esa-sceva/satcom-chunk-collection huggingface dataset:
# A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
## 5 Discussion
### Effects on Estimation of Phase Height  
As mentioned in Section 3, the TSVD-based method first estimates the pure volume coherence from the complex interferometric coherence and then extracts the forest height. The phase height, which is very important for the inversion of forest height [34; 35], was also computed from the estimated pure volume coherence and compared with the result of the three-stage method. The phase heights calculated from the estimated pure volume coherence obtained by the three-stage method and TSVD are shown in Figure 9a,b.  
From Figure 9a,b, it can be seen that the estimated phase heights in Figure 9a are higher than those in Figure 9b. We subtracted the phase heights in Figure 9b from those in Figure 9a and display the results in Figure 9c. Clearly, most of the values in Figure 9c are positive numbers. This indicates that the phase height extracted by TSVD is a better fit for the theoretical phase height of the pure volume coherence than the phase height extracted by the three-stage method. For a further comparison, we computed the average phase height of each forest stand and show the results in Figure 10.  
Figure 10 clearly indicates that the phase heights estimated by the TSVD-based method are consistently higher than those estimated by the three-stage method in each forest stand. Therefore, the forest heights inverted by TSVD are more accurate than those inverted by the three-stage method  
Figure 10: The estimated phase heights for each forest stand.  
Figure 9: **(a)** the phase heights extracted from the pure volume coherence obtained by the three-stage method; **(b)** the phase heights extracted from the pure volume coherence obtained by TSVD; **(c)** the differences between the phase heights obtained by the three-stage method and TSVD.  
in the forest stands. To confirm this conclusion, we also used the least-squares method to extract the forest heights from the pure volume coherence estimated by the three-stage method, as mentioned in Section 3.2. The results are shown in Figure 11.  
Comparing Figures 11 and 5, which displays the inversion results of TSVD, clearly, the forest heights in Figure 5 are closer to the LiDAR forest heights than those in Figure 11. Therefore, we can conclude that the TSVD-based method has the capacity to improve the estimation of the pure volume coherence. Based on the improved pure volume coherence, the forest height can be extracted more accurately.
## 4 Experiments
### Comparison with Baselines
#### 4.4.1 Evaluation on PHOENIX-2014
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Dev**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-5}  & **del \(\downarrow\)/ins \(\downarrow\)** & **WER \(\downarrow\)** & **del \(\downarrow\)/ins \(\downarrow\)** & **WER \(\downarrow\)** \\ \hline HMM/CNN/RNN Based & & & & \\ \hline CMLLR [50] & 21.8/3.9 & 55.0 & 20.3/4.5 & 53.0 \\ SubUNets [2] & 14.6/4.0 & 40.8 & 14.3/4.0 & 40.7 \\ Staged-Opt [3] & 13.7/7.3 & 39.4 & 12.2/7.5 & 38.7 \\ Re-sign [4] & -/- & 27.1 & -/- & 26.8 \\ LS-HAN [32] & -/- & - & - & 38.3 \\ CNN-HMM [19] & -/- & 31.6 & -/- & 32.5 \\ DenseTCN [16] & 10.7/5.1 & 35.9 & 10.5/5.5 & 36.5 \\ CNN-LSTM-HMM [20] & -/- & 26.0 & -/- & 26.0 \\ DNF(RGB) [37] & 7.8/3.5 & 23.8 & 7.8/3.4 & 24.4 \\ DNF (RGB + Flow) [37] & 7.3/3.3 & 23.1 & 6.7/3.3 & 22.9 \\ FCN [12] & -/- & 23.7 & -/- & 23.9 \\ SMC [7] & 7.6/3.8 & 22.7 & 7.4/3.5 & 22.4 \\ VAC [51] & 7.9/2.5 & 21.2 & 8.4/2.6 & 22.3 \\ mLTSF-Net [9] & 8.5/3.3 & 23.8 & 8.3/3.1 & 23.5 \\ CVT-SLR [52] & -/- & 21.8 & -/- & 22.0 \\ MSKA-SLR [53] & -/- & 21.7 & -/- & 22.1 \\ \hline Transformer Based & & & & \\ \hline REINFORCE (SL) [34] & 5.7/6.8 & 39.7 & 5.8/6.8 & 40.0 \\ REINFORCE (RL) [34] & 7.3/5.2 & 38.0 & 7.0/5.7 & 38.3 \\ SAFI [54] & 16.6/1.8 & 31.7 & 15.1/1.7 & 31.3 \\ SL-Transformers [18] & 39.3/2.8 & 50.18 & 37.0/2.8 & 47.96 \\ SL-Tramsformers(pre) [18] & 13.5/5.7 & 26.7 & 13.8/6.4 & 27.62 \\ PiSLTRc-R [55] & 8.1/3.4 & 23.4 & 7.6/3.3 & 23.2 \\ signBERT+ [56] & -/- & 34.0 & -/- & 34.1 \\
**MSF-ET (Ours)** & 7.4/3.3 & 22.4 & 7.3/3.2 & 22.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of our proposed method with existing approaches on the PHOENIX-2014 dataset, where lower values indicate better performance. “RGB” and “Flow” in the table refer to frame images and optical flow, respectively. “SL” stands for supervised learning and “RL” represents reinforcement learning. The \(\downarrow\) means the lower the better. Within the Transformer-based sequence modeling methods, it is worth noting that the self-attention-based approach consistently falls short compared to CNN or RNNs. Analyzing and addressing this phenomenon also constitutes one of the contributions of this work. As observed in Table 5, the application of Transformer to this task results in a WER metric of 30 or even higher. REINFORCE attempts to improve the encoder-decoder structure using reinforcement learning techniques. SL-Transformers employ pre-trained features from LSTM-HMM, reducing the WER from 50.18 to 26.7. In contrast to these models, our proposed method significantly lowers the WER metric, decreasing it by 15.6 points compared to the reinforcement learning approach, and even achieving a reduction of over 50% relative to the untrained SL-Transformers. The comparison with baseline models attests to the effectiveness of our proposed method, which not only achieves state-of-the-art performance among Transformer-based approaches but also rivals CNN/RNN models.
## 2 Materials and Methods
### Digital Holographic Cytometry (DHC) and Cell Tracking  
RAW 264.7 cells were seeded in a Sarstedt lumox(r) 96-well plate (Sarstedt, Germany) with 2 \(\times\) 10\({}^{4}\) cells per well and incubated overnight at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\) allowing the cells to adhere. The medium in each well was renewed with either 40 \(\upmu\)g/mL SA-MIPs, for treated cells, or culture medium for untreated cells. The lid was changed to Hololids for 96-well plates (PHIAB, Lund, Sweden), which enable cell imaging by the HoloMonitor M4 digital holographic cytometer (DHC) (PHIAB). The plates were placed on the motorized stages of HoloMonitor M4 in a standard 37 \({}^{\circ}\)C with 5% CO\({}_{2}\) incubator. Triplicated experiments were performed using 2 wells of treated and untreated cells in each experiment. Five positions in each well were chosen and images captured every 15 min for 48 h. Time-lapse imaging, image processing, segmentation and analysis were conducted with the App Suite software package (v3.5.0, PHIAB, Lund, Sweden) based on absolute values of refractive indexes for cells and culture medium. As the laser intensity is approximately 10 W/m\({}^{2}\) during imaging, and exposure time is less than 10 ms, it is assumed that the laser irradiation has only minimal effect on the physiological functions of the cells. App Suite was used for the tracking of individual cells. For each capturing position, cells were selected at time-point 0 h, which is the first image acquired in the 48 h time-lapse. When the cells divided, the two daughter cells were selected for further analysis and connected to the mother cell. This enables the calculation of the time of each cell division for individual cells, [25; 26]. Based on known time-points for cell divisions and from which cell the daughter cells originated, a cell family tree was drawn for each cell selected at time-point 0 h using RStudio software (v.2021.09.1, RStudio, Boston, MA, USA).
# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 3 Methodology
### Improved YOLOv5s
#### 3.2.2 Optimized Anchor Generation  
The initial dimensions of the anchor box in YOLOv5 are determined through edge clustering using the K-means algorithm [39] on the MS COCO dataset [40]. However, the MS COCO dataset predominantly comprises large and medium-sized targets. In contrast, this study's Road Damage Detection dataset encompasses small and medium-sized targets that deviate from the COCO dataset. Hence, the initial dimensions of the anchor box in YOLOv5 are deemed unsuitable for the RDD-2022 dataset samples. To address this issue, The K-means\({}^{++}\) clustering algorithm was used instead of the K-means algorithm to reconfigure the anchor box's dimensions for the samples present within the RDD 2022 dataset. K-means\({}^{++}\) is an enhanced variant of the K-means clustering algorithm that addresses certain limitations of the original K-means algorithm [41]. These limitations include random initialization of centroids, slower convergence speed, and reduced accuracy. The present study employs the K-means\({}^{++}\) algorithm to perform clustering on the training set, acquiring 12 anchor boxes that serve as the initial clustering boxes. Upon completing dataset reclustering, a novel anchor box was discovered, comprising the following coordinates: '15, 15, 44, 26, 33, 63, '116, 24, 81, 62, 44, 120', '228, 36, 92, 142, 175, 97', and '191, 190, 126, 464, 410, 199'.
## 2 Background  
### Sensor Signal Model  
For medical ultrasound imaging, we use adaptive beamformers to process ultrasonic data that has achieved transmitted and received focusing by SA. For a linear array consisting of M transducer elements, the output of the beamformer can be expressed as [20]:  
\[\mathrm{y}(\mathrm{k})=\mathbf{\omega}^{\mathrm{H}}(\mathrm{k})\mathbf{X}(\mathrm{ k})=\sum_{\mathrm{i=1}}^{\mathrm{M}}\mathbf{\omega}_{\mathrm{i}}^{*}(\mathrm{k}) \mathrm{x}_{\mathrm{i}}(\mathrm{k}) \tag{1}\]  
where \(\mathrm{k}\) is the time index. \(\mathbf{X}(\mathrm{k})\) consists of M time-delayed echo data, \(\mathbf{X}(\mathrm{k})=[\mathrm{x}_{1}(\mathrm{k}),\mathrm{x}_{2}(\mathrm{k}),\cdots,\mathrm{x}_{\mathrm{M}}(\mathrm{k})]^{\mathrm{T}}\), and \(\mathbf{\omega}(\mathrm{k})=[\mathbf{\omega}_{1}(\mathrm{k}),\mathbf{\omega}_{2}(\mathrm{ k}),\cdots,\mathbf{\omega}_{\mathrm{M}}(\mathrm{k})]^{\mathrm{T}}\) is the weight vector of the beamforming. \(*\) represents complex conjugate, \((\cdot)^{\mathrm{T}}\) and \((\cdot)^{\mathrm{H}}\) represent matrix transpose and matrix conjugate transpose, respectively. For adaptive beamformers, we artificially divide \(\mathbf{X}(\mathrm{k})\) into three parts as follow:  
\[\mathbf{X}(\mathrm{k})=\mathbf{S}(\mathrm{k})+\mathbf{I}(\mathrm{k})+\mathbf{ N}(\mathrm{k}) \tag{2}\]  
where \(\mathbf{S}(\mathrm{k})\) represents the desired signal, which is reflected by the detection point, \(\mathbf{I}(\mathrm{k})\) is the echo signal introduced from the sidelobe direction called interference signal, and \(\mathbf{N}(\mathrm{k})\) represents the noise signal, including thermal noise and scattering noise.  
For adaptive beamforming, the weight vector \(\mathbf{\omega}(\mathrm{k})\) is equivalent to a spatial filter, which can suppress interference and noise signals while retaining the desired signal. Since the weight vector \(\mathbf{\omega}(\mathrm{k})\) is calculated from the echo data, it can vary with echo data adaptively. Thus, it can improve the quality of echo images.  
Similarly, Equation (1) can also represent non-adaptive beamforming algorithms if the vector \(\mathbf{\omega}(\mathrm{k})\) is preset. For example, we can use the window functions, such as Hamming, Hanning, and Blackman, etc., which is called the apodization technique [21]. Especially, the output of the beamformer will be the result of the synthetic aperture (SA)when \(\mathbf{\omega}(k)=\left[1,1,\ldots,1\right]^{T}\). It is equivalent to adding a rectangular window to the beam. In this situation, the \(\mathbf{\omega}(k)\) can also be expressed as [22]:  
\[\mathbf{\omega}=\frac{\mathbf{I}^{-1}\mathbf{a}}{\mathbf{a}^{H}\mathbf{I}^{-1} \mathbf{a}} \tag{3}\]  
where \(\mathbf{I}\) is an identity matrix and \(\mathbf{a}\) denotes a steering vector.
# A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method
## 6 Experiments and Results  
### Experimental Data and Setup  
To verify the effectiveness of the proposed rolling bearing fault diagnosis and severity analysis method, the experimental data selected in this paper are all obtained from the Bearing Data Center of Case Western Reserve University and the rolling bearing experiment system and its sketch are shown in Figure 4. The accelerometers were placed at two positions, one at the drive end of the motor and the other at the fan end. Faults were introduced separately at the inner raceway, outer raceway, and rolling element by using electro-discharge machining. The vibration signals of the rolling bearing included four types: Normal, ball fault (BF), inner race fault (IRF), and outer race fault (ORF). The rolling bearing was tested under four different loads of 0, 1, 2, and 3 horse power (hp), each type of fault ranging from 0.007 inches to 0.040 inches in diameter. Motor bearings were seeded with faults using electro-discharge machining (EDM). Faults ranging from 0.007 inches in diameter to 0.040 inches in diameter were introduced separately at the inner raceway, rolling element (i.e., ball), and outer raceway. Faulted bearings were reinstalled into the test motor and vibration data was recorded for motor loads of 0 to 3 horsepower (motor speeds of 1797 to 1720 rpm). The sampling rates of the data were 12 kHz and 24 kHz. The drive-end and fan-end bearing specifications, including the bearing geometry and defect frequencies are listed in Tables 1 and 2, respectively. Figure 5 shows the vibration signals collected under different rolling bearing conditions for a load of 0 hp. Further details can be found on the Case Western Reserve University website [35].  
In this study, the sampling rate was set to 12 kHz and the drive-end rolling bearing data under four different loads of 0, 1, 2, and 3 hp with fault diameters of 0.007 in, 0.014 in, 0.021 in, and 0.028 in were collected. The working conditions of the collected rolling bearing data are illustrated in Table 3, where \"\(\diagup\)\" indicates the data sets that are selected and \"\(\diagup\)\" indicates the data sets that are not available.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Inner Race** & **Outer Race** & **Cage Train** & **Rolling Element** \\ \hline
5.4152 & 3.5848 & 0.39828 & 4.7135 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fan-end bearing defect frequencies (multiples of the running speed in Hz).  
Figure 4: The rolling bearing experiment system and its sketch of Bearing Data Center of Case Western Reserve University.  
Figure 5: Vibration signals collected under different rolling bearing conditions for a load of 0 hp.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Inner Race** & **Outer Race** & **Cage Train** & **Rolling Element** \\ \hline
4.9469 & 3.0530 & 0.3817 & 3.9874 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fan-end bearing defect frequencies (multiples of the running speed in Hz).
## 3 Results and Discussion
### Content of Active Ingredients in Plant Powders
[10] studied the content of polyphenols in unpeeled burdock root powder (BRP), peeled burdock root powder (PBRP), and burdock root peel powder (BRPP). The experiments revealed that the total polyphenol content varied depending on the part of the plant used for obtaining the powder: 15,131 [mg GAE/100 g DM] for BRP; 12,753 [mg \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{3}{*}{**Plant Powder**} & \multicolumn{2}{c}{**Total Polythenolic Content**} & \multicolumn{2}{c}{**Oxidative Activity**} & \multicolumn{2}{c}{**Vitamin C Content**} \\  & **[mg GAE/100 g DM]** & \multicolumn{2}{c}{**[\% Inhibition]**} & \multicolumn{2}{c}{**[mg/100 g DM]**} \\ \cline{2-7}  & **Mean Value** & **SD** & **Mean Value** & **SD** & **Mean Value** & **SD** \\ \hline
**Black radish** & 15.46 & 1.23 & 13.83 & 0.57 & 266.34 & 0.000 \\
**Burdock** & 41.52 & 1.25 & 68.75 & 2.91 & 4.14 & 0.447 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of measurements of total polyphenic content, oxidative activity, and vitamin C content. GAE/100 g DM] for PBRP; and 72,214 [mg GAE/100 g DM] for BRPP. The results show that the highest content of polyphenol compounds was present in the root peel of _Archium lappa_, while the lowest value of the parameter was measured in peeled burdock root. Moreover, [PERSON] et al. (2014) found that the oxidative activity of the burdock extracts obtained in their study ranged from 12.61 to 46.41%, which is nearly six times lower than the oxidative activity determined in the study reported here. The content of vitamin C in burdock powder obtained by low-temperature drying with simultaneous micromization reported in our work is not high compared to the literature-reported data. For example, the study conducted by [PERSON] et al. (2010) to determine the content of vitamin C in whole burdock root powder (BRP), peeled burdock root powder (PBRP), and burdock root peel powder (BRPP) found a high content of this active ingredient in the examined burdock material, ranging from 149 to 1231 [mg/100 g DM]. The results obtained are significantly influenced by factors including the form of the black radish plant material (dried, frozen, fresh), whether the plant or extract was tested, and the specific part of the plant studied. It is evident from the literature data that the highest content of active ingredients is typically obtained from fresh black radish plant material. Regarding burdock, discrepancies between the study's results and those in the literature could be attributed to factors such as the plant's country of origin, cultivation location, and other variables.
# Small Target Radiometric Performance of Drone-Based Hyperspectral Imaging Systems
###### Abstract  
Hyperspectral imaging systems frequently rely on spectral rather than spatial resolving power for identifying objects within a scene. A hyperspectral imaging system's response to point targets under flight conditions provides a novel technique for extracting system-level radiometric performance that is comparable to spatially unresolved objects.The system-level analysis not only provides a method for verifying radiometric calibration during flight but also allows for the exploration of the impacts on small target radiometry, post orthorectification. Standard Lambertian panels do not provide similar insight due to the insensitivity of orthorectification over a uniform area. In this paper, we utilize a fixed mounted hyperspectral imaging system (radiometrically calibrated) to assess eight individual point targets over 18 drone flight overpasses. Of the 144 total observations, only 18.1% or 26 instances are estimated to be within the uncertainty of the predicted entrance aperture-reaching radiance signal. For completeness, the repeatability of Lambertian and point targets are compared over the 18 overpasses, where the effects of orthorectification drastically impact the radiometric estimate of point targets. The unique characteristic that point targets offer, being both a known spatial and radiometric source, is that they are the only field-deployable method for understanding the small target radiometric performance of drone-based hyperspectral imaging systems.  
small target radiometry; radiometric performance; point targets; hyperspectral; imaging; small unmanned aircraft systems; UAS; UAV; convex mirrors 2024
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 2 Materials and Methods
### Semantic Segmentation Model  
We mainly investigated the expansion of color images with the corresponding predicted depth in improving the performance of semantic segmentation. The learning-based depth estimation method is divided into two domains: supervised and unsupervised learning methods. The depth estimation models used in this paper originate from our previous related work [25; 26]. Figure 1 outlines a summary of our semantic segmentation model with an encoding\(-\)decoding structure fusing depth information. For the encoder, the representative features of the RGB and predicted depth images were automatically extracted using our backbone network model, which was based on the improved residual network--IResNet [28]. The effective fusion of image and depth information was implemented as the input of the next layer followed by a fusion module. For the decoder, a series of up-sampling structures were used to gradually restore the resolution of the output feature map. The feature pyramid attention (FPA) network builds a bridge between the encoder and decoder modules, which mainly extracts multiscale information from the features output by the encoder module as the input to the decoder module. Some shortcut connections (red arrows) were also added between the corresponding layers in the encoder and decoder to enhance the information flow.
# A Reliable Routing Algorithm Based on Path Satisfaction in the Energy Internet
## 3 Network Architecture and System Model
### System Model
#### 3.2.1 Link Model
The relationship is intuitive in that the link with a higher spectrum connectivity contains more free FSs and a higher spectrum adjacency. Hence, it is more likely to be selected as the next hop in the routing selection process. SRLGs are marked with a red circle as shown in Figure 2. Anyway, we can find that an SRLG may be composed of one or many links, and a link may belong to many SRLGs, e.g., the link EF appears in two SRLGs [42]. As links occurring in the same SRLGs increase the correlated failure probability, these links should be bypassed as much as possible in the routing calculation process. Let \(\mathbb{F}\) be the set of SRLGs, and the SRLG attribute of the link \(e\) can be denoted as a constant \(\delta_{e}\) such that, as follows:  
\[\delta_{e}=\left\{ \begin{array}{ll}1,&\text{if }e\
otin\mathbb{F}\\ 0,&\text{Otherwise}\end{array} \right.. \tag{16}\]  
Therefore, we calculate the link satisfaction for each link in the EI in terms of (9). Subsequently, path satisfaction can be obtained as follows:  
\[\vartheta_{p}=(1-\frac{\sum_{e\in p}\delta_{e}}{n_{hop}(p)})\prod_{e\in p} \vartheta_{e} \tag{17}\]  
where \(n_{hop}(p)\) is the total number of hops along with the path \(p\). \(\delta_{e}\) as a binary variable. It takes on the value of 1 if the link \(e\) of the path \(p\) is in the set of SRLGs, and 0 if otherwise.
## 3 Results and Discussion
### Numerical Model Validation
#### 3.1.2 FSI Model Validation  
The maximum induced stresses due to the effect of the slug flow characteristics on the piping were used to validate the FSI model in the current work. Cases 1, 2 and 4 from the CFD model were utilised for the FSI model validation, and the results were compared against the findings of [PERSON] [19]. Figure 8 illustrates the maximum induced stresses plotted against the water superficial velocity and the quantitative comparison.  
Figure 7: Quantitative validation of air–water two-phase flow in the present model against the experimental work of [PERSON] [19]. Validation of (**a**) slug initiation position; (**b**) slug translational velocity; (**c**) slug liquid length; (**d**) slug frequency.  
Through the comparison of the results from the present model with those by [PERSON] [19], the deviations for Cases 1, 2 and 4 are recorded as 9.5%, 8.7% and 8.8%, respectively. The deviations indicate the reasonable agreement of the results compared.