esa-sceva/satcom-chunk-collection huggingface dataset:
# Refining Zero-Shot Text-to-SQL Benchmarks via Prompt Strategies with Large Language Models
## Appendix A Examples of Errors and Inconsistencies in the Original BIRD Dataset
### Logical and Structural Errors in SQL  
Example (Overly Complex SQL--Figure A1): Question 477 asks for artists who designed a specific card. The original SQL provided in BIRD uses repetitive OR conditions combined with GROUP BY, resulting in inefficient execution (287 ms). A simpler, logically equivalent query using IN and DISTINCT achieves the same result much faster (100 ms). This indicates the original dataset sometimes provides suboptimal or unnecessarily complex ground truth SQL.  
Example (Misaligned SQL Logic--Figure A2): Question 94 asks for account numbers of female clients who meet specific criteria (oldest with lowest average salary) and the salary gap. However, the provided SQL incorrectly filters based on the district of a single oldest female client, failing to capture the broader intent of identifying relevant clients across districts based on combined age and salary criteria. This misalignment stems from ambiguity in the question, potentially leading to incorrect SQL logic.
# Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions
## 3 Experiment and Results
### Experimental Loss Function  
IoU (Intersection over Union) [42] is produced by the interaction between the predicted box and the ground truth box. That is, it is a value representing the size of the predicted Bounding Box and Ground Truth in the field of object detection as a value between zero and 1. The formula is as follows. A is the predicted box, and B is the ground truth box. C box is the smallest box, including A and B box, and \(C\backslash A\cap B\) is the area in which the sum of A and B box is subtracted from the C box area. The GIoU (Generalized IoU) is the value obtained by subtracting the ratio of areas that do not overlap with both A and B in the C box. The larger the GIoU, the better the performance.  
When \(1-GIoU\) is used as loss in object detection (the range of the loss value is zero \(\sim\)2), the bounding box prediction process of GIoU loss according to Iteration is performed by expanding the B box area to overlap with GT and then reducing the B box area to increase IoU. This can improve the gradient vanishing problem for non-overlapping boxes, but there is a problem that the convergence rate is slow and the box is predicted incorrectly. To solve this problem, we use CIoU (Complete-IoU) in this paper to compare the loss function of the Original YOLOv5 model with the YOLOv5_Ours. In other words, the experiment is conducted under the condition that CIoU is applied equally to two models.  
* IoU: \[\text{IoU}=\frac{|A\cap B|}{|A\cup B|}\] (13)
* GIoU: \[\text{GIoU}=\text{IoU}-\frac{|C\backslash A\cap B|}{|C|}\] (14)
* LGIoU: \[\text{L}_{\text{GIoU}}=1-\text{GIoU}\] (15)As can be seen from Equation (18), w is the width, and h is the height of the prediction box. Additionally, \(w^{gt}\) and \(h^{gt}\) are the width and height of the ground truth box. \(v\) measures the consistency of the aspect ratio of the two boxes, \(\alpha\) is a positive trade-off parameter to adjust the balance between the non-overlapping case and the overlapping case. In particular, in the case of non-overlapping, the overlap area factor gives a higher priority to regression loss.  
* \(\mathrm{L_{CIOU}}\): \[\mathrm{L_{CIOU}}=1-\mathrm{IoU}+\frac{\rho^{2}\big{(}b,\ b^{gt}\big{)}}{c^{2} }+\alpha v\] (16)
* \(\alpha\): \[\alpha=\frac{v}{1-\mathrm{IoU}+v}\] (17)
* \(v\): \[v=\frac{4}{\pi^{2}}\big{(}\mathrm{arc}\tan\frac{w^{gt}}{h^{gt}}-\mathrm{arc} \tan\frac{w^{p}}{h^{p}}\big{)}^{2}\] (18)
## 2 Materials and Methods
### Data Sets
#### 2.2.4 Gross Primary Production Data  
The 8-day interval-gridded gross primary production (GPP) data set from Global Land Surface Satellite (GLASS) products was used. This database covers 1982 to 2015 and globally has a 0.05\({}^{\circ}\) spatial resolution in vegetation areas. The GPP product was simulated from the integration of eight light-use efficiency models by the Bayesian model averaging method. Its accuracy has been validated by 155 flux sites around the world [37; 38].
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 5 Prototype
### Sensor Node Lifetime  
The goal of the second experiment was to demonstrate the lifetime of a node running a simple application, which wakes up to sense temperature and then sleeps every 5 s. In this experiment, the harvester board with a 50-mF capacitor is first charged to 1.25 V by a 33-dBm 866-MHz source and then connected to a sensor node with the lower threshold voltage set to 1.02 V. The experiment has been repeated with a 200-mF capacitor. The voltage across the \(V_{out}\) and \(GND\) terminals of the board have been measured with a multimeter. As can be seen from Figure 10, the maximum sensor lifetime was measured as 3 s and 7 s with 50-mF and 200-mF capacitors, respectively. The experiment was conducted for a single capacitor configuration.  
Figure 10: Node lifetime.  
Figure 9: The charging time duration for 50-mF and 200-mF supercapacitors increases exponentially with charging distance. Practical applications that require longer charging range would thus require a very small capacitor.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 3 Metal Selenide Thin Films
### Cobalt Selenide Films  
Films were prepared on a soda lime glass by using sodium selenite (Na\({}_{2}\)SeO\({}_{3}\)) and cobalt (II) chloride (CoCl\({}_{2}\)). Based on the FESEM studies, the uniform grain size was observed when the anionic immersion time was 30 s [104]. Optical properties revealed a band gap of about 2.1 eV [105]. The microscope glass slide was employed as the substrate during the preparation of thin films under a rinse time of 10 s, an immersion time of 30 s, and 12 deposition cycles [106]. The XRD studies revealed a cubic phase of Co\({}_{9}\)Se\({}_{8}\), with lattice parameter values are a = b = c = 10.431 A. The influence of the deposition  
Figure 5: I-V characteristics for n-Bi\({}_{2}\)Se\({}_{3}\)/polysulfide PEC cell in dark and under illumination reproduced with permission from [98], Elsevier, 2002.  
cycles on the compositional of the films was studied [107]. The atomic percentages for cobalt (Co) and selenium (Se) were 53.52%:46.48%, 27.01%:72.99%, 39.73%:60.27%, under 10, 15, and 20 cycles, respectively. The CoSe\({}_{2}\) films were produced by [PERSON] and co-workers [108] at room temperature. Film thicknesses and crystallite sizes were 92.96 to 225.63 nm and 7.63 to 13.07 nm, respectively. They observed that the extinction coefficient and absorbance value reduced when the wavelength increased.
## 3 Results
### Recommended Sampling Rates to Compensate Performance Degradation of RP-MPSK  
The trade-off between data rate and BER is the key design aspect in PHY design as highlighted in Section 1. The aim of simulation in this part was to identify the best value of \(N_{p}\) for RP-8-PSK and RP-16-PSK that can deliver the same BER as that of RP-BPSK and RP-QPSK against the same values of \(\alpha\). For consistency with previous results, we take \(N_{p}=3\) as the reference value for RP-BPSK and RP-QPSK. To observe how the error-performance of RP-MPSK depends on \(N_{p}\), BER of RP-8-PSK and RP-16-PSK for several values of \(N_{p}\) was investigated. Figure 8 shows that the BER of RP-8-PSK and RP-16-PSK when their BER closely resembled the BER of RP-BPSK and RP-QPSK. The corresponding values of \(N_{p}\) are 6 and 16 for RP-8-PSK and RP-16-PSK, respectively. BER for other values of \(N_{p}\) have been omitted to confine our discussion according to the context.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Modulation** & **LPWAN** \\ \hline DBPSK & SigFox, DASH7 \\
8-PSK & EC-GSM \\ BPSK, QPSK & NB-IoT, LTE-M \\
16-QAM, 64-QAM & LTE-M, WiFi-HaLow \\ CSS & LoRa \\ BFSK & LoRa \\ RP-16-PSK & RPW \\ \hline \hline \end{tabular}
\end{table}
Table 2: LPWAN Modulation Schemes.  
Figure 7: Comparison of RP-MPSK with other LPWAN modulations.  
Another interesting finding is extracted from this simulation. For the values of \(\alpha\) exceeding 4 dB, the performance of RP-8-PSK and RP-16-PSK supercedes the performance of both RP-BPSK and RP-QPSK. This is promising yet a rational result. The performance of MFSK improves with higher order of modulation. A similar argument can be made about RP-MPSK. Since RP-MPSK relies on the value of \(N_{p}\) to enhance the reliability, arbitrary higher sampling rate has shown the improved performance of RP-8-PSK and RP-16-PSK. However, unlike MFSK, channel bandwidth is not increased with higher order modulations. Thus, we deduce that RP-MPSK has a higher spectral efficiency than MFSK, while it can potentially approach the BER of MFSK.
# A Strong Noise Reduction Network for Seismic Records
## 3 Results and Discussion
### Model Training  
In this experiment, the network was constructed utilizing the PyTorch (1.12.1) framework. The model underwent training, validation, and testing on an NVIDIA GEFORCE RTX3090 (Nvidia, Santa Clara, CA, USA). The training process was conducted with a learning rate of 0.0001 and a batch size of 32. Parameter optimization was achieved through the Adam optimization algorithm [52]. The mean square loss function (MSELoss) served as the training loss function, for which the formula is as follows. The model completed 100 epochs of training, resulting in the acquisition of the training and validation loss function descent curves, as depicted in Figure 3.  
\[\text{MSELOSS}=\frac{1}{\text{n}}\underset{i=1}{\overset{\text{n}}{\sum}} \left(\text{y}_{\text{i1}}-\text{y}_{\text{i2}}\right)^{2} \tag{10}\]
# Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs
###### Abstract  
Due to the limitations of the urban environment, the data transferred between vehicles can only change direction at the intersections. Therefore, the routing decision at an intersection will largely affect the overall routing decision. In this article, we propose an Intersection-Based Routing with Fuzzy Multi-Factor Decision (IRFMF), which utilizes several factors to decide the next road segment. In the scheme, each intersection introduces three factors including the direction, the number of lanes, and the traffic. After the fuzzification and defuzzification of these factors, the candidate segment with the highest evaluation will be selected. The simulation shows a significant improvement of VANETs performance on packet delivery ratio and end-to-end delay.  
VANET; multi-factor; fuzzification; defuzzification; intersection; routing protocol Article
## 3 Results and Discussion
### N-Glycan Data from the Reduction Method  
In order to analyze N-glycan at the subunit level instead of the intact protein level, DTT was employed to reduce mAb A to generate HC, which was used for further N-glycan analysis at the HC level. Figure 3A shows representative deconvoluted MS spectra of the HC of mAb A from the reduction method. The six major glycans were identified as G0F-GN, G1F-GN, G0F, G1F, G2F, and Man5, with small mass errors compared to theoretical molecular weight. Additionally, C-terminal lysine truncation and N-terminal glutamine pyrid glutamic acid cyclization were the main PTMs. Representative raw MS spectra of the heavy chains of mAb A from the reduction method is shown in Supplementary Materials, Figure S1A. The method was run in triplicate, and detailed information are shown in Supplementary Materials, Table S1. The average glycan ratios (n = 3) of G0F-GN, G1F-GN, G0F, G1F, G2F, and Man5 were 1.75%, 1.97%, 44.31%, 40.91%, 9.34%, and 1.73%, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Glycan Name** & \multicolumn{4}{c}{**Percentage/\%**} \\  & **Run 1** & **Run 2** & **Run 3** & **RSD** \\ \hline G0F-GN & 1.92 & 1.76 & 1.86 & 4.19\% \\ G0F & 41.33 & 41.06 & 41.27 & 0.34\% \\ Man5 & 4.22 & 4.40 & 4.29 & 2.09\% \\ G1F & 29.05 & 29.10 & 29.02 & 0.14\% \\ G1F’ & 9.40 & 9.46 & 9.41 & 0.38\% \\ G2F & 9.58 & 9.55 & 9.46 & 0.68\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The percentages of major N-glycans of mAb A from rapid 2-AB method.  
Figure 2: Representative chromatogram of N-glycan profiles of mAb A from rapid 2-AB method.
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 7 Conclusions  
In this paper, a phased FTA-based approach was suggested as a means of evaluating the system's performability, with the AUV functioning as a phased mission system. We designed FTs that account for component failure and performance for every task that sets the AUV apart, including charging, exploration, and launch/recovery. Based on an FT for every mission, TDLR was used to define the component's performance ranking. The weights of each performance were defined using the rank summed weighting technique, and the sub-FT performance was computed using the weighted arithmetic mean. Three categories were defined based on the AUV accomplishment level, and each level's performability was assessed. The EIP and performability of the entire system were computed using the UGF based on the reliability, performance, and performability of each sub-FT.  
It is feasible to ascertain whether the current mission can be completed by assessing the performability and EIP for each AUV mission. A performability analysis for a phased mission system calculates the performabilities of other missions and determines whether the AUV can perform additional missions, recoveries to the USV, etc. Even in the event that a malfunction occurs and the AUV cannot properly perform its current mission, it is possible to obtain information for future mission planning and decision making. In the future, we will verify the algorithm by implementing it on heterogeneous underwater search fleets made up of actual systems. Additional work is also necessary to more precisely depict the system's performance based on an FT.  
Conceptualization, S.B. and D.L.; methodology, S.B.; software, S.B.; validation, S.B. and D.L.; formal analysis, S.B.; investigation, S.B.; resources, S.B. and D.L.; data curation, S.B.; writing--original draft preparation, S.B. and D.L.; writing--review and editing, S.B. and D.L.; visualization, S.B.; supervision, D.L.; project administration, D.L.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Unmanned Vehicles Core Technology Research and Development Program through the National Research Foundation of Korea (NRF), the Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science and ICT, the Republic of Korea (NRF-2020M3C1C1A02086313).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
This research received administrative and technical support from [PERSON].  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AADL & Architecture Analysis and Design Language \\ AUV & Autonomous Underwater Vehicle \\ BDD & Binary Decision Diagram \\ EIP & Expected Instantaneous Performance \\ FE & Fault Effect \\ FHA & Fault Hazard Analysis \\ \end{tabular}
# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
## 1 Introduction
The main contributions of this paper are as follows. * A global-local blur feature disentangling network is proposed. The network adopts a parallel dual-branch architecture to decouple the global and local blur features. A branch attention mechanism is designed to dynamically fuse dual-branch features. * A phased training strategy with task-specific datasets was introduced to train different branches and the branch attention model. We train the branch modules to extract the global and local blur features at first; then, we fix the branch parameters and train the attention module to adaptively restore the complex blurry images. * A comprehensive comparison was conducted. With the guidance of the global and local blur feature disentangling branches and the branch attention mechanism, the network can reconstruct sharp edges and clear structures. The feature maps of the dual-branch network show that our method can successfully disentangle the global and local blur features. The restoration results show that the method proposed in this paper has achieved state-of-the-art subjective and objective performance compared with the existing dynamic scene deblurring methods.
## 6 Simulation Environment and Results
### Simulations Results
#### 6.2.3 Service Discovery Ratio  
In this section, we compare the service discovery ratio in OLSRv1 and in OLSRv2 according to the node speed and the number of nodes offering the same service in the network. We do the comparison in simulation scenarios with 50 nodes. The parameters for sending periodic HELLO and TC messages are the ones established by OLSRv1 and OLSRv2 RFCs. SDM INTERVAL_TIME to announce services is set to 5 s.  
In Figure 15, we can see that the service discovery ratio is slightly better in OLSRv2 than in OLSRv1. The basis for the service discovery mechanism is the same in both protocols, and this improvement is due to the advantages of the OLSRv2 itself, as it has better average end-to-end delay and throughput than OLSRv1.  
Like the results obtained for service discovery delay, when the network is more mobile, the service discovery ratio decreases. Furthermore, when the number of servers increases, the service discovery ratio increases.  
Figure 14: Service discovery delay _vs._ average speed (**left**) _vs._ service redundancy (**right**) in SD-OLSRv2 and SD-AODV.  
Figure 15: Service discovery ratio _vs._ average speed (**left**) _vs._ service redundancy (**right**).  
The comparison with SD-AODV gives us the results shown in Figure 16. For both SD-OLSRv2 and SD-AODV without an SREQ limited scope, the service discovery ratio is almost the same. If we limit the SREQ flooding scope, the service discovery ratio decreases significantly.
## 2 Application of Ribosome Engineering to Increase Antibiotic Production
### The Application of Ribosome Engineering in Other Bacteria and Fungi  
Ribosome engineering has also been used in other bacterial species, including several _Bacillus_ strains, _Escherichia coli_, and _Paenibacillus agaridevorans_, and in some fungi. For example, the introduction of three drug-resistance mutations in _rsmG_, _rpsL_, and _rpoB_ in _P. agaridevorans_ markedly enhanced the productivity of cycloisomaltooligosaccharide glucanotransferaseer (CITase) by more than 1100-fold as compared to the wild-type strain [60]. [PERSON] et al. [58] introduced limomycin- and kanamycin-resistance into _E. coli_ K12 AA23 pPSX-vioABCDE opv-1 to produce 41-fold higher expression of violacein (**24**). Finally, ribosome engineering is used not only to increase the production of secondary metabolites but also to increase the yield of \(\alpha\)-amylase [61], xylanase [62], vitamins [63], \(\varepsilon\)-poly-L-lysine [64; 65; 66; 67], L-isoleucine [68], and fuels including butanol [69] and ethanol [70] (Table 3).
# Stability of Feature Selection in Multi-Omics Data Analysis
## 2 Materials and Methods
### Classifiers with Embedded Feature Selection
#### 2.1.3 Lasso Regression  
Lasso (Least Absolute Shrinkage and Selection Operator) regression [43] is a linear regression method that incorporates L1 regularization to prevent overfitting and enhance feature selection.  
\[\hat{y}_{i}=\mathbf{w}^{T}\mathbf{x}_{i}+\theta \tag{6}\]  
\[error_{i}=y_{i}-\hat{y}_{i} \tag{7}\]  
In our binary classification problem, where \(y_{i}\in\{0,1\}\), we use a threshold of 0.5 to assign the class \(y_{i}=1\) if \(\hat{y}_{i}>0.5\) and the class \(y_{i}=0\) otherwise.
## 2 Brief Review of MIMO-NARX Modeling
### MIMO-NARX Model for MDOF System
The corresponding NARX coefficients can then be calculated based on the OLS method:  
\[\mathbf{\Theta}_{j,k}=\left[\mathbf{Z}_{j,k}^{\rm T}\mathbf{Z}_{j,k}\right]^{-1}\mathbf{Z}_{ j,k}^{\rm T}\mathbf{X}_{j}^{\rm T} \tag{5}\]  
where \(\mathbf{Z}_{j,k}\) is the terms matrix of the \(k\)th candidate NARX model; and \(\mathbf{X}_{j}\) is the response time series of the \(j\)th degree of freedom. The most appropriate MIMO-NARX model is selected after the terms and coefficients of the candidate NARX models are determined. An appropriate error measure is conducted by decoupling the identification of the NARX models for each degree of freedom. For the \(j\)th MIMO-NARX model, only the outputs of the \(j\)th degree of freedom are estimated by recursively running the model, while the outputs of the other degrees of freedom are directly obtained from the simulation results. The error measure is defined as:  
\[\widetilde{\mathbf{\epsilon}}_{\text{SE},j,k}=\frac{||\mathbf{X}_{j}-\widetilde{\mathbf{ X}}_{j,k}||^{2}}{||\mathbf{X}_{j}^{\rm T}-t\mathbf{\Sigma}_{t}\left[\mathbf{X}_{j} \right]||^{2}} \tag{6}\]  
where \(\mathbb{E}_{t}[\mathbf{X}_{j}]\) is the mean value of \(\mathbf{X}_{j}\); and \(\widetilde{\mathbf{X}}_{j,k}\) represents the response time series of the \(j\)th degree of freedom estimated from the \(k\)th candidate NARX model. The optimal model with model terms \(\mathbf{g}_{j}(\cdot)\) and coefficients \(\mathbf{\Theta}_{j}\) is selected as the candidate, which reaches a sufficiently small total error by satisfying a predefined threshold value. It is worth noting that as few as possible numbers of NARX terms have been found to avoid deleterious effects on the accuracy of the model, for example, overfitting and spurious dynamics [18; 22; 23].
# An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals
## 5 Conclusions  
In this paper, we have described a novel mechanism to derive a realistic, multi-scale and overcomplete dictionary from recorded real-world ECG signals. The dictionary was constructed offline, thus avoiding the computational burden of on-line approaches and ensuring the scalability of the proposed methodology for large datasets with many individuals and/or sample sizes. The obtained dictionary was been used to perform an accurate sparse representation of several ECGs recorded from healthy patients, showing that it can properly capture all the QRS complexes without introducing false alarms. Potential future lines include testing the proposed approach on a larger number of patients (especially including subjects with cardiac pathologies), the construction of dictionaries composed of multiple waveforms (e.g., P and T waveforms), and the combination of waveforms extracted from real patients with synthetic waveforms.  
Conceptualization, D.L.; methodology, D.L. and D.M.; software, D.L., D.M. and T.T.; validation, D.L.; data curation, D.M.; original draft preparation, D.L.; and review and editing, D.M. and T.T.  
This research was funded by Ministerio de Economia y Competitividad (Spain) through the MIMOD-PLC project (grant number TEC2015-64835-C3-3-R).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.