esa-sceva/satcom-chunk-collection huggingface dataset:
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 3 Methodology
### Multiscale Back-Projection Feature Fusion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Layer** & **Input Size** & **Filter Size** & **Stride** & **Activation Function** & **Output Size** \\ \hline \multirow{2}{*}{1} & Convolution layer & \(256\times 256\times 3\) & \(11\times 11\) & 1 & ReLU & \(256\times 256\times 16\) \\  & Convolution layer & \(256\times 256\times 16\) & \(3\times 3\) & 2 & ReLU & \(256\times 256\times 16\) \\ \hline \multirow{2}{*}{2} & Convolution layer & \(256\times 256\times 16\) & \(3\times 3\) & 2 & ReLU & \(128\times 128\times 32\) \\  & Encoder \(EB^{1}\) & \(128\times 128\times 32\) & \(3\times 3\) & 2 & ReLU & \(128\times 128\times 32\) \\ \hline \multirow{2}{*}{3} & Convolution layer & \(128\times 128\times 32\) & \(3\times 3\) & 2 & ReLU & \(64\times 64\times 64\) \\  & Encoder \(EB^{2}\) & \(64\times 64\times 64\) & \(3\times 3\) & 2 & ReLU & \(64\times 64\times 64\) \\ \hline \multirow{2}{*}{4} & Convolution layer & \(64\times 64\times 64\) & \(3\times 3\) & 2 & ReLU & \(32\times 32\times 128\) \\  & Encoder \(EB^{3}\) & \(32\times 32\times 128\) & \(3\times 3\) & 2 & ReLU & \(32\times 32\times 128\) \\ \hline \multirow{2}{*}{5} & Convolution layer & \(32\times 32\times 128\) & \(3\times 3\) & 2 & ReLU & \(16\times 16\times 256\) \\  & Encoder \(EB^{4}\) & \(16\times 16\times 256\) & \(3\times 3\) & 2 & ReLU & \(16\times 16\times 256\) \\ \hline \multirow{2}{*}{6} & Decoder \(DB^{1}\) & \(16\times 16\times 256\) & \(3\times 3\) & 2 & ReLU & \(16\times 16\times 256\) \\  & Deconvolution layer & \(16\times 16\times 256\) & \(3\times 3\) & 2 & ReLU & \(32\times 32\times 128\) \\ \hline \multirow{2}{*}{7} & Decoder \(DB^{2}\) & \(32\times 32\times 128\) & \(3\times 3\) & 2 & ReLU & \(32\times 32\times 128\) \\  & Deconvolution layer & \(32\times 32\times 128\) & \(3\times 3\) & 2 & ReLU & \(64\times 64\times 64\) \\ \hline \multirow{2}{*}{8} & Decoder \(DB^{3}\) & \(64\times 64\times 64\) & \(3\times 3\) & 2 & ReLU & \(64\times 64\times 64\) \\  & Deconvolution layer & \(64\times 64\times 64\) & \(3\times 3\) & 2 & ReLU & \(128\times 128\times 32\) \\ \hline \multirow{2}{*}{9} & Decoder \(DB^{4}\) & \(128\times 128\times 32\) & \(3\times 3\) & 2 & ReLU & \(128\times 128\times 32\) \\  & Deconvolution layer & \(128\times 128\times 32\) & \(3\times 3\) & 2 & ReLU & \(256\times 256\times 16\) \\ \hline \multirow{2}{*}{10} & Convolution layer & \(256\times 256\times 16\) & \(3\times 3\) & 2 & ReLU & \(256\times 256\times 16\) \\  & Convolution layer & \(256\times 256\times 16\) & \(3\times 3\) & 1 & ReLU & \(256\times 256\times 3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of the models used in our method.
# Effect of Osteoporosis on Well-Integrated Bone Implants
###### Abstract  
The installation of dental implants has become a common treatment for dertulous patients. However, concern exists about the influence of osteoporosis on the final implant success. This study evaluated whether an ovariectomy (OVX)-induced osteopororotic condition, induced eight weeks postimplantation in a rat femoral condyle, influences the bone response to already-integrated implants. The implants were inserted in the femoral condyle of 16 female Wistar rats. Eight weeks postimplantation, rats were randomly ovariectomized (OVX) or sham-operated (SHAM). Fourteen weeks later, animals were sacrificed, and implants were used for histological and histomorphometric analyses. A significant reduction in the quantity and quality of trabecular bone around dental implants existed in OVX rats in comparison to the SHAM group. For histomorphometric analysis, the bone area (BA%) showed a significant difference between OVX (34.2 \(\pm\) 4.3) and SHAM (52.6 \(\pm\) 12.7) groups (_p_ < 0.05). Bone-implant contact (BIC%) revealed significantly lower values for all implants in OVX (42.5 \(\pm\) 20.4) versus SHAM (59.0 \(\pm\) 19.0) rats. Therefore, induction of an osteopororotic condition eight weeks postimplantation in a rat model negatively affects the amount of bone present in close vicinity to bone implants.  
Keywords:dental implants; osseointegration; osteoporosis; ovariectomy; rat femoral condyle; animal model +
Footnote †: journal: Applied Sciences
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 4 Conclusions and Perspective  
A transient CFD analysis of an exhaust manifold was performed using five different turbulence models. The objective was to compare and analyze the performance of these models in predicting key flow parameters to identify the most accurate models for capturing flow characteristics.  
Mesh independency, temporal discretization, and the characteristics of different turbulence models were systematically evaluated. After determining optimal simulation parameters, the models were compared under consistent conditions to ensure a fair result.  
The k-kl-\(\omega\) model gave the highest maximum velocity and exhibited the widest temperature range, underlining its advanced capability to capture transitional flow effects using the length scale variable, which enhances its ability to handle the flows with the manifold, considering the curvature of the inlets. The k-\(\omega\) STD and SST transition models presented notably higher TKE values, denoting their advanced capabilities in modeling complex turbulent and transitional flows. These models regularly predicted heightened turbulence levels, particularly in the latter part of the manifold. Conversely, the Reynolds stress and RNG k-epsilon models displayed lower TKE values, suggesting a more subdued turbulence prediction. Despite these differences, all models exhibited similar trends in pressure drop, with a noticeable increase near the midpoint of the manifold, suggesting a common point of flow convergence or resistance.  
Comparing simulation results to experimental data is essential for thoroughly assessing their veracity. This will serve as a benchmark for testing the precision and reliability of each turbulence model when analyzing the complexities of fluid flow within the exhaust manifold.  
Conceptualization, O.N. and M.H.; Methodology, O.N., M.H., M.L.S. and L.J.; Software, O.N. and M.H.; Validation, O.N. and M.H.; Formal analysis, O.N., M.H. and M.L.S.; Investigation, O.N., M.H. and M.L.S.; Resources, O.N., M.H., M.L.S. and L.J.; Data curation, O.N., M.H. and M.L.S.; Writing--original draft, O.N. and M.H.; Writing--review & editing, M.L.S. and L.J.; Visualization, M.L.S. and L.J.; Supervision, L.J.; Project administration, M.L.S. and L.J.; Funding acquisition, M.L.S. and L.J. All authors have read and agreed to the published version of the manuscript.  
The APC was founded by Transilvania University of Brasov.  
Not applicable.  
No new data were created or analyzed in this study.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Grid LSTM Model and Baseline  
We first compared the GridLSTM and other baseline models on the train-clean-100 dataset in the LibriSpeech corpus. In this experiment, each loop layer was set to two layers, and the output layer was a fully connected layer. The speech recognition evaluation indicators of different models are shown in Table 1. GridLSTM has the lowest CER on the LibriSpeech corpus and the highest comprehensive index F1. The sensitivity and specificity values of GridLSTM are larger than other models. Specifically, when compared to LSTM, the CER of GridLSTM can achieve a relative gain of 5.55%. In addition, it can be seen that the LSTM speech recognition task on the LibriSpeech corpus is more advantageous than the LSTM variant GRU. The results show that the GridLSTM model better exploits the benefits of introducing gated linear dependence across depth dimensions. This result is consistent with the results in [20].
## 2 Results and Discussion
### _Cp12 Binds Calcium with High Affinity_  
To validate whether CP12 is indeed a calcium-binding protein, a calcium overlay assay was performed with recombinant protein (Figure 3). The mature protein, without targeting peptide, was cloned into the pTWIN1 vector and purified from _E. coli_ under native conditions. The well-established calcium-binding protein aequorin (Aeq) and cytochrome C (CytC) were used as positive and negative controls, respectively.  
Purity of the isolated proteins was confirmed by SDS-PAGE (Figure 3B). Calcium-overlay assays with \({}^{45}\)Ca were performed by direct spotting of the proteins onto a PDVF membrane and calcium-binding was analyzed by autoradiogram (Figure 3A, upper panel).  
Aequorin, which contains  
Figure 3: CP12 is a calcium-binding protein. (**A**) Autoradiogram of calcium-overlay assay using recombinant CP12 (upper panel). Aequorin (Aeq) and cytochrome C (CytC) were used as positive and negative controls, respectively. Four micrograms of each protein were spotted onto a PVDF membrane and post stained by coomassie brilliant blue (lower panel). (**B**) Purity of recombinant proteins used for calcium-overlay assays was assessed by SDS-PAGE and stained by coomassie bi  
Figure 2: Deduced amino acid sequence of Arabidopsis CP12. Grey bars indicate peptides found by tandem mass spectroscopy that matched to this protein. An arrow indicates the potential cleavage site of the transit peptide [24]. Four conserved Cys residues of CP12 are underlined.  
three calcium-binding EF domains shows the strongest signal even though less protein was apparently present on the membrane (Figure 3A). CytC does not display any calcium binding showing that it does not occur unspecifically with any protein. On the other hand, CP12 shows strong calcium binding albeit slightly weaker than aequorin. To further confirm specific binding of calcium to CP12, binding assay were performedin the presence of other divalent cations (Figure S1). Neither Zn2+, Ni2+ nor Cd2+ were able to compete for binding of 45 Ca. All together these results indicate that CP12 is a novel chloroplast calcium-binding protein. CP12 does not contain typical calcium binding EF-hand motifs, so the exact molecular base for calcium binding needs to investigated. However, based on the protein sequence one can speculate that conserved negative charged amino acid residues that occur in abundance in the \(C\)-terminal part of the protein could be involved in this process.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 3 Proposed Ship Classification Method
### Fine-Tuning Deep CNN
#### 3.4.2 Fine-ResNet-18  
The convolution kernel size of the first convolutional layer (i.e., Conv1) equaled \(3\times 3\). Since the output size of Conv5_x equaled \(8\times 8\times 512\), the kernel size of the global average pooling layer was fine-tuned to \(8\times 8\), and the number of output layers within the fully connected layer (i.e., FC1) equaled 4 or 6.
## 4 Results and Discussion  
### Determination of the Design Compaction Level  
#### 4.1.1 Determination of Specim Weight  
It is necessary to specify a certain mixture weight in a mold or a specimen height, because the compaction energy applied to the specimen is not uniformly distributed [36], resulting in some differences in mixture performance in the height direction [37].  
To test hot mix asphalt, AASHTO T312 specifies placing 4500 g to 4700 g of asphalt mixture into the gyratory mold in one lift [38]. Supposing the Marshall density of the asphalt mixture to be 2.45 g/cm\({}^{3}\), the ultimate height of the asphalt mixture specimen was calculated to be approximately 108 mm. Therefore, the SGC specimen height was controlled at 110 mm to reduce the vertical difference of the compaction energy.  
The mixtures of Aggregate A with different weights were compacted for 160 cycles by SGC (TB-047/02 CONTROLS, made in Italy (Figure 4)). The compaction curves are shown in Figure 5.  
According to Figure 5, the ultimate specimen height was calculated, and its variation with mixture weight is shown in Figure 6.  
As the specimen height was determined to be 110 mm, the mixture weight in one specimen for Aggregate A was determined to be 4700 g. In the same way, the mixture weight in one specimen for Aggregate B was determined to be 4200 g.
On the other hand, recent evidence suggests that differences in language models may not be as pronounced when neural model parameters are analyzed alongside EEG or brain data [11; 12]. However, it is worth noting that no prior studies have explored text summarization from a cognitive neuroscience perspective. Given the significance of text summarization in facilitating quick information extraction and efficient knowledge management, as well as the current limitations regarding factual accuracy within existing models, this study aims to apply theories and methods from cognitive neuroscience to establish correlations between human brain activities and ABS model performance. It is important to note that this study is fundamentally exploratory in nature, without a predefined hypothesis. This approach is intended to enhance interpretability in text summarization and advance interdisciplinary research in these fields. Specifically, the main contributions of this study are:  
1. This study pioneers using EEG signals from a cognitive neuroscience perspective to investigate factual issues of ABS, offering novel insights into the relationship between models and human brain activity in language tasks;
2. This study compares variations in EEG signals corresponding to factual word phrases and non-factual word groups obtained through different fact term extraction methods (dependency relation, named entity, part-of-speech tagging, and TF-IDF). It was found that the distinctions in EEG were most significant for factual and non-factual word groups using named entity recognition and dependency relations. This lays a foundation for integrating extraction methods to better simulate human text processing;
3. This study employs Representational Similarity Analysis (RSA) to compare the correlation between typical deep language models (GloVe, BERT, GPT-2) and human brain activities, revealing significant differences in RSA scores among different models and periods of human brain activity under specific conditions. These findings suggest potential adjustments to enhance the model's resemblance to the functioning of the human brain and facilitate a deeper understanding of mechanisms involved in language processing tasks within the human brain.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 5 Effect of Metalloid-Based NPs on Medicinal Plants
### Silicon and Silica NPs
#### 5.1.3 Attenuation of Abiotic Stress  
SiNPs effectively reduced adverse effect of saline stress on leaf dry and fresh weight and Chl content in _Ocimum basilicum_ plants and increased proline levels at exposure to SiNPs suggested induction of the tolerance in basil plants [289]. Exposure of _Allium cepa_ roots to non-irradiated suspensions of SiO\({}_{2}\) NPs and those irradiated with UV-A light did not lead to the adsorption of SiO\({}_{2}\) NPs on the root surface and no considerable adverse impact was observed even at high exposure concentrations. Consequently, it can be supposed that cell wall shielded the cell membrane from direct contact with NPs. It could be mentioned that similar results were obtained also with application of TiO\({}_{2}\) NPs showing photocatalytic potential [290]. Priming of _Calendula officinalis_ L. seeds with 200 mg/L SiNPs greatly enhanced antioxidant activity and TFC of plants exposed to drought stress of 25% FC [287].  
Production of rosmarinic acid elicited by SiO\({}_{2}\) NPs in HR of _Dracocephalum kotschvi_ achieving up to 8.26-fold higher yield compared to control can be considered as very promising [163]. However, also beneficial impact of foliar treatment of _Mentha piperita_ plants with 100 mg/L SiNPs resulting in nearly double enhancement of menthol, menthene and menthyl-acetate in plants seems to be convenient for application also in the field [152] (Table 1).
## 4 LEACH Protocol Improvement Based on the Differential Evolution Algorithm  
The LEACH protocol improvement based on the differential evolution algorithm executes in rounds the same as the traditional LEACH protocol. Each round execution is divided into four phases: (1) partitioning of initial clusters, (2) collecting status information about the nodes inside clusters by auxiliary cluster head nodes, (3) optimizing and selecting cluster heads with differential evolution algorithms, and (4) forming optimized clusters. Among them, the third one is the key to the improvements proposed in this paper, and will be described in detail.
## 6 DRRF and Real-time Traffic
### Earliest Deadline First with DRRF (EDF-DRRF)  
For a combination of EDF and DRRF, called EDF-DRRF, we first apply EDF, and then regulate the packet stream with DRRF. In other words, the packets are sorted according to the deadline, and then DRRF is used to decide whether the packet with the earliest deadline is eligible for transmission without exhausting the flow's credits (deficits). Again, we allow fragmented packets to be transmitted for frame utilization purposes.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Beyond IoT Technologies
#### 2.6.3 Software-Defined Networking  
Software-Defined Networking (SDN) improves the security of smart grids by providing centralized, programmable control over network flows [60]. This enables dynamic rerouting in the event of network congestion or security breaches. To guarantee that vital energy processes continue, it separates hacked parts and optimizes traffic. SDN provides fine-grained control over device communication pathways when used with IoT networks, thus lowering attack surfaces and enhancing grid scalability and efficiency in general [61].
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 6 Conclusions  
The impact of continental drift on location measurements on a farm across time can be addressed by the use of a local datum, i.e., a local reference position. However, the likely availability of mass-market RTK/PPP devices, driven by the availability of services, such as Ginan (in Australia), and the increasing ubiquity of farm connectivity, driven by the availability of services, such as Starlink, should drive the use of the national official static datum. For example, the national static datum is GDA2020 in Australia and ETRS89 in Europe. Another advantage of using the local official datum is compatibility for access to government-curated data layers, e.g., road networks. Applications requiring high spatial accuracy should avoid the use of publicly available web maps through the use of alternate imagery, e.g., georeferenced aerial (UAV) imagery can be collected for the farm. This solution adds cost in the form of drone imaging but it decreases operational complexity by avoiding the need for ongoing corrections. The geodetic field is in a state of flux as the availability and use of high spatial resolution data increases. Users providing applications targeted to farm operations, e.g., autonomous vehicles using GNSS, should be aware of likely changes. To support the accurate use of collected data, it is essential to record the date of data capture and the coordinate reference station and system used, including the realization of WGS84 if employed, and any transformations undertaken. There is a pressing need to update metadata recording within data formats, such as JSON, to capture data for the documentation of datum and coordinates systems used in data capture, processing, storage/management, and delivery, with attendant location error estimation. Failure to document such metadata provides the user with an illusion of accuracy and compromises the future use of quality data. Conceptualization, H.K.D., K.B.W. and T.B. ; methodology, H.K.D. and K.B.W. ; formal analysis, K.B.W. ; data curation, K.B.W. ; writing--original draft preparation, H.K.D. ; writing--review and editing, K.B.W. and T.B. ; visualization, H.K.D. ; supervision, K.B.W. and T.B; project administration, K.B.W. ; funding acquisition, K.B.W. All authors have read and agreed to the published version of the manuscript. H.K.D. acknowledges receiving a CQUUniversity Research Stipend Scholarship and a CQU International Excellence Award. All data used in this study are tabulated within the manuscript. We acknowledge support from Mangoes Mapping (Atherton, Australia) and Dynamic Solutions (Rockhampton, Australia) in survey work. The authors declare 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. **Table A1. **_Cont._
# Edge Convolutional Networks for Style Change Detection in Arabic Multi-Authored Text
## 5 ECNN-ASCD Evaluation
### Results and Discussion
#### 5.2.2 Ablation Experiments
Fifth, incorporating the attention mechanism achieved promising results in previous SCD works ([PERSON] et al., 2018; [PERSON] et al., 2018). In this study, the attention mechanism was incorporated as straightforward learnable weights to identify the relative importance of nodes. The weights are learned using an FC layer. However, this incorporation did not bring stable behavior. The paired \(t\)-test results revealed that the fluctuating attention scores led to inconclusive additive results. Moreover, incorporating attention increases the learnable parameters and the model complexity without clear benefits. This case prompted us to exclude the attention component from the final model. Further research is recommended to investigate attention mechanisms along with GNN-based models for SCD. Sixth, the convergent case means training the best accumulated components until the convergence state is reached. This state yielded a substantial performance improvement. Developing a model with an acceptable complexity level enables continual learning for \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Components** & **Easy (F1)** & **Medium (F1)** & **Hard (F1)** & **Decision** \\ \hline Basic GCN & 0.7968 & 0.6958 & 0.3640 & - \\ Basic EdgeConv & 0.9714 \(+\)0.1745 (\(\mathcal{S}\)) & 0.8356 \(+\)0.1398 (\(\mathcal{S}\)) & 0.3640 \(+\)0.0000 (\(\mathcal{NS}\)) & Added \\ Warmup & 0.9835 \(+\)0.0121 (\(\mathcal{S}\)) & 0.9081 \(+\)0.0724 (\(\mathcal{S}\)) & 0.8180 \(+\)0.4540 (\(\mathcal{S}\)) & Added \\ Prev-X & 0.9851 \(+\)0.0016 (\(\mathcal{S}\)) & 0.9168 \(+\)0.0087 (\(\mathcal{S}\)) & 0.8426 \(+\)0.0246 (\(\mathcal{S}\)) & Added \\ X0 & 0.9918 \(+\)0.0068 (\(\mathcal{S}\)) & 0.9214 \(+\)0.0046 (\(\mathcal{S}\)) & 0.8684 \(+\)0.0259 (\(\mathcal{S}\)) & Added \\ Attention & 0.9904 \(-\)0.0015 (\(\mathcal{NS}\)) & 0.9247 \(+\)0.0034 (\(\mathcal{S}\)) & 0.8453 \(-\)0.0231 (\(\mathcal{S}\)) & Removed \\ X0-Convergent (ECNN-ASCD) & 0.9945 \(+\)0.0026 (\(\mathcal{S}\)) & 0.9381 \(+\)0.0167 (\(\mathcal{S}\)) & 0.9120 \(+\)0.0436 (\(\mathcal{S}\)) & Added \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of the ablation experiments. extended epochs within an acceptable time. As shown in Figure 4, the F\({}_{1}\) scores increase across epochs till depicting flattened curves. Therefore, the approved version of ECNN-ASCD was obtained.