esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measuring Devices and Systems
### Optical-Type Measurement System  
The optical shaft power meter uses LED sensors to measure shaft torque and thrust. The system consists of an LED sensor, detector arm, mounting ring and power transmission  
Figure 1: Configuration of the strain gauge-type measurement system.  
Figure 3: Schematic representation of strain gauges.  
Figure 2: Installation for the strain gauge system.  
coil on the shaft. The measured values are transferred continuously from the rotating shaft to the stator part through wireless data connection. Power transmission from the stator to the rotating shaft is performed by means of induction. The stator part consists of a power transmission coil, a data signal receiver and a control box equipped with digital output connections. The general working principle of the system [24] is shown in Figure 4.  
The sensors are mounted on propeller shafts between the propeller and the thrust bearing as shown in Figure 5. When a shaft is subject to thrust and torque, this results in a small compression and torsion of the shaft. The optical sensor measures this shaft compression and torsion over a shaft with a length of typically 200 mm. This relatively long measuring area of the shaft, compared with the conventional strain gauge-type measurement system, increases the measurement accuracy. The optical sensors detect the small displacements over the shaft length, in both axial and tangential directions, corresponding to the compression (thrust) and torsion (torque) of the propeller shaft [25].  
In the optical-type measurement system, for the accurate and reliable measurement of thrust and torque, it is essential to determine the zero level very carefully. The zero setting should be performed on the condition that shaft is stationary with no residual forces or moments acting on the shaft. In this study, the zero level was adjusted after the installation during the ship was mooring on a quay side of the shipyard. Therefore, the zero setting was carried out rotating the propeller shaft clockwise and counterclockwise with the shaft revolution lower than 1 RPM to minimize the effect by the static deflection.
# Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity
## 3 Problem Description and Model Used
### Metrics of the Prediction Error  
The error measurement metrics reflect the distance between the actual and the predicted values. Two of the most widely used error metrics, with their definitions, are shown in the following equations:  
\[\text{Mean Absolute Error}\quad MAE=\frac{1}{N_{test}}\sum_{p=1}^{N_{test}} \left|y_{i}(p)-y_{o}(p)\right| \tag{3}\]  
\[\text{Coefficient of Determination}\quad R^{2}=1-\frac{\sum\limits_{p=1}^{N_{test}} \left[y_{i}(p)-y_{0}(p)\right]^{2}}{\sum\limits_{p=1}^{N_{test}}\left[y_{i}( p)-y_{i,mean}(p)\right]^{2}} \tag{4}\]  
where \(y_{i}(p)\) is the actual path loss value, \(y_{i,mean}(p)\) is the mean actual path loss value and \(y_{o}(p)\) is the predicted path loss value. \(N_{test}\) is the number of test patterns, while \(p\) represents the input according to which the prediction is made.
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 3 Data Collection and Processing  
### Original Data  
Due to time and equipment constraints, this study used data collected by [PERSON] from the Federal University of Rio de Janeiro in Brazil. The data is publicly available via Kaggle, a website that hosts many datasets for the Machine Learning and Data Science Community [26].  
The data was collected on SpectraQuest's Machinery Fault Simulator (MFS) Alignment-Balance-Vibration (ABTV) with different fault conditions. Figure 4 shows the data collection setup [10]. The specifications of the simulator can be found in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline
**Specification** & **Value** & **Unit** \\ \hline Motor & 1/4 & CV DC \\ \hline Frequency range & 700–3600 & rpm \\ \hline System weight & 22 & kg \\ \hline Axis diameter & 16 & mm \\ \hline Axis length & 520 & mm \\ \hline Rotor & 15.24 & cm \\ \hline Bearings distance & 390 & mm \\ \hline Specification & Value & Unit \\ \hline Number of balls & 8 & \\ \hline Balls diameter & 0.7145 & cm \\ \hline Inner diameter & 2.8519 & cm \\ \hline FTF & 0.375 & CPM/rpm \\ \hline BPFO & 2.998 & CPM/rpm \\ \hline BPFI & 5.002 & CPM/rpm \\ \hline BSF & 1.871 & CPM/rpm \\ \hline \end{tabular}
\end{table}
Table 1: Specification of MTS ABTV Simulator.  
Figure 3: Bearing faults: inner race fault (**left**), outer race fault (**center**), and ball fault (**right**) [8].  
There are 1951 sequences, each presenting 10 different conditions of a rotating machine. Each sequence data was collected with a 50 kHz sampling rate for five seconds total, which generates 250,000 observations or records per sequence. Table 2 lists the 10 conditions for fault detection. The total observations for all ten conditions are 487,750,000 records, which is a huge data set. The total size of this whole data set is 13 Gigabytes. This is a lot of data to process, which became a problem during the study due to the constraints of MATLAB and computational power.  
The data was organized in 8 columns or 8 predictors: tachometer data, first accelerometer data in the x-axis, first accelerometer data in the y-axis, first accelerometer data in the z-axis, second accelerometer data in the x-axis, second accelerometer data in the y-axis, second accelerometer data in the z-axis, and microphone data.
## 3 Methodology
### Dry Beach Slope Measurement
#### 3.3.1 Dry Beach Point Cloud Profile Denoising  
The ideal dry beach surface point cloud model should consist of a layer of points with no thickness. However, due to the influence of the image quality, shooting environment, surface characteristics, and other factors, there are many noise points surrounding the reconstructed point cloud, resulting in the point cloud model of the dry beach with thickness. Therefore, it is necessary to denoise the point cloud before performing subsequent calculations.  
In this study, to measure the slope of the dry beach at a certain point, a point cloud profile is divided parallel to the dam axis through this point, and then, the profile is denoised. According to the research idea and the characteristics of point clouds, a sliding window filter denoising method for point cloud profiles is proposed. The difference between noise points and non-noise points mainly lies in the difference in Z coordinates. Along the Z axis, noise points are evenly distributed on both sides of the non-noise point cloud of the dry beach. Therefore, the 3D point cloud can be projected onto the XZ plane and denoised based on the difference in Z coordinates. The flow of the filtering algorithm is shown in Figure 9. The specific steps are as follows:  
1. A 3D point cloud is projected onto the XZ plane to obtain 2D point cloud data;
2. Set the window size \(W\);
3. Initialize the sliding window: place the initial position of the sliding window at the minimum X coordinate of the point cloud profile;
4. Calculate the Z-coordinate average of all points in the sliding window;
5. Removing noise points: the Z-coordinates of all two-dimensional point clouds falling within the window minus the Z-coordinate average. When the absolute difference exceeds the threshold \(T\), the point will be removed. Otherwise, it will be retained;
6. Window slide: move the window along the right, setting the step size of each move to \(S\), and add the unprocessed data points;
7. Repeat steps 4 to 6 until the sliding window reaches the maximum X value;
8. Project the selected non-noise point cloud into the three-dimensional space, completing the filtering of the profile point cloud.  
Figure 8: Dry beach length.
# The Influence of Immersive Virtual Reality Systems on Online Social Application
## 3 Developing RVM-IVRSA
### Scene Synchronization  
Different from traditional network data communication, RVM-IVRSA synchronization data are not text, pictures, or files, but are a virtual environment with multiple 3D models. Therefore, scene synchronization should be considered in the design of a test platform.  
The test platform adopts ECS (entity component system) architecture [31], whose pattern follows the principle of composition over inheritance. Each basic unit in scene is an entity, and each entity is composed of one or more components. Each component only contains data that represents its characteristics. For example, MoveComponent contains properties such as speed, location, etc. Once an entity owns MoveComponent, it is considered to have the ability to move. System is a tool for dealing with a collection of entities that have one or more of the same components. In this example, moving system is concerned only with moving entities, and it will walk through all entities that have the MoveComponent component and will update the location of entities on the basis of relevant data  
Figure 5: User using RVM-IVRSA.  
(speed, location, orientation, and so on). Entities and components are a one-to-many relationship. What capabilities an entity has depends entirely on what components it has. By dynamically adding or removing components, behavior of entity can be changed at run time [32; 33].  
On the basis of ECS architecture of the test platform, we adopted a mechanism of deterministic lockstep synchronization to realize scene synchronization [34]. When operation data of a client starts to upload to the server, the server will lock the current frame all the time, and the server will not start to simulate the scene process until all data of the terminal has been uploaded. In the simulation process, the server will process simulation results into the instruction data of the client, which will be forwarded to all user terminals. Finally, user terminals will start their own simulation process on the basis of the forwarding instruction just received. The schematic diagram of the deterministic lockstep synchronization mechanism is shown in Figure 6.  
Here, we used two PCs as terminals along with Alibaba's ECS cloud server. The data transmitted were input data for terminal users, namely, real-time position parameters of HMD, key parameters of handles, and voice files. The values of the operation parameters from the two terminals were first consolidated into two sets of packets, each containing a token value to mark the number of the source terminal (this int variable is called \"T number\", assigning values 1 and 2 to terminal 1 and 2, respectively). Then, they were packed one at a time and sent to both terminals. The terminal will filter on the basis of the value of \"T number\", retaining the value of the operation parameter from another terminal to assign the character model B in the scene.
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 4 Protocol Description
### Selecting the Optimal Relay and Data Forwarding
In scenarios characterized by suboptimal link quality, elevating the weighting of subsequent-to-subsequent hop link quality ensures the protocol's reliability. As shown in Figure 6, node S is the forwarding node, and node A, node B, and node R are neighbors of node S. When node S needs to send data, it first sends HANDSHAKE-S. After neighbor nodes A, B, and R receive HANDSHAKE-S, they first check whether their hop count is greater than the hop count carried in HANDSHAKE-S. As can be seen from the figure, node B has a larger number of hops than node S. Therefore, node B discards the HANDSHAKE-S, node A and node R send HANDSHAKE-R to node S, and node S selects the node with the highest link quality that is greater than the threshold from the nodes sending HANDSHAKE-R as the best relay. The link quality of the three is calculated to be 0.77 and 0.65, respectively. According to the forwarding policy of WLQRP, the node R with the link quality of 0.77 is selected as the next hop node. When calculating the link quality between node S and nodes B and R, not only the link situation between node S and node R is considered, but also the link situation between nodes R and their neighbors. This also demonstrates that only the link condition between the node and the next hop node is good enough to make the next hop node a relay node, and the neighbor status of the next hop node is also an important judgment basis. This can effectively avoid the route falling into a void. Once the optimal relay is determined, node x proceeds to forward a set of data to node y. Upon successful data reception, node y sends an acknowledgment (ACK) message back to node x. If node x does not receive an ACK confirmation from node y within the specified wait time (round-trip time of the data packet), x will resend the data. The maximum number of retransmissions is 3. If x does not receive the ACK packet after retransmission, it proceeds to select the second-highest link quality node for data transmission, and this process continues iteratively. If the link quality of all neighboring nodes of node x falls below a predefined threshold, the node temporarily stores the packet in a cache queue for the next send. Once the packet is successfully sent, it will be removed from the cache queue. If the cache is full, the packets are dropped one-by-one in a first-in, first-out order.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 3 GAN-Enhanced CNN for Voiceprint Recognition
### Feature Extraction--MFCC
#### 3.2.2 Formatting  
All extracted audio features were padded to the same length to prepare the data for CNN model training. Padding, rather than cropping, was used to ensure that no important features were lost. After padding, the data were reshaped into a four-dimensional array to meet the input requirements of the CNN. This process ensures that the features are complete and ready for training.  
MFCCs were selected as the primary features for voiceprint recognition due to their established effectiveness in capturing speech characteristics. MFCCs simulate the human auditory system's perception of sound frequencies, making them particularly well-suited for distinguishing vocal patterns. They are widely used in speech and speaker recognition tasks because of their ability to represent both static and dynamic speech information when combined with delta and delta-delta coefficients.  
While MFCCs offer robust feature extraction, alternative features like linear predictive cepstral coefficients (LPCCs) also have potential for voiceprint recognition. LPCCs emphasize speech production characteristics and could complement MFCCs to capture additional vocal nuances. However, this study focused exclusively on MFCCs to maintain a targeted scope and streamline the experimental design. Future work will explore the integration of LPCCs and other advanced acoustic features to further enhance model performance and generalizability.
# Transport-Layer Limitations for NFV Orchestration in Resource-Constrained Aerial Networks
## 3 Orchestration in Intermittently Available Platforms
### Limited Capacity of NFVI Nodes  
It is important to emphasize that SUAVs are smaller than regular aircraft. In consequence, their payloads must be reduced in weight and size to be onboarded without harming their flight capabilities. This impacts the nature and the capacity of the available resources in every NFVI unit provided by the SUAVs. This issue has been explained in more detail in [5], where authors encourage the use of lightweight VNFs with container virtualization instead of the traditional hypervisor-based one to overcome this resource limitation. An alternative to these lightweight containers could be the one seen in [20], where authors propose using paravirtualization instead. This technique allows a VM to directly communicate with the OS directly, as opposed to hypervisor virtualization where the VM communicates with its \"virtualized kernel\". This direct communication speeds the information exchange between the host and the VM (or in this case VNF), increasing orchestration efficiency in the process.  
SUAVs limitations are not exclusive to the equipment being used on their payloads: how VIMs exchange information with NFVI units also plays an important role in their communication performance. Most commercial VIMs, which are highly based on OpenStack [25], use HTTP as their default application-layer protocol for exchanging information related with their NFVI resources, getting information about the status of the resources and/or sending actions to be applied on both resources and/or VNFs. With this context in mind, it is understandable the selection of this protocol for this purpose: not only is one of the most used application-layer protocol on the Internet, but also the HTTP embedded Representational State Transfer (REST) model perfectly fits with its information exchange that has to be performed between the aircraft and the GCS. However, this protocol was never intended to operate as a lightweight protocol, as it can be considered a process-intensive protocol compared to alternatives such as the Constrained Application Protocol (CoAP) [26] or Message Queuing Transport (MQTT) [27]. This could be a problem in SUAV networks, since the resources that can be onboarded cannot be very powerful due to both its limited size to fit equipment and the necessity of saving precious battery lifetime. Therefore, some of the aforementioned protocols could be used as alternatives to reduce message overhead for a more cost-effective solution to save battery lifetime and computational resources needed to process the messages.
## 3 Results and Discussion
### Parameter Optimization
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \hline  & **CH\({}_{2}\)Cl\({}_{2}\)** & **CHCl\({}_{3}\)** & **CCl\({}_{4}\)** & **CH\({}_{2}\)ClCH\({}_{2}\)Cl** & **CHCl\({}_{2}\)CHCl\({}_{2}\)** \\ \hline ASA & 82.5 (2.5) & 79.2 (3.2) & 74.1 (2.7) & 76.1 (2.1) & 74.2 (3.0) \\ IBP & 94.6 (3.0) & 81.7 (2.6) & 82.3 (3.0) & 83.2 (2.7) & 85.2 (2.9) \\ NAP & 96.8 (2.9) & 83.4 (3.1) & 84.5 (3.1) & 85.4 (3.0) & 86.3 (3.1) \\ KPF & 98.5 (2.8) & 85.2 (3.0) & 86.5 (2.9) & 89.1 (2.9) & 86.2 (2.9) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of different extraction solvents on the NSAID recovery accuracy (%). The conditions were as follows: 9 mL of simulated urine samples spiked with NSAIDs (20 ng mL\({}^{-1}\) of each), 1 mL of acetone, 300 \(\mu\)L of extraction solvent, and 5 \(\mu\)L of anthracene (LS. ; 60 ng \(\mu\)L\({}^{-1}\)). In brackets are reported the relative standard deviations (RSDs, %); each analysis was in triplicate. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compound** & \multicolumn{2}{c}{**Recovery (\%)**} \\ \hline  & **200 \(\mu\)L** & **250 \(\mu\)L** & **300 \(\mu\)L** \\ \hline ASA & 82.3 (3.1) & 94.2 (2.8) & 82.5 (2.5) \\ IBP & 95.2 (2.7) & 100.1 (3.1) & 94.6 (3.0) \\ NAP & 96.8 (2.9) & 99.8 (2.5) & 96.8 (2.9) \\ KPF & 95.1 (3.2) & 101.2 (3.0) & 98.5 (2.8) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of different volumes of CH\({}_{2}\)Cl\({}_{2}\) on the NSAID recoveries (%). The conditions were as follows: 9 mL of simulated urine samples spiked with NSAIDs (20 ng mL\({}^{-1}\) of each), 1 mL of acetone, different volumes of CH\({}_{2}\)Cl\({}_{2}\) as extraction solvent, and 5 \(\mu\)L of IS (60 ng \(\mu\)L\({}^{-1}\)). In brackets are reported the RSDs (%); each analysis was in triplicate. Finally, the effect of different NaCl quantities on the NSAID recoveries was evaluated. Table 5 shows that the salt decreased the NSAID solubility (salting out) below and above 13 g L\({}^{-1}\) concentration. Further, the decision to perform the whole study at NaCl concentration of 13 g L\({}^{-1}\) was essentially due to two considerations: (1) this concentration was the average of those reported in the real urine samples, which was between 10 and 16 g L\({}^{-1}\) of NaCl [55]; (2) the percentage NSAID recoveries obtained and reported in Table 5 were very similar to each other for NaCl concentrations between 10 and 15 g L\({}^{-1}\). Finally, it should be highlighted that two other interesting parameters, such as vortex time and ultrasonication time, were extensively studied in previous papers by this group [41; 42; 43; 44].
# Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties
## 1 Introduction
Nevertheless, the linkage between satellite-derived CUE and WUE, and soil properties have been rarely examined. This work builds on previous studies of plant-nutrient limitations by exploring multiple nutrient effects, variable soil profiles, and interactions between these factors. The larger effects of multiple nutrient additions (N, P, calcium, or Ca) on forest productivity than a single element was, revealed in a recent study of nutrient availability in northeastern US hardwood forests [27]. Additionally, a strong relationship between satellite-derived canopy greenness and soil N, aluminum (Al), and P was observed in tropical forests [52]. Moreover, many modeling experiments considered soil as a mixed column and thus relationships between soil nutrients and vegetation growth cannot be inferred for soil depth. Here, we examine how relationships of soil variables with CUE, WUE, and greenness indices vary with soil depth and with satellite sensors' spatial resolutions. We present a novel approach combining CUE and WUE estimates from satellite GPP, NPP, ET, and SIF with soil properties to understand the regulatory mechanisms and effects of the nutrient cycles. Previous studies have shown that WUE varies with land use type, with forest having the highest WUE and croplands having the lowest WUE [39; 53; 54]. The relationship between CUE and land use type is more complex and is driven by climate controls, such as temperature and precipitation; CUE is higher in a drier and cooler climate than in a wet and warm climate [55]. In general, forest has lower CUE than grasslands, and crops have medium CUE values [55; 56]. Based on previous studies, we hypothesize that the CUE and WUE depend on land use and depth-related soil properties. Moreover, this work elucidates how ecosystems are structured and function based on connections between aboveground vegetation productivity and soil properties.