esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Existing studies need to include analyses of the interaction patterns of encrypted malicious traffic. To study the interaction patterns of different attacks, we propose a malicious attack identification method named G-TIAG. It combines spatial features of graphs and statistical features of flows to learn the TIAG structure from large-scale network traffic and represent the differences in interaction patterns of different attacks. Our classification model includes both feature extraction procedure and model construction procedure.  
In the feature extraction procedure, we justify using graphs to represent different attacks and propose a rule for the construction of TIAG. Then, considering the statistical features of flows, we select nine attributive features for each node in TIAG. Last, we quantitatively demonstrate the effectiveness of TIAG. The results show that TIAG can effectively represent benign data, DDoS, DoS, and Portscan data. However, TIAG is less capable of representing Bot and Web attacks because there are fewer data in these two categories.  
In the model construction procedure, the classification of attacks is transformed into the classification of graphs by TIAG. G-TIAG captures the spatial features using GCN, the temporal features using GRU, and the differences in the importance of temporal features using self-attention. Furthermore, our model accurately identifies malicious traffic by using TIAGs as the input of the GCN model. Experimental results show that G-TIAG can reach good classification effects and perform better than other methods.  
In the future, we will further work on the identification method for malicious attack types with fewer data. In addition, to address the lack of data for some attack categories, we can focus more on the one-class classifier to identify only normality in the future and improve the adaptability of classification methods in the real network.  
Conceptualization, G.R. and G.C.; methodology, G.R.; software, N.F.; validation, G.R.; formal analysis, G.R.; investigation, N.F.; resources, G.R.; data curation, G.R.; writing--original draft preparation, N.F.; writing--review and editing, N.F.; visualization, N.F.; supervision, G.C.; project administration, G.C.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the General Program of the National Natural Science Foundation of China under grant number 62172093.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization
## 3 Adaptive GNSS Uncertainty Estimation by CNN
### Uncertainty Adaptation  
If GNSS 2D positioning solutions are not available, the INS measurement still propagates the receiver's location. When having GNSS information, the algorithm employs loose coupling between GNSS and INS data. Unlike the fixed integration, which uses the same uncertainty value throughout all the integration epochs, adaptive integration applies  
Figure 3: Overview of the proposed CNN network, where \(P_{CNN}\) denotes output from the CNN model.  
different uncertainty in different epochs. It is noted that two kinds of extreme uncertainty are designed during this process. \"Max uncertainty\" performs as the upper bound used when extremely large pseudo-range rate consistency measurements exist. It is embodied in epochs with GNSS positioning whilst without valid CNN prediction because of the abnormal pseudo-range rate consistency (i.e., extreme values occur in Feature 7 and 8). In contrast, the \"Min uncertainty\" is assumed to be the lower limit for predictions, and any predicted uncertainty below this threshold is adjusted to the value of \"Min uncertainty\". Both \"Max uncertainty\" and \"Min uncertainty\" are user-defined fixed values and vary according to different types of receivers. Apart from the extreme cases, all other epochs take the corresponding GNSS uncertainty based on CNN predictions, where the estimated 2D positioning error is considered equivalent to the GNSS uncertainty. The logic of adaptive uncertainty adjustment can be shown in the flow chart Figure 4 below, where \(P_{\text{CNN}}\) denotes the CNN-predicted uncertainty. Along with the fixed uncertainty, parameters in adaptive integration are set differently across the receiver type, which will further be specified in the experimental part.
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 4 Preliminaries
### Bilinear Pairing  
Let \(G_{1}\), \(G_{2}\) be the finite additive groups and \(G_{T}\) be the finite multiplicative group with same order \(p\) where \(|G_{1}|=|G_{2}|=|G_{T}|=p\), the bilinear pairing \(e:G_{1}\times G_{2}\to G_{T}\) satisfies the following properties [25]:  
* **Bilinearity:** The mapping \(e:G_{1}\times G_{2}\to G_{T}\) is said to be bilinear if the following relation holds: \(e(h_{1}^{a},h_{2}^{b})=e(h_{1},h_{2})^{ab}\), \(\forall h_{1}\in G_{1}\), \(\forall h_{2}\in G_{2}\) and \(\forall a,b\in Z_{p}\).
* **Non-degeneracy:** There exists \(h_{1}\in G_{1},h_{2}\in G_{2}\) such that \(e(h_{1},h_{2})\) is not the identity of \(G_{T}\).
* **Isomorphism:**\(\psi\) is an isomorphism from \(G_{2}\) to \(G_{1}\), with \(\psi(h_{2})=h_{1}\)
* **Computability:** The bilinear map \(e:G_{1}\times G_{2}\to G_{T}\) can be computed efficiently.
# Research on Lightweight Lithology Intelligent Recognition System Incorporating Attention Mechanism
## 5 Conclusions  
A lightweight lithology intelligent recognition model is proposed in this paper by using the MobileNetV2 network as the basic framework and improving the network by incorporating channel attention and spatial attention mechanisms. Additionally, two major aspects of model recognition accuracy and speed are explored and analyzed, and the following conclusions are drawn as a result of these investigations and analyses.  
(1) The enhanced MobilNetV2 model, which is capable of extracting more abstract and complex petrographic features and weakening the interference of non-rock features by fusing the inverse residual network of attention mechanism, is compared and analyzed with the Resnet101, InceptonV3, and MobileNetV2 models. The training results show that the training time of the model while ensuring the recognition accuracy and model size are less, which can effectively realize lightweight occlusion detection.  
(2) The MobileNetV2 model that incorporates the attention mechanism has an average recognition accuracy of 85% or more for the lithology of Class I-III rocks in the self-constructed dataset, and the recognition rate of some rocks is greater than 95%. Compared with the Resnet101, InceptonV3, and MobileNetV2 models, the model in this study has a better recognition effect and better stability for the overall dataset, and there are no significant differences between the models in terms of the accuracy with which it recognizes individuals. There is no recognition rate that is lower than 70 percent.  
(3) The consequences of the Grad-CAM interpretability analysis show that this model has a broader and more uniform focus on the rock feature area. Additionally, this model has a stronger anti-interference ability, which further illustrates the superiority of the robustness and generalization ability of this model.  
Investigation, Y.Z.; Supervision, H.L.([PERSON] ); Validation, Z.L.; Writing--original draft, H.L. ([PERSON]); Writing--review & editing, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NO. 52064025) and the Major Science and Technology Special Program in Yunnan Province (NO. 202102 AG050024).  
Not applicable.  
Data available on request from the authors.  
The authors declare no conflict of interest.
# H-KPP: Hypervisor-Assisted Kernel Patch Protection
## 2 Background
### Execution Prevention  
The execution of an unauthorized code is one of the attackers' main vehicles in their malicious actions. Multiple solutions have been proposed to prevent these attacks at various stages. For example, Microsoft's Data Execution Prevention (DEP) technology preventsthe execution of instructions from unauthorized pages by utilizing the page table's NX bit introduced by Intel. Linux also utilizes the NX bit on processors that support it starting from version 2.6.8 [11]. Unfortunately, this protection can be bypassed [3].  
Another example is the driver signing facilities available on Windows [2], and Linux [12]. These facilities either prevent or issue a warning upon an attempt to load an unsigned driver (kernel module). These facilities aim to prevent malicious code from even getting into the kernel. Unfortunately, [PERSON] demonstrated [4] that driver signing can be bypassed.  
Microsoft's Kernel Patch Protection (KPP) is an obfuscated integrity checking mechanism for the Windows kernel. For example, this mechanism prevents patching of the System Service Descriptor Table (SSDT), which holds the addresses of the system-call handlers. KPP consists of multiple verification code pieces that form a complete algorithm for verifying the kernel's critical data structures. In addition, the code pieces verify the integrity of themselves in order to prevent their circumvention. As with the previously described security measures, KPP was reverse-engineered and bypassed [5]. H-KPP is most closely related to KPP in its goals. However, unlike KPP, H-KPP protects its verification procedure by moving it from the kernel mode to the more secure root mode.
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 2 Reference Assumption PLL Features
### The Parameter Design of SSV RRAM  
The carrier tracking loop measurement errors consist of two portions, phase jitter (\(\sigma_{j}\)), and line-of-sight (LOS) dynamic stress error \(\theta_{e}\). The former is subdivided into three parts: the thermal noise \(\sigma_{lPLL}\), the vibration-induced oscillator jitter, and the Allan variance-induced oscillator jitter. When a user is transferred from a normal condition into a weak-signal and high-dynamic environment, the thermal noise increases as the C/N\({}_{0}\) decreases, while the dynamic stress error increases as the dynamics become greater. Apparently, for SSV users, the thermal noise and dynamic stress error are the two dominant PLL error sources. Both of them are far greater than the two kinds of oscillator jitters, so the influence of oscillator jitters can be ignored herein. Acting as the major constituent of phase jitter, the thermal noise is usually expressed in the following equation according to [12]:  
\[\sigma_{lPLL}=\frac{180}{\pi}\sqrt{\frac{B_{L}}{C/N_{0}}(1+\frac{1}{2T_{coh} \cdot C/N_{0}})} \tag{1}\]  
where \(B_{L}\) represents the LNBW, and \(T_{coh}\) represents the CIT. It must be stressed that \(C/N_{0}\) remains unchanged before and after coherent integration.  
For the SSV RRAM, the steady state error caused by LOS dynamic stress is written as:  
\[\sigma_{d}=\frac{1}{\omega_{n}^{3}}\frac{d^{3}R}{dt^{3}}=\frac{0.7845^{3}}{B_ {L}^{3}}J_{LOS}(m)=\frac{0.7845^{3}}{B_{L}^{3}}J_{LOS}\cdot\frac{360f_{L1}}{c }(\text{deg}) \tag{2}\]  
where \(c\) is the speed of light and \(f_{L1}\) is the carrier frequency of L1 signal. \(\frac{d^{3}R}{dt^{3}}\) is the third-order derivative of LOS range with respect to time, which is equivalent to \(J_{LOS}\), i.e., the projection of jerk between navigation satellite and user vehicle in the direction of LOS. \(\omega_{n}\) represents the natural frequency, and has a definite relationship with \(B_{L}\) in the third-order PLL:  
\[\omega_{n}=\frac{B_{L}}{0.7845} \tag{3}\]
# Concrete Silos: Failures, Design Issues and Repair/Strengthening Methods
## 4 Repair and Strengthening Methods for Reinforced Concrete Silos
### Strengthening of RC Silos
#### 4.2.2 External Pre-Stressing for Repair/Strengthening
Following the consideration of the cost benefits of external pre-stressing using FRP tendons, the adoption of the technique in repair and strengthening would involve a tradeoff with environmental degradation and sustainability, considering that the materials are easily corroded, and there is a risk of brittle catastrophic failure. From a long-term view, the benefits outweigh the limitations considering that the tendons can be easily repaired; friction losses and dead loads are diminished, and the anchorages are customizable.
# Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks
## 3 RAN Methodology to Simulate Recall Operations
### Abstract Concept Modeling with RAN
#### 3.2.5 Step 4a: Convex Abstract Concept Upward Activation Propagation (CACUAP)  
This step is used to propagate i-dimensional input data vector \(\text{a}_{i}\) to the CAC layer and to obtain k-dimensional data vector \(\text{A}_{k}\). This mechanism is used in two stages: in the first stage, Euclidean distance is calculated among the input data \(\text{a}_{i}\) and all the CCILWs \(\text{W}_{k,i}\). This distance is further normalized (using the denominator in Equation (3)) to obtain distance in the range [0, 1](in RAN's modeling, the activation values of are, by definition, real values in the [0, 1] interval--and in such a setting, in an \(n\)-dimensional space, the maximum possible Euclidean distance between any two points is \(\sqrt{\sum_{i=1}^{n}(a_{i}-0)^{2}}\)=\(\sqrt{n}\), where \(a_{i}=1\).).  
\[d_{k}=\frac{\sqrt{\sum_{i=1}^{n_{a}}(W_{k,i}-a_{i})^{2}}}{\sqrt{n_{a}}} \tag{3}\]  
In the second stage, the normalized distance obtained from Equation (3) is transformed non-linearly, establishing a similarity relation conforming to the following three conditions: (1) \(f(d=0)=1\), i.e., when distance is 0, similarity is 100%; (2) \(f(d=1)=0\) i.e., when distance is 1, similarity is 0%; and (3) \(f(d=x)\) is continuous, monotonous, and differentiable in the [0, 1] interval.  
\[f(x)=(1-\sqrt[3]{x})^{2} \tag{4}\]  
This similarity relation equation (Equation (4)) transforms the distance values observed at each node in the CAC layer into its similarity value. These similarity values act as degree  
Figure 5: The concept similarity relation learning (CSRL) weight matrices learned with Toy-data and their corresponding impact factor (\(\sigma\)) at layer 0 and layer 1. \(\sigma\) is calculated using Equation (5).  
of confidence (DoC) values to recognize the category being represented by the nodes at the CAC layer. Upon propagating all input values to the CAC Layer, the observed outputs A\({}_{k}\) are used to perform concept similarity relation learning (CSRL), as shown in Figure 4, step 4b. After completing step 4b, the RAN modeling procedure terminates, and a model is obtained, as shown in step 3 in Figure 4.  
In order to build more than one layer, all of the steps are repeated iteratively, and the output of all the intermediate CAC layers is pipelined as input to the new layer being built.
## 2 Material and Methods
### Measurement of Dielectric Properties  
To investigate the dielectric characteristics of different materials, appropriate measurement techniques are required to evaluate their ability to store charge and conduct electricity. Dielectric measurements are essential for determining how materials respond to electric and electromagnetic fields, providing critical information on their capacity to store charges, insulate, and dissipate energy as heat. These measurements can be applied to materials in various states, including solid, liquid, or semi-solid.  
Several techniques are available for evaluating dielectric properties, such as coaxial probes, transmission lines, and resonant cavity studies, each suited to specific material types and frequency ranges. For instance, the coaxial probe method can measure dielectric characteristics over a frequency range of 20 MHz to 20 GHz. To fully understand a material's properties and potential applications, it is crucial to use precise and accurate measurement methods.  
The change in the dielectric constant with frequency in an electric field is caused by the alignment of charges through polarization and the oscillations that occur when the electric field reverses. The speed of these oscillations depends on both the frequency and the temperature. This behavior can be described using the Debye equation, which is used to calculate the dielectric constant of polar materials, as shown in Equation (3) [55; 56; 57; 58; 59].  
\[\varepsilon=\varepsilon_{\infty}+\frac{\varepsilon_{s}-\varepsilon_{\infty}}{1 +j\omega\tau} \tag{3}\]  
where \(\varepsilon_{s}\) represents the dielectric constant, \(\varepsilon_{\infty}\) is the dielectric constant at high frequency, \(\omega\) is the angular frequency, and \(\tau\) is the relaxation time. Equations (4) and (5) can be divided into two main parts: \(\varepsilon^{\prime}\), the dielectric constant, and \(\varepsilon^{\prime\prime}\), the dielectric loss factor, as follows [60].  
\[\varepsilon^{\prime}=\varepsilon_{\infty}+\frac{\varepsilon_{s}-\varepsilon_{ \infty}}{1+\left(j\omega\tau\right)^{2}} \tag{4}\]  
\[\varepsilon^{\prime\prime}=\frac{\left(\varepsilon_{s}-\varepsilon_{\infty} \right)\omega\tau}{1+\left(j\omega\tau\right)^{2}} \tag{5}\]
# Intrinsic Interference Cancellation Scheme for FBMC-OQAM Systems Based on Power Multiplexing
###### Abstract  
As a competitive candidate of orthogonal frequency-division multiplexing (OFDM), filterbank multicarrier (FBMC) has significant advantages in low spectrum leakage, relaxed synchronization requirements and high spectral efficiency. However, the loss of orthogonality leads to intrinsic interference in FBMC, whereas most methods eliminate it by sacrificing data symbols, resulting in a non-negligible decrease in spectral efficiency. In this context, we propose a new method that eliminates intrinsic interference completely without sacrificing any data symbols thanks to the power multiplexing in the transmitter and the successive interference cancellation scheme in the receiver, contributing to a higher spectral efficiency compared with previous methods. Both the analytical and simulation results demonstrate that the proposed method has higher spectral efficiency and similar power efficiency compared with the traditional coded auxiliary pilot (CAP) method without a notable decrease in bit error rate (BER) performance.  
filter-bank multicarrier (FBMC); intrinsic interference; power multiplexing; successive interference cancellation; coded auxiliary pilot (CAP)
# Damage Detection Using Modal Rotational Mode Shapes Obtained with a Uniform Rate CSLDV Measurement
## 4 Experimental Validation
### Results and Discussions of Three Damaged Cases
#### 4.2.3 Case 3: Slight Crack  
Investigations showed that only the torsional modes were sensitive to the crack damage. For a slight crack with 6 mm length, the measurement was undertaken with the scan rates of 0.02 Hz in the x direction and 4.0 Hz in the y direction at the frequencies of the first two torsional modes that are 233.43 Hz and 750.94 Hz, respectively. The same procedure was applied to obtain the two mode shapes and corresponding rotational damage indicators. The corresponding rotational damage indicators in the y direction are plotted in Figure 12(a),b, respectively. Both figures clearly and correctly reveal the location and span direction of the slight crack.  
Actually, the scan frequency rate has a significant influence on measurement of the local slight change of the mode shape. The higher scan rate means the laser spot across the damage area very quickly and may not be able to capture the tiny local anomaly caused by damage. Moreover, the spatial resolution of measured shape data with a higher scan frequency rate also may not be able to reflect any change of the mode shape. In order to analyze the impact of the scan frequency rate, the scan frequency in the x direction was increased from 0.02 Hz to 0.05 Hz and the y direction increased from 4.0 Hz to 10.0 Hz to perform the measurement. The rotational damage indicators of the two torsional modes in the y direction are plotted in Figure 14. From the first plot, the crack position can be roughly located but the noise has some interference to the results. Compared with the results of 0.02 Hz scan rate, the impact of scan rates of the second torsional mode is much more serious, as shown in Figure 13(b). According to these figures, the crack cannot be identified clearly. The results show that the lower scan rates are preferred for slight crack damage detection.  
Figure 12: The RDIs surface plot of the second torsional mode in the y direction for the multi crack plate.  
Figure 13: The RDIs colored plot of the torsion modes in the y direction for a slight crack plate. (**a**) RDIs of the first torsion mode of 233.43 Hz. (**b**) RDIs of the second torsion mode of 750.94 Hz.
## Future Work and Research Outlook  
Several directions for future research have been identified to enhance the modelling framework and its decision-support capabilities:  
1. Methodological enhancement through optimisation and hybrid approaches  
To improve model adaptability and strengthen its decision-making value, future work will integrate optimisation algorithms and hybrid data-driven methods. These enhancements will enable automated identification of optimal system configurations and control strategies under varying boundary conditions, potentially improving both energy efficiency and economic outcomes.  
2. Advanced financial modelling under dynamic market conditions  
The current financial analysis assumes self-financing and static energy prices. Future studies will incorporate more realistic scenarios, including mixed financing models (e.g., loans, subsidies) and dynamic electricity and gas pricing. This will offer a more robust foundation for investment planning in an evolving energy market and policy landscape.  
3. Progressive integration into fourth-generation DH paradigms  
While this study supports the transition toward low-temperature DH through the inclusion of HPs, achieving full alignment with fourth-generation DH principles will require further development. These include lowering building-level supply temperatures and integrating on-site renewable electricity sources, such as PV, to power HPs. Future research will address these aspects to support broader decarbonisation and system flexibility.  
Conceptualisation, D.P., B.D., B.P., and V.M.-V.; methodology, D.P. and B.D.; software and simulation, D.P., B.D., and V.M.-V.; formal analysis, D.P.; investigation, D.P.; resources, D.P. and B.D.; writing--original draft preparation, D.P., B.D., and V.M.-V.; writing--review and editing, D.P., B.D., and V.M.-V.; visualisation, D.P., B.D., and V.M.-V.; supervision, B.D.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been supported by the University of Rijeka under the project Investigating the potential for the renewal of district heating systems using dynamic simulations (uniri-iskusni-tehnic-23-146).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Dataset available on request from the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.