esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Program
### Instrumentation  
Specimens S1A, S1B, and S5 were instrumented with a total of 68, 64, and 67 sensors, respectively. All specimens featured a similar instrumentation layout: LVDTs were mounted on the beams' web, with 28 LVDTs on S1A and S1B and 30 on S5. Half of these LVDTs measured flexural deformations parallel to the beams' longitudinal axis, while the other half were mounted in an X configuration to measure shear deformations. Additionally, five to seven string potentiometers (SP) were attached to the strong floor and connected along the beams' softit to capture beam deflection profiles. Two extra SPs were connected to the top and bottom of the reaction block to monitor potential displacements and rotations, which were found to be negligible. Finally, strain gauges (SGs) were installed along the main longitudinal top and bottom rebars, as well as on selected steel hoops, with 28 SGs on each specimen. In specimens S1A and S1B, only the end and middle small hoops within the chord members above and below the openings were instrumented (six hoops total); in S5, SGs were provided on the second and third stirrups within the end hinging region. Figure 9 provides a schematic of the instrumentation layout for specimens S1B and S5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Drift**} & [\%] & 0.1 & 0.2 & 0.3 & 0.4 & 0.6 & 0.8 & 1.0 & 1.5 & 2.0 & 3.0 & 4.0 & 6.0 \\ \cline{2-13} \multirow{2}{*}{**S1A**} & **Displ** & **[cm]** & 0.4 & 0.7 & 1.0 & 1.3 & 2.0 & 2.6 & 3.4 & 5.2 & 6.8 & 10.2 & 13.7 & 20.5 \\ \cline{2-13}  & **Load Down** & **[kN]** & \(111\) & \(211\) & \(295\) & \(364\) & \(470\) & \(568\) & \(606\) & \(654\) & \(682\) & \(722\) & \(698\) & \(392\) \\ \cline{2-13}  & **Load Up** & **[kN]** & \(-92\) & \(-182\) & \(-272\) & \(-354\) & \(-468\) & \(-558\) & \(-574\) & \(-621\) & \(-656\) & \(-711\) & \(-695\) & \(-545\) \\ \hline \multirow{4}{*}{**S1B**} & **Drift** & **[\%]** & 0.1 & 0.2 & 0.4 & 0.6 & 0.8 & 1.1 & 1.6 & 2.3 & 3.3 & 4.5 & 6.0 & \\ \cline{2-13}  & **Displ** & **[cm]** & 0.3 & 0.7 & 1.3 & 2.0 & 2.6 & 3.8 & 5.5 & 7.8 & 11.3 & 15.3 & 20.4 & \\ \cline{2-13}  & **Load Down** & **[kN]** & \(134\) & \(202\) & \(322\) & \(448\) & \(541\) & \(617\) & \(668\) & \(713\) & \(749\) & \(562\) & \(400\) & \\ \cline{2-13}  & **Load Up** & **[kN]** & \(-177\) & \(-237\) & \(-369\) & \(-477\) & \(-552\) & \(-571\) & \(-624\) & \(-669\) & \(-715\) & \(-631\) & \(-552\) & \\ \hline \multirow{4}{*}{**S5**} & **Drift** & **[\%]** & 0.1 & 0.2 & 0.4 & 0.7 & 0.9 & 1.2 & 1.6 & 2.1 & 2.9 & 3.4 & 3.8 & 4.5 & 5.3 \\ \cline{2-13}  & **Displ** & **[cm]** & 0.4 & 0.7 & 1.4 & 2.1 & 2.8 & 3.8 & 5.1 & 6.7 & 9.2 & 10.8 & 12.2 & 14.3 & 16.7 \\ \cline{2-13}  & **Load Down** & **[kN]** & \(199\) & \(302\) & \(491\) & \(705\) & \(805\) & \(856\) & \(886\) & \(926\) & \(979\) & \(986\) & \(971\) & \(646\) & \(431\) \\ \cline{2-13}  & **Load Up** & **[kN]** & \(-233\) & \(-304\) & \(-464\) & \(-580\) & \(-624\) & \(-652\) & \(-664\) & \(-694\) & \(-735\) & \(-741\) & \(-734\) & \(-602\) & \(-406\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Load protocol.  
Figure 9: _Cont._
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 9 Synergistic Interactions
Again, combinations appeared to have no effect on nematode reproduction,suggesting that increases in mortality were due to a weakening of the host rather than increased infectivity. A subsequent field trial with a _M. anisopliae_ and _H. bacteriophora_ combinations in turf also showed additive/synergistic effects, producing 95% grub mortality at a 4-week nematode application interval, which was comparable to a tested chlorpyrifos treatment [262]. It has also recently been shown that nematodes can help to disseminate bacterial for fungal entomopathogens in the soil. [PERSON] et al. [263] showed that _S. feltiae_ and _H. bacteriophora_ were capable of the moving the fungus _I. fumosroscea_ across different substrates. Some studies have also shown synergistic interactions between nematodes and chemical insecticides, such as chlorpyrifos, imidacloprid, and diazinon [264; 265; 266]. While this may be a good option in agricultural applications, incorporating chemical controls into biocontrol programs, even at low concentrations, puts non-target organisms at risk, and counteracts a primary objective of biocontrol. The use of entomopathogens can also be combined with other non-microbial control tactics to increase effectiveness. For instance, several studies have found that combining diatomaceous earth with _M. anisopliae_ and _B. beauveria_ treatments increase mortality for stored grain pests [267; 268; 269]. While no studies have been carried out on scrap pests, these combinations would likely be effective on white grubs, as diatomaceous earth can be easily incorporated to soil. Insect growth regulators have also been used to slow insect development, reducing cuticle shed and increasing the infectivity of entomopathogenic fungi [270; 271]. Again, more work is needed to determine whether this combination would work for scarab pests, but the growth regulator halofenoxide is effective on Japanese beetle, European chater, and oriental beetle [272; 273]. Indirect interactions between entomopathogens and natural enemies can also augment control [274]. The presence of predators or parasitoids can affect host movement patterns in ways that increase the dispersal of pathogens, and this has been demonstrated in several systems [275; 276; 277]. Natural enemies other organisms, such as earthworms, can also move viruses [278; 279; 280; 281; 282], bacteria, fungi [283; 284; 285; 286], and nematodes [287; 288; 289] throughout their environment on their cuticle or as vectors. Unfortunately, little research has been conducted on the potential impacts of non-host vectors in creating epizotics in scarab populations or in underground soil systems.
## 3 Results
### Simulation Validation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Actual execution of work orders (manual input)**} \\ \hline
**Code** & **Workstation** & **Actual start** & **Actual stop** & **Off** & **Short stop** & **Long Stop** & **Working** & **Quantity/m2** \\ \hline Micro-batch- & Plotter & 16/10/2024 & 16/10/2024 & 00:00.00 & 00:01:12 & 00:00.00 & 01:34:07 & 115.9 m2 \\
44-025-CAA & K-PLD40 & 556/20 & 7/3:21 & & & & & \\ Micro-batch- & Plotter & 16/10/2024 & 16/10/2024 & 00:00.00 & 00:01:02 & 00:03:23 & 00:55:14 & 73.88 m2 \\
44-032-CA & K-PLD40 & 7/3:218 & 8/3:58 & 00:00.00 & 00:00.00 & 00:05:04 & 01:45:01 & 148.58 m2 \\ Micro-batch- & Plotter & 16/10/2024 & 16/10/2024 & 00:00.00 & 00:00.00 & 00:00.00 & 00:50:04 & 01:45:01 & 148.58 m2 \\
44-034-CAA & K-PLD40 & 14/3:31 & 16/3:23:18 & & & & & \\ Micro-batch- & Plotter & 16/10/2024 & 16/10/2024 & 00:00.00 & 00:01.08 & 00:00.20 & 02:09:09 & 164.1 m2 \\
44-025-CA & K-PLD40 & 10/2026 & 16/10/2024 & 00:00.00 & 00:00.48 & 00:00.00 & 01:28:23 & 104.03 m2 \\
44-025-CA & K-PLD40 & 85/2:37 & 10/21:49 & & & & & \\ Micro-batch- & Plotter & 16/10/2024 & 16/10/2024 & 00:00.00 & 00:44 & 00:00.00 & 01:54:46 & 169 m2 \\
44-037-CM & K-PLD40 & 12/3:57:27 & 14:32:58 & & & & & \\  & & & **TOTAL** & 00:00.00 & **00.455** & **0.0849** & **9.4643** & **775.49 m2** \\ \hline \multicolumn{1}{c}{**Virtual factory simulation data**} & & & & & & & & \\ \hline
**Shift** & **Workstation** & **Start** & **End** & **Off** & **Short stop** & **Long Stop** & **Working** & **Quantity/m2** & **Availability** & **Performance** & **OEE** \\ \hline
**17.10.2024** & Plotter & 16/10/2024 & 16/10/2024 & 00:00.00 & **00:00.01** & **02:39.59** & **05:020.00** & 400 & 67\% & 100\% & 67\% \\  & K-PLD40 & 7:00.00 & 150.000 & & & & & & & \\ \hline
**Shift** & **Workstation** & **Start** & **End** & **Off** & **Short stop** & **Long Stop** & **Working** & **Quantity/m2** & **Availability** & **Performance** & **OEE** \\ \hline
**17.10.2024** & Plotter & 16/10/2024 & 16/10/2024 & 00:00.00 & **00:04:55** & **01:56:13** & **09:58:51** & 775 & 83\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Plotter workstation activity on 16 October 2024, based on manually reported task feedback, simulation data, and DMUSA sensor logs-
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 5 Conclusions  
In this study, we present PCCAU-Net, an advanced deep learning architecture engineered for the efficient and accurate extraction of roads. Extensive evaluations on the Massachusetts Roads Dataset and the DeepGlobe Roads Dataset reveal that PCCAU-Net consistently outperforms extant cutting-edge methodologies, including U-Net, ResU-Net, DeepLab V3+, and SDUNet, across multiple evaluation metrics. The efficacy of PCCAU-Net is attributed to its innovative Pyramid Pathway Input and DCA modules, explicitly tailored to handle the complex and diverse scenarios inherent in remote sensing imagery. Ablation studies further validate the positive impact of these modules on the model's performance. However, it is imperative to acknowledge the model's computational complexity as a limitation, rendering it potentially unsuitable for real-time or high-throughput applications. Future work will be directed towards optimizing the model to meet these operational constraints. Overall, the findings not only advance the field of automated road extraction, but also offer valuable insights for additional applications in remote sensing image analysis.  
Conceptualization--X.X.; Methodology--C.R. and Y.Z.; Software--C.R., A.Y. and C.D.; Validation--A.Y. and J.L.; Formal analysis--X.X., Y.Z. and Y.L.; Resources--X.X. and C.R.; Data curation--X.X.; Writing--original draft, X.X. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters (M)** & **FLOPs (GLOPS)** \\ \hline U-Net & 29.97 & 5.63 \\ \hline Resunet & 25.57 & 5.42 \\ \hline DeeplabV3+ & 5.88 & 6.60 \\ \hline SDUNet & 25.90 & 62.30 \\ \hline PCCAU-Net & 24.35 & 60.17 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of computational efficiency of different methods.  
**Funding:** This research was supported by the National Natural Science Foundation of China (Grant No. 42064003).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Preliminaries
### Design Goals
#### 3.2.2 (Near) Real-Time Streaming Support  
In the previously mentioned tasks, real-time streaming support is required to effectively derive insights from data captured by aerial drones at scale. The actual latency requirements may vary depending on the specific use case. For example, emergency response drones should be able to deliver low-latency video streams with a strict upper bound, whereas delivery drones may not have such stringent requirements but should still provide video streams with an acceptable delay.
## 4 Numerical Calculation of Slopes Based on Weibull Parameter Random Distribution Model
### Mining Slope Stability Analysis Lower Extension Mining
#### 4.2.3 Plastic Zone and Maximum Shear Strain Analysis  
As shown in Figure 10a,b, the plastic zone cloud maps and maximum shear strain cloud maps after slope excavation are presented, respectively, while Figure 10c shows the plastic zone volume curve at different excavation stages throughout the excavation process. Analysis reveals that a fully penetrative plastic zone developed in the platform area between elevations 1195 m and 1240 m, indicating severe instability in the soil within this region. The plastic zone volume curve, calculated using Fish scripting, shows a sudden increase in volume. Although a localized plastic zone also formed at the slope toe during mining, it has not yet fully penetrated, displaying localized damage characteristics. The maximum shear strain analysis further supports this finding, as the maximum shear strain in this area also reached a fully penetrative state. These results strongly suggest that excavation activities at the 1195 m to 1240 m platform caused significant disturbance, ultimately leading to the loss of slope stability and subsequent slope failure.  
Figure 8: Dissipated energy cloud during slope excavation: Step 2, Step 4, Step 10, Step 13, and dissipated energy monitoring histogram. (**a**) Dissipated energy cloud in Step 2 of excavation; (**b**) dissipated energy cloud in Step 4 of excavation; (**c**) dissipated energy cloud in Step 10 of excavation; (**d**) dissipated energy cloud in Step 13 of excavation; (**e**) bar chart of dissipated energy for different excavation steps.
## 4 Discussion
In the current study, the MnOBs were commissioned and used as part of a more complex integrated system for removing (and recycling) different metals from a specific mine water, though the flexible modular design would have more generic application for mitigating contaminated metal-rich water bodies. Their primary role was to lower concentrations of soluble Mn to below prescribed levels for discharging into the wider environment. Regulation N\({}^{\text{o}}\) 430/2011 set by the National Environmental Council (CONAMA, Brazil) [25] has delineated a maximum contaminant level for industrial effluent discharge for manganese of 1.0 mg L\({}^{-1}\), which means that, for this particular case, at least 90% of the manganese present in the Mn-containing mine water would need to be removed prior to discharge, and this was achieved when the MnOBs were performing effectively. These bioreactors also had a secondary role in improving water quality on both lowering the DOC of the processed water and moderating numbers of bacteria. There was good evidence to support the former role, with the final effluents containing <3.0 mg DOC L\({}^{-1}\). Concentrations of DOC found in rivers can vary from 1.0 (e.g., mountainous Alpine environments) to 20 mg L\({}^{-1}\) (e.g., taiga rivers), and a world DOC average for rivers was established as 5.75 mg L\({}^{-1}\)[26], which is above that of the processed synthetic mine water generated in the present study. Addition of organic substrates to improve Mn removal in the MnOBs could lead to an increase in the DOC of processed liquors, though most of the added DOC would be anticipated, in a controlled system, to be metabolized within the bioreactors themselves. Numbers of planktonic cells in the effluent liquor from the MnOB were generally lower than those in the feed liquors, possibly due to microorganisms becoming attached to the pebbles. What percentage of these were viable cells is unknown, and it would be anticipated that many of the anaerobic bacteria entering the MnOBs in liquor generated in the upstream sulfidogenic bioreactor would be killed in the aerated reactors.
# Sequential Polar Decoding with Cost Metric Threshold
## 3 Our Contribution
### SC-Creper and Additional Threshold
It allows the decoder to backtrack to previous decision points and explore different branches of the decoding tree, increasing the likelihood of finding the correct solution. Additional procedures for the refining of movement by nodes significantly refine the choice of the final path in the Creeper algorithm. Since backward tree movement in the case of polar codes is not as reliable a procedure as forward tree movement, an additional metric is introduced to control backward movement. Thus, in addition to the more accurate decoding of nodes and a more complete traversal through the tree, it is possible to obtain optimization in two parameters at once. * Number of operations. If, in the original version of decoding, the number of passes through the different nodes of the tree (as well as the T-node architecture as a whole) depended only on the step width \(\Delta\), then, here, it becomes possible to avoid unnecessary passes and cycles, reducing the number of operations (since the cost function will signal that the cyclic node does not need to be entered again). * Performance. Since searching by the Fano metric requires a certain level of precision and an understanding of the \"closeness\" in decoding, the cost function can also point to those paths that contain the smallest value of the \(\Gamma\) metric. Recall that information about the old node is stored until the metrics for each possible path are calculated, i.e., at each stage, there is either a \"bypass\" (if the metric is too high) or a bypass. This allows us to construct the most probable codeword in the most accurate way. Importantly, while the algorithm's sophistication and effectiveness are heightened, its memory consumption remains consistent. This is attributable to the utilization of the same two stacks employed in the original Creeper algorithm, ensuring the efficient use of the computational resources without necessitating additional memory overhead. However, the decoding efficiency is strongly tied to how the threshold and cost functions are defined. Thus, for example, with an exponential threshold and linear cost functions, it is impossible to build an effective decoding model, since most nodes will not be considered during such a traversal (e.g., they will not satisfy the threshold condition).
## 5 Conclusions  
As mentioned earlier, this study's main objective was to distinguish between an anthropogenic coastal landform (reclamation) coastline and a natural one using the GPR technique and sediment analysis method. The result of this study gives excellent feedback to achieve this objective. Based on Penang Island's GPR technique, the results show that the coastline in Persiaran Bayan Indah, specifically in front of the Queensbay Mall, has the characteristics of reclamation land such as undulating and clear reflection events, which are very different from the Batu Ferringhi coastline, a natural coastal landform with flat compaction along with profiles, strong and continuous reflectors. Sieve analysis shows the sediment grain size ranges widely from medium sand to gravel size with evenly distribution on the Batu Ferringhi coastline and uneven grain size distribution on the Persiaran Bayan Indah coastline.  
These findings can be applied further as a model to recognize both natural and anthropogenic sedimentation because both coastal landforms have different physical and engineering properties (material composition, compaction, sedimentary structures, and textures). These models might help engineers delineate the hazard-prone zones of reclaimed land due to groundwater salinity, ground instability, and polluted land and differentiate it from the natural coastal land. Besides GPR and sieving analysis, possible trenching and groundwater-well sampling of the study area will be helpful to enhance this comparison model in the future.  
[PERSON] is the first author. She conceptualized the research idea, provided the theoretical framework, involved in site survey planning, including desk study and data acquisition, performed data analysis, drafted and revised the manuscript. [PERSON] is the corresponding author. She was involved in data acquisition in the field, supervised the research progress, anchored the review, revisions and approved the article submission. [PERSON] performed the data acquisition, data analysis, and processed experimental data. [PERSON] and [PERSON] aided in theoretical framework, revised, and polished the final version of the manuscript. All authors provided critical feedback and helped shape the research, analysis and manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universiti Sains Malaysia (USM), short-term grant number \"304/PFIZIK/6315230\".  
Not applicable.  
We would like to thank [PERSON] for her contributions in the field during data acquisition and in the laboratory during sample analysis. This work is part of an urban geology Ph.D. study series funded by the Indonesian Regular Education Scholarship LPDP--Indonesian Ministry of Finance and supported by the short-term grant of Universiti Sains Malaysia (USM).  
The authors declare no conflict of interest.
###### Abstract  
In this research, a multi-channel target speech enhancement scheme is proposed that is based on deep learning (DL) architecture and assisted by multi-source tracking using a labeled random finite set (RFS) framework. A neural network based on minimum variance distortionless response (MVDR) beamformer is considered as the beamformer of choice, where a residual dense convolutional graph-U-Net is applied in a generative adversarial network (GAN) setting to model the beamformer for target speech enhancement under reverberant conditions involving multiple moving speech sources. The input dataset for this neural architecture is constructed by applying multi-source tracking using multi-sensor generalized labeled multi-Bernoulli (MS-GLMB) filtering, which belongs to the labeled RFS framework, to obtain estimations of the sources' positions and the associated labels (corresponding to each source) at each time frame with high accuracy under the effect of undesirable factors like reverberation and background noise. The tracked sources' positions and associated labels help to correctly discriminate the target source from the interferers across all time frames and generate time-frequency (T-F) masks corresponding to the target source from the output of a time-varying, minimum variance distortionless response (MVDR) beamformer. These T-F masks constitute the target label set used to train the proposed deep neural architecture to perform target speech enhancement. The exploitation of MS-GLMB filtering and a time-varying MVDR beamformer help in providing the spatial information of the sources, in addition to the spectral information, within the neural speech enhancement framework during the training phase. Moreover, the application of the GAN framework takes advantage of adversarial optimization as an alternative to maximum likelihood (ML)-based frameworks, which further boosts the performance of target speech enhancement under reverberant conditions. The computer simulations demonstrate that the proposed approach leads to better target speech enhancement performance compared with existing state-of-the-art DL-based methodologies which do not incorporate the labeled RFS-based approach, something which is evident from the 75% ESTOI and PESQ of 2.70 achieved by the proposed approach as compared with the 46.74% ESTOI and PESQ of 1.84 achieved by Mask-MVDR with self-attention mechanism at a reverberation time (RT60) of 550 ms.  
SRP-PHAT; deep learning; microphone array; MS-GLMB filtering; beamforming +
Footnote †: journal: Applied sciences  
Article  
Multi-Channel Speech Enhancement Using Labelled Random Finite Sets and a Neural Beamformer in Cocktail Party Scenario. _Appl. Sci._**2025**, 15, 2944. [[https://doi.org/10.3390/appl/15062944](https://doi.org/10.3390/appl/15062944)]([https://doi.org/10.3390/appl/15062944](https://doi.org/10.3390/appl/15062944))