esa-sceva/satcom-chunk-collection huggingface dataset:
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 3 Results and Discussion
### Acoustic Effects of Heavy Rain  
Lastly, the sound of heavy rainfall hitting the hive boxes can also cause sufficient changes in the recorded audio to make monitoring systems fail. In our experiment, we relied on weather information to pinpoint periods of the day during which precipitation amounts greater than 9 mm were present. Figure 13 shows a comparison between the spectrograms of a hive during a typical night (plot a) and the same hive under heavy rainfall (plot b). As can be seen, rainfall sounds can severely affect multiple frequency ranges of the hive, increasing the recorded sound in some bands by as much as 10 dB, and thus is likely to deteriorate monitoring performance. The histogram of the distribution of the first MFCC shown in Figure 14 shows the impact that rainfall can have on this parameter. Lastly, Figure 15 shows the changes in the DWT components with and without rain artifacts. Unlike the urban and speech effects, which were transient and lasted a few seconds, rain sound can be much longer, potentially contaminating several hours of data, thus severely affecting the automated monitoring system.  
Figure 14: Comparison of the first MFCC histogram with and without rain.  
Figure 13: Spectrogram comparisons (**a**) without and (**b**) with heavy rainfalls.
# Sensing Hydrogen Seeps in the Subsurface for Natural Hydrogen Exploration
## 2 Materials and Methods
### Sensor Stability  
All of the hydrogen sensing measurements were performed using the GA5000 sensor (Geotech, Coventry, UK). The sensor was initially calibrated by connecting multicomponent standard (supplied by BOC 89 ppm \(\pm\) 4 ppm hydrogen, 93 \(\pm\) 5 ppm methane, 91 \(\pm\) 5 ppm helium, 508 \(\pm\) 10 ppm carbon monoxide) to the inlet via a demand flow regulator (Scientific Gas Australia Pty Ltd., Banksmeadow, Australia). Regular measurements were then made to investigate the stability of the instrument using the same gas standard. The measurements were run for long enough for the readings to stabilize (\(\sim\)90 s).
## 1 Introduction
That means the probability density of power (PDP) of the satellite signal at the same position on the ground would vary with time, and the corresponding capacity of each Ser-Beam would also change. Existing research on load balancing schemes for satellite networks focuses on Sat-Beam scenarios without intra-Ser-BH, and users with unified uniform distribution in the time and space domains [8; 9; 10; 11; 12; 13; 14; 15]. Optimization of satellite network routes is the prevailing design for achieving load balancing in this research. However, the DoU are spatio-temporal non-uniform; none of the research can solve the problems of global load balancing based on local balancing schemes and the selection of access to Ser-Beams under changeable DoU. Considering the DoU have a serious relationship with geography, the users of S-IoT-N can be divided into different grids on the surface of the earth. After combining the grids with global population distributions, we can obtain multi-grids with different DoU densities. We can operate the load balancing according to the density variances of users under different movement directions from one grid to another grid. Though the densities of grids are different around the world, all scenarios can be taken as a limited set for density variances of users. Then, the basic load balancing schemes become the original reference for global schemes and different times, which can be taken as the gene of the genetic algorithm (GA). After modeling and improving the mutation and crossover of the gene, it can effectively solve the problem of the adaptation from a local balancing scheme to global balancing and the selection of Ser-Beams to access. Finally, this paper proposes a novel load balancing scheme of adjacent beams for S-IoT-N based on the modeling of spatial-temporal DoU and advanced GA [16; 17; 18; 19]. The main contributions of this paper are summarized as follows:  
* In contrast to existing research on load balancing under DoU of uniform, we are the first to improve these schemes by modeling the density variances of users under different moving directions. This can solve load balancing problems under spatio-temporal non-uniform DoU;
* Fully considering the prior periodicity of satellite movement and the similarity of DoU in different areas, we propose the adaptive inheritance iteration to optimize the crossover factor and mutation factor for GA for the first time. This can enhance the efficiency and convergence speed of GA for S-IoT-N scenarios;
* The Ser-BH scenario is totally new to SIN. We propose a load balancing scheme based on non-uniform spatial-temporal DoU and advanced GA, which can achieve better performance of total throughput for adjacent Ser-beams. The rest of this paper is organized as follows: In Section 2, the relative works are introduced. In Section 3, the system model and problem formulation are described. Section 4 is devoted to the load balancing scheme of adjacent beams for S-IoT-N based on the modeling of spatial-temporal DoU and advanced GA. The simulations and analysis are discussed in Section 5. The summaries are provided in Section 6.
## 4 Results
### Karst Features Delineation
#### 4.3.2. Karst Features in Outcrops  
Based on the geological observations made during the fieldwork, the karst features observed in the field are categorised into several characteristics and features. These characteristics are as follows:  
1. Isolated limestone hill: Formed due to paleo-weathering and dissolution (Figure 9a).
2. Cavern with stalagmites, stalactites, and pillars: Formed due to calcite precipitation (Figure 9c,e,f).
3. Vertical grooves: Formed due to dissolution (Figure 9d).
4. Water table: Formed due to the dissolution and erosion by the drainage leaving the hard bedrock (Figure 9g).  
Figure 9: (**a**) Isolated limestone hill. (**b**) Dolines in the ceiling of Great Cave. (**c**) Active stalagmites. (**d**) Vertically dissolution-enhanced groove of Great Cave. (**e**) Inclined ceiling of the Traders’ Cave with boulders. (**f**) General view of one of the entrances of the Great Cave with pillars. (**g**) Stream along the timber walkway towards the cave entrance with the appearance of a water table.
## 3 Results and Discussion
### Effect of DB and LTGN on Fatigue Behavior of 304 CNASS
#### 3.3.2. Fractography
Figure 15 shows the fracture surface of the fatigue specimen from Group III (stress amplitude of 610 MPa; number of cycles to failure: 78,200). The center of macrocrack formation and two main zones are clearly distinguishable (Figure 15a). Zone 1 shows the inhomogeneous movement of the cyclic wave (indicated by red arrows), creating a condition for extrusion. In zone 2, the cyclic waves also have different directions. As a result, static local regions are formed, which create local supercritical stresses. In these static regions of impeded plastic deformation, intercrystalline cracks are observed (Figure 15b). In zone 1, the cracks are smaller, but mixed-type (inter- and transcrystalline) cracks are also observed. The mechanism of macrocrack formation and development differs to some extent compared to the specimen in Figure 14. The initial stage of crack formation involves sliding along different crystallographic planes and an increase in dislocation density around the intersection of the two planes. The hardening of DB significantly increases the dislocation density along the favorable grain polygons formed. Cyclic loading causes alternating stresses. When the tensile stresses reach a limit value, they cause inter-boundary rupture of the bonds between the grains. The macrocrack formed at the boundary between the AL and bulk material causes a transcrystalline crack in the AL. In the vicinity of the formed macrocrack, a static zone is observed, resulting from the contact of cyclic wave A with cyclic wave B at right angles to each other. The meeting of the two waves is unfavorable and causes the formation of both trans- and intercrystalline cracks. In general, it can be assumed that the destruction has a plastic character, regardless of the fact that in individual local areas, it is hybrid. Figure 15: _Cont._
# Design and Implementation of a Smart Intercom System through Web Services on Web of Things
## 4 Software Framework
### Function Flow of the Chatbot Service  
The resident can click on one of the \"Open Door\", \"Watch\", \"Alarm\", and \"Busy\" menus on the instant messaging software, and the system will perform the corresponding programming functions of the chatbot server through the instant messaging platform.  
These functions of the chatbot service are described as follows:  
1. Door lock control function: when the chatbot server receives the \"Open Door\" message from the resident, the system can successfully drive the relay to control the door lock. The door can be locked automatically when the door is closed.
2. Camera function: when the chatbot server receives the \"Watch\" message from the resident, the system can drive the camera to take pictures and send back the doorway photos to the resident in real time.
3. Alarm function: when the chatbot server receives the \"Alarm\" message, the buzzer can make a sound in time.  
Figure 6: Function flow of the intercom.  
4. Touch and display function: when the chatbot server receives a \"busy\" or not reserved system message from the resident, the system can display the message directly on the touch screen of the intercom. After executing the above actions, the chatbot service will return to a standby mode. The function flow of the chatbot service is shown in Figure 7.
# Path Planning Based on Artificial Potential Field with an Enhanced Virtual Hill Algorithm
## 3 Virtual Hill Concept and Open Path Indicator, _New e\({}_{b}\)_  
The virtual hill concept allows the robot to escape from the local minimum by generating extra force instead of attractive force when in the local minimum area. As explained in  
Figure 1: MATLAB 2022b simulation environment: (**a**) mobile robot model, linear velocity \(v\), angular velocity \(\omega\); (**b**) visualizer of the Mobile Robotics Simulation Toolbox.  
Section 3.1, the extra force follows the direction of the unit tangent vector \(\mathbf{e}_{t}\) of \(\gamma\), which represents the trajectory of the nearest obstacle while the robot is moving within the local minimum. The unit tangent vector is determined as the cross product of the unit normal vector \(\mathbf{e}_{n}\) and the unit binormal vector \(\mathbf{e}_{b}\). Since \(\mathbf{e}_{n}\) is the vector between the nearest obstacle and the robot, the decisive factor that determines the robot's driving direction is \(\mathbf{e}_{b}\). Unlike the previous \(\mathbf{e}_{b}\) determined according to the relative positions of the robot, the closest obstacle, and the goal, the _new_\(\mathbf{e}_{b}\) proposed in this paper operates as an open path indicator. _new_\(\mathbf{e}_{b}\), which is determined by the shape of surrounding obstacles, makes the robot travel on a more open path, improving path efficiency.
## 2 Materials and Methods  
### Data Sources  
To extract phenological metrics, the normalized difference vegetation index (NDVI) dataset from MODIS (MYD13C1) was used. The temperature (Tem) datasets used in the autumn phenology model and NEP simulation were also obtained from MODIS (MOD11C2). GPP data used to improve the phenology model came from the revised EC-LUE (EddyCovariance Light Use Efficiency) model [16], and GPP estimates based on satellite NIRv (near-infrared reflectance) [17]. Due to the highly consistent interannual variations between the two datasets (Figure S1), we averaged them for model improvement.  
The fraction of absorbed photosynthetically active radiation (FAPAR) and photosynthetically active radiation (PAR) required for the CASA model were obtained from Global Land Surface Satellite (GLASS) products. Monthly actual evapotranspiration (ET) was sourced from the ECMWF Reanalysis v5 (ERA5) dataset. Potential evapotranspiration (PET) and precipitation (Pre) were obtained from the China 1-km monthly potential evapotranspiration and precipitation datasets [18; 19].  
For the land cover data, the MODIS Land Cover Climate Modeling Grid (MCD12C1) Version 6 product was used. This study focused on nine major and widely distributed vegetation types in China, including evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), deciduous needleleaf forest (DNF), deciduous broadleaf forest (DBF), mixed forests (MF), shrublands (SHB), savannas (SAV), grasslands (GRA), and croplands (CRO), with their distribution shown in Figure 1.  
We used satellite data products from 2003 to 2021 to estimate NEP and test the feasibility of the new NEP simulation framework. All satellite data were standardized to a spatial resolution of 0.05\({}^{\circ}\) using the arithmetic average. The spatiotemporal resolution and data acquisition details of the satellite data used in this study are shown in Table 1.  
Figure 1: Distribution of the landcover types, flux tower sites, and four economic regions across China.  
To test the performance of NEP simulation, we obtained 52 site-year NEP flux records across 7 flux tower sites in China (Figure 1), downloaded from the National Science & Technology Infrastructure platform ([[https://www.nesdc.org.cn/](https://www.nesdc.org.cn/)]([https://www.nesdc.org.cn/](https://www.nesdc.org.cn/)) (accessed on 29 January 2025)). Finally, to evaluate the synergistic variation trend between fossil fuel carbon emissions and ecosystem carbon sequestration, we obtained apparent carbon emission data for China's four economic regions (eastern, central, western, and northeastern) from 2003 to 2021 from the Carbon Emission Accounts and Datasets (CEADs) [20; 21].
## 2 Materials and Methods
### Training and Evaluation  
For the purpose of classifier training, the cross-entropy loss function was employed. The optimization algorithm used was Adam [21], with a mini-batch size of 58 and a learning rate of 0.0001. The parameters were updated a total of 1000 times, without alteration to the learning rate or cessation of the training process.  
We conducted two types of evaluations: cross-validation within the BCI competition dataset training data, and unseen test data evaluation. In the cross-validation within the training data, a 5-fold cross-validation was conducted 20 times for each individual. This evaluation examined the performance (accuracy) by eliminating the effect of dataset selection. Only the original training data of the BCI competition dataset (288 trials for each subject) were used. In the unseen test data evaluation, the classifier was trained using the training data (288 trials for each subject), and the classification accuracy of the test data (288 additional trials for the same subject) was determined. This procedure was repeated 100 times by changing the initial weights in the model, and the average accuracy was  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & \multicolumn{2}{c}{**2D CNN Model after STFT**} \\  & **Structure** & **Output Shape** \\ \hline
**Input** & **-** & **(251, 80, 22)** \\ \hline  & Convolution [3; 3] 8 ch & \\
1 & BatchNormalization & (249, 78, 8) \\  & LeakyReLU (\(\alpha\) = 0.2) & \\ \hline  & Convolution [3; 3] 16 ch & \\
2 & BatchNormalization & (247, 76, 16) \\  & LeakyReLU (\(\alpha\) = 0.2) & \\ \hline  & Convolution [3; 3] 32 ch & \\
3 & BatchNormalization & (245, 74, 32) \\  & LeakyReLU (\(\alpha\) = 0.2) & \\ \hline  & Convolution [3; 3] 32 ch & \\
4 & BatchNormalization & (243, 72, 32) \\  & LeakyReLU (\(\alpha\) = 0.2) & \\ \hline
5 & Global Average Pooling & (32) \\ \hline  & Dense [4] & (4) \\
6 & Softmax & (4) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The model structure 2D CNN after STFT.  
calculated. The data used for the training and test data evaluations were unchanged. In addition to the five-fold cross-validation accuracy and unseen test evaluation data accuracy, we calculated the kappa values and compared the results of our proposed method with those reported in previous studies. Since the numbers of samples in this data set are equally distributed across classes, using the number of classlabels \(M\), or the chance level \(p_{e}\) (reciprocal of \(M\)), the kappa value \(\kappa\) can be expressed by the following equation [22]:  
\[\kappa=\frac{Accuracy-p_{e}}{1-p_{e}}=\frac{M\times Accuracy-1}{M-1}. \tag{2}\]  
To check the statistical significance of the accuracy improvement, we performed a Wilcoxon signed-rank test and calculated the \(p\)-values between the transposed convolution model and the other methods. A \(p\)-value of 0.05 was assumed to be the threshold for assessing statistical significance.
## 3 Results and Discussion
### Constant Wind Speed and Variable Wind Directions  
Next, the ideal wind condition of constant wind speed and variable wind directions was studied. The wind condition is shown in Figure 6, and the optimization results of total wind farm power output and power losses as a function of wind turbine number are shown in Figure 7. As can be seen, unlike the above case, both the total wind farm power output and total power losses showed a linear increase with an increasing number of wind turbines. Figure 8 shows the optimal wind farm layout with 40 wind turbines optimized. In general, the wind turbines were uniformly distributed inside the wind farm. The wind farm boundary was fully saturated with distributed wind turbines while the rest of the wind turbines were staggered inside the wind farm area. Due to the characteristics of this ideal wind condition with the equal probability of 360\({}^{\circ}\) wind direction, overall the optimal wind turbine distribution seemed much more uniform than the above case. Similar to the above case, minimum distance apart for any two wind turbines was 7 turbine diameters and the average distance of all wind turbines was 36.27 turbine diameters.  
Figure 4: Results of wind farm layout optimization with different number of wind turbines under constant wind speed and constant wind direction: (**a**) wind farm power production and (**b**) wind farm power losses.  
Figure 5: Optimal wind farm layout with 40 wind turbines under constant wind speed and constant wind direction.  
Figure 6: Probability of constant wind speed and variable wind directions.
## 3 Intersection Delay Fairness Analysis
### Delay Model Fairness Analysis  
#### 3.1.1 Synthetic Sample Generation  
(1) Basic situation of the intersection  
The study focuses on a typical four-lane intersection with east-west and north-south directions, each with separate left-turn lanes. Right-turn movements are not considered in this analysis.  
(2) Traffic flow setting  
Based on the \"Urban Road Capacity Table of Various Levels\" in China and empirical data, the saturation capacity for each lane is set at 1200 pcu/h/lane, which is appropriate for urban trunk roads. The traffic volume for each lane is randomly generated within the range of [0, 1200] pcu/h.  
(3) Phase setting  
Considering the crossover practical situation, this paper chooses the classical opponent four-phase, and the following Figure 2 gives the schematic diagram.  
(4) Arithmetic sample generation  
Using Python 3.10.5, 2500 sets of traffic flow data are randomly generated for each lane. Focusing on low-saturation intersections, 362 datasets with intersection saturation between 0.0 and 0.8 are selected for analysis.  
(5) Sample description  
In this case study, the total saturation degree is set between 0.05 and 0.8 with a gradient interval of 0.015. The total intersection traffic flow ranges from 600 to 5600 vehicles per hour (veh/h) with an interval of 100 veh/h, generating 2500 sets of basic cycle data. These data sets are traversed to calculate the coefficient of variation for average vehicle delay per phase and phase saturation degree.  
Since the selected valid datasets are randomly generated, the distribution is relatively uniform. The average intersection traffic volume is 8448 pcu/h, with a maximum of 14,976 pcu/h and a minimum of 960 pcu/h; the corresponding average intersection saturation is 0.45, with a maximum of 0.78 and a minimum of 0.07, used for further analysis.  
(6) Sample of generating code logic  
Therefore, this paper uses a Python program to validate the model by employing the built-in scipy.optimize.minimize library to solve the constrained nonlinear objective function. The specific programming logic is as follows:  
1. Since the research subject of this chapter is low-saturation urban intersections, the total intersection saturation range is set between 0.05 and 0.8 with a gradient interval of 0.015; the total traffic flow ranges from 600 veh/h to 5600 veh/h with a gradient interval of 100 veh/h. These are paired to form a set of basic data, totaling 2500 sets.
2. Under the constraints of not exceeding the phase saturation flow and meeting the specified intersection saturation and total traffic flow, random values are assigned to the actual flow of each phase. The generated phase flow values are used for model traversal and solution.  
Figure 2: Classic opposite four-phase indication.  
The sample plays a crucial role in validating our theory and approach. The detailed generation and solution of this instance can be found in the pseudo-code for instance generation and model solution in Supplementary Materials. Subsequently, we will delve deeper into the analysis of this instance. The sample features are shown in Tables 1 and 2.
## Robust Analysis of Network-Based Real-Time Kinematic for GNSS-Derived Heights  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3 and [PERSON] 3**  
1 Department of Geoinformation Engineering, Sejong University, Seoul 143-747, Korea  
2 Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA; E-Mail: [EMAIL_ADDRESS]  
3 National Geodetic Survey Department, Silver Spring, MD 20910, USA; E-Mails: [EMAIL_ADDRESS] (G.M.); [EMAIL_ADDRESS] (M.D.)
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-2-3408-3231; Fax: +82-2-3408-4341.  
Academic Editor: [PERSON]  
_Received: 21 August 2015 / Accepted: 20 October 2015 / Published: 26 October 2015_  
**Abstract:** New guidelines and procedures for real-time (RT) network-based solutions are required in order to support Global Navigation Satellite System (GNSS) derived heights. Two kinds of experiments were carried out to analyze the performance of the network-based real-time kinematic (RTK) solutions. New test marks were installed in different surrounding environments, and the existing GPS benchmarks were used for analyzing the effect of different factors, such as baseline lengths, antenna types, on the final accuracy and reliability of the height estimation. The RT solutions are categorized into three groups: single-base RTK, multiple-epoch network RTK (mRTN), and single-epoch network RTK (sRTN). The RTK solution can be biased up to 9 mm depending on the surrounding environment, but there was no notable bias for a longer reference base station (about 30 km) In addition, the occupation time for the network RTK was investigated in various cases. There is no explicit bias in the solution for different durations, but smoother results were obtained for longer durations. Further investigation is needed into the effect of changing the occupation time between solutions and into the possibility of using single-epoch solutions in precise determination of heights by GNSS.  
keywords:GNSS; real-time kinematic (RTK); network RTK; GNSS-derived heights; GPS benchmarks +
Footnote †: journal: Journal of Scientific Computing