esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture and Test Environment
### Vehicle Tracking  
In object tracking applications, there is the problem of correctly identifying the same object over multiple frames of a video. Deep SORT addresses this issue by assigning a unique identification (ID) to each detected object and by using a combination of appearance and motion information to track the object over time. To achieve the objective of our project, which is the counting of vehicles, we need to track all the vehicles accurately, which is a crucial task in this type of application. The tracking of objects involves detecting the object, identifying it in a frame, and tracking it across all sequences until it leaves the scene. To achieve this task, we chose to use the Deep SORT algorithm (simple online and real-time tracking with a deep association metric), which is a multiobject tracking method. The Deep SORT algorithm uses the spatial and temporal characteristics of the targets to track and maintain the identification of all moving objects in all sequences [18] (as shown in Figure 5). By assigning each detected object a unique ID, Deep SORT ensures that the same vehicle is consistently identified and tracked across multiple frames, allowing for accurate vehicle-counting.  
The Deep SORT algorithm consists of the following steps (as is shown in Figure 6): First, the vehicles are detected using the YOLOv7-tiny. Then comes the multiobject tracking step, where the Kalman filter predicts the trajectories based on the position and speed of the target object; meanwhile, the Hungarian algorithm measures the correlation [19].  
Deep SORT distinguishes itself by using a combination of two convolutional layers, followed by six residual blocks and an appearance descriptor [19]. This makes the system more robust against missing-object occlusions by significantly reducing the number of identity changes. This combination makes Deep SORT an appropriate choice for applications requiring real-time monitoring. Furthermore, Deep SORT has been shown to be effective in handling complex scenarios with significant occlusion, which is a common challenge in tracking vehicles that are using a roundabout. The algorithm is also capable of tracking vehicles with varying speeds and trajectories, making it ideal for real-world applications.  
Additionally, the ability of Deep SORT to maintain the identity of each vehicle as it moves throughout the roundabout helps to improve the accuracy of the vehicle-counting system. Overall, the combination of traditional tracking methods with deep-learning techniques, the ability to handle complex scenarios, and the ability to maintain the identity of each vehicle makes Deep SORT a suitable choice for vehicle tracking for vehicles  
Figure 5: Deep SORT algorithm result, a unique ID is given to each class of detected vehicle.  
using a roundabout, and it contributes to the high accuracy of the proposed vehicle-counting solution.
## 3 Results and Discussion  
### Characteristics of Temporal Variation in NPP, ET, WUE, and TVDI  
Based on Figure 2, the annual values of NPP, ET, WUE, and TVDI during 2000-2020 were obtained for temporal analysis. Annual NPP presented a significant increase in the TMS, with a slope of 1.2 gC\(\cdot\)m\({}^{-2}\) per year (Figure 2a). \(NPP\) reached a maximum value of 84.9 gC\(\cdot\)m\({}^{-2}\) in 2016. Annual ET also exhibited an increasing trend in the study region during 2000-2020, with a slope of 1.6 mm per year (Figure 2b). \(NPP\) displayed a significantly increasing trend, which was in line with the results of previous research that observed improvements in vegetation across Central Asia [39; 40]. These variations have affected ecosystems through various processes, and the increasing temperature and humidification of the climate have led to improvements in vegetation growth [40]. The highest and lowest values of ET appeared in the year 2016 (285.8 mm) and 2008 (208.5 mm), respectively. After 2016, ET showed a decreasing and fluctuating trend. The trend increases obtained for NPP and ET in the TMS are obviously greater than those in Central Asia, which can be highly correlated with the vegetation cover [31]. Moreover, the increase in runoff over the TMS has increased the available water, which can improve both NPP and ET to some extent.  
Annual WUE and its trend during the past 20 years (2000-2020) are shown in Figure 2c. The extreme low value of WUE was 0.66 gC\(\cdot\)kg\({}^{-1}\).H\({}_{2}\)O in 2005 and the extreme high value was 0.78 gC\(\cdot\)kg\({}^{-1}\).H\({}_{2}\)O in 2011. WUE was at a higher level from 2000 to 2011 than during 2012 to 2020. WUE started to decrease in 2011, after the maximum value was reached. Overall, the regional annual series for WUE displayed an unremarkable decreasing trend. The decrease in WUE can be explained by the faster rate of increase in ET than that in NPP. In addition, the large areas covered by cotton, and the changes in cotton planting areas and irrigation patterns, may be the dominant reasons for the decrease in WUE [31]. The results for TVDI show that the TMS experienced a decreasing trend in drought events in terms of frequency and intensity. The study area was categorized as arid during 2000-2010, with the average TVDI value (0.62) being between 0.6 and 0.8, and the changed to slight aridity during 2011-2020, when the TVDI value was between 0.4 and 0.6.  
Figure 2: _Cont._
# Changes in Propeller Shaft Behavior by Fluctuating Propeller Forces during Ship Turning
## 2 Approaches Adopted
### Measurement and Data Analysis Procedures
#### 2.3.2 Measurement Procedures  
The test conditions of the case study ship were established based on SOLAS (the Marine Life Safety Convention) (IMO, 2014) II-1, Chapter 29, Paragraph 3 (rudder test).  
However, the purpose of this test was not to verify the rudder's torque capability, but to evaluate the impact of the eccentric propeller thrust fluctuation. This is caused by the sudden change in the stern's wakefield when the ship moves forward and has an effect on the propulsion shaft's stability.  
\begin{table}
\begin{tabular}{c c} \hline
**Sensor Type** & **Sensor Name and Mounted Position** \\ \hline \multirow{3}{*}{Laser sensor} & LA (in the vicinity of the forward-stern tube seal box) \\  & VDM18-300/32/105/122 of PEPPERL+FUCHS. \\  & Accuracy: \(\pm\)0.2 mm. \\ \hline \multirow{3}{*}{Strain gauge} & SG (in the vicinity of the forward-stern tube seal box) \\  & WFLA-3 of the Tokyo Measuring Instrument Lab. \\ \cline{1-1}  & Gauge factor: \(2122.12\pm 1\%\). \\ \hline \end{tabular}
\end{table}
Table 2: Sensor connections.  
Figure 5: Location of the strain gauge and laser sensor.  
Therefore, as given in Table 3 and Figure 7, the measurement was made by going straight ahead (at a 0\({}^{\circ}\) rudder angle) and then turning rapidly (at a 12\({}^{\circ}\) rudder angle) until the head of the ship changed by 90\({}^{\circ}\), and then the ship went back to going straight ahead (at a 0\({}^{\circ}\) rudder angle).  
Figure 6: Installation of the sensors and the telemetry system in the engine room. (**a**): Laser displacement sensor and strain gauge installation. (**b**): Strain gauge telemetry transmitter installation.  
Figure 7: Sea-trial procedure for the case study ship.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test No.** & **Condition** & **Load** & **Rpm** \\  & & & **(Approximately)** \\ \hline
1 & Static & 0 & 0 \\
2 & Steady state & 25\% & 57.3 \\
3 & Steady state & NCR & 78.3 \\
4 & Steady state & 85\% & 90.3 \\
5 & Steady state & MCR & 95 \\
6 & Straight ahead (Midship 0\({}^{\circ}\)) & NCR & 83.7 \\
7 & Command rudder angle port 12\({}^{\circ}\) & NCR & 84.9 \\
8 & Ship turning (Rudder angle port 12\({}^{\circ}\) steady) & NCR & 84.9 \\
9 & Ship’s heading 90\({}^{\circ}\) (Then midship 0\({}^{\circ}\)) & NCR & 84.9 \\
10 & Straight ahead (Midship 0\({}^{\circ}\)) & NCR & 84.9 \\
11 & Command rudder angle starboard 12\({}^{\circ}\) & NCR & 84.9 \\
12 & Ship turning (Rudder angle starboard 12\({}^{\circ}\) steady) & NCR & 84.9 \\
13 & Ship’s heading 90\({}^{\circ}\) (Then midship 0\({}^{\circ}\)) & NCR & 84.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test conditions of the case study ship.  
In addition, the ship's speed was limited to the NCR in consideration of the ship's operational conditions, the sea states, and the ship's seaworthiness.
## Nomenclature  
\begin{tabular}{l l l l} ATF & lubrication oil & \(R_{i}\) & inner radius of friction pair (m) \\ CFD & computational fluid dynamics & \(R_{o}\) & outer radius of friction pair (m) \\ NUC & non-uniformity coefficient & \(T\) & temperature (\({}^{\circ}\)C) \\ \(A_{\rm red}\) & non-groove area ratio & \(V\) & linear velocity difference (m/s) \\ \(c_{s}\) & damping coefficient (N\(\cdot\)s/m) & \(x\) & displacement (m) \\ \(d_{m}\) & thickness of friction material (m) & \(\dot{x}\) & velocity (m/s) \\ \(e\) & recovery coefficient & \(\ddot{x}\) & acceleration (m/s\({}^{2}\)) \\ \(E^{\prime}\) & Young's modulus (Pa) & \(Z\) & number of friction pairs \\ \(h\) & nominal oil film thickness (m) & \(\beta\) & asperity radius (m) \\ \(H\) & film thickness ratio & \(\delta\) & gap (m) \\ \(H_{fd}\) & thickness of friction disc (m) & \(\Delta\omega\) & angular speed difference (rad/s) \\ \(H_{sd}\) & thickness of separator disc (m) & \(\xi\) & local relative indentation (m) \\ \(I_{f1}\) & inertia of driven end (kg\(\cdot\)m\({}^{2}\)) & \(\dot{\xi}^{(-)}\) & initial indentation velocity (m/s) \\ \(K_{0}\) & stiffness of impact (N/m) & \(\eta\) & dynamic viscosity (Pa\(\cdot\)s) \\ \(K^{\prime}\) & contact coefficient & \(\mu\) & coefficient of friction \\ \(m_{0}\) & weight of piston (kg) & \(\mu_{\rm spline}\) & spline friction coefficient \\ \(m_{1}\) & weight of separator disc (kg) & \(\sigma\) & roughness (m) \\ \(m_{2}\) & weight of friction disc (kg) & \(\phi_{r}\) & pressure flow factor \\ \(M_{\rm R}\) & resistance torque (N\(\cdot\)m) & \(\phi_{f}\),\(\phi_{f_{\rm S}}\) & shear stress factors \\ \(n\) & impact coefficient & \(\ddot{\Psi}\) & permeability (m\({}^{2}\)) \\ \(N\) & asperity density (/m\({}^{2}\)) & \(\kappa\) & plastic deformation coefficient \\ \(P\) & applied pressure (Pa) & \(\rho\) & density (kg/m\({}^{3}\)) \\ \end{tabular}
# Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
## 4 Experimental Study
### Performance Evaluation Metrics  
The bearing life prediction problem is a regression problem in which the RUL of a bearing is the value of some continuous response variable. For better evaluation of the predictive effectiveness of rolling bearing RUL, mean absolute error (MAE), root mean squared error (RMSE) and scoring function are used as evaluation metrics to assess the effectiveness of the model; the formulas are as follows:  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\left|f(i)-\hat{f}(i)\right| \tag{12}\]  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left[f(i)-\hat{f}(i)\right]^{2}} \tag{13}\]  
\[Error_{i}=\frac{RUL_{i}-\widehat{RUL_{i}}}{RUL_{i}}(100\%) \tag{14}\]  
\[A_{i}=\begin{cases}exp^{-ln(0.5)\cdot(Error_{i}/5)},\ Error_{i}\leq 0\\ exp^{ln(0.5)\cdot(Error_{i}/20)},\ Error_{i}>0\end{cases} \tag{15}\]  
\[Score=\frac{1}{N}\sum_{i=1}^{N}A_{i} \tag{16}\]  
where \(N\) denotes the count of samples, \(f(i)\) denotes the true RUL value in the \(i\)-th sample, \(\hat{f}(i)\) denotes the prediction value of RUL in the \(i\)-th sample, \(RUL_{i}\) represents the true RUL value in the \(i\)-th sample, \(\widehat{RUL_{i}}\) denotes the predicted RUL value in the \(i\)-th sample, \(Error_{i}\) represents the RUL percentage error in the \(i\)-th sample.  
The image of the functional relationship _Score_ between and _Error_ is illustrated in Figure 10. According to the graph, it is clear that the function values of positive and negative errors are asymmetric. The positive errors score higher than negative errors. In bearing RUL assessment, positive errors represent predicted RUL lower than actual RUL,i.e., life underestimation, and negative errors represent predicted RUL higher than actual RUL, i.e., life overestimation. The overestimation of bearing RUL will have risk, so life underestimation is more realistic than life overestimation.
## 2 Materials and Methods
### Thermal Effusivity  
As a good alternative for obtaining the thermal parameters of different substances, photothermal techniques (PT) have been used to obtain properties such as thermal effusivity (\(e\)), diffusivity (\(\alpha\)), conductivity (\(\kappa\)), and specific heat (\(c\)). In the case of bio-oils, previous knowledge of thermal performance allows the prediction of specific conditions. Thus, the thermal parameters of a wide variety of samples could be obtained by using the photoproelectric (PPE) technique, which is commonly used for thermal characterization as it uses a frequency-modulated light and a pyroelectric sensor as a thermal wave detector [7,8].  
When using PPE techniques, it is possible to obtain the effusivity and thermal diffusivity of liquid samples through reliable and straightforward configurations of techniques such as those known as the inverse photoproelectric technique (IPPE) and thermal wave resonance cavity (TVRC). Therefore, different samples can be studied by using these configurations in liquids such as oils or mixtures [7,8,29].  
As seen in Figure 1, the inverse photoproelectric configuration (IPPE) was used to obtain the thermal effusivity of the sample. This technique is based on the incident radiation measured directly on the surface of a pyroelectric (PE) sensor, in which the light is modulated by the internal oscillator of a lock-in amplifier. The IPPE signal can be obtained as a function of the light modulation frequency (\(f\)), which can be varied in a specific range. When the frequencies are such that the sample is considered to be thermally thick (\(l_{s}>\mu_{s}\)) and the PE sensor is optically opaque, the IPPE signal is expressed as shown in Equation (1) [8,29]. It is also important to know that the experimental setup was always temperature controlled.  
\[\theta(\omega)\ =\ \frac{\Big{(}1-e^{\sigma_{p}l_{p}}\Big{)}(1+b)+\Big{(}e^{- \sigma_{p}l_{p}}-1\Big{)}(1-b)}{(g-1)e^{-\sigma_{p}l_{p}}(1-b)+(1+g)e^{\sigma _{p}l_{p}}(1+b)} \tag{1}\]  
where \(\omega\ =\ 2\pi f\), \(\sigma_{j}\ =\ (1+i)/\mu_{j}\), with the thermal diffusion length defined as \(\mu_{j}\ =\ (\alpha_{j}/(\pi f))^{1/2}\), where \(\alpha_{j}\) and \(l_{j}\) are the thermal diffusivity and thickness of the \(j-th\) element in the PE cell, respectively;\(f\) is the light modulation frequency, and \(b\ =\ e_{s}/e_{p}\), \(g\ =\ e_{g}/e_{p}\) with \(e_{s}\), \(e_{g}\) and \(e_{p}\) are the thermal effusivities of the sample, gas (air), and PE sensor, respectively.  
Figure 1: IPPE technique: experimental setup.  
Pyroelectric signals obtained from experimentation with air, the analyzed sample, and a specific frequency range were used to calculate the thermal effusivity by using Equation (1). Therefore, the experimental data and Equation (1) were used to calculate the thermal effusivity values [8,29].
# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 4 System Implementation
### Data Preprocessing and Quantity Inventory  
After multiple rounds of driving, we accumulated a substantial number of videos containing pedestrian crossings. However, these raw video files require preprocessing before they can be used for model training. As an initial step, each video is converted into still images at a frame rate of 30 frames per second as image-based input is required for deep learning. To minimize dataset noise, frames lacking visible pedestrian crossings are manually excluded prior to annotation. During manual labeling, only unobstructed regions of pedestrian crossings--those not occluded by vehicles or pedestrians--are annotated to avoid introducing ambiguity. These annotations are created using the LabellImg tool, with each visible segment labeled with a bounding box and assigned the class 'pedestrian crossing'. In the subsequent detection phase, fragmented bounding boxes corresponding to the same crossing are merged to form complete representations of pedestrian crossings.  
An analysis of the collected dataset reveals a significant class imbalance in terms of weather conditions. As shown in Figure 5, images captured on sunny days total 41,912, representing 94.06% of the dataset, while only 2648 images (5.94%) were recorded on rainy days. This severe imbalance is attributed to the practical challenges of collecting data under adverse weather conditions, such as safety concerns and limited visibility during driving. To address this imbalance and ensure the model performs reliably across diverse scenarios, we employ CycleGAN to generate synthetic images representing rare conditions--such as rain, nighttime, and glare. These generated samples serve to augment the training dataset, increasing its diversity and improving the model's generalization capabilities. Balancing the dataset is essential for achieving consistent detection accuracy across varying real-world environments.
## 4 Simulation Model
### Performance Metrics
#### 4.5.4 Comfort  
Comfort was evaluated by the degree of discomfort of passengers proposed by [PERSON] et al. [31]. They showed that a passenger's ride comfort due to speed fluctuations depends on both acceleration and jerk (change in acceleration rate) and used these factors to derive a discomfort index. The discomfort level \(d(t)\) at time \(t\) is given by  
\[d(t)=\beta_{1}a_{p+}(t)+\beta_{2}a_{p-}(t)+\beta_{3}j_{r+}(t)+\beta_{4}j_{r-}(t), \tag{11}\]  
where \(a_{p+}(t)\left[\text{m}/\text{s}^{2}\right]\) is the positive peak value of acceleration between \(t-3\left[\text{s}\right]\) and \(t\), \(a_{p-}(t)\left[\text{m}/\text{s}^{2}\right]\) is the absolute value of the negative peak value of acceleration between \(t-3\left[\text{s}\right]\) and \(t\), \(j_{r+}(t)\left[\text{m}/\text{s}^{3}\right]\) is the effective value when the average jerk between \(t-3\left[\text{s}\right]\) and \(t\) is positive, and \(j_{r-}(t)\left[\text{m}/\text{s}^{3}\right]\) is the effective value when the average jerk between \(t-3\left[\text{s}\right]\) and \(t\) is negative. In [31], two combinations of coefficients for each term were derived, one for a passenger in a resting posture and the other for a passenger in a reading posture. In this study, we used the coefficient combination for the reading posture, \(\beta_{1}=0.19\), \(\beta_{2}=0.53\), \(\beta_{3}=0.27\), and \(\beta_{4}=0.34\). Since the units of acceleration and jerk are different, the definition of \(d(t)\) is tricky, and the unit of \(d(t)\) generally cannot be given. We treat \(d(t)\) as a unitless number as [31] does.  
To calculate \(d\) using Equation (11), we calculated the acceleration by firstly applying the least-squares method to smooth the speed samples obtained from SUMO [32] and differentiating the result numerically using the central difference method. Then, we calculated the jerk by differentiating the acceleration in the same way. We calculated the discomfort per vehicle by averaging the cumulative discomfort of all vehicles arriving at the endpoint. Since we wanted to focus on cases where the passengers feel high levels of discomfort, we obtained the discomfort level \(D\) in each simulation as follows:  
\[D=\frac{1}{\left|A\right|}\sum_{i\in A}\int_{t_{\text{departure},i}}^{t_{ \text{arrival},i}}\max\{d_{i}(t)-d_{\text{thresh}},\ 0\}dt, \tag{12}\]  
where \(A\) is the set of vehicles arriving at the endpoint, \(t_{\text{departure},i}\) is the time when vehicle \(i\) departed from the warm-up zone, \(t_{\text{arrival},i}\) is the time when vehicle \(i\) arrived at the endpoint, and \(d_{\text{thresh}}\) is a threshold of high discomfort. In this study, \(d_{\text{thresh}}=4\).
## 4 Experiments and Analysis
### Ablation Studies  
In this section, we describe the investigation of the performance of the various proposed strategies. The Cityscapes converted to Foggy Cityscapes were used to evaluate our model. The Cityscapes dataset was used as the source domain, while the Foggy Cityscapes dataset was used as the target domain. For fair comparison, all experiments were performed under the same settings, and we refer the to Gain columns.  
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline
**Methods** & **Detector** & **Person** & **Rider** & **Car** & **Truck** & **Bus** & **Train** & **Motor** & **Bike** & **mAP** \\ \hline DA-Faster [1] & Faster R-CNN & 31.9 & 41.6 & 46.4 & 20.1 & 32.0 & 17.5 & 23.1 & 34.6 & 30.9 \\ GPA [27] & Faster R-CNN & 32.9 & 46.7 & 54.1 & 24.7 & 45.7 & 41.1 & 32.4 & 38.7 & 39.5 \\ SFA [23] & Faster R-CNN & 46.5 & 48.6 & 62.6 & 25.1 & 46.2 & 29.4 & 28.3 & 44.0 & 41.3 \\ DIR [28] & Faster R-CNN & 36.9 & 45.8 & 49.4 & 28.2 & 44.6 & 34.9 & 35.1 & 38.9 & 39.2 \\ MS- DAYOLO [17] & YOLOv4 & 8.6 & 45.5 & 55.9 & - & - & - & 28.8 & 36.5 & 41.1 \\ SSDA-YOLO [29] & YOLOv5-s & 43.8 & 44.9 & 53.8 & 27.3 & 45.6 & 34.7 & 34.3 & 38.8 & 40.4 \\ \hline _Source only_ & YOLOv5-s & 37.6 & 38.7 & 59.7 & 14.3 & 33.5 & 11.3 & 2.9 & 31.5 & 28.7 \\ _Ours w//\(L_{cls}\)_ & YOLOv5-s & 48.0 & 50.0 & 67.5 & 18.8 & 42.8 & 12.5 & 22.2 & 42.1 & 38.0 \\ _Ours w/trans_ & YOLOv5-s & 52.9 & 56.5 & 70.0 & 17.6 & 39.7 & 13.8 & 28.2 & 48.0 & 40.9 \\ CAST-YOLO(Ours) & YOLOv5-s & **54.0** & **58.9** & **70.1** & 21.4 & 43.2 & 19.2 & 28.6 & 51.1 & **43.3** \\ \hline _Oracle_ & YOLOv5-s & 51.2 & 49.2 & 71.9 & 40.1 & 57.7 & 56.3 & 40.1 & 42.3 & 51.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of different methods on the Foggy Cityscapes validation set for Cityscapes \(\rightarrow\) Foggy Cityscapes transfer.  
Figure 6: Confusion matrix. The horizontal axis from left to right indicates the ground truth for ‘car’, ‘truck’, ‘person’, ‘mbike’, ‘train’, ‘rider’, ‘bike’, ‘bus’, and ‘False Positive’, respectively. The vertical axis from top to bottom indicates the prediction of ‘car’, ‘truck’, ‘person’, ‘mbike’, ‘train’, ‘rider’, ‘bike’, ‘bus’, and ‘False Negative’, respectively.
# Recent Advances in Black Silicon Surface Modification for Enhanced Light Trapping in Photodetectors
## 2 Wet Methods for Black Silicon Fabrication
### Metal-Assisted Chemical Etching (MACE)
#### 2.1.2 Copper-Assisted Chemical Etching  
In their respective studies, [PERSON] et al. and [PERSON] et al. explored the impacts of MACE on the optical characteristics of BSi [31; 32]. It was found that Cu-assisted chemical etching achieved a reflectivity of 5.92% after 90 min, although this result varied across the sample as etching time increased. Notably, a lower reflectivity of 3.96% was observed after 5 min of etching, attributed to the formation of more homogenous pores that minimized reflectance. Similarly, [PERSON] et al. employed Cu-assisted chemical etching to engineer various surface textures on monocrystalline Si solar cells [32]. Their method effectively produced an array of structures including inverted pyramids, nanopores, upright pyramids, V-grooves, and hybrid structures. This innovative one-step technique not only facilitated the creation of random inverted-pyramid structures on Si samples but also enhanced light-trapping capabilities, significantly reducing reflectance and lowering production costs. Together, these studies underscore the Cu-assisted chemical etching potential to optimize the surface properties, thus improving the light trapping of Si-based solar cells.