esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Anifrolumab Mechanism of Action and Clinical Trials
### Clinical Trial Data  
In this section, we will review the available clinical trial data from phase I-III clinical trials of ANI, which resulted in the approval for the treatment of moderately-to-severely active SLE in addition to standard therapy. Figure 4 shows a timeline of major clinical trials and approval dates of ANI in non-renal SLE. Of note, a phase II clinical trial in LN has been published [14]. However, we will not further analyze this trial since the focus of this review is the cutaneous domain.  
Figure 3: Mechanism of action of anifrolumab in the context of the hypothesized systemic lupus erythematosus (SLE) pathogenesis [3, 4, 6]. Genetic, epigenetic, environmental, and hormonal factors (**1**) lead to an increased rate of apoptosis. Autoreactive B and T cells specific for self-nuclear antigens recognize and process these antigens (**2**), which, in turn, leads to autoantibody and immune complex generation (**3**). Toll-like receptor signaling in B cells and pDCs (not shown) results in increased levels of type 1 interferons, mainly produced by pDCs (**4**). Type 1 interferons further stimulate B cells in an autocrine loop, and B cells exhibit class switching, which leads to a persistent production of autoantibodies (**5**). Anifrolumab binds to IFNAR1, thus inhibiting dimerization and subsequent intracellular signaling mechanisms mediated by STAT1/2 and IRF9. The net result is a decreased transcription of proinflammatory genes (the so-called interferon-gene signature, IGS) in cells of both the innate and adaptive immune systems [10]. Created with biorender.com. EBV, Ebstein–Barr virus; IFNAR, interferon receptor subunit; IGS, interferon gene signature; IRF9, interferon regulatory factor 9; JAK, Janus kinase; pDC, plasmacytoid dendritic cells; STAT, signal transducers and activators of transcription; TYK, tyrosine kinase; UV, ultraviolet.
# Low Ionosphere under Influence of Strong Solar Radiation: Diagnostics and Modeling
## 3 Results
### Intense Solar Radiation: Consecutive Strong SF
The change in distribution of ionization at the D-region due to flare  
Figure 8: Simultaneous variations of X-ray flux, effective reflection height \(H^{\prime}\), and the sharpness \(\beta\) during the occurrence of five successive flares on 10 June 2014 are shown in panels from upper to lower one, respectively. existence can be illustrated by the electron density altitude profile changes. We present the height profile of electron density at peak time of five successive flares on 10 June 2014 in Figure 9. At unperturbed ionospheric conditions (black line), there is a moderate increase in electron density from 2 \(\times\) 10\({}^{7}\) m\({}^{-3}\) at 60 km height, to about 2 \(\times\) 10\({}^{9}\) m\({}^{-3}\) at the upper barrier of the D-region i.e., at 90 km height. These three lines have almost the same slope. Totally different behavior and slope have lines at peak times of X-class SF (dashed orange and pink lines) are already 2 \(\times\) 10\({}^{9}\) m\({}^{-3}\) at 60 km height. These variation in altitude profile of electron density are essential for the VLF propagation characteristics.
## 2 Theoretical Background  
### Synchrosqueezed Wavelet Transforms  
Bearing degradation signal is complex and nonstationary, time-frequency domain features can represent such kind of information effectively. Wavelet transform (WT) is a common time-frequency analysis technique, which is widely applied in the condition monitoring of rotating machinery. However, the traditional WT has the disadvantages of low time-frequency resolution, and the signal features are fuzzy in time-frequency domain, which has a negative impact on the degraded feature extraction of bearings.  
Synchrosqueezed Wavelet Transforms (SWT) [20] is a time-frequency rearrangement method based on traditional wavelet transform (WT), using the characteristic that the phase of the signal is not affected by the scale transformation in the frequency domain after WT to obtain the corresponding frequency at each scale. Then, the scale of the same frequency is added, and the wavelet coefficients obtained by WT are redistributed and compressed so that the value near the frequency is compressed into the frequency. SWT mainly includes the following steps. Firstly, the continuous wavelet transform is applied to the signal  
\[W_{x}(a,b)=a^{-\frac{1}{2}}\int_{-\infty}^{+\infty}x(t)\overline{\psi}\left( \frac{t-b}{a}\right)dt \tag{1}\]where \(a\) is the scaling parameter, \(b\) is the translating parameter, \(x(t)\) is a 1-D degradation signal. \(\psi(t)\) is the complex conjugate of mother wavelet function. Then instantaneous frequency \(\omega_{x}(a,b)\) for the signal \(x\) is calculated by  
\[\omega_{x}(a,b)=-i(W_{x}(a,b))^{-1}\frac{\partial}{\partial b}W_{x}(a,b). \tag{2}\]  
The information is transferred from the time-scale plane to the time-frequency plane, according to the map \((b,a)\rightarrow(b,\omega_{x}(a,b))\). \(W_{x}(a,b)\) is computed only at discrete values \(a_{k}\), with \(a_{k}-a_{k-1}=(\Delta a)_{k}\), and its synchrosqueezed transform \(T_{X}(\omega_{l},b)\) was likewise determined only at the centers \(\omega_{l}\) of the successive bins \([\omega_{l}-\Delta\omega/2,\omega_{l}+\Delta\omega/2]\), with \(\omega_{l}-\omega_{l-1}=\Delta\omega\). This process is expressed as  
\[T_{x}(\omega_{l},b)=(\Delta\omega)^{-1}\sum_{a_{k}:|\omega_{x}(a,b)-\omega_{l }|\leq\Delta\omega/2}W_{x}(a,b){a_{k}}^{-3/2}\Delta a_{k}. \tag{3}\]  
In this way, each frequency component in the signal is compressed in the frequency domain direction, and the problem of the low resolution of the traditional WT in the time-frequency domain is solved.
# Weight-Based PA-GPSR Protocol Improvement Method in VANET
## 5 Simulation and Performance Analysis
### Result Analysis
#### 5.2.2 Throughput  
Figure 10 shows the throughput of each routing protocol when changing the number of nodes and CBR connections. When the number of nodes increases, the throughput performance is improved. This result occurs because the connectivity between nodes increases with the increase in nodes, reducing the rate of redundant packet transmission and packet loss. The main reason for limiting the performance of routing protocols in sparse networks is insufficient network connections caused by the small number of nodes. The location-based greedy forwarding strategy is very suitable for well-connected networks because the scheme aims to obtain the least number of hops. In Figures 9 and 11, it can be seen that W-PAGPSR has a lower packet loss rate and lower end-to-end delay. Compared to GPSR, MM-GPSR, and PA-GPSR under all CBR connections, the W-PAGPSR proposed in this paper has higher throughput in sparse environments. The proposed routing protocol uses a variety of indicators as routing measures during packet transmission; this, the node can estimate the link stability and select the most stable relay node during packet transmission.  
As shown in Figure 10a, the average throughput of W-PAGPSR is 29.61%, 36.70%, and 15.01% higher than those of GPSR, MM-GPSR, and PA-GPSR, respectively. Figure 10b shows that the average throughput of W-PAGPSR is 37.72%, 44.45%, and 16.05% higher than those of GPSR, MM-GPSR, and PA-GPSR, respectively. As shown in Figure 10c, the average throughput of W-PAGPSR is 75.89%, 89.33%, and 38.36% higher than those of GPSR, MM-GPSR, and PA-GPSR, respectively. Figure 10d shows that the average throughput of W-PAGPSR is 47.47%, 59.89%, and 11.89% higher than those of GPSR, MM-GPSR, and PA-GPSR, respectively. Since W-PAGPSR considers link quality and uses a weight model when selecting the next hop forwarding node, it shows better network resource utilization than other protocols.  
Figure 10: The throughput under different number of nodes and CBR connections: (**a**) 5 CBR connections; (**b**) 10 CBR connections; (**c**) 15 CBR connections; (**d**) 20 CBR connections.  
As shown in Figure 10, from the overall trend point of view, from 30 to 70 nodes, the throughput advantage of W-PAGPSR is not obvious, which is because the insufficient number of nodes leads to a small number of packets successfully reaching the destination node. From 70 to 90 nodes, the throughput advantage of W-PAGPSR is obvious. This result occurs because the use of improved greedy forwarding and perimeter forwarding strategies to select relay nodes reduces the number of data retransmissions, which saves network bandwidth and can be used to transmit more data packets, thereby improving network throughput. From 90 to 110 nodes, the increase in throughput slows down. This result occurs because the probability of data congestion caused by higher node density increases, which increases the packet loss rate.
# Off-Cloud Anchor Sharing Framework for Multi-User and Multi-Platform Mixed Reality Applications
###### Abstract  
This article presents a novel off-cloud anchor sharing framework designed to enable seamless device interoperability for Mixed Reality (MR) multi-user and multi-platform applications. The proposed framework enables local storage and synchronization of spatial anchors, offering a robust and autonomous alternative for real-time collaborative experiences. Such anchors are digital reference points tied to specific positions in the physical world that allow virtual content in MR applications to remain accurately aligned to the real environment, thus being an essential tool for building collaborative MR experiences. This anchor synchronization system takes advantage of the use of local anchor storage to optimize the sharing process and to exchange the anchors only when necessary. The framework integrates Unity, Mirror and Mixed Reality Toolkit (MRTK) to support seamless interoperability between Microsoft HoloLens 2 devices and desktop computers, with the addition of external IoT interaction. As a proof of concept, a collaborative multiplayer game was developed to illustrate the multi-platform and anchor sharing capabilities of the proposed system. The experiments were performed in Local Area Network (LAN) and Wide Area Network (WAN) environments, and they highlight the importance of efficient anchor management in large-scale MR environments and demonstrate the effectiveness of the system in handling anchor transmission across varying levels of spatial complexity. Specifically, the obtained results show that the developed framework is able to obtain anchor transmission times that start around 12.7 s for the tested LAN/WAN networks and for small anchor setups, and to roughly 86.02-87.18 s for complex physical scenarios where room-sized anchors are required.  
mixed reality; multi-user; multi-platform; collaborative applications; spatial anchor sharing; Microsoft HoloLens
## 4 Propagation Model
### Overall Path Loss  
By utilizing (23) we can complete the parametrization of the model. Observe that the number of cloud layers blocking the propagation path follows multinomial distribution making calculation of the mean cloud impairments in the right-hand side of (13) a complex procedure. Instead, one may utilize the direct approach involving probability generating function (PGF) as follows.  
Let \(G_{L_{C}(f,d)}(z)\) denote PGF of the overall cloud impairments, that is,  
\[G_{L_{C}(f,d)}(z)=E\Big{[}z^{L_{C}(f,d)}\Big{]}=E\Big{[}z^{\sum_{k=1}^{K}l_{k }L_{k}}\Big{]}, \tag{24}\]  
where \(l_{k}\) is the indicator function.  
Since the probability of ground-to-aircraft path blockage at layer \(i\) is independent from blockage at layer \(j\) and the impairment at each layer is assumed to be constant, we can write  
\[G_{L_{C}(f,d)}(z)=G_{I_{1}}(z^{L_{1}})G_{I_{2}}(z^{L_{2}})\times\cdots\times G _{I_{N}}(z^{L_{N}}), \tag{25}\]  
where \(N\) is the overall number of cloud layers, \(G_{I_{k}}(z^{L_{k}})\) is the PGF of Bernoulli distribution characterizing the impairment at layer \(k\). the latter is known to be  
\[G_{I_{i}}(z)=p_{k}z+(1-p_{k}). \tag{26}\]  
The use of (24)-(26) allows for simple calculation of (13).
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
## 5 Performance Evaluation
### Scenario 3: Grid Topology  
In Scenario 3, there are nine networks installed in a grid shape. Figure 7a-c show the network performance as a function of offered load. Figure 7a shows that similar to the string topologies the throughputs increase in proportion to the offered load in the range of \(0\leq O\leq 13.6\) Mbps. Within that range, the frame-existence probabilities of all the nodes are less than one because all the networks are in non-saturation state as in Figure 7b. In this case, the frame-existence probability  
Figure 5: Network characteristics of analytical (lines) and simulation (plots) results as a function of the offered load for Scenario 1. **(a)** Throughput. **(b)** Frame-existence probability. **(c)** Carrier-sense airtime.  
Figure 6: Network characteristics of analytical (lines) and simulation (plots) results as a function of the offered load for Scenario 2. **(a)** Throughput. **(b)** Frame-existence probability. **(c)** Carrier-sense airtime.  
of Network 2 reaches one at \(O=12.75\) Mbps, and Network 5 reaches one at \(O=13.6\) Mbps. However, the throughput of Network 5 continues to increase as the offered load increases although the frame-existence probability of Network 5 reaches one at \(O=13.6\) Mbps. Because Network 2 loses the transmission opportunities when the offered load increases, Network 5 gets the transmission opportunities. For \(O\geq 13.6\) Mbps, the frame-existence probabilities of networks except Network 2 and 5 still increase. Therefore, the carrier-sensing probabilities increase, which can be confirmed from Figure 7c. As a result, the throughput of Network 2 decreases as offered load increases in the saturation condition. It is also seen from Figure 7b that all networks are saturated at \(O=26.0\) Mbps. From Figure 7c, it is confirmed that the degradation of throughput in Network 2 is also caused by bias of carrier-sensing. However, we observe the throughput of Network 5 does not increase markedly at \(12.75\) Mbps \(\leq O\leq 13.6\) Mbps. Because the frame-existence probability of Network 5 reaches one at \(O=12.75\) Mbps first. It is faster than Network 2. The carrier sensing duration of Network 5 decreases and Network 2 increases at \(O=11.8\) Mbps. Although the frame-existence probability of Network 5 reaches one first, Network 5 gets more the transmission opportunities at \(O=11.8\) Mbps. When the frame-existence probability of Network 2 reaches one at \(O=13.6\) Mbps, the throughput of Network 5 increases markedly.  
It is seen that in all the Figures 5-7 the analytical predictions well agree with the simulation results. That confirms the validities of the analytical expressions in this work. Not only that the results in all the scenarios show the large variation of network throughput caused by the carrier-sensing effect. Therefore, our findings can provide a convenient tool to understand the variation.
## 1 Introduction
[12] proposed a method for classifying and identifying target and jamming signals based on entropy features for amplitude modulation sweeping frequency jamming, which is more difficult to suppress with frequency modulation fuze. The Shannon entropy and singular spectrum entropy of the fuze output signal are extracted, and the target and jamming signals are classified using a support vector machine classifier. [PERSON] et al. [13] aimed at the problem of detecting and classifying radar target and the jamming signal produced by an electronic countermeasure system; the disturbance is modeled as a complex, correlated Gaussian process, and the jamming signal is modeled as a signal belonging to a cone whose axis is the true target signal. Two different approaches are analyzed, based on the adaptive coherent estimator and the generalized likelihood ratio test. [PERSON] et al. [14] considered the problem of anti-noise amplitude modulation jamming in triangle wave frequency modulation fuze and presented an anti-jamming method based on fractional Fourier transform (FrFt) to suppress the noise amplitude modulation jamming. The above methods allow an accurate classification of targets and jamming signals within a certain range. However, a common limitation of these methods is that the signal is only processed in the time-or frequency-domain. There is little information available, and when used to classify and identify the most threatening types of jamming on FMCW fuze, the identification accuracy is low and the anti-jamming ability is weak [3; 15; 16]. Thus, there are still challenges to overcome to improve the ability of FMCW fuze in anti-frequency sweeping jamming. In this paper, we present a wavelet-packet-based approach to classifying FMCW fuze targets and frequency sweeping jamming signals. Fuze anti-jamming is achieved by classifying and identifying targets and jamming signals. This approach is based on a combination of the output of the FMCW fuze output signal features extraction and machine-learning technologies. First, the output signals of the FMCW fuze under the action of a target and frequency sweeping jamming signals are collected. Second, the signals collected from the actual experiment were analyzed and wavelet packet features were extracted. Finally, based on the results of the feature extraction experiments, features that can significantly distinguish between the target and jamming signals form feature vectors that are fed into a support vector machine classifier for classification and recognition. To the best of our knowledge, this is the first time that wavelet packet features and support vector machines are used to classify targets and jamming signals for radio fuze. The method proposed in this paper is significantly more resistant to jamming than traditional radio fuze anti-sweep methods, and is highly robust, outperforming other methods in terms of recognition accuracy and stability. The experimental results show that the proposed method can achieve a high recognition accuracy.
## 6 Conclusions  
In summary, two large EQs, the Maduo \(M_{\rm S}\) 7.4 EQ on 21 May 2021 and the Menyuan \(M_{\rm S}\) 6.9 EQ on 8 January 2022, were successfully recorded on the Chinese mainland, after the data of the CSES satellite were put into service for EQ prediction work on the Chinese mainland. Effective ionospheric perturbations were reported immediately after they were found, respectively, two weeks and one week prior to these two events, which reconfirms that ionospheric information can be utilized for short-term EQ prediction. However, this work limited due to some uncertainty, including the correct location and the magnitude of the impending event. So, more EQ prediction practice and deeper investigations will be needed in the future.  
**Author Contributions:** Data collection, writing, and investigation, M.L.; review and editing, H.W., J.L., and X.S. All authors have read and agreed to the published version of the manuscript.
* This work was funded by the National Natural Science Foundation of China (NSFC) under Grant Agreement No. 41774084 and the National Key R&D Program of China under Grant No. 2018 YFC1503506.
* Institutional Review Board Statement: Institutional review board approval of our center was obtained for this study.
* Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
* All data used in this paper can be available at the website [[https://www.leos.ac.cn/](https://www.leos.ac.cn/)]([https://www.leos.ac.cn/](https://www.leos.ac.cn/)).  
**Acknowledgments:** This work used data from the CSES mission, a project funded by the China National Space Administration (CNSA) and the China Earthquake Administration (CEA). This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Agreement No. 41774084 and the National Key R&D Program of China under Grant No. 2018 YFC1503506.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Requirements of 5G-Based V2X Use Cases
### QoS Requirements
#### 2.1.2 Latency Requirements  
Usually, the latency requirements are defined somehow as the time between the sending and the receiving of a specific packet. In the case of V2X applications--or any application--latency requirements are defined as the maximum tolerable elapsed time from the instant a data packet is generated at the source application to the instant it is received by the destination application. So, the latency requirements towards the cellular network can be different from the end-to-end application latency requirements. The latency has many aspects in regular networks, including the jitter, the average latency, or the chances of getting very high latency values [5; 28]. Furthermore, in V2X communication, we instead opt for information latency. This means that the information in a packet is frequently updated, and the receiver is always interested in the most recent information available. Consequently, we might drop a packet and rely on the reception of the next packet instead of spending valuable time on the retransmission of an old packet. Detailed latency requirements are defined in [1; 2].
## 2 Methods
### Apparatus
#### 2.2.2 Eye-Tracker  
This experiment utilized Tobii Pro Glasses 2 as an eye-tracking system. Tobii Pro Glasses 2 is a wearable eye-tracking tool that allows researchers to capture truly objective and deep insights into human behavior in any real-world environment. Tobii Pro Glasses 2 consists of a head unit, a recording unit, and controller software. The gaze sampling frequency of Tobii Glass 2 is 100 Hz. Corneal reflection and dark pupil tracking techniques were used in this system. The average binocular accuracy associated with the eye-tracking system is 0.62\({}^{\circ}\) (SD = 0.23) in optimal condition (lighting condition: 300 lux, distance to target: 1.5 m, gaze angle: \(<\)15\({}^{\circ}\), black target/white background). Accuracy was defined as the average difference between the fixation target location and the measured gaze location on the screen. The precision was 0.05\({}^{\circ}\) (SD = 0.10). Precision was defined as the ability of the eye-tracker to reproduce the same gaze point measurement reliably and is calculated via the root mean square (RMS) from the successive data points. The detected gaze, defined as the percentage of gaze samples reported by the eye-tracker during fixation on the target, was 99% (SD = 1.7).  
To collect accurate eye-tracking data, Tobii Pro Glasses 2 must be calibrated individually for each participant. During the calibration process, the participant had to wear the head unit while focusing on the center of the calibration target. The calibration target was printed on the supplied calibration cards and calibration stickers. The calibration card was held flat against a wall (otherwise straight) towards the participant. The distance between the participant and the calibration card was between 0.75 and 1.25 m (2.5 and 4.1 feet).  
While the participant was looking at the calibration target, he/she tipped the calibrate icon in the bottom right-hand corner of the live-view window. Once the calibration was complete, the results were displayed in the calibration results box.  
Participants were asked to make themselves comfortable before the calibration and the session to ensure that they would stay within these bounds. In the experiment with Tobii Glasses 2, no chinrest was used.  
In order to analyze the gaze movement, it was analyzed that saccade occurred when the eye movement angle was greater than 100 degrees in 1 s, and fixation occurred when the gaze stayed at a specific point for more than 60 ms.