esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 1 Introduction
Our monitoring approach is evaluated using a mobile network testbed and some selected machine learning algorithms for anomaly detection. The framework leverages Software-Defined Network (SDN) and Network Function Virtualization (NFV) concepts as well as big data processing and analytic engines (such as Apache Kafka and Spark) to increase the accuracy, flexibility, and scalability of monitoring. Our hypothesis is that the framework provides, when compared to monitoring of heterogeneous data flows combining traffic from various 4G applications, more customized and accurate information of networks' trust levels to operators and user organizations in a real-time mode. With software networks, we isolate application-specific data streams and thus enable the monitoring system to focus on specific traffic patterns that are homogeneous. Thus, the security monitoring system can apply machine learning algorithms and control strategies that are optimal for the application. In addition, by correlating security event information from various 5G domains, we are able to capture threats that were previously hard to address. Virtualization and sharing of security information enable service providers to extend their monitoring landscape to the 5G systems. Hence, security awareness improves, and mitigations speed-up and automatize when application providers receive security information from operators' and infrastructure providers' domains. Figure 1 illustrates our contributions and how the monitoring landscape will evolve when entering the 5G era. The figure illustrates the shift from the generic security monitoring approaches of 4G to customized monitoring and cross-domain sharing of trust relevant information in virtualized 5G networks. The rest of this paper is structured as follows. First, in Section 2, we survey related work on 5G security monitoring and explain how our contributions map to the 5G security landscape. We also classify security and trust parameters shared over multiple domains and give an overview of software networking technologies that our work builds on. In Section 3, we describe requirements for the monitoring framework and enablers. Section 4 discusses two specific threat scenarios as well as mechanisms for detection and mitigation. It illustrates the advantages of multi-domain data sharing and correlation analysis. Section 5 describes our implementations and technology choices for the framework as well as the testbed. Section 6 studies characteristics of data in SDNs dedicated for different applications. In particular, we illustrate and study how homogeneity of data affects machine learning based on a clustering algorithm. Section 7 discusses the applicability and limitations of the proposed approach and, finally, Section 8 concludes the paper.
## 4 Algorithm Design
### Adaptive Fuzzy PID for Coupling System
#### 4.2.1 Domain of Discourse and Fuzzy Subset Determination  
In this paper, we defined the traveling speed deviation \(e\) and the rate of change in deviation \(e_{c}\) as the input variables. We also defined the corrections of the three PID control parameters \(\Delta K_{p}\), \(\Delta K_{i}\) and \(\Delta K_{d}\) as the output variables. The constant parameters of the conventional PID controller were as follows: \(K_{p}=3\), \(K_{i}=0.055\) and \(K_{d}=0.01\). This was designed according to the transfer function and dynamic characteristics of the system. Additionally, MATLAB was used for trial control and optimization.  
We modified the experimental vehicle with a feed detection function. Simulation tests were performed with the manual feeding of rice. At the same time, we performed a conventional PID simulation of the feeding volume-traveling speed coupling system. By referring to the variation characteristics of the input variables in the simulation tests, the basic domain of each variable was finally determined (Table 1).  
After determining the fuzzy domain, the variables were divided into several fuzzy sets using fuzzy linguistic values. Each variable value corresponded to a set of fuzzy language values, and the number of fuzzy sets was selected according to the control accuracy. The more accurate the number, the more complex the corresponding control rules. In this paper, we selected seven levels of fuzzy language values {NB, NM, NS, ZO, PS, PM, and PB} based on the actual harvesting conditions.
# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 2 Related Work  
Within the realm of computer vision, explainable artificial intelligence (XAI) has emerged. [PERSON] et al. [9] offer a detailed exposition of XAI from the perspective of visual analysis and review post hoc explanation methods, including LIME [10], SHAP [11], and CAM [4].  
In medical image analysis, there have been some advancements in interpretability research as well. [PERSON] [12] performed an extensive review of XAI research in healthcare, encompassing technologies such as SHAP, LIME, Grad-CAM, LRP, fuzzy classifiers, EBM, CBR, and rule-based systems. Their focus was primarily on providing a comprehensive overview of computational and visual explainable methods, though they overlooked the detailed analysis of these methods' applications in the medical field. [PERSON] [13] carried out a more detailed exploration of explainable methods in the healthcare sector, aiding in the comprehension of interpretability within radiology, with computer-aided diagnosis research employing end-to-end deep learning. Their study employed methods like CAM, feature activation mapping, and t-distributed stochastic neighbor embedding (t-SNE). Similarly, [PERSON] [14] outlined the categorization of explainable methods and reviewed the most frequently employed XAI methods in specific medical supervised machine learning, primarily focusing on Shapley Additive Explanations (SHAP) and Grad-CAM. However, these two studies primarily focused on introducing methods, overlooking an exhaustive examination of the progress and real-world implementations of these methods in the field of medicine.  
In summary, existing review articles have offered broad introductions to the mechanisms of XAI methods. Still, with the growing prevalence of CAM techniques in medical applications, there is a pressing need for a comprehensive retrospective analysis of the specific developments in the application of CAM-based methods in the medical field.
## 1 Introduction
Besides, overcoming that value too much could lead to a very high quality factor of the resulting DRA that would determine a very narrow band, which is not always desirable. In the following section, a new and unconventional design for a wideband DRA, operating between 2.4 and 3.8 GHz (covering WiFi/Bluetooth as well as 5G-NR band) has been simulated, realized, and measured. The manufacturing process has been carried out using FFF technology, exploiting one of the mentioned prototypal materials to realize the main component of the resonating structure. Specifically, the material chosen for the scope was a PLA-based filament enhanced with barium titanate at 17.5% in volume, exhibiting a dielectric constant of 5 and a loss tangent of 0.015. Those values have been measured using a lab-made T-resonator, realized by some of the authors and described in [23], to measure the characteristics of a 100 \(\times\) 100 \(\times\) 2 mm\({}^{3}\) substrate printed with a standard FFF printer and a 100% infill.
## Nomenclature  
\(P_{PV}\): The output power of PV  
\(G_{AC}\): Light intensity  
\(P_{STC}\): Maximum test power under the standard testing environment  
\(G_{STC}\): Light intensity under the standard testing environment  
\(k_{t}\): Power temperature coefficient  
\(T_{c}\): Temperature of the photovoltaic cell  
\(T_{r}\): Reference temperature  
\(P_{WT}\): The output power of WT  
\(P_{r}\): Rated power of WT  
\(a\), \(b\), \(c\),\(d\): Fit parameters  
\(v_{\rm ci}\): Cut-in wind speed  
\(v_{\rm r}\): Rated wind speed  
\(v_{\rm co}\): Cut-out wind speed  
\(P_{uc}\): The output power of RESs  
\(I\): The investment cost of BESS  
\(p\): The discharging power of BESS  
\(\Delta T\): The length of the time period  
\(Q\): The battery capacity  
\(A_{total}\): The total cumulative Ah throughput in the life cycle  
\(S_{OCinit}\): The initial state of charge  
\(C_{dp}\): The fuel cost of CHP  
\(p_{gas}\): The price of natural gas  
\(P_{dp}\): The electric power of CHP  
\(L_{HVNG}\): Low calorific value of natural gas  
\(H_{chp}\): The heat power of CHP  
\(\eta_{L}\): Heat loss coefficient  
\(\delta_{heat}\): Heating coefficient of the LiBr chiller  
\(p_{loss}^{loss}\): The total power loss  
\(p_{ss}^{loss}\): The power loss over the distributed line between MG \(n\) and MG \(m\)  
\(p_{ss}^{loss}\): Power loss over the distributed line between MG \(n\) and the main grid  
\(R_{nm}\): The resistance of the distribution line between MG \(n\) and MG \(m\)  
\(R_{n0}\): The resistance of the distribution line between MG \(n\) and the main grid  
\(C_{S}^{loss}\): The total power loss cost  
\(\lambda\): Price per unit of power energy  
\(x_{BESSn}\): The charge-discharge power of BESS in MG \(n\)  
\(x_{gridn}\): The exchange power between MG \(n\) and the main grid  
\(P_{gs}\): The selling price of the main grid  
\(d_{n}\): Load demands of MG \(n\)  
\(x_{n}^{lit}\): The exchange power between MG \(n\) and other MGs  
\(\eta\): Charge-discharge efficiency of BESS  
\(p_{max}^{max}\): \(p_{\rm discharge}^{max}\): Maximum and minimum charge-discharge power  
\(S_{OCmax}\), \(S_{OCmin}\): The upper and lower bounds of SOC  
\(\rho\): Penalty parameter  
\(e^{pri}\): \(e^{dual}\): The convergence error of the primal residual and dual residual
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
## 3 IoT-over-Satellite Performance Modeling
### Satellite Channel Model
#### 3.2.1 Air Absorption  
Electromagnetic transmission through the atmosphere experiences attenuation caused by the resonance of gas molecules and water vapor between the Earth's surface and the satellite. The approximate region of the atmosphere effect is around 100 km above the Earth's surface. The atmosphere components considered by this study are oxygen and water vapor. We implement the framework recommended by the International Telecommunication Union (ITU) in Reference [20], where \(L_{\text{Air}}\) depends on the pressure of air molecule and water vapor together, and it is based on a series of functions and the air pressure parameters measured by ITU. By giving the temperature, dry air pressure, and air humidity to the ITU model, it can return the approximate air absorption attenuation in dB. We implement the ITU air absorption model as a Matlab function with the following structure,  
\[L_{\text{Air}}=\text{AAM}(f,h_{o},\text{Lat}_{o},\text{Lon}_{o},\theta_{e}, \text{Month},\text{Date}), \tag{5}\]  
where \(h_{o}\), \(\text{Lat}_{o}\), \(\text{Lon}_{o}\) are representing respectively the height, latitude, and longitude of the measurement point on the ground. An illustration of the air-absorption effect is shown in Figure 4.  
Figure 3: Explanation of the satellite down-tilt angle shown in (3). Since the satellite object is moving, the \(\theta_{d}\) becomes a time varying function.
## 3 Results
### Verification of the HGsT10 Expression Patterns  
After the ten hub genes (MMP9, MMP3, MMP1, CXCL8, SPP1, PLAU, MMP13, COL1A1, COL1A2, and PLAUR) were screened out by the CytoHubba plugin, we verified the expression levels of the HGsT10 among the ESCC patient's sample with the GEO dataset GSE161533. The HGsT10 expression was demonstrated among the ESCC patient's sample of the TCGA database (Figure 3).  
Figure 2: Visualization of the top 250 genes interaction as protein–protein interaction network and the HGsT10 genes, (**A**) PPI network of the genes interacted with two co-expression modules nodes and edges. Edges represent the HGsT10 genes’ interactions. Edges indicate interaction associations between nodes, (**B**) The predicted HGsT10 gene using MCC algorithm. Genes with a higher MCC score are shown by red nodes, whereas yellow nodes represent genes with a minimal MCC score.  
Figure 3: The gene expression analysis for the predicted HGsT10 genes. (**A**) The HGsT10 gene expression at ESCC tumor condition was compared with normal samples showed the upregulation. (**B–K**) The HGsT10 genes expression were validated with TCGA samples with the log-transformed expression value, which includes 13 normal and 182 tumor samples. * denotes statistical significance (\(p<0.05\)).  
As shown in Figure 3A the MMP9 expression was noted maximum (198.822 TPM) in normal tissues (sample id GSM4909563). The samples GSM4909612, and GSM4909628 showed the expression of 2204.43, and 2231.53 TPM, respectively. Likewise, MMP3 (normal tissue sample max (NTSM), GSM4909557 = 9.8356; tumor tissues sample max (TTSM), GSM4909617 = 987.431), MMP1 (NTSM, GSM4909563 = 128.084; TTSM, GSM4909619 = 3143.52), CXCL8 (NTSM, GSM4909578 = 153.367; TTSM, GSM4909619 = 3034.75), SPP1 (NTSM, GSM4909566 = 106.629; TTSM, GSM4909612 = 2348.91), PLAU (NTSM, GSM4909569 = 133.366; TTSM, GSM4909632 = 1186.85), MMP13 (NTSM, GSM4909568 = 9.7126; TTSM, GSM4909629 = 313.765), COL1A1 (NTSM, GSM4909553 = 38.4753; TTSM, GSM4909622 = 1844.38), COL1A2 (NTSM, GSM4909566 = 2420.06; TTSM, GSM4909622 = 8155.97), and PLAUR (NTSM, GSM4909566 = 110.395; TTSM, GSM4909636 = 488.016 TPM) was upregulated in tumor tissues samples aberrantly, which might be a major cause in the ESCC tumor development. Furthermore, Figure 3B, describes that all of the HGsT10 was significantly upregulated in ESCC patient's samples compared with normal subjects.
## 3 Derivation of Water Quality Criteria and Relevant Fish Assay
### Japan  
The Ministry of the Environment Government in Japan presented the guidance on the derivation of WQS for protecting aquatic life from 2003 [5]; aquatic organisms are mainly focused on fish or shellfish, which are considered as seafood resources, and feed organisms consumed by seafood organisms are also objects for consideration [5]. The process of WQS derivation for aquatic life is separately conducted in seafood and feed organisms, and this study reviews the fish-related contents. Targeted fish data are collected in freshwater or oceanic groups; the top 20 catch-and-release fish species or indigenous species used in the international test methods, such as the Japanese medaka (_O. latipes_) in freshwater, and the top 20 hauling fish in the marine environment, are subjected to toxicity testing.  
The acute effects on fish include the signs of survival, swimming, and growth inhibition after 48-96 h of exposure to chemicals or targeted contaminants. Related test guidelines are the acute fish tests (OECD TG 203 and EPA OPPTS 850.1075) and fish life cycle toxicity test (EPA 850.1500) [9; 24; 40]. The results of acute fish toxicity assessments are calculated as LC\({}_{50}\) or EC\({}_{50}\) and used to derive WQS [5].  
Chronic effects are related to maturation, reproduction for adult fish, and survival or growth inhibition at embryonic- or larval-staged organisms. Exposure durations are specified to cover the embryo stage to the larval stage or pre-mature to reproductive stages of fish. The observative results of reproduction, mortality, immobilization, and growth inhibition are used to calculate the toxicity values (lowest observed effect concentration, LOEC, NOEC; maximum allowable toxic concentration, MATC). Related test methods include fish early-life-stage toxicity assays (OECD TG 210, EPA OPPTS 850.1400) [5; 10; 13]. Based on the acute and chronic toxicity to fish, the ACR is calculated to confirm the final chronic toxicity value; each final value of chronic toxicity in seafood or feed organism group is compared to the others; the lowest value is confirmed as the WQS value to protect the overall aquatic eco-receptor [5].
## 3 Results and Discussion
### Stress Profiles at Strain Gage of Incident Bar  
The stress profiles monitored at the strain gauge position in the incident bar over a range of flange lengths (\(L_{\mathrm{f}}\)) are presented in Figure 4. In Figure 4a (\(D_{\mathrm{b}}\) = 10 mm; \(A_{\mathrm{s}}\)/\(A_{\mathrm{b}}\) = 1.0), the case where \(L_{\mathrm{f}}\) = \(D_{\mathrm{b}}\) (black curve) yields a single (pseudo-one-step) tensile pulse starting at approximately 0.19 ms. However, two-step pulses are monitored when \(2D_{\mathrm{b}}\)\(\leqq L_{\mathrm{f}}\)\(\leqq\)\(10D_{\mathrm{b}}\). The reason for this observation is explained below.  
When \(2D_{\mathrm{b}}\)\(\leqq L_{\mathrm{f}}\)\(\leqq\)\(10D_{\mathrm{b}}\), the first stress pulse rise is identical to that when \(L_{\mathrm{f}}\) = \(D_{\mathrm{b}}\), while the second rise starts later (as \(L_{\mathrm{f}}\) increases). Focusing on the plateau region after the first rise in the stress signal, the peaks and valleys in the plateau region are identical for different \(L_{\mathrm{f}}\) values before the second rise starts. When the pulse duration ends, the tensile pulse also decays in a stepwise manner; such stepwise decay starts later as \(L_{\mathrm{f}}\) increases.  
When \(L_{\mathrm{f}}\) is \(50D_{\mathrm{b}}\) (purple curve), two separate tensile pulses appear: the magnitude of the first (marked as **B**) is smaller than that of the second (marked as **R1**). The definitions of these notations are described in the next section. At a smaller \(L_{\mathrm{f}}\) value of \(30D_{\mathrm{b}}\) (green curve), the two tensile pulses (**B** and **R1**) are on the verge of separation.  
In Figure 4b (\(D_{\mathrm{b}}\) = 20 mm; \(A_{\mathrm{s}}\)/\(A_{\mathrm{b}}\) = 1.0), as \(L_{\mathrm{f}}\) increases up to \(10D_{\mathrm{b}}\), the exhibited pulse magnitudes are the same as those in Figure 4a (\(D_{\mathrm{b}}\) = 10 mm; \(A_{\mathrm{s}}\)/\(A_{\mathrm{b}}\) = 1.0). However, at a given \(L_{\mathrm{f}}\) value (\(2D_{\mathrm{b}}\)\(\leqq L_{\mathrm{f}}\)\(\leqq\)\(10D_{\mathrm{b}}\)), the rise in the second step starts later in Figure 4b than that in Figure 4a. For the case where \(L_{\mathrm{f}}\) = \(30D_{\mathrm{b}}\) (green curve), the first (**B**) and second (**R1**) tensile pulses have already separated; this observation differs from the case in Figure 4a. When \(L_{\mathrm{f}}\) = \(50D_{\mathrm{b}}\) (purple curve), the starting time of the second tensile pulse (**R1**) is further delayed and superposed on the compressive pulse from the free end on the right side of the incident bar.  
Figure 3: Contours of the axial stress component (\(\sigma_{yy}\)) around the flange structure region for the finite element model shown in Figure 2: (**a**) 1.375, (**b**) 3.392, and (**c**) 5.337 \(\upmu\)s after the striker impact. The unit in the legend is Pa.  
202.36 MPa is explained on a quantitative basis later (see Section 3.4.1), using Equation (4). The rise and decay of the pulses with time in Figure 4c are similar to those in Figure 4b.
# Multi-Material Optimization for Lattice Materials Based on Nash Equilibrium
## 4 Numerical Examples and Tests
### L-Shaped Beam  
In this example, the L-shaped beam to be optimized is shown in Figure 12. The parameters of the beam are L1 = 100 mm, L2 = 60 mm, H1 = 100 mm, and H2 = 60 mm. The upper edge of the structure is constrained, while a unit vertical downward load is applied to the lower right corner. We select the cross-sectional area of two materials as the design variable, with a variation range of 0-0.2. The design domain is discretized into 6400 elements, with volume constraints of 0.1 for each of the two materials. Similarly, we employ three methods: Material 1, Material 2, and a combination of materials, to optimize the design.  
In Figure 13, it is evident that all three optimization problems converge after 70 iterations, with objective functions showing a significant decrease in the initial 20 iterations, followed by a slower convergence rate.  
Figure 11: Blue represents material 1, red represents material 2, and optimized cantilever beam that achieves reasonable distribution of two materials.  
Figure 10: The best solution of the cantilever beam is that mixed materials (0.182 J) are 6.67% stronger than Material 1 (0.195 J) and 20.5% stronger than Material 2 (0.229 J).  
In terms of computational cost, the Nash equilibrium method applied in this article is utilized to concurrently solve the equivalent stiffness matrix for different materials.  
Figure 12: L-shaped beam structure with L1 = 100 mm, L2 = 60 mm, H1 = 100 mm, and H2 = 60 mm.The top is constrained by a fixed support, and the red arrow represents a unit vertical downward force.  
Figure 13: The optimization process of L-shaped beam. The optimized objective function values of the three methods: mixed material, 0.315 J; Material 2, 0.319 J; Material 1, 0.332 J.  
As depicted in Figure 14, there is minimal disparity in the computational costs among the three methods, averaging 155 s per iteration step.  
Figure 15 shows the optimization results and the distribution of materials within the design domain. It is evident that Material 1 (blue in the figure), possessing a higher equivalent elastic modulus, is strategically distributed in areas experiencing significant tensile and compressive loads within the structure. Conversely, Material 2 (red in the figure), having a higher shear modulus, is allocated in regions where shear stress is predominant. This rational distribution of the two materials within the design domain results in an optimized structure with increased stiffness.  
Figure 14: The iteration time for each step of the three methods is between 150 s and 166 s, so there is not much difference in the optimization time cost among the three methods.  
Figure 15: Optimized L-shaped beam structure, where two materials achieve a reasonable distribution within the design domain based on their own mechanical properties.