esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Parametric Study on Tunnel Displacement
### Effect of \(K_{0}\) (Horizontal/Vertical Stress Ratio)  
It is well recognized that the coefficient of lateral pressure (the horizontal/vertical stress ratio, \(K_{0}\)) may lead to different mechanical responses of underground engineering structures [38; 39]. In this section, the effect of varying \(K_{0}\) values on tunnel displacement responses is examined, thereafter the \(K_{0}\) values of 0.5, 1, and 2 are considered, with the tunnel and excavation geometries in prototype scale being modeled. The calculated tunnel heave and horizontal displacement at different stages with varying \(K_{0}\) values are shown in Figure 19. This indicates that increasing the \(K_{0}\) significantly increases the effect of the excavation on the tunnel displacements. At each \(K_{0}\), excavation-induced tunnel heave and horizontal displacement are both increased as the excavation proceeds, and the maximum values are obtained at the last stage, as expected. At each stage, the tunnel heave and horizontal displacement both experience increases at an increased rate as the \(K_{0}\) values increase from 0.5 to 2.0. The terminal values also increase with increasing \(K_{0}\) values, so the maximum tunnel heave and horizontal displacement are simultaneously obtained when the \(K_{0}\) is 2. Increasing \(K_{0}\) results in a more significant increase in tunnel heave than horizontal displacement.  
Figure 18: Influence of unloading ratio on tunnel displacement.
## 3 Proposed BWO-ICEEMDAN-iTransformer Model  
The process of the proposed model is shown in Figure 6.  
1. The raw power load data contains some anomalies and missing values that need to be preprocessed. For missing values, fill them with the average value of the same time point over the previous and subsequent three days. For anomalies, apply the three-sigma rule for screening and treat them as missing values.
2. Optimize the two parameters of ICEEMDAN, noise standard deviation (Nstd) and the number of realizations (NR), using BWO to maximize the decomposition effect.
3. Use ICEEMDAN to decompose the preprocessed electricity load data, generating modal components and residuas.
4. Use SCC to select factors with high correlation to electricity load, including electricity price, related humidity (RH), and temperature (T).
5. Reconstruct the decomposed subsequences with the selected relevant factors.  
Figure 5: The structure of the iTransformer.  
Use the iTransformer model to obtain the predicted results for each subsequence and then combine them to obtain the final prediction.
## 3 Calculation Method of the Restraint Ratio of CSA-BRB
the external restraining members, it is not less than the bending moment \(M_{e}\) generated by the external load in the mid-span section, that is:  
\[M_{u}\geq M_{e} \tag{3}\]  
Similar to the BRB of the all-steel solid-web section, the flexural bearing capacity \(M_{u}\) of the cross-section of the CSA-BRB external restraining members is determined based on its edge yield criterion, which can be expressed as:  
\[M_{u}=W_{e}f_{y,s} \tag{4}\]  
where \(M_{u}\) is the sectional resistance moment of the external restraining members. Borrowing from the expression for the bending moment in the span section of a single-cored BRB under axial load \(P\), the bending moment \(M_{e}\) in the span is given for a CSA-BRB core with an axial compression strain of 0.02 and taking into account the effect of the initial defect (Equation (5)), where \(\eta\) is the strengthening factor considering the core material, \(\alpha\) is the adjustment parameter determined when the restraint ratio threshold is determined later and \(v_{0}\) is the initial defect of CSA-BRB, which is taken as \(l/500\). \[M_{e}=\frac{\alpha\eta P_{y,c}v_{0}}{1-\eta P_{y,c}/P_{cr}} \tag{5}\]  
Substituting Equations (4) and (5) into Equation (3) and introducing the restraint ratio \(\zeta=P_{cr,\varepsilon}/P_{y,c}\), the restraint ratio threshold \(\left[\zeta\right]_{\eta}\) for a bearing CSA-BRB considering the strengthening factor of the core material under monotonic axial compression load is obtained in Equation (6), where \(f_{y,s}\) is the yield strength of the steel in the externally restrained combined section. \[\left[\zeta\right]_{\eta}=\frac{\eta W_{e}f_{y,s}}{W_{e}f_{y,s}-\eta\alpha P_{ y,c}v_{0}} \tag{6}\]  
Under the action of axial cyclic load, the steel tube restrained kernel separated BRB has to meet the energy dissipation requirement, the cumulative plastic deformation factor of the CSA-BRB under repeated load \(\mu_{c}\geq 200\), so the restraint ratio threshold of the energy dissipating BRB should be more stringent than that of the bearing type. Referring to the American Code for Seismic Steel Construction AISC341-05 [25], considering the strain strengthening adjustment factor \(\omega\) for the core steel, the steel tube restrained core separated BRB energy dissipative restraint ratio threshold \(\left[\zeta\right]_{\omega}\) is calculated according to Equation (7). \[\left[\zeta\right]_{\omega}=\frac{\omega W_{e}f_{y,s}}{W_{e}f_{y,s}-\omega \alpha P_{y,c}v_{0}} \tag{7}\]
###### Abstract  
The active jet noise control received significant attention due to the little influence it has on the engine performance. The active jet noise control is a multivariable problem because it needs to achieve the simultaneous closed-loop control of jet noise and engine performance. Model predictive control (MPC) has great application potentials in the field of multivariable control of aero-engines, but the real-time performance of MPC is intractable. This paper proposed an active jet noise controller of a turbofan engine, based on explicit model predictive control (EMPC). An integrated model of turbofan engine and jet noise, which calculates the engine parameters and jet noise in real time, was established. The online computational burden of MPC was transferred to offline computation using multi-parametric quadratic programming (MPQP). To improve the efficiency of the online positioning algorithm, the sequence search method was replaced by a binary search tree. Step simulations were performed to test the effectiveness of the proposed controller. The results show that the proposed EMPC controller not only achieves the simultaneous control of jet noise and the turbofan engine, but also improve the real-time performance greatly.  
turbofan engine; jet noise; active control; explicit model predictive control; binary search
# PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
## 5 Results
### Entropy Detection of PUE Attack  
The first part of the model is the energy detector, which is implemented in GNURadio. The 100 positions of the grid are defined to measure the energy of one _PUE attack_ when it is active or not. As shown in Figure 8, there is a difference in the power when it is in the two states.  
The energy is detected by averaging 100 samples of the measured energy each time. Results are taken when the _PUE attack_ is in a fixed position. In Figure 9, an example of the received power is shown when there is a PUE active or not in two different positions near and far from a SU.  
The entropy is calculated with 100 samples of the energy. Figure 10 shows an example of the measured entropy when there is a PUE active or not close to the SU.  
Figure 8: Example of Power Received in GNURadio.  
Figure 9: Power in UL when there is an active/inactive _PUE attack_.  
Now that the entropy values have been calculated, we can run the learning algorithm for the DNN. According to Table 1, the SNR is estimated for GNURadio.  
For the experiments, we use GSM-850 MHz, which is assigned to an operator in the laboratory's coverage zone. A site survey was made, and we found that ARFCN 166 is not used. This corresponds to a DL frequency of 876.8 MHz and a UL frequency of 831.8 MHz. However, during all the experiments, the MCRN was configured to release the channel if a _PU_ was detected.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 10 Determining Frequency of APs in Enterprise Wi-Fi  
The frequency of each AP needs to be known in order to construct an RTT ranging request. While APs in some home Wi-Fi systems operate on one (or two) fixed frequencies, in enterprise-level installations, some central optimization periodically (often overnight) reassigns frequencies to improve throughput and reduce conflicts between APs.  
One way to obtain the necessary information is to scan the Wi-Fi bands and determine the frequencies of APs within range. Wi-Fi scans take the order of 100 ms per channel This means that it takes about 4 to 5 s to perform a Wi-Fi scan covering the 2.4 GHz band (11 possible channels) and the 5 GHz band (25 possible channels)--longer if the 6 GHz band (15 preferred scanning channels) is also supported. At an average pace of 1.4 m/s, a person walks 6 to 7 m during a Wi-Fi scan. So, one cannot afford to simply stop RTT ranging to wait for Wi-Fi scan results, and, at this point in time there does not appear to be a way to reliably interlace RTT ranging and Wi-Fi scanning on certain platforms (nor a way to restrict Wi-Fi scanning to specific channels).  
An alternative is to probe the different possible frequencies for each AP using range requests. A trial frequency is verified if there is a valid RTT response (while lack of a response may be due to any number of reasons, not just that the wrong frequency was selected). This may be a reasonable alternative since RTT ranging responses are much faster (50-400 ms--depending on burst size, number of responders, radio model, and frequency) than Wi-Fi scans (4-5 s). It helps that often enterprise Wi-Fi systems limit the number of channels used (e.g., using only channels 1, 6, and 11 in the 2.4 GHz band, and only 8 of the 25 channels in the 5 GHz band). This speeds up the search for the correct frequencies.
## 3 Results and Discussion
### Online University Students' Awareness and Understanding of Plagiarism  
The first question that the students were asked to write about was focused on their awareness and understanding of plagiarism: what was plagiarism to them? In order to establish how relevant and appropriate students' awareness and understanding of plagiarism was, it was important to first take one definition as the standard. After reviewing the literature, the researchers decided to use the definition in the 7 th edition APA publication manual [5] as a standard because of its clarity, comprehensiveness, relevance, and significance. It is also used widely in most of the universities in Pakistan and abroad. It has three important components: (i) copying or lifting text, (ii) using not only words but also ideas of other authors, and (iii) presenting work as your own (not citing the original work properly). When a student showed a limited idea or understanding of plagiarism, but not the whole idea (e.g., using only the term \"copy and paste\"), it was categorized as a poor/incomplete understanding.  
The analysis of students' responses showed that out of 267 students, only 41 (15.36%) could define it properly. The rest either had a completely wrong idea about plagiarism (\(n=37\), 13.86%) or an incomplete idea (\(n=189\), 70.79%).  
An overwhelming majority (41%) used the term \"copy and paste\" to describe plagiarism. Other terms used by the students were \"copy\" (26%), \"cheating\" (15%), \"writing without citations\" (11%), and others (7%). Among others, \"stealing others' ideas\", and \"presenting others' work as your own\" were prominent terms used by the students. One student wrote, \"... [[I]] copy something and then paste it in my assignment, then it is plagiarism\". Another wrote, \"Plagiarism is copying many lines from [the] Internet\". Some of the students' understanding of plagiarism appears to be defined by how different software is used to work to check plagiarism. They mentioned lifting five consecutive words as plagiarism. These students did not seem to realize that plagiarism was not only copying and pasting the text, but also using others' ideas without giving proper credit to them. Some did not see that copying and pasting could be academically ethical if it is cited properly.  
The literature also points out that most of the students do not have a proper awareness and understanding of plagiarism [42] and citing conventions [13; 43]. This shows that, if students do not know the meaning of plagiarism, it is difficult for them to avoid it. There seems to be either an inability of the universities to increase awareness and understanding or an unwillingness of the students to learn about it.
# Achievable Rate of NOMA-Based Cooperative Spectrum-Sharing CRN over Nakagami-\(m\) Channels
## 2 Related Work
These propagation conditions can be attained by varying the fading severity parameter, \(m\), in Nakagami-\(m\) fading channels. Another useful application of these derived expressions is that the transmission power's optimization can be performed on NOMA-based CRNs where the knowledge of instantaneous channel state information (CSI) is not available at the transmitter. The paper is organized as follows. Section 3 describes the system's model of the NOMA-based cooperative CRN. Section 4 presents the achievable rate analysis of both primary and secondary users. Section 5 provides a discussion on the numerical results. Finally, Section 6 presents the conclusions of the paper.
# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 4 Experimental Studies
### Dataset Description
#### 4.1.7 Mutagenesis3_Bonds Dataset  
The mutagenesis3_bonds dataset aims to predict the mutagenicity of molecules based on bond-level information. It contains 188 molecules, 125 of which are labeled as mutagenic (positive) and 63 as non-mutagenic (negative). The dataset includes 16 features related to molecular bonds, such as bond type and interaction. This dataset is critical in toxicology and drug development, where understanding molecular bonds helps identify harmful chemicals that may cause genetic mutations.
# The Shared Bicycle and Its Network--Internet of Shared Bicycle (IoSB): A Review and Survey
## 2 Background
### Comparisons  
Inspired by some investigation work in [11], we presented the performance of current brands of shared bicycle in five aspects to have an intuitive comparison, which are convenience, user fees, cycling comfort, stability and security that can greatly influence users' willingness to ride. In this context, these five aspects are compared among HZBike, OFO, and Mobike. Here we use a score between 1 and 3 to evaluate the performance of every brand in every aspect, which means that the higher score indicates the better performance.
# CubeSat Mission Scheduling Method Considering Operational Reliability
###### Abstract  
Mission scheduling is an effective method to increase the value of satellite missions and can greatly improve satellite resource management and quality of service. Based on the priority-based task scheduling model, this paper proposes a CubeSat scheduling method that takes operational reliability into account, considering the impact of scheduling results on reliable operation. In this method, the available energy and the time window are used as scheduling resources, and the average state of charge of the lithium battery and the number of task start-ups are defined as two indices to measure its reliability. To meet the mission requirements and energy availability of photovoltaic (PV) solar panel and battery constraints, the scheduling model is constructed with an objective function that includes mission priority and reliability index. The branch and bound (BB) method and analytical hierarchy process (AHP) method are used to solve the scheduling problem. The example analysis compares different scheduling results and verifies the effectiveness of the proposed scheduling method. Compared with the existing methods, it comprehensively considers the mission value and operational reliability of the CubeSat, improves the energy reserve level of the CubeSat, and reduces the surge current caused by the start-up of tasks.  
CubeSat operational reliability; mission scheduling model; BB method; optimization 202417490. [[https://doi.org/10.3390/en17020490](https://doi.org/10.3390/en17020490)]([https://doi.org/10.3390/en17020490](https://doi.org/10.3390/en17020490))  
1  
CubeSat operational reliability; mission scheduling model; BB method; optimization
# An Adaptive Throughput-First Packet Scheduling Algorithm for DPDK-Based Packet Processing Systems
## 5 Conclusions and Discussion  
To handle the ever-increasing network traffic, high-performance packet processing systems based on multi-core processors are a promising option. However, how to schedule packets to each processing core is crucial to make full use of the processing capacity of the system. In this paper, we propose an adaptive throughput-first packet scheduling algorithm for DPDK-based packet processing systems. Combined the burst-oriented packet receiving and transmitting, we propose to use the Subflow as the scheduling unit as well as the adjustment unit. By monitoring the occupancy of the core's queue, the system makes a scheduling decision as well as an adjustment decision if needed. With Subflow-based scheduling and adjustment, the proposed method gives the overloaded cores some gaps to breathe, thus reducing packet loss rate and improving the throughput of the system. The experimental results show that, compared with HRW, the throughput of our method achieves an average improvement of 0.77 times. Compared with RR and CRC32, the improvement is 4.75 and 0.64 times, respectively. Under some special cases, when only one core or two cores is overloaded, our method can achieve zero packet loss rate even under the throughput of 2400 Mbps, while CRC32 and HRW drop packets when the throughput is 300 Mbps. The extra overhead incurred by the adjustment is 0.29 times that of HRW, which is acceptable considering the improvement in throughput and packet loss rate.  
It is well known that the adjustment of the scheduling decision may incur out-of-order packets. We assume that the out-of-order problem is solved by the Log module by merging these logs from the same flow in this paper. However, the passive effects on the Log module should be further evaluated in future work. At the same time, when the adjustment is needed, some other factors such as the protocol the target core must process should be considered to maintain cache affinity as much as possible. Moreover, with different configurations of hardware and software platforms, the effectiveness of the proposed method should be further evaluated in future works since these factors affect the performance of packet processing systems.  
Conceptualization, C.L.; methodology, C.L.; software, C.L.; validation, C.L., L.S.; writing--original draft preparation, C.L.; writing--review and editing, C.L., L.S. and X.Z.; visualization, C.L.; supervision, L.S.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Major Project, grant number XDC02070100.  
**Data Availability Statement:** Not applicable.  
Figure 6: Average time spending on distributing a packet of different methods.  
**Acknowledgments:** We also gratefully acknowledge the editor and all reviewers for their valuable suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Multiobjective Optimization Algorithm
#### 2.3.2 Dmopso  
Stochastic approaches for solving MOPs usually suffer from high computational complexity. The Penalty Boundary Intersection approach proposed by [PERSON] and [PERSON] [13] decomposes an MOP intonumerous subproblems and optimizes each subproblem according to its neighboring subproblems. This utilizes a weighted vector \(w\) and penalty value \(\theta\) for minimizing the distance to the ideal objective vector \(d_{1}\) and direction error to the weighted vector \(d_{2}\) from the objective function vector \(F(x)\). Therefore, the optimization problem can be expressed using Equation (12), in which \(d_{1}\) and \(d_{2}\) are determined using Equation (13).  
\[\text{Minimize }g^{pi}(x|w,z)=d_{1}(F(x)\big{|}w,z)+\theta d_{2}(F(x)\big{|}w,z) \tag{12}\]  
\[d_{1}(f|w,z)=\frac{\parallel(f-z)^{T}w\parallel}{\parallel w\parallel} \tag{13}\]  
\[d_{2}(f|w,z)=\parallel f-\left(z+\frac{d_{1}}{\parallel w\parallel}w\right) \parallel,\]  
where \(z\) is the ideal point with minimum value in each objective and \(T\) is the neighborhood size.  
The procedure for OSP in SHM when using the proposed DMOPSO algorithm comprises two major blocks. The initialization block involves the following steps:  
1. Generate a well-distributed weighted vector \((W=\left\{w^{1},\cdots,w^{N}\right\}^{T})\).
2. Initialize neighborhood \(N\) according to the Euclidean distance (i.e., \(N=\left\{n^{1},\cdots,n^{N}\right\}^{T}\), \(n^{i}=\left[n_{1},\cdots,n_{T}\right]\)).
3. Set the initial reference point \(z^{id}\).
4. The personal best position initialization \(\textit{Pbest}=\left\{\textit{pbest}^{1},\cdots,\textit{pbest}^{N}\right\}^{T}\), \(\textit{pbest}^{i}=x^{i}\).
5. Set \(t=0\). The loop block involves the following steps when \(t<\textit{MaxIter}\).
1. Randomly select one particle from the neighbors as the _Gbest_ particle.
2. Calculate the new position \(x_{i}^{t+1}\).
3. Compute the objective function vector \(F\).
4. Update the neighborhood solutions. For the \(j\)th particle in the neighborhood of the \(i\)th particle, \(x_{i}^{t}=x_{i}^{t+1}\) if \(g^{pi}\big{(}x_{i}^{t+1}\big{|}w_{j},z\big{)}\leq g^{pi}\big{(}x_{i}^{t}\big{|} w_{j},z\big{)}\).
5. Update reference point \(z\).
6. Update the personal best solution _Pbest_.
7. Set \(t=t+1\).
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## Security Issues and Challenges in WSN-IoT
### Privacy  
Privacy and confidentiality are significant issues in WSN-IoT gadgets and frameworks under the IoT systems' universal character. Entities are linked, and information is conveyed and exchanged via the Internet, delivering client protection and causing various risks to sensitive information in many ways. So that the exploration issues are satisfied, knowledge acquisition security is just as important as information sharing security. Information protection is one of the primary uncertainties in the WSN-IoT because of the high chance of security vulnerabilities, such as sniffing and spoofing, unapproved access, data altering, and forgery with the unapproved altering of IoT nodes [53]. An aggressor can exploit numerous WSN-IoT administrations and applications to store sensitive and personal data, and if they are exposed, unstable and sensitive data can be exposed to outsiders [50; 54].