esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The oil-film bearing test rig can realize the monitoring of operating performances of oil-film bearings with thermocouples, pressure transducers, and displacement transducers installed on the test bearings. From the above analysis, several conclusions can be obtained as follows:  
Figure 14: The comparison of minimum oil-film thickness between S220 and M220 at 150 rpm-6 MPa. (**a**) S220; (**b**) M220.  
Figure 12: The variation of oil-film thickness from displacement transducer H2 with time at 150 rpm-6 MPa. (**a**) S220; (**b**) M220.  
Figure 13: The variation of oil-film thickness from displacement transducer H3 with time at 150 rpm-6 MPa. (**a**) S220; (**b**) M220.  
1. All property indexes of an oil-film bearing test rig can meet the specified requirements, and the test rig can guarantee the performance test of oil-film bearing oil.
2. The temperature performance of S220 is generally equal to that of M220 and both have the equivalent operation stability. The oil-film temperature performance conforms to Formula (1), and can be predicted at given load case through Formula (1).
3. Although the oil-film pressure of M220 is a little more stable, the oil-film pressure performance of S220 is similar to that of M220.
4. S220 and M220 have the same performance of oil-film thickness, and the oil-film thickness of S220 is slightly smaller than that of M220, especially at the beginning of operation.
5. From the comprehensive three months of comprehensive testing, S220 is evaluated to be suitable for further engineering application.  
This work is supported by the Natural Science Foundation of Shanxi Province (No. 201601D011049), Shanxi Patent Generalization and Implementation of Special Funding (No. 2016005), and Shanxi Provincial Key Research and Development Project (201603D111017).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
The following symbols are used in this manuscript:  
\begin{tabular}{l l} \(T\) & Oil-film temperature, \({}^{\circ}\)C \\ \(A_{1},A_{2},t_{a},p\) & constants of which the value can be fitted based on the test data, \({}^{\circ}\)C, \({}^{\circ}\)C, min, 1 \\ \(t\) & operating time, min \\ \(T_{\text{S220}}\) & temperature of oil S220, \({}^{\circ}\)C \\ \(T_{\text{M220}}\) & temperature of oil M220, \({}^{\circ}\)C \\ \(T_{\text{test}}\) & test temperature, \({}^{\circ}\)C \\ \(T_{\text{fitted}}\) & fitted temperature, \({}^{\circ}\)C \\ \(\xi\) & relative error, 100\% \\ S220 & New type oil-film bearing oil of brand type S with viscosity 220 \\ M220 & existent oil-film bearing oil of brand type M with the same viscosity, which has \\  & been widely applied in engineering \\ \end{tabular}
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 5 Sensitivity of Pile Head Displacement under Scour
### Wall Thickness  
The influence of wall thickness on normalized pile head horizontal displacement under scour is shown in Figure 18. When the wall thickness increases from 100 mm to 150 mm, the displacement of pile head only decreases by 2.4 mm (\(0.6\times 10^{-3}\)), while the scour sensitivity increases from 2.35 to 2.42. That is to say, increasing the wall thickness has little effect on reducing the horizontal displacement and results in increased susceptibility to scour. So, increasing the wall thickness of monopiles to restrict the pile displacement is unreasonable when taking scour into consideration.  
Figure 16: Pile displacement and scour sensitivity variation with pile diameter.  
Figure 17: Pile displacement and scour sensitivity variation with embedment depth.  
Figure 18: Variation of pile head displacement and scour sensitivity with pile wall thickness.
## 2 Preliminary  
### PCA  
Let \(n_{0}\), \(m\) be the numbers of samples and variables of a normal data matrix \(X\in R^{n_{0}\times m}\), with \(X=[x_{1},\cdots,x_{k},\cdots,x_{m}]\). In general, each column of the original normal data \(X\) is standardized to eliminate the impact of different ranges of variables.  
\[\overline{x}_{k}=\frac{x_{k}-E(x_{k})}{\sigma(x_{k})},k\in[1,m] \tag{1}\]  
\(E(x_{k})\) and \(\sigma(x_{k})\) in Equation (1) are the mean and standard deviation of the kth column in the matrix \(X\), respectively. The covariance matrix \(\Sigma\in R^{m\times m}\) of the standardized normal data matrix \(\overline{X}\) is then extracted and the singular value decomposition (SVD) is performed.  
\[\Sigma=\frac{\overline{X}^{T}\overline{X}}{n_{0}-1}=V\Lambda V^{T}=\left[ \begin{array}{cc}P&\stackrel{{\sim}}{{P}}\end{array}\right] \left[\begin{array}{cc}\Lambda_{pc}&0\\ 0&\Lambda_{res}\end{array}\right]\left[\begin{array}{cc}P&\stackrel{{ \sim}}{{P}}\end{array}\right]^{T} \tag{2}\]  
The matrix \(V=[v_{1},\cdots,v_{m}]\in R^{m\times m}\) in Equation (2) is a set of standard orthogonal bases in \(R^{m}\) space. The space \(P=[v_{1},\cdots,v_{l}]\in R^{m\times l}\) consists of the first \(l\) column vectors in the matrix \(V\), is the principal component subspace, and the space \(\stackrel{{\sim}}{{P}}=[v_{l+1},\cdots,v_{m}]\in R^{m\times(m-l)}\) which consists of the last \(m-l\) column vectors is the residual subspace [26].  
The PCA based fault detection method usually uses the two statistics \(T^{2}\) and SPE, for fault detection. The calculations of the \(T^{2}\) statistic and the SPE statistic are shown in Equations (3) and (4), respectively. A fault is considered to have occurred when either the \(T^{2}\) or the SPE statistic has exceeded its corresponding detection threshold of \(\delta_{T}^{2}\) or \(\delta_{SPE}^{2}\), respectively [27].  
\[T^{2}=\left\|\Lambda_{pc}^{-\frac{1}{2}}P^{T}\overline{X}^{T}\right\|^{2}\leq \delta_{T}^{2} \tag{3}\]  
\[SPE=\overline{X}\left(I_{m\times m}-PP^{T}\right)\overline{X}^{T}\leq\delta_ {SPE}^{2} \tag{4}\]
## 4 Discussion
### Limitation  
While this study has provided valuable insight, there are some limitations that should be considered. One of the limitations is the small sample size, which consisted of only 10 participants and did not include any female participants. This suggests caution in generalizing the findings to larger populations. Future studies should consider larger sample sizes and more diverse samples, including both male and female participants to ensure greater representation. Additionally, the study was conducted in a controlled environment, which may not be representative of all outdoor environmental conditions. Thus, the generalizability of the findings to uncontrolled environments should be considered with caution and future studies should consider longer periods of time and different temperature conditions to improve external validity. Finally, the study only considered young male participants, which may not be representative of diverse populations. Therefore, future studies should include participants of different ages, gender, and socio-demographics to gain a more comprehensive understanding of the effectiveness of the wearable sensors.  
The duration of the repetitive task performed by the participants in this study was relatively short compared to the typical 8 h workday experienced by industry workers. Additionally, the workout outfits worn by the participants were chosen to maintain consistency and minimize confounding factors that could affect heat dissipation and measurement accuracy. However, these outfits may not accurately represent the personal protective equipment (PPE) worn by industry workers during their job tasks. Therefore, future studies should explore longer durations of repetitive tasks to better simulate the working conditions of industry workers. Furthermore, incorporating the use of appropriate PPE in future studies will provide a more realistic assessment of wearable core temperature measuring devices' performance under real-world working conditions.  
Future studies should aim to include a more comprehensive assessment of individual differences with larger and more diverse participants. This could involve investigating factors such as hydration status, fitness level, acclimatization to heat, resting metabolism rate, and other relevant variables that may contribute to variations in SS and Zephyr CT measurements. By incorporating a wider range of factors, a more comprehensive understanding of the relationship between these variables and CT measurements can be achieved.
## 3 The Proposed Fault Diagnosis Methodology
### Alarm Data Preprocessing  
#### 3.1.1 Segmenting Alarm Sequences  
As mentioned above, one fault event of a wind turbine will trigger several alarms. These alarms are recorded continuously without distinguishing which fault event they belong to. First, the alarms that belong to the same fault events need to be selected.  
In an alarm system, information alarms are generally to communicate changes in certain operating conditions. I2 is an information alarm, which indicates the changes in the wind turbine's operational condition. As shown in Figure 1, when a wind turbine is started from a shutdown, the alarm I2 with the flag of start is sent out. When a wind turbine is shut down from running, the alarm I2 with the flag of the end is sent out. Therefore, the alarms related to a fault event are generated between the start of I2 and the end of I2. Accordingly, the continuous alarms are segmented into alarm sequences using I2. The alarms in one obtained alarm sequence belong to the same fault events.
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 2 Materials and Methods
### The Model of Short-Term Traffic State Prediction
#### 2.3.4 Attention Mechanism Module  
To make use of the spatiotemporal characteristics of the historic traffic state at urban intersection, the short-term prediction of traffic state is carried out through the redistribution of weight by attention mechanism. First, the attention coefficient of each time series needs to be calculated for the predicted target. Then these coefficients are used to evaluate the prediction results. Finally, when predicting the traffic state, the state values of vertexes with stronger correlation are calculated by the soft-attention mechanism.  
The structure of the attention mechanism is shown in Figure 8, where the output of the last layer of GRU model is the input of the soft-attention mechanism. For different time series, the weight of each time series feature is calculated by normalization of the SoftMax function, which can be expressed as Equations (13) and (14):  
\[e_{i}=w\times x_{i}+b_{i} \tag{13}\]  
\[\alpha_{i}=softmax(e_{i})=\frac{\exp(e_{i})}{\sum_{k=1}^{n}\exp(e_{i})} \tag{14}\]  
where \(w\) is weight, \(b\) is bias.  
Figure 7: The structure of GRU unit.  
Finally, the traffic state prediction result of the whole network is calculated, as shown in Equation (15):  
\[Y_{t}=\sum_{i=1}^{n}\alpha_{i}*x_{i} \tag{15}\]
## 3 Model-Driven Approach to QoS Estimation Using Neo4j Graph Database Aided by ChatGPT
### Neo4j and Graph Data Science Library  
Neo4j [19] is a database management system based on a graph data model. The underlying data representation model consists of the following main concepts: (1) node--an entity that corresponds to an object or event within some domain of interest considered, (2) link--a relationship that connects two node elements, and (3) property--a pair in the form of (key, value) describing the characteristics of interest of a node or link. Additionally, in order to perform data management or retrieval based on such representation in Neo4j, Cypher query language [20] is used for this purpose. Additionally, an add-on called the Graph Data Science Library [21] was also introduced as an optional plug-in in Neo4j, integrating a low-code interface to a set of machine learning-oriented functionalities against the data graph. The main focus of the available algorithms forming GDS is on two tasks: classification and relationship discovery.  
Despite the limited set of machine learning techniques that are supported by Neo4j, their implementation has been shown to be faster in training and more effective (higher prediction accuracy) compared to deep learning and other more complex neural network-based approaches in the case of simpler numeric data-based predictions, without involving audiovisual signals [22]. Considering that many cases rely on numeric data only, while the execution speed is crucial for real-time scenarios, Neo4j's GDS library represents a reasonable choice when it comes to the adoption of AI in the area of wireless networks.  
In order to train a classification model in GDS, we make use of the gds.alpha.ml.node-Classification.train [23] procedure for this purpose.  
Table 3 provides an overview of the queries used within the steps of the classification model creation. In this study, these queries were generated automatically using ChatGPT,  
starting from the model-based representation of a problem. The examples of queries are given in the case of a dataset for our QoS prediction case study.
## 3 Background and Definitions
### Background  
**The Happened Before Relation (HBR)** determines the causal precedence dependencies for a set of events. HBR has a strict partial order (i.e., it is transitive, irreflexive and antisymmetric). [PERSON] defined the HBR in the following way [26]:  
**Definition 1**.: _HBR, denote by \(\rightarrow\), is defines the smallest relationship for a set of events \(E\), established by three rules, as follows:_  
1. _Let_ \(a\) _and_ \(b\) _be events in the same process. Thus, if an event_ \(a\) _occurs before_ \(b\)_, then_ \(a\to b\)_._
2. _If_ \(a\) _stands for the event_ \(\text{send}(m)\)_, and_ \(b\) _for the event_ \(\text{delivery}(P_{i},m)\)_, then_ \(a\to b\)_._
3. _If_ \(a\to b\) _and_ \(b\to c\)_, then_ \(a\to c\)_._  
In practice, HBR is expensive, since it accounts for the precedence of every pair of events. We mitigate expensiveness by introducing a stronger relationship; identifying and attaching the minimal amount of control information per message to ensure causal ordering.  
**Immediate Dependency Relation (IDR)**. The IDR is expressed by \(\downarrow\), and it is the transitive reduction of the HBR. We define the IDR in the following way [27]:  
**Definition 2**.: _Two events, \(a,b\in E\), are related by the IDR, \(a\downarrow b\), if:_  
\[a\downarrow b\Leftrightarrow[a\to b\wedge\forall c\in E,\
eg(a \to c\to b)]\]  
Where \(E\) stands for a set of events.  
**Checkpoint and communication pattern (CCP)**: A CCP contains all information about processes local checkpoints as well as their transitive messages, i.e., incoming and outgoing messages towards other processes; the CCP has the following definition [28]:  
**Definition 3**.: _A CCP is a pair \((\widehat{E},E_{i})\)_  
where \(\widehat{E}\) is a partially-ordered set modelling a distributed computation and \(E_{i}\) is a set of local checkpoints defined on \(\widehat{E}\).  
Figure 1 illustrates a CCP and all associated checkpoints, intervals, and exchanged messages. For example, _checkpoint interval_ is illustrated as \(l_{k}^{x},\ (x\
eq 0)\), representing that a checkpoint belongs to process \(P_{k}\). A checkpointing interval represents events that happened before another checkpoint in \(P_{k}\), from \(C_{k}^{x-1}\) to \(C_{k}^{x},\ (x\
eq 0)\).  
Figure 1: An example of a CCP, where \(m_{i}\in M\ (i\in\{1,\dots,8\})\) represent messages; being \(M\) all messages, and each \(C_{k}^{x}\) represents a process local checkpoint.
## 5 Conclusions  
For many existing session recommendation models, more consideration is given to the order transition within the session, ignoring the impact of similar sessions on the recommendation performance. Some intra-session transition relationships captured by the GNN are not conducive to recommendation. In this paper, an improved session recommendation model based on GNN and attention networks, namely a session-enhanced graph neural network (SE-GNNRM), is proposed. Firstly, the model is modeled based on the session sequence, and the initial session sequence is encoded by GNN and SAN, respectively, and the obtained item vector takes into account the complex item transition and the importance of the item at the same time. Secondly, in order to enable the model to learn similar session information, a session interaction layer is designed. A global session graph is constructed, and the features between similar sessions are learned through an attention mechanism that fuses the similarity information between sessions. Extensive experiments on three real data sets demonstrate the effectiveness of the model. In the future, it is hoped that the recommendation performance of the model can be improved through more efficient graph representation learning and the introduction of more meaningful auxiliary information.  
Methodology, P.C.; software, L.Y.; validation, L.Y., P.C. and G.Z.; formal analysis, G.Z.; writing--original draft preparation, L.Y.; writing--review and editing, G.Z.; visualization, P.C.; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China grant number [61771087].  
Not applicable.  
The authors declare no conflict of interest.  
Figure 2: The effect of co-occurrence parameter \(\phi_{ij}\) in three. (**a**) The effect of co-occurrence parameter \(\phi_{ij}\) on P@20. (**b**) The effect of co-occurrence parameter \(\phi_{ij}\) on MRR@20.
## 1 Introduction
The main reflectarray has been oversized in the same dimension where the beams are going to be compressed in order to reduce the spillover, resulting in an elliptical reflectarray of dimensions \(3.5\) m \(\times\)\(1.8\) m. The interleaved beams required to provide the full coverage will be generated in the orthogonal polarization by exploiting the dual-polarization capability of the reflectarray elements. The proposed bifocal DRAA represents an improved solution with respect to the 4 m oversized SFPB reflector [5], since it requires a smaller aperture size, employs a lower number of feeds, and shows larger radiation efficiency. The replacement of the standard multi-reflector architecture by the proposed bifocal DRAA could enable a significant reduction in cost, volume and weight of the antenna farm for HTS systems operating in the Ka-band.
# Research on Phase Stabilization Algorithm of Femtosecond Timing System
## 3 Phase Correction Module Design
### FPGA Design  
For the data interface conversion module, we employ the Xilinx Artix 7 series FPGA (AMD, Santa Clara, CA, USA), specifically the model XC7A35 TFGG484-2. The design of the interface conversion module mainly includes a serial data reception module, a data packaging module, a parallel data transmission module, and circuits for clock, power, and reset. The overall block diagram of the FPGA conversion module system is shown in Figure 3, implemented using Verilog.  
Figure 2: Schematic diagram of phase correction module.  
The serial data reception module implements the UART (Universal Asynchronous Receiver-Transmitter) protocol and is responsible for receiving serial data from the host PC. The primary function of the data packaging module is to merge the received 8-bit wide data units into a 16-bit wide data frame. Finally, the parallel data transmission module is responsible for packaging the formatted 16-bit data frame and efficiently transmitting it via a set of parallel lines.  
The workflow is illustrated in Figure 4, detailing the operations of each module. First, a classic edge detection strategy is employed to monitor the transmission start signal, referred to as start_flag. When start_flag is activated, a counter is initiated, and upon reaching a preset value, the uart_done signal is set high, indicating the completion of data reception. In the data packaging module, the primary task is to detect the uart_done signal. When this signal is activated, the module stores the received data. If the uart_done signal is detected as high in two consecutive instances, the module merges these two data units to form a 16-bit data frame. The parallel data transmission module is responsible for monitoring the data_ready signal from the data packaging module. Upon detecting the activation of the data_ready signal, the module sends the data frame to the target device, completing the transmission.  
After the complete design, simulation verification of the module is required. Figure 5 presents the verification result waveform of the interface conversion module, from which the specific signal changes during the operational process can be observed.  
The module receives continuous serial input signals, with the first received data being the hexadecimal number AB, followed by another hexadecimal number CD. Through the designed algorithms and logic, the module is able to correctly package these two data units, generating a new hexadecimal number CDAB. Subsequently, the module transmits the packaged data to the designated target device. The functionality of the module has been validated through functional simulation.  
Figure 3: Schematic diagram of FPGA interface conversion module structure.
## 4 Tests Description and Data Analysis
\[\text{COP}=\frac{\dot{Q}}{\dot{P}} \tag{1}\]  
\[\dot{Q}=\frac{n}{3600}V\cdotp\cdotp C_{p}(T_{out}-T_{in}) \tag{2}\]  
with,  
\(C_{p}\) is the specific heat capacity of air (J.kg\({}^{-1}\).K\({}^{-1}\));  
\(\Delta T=(T_{out}-T_{in})\) is the EAHE inlet/outlet temperature difference (\({}^{\circ}\)C);  
\(\rho\) is the air density (kg.m\({}^{-3}\));  
\(n\) is the number of air changes per hour;  
\(V\) is the volume of each greenhouse (m\({}^{3}\)). \[\dot{P}=I\cdot U \tag{3}\]  
with,  
\(I\) is the electrical current used by the fan (A). \begin{table}
\begin{tabular}{c c c c c} \hline
**Test** & \(T_{\text{set}}\) & **Air Changes** & **Air Flow Rate in** & **EAHE Fan Electrical** & \\
**(n\({}^{\circ}\))** & **(\({}^{\circ}\)C)** & **(h\({}^{-1}\))** & **EAHE (m\({}^{3}\).h\({}^{-1}\))** & **Power (W)** & **EAHE Loop** \\ \hline A1 & - & 1 & 60 & 48 & Close \\ A2 & - & 3 & 60 & 48 & Close \\
**A3** & - & **3** & **15** & **62** & **Open** \\
**A4** & - & **12** & **60** & **48** & **Open** \\ B1 & 18 & 3 & 60 & 48 & Close \\
**B2** & **18** & **3** & **15** & **62** & **Open** \\ B3 & 20 & 3 & 60 & 48 & Close \\ B4 & 15 & 3 & 60 & 48 & Close \\
**B5** & **15** & **3** & **15** & **62** & **Open** \\
**B6** & **15** & **12** & **60** & **48** & **Open** \\ \hline \end{tabular}
\end{table} 
Table 3: Tests table, with 24 h duration for each experience, where tests A considered the heating OFF, and tests B considered the heating ON. \(U\) is the fan supply voltage (V). The uncertainty values related to \(\dot{Q}\), \(\dot{P}\), and COP were found as 2.50%, 2.64%, and 3.64%, respectively.