esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Stability Evaluation Based on Genetic Algorithm and Discrete Element Analysis
The root mean square error (RMSE) was calculated every 5000 h, and the results for the four monitoring points are shown in the figure below. As shown in Figure 11, although fluctuations in RMSE were observed during the iterations of the genetic algorithm, an overall decreasing trend is evident. Moreover, the RMSE values at all four monitoring points during the final 5000 h are relatively small. This indicates that the proposed method is both reasonable and effective, significantly improving the accuracy of parameter selection in the discrete element numerical model and providing a solid foundation for subsequent long-term stability evaluations. In Figure 10, it can be seen that the genetic algorithm objective function converges to a stable, smaller value. At this point, the constitutive parameters obtained through optimization using the genetic algorithm are assigned to the numerical analysis model. The numerical results show a good trend match with the field monitoring results, with small differences. Therefore, it is reasonable to use the generalized Kelvin constitutive model and this set of creep parameters for long-term stability evaluation. The analysis time for the  
Figure 11: The RMSE between the displacements from the numerical model and the actual monitored displacements. (**a**) RMSE at monitoring point TPZQX4-3; (**b**) RMSE at monitoring point TPZQX4-4; (**c**) RMSE at monitoring point TPZQX4-5; (**d**) RMSE at monitoring point TPZQX4-6. numerical simulation is set to 90,000 h, and the numerical calculation results are shown in Figure 12.  
In Figure 12, it can be seen that when the creep time is set to 90,000 h (approximately 10 years), the slope displacement of the anchored rock mass stabilizes, with no significant fluctuations or failures observed. Therefore, it can be concluded that the stability evaluation result of the left bank water cushion rock slope anchoring system is stable. This evaluation result is completely consistent with the trend shown by the actual monitoring data of the rock slope anchoring system, indirectly confirming the rationality of the method.
# An FDM-Based Dynamic Zoning Method for Disturbed Rock Masses above a Longwall Mining Panel
## 3 Case Study
### Numerical Simulation Results
#### 3.4.2 Dynamic Development Characteristics of the Four Zones in Longwall Mining  
Figure 14 illustrates the dynamic development characteristics of the four zones when the excavation face is extended to the positions of 20 m, 30 m, 40 m, 50 m, 60 m, 70 m, 90 m, and 100 m. At first, the impact of the longwall mining on the overlying strata was quite small. According to the principal plastic strain \(\varepsilon^{p}\), the overlying disturbed rock masses were in the post-peak softening stage. When mining to the position of 30 m, the scope of the overlying disturbed rock masses was suddenly expanded while the first roof caving occurred. From bottom to top, the overlying disturbed rock masses were in the stage of caved material deformation (Stage IV), residual strength (Stage III), and post-peak softening (Stage II). According to the zoning criteria, the overlying disturbed rock masses belong to the caved, fractured, and continuous deformation zones. The scope of the overlying disturbed rock masses was expanded periodically when further mining to the positions of 50 m, 70 m, and 100 m. Correspondingly, the four zones periodically expanded upwards and forwards.  
Figure 14: Dynamic evolution laws of the four zones due to excavation (20–100 m).  
With the advance of longwall mining, the scope of the overlying disturbed rock masses is gradually expanded (see Figure 15). When mining to 200 m, the influence of the excavation developed to the ground surface, and the four zones were formed. As shown in Figure 16, when mining further, the heights of the four zones are nearly constant, and the four zones will only develop along the mining direction. After the completion of longwall mining, the heights of the four zones are approximately 12 m, 102.8 m, 380 m, and 50 m, respectively.  
The above process indicates that the formation of the four zones is a dynamic process and of strong relevance to coal-seam roof caving. With the advancement of the excavation face, the coal-seam roof periodically caves, and the scope of the overlying disturbed rock masses expands periodically. The four zones will also periodically expand upwards and forwards. When the mined-out area reaches a certain extent, the heights of the four zones are nearly constant at maximum, and then the four zones will only develop along the mining direction.  
In addition, as shown in Figures 14 and 15, the advance influence of longwall mining on the overlying strata and the surface in front of the excavation face can be observed in the mining process. The overlying strata and surface in front of the mined-out area are influenced by mining, and the closer to the excavation face, the more significant the influence of the longwall mining
## 3 Analysis of Dynamic Performance
### Structural Dynamics  
The basic principle of structural dynamics for the SBG transmission is shown in Figure 3.  
1. With the help of the basic theory of vector structural mechanics, a simplified calculation model of a three-dimensional (3D) hexahedron element is constructed, and the calculation expression of the hexahedron element is derived by combining the solution method of reverse motion.
2. Aimed at the characteristics of high-speed ball bearings in helicopter transmission systems, a ball-bearing unit model is proposed, which simplifies the bearing balls into ball units, whose position information is determined by the centre of the circle.
3. Drawing on the spatial contact algorithm and combining the contact behaviour characteristics of key components such as high-speed SBG transmissions and bearings, a contact calculation model is constructed. The primary-secondary algorithm is used for a global search, and internal and external algorithms are used for a local search. The spatial contact algorithm equation suitable for vector finite elements is derived.
4. Drawing inspiration from the meshing damping model in the theory of gear system dynamics, the relative speed of gear and pinion is used as the calculation basis for the meshing damping force to define damping and propose a variable damping model. Based on the variable damping model, the dynamic control equation is derived.
5. According to the machining principle of the SBG transmissions, parameterised tooth surfaces and transition surfaces are generated based on tooth surface equations with machining parameters as variables, and high-precision finite element 3D models of gear and pinion are constructed.
6. Based on the relative position and contact relationship between various components, the node coordinates are transformed from the local coordinate system to the assembly coordinate system, completing the assembly of the SBG transmission.
7. The parameterised model of SBG transmission is combined with vector dynamics to establish a model of vector dynamics of the high-speed SBG transmission system for helicopters.
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 3 Methodology
### EfficientNet  
To enhance the performance of earlier ConvNets, a combination of any two or of only one dimension (i.e., depth, width, and resolution of the networks) is scaled up. While scaling the model, adjusting all three network dimensions is essential for achieving a better efficiency and accuracy. EfficientNets introduced in 2019 [27] have utilized compound scaling to uniformly scale the network's depth, width, and resolution principally, and they have attained competent results. These models are optimized for parameters and FLOPs efficiency and they have different versions, from B0 to B7. EffiecientNet-B0 has fewer parameters and FLOPs, while the EfficientNet-B7 is computationally complex with the highest accuracy among the EfficientNet family. Inspired by the computational efficiency of EfficientNets and their reliable performance, we employed the EfficientNet-B4 network from the EfficientNet family in the proposed framework for classifying multiple wound types. We chose the EfficientNet-B4 based on the transfer learning experiment that we performed to analyze the different versions of EfficientNet while classifying the chronic wounds. The details of the experiment are provided in Section 4.5. EfficientNet-B4 has a series of MBConv1 and MBConv6 blocks. These blocks further have Expansion, Depth-wise, squeeze-and-excitation (SE), and output blocks. The expansion block contains Conv2D, Batch Norm, and Activation functions. However, the Depth-wise block  
Figure 2: Augmented Wound Image Samples.  
involves DepthConv2D, Batch Norm, and Activation function. In the SE block, the squeeze operation involves a global average pooling layer, and the excitation operation includes two fully connected layers, one with the ReLU and other employing a sigmoid activation function. After that, the obtained attention weights are multiplied element-wise with the input features. Both blocks (MBConv1 and MBConv6) are different in the output blocks. In MBConv1, the output block incorporates Conv2D and Batch Norm, while the output block of MBConv6 encompasses Conv2D, Batch Norm, and Dropout. Notably, the main building block of the EfficientNet-B4 is the MBConv having the SE optimization blocks. The SE blocks allow the model to learn to adjust the channel-wise feature maps via a gating mechanism. This enables the network to selectively emphasize more informative features by learning channel-wise weights and thus enhancing the accuracy.
## 5 Experiments and Results
### Interactions and Attention Mechanism Evaluation  
There is a common phenomenon in which the model usually lacks sufficient explanations for black-box features in deep-learning. Ref. [43] Since our scheme is thoroughly designed on basis of the graph, the operating mechanisms are generally interpretable in figures. As emphasized above, our proposal imposes an attention mechanism to the adjacency matrix to learn the heterogeneous intensities of interactions, which makes the graph dynamic in edges and nodes simultaneously with the purpose of capturing spatial-temporal features. To visually represent these relationships, we take the sample of \(Vehicle\_Id=856\) on Peachtree Street as the target vehicle and build its interaction diagrams in Figure 11. The left side of Figure 11 is the real-world traffic scene, while the middle denotes the interaction dependency that shows the attention weights and connections. Meanwhile, the distance between nodes is balanced by the length of the connecting line.  
The target vehicle movements impacted by the surrounding agents and historical features along with traffic rules can be clearly observed in Figure 11. When the vehicle travels through the intersection, its previous status has the greatest impact on the trajectory, since the drivers tend to have moderate behavior. And this is what most existing works focused on in trajectory anticipation problems. Interestingly, the traffic rule comes third among all potential factors, indicating that the validity of our scheme positively benefits from the traffic rule attributes, and these features contain a lot of information. Surrounding agents also show strong interaction with the target vehicle, especially for \(Vehicle\_ID=850\), as it is close to the object and in the same lane. The above illustrations demonstrate our framework working processes and how our proposal effectively extracts multilevel features, which further verifies the performance of the developed model in producing accurate prediction results.  
Figure 10: Results of the prediction using TPASTGCN with 3 s windows input and 5 s trajectories output, where (**a–d**) display different movements and (**e–h**) contain the description of the results index (**c**,**d**) in frames.
# Simulation of AEBS Applicability by Changing Radar Detection Angle
## 2 Preparation of Simulation
### AEBS Configurations  
AEBS is one of the safety devices of the ADAS (Advanced Driver Assistance System) that is expected to effectively reduce traffic accidents. As shown in Figure 1, after monitoring the road conditions, AEBS 1 measures the distance to the front object, 2 informs the driver of a collision when the distance to the front object approaches, and 3 when the driver does not brake despite the warning, it automatically brakes to avoid collision or mitigate impact. For this, AEBS is equipped with a camera sensor and a 77 GHz radar sensor, as shown in Figure 2.  
The camera detects whether the front object is a vehicle, a pedestrian, or a structure; the radar measures the distance to the front object, and the ECU determines if control is necessary and brakes using an actuator. Figure 3 shows the algorithm for AEBS operation [10].  
Here, TTC (Time to Collision) represents the collision time as the ratio of the relative speed and the relative distance between the target vehicle and the front vehicle, and \(TTC_{min}\) represents the minimum time at which collision avoidance is possible as the ratio of the relative speed and the braking distance between the two vehicles (see Equations (1) and (2)). The core of the algorithm is that the brake is activated when \(TTC\) is less than \(TTC_{min}\) when comparing them.  
\[TTC\ (\text{sec})=\frac{Relative\ Distance}{Relative\ Speed} \tag{1}\]  
\[TTC_{min}\ (\text{sec})=\frac{Braking\ Distance}{Relative\ Speed} \tag{2}\]  
Figure 1: AEBS operations.  
Figure 3: AEBS algorithm.  
Figure 2: AEBS (Autonomous Emergency Braking System) Sensor Devices.  
Table 1 summarizes the specifications of radar sensors used as conditions for simulation in this study. Since the maximum detectable distance of radars varies from 100 m to 200 m, for this study, we fixed the detectable distance as 100 m, and increased the detection angle by \(\pm 2^{\circ}\) from a minimum \(\pm 4^{\circ}\) to a maximum \(\pm 10^{\circ}\).
## 3 Results and Discussion
### Asphalt Mixture Performance Analysis
#### 3.2.4 Comparison with Commercial BJ200 Material  
In order to obtain a clearer picture of the combined performance of SSEA mixes with BJ200, the various properties were summarized and the results are shown in Table 5.  
As shown in Table 5, the SSEA mixture offers several significant advantages over the commercially available BJ200 material. Firstly, compared to BJ200, SSEA mixtures exhibit superior high-temperature performance, as evidenced by higher dynamic stability and lower rutting depth under heavy traffic loads. The inclusion of SBR and SBS modifiers enhances the material's resistance to deformation and shear at elevated temperatures, as demonstrated by cone penetration and Marshall stability tests. These properties make SSEA mixtures more reliable for use in environments with extreme temperature conditions, offering improved durability, longer service life, and reduced maintenance needs compared to BJ200. Next, the enhanced water stability of the SSEA mixtures, as demonstrated by its higher freeze-thaw splitting strength ratio (TSR), ensures better performance under high-humidity or water-exposed conditions, outperforming BJ200 in similar environments. Then, although the SSEA's low-temperature performance is lower than the BJ200's overall, the optimized SBR/SBS modifiers provide superior low-temperature flexibility and stress relaxation capacity, which are essential for reducing the risk of cracking in regions with extreme temperature variations.  
In addition, the SBR/SBS composite modification improves the balance between high and low-temperature performance, offering greater adaptability for use in subtropical regions. While BJ200 exhibits acceptable performance, it lacks the customizability provided by the SBR/SBS-modified mixture, which can be tailored to meet specific project requirements by adjusting the modifier ratio. Finally, a combination of high- and low-temperature and water stability properties results in the most suitable performance of SSEA-3.
# A Cooperative Transmission Scheme in Radio Frequency Energy-Harvesting WBANs
###### Abstract  
Wireless Body Area Network (WBAN) plays an important role in e-health, sports training, and entertainment to monitor human bodies wirelessly and remotely. One critical challenge for WBAN is to guarantee the quality of user experience and improve the network performance within such a resource-constrained and dynamic network. In the proposed paper, we investigate a cooperative radio frequency energy harvesting-based WBAN. Herein, we primarily focus on improving the energy efficiency and network performance through intelligent cooperation among nodes, allowing sensors with sufficient energy to assist other sensors in data uploading. We propose a relay selection method that considers both energy demand and energy harvest efficiency. Each sensor calculates the transmission power threshold required for data uploading based on the perceived channel state and determines whether it can act as a potential relay node in conjunction with its own energy harvest efficiency. The coordinator is responsible for optimizing collaborative transmission plans based on real-time network status. Experimental results show that the cooperative scheme performs better than the common single-hop scheme in terms of packet reception rate and packet arrival rate. In a network consisting of 10 sensors, the increase in packet reception rate ranges from 4.9% to 7.8% when the sensors are placed in preset fixed positions. When the sensors are randomly placed, the increase in packet reception rate ranges from 0.9% to 7.9% and from 0.7% to 7.4%, corresponding to \(\delta\) values of 0.7 and 0.9, respectively.  
wireless body area network; radio frequency; energy harvesting; cooperative transmission 2023158367. [[https://doi.org/10.3390/su15108367](https://doi.org/10.3390/su15108367)]([https://doi.org/10.3390/su15108367](https://doi.org/10.3390/su15108367))  
1
# Bot Datasets on Twitter: Analysis and Challenges
## 3 Database Analysis
### [PERSON] et al. Database (2015)  
[PERSON] et al.'s [34] 2015 public database is a well-known collection (with a Field-weighted Citation Impact [50] of 5.33 for its article in Scopus) composed of five datasets extracted from Twitter's API via crawling: the TFP dataset (real accounts following Twitter account _@TheFakeProject_ validated through CAPTCHA), E13 dataset (real accounts from hashtag _#elezioni2013_ manually verified by sociologists), FSF dataset (fake accounts bought from public website _fastfollowerz_), INT dataset (fake accounts bought from public website _intertwitter_), and TWT dataset (fake accounts bought from public website _twittertechnology_). Each dataset contains public information of the crawled profiles, which are their account and tweet features, as well as their direct relationships in the form of followers and followings.  
From all five datasets, we have selected two for our analysis: the TFP dataset containing certified human accounts and the FSF dataset storing fake accounts bought online. We preferred the TFP dataset because it is a mixture of researchers and journalists from North America and Europe, as the E13 dataset only consists of active Italian Twitter users. Concerning the FSF dataset, there is no apparent difference between the three datasets of fake accounts other than the site the authors of [34] obtained them from. Therefore, we selected it for the number of accounts and relationships present in the collection, which is smaller and thus more manageable than the others.  
On the one hand, the TFP dataset comprises 469 Twitter real accounts, 258,494 follower relationships, 241,710 following relationships, and 563,693 tweets. On the other hand, the FSF dataset has 1169 Twitter fake accounts, 11,893 follower relationships, 253,026 following relationships, and 22,910 tweets. When contrasting both datasets, fake accounts outnumber real ones by 700 profiles but have about 25% fewer tweets and 22% fewer followers; however, they almost have the same amount of followers, with a difference of about 1%.
# A Novel ICT Framework for Sustainable Development Goals
## 5 The ICT Framework Description
### Sustainability Module
The outcomes in this framework include social performance measures (efficiency, equity, accountability, sustainability) and ecological performance measures (over-harvesting, resilience, biodiversity, sustainability). Therefore, the sustainability module transforms outcomes of the specific socio-ecological system to the (dis-aggregated) indicators of the Agenda 2030. The fifth subsystem _Interaction_ refers to social, economical, and environmental processes through which interactions lead to outcomes. Depending on the considered system, different models could be employed [183]: (1) System dynamics assume formalism based on ordinary differential (or difference) equations, representing causal relationships, feedback loops, delays, and decision rules of variables that generate system behavior; (2) Graphical models assume that variables are represented by nodes connected by links (random Markov fields) or arrows (Bayesian networks) representing dependences (or casual dependences in the case of Bayesian networks); (3) Agent-based models focus on interactions between autonomous entities (agents), representing individuals or groups, as well as the links between agents and their behavioral patterns; (4) Knowledge-based models are (computer) programs that use _knowledge base_ (a method/technology for storing structured and unstructured information) for automated reasoning and inferring conclusions (_inference engine_), adopting rule-based methods (\"if-then-else\" rules) or logic-based methods, formalized according to a logic system; and (5) Hybrid models involve combining models for producing an integrated outcome, including the previous four modeling approaches. Although SESF framework has proven capabilities of addressing common resources as documented in many works of [PERSON] and her colleagues, nevertheless, \"current analytical and quantitative modeling capabilities fall short of being able to capture all 17 SDGs and their targets\" [63]. Moreover, Shared Socio-economic Pathways (SSPs), established by the climate change research community for enhancing and facilitating the integrated analysis of future climate impacts, do not aim to address all SDGs. For example, even the most advanced pathways such as SSP1/SSP1-2.6, do not meet all SDGs and do not provide information on some of them [63]. Integrated assessment models used for the development of the SSP scenarios include:  
* IMAGE, the Integrated Model to Assess the Global Environment [186];
* MESSAGE-GLOBIOM, the Model for Energy Supply Strategy Alternatives and their GeneralEnvironmental Impact combined with the Global Biosphere Management Model [187];
* AIM, the Asia-pacific Integrated Model [188];
* GCAM4, the Global Change Assessment Model [189];
* REMIND-MAgPIE, the Regionalized Model of Investments and Development combined with the Model of Agricultural Production and its Impact on the Environment [190]; and
* WITCH-GLOBIOM, the World Induced Technical Change Hybrid model combined with GLOBIOM [191].