esa-sceva/satcom-chunk-collection huggingface dataset:
# A Process-Oriented Method for Tracking Rainstorms with a Time-Series of Raster Datasets
## 4 Results and Discussion  
In this section, two cases are used to evaluate our proposed PoAIR algorithm. In one, a process-oriented rainstorm dataset is simulated to investigate the performance of PoAIR compared to that of the original TITAN algorithm [8]. In the other, a real precipitation dataset is used to analyze the dynamic characteristics of a given rainstorm process.
# A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars
## 3 Backgrounds
### Mobile Mode Analysis for Traffic Multi-Agent  
For the mixed traffic flow, [PERSON] et al. [19] have already explained the concept of which components are the various categories of vehicles. [PERSON] et al. consider the design of a real-time cooperative eco-driving strategy for a group of vehicles with mixed automated vehicles (AVs) and human-driven vehicles (HVs), proposing a receding horizon model predictive control (MPC) method to minimize the fuel consumption for platoons and drive the platoons to pass the intersection on a green phase [20]. To better integrate ITS into a whole, first, it is necessary to analyze and summarize the information characteristics of the user side, as shown in Figure 2.  
Figure 2 illustrates a mixed vehicle traffic flow consisting of terms, ordinary vehicles, and autonomous vehicles, where ordinary vehicles and autonomous vehicles can be divided into communication with the RSU and no communication. The wireless network provided by RSU can provide communication services for many mobile users. Different types of vehicles communicate with the RSU through a wireless link, forming multiple mobile units, and all RSUs are connected by high-speed wired Ethernet. Vehicles in the RSU coverage area can use V2I communication to communicate with the RSU, while vehicles outside the RSU coverage area can forward information with V2V communication of other vehicles. The movement pattern of different vehicles in mixed traffic flow is an important factor affecting the design of urban traffic-communication systems.  
For the proposed layered hybrid roadside unit deployment scheme with t-RSU, the problem of how to select the appropriate vehicle as t-RSU is first addressed. Then, the existing t-RSU is dynamically updated. Since t-RSUs are not always static and the number and location of parked vehicles may change over time, sufficient connection robustness is required in this case.
## 7 Conclusions  
Overall, during multi-to-multi space collaborative detection in geostationary orbit, intelligent satellites avoided collisions with detection satellites through strategy optimization, while detection satellites achieved comprehensive observation of intelligent satellites through efficient mission planning and optimization strategies, demonstrating their maneuverability and collaborative detection advantages in complex space environments. Furthermore, a good balance was achieved between detection success rate, fuel consumption, mission time cost, and target coverage. The proposed algorithm demonstrated high applicability and robustness under complex constraints, validating the performance and application value of the Nash equilibrium-based distributed heterogeneous cluster task planning method in complex detection scenarios.  
The results clearly demonstrate the strong capability of the Nash equilibrium-based distributed task planning method in coordinating heterogeneous satellite cluster missions. The algorithm not only maximizes detection success rate but also significantly optimizes key metrics such as fuel consumption and mission time cost. By introducing a weighted optimization mechanism for comprehensive performance metrics, the proposed method exhibits high adaptability and robustness under multi-objective constraints, ensuring balanced optimization of all mission metrics when facing complex space mission requirements. This fully proves the broad application potential of the Nash equilibrium-based distributed task planning method in complex space detection missions. Particularly for future more complex dynamic space missions requiring efficient cluster coordination and intelligent mission planning, this study provides an effective method with significant academic value and practical importance.  
Conceptualization, H.G., J.W., Q.Y. and D.Z.; methodology, H.G., P.W. and D.Z.; software, H.G., J.W., P.W. and D.Z.; validation, X.X., J.W., Q.Y., P.W. and D.Z.; formal analysis, H.G., X.X. and Q.Y.; investigation, [PERSON], [PERSON], [PERSON] and Q.Y.; resources, [PERSON], [PERSON], [PERSON] and Q.Y.; data curation, [PERSON], [PERSON], P.W. and D.Z.; writing--original draft preparation, [PERSON], [PERSON], P.W. and D.Z.; writing--review and editing, [PERSON], [PERSON], P.W. and D.Z.; visualization, [PERSON], [PERSON], P.W. and D.Z.; supervision, [PERSON] and D.Z.; project administration, [PERSON], Q.Y. and D.Z.; funding acquisition, [PERSON], Q.Y. and D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grants 52377215, 51977177); Northwestern Polytechnical University Education and Teaching Reform Research Project (2025)GG03); Shanghai Academy of Spaceflight Technology Funded project (SAST2023-088); the \"Double world-class project\" discipline construction fund of Northwestern Polytechnical University (WI00001049).  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.
# Accuracy Analysis of the Measurement of Centre of Gravity and Moment of Inertia with a Swing
## 2 Materials and Methods
### Basic Equations
The period \(T\) of a damped oscillation can be evaluated based on:  
\[y(t)=\alpha(t)=A\ e^{-\lambda t}\ \ \cos(\frac{2\pi t}{T}+\phi)\to T=2\pi t\ \ \left(\arccos(\frac{\alpha}{A\ e^{- \lambda t}})-\phi\right)^{-1} \tag{5}\]  
In this case, the time depending amplitude \(y(t)\) is identical to the measured angle \(\alpha\). \(t\) represents the time. The oscillation starts with the initial amplitude \(A\). Two further constants \(\lambda\) and \(\phi\) represent the decay constant and the phase angle. The dynamic measurement provides a time series of angles \(\alpha\) in relation to the time vector \(t\), which can be used to fit Equation (5). For the further analysis, only the time period \(T\) is needed and all other constants can be used as control values. The resulting moment of inertia output based on Equation (4) is referenced around the pivot point and, to compare both pivot axes, the parallel axis theorem is used to  
Figure 3: Schematic visualisation of the acting forces \(F\) and distances \(d\) from the pivot point for the swing, additional model and masses: (**a**) empty swing, which is in balance represented by an angle \(\alpha=0^{\circ}\) (**b**) exemplary mass added so that \(\alpha=10^{\circ}\). calculate the MI around the centre of gravity \(I\) in relation to the \(I_{P}\), mass \(m\) and distance \(d_{S}\) as following:  
\[I_{P}=I+m\ d_{S}^{2}\to I=I_{P1}-m\ d_{SP1}^{2}=I_{P2}-m\ d_{SP2}^{2} \tag{6}\]  
This connection is further used to calculate the MI of the empty swing (Section 3.3). The specific MI around the two different pivot points are important for the further usage of this experimental set-up to evaluate the MI of added model \(I_{M}\). The connection is shown in Equation (7) for the pivot point P1 and can be transformed to alternative reference points based on the parallel axis theorem Equation (6). \[I_{P1 mes}=\frac{T^{2}}{4\tau^{2}}\ \ (m_{S}+m_{M})\ g\ d_{Smes}=I_{P1}+I_{MP1} \tag{7}\]  
The presented equations for the calculation of the individual vales for CG as well as the MI are further used to analyse the conducted measurements. All those analyses are conducted with the software MATLAB.
## 5 Comparison  
### Comparison between Different Controllers with Time History Performance  
Figure 14 illustrates the effectiveness of various control methods in mitigating vibrations under the most challenging conditions. The comparison encompasses positive position feedback (PPF), negative velocity (NVC), and propositional-derivative (PD) controllers. The results demonstrate that the PPF proves most effective in controlling and minimizing vibrations.
# Fast Conflict Detection for Multi-Dimensional Packet Filters
## 4 An Efficient Conflict Detection Algorithm for Non-Prefix Fields
### Combined Detection Method for 5D Conflict Detection
Based on the analytical results, all conflicting filter pairs can be divided into the three combinations listed in Table 3. The sets of non-prefix field combinations of Combinations 1-3 are expressed as 3_Dset1_, 3_Dset2_, and 3_Dset3_, and the relationship between these sets is 3_Dset1_\(\supset\) 3_Dset2_\(\supset\) 3_Dset3_. From this relationship, we can determine the following:  
1. When the non-prefix field combinations belong to 3_Dset3_, it means that we can find any two non-prefix fields \(s\) and \(t\) of filters \(F\) and \(G\) which satisfy Condition 2 defined in Section 3. Regardless of the combination to which the prefix field comparison results belong (except for the empty set), conflicts occur after combined detection. 2. When the non-prefix field combinations belong to 3_Dset2_, regardless of whether the prefix field comparison results belong to Combination 1 or 2, conflicts occur after combined detection because we can find at least one prefix field \(x\) and non-prefix field \(s\) of filters \(F\) and \(G\) which satisfy Condition 2. 3. When the non-prefix field combinations belong to 3_Dset1_, conflicts may not occur after combined detection with the prefix field comparison results that belong to Combination 2 because some non-prefix field combinations belong to 3_Dset1_ but do not belong to 3_Dset2_. Similarly, when the non-prefix field combinations belong to 3_Dset2_, conflicts may not occur after the combined detection with the prefix field comparison results that belong to Combination 3. In this situation, we may not find any two fields \(i\) and \(j\) of filters \(F\) and \(G\) which satisfy Condition 2. Our combined detection method analyzed all combinations for 5D filter conflict, which can reduce the complexity of the original 5D conflict detection according to the predefined information of prefix fields and non-prefix fields sets. It can also be applied to any existing 2D filter conflict detection algorithm that extends to 5D conflict detection because the combined detection method compares non-prefix fields based on the results of prefix-field comparison.
# Quality of Experience Experimentation Prediction Framework Through Programmable Network Management
## 5 Discussion and Data Analysis
### Models Description and Neural Network  
As described earlier, for each architecture we tried different hyper-parameters to see the best fit for the proposal. More precisely, we varied the following: 1. the use of cross-validation folds to protect against overfitting by partitioning the dataset into folds and estimating accuracy on each fold (from 2 to 50 folds); 2. number and types of splits (diversity indexes, surrogate decision splits); 3. training algorithm; 4. number of layers in a network; and 5. number of neurons for a layer. A total of 30% of our data was randomly taken out of the training phase to use for validation. Ultimately, we considered the classification of all features and their classes as shown in Table 8 and the use of a neural network on MOS prediction using the Bayesian regularization with 2 layers and 10 neurons, using mean squared error for performance rating. This resulted in a 0.999 regression value which means that the correlation between the output and the target is highly accurate. So, after comparing and contrasting the classification metrics used, we recommend the mentioned models above for each one of the features as an accurate prediction method. The study shows that these models are quite accurate when it comes to DASH-related streaming data and real user MOS. The neural network method with the selected options stated above tend to be more accurate towards small or noisy datasets. However, it takes more time and computing power and will be inefficient to be placed within a test bed for auto prediction and redirection of resources. For such noisy data, prediction methods must be tested and uniquely selected for the prediction of each and every feature that is dependent on all other features, Figure 10 shows the framework bit rate precision result after choosing bagged trees, whereas Figure 11 shows the framework's resolution prediction results after choosing fine tree. The main takeaway from these two figures is to show the differences between the two methods on the same data. This helps our research by identifying and reading correctly predicted true classes and how the methods react to noisy data such as ours.
# Effect of Slag Particle Size on Fracture Toughness of Concrete
###### Abstract  
The effects of particle size of ground granulated blast furnace slag (GGBS) on the fracture energy, critical stress intensity, and strength of concrete are experimentally studied. Three fineness levels of GGBS of 4000, 5000, 6000 cm\({}^{2}\)/g were used. In addition to the control mixture without slag, two slag replacement levels of 20% and 40% by weight of the cementitious material were selected for preparing the concrete mixtures. The control mixture was designed to have a target compressive strength at 28 days of 62 MPa, while the water to cementitious material ratio was selected as 0.35 for all mixtures. Test results indicate that using finer slag in concrete may improve the filling effect and the reactivity of slag, resulting in a larger strength enhancement. The compressive strength of slag concrete was found to increase in conjunction with the fineness level of the slag presented in the mixture. Use of finer slag presents a beneficial effect on the fracture energy (G\({}_{\text{F}}\)) of concrete, even at an early age, and attains a higher increment of G\({}_{\text{F}}\) at later age (56 days). This implicates that the finer slag can have a unique effect on the enhancement of the fracture resistance of concrete. The test results of the critical stress intensity factor (K\({}^{\text{S}}_{\text{IC}}\)) of the slag concretes have a similar tendency as that of the fracture energy, indicating that the finer slag may present an increase in the fracture toughness of concrete.  
fracture toughness; blast furnace slag; particle size; compressive strength; concrete +
Footnote †: journal: Applied Physics Letters  
_Article_  
Effect of Slag Particle Size on Fracture Toughness of Concrete
## 3 Results  
### Coastline Detection  
An overview of the coastline change classification on Level 2 (see Section 2.3.4.) is given in Figure 7. It shows the distribution of classification transects ranging from extreme erosion with change rates of less than \(-5\) m/year (in red) to extreme accretion representing prograding coastlines with change rates over \(+5\) m/year (in green). A quantitative analysis of countrywide coastline change is provided in Table 3, showing that 45.2% of the Vietnamese coast experienced no significant change (i.e., \(-0.5\) m/year and \(+0.5\) m/year) over the period from 1986 to 2021, while 27.7% of the coast was affected by erosion, and 27.1% by accretion. Within the erosion class, 39.2% of the coast experienced extreme erosion. The coastal accretion class shows a similar pattern, with 45.5% of the coasts being exposed to extreme accretion. Overall, the coastline dynamics of Vietnam show a threefold picture of unchanged conditions, extreme erosion, and extreme accretion.
## 3 Antwerp Parks Survey Campaign  
### Study Area  
This study was conducted during 22 days in August and September 2013. The data were gathered in eight different urban parks located in the city of Antwerp (Figure 1). Antwerp has a population of about 500,000 people and an economy centered around an important European harbor. Therefore, the traffic going to and from the city has a big impact on the urban sound environment.  
The investigated parks, chosen in cooperation with the city council environmental authority, were expected to thoroughly represent the Antwerp park environment. They were located in different urban areas: city center (Stadspark), near the big highway junction (Rivierenhof) and in various living districts (six other parks). Moreover, their sizes also varied considerably from the smallest park (Bischoppenhof, around 0.03 km\({}^{2}\) in size) to the largest park (Rivierenhof, around 1.3 km\({}^{2}\) in size).
# Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
## 2 Materials and Methods
### Yield Data and Predictive Models
To find the best _ntree_ value, the prediction error rate for a range of 0 to 500 was calculated, and the one that presented the lowest error before stabilization was selected (\"elbow rule\") [48]. For this study, the setting 100 for _ntree_ was chosen for both the spectral bands and VIs models. For _mtry_, values from 2 to 45 (maximum number of predictors variables, excluding those where variance was close to zero) were applied for the spectral bands with 22 being used. For the VIs models, values from 2 to 9 were tested and selected. In both cases, the _mtry_ selection was the one that presented the lowest RMSE. For both models (RF and MLR), the independent variables were the reflectance values of the spectral bands (totaling 45 spectralband variables, 5 bands times 9 dates) and derived VIs (totaling 36 VIs variables, 4 VIs times 9 dates). The observed sugarcane yield (measured by the on-board sensor-system) was the dependent variable to be predicted. All models were compared considering the R\({}^{2}\), RMSE, and MAE.
# Measurement and Modeling of Narrowband Channels for Ultrasonic Underwater Communications
## 6 Conclusions  
In this paper, the channel in ultrasonic UAC is studied, an essential point to foster the development of more efficient ultrasonic underwater modems that can be employed in sensor nodes and sensor networks. A campaign of measurements in Mediterranean shallow waters is presented and analyzed to extract parameters like the channel attenuation, Doppler spread and the fading statistics. The problem of modeling the fading in such channels is also addressed, and a review of different classical statistical models is provided, with emphasis on a model that has been recently proposed for this environment: the \(\kappa\)-\(\mu\) shadowed distribution. The measurement results are used to validate the usefulness of this model in the ultrasonic band. Moreover, a closed-form expression obtained from the model has been used to calculate the ergodic capacity of the channels.  
This work has been partially supported by FEDER and the Spanish and Andalusian Governments, under Projects TEC2014-57901-R and P11-TIC-8238, respectively; and by the Spanish company Sociedad Anonima Electronica Submarina (SAES). [PERSON] is the initiative and project manager of the work. The measurement campaign was carried out by [PERSON], [PERSON], [PERSON] and [PERSON]. The data analysis, fading modeling and capacity estimation were conducted by  
Figure 7: Evaluation of the ergodic capacity (by means of the model) for the measured channels with TS = 200 m and ASD = 25 m.  
[PERSON], [PERSON] and [PERSON]. The initial manuscript was written by [PERSON] and [PERSON]. All of the authors were responsible for the development, revision and proofreading of the final manuscript.
## 2 Applications and Background of Uwsn
### Applications of Uwsn
#### 2.1.3 Environmental Monitoring  
[PERSON] and [PERSON] propose a underwater pollution detection system with decentralized ad-hoc Uwsn [15]. The Authors improved the life time and reliability of the detection system and have shown the necessary simulation results with different case studies. A real-time research is done on Underwater Acoustic Network (UAN) test bed for ecological monitoring in Qinghai Lake from which information about water course, nature and living life of the lake can be achieved [16]. As it's a salt water lake and also undergoes many natural hazards, it is closest to the real-time deployment of Uwsn in the marine environment. It is another major step in the real-time surveillance of sea area which covers the most part of the water bodies. Researchers in [17] have proposed an underwater group based sensor network for marine farms which can accurately measure the amount of food wasted and dirt deposited on the sea bed.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 2 K-L-Based Local Path Planning Algorithm
### Adaptive Clustering Method Based on Kohonen Network
#### 2.2.2 Analysis of Discrete State  
The states in a dynamic environment are discrete states. Setting up discrete states is usually achieved by dividing them based on the maximum distance sensed by the sensors and the distance between the sensors and the obstacles. Excessively coarse segmentation can lead to suboptimal path planning for AGVs, whereas excessively fine segmentation can lead to \"dimensional disaster\" problems and slow down the convergence of the algorithm. For example, when the shape of the obstacle is regular, the sensor and the obstacle distance generally only need to be divided into four states, indicating that the obstacle is one-third distance or two-thirds distance from the sensor, as well as the maximum measurement distance of the sensor and the range beyond. However, when the shape of the obstacle is irregular, the division accuracy needs to be continuously improved to ensure the learning efficiency of the algorithm.  
The following table shows the \"state action\" pairs of the Q-learning algorithm for AGVs with varying segmentation accuracies. In this case, the number of sensors of the AGV is five, and the number of allowed actions is three. As shown in the table below, the \"state-action\" pairs of the Q-learning algorithm increase dramatically with the number of divisions, making it difficult to determine which division is the most appropriate. Moreover, in the actual learning process, there are many similar states, but because each state is learned and an action is selected by looking up the table to traverse each state, this considerably affects the learning efficiency of the algorithm.  
Given the above problems, we use automatic environment recognition and the automatic environment information classification function of a Kohonen network to classify the environmental information and reduce the size of Q-table as much as possible to speed up the convergence of the algorithm.  
Assuming that the number of neurons in the output layer of the Kohonen network is 50 and the number of AGV sensors is 5, according to Section 2.2.1, we can calculate the total number of states as 300. Table 1 shows that the current total number of states is significantly reduced. In addition, with the help of the Kohonen network, there is no need to consider how to divide the accuracy more reasonably.