esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### MSF Suppressed LPS-Induced Liver Damage  
Next, we examined whether MSF inhibited LPS-induced liver damage in mice. LPS injection triggered an increase in TNF-\(\alpha\) and IL-1\(\beta\) in the liver; however, pre-injection with MSF lowered the expression of these cytokines. The levels of TNF-\(\alpha\) and IL-1\(\beta\) were lower in the MSF group than in the K8 lysates group. The levels of TNF-\(\alpha\) and IL-1\(\beta\) in the _F. glaberrima_ group were similar to those in the LPS group (Figure 8A,B). Similar to the results of in vitro experiments, pre-injection with MSF and re-injection with LPS increased protein expression of the negative regulator A20 (Figure 8C). Immunohistochemistry (IHC) with an antibody for the mouse macrophage receptor F4/80 was performed to evaluate macrophage infiltration. The black part in Figure 8D represents F4/80, and Figure 7E represents the relative intensity of the F4/80 area in Figure 8E. An 80% reduction in macrophage infiltration was observed in mice pre-injected with MSF compared with mice that only received LPS injection. Likewise, macrophage infiltration in the MSF group was lower than that in the K8 lysate group. In an additional study, hepatocyte swelling was observed after LPS injection. However, in mice with MSF pre-injection, the cells were less swollen compared with those in the LPS and K8 lysates groups (Figure 8F). These  
Figure 7: MSF reduced LPS-induced systemic inflammation. BALB/c mice (7 weeks old, 4 mice/group) serum was collected 8 h after i.p. injection of 20 mg/kg LPS following pre-injection of PBS, 400 mg/mouse MSE, 400 mg/mouse K8 lysates, or 5 mg/mouse of _F. glaberrima_ for 24 h. TNF-\(\alpha\) (**A**) and IL-1\(\beta\) (**B**) levels in the serum were measured by ELISA. Serum AST (**C**) and ALT (**D**) levels were observed. Statistical analyses were conducted with an unpaired two-tailed _t_-test. *_p_ < 0.05; **_p_ < 0.01; ***_p_ < 0.001.  
results suggest that MSF reduced the lethality of endotoxin shock in mice by inhibiting inflammatory responses, exhibiting a better preventive effect than K8 lysates.
## 2 Materials and Methods
### Pre-Processing of Change Detection Results
#### 2.3.2 Object-Level Mean Confidence Score Calculation  
The bi-temporal images are firstly processed by the deep CNN change detection model. Figure 2(a) shows the pixel-level probability distribution map output by the Softmax classifier, with different grayscale values representing different change probabilities. The greater the brightness, the greater the probability of change in that pixel. Thereafter, a certain probability threshold is determined to obtain the initial result of the automatically detected change parcel in Figure 2(b).  
To facilitate the subsequent grading and filtering of the automatically extracted parcels, a confidence score is calculated for each closed parcel as a unit. A region-growing algorithm uses pixels with non-zero values as seed points to perform neighborhood expansion across the whole image. The average probabilistic value of pixels in each parcel is calculated as the confidence score of the parcel (Figure 2(c)).
## 3 Experimental Results
### Stress and Strain Curves  
Taking specimen RS-IS6-60 (Figure 13) as an example, the entire section of the steel pipe was compressed at the initial stage of loading, producing the negative longitudinal strain \(\varepsilon_{v}\) and the positive hoop strain \(\varepsilon_{h}\) with \(\varepsilon_{v}/\varepsilon_{h}\) equivalent to Poisson's ratio of steel. Figure 14 indicates that the transverse deformation coefficient \(\varepsilon_{v}/\varepsilon_{h}\) was slightly larger than Poisson's ratio of the steel, which illustrates that the steel pipe was not in an absolute uniaxial compression state, and the bond between the steel and RPC was relatively firm.  
The initial pressure made the steel enter the elastoplastic state earlier, and the specimen bent toward survey points 1 and 2 so that the longitudinal strain of these two points began to slowly change from the compression state to the tension state, while the hoop tensile strain changed in the opposite direction. Survey points 3 and 4 were still in the longitudinal compressive and hoop tensile states, and the \(\varepsilon_{v}/\varepsilon_{h}\) began to increase at this point. The hoop strain accounted for the main part, and the constraint effect of the steel pipe on the RPC was strengthened. The strain at survey points 1 and 2 was still in longitudinal  
Figure 13: Relationship of \(N_{e}\) to longitudinal and hoop strain (RS\(-\)IS6\(-\)60).  
compressive and hoop tension state at the peak load, but the bending degree of the specimen towards survey points 1 and 2 increased rapidly after that. Finally, the longitudinal strain at measuring points 1 and 2 was converted to tensile strain, and the hoop strain was converted to compressive strain. The specimen was then unstable in the directions of measuring points 1 and 2.
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 2 Geodetic and Satellites Input Data  
### Continuous GPS Stations REFUIGIO GREVE and GORRA BLANCA: Field Activities  
The continuous GPS stations are part of a local network located in the high plateau at the northern zone of the SPI. The GRCS station is placed in the accumulation area (see Figure 1) at 1438 m a.s.l., and it was installed on a runatak. A Trimble receiver model NetR9 and a Trimble Zephyr geodetic antenna (TRM41249.00), without a radome was used. The system was powered by a solar panel installed and mounted on a 1.7 m steel pole attached to the rock. On the other hand, the GBCS station was located on the edge of the high plateau, in a rocky outcrop at approximately 1600 m a.s.l. (see Figure 1). A Trimble receiver model NetRS and a Trimble Zephyr geodetic antenna (TRM41249.00) without a radome was used. A similar solar panel was installed and mounted on a 1.7 m steel pole similar to the GRCS. Both stations were configured with a logging interval of 15 s and elevation mask of 5\({}^{\circ}\).  
In total, the GRCS recorded 378 discontinuous days from 25 October 2015 to 18 June 2018 when the station ceased operation due to low battery charge and damage to the solar panel due to strong winds. GBCS recorded in total 911 days from 2015 to November 2020 (see Table 1) with some interruptions due to energy failures. These data collection interruptions were a consequence of the extreme weather conditions prevailing in the area.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 4 Challenges and Development Trends in Reflector Antenna-Based SAR Technology
### Risks for Reflector Antenna-Based SAR
#### 4.3.3 Risk Mitigation Measures for Future Systems  
To address these risks, the future development of reflector antenna-based spaceborne SARs should focus on the following:  
(1) Refined Ground Verification: Investing in more advanced ground simulation test facilities (e.g., suspension systems, air-bearing tables) to better replicate the space environment for rigorous testing and validation of deployment mechanism reliability, post-deployment surface accuracy stability under thermal loads, and thermal control performance.  
(2) Distributed Feed Architectures: Actively adopting multi-beam feeds or more advanced phased array feed (PAF) technologies (as discussed in Sections 3.2.2 and 3.2.3 of the manuscript). Distributing the total transmit power across multiple feed elements significantly reduces the power level per channel, thereby fundamentally mitigating thermal stress and multipactor risk (e.g., as planned for the HJ-2-E/F satellites). Furthermore, PAF offers flexible beam control capabilities (as mentioned in Section 4.1.1 DBF and Section 4.1.3 SweepSAR), which can help compensate for potential reflector surface deformations.
###### Abstract  
Background: Understanding the anatomy of the incisive canal is crucial for effective diagnosis and treatment planning in clinical orthodontics. This is because, during orthodontic tooth movement, there is a risk of contact between the roots of the upper central incisors and the incisive canal. Objective: The aim of this study was to assess the anatomical variability of the incisive canal using cone beam computed tomography (CBCT), as well as to evaluate its correlation with age, sex, and the position of the maxillary central incisors. There are only a few studies on this topic. Materials and methods: We analysed CBCT data from 67 patients aged from 13 to 49 years. This study was conducted at the Wroclaw Medical University. Measurements were performed twice by two independent researchers, and intra-observer error and correlation were calculated. The mean difference between the first and second observations and between observers was also assessed. We examined the dimensions of the incisive canal and its relationship to the roots of the upper central incisors in relation to age and gender. Results: Our study results revealed a significant correlation between the width and length of the incisive canal. Males exhibited a significantly greater canal length at the lowest point of the incisive canal on the palatal wall. Additionally, males had wider canals compared to females. The analysis of canal width and distance between the most mesial point of the root and the line passing through the most anterior point of the incisive canal showed a negative correlation in all age groups of men. The analysis of incisal inclination and incisal canal inclination showed a very strong relationship, especially in the age group of 13 to 20 years. Several potential risk groups of contact between the roots of central incisors and the incisive canal have been identified based on their structure and the planned incisors' orthodontic movement. Conclusions and implications: Knowledge of the anatomy of the incisive canal and the use of 3D imaging in high-risk patients can prevent resorption of the incisor root by considering the individual anatomical conditions of the patient when planning orthodontic tooth movement. We recommend performing a CBCT scan before starting orthodontic treatment in the case of moderate and significant retraction of the incisors, or a significant change in their inclination due to the wide anatomical diversity of the incisive canal, especially in adult patients.  
anatomy; CBCT; root resorption; incisive canal; nasopalatine canal; retraction; radiology; orthodontic treatment; orthodontic diagnosis; orthodontic treatment planning
# EEG-Based Attention Classification for Enhanced Learning Experience
## 2 Materials and Methods
### Noise Removal and Artifact Correction  
EEG preprocessing was conducted using the EEGLAB toolbox (version 2022.1) implemented in MATLAB R2022a, following standardized pipelines for artifact removal and signal enhancement.  
* **Filtering:** A 4 th-order Butterworth bandpass filter (1-40 Hz) was applied to the EEG signals to retain relevant frequency components while removing low-frequency drifts and high-frequency noise. Additionally, a 50 Hz notch filter was used to suppress power line interference.  
Figure 4: EEG signal from channel P3 during high attention (HA) and low attention (LA) conditions, after denoising.  
Figure 3: Preprocessing steps, feature extraction, feature reduction, and feature classification pipeline used in this study.  
* **Bad Channel Detection:** Noisy EEG channels were identified through a combination of visual inspection and automated thresholding, using an absolute amplitude criterion of \(\pm 80\) uV.
* **Independent Component Analysis (ICA):** ICA decomposition was performed using the runica algorithm in EEGLAB. The number of components was set equal to the number of EEG channels (after bad channel exclusion). Components with high correlation (\(r>0.8\)) to vertical and horizontal EOG signals were removed to eliminate ocular artifacts, following the guidelines of [PERSON] et al. (2007) [PERSON] et al. (2007).
* **Channel Interpolation:** Following ICA, bad channels were interpolated using spherical spline interpolation in EEGLAB. This order was chosen to preserve the full rank of the data, ensuring accurate component separation, as recommended by [PERSON] et al. (2023) [PERSON] et al. (2023) [PERSON] et al. (2023).
* **Baseline Correction:** Baseline correction was applied using the \(-100\) to \(0\) ms prestimulus interval relative to stimulus onset, to eliminate low-frequency drifts and enable consistent epoch comparisons.
* **Epoch Rejection:** EEG epochs exceeding an absolute amplitude of \(\pm 100\) uV were automatically rejected. This step removed approximately 5% of the total data.
* **Re-referencing:** All EEG signals were re-referenced to the common average reference to reduce spatial bias and enhance overall signal quality.
## 4 Discussion
By reconstructing B-mode images based on the spatial position of surface echo points, this requirement was achieved [23]. The major challenge of ultrasound technology is to develop a probe with sufficient accuracy and efficacy to scan intraoral tissues. The ultrasound scanner used in this study is a latest generation scanner developed specifically for this purpose. High lateral and axialresolution and accuracy are achieved by using a focused high-frequency single element transducer [33]. Furthermore, in order to increase the depth of field, a synthetic aperture focusing technique was used [34; 35]. Whereas the intraoral scanner showed the lowest measurement error, the highest deviation from the reference dataset was shown in laboratory scans. This deviation may possibly be related to the application of powder prior to scanning. However, powder is not mandatory for all laboratory scanners. In this study, the powder was applied to facilitate the detection of the acrylic resin surface since the material can show high reflectivity [36]. Likewise, these findings should be carefully interpreted since they do not represent a clinical situation. With regard to ultrasound, the main challenge is to maintain uniformity of the scan since the technique is sensitive to environmental variations, such as the variation on tissue surfaces [37]. Another limitation of this study was the small sample size. Although clinical features must be taken into consideration in future studies, ultrasound accuracy under different conditions was beyond the scope of this study. In this regard, some factors must be considered when performing ultrasound scanning. First, during the scanning, movements must be suppressed. For this purpose, an individual tray was developed containing a fixation structure that had exactly the same dimensions as those of the ultrasound probe. Second, the high difference in impedance between air and tissue was avoided with the use of an acoustic coupling. This was possible with the application of an ultrasound gel on the scanned site. This step has to be accurately performed to avoid the formation of bubbles, which may hamper the scanning process [35]. The time required for ultrasound scanning was two minutes, which can be considered acceptable for scanning one single dental implant. However, these findings are still not appropriate for the scanning of multiple dental implants. As recommendations for further studies, the simulation of clinical conditions should be investigated.
## 3 Results and Discussion
### Properties of Slag Cements (SCs) manufactured by 40% Slag
#### 3.3.4 Grinding Time  
Concerning the grinding time of the slag cements, it was generally observed to be higher than that one required for the OPC and the corresponding CPCs. Exceptions are the mixtures of slag 3-2a and 4-2a, for which reduction in grinding time was observed in case of 40% slag content. It is noticeable that these slags exhibited the highest grinding time among the cements with 20% slag. In addition, for 4-2b slag, which presented a relatively high grinding time in case of 20%, in the case of 40% the difference is only two min. The combination of these results indicates that when the content of slag of relatively high hardness increases in the mixture, a \"self-grinding\" phenomenon takes place, where the slag itself contributes to its grinding, thereby reducing the time required in order for the corresponding cement mixture to achieve the same Blaine. This observation is particularly important in terms of energy consumption.
## 5 Discussion
### Comprehensive Evaluation of Microscope Sealing Ability  
Although gypsum-salt rock has good hydrocarbon sealing ability, the areas where gypsum-salt rocks exist are not necessarily favorable areas for oil and gas sealing. As mentioned above, their sealing properties are influenced by factors such as their development scale, thickness, continuity, tectonic activity, and the stage of diagenesis. To better evaluate the sealing performance of the Middle Cambrian cap rock in the platform area of the Tarim Basin, a comprehensive quantitative evaluation method for the sealing performance of the cap rock has been established (Table 1).  
The comprehensive evaluation of the Cambrian caprock in each well can be calculated through the formula \(\text{E}=\sum a_{i}\times b_{i}\), where E is the comprehensive evaluation weight of caprocks, \(a_{i}\) is the evaluation parameter of \(i\), and \(b_{i}\) is the evaluation parameter weight of \(i\). When the comprehensive evaluation weight of the caprock is 3-4, 2-3, 1-2, and 0-1, the corresponding quality is very good, good, fair and poor, respectively. The comprehensive caprock evaluation of 14 wells is shown in Table 2. However, when faults break through the stratum, the comprehensive evaluation result will be poor even if the other indexes are good.  
As shown in Table 2, most indexes of well ST 1 and well F 1 are good. However, due to the presence of faults that penetrate the caprock, the caprock in both wells does not have sealing ability. Also, the lithology of the Cambrian strata in well ST 1 is mainly gypsum. When buried at a shallower depth, the gypsum will transform into anhydrite, leading to an increase in porosity and a decrease in sealing ability [43]. The comprehensive evaluation of well TC 1 is average, while the evaluation of wells LT 1, \(Z\) 4, T 1, H 4 and BD 4 is very good, and the evaluation of other wells is good.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Parameter** & **Weight** & **3–4** & **2–3** & **1–2** & **0–1** \\ \hline Caprock Lithology & 0.2 & Gypsum-salt Rock & Argillaceous & Argillaceous & Dolomite \\ Cumulative Thickness of & & & Mudstone & Dolomite & \\ Caprock/m & 0.15 & \(>\)300 & 150–300 & 50–150 & \textless{}50 \\ Single-Layer Thickness & & & & & \\ of Caprock & & & & & \\ The ratio of caprock to & & & & & \\ stratum thickness/\% & & & & & \\ Degree of Fracture & & & & & \\ Destruction & & & & & \\ Continuity & 0.1 & Completely & Continuous & Continuous & Discontinuous \\ Diagenesis Evolution & & & & & \\ (Burial Depth/m) & 0.1 & Plastic (\textgreater{}3300) & Brittle–Plastic & Plastic–Brittle & Brittle (\textless{}650\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comprehensive quantitative evaluation table of sealing ability of Middle Cambrian caprock in platform basin area of Tarim Basin.
## 4 Performance Evaluation
### Queue Overflow  
Figures 11 and 12 show the results in terms of packet loss due to the queue overflow. We observe high rate of queue overflow in HMC-MAC protocol because of the limited capacity of the packet queues especially on the nodes situated near the sink. Indeed, HMC-MAC is able to route packets with much less loss on the medium than the other protocols. This leads to accumulation of packets in the nodes that are situated near the sink and that have a high number of descendants. These nodes are thus unable to transmit these accumulated traffic and suffer from high rates of queue overflow. This phenomena is also known as the funneling effect.  
Figure 11: Packet loss due to queue overflow for periodic traffic.  
Figure 10: Delivery ratio at the sink for burst traffic.
# Optimized Unconventional Geometric Gates in Superconducting Circuits
## Appendix D DRAG Correction  
We here give the details of the calculation of the DRAG correction waveform in Equation (16). In fact, because the coupling of higher-excited states is not negligible, we cannot get the ideal two-level model. Here, we consider the influence of the second excited state, which is the main leakage source due to the anharmonicity. Therefore, we apply the DRAG technology to suppress the information leakage. To this end the Hamiltonian describing the three-level anharmonic oscillator is written as  
\[H(t)=\frac{1}{2}\mathbf{B}(t)\cdot\mathbf{S}+\delta\left|2\right\rangle \left\langle 2\right| \tag{12}\]  
where \(\delta\) is an anharmonic parameter. The vector operator \(\mathbf{S}\) is given by  
\[S_{x} =\sum_{0,\mathbf{I}}\sqrt{n+1}(\left|n+1\right\rangle\left\langle n \right|+h.c)\] \[S_{y} =\sum_{0,\mathbf{I}}\sqrt{n+1}(i\left|n+1\right\rangle\left\langle n \right|+h.c)\] \[S_{z} =\sum_{0,\mathbf{I},2}(1-2n)\left|n\right\rangle\left\langle n\right| \tag{13}\]  
The vector \(\mathbf{B}=\mathbf{B}_{0}+\mathbf{B}_{d}\) is the total controlled microwave field, where \(\mathbf{B}_{0}=(B_{x},B_{y},B_{z})\) and \(\mathbf{B}_{d}=(B_{d,x},B_{d,y},B_{dz})\) are the initial microwave field and the DRAG correction term. The correction term is written as  
\[B_{d,x} =\frac{1}{2\delta}\big{[}\hat{B}_{y}(t)-B_{z}(t)B_{x}(t)\big{]}\] \[B_{d,y} =-\frac{1}{2\delta}\big{[}\hat{B}_{x}(t)+B_{z}(t)B_{y}(t)\big{]}\] \[B_{d,z} =0 \tag{14}\]  
According to the Hamiltonian Equation (15), we have \(\mathbf{B}_{0}=(\Omega(t)\cos\mu(t),\Omega(t)\sin\mu(t),\\ -\Delta)\) and \(\delta=\alpha^{\prime}\). We obtain that  
\[B_{d,x} =-\frac{1}{2\alpha^{\prime}}\big{[}\Omega\sin\mu+\Omega\dot{\mu} \cos\mu+\Delta\Omega\cos\mu\big{]}\] \[B_{d,y} =\frac{1}{2\alpha^{\prime}}\big{[}\Omega\cos\mu-\Omega\dot{\mu} \sin\mu-\Delta\Omega\sin\mu\big{]}\] \[B_{d,z} =0 \tag{15}\]Accordingly, the corrected Hamiltonian is  
\[H(t) =\frac{1}{2}\{\Delta(t)(\left|1\right\rangle\left\langle 1 \right|-\left|0\right\rangle\left\langle 0\right|+3\left|2\right\rangle \left\langle 2\right|)\}-\alpha^{\prime}\left|2\right\rangle\left\langle 2\right| \tag{10}\] \[+\frac{1}{2}\{\Omega_{D}(t)e^{-i\mu(t)}(\left|0\right\rangle \left\langle 1\right|+\sqrt{2}\left|1\right\rangle\left\langle 2\right|)+\text{H.c}\}\]  
and the corrected Rabi frequency is written as  
\[\Omega_{D}(t)=\Omega(t)-\{i\Omega(t)+[\dot{\mu}(t)+\Delta(t)]\Omega(t)\}/2 \alpha^{\prime} \tag{11}\]