esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability and Efficiency of Metamodel for Numerical Back Analysis of Tunnel Excavation
## 5 Results and Discussions
### Global Sensitivity Analysis (GSA)  
The GSA was performed using the Vb method to analyse the effects of the five ground parameters on the vertical displacement of the tunnel crown. The 100,000 datasets of Group E were used, and the results are shown in Figure 6. The \(S_{T_{i}}\) of \(\gamma\), UCS, m\({}_{i}\), GSI, and K are 0.015, 0.024, 0.045, 0.929, and 0.995, respectively. Among them, \(S_{T_{i}}\) of GSI and K show extremely high values (exceeding 0.9) that exceed the average of \(S_{T_{i}}\) of other parameters. This result means that it is enough to consider only GSI and K among the five parameters when tunnel engineers conduct a back analysis. In the numerical analyses in this study, the elastic modulus value was calculated from the GSI. Therefore, this result is consistent with the selection of the elastic modulus and lateral pressure coefficient as the parameters of the back analyses, considering the vital parameters of tunnel displacements in existing studies [4; 5; 6].
# Formal Modeling and Improvement in the Random Path Routing Network Scheme Using Colored Petri Nets
## 2 Related Work
### Source-Location Privacy Addressing Scheme
#### 2.2.2 Shortest-Path Routing (SPR)  
SPR was also introduced for source-location privacy [14]. In shortest-path routing, a single path is carried out between the source and a destination node. In shortest-path routing, the packet has the shortest distance to travel, and in this scheme, packets are always moving to the next-hop node. Packets are moving along the shortest path between the sink node and the source note. It consumes very little energy, and the delivery ratio with shortest-path routing is very high but provides very low source-location privacy. Additionally, the drawback of this scheme is that it uses network configuration, which is not possible in real-world scenarios.
## 3 Results and Discussion
### QSAR Studies  
The physico-chemical properties of the FLP in correlation to biological activity as computed from their QSAR parameters are presented in Table 4.  
Figure 3: Three-dimensional representation of post-docking interactions of (**A**) \(\beta\)-Selinene, (**B**) Sulcatoene, (**C**) \(\beta\)-caryophyllene, (**D**) \(\alpha\)-ylangene (**E**) all lead phytochemical in the active pocket, and (**F**) 3D orientation of all lead phytochemicalinal with _A. gambiae_ OBP 7.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Compounds** & **LE** & **LE\({}_{\text{scale}}\)** & **LLE** & **FQ** & **LELP** \\ \hline Sulcatoe & 1.2111 & 0.6269 & 7.6746 & 1.9320 & 0.2876 \\ \(\alpha\)-ylangene & 0.6213 & 0.5271 & 6.3137 & 1.1763 & 0.8572 \\ \(\beta\)-caryophyllene & 0.6333 & 0.5271 & 6.4752 & 1.2016 & 0.8166 \\ \(\beta\)-selinene & 0.8133 & 0.5271 & 8.4638 & 1.5431 & 0.6343 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ligand efficiency metrics.  
Sulcatone and beta-selinene presented the best QSAR parameters. For instance, both compounds exhibited the highest partition coefficient log \(p\)-value, which is important in evaluating the rate of permeability into cell membranes [50].
## 2 Materials and Methods
### Color Measurement  
Color of homogenized meat products surface was measured directly after production process and after 14 d of chilled storage by using a reflectance colorimeter Minolta CR-400 (Konica Minolta, Osaka, Japan). Color values presented in this paper were expressed in CIE Lab system (L*--lightness, a*--redness, b*--yellowness). Samples of HMP were cut crosswise into 10-15 mm thick slices before each measurement. For each treatment, six measurements were made.
## 3 Results
### Co-Activation Differences with Vibration Conditions and Phases of Gait Cycle  
The change in co-activation with vibration conditions was different for each gait phase, as shown in Figures 4 and 5. Co-activation was mostly at its highest in the PU phase and at its lowest in the FC phase. At the threshold intensity (Figure 4), the TA-GL group showed reduced co-activation during the PU and the FCN phases. During the PU phase, Th-Max decreased by 14.1% and Th-Min by 5.6% (\(p<0.05\)). In the FCN phase, Th-Max was reduced by 5.7% and Th-Min by 4.4% compared with None, but was not significant. For the TA-GM group, co-activation decreased in the WA, the PU and the FP phases. Th-Max decreased by 6.2% for the PU phase and by 2.9% for FP phase (\(p<0.05\)). Th-Min decreased by 4.6% for the WA phase, 5.5% for the PU phase, and 2.7% for the FP phase, respectively (\(p<0.05\)). Co-activation in the TA-SOL group was reduced by 7.3% in Min during the WA phase (\(p<0.05\)). Also, during the PU phase, Th-Max and Th-Min decreased by 12.4% and by 11% compared with None, respectively (\(p<0.05\)). At Sub-threshold intensity (Figure 5), the TA-GL group showed increased co-activation in the Sub-Min during the PU phase (by 4.4%, \(p<0.05\)), while co-activation in  
Figure 3: Mean of EMG activity of all participants in vibration stimulation conditions at sub-threshold intensity: (**a**) Tibialis anterior, (**b**) Gastrocnemius lateralis, (**c**) Gastrocnemius medialis, (**d**) Soleus. ‘Sub-Max’ means a vibration of 140 Hz was applied at sub-threshold intensity, while ‘Sub-Min’ means a vibration of 210 Hz was applied at sub-threshold intensity. The solid line indicates the gait phase (Weight Acceptance (WA, 0%–14%), Pull-Up (PU, 15%–32%), Forward Continuance (FCN, 33%–64%), Foot Clearance (FC, 65%–82%), Foot Placement (FP, 83%–100%)).  
the FC phase was reduced by 3.2% in the Sub-Max (\(p<0.05\)). For the TA-GM group, the Sub-Max and Sub-Min in the PU phase increased by 4.3% and by 11.2% compared with None, respectively (\(p<0.05\)). Co-activation of Max in the FC phase decreased by 3% (\(p<0.05\)). TA-SOL group showed reduced co-activation in the Sub-Max (by 8.1%) and Sub-Min (by 11.5%) compared with None during the PU phase and Sub-Min resulted in the lowest co-activation (\(p<0.05\)).  
Figure 4: Mean value (\(\pm\) standard deviation) of co-activation of each gait phase according to the vibratory stimulation conditions at threshold intensity of each muscle group: (**a**) TA-GL group, (**b**) TA-GM group, (**c**) TA-SOL group, * indicates statistical significance through post-hoc tests between vibratory stimulus conditions (\(p<0.05\)).
## 3 Results
These results are corroborated by the Bland\(-\)Altman analysis provided in Figure 2, in which we detect a low bias between the observed and the predicted RPE (0.01 \(\pm\) 1.20 arbitrary units (AU); see Figure 2a) and between the observed and the predicted S-RPE (0.002 \(\pm\) 1.57 AU; see Figure 2b). Moreover, the Bland\(-\)Altman analysis also shows a lower systematic error in S-RPE (r = \(-\)0.13; see Figure 2b) compared to RPE (r = \(-\)0.30; see Figure 2a). The negative value of systematic error suggests that the predicted RPE and S-RPE are higher than the observed ones when players perceived low RPE while it is lower when the players perceived a high RPE. Figure 1: Correlation analysis. (**a**) Relationship between the observed and the predicted rate of perceived exertion (RPE). (**b**) Relationship between the observed and the predicted training load (S-RPE). The blue area refers to observed values lower than 4, the green area refers to observed values between 4 and 7, and the red area refers to observed values higher than 7. The values provided in the boxes reflect the percentage of overestimations and underestimations above 1 standard deviation (grey line) of the ordinal regressor in relation with the observed values. The red line refers to the trend line of the relationship between the values observed and predicted. Finally, Figure 3 shows that our model understates the RPE and S-RPE class when they show a value higher than seven in days close to the match, while it overestimates the players' effort in the day before a match when players perceived less than four. A similar distribution of both underestimations and overestimations was recorded when the players' perceived effort was between four and seven.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 2 Theoretical Background of TD-NIRS
### Time-Resolved (TR) Mean Depth of Light Propagation  
From sensitivity to absorption, \(H(\textbf{r}_{d},\textbf{r}_{s},\textbf{r},\,t)\), it is possible to calculate the TR mean depth of light propagation, \(<\)z\(>\)(\(\textbf{r}_{d},\textbf{r}_{s},\,t\)), in a semi-infinite homogeneous medium by,  
\[<z>(\textbf{r}_{d},\textbf{r}_{s},t)=\frac{\int_{V}zH(r_{d},r_{s},r,t)dr}{\int _{V}H(r_{d},r_{s},r,t)dr}=\frac{\int_{V}zdr\int_{0}^{t}dt\
u\{G(r,r_{s},tt)G(r, r_{d},t-t)\}}{\int_{V}dr\int_{0}^{t}dt\
u\{G(r,r_{s},tt)G(r,r_{d},t-t)\}}, \tag{21}\]  
where the integral with respect to position is over the volume, \(V\), with finite values of \(H(\textbf{r}_{d},\textbf{r}_{s},\textbf{r},\,t)\).  
Figure 5: (a) Light propagation from the source position, \(\textbf{r}_{s}\), to the detector position, \(\textbf{r}_{d}\), through an arbitrary position, **r**. (b) Light propagation from the source position, \(\textbf{r}_{s}\), to the position, **r**, and from the detector position, \(\textbf{r}_{d}\), to the position, **r**.  
Figure 6: Distributions of the time-resolved sensitivity of the reflectance from a homogeneous semi-infinite medium with the source-detector (SD) distance, \(\rho\), of 30 mm, \(\mu_{a}\) = 0.01 mm\({}^{-1}\), and \(\mu_{s}\) = 0.5 mm\({}^{-1}\) for different delays of the time windows of 0.86 ns, 1.66 ns, and 2.46 ns from left to right. Reproduced from Reference [30].  
As a note for the CW domain, the mean depth (or penetration depth) in semi-infinite homogeneous media with the source-detector (SD) distance of \(\rho\) is approximately expressed using the effective attenuation coefficient, \(\mu_{\textit{eff}}=(3\mu_{s}{{}^{\prime}}\mu_{a})^{1/2}\), as \(<\)\(>>(\rho)=(1/2)(\rho/\mu_{\textit{eff}})^{1/2}\)[31].
## 5 Design Considerations
### Proposed Acceptance Criteria for GRM Fill Materials (Static Loads)  
In the case of most granular soils, design criteria based on frictional shear strength, bearing capacity, and permeability are used to evaluate their suitability as structural fill materials for geotechnical applications. It is often required that fills should possess a friction angle greater than or equal to 30\({}^{\circ}\) (and/or a California Bearing Ratio (CBR) \(>\) 10%), to guarantee a satisfactory shear resistance and to minimize post-construction settlement [17]. In addition, it is also recommended that fill material should have a permeability coefficient similar to that of sandy fills to ensure rapid dissipation of excess pore water pressure and to minimize internal erosion phenomena.  
Because of the critical aspects identified in this study such as long-term creep compressibility and leaching of heavy metals, design criteria simply based on strength and permeability may not be sufficient to fully judge whether or not a GRM meets all the geotechnical requirements for structural fills. To overcome this issue, a modified framework with additional geotechnical design criteria including three levels of acceptance is proposed for GRMs (Figure 12):  
* Level 1: _Frictional shear resistance/bearing capacity._ When used as structural fills, GRMs should possess adequate shear strength and bearing capacity to guarantee a satisfactory stability and reduced post-construction settlement. Specifically, GRMs should have a friction angle \(\phi^{\prime}\geq 30^{\circ}\) and/or a CBR \(\geq\) 10%. Otherwise, they should only be recommended as general fills.
* Level 2: _Compressibility (most critical level)._ Since compressibility could continue for a long period of time, it is suggested that GRMs be accepted as structural fill only if the volumetric strain (\(\varepsilon_{v}\)) is \(\leq\) 3%. Otherwise, they should only be recommended as general fills.
* Level 3: _Permeability (not a critical level)._ In order to guarantee rapid dissipation of excess pore water pressure (i.e., maintaining relatively free-draining), it is recommended that GRM structural fills should have a permeability coefficient similar to that of sandy/gravelly fills (i.e., 1 \(\times\) 10\({}^{-6}\) cm/s \(\leq\) \(k\)\(\leq\) 1 \(\times\) 10\({}^{-4}\) cm/s). If \(k\)\(>\) 1 \(\times\) 10\({}^{-4}\) cm/s, GRMs could be used as general fills.  
In addition to the above proposed geotechnical criteria, GRMs must also meet environmental standards (e.g., New Zealand Drinking Water Standards and New Zealand Landfill Waste Acceptance Criteria). Alternatively, pre-treatment or confinement of leached chemicals may be required.
# Immersive Two-Channel Recordings Based on Personalized BRIRs and Their Applications in Industry
## 6 Review of Industrial Applications
### Music Streaming Services  
Remaining in the music industry, the popularity of music streaming services is skyrocketing as more and more people are discovering the benefits of loss-free audio in their reach everywhere and at any time. Currently nearly 80% of people use streaming services [58]. But the rapid changes in the management and evolution of streaming applications also contribute to this, where every year services race to live up to the expectations of their users. There are multiple techniques set to provide a distinct aural experience for the listener [51]. Unfortunately, not all of the services really take into consideration headphone listeners when providing new application features and updates. Even if headphone listeners are more than an afterthought, not every new development is truly suited to their needs. The main issue here stands with the externalization effect and how it can be easily suppressed in available immersive audio recordings.  
Among the most popular and commonly used audio technologies, Dolby Atmos ranks first. Since 75% of their users use headphones, Atmos Renderer for binaural rendering was created. This allows for listening to even a 7.1.4 Atmos mix through headphones by converting it to an Atmos binaural format in real time. The Dolby Atmos Renderer processes this using the provided metadata stream from the audio objects. To enhance the accuracy and effectiveness of the mixes, it is recommended to experiment with object positions and binaural render modes to find the optimal values for each component [52]. The renderer in 2022 introduced a beta of Dolby Atmos Personalized Rendering that uses the PHRTF (Personalized Head-Related Transfer Function). The connected app allows for head scanning to create PHRTFs in addition to virtual object sound-source positioning in the original renderer. Both of these functions combined constitute what typical personalized BRIRs stand for, but their availability is not widespread and there are not any tests comparing them to other techniques, so it is difficult to objectively evaluate how they work.  
Within the industry, audio technologies spread even beyond music services. Extended reality applications incorporating interactive graphic and auditory elements allows artists to express their imaginative intentions with great accuracy. [PERSON] et al. [59] conducted research that inspects the combination of auditory experiences of immersive music videos with haptic elements. Immersive sound heightened with musical haptics can help communicate performance information between artists and listeners. [PERSON] et al. [60] have created a real-time, multi-site, interactive and collaborative environment called DIP (Distributed Immersive Performance), which allows interactive musical performance. The idea was originally based on a complex acquisition and rendering but could possibly be improved with a pre-existing database of room impulse responses that are convoluted with desirable audio.
## 3 Results and Discussion
### Analytical Application
#### 3.2.3 Analytical Performance of the Recommended Procedure
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Chromogenic** & **\(\mathbf{\lambda_{max}}\)** & **pH** & **Molar Absorptivity,** & **Linear Dynamic Range** & **Detection Limit, (LOD),** & **Interferences** & **Ref.** \\
**Reagent** & **(nm)** & **pH** & **L/mol.cm** & **ug/mL** & **(ug/mL)** & **Interferences** & **Ref.** \\ \hline Cefkime & 336 & 4.68 & \(8.29\times 10^{3}\) & 1.015–8.122 & 0.032 & H\({}^{2+}\),Al\({}^{3+}\),Zn\({}^{2+}\),Fe\({}^{3+}\),Mn\({}^{2+}\) & [13] \\ BTAS & 485 & 5.0 & \(2.35\times 10^{4}\) & 0.63–0.504 & \(7\times 10^{-3}\) & Spin\({}^{2+}\),Cd\({}^{2+}\),Co\({}^{2+}\) & [14] \\
2,4-dinito AYFP & 445 & 8.7 & \(0.87\times 10^{3}\) & 10–80 & 1.72 & Sb\({}^{3+}\),Mn\({}^{2+}\),Pd\({}^{2+}\),Co\({}^{2+}\),Hg\({}^{2+}\) & [15] \\ DDTC & 435 & 5 & \(2.86\times 10^{5}\) & 0.2–12 & 0.2 & Fe\({}^{3+}\),Ni\({}^{2+}\),Pb\({}^{2+}\),Mn\({}^{2+}\) & [16] \\ DDTC & 435 & 5 & \(3.61\times 10^{5}\) & 0.02–12 & 0.029 & \({}^{3+}\),Pb\({}^{2+}\),Pb\({}^{2+}\) & [17] \\ ATAP & 608 & 4.5 & \(4.37\times 10^{5}\) & \(4\times 10^{-3}\),-0.115 & \(1.2\times 10^{-3}\) & Fe\({}^{3+}\),Sb\({}^{3+}\),Nb\({}^{3+}\),Nb\({}^{3+}\),\({}^{3+}\) & [18] \\ HBITSC & 510 & 4 & \(2.5\times 10^{3}\) & 1–8 & NM & Co\({}^{2+}\),Fe\({}^{3+}\),Fe\({}^{3+}\),Nb\({}^{3+}\),Cr\({}^{3+}\) & [19] \\ NBIMMT & 470 & 6.2 & \(2.8\times 10^{3}\) & 4.75–16.13 & NM & Pb\({}^{3+}\),Cd\({}^{2+}\),Zn\({}^{2+}\),Zn\({}^{2+}\),Ag\({}^{+}\),Al\({}^{3+}\) & [20] \\
2,4-PIT & 370 & 8 & \(2.14\times 10^{4}\) & 0.16–1.3 & 0.053 & NM & NM \\ H2 IF & 414 & 4.8 & \(1.46\times 10^{5}\) & \(6.35\times 10^{-3}\),-0.318 & \(6.38\times 10^{-3}\) & There is no interference at1:10 ratio & [22] \\ SM, 3H-BR & 430 & 5.5 & \(0.603\times 10^{4}\) & 0.05–13 & NM & There is no interference at1:10 ratio & [23] \\ Rubenamic acid & 380 & 3.5 & \(1.01\times 10^{4}\) & 0.65–2.65 & NM & Co\({}^{2+}\),Ni\({}^{2+}\) & [4] \\ CBIMMT & 414 & 4.2 & \(3.38\times 10^{3}\) & 5–17.5 & NM & Ni\({}^{2+}\),Pb\({}^{2+}\),Al\({}^{3+}\),Bi\({}^{3+}\) & [37] \\ AMT & 434 & 4–6 & \(1.9\times 10^{4}\) & 0.7–25 & 0.011 & Mn\({}^{2+}\),Al\({}^{3+}\) & PW \\ \hline \hline \end{tabular}  
* CBIMMT: 4(* chlorobenzylideneimino)-3-methyl-5-mercapto-1,2,4-triazole; NM not mentioned; PW: present work.  
\end{table} 
Table 5: Comparison between the proposed method and previously-published spectrophotometric methods to estimate Cu(II) \({}^{*}\)
## 5 Proposed Hardware Architecture
### Memory Unit  
The proposed architecture utilizes a memory unit with a size of \(10\times m\) to store intermediate and final results, as illustrated in Figure 1. Here, the value 10 refers to the number of memory locations, while \(m\) represents the width of each memory location. The initial projective points are stored in storage elements \(W_{1}\), \(Z_{1}\), \(W_{2}\), and \(Z_{2}\), while the updated projective points are stored in \(T_{4}\), \(T_{1}\), \(W_{0}\), and \(T_{3}\). Additionally, the intermediate results, as presented in Table 2, are stored in \(Z_{0}\) and \(T_{2}\).  
To store these storage elements in memory locations, a \(1\times 10\) demultiplexer (DEMUX) with control signal WRITE_ADDR is employed. Two multiplexers, RF1 and RF2, are utilized to retrieve storage elements from the memory unit for further processing, with control signals RF1_ADDR and RF2_ADDR. The size of RF1 and RF2 is \(10\times m\). The output of RF1 is RF1, while the output of RF2 is RF2.
# A Modality-Enhanced Multi-Channel Attention Network for Multi-Modal Dialogue Summarization
## 4 Experiment
### Error Analysis  
Multi-modal dialogue is more difficult for models to learn and generate summarization than textual dialogue. There are complex correlations among the data across modalities. As shown in Table 1, sometimes, attaching multi-modal data even results in performance degradation. Different modalities of data supplement more information and can cause conflict. Table 3 shows that the main problem of existing dialogue summarization models is their inconsistency. To obtain a higher score on word-overlap metrics, summarization models tend to generate a longer summary to cover more information. Given the lack of constraints on factual inconsistencies, models generate plenty of errors. Therefore, we conducted an error analysis and for a case study on MDS to analyze these challenges.  
Factual inconsistencies are often found in abstractive summarization and dialogue summarization. Several generated summaries with high ROUGE and BLEU scores contain considerable factual inconsistencies. We selected 50 of them and conducted a quantitative analysis. There are eight factual inconsistencies in abstractive summarization [31], and Table 4 shows the six most frequent types and their rates.  
Generally, MEMA performs better than T5 and MDialBART, as MEMA makes fewer errors than T5 and MDialBART regarding the following four factual inconsistencies: missing information, redundant information, wrong reference, and object error. Specifically, T5 and MDialBART have high error rates of missing information (78% and 84%, respectively). At the same time, MEMA can reduce the error rate to 26%, indicating that MEMA has a a greater probability of generating a complete summary than T5 and MDialBART. Furthermore, MEMA significantly decreases object error compared to T5 (\(-\)52%) and MDialBART  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **INFOR.** & **CONSIS.** & **EXP.** & **OVER.** \\ \hline Transformer & 1.92 & 1.70 & 2.20 & 1.42 \\ T5 & 2.84 & 1.12 & 1.12 & 1.08 \\ MDialBART & 3.32 & 1.22 & 2.66 & 1.26 \\ VMSMO & 1.02 & 1.30 & 1.00 & 1.00 \\ MEMA & **3.50** & **2.66** & **3.94** & **2.42** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Human evaluation scores on four measures of informedness (INFOR.), consistency (CONSIS.), expressiveness (EXP), and overall evaluation (OVER.).  
(\(-40\%\)), showing that MEMA possesses a more vital ability to generate correct objects in summary with audio-visual accompaniment. MEMA also enhances the performance of the problem of redundant information and incorrect references to some extent. As for circumstantial and negation errors, although MEMA tends to have a slight disadvantage, all three models maintain low error rates and can alleviate those two factual inconsistencies. Overall, error analysis shows that MEMA understands modality interaction and generates more faithful information than the compared models.