esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Code Auto-Completion Implementation for Firmware Developers
## 1 Introduction
### Background  
With the advent of machine learning (ML) techniques, the research paradigm in various engineering areas has been recently transitioned from theory-based to data-driven approaches [1]. There have already been many studies asserting that ML techniques outperform traditional statistical methods. A language model is no exception to this paradigm shift. Many research groups have been committed to developing a language model using ML techniques. For example, Google developed the bidirectional encoder representations from transformers (BERT) [2] that mainly use transformer encoder blocks. OpenAI released the generative pre-trained transformer (GPT) models [3, 4] such as the GPT-2 models.  
The GPT models are transformer-based language models trained on a massive text dataset from the websites. Depending on the size of neural network weight parameters, the GPT-2 models are classified into small (i.e., 117 M), medium (i.e., 345 M), and large (i.e., 762 M) pre-trained models, as shown in Figure 2. Here, 117 M means that there are 117 million parameters of the neural network model. The obvious upside of the GPT models is that the pre-trained models can be easily tailored to various domain-specific language modeling tasks, given that the models are fine-tuned with domain-specific training datasets. For this reason, the GPT models have been widely used for a variety of domain-specific tasks such as speech recognition and language translation.  
In fact, the GPT models have stunned the world by demonstrating the impressive capability that may exceed the current language models. One example is the Allen AI GPT-2 Explorer [6] as shown in Figure 3, where it uses the GPT-2 345M model to predict the most likely next word alongside their probability score. In this example, it appears that the model generates a list of candidates for the new few words (e.g., Electronics Co., Ltd.) once some initial text (e.g., I am currently working at Samsung) is provided.  
Figure 1: Notional sketch of necessity for developing a code auto-completion framework.  
Figure 2: GPT-2 models (reproduced from [5]).  
Given the aforementioned observations, it can be hypothesized if the pre-trained GPT-2 models are fine-tuned with SSD firmware source codes, the fine-tuned model predicts the most likely next code element. To that end, this research aims to develop a framework that deploys the GPT-2 models to help SSD firmware developers write code in a more efficient manner. The remainder of this paper consists of the following: Literature Review, Proposed Methodology, Results and Discussion, and Conclusion.
# A Transplantatable Frequency Selective Metasurface for High-Order Harmonic Suppression
## 4 Conclusions  
A transplantable harmonic suppression FMSSE is designed and analyzed, which can be applied to arbitrary FSSs operating at the same frequency band. And the designed HS-FMSSE was integrated  
Figure 14: Comparison of the measured transmission coefficients between the non-resonant FSS with HS-FMSSE and without HS-FMSSE for oblique incidence angles for the (**a**) TE polarization and (**b**) TM polarization (The green shadow represents suppressed harmonic transmission bands).  
Figure 12: Geometry of the fabricated multilayer patch-grid FSS integrating with HS-FMSSE, (**a**) total view, (**b**) patch layer, (**c**) grid structure layer, (**d**,**e**) harmonic suppression FMSSE layers.  
Figure 13: Measurement system setup for the FSS in the near field region.  
with two kinds of different FSSs: the sub-wavelength patch-grid FSS and the complementary resonant loop FSS. The simulation and measurement results show that the proposed HS-FSMSE can effectively remove the spurious transmission windows outside of the desired operation frequency range. Using the HS-FSMSE, the two FSSs operating at 10 GHz are free of high-order harmonic transmission bands up to 30 GHz without deteriorating the operating frequency of the pass band. The transplantable HS-FSMSE also shows good polarization and angle stabilities. The most essential cause of its flexibility lies in that the harmonic suppression FMSSE is designed independently from the original FSS. Additionally, the narrow band pass feature is taken in consideration when designing the harmonic suppression FMSSE. In other words, the design procedure of the original band pass FSS and harmonic suppression FMSSE are independent of one another. This makes the harmonic suppression FMSSE transplantable to different kinds of FSSs. What's more, the period of the harmonic suppression FMSSE is less than 0.2 wavelengths at band pass operating frequency (In this paper, the period of the harmonic suppression FMSSE is 0.11 wavelengths at band pass operating frequency). The small period size could be used for arranging most of the band pass FSS directly without cutting out both the harmonic suppression FMSSE and the original FSS.  
This work is supported by National Natural Science Foundation of China under Contract No. 51477126, and supported by Technology Explorer and Innovation Research Project, and Fundamental Research Funds for the Central Universities. All authors contributed substantially to the reported work. [PERSON] conceived the idea of transplantable high-order harmonic suppression frequency selective surfaces and performed the simulations and experiments; [PERSON] provided the main instructions of experiments; [PERSON] provided the main instructions of this study and revised the paper.  
The authors declare no conflict of interest.
# Review of Recent Automated Pothole-Detection Methods
## 3 The Three Types of Recent Automated Pothole-Detection Methods
### Vibration-Based Method (Type B)
The random forest (RF) has the best performance among the testing classifiers, with a precision of 0.885 and recall of 0.750. Pothole-detection techniques of vibration-based methods can be summarized as shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Authors** & \multicolumn{2}{c}{**Detection Techniques**} & **Dataset** & **Performance** & \multicolumn{2}{c}{**Experimental Circumstance**} \\ \hline \multirow{4}{*}{B1} & [PERSON] & \multirow{4}{*}{} & Data preprocessing: Butterworth filter & Training data sample & \multirow{4}{*}{} & \multirow{4}{*}{} & \multirow{4}{*}{} & \multirow{4}{*}{} & \multirow{4}{*}{} \\  & [PERSON] & & Road-surface recognition improved & - & Bump: 118, Flat 174, Fedue: 103 & & & & \\  & & Gaussian model & - & Synthetic: 0.9603 & & & & \\  & & Road-surface classification: k-nearest & - & Testing data sample & - & Bump: 0.9412 & & \\  & & neighbor & - & Bump: 151, Flat 583, Probable 68 & & & & \\ \cline{2-7}  & & \multirow{4}{*}{} & Data preprocessing: low pass filter & \multirow{4}{*}{} & Classification accuracy & \multirow{4}{*}{} & \multirow{4}{*}{} & \\  & & Feature extraction and selection: Fourier & & - & B2:1-0.9860, B2-2-0.9525, B2-0.9600 & & & \\ \cline{2-7}  & & \multirow{4}{*}{} & Transformation, correlation & - & B2:1-0.9860, B2-2-0.9525, B2-0.9600 & & & \\ \cline{2-7}  & & & Classification & - & - & Precision & & \\ \cline{2-7}  & & - & C4S detection rate (B2-1) & - & B2:1-0.985, B2-0.951, B2-0.975 & & & \\ \cline{2-7}  & & Support vector machine (B2-3) & & - & Recall & & & \\ \cline{2-7}  & & - & Naive Bayes (B2-3) & & - & B2:1-0.985, B2-0.953, B2-0.9609 & & \\ \hline \multirow{4}{*}{B3} & \multirow{4}{*}{} & Data preprocessing: sliding window & \multirow{4}{*}{} & Accuracy & \multirow{4}{*}{} & \multirow{4}{*}{} & \multirow{4}{*}{} \\  & & Feature extraction and selection: Fourier & - & - & B3:1-0.952, B3-2-0.948, B3-0.957 & & & \\ \cline{1-1}  & & transform, correlation & - & Normal windows: 3061 & & & \\ \cline{1-1}  & & Training and testing & - & Probable windows: 424 & & & \\ \cline{1-1}  & & - & Improved version (B3-1) & - & Transverse windows: 13 & & \\ \cline{1-1}  & & - & Random forest (B3-3) & & - & B2:1-0.748, B3-2.642, B3-0.750 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection techniques and characteristics of vibration-based probe-detection method.
## 3 Materials and Methods  
### Rainfall Datasets  
#### 3.1.1 GPM Data  
The Global Precipitation Measurement (GPM) mission is a newly launched international satellite network providing global rainfall and snow observations. Compared with previous satellite precipitation products, GPM has higher accuracy and spatiotemporal resolution. On the basis of TRMM's success, it deployed a \"core\" satellite with an advanced radar/radiometer system to measure spatial precipitation and serves as a reference standard for unified research and operational satellite constellation precipitation measurements to improve our understanding of the Earth's water and energy cycle and our ability to predict extreme events for the benefit of society.  
The GPM satellite carries two main sensors: the GPM Microwave Imager (GMI) and a Dual-Frequency Precipitation Radar (DPR) [32]. The input precipitation estimates are calculated by various passive microwave and infrared satellite sensors. According to the demand, it has three different modes: an early, late and final run. Among them, the final-run dataset introduces monthly gauge analysis for deviation correction, which is generally considered to be more accurate than early and late run, and is widely used in hydrological and climate research [19].  
IMERG data are one type of GPM data. The latest version of the IMERG final-run product V06 is used, which was launched in April 2014. This product has a spatial resolution of \(0.1^{\circ}\times 0.1^{\circ}\) and a coverage of \(60^{\circ}\) N-\(60^{\circ}\) N. (Source: [[https://gpm.nasa.gov/data/directory](https://gpm.nasa.gov/data/directory)]([https://gpm.nasa.gov/data/directory](https://gpm.nasa.gov/data/directory))).  
Figure 2: Spatial distribution of annual and seasonal total precipitation (mm) by the China Gauge-based Daily Precipitation Analysis (CGDPA) (**a**,**d**,**g**,**j**,**m**), the Global Precipitation Measurement (GPM) (**b**,**e**,**h**,**k**,**n**), and the Tropical Rainfall Measuring Mission (TRMM) (**c**,**f**,**i**,**l**,**o**) in the Tianshan Mountains (TSM) of China from 1 April 2014 to 31 March 2015. Year (**a**–**c**), spring (**d**–**f**), summer (**g**–**i**), autumn (**j**–**l**), winter (**m**–**o**).
# Nanoformulation of Peptides for Pharmaceutical Applications: In Vitro and In Vivo Perspectives
## 3 Nanoformulation of Peptides
### Silk Fibroin Nanoparticles  
Silk fibroin nanoparticles (SF-NPs) have been identified to possess excellent biocompatibility and degradability as well as conjugating abilities with other active molecules. The enhanced binding capacity toward distinct drugs, controlled release capability, and mild preparation approach make SF-NPs effective drug delivery carriers [102]. Recently, [PERSON] et al. (2021) successfully developed biomimetic SF-NPs against breast cancer by coating SF-NPs with polydopamine (PDA), paclitaxel (PTX), peptides (iRGD), and transformed them into iRGD-PDA-PTX-SF-NPs. This new biomimetic peptide-based nanoformulation acted as an effective drug nanocarrier to target cancer cells overexpressing integrin (selective targeting of tumor). The SF-NPs showed improved intra-tumoral penetration and accumulation. The better drug entrapment also helped to reduce the dose frequency and drug intake time into the human body for complete recovery, thus, lessening the burden to the patient. Further, [PERSON] et al. (2022) formulated SF-NPs, where doxorubicin and atovaquone were encapsulated with Arg-Gly-Asp-SF-polymactic acid (RSA) to improve the chemotherapy treatment. The treated mice (injected with breast 4T1 cancer cells) showed higher inhibition rates, compared to phosphate buffer saline (PBS) and RSA alone, with minimal changes to the weights exhibited in the treated mice. This SF-based targeted drug carrier can alleviate the hypoxia microenvironment by suppressing mitochondrial respiration, which means it may suppress tumor development [103].
# Smart Grid Protection, Automation and Control: Challenges and Opportunities
## 3 Evolution of Substation Architectures and Digital Modernizations for Smart Substations
### Virtualization of Protection, Automation and Control (VPAC)
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Consideration Area** & **Description** & **Additional Details** \\ \hline Data Integrity and Isolation & Protection: Ensure that data in a virtualized environment are properly isolated and protected against unauthorized access through network segmentation and data encryption in transit and at rest. & Network Segmentation: Divide the network into isolated segments to enhance security. \\ \hline  & Type-1 Hypervisors: Run directly on the hardware, providing more predictable performance. & Type-1: Lower overhead, higher performance. Ideal for production environments. \\ Hypervisor Types and Solutions (Hardware-Based Virtualization) & Kernel-Based Virtualization (KVM): Enables live migration and resource scheduling, though it may not be ideal for critical traffic due to switching times. & Type-2: Easier to implement but less efficient for critical applications. \\  & OS-Level Virtualization: Offers low resource overhead and additional services but provides less isolation. & IV: Suitable for diverse workloads, but live migration requires caution. \\ \hline  & Usage: Hardware acceleration is preferred for PAC virtualization because it provides guaranteed service for SMV and GOOSE traffic. Type-1 Hypervisors: Utilize hardware acceleration through the host BIOS. Type-2 Hypervisors: Dependent on the host operating system for acceleration, potentially resorting to software emulation if native support is unavailable. & Advantages: Improves performance and efficiency for intensive tasks. \\ \hline  & Real-time traffic handling: Crucial for VPAC applications that rely on SMV and GOOSE data. Technologies: NIC Virtualization—Utilizes features like guaranteed bandwidth for SMV traffic. SR-IOV—PCI Express extension for virtualizing NIC resources. Potential issues: Risk of broadcast storms and internal latencies. & NIC Virtualization: Allows reservation of specific bandwidth for critical traffic. \\ \hline Access Control Management & Establish robust access control to restrict system access to authorized personnel. & Utilize Role-Based Access Control (RBAC) with LDAP integration. Automation of access rights management can reduce unauthorized access risks. \\ \hline  & Develop and maintain comprehensive backup and disaster recovery plans for rapid system recovery. & Ensures rapid system recovery during failures. The regular testing of backup systems is necessary to validate the reliability of disaster recovery protocols. \\ \hline \hline \end{tabular}
\end{table} 
Table 11: _Cont._
## 4 VoLTE Scenario and Simulation Report
### Simulation Conclusions  
The statistical results show that in the single eNB scenario, the simultaneous communication of 10 UEs inside the specific area shows weaknesses in both QoS and QoE due to network congestion. The dual eNB scenario seems to be more stable. Latency remains low, and the frame loss is sustainable to non-existent for our simulated devices. Likewise, jitter remains acceptable to promise good voice quality. In cases where we must cover small areas and a limited number of users, the dual eNB proposal is more effective. Moreover, an optimal eNB placement and TX power amplification will provide better QoS and QoE results. Therefore, according to the simulation results, our low-cost, open-source SDR-based system proves to be a promising solution for reliable and practical implementations, such as emergency scenarios. Moreover, it is scalable, fulfilling the communication network needs within specific deployment areas.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline MOS/UE & UE[0] & UE[1] & UE[2] & UE[3] & UE[4] & UE[5] & UE[6] & UE[7] & UE[8] & UE[9] \\ \hline
**Mean** & 3.33 & 1.96 & 1.21 & 3.13 & 1.83 & 3.02 & 3.38 & 3.60 & 3.85 & 1.27 \\
**Standard Deviation** & 1.32 & 1.31 & 0.79 & 1.32 & 1.35 & 1.39 & 1.27 & 1.13 & 1.02 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Single eNB MOS values.  
Figure 9: UEs’ simulated jitter delay (dual eNB).
## 4 Experiments
### Subjective Evaluation  
Subjective evaluations of the sound quality and similarity for the proposed wav2 wav and the baseline MaskCycleGAN were conducted using the MOS and XAB tests, respectively. A total of 80 utterances (4 utterances \(\times\) 8 conversion directions \(\times\) 2 methods + 16 original utterances) were used for the sound quality test, and 96 utterances (8 utterances \(\times\) 4 conversion directions \(\times\) 2 methods + 32 original utterances) were used for the similarity test. A total of 10 listeners participated in the experiment. For the sound quality test, the participants were asked to rate each utterance on a scale of 1 to 5, with 1 being \"very bad\" and 5 being \"very good\". For the similarity test, the participants were asked to indicate which of the two converted speeches from the two methods sounded more similar to the target speaker's utterance. Each time, a target speaker's utterance was played first, then the two converted speeches were played in random order.  
Figure 6 summarizes the sound quality test results. It shows that the proposed method significantly improved the sound quality of the converted voice compared with the baseline method, regardless of the conversion directions and conversion pairs. The proposed method achieved an average MOS of 3.82, which was significantly higher than the baseline score of 2.76. Figure 7 shows how similar the converted voice was to the target speaker's voice. The numbers in the graph indicate the proportion chosen as more similar to the target speaker's voice. On average, 92% of the utterances converted by the proposed method were selected as being more similar to the target speakers' voices. Some sample speech waveforms produced by wav2 wav are available online at [https://wav2 wav.github.io/wav2 wav](https://wav2 wav.github.io/wav2 wav) (accessed on 20 April 2024).  
Figure 5: Mel-filterbank coefficients (top) and the learned weights of the feature-extracting CNN (the rest). The larger weight values are displayed in a darker color.  
Figure 6: Sound quality test result. MOS and 95% confidence intervals of the original speech and the converted speeches by the baseline MaskCycleGAN and the proposed wav2 wav.  
Figure 7: The results of the similarity test (XAB) between the original and two types of converted voices using the baseline MaskCycleGAN and the proposed wav2 wav.
## 3 Results and Discussion
### Factors Affecting Nitrogen Removal by Strain F19
#### 3.2.3 Effects of Carbon Sources  
The effects of different carbon sources on cell growth and nitrogen removal efficiency were assessed. As shown in Figure 3, cell growth of strain F19 was obviously restrained with ethanol or trisodium citrate as the sole carbon source (Figure 3A), and the corresponding removal efficiencies were significantly decreased to 39.90% and 22.27%, respectively (Figure 3B). When bicarbonate was used as a carbon source, the nitrogen removal rate was only 48.03%. With sodium acetate or glucose as the sole carbon source, the concentration of ammonium decreased from 43.35 to 2.25 mg/L, and from 44.33 to 9.35 mg/L, respectively, with corresponding nitrogen removal efficiencies of 94.81% and 78.91%, respectively (Figure 3B). The highest ammonia nitrogen removal rate was obtained with sodium acetate as the carbon source, and this was used in subsequent experiments.
## 3 Underwater Routing Protocols
### EGRCs  
An energy-efficient grid routing based on 3D cubes (EGRCs) addresses the inherent challenging properties of the underwater medium [29]. The entire network is considered as a cube that is further divided into smaller cubes called clusters. A cluster head is selected based on its residual energy and location information. Every cluster head further selects a relay node based on residual energy, end-to-end delay and location. The protocol improves end-to-end delay, energy consumption and throughput. However, the death of a cluster head severely degrades system performance.
# False Alarm Reduction Method for Weakness Static Analysis Using BERT Model
## 2 Related Work and Background  
### Software Weakness  
Software weaknesses in open sources reported over the past decade have topped the lists with C/C++ accounting for 50% of the seven languages. C/C++ is a powerful programming language used in many practical applications, and software developers often use it to build operating systems, embedded systems, system components, and games [14]. Typical C/C++ projects include Git [15], OpenSSH [16], PuTTY [17], and FileZilla [18]. The C/C++ language has been in use for a longer time than other languages and contains the most code. The higher the amount of code, the more weaknesses occur, and the reasons for the weaknesses in the C language are its more complex syntax, lack of exception processing, and difficulty managing memory compared to some modern languages. In addition, the use of pointers and global variables in C++ language observation has increased, resulting in easy memory corruption and less flexibility in logic processing. Common software weaknesses in the C and C++ programming languages include buffer overflow, string vulnerability, pointer substitutes, dynamic memory management, and race conditions. Projects related to CWE [19], CAPEC [20], and CVE [21] have emerged to define or evaluate the weaknesses of this software. In addition, the CWE Top 25 [22] and OWASP Top 10 [23], which are the results of the evaluation of the risk and occurrence of software weaknesses, are disclosed using the self-evaluation prevention theory of each project. The CWE Top 25 is a list of 25 software weaknesses that can lead to serious weaknesses in software. The OWASP Top 10 is the top 10 major software weaknesses that occur in the web environment.  
Command Weakness Enumeration (CWE) is a system for classifying weaknesses in software and hardware [19]. This is maintained by a community project aimed at understanding software flaws and creating automation tools to identify, correct, and prevent flaws. The CWE classification is hierarchically organized based on universality and specificity. Currently (in December 2022, version 4.9), there are 933 weaknesses in the inventory such as Stack-based Buffer Overflow (CWE-121), Improper Limitation to a Restricted Directory ('Path Traversal') (CWE-22), and Use of Hard-coded Credential (CWE-798), etc.  
In this study, software weaknesses are analyzed for the C/C++ programming language, which has a complex syntax or semantics and the largest number of weaknesses among programming languages. In addition, to standardize the weaknesses that each tool can analyze, it is systematized by referring to the CWE system.