esa-sceva/satcom-chunk-collection huggingface dataset:
# Sub-Milliwatt Transceiver IC for Transcutaneous Communication of an Intracortical Visual Prosthesis
## 7 Conclusions  
A sub-milliwatt transceiver IC for the implant side of an intracortical visual prosthesis was designed, fabricated and measured. It delivers 1 Mbps for the downlink (for stimulation) and 50 Mbps for the uplink (for recording), using a non-coherent BPSK demodulator and an IR-UWB transmitter, respectively. Its predominately digital components and adjustability lead to the low power consumption of 0.2 mW for the BPSK receiver and 0.3 mW for the IR-UWB transmitter at 1.3 V supply on 180 nm CMOS technology. Based on our transceiver IC in the implant, the system link budget analysis for both uplink and downlink show achievable figures: there is 18-23 dB excess margin for uplink, and 12-26 dB for downlink. These figures show that the link can be closed with an excess margin for the antenna/coil pair to communicate through 3-7 mm skin, which is the transcutaneous interface between the implant side and outer side, while achieving good BER performance as a result of closing the link with an excess margin.  
In the future, scaling to more advanced processes that use lower supply voltages will further drastically reduce power consumption due to the predominately digital architecture. Furthermore, the design of antennas/coils and a transceiver IC on the outside, in order to realize the system link budget, is the logical next step.  
Investigation, A.O. and G.R.; Conceptualization and methodology, A.O. and G.R.; Supervision, R.M. and M.B.; Funding acquisition, M.B.; Data curation, A.O.; Writing--original draft, A.O.; and Writing--review and editing, A.O., R.M., G.R. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Dutch Technology Foundation STW, which is part of the Netherlands Organization for Scientific Research (NWO), project 5 of the NESTOR program (P15-42).  
Data Availability StatementData is contained within the article tables and figures and is available on request.  
The authors would like to thank [PERSON] from the Eindhoven University of Technology for his reviews and support during the IC design and fabrication process. This research is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organization for Scientific Research (NWO), project 5 of the NESTOR program (P15-42).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ISM & Industrial, Scientific, and Medical \\ IC & Integrated Circuit \\ SAR & Specific Absorption Rate \\ NESTOR & NEuronal Stimulation for Recovery of Function \\ BPSK & Binary Phase Shift Keying \\ PLL & Phase Locked Loop \\ \end{tabular}
# Research of a Multi-Frequency Waveform Control Method on Double-Wire MIG Arc Welding
## 3 Proposed Multi-Frequency Double-Wire Impulse Welding Method
In this method, the trailing wire current can be switched between peak stage and base stage with the frequency of \(f_{T}\)_th_, which can help improve the cover of the welding seam and form regular fish-scale grain. 2. The process of switching between peak stage and base stage of the trailing wire can stir the welding pool, the gas inside the pool would be much adequately discharged, hence the air holes could be decreased. In addition, the heat delivery for the parent metal was also decreased, and it can prevent the excessive inflation of surface of the parent metal. Moreover, it can make the makeup of the welding seam more regular and compact. 3. This method can decrease the duration when both of the leading wire current and trailing wire current were in the base stage; meanwhile, the duration may be more dispersive by means of high frequency modulation. It can decrease the possibility of appearance of some unsteady phenomena, such as arc interruption. Hence, the stability of welding process can be improved. 4. Because the frequency at which the peak stage of trailing wire current appears was increased, the heat dissipation when the leading wire current was in the base stage was decreased, which can make the leading wire hold a continuous thermal supply and increase the deposition rate during the process. Hence, this method can improve the thermal efficiency under the condition that current peak and current base were unchanged.
## 2 Basic Theory
### Experimental Modal Analysis
#### 2.2.2 Experimental Modal Analysis
The eigenvalue decomposition of the characteristic matrix \(\mathbf{A}\) is as follows:  
\[\mathbf{A}=\mathbf{\phi}\cdot\mathbf{\Lambda}\cdot\mathbf{\phi}^{-1}. \tag{10}\]  
The eigenvalues \(\lambda_{r}\) are discretely obtained from the matrix \(\mathbf{\Lambda}\), and the system's eigenvalues \(\mu_{r}\) can be calculated using the following formula:  
\[\lambda_{r}=\mathrm{e}^{\mu_{r}\Delta t}\Rightarrow\mu_{r}=\sigma_{r}+i \omega_{r}=\frac{1}{\Delta t}ln(\lambda_{r}) \tag{11}\]  
where \(\sigma_{r}\) is the damping factor and \(\omega_{r}\) is the damped natural frequency of the rth mode; the damping ratio \(\xi_{r}\) is given by the following expression:  
\[\xi_{r}\ =\ \frac{-\sigma_{r}}{\sqrt{\sigma_{r}^{2}+\omega_{r}^{2}}}. \tag{12}\]  
The mode shape of the rth mode, \(\mathbf{\varphi_{r}}\), is the observable part of the system's eigenvector \(\mathbf{\phi_{r}}\), as represented below:  
\[\mathbf{\varphi_{r}}=\mathbf{C}\cdot\mathbf{\phi_{r}}. \tag{13}\]  
In the field of linear structural parameter identification, especially under stable excitation conditions, the stochastic subspace method has demonstrated its potential as an efficient technique, particularly in its outstanding capability to counteract output noise. The most prominent feature of this method is its ability to estimate parameters independently of the input data, making it extremely suitable for response mode analysis. By implementing a comprehensive approximation strategy, this technique achieves synchronized approximation through the integrated analysis of output waveform data from multiple measurement points. Hence, it maintains a high degree of identification accuracy, even in scenarios where modal frequencies are close or intrinsic frequencies are densely concentrated.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 4 Performance Metric and Simulation Setup
### Performance Evaluation Metrics
#### 4.1.2 End-to-End Delay  
End-to-end delay refers to the sum of all delays in the link caused by intermediate nodes--the total time taken by a packet from sender to receiver vehicle. It is computed as the sum of all communication delays. It is measured in sec, ms, or ns. A less delay infers better network performance.  
\[EED=(T_{delay}+P_{delay}+Q_{delay}+Proc_{delay})/totalP_{r} \tag{6}\]
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 4 Experiments
### Ablation Studies  
#### 4.4.1 Effect of \(\lambda\)  
In this study, we employed a noise-to-norm reconstruction paradigm. To validate the effectiveness of adding noise and the effect of the noise coefficient (\(\lambda\)) on the detection results, we conducted comparative experiments. The results, as shown in Table 4, demonstrate that the overall detection performance was the best when \(\lambda=0.3\). Compared to that  
Figure 4: Visualization of examples on MPDD. (**a**) Original image (**b**) Ground truth (**c**) Reconstructed image (**d**) Anomaly map (**e**) Prediction of anomalous regions (**f**) Prediction of anomalous regions on the original image.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **DRAEM** & **RD4 AD** & **PaDiM** & **CFLOW** & **FastFlow** & **PatchCore** & **Ours** \\ \hline Candle & 94.4 & 92.2 & 91.6 & 97.0 & 92.8 & **98.6** & 83.7 \\ \hline Capsules & 76.3 & 90.1 & 70.7 & 93.0 & 71.2 & 81.6 & **93.3** \\ \hline Cashew & 90.7 & **99.6** & 93.0 & 90.9 & 91.0 & 97.3 & 93.4 \\ \hline Chewing gum & 94.2 & **99.7** & 98.8 & 98.3 & 91.4 & 99.1 & 97.7 \\ \hline Fryum & 97.4 & 96.6 & 88.6 & 91.1 & 88.6 & 96.2 & **97.3** \\ \hline Macaroni1 & 95.0 & **98.4** & 87.0 & 69.6 & 98.3 & 97.5 & 91.6 \\ \hline Macaroni2 & 96.2 & **97.6** & 70.5 & 77.2 & 86.3 & 78.1 & 91.5 \\ \hline PCB1 & 54.8 & 97.6 & 94.7 & 91.4 & 77.4 & **98.5** & 94.7 \\ \hline PCB2 & 77.8 & 91.1 & 88.5 & 96.7 & 61.9 & **97.3** & 95.6 \\ \hline PCB3 & 94.5 & 95.5 & 91.0 & **99.6** & 74.3 & 97.9 & 98.7 \\ \hline PCB4 & 93.4 & 96.5 & 97.5 & 94.2 & 80.9 & **99.6** & 98.2 \\ \hline Pipe fryum & 99.4 & 97.0 & 97.0 & 99.0 & 72.0 & **99.8** & 92.6 \\ \hline Avg. & 88.7 & **96.0** & 89.1 & 91.5 & 82.2 & 95.1 & 94.0 \\ \hline \hline \end{tabular} Best results are highlighted in bold.  
\end{table}
Table 3: Comparison of image-level detection results (AUROC%) on the VisA dataset.  
of the case without added noise (\(\lambda=0\)), the detection accuracy increased by 22.28%, and the segmentation accuracy increased by 9.55%. Therefore, we finally set \(\lambda=0.3\). These experimental results confirm the significant improvement in anomaly detection achieved using the noise-to-norm reconstruction approach.
## 2 Methods
### Fracture Cavity Characterization
The ultra-thin square plate filling method describes interconnected pore space's three-dimensional irregular cavity distribution. The spatial heterogeneity fractal dimension can be obtained by calculating the number of ultra-thin square plates at different side lengths. This parameter reflects the changes in spatial dimension, connectivity, and symmetry. This quantitative method can better understand the complexity and heterogeneity of the fracture space cavity distribution. Based on the above research, the application of spatial fractional dimension can be further expanded to describe the complexity of fracture space cavity distribution under non-matching conditions of fracture surfaces. The specific method is as follows:Based on the high-precision mesh fitting of the actual surface morphology mentioned before, ultra-thin square plates with a width of \(\delta\) and a thickness of \(\xi\) are filled at the mesh nodes. Therefore, the number of thin square plates covering the fracture space at the mesh nodes can be determined by Equation (3). \[N_{i,j}=INT\left(\frac{\left[\max\!\left(z_{(i,j)^{\prime}}^{+}z_{(i+\delta,j)}^{ +},z_{(i,j+\delta)^{\prime}}^{+}z_{(i+\delta,j+\delta)}^{+}\right)-\max\!\left( z_{(i,j)}^{-},z_{(i+\delta,j)^{\prime}}^{-}z_{(i,j+\delta)}^{-},z_{(i+\delta,j+ \delta)}^{-}\right)\right]}{\xi}+1\right) \tag{3}\]  
where the different elevations of the four mesh nodes are represented by \(z_{(i,j)}\) and \(+\) and \(-\) denote the upper and lower surfaces, respectively. Here, \(i\) and \(j\) represent the position indices along the width and length directions of the ultra-thin square plate, and \(INT(X)\) is the function that returns the largest integer not greater than \(X\). Therefore, the total number of plates filling the entire interconnected fracture space at a given width can be calculated using Equation (4). \[N_{sum}(\delta)=\sum_{i,j=1}^{n-1}N_{i,j} \tag{4}\]  
By continuously changing the size of the mesh nodes through Kriging interpolation, the corresponding adjustment of the ultra-thin square plate width \(\delta\) is made to match the mesh nodes. The total number of ultra-thin square plates also changes accordingly. The relationship between the two can be plotted in a double-logarithmic coordinate system, and the slope of the curve is considered as \(-D\), representing the calculated spatial fractal dimension, as shown in Equation (5). \[-D=\frac{\log_{10}N_{sum}(\delta)-\log_{10}k}{\log_{10}\delta} \tag{5}\]  
where \(k\) is a proportionality constant, and \(D\) represents the corresponding spatial heterogeneous fractional dimension, which describes the heterogeneity of the distribution of interconnected cavity channels in the fracture space. Based on this, by comparing parallel plates with the same mechanical aperture, \(D_{\Delta}=D-D^{\textit{pref}}\) can be obtained. This represents the relative heterogeneous fractional dimension and is used to characterize the complexity of the fracture spatial void distribution. Figure 5: Ultra-thin square plate filling method for the spatial fractional dimension of the void within a fracture.
## 3 Results and Discussions  
### Validation of Simulation Results  
It has been proved in previous study that the simulated heat loss had a good agreement with experimental data carried out by National Renewable Energy Laboratory [15]. In this paper, the simulated energetic efficiency results implemented by the C++ language program (Visual C++ 11.0, Microsoft, Redmond, Washington, USA, 2012) are compared with experimental data from outdoor testing carried out by [PERSON] et al. [24]. All parameters used in simulation are same with parameters in experiments, namely, DNI of 864 W/m\({}^{2}\), ambient temperature of 32.8 \({}^{\circ}\)C, and HTF flow rate of 3 kg/s. The simulated and experimental energetic efficiencies are presented in Figure 5. It is noted that the units of variable of (\(\Delta T\)) and \(Q_{\text{solar}}\) is \({}^{\circ}\)C and W/m\({}^{2}\), respectively. In this study, root-mean-square deviation (RMSD) and mean bias error (MBE) [28] are employed to verify the simulation precision. MBE is expressed as follows:  
\[MBE=\sum_{i=1}^{N}{(X_{simulated,i}-X_{experimental,i})}/N, \tag{10}\]  
where \(X_{simulated}\) and \(X_{experimental}\) represent the simulated and experimental values, \(N\) refers to the total number of observations. It is calculated that the RMSD value remains within 6.0% and the MBE value is about \(-0.8\%\), which demonstrates that the simulated result yields satisfactory consistence with the experimental data.  
Figure 5: Modeled energetic efficiency versus experimental data.
## 3 Proposed Work
### Use Case  
The proposed group management framework is applied to smart home applications, as depicted in Figure 2. The smart home application is built up of embedded devices and sensors that collaborate via LAN or WAN connections to improve the comfort of the house members. The components of a smart home system include an entertainment system consisting of a music system, home theater, TV, etc., a security surveillance system with cameras, alarms, door locks, etc., and an energy management system with a connection to a smart grid to optimize electricity usage [45,46]. The segregation of smart home applications into groups helps prevent the spread of attacks from a vulnerable part of a system to other parts of the system. An authenticated key agreement protocol for groups in home-based sensor networks is proposed in [47], which uses an authentication token for authentication between mobile stations and serving networks. The scheme shows robustness when the mobile stations move between different serving networks. Another group-based authenticated key agreement protocol for machine-to-machine communication is proposed in [48], which selects a leader device first to perform full authentication. The remaining member devices authenticate using the vector provided by the leader device. The proposed work assumes that the smart home application contains an admin device that is trustable with enough resources to communicate with service functions and establish a secure connection. The steps involved in group initialization are depicted in Figure 3. Resource-constrained smart home devices can connect with the admin device via 5G networks or Bluetooth LE that supports device authentication. After mutual authentication, the admin devices send the device IP, MAC address, ID, Group ID, individual key \(\mathrm{K_{L}}\), and adjacent shared secret \(\mathrm{K_{a}}\) to the authentication service function encrypted using the key shared between them. After authentication, the authentication service function forwards the request to the key management service function. The key management service function forwards the adjacent shared secret and key materials needed to obtain the group key to the device encrypted using \(\mathrm{K_{L}}\), which was already obtained by the device from the admin. This ensures the message is from a trusted source. The member devices only know the group key of their respective groups. But the admin will have all the group keys as they generate \(\mathrm{K_{a}}\), which is consequently used to extract the group key in the proposed group key management approach. The flow rules are set in the switches and routers using an SDN controller so that only devices in the same groups communicate. Thus, if a malicious person compromises the music system, they cannot proceed further to compromise the door lock since the device will be in another group, and using SDN, these devices can be segmented.  
Figure 2: Grouping of the smart home system.
# Active Learning for Biomedical Article Classification with Bag of Words and _FastText_ Embeddings
## 3 Results
### SLR Workload Reduction  
Experiments to estimate the SLR workload reduction using the WSS measure were performed only for the generally most successful active learning setups: the SVM algorithm with the _FastText_ representation and the random forest algorithm with the bag of words  
Figure 3: Learning curves with stability-based stopping criteria marks for SVM-FT and RF-BOW with random initialization and uncertainty sampling.  
representation; both used with random initialization and uncertainty sampling query selection only. Figure 4 presents the obtained learning curves, plotting the WSS value obtained for the default decision threshold of predicted relevant class probability equal 0.5 versus the percentage of data labeled so far. Vertical dashed lines mark iterations when a recall value of 0.95 or higher were first achieved. The corresponding WSS@995% values are presented in Table 9.  
Figure 4: WSS learning curves for SVM-FT and RF-BOW with uncertainty sampling.  
The following observations can be made.  
* The highest workload reduction is typically achieved after labeling 20-40% of articles and ranges from about 0.2 to about 0.7.
* The recall level of 0.95 is typically achieved after labeling 60-80% of articles, which corresponds to WSS@95% values between 0.04 and 0.56 for SVM-FT (0.28 on average) or between 0.11 and 0.65 for RF-BOW (0.26 on average).
* With respect to peak WSS values, RF-BOW outperforms SVM-FT for \(four\) datasets, SVM-FT outperforms RF-BOW for \(three\) datasets, and they perform similarly to each other for eight datasets.
* With respect to WSS@95%, SVM-FT outperforms RF-BOW for \(eight\) datasets, RF-BOW outperforms SVM-FT for \(four\) datasets, and they perform similarly to each other (within a 0.02 margin) for \(three\) datasets.  
Table 10 shows how the WSS@95% values from Table 9 are related to the size and relevant class percentage of particular datasets according to the Spearman rank correlation. Unlike the results previously observed for prediction quality measured using the area under the precision-recall curve, workload reduction is positively correlated with data size (although this effect only occurs for SVM-FT) and negatively correlated with the relevant class percentage. This means that more savings in the SLR process are obtained for datasets that are harder to classify (bigger and more imbalanced). Even though the classification performance for such datasets is worse, they require considerably more human labeling effort, and a greater part thereof can be avoided due to active learning.
## 2 Materials and Methods
### Data Used  
Due to the complexity of each ecosystem, a large number of data were used from different national and international sources available for the entire territory of Romania. Satellite images and satellite imagery products such as MODIS, Landsat and Sentinel 2 were used. Furthermore, vector data such as land use limits (CORINE Land Cover or Land parcel identification system), limits of territorial administrative units (TAUs), limits of protected natural areas, the hydrographic network, the road network, pollution sources, soil types, etc. and statistical data such as livestock and the number of inhabitants were used. The data sets used for each ecosystem are described in Table 1.  
Figure 1: Location of Romania and major landforms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Major Ecosystem Category**} & **Ecosystem Type for** & \multirow{2}{*}{**Name of Datasets**} & **Resolution/Minimum Mapping Unit** & \multirow{2}{*}{**Time Reference**} & \multirow{2}{*}{**Source**} \\  & **Mapping and Assessment** & & & & \\  & & & & & \\  & & & & & \\  & & Land-pareol identification system (LPS) & NA & 2013 & Land-pareol identification system from Romania [35] \\  & Forest & Vegetation Continuous Fields (MOD4B) & 250 m & 2000–2013 & LADS DAC [56] \\  & & Hansen Global Forest Change & 30 m & 2000–2013 & Global Forest Change [37] \\ \cline{2-6}  & & Land-pareol identification system (LPS) & NA & 2017 & Land-pareol identification system from Romania [38] \\  & & Limits of territorial administrative units (TAUs) & NA & 2016 & National Agency for Calastre and Land \\  & Grassland & Digital surface model (EU-DEM) & 25 m & 2011 & Copernicus Land Monitoring Service [40] \\  & & Livestock numbers and types from TAUs & NA & 2010 & National Statistics Institute of Romania [41] \\  & & European Settlement Map & 10 m & 2010–2013 & Copernicus Land Monitoring Service [42] \\  & & Sentinel 2 images & 10 m & 2015–2017 & Copernicus Open-Access Hub [43] \\ \cline{2-6}  & & Natura 2000 (N2K) & NA & 2017 & Ministry of Environment, Waters and Forests [44] \\  & & Orthophotes & 0.5 m & 2016 & National Agency for Calastre and Land \\  & & CORINE Land Cover (CLC 2012) & 100 m & 2011–2012 & Copernicus Land Monitoring Service [45] \\  & & European Settlement Map & 10 m & 2010–2013 & Copernicus Land Monitoring Service [42] \\  & & European catchments and Rivers network system (ECHNS—dams on rivers) & 1250,000 & 2012 & European Environment Agency [46] \\  & & Rostds and railways & NA & 2016 & Open Street Map [47] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Used geo-database.