esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Description
### Typical Gravity Models  
We introduced three models for comparison and verification. First, the recently published gravity field model XGM2019 was used to compare vertical deflections computed by the HY-2 missions. It is a high-resolution model (d/o 2190) that integrates multiple satellite data sources and ground observations: gravity, elevation and topographic information [26]. Over the oceans, the model exhibited enhanced performance (equal or better than preceding models), which was confirmed by the authors.  
The second introduced model was the DTU21 gravity model, which was recently released by the Technical University of Denmark (DTU). The third model, V31.1, the latest version of the S&S series model and released by the Scripps Institution of Oceanography (SIO), was also used for comparison and verification. Incidentally, the DTU and S&S series models are the most well-known and widely accepted in the altimetry community as typical altimetry-derived marine gravity models. With the acquisition of additional data from satellite altimetry, new reference gravity fields, waveform retracking and improved data processing, DTU and SIO continuously publish new versions of global marine gravity models, so to some extent, DTU21 and S&S V31.1 represent the highest attainable accuracy [27; 28; 29].
###### Abstract  
Event cameras are bio-inspired devices that have revolutionized the acquisition of visual information by mimicking the neural architecture of the eye. These cameras respond asynchronously to changes in scene illumination at the pixel level, providing high-precision time information with low latency, typically in the order of microseconds. In this work, we experimentally evaluate an optical camera communication (OCC) link using an LED-based transmitter and an event camera as the receiver. We propose n-pulse modulation to encode data, adapting the system to the specific characteristics and operational principles of event cameras. The proposed scheme significantly reduces the demodulation complexity compared to other alternatives found in the literature. Furthermore, a set of experiments considering different camera bias sensitivities, encoding duty cycles, and LED radiant fluxes were carried out. The results showed that the BER performance was strongly dependent on the received optical power and the bias sensitivity. In addition, duty cycles between 0.3 and 0.7 at a 200 Hz transmission frequency presented the best performance, with a BER below \(1.25\times 10^{-4}\), which is under the forward error correction (FEC) limit. This work showcases the cutting-edge capabilities of event-camera-based OCC technology and contributes to the ongoing revolution in optical wireless communication (OWC).  
optical camera communication; event camera; optical modulation waveform Article
# Characteristics of Photon Beam and Preservation of Coherence in Fourth-Generation Light Sources
## 4 Characteristics of Photon Beams  
The quality of SR can be evaluated by many variables, of which flux, brilliance, and transverse coherence are important. To compare three important characteristics of PLS-II and Korea-4 GSR, three representative insertion devices, which have already been operated in PLS-II, are considered with two different bending magnets to evaluate photon beam characteristics (Table 3).  
The total flux of a synchrotron light source is defined as the total number of photons emitted per unit of time at a given spectral bandwidth. Therefore, the total flux is independent of beam emittance and strongly depends on the current and energy of the electron beam. Compared to the PLS-II, the flux curve for Korea-4 GSR is shifted to the right due to an electron beam energy increase (Figure 5a).  
The brilliance is defined as the total number of photons emitted per unit of time, and per unit of area in the transverse direction at a given spectral bandwidth. In the case of bending magnets, the horizontal aperture was set to 5 mrad for PLS-II and Korea-4 GSR. At 10 keV, Korea-4 GSR is 100 times higher than PLS-II, because the emittance of the Korea-4 GSR is reduced (Figure 5b).  
The photon-density distributions in phase space were obtained (Figure 6) for the bending magnet, wiggler, and out-vacuum undulator and in-vacuum undulator in PLS-II and Korea-4 GSR. The increased density of distribution (i.e., improvement) of Korea-4 GSR was particularly extreme in beam size and divergence in the horizontal plane. This improvement is a result of the smaller electron emittance and zero dispersion in straight sections in Korea-4 GSR than in PLS-II. The wiggler radiation assumes a toroid shape in the  
Figure 5: Comparison of (**a**) photon beam flux and (**b**) brilliance of Korea-4 GSR with PLS-II for different radiation sources. For each color, the solid line indicates PLS-II, and the dotted line indicates Korea-4 GSR. Bending magnet (**black**), wiggler (**blue**), out-vacuum undulator (**pink**) and in-vacuum undulator (**red**).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Wiggler** & **EPU** & **In-Vac. Undulator** \\ \hline Total length (m) & 2.0 & 3.6 & 1.3 \\ Peak field (T) & 1.81 & 0.89 & 0.97 \\ Period of ID (cm) & 10 & 11.4 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Major parameters of three representative insertion device in PLS-II.  
phase-space photon beam density of Korea-4 GSR, because wiggling motion is dominated by electron beam emittance reduction (Figure 6b). This specific shape in phase space should be included in the beamline design. In addition, the vertical photon-density distribution of the soft X-ray region of both PLS-II and Korea-4 GSR represents a diffraction-limit pattern close to the SR pattern emitted from single electron (Figure 6o,p).
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 5 Experiments
### Experiment I
#### 5.4.2 Numerical Results  
Table 3 compares the performance of various ML algorithms on the NSOC2017 dataset. The compared algorithms include unsupervised learning methods [40; 41], the common supervised learning methods in Section 3.3, oversampling methods [59; 70], and cost-sensitive methods, including the WSVM and IWSVM methods (Section 4.1.1). Note that the unsupervised methods [40; 41] (IF and IF + DC) were not designed for the heterogeneous alerts in the NSOC2017 dataset; however, we refer to their results for reference (even though they are not directly comparable to the classification methods used in this study).  
The results clearly demonstrate that the highly imbalanced class distribution causes poor generalization performance with the existing classifiers. In terms of the F1-measure,  
Figure 5: Visualization of unique alerts using \(t\)-SNE. The size of the disks is directly proportional to the weight of the sample being evaluated. The six NIDSs (A to F) included in the NSOC2017 dataset I are differentiated by the unique colors assigned to them. Disks that are associated with critical alerts are encompassed by a prominent dark boundary.  
the top-three performing algorithms are IWSVM, WSVM, and AdaBoost, which outperformed the other methods by a substantial margin.  
Among the supervised learning methods, the LDA and AdaBoost methods obtained a very high recall rate of 99.732%, with slightly higher FPR values of 0.016% and 0.002%, respectively. The SVM method obtained the lowest FPR of 0.001% with a relatively low recall rate of 97.901%.  
For the oversampling-based methods, the combination of random forest (RF) and SVMSMOTE yielded comparatively good results, while the DT method did not exhibit significant improvement. The XGBoost-MAS-OSS algorithm [59] leverages four distinct oversampling methods to generate more diverse artificial data and utilizes data cleaning for preprocessing, which results in notable performance enhancements.  
The WSVM assigns a high weight to positive samples to address imbalanced class distribution, which allowed it to outperform the conventional SVM method on all seven evaluation criteria, achieving a recall rate of 99.598% and the lowest FPR of 0.001%. IWSVM, which extends the WSVM method by assigning different weights to samples with different importance, further improved the recall rate to 99.643% with the lowest FPR value. In addition, the WSVM and IWSVM methods both achieved recall rates greater than 99.5%, indicating that very few critical alerts were missed by the filtering process, which is a significant advantage for SOC operation. The extremely low FPR values obtained by WSVM and IWSVM suggest that false alerts were minimized to ensure efficient incident response.
# Road Extraction Based on Improved Convolutional Neural Networks with Satellite Images
## 4 Results and Discussion
### Results Using the Public Dataset  
To further verify the effectiveness of the proposed improved network, the Massachusetts Roads Dataset was also applied for network evaluation. The test images are divided into two levels--general and complex scenes--following the position of the image contents. The sample results of the two-level images are shown in Figures 12 and 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model’s Description** & **Model Size (MB)** & **IoU (\%)** \\ \hline D-LinkNet (50) + Initial Block + D-Block & 792 & 51.370 \\ \hline D-LinkNet (101) + Initial Block + D-Block & 980 & 52.360 \\ \hline DenseNet (k = 24) + Initial Block + D-Block & 82.6 & 55.420 \\ \hline DenseNet (k = 32) + Initial Block + D-Block & 139 & 53.390 \\ \hline DenseNet (k = 24) + Stem Block + D-Block & 81.7 & 55.580 \\ \hline DenseNet (k = 32) + Stem Block + D-Block & 137 & 54.610 \\ \hline D-DenseNet (k = 24) + Stem Block + D-Block & 119 & 57.960 \\ \hline D-DenseNet (k = 32) + Stem Block + D-Block & 152 & 57.650 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results on our test set of different models.  
Figure 12: Results in the general scenes: (**a**) original image; (**b**) label; (**c**) D-LinkNet; (**d**) DenseNet (k = 24) + stem block.  
Figure 13: Results in the complicated scenes: (**a**) original image; (**b**) label; (**c**) D-LinkNet; (**d**) DenseNet (k = 24) + stem block; (**e**) D-DenseNet (k = 24) + stem block + D-Block.  
For the complicated scenes (Figure 13), the road area includes various-level roads and flyover roads, which would seriously affect the results from each network model. However, D-DenseNet (k = 24) + stem block + D-Block shows better performance for extraction including for the shadow-covered road.  
After the experiments of model training and test, D-LinkNet, DenseNet and D-DenseNet model size and IoU index were collected (Table 2). We found that the model size of the network (the Dense-block and the D-Dense block) is much smaller than of those with the initial block. Further, a lower size obtains higher IoU scores. In addition, the improved D-DenseNet (k = 24) with D-Block has higher IoU scores than other networks, as shown in Table 2.
# Privacy-Aware Cloud Architecture for Collaborative Use of Patients' Health Information
## 1 Introduction  
Recently, the healthcare sector has shown a growing interest in information technologies to facilitate new methods of collecting, managing, and analyzing health-related information. In fact, the healthcare sector is under pressure to embrace many new technologies that are available on the market, such as the Internet of Things (IoT) [1], and as consequence to such embracement, the amount of healthcare information is rapidly growing in detail and diversity, driven by record keeping, compliance, regulatory requirements, and of course, patient care [2]. Such information generates special value when it is exchanged and collaboratively used among different parties involved in the healthcare area [3]. Several researchers consider immediate access to previously generated medical records during healthcare service delivery highly important, it leads to effective ways of preventing and managing illnesses, as well as the discovery of new drugs and therapies [4].  
Patients choose medical providers such as hospitals and pharmacies based on proximity, quality, cultural fit, and bedside manner. This fragments patient data in heterogeneous systems. Most of this data are housed in private, heterogeneous, dispersed health information systems [5]. Thus, health-related data in these systems cannot be easily accessible to provide a complete patient profile. For instance, when a patient visits a general practitioner, they often need additional medical services over time, such as magnetic resonance imaging scans or cholesterol and blood sugar tests.  
Sharing healthcare information improves patient care by better understanding health requirements [6]. For that, the seamless exchange of multimedia clinical information is considered a fundamental requirement. Different technological approaches can be adoptedfor enabling the communication and sharing of health record segments [7]. However, interoperability and data privacy are important obstacles to maximizing healthcare information sharing. Interoperability means two or more systems can exchange and use information [8]. It is the ability to share and use information across multiple system technologies seamlessly. It is a fundamental requirement for the health care system to derive the societal benefits promised by the adoption of electronic healthcare records [7].
# Biofilm Formation as a Complex Result of Virulence and Adaptive Responses of _Helicobacter pylori_
## 4 Phenotypic Variability as a Modulator of _H. pylori_ Biofilm Structure
### Quorum Sensing and a Role of Flagella in Biofilm Formation  
When describing the genes coordinating the transition of microorganisms from the logarithmic growth phase to the stationary phase, one should also mention _luxS_, a gene involved in the production of AI-2 and microbial communication (the so-called quorum sensing) [110; 111].  
For _H. pylori_, it has been shown that the expression of _luxS_ increases in the initial stages of growth, reaches its maximum during the late logarithmic phase (around the second day), and then gradually decreases, presenting a minimal level of expression during the stationary phase [46; 58; 59; 112]. This was also confirmed by the transcriptomic analysis of biofilm _H. pylori_ forms showing a reduction in the expression of _luxS_ and other genes related to communication with AI-2, i.e., HPG27_227 (_aibA_; a periplasmic uptake protein AI-2) and HPG27_526 (an exporter of AI-2) [22]. The potential inverse relationship between LuxS production and the transition to the biofilm phase was demonstrated with the use of \(\Delta luxS\) mutants, which revealed several times higher biofilm production compared to the wild strain [26; 40; 48]. The observations of increased biofilm formation in _luxS_-nonproducing strains were associated with a reduction in the amount of Al-2, which is a stimulus to chemorepulsion (negative chemotaxis), and thus, an enhanced sedentary _H. pylori_ lifestyle [26; 48]. These suggestions were indirectly confirmed later when the decreased expression of genes encoding flagella components (including _flaA_, _flgE_, _flil_, _motA_, and _motB_) was noticed in \(\Delta luxS\) mutants [26].  
New light was shed on the significance of flagella by [PERSON] et al. [21; 22], who showed, during the transcriptomic analysis of planktonic and biofilm _H. pylori_ forms, higher flagella gene expression in the latter group (even seven times higher for some genes). The obtained results were confirmed by this team during the observation of biofilm production in the \(\Delta fliM\) (lack of flagella) and \(\Delta motB\) (flagella present, but lack of motility) mutants, for which a 25-fold and 6-fold reduction in the amount of biofilm were shown, respectively [21]. On this basis, it was concluded that flagella is an important structural component of _H. pylori_ biofilms [21; 22].  
The above results show the multitude of interactions taking place during the formation of the microbial biofilm and the difficulties in unambiguously assessing the usefulness or non-usefulness of _H. pylori_ genes during the formation of this complex structure.
# Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
## 5 Numerical Experiments
### Experiments and Implementation Details  
On the tensor recovery problem, we compare LRANNT with TNN, UTNN, FTNN, and NTTNN. These experiments were conducted on grayscale video, color images, and MRI datasets, and we demonstrate our comparative results from both visual and numerical perspectives, which is valuable in practical applications. Among these, the primary numerical comparison metrics we used were the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The PSNR and SSIM were used to evaluate the image restoration quality of different methods [42]. The definitions are as follows:  
\[\text{PSNR}=10\log_{10}\frac{\text{MAX}_{\mathbf{X},\mathbf{X}^{*}}^{2}}{\left\| \mathbf{X}-\mathbf{X}^{*}\right\|_{F}^{2}},\quad\text{SSIM}=\frac{(2\mu_{ \mathbf{X}}\mu_{\mathbf{X}^{*}})(2\sigma_{\mathbf{X}\mathbf{X}^{*}}+c_{2})}{ \left(\mu_{\mathbf{X}}^{2}+\mu_{\mathbf{X}^{*}}^{2}+c_{1}\right)\left(\sigma_{ \mathbf{X}}^{2}+\sigma_{\mathbf{X}^{*}}^{2}+c_{2}\right)},\]  
where \(\mathbf{X}^{*}\) represents the true image and \(\mathbf{X}\) is the restored image. \(\mu_{\mathbf{X}}\) and \(\mu_{\mathbf{X}^{*}}\) denote the mean values of \(\mathbf{X}\) and \(\mathbf{X}^{*}\), respectively, while \(\sigma_{\mathbf{X}}\) and \(\sigma_{\mathbf{X}^{*}}\) are their standard deviations. The covariance between \(\mathbf{X}\) and \(\mathbf{X}^{*}\) is denoted as \(\sigma_{\mathbf{X}\mathbf{X}^{*}}\), and \(c_{1}\) and \(c_{2}\) are constants. Lastly, the relative error between consecutive iterations of the tensor \(\mathbf{X}\), defined as \(\frac{\left\|\mathbf{x}^{k+1}-\mathbf{x}^{k}\right\|_{F}}{\left\|\mathbf{x} ^{k}\right\|_{F}}\leq 10^{-4}\), is used as the stopping criterion.  
Figure 2: The algorithm calculates the values of PSNR and SSIM at each iteration in the completion problem.
# Experimental Investigation on a Throttleable Pintle-Centrifugal Injector
## 3 Experimental Investigation on the Pintle-Centrifugal Injector
### Results and Discussion
#### 3.3.1 Analysis of Injection Pressure Drop
In the fixed structure injector, according to Equation (1), the injection pressure drop is proportional to the square of the mass flow rate of the propellant. This results in an injection pressure drop variation of approximately 46.799 for a fixed structure injector when the mass flow rate is adjusted to 6.841. This will place a huge burden on the propellant delivery system of the hybrid rocket motor. In summary, the throttleable pintle-centrifugal injector can be effectively adjusted for injection pressure drop. \[\dot{m}=\mu A\sqrt{2\rho(P_{1}-P_{2})}=\mu A\sqrt{2\rho\Delta P} \tag{1}\]  
A similar pattern of change was observed in the throttleable pintle-centrifugal Injector 2. As illustrated in Figure 7, the throttleable pintle-centrifugal injector was able to effectively regulate pressure drop by modifying the stroke in response to varying operational conditions, demonstrating a capacity to maintain control when the flow rate was altered from 56.8 g/s to 382.3 g/s. It is noteworthy that the pressure drop observed in the test of Injector 2 is considerably higher than that of Injector 1, which can be attributed to the smaller tangential hole size of the latter. The pressure drop of the injector, as measured in the course of our tests, can be attributed to two distinct effects: the throttling effect of the tangential hole and the throttling effect of the orifice. The test results demonstrate that these two throttling effects can be flexibly allocated for different flow condition ranges, thus further enhancing the performance of the throttleable pintle-centrifugal injector.
# A New Concept of Instantaneous Whirling Speed for Cracked Rotor's Axis Orbit
## 3 The Definition of Instantaneous Whirling Speed
The controller supplies power to the motor and controls the speed of the motor. The displacement probe collects the lateral vibration signal of the shaft, and is connected with DAS through the signal regulator. The displacement signals in two directions (x and y) are acquired through the displacement probes, and the curves of RWS are plotted according the diagram in Figure 5. As shown in Figure 7, the RWS curve of the system with no rotor cracks (Figure 7a) fluctuates in a small range but the RWS curve of the system with rotor cracks (Figure 7b) fluctuates in a wide range. The RWS shows distinguished differences between normal and cracked rotors and that is qualitatively consistent with the simulation results as shown in Figures 3b and 4b. On the other hand, the real rotor system is not an ideal normal  
Figure 5: The plot diagram of relative instantaneous whirling speed (RWS). system and has many differences with the system simulated by the dynamic model, suggesting that the RWS of experimental data is quantitative different with that of the simulated system. The RWS of a normal real system is not a constant like that shown in Figure 3b but fluctuates in a small range. The comparison results show that RWS can distinguish the normal and cracked states of real rotor systems. However, real rotor systems inevitably exhibit a certain extent of compound faults, such as misalignment, oil whirl, rubbing, and other faults, which may lead to the confusion of instantaneous whirl characteristics.
# SITAEI HC1 Low-Current Hollow Cathode
## 4 Results
### Cathode Performance Tests  
The barium-based emitter requires an activation process to force the emitting compound to diffuse toward the inner surface of the emitter. The reaction of the impregnant with the tungsten matrix releases barium atoms, which pass through the pores to combine with oxygen, thus creating the superficial monolayer of BaO [6]. In addition, the emitter must be allowed to soak at two temperatures long enough to allow complete out-gassing of the water vapor.  
With the emitter temperature mapping obtained from the thermal test, the heater power values required to reach the two soak temperatures were identified and included in the cathode activation procedure. This procedure lasts several hours and must be performed each time the cathode is ignited after air exposure, as well as for the very first ignition. During the heating ramp, the cathode starts emitting an electron current due to the thermionic effect. In this phase, the emitted current was measured with the aid of a multimeter, as the keeper was biased relative to the cathode at 100 V. An example of the measured values is shown in Figure 7, where the emitted current is reported as a function of heater power. Variability in the emitted current values of about 20% among different cathodes was observed, which is ascribed to the measurement uncertainties of such low current values. Nevertheless, the emission curve remains an important indication in terms of the emitted current trend with increasing heater power to evaluate the success of the cathode activation. After the activation phase, the cathode could be ignited, with a heater power of less than 50 W and a mass flow rate of between 0.1 and 0.2 mg/s (multiple ignitions were found to be successful in the indicated flow range). A keeper voltage of 100 V is normally applied before setting the final value of the heater power, and lower values (\(<\)50 V at the beginning of life) were found to be sufficient for ignition. The cathode's electrical characteristics with 0.1 mg/s Xe are reported in Figure 8, together with the corresponding theoretical results. The model is in excellent agreement with the experimental data at the higher currents, whereas a maximum difference of about 4.5 V is found at 0.5 A [13].  
Figure 7: Emitted current as a function of heater power.