esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Physical Model Test Results
### Temperature Field Analysis of Freeze-Thaw Rock Mass  
Figure 5 shows the law of temperature change at measuring points at different depths during the freeze-thaw cycle. During the cooling process, it was discovered that: (a) the temperature in the cold storage drops to around \(-13\)\({}^{\circ}\)C within 20 h and then fluctuates slightly, with a fluctuation range of \(\pm 2\)\({}^{\circ}\)C; (b) the temperature curves at different depths exhibit essentially the same drop law during the cooling process, showing that heat is uniformly transmitted to the outside; (c) the temperature evolution law at the slope surface and the slope buried depth of 8 cm is very similar to that in cold storage. However, the temperature of the measuring point at the buried depth of 24 cm decreases slowly, falling to just about 0 \({}^{\circ}\)C after 120 h. The temperature change of the measuring points with a buried sensor depth of 24-32 cm presents a smooth curve and is not sensitive to the fluctuation of indoor temperature. It can be seen that with the increase in buried depth, the influence of external temperature changes on the interior of the rock mass will be weaker, and the cooling rate will also slow down. (d) Affected by the latent heat of pore water in the model, when the slope surface temperature drops to 0 \({}^{\circ}\)C, the temperature curve will have a plateau period of about 25 h before continuing to decline. In the heating process, (a) the change rate of indoor temperature changes from fast to slow, and the slope of the corresponding time-varying curve changes from large to small; (b) the response of slope body temperature lags far behind, and slope body must be preheated for an extended period of time. Using the slope surface as an example, it takes approximately 28 h, and the interior of the slope body takes even longer; (c) the heating rate will accelerate after the slope has been preheated, and the trend will be linear.  
Figure 5: The temperature change curves during the freeze–thaw processes: (**a**) cooling; (**b**) heating.
## 2 Materials and Methods
### Gene Expression Analysis by qRT-PCR  
Total RNA was extracted from frozen grape samples using an RNA extraction kit (Bioteke, Beijing, China). The total RNA concentration was determined, and the RNA quality was assessed using a spectrophotometer (Biodrop, Cambridge, UK) and by agarose gel electrophoresis. The purified RNA (1 \(\upmu\)g) was then used as the template to synthesize cDNA with the cDNA synthesis kit (Vazyme Biotech, Nanjing, China). The expression levels of specific genes were determined by quantitative real-time PCR (qRT-PCR) with the QuantStudio 6 (Life Technologies, Carlsbad, CA, USA). Primers for _VvDXS1_, _VvDXS3_, _VvDXR_, _VvHDR_, _VvGPPS_, and _VvFPPS_ were from a previous study by [13], whereas those for _VvGT7_, _VvGT14_, _VvGT9_, _VvGT5_, and _VvGT6_ were from [22; 23]. Finally, the primers specific for _VvActin_, _VvCCD4a_, _VvCCD4b_, _VvCCD1_, _VvLinNer1_, _VvLinNer2_, _VvLOXA_, _VvADH1_, _VvADH2_, and _VvADH3_ were designed using NCBI. The _VvActin_ gene was used as a reference control. Gene expression levels were determined with the \(2^{-\Delta\Delta\text{CI}}\) method, with the first control own-rooted Merlot and Marselan sample serving as the reference.
# Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography
## 2 Materials and Methods
### Cross-Sectional Optoacoustic Tomography with a Ring Array  
The system layout is depicted in Figure 1a (bottom) while its detailed description is available in [33]. Briefly, the ultrasound array (Imasonics SaS, Voray, France) consists of an 80 mm diameter ring having 512 ultrasound individual detection elements with 5 MHz central frequency and \(-6\) dB bandwidth of \(\sim\)80%. Each element is cylindrically focused at a distance of 38 mm to selectively capture signals from the imaged cross-section. In the experiments, light excitation was provided by a short-pulsed (\(<\)10 ns duration pulses at a wavelength of 1064 nm with \(\sim\)100 mJ per-pulse energy and 15 Hz pulse repetition frequency) Nd:YAG laser (Spectra Physics, Santa Clara, California). The laser beam was guided with a fiber bundle (CeramOptec GmbH, Bonn, Germany) having 12 output arms placed around the circumference of the ring transducer with an angular separation of 60\({}^{\circ}\) between the arms. Much like the SVOT system, signals detected by all the array elements were simultaneously digitized at 40 megasamples per second using custom-made DAQ (Falkenstein Mikrosysteme GmbH, Taufkirchen, Germany), triggered with the Q-switch output of the laser. The data was transmitted to a PC via Ethernet. In total, 100 frames were recorded with the array positioned at two distinct regions of the mouse.
###### Abstract  
Reducing losses in water distribution networks is a worldwide challenge and all utilities are developing proper strategies for the active control of leaks. Temporary or permanent grids of units for the continuous monitoring of pipelines through vibro-acoustic measurements are probably the most commonly adopted leak detection systems. Such systems generally rely on the definition of proper thresholds to detect increments in the vibration levels associated with leaks. Since the thresholds are strongly dependent on the local boundary conditions of the monitored network, the initial setup is costly and time consuming, and the risk of undetected leaks or false alarms increases. This work aims to investigate leak detection methods based on the inherent properties of the measured signals instead of their relative amplitude. In particular, the possibility of detecting water leaks in small-diameter plastic pipes by analyzing the autocorrelation function of vibro-acoustic signals is assessed. An experimental campaign is conducted in a full-scale test facility that simulates the actual network. The measurements concerning artificially generated leaks are attained by two accelerometers and one hydrophone. The experimental results confirm the effectiveness of the proposed approach, which is therefore proven as a promising tool for leak detection.  
leak detection; plastic pipe; vibration measurement; acoustic signal; autocorrelation function +
Footnote †: journal: Applied Physics Letters B
## 3 Methodology
### Preliminaries
#### 3.1.2. Quantization  
Quantization is a fundamental process involving the approximation of continuous signal values or a vast array of discrete values into a finite and often reduced set of discrete values [24]. It is used to ensure the fixed accuracy goal required in the problem while eliminating the unnecessary precision of the data.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADTM & Algorithmic Dynamic Topic Models \\ BOW & Bag-of-words \\ BERT & Bidirectional Encoder Representations from Transformers \\ CBOW & Continuous Bag of Words \\ DBCV & Density-Based Clustering Validation \\ DL & Deep Learning \\ DTM & Dynamic Topic Models \\ EAI & Enhanced Air Interface \\ HDBSCAN & Hierarchical Density-Based Spatial Clustering of Applications with Noise \\ ISAC & Integrated Sensing and Communication \\ ISAGSC & Integrated Space-Air-Ground-Sea Communication \\ KEM & Keyword Emergence Maps \\ KIM & Keyword Issue Maps \\ LC & Link Clustering \\ LDA & Latent Dirichlet Allocation \\ LLMs & Large Language Models \\ MIMO & Multiple-Input Multiple-Output \\ NPMI & Normalized Pointwise Mutual Information \\ NSTs & New Spectrum Technologies \\ OAM & Orbital Angular Momentum \\ PDTM & Probabilistic Dynamic Topic Models \\ PMI & Pointwise Mutual Information \\ POS & Part-Of-Speech \\ RIS & Reconfigurable Intelligent Surface \\ SBERT & Sentence-BERT \\ TC & Topic Coherence \\ TD & Topic Diversity \\ TEMs & Topic Emergence Maps \\ TF-IDF & Term Frequency-Inverse Document Frequency \\ UMAP & Uniform Manifold Approximation and Projection \\ \end{tabular}
## 4 Conclusions  
This study evaluated the corrosion behavior of FCD-500 in simulated cooling water conditions in a marine engine, focusing on the combined effect of the nitrite-based inhibitor dosage and the Cl\({}^{-}\) level. The results obtained from the immersion coupon test and electrochemical analysis were as follows:  
1. In the absence of Cl\({}^{-}\), general and localized corrosion were effectively inhibited at both 50 \({}^{\circ}\)C and 80 \({}^{\circ}\)C when the inhibitor dosage exceeded 5000 ppm in the immersion coupon test;
2. In 100 ppm of Cl\({}^{-}\), the inhibitor required a concentration above 13,000 ppm to ensure comparable corrosion inhibition performance;
3. FCD-500 showed uniform corrosion under uninhibited conditions, whereas insufficient inhibitor concentrations led to localized corrosion. It was primarily observed around the matrix near the graphite nodules;
4. With a high level of Cl\({}^{-}\) (500 ppm), visible rust was observed even at 17,000 ppm, although the corrosion current density remained below 6.42 \(\mu\)A/cm\({}^{2}\) in the electrochemical reaction;
5. Electrochemical impedance spectroscopy (EIS) confirmed the formation of a stable passive film with an increasing inhibitor concentration. The highest inhibition efficiency, calculated from the EIS parameter, was approximately 97.3% at 17,000 ppm inhibitor dosage under 100 ppm of Cl\({}^{-}\);
6. Overall, the inhibition performance was well-maintained within the inhibitor concentration range from 11,000 to 17,000 ppm under chloride ion concentrations below 200 ppm. However, in high-chloride environments (500 ppm), the inhibition performance became unstable or declined.  
Conceptualization, K.-H.J.; methodology, W.-S.J. and K.-H.J.; software, [PERSON]-S.J. and [PERSON]-H.J.; validation, [PERSON]-S.J. and K.-H.J.; formal analysis, [PERSON]-S.J. and K.-H.J.; investigation, [PERSON]-S.J. and K.-H.J.; resources, [PERSON]-S.J. and [PERSON]-H.J.; data curation, [PERSON]-S.J. and K.-H.J.; writing--original draft preparation, [PERSON]-S.J. and K.-H.J.; writing--review and editing, [PERSON]-H.J.; visualization, [PERSON]-S.J. and K.-H.J.; supervision, K.-H.J.; project administration, [PERSON]-S.J. and [PERSON]-H.J.; funding acquisition, [PERSON]-H.J. All authors have read and agreed to the published version of the manuscript.  
Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. Conflicts of Interest: The authors declare that there are no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript: CSW Cooling Water System CW Cooling Water FCD Ductile Cast Iron CPE Constant Phase Element
# 3D Printed High Gain Complementary Dipole/Slot Antenna Array
###### Abstract  
By employing the complementary dipole antenna concept to the normal waveguide fed slot radiator, an improved antenna element with wide impedance bandwidth and symmetrical radiation patterns is developed. This is achieved by mounting two additional metallic cuboids on the top of the slot radiator, which is equivalent to adding an electric dipole on top of the magnetic dipole due to the slot radiator. Then, a high-gain antenna array was designed based on the improved element and fabricated, using 3D printing technology, with stable frequency characteristics operated at around 28 GHz. This was followed by metallization via electroplating. Analytical results agree well with the experimental results. The measured operating frequency range for the reflection coefficient \(\leq\)\(-\)15 dB is from 25.7 GHz to 29.8 GHz; its corresponding fractional impedance bandwidth is 14.8%. The measured gain is approximately 32 dBi, with the 3 dB beamwidth around 4\({}^{\circ}\).  
waveguide slot antenna; antenna array; complementary antenna; 3D printing; electroplating Article
# Modeling of Geophysical Flows through GPUFOW
## 4 Results
### Simulation of the 2018 Etna Eruption  
The December 2018 Etna eruption started from a fissure that opened on the eastern side of Etna's South-East Crater (SEC) and propagated to the south-east, overcoming the edge of the western wall of the Valle del Bove (VdB), reaching a total length of \(\sim\)2 km. The lava flow, which lasted only three days, reached a distance of about 4.2 km and covered an area of \(\sim\)1 km\({}^{2}\). The time-averaged discharge rate (TADR) and the volume were estimated by using mid- and thermal infrared data acquired by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) through the HOTSAT system [46; 47].  
Lava flow was simulated on a 10-m DSM of Etna that covers the summit craters and a portion of the southeastern flank of the volcano, obtained by processing tri-stereo Pleiades 1A imagery acquired on 18 July 2016 using the MicMac photogrammetric library [48]. The eruptive vents were located along the vast N-S to NW-SE fracture that opened on the upper SE flank [47]. For the rheological properties, we used the typical parameters of the Etnean lava, e.g., 1360 K and 1143 K as extrusion and solidification temperatures, respectively, 2600 kg\(\cdot\)m\({}^{-3}\) as density, and 0.05 wt% as the water content.  
The lava flows simulated by GPUFLOW for the 2018 flank eruption are reported in Figure 5, together with the actual field retrieved from the PlanetScope image acquired at  
Figure 4: GPUFLOW simulations of the 2006 debris flow in the Dolomites without (**a**) and with (**b**) the bias correction. Colors represent the deposit thickness in meters. The actual debris field was redrawn from [44].  
09:19 GMT on 29 December 2018 (spatial resolution of 3 m). Lava flows were driven by using the maximum effusion rate derived from the SEVIRI data (see Figure 8 in [47]). The simulation run without the bias correction (Figure 5a) had a total length of 4.5 km and an extension of 1.35 km\({}^{2}\), while the one executed with the \(\alpha=2^{-6}\) bias correction (Figure 5b) had a length of 4.1 km, covering an area of 1.23 km\({}^{2}\). The fitting obtained was 74% for the simulation without the bias correction and 75% for the one with the bias correction. Refining the DSM resolution to 5 m improved the fit to 77%, at the cost of an order of magnitude longer runtime.
## 5 Comparative Studies  
One of the most important decisions when designing an evaluation study is whether to compare the new protocol against some existing ones. The researcher potentially weakens her protocol and its chances to be published if no such comparison is provided. However, comparing against many different protocols also does not make a lot of sense and costs a lot of efforts, since most of the protocols are not readily available.  
Thus, researchers often opt for a tradeoff, where they compare their new protocols against available and well explored ones. In the case of OppNets, these protocols are typically Epidemic, ProPhet, Spray & Wait, MaxProp and BubbleRap, as shown in Figure 4. It can be seen clearly in Figure 4 that the most widely used protocols for comparison are Epidemic, Prophet, Spray & Wait, Maxprop and BubbleRap and this trend stays the same even in the recent studies during 2015-2018.  
Figure 3: Simulation scale for different simulation environments, for all papers in our surveys which declare the number of nodes.  
We also present these results extensively in Figure 12 in Appendix A, where we have marked all protocols which have been used for comparison from other protocols. For example, the SEPR protocol from 2003 (Line 3 in the figure) has been compared against Epidemic. It can be clearly seen that freely available implementations of protocols (marked blue in the figure) are preferred options to compare against. Furthermore, the matrix in Figure 12 gets \"thinner\" towards the right bottom corner, while it should be actually filled under its diagonal.  
This comparison strategy has one important disadvantage: it does not allow the research community to evaluate newer protocols against each other. For example, both Predict & Forward from 2018 and PathSampling from 2016 were compared against Epidemic and Spray & Wait. However, which is better or to be preferred in which situations? They are obviously close enough in their application scenarios to each other, since both selected the same set of comparative protocols.  
The observations from this section lead us to only one possible way out of this problem: new protocols need to be implemented in standard simulators and their code needs to be published. This is the only possibility to enable future studies to use the most recent advances in the area. This would also have the positive side effect of researchers reproducing and confirming the results of each other and thus pushing the state of the art forward faster. Comparative studies against traditional protocols such as Epidemic can still be very valuable to explain better the new properties or to set the new protocol in context.  
_Take home message: New protocols need to be compared against recent advances and their code needs to be freely available._
# An Activity Theory-Based Approach for Context Analysis, Design and Evolution
## 1 Introduction
### Redefining Approaches through Activity Theory  
This subsection addresses the following questions: _Why use activity theory as an instrument for context modeling? How is this theory linked to software related issues?_ Given the interaction level that technology has achieved and its increasing coupling with the surrounding context, previous context modelling approaches are insufficient, as discussed previously. Thus, the need to provide a helpful instrument for designers and developers is an opportunity for multidisciplinary and for the application of conceptual tools already proved in other fields of study.  
In this regard, activity theory has inspired deep theoretical reflections in a variety of fields such as, for example, psychology [21], education [22], or management [23]. In terms of software research, activity theory has been applied to proposals in human-computer interaction (HCI) to improve development processes based on activity analysis for design rationalization [24]. Additionally, in the field of information systems, activity theory has been used to describe how the development process is carried out [25; 26].  
Activity theory can be flexibly used in different disciplines due to its technology-agnostic nature and its adaptability to different situations under study. Several researchers have recognized that activity theory is holistically rich for understanding how people do tasks together and, or, with the help of sophisticated tools, even in intricate and dynamic environments [27; 28; 29]. These references hint at how helpful activity theory may be for software design and development. In summary, activity theory provides an exceptionally comprehensive basis for understanding complex human, technological, evolutionary, and organizational issues.
## 6 Conclusions  
This paper addresses the task offloading and resource allocation problem in multi-UAV mobile edge computing (MEC) systems, where UAVs provide communication and computational services to mobile devices. The study focuses on jointly optimizing mobile device selection, multi-UAV mobility control, UAV CPU frequency adjustment, and task offloading allocation to minimize the weighted sum of task processing delay and UAV energy consumption. To tackle this non-convex optimization problem, a novel task offloading and resource allocation algorithm based on MATD3-TORA (Multi-Agent Twin Delayed Deep Deterministic Policy Gradient for Task Offloading and Resource Allocation) has been proposed. The MATD3-TORA algorithm demonstrates superior performance by effectively reducing the weighted sum of task processing delay and UAV energy consumption, outperforming traditional benchmark methods.  
Through extensive simulations, the convergence performance of MATD3-TORA was rigorously compared with benchmark methods, highlighting its faster convergence and greater stability. The algorithm's robustness was further validated by analyzing its performance under varying learning rates, exploration rates, and training strategy parameters, which consistently show its adaptability to dynamic environments. Additionally, the study examines the algorithm's performance under different task volumes, transmission bandwidths, and weight factors, demonstrating its ability to maintain optimal efficiency across diverse operational scenarios. The MATD3-TORA algorithm's superiority lies in its ability to balance exploration and exploitation, leveraging dual-critic networks, delayed policy updates, and target policy smoothing to achieve more stable and efficient optimization. These features enable MATD3-TORA to outperform traditional methods such as Edge-only, Local-only, and MADDPG, particularly in complex and dynamic multi-UAV MEC systems. This research not only validates the effectiveness of the proposed algorithm but also provides valuable insights into its potential for real-world applications requiring adaptive and robust resource management.  
Conceptualization, S.X.; methodology, Q.L.; software, X.W.; validation, C.G.; investigation, X.W.; resources, Q.L.; writing--original draft preparation, S.X.; writing--review and editing, X.W.; supervision, Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Joint Funds of the National Natural Science Foundation of China under Grant U24B20173.  
Not applicable.  
Data Availability Statement: Data available on request due to restrictions. Conflicts of Interest: The authors declare no conflicts of interest.
## 3 The Proposed Framework  
The proposed framework for the simultaneous tracking and recognition of drone targets using mmWave radar is presented in this section. A front-end mmWave radar system with three transmitting antennas (Tx) and four receiving antennas (Rx) is shown in Figure 1. The mmWave radar transmits multiple frequency-modulated continuous waveform (FMCW) chirps. These signals are received by the receiving antennas after being reflected from multiple targets in the FoV. The radar then combines the Tx and Rx signals to demodulate the FMCW signals and generate intermediate-frequency (IF) signals, creating a time-stamped snapshot of the FoV [36]. The collected sequence of IF signals is insufficiently informative and, hence, was applied to preliminary preprocessing to extract some features of the target, such as the range, velocity, and angle [20; 36]. Figure 2 shows the proposed framework, which consists of four modules, which operate using a pipelined approach as follows:  
1. Signal preprocessing: This module translates the raw information collected by the mmWave radar into sparse point clouds and eliminates the points associated with interference noises and static objects (i.e., points that appeared in the previous frame), which reveal the existence and movement of targets.
2. Clustering: This module detects different moving targets and merges the related point clouds into clusters, where each cluster represents a single moving target.
3. Tracking: This module estimates a target track in successive frames and applies an association algorithm to track multiple targets' paths.
4. Recognition: This module utilizes a CNN model to extract representative spatiotemporal features from cloud points and then classifies the detected targets as drone and non-drone targets.  
The four modules of the proposed framework are explored in detail in the following sections.