esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Specific Application of Topic Prediction Models and Algorithms
### Deep Learning or Machine Learning Analysis Models and Algorithms Based on Time Sequence
#### 6.1.1 LSTM Model  
The LSTM model is a long short-term memory network proposed by [PERSON] et al., which introduces the gate nodes of the forget gate, the input gate, and the output gate on the basis of the RNN recurrent neural network, and processes the long-distance dependent time series data through gate control. The LSTM is used in a large number of tasks related to sequence learning, and it is also one of the most widely applied methods in trend prediction.  
[PERSON] added the three characteristics of topic intensity, keyword frequency and literature number, and proposed a technical topic prediction model based on the LSTM neural network [53]. [PERSON] et al. addressed topic relevance, novelty degree and migration degree as the predictors of the LSTM model in order to predict the evolution trends of privacy research topics [36]. [PERSON] and [PERSON] established an EMD-LSTM technology prediction model based on empirical mode decomposition (EMD) and LSTM to predict developments in the robotics field [5]. [PERSON] et al. constructed a subject topic popularity prediction model, including subject topic extraction, subject topic popularity calculations, and subject topic popularity prediction, and predicted topics' popularity in the LIS field [76]. [PERSON] et al. carried out the evolution prediction of LIS subject topics at home and abroad based on the TPP-LSTM subject popularity prediction model [9]. [PERSON] proposed an EEMD-LSTM technology prediction model combining collective empirical mode decomposition (EEMD) and LSTM to predict the key technologies in the field of lithium-ion battery use in new energy vehicles [59].
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 4 Embedded Programming
### Robustness Enhancement of Kalman Prediction
#### 4.3.3 Kalman Prediction Related Code  
This section details the Kalman filter implementation for position prediction during GPS signal loss. When signal interruption occurs, the system uses the average velocity  
Figure 6: Kalman prediction flow chart. Blue represents normal GNSS positioning flow; Orange indicates Kalman prediction mode; Green dashed lines denote decision nodes; Red highlights mode switching critical points.
# Regional Traffic Event Detection Using Data Crowdsourcing
## 3 Proposed Regional Traffic Event Detection Technique
### Event Type Extraction

Table 1: Event types and their meanings. Figure 3: Event selection example. After Event Selection, the Categorize Selected Event Types stage classifies events according to event type. Figure 4 shows an example of the Categorize Selected Event Types stage, depicting multi-class classification. The vector values of posts selected via Event Selection for their relevance to traffic events were input into the multi-class classifier. When the results are output by the multi-class classifier, they are converted to text referring to these event types. In this study, we performed classification using six models: naive Bayes, random forest, SVC, linear SVC, BiLSTM, and TextCNN, which is a CNN for text classification [25; 26]. Multi-class classification refers to items classified into three or more classes. The multi-class classification includes the one-versus-all (OvA) and one-versus-one (OvO) strategies. The OvA strategy creates a binary classifier for each class and selects the class that produces the highest score. By contrast, the OvO strategy creates binary classifiers for all possible combinations of two classes and selects the class classified as the most positive.
## 1 Introduction
In this way, generic pre-training tasks can be replaced by specific rules able to perform detection more efficiently in the operational environment. The extensive evaluation of our approach (Section 4), in comparison with established solutions, shows that we can offer a similar or better detection accuracy and positioning precision with a reduced processing time. As a result, the main contributions of this work are the following:  
* We propose different classes of segmentation methods to compute semantic maps with low-density point-clouds; \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sensor** & **Scope** & **Vertical** & **Horizontal** & **Samples** & **Points** & **Max. Detection** \\  & & **FoV** & **FoV** & \(\times\)**Second** & \(\times\)**Sample** & **Distance** \\ \hline \multicolumn{7}{c}{Camera} \\ \hline Astra camera \(640 px\times 480 px\) & 3D & 49.5\({}^{\circ}\) & 60\({}^{\circ}\) & 33 & 307,200 & 6–8 m \\ \multicolumn{7}{c}{(160 px\times 120 px) [8]} \\ \hline \multicolumn{7}{c}{LiDAR} \\ \hline UST-10 LX [9] & 2D & N/A & 270\({}^{\circ}\) & 40 & 1440 & 30 m \\ Veldoryne VLP-16 [10] & 3D & 30\({}^{\circ}\) & 360\({}^{\circ}\)(60\({}^{\circ}\)) & 10 & 30,000 (5000) & 100 m \\ Cube 1 [11] & 3D & 30\({}^{\circ}\) & 70\({}^{\circ}\) & 10 & 13,125 & 75 m \\ CE30 [12] & 3D & 9\({}^{\circ}\) & 132\({}^{\circ}\) & 20 & 7680 & 4 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of exemplary sensors used for perception by service robots. * Our object detection methods are effective with a performance similar to those of more complex approaches, but using less processing time;
* We present experimental results comparing the performance of our approach with different semantic mapping solutions for different sensor types as well as point-cloud densities. In conclusion (Section 5), we show that semantic mapping for indoor robot navigation can be performed effectively even without high-end processing resources or high-density point-clouds. This poses the basis for privacy preserving, low-end robotic platforms able to coexist and cooperate with humans in everyday indoor scenarios.
# An Estimation Method of River Dry Runoff Alteration after Upper New Reservoirs Storage
## 2 Background and Methodology
### Estimation Method Development
#### 2.3.2 Method of River Runoff Increment Estimation during the Dry Season  
Based on the correlation between downstream river hydrological alterations and reservoir operation during the dry period, an estimation method was developed to predict the increment of river runoff in the dry season when additional dams are constructed in the future. The framework of this estimation method is illustrated in Figure 6.  
To address the concerns regarding ship navigation and ecology restoration, the estimation method focused on the minimum runoff. The following steps outline the details of the estimation method, using the month with the minimum runoff as an example:  
Firstly, based on the observed alterations during the dry period after the construction of previous dams, the percentage increment of the driest month within the total increment during the dry period (\(P_{0}\)) can be calculated by  
\[P_{0}=\Delta Q^{0}_{driest\ Month}\ \ /\Delta Q^{0}_{dryPeriod} \tag{1}\]  
where \(\Delta Q^{0}_{driest\ Month}\) is the practical increment of the driest monthly average runoff and \(\Delta Q^{0}_{dryPeriod}\) is the sum of the monthly average runoff increment during the dry period.  
Secondly, the theoretical increment of the driest monthly average runoff after the previous dams were built (\(\Delta Q^{0}_{driest\ Month}\)) can be calculated by  
\[\Delta Q^{0^{\prime}}_{driest\ Month}\ \ =P_{0}\times\sum_{djustable}V^{0}_{djustable }/(D_{drisetMonth}\times 24\times 3600) \tag{2}\]where \(\sum V^{0}_{adjustable}\) is the sum of the adjustable capacity of upstream cascade reservoirs and \(D_{dristMonth}\) is the total days in the driest month.  
Then, the \(Dev_{0}\), which is the bias between the practical runoff increments and the theoretical in the driest month, can be expressed as  
\[Dev_{0}=(\Delta Q^{0}_{driest\ Month}\ -\ \Delta Q^{0}_{driest\ Month})/\Delta Q^{0}_{driest\ Month} \tag{3}\]  
Thirdly, according to Equation (2), the theoretical increment of the driest monthly average runoff after the future dams are built (\(\Delta Q^{1^{\prime}}_{driest\ Month}\) ) can be obtained by  
\[\Delta Q^{1^{\prime}}_{driest\ Month} =P_{0}\times\sum V^{1}_{adjustable}/(D_{dristMonth}\times 24\times 3600) \tag{4}\]  
where \(\sum V^{1}_{adjustable}\) is the sum of the adjustable capacity of upstream cascade reservoirs in the future.  
By following the above steps, the predicted increment of the driest monthly average runoff after the future dams built, \(\Delta Q^{1}_{driest\ Month}\), can be obtained by  
\[\Delta Q^{1}_{driest\ Month} =\Delta Q^{1^{\prime}}_{driest\ Month}\ \ \times(1-Dev_{0}) \tag{5}\]
## 4 Results and Discussion
### Permeability Quality Evaluation Test Results
\[CV=\frac{\sigma}{\mu^{\prime}} \tag{11}\]  
where \(\sigma\) is the data standard deviation and \(\mu^{\prime}\) is the data average. The \(CV\) of test results for samples gradually increases with the cycles, and the \(CV\) of the test results for samples is smallest when the cycle time is zero. This indicates that the physical clogging of the permeable asphalt mixture is a random phenomenon, and the longer the cycle time, the greater the discreteness of the test data. The CV of all test results measured in this paper is within 20%, which proves that the porosity test has good repeatability. According to the grey system theory, the clogging coefficient of the three mixtures with different cycles is calculated, and the results are shown in Figure 11.  
As can be seen from Figure 11, the clogging coefficient of the three mixtures increases as the cycles increase, which indicates that the clogging state of the permeable pavement becomes increasingly serious. For the same cycles, the clogging coefficient of SBS-OGFC is always higher than that of the other two mixtures, which demonstrates that the larger the porosity of permeable asphalt pavement, the higher the probability that the clogging materials will enter the pores and cause the clogging behavior. The clogging coefficient of OGFC showed almost no charge after 12 cycles, which indicated that the rapid clogging process in spring was basically completed before 12 cycles, followed by the slow clogging process. The effect of the slow clogging process on the clogging coefficient index is not obvious. The clogging coefficient of COF-OGFC showed little change after 16 cycles, i.e., the rapid clogging process of COF-OGFC had been completed before 16 cycles. It can be seen from Figure 11b that the clogging coefficient of three asphalt mixtures increases steadily with the increase of summer clogging cycles in the first 40 cycles, and then the clogging coefficient of three asphalt mixtures enters the stable stage, which is almost consistent with the permeability coefficient, porosity and dry weight change trend. This indicated that the clogging coefficient as an indicator for evaluating the clogging state of the actual permeable pavement is an objective and reliable application. The clogging coefficients of three permeable pavement are similar in each period. This is because the gradation of the three permeable pavements is the same. After 50 summer clogging cycles, the clogging coefficient of SBS-OGFC was higher than OGFC and COF-OGFC. This is because the porosity of SBS-OGFC is the largest, and it can accommodate more clogging materials. In the last 10 summer clogging cycles, the clogging coefficient of three asphalt mixtures has almost no change, which also shows that the clogging materials content of permeable pavement almost reached saturation after 40 summer clogging cycles. Figure 11: Clogging coefficient of three mixtures with different cycles. (**a**) Spring clogging procedure; (**b**) Summer clogging procedure.
# One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation
## 2 Related Work
### Transfer Learning and Multi-Task Learning  
**Transfer Learning** refers to the problem in the machine learning field that focuses on storing knowledge obtained from a task and applying it to a different but related task [36]. Transfer learning has been demonstrated on various computer vision-related tasks from image recognition [27; 37] to detection and segmentation [7; 38]. The reason why it works is that features learned by Deep ConvNets are hierarchical [27]. Both object detection and semantic segmentation need Deep ConvNets as the infrastructure, hence these two tasks require similar features.  
**Multi-Task Learning** (MTL) is the strategy that aims to leverage information among multiple related learning tasks to improve the performance of all those tasks [39]. MTL obviously utilizes fusion features because it learns features useful for every related task. Simultaneous detection and segmentation can be seen as an application of multi-task learning, and more details will be given in the next section. The OFA-Net leverages this idea and demonstrates that by training detection and segmentation alternately, it is able to better the performance on both tasks in some aspects.
## 3 Application to the Resource Allocation Problem in the Mobile Harbor System
### Market Conditions  
The Port of Surabaya in Indonesia can be reached via channel 40 km from the open sea; Figure 4 depicts the layout of this port. As demands for the port's services increases, the port plans to provide future service to container ships with capacities greater than 2500 TEU. In order to serve large container ships using the quay cranes on land, the port must enlarge and dredge the 40 km entrance channel so that the ships can approach the port. The depths of the port's berths must also be increased. An alternative is to serve these large container ships offshore via a fleet of mobile harbors and build shallow 5 m berths for the mobile harbors to use in loading and unloading containers at the land-sea interface. This would be possible without expanding and dredging the current channel.  
The anticipated yearly throughput at the Port of Surabaya was provided in [34]; see Table 1. There are five classes of ships anticipated. The class of a ship is based on its TEU capacity. The number of ships expected per year in each class is provided in Table 1. The total expected throughput per year, brought by ships in each category, is also listed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Ship Size (TEU)** & **\# of Visiting Ships** & **Throughput (TEU)** \\ \hline
1 & 0–499 & 39 & 9750 \\
2 & 500–999 & 108 & 81,000 \\
3 & 1000–2499 & 1080 & 1,512,000 \\
4 & 2500–3999 & 589 & 382,850 \\
5 & 4000–5999 & 147 & 73,500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Expected throughput pattern for the Port of Surabaya.  
Figure 4: Layout of the Surabaya port.
# Progress in Phase Calibration for Liquid Crystal Spatial Light Modulators
## 3 Interferometric Phase Calibration Method  
The SLM phase calibration could be treated as a modulated phase detection process with a foregone changing grayscale pattern uploaded on SLM. Due to the extremely low detection rate of the electrical photo-electric detector compared to the frequency of the light, directly detecting the phase of the light is an impossible mission using an available detector which could record the intensity only. The interferometric technique could be utilized to record and reconstruct both the intensity and phase by mathematical methods. For instance, the interference fringes of two monochromatic plane waves with the same direction of polarization could be described as:  
\[I(x,y)=I_{1}(x,y)+I_{2}(x,y)+2\sqrt{I_{1}(x,y)\cdot I_{2}(x,y)}\cdot\cos\delta \tag{3}\]  
where \(I_{1}(x,y)\) and \(I_{2}(x,y)\) are the intensity distributions on the detection plane of two beams, \(\delta\) is the phase difference between the two beams, and \(I(x,y)\) is the interference pattern distribution. Holding one beam and introducing a shifted phase to the other, the shifted phase difference of the two beams could lead to the displacement of interference fringes. For phase calibration of the SLM, the interference fringes could be generated by superposing the modulated beam from the SLM with a foregone reference beam. By changing the grayscale of the pattern uploaded on SLM, the modulated phase could be derived from the displacement of the fringes or other changes on the interferograms. In the interferometric methods, the reference beam could come from a certain zone or outside of SLM. The measured phase values are the relatively shifted phase introduced by changing the uploaded grayscale pattern on SLM, instead of the absolute phase of the modulated beam. For the interferometric calibration methods, the beams with approximately equal intensity are needed for good contrast on the interferograms.
# Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System
## 5 Results of Experiments and Discussion
### Quantitative Evaluation
\begin{table}
\begin{tabular}{c c c c c} \hline
**Image No.** & **Metric** & **Proposed** & **PredRNN** & **Better** \\ \hline \multirow{3}{*}{1} & PSNR & 14.14 & 13.14 & Proposed \\  & SSIM & 0.72 & 0.74 & PredRNN \\  & LPIPS & 0.16 & 0.20 & Proposed \\ \hline \multirow{3}{*}{2} & PSNR & 25.30 & 19.55 & Proposed \\  & SSIM & 0.95 & 0.90 & Proposed \\  & LPIPS & 0.03 & 0.06 & Proposed \\ \hline \multirow{3}{*}{3} & PSNR & 13.42 & 21.63 & PredRNN \\  & SSIM & 0.66 & 0.91 & PredRNN \\  & LPIPS & 0.27 & 0.07 & PredRNN \\ \hline \multirow{3}{*}{4} & PSNR & 14.59 & 20.63 & PredRNN \\  & SSIM & 0.69 & 0.89 & PredRNN \\  & LPIPS & 0.24 & 0.07 & PredRNN \\ \hline \multirow{3}{*}{5} & PSNR & 14.00 & 17.57 & PredRNN \\  & SSIM & 0.68 & 0.79 & PredRNN \\  & LPIPS & 0.25 & 0.14 & PredRNN \\ \hline \multirow{3}{*}{6} & PSNR & 21.10 & 19.38 & Proposed \\  & SSIM & 0.88 & 0.83 & Proposed \\  & LPIPS & 0.07 & 0.10 & Proposed \\ \hline \multirow{3}{*}{7} & PSNR & 19.41 & 20.01 & PredRNN \\  & SSIM & 0.83 & 0.85 & PredRNN \\  & LPIPS & 0.12 & 0.10 & PredRNN \\ \hline \multirow{3}{*}{8} & PSNR & 30.82 & 20.54 & Proposed \\  & SSIM & 0.96 & 0.87 & Proposed \\  & LPIPS & 0.02 & 0.08 & Proposed \\ \hline \multirow{3}{*}{9} & PSNR & 14.78 & 16.94 & PredRNN \\  & SSIM & 0.73 & 0.82 & PredRNN \\  & LPIPS & 0.20 & 0.14 & PredRNN \\ \hline \multirow{3}{*}{10} & PSNR & 14.86 & 19.69 & PredRNN \\  & SSIM & 0.70 & 0.85 & PredRNN \\  & LPIPS & 0.26 & 0.10 & PredRNN \\ \hline \end{tabular}
\end{table} 
Table 3: Quantitative comparison of final images: proposed model (noise-augmented) vs. PredRNN. Best values are highlighted in red. The results showed that PredRNN achieved a lower FID score (242.8) than the noise-augmented training model (311.5), indicating that PredRNN's generated images were closer in distribution to the target images. This aligns with the LPIPS results, reinforcing that PredRNN better preserves high-level perceptual and semantic coherence across frames. Note that the FID scores were computed on a limited set of 10 final-frame images per model, which may introduce some variability and should be interpreted as indicative rather than definitive.
## 2 Analysis of the Relationship between Beam Walk and DWS Signal
### DWS Interference Signal Model in Practical Situation
The preceding two integral expressions can be succinctly denoted as follows:  
\[F_{left}=E_{0}\cdot B_{1}\cdot B_{2}\cdot B_{3} \tag{5}\]  
\[F_{right}=E_{0}\cdot B_{1}\cdot B_{2}\cdot B_{4} \tag{6}\]  
where \(B_{1}\), \(B_{2}\), \(B_{3}\), and \(B_{4}\) can be determined as follows:  
\[B_{1}=\frac{A_{0}}{\omega(z_{g})}\cdot e^{[-i\varphi(z_{g})-ikz_{g}-ik\sin( \varphi)x_{0}+ikz_{f}]} \tag{7}\]  
\[B_{2}=\int_{-\omega_{f}}^{\omega_{f}}e^{-\frac{y^{2}}{\omega^{2}(z_{g})}-\frac {3 ky^{2}}{2R(z_{g})}}dy \tag{8}\]  
\[B_{3}=\int_{x_{0}-\frac{\omega_{f}}{\cos(\varphi)}}^{-h}e^{-\frac{x^{2}}{ \omega^{2}(z_{g})}-\frac{3 ky^{2}}{2R(z_{g})}+ik\sin(\varphi)x}dx \tag{9}\]  
\[E_{ft(x,y,z)}=E_{0}e^{-ikz/\varpi(x^{2}+y^{2}\leq\omega_{f}{}^{2})} \tag{2}\]0  
The DWS signal is shown as follows:  
\[E_{ft(x,y,z)}=E_{0}e^{-ikz/\varpi(x^{2}+y^{2}\leq\omega_{f}{}^{2})} \tag{2}\]1  
From the provided equation, the DWS signal prominently hinges on the beam angle \(\varphi\), QPD quadrant gap \(h\), the spot size \(\omega_{f}\), and the Gaussian flat-top spot offset \(x_{0}\) on the QPD detection surface. Conversely, the detector size \(L\) bears no relevance to the analysis.
# Volatile Composition of Industrially Fermented Table Olives from Greece
## 3 Results and Discussion  
### Volatile Composition of Table Olives  
In total, 126 volatile compounds were identified in the three types of table olives used. These comprised 11 acids, 30 alcohols, 50 esters, 10 carbonyls, 4 hydrocarbons, 6 phenols, 8 terpenids and 7 miscellaneous compounds (Table 2). It is noteworthy that the volatile profile of Kalamata (KLM) table olives was characterized by a smaller number of compounds as compared to Halkidiki (HLK) and Conservolea (CNS) samples. The majority of the identified compounds (105 out of 126) have already been reported in various types of table olives, including Californian-style from Spanish cultivars [16; 17; 18], Spanish-style from Spanish cultivars [24; 25; 26; 27; 28; 29; 30; 35], Greek-style from Italian cultivars [19; 20; 21; 22; 23], _alcaparras_ stone table olives from Portugal [14], Sicilian green table olives [15] and Tunisian olives [36]. However, the data are scarce in the literature as regards the volatile composition of table olives produced from Kalamata, Halkidiki and Conservolea cultivars [12; 31; 32]. The total relative content (%) of each chemical class is presented in Figure 1. The volatile profile of the three cultivars was predominated by esters (-31-45%), alcohols (-20-37%) and acids (-22-27%), whereas the content of the remaining classes was quite lower (<12%). The highest and lowest content of esters (\(p<0.05\)) was observed in CNS (45.2%) and KLM (31.2%) samples, respectively. The opposite was observed for alcohol content (CNS: 19.7%, KLM: 37.3%).