esa-sceva/satcom-chunk-collection huggingface dataset:
# Compressive Behaviors of Prisms Made of Demolished Concrete Lumps and Fresh Concrete
## 3 Results and Discussion
### Mechanical Properties of Specimens Made of DCLs and FC
#### 3.3.1 Combined Compressive Strength
The larger the replacement ratio, generally the greater the discrepancy between the predicted and measured strengths. This might be explained in the same way. With increasing replacement ratio, the number of spaces between the mold and the DCLs and the number of gaps between adjacent lumps increased. But the spaces and gaps generally became narrower, so the heterogeneity of the compound concrete would increase if the aggregate was coarse. With the test results listed in Table 4 as a reference, a modified Equation (8) can be proposed for predicting the compressive strength of compound concrete made with normal-strength DCLs and FC with a maximum aggregate size not larger than 35 mm:  
Figure 7:2  
Figure 7:3 \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Notation**} & \multirow{2}{*}{\begin{tabular}{c} \(f_{*,300,\text{DC}}\) \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} \(f_{*,300,\text{FC}}\) \\ **(MPa)** \\ \end{tabular} } & \multicolumn{6}{c}{\
\begin{tabular}{c} \(f_{*,300,\text{FC}}\) \\ **(MPa)** \\ \end{tabular} } & \multicolumn{6}{c}{
\begin{tabular}{c} \(f_{*,300,\text{CC}}\) \\ **(MPa)** \\ \end{tabular} } \\ \cline{3-10}  & & & & & & & & & & \\ \hline \multirow{3}{*}{Group 2} & \multirow{3}{*}{CU300-100-0.3} & \multirow{3}{*}{33.6} & \multirow{3}{*}{43.6} & \multirow{3}{*}{43.6} & \multirow{3}{*}{41.6} & \multirow{3}{*}{8.3} & \multirow{3}{*}{38.6} & \multirow{3}{*}{0.5} \\  & & & & & & & & & \\ \cline{3-10}  & & & & & & & & & \\ \hline \multirow{3}{*}{Group 3} & \multirow{3}{*}{CY300-100-0.2} & \multirow{3}{*}{28.8} & \multirow{3}{*}{37.5} & \multirow{3}{*}{31.7} & \multirow{3}{*}{34.9} & \multirow{3}{*}{10.0} & \multirow{3}{*}{32.3} & \multirow{*}{1.8} \\  & & & & & & & & & \\ \cline{3-10}  & & & & & & & & & \\ \cline{3-10}  & & & & & & & & & \\ \hline \multirow{3}{*}{Group 4} & \multirow{3}{*}{PR300-100-0.3} & \multirow{3}{*}{28.8} & \multirow{3}{*}{37.4} & \multirow{3}{*}{33.4} & \multirow{3}{*}{37.5} & \multirow{3}{*}{12.4} & \multirow{3}{*}{33.4} & \multirow{3}{*}{0.1} \\  & & & & & & & & & \\ \cline{3-10}  & & & & & & & & & \\ \cline{3-10}  & & & & & & & & & \\ \hline \hline \end{tabular} Note. _Mo., measured value; Cal., calculated value._  
\end{table} 
Table 5: Comparison between predicted and measured combined compressive strengths for specimens in Groups 2, 3, and 4.  
where \(\lambda\) is a reduction coefficient not larger than 1.0, \(\alpha\) = 0.68 for a specimen with a lateral dimension of 300 mm, and \(s_{\text{max}}\) (in mm) denotes the maximum aggregate size of the fresh concrete; \(\lambda\) = 1.0 when \(s_{\text{max}}\leq\) 25 mm. In that case, Equation (8) reduces to Equation (4). The combined compressive strengths of the specimens in Groups 2, 3, and 4 predicted using Equations (8) and (9) are listed in Table 5. The predicted results clearly agree well with the measured data on the whole.
## 1 Introduction  
Time series data are generated in various fields such as social sciences [1], meteorological industry [2, 3], financial market [4], modern agriculture [5, 6], electric power field [7], etc. Especially in the Big Data and IoT industry [8], it is essential to analyze the regular patterns of time series data and predict the trends for system management and control. Time series prediction can help to sense and control the systems in advance. However, the time-series data in all systems are often highly nonlinear, nonstationary, and massive [9], making it very difficult to predict trends accurately. Therefore, the prediction of complex time series data has become a hot research topic in data mining [10].  
Scholars have explored different methods for the nonlinear and nonstationary problems in time series data prediction. The existing prediction methods are mainly classified into statistical methods [11, 12], shallow machine learning methods [13, 14], deep learning methods [15, 16], and broad learning methods [17, 18]. The main statistical prediction methods are the autoregression model (AR), moving average model (MA), autoregression moving average model (ARMA), and differential auto regression integrated moving average model (ARIMA). The methods above mainly transform the nonstationary time series into the stationary time series by variance or integration. It is difficult to reduce the loss function value with stable data transformation, so it is unsuitable for practical applications. Deep learning methods develop from external neural networks. Deep learning forms a vertically deepened network structure, which improves the network's learning ability. However, the time and computational costs are high since all layers need the training simultaneously. Meanwhile, the gradient disappearance and explosion can occur in deep the emerging models in the recurrent networks with a lightweight training mode. The BLS is proposed to reduce the network scale of the deep learning networks. There is no ideal network to cover the strong learning ability and appropriate structure [31]. Then we try to propose a new model which combines the BLS and ESN to take advantage of different networks. The new network is studied to balance the model learning ability and the computation resource occupancy.
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 3 Cost Model
### Cost Components in a Cloud
#### 3.3.1 Servers, Switches, Racks, and Cabling  
Servers are assumed to have a lifecycle of five years, thus needing renewal every five years [34]. In the model, the server investments are made based on the forecasted demand. Since server investments are assumed to be made every five years, the total number of servers required to meet the demand of 2025 are bought in 2021, then the total number of servers required to meet the demand of 2030 are bought in 2025. While the computing capacity improves over generations of servers, for simplicity, it is assumed that that servers with the same capacity are bought at inflation-adjusted prices. The average costs of servers, racks and cabling as well as switches (including installation costs) in a telecom operator's cloud are based on [32]. Switches are assumed to have a lifecycle of five years, the same as that of servers, while the CAPEX is calculated in the same manner as the servers. Top-of-rack switches are assumed, thus requiring a minimum of one switch per rack. For redundancy, two switches are assumed per rack, and both contribute to the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Cost Item** & **Cost** & **Source** \\ \hline Number of Servers, Ns & Server, C\({}_{\text{S}}\) & EUR 10,000 & [31] \\ Number of Racks, N\({}_{\text{R}}\) & Racks and cabling, C\({}_{\text{R}}\) & EUR 20,000 & [31] \\ Number of Switches, N\({}_{\text{SW}}\) & Switch, C\({}_{\text{SW}}\) & EUR 10,000 & [31] \\ - & Power per server, P\({}_{\text{S}}\) & 400 W & [31] \\ - & Power per switch, P\({}_{\text{SW}}\) & 400 W & [31] \\ - & Electricity price, E & 0.1 euro/kWh & [32] \\ - & Software license per server, C\({}_{\text{SL}}\) & 1300 EUR/year & [31] \\ Number of VNF licenses, N\({}_{\text{VNF}}\) & VNF license, C\({}_{\text{VNF}}\) & 85 EUR/year & 100 USD/year [33] \\ - & Maintenance, C\({}_{\text{M}}\) & 10\% of CAPEX & Assumption \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cost Inputs.  
annual OPEX. A rack housing 12 servers is used for calculation, and the racks and cabling are assumed not to need renewal within the scope of ten years.  
\[\text{No. of Racks},\ N_{R}=\text{Ceil}\bigg{(}\frac{N_{S}}{12}\bigg{)} \tag{5}\]  
\[\text{No. of Switches},\ N_{SW}=2\times N_{R} \tag{6}\]  
Furthermore, software licenses are assumed per server and VNF licenses are assumed per VNF. The power consumption, maintenance, and software/VNF licenses contribute to the server OPEX. The redundancy for servers is determined according to the slice requirements; different redundancies can be applied during calculation per slice type.
###### Abstract  
In this study, a finite element (FE)-based machine learning model was developed to predict the mechanical properties of bioglass (BG)-collagen (COL) composite hydrogels. Based on the experimental observation of BG-COL composite hydrogels with scanning electron microscope, 2000 microstructural images with randomly distributed BG particles were created. The BG particles have diameters ranging from 0.5 \(\upmu\)m to 1.5 \(\upmu\)m and a volume fraction from 17% to 59%. FE simulations of tensile testing were performed for calculating the [PERSON]'s modulus and Poisson's ratio of 2000 microstructures. The microstructural images and the calculated Young's modulus and Poisson's ratio by FE simulation were used for training and testing a convolutional neural network regression model. Results showed that the network developed in this work can effectively predict the mechanical properties of the composite hydrogels. The R-squared values were 95% and 83% for [PERSON]'s modulus and Poisson's ratio, respectively. This work provides a surrogate model of finite element analysis to predict mechanical properties of BG-COL hydrogel using microstructure images, which could be further utilized for characterizing heterogeneous materials in big data-driven material designs.  
composite hydrogels; mechanical properties; machine learning Article
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 4 Discussion
### Results from Emulation Experiment  
For this experiment, we obtained a \(\eta\overline{\otimes}\) comparison between Generic TCP and TCP-NACK as a function of the error packet probability. Figure 9 shows the generated curves for each emulated protocol. We obtained the CWND size as a function of the BER value through time and calculated the area under each CWND curve. Hence, we obtained the \(\overline{\partial}\) values shown in Table 6. We found that the CWND size of TCP-NACK surpass the CWND size of Generic TCP in 2 average packets, leading to a higher area under the curve, or a \(\overline{\partial}\) increment. The throughput of the TCP-NACK was better than TCP-Reno and if we compare the results presented in Figure 8, the throughput of the TCP-NACK also appear better than TCP-Westwood, and the algorithm of the TCP-NACK has less complexity than TCP-Westwood. Therefore, TCP-NACK will be used in practical experiments to validate its performance.  
Figure 8: Theoretical \(\eta\overline{\otimes}\) as a function of the error packet probability.
# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 3 Proposal of Multilayered Network Model for Mobile Network Infrastructure Disruption
### MANET Layer  
The MANET network is usually composed of devices such as smartphones, tablets, laptops and so on. MANET layer in the MNM is composed of mobile smart sensors with communication based on IEEE 802.11 Wi-Fi using an Ad-Hoc mode. The advantages of the MANET network are the autonomous and self-organized network mobile nodes. Therefore, the establishment of the network is quick without needing fixed infrastructure, which enables MANET to be used in different scenarios and environments. The reason MANET is chosen as the second layer in MNM is due to the fact that MANET nodes are not strictly resource-constrained and offers longer radio ranges along with higher data rates. Standards like 802.11n offer data rate range from 54 Mbps to 600 Mbps with outdoor radio range up to 250 m [24]. With mobility, it is possible to send urgent data from the WSN layer through the MANET layer to the nearest functional AP.  
The crucial part of MANET layer is communication without interference with other devices and with high spectrum efficiency in the highly congested 5G environment. One of the solutions to achieve higher spectral efficiency in 5G environment is D2D communication. [PERSON] et al. [25] categorize D2D communication as Inband (licensed) and Outband (unlicenced) on the bases of spectrum in which D2D communication occurs. In Inband communication, D2D users share cellular resources, while in Outband communication is used to eliminate interference between D2D users and cellular users. It works in the unlicensed spectrum where Wi-Fi, Bluetooth and ZigBee operates. In terms of MANET networks, the solution to these problems is Cognitive Radio of Cognitive Radio Ad-Hoc Networks (CRAHN).
## 3 Results and Discussion
### Emission Factor of Tepenes
molle_, EF of terpen other than isoprene showed little change throughout the day except for limone. Monoterpene emissions are known to be variable throughout the  
Figure 4: Standardized emission factor, expressed as \(\mathrm{\SIUnitSymbolMicro g_{ldw}}^{-1}\) h\({}^{-1}\) during the day and standard deviation of monoterpens, transcaryophilene and alfa-humelene for _Platanus \(\times\) accerifolia_ (**left**) and _Schinus molle_ (**right**). day and among days, the season, as well as the age of the individual [22]. However, the 1-8 cineole, followed by \(\beta\)-myrrene and limonene are the species with the highest EF from _S. molle_ according to [PERSON] et al. (2013a) [25]. The terpene 1-8 cineole was not measured in this study. Limonene exhibited the highest EF of monoterpenes followed by terpinolene.
## 3 Overview of Join Processing in WSNs
### Requirements and Challenges  
Due to the constraints of WSNs stated in Section 2.1, join implementation in WSNs is very complicated, whose optimization is very difficult. Major requirements and the resulting challenges are described below.  
**Stream processing.** In the conventional databases, join operand relations are stored ones. Access paths to and the statistics on them including the statistics on the joining attributes are available. In WSNs, join operand relations are streams of sensor readings. The challenges are to execute a stream join using limited resources of sensor nodes, and to collect the statistics on the sensor readings for cost-based query optimization.  
**Distributed processing.** Because of the memory constraints in the sensor nodes, it would not be possible to collect all the tuples at one designated node in the network for join processing. Thus, distributed processing is required. As stated in Section 2.1, in WSNs, each node has limited knowledge of other nodes and the network connectivity. Therefore, the problem of generating an efficient distributed strategy is a difficult challenge.  
**Protocol-oriented implementation.** Since the sensor nodes in WSNs are only with constrained resources, a join implementation which is free from any physical error that might be caused due to the resource constraints while guaranteeing algorithmic correctness of the join result is required. In transmitting data among the sensor nodes, for example, an overflow at a receiving node due to its memory constraints should be avoided. A carefully designed protocol by which data transmission as well as query operations are synchronized is required. Such a protocol-oriented implementation under the resource constraints is a crucial challenge.  
**Fault-tolerance.** As stated in Section 2.1, WSNs are often unstable. Node or link failures can occur or several reasons. Even if just a small number of joinable tuples are missing because of a failure, it could have a great effect on the join result. Dealing with failures is a challenge because fault tolerance with duplicate transmissions, retransmissions, and exchanges of control information that can help detect failures would not only increase the complexity of join processing but requires more communication.
## 5 Discussion
### Comparison with Other Commonly Used Object Models  
The purpose of this experiment was to compare our improved YOLOv5 model with Faster R-CNN, YOLOv4, and YOLOv5l by training the kumquat data under different  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Growth Stage**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Ground Truth** \\ **Count** \\ \end{tabular} } & \multicolumn{2}{c}{**Correctly Identified**} & \multicolumn{2}{c}{**Falsely Identified**} \\ \cline{5-8}  & & & **Amount** & **Rate (\%)** & **Amount** & **Rate (\%)** \\ \hline \multirow{3}{*}{Growing} & YOLOv5l & 300 & 243 & 81.00 & 10 & 3.3 \\  & Ours & 300 & 261 & **87.00** & **3** & **1** \\ \cline{1-1} \cline{2-8}  & YOLOv5l & 281 & 259 & 92.17 & 15 & 5.3 \\ \cline{1-1} \cline{2-8}  & Ours & 281 & 270 & **96.08** & **5** & **1.7** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison results of our improved model and YOLOv5l in detecting citrus fruits under different growth periods.  
Figure 9: The comparison of detection effects in the kumquat images. (**a**) Ground truth of samples; (**b**) YOLOv5l; and (**c**) improved YOLOv5.  
growth stages. Table 5 provides the details of the experimental results. This demonstrated that the improved YOLOv5 model achieved better detection performance for \"Kumquat\" fruits at both growth stages. According to the detected images and Table 5, for growing \"Kumquat\" fruits, the improved YOLOv5 method detected a larger number of citrus fruits, while Faster R-CNN and YOLOv4 detected relatively fewer fruits, along with a small number of identification errors. For mature \"Kumquat\" fruits, the improved YOLOv5 model was significantly better than the other three methods. During the growing period, the \"Kumquat\" fruits were small in size, the fruits and leaves were green, and the color characteristics were not obvious. It could be seen from the chart that the improved method significantly improved the detection ability of the YOLOv5 model, and the recognition accuracy was not affected by the color or individual characteristics of the growth stage. After many comparison experiments, the results showed that the improved YOLOv5 method was better than the other three methods, and there was no loss of detection speed after combining the CA attention mechanism and BiFPN. Figures 10 and 11 show the detection effects of the four models on the growth stage and maturity stage.
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 4 Experiments
### Performance Evaluation
#### 4.4.5 Performance Comparison  
Table 6 presents a performance comparison of the proposed approach and the existing state-of-the-art security alert data analysis approaches. Unsupervised learning methods from [39,40], isolation forest (IF), and IF with day-forward chaining (IF-DC) were used for comparison. The results from [13], obtained using DT combined with SVMSMOTE (DT-SVMSMOTE), are also included for reference. Deep learning methods, CNN, and long short-term memory (LSTM), combined with MAS-OSS, were also used for comparison. Deep learning (CNN-MAS-OSS and LSTM-MAS-OSS) and unsupervised learning approaches (IF and IF-DC) yielded high FPRs, resulting in a large number of false alerts that require manual investigation. The proposed approach, XGBoost-MAS-OSS, yielded an FPR of 0.119%, outperforming all compared security alert analysis approaches by a large margin. Table 7 presents a performance comparison of the proposed approach with the deep learning approaches evaluated on the UNSW-NB15 dataset. The dataset is not highly skewed, and therefore, most of the cited approaches do not require techniques to handle class imbalance; hence, they did not employ such techniques. Therefore, the results are not directly comparable, as we sampled the attack data to obtain a class distribution closely representative of our highly skewed enterprise dataset. However, the proposed approach achieved superior scores in all evaluation metrics. These findings highlight the potential of the proposed approach in solving the highly skewed class distribution problems commonly  
Figure 6: Precision–recall curves for different sampling settings on enterprise dataset.  
encountered in cybersecurity scenarios, thus paving the way for more effective and efficient AI-assisted security operations.
## 2 Handcrafted Representation-Based Approach
### Space-Time-Based Approaches
#### 2.1.1 Space-Time Volumes (STVs)  
The features in the space-time domain are represented as 3D spatio-temporal cuboids, called space-time volumes (STVs). The core of STV-based methods is similarity measure between two volumes for action recognition. In [32], an action recognition system was proposed using template matching, instead of using space-time volumes, they used templates composed of 2D binary motion-energy-image (MEI) and motion-history-image (MHI) for action representation followed by a simple template matching technique for action recognition. This work was extended in [33] where MHI and two appearance-based features namely foreground image and histogram of oriented gradients (HOG)  
Figure 5: Components of space-time-based approaches. ESURF: Enhanced Speeded-Up Robust Features; HOG: histogram of oriented gradients; HOF: histogram of optical flow; MBH: motion boundary histogram; BOW: bag-of-words; BoVW: Bag-of-Visual-Words; FV: Fisher Vector; SFV: Stacked Fisher Vector; HMM: Hidden Markov Model; DBAN: Dynamic Bayesian Action Network; SVM: support vector machine; ANN: Artificial Neural Network; LDA: Latent Dirichlet Allocation; SOM: Self Organizing Map; VQ: vector quantization.  
were combined for action representation, followed by simulated annealing multiple instance learning support vector machine (SMILE-SVM) for action classification. The method proposed in [34] also extended the [32] from 2D to 3D space for view-independent human action recognition using a volume motion template. The experimental results using these techniques are presented in Table 1.
## 3 The LOBIN Prototype: A Practical Study Case
### System Validation and Tests  
During the validation of the system, each subsystem was tested first separately for debugging and tuning purposes. Finally, the overall system was tested in a real scenario (namely, as a pilot scheme deployed in the Cardiology Unit of La Paz Hospital, Madrid, Spain).
# Fragility Assessment and Reinforcement Strategies for Transmission Towers Under Extreme Wind Loads
## 2 Project Overview
### Transmission Line Design and Damage Condition  
The transmission line was originally designed for a maximum wind speed of 33 m/s, corresponding to a 15-year return period, measured at 10 m above ground level. The design also specified a minimum ambient temperature of \(-5\,^{\circ}\mathrm{C}\), with no consideration for ice accretion. Towers #51 and #52 on Huibian III Road were equipped with double-split LGJ-300/25 steel-core aluminum stranded conductors and designed with a safety factor of 2.5. Under maximum working conditions, these conductors experience a tensile stress of 100 MPa, while the average operational stress is 62.56 MPa--approximately 25% of the conductor's breaking strength.  
Originally, the towers were outfitted with two GJ-50 galvanized steel strand ground wires. Following a technical upgrade, these were replaced with JLB-40/80 aluminum-clad steel strands, offering a higher safety factor of 3.0. The upgraded ground wires have a maximum working stress of 204 MPa and an average operational stress of 153.01 MPa, also corresponding to 25% of their breaking strength.  
The towers that collapsed include Huibian III Road #51 tower (designated ZM3-24), #52 tower (ZM3-33), and Huizhai Line #21 tower (ZM2-24). As illustrated in Figure 1, on-site forensic investigations revealed that failure in the #52 tower initiated in the third segment above the base legs. The collapse mechanism involved bending instability in the primary load-bearing members, torsional deformation of the cross-bracing, and detachment of auxiliary structural components, culminating in a progressive structural failure.  
Figure 1: Examples of transmission tower damage conditions.