esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Challenges and Limitations  
Despite the advances in employing carbon-based or metal-oxide nanomaterials in treating wastewater, a few challenges and limitations need further evaluation. Understanding the behavior, consequences, and long-term ecological effects of nanoparticles in aquatic environments is critical. Studies have shown that nanoparticles can undergo aggregation, dissolution, or chemical transformations in water, altering their properties and potential toxicity [173]. Establishing robust monitoring systems and risk assessment protocols is essential to ensure their safe application and disposal, minimizing adverse environmental consequences [174].  
Additionally, nanomaterials may degrade under specific conditions, reducing their efficacy in wastewater treatment. For example, photocatalytic nanoparticles like TiO\({}_{2}\) can lose activity due to fouling or changes in water chemistry. Developing advanced nanomaterials or applying surface modifications could improve durability and functional performance under harsh conditions. Addressing these challenges requires innovative research to enhance the stability and resilience of nanomaterial-based systems. Furthermore, the potential environmental release of nanomaterials during wastewater treatment operations raises concerns about their impact on aquatic ecosystems and human health [175]. Lastly, the other crucial issue limiting the usage of CNTs is their high cost in comparison to traditional adsorbents for full-scale water treatment applications. To solve this issue, it is necessary to carry out efficient and economical synthesis methods to modify and functionalize CNT materials to produce them on a wide scale.  
Most of the research work on carbon nanotube adsorbents is completely focused on laboratory experiments. To fully exploit the potential of CNTs and CNT membranes, studies on long-term performance, real wastewater, and the transition to large-scale applications are mandatory. In this direction, CNT production costs play a predominant role, and the annual production price cut recently experienced can support the design and investigation of low-cost methods in the next few years [176].
## 4 Conclusions and Future Work  
This paper investigated the problem of failure detection and diagnosis in grid-connected PV (GCPV) systems. The developed methodologies were based on Neural Network (NN), multiscale representation, and principal component analysis (PCA) tools. A multiscale PCA strategy was used to remove noise and extract and select more-relevant features. After that, the extracted features were fed as inputs to the NN classifiers in order to detect, classify, and distinguish between the different working conditions. After that, the extracted features were fed as inputs to the NNs classifiers in order to detect, classify, and distinguish between the different working conditions. In this work, we consider the diagnosis of all potential and frequent faults that may occur in GCPV systems in order to establish a comprehensive analysis and guarantee the efficiency and safety of such systems. Therefore, 21 faulty scenarios, including line-to-line, line-to-ground, connectivity faults, and faults that can affect the normal operation of the bay-pass diodes, were introduced. These faulty scenarios comprise various conditions: Simple, multiple, and mixed faults are injected at different levels and locations. To evaluate the robustness of the proposed strategy, various cases were investigated. The suggested solutions were sufficient for diagnosing the characteristics of GCPV operating conditions in both normal and abnormal modes. Nevertheless, the obtained fault diagnosis accuracy presented when applying the established approach demonstrated some missed detection and false alarm rates, thereby some faulty conditions not being correctly labeled. Accordingly, one future work aspect is to employ an online and adaptive NN-based tool to enhance the model, which can provide a reduced missed classification rate. Another direction of work is to develop adaptive NNs-based techniques to address and avoid uncertainties in PV systems using the interval-valued dataset representation. Indeed, an ensemble NNs-based model will be improved using multiple NNs-based strategies to raise the precision of the decision-making.  
Methodology, software, writing--original draft preparation, K.A.; validation, investigation, supervision, writing--review and editing, M.M., A.K. and M.H.; investigation, supervision, H.N. and K.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request.  
The authors declare no conflict of interest.
# Dust Emission Thresholds in Loess Soil Under Different Saltation Fluxes
## 5 Conclusions  
This study examined empirically the dust emission thresholds in loess soils with different content of sand-sized particles. The significance of this study was to provide empirical information on the link between soil particle size distribution and saltation and dust emission thresholds. The results of the wind tunnel experiment show that dust PM10 emission threshold is strongly associated with the saltation threshold. As such, the direct aerodynamic lifting of loose dust particle was not distinct in this study. Under conditions of dust emission: (i) the dust flux increases as the amount of the clay-silt fraction in the soil is higher, (ii) there is a logarithmic increase of dust flux with the of the shear velocity, (iii) the rate of the dust flux under certain saltation is subjected to the available dust amount in the soil during a wind event, and (iv) in soil with non-limited dust supply in the soil, the maximum rate of the dust flux is limited to the capacity of the saltation flux under certain shear velocity. The results of the sandblasting efficiency highlight the significance of the soil disturbance and aggregate disintegration in increasing dust emission from loess soils. The combination of loose sand particles in soils containing distinct amount of dust particles (>10%) is favor for dust emission. The findings of this study can be processed into parametrization in dust emission models. They can also support management strategy of soils in preventing dust emission by keeping the soil aggregation.  
Conceptualization, I.K.; methodology, I.K. and A.R.; formal analysis, A.R.; writing--original draft preparation, A.R.; writing--review and editing, A.R., M.B.-H. and I.K.; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# Overview and Perspectives for Vehicle-Integrated Photovoltaics
## 2 Results and Discussions
### Examination and Outlook of PV Technologies for PV-Powered Vehicles
#### 2.2.2 Perspectives of the PV Technologies and Their Performance for Implementation in EVs  
Different types of lightweight and/or flexible/curved PV modules already exist, including curved PV modules with a similar structure to a flat panel (2-3 mm thick front glass), flexible PV modules which are lightweight (often glass free), or light PV modules with a rigid structure. Examples of such PV technologies are presented in Figure 5. The characteristics of PV technologies vary depending on the application. Integration of the module-level electronics (usually bypass diodes or MOSFETs) into the module laminate could facilitate the placement of modules in/on EVs. An electrical architecture more resilient to shadowing is recommended for EV applications due to the higher frequency of partial shadowing compared to a power plant. One possibility is to increase the number of bypass diodes in a module of cells in series, whereas an alternative layout could include a parallel connection. Numerous solutions are under investigation for BIPV, which could inspire module design for EVs [72; 73]. A noncomprehensive list of commercially available low-weight and/or bendable modules is presented in Table 2, including information such as module yield, cell technology, weight, model, and manufacturer. Figure 4: Best cell and module efficiencies found in the literature. Data from [PERSON] et al. [69]. Figure 5: (**a**) Curved Panasonic module for Toyota Prius [74]; (**b**) Solbian flexible solar panels [75]; (**c**) Sunman light solar panel [76]. Among the projects listed in Table 2, 50% are flexible PV modules (Figure 6), representing the most active module technology. As flexible modules can be added to existing vehicles (e.g., the roof), in addition to other applications with limited added weight, they represent one of the fastest ways to integrate photovoltaics. On the other hand, curved photovoltaic modules with a 2-3 mm thick glass cover have significant added weight compare to the metal roof. This may explain the lower number of projects listed. Cur
# An Intrusion Detection System for RPL-Based IoT Networks
## 4 IDS Design
### Placement Strategies  
With respect to the placement strategy of the IDS modules, three options can be distinguished: distributed, centralised, and hybrid placement. A distributed placement strategy consists in installing and configuring an IDS detector in every network node in order to increase the chance of detecting an attack as much as possible. However, this strategy will result in higher power consumption at the nodes. On the other hand, a centralised placement strategy implies using one powerful node as the IDS (e.g., at the BR). This strategy does not involve the rest of the nodes in the network when detecting possible attacks; hence, this consumes less power. Nevertheless, the centralised strategy is only able to detect those attacks that are in range or those that need to send malicious packets to external networks through the BR. Finally, the hybrid placement combines both strategies. For example, it has one node acting as the central IDS, and multiple detectors (in charge of monitoring some areas of the network) gathering information on the network that the central IDS is not capable of obtaining.  
In general, a hybrid-based placement approach seems to be the best and the most flexible choice as it allows the IDS to detect more attacks by increasing the number of nodes involved in the monitoring and detection process, but it does not consume as much power as a distributed strategy. Hence, in our design, two types of nodes are used to perform the IDS tasks: a central IDS (which is typically placed in the DODAG root or BR node) and multiple detectors. Figure 1 represents a possible architecture to exemplify the hybrid design. As can be seen, the root node (aaaa::1), which is the one acting as the central IDS, does not have a large range. In fact, only nodes aaaa::2, aaaa::3, and aaaaa::4 are in transmission (TX) and reception (RX) range. Therefore, if node aaaa::1 acts as a malicious node performing a Hello flood attack, it will send a large number of Hello messages to the nodes in range (aaaa::8, aaaa::10, and aaaa::9). Consequently, the central IDS (aaaa::1) will never be able to detect such an attack. On the other hand, by having multiple detectors in the network, the possibilities of detecting an attack increases. In this example, detector node aaaa::8 receives the attack from aaaa::11 and is able to send an alert to the central IDS for further actions.  
Figure 1: Hybrid IDS design example. The shaded node (aaaa::1) is the root, and it hosts the central IDS. The doted rectangle in black represents the nodes in TX/RX range from the root node. Nodes in green (aaaa::2 and aaaa::8) represent IDS detectors. The doted rectangle in green represents the nodes in TX/RX range from the IDS detector nodes.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Bionic Graphene ECG Electrodes
#### 2.1.2 Amphibian- and Octopus-Inspired ECG Electrodes  
Human skin is a rough surface with a certain degree of wetness, and ECG electrodes are prone to sweat interference during long-term wearing [37]. The adhesion systems of amphibians can enhance the adhesion forces on a wet or rough surface through their hexagonal structure [38]. Moreover, the convex suction cups of an octopus enable strong adhesion to various surfaces in dry and wet conditions [39]. Inspired by the microchannel network in the toe pads of tree frogs and convex cups in the suckers of octopi, [PERSON] et al. proposed a water-drainable and air-permeable graphene ECG electrode [40]. Based on silicon holds with micro-hole patterns, the frog- and octopus-inspired structures were replicated using PDMS. Later, the PDMS structure was covered by reduced graphene oxide (rGO) nanoplatelets through spray-coating (Figure 1c). Inspired by rain frogs that can attach to rough surfaces by routing water through their pads with hexagonal channels, electrodes with hierarchical architecture show high peeling energy on wet surfaces. To further increase wet adhesion of the electrode in different directions, inspired by the protuberant structures of the octopus's suckers, microscale cylindrical holes were stamped and molded on the contact surfaces of the electrode. Due to the suction effect of the microstructure, the electrode patches realized 6.6 N\(\cdot\)cm\({}^{-2}\) adhesion strength on dry surfaces, 5.3 N\(\cdot\)cm\({}^{-2}\) on wet surfaces, and 4.5 N\(\cdot\)cm\({}^{-2}\) in underwater conditions. After coating with ultrathin rGO nanoplates to form the conductive layer, the realized ECG electrode could monitor ECG signals on sweaty skin during exercise or even under flowing water.
## 2 Sample Processing and Testing Equipment
### SHPB Testing Device  
The impact compression test was conducted using an SHPB test device from the State Key Laboratory for Mining Response and Disaster Prevention and Control (as shown in Figure 3); the device consists of pressure bar and data acquisition systems. The device has a yield strength > 800 MPa, an equipment pressure rod diameter of 50 mm, an incident rod length of 2 m, and a transmission rod length of 1.5 m. An impact air pressure of 0.4 MPa was selected to test the specimens, the speed at impact was about 18 m/s, and the incident energy was about 119.54 MPa. The voltage measured using the strain gauges of both the  
Figure 1: Location of the prefabricated cracks in sandstone specimens. (**a**) Schematic diagram of prefabricated cracks. (**b**) Sample diagram.  
incident and projection bars and the dynamic mechanical parameters were calculated using the three-wave method.  
Compression tests were conducted on intact sandstone samples and sandstone samples with different prefabricated crack thicknesses to determine their impact. Figure 4 shows the sketch of specimen clamping.  
Figure 4: Sketch of specimen clamping.  
Figure 3: The SHPB device.  
Figure 2: Diagram of the fractured sandstone specimens.  
Using the device, electrical signals were measured on the strain gauges, and the obtained test data were processed using the three-wave method (as shown in Equation (1) to obtain the basic dynamic parameters of the specimen).  
\[\begin{array}{l}\sigma(t)=\frac{E_{0}A_{0}}{2A_{\mathrm{S}}}[\varepsilon_{ \mathrm{I}}(t)+\varepsilon_{\mathrm{R}}(t)+\varepsilon_{\mathrm{T}}(t)]\\ \varepsilon(t)=-\frac{C_{0}}{I_{\mathrm{S}}}\int_{0}^{T}[\varepsilon_{\mathrm{ I}}(t)+\varepsilon_{\mathrm{R}}(t)-\varepsilon_{\mathrm{T}}(t)]dt\\ \dot{\varepsilon}(t)=-\frac{C_{0}}{I_{\mathrm{S}}}[\varepsilon_{\mathrm{I}}(t)+ \varepsilon_{\mathrm{R}}(t)-\varepsilon_{\mathrm{T}}(t)]\end{array} \tag{1}\]  
where \(A_{0}\) indicates the cross-sectional area of the lever; \(A_{S}\) indicates the cross-sectional area of the specimen; \(\varepsilon_{I}\), \(\varepsilon_{R}\), and \(\varepsilon_{T}\) denote the incident, reflected, and transmitted strains, respectively; \(C_{0}\) indicates the longitudinal wave velocity of the compression rod; \(\tau\) indicates the duration of stress waves; and \(L_{\mathrm{S}}\) indicates the length of the rock specimens.
## 2 Materials and Methods
### ECA-DenseNet  
The core innovation of DenseNet lies in its dense connection mechanism, which promotes feature reuse, mitigates gradient vanishing, and reduces the number of parameters. It is composed of alternating Dense Blocks and transition layers [18]. However, DenseNet is primarily designed for two-dimensional (2D) data-like images, presenting limitations when processing one-dimensional (1D) spectral data of tea tree seeds: 2D convolutional operations may fail to effectively capture key features in 1D data, while 2D pooling operations can disrupt the wavelength order and spectral feature information. Additionally, its complex network structure and excessive parameters may lead to overfitting.  
To address these challenges, this study proposes improvements to DenseNet-121. The improved ECA-DenseNet is illustrated in Figure 4, consisting of an input layer, 1D convolutional layer, three Batch Attention-Dense Block (BA-Dense Block), transition layers, an ECA attention layer, an upsampling layer, and a classification layer, with dense connections implemented in a feedforward manner between the layers. The input spectral data is first normalized by the input layer, then processed through Conv1 with 1D convolution and Batch-Channel Normalization (BCN) to enhance the robustness and generalization, followed by Rectified Linear Unit (ReLU) activation and max pooling for initial feature extraction. The features then enter the BA-Dense Blocks, where multiple convolutional operations and Mixed Self-Attention and Convolution (ACMix) modules are used to capture local and global feature dependencies, strengthening the feature correlations. After dimensionality reduction via transition layers, the features are processed sequentially by  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Noise Standard** & **Feature Peak** & **Peak Position** & **Baseline Fluctuation** \\
**Combination** & **Deviation (\(\sigma\))** & **Retention Rate (\%)** & **Shift (nm)** & **Amplitude (\(\Delta\)R)** \\ \hline w = 1, \(p\) = 1 & 0.042 & 98.7 \(\pm\) 1.2 & \(\pm\)0.5 & 0.025 \\ w = 3, \(p\) = 1 & 0.018 & 95.3 \(\pm\) 2.1 & \(\pm\)1.0 & 0.008 \\ w = 5, \(p\) = 1 & 0.009 & 82.6 \(\pm\) 3.8 & \(\pm\)2.3 & 0.005 \\ w = 3, \(p\) = 0 & 0.021 & 93.1 \(\pm\) 2.8 & \(\pm\)1.2 & 0.032 \\ w = 3, \(p\) = 2 & 0.017 & 92.4 \(\pm\) 3.5 & \(\pm\)1.1 & 0.019 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison Table of the Influence of SG Filter Window Length and Polynomial Order Parameter Combinations on Spectral Data Processing Indicators.  
Figure 3: (**a**) Raw acquired spectrogram; (**b**) spectrogram smoothed by SG filtering; (c) final processed spectrogram.
# Evaluating Cluster-Based Synthetic Data Generation for Blood-Transfusion Analysis
## 2 Materials and Methods
### Original Analysis by [PERSON] et al. (2022): Predicting Deferral of Blood Donors  
The Dutch blood bank tests donor hemoglobin (Hb) before each donation, so that donors with low Hb levels can be deferred. This protects donor health and ensures quality of the provided transfusions. Because on-site donor deferral forms a demotivating experience for the donor as well as an unnecessary waste of resources, [PERSON] et al. [26] trained machine learning models (specifically support vector machines, SVMs, see below) to predict whether donors will be deferred. Using these predictions, donors with a high probability of having a low Hb can be deferred on paper and requested to return at a later time; when their Hb is expected to be sufficiently high.  
[PERSON] et al. [26] performed the analysis with 250,729 donation records from the Dutch blood bank. The donation records contained the variables Sex, Age (years), Time of day of the donation (hours), Month of donation, Last ferritin level (a protein that indicates iron storage, measured in ng/mL), Time since last ferritin measurement (days), Previous Hb level (mmol/L), Time since previous Hb measurement (days). Additionally, the donation records contained the binary variable Deferral Status, i.e., whether a donors' Hb was sufficiently high for donation or not. The Dutch blood bank defines a sufficiently high Hb as above 7.8 mmol/L for females and 8.4 mmol/L for males.  
Predictions were made with two SVMs; one for male donors and one for female donors. Donations from 2017 up to 2020 served as the _training data_. The SVMs predict Deferral Status, based on the predictors Time, Age, Month, Last ferritin, Time since last ferritin measurement, Previous Hb level, and Time since previous Hb measurement. Their analysis code is available online ([[https://zenodo.org/record/6938113#.Y67A13](https://zenodo.org/record/6938113#.Y67A13) bMjsY]([https://zenodo.org/record/6938113#.Y67A13](https://zenodo.org/record/6938113#.Y67A13) bMjsY), accessed on 19 January 2022). A separate _test data set_ was used to assess whether donor Hb levels for donations in 2021 could be correctly predicted. This data set contained the same variables for 183,867 donations. Predictive performance was quantified by computing precision and recall scores. Precision is the proportion of deferred donors among donors predicted to be deferred, while recall is the proportion of donors correctly predicted to be deferred among donors who were in fact deferred. The SVMs achieved precision scores of 0.997 and 0.987 and recall scores of 0.739 and 0.637 for males and females, respectively.
# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 6 Related Work
Second, they developed a heterogeneous data access metamodel for remote sensing observation management in Precision agriculture, integrating IoT and remote sensing technologies to streamline data management and facilitate precision agriculture. Finally, they proposed a metadata model for air quality monitoring, leveraging a heterogeneous Key Performance Indicator (KPI) framework. This model utilizes continuously collected air quality data from IoT sensors to provide robust and efficient air quality detection and analysis. While previous studies have made significant advancements in bug priority prediction, the proposed Priority-Sensitive LSTM-Attention mechanism in this study offers unique contributions that differentiate it from existing research:  
* Integration of Priority-Based Feature Selection: Many prior works focus solely on textual or emotional features extracted from bug reports. This study incorporates priority-specific feature selection, emphasizing product and component attributes that are often overlooked. By tailoring the feature extraction process to priority levels, the proposed model provides a more focused and relevant input to the prediction framework. * Hybrid LSTM-Attention Architecture: Unlike models that rely solely on CNNs, RNNs, or traditional ML algorithms, the proposed approach combines LSTM's capability to capture sequential dependencies with an attention mechanism that dynamically focuses on the most relevant features. This hybrid architecture improves interpretability and prediction accuracy. * Statistical Validation: While many studies report performance improvements, this study performs rigorous statistical testing to confirm the significance of the results. This ensures that the observed differences between the proposed model and baselines are not due to random variation. * Comprehensive Evaluation Across Datasets: The proposed model is evaluated on two widely used datasets (Eclipse and Mozilla), showcasing its robustness and generalizability. Many previous studies focus on a single dataset or domain, limiting their applicability. * Addressing Data Imbalances: The study identifies challenges related to imbalanced priority distributions and highlights potential solutions, such as normalization and resampling, for future improvements. Previous works often overlook these data-related limitations. * Comparison with Broader Baselines: The study benchmarks the proposed model against a wide range of traditional and deep learning models, including SVM, NB, CNN, LSTM, and CNN-LSTM, providing a holistic assessment of its effectiveness.