esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Heat Pump Systems in Buildings by Minimizing Costs and CO\({}_{2}\) Emissions
## 3 Case Study
### Assumptions for Heat Pumps and Electric Heaters
#### 3.2.4 Coefficient of Performance of Heat Pumps  
The energy transformation coefficient, also known as the coefficient of performance (COP), is a metric used to measure the efficiency of HPs. It is calculated as the ratio of the amount of heat obtained to the amount of electricity used by the heat pump. The COP of HPs is calculated by the manufacturer under certain standardized field and indoor air conditions. However, it can vary depending on the unique system of each user. Factors such as primary source temperature and heating water temperature that is fed into the heating system greatly affect COP. In this analysis, COP values for GSHPs were evaluated for each month by using tables and graphs provided in the specifications [45; 46; 47] of the HPs and considering varying primary source (ground soil) temperatures and a constant heating water temperature of 55 \({}^{\circ}\)C.  
Similarly, COP values for air-to-water HPs were determined by dividing the maximum heat pump power by the input power, taking into account the varying average outdoor air temperatures and a constant indoor temperature of 20 \({}^{\circ}\)C. These COP values were then used to calculate the hourly electricity consumption for heating. The electricity consumption for heating of hybrid HPs was calculated using the parameters provided in the specifications [40] of HPs. Additionally, over a 10-year period, the COP values of ground source and air-to-water HPs were assumed to decrease by 0.5% annually, while the energy consumption parameters of hybrid pumps may increase due to factors such as deterioration and natural degradation of the components over time. Therefore, it is crucial to consider the long-term COP values in the selection and maintenance of HPs to ensure that they remain energy efficient over time.
# An Introduction to Nonlinear Integrated Photonics: Structures and Devices
## 2 Integrated Photonic Structures  
Influenced by the general trend of sciences towards the nano-world, development of integrated photonics, begun with cm-long circuits, went through microphotonics and finally came down to nanophotonics. Guided wave structures, resulting from the coupling between an electromagnetic field and some resonance, are the basic blocks that have accompanied this process.
# Factors Affecting the Efficiency of Hydrophobic Coatings--Experience from Application on Sandstone
## 4 Discussion  
Sufficient penetration of the hydrophobic medium into the material that should be protected is the key factor of coatings' long-lasting efficiency. It is necessary to find out how much of certain coating types is needed to reach the required penetration depth. With such knowledge, both longevity and cost savings can be secured [17]. However, there is still no standard method of how to evaluate the penetration depth [24]. The procedure used in this research is the simplest and the most often applied method [6; 14; 15; 24; 29]. It showed deeper penetration when more coating layers were applied. Similar statements were published indicating that deeper penetration can be achieved either by applying more of the impregnation medium or by increasing its concentration [6; 13; 15]. The producer of Antipluviol S also informs the users that the efficiency of the coating depends on the porosity of the treated material and on the amount of the applied coating medium. He recommends a penetration depth of 4 mm. However, as is evident from Figure 6, this was not reached with either of the recommended two or three layers of coating. This may explain why higher penetration with three layers did not show much better efficiency than with two layers--it was still insufficient and more layers should be applied on this very porous stone. However, deeper penetration does not necessarily mean higher efficiency [14]. The curing time proved to be more important for the coating's efficiency than the number of coating layers (at least in this case study). Unfortunately, no recommendation regarding the curing time is given for the tested products. Two weeks are recommended for another hydrophobic product called Wacker [6]. Several authors confirm that longer curing time creates a better hydrophobic effect [6; 13; 15]. In the presented study, 2 weeks were sufficient for the curing of Antipluviol S to protect the Horice sandstone from water infiltration; the water absorption by capillarity of coated samples was circa 10% of that of untreated samples, meaning cca. 90% improvement of this stone property. Under same conditions, results with Funcosil SNL were not so satisfying, reaching only 20% improvement. This coating type probably needs more layers applied and/or longer time for curing--further research is necessary. The leaflet provided by the producer states that the surface treated with Funcosil SNL must be protected from water at least for 5 h to secure the optimum impregnation. It is now evident that even 48 h is insufficient. Furthermore, if fully immersed in water, the coating may totally disappear. This must be taken into account in case of floods--a new coating application may be needed after drying the flooded walls to protect them from rain again. However, the flood will probably not take 30 days as the laboratory tests did, which should be regarded. The producer of the coating Funcosil SNL claimed it had a positive impact upon frost resistance, which was not tested in this study.
## 3 System Design
### Receiver Operating Modes  
The proposed concept demonstrator implements two operating modes: high-sensitivity mode and normal-sensitivity mode. High-sensitivity mode enables the processing of weak Galileo E1b/c signals with a \(C/N_{0}\) down to 20 dB-Hz. Normal-sensitivity mode enables the processing of both Galileo signals and GPS signals received at nominal \(C/N_{0}\) levels. The default configuration of the receiver is set to operate in high-sensitivity mode for Galileo E1b/c signals, and concurrently in normal-sensitivity mode for Galileo E5a, GPS L1 C/A, and GPS L5 signals, as illustrated in Table 1. This dual-mode setup is designed to enhance receiver availability whenever possible, given that high-sensitivity mode is currently only supported for Galileo E1b/c signals.
## 3 Experimental Analysis
### Component Prediction
#### 3.4.2 Seasonal Component Prediction Based on CNN  
In the proposed combined method, the seasonal component is first reshaped into a two-dimensional structure and then input into the CNN model for feature extraction and downsampling through convolutional and pooling layers. Subsequently, the fully connected layer processes the feature map and outputs the forecasted results of the seasonal component. Overall, in the CNN model, the load data undergo processing through modules such as convolution, pooling, flattening, and fully connected layers, enabling the prediction of the future load seasonal component. In the CNN-based seasonal component prediction, both convolutional layers utilize 32 filters with a filter size of 3, a stride of 1, and'same' padding, with a tanh activation function. A max pooling layer with a pool size of 2 is added between the two convolutional layers. The fully connected layer has 50 neurons with a tanh activation function, and the output layer has 1 neuron.  
Figure 7 shows the prediction results of the seasonal component of the test set load data for the first two weeks (336 h). It can be observed that the CNN model aptly captures the regularity and periodicity of the seasonal component, enabling effective forecasting for this component.  
To further demonstrate the precision of the CNN in predicting the seasonal component, Figure 8 displays the MAPE values of the seasonal component at each moment during  
Figure 6: MAPE values of trend component at each timestamp for the previous 336 h.  
Figure 7: Predicted results of seasonal component for the previous 336 h.  
the initial two weeks (336 h). It illustrates that the forecast results have MAPE values ranging from 0 to 0.4, with most values falling within the 0-0.1 range, indicating a high level of prediction accuracy. Additionally, the MAPE value curve shows a certain degree of periodicity.
# Analysis of the Aroma Chemical Composition of Commonly Planted Kiwifruit Cultivars in China
## 3 Results and Discussion
### HS-SPME-GC-MS Analysis
#### 3.1.4 Classification of Kiwifruit Samples Based on Volatile Components  
To better understand the relationship of aroma chemical compositions among kiwifruit samples, the kiwifruit samples were further classified by cluster analysis. Figure 2A shows the cluster heat map analysis of kiwifruit samples based on the types and concentrations of volatile components. Figure 2B extracts the clustering part from Figure 2A and draws a simplified diagram to make the results clearer and more intuitive.  
Through cluster analysis, 23 kiwifruit samples were preliminarily divided into three major categories. The first category contains five kiwifruits, namely, Xuxiang, Jinhong No. 1, Huayou, Jinyan, and Qinmei. The second category contains 10 kiwifruits, namely, Yate, Cuixiang, Hayward, Guichang, Jinfu, Ruiyu, Puyu, Jintao, Jinshi, and Sungold. There were eight kiwifruits in the third category, namely, Jingong No. 7, Oriental Red, Xixuan No. 2, Hongyang, Hongshi No. 2, Jinhong 50, Hort16A, and Qihong. The second category contains the most kiwifruit cultivars. The imported cultivar Sungold and one of the introduced cultivars, Hayward, belong to the second category, while Hort16A, the other introduced cultivar, belongs to the third category. The results showed that there were no significant differences in aroma chemical compositions between imported and introduced cultivars and new Chinese breeds. In addition, PCA was performed based on GC-MS data (Table S4), and the cumulative variance of the first three principal components was 45.643%, which was less than 80%. Therefore, the results of GC-MS cannot be used to distinguish different kiwifruit cultivars, which is consistent with the clustering results.
## 6 Abuse Attack Techniques
### Encoding
#### 6.2.2. CPLS as Redirector to C&C Domain  
ESET researchers discovered a Korplug variant [50] used by the Advanced Persistent Threat group, the Winnti group. This variant exploited publicly shared Google Docs files to extract its C&C address from a seemingly legitimate block of text using the well-known DZKS and DZJS delimiter strings.  
An APT group named Turla used a trojan backdoor housed in a Firefox extension to retrieve the C&C URL. Turla APT is a cyberespionage group with over a decade of activity. In the analyzed sample of this threat report [29], the C&C domain was obscured using an encoding technique in a well-known celebrity's Instagram comment. More specifically, the Firefox extension parsed the photo comments on the official Instagram account and calculated a custom hash value. If the comment hash value equaled 183, a regular expression (?:\u200d(?:#1@)(\w) was then executed against the matched comment to obtain the C&C shortened URL path, which subsequently led to the actual C&C URL.  
Stantinko [42] is a remotely configured cryptomining module that utilizes most of the compromised machine's resources. Stantinko malware interacts with its mining pool indirectly, through an IP address collected from YouTube video descriptions. These C&C IP addresses are concealed in a hexadecimal format in the video description string.  
Janicab malware has exploited YouTube [43] to obtain the C&C IP. To retrieve the actual C&C IP, the malware navigates through comments on specific YouTube videos. If a format such as _\"our 50380702789658 th psy anniversary\"_ is matched, the obscured number from the satisfactory comments, leading to the C&C IP, is extracted and converted to an actual IP.  
Palo Alto [46] documented two similar malware variations, CONFUCIUS_A and CONFUCIUS_B, which abused legitimate websites to retrieve C&C server IP addresses instead of using DNS lookups. To illustrate how the IP for the C&C domain is decoded, CONFUCIUS_A and CONFUCIUS_B use Yahoo and Quora to circumvent traditional mechanisms, parsing for keywords between specific phrases previously posted by the threat actor. The malware then decodes the interim phrase by substituting words for components of an IP address. A basic lookup table is used to decode and derive the C&C IP address.  
Palo Alto also analyzed a new variant of the SunOrcal malware family [47], which exploited the GitHub service by employing a de-obfuscation process of Base64 and XOR decoding to extract a C&C server. Strategically, this sample was configured to connect to a specific file hosted on the GitHub repository to extract the data leading to the real C&C server. Text between two particular strings within this target file is parsed and encoded to derive the C&C URL.  
The Scote backdoor is malware, discovered by Palo Alto Networks [58], that misuses legitimate third-party web platforms like Pastebin and Google+ as covert C&C communication channels. The Scote payload establishes a connection to these legitimate platforms' URLs to retrieve data and parse for specific commands to be executed by the compromised machine.
### Kernel Density Estimation for Solving the Probability Density Function of Chaotic Signals  
Here, we use a sample of size \(N\) independently and identically distributed from a population with probability density function \(f_{x}(x)\) and distribution function \(F_{x}(x)\). The sample values are \(x_{1}\), \(x_{2}\),..., \(x_{i}\),..., \(x_{N}\), where \(i=1,2,\cdots,N\). Then, the estimation of the probability density function \(f_{x}(x)\) is  
\[\hat{f}_{x}(x)=\frac{1}{Nh}\sum_{i=1}^{N}K\bigg{(}\frac{x-x_{i}}{h}\bigg{)} \tag{1}\]  
where \(K(\cdot)\) is the kernel function and \(h\) is the bandwidth.  
The distribution function \(F_{x}(x)\) is  
\[F_{x}(x)=\int\limits_{a}^{x}f_{x}(\tau)d\tau,x\in(a,b) \tag{2}\]  
where the range of values for \(x\) is between \((a,b)\).  
The kernel function \(K(\cdot)\) must satisfy the normalization condition:  
\[\left\{\begin{array}{l}K(x)\geq 0,\int_{-\infty}^{+\infty}K(x)dx=1\\ \sup K(x)<+\infty,\int_{-\infty}^{+\infty}K^{2}(x)dx<+\infty\\ \lim\limits_{x\rightarrow\infty}K(x)\cdot x=0\end{array}\right. \tag{3}\]  
Common types of kernel functions include the Gaussian kernel function, uniform kernel function, triangular kernel function, and Epanechnikov kernel function, etc. The kernel density estimation results are shown in Figure 1.  
Figure 1 shows that the probability density functions obtained by four different kernel functions overlap significantly and are almost indistinguishable, indicating that the kernel density estimation algorithm is not sensitive to the choice of kernel functions. Therefore, the Gaussian kernel function is used in the examples of this study.  
The smoothness of kernel density estimation is closely related to the bandwidth \(h\). Two common methods for choosing the bandwidth \(h\) are as follows:  
\[\left\{\begin{array}{l}h_{1}=1.059 sN^{-0.2}\\ h_{2}=0.785(q_{0.75}-q_{0.25})N^{-0.2}\end{array}\right. \tag{4}\]  
where \(s\) is the sample standard deviation and \(q_{0.75}\) and \(q_{0.25}\) are the 0.75 quantile and 0.25 quantile, respectively. The kernel density estimates for the two commonly used bandwidths are shown in Figure 2.  
Figure 2 shows that for irregular distributions with multiple peaks in the probability density function, the approximation degree of the bandwidth \(h\) shown in Equation (4) is not ideal.  
After balancing between smoothness and error, this paper takes one-thousandth of the range width of the chaotic signal \(x\) as the value of the bandwidth \(h\), as shown in Figure 3.  
Figure 1: PDFs of different kernel functions.  
Figure 3: PDFs of the proposed bandwidth.  
Figure 2: PDFs for the two common bandwidth selections.  
Figure 3 shows that the proposed method can provide a good approximation for probability density functions with multiple peaks. In the details, it is shown that the probability density function obtained by the kernel density estimation algorithm is smoother than that obtained by the histogram method.  
This paper selects the second component of the [PERSON]'s chaotic system (see Appendix A) as the intrinsic signal \(x\), whose probability density function and distribution function are shown in Figure 4.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Controller Functions  
#### 5.4.1 Data Collection  
In KDN architecture, the control plane collects data to aid in decision-making in the control plane (either using an information-centric approach using direct data/information or using a knowledge-centric approach by generating knowledge and rules from the collected data), whereas management-related data (data for network monitoring and configuration) and data specifically for knowledge generation are collected by the management plane. Note that most of the data are collected by the management plane, while only specific data that is required to aid control decision-making is collected by the controller in the KDN architecture. However, some data, such as data related to network topology and network traffic flow, will be jointly collected by the control and management planes in order to develop a global view for making management and control decisions.  
The control plane collects detailed statistics about the network traffic, such as the number of packets and bytes transmitted, the rate of traffic flows, and the types of protocols used. By collecting detailed traffic statistics, greater visibility into the behavior of the network can be obtained, which will allow early detection of issues before they become critical. By analyzing network statistics, the control plane can identify congested links and adjust traffic flows to balance network utilization and improve performance. Network statistics can be utilized to be confident that network traffic is prioritized according to QoS demands [377].  
The control plane can collect QoS data such as latency, throughput, packet loss rate, bandwidth utilization, etc. in order to make sure that network traffic is prioritized and delivered adhering to user requirements [378].  
The control plane may collect data regarding policies of the data plane elements, such as access control policies, QoS policies, routing policies, and security policies, in order to verify policies [262].  
The control plane needs to collect data on security events such as network attacks and intrusion attempts. These data will be used to identify potential security threats and trigger policies that can mitigate the impact of these events, as the control plane is responsible for enforcing security policies and responding to security events in real time. Furthermore, it can provide security-related data to the knowledge plane to generate knowledge and rules to refine application policies related to security [379].  
The control plane may need to collect information about routing protocols, including the configuration and behavior of the network devices, which will be necessary for traffic engineering using the controller [380].
# A Maximum Likelihood Estimation Method for Underwater Radiated Noise Power
## 2 Data Model  
Assuming that the underwater radiated noise source can be approximated as a point source, and considering the effects of shallow-water sound propagation, the time-domain data \(x(t)\) received by a single hydrophone can be expressed as the convolution of the channel impulse response and the radiated noise [35]:  
\[x(t)=g(\mathbf{p},t)\otimes s(t)+n(t), \tag{1}\]  
where \(\otimes\) denotes convolution, \(s(t)\) represents the radiated noise at a distance of 1 m from the source, \(g(\mathbf{p},t)\) denotes the channel impulse response from the source to the hydrophone at the position \(\mathbf{p}\), and \(n(t)\) represents the ambient noise received by the hydrophone. In this study, it is assumed that both the radiated noise and the ambient noise are characterized as white Gaussian noise and are mutually independent.  
By segmenting the received time-domain data and applying the Discrete Fourier Transform (DFT), the frequency-domain received data is obtained and expressed as  
\[X(k,f)=G(\mathbf{p},f)S(k,f)+N(k,f), \tag{2}\]  
where \(k\) denotes the time-segment index, \(f\) denotes the signal frequency, \(X(k,f)\), \(S(k,f)\) and \(N(k,f)\) represent the DFTs of the hydrophone data \(x(t)\), the underwater radiated noise \(s(t)\), and the ambient noise \(n(t)\), respectively, and \(G(\mathbf{p},f)\) is the channel transfer function from the radiated noise source to the hydrophone at frequency \(f\).  
In practice, an array composed of multiple hydrophones is employed for data acquisition, thereby achieving beamforming gain to enhance the SNR of the received data. For an \(M\)-element hydrophone array, the received data can be expressed as  
\[\mathbf{X}(k,f) =\left(\begin{array}{c}X_{1}(k,f)\\ X_{2}(k,f)\\ \vdots\\ X_{M}(k,f)\end{array}\right)=\left(\begin{array}{c}G(\mathbf{p}_{1},f)\\ G(\mathbf{p}_{2^{\prime}},f)\\ \vdots\\ G(\mathbf{p}_{M},f)\end{array}\right)S(k,f)+\left(\begin{array}{c}N_{1}(k,f)\\ N_{2}(k,f)\\ \vdots\\ N_{M}(k,f)\end{array}\right) \tag{3}\] \[=\mathbf{G}S(k,f)+\mathbf{N}(k,f),\]  
where the subscript \(m=1,2,\ldots,M\). indicates the index of each hydrophone.  
Due to the influence of the ocean channel, the signal power received by various array hydrophones may vary. Consequently, the SNR of the array received data is defined by utilizing the average power of the received signal and the ambient noise across the entire array as  
\[\text{SNR}=10\text{lg}\left(\frac{\left\|\mathbf{G}S\right\|_{2}^{2}}{\frac{ \left\|\mathbf{N}\right\|_{2}^{2}}{M}}\right)=10\text{lg}\left(\frac{\left\|\mathbf{G }\right\|_{2}^{2}}{M}\frac{\sigma_{s}^{2}}{\sigma_{n}^{2}}\right), \tag{4}\]  
where \(\left\|*\right\|_{2}^{2}\) denotes the square of the \(l_{2}\) norm of a vector, \(\sigma_{s}^{2}=\left|S\right|^{2}\) represents the power of the radiated noise, and \(\sigma_{n}^{2}=\left\|\mathbf{N}\right\|_{2}^{2}/M\) represents the average power of the ambient noise received by the \(M\)-element array.