esa-sceva/satcom-chunk-collection huggingface dataset:
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 4 Our Approach
### LSTM-Attention Algorithm  
A schematic representation of the proposed model is shown in Figure 5, illustrating its core components and processes. At the heart of the model is the Long Short-Term Memory (LSTM) algorithm [42], a specialized type of recurrent neural network (RNN) designed to handle both long-term and short-term dependencies effectively. Dissimilar to traditional RNNs, which struggle to retain information over longer sequences due to issues such  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Target Code** & **TOP1** & **TOP2** & **TOP3** \\ \hline MongoDBDataSetWizardPage.java & OlapQueryUtil.java (36.86) & QueryProperties.java (36.06) & MongoDBDriver.java \\  & & & (35.66) \\ \hline DataSetParametersPage.java & Regression.154987.java (89.65) & ParameterComboCellEditorjava (85.39) &
\begin{tabular}{l} IRportClasspathResolver.java \\ (84.71) \\ \end{tabular} \\ \hline BindingDialogHelper.java & ElementExportUtil.java (77.75) & ElementExporter.java & IRportClasspathResolver.java (69.68) \\ \hline CategoryWrapperjava & AbstractScriptHandler:java (65.16) & FunctionProviderBaseImpl.java & CrosstabPreparationHandler:java (56.29) \\ \hline \hline \end{tabular}
\end{table}
Table 2: An Example of Similar Codes in Birt.  
as vanishing gradients, LSTM mitigates these limitations by utilizing its unique gating mechanism to retain or discard information as needed selectively.  
To further enhance the model's capability, an attention mechanism is integrated. This mechanism addresses two critical challenges typically encountered by sequence-to-sequence (Seq2 Seq) models: (1) performance degradation when processing lengthy input sequences and (2) difficulty in focusing on the most relevant portions of the input. The attention mechanism overcomes these issues by dynamically assigning importance scores to individual words or tokens in the input sequence. These scores are calculated using a softmax function and are then used to compute a weighted sum of the encoder's hidden states, producing a context vector that highlights the most relevant information in the sequence.  
In this study, the LSTM-Attention model was employed to process the top-K code similarity results derived from the BM25 algorithm. The top-K code similarities, which represent the most relevant source code files associated with a given bug report, were fed as input into the LSTM algorithm. The output of the LSTM, capturing sequential relationships within these input data, was subsequently passed to the attention mechanism. This combined approach enabled the model to focus on the most critical elements of these data, ultimately predicting the source code file most likely to contain the bug.  
By leveraging both the ability of the LSTM to capture long-term dependencies and the attention mechanism's capacity to highlight relevant features, the proposed model effectively improves the accuracy and robustness of bug localization.
# Instantaneous Networking Service Availability for Disaster Recovery
## 3 System Architecture
### System Overview  
An LI-Cloud system that is introduced in this paper provides networking services such as messengers and voice communications. Hence, even when there are no global networking services, the launching of an LI-Cloud allows users in the cloud to communicate with each other.  
Multiple LI-Clouds will be deployed at different places in a disaster region. The correlation of launching times may exist among multiple LI-Clouds according to their deployment sequence. When multiple clouds are deployed independently and in parallel, their launching times are independent of each other. When the LI-Clouds are deployed in sequence, the launching times are correlated in a time sequence. The global connection of each LI-Cloud is also carried out during the restoration.  
Figure 1 shows an example of a resilient network based on LI-Cloud service in disaster areas. In the example, there are three areas--A, B, and C. In conventional restoration, users can communicate with other users only when both local network and global network connections are recovered. By utilizing LI-Cloud, as shown in Figure 1, a user is able to communicate with other users in the same local area before the connection to the global network is recovered. When a disaster area is recovered, the users in the area are able to communicate with the users in other areas that are connected to the global networks.
## 2 NB-PLC and RF Characterization
### NB-PLC Characterization
#### 2.1.2 NB-PLC Noise  
According to [34; 35; 36], PLC noise may be classified into three types: (1) periodic impulsive noise characterized by its cyclostationarity in both the time and frequency domain; (2) background noise where its power spectral density (PSD) decreases exponentially; and (3) the asynchronous impulsive noise known as random impulses. Since the periodic impulsive noise is the dominant noise component of NB-PLC, authors select it to be the noise model for the power line channel. In this paper, following the IEEE 1901.2 standard, the linear periodical time-varying (LPTV) system models the periodic impulsive noise [37]. Given this model, the noise samples are written as in Equation (6) as follows:  
\[n_{k}=\sum_{i=1}^{N}1_{k\in R_{i}}\sum_{\tau}h_{\tau}^{(i)}v_{k-\tau} \tag{6}\]  
where \(1_{k}\) is the indicator function, \(h_{\tau}^{(i)}\) is the impulse response of the LTI filter in the interval \(R_{i}\), and \(v_{k}\) obeys the Gaussian distribution. The generation of periodic impulsive noise process is summarized in Figure 6. First of all, the authors determine the number of L samples according to the desired sampling rate and mains period. Then, they determine the parameters of each filter corresponding to each region interval. Afterward, a generation of L-length AWGN with zero mean and unit variance is essential to pick the appropriate frequency response of each filter. Finally, the filters' outputs are concatenated in order to generate noise samples for one period.  
In Figure 7, the authors present the periodic impulsive noise wave for four periods. This type of noise is essentially characterized by three parameters: number of regions (N\({}_{\text{R}}\)), region interval (R\({}_{\text{j}}\)), and the LTI filters. All three parameters are parametrized by their corresponding noise PSDs. The authors summarize the percentage of each region's intervals in Table 2.  
Figure 5: Measurement versus simulation of NB-PLC channel.  
According to Table 2, the first region corresponds to the biggest number of OFDM symbols. This corresponds to the duration of the first filter (6.6 ms) used for noise shaping in the AC cycle being the highest compared to the two regions R\({}_{2}\) (1.38 ms) and R\({}_{3}\) (0.26 ms).
## 6 Conclusions  
1. Through a decomposition algorithm and principal component analysis, redundant noise is removed and the main features of the initial data are obtained, which reduces the computational complexity of the whole process and improves the prediction performance of the constructed model.
2. A stratified sampling method is used to divide the training set and test set, which effectively avoids the large deviation caused by random sampling and ensures the reliability and objectivity of the prediction results.
3. A back propagation neural network based on a modified particle swarm optimization algorithm is proposed. Compared with the traditional particle swarm optimization algorithm, the modified algorithm has the advantages of fewer parameters needing to be adjusted, higher efficiency, and a greater possibility of jumping out of the local optimum.
4. Combining the data decomposition algorithm with a back propagation neural network based on the improved particle swarm algorithm, a novel hybrid prediction model for calculating natural gas pipeline corrosion rates is proposed. Based on the evaluation modules of R\({}^{2}\), MAPE, RMSE, MAE, U1, U2, RE, forecasting effectiveness, Grey Relational Analysis, and interpretability research, combined with actual pipeline cases, the constructed model is verified to have good prediction performance. The constructed model can provide a basis for decision-making and technical support for pipeline system corrosion control.  
Figure 9: Partial Dependence Plots (PDP) and Individual Conditional Expectation (ICE) plots.  
**Author Contributions:** Methodology, H.L. and X.M.; Software, H.L.; Validation, X.C.; Formal analysis, X.C.; Investigation, X.M.; Resources, X.C.; Data curation, H.L.; Writing--review & editing, X.C.; Supervision, H.L. and X.M.; Project administration, X.M.; Funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Shaanxi Province key research and development plan--Intelligent pipeline patrol management cloud platform (2024 GX-YBXM-035) and the Shaanxi Province key research and development plan--Research on domestic application of natural gas energy metering and valuation platform (2024 GX-YBXM-157).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in this article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Shaanxi Provincial Natural Gas Co., Ltd. Author [PERSON] was employed by the company Shaanxi Gas Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods  
### Isolation of HFMSCs  
To acquire HFMSCs, donor hair follicles were uniformly obtained from the adult occipital scalp by the ARTAS(r) robotic system (San Jose, CA, USA). Isolation techniques were designed according to the previously established protocol with some modifications. First, the biopsy sample of hair follicles was thoroughly rinsed with phosphate-buffered saline (PBS; Sigma, Taipei, Taiwan) containing penicillin/streptomycin (P/S), then the excess adipose tissues were removed. After that, the samples were treated with trypsin/ethylenediaminetetraacetic acid (EDTA; Sigma, Taipei, Taiwan) at 37 \({}^{\circ}\)for 30 min, and then digested with dispase at 4 \({}^{\circ}\)C for over 16 h. After the digestion procedure, the hair and scalp portions were separated from the hair follicles using fine forceps, then filtered through a 70 \(\upmu\)m cell strainer, and washed extensively with PBS. Prior to the proliferation assay, the cells were cultured in stromal medium (Dulbecco's Modified Eagle Medium and Ham's F-12 medium (DMEM-F12; Gibco, Taipei, Taiwan)) and 10% fetal bovine serum (FBS; Gibco, Taipei, Taiwan) for seven days. After seven days, cells from individual wells were harvested by 0.25% trypsin in 0.01% EDTA following centrifugation at 800 rpm for 5 min. The centrifuged liquid was filtered through a 100 \(\upmu\)m cell strainer. Then, the hair and scalp were separately transferred into a sterilized 6-well plate and maintained in MSCs culture medium (100 \(\upmu\)L DMEM, 10% FBS, 1% P/S, 2.45 g/L sodium bicarbonate, and 10 ng/mL human epidermal growth factor). The cells were maintained in a culture dish for two weeks and the medium was replaced every three days.
## 4 Results
### Interaction with an Agent
#### 4.3.2 IoT Devices  
There are three commonly used interactions between an agent and an IoT device: (1) IoT devices are used as identifiers for AR when printed AR markers are not used, (2) IoT devices are manipulated by the agent, and (3) IoT devices collect and send data to a server in order to facilitate further interactions. As an example of the first and second interactions, the interaction device in the MI system proposed by [PERSON] et al. [12] is an HMD that can identify IoT devices (i.e., smart speaker and smart light bulb) using object detection. The agent can use predefined hand gestures to trigger a number of actions on the detected IoT devices, such as playing a song, increasing the volume, selecting music, changing the color of light, and managing the light bulb power. Whereas, the MI system that was proposed by [PERSON] et al. [12] enables remote control of IoT devices by the agent, the MI system proposed by [PERSON] et al. [44] involves the second interaction in a way that requires the agent to have physical contact with the IoT devices. The positions of the IoT devices are spatially registered in the MI system, and a visual notification is provided when the IoT devices are moved from the original location. This visual notification conveys information on the current location of the moved IoT devices using arrows. As an example of the third interaction, [PERSON] et al. [45] used Microsoft HoloLens to predefine the locations of AR content that remained in the same position, regardless of the field of view of the agent. Therefore, the AR marker is not used; instead, pre-positioned AR arrows are used to guide the agent to the destination as an application of indoor navigation. Indoor navigation is initiated when the agent looks for a tool that is placed in another room. When the agent reaches the location of the tool he or she is looking for and picks it up, the MI system determines whether the target tool is collected by the agent using IoT devices that are installed in the target tool and on the agent's wrist. If the MI system determines that the agent successfully obtained the tool, then the return path or the path to the next tool is presented on HoloLens.
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 4 Computer Algorithms
### Compensation of Frequency Shift (\(f_{c}\))  
As stated in Section 3.2, it is fundamental to compensate the shift in cutoff frequency. Thus, it is necessary to apply an algorithm to generate a filter with exact real \(f_{c}\). This algorithm does not affect the length \(l_{20}\), only the lengths \(l_{L}\) and \(l_{C}\). Before designing the algorithm, it is important to establish the attenuation that the filter must have at \(f_{c}\). In the theory of filter design, the attenuation at \(f_{c}\) for a first order linear filter is approximately \(3\) dB [25]. Therefore, Algorithm 4 will guarantee that the proposed low-pass filter effectively decays \(3\) dB at the desired \(f_{c}\), no matter the type of filter or its order \(n\). All filters designed with MicroStripOptim will give values considering frequency compensation.  
The operation of Algorithm 4 is as follows. The desired cutoff frequency is \(F_{c}\), and initially, \(f_{c}=F_{c}\) is assigned. The function computeLengths(\(params,W_{0}\)) calculates the set of lengths \(l_{C}\) and \(l_{L}\) according to the steps described in Section 2.1. Using these lengths, the function scattering(\(params,Z_{0},l_{L},l_{C},F_{c}\)) generates the \(S_{21}\) parameter data precisely at the frequency \(F_{c}\), following the steps described in Section 2.3. The variable \(e_{f_{c}}\) represents the difference between the attenuation \(S_{12F_{c}}\) and \(-3\) dB. While the error \(e_{f_{c}}\) remains greater than \(0.001\) dB, \(f_{c}\) is adjusted as \(f_{c}=f_{c}+Ke_{f_{c}}\), where \(K\) is a constant. The lengths \(l_{C}\) and \(l_{L}\) are recalculated using the \"compensated\" frequency \(f_{c}\), to obtain a new value of \(S_{12F_{c}}\). In summary, the filter is iteratively redesigned (recalculating the values of \(l_{C}\) and \(l_{L}\)) until the \(S_{12}\) response decays to \(-3\) dB exactly at \(F_{c}\).  
```
0:\(W_{0},W_{L},W_{C},f_{c},n,h,e_{r}\), filter type
0:lengths of microstrip lines
0:\(params\equiv f_{c},W_{L},W_{C},n,h,e_{r}\), \(f_{c}\gets F_{c}\) \(l_{L},l_{C}\leftarrow\text{computeLengths}(params,W_{0})\) \(S_{12F_{c}}\leftarrow\text{scattering}(params,Z_{0},l_{L},l_{C},F_{c})\) \(e_{f_{c}}\leftarrow-3-S_{12f_{c}}\) while\(|\)\(e_{f_{c}}\)\(|>0.01\)do \(f_{c}\gets f_{c}+K*e_{f_{c}}\) \(l_{L},l_{C}\leftarrow\text{computeLengths}(params,W_{0})\) \(S_{12F_{c}}\leftarrow\text{scattering}(params,Z_{0},l_{L},l_{C},F_{c})\) \(e_{f_{c}}\leftarrow-3-S_{12f_{c}}\) endwhile
```  
**Algorithm 4** Algorithm for \(f_{c}\) shift compensation
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 4 Security Solutions
### Machine Learning and Deep Learning Solutions for VANETs  
Among the available solutions for VANETs, machine learning is well established and is proven to be very beneficial for prediction and analysis of the attacks. The machine learning and deep learning approaches are mostly used in preventing Denial of Service (DoS) and its variants. Various machine learning algorithms such as: Random Forest and Naive-bayes [108; 109; 110], Support Vector Machine (SVM) [111; 112; 113; 114], Artificial neural network (ANN) [115], K-clustering [109; 116], neuro-fuzzy algorithms [117], decision tree based on features extracted [118], Markov chain integrated with ant-colony optimization [119], DeepVCM using CNN and LSTM [120; 121] are used. Besides, some adaptive algorithms are also seen for intrusion detection in VANETs. For example, Heuristic-based adaptive IDS in [122]. Knot flow classification with spline implementation is shown in [123]. All these algorithms' performance are quite significant as they achieve approximately 90% of their accuracy related to their classification and detection of attacks. However, some of the limitations are also observed in these approaches. These solutions are summarized in Table 7.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference(s)** & **Year** & **Service** & **Attack Type Handling** & **Research Gap** \\ \hline [PERSON] et al. [103] & 2002 & Authentication, Non-repudiation & Replay attack, Impersonation, Eavesdropping & Delays in route discovery \\ \hline [PERSON] et al. [104] & 2002 & Availability, Non-repudiation & DoS, Routing attack, Replay attack & More efficiency is required in PDR and computational overheads \\ \hline [PERSON] and [PERSON] [105] & 2003 & Authentication, Availability & DoS, Routing attack, Impersonation & Higher latency and overheads need to be improved. \\ \hline [PERSON] et al. [44] & 2008 & Confidentiality & Traffic Analysis Attack & Should be applicable in inter vehicle communication \\ \hline [PERSON] et al. [43] & 2012 & Confidentiality & Man-in-the- Middle & Requirement of focus on time constraint \\ \hline \hline \end{tabular}
\end{table}
Table 6: Authentication and authorization techniques for VANETs
# Investigation of the Environmentally-Friendly Refrigerant R152a for Air Conditioning Purposes
## 3 Results and Discussion
### Comparative Study with Other Refrigerants  
The last step in this work is the comparison of the R152a with other refrigerants. The R152a is compared with the natural refrigerants R600a and R290, with the harmful refrigerants R134a and R404a, as well as with the promising refrigerants R32 and R1234 yf. Figure 20 and Table 4 give the comparative results. These results are given to different ambient temperatures and for cooling production equal to 5 kW. The rotational speed is different among the examined cases in order to achieve this cooling capacity. Moreover, it has to be said that the indoor temperature has the default value of 25 \({}^{\circ}\)C.  
It is found that the R152a has the highest COP among the examined cases, which makes it a promising working fluid in the heat pumps. For all the ambient temperatures, the R152a has the highest performance, with the R600a to be the second case and the R404a to be the worst working fluid. The mean enhancement in the COP with the R152a is 1.14% compared to the R600a, while it is 4.36%, 5.51%, 9.50%, 8.65% and 20.20% compared to R134a, R290, R1234 yf, and R404a respectively. At this point, it can be said that the found results are in accordance to the literature because the studies [12; 13; 14] have found the R152a to be more efficient than other refrigerants such as R134a and R32.  
Figure 20: Comparison of different working fluids for \(Q_{\rm e}=5\) kW and \(T_{\rm ind}=25\)\({}^{\circ}\)C.  
Therefore, it can be said that the R152a has great potential as a working fluid by the energetic point of view. Taking into consideration is relatively low GWP = 138 which is lower than 150, the R152a can be a choice for the future of the cooling systems. The medium flammability problems have to be taken into consideration for safety reasons and these are the only limitations of this refrigerant. Lastly, it has to be stated that the COP values are relatively high in low ambient temperatures and this fact indicates that the examined system is a highly efficient air conditioner.  
About the future studies, the R152a refrigeration system can be examined in a dynamic model coupled with a building with real cooling loads. A more detailed model is used to be developed. Also, stochastic, adaptive, and dynamic control techniques [35, 36] can be applied for the proper optimization of the overall configuration.