esa-sceva/satcom-chunk-collection huggingface dataset:
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 6 Discussion
### Threats and Validity  
The evaluation of the proposed LSTM-Attention-based bug localization model was conducted using six open-source projects: Tomcat, AspectJ, Birt, Eclipse UI, JDT, and SWT. While the experimental results showed significant performance improvements compared with existing baseline methods, there are several factors that may limit the generalizability of these findings.  
One potential threat to validity is dataset dependency. The selected projects represent a diverse range of software domains, including web servers, aspect-oriented programming, data visualization, integrated development environments (IDEs), and graphical user interfaces; however, the model's effectiveness in other types of software systems, such as embedded systems, mobile applications, or large-scale enterprise software, remains unverified. To improve generalizability, future research will extend the validation process to a broader and more diverse set of open-source projects.  
Another consideration is hyperparameter selection. In this study, the LSTM-Attention model was trained using a fixed set of hyperparameters, including hidden layer size, sequence length, learning rate, and number of layers. While these parameters were chosen based on prior research and empirical evaluation, they may not be optimal for all software projects. Variations in code structure, bug report characteristics, and repository size could influence the effectiveness of specific hyperparameter configurations. Future work will explore automated hyperparameter tuning techniques, such as Bayesian optimization or reinforcement learning-based adjustments, to enhance model adaptability across different datasets.  
Additionally, the evaluation metrics used in this study (accuracy, MRR, and MAP) provide insights into the model's ranking and retrieval performance; however, these metrics do not fully capture real-world debugging efficiency, as actual developer interactions with bug localization tools were not analyzed. Conducting user studies or integrating the model into real-world debugging workflows could provide further validation of its practical applicability.  
Last, while statistical significance testing confirmed that the performance improvements observed in the proposed model were meaningful, further verification on different types of datasets and software repositories is required to establish broader applicability. Future research will aim to minimize biases introduced by dataset selection and experimental conditions by evaluating the model on additional large-scale and industrial codebases.  
Despite these limitations, the proposed LSTM-Attention model with top-K code similarity learning consistently outperformed existing methods, showing its potential for improving automated bug localization. Addressing these validity concerns in future research will further refine and enhance the applicability of the proposed approach in diverse software maintenance environments.
## 4 Emerging Lightweight Security Approaches
### Symmetric Encryption--Lightweight AES Approaches
#### 4.1.7 ISA Extension for AES and SM4  
An ISA is an instruction set architecture, where very low-level code (such as assembly code) defines operations closely related to given microprocessor architecture. In the work by [PERSON] [82], a new ISA extension was created for the open-source architecture called RISC-V. For this reason, this new AES lightweight approach is classified in Table 7 as a custom CPU for increased performance. Another reason is that it is a hardware and software platform that applies to low-power devices.  
The ISA extension proposed in [82] not only supports AES but also SM4, an encryption algorithm that is widely adopted in China. SM4 is also a 128-bit block cipher with a 128-bit key. It uses a non-linear transformation similar to the SubBytes transformation.  
Most CPU architectures, such as Intel and ARM, have instructions to support AES encryption. However, they use a type of parallel processing using single instruction, multiple data (SIMD) registers that may not be available in less powerful CPUs. Thus, the work in [82] proposes a new architecture. It has two source registers, a destination register, and instructions that incorporate several AES transformations. For instance, the instruction AES-FN-ENC performs all the following tasks as a single instruction for one byte of the 16-byte block:  
* [no  
mentation, the authors explain that some AES transformations can be done out of order, e.g., the order of the ShiftRows transformation and the SubBytes transformation can be changed without affecting the result.  
Finally, after comparing three different low-area AES implementations and their architectures, the authors proposed their eight-bit implementation, called Quark-AES. They use only one S-Box for encryption and key expansion; they use a lightweight MixColumns approach that is a serial version of MixColumns that needs only two registers, and every component is serial. They implemented Quark-AES on an ST 65 nm processor and were able to use only 1960 GEs, with a latency of 216 clock cycles to encrypt and decrypt.
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 2 Open Source Ecosystem for the IoT
### IoT Device Management
#### 2.5.2 LwM2M Technology  
The LwM2M is a lightweight protocol based on the RESTful style for managing sensor devices and M2M devices. It defines a scalable resource or data model and implements the remote management of devices by the CoAP protocol. It is standardized by the OMA device management group [47]. Open source implementations of this protocol include Betwixt [48], based on Go, for LwM2M server and client; Eclipse Wakaama [49] and AwaLWM2M [50], based on C; and Eclipse Leshan [51] based on the Java language.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Resource Discovery** & **Supported Protocol** & **Communication Mode** & **Openness** & **Security** & **Scenario** \\ \hline \multirow{2}{*}{Modbus} & \multirow{2}{*}{-} & \multirow{2}{*}{TCP/Serial/Ethernet} & Master-slave coupling, Request/response, & No open API and requiring & & Industrial automation, \\  & & & Request/response, & professional knowledge for & - & monitoring and data \\  & & & and 1-to-n & development & & acquisition \\ \multirow{2}{*}{OPC-UA} & Local or network server discovery & Protocol independent & Publish/subscribe and event-based notification & Device integration with & & \\  & & & & enterprise application, & encryption/audit/authentication & Industrial automation \\  & & & & No high-level API & & \\ \multirow{2}{*}{MQTT} & \multirow{2}{*}{-} & \multirow{2}{*}{TCP/IP, WideSocket} & \multicolumn{2}{c}{_1-to-n based on publish/subscribe mode_} & Strong openness & encryption/authorization/authentication & \begin{tabular}{c} Loosely coupled \\ integration between \\ applications \\ \end{tabular} \\ \hline \multirow{2}{*}{CoAP} & Resource discovery and storage & UDP & \
\begin{tabular}{c} Request/response, \\ publish/subscribe, \\ Multicast \\ \end{tabular} & Strong openness & encryption &
\begin{tabular}{c} Device integration \\ with WWW \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of IoT application layer protocols.
## 4 Discussion  
To quantify and compare the effect of various gap conditions on the strength of CCFST structures, the strength index (\(SI\)) was introduced to normalize the strength parameter, and it can be defined by Equation (23), as follows:  
\[SI=\frac{F_{max-gap}}{F_{max-intact}} \tag{23}\]  
where \(F_{max-gap}\) is the maximum force in the loading process of the CCFST with a gap; \(F_{max-intact}\) is the maximum force in the loading process of the intact CCFST.  
In Eurocode 4, it is stated that the design of steel-concrete composite structures needs to consider the geometric defects [64]. Therefore, it can help guide the design of engineering projects and provide theoretical basis for eliminating the specific effect of geometric defects to exploring the influence of gap location and distance on the flexural bearing capacity of CCFSTs. In this study, the \(SI\) of the CCFST is introduced, and its correlation with different loading angles and clearance distances are shown in Figure 17. When the coronal gap distance remained constant, the bearing capacity of the CCFST structure with only friction at the interface between the steel tube and concrete was the worst, with an \(SI\) between 0.789 and 0.814. In this scenario, the influence of the angle between the coronal gap and the loading direction on the flexural performance was relatively small. However, for the CCFST  
Figure 16: The load–displacement curves of the CCFST with different gap heights.  
Figure 15: Equivalent plastic strain contours of the CCFST with different gap heights.  
structure with cohesive bonding, the \(SI\) ranged from 0.784 to 0.868. The angle between the coronal gap and the loading direction had a significant effect on the flexural performance, and the optimal angle for the structural strength was 45\({}^{\circ}\). The change in the coronal gap distance had the most significant impact on the flexural bearing capacity of the CCFST, and the \(SI\) decreased quickly with the increase in the gap distance. In summary, coronal gap has a great impact on the performance of the CCFST structures. If coronal gap cannot be completely avoided, the designed bending capacity of the structure will decrease.
## 5 Conclusions and Future Work  
In the actual BVR air combat, it is necessary to have a crucial process of attack guidance and aiming of the UCAV to effectively connect the two missions of occupying a superior situation and BVR AAMs' guidance. The best launching conditions of the BVR AAM can be formed through the process of attack guidance and aiming, which can also give full play to the combat effectiveness of the BVR AAM to the greatest extent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(I_{e}(\mathbf{rad})\) & \(I_{d}(\mathbf{s})\) & \(I_{e}\) \\ \hline PID method & 0.1513 & 0 & 3.5882 \\ AFC method & 0.0927 & 0.15 & 3.7476 \\ The proposed method & 0.0208 & 106.35 & 3.4938 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of quantitative evaluation indexes of the three methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PID Method** & **AFC Method** & **The Proposed Method** \\ \hline Average calculation time in single step (ms) & 0.039 & 0.167 & 0.462 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons of quantitative evaluation indexes of the three methods.  
For the problem of autonomous attack guidance for the UCAV to intercept the highly maneuvering aerial target in BVR air combat. The characteristics of high dynamic, nonlinear, and model uncertainty of the guidance system are fully considered in this paper, and an adaptive fuzzy controller based on the MPC framework is used to design the autonomous attack guidance method. To reduce the predicted aiming error angle based on the target motion prediction information, the algorithm iteratively optimizes and updates the actual guidance control variable online.  
Several simulation experiments are used to demonstrate the feasibility and effectiveness of the proposed method, which is compared with the other two classical methods by designed several quantitative evaluation indexes. The simulation results show that the proposed method is very effective for solving the autonomous attack guidance problem, which has the advantages of adaptivity, high timeliness, and high aiming precision when intercepting the highly maneuvering aerial target.  
Future research directions will mainly include improving the target motion prediction model, considering the uncertainty of the target motion information, building more real and accurate models, and further enhancing the computational efficiency of the method.  
Conceptualization, Z.Y.; Methodology, Z.Y. and Y.Z.; Data curation, Z.S. and H.P.; Software, Z.Y.; Formal analysis, Z.S. and W.K.; Project administration, H.P. and D.Z.; Writing--original draft, Z.Y.; Writing--review and editing, Y.Z. and K.Z.; Funding acquisition, Z.S. and D.Z. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China under Grant 61603299 and Grant 61612385, and in part by the Fundamental Research Funds for the Central Universities under Grant 3102019 ZX016. The authors declare no conflict of interest.
## 2 Related Work
### CAT Tools Among Arab Translators  
Despite the increased use of CAT tools among translators and language service providers around the world, several researchers have noticed reluctance in adopting these technologies in the Arab world [8; 9; 10; 11; 12; 13].  
[PERSON] [10] argues that this could be related to the negative attitudes toward Arabic machine translation that has failed to produce fully automated, high-quality outputs in the past. As a result, this reinforced the idea that there is no role for technology in the translation process. The complications of these tools can hinder their use among Arab translators. [PERSON] [13] suggests that these tools sometimes fail to handle Arabic owing to its complex nature. Arabic has a rich morphology system in which words contain complex inflections. Unlike European languages, Arabic has a unique diacritic mark system, a text direction from right-to-left, and a different syntactic structure compared with other languages that render computer-assisted translation challenging and unreliable. In addition, earlier versions of CAT tools had several incompatibility issues with non-Roman character sets. The accuracy of Arabic OCR, segmentation, and alignment tools was disappointing and required a large amount of post-editing. These challenges were reported in several studies. In the study conducted by [PERSON] [9] to evaluate the challenges faced by Arab translators when they used CAT tools, the researcher highlighted several linguistic and technical limitations that included the inability to manage Arabic diacritics, morphological analysis, matching, and segmentation. The inability to convert files to readable forms, differences in text directions, alignment tools, and the ability to deal with tags were among the technical limitations reported in other studies [8; 12; 13].  
Over the years, researchers and companies have worked closely on enhancing the compatibility of these systems and many of these tools can now handle various languages with acceptable levels of accuracy. However, more research is still needed to assess the usability and functionality of these tools with various users and in different contexts.
## 2 Wind-Resistant Design Parameters and Modal Analysis  
### Determination of Basic Design Parameters  
The bridge is located about 40 km from the Pearl River estuary, in the western Pacific Ocean's strong wind belt, with six typhoons impacting the region in 2018, which could have a substantial impact on the bridge's construction and service states. The wind parameters required for wind resistance design are determined according to \"wind-resistant design specification for highway bridges\" (Chinese Specification' for short). The yearly-maximum 10 min averaged mean wind speed at 10 m height corresponding to 100-year return period, defined as basic wind speed at the bridge site (\(U_{10}\)), is 32.8 m/s, according to the general condition of the bridge and Chinese Specification. The exponent of mean wind profile at the bridge site is about \(\alpha_{0}\) = 0.16. The wind speed was calculated using the exponential distribution along the height of the bridge deck, thus the design reference wind speed (\(U_{\text{d}}\)) at deck level, which is 25 m above normal water level, could be calculated using Equation (1). According to the Chinese Specification, the flutter checking wind speed was calculated by Equation (2).  
\[U_{\text{s10}}=k_{\text{c}}U_{\text{10}},\ \frac{U_{\text{d}}}{U_{\text{s10}}}=k_ {\text{f}}\left(\frac{Z}{10}\right)^{\alpha_{0}} \tag{1}\]  
\[U_{\text{f}}=\gamma_{\text{f}}\gamma_{\text{r}}\gamma_{\text{a}}U_{\text{d}} \tag{2}\]  
where \(k_{\text{c}}\) is conversion coefficient of basic wind speed surface category, and is set to 1.0 here according to the Chinese Specification. \(k_{\text{f}}\) is the wind resistance risk factor, according to the Specification, the service stage is set to 1.05, construction stage is set to 0.88. \(U_{\text{f}}\) is the flutter checking wind speed. \(\gamma_{\text{f}}\) is the partial coefficient of flutter stability, when the wind  
Figure 1: Elevation of the bridge (unit, m).  
Figure 2: Typical cross section of the bridge (unit, m).  
tunnel test method is used to obtain the flutter critical wind speed, it is taken as 1.15. \(\gamma_{\text{t}}\) is the partial coefficient of spatial influence of wind speed fluctuation, according to the main span of the bridge and surface type, 1.27 is taken according to the Chinese Specification. \(\gamma_{\alpha}\) is the partial coefficient of attack angle effect, when the wind angle of attack \(\alpha\) is +3\({}^{\circ}\), 0\({}^{\circ}\), -3\({}^{\circ}\), the value is taken as 1.0. The wind speed design parameters are shown in Table 1.
# Differential Signalling in Free-Space Optical Communication Systems
## 3 Lossy Channel and Background Noise
### Background Noise  
Considering the background noise \(n_{\text{bg},i}\) (i.e., due to sun and artificial lights) [22] the optimal DTL in Label (5) can be reformulated as  
\[V_{thresh}=0.5S_{thresh}(\Gamma_{1}h_{1}\mathcal{R}_{1}G_{1}-\Gamma_{2}h_{2} \mathcal{R}_{2}G_{2})+\Big{(}n_{1}+n_{bg,1}\Big{)}-\Big{(}n_{2}+n_{bg,2}\Big{)}. \tag{13}\]  
Therefore, the variance of DTL in Label (6b) is given as  
\[Var(V_{thresh})=\sigma_{n,1}^{2}+\sigma_{n,2}^{2}+\sigma_{bg,1}^{2}+\sigma_{bg,2}^{2}-2\rho_{bg}\sqrt{\sigma_{bg,1}^{2}\sigma_{bg,2}^{2}}, \tag{14}\]  
where \(\rho_{\text{bg}}\) is the correlation coefficient between \(n_{\text{bg},1}\) and \(n_{\text{bg},2}\). Using the differential signalling scheme proposed in [22] for the case of using two close wavelengths, here we have \(\sigma_{\text{bg},1}^{2}=\sigma_{\text{bg},2}^{2}\) and \(\rho_{\text{bg}}\approx 1\). Assuming the same photodetector parameters at the receiver for the two wavelengths, we have \(\text{Var}(V_{thresh})=2\sigma_{n}^{2}\). Under these conditions, the background noise can be effectively rejected after differential detection, and we can obtain the BER formula for the corresponding FSO system with DSS similar to the case in the previous subsection, as presented in Table 1. Note that this BER expression is valid as long as the Rx is not saturated by the ambient light [22].
Improving the frequency drag span could reveal hidden significant differences in vibrational frequencies between ripening stages [29]. Recent works have applied low and high-frequencies and video magnification analysis for colorimetric characterized coffee ripening stages [29]. However, the coffee time-frequency signals present noise and abrupt changes that hinder the frequency characterization [30]. Consequently, to accurately analyze frequency signals with sudden fluctuations, we require functions that are well localized in time and frequency as wavelets [31]. This approach was implemented for almond time-frequency signals where wavelet filtering improved the displacement estimate [32]. In this work, the oscillatory behavior in coffee time-frequency signals has been characterized using traditional frequency, time analysis, and continuous wavelet transform (CWT). The substructure fruit-peduncle dynamic behavior was characterized over low (10 to 100 Hz) and high frequencies (100 to 1000 Hz). Time and frequency signals have been characterized by applying traditional fast Fourier transform (FFT) to velocity response over two coffee fruit orthogonal directions [16]. Wavelets analysis was performed to determine frequency-time magnitude scalograms [33]. The present work aims to identify a \"vibrational fingerprint\" of each _Coffea arabica var_. Castillo fruit-ripening stage identified by colorimetric techniques [34]. These findings are a crucial step toward selective harvesting frequencies that could finally improve the effectiveness of semi-mechanized tools. There are no studies exposing coffee branch fruits to high frequencies where fatigue and fracture phenomena could occur. Specific vibrations that transmit maximum oscillations in the fruit-peduncle substructure could promote the ripe fruit detachment [15]. It is crucial to highlight that a slight change in frequency promotes ripe fruit detachment instead of unripe [29]. A robust vibrational analysis might characterize each ripening stage employing a complete frequency drag. In the materials and methods section, the description of the theory of CIELab chromaticity is given to classify of the coffee-ripening stage. The wavelet theory is also shown for carrying out the vibration analysis, which is the primary goal of our study. Then, the experiment for the vibration analysis carried out in selected fruit samples is detailed in the experimental setup. Finally, the results are presented, and a discussion is done concerning the obtained vibrations scalograms. Several conclusions are highlighted, arguing the main differences in the dynamics of the tested ripening stages. Finally, prominent future research and applications are suggested.
# Online Streaming Feature Selection via Conditional Independence
## 3 Framework for Streaming Features Filtering
### Framework for Filtering Conditional Independence
#### 3.2.1 Filtering of Null-Conditional Independence  
We use the filtering of null-conditional independence to identify and remove irrelevant features from streaming features. If an incoming feature is relevant to the class attribute \(C\), the feature is added into _CFS_. If not, the feature would be discarded due to its irrelevance with \(C\).  
**Proposition 1**.: _The features filtered by null-conditional independence are irrelevant features._  
Proof.: When one assumes \((x,y)\in CFS\), and considers **Definitions 1** and **6**, the following holds:  
\[\begin{array}{ll}x\bot y|[]&\Rightarrow P(x|y,[])=P(x|[])\Rightarrow P(x|y)= P(x)\\ &\Rightarrow\frac{P(x,y)}{P(y)}=P(x)\Rightarrow P(x,y)=P(x)P(y)\Rightarrow x \bot y\end{array}\]  
Therefore, \(x\) and \(y\) are non-conditionally independent and irrelevant to each other.