esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Discussions  
In this section, we demonstrate the effectiveness of our proposed method and evaluate it against advanced techniques using a newly created dataset from the GaoFen-4 satellite. This study aims to detect ships in optical satellite images affected by clouds and fog. We begin by detailing the experimental setup, including dataset specifics, evaluation metrics, implementation details, and baseline methods. Next, we present the detection results on the constructed dataset and compare them with those of advanced methods, providing analysis and visual representations of the results. Finally, we conduct ablation studies to assess the individual contributions of each component of our method to the overall detection performance.
# Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems
## 4 Proposed BGRR Scheme for Reduction of Overhead of RS
Figure 6 shows an example of using the BGRR algorithm. The LS-MIMO only can be used for the static or nomadic UEs due to the burden of the RS overhead. In this regard, the BGRR is also valuable for the static or nomadic UEs. To use the proposed BGRR, we should first know the UE locations to guarantee the space separability among different groups. There are many positioning schemes, and the 3 GPP standard already provides the LTE Positioning Protocol (LPP) and continuously trys to improve it. High precision GPS or long-term RS also can be helpful for this. If the rough UE locations are recognized, the virtual sectors can be determined for high beam separability. We can use the UE based or the angle based selection as mentioned previously. Based on the location information, we can generate beams using location based precoding. With the location based precoding, we can send RS or short-term RS to get the channel information. Obviously, the short-term RS resources should be orthogonal with each other in a virtual sector, but can be reused for different sectors. In the case of the WCR, UEs can send the sounding reference signals, and RX beamforming can be used at the BS. After CSI acquisition, the location and the inter-user interference (IUI) reduction precodings are used together to send the data signal. More details will be shown in the following section. There could be many modifications to improve the BGRR. As one example, we can increase beam separability among groups by using a iterative solution. The flow chart of this example is shown in  
Figure 6: An example of using the grouping resource reuse (BGRR) algorithm. Figure 7. In this flow chart, we can see that if SINR is not in the satisfactory region, we can adjust the virtual sectors to increase SINR, which can guarantee the high space separability.
## 5 Experimental Data Analysis
### Total Station Data Analysis
#### 5.3.1 Scatter Plot Analysis of Measurement Points  
After each loading cycle, observations were made for all points using the total station. The distribution of measurement points after the experiment was compared to their distribution before loading. Figure 13b indicates that after the 20 th loading cycle (with a maximum load of 15 kN), two vertical cracks appeared on the left side of the model, indicating that the model had suffered complete failure. Additionally, significant changes in subsidence were observed at each measurement point compared to the 19 th loading cycle.
# Influence of Cavitation and Shaft Deformation in the Analysis of Lubrication of the Stern Bearing
## 5 Results and Analysis
### The Mixed Lubrication State  
The basic parameters of the bearing are presented in Table 2.  
The lubrication state of the bearing under deflection is analyzed with the proposed model. The calculated results are shown in Table 3.  
It can be seen from the table that the lubrication state gradually transits from mixed lubrication to liquid lubrication. The proportion of liquid film load increases rapidly, along with the increments of rotational speed. After considering the deflection deformation and surface roughness, the contact force can be accurately calculated.  
\begin{table}
\begin{tabular}{c c c c} Inner radius of bearing (mm) & 100 & Outer radius of bearing (mm) & 160 \\ Radius clearance (mm) & 0.3 & Width of bearing(mm) & 240 \\ Rotational speed (ppm) & 20–60 & Lubricating oil viscosity (Pa-s) & 0.082 \\ Roughness of bushing (10\({}^{-6}\) m) & 8 & Roughness of journal (10\({}^{-6}\) m) & 2 \\ Elastic modulus of bushing (GPa) & 100 & Elastic modulus of journal (GPa) & 210 \\ Poisson ratio of bushing & 0.29 & Poisson ratio of journal & 0.3 \\ Load of bearing (N) & 38,000 & Initial Bearing-journal contact friction coefficient & 0.1 \\ \end{tabular}
\end{table}
Table 2: Parameters of a ship stern bearing for investigation of deformation of bearings.  
Figure 8: Effects of cavitation on the maximum pressure in journal bearings.
# Research on Digital Forensics Analyzing Heterogeneous Internet of Things Incident Investigations
## 3 Proposed Methodology
### Network-Based Incident Investigation Process  
If the device remains active after the application-side incident investigation, a network-side incident investigation is conducted. This investigation involves obtaining information from the activated device, allowing for the collection of volatile data. In addition, as IoT devices often communicate within internal networks using routers, access to the internal network is crucial for a thorough network investigation. However, in cases where low-power protocols like smart sensors are used for communication, packet collection can be achieved through sniffing tools without accessing the internal network. The investigation from the network perspective involves collecting the following information:  
* Network packets;
* Active services;
* Device system information through remote access.  
This information can be collected when the device is active. It is critical for identifying and assessing security breaches.  
Most IoT devices have encrypted communications, making it challenging to collect data from packets. However, even if the packet content is encrypted, network traffic collection through sniffing provides crucial data beyond the packet content. For example, metadata, such as internet protocol (IP) addresses, port numbers, and packet characteristics, are indispensable for understanding activities and traffic flow within the network. IP addresses and port numbers identify specific network devices and services, and analyzing this information can determine which devices are active on the network and the services with which they are communicating. Additionally, analyzing the packet characteristics, such as the size, frequency, and traffic pattern, can detect abnormal device activities or potential security threats. For instance, unusually sizable data transfers or consistent traffic to a specific destination can indicate that a device is infected with malware.  
Figure 4: Application-based data acquisition process.  
Furthermore, if remote access services (e.g., telnet) are available or if management services exist, they can be identified through port scanning to obtain additional information. Remote access services allow administrators or investigators to directly access a device over the network and inspect system settings, log files, and running processes. The information available through management ports varies depending on the device and is primarily used to acquire device logs or system setting information. Access through these ports can provide detailed operating status, system logs, and configuration settings of each device, allowing for a deeper understanding of the system information. Figure 5 presents a flowchart of the network aspect of incident investigation methods.
## 3 Results and Discussion
### Predicting Lifetime  
Table 6 provides a mathematical model based on the graphs fitted in Figure 7. The PE pipes failure time was used as the dependent variable y and the solution PH as the independent variable x in the development of this model. In other words, for a specific pH value of the solution, we may use this mathematical model to forecast the value of the time till failure. It is crucial to remember that this mathematical model is based on a fit to the data presented in Figure 7; as a result, care must be taken to ensure that the data are accurate before using the model.  
As seen in Table 6, the quadratic regression curve algorithm has an R-squared of 1, which indicates that the model fit by serial number 5's quadratic regression curve Equation (7).  
\[y=-1.19352+1.29333\times x-0.0947\times x^{2} \tag{7}\]  
fits the data very well and the independent variable fully accounts for all variation in the dependent variable. This outcome is highly encouraging and demonstrates that the generated lifespan prediction equation's quadratic regression curve may adequately reflect the relationship between the data.
## 3 Results and Discussion
### Sugar Content  
The predominant sugars found in apples are glucose, fructose, sucrose and sorbitol, which contribute to the sweetness and flavor of the fruit [79]. Figure 5 shows the composition of soluble sugars (mg/100 g d.m.) of the samples studied.  
The most abundant sugar in all tested samples was fructose, followed by sucrose or glucose and sorbitol. In apples, only a small fraction of fructose is incorporated into starch. Instead it accumulates in the vacuoles of apple cells, and as a result, fructose is always higher than glucose in fruit tissue [80].  
In terms of glucose, fresh apple has the biggest amount followed by CF, OD65\({}^{\circ}\), SD, OD85\({}^{\circ}\) and CC (\(p\) < 0.001175) (Tables S29 and S30). For fructose, the highest value was also registered in fresh apple, then CF, OD65\({}^{\circ}\), OD85\({}^{\circ}\), CC and SD, with no significant differences (Tables S31 and S32). This tendency is switched for sucrose content, where the highest value was registered in CC, followed by fresh apple, OD65\({}^{\circ}\), OD85\({}^{\circ}\), SD and CF (\(p\) < 0.000010) (Tables S33 and S34).  
Figure 4: Antioxidant activity (AA) of fresh apple, samples dried with different methods (SD—solar; OD65\({}^{\circ}\)—oven at 65\({}^{\circ}\); OD85\({}^{\circ}\)—oven at 85\({}^{\circ}\)) and commercial samples (CC—commercial white label; CF—commercial label). (i) DPPH analysis; (ii) ABTS analysis. Data are expressed as mean plus/minus standard deviation (n = 3). Values with the same lowercase letters are not significantly different (\(p\) > 0.05).  
In relation to sorbitol, the results are 1.18 mg/100 g d.m. for CF and 0.95 mg/100 g d.m. for CC, showing that commercial samples have the highest amount. For fresh samples the sorbitol content was 0.78 mg/100 g d.m., followed by OD65\({}^{\circ}\), SD and OD85\({}^{\circ}\) (\(p<0.000519\)) (Tables S35 and S36).  
Comparing the sum of the sugars in the different samples, fresh _Royal Gala_ has a total of 43.25 mg/100 g d.m., followed by CC with 33.81 mg/100 g d.m., OD65\({}^{\circ}\) with 33.37 mg/100 g d.m., CF with 31.56 mg/100 g d.m., OD85\({}^{\circ}\) with 25.92 mg/100 g d.m. and SD with 25.01 mg/100 g d.m. Despite the variation of sugar content related to the drying process, there is no clear relationship with browning index. We expected to obtain a concentration effect because the drying process typically concentrates sugars by removing water, leading to an increase in sugar content [81]. However, it does not occur in our study, which can be explained by several factors, such as the exposure of sugars to heat and air during drying, which can cause its degradation. The degradation processes, like caramelization or Maillard reactions, can cause a reduction in the measurable sugar content and contribute to the color and flavor changes in dried apple [82].
# Chinese News Text Classification Method via Key Feature Enhancement
## 3 Key Feature-Enhanced Chinese News Text Classification Method
### Data Augmentation
#### 3.1.2 Key Feature Semantic Extensions  
As can be seen from the flowchart in Figure 2, the entire key feature semantic extension only performs secondary data enhancement for samples whose original text length is less  
Figure 3: The flowchart of the key feature extraction process.  
than 300 and automatically skips this step if the original text length is not less than 300. The detailed semantic extension steps are as following:  
Step 1: Input a sample whose original text length is less than 300 and initialize an empty semantic extension list SE, then go to Step 2;  
Step 2: Use the key feature extraction algorithm in Section 3.1.1 to extract a list of all key feature words from the original text, then go to Step 3;  
Step 3: Loop through the list of key feature words in Step 2, use each word as a query, input the large-scale word2 vec word vector tool that is open source in the literature [21] and call the similar_by_word (query, 5) method to return the current query and the top-five most semantically-relevant feature words and their corresponding semantic similarity dictionary, T, then go to Step 4;  
Step 4: If T is empty, go directly to Step 3 to start the expansion of the next query;  
Step 5: If T is not empty, loop through the dictionary T and then add all the words in T with a semantic similarity value greater than S (in the experiment, S is set to 0.85) into the semantic expansion list SE, then go to Step 3 and start the next query extension;  
Step 6: After traversing all queries, output the SE list as the final semantic extension features list.  
The semantic extension of all key features can be realized by using the key feature semantic extension method in Steps (1) to (6). Then, this semantic extension feature is combined with the key features extracted and concatenated to the back of the original text to generate new training samples to achieve the final data enhancement. It is worth noting that the semantic similarity calculation method in Step 5 uses the cosine similarity to calculate the cosine distance between two k-dimensional vectors and the relevant calculation principle is shown in Formula (1):  
\[S=\frac{\sum_{k=1}^{n}(x_{k}\times y_{k})}{\sqrt{\sum_{k=1}^{n}(x_{k})^{2}} \times\sqrt{\sum_{k=1}^{n}(y_{k})^{2}}} \tag{1}\]
# Identification of High-Order Nonlinear Coupled Systems Using a Data-Driven Approach
## 2 Mathematical Tools
### Simulink Support Package for Parrot Minidrones
So, to correctly formulate Equation (4), consider that the matrix \(\dot{X}\) is:  
\[\dot{X}=[\dot{x}_{1},\ldots,\dot{x}_{12}], \tag{23}\]  
while the matrix \(\Theta(X,U)\) is  
\[\Theta(X,U)=[f_{1}\;f_{2}\;\ldots\;f_{16}], \tag{24}\]  
with \(f_{1}=1\), \(f_{2}=x_{7}\), \(f_{3}=x_{8}\), \(f_{4}=x_{9}\), \(f_{5}=x_{10}\), \(f_{6}=x_{11}\), \(f_{7}=x_{12}\), \(f_{8}=\beta_{1}\cdot(\sin(x_{4})\cdot\sin(x_{6})+\cos(x_{4})\cdot\cos(x_{6}) \cdot\sin(x_{5}))\), \(f_{9}=\beta_{1}\cdot(\cos(x_{4})\cdot\sin(x_{6})-\cos(x_{6})\cdot\sin(x_{4}) \cdot\sin(x_{5}))\), \(f_{10}=\beta_{1}\cdot\cos(x_{5})\cdot\cos(x_{6})\), \(f_{11}=\beta_{2}\), \(f_{12}=x_{11}\cdot x_{12}\), \(f_{13}=\beta_{3}\), \(f_{14}=x_{10}\cdot x_{12}\), \(f_{15}=\beta_{4}\), \(f_{16}=x_{10}\cdot x_{11}\), where, as mentioned above, \(x_{1},\ldots,x_{12},f_{1},\ldots,f_{16}\in\mathbb{R}^{2001}\). Thus, the unknown matrix \(\Xi\) has a dimension of \(16\times 12\). The MATLAB code for the classical SINDY algorithm, with \(Theta=\Theta(X,U)\), \(dXdt=\dot{X}\), \(lambda=1\times 10^{-3}\), and \(n=12\), is given in Appendix A.2. The matrix \(\Xi\) produced is:  
\[\Xi=\left[\begin{array}{cccccc}0&0&0&0&0&0\\ 1&0&0&0&0&0\\ 0&1&0&0&0&0\\ 0&0&1&0&0&0\\ 0&0&0&0&0&1\\ 0&0&0&1&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0 It is noteworthy that the obtained identification does not exhibit sparsity, as each function considered in \(\Theta(X,U)\) emerges between the seventh and twelfth equations in the identified model, even when the parameter lambda is not very small (e.g., \(1\times 10^{-3}\)). However, even if one were to consider the identified model as a potential representation of the nonlinear dynamics of the drone, a significant limitation becomes apparent: the resulting model is non-controllable. This poses a fundamental issue, given that drones are recognized as controllable systems in the literature [31]. This discrepancy underscores the need for further refinement in the modeling approach to ensure an accurate representation of the controllable nature inherent to drone dynamics. This example demonstrates that, in general, it is not sufficient to know the functions appearing in the model to be identified for SINDY to yield correct results. The following section addresses and mitigates this drawback. Identification of a High-Order Nonlinear Systems with Coupled Dynamics through the Modified SINDY Algorithm
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 2 Homomorphic Encryption
### Processing Other Users  
The same process is applied to [PERSON] and [PERSON]'s audio data, resulting in their respective encrypted voice data, which are then transmitted to the server, as shown in Figure 2.
## 3 Development of RF WPT-Enabled IoT Application
### Target Application and Requirements  
As a target scenario for our illustrative WPT implementation, we selected a rather common industrial use case coming from the field of mechanical engineering, which is the measurement of the acceleration of the rotating mechanical structures. The need for such a solution arises in production (e.g., consider a metalworking lathe [55; 56]), power generation (e.g., generator turbines [57]), transportation (e.g., the ship or aircraft screw propeller or propeller shaft--[58]) and many other domains. Even the conventional cars may benefit from having a reliable system monitoring the parameters of their wheels [59].  
This is obvious that a cable connection to the sensors located on a rotating structure to read their data and provide them the power is hardly feasible. Therefore, up to now, two different approaches were primarily employed to address this challenge. The former one bases on the use of static sensors located around the rotating structure (e.g., cameras, microphones, magnetic or vibration sensors), the data from which are analyzed to determine the parameters of interest. Another option is mounting a battery-powered sensor to a rotation structure directly. In what follows, we attempt to address the very same challenge in a new way, employing the RF-based WPT.  
The key requirements for our use case are as follows. Given a lathe with a disc with a diameter of 30 cm, rotating around the horizontal axis with the speed of 60 rpm. The lathe is open from one side, providing a line-of-sight towards the rotating disc. A battery-free sensor device constantly measuring and reporting over a wireless channel at least twice per second the temperature and the acceleration must be mounted on the disc. The sensor should be fully powered with RF-based WPT, with WPT transmitter located at a distance of at least one meter from it.
# Sustainable Urban Water Management in China: A Case Study from Guangzhou and Kunming
## 5 SWOT Analysis in the Two Cities
### SWOT Analysis for Kunming City
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline  & **Strength** & **Weakness** \\ \hline \multirow{6}{*}{Urban drainage system} & \(\bullet\) & The separate drainage system in the new area & \(\bullet\) & Inadequate supervision of leakage Disordered drainage systems in agricultural areas \\  & \(\bullet\) & Kunming’s developed a water system that can carry runoff and rainwater & \(\bullet\) & The sensitive urban area is a combined drainage system \\  & \(\bullet\) & Complete flood warning system & \(\bullet\) & Inability to cope with extreme weather \\ \hline \multirow{6}{*}{Water pollution treatment system} & \(\bullet\) & Sewage treatment plants of different nature & \(\bullet\) & Insufficient sewage treatment in agricultural areas \\  & \(\bullet\) & 27 sewage treatment plants in the Dian Lake Basin & \(\bullet\) & The low sewage collection rate in the main urban area, only about 81\% \\  & \(\bullet\) & Complete local sewage treatment standards & & \\ \hline \multirow{6}{*}{Economy and society} & \(\bullet\) & The trade center of Southeast Asian countries & \(\bullet\) & Low overall education level Residents’ low participation in the decision-making process \\  & \(\bullet\) & Good economic foundation & \(\bullet\) & decision-making process \\  & \(\bullet\) & Increased education level & & \\ \hline \multirow{2}{*}{Ecology and environment} & \(\bullet\) & Suitable weather and climate for the growth of various plants & \(\bullet\) & Poor water quality \\  & \(\bullet\) & High environmental quality & & \\ \hline \multirow{6}{*}{Urban drainage system} & \(\bullet\) & **Opportunity** & **Threat** \\ \cline{2-5}  & \(\bullet\) & Advanced technology that can monitor the pipeline conditions in real-time & \(\bullet\) & Hardened ground Uneven precipitation \\  & \(\bullet\) & High building efficiency & \(\bullet\) & Low community resilience Different companies have different construction effects \\  & \(\bullet\) & Increasing economic investment & \(\bullet\) & Rapid population growth \\ \hline \multirow{6}{*}{Water pollution treatment system} & \(\bullet\) & The separate drainage system in the new construction area & \(\bullet\) & In the rainy season, high overflow rate of combined sewage, about 90\% \\  & \(\bullet\) & Ecological methods to treat wastewater Various technologies combine to treat wastewater & \(\bullet\) & Dian Lake carries more massive sewage discharge \\ \cline{1-1} \cline{2-5}  & \(\bullet\) & Various technologies combine to treat wastewater & & \\ \cline{1-1} \cline{2-5}  & \(\bullet\) & & & sewage discharge \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SWOT analysis in Kunming.
# Accurate Needle Localization in the Image Frames of Ultrasound Videos
## 2 The Proposed Method  
Figure 1 provides an overview of the proposed method. As shown in the figure, the method consists of three phases. The subsequent subsections provide a detailed description of these phases.  
Figure 1: An overview of the proposed method. **(a,b)** Two ultrasound images that are acquired at **(a)** the \(i\)-th and **(b)** the \((i+1)\)-th image frames, respectively. The gold standard needle trajectory and tip in **(a)** are indicated using yellow arrows. Phase I—Generating the QUS image: **(c)** The speckle variations feature. **(d)** The local phase feature. **(e,f)** The **(e)** sum average and **(f)** sum variance GLCM texture features. **(g)** The QUS image that is generated by the LDA classifier using the selected features. Phase II—Analyzing the QUS image to identify the ROI that includes the needle: **(h,i)** The initial prediction of the needle trajectory (shown as a red line) overlayed on **(h)** the QUS image and **(i)** the ultrasound image. **(j)** The ROI in the ultrasound image that includes the needle. Phase III—Analyzing the ROI that includes the needle to accurately localize the needle trajectory and tip: **(k)** The transformed ROI obtained using the customized RanT. **(l,m)** The accurately localized needle trajectory (shown as a red line) overlaid on **(l)** the transformed ROI and **(m)** the ultrasound image. **(n)** The localized needle trajectory (shown as a red line) and needle tip (shown as an orange circle) overlaid on the ultrasound image.