esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantifying the Occurrence of Multi-Hazards Due to Climate Change
## 3 Results and Discussion
### Wind  
During the historical period, the areas of high wind-speed (exceeding 15 m/s) probability are found to be consistent with other studies in the literature [45; 55; 56] (Figure 6a) and in accordance with the known synoptic atmospheric systems associated with the prevailing wind patterns. More particularly, the areas of the northeastern Aegean Sea are impacted by north-easterlies and the central Aegean by the Etesians, whereas the topography of Crete significantly amplifies the patterns of the extreme winds.  
Figure 5: (**a**) Spatial distribution of probability of RR exceedance above 10 mm/h calculated using EC–Earth–WRF downscaled data for the historical period 1980–2004. Differences (future–historical) in the probability of RR exceedance for: (**b**) RCP4.5 in near future (2025–2049), (**c**) RCP8.5 in near future (2025–2049), (**d**) RCP4.5 in far future (2075–2099) and (**e**) RCP8.5 in far future (2075–2099).  
Figure 6: (a) Spatial distribution of probability of wind speed exceedance over the threshold (15 m/s) calculated using EC–Earth–WRF downscaled data for the historical period 1980–2004. Differences (future–historical) in the probability of wind speed exceedance for: (b) RCP4.5 in near future (2025–2049), (c) RCP8.5 in near future (2025–2049), (d) RCP4.5 in far future (2075–2099) and (e) RCP8.5 in far future (2075–2099).  
In the near future and under both scenarios, the probability of the occurrence of windstorms is amplified almost all over the domain and mostly over the sea areas and mountain ridges (Figure 6b,c). The Ionian and central Aegean Seas are projected to exhibit the highest changes in the probabilities in the RCP4.5 scenario.  
In the far future, both scenarios show a decrease in the probability of extreme winds over the northeastern Aegean and south Ionian Seas (Figure 6d,e). On the other hand, the mountainous areas, the central Aegean Sea and Crete present an increase in the probability of extreme winds. The increases in extreme winds associated with the Etesians over the Aegean Sea projected by both RCPs are in agreement with the findings of [57].
## 1 Introduction
The first involves  
Figure 1: Temperature ranges for different electronics applications. employing the classic ZTC voltage bias technique, where the gate voltage of the MOS transistor is biased at the ZTC point to mitigate the temperature dependence of the LNA's performance. While this method can reduce temperature-related variations to some extent, not all transistors within the same process exhibit a well-defined and convergent ZTC point. Moreover, in many cases, locating such a point is challenging, limiting the effectiveness of this approach. The second approach dynamically compensates for temperature-induced changes in transistor characteristics by generating bias currents or voltages proportional to temperature. This method offers greater adaptability but is sensitive to manufacturing variations, which can compromise the accuracy of the compensation and, consequently, the performance of the amplifier. Both of these mainstream solutions provide the partial stabilization of gain and noise performance during temperature variations, but they remain constrained by inherent limitations. Therefore, further research is required to develop improved and optimized techniques that enable LNAs to function reliably over broader temperature ranges while maintaining stable performance metrics across varying thermal conditions. The remainder of this article is organized as follows. Section 2 reviews the issues related to the design of low-offset operational amplifiers in high-temperature environments. Section 3 provides a detailed description of the implemented circuit. Section 4 presents the measurement results of the proposed operational amplifier. Finally, Section 5 concludes the paper.
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 6 Results  
In this section, the simulation and hardware testbed results are presented, compared, and discussed. To achieve accurate statistics, all the experiments are repeated 40-50 times with the testbed and the simulator. The experimental results are represented as average values of the sample runs with error bars. The length of the error bar indicates the standard deviation around the mean. The absence of any overlap between the error bars indicate a statistically significant difference.  
Our study focuses on the following performance aspects of the DCE implementation of the MPTCP protocol in the ns-3 simulator. First, we explore the uplink/downlink MPTCP achievable throughput with the aforementioned congestion control algorithms. After that, we study the MPTCP round trip time (RTT) values. Next, we evaluate the MPTCP performance during LTE handover.  
In all the aspects mentioned above, the performance of MPTCP in the ns-3 simulator is compared with the hardware testbed. The MPTCP throughput is measured by the time required to transmit data between the sender and receiver applications. Throughout this data transfer, the RTT is measured by calculating the time taken to send packets and receive acknowledgments. We focus on two data (flow) sizes unless otherwise mentioned. One represents a relatively short flow (20 MB), whereas the other is a long-lived flow (75 MB). Using a larger data volume leads to no noticeable impact on the experiment outcomes except increasing the file transmission time, which is prohibitive to repeating each experiment for a sufficiently large number of samples in a reasonable time. The two flow sizes can emulate different traffic patterns [43] and their impact on throughput. For instance, short-lived flows emulate the transmission of a relatively small chunk of data followed by a relatively long period of no data transmission, whereas long-lived ones imitate transferring a large amount of data, such as transferring a large file or browsing a large website. During LTE handover, the MPTCP performance evaluation is conducted for the S1-based handover in the testbed and the X2-based handover in the simulator. Note that all results are obtained with similar configuration parameters of Wi-Fi and LTE networks in the simulator and testbed, as discussed in Sections 4.2 and 5.
## 4 Discussion
We can estimate the isotopic composition of the soil moisture by the composition of rainfalls, which was enriched in heavier isotopes, compared with the snowfalls and had a negative d-excess (Table 2). Therefore, the input of such soil moisture can explain the change in the isotopic composition in the bottom layers of the snowpack on the non-frozen soil relative to the bulk samples, while its input is insignificant for the bottom layers on the frozen soil. The difference in the isotopic composition of the bottom layers of the snowpack on unfrozen soil is clearly shown by their local line of meteoric waters (Figure S2a, green line), the slope of which isless than the LMWL for bulk samples of snowpack and samples of the bottom layer on frozen soil. Comparison of the isotopic composition in the bottom layers of snowpack on the frozen soil with the precipitation-weighted mean value of the isotopic composition of snowfalls over the same period (Table 2) showed that the bottom layers on frozen soil were less enriched (by 1.7% for \(\delta^{18}\)O and by 15% for \(\delta\)D) relative to the initial atmospheric precipitation, which is in good agreement with the data for the cold season 2020-2021 (Figure 3b,d) and can be explained by the partial melting of the first snowfalls. At the same time, the deuterium excess of the bulk samples and the bottom layer of the snowpack deposited on the frozen soil indicates a weak sublimation of snow relative to the initial atmospheric precipitation. Some studies showed that, with an increase in solar radiation for regions with solar and snow conditions similar to the Altai region, sublimation from the top layer enriches the snowpack in heavier isotopes in the daytime [29; 33; 43; 61]; in contrast, night condensation of air moisture, which is more depleted in heavy isotopes, might fully compensate for the effects of the daytime sublimation [33; 61; 62]. In our opinion, before the beginning of the active snow melting, the resultant multidirectional processes of moisture exchange between the top layer of snow and the atmosphere depends on the difference between day and night temperatures. For example, in 2019-2020, the mean value of difference between day and night temperatures during the formation of the upper layer of the snowpack (No. 11) did not exceed 6 degrees (Table S2), and we noted some enrichment of snow in heavier isotopes with the decreasing of d-excess value relative to the initial snowfalls (Figure 3), that indicates the predominance of the sublimation process for this period. However, in 2020-2021, during the formation of the upper layer, the difference between day and night temperatures was already significant (mean value was 14 and max value reached 30 degrees), and the depletion in heavy isotopes of the snow layer relative to the initial snowfalls indicates the predominance of the night air moisture condensation over the process of daytime sublimation of the upper snow layer (Figure 3).
## 3 Results
### Acid Value of Interesterification Products  
The obtained results of acid values for the individual oil mixtures and oils from the raw materials are presented in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample Type**} & \multicolumn{2}{c}{**Endothermic Peak 1**} & \multicolumn{2}{c}{**Endothermic Peak 2**} & \multicolumn{2}{c}{**Endothermic Peak 3**} \\ \cline{2-7}  & **Minimum Temperature (\({}^{\circ}\)C)** & **Temperature Range (\({}^{\circ}\)C)** & **Minimum Temperature (\({}^{\circ}\)C)** & **Temperature Range (\({}^{\circ}\)C)** & **Temperature Range (\({}^{\circ}\)C)** \\ \hline A25B75 & \(16.1\) & \(-13.1\) to \  
According to the obtained acid value data, distinct differences between oils from the raw materials and prepared oil mixtures were determined. The acid values of tomato seed oil and coconut oil were below 0.1 mg KOH g\({}^{-1}\) oil, while the interesterified oil blend samples showed acid values in the range of 9.8 to 13.3 mg KOH g\({}^{-1}\) oil. The sample with the highest level of acid value was sample A25B75 (13.3 mg KOH g\({}^{-1}\) oil).
## 5 Evaluation
### Cold Starts  
Cold starts are one of the most critical performance challenges in FaaS applications due to its overwhelmingly expensive latency caused by the booting time, which can easily dominate the function's total execution time [12].  
The time it takes for an inactive container to be deallocated varies depending on the cloud provider. For instance, MsAzure offers three types of hosting plans for azure functions that affect the frequency of cold starts: consumption plan, premium plan and dedicated (App Service) plan [67]. The hosting plan that best suits our needs is the consumption plan, given that the _search blobs_ use case does not require heavy operations for execution, and we also do not want to avoid cold starts for the purposes of this evaluation, like the premium plan does. When using the consumption plan, containers are deallocated after roughly 20 min of inactivity, meaning that the next invocation will result in a cold start [68].  
Even though there is no official documentation from Google Cloud Platform specifying for how long a container stays in an idle state (_idle timeout_) before being offloaded, some studies have argued that container instances are recycled after 15 min of inactivity [69].  
This evaluation will enable us to verify whether the usage of cloud-agnostic libraries, in addition to the execution of a few extra operations in template functions, have an evident impact on the function's performance when cold started.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 6 Performance Evaluation and Benchmarking
### Research Challenges and Future Work  
Despite the multitude of proposed solutions for VLC-based IPSs, there are still several areas that require additional attention and further research.
## 3 Results
### Model Validation Based on DNN  
Figure 6 shows a structure of intelligent camera-based queue length estimation. A field test in the campus thoroughflare has been conducted by this platform, whose structure is similar to [52]. Smart sensors and connected devices are used to communicate with ThingSpeak and workstation, both of which are developed by MATLAB.  
Figure 5: Shockwave-based queue length estimation results when penetration rate \(\rho\) = 10% (**a**), penetration rate \(\rho\) = 30% (**b**), penetration rate \(\rho\) = 50% (**c**) and penetration rate \(\rho\) = 70% (**d**).  
Figure 6: Structure of test for DNN-based queue length estimation.  
The cameras were mounted close to one of the campus thoroughfares. The image captured by cameras is sent to the edge server, which is Raspberry Pi 3b+ with a Broadcom BCM2837B0 SoC with a 1.4 GHz 64-bit quad-core ARM Cortex-A53 processor, GPU, and 1 GB memory. SIMULINK is utilized to provide a modeling environment that can automatically generate a code that can run on Raspberry Pi 3b+. With the DNN algorithm deployed on the Raspberry Pi device, the raw data sent by the Raspberry Pi 3b+ to ThingSpeak is analyzed by fetching it from thingspeak.com. The test reveals that it takes about 3 s for Raspberry Pi 3b+ to tackle the pictures.  
It is obvious that the connected vehicles' penetration rates will not affect the result based on DNN but the intensity of illumination. This is the advantage of image-based queue length estimation. In this work, a normal case is considered, and the light will not affect the classification result of DNN. Aiming to verify the performance of the model, we set the time usage by DNN to 3 s and the penetration rates of connected vehicle is 10%. The estimation result based on DNN is depicted in Figure 7. It reveals that the queue length estimation result of many cycles is not equivalent to queue length at the intersection. The reason is that the time delay is caused by an image process and the traffic flow is variable. To classify the vehicles in the image by DNN, it takes some time to compute due to several layers of the neuron. To promote the estimation accuracy, it is better to combine the DNN with shockwave.
## 1 Introduction
Their results showed that the DORC can enhance 2.824% propulsion efficiency in comparison with the original engine. [PERSON] et al. [29; 30] conducted a parametric analysis of the DORC system; these authors selected cyclohexane, toluene, and water as working fluids for HTLs, and used R245 fa, R236 fa, and R123 for LTLs. Their conclusion showed that the DORC can increase the net power output by 11.6% in comparison with an original diesel engine. A few researchers have conducted parameter optimization for DORCs. [PERSON] et al. [31] explored DORC parameter optimization using R245 fa for maximizing net power output and minimizing heat transfer area and used optimization parameters, such as evaporation pressure, superheat degree, and condensation temperature. Their results indicated that the maximum net power output can reach 23.62 kW. Considering that exhaust gas is the main heat source of ICE waste heat, the exhaust gas temperature will considerably affect the working fluid selection for the two loops, the system thermal performance, and the operating parameters. Many works on the DORC have been based on fixed exhaust gas temperature, and a few studies have discussed the influence of exhaust gas temperature on the working fluid selection for two loops. In addition, HTL evaporation temperature, condensation temperature and superheat degree are important operation parameters. Many studies have been conducted using a saturated cycle or fixed HTL condensation temperature, but a few studies have considered these operational parameters together for different working fluid systems. The change in engine exhaust gas temperature will affect the exergy destruction rate, and thus this change must be analyzed. The engine exhaust gas temperatures of 523.15-623.15 K are considered in the present work because these temperatures are common and suitable for ORC application [6; 29; 30]. Cyclopentane, cyclohexane, benzene, and toluene [12; 18; 29; 32; 33; 34; 35; 36] are selected as the HTL working fluids because they possess efficient thermal performance with high temperature and favorable environmental and high decomposition temperature. Furthermore, R1234 ze(E), R600a, R245 fa, and R601a are selected as working fluids for LTLs [8; 37; 38; 39; 40; 41] given the efficient environmental and thermal performances with low temperature. The working fluid properties are listed in Table 1[8; 12; 29; 38], ODP is the ozone depletion potential; GWP is the global warming potential. By considering the exhaust gas acid dew point temperature limit, the present work optimizes HTL evaporation temperature, condensation temperature and superheat degree for maximizing net power output with 523.15-623.15 K engine exhaust gas through a genetic algorithm. Moreover, the effects of ICE exhaust gas temperature on net power output, thermal efficiency, exergy efficiency, HTL evaporation temperature, HTL condensation temperature, HTL superheat degree, exhaust gas temperature at the exit of an HTL evaporator, heat utilization ratio and exergy destruction rate of the components are studied.
## 1 Introduction
The significance of this study is that it proposes a method for automatically estimating the imaging angles of lumbar oblique images using deep learning. This method will enable the determination of the imaging angle based on objective data, which until now has relied heavily on the radiological technologist's experience and intuition. This is expected to improve the reproducibility and consistency of imaging and lead to improved diagnostic accuracy. The findings obtained in this study can also be applied to radiographic imaging of other parts of the body and are expected to contribute widely to the optimization of radiographic examinations. Furthermore, this study is also significant in terms of the application of artificial intelligence (AI) in the medical field. AI is being applied to various aspects of medicine, but it has not yet been fully utilized in the field of radiological examinations. This study shows one way to improve the quality of radiological examinations using AI and expands the possibilities for medical applications of AI. As described above, this study proposes a new approach to the global health problem of low back pain using deep learning. The results of this study are expected to contribute to the optimization and efficiency of lumbar oblique radiography, thereby reducing the burden on patients and improving the quality of medical care. Furthermore, this research demonstrates the potential for medical applications of AI, and it is expected to help promote the use of AI in the medical field. To provide a clear understanding of the research presented in this article, the structure is organized as follows. Following this introduction, Section 2 describes the datasets used, the preprocessing steps, and the deep learning models developed. Section 3 presents the findings of the CNN's performance in estimating the angles of lumbar spine oblique images. Subsequently, Section 4 interprets these results, comparing them with existing methods, and explores the implications for clinical practice. Finally, Section 5 summarizes the study's contributions and outlines future research directions.
## 5 Results
### Proton and Alpha Cluster Measurements
As anticipated, this angular sweep is necessary for the experimental estimation of the dead layer of the sensor, exploiting the fact that for larger angles, the protons are depositing a larger fraction of energy into it (proportionally to \(1/\cos\theta\)) before reaching the active volume. The choice of using the lowest available proton energy for the dead layer measurement is motivated by 8 \(\upmu\)m proton range at this energy in silicon (as shown in Figure 4), which is comparable in size to the expected dead layer thickness (expected to be around 1 \(\upmu\)m or smaller), such that the fraction of energy lost in the dead layer is non-negligible. Indeed, this is what ensures that the mean ToT values of the peaks in Figure 11 are well distinguishable. Figure 10: Timepix3 Radiation Monitor measurements for (**Top**) proton runs and (**Bottom**) alpha runs with orthogonal incidence and for different energies, displaying the distributions of total cluster ToT (**left**) and number of pixels (**right**) per cluster. The measured peaks (continuous lines) are fitted with Gaussian functions (dashed lines). For the 5.6 MeV alphas, the right slope of the data has been fitted, a choice further motivated in the discussion (Section 6). As outlined in Section 4.2, the dead layer estimation procedure requires the conversion of the mean ToT values of the above peaks into deposited energies using a pre-established calibration. The values are then plotted against the inverse of the cosine of the irradiation angle \(\theta\), as shown in Figure 11 (right). By performing a linear fit of this graph, it is then possible to obtain the values of the coefficients of Equation (4) (notably the slope \(m\)), from which the value of the dead layer can be directly derived. Since this procedure requires a calibration, and in turn, the calibration requires the knowledge of the dead layer thickness, the two quantities are evaluated iteratively, as further outlined below.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 2 Related Work
### Monocular 3D Object Detection Methods  
Image-based 3D object detection leverages monocular or stereo images to estimate depth and generate 3D proposals. Approaches like MONO3D [13], MLF [19], and Frustum PointNet [20] use depth maps or disparity estimates to compute 3D coordinates, often integrating them into 2D detection pipelines. However, poor depth representations in these methods limit the ability of convolutional networks to accurately localize objects, especially at greater distances, leading to performance gaps compared to LiDAR-based systems. Depth-based methods aim to bridge this gap by explicitly or implicitly leveraging depth information. One common approach is to estimate pseudo-depth maps from monocular images and utilize them for 3D object detection. For instance, Pseudo-LiDAR [5] and F-PointNet [12] rely on pre-trained depth models to generate point clouds, which are then processed with LiDAR-style 3D detectors. While these methods improve spatial reasoning, they suffer from depth estimation errors and increased computational costs due to reliance on external depth estimators.  
Another line of work focuses on integrating monocular image features with estimated depth information in a feature fusion framework. Methods like D\({}^{\text{d}}\)LCN [13] and CaDDN [21] incorporate depth-sensitive features into the 2D detection pipeline to enhance accuracy. However, these approaches are constrained by the quality of the estimated depth maps and often rely on complex architectures, limiting their real-time applicability. In contrast, our proposed method avoids reliance on external depth estimators by directly learning depth-sensitive features through the Auxiliary Depth Feature (ADF) module. This design allows AuxDepthNet to achieve high accuracy while maintaining computational efficiency.