esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Retail Data Pre-Processing  
After acquiring the retail sales data from the Rossmann and Walmart datasets, data pre-processing was carried out to eliminate unreliable samples for better forecasting. In this scenario, the data pre-processing step included the processes of interpolating missing data, outliers' removal, normalization, and de-normalization [31, 32].  
In the initial process, identifying outliers is important in statistics and data analysis because it has a significant impact on the results of statistical analyses. Removing the outliers involves excluding data points significantly deviating from the norm to enhance the model's accuracy and generalization on new data. The outliers are the instances which are highly deviated from other instances in the Rossmann and Walmart datasets. In this study, the instances, \(s_{i,j}\), are considered as the outliers in the time-series data, \(S_{j}\). When the following condition is satisfied, \(abs\big{(}s_{i,j}-mean\big{(}S_{j}\big{)}\big{)}>n\times std\big{(}S_{j}\big{)}\), the instances \(s_{i,j}\) are eliminated from the time-series data, \(S_{j}\). Here, the absolute value function is \(abs(.)\), mean function is represented as \(mean(.)\), \(n=3\), and the standard deviation function is denoted as \(std(.)\). After the elimination of outliers, the missing data are interpolated because they affect the validity and generalizability of the data, which depend on the quantity, category, and pattern. The missing data are supplied by computing the mean value of two nearest neighbor data.  
Following that, the data normalization is performed by implementing the z-score normalization technique, where the normalization process speeds up the training time of the Bi-GRU model. The key concept of normalization is to remove potential biases and distortions that occur because of the dissimilar feature scales. The z-score normalization  
Figure 1: Workflow of the proposed automated regression model.  
technique normalizes the input and the output variables based on Equation (1), and the de-normalization is mathematically formulated in Equation (2).  
\[{S^{\prime}}_{j}=\frac{{{S}_{j}-mean\big{(}{{S}_{j}}\big{)}}}{{std\big{(}{{S}_{j} }\big{)}}} \tag{1}\]  
\[{S}_{j}=mean\big{(}{{S}_{j}}\big{)}+{S^{\prime}}_{j}\times std\big{(}{{S}_{j} }\big{)} \tag{2}\]
## 3 Methodology
### U-Former Deraining Subnetwork  
The U-Former proposed in this paper is a symmetric encoder-decoder structure. The encoder processes the input data, increasing the number of channels through stacked convolutional layers to extract image features, while the decoder uses transpose convolutions to reconstruct the image. According to Figure 6, the deraining subnetwork includes three encoders, three decoders, and a bottleneck module. The U-Former uses Swin Transformer modules (Swin Transformer block, STB) to replace the 3 \(\times\) 3 convolutional layers in the original UNet, with each STB containing two submodules. Each submodule includes two LayerNorm layers, one MLP layer, and the concentration of many brains module is  
Figure 5: Structure of IMSAR.  
window-based. Additionally, to prevent the extracted features from being confined to small windows, the self-attention in the second submodule uses shifted window self-attention to allow feature information exchange between different windows.  
After processing with the IMSAR, the feature-fused image is concatenated with the approximate rain streak map obtained from RSENet and input into the U-Former deraining network, producing derained images at multiple scales. Given an input component \(X_{ip}\in R^{\frac{H}{2}\times\frac{W}{2}\times 3}\), the U-Former first fuses the approximate rain streak map from the RSENet module, and then uses two convolution layers and two ReLU functions (Conv3\(\times\)3--ReLU--Conv3\(\times\)3--ReLU) to excerpt shallow characteristics \(F_{sf}\). The shallow features \(F_{sf}\) are then fed into the the symmetric encoder-decoder structure to extract deeper features. Each encoder-decoder layer contains \(N_{i}(i=1,2,3)\) STBs. During the decoding phase, the output of each decoder layer includes both the STB output and a skip connection that merges the corresponding encoder output. Finally, the feature information from the encoder-decoder structure is processed through two convolution layers and two ReLU functions to obtain \(F_{op}\in R^{\frac{H}{2}\times\frac{W}{2}\times 3}\), which is then passed through a residual connection to learn residual information and produce the final output \(X_{op}\).
# Toward an SDN-Based Web Application Firewall: Defending against SQL Injection Attacks
## 2 Background
### Web Application Firewall  
WAFs act as an intermediate layer of protection between users and web application servers [20]. The effectiveness of WAFs relies on the configured rules, as they inspect web traffic to detect patterns or texts specified in these rules. These rules typically contain malicious patterns or texts that are commonly used by attackers to exploit vulnerable services or known software vulnerabilities. Additionally, WAF rules define the action to be triggered upon a match, and decisions are made based on security policies. Traffic can be modified, blocked, or ignored, and a warning may be sent to the network administrator. Firewall rules can be based on blacklists (blocking traffic that matches the rules), whitelists (allowing only traffic that matches the rules), or a combination of both.  
A typical workflow of a WAF includes the following stages. (i) Parsing requests/traffic: WAFs parse and normalize the incoming traffic to ensure a unified format, making further processing easier. (ii) Normalization: WAFs normalize the parsed data to eliminate inconsistencies and reduce the risk of evasion techniques used by attackers. (iii) Matching: WAFs perform matching based on their configured rules, which can be either signature-based or anomaly-based. These rules are designed to detect known patterns or anomalies that may indicate an attack. (iv) Logging: Upon a match, important information about the detected event is logged for further inspection by security staff. This helps in analyzing and investigating potential security incidents.
# The Use of Wireless Networks and Mobile Applications in the Control of Electric Machines
## 6 Conclusions  
The performed tests have shown that it is possible to smoothly control the rotational speed of a PMDC motor supplied by a three-phase thyristor rectifier by remote control system using wireless communication. Both Wi-Fi and Bluetooth methods ensure the same level of control accuracy. The module communicating via Wi-Fi offers the ability to control the system from anywhere and covers longer distances than Bluetooth. The only necessary condition to connect to the controlled object is to have a smartphone with a mobile application and access to the internet. Mobile applications are becoming the basic GUI interface for modern technical systems from various fields of engineering. The proposed shape of the application indicates important aspects of its design and ergonomics. The proposed remote-control system could be suitable for some industrial requirements and locations.  
Conceptualization, A.M.; methodology, A.M., A.B. and J.I.; formal analysis, J.I. and A.M.; investigation A.M. and A.B.; writing--original draft preparation, J.I., A.M. and A.B.; supervision, A.M. All authors have read and agreed to the published version of the manuscript.  
This project is financially supported under the framework of a program of the Ministry of Science and Higher Education (Poland) as \"Regional Excellence Initiative\" for the years 2019-2022, project number 006/RID/2018/19, with an amount of funding of 11,870,000 PLN.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distance** & **3 [m]** & **7 [m]** & **11 [m]** \\ \hline
**Object visibility** & **Very good** & **Good** & **Poor** \\ \hline  & & Intermittent & \\  & & connection. & \\  & & Intermittent control. & No connection and no \\  & & Control parameters & \\
**Bluetooth control** & Continuous & were not updated on & No information about \\  & connection. & a regular basis. & control parameters. \\  & Smooth control. & Full control over the & There was no control \\  & Control parameters & object was not & over the object. \\  & are updated on a & maintained & \\ \hline  & regular basis. & & \\
**Wi-Fi control** & Full control over the & & Continuous connection. Smooth control. \\  & object. & Control parameters are updated on a regular & \\  & & basis. Full control over the object. \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of system performance quality indicators depending on the distance.
## 3 Results and Discussion
### Thickness, Opacity and Light Transmittance of Films  
The addition of the nanoemulsion of TC influenced film thickness. The results are shown in Table 2. Higher concentrations of TC resulted in thinner films due to the reduction of the solid content. The film thickness varied between 63 and 91 \(\upmu\)m from P/NE:1 to P/NE:4 ratios, while the thickness of pure pectin film was 102 \(\upmu\)m.  
Transparency in food packaging is important because it influences the choices consumers make [43]. The higher the opacity value, the lower the transparency. Table 2 shows the opacity values of the films. The results showed that the opacity of the pectin films increased from 1.21 to 2.37 A \(\times\) mm\({}^{-1}\), giving more opaque films compared to the neat pectin films with an opacity of 0.73 A \(\times\) mm\({}^{-1}\). This is due to the dispersion of the nanoemulsion of TC into the film matrix. In fact, the scattering of lipid particles plays a role in coalescence, light-scattering, and cream phenomena throughout the drying process. It caused the surface coarseness of the film and more opaque films [44]. A higher concentration of oil caused a higher value of opacity, except for P/NE:1 which can be explained by its thinner thickness and the very high concentration of TC (5%). The films with clove essential oil emulsions also showed a reduction in film opacity by adding essential oil emulsions [45]. The percentage of light transmittance at the wavelengths of 280 and 600 nm is shown in Table 2. The transmittance at a wavelength of 280 nm (UV range) was investigated to evaluate the UV barrier properties of the films. UV radiation can cause the oxidation of food products. Therefore, this parameter is an important factor when these films are used for food packaging in order to improve food preservation [46]. The addition of a TC nanoemulsion resulted in a complete UV barrier for pectin films. Pure pectin films had a transmittance of 6.49%, while all pectin films that incorporated a nanoemulsion of TC had a transmittance of zero percent at 280 nm. Transmittance at a wavelength of 600 nm (visible range) was evaluated as a transparency factor and light transmission. A significant reduction was observed in transmittance at a wavelength of 600 nm with the addition of a TC nanoemulsion. The pure pectin films showed a high transmission of 83.83%, while the pectin film containing a nanoemulsion of TC had lower transmittances ranging between 11.51 and 2.15%. The reduction in transmittance at both the 280 and 600 nm wavelengths could be attributed to the change in light absorption when combined with the nanoemulsion in the pectin film formulation and the effect of oil/lipid addition in the film matrix as well as the creation of irregularities that reduce specular light reflection [47]. The transmittance value of the samples at 600 nm was statistically different (\(p<0.05\)) and followed the decreasing order P/NE:1 \(>\) P/NE:4 \(>\) P/NE:3 \(>\) P/NE:2. This tendency confirmed the reduction in light transmittance due to the presence of TC, except for P/NE:1. Surprisingly, P/NE:1
## 4 Field Test Results and Discussion
### RSSI Measurements  
As the optional section of the radio transmission layer, RSSI is an indicator of the received signal strength to determine whether to increase the strength of the transmission power. Besides the transmission distance, which is the significant influential factor, the factors leading to changes of RSSI also include the directivity and gain of antenna, application environment, and the placement of the sensor node. In the field test, the CO\({}_{2}\) sensor was placed in an 8 \(\times\) 84 m\({}^{2}\) watermelon greenhouse to carry out communication tests. The wireless communication module was powered by a standard 5 V voltage and the matched vertically-installed antenna was a common single whip type. Since the RF signal wavelength was affected by surface reflections, the sensor node was placed at half a meter from the ground, thus expecting to achieve the best communication performance. The initial transmission power was set to 20 dBm (100 mW). The field test results are shown in Figure 14. From Figure 14a, the relationship between the RSSI value and communication distance are basically consistent with the theoretical anti-exponential curve, with some fluctuation caused by the combined effect of regional plant shelter and inherent fluctuations. According to the preset feasible RSSI range, the corresponding reasonable transmission distance with 20 dBm transmission power is 58-80 m, which is able to satisfy the application in such a greenhouse. These test results were measured during the growth stage of the plants, and more serious signal attenuation in the harvest period from the relatively strong plant shelter influence should be paid more attention. Besides the transmission distance and plant shelter, the movement of mangers between the terminal PC and sensor nodes will bring nearly 10 dBm RSSI value attenuation, which is the tolerance range in the automatic transmission power regulation. In order to deal with the unpredictable influence on wireless communication from the environment, like humidity, temperature, and different plant shelter influences in different growth stages, the sensor nodes should intelligently adjust the transmission power to optimize the transmission performance, including saving power, ensuring link quality and avoiding co-channel interference.  
Figure 13: The measured CO\({}_{2}\) concentration under dynamic Fuzzy-PID control.  
However, there were undesired detection errors in the RSSI test and the detection errors increased with the transmission distance, as shown in Figure 14b. When the transmission distance is longer than 80 m, the detection errors reach 10%, implying that the method of using RSSI to evaluate link quality is not applicable in long-distance transmission in a greenhouse.
## 1 Introduction
A similar strategy has been used in [26], where a particle swarm algorithm is employed to optimize the control settings of air-handling units in an office building to obtain a good trade-off between energy consumption and thermal comfort. However, in case of residential buildings, the most important aspect is to satisfy the inhabitants requirements and then try to reduce as much as possible the total energy consumption. In [27] authors propose a methodology that, thanks to the usage of GA, identifies the building thermal parameters subject to multi-objective cost functions and constraints. In this case, the optimization also considers the control of the employed HVAC systems and the occupants comfort requirements to obtain optimal building thermal parameters. However, this methodology is quite complex and computationally heavy and, furthermore, it must be applied to new buildings. The methodology proposed here exploits a recently developed temperature modelling technique that provides a sufficiently accurate model of the temperature of each room (by also considering the heat exchange between adjacent rooms) without any prior knowledge of the structure of the building [28], which can be already existent. Based on this model and on the user requests, in terms of temperature values, the reference signal for each room during the day is computed to minimize the energy consumption. To reduce the computational burden, only ramp and step reference signals are considered. Although the obtained solution is therefore suboptimal from a theoretical point of view, the solution can be determined in a relatively short time interval before the resulting planning is applied (for example, it can be calculated during the night for the day after). Both the cases where fan-coil units and radiators are employed have been analyzed. Furthermore, the case where a room is unoccupied and therefore the value of the temperature is not relevant for the user has been addressed, showing again the effectiveness of the devised centralized approach. The paper is organized as follows. The novelty of the developed approach is presented in Section 2. The problem is formulated in Section 3, while the methodology proposed to solve it is presented in Section 4. The model of the residential building used to illustrate the method is described in Section 5 and simulation results are in Section 6. The case of unoccupied rooms is elaborated in Section 7 and, a comparison between the proposed method and the well-known Model Predictive Control is shown in Section 8. Final discussions and conclusions are given respectively in Sections 9 and 10.
## 1 Introduction
In [19], homomorphic encryption is used to protect the ballots. The organizer can verify the ballot when tallying the votes without obtaining any details about the content of the ballot. However, the scheme suffers from high computation and communication overheads due to the time and computational power required to aggregate homomorphic encrypted ballots and to decrypt the large ciphertexts that were produced by the scheme. In this paper, we propose an electronic voting scheme using IBM Cloud-based Hyperledger Fabric. We opt to develop our e-voting scheme using the Hyperledger Fabric blockchain for two main benefits. Firstly, Hyperledger Fabric is a private blockchain, meaning only identified nodes can participate in the network, and these known participants can access the data stored on each block. Secondly, using IBM cloud-based Hyperledger Fabric removes the burden needed to set up and run a private local blockchain. Self-hosting a private blockchain requires thousands of dollars worth of hardware and a great wealth of technical knowledge to set up and maintain [20]. Our developed scheme is unique, as most existing works depend on using _public or locally-hosted blockchains_ to run an e-voting scheme. Moreover, ours is not a costly solution, as elections only run on a scheduled basis, e.g., once a year or once every four years [12; 13; 15; 21; 22; 23]. Our proposed e-voting scheme allows small businesses and governments alike to run an election in a decentralized, transparent, and secure manner. We developed a custom smart contract with four custom attributes to drive all of the functionality required to host an election. The smart contract handles the creation of the election, registering of voters, casting of votes, and tallying of votes. In our voting scheme, the blind signature cryptoscheme is leveraged so that voters can obtain anonymous voting tokens so they can cast their votes to the blockchain while maintaining the privacy of their real identity, which is not shared with the network. To evaluate our proposed scheme, we have used Hyperledger Caliper to measure and evaluate the performance in terms of throughput, latency, and error rate. The results indicate that the use of the IBM Cloud to remotely host a blockchain-based e-voting scheme is feasible and scalable to elections involving 10,000 voters. The rest of this paper is organized as follows. In Section 2, we perform a literature review and discuss related works. Next, in Section 3, we present the considered scheme model followed by the design goals of our proposed scheme. Section 4 gives an overview of the preliminaries used in our paper. Section 5 discusses the considered blockchain design and our developed smart contract. In Section 6, we present the performance evaluations of our proposed scheme. An analysis of the proposed scheme is given in Section 7. Finally, we give concluding remarks in Section 8.
## 3 Methodology
### Experimental Set-Up
#### 3.3.1 XGBoost and LightGBM  
To perform extreme gradient tree boosting algorithm, we used standard XGBegressor from Python package called xgboost version 0.90 and LGBRegressor from lightgbm Python package version 2.1.1 for the implementation of LightGBM model. To get better results from tree-based models, we needed to find best parameters for each model by using customized search approach. The best parameters for XGBoost model are: n_estimators = 70, max_depth = 2, min_child_weight = 1, learning_rate = 0.2, gamma = 0, colsample_bytree = 1, alpha = 10, and objective = reg-squarederror, with all other parameters set to default. For LightGBM, the parameter setting is: learning_rate = 0.1, max_depth = -1, metric = {'11', '12'}, num_leaves = 255, colsample_bytree = 1.0, objective = regression, subsample = 0.6, and seed = 10. Training the model for the best number of iterations while using early stopping patience until 5 epochs to prevent the model from overfitting gives best results at 28 epochs.
# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 4 Results and Discussion
### Results  
#### 4.4.1 14-Day Temperature Prediction Experiment  
To evaluate the performance of the model for short-term temperature prediction and the prediction performance differences among each model, the temperature prediction experiments are set up for the next 14 days, as follows in this research. We took Nanjing city as the research area and set the number of time-sliding windows to 30. The number of input nodes is 30 \(\times\) 8, a total of 240, and the number of output forecast days is 14 days. We use training and validation datasets to train GCN-BiLSTM, DFN, and LSTM, respectively. Then, three baseline models and GCN-BiLSTM are employed to forecast 14-day temperatures by selecting 10 random time points. The RMSE and MAE of each model are calculated by taking the average results of 10 experiments.  
The experimental results of temperature prediction for the next 14 days are shown in Table 7.