esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Rigid Model Pressure Measurement Test
### Measuring Point Layout and Test Conditions  
The large-span roof structure is divided into the first phase and the second phase, and the two parts of the building are centrally symmetrical (the pressure measurement model and the pseudo model in Figure 2. Therefore, only half of the structure was selected for the measuring point arrangement and data processing in the test. Because the roof is large and complicated in form, in order to facilitate the measurement, the pressure test was carried out after the roof was zoned. A total of 2010 measuring points were arranged on the structure, of which 1402 measuring points were arranged in the roof part. The layout of the roof surface zoning and measuring points is shown in Figure 3.  
According to the layout of the wind pressure measuring points, the drill tool was used to drill the corresponding points. One end of the extremely tough rubber tubes was glued to the model, and tools such as blades were used to flatten the surface of the model. The other end of the rubber tubes was connected to the pressure measuring line of the scan  
Figure 3: Layout of zoning and wind pressure measuring points on roof surface.  
Figure 2: Wind tunnel test for rigid model pressure measurement: (**a**) front view; (**b**) side view.  
valve. Finally, each scanning valve was connected to the corresponding channel on the collecting box.  
Before the formal test, the wind pressure collection data under no wind were checked to ensure that the collection equipment is in normal working condition. A total of 24 working conditions were selected for the test wind direction angle. A test wind direction angle was set every 15\({}^{\circ}\) from 0\({}^{\circ}\) to 345\({}^{\circ}\), i.e., \(\Delta\alpha=15^{\circ}\). The incoming flow direction directly in front of the railway station square was defined as \(\beta=0^{\circ}\), as shown in Figure 4. The test reference point was taken at the height of the roof top surface (about 43 cm). According to the basic wind speed of the city and region where the structure is located, the wind speed was 8 m/s. Comprehensively taking into account the frequency setting of the acquisition equipment and the effectiveness of data processing, the sampling frequency was 200 Hz, and the sampling time was 60 s.  
In the test, the Scanivalve DSM4000 electronic pressure scanning valve system was used to collect the pressure synchronously at the wind pressure measuring points on the roof surface. Data were checked after repeating the measurement twice for each test condition to avoid failed data acquisitions. In addition, before the test of each group of working conditions was carried out, there will be a few minutes of waiting before the collection to make the flow field reach a stable state and reduce the measurement error.
# Numerical Simulation Study on the Volumetric Efficiency Loss of CO2 Swash Plate Axial Piston Pumps
## 4 Numerical Simulation Method and Simulation Method Validation
### Numerical Simulation Method for Piston Pump Fluid Domain  
A transient numerical simulation was used to open the energy equation; the inlet and outlet boundary conditions of the fluid domain of the piston pump were set as pressure boundaries of 3 MPa and 12 MPa, respectively; the operating temperature was 250K; the inlet and outlet pipelines were set as static areas; the plunger followed the rotational movement of the cylinder around the axis; the slip mesh was set to the fluid domain of the plunger cavity (mesh motion); and the operating speed was 1000 rpm. Reciprocating motion was applied in the plunger cavity. For this part of the motion, a User-Defined Function (UDF) was used to impose the motion velocity. An expression of the reciprocating motion of the plunger relative to the cylinder speed is shown in Equation (5). Motion mesh technology was used to choose the layer. The actual motion conditions of the plunger pump were realized via the synergistic application of the slip mesh and UDF.  
\[\mathrm{v}=\omega D\tan\beta\sin\biggl{(}\omega t+\frac{2\tau n}{Z}\biggr{)} \tag{5}\]  
where v is the axial movement speed; \(\omega\) is the angular velocity; \(\beta\) is the inclination angle of the swashplate; and \(Z\) is the number of plungers. The value range of n is 0, 1, 2, \(\ldots\), z \(-\) 1, and n = 0 describes the initial plunger.
###### Abstract  
Nonalcoholic fatty liver disease (NAFLD), which is associated with obesity, is the most common reason for chronic hepatitis in developed countries. Its therapy includes a low-energy diet, physical activity and also various antioxidative compounds, such as milk thistle (_Silybum Marianum_) seeds, which are a natural source of silymarin. The aim of the study was to assess the influence of 3 months of therapy including dietary and physical activity recommendations combined with a daily milk thistle supply on the biochemical parameters of NAFLD patients. The study was conducted on a group of 20 patients (11 females, 9 males) with NAFLD and excessive body mass diagnosed. For 3 months, participants received daily doses of 7.1 g of milk thistle as a source of silymarin (210 mg a day). They also received dietary and physical activity recommendations and were monitored every 2 weeks. Their diet, body composition and biochemical parameters were assessed. It was observed that the studied patients did not follow their dietary recommendations, as when comparing the baseline diet and the diet after 3 months, there were no differences (for the vast majority of nutrients; \(p<0.05\)). Patients also did not follow their physical activity recommendations, as when comparing baseline results with those after 3 months, there were no differences (for the vast majority of parameters; \(p<0.05\)). However, when comparing baseline biochemical parameters and those after 3 months, it was indicated that blood triglyceride levels (140.36 \(\pm\) 14.92 vs. 110.04 \(\pm\) 37.23 mg/dL; \(p=0.0017\)) and gamma-glutamyltransferase (GGT) activity decreased (23.03 \(\pm\) 12.87 vs. 12.77 \(\pm\) 5.24 U/L; \(p=0.0008\)). As the diet and physical activity of the studied patients were not changed within the studied period, it may be supposed that the beneficial decrease in blood triglyceride levels and GGT activity were obtained due to the milk thistle supply as a source of silymarin. It may be indicated that including milk thistle in an everyday diet may be an additional recommendation for NAFLD patients, as it allows one to obtain beneficial effects, even if the other recommendations are not followed.  
nonalcoholic fatty liver disease; milk thistle; _Silybum Marianum_; silymarin; obesity; diet, body mass; lipid profile; liver enzymes
## 3 FKP-DGPS Algorithm  
### RTCM Version 2.3  
The development of the real-time FKP-DGPS algorithm is based on the DGPS messages, which are broadcasted in the RTCM version 2.3 format through the NTRIP. The broadcasting information in the RTCM version 2.3 contains various messages regarding the DGPS correction and network RTK. Among the various messages, the low-cost GPS receiver requires several messages such as message types 1, 2, 3, and 9 to obtain the position of the DGPS more accurately, instead of only a standalone position [(23; 24)]. Including the u-blox receiver, which is a typical low-cost GPS receiver, the commercial low-cost L1 single-frequency GPS receivers have an interface to receive the RTCM version 2.3 messages for the DGPS. Figure 4 shows the structure of the message type 1 in the RTCM version 2.3. The structure comprises various information, such as pseudorange correction (PRC) and range-rate correction (RRC), which is expressed as pseudorange correction change rate in Figure 4, issue of data (IOD), and user differential range error (UDRE). The DGPS correction for each satellite in message type 1 is approximately 40 bits except for the parity data, and it is usually broadcasted every second. The PRC is the main information for the DGPS, and we explain the method of integrating the modified FKP corrections with the PRC information of the message type 1 in Section 3.3.
# Biosignals Monitoring of First Responders for Cognitive Load Estimation in Real-Time Operation
## 3 Materials and Methods
### System Integration  
As previously stated, the biosignals acquisition module consists of all the chosen wearable devices that collect the data from the user (i.e., E4 wristband, Zephyr chest band, HoloLens glasses, Myoware muscle sensor and a thermocouple) along with a mobile phone that connects with the wristband and chest strap, and a microcontroller that obtains information from the muscle sensor and the thermocouple (see Figure 2).  
Therefore, the biosignals monitoring system is made up of three main parts. Firstly, the biosignals acquisition, where the biosignals data are collected by the wearable devices and sent to the computer following different protocols. Secondly, the control interface, where the user identifier and a control variable, that subsequently, allows or denies the data  
Figure 2: Sensors’ architecture and connection to the embedded computer.  
collection of all the biosignals, are inserted into the database. Finally, the actual storage of biosignals data into a database.  
Once the data are extracted, the microcontroller, phone and HoloLens are the devices responsible for sending the biosignals data to the embedded computer following different protocols: serial, MQTT, and HTTP, respectively. Figure 3 shows a diagram of the architecture.  
To integrate the reception of the biosignals data in the computer as independently as possible, the system is completely dockerized [69]. The control interface and the database modules are also running in separate docker containers inside the computer. Therefore, five different docker containers are created:  
* Docker-phone receives the data of the wristband and chest band sent by the phone.
* Docker-holo receives the data sent by the HoloLens.
* Docker-serial receives the data of the facial information sent by the microcontroller.
* Docker-mongo runs the MongoDB database where all the incoming data are stored.
* Docker-flask is a flask web interface to externally monitor all the processes.  
Finally, the processing of the data and the assessment of the cognitive load will be carried out in a high-end computer using the database that is stored in an external docker volume during the tests.
# Quasi-Shor Algorithms for Global Benchmarking of Universal Quantum Processors
## 2 Quasi-Shor and Bell-Shor Algorithms  
[PERSON]'s factoring algorithm involves two registers in the quantum processors [19; 59]. The first one contains \(t\) qubits set in \(\left|0\right\rangle\) state initially. The \(t\) relies on the number of digits of calculated accuracy and the probability of success of the phase estimation process. The second register starts with \(\left|1\right\rangle\) state, which should have enough qubits to store quantum information.  
As shown in Figure 1a, quasi-Shor algorithms replace the modular exponentiation in [PERSON]'s algorithm with unitary operations \(M_{i}\)\((i=1,2,3\dots)\). In the first register, the circuit contains a series of Hadamard gates, which are followed by controlled-\(M_{i}\). Then inverse quantum Fourier transformation (QFT\({}^{\dagger}\)) is applied to the first register. It still holds the analog frame of the quantum circuit with [PERSON]'s algorithm. The measurement of all these qubits after QFT\({}^{\dagger}\) could be exploited to benchmark the performance of the quantum processor.  
Figure 1b gives the schematic of the Bell-Shor algorithm based on five qubits, which are divided into two registers with three and two qubits, respectively. This combination keeps a certain complexity by retaining three computation periods with three controlling qubits in the first register while simplifying the varieties of entangled states through Bell states with two computing qubits. The quantum operation \(M\) is set as a combination of a Hadamard gate and a CNOT gate, which is denoted as a Controlled-Bell (C-Bell) quantum operation:  
\[M=\text{CNOT}\ast(H\otimes I). \tag{1}\]  
As a case study, when \(M_{i}\)\((i=1,2,3)\) satisfy \(M_{i}=M^{2^{i-1}}\)\((i=1,2,3)\), the quasi-Shor algorithm could be utilized to complete phase estimation. For further simplification, \(M_{i}\)\((i=1,2,3)\) satisfy \(M_{i}\equiv M\)\((i=1,2,3)\), the brief quantum operations of Bell-Shor algorithm are displayed in Figure 1b. The quantum states along the quantum circuit are denoted as \(\phi_{j}\)\((j=1,2,...,5)\). The first register is linked to the cycle index of the C-Bell operations and it is adjustable by changing the number of controlling qubits to alter the periodic operations in the second one. While for the computing qubits in the second register, \(M_{i}\) is not limited by the quantum circuits for generating Bell states. It is also possible to increase the number to generate Greenberger-Horne-Zeilinger (GHZ) states [60] and W states [61] on more computing qubits. Furthermore, if \(M_{i}\) is set as the Grover iteration \(G\), the quasi-Shor algorithm would transform into an approximate quantum counting circuit [59].
## 3 Data
The detailed vegetation database, which is in a geographic information system, was developed by the Center for Remote Sensing and Mapping Science at The University of Georgia and the South Florida Natural Resources Center, Everglades National Park [3, 45]. Conventional visual interpretation techniques were used with optical airborne- and spaceborne-based observations to generate the vegetation map. Although the map is based on remote sensing data acquired two decades ago, it is still widely used as a reference map and is the only available map. We used the Vegetation Map and Digital Database of South Florida's National Park Service Lands as reference maps to evaluate our classification results. However, because the information represented in the map was dated, we examined possible vegetation changes by comparing multi-temporal Landsat-TM images collected from 1994 to 2011. In addition, we conducted a helicopter field survey in 2014 to verify the current vegetation distribution in some representative areas (Figure 2).
## 3 The Fully Automated Cardiac Free-Running Framework
### Acquisition  
The FRF utilizes a sequence which continuously acquires 3D radial k-space lines, also known as radial spokes, with each passing through the centre of k-space. All these radial spokes can be segmented into consecutive interleaves that each follow a phyllotaxis pattern and are rotated by the golden angle (\(\sim\)137.5\({}^{\circ}\)) from the previous one (Figure 2A) [6]. Two important parameters that define the phyllotaxis trajectory are the number of radial spokes acquired per interleave (also referred to as the number of segments), and the total number of interleaves needed to complete data collection. The multiplication of these two parameters results in the total number of radial spokes to be acquired in the sequence. With each radial spoke passing through the centre of k-space, a 3D kosaball is acquired and an image with an isotropic spatial resolution and isotropic field-of-view is typically generated (Figure 2B). The (isotropic) spatial resolution is usually set between 1.0 and 2.0 mm, where a higher resolution requires an increased number of interleaves to obtain sufficient image data for reconstruction. The readout of the radial spokes can be performed using either balanced steady-state free-precession (bSSFP) or regular gradient echo (GRE), of which the former is preferred at 1.5 T given its increased signal while the latter is the method of choice at 3 T to prevent banding artefacts.  
To enable total SG, an additional radial spoke in the SI direction (SI spoke) is periodically inserted at the beginning of each interleaf for subsequent extraction of the cardiac and respiratory SG signals (Figure 3). With typical sequence parameters, such as a repetition  
Figure 2: Panel (**A**) Schematic overview of 3D radial sampling using a spiral phyllotaxis readout pattern (also known as an interleave). When the pre-defined number of radial spokes of a single interleave has been acquired, the next interleave will start which is rotated about the golden angle (\(\sim\)137.5\({}^{\circ}\)) from the previous one. Panel (**B**) With an increasing number of interleaves, an increasingly dense 3D kosaball of data points is obtained. After a pre-defined number of interleaves, the free-running acquisition is finished.  
time of 3.5 ms and 20 segments (including the first SI spoke), an SG signal is acquired every 20 \(\times\) 3.5 = -70 ms.
# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 2 Materials and Methods
### Machine Learning Methods
#### 2.4.2 Linear Discriminant Analysis (LDA)  
Linear discriminant analysis (LDA) serves as a linear model employed for both classification and dimensionality reduction [32]. In the context of classification, LDA facilitates the projection of data from a D-dimensional feature space to a D' (D > D') dimensional space, with the objective of maximizing variability between classes while concurrently minimizing variability within classes.
## 6 Clustering, Virtual Backbone and Minimum Connected Dominating Set  
Simply maintaining the connectivity of a WSN is not sufficient for data dissemination. Routing must also be considered. This section will consider connectivity issues arising in one popular approach:cluster-based routing.  
Routing is a major challenge for WSNs. The large number of sensors involved means that efficient routing is required. However, their typical data rates are much lower than other large networks such as the internet. Hence the relative cost of the signalling overhead of standard routing algorithms such as distance vector routing [72] or link state routing [73] can be excessive. This is exacerbated by the fact that nodes have very little computational power, but each node typically has a very large number of neighbours because wireless is a broadcast medium. Moreover, the set of neighbours is rather nebulous because wireless links vary in their reliability. Using reliably, short hops causes an excessive number of transmissions and hence excessive interference; conversely, using longer hops risks the need for retransmissions. (This can be addressed by opportunistic scheduling [74; 75], which is out of scope of this paper.) Finally, unstructured routing protocols require all potential relays to have their radio circuitry powered all of the time, in case they are called upon to relay data. This is a significant overhead in the ultra-low-power designs envisioned for WSNs.  
One common solution to these problems is to perform routing through a specific subset of nodes, called a virtual backbone, to which all other nodes connect in a single hop. A good virtual backbone can simplify the routing process and can reduce the overall network energy consumption in two ways. First, as only nodes in the virtual backbone forward packets, non-backbone nodes can spend more time in a low-power idle mode. Second, all sensor nodes need to perform in-network processing and data aggregation. Doing this within virtual backbone nodes can eliminate redundant data and relax the packet transmission burden, which leads to energy saving.  
For a given WSN, we typically wish to find a virtual backbone with the minimum number of nodes, to maximise the potential energy saving. However, successful packet delivery requires that the nodes in the virtual backbone remain connected, and that every other node is within range of a backbone node.  
In this section we first introduce cluster-based routing, which groups nodes according to their geometric positions, and selects a head node for each group (i.e., cluster). All the cluster heads form a virtual backbone. We also discuss a graph theoretic concept, Minimum Connected Dominating Sets (MCDS), which model a small connected relay set. We also cover the state-of-the-arts algorithms for MCDS construction.
# Turning Video Resource Management into Cloud Computing
## 3 The Video Resource Management System in HDFS-Based Cloud Computing
### HDFS-Based Architecture of Video Resource Management  
The model of video resource management systems (Figure 4) consists of clients, web clusters, streaming media servers, and HDFS clusters. These components work cooperatively. Firstly, a user sends a video playing request to web clusters by selecting a video listed on a web page through the HyperText Transfer Protocol (HTTP); secondly, web clusters search the video information according to the user's request from the MySQL database, and return the search results to the HDFS clusters to retrieving the video files; thirdly, video HDFS clusters send the video information to streaming media servers; fourthly, streaming media servers read, compress, and fetch the video streaming data from the HDFS clusters; finally, clients get the continuous video stream from the streaming media servers.  
Figure 4: HDFS-based architecture of video resource management.  
Figure 3: HDFS-based five-layer model for video resource management.
## 4 Ad Hoc Routing: Configuration, Scenarios, and Results
### Performance Analysis  
It is always a good idea to compare different scenarios and to produce conclusive results, in order to better understand the processes of any system. This analysis not only helps in decision-making, but also helps to find solutions for any current or future issue. We plotted the average values of the Round Trip Time (RTT) taken by sent packets in various executions of the system, versus the number of hops they traversed. Obviously, the greater the number of hops, the more time it takes for the data to reach its destination. We witnessed that by executing the same operation by changing the positions of the users (robot cars, in our case) every time, the values for the RTT changed accordingly, as depicted in Figure 18. This change is mainly due to the mobility of the cars, but is due also in part to the disturbances in the wireless environment (e.g., scattering effects, interference with other signals).  
Additionally, we performed three different tests by executing the proposed applications on the TestBed. 100, 150, and 200 queries were generated for each hop-count value (i.e., \(1,2,3,4,5,6\) in the first, second, and third test, respectively). In each test, the Packet Delivery Ratio (PDR) was observed by increasing the hop-count value, as shown in Figure 19. It was observed that PDR was 100% with a hop-count value from 1 to 4, which shows that all the queries that were sent to the intended destination were successfully received. However, with an increase in the number of hop-counts greater than 4, the PDR decreased to some extent, showing that there was a loss in the received queries which were sent by the sender. This happened possibly because of interference due to the increase in the number of nodes in any wireless communication.  
Figure 18: Analysis: Round Trip Time (RTT) vs Hop Count.  
It is to be noted that safety and emergency applications require a 20-100 ms delay. Though the proposed application is designed to meet the communication requirement in an emergency situation, the nature of the application can tolerate little delay. During the testing phase, the average delay was evaluated for the queries. Figure 20 presents the average delay comparison with the number of hops. The TestBed encompasses a limited number of vehicular nodes, and the observed delay is below the minimum requirement of the safety application.  
This concludes our work toward presenting a real-world prototype, which comes with its own set of advantages and disadvantages. This work can be considered to realize even better systems that can be commercially used in rescue-and-relief efforts in disaster-management areas.  
Figure 19: The trend of packet delivery ratio (%) with increasing hop count.  
Figure 20: Comparison of Delay with Increasing Hop Count.