esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure
## 2 Materials and Methods
### Labeling Strategy  
For determining the left and right feet, a senior expert in plantar pressure images with more than 15 years of experience in footprint imaging supervised the process (the 487 left foot images 487 and right foot images). The abnormal foot progression angle in the plantar pressure image may complicate the recognition of the left and right feet. Furthermore, the defective footprint features may introduce complications due to an incomplete footprint pattern, particularly in the plantar region, that challenge the prediction of the left and right feet [30]. Nevertheless, object detection achieved good results in medical images [22]. For the plantar pressure, this study used bounding box annotation to determine the left and right feet through different object detection models to achieve better accuracy [18; 24]. The bounding box is used to localize and classify the left and right feet based on manual labeling to obtain a prediction.  
The labeling process depends on the footprint features; labeling in the maximum area of plantar pressure images is recommended to make predictions more precise [29] (Figure 4A). However, abnormal foot progression angle and defective footprints proved challenging. Part of the plantar pressure images was an abnormal foot progression angle wherein the direction of foot placement was more or less than 15\({}^{\circ}\)[18]. On the other hand, defective footprint patterns cannot provide the full pressure distribution of the footprint pattern, and sometimes the plantar pressure images cannot provide full footprint patterns during locomotion [31]. For example, plantar pressure images with pressure in the rearfoot and forefoot in the metatarsal have limitations such as incomplete toe or forefoot area (Figure 4B) and abnormal foot progression angle with high pressure on the forefoot and midfoot and less pressure on the rearfoot (Figure 4C). Defective footprint images with high pressure on heels lost pressure in the midfoot and on the rear and forefoot (Figure 4D).  
Figure 3: The illustration of foot profile box for left and right feet in plantar pressure image detection with the ResNet-50, DenseNet, YOLOv3, YOLOv4, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x models.
# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 3 Preliminaries
### Evaluation Metrics
#### 3.6.1 Confusion Matrix and Thresholds  
The confusion matrix is a tool commonly used in machine learning to display statistical information on the predictions of a model compared to the actual classifications [55], which refers to the actual similarity score generated by benchmark. It reports four primary values: true positive (TP), true negative (TN), false positive (FP), and false negative (FN). TP represents the number of instances that the machine learning model correctly predicted as belonging to a specific class, which aligns with the actual results of the reference alignment. Conversely, TN indicates the number of instances that the model correctly predicted as not belonging to a specific class, in agreement with the actual reference alignment results. FP represents the number of instances that the model predicted as belonging to a particular class, but the reference alignment results indicate otherwise. FN indicate the number of instances that the model predicted as not belonging to a specific class, whereas the actual results of the reference alignment indicate the opposite.  
In this study, we created confusion matrices to examine the performance of the proposed method at three thresholds of 0.70, 0.80, and 0.90. In the context of ontology alignment evaluation, a threshold refers to the optimal similarity score value that enables us to assess whether aligned ontologies are similar or not. A higher threshold value indicates a stricter evaluation criterion, leading to more dependable final outcomes. Conversely, a lower threshold value results in a more lenient evaluation criterion, leading to less trustworthy results.
## 2 Underwater Sensor Networks
### Architecture  
UWSNs have a number of limitations, such as their harsh environment, high mobility, long propagation delays and limited communication bandwidth. Under such conditions, communication failures arise more frequently in UWSNs than they do in TWSNs. Therefore, a technique is needed for alternating the direct communication between the sensor nodes and surface station. A clustering or hierarchical protocol can improve scalability, energy efficiency and the lifetime of the network in UWSNs.  
The architecture of underwater wireless sensor networks is presented in Figure 5[1]. A group of sensor nodes is anchored to the bottom of the ocean. Sensor nodes are interconnected to one or more head nodes via wireless acoustic communication. Head nodes are devices in charge of relaying data from sensor nodes located at the bottom of the ocean to AUVs indirectly or a surface sink directly. AUVs dive between the surface and go to the bottom of the ocean. They communicate and relay the collected data sent from head nodes to the surface sink to reduce the energy consumption of the head nodes and the data transmission latency. The surface sink is equipped with an acoustic transceiver  
Figure 4: Underwater platforms.  
capable of handling multiple communication instances with AUVs. The surface sink also supports a long-rage RF and satellite transmitter to communicate with the onshore control center. The onshore control center collects data from the surface sink and manages the underwater network.
# The Development of the Pipe Jacking Guidance Technology
## 3 Laser Target Method
### Research Status  
Lixin Measurement (Shanghai, China) Co., Ltd.'s RMS-D (linear) automatic guiding system adopts fully automatic measurement technology, ensuring high measurement accuracy. The instruments are characterized by their affordability, compact size, minimal pipe aperture requirements and wireless communication capabilities. See Table 1 for details. However, since the laser can only propagate along the straight line, and with the increase of distance, resulting in laser spot discoloration, it seriously affects the guiding accuracy, so the method is suitable for short-distance straight-line construction. The TUnIS Navigation MT\({}^{\rm{Laser}}\) from VMT Germany is suitable for short straight-line pipe jacking construction where the length of the pipe jacking is less than 250 m. TUnIS Navigation MT\({}^{\rm{HydroLLevel}}\) adds a static leveling to TUnIS Navigation MT\({}^{\rm{Laser}}\). It calculates the vertical position changes of the pipe jacking machine in real time during the jacking process and integrates the vertical position data information into the data of the laser target. This improves the  
Figure 7: Schematic diagram of the horizontal and vertical deviation principle.  
guidance accuracy and the pipe jacking distance. The system can meet the requirements of straight pipe jacking guidance within 400 m.  
In order to improve the guidance measurement accuracy of this method, the research focuses on laser targets. The ZED's laser target utilizes photoelectric sensor measurement technology inside. The laser target developed by Huazhong University of Science and Technology (HUST) and Shanghai Tunneling Engineering Company Limited (STEC) adopts a non-contact photoelectric detection method. It is inevitable that the laser spot will suffer from aberrations, lack of shape and other phenomena due to problems such as excessive distance during laser propagation, high humidity in the pipeline and deviations in the placement of the CCD camera. [PERSON] [9] established a geometric transformation correction model to improve the laser spot perspective distortion caused by the skewed installation position of the industrial camera. The grayscale centroid algorithm was used to calibrate the center coordinate position, and the actual coordinate position was obtained through coordinate transformation. Experiments showed that the system accuracy of this method was better than 0.1\({}^{\circ}\). [PERSON] [10,11] proposed a dual-screen visual target system, which used a front and rear dual imaging screen, two industrial cameras arranged relative to each other and a built-in dual-axis inclinometer to measure roll and pitch angle information.
# A Study on Analyzing Travel Characteristics of Micro Electric Vehicles by Using GPS Data
## 3 Methodology
### Travel Behavior Analysis  
#### 3.1.1 GPS Data Collection  
In the experiment analyzing the travel characteristics of micro-EVs, the vehicles were utilized across three service types: a shared transport service, delivery service, and public service. The micro-EV service was planned around the service operation organization's base, and accordingly, micro-EVs were operated in different areas depending on the service type. The shared transport service was provided in regions where high user demand was anticipated, such as Jeju Island, local governments in the Jeolla province (including Mokpo, Yeonggwang, Muan, etc.), and the capital city of Seoul and surrounding cities, with the purpose of integrating with public transportation. Specifically, in Jeju Island, the service was provided to improve the commuting convenience for students and staff of schools in the area. The delivery service was provided in the Yeonggwang and Muan regions of Jeollanam-do. Although the area is not large, the presence of industrial complexes and a nuclear power plant was expected to generate considerable service demand. The micro-EVs used for the delivery service were utilized by workers who previously performed deliveries using motorcycles. Micro-EVs for public service were provided to government employees working in various local governments, ensuring alignment with the service's purpose.  
The number of micro-EVs differed by service type in the empirical study; fifty-seven micro-EVs were used for the shared transport service, thirteen micro-EVs were used for the delivery service, and thirty-six micro-EVs were used for the public service. Every micro-EV was equipped with a GPS device, and various data, including the location, speed, and gyroscope of the micro-EVs, were collected every second. Among the micro-EVs, GPS data from a few vehicles were not collected due to device issues. As a result, vehicles without GPS data were excluded from the analysis. The number of vehicles for each service type and micro-EV model is shown in Table 3. Specifically, two micro-EVs from the shared transport service and one micro-EV from the public service were excluded from the analysis.  
The structure of the GPS data is shown in Table 4. Figure 1a shows the locations of the micro-EV every second in the area where the micro-EV was driven, and Figure 1b shows the enlarged part of the path where the micro-EV was driven. Every dot in Figure 1 indicates the position of the micro-EV every second. The GPS data of micro-EVs were collected from January to October 2021 for all service types and used in this study to derive the travel behaviors of the devices. The GPS data were transformed into the dataset to analyze micro-EVs' travel behaviors because the dataset was initially inadequate for the purposes of directly exporting trip and trip chain characteristics. Details of the data transformation will be described in a later chapter.
# Dual Roles of Water Availability in Forest Vigor:
###### Abstract  
Water availability is one of the most important resources for forest growth. However, due to its complex spatio-temporal relationship with other climatic factors (e.g., temperature and solar radiation), it paradoxically shows both positive and negative correlations (i.e., dual roles) with forest vigor for unknown reasons. In this study, a multiperspective analysis that combined the deficit of the Normalized Difference Vegetation Index (_d NDVI_) and multitimescale Standardized Precipitation Evaptotranspiration Dirac (_SPEI_) was conducted for the forests in China, from which their correlation strengths and directions (positive or negative) were linked with spatio-temporal patterns of environmental temperature (T) and water balance (_WB_) (i.e., precipitation minus potential evapotranspiration). In this way, the reasons for the inconsistent roles of water were revealed. The results showed that the roles of water availability greatly depended on \(T\), _WB_, and seasonality (i.e., growing or pregrowing season) for both planted and natural forests. Specifically, a negative role of water availability mainly occurred in regions of \(T\) below its specific threshold (i.e., \(T\leq T_{\text{threshold}}\)) during the pregrowing season. In contrast, a positive role was mainly observed in warm environments (\(T>T_{\text{threshold}}\)) during the pregrowing season and in dry environments where _WB_ was below its specific threshold (i.e., \(WB\leq WB_{\text{threshold}}\)) during the growing season. The values of \(T_{\text{threshold}}\) and \(WB_{\text{threshold}}\) were related to the vegetation type, with \(T_{\text{threshold}}\) ranging from 1.3 to 4.7\({}^{\circ}\)C and \(WB_{\text{threshold}}\) ranging from 129.1 to 238.8 mm/month, respectively. Our study revealed that the values of \(T_{\text{threshold}}\) and \(WB_{\text{threshold}}\) for a specific forest were stable, and did not change with the _SPEI_ time-scales. Our results reveal the dual roles of water availability in forest vigor and highlight the importance of environmental climate and seasonality, which jointly affect the roles of water availability in forest vigor. These should be considered when monitoring and/or predicting the impacts of drought on forests in the future.  
forest vigor; water availability; positive and negative correlations; environmental climate data; temperature and water balance thresholds +
Footnote †: journal: remote sensing
###### Abstract  
In this paper, we present a novel autonomous vehicle (AV) localization design and its implementation, which we recommend to employ in challenging navigation conditions with a poor quality of the satellite navigation system signals and computer vision images. In the case when the GPS signal becomes unstable, other auxiliary navigation systems, such as computer-vision-based positioning, are employed for more accurate localization and mapping. However, the quality of data obtained from AV's sensors might be deteriorated by the extreme environmental conditions too, which infinitely leads to the decrease in navigation performance. To verify our computer-vision-based localization system design, we considered the Arctic region use case, which poses additional challenges for the AV's navigation and might employ artificial visual landmarks for improving the localization quality, which we used for the computer vision training. We further enhanced our data by applying affine transformations to increase its diversity. We selected YOLOv4 image detection architecture for our system design, as it demonstrated the highest performance in our experiments. For the computational platform, we employed a Nvidia Jetson AGX Xavier device, as it is well known and widely used in robotic and AV computer vision, as well as deep learning applications. Our empirical study showed that the proposed computer vision system that was further trained on the dataset enhanced by affine transformations became robust regarding image quality degradation caused by extreme environmental conditions. It was effectively able to detect and recognize images of artificial visual landmarks captured in the extreme Arctic region's conditions. The developed system can be integrated into vehicle navigation facilities to improve their effectiveness and efficiency and to prevent possible navigation performance deterioration.  
computer vision; robust vehicle localization; data quality; machine learning +
Footnote †: journal: Applied sciences
# Possibility of Bridge Inspection through Drive-By Vehicles
## 5 Effect of Road Roughness on Drive-By Bridge Inspection  
According to the ISO standard 8608, road surface profiles can be represented with a zero-mean normal stationary ergodic random process described by their Power Spectral Density. The following function (Equation(1)) can describe the PSD:  
\[G(n)=G(n_{0})\bigg{(}\frac{n}{n_{0}}\bigg{)}^{-2}. \tag{1}\]  
The \(G(n)\) is the power spectral density for the spatial frequency \(n\) and \(G(n_{0})\) is the power spectral density for the reference spatial frequency \(n_{0}\) = 0.1 m\({}^{-1}\). The value of \(G(n_{0})\) represents the road profile class, as shown in Table 8.  
Road profiles could be generated as the sum of a series of harmonics (Equation (2)):  
\[y_{1}(x)=\sum_{i}^{N}\sqrt{2G(n_{i})\Delta n}cos(2\pi n_{i}x+\varnothing_{i}) \tag{2}\]  
where \(\phi_{i}\) is the random phase angle uniformly distributed from 0 to 2\(\pi\). This expression employs \(N\) frequencies between two values \(n_{min}\) and \(n_{max}\) and the increment value (\(\Delta\)n) is defined as Equation (3),  
\[\Delta n=\frac{n_{max}-n_{min}}{N} \tag{3}\]  
In this study, Class B road profile was chosen to be simulated in the 3D VBI model. The profile was generated through Matlab, then written into the 3D bridge model through the ABAQUS input file. The steps used in road roughness generation could be summarized as follows:  
1. Establish the original bridge in ABAQUS, and generate an input file of the model;
2. Generate a 2D roughness profile in the ABAQUS with the same node points as the ABAQUS bridge model. The profile is generated line by line in the vehicle moving direction using Equation (2). Each line is different from the other, but the trend of each line is the same.
3. Reshape the roughness amplitude matrix into one column and replace the node coordinate inputs in the ABAQUS input file with the generated roughness node coordinates.  
Figure 18 shows a generated road roughness from Matlab for a class B road roughness. Figure 19 shows the power spectrum density (PSD) plot of the generated roughness, which displays a fitted linear relationship between spatial frequencies and roughness profile PSDs with a fitting coefficient close to \(-2\). The \(-2\) slope obtained verifies Equation (1) and the road profile generated.  
\begin{table}
\begin{tabular}{c c} \hline
**Road Class** & \(G(n_{0})\) **(m\({}^{3}\))** \\ \hline A (very good) & \(1.600\times 10^{-5}\) \\ B (good) & \(6.400\times 10^{-5}\) \\ C (medium) & \(2.560\times 10^{-4}\) \\ D (poor) & \(1.024\times 10^{-3}\) \\ E (very poor) & \(4.096\times 10^{-3}\) \\ \hline \end{tabular}
\end{table}
Table 8: Road roughness classification.
## 4 Results
### High-Sensitivity Acquisition Latency
#### 4.2.1 Tests Results Using the TCXO
In line with this, the acquisition was configured as shown in Table 11.  
When utilizing a TCXO with the configuration outlined in Table 11, the acquisition process takes a maximum of 14.1 s to transition from stage 1 to stage 2 in the worst-case scenario. Considering that the worst-case Doppler shift rate experienced by a static receiver on the Earth's surface is approximately 1 Hz/s [48], the Doppler frequency of the received signal may change by 14.1 Hz during the transition form stage 1 to stage 2. The Doppler search space used in the second stage is \(\pm\)15 Hz, which is larger than 14.1 Hz. Therefore, any signal detected in the first stage can be detected in the second stage. The limitation of the Doppler search space to \(\pm\)100 Hz necessitates the use of CFO compensation and correction, due to the large CFO observed in TCXO 2 and TCXO 3. For this reason, the receiver was tested using the two-stage acquisition process configured according to Table 11, along with the CFO estimation and correction procedure. The receiver successfully acquired the weak GNSS signals listed in Table 12, initiated tracking of them, and detected their telemetry preambles. The advantage of utilizing the second acquisition stage became evident, as the receiver failed to track the detected signals if the second stage was disabled. When using the TCXOs, some channels experienced occasional loss of lock during tracking. This issue was related to the configuration of the tracking loops (see Appendix A.1) and the measured TCXO frequency stability shown in Table 10. This problem did not occur when using the OCXO (see Section 4.2.2). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage** & **Doppler Search Space** & **Measured Latency** \\ \hline \multirow{3}{*}{Stage 1} & \(\pm\)100 Hz around the Doppler frequency & initial part of sample capture: 100 ms \\  & predicted using assistance data, in steps & 7 non-coherent combinations: 14 s \\  & of 5 Hz & Total: 14.1 s \\ \hline \multirow{3}{*}{Stage 2} & \(\pm\)15 Hz around the Doppler frequency & initial part of sample capture: 100 ms \\  & estimated in step 1, in steps of 5 Hz & 7 non-coherent iterations: 2 s \\ \cline{1-1}  & & Total: 2.1 s \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Acquisition latency using a TCXO.
## 4 Analysis  
From Figure 3, the spindle reliability curve based on the model presented in this paper \(R(t|X(t))\) declines slower than the curve established by the modified maximum likelihood parameter estimation method \(R(t)\). Assuming that the motorized spindle works 8 h a day and 252 days a year, the reliability of the motorized spindle is 0.575 and 0.4944, respectively, when it works for 1 year. It indicated that the reliability calculated by the competing risk model established in this paper is higher than the other one. It is more coincides with the high-reliability characteristic of the motorized spindle.  
From Figure 4, it can be seen that the difference increases at first and then decreases, reaching its maximum at about 2200 h with the value 0.08, which is very small, indicating the rationality of the proposed method. In Figure 5, \(R(t)\) is also within the confidence interval of product reliability obtained by the method proposed in this paper. In addition, according to the correlation index test method, the correlation index of the model proposed in this paper is 0.79, and the correlation index of the model obtained by the modified maximum likelihood estimation method is 0.72. This shows that the fitting of the method in this paper is closer to reality.  
As can be seen from Table 3, \(T_{R(t|X(t))}\) is larger than \(T_{R(t)}\) at each reliability value. When the reliability is 0.368, spindle characteristic lifetime can be obtained, and are 2815 h and 2490 h, respectively, it means that about 36.8% of the spindles with the same type have a lifetime of more than 2815 h according to the model proposed in this paper. When the reliability is 0.5, spindle median lifetime can be obtained, and are 2285 h and 1997 h,  
Figure 5: The curves of \(R_{L}(t|X(t))\), \(R_{U}(t|X(t))\), \(R(t|X(t))\), and \(R(t)\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reliability**} & \multicolumn{2}{c}{**Reliability Life/h**} & \multicolumn{2}{c}{**Reliable Life Interval Estimation/h**} \\ \cline{2-5}  & \(T_{R(t)}\) & \(T_{R(t|X(t))}\) & \(T_{L}\) & \(T_{U}\) \\ \hline
0.368 & 2490 & 2815 & 1985 & 4288 \\
0.5 & 1997 & 2285 & 1630 & 3454 \\
0.6 & 1659 & 1902 & 1377 & 2890 \\
0.7 & 1332 & 1570 & 1130 & 2340 \\
0.8 & 1000 & 1200 & 870 & 1770 \\
0.9 & 636 & 770 & 570 & 1140 \\
0.95 & 411 & 515 & 385 & 750 \\
0.98 & 233 & 305 & 225 & 432 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reliability life comparison table.  
respectively, which means that about half of the spindles with the same type have a lifetime of more than 2285 h according to the model proposed in this paper, which coincides with the long-life characteristic of the motorized spindle.  
According to the characteristics of high reliability and the long life of motorized spindles, the method proposed in this paper is more in line with engineering practice.