esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advancement in Assessment and Control of Structures under Multi-Hazard
## 5 Multi-Hazard Assessment and Vibration Control of Buildings
Seismic loads, however, might excite higher vibration modes of such structures, resulting in high floor accelerations. This implies low inter-story drift and, therefore, lower risk of structural damage, but high floor acceleration can be critical for non-structural components [6, 9, 127, 128]. From a structural point of view, wind loads are, therefore, critical for flexible structures, while seismic loads are more demanding on stiff structures. Occupant comfort and safety is another consideration when it comes to the response of buildings to wind and earthquake loads. Large floor accelerations can cause discomfort to occupants and may pose a safety threat due to moving objects. Floor accelerations in tall buildings are typically higher during moderate to strong ground shaking than during strong wind. Strong seismic loading is, however, typically less frequent than strong wind. From a serviceability point of view, wind action is, therefore, more critical for occupant comfort. Multi-hazard effects in buildings also need to be looked at from a life-cycle cost perspective and accumulation of damage due to multiple events: for example, wind response of a structure partially damaged by an earthquake or vice versa. Damage accumulation and fatigue due to repeated loading from frequent actions, such as moderate to strong wind, is also an important consideration.
# Streaming-Based Anomaly Detection in ITS Messages
## 2 Materials and Methods
### Performance Indicators  
In the unsupervised outlier detection setting, it is often problematic to judge the effectiveness of the algorithms in a rigorous way. The majority of the outlier-detection algorithms output an outlier score, which is converted to a label based on a threshold. If the threshold selection is too restrictive (to minimise the number of declared outliers), then the algorithm will miss true outlier points (false negatives). On the other hand, too many false positives will be generated if the algorithm declares too many data points as outliers. This trade-off can be measured in terms of precision and recall, common measures of effectiveness.  
In order to evaluate the performance of the different approaches, we use the area under the curve of the receiver operating characteristic (AUC-ROC) [47] and the area under the curve of precision-recall (AUCPR) [48]. Both indicators are based on the following concepts:  
* True positive (TP): True positives are correctly identified anomalies.
* False positive (FP): False positives are incorrectly identified normal data.
* True negative (TN): True negatives are correctly identified normal data.
* False negative (FN): False negatives are incorrectly rejected anomalies.  
The true positive rate (TPR), or recall, is  
\[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}}.\]  
The false positive rate (FPR) is  
\[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}}.\]  
The AUC-ROC receiver operating characteristics are TPR and FPR. The higher the AUC-ROC, the better the detection. AUC-ROC is the most popular evaluation measure for unsupervised outlier detection methods [49].  
AUCPR uses precision and recall. Precision is the fraction of retrieved instances that are relevant [50]. Recall or sensitivity is the ability of a model to find all the relevant cases within a dataset:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]  
The AUCPR baseline is equivalent to the fraction of positives [51]:  
\[\text{AUCPR}-\text{baseline}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}+ \text{TN}}\]  
Figure 4: Anomaly detection framework.  
AUCPR is a highly effective assessment metric that performs well for a variety of classification tasks. It is especially beneficial when dealing with imbalanced data when the minority class is more significant, like in anomaly detection. As a result, we used these metrics to assess the efficacy of anomaly detection algorithms in this study.
## 1 Introduction
This kind of model was the former one used for the forecasting of meteorological variables and PV generation. They are more trustworthy in the long-term forecast when weather conditions are more stable, as they do not behave well in the face of sudden changes. A drawback of physical techniques is that a thorough knowledge of physics is essential. Among the most know methods are numerical weather prediction (NWP) [12] and sky imagery [13]. As for recurrent methods, they have been the most widely used for time series forecasting for several years. Nevertheless, recurrent models can show problems in dealing with the non-linearity and seasonality properties [14]. Finally, AI methods, contrary to recurrent methods, are indeed able to handle non-linear problems; thanks to this ability and to learning algorithms, AI methods can provide precise predictions and react to sudden meteorological changes. The researchers of [15] provide an extensive review of AI-based solar energy prediction. They conclude that the most frequently applied techniques are artificial neural networks (ANN) in first place, followed by support vector machine (SVM) techniques. Finally, another term by which PV generation predictors are also divided is by time horizon. The time horizon is the amount of time to forecast. In the literature, they are usually divided into three categories [16]; (1) Short-term forecasting is considered to be between a few minutes and a week. Its function is to schedule energy transfer, demand response, and economic dispatch of load [17]. (2) Medium-term forecasting is typically between 1 month and one year and is used to plan the next energy plans. (3) Long-term forecasting is usually considered when it is longer than one year, usually to plan the power plant to meet future needs and cost efficiency [18]. This article presents four short-term PV generation, prediction models. One of them is an analytical model, and the other three are AI-based models. The overall objective of this paper is to design and compare different prediction models, with the goal of obtaining an accurate model that can be integrated into an EMS. A detailed explanation of how the models have been developed is given. The results are analyzed in different ways. On the one hand, the general behavior of the four models during one month is studied and compared to real data, and on the other hand, the analysis of the behavior of the models for different types of days, differentiating between sunny and cloudy days, is presented. The remaining sections of the current article are organized as follows: Section 2 describes the case study for which the four PV energy generation forecasters have been implemented. Afterward, Section 3 explains the theory behind the techniques used to build the models. The development of the four models is described in Section 4. Section 5 presents the results obtained. Finally, Section 6 discusses the obtained conclusions.
# Experimental Studies on the Spraying Pattern of a Swirl Nozzle for Coal Dust Control
## 1 Introduction
\[d_{pmin}=\sqrt{\frac{9\mu D_{w}S_{ik}}{\rho_{p}v_{0}}} \tag{1}\]  
where \(d_{pmin}\) represents is the dust particle diameter (m), \(\mu\) is dynamic viscosity, \(D_{w}\) is a droplet diameter, \(S_{ik}\) is the stokes number, \(\rho_{p}\) is the dust density, and \(v_{0}\) is the air flow rate. Equation (1) shows that the square of the particle diameter \(d_{pmin}\) is proportional to the droplet size \(D_{w}\). When the other parameters in Equation (1) are fixed, \(d_{pmin}\) depends only on the droplet diameter \(D_{w}\). In other words, to capture the smaller particle size, the size of the fog particles \(D_{w}\) should be reduced accordingly. Since the respirable dust that causes pneumoconiosis is very small (less than 10 microns) [1], it is expected that the nozzle can spray out more tiny droplets to reduce the respirable dust more effectively. Therefore, it is necessary to study the atomization characteristics of the droplet particles for spraying dust. According to the tests performed with swirl nozzles [34], the relationship between the water consumption rate of the nozzle and the pressure of water is determined to be  
\[Q=kD\sqrt{P_{w}}\times 10^{-6}/60 \tag{2}\]  
where \(Q\) is the conical nozzle flow rate (m\({}^{3}\)/s), \(D\) is the nozzle diameter (m), \(P_{w}\) is the water pressure (MPa), and \(k\) is the test coefficient (\(k\) = 1.34). At the same time, nozzle flow rate \(Q\) can be described as follows. \[Q=v\cdot\pi\cdot(\frac{D}{2})^{2} \tag{3}\]  
where \(v\) represents water jet velocity (m/s) and \(D\) is nozzle orifice diameter (m). Substituting Equation (3) into Equation (2) the following expression is obtained. \[v=\frac{200\cdot k}{3\cdot\pi}\sqrt{P_{w}} \tag{4}\]  
It can be seen from Equation (4) that the water jet velocity \(v\) is proportional to the square root of the water pressure \(P_{w}\). As the spray pressure \(P_{w}\) increases, the axial velocity \(v\) of the fog increases accordingly. Thus, the relative velocity of the fog particles with the ambient air increases (weber numbers increase), which in turn promotes further atomization of the droplets. Figure 1: Tangential injection swirl nozzle (A is cross section symbols for location).
## 2 Related Work
An introduction of different technologies used in machine-to-machine (M2M) home automation environments is done by Starsinic [19]. The choice of a communication technology for a given WHAN environment will depend on the specific requirements of the latter. Among the alternative technologies for WHANs, a promising one is low-power Wi-Fi, given the widespread availability of Wi-Fi infrastructure in home scenarios. For example, [PERSON] _et al._ show  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline  & ZigBee & Z-Wave & BLE & Wi-Fi \({}^{a}\) \\ \hline RF Band & \multirow{2}{*}{868/915/2400} & \multirow{2}{*}{868/908/2400} & \multirow{2}{*}{2400} & \multirow{2}{*}{2400} \\ (MHz) & & & & \\ \hline Bitrate & \multirow{2}{*}{20/40/250} & \multirow{2}{*}{9.6/40/200} & \multirow{2}{*}{1000} & \multirow{2}{*}{\(\leq 54\times 10^{3}\)} \\ (kbps) & & & & \\ \hline Receiver & \(\leq\) –85 at 2.4 GHz & \multirow{2}{*}{\(-\)101 (at 40 kbps)} & \(\leq\) –70 (required) & \multirow{2}{*}{–68} \\ sensitivity (dBm) & \(\leq\) –92 at 868/915 MHz & & –87 to –93 (typical) & \\ \hline Max. msg. size (bytes) & \multirow{2}{*}{127} & \multirow{2}{*}{64} & \multirow{2}{*}{47} & \multirow{2}{*}{2346} \\ \cline{1-1} \cline{  
that an energy-efficient connection of devices to the Internet of Things over devices that use low-power Wi-Fi is possible, even when using standards like HTTP and TCP/IP [20]. [PERSON] _et al._ state that Wi-Fi technology is adequate to serve as the interface for home area networks to smart meters in smart grid environments [21]. While the market share of WHAN technologies, such as low-power Wi-Fi and also BLE, is growing steadily, ZigBee currently dominates the WHAN market [22]. Table 1[23] shows a comparison of the key features, such as Bitrates or message (msg.) sizes used by several WHAN low-power technologies.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 5 Experimental Results
### Results of Federated Learning with Cyclical Learning Rate
#### 5.2.3 CLR Results on CIFAR-10  
Finally, we turn to the CIFAR-10 dataset to further examine the benefits of employing a cyclical learning rate in the context of non-IID conditions throughout the federated learning process.  
As with the other datasets, we started by conducting the Learning Rate Range Test (LRRT) with SGD as the optimizer:  
As depicted in Figure 10, the learning rate range test identified 0.02 as the minimum learning rate, where the loss began to decrease, and 0.2 as the maximum, where the loss started to become ragged and increase, indicating potential instability. These bounds were then used for the CLR strategy during the federated training process.  
We then compared the performance of FedAvg with a fixed learning rate and FedAvg with CLR on the CIFAR-10 non-IID dataset.  
Figure 11 displays the evaluation accuracy over the training rounds for FedAvg using a fixed learning rate of 0.02 and FedAvg using a cyclical learning rate policy, with the learning rate varying between a minimum of 0.02 and a maximum of 0.2.  
Table 8 illustrates the differences in evaluation accuracy between these two strategies per 10 rounds of training.  
Applying the cyclic learning rate strategy helped the model achieve a higher accuracy of 48.8% in 200 rounds, compared to 37.8% with a constant learning rate.  
As presented in Table 9, CLR enabled the model to attain the target accuracy in considerably fewer rounds than with the fixed learning rate, highlighting a notable improvement in training efficiency.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Max Accuracy (\%)** & **Rounds to 71\%** & **Speedup (Rounds)** \\ \hline fmnist\_sgd\_fixed & 71.2 & 160 & x1 \\ \hline fmnist\_sgd\_clr & 78.3 & 69 & x2.3 \\ \hline \end{tabular}
\end{table}
Table 7: Maximum accuracy and rounds to 71%.  
Figure 10: Learning rate range test for CIFAR-10.  
Figure 11: CIFAR-10 non-IID with fixed learning rate vs. CIFAR-10 non-IID with CLR.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Communication Rounds** & **SGD with Fixed Lr** & **SGD with Cyclical Lr** \\ \hline \(1\) & 0.09980 & 0.09620 \\ \hline \(10\) & 0.14370 & 0.15940 \\ \hline \(20\) & 0.14920 & 0.10310 \\ \hline \(30\) & 0.20900 & 0.20790 \\ \hline \(40\) & 0.24100 & 0.23200 \\ \hline \(50\) & 0.17450 & 0.30710 \\ \hline \(60\) & 0.24200 & 0.31320 \\ \hline \(70\) & 0.22480 & 0.26330 \\ \hline \(80\) & 0.17260 & 0.27600 \\ \hline \(90\) & 0.17400 & 0.37130 \\ \hline \(100\) & 0.21020 & 0.42310 \\ \hline \(110\) & 0.22460 & 0.33850 \\ \hline \(120\) & 0.27970 & 0.40440 \\ \hline \(130\) & 0.26620 & 0.42730 \\ \hline \(140\) & 0.30510 & 0.43270 \\ \hline \(150\) & 0.31660 & 0.37720 \\ \hline \(160\) & 0.20150 & 0.42310 \\ \hline \(170\) & 0.36300 & 0.46350 \\ \hline \(180\) & 0.28750 & 0.40060 \\ \hline \(190\) & 0.30010 & 0.41100 \\ \hline \(200\) & 0.32790 & 0.47660 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation accuracy differences per 10 training rounds.
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 3 Related Work and Introduction of a Novel Approach
All the state-of-the-art works mentioned above primarily focus on modeling the LiDAR signal attenuation and stochastic simulation of backscattering (false positives) caused by rain and fog droplets. However, most authors did not quantify these errors or validate their modeling approach by comparing the simulations with the real measurements. Some authors obtained the real LiDAR point clouds under normal environmental conditions and stochastically implemented a false positive effect caused by rain, fog, and snow. These approaches are helpful for use cases where the data from rain and fog are required for ADAS testing, regardless of their accuracy; however, they do not fit very well in the usecases where a high-fidelity virtual LiDAR sensor output is needed under adverse rain and fog conditions because the actual LiDAR sensor's inherent optical detector and peak detection algorithm behavior significantly change depending on the rain intensity and fog visibility distance \(V\). For instance, the LiDAR rays that are backscattered from the rain or fog droplets also increase the background noise of the detector, and the low-reflective targets close to the LiDAR sensor may be masked under the noise. This effect produces a false negative result, significantly decreasing the LiDAR sensor detection rate. Therefore, it is not the case that every backscattered LiDAR ray from the rain or fog droplets appears as a false positive scan point in the point cloud. Moreover, the scattering from the rain and fog droplets misaligns the LiDAR rays, shifts the target peak location, and ultimately results in the ranging error \(d_{error}\). An overview of the features, output, and validation approaches of the state-of-the-art works mentioned above are tabulated in Table 1.  
In this work, we introduce a novel approach to model the effect of rain and fog on the performance of the LiDAR sensor. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. This methodology can be used to analyze the behavior of LiDAR detectors and peak detection algorithms under adverse rain and fog conditions. We have compared the simulation and real measurements on the time domain to quantify LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. The detection rate (DR), false detection rate (FDR), and ranging error \(d_{error}\) due to rain and fog droplets are evaluated
## 2 Background and Related Work
### Researches in Caching Field  
#### 2.2.1 Present Research Situation  
Because of these new features and challenges, there is a wide variety of research on ICN caching. At present, these research works are roughly divided into two categories as shown in Figure 1: one is to optimize the performance of the cache network system; the other is to establish and analyze the theoretical model of the cache network. The former tends to be specific and optimizes the overall performance of the cache system from different perspectives. The latter is to abstract and simplify the network to improve the theoretical support for understanding the behavior of the network system.  
While less work has been done on cache size allocation in existing studies [22], this does not mean that it is unimportant. The size of the cache storage space will undoubtedly affect the performance of the entire cache system. When the other settings of the system are the same, the nodes with larger cache size can cache more objects, which will improve the  
Figure 1: Present Research on Cache in ICN.  
cache hit ratio for the entire system. At the same time, the cost of query caching increases as the cache space increases. ICN requires nodes to execute at line speed, which limits the maximum cache size that a node can support. To optimize the performance of the cache network from the perspective of cache size, the main problem to be solved is the allocation of cache resources in different nodes. That is, which nodes should be allocated more cache space within the total cache budget to improve the overall performance of the cache system.
## 2 Data Description and Data Preprocessing
### Power Spectral Density (PSD)  
To compensate for the deficiencies in amplitude domain features, this paper introduces frequency-domain features by calculating the power spectral density of the acceleration signal. Segments of 2048 sampling points are used as segments, with an overlap length of 1024 to calculate the power spectral density, and the amplitude of the power spectrum is normalized using the maximum value. Figure 3 illustrates the power spectral features of various data patterns. In the normal category, peaks of varying heights appear in the 0-10 Hz range, reflecting the inherent dynamic characteristics of the structure. The minor category exhibits two abnormal modes: One involves abnormal amplitudes but normal vibrations, resulting in relatively normal peak values in the power spectral density due to lower vibrational energy. However, some high-frequency noise effects are accentuated due to the smaller vibrational energy. The other mode occurs when data amplitudes fluctuate near the data storage precision, leading to square wave characteristics in the time domain and completely abnormal spectral characteristics. For outlier patterns, high-energy disturbances lead to energy concentration in the power spectrum around 0. Although the biased class shows peaks in the 0-10 Hz range, these frequencies do not reflect the inherent dynamic characteristics of the structure when compared to normal acceleration data. Lastly, the noise type, due to high-energy noise interference, predominantly showcases noise frequency components within its spectrum, completely submerging the normal frequency components of the signal.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 6 Service-Oriented Middleware (SOM) Architectures Approaches for WSNs
### HealthCare Approaches  
Within the healthcare industry, SOA is widely used to improve the transmission of important patient information. By linking the data to the healthcare community, doctors and caregivers have remote access to all of their patients' daily activities.  
The monitoring system for a patient using SOA as shown in Figure 10[41], An SOA approach is applied into WSNs to design different applications to monitor the patients for long periods of time [20]. Through SOA, the sharing of patient data has become cost-effective and secure. In [41], WSNs are introduced as an integrated with a web service, using context-aware SOM architecture that increases system flexibility. A web service combined with Radio Frequency Identification (RFID) is necessary to manage patient information. It is responsible for collecting, storing, and making clinical data available [41]. The context-aware service searches the patient information and obtains the most accurate output without errors. In its own capacity, RFID can access secured patient information. RFID is designed as a smart card accompanied with a verifiable, individual patient photo ID to obtain patient history that helps doctors give accurate diagnoses with less fault detection. This process produces an improved QoS and reduces costs.
## 3 Experiments and Discussions
### Discussion of the Volumetric Errors  
As defined in Section 2.4, the volumetric errors can be presented as the differences between the modeled and calibrated positions of the fiducials. According to the measured results shown in Table 2, an easy-to-digest figure as shown in Figure 7 is presented with vector representations of the errors at the positions in the coordinate of machine tool. The length of the arrow represents the value of the error, and the direction of the arrow is decided by the integrated value of three coordinates. The error vectors are multiplied by 10,000. The mean of the volumetric errors is 4.5 \(\upmu\)m.  
A total of nine locations of the artefact are measured and three measurement tasks are performed to enhance the measurement accuracy. Figure 7 gives the detailed information of the second of the three measurement tasks. The maximum standard deviation between the datasets at different positions is 0.8 \(\upmu\)m. It is clear that the large BC offset has a large effect on the volumetric error of the ultra-precision machine tool.  
In the current study, the standard ball served as a fiducial and was mainly used in the experiments. Not only is it easy to be measured by the developed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **x (\(\upmu\)m)** & **y (\(\upmu\)m)** & **z (\(\upmu\)m)** \\ \hline Sphere 1 & 0.4 & 0.9 & 0.2 \\ Sphere 2 & 0.5 & 0.8 & 0.4 \\ Sphere 3 & 0.7 & 0.8 & 0.3 \\ Sphere 4 & 0.7 & 1.0 & 0.3 \\ Sphere 5 & 0.6 & 0.7 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The sphere diameter deviations.  
Figure 7: Distribution of the volumetric errors of the multi-axis machine tool occupied by the workpiece at nine locations by rotating the b and c axes: (**a**) xy view, (**b**) xz view, (**c**) 3D view, the errors are multiplied by 10,000.  
system from multiple locations, but also the cost of the standard ball bought from the market is low. Furthermore, it may transfer the accuracy of an off-machine measurement system to the working machine tool. In our future research, the performances of other types of fiducials and artefacts may be investigated.