esa-sceva/satcom-chunk-collection huggingface dataset:
# Total Ionizing Dose Effects on a Delay-Based Physical Unclonable Function Implemented in FPGAs
## 4 Experimental Results and Discussion
### Result Analysis  
The increase in the core currents are consistent with other results reported in the literature for the same FPGA [17]. In the case of FPGA1, the first failure occurred at 410 krad(Si), which is also similar to the first failure reported in [17], where the DUT worked properly until 345 krad(Si). Regarding the RO-frequencies, the experiments have shown that there is a good stability on the frequencies and the changes due to the accumulated dose are negligible.  
On the other hand, the quality metrics show that the accumulated dose makes the responses produced by the PUF unreliable. Nonetheless, as the uniformity metric shows, the randomness of the response remains unaffected by the total dose. This may be due to the decrease of the noise parameters that have a direct influence on the randomness. Reliability is the key metric to evaluate after the deployment of PUFs in space. If at pre-irradiation conditions all the metrics have acceptable values, only a decrease in the reliability due to ionizing radiation can affect the rest of the metrics. In this case, the reliability metric shows a little degradation that can be corrected using some typical countermeasures such as using only RO pairs with maximal frequency difference [2] or using quantizers with reliability guarantees [18]. Regarding uniqueness, as a collateral effect of unreliability, the results also show a degradation of the metric.  
Figure 11: Uniqueness vs. radiation.  
Figure 10: Reliability vs. radiation.  
All in all, we can conclude that total ionizing dose has a perceptible effect on the quality of the RO-PUF response. However, with some post-processing, this RO-PUF could be used for space applications.
# Evaluation of Short-Term Aging Protocol for Asphalt Mixtures
###### Abstract  
Asphalt mixtures are subjected to short-term aging during the production, placement, and compaction processes. Proper evaluation of asphalt pavement performance relies on the accurate characterization of asphalt mixtures during the design stage. In this study, three different loose asphalt mixtures often used in Qatar were evaluated to develop a laboratory short-term aging procedure. Sample mixtures 1 and 3 were collected from a construction site, while mixture 2 was obtained from an asphalt plant. Virgin aggregates and binders were also collected to reproduce the mixtures in the laboratory. Laboratory-produced mixtures were conditioned at 135 \({}^{\circ}\)C using various time durations. The mechanical properties of laboratory-produced mixtures were compared to those of mixtures produced on site. The results of the mechanical and binder testing demonstrated that the proper short-term aging protocol for asphalt mixtures often used in road construction in the State of Qatar would involve heating asphalt mixtures for 4 h at 135 \({}^{\circ}\)C before laboratory compaction.  
Keywords: asphalt mixture; short-term aging; dynamic modulus; superpave binder testing; asphalt mixture performance tester (AMPT) +
Footnote †: journal: Applied Sciences
# High Resolution Brillouin Sensing of Micro-Scale Structures
## 6 Conclusions and Outlook  
In this paper, we provided a summary of various Brillouin sensing techniques in micro-scale waveguides capable of detecting small transverse and longitudinal features in these structures. We gave an overview of different techniques for mapping the longitudinal features along the waveguide using distributed Brillouin scattering and showed that the highest spatial resolution among these techniques is offered by BOCDA. We then highlighted applications of Brillouin sensing in detecting various geometrical features in PCFs, microfibres, and on-chip waveguides using non-distributed measurement with high sensitivity and mapping the opto-acoustic responses using distributed SBS measurements with high spatial resolution, featuring today's state-of-the-art spatial resolution. The potential to further increase the spatial resolution to the scale of molecular vibrations (approximately 100 \(\upmu\)m) [88] will enable the study of the phonon-photon interaction at this limit.  
In addition to the discussed distributed Brillouin measurements techniques, which deal with the detection of backward Brillouin scattering, the distributed forward Brillouin measurement was reported recently for the first time by [PERSON] et al. [69] and [PERSON] et al. [70] with a spatial resolution of 100 m and 15 m, respectively. The main novelty in both approaches is the simultaneous activation (in the forward direction) and detection (in the backward direction) of the transverse acoustic waves, which involves heavy data processing and dealing with very weak SNR. These experiments open up new opportunities for characterizing on-chip silicon devices in the future, which was not possible using Brillouin distributed measurement before. This was mainly due to the fact that silicon as one of the main platforms of photonic integration exhibits strong forward Brillouin scattering, which has found applications in lasers [28], non-reciprocal modulators [107], and RF photonic filters [108], and therefore, opto-acoustic characterization of such devices is of great interest in this field. While the spatial resolution demonstrated by these techniques is not sufficient for monitoring cm- and mm-scale waveguides, there is room for spatial resolution improvement, as suggested by the authors.  
**Funding:** This research was funded by the Australian Research Council (ARC) Laureate Fellowship (FL120100029) and the Centre of Excellence program (CUDOS CE110001018).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Main Research
#### 3.2.5 Water Permeability Tests  
The results of the tests are presented in Figure 8. Figure 8a shows the results of the water pressures at which the signs of wet spotting on the opposite side of the test samples were detected, as well as the coefficients of variation corresponding to each sample type. Figure 8b shows the normalized water permeability levels corresponding to the maximum pressure at which complete water penetration occurred, as well as the deviations in the pressure values from the reference sample [23].  
According to the test results, the average maximum water pressure for the reference sample varied from 0.6 to 0.8 MPa, for which the variation coefficient was 14.9%. When including the two-component binder, Types 2-4), the pressure increased from 35 to 44% of the reference sample. The average values of the maximum pressure were 0.89, 0.91 and 0.94 MPa, and the corresponding variation coefficients were 12.1, 11.7 and 10.4%. A significant increase in pressures, compared to the reference sample, was observed in samples with a three-component binder (Types 5-7), amounting to 65 to 74%. The values of the average maximum pressures were 1.09, 1.11 and 1.14, and the corresponding variation coefficients were 9.8, 9.6 and 8.5%. Unlike water absorption, packing density plays a significant role in improving water permeability: if, for the reference sample water permeability level was W6, then for samples with a two-component binder it was W8, which corresponds to a filtration coefficient in the range from 6 \(\times\) 10\({}^{-10}\) to 1 \(\times\) 10\({}^{-10}\) cm/s; for samples with a three-component binder, the water permeability level reached W10, which corresponds to a filtration coefficient in the range from 1 \(\times\)10\({}^{-10}\) to 5 \(\times\) 10\({}^{-11}\) cm/s. The variation coefficients also showed a tendency to reduce the statistical deviations of more stable polyfractional compositions.  
Figure 7: Freeze–thaw resistance levels of concrete series: (**a**) Average water absorption values of the compared sample types and their coefficients of variation; (**b**) Reduction in water absorption values relative to the reference sample and the corresponding percentage deviations.
## 4 Analysis and Discussion
### Performance Evaluation by AVI Task
it features the second-highest median F1-score and the narrowest spread. Faster RCNN, FCN, attention CNN, CNN LSTM, DDETR, and custom transformer models have between 1 and 3 reported performances within the range of the best-performing model, Mask RCNN. Further investigations on those models will yield more general insight into their promising performance. There are mostly segmentation models found in the mIoU boxplots in Figure 14, as it was reported as a metric for other CV tasks less than five times. The reported values are generally lower than mAP and F1-score due to the higher difficulty of the segmentation task compared to object detection. There are also only five models with more than one sample, which are UNet, ensemble models, DeepLab, ViT, and custom encoder-decoder-CNNs. The best of those five regarding the median mIoU are ensemble models. DeepLab shows the second-highest median mIoU with a slightly larger min-max spread compared to ensemble models. Custom encoder-decoder CNNs achieve a median mIoU 5%p lower but with a slightly lower spread. The UNet model, which occurred the most, also has the worst performance regarding the median, as well as the interquartile range and min-max spread. Of the seven models with only one sample, an attention-enhanced CNN, DenseNet, and a custom transformer yield a performance that is more than 10%p better than the median of DeepLab. Further investigation of these architectures on different datasets seems promising. Based on our surveyed literature, we would recommend using Mask RCNN or YOLO models for localization tasks solved with object detection models and DeepLab, Mask RCNN, or UNet for segmentation. Despite a higher time expenditure for implementation and training, the use of customized CNN models can be justified and yield similar performance to the aforementioned models. However, it should be noted that, on the one hand, the necessary expertise must be available and, on the other hand, the amount of data must be sufficient. CNN LSTM, DDETR, custom transformer, and FCN look promising, as well, but have to be investigated more thoroughly to make a more informed statement.
# Early Ransomware Detection with Deep Learning Models
## 2 Background
The dataset spans various malware types, and it includes dynamic API call sequences from malware, capturing only the names of the API calls while omitting additional details such as call results or timestamps. API call sequences are mapped from strings into vectors using an API2 Vec vectorization method based on Word2 Vec [28]. The LSTM-based model of [27] achieved an F1-score of 0.9402 on the test set, and it was shown to be notably superior to traditional machine learning models. The paper [29] introduces an innovative approach to malware detection using deep graph convolutional neural networks (DGCNNs) [30]. It focuses on the capabilities of DGCNNs to process and analyze API call sequences. The dataset used in this work comprises 42,797 malware API call sequences and 1079 goodware API call sequences; only the API call names were recorded. DGCNNs demonstrated comparable accuracy and predictive capabilities to LSTMs, achieving slightly higher F1 scores on the balanced dataset but performing less well on the imbalanced dataset. The work [31] concentrates on the behavioral analysis of both malicious and benign software through API call monitoring. Instead of analyzing the sequence of API calls, this study employs advanced machine learning techniques to assess the overall frequency and type counts of these API calls. The authors developed two distinct datasets that include a wide variety of ransomware families. The datasets contain only API call names. Several ML algorithms were tested, including k-nearest neighbors (kNNs) [32], random forest (RF) [33], support vector machine (SVM) [34], and logistic regression (LR) algorithms [35]. Both LR and SVM exhibited exemplary performance, achieving perfect precision scores of 1.000 and the highest recall rates of 0.987, which correspond to an F1-score of 0.994. In the paper [29], the authors propose a novel behavioral malware detection method based on deep graph convolutional neural networks (DGCNNs) to learn directly from API call sequences and their associated behavioral graphs. They use longer call sequences (100 API calls) to achieve high classification accuracy and F1 scores on a custom dataset of over 40,000 API call sequences. The goal of this paper is to improve detection skills by combining deep learning with the fundamentals of behavioral analysis. This will lessen the possibility of false positives and improve the identification of zero-day threats.
## 3 Results  
### Effects of \(\beta\)-Lactolin on ROS Production in Astrocytes  
To evaluate ROS production in astrocytes, murine astrocytes were treated with LPS and IFN-\(Y\). Intracellular ROS levels were determined using the relative fluorescence intensity of CM-H2 DCFDA. Referring to data from the literature [25; 29], the used concentrations of LPS were as follows: 10, 50, and 100 ng/mL. Treatment with LPS caused a significant increase in the intracellular ROS levels compared to the control (control: 2077 \(\pm\) 34.94 RFU; LPS 10 ng/mL: 2688 \(\pm\) 60.58 RFU, \(p\) = 4.30 \(\times\) 10\({}^{-6}\); LPS 50 ng/mL: 2652 \(\pm\) 77.46 RFU, \(p\) = 8.72 \(\times\) 10\({}^{-6}\); and LPS 100 ng/mL: 2710 \(\pm\) 107.3 RFU, \(p\) = 3.41 \(\times\) 10\({}^{-6}\); Figure 1A,B). To investigate the effects of \(\beta\)-lactolin on ROS production in astrocytes, cells were treated with \(\beta\)-lactolin for 6 h after LPS stimulation. LPS treatment significantly increased the intracellular ROS levels (control: 3371 \(\pm\) 38.43 RFU; LPS: 4687 \(\pm\) 81.89 RFU, \(p\) = 2.76 \(\times\) 10\({}^{-6}\)), whereas treatment with \(\beta\)-lactolin significantly reduced intracellular ROS levels (\(\beta\)-lactolin 1 uM: 4092 \(\pm\) 89.52 RFU, \(p\) = 3.35 \(\times\) 10\({}^{-6}\); 5 uM: 4098 \(\pm\) 60.45 RFU, \(p\) = 2.91 \(\times\) 10\({}^{-6}\); and 10 uM: 3771 \(\pm\) 45.65 RFU, \(p\) = 2.76 \(\times\) 10\({}^{-6}\); Figure 1C,D). Of note, \(\beta\)-lactolin treatment alone  
\begin{table}
\begin{tabular}{c c c} \hline
**Gene** & **Forward Primer Sequence (5\({}^{\prime}\)-3\({}^{\prime}\))** & **Reverse Primer Sequence (3\({}^{\prime}\)-5\({}^{\prime}\))** \\ \hline _Gapdh_ & GTCATCCCAGAGCTGAACGG & ATACTTGCAGGTTTCTCACG \\ _Mao-b_ & GCCCATTTCCACCAGTATGGA & CTGGGAATCTTGCCCATC \\ _Cont_ & GGTTGTTGTTGCAG & TTTGTTGTTGCTGACATG \\ \hline \end{tabular}
\end{table}
Table 1: Primer list.  
did not induce a significant change in the intracellular ROS levels compared to the control (Supplemental Figure S1A,B). These results indicate that \(\beta\)-lactolin treatment suppresses intracellular ROS production in LPS-treated astrocytes.
## 5 Problems and Limitations  
The 3D printable graphene resins described in previous sections comprise three major components: (1) host polymer, (2) solvent or diluent and (3) graphene/GO in the form of powder or nanoplates. The starting resins are usually of low viscosity (0.25-10 Pa\(\cdot\)s) [133]. Commercial resins provided by Formlabs and Covestro uses viscosities between 0.3 and 1.6 Pa\(\cdot\)s. In general, is recommended a viscosity lower than 2 Pa\(\cdot\)s for SLA 3D printers [134] in order to allow recoating of the resin during SLA 3D printing [135]. Controlling and tuning rheological features is essential to enable 3D printing and requires the incorporation of additional materials, including polymers (polylactic acid, polybutylene terephthalate, lignin) or fumed silica. Binders are also added to provide mechanical integrity to the built objects. 3D printing of graphene-based materials is facing several problems and limitations that still hamper the manufacturing at industrial levels of a large variety of medical or electrical devices, thin films and elastic structures. More specifically, the primary requirement refers to the stability and homogeneity of the dispersions without graphene agglomerates. Secondly, matching the polarity between the polymer and filler makes the interaction between graphene and the polymer matrix stronger [136], which results in superior properties. As for the bioinks, the main drawback is represented by the incorporation of cells into printing materials and their survival after the fabrication process [137]. Ultimately, the cost-effectiveness of the 3D-printed objects. The 3D-printed structures should reproduce the natural tissue at all levels, i.e., physical and biological, with similar morphological and mechanical characteristics, capable of promoting cells attachment, growth and differentiation. As a result, building biomimetic scaffolds for tissue engineering is not an easy task, and one of the issues relates to porosity and degradability. An interconnected porosity will provide the optimal environment to promote tissue ingrowth but will affect the mechanical strength of the scaffold. AM techniques face some limitations in achieving high porosity in the fabrication of complex 3D scaffolds for tissue engineering. Particularly in SLA, the porosity of the building blocks is usually smaller than the original design due to overcuring in both depth and width [138]. Probably the most important limitation of the conventional stereolithography process is the adherence of the polymerized layers to the solid constraining interface. Some mitigation actions to prevent these stiction forces have been explored, including the use of
## 6 Discussion  
The experimental data from comparative trials comparing BMFA-Net with other segmentation models demonstrated the effectiveness of the BMFA-Net design. This confirmed that the utilization of the multi-level feature aggregation (MFA) mechanism and the design of the efficient atrous convolution attention (EACA) module in BMFA-Net, when compared with the single-branch CaraNet, could better capture local information and surrounding contextual information across different receptive fields, effectively reducing both the missed detection and false detection rates of polyps. The experimental data from ablation studies consistently highlighted the boundary constraint reverse attention (BCRA) module as a key component in BMFA-Net, emphasizing its role in focusing on edge information and enhancing the segmentation performance. These research findings aligned closely with those of BCNet, yet it should be noted that BCNet applies boundary constraints to joint feature maps using shallow features in an attempt to improve the quality of boundary segmentation. However, joint feature maps can be susceptible to edge information loss  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Mean Dice**} \\  & **CVC-300** & **CVC-ColonDB** \\ \hline Backbone & 0.871 & 0.709 \\ EACA + backbone & 0.903 & 0.772 \\ MFA + backbone & 0.886 & 0.737 \\ EACA + MFA + backbone & 0.919 & 0.805 \\ BCRA + EACA + MFA + backbone & 0.922 & 0.841 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation experiment on two datasets.  
Figure 8: Visualization results of ablation experiment.  
due to the iterative nature of multiple joint operations on deep layers, where directly incorporating shallow features, which often contain abundant noise, may not sufficiently mitigate edge information loss. Conversely, during the segmentation process, BMFA-Net employs bilateral constraints on deep features and reverse attention features using shallow features. This approach not only effectively resolves the edge information loss caused by zero padding at the edge of convolutional kernels in the EACA module but also addresses the issues of the under-segmentation and over-segmentation of segmentation results at the edges during the reverse erasure of foreground in the reverse attention module.  
Extensive experiments showed that the proposed BMFA-Net model achieved better performance on five challenging datasets compared with some state-of-the-art methods, especially on the CVC-300 dataset. It still has certain limitations. First, the current model encountered specific failure scenarios, as shown in Figure 9. These limitations mainly existed in the case of multiple polyps in an image; where the human eye finds it difficult to distinguish polyp regions from normal tissue, the model may miss detection. It is worth noting that the model proposed in this paper mainly focuses on polyp segmentation, and the experiments were not extended to other medical objects. Therefore, the next research stage is to apply the innovative method proposed in this paper to tumor segmentation in different anatomical regions of the human body.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 2 Materials and Methods
### Data Preprocessing Pipeline
#### 2.3.2 Data Cleaning and Imputation  
Missing data is a common issue in sensor-based time-series datasets. To address gaps in the time series, a learned imputation approach was employed. For each reservoir's time series, a pre-trained Bi-LSTM model was used to predict and fill missing values by leveraging temporal patterns from neighboring points.  
The Bi-LSTM imputer, trained on historical data, captured both forward and backward temporal dependencies, ensuring plausible value reconstruction for missing entries. The imputation stage was parallelized, with multiple MPI ranks, each handling a subset of reservoir time series, loading a shared pre-trained model to generate imputations concurrently.  
This parallelization enabled imputation across all 48 reservoirs simultaneously while leveraging GPU acceleration--each MPI rank offloaded its imputation task to a GPU via TensorFlow, significantly reducing processing time. As a result, even in cases where 5-10% of values were missing in some time segments, the pipeline was able to correct them within seconds. The effectiveness of LSTM-based imputation in preserving the integrity of subsequent forecasts is supported by prior research [16].
# Reliability-Based Optimization of Impeller Structure Using Exponential Function Approximation
## 1 Introduction
Unlike adaptive surrogate methods that require iterative model updates or hybrid single-loop methods dependent on derivative calculations, the proposed first-order exponential approximation decouples reliability analysis and optimization by constructing an explicit failure probability function through a single reliability evaluation. This eliminates repeated reliability analyses and sensitivity iterations, making it computationally efficient and practical for complex engineering systems. It first expresses the relationship between the relevant design variables and failure probability using a first-order exponential function approximation, and then applies this approximated function in an iterative decoupling process in optimization to refine the design solution. The proposed optimization method was applied to the design of a centrifugal compressor impeller; the results indicate significant improvements in computational efficiency using the adopted method when compared to traditional double-loop methods. The remainder of this paper is organized as follows: Section 2 details the proposed reliability-based optimization methodology, Section 3 applies the proposed optimization method to design an impeller, and Section 4 concludes the study.