esa-sceva/satcom-chunk-collection huggingface dataset:
# A Sparsity-Invariant Model via Unifying Depth Prediction and Completion
## 6 Conclusions  
In conclusion, we have presented a depth completion model with high generalization capability, aimed at handling sparse depth maps with different sparse levels and irregular distributions, or even in cases where depth is extremely sparse or completely absent. Recognizing that as the number of valid depth points decreases, the depth completion task becomes a monocular depth estimation task, we combined the strengths of mainstream methods in both tasks and designed a dual-branch network structure consisting of the prediction branch and the completion branch, and we combined this with the commonly used 3D plane-related designs from the monocular depth estimation task, the multi-scale local plane guidance module and the mean normal loss function. These designs significantly improved the model's performance in handling sparse depth inputs with different distributions. Finally, we conducted experiments on the benchmark NYU Depth V2 and the OPPO prototype dataset equipped with the Spot-iToF316 sensor in real-world applications. Our experiment demonstrates the effectiveness of our model, particularly in cases with limited, irregularly distributed or absent depth information.  
S.W.: Conceptualization, methodology, software, validation, formal analysis, investigation, writing--original draft preparation, writing--review and editing, visualization. F.J.: formal analysis, writing--review and editing, X.G.: resources, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Representative data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 8: Visualization results of the OPPO prototype equipped with Spot-iToF316. (**a**): Color image and corresponding sparse depth (a dilation operation on the sparse depth map was performed for better visualization). (**b**): Predicted depth map.
## 6 Discussion
### Potentialities of HCA
#### 6.1.2 Case Study for Semantic Dependency Parsing  
For semantic dependency parsing, we employed the state-of-the-art AMR parser proposed in [2] to predict the semantic dependency graph (SDG) of the exemplified sentence in Section 1. As shown in Figure 10, three dotted red edges were missed by the parser when compared with the gold SDG.  
As discussed in Section 6.1.1, inter-clause relations among three clauses mapping to subgraphs \(G_{2}\), \(G_{4}\), and \(G_{5}\) can be derived by the following transformation rules:  
Figure 10: Semantic dependency graph (SDG) predicted by the state-of-the-art semantic dependency parser, DynGL-SDP. Dotted red dependency edges, which were missed by the parser, can be recovered by transformation rules derived from the HCA tree.  
Figure 9: Abstract meaning representation (AMR) graph predicted by the state-of-the-art AMR parser. Red dotted relation edges, which were missed by the parser, can be recovered by transformation rules derived from the HCA tree. Red solid relation edges, which were mistakenly predicted by the parser, can be deleted by transformation rules derived from the HCA tree.  
1 Transform the inter-clause relation but to a dependency edge between root nodes (i.e., anxious and much) of \(G_{2}\) and \(G_{4}\).
2 Transform the inter-clause relation resultative to a dependency edge between root nodes (i.e., much and can) of \(G_{4}\) and \(G_{5}\).  
Moreover, the relative subordinate clause _\"which does sort of go away after 15-30 min\"_ mapping to subgraph \(G_{3}\) modifies the complement \"axixous\" in the matrix clause _\"I get very anxious\"_ mapping to subgraph \(G_{2}\). Thus, a new transformation rule can be derived:  
1. Transform the inter-clause relation relative to a dependency edge between the root node go \(G_{3}\) and the node anxious) of \(G_{2}\).  
With these three transformation rules, we can add the dotted red edges, which were missed by the parser.  
To sum up, we provided two case studies demonstrating the potentialities of HCA in semantic parsing, where transformation rules derived from the HCA tree were applied to modify two state-of-the-art parsers of the AMR parsing and semantic dependency parsing tasks.
# Chemical Characterization of Cider Produced in Hardanger--From Juice to Finished Cider
## 2 Materials and Methods
### GC-MS Analyses of Volatile Compounds  
In juice and ciders, we determined selected esters and C6-alcohols using an automatic robotic system for head space (HS) solid-phase microextraction (SPME) combined with gas chromatography coupled to mass spectrometry (GC-MS). The compounds were quantified and identified using validated MS-SPME_GC-MS methods described elsewhere [11].  
The samples were extracted using headspace solid-phase micro extraction (HS-SPME) using SPME Fiber Assembly (50/30 \(\upmu\)m DVB/CAR/PDMS, Stableflex, 24 Ga, Autosampler, Gray (Supelco, St. Louis, MO, USA)). Into a 20 mL SPME vial, 1 mL of juice and cider samples was added to 2 g of NaCl and 9 mL of deionized water and spiked with 20 \(\upmu\)L of internal standard solution. The vials were tightly sealed with a PTFE-lined cap. The solution was then homogenized using a vortex mixer, and the samples were loaded onto a Gerstel MPS Robotic autosampler (CTC Analytics AG, Zwingen, Switzerland). The fibre was inserted into the SPME Arrow Conditioning Module for 2 min at 270 \({}^{\circ}\)C. Thereafter, the fibre was inserted into the headspace of the sample vial for 30 min at 40 \({}^{\circ}\)C with simultaneous sample swirling at 250 rpm using the Agigator module. Afterwards, the fibre was transferred to the injector for desorption at 250 \({}^{\circ}\)C for 15 min. The sample injection time into the GC column was set to 30 s, followed by fibre cleaning in the SPME Arrow Conditioning Module for 10 min at 270 \({}^{\circ}\)C.  
HS-SPME-GC-MS analyses were carried out on an HP 8890 GC System coupled to a 5977 B GC/MSD quadrupole mass spectrometer (Agilent, Santa Clara, CA, USA) equipped with a Gerstel MPS Robotic autosampler. For quantification, a Mass Hunter Workstation Software (Version 10.1) (Agilent Technologies) was used. Identification of all analysed compounds was performed by comparing the retention times and mass spectra with the pure analysed standards. Library from the NIST Mass Spectral Search Program(Version 2.4, 2020) was also used for additional confirmation of the analysed compounds. For the purpose of quantification of analysed compounds, internal standards were spiked accordingly. Each internal standard was dissolved in ethanol solution separately to obtain a stock solution.  
Injections were performed in the splitless mode for 0.5 min, using ultra-inert inlet single taper liner. A DB-WAX UI capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 um film thickness, Agilent Technologies, USA) was used, and the carrier gas was helium (H 6.0) with a flow of 3 mL min\({}^{-1}\). The oven temperature was programmed at 40 \({}^{\circ}\)C for 5 min, then raised to 200 \({}^{\circ}\)C at 3 \({}^{\circ}\)C min\({}^{-1}\), further increased to 240 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C min\({}^{-1}\), and held for 10 min. The mass spectrometer was operated in electron ionization mode at 70 eV with selected-ion-monitoring (SIM) mode.
# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 1 Introduction
Principal component analysis (PCA) was used with the LRCF to reduce the dimensionality in the translation and scale estimation phase to improve the algorithm speed. 2. The memory templates were updated at regular intervals, and the existing and initial templates were re-matched every few frames to maintain template accuracy. Figure 1: Typical environments in underground coal mine. (**a**) is a main roadway of a coal mine. (**b**) is the shaft bottom of coal mine. (**c**) is general roadway in coal mine. (**d**) is a haulage roadway. The contribution of this study and the improvements are as follows:  
1. This work designs a target tracker framework in a low-illumination coal mine environment. The environmental illumination is detected before the DCF extracts the training sample. An image enhancement module is incorporated. 2. A local illumination detection method is proposed. A pre-set padding area around the target bounding box (BB) is cropped and used as the area of local illumination detection. The illumination intensity in this area, instead of the global illumination, determines whether image enhancement is performed. 3. A fast and efficient image enhancement method is proposed because most image enhancement algorithms are computationally expensive and have low real-time performance. Excellent image enhancement algorithms bring expensive computational costs and perform poorly in real-time performance. This method is well-suited to object tracking by robots. 4. A tracking system based on a robot operating system (ROS) is designed. The proposed tracker installed in a drilling robot is evaluated in a field experiment conducted in an underground coal mine. The remainder of this paper is organized as follows. Section 2 discusses previous work related to the proposed trackers. The proposed DCF tracking method and the principle of the image enhancement module are introduced in Section 3. The experimental results are analyzed and discussed in Section 4. Finally, the conclusion is presented in Section 5.
# Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection
## 4 Experiments
### Ablation Study
#### 4.3.2 Fast Adaptive Pooling  
Our model incorporates faster pooling methods to extract adaptive noise, providing flexibility in feature processing. We explored both max pooling and bilinear average pooling to handle feature maps. In bilinear average pooling, the feature map is first resized using bilinear interpolation, maintaining spatial information at a fixed intermediate resolution. This is followed by two rounds of average pooling to progressively reduce the dimensions. While bilinear average pooling offers smoother transitions and preserves more spatial coherence during resizing, our experiments demonstrate that max pooling yields superior results and enhances the model's ability to filter important features, reaching 58.2% AP in the experimental setting, as shown in Table 3, compared to 57.9% AP for bilinear average pooling. Thus, max pooling remains our default choice due to its stronger performance in terms of both overall accuracy and small object detection.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Method** & **Backbone** & **AP** & \(AP_{50}\) & \(AP_{75}\) & \(AP_{s}\) & \(AP_{m}\) & \(AP_{l}\) \\ \hline FCOS [15] & ResNeXt101 & 50.7 & 79.5 & 54.0 & 18.0 & 40.0 & 56.2 \\ Faster R-CNN [13] & ResNeXt101 & 52.8 & 81.8 & 57.5 & 17.2 & 40.9 & 58.2 \\ Cascade R-CNN [43] & ResNeXt101 & 54.8 & 81.1 & 59.7 & 16.8 & 42.2 & 60.6 \\ Libra R-CNN [50] & ResNeXt101 & 54.8 & 82.8 & 60.5 & 16.5 & 43.1 & 60.6 \\ Sparse R-CNN [23] & ResNet50 & 55.8 & 81.6 & 60.6 & 17.8 & 40.7 & 61.8 \\ Deformable DETR [20] & ResNet50 & 57.2 & 84.4 & 62.7 & 15.6 & 42.1 & 63.1 \\ DiffusionDet [24] & ResNet50 & 57.5 & 82.1 & 62.2 & 18.3 & 42.2 & 64.0 \\ \multicolumn{8}{l}{DiffAda} \\ \hline \hline \multicolumn{8}{l}{The main results for the RUOD test set are compared across various methods. The number of noises is set to \(100\)} \\ by default, and all comparison methods were reproduced using mmdetection [44]. Specifically, the deformable DETR initialization weights are loaded with COCO pretrained weights.} \\ \end{tabular}
\end{table}
Table 2: RUOD results.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**FAT Mode** & **AAFI** & **Ada-DN** & **AP** & \(AP_{50}\) & \(AP_{75}\) & \(AP_{s}\) & \(AP_{m}\) & \(AP_{l}\) \\ \hline b-a & \(\times\) & \(\times\) & 57.9 & 82.9 & 62.8 & 17.4 & 43.1 & 64.1 \\ max & \(\times\) & \(\times\) & 57.9 & 82.9 & 62.8 & 18.5 & 43.3 & 64.2 \\ b-a & \(\times\) & ✓ &
# A Review of Deep Learning Applications for Railway Safety
## 6 Operation Safety
### Wind Risk  
High-speed railways are susceptible to strong winds, which can pose a major threat to train safety. In order to ensure train safety, it is necessary to measure the wind speed of the preceding area in real time or to inform the train of the information in advance by short-term prediction. However, measured and predicted wind speed alone are not sufficient to explain wind conditions. For example, if the expected wind speed is slightly lower than the strong wind threshold, it is difficult to estimate whether a substantial wind accident can occur. [PERSON] et al. [83] proposed a multiple attention layer based multi-instance  
Figure 8: Example of railroad trespassing detection. Reproduced with permission from IEEE [80].  
learning (MAL-MIL) model to predict substantial wind risk alongside a high-speed railway (HSR). Based on attention mechanisms and LSTM networks, the model extracted features of the future wind status and identified the relationships between the current features and strong wind incidents.
## 7 System Performance and Evaluation  
### Calibration  
The linear correlation between the sensors readings of our system (OS) and their commercial equivalents at mine face 1 and the midway point of the mine are shown in Figure 7. Figure 7a-d show strong correlations between the temperature and humidity measured by Arduino-based sensors and commercial equivalents. The regression constant obtained for temperature and humidity at both of these localities was always greater than 0.96. The mean square error for Figure 7a-d was found always to be less than 0.581 and the root mean square error was observed to be always less than 0.762. Figure 7e shows the linear correlation constant (R\({}^{2}\)) between CH\({}_{4}\), monitored by OS and the Gas Central CH4C 100, which was 0.9715 with a slope of 1.018 across the range of 750\(-\)1850 ppm. Moreover, an \(R^{2}\) of CH\({}_{4}\) (0.9962) at the midway mine point is shown in Figure 7f. The correlation constant coefficient between CO\({}_{2}\) concentration measured by OS and Telaire 7000 was found to be (\(R^{2}\) = 0.9857) having a slope = 1.01. Similarly, the R\({}^{2}\) and slope values of CO\({}_{2}\) for midway mine are illustrated in Figure 7h. However, the mean square error for the cases Figure 7e-h were quite high because of the abrupt changes and large variations in the values of CH\({}_{4}\) and CO\({}_{2}\). Minimum root square error for these cases was found to be greater than 4.33. These regressions show that OS is a good replacement for expensive and less efficient mine monitoring instruments for these specific parameters.
# Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks +
## 3 Extended Hybrid Message Passing Algorithm
* All Lambda messages from children of the \(j\)-th node are computed to calculate the Lambda value \(\lambda_{j}\). If the RV is discrete, Equation (4) is used, while if it is continuous Equation (19) is used. * A Pi message is sent from the \(j\)-th node to its children. If the node is discrete, Equation (5) is used, while if it is continuous Equation (20) is used. * A Lambda message is sent from the \(j\)-th node to its parents. If the node is discrete, Equation (6) is used, while if it is continuous and has only discrete, only continuous or hybrid parents, Equation (15), (21) or (22) (for continuous parent)/(23) (for discrete parent) is used, respectively. For each of these functions in Lines 4, 5, 6 and 7, if the current execution time exceeds the maximum execution time, the result from the function is not updated and the for-loop in Line 3 of the HMP-GMR procedure is stopped. * After all nodes have passed their messages (i.e., Line 3 \(\sim\) 7), the belief function is computed for all nodes. * The Lambda and Pi values are multiplied and normalized for all nodes to calculate the belief function \(bel_{\text{ij}}\). * The difference \(diff_{\text{ij}}\) between the current and previous beliefs are computed for all nodes. * The maximum difference \(max\_diff\) between current and previous belief is selected. * If the maximum difference \(max\_diff\) is less than the maximum precision \(max\_prcs\), the iteration of the message passing is stopped. * Upon stopping, the algorithm outputs approximate posterior marginal distributions for all nodes. There are three exit points from the iteration: (1) when the iteration reaches the maximum number of allowable iterations, (2) when the maximum difference is less than the maximum precision and (3) when the current execution time for the algorithm exceeds the maximum execution time.
# Recent Applications of Solid Phase Microextraction Coupled to Liquid Chromatography
## 2 Applications
### Biological
#### 2.1.3 Metabolomic
A new SPME-LC-qTOF-MS method for the solventless extraction of human milk lipidome has been reported by [PERSON] et al. [53]. Almost 70 lipids were detected and identified through an automated search of a lipid database, with the goal to properly understand the health benefits of human milk and to improve the quality and composition of artificial formulas. In a further work, SPME directly coupled to ESI-QqQ mass spectrometer provided similar ions profiles as LC-MS/MS for the screening of secondary metabolites in actinobacteria solid medium [54]. In this case, SPME was performed by means of nitinol wires coated using a slurry composed by hydrophilic-lipophilic balance (HLB) particles (5 \(\upmu\)m) and polyacrylamide(PAN).
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 5 Result and Analysis
### Energy Consumption at Data Phase  
Energy utilization is a critical metric, as sensors have limited energy. Effectively handling this energy is vital for elongating the overall network lifetime. During the process of data transmission, sensor nodes utilize high energy. The energy consumption per bit is 5 nJ. For instance, if 6 nodes are generating single-part data, resulting in 16 \(\times\) 6 = 96 bits. The energy consumption is calculated as 96 bit \(\times\) 5 nJ/bit = 480 nJ. Similarly, when sensor nodes generate data with two parts, producing data of 32 bits, consuming 32 \(\times\) 6 = 192 bits. So, the energy utilization in this situation is 192 bit \(\times\) 5 nJ/bit = 1152. In resource-constrained environments, the dissipation of this much energy leads to the earlier death of nodes. As the number of attributes increases, energy consumption further escalates.  
To address this issue, the proposed EE-FDAS scheme adopts a strategy during the data phase. It involves transmitting a Boolean digit \"0\" whenever sensors produce regular data readings. This Boolean digit only takes 1 bit. Normally, patients exhibit fluctuations in 1 or 2 sensors. By sending complete values for these sensors while sending \"0\" digits for normal data, the resultant data size is considerably reduced. So, the scheme's design ensures that the non-critical information including routine data, is efficiently compressed without substantial loss while optimizing energy consumption. This decrease in data size finally leads to noticeably lower energy depletion, as compared to the DICA and DICA_EXTENSION techniques. The simulation results specify that FDAS and EHDA attain lower energy consumption. FDAS accomplishes this by reducing data size through the transmission of \"0\" at level 2 for Boolean data, while EHDA sends compressed data to  
Figure 3: The impact of the Number of Attributes on Average Aggregation is explained in (**a**) while Energy Consumption during the data phase is shown in (**b**).  
the Fog server. In contrast, DICA and DICA_EXTENSION do not include any procedure to cope with high energy consumption.  
Figure 2(b) illustrates the association between the number of attributes and energy consumption. The proposed scheme EE-FDAS validates the lowest energy consumption at 24, followed by FDAS at 38, EHDA at 36, DICA at 42, and DICA_EXTENSION at 47.
## 3 Results and Discussion
### Selective Chemical Extractions  
The SAM and HCl extraction results are given in Table 4. The percentage of mass dissolved by SAM extraction in OPC paste compared with that of AA-WBA binders was higher due to its composition based in C-S-H phases. The main contribution of C-(A)-S-H phases dissolved in WBA powder probably came from the residues of small construction works in the domestic sphere. The most C-(A)-S-H formation in AA-WBA occurred in the AA-WBA-6M sample, as it showed the maximum value of dissolved mass by SAM extraction. A slight reduction of dissolved mass by SAM extraction in the AA-WBA-8M sample compared with that of the AA-WBA-6M sample was also observed. These results reveal that the NaOH 6M was the maximum NaOH concentration from which the precipitation of Ca\({}^{2+}\) in form of C-(A)-S-H was disadvantaged against the formation of aluminosilicate gels [49]. The HCl extraction results show that increases in the NaOH concentration of AA-WBA binders resulted in an increase in the dissolved mass. The selective chemical extraction by HCl led to the dissolution of calcium and sodium carbonate phases [50], sodium aluminosilicate gels, and zeolites [44], as well as C-(A)-S-H phases leading to the removal of Ca\({}^{2+}\) and leaving silica gel as an insoluble residue [46]. The results of HCl extraction indicate that the higher the NaOH concentration, the higher mass dissolved. This fact is due to the formation of C-A-S-H gels as demonstrated in SAM extraction, as well as the increased presence of sodium carbonate phases as the alkali dosage was increased (Table 2).
## 5 Discussion
With PALSAR we obtain velocities upstream of 15 km. The impact of sensor resolution for retrieving ice velocities on this fast flowing ice stream with strong shear is obvious. The TerraSAR-X high-resolution Strip Map images, acquired with 11-day repeat, deliver high quality velocity over the entire terminus. For PALSAR the spatial resolution in LOS is slightly lower than for S1 IW mode data, but the resolution in azimuth is four times higher (Table 1). In spite of the longer repeat cycle, PALSAR provides velocities slightly further downstream than S1. One reason for this could be the unfavorable orientation of the main velocity gradient with respect to the S1 track, which is aligned approximately perpendicular to the azimuth direction in the S1 images. Because of the lower spatial resolution in azimuth, the IW mode data are less sensitive to velocity gradients in azimuth than across track. The S1 Strip Map mode data, with nominal spatial resolution of 5 m\(\times\) 5 m, would offer the possibility for closing gaps on fast flowing glaciers. However, the S1 operation strategy allows only in exceptional cases (e.g., emergency requests) for Strip Map mode acquisitions to supersede the systematic acquisition plan [5]. On the other hand, the availability of six-day repeat pass data after the launch of Sentinel-1B will offer the opportunity to largely reduce the (rather limited) gaps in velocity coverage over fast flowing glaciers with IW mode data. The shorter repeat observations will also be of interest for interferometric retrievals of ice motion and deformation with S1 IW mode data, in particular for slowly moving ice.