esa-sceva/satcom-chunk-collection huggingface dataset:
# Floral Scent Evaluation of Three Cut Flowers Through Sensorial and Gas Chromatography Analysis
## 4 Discussion
These two monoterpenes (origanene and D-limonene) are very common compounds present in floral scent, present in more than 70% of families of plants [8], and have both been described as important contributors to the floral scent in attracting pollinators [50]. As mentioned above, the scent of chrysanthemum flowers contained the highest number of volatile compounds, followed by freesia and oriental lily, but oriental lily showed the highest total amount of volatiles followed by freesia and chrysanthemum. The highest score for floral scent liking was observed in the evaluation of freesia, which was close to 7 ('like moderately'), compared to chrysanthemum and oriental lily, with values close to 6 ('like slightly'). This might be due to the aromatic description of the major VOC of freesia, linalool, which is described as 'flower/lavender', whereas the major VOCs of oriental lily and chrysanthemum are descried as 'warm herbaceous/green/terpenic' (\(\beta\)-cis-ocimene) and 'pine/resin/turpentine' (\(\beta\)-pinene), respectively. Thus, the association the evaluators made between freesia and a 'flower/lavender' fragrance could have induced them to appreciate the floral scent of this species more. Sensorially, the floral scent of oriental lily did not show significant differences in terms of intensity with freesia (Figure 2), despite the fact that its relative area total sum of the VOCs (84,791) was about 25 times higher than the total sum of the VOCs of freesia (3278) (Table 2). This result suggests that when analyzing floral scent, the total VOC abundance is not necessarily associated to intensity perception. VOCs only participate in the aroma that is perceived by humans if they are above a certain odor threshold [51] and it is possible that the compounds detected in oriental lily were not more highly perceived by the evaluators, despite their higher concentration compared to freesia. In fact, linalool, the major VOC present in freesia, has been reported to have a lower ODT (1.0) compared to \(\beta\)-cis-ocimene (34) and \(\beta\)-pinene (1500) [33], the major VOCs of oriental lily and chrysanthemum, respectively. However, it has been reported that odor receptors may participate by interacting among aroma compounds, showing no-effect, a masking effect, additive action, and a synergistic effect [52; 53]. Furthermore, even compounds at sub-threshold concentrations have been found to interact with other VOCs to contribute to the aroma [54].
# Learning and Compressing: Low-Rank Matrix Factorization for Deep Neural Network Compression
## 4 Experiments
### Experimental Setup
#### 4.1.2 Low-Rank Parametrization of Convolutional Layers  
The convolutional layer is a four-dimensional tensor of size \(n\times c\times d\times d\); that is, \(n\) filters with \(c\) channels of \(d\times d\) spatial resolution. In experiments, we decompose the convolutional layer using a low-rank structure scheme [44; 45] into two low-rank matrices with \(r\times cd^{2}\) and \(n\times r\), respectively. In networks, they can be viewed as two convolutional layers of \(r\) filters with \(c\) channels of \(d\times d\) spatial resolution and \(n\) filters with \(r\) of \(r\times 1\times 1\) spatial resolution.
## 4 Method
### Normalized Virtual Softmax Loss
In the next training round, the virtual class of the previous round is discarded and new training sample becomes the anchor of the new virtual class, thus preventing the situation of incorrect insertion in which two virtual class are neighboring. The process is illustrated in Figure 9b, and a more compact feature space is created in which \(\theta_{1}^{{}^{\prime}}<\theta_{1}\). L is the sum of each \(L_{k}\) in a training batch, so for convenience we focus only on \(L_{k}\). The loss back propagates, changing the network parameters, acts on the feature extraction network and FC layer as shown in Equation (15), Equation (16) and Equation (17), respectively. Here, it needs to be emphasized that (1) \(\mathbf{X}^{*}\), \(\mathbf{W}_{y_{i}}^{*}\) and \(\mathbf{W}_{vir}^{*}\) are all unit vector, so some of the calculation is easy such as \(\mathbf{X}^{*}\cdot\mathbf{X}^{*T}=1\) in Equation (16); (2) the derivative of the modulus of a vector with respect to itself is equal to the transpose of the unit vector corresponding to this vector, which means \(\frac{\partial|\mathbf{W}_{y_{i}}^{*}|}{\partial\mathbf{W}_{y_{k}}^{*}}= \mathbf{W}_{y_{k}}^{*T}\) in Equation (16). Comparing to the original virtual softmax proposed by [26], the operation of normalization not only eliminates the bias coming from the network, but also simplifies the calculation of back propagation above. However, we don't do the simplification because we hope our proof to be more generalized as a reference for original virtual softmax loss. Furthermore, normalized virtual softmax loss has a more specific geometric meaning than original virtual softmax loss, where the dot product of the feature vector \(\mathbf{X}\) and class anchor \(\mathbf{W}\) degenerates into the calculation of the angle between them, for \(|\mathbf{X}|=|\mathbf{W}|=1\). \[\mathbf{X}^{*}=\frac{\mathbf{X}}{|\mathbf{X}|} \tag{8}\]  
\[\mathbf{W}^{*}= \begin{bmatrix}\mathbf{W}_{1}^{*}\\ \mathbf{W}_{2}^{*}\\ \vdots\\ \vdots\\ \mathbf{W}_{i}^{*}\\ \vdots\\ \mathbf{W}_{C}^{*}\end{bmatrix} = \begin{bmatrix}\mathbf{W}_{1}/|\mathbf{W}_{1} |\\ \mathbf{W}_{2}/|\mathbf{W}_{2}|\\ \vdots\\ \vdots\\ \mathbf{W}_{i}/|\mathbf{W}_{i}|\\ \vdots\\ \mathbf{W}_{C}/|\mathbf{W}_{C}|\end{bmatrix} ,\textit{where}i=1,2,\cdots,C; \textit{Cisthenumberofclasses} \tag{9}\]  
\[\mathbf{W}_{v\_add}^{*}=\mathbf{X}^{*} \tag{10}\]  
\[\mathbf{W}_{vir}= \begin{bmatrix}\mathbf{W}^{*}\\ \mathbf{W}_{v\_add}^{*}\end{bmatrix}  \tag{11}\]  
\[\mathbf{X}\cdot\mathbf{W}_{vir}^{T}= \begin{bmatrix}s_{1}&s_{2}&\cdots&s_{i}& \cdots&s_{C}&s_{vir}\end{bmatrix} =\mathbf{S}_{vir} \tag{12}\]  
\[\mathbf{S}_{vir}^{*}= \begin{bmatrix}s_{1}^{*}&s_{2}^{*}&\cdots&s_{i}^{*}&\cdots &s_{C}^{*}&s_{vir}\end{bmatrix}
# An Interoperable Component-Based Architecture for Data-Driven IoT System
## 3 Component-Based IoT Architecture
### Software Application Component  
The software application component consists of two main sub-components: (i) configurable data visualization platform; and (ii) database management system:  
* Configurable data visualization platform: The configurable data visualization platform is a data visualization web application and graphical user interface (GUI) for data monitoring panel controls. To enable system interoperation under one platform and reusable for different application, the data visualization module provides a modifiable data presentation platform to suit the requirement of multiple application domains. Furthermore, considering the wide adaption and usage of Business Intelligence (BI) data analytic tools such as Microsoft's Power BI and SAP Business Intelligence, customizable and flexible data visualization platform is useful for a more comprehensive data presentations. However, it is also crucial to reduce the complexity of data pre-processes to increase the platform's usability. Therefore, this component aims to maximize the customizability of the data visualization platform and to minimize the requirement of complicated data pre-processes by providing a customizable and flexible data visualization platform that can directly interface with the databases of the IoT systems.
* Database management system: The database management system is a generic database system with an essential feature: automatic database generation for new incoming data. The central control system identifies the databases and data-driven models for incoming data based on their device ID and determine whether the database for the data from this device has already been created.  
Overall, the heterogeneous data are presentable on one single platform for easier data analysis and information extraction.
## 3 Packet Delays within a Router
### Delays within a Router  
A simple laboratory network was set up with the use of a dual ported Linux machine running Wireshark as a method of measuring delays across a router. An initial experiment was conducted to identify the accuracy of the measuring system by passing packets into a 100 Mbps hub and measuring the delay experienced on two of the outputs. Since a hub is a very simple device with no buffering, results from the experiment show that the average delay was only 9 usecs. This would be the error bar for a 100 Mbps network and should be applied to all results but is not significant in the overall values obtained.
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 6 Conclusions  
Underwater object detection faces complex challenges, such as uneven lighting, low contrast, dense object distributions, occlusions, and constraints on computational resources and storage in underwater embedded devices, leading to limitations in the performance of conventional object detectors. This work enhances the YOLOv8 algorithm, a cutting-edge object detection approach, with four modules aimed at lightweight and enhancing small-object detection accuracy. According to experiments, adding the prediction head for small objects module is the most direct and effective method to improve the accuracy of detecting small objects. The use of Deformable ConvNets also significantly enhances the detection of small objects. The results demonstrate that our approach achieves a state-of-the-art (SOTA) performance on the UTDAC2020 underwater dataset, maintaining lightweight parameters (8.5 M), low flops (25.5 B), and a small model size (18 MB), while achieving an accuracy of AP 53.18% and AP50 86.21%. Notably, it even surpasses the larger YOLOv81 model with an AP of 51.69%. Additionally, parallel experiments on the Pascal VOC dataset show  
Figure 10: The prediction results of our method (640) on the unseen underwater dataset UODD.  
Figure 9: The feature maps visualizing the image in Figure 8 using our method (1280) at different scales. **Column 1**: feature map of 320 \(\times\) 184. **Column 2**: feature map of 160 \(\times\) 92. **Column 3**: feature map of 80 \(\times\) 46.  
the generalization of our approach, outperforming many well-established detectors while maintaining a lightweight model.  
In future work, we will continue to explore models that balance model size and detection accuracy to further advance the field of underwater object detection. Additionally, the scarcity of underwater datasets remains a challenge, requiring more high-quality data to effectively improve model performance in underwater environments.  
Conceptualization, W.S.; methodology, M.Z. and Z.W.; validation, Z.W.; writing--original draft preparation, Z.W.; writing--review and editing, M.Z., Z.W., W.S., D.Z. and H.Z.; investigation, M.Z.; supervision, W.S. and M.Z.; funding acquisition, W.S. and M.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (61972240), and the Young Scientists Fund of the National Natural Science Foundation of China (42106190).  
Not applicable.  
The UTDAC2020 underwater dataset presented by [PERSON] et al. and the dataset is available at [[https://doi.org/10.48550/arXiv.2206.13728](https://doi.org/10.48550/arXiv.2206.13728)]([https://doi.org/10.48550/arXiv.2206.13728](https://doi.org/10.48550/arXiv.2206.13728)), accessed on 11 December 2023.  
This research was supported by the Digital Oceanography Institute of Shanghai Ocean University.  
We declare no conflicts of interest.
# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
## 3 Methods  
As described in Section 1, load is influenced by various variables, rendering it non-linear and non-stationary in its typical characteristics. The causal effects of different variables on load had to be considered, so as to allow for accurate prediction of future load. Causality is a type of correlation relationship. In the present study, the correlation relationships between external variables and load were first screened by the removal of low-information factors, as described in Section 3.1. Informer-based time series forecasting was implemented for medium- and long-term load forecasting. In addition, several improvements were made to the Informer model to enhance its performance and adapt it better to non-stationary data forecasting scenarios, which is presented in Section 3.2. The causal inference method, described in detail in Section 3.3, was used to determine the causal effects of different variables on load. Given the important role of external factors, the time series forecasting module was combined with the causal inference module in order to obtain accurate forecasting results. The method is explained in Section 3.4. The complete flow of the proposed method is shown in Figure 2.
# Performance Optimization Design of Diagonal Flow Fan Based on Ensemble of Surrogates Model
## 4 Ensemble of Surrogate Model and Multi-Objective Optimization
### Multi-Objective Optimization of Diagonal Flow Fan Blade  
The NSGA-II algorithm is an efficient and fast sorting algorithm based on a genetic algorithm. It does not need any transformation of design objectives, and can achieve the purpose of screening individual advantages and disadvantages only according to the dominance relationship between individuals [18]. The algorithm uses the crowding distance evaluation method to replace the commonly used shared function method, so that it can control the distribution of individuals without determining a shared parameter. In this paper, four variables of CST parametric design are used to determine the maximum total pressure efficiency of the fan \(\eta_{B}\) and maximum flow \(q_{v}\). The mathematical model of performance optimization of the diagonal flow fan is as follows:  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(Q_{v}\) & \(\eta\) \\ \hline \(R^{2}\) & 0.973 & 0.966 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ensemble of surrogates model \(R^{2}\).  
Figure 11: Optimization parameters and optimization target fitting surface. (**a**) \(v_{0}\) and \(v_{1}\) fit surfaces with efficiency (%). (**b**) \(v_{2}\) and \(v_{3}\) Fitting surfaces with efficiency (%). (**c**) \(v_{0}\) and \(v_{1}\) fit surfaces with flow rate (m\({}^{3}\)/min). (**d**) \(v_{2}\) and \(v_{3}\) fit surfaces with flow rate (m\({}^{3}\)/min).  
\[\left\{\begin{array}{l}\max(q_{v}(v_{0},v_{1},v_{2},v_{3}))\\ \max(y_{B}(v_{0},v_{1},v_{2},v_{3}))\\ 0.3\leq v_{0}\leq 0.7\\ 2\leq v_{1}\leq 4\\ 1\leq v_{2}\leq 3\\ 0.4\leq v_{3}\leq 0.8\end{array}\right. \tag{13}\]  
The Ensemble of surrogates model is solved by NSGA-II to obtain the Pareto solution set, as shown in Figure 12.  
The Pareto frontier solution set obtained through optimization is shown in Figure 12. It can be seen that each solution set is relatively close. In engineering problems, there is a constraint relationship between some optimization target parameters. The improvement of one parameter target often leads to the decrease in another parameter index. Considering the flow improvement and efficiency of the diagonal flow fan, the efficiency maximization is sought under the flow requirement, and the optimization scheme of \(0.5\eta_{B}+0.5\eta_{v}\) is determined. Comparing the blades before and after optimization, it is found that under the design conditions, the effective air volume of the fan increases by 2.0 m\({}^{3}\)/min to 21.4 m\({}^{3}\)/min, and the total pressure efficiency increases by 2.8% to 38.9%. Table 3 shows the comparison of parameters before and after arc optimization in the blade.
## 3 Background
### Power Sub-System Overview
#### 3.2.1 Power Generation  
Solar cells typically act as the electrical power generation source for CubeSats as they can use the photovoltaic effect to harness the perpetual energy of the incoming solar radiation incident upon the craft [21]. This provides a renewable source of energy for the craft extending the mission lifecycle, compared to utilizing a finite source [23]. Photovoltaic cells are constructed from extremely thin semi-conductor wafers which generate an electrical current when exposed to incoming light [28]. The amount of incoming light depends on the distance of the craft's orbit from the Sun as the intensity varies per the inverse square law, and the projected surface area of panel exposed to the Sun as this varies throughout orbit depending on the cosine angle of the panel with respect to the Sun's absolute position.  
A large market share of available PV cell products employs single junction cells; however, the efficiency of these products is approximately 20%, which is a relatively low value compared to multi-junction cells [23]. The multi-junction incorporates multiple layers of materials with altering band gaps, allowing the utilization of a wider spectrum of solar radiation. In the aerospace industry triple junction cells are commonly used due to their ideal efficiency to cost ratio. The significant downfall of solar cells is that they have a high mass and surface area, are unable to generate power in the CubeSat's eclipse period of orbit, have high initial cost, and degrade over time [23]. Figure 7 highlight typical solar cell efficiencies of various manufacturers.  
Solar cells are typically constructed from semiconductors including Silicon (Si) and Gallium Arsenide (GaAs) [3]. Solar arrays are readily available off the shelf from a range of large providers including ISIS and Clyde Space.  
Figure 6: Simplistic Overview of a CubeSat Power Sub-System [27].  
Many cutting-edge energy sources are under development for space applications including hydrogen fuel cells, thermo-voltaic batteries, and flexible solar cell films [23].
## 1 Introduction  
Since [PERSON] [1] proposed the Whipple shield in 1947, many scholars have continued to study and optimize the shield, including Whipple, Nextel/Kevlar Stuffed Whipple and multi-wall shields in general [2]. Normally, the shield design with a lower surface density is better when the protection requirements are met. To achieve this, some innovative materials, such as ceramic materials, polymer materials, and composites, have been developed and ballistic experiments of these shielding materials have been carried out for potential applications [3; 4; 5]. A \"small debris\" with a size of 1-10 cm cannot be monitored and tracked due to detection technology limitations. In addition, because of its large size and high kinetic energy, \"small debris\" poses the greatest threat to spacecraft [6]. However, the existing research on space debris protection is mostly focused on micro-sized space debris with a diameter of less than 1 cm. There are few studies on the impact and protection effects of cm-size fragments. As a practical matter, most of the protective shields have been investigated from ballistic impact databases involving only spherical projectiles. However, debris can take any shape. Furthermore, it has been demonstrated that non-spherical projectiles can be more destructive than equal mass spherical projectiles under the same impact conditions [7; 8]. Therefore, ballistic impact tests of non-spherical projectiles are valuable and necessary for the study of debris protection.  
Compared with conventional ceramic materials, boron carbide (B\({}_{4}\)C) exhibits not only higher hardness and lower density properties, but also possesses a better chemical stability at high temperatures,which has been used in civil, aerospace and military applications [9]. Ultra-high molecular weight polyethylene (UHMW-PE) has a good impact resistance and a great energy absorption performance [10]. Some scholars [11; 12] have applied UHMW-PE to the design of Whipple protection structures and conducted extensive research. In this paper, the multi-wall shield of high-velocity cm-size debris was designed and studied. The cylindrical Al 7075-T651 projectile was launched by a two-stage light-gas gun and impacted the multi-wall shield in the velocity range of 1.6-1.9 km/s. Three effective shield configurations were obtained through experiments. The best performing shield configuration with the B\({}_{4}\)C/UHMW-PE composite plate rear wall, as well as the comparison configuration with the aluminum rear wall, was further analyzed by a numerical simulation. Meanwhile, the mechanism of the multi-wall shield against the projectile was investigated.
# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 6 Discussion and Findings  
The parameters in table below defines the simulation to generate the UE dataset (see Table 1). Table 2 shows the compositions of clustering output for the first instance. The percentage in each cluster indicates the types of UE traffic classification in the clusters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Simulation Parameters** & **Method** & **Remarks** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Total No. of UEs} & \multirow{2}{*}{Random} & Total UEs: average UEs (5379) \\  & & & 1 st Instance = 5443 \\  & & & 60 th Instance = 5250 \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{Traffic Classification} & \multirow{4}{*}{Random} & Based on the randomly generated percentage applied to the total number of UEs. Sampled from 1 st Instance. \\  & & & 1. Normal UEs: 2352 \\  & & & 2. UEs classified in Specific 5G Use Cases: 3091 \\  & & & i. Total eMBB UEs: 1241 \\  & & & ii. Total URLLC UEs: 250 \\  & & & iii. Total mMTC UEs: 1600 \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Types of UEs} & \multirow{2}{*}{Defined} & 1. Static UEs such as IoT and fixed workstations from gaming and streaming devices. \\  & & & 2. Dynamic UEs such as pedestrians, vehicles, IoT, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The list of parameters for UE and transport network simulations.  
Figure 5: The end-to-end flow of the resource assignment process based on various UE types.