esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Embedding of Semantic and Statistical Features for Effective Code Search
## 4 Experiment and Analysis
### Results Analysis
#### 4.4.1 RQ 1: Effectiveness Evaluation of Code Search Approaches
\begin{table}
\begin{tabular}{c l} \hline \hline
**Query ID** & **Query Description** \\ \hline
1 & create a folder \\
2 & queue an event to be run on the thread \\
3 & get current time and date \\
4 & how to split string into words \\
5 & read line by line \\
6 & how to deserialize XML document \\
7 & open a URL in Android’s web browser \\
8 & convert an input stream to a string \\
9 & open url in html browser \\
10 & copy paste data from clipboard \\
11 & converting String to DateTime \\
12 & how to delete all files and folders in a directory \\
13 & how to generate random int number \\
14 & how to execute a sql select \\
15 & if a folder does not exist create it \\
16 & how to get mac address \\
17 & how to save image in png format \\
18 & remove cookie \\
19 & count how many times same string appears \\
20 & how can I test if an array contains a certain value \\
21 & append string to file \\
22 & how can I convert a stack trace to a string \\
23 & how to convert a char to a string in java \\
24 & convert input stream to byte array in java \\
25 & ping a hostname on the network \\
26 & how to modify a char in string \\
27 & how to read a large text file line by line using java \\
28 & converting string to int in java \\
29 & read an object from an xml file \\
30 & get content of an input stream as a string using a specified character encoding \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The collected query descriptions in real code search attempts. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Query ID** & \multicolumn{2}{c}{**FRank**} & \multirow{2}{*}{**Query ID**} & \multicolumn{2}{c}{**FRank**} \\
**JessCS** & & & **UNIF-Based** & & **JessCS** & **UNIF-Based** \\ \hline
1 & 1 & 1 & 16 & 1 & 1 \\
2 & 1 & 3 & 17 & 4 & 3 \\
3 & 5 & 1 & 18 & 1 & 1 \\
4 & 1 & 1 & 19 & NF & NF \\
5 & 9 & NF & 20 & 1 & 7 \\
6 & 8 & 2 & 21 & 1 & 1 \\  
From the data in Table 4, we can find that JessCS performs more effective on all the indicators used in our experiments. For JessCS, the query success rate of returning TOP-10 results reaches 0.90 and 63% of the queries found the expected code snippet in the first search result. JessCS obtains 47%, 19%, 13%, and 27% improvements on SucRate@1, SucRate@5, SucRate@10, and MRR, respectively. The improvements come from the quality of _Javadoc_ comments since we set a filter on the projects selection. _Javadoc_ comments are widely used in the real projects, especially for the large-scale projects, so we can claim that Jess is effective. **Finding 1. **_JessCS performs more effective then UNIF-based approach in our experiments, with at least 13% improvements on the metrics_.
## 3 Spectrum Management
### Shared Spectrum for Mixed Use Case or Traffic  
This spectrum management scheme allows total available bandwidth to be shared by both human type and machine type communications. This scheme allows the human mobile and IoRT devices to compete for the same pool of resource blocks and depending on the scheduling metric, the available resource blocks can be assigned to either one of the IoRT or human mobile devices. This scheme does not guarantee resource blocks to IoRT devices. Hence, the scheduling algorithm will play a vital role in ensuring that IoRT devices are to a large extent fairly treated in terms of resource allocation. However, this scheme allows unused spectrum by human type communications to be used for machine type communications.  
\begin{tabular}{l} \hline \hline
**Shared spectrum management** \\ \hline
1. Initialize \(B_{T}\) \\
2. Initialize \(S_{IoRT}=\{1,2,3,\ldots\ldots\ldots N_{s}\}\) and \\ \(S_{n}=\phi\) for all selected UEs (including IoRT devices) \(n\) \\
3. While \(S_{T}\
eq\varnothing\) \\
4. For UE \(i=1\) to \(I\) \\ Compute scheduling metric for each RB \(j\) \\
5. \(m_{i}=argmax\,m_{i}(j)\) \\
6. \(S_{T}=S_{T}-i_{n}\) \\
7. \(S_{n}=S_{n}+j_{n}\) \\ end \\ \hline \hline \end{tabular}  
Where \(B_{T}\) is the total bandwidth, \(S_{T}\) is the available resource blocks for UE including IoRT devices, \(S_{n}\) is the allocated resource blocks that have been assigned to the scheduled or selected UEs, \(I\) is the number of UEs and \(m_{i}(j)\) is the scheduling metric for UE \(i\) for RB \(j\).
# Formal Safety Assessment and Improvement of DDS Protocol for Industrial Data Distribution Service
## 4 DDS Protocol Modeling
### System Initialization  
In the initialization phase, the certificate is generated by the Certificate Authority (CA), and the identity of the participant is verified according to the certificate. Additionally, the CA is responsible for generating an asymmetric key pair, public key and private key, for each certificate. After confirming the identities of both participants, the identity information of the participants is read from the digital certificate. Only through CA certification can the CA issue certificates to both participants. Afterwards, a shared key is generated. The establishment of the shared key in the protocol adopts the Elliptic Curve Diffie-Hellman (ECDH) key agreement method. The specific scheme is as follows:  
Each participant has a public-private key pair based on elliptic curves, and establishes a shared key through an insecure channel. Suppose [PERSON] wants to establish a shared secret with [PERSON], but the channel available to them is not secure. Initially, the domain parameter (\(p\), \(a\), \(b\), \(G\), \(n\), \(h\)) must be agreed upon. Additionally, each party must have a key pair suitable for elliptic curve cryptography, consisting of a private key \(d\) (\(d\)\(\in\)\([1\), \(n-1]\)) ) and a public key \(Q\), where \(Q=dG\). [PERSON]'s private key and public key are (_SKm_, _PKm_), [PERSON]'s key pair is (_SKs_, _PKs_). M computes _SKmPKs_ to generate a shared key, due to the unequal status of the communication parties in the original scheme of the DDS protocol, that is, the shared key is only generated by the communication party M, so the other party S just passively accepts.
###### Abstract  
This paper presents a rolling shutter orthogonal frequency-division multiplexing (RS-OFDM) optical camera communication higher rate longer range proposed in IEEE 802.15.7a Task Group (TG7a) using an image sensor as a receiver. OFDM is a digital multi-carrier modulation scheme deployed for broadband wireless communication to resolve the inter-symbol interference (ISI) effect caused by the multipath channel. In optical wireless communication systems, OFDM was applied widely for indoor applications: internet of things, e-health, vehicular, and localization systems. The mobility scenario is a big problem for OWC systems, which reduces the system performance due to the optical channel variation in the processing time. In addition to that, signal detection should be considered in the mobility environment to improve the signal-to-noise ratio of OWC systems. In this paper, we proposed the convolution neural network (CNN) for LED detection in the RS-OFDM system, considering the mobility effect. In addition to that, the deep neural network was applied to detect the start of OFDM frame instead of conventional technology (Van De Beek algorithm). By applying our approach, the RS-OFDM system can achieve long communication (18 m distance) with a low error rate in the 2 m/s velocity environment.  
rolling shutter OFDM; RS-OFDM; deep learning for OCC system Article
# Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation
###### Abstract  
In mobile communications systems, fast link adaptation (FLA) aims to achieve high system throughput of each user by accurately determining a channel quality indicator (CQI) for feedback, which predicts the next channel based on the current channel state information. In this paper, we propose an improved calibration method of mutual information effective signal-to noise-ratio mapping (MIESM) to determine an accurate CQI feedback value in FLA. Our proposed calibration method derives the optimal calibration factors by considering various channel environments and setting the effective interval of effective signal-to-noise ratio, which is a single value compressing the information of channel characteristics at a time. The simulation is performed in various signal-to-noise ratio (SNR) ranges to account for the actual environments, and the calibration factors are derived from the proposed calibration method. The results show that the CQI feedback value from the derived calibration factors are more accurate than the existing calibration factors. In addition, we discuss a study regarding the time-coherence-based CQI feedback bit reduction scheme. Assuming that each channel is correlated to the previous and subsequent channels, we propose a method to reduce the number of CQI feedback bits adapted to the corresponding SNR regime. Through the simulation, we compare the system throughputs of the proposed adaptive CQI feedback and the conventional CQI feedback scheme. As a result, the proposed CQI feedback has almost the same system throughput as the conventional CQI feedback scheme, but the average number of feedback bits is reduced, thereby improving the efficiency of the communication.  
fast link adaptation (FLA); channel quality indicator (CQI); calibration method; calibration factor; CQI feedback +
Footnote †: journal: Electronics Electronics
## 5 Experiment Results  
For validating the effectiveness of the proposed algorithm for harmonic identification further, the real experiment is designed to test its identification precision. The initial values of both \(\omega_{ai}(k)\) and \(\omega_{bi}(k)\) are chosen as 0.05. Its sampling frequency is also 1000 Hz. During the experiment, the Adaline-LMM parameters are kept as the same as the simulation except \(L_{w}\) which is set as 16 because there are eight harmonics, including fundamental response, existing in the system response and C\({}_{2}\) which is set as 1.071. The estimation error is displayed in Figure 14 which is used to demonstrate the estimation accuracy. It is noted that the estimation error fluctuates largely at the beginning of the identification but it is rapidly converged to a relative small range (within 0.08) after 2 s. There are two different lines, the dashed one and the red line contained in Figure 15. The dashed line is the actual system response and the red line presents the identified signal. Initially, the red line has a wide fluctuation, however, after the Adaline-LMM algorithm has been trained, the identified signal is ultimately well converged to the actual signal within 2 s. That is, the identified signal matches the actual signal very well and the harmonic identification precision of the proposed algorithm is rather successful.  
The estimation amplitude results are displayed in Figure 16. It is noted that all amplitude values are converged to determined values which match well with the FFT-computed values shown in Table 2.  
Figure 14: The estimation error of experiment result.  
Figure 15: The process of harmonic identification in experiment.  
Figure 17: The estimation phases of experiment result.
# Mathematics-Driven Analysis of Offshore Green Hydrogen Stations
## 2 Materials and Methods
### PHASE II: Modeling of Solar Electricity Generation
#### 2.5.2 Modeling of Perpendicular Solar Radiation Received on the Surface of Photovoltaic Panels
 \tag{23}\]  
In this way, the value of the direct and perpendicular solar radiation received on the panels' surface (\(I_{n\_direct}\)) is modeled using the following expression:  
\[I_{n\_direct}=I_{attenuated}\times K_{direct} \tag{24}\]  
Symmetrically, expression (Equation (25)) presents the geometric conversion factor necessary to normalize the reflected solar radiation density (Equation (20)) impactingthe panel surface (\(K_{reflected}\)). This normalization depends on \(\alpha_{solar\_elevation}\) (Equation (8)), \(\alpha_{solar\_ azimuth}\) (Equation (9)), the panel inclination angle relative to the ground (\(\beta_{inclination}\)), and the panel azimuth angle (\(\beta_{azimuth}\)) relative to the north or south, depending on the hemisphere where the installation is located:  
\[ \begin{split} K_{reflected}&=sin(-\alpha_{solar\_ elevation})\times sin(\beta_{inclination})+cos(-\alpha_{solar\_elevation})\\ &\times cos(\beta_{inclination})\times cos(\alpha_{solar\_ azimuth}-\beta_{azimuth})\end{split}  \tag{25}\]  
In this way, the value of the reflected and perpendicular solar radiation received on the panels' surface (\(I_{n\_reflected}\)) is modeled using the following expression:  
\[I_{n\_reflected}=I_{reflected}\ \times\ K_{reflected} \tag{26}\]  
Using expressions (Equations (24) and (26)), and considering that the value of \(I_{diffused}\) is equal to zero, the expression for \(I_{total}\) (Equation (22)) can be reformulated to calculate the total solar radiation density received perpendicularly on the photovoltaic panels (\(I_{n\_total}\)):  
\[I_{n\_total}=I_{n\_direct}+I_{n\_reflected} \tag{27}\]  
At this point, knowing \(I_{n\_total}\), considering the photovoltaic panels' capacity to convert solar radiation density per square meter (\(K_{panels}\)), and the available panel surface area (\(M_{panels}\)), the total calculation of the electrical power generated by the photovoltaic panels (\(PV_{w}\)) is modeled as follows:  
\[PV_{w}=I_{n\_total}\times K_{panels}\times M_{panels} \tag{28}\]
# Unidirectional Slow Light Transmission in Heterostructure Photonic Crystal Waveguide
## 3 Model Design and Simulation Results
### Backscattering-Immune Feature
Figure 7: The electric field distributions for \(E_{z}\) at 4.2926 GHz in the unidirectional slow light PCW for parameters \(r_{1}=0.078a\), \(r_{2}=0.115a\) (**a**) in the absence of the obstacles; (**b**) with an inserted metal reflector (indicated by the yellow rectangle); (**c**) with two YIG rods replaced by Copper rods (circled in the black rectangles) of the same size; (**d**) with a \(Z\)-shaped interface; (**e**) with rod defects (circled in the black rectangles and the corresponding rod radii are \(r_{3}=0.08a\) and \(r_{4}=0.11a\) respectively). The top and the bottom plots in (**a–e**) correspond to the results of the externally applied magnetic field along \(+z\) and \(-z\) direction, respectively. The point source is indicated by the black star. Blue and red colors represent negative and positive field strength values. Notably, as illustrated in the top plots of Figure 8, in all five cases, the power flow is cycling anticlockwise on the edge of the Alumina PC and clockwise on the edge of the MOPC. The opposite cycling direction of the power flow on the upper and the lower boundaries of the waveguide core may indicate the slow light properties of the unidirectional waveguide. Under the reversed externally applied magnetic field in the \(-z\) direction, the power flow is switched to the opposite direction accordingly as demonstrated in the bottom plots in Figure 8. The results in Figures 7 and 8 are obtained by using a finite-element frequency domain analysis. The point source is located in the interface between two types of PC structures and perfectly matched absorbing boundary layers are applied to the surroundings of the structure in the FEM simulation. Figure 8: The Poynting vectors distributions at 4.2926 GHz in the unidirectional slow light PCW for parameters \(r_{1}=0.078a\), \(r_{2}=0.115a\) (**a**) in the absence of the obstacles; (**b**) with an inserted metal reflector (indicated by the yellow rectangle); (**c**) with two YIG rods replaced by Copper rods (circled in the black rectangles) of the same size; (**d**) with a Z-shaped interface; (**e**) with rod defects (circled in the black rectangles and the corresponding rod radii are \(r_{3}=0.08a\) and \(r_{4}=0.11a\) respectively). The top and the bottom plots in (**a–e**) correspond to the results of the externally applied magnetic field along \(+z\) and \(-z\) direction, respectively.
# Texture Analysis is a Useful Tool to Assess the Complexity Profile of Microcirculatory Blood Flow
## 1 Introduction
TA has been used in medical imaging with the purpose of improving diagnostic capacity, given that human visual inspection constitutes a process that is expensive, time-consuming and prone to interpretation errors [21]. Thus far, TA in medical imaging has been more frequently applied to mammography [22], ultrasonography, computerized tomography (CT) [23], and magnetic resonance imaging (MRI) [24] techniques [25], as well as to dermatoscopy [26] and microscopy images [27]. In particular, TE has been used to explore immune competence of lymphoid tissue in microscopic images [28], to assess the onset organ (liver) disease and tumor (colorectal cancer) evolution in CT scans [29; 30; 31], and to assess bone regeneration in fractures [32] and osteoarthritis [33], among others. In medical imaging, TE does not necessarily decrease when images display abnormal features. In fact, since abnormal or dysmorphic features often increase the coarseness of smooth or uniform images from healthy subjects, increases in TE are often seen in pathological images, such as in the case of aged tissues and organs [34] as well as the case of neoplastic formations [30; 31]. In these situations, a richer texture and therefore, higher entropy, is associated with dysfunction. Although currently underexplored in vascular physiology, the complexity assessment of microcirculation signals, both raw and decomposed, may deepen our knowledge of the mechanisms underlying perfusion regulation. Microcirculation signals are easily decomposed into their frequency components by the wavelet transform (WT) [4]. One of the WT main outputs is a scalogram, i.e., a multi-patterned image representation of the time evolution of all components of the decomposed signal. Given their textural richness, WT scalograms seem suitable candidates for TE analysis, however, to our knowledge this analysis has not been previously attempted. Since WT scalograms are not medical images, the interpretation of TE in this context must be necessarily different from the one performed in medical diagnosis. Given the fact that a WT scalogram is a 2D projection of a 3D frequency spectrum, (i.e., the 3D spectrum seen from above), it is only logical to assume that a change in the entropy of the numerical series that constitute the 3D spectrum would also be observed in corresponding WT scalograms. Therefore, the assumption that similar trends in complexity would be observed with MSE and TA seems legitimate. Our objective was to compare the complexity of microcirculation PPG signals calculated with TE against MSE during a typical venoarteriolar reflex (VAR).
# Vibroacoustic Optimization Study for the Volute Casing of a Centrifugal Fan
## 3 Vibroacoustic Aerodynamics
### CFD Validation
#### 3.2.3. Unsteady Pressure Fluctuation Validation  
The pressure fluctuations on the solute casing induced by the unsteady flow at the impeller outlet are the significant vibration noise sources; thus, the validation of the unsteady aerodynamics is very important. In this study, to confirm the simulation accuracy, a comparison analysis between the  
Figure 6: Sketches of the fan with the measurement points.  
Figure 7: Comparison results between numerical and experimental curves.  
dynamic pressure measurement results of solute casing and the numerical calculation was conducted. Due to the similar spectrum signals of the different measurement points on the solute surface, only a few typical points were selected for analysis and confirmation. Figure 8 shows the power spectrum of the volume pressure fluctuations at the BEP at the typical selected measurement points: point 01, point 06, and point 09. The left figure shows the experimental measurement results, and the right figure shows the numerical results. It can be found that the numerical and experimental amplitude of the blade-passing frequency (BPF), which presented the predominant frequency component, were in good agreement at the three most important axial positions (\(Z/B=0.17\), 0.27, and 0.34). More details about the results discussion have been reported in the previous work ([PERSON] et al.) [13].  
Figure 8: Power spectrum of solute pressure fluctuation at three measurement points with the flow rate best efficiency point (BEP); (**a**) Point 01 (exactly at the tongue); (**b**) Point 06 (52\(\circ\) from the tongue); (**c**) Point 09 (187\(\circ\) degree from the tongue).
# Development of Intelligent Core Network for Tactile Internet and Future Smart Systems
## 5 Conclusions  
SDN, NFV, and MEC technologies are solutions to overcome the challenges associated with the realization of Tactile Internet system, especially the 1 ms round trip latency. Employing SDN at the core network of cellular system reduces the latency and efficiently improves the system performance. This is because SDNC can efficiently manage and establish an efficient and flexible routing path between any two end points. Thus, it can reduce the number of intermediate nodes that are involved in communication process. The proposed structure for the Tactile Internet system, which employs SDN at the core of the network and multi-level cloud units, poses a round trip latency of orders of ms. Thus, this structure is helpful and effective for building the Tactile Internet system.  
**Acknowledgments:** The publication was financially supported by the Ministry of Education and Science of the Russian Federation (the Agreement number 02.a03.21.0008). The authors are grateful to the Middle East University, Amman, Jordan for the financial support granted to cover the publication fee of this research article.  
**Author Contributions:** [PERSON] and [PERSON] built the network model and perform the simulation process. [PERSON] and [PERSON] analyze the existing methods and the issues related to the OpenFlow protocol. [PERSON] and [PERSON] wrote the paper and revise the whole work.  
**Conflicts of Interest:** The authors declare no conflict of interest.