esa-sceva/satcom-chunk-collection huggingface dataset:
# Stride-TCN for Energy Consumption Forecasting and Its Optimization
## 3 Methodology
### Bayesian Optimization  
The Bayesian optimization technique probabilistic model \(p(\theta\mid\lambda\;)\) of the configuration performs on an evaluation index \(\theta\) (i.e., loss or accuracy of the test), given a set of hyperparameters \(\lambda\)[16]. Bayesian optimization uses a surrogate model to estimate the function to be optimized, as demonstrated in Algorithm 1.  
```
Input: Search space \(\Lambda\), black-box function \(F\), acquisition function \(S\), maximal number of function evaluations \(m\)
1. \(D_{0}=\) initialize(\(\Lambda\))
2. forn = 1 to \(m-|D_{0}|\)do
3. \(p(\theta|\lambda,\;D)=\) fit predictive model on \(D_{n-1}\);
4.  select \(x_{n}\) by optimizing \(\lambda_{n}=arg\underset{\lambda\in\Lambda}{\max}\{\lambda;\;D_{n-1},\;p( \theta|\lambda,\;D)\}\) ;
5. endfor
6. Query \(\theta_{n}\;:=F(\lambda_{n})\);
7. Add observation to data \(D_{n}=D_{n-1}\;\cup\;\{\langle\lambda_{n},\;\theta_{n}\rangle\}\);
8. return Best \(\lambda^{*}\)
```  
**Algorithm 1** Bayesian Optimization (BO)  
Bayesian optimization determines an optimized \(\lambda^{*}\) of the function \(F:\;X\rightarrow\mathbb{R}\) to denote a black-box function. Bayesian optimization performs an iterative process to determine the probabilistic \(p(\theta|\lambda,\;D)\) based on the previous observation \(D=\{(\lambda_{0},\;\theta_{0}),\;(\lambda_{1},\;\theta_{1}),\ldots,\;( \lambda_{n-1},\;\theta_{n-1})\}\), where it is assumed to only access noisy observations \(\theta=F(\lambda)+\varepsilon\) with \(\varepsilon\sim\mathcal{N}\big{(}0,\sigma_{noise}^{2}\big{)}\). To select the next \(\lambda\), the acquisition function-expected improvement \(S\)[17] is used to determine a point that maximizes it. Then, it evaluates \(F\) at \(\lambda_{n}\), obtains \(\theta_{n}\), updates the probabilistic model, and iterates [18].
## 6 Discussion
### M2M Security  
There are huge amount of technologies, which could be applied for enabling M2M security as shown by the review. There is very strong need for defining guidelines for the use of M2M security technologies also because M2M applications are usually very sensitive on misuse there are high requirements for reliability. When both ends of the communication are mobile, there is need e.g., for advanced credential management, creation of trust, bridging mobile asset network and WAN security, adaptive M2M secure elements and horizontal end to end security solutions. For example, creation of trust is traditionally established in hop by hop manner between M2M asset devices and M2M applications. This kind of model can be challenging in M2M systems, because of M2M information content may be business critical and it may contain high privacy requirements. Therefore, these areas are also open for future research and novel solutions.
## 4 Problem Formulation  
According to the system model, the task processing delay of vehicle \(un\) is defined as  
\[t_{un}=\begin{cases}t_{un}^{vch},&\text{if }\eta_{un}=0,\\ t_{un}^{v,su},&\text{if }\eta_{un}=n,\\ t_{un}^{v,su},&\text{if }\eta_{un}=v,\,v\
eq n.\end{cases} \tag{8}\]  
According to (8), vehicles make different task offloading decisions, i.e., local computation, offloading to the associated RSU, or offloading to other RSUs via the associated RSU, resulting in varying task processing delays. Therefore, we need to find the optimal task offloading decision to minimize the task processing delay for the vehicles. Additionally, the communication resources allocated by the RSU to offloading vehicles also influence the task processing delay. Thus, it is essential to efficiently allocate the limited RSU communication resources to the vehicles requiring task offloading to reduce task transmission delay.  
Let \(\mathbf{\eta}=\{\eta_{un}\}_{u\in\mathcal{U}_{n},n\in\mathcal{N}}\) and \(\mathbf{\beta}=\{\mathbf{\beta}_{un}\}_{u\in\mathcal{U}_{n},n\in\mathcal{N}}\) denote the task offloading decision and bandwidth allocation ratio variables, respectively. The optimization problem is formulated to jointly optimize task offloading decisions and communication resource allocation to minimize the total task processing delay for all vehicles while satisfying the task processing delay constraints for each vehicle and ensuring the stability of the task queue. Thus, we construct the optimization problem as  
\[\mathcal{P}1 :\min_{\mathbf{\eta},\mathbf{\beta}}\sum_{n\in\mathcal{N}}\sum_{u\in \mathcal{U}_{n}}t_{un}\] s.t. \[\text{C}1:\eta_{un}\in\{0,1,2,\dots,N\},\forall u\in\mathcal{U}_{ n},\forall n\in\mathcal{N},\] \[\text{C}2:\beta_{un}\in[0,1],\forall u\in\mathcal{U}_{n},\forall n \in\mathcal{N},\] \[\text{C}3:\sum_{u\in\mathcal{U}_{n}}\beta_{un}\leq 1,\forall n \in\mathcal{N},\] \[\text{C}4:t_{un}\leq\lambda_{un}t_{un}^{vcl},\forall u\in \mathcal{U}_{n},\forall n\in\mathcal{N},\] \[\text{C}5:C_{n}-\sum_{k\in\mathcal{N}}\sum_{l\in\mathcal{U}_{k}} \left((f_{lk}+O_{lk}X_{tra})d_{lk}\lambda_{lk}\right)>0\] \[\forall l\in\mathcal{U}_{k},\forall n,k\in\mathcal{N},\]  
where the constraint \(\text{C}1\) represents the set of available task offloading decisions for the vehicle. The constraints \(\text{C}2\)-\(\text{C}3\) ensure that the total bandwidth allocated by the RSU to associated vehicles cannot exceed its maximum available bandwidth. The constraint \(\text{C}4\) ensures that the task processing delay does not exceed the maximum tolerable delay. The constraint \(\text{C}5\) dictates that the computational workload of tasks processed on the RSU must be less than its computing resources to ensure the stability of the task queue.
## 3 Results
### Effect of Operating Parameters on Specific Energy of R\({}_{\rm VA}\)
#### 3.1.1 Effect of Depth  
The results of FFD ANOVA indicate that variation in SPE was dependent on DO (F\({}_{2,80}\) = 363.5, \(p\) < 0.0001). As also observed in Figure 7, the SPE of R\({}_{\rm VA}\) unit increased with the increase in DO. During the trials, the mean SPE required by R\({}_{\rm VA}\) was in the ranges of 0.061 to 0.87 kWh ha\({}^{-1}\). As the DO increased from 20 to 60 mm, SPE increased from 0.19 to 0.45 (SE: \(\pm\)0.048) kWh ha\({}^{-1}\), 0.24 to 0.60 (SE: \(\pm\)0.053) kWh ha\({}^{-1}\), and 0.33 to 0.69 (SE: \(\pm\)0.059) kWh ha\({}^{-1}\) at CIs of 300, 400 and 500 kPa, respectively, at the lowest \(u/v\)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **Degrees of Freedom** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline Model & 2.74 & 26 & 0.11 & 44.18 & \(<\)0.0001 \\ A: Depth & 1.74 & 2 & 0.87 & 363.50 & \(<\)0.0001 \\ B: Cone index & 0.88 & 2 & 0.44 & 183.19 & \(<\)0.0001 \\ C: \(u/v\) & 0.013 & 2 & 0.0065 & 3.72 & 0.03065 \\ A x B & 0.055 & 4 & 0.0138 & 5.76 & 0.0006 \\ A x C & 0.025 & 4 & 0.00633 & 2.65 & 0.0429 \\ B x C & 0.020 & 4 & 0.00489 & 2.05 & 0.1004 \\ A x B x C & 0.019 & 8 & 0.002385 & 1.00 & 0.4484 \\ Error & 0.13 & 54 & 0.002389 & & \\ \hline Total & 2.87 & 80 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis of variance formulated for specific energy of vertical axis rotor (R\({}_{\rm VA}\)).  
Figure 6: Variation of specific energy at different operational parameters.  
ratio of 1.7, while at the highest \(u/v\) ratio of 3, when the DO increased from 20 to 60 mm, SPE increased from 0.084 to 0.41 (SE: \(\pm\)0.037) kWh ha\({}^{-1}\) for CIs of 300 kPa, 0.21 to 0.65 (SE: \(\pm\)0.063) kWh ha\({}^{-1}\) for CI of 400 kPa, and 0.34 to 0.73 (SE: \(\pm\)0.11) kWh ha\({}^{-1}\) for CI of 500 kPa. As the DO was increased during the trials, SPE increased by 60-70%.
# Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
###### Abstract  
Single-pass soil moisture retrieval has been a key objective of Global Navigation Satellite System-Reflectometry (GNSS-R) for the last decade. Achieving this goal will allow small satellites with GNSS-R payloads to perform such retrievals at high temporal resolutions. Properly modeling the soil surface roughness is key to providing high-quality soil moisture estimations. In the present work, the Physical Optics and Geometric Optics models of the Kirchhoff Approximation are implemented to the coherent and incoherent components of the reflectometry measurements collected by the SMAP radar receiver (SMAP-Reflectometry or SMAP-R). Two surface roughness products are retrieved and compared for a single-polarization approach, critical for single-polarization GNSS-R instruments that target soil moisture retrievals. Then, a polarization decoupling model is implemented for a dual-polarization retrieval approach, where the ratio between two orthogonal polarizations is evaluated to estimate soil moisture. Differences between linear and circular polarization ratios are evaluated using this decoupling parameter, and the theoretical soil moisture error with varying decoupling parameters is analyzed. Our results show a 1-sigma soil moisture error of 0.08 cm\({}^{3}\)/cm\({}^{3}\) for the dual-polarization case for a fixed polarization decoupling value used for the whole Earth, and a 2-sigma error of 0.08 cm\({}^{3}\)/cm\({}^{3}\) when the measured reflectivity and the VOD are used to estimate the polarization decoupling parameter.  
soil moisture; GNSS-R; polarimetry; roughness Article
# Second-Order Text Matching Algorithm for Agricultural Text
## 1 Introduction
NLP technologies further enhance the understanding of agricultural text and context, improving the accuracy of agricultural decisions. However, the integration of text matching technology in agriculture still faces a number of challenges. Most existing research focuses on improving accuracy, but ignores the efficiency problem of the relevant models. What's more, the efficiency problem is a very important and cannot be ignored issue brought by deep learning [17]. On the other hand, text matching models based on deep learning tend to concentrate on information at a particular granularity, potentially giving less direct attention to details at other granularities. To a certain extent, this may impact the ability to fully capture all potentially useful information within the text. In this paper, a second-order text matching (SOTM) algorithm is designed for agricultural text. The first phase is referred to as the recall phase. For the text to be matched, we first use BM25 algorithm to select a part of the constructed database as a candidate set. The second phase is referred to as the rearrangement phase. The candidate set is filtered using BERT model to select the text with the highest match. SOTM algorithm combines the fast retrieval speed of BM25 with the high retrieval accuracy of BERT model. The result is high quality results with a reduction in the cost of text matching. In summary, the primary contributions of this paper can be outlined as follows:  
* SOTM algorithm combines the advantages of fast retrieval speed of BM25 algorithm and high retrieval accuracy of BERT model. It is not only significantly reduced runtime for deep learning models but also achieves breakthroughs in accuracy. * BM25 algorithm is based on words, while BERT model used in this paper (BERT-Chinese) is based on characters. This multi-granularity information fusion has higher accuracy compared to single-granularity algorithms or models, demonstrating the effectiveness of multi-granularity information fusion. * In the construction of the dataset, this paper utilizes several experimentally proven effective methods, enabling BERT model to acquire more fundamental and essential knowledge. The rest of this paper is structured as follows. Section 2 describes the related work in text matching. Section 3 describes the related algorithm and model used in SOTM algorithm. Section 4 describes the overall composition of SOTM algorithm, with a description of the recall and rearrangement phases, respectively. Moreover, parameter selection and complexity analysis are also discussed in this section. Section 5 describes the experimental setup, optimal parameter determination, performance comparison analysis and summary. Section 6 summarises the paper. It shows the accuracy and efficiency of SOTM algorithm, indicates the application value of the algorithm, and puts forward the future research direction.
# A Bayesian Control Chart for Monitoring Process Variance
###### Abstract  
Automation in the service industry is emerging as a new wave of industrial revolution. Standardization and consistency of service quality is an important part of the automation process. The quality control methods widely used in the manufacturing industry can provide service quality measurement and service process monitoring. In particular, the control chart as an online monitoring technique can be used to quickly detect whether a service process is out of control. However, the control of the service process is more difficult than that of the manufacturing process because the variability of the service process comes from widespread and complex factors. First of all, the distribution of the service process is usually non-normal or unknown. Moreover, the skewness of the process distribution can be time-varying, even if the process is in control. In this study, a Bayesian procedure is applied to construct a Phase II exponential weighted moving average (EWMA) control chart for monitoring the variance of a distribution-free process. We explore the sampling properties of the new monitoring statistic, which is suitable for monitoring the time-varying process distribution. The average run lengths (ARLs) of the proposed Bayesian EWMA variance chart are calculated, and they show that the chart performs well. The simulation studies for a normal process, exponential process, and the mixed process of normal and exponential distribution prove that our chart can quickly detect any shift of a process variance. Finally, a numerical example of bank service time is used to illustrate the application of the proposed Bayesian EWMA variance chart and confirm the performance of the process control.  
average run length; beta-binomial distribution; service automation; time-varying process distribution; variance control chart +
Footnote †: journal: Applied Sciences  
16 February 2021  
Accepted: 13 March 2021  
Subject head: **Publisher's Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Post-FEC Performance of Pilot-Aided Carrier Phase Estimation over Cycle Slip
## 4 Conclusions  
In this paper, we have experimentally investigated the PRE-FEC BER and POST-FEC BER performance of the CPE + PAPU scheme and the CPE scheme on various CPE filter lengths in 56 Gbit/s QPSK systems. Experimental results show that, compared with CPE, CPE + PAPU can achieve the required PRE-FEC OSNR about 3.1 dB, 1.3 dB, and 0.6 dB at BER of \(1.8\times 10^{-2}\) under the CPE filter length of 8, 16, and 20, meanwhile, the CPE + PAPU scheme respectively obtained 3 dB, 1 dB, and 0.5 dB POST-FEC OSNR gain and a large reduction of the CS rate compared with the CPE scheme, with the CPE filter length of 8, 16, and 20. The experimental results also prove that the CPE + PAPU scheme respectively improve the FEC limit from \(2.5\times 10^{-3}\) to \(1.4\times 10^{-2}\), from \(8.9\times 10^{-3}\) to \(1.8\times 10^{-2}\), and from \(1.6\times 10^{-2}\) to \(1.9\times 10^{-2}\) compared with the CPE scheme, with the CPE filter length of 8, 16, and 20. The experimental results show the CS-rate increases with deterioration of OSNR. On the lower OSNR region, CPE shows high residual CS at short filter length and low residual CS at long filter length, while CPE + PAPU can effectively reduce CS at all the filter lengths. Our experiment results prove that the CPE + PAPU scheme combined with a concatenated soft decision (SD)-LDPC coding can effectively reduce the CS induced by phase noise, which will be a promising candidate for next-generation high-speed coherent optical communications.  
Figure 7: The relationship between POST-FEC BER and PRE-FEC BER.  
**Author Contributions:** This paper was mainly wrote by [PERSON] ([PERSON]) and [PERSON], [PERSON] demonstrated the experiment and [PERSON] revised the article. [PERSON], [PERSON] ([PERSON]), [PERSON], [PERSON] and [PERSON] contributed to the reviewing and editing of the manuscript. [PERSON] supervised overall project.  
**Funding:** This work was partly funded by 863 program 2015 AA015503, NSFC program 61475022, 61505011, 61675034, 61331008, 973 project 2014 CB340102, Fund of state key laboratory of IPOC (BUPT), and the fundamental research funds for the central universities.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
## 4 Experimental Evaluations
### Experimental Datasets and Settings  
This research utilized several features that were mentioned in Section 3.2 to reveal the result of a PM2.5 value forecast. AirBox data were selected for experiments [1]. AirBox was provided by the Taipei government (Taiwan, R.O.C). AirBox also cooperates with Environmental Protection Administration Executive Yuan as an air quality sensor IoT system; data from AirBox were much more abundant than from national sensors. In Figure 7, the blue dots represent 146 AirBoxes in Taipei, and the purple circles represent 21 weather stations around Taipei. To prevent inaccuracies in the weather forecast data, weather forecast data were replaced with monitored meteorological data. AirBox data were combined with the nearest weather station data every hour. The spatial data provided by Data.Taipei were used for this study. Due to AirBox users' private rights, the coordinates of consumers were rounded to three decimal places; therefore, the location error could be about 75 m.  
1. AirBox: An AirBox is a device that can monitor PM2.5 using the principles of optics, temperature, and relative humidity. It measures the number of particles and evaluates the concentration of PM2.5. Edimax Technology and Academia Sinica produce AirBox, and make it available to schools and citizens. Each device monitors data every 5 min; however, weather data are monitored every hour. We averaged the monitored data from AirBox every hour to combine it with the weather data. As long as AirBox users agree, the monitoring data are uploaded to the Edimax Internet-of-Things platform and will be made available as an open data download. This was provided by Data.Taipei.
2. Weather station: The Central Weather Bureau provides meteorological data and weather forecast data. They consist of temperature, relative humidity, wind speed, wind direction, atmospheric pressure, and daily rainfall. It monitors data once an hour. We received the weather data from data.gov.  
Figure 6: Schematic diagram of the smooth approach.  
3. Location information: Location data include the latitude and longitude of each AirBox device. Due to users' private rights, some location coordinates were only accurate to the third decimal place. These data were also received from Data.Taipei.  
The experiment data were collected from March 2017 to May 2018. The data were divided into a historical part and a test part. The data before January 2018 were defined as history data, and the data from January 2018 were to be forecasted. After 48 h of forecast completion, the following 48 h of the next hour were forecasted, and the last hour would be added into the historical data. For the forecasting day for every 48 h, the performance of the forecast method was analyzed using the RMSE per hour.
## 6 Conclusions  
To fully obtain the advantageous features of OFDM in the HomePlug GP system, it is very important to maintain the frequency synchronization between the transmitter and the receiver. To address this issue, this paper proposed an efficient SFO estimation scheme in the OFDM-based HomePlug GP system without resorting to pilot symbols. To enable pilot-less estimation of SFO, the proposed estimation scheme was designed to use a repetitive structure due to the redundant repetition coding adopted in the HomePlug ROBO mode. The subset selection according to tone mask requirements played an important part on minimizing an error floor from the presence of SFO and making the estimation scheme to be less computationally intensive. In order to verify the feasibility of the proposed scheme, the MSE is numerically derived. We have demonstrated from simulation and analytical results that the proposed estimation scheme reduces the computational burden and offers greater immunity to SFO in comparison to the conventional DD estimation methods. The HomePlug GP signal was proven to contain rich structural information for synchronizing systems without any  
Figure 3: Bit error rate (BER) comparison between the proposed and conventional estimation schemes versus SFO: (**a**) High-speed ROBO mode (**b**) Standard ROBO mode.  
Figure 2: MSE comparison between the proposed and conventional estimation schemes versus sampling frequency offset (SFO): (**a**) High-speed ROBO mode (**b**) Standard ROBO mode.  
need for dedicated pilots. For future works, the closed-form of the MSE could be straightforwardly extended to other PLC systems. Additionally, the performance of the OFDM HomePlug system under realistic indoor channel conditions would strengthen the theoretical analysis, especially considering the long-term and short-term time-varying nature of the PLC channel.  
[PERSON] and [PERSON] designed the proposed estimation algorithm. [PERSON] supplemented the proposed algorithm. [PERSON] gave feedbacks about an enhanced algorithm and all simulation results. [PERSON] and [PERSON] wrote the paper jointly.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2018R1D1A1B07048819).  
We would like to thank the anonymous referees and editors for their careful reading on the manuscript and providing constructive comments.  
The authors declare no conflict of interest.