esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Guide Set-Based Particle Swarm Optimization for Multi-Objective Portfolio Optimization
## 1 Introduction
Furthermore, the proposed approach identifies subsets of assets to be included in the portfolio, leading to a reduction in the dimensionality of the problem. Lastly, MGSBPSO is the first MOO approach to SBPSO. The performance of MGSBPSO for portfolio optimization is investigated and compared with that of other multi-objective algorithms, namely, MGPSO, non-dominated sorting genetic algorithm II (NSGA-II) [12], and strength Pareto evolutionary algorithm 2 (SPEA2) [13]. The single-objective SBPSO is also included in the performance comparisons as a baseline benchmark and to evaluate whether SBPSO is competitive amongst multi-objective algorithms. NSGA-II and SPEA2 were selected to compare with MGSBPSO, since these algorithms had been used extensively for portfolio optimization before [14; 15; 16; 17; 18; 19; 20]. It should also be noted that this paper is the first to apply MGPSO to the portfolio optimization problem. The main findings of this paper are:  
* MGSBPSO is capable of identifying non-dominated solutions to several portfolio optimization problems of varying dimensionalities. * The single-objective SBPSO can obtain results (over a number of runs) that are just as good as those obtained by multi-objective algorithms. * NSGA-II and SPEA2 obtain good-quality solutions, although they are not as diverse as the solutions found by SBPSO, MGPSO, and MGSBPSO. * MGPSO using a tuning-free approach [21] performs similarly to NSGA-II and SPEA2 using tuned control parameter values. * MGSBPSO scales to larger portfolio problems better than MGPSO, NSGA-II, and SPEA2. The remainder of this paper is organized as follows: The necessary background for the portfolio optimization is given in Section 2. Section 3 details the algorithms used in this paper. Section 4 proposes MGSBPSO for portfolio optimization. The empirical process for determining the performance of the proposed approach is explained in Section 5, and the results are presented in Section 6. Section 7 concludes the paper. Ideas for future work are given in Section 8.
## 4 Conclusions  
In this work, we studied the drag and flow noise reduction effects of the superhydrophobic surfaces with different GF and strip numbers under the same Reynolds number. We used the boundary conditions of alternating free-slip and no-slip surfaces to simulate the superhydrophobic surfaces. The study focuses on the scenario that the Reynolds number is 4200 and the friction Reynolds number is 180. \(GF=0.5,0.6,0.7,0.8\) and \(n=1,2,4,8\) are considered on the superhydrophobic surfaces to study noise and drag reduction effect. We have used the large eddy simulation for the simulation of the flow domain. We used the Lighthill acoustic analogy combined with finite element and infinite element method for the calculation of the flow noise. The results show that the superhydrophobic surface has noise reduction effect. GF and strip number have a significant influence on the noise reduction effect of the superhydrophobic surface. When GF is small(\(GF=0.5\)), superhydrophobic surface will produce a small slip velocity. As a result, the flow is relatively stable and quiet. When GF becomes larger, the bulk velocity in the channel rises and the noise increases due to the increase of slip velocity. When the GF is constant, if \(n\) is increased, the fluid in the channel will increase the lateral energy exchange due to the alternating  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strips Number** & **1** & **2** & **4** & **8** \\ \hline GF = 0.5 & 84.6 & 81.5 & 79.2 & 82.6 \\ GF = 0.6 & 83.1 & 87.1 & 85.9 & 81.2 \\ GF = 0.7 & 86.5 & 88.6 & 87.5 & 82.5 \\ GF = 0.8 & 86.2 & 93.5 & 91.1 & 90.8 \\ \hline no-slip & & & 89.9 dB & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall sound pressure level of different scenarios (dB).  
Figure 19: Comparison of frequency spectra for GF at \(Strip\ number=1,2,4,8\). **(a)**\(GF=0.5\). **(b)**\(GF=0.6\). **(c)**\(GF=0.7\) **(d)**\(GF=0.8\).  
free-slip and no-slip boundary conditions. As a result, more lateral vortex structures are generated and the effect of drag reduction and noise reduction is weakened. In our results, when strip number is 4 and GF is 0.5, the optimal noise reduction effect is obtained as 10.7 dB. The optimal drag reduction effect is 32%, and it is obtained under the scenario of \(GF=0.8\) and \(Strip\ number=1\). In summary, the choice of GF and the number of strips is comprehensively considered to guarantee the performance of drag reduction and noise reduction in this work. This work can provide a new direction for flow noise control on sonar platform.  
Methodology, C.N., Y.L., D.S, C.Z.; software; C.N.; writing--original draft preparation, C.N.; writing--review and editing, C.N., Y.L., D.S., C.Z.; supervision, D.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Defense Basic Scientific Research Program of China, grant number JCKYS20206048 SJS001. The authors would like to thank the College of Underwater Acoustic Engineering Laboratory.  
The authors declare no conflict of interest.
## 3 Results
### Comparisons Between the Impacts of Forest Cover Change and CDHEs on Forest Carbon Dynamics  
In Nanjing, the net carbon loss caused by forest cover change (0.222 TgC) was more significant than that caused by CDHEs (0.186 TgC) during 2000-2022, indicating that the carbon loss caused by forest in Nanjing was dominated by human activities (Figure 12). Conversely, the net carbon loss caused by forest cover change in Shaoguan (1.293 TgC) was smaller than that caused by CDHEs (3.219 TgC), indicating that extreme climate was the dominant factor causing forest carbon loss in Shaoguan. Both factors caused the most forest carbon loss in Shaoguan, especially CDHEs. The effects of CDHEs and forest cover change on forest carbon dynamics had two sides. Afforestation can increase forest carbon storage, while deforestation can cause forest carbon emissions. Moderate heat events caused an increased photosynthesis effect, increasing forest carbon sequestration capacity and forest carbon storage. In contrast, extreme heat and drought affected vegetation photosynthesis and even led to vegetation death, decreasing forest carbon storage. However, there are differences in how forest cover change and CDHEs affect forest carbon dynamics. Forest cover change directly led to the decrease or increase in forest carbon storage through the decrease or increase in forest area. The carbon loss caused by CDHEs was mainly due to the decrease in forest carbon sink capacity.  
Figure 12: Forest cover change and CDHEs caused net carbon loss in the two regions during 2000–2022.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year/Month** & **Nanjing** & **Shaoguan** \\ \hline
2003/07 & & \(-1.456\) \\
2005/06 & \(-0.0374\) & \\
2017/07 & \(-0.0645\) & \\
2021/09 & & \(-0.705\) \\
2022/08 & \(-0.0844\) & \(-1.058\) \\ Total carbon loss & \(-0.186\) & \(-3.219\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Forest carbon loss caused by CDHEs in two regions (unit TgC).
# Material Characterization for Sustainable Concrete Paving Blocks
## 2 Experimental Program
### Mix Proportion  
In this project, the original mix proportion was designed according to the \"mass method\" in Chinese standard JGJ55-2011 [20]. The particular mix proportions for this series of experiments are shown in Table 4. The range of replacement levels for each recycled material was determined as follows:  
Recycled concrete coarse aggregate, or RCCA for short, is one of the most common recycled waste used for recycled aggregate concrete (RAC) products. According to the previous articles and experiments' results, five replacement percentages of RCCA were selected in this experiment. The concrete paving blocks which were cast by replacing the natural coarse aggregate with different replacement levels of 20%, 40%, 60%, 80%, and 100% [21] by weight of the total coarse aggregate content.  
Recycled concrete fine aggregates (RCFA) were also considered as a type of recycled wastes to be used in this experiment. Unlike the RCCA, previous researchers and experimental results [6] indicated that a high replacement level of RCFA would lead to a sharp decline of concrete product's properties. The limitation of the replacement percentage of RCFA was suggested as 30% [22]. Therefore, the replacement levels of RCFA in the experiment were determined as 10%, 20% and 30%.  
Both crumb rubber (CR) and crushed glass (CG) were barely used as aggregates in the concrete paving blocks. Therefore, several trial experiments were undertaken to estimate the ranges of replacement levels. Due to the rubber particles having strong rebound resilience [23], the specimens cannot maintain the original dimensions after being taken out from the brick making machine if the rubber content is too high, considering which the replacement levels of CR were selected as 1%, 2%, and 3%. Similarly to RCCA, crushed glass was also used to replace the coarse aggregates in the blocks. According to the previous articles and trial test results [24], the final replacement levels of CG were decided as 10%, 20%, 30% and 40%. GGBS was used to replace the cement with replacement levels selected as 30%, 50% and 70% according to the previous test results [14; 25].  
In Table 4, \"RL\" represents \"replacement level\", \"NCA\" represents \"natural coarse aggregate\", and \"NFA\" represents \"natural fine aggregate\". The replacement levels of RCCA in the experiment were determined as 20%, 40%, 60%, 80%, and 100%, labeled as RCCB-X, with \"X\" indicating the replacement level of RCCA in the blocks. The replacement levels of RCFA in the experiment were determined as 10%, 20%, and 30%, labeled as RCFB-X. The replacement levels of CB were selected as 1%, 2%, and 3%, and labeled as CRB-X. The replacement levels of CG were decided as 10%, 20%, 30%, and 40%, and named as CGB-X. The replacement levels of GGBS were selected as 30%, 50%, and 70% and labeled as GGBS-X.
# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 4 Experimental Setup  
### Dataset  
To evaluate the proposed models, the ArSL dataset presented in ref. [12] was utilized in this work. The dataset contains 54,049 images distributed around 32 classes of Arabic signs. The dimensions of the images are 64 \(\times\) 64, and many variations of images are presented using different lighting and backgrounds. A sample from the dataset were randomly generated as can be seen in Figure 2. One observation noted was that the number of images per each class was not balanced. Table 5 shows the classification of the Arabic alphabet signs, with labels, the English transliteration of each alphabet, and the number of images, which is also visualized in Figure 3. It has been claimed in ref. [12] that this dataset is sufficient for the training and classification of ArSL alphabets. We did not make any attempts to identify and eliminate the noisy features, as we think their inclusion would enhance the model's resilience and ability to apply them to various situations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Input Shape**} & \multirow{2}{*}{**No Convolution Layers**} & \multirow{2}{*}{**Conv. Filters**} & \multirow{2}{*}{**Conv. Size**} & \multirow{2}{*}{**Max Pool**} & \multirow{2}{*}{**Trainable**} & \multirow{2}{*}{**\(\mathbf{\approx}\)Time (s)**} \\ \cline{5-8} \multirow{3}{*}{CNN-2} & CNN-2V1 & 224 \(\times\) 224 & conv1, conv2, conv3, & 224, 112, 56, & & \multirow{3}{*}{\(3\times 3\)} & \multirow{2}{*}{\(2\times 2\)} & \multirow{3}{*}{328,346} & \multirow{2}{*}{4262} \\  & & & conv4, conv5 \(\times\) leaky & & 28, 14 & & & \\ \cline{2-8} \multirow{3}{*}{CNN-2} & CNN-2V2 & 224 \(\times\) 224 & ReLU, fully connected layer + softmax & 64, 64, 64, 64 & & \multirow{3}{*}{\(3\times 3\)} & \multirow{2}{*}{\(2\times 2\)} & \multirow{2}{*}{249,888} & \multirow{2}{*}{1128} \\ \hline \multirow{5}{*}{CNN-3} & CNN-3V1 & 224 \(\times\) 224 & conv1, conv2, conv3, conv4, conv5 + leaky & 64, 64, 64 & & \multirow{3}{*}{\(3\times 3\)} & \multirow{2}{*}{\(2\times 2\)} & \multirow{2}{*}{250,144} & \multirow{2}{*}{1793} \\  & & & ReLU, fully connected layer + softmax, data augmentation & 224, 112, 56, & & & & \\ \cline{2-8} \multirow{3}{*}{CNN-3} & CNN-3V2 & 224 \(\times\) 224 & \begin{tabular}{c} +1 batch normalization, \\ 1 dropout \\ \end{tabular} & \begin{tabular}{c} 256, 126, 64, \\ 62, 16 \\ \end{tabular} & \begin{tabular}{c} 5 \(\times\) 5, 3 \(\times\) \\ 3 \\ \end{tabular} & \multirow{2}{*}{\(2\times 2\)} & \multirow{2}{*}{430,798} & \multirow{2}{*}{3852} \\  & & & & & & \\ \cline{2-8} \multirow{3}{*}{CNN-3V4} & CNN-3V4 & 224 \(\times\) 224 & \
\begin{tabular}{c} +1 batch normalization, \\ 1 dropout (30 epochs) \\ \end{tabular} &
\begin{tabular}{c} 256, 126, 64, \\ 62, 16 \\ \end{tabular} & \(3\times 3\) & \(2\times 2\) & 431,146 & 11,322 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(Cont.\)  
Figure 2: Arabic Sign Language dataset samples.
# A Latent-Factor System Model for Real-Time Electricity Prices in Texas
## 2 Data and Variables
### Sample Period and Variables
We use the Henry Hub price instead of the local natural gas price (e.g., Houston Ship Channel) since the Henry Hub price is highly correlated with the local natural gas price (\(r>0.95\)). Finally, the latent factor variable, which is estimated from within the system endogenously, appears as an exogenous variable in the observation equation. All variables are on the natural log scale except, of course, the dummy variable. _Latent Factor Equation Exogenous Variables._ Recall that the latent factors are unobserved variables; there is no data for them. The parameters corresponding to these variables are recursively estimated from within the system at each point in time, which leads to the following intuition: if one could observe these latent causes, then they are most likely going to be related to load and prices. For instance, power outages, erroneous short-term weather forecasts, unanticipated transmission bottlenecks, etc., would most certainly impact demand and price distributions across ERCOT. Therefore, we use system-wide load (MWH) and lagged weighted price ($/MWH) across all eight zones as the exogenous factors that could likely associate with the unobserved factor variables. Additionally, a first-order autoregressive process for the latent factor is used. This allows us to capture the potential lingering effects of hidden variables over time. As described in the next section, while we could use higher-order lags, we do not do so in the interests of parsimony. Also, the lagged weighted prices do capture some of the previous time period's effect on the latent factor. Note that the system-wide load is, in one sense, endogenous to the observation equation via the latent factor. Finally, we work with the natural logs of all these variables.
# Influence of the Parameters of an Agricultural Biogas Plant on the Amount of Power Generated
## 4 Discussion  
Correctly selecting the essential components in an agricultural biogas plant is a fundamental factor in this type of investment's economic and functional success. It must also withstand the changing parameters of the anaerobic digestion process. In the biogas plant analysed, the temperature of the digestion mass varied between 34 and 37 \({}^{\circ}\)C, which, according to [22; 23], qualifies the process as mesophilic. However, this is significantly lower than the 42 \({}^{\circ}\)C indicated in the technical documentation. Studies have shown that changing the temperature value during digestion (within the recorded limits) does not directly affect the amount of biogas produced or the amount of power generated in the generator [10]. Properly designed digesters do not allow biogas shortages or excesses to occur. This was the case for the agricultural biogas plant studied. The selected digester volume and the programmed slurry flow allowed biogas to be produced in such quantities (the biogas horizon during the recording period ranged from about 2.1 to 3.7 m) as to ensure that there was sufficient biogas, regardless of the slurry and digestate flow, while maintaining constant electricity generation. The correct setting of the slurry and digestate flows also ensures that the amount of digestate present in the fermenter varies little (its height in the biogas plant analysed varied between 1.55 and 1.75 m).  
The chemical composition of the biogas produced in the biogas plant was also checked during the study. The most significant proportion of methane in the biogas studied was 50.8%, at the lower end of the range of methane in biogas reported in the literature [37; 48; 75]. The carbon dioxide content was as high as 44.3% and was higher than the ranges reported in other publications [48; 76]. Oxygen was only 0.7%, and hydrogen sulphide was 28 ppm. The above parameters were measured before the biogas was subjected to the treatment process. These values indicate that the biogas production process in the agricultural biogas plant under investigation should be optimised, e.g., using the solutions described in [37]. The authors will pursue this topic in further studies.
## 8 Conclusions  
Network virtualization together with the service-oriented architectural principle enable the trend of cloud-native network design that is transforming communication networks from a data transport infrastructure to a versatile service platform that supports the convergence of networking and cloud/edge computing. Intelligent and autonomous management of network operations and service provisioning play a critical role in cloud-native future networks. The wide variety of AI/ML-based technologies developed for addressing different management aspects and the multi-tenant multi-domain nature of future networking call for standard management architectures that may integrate various management technologies within a common holistic framework. This paper surveyed the current status of the standardization of management architectures for enabling intelligent and autonomous management in cloud-native future networks. The notion of cloud-native network design and its impacts on future networking were first introduced. Then, the paper reviewed the latest developments of representative management architectures, including the 3 GPP Network and Management Data Analytics Function (NWDAF/MDAS), ETSI Experiential Networked Intelligence (ENI), the ITU-T Unified ML Architecture in future networks, and the ETSI Zero Touch Network and Service Management (ZSM), and analyzed how cloud-native network design facilitates the management architecture developments. The paper also discussed open issues related to intelligent and autonomous management from an architectural perspective and identified some possible directions for future research and development.  
This research received no external funding.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 2 Related Work
### Packet Loss Measurement  
Packet loss measurement of TCP has been studied in a number of works ([PERSON] et al., 2014; [PERSON] et al., 2015; [PERSON] et al., 2016). [PERSON] et al. (2015); [PERSON] et al. (2016) predict the packet loss rate with implementations in routers where they can acquire the low level sequence and acknowledge numbers of the TCP/IP stack which cannot be obtained by applications. [PERSON] et al. (2017) provide a application layer estimation on packet loss rate. However, it assumes the RTT is a constant. In addition, it mainly aims at end users who download stream from a remote server, which is different from our work.
# Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data
## 4 Discussion
The results also showed that MAEs were improved when the data collected in different nearby locations were used. However, negative effects on MAEs arose when data obtained from widely-separated locations were used. The difference in variation at each hour, shown in Figure 13, was considered the possible cause. This difference in variation reflects the different environments of Tokyo and Saga prefectures. For example, the solar radiation at 15:00 in Saga and 16:00 in Tokyo was very similar, but the variation in temperature was clearly different. Our method performed poorly when data obtained from totally different environments were used for training. These results suggest the need to prepare datasets obtained from a range of environments and a dataset measured under similar environments as a test case in practical applications. Even though such data collection requires time and labor costs, long-term cost should be less than using expensive high-accuracy sensors. This is because, once training data are collected at a location, the same model can be applied even when the low-cost sensor is broken and renewed at the location. Our approach also has an advantage over expensive high-accuracy sensors in terms of flexibility; the combination of our approach and low-cost sensors without the forced aspiration system does not require a large amount of electric power, which is often a challenge for use at outdoor sites, but achieves higher accuracy. The calibration results showed that our method also improved the daily average temperature and the diurnal range of temperature. These are especially important values in crop modeling, because the accumulated value of the former is used as the index of flowering and harvesting, and the latter indicates the balance between photosynthesis and transpiration. Therefore, we expect that our method has the ability to improve the accuracy of crop modeling. Significant research has been conducted on sensor observation systems, to ensure interoperability among different WSNs. One of the objectives of such research is to make data on a range of climatic factors, measured by sensors of different accuracy in different parts of the world, accessible to improve crop modeling. However, as shown in this study, data measured by low-cost sensors are less reliable. Research is needed both on the acceleration of data accessibility and improvement of data accuracy, to drive data utilization in agricultural studies. This study calibrated the air temperatures measured by low-cost sensors using a software-based approach. Several hardware-based approaches are available for improving the accuracy of low-cost sensors, such as the use of radiation shielding or heat-resistant coatings. The performance of these has already been demonstrated [19; 21; 22; 23; 25; 26; 27; 31]. In future studies, a combination of software- and hardware-based approaches may be demonstrated to offer further improvements in accuracy.