esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method  
### Method Overview  
First, as shown in Figure 2, a simulator was built for the vibration system with an FIR filter. The vibration system has 5 inputs and 4 outputs. The 5 inputs refer to the forces of the vibration exciter and 4 electromagnetic actuators. The 4 outputs refer to the 4 acceleration signals. In detail, we used experimental data to identify an FIR filter, which acted as the simulator. We gave specified force signals to the vibration exciter and electromagnetic actuators, which applied forces to the vibration system. Both the force signals and corresponding acceleration signals were recorded and used to identify the FIR filter.  
Figure 1: Schematic diagram of vibration-control system.  
Then, an RL algorithm collected data and found a control policy through the interaction with an environment, as shown in Figure 3. The simulator, which had enough physical fidelity, was used to describe the evolution of the acceleration signals. The RL algorithm used the collected simulator data to find a near-optimal policy with respect to the specified reward function.  
Many RL algorithms have been presented to solve the optimal solution, including value-based algorithms, such as the DQN [29], DDQN [30], prioritized DDQN [31], dueling DDQN [32], distributional DQN [33], noisy DQN [34] and Rainbow [35] algorithms. These value-based algorithms are suitable for problems with discrete and low-dimensional action spaces, such as electronic games, and therefore they were not used in this work. The DDPG [36] algorithm was chosen for the vibration control in this work, as it is suitable for high-dimensional continuous-action spaces. The DDPG is a model-free off-policy actor-critic algorithm that uses deep networks and that can learn the control policy. The representation of the control policy of the actor is restricted, as it must run on hardware with real-time guarantees. Therefore, we used a fast five-layer feed-forward neural network in the actor. The actor was trained by the PyTorch software package.  
Finally, the control policy was bundled with the associated experiment control targets into an executable using a compiler tailored towards real-time control at 1 kHz. The deterministic nature of the DDPG gives a deterministic policy to execute on the plant. Although the actor is trained by the PyTorch software package, the well-trained actor is rewritten by the Simulink and NI/Veristand software packages, where the NI/Veristand  
Figure 3: The RL algorithm and control policy. (**a**) Depiction of the learning loop. The controller sends force commands on the basis of the acceleration state and control targets. (**b**) The control policy is a feed-forward neural network, with three hidden layers, which takes measurements and outputs force commands.  
Figure 2: Depiction of the simulator for the vibration system. The simulator, which has enough physical fidelity, receives force signals and outputs acceleration signals.  
acts as a plug-in of Simulink. The actor written in Simulink is then compiled into binary code. Finally, the binary code is run on the NI controller hardware.
# Enhanced Retrieval-Augmented Generation Using Low-Rank Adaptation
## 3 Proposed Method
### Enhanced Retrieval Strategy
#### 3.2.1 Transformer Attention-Based Similarity Adjustment  
Our model improves retrieval precision by integrating transformer attention scores from the LoRA-enhanced BERT-based uncased model. While traditional RAG models often fail to distinguish between road traffic legal clauses that share similar terminology but have different legal implications, our approach prioritizes documents with stronger semantic relevance.  
The transformer attention mechanism provides three key advantages for improving retrieval accuracy:  
* It captures contextual relationships between the terms in both the query and documents, enabling the model to better understand how terms relate to each other within the specific context of road traffic regulations.
* It gives higher weight to semantically significant terms based on their contextual importance rather than treating all terms equally or relying solely on frequency-based measures.
* It considers the full context of both the query and document when determining relevance, rather than relying on isolated word matches.  
\[\text{Sim}^{\prime}(E_{q},E_{d})=\alpha\cdot\text{Sim}(E_{q},E_{d})+\beta \cdot A_{qd} \tag{3}\]  
By incorporating high-level attention scores from the upper layers of BERT's transformer, we ensure that retrieved road traffic-related laws and regulations align more effectively with the query intent. The adjusted similarity score is computed as follows: where \(A_{qd}\) represents the transformer attention score between query and document chunk, and \(\alpha\) and \(\beta\) are weighting factors that balance the contribution of cosine similarity and attention-based similarity.
## 4 Performance Evaluation and Discussion
### LFU Communication Performance

Table 1: Comparison of the average successful transmission rate (STR) (%) for the proposed audio marker system at different distances and azimuths. Figure 8: Experimental setup for the second experiment: (**a**) configuration of two laptop computers (receiver) located inside and outside of the maximum transmission zone each and a COTS loudspeaker (transmitter); and (**b**) two different distances and five different azimuths for the placement of laptop computers. Figure 7: Experimental setup for the first experiment: (**a**) configuration of a laptop computer with an embedded microphone (receiver) and a commercial off-the-shelf (COTS) loudspeaker (transmitter); and (**b**) different distances and azimuths between the receiver and transmitter.
## 6 Statistical Analysis
### Statistics in Olfactoryometry and Behavior  
#### 6.1.1 Generalities  
Olfactometry itself is a crucial assay for investigating chemical ecology interactions mediated by VOCs, because it provides information about insect behavior in response to a particular olfactory stimulus. Behavioral analysis must be performed with various repetitions in enough quantities according to the experimental design and scope to ensure sufficient data, and then the appropriate statistical model leads to successful interpretations. Although this notion is widely known, it is also common that related and necessary concepts of statistical analysis are misunderstood or even unfamiliar for many researchers, and then mistaken conclusions about assay results are made. After this, it is valid to consider some management steps for the statistics of olfactory and behavioral studies, among which the following are the most important:  
1. Experimental design and scope: The utilization of robust experimental designs provides better accuracy for making conclusions about the results of an experiment, but it should be taken into account that incorrect interpretations of data by the researcher are always possible [203].
2. Sample size and statistical power: Corresponding to optimal numbers of measurements, treatments and repetitions performed during the experimentation stage [204] should be sufficient to provide significant differences between the null hypothesis and alternative hypothesis [203].
3. Pseudoreplication: Pseudoreplication is one of the most extensive problems in olfactory and, subsequently, in associated reports [14; 203; 205; 206]. It is strongly advisable to avoid this when possible. This consists of the use of non-independent experimental units during assays, generating a result bias and incorrect statistical conclusions. Often this is derived from incorrect experimental unit definitions (border effects, lack of randomization, poor variable control, etc.) or management (reutilization of the VOC source, insects, materials, and more), producing a \"residual\" effect amongst a unit and its replicates [205; 206].
4. Unbalanced data: This refers to data deficits due to unforeseen experimental events, such as difficulty measuring some variable, atypical or out-of-range values, etc., which produce an incomplete data matrix for analysis. In some cases, this can be corrected through two strategies: data deletion when experimental data are sufficient and insignificant information is lost or data filling for situations in which the data behavior is well known. Unfortunately, these approaches are not always feasible [206].
## 3 Results
### Surface Roughness (Ra)
The mean \(\pm\) SD and median (minimum-maximum) values of the surface roughness values obtained after glazing and mechanical polishing of the materials and the differences between the groups are presented in Table 4. A statistically significant difference was observed in the surface roughness values of all lithium dissilicate ceramic groups, depending on whether glazing or polishing was applied (\(p<0.05\)). The median surface roughness values in the mechanical polishing groups were lower than those in the glazing groups. No statistically significant difference was found in the surface roughness values of the KAT ceramic following the different surface finishing procedures (\(p=0.677\)). Two-way analysis of variance (ANOVA) revealed that neither the ceramic type (\(p=0.766\)) nor the interaction between ceramic type and surface treatment (\(p=0.217\)) had a statistically significant effect on the surface roughness value. However, the surface treatment protocol itself showed a statistically significant effect on the Ra value (\(p<0.001\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Glazing**} & \multicolumn{3}{c}{**Mechanical Polishing**} & \\ \cline{2-7}  & \multicolumn{1}{c}{**Mean \(\pm\) SD**} & \multicolumn{1}{c}{**Median**} & \multicolumn{1}{c}{**Mean \(\pm\) SD**} & \multicolumn{1}{c}{**Mean \(\pm\) SD**} & \multicolumn{1}{c}{**Test Statistic**} & \multicolumn{1}{c}{\(p\)**} \\ \hline CT & 0.13 \(\pm\) 0.02 & \begin{tabular}{c} 0.13 \\ (0.11–0.16) \\ \end{tabular} & 0.09 \(\pm\) 0.01 & \begin{tabular}{c} 0.09 \\ (0.07–0.11) \\ \end{tabular} & U = 0.000 & **\textless{}0.001** \\ \hline LS Block & 0.12 \(\pm\) 0.01 & \begin{tabular}{c} 0.13 \\ (0.11–0.14) \\ \end{tabular} & 0.09 \(\pm\) 0.02 & \begin{tabular}{c} 0.08 \\ (0.06–0.13) \\ \end{tabular} & U = 5.000 & **0.001** \\ \hline EC & 0.12 \(\pm\) 0.03 & \begin{tabular}{c} 0.13 \\ (0.09–0.18) \\ \end{tabular} & 0.09 \(\pm\) 0.02 & \begin{tabular}{c} 0.08 \\ (0.07–0.13) \\ \end{tabular} & U = 14.000 & **0.006** \\ \hline KAT & 0.11 \(\pm\) 0.03 & \
\begin{tabular}{c} 0.11 \\ (0.07–0.14) \\ \end{tabular} & 0.11 \(\pm\) 0.02 &
\begin{tabular}{c} 0.12 \\ (0.09–0.13) \\ \end{tabular} & U = 44.500 & 0.677 \\ \hline Test Statistic & \multicolumn{1}{c}{\(\chi^{2}\) = 5.587} & \multicolumn{1}{c}{\(\chi^{2}\) = 11.744} & \multicolumn{1}{c}{} \\ \hline \hline \(p\) & 0.134 & \multicolumn{3}{c}{**0.008**} & \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of roughness values according to the ceramic and surface finishing process.
## 4 Results
### Monthly Variation of NPP Value  
Monthly distributions of NPP reflect interactions between physical (e.g., FPAR, precipitation) and biological (e.g., species composition, microbial activity, and ecological interactions) processes. The total NPP in the study area accumulated mainly from May to September (accounting for 84.2% of the annual NPP) and peaked in July and August (accounting for 44.3% of t annual NPP) (Figure 5). The following are three possible explanations for the seasonal distributions: (1) rainfall was not a limiting factor for vegetation growth in summer; (2) the temperature was closer to optimum in summer than in other seasons; and (3) FPAR peaked in summer, which led to the greatest fraction of APAR.  
Calculated by the ratio between the months NPP difference for 11 years, the monthly rate change of NPP was obtained. From the change rate of monthly NPP (Figure 6), large variations occurred from March to May, and this period is the fastest growing period of NPP in Chongqing, with significant changes, whereas there is little change in the summer, and stability after September. Among the 11 years, the change rate of NPP in March reached 62.28%, while the lowest is 8.28% in August. This phenomenon indicates that NPP is stable in summer and the monthly variation of NPP in spring is larger, and the result may be caused by meteorological conditions in different seasons.  
Figure 4: The NPP value from the year 2001 to 2011.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Vogeltions Types** & **Evergen Confirions Forest** & **Evergen Desiderboard Forest** & **Decideons Centers Forest** & **Decidons Road-Level Forest** & **Shrob** & **Gaussian** & **Framulated** & **Ukbm-land** & **Extended** \\ \hline Area (Swr) & 8066 & 5076 & 48 & 12206 & 10202 & 7865 & 2008 & 603 & 57 \\
2001 & 598.69 & 771.2 & 596.2 & 633.19 & 495.23 & 348.96 & 328.83 & 215.61 & 143.95 \\
2002 & 671.15 & 835.44 & 733.23 & 722.13 & 807.13 & 402.35 & 308.07 & 224.28 & 172.24 \\
2003 & 900.81 & 1275.1 & 786.46 & 942.75 & 683.76 & 314.25 & 327.58 & 71.08 & 86.20 \\
2004 & 682.83 & 765.14 & 443.83 & 705.52 & 832.02 & 396.41 & 339.64 & 207.44 & 97.36 \\
2005 & 615.72 & 769.80 & 473.97 & 689.29 & 514.29 & 351.79 & 356.01 & 220.30 & 166.49 \\
2006 & 603.19 & 815.21 & 401.34 & 722.08 & 584.00 & 364.19 & 353.28 & 175.98 & 134.21 \\
2007 & 693.62 & 813.52 & 515.28 & 723.79 & 584.03 & 376.99 & 360.18 & 209.72 & 128.95 \\
2008 & 626.54 & 720.46 & 792.01 & 722.17 & 566.49 & 723.31 & 360.12 & 200.11 & 113.42 \\
2009 & 634.47 & 750.70 & 499.43 & 696.80 & 584.97 & 340.22 & 350.26 & 175.21 & 56.39 \\
2010 & 636.24 & 768.50 & 294.02 & 718.39 & 584.55 & 386.89 & 334.85 & 204.09 & 80.83 \\
2011 & 639.53 & 764.62 & 500.47 & 728.44 & 581.44 & 364.48 & 337.31 & 194.21 & 70.68 \\ \hline Average(C/m\({}^{2}\)s) & 663.51 & 830.4 & 525.81 & 726.74 & 584.43 & 362.40 & 367.93 & 203.32 & 110.68 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The statistics of average NPP in different vegetation types.
## 5 Instrumental Analysis
### Detection Methods
In the first place, ESI, being predominant in LC-MS workflows, ionizes polar and high-molecular-weight compounds through protonation ([M + H]+) or deprotonation ([M - H]-), making it ideal for additives, oligomers, and acidic/basic compounds. For instance, the ESI+ mode is widely used to detect additives, like erucamide, oleamide, phthalates, or antioxidant degradation products, while ESI- excels for acidic species, such as palmitic acid or stearic acid [63, 68]. The soft ionization nature of ESI preserves molecular ions, enabling HRMS characterization of labile compounds. In GC-MS, EI (70 eV) remains the standard, generating reproducible fragment-rich spectra for library matching (e.g., NIST). This technique is indispensable for volatile NIAS like aldehydes and hydrocarbons [24] or antioxidants and volatile additives [36]. However, the extensive fragmentation patterns of EI make the differentiation of isomers or structurally related substances difficult, where orthogonal separation (e.g., GC \(\times\) GC) offers significant advantages, as has been previously mentioned [13]. Finally, APCI ionizes non-polar and thermally stable compounds via gas-phase reactions, reducing in-source fragmentation compared to EI. In GC workflows, APCI enhances the identification capabilities by preserving more precursor ions, as shown in the work conducted by [PERSON] et al. [54]. However, commercially available libraries for APCI fragmentation patterns are scarce, requiring significant efforts for structural elucidation. While chromatographic techniques are predominant in the analysis of NIAS in plastic FCMs, a minority of studies employed alternative methods such as spectroscopy or elemental analysis. For example, [PERSON] et al. [49] investigated inorganic residues and contaminants in LDPE pellets and films using a multi-technique approach combining Raman spectroscopy, micro-Raman imaging, and inductively coupled plasma mass spectrometry (ICP-MS). The study highlighted the presence of unintended inorganic additives or contaminants, likely introduced during polymer processing, and emphasized the complementary role of Raman spectroscopy and ICP-MS in characterizing both organic and inorganic NIAS, complementing the broader reliance on LC-MS and GC-MS approaches.
# Recycling of Low-Quality Carbon Black Produced by Tire Pyrolysis
## 3 Results and Discussion
### Enhancing Calorific Value of Natural Fuels  
Given the exceptionally high calorific value of pure carbon (32.8 MJ/kg), the augmentation of natural fuels' calorific value is achievable through the incorporation of rubber-derived carbon black. In our experiments, well-decomposed peat sourced from the Elvand Rannu deposit (H(-H9)) served as a representative natural fuel. The highly decomposed (\"black\") peat is a mining residue that remains in the deposits due to low demand. As the water table in the peat mining areas is lowered, the remaining peat is exposed to air and thus undergoes decomposition, causing significant greenhouse gases emissions. Due to its high acidity, it inhibits the recovery of the natural state of peatland. For this reason, it is recommended to remove these layers in the depleted peat mines for any purpose, including their use as fuel [36]. The initial calorific value of peat (dry matter) stood at 14.5 MJ/kg, with an approximate moisture content of 55%. rCB was introduced to peat in ratios of \(1+9\), \(1+4\), and \(1+1\).  
Various mixtures were pressed into briquettes, and their combustion properties were meticulously examined at the Estonian University of Life Sciences' biofuels laboratory following standard methods (EN 14918) [28]. Briquettes with a \(1+9\) ratio demonstrated ro  
Figure 4: Concrete specimens with different contents of rCB.  
\begin{table}
\begin{tabular}{c c c} \hline
**Property/Ingredient** & **Unit** & **Value** \\ \hline Particle size \textgreater{} 10 μm & \% & \textless{}1 \\ pH & - & 6.8 \\ Volatiles content & wt\% & \textless{}3 \\ Moisture content & wt\% & \textless{}1 \\ Ash content & wt\% & 27 \\ PAH content & ppm & \textless{}120 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._bust cohesion, while others tended to disintegrate upon mold removal. The comprehensive results (on a dry matter basis) are presented in Table 4.  
Notably, carbon black achieves complete combustion when included in the briquette mixture at a 1 + 9 ratio, significantly enhancing the resulting briquette's calorific value. This is evident both in the table and visually through the resulting ash. However, a higher rCB content leads to incomplete carbon combustion, leaving unburned residues in the ash composition. Moreover, briquettes with an elevated rCB content lack structural integrity and exhibit a notable increase in sulfur emissions. The briquettes may break while reloading; thus, we recommend not using an rCB-to-beat ratio higher than 1 + 4.
# Identification of Damage in Beams by Modal Curvatures Using Acoustic Beamformers
## 4 Inverse Problem
### Experimental Tests
#### 4.2.2 Damage Located in between Sensors: Case B  
In the four scenarios D1.B-D4.B, the damage is located in between sensors #3 and #4. The contourplots and estimates obtained from the objective function, Bartlett beamformer, and MVDR processor are presented in Figures 16-19. The errors on the identified damage parameters are reported in Table 8. Since damage is in between sensors, no sharp peak of the curvature can be captured, which makes the identification of the correct damage parameters more difficult than in case A. This time, the estimation is based on the curvatures away from damage, which differ slightly from the undamaged value as shown in Figure 2d-f.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**D1.A**} & \multicolumn{2}{c}{**D2.A**} & \multicolumn{2}{c}{**D3.A**} & \multicolumn{2}{c}{**D4.A**} \\ \hline  & \(\mathbf{s^{D}}\) & \(\mathbf{h^{D}}\) & \(\mathbf{s^{D}}\) & \(\mathbf{h^{D}}\) & \(\mathbf{s^{D}}\) & \(\mathbf{h^{D}}\) & \(\mathbf{s^{D}}\) & \(\mathbf{h^{D}}\) \\ \hline \(H\) & 100 & \(-12.7\) & 0.0 & 6.9 & 0.0 & 11.3 & 0.0 & 14.3 \\ \hline Bartlett & 0.0 & 5.8 & 0.0 & 6.9 & 0.0 & 11.3 & 0.0 & 14.3 \\ \hline MVDR & 0.0 & 5.8 & 0.0 & 6.9 & 0.0 & 11.3 & 0.0 & 14.3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Error % on the identified damage parameters for damage case A.  
Figure 14: Contour plots of the estimators for damage scenario D3.A (**(a)** objective function, (b) Bartlett, (c) MVDR) (circle: correct damage parameters; cross: identified parameters).  
Figure 15: Contour plots of the estimators for damage scenario D4.A (**(a)** objective function, (b) Bartlett, (c) MVDR) (circle: correct damage parameters; cross: identified parameters).
## 2 Materials and Methods
### Data Processing and Assessment  
In order to evaluate the trueness of each test group, the trueness of the prealignment and postalignment data were evaluated. The reference file and each test file were aligned with a best-fit algorithm in 3D analysis software (Geomagic Control X, 3D Systems, Morrisville, SC, USA) to perform superimposition. After alignment, ISB areas in the prealignment data and abutment areas in the postalignment data were extracted to obtain the root-mean-square error (RMSE) value calculated according to the following formula:  
\[\text{RMSE}=\frac{1}{\sqrt{n}}*\sqrt{\sum_{i=1}^{n}\left(\chi_{1,i}-\chi_{2,i }\right)^{2}}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scanner** & **Manufacturer** & **Scanning Technology** & **Scanning Tip Size** \\ \hline T500 & Medit & Phase-shifting optical triangulation & - \\ \hline TRIOS 3 & 3 Shape & Confocal microscopy, continuous imaging & 16 mm \(\times\) 20 mm \\ \hline CS3600 & Carestream Dental & Structured light-active Speed 3D Video\({}^{\text{TM}}\) & 16 mm \(\times\) 12 mm \\ \hline i500 & Medit & 3D-in-motion video technology & 19 mm \(\times\) 15.2 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scanners used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scanner** & **ISB Orientation** & **Scanning Method** \\ \hline T500 & Buccal & Automatic scan \\  & Proximal & Automatic scan \\ \hline TRIOS 3 & Buccal & Full scan \\ CS3600 & Proximal & Partial scan \\ i500 & Buccal & Full scan \\  & Proximal & Partial scan \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental group design.  
Figure 2: In order to compare the trueness according to the ISB orientation, the flat surfaces of the ISBs faced either the buccal (**A**) or proximal (**B**) direction.  
where \(\chi_{1,i}\) is the reference scan value at measurement point \(i\), \(\chi_{2,i}\) is the evaluated scan value at measurement point \(i\), and \(n\) is the total number of measurement points in the analysis.