\) & \(0.458\pm 0.036\) & \(36,706\pm 4812\) & \(0.689\pm 0.090\) & 0.733 \\ DSNet & \(0.612\pm 0.022\) & \(0.481\pm 0.024\) & \(30,244\pm 4362\) & \(0.568\pm 0.082\) & 0.367 \\ DSNet(w/o DC) & \(0.607\pm 0.020\) & \(0.487\pm 0.036\) & \(47,484\pm 6239\) & \(0.892\pm 0.117\) & 0.343 \\ DSNet(w/o AG) & \(0.611\pm 0.019\) & \(0.476\pm 0.019\) & \(48,069\pm 5959\) & \(0.903\pm 0.112\) & 0.354 \\ DSNet(w/o DC+AG) & \(0.606\pm 0.018\) & \(0.492\pm 0.061\) & \(48,505\pm 6154\) & \(0.911\pm 0.116\) & 0.330 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Registration results of MCANet ablation studies.  
Figure 5: Visualization results of several registration methods employed in this paper. The first row shows the fixed image and the deformed images warped by SyN, LDDMM, VoxelMorph-1, VoxelMorph-2, FAIM, Transmorph, and MCANet. The second row shows the moving image and the gridded deformation fields produced By SyN, LDDMM, VoxelMorph-1, VoxelMorph-2, FAIM, Transmorph, and MCANet.
## 1 Introduction
Only [PERSON] and [PERSON] found a datum deviation of 3.8 ns between BDS-2 and BDS-3 by comparing the differential code bias (DCB) and time group delay (TGD) of BDS-3 satellites [10]. [PERSON] and [PERSON] further attributed this inconsistency to satellite-dependent TGD bias of the BDS broadcast ephemeris, and corrected this inconsistency by calculating TGD bias [11; 12]. Although the corrections of TGD bias can weaken the impact of the inconsistency of the datum of satellite clock offsets on the assessment of BDS-2 and BDS-3 signal-in-space accuracy, the inter-system bias (ISB) between BDS-2 and BDS-3 is affected by both the datum of satellite clock offsets and receiver pseudorange bias [13; 14; 15; 16]. The receiver pseudorange bias and residuals of these corrections affected by the number of tracking stations and the accuracy of satellite orbits and ionospheric productswill continue to affect the assessment of BDS-2 and BDS-3 signal-in-space accuracy and single point positioning (SPP). With this background, we give the evaluation methods of broadcast clock offsets and SISRE and the new BDS-2 and BDS-3 combined SPP model in Section 2. The datasets needed for the assessment of BDS broadcast ephemeris and the SPP processing strategy are presented in Section 3. In Section 4, we first analyze the accuracy of broadcast orbit and clock offsets, SISRE and orbit-only SISRE, and prove that the datum of broadcast clock offsets for BDS-2 and BDS-3 are inconsistent by comparing average broadcast-minus-precise (BMP) clock values of BDS-2 and BDS-3 during the assessment of broadcast clock offsets. Then, we investigate the impact of the inconsistent datum of broadcast clock offsets on BDS-2 and BDS-3 combined SPP. Finally, some conclusions and recommendations are drawn.
## 3 Proposed Algorithm and Analysis
### Second-Level Clustering: Angular Distance Estimation  
An angle \(\theta\) between BS and node is introduced in second-level clustering to further enhance clustered network efficiency. Figure 3 shows ten randomly deployed sensor nodes and their corresponding location and angles with BS. To show how the value of  
Figure 2: Flowchart showing the working mechanism of the proposed TLC.  
\(\theta\left(0\leq\theta\geq\pi/2\right)\), as all the nodes are assumed to be in the first quadrant) alters the network performance, three CHs, N\({}_{2}\), N\({}_{3}\), and N\({}_{8}\), are formed according to the criteria defined in Equation (12). The rest of the sensor nodes are represented as the ordinary nodes.  
The ordinary node \(N_{7}\) is located in the outer range of CH nodes \(N_{3}\) and \(N_{8}\). \(N_{7}\) computes the energy needed to send its data to BS via any CH present in the network. The previously formed cluster by \(N_{8}\) is suitable to join, as the distance is only 4.1 units. However, it chooses a CH with the following condition:  
\[(d_{N})_{ci}=(d_{N})_{ci}+(d_{BS})_{ci}cos\theta,\;0\leq\theta\leq\pi/2 \tag{13}\]  
In Equation (13), nodal distance (\(\left(\mathrm{d_{N}}\right)_{ci}\)), distance with BS \(\left(\left(\mathrm{d_{BS}}\right)_{ci}\right)\), and \(\theta\) computed at the very beginning and remains the same until the very end. After some rounds of information exchange, the network starts showing heterogeneous characteristics, i.e., the energy level of each node is different; this is due to the position of some nodes being farther or nearer to CH. Hence, it is necessary to define a boundary condition in addition to the remaining energy and the nearest distance. For some nodes like N\({}_{7}\) which is yet to be included to any cluster, makes decision on its projection angle with BS. The number of rounds \(r\) for a sensor node in the boundary is defined as follows:  
\[r=\frac{E_{t}}{E_{rounds}} \tag{14}\]  
\(r\) plays a vital role in determining the efficiency of the network. A network system that transmits data to BS for a greater number of \(r\) is energy efficient. We calculate the values of \(E_{rounds}\) and compare these values to different algorithms to find the network efficiency. The value of \(r\) is set from 200 to 2000 for our simulation to calculate different network metrics.
# SACRB-MAC: A High-Capacity MAC Protocol for Cognitive Radio Sensor Networks in Smart Grid
## 1 Introduction
In [22], Spectrum-aware and Cognitive Sensor Networks (SCSNs) are proposed for CRSN-based smart grid applications, which can overcome spatio-temporally varying spectrum characteristics and harsh environmental conditions. Therefore, there is a great potential of using spectrum aggregation for CRSNs in a smart grid. However, the utilization of spectrum aggregation-based CRSNs in a smart grid is still unexploited. It requires optimizations and enhancements for each layer of the protocol stack, especially the Medium Access Control (MAC) protocol. Against this background, this paper aims to propose a high-capacity MAC protocol with novel modifications especially tailored for CRSNs in smart grid. In this regard, the SACRB-MAC (Spectrum Aggregation Cognitive Receiver-Based MAC) protocol is proposed. SACRB-MAC is a spectrum aggregation-based and receiver-based MAC protocol designed with special emphasis on reliability and capacity requirements of CRSNs in smart grid. It adopts spectrum aggregation technique to improve the available bandwidth and utilizes wireless medium broadcast nature to improve the reliability of CRSNs. The rest of the paper is organized as follows. Section 2 describes the framework for SACRB-MAC including overview, system model and the protocol description. In Section 3, analytical models for different performance metrics are discussed, followed by performance evaluation in Section 4. Finally, the paper is concluded in Section 5.