esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
Furthermore, each event has a _code_ denoting the kind of event and an associated value, and the API enables the tracer program to customize the type and number of traced events based on both the event code and value. There is a direct relationship between the number of traced events and the \begin{table}
\begin{tabular}{l l l} \hline \hline
**Event Type** & **Source-Code-Level Events (_Event Code_)** & **\#** \\ \hline Variable: & _E_Assign_,_E_Deref_ & 2 \\ \hline Expression: & _E_Exall_, _E_Fresum_, _E_Fail_, _E_Frem_, _E_E_Susp_ & 5 \\ \hline List/Array: & _E_List_, _E_Lelem_, _E_Lsub_, _E_Lpush_, _E_Lput_, _E_Lpop_, _E_Lpull_ & 7 \\ \hline Dictionary: & _E_Table_, _E_Telem_, _E_Tusubs_, _E_Totbl_, & \\  & _E_Tsub_, _E_Tkey_, _E_Tinsert_, _E_Tdelete_, _E_Tmember_ & 9 \\ \hline Set: & _E_Set_, _E_Slem_, _E_Slots_, _E_Cset_, _E_Sinsert_, _E_Sdelete_, _E_Smember_, _E_Sval_ & 8 \\ \hline Sting: & _E_String_, _E_Snew_, _E_Sresum_, _E_Sfail_, _E_Sususp_, _E_Spos_, _E_Srem_ & 7 \\ \hline Reg. Exp.: & _E_PatAttempt_, _E_PatMatch_, _E_PatFail_, _E_PlemAttempt_, _E_PlemMatch_, & \\  & _E_PlemFail_, _E_PatCode_, _E_PatArg_, _E_PatVal_, _E_PatPush_, _E_PatPop_ & 11 \\ \hline Struct/Object: & _E_Record_, _E_Rcreate_, _E_Rsub_, _E_Rref_ & 4 \\ \hline Procedure: & _E_Pcall_, _E_Presum_, _E_Pfail_, _E_Pret_, _E_Pususp_ & 5 \\ \hline Function: & _E_Fcall_, _E_Fresum_, _E_Fail_, _E_Fret_, _E_Fususp_ & 5 \\ \hline Line \& Syntax: & 2 \\ \hline Miscellaneous: & _E_File_, _E_Lright_, _E_Real_, _E_Refresh_, _E_Coexpr_ & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The mnemonic codes for the kinds of source-code-level runtime events that are used to capture the code structure and execution behavior of test cases. A total of 70 different kinds of events are used. length of the resulting execution trace. The test engineer can adjust this length by changing the number of monitored events. For example, events such as _E_Line_ can provide insights about the executed line numbers and source-code coverage statements. Such events can be filtered based on a specific source-code file or user-defined procedure. Events reported from user-defined procedures and built-in runtime functions can assist in various control-flow coverage criteria [20]. Whereas, events depicting variable references such as _E_Assign_ and _E_Deref_ can be used to assist in the data-flow coverage criteria, in which the _E_Assign_ can be used to detect variables' _defs_ and _E_Deref_ can detect variables' _uses_ or reads [21]. This monitoring framework facilitates the source-code instrumentation with no intrusion of the program under test.
## 4 Discussion
### Limitations  
In the reactive agility tests, high-speed cameras from consumer-grade devices were used (iPhone/iPad). Even if this equipment can record a video at 240 frames per second, measurement errors should be taken into consideration and should be improved in future tests.  
The types of terrain in trail running are very variable. We have tested only one. Also, the downhill slopes can range from 0 to \(-40\%\) and sometimes even more. We tested an average gradient of \(-10\%\), with the maximum being \(-17.9\%\). Another limitation is the number of studied participants and the fact that the participants are amateur, which could incur differences in physiological, biomechanical, and perceptual-motor expertise compared to those of professionals. Additionally, it is important to remember that correlation studies do not infer cause/effect relationships. Considering this, caution should be taken when extrapolating conclusions.
# Real-Time Communication Aid System for Korean Dysarthrie Speech
## 4 Experiments
### Experimental Results
#### 4.2.1 ASR Model  
The Korean dysarthric ASR model was trained for a total of 35 epochs, with a batch size of 20, and 23,450 training steps. It was performed based on 2309 byte-pair encodings. The training took approximately 20 h, and the results presented an accuracy of 98.1% for the training data and 94.4% for the validation data.  
To evaluate the performance of the proposed model, we conduct a performance evaluation by comparing it with the state-of-the-art Korean dysarthric ASR model based on the hybrid CTC/attention transformer encoder-decoder structure in [27]. The hybrid CTC/attention learning method, which incorporates both CTC and attention losses, is the same as the proposed model. However, in this existing model, VGG for local pattern learning of input data is performed independently from the transformer, and the CTC algorithm follows the conventional approach. For evaluation, a dataset of approximately2500 sentences not used in training was employed. The evaluation metrics included the Character Error Rate (CER) and Word Error Rate (WER), which are the most commonly used metrics for assessing ASR model performance. These metrics are particularly suitable for evaluating Korean, as meaning is constructed at the syllable and word levels. Additionally, to measure the performance improvement over the baseline model for each error rate, the Error Reduction Rate (ERR) was also evaluated.  
Table 2 shows the performance evaluation results. The results show that the performance improvement is about 42% for CER and about 38% for WER compared to the existing model. Consequently, this indicates the superiority of the proposed conformer-based GTN-CTC application model for Korean dysarthric speech over the existing ASR model.
## 1 Introduction
* In positioning, an improved adaptive method that produces solutions with lower error levels has been used. * In the positioning process, feedback has been received according to the lines by making predictions for the moving nodes that enter the area of the anchor. The remaining sections of this paper are designed as follows: Section 2 presents a concise literature review concentrating on the localization of sensor nodes within a 3D environment. Section 3 offers a detailed description of the proposed method. Section 4 tests the statistical performance of the method and applies it to the relevant problem. Section 5 evaluates the obtained results, and Section 6 concludes this study and discusses future research plans.
# An Innovative and Efficient Method for Reverse Design of Wheel-Rail Profiles
## 5 Application Example
### Wheel-Rail Contact Stress and Contact Patch Distribution  
The wheel-rail contact patch shape and contact stress distribution when different wheel and rail profiles match are described by taking the lateral wheelset displacements of 0 mm, 3 mm and 6 mm as the examples. Figures 19 and 20 show the distribution of contact stress when different wheel and rail profiles match; for the comparison of maxima, see Table 1.  
Figure 17: Wheel-rail contact distribution. (**a**) LMa and CHN60; (**b**) Optimized wheel and CHN60.  
Figure 18: Contact bandwidth and contact bandwidth ratio. (**a**) Contact bandwidth; (**b**) Contact bandwidth ratio.  
As shown in Figures 19 and 20, the contact patch shapes on the rail do not demonstrate much difference when the lateral wheelset displacement is 0 mm and 3 mm but differ obviously when the lateral wheelset displacement is 6 mm. This is mainly dependent on the corresponding wheel-rail contact geometry, showing wheel-rail matching is crucial to the contact pressure. Figure 21 shows the maximum wheel-rail contact stress at different wheelset displacement.  
As shown in Table 1, when the lateral displacement is 0 mm, the contact patch shapes and areas do not change much and the maximum contact stress values are close to each other. When the lateral displacement is 3 mm, the contact patch shapes do not change much, the contact patch areas are large when CHN60 matches LMa and the degree of concentration of maximum compressive stresses is low. When the lateral displacement is 6 mm, the contact patch shapes are dumbbell-like when CHN60 matches LMa and the degree of concentration of maximum compressive stresses is high, because the wheel-rail contact points are dispersed and the contact bandwidths are wide. The contact patches are round when CHN60 matches optimized wheels and the degree of concentration of maximum compressive stresses is low, because the wheel-rail contact points are concentrated and the contact bandwidths are narrow.  
Figure 19: LMa matching CHN60. (**a**) Lateral displacement 0 mm; (**b**) Lateral displacement 3 mm; (**c**) Lateral displacement 6 mm.  
Figure 20: Designed wheel matching CHN60. (**a**) Lateral displacement 0 mm; (**b**) Lateral displacement 3 mm; (**c**) Lateral displacement 6 mm.
# Improved Sea Ice Image Segmentation Using U\({}^{2}\)-Net and Dataset Augmentation
###### Abstract
In this context, to obtain the segmentation of sea ice remote sensing images under limited sample conditions and achieve good generalization of the semantic segmentation model, this study improved two aspects of data augmentation and network structure, and used a test set to check the accuracy of the improved model. The experimental area is located at lower latitudes (where data are easier to access and process). We hope to build richer training sets to train lightweight neural networks in the future to obtain more robust deep learning models, which will later realize automated sea ice extraction and operational release of satellite-based remote sensing data. In the next step, we will realize a more detailed sea ice segmentation, which classifies sea ice into new ice, one-year ice, multiyear ice, etc. In addition to this, we will fuse more remote sensing data (e.g., synthetic aperture radar (SAR)) and use this method for the study of sea ice in the polar regions.
## 3 Analysis of Roll Modulation Signals  
After analyzing the coordinate transformations, we have to construct roll modulation signals, as shown in Figure 3, to get relative roll angle \(\alpha\), so that we can obtain the true roll angle from it. In this paper, a magnitude extraction method has been put up, wherein the theoretical derivation and SNR features are also studied.
# An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems
## 4 Experimental Study
### Experimental Results and Analysis
#### 4.4.1 MOEA/D-WST Compared with MOEA/D-WS and MOEA/D-TCH  
Since the proposed WST approach is a combination of the weighted sum and the Tchebycheff approach, we have tested the MOEA/D-WST, MOEA/D-WS and MOEA/D-TCH on DTLZ1-DTLZ4 with 4-6 objectives. These three algorithms function with the same condition without the decomposition approach. Table 2 shows the IGD metric values obtained by three algorithms, including the mean, the best and the standard values of the 20 IGD metric results. Table 3 shows the GD metric values obtained by the three algorithms, including the mean, the best and the standard values of the 20 IGD metric results, as well. We can find from these two tables that the MOEA/D-WST has better performance on both the IGD metric and the GD metric values for most of the instances. This is because the WST approach has the features of both the weighted sum approach and the Tchebycheff approach. Therefore, MOEA/D with the WST approach has some certain superiorities over MOEA/D with the weighted sum approach and MOEA/D with the Tchebycheff approach.
# 2 CA-R2: A Hybrid MAC Protocol for Machine-Type Communications
## 6 Simulation Experiments and Results
### Metrics of Performance  
In this section, we define the metrics of performance to be used in the evaluation. Note that by central station we refer to an access point, which is the common term used in Wi-Fi.  
* Network throughput (\(T_{N}\)). It is the total number of bits received at the central station (\(\#bits\)) divided by the simulation time (\(t_{s}\)). That is, \[T_{N}=\frac{\#bits}{t_{s}}.\] (8)
* Access delay (\(D_{A}\)). This is the time that elapses from the instant a packet is generated at a network node (\(t_{g}\)) to the instant the corresponding acknowledgement (returned by the central station) is received (\(t_{r}\)). \[D_{A}=t_{r}-t_{g}.\] (9)
* Queuing delay (\(D_{Q}\)). This is the time that elapses from the moment a packet is generated at a node of the network (\(t_{g}\)) until the beginning of its first transmission attempt (\(t_{a}\)) (that is, until there is no other packet ahead of it in the queue). \[D_{Q}=t_{a}-t_{g}.\] (10)
* Percentage of dropped packets (\(\%p_{d}\)). It is computed as the number of dropped packets (\(\#p_{d}\)) divided by the total number of packets transmitted in each simulation (\(\#p_{Tx}\)). That is, \[\%p_{d}=\frac{\#p_{d}}{\#p_{Tx}}.\] (11)
# EmuCD: An Emulator for Content Dissemination Protocols in Vehicular Networks
###### Abstract  
The development of protocols for mobile networks, especially for vehicular ad-hoc networks (VANETs), presents great challenges in terms of testing in real conditions. Using a production network for testing communication protocols may not be feasible, and the use of small networks does not meet the requirements for mobility and scale found in real networks. The alternative is to use simulators and emulators, but vehicular network simulators do not meet all the requirements for effective testing. Aspects closely linked to the behaviour of the network nodes (mobility, radio communication capabilities, etc.) are particularly important in mobile networks, where a delay tolerance capability is desired. This paper proposes a distributed emulator, EmuCD, where each network node is built in a container that consumes a data trace that defines the node's mobility and connectivity in a real network (but also allowing the use of data from simulated networks). The emulated nodes interact directly with the container's operating system, updating the network conditions at each step of the emulation. In this way, our emulator allows the development and testing of protocols, without any relation to the emulator, whose code is directly portable to any hardware without requiring changes or customizations. Using the facilities of our emulator, we tested InterPlanetary File System (IPFS), Sprinkler and BitTorrent content dissemination protocols with real mobility and connectivity data from a real vehicular network. The tests with a real VANET and with the emulator have shown that, under similar conditions, EmuCD performs closely to the real VANET, only lacking in the finer details that are extremely hard to emulate, such as varying loads in the hardware.  
network emulation; VANET; content dissemination; scalability +
Footnote †: journal: Future Internet
## 3 Experimental Results
Figure 8: Damping coefficients of the third mode of vibration for the different configurations. Figure 7: Damping coefficients of the second mode of vibration for the different configurations. However, for mode 1 (the most comfort-relevant mode), 100% coverage is detrimental: modal damping is reduced by 10% when using full coverage. Comparing configurations B1 and B3, higher coverage is now not detrimental, but the improvement is mild: it improves damping by only 20% despite doubling the mass. In fact, in terms of specific damping, partial coverage is always preferable to full coverage (for mode 1). Indeed, C1 is the configuration that provides the greatest specific damping, more than 2.2 times higher than C3. Finally, the continuity of the CLD patch has also been studied and can be analysed by comparing configurations B3 and B4. In all modes, a higher continuity of the constraining layer (2 segments versus 4) increases the dissipation of the viscoelastic layer and even triples the damping of the first two flexible modes. Conclusions may differ depending on the requirements. If a solution is required to locally increase the structural damping of an existing lightweight structure without compromising its weight, C1 provides the highest specific damping (per unit mass): partial coverage centred on the most significant antinode via CLD patches with thin layers of both the viscoelastic and the honeycomb layers. However, if the search for higher structural damping is pursued from the design stages, the constraining layer can be enhanced without being concerned about its weight by incorporating its stiffness into the overall stiffness of the structure. In this case, C6 is the most suitable: full coverage via CLD patches with thick viscoelastic layer and a honeycomb constraining layer with a high cross section moment of inertia. Figures 9 and 10 show the Frequency Response Function (FRF) of an accelerometer for the configurations with uniform (B) and honeycomb (C) constraining layers, respectively. Figure 11 shows the FRF of an accelerometer for the two selected cases (C1 and C6) and the reference beam without CLD. The improvement of C6 over C1 is clear in these figures.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 5 Intrusion Detection in Fog, Edge, and Cloud Computing
### Open-Successes for Fog Computing in the IoT Environment
#### 5.2.1 Latency Constraints  
Fog computing meets the standard of latency constraints from IoT applications by performing all-time sensitive actions near the end-users.
# Design Methodology and Application Dynamics of Compact Quasi-Zero Stiffness Isolators
## 3 Compact Design of QZS Isolation
### Special Spring Type
#### 3.1.3 Planar Springs  
[PERSON] et al. [45] proposed a special planar spring structure, as Figure 6 demonstrates, which can be designed for stiffness by adjusting the thickness of the face wall of the planar spring, and proposed a lateral adjustment mechanism to adjust the amount of preload to accommodate different load variations. It was shown to be effective in attenuating external excitation as low as 2.3 Hz under load conditions of 0-6 kg.
# Fatty Acid Composition and Volatile Profile of _M. longissimus thoracic_ from Commercial Lambs
## 2 Materials and Methods
### Fatty Acid Composition  
Prior to fatty acid composition analysis, all samples were freeze-dried (Cuddon Freeze Drier, Blenheim, New Zealand) and ground into a fine powder. Fatty acid concentrations were measured using trans-methylation of the fatty acids and quantification by gas chromatography (GC), as described by [PERSON] et al. [20]. Freeze-dried ground meat (300 mg), 4 mL of toluene, 0.3 mL of internal standard (C11 triglyceride in toluene) and 4 mL of 5% of sulphuric acid in methanol were mixed thoroughly by vortex and incubated at 70 \({}^{\circ}\)C for 2 h, with mixing every 30 min during the incubation time. After 25 min of equilibration at room temperature, 5 mL of saturated NaCl was added, mixed and then centrifuged at 2300 rpm for 2 min to separate solvent layers. The top layer containing the fatty acid methyl esters (FAME) was transferred into a 1.5 mL GC autosampler vial (Hewlett Packard, model 6890). The GC was a Shimadzu GC-2010 plus (Shimadzu Corporation, Kyoto, Japan) with a flame ionisation detector (FID). The column was a Restek RTX 2330 column of 105 m length, 0.25 mm i.d., and 0.20 \(\upmu\)m film thickness (Restek Corporation, Bellefonte, PA, USA). The thermal program used an initial temperature of 175 \({}^{\circ}\)C for 17 min, which was increased to 220 \({}^{\circ}\)C at a rate of 6 \({}^{\circ}\)C per min and held for 10 min. The carrier gas was hydrogen with a linear velocity of 50 cm/s. The injection volume was 1 \(\upmu\)L, with a split ratio of 80:1. The injector temperature was 260 \({}^{\circ}\)C and the detector temperature was 300 \({}^{\circ}\)C.  
The peak areas obtained from the GC were integrated using the Shimadzu Lab-solution software (version 4.20, Shimadzu, Kyoto, Japan) in a post-run analysis. Peaks were identified by comparison of their retention times with those of commercial standard mixtures (FAME mix 37 components from Supelco Inc., Bellefont, PA, USA) and quantified by using the internal standard (C11:0) and theoretical FID response factors [20]. The equations for generating the response and conversion factors to quantify individual fatty acids from FAME were obtained from American Oil Chemists' Society 6 th edition (AOCS Ce1f-96, Ce 1 h-05 and Ce 1i-07). Individual fatty acid content was expressed as mg per 100 g of raw meat (_M. longissmus thoracic_) and as a percentage of total fatty acids. For 10% of the samples, a randomly allocated duplicate sample was analysed to verify consistency. Major fatty acid groups and PUFA-SFA ratio, n-6:n-3 ratio, thrombogenic index [21], atherogenic index [21], hypocholesterolaemic/Hyperdolesterolaemic (h/H) ratio [22] and nutritional ratio [23] were calculated from the relevant individual fatty acids to enable comparison with human nutritional guidelines.
## 2 Related Works
### Application of Multimodal Deep Learning  
#### 2.2.1 Application of Multimodal Techniques in the Field of Software Engineering  
Several studies in software engineering proposed multimodal techniques and successfully achieved higher performance than the unimodal techniques. They utilized multimodal approaches in bug localization [14], code editing [15], and bug report triage [16].  
[PERSON] et al. [14] proposed a multimodal bug localization approach called NetML. NetML comprises three main components: feature extraction, graph construction, and integrator. The feature extraction component extracts multimodal input features from a bug report and a method. The graph construction component computes the similarity graph among bug reports and methods. The integrator component combines the input features and the similarity graph to generate a ranked list of methods based on relevancy scores. The experimental evaluation with 355 real bugs showed that NetML successfully localizes significantly more bugs than the four baseline approaches.  
[PERSON] et al. [15] proposed an automated code editing tool called MODIT based on a multimodal neural machine translator. MODIT uses three modalities of information: edit location, edit code context, and commit messages. Given a code fragment that needs to be edited, the surrounding method body, and a commit message, MODIT automatically generates a set of patches. The evaluation showed that MODIT achieves better accuracy than applying the existing deep learning models of CodeBERT, GraphCodeBERT, and CodeGPT for code editing.  
[PERSON] et al. [16] proposed SusTriage, a multimodal deep learning-based bug report triage system. SusTriage extracts the textual data and metadata from bug reports. Using the extracted dataset, SusTriage builds three recommendation models that classify developers into core, active, and peripheral developers. Then, SusTriage combines the three models based on ensemble learning. Finally, given a pair of bug reports and developers, SusTriage recommends suitable developers to resolve the bug report. The evaluation with Eclipse and Mozilla bug report datasets showed that SusTriage significantly outperforms state-of-the-art techniques on both accuracy and sustainability.  
In software engineering, researchers have applied multimodal deep learning to achieve state-of-the-art results. They incorporated supplementary information to enhance task performance. For instance, [PERSON] et al. [16] utilized textual data and metadata to enhance their model's comprehension of bug reports, but they did not fully exploit the comprehensive nature of bug reports beyond textual information. In contrast to those studies, we propose MulDIC, which utilizes comprehensive information of an issue. MulDIC leverages three modalities of text, code, and image, considering the characteristics of each modality.