esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data
###### Abstract  
The 5G core network adopts a Control and User Plane Separation (CUPS) architecture to meet the challenges of low-latency business requirements. In this architecture, a balance between management costs and User Experience (UE) is achieved by moving User Plane Function (UPF) to the edge of the network. However, cross-UPF handover during communication between the UE and the remote server will cause TCP/IP session interruption and affect continuity of delay-sensitive real-time communication continuity. Information-Centric Networks (ICNs) separate identity and location, and their ability to route based on identity can effectively handle mobility. Therefore, based on the 5G-ICN architecture, we propose a seamless mobility support method based on router buffered data (BDMM), making full use of the ICN's identity-based routing capabilities to solve the problem of UE cross-UPF handover affecting business continuity. BDMM also uses the ICN router data buffering capabilities to reduce packet loss during handovers. We design a dynamic buffer resource allocation strategy (DBRAS) that can adjust the buffer resource allocation results in time according to network traffic changes and business types to solve the problem of unreasonable buffer resource allocation. Finally, experimental results show that our method outperforms other methods in terms of average packet delay, weighted average packet loss rate, and network overhead. In addition, our method also has good performance in average handover delay.  
5G-ICN; mobility; seamless handoff; resource allocation; real-time communication +
Footnote †: journal:  
[PERSON], [PERSON], [PERSON] A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data. _Future Internet_**2024, 16**, 96. [[https://doi.org/10.3300/616030096](https://doi.org/10.3300/616030096)]([https://doi.org/10.3300/616030096](https://doi.org/10.3300/616030096))
## 3 Design Challenges of MAC Protocol for IBSN
### Types of Access Mechanisms Recommended for IBSN
#### 3.2.4 Adaptive Access Mechanisms  
Adaptive access mechanisms are improvised hybrid access mechanisms, which are adaptive to the dynamic changes of the system. The underlying working principle of them is based on either contention-free access, contention-based access or both. Unlike hybrid access mechanisms, adaptive access mechanisms incorporate learning techniques to adapt the underlying access mechanisms, based on continuous feedback from the system. For example, consider a simple frame-based contention access mechanism, in which each node determines its own wake-up schedule based on the real-time requirements of the node. Such a non-fixed adaptive duty-cycle not only improves energy-efficiency, but also improves network performances over time. The node learns about the flow of data over time, and using learning algorithms, the node can teach itself to derive an optimum schedule of duty-cycles. Also, in the case of IBSN, the dynamicity in the flow of data can be handled easily with adaptive access mechanisms. However, these adaptive access mechanisms are still in their infancy, where learning algorithms require large memory and computing power to operate. In IBSN, the amount of resources is stringent, which widely limited its direct application in MAC protocols for IBSN.
# MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
## 5 Conclusions  
To overcome the dual challenges of small sample data and variable operating conditions, this paper proposes a novel bearing fault diagnosis method that combines physics-guided two-dimensional signal analysis with the MTC-GAN model. First, the physical characteristics of vibration acceleration signals are analyzed to generate two-dimensional vibration signal images. Then, the Local Binary Pattern (LBP) operator is employed to extract the micro-texture features of the images, thereby constructing highly discriminative fault feature signatures. Finally, the MTC-GAN model is utilized for data augmentation, generating high-quality synthetic samples to enhance the generalization capability under small sample conditions, and the model's discriminator is used to perform fault classification tasks. This method significantly improves the model's adaptability and classification accuracy under small sample sizes and varying operating conditions.  
The experimental results demonstrate that the proposed method maintains excellent classification accuracy and robustness, even under limited training data, in both single and variable operating conditions. Compared to traditional fault diagnosis methods, the proposed approach exhibits superior diagnostic accuracy and efficiency. Future research could focus on further exploring bearing fault diagnosis under complex scenarios involving small samples and variable operating conditions, by developing more advanced feature extraction techniques and optimizing model architectures to enhance fault detection accuracy and model adaptability in diverse working conditions.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and J.L.; software, [PERSON] and [PERSON]; validation, [PERSON] and J.L.; formal analysis, [PERSON]; investigation, J.L.; resources, [PERSON]; data curation, [PERSON] and J.L.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Classification Accuracy (\%)** & **Training Time (s)** & **Testing Time (s)** \\ \hline SVM & 65.83 & 144 & 102 \\ CNN & 80.47 & 962 & 233 \\ LSTM & 83.64 & 1089 & 278 \\ DTL & 91.56 & 705 & 254 \\ DAN & 93.43 & 682 & 194 \\ Proposed Method & 98.50 & 182 & 131 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative test results between different models under varying-condition conditions.  
**Funding:** This work was supported by the Scientific Research Fund of Department of Education of Liaoning Province, China (LJKZZ20220037).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The raw/processed data needed to reproduce these findings cannot be shared publicly at this time, as they are also part of ongoing study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Research on Brake Energy Recovery Strategy Based on Working Condition Identification
## 4 Construction of Regenerative Brake Strategy Based on Condition Identification
### Front and Rear Axle Brake Force Distribution Strategy
#### 4.1.2 Front and Rear Axle Brake Force Distribution Strategy  
According to the level of braking intensity, braking is divided into four modes: light, medium, heavy, and emergency braking. As the rear wheel first locking in the braking process represents an unstable condition, an empty-, half-, and full-load/front- and rear-axle braking force distribution strategy was developed, based on the comprehensive consideration of the f-group line (the front wheel first locking and the rear wheel not being wrapped), ECE braking regulations, and the ideal braking force distribution curve of different loads. Brake force distribution curves for the front and rear axles are shown in Figure 12.  
As shown in Figure 12, the brake force distribution rules are as follows:  
1. Light braking mode: When the braking intensity is between 0 and 0.15 (\(0\leq z<0.15\)), regardless of the current vehicle load, all of the braking force is distributed to the rear axle according to the AB curve. The maximum braking force is provided by the electric motor, and the mechanical brake plays a complementary role.
2. Medium braking mode: The vehicle distributes the braking force according to the BC curve, the braking force of the rear axle remains unchanged, and the braking force of the front axle increases linearly with the increase in braking intensity, until the no-load threshold \(z_{E1}=0.42\), half-load threshold \(z_{H1}=0.29\), and full-load threshold \(z_{F1}=0.23\) are reached.
3. Hard braking mode: The vehicle distributes the brake force along the CD curve, and the brake force distribution curve is always below the ideal brake force distribution coefficient \(I\) curve and close to the I curve, until it coincides again with the ideal brake force distribution coefficient \(I\) curve, at which point \(z_{E2}=z_{H2}=z_{F2}=0.7\).
4. Emergency braking mode: If \(z>0.7\), no regenerative braking is performed under any load condition, and the braking force is fully distributed to the mechanical brakes of the front and rear axles, according to the DE curve.
## 5 Results and Discussion
### Parameterization Results  
Firstly, the proposed methodology was applied for parameterizing the active power model and the THD model, using the piecewise approach previously described. In Table 1, the weights \(w_{1}\), \(w_{2}\), \(w_{3}\), and \(w_{4}\), delivered by the GA using the 24 days of the parameterizing dataset for the four sections of the active power estimation, are summarized. It is observed in Table 1 that the weight values are very different from one section to another; thus, it can be inferred that using the whole signal, instead of sectioning it, may compromise the accuracy of the results. Additionally, the resulting 14 weights for the four sections of the THD signals are presented in Table 2. Once again, the parameterization dataset was also used to obtain these weights. Just as in the previous case, all the values significantly vary from one section of the THD signal to another, confirming the fact that the piecewise approach implemented in this work helps to increase the reliability of the methodology. As aforementioned, the behavior of the PV system is different depending on the operating conditions [33], i.e., the behavior of the system is nonlinear. This nonlinearity is the main reason for the variation in the weights presented in the different sections of the estimated models.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 6 Performance Evaluation
### Experimental Results
#### 6.2.2 Resource Sharing with TCP Traffic  
In a second experiment series, both clients send TCP traffic. We vary the number of saturated TCP connections of Client 0 while Client 1 has only a single saturated TCP connection. Thus, Client 0 is a heavy user while Client 1 is a light user. Figure 5 shows the obtained goodput for both clients.  
Without ABC, the goodput of Client 0 increases with increasing number of TCP connections while the goodput of Client 1 significantly decreases. With ABC, the goodput of both clients remains in the order of 25 Mb/s if the number of TCP connections for Client 0 increases. Thus, fair resource sharing is approximated.  
Figure 4: Resource sharing with constant bit rate (CBR) traffic; Client 0 sends CBR traffic as indicated on the \(x\)-axis and Client 1 sends CBR traffic at 40 Mb/s.  
Figure 5: Resource sharing with TCP traffic; the number of saturated TCP connections of Client 0 is indicated on the \(x\)-axis while Client 1 has only one saturated TCP connection.
# Secure Delivery Scheme of Common Data Model for Decentralized Cloud Platforms
## Materials and Methods
### Methods for Adapting to CDM Model
#### 2.2.3 Applied Security and Privacy Method in SC-CDM  
This section presents a method for handling the security and privacy concerns in the SC-CDM, including the model for secure access of CDM data. CDM is represented as a digital asset concerned in a blockchain. The participants of the blockchain are the stakeholders of CDM, which consists of a CDM consumer, a CDM provider, and a service broker. The service broker plays a role as a mediator that preserves a trustworthy delivery of CDM between the CDM consumer and CDM provider. In the CDM blockchain, the transaction can be explained as three operations related with CDM. To access the permissioned CDM blockchain (represented as a node in Figure 3), X.509 is used for defining the format of public key certificates. Node A represents a CDM provider, and Node B represents a CDM consumer. Two trust managers located in the service broker play role as agents of the CDM provider operated in Node A and the CDM consumer operated in Node B for delivering the CDM trustily (represented as \(\text{CDM}_{\text{A}\rightarrow\text{B}}\) in Figure 3).  
When a certificate is signed by a trusted certificate authority in SC-CDM, the CDM consumer accesses the public key of the CDM provider. The SC-CDM can establish secure communications with  
Figure 3: CDM delivery process.  
Figure 2: Concept of the Secure-Cloud Common Data Model (SC-CDM) for building \(\text{CDM}_{\text{A}\rightarrow\text{B}}\).  
the components and validate \(\text{CDM}_{\text{A}\rightarrow\text{B}}\), which is digitally signed by the corresponding private key owned by the CDM provider. Table 1 shows the basic definition of the components of SC-cloud.  
Table 2 summarizes the qualitative comparison of the characteristics of the baseline CDM model and the proposed cloud CDM model, SC-CDM. In terms of the fault tolerance, data sharing, privacy, and data integrity, SC-CDM is an improvement over the baseline CDM model.
## 4 Simulation and Results
### Our Proposed Model with Media Noise  
To approximate the actual condition, we simulated the BPMR system with position fluctuation. Based on the BPMR channel in Section 3.1, in Figure 20, we performed a simulation with 6% position fluctuation (\(\Delta_{x}\
eq 0\) and \(\Delta_{z}\
eq 0\)) [19]. In addition, we simulated the third model with position fluctuation. The neural network was trained at an SNR of 11 dB.  
Figure 19: BER versus TMR at 15 dB.  
Figure 20: BER versus SNR for 6% position fluctuation.  
Finally, Table 7 compares the complexity among the proposed model and some previous studies [31; 32] that applied the neural network for the TDMR channel. We use the third model with two hidden layers for comparison. We can see that the proposed model achieves manageable complexity.
# Policy-Compliant Maximum Network Flows
## 4 Policy-Compliant Paths and Algorithms
### Finite State Automata  
We now proceed to define finite state automata [5; 6], which are the simplest of all classic automata. Note that we will only consider deterministic automata, although for finite state automata we have that non-determinism does not add expressive power, i.e., the class of deterministic finite state automata is exactly the same as the class of non-deterministic finite state automata.  
We define a deterministic finite state automaton as \(M=(Q,\Sigma,\delta,q_{0},F)\), where  
* \(Q\) is a finite set of internal states,
* \(\Sigma\) is a finite set of symbols, called the input alphabet,
* \(\delta:Q\times\Sigma\to Q\) is the (total) transition function,
* \(q_{0}\in Q\) is the start (or initial) state,
* \(F\subseteq Q\) is the set of final states.  
Such an automaton works as follows. Initially, we start with an input word \(w\in\Sigma^{*}\), where \(\Sigma^{*}\) is the set of strings obtained by concatenating zero or more symbols from \(\Sigma\). Each of these symbols of the word \(w\) gets consumed, from left to right, while the automaton changes its state accordingly to its transition function, taking as input the current state and the symbol just read. If the automaton starts in state \(q_{0}\), and finishes reading the word \(w\) while reaching state \(q\), then we say that the word \(w\) is accepted by the automaton if and only if \(q\in F\). For convenience, we introduce the extended transition function \(\delta^{*}:Q\times\Sigma^{*}\to Q\), where the second argument is a string rather than a single symbol. We define \(\delta^{*}\) recursively as follows:  
\[\delta^{*}(q,w)=\begin{cases}q&\text{if }w=\lambda,\\ \delta^{*}(\delta(q,a),v)&\text{if }w=av,\end{cases} \tag{13}\]  
where \(a\in\Sigma\) and \(v,w\in\Sigma^{*}\). Note that we also introduced the empty word \(\lambda\) out of convenience for this definition. We won't need it any further in this work.
## 2 System Model
### Wireless System with Shannon Capacity Links  
If the receiver of interest is part of a wireless network with Shannon capacity links, then \(R_{b}=BW\cdot W\,i_{2}(1+\gamma)\) and thus the SINR threshold \(\gamma\), for \(R_{b}\) is equal to is \(\gamma=2^{\frac{R_{b}}{BW}}-W\). Therefore the criterion for successful communication is: \(\frac{P_{1}G_{il}}{(\sum_{j\
eq i}P_{j}G_{ij})+n_{i}}\geq 2^{\frac{R_{b}}{BW}}-W\). Following the analysis presented before, and taking into account the interference calculations and the criterion for successful communication in this case the interference limited communication region is given by:  
\[d_{ILR}=\sqrt[a]{\frac{1}{2^{\frac{R_{b}}{BW}}-1}}\cdot\frac{P_{i}\cdot D}{( \sum_{j\
eq i}P_{j}\cdot D\cdot d_{(j,i)}^{-a})+n_{i}}\approx a\sqrt[a]{\frac{1}{2 ^{\frac{R_{b}}{BW}}-1}}\cdot\frac{P\cdot D}{(\sum_{j\
eq i}P\cdot D\cdot d_{(j,i)}^{-a})} \tag{9}\]  
We distinguish two cases:  
\[d_{ILR}=\left\{\begin{aligned} &\sqrt[a]{\frac{r_{\max}^{2}-r_{\min}^{2}}{2( \ln r_{\max}-\ln r_{\min})}}\cdot\sqrt[a]{\frac{1}{2^{\frac{R_{b}}{BW}}-1}} \cdot\frac{1}{N_{t}},\,\text{for $a=2$}\\ &\text{a}\sqrt[a]{\frac{1}{2^{\frac{R_{b}}{BW}}-1}}\cdot\frac{1} {N_{t}}\cdot\frac{(a-2)(r_{\max}^{2}-r_{\min}^{2})}{2\left(\frac{1}{r_{\min}^ {a-2}}-\frac{1}{r_{\max}^{a-2}}\right)},\,\text{for $a>2$}\end{aligned}\right. \tag{10}\]  
Figure 4 shows that the \(d_{ILR}\) has the same behavior as before (Figure 3) but its values are much higher (as expected because of the much better performance of the link). For small number of transmitters the \(d_{ILR}\) with \(a=2\) is larger than the \(d_{ILR}\) with \(a>2\). The rate of convergence to zero is the same as before.  
Figure 4: Interference limited communication range for Shannon capacity link.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 2 System Model
### 5G-and-Beyond Channel Model
By utilizing the channel model in (8) and the transmitted signal in (4), the received signal after passing through the 5G multi-path channel is formulated as  
\[\mathbf{\tilde{y}}_{gb}(t)=\sum_{a=1}^{N_{T}}\mathbf{\tilde{h}}_{a,b}(\tau,t) \otimes\mathbf{\tilde{x}}_{ga}(t)+\mathbf{\tilde{n}}_{b}(t), \tag{9}\]  
where \(\mathbf{\tilde{h}}_{a,b}(\tau,t)=[h_{a,b}(\tau_{1},t),\dots,h_{a,b}(\tau_{L},t)]\); \(\mathbf{\tilde{n}}_{b}(t)\) is the additive noise vector, whose elements are independent and identically distributed random variables following a circularly symmetric complex Gaussian distribution with zero-mean and variance \(\sigma_{n}^{2}\). From the received signal in Equation (9), we are able to estimate the propagation channels and analyze the system performance as shown below.
# Generating Transparency in the Worldwide Use of the Terminology Industry 4.0
## 3 Results
### Results of the Correlated Analysis  
#### 3.2.1 International Analysis  
The international analysis on the worldwide scale in Figure 7 clearly displays Europe's dominant role in the correlated analysis. In comparison to the USA and China, Europe produces 78% of all papers linked to the category CPS, 65% of all papers referring to data management, 83% of all papers that fall within the category smart, and even 93% of all papers dealing with human-machine interaction, in correlation to the term Industry 4.0. Meanwhile, the USA shows very little to no use of the term in correlation to the selected keywords. China has some contributions, with 14 papers identified in the data management category, followed by CPS (7) and smart (6). Publications around the topic human-machine interaction are of no special interest in the USA (two) and China (one).  
On a European scale, the dominant use of the term Industry 4.0 within Germany becomes evident (cf. Figure 8). The country is particularly focused on CPS (31) and human-machine interaction (31), while data management (13) and smart (19) are falling behind. Only two more countries appear to implement the term Industry 4.0 to at least some degree: Spain and the UK. The latter seems to focus on data management (four), followed by human-machine interaction (two) and smart (two). Spain, on the other hand, published most of its studies around CPS (five), followed by smart (two), data management (two), and human-machine interaction (two). Apart from these three main countries, Austria, Portugal, Hungary, and Italy have also conducted some amount of research in the aforementioned fields in relation to the term Industry 4.0. The rest of Europe does not appear to use this term to any significant extent in the context of the selected keywords.
# Three-Dimensional Probabilistic Semi-Explicit Cracking Model for Concrete Structures
###### Abstract  
This paper introduces a three-dimensional (3D) semi-explicit probabilistic numerical model for simulating crack propagation within the framework of the finite element method. The model specifically addresses macorrack propagation using linear volume elements. The criteria governing the macorrack propagation is based on the softening behavior observed in concrete under uniaxial tension. This softening behavior corresponds to a dissipated cracking energy that is equal to the mode I critical fracture energy (\(G_{IC}\)) used in the Linear Elastic Fracture Mechanics theory (LEFM). The probabilistic nature of this model revolves around the random distribution of two mechanical properties: tensile strength (\(f_{t}\)) and fracture energy, which varies based on the volume of finite elements. The scattering of the fracture energy increases as the volume of finite elements decreases in order to consider the strong heterogeneity of the material. This work primarily aims to estimate the relationship between the standard deviation of \(G_{IC}\) and the volume of finite elements through the development of the numerical model. For this purpose, an inverse analysis is conducted based on a fracture mechanical test simulation. This test involves macorrack propagation in a large Double Cantilever Beam (DCB) specimen with a crack length exceeding two meters. The proposed inverse analysis procedure yields highly significant results, indicating that the numerical model effectively evaluates both crack length and crack opening during propagation.  
numerical model; probabilistic characteristics; macorrack propagation; volume effect; finite element method +
Footnote †: journal: Applied Sciences