esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study introduces an artificial neural network (ANN) model for optimizing process parameters to reduce the chances of delamination in carbon fiber-reinforced polymer (CFRP) materials during abrasive water jet (AWJ) piercing. AWJ is a proper method for cutting CFRP. The initial step in this process is AWJ piercing, which creates entry holes in the material to facilitate further cutting operations. However, AWJ piercing is particularly challenging due to the high energy applied to the material. If it is not properly controlled, this high-energy impact can cause material delamination. Avoiding CFRP delamination is a critical aspect when expensive parts are processed with AWJ, particularly in the aerospace and automotive industries. This can compromise the CFRP workpiece, and this induces extra costs for rework. The ANN model was trained using backpropagation to predict delamination. It features a feed-forward architecture that balances model complexity and performance. Validation showed that the ANN model effectively predicted optimal process parameters, eliminating delamination in machined CFRP parts. This study underscores the potential of ANNs in enhancing AWJ piercing processes and provides a robust and reliable method of improving the quality of CFRP parts.  
abrasive water jet cutting; piercing; composite materials; CFRP; artificial intelligence; machine learning; predictive model +
Footnote †: journal: Applied Sciences  
Article Intelligence Model Used for Optimizing Abrasive Water Jet Machining Parameters to Minimize Delamination in Carbon Fiber-Reinforced Polymer
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Application Layer
#### 3.1.1 Constrained Application Protocol  
CoAP is a lightweight protocol apt for the ever-increasing Machine-to-Machine (M2M) communications in resource-constrained environments [51]. Supporting specific features suitable for V2I communication, this protocol implements a request-response architecture between application endpoints, which supports QoS that ensures reliable transmissions, simple congestion control, and flow control. In addition, CoAP underpins asynchronous message exchange and offers a built-in discovery of services and resources. Furthermore, the protocol implements an optional subscription mechanism for resource observation. The CoAP protocol operates a client-server model like TCP and relies on UDP as the underlying transport protocol, establishing a robust connection that ensures reliable communication even in dynamic and intermittent connected environments, such as those with high-speed connectivity and rapidly changing topologies. CoAP's compatibility with mobility management [52] further enhances its capability to handle high mobility-related and handover challenges. Refer to Figure 3 for CoAP architecture.  
Figure 2: IoT Protocol Stack.
# Welding Image Data Augmentation Method Based on LRGAN Model
## 4 Experimental Results and Discussion
### Comparison of Images Generated by Different Models
#### 4.2.3 Quantitative Analysis  
In addition to analyzing the model's generation performance using the PSNR, SSIM, and FID evaluation metrics, we can also assess the LRGAN model's performance from the perspective of large data volumes and manifold structure evaluation by introducing the GAN_train and GAN_test metrics. These metrics, obtained by training on the VGG19 classification network, are mainly used to evaluate the manifold structure similarity between datasets. The higher the GAN_train and GAN_test values, the more similar the manifold structures of the datasets. The results are shown in Table 4 and Figure 16.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **FID** & **PSNR** & **SSIM** & **GAN\_Train** & **GAN\_Test** \\ \hline WGAN & 87.93 & 76.03 & 52.01 & 57.67 & 67.25 \\ LSGAN & 56.52 & 86.54 & 74.50 & 88.00 & 43.33 \\ CycleGAN & 48.32 & 60.02 & 80.33 & 66.71 & 57.30 \\ LRGAN & 42.11 & 90.31 & 89.72 & 92.00 & 70.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative analysis of different models.  
Figure 16: Comparison of model effectiveness by different models.  
Figure 15: 3D surface plot visualization of different models.  
Through quantitative analysis, it can be seen that the proposed model outperforms the other three models in terms of having the smallest FID, the highest PSNR, and SSIM closest to 1 (100%), as well as achieving the highest manifold structure similarity evaluation (GAN_train and GAN_test). Based on these five objective evaluation metrics, it can be concluded that the LRGAN model better preserves the feature distribution and structural similarity between the generated welding image dataset and the original welding image dataset, resulting in the best image generation effect, which is closest to the original welding images.  
Furthermore, based on the 3D area plot in Figure 17, it can be concluded that the area blocks corresponding to the conditions of small FID values and large PSNR, SSIM, as well as GAN_train and GAN_test values, are primarily concentrated around LRGAN (i.e., the proposed model in this paper). This indicates that the number of area blocks meeting these conditions is highest for the proposed model. Therefore, this further confirms that, among the four models, the LRGAN model proposed in this paper generates welding images that are the most similar to the original welding samples, demonstrating the best performance.
## 4 Materials and Methods
### Analysis of Volatile Metabolites
#### 4.3.4 Data Analysis  
Raw GC-MS data generated from SPME analysis were processed through the Automated Mass spectral Deconvolution and Identification System (AMDIS) software. Identification of compounds was based on the National Institute of Standards and Technology (NIST) 2017 mass spectral library, only considering those with a match quality above 90%. Compounds were considered tentatively identified (putative identification). The R package MassOmics was used for automated integration of reference ion peak area [41]. Each identification was individually screened, and manual retention time correction and subsequent reintegration were performed where required. To enable comparisons between samples, the data were normalised by the internal standard peak intensity and log-transformed to fit normal distribution criteria for downstream statistical analyses. Two silence samples were removed due to mishandling during the sample preparation stage. The relative abundances of each compound were analysed for differences between sound treatments using ANOVA followed by post hoc Tukey multiple comparisons to isolate significant means whilst using a correction for \(p\)-values for multiple comparisons. To attempt to distinguish samples among classes (sound treatment), a linear discriminant analysis (LDA) was used as LDA maximises separation between classes, rather than a PCA which maximises variation in the reduced dimension [47]. We applied LDA to visualise samples using the top 20 identified common aroma compounds, by relative abundance, detected across all samples and all conditions. The LDA results were then validated using leave-one-out cross-validation (LOOCV) [13]. All statistical analyses were carried out using RStudio 3.4.1.
# A Survey on Network Optimization Techniques for Blockchain Systems
## 5 Network Optimization Techniques Used in Blockchain Networks
### Consensus Layer
#### 5.2.2 Communication Complexity  
The authors in this category proposed consensus algorithms with a reduced number of communications required. For example, [PERSON] et al. [87] presented a Byzantine Agreement-based consensus algorithm that requires \(1/4\) nodes and two rounds of communication to reach a consensus. The reduced number of steps should translate to a smaller latency and bandwidth consumption; however, the results presented suggest that effective throughput was reduced when the number of peers increased, and, hence, it is not scalable.  
[PERSON] et al. [88] proposed a consensus algorithm called High-Performance and Scalable Byzantine Fault Tolerance (HSBFT). Although no evaluation was performed for the proposed technique, it may have a reduced bandwidth usage since communication complexity was reduced from \(O(n^{2})\) to \(O(n)\).
## 3 Results  
### Assessment in Accordance to ISO 10140  
Figure 2 shows the sound insulation spectrum of the two walls as obtained by ISO 10140-2. The dip around \(f_{0}\approx\) 125 Hz in the ETICS treated wall, caused by the reduced acoustic impedance (mismatch with the air) of the multilayer at resonance, agrees with Equation (9), using the following known values  
for the involved material properties: \(m_{1}\) = 375 kg/m\({}^{2}\), \(m_{2}\) = 28 kg/m\({}^{2}\), and \(s^{\prime}\) = 15 MN/m\({}^{3}\)[13; 42]. In addition, other features of the ETICS effect are as expected. On the one hand, below resonance, the added mass of the plaster and mineral wool layer is too small to significantly increase the sound insulation. On the other hand, above the dip, the addition of a (thin) mass layer and cavity leads to a dual mass law behavior of 12 dB/octave-behavior, in contrast with the single mass law behavior of 6 dB/octave-behavior of the bare Wall 1. Both spectra also show a dip at \(f_{c}\approx 160\) Hz due to the coincidence effect. However, in the case of ETICS Wall 2, the mass-spring-mass dip is clearly dominant. Figure 2 illustrates that, despite the drastic differences between the insulation curves of both walls, the effect on the respective shifted \"matching\" reference curves is pretty small.  
In theory, the resonance frequency of an ETICS system can be roughly estimated by [42]:  
\[f_{0}\ =\ \frac{1}{2\pi}\sqrt{s^{\prime}\Bigg{(}\frac{1}{m_{1}^{\prime}}+ \frac{1}{m_{2}^{\prime}}\Bigg{)}} \tag{11}\]  
where \(m_{1}\) is a total mass per unit area of Wall 1 (kg\(\cdot\)m\({}^{-2}\)); \(m_{2}\) is mass per unit area of the used plaster (kg\(\cdot\)m\({}^{-2}\)); and the \"spring\" of the system, (i.e., mineral wool placed in the cavity between the two walls) is characterized by its dynamic stiffness \(s^{\prime}\) (N\(\cdot\)m\({}^{-3}\)) measured in accordance with EN 29052-1 [55]. Typical values are given in the Section 2 of this article. In addition to the properties of materials used for ETICS, the sound insulation is also affected by aspects of workmanship, e.g., related to how the insulation slabs are mounted onto the supporting wall.  
Figure 2: Airborne sound insulation curves of Walls 1 and 2 obtained by ISO 10140-2, together with the respective shifted reference curves [26].  
Consequently, the effect on the single number ratings (Table 1) is quite small: application of ETICS in this case deteriorates \(R_{W}\) (ISO 717-1 [20]) by 1 dB. Considering spectrum adaptation terms (\(C\); \(C_{tr}\)), the single number sound insulation decreases by amounts between 2 and 4 dB.
# Parameterized Modeling and Planning of Distributed Energy Storage in Active Distribution Networks
## 3 Parameterized Modeling of Optimal DES Planning in Active Distribution Networks (ADNs)
### DES Planning Constraint  
\[\sum\
olimits_{m\in\Omega_{\rm type}}\sum\
olimits_{i\in N_{\rm N}}y_{i,m}S_{m}^ {\rm unit}\leq S^{\rm BGT} \tag{21}\]  
\[\sum\
olimits_{m\in\Omega_{\rm type}}\sum\
olimits_{i\in N_{\rm N}}z_{i,m}E_{m} ^{\rm unit}\leq E^{\rm BGT} \tag{22}\]  
\[\frac{y_{i,m}S_{m}^{\rm unit}}{S^{\rm BGT}}\leq\delta_{i} \tag{23}\]  
\[\sum\
olimits_{i\in N_{\rm N}}\delta_{i}\leq n^{\rm DES} \tag{24}\]  
where \(S^{\rm BGT}\) and \(E^{\rm BGT}\) are the maximum power capacity and energy capacity of DES in planning. \(\delta_{i}\) is a binary variable indicating the location of DES at node \(i\). \(n^{\rm DES}\) is the maximum number of DES installing nodes.  
Constraints (21) and (22) represent the maximum limits on power and energy capacity of DES in planning. The constraint that represents whether DES is installed at node \(i\) can be described as (23). When \(\delta_{i}=1\), DES is installed at node \(i\); when \(\delta_{i}=0\), no DES is installed at node \(i\). The limit on the maximum number of nodes that DES can be installed is expressed as constraint (24).  
As a consequence, parameterized model for optimal DES planning in ADN is finally expressed as (25), which is a mixed-integer non-linear programming (MINLP) model.  
MINLP Model : \(\min C_{1}\) or \(\min C_{2}\)  
s.t. (6)-(24)
###### Abstract  
Changes in vegetation dynamics play a critical role in terrestrial ecosystems and environments. Remote sensing products and dynamic global vegetation models (DGVMs) are useful for studying vegetation dynamics. In this study, we revised the Community Land Surface Biogeochemical Dynamic Vegetation Model (referred to as the BGCDV_CTL experiment) and validated it for the Tibetan Plateau (TP) by comparing vegetation distribution and carbon flux simulations against observations. Then, seasonal-deciduous phenology parameterization was adopted according to the observed parameters (referred to as the BGCDV_NEW experiment). Compared to the observed parameters, monthly variations in gross primary productivity (GPP) showed that the BGCDV_NEW experiment had the best performance against the in situ observations on the TP. The climatology from the remote sensing and simulated GPPs showed similar patterns, with GPP increasing from northwest to southeast, although the BGCDV_NEW experiment overestimated GPP in the semi-arid and arid regions of the TP. The results show that temperature warming was the dominant factor resulting in the increase in GPP based on the remote sensing products, while precipitation enhancement was the reason for the GPP increase in the model simulation.  
Keywords:dynamic vegetation; gross primary productivity; regional climate change; remote sensing products; Tibetan Plateau +
Footnote †: journal: _Frontee sensing_
## 1 Introduction
[PERSON] et al. [19] conducted triaxial compression tests on rocks in a reservoir area and found that dry and wet cycling reduced the peak stress, residual stress, and elastic modulus of their specimens. [PERSON] et al. [20] studied the static splitting mechanics of marble ring samples with different inner diameters. [PERSON] et al. [21] studied the influence of dry-wet cycling on the fracture toughness and tensile strength of sandstone, and the results showed that the deterioration in the fracture toughness of sandstone samples after dry-wet cycling was greater than their tensile strength. [PERSON] et al. [22] carried out a Brazilian disc-splitting test on sandstone rings with different inner diameters and suggested that tensile stress could not be used as a failure index. [PERSON] et al. [23] used the MTS 815 rock mechanics test system to study the mechanical rules of sandstone rings under multi-stage cyclic loads. [PERSON] et al. [24] analyzed the crack development of an eccentric ring specimen following the use of the SHPB dynamic test. [PERSON] et al. [25] revealed the temporal and spatial evolution characteristics of microcracks in granite ring specimens through the use of a uniaxial compression test. In summary, fruitful results have been achieved in the study of rock subjected to dry-wet cycling or ring specimens. However, there are few studies on the dynamic mechanical properties of ring specimens following dry-wet cycling. In this paper, we will present, in detail, the dynamic characteristics and energy consumption analysis of sandstone ring specimens with different inner diameters following dry-wet cycling. First, the basic physical parameters of the sandstone ring specimens were determined before and after dry and wet cycle treatment, and changes in the mass, density, and volume of the specimens were compared. Afterward, the impact compression test on sandstone rings with different inner diameters following dry and wet cycling was carried out using an SHPB test device, and their dynamic characteristics, such as dynamic stress, dynamic strain, and elastic modulus, are discussed with respect to the inner diameter of the sandstone rings. Lastly, according to the energy change and crushing form of the sandstone ring specimen, the influence of the inner diameter of the sandstone ring specimen on the deterioration of its dynamic properties is analyzed.
## 4 Experiments
### Performance on TISs Experimentally Validated via Ribosome Profiling  
To rigorously evaluate cross-species generalization capability, we applied NeuroTIS+ trained exclusively on human transcriptome data without fine-tuning to a curated _A. thaliana_ dataset containing 6947 Ribo-seq validated translation initiation sites (TISs) and 258,553 negative TISs. The model demonstrated remarkable performance across all evaluation metrics, achieving near-perfect detection of experimentally verified TISs with 99.49% sensitivity while maintaining 99.12% specificity. This robust performance was further evidenced by outstanding metric scores: near-perfect class separation (auROC = 0.9996), exceptional precision-recall balance under extreme class imbalance (auPRC = 0.9953), and strong overall prediction consistency (MCC = 0.944). The remarkable performance underscores NeuroTIS+'s utility as a universal TIS prediction tool capable of accurate annotation in uncharacterized plant transcriptomes without species-specific retraining, which verifies its biological generalizability.
## 4 Results and Analysis  
### Unconfined Compressive Strength (\(q_{u}\))  
Figure 5a portrays \(q_{u}\) as a function of porosity/cement index (\(\eta\)/\(C_{it}\)) (stated as porosity (\(\eta\)) divided by the volumetric cement content (\(C_{it}\)), the latter expressed as a percentage of cement volume to the total volume of the iron tailings-Portland cement mixes [33]) for the curing periods studied (2, 4, 7, 28 and 90 days). [PERSON] et al. [34] carried out the theoretical approach validating the shape of the equation. Figure 5a indicates that the \(\eta\)/\(C_{it}\) index is useful in normalizing strength results for iron ore tailings-Portland cement blends. The results indicate that the behaviour of the studied blends presents the same trend, thus generating a single equation (Equation (2)).  
\[q_{u}(\mathrm{kPa})=A\times 10^{4}\times\left[\frac{\eta}{C_{it}}\right]^{-D} \tag{2}\]  
Scalar \"\(D\)\" has been found to be a constant (\(D=1.3\)) to all curing times studied (from 2 to 90 days), while scalar \"\(A\)\" increases with curing time, as shown in Table 2. \"\(A\)\" changes from 1.63 (for 2 days of curing) to 4.89 (for 90 days of curing) and the coefficient of determination (R\({}^{2}\)) varies in the range 0.92 to 0.97. From 2 days of curing to 4, 7, 28 and 90 days of curing, the strength increase percentages were of 63.2%, 82.8%, 147.9% and 200.0%, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Curing Period**} & \multicolumn{2}{c}{**Strength Data—\(q_{u}\)**} & \multicolumn{2}{c}{**Stiffness Data—\(G_{0}\)**} \\ \cline{2-5}  & \({}^{\prime\prime}\)\(A\)” & \multicolumn{2}{c}{**Coefficient of**} & \multicolumn{2}{c}{**Coefficient of**} \\  & \({}^{\prime\prime}\)**\(A\)” & \multicolumn{2}{c}{**Determination (R\({}^{2}\))**} & \multicolumn{2}{c}{**Determination (R\({}^{2}\))**} \\ \hline
2 days & 1.63 & 0.92 & 1.46 & 0.86 \\
4 days & 2.66 & 0.96 & 2.98 & 0.92 \\
7 days & 2.98 & 0.97 & 4.11 & 0.97 \\
28 days & 4.04 & 0.94 & 4.53 & 0.96 \\
90 days & 4.89 & 0.96 & 6.04 & 0.96 \\ \hline \end{tabular}
\end{table}
Table 2: \"\(A\)” and “C” scalars for Equations (2) and (3), respectively.  
Figure 5: Compacted (\(\gamma_{d}=17\) kN/m\({}^{3}\), \(\gamma_{d}=18\) kN/m\({}^{3}\), \(\gamma_{d}=19\) kN/m\({}^{3}\)) filtered iron mining tailings treated with early strength Portland cement (from 1% to 5%): (**a**) unconfined compressive strength (\(q_{u}\)) versus porosity/cement index (\(\eta\)/\(C_{tip}\)) considering distinct curing time periods and (**b**) initial shear stiffness (\(G_{0}\)) versus \(\eta\)/\(C_{tip}\) taking under consideration different curing time periods (2, 4, 7, 28 and 90 days of curing).
## 1 Introduction
### The Internet of Things  
The Internet of Things is an emerging paradigm in communication networks, commonly known as the Third Wave. The IoT is the interconnection of uniquely identifiable physical things with embedded systems connected to the Internet [1] that ideally interact with one another without any human intervention. The \"things\" in IoT refers to a wide variety of devices, such as smartphones, sensors, measurement devices, and laptops. As the concept of IoT slowly assimilates into our daily life,its application is similarly extended to remote environmental monitoring systems, such as agricultural sensing and Supervisory Control and Data Acquisition (SCADA) [2; 3] data logging. IoT sensors, devices, and controllers are being deployed over large geographical areas that require continuous connection to the network to gather environmental sensing data. However, network connectivity may not always exist due to the lack of network infrastructure in remote regions. The end-to-end network link between data sources and destinations is not available or is available only periodically. Furthermore, IoT devices usually have limited storage and battery life, and they operate using low-power short-range wireless technologies. They are periodically scheduled to sleep until a network link is available to conserve energy, and buffer management can reduce storage consumption in IoT application deployment. A wide distribution of nodes with limited wireless radio range, storage resources, and battery life leads to a greater chance of limited network connectivity.
### The Contribution of the Work in This Paper  
The contribution of the work in this paper can be summarized as follows:  
1. We introduce a novel approach called the LVAE to identify unknown attacks. We developed this approach by incorporating an effective reconstruction loss term that utilizes the logarithmic hyperbolic cosine (log-cosh) function. This function accurately captures the intricate distribution of actual attack data, enabling the simulation of discrete features to model and enhance the detection of novel, unknown attacks.
2. We employ eight different techniques for feature extraction and data classification. Throughout the experimentation process, we select the most accurate approach to ensure exceptional performance in identifying unknown attacks.
3. Our model is trained using the latest CICIDS 2017 dataset, which includes a variety of real and unknown-type attacks. In comparison to several state-of-the-art methods, our LVAE approach demonstrates superior performance, surpassing recent advancements and significantly improving the detection rate of unknown attacks.
4. Our method exhibits fast convergence and effectively minimizes the loss function within a short learning epoch.
## 3 Technical Background
The R-SFCrs that cannot be mapped on the MDC because of the shortage of resources are left to be mapped on a subsequent MDC. However, in this case, the R-SFCrs will be processed with equal priority as the P-SFCrs in the new MDC. As we will describe in more detail in Section 5, this priority redefinition of R-SFCrs in a full MDC to be considered as equal-to-P-SFCrs in the subsequent MDCs incurs a penalty to R-SFCrs of those MDCs. The impact of this penalty, which is reflected in the number of SFCrs that cannot be allocated, increases as the percentage of P-SFCrs increases. In addition to the identification and description of this problem, we propose a Modified Priority-based Greedy (ModPG) heuristic that deals with this problem, improving the number of successfully mapped SFCrs. Figure 1: Problem outline using the example described in [13]. Starting from the priority-based mapping results, a VNFr merging algorithm is executed. The objective of this second algorithm is to reduce the number of VNF instances to reduce the BRCs, the number of activated MDCs, and the total cost.