esa-sceva/satcom-chunk-collection huggingface dataset:
# Frost Resistance Prediction of Concrete Based on Dynamic Multi-Stage Optimisation Algorithm
## 1 Introduction
[17] established six ML models, and the performance statistics show that the XGBoost ensemble learning algorithm based on boosting has the best prediction performance for the frost resistance of rubber concrete. However, the existing study based on ensemble learning models such as RF and SVM still has some drawbacks. These models are prone to errors when dealing with imbalanced sample distributions and strong nonlinear correlations among variables. Moreover, the performance of these models is highly sensitive to the setting of hyperparameters, which requires manual adjustment based on experience and thus cannot guarantee optimal performance in multi-feature complex datasets. Meanwhile, such methods often lack a global search mechanism, making them prone to getting stuck in local optima, resulting in poor stability of the prediction results and limited generalization ability. The application of an intelligent optimisation algorithm can somewhat overcome these problems. [PERSON] et al. [18] adopted the Bayesian optimisation random forest ensemble learning method to establish a three-stage frost resistance prediction model for an accurate and quick prediction of the frost resistance of concrete. [PERSON] et al. [19] proposed the use of NSGA-II in optimising the random forest model to improve the frost resistance of concrete in severe cold areas, realise the economic and environmentally friendly production of concrete, and improve the safety performance and service life of the project. Although intelligent optimisation algorithms have begun to be applied in the prediction of concrete frost resistance, relatively few related studies focus on their utilisation; thus, further exploration is needed. In this paper, a dynamic multi-stage optimisation algorithm (DMSOA) is used to optimise the ensemble learning algorithm to predict the frost resistance of concrete. More than 7090 sets of experimental data are collected, and nine attributes of cement, water, fly ash, coarse aggregate, fine aggregate, mineral powder, water-reducing agent, initial air content of concrete and freeze-thaw cycle times are utilised as input variables. Considering that RF, AdaBoost, CatBoost, and XGBoost are widely used and perform well in relevant studies, this study chooses them to construct benchmark models for comparison. DMSOA and other optimisation algorithms are also used to optimise and establish optimisation models. In addition, all models are evaluated to provide the most accurate prediction model for the frost resistance of concrete. This process not only can greatly save on engineering cost but also has an important guiding significance for engineering design.
# GNSS Timing Performance Assessment and Results Analysis
###### Abstract  
Global Navigation Satellite System (GNSS) timing is a main service function. Each GNSS has its own time performance specification. However, a uniform timing performance assessment methodology and its outcomes do not exist. Firstly, the timing performance specifications of each GNSS are analyzed. Then, time transfer accuracy is considered as the key GNSS timing performance indicator. Secondly, an assessment method for the Coordinated Universal Time (UTC) published by the Bureau International des Poids et Mesures (BIPM) and UTC kept by the National Time Service Center of China (UTC(NTSC)) is proposed. Thirdly, the uncertainty budget of the assessment method is given. The timing performances of BDS, GPS, GLONASS, and Galileo are assessed and compared. The results show that the time transfer accuracy of BDS, GPS, GLONASS, and Galileo was 13.8 ns, 4.5 ns, 16.8 ns, and 4.2 ns, respectively, in 2021, meeting their performance requirements specified by GNSS (30 ns or 40 ns). Meanwhile, the assessment results of GPS and Galileo are much better than requirements, while the assessment results of BDS and GLONASS show fixed time offset and can still be improved further. If the local reference time of GNSS users can be connected with UTC, this assessment method can be used.  
GNSS; timing performance; time transfer accuracy; assessment +
Footnote †: journal: Sensors
## 3 Experimental Test and Result Analysis
### Experimental Test
#### 3.4.3 Comparative Experiment
The experimental results reveal the specific effects of different attention mechanisms on model performance. The model with SE and CBAM attention mechanisms suffered a 0.4% and 0.2% decrease in detection accuracy, respectively, indicating that the two mechanisms did not effectively improve performance on the current dataset. In contrast, when the model combines the CA, ECA, and EMA attention mechanisms, the detection accuracy is improved by 0.4%, 0.3%, and 1.1%, respectively. For the latter two attention mechanisms, the detection accuracy is significantly improved. On the whole, the introduction of the EMA attention mechanism not only accelerates the detection speed but also effectively improves the detection accuracy of the model, which makes it more advantageous in practical applications. In order to verify the performance of the ROS-YOLOv5-FleetEMA model proposed in this paper, the model is compared with the traditional YOLOv5-Lite model based on deep learning. In the case of using the same dataset and experimental environment, the average accuracy improvement effect is shown in Table 4.  
Through the analysis of the results, the mAP @ 0.5 of the ROS-YOLOv5-FleetEMA model proposed in this paper is 2.7% higher than that of the traditional YOLOv5-Lite model, and in a wider accuracy range mAP @ 0.5-0.95, the ROS-YOLOv5-FleetEMA model proposed in this paper is 4.3% higher than the traditional YOLOv5-Lite model. In order to evaluate the lightweight improvement effect of the ROS-YOLOv5-FleetEMA model more comprehensively, this paper introduces the traditional YOLOv5 s model as the comparison benchmark. The experimental results are shown in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP @ 0.5** & **mAP @ 0.5–0.95** & **Precision** & **Recall** \\ \hline - & 0.765 & 0.515 & 81.2 & 66.8 \\ SE & 0.761 & 0.523 & 78.4 & 68.1 \\ CBAM & 0.763 & 0.505 & 83.6 & 60.5 \\ ECA & 0.768 & 0.525 & 83.2 & 63.9 \\ CA & 0.769 & 0.521 & 73.7 & 66.3 \\ EMA & 0.776 & 0.534 & 85.6 & 69.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of attention improvement effects. The results show that the ROS-YOLOv5-FleetEMA model proposed in this paper has achieved significant optimization in the two key indicators of GFLOPS and Param. Compared with the traditional YOLOv5 model, the GFLOPs of the ROS-YOLOv5-FleetEMA model are reduced by 79.3%, and the parameter amount is reduced by 81.1%. This optimization not only reduces the consumption of computing resources but also makes the model more suitable for deployment on resource-constrained devices. At the same time, compared with the YOLOv5-Lite model, the GFLOPs of the ROS-YOLOv5-FleetEMA model are reduced by 13.2%, and the amount of parameters is reduced by 15.1%. By comparing the experimental results, it is verified that the ROS-YOLOv5-FleetEMA model shows significant advantages in computational efficiency and resource consumption while maintaining high detection accuracy, which proves its practicability and effectiveness in a resource-constrained environment.
# Artificial Intelligence-Based Semantic Internet of Things in a User-Centric Smart City
## 2 Related Work
built a smart collaborative caching scheme in IoT through high-level Information Centric Networking (ICN) principles. Through the analysis of typical scenarios, it is concluded that the scheme optimizes the total packet number and average transmission latency [41]. [PERSON] et al. proposed a green survivable virtual network embedding (GSVNE) for the collaborative edge computing in SC to guarantee the network survivability [42]. In the method, the number and geographical locations of backup Edge devices are determined by resource-division methods based on heuristic strategies and the GSVNE will ensure the maximal sharing degree of backup resource. [PERSON] et al. [43] mentioned that Information Communication technology (ICT) for disaster mitigation and SC research problems that are expected to develop in the next ten years are enumerated, so as to build a safe and intelligent city against disasters. The development of Internet, smartphones, IoT devices has brought great changes to the collection and distribution of disaster information, however, there is still room for development if we combine multiple technologies to support disaster. [PERSON] et al proposed a SC architectural model with mobile edge computing and fog computing exploits Mobile Edge Computing (MEC) concept [44]. The approach distributes the computational load onto the network equipment and the program that leveraging nodes to deploy service for SC improves the user experience. [PERSON] et al. proposed a fog-computing framework that enables 5G enabled SC with autonomous management and orchestration functions [45]. The framework fully integrated fog node management system and Open Shortest Path First (OSPF) routing protocol applied to exchange information between fog nodes. Evaluation results show that network bandwidth usage and latency reduced obviously. Reference [46] proposed a follow-me cloud-cloudlet approach in fog-computing-based radio access networks for SC which can reduce the latency of data transmission in SC. It is noted that the approaches discussed above do not integrate AI and semantic models based on natural language. Moreover, the ontology models are limited by the formulation structure and are therefore lack sufficient flexibility towards the AI system. Motivated by these observations, in this paper, we proposed the AI-SIoT to address the challenges confronted by the existing systems.
## 3 Indices Related to Plant Productivity
[12] studied the relationship between the topography of the area, NDVI, NDVIIre, and simple ratio (SR) indices and the yield of maize and soybeans. These crops were grown in Eastern Ontario, Canada, and remote sensing data were acquired from June to August of each study year. The research proved that the MLP network developed by the authors was more effective in predicting the yield of maize than that of soybean. The RMAE for the forecasting of maize for the two tested years (2011-2012) did not exceed 15%. In contrast, the RMAE for soybeans was less than 20%. [PERSON] et al. [147] used a back-propagation error ANN to predict maize seed yield. The network was trained using the conjugate gradient method algorithm in farmland located near Ottawa, Canada. In the above study, the authors used independent topographic features (slope and aspect), vegetation indices (including NDVI, WDVI, and SAVI) and textural indices (including homogeneity, contrast, and entropy) as variables. Various combinations of these variables were analyzed in the development of the predictive model. The research showed that the model containing all explanatory variables was more accurate than the other models. The RMSEs for the training and validation set were 0.36 and 0.42 t-ha\({}^{-1}\), respectively. [PERSON] et al. [141] also tested predictive models that included different vegetation indices as independent variables. In the work on forecasting the yield of maize cultivated in North Dakota (USA), four indicators were considered: NDVI, global vegetation index (GVI), perpendicular vegetation index (PVI), and soil-adjusted vegetation index (SAVI). Back-propagation neural network models were developed, producing a total of 16 models (four vegetation index \(\times\) four years of research, including data from interconnected years). The model that used PVI as a predictor exhibited the best forecast accuracy compared with the other models. The average accuracy of the estimation of the size of maize yields was 83.5%, 93.0%, and 96.0% for 1998, 1999, and 2001, respectively. According to the authors, the high accuracy of the model was due to the PVI being better at reducing noise caused by bare soil information present in spectral images. [PERSON] et al. [148] used 40 indices related to plant productivity, including MTCI, NDVI, and EVI, to predict alfalfa (lucerne) yield, located in the state of Wisconsin (north-central United States). The crop was sown in May 2018 and 2019, whereas the measurements were recorded on 25 July and 19 August 2019. The most accurate model obtained by the researchers was characterized by a R\({}^{2}\) of 0.87. As demonstrated above, progress in remote sensing techniques has allowed for the application of multispectral images as an effective tool for predicting plant productivity [149].
# Large Field-Size Elliptic Curve Processor for Area-Constrained Applications
## 3 Proposed Crypto Processor Architecture
### Data Buffers  
The orange color in Figure 1 contains four \(m\)-bit buffers and a \(3\times 1\) multiplexer \(M1\). The \(x_{p}\) and \(y_{p}\) buffers contain the \(x\) and \(y\) coordinate of initial point \(P\). A buffer \(b\) holds an \(m\)-bit elliptic curve constant. The _KeyReg_ buffer keeps an \(m\)-bit scalar multiplier. Whereas \(m\) determines the scalar multiplier length (i.e., \(k\)). All these parameters (\(x_{p}\), \(y_{p}\), \(b\) and _KeyReg_) are input to our proposed architecture. The corresponding values for \(x_{p}\), \(y_{p}\) and \(b\) are selected from a standardized document by NIST.
## 2 Mathematical Formulation
### Model Interpretation  
In Equations (1)-(8), the mathematical model is described, which includes continuous variables related to the magnitudes, angles of voltages, and active and reactive power production. This is a MINLP model that includes binary variables associated with the placement of the D-STATCOMs in the network. In addition, due to active and reactive power balance constraints and the trigonometric functions and voltage products, this model is nonlinear [2].  
To understand the MINLP model described in Equations (1)-(8), some aspects must be clarified. Equation (3) describes the total costs of one year of operating the distribution network, which consists of Equation (1), representing the yearly cost of power purchased at conventional generation node terminals, and Equation (2) expresses the costs of installing the D-STATCOMs. Equations (4) and (5) are observed for each node and period of the reactive and active power balance. Variable \(Q_{k}^{DS}\) is considered in Equation (5), which injects the reactive power of the D-STATCOMs positioned at the nodes. It is essential to clarify that the power varies as a function of the daily demand curve. As imposed by the regulatory authorities of the electricity sector [41], (6) is the inequality that defines, for each node, the lower and upper voltage limits of each period. The nominal reactive power limits for each D-STATCOM and variable \(Z_{k}\), which is binary, indicate whether the D-STATCOM is located at node \(k\). This is observed in constraint (7). The limit of D-STATCOMs installed in the grid is defined by inequality (8), where \(N_{A}^{DS}\) is the maximum number of D-STATCOMs allowed.
# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 3 Results
### Unsupervised ML (Random Forest)  
[PERSON] et al. (2020) [PERSON] et al. (2020) examined the relationship between self-reported non-pathological fatigue and multimodal sensor data by comparing supervised and unsupervised ML techniques (after imputing missing time-series data) from a multi-sensor wearable device using a recurrent NN-based algorithm. The analysis was carried out on 405 recording days and 27 healthy participants. Continuous multimodal wearable sensor time series for vital signs, physical activity and other physiological parameters were recorded, as were three daily questionnaires on physical fatigue, mental fatigue and the visual analog scale (VAS).  
Physical tiredness labels provided by the subjects were used to train a Random Forest classifier, and the causal convolutional NN model for the unsupervised representation learning of multivariate sensor data produced the best results (weighted precision of 0.70 \(\pm\) 0.03 and recall of 0.73 \(\pm\) 0.03). Physical activity (energy expenditure, intensity of motion and number of steps) and vital signs (HR, HRV and respiratory rate) were both crucial parameters for training the Random Forest (weighted precision of 0.70 \(\pm\) 0.05 and recall of 0.72 \(\pm\) 0.01).
## 5 Results and Discussion
### Calculation of Information Quantity of Evaluation Factors
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Primary Impact** & **Secondary** & **Disaster Spot** & **Area (km2)** & **Disaster Spot** & **Weight** & **Weighted** \\
**Factor** & **Impact Factor** & **(Location)** & **Area (km2)** & **Density (km2)** & **Weight** & **Information Quantity** \\ \hline \hline \multirow{8}{*}{Slope orientation} & Plain & 0 & 170.2992 & 0.0000 & & 0.00000 \\ \cline{2-6}  & Northeast & 29 & 1112.3845 & 0.0261 & & 0.00455 \\ \cline{2-6}  & East & 18 & 1148.7739 & 0.0157 & & \(-\)0.01409 \\ \cline{2-6}  & Southeast & 10 & 1099.4645 & 0.0091 & & \(-\)0.03399 \\ \cline{2-6}  & South & 10 & 862.0239 & 0.0116 & & \(-\)0.02509 \\ \cline{2-6}  & Southwest & 21 & 936.3509 & 0.0224 & & \(-\)0.00096 \\ \cline{2-6}  & West & 34 & 1137.3806 & 0.0299 & & 0.00956 \\ \cline{2-6}  & Northwest & 46 & 1234.6095 & 0.0373 & & 0.01762 \\ \cline{2-6}  & North & 31 & 941.6163 & 0.0329 & & 0.01309 \\ \hline \multirow{8}{*}{Relief amplitude} & 0–9 & 121 & 3338.7381 & 0.0362 & & 0.02590 \\ \cline{2-6}  & 9–18 & 59 & 2548.4577 & 0.0232 & & 0.00031 \\ \cline{2-6}  & 18–30 & 14 & 2005.3339 & 0.0070 & & \(-\)0.06814 \\ \cline{2-6}  & 30–51 & 4 & 641.3795 & 0.0062 & & \(-\)0.07458 \\ \cline{2-6}  & 51–286 & 1 & 108.9942 & 0.0092 & & \(-\)0.05254 \\ \hline \multirow{8}{*}{Fault buffer zone} & 0–1000 & 63 & 3173.4739 & 0.0199 & & \(-\)0.00691 \\ \cline{2-6}  & 1000–2000 & 37 & 2066.5031 & 0.0179 & & \(-\)0.01172 \\ \cline{2-6}  & 2000–3000 & 12 & 1289.6542 & 0.0093 & & \(-\)0.04222 \\ \cline{2-6}  & 3000–4000 & 15 & 850.8013 & 0.0176 & & \(-\)0.01244 \\ \cline{2-6}  & 4000–5000 & 19 & 503.0083 & 0.0378 & & 0.02307 \\ \cline{2-6}  & \(>\)5000 & 53 & 759.4627 & 0.0698 & & 0.05167 \\ \hline \multirow{8}{*}{Engineering geological rock formations} & A & 32 & 250.8534 & 0.1276 & & 0.14005 \\ \cline{2-6}  & B & 22 & 1383.5292 & 0.0159 & & \(-\)0.03028 \\ \cline{2-6}  & C & 46 & 5075.4648 & 0.0091 & & \(-\)0.07627 \\ \cline{2-6}  & D & 27 & 1259.8809 & 0.0214 & 0.0818 & \(-\)0.00587 \\ \cline{2-6}  & E & 48 & 514.3091 & 0.0933 & & 0.11448 \\ \cline{2-6}  & F & 10 & 105.2644 & 0.0950 & & 0.11594 \\ \cline{2-6}  & G & 14 & 53.6016 & 0.2612 & & 0.19867 \\ \hline \multirow{8}{*}{Language type} & Paddy field & 0 & 1.2348 & 0.0000 & & 0.00000 \\ \cline{2-6}  & Dry land & 21 & 433.5838 & 0.0484 & & 0.12173 \\ \cline{2-6}  & Forest land & 22 & 1033.6438 & 0.0213 & & \(-\)0.01287 \\ \cline{2-6}  & Shrub & 3 & 24.6384 & 0.1218 & & 0.27264 \\ \cline{2-6}  & Sparse forest land & 0 & 91.5045 & 0.0000 & & 0.00000 \\ \cline{2-6}  & Other forest lands & 0 & 8.7031 & 0.0000 & 0.1637 & 0.00000 \\ \cline{2-6}  & High coverage grassland & 112 & 2862.3880 & 0.0391 & & 0.08681 \\ \cline{2-6}  & Medium coverage grassland & 36 & 2191.7117 & 0.0164 & & \(-\)0.05529 \\ \cline{2-6}  & Low coverage grassland & 0 & 49.7048 & 0.0000 & & 0.00000 \\ \cline{2-6}  & \multicolumn{1}{c}{} & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Federated Learning-Based Analysis of Human Sentiments and Physical Activities in Natural Disasters
## 4 Experimental Results
### Assessment of the Trade-off between the Training Data and Accuracy  
Another experiment was performed, in which we distributed an increased number of training instances of both datasets with manually labeled instances to connected nodes to see how it impacts the accuracy in the FL environment by keeping the connected nodes constant. The training sets of both datasets contained a thousand images each, which were divided up and given to each of the five nodes to guarantee that each node would obtain an adequate quantity of training examples. We continued to expand the training set of each node by introducing an additional 50 images to each node's dataset. This resulted in an increase of 230 training instances across all clients. Figure 7 demonstrates that the predictive accuracy improved when the number of training samples was increased. This was the case before and after the expansion. Early in the process, there was a great degree of diversity; however, as the number of training datasets obtained by each node grew, the pace of the improvement in their performance slowed down.  
Tables 7 and 8 show the assessment/evaluation of the two baseline methods with regard to the level of accuracy and F1-score. (The Baseline 1 scenario is the ideal situation, in which each image is manually evaluated and labeled; the Baseline 2 scenario is a training set that contains unrelated data.) The suggested technique yields encouraging outcomes; on  
Figure 6: Impact on accuracy by expanding the number of participating client nodes in the FL environment: (**a**) human sentiment; (**b**) human physical activity.  
Figure 7: Accuracy and training sample size in FL is a trade-off during this experiment as there will only be a certain number of clients (i.e., six). As the number of training samples per client grows, so does the global model’s accuracy. (**a**) Human visual sentiments dataset; (**b**) Human physical activities dataset.  
both datasets, it performs better than Baseline 2, while maintaining a level of performance that is similar to Baseline 1.
## 2 Materials and Methods
### Chemicals and Reagents  
An analytical standard of arachidonic acid (cis, cis, cis, cis-5,8,11,14-Eicosatetraenoic acid, CAS No 506-32-1, MW 304.47, purity \(\geq\) 97.0%) and octadeuterated arachidonic acid (cis, cis, cis, cis-5,8,11,14-Eicosatetraenoic acid-5,6,8,9,11,12,14,15-d8, CAS No 69254-37-1, MW 312.52, \(\geq\)98 atom % D, purity \(\geq\) 98%) used as an internal standard (IS) were purchasedfrom Merck Sigma-Aldrich, Co., St. Louis, MO, USA. LC-MS grade acetonitrile, methanol, ethanol, and 2-propanol were purchased from Fisher Scientific, Leics, UK; LC-MS grade formic acid and ammonium formate were obtained from Fisher Chemicals, Brno, the Czech Republic, and ethanol MS grade was obtained from Fisher Scientific, St. Louis, MO, USA. ACS Reagent Grade dichloromethane (Merck Sigma-Aldrich, St. Louis, MO, USA) was used for sample preparation. Bovine Serum Albumin, CAS No 9048-46-8, was purchased from Sigma Aldrich, Co., St. Louis, MO, USA. For the preparation of pooled human serum, venous blood was drawn from 30 different healthy adult donors in Serum Clot Activator tubes. After centrifugation at 490\(\times\)\(g\) for 15 min, at 20 \(\pm\) 5 \({}^{\circ}\)C the serum was separated. Pooled human serum was prepared by mixing equal aliquots of 2 mL from each donor serum and used to prepare calibration curves and quality control (QC) samples. Aliquots of the pooled serum were stored at -18 \({}^{\circ}\)C for validation experiments.