esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Resistant Composite Membranes for Carbon Dioxide Separation from Methane
###### Abstract  
Membranes that are resistant to water vapor permeation have potential in natural gas sweetening by reducing the need for pretreatment. The perfluorinated polymer Teflon AF1600 has proven resistance to water vapor, which is adapted here in the form of composite membranes consisting of a Teflon AF1600 protective layer on membranes of the polyimide 4,4\({}^{\prime}\)-(hexafluoroisopropylidene) diphthalic anhydride 2,3,5,6-tetramethyl-1,4-phenylenediamine (6 FDA-TMPDA) as well as Polymer of Intrinsic Micro-porosity (PIM-1). The permeability of CO\({}_{2}\) and CH\({}_{4}\) through the composite membranes was shown to be a function of the respective permeabilities of the individual polymer layers, with the Teflon AF1600 layer providing the majority of the resistance to mass transfer. Upon exposure to water, the composite membranes had reduced water permeation of 7-13% compared to pure membranes of 6 FDA-TMPDA and PIM-1, because of the water resistance of the Teflon AF1600 layer. It was observed that water permeated as clusters through the composite structure. Under CO\({}_{2}\)-CH\({}_{4}\) mixed gas conditions, 6 FDA-TMPDA layer permselectivity performance was reduced and became comparable to Teflon AF1600, while the PIM-1 layer retained much of its high permselectivity performance. Importantly, at water activities below 0.2 the PIM-1 composite membrane achieved higher permeability for CO\({}_{2}\) compared to water.  
composite membranes; Teflon AF1600; PIM-1; polyimide; water; CO\({}_{2}\)/CH\({}_{4}\) separation +
Footnote †: journal: Applied Sciences
# A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes
## 3 The Proposed HCD Algorithm
### Network Model  
An IoT based on wireless nodes and a BS with an unlimited power supply connected to the network are the primary considerations in our model. Data is sampled by sensor nodes and they are routed in order to be sent to the BS. Also, each node can perform as a CH or non-CH node. Some assumptions of the network model are as follows.  
* \(N\) wireless nodes are randomly distributed in \(M^{\star}M\) environment.
* Nodes are heterogeneous.
* All nodes and the BS are fixed.
* All nodes can set transfer power in terms of the distance between nodes.
* CH can aggregate data.  
The first order radio model of energy consumption in this proposed method is similar to the LEACH protocol [3]. Energy consumption transfer is defined as Equation (1).  
\[E_{TX}(i,K,d_{ij})=\left\{\begin{array}{ccc}E_{dec}K+E_{fs}{Kd_{ij}}^{2}&if&d _{ij}\leq d_{o}\\ \\ E_{elee}K+E_{mp}{Kd_{ij}}^{4}&if&d_{ij}>d_{o}\end{array}\right. \tag{1}\]  
where \(K\) is the number of data bits and \(d_{ij}\) is the distance between two nodes--\(i\) and \(j\). \(E_{dec}\) is energy consumption in sender or receiver circuit to send a data bit. \(E_{fs}\) and \(E_{mp}\) are energy consumptions in the sender amplifier for sending a data bit in terms of distance between the receiver and sender. Also, \(d_{0}\) value, which is a threshold distance, is obtained from Equation (2).  
\[d_{0}=\sqrt{\frac{E_{fs}}{E_{mp}}} \tag{2}\]  
In an energy model, a receiver energy consumption is defined as Equation (3).  
\[E_{RX}(i,K)=E_{elec}K \tag{3}\]  
According to the data aggregation model used in our simulation, it is assumed that the information collected by a set of \(N\) nodes can be packed in a \(k\) bit package.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 7 Multi-User Multiple-Input Multiple-Output (MU-MIMO)
### Related Works
#### 7.2.1 User Selection  
The access point must select the stations that can transmit simultaneously in UL MIMO. MUSE [161] improved the throughput by selecting the most accurate set of transmitters in the uplink. Primarily, the access point initiates an uplink OFDMA transmission and records the Channel State Information (CSI) from each station. Regarding the obtained information, the access point selects the optimal set of users for MU MIMO. MUSE proposed to use EDCA to reduce the number of collisions when sending the CSI.  
The authors of [162] applied reinforcement learning techniques for efficient MU MIMO user selection. They considered a Software-Defined Networking (SDN) controller in charge of the learning process that monitors the network to reduce the volume of control packetscompared with a distributed monitoring process. The requests from stations are forwarded to the controller, which decides how to allocate resources and configure the link parameters dynamically depending on the load.  
[PERSON] et al. [163] formulated a throughput maximization problem in MIMO WLAN. They mitigated the hidden node problem by optimizing the CCA threshold. Finally, they maximized the average number of successful transmissions with respect to the density of nodes, multi-antenna configuration, channel fading, and path loss.  
[PERSON] et al. [67] proposed a new MU MIMO frame format in both uplink and downlink to maximize the transmission efficiency. They formulated the problem as NP-complete and considered the status of the buffer and the bitrate of the stations. They solved the problem to find the optimal length of the multi-user frame.
# Refining Zero-Shot Text-to-SQL Benchmarks via Prompt Strategies with Large Language Models
## 2 Related Works  
High-quality datasets are crucial for training and evaluating large language models (LLMs) [8]. Foundational datasets in the Text-to-SQL field include WikiSQL [1], ATIS [18], SParC [16], and CoSQL [17]. WikiSQL focuses on simple queries derived from Wikipedia tables [1], lacking complexity like JOINs [2]. ATIS offers examples in the air travel domain but its simple queries limit generalizability [18]. SParC emphasizes conversational parsing across topics [16], while CoSQL targets dialogue scenarios using Spider's databases [17; 19]. These datasets, while foundational, are increasingly inadequate for modern LLMs due to limited complexity and scope [7].  
The advent of neural approaches marked a shift from rule-based systems. Seq2 SQL [1] introduced reinforcement learning, followed by SQLNet [2], which improved efficiency without RL. SyntaxSQLNet [3] enhanced cross-domain performance using syntax tree networks. These methods laid the groundwork for LLM-based solutions like DAIL-SQL [4]  
Figure 1: Example from the BIRD dev dataset (Question 608) illustrating a logical misalignment. The question requests the comment created on 19 July 2010 at 7:25:47 PM, but the provided SQL answer matches the content created on 19 July 2010 at 19:16:14.0, returning incorrect data. This highlights a common issue in BIRD, where approximately 30% of queries fail to align with their SQL outputs, complicating zero-shot Text-to-SQL evaluation.  
and SQLPaLM [5], leveraging pretraining and dynamic context learning [6]. PICARD [20] introduced constrained decoding to refine autoregressive outputs.  
Today, Spider [14] and BIRD [12] are prominent benchmarks. Spider targets complex, cross-domain parsing [14], but LLMs like LLaMA [10] and PaLM [21] have diminished this challenge. Zero-shot prompting, pioneered by GPT3 [8], has gained traction, with GPT-4 [9] and LLaMA excelling without training [11], though logical reasoning gaps persist [22]. Recent advancements include multi-agent frameworks like MAC-SQL [23] and SQLFixAgent [24], chain-of-thought prompting [25], and open-source efforts like CodeS [26]. Techniques such as SEA-SQL [27], QDA-SQL [28], OpenSearch-SQL [29], MCS-SQL [30], and Mag-SQL [31] enhance semantic accuracy, few-shot learning [32], and generative approaches. Surveys like [6; 7; 33] highlight trends toward real-world complexity, yet ambiguous 'dirty' data remain a challenge [23; 34]. Our work refines BIRD into a clear, complex benchmark for zero-shot LLM evaluation, building on prompt design insights [35] and addressing data quality issues [9; 36] to test reasoning robustly.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 5 Evaluation
### Deep Learning Techniques  
The development of DL techniques was divided into the optimal design and training of two ANN models, using two different libraries: _Scikit-Learn_ and the _Keras_ module of _TensorFlow_. ANNs, and particularly Multilayer Perceptrons (MLPs), are built from various layers of nodes and connections that replicate the neural structure of the human brain [52].  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{\(\mathbf{C}\)} & \multicolumn{3}{c}{_Kernel_} \\ \cline{2-4}  & _poly_ & _rbf_ & _sigmoid_ \\ \hline
0.25 & 97.65 & 97.66 & 95.96 \\ \hline
0.5 & 97.65 & 97.71 & 95.94 \\ \hline
0.75 & 97.65 & 97.75 & 95.82 \\ \hline
1 & 97.65 & 97.78 & 95.81 \\ \hline \end{tabular}
\end{table}
Table 7: Accuracy (\(\%\)) (_mean_test_score_) of _cv_results_ attribute from the _Grid Search_ in the SVM.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{\(\mathbf{C}\)} & \multicolumn{3}{c}{_Kernel_} \\ \cline{2-4}  & _poly_ & _rbf_ & _sigmoid_ \\ \hline
0.25 & 21,030 & 13,876 & 14,659 \\ \hline
0.5 & 28,807 & 10,277 & 18,695 \\ \hline
0.75 & 42,183 & 9328 & 12,235 \\ \hline
1 & 47,460 & 9446 & 12,869 \\ \hline \end{tabular}
\end{table}
Table 8: Time (s) (_mean_fit_time_) of _cv_results_ attribute from the _Grid Search_ in the SVM.  
Regarding their development, it is important to note that the feature scoring and dimensionality reduction steps applied to the ML models in Section 5.3 were omitted. This is because ANNs internally analyze and score the importance of features within the network, so these steps are not necessary to include [53].
# Experiment and Analytical Model for Pore Structure of Early-Age Composite Cement Pastes by LF-NMR
## 2 Experimental Materials and Methodology
### Experiment Method  
In this experiment, the PQ001 LF-NMR spectrometer in Figure 2, manufactured by Niumag Corporation (Suzhou, China), was utilized. The equipment consists of a control unit, a magnet unit, an industrial control computer integrated with a spectrometer system, and a temperature control unit. Specimens were inserted into cylindrical glass tubes measuring 27 mm in diameter and 200 mm in length for LF-NMR analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Mixture Parameters**} & \multicolumn{4}{c}{**Quantity of Experimental Substances (kg/m\({}^{3}\))**} \\ \cline{2-10}  & **w/b** & **Slag** & **Fly Ash** & **Silica Fume** & **Content \%** & **Slag** & **Fly Ash** & **Silica Fume** & **Water** \\ \hline WC30 & 0.30 & 0 & 0 & 0 & 1628 & 0 & 0 & 0 & 488 \\ WC35 & 0.35 & 0 & 0 & 0 & 1505 & 0 & 0 & 0 & 527 \\ WC40 & 0.40 & 0 & 0 & 0 & 1399 & 0 & 0 & 0 & 560 \\ FA15 & 0.40 & 0 & 15 & 0 & 1154 & 0 & 204 & 0 & 543 \\ FA30 & 0.40 & 0 & 30 & 0 & 922 & 0 & 395 & 0 & 527 \\ SL15 & 0.40 & 15 & 0 & 0 & 1187 & 209 & 0 & 0 & 558 \\ SL30 & 0.40 & 30 & 0 & 0 & 974 & 417 & 0 & 0 & 557 \\ SI5 & 0.40 & 0 & 0 & 5 & 1316 & 0 & 0 & 69 & 554 \\ SI10 & 0.40 & 0 & 0 & 10 & 1236 & 0 & 0 & 137 & 549 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Blend ratios of cement slurries.  
Figure 2: Low-field nuclear magnetic resonance instrument. (1 magnetic box; 2 spectrum system and controlling machine; 3 console unit; 4 displayer).  
After powering on, the free induction decay (FID) sequence was first used to calibrate the standard oil sample, obtaining the frequency offset (O1) of the radio-frequency signal and the 90\({}^{\circ}\) pulse width (P1). Subsequently, the transverse relaxation time (T2) of the samples was measured using the LF-NMR spectrometer. Before testing, the parameters of the FID and Carr-Purcell-Meiboom-Gill (CPMG) sequences were configured, as detailed in Table 3.  
Studies by [PERSON] et al. [31] and [PERSON] et al. [33] indicated that the pore diameter of cement pastes can be determined using the LF-NMR transverse relaxation time \(T_{2}\), as represented by the following equation:  
\[d=CT_{2} \tag{1}\]  
where \(d\) represents the pore diameter, nm; \(T_{2}\) denotes the transverse relaxation time of the water molecules at the pore surface, ms, and \(C\) is the conversion factor--48 nm/ms.
# A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance
## 2 Related Works
### Multi-Scale Blocks  
Multi-scale blocks are usually employed in both the encoder and decoder [3; 12]. DeepLabv3+ [4], for instance, utilizes dilated convolutions at different rates in the encoder to achieve multi-scale feature extraction. However, feature extraction at different scales lacks a well-defined fusion mechanism, often relying solely on simple concatenation. Unlike previous methods, MCAG not only captures multi-scale features in the encoder but also introduces spatial and channel reconstruction modules to better fuse features. Additionally, two branches are introduced to further integrate features at larger scales. These advancements enable our model to achieve higher performance than many existing segmentation methods.
# Development and Characterization of High-Fiber, Gluten-Free Pasta for Celiac Disease Patients
## 3 Results and Discussion
### Chemical Composition of the Pasta Treatments
#### 3.1.2 Ash Content  
The percentages of ash content in the control sample and pasta treatments were 1.28 for the control and 1.17, 1.41, 1.49, 1.50, 1.47, and 1.65 for T1, T2, T3, T4, T5, and T6, respectively. Compositional differences between the flour blends resulted in differences in pasta ash content [22]. The results of the ash content indicated that there are significant differences between the control and gluten-free pasta treatments, except for T1. The ash contents of the gluten-free pasta treatments were generally higher than those of the control group. It is shown that T6 showed the highest ash content and was significantly higher (\(p<0.05\)) compared to other treatments and the control sample. The ash content of the pasta treatments increased with the addition of lupin flour and flazseed, since the average ash content in lupin was 2.65% [23] and 3-4% in flazseed [24].
## 2 Improved Wavelet Threshold Denoising Method
### Basic Wavelet Threshold Denoising Method  
For many structural signals, the low-frequency component is more informative as it contains the underlying character of the structure while the high-frequency component shows the details of the signal and includes lots of noise. The wavelet threshold denoising decomposes the signal into different frequency scales and removes the noise from the high-frequency component at various decomposition levels.  
As shown in Figure 2, the detailed process of wavelet threshold denoising is described as follows:  
1 Wavelet decomposition: Select the appropriate wavelet basis function and the number of decomposition layers \(n\). Conduct wavelet decomposition for \(n\) times and obtain the detail coefficients cD\({}_{i}\) and appropriate coefficients cA\({}_{i}\);  
2 Threshold setting: Select appropriate thresholds to process the decomposed detail coefficients (high-frequency signals) of each layer. The detail coefficients lower than the threshold are viewed as meaningless noise and set to zero, whereas the detail coefficients higher than the threshold value are retained;  
3 Wavelet reconstruction: The denoising sequence is reconstructed by an inverse wavelet operation using the detail coefficients of each layer and the appropriate coefficients of the \(n\)th layer.  
Figure 1: Schematic diagram of the Mallet algorithm.
## 3 Results
### Ecosystem Resilience to Drought in China  
Figure 6a shows the spatial distribution of ecosystem resilience to drought in China from 1982 to 2015, which evaluated the ability of an ecosystem to absorb and recover from the drought disturbances. On the whole, the terrestrial ecosystems were mostly resilient to drought in southeastern part of China and non-resilient to drought in northwestern China. Ecosystems in approximately 36.0% of Chinese territory were resilient to drought, which were mainly distributed in central parts in Northeast China, east parts in Inner Mongolia and most parts in Central China and Southwest China. On the contrary, about 21.7% of Chinese territory showed severely non-resilience, mainly located in almost all of Northwest China, central parts in North China, Tibetan Plateau and some regions in Inner Mongolia. Southwest China had the highest \(\mathrm{R_{d}}\) (\(\mathrm{R_{d}}\) = 1.0142) compared with other regions, followed by Northeast China and Central China. Moreover, ecosystems in Southeast China, North China and South China were slightly non-resilient to drought with \(\mathrm{R_{d}}\) values of 0.9829, 0.9722 and 0.9032, respectively. Inner Mongolia was found to be moderately non-resilient, Northwest China (\(\mathrm{R_{d}}\) = 0.4509) and Tibetan Plateau (\(\mathrm{R_{d}}\) = 0.7677) had relatively lower \(\mathrm{R_{d}}\) values, which meant that ecosystems in both regions were fragile and difficult to adapt to drought disturbances.  
Figure 5: The spatial distributions of correlation coefficient between WUE and previous-year SPEI (**a**) and of difference in Akaike Information Criterion value (\(\Delta\)AIC) between Equations (1) and (2) (**c**) in China during the period 1982-2015 (white color indicates non-vegetated regions, and the black and red lines show the boundaries of sub-regions and eco-climatic zones). The regional mean correlation coefficients for different vegetation types (CRO: croplands, EBF: evergreen broadleaf forests, DBF: deciduous broadleaf forests, ENF: evergreen needle-leaf forests, DNF: deciduous needle-leaf forests, MF: mixed forests, SHR: shrub lands and GRA: grasslands) between WUE and current-year (previous-year) SPEI are exhibited in (**b**). The error bars in (**b**) were standard deviation of Pearson correlation coefficient in each vegetation type.  
At vegetation type scale (Figure 6c), DNF, DBF, EBF and ENF exhibited relatively higher ecosystem resilience to drought (\(\text{R}_{\text{d}}\geq 1\)) during the past 34 years. The croplands, shrub lands and mixed forests showed slightly non-resilient to drought with \(\text{R}_{\text{d}}\) values varying from 0.9 to 1. Grasslands had the lowest \(\text{R}_{\text{d}}\) (\(\text{R}_{\text{d}}=0.8148\)), which meant that grassland was relatively more vulnerable to drought among all vegetation types.
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Adaptive Filtering Methods
#### 2.3.2 Recursive Least Squares (RLS) Filter  
The Recursive Least Squares (RLS) filter is an adaptive method that adjusts the filter coefficients in real time to minimize the error between the target and the acquired signal. This technique provides excellent adaptive performance, being particularly useful in dynamic environments where signal characteristics can vary rapidly. The RLS filter minimizes a weighted root mean square error function recursively using an iterative estimation technique.  
The RLS filter can be used to remove noise and artifacts from the ECG signal while maintaining real-time adaptive performance. A notable example from the literature is [41], who showed that the RLS filter can be used to filter muscle noise and other interference from ECG signals, improving the signal for accurate detection of arrhythmias, including ventricular fibrillation.
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 3 Programmable Deterministic Multi-Queue Accelerator Model
### Deterministic DMA for Serverless  
The deterministic DMA module consists mainly of the DMA component, the tx engine (transmission engine), the Tx scheduler(transfer scheduler) [28], and the PTP hardware clock(HC) [29]. The queue transmission engine is specific to a hardware queue and processes transmission instructions from the transmission scheduler. It is responsible for coordinating the operations required to transmit packets while controlling the enabling/disabling of the queue by writing registers. The PTP hardware clock provides the accelerator with high-precision timing with sub-microsecond errors from the host, which, combined with the Tx scheduler's control of the transmission engine, enables on-time and accurate transmission.  
When the application makes a deterministic sending request for transmission, the driver writes the timestamp along with the description information into the descriptor ring, and the DMA controller detects the change in the queue tail pointer of the Descriptor Ring and finds the next available descriptor in the transmission ring. The controller first reads the timestamp and, while copying the data in the buffer pointed by the descriptor to the specified transfer queue of the Accelerator via the PCI bus, communicates the read timestamp information to the transfer scheduler. At this point, the Tx scheduler obtains the current time by timing it with the PTP hardware clock. It issues a command for the transmission engine corresponding to the transmission queue at the time specified by the timestamp. Then, the transmission queue forwards the data out through the MAC chip. After sending, the Accelerator updates the head pointer of the ring, and the DMA controller initiates a hard interrupt to notify the CPU to release the packets in the buffer.  
Figure 3: Multi-queue receiving process.  
The above transferring of the control of packet sending to the tx engine of the queue through timestamps set by the serverless node combined with the precise timing function of the PTP hardware clock simplifies the operation of the serverless node and provides deterministic control of the transmission.
###### Abstract  
To localize a moving source in shallow water with a single hydrophone, a passive range localization method based on synthetic aperture beamforming is proposed. First, the horizontal wavenumber spectrum excited by the source is obtained by synthetic aperture beamforming. Then, according to the theoretical derivation (when the integration time is short, the maximum value of the horizontal wavenumber spectrum is related to the average horizontal wavenumber and the radial velocity of the source), the radial velocity can be obtained after obtaining the average horizontal wavenumber. Finally, in the case where there is a closest point of approach (CPA), the range can be recovered from estimation of the range and time of CPA, and from the constant source speed alone the linear track by fitting the source velocity with the model of radial velocity. The only a priori information required is the sound velocity in water. The processing results using simulated data and SWellEx-96 experimental data show that the proposed method can effectively estimate the range of a moving source in shallow sea.  
range localization; shallow water; single hydrophone; synthetic aperture beamforming 2020