esa-sceva/satcom-chunk-collection huggingface dataset:
# MIMO Over-the-Air Computation for Distributed Estimation
## 5 Performance Evaluation
In particular, the MIMO-AirComp remarkably outperformed the TDMA scheme when the sensing noise \(\sigma_{\text{sn}}^{2}=0.9\) was high but the channel noise was low, \(\sigma_{\text{cn}}^{2}=0.5\) for \(K\geq 6\). On the other hand, under the harsh channel condition \(\sigma_{\text{cn}}^{2}=1\), both the average service delay and the RMSE of TDMA were prominent compared to that using the MIMO-AirComp for the limited number of nodes \(K\leq 10\). The TDMA reliably delivered the sensing data over the harsh wireless channel at the cost of the time delay to collect each measurement dependent on the node population. While the global estimation of Equation (11) reduced the impact of the sensing noise, the time delay still heavily affected the overall service delay and the RMSE. On the other hand, the MIMO-AirComp compensated for both the channel and measurement noise by aggregating the sensing data each time, while it was still vulnerable to the channel noise. The noise suppression gain of the MIMO-AirComp was strong under the favorable wireless condition (i.e., low amount of channel noise) and weak under the hostile one (i.e., high amount of channel noise). The adverse effects of the different wireless conditions diminished with the increased node population. Thus, the performance gain of the MIMO-AirComp compared to the TDMA increased when the network consisted of a large number of noisy measurements under the fair channel condition. Our framework efficiently computed the target multifunction relying on massive IoT networks, where the densely deployed wireless nodes are typically built on low-cost sensors. Next, we investigated how the MIMO configuration affected the service delay and the RMSE of both the MIMO-AirComp and TDMA for the limited number of beacon nodes. Figure 4 presents the average service delay and the RMSE using MIMO-AirComp and TDMA with different \(N_{\text{tx}}=N_{\text{rx}}=3,4,5\) for various amounts of channel noise \(\sigma_{\text{cn}}^{2}=0.1,\ldots,1\). We set \(\sigma_{\text{sn}}^{2}=0.6\) and \(K=6\). The TDMA scheme provided an almost flat service delay and RMSE independent of the channel noise and the MIMO configuration, since it already achieves extremely reliable performance even with \(N_{\text{tx}}=N_{\text{rx}}=3\). On the other hand, the average service delay and the RMSE using the MIMO-AirComp increased linearly with the channel noise. Adding more MIMO antennas \(N_{\text{tx}}=N_{\text{rx}}=4,5\) considerably reduced both the average service delay and the RMSE of the MIMO-AirComp by effectively compensating for the harsh channel condition.
## 5 Conclusions  
Targeted at the lack of ecological sensitivity assessment work in the Tumen River Basin in China, we selected 3 types of 11 factors closely related to the ecological sensitivity of China's Tumen River Basin, established an ecological sensitivity evaluation factor system, and used GIS technology combined with the AHP model to evaluate the ecological sensitivity of China's Tumen River Basin. Finally, we obtained the ecological sensitivity evaluation results of the Tumen River Basin and analyzed its spatial pattern. Here, we present the main conclusions of the research.  
The comprehensive ecological sensitivity levels of the Tumen River Basin include highly sensitive, moderately sensitive, and slightly sensitive areas, while there are no extremely sensitive or insensitive areas. The proportion of highly sensitive areas is very small, and their distribution scope is mainly concentrated in lakes, rivers, reservoirs, and other areas in the study area. The moderately sensitive area is the largest, accounting for 54.12% of the total area, and its distribution is concentrated in the middle of the Tumen River Basin. The slightly sensitive area is mainly distributed in the northern and southern mountainous areas of the study area.  
Among the various land resource types, cultivated land is dominated by moderately sensitive areas, woodland has a close proportion of slightly and moderately sensitive areas, grassland has 82.07% of moderately sensitive areas, construction land is extremely concentrated in moderately sensitive areas, and bare land has 78.78% of moderately sensitive areas, while water bodies are mostly distributed in highly sensitive areas, and the wetlands are all moderately sensitive.  
As the scope of human activities in the Tumen River Basin continues to expand and the intensity of development continues to increase, the threats to the ecological environment will intensify. For highly sensitive areas, we suggest that development should be strictly controlled, while ecological risk assessment should be performed and ecological risk monitoring and prevention efforts should be strengthened. The moderately sensitive areas and the slightly sensitive areas, with a certain resistance to disturbance and with ecological self-healing functions, can be developed appropriately, but attention should be paid to reasonable protection, and development should be carried out under the guidance of ecological protection.  
Formal analysis, visualization, and writing--original draft, H.F.; methodology, X.Z.; Investigation, writing--review and editing, Y.N.; software, D.Z.; supervision, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 42067065), the National Natural Science Foundation of China (Grant No. 42271314), and the Jilin Science and Technology Development Program (Grant No. 20210203138 SF).  
Not applicable.  
Data Availability Statement: Data are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 3 Results
### Larvated Eggs (LE)  
The concentrations of 40, 20, 10, 5, 2.5, 1.2, 0.6, and 0.3 mg/mL, as well as the factors of extract partition, plant, concentration, and extract interactions between partition-plant, plant-concentration, and extract partition-concentration were analyzed. It was necessary to transform the original data to the square root, and the Fisher test values and their significance at \(\alpha=0.05\) were shown for the comparison of transformed data. For the extract  
Figure 4: Plant–concentration effect on the percentage of morulated eggs.  
partition factor, significant differences were found (_p_ < 0.05) between the four extract partitions. The decreasing order of activity over the percentage of LE was DCM (20.6%), EtOAc (20.5%), W-EtOH (15.6%), and Hex. (12.5%). For the plant factor, _G. sepium_ and _P. dulce_ presented the highest values (19.4 and 17.8%, respectively) without significant differences (_p_ > 0.05). For the concentration factor, significant differences (_p_ < 0.05) were observed between most of the concentrations, except for 0.3 and 20 mg/mL. The highest percentages of LE (36.9, 33.4, 26, 4, and 18.1) were obtained in concentrations of 5, 2.5, 1.2, and 10 mg/mL, respectively (Figure 5). It was observed that the extract partitions of plants at lower concentrations produced a lower effect on hatching, and consequently, a higher percentage of larvae, contrary to what occurs at higher concentrations, where a greater effect on hatching produces a greater percentage by ME.  
Figure 6 shows the extract partition-plant interaction. The highest percentage of LE was obtained for the EtOAc and DCM extract partitions of _G. sepium_, (26.9% and 24.6, respectively), without significant differences between them (_p_ > 0.05). The immediately smaller effect with significant differences (_p_ < 0.05), was obtained by the EtOAc and DCM extract partitions of _L. leucocephala_ (20 and 16.6%, respectively) and the W-EtOH and DCM extract partitions of _P. dulce_ (19.6 and 18.7%, respectively), without significant differences between them (_p_ < 0.05).  
Figure 7 shows the plant-concentration interaction. _G. sepium_, _L. leucocephala_, and _P dulce_ did not present significant differences (_p_ > 0.05) at 40 mg/mL, which coincided with the highest percentages of ME at the same concentration. The LE percentages of _G. sepium_ and _L. leucocephala_ at 5 mg/mL and _P. dulce_ at 2.5 were the highest (39.3%, 36.6%, and 35.8), which translated into the points of greatest inhibition during the larval hatching.  
For the extract partition-concentration interaction, the highest percentages of LE (52.5%, 49.6%, 47.7%, and 47.5%) were obtained with the EtOAc extract partition at concentrations of 2.5 and 1.2 mg/mL, Hex. at 5 mg/mL, and DCM at 2.5 mg/mL, respectively. There were no significant differences (_p_ > 0.05) between the percentages obtained with the first two extract partitions; however, significant differences were observed between these and the remaining two (_p_ < 0.05).
## 4 Analysis of the Vibration Suppression Mechanism of the Deflector Plate
### Boundary Conditions and Meshing  
The computational domain of this study consisted of two parts: the internal region (2B long and 2D high) and the external region (15B long and 15D high), as shown in Figure 21. The model blockage rate in the computational domain was 0.5%, less than the allowable value of 5% [42; 43; 44; 45]. The internal region of the computational domain was divided by an unstructured mesh, and the external region grid was divided by a structured mesh [46; 47].  
Figure 21: The computational domain.  
Both the computational efficiency and the accuracy of the numerical analysis results were affected by the meshing quality, so the meshing needed to be appropriate. In order to calculate the displacement of the model in the x and y directions by using a dynamic mesh, an overlapping grid was used for all numerical analyses in this study. Figure 22 shows the mesh of the computational domain of the original section of the truss girder.  
When dividing the mesh, the value of y-plus was assumed to be 1, and the height of the boundary layer of mesh was calculated to be 0.05 mm according to the set wind speed, air density, and dynamic viscosity. The main girder section adopted an unstructured mesh and was divided into three parts. The upper and lower deck parts were set as UL, the web part was set as W, and the railing part was set as R. The maximum size of the mesh was set to 0.04 mm. For the parts of UL and W, the mesh size was set to 0.035 mm, the boundary layer was ten layers, and the linear growth rate was set to 1.1 [48]. For the R part, which had a smaller section size, the mesh size was set to 0.02 mm, the boundary layer was ten layers, and the linear growth rate was set to 1.1. The background mesh adopted a structured mesh, and the mesh size was close to the maximum size of the truss girder. In order to ensure the reliability of grid size selection, a mesh independence check was carried out, and the total number of elements was taken as 148,367.
## 3 Results and Discussion
### Effect of Varying Crude Oil Grades on Slug Characteristics
#### 3.2.4. Slug Frequency  
Figure 14 illustrates the effect of varying crude oil grades on slug frequency.  
The slug frequency increases when the crude oil density increases. Moreover, as the density of crude oil increases, the difference in slug frequency values decreases at different fluid and gas velocities. For instance, the slug frequency for very heavy crude oil fluctuates between 1.89 and 2 Hz for all examined superficial velocities.  
Figure 14: Effect of varying crude oil grades on slug frequency.  
Figure 13: Effect of varying crude oil grades on slug translational velocity.  
For light crude oil, the slug frequency is highly influenced by the superficial velocity of the gas and liquid phases, and the variation is more noticeable than that of heavy crude oil. When the gas and liquid superficial velocities are 0.7 and 0.93 m/s, respectively, the slug frequency for light crude oil is 1.17 Hz. As the gas and liquid superficial velocities increase to 2.44 and 1.0 m/s, respectively, the slug frequency noticeably decreases to 0.6 Hz. By maintaining the gas phase superficial velocity at 2.44 m/s and decreasing the crude oil superficial velocity to 0.86 m/s, the slug frequency further decreases to 0.53 Hz.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Neon Ion Irradiation  
[PERSON] et al. [92] also irradiated CVD-grown MoS\({}_{2}\) mono-layers with Ne\({}^{+}\) ions with an energy of 30 keV. With increasing Ne\({}^{+}\) ion doses, the \(A_{1g}\) and \(E_{2g}\) modes were quenched and broadened, indicating the growing disorder induced by the Ne\({}^{+}\) ions.
# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
###### Abstract
In the second stage, either the EfficientNet or ConvNeXt model is applied to classify ROIs as benign or malignant. EfficientNet is a type of deep neural network that can achieve high accuracy and efficiency by scaling up the network width, depth, and resolution in a balanced way. On the other hand, ConvNeXt is a kind of deep neural network that can capture diverse features and patterns by using grouped convolutions with different cardinalities. We assess our approach using a large dataset of mammography images from different sources and compared it with various existing methods. Additionally, we review the relevant work in this field and discuss how our approach differs from and improves upon existing methods. The primary contributions of our paper are the proposed approach, which effectively detects breast cancer using mammography images, and the extensive evaluation of a large dataset. * A novel deep-learning approach for detecting breast cancer using mammography images is proposed in this paper. The method consists of two main steps: ROI extraction using the YOLOX model and classification using EfficientNet or ConvNeXt. * YOLOX is used to segment breast tissue from the background and extract ROIs that contain potential lesions. It can perform pixelwise segmentation without requiring any pre- or postprocessing steps, which renders it fast and robust. * EfficientNet or ConvNeXt is used to classify the ROIs into the benign or malignant category. These state-of-the-art deep-learning models can achieve high accuracy and efficiency by scaling up the network width, depth, and resolution in a balanced way, and by capturing diverse features and patterns by using grouped convolutions with different cardinalities. * Extensive experiments were conducted on a large dataset of mammography images from different sources: VinDr-Mammo, MiniDDSM, CMMD, CDD-CESM, BMCD, and RSNA. The approach is compared with several baseline methods. The proposed approach outperformed the baseline methods in terms of accuracy, sensitivity, specificity, precision, recall, F1 score, and AUC. * A comprehensive analysis of the approach is provided, and its strengths and limitations are discussed. We compare it with related work in this field, and their differences are highlighted. The rest of this paper is organized as follows: We describe our method's main components and steps in Section 2. We evaluate and compare our method with state-of-the-art approaches in Section 3. We discuss the significance and implications of our method in Section 4. We conclude the paper and outline future work in Section 5.
## Materials and Methods
### In Vitro Experiments with the Glycosic Extract (GP) and Human Saliva  
The glycosidic precursor extract (GP, 4.1 g/Kg fruit) from _P. friedrichsthalianum_ was obtained following the procedure reported by [PERSON] et al. [1] by selective adsorption of fruit polar extract on Amberlite XAD-4. The experiments were set up by placing 24 mg of GP with 12 mL of saliva inoculum (2 mL of saliva mixture/10 mL of SHI medium) in 50 mL sterile Falcon tubes [16]. Additionally, three different control experiments were performed: (A) incubation of the oral microbiota in the medium, but without the GP, to evaluate the volatile compounds derived from basal metabolism (A3); (B) incubation of the GP in the medium but without the oral microbiota, to determine the changes due to chemical transformation under the conditions of the incubation medium (A2); and (C) incubation of the positive control, using octyl-\(\beta\)-D-glucopyranoside (20 ppm), the SHI medium, and oral bacteria, to verify the hydrolytic activity of bacteria by releasing 1-octanol. All the cultures were made at 37 \({}^{\circ}\)C for a 72 h period, with constant agitation at 140 rpm, in duplicate. A 4 \(\times\) 2 random experimental design was performed, with four sample collection times that were representative of the original sample (0 h, T1), the initial stage of hydrolysis (2 h, T2), the hydrolysis time (24 h, T3), and the post hydrolysis time (72 h, T4), and two microbial growth conditions: aerobic (C1) and anaerobic (C2). At each time, 2 mL samples were collected in glass vials (for HS-SPME, 20 mL volume); these were used for the analysis of volatile compounds by GC-MS. Likewise, samples were taken in sterile Eppendorf tubes (1.5 mL) at the indicated times and stored with aqueous 40% glycerol (1:1) at -70 \({}^{\circ}\)C.  
HS-SPME (Headspace Solid-Phase Microextraction) of Volatile Compounds after Incubation of the P. friedrichsthalianum Aroma Precursors with Human Saliva  
The procedure was adapted from that described by [PERSON] et al. [13]. In vials, 40 \(\mu\)L of the internal standard for quantification solution (3-octanol 10 ppm) was added to the sample (2 mL), as well as 0.5 g of NaCl. The extraction procedure was automatically performed using a CombiPal system (CTC Analytics AG, Zwingen, Switzerland) with a 2 cm long DVB/CAR/PDMS fiber (Supelco, Bellefonte, PA, USA). Samples were preincubated for 10 min at 35 \({}^{\circ}\)C, and the extraction was done in the headspace of the vial for 5 min at 35 \({}^{\circ}\)C. The desorption was performed in a gas chromatograph injector (Agilent 6890N, Agilent, Palo Alto, CA, USA) in splitless mode for 90 s at 270 \({}^{\circ}\)C. After each injection, the fiber was cleaned for 10 min to remove any residual volatile compounds. To quantify the compounds, the internal standard method was used.
## 5 Experimental Validation and Discussions
### Modal Experiment  
Based on the basic principle of modal analysis, the method of force-hammer excitation was adopted in the experiment. The pipeline is full of fluid and sealed by screw plugs, and it is suspended in the air with an elastic string to simulate the free state of the pipeline. The pipeline is hammered by the force hammer, causing the pipeline excited by pulse and the impact direction of the pulse is the radial direction of the pipeline. The vibration response of the pipeline will occur under the ideal unit impulse excitation. The amplitude of the harmonic component near the natural frequency is the largest, so the natural frequency can be obtained according to the frequency response.  
The signal of the force hammer is shown in Figure 10, the excitation bandwidth satisfies the test require and can be considered as the ideal pulse excitation signal.  
The axial vibration velocity response of the two measuring points on the pipeline based on the above excitation signal is shown in Figure 11.  
Figure 10: The signal curve of the force hammer.  
Figure 9: The overall experimental apparatus.  
The first four experimental modes of the two measuring points and the error compared with simulation are described in Table 4.  
The comparison between Tables 2 and 4 shows that the maximum error value between simulation and experiment is less than 2.1%, the good consistency of simulation and experiment could indicate that the modals and the solution process were correct and valid.
## 6 General Discussion
Following eight years of investigations and research (2003-2011) funded by the US DOE at the Dallas Centre, Iowa USA, the Iowa Energy Storage Project, which aimed to develop a utility-scale, bulk energy storage facility linked to renewable wind energy, was shelved [12]. All of the above porous rock projects encountered problems with one of more of the following: pressure anomalies, variations in reservoir quality and performance, 'air bubble' formation in the reservoir, reaction between the oxygen of the injected air and minerals in the reservoir rock leading to oxygen depletion and/or potential for bacterial/micro-organism growth and porosity reduction. Aquifer storage for the UK, which would be remote offshore, thereby increasing costs, thus seems less likely than salt cavern storage, at least in the short term. By contrast, our results illustrate the main halite-bearing strata of the UK onshore and East Irish Sea areas offer very significant CAES exergy storage possibilities and capacity, which being closer to demand could play a major role in grid support, load-levelling, and helping to meet the UK's annual electricity demand, which is currently at a level of \(\approx\)300 TWh [44]. Such resources in combination with renewable energy generation, particularly solar and wind, could replace the current flexible power generation at a national scale. Open-source data [63] illustrate that although the UK has achieved substantial carbon emission reductions in its power sector in the last decades by reducing the coal-based generation by almost 95%, from about 100 TWh in 2009 to 6 TWh in 2019, gas power is still an essential source in offering flexibility to maintain the second-by-second balance between the power supply (including intermittent renewable power) and varying demand. In 2019, gas power provided 114 TWh electricity that is 42% of all the electricity generated. To decarbonise the gas power and provide the flexibility sacrificed, energy storage will play a significant role and the use of salt cavern-hosted CAES could underpin decarbonisation of the UK power system by offering large-scale flexibility over multiple timescales.