esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant Design for the Conversion of Plastic Waste into Valuable Chemicals (Alkyl Aromatics)
## 2 Materials and Methods
### Pretreatment of the Plastic  
Plastic waste production is a problem worldwide, and its production has been increasing with time. According to reports, in the year 2020, more than 380 million tons of plastic waste were produced worldwide. In Oman, about 3.8 million tons of plastic waste were produced in the same year, which shows that the availability of plastic is not an issue. It can be easily gained from landfills and other dumping areas. The next step is the pretreatment of the plastic, which is further divided into three steps [13].  
* Cleaning  
The methods for dirt and dust particle removal when recycling HDPE and LDPE plastics include sorting and prewashing to manually remove large debris and contaminants, as well as rinsing with water or mild detergent. Mechanical cleaning methods like sieving or screening with vibrating screens or sieves can separate plastic particles from dirt. Washing and rinsing with water and detergents, along with agitation, can dislodge stubborn dirt particles. Floatation or density separation techniques using water or air can separate plastic from heavier contaminants. Air classification systems use air currents to blow away fine  
Figure 3: Process flow diagram.  
particles, and electrostatic separation utilizes electrical charges to attract or repel dust particles from the plastic waste.
* Drying  
Since dry material melts and degrades easily, the plastic is dried to boost the thermal efficacy of the reaction. Drying the plastic in open sunlight is possible, but this may again introduce dust into the plastic. Drying may also be achieved by introducing hot air to plastic. This is a somewhat pricey but practical approach.
* Shredding  
Most of the plastic collected from landfills is in the shape of large lumps. These lumps can delay the pyrolysis process by generating resistance in heat breakdown and causing an increase in residence time. To lessen this issue, plastic trash is shredded into smaller pieces, which decreases the aggregated material into smaller and separated particles with more surface area. These particles can readily transmit heat and are thermally destroyed quickly.
## 4 2.5D Map Representation and Relocation
### Traditional Grid Map and Feature Map  
Occupy grid map is wildly used in LiDAR-SLAM, it is a simple kind of 2D map that represents the obstacles on the LiDAR plane:  
\[M_{grid}=\{m_{g}(x,y)\}, \tag{18}\]  
where \(m_{g}(x,y)\) denotes the possibility if the grid \((x,y)\) is occupied. Generally, the value of \(m_{g}(x,y)\) can be 1 (the grid \((x,y)\) is occupied) or 0 (the grid \((x,y)\) is not occupied).  
Feature map is another kind map generated by most feature-based Visual-SLAM approaches, it can be represented as:  
\[M_{feature}=\{f(x,y,z)\}, \tag{19}\]  
where \(f(x,y,z)\) denotes that on the world position \((x,y,z)\), there is a feature \(f(x,y,z)\), for real applications,\(f(x,y,z)\) could be a descriptor to the feature in a dictionary like BoW.  
While Visual-SLAM processing, key-frames with bag-of-feature description and poses at where they were observed are usually stored as an accessory for feature maps. For relocation applications, searching and matching in key-frames can greatly improve the processing speed and reliability.  
As a result, a feature map is a sparse map which only has value on the position which has features. This makes a feature map is reliable for localization, but not good for navigation and path-planning.
## 3 Materials and Methods
### Materials
#### 3.1.2 Implementation Details  
All experiments were conducted on a system equipped with an AMD Ryzen 7 7800X3D processor (8 cores, 16 threads), 32 GiB of RAM, and an NVIDIA GeForce RTX 4070 Ti SUPER GPU. The operating system used was Ubuntu 22.04.5 LTS (64-bit). All hyperparameters listed in Table 2 were optimized individually for each model through validation-based tuning. For classification models (EfficientViT, VGG-16, ResNeXt-50, DenseNet-121), we applied early stopping with a patience of 20 epochs to ensure convergence while avoiding overfitting. For segmentation models used in the 2-stage pipeline, a more aggressive early stopping with a patience of 10 epochs was employed due to the faster saturation of IoU-based validation metrics. These strategies ensured stable and efficient training across all architectures.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model Type** & \begin{tabular}{c} **Batch** \\ **Size** \\ \end{tabular} & \
\begin{tabular}{c} **Epochs** \\ **Rate** \\ \end{tabular} &
\begin{tabular}{c} **Learning** \\ **Optimizer** \\ \end{tabular} \\ \hline U-Net, SegNet, EfficientPS & 8 & 200 & 0.001 & Adam \\ Attention U-Net, DeepLabV3+, MobileNetV2 & 8 & 200 & 0.0001 & Adam \\ ResNeXt-50, DenseNet-121, VGG-16 & 8 & 400 & 0.0001 & SGD \\ EfficientViT & 8 & 400 & 0.0005 & AdamW \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training configurations for segmentation and classification models.  
Figure 2: Examples of beef carcass cross-section images from the dataset used in this study. The dataset includes various quality grades, where the distribution of intramuscular fat plays a critical role in determining the grade.
## 2 Related Research
### Position Fingerprinting Method
#### 2.1.3 SVM-Based Fingerprinting Method  
SVM (Support Vector Machine) [17] is an algorithm capable of handling classification and regression with excellent performance for high-dimensional, nonlinear pattern recognition problems [25]. Data classification is achieved by adding a kernel function \(K(x,y)\) to the SVM to construct a partitioned hyperplane that maximizes the geometric interval in the high-dimensional space. The SVM is trained in the offline phase using data from the fingerprint database to determine the nonlinear relationship between RSSI values and location coordinates, and then the localization calculation is performed in the online phase.  
The problem is that accuracy is affected by the kernel function and the number of parameters, and as training data increases, the computational complexity becomes enormous.
## 3 Method  
### Framework Overview  
The domain adaptive method proposed in this paper involves two key domains: a source domain \(S\) for pre-training, which can be any normal lighting scene, and a target domain \(T=\{T_{d},T_{n}\}\) containing two roughly aligned subdomains, \(T_{d}\) and \(T_{n}\), representing daytime and nighttime scenes, respectively. In the pre-training phase, a labeled image set \(S\triangleq\{X_{S},Y_{S}\}\) from the source domain was used to optimize the semantic segmentation network parameters. Subsequently, two discriminators, \(D_{S\to T_{d}}\) and \(D_{S\to T_{n}}\), were employed to bootstrap the domain adaptive model transfer from \(S\) to \(T_{d}\) and from \(S\) to \(T_{n}\) to efficiently model semantic segmentation of the nighttime scene \(T_{n}\) in the target domain. The domain adaptive semantic segmentation network in this paper comprises three modules: (1) a low-light image enhancement network \(N_{en}\), (2) a pre-trained semantic segmentation network \(G_{pre}\) and a transfered semantic segmentation network \(G_{seg}\), which decouples the body and edge during segmentation and provides predicted image dimensions of \(\mathbb{R}^{H\times W\times C}\), with \(C\) denoting the total number of image categories, and (3) a segmented mask activation network \(N_{Y}\), which consists of a convolutional layer and a sigmoid normalization function, as shown in Figure 1. The network input contains the source domain image \(X_{S}\) and the target domain images \(X_{T_{d}}\) and \(X_{T_{n}}\), consisting of three types of domain samples. Among them, \(X_{T_{n}}\) was additionally passed through a nighttime (low-light) enhancement network \(N_{en}\), which generated an enhancement loss \(L_{en}\) to optimize the enhancement result and brought the output closer to the daytime domain. The network uses image annotations \(X_{S},Y_{S}\) in the source domain \(S\) dataset to compute the segmentation loss \(L_{seg}\) and then obtains the segmentation prediction masks \(\hat{F}=\{\hat{F}_{S},\hat{F}_{T_{d}},\hat{F}_{T_{n}}\}\) and segmentation loss \(L_{de}\) by \(G_{seg}\). After that, two discriminators, \(D_{S\to T_{d}}\) and \(D_{S\to T_{n}}\), perform adversarial transfer learning, and the final segmentation masks \(\hat{Y}=\{\hat{Y}_{S},\hat{Y}_{T_{d}},\hat{Y}_{T_{n}}\}\) are obtained via activating the network \(N_{Y}\), i.e., \(\hat{Y}=\{N_{Y}(\hat{F}_{S}),N_{Y}(\hat{F}_{T_{d}}),N_{Y}(\hat{F}_{T_{n}})\}\). The whole network guides the domain adaptive alignment of the model based on the composite total loss \(L_{total}\).
## 2 Architecture of VANETs  
VANETs adopt similar concepts of connectivity and design as MANETs. VANET communication can be divided into three major categories: (1) V2V communication, where vehicles can link immediately to disseminate messages to each other; (2) V2I communication [8], in which the vehicle can connect with infrastructure-based networks for exchanging data wirelessly; and (3) Infrastructure-to-Infrastructure (I2I) networks to contribute to major vehicular applications, as shown in Figure 2. A wireless connection exists between the infrastructure and nearby vehicles, where it can relay data in both directions (e.g., V2I and I2V) [9]. The infrastructure offers up-to-date information and internet access to vehicles through this connection. As a result, they will receive major updates on current events as well as traffic on nearby highways. VANET architecture includes communication between the onboard unit (OBU) installed on vehicles with the Roadside Unit (RSU), which are mostly static in nature and are installed at the roadside. The synchronization between all the units provides improved results in the form of delivery, throughput, and efficiency.  
Figure 2: VANET Architecture.  
The Roadside Unit (RSU) and an onboard unit (OBU) are the key elements used for VANET [10]. Normally, the RSU is static all along the paths, while the OBU is housed inside the vehicle. All RSUs are interconnected with each other along the route. The key RSU functions include: (i) Expanding the range of VANET communication [11] by sending messages to another OBUs and RSUs. (ii) Applications for running protection, such as traffic situation coverage or accident alerts. (iii) Supplying OBUs with internet access.  
The OBU, on the other hand, handles contact between vehicles and the RSUs on the network [12], as shown in Figure 3. An OBU comprises a processor, memory, network unit, and sensors for resource commands. Later, the OBU observes and collects the data to create messages delivered via wireless media to nearby vehicles [13].
## 4 Conclusions  
RAFT polymerization of DMAEMA has been successfully carried out in the presence of DBTTC, MTTCD, BDATC and CPTCD as the chain transfer agents and AIBN as the initiator. The results showed that CPTCD was the effective RAFT agent for the RAFT polymerization of DMAEMA. The polymerization of DMAEMA with CPTCD as the chain transfer agent showed \"living\"/controlled characteristics and exhibited pseudo-first-order kinetics. Star PDMAEMA has been successfully prepared by the RAFT polymerization of DVB using PDMAEMA-CPTCD as macro RAFT agent and the pure star PDMAEMA was also obtained by the precipitation fractionation technique. We can see from the results that the polydispersity index of SPDMAEMA-8 (PDI = 1.43) was apparently lower than SPDMAEMA-4 (PDI = 2.10, DVB/PDMAEMA-CPTCD = 45). The chemical composition and structure of the linear and star polymers were characterized by GPC, FTIR, \({}^{1}\)H NMR, XRD analysis.  
For the pure Cs membrane, CO\({}_{2}\) permeation rate and ideal selectivity of the blending composite membrane both have a great improvement with increasing the content of SPDMAEMA-8. When at a feed gas pressure of 37.5 cmHg and 30 \({}^{\circ}\)C, the blinding composite membrane (Cs:SPDMAEMA-8 = 4:4) had a CO\({}_{2}\) permeation rate of 8.54 \(\times\) 10\({}^{-4}\) cm\({}^{3}\) (STP) cm\({}^{-2}\)-s\({}^{-1}\)-cm\(\cdot\)Hg\({}^{-1}\) and a N\({}_{2}\) permeation rate of 6.76 \(\times\) 10\({}^{-5}\)cm\({}^{3}\) (STP) cm\({}^{-2}\)-s\({}^{-1}\)-cm\(\cdot\)Hg\({}^{-1}\), and an ideal CO\({}_{2}\)/N\({}_{2}\) selectivity of 35.2.
## 5 Parameters Influencing Impedance Frequency Response
### Switching Frequency  
We have also studied the influence of the inverter switching frequency on the accuracy of the proposed impedance identification method. Since it is the various multiples of the switching frequency that are intrinsically excited, this means that for a fixed measurement frequency range, the characteristic obtained is enriched or depleted, depending on the switching frequency used. This assertion is confirmed by comparing the cleaned impedance characteristics shown in Figure 27a, obtained with \(f_{PWM}=15\) kHz and \(f_{PWM}=30\) kHz. The number of blue points is twice as high when the switching frequency is set to 15 kHz.  
This increased point density provides a better approximation of the different series and parallel resonance frequencies. As a result, their estimation is all the more accurate and reliable. However, the interpolations presented in Figure 27b show that the difference in accuracy obtained with these two switching frequencies is not significant enough to bias the characterization, as the deviation is less than 3% (the uncertainty range calculated in Section 4.3 during reliability tests of the method).
## 2 Data  
The dataset used in this study was obtained from the UCI Machine Learning repository. It comprises data from a CCPP, which consists of two gas turbines, two Heat Recovery Steam Generators (HRSGs), and one steam turbine. The gas turbines consist of a compressor, combustor, and turbine. The compressor combines compressed air with fuel to generate a high-temperature flow through the combustor. The high-temperature flow drives the turbines, generating electrical power. The HRSGs capture heat from the gas turbines' hot exhaust gasses to produce steam, which is then supplied to a steam turbine to generate additional electrical power. Consequently, electricity is generated by both the gas and steam turbines within the CCPP. According to [PERSON] and [PERSON] [19], the performance of the gas turbine is affected by anything that changes the density and/or mass flow of air intake to the compressor such as ambient temperature, pressure, and humidity. The exhaust steam pressure from the steam turbine is converted back into water through the condenser. The higher the vacuum inside the condenser, the lower the outlet pressure of the steam turbine, and more steam is moved to the condenser. Thus, this enables the steam turbine to generate maximum power output. However, abnormal vacuum pressure can hinder the condensation process, ultimately resulting in decreased power output [20]. Therefore, a vacuum level must be maintained in the condenser so that the generator can work optimally.  
The dataset contains 9,568 data points collected over six years (2006-2011), specifically during the 674 days when the CCPP operated at full load. Input variables, namely ambient temperature (AT), atmospheric pressure (AP), relative humidity (RH), and vacuum (V), were calculated as an average per hour and obtained from various sensors located around the plant that record the ambient variables every second. We use these input variables to predict the average hourly full-load electrical power output (\(P_{E}\)) received from a control system when the power plant is at base load. All the input variables and target variable are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Variable** & **Range** \\ \hline \multirow{4}{*}{Input} & Ambient temperature (AT) & 1.81–37.11 \({}^{\circ}\)C \\  & Atmospheric pressure (AP) & 992.89–1033.3 mbar \\  & Relative humidity (RH) & 25.56–100.16\% \\  & Vacuum (V) & 25.36–81.56 cm Hg \\ \hline \multirow{2}{*}{Target} & Full-load electrical power & \\  & output (\(P_{E}\)) & 420.26–495.76 MW \\ \end{tabular}
\end{table}
Table 1: The hourly average ambient input variables and target variable in the dataset.  
Figure 1 shows correlation coefficients among the variables. Among the input variables, AT and V exhibit a strong positive correlation of 0.844, indicating a significant positive relationship between them. The target variable (\(P_{E}\)) displays a strong negative correlation with AT and V and a weak positive correlation with AP and RH. Nevertheless, it suggests that they might all affect the power output.
## 2 Materials and Methods
### Instruments and Data Analysis  
A wireless bipolar surface electromyograph (Cometa systems inc., Milano, Italy; resolution: 16-bit; gain: 1000; impedance: 10\({}^{5}\)\(\Omega\)) was used to record the muscles' signals at a 2000 Hz sampling frequency. The skin areas under the EMG electrodes were shaved, cleaned and prepared with conductive cream. After a careful preparation of the skin, two Ag/AgCl electrodes were placed on 8 trunk/shoulder muscles on the left side of the athletes' bodies. Data from the biceps brachii (BIC), middle trapezium (TRA), posterior deltoid (DEL) and triceps brachii long head (TRI) were collected following European SENIAM procedure [10]. EMG activities of the pectoralis major sternocostal part (PEC), serratus anterior (SER), infraspinatus (INF) and latissimus dorsi (DOR) were collected based on previous literature [11; 12; 13; 14]. Physical verification tests were executed to ensure accurate electrode positions. The raw signals were filtered with a 10-500 Hz band-pass filter. The root mean square (RMS) values of the EMG signals were calculated for each muscle in each exercise with a window of 25 ms and a step of one sample. The ratio between the RMSs of the PSEs and the IS, expressed in percentage, was used to calculate the muscular excitation of the PSEs relative to the main element [7]. The signal-to-noise ratio was controlled using the Agostini procedure [15]. When the signal-to-noise ratio value guaranteed the good behavior of the synergy extraction algorithm [16], the muscle synergies were identified for each muscle and exercise following the procedure suggested by [PERSON] and colleagues [17; 18]. After applying the previously described band-pass filters, the EMG signals underwent a series of processing steps, including high-pass filtering (8 th-order zero-lag IIR Butterworth digital filter, cut-off frequency of 35 Hz), rectification and low-pass filtering (4 th-order zero-lag IIR Butterworth digital filter, cut-off frequency of 12 Hz). The EMG envelope was then amplitude-normalized to the maximum value recorded across all muscle signals for each participant. To extract a set of N time-invariant muscle synergies that minimized the reconstruction error, the Non-Negative Matrix Factorization algorithm was applied [18]. The \"nnmt\" MATLAB(r) function was executed, varying the number of muscle synergies from 1 to 8 and using parameters optimized in previous studies: the multiplicative update algorithm, a function tolerance of \(1\times 10^{-6}\), 50 replicates and a maximum of 1000 iterations [18]. For each number of muscle synergies,
# Performance Analysis of a WPCN-Based Underwater Acoustic Communication System
## 5 Conclusions  
In this paper, we have presented the application of WPCN to the UWAC systems and investigated the performance of the WPCN-based UWAC systems. We have derived tractable and novel closed-form expressions for the PDF of the instantaneous receiving SNR at the H-AP, as well as the average BER and outage probability for the systems. These expressions have been verified by Monte-Carlo simulations. We have also investigated the impacts of various system and channel parameters on the system's performance. These parameters include transmission distance, spreading factor, signal frequency, time allocation factor, and Doppler shift. The results have demonstrated that the average BER and outage probability are affected by these parameters. It has also been demonstrated that there exist optimal values of signal frequency that minimize the average BER and outage probability, as well as optimal values of time allocation factor that maximize the achievable information rate. In this paper, we assume that all the energy harvested in the downlink is used for information transmission in the uplink. Energy-efficient optimization design for the proposed WPCN-based UWAC systems will be left for future work. Also, we assume a simple binary constellation and perfect CSI for the proposed analysis in our setup, and Monte-Carlo simulation is performed to validate the results. Different modulation, coding, channel estimation, equalization schemes, and field experiments for the proposed analysis can be further investigated. New subjects can be investigated to expand the work in this paper to the scenario of numerous users.  
Conceptualization and methodology, Y.F., R.X. and Y.Z.; software and validation, R.X. and Y.Z.; formal analysis and data curation, R.X. and Y.Z.; writing--original draft preparation, Y.F. and R.X.; supervision, Y.F. and F.J. All authors have read and agreed to the published version of the manuscript.  
Figure 7: The average BER versus Doppler shift for various signal bandwidths (\(P_{d}=170\) dB re \(\upmu\)Pa and \(d=60\) m).  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant numbers 62171187 and 62192712, and the Guangdong Basic and Applied Basic Research Foundation, grant number 2022A1515011476.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this paper are available upon request from the corresponding author.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their careful reading and valuable comments.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
###### Abstract  
This paper reports on an efficient lossless compression method for periodic signals based on adaptive dictionary predictive coding. Some previous methods for data compression, such as difference pulse coding (DPCM), discrete cosine transform (DCT), lifting wavelet transform (LWT) and KL transform (KLT), lack a suitable transformation method to make these data less redundant and better compressed. A new predictive coding approach, basing on the adaptive dictionary, is proposed to improve the compression ratio of the periodic signal. The main criterion of lossless compression is the compression ratio (CR). In order to verify the effectiveness of the adaptive dictionary predictive coding for periodic signal compression, different transform coding technologies, including DPCM, 2-D DCT, and 2-D LWT, are compared. The results obtained prove that the adaptive dictionary predictive coding can effectively improve data compression efficiency compared with traditional transform coding technology.  
Keywords: lossless compression; periodic signals; adaptive dictionary; predictive coding; transform coding +
Footnote †: journal: Applied sciences