esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 5 The Layered Architecture of the IoV and Emerging Technologies in Smart Cities
### Application Layer  
This layer is devoted to the deployment of smart applications, which make transportation better in terms of traffic safety and web-based utilities. By utilizing information processed and analyzed in the AI layer, it uses data to offer advanced services at the end-user level. The applications are meant to make transportation more efficient, secure, and user-friendly. The application layer helps achieve the overarching goal of building a more connected smart city, and helps promote a more connected and responsive vehicular ecosystem by generating predictive understanding achieved by utilizing the power of real-time data and intelligent algorithms.
# A Survey of Planning and Learning in Games
## Planning and Learning in Games
### Learning Applied to Planning
#### 5.2.1 Model-Based Reinforcement Learning  
As previously stated, model-based RL can be used for planning. A concrete example of this is the Imagination-Based Planner (IBP) [232], an algorithm devised to learn all the components of the planning process, more specifically, how to construct, evaluate, and execute plans, solely from experience. During this learning process, IBP is free to decide whether to act or imagine, and even how to imagine. Plans are derived by proposing imagined actions and recording the expected quality of possible sequences of these actions. When a sequence of actions (plan) with an expected high reward is encountered, IBP may decide to perform those actions on the environment (i.e., act).  
At a high level, IBP works as follows: at each step a manager decides whether to act or imagine. When acting, the controller performs an action on the environment (real-world), whereas when imagining (i.e., during an imagination step), the controller proposes an imagined action that is subsequently evaluated via a model of the world. The manager can decide on the number of imagination steps to perform and also on which previous imagined or real state to imagine from.  
All data generated during these steps, whether acting or imagining, is aggregated in a memory that is used to influence future decisions; see Figure 9.  
The Collaborative Action Planning with Intention Recognition (CAPIR) [233] architecture is yet another example. The focus of CAPIR is to provide a methodology to derive NPCs capable of assisting human players in collaborative games. At a very high level, the action planning process in CAPIR is implemented as follows. During the subtask planning phase (performed offline), the main task is subdivided into subtasks encoded as MDPs. VI is then used to derive the collaboratively optimal action policy for each of these MDPs. During runtime and at each step, Bayesian inference is used to infer the subtask currently being pursued by the human player, a process referred to as intention recognition. Given this knowledge, the NPC can use the appropriate action policy learned previously to determine the most suitable actions to be carried out in order to assist the human player in the intended subtask.
# Inter-System Differencing between GPS and BDS for Medium-Baseline RTK Positioning
###### Abstract  
An inter-system differencing model between two Global Navigation Satellite Systems (GNSS) enables only one reference satellite for all observations. If the associated differential inter-system biases (DISBs) are priori known, double-differenced (DD) ambiguities between overlapping frequencies from different GNSS constellations can also be fixed to integers. This can provide more redundancies for the observation model, and thus will be beneficial to ambiguity resolution (AR) and real-time kinematic (RTK) positioning. However, for Global Positioning System (GPS) and the regional BeiDou Navigation Satellite System (BDS-2), there are no overlapping frequencies. Tight combination of GPS and BDS needs to process not only the DISBs but also the single-difference ambiguity of the reference satellite, which is caused by the influence of different frequencies. In this paper, we propose a tightly combined dual-frequency GPS and BDS RTK positioning model for medium baselines with real-time estimation of DISBs. The stability of the pseudorange and phase DISBs is analyzed firstly using several baselines with the same or different receiver types. The dual-frequency ionosphere-free model with parameterization of GPS-BDS DISBs is proposed, where the single-difference ambiguity is estimated jointly with the phase DISB parameter from epoch to epoch. The performance of combined GPS and BDS RTK positioning for medium baselines is evaluated with simulated obstructed environments. Experimental results show that with the inter-system differencing model, the accuracy and reliability of RTK positioning can be effectively improved, especially for the obstructed environments with a small number of satellites available.  
GPS and BDS; inter-system differencing; differential inter-system biases (DISBs); real-time kinematic (RTK) positioning; medium baselines +
Footnote †: journal: remote sensing
###### Abstract  
The feasibility of using hyperspectral imaging with convolutional neural network (CNN) to identify rice seed varieties was studied. Hyperspectral images of 4 rice seed varieties at two different spectral ranges (380-1030 nm and 874-1734 nm) were acquired. The spectral data at the ranges of 441-948 nm (Spectral range 1) and 975-1646 nm (Spectral range 2) were extracted. K nearest neighbors (KNN), support vector machine (SVM) and CNN models were built using different number of training samples (100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 and 3000). KNN, SVM and CNN models in the Spectral range 2 performed slightly better than those in the Spectral range 1. The model performances improved with the increase in the number of training samples. The improvements were not significant when the number of training samples was large. CNN model performed better than the corresponding KNN and SVM models in most cases, which indicated the effectiveness of using CNN to analyze spectral data. The results of this study showed that CNN could be adopted in spectral data analysis with promising results. More varieties of rice need to be studied in future research to extend the use of CNNs in spectral data analysis.  
hyperspectral imaging; variety identification; rice seed; convolutional neural network +
Footnote †: journal: Applied Sciences
## 3 Results
### Growth Status of Rice Seedlings  
Two years of experimental data indicate that, overall, as the years progressed, the dry weight, plant height, and root length of seedlings treated with JCW exhibited a decreasing trend (Figure 6). In contrast, the dry weight and plant height of seedlings treated with JCWH and JCWF showed significant increases. Notably, the root length of seedlings under the JCWF treatment was longer in 2022. The variation in leaf age across all amendment treatments was minimal. Compared to the control group (JCK), all amendment treatments significantly enhanced the dry weight of seedlings by 67.31% to 149.21%, increased root length by 15.38% to 62.26%, and elevated plant height by 71.7% to 133.5%. After two years, seedlings treated with JCWH reached a height of 20.91 cm and a weight of 24.83 mg, demonstrating no significant difference when compared to the farmer's seedling-raising soil ZCK (\(p>0.05\)).  
A correlation analysis can further elucidate the relationship between the physical and chemical properties of amended soils, the soil organic carbon and its components, and the growth parameters of rice seedlings (Figure 7). All amendment treatments, including the application of straw, gypsum, sulfuric acid, and chemical fertilizers in soda saline-alkali soil, have demonstrated significant effects in improving the soil physical and chemical properties, enhancing soil fertility, and promoting seedling growth. The seedling growth parameters, such as seedling height (SH), leaf age (LA), and seedling dry weight (SDW), exhibited a negative correlation with the physical and chemical properties of soils, including total alkalinity (TA), exchangeable sodium (ES), and pH, while showing a significantly positive correlation with soil EC. Furthermore, the seedling growth parameters (SH, root length (RL), LA, and SDW) displayed a positive correlation with soil-fertility-related parameters, including SOC, HAC, FAC, HA_AlgK_, and PQ values, while demonstrating a negative correlation with FA_AlgK.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Treatment** & **PQ (\%)** & **HA\_AlgK** & **FA\_AlgK** \\ \hline (a) 2021 yr & & & \\ JCK & 44.04 \(\pm\) 0.54 c & 0.59 \(\pm\) 0.01 d & 0.96 \(\pm\) 0.01 a \\ JCW & 50.46 \(\pm\) 2.04 a & 0.71 \(\pm\) 0.02 b & 0.94 \(\pm\) 0.02 ab \\ JCWH & 47.16 \(\pm\) 1.14 b & 0.74 \(\pm\) 0.01 a & 0.92 \(\pm\) 0.02 b \\ JCWF & 47.64 \(\pm\) 1.18 b & 0.66 \(\pm\) 0.00 c & 0.91 \(\pm\) 0.00 b \\ \hline (b) 2022 yr & & & \\ JCK & 43.44 \(\pm\) 0.13 b & 0.58 \(\pm\) 0.01 c & 0.96 \(\pm\) 0.01 a \\ JCW & 50.30 \(\pm\) 0.30 a & 0.73 \(\pm\) 0.01 b & 0.93 \(\pm\) 0.06 a \\ JCWH & 50.18 \(\pm\) 0.35 a & 0.79 \(\pm\) 0.00 a & 0.85 \(\pm\) 0.01 b \\ JCWF & 45.38 \(\pm\) 2.22 b & 0.72 \(\pm\) 0.01 b & 0.83 \(\pm\) 0.01 b \\ \hline \end{tabular} Figures sharing similar small letters in columns in a year are not statistically different at \(p=5\%\).  
\end{table}
Table 2: The effects of different treatments on seedling-soil PQ value and \(\mathcal{A}\)lgK value.
# A Parametric Identification Method of Human Gait Differences and its Application in Rehabilitation
## 4 Discussion  
### Effects of Shape Parameters in Rehabilitation  
According to the abnormal shape parameters of the patient group, besides representing abnormal gait, they have some differences in the excess degrees from normal ranges in different rehabilitation phases, which indicates that parameters could assist in evaluating the recovery status of patients. Furthermore, from the graphical results, the largest differences from the normal distribution areas were the second and third amplitude values for the hip, the third for the knee, and the first and fifth for the ankle. These outcomes were the same with the distributions of the primary abnormal parameters of the three joints, so it could demonstrate that the weights of these primary abnormal parameters here were greater than that of other abnormal parameters.  
In line with this idea, it can be considered that when these weighted abnormal parameters exceed the normal ranges, the recovery situation is not ideal; if they belong to the normal ranges while other abnormal parameters slightly exceed the normal ranges, the recovery state can be considered good. Moreover, the higher the abnormal parameters exceed the values, the farther they are away from the normal ranges. With this information, patients with unsatisfactory recovery can be further judged. For the hip joint, patients E and F basically restored normal hip rotation, and the joint function of patient C was basically normal while the hip function of patients A, B, and D still seriously suffered due to the ankle injury. It should be noted that patient D was less influenced than patient B than patient A. For the function of the knee joint, the influence degrees of patients D, E, and F were smaller compared with patients A, B, and C. Furthermore, patient C was less than patient B than patient A. For the ankle joint, the recovery of patients E and F was better than patients C and D while patients A and B were relatively poor in rehabilitation.  
All in all, the ankle function of patients A and B was still out of condition since they were in the second month of convalescence, and the knee and hip function were significantly affected. In the third month of rehabilitation, although the ankle function of patients C and D improved, it still mainly influenced the hip joint. By the sixth month of the recovery period, the three joint functions of patients E and F were basically restored to normal.
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 4 Methodology
### Ship Detection Based on One-Stage Object Detection Model
#### 4.1.2 Squeeze-And-Excitation (SE) Module  
Traditional object detection algorithms primarily focus on extracting spatial dimensional features, often neglecting the dependencies between channels. This limitation may hinder the performance of the network in complex tasks, particularly in the context of intricate waterway scenarios. To address this, this paper proposes the use of the squeeze-and-excitation (SE) network module to dynamically adjust the feature weights between channels, thereby enhancing the representational power of the convolutional neural network (CNN). The SE module introduces a feature recalibration mechanism, dynamically adjusting the weights of each channel. This enables the network to focus more on valuable features and suppress redundant or irrelevant information, ultimately improving the overall performance of the model.  
The workflow of the SE module can be summarized as two core operations. First, the input \(X\) is a multi-dimensional tensor, typically represented as \(X\,\in R^{H^{\prime}}\times W^{\prime}\times C^{\prime}\). Then, the input \(X\) undergoes a convolution operation, \(F_{tr}\), to generate the output feature map, \(U\,\in R^{H\,\times\,W\,\times\,C}\), as follows:  
\[u_{c}=v_{c}\,*X \tag{5}\]  
where \(u_{c}\) denotes the feature map of the c-th channel, and \(v_{c}\) represents the convolutional kernel for the c-th channel.  
In the context of inland waterway scenarios, the SE module replaces traditional global average pooling with global weighted pooling to capture global information and emphasize key regions, such as vessels and obstacles. For each channel, \(c\), global weighted pooling computes a channel descriptor, \(Z_{c}\), which represents the global information of that channel. The specific formula is as follows:  
\[Z_{c}=F_{\text{sq}}(u_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}w(i,j)\cdot u_{c}(i,j) \tag{6}\]In this case, \(\rm{w(i,j)}\) represents the spatial weights, which effectively increase the importance of specific regions in waterway monitoring. The SE module then constructs a nonlinear mapping through two fully connected layers, transforming these global descriptors into weight coefficients for each channel. Finally, feature dynamic recalibration is achieved by applying the weight coefficients to the input features.
# A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
## 2 Materials and Methods
### Methodology--Model Development
#### 2.3.2 Water Column Correction  
As solar radiation penetrates the sea surface, it interacts with the water column and its constituents. This kind of light propagation reduces its intensity before the radiation reaches the sea bottom [18]. The decreased irradiance energy, usually called light attenuation, is dependent on the degree of water absorption and scattering processes. [PERSON] (1981) [19], introduced an image-based method (water column correction) for compensating for the effect of light attenuation due to absorption and scattering caused by depth variability. In essence, this technique equalizes the spectral signature of the sea bottom by producing a depth-invariant index determined from pairs of spectral bands. The [PERSON] method can be used instead of estimating the seabed reflectance; this technique was implemented in the particular marine area of our study, which consists of the various seabed sediments and depth variability.  
Three areas were selected (indicated with the letters (A), (B), and (C)) to correct the Sentinel-2A image for the water column effect, with depth ranges of (A) 8-15 m; (B) 2-9 m; and (C) 10-15 m (Figure 5). Subsequently, the sunlight corrected reflectance of Sentinel-2A spectral bands (\(Degl(R_{Bi})\)) were re-modelled using natural logarithms (Equation (2)), in order for their variation to become linear:  
\[X_{Bi}=ln(Degl(R_{Bi})) \tag{2}\]  
The ratio of attenuation coefficients \(K_{d}(\text{Bi})\) was then calculated between pairs of spectral bands, using data within each marine area of the same seabed sediment, but with different depths. The ratio pairs validated in this study were B2/B3, B2/B4, and B3/B4, with the ratio of each band pair determined by Equations (3) and (4):  
\[\frac{K_{Bi}}{K_{Bj}}=a+\sqrt{(a^{2}+1)} \tag{3}\]  
\[a=\frac{\sigma_{ii}-\sigma_{jj}}{2\times\sigma_{ij}} \tag{4}\]  
where \(\sigma_{ii}\), \(\sigma_{jj}\) in Equation (4) are the variances of the deglinted reflectances of bands \(B_{i}\) and \(B_{j}\) (i.e., B2 and B3) and \(\sigma_{ij}\) is the covariance between bands \(B_{i}\) and \(B_{j}\).  
Figure 4: The original Sentinel-2A image (left) and the deglinted image (right), for the study area shown in Figure 1.  
The depth-invariant index (DII) is a parameter that represents the image correction due to the water column interaction (i.e., absorption and scattering) and was computed for each band pair using Equation (5):  
\[DII=ln(Degl(B_{i}))-\left[\left(\frac{K_{Bi}}{K_{Bj}}\right)\times ln\left(Degl \left(B_{j}\right)\right)\right] \tag{5}\]  
Figure 6 presents the comparison between the DII and the deglinted image, indicating the spectral contrast of the seabed texture, which is better in the case of the water column corrected image (Figure 6, right).
# A Mobility-Aware Adaptive Duty Cycling Mechanism for Tracking Objects during Tunnel Excavation
## 2 Background and Related Work
### Adaptive Duty Cycling Based on Mobility
A Broader Node (BN) is kind of a sink node that gathers information about the target region and communicates with other broader nodes as necessary. BN-MAC tries to guarantee end-to-end (source node to BN) reliability. Topology changes do not affect the performance of BN-MAC because all nodes maintain the information of only one-hop neighbor nodes that rarely changes. During end-to-end data transmission, BN-MAC tries to reduce the number of same data retransmission by sender nodes using an automatic packet buffering process. Therefore, intra-cluster communication between BN and member nodes is managed with an asynchronous method and inter-cluster communication among BNs is managed with a synchronous method. Our mechanism also considers the mobility and trade-off between energy efficiency and data transmission latency. However, the main difference between our mechanism and previous studies is that the proposed mechanism adaptively adjusts the node's active period as its mobility. If a node quickly moves, its active period increases to track its location.
# Kinematic Analysis and Verification of a New 5-DOF Parallel Mechanism
## 2 Design and Verification of the Scheme
### Verification of the Primary Scheme  
#### 2.2.1 Verification of Degree of Freedom by Grubler-Kutzbach Formula  
The number of degrees of freedom determines whether the designed mechanism meets the requirements, so the analysis of the degrees of freedom is particularly important. The traditional Grubler-Kutzbach method is relatively simple, it only depends on the number of components and kinematic pairs of the mechanism, and the degree of freedom of the mechanism can be obtained only through the most basic mathematical calculations. Therefore, this paper firstly uses Grubler-Kutzbach method to verify the degree of freedom of the mechanism. The main contents of Grubler-Kutzbach method are as follows:  
In three-dimensional space, when a component is free before it is connected with other components by kinematic pairs. The \(n\) components including the frame have \(6(n-1)\) degrees of freedom. When one component is connected to another component with a kinematic pair, the component is constrained to a certain extent and its degree of freedom is reduced. When the two components are connected by a kinematic pair with \(f_{i}\) degrees of freedom, the degree of freedom of the original system is reduced by \(6-f_{i}\) due to increased  
Figure 1: Design sketch of parallel mechanism.  
constraints. When the number of kinematic pairs continues to increase to \(g\), the degree of freedom of the system is reduced by \((6g-\sum\limits_{i=1}^{g}f_{i})\)  
At this time, the degree of freedom of the mechanism is  
\[M=6(n-g-1)+\sum\limits_{i=1}^{g}f_{i} \tag{1}\]  
where \(M\) is the degree of freedom of the mechanism (Mobility); \(n\) is the total number of components of the mechanism; \(g\) is the number of kinematic pairs of the mechanism; \(f_{i}\) is the number of degrees of freedom of the \(i\)th kinematic pair.  
As shown in Figure 1, the mechanism is composed of one fixed platform, one moving platform, seven connecting links, and five sliders, so the total number of mechanism components is \(n=14\). The mechanism has seven Hook joints, six mobile pairs, and five spherical hinges, so the number of moving pairs is \(g=18\). The degree of freedom of each Hook joint is two, the degree of freedom of the mobile pair is one, the degree of freedom of the spherical hinges is three, and the sum of the degrees of freedom of the moving pair is \(\sum\limits_{i=1}^{g}f_{i}=2\times 7+1\times 6+3\times 5=35\), so  
\[M=6(n-g-1)+\sum\limits_{i=1}^{g}f_{i}=6\times(14-18-1)+35=5 \tag{2}\]  
That means the mechanism has five degrees of freedom, the five sliders are used as the driving part, and the mechanism can achieve a certain movement.
# Study on the Method of Moving Load Identification Based on Strain Influence Line
###### Abstract  
In order to improve the accuracy of load identification and study the influence of transverse distribution, a novel method was proposed for the moving load identification based on strain influence line and the load transverse distribution under consideration. The load identification theory based on strain influence line was derived, and the strain integral coefficient was proposed for the identification. A series of numerical simulations and experiments were carried out to verify the method. The numerical results showed that the method without considering the load transverse distribution was not suitable for solving the space problem, and the method with the load transverse distribution under consideration has a high identification accuracy and excellent anti-noise performance. The experimental results showed that the speed identification error was smaller than \(\pm\)5%, and the vehicle speed had no obvious influence on the identification results of the vehicle weight. Moreover, the average identification error of the vehicle weight was smaller than \(\pm\)10%, and the error of more than 90% of samples was smaller than \(\pm\)5%.  
moving load identification; strain influence line; load transverse distribution; strain integral coefficient; identification error