esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Spatio-Temporal Distribution Characteristics of PMGIM
### Analysis of Microseismic Correspondence in Grouting Disturbance Area  
Mining at the 182602 working face commenced on 7 November 2018. The microseismic monitoring system was established as early as November 2017 and has been continuously operational for 11 months, providing crucial data for pre-mining safety assessments. During this period, the regional grouting control project triggered varying intensities of microseismic events between the 2# coal seam floor and the Ordovician limestone karst aquifer, forming the focus of this study known as \" PMGIM\".  
Through detailed data analysis, we exclude microseismic interference induced by mining activities in the adjacent 182101 face and observe the distribution characteristics of microseismic events over different time periods:  
In Figure 6a, a total of 40 microseismic events were detected between January and February 2018, with 70% (28) concentrated within the grouting disturbance area. In subsequent periods (as depicted in Figure 6b-f), spanning January through September 2018, the number of detected microseismic events rose steadily to 337. During this time, the propor  
Figure 5: The 182602 working face overview.  
tion concentrated within the grouting disturbance area fluctuated slightly but generally stabilized between 62.0% and 67.9% as follows: 66.2% from January to February, 66.8% in April, 67.9% in May, 67.6% in July, and 62.0% in September.  
Furthermore, microseismic events outside the grouting disturbance area exhibited a scattered distribution without a clear clustering trend. The growth rate of these events decreased notably in the later stages of the grouting process (May 2018, September 2018), suggesting effective sealing of cracks as grouting advanced.  
From the above analysis, it is evident that there is a strong correlation between primary microseismic events and the grouting disturbance area. Continuous seepage and fracturing caused by grouting lead to the destruction of water storage spaces and karst caves, disrupting the balance of the Austrian ash aquifer and releasing high strain energy. Simultaneously, this process promotes the expansion and propagation of primary and secondary cracks in the rock mass, triggering the clustering of microseismic events. Beyond the grouting disturbance area, as the influence on the Austrian limestone aquifer diminishes, the degree of fracture expansion decreases, resulting in a discontinuous microseismic distribution.
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 4 Classification of ML Models Applied in the Optimization of Wastewater Treatment Processes
### ML Models Applied in the Optimization of Wastewater Treatment Processes
#### 4.2.6 Advanced AI Techniques for Dynamic Optimization  
Reinforcement Learning and graph neural networks are some of the advanced AI techniques that can deployed in wastewater treatment plants in order to save energy. Both of these methods can be combined to achieve both real-time control (RF) and system-level optimization (GNN).  
Reinforcement learning is an ML method in which the agent learns the best control policies through environmental interaction. Agents are rewarded or punished according to the consequences of their actions. With continuous multi-objective RL, RL agents can be trained to operate treatment plants in real time, with instantaneous decisions on aeration rates, the addition of chemicals, and sludge retention times, to achieve long-term operation goals and respond to variable influent characteristics and environmental conditions. Recent investigations reveal a cost reduction of between 12% and 937%, depending on three diverse weather cases, with RL agents for system control with respect to traditional PID controllers [87].  
Graph neural networks are employed to characterize relationships and dependencies in complex interconnected systems. This feature makes them suitable for WWTPs, where various operations are interconnected through process flows and feedback control. GNNs can naturally capture spatial dependency and temporal representation. A specific model, the NLCGNN, has also been developed, which is used to predict parameters such as COD, BOD, and TSS [88]. RL-GNN integration is an important step towards the dynamic optimization of wastewater treatment and can solve three problems:  
* How to perform adaptive control under uncertainty;
* Multi-timescale optimization;
* Scalability of dynamic optimization.
## 3 Experiment and Results Analysis
### Comparison of Model Experiments  
#### 3.3.1 Experimental Results and Analysis on the Cityscapes Dataset  
To verify the performance of the proposed ESNet for segmentation, six real-time semantic segmentation models with higher accuracy are selected for comparison with the proposed network model, namely SegNet, ENet, ESPNet, CGNet, ERFNet, and ICNet, etc. The experimental results obtained are shown in Figure 14.  
Figure 13: Curve graph of the training set Loss and the learning rate and the number of training epochs.  
The experimental results obtained are shown in Figure 14. The Cla accuracy refers to the average IoU of seven segmentation methods in 19 semantic categories, such as bicycle, motorcycle, train, bus, truck, car, rider, person, sky, terrain, vegetation, traffic signs, traffic lights, poles, fences, walls, buildings, sidewalks, roads, etc. Cat accuracy is the average IoU of seven segmentation methods under seven large grouping categories: people, objects, sky, vehicles, natural, buildings, planes, etc.  
In Figure 14, on the Cityscapes dataset, the ESNet structure achieves a segmentation accuracy of 70.7% for the 19 semantic categories set and 87.4% for the seven large grouping categories. Compared with other algorithms, all have different degrees of increase. Compared with a complex ICNet, the segmentation accuracy of the proposed ESNet structure is only 0.4% lower than it in each semantic category. However, its segmentation accuracy is improved by 0.4% under large grouping categories. The recognition results of the seven segmentation models for semantic categories such as roads, sidewalks, buildings, walls, fences, and poles are shown in Figure 15.  
Comparing the recognition results of the above 13 categories by different segmentation models indicates that the constructed ESNet structures have achieved better segmentation results. Compared with the ENet structure, the ESNet structure improves the segmentation accuracy of trucks by 22.9% and the segmentation accuracy of cars by 29.6%, which has obvious advantages. High recognition accuracy is still achieved for pixel categories that are difficult to identify, such as fences, poles, and traffic facilities. This demonstrates that the recognition accuracy of different network structures for small target objects such as traffic lights and traffic signs is different, but the ESNet network proposed in this paper can still better complete the task of semantic segmentation.  
Figure 14: Comparison results of various segmentation methods.
## 6 Results and Discussion
### Comparative Discussion  
Table 13 portrays a comparative discussion of the developed classification scheme. By considering 100 nodes, the testing accuracy, sensitivity, and specificity observed by the proposed method are 0.931, 0.953, and 0.915, respectively, which shows the higher performance when compared with existing models. With 150 nodes, testing accuracy, sensitivity, and the specificity measure computed by the proposed model are 0.928, 0.949, and 0.910, respectively.
## 4 Self-Healing Approaches
### Self-Healing Models and Frameworks
#### 4.1.2 QoS Model  
The quality of service (QoS) model is described by [17] as the expression of the correlation between QoS changes and its environment primitives (EPs) or control primitives (CPs). The QoS models can be a powerful tool to automatically assist cloud providers in adapting cloud-based services and applications. They help determine the extent to which services and applications can sufficiently exploit CPs to support QoS objectives and consider the QoS sensitivity of both EPs and CPs, as noted by [37]. A self-healing QoS model can automatically detect and correct errors in a system without human intervention. It is a model designed to maintain high accuracy and reliability despite unexpected events or changes. One approach to building a self-healing ML QoS model combines supervised and unsupervised learning techniques. Supervised learning is used to train the model on a set of labelled data, while unsupervised learning is used to identify patterns and anomalies in the data.
## 2 Materials and Methods
### Our Proposed Solution: A Combination of CR and MODCOD Techniques and Its Applications
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Overall Link Budget** & **QPSK \(\downarrow\)** & **QPSK \(\downarrow\)** & **QPSK \(\downarrow\)** & **QPSK \(\downarrow\)** & **QPSK \(\downarrow\)** & **SPSK \(\downarrow\)** & **SPSK \(\downarrow\)** & **16 APSK \(\downarrow\)** \\  & **ID = 9** & **ID = 8** & **ID = 7** & **ID = 6** & **ID = 5** & **ID = 4** & **ID = 2** \\ \hline \hline Received C/\(N_{0}\) [dBHz] & & & & & 95.5 & & & \\ Received \(E_{b}/N_{0}\)—loss [dB] & & & & 15.4 & & & \\ Implement. Loss [dB] & & & & 3.0 & & & \\ Demodol. Loss [dB] & & & & 3.0 & & & \\ \(E_{b}/N_{0}\) @ BER = \(1\times 10^{-6}\) [dB] & 0.6 & 1.5 & 3.0 & 3.7 & 4.4 & 5.4 & 6.4 \\ \(C/I\) [dB] & 1.9 & 5.4 & 8.4 & 9.6 & 11.3 & 12.8 & 15.2 \\ \(E_{b}/I_{0}\) [dB] & 3.9 & 4.9 & 6.5 & 7.6 & 8.2 & 9.4 & 10.7 \\ Received \(E_{b}/(N_{0}+I_{0})\) [dB] & 3.58 & 4.48 & 5.98 & 6.91 & 7.45 & 8.42 & 9.43 \\ Spec Margin (M) [dB] & 2.99 & 3.00 & 2.99 & 3.01 & 3.02 & 3.02 & 3.001 \\ \(INR_{max}=I_{0}/N_{0}\) [dB] & 14.62 & 13.69 & 12.10 & 11.14 & 10.56 & 9.49 & 8.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the link budget parameters from the implemented MODCOD. Figure 6: Curve of _INR_ variation versus applied MODCOD.
# Investigation on Eddy Current Sensor in Tension Measurement at a Resonant Frequency
## 3 Finite Element Analysis  
A 2D axis-symmetric model of eddy current sensor was established using finite element analysis software ANSYS 15.0 (ANSYS, Pittsburgh, PA, USA). The physical field was selected as the magnetic field under AC excitation and then solved using the harmonic solver [27]. The important parts were modeled with relatively fine mesh while the parts which do not need attention were modeled with relatively coarse mesh [28]. To investigate the influence of sleeve structure on the magnetic field strength distribution of the test specimen, two models with and without the sleeve structure were established. The simulation parameters were set as follows: the turns of coil were 204 and the diameter of enameled wire was 1 mm. The inner diameter and length of coil were 16 mm and 100 mm, respectively. The industrial pure iron DT4 was fabricated into the sleeve structure. The magnetic field lines and magnetic field strength distributions were acquired through the solution as shown in Figures 4 and 5, respectively.  
Figure 3: Inductor–resistor–capacitor (LRC) model of eddy current sensor.  
It can be observed in Figure 4 that the utilization of a sleeve structure with the high permeability material concentrates the magnetic field and makes the magnetic field lines pass through the test specimen. In Figure 5, it is possible to observe that the magnetic field strength of the test specimen with the sleeve structure was higher than that without the sleeve structure. The simulation results demonstrate that the sleeve structure reduces reluctance in the magnetic circuit and provides a higher permeability path to magnetic field lines than the non-sleeve structure, thus reducing the loss of magnetic field.
## Magnetic Nanoparticles in the Imaging of Tumor Angiogenesis
Necrotic areas exhibit sparse and degraded vessels, whereas angiogenic areas are characterized by high vessel density [18], large vessel diameters [19], and increased permeability. _In vivo_ imaging methods for monitoring microvasculure non-invasively are therefore of major interest for both tumor detection and the development of surrogate markers for assessing response to treatment (Figure 1). Imaging angiogenesis has been focused into three different arenas: (1) steady state blood volume determinations of neovascular density using magnetic nanoparticles; (2) kinetic imaging using dynamic tracking of contrast administration by ultrasound, computed tomography or magnetic resonance imaging; and (3) specific molecular markers of angiogenesis. This article will focus on steady state imaging of angiogenesis using magnetic nanoparticles, which as described below, does not suffer from some of the pitfalls associated with kinetic techniques (_i.e._, two _vs._ three compartment modeling, tradeoffs between spatial and temporal resolution, ability to image in areas constrained by motion), and offers high spatial resolution imaging of the blood pool with sophisticated analyses of microvascular volume and vessel size. Figure 1: Effect of treatment on tumors in patients who completed entire combined treatment regimen, and surgery. (**a**) Endoscopic and pathological evaluation of rectal tumors. Surgical specimens showed grade II tumor regression in patients 1–5 and grade III in patients 6, by Mandard criteria. Endoscopic image (instead of surgical specimen) was taken for patients 6, 3.5 weeks before surgery. BV, bevacizumab. (**b**) Representative functional CT images of blood perfusion before treatment (day 0), after bevacizumab (day 12) and after completion of treatment (day 104) in patient 5. (**c**) Tumor FDG uptake before treatment (pretreatment), 12d after bevacizumab treatment and 6–7 weeks after completion of all neoadjuvant therapy (presurgery). Saggital projections of FDG-PET scans for patient 1 are shown. Tumor is outlined in box, posterior to the bladder. Reprinted with permission from Macmillan Publishers Ltd: Nature Medicine, [PERSON] _et al_. Direct evidence that the VEGF-specific antibody bevacizumab has antivascular effects in human rectal cancer. 2004, _10_, 145–147.
## 3 VO\({}_{2}\)-Implanted THz Metamaterials
The quality and uniformity of the prepared VO\({}_{2}\) films can be accurately characterized by high resolution X-ray diffraction (XRD), Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR). By means of photolithography and a micro-fabrication process, the VO\({}_{2}\)-implanted THz MMs structure can be easily obtained. First, the VO\({}_{2}\) layer is patterned via reactive ion etching after photolithography. Gold films are then deposited on the samples by electron beam evaporation following the second photolithography. Eventually, the samples are fabricated using the lift-off process [37]. To accurately characterize the switching behavior of the metadevice, we can employ traditional THz time-domain spectral (THz-TDS) based on femtosecond laser [38]. The measured sample should be placed in a temperature control box so that its surface temperature can be adjusted in real time. At the same time, the area of sample is much larger than that of the THz beam spot, which can ensure the validity of the measurement. The THz time-domain signal and Fourier spectrum information of air reference and sample can be extracted, then the transmission spectrum corresponding different frequencies can be obtained. To eliminate the influence of water vapor noise on the experimental results, the whole experimental measurement must be carried out in nitrogen atmosphere. The thin and transparent polymer substrate in the MM structure will greatly reduce the interference of the substrate to the measured THz spectrum. It should be emphasized that the frequency resolution of THz-TDS will have a great influence on the measured resonance intensity and Q-factor. Therefore,  
Figure 2: Graphical representation of the VO\({}_{2}\)-implanted terahertz (THz) asymmetric double C-shaped metamaterial (MM) sample under the thermal pump and THz pulse irradiation. The upper right inset shows the detailed geometry dimensions of the unit cell with period length \(Px=Py=180\)\(\upmu\)m, resonator length \(lx=140\)\(\upmu\)m and \(ly=120\)\(\upmu\)m, resonance linewidth \(w=15\)\(\upmu\)m, VO\({}_{2}\) rectangular strip length \(l=32\)\(\upmu\)m and gap \(d=15\)\(\upmu\)m, (asymmetric parameter) offset length \(a=16\)\(\upmu\)m  
based on the above analysis, we preliminarily predict that as long as the frequency resolution of system is high enough, the results of experiments and simulations will be in good agreement [39].
## 3 Results and Discussion
### Dimensional Stability, Porosity and Phases Formed
Second, carbonates have proven to be a more effective activator than nitrates, as the former is able to cause a greater decrease in the porosities than the latter in similar concentrations (Figures 3 and 4). This denser microstructure will present lower water permeability and can lead to higher pore pressure and stress concentrations during the drying step of the tests [54]. The stresses in the pores due to drying can easily increase the size of these microcracks in HC mortars and make them more relevant. Thus, drying periods can potentially favor the nucleation and/or growth of cracks. Previously, other authors [15; 28; 31] have already shown that the formation of carbonated-based hydrates or dense microstructure early on can induce internal pressure or restraint shrinkage phenomena that promote microcracking. The higher incidence of microcracks in HC-6% mortars (Figure 5) correlates with the higher activator content and the more intense expansion detected, reinforcing the hypothesis that these early chemical processes contribute directly to microstructural damage. To understand the porosity results of HN (Figures 3 and 4), DTA analyses were carried out to evaluate the effect of nitrates on the formation of certain hydration products, and confirm the formation of nitrate-Aft reported in previous bibliography [38; 52] in hybrid mortars. The obtained DTA results can be seen in Figure 6. All the studied mortars show an endothermic peak of about 100-150 \({}^{\circ}\)C, which corresponds to the loss of free water or partial dehydration of hydrated aluminosilicates (mainly CSH decomposition [55] and/or Aft phases) [56]. OPC also shows another endothermic peak at 400-500 \({}^{\circ}\)C, which corresponds to CH decomposition [56]. This peak has much lower intensity for hybrid mortars, confirming their lower alkaline reserve, which is clearly related to their reduced initial CaO content and its consumption in the formation of hydration products other than portlandite. An important fact that can be observed in Figure 6 is the presence of two endothermic peaks for HN between 250 \({}^{\circ}\)C and 400 \({}^{\circ}\)C. Both peaks are related to the formation of hydrolatolites. Hydrolatolites are laminar double hydroxides (LDH), and the NO\({}_{3}\)-AFm that can be formed with the addition of nitrates to cement has this structure [56]. The endothermic peak at approximately 325-330 \({}^{\circ}\)C can be related to decomposition of interlayer carbonates of hydrolatolites [57], while the other peak is related to the dihydroxylation of lamellar hydroxides (e.g., NO\({}_{3}\)/NO\({}_{2}\)-AFm) [56].
## 2 Methods of Atmospheric Density Inversion for Low Earth
### Comparison of Three Inversion Methods
Its gravity gradiometer was installed at the center of mass and consisted of three pairs of highly sensitive accelerometers [34]. These acceleration components need to be calibrated before they can be used. After calibrating the scale factor and deviation factor of the accelerometer data in the tangential direction of the satellite, as well as the temperature factor of the accelerometer carrier, the atmospheric density can be calculated. Equation (10) describes the linear calibration of the accelerometer data using precision track data. Atmospheric density can be calculated by calibrating the accelerometer data in the tangential direction of the satellite to the scale factor and bias factor of the sensor, the temperature factor of the accelerometer carrier, and so on (Equations (11)-(13)). \[a_{IFX}=Scale*a_{ACC}+Bias \tag{10}\]  
\[a_{IFX}=S(a_{ACC})+Bias+n \tag{11}\]  
\[a_{IFX}=Scale(T)*a_{ACC}+Bias(T)+n \tag{12}\]  
\[a_{NG}^{GPS}=Scale*a_{ACC}^{UNCAL}+Bias+Q*T(t+F)+\varepsilon \tag{13}\]  
\(a_{IFX}\) represents the equivalent non-conservative force acceleration inverted using precision orbital data, \(a_{ACC}\) represents accelerometer measurements, _Scale_ and _Bias_ represent scale and bias calibration factors, and \(T\) is used to correct temperature. The onboard accelerometer measures instantaneous acceleration, with high accuracy but slow update speed. The calibrated accelerometer data are still blank most of the time and the accelerometer calibration data are only provided for a few months; for example, Swarm satellite publishes acceleration data for March of a certain year. So there is an urgent need to develop new atmospheric density products based on Low Earth Orbit satellite data to provide support for the development of Low Earth Orbit satellites [35]. Under the aforementioned methodologies for determining atmospheric density, which differ from those outlined in Table 1, the corresponding inversion process is illustrated in Figure 2.  
Figure 2: Schematic diagram of inversion of orbit data and accelerometer data. The orbit data adopts the specific steps of semi-long-axis attenuation method and energy conservation method, and the accelerometer data are calibrated and inverted.