esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### The Effects of the Material and Riblets on Cavitation-Induced Erosion Depth and Cross-Section
The widths of erosion parallel to the riblet lines are 350 \(\upmu\)m and 322 \(\upmu\)m for Al and brass with riblets, respectively. Comparing these values with the width of erosion for Al (350 \(\upmu\)m) and brass (320 \(\upmu\)m) without riblets shows that riblets along their lines do not affect water vortices and thereby do not decrease erosion width. More information regarding water vortices can be found elsewhere [21; 41; 42]. On the other hand, completely different results are observed perpendicular to the riblet lines. It is evident that the width of erosion perpendicular to the riblet lines for Al and brass with riblets are 245 \(\upmu\)m and 225 \(\upmu\)m, respectively. Intercomparison of these results with the values for Al (300 \(\upmu\)m) and brass (310 \(\upmu\)m) without riblets signifies that the presence of riblets reduces the width of erosion by 18.3% and 27.4% for Al and brass, respectively. This outcome is attributed to the riblets, as perpendicular to riblet lines, the water vortices separate, and the impulse of the water hammer decreases along this axis, thereby reducing the width of erosion. This section outlines the significant effects of material type and riblets on erosion. Brass exhibits 21.6% greater erosion resistance compared to Al due to its mechanical stability. In other words, brass experiences less erosion than Al because of its inherent physical properties. Riblets reduce the depth and width of erosion by 3.8% and 18.3%, respectively, for Al. In the case of brass, riblets decrease the depth and width of erosion by 7.9% and 27.4%, respectively. The width of erosion perpendicular to the riblet lines decreases more than that for erosion parallel to riblet lines due to the separation effects of water vortices. Therefore, it can be concluded that riblets can provide anti-erosion properties for materials against fluid and cavitation damage.
# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 4 Data for Machine Learning  
### Data for Machine Learning  
This study analyzed 78 buildings completed by the Korea Public Procurement Service, with a 'Total Construction Cost' of less than 20 billion. The survey collected data on the total floor area, total construction costs, and construction costs per total floor area. The average total floor area of the surveyed buildings was 3536.10 m\({}^{2}\), with a minimum of 387.25 m\({}^{2}\) and a maximum of 12,041.58 m\({}^{2}\). The average total construction costs were KRW 71.31 billion, with a minimum of KRW 63.7 billion and a maximum of KRW 199.61 billion. The average construction costs per total floor area were KRW 2,040,000, with a minimum of KRW 900,000 and a maximum of KRW 3,360,000. The distribution of the collected data is shown in Figure 2.
# Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks
## 4 Results
There exists a optimal value of the tradeoff coefficient where the energy efficiency and spectral efficiency of the system remain constant, and the maximum energy efficiency can be attained while fulfilling the spectral efficiency requirements. However, beyond this value, increasing the tradeoff coefficient will result in the system sacrificing some energy efficiency to achieve the desired spectral efficiency. Thus, a suitable tradeoff coefficient should be chosen based on the specific requirements of the system. In addition, Figure 5 shows that the flight altitude of UAVs has an impact on the EE. It can be seen that the higher the flight altitude of the aircraft, the lower the energy efficiency of the system. An increase in aircraft altitude results in a decrease in energy efficiency because an increase in altitude results in a decrease in the channel gain between the IoT devices and the UAVs. IoT devices need to increase its transmission power to maintain a reliable connection, which leads to a decrease in EE. Figure 6 shows the impact of flight altitude on SE. In contrast to the impact on EE, an increase in flight altitude will lead to an increase in SE, which is also consistent with theoretical derivation. Figure 4: The total rate varies with the number of iterations of different \(\epsilon\), when \(U=2\), \(K=4\), \(I=8\). Figure 5: The energy efficiency varies with \(\epsilon\) of different \(h_{\rm U}\), when \(U=2\), \(K=4\), \(I=8\). Figure 6: The spectral efficiency varies with \(\epsilon\) of different \(h_{\rm U}\), when \(U=2\), \(K=4\), \(I=8\). power beyond this optimal point will not bring about any performance improvements in terms of EE. The curves in Figure 8 illustrate the variation of SE concerning different UAV transmission powers and flying heights. It can be seen that with the increase of the transmission power of the UAV, SE increases until it reaches the optimal value and remains unchanged. Figure 8: The spectral efficiency varies with UAV transmission powers of different \(h_{\rm U}\), when \(U=2\), \(K=4\), \(I=8\). Figure 7: The energy efficiency varies with UAV transmission powers of different \(h_{\rm U}\), when \(U=2\), \(K=4\), \(I=8\).
## 3 Study Area and Data  
### Study Area  
The study area selected for this paper is the Kowloon Peninsula of Hong Kong, located in the northern part of the Hong Kong Special Administrative Region, a major administrative region and commercial center of Hong Kong, with a total area of about 46.93 square kilometers, including five districts, namely Yau Tsim Mong, Sham Shui Po, Kowloon City District, Wong Tai Sin, and Kwun Tong, which is one of the three major districts of Hong Kong (Figure 1). With a more developed economic and transportation network, and many  
\begin{table}
\begin{tabular}{c c} \hline
**Country (Region)** & **Definition of Road Black Spots** \\ \hline \multirow{2}{*}{Austria} & Locations with at least three traffic fatalities and injuries in a 3-year period and an accident factor of not less than 0.8 \\ \hline \multirow{2}{*}{Denmark} & Locations where the number of accidents exceeds the number predicted by statistical laws \\ \hline \multirow{2}{*}{Belgium} & Locations with three or more traffic accidents in a 3-year period, or with a priority factor S exceeding 15 \\ \hline \multirow{2}{*}{Germany} & Accident black spots are defined by the number of accidents on a road section or at a location that exceeds the identification threshold within a certain period of time \\ \hline \multirow{2}{*}{Hungary} & Road segments within the urban area with at least four traffic accidents in 3 years and a length of up to 1000 m, or outside the urban area with at least four traffic accidents in 3 years and a length of up to 100 m \\ \hline \multirow{2}{*}{Norway} & Any point with at least four traffic fatalities and injuries within 5 years and not exceeding 100 m in length, and road sections with at least ten fatalities and injuries within 5 years and not exceeding 1000 m in length \\ \hline \multirow{2}{*}{Portugal} & Intersections are road segments with at least five crashes with a severity index of more than 20 in a given year and a length of no more than 200 m \\ \hline \multirow{2}{*}{Switzerland} & Locations where the accident rate exceeds this range by calculating the average accident rate for road segments and intersections and setting a maximum probability of deviation \\ \hline \end{tabular}
\end{table}
Table 1: Definition of road black spots in Western countries.  
famous scenic spots as well as major roads, it is an important convergence of logistics and people flow. As a typical urban area in Hong Kong, the Kowloon Peninsula has a very high traffic density and traffic congestion is very common, especially during the morning and evening peak periods.
# Application of Spinel and Hexagonal Ferrites in Heterogeneous Photocatalysis
## 1 Introduction
### Spinel Ferrites  
Spinel ferrites with the general chemical formula of MFe\({}_{2}\)O\({}_{4}\), where M states for metallic cation of Fe, Mg, Mn, Co, Zn, Ni, etc., are characterized by a cubic shape crystal structure, as presented in Figure 2. The distribution of M\({}^{2+}\) and Fe\({}^{3+}\) in the crystallographic sites (tetrahedral and octahedral) depends mainly on the M-cation radius and its charge, which obviously influence the final material properties [60; 61]. In the case of normal spinel structures, M\({}^{2+}\) cations occupy only tetrahedral sides, leaving octahedral ones for Fe\({}^{3+}\). Zinc ferrite (ZnFe\({}_{2}\)O\({}_{4}\)) is an example of this. In contrast, M\({}^{2+}\) cations could also occupy only octahedral sides, forcing the partial migration of Fe\({}^{3+}\) ions into the tetrahedra position, which is known as the inverse spinel structure (Fe\({}_{3}\)O\({}_{4}\), CoFe\({}_{2}\)O\({}_{4}\), NiFe\({}_{2}\)O\({}_{4}\), etc.) [62].  
Magnetic properties of spinel ferrites strongly depend on their dimensions, i.e., the bigger the NP is, the higher is the value of magnetization saturation (M\({}_{\text{s}}\)). Furthermore, superparamagnetism appears as a consequence of a decrease in ferromagnetic particle size to the nanometric scale. Usually, this occurs in NPs of the order of several nanometers to several dozens of nanometers, but the critical particle size is different for each material and is conditioned by magnetic anisotropy as well as chemical composition, crystal structure, and morphology of the ferrite [64]. As an example, magnetite NPs become superparamagnetites when their diameter is about 10-12 nm [65]. It should be pointed out that NPs lose their magnetization when the magnetic field is turned off.
# A Dynamic Adjusted Aggregate Load Method to Support Workload Control Policies
## 6 Conclusion and Future Development Paths  
The workload control model is a control approach for production planning in job-shop systems.  
The WLC includes how to release the orders, the dispatching rules for the pre-shop queue, and how to compute the aggregate workload of the machines. The major part of the research used the adjusted workload aggregate computation by [PERSON] et al. [11], and other studies on this issue  
Figure 8: Total lateness.  
Figure 7: Bottleneck shiftness.  
are limited. This study evaluates a modified computation of the workload, considering the dynamic adjustment of the workload that can use the development of IoT technologies.  
In response, our first research question asked is: Can the proposed dynamic adjusted workload computation improve some performance measures of the manufacturing system?  
The simulation results demonstrated how the proposed approach can significantly improve the performance as it relates to the ability to deliver the job on time, achieving a more uniform distribution of the direct workload and reduction of the shop time of the jobs. The uniform distribution of the direct workload can have a relevant impact on the maintenance policy of the manufacturing system.  
The second research question asked: Is the performance improvement affected by the dispatching rules, norm approach and the number of bottlenecks?  
The proposed model has demonstrated the robustness to the change of dispatching rules, norm evaluation and when the number of bottlenecks changes. Moreover, the better value for the norm limitation is the same as with the classical approach.
## 5 Complexity and Latency  
### Complexity  
In this section, we consider the complexity of the proposed model and models used in previous studies. In addition, the complexity was calculated from the equalizer to detection developed in this study. The numbers of operations per bit are listed in Table 4.  
As can be seen in Table 4, the IRCSDFA is an iterative row-column soft decision feedback algorithm. This model uses the maximum a posterior (MAP) algorithm to design detection. When calculating the complexity of this algorithm, we simply computed for one iteration and a mask with a size of 3 \(\times\) 3. Similarly, the IRSDF-Gaussian approximation (IRSDF-GA) is also an iterative row-column soft decision feedback algorithm, which applies a Gaussian approximation to reduce the complexity of the IRSDFA. When computing the complexity of the IRSDF-GA, we calculated for only one iteration. For the training process, we estimated three GPR targets in our proposed model. However, the calculation of the three GPR targets was performed simultaneously using different target constraints. Based on the multi-GPR targets in the testing process, the complexity was significantly reduced. By supplying the interference information for detection, we can convert 2D detection into 1D detection (via the VA). Designing the trellis of a 1D VA is simpler than  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ModelOperator** & **Multiplication** & **Addition and** & **Exponential and** \\  & **and Division** & **Subtraction** & **Logarithmic** \\ \hline
**1D Viterbi [15]** & 33 & 48 & 0 \\
**MVA [16]** & 49 & 96 & 0 \\
**3-way MVA [21]** & 50 & 97 & 0 \\
**IRCSDFA [24] (3 \(\times\) 3)** & 1935 & 3398 & 391 \\
**IRSDF-GA [25]** & 74 & 60 & 9 \\
**Serial detection [22]** & 249 & 696 & 0 \\
**Serial detection [23]** & 252 & 700 & 0 \\
**Our proposed model** & 99 & 146 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Complexity of detections.  
Figure 13: BER vs. position fluctuation from 6% to 16%.  
that of 2D detection. The three detectors have similar structures and different output values. Therefore, with high performance, our proposed model can significantly reduce complexity.
# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
## 4 Discussion  
### Temporal and Spatial Distribution of Precipitation  
During the summer months, the Parana State region typically receives a humidity air mass that moves from the Amazon to the southwestern Atlantic region, defined as the South Atlantic Convergence Zone (SACZ) [48], which is directly connected to the South American monsoon system along a northwest-southeast axis. The precipitation observed in the southwest region in October, on the other hand, is related to convective and frontal complexes [32; 49]. The definition of the summer as the wet season and the spatial and temporal distribution in the study area presented here is corroborated by previous studies [32; 49; 50; 51], confirming the precipitation estimates by IMERG through remote sensing approaches.
## 4 Experimental Evaluation
### Compared with the Experimental Results of Other Advanced Models  
To further compare the performance of our model, we also assessed our model framework with various other methods, including  
* Rule-Based Method: FlowPrint [28];
* Statistical-Feature-Based Method: APPScanner [5];
* Machine-Learning-Based Method: SVM [11];
* Semi-Supervised and Deep Learning-Based Method: MT-CNN [24].  
To better balance the performance of our model with others, the experiment used 20% labeled traffic. From Table 4, it is obvious that compared to the other models, the MT-CNN and CLSTM-MT models exhibit superior overall classification performance. FlowPrint relies on predefined rule sets, which perform poorly when dealing with complex and variable encrypted traffic. Since rules need to be manually formulated and updated, they struggle to adapt to new or unknown traffic patterns, resulting in lower classification accuracy. APPScanner extracts statistical features from traffic for classification. While this method can identify traffic types to some extent, its ability to capture temporal sequence features is limited. SVM have limited capability in processing non-linear data, particularly in the context of complex encrypted traffic data. This often necessitates extensive feature engineering, increasing the complexity and difficulty of parameter tuning. However, one limitation of MT-CNN is that it focuses solely on converting network traffic into a graph for type identification, without considering the temporal sequence features of the traffic. This leads to lower recall rates, lower F1 scores, and relatively poorer classification performance compared to the proposed CLSTM-MT model.
## 2 Study Area and Data Preparation  
### Study Area  
The research area is located in Lishui (27\({}^{\circ}\)25\({}^{\prime}\)-28\({}^{\circ}\)57\({}^{\prime}\) N, 118\({}^{\circ}\)41\({}^{\prime}\)-120\({}^{\circ}\)26\({}^{\prime}\) E), a city in the southeast part of China, covering a total area of 17,287.13 km\({}^{2}\). The study area lies within the Zhejiang-Fujian Uplift Zone, characterized mainly by moderately mountainous and hilly terrain. Approximately 88.42% of the area is mountainous, sloping from the southwest to the northeast, with elevations ranging from 2 m to 1890 m, resulting in a relative height difference of 1888 m (Figure 1). The study area experiences a subtropical monsoon climate with an average annual temperature of 19 \({}^{\circ}\)C. It receives abundant rainfall, with an average annual precipitation of 1865.9 mm, with the rainy season concentrated between March and September each year.  
The study area is mainly characterized by the development of Mesozoic and Paleozoic strata [38]. The Mesozoic strata comprise Middle Jurassic, Late Jurassic, and Cretaceous formations, dominated by volcaniclastic and sedimentary rocks, covering approximately 85% of the total study area. The Paleozoic rocks consist mainly of slate, schist, and mixed rock formations, with a distribution area exceeding 1200 km\({}^{2}\). Additionally, the study area is marked by the presence of two sets of fault zones. The two sets of faults are both NE oriented; one is the Lishui-Yuyao fault zone and the other is the Songyang-Pingyang fault zone. These two fault sets traverse the entire research area. The study area is prone to frequent geological hazards such as landslides, influenced by factors such as typhoons, heavy rainfall, lithology, and geological structures.
## 2 Related Work
### Optimization Strategies for Lightweight Model Performance  
Frequent memory accesses and data transfers can considerably impede inference speeds, undermining the real-time detection capabilities of unmanned systems [29]. Furthermore, balancing model complexity with detection precision remains a critical challenge; overly simplifying the model can result in substantial performance degradation. In practical applications, real-time performance is a crucial metric for UAV object detection algorithms [30]. To enhance detection speed, researchers have proposed various optimization strategies, such as using lightweight network architectures, pruning, and quantization techniques [31]. For instance, SlimYOLOv3 balances the number of parameters, memory usage, and inference time to achieve real-time object detection [32]. Although depthwise separable convolutions (DSCs) [33] significantly reduce the number of parameters and computational load of CNNs, their deployment on performance-limited embedded devices does not always result in faster operation than conventional convolutions. For example, [PERSON] analyzed that ShuffleNetV2's operation speed on a CPU is slower than ResNet-18 due to the frequent memory accesses required by DSCs [34].  
Despite advancements in lightweight models, balancing model complexity with detection precision remains a critical challenge [31; 35]. Overly simplified models can suffer from performance degradation, while the high frequency of memory accesses in DSCs can slow down inference speeds. The design of memory-efficient models like PConv offers a promising solution by optimizing computational resources and enhancing real-time detection capabilities [36; 37].
## 3 Prediction of AWTT
### Prediction Method for AWTT  
As shown in Figure 4, the method for predicting AWTT involves the factor analysis and the fully connected neural network. Firstly, the factor model is established among ambient temperature, load current, and top oil temperature. Then, the above parameters are decoupled based on the factor analysis. Next, taking the factor score as the input and the AWTT as the output, the AWTT prediction model based on the fully connected neural network is constructed, and the hidden layers and the number of nodes are adjusted. Finally, the model trained with the Bayesian regularization optimization algorithm is applied to predict the AWTT.  
Figure 3: The structure of the fully connected neural network.
## 3 Experiments
### Experiment Settings
Instead of dividing the dataset into a training set and a test set by a certain fixed time point or using cross-validation, we used back-testing by day to compare the accuracy of each of the four pooling operations when incorporated into our proposed traffic congestion representation framework for traffic congestion prediction [30]. Traffic congestion levels in the morning rush hours between 07:00 and 10:00 on each of the 20 working days from 3 September 2019 to 30 September 2019 were tested. Traffic congestion data from the past 133 consecutive working days before each tested working day were used as training data. We used mean absolute error (MAE), mean squared error (MSE), and roads-only mean absolute percentage error (roMAPE) defined respectively by Equations (6)-(8) as the accuracy metrics to evaluate the performance of the traffic congestion prediction when using different pooling operations inside our proposed representation framework in this paper. Because nonroad areas including background and other elements were converted to 0 in the original traffic congestion matrices, roMAPE only considers errors for \(c_{ij}^{t}\) which corresponds to an original traffic congestion matrix's grid containing road segments marked with congestion levels. In these three equations, \(c_{ij}^{t}\) and \(\widehat{c}_{ij}^{\widehat{t}}\) respectively denote a ground-truth traffic congestion level and a predicted traffic congestion level at time \(t\) for an element indexed by \((i,\ j)\) in a compressed traffic congestion matrix \(C_{t}\). In Equation (8), \([P]\) is the Iverson bracket which converts a logical proposition \(P\) to either 1 or 0 according to whether \(P\) is true or false. \[MAE=\frac{1}{R\times C}\sum_{i=1}^{R}\sum_{j=1}^{C}\left|c_{ij}^{t}-\widehat{c }_{ij}^{\widehat{t}}\right| \tag{6}\]  
\[MSE=\frac{1}{R\times C}\sum_{i=1}^{R}\sum_{j=1}^{C}\left(c_{ij}^{t}-\widehat{ c}_{ij}^{\widehat{t}}\right)^{2} \tag{7}\]  
\[roMAPE=100\times\frac{1}{\sum_{i=1}^{R}\sum_{j=1}^{C}[c_{i,j}^{t}>0]}\sum_{i=1 }^{R}\sum_{j=1}^{C}\left(\frac{[c_{i,j}^{t}>0]\times\left|c_{ij}^{t}-\widehat{ c}_{ij}^{\widehat{t}}\right|}{[c_{i,j}^{t}>0]\times c_{i,j}^{t}+1-[c_{i,j}^{t}>0]}\right) \tag{8}\]