esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 3 System Model: Instantiating the vBBU within the Linux Kernel
### Mitigating Impact on vBBU RT Performance: Exploring CPU Isolation with Collocated Processes  
CPU isolation is a well-known practice in RT systems aimed at preventing process interference [17]. This technique involves dedicating a specific subset of CPUs exclusively to a particular process, ensuring that no other user-space processes, and, in certain scenarios, no Kernel threads, are allocated CPU time on the same subset of CPUs. Implementing CPU isolation in this manner helps RT systems avoid processing interference from collocated workloads, contributing to improved performance and predictability.  
We explore two CPU isolation approaches for the vBBU: Shielding (soft isolation) and isolCPU (hard isolation). Shielding involves isolating a specific subset of CPUs from user-space processes not intended to run within the shield. This isolation is achieved through the Kernel's cpuset subsystem [64], which assigns individual CPUs and memory nodes to control groups (Cgroups). Resources allocated to Cgroups are only visible to the Cgroup members or their parents, meaning that certain system Kernel threads may not be moved outside the shield. Thus, Shielding is categorized as a soft CPU isolation approach. Our methodology for studying the vBBU under isolated CPUs using the Shielding approach involves adopting the CPU affinity allocation strategies IA-WA and IA-TA.  
In contrast, isolCPU provides a more stringent form of CPU isolation by completely excluding a subset of CPUs from the RT-Kernel Scheduler [65]. Configured at boot time using the isolcpus feature [66], isolated CPUs cannot be allocated to any user-space process or Kernel-space threads, unless the CPU affinity of a process specifies that it should run on the isolated CPUs [17]. Because isolCPU fully prevents the Kernel Scheduler from allocating CPU time to isolated CPUs, it is considered a hard isolation approach [67]. Consequently, only specified processes can run on isolated CPUs, achieved by allocating CPU affinity. The methodology for studying the vBBU when running on isolated CPUs through the isolCPU approach consists of adopting CPU affinity allocations IA-WA and IA-TA.  
In this section, we have outlined a comprehensive system model tailored to evaluate resource sharing and CPU management for executing vBBUs on edge servers. Ouranalysis incorporated the implementation of CPU affinity and isolation strategies aimed at mitigating the impact of resource contention on vBBU performance. This model serves as the cornerstone for our empirical evaluation, laying the groundwork for the subsequent performance assessments outlined in this paper.
# An Iterative Closest Point Method for Lidar Odometry with Fused Semantic Features
## 2 Related Work  
### Lidar Odometry Algorithm  
With the capability of directly acquiring spatial distance information and working at night, 3D Lidar provides significant value and prospects for applications in earth observation missions. Therefore, it is crucial to increase the precision of LO and reduce its accumulated drift error. The current conventional LO methods generally use geometric feature matching to solve six-degrees-of-freedom pose information in three-dimensional space. Planar and edge points are generally extracted from the point cloud as geometric features [12; 13; 14]. Geometric features such as lines and cylinders can also be extracted from special environments [15]. Compared to cameras, Lidar produces a sparse, unstructured point cloud that lacks texture features, but it can be used to accurately characterize 3D spatial structures. With the development of deep learning, much inspiring work has been achieved in the semantic segmentation of images and Lidar point clouds [16; 17; 18]. The related work on LO algorithms that incorporate semantic information is also gradually unfolding [19; 20].
## 1 Introduction
### Background  
As shown in Figure 1, the study area of this paper was the Guangdong section of the Xijiang River Basin. It consists of the Xijiang River Basin Authority area that starts from the provincial boundary of Guangdong Province (vertically) and ends at the Doumen District of Zhuhai City. It has a total length of 342 km, a rainfall catchment of 17,960 km\({}^{2}\), an average annual rainfall of 1577 mm, and an average annual flow rate of 14.96 billion m\({}^{3}\). The study area is rich in water conservancy and hydroelectricity; it has a high shipping capacity and has important resources for the development of agriculture along the coasts and rivers. The cities of Zhaoqing, Foshan, Guangzhou, Jiangmen, Dongguan, Shenzhen, Zhongshan, and Zhuhai, through which the Guangdong section flows, belong to the Pearl River Delta urban agglomeration, which has the largest concentration of population, the strongest innovation capacity, and the strongest comprehensive strength in China, and it is also an important part of the Guangdong-Hong Kong-Macao Greater Bay Area, which has been a significant leader in China's economic development [35]. Relevant studies show that during 1980-2018, due to accelerated urbanization processes, the ecological land on the west bank of the Pearl River Delta has become heavily crowded out, the land use types along the basin are constantly changing, and the functions of their corresponding ecosystems and the ESV per unit area are also changing [13], and this is a typical area where basin ecosystems are changing in a gradient. The buffer zone (i.e., the land-water intersection zone) within 10 km from both sides of the Guangdong section of the Xijiang River Basin was designated as the study area of this paper.  
Figure 1: Map showing the location of the water–land ecotone of the Guangdong section of the Xijiang River.
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 3 Methodology
### Active Learning Query Strategy
#### 3.2.6 Information Density Weighted Strategy  
The information density weighted strategy [42] is another combination of informative and representative strategies. Informative-based strategies may tend to select unlabeled instances that lie along the classification boundaries even when these instances are outliers that are not representative of the broader distribution in the input space. This strategy introduces the concept of information density (ID), as defined Equation (5):  
\[\phi_{ID}(x)=\phi_{SE}(x)\times\left(\frac{1}{|D_{u}|}\sum_{i=1}^{|D_{u}|}sim (x,x_{u})\right)^{\beta}, \tag{5}\]  
where \(\phi_{SE}(x)\) measures the \"base\" informativeness of an unlabeled instance \(x\); the terms in parentheses in Equation (5) represent the similarity of \(x\) to all other unlabeled instances in \(D_{u}\), while the parameter \(\beta\) controls the relative importance of the representative term. The information density weighted strategy effectively combines uncertainty and diversity in active selection.
## 3 Rule-Based Classification
### Definitions of Data Anomaly Classes
#### 3.1.6 Drift Class  
The _Drift_ class is characterized by nonstationary vibrations with random drift, as illustrated in Figure 8. To identify the _Drift_ class, the measured data were partitioned into equally sized segments. Here, we define the following index to determine the _Drift_ class:  
\[d_{drift}=\frac{\sigma_{\mu_{s}}}{\mu_{\sigma_{s}}} \tag{5}\]  
where \(\sigma_{\mu_{s}}\) is the standard deviation of the mean of the segments and \(\mu_{\sigma_{s}}\) is the mean of the standard deviation of the segment. If the index \(d_{drift}\) is greater than a pre-defined threshold, the signal is classified as _Drift_. \(\sigma_{\mu_{s}}\) indicates the level of variation in the segmented signals. By dividing \(\sigma_{\mu_{s}}\) by \(\mu_{\sigma_{s}}\) which is the overall amplitude of the original signal, the index \(d_{drift}\) can be used as a measure of the random drift in a signal.
## 5 Experiments
### Experimental Results and Analysis
#### 5.2.2 Comparison with Other Mainstream Re-ID Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Market1501**} & \multicolumn{2}{c}{**DukeMTMC-reID**} & \multicolumn{2}{c}{**MSMT17**} \\ \cline{2-7}  & **Rank-1** & **mAP** & **Rank-1** & **mAP** & **Rank-1** & **mAP** \\ \hline SVDNet [53] (2017) & 82.3 & 62.1 & 76.7 & 56.8 & - & - \\ PDC [42] (2017) & 84.1 & 63.4 & - & - & - & - \\ Mancs [38] (2018) & 93.1 & 82.3 & 84.9 & 71.8 & - & - \\ HA-CNN [39] (2018) & 91.2 & 75.7 & 80.5 & 63.8 & - & - \\ PCB [6] (2018) & 92.3 & 77.4 & 81.8 & 66.1 & - & - \\ PCB+RPP [6] (2018) & 93.8 & 81.6 & 83.3 & 69.2 & - & - \\ APR [50] (2018) & 84.3 & 64.7 & 70.7 & 51.9 & - & - \\ GLAD [43] (2018) & 89.9 & 73.9 & 80.0 & 62.2 & - & - \\ MGCAM [12] (2018) & 83.8 & 74.3 & - & - & - & - \\ MHN-6(PCB) [40] (2019) & 95.1 & 85.0 & 89.1 & 77.2 & - & - \\ BAT-net [41] (2019) & 95.1 & 84.7 & - & - & - & - \\ PAN [54] (2019) & 82.8 & 66.1 & 71.6 & 51.5 & - & - \\ PAAN [44] (2019) & 91.9 & 77.6 & 81.7 & 64.5 & - & - \\ PBB [45] (2019) & 74.4 & 59.2 & - & - & - & - \\ AFFNet [51] (2019) & 93.7 & 81.7 & 84.6 & 70.7 & - & - \\ MaskReID [27] (2019) & 93.4 & 82.4 & 78.9 & 61.9 & - & - \\ IANet [55] (2019) & 94.4 & 83.1 & - & - & 75.5 & 46.8 \\ JJCL [56] (2019) & 94.8 & 86.0 & - & - & 77.2 & 52.3 \\ OSNet [57] (2019) & 94.8 & 84.9 & - & - & 78.7 & 52.9 \\ AANet [46] (2020) & 93.9 & 82.5 & 86.4 & 72.6 & - & - \\ APDR [52] (2020) & 93.1 & 80.1 & 84.3 & 69.7 & - & - \\ IMGPPnet [47] (2020) & 87.7 & 72.1 & 79.0 & 62.1 & - & - \\ MCAM [48] (2020) & 94.1 & 83.7 & 87.5 & 74.3 & - & - \\ SAN [58] (2020) & 96.1 & 88.0 & - & - & 79.2 & 55.7 \\ GASM [59] (2020) & 95.3 & 84.7 & - & - & 79.5 & 52.5 \\ FA-Net [49] (2021) & 95.0 & 84.6 & - & - & 76.8 & 51.0 \\ MAPN [24] (2022) & 94.5 & **92.1** & 90.0 & **86.5** & - & - \\ ISPE [60] (2023) & 94.4 & 85.2 & 87.1 & 74.3 & - & - \\ Ours & **96.7** & **90.1** & **90.3** & 81.4 & 79.2 & 55.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of our method with current representative methods on Market-1501, DukeMTMC-reID, and MSMT17. The best results are shown in red bold, and the second-best in blue bold. tility of the method, which lays a solid theoretical foundation and technical support for its implementation in the real-world scene.
# BN-RA: A Hybrid Model for Risk Analysis of Overload-Induced Early Cable Fires
## 3 Construction of a BN
### Determination of the BN Parameters
#### 3.3.1 Prior Probabilities of Root Nodes  
Root nodes with no parent nodes are the most basic nodes in a BN. The initial state of these root nodes must be determined through empirical decision-making and historical data. In this study, the prior probabilities of root nodes were obtained from the monitoring records of a tobacco distribution center and the historical data of electrical inspection (Table 1). Establishing a CPT relies on the following five rules:  
1. Because of the different properties of the nodes, these nodes are divided into discrete and continuous nodes. To unify the construction of a BN, continuous variables should be discretized and divided into different discrete intervals, where each discrete interval represents the state of the corresponding node.
2. Cable current node reflects the current through the 0.75 mm\({}^{2}\) cross-sectional area of the cable. According to an experimental study on the cable temperature rise, the rated current for this type of cable during normal operation is approximately 7 A.
3. The degree of mechanical damage indicates the original damage of the cable, that is, the mechanical damage caused by wiring, operation, or animals' gnawing.
4. Cable aging is quantified by the aging time. In the temperature rise experiment, the cables aged according to the Arrhenius high-temperature aging model [44], yielding cables with different aging degrees.
5. Cable working environment temperature is derived from historical records of a tobacco storage and distribution center.  
Figure 3: Structure of the BN for overload-induced early cable fire risk.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 5 Materials and Methods
### Relative Incident Angle Calculation  
The relative incident angle is one of the necessary factors for our localization method. It is defined as the angle \(\theta\) between the antenna direction and Y-axis. As shown in Figure 12, \(\theta_{1}\), \(\theta_{2}\), \(\theta_{3}\), and \(\theta_{4}\) are the relative incident angles of the four quadrants in the XOY plane, respectively. Note that \(T_{S}\) and \(T_{E}\) are the start and end time stamps during rotation, respectively. \(T_{\mathit{T_{T_{T_{S}}}}}\) is the timestamp when the antenna direction is facing a tag in a straight direction, and the angle at which the antenna has rotated at this time \(\phi\) is:  
\[\phi=2\pi\times\frac{T_{T_{T}g}-T_{S}}{T_{E}-T_{S}} \tag{8}\]  
In actual experiments, \(T_{S}\) and \(T_{E}\) acquisitions have certain errors due to various hardware delays, software delays, and artificial delays, and this will directly affect the calculation of \(\theta\), so we correct according to the reference tag, and record \(T_{R1}\) and \(T_{R2}\) as the theoretical timestamp and the collected timestamp when the antenna direction is facing the tag straightforward, so \(\phi\) can be corrected to:  
\[\phi=2\pi\times\frac{\left(T_{\mathit{T_{T}g}}-T_{S}\right)+\left(T_{R1}-T_{R2 }\right)}{T_{E}-T_{S}} \tag{9}\]  
In the process of rotating the antenna by 360 degrees, if \(\phi<\pi\), the tag is in tags array I, otherwise in tags array II. Make the following convention in order to facilitate subsequent calculation: the Y-axis coordinates of tags array I is negative. Y-axis coordinates of the tags array II are positive. \(\theta\) of the first and fourth quadrants are positive, so \(\theta_{1}\) and \(\theta_{4}\) are positive, and \(\theta\) of the second and third quadrants is negative, so \(\theta_{2}\) and \(\theta_{3}\) are negative, then:  
\[\theta=\left\{\begin{array}{ll}\frac{\pi}{2}-\phi,\phi<\pi\\ \frac{3\pi}{2}-\phi,\mathit{else}\end{array}\right. \tag{10}\]  
Figure 11: Least squares fitting.  
Figure 12: Relative incident angle analysis.
## 3 Proposed Model
### Model Phases
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## 4 BER Analysis  
In this section, computer simulation results of bit error rate (BER) are given to illustrate the good anti-interference characteristics of the EBPSK system, where the transfer function of the filter is chosen as (2), _i.e.,_ simulations are based on the filter having three conjugate poles and one conjugate zero. On the other hand, just co-channel interference is considered in this paper. In our simulation, the other parameters are chosen as follows: \(f_{c}=930 kHz\), \(N=20\), \(K=2\), \(A=B=1\), \(\theta=\pi\).
## 2 Materials and Methods  
### Experimental Design  
This study is characterized as experimental scientific research involving C2C12 undifferentiated muscle cells. Initially, the ideal doses for PBMT/sMF irradiation (Table 1) were determined in vitro, as well as the evaluation of H\({}_{2}\)O\({}_{2}\) cytotoxicity on the cell line. To obtain a concentration-response curve, the cells were exposed to H\({}_{2}\)O\({}_{2}\) (200-1000 \(\upmu\)M; 1 h), an oxidative/stress inductor agent. Furthermore, after standardizing the optimal concentration of H\({}_{2}\)O\({}_{2}\) and PBMT/sMF, the efficacy of PBMT/sMF against H\({}_{2}\)O\({}_{2}\)-induceddamage was tested at different time points, assessing cytoprotection through colorimetric and fluorometric assays.  
\begin{table}
\begin{tabular}{l c} \hline
**Class** & **1M** \\ \hline
**Number of lasers** & **1 Super-pulsed infrared** \\ \hline Wavelength (nm) & 905 (\(\pm\)1) \\ Frequency (Hz) & 1–250 \\ Peak power (W) & 25 \(\pm\) 5 \\ Average mean optical output (mW) & 0.75 \\ Power density (mW/cm\({}^{2}\)) & 1.17 \\ Energy density (J/cm\({}^{2}\)) & 0.031 (26.7 s)/0.104 (88.8 s)/0.210 (178.6 s)/0.421 \\  & (357.1 s)/0.736 (625 s)/1.052 (892.9 s) \\  & 0.020 (26.7 s)/0.066 (88.8 s)/0.133 (178.6 s)/0.267 \\ Dose (J) & (357.1 s)/0.468 (625 s)/0.669 (892.9 s) \\ Spot size of laser (cm\({}^{2}\)) & 0.44 \\ \hline
**Number of red LEDs** & 3 Red \\ \hline Wavelength of red LEDs (nm) & 640 (\(\pm\)10) \\ Frequency (Hz) & 2 \\ Average optical output (mW)—each & 50 \(\pm\) 8 \\ Power density (mW/cm\({}^{2}\))—each & 78.61 \\ Energy density (J/cm\({}^{2}\))—each & 2.099 (26.7 s)/6.981 (88.8 s)/14.038 (178.6 s)/28.071 \\  & (357.1 s)/49.133 (625 s)/70.181 (892.9 s) \\  & 1.335 (26.7 s)/4.44 (88.8 s)/8.93 (178.6 s)/17.855 \\ Dose (J)—each & (357.1 s)/31.25 (625 s)/44.645 (892.9 s) \\ Spot size of red LED (cm\({}^{2}\))—each & 0.9 \\ \hline
**Number of infrared LEDs** & **3 Infrared** \\ \hline Wavelength of infrared LEDs (nm) & 875 (\(\pm\)10) \\ Frequency (Hz) & 16 \\ Average optical output (mW)—each & 62.5\(\pm\)8 \\ Power density (mW/cm\({}^{2}\))—each & 98.274 \\   Energy density (J/cm\({}^{2}\))—each & 2.623 (26.7 s)/8.728 (88.8 s)/17.55 (178.6 s)/35.090 \\  & (357.1 s)/61.417 (625 s)/87.741 (892.9 s) \\ Dose (J)—each & 1.668 (26.7 s)/5.55 (88.8 s)/11.162 (178.6 s)/22.318 \\  & (357.1 s)/39.062 (625 s)/55.806 (892.9 s) \\ Spot Size of LED (cm\({}^{2}\))—each & 0.9 \\ \hline
**Magnetic Field (mT)** & 35 \(\pm\) 10 \\ \hline Irradiation time & 26.7 \\ Irradiation time per site (s)—30 J & 88.8 \\ Irradiation time per site (s)—60 J & 178.6 \\ Irradiation time per site (s)—121 J & 357.1 \\ Irradiation time per site (s)—211 J & 625 \\ Irradiation time per site (s)—302 J & 892.9 \\ \hline \end{tabular}
\begin{tabular}{l c} \hline
**Aperture of the device (cm\({}^{2}\))** & 0.636 \\ \hline \end{tabular}
\end{table}
Table 1: Irradiation Parameters.
## 4 Results and Discussion
### Uncertainty Analysis of CERES-Wheat Model with POD4 DVar  
Imperfect predictions are often unavoidable because of uncertainty about future weather conditions and because of imperfect crop models that are limited due to insufficient detailed knowledge of the crop growth process, but the uncertain simulations appear to be well corrected through the use of data assimilation technology. When crop models with data assimilation are employed to estimate regional crop yields and other growing state variables, several uncertainties in the model inputs, data assimilation algorithms, and assimilated observations must be addressed.  
It is essential to obtain high-quality model inputs of soil, weather, and field management information because these data may be used for the calibration and validation of crop models. Nevertheless, it appears to be impossible to collect all of these data on a regional scale, especially for  
Figure 5: Comparisons between the real and estimated yields on (**a**) pixel and (**b**) county scales.  
regional field management information, which is often parameterized as a general value in an agricultural region. This issue is present in the current study, but a data quality control was performed in the data preparation stage. This procedure reduced the uncertainty due to the model inputs in the data assimilation.  
The data assimilation performance of the CERES-Wheat model appears to rely heavily on the parameters of the POD4 DVar algorithm and the spatiotemporal scales of the assimilating observations. As in the EnKF method, the state ensembles are used in the POD4 DVar method to describe the possibility of real states in crop growth simulations. This characteristic suggests that the quality of the results depends on the perturbed ensembles when POD4 DVar is used [54]. Hence, the state ensembles must be generated in a reasonable manner to reduce the prevalence of uncertain simulations.  
The assimilated observations also result in uncertainties. The purpose of data assimilation is to employ the observational data to correct simulations of the crop growing process. A significant improvement at the regional scale could be achieved if remotely sensed observations with high  
Figure 6: Estimation of additional information in the CERES-Wheat model with POD4 DVar-based data assimilation. (**a**) Above-ground biomass; (**b**) Harvest index; (**c**) Evapotranspiration; (**d**) Soil organic carbon.  
spatiotemporal resolution were assimilated into the crop model. However, these satellite-based observations during the crop growing season are usually limited by frequent cloud coverage, long revisit periods, and/or mixed-pixel errors [56]. These limitations and errors are important factors that result in uncertainty for regional crop yield estimation. This aspect also requires further investigation.