esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Odometer-Aided BSSD PPP/INS Tightly-Coupled Integration  
In motion scenarios, the vehicle does not slip sideways and upward, which means that the velocity on lateral and vertical will be close to zero and only the forward speed exists [31; 32]. Meanwhile, the forward velocity obtained from an odometer can be used as a pseudo-measurement. However, the odometer-measured velocity will be influenced by the scale factor error  
\[v_{o}\approx\left[v_{o}^{p}/(1+S_{o})\ \ 0\ \ 0\right]^{T} \tag{22}\]  
where \(v_{o}^{p}\) is the forward vehicle velocity measured by the odometer in the vehicle frame (\(v\)); \(S_{o}\) represent the scale factor which can be modeled as a random walk process.  
The innovation vector of the odometer can be calculated by the difference between the velocity measured by the odometer and predicted by INS predicted one. However, the \(v\)-frame and the \(b\)-frame are not usually aligned theoretically. Therefore, the innovation vector (\(Z_{o}\)) can be described by  
\[Z_{o}=v_{o}^{p}-C_{b}^{p}v_{INS}^{b}\approx\begin{bmatrix}v_{o}^{p}/(1+S_{o}) \\ 0\\ 0\end{bmatrix}-\begin{bmatrix}v_{INS}^{F}\\ v_{INS}^{R}\\ v_{INS}^{D}\end{bmatrix}+\eta,\eta\sim N(0,\sigma_{o}^{2}) \tag{23}\]  
where \(C_{b}^{v}\) denotes the rotation matrix from \(b\)-frame to \(v\)-frame; \(\eta\) is the vector of odometer innovation noise with the prior variance of \(\sigma_{o}^{2}\).  
According to Equation (11), \(Z_{BSSD,TCI,k}\) can be furtherly expressed as  
\[Z_{BSSD,TCI,k}=\begin{bmatrix}SSDM\times Z_{TCI,k}\times SSDM^{T}\\ v_{o}^{p}-C_{b}^{p}v_{INS}^{b}\end{bmatrix} \tag{24}\]  
with the linearized form of  
\[\delta Z_{o}=v_{o}^{v}\delta S_{o}-C_{b}^{v}\Big{(}C_{n}^{b}\delta v_{INS}^{n }-C_{n}^{b}(v_{INS}^{n}\times)\delta\psi-\Big{(}l_{o}^{b}\times\Big{)}\omega_{ ib}^{b}\delta S_{g}\Big{)} \tag{25}\]  
where \(\delta S_{o}\) is the correction of the odometer scale factor; \(l_{o}^{b}\) denotes the lever-arm between the reference center of the odometer and IMU in the \(b\)-frame. The corresponding BSSD state vector can be expressed as  
\[X_{BSSD,TCI,k}=\left[\delta p_{INS}^{n}\quad\delta v_{INS}^{n}\quad\delta\psi \quad\delta B_{a}\quad\delta B_{g}\quad\delta S_{a}\quad\delta S_{g}\quad \delta S_{o}\quad\delta d_{wet}\quad\delta N_{IF}^{km}\right] \tag{26}\]  
the state vector can be estimated by the EKF [41; 42]  
\[X_{BSSD,TCI,k}=X_{BSSD,TCI,k,k-1}+K_{k}(Z_{BSSD,TCI,k}-H_{BSSD,TCI,k}X_{BSSD,TCI,k,k-1}) \tag{27}\]  
\[P_{BSSD,TCI,k}=(I-K_{k}H_{BSSD,TCI,k})P_{BSSD,TCI,k-1}(I-K_{k}H_{BSSD,TCI,k})^{ T}+K_{k}R_{BSSD,TCI}K_{k}^{T} \tag{28}\]
# Water Segmentation for Unmanned Ship Navigation Based on Multi-Scale Feature Fusion
###### Abstract  
The segmentation of the navigation area from water images is of great significance in the safe and automated navigation of unmanned vessels. However, accurate segmentation of water boundaries in real time under the interference of water-surface light changes is still a challenging problem. In this study, a river water segmentation method based on a multi-scale feature fusion network (Fuse-ResNet), which improves the segmentation of river water boundaries by focusing more on the target features, is proposed. An Atrous Spatial Pyramid Pooling (ASPP) mechanism is introduced to expand the sensory field and enhance the relevance of contextual information at different scales. Experimental results based on the USVInland unmanned vessel dataset show that the proposed method achieves a mean Pixel Accuracy (mPA) of 99.25% and a mean Intersection over Union (mIoU) of 98.45% in the water category, which are better than those achieved by mainstream models. The interference of background noise resulting from water surface reflection can be suppressed to improve the accuracy of water segmentation for unmanned vessel navigation.  
unmanned ships; feasible domain segmentation; ResNet-50; multi-scale feature fusion; pyramid structure +
Footnote †: journal: Applied sciences  
Article
## 3 Results
### Operator
#### 3.4.6 Supporting Operators with Industry 4.0 Technologies  
Industry 4.0 technologies offer new ways to support human operators in their duties--see Table 3: training can be made easier with Virtual Reality (VR), Augmented Reality (AR) and motion tracking [74; 75; 76]; instructions can be generated in real time and displayed using AR [77; 78; 79]; or projection AR can be used to provide process information [80], assembly assistance [81], safety in HRC \"chaotic\" smart warehouses [224], shipyard worker assistance [225] or to enhance the operator's capabilities and competencies [82]. In general, human operators are positive about the use of AR for assembly support [226]. The technology-enhanced operator is a growing field of research, with many other Industry 4.0 KET involved in achieving varied goals: IoT-based human-Cyber Physical Systems for providing feedback to operators working in an intelligent space [72]; reducing big data to smart data to assist people [60]; software robots (softbots) to interface between machines and computer information systems [227]; mobile devices in order to allow dynamic job rotation in multi-variant assembly lines [228]; verbal and visual prompts for assisting workers with intellectual disabilities [229]; wearables for audio commands [230] or detecting potentially hazardous or risky situations [231]; or a combination of many technology-enabled tools [232; 233; 234].  
**In conclusion**, varied Industry 4.0's Key Enabling Technologies can be used to support production operators to obtain different benefits. In particular, Virtual and augmented reality and wearable devices have attracted great attention. Operators can be supported with assembly instructions, quality control,assembly details prompts or enhanced training programs, which can be provided in a way that is satisfactory for the users.
## 3 Intrinsic and Extrinsic Factors Associated with Hair Graying
### Nutrition  
A study analyzing serum levels of trace elements in patients with premature hair graying demonstrated lower copper levels and higher iron levels in the patient group compared to the control group while zinc levels did not differ between the two groups [64]. In another study, iron, copper, and calcium were all lower in the patient group than in the control group [116]. An 11-year-old male with slowly graying hair had low serum ferritin and hemoglobin levels, and five months of iron supplementation (oral ferrous sulfate, 40 mg daily) normalized serum ferritin and hemoglobin levels and recovered black hair color in this patient [117]. Epidemiological or cross-sectional studies also suggested that premature hair graying is related to low serum ferritin and calcium levels [118; 119; 120]. On the other hand, serum levels of selenium and lead did not significantly differ between the two groups with and without premature hair graying [121].  
Hair loss and graying hair were observed in patients receiving intravenous fat-free fluid for a long period. These symptoms were related to a deficiency of essential fatty acids and were cured by topical safflower oil containing high linoleic acid levels [122]. Several studies investigated the link between hair graying and certain vitamins, including vitamin B12 and vitamin D, and most, but not all, results supported a possible correlation [115; 118; 119; 120; 123; 124].
## 3 Development of Wireless Laser Displacement Monitoring Equipment
### Indoor Static Loading Test for Verification
#### 3.3.2 Analysis of Experimental Data  
Before the test started, the distance measurement error of the laser displacement monitoring equipment was measured. The error calculation can be obtained through the static distance measurement test. When no load is applied, the distance measurement data can be collected for a period of time. Figure 8 shows the time-history curve of the measurement distance. The range is between 8.2946 m and 8.2966 m, and the fluctuation range is approximately 2.0 mm. Generally, the random error of measurement conforms to a Gaussian distribution. The greater the number of measurements, the closer the average value of the measurement is to the true value. The average distance value in the test period is subtracted from the measured distance value, and the probability distribution of the distance measurement error can be further obtained, as shown in Figure 9. It can be seen from the figure that the error distribution is in a certain interval near 0 (\(-\)1.2 mm\(\sim\)0.8 mm), \(\Delta\)lmax \(\approx\) 2.0 mm, and the long-term ranging error probability of the laser displacement monitoring equipment conforms to the Gaussian distribution, which can be obtained by fitting (\(\mu\) = 0.0592, \(\sigma\) = 0.6052).  
After the static load test equipment was installed, the installation angle of the measurement sensor was approximately 5.2\({}^{\circ}\), and the static average measurement distance was 8.2957 m. The deformation time-history curve of the test equipment (laser displacement monitoring equipment, red curve) and the comparison equipment (dial indicator, blue curve) taken during the test are shown in Figure 10. It can be seen from the figure that, compared with the basically flat curve of the dial indicator, the fluctuation range of the deformation measurement of the laser displacement monitoring equipment was within 0.2 mm.  
Figure 8: Static ranging test of laser displacement monitoring equipment.  
Figure 9: Fitting of the measurement error distribution (a = 0.11, \(\mu\) = 0.0592, \(\sigma\) = 0.6052).  
Theoretical error \(\Delta d_{max}\) is calculated by the following formula:  
\[\Delta d_{max}=\Delta l_{max}\cdot\sin\alpha \tag{14}\]  
The maximum theoretical error obtained by calculation is 0.18 mm, and the maximum error of the measured value is relatively close to the maximum error of the theoretical value. Although this method was validated in the laboratory, on site, it may be limited by light or installation conditions and the relevant data may be uncertain, which means it requires further validation in the future.  
In view of the stability of laser intensity and laser spot, the current test results show that the stable measurement results can be acquired in 25 m, and the corresponding bridge span is about 50 m.
# Effect of Using Plastic Waste Bottles on Soil Response above Buried Pipes under Static Loads
## 3 Results and Discussion
### Relative Density of Backfill Soil (DR) Effect  
Figure 12 depicts the effect of the relative density of the soil on the bearing pressure of a model footing supported on a bottle-reinforced sand bed. Figure 12a-e show how the applied load varies with the surface settlement on the unreinforced sand bed at a relative density of 80% (the dotted line in each of these diagrams represents a baseline load capacity that can be used to measure the performance improvements from the reinforcements) and the variation of the applied pressure on the model footing on a reinforced sand bed with the surface settlement at relative densities of 30, 60 and 80%. It is clear from this diagram that soil density has a significant impact on the footing's bearing capacity and settlement. Increases in relative soil density resulted in more bearing pressure and less footing settlement than those seen with any of the reinforcing conditions, as seen by the stiffer pressure-settlement reactions. The shear strain created before failure in the failure  
Figure 10: Applied pressure with footing surface settlement curves for various reinforcement widths reported at (**a**) DR = 30%, (**b**) DR = 60%, and (**c**) DR = 80%.  
zone increased due to this behavior [3]. This was because denser sand layers had higher reinforcing performance.  
Figure 11: Variation of BCR and PRS with different reinforcement widths reported at (a) DR = 30%, (b) DR = 60%, and (c) DR = 80%.  
Figure 12: _Cont._  
Figure 12 also illustrates that higher applied pressure occurred at lower settlements as the relative density of the soil bed increased. For example, the ultimate bearing capacity values for relative densities of 30, 60, and 80% with bottle reinforcement (u/B = 0.5 and b/B = 2.08) were 45.3, 139, and 337.3 kPa at a settlement level (S/B) of 10%. Moreover, the footing surface settlement at a given bearing pressure decreased as the relative density of the soil increased [35]. A similar reduction in the soil surface settlement, irrespective of the reinforcement condition, has been reported by [PERSON] and [PERSON] [36] from experimental tests on buried pipes backfilled with a geogrid-reinforced sand bed.
# Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images
## 3 Method
### Semantic Fusion  
With the learning 2D segmentation and depth estimation, pixel-wise 2D semantic labels and depth maps are obtained for each view. However, because of the occlusions, complexities of environments, and the noise of sensors, both image segmentation results and depth maps might have a large number of inconsistencies between different views. Thus, we further cross filter the depth maps by their neighbor views, and then produce the 3D semantic point clouds by combining 2D segmentation and depth maps with multi-view consistency.  
Similar to other depth-map-based MVS methods [6; 29], we utilize geometric consistency to cross filter the multi-view depth data. Given the pixel \((x,y)\) from image \(I_{i}\) with depth \(d(x,y)\), we project \((x,y)\) to the neighbor image \(I_{j}\) through \(d(x,y)\) and camera parameters. In turn, we re-project the projected pixel back from the neighbor image \(I_{j}\) to the original image \(I_{i}\); the re-projected depth on \(I_{i}\) is \(d_{reproj}\). We consider the pixel consistent in the neighbor view \(I_{j}\) when \(d_{reproj}\) satisfies:  
\[\frac{\left|d(x,y)-d_{reproj}\right|}{d(x,y)}<\tau. \tag{3}\]  
According to the geometric consistency, we filter the depths which are not consistent in more than \(k\) views. In this paper, we take \(\tau=0.01\) and \(k=3\).  
After cross filtering, the depths are projected to 3D space to produce 3D point clouds. Since our purpose is to assign point-wise semantic labels for the 3D model, we propose a probabilistic fusion method to aggregate multi-view 2D semantic information. With the fine-tuned CNN, a pixel-wise label probability distribution \(P(\mathcal{L})\) has been calculated for each source image. Given a 3D point \(X\) which is visible in \(N\) views, the corresponding probability on view \(i\) for label \(l_{j}\) is \(p_{i}(l_{j})\); we accumulate the multi-view probability distribution of each view as follows:  
\[P(l_{j})=\frac{1}{N}\sum_{i=1}^{N}p_{i}(l_{j}),l_{j}\in\mathcal{L}, \tag{4}\]  
Figure 2: Visualization of the depth map estimated by the learning-based MVS method. (**a**) The input image. (**b**) Depth estimation by R-MVSNet [6]. (**c**) Refined depth by bilateral filtering.  
where \(P(I_{j})\) denotes the probability of point \(X\) labeling by \(I_{j}\). In this way, we transfer the probability distribution of multi-view images into 3D space. Generally, the predicted 3D semantic label can be determined by the Argmax operation as:  
\[l(X)=\underset{I_{j}}{argmax}(P(I_{j})),I_{j}\in\mathcal{L}, \tag{5}\]  
where \(l(X)\) is the 3D semantic label of \(X\). As depicted in Figure 3, the probabilistic fusion method effectively reduces errors since it integrates information from multiple images.
## 4 Enrichment Ratio for Rill versus Inter-Rill Erosional Processes
### Stage I: Detachment and Disruption of Soil Aggregates  
Raindrop-induced disruption of soil aggregates, the first stage of the erosional process (Figure 1), is the dominant cause of splash, or inter-rill soil erosion. The SOC entrapped within the aggregates is exposed to microbial processes by the disruption of aggregates and the liberation of some materials. Change in pore-size distribution and soil-water matrix potential may also affect microbial activity and thus, the vulnerability of the liberated material to decomposition and emission of GHGs. [PERSON] et al. [22] indicated that the mechanical disturbance of the soil structure can alter pore systems and thus, the CO\({}_{2}\) effluxes. Physical influences may be overlapped by biological effects due to the higher energy supply to the microbes. Thus, the susceptibility of physical protection mechanism to SOC by the disruption of aggregates (structure) may also aggravate CO\({}_{2}\) effluxes [22]. [PERSON] et al. [23] used the event-based multi-class sediment transport model regarding a 100-year time series of rainfall observation on a 3-ha agricultural catchment in Belgium. Based on the modeling analysis, [PERSON] et al. [23] concluded that: (a) inter-rill erosion is a selective process which erodes primary particles, (b) rill erosion is non-selective and erodes secondary particles (aggregates), (c) particulate organic carbon (POC) is mostly encapsulated within aggregates, and (d) the export of SOC-ER is the highest in events comprised primarily of inter-rill erosion. [PERSON] and [PERSON] [15] observed that the breakdown of aggregates by rainfall energy may release POM, which is then preferentially eroded. Thus, SOC and plant nutrients are selectively moved during erosion.
# Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
## 6 Results and Discussions
### Quantization Results  
Table 4 and Figure 12 reports the top-1 accuracy (%), energy (in pJ), and model size (in bits) with 2-bit, 4-bit, 8-bit, 16-bit, and 32-bit precision for the model parameters. For comparison, we have included results using the baseline 1 DCNN, which uses full 64-bit precision for the model parameters. We make the following three key observations.  
First, the top-1 accuracy reduces with a reduction in the bit precision (higher accuracy is better for respiratory classification in premature newborn infants). With 2-bit, 4-bit, 8-bit, 16-bit, and 32-bit precision, the top-1 accuracy is lower than the baseline 64-bit precision by 8.5%, 8.4%, 4.3%, 0.6%, and 0.1%, respectively. The top-1 accuracy with 32-bit precision is comparable to 64-bit precision.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Quantization** & **Top-1 Accuracy** & **Energy (pJ)** & **Model Size (bits)** \\ \hline
2-bit/parameter & 88.93\% & 7089 & 92,258 \\
4-bit/parameter & 88.98\% & 15,994 & 184,516 \\
8-bit/parameter & 93.00\% & 29,871 & 369,032 \\
16-bit/parameter & 96.55\% & 57,640 & 738,064 \\
32-bit/parameter & 97.03\% & 113,386 & 1,476,128 \\ \hline Baseline 1 DCNN & 97.15\% & 134,613 & 2,952,256 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model quantization results.  
Figure 11: Confusion matrix for the 1 DCNN model.  
Second, energy reduces with a reduction of the bit precision (lower energy is better for respiratory classification in wearables due to limited battery, as we mentioned in Section 1). With 2-bit, 4-bit, 8-bit, 16-bit, and 32-bit precision, energy is lower than the baseline 64-bit precision by 94.7%, 88.1%, 77.8%, 57.2%, and 15.8%, respectively. These results show the significant reduction in energy achieved using quantization. Lower energy leads to longer battery life in wearables. Finally, model size also reduces with a reduction in the bit precision (lower model size is better for wearables due to their limited storage availability). With 2-bit, 4-bit, 8-bit, 16-bit, and 32-bit precision, energy is lower than the baseline 64-bit precision by 96.8%, 93.7%, 87.5%, 75.0%, and 50.0%, respectively.  
We conclude that to reduce energy and model size for respiratory classification in wearables, quantization techniques can lead to a significant reduction in accuracy.
## 2 Thermal Signature of Malignancy  
### Skin Cancers  
Skin cancers (also known as skin neoplasms) are generally classified based on the kind of skin cell from which they arise. Basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma (a malignant transformation of melanocytes) are the most common skin cancers by the number of diagnosed cases. Each type of these skin neoplasms exhibits a characteristic thermal signature, as shown by [PERSON] et al. [16]. These authors examined 30 patients with a reported diagnosis of melanoma (in 6 cases) and non-melanoma (18 BCC and 6 SCC) skin neoplasms using an FLIR 1400 IR camera (320 \(\times\) 240 Focal Plane Array, with a spectral range of 7.5-13 \(\upmu\)m and a thermal sensitivity of 50 mK at 30 \({}^{\circ}\)C). Based on the acquired thermal images, they found that BBCs show negative temperature differences between the cancer lesion and the healthy skin, while SCCs and melanomas exhibit positive differences instead [16]. These thermal signatures were also studied in relation to the vascularity of the skin lesion. In particular, for BCCs, a strong correlation between local temperature lesion and vascularity was found, suggesting a low metabolic heat production due to the tumor with respect to the variation of its vascularity; for SCCs, the results showed that there is a constant ratio between metabolic heat production and vascularity; while for melanomas, the local temperature increases even though the vascularity of the tumor does not increase, suggesting that there is a higher variation in metabolic heat produced by the tumor [16]. These conclusions are not only useful to correctly identify different kinds of skin cancers through thermal imaging, but they also provide further information for thermal modeling of skin neoplasms.
## 3 Monitoring and Verification
### Simulation Analysis
#### 3.1.1 Stress Field Analysis  
Stress contour maps are acquired by slicing along the tunnel's longitudinal section and excavation direction to comprehend the stress field alterations and distributions post-simulation of tunnel excavation and support. Figure 6 depicts the stress distribution from a cross-sectional perspective.  
Figure 6 shows that the principal stresses on the tunnel excavation face are symmetrical on both the left and right sides. The crown and bottom demonstrate a lenticular distribution, while the arch ribs display a semi-circular distribution on both sides. Stress concentration zones are evident at the crown and arch ribs. The contour map indicates that the stress values at the arch ribs on both sides are comparable and reach a maximum relative to other areas, with values of \(-23.22\) and \(-20.89\) MPa for the left and right sides, respectively. It is also evident from Figure 6 that as tunnel excavation progresses, the stress values around the tunnel cavity remain relatively high, gradually decreasing and spreading outwards. The area behind the tunnel face experiences excavation disturbance, leading to a distinct stress concentration zone with a stress value of \(-15.66\) MPa. Thus, during the excavation process, the stress concentration zone behind the tunnel face requires careful consideration, and implementing appropriate advance support measures becomes imperative.  
Figure 6: ZZ direction stress cloud of section and excavation.