esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence
## 3 the Proposed Method
### Global Visual Network  
The global visual network aims to obtain a vector representation that is a general visual feature over the video frames. We divide the input video frames into \(N\) chunks, and one frame is sampled from each chunk by random sampling. We set \(N=20\). We heuristically determined the chunks as \(N=20\). Although there are no specific reasons, the experimental results show that the \(N\) worked well. We extract visual features from the sampled frames using the ResNet-152 pre-trained on the ImageNet dataset [35]. Specifically, we resize the sampled frames \(320\times 240\) to \(224\times 224\), and then the ResNet encodes them, resulting in 2048-dimensional \(N\) vectors. Note that we extract the vectors directly from the last fully connected layer of the ResNet. Subsequently, we apply average-pooling to the \(N\) vectors to merge them. Consequently, we obtain a feature vector \(\psi_{g}(x)\in\mathbb{R}^{2048}\) containing a global visual feature in the video.  
We learn a joint embedding space of the global visual and textual networks. As defined in Equations (1) and (2), we use embedding functions to embed \(\psi_{g}(x)\) and \(\phi(y)\) into a \(D\)-dimensional space.  
\[\mathrm{f}_{g}(x) =W_{g}\psi_{g}(x)+b_{g} \tag{1}\] \[\mathrm{g}_{g}(y) =\hat{W}_{g}\phi(y)+\hat{b}_{g} \tag{2}\]  
There are learnable parameters \(W_{g}\in\mathbb{R}^{2048\times D}\), \(\hat{W}_{g}\in\mathbb{R}^{H\times D}\), and \(b_{g},\hat{b}_{g}\in\mathbb{R}^{D}\). We set \(D=512\) in this paper.  
We use cosine similarity to measure the similarity \(\mathrm{s}_{g}(y,x)\) between the video and sentence in the joint embedding space.  
\[\mathrm{s}_{g}(y,x)=\frac{\mathrm{f}_{g}(x)\cdot\mathrm{g}_{g}(y)}{\parallel \mathrm{f}_{g}(x)\parallel\parallel\mathrm{g}_{g}(y)\parallel} \tag{3}\]
# Sparse-View Artifact Correction of High-Pixel-Number Synchrotron Radiation CT
## 3 Results  
### Comparison of Artifact Correction Effects of Different Methods  
To verify the performance of our proposed method, we compare it with the typical deep learning methods FBPConvNet [17] and DD-Net [19] under 200 views and 400 views. Figure 4 shows the artifact correction effects of several methods under 200 views. Figure 4(a1-e1) is an enlarged view of the area of the red box in Figure 4a-e. Figure 4a is a directly reconstructed slice using FBP, with details on the image destroyed by artifacts. After applying the DD-Net (Figure 4(b1)) and FBPConvNet (Figure 4(c1)) algorithms, the image features appear blurred and the internal structure is indiscernible. However, our proposed method retains more information than other methods while removing artifacts (Figure 4(e1)). Figure 4(a2-d2) depicts the absolute error images of Figure 4a-d and the full-view slice (Figure 4e), with brighter colors indicating larger errors. Our proposed method demonstrates the smallest absolute error, followed by the FBPConvNet method and the DD-Net method in descending order. The data at the top of (a2-d2) represent the average values of the absolute error images.  
Figure 5 depicts the artifact correction effects of various methods at 400 views. Sequentially, Figure 5a-e presents the following: the slice directly reconstructed by FBP, the DD-Net method, the FBPConvNet method, our proposed method, and the slice directly reconstructed by full-view (1200 views). Additionally, Figure 5(a1-e1) provides a zoomed view of the area within the red box in Figure 5a. The input image of the network (Figure 5(a1)) exhibits a low signal-to-noise ratio and noticeable noise, which can impact subsequent image analysis. The DD-Net method still introduces excessive smoothing (as indicated by the blue arrow in Figure 5(b1)). Upon observing the region highlighted by the red arrow in Figure 5(c1), it is evident that the aliasing artifacts persist in the image corrected by the FBPConvNet method. In contrast, our proposed method demonstrates advantages in recovering fine details and closely approximates the label image. This is further supported by the absolute error image in Figure 5(a2-d2).
## 2 Materials and Methods
### Quality Control  
Quality control in this study was ensured through the use of reagent blanks, certified soil reference materials (GBW07410, provided by the National Research Center for Certified Reference Materials, Beijing, China), and duplicate samples analyzed alongside the soil samples. The certified reference materials included GSS-2A, GSS-3A, and GSS-5A, with certified Se concentrations and associated uncertainties of 0.26 \(\pm\) 0.03 \(\upmu\)g/g, 0.12 \(\pm\) 0.03 \(\upmu\)g/g, and 0.75 \(\pm\) 0.12 \(\upmu\)g/g, respectively. The average recovery rate for Se was 107%, indicating acceptable analytical accuracy.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 4 Supporting TSN in WiFi 7
### Ultra-Reliability  
Ultra-reliability in TSN is responsibility of the IEEE 802.1 CB frame replication and elimination for reliability (FRER) sub-standard, which sends duplicate copies of each frame over disjoint wired paths to provide proactive seamless redundancy. To minimize network congestion, FRER can also be applied only on determined traffic classes (e.g., critical or time-sensitive traffic) and paths.  
Figure 5: Frame preemption mechanism [48], being BE: Best-effort frame, and TS: Time-sensitive frame.  
The potential incorporation of IEEE 802.1 CB into the IEEE 802.11 ecosystem relies on the IEEE 802.11 ak amendment, which is able to create link-disjoint or node-disjoint paths. However, to actually improve wireless path reliability TGbe is considering other complementary enhancements [49]:  
* Multi-link operation (i.e., frequency diversity): use of separated bands to transmit the same frame between multi-link devices. In fact, a first testbed has proven the latency reduction associated to the transmission of the same time-sensitive traffic flow over two different channels in presence of interfering traffic [50].
* Multi-AP operation: use of joint transmission (JTX) from different APs to improve reception probability in the DL. Particularly, JTX provides a better performance gain compared to other multi-AP transmission schemes at the expense of adding complexity for synchronization [51].
## 4 ASL Extensions
### Extended Data Enumerations  
Another extension added to ASL is the support of key/value pairs of strings to define data enumerations. Previously, ASL only supported the specification of data enumerations with values (without an associated unique identifier). As discussed in Section 3.1, using this unique identifier for the data enumeration element brings some advantages. For example, it makes it possible to uniquely identify an element in multi-language systems where the associated value is translated according to the user's preferred language. Additionally, it contributes to keeping the size of databases as low as possible since the _keys_ are potentially significantly smaller than their associated _values_.  
Spec. 5 shows an example specification of the data enumeration \"Document Type\", using the newly added unique identifiers.  
```
1 MatEnumerationDocumentType\"DocumentType\"values(
2 SI\"StandardInvoice\",
3 CI\"CreditInvoice\",
4 DI\"DebitInvoice\",
5 MI\"MixedInvoice\"
6)
```  
**Spec. 5.** Example specification of the data enumeration \"Document Type\".
# Estimating Recycling Return of Integrated Circuits Using Computer Vision on Printed Circuit Boards
## 5 Discussion  
The proposed economic evaluation output was a total of USD7.44 (USD8.55 with IC detector model) with USD0.59 as the highest value for a PCB and (USD0.69 with IC detector model estimative), and the average return was USD0.05 (standard deviation of USD0.06). However, precious metals (Au, Pd, Pt, and Ag) are a small fraction of the ICs' mass and  
\begin{table}
\begin{tabular}{c c c c} \hline
**Metric** & **Predicted** & **Ground Truth** & **Error** \\ \hline Total IC Area (mm) & 525,828.2 & 457,667.37 & 14.89\% \\ \hline Total IC Weight (g) & 1239.9 & 1079.18 & 14.89\% \\ \hline Average RMSE (mm) & & 477.12 (698.61) & \\ \hline \end{tabular}
\end{table}
Table 4: WPCB’s total IC area and weight.  
Figure 8: Root mean squared error (RMSE) for every WPCB in the dataset (**a**) and IC area percentual error for every WPCB in the dataset (**b**).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metal**} & **Metal Value** & **Recycling Rate** & \multicolumn{2}{c}{**IC Detection Model**} & \multicolumn{2}{c}{**Ground Truth**} \\  & **(USD/Ton)** & **(g of Metal/Kg of IC)** & **g of Metal** & **USD Return** & **g of Metal** & **USD Return** \\ \hline Fe & 100 & 68.88 & 85.40 & 0.00854 & 74.33 & 0.00743 \\ Ni & 13,887 & 31.2 & 38.68 & 0.53722 & 33.67 & 0.46758 \\ Cu & 6440 & 331.5 & 411.03 & 2.64701 & 357.74 & 2.30390 \\ Si & 10,500 & 411.6 & 510.34 & 5.35860 & 444.19 & 4.66400 \\ \hline Total & - & - & 1045.46 & 8.55 & 909.94 & 7.44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Total IC recycling estimation outcomes.  
were not considered in this small sample. As precious metals are responsible for most of the financial return of WPCB recycling, an increase in the gross income of IC recycling is expected when also recovering precious metals like gold and silver.  
The complete estimation of WPCB compositions depends on more data regarding the composition of each electronic component. Research in these directions could reduce the uncertainty in the evaluation of a WPCB composition.  
A first application of the proposed method is to cluster PCBs with similar IC densities. Those similar WPCBs could suffer the same recycling process because they probably have similar compositions. Another critical component is the market value of the metal. The market value is positively affected by the purity of the metal. Therefore, depending on the used recycling technique, the gross outcome may be different.
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 4 Experimental Results and Analysis
### Accuracy and Robustness Performance
The maximum error of SDPL-VIO is 0.73 m in the sequence of MH05, while that of PCSD-VIO is 0.85 m, that of PL-VIO is 0.78 m and that of VINS-Mono is 0.90 m, respectively. For the V202 dataset, SDPL-VIO still performs better than the other three. The maximum error of SDPL-VIO is 0.33 m, while that of PCSD-VIO is 0.37 m, that of PL-VIO is 0.36 m and that of VINS-Mono is 0.42 m, respectively. From Figures 11 and 12, it can been seen that, due to the semi-direct method tracking point and line features, SDPL-VIO has a better performance than the other three methods in challenging sequences, such as fast motion, large illumination changes or poorly textured environments, etc. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sequence** & **SDPL-VIO** & **PCSD-VIO** & **PL-VIO** & **VINS-Mono** & **SVO** \\ \hline V103 & 0.13 & 0.18 & 0.14 & 0.20 & N/A \\ V201 & 0.08 & 0.09 & 0.07 & 0.09 & 0.30 \\ V202 & 0.11 & 0.14 & 0.12 & 0.16 & 0.47 \\ V203 & 0.22 & 0.26 & 0.24 & 0.28 & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 10: RMSE (m) of the five methods. Figure 11: The comparison of trajectories on the MH01, MH05 and V202 datasets.
# Simultaneous Measurement of Refractive Index and Flow Rate Using a Co\({}^{2+}\)-Doped Microfiber
###### Abstract  
This paper has proposed and experimentally demonstrated an integrated Co\({}^{2+}\)-doped microfiber Bragg grating sensor (Co-MFBGS) that can measure the surrounding liquid refractive index (LRI) and liquid flow rate (LFR) simultaneously. The Co-MFBGS provides well-defined resonant modes of core and cladding in the reflection spectrum. By monitoring the wavelength of the cladding mode, the LRI can be measured; meanwhile, by monitoring the wavelength of the core mode caused by the heat exchange, the LFR can be measured. The LRI and LFR can be distinguished by the wavelength separation between cladding mode and core mode. The experimental results show that in aqueous glycerin solution, the maximum measurement sensitivity for LRI detection is \(-7.85\) nm/RIU (refractive index unit), and the LFR sensitivity is \(-1.93\) nm/(\(\mu\)L/s) at a flow rate of 0.21 \(\mu\)L/s.  
Co\({}^{2+}\)-doped microfiber grating sensor; refractive index; flow rate; simultaneously measurement; cross-impact +
Footnote †: journal: Applied Sciences
###### Abstract  
The key features for future machine-type communication cellular systems facilitate low-power, low-cost, narrow-band, and wide-area systems. One of the challenging tasks to realize these requirements in machine-type communication cellular networks is a low-cost initial synchronization. To address this issue, this paper presents a reduced-complexity joint estimation of integer carrier frequency offset and cell identity for machine-type communication in the long-term evolution system. Using the conjugate property of a primary synchronization signal, joint search space of a number of hypotheses is reduced by two-thirds, facilitating low-complexity detection. The performance is assessed in terms of theoretical probability of detection failure. Simulation results confirm that the proposed estimation scheme achieves approximately the same performance as the conventional scheme with notably low complexity.  
long-term evolution; machine-type communication; cell search; synchronization signal Article
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 4 Experiments and Results
### Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**(a) PESQ**} & \multicolumn{6}{c}{**(b) segSNR**} \\ \hline
**SNR (dB)** & \(noisy\) & \(sT\) & \(snT\) & \(nDT\) & \(snDT\) & **SNR (dB)** & \(noisy\) & \(sT\) & \(snT\) & \(nDT\) & \(snDT\) \\ \hline \(-6\) & 1.33 & 1.68 & 1.77 & 1.79 & **1.90** & \(-6\) & \(-6.59\) & \(-0.86\) & 1.78 & 1.70 & **1.90** \\ \(-3\) & 1.55 & 1.93 & 2.02 & 2.02 & **2.13** & \(-3\) & \(-5.08\) & 0.81 & **2.85** & 2.72 & 2.81 \\ \(0\) & 1.77 & 2.16 & 2.25 & 2.27 & **2.35** & 0 & \(-3.35\) & 2.58 & 3.50 & 3.47 & **4.04** \\ \(3\) & 1.98 & 2.38 & 2.46 & 2.44 & **2.55** & 3 & \(-1.48\) & 4.16 & 4.97 & 4.89 & **5.69** \\ \(6\) & 2.20 & 2.59 & 2.67 & 2.65 & **2.75** & 6 & 0.64 & 5.82 & 6.64 & 6.60 & **7.44** \\ \(9\) & 2.41 & 2.78 & 2.86 & 2.83 & **2.93** & 9 & 2.91 & 7.29 & 8.11 & 8.08 & **8.97** \\ \hline Aver. & 1.88 & 2.25 & 2.34 & 2.33 & **2.43** & Aver. & \(-2.16\) & 3.30 & 4.64 & 4.58 & **5.14** \\ \hline \hline \multicolumn{11}{c}{} \\ \hline
**(c) eSTOI** & \multicolumn{6}{c}{**(d) SDR**} \\ \hline
**SNR (dB)** & \(noisy\) & \(sT\) & \(snT\) & \(nDT\) & \(snDT\) & **SNR (dB)** & \(noisy\) & \(sT\) & \(snT\) & \(nDT\) & \(snDT\) \\ \hline \(-6\) & 0.39 & 0.46 & 0.48 & 0.48 & **0.51** & \(-6\) & \(-6.00\) & 1.96 & 2.44 & 2.20 & **2.59** \\ \(-3\) & 0.47 & 0.55 & 0.58 & 0.57 & **0.60** & \(-3\) & \(-3.11\) & 4.89 & 5.37 & 5.21 & **5.57** \\ \(0\) & 0.55 & 0.63 & 0.66 & 0.66 & **0.68** & 0 & \(-0.17\) & 7.89 & 8.37 & 8.26 & **8.61** \\ \(3\) & 0.63 & 0.71 & 0.74 & 0.73 & **0.75** & 3 & 2.79 & 10.50 & 10.92 & 10.78 & **11.17** \\ \(6\) & 0.71 & 0.77 & 0.80 & 0.80 & **0.81** & 6 & 5.78 & 13.01 & 13.41 & 13.24 & **13.66** \\ \(9\) & 0.78 & 0.82 & 0.84 & 0.84 & **0.86** & 9 & 8.78 & 15.11 & 15.52 & 15.37 & **15.82** \\ \hline Aver. & 0.59 & 0.66 & 0.68 & 0.68 & **0.70** & Aver. & 1.34 & 8.89 & 9.34 & 9.18 & **9.57** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of PESQ, segSNR, eSTOI, and SDR values of the proposed and baseline networks in the mismatched noise conditions, where \(-6\) and \(9\) dB cases are unseen SNR conditions. Figure 4: (From top to bottom) The spectrograms of noisy speech degraded by \(metro\) noise with \(-3\) dB signal-to-noise ratio (SNR), enhanced speech by the \(snT\) model, enhanced speech by the \(snDT\) model, and the corresponding clean speech, respectively.
# Deformation Characteristics of Mechanical Seal Face under Alternating Loads
## 2 Materials and Methods  
### Geometric Model  
The two-dimensional (2-D) geometric model shown in Figure 1 is used as the computational domain in the research due to the axisymmetric structure of the seal ring. The model includes a rotating ring, a stationary ring, and the fluid medium between the seal rings. The inner, outer, and balance radii are \(r_{1}\) = 40 mm, \(r_{0}\) = 50 mm, and 42.6 mm, respectively.  
In Figure 1, the rotating ring as a primary ring is in close contact with the stationary ring under the action of the spring force and the medium pressure \(p_{\mathrm{o}}\), the bottom surface (S\({}_{11}\)) of the stationary ring is fixed and assumed as an adiabatic boundary, the inner peripheral surfaces (S\({}_{2}\)-S\({}_{10}\)) of the seal rings are in direct contact with the atmosphere (\(p_{\mathrm{i}}\) is presented as atmospheric pressure), and the outer peripheral surfaces (W\({}_{1}\)-W\({}_{7}\)) of the seal rings are in direct contact with the sealing medium and conduct convection heat transfer with the sealing medium. The seal faces are affected by the fluid film pressure, contact pressure, and heat flux density. The specific boundary conditions of the seal rings are described in detail in the previous work [15].  
The materials of the rotating and stationary ring are carbon graphite impregnated with furan resin (M106K) and pressure-free sintered silicon carbide (SSiC), respectively. The fluid medium is water at normal atmospheric temperature (26 \({}^{\circ}\)C).
# Hailstorm Detection by Satellite Microwave Radiometers
###### Abstract  
Passive microwave measurements from satellites have been used to identify the signature of hail in intense thunderstorms. The scattering signal of hailstones is typically observed as a strong depression of upwelling brightness temperatures from the cloud to the satellite. Although the relation between scattering signal and hail diameter is often assumed linear, in this work a logistic model is used which seems to well approximate the complexity of the radiation extinction process by varying the hail cross-section. A novel probability-based method for hail detection originally conceived for AMSU-B/MHS and now extended to ATMS, GMI, and SSMIS, is presented. The measurements of AMSU-B/MHS were analyzed during selected hailstorms over Europe, South America and the US to quantify the extinction of radiation due to the hailstones and large ice aggregates. To this aim, a probabilistic growth model has been developed. The validation analysis based on 12-year surface hail observations over the US (NOAA official reports) collocated with AMSU-B overpasses have demonstrated the high performance of the hail detection method in distinguishing between moderate and severe hailstorms, fitting the seasonality of hail patterns. The flexibility of the method allowed its experimental application to other microwave radiometers equipped with MHS-like frequency channels revealing a high level of portability.  
passive microwaves; satellite; hail +
Footnote †: journal: Accepted: 11 February 2020; Published: 13 February 2020
# Indoor Carrier Phase Positioning Technology Based on OFDM System
## 4 Numerical Results  
In this paper, MATLAB is used to verify the algorithm. Furthermore, one PRS sub-frame is used in each PRS positioning occasion. Perfect muting is assumed in the simulation. The positioning scene is shown in Figure 3, where six BSs are regularly distributed in the building, and the reference device is located in the center of the scene. In the simulation, it is assumed that there is a synchronization error in Network. Therefore, there are time-varying synchronization errors at the BS side and the terminal side. The detailed simulation parameters are listed in Table 1. For other parameters including the number of multipath in the indoor scenarios, the criteria for generating LOS/NLOS, and the path loss, please refer to [36]. Figure 2: Flowchart of TOA/Carrier Phase combined Real-Time Kinematic. In the simulation, moving speed of the terminal is 1 m/s, and the position solution interval is 0.1 s. The terminal moves according to a specific track in which the length is 60 m, and the number of sampling epoch is 600. High accuracy localization using the carrier phase requires a fast and accurate solution of the integer ambiguity. Therefore, we use the following perspectives to evaluate the effectiveness of the algorithm:  
* The accuracy and convergence speed of the integer ambiguity. * The terminal that can judge whether the solved integer ambiguity is reliable or not. * The real-time positioning accuracy of the terminal position. * The cumulative distribution curve of positioning error. Equation (25) shows that the SD carrier phase measurement \(\Phi_{r}^{ij}\) contains the DD integer ambiguity \(N_{ru}^{ij}\). Therefore, we use the DD integer ambiguity for performance comparison. Define the integer ambiguity estimation error as:  
\[e_{N}=|N_{ru,ture}^{ij}-N_{ru}^{ij}| \tag{46}\]  
Figure 4 illustrates the four DD integer ambiguity estimation errors. When \(e_{N}=0\), it represents that the estimated integer ambiguity is the same as the actual ambiguity. Furthermore, the first BS is used as the reference BS in our experiment. All integer ambiguity errors were significant at epoch 0 due to the sizeable initial position estimation error. In the 92\(nd\) epoch, BS21, BS41, and BS51 all estimate the integer ambiguity correctly and remain unchanged in the subsequent epochs; BS31 always has an error of 1 circle during  
Figure 3: Terminal positioning environment. the experiment. The mistake of BS31 did not affect the localization accuracy because the other integer ambiguities were correctly estimated.
## 3 Datasets and Evaluation Metrics
### HDR-VDP2  
A widely used successor, HDR-VDP2, refines the HDR-VDP model using improved calibration against psychophysical data. It simulates key aspects of the human visual system (HVS), including local adaptation, contrast sensitivity, and masking effects across different visual channels. This allows it to predict the visibility of differences between a reference and a test image or to estimate an overall quality score. The calculation involves pooling distortion signals computed internally within the HVS model and potentially mapping this result to align with subjective scales like Mean Opinion Scores (MOSs), often using functions calibrated against subjective datasets. While the underlying HVS model is complex, its calibration aims to provide results that correlate well with human perception. The full model details can be found in [35].