esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Aim** & **SC Technique** & **R or R\({}^{2}\)** \\ \hline [PERSON] & To forecast the uplift capacity of suction & & Various R\({}^{2}\) are available. Highest \\ et al. [14] & caissons employing an ANN model. & ANN & train R\({}^{2}\) = 0.975 \\  & & & Highest test R\({}^{2}\) = 0.996 \\ \hline [PERSON] [45] & To predict the uplift capacity of suction & & NGN & - \\  & caissons using a neuro-genetic network. & & & \\ \hline [PERSON] et al. [46] & To obtain an empirical model for evaluating & & & \\  & the complicated behavior of suction caissons’ & & GP-SA & R\({}^{2}\): Train = 0.858; test = 0.759 \\  & uplift capacity by the hybrid GP-SA technique. & & & \\ \hline [PERSON] et al. & To examine the robustness of the conventional & & Tree-based genetic & \\  & tree-based GP and its suitable modifications, & & programming (TGP), & \\  & i.e., LGP and GEP, for evaluating & & linear genetic & \\  & complicated behavior of the uplift capacity of & & programming (LGP), and & \\  & suction caissons. & & gene expression & \\ \hline [PERSON] et al. [48] & To assess the complex behavior of the uplift & & & \\  & capacity of suction caissons employing a useful & & & \\  & variant of GP, namely MEP. & & \\ \hline [PERSON] et al. & To determine the uplift capacity of suction & & & \\  [49] & caisson in clay adopting the MARS model. & MARS & R: train = 0.998; test = 0.997 \\ \hline [PERSON] et al. [50] & To forecast the uplift capacity of suction & & GP R: test = 0.997 \\  & caisson in clay, based on the literature’s & SVM-ANN & ANN R: test = 0.991 \\  & measured data. & & SVM R: test = 0.989 \\ \hline [PERSON] et al. & To predict the uplift capacity of suction & & & \\  [51] & caissons using an IFRIM model. & IFRIM & IFRIM R\({}^{2}\): train = 0.996; test 0.979 \\ \hline [PERSON] et al. & To develop a model that assesses and forecasts & & \\  & the condition of offshore oil and gas pipelines & & Various R\({}^{2}\) are available. Highest \\  & based on numerous factors in addition to & & & R\({}^{2}\) = 0.995 \\  & corrosion. & & \\ \hline [PERSON] et al. [13] & To develop a model to obtain the minimum & ANN-particle swarm & \\  & liquefaction potential. & optimization (PSO) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of soft computing techniques for the prediction of the peak uplift resistance of buried pipes.
## 3 Results and Discussion
### Color Parameters  
Figure 1 illustrates the color difference, expressed as \(\Delta\)E* values, derived from L*, a*, and b* for different packaging materials over the course of eight-months storage, with the initial month (month 0) considered as the basis for comparison. Before the storage the values of kernel color parameters among treatment and packaging materials ranged approximately from 25.94 (PLA-A-E-) to 27.33 (PP-A-E-) for L*, -1.06 (PE-A-E-) to -0.21 (PLA-A-E-) for a*, and 12.17 (PP-A-E-) to 16.27 (PLA-A-E-).  
In relation to color change for different packaging materials, a relatively minor change was observed for MPP (0.2-3.3), followed by a somewhat more pronounced alteration for PLA (0.7-3.6) and PE (1.04-3.9) after an eight-month period. The most considerable differences were identified for PP (1.2-4.1). During the experiment both increasing and decreasing trends were observed in the \(\Delta\)E* values. Based on the measured color parameters, the color difference values were changed mainly due to the decrease of L* at the end of eight months, especially in the case of PP packaging material. a* and b* values were increased during the storage which are in accordance with the study of [PERSON] et al. and [PERSON] et al. (2014, 2015). Based on this, it can be concluded that the walnut samples had lost their color intensity.  
Figure 1: \(\Delta\)E* values for the different packaging treatments and packaging materials for ‘Milotai 10’ cultivar. PE (polyethylene), PP (polypropylene), MPP (metallized polypropylene), PLA (polylactic acid), A–E- (air without edible coating), A–E+ (air, with edible coating), N–E- (nitrogen without edible coating).  
In comparison with another study, [PERSON] et al. [48] observed that the \(\Delta\)E* values of the Tulare variety increased during the storage period, while a decreasing trend was noted for the Howard variety.
## 5 Implementation and Experiments
### Assessment through Personalized Content
#### 5.2.2 Experiment of Personalizing Examples for Different Domains  
This experiment generates examples of looping for a student in the academic domain and for another student in the finance domain. The examples of looping generated from GPT for the student in academia are shown in Figure 13.  
As shown in the screen dump, the example of computing the total and average of some numbers is simple enough for any student to understand.  
Now, the foundation generated from GPT for the student in the finance domain is shown in Figure 14.  
As  
The generated example uses a loop for calculating the total amount of deposits made on bank accounts. This example is intuitive and easy to understand for users in the finance domain.  
Through the two contents of examples generated for different domains, we can clearly observe that the GPT content for generated examples is well personalized for each domain of interest.
## 4 Proposed RL-Based Antenna Beamwidth and Beam Alignment Interval Optimization  
### Problem Formulation  
Let \(\lambda_{j}^{k}\) be the beam alignment indicator in the \(k\)th V2I link. If the beam alignment is performed at the beginning of time slot \(j\), \(\lambda_{j}^{k}=1\); otherwise, \(\lambda_{j}^{k}=0\). Moreover, for the simplicity, we assume that the channel does not change during the slot time. The data rate at time slot \(j\) in the \(k\)th V2I link can then be expressed as follows:  
\[R_{j}^{k}=\left(1-\lambda_{j}^{k}\frac{t_{k}^{k}(\varphi_{j}^{k})}{T_{s}} \right)\cdot W\cdot\log_{2}(1+\text{SNR}_{j}^{k}), \tag{5}\]  
where \(W\) is the system bandwidth, \(\varphi_{j}^{k}\) is the antenna beamwidth updated in the beam alignment process, and \(\text{SNR}_{j}^{k}\), which is the received SNR at time slot \(j\), is obtained from (4).  
Our objective is to maximize the amount of data the vehicles receive from the RSU while moving along the road. The optimization problem that determines the beamwidth and beam alignment interval can then be expressed as follows:  
\[\max_{\Phi^{k},\mathcal{L}^{k}}\ \ \sum_{k=1}^{K}\sum_{j=1}^{N}S_{j}^{k}, \tag{6}\]  
where \(S_{j}^{k}\), which is the amount of received data in time slot \(j\) in the \(k\)th V2I link, is given by \(S_{j}^{k}=R_{j}^{k}T_{s}\); and \(N^{k}\), which is the total number of time slots while the vehicle moves along the road, is given by \(N^{k}=\lfloor D^{k}/(v^{k}T_{s})\rfloor\). We need to find the optimal parameters, namely the set of beamwidths \(\Phi^{k}=\{\varphi_{1}^{k},\dots,\varphi_{N^{k}}^{k}\}\) and the set of beam alignment intervals \(\mathcal{L}^{k}=\{L_{1}^{k},\dots,L_{N^{k}}^{k}\}\) for all \(K\) V2I links [47].  
However, there exist tradeoffs among the antenna beamwidth, the beam alignment interval, and the amount of received data. The narrower the antenna beamwidth, the higher the received SNR of the vehicle, the shorter the beam alignment interval, and the shorter the data transmission time. Moreover, because the antenna beamwidth and beam alignment interval determined at the current time will affect the future performance, it is a noncausal system. For this reason, finding the optimal parameters is NP-hard; therefore, we propose an RL-based approach to solve the optimization problem.
## 5 Discussions  
With the continuous advancement of WLAN technology, the demand for wider bandwidth (320 MHz or higher) and more intricate modulation schemes (4096 QAM or higher) has emerged to achieve greater data throughput. Within the transmitter, the power amplifier (PA) plays a critical role in determining the efficiency and linearity of the entire transmission path. To satisfy the system's error vector magnitude (EVM) requirements, the power amplifier necessitates exceptionally high linearity and efficiency. Consequently, the design of CMOS PAs faces increasingly demanding challenges.  
Looking ahead, the future research direction of CMOS PAs for WLAN primarily revolves around the exploration of high linearization techniques and the enhancement of deep back-off efficiency. These areas of focus aim to address the need for improved linearity performance and increased efficiency, aligning with the evolving requirements of WLAN systems.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Freq**} & \multirow{2}{*}{**Supply**} & \multirow{2}{*}{**Process**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**HPM**} & \multicolumn{2}{c}{**LPM**} & \multicolumn{2}{c}{**CW PAE**} & \multirow{2}{*}{**Linearity**} \\ \cline{5-10}  & & & & **OP1 dB** & & & **PAE** & & **OP1 dB** & & **PAE** \\ \hline
[40] & 2.4G & 5.6 V & \
\begin{tabular}{c} 180 \\ nm-CMOS \\ \end{tabular} & Mode Switching & 27 & 26.1 & 22 & 21.8 & x2 &
\begin{tabular}{c} EVM-15.8@26 dBm \\ power, 802.11n \\ \end{tabular} \\ \hline
[42] & 2.4G & 3.3 V &
\begin{tabular}{c} 90 \\ nm-CMOS \\ \end{tabular} & Turn-off one -stage & 22.7 & 12.4 & 18 & 10.4 & x1.6 & - \\ \hline
[43] & 2.4G & 3.3 V & \begin{tabular}{c} 55 \\ nm-CMOS \\ \end{tabular} & \
\begin{tabular}{c} Dual Mode with \\ Linearizing Body \\ Network \\ \end{tabular} & 22 * & 14 * & 21 * & 11 * & - &
\begin{tabular}{c} EVM-27 dB @17.3 dBm \\ power, 802.11n \\ \end{tabular} \\ \hline
[44] & 2.4G & 3.3 V & \begin{tabular}{c} 180 \\ nm-CMOS \\ \end{tabular} & \
\begin{tabular}{c} Dual Mode with \\ Tunable Matching \\ Network \\ \end{tabular} & 22.3 & 40 & 16.0 & 28 & - &
\begin{tabular}{c} EVM-25 dB @15.7 dBm \\ power, 802.11n \\ \end{tabular} \\ \hline
[7] & 2.5G & 3.3 V & \begin{tabular}{c} 180-nm \\ CMOS \\ \end{tabular} & \begin{tabular}{c} Discrete resize \\ and PCT \\ \end{tabular} & 27.5 & 24 & 20.5 & 15 & x1.5 & - \\ \hline This work & 2.4G & 3.3 V & \
\begin{tabular}{c} 55-nm \\ CMOS \\ \end{tabular} & Mode Switching & 27.6 & 32.7 & 17.7 & 10 & x1.5 &
\begin{tabular}{c} EVM-27 dB @19 dBm \\ power, 802.11n \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with previous works.  
Figure 25: Measured PA performance for 802.11n.
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
###### Abstract  
Haze imagery suffers from reduced clarity, which can be attributed to atmospheric conditions such as dust or water vapor, resulting in blurred visuals and heightened brightness due to light scattering. Conventional methods employing the dark channel prior (DCP) for transmission map estimation often excessively amplify fogged sky regions, causing image distortion. This paper presents a novel approach to improve transmission map granularity by utilizing multiple \(1\times 1\) DCPs derived from multiscale hazy, inverted, and Euclidean difference images. An adaptive airlight estimation technique is proposed to handle low-light, hazy images. Furthermore, an adaptive gamma correction method is introduced to refine the transmission map further. Evaluation of dehazed images using the Dehazing Quality Index showcases superior performance compared to existing techniques, highlighting the efficacy of the enhanced transmission map.  
haze removal; transmission map; dark channel prior; Euclidean difference image; adaptive gamma correction +
Footnote †: journal: Applied sciences  
0
Footnote 0: Copyright** © 2024 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities
## 2 Improved Ant Colony Mapping Algorithm
### Solution Construction  
In the traditional ant colony algorithm, the probability of t (that the ant \(k\) maps the neuron \(v_{i}\) to the node \(r_{j}\) in the first iteration) is:  
\[p_{i,j}^{k}(t)=\left\{\begin{array}{c}\frac{\left[\tau_{i,j}(t)\right]^{ \alpha}\times\left[\eta_{i,j}\right]^{\beta}}{\sum_{j\in allow}\left[\tau_{i,j }(t)\right]^{\alpha}\times\left[\eta_{i,j}\right]^{\beta}},j\in allow\\ \\ 0,\qquad j\
otin allow\end{array}\right. \tag{10}\]  
In the above formula, \(\tau_{i,j}(t)\) is the \(t\) cycle, \(R_{j}(t)\) is the occupancy of node \(r_{j}\) in the \(t\) cycle, and \(r_{j}\) is the normalized pheromone concentration of the node.  
\[p_{i,j}^{k}(t)=\left\{\begin{array}{c}Normalize\Big{[}\left[\tau_{i,j}(t) \right]^{\alpha}\times\left[\eta_{i,j}\right]^{\beta}\times R_{j}(t)\Big{]},j \in allow\\ \\ 0,\qquad j\
otin allow\end{array}\right. \tag{11}\]  
In this paper, some improvements have been made to the above decision probability. Compared to the traditional ant colony algorithm, \(R_{j}(t)\) is added to the probability, representing the occupancy rate of node \(r_{j}\) in the \(t\)-th cycle. That is, it is more inclined to deploy neurons on nodes with high occupancy, and deploying different neurons on the same node directly avoids the traffic between them. When the occupancy is 0, \(R_{j}(t)\) is placed as the same as deploying a neuron.  
\(\alpha\) and \(\beta\) mean the weight. The larger the value of \(\alpha\), the more likely the ants choose to take the path with high pheromone concentration; that is, the greater the possibility of choosing the path that has been tried before, thus reducing the randomness of the search. With larger \(\beta\) values, it is easier for colonies to choose locally shorter paths, which can accelerate convergence, but are easier to fall into local optima.  
\(allow\) is the set of nodes that can still deploy neurons at present. Because the capacity of nodes is limited, some nodes will be filled with the deployment process of neurons, so only nodes that are not full can be selected.
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 5 Results and Analysis  
### Correlation Analysis of Model Driving Factors  
Table 3 lists the Pearson correlation coefficients between the selected 14 driving factors and above-ground biomass (AGB) in grasslands. Significant factors include MVI, Clg, precipitation, and organic carbon content, indicating that these variables have a critical influence on AGB variations.  
Among the vegetation indices, MVI showed the highest correlation with AGB (\(p<0.01\)), reflecting the high sensitivity of red, near-infrared, and shortwave infrared bands to vegetation growth conditions. This makes it particularly suitable for monitoring grassland growth in arid and semi-arid regions. Among the meteorological data, precipitation exhibited a correlation coefficient of 0.597, highlighting that water availability is the primary limiting factor for grassland growth in this area. Additionally, the positive correlation between soil organic carbon content and biomass emphasizes the importance of soil nutrients for vegetation health.  
Overall, the combination of these variables effectively enhances the accuracy of the inversion model [37].
## 4 Multi-Hop Data Forwarding Strategy Based on Annulus-Sectors
### The Length of the Communication Radius of Sensor Node  
Obviously, in AEBDC, it is necessary to ensure that each non-CH can upload data to the CH within one hop. As mentioned above, the area of the annulus-sector in the \(k\)-th annulus is the largest. Therefore, one only needs to ensure that the distance between any node and the CH in this annulus-sector is no longer than the length of the communication radius (denoted as \(r_{t}\)).  
Like in Section 4.2, the Euclidean distance from any node in the \(k\)-th annulus to the center of its RCCH is defined as \(d_{s}\). As shown in Figure 6, the value of \(d_{s}\) is maximal if and only if this node is located at point A or B. In this case, the maximum value is:  
\[d_{s}=\sqrt{R^{2}+(2k-1)^{2}(R/2k)^{2}-(2k-1)(R^{2}/k)\cos(\pi/n)} \tag{22}\]  
Moreover, no matter where the cluster header is located at, the node in the same annulus-sector should be able to upload data to it within one hop. Thus, the following requirement should also be met:  
\[r_{t}\geq d_{s}+r_{k} \tag{23}\]  
On the other hand, the CHs in two adjacent annulus-sectors of two different annuli should also be able to communicate with each other within one hop to ensure that data can eventually be uploaded to BS. According to Section 4.3, the closer the annulus-sector is to the BS, the larger the area of its RCCH is, so it is not difficult to know that the furthest distance between two communicable CHs is equal to the distance from \(c_{1}\) (the CH in the first annulus) to \(c_{2}\) (the CH in the second annulus), as shown in Figure 6. That is to say:  
\[r_{t}\geq(R/k)+r_{1}+r_{2} \tag{24}\]  
In summary, the value of \(r_{t}\) should meet the following equation:  
\[r_{t}\geq\text{Max}(d_{s}+r_{k},\ (R/k)+r_{1}+r_{2}) \tag{25}\]  
Figure 5: The maximal value of \(r_{1}\).
###### Abstract  
Facing a significant decrease in economic working processes, Off-Site Construction (OSC) methods have been frequently adopted in response to challenges such as declining productivity and labor shortages in the construction industry. Currently, in most OSC applications, the assembly phase is traditionally managed based on the personal experience and judgment of the site managers. This approach can lead to inaccuracies or omissions, particularly when dealing with a large amount of information on large, complex construction sites. Additionally, there are limitations in exploring more efficient and productive alternatives for rapidly adapting to changing on-site conditions. Given that the assembly phase significantly affects the OSC productivity, a systematic management approach is crucial for expanding OSC methods. Some initial studies used computer algorithms to determine the optimal assembly sequences. However, these studies often focused on geometrical characteristics, such as component weight or spatial occupancy, neglecting crucial factors in actual site planning, such as the work radius and component installation status. Moreover, these studies tended to prioritize the generation of initial assembly sequences rather than providing alternatives for adapting to evolving on-site conditions. In response to these limitations, this study presents a systematic framework utilizing a Building Information Modeling (BIM)-Genetic Algorithm (GA) approach to generate Precast Concrete (PC) component installation sequences. The developed system employs Genetic Algorithms to objectively explore diverse assembly plans, emphasizing the flexibility of accommodating evolving on-site conditions. Real on-site scenarios were simulated using this framework to explore multiple assembly plan alternatives and validate their applicability. Comprehensive interviews were conducted to validate the research and confirm the system's potential contributions, especially at just-in-time-focused PC sites. Acknowledging a broader range of variables such as equipment and manpower, this study anticipates fostering more systematic on-site management within the context of a digitized construction environment. The proposed algorithm contributes to improving both productivity and sustainability of the construction industry by optimizing the management process of the off-site construction projects.  
off-site construction; BIM; genetic algorithm; precast concrete; prefab assembly Article
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 4 Functional Blocks of the Proposed System
### The DEMUX Block  
The DEMUX block is a data-distributor device composed of one input and \(b\) outputs. Input data are switched to any one of output lines based on the boolean value of the selection variables. These variables are outputs of a modulus \(b\) counter provided with a ripple carry output, too.
# Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification
## 2 Literature Review
FCMs add fuzzy logic to conventional cognitive maps. Due to their capacity to describe any complicated system, FCMs have attracted numerous researchers, and have been effectively used in a wide range of scientific domains. Using FCMs in the medical field, [PERSON] et al. [24] studied the many causes of diabetes, [PERSON] et al. [25] detected obesity through psychological behavior, and [PERSON] et al. [26] used FCMs to investigate whether a person's propensity to acquire breast cancer is affected by their family history of the condition. For crisis management decision making, [PERSON] et al. [27] suggested using FCMs as a tool for modeling political and strategic challenges. [PERSON] et al. [28] performed a credit risk assessment using FCMs [22]. The existing literature suggests many FCM expansions. [PERSON] and [PERSON] [29] presented a rule-based fuzzy cognitive map as an expansion of FCMs to include methods for dealing with feedback. Cognitive maps that deal with diverse meaning contexts were postulated by [PERSON] [30]. Intuitionistic Fuzzy Cognitive Maps (iFCM) were developed by [PERSON] and [PERSON] [31] to address experts' reluctance in making decisions. [PERSON] et al. [26] suggested an FCM variant that allows for the identification of dynamic causal links between the concepts. To represent dynamic systems, [PERSON] [32] came up with the idea of dynamic random fuzzy cognitive maps (DRFCMs). The model of FCNs (fuzzy cognitive networks) was first proposed by [PERSON] et al. [33] based on the idea that equilibrium points always exist, while [PERSON] [34] presented Rough Cognitive Maps, a fuzzy cognitive map based on Rough Set Theory. By building upon the relevant literature, this paper proposes an approach for the selection of features using distributed FCMs. The material and methods connected to distributed-FCM-based feature selection are presented in the next section. Next, the application of distributed-FCM-based feature selection is described and the obtained results are discussed. In the final section, conclusions and future challenges related to this issue are highlighted.