esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Pearson Correlation Coefficient, Anomaly Correlation, and Taylor Diagram
Moreover, the precipitation error is reduced when flashes are assimilated, because the points referring to LDA are closer to the observed point along the \(x\) axis (point of coordinates (1,0)). This improvement is larger over the sea than over land. Figure 5 shows that the observation standard deviation is well represented by WRF over land, while it is underestimated over the sea. This result can be determined not only by an underestimation of the precipitation field standard deviation of WRF but also by an overestimation of the precipitation standard deviation given by the IMERG dataset. The IMERG Final Run dataset is calibrated using the GPCC ([[https://www.dwd.de/EN/ourservices/gpcc/gpcc.html](https://www.dwd.de/EN/ourservices/gpcc/gpcc.html)]([https://www.dwd.de/EN/ourservices/gpcc/gpcc.html](https://www.dwd.de/EN/ourservices/gpcc/gpcc.html)); last access on 12 February 2021) monthly gauge analysis  
Figure 4: AC of the 3 h precipitation for the whole month of November 2019 for (**a**) land grid points and (**b**) sea grid points. CTRL is in red, LIGHT is in blue, and the difference between LIGHT and CTRL, in this order, is in green. A positive value of the green curve shows a better performance of the LIGHT forecast compared to CTRL. available (mostly) over land. The presence of intense precipitation gradients across the coastline (see Figure 2 or Section 3.3) reveals a possible overestimation of the IMERG precipitation over the sea because the values over land are corrected by the calibration with rain gauge measurements. As a matter of fact, other studies [86] have reported the overestimation of the uncalibrated IMERG product with respect to the calibrated product. Larger values of rainfall give, usually, larger values of precipitation standard deviation, and this could partially explain the WRF underestimation of the precipitation standard deviation over the sea. The intense precipitation gradients across the coastline are not present if the Early Run of IMERG (uncalibrated) is considered, and we noticed this difference also for other case studies. In this regard, we plan to perform a comparison with other precipitation products (e.g., MW-based products provided within the EUMETSAT H SAF project, [[http://hsaf.meteoam.it](http://hsaf.meteoam.it)]([http://hsaf.meteoam.it](http://hsaf.meteoam.it)); last access on 12 February 2021) that will become operational in the near future and that will provide daily precipitation rate over a regular grid. Overall, the above results indicate that LDA improves the representation of the rainfall field over both land and sea.
# A Joint Detection and Decoding Scheme for PC-SCMA System Based on Pruning Iteration
## 3 Proposed Algorithm for Joint Detection and Decoding
### Joint Detection and Decoding Scheme Based on Pruning Iteration PI-JDD  
Each iteration of the PI-JDD algorithm mainly has two phases: during the first phase, the RNs are updated sequentially, and the message confidence stability \(S_{r_{k}\to u_{j}}\) of all branches belonging to the RNs are calculated in two adjacent iterations. In the next iteration, the branch with the largest \(S_{r_{k}\to u_{j}}\) is removed from the factor graph, and does not perform the updating of this RN anymore. In the next phase, the updated messages from the RNs are de-interleaved and then the polar decoder is input as a prior probability for SCAN decoding. The soft message output by the polar decoder is used as a prior message for the RNs updating. The message updating process for the first three iterations is shown in Figure 3.
## 3 Results and Discussions
### The Influence of the Rotational Speed  
The dynamic responses of the fan of the rotor system under three other rotational speeds are demonstrated in Figure 15, then a comparison between the dynamic responses was performed in conjunction with the calculation at 3772 r/min in Figure 12. The time history variations of displacement (Figure 15a-c) have the same trend as that presented in Figure 12. Due to the different magnitude of the bird strike force, the maximum value of each displacement has obvious differences and increases with rotational speed. Similar patterns can be observed in the axis orbit diagram (Figure 15d-f). The range of the axis orbit motion increases significantly with the increase in rotational speed. It can be seen from the phase diagram shown in Figure 15g-that the higher the speed is, the higher the number of limit rings would be. It can be seen in the Poincare section diagram (Figure 15j-l) that, as the speed increases, more points appear in the diagram, so more periods of motion exist. From what was said above, it can be concluded that as the rotor speed increases, the impact of bird strike becomes more drastic and the time required to go back to the steady state becomes longer. From the frequency spectrum diagram shown in Figure 15m-o, the fundamental frequency of the rotor system is always present with or without bird strike, and that bird strike only causes small fluctuations in the amplitude. It is noteworthy to say that the vibrational peak occurred at around 43 Hz. As the natural frequency of the rotor is also around 43 Hz, the first-order mode of the system is excited by the bird strike, which can cause serious damage to the engine. In Figure 15p-r, the time-frequency graphs show the same results. At different speeds, first-order frequencies are excited except for the fundamental frequency. From what was said above, it can be concluded that the effect of bird strikes on the rotor system only differs in magnitude at different speeds, and there is no significant difference in vibration characteristics.  
Figure 14: The displacements of rotor system at different times.  
Figure 15: _Cont._Figure 15: _Cont._  
Figure 15: The dynamic responses of rotor system fan after bird strike at 836, 1984, and 3344 r/min: Time history of displacement at **(a)** 836 r/min, **(b)** 1984 r/min, and **(c)** 3344 r/min; axis orbit at **(d)** 836 r/min, **(e)** 1984 r/min, and **(f)** 3344 r/min; phase diagram at **(g)** 836 r/min, **(h)** 1984 r/min, and **(i)** 3344 r/min; Poincaré section at **(j)** 836 r/min, **(k)** 1984 r/min, and **(l)** 3344 r/min; frequency spectrum at **(m)** 836 r/min, **(n)** 1984 r/min, and **(o)** 3344 r/min; time–frequency graph at **(p)** 836 r/min, **(q)** 1984 r/min, and **(r)** 3344 r/min.
# On Simulating the Propagation and Countermeasures of Hate Speech in Social Networks
## 2 Related Work
### Models of Opinion Diffusion  
When modelling social networks, researchers use a mathematical graph abstraction. Thus, a social network is built as a graph \(G=(E,V)\) composed of a set of vertices \(V\), which correspond to users, and a set of edges \(E\) representing how they relate (and communicate). Usually, individual opinions about a given topic are represented as numerical values in the interval \([0,1]\). Both limits of the interval are associated with the extreme stances about the considered topic. In the case of hatred, 0 stands for a very non-hateful opinion whereas 1 stands for the most hateful opinion. Users influence one another by sending messages--through their connecting edges--that lead to a change on their opinion.  
The literature has proposed different models for opinion change/diffusion. Here we just introduce a comprehensive (but not exhaustive) set of works. On the one hand, some models are based on the concept of consensus. Thus, [PERSON] et al. [26] uses Average Consensus Gossiping (ACG) to force the entire network to converge to the average of all initial opinion values. Alternatively, the aim of DeGroot model [27] is to come to a consensus by using trust as a means to induce differences in the influence of users. DeGroot model was recently applied to the research on hateful behaviours on Twitter and Gab platforms to adjust the score for hate intensity of users [6; 21]. On the other hand, bounded confidence models are proposed to follow the intuition that people usually do not accept opinions too far from their own, which is known as _confirmation bias_. For instance, [PERSON] and [PERSON] [28] add some kind of stubbornness, distinguishing between an intrinsic initial opinion, which remains the same, and an expressed opinion, which changes over time. In contrast, [PERSON] and [PERSON] (HK) [29] introduce confidence level--a threshold for opinion difference. [PERSON] and [PERSON] (DW) [30] were the first to use asynchronous random opinion updates of two users considering the confidence level. Finally, [PERSON] et al. [31] conducted extensive simulations showing that [PERSON] and Deffuant-Weisbuch outperform other models in describing the spread of hateful content on Twitter.
###### Abstract  
Since BeiDou Navigation Satellite System (BDS) and Japan's Quasi-Zenith Satellite System (QZSS) have more visible satellites in the Asia-Pacific region, and navigation satellites of Global Positioning System (GPS), Galileo satellite navigation system (Galileo), and GLONASS satellite navigation system (GLONASS) are uniformly distributed globally, the service level of multi-mode Global Navigation Satellite System (GNSS) in the Asia-Pacific region should represent the best service capability. Based on the observation data of 10 Multi-GNSS Experiment (MGEX) stations, broadcast ephemeris and precision ephemeris from 13 to 19 October 2021, this paper comprehensively evaluated the service capability of multi-GNSS in the Asia-Pacific region from three aspects of observation data quality, broadcast ephemeris performance, and precision positioning level. The results show that: (1) the carrier-to-noise-density ratio (C/N0) quality of the GPS and Galileo is the best, followed by BDS and GLONASS, and QZSS is the worst. GPS, BDS-2, GLONASS, and QZSS pseudorange multipath values range from 0 to 0.6 m, while Galileo system and BDS-3 pseudorange multipath values range from 0 to 0.8 m. (2) In terms of broadcast ephemeris accuracy, BDS-3 broadcast ephemeris has the best orbit, and the three-dimensional (3D) Root Mean Square (RMS) is 0.21 m; BDS-2 was the worst, with a 3D RMS of 1.99 m. The broadcast ephemeris orbits of GPS, Galileo, QZSS, and GLONASS have 3D RMS of 0.60 m, 0.62 m, 0.83 m, and 1.27 m, respectively. For broadcast ephemeris clock offset: Galileo has the best performance, 0.61 ns, GLONASS is the worst, standard deviation (STD) is 3.10 ns, GPS, QZSS, BDS-3 and BDS-2 are 0.65 ns, 0.75 ns, and 1.72 ns, respectively. For signal-in-space ranging errors (SISRE), the SISRE results of GPS and Galileo systems are the best, fluctuating in the range of 0 m-2 m, followed by QZSS, BDS-3, Galileo, and BDS-2. (3) GPS, BPS, GLONASS, Galileo, GPS/QZSS, and BDS/QZSS were used for positioning experiments. In static PPP, the convergence time and positioning accuracy of GPS show the best performance. The positioning accuracy of GPS/QZSS and BDS/QZSS is improved compared with that of GPS and BDS. In terms of kinematic PPP, the convergence time and positioning accuracy of GPS/QZSS and BDS/QZSS are improved compared with that of GPS and BDS. In addition to GLONASS and Galileo systems, the other combinations outperformed 3 cm, 3 cm, and 5 cm in the east, north, and up directions.  
C/N0; pseudorange multipath; broadcast ephemeris clock offset accuracy; broadcast ephemeris orbit accuracy; SISRE; PPP +
Footnote †: journal: remote sensing
## 3 The SMART Dataset  
The proposed methodology for detecting failing hard disks was tested on a public dataset of SMART attributes that was collected by a commercial datacenter [5]. The dataset used in this study was collected for more than \(40,000\) HDD, comprising 26 different models and with storage capacities ranging from 1.0 terabyte to 8.0 terabytes, over a span of 273 days. As disks that were determined to have failed on a given day were removed the next day, and new disks were constantly being added to the datacenter, the number of disks was almost always changing, though it remained in excess of \(40,000\) at all times. However, the proposed methodology wasn't tested on the entire dataset because of several caveats in the data. First, although values of 45 SMART attributes were to be recorded for more than \(40,000\) HDD each day, not every HDD reported values for all the SMART attributes every day; a few attributes weren't reported at all. Therefore, the data were rife with blank fields, requiring the removal of records for disks with blank fields. Moreover, for some SMART attributes, the reported values were way out of bounds, e.g., for some drives, the raw value of SMART attribute \"9\" (9 is the ID of the SMART attribute that stores the _total power-on hours_ for a HDD) suggested a drive life of more than 10 years, which was not correct. Hence, such records had to be removed from the dataset to ensure that the results and conclusions are based on reliable data. Furthermore, an average ARR of around 2-4% implies that the available SMART datasets would almost always be imbalanced, i.e., more data are available for healthy drives than for failed drives; class imbalance skews the classifiers in favor of the majority class, i.e., the healthy drives in this case, which leads to overfitting. Different approaches have been proposed to mitigate the effects of class imbalance. The simplest approaches involve either over-sampling the minority class, i.e., the failed hard disks in this case, or under-sampling the majority class, i.e., the healthy disks [37; 38; 39; 40]. In this study, the majority class is under-sampled, i.e., data for an appropriate number of randomly selected healthy hard disks are removed to make the two classes more balanced. The SMART attribute values for all the failed drives are selected for training the NB classifier, i.e., a total of 565 drives failed during the period for which the data were considered. In order to have a reasonable balance between the two classes, SMART data for only 1500 randomly selected samples of healthy hard drives were added to the final dataset, which reduced training time and helped avoid overfitting. Hence, the final dataset that was used for training and testing the classifier contained values of 42 SMART attributes for 2065 hard disks, for a period of approximately nine months.
## 3 SSFXG: System Overview and Contributions  
The SSFXG framework introduces a new approach to wireless communication, addressing critical concerns surrounding identity management. By separating the identity management layer from the control of MNOs, it gives network subscribers greater authority over their profile information, exchanged data, and behavioral patterns. This decentralized model enables users to take charge of their subscription identity and service details while fostering trust and security across the system [11; 20].  
SSFXG aims to decentralize the network management layer into multiple distributed components, redistributing control and establishing mutual trust among participants. This user-focused model provides subscribers with the flexibility to join networks of their choice and acquire services from various operators globally. Introducing a provider-independent service approach ensures consistency in service offerings while allowing operators to maintain responsibility for managing and securing network resources.  
This framework achieves its objectives through a structured process comprising three main steps. First, trusted network authorities issue unique subscription identities and credentials to subscribers. These credentials are then securely managed using a self-directed identity system and stored within distributed storage capsules hosted in cloud environments [20]. During the subscription verification step, operators validate these anonymized credentials, authenticating users without exposing sensitive information. Thisprocess enhances privacy while building a trustworthy relationship between users and service providers.  
A key feature of SSFXG is its decentralized storage system. This system ensures the secure management of subscribers' identity credentials, serving as a digital extension for users. By using distributed storage capsules, the design safeguards the privacy and integrity of user credentials while enabling anonymous verification during service authentication. This eliminates the need to expose sensitive subscriber data and strengthens the system's dependability.  
To support the decentralized structure, SSFXG incorporates blockchain technology to manage federated operations and agreements. Blockchain's inherent properties, such as its resistance to tampering and ability to preserve data integrity, protect the interactions and agreements between network nodes with differing interests. This integration secures operations across the federated NextG communication network and ensures transparency for all participants.
# Key Bit-Dependent Side-Channel Attacks on Protected Binary Scalar Multiplication +
## 5 Experimental Results
### Key Bit-Dependent Power/Electromagnetic Attack on Hardware Implementation
**(5)** Figure 4a shows the PoIs chosen from electromagnetic subtraces when we targeted _Case Study 1_. Although it was not easy to clearly divide them into two groups via SPA-VI, we could classify \(p_{i}\) into two groups with a 100% success rate using the differences from an average trace. Accordingly, the classification success rate based on k-means clustering algorithm was also 100%; thus, we could find the entire secret scalar bits \(d_{i}\) based on Property 1. **(6)** Moreover, if we could use the low-pass filter to increase the signal-to-noise ratio, we could extract whole secret scalar bits through SPA-VI as shown in Figure 4c. **(7)** Figure 4b shows the PoIs chosen from electromagnetic subtraces when we targeted _Case Study 2_. Unlike the result of **(3, 4)**, it was not easy to clearly divide them into two groups via SPA-VI. However, it was possible to divide \(p_{i}\) into two groups based on the differences from an average trace. Thus, the classification success rate based on k-means clustering algorithm was also 100%; therefore, we could find all the secret scalar bits \(d_{i}\) based on Property 3 (\(0\leq i\leq\lambda-1\)). **(8)** Moreover, if we could use the low-pass filter to increase the signal-to-noise ratio, we could extract whole secret scalar bits through SPA-VI as shown in Figure 4d. To sum up, we also showed that we could recover all secret scalar bits using only one electromagnetic trace. Compared to the key bit-dependent power attack, the secret scalar bits could be recovered with a 100% success rate based on Property 1. We define attacks such as in Steps (5) to (8) as key bit-dependent electromagnetic attacks (KBEA). Figure 9: Hardware implementation: one of the subtraces (**top**) and the SOST value between two subgroups (**bottom**).
# Multimode Physics in the Mode Locking of Semiconductor Quantum Dot Lasers
## 3 Towards Transform-Limited Performance
### Linewidth Enhancement Factor
From the above equation, the corner frequency \(f_{c}\) -21 GHz >>\(f_{m}\) is calculated. Then, the \(\alpha_{H}\)-factor is obtained from the above equation. The spectral dependence of the \(\alpha_{H}\)-factor extracted for 19 longitudinal modes near the gain peak is illustrated with the red dots in Figure 10b. The \(\alpha_{H}\)-factor of this silicon-based epitaxial QD laser exhibits values ranging between 1.0 and 1.6 across the entire optical spectrum at twice the threshold current. Interestingly, we can compare such values with those measured on a QW laser heterogeneously grown on silicon. We observe that the \(\alpha_{H}\)-factor extracted from the ASE at the threshold is more than three times larger than that of the QD laser measured well above the threshold. The asymmetric variation of the gain and refractive index of the quantum well in relation to the carriers does contribute to the widely varying \(\alpha_{H}\)-factor, and optimizing the material and structural parameters of QD lasers has the potential to obtain an \(\alpha_{H}\)-factor close to zero [37]. Therefore, we can conclude that silicon-based QD lasers have much better physical parameters thanks to the near-zero \(\alpha_{H}\)-factor offered by the QDs, hence offering multiple ways for developing highly performing OFC generators. In a two-section laser, there is flexibility in increasing the saturable absorption to compensate for dispersion. With a single-section laser, the ability to increase the relative phase angle terms to increase mode locking is constrained because they couple strongly to other gain medium nonlinearities, such as mode competition. This leaves reducing dispersion as the only avenue. Fortunately, with quantum dots, the carrier-induced refractive index may be minimized with the laser design. Experiments and calculations with the linewidth enhancement factor \(\alpha_{H}\) found that QD lasers may be configured to operate with vanishingly small carrier-induced refractive index changes. The mapping shown in Figure 11a depicts the threshold gain at gain peak \(G_{th}\) as a function of the inhomogeneous width \(\Delta_{inth}\) and the p-dope density \(N_{p}\). The corresponding mapping of the minimum linewidth enhancement factor at gain peak \(|\alpha_{H}(v_{pk})|_{min}\) with the same combinations is shown in Figure 11b. The calculated results show that a sizable region with a zero linewidth enhancement factor at the gain peak for combinations of inhomogeneous linewidth, p-dope density and threshold gain are reachable by present QD lasers. Figure 10: (**a**) Normalized spectra of one longitudinal mode under four delays for the QD laser; (**b**) spectral dependence of the \(\alpha_{H}\)-factor measured for both QD (red) and QW (blue) lasers.
## 4 Experimental Applications
### Configurations of Inspection  
Hereafter, two samples lent by EDF and extracted from the same weld are inspected. Each of them comprises two SDH of 1 mm diameter at different positions: in the first sample the defects are located at the same positions as in the numerical proof of concept; in the second sample the reflectors are on the right chamfer at 10 mm and 20 mm depths. Consequently, the first sample is inspected with a OL0\({}^{\circ}\) inspection and the second one with a OL60\({}^{\circ}\) inspection (the probe is tilted in the water so that the QL wave propagation in the base material is refracted with a 60\({}^{\circ}\) angle). The configurations of inspection are illustrated in Figure 6. A macrograph of the weld and two photographs of the samples are given in Figure 7. The two samples have the same height.  
Figure 5: Slowness curves of QL waves and estimation error of the optimization procedure (**a**): actual material properties (green) and estimation by the optimization procedure (red); estimation error of slowness with PSO (dashed blue). Table of actual and estimated material properties (**b**).  
Figure 6: Configurations of inspection of the two 316L stainless steel V-shape weld samples: OL0\({}^{\circ}\) inspection (**a**) and OL60\({}^{\circ}\) inspection (**b**). Each sample contains two 1.0 mm diameter SDH at 10 mm (1) and 20 mm (2) depths. The reflectors of the first sample are vertically aligned in the middle of the weld and those of the second sample are located on the right chamfer.  
The data acquisitions were performed with a multichannel MultiX++TM (Eddyfi Technologies, Quebec, QC, Canada) and a 5 MHz linear array (Imasonic, Voray-sur-l\({}^{\prime}\)Ognon, France) composed of 64 elements of 0.5 mm width and with a 0.6 mm pitch. The array elements were excited with a rectangular-type electric pulse whose time length is 0.2 us, and signals were digitized with a sampling frequency of 100 MHz.