esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
8. Hybrid model, a combination of any of the other models [21]. In [22], the authors propose an approach based on objective methods known as instrumental QoE models. With this approach, the model obtained uses QoS parameters, subjective data or certain other model outputs to obtain a score that represents the user QoE. This approach is effective because it predicts the QoE perceived by users in live transmissions, without the requirement for user subjective tests [23]. Each of the methods presented has its advantages and disadvantages. For example, subjective methods have become a standard in testing the performance of the different types of the proposed models; however, their implementation is expensive in terms of resources and time [24]. However, objective methods require high processing power, and a few of them are not implemented in commercial software. On the other hand, the proposals for evaluating QoE based on instrumental QoE models present advantages over approaches using subjective and objective methods [25]. Evaluating QoE combines nontechnical parameters, such as user perception, experience and expectations with the technical parameters of network QoS [26]. The remainder of the paper is organized as follows: Section 2 presents related works. Section 3 describes the formulation of the models. Section 4 describes the experiment designed to obtain the necessary data to create our models. Section 5 describes our analysis of the data obtained and our proposed models. Section 6 provides an analysis of the performance evaluation of our proposed models. Section 7 presents and analysis of the results. In Section 8, the algorithm to achieve quality is explained. In Section 9, we present future trends. Finally, our conclusions and future work are discussed in Sections 10 and 11.
## 6 Results
### Equalization by the CMA Algorithm  
The blind channel equalization or constant modulus algorithm was applied to the received data, as described by Section 3.2. The distorted OFDM subcarrier signals with the QPSK modulation format resulting in the constellation diagram and EVM calculations are shown in Figures 13 and 14. The implementation of the proposed CMA algorithm is examined with different fiber lengths in the presence of dispersion.  
It was observed that for QPSK modulation in OFDM subcarriers and a maximum fiber length of 100 km, we obtained an EVM of 15% after equalization. We obtained an EVM improvement of 3.7% for QPSK after CMA equalization for the same fiber lengths. When we compared the CMA with the LMS algorithm in terms of EVM, in the case of CMA we obtained a 2% further improvement in EVM.  
Figure 11: EVM results obtained before and after equalization vs. the fiber lengths for a QPSK constellation.  
Figure 12: EVM results obtained before and after Least Mean Square (LMS) equalization vs. the fiber lengths for a 16 QAM constellation.
# Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition
## 3 Methods
### Model Selection and Architecture
#### 3.1.3 Hybrid Model: 1D CNN + LSTM  
1D CNN and LSTM have been used sequentially to build a hybrid model for human recognition [18]. Figure 4 shows the structure of the hybrid model. 1D CNN is used for feature extraction from input data before the LSTM layers to support sequence prediction. 1D CNN layers process the input  
Figure 3: Architecture of the one dimensional convolutional neural networks (1D CNN) model for human activity recognition.  
Figure 2: Architecture of the LSTM model for human activity recognition.  
sub-sequences of human activities independently. The sub-sequences of human activities in 1D CNN are not sensitive to the time step order, which is opposite to LSTM. Moreover, 1D CNN layers are often used when an RNN model cannot realistically process and recognise long temporal sequential data. In such cases, the hybrid model could be used as a solution where 1D CNN can be applied as a pre-processing step to make the long temporal sequential data shorter through down-sampling by extracting higher level features. Subsequently, the 1D extracted features as input are fed to the RNN layers [44]. 1D CNN layers as a pre-processing step of RNN layers is particularly important to make the long sequences smaller when order sensitivity is not needed. However, order sensitivity is the key in activity recognition systems, hence the hybrid of 1D CNN and LSTM is not an optimal solution to induce order sensitivity. In this paper, the hybrid model is built by combining 1D CNN and LSTM where a 1D CNN layer is used as a pre-processing step to an LSTM layer. Specifically, the 1D CNN layer followed by a max-pooling layer with the size of the window is equal to 2. Afterwards, the LSTM layer and flattened layer are stacked, followed by fully-connected layers, i.e., a dense layer with ReLU activation function and a soft-max layer.
# DCS-TransUperNet: Road Segmentation Network Based on CSwin Transformer with Dual Resolution
## 4 Experiment
### Datasets  
In order to estimate the performance of the designed DCS-TransUperNet, we perform enough comparative experiments on the Massachusetts road dataset and the DeepGlobe dataset.  
The Massachusetts roads dataset [36] widely is applied for model verification of various road extraction. The dataset consists of 1171 images with a resolution of 1 m. Each one has a corresponding binary label whose resolution is 1500 \(\times\) 1500 pixels, like the image. We randomly split the dataset into three parts, including 1108 training images, 14 validation images and 49 test images. Before training, we perform data enhancements, such as random clipping and random rotation with different angles. Finally, we obtain 110,800 training samples at 256 \(\times\) 256 pixels in size, 1400 validation samples and 49 original testing samples after data pre-processing.  
The DeepGlobe dataset [37] comes from the CVPR DeepGlobe 2018 road extraction challenge. It contains 8570 images with the size of 1024 \(\times\) 1024 pixels and a resolution of 0.5 m, which are split into 6626 images for training, 1243 images for validation and 1101 images for testing. Since the only binary labels of the training set are available after the challenge, we select 6626 training set images and corresponding labels as our experimental data. We randomly divide the dataset according to the ratio of 8:1:1 and then perform the same data enhancements as the Massachusetts road dataset.
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 3 Time Series Forecasting Experiments
### Presentation of Prediction Results and Analysis
#### 3.5.3 Multi-Step Prediction  
Forecasting with multiple steps in advance gives the scheduling mechanism more time to schedule and execute the production plan. Different machine-learning models were used to implement multi-step forecasting with one to six steps, and the evaluation metrics are shown in Table 4. Figure 17 reflects the error variation law of the top ten multi-step prediction results. It can be seen that prediction errors generally tend to increase as the time step increases, so in order to obtain timely and valid prediction information, it is necessary to keep the error in each step as small as possible.  
The LSTM controls the flow of information through a gating mechanism, thus avoiding the problem of vanishing or exploding gradients, and this allows the network to be more stable in dealing with long-term dependencies and produce better predictions when dealing with time-series data. In this study, the LSTM made full use of its strengths in this area by the time series modelling of meteorological parameters. Further data optimisation work was also implemented, with Savitzky-Golay filters optimising the expected output of the  
Figure 15: Schematic diagram of power prediction results.  
training set, and the most efficient configuration parameters were identified in the previous subsection. The improved data quality prevented the model from learning insignificant fluctuating features or noise, resulting in better generalisation performance. On the basis of these, the model achieved better results than other methods at all stages of the multi-step prediction. The normalized RMSEs for all six steps are 0.0485, 0.0772, 0.0932, 0.0109, 0.1168, and 0.1299, respectively, and the results of each step are the best among all the compared models. It can still maintain the prediction result at the fifth-step stage with a determination coefficient (\(R^{2}\)) of 0.91. This accurate multi-step forecasting ensures the timeliness of effective forecasting information.  
Figure 16: Diagram of the scattering of predicted values.  
Figure 17: Error comparison chart of multi-step forecast.
# A New Observation in Spiral and Curl Antenna Configurations above the Ground Plane
## 3 Curl Antenna  
So far, we have obtained low-profile spiral antennas with \(h<0.25\lambda_{0}\) by optimizing the configuration parameters for CP radiation. This section realizes a low-profile curl antenna, as for the spiral antenna. Two types of feed systems are considered for the input impedance characteristics.
## 1 Introduction
In this work, we propose a geo-sensor framework that can be used by multiple clients to deploy their own geo-sensor networks, bind their sensor objects to desired locations based on DIY, generate geo-sensor services for the uploaded networks, and manage the services with a geo-sensor composite toolbox. The availability of such a platform will allow the ease of deployment and management for the geo-sensor networks and also the reuse of geo-sensor services. The deployed geo-sensor platform and its services can be reused by authorized users in different scenarios, with the online availability of the geo-sensor framework. This also reduces the deployment cost in scenarios where such deployed networks and services can be reused, as an alternative to additional deployments. It will also reduce the deployment redundancy. The proposed work offers the following novelty points as  
* [noitemsep,topsep=0 pt]
* Ease of deployment for geo-sensor networks based on DIY,
* Multi-platform geo-sensing services generation,
* Independent geo-sensor composite toolbox for ease of management,
* Visualization tool for users,
* Removing redundancy. The multiple spatial context service platforms use the sensor profile information and geographical context of the service to create geo-sensor services. We present the detailed system design for the deployment of geo-sensor framework, geo-sensor composition toolbox, and geo-sensor multiple spatial services platforms for the wireless sensor networks. The proposed system is implemented using both the RESTful web services and SOAP web services. RESTful web services allocate URI (Uniform resource identifier) to each resource and use basic CRUD operations over HTTP for making the interface uniform throughout and are simpler to implement. The rest of the paper is divided as follows; Section 2 gives an overview of related work. Section 3 presents the system design for a geo-sensor framework and multiple platforms deployment based on the geo-sensor framework. In Section 4, the implementation of the proposed framework is presented. In Section 5, we give a performance analysis of a system based on system queries and access times, and Section 6 concludes the paper.
## 5 Conclusions and Future Research Details
Streamlining the iterative optimization process integral to GS, aiming for expeditious and efficient constellation structure optimization tailored for the PCPE algorithm; 2. Studying the Odd-bit QAM GS scheme based on 4D modulation [30], to realize the constellation structure design that can be adapted to the dual-polarization joint PCPE algorithm; 3. Optimizing the implementation of the PCPE algorithm, and attempting to achieve compatibility with odd-bit QAM signals through the improvement of the algorithm implementation side. Conceptualization, X.W. and K.L. ; Data curation, X.W. and L.Z. ; Formal analysis, K.L. and L.Z. ; Funding acquisition, X.X. ; Investigation, K.L. and L.Z. ; Methodology, X.W. ; Project administration, Q.Z. ; Resources, Q.Z. and X.X. ; Software, X.W. ; Supervision, K.L. and L.Z. ; Validation, X.W., K.L. and Q.Z. ; Visualization, X.W. ; Writing--original draft, X.W. ; Writing--review & editing, Q.Z., L.Z. and X.X. All authors have read and agreed to the published version of the manuscript. This research was funded by National Key R&D Program of China: 2022 YFB2903104, National Natural Science Foundation of China, grant number 62305027, China Postdoctoral Science Foundation funded project, grant number 2023M730242, and Open Fund of IPOC (BUPT), grant number IPOC2022A04. Not applicable. Data Availability Statement: The data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request. The authors declare no conflict of interest.
## 5 Robust Optimization  
### Uncertainty Modeling  
Uncertainty modeling is the first step of robust optimization, which includes identifying the uncertainty source and selecting the appropriate description. For the problem studied in this paper, the source of contamination on the airfoil surface is set as the roughness bands, the position of the roughness bands is set as the uncertainty variable of the problem, and the transition occurs once the roughness bands are passed. The probability distribution range of the roughness bands is set between 0-0.7 times the chord length, considering the fact the transition positions of laminar flow airfoil are mostly within 0.7 times the chord length. Case 1 assumes that the uncertainty variable follows a triangular distribution considering the linear superposition of perturbation which is caused by uniform surface contamination effects, while case 2 takes a uniform distribution in order to increase the diversity of the assumptions and the persuasiveness of the final results, as shown in Figure 12.  
Figure 11: Comparison of aerodynamic performance after normal optimization.  
Figure 12: Roughness bands location probability distribution.  
Figure 13 shows the results of 200 sets of roughness bands on the upper and lower airfoil surface sampled by Monte Carlo descriptive sampling method according to the triangular distribution, and the corresponding aerodynamic calculation results. It can be seen that the distribution of sample points is consistent with the assumed probability distribution. It is also found that the drag coefficient characteristics are strongly associated with the location of the transition point on the upper surface, which shows an obviously linear relationship.
## 4 Light Intensity- and Spectrum-Associated Adjustment of Metabolism
### Light Control of Carbohydrate Metabolism
#### 4.1.1 Light Intensity-Associated Regulation of Carbohydrate Metabolism
In this mutant HL activation of RPK, Rubisco small subunits (Rbcs 1A and 3B), phosphoglycerate kinase 1 (PGK1), GAPDH B, triose phosphate isomerase (TPI), transketolase 1 (TKL1), SBP, ribulose 5-phosphate epimerase (RPE), and ribose 5-phosphate isomerase (RPI) were diminished, too. Interestingly, the short-term exposure (15-20 min) of the dicot _Arabidopsis_ and monocot rice to increasing light intensities affected the CBC differently in the 2 species [132]. The balance between subprocesses was different since 3-phosphoglycerate reduction was favoured in _Arabidopsis_, and ribulose 1,5-bisphosphate regeneration was favoured in rice in HL. These observations indicate that the results obtained in _Arabidopsis_ cannot be generalized, and several plant species should be used for the creation of models describing the metabolic effect of light intensity. Similar to carbon assimilation, carbohydrate catabolism also changes with alterations in the light conditions. The effect of light-induced redox changes on glycolysis was shown in the case of GAPC, a key enzyme in glycolysis, since it was inactivated by H\({}_{2}\)O\({}_{2}\)-mediated oxidation [133]. GAPC-SOH may undergo glutathionylation by GSH, which would lead to the formation of GAPC-SSG. GAPC-SSG may be restored to GAPC by Trxs or glutaredoxins (Grxs) [133]. Another glycolytic enzyme, phosphofructokinase (PFK), is deactivated via the Fd/Trx pathway during the light-to-dark transition in _Arabidopsis_ and activated by the Trx-like 2 (TrxL2) pathway during the night [134]. Since Trxs regulates not only the enzymes of CBC but also the enzymes of the TCA cycle, glycolysis, and photorespiration [35; 135], these pathways may be involved in the coordination of these processes. Generally, most studied metabolites of the tricarboxylic (TCA) cycle were increased under HL, and this effect was redox-dependent [136]. Correspondingly, modifying the redox environment by antisense expression of pumpkin or tobacco ascorbate oxidase expression decreased malate accumulation and alleviated the HL enhancing effect on fumarate in tobacco plants. The effect of HL on the TCA cycle is mediated by the HY5 transcription factor, as shown for the pyruvate dehydrogenase complex (PDC), aconitase, fumarase (FUM), and NAD-dependent malic enzyme (NAD-ME) (Figure 4) [123]. During the TCA cycle, the enzyme activities of PDC, citrate synthase (CS), aconitase, and NAD-dependent isocitrate dehydrogenase (IDH) were dark-enhanced, and FUM and malate dehydrogenase 2 (MDH2) activity increased during the day. Interestingly, NAD-ME activity changed during the light-to-dark and dark-to-light transitions [137]. The light control of the activity of these NAD-dependent enzymes can be likewise mediated by the mitochondrial alternative oxidase (one of the terminal oxidases of the plant mitochondrial electron transport chain which are also light-responsive) [138].
# PrivacyGLUE: A Benchmark Dataset for General Language Understanding in Privacy Policies
## 7 Conclusions and Further Work  
In this paper, we described the importance of data privacy in modern digital life and observed the lack of an NLP benchmark in the privacy language domain despite its distinctness. To address this, we proposed PrivacyGLUE as the first comprehensive benchmark for measuring general language understanding in the privacy language domain. We released benchmark performances from the BERT, RoBERTa, Legal-BERT, Legal-RoBERTa and PrivBERT transformer language models. Our findings showed that PrivBERT outperforms other models by an average of 2-3% over all PrivacyGLUE tasks, shedding light on the importance of in-domain pretraining for privacy policies. We applied model-pair agreement analysis to detect PrivacyGLUE examples where PrivBERT's pretraining provides a competitive advantage and disadvantage. By benchmarking holistic model performances, we believe PrivacyGLUE can accelerate NLP research into the privacy language domain and ultimately improve general language understanding of privacy policies for both humans and AI algorithms. Ultimately, this will support practitioners in selecting the best models to use in applications that simplify privacy policies. An example of such an application could be a browser plugin that actively condenses privacy policies into their most important parts before presenting them to the user for their consent.  
Looking forward, we envision several ways to further enhance our study. Firstly, we intend to apply deep-learning explainability techniques, such as Integrated Gradients [37] on examples from Table 4, to explore PrivBERT's and BERT's token-level attention attributions for categories P and O. Additionally, we intend to benchmark large prompt-based transformer language models such as T5 [38] and T0 [39], as they incorporate large amounts of knowledge from the various sequence-to-sequence tasks that they were trained on. Fi  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Category P** & **Category O** \\ \hline
**ID:** 1978 & **ID:** 33237 \\
**Question:** Who can see my information? & **Question:** Could the wordscapes app contain malware? \\
**Answer:** We do not sell or rent your personal information to third parties for their market- & **Answer:** We encrypt the transmission of all information using secure socket layer technology (SSL). \\
**Label:** Relevant & **Label:** Relevant \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test-set examples from PrivacyQA that fall under categories P and O for PrivBERT vs. BERT.  
Figure 4: Model–pair agreement analysis of PrivBERT against other models over all PrivacyGLUE tasks; bars represent proportions of examples per model–pair and task which fell into categories P and O; all models on the x-axis are compared against PrivBERT.  
nally, we plan to continue maintaining our PrivacyGLUE GitHub repository and host new model results from the community.
## 3 Theoretical Framework
### Software Tools for Research and Performance Measurements
It is used in this application in order to store the publications generated in the stress tests. **MongoDB** is a NoSQL database system, which is a document-oriented and open source for querying and indexing. In this project, we used it to store the information generated for the Threads and Users. During the conduction of the tests, we used JMeter [37] and NewRelic [38] applications. Both support data collection in real time of the performance of the resources that need to be analyzed. Hereby, the tests focus on CPU and Memory consumption, hard disk drive reading and writing speed, network, and application performance. Following is a brief description of the mentioned tools:  
**Apache JMeter** is a Java-based open-source software tool designed to load the test's functional behavior and measure performance. We used this tool to configure stress tests and collect information on the application's performance and response in real time. With JMeter, the scripts were generated for stress tests, allowing them to specify the request conditions in each environment. Also, Server Agent version 2.2 was included as a component of JMeter to collect performance information. The current project was used as a service that supports the collection and interpretation of data generated during stress tests. **Server Agent** is a component of JMeter in order to collect performance information. Hereby, we used it as a service that helps to collect and interpret data generated during stress tests. **NewRelic** provides a performance analysis service that allows viewing and analyzing amounts of data and obtaining useful information in real time. In the current study, it is used such as JMeter. However, in this case, it predominantly focuses on collecting information about the performance of the environment where the application runs during the stress tests and later analyzing them in real-time graphs.