esa-sceva/satcom-chunk-collection huggingface dataset:
# Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES
###### Abstract  
The China Seismo-Electromagnetic Satellite (CSES-01) is the first satellite of the space-based observational platform for the earthquake (EQ) monitoring system in China. It aims to monitor the ionospheric disturbances related to EQ activities by acquiring global electromagnetic fields, ionospheric plasma, energy particles, etc., opening a new path for innovative explorations of EQ prediction. This study analyzed 47 shallow strong EQ cases (Ms \(\geq\) 7 and depth \(\leq\) 100 km) recorded by CSES-01 from its launch in February 2018 to February 2023. The results show that: (1) For the majority (90%) of shallow strong EQs, at least one payload onboard CSES-01 recorded discernible abnormal signals before the mainshocks, and for over 65% of EQs, two or three payloads simultaneously recorded ionospheric disturbances; (2) the majority of anomalies recorded by different payloads onboard CSES-01 predominantly manifest within one week before or on the mainshock day, or occasionally about 11-15 days or 20-25 days before the mainshock; (3) typically, the abnormal signal detected by CSES-01 does not directly appear overhead the epicenter, but rather hundreds of kilometers away from the epicenter, and more preferably toward the equatorward direction; (4) the anomaly recognition rate of each payload differs, with the highest rate reaching more than 70% for the Electric Field Detector (EFD), Search-Coil Magnetometer (SCM), and Langmuir Probe (LAP); (5) for the different parameters analyzed in this study, the plasma density from LAP, and electromagnetic field in the LUF band recorded by EFD and SCM, and energetic electrons from the High-Energy Particle Package (HEPP) show a relatively high occurrence of abnormal phenomena during the EQ time. Although CSES-01 has recorded prominent ionospheric anomalies for a significant portion of EQ cases, it is still challenging to accurately extract and confirm the real seismic precursor signals by relying solely on a single satellite. The combination of seismology, electromagnetism, geodesy, geochemistry, and other multidisciplinary means is needed in the future's exploration to get infinitely closer to addressing the global challenge of EQ prediction.  
China Seismo-Electromagnetic Satellite (CSES); payload; seismic ionospheric anomaly; retrospective study; anomaly recognition rate 2024164426[[https://doi.org/10.3390/rs16234426](https://doi.org/10.3390/rs16234426)]([https://doi.org/10.3390/rs16234426](https://doi.org/10.3390/rs16234426))
# Combining Supervised and Reinforcement Learning to Build a Generic Defensive Cyber Agent
## 4 Performance Evaluation
### Results Based on Training Topology
The proposed framework trains a single blue agent to handle both _b_line_ and _red_meander_. The results in Table 7 indicate that agents trained using the proposed framework possess generalizability, enabling them to effectively handle both types of red agents. Previously, all results were based on episodes of 100 steps. Here, we present results from another set of experiments with an episode length of 50 steps. Prior findings have shown that retraining agents is counterproductive, so retrained agents are not considered in this analysis. Figure 8 shows the mean total rewards, along with 95% CIs, for different blue agents. The performance of the blue agents against _b_line_ is similar to that shown in Figure 7. VAE-V demonstrates the best mean reward, followed by MLP and SVM. However, the performance difference between the blue agents trained using the proposed framework and BL is not statistically significant. The RED agent shows the worst performance against _b_line_, as it is specifically trained against _red_meander_. Most HAs also demonstrate worse mean rewards compared to BL and the blue agents trained using the proposed framework, with the differences in performance for VAE-V and HA20 being statistically significant. The blue agents' performance against _red_meander_ is, again, similar to what is shown \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Agent Type** & **BL** & **RED** & **BL-RED** & **RED-BL** & **MLP** & **SVM** \\ \hline
**Total Mean Reward** & \(-140.88\) & \(-122.96\) & \(-151.33\) & \(-198.75\) & \(-117.70\) & \(-124.61\) \\ \hline
**Agent Type** & **VAE-V** & **HA5** & **HA10** & **HA15** & **HA20** & \\ \hline
**Total Mean Reward** & \(-\textbf{110.90}\) & \(-117.60\) & \(-123.10\) & \(-126.60\) & \(-127.43\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Total mean reward—100-step episode. Figure 7: Performance comparison using training network topology—100-step episode. in Figure 7. The RED agent demonstrates the best performance, with VAE-V and MLP showing comparable performance to RED, as the differences are not statistically significant. BL performs the worst, since it is trained for _b_line_. The mean reward for HAs decreases as the percentage error in selecting the appropriate blue agent increases, although their performance is generally similar to RED. Table 8 lists the mean total rewards as demonstrated by the blue agents against both red agents. Among the blue agents trained using the proposed framework, VAE-V achieves the highest total mean reward, indicated by the bold font. HA5 achieves the same total mean reward, while MLP and SVM also exhibit comparable total mean rewards. The results in Table 8 indicate that agents trained with the proposed framework exhibit a certain degree of generalizability, enabling them to effectively handle both types of red agents using a single blue agent.
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 1 Introduction
At a fundamental level the smart meter consists of sensing circuitry, a data logging and reporting interface, and a communication interface. In the following sections, we review each of the components in turn in the setting presented here. The paradigm of smart energy has evolved to include electricity, heat, and gas. While most of the smart meters are focused on electricity measurement, it is pertinent to review other sensing circuitries and their integration in the smart meter as well. In Section 2, we review the different sensing techniques. Section 3 reviews the hardware/software platforms and communication systems for the IoT domain and their implementation status for the smart meters. Section 4 concludes the review by presenting a brief outline of the several opportunities available in the current technology landscape for smart meters.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 2 Methods
### Features Integration  
After the data imputation, all three sensor excerpts of all datasets along with their activity labels were integrated to make a feature vector. The features extracted for each sensor are the five greatest distances between the maximum peaks, the average, standard deviation, variance, and median of the maximum peaks, and the standard deviation, average, maximum value, minimum value, variance, and median of the raw signal.
## 2 Antenna Design Approach
### Silicon-Based Antenna Design with Enhanced Bandwidth
Antenna III etches double U-shaped slots on the lower patch. Figure 8 shows the MS curves and far-field patterns of antenna I to antenna III. The results were obtained  
Figure 5: Characteristic currents and characteristic fields of four modes. (a) J1; (b) J2; (c) J3; (d) J4. Figure 6: Simulated S\({}_{11}\) of antennas in the four cases. without the feeding structure, since MS is independent of any sources. It is seen that the MS curve of Antenna I and Antenna III only had one resonant mode radiating unidirectionally, while the MS curve of Antenna III showed two desired modes in this frequency band. Figure 9a shows the MS of antenna III with a probe-feed structure. Compared with Figure 8c, the MS curves shifted to higher frequencies, due to the introduction of the feeding probe. From Figure 9b, we can see there are two modes excited (J1 and J2). Figure 10 shows the characteristic currents and far-field radiation pattern when J1 and J2's MS = 1, respectively. Note that the characteristic currents near resonance for J1 and J2 show in-phase and anti-phase relationships, while both modes present the maximum radiation in boresight. In our opinion, the far-field radiation depended mainly on the top patch currents coupled by the bottom patch, which serves as a \"double tuned\" matching [24] resonator. The simulated results using full-wave simulation are shown in Figure 5.0 Compared with Antenna I and II, the S\({}_{11}\) curve of Antenna III had a double-resonant response and realized a 10 dB impedance bandwidth of 16.5%. Within this bandwidth, a gain performance of more than 5.5 dBi in boresight was realized for the three antennas, due to the high resistivity of silicon. Note that the boresight gains of Antenna II and Antenna III both had nulls near 24 GHz and 32 GHz beyond the band. This was caused by the opposite current distribution in the y direction. Figure 5.1 MSs and corresponding radiation patterns of the three source-free antennas. (**a**) Antenna I; (**b**) Antenna II; (**c**) Antenna III. Figure 5.2 Configurations of antennas I to III.
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 5 Discussion
### Can Geology and Land Use Affect the Assimilation?
The karst area of Larzac and Cevernnes and the flood irrigated area of Camargue are shown (“1” and “2”, respectively). can be observed for the ubRMSD difference, Figure 12d having more similarities with Figure 13d (\"LAI\") than with Figure 13b (\"SSM\"). Over the Camargue, the improvement in consistency between the S1 SSM and the modeled WG2 was limited to a few grid cells in the \"LAI\" and \"SSM\" experiments. The more extensive improvement obtained with the \"SSM and LAI\" experiment over the Camargue demonstrates the synergy between S1 SSM and LAI observations in the assimilation over intensively irrigated areas. For the other two regions, the same study case as for Montpellier was performed for July 2018 and July 2019. For the Toulouse region, July 2019 was also drier than July 2018. As for Montpellier, the assimilation of S1 SSM alone did not have much impact on WG5. The assimilation of LAI and LAI together with S1 SSM had more impact with respect to OL, especially in agricultural areas. This can be explained by a possible irrigation relationship, as observed in the Montpellier region, and more accurate S1 SSM retrievals in agricultural areas [71]. Similar results were observed over Salamanca. Assimilation results for Toulouse and Salamanca regions can be found in the Supplementary Materials of this paper. Figure 13: Impact of SSM assimilation alone (**a,b**) and LAI assimilation alone (**c,d**): (**a,c**) difference between correlation of S1 SSM vs. SSM and LAI analysis WG2 and correlation of S1 SSM vs. OL WG2; (**b,d**) difference between ubRMSD of S1 SSM vs. SSM and LAI analysis WG2 and ubRMSD of S1 SSM vs. OL WG2. The karst area of Larzac and Cevennes and the flood irrigated area of Camargue are shown (“1” and “2”, respectively).
# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
## 2 Materials and Methods
### Leaf Sampling and Molecular Detection of Phytoplasmas  
The actual presence or absence of phytoplasmas in grapevine tissues was checked by nested-PCR on 60 plants, 42 symptomatic and 18 asymptomatic. For each plant, 10 leaves with petioles were collected from the median sector of 3 different twigs, immediately refrigerated, and transferred to the laboratory. Petiole and midribs (1.5 g per sample, in 3 replicates) were separated from the lamina with a sterile razor blade and stored at \(-20^{\circ}\).  
DNA extraction was performed by the CTAB method [24; 25; 26]. Briefly, midribs and petioles were ground with pestle and mortar in liquid nitrogen and the powder was transferred to a 1.5 mL plastic tube with 800 L of 3.5% ethyl-trimethyl-ammonium bromide in 1 M Tris-HCl pH8, 10 mM EDTA, 1.4 M NaCl (CTAB buffer). The mixture was then transferred into a 2 mL clean tube and centrifuged at 1000\(\times\)\(g\) for 10 min. After the transfer of 1 mL of the suspension to a new tube, 2-marcaptoethanol was added to a final concentration of 0.2%), and tubes were incubated for 20 min at 65 \({}^{\circ}\)C. Then, an equal volume of chloroform: isoamyl alcohol (24:1) was added, followed by vortexing and centrifugation at 10,000\(\times\)\(g\) for 10 min. The aqueous phase was separated, and nucleic acids were precipitated with an equal volume of cold isopropanol by centrifugation at 10,000\(\times\)\(g\) for 15 min. The pellet was washed with 70% ethanol, dried, and dissolved in 100 L of nuclease-free water.  
Nucleic acid concentration was determined with a Nanodrop (ThermoFisher Scientific, NJ, USA) and adjusted to 20 ng/uL. Purity was assessed by calculating the ratio of the absorbance at 260 nm over the absorbance at 280 nm and at 230 nm.  
Detection of FDp or BNp was performed according to the EPPO guidelines for FD [25] by nested PCR, performing the first round of amplification with the generic P1/P7 primer couple [27]. The subsequent nested amplifications were carried out from the first diluted PCR products (1: 40) with specific primers, either for FD (R16(V)F1-R1) or for BN (R16(U)F1-R1) phytoplasmas [28]. Amplification products were run on 1% agarose gel in Tris-acetate-EDTA. The expected amplicon size was 1800 bp for the first amplification round and 1100 bp for both nested reactions. In parallel, controls of DNA extraction and positive and negative controls of the amplifications were also included in each round of experiments to exclude possible contaminations.
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 2 Preliminaries
### Insecure Deserialization  
In Java, object serialization and deserialization are two fundamental mechanisms. Serialization allows a Java object to be transformed into a byte stream, while deserialization reconstructs the Java object from the byte stream. Benefiting from these mechanisms, objects serialized on one platform can be deserialized on another platform, which is the embodiment of the portability of Java.  
RMI (Remote Method Invocation) is a mechanism that enables an object in one JVM (Java Virtual Machine) to invoke methods of an object running in another JVM. Dynamic class loading is an essential feature of RMI. When a JVM does not contain the definition of a class, it will download the class from a remote URI (Uniform Resource Identifier). If the attacker can indicate where the JVM downloads a remote class, the IDSER can be realized.  
Figure 4 depicts an example of an attacker implementing an IDSER attack by loading a remote class through RMI. Figure 5 shows the malicious class file constructed by the attacker. The attacker uses the vulnerability of the victim server during the JSON data parsing to indicate the address of this malicious class file through the dataSourceName field, forcing the server to download the malicious class file. Then, the server performs deserialization and executes the malicious behavior set in the static code block.  
Figure 4: An example of loading the remote class by RMI.  
Figure 3: An ELi example.
# The Design of a High-Intensity Deuteron Radio Frequency Quadrupole Accelerator
## 6 Beam-Loading Effect and Power Coupler Design  
### Beam-Loading Effect [31; 32]  
The existence of beam-loading effects represents a significant challenge for achieving stable operation in high-current RFQs. This factor demands diligent consideration, particularly in relation to power-coupling strategies. Otherwise, the beam-loading effects lead to an amplitude decrease and a phase offset of the electric field in the cavity. For most RFQs, the current is low enough that the beam power consumption is less than that of the cavity, allowing loading effects to be ignored. In our case, however, the beam power consumption (Pb) is approximately 21 kW, whereas the simulated cavity power consumption (Pc) is 61 kW for a beam load ratio (Pb/Pc) of about 0.34. This relatively high beam load ratio is compensated for by adjusting the coupling coefficient. If detuning occurs, its effects can be counteracted by adjusting the driving frequency. The detuning frequency is calculated below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Ambient temperature & 20 & \({}^{\circ}\)C \\ Water temperature & 20 & \({}^{\circ}\)C \\ Flow velocity & 2.5 & m/s \\ Maximum temperature rise & 18.1 & \({}^{\circ}\)C \\ Maximum deformation & 86.1 & \(\upmu\)m \\ Maximum stress & 53.5 & MPa \\ Frequency drift & 9.9 & kHz \\ \hline \hline \end{tabular}
\end{table}
Table 10: Result of the multiphysics analysis.  
Figure 26: Structural deformation of the RFQ (the fixed surface is the ending plate for both sides).  
Figure 25: Temperature distribution of the RFQ.  
The power consumption of the cavity and the external power consumption are denoted by \(P_{c}\) and \(P_{ex}\), respectively. The coupling coefficients of the coupler the cavity are then defined as a ratio of their power consumption,  
\[\beta=\frac{P_{ex}}{P_{c}} \tag{10}\]  
By the definition of the Q value, the relationship between on-load \(Q_{L}\), no-load \(Q_{0}\), and external \(Q_{ex}\) is  
\[\frac{1}{Q_{L}}=\frac{1}{Q_{0}}+\frac{1}{Q_{ex}} \tag{11}\]  
\[Q_{0}=(1+\beta)Q_{L},\ \beta=\frac{Q_{0}}{Q_{ex}} \tag{12}\]  
Due to microwave effects, the voltage reflection coefficient \(\Gamma\) of the cavity load is expressed as  
\[\Gamma=\frac{\beta-1}{\beta+1} \tag{13}\]  
In order to feed all of the power from the source into the resonant cavity, the reflection coefficient should be zero. According to the formula, the coupling coefficient is then one. When the cavity resonates, the equivalent circuit becomes a resistance circuit. For an input power Pg, the cavity power is  
\[\mathrm{P_{C}}=\mathrm{P_{g}}\Big{(}1-\Gamma^{2}\Big{)}=\mathrm{P_{g}}\frac{4 \beta}{(1+\beta)^{2}} \tag{14}\]  
For a loaded beam, the optimal matching parameters for a single coupler are listed in Table 11. The beam-loading effect for a beam load ratio of 0.34 is compensated for by adjusting the optimal coupling coefficient to 1.34 and raising the driving frequency by 1.225 kHz.
## 2 Materials and Methods
### Coarse Segmentation using Fuzzy C-Means Clustering  
FCM clustering is an overlapping clustering algorithm, where each point may belong to two or more clusters with different degrees of membership. The features (discussed in Section 2.3) with close similarity in an image are grouped into the same cluster. The similarity is defined by the distance of the features vector to the cluster centers. Euclidean distance is used to measure this distance and data will be associated to an appropriate membership value [24, 29, 30]. The cluster center is updated until the difference between adjacent objective function, as displayed in Equation 1 is close to zero or practically less than a predefined small constant:\[J_{m}=\sum_{i=1}^{M}\sum_{j=1}^{C}u_{ij}^{m}\left\|x_{i}-c_{j}\right\|^{2} \tag{1}\]  
where \(m\) is an exponential weighting function that controls the fuzziness of the membership function, it is set to 2 by [PERSON] [20]. \(M\) is number of features. \(C\) is number of clusters. \(u_{ij}\) is the degree of membership of \(x_{i}\) in the cluster \(j\), \(x_{i}\) is the \(i\)th of d-dimensional measured data, \(c_{j}\) is the d-dimension center of the cluster, and \(\left\|*\right\|\) is any norm expressing the similarity between any measured feature and the center.  
Fuzzy partitioning is carried out through an iterative optimization of the objective function shown above, with the update of membership \(u_{ij}\) and the cluster centers \(c_{j}\) by Equations 2 and 3:  
\[u_{ij}=\frac{1}{\sum_{k=1}^{c}\left(\left\|x_{i}-c_{j}\right\|\right)^{2}=1} \tag{2}\]  
\[c_{j}=\frac{\sum_{i=1}^{M}u_{ij}^{m}x_{i}}{\sum_{i=1}^{M}u_{ij}^{m}} \tag{3}\]  
The iteration will stop when Equation 4 is satisfied:  
\[\max_{ij}\left\{\left|u_{ij}^{(k+1)}-u_{ij}^{(k)}\right|\right\}<\varepsilon \tag{4}\]  
where \(\varepsilon\) is a termination criterion, 0.00001 for our case. \(k\) is the iteration number, it is set to a maximum of 200 for our case. This procedure converges to a local minimum or a saddle point of \(J_{m}\).  
The input to the FCM algorithm is a set of features. Rhe algorithm is composed of the following steps:  
Step 1: Initialize the fuzzy partition matrix \(U=\{u_{ij}\}\) (\(U^{(0)}\)) by generating random numbers in the range 0 to 1 subject to Equation 5:  
\[\sum_{i=1}^{M}\sum_{j=1}^{C}u_{ij}=1 \tag{5}\]  
Step 2: At \(k\)-step: calculate the centers vectors \(C^{(K)}\)=[\(c_{j}\)] with \(U^{(K)}\) according to Equation 3.  
Step 3: Update the fuzzy partition matrix \(U^{(K)}\), \(U^{(K+1)}\) by the new computed \(u_{ij}\) according to Equation 2.  
Step 4: Compute the objective function according to Equation 1. If the difference between adjacent values of the objective function is less than termination criterion (\(\varepsilon\)) then stop the iteration; otherwise return to step 2.  
The output from FCM clustering is a list of cluster centers and \(n\) membership-grades for each pixel, where \(n\) is a number of desired clusters. A pixel will be assigned to the cluster with highest membership-grade.
# Learning Kernel Stein Discrepancy for Training Energy-Based Models
## 5 Training Unnormalized Models with KSD
### Generative Adversarial Network (GAN)  
The generative adversarial network is a popular method for training generative models. The generative adversarial network (GAN) [13] involves a generator \(G\) and a discriminator (or critic) \(D\). The goal of the generator is to transform latent codes \(z\sim p(z)\) into generated samples, which the discriminator cannot distinguish from real data samples, and the goal of the discriminator is to distinguish real data samples and generated samples as well as possible. With i.i.d. data samples, the optimal generator parameter \(\theta_{g}\) and discriminator parameter \(\theta_{d}\) are the solution to the following minmax game:  
\[L=\underset{\theta_{g}}{\text{\emph{minimax}}}V\big{(}\theta_{g},\theta_{d} \big{)}=\text{E}_{x\sim q\ (x)}\big{[}\log D(x,\theta_{d})\big{]}+\text{E}_{z\sim q(z)}\big{[}\log \big{(}1-D\big{(}G\big{(}z,\theta_{g}\big{)},\theta_{d}\big{)}\big{)}\big{]} \tag{16}\]  
The minimax game highlights the adversarial nature of the training process. The discriminator, parameterized by \(\theta_{d}\), aims to maximize its ability to differentiate between real and generated data, while the generator, parameterized by \(\theta_{g}\), endeavors to minimize the discriminator's accuracy by producing increasingly realistic data. \(\text{E}_{x\sim q\ (x)}\big{[}\log D(x,\theta_{d})\big{]}\) term signifies the expected log probability over real data samples \(x\) drawn from the true distribution \(q(x)\) that the discriminator D correctly identifies as genuine. The goal for the discriminator is to minimize this value, ensuring a high probability of correctly classifying real samples. \(\text{E}_{z\sim q(z)}\big{[}\log\big{(}1-D\big{(}G\big{(}z,\theta_{g}\big{)},\theta_{d}\big{)}\big{)}\big{]}\) denotes the expected log probability, for noise variables \(z\) sampled from a distribution \(q(z)\), that the discriminator D correctly labels the data generated by the generator G (using the noise variables \(z\)) as fake. The discriminator aims to maximize this value to efficiently identify and label the artificially generated data, while the generator seeks to minimize this, thereby improving its ability to generate data that the discriminator misclassifies as real. Together, these terms formulate the objective function for the GAN, capturing the adversarial relationship between the generator and discriminator during the training process.