esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Ground Temperature Monitoring Data
### Heat Flux Near the Permafrost Table  
Temperature field changes in the permafrost regions are mainly affected by surface radiation and earth heat flow. External energy enters the interior of the subgrade through the active layer, affecting the development of the permafrost table within the subgrade. In the study of temperature field changes and the distribution of subgrades in cold regions, the heat flow changes in permafrost are often estimated by calculating heat fluxes near the permafrost table [21].  
According to the principle of heat conduction, the heat flux into the depth range in the one-dimensional vertical direction can be approximately described as:  
\[Q=\sum_{i=1}^{12}q_{i} \tag{1}\]  
\[q_{i}=\lambda\frac{\partial T}{\partial H}T_{i}\approx\lambda\frac{T_{1}-T_{2} }{\Delta H}T_{i} \tag{2}\]  
where Q is the heat flux into the set depth range in one year; q\({}_{i}\) is the heat flux in month i; \(\lambda\) is the thermal conductivity of the soil, which is taken as 1.19 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) for melting and 1.43 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) for freezing [22]; Ti is the length of time in month i; T\({}_{1}\) and T\({}_{2}\) are the ground temperatures at the depth near the permafrost table in the medial divider and the right natural hole, respectively; H is the depth of permafrost; \(\Delta H\) is the calculated depth range of the heat flux, taken as 0.5 m.  
Figure 6 shows the heat flux of the soil beneath the medial divider and the right natural hole, calculated according to the ground temperature monitoring data from 2020 to 2023. It can be observed that there is essentially no difference in the trend of heat flux change between the medial divider and the right natural hole. Both of the lower soils' heat fluxes exhibit cyclical patterns. The heat-absorbing period is relatively shorter, and the trend of the change is more intense. The heat-exporting period is relatively longer, and the trend of the change is milder.  
Figure 5: Distributions of ground temperature along the depth at different locations in 2020.  
During the monitoring period, the medial divider and the right natural hole at this depth were heat-absorbing. For instance, in 2023, the right natural hole had a total heat flow of 131.85 W/m\({}^{2}\), while the medial divider had a total heat flux of 171.07 W/m\({}^{2}\). The permafrost in the bottom portion of the medial divider is more unstable than that in the right natural hole, as indicated by the greater vertical heat absorption rate in the medial divider compared to the right natural hole. The primary cause is that, at the same depth below the ground surface, the medial divider is more susceptible to the effects of engineering disturbance and external ambient temperature.
## 5 M-Estimators Loss Function  
M-estimators play a vital role in NN society and constitute a broad group of estimators; maximum likelihood estimation and nonlinear least-squares methods are special cases. M-estimators are an effective mechanism in robust statistics that is resistant to outliers. M-estimators are robust statistical estimators usually used as an alternative to the least-squares estimator (LS) when the data contain outliers or extreme observations or do not conform to  
Figure 1: The flowchart of the suggested DNN for estimating V2X performance.  
a normal distribution. The M-estimator is advantageous when the data have outliers and are contaminated by a single outlier (or an error with a strong tail). Even a single outlier can destroy the least-squares estimator and lead to its breakdown. Therefore, there are two options: 1. eliminate outliers that give a bad result; 2. use the robust M-estimators.  
Recently, several researchers have proposed M-estimators as loss functions to improve the learning process of NNs. The M-estimators use multiple loss functions that are less increasing than the LS estimators. The M-estimator restricts the response once the fitting error surpasses a threshold, i.e., the fitting error is not zero. Hence, the M-estimator loss function has greater reliability, robustness, and flexibility when the data contains outliers than the conventional MSE loss function, which is highly susceptible to outliers; thus, this set of estimators should be a suitable replacement for the MSE loss function outliers.  
In this work, the traditional MSE loss function has been replaced by M-estimator loss functions to improve the training process and increase the learning of NNs. Assume that e\({}_{i}\) is the residual of the ith data point that is the difference between the observed and estimated values. The least-squares method attempts to reduce the error \(\sum_{i}e_{i}^{2}\) that is unstable with outliers. Outlier data has extreme values that can spoil the estimation of the parameters. The M-estimators attempt to minimize the effects of outliers by exchanging the squared residuals \(e_{i}^{2}\) with various fitting error functions, resulting in:  
\[min\sum_{i}\rho(e_{i}) \tag{1}\]  
* \(\rho(e)\) is a positive-definite symmetric function, where \(\rho(e)=\rho(-e)\) for all \(e\).
* \(\rho(e)\geq 0\) for all e and has a unique minimum at \(e=0\).
* \(\rho(e)\) increases when e increases from 0 but does not become too large when e increases and is selected to be less increasing than square.  
Table 1 illustrates the used M-estimators, and their influence function can be represented as follows [36; 38].
## 2 Background
### Software Tamper-Proofing  
Software programs are increasingly used to execute critical tasks. At the same time, their protection against undesired modification is very important. Much research has been done in the field of tamper-proofing. The most classical one is based on the seminal research by [PERSON] [11]. However, in total, program protection techniques can be widely classified into two sides: hardware and software approaches.  
Hardware-based approaches are somewhat monetary cost and cannot be circle used basically. However, on the other hand, these approaches are more difficult to break. Now, there are many hardware solutions that have been proposed to against physical and software attacks. Dongles is a very common device that is distributed to the copy of the software and can be attacked to a computer. The program will invoke a callback discontinuously from dongles during the execution progress of the program. If the reply has no sign of abnormal, the program will continue to run, and, if not, it ceases to run. It is more difficult to debug the dongles. That is to say, anyone who wants to copy the dongles is illegal. However, if an adversary can obtain the security function from the dongle, he can also bypass the protection using emulation. As the time goes by, modern processors possess their own protection functionality in hardware (e.g., the Cell Broadband Engine, which provides the Runtime Secure Boot feature, is used to verify an application that has not been tampered with at start up [12]). Other hardware solutions involve the use of trusted computing platforms such as TPM (TPM2.0, Infineon, Munich, Germany) and Microsoft's NGSCB (Microsoft Corporation, Redmond, WA, USA) [13]. Finally, there are customized hardware techniques to prevent tampering, such as execute-only memory [14] and systems based on secure processors [15].  
On the contrary, software-based approaches are used more generally. [PERSON] [16] proposed the original guards used to tamper-proof, which employed the idea of checksum guards to protect the integrity of an application. After that, [PERSON] [17] et al. came up with the concept of tester, which was similar to the guards. It consists of a sequence of instructions that calculated a hash value of fixed length addresses and compared it with a corrector during the program runtime.  
Obfuscation techniques are also widely used in the area of software tamper-proofing so that it is difficult for an adversary to understand and analyze the original code of the application. [PERSON] et al. provided a number of obfuscation techniques to obscure the code [18; 19]. [PERSON] et al. designed a novel technique to disturb the disassembly of the code [20]. [PERSON] et al. proposed the use of indirect branches to obfuscate the control flow of a program. Encryption of the program code is another way to consolidate the program. The specific hardware can decode the code (i.e., a secure co-processor [21]). However, as we mentioned before, they cost a lot than other software-based approaches. Nevertheless, these static obfuscation techniques are vulnerable to some dynamic analysis techniques [22].
## 3 Demonstration Problem: Design of Aluminum Foam-Cored Sandwich Panel  
### Problem Formulation  
For demonstration, we designed a sandwich panel with an aluminum foam core for blast resistance applications, as illustrated in Figure 5. Aluminum foam is a useful material that is capable of absorbing blast energy or projectile impacts because of its high specific strength, low relative density, and high energy-absorbing capacity [18; 19; 20; 21]. Given the excellent material properties, aluminum foam-cored sandwich panels (AFSPs) can be widely applied as energy absorbers in a wide range of impact- or blast-protection applications, such as crash barriers, blast mitigators, and munitions packaging. In this study, we designed a circular AFSP (2 m diameter, 100 mm fixed total thickness) capable of satisfying the performance requirements for deflection and structural mass subjected to a blast impact of 15 kg of TNT at a distance of 1 m (Figure 5).  
The hierarchical design flow of the sandwich panel is shown in Figure 6. The sandwich panel problem can be decoupled into three levels: Processing level, property level, and performance level. The variables designed here were the processing parameters for the aluminum foam that affect the structural/mechanical properties of the core as well as the foam core thickness (\(T_{c}\)). With the given sandwich panel geometry and material properties, the final performances were maximum deflection due to blast impact and mass/area of the panel. A response surface model links the processing and property levels and is established to represent the influence of two processing parameters (amount of TiH\({}_{2}\) and holding time) on the relative density of the aluminum foam [22]. The deflection and mass/area as functions of core density and thickness were calculated by a kriging model and mass/area equation, respectively. Additional information on the models that linked the design levels as well as model validations are discussed in detail in our previous study [9]. The performance requirements were established as a maximum deflection of \(\leq\)80 mm and mass/area value of \(\leq\)20 g/cm\({}^{2}\).  
Figure 5: Aluminum foam-cored sandwich panels subjected to blast impact [9].
# Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network
## 4 Numerical Results and Discussions  
We use CPLEX to implement the three programs (LBO, LB&NFP, LB&LFP). We evaluate our ILP models on two sets of topologies. The first topologies come from four well-known networks shown in Figure 7. The link capacity for every link is 100 units. Each node pair in the network has a one-unit demand. The topologies in the second set are randomly generated. The connectivity of each topology is at least 2-Vertex and 3-Edges (2V3E). Each node pair in the network is assigned a random demand with demand volume obtained from the Bimodal Traffic Matrices (TMs) [15]. We combine the two Gaussians. The first is (\(\mu_{1}=15,\,\sigma_{1}=2\)) and the second is (\(\mu_{2}=85,\,\sigma_{2}=2\)), to generate Bimodal traffic demands. The first Gaussian generates small traffic demand, and the second Gaussian generates large (big) traffic demand. A biased coin is flipped for every OD pair, for which the chances (probability) of head and tail are 0.8 and 0.2, respectively. The random number is produced using the first Gaussian (\(\mu_{1},\,\sigma_{1}\)) if a flipped coin shows heads; otherwise, second Gaussian TMs are used. Thus, we have 80% small demand and 20% large (big) demand. We set the value of the error margin as two.  
Figure 7: Well-known benchmark networks [12]. (**a**) NSF network; (**b**) EON network; (**c**) COST239 Network; (**d**) USA network.
## 5 Discussion
### Limitations of the Study
#### 5.2.1 FEA Simplifications  
One of the main limitations of this study lies in the simplifications made during the FEA simulations. To ensure computational efficiency, the material properties were modeled using homogeneous assumptions, neglecting potential microstructural variations within the material. While the Johnson-Cook material model was used to represent dynamic material behavior, this approach may not fully capture the complex real-world interactions that occur at the micro-scale, particularly in high-strength materials like UIC 860 Grade 900A railway rails. This limitation is consistent with challenges identified in similar studies. For instance, [PERSON] et al. [34] acknowledged that their FEA simulations lacked the ability to fully model microstructural changes during the deep rolling process. As a result, predictions regarding stress distribution and deformation depth may be less accurate than those obtained through experimental analysis. Our results align with this observation, as small discrepancies between the FEA-predicted and experimentally measured residual stress values were noted, particularly in deeper material layers. [PERSON] et al. (2019) also reported similar limitations in their simulation work, suggesting that more detailed material models, incorporating grain structure and anisotropic behavior, could improve the predictive accuracy of FEA.
# Comparison of the Heights of Sporadic E Layers and Vertical Ion Convergence Parameters
## 3 Results and Discussion  
### Comparison of the Heights of Es Layers and Vertical Ion Convergence Parameters  
Figure 2 presents scatter density figures illustrating the relationship between the heights of Es layers and IVN at different latitude zones and seasons. In each subfigure, a regular triangle symbolizes an Es-IVN data point, with color indicating the relative density of points. Every subfigure contains a few to over ten thousand points covered over all longitudes. Additionally, each subfigure in Figure 2 provides the diagonal lines (red dashed lines) and the difference between the heights of Es layers and IVN (red characters, the unit is kilometer). The results reveal that both the heights of Es layers and IVN predominantly center around 90-110 km, which is consistent with previous studies [6; 36]. The heights of Es layers tend to be lower than that of IVN by several kilometers, approximately 2-6 km. It is easy to see that most points of Es-IVN center at the diagonal lines, indicating good agreements between the heights of Es layers and IVN at low-latitudes (<20\({}^{\circ}\)) and mid-latitudes (20-50\({}^{\circ}\)).  
Figure 3 is the same as Figure 2, but inverted triangles in Figure 3 denote data points for Es-VICmax. It is evident from Figure 3 that the height of VICmax predominantly falls within the 110-130 km range, indicating a higher altitude than that of Es layers by approximately 20 km on average at different latitudes and seasons. At the same time, the distributions of the points of Es-VICmax deviate obviously from the diagonal lines at different latitudes and seasons. Upon comparison of the results presented in Figures 2 and 3, it becomes evident that the heights of IVN exhibit stronger agreement with the heights of Es layers observed by COSMIC-1.  
For IVN, the vertical ion velocity at this node is zero, resulting in stationary convergent thin layers after Es layer formation. However, the vertical velocity of VICmax may not necessarily be zero. This implies the presence of a thin ion layer undergoing vertical motion, deviating from the height of VICmax. According to the simulation findings [22; 23], the Es layer is expected to manifest at the height of IVN. In scenarios where IVN is absent, Es would emerge at the height of VICmax and subsequently move towards a height where both the vertical ion velocity and vertical ion convergence approach zero. Thus, in theory, Es is more closely aligned with the height of IVN. Our results supported this conclusion.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 2 Grid Planning
### Reliability and Resilience Planning
#### 2.3.3 Role of Smart Networks to Enhance Reliability and Resilience  
Advanced control technologies in smart distribution networks significantly shorten outage times and improve grid resilience, enabling quick responses to disruptions. This automation ensures reliable power distribution and reduces the impact of interruptions. Smart distribution systems (SDSs) enhance grid reliability and resilience, leveraging technologies like smart meters and remote-controlled switches for sophisticated outage management and fault location, isolation, and service restoration (FLISR) techniques [23].
## 4 Experiments
### Ablation Study  
#### 4.5.1 Analysis of Different Representation Layers  
To further investigate the impact of four modules in representation layers on model performance, we conduct ablation experiments. Acknowledging character embedding (char) as the most basic representation, we make the deliberate decision to retain it consistently in each experiment. Subsequently, we progressively eliminate either one or both bichars, SoftLexicon, and RoBERTa. Table 7 shows the specific experimental results.  
In the representation layer of our SLRBC, char, bichar, SoftLexicon, and RoBERT are used simultaneously to achieve the best results on both datasets, once again verifying the validity of SLRBC. The removal of SoftLexicon or RoBERTa results in a significant decrease in F1 compared to SLRBC. F1 reaches its lowest point when both are removed simultaneously, providing conclusive evidence that these two modules play a crucial role in the representation layer of SLRBC. However, when bichar is removed, F1 does not decrease significantly compared with SLRBC. This is due to the fact that bichar, serving as an adjunct to char, makes a relatively smaller contribution to the representation. Experiments conducted on the two datasets reveal that the F1 for CCKS2018 exhibited less fluctuation compared to that of CCKS2019. This result indicates that when the entity annotation of training data is clearer, the change in the representation layer has less influence on the final recognition performance.
## 4 System Model and Simulation Setup  
This research focuses on a collection of drones that could freely travel across a UANET employing an 802.11 g radio transceiver for wireless transmission. A random waypoint mobility model is applied in which UAVs move randomly towards the determined destination in the network with a steady speed (30 m /s and 40 m /s). The take-off and landing scenarios for UAVs are not considered in this research. Energy constraints are not considered as presumed rechargeable batteries for UAVs that can be recharged from renewable energy sources [40]. As most UAV implementations do not fly at high altitudes, we also consider that UAVs fly at a low and consistent altitude during their flight. An IPv4 auto-assign address is applied to each UAV, making it identifiable by its node address.
## 5 Results and Discussion
### Sentiment Classification
#### 5.1.4 Social Media App Review Dataset  
Table 4 represents a comparative analysis of the Sentiment Classification model for several performance metrics through altering training data. The analysis of TD-Spider TaylorChoA-GRU for the precision, recall and F-measure is listed below. In 90% of training data, the precision of the developed TD-Spider TaylorChoA-GRU is 0.897. The recall of TD-Spider TaylorChoA-GRU is 0.906 for 90% of training data and, considering 90% of training data, the F-measure of the developed model is 0.902.  
Figure 7 represents the accuracy and loss for the Sentiment Classification model. Here, the proposed method also has the highest accuracy and minimum loss.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Metrics** & **Data (\%)** & **HSACN** & **MCNN** & **DCBVN** & [3] & [34] & [17] & **Proposed** \\ \hline \multirow{4}{*}{Precision} & 60\% & 0.741 & 0.769 & 0.807 & 0.816 & 0.834 & 0.848 & 0.858 \\  & 70\% & 0.768 & 0.785 & 0.821 & 0.837 & 0.854 & 0.865 & 0.875 \\  & 80\% & 0.785 & 0.807 & 0.841 & 0.859 & 0.864 & 0.875 & 0.887 \\  & 90\% & 0.798 & 0.814 & 0.865 & 0.869 & 0.874 & 0.887 & 0.897 \\ \hline \multirow{4}{*}{Recall} & 60\% & 0.754 & 0.778 & 0.814 & 0.827 & 0.841 & 0.858 & 0.865 \\  & 70\% & 0.778 & 0.799 & 0.829 & 0.848 & 0.862 & 0.876 & 0.887 \\  & 80\% & 0.798 & 0.814 & 0.832 & 0.859 & 0.878 & 0.887 & 0.897 \\  & 90\% & 0.801 & 0.825 & 0.848 & 0.865 & 0.887 & 0.898 & 0.906 \\ \hline \multirow{4}{*}{F-measure} & 60\% & 0.748 & 0.774 & 0.811 & 0.821 & 0.838 & 0.853 & 0.862 \\  & 70\% & 0.773 & 0.791 & 0.825 & 0.842 & 0.858 & 0.870 & 0.881 \\ \cline{1-1}  & 80\% & 0.791 & 0.811 & 0.837 & 0.859 & 0.871 & 0.881 & 0.892 \\ \cline{1-1}  & 90\% & 0.800 & 0.820 & 0.856 & 0.867 & 0.881 & 0.892 & 0.902 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of Sentiment Classification for social media app review dataset in terms of precision, recall and F-measure.  
Figure 6: Performance analysis based on (a) accuracy and (b) loss for Sentiment Classification using Yelp 2014 dataset.