esa-sceva/satcom-chunk-collection huggingface dataset:
# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 1 Introduction
### Medical Imaging  
Medical imaging techniques like X-rays, MRI, and CT require subjective analysis by radiologists. With increasing techniques, manual analysis becomes time-consuming. Machine learning models mimic human visual perception, enabling the automatic classification of images as diagnostic aids. Advances in AI and computing hardware have made computer-assisted methods more powerful, making them useful for diagnosticians. Consequently, CAD has acquired theoretical and practical significance as a significant trend in medical science. Using computer vision to automatically analyze and process medical images has many distinct benefits [13; 20; 21; 22]. Utilizing medical images like PET, SPECT, MRI, and CT offers vital insights into head and neck cancer's dimensions, site, structure, and metabolism [23]. Navigating clinical outcome prediction proves intricate yet pivotal for guiding treatment strategies [24]. In contrast to standard regression tasks like predicting clinical scores, clinical outcome prediction presents greater complexity due to survival data censoring, causing event times to be indeterminate for certain patients [25]. Accurate kidney tumor segmentation is pivotal in advancing kidney tumor diagnosis and treatment strategies through early detection, treatment planning, and risk assessment. However, real-time medical image analysis faces challenges like poor image quality, diverse protocols, and patient variations. Modern hardware's computational power enables rapid analysis and processing, preventing fatigue and cognitive issues. Computer technologies facilitate data transmission, enabling accurate diagnosis of patients in remote areas. Machine learning algorithms and artificial intelligence classifiers improve prediction accuracy for kidney stone patients [20]. Since the introduction of the first (CNN) (LeNet-5 [26]) in 1989, CNNs exhibited exceptional performance in medical image processing [27; 28]. The evolution of these models has also contributed to the precision of CAD techniques [17; 29].
# Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022
## 2 Materials and Methods
### Discharge Analysis  
The level of the Po River has been gauged at various stations since the beginning of the 19 th century. In this study, the daily flow dataset from 1961 to 2023 measured by the Pontelagoscuro hydrometer (located 83 km from the river mouth, see Figure 1) has been analyzed. Po River discharge data were collected from [[https://simc.arpae.it/dext3r/](https://simc.arpae.it/dext3r/)]([https://simc.arpae.it/dext3r/](https://simc.arpae.it/dext3r/)) (accessed on 11 September 2024). The average monthly runoff was calculated for each year, and then two different indices were quantified on a monthly basis to detect anomalies in Po River runoff: the coefficient of variation (CV) and the Streamflow Drought Index (SDI) [43].  
Figure 1: Study area. The orange rectangles represent the extent of the 5 satellite image tiles used for the analysis (the same orange rectangles are used in the overview map in the following figures). The focus areas extent are represented by the green rectangles. The location of the Pontelagoscuro hydrometer is represented by a black triangle.  
For each month, CV was calculated to assess the variability of river discharge during the considered year according to Equation (1).  
\[CV_{t}=\frac{\sigma_{t}}{\mu_{t}} \tag{1}\]  
where \(t\) is the considered year, \(\sigma_{t}\) is the standard deviation of the considered year, and \(\mu_{t}\) is the average discharge of the considered year.  
SDI was computed using the same approach as the Standardized Precipitation Index (SPI), which is commonly used for the detection of meteorological droughts [44]. SDI, defined in terms of discharge, was calculated for the period from 1961 to 2022 to quantify the intensity and duration of the hydrological drought by comparing runoff anomalies to historical averages. For each month, the discharge data were fitted to a long-term reference period (1961-2021) to determine the mean and standard deviation. These values were then used to standardize the discharge (Equation (2)), resulting in monthly SDI values.  
\[SDI_{t}=\left(X_{i,t}-\mu_{i(t_{0},t_{-1})}\right)/\sigma_{i,(t_{0},t_{-1})} \tag{2}\]  
where \(X_{i,t}\) is the mean monthly discharge over the considered year t, and \(\mu_{i(t_{0},t_{-1})}\) and \(\sigma_{i,(t_{0},t_{-1})}\) are, respectively, the monthly discharge average and the monthly standard deviation of a multi-year time series, corresponding to the reference period. The SDI values were then used to classify the wet and drought periods according to Table 1[43]. A positive SDI value typically outlines conditions that are wetter than average, whereas values around zero indicate conditions that are at the median level. Conversely, negative SDI values are indicative of drier-than-average conditions [43; 44].  
Considering the analyzed case study and the available discharge dataset, the following time periods were used to calculate CV and SDI: t = 2022; t\({}_{-1}\) = 2021; and t\({}_{0}\) = 1961.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 4 Hyperbranched Polymer Toughening
### Toughening Mechanism  
When hyperbranched polymers (HBPs) are used as non-reactive modifiers, phase separation occurs, and the toughening mechanism resembles that of rubber toughening. For cases where phase separation does not occur, researchers have conducted in-depth studies. [PERSON] et al. [5] synthesized terminal epoxy hyperbranched aromatic polyethers (EHBPEs) to toughen epoxy resins. They found that adding 5% EHBPE optimized the material performance. They proposed a toughening mechanism wherein the introduction of HBPs expands the material's free volume and enhances the mobility of the molecular chains. Additionally, the nearly spherical structure of HBPs effectively absorbs impact energy from various directions, thereby significantly improving the resin's toughness and impact strength.  
[PERSON] et al. [76] conducted an in-depth study on the modification of epoxy resins with hyperbranched polymers (HBPs) and proposed an in situ uniform reinforcement and toughening mechanism. In this mechanism, the terminal groups of HBPs participate in the curing reaction of the epoxy matrix, while HBPs can disperse and permeate into the molecular chains of the resin matrix. This interaction increases the molecular chain interactions during the curing process, ultimately enhancing the tensile and flexural strengths. During impact, numerous non-crosslinked molecular voids absorb energy, deform, and form fibrils, exhibiting high toughness. Consequently, HBPs provide a dual effect on epoxy resins by both reinforcing and toughening them.
## 4 Conclusions  
The 22 fungal strains were tested for their ability to transform three halolactones **2-4**. Only two of these strains (_C. japonica_ AM472 and _F. culmorum_ AM10) were able to conduct the reaction of hydrolytic dehalogenation of the chosen substrates, giving hydroxylactone as a product. The best result was obtained when bromolactone **3** was transformed by the _F. culmorum_ AM10 strain. Enantiomeric excesses were low, but in all cases the (+)-enantiomer was produced in excess. Chloro- **2**, bromo- **3**, iodo- **4** and hydroxylactone **5** were then subjected to biological tests. The growth inhibition tests were conducted on the chosen strains of bacteria, yeast and filamentous fungi. Complete inhibition of growth of _S. aureus_ strain was caused by lactones **2**, **3** and **4**. Lactone **2** caused complete inhibition of growth of _C. albicans_ strain. The same effect was observed for lactones **3** and **4** when _F. linii_ strain was tested. Lactones **2**, **3**, **4** limited the increase in the biomass of _E. coli_, _P. fluorescens_ and _A. niger_ strains. Lactone **5** also limited the increase in the biomass of _S. aureus_ and _P. fluorescens_ strains. Other assays were performed on the peach potato aphid. These tests showed that lactone **4** was inactive against _M. persicae_. Lactones **2** and **3** showed deterrent activity. Indices of deterrence after 24 h reached the relatively high values of 0.5 and 0.4 for compounds **2** and **3**, respectively. Lactone **5** showed a weak attractant activity after 24 h.  
**Supplementary Materials:** The following are available online at www.mdpi.com/2076-3417/7/1/12/s1.  
**Acknowledgments:** Publication supported by Wroclaw Centre of Biotechnology, programme The Leading National Research Centre (KNOW) for years 2014-2018.  
**Author Contributions:** [PERSON] conceived, designed the experiments and analyzed the data, [PERSON] and [PERSON] performed the experiments, [PERSON] and [PERSON] wrote the paper, [PERSON], [PERSON] and [PERSON] performed the biological analysis, [PERSON] performed the high resolution mass spectrometry analysis, [PERSON] contributed for the discussion of results. All authors read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Numerical Simulation and Experimental Verification
### Image Evaluation Metrics  
(1) Relative Error of Reconstructed Images (IRE)  
The fidelity of the reconstructed image compared to the ground truth image is a crucial evaluation criterion for image reconstruction. The relative error of the reconstructed image (IRE) captures the errors between the reconstructed image and the ground truth image in terms of shape, area, and other aspects, effectively reflecting the differences between the two. The calculation formula for IRE is given as follows.  
\[\text{IRE}=\frac{\sqrt{||g-g_{0}||}}{\sqrt{||g_{0}||}} \tag{24}\]  
In the above equation, \(g\) represents the reconstructed medium distribution obtained through the imaging reconstruction algorithm, and \(g_{0}\) represents the actual medium distribution in the field in the simulation experiment. A smaller value of the relative error indicates a higher image quality.  
Figure 7: A three-dimensional finite element model of the human thoracic field with 16-electrode configuration.  
Image Correlation Coefficient (ICC)  
The image correlation coefficient is an important metric for evaluating the degree of correlation between the reconstructed image and the ground truth image. It represents the correlation between the actual distribution and the reconstructed image. The calculation formula for the image correlation coefficient is given as follows.  
\[\text{ICC}=\frac{\sum_{i=1}^{n}(g-g^{\prime})(g_{0}-g_{0}^{\prime})}{\sqrt{\sum_{ i=1}^{n}(g-g^{\prime})^{2}\big{(}g_{0}-g_{0}^{\prime}\big{)}^{2}}} \tag{25}\]  
In the above equation, \(g^{\prime}\) represents the mean value of the reconstructed image's medium distribution, \(g_{0}^{\prime}\) represents the mean value of the medium distribution in the region of interest in the ground truth image, and \(n\) represents the number of pixels in the image.  
(3) Structural Similarity Index (SSIM)  
SSIM is a metric for measuring the similarity between two images in terms of their structure. It is introduced as a standard to evaluate the similarity between the reconstructed image and the ground truth image. The calculation formula for SSIM is given as follows.  
\[\text{SSIM}(x,y)=\frac{\big{(}2u_{x}u_{y}+c_{1}\big{)}\big{(}2\sigma_{xy}+c_{2} \big{)}}{\Big{(}u_{x}^{2}+u_{y}^{2}+c_{1}\big{)}\Big{(}\sigma_{x}^{2}+\sigma_{ y}^{2}+c_{2}\Big{)}} \tag{26}\]  
In the above equation, \(u_{x}\) and \(\sigma_{x}^{2}\) represent the mean and variance of the reconstructed image, while \(u_{y}\) and \(\sigma_{y}^{2}\) represent the mean and variance of the ground truth image. \(\sigma_{xy}\) represents the covariance between the reconstructed image and the ground truth image. \(c_{1}\) and \(c_{2}\) are constants, usually set to very small values to avoid division by zero. A larger value of SSIM indicates a higher structural similarity between the two images and a better result in the image reconstruction.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## Appendix A Artifacts Correction by Iterative Algorithms
### Averaged Time Reversal  
The averaged time reversal method is proposed in [12] as a remedy to the conventional TR to remove the reflection artifacts in the latter. The essential improvement comes from the introduction of an averaging process to the measured data before reversing the time. The rational, roughly speaking, is that artifacts generated by acoustic reflections have either positive amplitude or negative amplitude, depending on the number of times they touch the boundary. A suitable averaging process along the time direction is thus able to annihilate reflected artifacts with opposite signs. The process can be viewed as a pre-conditioning to the TR. To be a bit more precise, let \(\mathbf{\Lambda}\) be the linear operator  
\[\mathbf{\Lambda}:\qquad p_{0}\mapsto u|_{[0,T]\times\partial\Omega} \tag{1}\]  
where \(u\) is the solution of the forward model (1). In other words, \(\mathbf{\Lambda}\) sends the initial pressure to the measured data. This is a linear operator that is completed determined by the form of the partial differential equation in (1). If the forward model is discretized, \(\mathbf{\Lambda}\) would just be a matrix. Reconstructing the initial pressure from the ultrasound signals amounts to inverting the matrix \(\mathbf{\Lambda}\). Direct inversion is normally impossible due to the large dimensionality of this matrix. Instead, it is shown in [12] that one can introduce an averaged time reversal process \(\mathbf{A}\) such that the initial pressure \(p_{0}\) can be reconstructed iteratively from the measured data \(u|_{[0,T]\times\partial\Omega}\) by the relation\[p_{0}=\sum_{k=0}^{\infty}(\mathbf{A}\boldsymbol{\Lambda}-\boldsymbol{Id})^{k} \mathbf{A}(u|_{[0,T]\times\partial\Omega}) \tag{10}\]  
where \(\boldsymbol{Id}\) is the identity operator (or identity matrix if the forward model is discretized). This algorithm is known as the averaged time reversal (ATR). It is mathematically proved in [12] that ATR can correct the reflection artifacts caused by conventional time reversal methods. It is one of the baseline methods we used in the paper to compare with the DL reconstruction.
# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 2 Materials and Methods  
### Study Area  
The Mekong River is the largest river in South East Asia, flowing 4909 km with a total basin area of 795,000 km\({}^{2}\) and an average discharge of 14,500 m\({}^{3}\)/s, distributed unevenly across six countries: China (21%), Myanmar (3%), Lao People's Democratic Republic (Lao PDR) (25%), Thailand (23%), Cambodia (20%), and Vietnam (8%) [32]. The MRB ranges from temperate to tropical monsoon climate. The Upper Basin in China (known as Lancang) is glaciated, starting in the Tibetan Plateau (Figure 1). The Lower Mekong Basin starts from Yunnan province downstream (China) and flows through the Golden Triangle tripoint to the South China Sea (known as the Lower Mekong Basin), and is classified as tropical monsoon [32]. The watershed has a complex topography, with the elevations in the basin ranging from above 6000 m in the Tibetan Plateau to less than a meter (0.3-0.7 m) above sea level in the downstream river delta, and with deep-cut valleys in the high mountains [32; 33].  
Overall, acrisols are the most common type of soil in the MRB, comprising around 65% of the total land area. Acrisols are common to humid tropical climates, and are a weathered-type, clay-rich, and low-nutrient soil, posing limitations for agriculture and thus being commonly forested. Lithosols are the second most common type of soil in the MRB, found in the upper, steep mountainous areas and common to highly erodible grassland [32; 33].  
Land use can be broadly divided into three major components: agriculture (32%), forested land (39%), and grassland (9%). The majority of the MRB is covered by forest, mainly located in Lao PDR and Cambodia. Grassland dominates the upper basin and agricultural land dominates the low-lying plains of the Chi-Mun Basin in northeast Thailand, the Vientaine plain in Lao PDR, the Tonle Sap Basin in Cambodia, and the delta in southern Vietnam (Figure 1) [32; 33].  
As can be seen from the spatial contrasts in rainfall, much of the monsoonal rain is captured in the Northern and Eastern Highlands, creating dry conditions across the Korat Plateau. The mean annual rainfall is 1300 mm, more than 70% of which falls in the summer season. The dry season lasts from December to May, and evapotranspiration is high during this time. The river flow has a distinct seasonal pattern, with high flows from June to November that account for 80%-90% of the total annual  
Figure 1: Map of the Mekong River Basin: (**a**) Rainfall and the hydrological station network; (**b**) mean annual rainfall; (**c**) land use map (Data source: MRC [33]).  
flow [34]. The annual flood season is especially important in the Lower Mekong Basin, where it has shaped the environment and its inhabitants.
# Design and Experimental Study of a Down-Drive Piezoelectric High-Frequency Fatigue Testing Machine
## 4 Trial Tests
### Tests of the Effect of Preset Loads on Specimen Forces  
In order to study the relationship between voltage and preset load on the stress acting on the specimen, based on the elastic ring with thickness \(T_{b}\) = 0.7 mm at the flexible place and the specimen with thickness \(w\) = 2 mm in the middle, when the counterweight mass block \(a\) was taken as 0 mm, 10 mm, 20 mm, and 30 mm, respectively, the effect of the preset load (\(F_{0}\) = 20 N, 40 N, and 60 N) and the voltage was tested on the stress acting on the specimen in the fatigue tester by setting up different preset loads. The relationship is shown in Figure 17.  
As can be seen from Figure 17, the stress amplitude acting on the specimen increases gradually with the increase in driving voltage, and the stress amplitude acting on the specimen increases gradually when the preset load increases at the same counterweight  
Figure 17: Stress amplitudes acting on the specimen as a function of voltage and preset load for different counterweight mass blocks. (**a**) a = 0 mm. (**b**) a = 10 mm. (**c**) a = 20 mm. (**d**) a = 30 mm.  
mass block and the same voltage. The stress amplitude acting on the specimen ranges from about 1 N to 32 N within a driving voltage peak-to-peak range of 1 V to 200 V.
# Power Consumption Modeling of Discontinuous Reception for Cellular Machine Type Communications
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
DRX Discontinuous Reception  
IoT Internet-of-Things  
HTC Human-Type Communication  
LTE Long-Term Evolution  
LTE-A Long-Term Evolution Advanced  
M2M Machine-to-Machine  
MTC Machine-Type Communication  
POMDP Partially Observable Markov Decision Process  
SNR Signal to Noise Ratio  
TPC Transmit Power Control  
UMTS Universal Mobile Telecommunication System
# Multi-Level Control and Utilization of Stormwater Runoff
## 2 Materials and Methods
### Data Analysis Methods  
#### 2.3.1 Efficiency Assessment  
There were large variations in the concentrations of pollutant in surface runoff over a rainfall event due to the influence of rainfall characteristics and pollutant properties. This present study adopted the event mean concentration (_EMC_) based on the experience of the United States Environmental Protection Agency (USEPA) [22] as a measure of pollutant concentration. The calculation of the _EMC_ was a measure of pollutant concentration. The calculation of the _EMC_ was as follows:  
\[EMC=\frac{M}{V}=\frac{\int_{0}^{T}c_{l}Q_{l}dt}{\int_{0}^{T}Q_{l}dt}\cong\frac {\sum c_{l}Q_{l}\Delta t}{\sum Q_{l}\Delta t} \tag{1}\]  
In Equation (1), _EMC_ is the event mean concentration of runoff pollutant (mg/L), \(M\) is the load of pollutant entering the system over the entire runoff process (mg), \(V\) is the total runoff volume (L), \(c_{t}\) is the concentration of a pollutant at time \(T\) (mg/L), \(Qt\) is runoff flow at time t (L/ min) and \(\Delta t\) is the sampling interval (min).  
Storm runoff pollution load refers to the total removal load discharged by runoff due to one or more rainfall events. The runoff pollution load per unit area of a pollutant can be expressed as follows:  
\[L=0.001\sum_{i=1}^{m}EMC_{i}\times R_{i}\times P_{i} \tag{2}\]  
\[R_{c}=Q\times\left[\sum_{i=1}^{n}(C_{oi}-C_{ei})\times t_{i}\right] \tag{3}\]  
In Equations (2) and (3), \(L\) is the runoff pollution load per unit area of a pollutant (mg:m\({}^{-2}\)), 0.001 is the unit conversion factor, m is the number of samples, \(R_{i}\) is the runoff coefficient of the ith rainfall event, \(R_{i}\) of the porous pavement was assigned a value of 0.8 based on experience [23], \(P_{i}\) is the rainfall capacity of the \(i\)th rainfall event (mm), \(R_{c}\) is the load of pollutant removed [g:(m\({}^{2}\)-d)], \(C_{oi}\) and \(C_{ei}\) are the average inlet and outlet concentrations (mg:L\({}^{-1}\)), respectively and \(t\) is the time interval between the sampling time (d).  
Removal rates (_RR_) of the major parameters of SS, TN, NH\({}_{4}\)+-N, TP, Pb and Zn were computed by the formulae given below:  
\[RR=\frac{C_{i}-C_{e}}{C_{i}} \tag{4}\]  
In Equation (4), \(C_{i}\) and \(C_{e}\) are the inflow and outflow concentrations in mg:L\({}^{-1}\).  
Hydraulic Loading Rates _HLR_ (L:m\({}^{-2}\cdot\)d\({}^{-1}\)) applied to the hydroponic green roof were determined using the equation:  
\[HLR=\frac{Q}{A_{w}} \tag{5}\]  
In Equation (5), \(Q\) is the flow (L:d\({}^{-1}\)) of stormwater runoff through the hydroponic green roof, \(A_{w}\) is the area (m\({}^{2}\)) of the given experimental pond of the constructed wetland.