esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 4 Design and Implementation
### Pre-Processing Module
#### 4.1.2 Multi-Layer Protocol Hierarchical Relationship Extraction  
A network communicating packet can be divided into seven layers according to the OSI (Open System Interconnection) model. To construct a complete packet that can be used  
Figure 3: Overall system architecture.  
for network protocol fuzzing, the syntax information of all concatenated protocols on top of the transport layer (TCP, e.g.) is needed. This means that we need to accurately identify and extract all protocol layer relationships from the transport layer to the application layer. For example, the S7 Communication packet contains three protocols above TCP: TPKT, COTP, and S7 Comm. Correctly identifying and extracting the multi-layer protocol hierarchical relationships can ensure the comprehensiveness of extracted protocol syntax information.  
The Multi-layer protocol hierarchical relationship extraction process is executed as follows: Firstly, if a dissector \"Da\" creates a dissector table \"Ta\" by calling a protocol registration function, the protocol \"Pa\" that is related to dissector \"Da\" is considered a lower-layer protocol, afterward, when another dissector \"Db\" adds entries to dissector table \"Ta\", the dissector Db's associated protocol \"Pb\" is considered an upper-layer protocol of \"Pa\". Secondly, if a dissector \"Dc\" explicitly calls an external dissector \"Dd\" by calling \"find_dissector()\" function, the called dissector \"Dd\" is the upper-level dissector of \"Dc\" and its corresponding protocol \"Pd\" is the upper-level protocol of \"Pc\". In this way, multi-layer relationships of protocols are extracted and written into the parser table. Afterward, the system will list all supported protocols for the user to select, when the user selects a special protocol, all associated protocols are processed in sequence according to the hierarchical relationship stored in the parser table.
# Seismic Performance Assessment of Wide Pile-Supported
## 2 Methods  
### Target PSW  
The target PSW is a rigid-frame structure with a steel pipe pile foundation and reinforced concrete deck built in Indonesia. Because of the mild slope of the seabed, the PSW was built far from the coast to meet the required water depth for container ships; therefore, a trestle bridge is needed to connect it to the land.  
The target PSW and ground configurations are depicted in Figure 1. The dimension of the wharf is 80 m \(\times\) 300 m, and it is connected to the trestle bridge of 85 m \(\times\) 13 m and the causeway of 60 m wide. The crown height of the wharf is +4.00 m low water spring (LWS), and the deck is supported by 15 rows of vertical steel pipe piles with diameters (\(\O\)) of 1100 and 1200 mm that rest on \(-\)13 m LWS seabed. The trestle bridge is composed of a reinforced concrete deck, and 17 rows of \(\mathcal{O}\)1000 mm steel pipe piles that sit on a mild soil slope with a slope ratio of 1:5. The trestle and wharf were combined and responded integrally against earthquakes.  
Table 1 shows the soil properties obtained from a soil investigation. The ground consists of four silty clay layers, where Clay 0 is a soft clay at the landslide ground surface, and Clay 3 is a stiff ground where the pile's bottom end rests. Each soil layer's shear wave velocity (Vs) was calculated using the correlation equation with the N-value observed from the standard penetration test (N-SPT) proposed by [PERSON] and [PERSON] [23]. The average shear wave velocity of the top 30 m (Vs30) of this site was 174.5 m/s; it falls into site class E (soft ground) according to the Indonesian seismic code SNI 1726 2019 [24]. The natural frequency of the ground was determined to be 0.94 Hz.
# Replacement of Natural Sand with Expanded Vermiculite in Fly Ash-Based Geopolymer Mortars
## 3 Results and Discussion  
### Flowability  
Figure 4 shows the flowability of different geopolymer mortar. As shown in the figure, the mixes with a 10 molar NaOH solution developed a larger flowability compared with those with a 15 molar NaOH solution. This observation agrees with previous studies on bottom ash-based geopolymer mortar [36] and FA-based geopolymer concrete [37; 38; 39], and this is due to the increase in the viscosity and leaching of the alumina and silica components of FA with increasing NaOH concentration, resulting in decreased flowability of the mixes [39]. The 10 molar NaOH mixes with 50% and 70% FA experienced 7-14% and 6-12% higher flowability than those companion mixes with a 15 molar NaOH solution, respectively. It can also be seen that, for a given vermiculite content and NaOH concentration, increasing the FA content caused increased flowability of the geopolymers. The higher flowabilities of the mixes with a higher FA content agree with previous studies on mortar [40; 41; 42; 43; 44] and concrete [45; 46; 47] and could be due to the morphology of FA, having a spherical particle shape and a smooth surface [48; 49]. For the 10 molar NaOH solution,  
Figure 3: (**a**) Compression, (**b**) ultrasound and (**c**) thermal conductivity tests on geopolymer mortar.  
70% FA mixes containing 0%, 10% and 30% vermiculite had 3%, 3% and 6% higher flowability, respectively, and for the 15 molar NaOH solution, they experienced 10%, 5% and 3% higher flowability than the companion 50% FA mixes, respectively.  
As shown in Figure 4, incorporating expanded vermiculite led to decreased flowability of the mixes for a given FA content and NaOH concentration. It is also shown that an increase in the vermiculite content led to decreased flowability of the mixes. These behaviors agree with those reported in previous studies on conventional mortar [12] and could be due to the porous structure of expanded vermiculite, which absorbed the water of the fresh mixture. At 50% FA, the mixes containing 15% and 30% vermiculite had approximately 5% and 9% lower flowability, respectively, and at 70% FA, they had approximately 7% and 10% lower flowability compared with the companion mixes without vermiculite, respectively.
# One-Dimensional Modeling of the Pressure Loss in Concrete Pumping and Experimental Verification
## 3 Concrete Pumping One-Dimensional Flow Model Construction
### Calculation of the Pressure Loss Based on [PERSON]'s Equation  
As the equivalent particle size of the coarse aggregates is larger than the thickness of the lubrication layer, the coarse aggregate particles in the lubrication layer are directly exposed, and the vortices around the coarse aggregate particles directly affect the flow of the mortar in the lubrication layer; thus, the flow pattern in the lubrication layer is turbulent. According to the turbulent friction loss coefficient calculation formula, [PERSON]'s formula is used to calculate the friction loss coefficient \(\lambda\). [PERSON]'s formula, as an empirical formula applicable to turbulent smooth zones, turbulent transition zones, and turbulent rough zones, is widely applicable, simple to calculate, and widely used in the field of fluid dynamics. According to the Reynolds number \(R_{e}\) and the relative roughness of the coarse aggregates \(K_{d}\), the resistance coefficient \(\lambda\) is  
Figure 3: 3D geometric reconstruction of coarse aggregates.  
\[\lambda=0.0055\Bigg{[}1+\Bigg{(}20,000K_{d}+10^{6}\bigg{/}R_{e}\bigg{)}^{1/3}\Bigg{]} \tag{25}\]  
Therefore, the pressure loss \(p_{f}\) in concrete pumping through cross-flow section 1 to cross-flow section 2 is  
\[p_{f}=0.0055\Bigg{[}1+\bigg{(}20,000K_{d}+\frac{10^{6}}{R_{e}}\bigg{)}^{\frac{1 }{3}}\Bigg{]}\frac{l\varrho_{s}v^{2}}{2d_{e}} \tag{26}\]  
The pressure difference \(\Delta p_{m}\) of concrete pumped through cross-flow section 1 to cross-flow section 2 is  
\[\Delta p_{m}=0.0055\Bigg{[}1+\bigg{(}20,000K_{d}+\frac{10^{6}}{R_{e}}\bigg{)}^{ \frac{1}{3}}\Bigg{]}\frac{l\varrho_{s}v^{2}}{2d_{e}}+(z_{2}-z_{1})\gamma \tag{27}\]
# Radio Channel Modelling for VHF System Operating in the Offshore Wind Farms Propagation Environment
## 2 Methods
### Modelling of the Radio Channel Based on Selected Parameters
* Coherence time (\(T_{c}\))--defines the period of time during which the channel remains invariant as a function of time (i.e., the time during which the impulse response of the channel is approximately constant). Channel coherence time is approximately the inverse of the Doppler band [23]: \[T_{c}\approx\frac{0.423}{B_{D}}=\frac{0.423}{f_{d\ max}}.\] (32) Based on the above parameters, four basic types of radio channels can be defined: 1. Flat fading channel--a frequency non-selective channel, the characteristics of which are constant throughout the entire band. Fading that can affect the entire band is called flat fading. This type of channel occurs when \[B_{s}\ll B_{c}\ \ \ \ \ \ or\ \ \ \ \ T_{s}\gg\sigma_{\tau}\] (33) where \(B_{s}\)--signal bandwidth and \(T_{s}\)--symbol duration. 2. Frequency selective fading channel--a channel that influences some frequency components more strongly than others. This happens when \[B_{s}>B_{c}\ \ \ \ \ \ or\ \ \ \ \ T_{s}<\sigma_{\tau}.\] (34) This type of channel introduces intersymbol interference (ISI). 3. Fast fading channel--the channel whose impulse response changes faster than the duration of the symbol. This is due to the displacement of one of the system components (Doppler spread). For a channel with fast fading, the following relationships are true: \[T_{c}<T_{s}\ \ \ \ \ \ or\ \ \ \ \ \ f_{d\ max}>B_{s}.\] (35)Slow fading channel--a channel for which the relations are opposite to those for point 3; i.e., the Doppler spread is much smaller than the signal bandwidth:  
\[T_{c}\gg T_{s}\qquad or\qquad f_{d\ max}\ll B_{s} \tag{36}\]  
Out of the above four cases, the most unfavorable impact on the resultant transmission quality occurs during the situation when the channel is frequency-selective and, additionally, it is characterized by fast fading. Selectivity introduces the need to implement complex channel characteristic equalizers, usually in the form of a filter. On the other hand, when a channel has fast fading, information about its state must be transmitted at high frequency. When the channel changes faster than the frequency of the channel estimation, the power control or adaptive modulation algorithms may not be efficient enough. As part of this article, a measurement campaign was carried out in the vicinity of a selected onshore wind farm, which allowed us to determine the power delay profile of the channel power taking into account the reflections from individual wind turbines. The coherence time and the channel coherence bandwidth were also determined. In the further part, based on the developed software, the results of the simulation calculations of the basic parameters of the radio channel for a wind farm located in the marine environment are also presented. The obtained results can be verified in the future during the measurement campaign organized in the vicinity of the offshore wind farm.
# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 1 Introduction
* Practical Implications: By automating bug priority prediction, the proposed method can enhance developer productivity and enable project managers to allocate resources more effectively, ultimately improving the efficiency of the software development process. This study shows the effectiveness of a priority-sensitive LSTM-Attention mechanism for automated bug priority prediction. The proposed model significantly outperforms existing methods, providing a reliable and scalable solution for managing the increasing volume of bug reports in large-scale software projects.
# Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
## 5 Discussion
### Limitations  
While we attempted to perform a comprehensive evaluation of the proposed MC-Net, our study also has several limitations. First, the range of imaging contrasts evaluated was focused on T1-weighted, more specifically MP-RAGE, and a few T2-weighted scans (FLAIR). Therefore, we do not know how our model performs for other contrasts, such as susceptibility weighted or conventional T2-weighted contrast. However, given the poor performance of MC-Net in processing FLAIR images, it is likely that the current network would not be beneficial for other contrasts either.  
Second, we did not include sagittal or coronal data in the training set or evaluate the corresponding performance since we focused on a scenario where only one source of data is available and sufficient to train the model, i.e., single-domain generalization [41]. These factors might be relevant in the clinical setting. We plan to include sagittal and coronal data to train the model for more comprehensive evaluation in the near future.  
The motion trajectories used in this work were based on real head motions measured with PACE-fMRI and are more realistic compared with options such as sinusoids and other regular patterns or random motion trajectories. However, the time scale of the motion trajectories was not resampled to match that of the MP-RAGE and FLAIR acquisitions. Consequently, movements in the simulations were to some degree slowed down or accelerated compared with the PACE measurements. Further, the amplitude of movements was magnified to create more challenging artifacts for MC-Net. Therefore, some degree of authenticity of the motion trajectories was sacrificed for higher temporal resolution and higher motion amplitudes. Also, the simulation of motion artifacts did not include the effects of movements on spin saturation across TR-periods. However, since MP-RAGE is a 3D acquisition, the spin-history effects are probably relatively minor; we did not model through-plane motion.  
It is worth noting that this model was not trained and tested for extremely large motions. Extreme cases can be added by fine-tuning the model with new training samples and intermediate samples, possibly to be generated using a Projection-On-Convex-Set-based cycle Generative Adversarial Network [11].
## 2 Materials and Methods
### Eb-Pbf  
EB-PBF is an AM process, which belongs to the powder bed fusion (PBF) processes. AM, and especially EB-PBF, is based on a cyclical process and consists of the following steps: lowering the building table--application of the powder--preheating--melting--reheating. How far the table is lowered at each step defines the layer thickness, which for the EB-PBF process can be between 50-200 \(\upmu\)m [33] (in this study a layer thickness of 70 \(\upmu\)m was used). Powder is applied onto the previous layer, raked even and afterwards pre-heated using a highly defocused electron beam. The pre-heat prevents the powder from smoking and therefore enables a stable process. The main parameters to optimise in an EB-PBF process are scanning speed, beam current, hatch spacing and focus offset. In previous work, stable process parameters for manufacturing Ti-5553 in the used ARCAM A2X were found [20]. One foremost problem when utilising PBF is associated process-inherent defects, which act as crack nucleation sites and tend to reduce the lifetime of AM parts. Depending on their origin, different defects are more or less critical [34]. Large, unregularly shaped defects such as those cause by lack of fusion, cracks, unmolten powder or delamination should be eliminated fully due to their negative impact on the respective mechanical properties [23; 35; 36]. Smaller, mostly round defects such as gas pores tend to be less important with regard to their impact [37]. The used specimens for the conventional and in situ samples were manufactured in two individual production runs. By using the same process parameters, it can be expected that the production runs will be comparable.
# Advances in IoMT for Healthcare Systems
### Machine Learning (ML)- and Artificial Intelligence (AI)-Driven Diagnostics  
The integration of machine learning (ML) and artificial intelligence (AI) is becoming increasingly important in the IoMT environment for several reasons. For example, ML and AI can aid in early detection, as such models can quickly analyze large amounts of medical data. ML and AI-driven diagnostics are error-prone and insightful, allowing for more effective and efficient decision-making. Medical robots also help allow us to streamline high-risk clinical tasks such as surgery and radiation therapy [8]. These technologies can predict disease progression and outcomes based on historical data patterns and facilitate real-time data collection and analysis, such as telemedicine and remote patient monitoring. However, the accuracy of ML- and AI-driven applications heavily depends on the quality of the training data, so bias in the training or learning data can lead to unreliable results. In addition, ML models are prone to overfitting or underfitting problems. The successful implementation of ML- and AI-driven applications in the IoMT environment is also hampered by the collaboration issue between machine and human.
## 2 Methods
### Methodology
#### 2.2.4. CAPAFE++ Upsampling Module  
To create feature maps with rich semantic information, the CAPAFE++ method [36] is employed as an upsampling operator to decrease the loss of upsampled information and enhance the receptive field. The content-aware reassembly submodule and the kernel prediction submodule make up the CARAFE++ module. The function of the kernel prediction submodule is to generate the upsampling reorganization kernel in a content-aware form so that the reassembly submodule can achieve the upsampling work. Suppose a feature map size is \(H\times W\times C\), and an upsampling ratio is \(\omega\). Firstly, the input feature map channel from \(C\) to \(C_{v}\) is compressed via a \(1\times 1\) convolution layer, reducing the parameters of the following steps. Secondly, based on the compressed feature map, a convolution layer of a kernel size of \(k_{e}\times k_{e}\) is used to generate the upsampled reassembly kernel. The channel dimension is expanded on the spatial dimension. The size of the reassembly kernel is \(\omega H\times\omega W\times C_{r}\). Here, \(\omega\) is the upper sampling rate. Finally, the values for the reassembly kernel with a size of \(k_{r}\times k_{r}\) are normalized using a softmax function so that the sum of the kernel values is \(1\). In the content-aware reassembly submodule, each position in the output feature is mapped to the input feature map to gain the feature \(N(\chi_{p},k_{r})\). Then, the region of each location center \(p=(i,j)\) is taken out for dot-product operation with the reassembly kernel \(k_{r}\). Then, the upsampling feature map with a size of \(\omega H\times\omega W\times C\) is obtained.
## 4 Experimental Results and Analysis
### Findings and Discussion
Additionally, although the current algorithm is implemented in a single thread, future efforts could explore improving the algorithm's ability to handle large-scale circuit optimization tasks through optimization of the algorithm itself and efficient scheduling strategies. Overall, the circuit partitioning optimization algorithm based on partition connectivity clustering and improved tabu search demonstrates good optimization effects in reducing cut edges and enhancing load balancing. Although the current algorithm is single-threaded, its stability and efficiency in scaling up circuits make it highly applicable, providing potential directions for further optimization and expansion in the future.
# Modulation of Solar Wind Impact on the Earth's Magnetosphere during the Solar Cycle
## 4 Conclusions  
In this study, the response of the magnetosphere to different solar wind conditions was investigated statistically by analyzing the correlation between the SW energy and the \(Dst\) index separately for periods of maximum and minimum solar activity. The results showed a roughly logarithmic decay of \(Dst\) versus \(E\), where higher values of the energy carried by SW correspond to severe storms detected by lower values of the \(Dst\).  
In addition, the decay observed in the \(Dst\) as the SW energy increases is different during solar maximum and minimum activity periods. The most severe perturbations, characterized by lower values of \(Dst\), occur mostly during solar maxima. However, this does not appear to depend on the SW energy level, as one could expect. We conjecture that this observation could be related to the rate of occurrence of the solar wind perturbations. Perturbations with sufficiently geo-effective energy content are more frequent during solar maxima with respect to solar minima periods. This could lead to the accumulation of perturbations during a shorter time period, which prevents the magnetospheric magnetic configuration from relaxing, therefore leading to severe events with large negative \(Dst\) indices. On the other hand, during the minimum period, the quiet time between successive events is longer, resulting in moderate \(Dst\), due to the slower accumulation of geo-effective events and the subsequent possibility for the magnetospheric currents system to recover the equilibrium configuration. This may be of relevance for space weather research, and, in particular,the determination of the effective duration of perturbations in response to the solar wind drivers.  
Conceptualization, F.C., D.T., E.Y. and L.S.-V.; methodology, F.C., D.T., E.Y. and L.S.-V.; data curation, D.T.; writing--original draft preparation, F.C., D.T., L.S.-V.; writing--review and editing, F.C., D.T., E.Y., L.S.-V. All authors have read and agreed to the published version of the manuscript.  
L.S.-V. and E.Y. were supported by SNSA grants 86/20 and 145/18.  
Not applicable.  
Wind spacecraft data can be obtained at: [[https://cdaweb.gsfc.nasa.gov](https://cdaweb.gsfc.nasa.gov)]([https://cdaweb.gsfc.nasa.gov](https://cdaweb.gsfc.nasa.gov)), (accessed on 24 April 2022); \(Dst\) data can be obtained at: [[https://izw1.caltech.edu/ACE/ASC/DATA/level3/icmetable2.html](https://izw1.caltech.edu/ACE/ASC/DATA/level3/icmetable2.html)]([https://izw1.caltech.edu/ACE/ASC/DATA/level3/icmetable2.html](https://izw1.caltech.edu/ACE/ASC/DATA/level3/icmetable2.html)), (accessed on 24 April 2022).  
The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.