esa-sceva/satcom-chunk-collection huggingface dataset:
# The Gyrotrons as Promising Radiation Sources for THz Sensing and Imaging
## 2 Advantages of the Gyrotrons as Powerful Radiation Sources for Sensing and Imaging
### Advanced Spectroscopic Techniques
#### 3.1.2 Measurement of the Hyperfine Splitting (HFS) of Positronium  
The positronium is a metastable bound state of one electron and a positron that forms an exotic hydrogen-like atom. It can exist in two states, namely ortho-positronium (o-Ps) and para-positronium (p-Ps). The energy splitting between o-Ps and p-Ps, i.e., the HFS is about 203.4 GHz. A significant  
Figure 4: \(0.8\) THz double-beam gyrotron with a 15 T cryo-free superconducting magnet at FIR UF.  
discrepancy of 3.9 standard deviations between the measured HFS values and the theoretical prediction of the quantum electrodynamics (QED) motivated the development of a novel method for the direct and precise evaluation of HFS [73]. In contrast to the previous indirect methods (e.g., measuring the Zeeman splitting in a static magnetic field) that are prone to systematic errors, the new approach relies on a stimulated transition between o-Ps and p-Ps states induced by irradiation with a strong electromagnetic wave with a frequency of about 203 GHz generated by a gyrotron. The experimental setup includes a transmission line that delivers and couples the wave beam to a high-finesse Fabry-Perot (FP) cavity in which a power of about 10 kW is accumulated, and it includes a gas chamber and a positron source as well as a set of detectors and an electronic control system. The positronium is formed in the cavity using a 22 Na source of positrons and nitrogen mixed by iso-butane as a stopping target. The schematics of this experimental arrangement are shown in Figure 5. Under the irradiation by a 203 GHz wave, some of the o-Ps (decaying into three photons) transit into p-Ps (decaying into two photons), and consequently, the ratio of two-photon events increases. This process is monitored by the photon detectors (LaBr3(Ce) scintillators) that are located around the cavity. In the measurements, the frequency of the gyrotron is varied within approximately 2 GHz in order to observe a Bret-Wigner resonance of the transition. The hyperfine transition has been observed with a significance of 5.4 standard deviations. The transition probability that has been measured directly for the first time is found to be \(A=3.1\pm^{1.6}_{1.2}\times 10^{-8}\) s\({}^{-1}\), which is in a good agreement with the theoretical value of \(3.37\times 10^{-8}\) s\({}^{-1}\)[74]. Recently, the whole Breit-Wigner resonance of the transition from o-Ps to p-Ps has been measured for the first time using a frequency-tunable millimeter-wave system and tuning the gyrotron in a very wide range from 201 to 205 GHz by changing successively several gyrotron cavities of different radii.
# Investigation of Mixed-Mode I/II Fracture under Impact Loading Using Split-Hopkinson Pressure Bar
## 2 Experiment
### Crack Propagation Gauge  
The crack extending velocity is an essential index to identify crack propagation behavior; especially for mixed-mode I/II crack whose running speed is hard to get during the experiment, measuring instant crack speed is challenging work. Therefore, the Crack Propagation Gauge (CPG) test system was applied to implement the task. In view of the trajectory irregularity of mixed-mode crack, a novel sensitive gauge, shown in Figure 3, was introduced in this experiment which was large enough to cover the potential area where the mixed-mode running crack might pass. Notably, this gauge has a very brittle glass-silk baseplate on which the resistance wires are uniformly spaced. When the gauges were tightly glued on the surface of the specimen, the fine resistance wires would be fractured synchronously with propagating crack passing by. Thus, the voltage would change correspondingly, shaping a stair step signal and the fracture time of each wire could be confirmed precisely with the help of the derivative of the CPG signal. Then by determining the distance between adjacent wires, the crack propagating velocity could be calculated.  
Figure 5 shows the CPG signal curves of specimen-0\({}^{\circ}\) and specimen-30\({}^{\circ}\), respectively. The specimen-0\({}^{\circ}\) is the typical mode I crack, the propagation time between adjacent wires stayed approximately the same after initiation, until a distinct time interval, 62.4 us, observed between the 16 th and 17 th fine wires. Based on the conclusion proposed by [PERSON] [36], there should be a crack arrest that appeared here at a very low speed. While for specimen-30\({}^{\circ}\) with mixed-mode I/II initiation in, the CPG signal curve presents a subtle change at 7 th-8 th fine wires, there were decelerations at the 16 th fine wire, and the crack initiation time of specimen-30\({}^{\circ}\) is later than mode I fracture. It is clear that the dynamic crack propagation behavior between mode I and mixed-mode I/II fracture has a difference.
## 4 Conclusions
Additionally, a higher revisit time for thermal images is expected by September 2021 with the launch of the Landsat 9 satellite-carrying onboard the Thermal Infrared Sensor 2 (TIRS-2), characterised by the same technology that was used for TIRS on Landsat 8. Conceptualisation, A.M. and C.P. ; methodology, A.M., C.P., G.D., F.C. and J.-P.M.; validation, A.M., C.P., G.D. and F.C. ; data curation, A.M., C.P. and G.D.; writing--review and editing, A.M., C.P., G.D., F.C. and J.-P.M.; supervision, J.-P.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Restrictions apply to the availability of meteorological data. Data were obtained from the dam management authority department and are available from the authors with the permission of the dam management authority department. Sentinel images can be found after registration at [[https://shihub.copernicus.eu/](https://shihub.copernicus.eu/)]([https://shihub.copernicus.eu/](https://shihub.copernicus.eu/)) (accessed on 29 March 2021). Landsat 8 TIRS data are available through the Google Earth Engine platform by requiring them at users/sofaermida/landsat_smw_lst:modules/Landsat_LSTj.s. GNSS data from AGRI CORS are owned by the University of Palermo. The data from the Geomax receivers were released to the University after a scientific partnership agreement signed with the University of Palermo. GNSS data support the findings of this study are available from the corresponding author upon reasonable request. The software used in this work is SARProZ(r), developed by [PERSON]. The authors are grateful to the developer also for his technical support. The authors would also like to thank the dam management authority for providing in situ water levels and air temperatures. The authors wish to thank the key contact people of the dam management authority department 'Dipartimento dell'Acqua e dei Rifituti-Assessorato Regionale all'Energia e ai Servizi di Pubblica Uilita' of the Sicilian Regional Government. In particular, the authors would like to thank [PERSON], head of the dam during the investigation period; [PERSON] and [PERSON] for documentation provided; and [PERSON], technical manager of the dam. J.-P.M. would like to acknowledge tthe support of the ESA-NRSCC DRAGON 4 programme for their support of work on InSAR. The authors declare no conflict of interest.
## 3 Results
### Effect of Compactness on Disintegration Characteristics
#### 3.3.3 Time-Stabilized Waste Soil  
Figure 12 shows the relationship between the residual weight and disintegration time of the silty clay with lime contents of 4%, 6% and 8%. Three disintegration stages were  
Figure 11: Disintegration curve of untreated silty clay: (**a**) Residual weight; (**b**) Disintegration ratio.  
observed for the disintegration of lime-stabilized soil compared with the untreated soil. The first two stages were consistent with the untreated soil in spite of the last phase of saturated dissolution, where the disintegration ratio of the soil decreases until complete disintegration [32; 34]. In addition, higher compactness increased the disintegration time of the treated soil under the same lime content, with a maximum increase of 15% for the soil with 8% lime content.  
The lime-stabilized soil with 90% and 100% compactness showed that the disintegration ratio accelerates significantly when the disintegration ratios were 8% and 12% on average, respectively, and this extended the water absorption phase, which was similar to the untreated soils. The disintegration ratio was basically identical to that of the untreated soils before the disintegration ratio exceeds 80%. Subsequently, the disintegration ratio slowed down in the phase of saturated dissolution, and it decreased with the larger lime content. This increased the disintegration time of the lime-stabilized soils, which is significantly different from the disintegration characteristics of untreated soil.  
The addition of lime contributes to cementing the fine soil particles into a mass, and the proportion of sticky particles gradually increases [26; 27]. The disintegration time of all the mixed soils with lime increased by 8%, corresponding to the addition of 1% in lime content. It should be noted that lime content should not be too large due to the marginal effects and engineering costs.  
From Figure 13, the stabilized silty clay showed an almost linear increase in the maximum disintegration time, considering varying contents of lime and different soil compactness. Hence, the relationship could be fitted as expressed:  
\[t_{\text{max}}=-12.7\text{nx}+23.78\text{x}+289.7\text{n}-112.59, \tag{6}\]  
where \(t_{\text{max}}\) is the maximum disintegration time (s), x is stabilizer content from 0 to 8% and n is the soil compactness between 90% and 100%.
## 2 Background and Problem Formulation
### Group/Extended Target State Modeling  
For current group/extended target tracking, a target state \(\xi\) involves a scalar measurement rate \(\gamma\), a kinematic target state \(\mathbf{x}\in\mathbb{R}^{n_{x}}\), and an extent matrix \(\mathbf{X}\in\mathbb{S}_{++}^{d\times d}\). Here, \(n_{x}\) represents the kinematic state's dimension, \(\mathbb{R}^{n_{x}}\) is a real column vector set, and \(\mathbb{S}_{++}^{d\times d}\) is a set of symmetric positive definite \(d\times d\) matrices. The measurement rate and the kinematic state are modeled using the gamma distribution \(\mathcal{GAM}(\gamma;\alpha,\beta)\) and Gaussian distribution \(\mathcal{N}(\mathbf{x};\mathbf{m},\mathbf{P})\), respectively. [PERSON] proposed using a random symmetric definite matrix to describe a target with an extended elliptical shape, which can be modeled using the inverse Wishart distribution [37]. Assuming that the moving state of the target and its extended shape are independent, the newly combined state can be described with the following probabilistic model [38]:  
\[\begin{split} f(\xi)&=\mathcal{GAM}(\gamma;\alpha, \beta)\mathcal{N}(\mathbf{x};\mathbf{m},\mathbf{P})\mathcal{IM}_{d}(\Sigma;v, \mathbf{V})\\ &=\mathcal{GGIN}(\xi;\xi).\end{split} \tag{1}\]  
This model is the gamma Gaussian inverse Wishart distribution (GGIN) state model of the targets, and we have the following:\[\mathcal{GAM}(\gamma;\alpha,\beta)=\frac{\beta^{a}}{\Gamma(\alpha)} \gamma^{a-1}\exp(-\beta\gamma),\alpha>0,\beta>0, \tag{2}\] \[\mathcal{N}(\mathbf{x};\mathbf{m},\mathbf{P})=\frac{\exp(-\frac{1}{ 2}(\mathbf{x}-\mathbf{m})^{\mathrm{T}}\mathbf{P}^{-1}(\mathbf{x}-\mathbf{m}) )}{(2\pi)^{\frac{n_{x}}{2}}|\mathbf{P}|^{\frac{1}{2}}},\mathbf{m}\in\mathbb{R} ^{n_{x}},\mathbf{P}\in\mathbb{S}^{n_{x}}_{+},\] (3) \[\mathcal{IW}_{d}(\Sigma;v,\mathbf{V})=\frac{2^{-\frac{v-d-1}{2}}| \mathbf{V}|^{\frac{v-d-1}{2}}}{\Gamma_{d}\Big{(}\frac{v-d-1}{2}\Big{)}| \Sigma|^{\frac{v}{2}}}\mathrm{etr}\left(-\frac{1}{2}\Sigma^{-1}\mathbf{V} \right), \tag{4}\]  
where \(\Gamma(\cdot)\) is the gamma function, \(\exp(\cdot)\) denotes the exponential function, \(|\cdot|\) is the determinant function of a matrix, \(\mathrm{etr}(\cdot)=\exp(\mathrm{Tr}(\cdot))\) is the exponential function of the matrix trace, and \(\Gamma_{d}(\cdot)\) is a multivariate gamma function.
## 1 Introduction
Hence, to enhance the signal susceptibility, minimise noise, and ensure swift switching without compromising the probability of intercept in the receiving network, we introduce a narrow-band RF switched filter bank using hairpin filters for radar applications. Filter banks are indispensable for maintaining the integrity of backend signal processing. Ineffective noise can undermine the usefulness of the signal, leading to ineffective detection. An optimal filter bank must exhibit zero insertion loss and a return loss exceeding 40 dB for each filter. Rapid switching is imperative to prevent delays, and power consumption should be minimised. The process of designing a hairpin filter bank is depicted in Figure 2.  
Figure 1: Block diagram of proposed filter bank.
## 5 Results
### Simulation 3: Noise-Effect Analysis
On the other hand, a Kalman filter with greater immunity to noise, such as the extended Kalman filter or some other type of hybrid Kalman filter, can be used to improve the noise immunity and the estimation precision.
## 2 Materials and Methods  
The PIN diode emitter was fabricated at Fraunhofer HHI in Germany [33]. A diagram of the emitter antenna is shown in Figure 1 with the E- and H- planes indicated. The emitter is mounted onto a hyper-hemispherical high-resistivity silicon (Si) lens with a diameter of 10 mm to reduce both beam divergence [32] and coupling losses (due to Fresnel reflections) from the InP substrate to air. The beam profiles were obtained using an angular mapping method, applied in two orthogonal orientations to yield beam profiles in the E-plane and H-plane. The frequency of the emitter was tuned from 100 GHz to 1 THz.  
The beam profile of an emitter can be described by adopting two different approaches: (i) lateral displacement or (ii) angular displacement of the emitter with respect to the receiver. For a collimated beam where lateral dimensions and profile are preserved with distance from the source, lateral mapping is appropriate to describe the beam profile. For a divergent source, angular mapping is more appropriate, because angular spread and variation is preserved with distance from the source. Since the PIN diode emitter examined here is a divergent source, its beam profiles were measured using an angular mapping method.  
The angular profiles of both orientations of the emitter were measured using the setup shown in Figure 2. The emitter was placed on a rotary stage and rotated with respect to the detector from \(-45^{\circ}\) to \(+45^{\circ}\) in \(0.2^{\circ}\) steps. The detector was a Tydex GC-1T Golay cell with an 11-mm aperture. The emitter was powered and controlled by a Toptica TeraScan system. The bias modulation frequency was set to 11.242 Hz, since this was the optimum modulation frequency to maximise the responsivity of the Golay detector. The output of the detector was read by a lock-in amplifier (Signal Recovery 7265 DSP).
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 1 Introduction
We build the decoder module to enhance the dual-channel communication capability, allowing the two adjacent layers of the ResNet50 network to guide each other for feature mining. And it helps in the recovery and fusion of the acquired image feature information by the decoder module. False color image synthesis and NDVI vegetation index are incorporated to enhance the identification of UGS boundary can effectively improve the segmentation accuracy of UGS. In summary, our main contributions are as follows:  
1. Design the MSPA module to extract the intra-contextual information of multiscale UGS, and then improve the relevance of the MAFANet model to capture the long-range feature information of UGS, thus improving the overall USG segmentation effect;
2. Designing the DE and BFF module construction new decoder to enhance the dual-channel communication capability, so that the two neighboring layers of ResNet50 network can guide each other in feature mining and improve the anti-interference capability of the MAFANet model;3. Introducing false color image synthesis and NDVI vegetation index to improve segmentation accuracy while proving that false color feature is better than the vegetation index in the process of UGS information extraction.
# Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
## 4 Experiments and Results
### Baseline Performance Assessment: Evaluating on Original Dataset
Transformer models stand out for their ability to manage complex data features \begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Category** & **Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **ROC AUC** \\ \hline \multirow{8}{*}{**ML**} & DT & 0.572 & 0.562 & 0.566 & 0.907 & 0.747 \\  & RF & 0.980 & 0.420 & 0.587 & 0.936 & 0.889 \\  & SVM & 0.989 & 0.342 & 0.506 & 0.928 & 0.890 \\  & NB & 1.000 & 0.023 & 0.046 & 0.894 & 0.630 \\  & LR & 0.907 & 0.194 & 0.312 & 0.910 & 0.878 \\  & ANN & 0.642 & 0.489 & 0.552 & 0.915 & 0.827 \\ \hline \multirow{4}{*}{**DL**} & CNN & 0.745 & 0.340 & 0.449 & 0.915 & 0.845 \\  & LSTM & 0.808 & 0.345 & 0.461 & 0.919 & 0.811 \\  & GRU & 0.719 & 0.319 & 0.375 & 0.904 & 0.806 \\  & BiLSTM & 0.656 & 0.428 & 0.518 & 0.915 & 0.820 \\ \hline \multirow{4}{*}{**Transformer**} & BERT & 0.733 & 0.650 & 0.689 & 0.940 & 0.922 \\  & ROBERTA & 0.830 & 0.409 & 0.548 & 0.930 & 0.928 \\  & DISTILBERT & 0.895 & 0.463 & 0.610 & 0.939 & 0.941 \\  & XLNET & 0.773 & 0.369 & 0.500 & 0.924 & 0.907 \\  & ALBERT & 0.843 & 0.557 & 0.671 & 0.944 & 0.928 \\  & ELECTRA & 0.912 & 0.409 & 0.565 & 0.935 & 0.919 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy metrics for different models over the original dataset across ML, DL, and Transformer categories. Figure 2: Performance evaluation of experimental machine-learning, deep-learning, and Transformer models on the original dataset.
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 4 Algorithm Description
### Clustering  
#### 4.1.1 Choice of Candidate Cluster Leaders  
Cluster head selection becomes more reasonable by introducing the concept of node density. The concept of node coverage redundancy is then introduced to improve the fault tolerance of the cluster head monitoring area. A node first broadcasts its residual energy, node ID and location information to its adjacent nodes within the communication radius. The weight of the node will become zero if its residual energy is lower than the average residual energy of all network nodes. Then, it directly drops out of the competition and becomes a member node. Nodes that are qualified for participating in the competition of candidate cluster heads calculate weight size by computing energy, density and coverage redundancy. The weight value of the node \(s_{n}\) is expressed as  
\[w_{n}^{c}=\begin{cases}a_{1}\frac{E_{n}^{c}-E_{n}^{env}}{E_{n}^{env}}+b_{1} \frac{Y_{n}}{Y_{max}}+c_{1}\frac{\Omega_{n}}{\Omega_{max}},&E_{n}^{r}\geq E_{n }^{av}\\ 0,&E_{n}^{r}<E_{n}^{av}\end{cases} \tag{11}\]where \(E_{n}^{r}\) and \(E_{n}^{av}\) denote the remaining and average remaining energy of nodes, respectively; Y\({}_{max}\) represents the maximum density of nodes; \(\Omega_{max}\) stands for the maximum coverage redundancy of nodes; \(a_{1}\), \(b_{1}\), \(c_{1}\) are adjustment factors, and \(a_{1}+b_{1}+c_{1}=1\).  
Node density refers to the density of a node, which is up to the number of neighboring nodes within its communication range and the distance from neighboring nodes to that node. The formula for node \(s_{n}\) density is expressed as  
\[\mathrm{Y}_{n}=\frac{V^{2}}{\sum_{\
u=1}^{V}D_{n\
u}}, \tag{12}\]  
where \(V\) is the number of nodes adjacent to the node; \(D_{n\
u}\) represents the distance from the node to its neighboring one \(s_{\
u}\).  
Node coverage redundancy is the degree to which the set of nodes adjacent to a node covers its sensing area. The distance to neighboring nodes and the sensing radius can be calculated to obtain the node coverage redundancy. The formula for coverage redundancy is given in the citation [17].  
A random number \(\mu_{n}\) is defined in the equation according to the method of (6) to determine whether it becomes a candidate cluster head. In this case, the nodes in the sparse region also have the possibility of becoming cluster heads, which avoids the concentration of all cluster heads in relatively dense places at the beginning of the network. From the formula, it can be seen that a node will be more likely to become a candidate cluster head when its weight is larger.
## 2 Materials and Data
### Dataset Acquisition  
As shown in Figure 1a, the NIR spectrometer utilized in this study is the Ocean Optics (USA) NIRQuest512 spectrometer, which has a detection wavelength range of 900-1700 nm, a spectral resolution of 3 nm, an indium gallium arsenic detector, and a full-signal signal-to-noise ratio of 4000:1. The spectral data should be collected by controlling the stability of the ambient temperature and humidity to eliminate the influence of the environment. Before the collection, the instrument should be calibrated. The environmental spectrum is first measured and stored as dark spectrum. Then, the whiteboard spectrum is measured as a bright spectrum, and the wood spectrum data is measured after subtraction. This can ensure that the collection is not affected by the instrument error. In this experiment, diffuse reflectance spectra were collected at three different locations in the upper and lower sections of the specimen. The average value was taken as the final spectrum of the specimen, and SpectraSuite (Ocean Optics, Dunedin, FL, USA) software was used to control the acquisition process. According to the standard GB/T 1935-2009 [20], the specimen is placed at the center of the support of the WDW-100 universal testing machine (Kexin, Changchun, China), Figure 1b shows the device. The load is applied at a uniform speed, the specimen is destroyed within 1.5 min to 2.0 min, and the damage load is recorded. The compressive strength is calculated as the damage load divides by the area under stress.