esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 3 Chlorophyll Fluorescence Detection near the Ground  
### Active Chlorophyll Fluorescence Measurements  
The active methods exploit the chlorophyll fluorescence kinetics to measure the relative parameters, including the modulated method (pulse-modulated chlorophyll fluorimeter [25; 26; 27; 28; 29; 30])  
Figure 1: Generation of chlorophyll fluorescence, shows that chlorophyll molecules on the excited state release energy to return to the ground state through heat dissipation, photosynthesis, and fluorescence [59].  
Figure 2: Chlorophyll fluorescence spectrum simulated by FluorMOD (the input parameters of FluorMOD are in default values). (**a**) The reflectance and fluorescence spectrum; (**b**) one convex at 690 nm in the actual reflectance; (**c**) one convex at 740 nm in the actual reflectanceand non-modulated method (non-modulated fluorimeter [31; 32]). The modulated and non-modulated chlorophyll fluorimeters are designed based on the Kautsky effect [53; 61; 62; 63].  
A modulated fluorimeter uses the modulation measuring light in the whole process. When the measuring light from a modulated fluorimeter has the same frequency as the fluorescence, the fluorescence values can be recorded in every physiological state, including under the strong light. Therefore, a modulated fluorimeter can be used in field experiments. The world's first pulse amplitude modulation (PAM) fluorimeter was designed and manufactured by [PERSON] (1983). Chlorophyll fluorescence induction dynamics parameters measured by PAM can reflect the variation of the vegetation [64] and are commonly used to detect the physiological state of the vegetation [25; 26; 28; 29; 30; 54; 65]. In addition, the chlorophyll fluorescence parameters are often combined with other related vegetation physiological parameters (stomatal conductance, leaf water potential, and so on) to analyze vegetation stress [66; 67].  
Non-modulated fluorimeters are designed to utilize a fast data acquisition system in order to record Kautsky induction or fast chlorophyll fluorescence induction [61], such as the Pochet plant efficiency analyzer (PEA), handy PEA, and multi-function PEA.  
Two different measurement methods can be used to obtain the chlorophyll fluorescence kinetic parameters. Non-modulated fluorimeters have a simple structure and are easy to operate. Continuous light is used in the whole measurement process. By contrast, modulated fluorimeters have a modulated light source to obtain chlorophyll fluorescence measurements in every physiological state. In fact, modulated fluorimeters are commonly used to detect vegetation stress [66; 68].
## 3 Results
### Release Profile Under Digestion Conditions  
To replicate the digestive conditions and analyze how CBG is released from CNC-CTAB in the gastrointestinal tract, an in vitro digestion model was used. This model followed the standardized European INFOGEST procedure for simulating static in vitro digestion [19; 20]. The experiment was conducted for both the encapsulated CBG and free CBG, with the release patterns displayed in Figure 7.  
CBG exhibited a biphasic release pattern, characterized by an initial slow release in the gastric phase followed by a sustained release in the intestinal phase. Approximately 10% of CBG was released within the first 2 h in the gastric environment, while an additional 25% was released over the subsequent 4 h in the intestinal phase (Figure 7). This release profile is particularly interesting for intestinal delivery of CBG, as it allows for prolonged exposure of the bioactive compound to intestinal cells, promoting its biological activities,  
Figure 7: Release profile of CBG during the in vitro simulation of gastrointestinal digestion using the CNC-CTAB delivery system. Variations in letters denote statistically significant differences (\(p<0.05\)) observed between the time points.  
such as antioxidant and anti-inflammatory effects. Additionally, the sustained release of CBG in the intestinal phase may enhance its antimicrobial activity against pathogenic gut microbiota, including _Campylobacter_ spp. The observed release pattern suggests that the Korsmeyer-Peppas model could be appropriate for describing CBG's release kinetics. This model is particularly appropriate for polymeric systems and can account for both Fickian diffusion and non-Fickian transport mechanisms. The Korsmeyer-Peppas model is especially useful for interpreting complex and time-dependent release patterns, such as the biphasic release observed with CBG in the gastric and intestinal phases [46]. The results highlight the potential of encapsulated CBG, due to its unique interactions with cannabinoid and serotonin receptors, for applications aimed at intestinal inflammation and microbial infections.
## 1 Introduction
Many strategies have been proposed in the literature to address the producer mobility issue, which can be divided into two broad approaches: Anchor-less [11; 12; 13; 14; 15; 16] and Anchor-based [17; 18; 19; 20; 21]. In the Anchor-based approach, a fixed network node is used as a Domain Name System (DNS) server, a mapping server, or a rendezvous node to redirect packets toward the Mobile Producer (MP) or as a depot to store the producer's data before a handoff occurs. In contrast, in the Anchor-less approach, producer mobility is generally supported using updates of the forwarding information base table in the routers either between producers and consumers or between the producer's new and old PoA after handoff occurs. This paper aims to design a strategy for supporting UAV mobility and ensuring seamless low-latency data delivery in NDN architecture. Table 1 lists the key abbreviations used in this article with their definitions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Acronyms** & **Definition** \\ \hline NDN & Named Data Networking \\ IP & Internet Protocol \\ UAV & Unmanned Aerial Vehicle \\ DMMS & Distributed Mobility Management Scheme \\ PoA & Point of Attachment \\ DNS & Domain Name System \\ MP & Mobile Producer \\ Inpt & Interest Packet \\ PIT & Pending Interest Table \\ FIB & Forwarding Information Base \\ CS & Content Store \\ AS & Autonomous System \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of abbreviations used throughout the paper. The main contributions of this work are as follows:  
1. We propose a low-overhead strategy based on strategically deployed decentralized cooperative Anchors, which proactively forwards the consumer's packet toward producers when handoff occurs to achieve seamless data delivery with minimum packet loss and delay. 2. We introduce a new forwarding strategy that combines the NDN's default forwarding and the location-based forwarding strategy. 3. A Kalman filter method is used to predict the UAV's location coordinates after the handoff. 4. Using a realistic scenario, we implemented and evaluated the performance of the DMMS against two well-known solutions, Kite [17] and MAP-ME [13]. This paper is structured as follows. Section 2 provides a concise overview of the NDN architecture, its forwarding pipeline, the associated mobility issues, and a review of the pertinent literature. In Section 3, the design and functionalities of DMMS are detailed. Section 4 offers an evaluation of our proposed solution, examining its performance and effectiveness. Lastly, Section 5 concludes the paper and outlines potential avenues for future research in this domain.
# Weather Radar in Complex Orography
## 4 Solutions and Results
### Design Considerations
Nevertheless, it is good practice to generate the radar products with a sufficiently high space-time resolution of the order of 1 km and a few minutes, and then to do the integration later, depending on the specific setting of the application. The Cartesian products in Switzerland are generated with a resolution of 1 km and updated every 2.5 min, a reasonable choice, considering the requirements listed in Section 2.
## 7 Limitations of Study  
Our study highlights that translation can introduce undesirable patterns in the data. While translation facilitates the creation of multilingual datasets, it can also result in superficial artifacts that affect model generalization. These artifacts may cause models to rely on surface-level patterns rather than deeper semantic understanding, impacting their performance on non-translated, naturally occurring data. However, there were certain limitations to our study.  
We attempted to fine-tune the Gemma 2B model; however, the results were suboptimal compared to mBART and XLM-Roberta. The Gemma 2 billion model required more fine-tuning than just a single epoch to yield competitive results, leading us to exclude it from our final evaluation.  
Additionally, due to resource constraints, we could only perform a single run of our experiments on the training dataset, which consisted of 150,000 sentences, and the testing dataset of 5010 evaluation sentences from XNLI. This approach restricted our ability to perform thorough hyperparameter tuning and validation, potentially affecting the generalizability of our findings.  
Maintaining linguistic patterns during machine translation, especially for low-resource languages, can be enhanced through advanced techniques such as hybrid model architectures and back-translation. For instance, Google Translate employs a combination of transformer encoders and RNN decoders to capture complex dependencies in the source text, improving translation quality. Additionally, back-translation uses monolingual data to generate synthetic parallel data, aiding in fluency and contextual accuracy for low-resource languages. Complementing these methods, we propose incorporating domain-specific fine-tuning, where models are pre-trained on large multilingual corpora and then fine-tuned on domain-specific data to better preserve linguistic nuances. This approach, combined with multilingual transfer learning through M4 modeling and sophisticated data filtering, can significantly enhance the maintenance of linguistic patterns in machine translation [19,20].
# Three Dimensional Digital Sieving of Asphal Mixture Based on X-ray Computed Tomography
## 1 Introduction
developed an image segmentation method for multi-size particles and calculated dimensions along three principal axes, which was determined from the analysis of moments [12]. The gradation curves based on traditional sieve analysis, equivalent diameter, maximum dimension, intermediate dimension, and minimum dimension were compared. However, these techniques, based on indirect parameters, utilized only partial characteristics of aggregate shape to evaluate gradation, making them unable to exactly capture the real sieve opening that the aggregate passed through. In addition, there is a need to study the potential error brought by indirect parameter in describing particle size. The aim of this study is to develop 3D digital sieving technique used to evaluate the gradation of the asphalt mixture in X-ray CT images, which considers both the shape of aggregate particles and the square opening of the mechanical sieve. For this purpose, the definition of digital sieve size was proposed and the corresponding program was developed using Matlab(tm) (Natick, MA, USA) to obtain the digital sieve size. After that, laboratory experiments consisting of epoxy-filled aggregate specimens were conducted to calibrate the program. Moreover, the digital sieving technique was used to estimate the gradation of stone mastic asphalt mixtures.
# Process Discovery for Event Logs with Multi-Occurrence Event Types
## 2 Related Works
### Graph Neural Networks in Process Mining
Unlike Euclidean domains, defining convolution on graphs involves complex theoretical considerations due to the lack of a straightforward definition. Two main approaches have emerged: spectral and spatial methods. Spectral Methods: These rely on the convolution theorem, which equates convolution in the spatial domain to a product in the frequency domain. Although the theorem is derived for Euclidean spaces, some approaches extend it to graphs by utilizing the graph. However, spectral methods face some limitations: (a) They are sensitive to changes in graph topology, as minor structural variations can significantly impact convolution results; and (b) the computation requires matrix diagonalization, which is computationally expensive due to the lack of an efficient graph-based fast Fourier transform (FFT). Spatial Methods: These approaches attempt to replicate the classical convolution operation directly on the graph's spatial domain. While they aim to preserve the graph's structural information, many methods simplify the graph too much, potentially overlooking critical details.
## 4 Results and Discussion  
In this section, a series of simulations that include ten connected homogeneous agents (Assumption 3) were assumed to be initially located at the planar coordinates (refer to Table 1). With optimal SPSA parameter settings and ET settings, as presented in Table 2, in a feedback control system in a broadcast and communication environment (Assumption 1), the agent was expected to reach consensus and meet at a rendezvous point at the desired target point located at (80, 80), as shown in Figure 6. The SPSA gain of \(a_{k}\) and \(c_{k}\) depended on Equations (17) and (18). The agent worked based on the \"objectives\" function determined in Equation (2) and the termination criteria, based on the measurement state error value, which should reach zero. The results obtained specifically in relation to the efficiencies of the time and iterations of this research were based on results obtained using MATLAB 2015B simulation software with the Intel(r) Core(tm) i5-2410M CPU @ 2.3 GHz processor, running on a 64-bit operating system.  
\[a_{k}=a/(t+1+A)^{a_{1}} \tag{17}\]  
\[c_{k}=c/(t+1)^{\gamma_{1}} \tag{18}\]  
The evaluation of agent performances in terms of time, iteration, trajectory and number of channels taken by the multi-agent to reach the rendezvous point in a few time runs were then recorded. This started with evaluation of the BET consensus controller, and the BTT consensus controller was then observed in order to compare the effectiveness of the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Agent** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Agent & & & & & & & & & & \\ position & 5,5 & 10,5 & 5,10 & 10,10 & 15,5 & 15,10 & 15,15 & 20,5 & 20,10 & 20,15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Agent state position.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SPSA Parameters** & **Values** \\ \hline \(a\) & 0.2 \\ \hline \(A_{1}\) & 30 \\ \hline \(\alpha_{1}\) & 0.6 \\ \hline \(c\) & 1 \\ \hline \(\gamma_{1}\) & 0.06 \\ \hline \(\sigma_{l}\) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal simultaneous perturbation stochastic algorithm (SPSA) and event-triggered (ET) parameter settings for the broadcast event-triggered (BET) controller.  
Figure 6: Agent 2D workspace (initial state position and desired target).  
proposed controller with the conventional sampling system. The results are divided into two sections, starting with the BET and followed by the performance comparison of the BET and BTT in finding the consensus in relation to the desired rendezvous target.
## 2 Materials and Methods  
### Datasets  
We conducted experiments on two widely used public breast ultrasound datasets, the BUSI dataset [19] and Dataset B [21]. The BUSI dataset contains 780 images acquired by two types of ultrasound equipment (LOGIQ E9 ultrasound and LOGIQ E9 Agile ultrasound system) in Baheya Hospital. The average image size of these images is \(500\times 500\) pixels. Radiologists manually segmented the boundaries of breast lesions in each ultrasound image using Matlab. Dataset B contains 163 breast US images from different women, which were scanned with a Siemens ACUSON Sequoia C512 system 17L5 HD linear array transducer (8.5 MHz) at the UDIAT Diagnostic Centre of the Parc Tau'II Corporation, Sabadell (Spain), in 2012. In the ground truth (GT) images, the boundaries of breast lesions were delineated by professional radiologists. The STU dataset comprises 42 BUS (breast ultrasound) images, each with an average size of \(128\times 128\) pixels. These images were obtained by the Imaging Department of the First Affiliated Hospital of Shantou University using a GE Voluson E10 ultrasonic diagnostic system. Due to the limited number of images in the STU dataset, it was solely employed as external validation data to assess the segmentation network's generalization performance.
## 4 Proposed Video Latency Measurement Technique
In this case, all video stream data are transferred over the UDP while TCP is used only for sending RTSP protocol packets. When TCP is selected all data is transferred over TCP connection only, greyed out UDP channels in Figure 2 are not used in this case. The encryption string length denotes the maximum amount of data which can be encrypted/decrypted at once. For this we used three sets of strings; 128 bytes, 256 bytes and 512 bytes. Additionally, there are two options available to select the encoding algorithm (i.e., MJPEG or H264). Encoding of the data was done by the IP camera, while the stream was decoded in the latency measurement application utilizing the FFMPEG library. Details of each node are given below:  
Figure 3: Screenshots of applications, (a) latency measurement and (b) timestamp sender. Figure 2: Software architecture.
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 2 Analysis of Seismic Damage Scenes in Highway Tunnels
### Analysis of the Influencing Factors of Earthquake Disasters in Portal Sections of Highway Tunnels
#### 3.4.1 Concrete Chipping and Lining Structure Collapse  
Concrete chipping and lining structure collapse are the most common types of damage in highway tunnel sections passing through fault crushing belts. The main reason for this is that the tunnel structure suffers from a large shear force due to the large fault movement caused by the earthquake, and the tunnel lining structure cannot resist this shear force. During the Wenchuan Earthquake, the Longxi Tunnel and Jiujiaya Tunnel passed through fault crushing belts with widths of 10 m and 64 m, respectively, and the lining structure collapsed with concrete chipping, which were the most seriously damaged parts of the two tunnels. The reason is that a large amount of elastic strain energy is released during an earthquake, and the upper and lower walls of the fault produce different displacement and stress responses due to different mechanical properties, resulting in mutual extrusion and fault movement, resulting in a large fault movement of the tunnel structure on the faulted surface to produce a large shear force. The lining structure of the tunnel itself cannot resist this external force, and the lack of seismic measures leads to the occurrence of this damage.  
The fault crushing belts of the two tunnels are wider, and the fault movement is greater when an earthquake occurs, which causes a large displacement between the lining structures of the tunnel in the crossing section, resulting in concrete chipping and lining structure collapse, which are engineering geological factors. The strength of the tunnel structure cannot resist the external force at the fault fracture zone, and no special seismic measures are set up, resulting in concrete chipping and lining structure collapse of the tunnel structure, which are tunnel structure factors.
## 3 Results
### Investigation of Synthesized CT Complex  
#### 3.2.1 Infrared  
**The FTIR spectrum of quinine-DDQ CT complex**: The FTIR spectrum of the quinine-DDQ complex is shown in Figure 5. The band attributed to the -OH stretching frequency originally appearing at 3285 cm\({}^{-1}\) in the pure sample shifted to 3247 cm\({}^{-1}\) in the quinine-DDQ complex. The 38 cm\({}^{-1}\) shift in the value of (O-H) band could be attributed to the stretching vibration of a proton attached to the donation site of the donor. In addition, the band located at 2223 cm\({}^{-1}\) stretching frequency assigned to the cyano group (C\(\equiv\)N) in free DDQ shifted to 2209 cm\({}^{-1}\). The characteristic band of the C=O appeared at 1674 cm\({}^{-1}\) in pure DDQ shifted to 1697 cm\({}^{-1}\). The band at 1508 cm\({}^{-1}\) which is assigned to the aromatic C=C stretching frequency in shifted to 1498 cm\({}^{-1}\). The C-O stretch at 1241 cm\({}^{-1}\) shifted to 1246 cm\({}^{-1}\). Finally, the C-N band previously located at 1095 cm\({}^{-1}\) exhibited a slight shift to 1110 cm\({}^{-1}\) for the complex. The shift of the IR bands revealed a donor-to- acceptor charge transfer of \(\pi\)-\(\pi^{\ast}\) interaction.  
**The FTIR spectrum of quinine-TCNQ CT complex**: The FTIR spectra of quinine-TCNQ complex are shown in Figure 6. Comparison of the individual spectrum revealed significant band shifts after complex formation. The quinine OH stretching band previously located at 3285 cm\({}^{-1}\) shifted to 3247 cm\({}^{-1}\) in the Q-TCNQ complex. The 38 cm\({}^{-1}\) shift in the value of (O-H) band could be attributed to the stretching vibration of a proton attached to the donation site of the donor. Another significant change is observed for the cyano group (C\(\equiv\)N) which shifted from 2257 to 2179 cm\({}^{-1}\). In addition, the band associated with the aromatic C=C stretch shifted from 1429 cm\({}^{-1}\) to 1433 cm\({}^{-1}\).  
In conclusion, comparing the FTIR spectra of free quinine, the electron donor, free DDQ, electron acceptor, and the Q-DDQ complex on one side and the Q-TCNQ complex on the other side has revealed that the IR spectra of the CT complexes exhibit a shift in the wave numbers of some bands as well as the superposition of other bands. It is also noticeable that some bands have become different in shape and intensity. This variation could be an indicative of molecular association leading to the formation of the CT pair. At the molecular level, the CT formation may be initiated by an electron transfer from either the aliphatic nitrogen atom of the quinuclidine subunit or the quinoline subunit in quinine to the electron-deficient DDQ or TCNQ counterpart.
## 3 Results
### Spatial Changes Analysis of RSEI in the Shendong Mining Area from 1990 to 2023  
As shown in Figure 4, from 1990 to 2023, the spatial distribution of _RSEI_ exhibits an overall trend of improvement, with a noticeable increase in areas classified as \"Good\" and \"Excellent\" over time. In 1990, large portions of the region were dominated by \"Poor\" and \"Fair\" _RSEI_ classifications, particularly in the central and southern areas. Specifically, in 1990, the area proportion classified as \"Good\" was 46.31%, while the proportion classified as \"Excellent\" was only 0.04%. By 2023, significant improvements are evident, with the \"Good\" classification area increasing to 74.83% and the \"Excellent\" classification expanding significantly to 23.83%, indicating a general enhancement in ecological conditions. The trend map for 1990-2023 reveals distinct spatial variations in _RSEI_ changes, supported by specific area proportions of different trends. Regions showing \"Highly significant increases\" account for 11.92% of the total area, while \"Significant increases\" and \"Marginally significant increases\" make up 8.04% and 23.43%, respectively. Areas with \"No significant increase\" dominate, covering 55.71% of the region. In contrast, regions with decreasing trends are minimal; \"Highly significant decreases\", \"Significant decreases\", and \"Marginally significant decreases\" account for only 0.017%, 0.025%, and 0.022% of the total area, respectively. Regions with \"No change\" occupy 0.14% of the area. These proportions indicate that the majority of the study area experienced stable or improving ecological conditions, with only a negligible portion showing a decline. Overall, the data highlight a positive trend in _RSEI_ across most of the region, reflecting successful ecological restoration or improvement efforts, with the majority of areas demonstrating stable or improved ecological quality over the 33-year period.