esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Using finite element analysis and an analytical solution, [PERSON] and [PERSON] [21] revealed that an accurate modulus can be obtained with a distance greater than 40 times the indentation depth for a spherical indenter and 10 times the indentation depth for a Berkovich tip on titanium and steel alloys. [PERSON] and [PERSON] [22] also found that a minimum indent spacing of 10 times the indentation depth can offer highly accurate results for a Berkovich indenter on several materials like silica, aluminum, polycarbonate, and copper. The authors also argued that there are no obvious differences in hardness values as a function of indenter orientation and the number of neighboring indents. No or very little FEA research considered the influence on the mechanical behavior due to pileup, sink-in, and crack formation during nanoindentation, especially for polymer materials. To our knowledge, no papers presently exist for analysis of the stress and strain based on the nanoindentation with Berkovich tip for polymers without considering uniaxial test. In [PERSON]'s and [PERSON]'s work [23], the accuracy of indentation stress-strain for [PERSON]'s and their method was confirmed by contrasting against the uniaxial stress-strain results. Although they applied the spherical tip and only considered metals, these two methods were applied in this paper due to the high result validity and the adjustable parameters. In this work, we investigate the influence of the separation and orientation between indents on the accuracy of mechanical properties for polystyrene at three different maximum depths with a view to find the minimum optimal separation. Both experimental and finite element methods were applied in these studies. An indentation stress constraint factor is introduced to define stress and strain directly from nanoindentation experiments. The loading-unloading curve from the finite element method was compared against that from the experiment to prove the accuracy.
## 4 Analysis and Discussion
### Discussion and Takeaways
#### 4.2.1 Points of Note for Digital CHComm Applications
It is worth stressing here that cutting down on the depth of the content to better serve an audience which may not be willing to assimilate even the reduced version is not advisable. The effort of a designer may be gainfully focused on engaging the willing and voluntary participants and this is where the bulk of the content design must initially be targeted. Encouraging user reflection and improving the wider accessibility to CHComm that deliberately includes values in their design is a need of the hour. When designers do not recognize the value of values, the willing visitor receives diminishing returns for their efforts. This can lead to an undesirable cycle that ends up eroding the value of cultural heritage itself. Cultural Heritage conservation and communication go hand in hand and are rightfully rooted in the CH value perceptions of historic and contemporary individuals and societies. The value associations of any one CH property are numerous and complex and it can be a daunting task to collect, filter and present them. We followed a broad typology for the classification of values and a model of significance assessment that was described as 'purposefully simple' by its authors. The typologies and significance assessment systems we followed do not claim to be definitive and we only present them as they were conducive to our operational framework. Even so, the result of this process is a rewarding experience for willing visitors and thereby a necessary exercise for the designers of digital CHComm. Figure 15: An illustrative map behind the concept of ‘Eretz Yisrael’ (**above**), the specific period of the artefacts in the museum highlighted on a complete timeline of the history of the contemporary state of Israel (**below**). over a period of three months and as such the time constraints made us limit our survey and collection of values to the more 'obvious' associations. We intend to expand the study to more collections and conduct a deeper analysis of value associations to enhance the quality of the content in the digital CHComm application with future iterations. Secondly, the control group that was used in this study was the users of the 'CHComm Info' version of the application. Assessing the value appreciation of the visitors to the museum who did not view any application would have provided us insights into the impact of a digital application all by itself without any intentional inclusion of CH values. As mentioned, we note that the users of the 'CHComm Info' application also stated that they learnt new information and the impact of the re-iteration of cultural knowledge by a digital application cannot be ignored even in the 'CHComm Info+Values' application. A refined and expanded study would ideally be able to control for the impact of the application being a digital summary of the artefacts that enables focused attention of the users by virtue of audio-visual engagement. Even so, as it stands our current results show the increased impact from the deliberate inclusion of CH values in a digital CHComm application.
# From Climate Conditions to the Numerical Slope Stability Analysis of Surface Coal Mines
## 5 Numerical Analysis of Slope Stability of Lignite Mines under Extreme Rainfall Events
### Numerical Model Overview
According to the van Genuchten-Mualem model:  
\[\mathrm{k_{w}}=\mathrm{k_{s}}\cdot\mathrm{S_{e}}^{\mathrm{g_{a}}}\cdot\left[1- \left(1-\mathrm{S_{e}}^{\frac{\mathrm{g_{a}}}{\mathrm{g_{a}}-1}}\right)^{ \frac{\mathrm{g_{a}}-1}{\mathrm{g_{n}}}}\right]^{2} \tag{2}\]  
Figure 6: Soil–water characteristic curve for different material parameters, \(\mathrm{g_{a}}\). where \(\rm g_{i}\) is a fitting parameter equal to 0.5, regardless of the soil type. The effective degree of saturation, \(\rm S_{e}\), expresses a normalized water content between the saturated and the residual state of the soil and equals:  
\[\rm S_{e}=\frac{S_{w}-S_{r}}{S_{s}-S_{r}} \tag{3}\]  
As a result of Equations (2) and (3), the unsaturated permeability, \(\rm k_{w}\), decreases with the decrease of the soil water content, \(\rm S_{w}\), and suction increases. Finally, the soil's strength is defined to complete the unsaturated zone's description. The unsaturated soil's shear strength can be written based on Bishop's effective stress as [57; 58]:  
\[\rm s=(\sigma+S_{e}\cdot|u_{w}|)\cdot tan\varphi^{\prime}+c^{\prime}\Leftrightarrow s =\sigma\cdot tan\varphi^{\prime}+S_{e}\cdot|u_{w}|\cdot tan\varphi^{\prime}+c^ {\prime} \tag{4}\]  
where \(\sigma\) denotes the total stress and \(\varphi^{\prime}\) and \(c^{\prime}\) are the effective soil shear strength parameters. The second term of the expanded form of Equation (4) (which contains the effective saturation degree, the matric suction \(\rm 1\,u_{w}\), and the effective friction angle) represents the effect of suction in the unsaturated soil shear strength.
## 5 Conclusions  
This paper addresses the challenges of severe interference and limited detection accuracy in advanced underground exploration by proposing an innovative cross-hole grounded-wire-source TEM. A detailed equipment arrangement scheme is presented, and the technique is validated using flexible unstructured FE methods for forward modeling. The following conclusions are drawn:  
1. Larger volumes, lower resistivity values, and closer proximity of anomalous bodies result in larger response amplitudes, thereby improving detection effectiveness.
2. High-resistivity and low-resistivity anomalous bodies produce opposite anomalies (positive and negative, respectively) in the electric field. The system is more sensitive to low-resistivity anomalous bodies, making it particularly useful for detecting water-bearing targets in coal mines.  
Figure 17: Contour plots of EMF obtained by (**a**–**f**) full-space model and (**g**–**l**) water-bearing trap column model.  
3. The position of the receivers has a greater impact on the final response strength than the source location. This suggests that the borehole-based arrangement proposed in this paper improves detectability.
4. The proposed setup can effectively determine key parameters, such as the anomalous body's size, resistivity contrasts, and spatial position by analyzing changes in the electric field and first-order differential derivatives. This allows for the approximate localization of the anomaly's boundary. Moreover, the cross-hole arrangement takes advantage of borehole spatial positioning, offering multi-directional information about the detected target.
5. In the collapse column model with multiple survey lines, both the electric field response and first-order differential results clearly delineate the boundary of the collapse column, demonstrating significantly improved detection accuracy over the traditional BRTEM method. Additionally, this configuration enables 2D/3D observations in roadway environments.  
Overall, the cross-hole grounded-wire-source TEM proposed herein demonstrates excellent detection performance, particularly in water-bearing target exploration in underground spaces. This study introduces the technical concept of the method and establishes a theoretical foundation for future work, which will involve field data acquisition and validation.  
Conceptualization, J.Z., Z.J. and M.L.; methodology, J.Z., Z.J. and M.L.; software, J.Z. and Z.J.; formal analysis, J.Z. and M.L.; investigation, J.Z., J.M., L.Z. and Z.G.; writing--original draft preparation, J.Z.; writing--review and editing, J.Z., M.L. and Z.D.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This paper is financially supported by the National Natural Science Foundation of China (42304085), the Natural Science Foundation of Jiangsu Province (BK20231086), and the Jiangsu Funding Program for Excellent Postdoctoral Talent (2023 ZB154). Data associated with this research are available and can be obtained by contacting the corresponding author.  
The authors declare no conflicts of interest.
# Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks
## 2 Method
### Cnn  
A 1-D convolutional neural network (CNN) structure for blade crack fault detection is designed in this research, as shown in Figure 4. The vibration signal of cracked blades, obtained by numerical simulation or experimental test, is the input of the CNN network. The kernel size of the downsampling layer is 11, the step size is 4, and the kernel channel size is 64. In this research, a 5-layer 1-D convolutional network with a kernel size of 5 is adopted. Multiple layers of small convolutional kernels can make the network deeper, resulting in better feature representation and contributing to better network performance [27]. The last layer of the fully connected layer consists of two parts: the upper layer with output size 4 is used to output the crack location feature, and the lower layer with output size 1 is used to output the crack depth feature. The weights for the loss functions of the classification and regression tasks are adopted to generate the loss function of the total task. Adam is used in the optimizer, and the initial learning rate is set to 0.001. MSE and CrossEntropyLoss are adopted as cost functions to predict the value and type, respectively.
# Congestion Control in CoAP Observe Group Communication
## 4 Evaluation Setup
### Simulation Setup  
To simulate the network and evaluate our congestion control mechanism, the algorithms were implemented using the Cooja Simulation [27] in the Contiki Operating System (ContikiOS), an open-source operating system for constrained devices that supports dynamic loading and the replacement of individual programs and services [28]. The advantage of Cooja was that the program can compile and upload into a real node.  
In this study, we selected a Z1 mote which was developed by Zolertia to be the principal node for simulations in the Cooja [29]. It was devised for a low-power wireless sensor network (WSN) which can communicate over the 6 LoWPAN protocol. The hardware specifications of the Z1 mote are provided in Table 1, and the details of the Cooja parameter setup are in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Note** & **RAM** & **ROM** & **MCU** & **Radio** \\ \hline Zolertia (Z1) & 8 KB & 92 KB & MSP430F2167 & CC2420 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware specification of the Zolertia Z1 wireless sensor node.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Settings** & **Value** \\ \hline Congestion mechanisms & Default CoAP, CoCo-RED \\ Routing protocol & Routing Protocol for Low-Power and Lossy Networks (RPL) \\ Max retransmissions & 4 \\ Wireless channel model & Unit Disk Graph model, transmission range = 15 m, interference range = 30 m \\ Distances between nodes & 10 m \\ Transport and network & UDP + ulPV + 6 LoWPAN \\ Media access control & (CSMA/CA) \\ Radio duty cycling (RDC) & Null-RDC \\ Radio band & 2.4 GHz \\ Physical & IEEE 802.15.4 PHY \\ Simulation time & 300 s \\ Max open transactions & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cooja parameter setup.  
Figure 7: An overview of the RTO used to maintain and update the RTO state information for a destination endpoint in CoCo-RED.
## 3 Results  
### Characteristic Analysis of POMs  
#### 3.1.1 Particle Size and EE  
The particle size significantly influences the flow characteristics and dispersion properties of microcapsule powders. At present, there are studies using sodium octenyl succinate starch (OSA starch) and tea polyphenols (TPs) in spray-drying to encapsulate PPO, and the EE is controlled at 88.13 \(\pm\) 1.48% [31]. In this paper, the particle size, surface oil content, and EE of POMs, prepared using different drying methods and encapsulating agents, were significantly improved compared with previous studies. The different preparation methods and materials had a significant influence on these parameters (\(p<0.05\)). The particle sizes of different microcapsules exhibited unimodal distributions, providing evidence for the uniformity of particle size. Additionally, spray-dried microcapsules demonstrated smaller particle sizes and lower surface oil contents, indicating more effective control over particle size and homogeneity. Moreover, they exhibited superior embedding effects for PPO compared to freeze-dried microcapsules, resulting in a higher encapsulation efficiency (Table 1).  
The EE appears to be related to the microcapsule drying method, the encapsulating agent, and the formulation of the microcapsules. The EE of spray-dried GA/MD microcapsules (SG) was 4.24% higher than that of spray-dried SPI and MD microcapsules (SS), apparently because of the higher viscosity of GA. The particle size of SG was 0.03 \(\upmu\)m smaller than that of SS, which may be due to the molecular weight of the raw material itself and the high temperature during spray-drying, causing the microcapsule surface depression [32].
# Embedded Based Real-Time Monitoring in the High-Pressure Resin Transfer Molding Process for CFRP
###### Abstract  
Carbon Fiber Reinforced Plastics (CFRP) is a material developed for its high strength and light weight in a broad variety of industries including aerospace, automotive, and leisure. Due to the rapid molding cycle time, high-pressure resin transfer molding (HP-RTM) processes are prone to molding defects and susceptible to various process variables such as the resin injection rate, pressure and temperature in the mold, vacuum, end-gap, pressing force, and binder. In recent years, process monitoring technology with various sensors has been applied to stabilize the HP-RTM process and control process variables. The field-programmable gate array (FPGA) based embedded monitoring system proposed in this study enabled high-speed real-time signal processing with multiple sensors, namely pressure, temperature, and linear variable differential transformer (LVDT), and proved feasibility in the field. In the HP-RTM process, the impregnation and curing of the resin were predicted from the cavity pressure and temperature variations during the injection and curing stages. In addition, the thickness of the CFRP specimen was deduced from the change in the end-gap through the detection of the LVDT signal. Therefore, the causes of molding defects were analyzed through process monitoring and the influence of molding defects on the molding quality of CFRP was investigated.  
carbon fiber reinforced composite material; high-pressure resin transfer molding; process condition monitoring; cavity pressure sensor; temperature sensor; embedded monitoring system Article
# Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm
## 4 Micro-Blog Sentiment Analysis Method Based on Personality and Bagging Algorithm
### Text Personality Classification  
In order to accurately assign micro-blog text to different collections, the personality characteristics of the text have to be predicted accurately. At present, personality prediction mainly involves three personality aspects in the Big Five model: extroversion, agreeableness, and conscientiousness. The two other dimensions, openness and neuroticism, are difficult to predict, as shown in previous studies [39; 43], and therefore would not be included in this study. In scoring the personality, each dimension of personality can either be high or low. However, due to the low number of low conscientiousness texts in micro-blog, this study considers only five aspects: high extroversion, low extroversion, high agreeableness, low agreeableness, and high conscientiousness.  
A rule-based personality classification method is used to evaluate the aspects of extraversion, agreeableness, and conscientiousness. The textual characteristics of each personality group reflect the commonalities of the corresponding emotional expressions. For example, conscientiousness expressions are often perceptions of achievement (e.g., results, persistence). Expressions of agreeableness are often related to love and praise (e.g., \"love\", \"beautiful\") and sympathy (e.g., \"poor\"). In contrast, the expressions of low-level agreeableness usually involve accusations or insults (e.g., \"fool\", \"stupid\"). Extraversion expressions convey positive (e.g., \"happy\") or negative (e.g., \"lonely\") emotions directly.  
For example, if the number of words (Cword) or emoticons (Cemoction) expressed by a high degree of conscientiousness (HC) in the text is high, the value of text conscientiousness would be considered as high. Table 1 presents the main text features of personality prediction.  
Figure 2: The sentiment classification process based on personality and bagging algorithm.  
Table 2 summarizes the set of rules for personality determination (where p1, p2... p10 are the thresholds used by the rule, and the size of the threshold is determined experimentally). Since the texts are evaluated using several aspects of personality, each text can belong to multiple personality sets.
# Experimental Study on the Effect of Wind Speed on Hail Impacts
## 2 Experimental Overview
### Determination of Experimental Parameters  
In nature, the relationship between the terminal velocity and size of freely falling hailstones is relatively consistent [35; 36]. Statistical analyses indicate that hailstones are predominantly spherical in shape [37]. Therefore, in this study, hailstones are assumed to be spherical for analytical purposes. When hailstones are modeled as spheres, the theoretical terminal velocity is calculated as follows:  
\[v=\left(\frac{4}{3}\frac{\rho_{i}}{\rho_{a}}\frac{g}{C_{D}}d\right)^{\frac{1}{ 2}} \tag{2}\]  
In the equation, \(v\): the terminal velocity of the hailstone (unit: m/s); \(g\): gravitational acceleration, taken as \(g\) = 9.8 m/s\({}^{2}\); \(\rho_{i}\): the density of the hailstone, taken as \(\rho_{i}\) = 0.9 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\); \(\rho_{a}\): air density at a standard temperature (20 \({}^{\circ}\)C), taken as \(\rho_{a}\) = 1.205 kg/m\({}^{3}\); \(d\): the diameter  
\begin{table}
\begin{tabular}{c c c} \hline
**Hail Diameter (cm)** & **Theoretical Mass (g)** & **Actual Mass (g)** \\ \hline
2.40 & 6.51 & 6.35 \\
2.80 & 10.33 & 10.12 \\
3.20 & 15.43 & 15.22 \\ \hline \end{tabular}
\end{table}
Table 1: Related parameters of hail with different sizes.  
Figure 3: The force measurement system consists of two parts: (**a**) hail-making mold interior and (**b**) nail-making mold shell.  
of the hailstone (unit: m); and \(C_{D}\): dimensionless drag coefficient. For smooth spherical hailstones, the empirical relationship \(C_{D}=0.41D^{0.185}\) applies when 0.5 cm \(\leq D<\) 5 cm, where \(D\) is the hailstone diameter in centimeters.  
The custom bracket was rigidly attached to the force balance and securely mounted on the balance while performing the experiment to prevent severe wind vibration from affecting the results. The ambient temperature was 20 degrees.  
Table 2 shows the theoretical terminal velocities of three different sizes of hailstones calculated according to Equation (2), and the actual mass of the hailstones.  
This study conducted a comparative investigation based on the theoretical terminal velocity of hailstones, utilizing three different hailstone diameters (d = 2.4, 2.8, 3.2 cm), four wind speeds (V\({}_{\mathrm{w}}\) = 0, 5, 9, 11 m/s), and varying launch velocities of hailstone particles. The systematic analysis focused on the impact behavior of hailstones under different wind speeds and particle parameters. The specific experimental conditions are summarized in Table 3.
# Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning
## 3 Methods
### Multi-Robot Collaborative Navigation
#### 3.3.1 Parallel Deep Deterministic Policy Gradient  
To address the aforementioned issues, we proposed a parallel deep deterministic policy gradient (PDDPG). Compared with the improved DDPG for a single robot mapless navigation task, the PDDPG algorithm trains the robots in parallel. The algorithm attains the target of a whole multi-robot system by sharing the experience memory data and the navigation policy. The robots of the system use the same policy module to make decisions, and the trajectories of robots will be saved in the shared experience replay buffer. The basic network structure unit of the PDDPG algorithm is the same as improved DDPG. We only modify the input dimension of lidar observations and the robot states.
## 5 Results and Discussion
### Simulation Settings  
We performed simulation experiments in Python 3.7 using pytorch. In our simulation, we set up one MEC server, and 10 task vehicles and 5 service vehicles were randomly distributed along a single-directional road stretching 1 km. The speed of each vehicle ranged between 40 and 50 km/h. Each task vehicle generates a task of a certain data size at each time slot with a probability of 0.4, and the time slot length is 10 ms. The size of the data size of each task is limited to [40, 50] kB, and its computational intensity is all 1000 cycles/byte. In addition, the bandwidth of each subcarrier is 180 KHz. The transmit power and CPU cycle frequency of each task vehicle were 0.2 W and [0.5, 1] GHz, the CPU frequency of the MEC server was selected at 30 GHz, and the CPU frequency of each SV was selected at [2, 4] GHz. Vehicles could communicate up to a maximum distance of 200 m. Regarding the proposed MHPPO method, two hidden layers were set up for both the actor network and the critic network. The first hidden layer comprises 256 units, followed by 128 units in the second layer. Additionally, the experience replay buffer maintains a capacity of 1000 transitions, with mini-batches of 128 samples randomly sampled during training. A discount factor of 0.99 is applied, while the Adam optimizer is employed to minimize the loss function throughout the training phase.