esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Motivations and Contributions  
The scenic route planning method calculates the scenic value of arc or POI in the road network through relevant crowdsourced data and maximizes the scenic score in the process of path planning. Such methods can often plan travel routes with large scenic scores for users. Route planning methods based on user preferences consider the visitor's personalized needs, but most of them require users to manually input their preferences and then connect as many POIs as possible to form the optimal path. The above two types of route planning methods aim to plan the route with the maximum scenic score for users, but they do not consider the urgency of users' travel and explore users' interests and preferences. Therefore, this paper proposes a personalized scenic route planning model based on urgency. This model takes tourism users as the object to provide tourism users with a multifunctional and personalized scenic route planning scheme. The goal is to plan an optimal driving route that matches urgency and scenic features with users' interests and preferences. Therefore, the main contributions of this paper are summarized as follows:  
1. A multifunctional and personalized time-dependent AOP is defined, which indicates different travel schemes at various departure times for several user preferences with specific travel urgency.
2. A personalized scenic tourism route planning model based on urgency is proposed to solve the above time-dependent AOP. Through the steps of user preference extraction, road network modelling and path optimization search, the model plans multifunctional and personalized scenic routes to meet the needs of users.
3. The test was carried out on the road network dataset of Xi'an and Wuhan. Compared with the three benchmark methods in the experiment, the proposed model can plan routes with various functions according to specific urgencies as well as plan routes with different neighbouring POIs for individual users. Therefore, the model has good performance in terms of effectiveness.
## 4 Analysis of Influencing Factors
### Influence of Meshing  
Keeping all other conditions the same, Figure 12 shows the inversion results of the elastic wave information when the inversion area grid size from left to right was 1.5 m \(\times\) 1.5 m, 1 m \(\times\) 1 m, and 0.5 m \(\times\) 0.5 m, respectively. When the grid size was 1.5 m \(\times\) 1.5 m, the resolution of the cloud map for the strata was low, and most of the strata were not reflected; when the grid size was 1 m \(\times\) 1 m, the accuracy of the inversion improved significantly, and most of the strata were reflected basically. However, compared to the inversion results of the 0.5 m \(\times\) 0.5 m grid size, the distribution of strata still seemed to be rough  
Figure 11: Comparison of results of different sampling densities.  
Figure 10: Comparison of results with different receiving angles.  
and fuzzy, flat and regular, and not as detailed as the latter. Therefore, the quality of the inversion becomes higher as the grid size is smaller and smaller, but the inversion time also increases gradually.
## 1 Introduction
The proposed configuration offers the advantage of combining desirable solid-state properties such as the tunability of pump lasers in the 1527-1565 nm range, the high output power of \(\sim\)980 nm signal laser module, high conversion efficiency offered by uniformly poled QPM crystal and wide input wavelength acceptance range due to a step-chirped (SC-PPLN) crystal. If, for instance, a broadband laser source is used rather than a narrow band highly coherent source for wavelength mixing, multiple wavelengths can be generated simultaneously in SC-PPLN. Up to 7.1 mW SFG output power was detected for the 50 mm PPLN crystal, 4.3 mW for the 20 mm SC-PPLN crystal, and 10 mW for the cascaded module corresponding to 7.4%/W, 4.5%/W, and 10.4%/W nonlinear conversion efficiencies of the total input power of the fundamental lasers, respectively. To the best of our knowledge, no work has been reported on the generation of an orange laser using MgO:PPLN bulk crystal in terms of cascaded single-pass sum frequency mixing of uniformly poled and SC-PPLN crystals, which has the advantage of tunability and high conversion efficiency.
## Data  
### Satellite Data  
COMS/MI has one channel in the visible spectrum (VIS, 0.55 \(\upmu\)m-0.80 \(\upmu\)m) and four infrared-sensing channels (shortwave (SWIR); 3.5 \(\upmu\)m-4.0 \(\upmu\)m, water vapor (WV); 6.5 \(\upmu\)m-7.0 \(\upmu\)m, infrared1 (IR1); 10.3 \(\upmu\)m-11.3 \(\upmu\)m, and infrared2 (IR2); 11.5 \(\upmu\)m-12.5 \(\upmu\)m). The spatial resolution of MI is 1 km \(\times\) 1 km in the visible channel and 4 km \(\times\) 4 km in the infrared channels at the nadir (Table 1). The COMS/MI provides visible and thermal IR radiance data at the top of the atmosphere every 3 h for the full disk and every 15 min for the regional area including the Korean Peninsula. We used the full-disk L1B data of COMS/MI from April 2011 to March 2015 to estimate the coefficients for SST retrieval and validate the calculated SST. These data were obtained from the Satellite Earth-Ocean science & edUcation Lab (SEOUL) of Seoul National University (SNU) and the National Meteorological satellite Center (NMSC) of KMA.  
NMSC/KMA performed COMS/MI infrared sensor calibration against the Infrared Atmospheric Sounding Interferometer (IASI) on board the Meteorology Operational Satellite-A (MetOp-A) [31] under the Global Space-based Inter-Calibration System (GSICS) [32] program. The correction coefficients of MI radiance data have been distributed through the NMSC website [33]. When comparing the BT between MI and IASI, the IR1 and IR2 channels used for SST calculation showed a positive bias of about 1 \({}^{\circ}\)C near 200 K in extreme cases; however, it was small and stable at a warm range of \(-\)25 \({}^{\circ}\)C with negligible biases such as 0.76 K, 0.34 K, 0.03 K at BT values of 220 K, 250 K, and 290 K, respectively [31]. Since the calculation range of SST is between \(-\)5\({}^{\circ}\)C-35\({}^{\circ}\)C, no additional correction of radiance measurements was performed in this study.
## 2 Materials and Methods  
Chemicals. Battery grade Li\({}_{2}\)CO\({}_{3}\) (purity \(\geq\) 99.99%) was supplied by China Shanghai Oujin Industrial Co., LTD. Other molten salt components, analytical-level Na\({}_{2}\)CO\({}_{3}\), K\({}_{2}\)CO\({}_{3}\), CaCO\({}_{3}\) and BaCO\({}_{3}\) (purity \(\geq\) 99.0%), were provided by the Changchun Chemical Reagent Plant of China. Nickel wire (2 cm \(\times\) 5 cm, active area = 10 cm\({}^{2}\)) and galvanized iron wire (1 cm \(\times\) 5 cm, active area = 5 cm\({}^{2}\)) from Hebei SSMP company were employed as anode and cathode to provide sites for electrochemical redox reactions. A corrosion-resistant crucible, produced by Tangshan Kai Ping Porcelain Chemistry Factory with Al\({}_{2}\)O\({}_{3}\) purity of 99%, was selected to contain molten salts at high temperatures.  
Methods. Li\({}_{2}\)CO\({}_{3}\), Na\({}_{2}\)CO\({}_{3}\), K\({}_{2}\)CO\({}_{3}\), CaCO\({}_{3}\) and BaCO\({}_{3}\) were dried under 150 \({}^{\circ}\)C in an air circulation oven for 12 h to avoid the effect of moisture, then mixed, ground evenly according to specific mass ratio and packed into the corundum crucible. The electrolyte was heated to 50 \({}^{\circ}\)C degrees above the melting point by the ceramic heating plate, equipped with a temperature controller, as Scheme 1 shows. After reaching the predetermined temperature, the molten salts electrolyzer needed to be stabilized for at least 0.5 h before electrolysis. The pretreated metal anode and cathode were inserted into the molten salts and current densities, or cell voltage was applied by high precision DC power supply (BK Precision 1715 A) across the two-electrode system. A lower current density of 15-25 mA/cm\({}^{2}\) was used to activate the Ni metal active site on the Fe cathode surface before expanding the current density to over 100 mA/cm\({}^{2}\) for rapid carbon deposition. Unless otherwise stated, the current densities described herein are expressed as cathode surface. The consumedenergy in heating and electrolysis could be substituted by wind, solar, tidal or other green renewable energy [21; 22; 23]. It was obvious that the cathode, removed from carbonate melts, was coated with a layer of solid black carbon after 1 A-h electric quantity. The deposited carbon was separated from the electrode surface by ultrasonic water bath initially. The carbon product was then washed in 1 M HCl for 48 h. The purpose of pickling was to remove the residual carbonates and dissolve the Fe or Ni that is reduced simultaneously with the carbon products. The pretreated carbon materials were to remove residual water content further via drying at 85 \({}^{\circ}\)C for at least 12 h.  
Characterization. To accurately observe the microstructure and analyze the elemental composition of the obtained carbon materials, a Zeiss SIGMA scanning electron microscope (SEM) and a British Oxford Instruments energy dispersive spectrometer (EDS) were employed to characterize the carbon products. And the specific surface area of new carbon-based materials was revealed by Brunauer Emmett Teller Tristar-3020 from Micromeritis Instrument Corporation.
# Design of Cloud-Based Real-Time Eye-Tracking Monitoring and Storage System
## 2 Materials and Methods
### Experiments  
Experimental sessions were carried out to assess the reliability of the proposed system architecture, comparing two different scenarios: local implementation (LI) and online implementation (OI). The local scenario involved configuring the system on the local computer, while the online scenario consisted of configuring the system on the online server.  
In order to measure the time delay of both scenarios (LI and OI), the timestamps of each platform were collected during a 100 s time window. The delay was calculated by subtracting the timestamp value of the experiment platform (A) from the timestamp received on the real-time results platform (B) (B-A), (i.e., arrival time-starting time). It is of note that platform (A) sends data to platform (B) at the frequency of 1 HZ (see Figure 1). The _Conse.log()_ function was used to visualize data in the experiment. Specifically, _Conse.log()_ is a function that allows the data given into the function to be seen outside the code environment. This function allowed us to capture the precise timestamps indicating the arrival and starting time of data effectively.  
Moreover, for the second experiment, 15 min of data were collected from the system at one-second intervals to understand how the system affects memory usage and how it changes over time while the eye-tracking model is performing real-life computations in the experiment platform. A _logMemoryUsage()_ function was used to record the memory usage measurements in real-time. Specifically, the _logMemoryUsage()_ function can be used for several purposes, such as analyzing the change in memory usage over time and detecting memory leaks or performance problems.
## 3 Results
### Driving Meteorological Factors of Carbon Fluxes
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Tasmax (\({}^{\circ}\)C)** & **Tas (\({}^{\circ}\)C)** & **Tasmin (\({}^{\circ}\)C)** & **Pr (mm)** \\ \hline \multirow{4}{*}{SSP1-2.6} & GPP & \(y=-4332.54+294.02x\)\({}^{**}\) & \(y=-4589.87+399.30x\)\({}^{**}\) & \(y=-1914.98+355.83x\)\({}^{**}\) & \(y=2233.66+0.14x\) \\  & ER & \(y=-4229.69+283.67x\)\({}^{**}\) & \(y=-4852.93+406.69x\)\({}^{**}\) & \(y=-2677.10+407.77x\)\({}^{**}\) & \(y=1921.450+0.29x\) \\  & NPP & \(y=-1397.67+98.07x\)\({}^{**}\) & \(y=-1292.52+122.26x\)\({}^{**}\) & \(y=-194.14+85.85x\)\({}^{**}\) & \(y=914.286+0.06x\) \\  & NEP & \(y=-102.84+10.36x\)\({}^{**}\) & \(y=263.07-7.39x\)\({}^{**}\) & \(y=762.12-51.94x\)\({}^{**}\) & \(y=312.215+0.16x\) \\  & GPP & \(y=-4306.76+290.88x\)\({}^{**}\) & \(y=-345.29+346.97x\)\({}^{**}\) & \(y=-2033.09+357.81x\)\({}^{**}\) & \(y=3129.95-0.71x\)\({}^{*}\) \\  & ER & \(y=-5199.02+324.58x\)\({}^{**}\) & \(y=-4729.06+396.08x\)\({}^{**}\) & \(y=-2944.22+423.13x\)\({}^{**}\) & \(y=2993.7-0.69x\)\({}^{*}\) \\  & NPP & \(y=-540.60+59.37x\)\({}^{**}\) & \(y=-312.96+64.839x\)\({}^{**}\) & \(y=-212.29+65.40x\)\({}^{**}\) & \(y=1069.232-0.23x\)\({}^{*}\) \\  & NEP & \(y=892.22-33.70x\)\({}^{*}\) & \(y=983.78-49.11x\)\({}^{*}\) & \(y=911.13-64.50x\)\({}^{*}\) & \(y=136.18-0.01x\) \\  & GPP & \(y=-2892.23+227.32x\)\({}^{**}\) & \(y=-2211.89+257.24x\)\({}^{**}\) & \(y=-997.61+270.36x\)\({}^{**}\) & \(y=451.02-0.09x\) \\  & ER & \(y=-3957.49+46.28x\)\({}^{**}\) & \(y=-3278.69+310.76x\)\({}^{**}\) & \(y=-1929.62+335.82x\)\({}^{**}\) & \(y=9151.51-0.09x\) \\  & NPP & \(y=120.29+30.24x\)\({}^{**}\) & \(y=299.88+329.26x\)\({}^{*}\) & \(y=521.22+42.00x\)\({}^{*}\) & \(y=2274.18-0.04x\) \\  & NEP & \(y=1065.26-40.93x\)\({}^{**}\) & \(y=1066.80-53.34x\)\({}^{*}\) & \(y=932.01-65.47x\)\({}^{*}\) & \(y=176.84-0.05x\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Trends and significance levels of carbon fluxes and meteorological factors.
## 3 Method
### CRF Layer  
Two common methods of sequence labeling are direct classification using softmax function and CRF model. Although the former can output the labels corresponding to the maximum probability of words, the labels are independent of each other and thus prone to unreasonable sequences. The CRF adds some constraints to the prediction labels to ensure their accuracy, so the CRF is chosen for sequence labeling in this paper.  
For a piece of text, the input text sequence is denoted by \(S=\{c_{1},c_{2},c_{3},\ldots,c_{n}\}\), and the output annotation sequence is denoted by \(y=\{y_{1},y_{2},\cdots,y_{n}\}\). For the prediction sequence \(y\), its score function is given by:  
\[Score(S,y)=\sum_{i=0}^{n}A_{y_{i},y_{i+1}}+\sum_{i=1}^{n}P_{i,y_{i}} \tag{15}\]  
where \(P_{i,yi}\) denotes the score of the \(y_{i}\) th tag of the \(i\) th character, \(A\) is the transfer score matrix, \(A_{y_{i},y_{i+1}}\) denotes the score transferred from tag \(y_{i}\) to tag \(y_{i+1}\), and \(y_{0}\) and \(y_{n}\) are the tags added at the beginning and end of the sentence. The output sequence with the highest score is predicted by Equation (9) at final decoding.  
\[y^{*}=\text{argmax}Score(S,y) \tag{16}\]  
The names of credit entities in the credit text of shipping enterprises are long and complex in composition, and there is the phenomenon of nested entities. For the characteristics of shipping enterprise credit text, a deep learning method based on multi-feature fusion is used to solve the above problems. BERT is introduced to enhance the character-level semantic representation; word-level features are obtained by reorganizing words through BMES after introducing stuttering subscripts, including word vector features, pos features, and word length features to fully extract text features and improve the entity recognition effect; and BiGRU is introduced to obtain the contextual features of the text to solve the recognition error problem caused by long entity names. Two one-way GRUs are combined together to form BiGRU to obtain the forward and reverse information, respectively, to fully extract the contextual features of the shipping enterprise credit text, and finally, by CRF into a sequence annotation task.
# Covert Communication for Integrated Satellite-Terrestrial Relay Networks with Cooperative Jamming
## 2 System Model and Problem Formulation
### Problem Formulation  
Since \(R\) always sends either the re-transmission or the jamming signals in the second time slot, we primarily pay attention to the first time slot. At this time, \(E\) attempts to use the related detection device to judge whether \(S\) transmits its source information to \(B\). Therefore, two types of detection error will occur inevitably, which are expressed as  
\[\begin{array}{l}P_{md}=\Pr\left(\,D_{0}|H_{1}\right)\\ P_{fa}=\Pr\left(\,D_{1}|H_{0}\right)\end{array}, \tag{2}\]  
where \(P_{md}\) and \(P_{fa}\) are the missed detection and false alarm probabilities, respectively.Clearly, \(D_{0}\) represents \(E\) judges that no transmission at \(S\), and \(D_{1}\) is the opposite.  
Without loss of generality, no matter what strategy \(E\) adopts, we can only realize the reliable covert communication if the following constraint is satisfied [32], namely  
\[P_{e}\overset{\Delta}{=}P_{md}+P_{fa}\geq 1-\varepsilon, \tag{3}\]  
where \(\varepsilon\) denotes any sufficiently small constant that satisfies \(\varepsilon>0\) and represents the requirement of covert communication, and \(P_{e}\) denotes PDE. The reliability of covert communication depends on whether (3) can be satisfied. For \(E\), both types of detection error are intolerable. On the contrary, \(S\), \(R\) and \(B\) tend to confuse \(E\) as much as possible to degrade its detection ability.  
In this letter, we pay attention to obtaining the maximum effective covert rate to fight against \(E\), which can be defined as  
\[R_{c}=(1-P_{out})R_{SB}, \tag{4}\]  
where \(R_{SB}\) denotes the required rate between \(S\) and \(B\), and \(P_{out}\) represents the OP for the ISTRN.  
Furthermore, we present a practical and effective scheme of power allocation for the ISTRN. We define the total transmitted power as \(P_{sum}=P_{S}+P_{R}\), which is fixed in our relay system, where \(S\) and \(R\) are treated as a whole. One variable factor \(\eta\) is set. \(P_{S}=\eta P_{sum}\), \(P_{R}=(1-\eta)P_{sum}\), and \(0<\eta<1\). Moreover, to ensure covertness, we should also consider the constraint of (3). Thus, we describe the optimization problem as  
\[\begin{array}{l}\underset{\eta}{\max}(1-P_{out})R_{SB}\\ s.t.\qquad P_{e}\geq 1-\varepsilon\\ 0<\eta<1\end{array}. \tag{5}\]
# Portrait Semantic Segmentation Method Based on Dual Modal Information Complementarity
## 5 Conclusions  
In this paper, we propose a new semantic segmentation method based on dual-modal information complementarity that is mainly designed based on the U-net basic model structure. This model uses two modal images, RGB image and gray image, which can solve the problem that the current U-net-based semantic segmentation model has poor segmentation accuracy in the face of different sizes of portrait edges and is prone to mis-segmentation phenomenon in low-light scenarios.  
We also propose the SK-ASSP module as a feature extraction module to extract the features of the two modal images respectively. The SK-ASSP module can adaptively adjust the convolutional kernel weights for different sizes of portraits. Meanwhile, direction mapping and distance mapping are used to effectively optimize the edge segmentation accuracy of the portrait. After that, we use the cross-modal complementary module to exchange information on the feature map of the two modes. In the skip connection stage, the encoder and decoder are connected by mixed-parallel attention module, and the feature maps are modeled in channel and coordinate dimensions, and mine the pixel context information of the portrait image in space and channel dimensions.  
Based on the above improvements, the model in this paper performs well in the enhanced dataset built under human matting, reaching 96.89% and 95.48% in PA and MIoU, which is a larger improvement than the benchmark model U-net in this paper, in both PA and MIoU; at the same time, it has a higher segmentation accuracy than the semantic segmentation models LinkNet, PSPNet, deeplab v3+, and Trans UNet. In the actual image segmentation effect, the model in this paper has a better segmentation effect on the details of portrait edge segmentation, such as fingertips. In the future, we will focus on how to lighten the model by reducing the number of parameters of the model while maintaining the high accuracy of the model, and obtain a portrait semantic segmentation model that is easier to deploy.  
Conceptualization, G.F.; methodology, C.T.; software, C.T.; validation, C.T.; resources, G.F.; writing--original draft preparation, C.T.; writing--review and editing, G.F. All authors have read and agreed to the published version of the manuscript.  
This research and the APC was funded by Guangdong Provincial Philosophy and Social Science Planning Project of China, Project Name: Personalized Guidance Research Based on Cross modal High Order Reasoning Model, grant number GD23 YJ708.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to contain portrait privacy information.  
The authors declare no conflict of interest.
## 3 Problem Formulation
### Communication Model  
Assume that in a three-dimensional Cartesian coordinate system, the UAV maintains a constant altitude \(H\), within which it can freely move on the corresponding horizontal plane. At time slot \(t\), the initial coordinates of the UAV are denoted as \(z(t)=[x(t),y(t),H]^{\top}\in R^{31}\), while its final position is represented as \(z(t+1)=[x(t+1),y(t+1),H]^{\top}\in R^{31}\). The position of the mobile device (MD) \(l\) is given by \(z_{l}^{md}(t+1)=[x_{l}^{md}(t+1),y_{l}^{md}(t+1),0]^{\top}\in R^{31}\). Thus, the Euclidean distance between UAV \(u\) and MD \(l\) at time slot \(t\) is expressed as  
\[d_{l,u}(t)=\sqrt{(x(t)-x_{l}^{md}(t))^{2}+(y(t)-y_{l}^{md}(t))^{2}+H^{2}}. \tag{9}\]  
Let \(\beta_{0}\) denote the channel gain at the reference distance \(d=1\) m. For the line-of-sight (LoS) link, the channel gain between UAV \(u\) and MD \(l\) is given by  
\[h_{l,u}(t)=\beta_{0}/(d_{l,u}(t)^{2}). \tag{10}\]  
In practical scenarios where obstacles may block the signal, the impact of such obstructions must be considered. The wireless transmission rate between UAV \(u\) and MD \(l\) is then expressed as  
\[r_{l,u}=Blog_{2}(1+P_{up}h_{l,u}(t)/(\sigma^{2}+P_{NLoS}b_{l,u}(t))). \tag{11}\]  
where \(B\) represents the signal bandwidth of the wireless communication, \(P_{up}\)denotes the uplink transmission energy of the MD, \(\sigma^{2}\) is the noise energy, and \(b_{l,u}(t)\) is a binary indicator of whether an obstruction exists between UAV \(u\) and MD \(l\) at time slot \(t\). Specifically, \(b_{l,u}(t)=0\) indicates no obstruction, while \(b_{l,u}(t)=1\) indicates the presence of an obstruction. \(P_{NLoS}\) represents the energy loss due to non-line-of-sight (NLoS) conditions caused by obstacles such as trees or hills in the environment. This model captures the dynamic nature of UAV-MD communication, incorporating both LoS and NLoS conditions to ensure accurate representation of real-world scenarios.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 8 Challenges and Future Directions
### Exploring Information Other Than RSL as the Basis of Retrieval  
At present, most of the studies on CML-based rainfall measurement use the maximum-minimum RSL, average RSL, instantaneous RSL, and other amplitude informations, and a few studies have used other informations, for example, the transmission error of a microwave link signal was used for the first time by [PERSON] et al. [130] to divide dry and wet weather. [PERSON] et al. [87] proposed a rainfall type distinguishing method by using the differential attenuation rate from multi-frequency and dual-polarization microwave links. In the research of using WIFI signals to identify indoor human activities, the channel state information (CSI) with phase information is commonly used as the retrieval basis. Therefore, CML-based rainfall measurement should also be considered to explore some phase information other than RSL as the basis of retrieval, which may be able to obtain more accurate and real-time rainfall spatial information.