esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 2 Rubber Toughening
### Mechanisms of Rubber Toughening
#### 2.1.1 Shear Yielding Theory  
In the 1960s, [PERSON] and colleagues, through the study of the deformation characteristics of rubber elastomers in ABS, found that rubber elastomers could significantly reduce the yield stress of materials and promote shear yielding. This led to the proposal that the toughening of the matrix resin by rubber elastomers is primarily due to the shear yielding of the matrix [22]. They posited that rubber elastomer particles act as stress concentrators within the matrix resin. When subjected to stress, these particles cause cavitation in the matrix, interface debonding, and the formation of crazes, which lower the glass transition temperature and make the matrix more prone to plastic deformation, thereby achieving toughening.  
Subsequently, [PERSON] and others [23; 24] observed in experiments that shear bands passed through rubber particles and that numerous crazes were terminated by these shear bands. This observation led to the conclusion that the combined action of crazing and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline Rubber Toughening [4; 13] & Modest increase in toughness; low cost & Poor compatibility with epoxy resin; significant reduction in thermal and mechanical properties of epoxy resin \\ \hline Thermoplastic Resin & Significant increase in toughness; does not affect modulus or heat resistance; low cost & Increased viscosity of the cured system, leading to reduced solubility; slight reduction in thermal and mechanical properties \\ \hline Nanoparticle Toughening [16] & Modest increase in toughness; improves fracture toughness and tensile strength; slight improvement in thermal and mechanical properties & Poor dispersion of nanoparticles; high cost \\ \hline Hyperbranched Polymer & Significant increase in toughness; slight improvement in mechanical properties & Complex synthesis and expensive \\ \hline Flexible Chain Segment & Significant increase in toughness; toughness & Reduction in heat resistance and tensile strength \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the advantages and disadvantages of various toughening methods.  
shear bands is the primary mechanism for material toughening. Based on these findings, [PERSON] and colleagues [25; 26] proposed that the stress induced by crazing in the matrix is proportional to the square root of the chain entanglement density, while the critical yield stress of the matrix is positively correlated with the characteristic ratio of unperturbed chains. From these relationships, it was deduced that, when the stress induced by crazing in the matrix is below its critical yield stress, the crazing mechanism dominates the toughening process. Conversely, if the crazing stress exceeds the critical yield stress, the shear yielding mechanism becomes the primary mode of action.
## 5 Real World Application
### Environment and Location Data  
Finally, we find equilibrium solutions informed by the data we intend to incorporate into the system. This allows us to understand challenges that come with using more complicated environments and computation times associated with more detailed environments and large \(N\). We use the location data, Figure 15a, take the kernel density estimation of the data, mollify it, and then add a small constant to avoid proximity to zero. Once again, this has no bearing on the results. The result is illustrated in Figure 15b. We use this as an initial condition. Then, for a fixed set of parameters, we find the equilibrium solutions using the two environment types, mollified with extended domains, \(U=\) EDGE, shown in Figure 14d, and \(U=\) SAND, shown in Figure 14c.  
Figure 16 shows equilibrium solutions with two different environments, as well as the location data. The figures were both generated with the same parameter values, but Figure 16a was generated with the environment seen in Figures 14d and 16b was generated with the environment seen in Figure 14c. Although similar, these different environments lead to different territories. The negative log-likelihood value was lower with a value of \(1.2232\times 10^{4}\) for the EDGE environment data than the SAND environment, which had a negative log-likelihood value of \(1.2284\times 10^{4}\).  
Figure 16: Equilibrium solutions to (2) with \(b=0.25\), \(a=0.003\), \(\eta=3\), \(u_{0}(x)\) from Figure 15b, two different mollified environment types from Figure 14, and the meerkat location data.  
Figure 15: (**a**) The location data of meerkats are presented within the same geographical area as depicted in Figure 14. Each data point corresponds to a sighting of a meerkat from a specific group, and the different colors represent distinct meerkat groups. (**b**) The distribution approximation of the meerkat location data via mollified kernel density estimation added to a small, positive constant.
## 1 Introduction
### Microgrid Control and Its Integration with the Utilities
It is also very versatile and flexible due to its hierarchical structure, so it can be applied over a wide range of power systems. While thermal and electrical energy management and control are modeled, analyzed and simulated based on a set of predefined rules and conditions studied before--they have been done so almost entirely in the context and scope of smart buildings technologies, and in advances in buildings' specific energy efficiency metrics. Hence, the salient and very distinct features and contributions of the present paper lie in the case study being presented, which is in the context and scope of energy homeostaticity for electrical and thermal energy management and intelligent control, allowing for the much needed electric utility-customer integration and coordination described, both of which cooperate in search for an optimal use of electrical and thermal energy in the residential building used in the example, and with the incorporation of Distributed Generation Systems (DGS) tied to the electrical distribution network, deploying a growing presence of renewable/green energies, which is what is of keen interest to electric utilities like ENEL. The aim is to simplify and expedite the energy management and power control of a grid-tied microgrid installed in a building, while at the same time, being able to improve real-time connectivity and communication with the electric utility to ensure optimal service management and efficiency from every angle of the triad: the electric utility, the customer and the building, not just the building alone when providing flexibility and ancillary services to the electric power distribution network. In this sense, the present paper applies this paradigm over a real case study, a microgrid installed in a university laboratory building in Santiago, Chile including a thermal study using temperature sensors. In Section 2, a study of the microgrid is done in order to determine which are the most interesting monitoring variables, and the control actions that could be applied to optimize system performance along with the rules for managing the system adequately. Using this information, Section 3 describes a control flow diagram for the EMS. In turn, Section 4 shows the results of the simulations applying the proposed homeostatic control algorithm. In Section 5, a brief discussion about the relation and dynamics between electric utilities and the customers are discussed. Finally, the conclusions are described in Section 6.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 3 Mobile Ad Hoc Networks
### Routing in MANETs
#### 3.1.2 Reactive Routing Protocol  
Reactive routing protocols, also known as on-demand routing, are resource-efficient. Unlike proactive protocols, they do not require nodes to maintain extensive routing tables. Instead, they discover routes only when needed, reducing memory and bandwidth consumption. However, this reactive approach introduces a delay as nodes need to actively find a route before forwarding data packets [18].
## 3 Results and Discussion
### Tribological Properties
#### 3.3.2 Wear Track Morphology  
To further investigate the structural response of Al-Si-(Cu)-Mg alloys during high-temperature sliding, three-dimensional optical profilometry was performed to quantify the wear track depth and width after ball-on-disk testing (Figure 7).  
Figure 6: (**a**) Friction coefficient variation and (**b**) average wear rates of Alloy A and Alloy B at RT, 150 \({}^{\circ}\)C, and 250 \({}^{\circ}\)C.  
At room temperature (25 \({}^{\circ}\)C), Alloy A exhibited a wear depth of 24.37 \(\upmu\)m and a track width of 1044.24 \(\upmu\)m, revealing a shallow and uniform wear morphology. This behavior is attributed to an effective shear resistance and load distribution, which were enhanced by the homogeneous Cu solid solution-induced lattice contraction and the stable dispersion of \(\alpha\)-Al(FeMn)Si phases within the Al matrix. In contrast, Alloy B displayed significantly deeper and wider wear tracks (depth: 44.52 \(\upmu\)m; width: 1362.51 \(\upmu\)m), suggesting an accelerated surface degradation due to the local stress concentration induced by acicular \(\beta\)-AlFeSi phases and agglomerated Mg\({}_{2}\)Si precipitates.  
At 150 \({}^{\circ}\)C, Alloy A maintained relatively controlled wear dimensions (depth: 32.80 \(\upmu\)m; width: 975.13 \(\upmu\)m), whereas Alloy B exhibited a severe wear progression (depth: 55.33 \(\upmu\)m; width: 1838.45 \(\upmu\)m). The pronounced damage in Alloy B is ascribed to the coarsening of Mg\({}_{2}\)Si and the thermal persistence of \(\beta\)-AlFeSi, which promote crack initiation, spalling, and lateral propagation.  
Under the 250 \({}^{\circ}\)C test condition, Alloy A continued to exhibit stable wear resistance (depth: 31.33 \(\upmu\)m; width: 1634.38 \(\upmu\)m), likely due to the microstructural stabilization arising from precipitate spheroidization and thermally stable Cu solute atoms. Meanwhile, Alloy B experienced ongoing structural degradation (depth: 53.12 \(\upmu\)m; width: 1618.64 \(\upmu\)m), indicating a further decline in microstructural integrity.  
These differences in the wear morphology are governed not merely by bulk hardness but more critically by the post-heat-treatment microstructural stability and the spatial distribution of intermetallic compounds. In Alloy A, the uniformly dispersed \(\alpha\)-Al(FeMn)Si phases--often in the Chinese-script morphology--and consistent Cu solubility enhanced the load bearing and deformation resistance. In contrast, Alloy B retained structurally vulnerable features, including clustered Mg\({}_{2}\)Si and acicular \(\beta\)-AlFeSi phases, which facilitated localized stress intensification and crack propagation.
## 3 Materials and Methods
### Adaptive Convolution Module  
The traditional 2D convolution uses a regular grid over the input feature map, which means that the sampling locations and receptive field sizes of regular convolution are fixed. For example, in regular convolution, a \(3\times 3\) kernel could be defined as  
\[p_{k}\in\{(-1,-1),(-1,0),\cdots,(1,1)\}. \tag{8}\]  
In this example, we let \(K=3\times 3\), which denotes the 9 sampling locations. Let \(x(p)\) denotes the features at location \(p\) from  
To solve the issues above, we propose an adaptive convolution module (ACM). For simplicity, the ACM is described in 2 dimensions. As illustrated in Figure 4, the regular convolution kernels in ACM could be adjusted with learnable offsets containing 4 elements \(\{\Delta x,\Delta y,\Delta w,\Delta h\}\). \(\Delta x,\Delta y\in(-0.5,0.5)\) and \(\Delta w,\Delta h\in(-1,1)\) are respectively used for adjusting the sampling locations and shapes (i.e., weight and height) of regular convolution kernels. The sampling location offsets can be expressed as  
\[\Delta xy=(\Delta x,\Delta y) \tag{10}\]  
The sampling shape offsets can be expressed as  
\[\Delta wh_{k}\in\{(-\Delta w,-\Delta h),(-\Delta w,0),(-\Delta w,\Delta h), \cdots,(\Delta w,\Delta h)\} \tag{11}\]  
Hence, in ACM, the features at location \(p\) from the output feature maps becomes  
\[y(p)=\sum_{k=1}^{K}w_{k}\cdot x(p+p_{k}+\Delta xy+\Delta wh_{k}) \tag{12}\]  
As \(\Delta xy\) and \(\Delta wh\) are typically fractional, the value of \(y(p)\) is calculated via bilinear interpolation, just as in [23]. In this way, for \(3\times 3\) kernel, ACM can adjust the regular convolution kernels with only 4-dimensional offsets. Therefore, no heavy computation is introduced by ACM. To further enhance ACM, the offsets are obtained via MFAF block with \(mid_{-}ch=8\). The spatial resolution of the offset field is the same as the input feature map. In the training, both the convolutional kernels and the offsets could be learned simultaneously. In this paper, ACM is utilized in all the \(3\times 3\) convolutional layers in conv3-conv5 stages in ResNet-50 [21].
# Assessing Cyber Challenges of Maritime Navigation
## 5 Conclusions
List of Acronyms  
Aids to Navigation  
AIS  
BeiDou  
China's BeiDou Navigation Satellite System  
CERT-EU  
Computer Emergency Response Team  
cPP  
contractual Public-Private Partnership  
CSIRT  
Computer Security Incident Response Teams  
ECDIS  
Electronic Chart Display and Information System  
EC3  
European Cyber Crime Centre at Europol  
eLORAN  
Enhanced LOng-RAnge Navigation  
EMODnet  
European Marine Observation and Data Network  
EMSA  
European Maritime Safety Agency  
ENISA  
European Union Agency for Cybersecurity  
ECSO  
European Cyber Security Organisation  
Galileo  
European Union's GNSS  
GLONASS  
Russia's Global Navigation Satellite System  
GNC  
Guidance, Navigation and Control  
GNSS  
Global Navigation Satellite System  
GPS, GPS/NAVSTAR  
Global Positioning System (USA's GNSS)  
Hybrid CoE  
European Centre of Excellence for Countering Hybrid Threats  
IEC  
International Electrotechnical Commission  
IMO  
International Maritime Organization  
INS  
Integrated Navigational System  
IoT  
Internet of Things  
ISAC  
Information Sharing and Analysis Centres
## 5 Conclusions and Future Works  
In this study, we proposed a target detection algorithm based on GF-YOLOv7 for the detection task of substation bounce locks. As a model for target detection algorithms, YOLOv7-tiny incorporates a self-attention mechanism into the task of target detection through the MobileViT module. This mechanism, utilizing the Transformer structure's capacity to capture global information, allows for the comprehensive extraction of both local and global data. By merging the separated CBAM attention mechanism with the feature fusion network, we designed the FF-FPN feature fusion network, ultimately leading to the construction of the GF-YOLOv7 target detection network. Our experimental validation revealed significant findings. In terms of detection accuracy, GAE-YOLOv7 exhibits a notably higher error rate than the other networks. Both YOLOv7-tiny and FF-YOLOv7 demonstrate roughly comparable detection accuracies, while the GF-YOLOv7 surpasses other networks with a detection accuracy of 97.8%. Regarding detection speed, all four models fulfill the criteria for real-time detection. The GF-YOLOv7 operates at a speed of 32.1 frames per second and boasts a model weight file size of 9.1 megabytes. These results indicate that the GF-YOLOv7 model meets the demands for detection accuracy and real-time performance. The model's lightweight file structure presents another advantage, rendering it more suitable for engineering applications.  
In the future, we plan to improve the detection accuracy and speed of the algorithm on small targets. The weight of the model further reduced to facilitate better deployment of identification tasks on the edge computing platform.  
Conceptualization, X.Z.; Project administration, L.L.; Writing--original draft, Y.W.; Methodology, P.Z.; Validation L.W.; Table, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (grant no. 41771487).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy of the subjects involved in study.  
The authors declare no conflict of interest.
## 4 Technology Evaluation Criteria
### Required Infrastructure and Intrisiveness  
Given definition:  
Required infrastructure refers to the hardware that needs to be installed to perform the communication tasks at hand. Intrisiveness refers to the degree to which the communication system interferes with personal or workflow activities.  
Additional commentary:  
The infrastructure is of central importance to differentiate the technologies under consideration. Technology options can be distinguished by the need for hardware installed within the environment to be connected at mobile or stationary systems. This contains possibly needed (wireless) network connection infrastructure and a permanent power supply. The introduction of some sort of communication is meant to have a positive change on the workflow and or process, but installation, reinstallation, and maintenance can be intrusive to existing processes and is in this case combined with the needed infrastructure.  
Suggested scale:Needed infrastructure and connected intrusiveness:  
1. Dense installations connected to (cable-based) network and power supply
2. Sparse installations connected to (cable-based) network and power supply
3. Decentralized installations without power supply (battery-powered)
## 4 Controller Design
### Stability Analysis of BSTDNDO  
The Lyapunov function is chosen to be: \(V_{i}=V_{4}+\frac{1}{2}e_{5}^{2}\) in order to evaluate the stability. This results in Equation (50).  
\[\dot{V}_{i}=-k_{1}e_{1}^{2}-k_{2}e_{2}^{2}-k_{3}e_{3}^{2}-k_{4}e_{4}^{2}-k_{5}e _{5}^{2}+e_{2}\overline{\Delta}_{2}+e_{3}\overline{\Delta}_{3}-e_{2}\widetilde {\alpha}_{1}-e_{3}\widetilde{\alpha}_{2}-e_{4}\widetilde{\alpha}_{3}-e_{5} \widetilde{\alpha}_{4} \tag{50}\]  
The error of TD is also bounded because selecting proper parameters bound the tracking error of NDO. According (50), Equation (51) is obtained.  
\[\dot{V}_{i}\leq-k(e_{1}^{2}+e_{2}^{2}+e_{3}^{2}+e_{4}^{2}+e_{5}^{2})+\left|e_ {2}\right|\left|\widetilde{\Delta}_{2}\right|+\left|e_{3}\right|\left| \widetilde{\Delta}_{3}\right|+\left|e_{2}\right|\left|\widetilde{\alpha}_{1} \right|+\left|e_{3}\right|\left|\widetilde{\alpha}_{2}\right|+\left|e_{4} \right|\left|\widetilde{\alpha}_{3}\right|+\left|e_{5}\right|\left|\widetilde{ \alpha}_{4}\right| \tag{51}\]  
Equation (51) can be rewritten, as follows.  
\[\dot{V}_{i}\leq-2(k-1)V_{i}+\frac{1}{2}\varepsilon^{2} \tag{52}\]  
where \(k=\min\{k_{1},k_{2},k_{3},k_{4},k_{5}\}\) and \(\varepsilon=\left|\widetilde{\Delta}_{2}\right|+\left|\widetilde{\Delta}_{3} \right|+\left|\widetilde{\alpha}_{1}\right|+\left|\widetilde{\alpha}_{2} \right|+\left|\widetilde{\alpha}_{3}\right|+\left|\widetilde{\alpha}_{4}\right|\).  
Solving the Inequality (52), the non-negative Lyapunov function \(V_{i}\) is bounded by  
\[V_{i}\leq[V_{i}(0)-\frac{\varepsilon^{2}}{4(k-1)}]e^{-2(k-1)t}+\frac{ \varepsilon^{2}}{4(k-1)} \tag{53}\]  
By choosing any positive number \(\varphi\) and \(k\geq 1+\frac{\varepsilon^{2}}{4\varphi}\), if \(V_{i}(0)\leq\varphi\), then \(V_{i}(t)\leq\varphi\) will be guaranteed for all \(t>0\). Therefore, the controller stability is proven, and the tracking error can converge to a sufficient small neighbor of zero.
# Study on the Bearing Characteristics of Composite Foundations with Permeable Concrete Piles
## 1 Introduction
Initially, the research examines the variations in compressive strength, permeability coefficient, and porosity of pervious concrete, investigating the effects of different water-cement ratios and designed porosities on its stability and permeability to determine the optimal mix proportion. Subsequently, numerical simulations using Flac3D 6.0 are conducted to explore the bearing characteristics of previous concrete piles in composite foundations. By comparing with traditional impervious piles, the study assesses the advantages and potential of pervious concrete piles in practical engineering applications, providing the scientific basis and technical support for infrastructure development in coastal areas.
# An Experimental Survey of Extended Resolution Effects for SAT Solvers on the Pigeonhole Principle
## 5 Experimental Results
### DRUP Analysis  
Tables 4 and 5 exhibit the results of DRUP analysis of PHP instances with full extension for MiniSAT and Glucose with/without our methods. The second to fourth columns (n, #V, and #C) indicate the number of pigeons, variables (including extended variables), and clauses (including extended clauses), respectively. Note that solvers with a DRUP output run slower due to the overhead of recording DRUP, and instances with more than 13 pigeons are omitted since these instances could not be solved within the time limit (the \"no-data\" stands for the time limit exceeded one). As mentioned in Section 4.3, we count the frequencies (the number of appearances) of each clause in the unsatisfiability proof by analyzing DRUP proof of the instance. Then, we sort the frequencies in descending order and see the top-k (\(k=10\) and \(100\)) of frequently used clauses for each instance. The values in columns top-k in the tables stand for the ratio of the number of extended clauses (clauses including at least one extended variable) in the top-k clauses. The column \"ECR\" indicates the ratio of the number of extended clauses to the number of whole clauses. Hence, if all the clauses uniformly contribute on the proof, the top-k ratio can be close to this rate. We can see that both solvers with the methods utilized lots of clauses including extended variables for almost all instances, while the original solvers hardly did. The column \"\(p\)-value\" stands for the result of the Wilcoxon rank-sum test [42], a nonparametric test of the null hypothesis that two sets of samples come from the same population. The first set is the clauses including (at least one) extended variables, and the second one is the clauses without extended variables. Note that in this test we observe all the clauses (not limited to top-k) used in the proof. In general, \(p\)-value less than 0.01 is considered to be statistically significant, which means that the null hypothesis should be rejected. In our experiments, we conduct the one-sided test where the alternative hypothesis is that clauses including extended variables are more frequently used in the proof. High correlation can be seen between the top-k ratio and \(p\)-value: higher top-k ratio gains lower \(p\)-value. From these results, we determine that our methods can firmly focus on the extended variables and shorten the processing time. Tables 6 and 7 exhibit the results of partial extension. Due to the few extended clauses (the ratio of extended clauses is under 0.06), these clauses appeared infrequently in the DRUP proof. Moreover, corresponding \(p\)-values are not low,