esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result
### Historical Relationships between Drought Index and SPI, SPEI and GPP  
Figure 4 depicts the spatial distribution of the correlation between the three indices and the meteorological drought indices (SPI and SPEI). The results show that the TVPDI has the best correlation with meteorological drought, followed by the SDCI and TVMPDI. The correlation of the TVMPDI with the SPI and SPEI was not as good as that with the SDCI and TVPDI (Figure 4), and the regions with low correlation were in the central and  
Figure 3: Temporal correlation between TVMPDI and several remote sensing drought indices, including VHI (**a**), SDCI (**b**), SMCI (**c**) and TVPDI (**d**).  
Figure 2: Scatter plot of the TVMPDI and SPEI at the meteorological station from April to October: (**a**) April; (**b**) May; (**c**) June; (**d**) July; (**e**) August; (**f**) September; (**g**) October.  
western regions of the study area (Figure 4a,b). The correlation of the TVPDI with the SPI and SPEI was the best in the whole region (Figure 4e,f).  
Figure 5 shows the correlation between GPP and the drought index. The area with a positive correlation between the TVMPDI and GPP accounts for 78.59%, and the significant area is mainly concentrated in the central and eastern part of the study area, which accounts for more than 20% (Figure 5e). In contrast, the area with a positive correlation between the TVPDI and GPP was only 47.5%, and the significant area accounts 5.7% (Figure 5f). The positive correlation area and significant area of the SDCI were not as high as that of the TVMPDI (Figure 5d). We also compared the correlation between the SMCI and GPP for cross-validation (Figure 5c).  
Figure 4: Temporal correlation between the TVMPDI (**a,b**), SDCI (**c,d**) and TVPDI (**e,f**) with SPI and SPEI.  
Figure 5: Temporal correlation between GPP and dryness indices, including SPI (**a**), SPEI (**b**), SMCI (**c**), SDCI (**d**), TVMPDI (**e**) and TVPDI (**f**).  
In addition, the relationship between GPP and drought indicators (SPI and SPEI) was negative in the middle of the study area (Figure 5a,b). In particular, the SPEI showed a strong negative correlation with GPP in areas where GPP and drought indices were positively correlated.
# DANet: Temporal Action Localization with Double Attention
## 2 Related Work
### Vision Transformer in Video Understanding  
Transformer [19] has shown superior performance in the field of natural language processing (NLP), which is at the center stage of data-driven artificial intelligence. As the key component in Transformer, the self-attention mechanism enables the establishment of long-term context reliance on the input sequence. Subsequently, many researchers in NLP [29; 30; 31; 32] incorporate Transformer into their methods to yield groundbreaking performance. Furthermore, to achieve better performance, many methods [33; 34] incorporate Transformer into the field of computer vision (CV) (e.g., Vision Transformer (ViT) [33]). Specifically, ViT transforms the input image into a sequence of tokens for the input model by dividing the image into small pieces, adding extra learnable class embeddings, and feeding them into stacked Transformer blocks. In addition, many Transformer-based models [35; 36] have been proposed to solve video understanding tasks.  
However, most Transformer-based methods adopt the global self-attention mechanism, which may ignore local variations and thus affect the inference of the boundary timestamps for action instances. Therefore, some Transformer-based methods adopt the local self-attention mechanism, rather than the global self-attention mechanism. However, adopting the local self-attention mechanism cannot accurately capture all relevant time intervals and action instances. Obviously, capturing both global and local information simultaneously is key. To capture the most critical local features and model global contextual of temporal information, we propose two kinds of mechanisms (see Section 3.2 for more details).
## 2 Materials and Methods
### GC-IMS Analysis  
#### 2.4.1 Instrument Parameters  
Analyses of GC-IMS were performed on a FlavourSpec GC-IMS from Gesellschaft fur Analytische Sensorysteme mbH (G.A.S., Dortmund, Germany). A gas chromatographic column was FS-SE-54-CB-1(5% phenyl, 1%vinyl, 94% methyl polysiloxane) capillary column (15 m \(\times\) 0.53 mm, 1 \(\upmu\)m film thickness). Nitrogen of 99.99% purity was used as a carrier gas at a programmed flow as follows: 2 mL min\({}^{-1}\) for 2 min, 20 mL min\({}^{-1}\) for 8 min, 100 mL min\({}^{-1}\) for 10 min, 150 mL min\({}^{-1}\) for 5 min. After injection, the carrier gas was passed through the injector in order to drive the sample into the column, which was kept at 60 \({}^{\circ}\)C for timely separation. The total GC runtime was 30 min. Data were acquired in thepositive ion mode. Data were viewed and processed by using the software LAV (version 2.2.1) from G.A.S. The identification of VOCs was based on comparing RI and the drift time with the GC-IMS library. For analysis, 2 g of sample was placed in a 20 mL vial that was then closed with magnetic caps. After 20 min incubation at 65 \({}^{\circ}\)C, 500 \(\upmu\)L of sample headspace was automatically injected into the heated injector (85 \({}^{\circ}\)C) of the device by means of a headspace autosampler from CTC Analytics (Zwingen, Switzerland). In order to exclude random errors of the system, each sample was determined twice in parallel.
## 3 Simulation and Optimization of the Joint
### Effect of the Structural Parameters
#### 3.6.2 Optimization Scheme of the Joint  
The simulation results show that the structural parameters of the thread have a great influence on the stress distribution. Therefore, these three parameters--the taper, thread height, and thread angle--are optimized to produce a more uniform stress distribution.  
* **Optimum scheme 1--Compound-tapered thread:** The simulations show that the stress during the first turn of the thread is the highest in any case. Therefore, reducing the stress in the first turn can make the overall stress distribution more even. The smaller the taper is, the smaller the stress, so the thread near the inner shoulder is designed to create a smaller taper. Cut the top surface of the thread near the inner shoulder to the same height so that the taper is zero and the rear thread taper is unchanged, as shown in Figure 16a. This turns the thread into a compound taper of 0 + 1:32. This turns the thread into a compound taper. The simulation results show that the thread stress concentration factor is 2.97, which is 15% lower than that before optimization.
* **Optimum scheme 2--Increased thread height:** The simulation results show that the stress concentration factor of the thread decreases as the thread height increases. By increasing the thread height to 2.44 mm while keeping the other parameters constant, as shown in Figure 16b, the thread stress concentration factor is 3.39, which is 1% lower than that before the optimization.
* **Optimum scheme 3--Reduced thread angle:** According to the simulation results, the stress concentration factor of the thread decreases as the thread angle decreases. By decreasing the thread angle from 30 degrees to 15 degrees while keeping the other parameters constant, as shown in Figure 16c, the thread stress concentration factor is 2.89, which is 18% lower than that before optimization.
# Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions
## 3 DFT-Based LS Channel Estimation Methods
### Improvements in the Related Methods
#### 3.2.2 Methods Controlled with Residual Error Energy  
Another approach to select the final set of the impulse response coefficients is based on the iterative testing of the residual error energy. This error is estimated as follows:  
\[\mathbf{R}^{(i)}=\mathbf{Y}-\mathbf{X}\mathbf{F}\hat{\mathbf{h}}_{R}^{(i)} \tag{15}\]and provides an instant but indirect information about reconstruction quality of the channel impulse response \(\mathbf{h}_{R}\) in the \(i\)th iteration step.  
Generally, the LS subvector \(\hat{\mathbf{h}}_{R}\) has nonzero values for the selected indices from the set \(\mathbb{M}\). The specific number of the index in the \(i\)th iteration is determined according to the formula:  
\[r^{(i)}=\operatorname*{arg\,max}_{r\in\mathbb{M}}\Bigl{(}|\mathbf{f}_{\mathbf{ r}}^{H}\mathbf{X}^{H}\mathbf{R}^{(i-1)}|\Bigr{)} \tag{16}\]  
and then appended to the set of coefficients' indices obtained in previous iterations. \(\mathbf{f}_{r}\) denotes the \(r\)th column of the Fourier Transform matrix \(\mathbf{F}\). The procedure of CIR estimation stops when the residual error energy drops below a predefined threshold. This working principle is typical for the OMP algorithm that belongs to the family of greedy methods in the compressed sensing domain [21]. If all N subchannels are active, the procedure can be significantly simplified. The nonzero part of \(\hat{\mathbf{h}}_{R}\) is formed by the smallest number of \(\hat{\mathbf{h}}_{C}\) coefficients with the largest amplitudes for which the residual error energy meets the following condition:  
\[||\mathbf{R}^{(i)}||_{2}^{2}\leq N\Bigl{(}N\sigma_{W}^{2}\Bigr{)}, \tag{17}\]  
where \(N\sigma_{W}^{2}\) is the noise variance in the frequency domain.  
The analysis conducted in [13] shows that the above simple selection rule may be inefficient in the case of compressible channel impulse response. The improvement introduced there relies on the idea that for such channels, the reconstructed impulse response coefficients should gather in groups around the dominant ones. The proposed modification has two distinct stages. In the first one, the largest elements of \(\hat{\mathbf{h}}_{C}(n)\) are selected until \(||\mathbf{R}^{(i)}||_{2}^{2}\leq 2N\bigl{(}N\sigma_{W}^{2}\bigr{)}\) is met [13]. Then, additional coefficients, adjacent to the dominant ones (selected in the current and previous iterations), are added. The final stop occurs when condition (17) or another one based on difference between the successive residual errors  
\[||\mathbf{R}^{(i)}-\mathbf{R}^{(i-1)}||_{2}^{2}\leq 10\Bigl{(}N\sigma_{W}^{2} \Bigr{)}. \tag{18}\]  
are met. The use of supplementary condition (18) can improve the accuracy of impulse response estimation but at the cost of increasing computational complexity of the already complex core of the greedy procedure.
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 6 Evaluation
### Chat from History Evaluation  
Further evaluation aimed to assess the ability of the _Json LLM_ to effectively reuse information from previous interactions in the chat history. The chatbot was tested on a series of questions within the Movie and CLEVR databases, categorized into zero-step, one-step, two-step, and three-step reasoning questions, as shown in Table 4. Throughout the process, the ability to recognize the question in the chat history is evaluated, not necessarily the correctness of the answer, meaning the results can be either true or false. For evaluating the chatbots capability to reference prior interactions, the following schema used was: \"Given the current [chat history] and [question], determine if the question can be answered solely using the chat history.\" Here, the ground truth answers were boolean values, indicating whether the required information was indeed present in the conversation history.  
The zero-step reasoning describes the posing of an initial question without any historical context for that question, implying for the model that it would correctly output \"false\" in such cases. In one-step reasoning, the question selected at the start is asked once again and then checked to see if it was recognized in the existing history, and the history would be used to answer it. In this case, the \"true\" result would be appropriate. The two-step reasoning now merges the first question with the second question in the chat history and asks a composite question. It then checks if the chatbot recognizes this combined question from the chat history and correctly handles it. Again, \"true\" would be the proper return value. With increasing complexity, an analogous approach was applied  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Selection Options**} & \multicolumn{2}{c}{**Metrics**} \\ \cline{2-3}  & **\# Questions** & **Selection Accuracy (\%)** \\ \hline Movie & 118 & 100 \\ \hline CLEVR & 100 & 92 \\ \hline Northwind & 30 & 100 \\ \hline None & 34 & 100 \\ \hline \(\Sigma\) & 282 & 96.45 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Database decision evaluation result.  
to three-step reasoning. For each reasoning step, 214 questions were asked, resulting in a total of 856 questions.  
The evaluation revealed varying patterns in response accuracy. The results revealed the model's ability to perform zero-step reasoning questions with 100% accuracy, demonstrating flawless decisions when there is no prior input. In contrast, one-step reasoning questions showed a decline in accuracy, with the model correctly using chat history 82.24% of the time. This trend continued with two-step reasoning questions, where the model's accuracy decreased to 79.44%. However, an interesting pattern appeared in the three-step reasoning category, where accuracy increased to 87.38%. This indicates that although the model struggles with intermediate complexity, it is better at handling more complex referencing tasks, which likely involve more robust integration of contextual information.
## 5 Filter Design, Simulation, and Experimental Results  
This section presents the performance characteristics of the implemented designs, which were previously simulated and subjected to minimum specifications to validate the core technology. The key requirements include the following:  
* Center frequency \(f_{0}\) =11.8 GHz.
* Bandwidth BW = 60 MHz.
* Filter topologies implementing either of the following:
* Transmission zeros (4-2-0 configuration).
* Equalization zeros (4-0-2 configuration).  
Figure 27: Screw depth variation versus frequency response.  
Using standard P-T-E notation (poles--transmission zeros--equalization zeros), Figure 28a shows the direct-coupled filter design (4-0-0 configuration), while Figure 28b demonstrates its physical implementation with a stepped mechanical housing for tuning. The fundamental difference between 4 and 2-0 and 4-0-0 configurations lies solely in the cross-coupling transmission line geometry--specifically, the clockwise or counter-clockwise arc orientation (\(\theta\)) that determines whether transmission zeros or equalization zeros are implemented, as previously explained.  
The filter designs were developed under stringent manufacturing constraints, with the aspect ratio reduced to AR = 0.48 due to material stock limitations from suppliers, resulting in a lower measured \(Q_{u}\) than simulated values. The implementation accounts for adhesive material uncertainties, including frequency-dependent electrical property variations and thickness tolerances, while accommodating for low-Q tuning elements that degrade the loaded quality factor (\(Q_{L}\)). A specialized cross-head assembly was developed to address both mechanical resonance in tuning elements and cavity resonance challenges. This integrated component controls enclosure resonances while providing secure screw fixation, effectively suppressing parasitic modes from both the housing structure and internal tuning components.  
Manufacturing imperfections were carefully considered, particularly hand-filing tolerances during dielectric resonator fabrication and assembly, along with the use of unfinished aluminum cavity surfaces. As a proof-of-concept (PoC), this implementation deliberately excluded surface treatments such as silver plating to focus exclusively on electrical performance validation, though at the expense of reduced quality factor and increased insertion loss.
## 2 Research Methodology
### Comparative Information of Reviewed Methods
First, classical approaches dominate the field, relying on physics-based models (e.g., FEM, modal analysis, FFT, Newmark-\(\beta\), Kalman filter). They mainly offer well-established reliability but often fall short in handling nonlinearity, high uncertainty, or real-time adaptability. Second, heuristic methods (e.g., SVD, genetic algorithms) are employed for optimization and model simplification but often lack robustness and are sensitive to initial conditions. Also, AI/ML techniques (e.g., CNNs, ANNs, GANs, and random forests) are increasingly adopted for fault diagnosis, classification, and pattern recognition. These offer higher adaptability and automation. However, they require large datasets and high computational resources. Finally, uncertainty methods (e.g., PCE, fuzzy logic, FFEM) deal with model inaccuracies and parameter variability. They are critical in modern reliability studies but are often computationally expensive or limited by assumptions (e.g., independent variables). Most discussed works assume improvement over classical modeling techniques, like finite element reduction or harmonic balance. They also propose data-driven approaches to addressing problems in multi-fault or highly nonlinear systems. For example, methods utilizing the incremental harmonic balance method (IHBM) are very accurate in modeling intricate bifurcation structures, but they depend significantly on the choice of initial conditions. This issue is addressed in several studies by presenting surrogate models or artificial neural networks to assist in initialization or speeding up convergence. Some techniques exist that can fully reconstruct periodic orbits, find unstable solution branches, and reduce computational needs. Some utilize new signal processing techniques (synchrosqueezing, Teager energy analysis, or ISOMAP) to simplify the outcomes or address noisy real-world vibration measurements. There are, nevertheless, apparent limitations. Some methods depend on unrealistically strong assumptions (e.g., the system behaves linearly in small areas), do not scale well, or need unreasonably large amounts of training data. Further, while data-driven approaches work well, the generality of the outcomes tends to depend on the quality and representativeness of the datasets used. The techniques outlined utilize diverse methods--from analytical and numerical methods to purely data-driven setups--showcasing various approaches. A comparison of the main methodological domains across key evaluation criteria is also summarized in Figure 37.  
AI/ML-based methods offer the most balanced and superior performance overall, especially in complex and data-rich environments. However, classical methods still hold value for their simplicity and analytical clarity, while heuristic and uncertainty-based approaches can complement specific scenarios like quick approximations or handling unknown variations.
## 4 Experimentation
### Evaluations
#### 4.4.4 Interface Satisfaction and User Experience Metrics  
The choice of UEQ-S and SUS as metrics for evaluating user experience and usability is justified by their ability to provide fast and effective evaluations of interactive products. The UEQ-S, with its focus on pragmatic and hedonic qualities, provides a holistic view of user experience, encompassing both usability and user experience goals. On the other hand, the SUS is a widely recognized and established tool that allows usability issues to be identified efficiently through its scoring scale and its focus on specific questions reflecting usability and feature integration [55; 61; 62]. Both tools are complementary and provide a holistic view of the user's interaction with the product, making them suitable for the objectives of this study that seek to assess both usability and user experience. In addition, the combination of quantitative and qualitative analysis offered by these metrics allows for a deeper understanding of user opinions and potential usability issues [33; 63].  
The SUS and UEQ-S were used to assess user satisfaction with the interface and verify whether it meets usability standards. Users were organized into two groups, with 30 completing the SUS and 45 answering the UEQ-S. The SUS provides a comprehensive  
Figure 7: Analysis of user interaction on the interface.  
The corresponding values of [PERSON]'s Alpha [64] were 0.84 (pragmatic quality) and 0.81 (hedonic quality), as seen in Table 10, suggesting that both scales have high reliability and that the items of each scale are consistently related to each other.
## 1 Introduction
Initial composition of this effluent in terms of color, AOX, and COD was: 4018 Pt-Co, 46.3 mg L\({}^{-1}\) and 1248 mg L\({}^{-1}\), respectively. Operating in batch mode and using three different dilutions of this effluent, resulting from the process of pulp production using red pine, the authors reported removal efficiencies of 84%, 80% and 58% for color, AOX, and COD, respectively. [PERSON] [13] aimed to evaluate the growth of microalgae on mixtures of municipal, dairy, and pulp and paper effluents to achieve the dual goal of nutrients removal and lipids production. The use of mixtures of pulp and paper industry effluents with municipal and dairy ones was to evaluate if these mixtures could (i) promote microalgal growth without previous dilution with freshwater; and (ii) provide the required nutrients for biomass production without the need for nutrients supplementation. Characterization of these mixtures revealed an ammonium-nitrogen (NH\({}_{4}\)-N) concentration ranging from 14.75 mg\({}_{\text{N}}\) L\({}^{-1}\) to 22.35 mg\({}_{\text{N}}\) L\({}^{-1}\), a nitrate-nitrogen (NO\({}_{3}\)-N) concentration between 1.6 mg\({}_{\text{N}}\) L\({}^{-1}\) and 10.1 mg\({}_{\text{N}}\) L\({}^{-1}\) and a phosphate-phosphorus (PO\({}_{4}\)-P) concentration ranging between 1.06 mg\({}_{\text{P}}\) L\({}^{-1}\) and 1.25 mg\({}_{\text{P}}\) L\({}^{-1}\). With this study, carried out in laboratory tubes, in batch mode, the authors demonstrated that the microalgae _Senedesmus_ sp., _Scenedesmus dimorphus_ and _Selenastrum minutum_ were able to achieve nitrogen and phosphorus removal efficiencies of 96%-99% and 91%-99%, respectively. Finally, in the study performed by [PERSON], et al. [1], a mixed microalgal culture (composed by two _Scenedesmus_ species) was grown in different dilutions (0%-95%) of a pulp and paper mill effluent, resulting from an Indian company, with the following composition: (i) 9.932 mg\({}_{\text{N}}\) L\({}^{-1}\) of NO\({}_{3}\)-N; (ii) 30.25 mg\({}_{\text{P}}\) L\({}^{-1}\) of PO\({}_{4}\)-P; (iii) 3000.15 mg L\({}^{-1}\) of COD; and (iv) 2944 mg L\({}^{-1}\) of biochemical oxygen demand (BOD). The experiments, aimed at evaluating both biomass production and nutrients removal efficiencies, were performed in batch mode, for 28 days, using open ponds as cultivation system (outdoor conditions). Regarding nutrients uptake, the most promising results were obtained for the 40% dilution: (i) 65% for NO\({}_{3}\)-N removal; (ii) 81.3% for PO\({}_{4}\)-P; and (iii) 75% for COD; and (iv) 82% for BOD. The main goal of the present study was to evaluate biomass production and phosphorus removal from a secondary-treated effluent of a Portuguese paper company using the microalga _Chlorella vulgaris_. Different dilutions were performed to evaluate possible inhibitory effects of the effluent on microalgal growth and phosphorus uptake ability.