esa-sceva/satcom-chunk-collection huggingface dataset:
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 2 Methodology
### Deployment Protection Methods
#### 2.3.2 Interference Mitigation Methods
In the antenna pattern (Cartesian coordinates) of the \(16\)\(\times\)\(16\) beamforming antenna, assuming the interference threshold is \(-10\) dB (the red line), the angles above the interference threshold need to be shut off. Figure 10(b) illustrates the angles that need to be shut off in the polar coordinate system. The angle shutoff is the sum of all the angles that exceed the interference threshold. It represents the total angular range that needs to be shut off in order to mitigate interference. The angle shutoff can be obtained by summing up the individual angles that exceed the interference threshold within the specified range. By calculating the angle shutoff, we can determine the extent of interference mitigation required to minimize the impact on radio altimeters.
## 5 Experimental Results
### Comparing the Behavior of the Specimens  
Figure 7 illustrates the hysteresis curves of the tested models. As shown, no degradation occurred in the M_P_St and S_P_St specimens up to the ultimate displacement. However, in the M_P model, at a displacement of around 5 mm, a decrease in strength could be noticed. In this model, stiffness also decreased at a displacement of 18 mm.  
Figure 6: Tearing of the plates.  
Comparing the hysteresis curves of specimens, Figure 8 reveals that M_P had lower strength and energy_dissipating capability than other models. However, a comparison of S_P_St and M_P_St showed that, by connecting the cross plate to the web plate, higher strength and lower ultimate displacement was achieved in contrast to specimens without connected plates.  
Figure 8: Comparing the test models.  
Figure 7: Damper: (a) M_P; (b) M_P_St (c) S_P_St.  
The ultimate strength and stiffness of the specimens are listed in Table 2. Results indicate that, by connecting the cross stiffeners to the web plate, the ultimate strength and stiffness increased considerably. Comparing the results of the specimen with (no connected to web plate) and without cross_flexural plates confirmed that the cross plate increased the ultimate strength by 2.65 and reduced the stiffness by 16%. The length of the web plate was more affected in terms of stiffness than thickness. Connecting the cross plate to the web plate improved the ultimate strength and stiffness by 84% and 3.9, respectively.  
Comparing the specimens with (not connected to web plate) and without a cross plate showed that cross plates considerably increase the ductility. Although connecting the cross plate to the web plate improved the ultimate strength and stiffness, it reduced the ductility by 2.25. It did not change the displacement corresponding to the yielding, but the maximum displacement was reduced by connecting the cross plate and web plates.  
Some of the most important parameters in all damping systems is damping are the damping constant and damping ratio. Thus, in this section, the damping ratio of the tested damper is discussed.  
The damping ratio \(D_{p}\) can be estimated on the basis of the hysteresis loop generated due to cyclic loading [51], where \(D_{p}\) is defined as follows:  
\[D_{p}=\frac{\Delta W}{4\pi W}, \tag{25}\]  
where \(\Delta W\) and \(W\) are the dissipated energy and the total energy, respectively, during a loading cycle. The ratio between \(\Delta W\) and \(W\) can be calculated as the ratio between the loop and triangle areas generated by the loading and unloading curves [52]. It is noteworthy that the dissipated area depends mainly on the plastic strain that remains after each loading cycle, which indicates how plastic properties of the subgrade soil can be used to mitigate the effect of cyclic excitation.  
The calculated damping rations for M_P, M_P_St, and S_P_St specimens were 0.26, 0.28, and 0.48, respectively. S_P_St presented a damping ratio around 84% and 71% greater than the M_P and M_P_St models, respectively.
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 4 Transparent Armor Nano-Ceramics
### MgAl\({}_{2}\)O\({}_{4}\)
The optical transmittance of the MgAl2O4 transparent nano-ceramics prepared by the SPS method reached 78% in the 400-800 nm band. In 2020, [PERSON] et al. [81] successfully prepared MgAl2O4 transparent nano-ceramics by air pre-sintering and HIP methods. They found that a small amount of CaO (0.1 wt.%) was very effective for the densification of ceramics. They also found that the relative density of MgAl2O4 transparent nano-ceramics changed from 86.3% to 99.4% when the pre-fired temperature was increased from 1450 \({}^{\circ}\)C  
Figure 11: Sample particle size distribution and XRD pattern; (**a**) FEG-SEM micrograph, (**b**) XRD pattern, (**c**) TEM micrograph, and (**d**) particle size distribution of as-received spinel powder SPI-P1 [77]. Reprinted with permission from Ref. [77]. Copyright 2021, copyright ELSEVIER. to 1550 \({}^{\circ}\)C. Optical experimental results demonstrated that the prepared large transparent nano-ceramics, with an average grain size of 1500 nm, exhibited high transmittances of 86.3% and 82.5% at 1100 nm and 600 nm, respectively. In 2020, [PERSON] et al. [82] used high-purity spinel nano powders, with a particle size of 55 nm and a specific surface area of 30 m\({}^{2}\)/g, to successfully prepare MgAl\({}_{2}\)O\({}_{4}\) transparent nano-ceramics by microwave sintering and HIP methods. They found that microwave-sintered MgAl\({}_{2}\)O\({}_{4}\) transparent nano-ceramics could achieve higher densities at lower temperatures and in a shorter time than conventional pressureless sintering. Experimental results showed that the in-line transmittances of MgAl\({}_{2}\)O\({}_{4}\) transparent nano-ceramics 1.5 mm thick that were obtained by microwave sintering (1400 \({}^{\circ}\)C, 80 min) and HIP (1650 \({}^{\circ}\)C, 180 MPa, 3 h) were 86.4% and 80.2% at 1064 nm and 400 nm, respectively. Therefore, MgAl\({}_{2}\)O\({}_{4}\) transparent nano-ceramics have excellent light transmission ability [83].
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 4 Experiments and Discussions
### Experimental Results
#### 4.4.5 Confusion Matrix Comparison  
Figure 8 shows the comparison of the proposed incremental learning method with the United-NME and iCaRL-NME methods confusion matrix on the test dataset, and this experiment is performed under a 5-phases incremental process. As shown in Figure 8, the iCaRL-NME method is greatly affected by the imbalance between the old and new classes, and the classification bias is more serious. It is more likely to classify the input test samples as the new classes, and the overall accuracy is poor. The United-NME method is able to better alleviate the class imbalance due to its use of cosine normalization, which solves this problem to some extent. In general, this paper uses knowledge distillation loss and class separation loss to reduce forgetting and confusion. It also corrects the bias of the fully connected layer, which better solves the bias problem caused by classes imbalance. As shown in Figure 8c, the proposed method identifies the old classes better, avoids the problems of catastrophic forgetting and classification confusion well, and solves the problem of class imbalance better.
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
## 3 Vehicular Communication
### Cellular Network  
In contrast to the decentralized approach in ad hoc networks, each cell within a cellular network is governed by a base station. This means that communication is only possible once infrastructure is available--at least in the classical sense, but more on that shortly. Limited cell size has the consequence that moving nodes may move outside the covered area, i.e., the cell. The new area may be part of a new cell or may not be covered at all. In the first case, cellular handover needs to be performed, which usually leads to delays of approximately 50 ms [27], where the node is not accessible. In the second case, communication is no longer possible. Connection can be established again upon re-entry into a cell. Communication within a cellular network needs to obey strict rules. Channels are divided into small resource blocks that are assigned to nodes that ask for communication permission. No node is allowed to send without permission. This increases the reliability enormously as packet collisions are mostly avoided and further, allows good network utilization [18]. Still, network coverage may not be always given.  
Typically, communication takes place over long distances--the radius of a macro cell can be up to 25 km [28], although in vehicular environments, most of the time, communication partners are located at short distances. Additionally, communication always runs via infrastructure nodes resulting in higher latency compared to ad hoc networks unable to meet the strict delay requirements for safety applications. A transmitting node sends its data as unicast to the base station. High vehicle density may overload this uplink channel. Once received by the base station, it either unicasts, multicasts (geocasts), or broadcasts the message for receiving nodes. Due to the size of cells, broadcasting may result in the reception of many irrelevant messages that need to be further processed by each vehicle. Multicasting comes along with high control signaling overhead and high latency. Once unicasting, e.g., to relevant vehicles only, the downlink channel becomes a bottleneck that already has a few vehicles [29].  
To overcome latency issues and avoid overloading the up- and downlink channels, cellular V2X (C-V2X) was added to the Long-Term Evolution (LTE) standard. C-V2X is specified in Release 14 in 2017 and enables two modes for direct communication between nodes in- and outside of network coverage: Mode-3 and Mode-4. Mode-3 targets direct communication under coverage and supports radio resource management. The latter is not offered by Mode-4, but this mode does not require network coverage [19].
# Comparison of Functional Connectivity Analysis Methods in Alzheimer's Disease
## 3 Functional Connectivity Analysis Methods
### Model/Seed Based Methods
#### 3.1.1 Cross-Correlation Analysis (CCA)  
This approach, first presented in 1999, was proposed using the BOLD time series to detect a correlation across each pair of functionally connected brain regions [21]. For a BOLD fMRI time course \(f_{x}\)_(_i_) with a seed \(f_{y}\)_(_i_) (referenced time course), the correlation at lag \(\mu\) can be calculated using CCA using equation [22]:  
\[\mu=\frac{Cov_{x,y}(\mu)}{\sqrt{Var(x)\ X\ Var(y)}} \tag{2}\]  
Here, \(Var(x)\) and \(Var(y)\) is the variance of \(f_{x}\)_(_i_) and \(f_{y}\)_(_i_), respectively.  
Cross covariance \(Cov_{x,y}(\mu)\) can be calculated using mean values (\(E\)) of \(f_{x}(i)\) and \(f_{y}(i)\) respectively using the following equation [23]:  
\[Cov_{x,y}(\mu)=E\big{\{}(f_{x}(i)E(f_{x}))\times\big{(}f_{y}(k)-E\big{(}f_{y} \big{)})\big{\}} \tag{3}\]  
The conceptual explanation of this method is that if one region of the brain is functionally-linked to a specific seed, there must be a correlation between their BOLD time courses.
# Development and Testing of a Railway Bridge Weigh-in-Motion System
## 2 RB-WIM Algorithm  
Most operational B-WIM algorithms work on the assumption of pseudo-static conditions (without considering the dynamic effects), similar to that proposed by [PERSON] [21]. This is built upon the observation that, during the passage of a truck, the bridge oscillates about a static response. The same concept is adopted here, using the well-established B-WIM approach, but allowing for non-constant train speed during the bridge crossing event. Assuming strain transducers attached to each bridge beam or strip of a slab at \(G\) measurement points, the average measured strain at time, \(t\), \(\overline{\epsilon}(t)\), can be found by averaging the individual values:  
\[\overline{\epsilon}\big{(}t_{j}\big{)}=\frac{1}{G}\sum_{i}^{G}\varepsilon_{i} (t)=C_{F}\sum_{i}^{G}\varepsilon_{i}(t) \tag{1}\]  
where \(\varepsilon_{i}(t)\) is the strain in the \(i\)th girder or section of slab at time \(t\), and \(C_{F}\) is a calibration factor. The calibration factor \(C_{F}\) can also incorporate strain transducer factors relating strain to voltage and is obtained experimentally by correlating the B-WIM results for some vehicles with their true axle loads, as measured on static scales.  
In the B-WIM algorithm, the number of unknowns for each vehicle is equal to the number of axles, \(N\), and these are determined by at least \(N\) different measurements recorded for different longitudinal positions of the vehicle along the bridge. Setting up the equations requires the strain influence lines, \(I(x)\). The procedures to calculate a bridge influence line based on measurements are described in Section 2.2. Taking the origin at the peak of the influence line and letting the first axle arrive at this point at time zero, the first axle is at \(x=vt\) at time \(t\), where \(v\) is velocity. Hence, the \(i\)th axle is at \(x=v(t-t_{i})\) at time \(t\), where \(t_{i}\) is the time interval between the arrivals of the 1 st and \(i\)th axles. The B-WIM weighing challenge is then to minimise the sum of squares of differences between the measured strains of Equation (1) and the theoretical equivalents, given as the sum of contributions from each axle:  
\[\min\sum\left\{\overline{\epsilon}(t)-\sum_{i=1}^{N}A_{i}I[v(t-t_{i})]\right\} ^{2} \tag{2}\]  
where summation is over the number of scans (each corresponding to a different point in time), \(A_{i}\) is the weight of axle \(i\), \(N\) is the number of axles, and \(I(x)\) is the influence line value. With a scan rate of 512 samples per second and vehicle passage duration of the order of seconds, the number of equations is typically one or two orders of magnitude greater than the number of unknowns. This over-determinedsystem of equations is solved for \(A_{i}\), in the least-square sense, with the use of the singular value decomposition algorithm [32].
## 4 Discussion
### Evaluation of BiomedCLIP Sensitivity in scoliosis Detection Across All Images  
The results of the BiomedCLIP model showed an overall correlation between Cobb angles and the probabilities predicted by the model, with \(r=0.37\), indicating moderate agreement with actual clinical outcomes (\(p<0.001\)). Despite statistical significance, this result does not suggest high sensitivity of the model's predictions in all cases. When analyzing differences based on scoliosis stage, the correlation in mild cases was low (\(r=0.24\), \(p=0.118\)), indicating limited effectiveness of the model in detecting mild cases. Similarly, in the moderate stage, the correlation was weak (\(r=0.19\), \(p=0.077\)), and in the severe stage, although statistically significant, the correlation was relatively low (\(r=0.18\), \(p=0.041\)). These findings suggest that the model struggles to predict scoliosis in mild and moderate stages, challenging the assumption of its high sensitivity in all situations. In conclusion, Hypothesis H1 was not confirmed. The results indicate that BiomedCLIP does not achieve  
Figure 5: ROC curve analysis for scoliosis types prediction using the BiomedCLIP model with AUC and 95% confidence interval.  
high sensitivity in predicting the presence of scoliosis across all posturographic images. While the model shows moderate effectiveness in more advanced cases and in predicting scoliosis types, its performance is weaker for mild and moderate stages, as well as in distinguishing scoliosis types, emphasizing the need for further optimization of the model.  
The limitations in distinguishing scoliosis types highlight the necessity for improving training datasets and advanced image processing techniques to enhance the model's precision [27]. Studies suggest that the integration of diverse imaging data could enhance AI performance in clinical environments [28]. Despite these challenges, ongoing advancements in AI and imaging techniques may ultimately lead to more reliable diagnostic tools for scoliosis.
# An Unambiguous Delay-And-Multiply Acquisition Scheme for GPS L1C Signals
## 3 Results
### Results of the Performance Analysis for the Proposed DM Acquisition Method
#### 3.3.3 Finding the Doppler Frequency  
In this section we obtain the Doppler frequency after finding the code phase as in Section 3.1.3. In Figure 7, after the code phase aligned, the multiplication of the received signal \(r(t)\) and \(g_{conv,P}(t)\) contains the Doppler frequency \(f_{D}\) as:  
\[\mathrm{r}(t)\cdot g_{conv,P}(t)\approx\pm\frac{1}{2}\sqrt{\frac{3}{2}}c\cdot \cos(2\pi f_{D}t). \tag{24}\]  
Thus we can obtain \(f_{D}\) using the FFT method. We use the received signal \(r(t)\) for 10 ms with 600,000 samples, i.e., \(M=600\),000 and multiply it with \(g_{conv,P}(t)\), and then average 1000 samples resulting in \(\overline{M}=600\) samples. The frequency resolution of the FFT is 100 Hz since 10 ms data is used. Figure 12 shows the FFT result from the 600 samples with Doppler frequencies, such as 1 kHz, 2 kHz, 3 kHz, and \(-1\) kHz. We can notice that the cases of \(f_{D}=1\) kHz and \(f_{D}=-1\) kHz show the same result because of cosine function in (24).  
Figure 11: \(\mathrm{P_{d}}\) with respect to \(\mathrm{C/N_{0}}\) and \(K\) for the DM method (\(\mathrm{P_{fa}}=0.001\)).  
Figure 12: _Cont._
## 3 Secretory Profile of Exosomes in Metabolic Tissues
### Pancreas  
The pancreas has mixed functions, encompassing both exocrine and endocrine roles. The exocrine role primarily comprises pancreatic acinar cells, which secrete digestive enzymes such as amylase, lipase, and proteases. Additionally, the ductal cells are responsible for bicarbonate secretion. The endocrine pancreas is composed mainly of Langerhans islets, which include alpha cells that release glucagon, delta cells that secrete somatostatin, PP cells responsible for pancreatic polypeptide secretion, and beta cells that produce insulin. The latter is of particular interest in this study due to its critical role in metabolic regulation [54; 61]. Mechanisms have been reported that help explain how exosomes secreted by various organs can affect the pancreas, specifically the Langerhans islets; however, there is limited information about how exosomes secreted by beta cells influence other tissues. There is evidence that exosomes secreted from beta cells can transport microRNAs such as miR-146a, miR-29a, that can modulate the immune response [55; 62]. Exosomes can also transport autoantigens such as GAD65, which activate T-lymphocytes and trigger an autoimmune response that can lead to type 1 diabetes [55; 63]. It has even been reported that exosomes secreted by pancreatic islets decrease apoptosis and promote angiogenesis, indicating a protective role in islet revascularization after transplantation in patients with type 1 diabetes mellitus [64].  
Considering the metabolic implication of this work, it is important to highlight the role of exosomes in the secretion of amylin (hIAPP), which is known to produce amyloid plaques in beta cells, inducing their apoptosis [57; 65]. According to a study conducted by Ribeiro D [58; 66], exosomes secreted from beta cells of healthy patients reduce the formation of amyloid plaques by hIAPP through a direct binding mechanism. This is in contrast to what occurs in exosomes from patients with DM2, where in the formation of these plaques is increased, suggesting that under physiological conditions these exosomes protect against the formation of amyloid plaques. In addition, evidence indicates that exosomes from hepatocytes can modulate beta-cell proliferation based on transplantation assays [59; 67]; however, no evidence of the inverse has been found.  
In addition, pancreatic beta cells (CbLs) can secrete exosomes loaded with miRNAs that can modulate the effect of insulin in metabolic organs such as the liver and skeletal muscle [25; 27]. Our group has characterized the metabolic effect of exosomes from beta cells in hepatocytes. For instance, our group analyzed the content of exosomes obtained from a rat CbL model stimulated with VLDL, and we reported the presence of HSP70 in the secreted exosomes (Personal communication, BCV-FMM-UABC).
# Ocean Wave Inversion Based on a Ku/Ka Dual-Band Airborne Interferometric Imaging Radar Altimeter
## 1 Introduction
A dual-band interferometric imaging radar altimeter (DInIRA) can address the above-mentioned shortcomings. In 2018, the \"Guanlan\" ocean science satellite project was proposed by the Pilot National Laboratory for Marine Science and Technology of China to more effectively observe ocean waves and sub-mesoscale ocean phenomena. Of note, the \"Guanlan\" satellite would take the two-side observation geometry with multiple Ku-band beams less affected by the atmosphere and multiple Ka-band beams insensitive to the ionosphere, which could achieve a wider swath and a higher altimetric accuracy [22] as compared with single-frequency radars. In this sense, [PERSON] et al., (2006) [23] compared the sea surface backscatter coefficients between the Ku and Ka bands for different incidence angles under special wind conditions, however, the results were not verified due to the lack of field observation data. [PERSON] et al., (2021) [24] also compared the dependence on vector winds and sea surface temperature in Ku and Ka bands, and showed that the Ka band had a greater dependence on vector winds and sea surface temperature than the Ku at the low-to-medium wind speed area. Since no field observation data were available and even few studies have been conducted on the simultaneous observation of ocean waves using Ku and Ka bands worldwide, the bulk of the related literature was merely based on simulations [25; 26]. The measurement mechanism of the InIRA has not been well established, and the performance of ocean wave inversion in Ku and Ka bands at small incidence angles is still unclear. As the airborne platform can verify the viability of InIRA technology in the ocean wave inversion, its application in theoretical assessments is crucial. This study conducted an airborne Ku/Ka dual-band simultaneous observation campaign on the sea area of Wappingkou, Rizhao, Shandong Province, China, and aimed to analyze the performance of ocean wave inversion in Ku and Ka bands at small incidence angles based on airborne data. The remainder of this paper is organized as follows: in Section 2, we introduce the airborne DInIRA campaign and then process the echo data; in Section 3, we show the inversion results of the ocean waves and verify the results using the in situ data; in Section 4, we discuss the reliability of the inversion results, and analyze the characteristics of the satellite-based DInIRA; and finally, in Section 5, we provide a summary of the study.