esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 6 Conclusions and Future Scope  
Researchers and scientists have developed good models for forecasting air pollution by using various state-of-the-art methods. From the experimental analysis, it is concluded that real-world datasets contain noisy data, and to achieve reliable and accurate forecasting of air pollution, handling of the missing data, outlier detection and removal, and preprocessing steps are of utmost importance. In this paper, these issues are addressed by using powerful data preprocessing steps.  
Similarly, effective evaluation measures, i.e., RMSE, MAE, and R\({}^{2}\), are used to compare and evaluate various prediction models on different datasets. The results show that the ET regressor outperforms other regressors, such as RF, LGBM, GB, and DT, for PM\({}_{2.5}\) prediction. The ET regressor was wisely chosen based on the experimentations for iterative imputation, as demonstrated in Table 3. Different models, such as univariate LSTM, univariate 1D CNN, univariate GRU, and multivariate LSTM, were used for forecasting the hourly value of PM\({}_{2.5}\). All these models were used in three cases: firstly, with raw data where all missing values were removed; secondly, with imputation; and finally, with the removal of anomalies. The proposed forecasting model, i.e., MIA-LSTM, is efficient and effective in predicting PM\({}_{2.5}\) concentration with the smallest error for noisy data. The proposed model shows reduced RMSE and MAE values for all the datasets used, as shown in Table 4.  
In the future, the existing work can be extended in the following ways:  
* Datasets from different locations with different pollutant concentrations can be harnessed to understand the behavior of air pollution in those particular locations.
* The time complexity is one of the important parameters for forecasting models. Reducing the time complexity without affecting the accuracy of the forecasting can be one of the key aspects of the proposed work.
* More complex models and algorithms, such as an ensemble and CNN-LSTM, can be utilized to further improve the accuracy of air pollution forecasting.  
Conceptualization, G.N.; methodology, G.N.; validation, G.N., A.H. and C.K.; formal analysis, B.T.; software, B.T.; writing--original draft preparation, G.N.; writing--review and editing, A.H. and C.K.; supervision, A.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not available.  
The authors declare no conflict of interest.
# An Efficient Probe-Based Routing for Content-Centric Networking
## 4 Performance Evaluation
### Experimental Results
#### 4.4.4 Analysis of Routing Complexity  
To quantitatively evaluate the routing complexity of our approach, the number of timeout interests and average response time are utilized as measurement parameters. In Section 3.3, the additional complexity of our approach combined with the algorithm was qualitatively explained in detail. Compared to the basic CCN, the additional time complexity of our approach are O(\(N_{CS}+N_{rt}+N_{FIB}*N_{SPT}\)) and O(\(N_{FIB}\)) on processing interest packets and data packets, respectively. However, the results in Figure 5(b),c show that our approach outperforms the basic CCN in terms of the number of timeout interest packets and average response time of the consumer. _PIT probe_ and _FIB probe_, respectively, reduce the number of timeout interest packets by 6.11% and 7.23%, and the average response time of consumer by 1.08 s and 0.64 s. The main reason is that the _probe_-based approach uses _probe_ to find the best content providers to improve the routing table's accuracy, allowing more interests to be satisfied by nearby routers and thereby reducing interest transmission. These benefits compensate for the additional computing overhead of packet processing in each router.
## 5 Results and Discussion
### Parameter Characterization  
The output histograms of C\({}_{s}\), D, and \(\alpha\) are also updated after each step of the procedure (Figures 6-8, respectively). The changes in Figures 6 and 7 after updating are due to the fact that in Step 2 we have added additional information on these two parameters for the loaded tests. These a posteriori histograms will be used to characterize the mean and standard deviation of the model parameters by considering loading conditions.  
Figure 5: Flowchart of the proposed methodology.  
Figure 6: Comparison of a priori and a posteriori histograms of C\({}_{s}\).  
The mean and standard deviation of C\({}_{\rm s}\), D, and \(\alpha\) obtained from the Bayesian networks are summarized in Table 5 for each case (without loading, static, and cyclic loading). For parameters C\({}_{\rm s}\) and D, the results of step 1 (without load) are very close to the values given in Table 2. It is also noted that the mean and standard deviation of C\({}_{\rm s}\) vary little from one loading case to another, which makes sense because all beams have been exposed to the same constant concentration of Cl\({}^{-}\). The parameters of D (\(\mu\) and \(\sigma\)), are close for all cases meaning that the proposed Bayesian methodology has well separated the effects of cracking from the diffusion phenomenon which depends on the formulation and fabrication (pouring, curing, etc.) of the concrete beams. The Bayesian model allows characterization of a factor that increases the chloride diffusivity into concrete due to the presence of load-induced cracks and has a higher value for the cyclic loading. Indeed, the mean values of this factor (\(\alpha\)) obtained from the Bayesian network are 1.73 and 1.77, respectively, for static and cyclic loading (Table 5). In both loading cases, the application of the two-step methodology makes it possible to further optimize the characterization of the parameters C\({}_{\rm s}\) and D by integrating the results of the tests with and without loading.
## 1 Introduction
### Research Motivation  
Over the past decade, CPSs have emerged as an attractive research topic [1; 2; 3; 4]. Considerable advancements have been made in the research of its theoretical basis and application extension [5; 6]. The National Science Foundation has listed CPS as a key research and funding project; and governments in Europe, South Korea, and Japan have also provided CPS-related research funding. At present, the government of Taiwan focuses on integrating CPS (e.g., network, manufacturing, and factories) in Industry 4.0. However, Industry 4.0 neglects the application of cloud computing and big data in architecture engineering construction [7] in conventional industries. Therefore, this study used relevant information technologies (such as software design, development and installation) and communications technologies (such as transmission access, network switching and private network communication) to develop a BIM-based CPS intelligent disaster prevention system, which integrated and promoted the disaster prevention management of related facilities such as the Artificial Intelligence of Things and fifth-generation cellular networks from the user end. The system developed in this study will provide relevant monitoring data on site in specific areas and disaster prevention and relief related information to the Intelligent Joint Emergency Operation Center in the form of two-dimensional/3D graphic display, shortening the response time of the Center and improving the efficiency of disaster relief in specific areas.
###### Abstract

Table 1: Comparison among Location-Aided Routing (LAR), Ad hoc On-demand Distance Vector (AODV), and Optimal Link State Routing (OLSR) protocols. Figure 1: Mobile Ad-hoc Network (MANET) categories. methodologies. One popular method involves using metaheuristic approaches, such as genetic algorithm [22] and particle swarm optimization (PSO) [23]. This paper focuses on data transfer between nodes of the actual MANET, which absorbs the battery level in the nodes and guarantees early shutdown of the node for the lifetime of the nodes in the network, which influences connectivity and maintains packet transmission to the nodes. Therefore, the protocol must be carefully modified to use the route and to transfer data with a high level of energy consideration by using Optimized PSO (OPSO) [24] and adapting [23,25] to be a benchmark for evaluating the performance of the proposed approach.
## 3 Results and Discussion
### Antioxidant Capacity
An increase in the number of herbal mixtures resulted in a decrease in the antioxidant capacity. Thus, it was the lowest in the four- and five-component mixtures. However, they reported high antioxidant capacity in a six-component mixture. The present results also demonstrated lower antioxidant capacity in three-component than in two-component mixtures or single herbal extracts. One explanation could be the interactions between the compounds present in the herbal mixtures that can create complexes and potentially decrease polyphenols' electron donation capacity, thus reducing their antioxidant activity. Another possible explanation for lower antioxidant capacity in herbal mixtures is the dilution effect--a greater amount of herbals that participated in the combinations resulted in less bioactive molecules, which are important for antioxidant capacity. Some of these bioactive molecules are previously mentioned (flavonols and phenolic acids) as well as the volatile compound 1,8-cineole with strong antioxidant properties [118]. The presence of 1,8-cineole was highest in \(L\) and two-component mixtures with \(L\). This may contribute to the higher antioxidant capacity of \(L\) and its mixtures, probably because of synergism with phenolic compounds. \(L\) contained a higher amount of hydroxybenzoic acids when compared to \(S\) and _WT_, in which hydroxycinamic acid extracts were more present. It was previously reported that gallic acid, one of the acids whose amount was higher in \(L\) compared to \(S\) and _WT_, showed the highest DPPH scavenging activity in relation to hydroxycinnamic acids [119]. Gallic acid also indicated a synergistic interaction with protocatechic acid, resulting in their higher antioxidant capacity [120,121]. High antioxidant capacity was also demonstrated in the synergistic action between gallic acid and caffeic acid [18], which was mainly found in the WT extract in the current study. It was expected that the _WTL_ mixture should also provide high antioxidant capacity. A contribution to the antioxidant capacity in the two-component _WTL_ mixture certainly was provided by the presence of catechins, which is related to their hydroxyl groups in their molecular structures [51,122]. There is a study about their synergistic antioxidant effect when supplemented with whole green tea extract [123] and synergism with protocatechic acid in antibacterial and antioxidant activities [124,125]. Catechins also impact sensory properties, enhancing the bitterness and astringency of the beverage models [122]. For a better understanding and determination of the biopotential of herbal extracts, it is important to know the composition of individual phenolic and volatile compounds since not all groups contribute equally to antioxidant capacity.
# Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine
## 3 Perturbation Observer-Based Nonlinear Adaptive Controller Design
### NAC Design of PMSG  
#### 3.2.1 Input/Output Linearization  
Input/output linearization of Equation (6) is represented as  
\[\left[\begin{array}{c}y_{2}^{(1)}\\ y_{3}^{(1)}\end{array}\right]=\left[\begin{array}{c}F_{2}(x)\\ F_{3}(x)\end{array}\right]+B2(x)\left[\begin{array}{c}u_{2}\\ u_{3}\end{array}\right] \tag{24}\]  
where  
\[F_{2}(x) = \frac{1}{L_{\rm md}}(-i_{\rm md}R_{\rm s}+\omega_{\rm e}L_{\rm mq }i_{\rm mq}) \tag{25}\] \[F_{3}(x) = -\frac{R_{\rm s}}{L_{\rm mq}}i_{\rm mq}-\frac{1}{L_{\rm mq}}\omega _{\rm e}(L_{\rm md}i_{\rm md}+K_{\rm e}) \tag{26}\]  
\[B2(x)=\left[\begin{array}{c}B_{2}(x)\\ B_{3}(x)\end{array}\right]=\left[\begin{array}{cc}\frac{1}{L_{\rm md}}&0\\ 0&\frac{1}{L_{\rm mq}}\end{array}\right] \tag{28}\]  
As \(\det[B2(x)]=\frac{1}{L_{\rm md}L_{\rm mq}}\
eq 0\), i.e., \(B(x)\) is nonsingular for all nominal operation points. Therefore, the FLC controller is represented as\[\left(\begin{array}{c}u_{2}\\ u_{3}\end{array}\right)=B2(x)^{-1}\left(\begin{array}{c}-F_{2}(x)+v_{2}\\ -F_{3}(x)+v_{3}\end{array}\right) \tag{29}\]  
\[B2(x)^{-1}=\left[\begin{array}{cc}L_{\rm md}&0\\ 0&L_{\rm mq}\end{array}\right] \tag{30}\]  
And the nonlinear system is linearized as  
\[\left[\begin{array}{c}y_{2}^{(1)}\\ y_{3}^{(1)}\end{array}\right]=\left[\begin{array}{c}v_{2}\\ v_{3}\end{array}\right] \tag{31}\]  
where  
\[v_{2} = \dot{y}_{2\rm r}+k_{21}e_{2} \tag{32}\] \[v_{3} = \dot{y}_{3\rm r}+k_{31}e_{3} \tag{33}\]  
where \(v_{2}\) and \(v_{3}\) are inputs of linear systems, \(k_{21}\) and \(k_{31}\) are gains of linear controller, \(y_{2\rm r}\) and \(y_{3\rm r}\) the output references. Define \(e_{2}=y_{2\rm r}-y_{2}\) and \(e_{3}=y_{3\rm r}-y_{3}\) as tracking errors, the error dynamics are  
\[\dot{e}_{2}+k_{21}e_{2}=0 \tag{34}\] \[\dot{e}_{3}+k_{31}e_{3}=0 \tag{35}\]
# A Packet Collision Reduction Based on Reselection for LTE V2X Mode 4
## 2 LTE V2X Mode 4
### Overlapping Selection Window  
In a resource selection by the SPS, the UEs select a resource to avoid packet collisions with each other. The resources used by other UEs are estimated based on a feature of using the same subchannel resources periodically. Hence, if a UE reselects a resource, other UEs cannot detect which resource was selected until the transmission using the newly selected resource is performed.  
Figure 7 shows an example of an overlapping selection window. This figure indicates that UE2 has reselected in subframe 950 and UE1 reselects in subframe 1000. The range of the selection window for UE2 is from a maximum of subframe 951 to 1050, and that for UE1 is from a maximum of subframe 1001 to 1100. In this case, the section between subframe 1001 and 1050 is overlapped, and UE1 may select the same resource as UE2.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 6 Conclusions and Future Work  
This paper presented an optimization framework for data collection in a hybrid SDVN architecture. For the hybrid SDVN architecture, we optimize data collection by modeling it as an integer quadratic programming problem. The results showed that the proposed optimization method has much lower communication cost and latency compared to the data collection for the centralized architecture for larger (well-connected) vehicular networks for any mobility scenario or for any optimization frequency. However, the proposed optimization should be done with a minimum optimization frequency of 4 Hz and a maximum heterogeneous network link entropy threshold of 0.005 in order to achieve the benchmark packet delivery ratio of 99.9% required for high-end vehicular network applications.  
In future work, one can evaluate the performance of data collection when sensor (RGB camera, LIDAR sensors) data are collected. Furthermore, this research lays a foundation for applying machine learning to making network decisions in an SDVN. Thus, this research opens avenues for data scientists to optimally collect data at the centralized controller for applying machine learning to the collected data, which will improve the performance of the existing SDVN paradigm. Moreover, the proposed data collection mechanism for the SDVN paradigm can be extended to the Knowledge Defined Vehicular Network (KDVN) paradigm for data-centric network applications.  
**Author Contributions:** Conceptualization--P.A.D.S.N.W., K.L.K.S., G.G.N.S. and P.H.J.C.; methodology--P.A.D.S.N.W., K.L.K.S., G.G.N.S. and P.H.J.C.; software--P.A.D.S.N.W.; validation--P.A.D.S.N.W. and K.L.K.S; formal analysis--P.A.D.S.N.W. and K.L.K.S; investigation--P.A.D.S.N.W.; resources--P.A.D.S.N.W.; data curation--P.A.D.S.N.W. and K.L.K.S.; writing--original draft preparation--P.A.D.S.N.W; writing--review and editing--K.L.K.S., G.G.N.S. and P.H.J.C.; visualization--P.A.D.S.N.W., K.L.K.S., G.G.N.S. and P.H.J.C.; supervision--KL.K.S., G.G.N.S. and P.H.J.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data and code are available for the journal to review. Data and code are available from the authors upon reasonable request after publication.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Performance Analysis
### Simulations Results Corresponding to BS Motion
#### 4.3.4 Analysis of \(\Delta D_{BA12}\)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**BS Motion**} & \multicolumn{2}{c}{\(\Delta D_{BA12}\) **\#SS3 (m)**} & \multicolumn{2}{c}{\(\Delta D_{BA12}\) **\#SS6 (m)**} & \multicolumn{2}{c}{\(\Delta D_{BA12}\) **\#SS8 (m)**} \\ \hline
**BS1** & **BS2** & **Mean** & **STD** & **Mean** & **STD** & **Mean** & **STD** \\ \hline F & F & 1.18 & 0.89 & 5.08 & 3.56 & 10.35 & 7.69 \\ F & R & \(-\)0.28 & 1.69 & \(-\)1.89 & 8.91 & \(-\)4.24 & 17.99 \\ R & F & 0.28 & 1.69 & 1.74 & 8.61 & 3.67 & 16.72 \\ R & R & \(-\)1.18 & 0.86 & \(-\)5.23 & 3.65 & \(
## Materials and Methods
### Establishment of the TRS Breast Imaging System  
A dual-channel three-wavelength time-resolved spectroscopy system (TRS-20, Hamamatsu Photonics K.K., Hamamatsu, Japan) was used to measure optical breast tissue parameters. The TRS-20 consists of two pulsed light sources, photomultiplier tubes (GaAs PMT, Hamamatsu Photonics K.K., Hamamatsu, Japan), a single photon counting (SPC) unit, and optical fibers. Each pulsed light source, called Picosecond Light Pulses (PLPs, Hamamatsu Photonics K.K., Hamamatsu, Japan), is composed of three laser diodes (760 nm, 800 nm, and 834 nm) which generate a light pulse with full-width at half-maximum (FWHM) of 70-100 ps at repetition frequency of 5 MHz, and has an average output power at the irradiation optical fiber end of ~100 \(\mu\)W. The SPC unit (custom-designed, Hamamatsu Photonics K.K., Hamamatsu, Japan), based on the time-correlated single-photon counting method (TCSPC) for measuring the temporal point spread functions (TPSFs) of tissue, is composed of a constant fraction discriminator (CFD), a time-to-amplitude converter (TAC), an analog-to-digital (A/D) converter, and a histogram memory. The irradiation fiber is a single fiber with a numerical aperture (NA) of 0.25 (GC.200/250L, FUJIKURA, Tokyo, Japan), and a fiber bundle 3 mm in diameter with NA of 0.29 (Sumita Opcical Glass, Inc., Saitama, Japan) is applied to the optical detection fiber. To estimate the absorption and reduced scattering coefficients, TPSF is first derived from analytical solution of the diffusion equation, on the assumption that the breast is a semi-infinite homogeneous medium. Then, the TPSF, which is convoluted with the instrumental response function, is fitted to the observed temporal profile. In the fitting procedure, the non-linear least-squares method is used [15; 16]. Oxy- and deoxy-hemoglobin concentrations are determined using an expression in which the absorption coefficient consists of a linear combination of the extinction coefficients and concentrations of hemoglobin. The TRS imaging system is shown in Figure 1.
## 3 Cryptanalysis of the [PERSON]'s Protocol
### Vulnerable to the Tracking Attack  
[PERSON] claimed that the protocol allows anonymous communication so that an adversary cannot link any communication session to another session of the same \(SN\). However, this claim is not true, based on the following facts.  
Every \(SN\) is registered to the system through one single \(FN\). The identity of the \(FN\), \(id_{N}^{\prime}\), is sent over the air in Step 2 of the authentication phase. Since \(id_{N}^{\prime}\) would not be changed in the protocol, adversary can be easily associated with two sessions with the same \(FN\) s. For an \(FN\) coordinating only one \(SN\), the adversary is allowed to link two sessions of the same \(SN\) by inspecting only Step 2. If the \(FN\) coordinates more \(SN\) s, the user's privacy/anonymity does not enhance as in some applications suggested in [PERSON]'s paper. Consider the medication, where the sensors of a patient are likely to be connected to a single \(FN\), e.g., his smart phone. Revealing the identity of the \(FN\) (smart phone) is even worse than revealing only the identity of an \(SN\) (a sensor).  
In certain applications, an \(FN\) may coordinate extremely large amount of \(SN\) s, where the identity of the \(SN\) is the only concern and an adversary is still able to link two sessions with the same \(SN\) s. Assuming that the adversary \(\mathcal{A}\) captures only the messages sent from the \(SN\) to \(FN\) and \(FN\) to \(SN\) at the time \(T_{1}\) and a later time \(T_{2}\), as  
\[\text{Capture at }T_{1}:\begin{cases}\langle tid_{1},y_{1},a_{1},b_{1},t_{1} \rangle\\ \langle a_{1},\beta_{1},\eta_{1},\mu_{1}\rangle\end{cases},\quad\text{Capture at }T_{2}: \begin{cases}\langle tid_{2},y_{2},a_{2},b_{2},t_{2}\rangle\\ \langle a_{2},\beta_{2},\eta_{2},\mu_{2}\rangle\end{cases}.\]To investigate if the messages captured at \(T_{2}\) is a subsequent login of the messages captured at \(T_{1}\), the \(\mathcal{A}\) simply computes \(a_{2}\oplus b_{2}\). If these two sessions are related, this value corresponds to \((\gamma_{1}\oplus\eta_{1})\oplus(\gamma_{1}\oplus\mu_{1})=\eta_{1}\oplus\mu_{1}\), which is indeed \(k_{HN}\oplus k_{N}\). Except for an extreme low probability of coincident \((2^{-\mathrm{length}(k_{HN})})\), comparing \(a_{2}\oplus b_{2}\stackrel{{?}}{{=}}\eta_{1}\oplus\mu_{1}\) will allow for determining if these two sessions are related.
# MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
###### Abstract  
With the increasing popularity of the Internet of Things (IoT), the proliferation of computation-intensive and timing-dependent applications has brought serious load pressure on terrestrial networks. In order to solve the problem of computing resource conflict and long response delay caused by concurrent application service applications from multiple users, this paper proposes an improved edge computing timing-dependent, task-offloading scheme based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that aims to shorten the offloading delay and improve the resource utilization rate by means of resource prediction and collaboration among multiple agents to shorten the offloading delay and improve the resource utilization. First, to coordinate the global computing resource, the gated recurrent unit is utilized, which predicts the next computing resource requirements of the timing-dependent tasks according to historical information. Second, the predicted information, the historical offloading decisions and the current state are used as inputs, and the training process of the reinforcement learning algorithm is improved to propose a task-offloading algorithm based on MADDPG. The simulation results show that the algorithm reduces the response latency by 6.7% and improves the resource utilization by 30.6% compared with the suboptimal benchmark algorithm, and it reduces nearly 500 training rounds during the learning process, which effectively improves the timeliness of the offloading strategy.  
edge computing; task dependency; computational offloading; resource prediction; deep reinforcement learning; multi-agent collaboration +
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