esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
At the ecosystem level, WUE is typically controlled by climate and soil variables, including precipitation, air temperature, vapor pressure deficit (VPD), and soil water content, due to their roles in energy partitioning and canopy conductance (e.g., Tong an Song [26; 27]), although the magnitude and direction of the response might be different or even opposite (e.g., [PERSON] [28]). As observed in many semi-arid ecosystems, studies on the dynamics of CO\({}_{2}\) exchange in the Caatinga biome are still incipient. Regarding the role of the Caatinga in the global and regional carbon balance, there is still need to corroborate in situ GPP measurements, to comprehend phenological controls and WUE patterns in the biome, and to better understand the Caatinga response to years of extreme drought such as 2015 [29]. Therefore, this study proposes an assessment of the seasonality of ecosystem production and its correlations with atmospheric controls (meteorological variables), vegetation indices (NDVI--Normalized Difference Vegetation Index and EVI--Enhanced Vegetation Index, which are the most used greenness proxies in the literature according to [PERSON] [30]), and GPP data estimated by remote sensing. Our objective is to answer the following questions: (i) what are the correlations, and which are the main controls and contributions of evaporative fluxes to the seasonality of Caatinga GPP? (ii) Can phenological patterns observed at the field level also be perceived at larger scales (MODIS NDVI and EVI)? (iii) What are the patterns of WUE in the Caatinga and how do they behave during climate extreme years such as 2015?
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 4 MAC Protocol Leveraging Wake-up Receivers  
This section introduces SNW-MAC [43], a protocol for data-gathering star networks that uses ULP WuRx. Traditional protocols use the duty-cycling approach to reduce energy consumption; however, this scheme does not eliminate the energy waste incurred by idle listening and overhearing. Moreover, these protocols are subject to collisions, which reduce their scalability and increase their energy consumption. SNW-MAC leverages ULP WuRx to enable asynchronous communication, minimizing the energy required to transmit a packet and making collisions impossible between packets sent by nodes belonging to the same SNW-MAC-based network. It is assumed that a Physical layer (PHY) providing an error detection mechanism is used. For example, the widespread IEEE 802.15.4 PHY provides a Cyclic Redundancy Check (CRC) error-detecting code.
## 3 Data and Methods  
### CNNs  
To analyze data from devices of the GIC monitoring system, we use a CNN for the binary classification of scalograms obtained after applying a CWT to the neutral currents. The choice of CNN is justified by the fact that this deep learning algorithm is the most popular, including for solving image classification tasks [18; 19]. Classification is a concept that categorizes a dataset, in our case, images, into classes. CNNs are a class of feedforward learning algorithm. They are designed to automatically and adaptively learn spatial hierarchies of features, from minor features to global ones. This aspect improves the generalization ability of such neural networks and makes them perfect for image classification.
## 3 Proposed Optimal Strategy
### Procedure of Optimization Strategy  
The proposed strategy focuses on the frequency regulation capability of the PMSG wind turbine. As shown in Figure 9, the procedure for the proposed optimal strategy is described as follows:  
1. Obtain the maximum power using the MPPT and pitch angle control based on the converter control.
2. Optimize the KE reserve to improve the frequency support capability of the PMSG wind turbine. The KE optimization problem is solved using the PSO algorithm, which maximizes the sum of the KE reserves to determine the de-loaded control power reference for each wind farm. If the number of iterations is maximized, the optimal decision value is used to maximize the KE reserve.
3. Operate at the de-loaded power point to maintain a certain power margin and ensure an additional KE reserve. The regulation margin is available to support the system frequency in a transient duration.
4. Check the change in the system frequency caused by the loss of generators and disturbance. In addition, monitor the frequency deviation of the power system. If the frequency deviation problem (low frequency) occurs in the power system, then go to Step 5. Otherwise, if the measured frequency is higher than the nominal frequency, the system operator provides a new command to the de-loaded control and reduces the output of the conventional generators. When the power system is stable, return to Step 3.
5. In the event of a frequency disturbance, switch the control of the trigger signal from the de-loaded control to the KE discharge control. The frequency regulation control utilizes the additional reserves due to the KE stored in the PMSG wind turbine and rotor.
6. If the operation in the power system is stabilized from the compensation, end the procedure.
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 5 Model Maintenance Algorithm
### Updating the Data Distribution Matrix D  
The better sensors have knowledge about the knowledge bases of their neighbors, the better decisions they can make. In this subsection, we focus on the data distribution updating approaches based on the data pieces sensors sense, broadcast and receive. Algorithm 2 presents the updating process of data distribution matrix \(D\) for sensor \(v_{i}\), where \(dataG\), \(dataB\), and \(dataR\) are data sets, which respectively store data sensed, data broadcast and data received by \(v_{i}\) as well as outdated data. First, for each data generated by \(v_{i}\) based on its own detection, obviously, it will update element \(D_{v_{i},d_{h}}\) to 1 (line 1-2). Second, for each data broadcast by \(v_{i}\), and for any neighbor \(v_{j}\), the probability of receiving this data is \(C_{v_{i}v_{j}}\). Therefore, according to the standard probability function, \(v_{j}\)'s probability of having data \(d_{h}\) should be updated, \(D_{v_{j}d_{h}}=1-(1-D_{v_{j}d_{h}})(1-C_{v_{i}v_{j}})\) (line 3-4). Third, for each data received by \(v_{i}\), \(v_{i}\), update \(D_{v_{i}d_{h}}\) to 1 (line 6). Also, all nodes on \(d_{j}.path\) have this data (line 8), and the nodes that are neighbors of nodes on the path have a probability of having this data, which can be calculated according to the standard probability function (line 9). Finally, for data, which relative information has been fused and outdated, its corresponding column in \(D\) should be deleted to save storage (line 10-11).
## 2 The Light Calibration Method for Line Structure based on Multiple Geometric Constraints
### Screening of Error Points According to Calibration Point Dependency  
According to the center point detection algorithm of the light strip, the pixels of the points were obtained from the grayscale distribution of the stripe in the image; these center points may not be on the same light plane because they are affected by the external environment and detection algorithm [16]. Line-structured light calibration is the process of obtaining model parameters using a statistical sample. During calibration, the sample should have enough data points, and the data points involved in the calculation need to conform to the statistical model. That is, all data points should be on the calibration plane. Since the coordinates  
\[A_{2}X_{C}+B_{2}Y_{C}+C_{2}Z_{C}-1000=0 \tag{7}\]  
According to the calibration optimization model, the light planes \(\pi_{1}\) and \(\pi_{2}\) are a pair of parallel planes. Therefore, an objective function is shown in Equations (8) and (9).  
\[F(A,B,C,D_{1},D_{2})=\min\sum\limits_{i=1}^{N}\left(F_{1}+F_{2}\right) \tag{8}\]  
\[\begin{cases}F_{1}(A,B,C,D_{1})=\min\sum\limits_{i=1}^{N}\left|AX_{Ci}^{1}+BY_{ Ci}^{1}+CZ_{Ci}^{1}+D_{1}\right|\\ F_{2}(A,B,C,D_{2})=\min\sum\limits_{i=1}^{N}\left|AX_{Ci}^{2}+BY_{Ci}^{2}+CZ_{Ci }^{2}+D_{2}\right|\end{cases} \tag{9}\]  
In the optimization objective function, the optimization parameters are the direction parameters \(A\), \(B\), and \(C\) of the parallel light planes and the constant terms \(D_{1}\) and \(D_{2}\) of the two plane equations. The initial parameters are the mean values of the corresponding parameters obtained by the least multiplication method at each position. The flow chart of the calibration method proposed in this paper based on the steps above is shown in Figure 4.  
The process of the optimization method with multiple parallel constraints is basically the same as the process of the optimization method with two parallel relationships. To improve the calibration accuracy, multiple parallel constraints can be obtained by moving the laser line multiple times. However, increasing the number of parallel geometric constraints leads to an increase in the running time of the algorithm, and locally optimal solutions can be achieved. The paper discusses the number of parallel geometric constraints through experimental methods and determines the number of constraints.  
Figure 3: Optimization model of structured light calibration with multiple geometric constraints.
## 5 Conclusions  
This paper addresses the task partitioning decision in HSR environments with MECSs. The proposed DRL-based task partitioning mechanism dynamically adjusts the task partitioning ratio in response to a real-time environment in which the number of HSTs changes every time slot, the density of passengers inside the HST, the server load of the MECS, and the data size of generated tasks change. The experimental results show that when the task occurrence rate is 20, the delay time is improved by about 5% compared to _random_ and about 13% compared to _no_partition_. When it is 50, there is no significant difference from _random_ and about 2% improvement compared to _no_partition_. There is almost no difference in task processing when it is 20. However, when it is 50, _random_ is much better. Future research will focus on integrating resource optimization and task partitioning mechanisms to develop a unified framework capable of dynamically adapting to HSR environments. This study supports uninterrupted services for passengers by ensuring stable task processing and QoS even during high-speed movement in the HSR system. Furthermore, by adjusting the task partitioning ratio to balance the load among MECSs, the approach increases the utilization of MEC system resources, reduces task execution delay, and enhances task throughput. This study also contributes to the development of scalable and intelligent transportation systems. It supports the implementation of scalable operations while maintaining QoS in environments characterized by high density and high-speed mobility. These contributions have far-reaching implications for the realization of smart cities and advancements in high-speed transportation technologies. Future research will focus on developing an integrated framework that dynamically adapts to HSR environments by combining resource optimization and task partitioning mechanisms. Given the high-speed nature of HSR environments, managing interference is critical. To address this challenge effectively, future work will also explore power resource allocation optimization, aiming to further enhance system performance under the unique constraints of HSR scenarios.  
Conceptualization, S.K. and Y.L.; methodology, S.K. and Y.L.; software, S.K.; writing--original draft preparation, S.K.; writing--review and editing, S.K.; visualization, S.K.; supervision, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 5: Average delay about number of server and task arrival rate: (**a**) Number of serving MECSs (K) (5, 8, 10); (**b**) Task arrival rate in a HST (20, 30, 40, 50).  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
This work was supported by the IITP (Institute of Information & Communications Technology Planning & Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD) grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2022-00156299).
# Beyond Manual Media Coding: Evaluating Large Language Models and Agents for News Content Analysis
## Appendix A
### Metrics  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Question** & **Type** & **M1** & **M2** & **M3** & **M4** & **M5** & **M6** & **M7** & **A** \\ \hline Q1 & categorical & 0.912 & 0.914 & **0.934** & 0.927 & 0.903 & 0.913 & 0.914 & 0.863 \\ Q2 & binary & 0.693 & 0.935 & 0.726 & 0.841 & 0.823 & 0.867 & 0.708 & **0.940** \\ Q3 & binary & 0.940 & 0.829 & 0.864 & **0.950** & 0.944 & 0.940 & 0.940 & 0.944 \\ Q4 & multi-label & 0.588 & 0.481 & 0.527 & 0.553 & 0.541 & **0.660** & 0.440 & 0.657 \\ Q5 & binary & **1.000** & **1.000** & 0.997 & **1.000** & **1.000** & **1.000** & **1.000** & 0.990 \\ Q6 & categorical & 0.753 & 0.738 & 0.732 & **0.772** & 0.699 & 0.754 & 0.640 & 0.742 \\ Q7 & binary & 0.995 & 0.997 & 0.995 & 0.995 & **1.000** & 0.997 & 0.947 & 0.990 \\ Q8 & categorical & 0.447 & 0.481 & 0.641 & 0.481 & 0.444 & **0.658** & 0.506 & 0.536 \\ Q9 & binary & 0.937 & 0.930 & **0.952** & 0.941 & 0.934 & 0.933 & 0.776 & 0.919 \\ Q10 & multi-label & 0.800 & 0.738 & 0.838 & **0.843** & 0.760 & 0.835 & 0.609 & 0.781 \\ Q11 & categorical & 0.632 & 0.813 & 0.810 & 0.804 & 0.793 & **0.841** & 0.517 & 0.809 \\ Q12 & binary & 0.781 & 0.868 & 0.800 & 0.915 & **0.929** & 0.904 & 0.807 & 0.896 \\ Q13 & binary & 0.780 & 0.888 & 0.856 & 0.851 & 0.920 & 0.910 & 0.733 & **0.925** \\ \hline \hline \end{tabular}
\end{table}
Table 10: F1-scores for binary, categorical and multi-label types of questions. All models were evaluated against the annotator’s values. The table shows each model by its abbreviation. **M1**: Meta-Llama-4-Maverick; **M2**: Qwen3; **M3**: Meta-Llama-3-3; **M4**: DeepSeek-R1; **M5**: DeepSeek-R1-Distill-Qwen; **M6**: Claude-3-7-Sonnet; **M7**:Meta-Llama-3-1; **A**:A**:**A**(Meta-Llama-4-Maverick). Bold indicates the highest value in each row.  
(MEDIA_QUESTIONS)  
Instructions:  
1. Read the provided article carefully  
2. Apply your media expertise to analyze the content  
3. Address each relevant question from your domain  
4. Provide detailed insights about journalistic approach, sourcing, framing, and presentation  
5. Focus on HOW the story is told, not just WHAT is told  
Provide a comprehensive analysis covering all relevant media and journalism aspects.
###### Abstract  
The large amount of information stored in audio and video repositories makes search on speech (SoS) a challenging area that is continuously receiving much interest. Within SoS, spoken term detection (STD) aims to retrieve speech data given a text-based representation of a search query (which can include one or more words). On the other hand, query-by-example spoken term detection (QBe STD) aims to retrieve speech data given an acoustic representation of a search query. This is the first paper that presents an internationally open multi-domain evaluation for SoS in Spanish that includes both STD and QBe STD tasks. The evaluation was carefully designed so that several post-evaluation analyses of the main results could be carried out. The evaluation tasks aim to retrieve the speech files that contain the queries, providing their start and end times and a score that reflects how likely the detection within the given time intervals and speech file is. Three different speech databases in Spanish that comprise different domains were employed in the evaluation: the MAVIR database, which comprises a set of talks from workshops; the RTVE database, which includes broadcast news programs; and the SPARL20 database, which contains Spanish parliament sessions. We present the evaluation itself, the three databases, the evaluation metric, the systems submitted to the evaluation, the evaluation results and some detailed post-evaluation analyses based on specific query properties (in-vocabulary/out-of-vocabulary queries, single-word/multi-word queries and native/foreign queries). The most novel features of the submitted systems are a data augmentation technique for the STD task and an end-to-end system for the QBe STD task. The obtained results suggest that there is clearly room for improvement in the SoS task and that performance is highly sensitive to changes in the data domain.  
search on speech; spoken term detection; query-by-example spoken term detection; international evaluation; Spanish language +
Footnote †: journal: _Applied Sciences_  
_Article_  
The Multi-Domain International Search on Speech 2020 ALBAYZIN Evaluation: Overview, Systems, Results, Discussion and Post-Evaluation Analyses
## 3 Results and Discussion
### Analysis of Influencing Factors on Carbon Emission Intensity
#### 3.2.3 Treatment Process
Conversely, the SBR process's intermittent operation and carbon resource competition result in higher overall carbon intensity, positioning the AAO configuration as a more environmentally sustainable option for wastewater treatment. To eliminate interference from other factors on carbon emission intensity and more intuitively compare the carbon emission levels of different treatment processes, this study selected four STPs with different treatment processes for comparative analysis. These STPs were chosen to be as comparable as possible in terms of design capacity, influent/effluent water quality, and power consumption per ton of water. Their basic information and carbon emission data are presented in Tables 6 and 7. The carbon emission intensity of the four STPs was ranked as follows: WWTPs4 (oxidation ditch process) < WWTPs2 (AAO process) < WWTPs3 (multi-stage AO process) < WWTPs1 (SBR process). However, due to differences in the treatment capacities of these four STPs, this ranking may not fully represent the actual carbon emission levels of each process. A larger sample size is required to draw more reliable conclusions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**WWTPs**} & \multirow{3}{*}{\begin{tabular}{c} **Design Scale/** \\ **(10\({}^{4}\) m\({}^{3}\).d\({}^{-1}\))** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Treatment** \\ **Scale/** \\ **(10\({}^{4}\) m\({}^{3}\).d\({}^{-1}\))** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Process** \\ **(10\({}^{4}\) m\({}^{3}\).d\({}^{-1}\))** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Influent Water** \\ **(Quality** \\ **(mg\(\cdot\)L\({}^{-1}\))** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Effluent Water** \\ **Quality** \\ **(mg\(\cdot\)L\({}^{-1}\))** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Power** \\ **Consumption of** \\ **per Ton Water/** \\ **(kW\(\cdot\)h/m\({}^{3}\))** \\ \end{tabular} } \\ \cline{4-7}  & & & & **COD** & **TN** & **COD** & **TN** & **(kW\(\cdot\)h/m\({}^{3}\))** \\ \hline WWTPs1 & 5 & 4.5 & SBR & 267.2 & 43.6 & 22.2 & 10.9 & 0.290 \\ WWTPs2 & 1 & 0.4 & AAO & 78.5 & 17.8 & 15.0 & 9.7 & 0.304 \\ WWTPs3 & 4 & 3.4 &
\begin{tabular}{c} Multi- \\ level AO \\ \end{tabular} & 311.1 & 45.3 & 16.7 & 6.5 & 0.337 \\ WWTPs4 & 4 & 3.8 & OD & 179.5 & 33.6 & 14.1 & 7.3 & 0.198 \\ \hline \hline \end{tabular} Note: Water quality is the annual weighted average.  
\end{table} 
Table 6: Basic information of four wastewater treatment plants with different treatment processes.
# Underwater Electromagnetic Sensor Networks--Part I: Link Characterization +
## 3 Results
### Validation of Simulations with Horizontal Variations  
In Figures 10-12, attenuation measured with loop antennas of 40 cm radius is shown, as well as simulation results for horizontal antenna movements. In Figure 10, a full sweep from 10 KHz to 1 MHz is presented. Very good agreement between simulation and measurement results is obtained, except for the areas of higher frequencies and short distances. In this case, the differences are caused by two factors: the struggle of locating antennas precisely at short distances where the errors are proportionally higher and the tremendous attenuation suffered in higher frequencies. As attenuation is greater in this band, a small error of position leads to enormous variation in attenuation values. It is important to notice that the instruments' noise floor is around \(-125\) dBm, measured with an RBW (Resolution Bandwidth) of 3 KHz and a 50 \(\Omega\) load. Loop antenna in air captures significantly more noise level while, submerged on the seabed, the noise level is around \(-125\) dBm again. It was found that the dominant noise is generated within the measuring equipment, and most ambient noise captured by the antenna in the seabed is below that limit. This noise floor limits the smallest signal power that can be measured. It was flat and maintained the same value during several measuring campaigns.  
Figure 10: Path loss (dB) comparison up to 8 m (10 KHz–1 MHz).  
Figure 9: Influence of loop radius (100 KHz–1 MHz).  
In Figure 11, a zoom in the lower band is presented to analyse the results at 3-8 m. There is a remarkable agreement between simulation and measurement results, which makes possible the validation of simulation tools. Even in Figure 12 (8-13 m), some interference appears around 90 KHz.
# Simultaneous Detection of Nine Key Bacterial Respiratory Pathogens Using Luminex xTAG(r) Technology
## 2 Materials and Methods
### Primers for Pathogen Amplification  
Nine sets of primers were used in this assay. The primers for these nine pathogens were selected from previously published studies, and primers were selected on the basis of having similar melting temperatures so that simultaneous amplification using the same conditions could occur for all multiplex reactions. The expected amplicon sizes were between 94 and 650 bp. It is commonly known that small amplicons provide improved signal intensity and hybridization efficiency in the MPLT system [16; 17]. Each forward primer targeting the nine pathogens was modified by a unique 24-base oligonucleotide \"TAG\" sequence at the 5' terminus and was used to connect with the MagPlex-xTAG Microsphere (Luminex Corporation, Toronto, ON, Canada). The MagPlex-xTAG Microspheres are MagPlex beads (6.5-micron superparamagnetic beads) where each bead region is covalently pre-coupled with the 24-base oligonucleotide \"anti-TAG\" sequence (complementary to the \"TAG\" sequence). A 12-carbon amine was incorporated between the \"TAG\" sequence and primer. All reverse primers were biotinylated at the 5' terminus. The primer sequences, their target genes, and the size of the resulting amplicons are shown in Table 2. All primers were synthesized by [PERSON] (Beijing, China). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Species** & **Strain ID and Description** \\ \hline
1 & _Streptococcus pneumoniae_ & ATCC 49619 \\
2 & _Haemophilus influenzae_ & L3 (clinical isolates from China) \\
3 & _Moraxella catarrhalis_ & ATCC 25238 \\
4 & _Pseudomonas aeruginosa_ & ATCC 27853 \\
5 & _Klebsiella pneumoniae_ & A915 (clinical isolates from China) \\
6 & _Staphylococcus aureus_ & ATCC 25923 \\
7 & _Legionella pneumophila_ & ATCC 33152 \\
8 & _Streptococcus pyogenes_ & ATCC 19615 \\
9 & _Mycoplasma pneumoniae_ & ATCC 29342 \\
10 & _Escherichia coli_ & ATCC 25922 \\
11 & _Bordettella pertussis_ & ATCC 9797 \\
12 & _Corynebacterium diphtheriae_ & CMCC 38106 \\
13 & _Mycobacterium tuberculosis complex_ * & H37R \\ \hline \end{tabular}  
* _Mycobacterium tuberculosis complex_ (20.1 ng/\(\mu\)L) and _M. pneumoniae_ (15.5 ng/\(\mu\)L) were supplied as DNA extracts by the Chinese Center for Disease Control and Prevention.  
\end{table} 
Table 1: List of bacterial pathogens used in this study.