esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating IP Mobility Management Protocols and MANET: A Survey
## 2 IP Mobility Management Protocols  
Several mobility management protocols have been proposed in order to support IP mobility in a wireless network. IP mobility protocols can generally be classified according to various criteria. They can be classified according to the network perspective into host-based mobility systems and network-based mobility systems. A host-based mobility approach is when a mobile node changes its point of attachment. Examples of this category are Mobile Internet Protocol version six (MIPv6), Hierarchical MIPv6 (HMIPv6) [13], and Fast Handover for HMIPv6 (FHMIPv6). Network mobility systems are concerned with cases where a whole subnet changes its point of attachment, and so mobile nodes are not involved in any mobility related signaling. An example is when a whole network containing a router and mobile devices changes its point of attachment. Network-based mobility scheme scenarios can be seen in military applications. Proxy MIPv6 (PMIPv6) and Fast PMIPv6 (FPMIPv6) are mobility solutions that support network-based mobility [14; 15].  
Host-based mobility and network-based mobility solutions address hierarchical networks and are logically centralized. They share common mobility management functions: Anchoring functions, internet work location management function, and forwarding management function [16].  
From a mobility perspective, mobility management systems can be classified into two main categories: IP macro mobility management protocols and IP micro mobility management protocols. The macro-mobility management approach is used to manage mobile device movement on a large scale between different network domains, while maintaining ongoing connections. Mobile IP [17] has different versions; Mobile IPv4 and Mobile IPv6 are the best known and adopted protocols for cross domain mobility management. IP micro mobility protocols involve the management of mobile nodes movement on a local scale within the local domain. Mobile IP is not a good solution for mobility management when the movement is relatively frequent, because of the mobility signaling overhead caused by this mechanism when Mobile IP tunneling introduces high network overheads. Micro mobility solutions therefore aim to minimize the handover delay and keep signaling overhead to a minimum. They manage the local movement of mobile nodes without registration between mobile nodes and other distant home agent nodes, and so they manage to reduce binding updates for the distant home agent, delay, and packet loss during handover procedure. Several protocols were proposed in this category, such as HMIP [13], HAWAII [18], and cellular IP [19]. Figure 1 shows HAWAII architecture that depicts macro and micro mobility levels of mobile nodes within and across different domains. In this paper, mobility management solutions are classified from the mobility point of view into macro mobility management and micro mobility management, which are introduced in the following subsections.
# Convert a Strongly Connected Directed Graph to
## 5 The Weak Model of Communication Graphs
\begin{array}{l}\mathcal{WM}=\{\{\
eg a,b,c\}:6,7,\;\{\
eg b,a,c,d\}:11,\; \{\
eg c,a,d\}:9,14,\\ \{\
eg a,-b,c,d\}:3,\;\{\
eg a,-b,-c,d\}:1,\;\{\
eg a,-c,b,d\}:5\}.\end{array}  \tag{5}\]  
Note that since \(d\) has no child node, it does not occur as a negative literal in the model. Note that since the communication graph from Figure 2 is not strongly connected, its weak model (5) is not a black and white SAT problem. For example, \(\{\
eg a,-b,-c,d\}\) is a solution of the SAT problem in (5). Figure 4: Two simple communication graphs. As a second example we show the weak model of the communication graph from Figure 3. In this graph we have six cycles:  
\[(a,b),(a,b,c),(a,c),(a,b,d),(a,c,d),(a,b,c,d).\]  
The last cycle contains all vertices, so it has no exit point; therefore, no clause is generated for it. Note that in this graph we have an edge from \(d\) to \(a\). The corresponding clauses are the last three clauses in this example; the last two clauses correspond to the new cycles. The remaining clause is the same as in the previous model. Thus, the weak model is (after each clause we list the indices of subsumed full-length clauses from (3)):  
\[ \begin{split}\mathcal{WM}&=\{\{-a,b,c\}:6,7,\ \{-b,a,c,d\}:11,\ \{-c,a,d\}:9,14,\\ \{-a,\
eg b,c,d\}:3,\ \{-a,\
eg b,\
eg c,d\}:1,\ \{-a,\
eg c,b,d \}:5\\ \{\
eg d,a\}:8,10,12,14,\ \{-a,\
eg b,\
eg d,c\}:2,\ \{-a,\
eg c,-d,b\}:4\}.\end{split}  \tag{6}\]  
Note that since the communication graph from Figure 3 is strongly connected, its weak model is a black and white SAT problem; i.e., the SAT problem in (6) has only these two solutions: \(\{a,b,c,d\}\) and \(\{-a,\
eg b,\
eg c,\
eg d\}\). Each cycle was a simple cycle in the above two examples. A cycle is simple iff only the first and last vertices are repeated. Thus, we might think that it is enough to consider simple cycles. The next example shows a case when we need to consider a non-simple cycle as well. In Figure 5 we can see a strongly connected communication graph which contains three simple cycles, \((a,b)\), \((a,b,c,d)\) and \((b,c)\), and one non-simple cycle, \((a,b,c,b)\). As a third example we give the weak model of this communication graph (after each clause we list the indices of subsumed full-length clauses from (3)):  
\[ \begin{split}\mathcal{WM}&=\{\{\
eg a,b\}:4,5,6,7,\ \{-b,a,c\}:10,11,\ \{-c,b,d\}:5,13,\\ \{-d,a\}:8,10,12,14,\ \{-a,\
eg b,c\}:2,3,\ \{-b,\
eg c,a,d\}:9,\\ \{-a,-b,-c,d\}:1\}.\end{split}  \tag{7}\]  
Note that the last clause is generated from the non-simple cycle, and that full-length clause is not subsumed by any other clause.
# Optimizing Activation Function for Parameter Reduction in CNNs on CIFAR-10 and CINIC-10
## 5 Conclusions and Future Work  
### Conclusions  
The presented system is lightweight and convenient to use on any device thanks to its efficient architecture and well-optimized learning algorithms. Despite its simplicity, the network achieves results comparable to those of leading models. This makes it suitable for a variety of applications that require fast image processing and real-time classification, such as driving cameras, web security, and various aspects of cybersecurity. In the context of cybersecurity, the system has the potential to be used in multiple critical scenarios, especially in automated threat detection in network traffic, according to the type of input formatting, and video stream analysis for real-time detection of suspicious activities, on devices ranging from small IoT systems to larger infrastructures. In addition, the system offers flexibility and adaptability, making it suitable for integration into existing infrastructures without the need for significant hardware or software changes. This is particularly important in the cybersecurity domain, where rapid response to new threats is essential. The ability to operate in real-time, combined with low resource requirements, makes the system ideal for deployment in scalable environments, such as large enterprise networks or even IoT devices. Also, its ease of use and high performance make it accessible to a wide range of users, from small enterprises to large organizations looking for effective security and data analytics solutions.  
Nevertheless, our system still falls short of the performance achieved by state-of-the-art models. Those advanced models are typically pre-trained on vast and diverse datasets like ImageNet, which allow them to learn intricate patterns that simply cannot be captured from a smaller dataset such as CIFAR-10 alone. In our case, the hardware limitations prevent us from pre-training on a large-scale dataset like ImageNet before fine-tuning on CIFAR-10, making a direct comparison between our model and these pre-trained giants somewhat unfair.
## Abbreviations  
AMQP Advanced Message Queuing Protocol ANN Artificial Neural Networks BERT Bidirectional Encoder Representations from Transformers BiLSTM Bidirectional Long Short-Term Memory CNN Convolutional Neural Network DoS Denial of Service FHMM Factorial Hidden Markov Model FL Federated Learning FN False Negative FP False Positive FPR False Positive Rate GA Genetic Algorithms GAN Generative Adversarial Network GPS Global Positioning System GRPC Google Remote Procedure Call GRU Gated Recurrent Units (GRUs) HIDS Host-Based IDS HTTP Hypertext Transfer Protocol ICMP Internet Control Message Protocol IDS Intrusion Detection System IID Independent Identically Distributed INF Infinite Value IoMT Internet of Medical Things IoT Internet of Things KDD Knowledge Discovery Dataset KNN K-Nearest Neighbor
## 4 Discussion
### Limitations  
Due to the cross-sectional design of the study, the SSBs consumption pattern of participants over time cannot be readily shown, nor any causal relationship. Although the study used a validated 24 hr dietary recall method for the collection of SSBs data, its reliance mostly on participants' memories may affect the accuracy of the results. The study did not control for potential confounders such as socioeconomic and lifestyle factors, such as physical activity, smoking, alcohol consumption, and energy intake, which is the main reason for the minimal effect after controlling by age. However, according to [PERSON] and colleagues [5], the advantage of adjusting with fewer confounders is generalizability to the real world, because study participants are prone to change their lifestyle without the investigator's knowledge and intervention. Only rural black adults were focused on in this study; therefore, this should be investigated further on other ethnic groups living in urban areas to allow comparison and prevent generalisation, since SSBs consumption and MetS differ by region, gender, ethnic group, and the criteria used to define it [47]. Additionally, the study only explored the association on young adults; thus, focus should be put on different age groups.  
The design of the study assisted in indicating short-term association between the consumption of SSBs and some MetS components. All the questionnaires that were used in the present study have been validated. To the best of the authors' knowledge, this was the first study to investigate the association between SSB consumption and MetS components in rural settings in South Africa.
# In-Orbit Results and Attitude Analysis of the SNUGLITE CubeSatellite
## 1 Introduction
A brief introduction to the SNUGLITE project is given, and the specification of the CubeSat, payloads, and the structure of broadcasting data are included in Section 2. The attitude system of the SNUGLITE CubeSat is described, and a method to analyze the performance of the attitude estimation is also presented in Section 2. Subsequently, the GPS data received from the SNUGLITE as well as the status of the CubeSat are presented in Section 3. In-orbit results of the attitude estimation are also evaluated and discussed in Sections 3 and 4, respectively. In Section 4 (Discussion), the attitude system is also compared with that of previous studies. Finally, Section 5 summarizes the study and provides suggestions for future work.
## 4 Analysis of the Properties of LCPSO
### Behavior of LCPSO  
We suppose that the agents' positions are taken in \(\mathbb{R}^{D}\), with \(D\) the dimension. \(S(t)\) is the set of agents' positions \(\mathbf{p}_{i}(t)\) of the swarm at time \(t\), \(i\in[1,\cdots,N]\). Let us note \(C(t)\) as a convex hull of \(S(t)\): it is a continuous subset of \(\mathbb{R}^{D}\). Then let us note \(\hat{C}(t)\) as the convex polygon of \(S(t)\). It is a manifold of dimension \(D-1\) which displays the surface taken by the swarm. We set \(\mathbf{y}(t)\in\mathbb{R}^{D}\) the optimum position of \(f\) at date \(t\): \(\mathbf{y}(t)=\arg\max_{\mathbf{y}}f(\mathbf{y},t)\). We suppose that this optimum is unique and is the position of the target \(\mathbf{p}_{s}(t)\) propagating information.  
We define the set \(B(t)\), which contains the best attractors of the swarm at time \(t\) which minimised the Euclidean distance with respect to \(\mathbf{y}(t)\).  
\[B(t)=\{\mathbf{p}(t)\in\mathbb{R}^{D}|\mathbf{p}(t)=\underset{\mathbf{p}(t) \in S(t)}{\arg\min}\|\mathbf{p}(t)-\mathbf{y}(t)\|\}. \tag{16}\]  
The set \(S(t)\) being discrete, we introduce the set \(\bar{S}(t)\), defined as the set of the points of \(S(t)\) which are in the convex polygon \(\bar{C}(t)\) of \(S(t)\). We summarize the behavior of our swarm with the following theorem:  
**Theorem 1**.: _We assume that each agent follows the LCPSO Equation (11), with \(r_{com}\to+\infty\). Then the center of gravity of the swarm is heading towards the attractor \(\mathbf{p}^{s}(t)\), and its velocity vector is equal to :_  
\[\bar{\mathbf{v}}(t+\Delta t)=c_{0}\bar{\mathbf{v}}(t)+c_{2}\big{(}\mathbf{p}^ {s}(t)-\bar{\mathbf{p}}(t)\big{)}. \tag{17}\]  
Hence, if the swarm is stable in the sense of Definition 2, all agents follow the speed vector of the center of gravity. Taking into account the inertia weighted by \(c_{0}\), the attractor position gives the direction that the swarm will follow. In the MOSL case, we have defined it as the agent that has measured the strongest information \(f(\mathbf{p}^{s},t)\) at time \(t\); it is thus the agent that is the closest to the target.  
We distinguish two particular states. The first one is the transition state, with \(\mathbf{y}(t)\
otin C(t)\). In this state, the attractors are necessarily and intuitively the agents located on the hull of the swarm: \(B(t)\subseteq\bar{S}(t)\). Trying to catch up with these attractors, the agents in the swarm will accelerate to their maximum speed and then remain at this speed, in a steady state. Thanks to (17) and \(r_{max}\), the maximal speed \(v_{max}\) is predictable. This state is illustrated in Figure 2b in dimension 2. The second case is the steady state, with \(\mathbf{y}(t)\in C(t)\). In this state, all agents of \(S(t)\) are potentially attractive, that is, \(B(t)\subseteq S(t)\). In this case, the swarm will follow a speed close to that of the target. Thus, the closer the attractor is to the center of gravity, the slower its speed will be. In the case of tracking a static target, our swarm will head towards the source, and will stop when \(\mathbf{y}(t)=\bar{\mathbf{p}}(t)\), shown in Figure 2a in Dimension 2.
## 2 Materials and Methods
### Climate Impact  
To calculate the climate impact of different aircraft configurations the non-linear climate-chemistry response model, AirClim [51, 52] is applied. AirClim is a surrogate model which combines air traffic emissions, calculated, e.g., by GRIDLAB, with pre calculated atmospheric impact data. For the pre calculated data, climate-chemistry simulations are performed, with normalised emissions in idealised emission regions. For the impact of NO\({}_{\text{x}}\) emissions on the O\({}_{3}\) and CH\({}_{4}\) concentration, and for the impact of H\({}_{2}\)O emissions, the complex climate-chemistry model E39C/A [53] was used. For the climate impact of CiC (contail induced cloudiness) simulations with the ECHAM4-CCMod model [54] were analysed. The climate impact of CO\({}_{2}\) is independent of the emission location due the long lifetime. Therefore, a green function based on [55] is used for the climate impact of CO\({}_{2}\). AirClim calculates the concentration change, RF (Radiative Forcing) and near surface temperature change of the individual species accounting for their different life time and dependency of the emission location. The AirClim model presented in [52] was generated for conventional fuel consumption. For alternative fuels such as sustainable aviation fuel (SAF) or hydrogen, the number of emitted soot particles is reduced, which influence contrail properties and RF. To account for this, a relation between relative change in particle emission and resulting change in RF is applied, which is received from simulations from [56]:  
\[\Delta RF^{\text{CiC}}=\frac{arctan(1.9\Delta pn^{0.740})}{arctan(1.9)} \tag{7}\]  
where \(\Delta pn\) is the relative change in particle number emissions (dimensionless value between 0 and 1) [57]. Using liquid hydrogen instead of conventional kerosene changes both the particle emission and the probability of contrail formation. The Schmidt-Appleman Criterion (SAC) suggests whether contails form and persist. This depends both on the ambient temperature and humidity on the slope of the G-factor [58]:  
\[G^{*}=\frac{EI_{H_{2}\text{O}}}{(1-\eta)LHV} \tag{8}\]  
Figure 6: Global distribution of flown distances covered by the underlying route network.  
where \(EI_{H_{2}O}\) is the emission index of water vapour, \(\eta\) is the overall propulsion efficiency, and LHV is the lower heat value of the fuel. If the slope increases, the probability of contrail formation increases. Additionally, \(\eta\) influence the contrail coverage by [47]  
\[F(LH2)=\frac{\eta}{\eta_{conventional}}. \tag{9}\]  
For assessing the climate impact of the different aircraft configurations, the average near surface temperature change over 100 years (Average Temperature Response, ATR100) is analysed. An entry into service of 2040 and a life time of 32 years is assumed. As background atmosphere, the RCP4.5 scenario is applied, which is a middle of the road scenario [59].
# Attention-Based Mechanism and Adversarial Autoencoder for Underwater Image Enhancement
## 3 Experimentation and Analysis
### Analysis of Objective Indicators
#### 3.3.1 Objective Metrics Analysis on Test Set A
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Image** & **Input** & **UDCP** & **HE** & **Ucolor** & **Fusion** & **FunieGAN** & **ULAP** & **Ours** \\ \hline image1 & 7.142 & 5.985 & 3.977 & 4.774 & 3.900 & 5.784 & 6.532 & **3.229** \\ image2 & 5.138 & 5.149 & 3.650 & 4.139 & 4.256 & 4.844 & 4.824 & **3.383** \\ image3 & 3.716 & 3.344 & 3.011 & 3.486 & **2.981** & 3.682 & 3.937 & 3.403 \\ image4 & 7.360 & 5.172 & 4.864 & 6.200 & 4.334 & 5.325 & 5.305 & **4.122** \\ image5 & 4.640 & 5.295 & 3.355 & 4.508 & 3.381 & 3.934 & 6.442 & **3.266** \\ image6 & 3.436 & 3.571 & 3.377 & 3.507 & 3.329 & 3.510 & 3.387 & **3.249** \\ image7 & 4.704 & 4.125 & **3.899** & 4.092 & 3.969 & 4.316 & 4.590 & 4.105 \\ Image8 & 3.576 & 3.411 & 2.956 & 3.117 & **2.883** & 3.393 & 3.294 & 3.100 \\ Image9 & 3.770 & 3.822 & 3.290 & 3.574 & **3.278** & 4.046 & 3.520 & 3.471 \\ Image10 & 4.604 & 4.138 & 8.281 & 8.875 & 3.496 & 4.309 & 4.565 & **3.386** \\ average & 4.809 & 4.401 & 4.066 & 4.627 & 3.581 & 4.314 & 4.640 & **3.471** \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the NIQE evaluation index results on test set A. Bold indicates the minimum value of NIQE for each group, and lower NIQE values indicate higher image quality. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Image** & **Input** & **UDCP** & **HE** & **Ucolor** & **Fusion** & **FunieGAN** & **ULAP** & **Ours** \\ \hline image1 & 0.476 & 0.469 & 0.491 & 0.517 & 0.469 & 0.492 & 0.538 & **0.543** \\ image2 & 0.513 & **0.633** & 0.534 & 0.550 & 0.541 & 0.583 & 0.566 & 0.466 \\ image3 & 0.341 & 0.449 & 0.460 & 0.449 & 0.436 & 0.370 & 0.537 & **0.580** \\ image4 & 0.355 & 0.496 & 0.526 & 0.448 & **0.565** & 0.555 & 0.486 & 0.427 \\ image5 & 0.338 & 0.429 & **0.476** & 0.378 & 0.443 & 0.435 & 0.455 & 0.443 \\ image6 & 0.351 & 0.450 & 0.450 & 0.406 & 0.430 & 0.460 & 0.462 & **0.483** \\ image7 & 0.327 & 0.549 & 0.492 & 0.435 & 0.445 & 0.538 & 0.350 & **0.582** \\ Image8 & 0.384 & 0.456 & **0.579** & 0.473 & 0.569 & 0.450 & 0.491 & 0.484 \\ Image9 & 0.357 & **0.521** & 0.506 & 0.447 & 0.476 & 0.503 & 0.483 & 0.494 \\ Image10 & 0.376 & **0.565** & 0.558 & 0.557 & 0.524 & 0.385 & 0.371 & 0.422 \\ average & 0.382 & 0.502 & **0.507** & 0.466 & 0.490 & 0.477 & 0.474 & 0.492 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of UCIQE evaluation index results on test set A. Bold indicates the maximum value of each group of UCIQE, and higher values of UCIQE indicate higher image quality.
## 2 Results
### Effect of Drying Temperature on the Bioactivity of LBT
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **DPPH** & **\(\alpha\)-Amylase** & **\(\alpha\)-Glucosidase** & **Inhibition Rate of Glucose** \\  & **IC\({}_{50}\) (\(\mu\)g/mL)** & **IC\({}_{50}\) (\(\mu\)g/mL)** & **IC\({}_{50}\) (\(\mu\)g/mL)** & **Absorption (\%)** \\ \hline
70 \({}^{\circ}\)C & 20.71 \(\pm\) 0.23 \({}^{\mathrm{d}}\) & 12.14 \(\pm\) 0.08 \({}^{\mathrm{b}}\) & 9.39 \(\pm\) 0.77 \({}^{\mathrm{b}}\) & 52.91 \(\pm\) 0.68 \({}^{\mathrm{a}}\) \\
80 \({}^{\circ}\)C & 21.02 \(\pm\) 0.18 \({}^{\mathrm{d}}\) & 12.38 \(\pm\) 0.03 \({}^{\mathrm{b}}\) & 9.18 \(\pm\) 0.27 \({}^{\mathrm{b}}\) & 49.67 \(\pm\) 0.14 \({}^{\mathrm{b}}\) \\
90 \({}^{\circ}\)C & 19.71 \(\pm\) 0.13 \({}^{\mathrm{e}}\) & 11.22 \(\pm\) 0.35 \({}^{\mathrm{b}}\) & 8.25 \(\pm\) 0.07 \({}^{\mathrm{c}}\) & 52.15 \(\pm\) 0.41 \({}^{\mathrm{ab}}\) \\
100 \({}^{\circ}\)C & 22.30 \(\pm\) 0.30 \({}^{\mathrm{a}}\) & 15.37 \(\pm\) 0.76 \({}^{\mathrm{a}}\) & 9.18 \(\pm\) 0.11 \({}^{\mathrm{b}}\) & 49.47 \(\pm\) 0.65 \({}^{\mathrm{b}}\) \\
110 \({}^{\circ}\)C & 21.93 \(\pm\) 0.22 \({}^{\mathrm{ab}}\) & 16.00 \(\pm\) 0.69 \({}^{\mathrm{a}}\) & 10.87 \(\pm\) 0.19 \({}^{\mathrm{a}}\) & 46.04 \(\pm\) 1.22 \({}^{\mathrm{d}}\) \\
120 \({}^{\circ}\)C & 21.44 \(\pm\) 0.22 \({}^{\mathrm{bc}}\) & 15.6 \(\pm\) 0.33 \({}^{\mathrm{a}}\) & 10.50 \(\pm\) 0.38 \({}^{\mathrm{a}}\) & 46.51 \(\pm\) 2.16 \({}^{\mathrm{d}}\) \\
130 \({}^{\circ}\)C & 22.29 \(\pm\) 0.31 \({}^{\mathrm{a}}\) & 16.45 \(\pm\) 0.33 \({}^{\mathrm{a}}\) & 11.25 \(\pm\) 0.44 \({}^{\mathrm{a}}\) & 47.76 \(\pm\) 0.68 \({}^{\mathrm{cd}}\) \\ \hline \hline \end{tabular}  
* Different lowercase letters in the same column indicate significant differences at \(p<0.05\).  
\end{table} 
Table 6: Scavenging effects on the DPPH radical and inhibitory effects on the \(\alpha\)-amylase, \(\alpha\)-glucosidase, and glucose absorption activity of the LBT at different drying temperatures. the black tea polysaccharide and the biological activity of the tested samples. Collectively, the decrease in the catechin and theaflavin content, along with an increase in the gallic acid content, may be attributed to the decline in the antioxidant and hypoglycemic activities of the LBT with increasing drying temperatures. This suggests that reducing the drying temperature appropriately is advantageous for preserving the biological activities of LBT.