esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automatic Participant Detection Framework for Event Tracking on Twitter
## 3 Materials & Methods
### Framework  
APD shares query expansion's objective to describe the input query, or the event description, in more detail. However, query expansion is insufficient in isolation because Twitter users do not only talk about relevant participants before the event starts. Therefore in this section, we present a framework that combines query expansion and entity set expansion.  
The APD framework receives as input a corpus of tweets collected shortly before the event. This period covers the time when users are likely to start discussing the event more intensely, and can be used to generate machine-readable information about the event in advance. The details about the datasets used in this research are shown in Table 1. APD processes these datasets in six steps to finally output a list of participants that could be involved in the event once it starts:1. Extract named entities, or candidate participants, from the corpus;
2. Score and rank the candidates;
3. Filter out the low-scoring candidates;
4. Resolve valid candidates to a semantic form so they become participants;
5. Extrapolate the seed set of participants; and
6. Post-process the participants.  
The first three steps correspond roughly to pseudo-relevance feedback in query expansion. The next steps overcome the challenges of APD that we observe in Section 4: Twitter's discussions mention many named entities that are not relevant to the event while missing many valid participants. Step 4 confirms which named entities are likely to be valid participants, and Step 5 is an entity set expansion technique that looks for the missed participants. Figure 2 shows the flow of this process from the collection of tweets, which are the algorithm's input, to the list of participants, or the output.  
Figure 2: The process of the APD (Automatic Participant Detection) framework takes as input a corpus of tweets collected before an event and outputs a list of event participants.  
We describe our implementation of this framework next. Due to the time-consuming nature of our evaluation, described in Section 4, we set the values of our implementation's parameters empirically. All tools and algorithms implemented for this article are available on GitHub [24].
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 4 Implementation and Analysis of Real-Time SR Algorithms in IoT Context  
### Implementation on IoT Hardware  
Deploying SR algorithms on IoT-specific hardware presents unique challenges and opportunities. To gain practical insights into real-world applications, we explored approaches conducted on microcontrollers and low-end FPGAs.  
For microcontroller implementations, processors like the ARM Cortex-M family offer a balance between power efficiency and computational performance. These processors are well suited for lightweight SR models, such as simplified versions of FSRCNN or ESPCN, which have shown potential in resource-constrained environments.  
FPGA-based solutions have emerged as a promising alternative for deploying DNN-based SR algorithms at the edge. For instance, [PERSON] et al. [84] proposed a lightweight version of the FSRCNN for FPGAs by replacing the deconvolutional layer with a convolutional layer, significantly reducing the memory footprint. However, this compression resulted in some performance degradation compared to the original FSRCNN. Additionally, NeuriCam [85] introduced a dual-mode IoT camera system that integrates a low-power (1.1 mW) grayscale camera with a duty-cycled high-power (100 mW) color camera. This system employs a neural network decoder on a nearby gateway to reconstruct HR color video from the LR grayscale stream supplemented by periodic HR keyframes.
# Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique
## 1 Introduction
By analyzing approximately four years (2019-2022) of magnetic field data from the first satellite of the China Seismo-Electromagnetic Satellite (CSES) constellation, we conducted a statistical study of whistler occurrences, revealing their spatial and temporal distribution within the middle-to-low latitude (from \(-40^{\circ}\) to \(40^{\circ}\)), top-side ionosphere. The manuscript is organized as follows: Section 2 describes the data and methods employed in this analysis; Section 3 presents the results of applying the WhISPER technique; finally, Section 4 provides a discussion of the results and presents the key conclusions drawn from this study.
###### Abstract  
This paper presents a novel energy management system for AC microgrids that integrates a parallel implementation of the Multi-Verse Optimizer (MVO) with the Successive Approximations method for power flow analysis. The proposed approach optimally schedules battery energy storage systems (BESSs) in both grid-connected and islanded modes, aiming to minimize energy losses and reduce CO\({}_{2}\) emissions. Numerical evaluations on a 33-node AC microgrid demonstrate significant improvements: in the grid-dependent mode, energy losses drop from 2484.57 kWh (base case) to 2374.85 kWh, and emissions fall from 9.8874 Ton(CO\({}_{2}\)) to 9.8693 Ton(CO\({}_{2}\)). Under the self-powered configuration, energy losses and emissions are curtailed from 2484.57 kWh to 2373.53 kWh and from 16.0659 Ton(CO\({}_{2}\)) to 16.0364 Ton(CO\({}_{2}\)), respectively. The results highlight that the proposed method outperforms existing metaheuristics in solution quality and consistency. This work advances microgrid scheduling by ensuring technical feasibility, reducing carbon footprint, and maintaining voltage stability under diverse operational conditions.  
AC microgrids; multi-verse optimizer (MVO); battery energy storage; CO\({}_{2}\) emissions minimization; energy loss reduction; dual-mode operation; power flow analysis Article
## 3 Results
### Osmotic Dehydration of PEF Pre-Treated Tomatoes
#### 3.2.2 Water Activity, pH and Titractable Acidity of PEF Treated Tomatoes during OD  
The results concerning water activity of untreated and PEF-treated tomato samples are presented in Figure 3a. Pulsed Electric Fields treatment led to a significant change in \(a_{w}\) evolution only at treatments exceeding 50 pulses. At 100 and 300 pulses, initial water activity (after treatment and instantaneous submersion in the osmotic medium) is significantly (\(p<0.05\)) lower than the corresponding value of the untreated sample. This observation signifies an instantaneous release of free water immediately after processing, caused by electroporation. A swift submersion in the osmotic medium is adequate to remove this released free water and decrease water activity. Although, overall water activity values are lower at more intense processing conditions, the temporal evolution (rate of \(a_{w}\) decrease) was not affected by the treatments. Finally, a treatment intensity exceeding 100 pulses did not lead to a significant (\(p>0.05\)) reduction in water activity during the course of dehydration. Water activity values below 0.91 are commonly adopted as minimum values where bacterial proliferation is significantly reduced [26]. In combination with a low pH value (less than 4.6), OD of PEF treated tomatoes at 100 pulses for 120 min or more is suitable for obtaining a product of high quality (Figure 3b).
# Parameterized Optimization in Uncertain Graphs--A Survey and Some Results
## 2 Distribution Models for Uncertain Graphs
### Set-Based Dependency (SBD) Model  
The uncertain graph \(\mathcal{G}=(V,E,p)\) satisfies the additional properties that \(E\) is partitioned into \(\{E_{1},E_{2},\ldots E_{t}\}\) of \(E\), for some \(t\geq 1\). Further, \(p\) satisfies the property that for any two edges \(e_{1}\) and \(e_{2}\) that belong to the same part in the partition, \(p(e_{1})\
eq p(e_{2})\). Typically, this partition is a fixed partition of \(E\) coming from the domain where the edge failures occur according to the SBD model. The sampling procedure definition of the SBD model is as follows: the edge sets are considered in order from \(E_{1}\) to \(E_{t}\), and for \(1\leq i<j\leq t\), the edges in the set \(E_{i}\) are considered before the edges in the set \(E_{j}\). For each \(1\leq i\leq t\), the edges in \(E_{i}\) are considered in decreasing order of the value \(p\). For each \(1\leq i\leq t\), when an edge \(e\in E_{i}\) is considered, it is sampled with probability \(p(e)\) independent of the outcome of the previous samples. If the outcome selects the edge \(e\) (that is, if \(e\) survives), then the next edge in \(E_{i}\) is considered. Otherwise, all the remaining edges of \(E_{i}\) that are to be considered after \(e\), are considered to have failed, and the set \(E_{i+1}\) is considered. The procedure terminates after considering \(E_{1},\ldots,E_{t}\). The edge subgraph consisting of the selected edges (survived edges) is the outcome of this sampling procedure. The number of possible worlds that have a non-zero probability of being an outcome of the sampling procedure is \(\prod_{i=1}^{t}(|E_{i}|+1)\). To summarize, under the SBD model, for each \(1\leq i\leq t\), the survival of an edge \(e\) in a set \(E_{i}\) implies that every edge \(e^{\prime}\in E_{i}\) with a greater survival probability than that of \(e\) would have survived. Further, the edge samples of edges in two different sets are mutually independent. As a consequence of this, we have the following observation.  
**Observation 2**.: _The RF model on an uncertain graph \(\mathcal{G}=(V,E,p)\) is identical to the SBD model on \(\mathcal{G}\) and \(E\) is partitioned into \(m\) sets each containing an edge of \(E\)._
## 3 Results and Discussion
### Sorption Capacity  
DSPE experiments were performed with different loading concentrations (10-500 mg L\({}^{1}\)) to investigate the influence of the standard concentration on the extraction efficiency. Results with the poly(n-VIM/C\({}_{6}\)-bis-VIM) polymer with a monomer to crosslinker ratio of 11.50 to 1 are displayed in Figure 5 and given in Table S11.  
It was observed that the concentration of phenolic acids in the sample had a direct influence on extraction recoveries. At a concentration of 10 mg L\({}^{-1}\) no phenolic acids were detected in the supernatant, indicating a quantitative binding; however, the highest recoveries could not be obtained in this particular case. It is possible that there existed a limited number of binding sites on the polymer that have very strong adsorption properties towards phenolic acids. Hence, a small proportion of analytes was strongly bound and was not eluted under the applied conditions. As a result, they remained attached to the polymer and significantly reduced the extraction recoveries. At analyte concentrations of 20, 50 and 100 mg L\({}^{-1}\), only CinA was detected in the supernatant and at 250 mg L\({}^{-1}\) all analytes were detected except for ChlorA. In contrast, at 500 mg L\({}^{-1}\) all analytes were visible in  
Figure 4: Analyte recovery results obtained from DSPE experiments with [C\({}_{6}\)-bis-VIM] [Br] crosslinked vinylimidazole polymers with different monomer to crosslinker ratios. Standard deviations are given as error bars.  
Figure 5: Extraction recoveries obtained from DSPE experiments with the poly(n-VIM/C\({}_{6}\)-bis-VIM) polymer with a monomer to crosslinker ratio of 11.50 to 1, by applying different loading concentrations. Standard deviations are given as error bars.  
the chromatogram. The extraction recoveries obtained from the DSPE experiments with the highest sample concentration (500 mg L\({}^{-1}\)) were limited by the loading capacity of the sorbent. Depending on the type of phenolic acid, 1.4 to 19.0% of the loaded concentration remained dissolved in the supernatant. These findings are in accordance with the results from Section 3.3, as the binding capacities were less than 50 mg g\({}^{-1}\) polymer, which indicated an overloading of the sorbent. Even though a high proportion of the bound analytes could be eluted from the polymer, the recoveries were still lower than at other concentration levels. The optimal concentration range for the extraction of phenolic acids with 10.0 \(\pm\) 0.2 mg of sorbent was between 20 and 250 mg L\({}^{-1}\), with the highest average recovery observed at 100 mg L\({}^{-1}\).
###### Abstract  
The transformer-based deep learning approach represents the current state-of-the-art in machine translation (MT) research. Large-scale pretrained transformer models produce state-of-the-art performance across a wide range of MT tasks for many languages. However, such deep neural network (NN) models are often data-, compute-, space-, power-, and energy-hungry, typically requiring powerful GPUs or large-scale clusters to train and deploy. As a result, they are often regarded as \"non-green\" and \"unsustainable\" technologies. Distilling knowledge from large deep NN models (teachers) to smaller NN models (students) is a widely adopted sustainable development approach in MT as well as in broader areas of natural language processing (NLP), including speech, and image processing. However, distilling large pretrained models presents several challenges. First, increased training time and cost that scales with the volume of data used for training a student model. This could pose a challenge for translation service providers (TSPs), as they may have limited budgets for training. Moreover, CO2 emissions generated during model training are typically proportional to the amount of data used, contributing to environmental harm. Second, when querying teacher models, including encoder-decoder models such as NLLB, the translations they produce for low-resource languages may be noisy or of low quality. This can undermine sequence-level knowledge distillation (SKD), as student models may inherit and reinforce errors from inaccurate labels. In this study, the teacher model's confidence estimation is employed to filter those instances from the distilled training data for which the teacher exhibits low confidence. We tested our methods on a low-resource Urdu-to-English translation task operating within a constrained training budget in an industrial translation setting. Our findings show that confidence estimation-based filtering can significantly reduce the cost and CO2 emissions associated with training a student model without drop in translation quality, making it a practical and environmentally sustainable solution for the TSPs.  
knowledge distillation; NMT; LLMs +
Footnote †: journal: Applied Sciences
# Verifying Mutual Exclusion Algorithms with Non-Atomic Registers
## 3 Uppaal Modeling with Atomic Registers
### Checking Algorithm's Correctness
#### 3.2.1 Mutual Exclusion Property  
The following query asks if it is always true that in all the states of the state graph the number of processes simultaneously found in their critical section, is less than or equal to 1:  
A[] (sum(i:pid)Process(i).CS) \(\leq\) 1
## 4 Results and Discussion  
### Spatial Patterns of Interannual CoV of the Productivity Variable  
Figure 6 shows the spatial distribution of the interannual CoV of cumulative NDVI over the study period. The lowest interannual CoV values (i.e., those less than 0.1) were concentrated along high elevations indicating that these areas had the most stability and least temporal changes over the study period. These areas are dominated by needleleaf forest. The interannual CoV generally increased with decreasing elevation. The range from 0.21 to 0.56 corresponds to the highest interannual variability, a sign of a greater variability and change in vegetation productivity. The areas corresponding to the highest interannual CoV values are mostly low to mid-elevation where shrubland is the dominant vegetation type. The boxplot in Figure 7 shows the interannual CoV of cumulative NDVI across these three dominant vegetation cover types in the area: needleleaf forest, shrubland, and grassland. The figure indicates that needleleaf forest shows least variability in comparison with shrublands and grasslands. This suggests that needleleaf forest productivity is less sensitive to ongoing droughs, and that shrubland and grassland are more sensitive. This is likely related to the level of resilience among these vegetation communities modulated by their deeper rooting systems and access to deeper water during drought episodes and during the dry season.
###### Abstract  
In this paper, we propose a deep learning method with convolutional neural networks (CNNs) using skip connections with layer groups for super-resolution image reconstruction. In the proposed method, entire CNN layers for residual data processing are divided into several layer groups, and skip connections with different multiplication factors are applied from input data to these layer groups. With the proposed method, the processed data in hidden layer units tend to be distributed in a wider range. Consequently, the feature information from input data is transmitted to the output more robustly. Experimental results show that the proposed method yields a higher peak signal-to-noise ratio and better subjective quality than existing methods for super-resolution image reconstruction.  
onvolutional neural networks; deep learning; super-resolution; image reconstruction; skip connection; layer group +
Footnote †: journal: Applied Sciences
# Dynamic Markov Model: Password Guessing Using Probability Adjustment Method
## 3 Method
### Defects in Markov Model  
3.2.1. Problems in Standard Markov Model  
Cracking passwords using the standard Markov model has the problem of generating large scale repeated passwords. To illustrate this problem, we utilized the RockYou [58] dataset to train the model. RockYou dataset is a large scale dataset that includes almost 21,000,000 real passwords, therefore it is widely used in password guessing tasks. We made the model generate \(10^{9}\) passwords, and we observed the result from two aspects: the number of repeated passwords and the repetition rate. The repetition rate expresses the proportion of repeated passwords in the generated passwords. The results (Table 1) show that there are only \(2.41\times 10^{8}\) unique passwords, and the other \(7.59\times 10^{8}\) passwords are all repeated passwords, which means the model wastes about 76% of computing resources. In addition, we find that there is a positive correlation between the number of generated passwords and the number and proportion of repeated passwords. More unique passwords are accompanied by more waste, which severely limits the cracking efficiency.  
Repeated passwords not only constrain efficiency, but also restrict the cover rate. The cover rate indicates the hit ratio of generated passwords to the passwords in the test set. One reason is that repeated passwords take up many appearance opportunities within  
Figure 2: Markov model training process (order of model is 2).  
limit generation time, so the other passwords that are possible to hit have no chance to be generated. Another reason is that the essence of the standard Markov model is equal to random sampling in the original distribution \(D_{ori}\). Due to the randomness of the sampling method, high-probability passwords will appear with high frequency, while low-probability passwords will not show up often or even disappear, and then passwords with low probability in the test set are hard to cover.
## 1 Introduction
As the total amount of heavy metals may be derived from anthropogenic and natural origins, it is difficult to determine a priori which of these two contributions will \"decide\" the variability [23]. Accordingly, the detection of contamination patterns with multivariate or multiple sample data involves determining which variables are of the most importance. Usually, the statistical methods of factor analysis (FA), principle component analysis (PCA), cluster analysis (CA), and correlation analysis have been widely used in evaluations of heavy metal pollution [24; 25; 26; 27]. Previous studies often focused on a single heavy metal or influencing factor, and little research has been conducted on a larger scale, considering the landscape characteristics, in-situ soil conditions, or land cover. It is usual that the variation in soil nutrient status across landscapes is often analyzed using such techniques as regression and geostatistical analysis [28]. Other new multivariate methods are also recommended [29; 30]. Recently, canonical correspondence analysis (CCA) has been widely applied in soil-environment research [31]. While there are many studies investigating roadside pollution in China, especially in urban areas, no studies thus far have demonstrated the spatial variability of heavy metals in tropical ecosystems [2; 32; 33; 34]. The variation and accumulation of metallic pollutants along roads in this region require further study. In this paper, we aim to verify the hitherto lacking evidence on the ability of vegetation characteristics and topographical factors to decrease pollutants in tropical regions in China. For this purpose, the main highway that connects the Puer and Jinghong cities in Yunnan Province, Southwest China, was chosen. In the current study, special emphasis is placed on the relative ability of different factors. Based on previous studies, we assumed that soil pollutants decrease with increasing distance from the road [16; 26], while the trends were not similar under different land uses. In addition, the pollutant accumulation and variations are related to the local landscape features [35; 36; 37; 38]; however, the tropical areas in China need to be further explored. We considered the vegetation coverage and relative elevation of soil samples to the road as influencing factors, according to the field investigation. Thereby, our study uses multivariate methods to investigate the relationship between heavy metal contamination in roadside soils and its influencing factors. The objectives are to (1) reveal the spatial distribution of heavy metals near different roads; (2) use the CCA method to discern the relative importance of different influencing factors to immigate heavy metal soil pollution.
# Classification Models for Nitrogen Concentration in Hop Leaves Using Digital Image Processing
## 2 Materials and Methods
### Obtaining Images of Hop Leaves  
Hop leaf collection for image processing (DIP) purposes was conducted in two distinct periods. The first collection took place at the beginning of 2022, 15 days after fertilization. In this case, three leaves from the upper third of each plant were selected and photographed using a mobile phone inside a dark chamber with known dimensions, illuminated by a LED light, generating images with a resolution of \(960\times 1280\) pixels, i.e., approximately 1.2 megapixels (MP). The hop leaves were photographed alongside a white square (2 cm per side), used as a reference for DIP calculations. In total, 72 images were generated and saved in JPEG format.  
The use of a mobile phone camera was intentional, aiming to evaluate the feasibility of using an accessible and cost-effective imaging system for nitrogen classification in hop plants. Mobile devices are widely available and have been increasingly employed in precision agriculture due to their portability, ease of use, and capacity to support real-time monitoring applications.  
The second collection took place one year after the first. In this instance, six leaves from the upper third of each plant were collected. If a plant had not reached maturity by the time of collection or had died, leaves were collected from the remaining developed plants within the same treatment, along with three additional leaves from a deficient plant. After collection, the leaves were scanned using an ECOSYS M6295 cidn printer (Kyocera Document Solutions Inc., Osaka, Japan), together with a white background and a black reference square (5 cm long), generating images with a resolution of 4960 \(\times\) 7014 pixels (-34.8 MP), totaling 150 images, which were saved in JPEG format.