esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Real-Time Travel Times to Estimate Fire Service Coverage Rate for High-Rise Buildings
## 5 Conclusions  
This study was aimed to answer whether high-rise buildings can be effectively covered by fire services. Firstly, we proposed that the minimum fire apparatus requirement for high-rise buildings is \"at least one water fire truck and one aerial fire truck should arrive at the fire scene of high-rise building within 4 min (240 s)\". Secondly, the dynamic estimation model of fire service effective coverage rate for high-rise buildings was established based on the minimum travel times of water fire trucks and aerial fire trucks. Thirdly, JavaScript was used to identify high-rise buildings in POIs, extracting real-time travel times from online maps, and analyzing and calculating based on the estimation model.  
In this study, 5146 identified high-rise buildings, 61 water fire trucks, and 56 aerial fire trucks in Changsha were selected as case studies. A total of 308,760 valid data in 30 estimation scenarios were obtained. Further calculation and analysis showed that the total effective coverage rate had a value of 20.43%, and the total average travel time had a value of 421.95 s in Changsha. There were 10,840 water fire trucks and 6192 aerial fire trucks in China. Based on the average number of water fire trucks and aerial fire trucks per high-rise building, it can be estimated that the total effective coverage rate in China may not exceed 60.00%.  
Due to limited fire resources and frequent traffic congestion, only partial high-rise buildings can be effectively covered by fire services, whether in Changsha or China. Therefore, more fire stationsequipped with water fire trucks and aerial fire trucks should be built, and China should make great efforts to reduce traffic congestion.  
Conceptualization and methodology, D.L. and Z.X.; validation and visualization, D.L., L.Y. and F.W.; Writing--original draft preparation, D.L. and L.Y.; writing--review and editing, Z.X. and F.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Nos. 51676210, 51906261, and 51974362). The authors declare no conflict of interest.
# Two-Dimensional Digital Beam Steering Based on Liquid Crystal Phase Gratings
## 2 Materials and Methods
### Electronic Driver  
An electronic driver has been specially designed for individual electronic control of every pixel of the LC cell (Figure 8). It employs a 12 bits PWM signal for each of the pixels of the two LC cells. The electronic driver is addressed using a standard SPI protocol. An Arduino UNO board acts as a USB-SPI for the communication between the controlling computer and the LC cell driver.  
The driver uses the 24 channel TLC5947 driver (PWM LED driver) from Texas Instrument (Dallas, TX, USA), with the output adjusted to TTL voltage levels by setting the control resistance to 18 k\(\Omega\) and employing a 10 k\(\Omega\) pull-up resistor. Each PWM voltage output is connected to the digital input of an analogue switch, ADG5413, from Analog Devices, which switches between an externally supplied AC signal and ground. The PWM cycle of approximately 1.3 us is sufficiently fast for the LC to perceive only the effective RMS signal of the supplied AC field, which is a 5V\({}_{\text{pp}}\) 1 kHz signal.  
Figure 6: Pixel layout of the cells showing the contact fan-out. The active areas are marked with red squares. When overlapped, they create the 2D steering: (**a**) Horizontal pixels; (**b**) Vertical pixels.  
Figure 7: View of the active area in the microscope between cross polarizers: (**a**) Horizontal active area with topology +12 (pattern repetition for every 6 pixels, 90 \(\upmu\)m); (**b**) Vertical active area with topology +3 (pattern repetition for every 24 pixels, 360 \(\upmu\)m). Inserts show areas of 360 \(\times\) 120 \(\upmu\)m\({}^{2}\), in which one may appreciate the individual pixels.  
The TLC5947 may be daisy chained. In the presented driver, three TLCs are combined per driver board, and two boards are daisy chained, providing individual control of all the 144 (72 + 72) pixels in the two LC devices. The power consumption of the driver is less than 2 W.
## 2 Materials and Methods
### Testing Procedures
#### 2.2.2 Isometric Hip Strength Assessment  
All hip strength assessments were conducted using an isometric dynamometer (Muscle Board, S2P, d. o. o, Ljubljana, Slovenija), which utilized U-shaped braces with single-point load cells. The procedure was recently assessed in terms of intra- and inter-day reliability, and most of the outcomes showed good to excellent reliability scores [27]. The braces could be rotated to accommodate the desired task. Hip flexion, adduction, and abduction maximal and explosive strength were measured in the supine position, while hip extension strength and explosive strength were assessed in the prone position, with the hip, knee, and ankle maintained in a neutral position. Testing positions were individually adjusted based on the participants' anthropometric characteristics. During all tests, ankles and knees were aligned hip-width apart. Leg lever arm was measured prior to each test to the nearest 0.5 cm by measuring the distance from the greater trochanter to the middle of the sensor attachment site. To stabilize the body during testing, the players held on to the sides of the plinths while a non-elastic strap was tightly secured across the pelvis. For each test, the participants performed three isometric repetitions using their preferred push-off leg. They were required to apply a 10 N pre-tension to the brace, positioned just above the ankle, and then press against the sensor with their knee extended as quickly and forcefully as possible [28]. They had to maintain maximal pressure on the sensor for an additional 5 s. The rest period between repetitions of each individual test was approximately 30 s, while the rest period between tests targeting different muscle groups was three minutes. To avoid systematic bias due to fatigue accumulation, the test order was counter-balanced.  
All measurements were recorded at a sampling rate of 1000 Hz. The signals were automatically processed using the manufacturer's software. A moving average filter with a 5 ms window was applied to the force signal. Maximal voluntary isometric contraction (MVIC) strength for adduction (MVIC ADD), abduction (MVIC ABD), flexion (MVIC FLEX), and extension (MVIC EXT) was determined as the highest force value within a one-second moving average window during MVICs. The rate of force development (RFD) was calculated within the first 100 ms of the explosive isometric contraction. Force values were multiplied by leg length and allometrically normalized by a factor of 0.67 to individual body weight, resulting in normalized peak torque (PT) (Nm/kg) and the rate of torque development (RTD) (Nm/s/kg) [29]. RTD was assessed for adduction (RTD ADD), abduction (RTD ABD), flexion (RTD FLEX), and extension (RTD EXT). MVIC measurements demonstrated a good to moderate reliability between three test repetitions (ICC = 0.913-0.580), while RTD variables exhibited a moderate to poor reliability (ICC = 0.546-0.396).
## 4 Results and Discussion
### Signify Qualitative Assessment  
As previously stated, the main objective of this work is the development of a serious game that can facilitate LIS learning for young children. To have a more robust evaluation of the application and to verify its usability and simplicity, a comprehensive assessment was required. In the previous section, the performance assessment of the automatic hand gesture recognition algorithm has been reported. In this section, the application has been evaluated in terms of usability, with a focus on its effectiveness, visual immediacy, and practical use in an educational context [52]. For this reason, the application was tested by a heterogeneous group of 11 children aged between 10 and 12 to evaluate the effectiveness of the game and its potential as a learning tool, under the supervision of three teachers. The most widely used evaluation methodologies for usability and engagement, System Usability Scale (SUS) [53] and User Engagement Scale (UES) [54], have been reworked to make them suitable for primary school children.  
As shown in Figure 11, all users appreciated the tutorial section, since none of them were familiar with sign language, and only one of them expressed the need to revise gestures several times due to unfamiliarity with technology. Given the engagement and the rapid learning curve shown by the children, the teachers reported the gamification technique very useful, expressing the desire to use it for other purposes as well.  
The graphical user interface was considered adequate by the majority of subjects, with 73% appreciating the UI and finding it effective, 18% finding it fairly effective, and the remaining 9% thinking it needs improvements. Eventually, 36% of the subjects considered the application easy to use, 28% encountered slight difficulties in use, and the remaining 36% found the application fairly easy to use (Figure 11).  
Therefore, unlike existing serious games in the literature, such as Magic Touch Math [18], the system for Portuguese sign language learning, and MatLIBRAS Racing [7], the proposed application has the great advantage of having been already validated from the actual end-users, children, and teachers. Indeed, the results have proven to be crucial in assessing the effectiveness of the proposed system, testing it directly on what would be the main target to highlight the feasibility of this approach. This difference is substantial in a context where automatic recognition has already proven to be particularly effective over the years [20] and, consequently, requires more attention from the perspective of integrating artificial intelligence with extended reality. In particular, it is this latter element that has already proven to be decisive in other contexts for facilitating the learning process through more engaging and immersive activities [55; 56].
## 4 Experimental Results and Analysis
### Experimental Results
Due to the inclusion of the feature pyramid, the detection time is slightly longer in the method than in the others adopting the original point cloud-based Complexer-YOLO and 3 DSSD algorithms, which is still a result better than those in the other mainstreams. The results produced by one algorithm individually in the original point cloud method, multiview method and image point cloud fusion method were selected to compare those produced in the method adopting our proposed algorithm, which is shown in Figure 16. In the cases with shadow occlusion, vehicle occlusion and distant vehicles, the false detection rate and missed detection rate are both lower and the 3D detection frame-matching accuracy of the vehicles is higher in the method adopting this algorithm than in the other three methods. The experimental results indicate that the proposed algorithm provides fast \begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Cycles** & **Network Losses** & **Testing Accuracy\%** \\ \hline
60 & 1.652 & 73.32 \\
80 & 1.271 & 79.46 \\
100 & 1.393 & 83.84 \\
120 & 1.132 & 84.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network training loss and detection accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Testing** & \multirow{2}{*}{**Algorithms**} & \multicolumn{4}{c}{**Precision\%**} & \multirow{2}{*}{**Time/s**} & \multicolumn{2}{c}{**Average**} \\ \cline{3-4}
**Methods** & & **Simple** & **General** & **Difficulties** & & **Accuracy\%** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Raw point \\ cloud \\ method \\ \end{tabular} } & Complexer-YOLO & 24.27 & 18.53 & 17.31 & 0.09 & 20.04 \\  & YOLO & 88.36 & 79.57 & 74.55 & 0.10 & 80.83 \\  & VOXEL3D & 86.45 & 77.69 & 72.20 & 0.24 & 78.78 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Multi-view \\ approach \\ \end{tabular} } & SARFNET & 85.63 & 76.64 & 71.31 & 0.12 & 77.86 \\  & SIE Net & 88.22 & 81.71 & 77.22 & 0.15 & 82.38 \\  & MVOD & 88.53 & 80.01 & 77.24 & 0.16 & 81.93 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Image point \\ cloud fusion \\ methods \\ \end{tabular} } & F-PointNet & 82.19 & 69.79 & 60.59 & 0.17 & 70.86 \\  & AVOD & 83.07 & 71.76 & 65.73 & 0.22 & 73.52 \\ \cline{1-1}  & MV3D & 74.97 & 63.63 & 54.00 & 0.36 & 64.20 \\ \cline{1-1}  & Text Algorithms & 88.75 & 85.52 & 79.86 & 0.14 & 84.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of time and accuracy of mainstream algorithms. Figure 15: Training loss chart. and accurate 3D vehicle detection in both natural and complex scenarios, meaning it is an effective and feasible method.
## 3 Model for Predicting the Health of an Unmanned Aerial Vehicle Data-Link System
### Constructing a Bayesian Network Root Node Prediction Model
In communications, the bit error rate is an important indicator that measures the accuracy of data transmission within a specified time. Often, bit errors are caused by a combination of factors, such as the decay of the signal transmission or pulse interference caused by noise, alternating current, lightning and equipment failure. Since the bit error rate is the number of bit errors divided by the total number of transferred bits during a studied time interval, a probability value can reflect the error (i.e., unhealthy state) of information transmission and therefore it can be used in a BN prediction model as a representation between the nodes that are associated with each of the error transmissions. In this way, we can modify the BN model to predict the UAV data-link health state not only considering the networking mode and we can add the bit error rate node to represent the data-link affected by the external environment. The BER value of the newly added root node can be simulated and generated. Each link generates a random bit stream for information transmission by means of the BPSK spread spectrum. The signal is processed by the method of generating the sixteenth order Walsh code and the simulation can be performed. As shown in Figure 5, interfering with the modulated bit stream generates random errors and statistics on the bit error rate, generating a bit error rate value BER\(n\) for each link, thereby obtaining a probability that the link information transmission is normal: \(P_{Error\_n}\ =\ 1\ -\ BER_{n}\). The method for predicting the health status of the UAV data-link system proposed in this paper can be summarized as follows:  
1. Determining nodes of the UAV data-link system;  
Figure 5: Simulation of bit error rate under different bit stream rates, transmission symbols and signal to noise ratio. Construct a DAG of the UAV data-link system and establish the CPT of the non-root nodes;
* Consider the impact of bit error rate, add the bit error rate nodes and establish the CPT;
* The JT estimation algorithm is used to solve the joint probability of relevant nodes, to update the conditional probability values of each node and to achieve the deduction of state probability of UAV data-link system nodes.
## 2 Background  
### Thermodynamic Processes of CAES  
The compressed air energy storage technology adopts the advanced-adiabatic process [20]. As illustrated in Figure 2, this process has four parts: (1) compression, (2) cooling, (3) heating, (4) expansion. The ambient air first goes through a compression process powered by the renewable energy available for storage. After compression, the pressure and temperature of the air increase significantly. The temperature (\(T_{2}\)), especially, can rise to more than 1000 \({}^{\circ}\)C, which is not possible for storing inside the concrete pile foundation. The cooling process is performed to extract the heat from the compressed air and store it in a separated heat storage medium, such as oils. However, the temperature (\(T_{3}\)) after the cooling cannot wholly drop back to the ambient temperature (\(T_{1}\)) due to the efficiency of the cooling process. Therefore, two options are available. The first option (Option 1) is to provide extra cooling to reduce the temperature closer to \(T_{1}\) before entering the pile foundation for storage. The other option (Option 2) is to directly store the air in the pile foundation without further cooling. The latter option is possible because \(T_{3}\) is typically around 150 \({}^{\circ}\)C, which has insignificant effects on the mechanical properties of the concrete [21]. When the energy is needed, the stored compressed air is released and goes through heating and expansion processes to produce electric power. The detailed explanation and equations derivation for the advanced-adiabatic process are discussed in [PERSON] et al. [7]. This paper investigates the temperature changes inside the pile foundation for Option 2.
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 4 Application of Image Processing Techniques to Underwater Crack Detection in Dams
### Image Processing of Underwater Cracks
#### 4.2.2 Underwater Crack Image Recognition  
Image pre-processing methods can significantly improve image quality and make images suitable for crack recognition. In order to assess the extent of the damage to the underwater portion of the concrete dam, it is necessary to identify cracks in pre-processed images of underwater cracks on the dam surface, so as to realize the accurate location and quantification of the underwater cracks on the dam surface. Existing optical image-based algorithms for underwater crack recognition fall into three main categories: traditional, deep learning, and hybrid algorithms. These image-based algorithms for underwater crack detection can be implemented programmatically using MATLAB or Python. Traditional algorithms are mainly based on prior knowledge of cracks to identify underwater cracks [25]: (1) the gray value of the image region where the crack is located is smaller than the background region; (2) the crack has a slender shape; (3) the possibility of cracks increases with the information entropy. The improved edge detection algorithm is applied to the crack detection of underwater dam surfaces based on the above theory. [PERSON] et al. [74] proposed an artificial ant colony algorithm for edge detection based on biological principles to detect underwater cracks in dams. [PERSON] et al. [77] proposed an adaptive underwater dam surface edge detection algorithm based on multi-structure and multi-scale elements. According to the differences between cracks and background regions in pre-processed images, crack detection algorithms based on local or global features have also been used to detect underwater cracks on dam surfaces. This approach typically divides the pre-processed image into several image blocks on average and uses the local features of each image block to identify crack and non-crack image blocks and further refine crack regions with global features. [PERSON] et al. [20] extracted crack image patches based on local feature clustering. This method only localizes image patches that contain cracks and does not identify specific regions of the cracks. In order to achieve the extraction of fine cracks, Shi [25], Fan [71], and Xin [46], etc., selected crack image patches and fine remove of the non-crack information based on the local features of image patches and the global features of connected regions, respectively. Due to the difference between the gray scale and shape of the crack region and the background region in the pre-processed underwater images, the researchers segmented the surface cracks of the underwater dam based on a morphological approach. [PERSON] et al. [78] distinguished cracks and background regions based on the curvature properties of the 3D spatial surface transformed from a 2D gray intensity map and achieved good results. Benefiting from the development of
# FocalMatch: Mitigating Class Imbalance of Pseudo Labels in Semi-Supervised Learning
## 2 Related Work  
### Semi-Supervised Learning  
Semi-supervised learning (SSL) is a popular machine learning technique that combines supervised and unsupervised learning, i.e., both labeled and unlabeled data are used for training. Typically, semi-supervised learning uses smaller labeled datasets because one of the main ideas behind SSL is to address the problem of insufficient labeled data [18, 19]. Semi-supervised learning has become increasingly important in many areas, for example, medical image analysis [20] and natural language processing [21] since labeled data in these scenarios is usually either expensive or hard to obtain. A detailed introduction to semi-supervised learning can be found in [22].  
In the study of semi-supervised learning, consistency regularization [23] is a commonly used technique. It assumes that distorted versions of the same input sample should yield similar predictions from the model. Consistency regularization has been applied in many state-of-the-art SSL methods. In order to generate distorted samples, data augmentation is usually beneficial. ReMixMatch [24] and UDA [25] both use strong data augmentations to improve the consistency regularization between different versions of images. Pseudo-labeling [14] is another widely used technique in SSL that generates hard (one-hot) artificial labels for unlabeled data from the model predictions. In several recently proposed SSL methods, pseudo-labeling is combined with consistency regularization. FixMatch [15] generates one-hot pseudo labels from predictions on weakly-augmented data with a pre-defined high threshold and ensures consistency against strongly-augmented data.  
Figure 1: Framework of FocalMatch. Unlabeled data is fed into the model, and the model generates pseudo labels based on the weakly-augmented unlabeled data. At early iterations, the model prefers certain classes for pseudo labels, which causes severe class imbalance. Then, the model makes predictions on the same unlabeled data but with strong augmentation. The model calculates the loss between predictions and pseudo labels via focal loss [17] to ensure consistency. Focal loss adjusts the weight of different data based on how close their predictions are to their pseudo labels. For well-classified data (i.e., the majority of pseudo labels), their loss contribution is reduced. Therefore, the model can rapidly eliminate class imbalance at early iterations.
## 2 Materials and Methods
### NMR Measurements
#### 2.2.2 Measurements and Data Analysis  
The Carr-Purcell-Meiboom-Gill (CPMG) sequence [24; 25] was applied for the NMR magnetization decay measurements. The durations of the 90\({}^{\circ}\) and 180\({}^{\circ}\) pulses for the \({}^{1}\)H were 4 \(\upmu\)s and 8 \(\upmu\)s, respectively. The relaxation delay was 5 s, and the number of scans was equal to 4. The echo time was 3 ms, and the number of echoes was 600. The resonance frequency was tuned before each CPMG sequence was run.  
As in the chosen settings, the NMR magnetization decays revealed only one component, and the NMR parameters were fitted to the decays using a mono-exponential function:  
\[\mathrm{A(t)}=\mathrm{A_{0}}\cdot\mathrm{e^{(-R_{2}t)}}+\mathrm{c_{0}}, \tag{2}\]  
where \(\mathrm{A_{0}}\) is the maximal signal magnitude, \(\mathrm{R_{2}}\) is the spin-spin relaxation rate, and \(\mathrm{c_{0}}\) is the signal baseline. \(\mathrm{R_{2}}\) is the inverse of the spin-spin relaxation time \(\mathrm{T_{2}}\).