esa-sceva/satcom-chunk-collection huggingface dataset:
# Asymmetric Propagation Delay-Aware TDMA MAC Protocol for Mobile Underwater Acoustic Sensor Networks
## 3 Simulation Results and Analysis
### Performance Degradation due to Transmission Order  
First of all, we verify that the performance degradation due to the employed transmission order is negligible. We define the normalized cycle duration loss as follows:  
\[\frac{D_{CYC}-D_{CYC,\ opt}}{D_{CYC,\ opt}}, \tag{16}\]  
Figure 5: Flow chart for the transmission scheduling algorithm of the APD-TDMA.  
in which \(D_{\text{CYC}}\) and \(D_{\text{CYC, }opt}\) are the cycle duration of the original APD-TDMA and the APD-TDMA with optimal transmission order, respectively. Note that \(D_{\text{CYC, }opt}\leq D_{\text{CYC}}\). Figure 6 shows the normalized cycle duration loss with respect to the data packet duration. Figure 6 is the average of 10-runs, and each run has 1000 cycles. We consider \(R\) of 1500 m and 3000 m, and \(N\) and \(Vmax\) are fixed at 10 and 7 m/s, respectively. It can be seen from Figure 6 that the performance loss of the APD-TDMA due to transmission order is less than 1%, which justifies the transmission order of the APD-TDMA.
# Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource
## 6 Conclusions  
A data preprocessing method is proposed based on the optimized Lomaofski model, considering the temporal-spatial correlation of wind speed series. The experimental and computational results show that this approach is effective and feasible.  
The prediction results are compared with those based on common similar day clustering. It can be seen that the results that the model put forward in this paper are better than those of the model based on the similar day clustering, and the average absolute error and root mean square error of the prediction results are reduced, which proves the validity of the model. When the wind speed and direction present a randomness and volatility fluctuation range in the training data, the results of the new method still show a good performance.  
We proposed a FIG-Elman wind power interval prediction method based on continuous-time similarity clustering analysis, which would be combined with data preprocessing and data classification. It has the advantage of dealing with small sample data and high-dimensional nonlinear problems. The performance of the model is evaluated by the interval prediction assessment indicators. By comparing the predictive value with the measured value, the prediction accuracy of the FIG-Elman wind power interval prediction method is significantly higher than that of the LUBE-Elman method, as well as more efficient.  
**Author Contributions:** Methodology, B.T. and Q.C.; software, B.T.; writing--review and editing, B.T., Q.C. and M.S.; project administration, Y.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Science and Technology Planning Project of Guangdong Province, grant number 2015B020240003, the National Natural Science Foundation of China, grant number 51976113, the National High-tech R&D Program of China (863 Program), grant number 2012 AA051301.  
**Acknowledgments:** The authors thank the anonymous referees for the thoughtful and conductive suggestions that led to a considerable improvement of the paper. This research is supported by Science and Technology Planning Project of Guangdong Province(2015B020240003), National Natural Science Foundation of China(51976113), National High-tech R&D Program of China(2012 AA051301).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**LUBE-Elman** & \(I_{\text{\bf{MAPE}}}\) **(\%)** & \(I_{\text{\bf{FICP}}}\) **(\%)** & \(I_{\text{\bf{FIAW}}}\) \\ \hline
2018.01.30 & 12.58 & 81.11 & 228.31 \\ \hline
**FIG-Elman** & \(I_{\text{\bf{MAPE}}}\) **(\%)** & \(I_{\text{\bf{FICP}}}\) **(\%)** & \(I_{\text{\bf{FIAW}}}\) \\ \hline
2018.01.30 & 10.64 & 89.89 & 185.26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of two models.
## 6 Analysis and Conclusions  
In this paper, for the unique operation and maintenance mode of the subway train system, the train braking system is modeled as a K-out-of-N model. A multi-phase Markov chain is applied to analyze its operation and maintenance process which contains fault states and two preventive maintenance modes, the mathematical description of its states is given, the braking distance of the braking system is coupled with its states, and the mathematical relationship between them is given. Finally, a numerical case is used to analyze the influence of the operation and maintenance parameters of this braking system on the braking distance and its safety level.  
By using this method, we established mathematical relationships between brake system operation and maintenance parameters and safety indicators such as braking distance. A framework for analyzing the physical performance of the braking system under the influence of faults and maintenance is built. It can be seen that by selecting a brake motor with a lower failure rate, the safe braking distance requirement can be achieved with a smaller number. Similarly, if more intensive overhauls are scheduled, the number of brake motors required can be reduced. Additionally, after the brake system design is completed, design compensation can also be achieved by rationalizing the sizing of routine maintenance and overhaul cycles. We can trade off the effects of the multiple PM intervals on train safety considering the redundancy structure of the braking system by the proposed model. This method can be extensively applied to the modeling and analysis of any other K-out-of-N system with such operational characteristics, allowing the analysis of the requirements decision of the relevant equipment reliability and the determination of the maintenance scheme parameters given a safety threshold.  
In future work, we will consider more operational scenarios. On the one hand, we can consider the downtime maintenance in addition to the preventive maintenances. Another possible extension direction is to consider a K-out-of-N system containing different components, such that the parameters of the K-out-of-N system becoming multidimensional and the system state dimension increasing.  
Figure 9: The average safety index with different overhaul interval.  
**Author Contributions:** Conceptualization, L.Z. and L.G.; methodology, L.Z. and Y.W.; investigation, L.Z.; writing--original draft preparation, L.Z.; writing--review and editing, L.G. and R.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Beijing Natural Science Foundation (L191010).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# MCST Scheme for UAV Systems over LoRa Networks
## 3 System Model
### Channel Model  
As the channel model for the transmission, the signal attenuation is based on the log-distance pathloss model with ITU recommendation [25]. The channel gain in decibel [dB] from transmitting node \(i\) to receiving node \(j\) of the ongoing transmission, where \(i,j\in\mathbb{N}\), is given as  
\[PL_{ij}=10\cdot\alpha\cdot\log_{10}(d_{ij})+\delta+10\cdot\gamma\cdot\log_{10 }\mathbb{F}+N(0,\sigma) \tag{1}\]  
where \(\alpha\) is the attenuation constant or coefficient associated with the increase of pathloss with distance, \(d_{ij}\) is the Euclidean distance between node \(i\) and node \(j\) in meter, \(\delta\) is the coefficient associated with the offset value of pathloss and \(\delta=29.2\), \(\gamma\) is the coefficient associated with the increase of pathloss with frequency and \(\gamma=2.11\), F is the operating frequency [GHz], and \(N(0,\sigma)\) is instantaneous fading, which is the zero mean Gaussian random variables with standard deviation \(\sigma\). Then, the received power ratio between node \(i\) and node \(j\) is  
\[G_{ij}=\frac{1}{10^{\binom{p_{ij}}{10}}} \tag{2}\]
## 4 Federated Client Selection Scheme
### Problem Formulation  
In the complex and changeable edge network, the client selection strategy should be able to adapt to dynamic changes in the environment state. DRL-based client selection frameworks can learn client selection strategies to maximize returns through continuous interactions with the environment. As shown in Figure 3, the client selection problem is formulated as MDP \(\langle S,A,R\rangle\), where \(S\), \(A\), and \(R\) refer to the state space, action space, and reward function, respectively.  
1. State space: The state space is defined as the union of the state set of the client: \[S=\bigcup_{n=1}^{N}S_{n}\] (17) where \(S_{n}\) is the set of states of client \(n\), \(S_{n}=\{|H_{n}|,f_{n},c_{n},T,r_{n}\}\), \(|H_{n}|\) denotes the dataset size of client \(n\), \(f_{n}\) denotes the CPU frequency, \(c_{n}\) denotes the number  
Figure 3: Client selection framework.  
of CPU cycles, \(T\) denotes the local model training and upload time, and \(r_{n}\) denotes the historical reputation of the device. To speed up the training of the model, we normalize each element in the state vector so that they are at the same scale. (2) Action space: The action space is the Cartesian product of the client's candidate actions: \[A=\prod_{n=1}^{N}a_{n}^{t}\] (18) where \(a_{n}^{t}\in\{0,1\}\) is defined as whether client \(n\) participates in the \(t\)-th round of global training. \(a_{n}^{t}=1\) indicates that client n was selected to participate in the \(t\)-th round of training. (3) Reward function: The reward function needs to take into account changes in accuracy, number of iterations, profit, and client reputation, i.e., maximize model accuracy and client reputation while minimizing device profit and number of iterations. Therefore, the reward function is defined as follows: \[R_{n}^{t}=-\left(\frac{\omega_{1}}{\sum_{n\in\mathcal{N}}a_{n}^{t}}L_{n}\left( x_{\text{test}}\,,y_{\text{test}}\,;\omega_{n}^{(T^{it})}\right)\right)+\omega_{2 }T_{n}^{iter}-\omega_{3}P_{n}^{t}+\omega_{4}r_{n}\] (19)
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 2 Related Works
### Managerial Aspects in RID  
In order to maintain relief items distribution to support the disaster victim's life, relief items distribution is planned, implemented and managed by decision-makers. For the effective distribution task, the RID schedules need to be generated based on the information from the disaster-affected regions. For example, the case study of San Francisco showed that in an emergency, relief item distribution needs to be well-managed in terms of information gathering to enhance the efficiency of the relief operations [22]. The analysis of this study has also shown that the RID model must cover the post-disaster relief operation management aspect to make the distribution task effective. In addition, the study also showed that all the information related to the disaster-affected regions, including road and transportation constraints, needs to be well-managed to minimise the relief items distribution time. Analysing another disaster, the Haiti earthquake showed that there had been a large number of casualties after the disaster, which had made the relief items distribution management even more complex during the post-disaster disaster relief operations [23].  
In a case study of the Nepal earthquake in 2015, it was shown that the relief items distribution management had not been completely straightforward as had been planned, as the expectations of disaster victims were not uniform [24]. A different study on the Nepal earthquake showed that not all the victims received all the relief items they needed because of the poor distribution design and management [25]. Some of the victims had received only one type of relief item whereas some had received all kinds of relief items they needed. Another study from the Nepal earthquake showed that there had also been a time lag between the event and the arrival of relief items and the disaster event [26]. In these studies, one of the managerial issues has been the time lag, which has a direct impact on disaster victims' recovery management since the disaster victims need timely relief support for their survival after the disaster. Consideration of the timing of relief items supplies to the disaster regions is crucial, especially when the distribution plan is being operated over multiple time periods. Inclusion of supply time or wait time has been missing in these approaches of humanitarian supply chain decision-making, which create bias in the distribution towards some disaster regions.
# Predicting Multi-Gene Mutation Based on Lung Cancer CT Images and Mut-SeResNet
## 5 Discussion  
### Impact of PatientPersonal Information on Results  
This paper delved further into the effect of adding a priori information (age, gender, and smoking status on the prediction results) to the multi-task prediction on predicting EGFR. In Figure 10, the effects of the three factors on the prediction results were in descending order: smoking status, gender, and age. In Table 3, the predicted mutation results increased by 0.03% after adding age information to the model. The accuracy prediction results increased by 0.29% after adding gender information to the model. The predicted accuracy increased by 0.295% after adding smoking status information to the model. Statistical analysis of the dataset in the experimental dataset preliminarily concluded that mutations were higher in females and smokers. In the studies of [PERSON] [34] and [PERSON] [35], the effect of smoking on carcinogenesis was also found to be more significant.  
The CNN classifier for EGFR and KRAS mutations showed good accuracy. Moreover, the sensitivity and specificity could be further improved by adding clinical features (age, gender, and smoking status). This paper showed that it is feasible to use CT images and CNN to identify EGFR and KRAS gene mutations in lung cancer. This medical image-based approach has many advantages over other testing methods:  
1. It has the potential to support treatment decisions when surgical resection and puncture biopsy are difficult;
2. It can be applied to the entire therapeutic EGFR mutation status, as CT images are repeatedly captured in clinical practice;
3. CNN tools analyze the entire tumor, not just the tissue sample. They may be able to overcome the problem of heterogeneity;
4. The use of CNN is a fast, easy, and low-cost method.  
Given that the field of radiomics is expanding, new methods for extracting information from medical images benefit disease diagnosis, treatment, and prognosis. Tumor phenotypes, including its aggressiveness, driver genes, heterogeneity, response to treatment, and recurrence, can be more precisely identified and defined with the help of quantitative analysis of the medical images.
# Transitioning Broadcast to Cloud
## 2 Processing Chains in Broadcast and Cloud-Based Online Video Systems
### Video Formats and Elementary Streams  
We next look at characteristics of video formats used in both broadcast and online video systems. The summary of this comparison is provided in Table 1. For simplicity, in this comparison, we only consider SD and HD TV systems. As shown in this table, SD systems almost always use interlaced, bottom-field first (bff) sampling format [26; 30; 41]. If the source is progressive (e.g., film) it is typically converted to interlaced form by so-called telecine process [1; 2]. HD systems also use interlaced formats, but with top-field first (tff) order. HD systems can also carry progressive formats (e.g., 720p). In contrast, in internet streaming, only progressive formats are normally used [42; 43; 44]. In terms of color parameters and Sample Aspect Ratios (SARs), streaming video formats are well aligned with HDTV systems. On the other hand, streaming of SD content requires both color- and SAR-type conversions. The primary reason why streaming systems are more restrictive is compatibility with wide range of possible receiving devices--mobiles, tablets, PCs, etc. [42]. In many such devices, graphics stacks are simply not designed to properly render interlace, or colors other than sRGB/ITU-R BT.709, or pixels that are non-square. This forces color-, temporal sampling type-, and SAR-type conversions. In Table 2, we further analyze characteristics of encoded video streams (or elementary streams) used for broadcast and streaming distribution. Again, consideration is limited to SD and HD systems. First, we notice that the number of encoded streams is different. In broadcast, each channel is encoded as a single stream. In streaming, each input is encoded into several output streams with different resolutions and bitrates. This is needed to accommodate adaptive bitrate (ABR) delivery. There are also differences in codecs, encoding modes, and codec constraints. For example, in broadcast, the use of constant bitrate (CBR) encoding is most common [24]. It forces codec to operate at a certain target bitrate, matching the amount of channel bandwidth allocated for a particular channel. The use of variable bitrate (VBR) encoding in broadcast is rare and only allowed in so-called statistical multiplexing (or statmux) regime [59; 60], where the multiplexer is effectively driving dynamic bandwidth allocation across all channels in a way that the total sum of their bitrates remains constant. In streaming, there is no need for CBR or statmux modes. All streams are typically VBR-encoded with some additional constraints applied on decoder buffer size and maximum bitrate [44].
## 3 Case Studies and Analysis  
### Settings of the System Operation Conditions and the Regulation Modes and Scenarios of CIVs  
Based on the mathematical models above, a case study for the PFG at each SCP is carried out in this section. First, several typical system operation conditions are assumed in Table 1. The operation conditions are mainly concerning the voltage of \(v_{\rm r}\) and the phase difference \(\delta\). The first three conditions belong to the normal conditions, while the last condition is assumed as the severe condition.  
In order to outline the regulation principles of the PFG to each CIV more comprehensively, the regulation modes of CIVs have also to be designed typically and effectively. The first two CIVs containing the magnitude and the phase angle \(\theta\) of OSIV -are assumed to be regulated smoothly and simultaneously, while the CIV3 of phase difference \(\delta\) is assumed as constant in one specific operation condition and changes to another value only when the system operation conditions changes. Here, the magnitude of OSIV is assumed to range from 0 p.u. to 4.0 p.u., and the phase angle \(\theta\) of OSIV is assumed to range from \(-\pi\) to \(\pi\). Then, when the CIV1 is being regulated to increment from 0 p.u. to 4.0 p.u., there are two possible regulation methods regarding CIV2. On the one hand, the CIV2 can be regulated to increment from \(-\pi\) to \(\pi\) step by step, which is assumed as Regulation 1 (Reg.1) mode. On the other hand, it can also be regulated to decrement from \(\pi\) to \(-\pi\) step by step which is assumed as Regulation 2 (Reg.2) mode. On the settings, the following case studies will be carried out by both the two regulation types containing Reg.1 and Reg.2 modes of OSIV. It should also be noted that if the magnitude of \(v_{12}\) become negative during the regulation process, it just means the OSIV are inserted into the system in the reverse directions.  
Furthermore, in order to depict the regulation principles of PFG to CIV3 in as many operation conditions as possible, the phase difference \(\delta\) must be assumed as continuous variables ranging from \(-\pi\) to \(\pi\). On this occasion, the CIV1 still keep the previous varying ranges and modes, whereas the CIV2 is specifically designed as four different values in four typical regulation scenarios, which is listed in Table 2. Based on the settings, the regulation principles of PFG to CIV3 can be figured out more comprehensively.  
Based on the above settings, the detailed regulation processes and principles can be outlined by the assumed regulation modes of CIV1 and CIV2, and the scenarios of CIV3 of UPFC. All the principles of the PFG to each CIV at different SCP will be tested and analyzed in the two-dimensional planes and three-dimensional spaces, respectively.
## 3 Construction of Integrated Network
### Establishment of the Highway Network  
To simplify the processing of the transportation network data and align with the reality of freight transportation, the expressway was primarily considered in this study. At present, the expressway network in China is laid out with radial lines and vertical and horizontal grids, which are composed of 7 radial lines starting from Beijing, as well as 9 north-south vertical lines and 18 east-west horizontal lines [34]. Compared with the railway transportation network, only cities directly connected by expressways are considered as the nodes of the highway network. Therefore, the cities connected by expressways are divided into segments. For example, the expressway between Beijing and Harbin passing through multiple node cities is divided into many subsections, such as Beijing-Tangshan, Tangshan-Shenyang, Shenyang-Changchun, and Changchun-Harbin. In addition, Dongguan is not connected by highway. Therefore, in this study, based on the above-mentioned factors, 79 connections among 43 node cities were identified. By using ArcGIS 10.2 software, a topological map of the expressway transportation network  
Figure 3: Topological map of the railway transportation network.  
was drawn based on the 79 connections among the 43 node cities, as shown in Figure 4, which indicates the highway link network among the 43 node cities connected by the highway network.
## 1 Introduction
(2) An improved proportional integral observer structure is proposed, which dynamically modifies the data compression matrix through matrix decomposition techniques to enhance the algorithm's ability to dynamically track time-varying parameters, and a subspace identification recursive algorithm based on forgetting factor correction is designed. (3) By constructing an EDWM and an improved proportional integral observer (PIO) and combining it with a dynamic correction recursive subspace identification algorithm (CFF-SIR), high-precision dynamic decoupling and robust estimation of tire longitudinal force are achieved. As a whole, this study provides a solution for longitudinal force estimation of four-wheel independent drive electric vehicles that balances accuracy, robustness, and engineering applicability, laying a theoretical foundation for vehicle dynamics control and intelligent driving technology. The structure of the rest of the full text is as follows: Section 2 elaborates on the mechanism modeling and data acquisition methods of an electric drive wheel; Section 3 proposes an improved observer design and a modified recursive subspace identification algorithm; Section 4 verifies the effectiveness of the algorithm through simulation and experimentation; and Section 5 summarizes the research results and looks forward to future directions.