esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Specimen Acoustic Emission Characterization  
The evolution of crack production, dispersion, extension, and penetration in the rock is found to be correlated with the fluctuation in cumulative ring count [37]. Therefore, the cumulative ringing count is utilized as a starting point to examine how fracture-grouted rock specimens are damaged and destroyed. Figure 8 displays the acoustic emission ring counts and stress curves over time for fracture-grouted rock specimens under uniaxial compression with varying numbers of dry and wet cycles.  
The image illustrates how closely the stress-strain curves of the fracture-grouted rock specimens and the acoustic emission ringing count curves correspond. When the rock sample's axial stiffness gradually increases, the ringing count decreases and the acoustic  
Figure 8: Acoustic emission characteristics of uniaxial compression of fracture-grouted rock specimens with different numbers of wetting–drying cycles. (**a**) 0 cycles. (**b**) 5 cycles. (**c**) 10 cycles. (**d**) 20 cycles.  
emission signal primarily has a low amplitude at the beginning of loading [38]. A minor amount of low-energy acoustic emission is produced during the closing process of the original crack within the fracture-grouted rock specimen, including during the destruction of a portion of the rough surface after closure and the slide between the surfaces of the closed fissures. At this time, the fracture-grouted rock specimen is in the pore-fracture-compacting stage, producing nonlinear deformation. The primary microcracks inside the fissure-grouted rock specimen have essentially closed. The fissure-grouted rock specimen enters the elastic stage with the test loading, and the acoustic emission signal steadily grows and intensifies. When the load steadily grows, cracks start to appear and expand, and the cumulative and auditory emission ringing counts progressively start to be active. As the loading continues, the interaction between cracks begins to intensify, and the microcracks coalesce, penetrate, and gradually form macroscopic cracks [37; 39]. Near the stress peak, the acoustic emission activity is exceptionally vigorous, and the acoustic emission ringing count is at its highest.  
The overall trend of the cumulative acoustic emission ringing counts of the specimens after 10 and 20 cycles is clearly different from the overall trend of the cumulative acoustic emission ringing counts after 0 and 5 cycles. With an increase in the number of wet and dry cycles, the acoustic emission ringing count peak increases significantly, which makes the cumulative acoustic emission ringing count jump significantly when the fractured-grouted rock specimen is damaged. The dry and wet cycling effect greatly reduces the integrity and stability of the specimen by reducing the fissure-grouted rock specimen. Moreover, the microcracks of the fissure of the grouting rock specimen are more easily sprouted, expanded, lapped, and connected. The continuous wet and dry cycles lead to a gradual increase in microcrack activities within the specimen and a gradual and evident development of damage to the specimen [40].
## 2 Experiments and Methods
### Simulation of Chloride Ion Transport in Concrete
#### 2.2.2 Calculation Conditions  
In actual engineering scenarios, the polarization potential caused by stray currents generally does not exceed 150 mV, and the chloride ion concentration in service environments typically ranges from 0.6 to 1.8 mol/L. Therefore, the calculations will simulate chloride ion penetration under four voltage values: 0 mV, 50 mV, 100 mV, and 150 mV, with varying chloride ion concentrations.
## 1 Introduction
### Contribution  
1. Bagging ensemble consisting of LR and SVR is firstly proposed to improve the forecasting accuracy by converting ML models from weak learners to strong learners.
2. Advanced DL models including DNN, LSTM, and CNN-LSTM are implemented along with tuning hyperparameters for STLF to handle back-propagation learning and time series problems.
3. A detailed comparative analysis of the proposed model and other DL models is provided and compared each other. The comparison is done by considering the mean absolute percentage error (_MAPE_), the mean absolute error (_MAE_), and the mean squared error (_MSE_) as the main performance metrics. These performance metrics are computed for the provided dataset for every month.
4. The used data in this work are obtained from EGAT and are first smoothed using the filtering technique. The filtering process is done to avoid missing values and outliers.
5. Different input features are applied for all models and are compared to check the correlation between load and external influential factors, because external factors like temperature, holidays, and months of the year commonly affect load demand.
# Role-Based Access Control Model for Inter-System Cross-Domain in Multi-Domain Environment
## 7 Conclusions and Future Work  
A role-based access control model for inter-system cross-domain in multi-domain environment (RBAC-IC) is proposed. This model is based on the traditional RBAC model, where roles are divided into abstract roles and specific roles play different functions; users are divided into three categories: platform administrators, domain administrators and ordinary users. This paper explains the concepts of the model through formal definitions, and it expounds upon different functions of abstract roles and specific roles. The execution process of RBAC-IC is described by pseudocode. RBAC-IC has four features that support role name repetition, platform-domain isolation management, inter-domain isolation management, and fine-grained cross-domain sharing. RBAC-IC carried out a typical case verification under the information service platform of the packaging can manufacturing group, and it conducted model security analysis by establishing security violation formulas. It is proved that RBAC-IC can run securely, and it is suitable to be used as the access control model of multi-domain information service platform to address the issue of inter-system cross-domain access control.  
The security analysis of the model still needs further research, especially in the aspect of cascading security threats [32]; for example, the security events of system X in domain A may affect other systems in other domains. In the future work, we will carry out more research in this area.  
Conceptualization, Y.L. and Z.D.; methodology, Y.L. and L.L.; software, Y.L.; validation, Y.L. and L.L.; formal analysis, Y.L. and L.L.; writing--original draft preparation, Y.L., L.L. and Z.D.; writing--review and editing, Y.L. and Z.D.; supervision, Z.D. and Y.F.; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shaanxi Natural Science Basic Research Project (Grant Number: 2021 KW-07) and the Shaanxi International Science and Technology Cooperation Program Project (Grant Number: 2022 QFY01-14).  
Not applicable.  
Figure 15: The platform administrator _pa_ creates an abstract role and domain administrator _da_ creates a specific role are allowed. However, platform administrators create specific roles, and domain administrators create abstract roles, which are not allowed.  
**Acknowledgments:** The authors thank Timechainer (Beijing) Technology Co., Ltd. and its general manager [PERSON] for their support in software development. The authors thank Taishan Zhizhen Packaging Technology Co., Ltd. and [PERSON] of North Automatic Control Technology Institute for their support in verification.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 5 Simulation Tests and Results
### Effect of the Size of the Scenario  
To illustrate the effect of the size of the scenario, this subsection reports the results of a battery of simulations, with 10 nodes deployed in a circular area and a radius ranging from 100 m to 2 km. A fixed frame size of 215 bytes is used. The results are represented in terms of the total update time. Each of the presented values is the average of 40 simulations, and 95% confidence intervals are also included.  
The _only unicast_ method results are shown in Figure 14a: the increase in the radius results in a higher loss probability and an increase in the total time required for the update.  
The benefit of the _broadcast + unicast_ method is clear for short distances In Figure 14b, for example, for 100 m, the time is reduced to 9.2% of that required by the _only unicast_ method. However, as the distance increases, it starts behaving similarly to the _only unicast_ method: for 2 km, the time required by the _broadcast + unicast_ is 35% of that required by the _only unicast_ method. The reason is that the broadcast stage is not very successful, so most of the chunks are sent in unicast frames.  
Figure 12: _Only broadcast_ firmware update of 10 peers with a maximum radius of 400 m: (**a**) RSSI of the frames sent by the gateway, containing the firmware chunks; and (**b**) RSSI of the frames received by the nodes which are missing them. The red line corresponds to the sensitivity of the receiver.  
Figure 14: Effect of the radius of the scenario on the total update time: (**a**) _Only unicast_ method; (**b**) _Broadcast + unicast_ method; and (**c**) _Only broadcast_ method. Note that the \(Y\) axis is in logarithmic scale.  
Figure 13: _Only broadcast_ firmware update of 10 peers with a maximum radius of 2 km: (**a**) RSSI of the frames sent by the gateway, containing the firmware chunks; and (**b**) RSSI of the frames received by the nodes which are missing them. The red line corresponds to the sensitivity of the receiver.  
Finally, the _only broadcast_ method (Figure 14c) is quite similar to the _broadcast_ + _unicast_ one for short distances (4.2% worse for 100 m), but it is able to maintain lower update times for long distances: for example, for 2 km, the time required by the _only broadcast_ method is 65.7% of the time spent by the _broadcast_ + _unicast_ one.
# An Image Compensation-Based Range-Doppler Model for SAR High-Precision Positioning
## 2 The Proposed Image Compensation-Based R-D Model
### R-D Model  
For any pixel \((c,r)\) in an image, the acquisition time can be determined from the image row coordinate \(r\). Using this time information, data such as sensor position and speed can be extracted from position and orientation system (POS) data. Additionally, the SAR measurement of distance from the target to the sensor can be calculated based on the column coordinate \(c\). The R-D model utilizes this information to establish geometric and physical relationships between the sensor and the ground target, forming the basis for the R-D equations, as follows:  
\[\begin{cases}(R_{0}+c\cdot M_{r})=\sqrt{(X-Xs)^{2}+(Y-Ys)^{2}+(Z-Zs)^{2}}\\ f_{D}=2[(X-Xs)V_{Xs}+(Y-Ys)V_{Ys}+(Z-Zs)V_{Zs}]/(\lambda R),\end{cases} \tag{1}\]  
where the parameter \(R_{0}\) represents the initial slant range corresponding to the first column of an image, Mr is the slant range resolution of the image, and \(\lambda\) represents the wavelength of the electromagnetic wave. The first equation represents the range equation, where the left side denotes the distance extracted from the SAR image, and the right side presents the spatial distance between the sensor coordinates (\(X_{S}\), \(Y_{S}\), \(Z_{S}\)) and the ground target coordinates (\(X\), \(Y\), \(Z\)). The second equation expresses the Doppler equation, with the left side representing the Doppler frequency \(f_{D}\) used in imaging, while the right side is the Doppler shift of the SAR electromagnetic wave obtained based on the sensor velocity [\(V_{Xs}\), \(V_{Ys}\), \(V_{Zs}\)]T and the relative position [\(X-X_{S}\), \(Y-Y_{S}\), \(Z-Z_{S}\)]T between the sensor and the ground target.  
High-precision positioning in the R-D model is typically achieved through optimizing the model parameters using reference points. In Equation (1), the sensor's position and velocity, imaging Doppler frequency, and initial slant range correction parameters (serving as their functional parameters) are unknown variables. Under the condition of having reference points for support, error equations can be constructed, and correction values for the initial values of the model parameters may be determined.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 4 Heuristic 5G Core Network Slice Provisioning Algorithm Design
### Heuristic Slice Provisioning
#### 4.2.2 Slice Link Provisioning  
In the slice link provisioning stage, since the slice link with a larger amount of bandwidth resource demand is more difficult to be provisioned, the slice links are first ranked according to the bandwidth requirements from large to small. The \(k\)-shortest path algorithm [30] is then implemented to provision slice links. Thus, our RT-CSP algorithm includes the heuristic slice node provisioning algorithm and the basic \(k\)-shortest path-based slice link provisioning algorithm.  
To further improve the performance of RT-CSP, we propose a novel strategy, called _minMaxBWUtilHops_, in the slice link provisioning stage. After the \(k\)-shortest path algorithm obtains \(k\) candidate physical paths for each slice link satisfying its bandwidth demand, _minMaxBWUtilHops_ evaluates each candidate physical path as follows:\[\Gamma_{p^{l}}=(1-\frac{b_{a}(e^{l})}{b_{0}(e^{l})})_{max}\cdot|L(p^{l})|. \tag{20}\]  
\(\Gamma_{p^{l}}\) is the product of the maximum link bandwidth utilization of the candidate physical path \(p^{l}\) and its hop counts. The candidate physical path with the smallest \(\Gamma_{p^{l}}\) is selected to host the slice link. The reason we propose this strategy is that the physical link with large bandwidth utilization in the physical path becomes the bottleneck of the path, which causes the provisioning of the other slice links to fail easily, resulting in a decrease in the slice acceptance ratio. In addition, selecting the physical path with fewer hops can reduce the provisioning cost. The slice link provisioning algorithm based on the _minMaxBWUtilHops_ is described in Algorithm 2. The slice provisioning algorithm with heuristic slice node provisioning algorithm and the _minMaxBWUtilHops_ based slice link provisioning algorithm is named as RT-CSP+.  
```
0: Infrastructure network \(G^{l}\), slice request \(G^{S}\), and slice node provisioning results \(sliceNodeProvisioningList\)
0: Slice link provisioning solution
1: Rank all the slice links in \(E^{S}\) based on bandwidth requirements from large to small.
2: Put the ranking results into \(sliceLinkRankList\).
3:for each slice link \(e^{S}\in sliceLinkRankList\)do
4:\(k\) shortest path algorithm is implemented to obtain the candidate substrate paths \(subPathList\) for \(e^{S}\) meeting its bandwidth demand.
5:if\(subPathList\) is not empty then
6:for each substrate path \(subPath\in subPathList\)do
7: Calculate \(\Gamma_{p^{l}}\) based on Equation (20).
8:endfor
9: Provision \(e^{S}\) onto the candidate substrate path with the minimum \(\Gamma_{p^{l}}\).
10:else
11:return\(sliceLinkProvisioningFailed\)
12:endif
13:endfor
14:return\(sliceLinkProvisioningList\)
```  
**Algorithm 2** Slice link provisioning based on _minMaxBWUtilHops_.
## 10 Future Directions
### Explainable Federated Learning  
Developing methods to make FL models more interpretable and explainable is another important direction. By understanding the decision-making process of these models, it becomes easier to detect and mitigate malicious behaviors. Explainability can also build trust among participants by providing transparency in how the aggregated model is being trained and updated.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 3 Proposed Methodology
### Multi-Scale Feature Fusion Model
#### 3.4.2 Cross-Scale Attention Mechanism  
While the FPN provides a hierarchical representation of the input image, not all scales are equally important for detecting objects of varying sizes. The MSFF module introduces a cross-scale attention mechanism that adaptively weighs the contributions of different scales based on their relevance to the detection task. \(F_{l}\) represent the feature map at level \(l\) in the pyramid. The cross-scale attention mechanism computes attention weights \(\alpha_{l}\) for each scale:  
\[\alpha_{l}=softmax(w_{a}^{\intercal}GAP(F_{l}))+b_{a}, \tag{24}\]  
where GAP\((\cdot)\) is the global average pooling operation that reduces the feature map \(F_{l}\) to a vector, \(w_{a}^{\intercal}\) is a learnable weight vector, \(b_{a}\) is a learnable bias term. The attention weights \(\alpha_{l}\) determine the importance of each scale, and the final fused feature map \(F_{fused}\) is computed as a weighted sum of the multi-scale feature maps:  
\[F_{fused}=\sum_{l=2}^{5}\alpha_{l}F_{l}, \tag{25}\]  
This fusion process ensures that the most relevant features from each scale are combined, enhancing the model's ability to detect objects of varying sizes.  
After the feature maps are fused, scale-specific detection heads are applied to the fused feature map to generate the final predictions. Each detection head is responsible for predicting the oriented bounding boxes and class scores for objects at a particular scale. \(H_{l}\) represent the detection head applied to the feature map at level \(l\). The detection head consists of a series of convolutional layers followed by output layers for bounding box regression and classification:  
\[\begin{split} B_{l}&=Conv_{b}\Big{(}F_{fused} \Big{)}\\ C_{l}&=Conv_{c}\Big{(}F_{fused}\Big{)}\end{split} \tag{26}\]  
where \(Conv_{b}\) and \(Conv_{c}\) are convolutional layers for bounding box regression and classification, respectively. \(B_{l}\) is the predicted bounding box for scale level \(l\), \(C_{l}\) is the predicted class score for scale level \(l\). The final detection output is obtained by aggregating the predictions from all scale levels:  
\[B=\sum_{l=2}^{5}B_{l},\qquad C=\sum_{l=2}^{5}C_{l} \tag{27}\]  
This aggregation ensures that the model can accurately detect objects at different scales, leveraging the information from all relevant feature maps. The MSFF module is implemented in a way that allows it to be seamlessly integrated into the RASST model. The attention mechanism is lightweight and adds minimal computational overhead, making it suitable for real-time applications. The use of scale-specific detection heads ensures that the model maintains high accuracy across all scales, particularly in challenging aerial imagery scenarios.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 3 Results and Discussions
### Sensitivity Analysis, Feature Importance, and Computational Complexity  
The Figure 14 illustrates the comparison analysis of training times of two machine learning models--Artificial Neural Network (ANN) and Random Forest (RF)--for different types of buildings: the LUCIA Building (nZEB) with special consideration for HVAC systems and the FUHEM Building for heating. For the LUCIA Building (a), the ANN takes significantly more training time compared to the Random Forest, owing to the complexity of neural network training, with its multiple epochs, backpropagation, and optimization of numerous weights and biases, which implies more computational demands. The RF model, however, which builds a lot of decision trees in parallel, completes its training much earlier. In the FUHEM Building (b), the same trend is noticed, but with a smaller time difference, which suggests that the efficiency of the models could be data-characteristic- or problem-complexity-dependent, while the performance of the ANN gives suitable results compared to the RF model [32].  
Figure 15 illustrates the sensitivity analysis results for the Artificial Neural Network (ANN) and the feature importance for the Random Forest (RF) model across two building types: the LUCIA Building (nZEB) and the FUHEM Building (Heating). In both cases, the sensitivity analysis for the ANN indicates that certain features have a significant impact on the model's predictions; for instance, operative temperature and air temperature are critical for LUCIA, and heat load/floor area and humidity for FUHEM, as their perturbations lead to substantial changes in the output. Similarly, the RF model's feature importance  
Figure 13: Performance for ANN: (a) (LUCIA Building, nZEB-HVAC), (b) (FUHEM Building-Heating).  
highlights temperature and nominal capacity as pivotal factors, consistently showing high importance scores. This alignment between the two models underscores the critical role of these features in influencing HVAC and heating demands, suggesting that they should be prioritized in building energy management strategies [32].
# Influence of Noble Metals on the Microstructure and Properties of Biodegradable Mg-Nd-Zr Alloy
## 3 Results and Discussion
### Mechanical Properties after Biocorrosion
Figure 5: Samples from a magnesium alloy with 0.1 wt.% Ag + 0.1 wt.% Au after 3 months of aging in Gelofusine®: (**a**) macrostructure; (**b**) microstructure. the alloy under study provided the maximum level of its strength and ductility with the addition of 0.1 wt.% Ag or Au, which is consistent with previous studies describing modification with other elements [36]. The results obtained show that at complex modification (0.1 wt.% Au + 0.1 wt.% Ag) of magnesium alloy, the sizes of its structural components significantly decreased, the strength of the alloy increased by \(\sim\)20% and ductility by \(\sim\)2 times due to the formation of intermetallic phases of complex composition. The conducted research may be of interest to manufacturers of castings from magnesium alloys. In addition, it should be noted that the modifiers used are non-toxic to the body compared to the use of, for example, rare earth elements such as yttrium or scandium [37; 38; 39]. The use of a biodegradable Mg-based alloy with an increased set of properties makes it possible to eliminate the preparatory operation of forming a canal and cutting threads with a tap in osseeous tissue during osteosynthesis, which reduces the risk of injury during the surgical intervention and significantly reduces the time of surgery. The results obtained make it possible to develop improved technologies for modifying cast magnesium alloys with noble metals, improving the quality of castings and increasing the mechanical properties of products. The results of these studies may be useful for aviation, automotive and other industries [40].