esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving IMES Localization Accuracy by Integrating Dead Reckoning Information
## 2 Related Studies  
Studies have already investigated hybrid positioning methods in which two or more positioning methods are combined to estimate a user's location. In particular, WiFi fingerprinting combined with PDR has been well studied. [PERSON] [18] developed a MEMS-based strapdown inertial navigation system with WiFi signal strength measurement. This system can be implemented in a modern smartphone with built-in MEMS sensors, and it does not require any additional special hardware.  
[PERSON] [19] and [PERSON] [20] used a similar approach. These studies used a Kalman filter to integrate the WiFi fingerprinting and IMU data. A particle filter has also been used to integrate information from multiple sources [21]. These WiFi-based methods achieved localization accuracy on the order of meters; however, they require a well-surveyed, pre-built radio strength map to obtain an accurate localization result. This map should be updated regularly because it could change easily upon an access point being newly added or removed and the change of the environment.  
In contrast, an IMES radio signal is more suitable for high accuracy indoor positioning, because it carries information of the transmitter location encoded in a GPS-compatible format. Therefore, if the radio propagation properties of IMES are known, a pre-built radio strength map is not required. Furthermore, information of the IMES transmitters' locations is highly reliable, because these locations are managed by a public organization [22]. The technical significance of this study is to investigate the IMES radio propagation properties, to develop a hybrid positioning method that combines IMES localization and dead reckoning information and to evaluate its positioning performance for mobile robot positioning and pedestrian positioning.
## 4 Evaluation
### Results
#### 4.3.1 Results of Experiment 1  
Experiment 1 was carried out on the network denoted by orange and labelled with 1 in Figure 2. The source and destination routers were R4 and R6, respectively. The path chosen by OSPF was R4-5-6, as labelled with \"Path \(i\)\", and the backup path was R4-1-2-6, labelled with \"Path \(j\)\". The evaluation results are shown in Figure 3a,b and Table 4. In Figure 3a, the real-life latencies of both paths (R4-1-2-6 and R4-5-6) are presented with curves in orange (R4-5-6) and blue (R4-1-2-6) colors. As indicated before, the current path chosen by OSPF was R4-5-6, the orange curve in Figure 3a, and the blue curve is the latency of the backup path R4-1-2-6. The value of \(PSI~{}I_{\Delta t}\) at each time point is shown as a gray spot, and a continuous 1 s of indicator \(I_{\Delta t}\) forms a window in Figure 3a. Inside the window the PSI is on, which means route should be switched to the backup path, and vice versa--outside the window the PSI is off, during which the route should be switched back to the current path, because it indicates that the current path has a better condition than the backup one according to our algorithm. The x-axis in Figure 3a is the number of steps of each measurement; each step is defined by the sampling interval \(\Delta t\), which was 5 s in this experiment.  
In the ideal situation, when \(PSI\) is 1, the latency of the current path should always exceed the backup one, and vice visa. However, in the real-life backbone network, especially during busy hours when the traffic peak is 10 times the bandwidth, as in this experiment, the channel situation is very dynamic, and the congestion situation in terms of the latency difference will oscillate between two paths, as shown with the green line in Figure 3b; if we simply swap the route to the one with less latency, the router will be busy jumping back and forth between two paths, and the expense of rerouting will cause more than the channel improvement the rerouting brings. Thus, a consistent routing selection indicator is required. The metric to measure the consistency of the indicator selected in this paper is the _time interval between path swaps \(T_{PSI}\)_; the time interval can stay unchanged once the value of the indicator is set. As shown in Table 9, the indicator for route consistency is calculated as Equation (11).  
\[\begin{array}{l}T_{PSI}=\frac{Total~{}Time}{Total~{}Num.~{}of~{}I_{\Delta t }~{}Swaps}\times\Delta t\\ \frac{3197}{21}\times\Delta t=152.23\times 5=761.15s\end{array} \tag{11}\]  
Figure 8: Summarization and comparison of the _accuracy improvement_ of each experiment.  
Equation (11) implies that it is evident that, on average, each time there is a PSI change, it can stay consistent for 761.15 s. This is quite stable compared to the route change time expense, which is at the scale of milliseconds.
# Influence of Material-Dependent Damping on Brake Squeal in a Specific Disc Brake System
## 3 Pad-on-Disc System Description
### Numerical Verification of the Proposed FE Model  
The FE model was verified by a set of experimentally obtained FRF data. A couple of (4,0) disc-bending-mode frequencies were compared. Table 4 summarizes the comparison of couples of eigenfrequencies \(f_{n1}\), \(f_{n2}\) for different boundary conditions: (1) free disc, (2) attached disc, and (3) attached disc with the pad in contact. Errors \(\Delta f_{n1}\) and \(\Delta f_{n2}\) range from 0.3% to 5.8%.  
According to the FE calculations, the system exhibited different damping ratios \(\zeta_{1}\) and \(\zeta_{2}\) for a couple of bending modes when the pad is in contact with the disc (case No. 3).  
This effect can be qualitatively verified from the experimental data shown in Figure 5b, where two neighboring eigenfrequencies exhibit different damping when the pad is in contact with the disc. Figure 9 shows a qualitative comparison of experimentally (Figure 9a) and numerically (Figure 9b) obtained mode shapes corresponding to the (4,0) bending mode of disc for the No. 3 boundary conditions. The experimental mode shape was reconstructed from the set of FRFs by EMA.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multicolumn{3}{c}{**Experiment**} & \multicolumn{3}{c}{**FEM**} & \multicolumn{3}{c}{**Error**} \\ \hline
**No.** & \(f_{n1}\) **(Hz)** & \(\zeta_{1}\) **(\%)** & \(f_{n2}\) **(Hz)** & \(\zeta_{2}\) **(\%)** & \(f_{n1}\) **(Hz)** & \(\zeta_{1}\) **(\%)** & \(f_{n2}\) **(Hz)** & \(\zeta_{2}\) **(\%)** & \(\Delta f_{n1}\) **(\%)** & \(\Delta f_{n2}\) **(\%)** \\ \hline
1 & 6000 & 0.4 & * & * & 5937 & 0.4 & 5960 & 0.4 & 1.1 & * \\ \hline
2 & 6140 & 0.4 & 6154 & 0.4 & 6122 & 0.4 & 6159 & 0.4 & 0.3 & 0.1 \\ \hline
3 & 6146 & * & 6180 & * & 6166 & 0.4 & 6540 & 1.2 & 0.3 & 5.8 \\ \hline \end{tabular} \({}^{*}\) value not specified  
\end{table}
Table 4: Comparison of experimentally and numerically obtained eigenfrequencies \(f_{n1}\) and \(f_{n2}\) and modal damping ratios \(\zeta_{1}\) and \(\zeta_{2}\) for couples of (4,0) disc-bending modes.  
Figure 8: Boundary conditions for static equilibrium analysis.
## 4 Conclusions  
The effect of solder joint preparation on the thermal properties of the IGBT soldered on FR4 substrate was investigated. It was found that both the soldering technology used and the thermal profile could change the thermal impedance \(Z_{th}(t)\) and thermal resistance \(R_{th}\). The differences were between 15% and 20% for \(Z_{th}(t)\) and \(R_{th}\), respectively. The best results were observed in the case of a batch oven with infrared heating. Although the ratio of the gas void in the solder joints varied between 3% and 30%, no correlation between the void ratios and \(R_{th}\) value increase was found. This means that, in the case of the power package TO263, the void ratio of the solder joint is not as important from the perspective of the proper working of the component, as was supposed by the microelectronics industry. The microstructure of the solder joint showed significant differences in IMC layer thicknesses in the case of the different soldering technologies, which correlated well with the time above liquidus during the soldering process. The thermal parameters of the IGBT soldered onto the FR4 substrate could be changed due to the increased thermal conductivity of the IMC layer as compared to that of the bulk solder joint. Our research highlighted the importance of the soldering technology used and the thermal profile in the case of IGBT components.  
Conceptualization (A.S. and P.G.); methodology (A.S. and P.G.); validation (P.G., A.P., S.W. and B.I.), investigation, (A.S., P.G., A.P., S.W. and B.I.); resources, (A.S., P.G. and A.P.); writing--original draft preparation, (A.S., P.G. and A.P.); writing--review and editing, (A.S. and P.G.); visualization, (A.S., P.G., A.P. and B.I.); supervision, (A.S.); funding acquisition, (A.S., P.G.) All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] and [PERSON] was financed within the program of the Ministry of Science and Higher Education called \"Regionalna Iniciyta Doskonalosci\" in the years 2019-2022, the project number 006/RID/2018/19, the sum of financing 11 870 000 PLN\". The work of [PERSON] was partially supported by the National Research Development and Innovation Office--Hungary (NKFIH) as part of project no FK 127970.  
Not applicable.  
**Data Availability Statement:** For detailed data presented in this study please contact the corresponding author.  
Figure 11: The IMC layer thicknesses of the solder joints soldered in different soldering ovens.
## 1 Introduction  
In the past decade, environmental research has increasingly shifted focus from conventional priority pollutants, such as polychlorinated biphenyls and polycyclic aromatic hydrocarbons, to emerging contaminants. Among these, pharmaceuticals and personal care products (PPCPs) have emerged as a critical group of contaminants of concern [1, 2]. PPCPs span a wide range of chemical substances, including pharmaceuticals for human and veterinary use, disinfectants, and personal care products like lotions, cleansers, and sunscreens. These chemicals enhance our daily lives, from disease prevention to household hygiene.  
PPCPs are often only minimally transformed or remain unchanged in wastewater treatment plants, immediately then entering into aquatic environments. Additional sources include direct inputs from aquaculture, indirect inputs through manure application in agriculture, and emissions from manufacturing sites, hospitals, and nursing homes. PPCPs have been detected globally [3, 4], raising concerns due to their large-scale production and usage, persistence in the environment, and toxicity to non-target organisms. Many PPCPs exhibit bioaccumulation potential across different trophic levels, and their continuous emission renders them pseudo-persistent pollutants. Since the mid-1990s, there has been a steady increase in studies assessing the environmental risks posed by PPCPs in both aquatic and terrestrial ecosystems [2].  
To address the challenge of assessing environmental risks, in silico modeling has been proposed as an efficient alternative to time-consuming and costly experimental methods, particularly under frameworks like the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) [5]. Recent investigations have demonstrated the utility of these in silico techniques in predicting the environmental toxicity of a diverse range of chemical compounds [6; 7; 8; 9; 10; 11; 12; 13; 14].  
Given the significance of PPCPs and the risks they pose to aquatic ecosystems, this work focuses on developing predictive and validated Multitasking Quantitative Structure-Toxicity Relationship (mt-QSTR) models. These models aim to predict the aquatic toxicity of PPCPs using a complex and curated dataset obtained from the ECOTOX database [15]. The dataset was refined through a newly developed in-house curation tool to ensure accuracy and consistency. To address experimental variations, a Box-Jenkins-based moving average approach was employed, integrating both structural attributes and experimental conditions into the in silico modeling process [16]. Additionally, a range of feature selection algorithms and machine learning (ML) techniques were applied to identify the most effective linear and non-linear mt-QSTR models, delivering robust predictions of PPCP toxicity.  
This work provides valuable insights and a robust framework for designing PPCPs with reduced environmental toxicity, offering a significant step forward in mitigating their impact on aquatic ecosystems.
# Photocatalytic Inactivation as a Method of Elimination of _E. coli_ from Drinking Water
## 4 Inactivation of _E. coli_ by Assisted Photocatalysis  
Most studies referred to this method as photoelectrocatalysis. This combined technique is advantageous because it clearly separates the site of oxidation from the site of reduction, which reduces the crossover of the products [120]. In addition, the measurement of voltage and current from electrochemical method can serve as parameter for quantifying the rate of photocatalysis [120]. _Pseudomonas aeruginosa_ and _Escherichia coli_ were removed from rainwater by combining both photocatalysis and electrochemical methods. This was achieved by designing a photoelectrochemical reactor, which was placed under the sun. Evidence from culture-based analysis showed that 5.5-log of _E. coli_ reduction was achieved [121]. The photoelectrocatalytic system made from nanotubes of titanium oxide that was doped with the combination of tin oxide, nanoparticles of silver and antimony was active against _E. coli_. Over 5-log bacteria elimination was reported when this system was used for inactivating _E. coli_ within 60 min in the presence of light [122]. [PERSON] et al. [123] also reported the use of photoelectrochemical approach to inactivate _E. coli_, but this was achieved by using titanium oxide composites as the photocatalyst, while RuOx/Ti was used as the anode. When only the electrochemical process was used, the active radical for the inactivation was hydroxyl radical. However, when it was combined with photocatalysis, the active species for the inactivation were chlorine species and hydroxyl radicals [124]. In addition to the use of electrochemical method for assisting photocatalysis, plasmon has also been used to aid photocatalysis for the inactivation of _E. coli_[125].
# Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index
## 4 Validation of the Selected Models  
After developing the models for predicting \(BI\) of the rock, they should be validated through the use of new datasets. Therefore, the authors decided to use 15 more empirical data from the same case study. In should be noted that these data were not used for training and testing phases. The authors used the selected models in previous section and run them using the new datasets for validation  
Figure 11: Testing and training results of KNN model to predict the \(BI\).  
Figure 12: The relationship between the predictors and K selection for predicting the \(BI\).  
Figure 13: Input variable’s importance to predict the \(BI\) derived from the KNN model.  
purposes. Then, the measured and predicted values of \(BI\) were evaluated considering the previous performance indices. Table 3 presents the results of performance indices for all 3 predictive models i.e., ANN, KNN and RF. According to this table, _a20-index_ is 1 for all predictive models which shows that m20 (values of experimental/predicted) is equal to N (total number of samples). It confirms that all models are able to provide good results for similar data as well. In addition, \(R^{2}\) results (0.971, 0.860, and 0.807) and _VAF_ results of (96.852, 85.633, and 80.642 %) were obtained for RF, ANN and KNN models, respectively in validation stage which indicate that RF model is better than the other 2 models. In terms of system error, RF model with _RMSE_ of 0.62 and _MAE_ of 0.46 received lower amount of error compared to ANN and KNN models. Figure 14 shows the measured and predicted \(BI\) values for the RF, ANN, and KNN models in validation phase. In addition, Figure 15 depicts predicted \(BI\) values by RF, ANN and KNN together with their measured \(BI\) for all 15 data samples assigned for validation stage. As it can be seen from these two figures, the \(BI\) values by RF model are closer to the measured \(BI\) values in comparison with the KNN and ANN models.  
As conclusion on this part, all models are able to provide a good prediction results for \(BI\) values when similar data will be available. However, RF can receive higher performance capacity if similar data will be available compared to ANN and KNN models. This means that if the other researchers or
## 4 Training and Performance Evaluation of the CCBA Model
### Performance Evaluation and Results
#### 4.3.2 Computational Efficiency on Jetson Nano
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Feature (or Equivalent)**} & \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Model FLOPs**} & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Parameters** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Time Cost on Feature** \\ **(Seconds)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Time Cost on** \\ **Classifier Using** \\ **CPU** \\ \end{tabular} } \\ \cline{5-8}  & & & & & **CPU** & **GPU** & **GPU (Seconds)** \\ \hline \multirow{3}{*}{\begin{tabular}{c} [24] \\ (Teacher) \\ \end{tabular} } & \multirow{3}{*}{SPWVD-MFCC} & \begin{tabular}{c} Dual-stream \\ ResNet50- \\ CNN-LSTM \\ \end{tabular} & 1.29 G & 24.5 M & 21.319 & - & 0.387 \\ \cline{1-1} \cline{5-8}  & \multirow{3}{*}{**CNN-Audio Block**} & \begin{tabular}{c} **CNN-** \\ **BiLSTM-** \\ **Attention** \\ \end{tabular} & **0.65 G** & **2.83 M** & **0.111** & **0.014** & **0.29** \\ \hline \multirow{3}{*}{\begin{tabular}{c} [21] \\ (replicated) \\ \end{tabular} } & \multirow{3}{*}{MFCC} & \multirow{3}{*}{CNN-LSTM} & \multirow{3}{*}{0.014 G} & \multirow{3}{*}{0.99 M} & \multirow{3}{*}{0.053} & \multirow{3}{*}{-} & \multirow{3}{*}{0.028} \\ \cline{1-1} \cline{5-8}  & & & & & & \\ \cline{1-1} \cline{5-8}  & \multirow{3}{*}{\
\begin{tabular}{c} STFT (\(n=512\))-MFCC \\ STFT (\(n=1024\))-MFCC \\ STFT (\(n=2048\))-MFCC \\ STKKwell-MFCC \\ \end{tabular} } & \multirow{3}{*}{8-stream CNN} & \multirow{3}{*}{4.60 G} & \multirow{3}{*}{0.97 M} & \multirow{3}{*}{\textgreater{300}} & \multirow{3}{*}{-} & \multirow{3}{*}{0.46} \\ \cline{1-1} \cline{5-8}  & & & & & & \\ \cline{1-1} \cline{5-8}  & \multirow{3}{*}{
\begin{tabular}{c} WVF (\(n=256\))-MFCC \\ WVF (\(n=512\))-MFCC \\ WVF (\(n=1024\))-MFCC \\ \end{tabular} } & \multirow{3}{*}{4.60 G} & \multirow{3}{*}{0.97 M} & \multirow{3}{*}{\textgreater{300}} & \multirow{3}{*}{-} & \multirow{3}{*}{0.46} \\ \cline{1-1} \cline{5-8}  & & & & & & \\ \hline \hline \end{tabular} Bold values indicate the results of the proposed CCBA model after two-stage training.  
\end{table} 
Table 2: Computational complexity and time costs of different approaches for classifying a 6.144 s audio segment on the Jetson Nano.
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 4 Performance Evaluation
### Results from a Random Topology
#### 4.2.1 The Impact on Throughput  
Figure 14a shows the total throughput of these three schemes under different numbers of flows. As the number of flows increased, the throughput of all the schemes gradually increased. However, when the number of flows exceeded 12, the throughput of DCAR and COPE + AODV tended to flatten and descend. This was because congestion appeared with the increased payload. In addition, the throughput of CCAR reached a peak value at 14 flows and began to stabilize. This shows that CCAR had the best robustness to the network congestion. It is worth noting that the throughput of  
Figure 11: Total throughput versus flow rate in the grid network. EXQL, expected transmission count based on queue length.  
Figure 12: Delay versus flow rate in the grid network.  
Figure 13: A random topology with 30 nodes.  
DCAR was larger than CCAR when the number of flow was less than eight, implying that DCAR can obtain more coding opportunities in a small-scale network.  
In addition, the total throughput is exhibited under different packet loss rates with 10 flows in Figure 13(b). We can observe that the total throughput of those three schemes decreased when the packet loss rate increased. CCAR had the largest throughput and best robustness compared to the other two schemes. This performance was due to the constrained coding condition that can guarantee decoding opportunities. Moreover, the value of DCAR declined fastest because the scheme had more encoded packets that could not be successfully decoded.
# Refining Zero-Shot Text-to-SQL Benchmarks via Prompt Strategies with Large Language Models
###### Abstract  
Text-to-SQL leverages large language models (LLMs) for natural language database queries, yet existing benchmarks like BIRD (12,751 question-SQL pairs, 95 databases) suffer from inconsistencies--e.g., 30% of queries misalign with SQL outputs--and ambiguities that impair LLM evaluation. This study refines such datasets by distilling logically sound question-SQL pairs and enhancing table schemas, yielding a benchmark of 146 high-complexity tasks across 11 domains. We assess GPT-4o, GPT-4o-Mini, Qwen-2.5-Instruct, llama 370b, DPSK-v3 and O1-Preview in zero-shot scenarios, achieving average accuracies of 51.23%, 41.65%, 44.25%, 47.80%, and 49.10% and a peak of 78.08% (O1-Preview), respectively. Prompt-based strategies improve performance by up to 4.78%, addressing issues like poor domain adaptability and inconsistent training data interpretation. Error-annotated datasets further reveal LLM limitations. This refined benchmark ensures robust evaluation of logical reasoning, supporting reliable NLP-driven database systems.  
Text-to-SQL; Natural Language Processing (NLP); large language models (LLMs); zero-shot prompting; dataset benchmarking 2020