esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Loads on Overhead Sign Structures: A Comparative Study
## 5 Discussion of Results  
In CFD simulations, data from laboratory testing or full-scale measurements can ascertain reliable simulations. For this study, no previous experimental data were readily available to compare with the CFD results. However, a bluff body under turbulent flow has an expected drag of 1.2, as indicated in previous work by others [26]. The results obtained from the grid-independence study gave a converged drag coefficient value of around 1.206, having almost the exact predicted value.  
It is worth mentioning that the wind force on a bluff body depends on the wind direction angle, among other factors. However, for a thin plate, like the overhead board considered in the current study, the maximum drag may be obtained when the wind is perpendicular to the board surface. Previous work by others shows that over a range of wind direction angles varying between 0\({}^{\circ}\) and 90\({}^{\circ}\), the drag force on a flat plate is maximum when the wind is perpendicular to the surface of the panel (90\({}^{\circ}\)) [26].  
The current study showed that the effects of porosity on the drag force are positive. Introducing porosity to solid objects can reduce wind forces. Not only does the porosity  
Figure 9: Path lines of airflow around: (**a**) standard panel, (**b**) 28 staggered holes, (**c**) 200 uniform square holes, (**d**) 10 rectangular slots, and (**e**) curved.  
reduce aerodynamic forces on bluff bodies, but also it decreases the wind velocity on neighbor structures due to the sheltering effects. Experimental and computational studies suggest Sierpinski Tetrahedrons are effective in windbreak applications [27]. Higher-order Sierpinski Tetrahedrons offer higher porosity, which leads to the lowest turbulence intensity and highest speed reduction. The wind speed reduction, the higher the pressure differences on the windbreak, leading to higher drag, which is consistent with the finding of this study when the small holes lead to higher drag forces.  
A previous study investigated perforated road signs with a porosity of 30% [28]. The drag coefficient of perforated boards did not show a clear dependence on the wind speed. The porosity and the size of the perforation should be determined so that the information displayed on the road sign has sufficient visibility and readability. Overall, the results show that a perforation with 25% porosity can enhance the resilience of sign boards by reducing wind loads. The study provides valuable information for decision-makers involved with the installation of road signs in wind-vulnerable areas to improve their resiliency and enhance safety on the roads.
## 4 The Proposed Method
### Overall System Architecture  
We implemented our work using FedScale [30], an open-source evaluation platform and benchmark designed explicitly for federated learning. Figure 5 illustrates the overall system architecture of our implementation.  
In this architecture, the user first submits the job, which includes the hyperparameter settings (e.g., clients per round and \(\mu\)), to the Parameter Aggregator (acting as the primary server) \(\boldsymbol{\mathfrak{T}}\).  
Additionally, the Parameter Aggregator receives feedback from the clients regarding the previous training round, updates the global model with the clients' updates, and collects relevant information from the clients \(\boldsymbol{\mathfrak{O}}\).  
Figure 5: The overall system architecture and the functional block diagram of our proposed framework.  
Afterward, the Oort Client Selector will select a certain percentage (k%) of preferable clients based on their computing capability, communication bandwidth, and data distribution. The remaining quota will be randomly selected from the pool of unselected clients \(\mathfrak{G}\).  
The Model Distributor receives the aforementioned information, including the updated global model, client information, and the selected clients. It utilizes the Tier Clustering Algorithm to group the selected clients into several clusters based on their characteristics. Then, it dispatches the suitable model to each client within their respective cluster, ensuring an efficient and tailored training process \(\mathfrak{G}\).  
The clients receive the assigned models through the Model Receiver and train with their local data in the Model Trainer \(\mathfrak{G}\).  
After completing the training process, the Client Collector collects the local model parameters and captures their corresponding status \(\mathfrak{G}\).  
Finally, the collected data, including the model parameters and their status, are transmitted and updated on the server \(\mathfrak{O}\).  
The entire process is executed iteratively, with periodic testing every few rounds until the desired number of target rounds is reached.
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
## 6 Data Analysis
### Simulation Design of Planar Array Antennas  
In this final example, a 16 \(\times\) 16 planar array antennas was simulated and compared with the GA in Ref. [40]. This GA employed four distinct techniques: Gaussian, Kaiser, Hamming, and Blackman, with the aim of SLL reduction. The planar array antenna has 256 elements optimized, and the mainbeam width was configured as 12\({}^{\circ}\). The population size of the NCGWO algorithm was set to 64, and the maximum number of iterations was set to 200.  
Table 8 shows the optimization results. The PSLL of the NCGWO algorithm was \(-34.8303\) dB, which was 12.0103 dB, 12.7973 dB, 12.0103 dB, 2.8233 dB, 6.8993 dB, and 4.2606 dB lower than those of the Gaussian, Kaiser, Hamming, Blackman, GWO, and IGWO, respectively. This Blackman method reduces the PSLL below 30 dB, but its mainbeam width widens by 4\({}^{\circ}\). The NCGWO algorithm mitigates the PSLL of the planar array antennas and keeps the beam width unchanged. Figure 17 presents a 3D representation of the planar array and the current amplitude of each array element.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Optimized Element Position** & **Peak SLL (dB)** & **Null Depth (dB)** \\ \hline \multirow{3}{*}{ACO} & 0.1500, 0.7500, 1.0500, 1.7500, & \multirow{3}{*}{\(-17.0990\)} & \multirow{3}{*}{\(-50\)} \\  & 2.2500, 2.5500, 2.9500, 3.7500, & & \\  & 4.1500, 4.5500, 4.7500, 5.3500 & & \\  & 6.0500, 7.0500, 7.7500, 8.4500 & & \\ \hline \multirow{3}{*}{CSO} & 0.2833, 0.6830, 1.1929, 1.5199 & \multirow{3}{*}{\(-18.1562\)} & \multirow{3}{*}{\(-80\)} \\  & 1.9768, 2.3247, 2.6886, 3.1362 & & \\  & 3.4848, 3.9538, 4.3822, 4.9252 & & \\  & 5.4817, 6.2091, 7.0412, 7.7500 & & \\ \hline \multirow{3}{*}{GWO} & 0.1943, 0.7407, 1.2492, 1.7476 & \multirow{3}{*}{\(-15.1287\)} & \multirow{3}{*}{\(-106\)} \\  & 2.2413, 2.7143, 2.9998, 3.4515 & & \\  & 3.7540, 4.2759, 4.7500, 5.2556 & & \\  & 5.7518, 6.4559, 7.2500, 8.0000 & & \\ \hline \multirow{3}{*}{IGWO} & 0.2505, 0.6570, 1.0574, 1.4622 & \multirow{3}{*}{\(-19.0584\)} & \multirow{3}{*}{\(-108\)} \\  & 1.9027, 2.3433, 2.7637, 3.1672 & & \\  & 3.5856, 4.0330, 4.5951, 5.0225 & & \\  & 5.5743, 6.5045, 7.3101, 8.2594 & & \\ \hline \multirow{3}{*}{NCGWO} & 0.2500, 0.6500, 1.0525, 1.4619 & \multirow{3}{*}{\(-20.9203\)} & \multirow{3}{*}{\(-116\)} \\  & 1.8950, 2.3419, 2.7771, 3.2000 & & \\ \cline{1-1}  & 3.6361, 4.1108, 4.6883, 5.2370 & & \\ \cline{1-1}  & 5.8100, 6.9328, 7.6841, 8.3345 & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results for the 32-element sparse linear array antennas.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Peak SLL (dB)** & **Mainbeam (\({}^{\circ}\))** \\ \hline Gaussian & \(-22.8200\) & \(12\) \\ [PERSON] & \(-22.0330\) & \(12\) \\ [PERSON] & \(-22.820\) & \(12\) \\ [PERSON] & \(-32.0070\) & \(16\) \\ GWO & \(-27.5697\) & \(12\) \\ IGWO & \(-30.5697\) & \(12\) \\ NCGWO & \(-34.8303\) & \(12\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results for planar array antennas.
###### Abstract  
This paper presents an experimental realization of multiband 5G new radio (NR) optical front haul (OFH) based radio over fiber (RoF) system using digital predistortion (DPD). A novel magnitude-selective affine (MSA) based DPD method is proposed for the complexity reduction and performance enhancement of RoF link followed by its comparison with the canonical piece wise linearization (CPWL), decomposed vector rotation method (DVR) and generalized memory polynomial (GMP) methods. Similarly, a detailed study is shown followed by the implementation proposal of novel neural network (NN) for DPD followed by its comparison with MSA, CPWL, DVR and GMP methods. In the experimental testbed, 5G NR standard at 20 GHz with 50 MHz bandwidth and flexible-waveform signal at 3 GHz with 20 MHz bandwidth is used to cover enhanced mobile broad band and small cells scenarios. A dual active Mach Zehnder Modulator having two distinct radio frequency signals modulates a 1310 nm optical carrier using distributed feedback laser for 22 km of standard single mode fiber. The experimental results are presented in terms of adjacent channel power ratio (ACPR), error vector magnitude (EVM), number of estimated coefficients and multiplications. The study aims to identify those novel methods such as MSA DPD are a good candidate to deploy in real time scenarios for DPD in comparison to NN based DPD which have a slightly better performance as compared to the proposed MSA method but has a higher complexity levels. Both, proposed methods, MSA and NN are meeting the 3 GPP Release 17 requirements.  
digital predistortion; magnitude selective affine; radio over fiber; neural network; error vector magnitude; adjacent channel power ratio +
Footnote †: journal: Applied Sci. 2021
## 2 Materials and Methods
### Precipitation Products
#### 2.3.2 GPCP-1 DD  
The GPCP was established under the World Climate Research Programme (WCRP) and is available in monthly, pentad, and daily form. The GPCP-1 DD provides precipitation on a 1\({}^{\circ}\) grid and daily scale starting from 1996 to the present. It uses the threshold-matched precipitation index (TMPI) to measure precipitation from IR, passive MW (PMW) rain estimates, and rain gauge analysis [38]. Similar to other SPPs, the 3-hourly images in each day (00:00, 03:00, 06:00, 09:00, 12:00, 15:00, 18:00, and 21:00 UTC) were summed to generate daily precipitation value [38]. The GPCP-1 DD version 1.2 used in this study was provided by the NASA/Goddard Space Flight Center's Mesoscale Atmospheric Processes Laboratory, which develops and computes the 1 DD as a contribution to the GEWEX GPCP [39].
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 2 PV-Powered Charging Station with V2G Service
### PV-Powered Charging Station with V2G Service with Energy Cost Optimization  
On the other hand, the PVCS can operate with energy cost optimization. In this case, Figure 3 describes the supervisory control system for the PVCS [44]. The supervisory control system of the PVCS is composed of four layers: prediction, energy cost optimization, operation, and HMI. The control block has been designed and implemented to interact with EV users and maintain power balance at the DC bus through the energy cost optimization and operation layers.  
The prediction layer utilizes weather forecasts. From the interaction with the HMI, the user of an EV \(v\) selects their desired SOC at departure charging mode, participates in the V2G service, and obtains the SOC of their EV at arrival in real-time. The energy cost optimization relies on the production prediction, which depends on the hourly solar irradiation predictions and consumption profile communicated from the HMI. Moreover, the power limits of the connected public grid, energy pricing, and energy system limits  
Figure 1: Scheme of PV-powered charging station with V2G service.  
are communicated. The MILP formulation is used to reduce the total energy cost of the PVCS. This supervisory control system has the advantage of interacting with EV users for optimization; yet, if the choices of the EV users are not practical, they must be adjusted in order to enable optimization [45]. For instance, if an EV user arrives at the charging station and requests fast charging, but the available power cannot support it, the HMI will communicate with the user to suggest charging in average or slow mode, waiting for available power, or leaving the charging station altogether. Similarly, if the EV user mistakenly inputs an invalid SOC at departure (e.g., 180%), the HMI will alert the user to enter a valid SOC interval below 100%. Whenever a new EV arrives, the optimization process is updated with the new inputs from the HMI and physical constraints. Therefore, the process is repeated each time a new EV arrives.  
Dealing with the unpredicted arrival of EVs is the main challenge. The power references for the public grid, stationary storage, and EVs are sent to the operation layer based on the optimization results. The operation layer is responsible for maintaining the power balance while respecting the system's constraints and physical limitations [46]; furthermore, it sets the PV power limitation and applies EV shedding if needed.  
Figure 2: PV-powered charging station energy management via V2G service without optimization.
# Instance Hard Triplet Loss for In-video Person Re-identification
## 3 Our Approach
### Unsupervised In-video ReID  
In practice, labeling cross-camera ReID data is expensive and time consuming, because it involves identifying a person from a camera network. Although our in-video ReID only requires associating a person from continuous frames, which is much easier for labeling training data, annotating for each new scenario still needs much manpower, and this deficiency limits the potential large-scale practical applications of in-video ReID.  
Can we train an in-video ReID model in an unsupervised way? We observe the fact that a person can only appear once in a frame; in other words, we will not see the same person twice at the same time. Therefore, we can guarantee that the detected persons within the same frame are different persons, and images of other persons in the same frame can be used as negative samples. Now, the problem is how to get positive samples of a person with the same identity. In this paper, we propose a Reciprocal Identity Association (RIA) method to associate the same person across frames automatically for in-video ReID.  
For a person \(p\) in a frame, we denote the \(k\)-nearest neighbors as:  
\[\mathcal{N}(p,k)=\{g_{1},g_{2},...,g_{k}\},|\mathcal{N}(p,k)|=k \tag{5}\]  
where \(g\) is the persons in the next frame, \(g_{i}\) is the \(i^{\text{th}}\) sample in the top \(k\) ranking list of \(p\), and \(|\cdot|\) is the number of samples. If \(g_{i}\) is in the \(k\)-nearest neighbors of \(p\), in turn, \(p\) should also be in the \(k\)-nearest neighbors of \(g_{i}\) if they are the same person, and the \(k\)-reciprocal neighbors [57] can be defined as:  
\[\mathcal{R}(p,k)=\{g_{i}|(g_{i}\in\mathcal{N}(p,k))\wedge(p\in\mathcal{N}(g_{i },k))\} \tag{6}\]  
Persons in \(\mathcal{R}(p,k)\) are potential positive samples. With a larger \(k\), it is more likely that \(\mathcal{R}\) contains the correct match, but it also brings more ambiguity into determining the corresponding identity. Because there is only one correct match in the next frame, we choose \(k=1\). If \(|\mathcal{R}(p,1)|=1\), we take it as a positive sample. In this way, some person associations will be missed (\(|\mathcal{R}(p,1)|=0\), no sample meets the condition), but the remaining association is more reliable. In our experiments, wrong associations with false supervision signals are more likely to cause model degeneration. Although we droppedsome potentially available associations, in a real scenario, we can usually collect many unlabeled data, which is much less expensive than collecting labeled data, so plenty of reliable associations can be automatically generated, and this problem could be compensated.
# An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization
## 5 Expanded Experiments, Results, and Discussion
### Random Forest Model Validation
To validate our feature selection and model design, we conducted an ablation study examining individual feature contributions and model component sensitivity (shown in Table 4). Individual feature analysis revealed that citations achieved the highest standalone performance (F1-score: 0.8690), significantly outperforming the Altmetric score (0.8246) and impact factor (0.8348), confirming citation patterns as the strongest predictor of retraction. Feature combination analysis showed that the optimal two-feature combination of citations + Altmetric score (F1-score: 0.9334) slightly outperformed our full three-feature model (0.9305), with feature removal analysis revealing that removing citations caused the largest performance drop (10.70%) while removing the Altmetric score decreased the performance by 4.58%. Notably, removing the impact factor actually improved the performance by 0.31%, suggesting that it introduces predictive noise; however, we retained the impact factor in our model because it represents an important journal-level quality assessment that complements article-level metrics, provides valuable context for understanding retraction patterns across different journal tiers, enables a comprehensive analysis of institutional factors in research integrity, and maintains consistency with existing retraction studies that consider journal prestige as a relevant factor. Parameter ablation confirmed that the performance stabilized at 100 estimators with minimal variation across different max_features settings, validating our parameter choices and demonstrating model robustness, while the marginal performance cost (0.31%) of including the impact factor was acceptable given its important analytical benefits beyond the pure predictive performance. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **CV F1-Score** & **Test F1-Score** & **AUC** & **Rank** \\ \hline Gradient Boosting & 0.9304 & 0.9336 & 0.9477 & 1 \\ XGBoost & 0.9296 & 0.9333 & 0.9452 & 2 \\ Random Forest & 0.9275 & 0.9305 & 0.9377 & 3 \\ Decision Tree & 0.9185 & 0.9266 & 0.8942 & 4 \\ AdaBoost & 0.9061 & 0.9118 & 0.8969 & 5 \\ K-Nearest Neighbors & 0.9055 & 0.9114 & 0.8931 & 6 \\ Neural Network & 0.9052 & 0.9092 & 0.8657 & 7 \\ SVM & 0.8706 & 0.8685 & 0.7956 & 8 \\ Logistic Regression & 0.8485 & 0.8481 & 0.7476 & 9 \\ Naive Bayes & 0.1227 & 0.1286 & 0.5821 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of machine learning models for retraction prediction.
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 4 Fog Data as a Service Delivery Model  
Because of the importance of data analysis in fog computing, we believe that data as a service (DaaS) needs to be considered as the fourth delivery model in fog-based environments. With the advent of the service-oriented architecture (SOA) and the proliferation of cloud-based data management and access solutions, data as a service (DaaS) has emerged as a data delivery model that continues to grow in popularity. DaaS relies on the same concepts as the other cloud delivery models (IaaS,PaaS, and SaaS). With the DaaS model, data are delivered to consumers on-demand regardless of their location [19; 25]. DaaS considers that the platform where the data resides is not essential to the consumer. The consumer application could access the data where they live. Figure 2 shows a DaaS-based system's components, including service-oriented architecture as the underlying platform, data ingestion and processing, microservices, applications, and visualization dashboards. The usage of this delivery model in fog computing is growing. For example, [PERSON] et al. [26] proposed a DaaS-based solution to support data delivery in a fog computing environment. The solution permits efficient data transfer between data stores owned by data providers and data consumers.  
DaaS provides the following benefits:  
* Agility: Since DaaS relies on a Service-Oriented Architecture (SOA), access to critical data through a cloud or fog service powered by DaaS provides great flexibility. Access to data is fast, because the architecture in which they exist is quite simplistic. Moreover, when the data structure needs to be changed or geographic needs arise, changes to the data are easy to implement.
* High-quality data: Implementing a rigorous process of data management and processing (acquisition, cleansing, aggregation, and enrichment) by the DaaS provider guarantees consumer access to high-quality data.
* Easy access: The DaaS model permits easy access to data using various devices like desktops, laptops, tablets, and smartphones anywhere and anytime.
* DaaS provider lock-in avoidance: The DaaS model allows data to be transferred quickly from one platform to another.  
In smart cities, data are fundamentally important for downstream analysis and decision-making. With the phenomenal growth of smart city data, providing access to data as a service (DaaS) over the Internet or through a network is of utmost importance. The DaaS service enables the consumer to have dynamic access to data on demand and provides up-to-date data that could be used by a wide range of smart city applications. Similar to other cloud and fog delivery models, DaaS adoption raises several concerns with regard to privacy, security, data governance, and ownership issues.  
Figure 2: Data as a service.
## 2 Materials and Methods
### Evaluation of Prediction Results  
In order to assess the predictive accuracy of the model, we chose mean absolute error (MAE), root mean square error (RMSE), average absolute percentage error (MAPE), and  
Figure 5: Heat map of correlation coefficients between different features of site S1.  
coefficient of determination (R\({}^{2}\)) as model evaluation metrics. The calculations are shown in Equations (16)-(19).  
MAE \[= \frac{1}{n}{\sum_{i=1}^{n}}{\left|{{y_{i}}-{\hat{y}_{i}}}\right|}\] (16) RMSE \[= \sqrt{\frac{1}{n}{\sum_{i=1}^{n}}{\left({{y_{i}}-{\hat{y}_{i}}} \right)^{2}}}\] (17) MAPE \[= \frac{1}{n}{\sum_{i=1}^{n}}\frac{{\left|{{y_{i}}-{\hat{y}_{i}}} \right|}}{{y_{i}}}\] (18) R\[{}^{2} = 1-\frac{{\sum_{i=1}^{n}}{\left({{y_{i}}-{\hat{y}_{i}}} \right)^{2}}}{{\sum_{i=1}^{n}}{\left({{y_{i}}-{\overline{y_{i}}}}\right)^{2}}}\] (19)  
where \(y_{i}\) denotes the true value, \({\hat{y}_{i}}\) denotes the predicted value, and \({\overline{y_{i}}}\) denotes the mean of all true values.  
Lower values of MAE, RMSE, and MAPE indicate diminished error levels, implying more accurate predictions. Meanwhile, as the R\({}^{2}\) value gets closer to 1, the better the model is fitted.