esa-sceva/satcom-chunk-collection huggingface dataset:
# Coalition Formation Game for Cost-Efficient Multiparty Payment Channel in Payment Channel Networks
## 4 Multiparty Payment Channel
### Concurrent Payments in Independent SPCs
For this MPC with parallel SPCs, either full or partial synchronization can be considered, similar to the linear MPC model. The full synchronization keeps users updating an MPC state from the previous round. Meanwhile, partial synchronization has an update delay for the final status at the cost of reducing the communication overhead. For example, A can finalize the \(T_{0}\) status at \(T_{2}\) as it receives \(v_{DE}\) from D at \(T_{1}\) and \(v_{CF}\) from F at \(T_{2}\) in Figure 6. Although this synchronization delay increases by log-scale \(\log V/2\), owing to \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Number of MPC Users (N)** & **4** & **6** & **8** & **10** & **20** & **40** \\ \hline Average SPCs & 1.3 & 2.43 & 3.56 & 4.66 & 9.85 & 19.93 \\ \hline Average rounds (\(r\)) & 4.5 & 6.17 & 7.84 & 9.65 & 19.29 & 39.14 \\ \hline Rounds by Algorithm 3 & 3 & 5 & 7 & 9 & 19 & 39 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Expected concurrent SPCs, scheduling period, and experimental result. the accumulated blocks in the local chain, a user can suffer from communication overhead to trace the balance of a counterparty. For dispute resolution in the concurrent SPCs, the smart contract also receives all users' previous \(V\) blocks (almost \([n-r,n-1]\) rounds). It investigates each round for update as in Algorithm 2. In contrast to the linear MPC, each round is subjected to an MPC update only when all SPC transactions of a round are verified successfully.
# Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016
## 3 Results
### Characteristics of Lagged Response of the Terrestrial Carbon Fluxes to Drought  
The responses of terrestrial carbon fluxes to drought were lagged at different temporal scales in different regions (Figure 3). In cold regions, such as northern Canada, central and eastern Eurasia, and the Qinghai-Tibet Plateau, the response of terrestrial carbon fluxes to drought lagged for a relatively longer period, mostly above 10 months. In humid regions, such as the eastern United States, western Europe, the Amazon basin, central and western Africa, and southeastern China, the response of terrestrial carbon fluxes to drought lagged for a relatively shorter period, generally less than 4 months. In arid and semi-arid regions, such as the western United States, southern South America, southern Europe, northeastern China, northern Africa, and western Australia, the lagged time for terrestrial carbon fluxes in response to drought ranged from 6 to 10 months. Overall, the lagged time of terrestrial carbon fluxes responding to drought was longer in areas with weaker responses of carbon fluxes to drought than in areas with stronger responses of carbon fluxes to drought.  
GPP, NPP and NEP had similar spatial distributions of lagged time in response to drought, while the lagged time of ER in response to drought differed from GPP, NPP, and NEP (Figure 3). This difference was partly reflected in the increased area of regions with longer lagged months to drought, and partly in the higher spatial heterogeneity of ER in response to drought. In arid and semi-arid regions such as the western United States, southern South America, western India, southern Africa, and western Australia, the lagged months of ER in response to drought were significantly longer than those of GPP, NPP,  
Figure 3: Lagged months of the strongest response of terrestrial GPP (**a**), NPP (**b**), ER (**c**), and NEP (**d**) to drought. Areas with no significant correlations between carbon fluxes and SPEI, and areas with desert, sea, and ice, are depicted in white.  
and NEP. In cold regions, such as northern Eurasia, northern North America, and the Qinghai-Tibet Plateau, the lagged months of ER in response to drought were also longer.  
Globally, the lag effects of drought on GPP, NPP, ER, and NEP were almost identical with longer time scales in semi-arid and semi-humul regions and shorter time scales in arid and humid areas (Figures 3 and 4). In areas with a negative annual water balance averaged over the period from 1981 to 2016, the lagged time of terrestrial carbon fluxes in response to drought increased with increasing annual water balance. In contrast, in areas with a positive annual water balance, the lagged time of terrestrial carbon fluxes in response to drought decreased with the increasing annual water balance.
# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 4 Assessing the vBBU Performance in the Linux Kernel
### Results and Discussion
#### 4.2.2 CPU Management Strategies
Based on the evidence presented thus far, the coexistence of user-space processes and Kernel threads sharing computing resources has a noticeable impact on vBBU performance. In this section, we explore the effectiveness of two CPU isolation mechanisms, namely, Shielding and isoCPU, as potential solutions to mitigate the influence of concurrently executing workloads. Since CPU isolation ensures that all user-space processes run on CPUs other than isolated ones, scenarios such as Idle and User exhibit no direct impact on vBBU performance. Consequently, our evaluation focuses exclusively on the Kernel scenario. To gain deeper insights into the advantages of Shielding compared to isoCPU for the vBBU, we analyzed the performance of vBBU's DL NACK procedures, as illustrated in Figure 7.  
The Shielding approach demonstrates better performance by producing fewer DL NACKs compared to isoCPU. Remarkably, Shielding outperforms even the results observed in Figure 5 for the Idle scenario, where the vBBU utilizes all available CPUs in the edge server. In this case, the allocation of CPU affinity through IA-WA enhances vBBU performance in terms of DL NACKs, as opposed to IA-TA. In contrast, when employing CPU isolation with isoCPU, the vBBU experiences a higher number of DL NACKs for both IA-WA and IA-TA, surpassing even the performance observed in a stressed system, such as in the User scenario depicted in Figure 5. This discrepancy is attributed to the implications of isolating CPUs in isoCPU, which involves migrating buffered data chunks. Potentially, these chunks may be in the cache of CPUs outside the isolated set, leading to degradation in the DL data path. Certainly, isolating CPUs mitigates the impact on vBBU scheduling latency caused by sharing computing resources. As shown in Table 5, both CPU isolation approaches result in lower vBBU scheduling latency compared to the results presented in Tables 2-4 for the Kernel scenario. However, the specific benefit of isoCPU over Shielding in terms of vBBU scheduling latency is not entirely clear. The observed difference in scheduling latency results between the two cases is not significant. Some related research [63] suggests that traffic-based applications benefit from hard isolation, reducing processing latency. However, because the vBBU involves various operations beyond packet processing, such as I/O, such a benefit may not be readily apparent. While isolating CPUs proves beneficial in mitigating the impact of sharing computing resources, it comes with the trade-off of resource underutilization. Therefore, before adopting this approach, a careful assessment of the trade-off between mitigating processing interference and maintaining resource efficiency is essential. Figure 7: vBBU’s number of DL NACKs computed over a set of six experiments (total observation time-span is 60 min). Evaluated scenarios: Kernel–CPU affinity allocation: IA-WA vs. IA-TA.
# An Experimental Study of the Performance of a Crossed Rib Diffuser in Room Acoustic Control
## 3 The Experiment Using a Small Meeting Room  
This section describes an experiment conducted with R-1, CRD and CRD-A using a small meeting room. Figure 7 portrays interior views of the small meeting room measured for this study. The meeting room had a gypsum board ceiling, a tile carpet floor, one gypsum board wall and three glass walls. A wooden table was placed at the center of the room with six chairs. Additionally, a wooden shelf was located at the back of the room. The room had slight sound absorption. Consequently, some flutter echo could be perceived by hand clapping. The room's impulse responses without specimens and with R-1, the CRD and the CRD-A were measured using an omnidirectional microphone. Then, reverberation parameters (reverberation time \(T_{20}\) and early decay time \(EDT\)) and a speech clarity parameter (definition \(D_{50}\)) were compared with each case to examine CRD and CRD-A effects on the room's acoustic parameters. Furthermore, sound intensity measurements were taken using a four-channel sound field microphone to investigate the structural differences of reflected sounds among a flat wall, the CRD and the CRD-A. Virtual source locations were visualized, revealing the described incident direction and intensity of reflected sounds incident to the microphone. To observe the effect of scattering reflection by the CRD and absorption by the CRD-A, the visualizations were conducted at 2 kHz octave bands because the CRD and the CRD-A represent a high scattering coefficient and a high absorption coefficient at the frequency band, respectively. As references, the cases at 500 Hz and 1 kHz octave bands were also visualized in Appendix A.  
Figure 7: Interior views of the small meeting room measured for this study.
# Unsupervised Image Segmentation on 2D Echocardiogram
## 4 Results
### Quantitative Analysis
#### 4.1.2 Ejection Fraction
Inter-observer variation in echocardiographic measurements has been reported to be as high as 13.9% [7], with slight deviations in segmentation boundaries being relatively common. For instance, EchoNet-Dynamic, a leading supervised model for echocardiogram analysis, demonstrated an area under the curve of 0.97 when classifying cardiomyopathy based on an ejection fraction threshold of 50%. This highlights the fact that even supervised models trained on large datasets experience similar levels of variance, particularly in edge cases. Our unsupervised approach, while exhibiting occasional segmentation inconsistencies due to image artifacts, remains competitive within this typical range of variation, offering promising results without reliance on annotated data.
## 1 Introduction
Additionally, there has been research on using manned [37; 38] and unmanned aerial vehicles (UAVs) [39] equipped with hyperspectral images for fire severity assessment. However, current satellite-based fire severity quantification research primarily relies on multispectral images [12; 25], whose broad spectral bands limit its ability to capture detailed wavelength information [40], making it difficult to accurately analyze fine-scale variations in fire severity with respect to wavelength. This limitation directly impacts the precision and stability of fire severity quantification, thereby constraining the practical application of satellite remote sensing for fire severity estimation. This issue becomes particularly pronounced when dealing with complex fire environments, such as mountainous regions, or when addressing multi-scale application demands. Thus, the use of data with higher spectral resolution to clarify the fire severity-spectral response mechanism is crucial for enhancing the potential of satellite remote sensing in precise fire severity estimation [41]. This study focused on the burned area of a forest fire that occurred on 11 April 2023, in Yuxi City, Yunnan Province, China. The data from ZY-1 hyperspectral data (ZY1E/F AHSI) was selected for analysis, with images captured pre-fire (6 February 2023, 12:07) and post-fire (21 April 2023, 11:53). Using spectral separability contrast and threshold segmentation methods, the sensitivity of fire severity estimation was analyzed for both the post-fire image and the pre- and post-fire image combination. The study investigated the relationship between changes in surface spectra and fire severity, systematically evaluating the ability of spectral bands and indices to identify fire severity. The aim is to provide technical guidance and theoretical references for the development of new fire severity estimation models, ultimately contributing to more accurate assessments of forest fire damage and vegetation recovery in mountainous regions.
# Assessment of the Impact of Electric Vehicle Batteries in the Non-Linear Control of DC Microgrids
## 3 Methodology
### Modeling of a Battery Cell
#### 3.1.2 SOC Calculation  
This model uses three initial parameters to calculate the real-time SOC: the initial \(SOC\) noted as \(SOC_{0}\), the discharging current \(I\), and the usable capacity \(C\). Based on [33], the \(SOC\) could be calculated, as per:  
\[SOC=SOC_{0}-\int_{0}^{t}\frac{I}{C\times 3600}dt \tag{32}\]
###### Abstract  
Downsampling, which aims to improve computational efficiency by reducing the spatial resolution of feature maps, is a critical operation in neural networks. Many downsampling methods have been proposed to address the challenge of retaining feature map information. However, some detailed information is still lost, even though these methods can extract features with stronger semantics. In this paper, we propose a novel downsampling method which combines feature slicing and depthwise separable convolution for information-retaining downsampling. It slices the input feature map into multiple non-overlapping sub-feature maps by using indexes with a stride of two in the spatial dimension and applies depthwise separable convolution on each slice to extract feature information. To demonstrate the effectiveness of SliceSamp, we compare it with classical downsampling methods on image classification, object detection, and semantic segmentation tasks using several benchmark datasets, including ImageNet-1K, COCO, VOC, and ADE20K. Extensive experiments demonstrate that SliceSamp outperforms classical downsampling methods with consistent improvements in various computer vision tasks. The proposed SliceSamp shows advanced model performance with lower computational costs and memory requirements. By replacing the downsampling layers in different network architectures (including ResNet (Residual Network), YOLOv5, and Swin Transformer), SliceSamp brings different degrees of performance gains (+0.54-3.64%) compared to these baseline models. Additionally, SliceUpsamp enables high-resolution feature reconstruction and alignment during upsampling. SliceSamp and SliceUpsamp can be plug-and-play-integrated into existing neural network architectures. As a promising downsampling alternative to traditional methods, SliceSamp can also provide a reference for designing lightweight and high-performance model architectures in the future.  
Keywords:model lightweighting; information-retaining downsampling; feature slicing; depthwise separable convolution; high-resolution feature reconstruction +
Footnote †: journal: Applied
## 2 Related Work and System Model
### Comparison of AN and SCO\(-\)AN  
The artificial noise must be in the null space of the CSI matrix, this condition makes the artificial noise design very challenging. Artificial noise is the solution of homogeneous linear equations \(\mathbf{H}_{k}\mathbf{w}_{k}=0\). If the rank of the matrix \(\mathbf{H}_{k}\) is r and the dimension is \(n\times m(n\geq m)\), only when \(r<m\), the homogeneous linear equation system \(\mathbf{H}_{k}\mathbf{w}_{k}=0\) has no solutions, when \(r=m\), the homogeneous linear equations have only zero solutions. In the environment of natural communication, the probability of occurrence of \(r=m\) is almost zero, that is to say, in the conditions of natural communication, the design of artificial noise is almost impossible.  
For example, in MIMO, when the number of transmitting antennas is less than the number of eavesdropping antennas, artificial noise cannot be designed; when the number of transmitting antennas is equal to the number of eavesdropping antennas, artificial noise can be designed under the condition \(\left|\mathbf{H}\right|=0\). When the number of transmitting antennas is greater than the number of eavesdropping antennas, artificial noise cannot be designed. This is exactly the opposite of the original intention of AN. AN is designed to solve the condition that the eavesdropping channel must be a weaken version of the legitimate channel.  
Therefore, the previous researches discussed some of the characteristics of AN theoretically and ignored its applicability.  
For SISO, \(H_{k}\) is a constant and \(w_{k}\) is a constant as well. If \(H_{k}W_{k}=0\) has a non-zero solution, H = 0 must be guaranteed. Therefore, AN is not applicable in SISO wireless communication system.  
SCO\(-\)AN is located in the range space of the legitimate CSI space, so, \(\mathbf{H}_{k}\mathbf{w}_{g}\
eq 0\).  
There are countless non-zero solutions to \(\mathbf{w}_{g}\), so we don't worry about to design \(\mathbf{w}_{g}\).  
We try to design AN under the condition of Rayleigh fading channels, and carry out a total of 1000 experiments, and all experiments fail. When we try to design SCO\(-\)AN, all experiments are successful.  
In Table 1, we compare SCO\(-\)AN and AN in detail, and briefly summarize the characteristics and applicability of SCO\(-\)AN and AN. It can be seen that SCO\(-\)AN is better than AN in every aspect.
## 2 Passive Radar Performance Principle  
In Figure 2, the basic geometry of a bistatic PR using an EO SAR sensor as the OI is presented. The system principle of operation is based on the correlation of the reference signal from the OI acquired by the reference channel (continuous orange arrow) and the target echoes acquired by the surveillance channel (continuous blue arrows). As a result of this coherent processing, the cross-ambiguity function (CAF) is generated.  
In Equation (1) the discrete time expression of the CAF is presented for a coherent integration time, \(T_{int}\) (s), and a sampling frequency, \(f_{s}\) (Hz), where:  
* \(N=T_{int}\cdot f_{s}\) is the number of samples.
* \(m\) represents the time bin associated with a delay \(\tau_{m}=\frac{m}{f_{s}}\).
* \(p\) is the Doppler bin corresponding to Doppler shift \(f_{D_{p}}=f_{s}\cdot(\frac{p}{N})\).
* \(s_{REF}[n]\) and \(s_{SURV}[n]\) are the reference and surveillance signals, respectively. \[s_{CAF}[m,p]=\sum_{n=0}^{N-1}s_{REF}^{*}[n-m]\cdot s_{SURV}[n]\cdot\exp(-j2 \pi\frac{p}{N}n)\] (1)  
For each target of the coverage area, the result of the CAF is the ambiguity function (AF) of the transmitted signal, scaled and shifted to be centered on the time delay and Doppler shift corresponding to the bistatic range and bistatic Doppler shift of the target. Figure 3 presents an example of CAF generated in a scenario with the following elements:  
* Two stationary targets are at bistatic ranges \(Rb1\) and \(Rb2\). These bistatic ranges are calculated as \(Rb=\tau_{bistatic}\cdot c\), where \(\tau_{bistatic}\) is the bistatic delay calculated in Equation (2) as a function of the target-OI, the target-PR and the OI-PR or baseline distances, denoted as \(R_{T},R_{R}\) and \(L\), respectively. \(c\) is the velocity of light. Stationary targets appear in the zero Doppler line of the range-Doppler map. \[\tau_{bistatic}=\frac{R_{T}+R_{R}}{c}-\frac{L}{c}\] (2)  
Figure 2: Example of passive radar geometry.  
* One moving target is detected at a bistatic range \(Rb1\). Its echo appears in the range-Doppler map at \((Rb1,fd_{b3})\), where \(fd_{b3}\) is the bistatic Doppler generated by target movement relative to the OI and the PR.
* The direct signal transmitted by the OI is captured by the reference and surveillance channels. The surveillance antenna is designed for rejecting this direct signal, but as it can be 100-80 dB higher than the target radar echoes, the level captured by the surveillance antenna can be significant compared to the target echo ones. This signal, known as the direct path interference (DPI) signal, correlates perfectly with the reference antenna signal, and as a result, a peak appears in the range-Doppler map of the CAF, located at zero bistatic range and zero Doppler.  
In the following subsections, the main parameters and tools that will be used in the feasibility study are presented: the bistatic range resolution, the bistatic Doppler resolution and the bistatic radar equation.
# A Simple and Compact Laser Scattering Setup for Characterization of a Pulsed Low-Current Discharge
## 3 Experimental Setup
### Test Object  
A commercially available pulsed plasma system, using a comparable working principle to that of other systems on the market [51, 52, 53, 54], is chosen to test the proposed scattering setup. The system consisting of a PG31 plasma generator and a PS2000 OEM power supply is produced by Relyon Plasma GmbH, Regensburg, Germany. Its main application fields are surface activation, decontamination or deposition of different materials [55, 53, 56, 57, 58].  
The PG31 generator has a conventional construction with a positively biased, finger-shaped non-refractory inner electrode and a copper alloy nozzle acting as cathode. Several nozzle geometries are available from the manufacturer--a conical shaped one of type A450 with an outlet diameter of 4 mm is used exclusively for the investigations. The generator is optimized for operation with molecular gases and gas flow rates between 35-60 standard liters per minute (short L/min) [56]. Accordingly, pure nitrogen at a flow rate of 35 L/min is used in the following experiments, regulated externally by a mass flow controller of type 8626 produced by Burkert GmbH & Co. KG, Ingelfingen, Germany. The effluent plasma jet operating at 60 kHz can be seen in Figure 5.  
The discharge is driven by unipolar, triangular current pulses with variable amplitude of maximal 1 A generated at the output of the PS2000 power supply. The power supply utilizes a single-phase full-bridge push-pull converter design with the rise and fall times of the triangular pulses fixed at 5 us, regardless of the selected pulse frequency. The power supply can be controlled via a PC software and allows an adjustment of the output power between 70% and 100%, which corresponds to a current amplitude of 0.7-1.0 A at the exit, whereas the pulse frequency can be varied between 40 kHz and 65 kHz in 1 kHz steps. In the experiments, the power setting was set to 100% (i.e., current amplitude of 1.0 A) and  
Figure 4: Average images of (**a**) stray light, (**b**) cold gas, and (**c**) plasma measurements obtained after pre-processing, and the corresponding spectral profiles for the marked pixel line used to determine the plasma parameters. The pure Thomson scattering signal extracted from these spectral profiles is shown in diagram (**d**).  
the operating frequency to 60 kHz. The typical current and voltage traces for these settings are presented in Figure 6.
## 4 Experimental Results and Analysis
### Performance Analysis and Comparison
#### 4.1.1 Pest Detection and Classification Results  
For multi-class pest detection and classification, model A is adopted. Figure 9 shows some examples of pest detection and classification results using the proposed model. The obtained results underline the validity of the method in locating pests, even if challenging images are tested. In fact, pests of varying sizes, shapes, and colors are accurately located.  
In Table 2, the ability of model A to localize pest and then recognize the insects inside the bounding box is indicated and compared with other recent methods indicated in the literature. Among all the recently released methods adopting the IP102 dataset  
Figure 9: Examples of pest detection and classification results on the validation set. The ground truth and corresponding predictions are shown.  
as a test bench, the comparison takes into account only frameworks performing both detection/localization and classification tasks.