esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Hard, Transparent and Hydrophobic Coating on PMMA Sheet
## 3 Results and Discussions
### Wettability of Coatings  
The wettability of the coating was evaluated by testing the average water contact angle of five samples. The wettability of coatings against nano SiO\({}_{2}\) content is shown in Figure 5. According to it, the average water contact angle of bare PMMA substrate was 79.3\({}^{\circ}\), while after coating, the surface water contact angle was improved above 100  
Figure 4: Surface morphology of nano SiO\({}_{2}\) particles modified coatings with different weight ratio (a) 0, (b) 0.5 wt%, (c) 1 wt%, and (d) 2 wt%.  
Figure 3: Schematic diagram of hydrolysis and condensation reactions of alkoxysilane monomers.  
showing hydrophobic properties. The greatest average water contact angle was 114.2\({}^{\circ}\) when the coating was modified with 0.5 wt% nano SiO\({}_{2}\) particles. However, the water contact angle decreased when the amount of SiO\({}_{2}\) continued to increase. It is generally accepted that surface micro-nano structure improves the hydrophobicity, which works similarly to lotus leaves. On the one hand, this contributed to the low surface energy of silicone coating and the micro-nano structure induced by nano SiO\({}_{2}\) particles, the surface water contact angle of PMMA increased after coating and showed hydrophobic properties, which can reduce the adhesion of marine microorganisms or make the pollutants easy to clean by seawater. On the other hand, due to the physical absorption of water on the SiO\({}_{2}\) particle surface [20], the water contact angle would decrease when massive nano SiO\({}_{2}\) particles were added.
###### Abstract  
Recent technological developments have led to the emergence of the next generation of industry--Industry 4.0. The Industrial Internet of Things (IIoT) is a key enabler of this new manufacturing paradigm where millions of interconnected smart devices, such as sensors and robots, manage massive amounts of data. Wireless sensor networks (WSNs), which allow the integration, flexibility, and scalability of the production line, thus avoiding the need for complex and expensive wired networks, are essential for IIoT. Nevertheless, the nonstop improvements of the smart industry have increased the amount of data transmitted by WSNs, making their nodes, which rely on small batteries, prone to exhaustion. In this scenario, where the transmission could be abruptly interrupted, losing time, information, and money, the development of energy-based management strategies for reducing the energy consumption of WSNs is urgent. In this paper, a software-defined network (SDN)-based energy-aware routing protocol is proposed to optimize the power consumption of WSNs within the framework of IIoT to support Industry 4.0. The SDN controller estimates the energy level of critical nodes in the WSN and decides the best routing path based on their energy consumption rather than on the widely used shortest-path criterion. Experimental results, obtained via a Mininet-Wifi simulation, show that the proposed approach prevents WSNs' nodes from draining their batteries and abruptly interrupting the data transmission. Hence, valuable retransmission time is saved, potential information loss is prevented, the need for replacing the node's battery is avoided, and the transmission lifetime is prolonged. In addition, the baseline shortest-path routing method is outperformed in terms of energy consumption and node failure, doubling its transmission time.  
software-defined network (SDN); energy-efficient consumption; wireless sensor network (WSN); Industrial Internet of Things (IIoT); Industry 4.0 +
Footnote †: journal: Applied Sciences
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 4 Results and Discussion
### Histogram with Probability Density Function
Moreover, normality tests (Shapiro-Wilk and Anderson-Darling) were conducted to determine whether the datasets followed Gaussian distributions, guiding the selection of appropriate statistical analyses. By applying these methods, the histograms and PDFsaccurately illustrate the statistical behavior of the network under jamming conditions before and after the mitigation strategy. The results offer valuable insights into how energy consumption was affected by interference and how the proposed mitigation mechanism effectively altered the network's statistical characteristics.
## 8 Evaluation  
### Setup  
We implemented our model on top of OpenWhisk, an open-source cloud platform. OpenWhisk consists of a REST interface for submitting functions and retrieving execution responses. The Controller is responsible for routing incoming function requests to the appropriate Invoker for execution. We modified the Controller by integrating both the Hybrid Predictor and the Skewness-Aware Scheduler. On each Invoker, we implemented the Buffer-Aware Scheduler, which manages the containers within the invoker. When a request arrives, we validate the API key and namespace permissions using OpenWhisk's built-in authentication system and then update the Function Log with the new request data. We replaced OpenWhisk's default Controller with a custom version that includes the Hybrid Predictor as a submodule. The Hybrid Predictor uses historical function logs (stored in CouchDB 3.3) to forecast the invocation count per function for the upcoming time window (5 min in our experiment). Additionally, we replaced OpenWhisk's default Sharding Container-Pool Balancer with our custom Global Scheduler, which retrieves real-time metrics (CPU, memory, execution duration) from all invokers to compute the Skewness Score, and routes requests to the invoker with the lowest score. We added a new module, the Invoker Metrics Collector, which uses custom HTTP endpoints on each invoker to collect real-time resource metrics and store them in Redis 7.2.4 for low-latency access. To improve communication efficiency, we replaced OpenWhisk's default pull-based model--where invokers fetch tasks from Kafka--with a push-based routing mechanism using gRPC v1.7 streams. This allows direct communication between the Controller, the Invoker Metrics Collector, and the Invokers. Additionally, on each invoker, we replaced the default ContainerPool with the Local Scheduler, which monitors the available buffer in each warm container. It assigns tasks to containers with sufficient buffer or triggers a cold start when necessary.  
The modified OpenWhisk setup runs on a single server equipped with a 32-core Intel Xeon Platinum 2.80 GHz CPU and 250 GB of RAM. The operating system used is Ubuntu Server 16.04, and all functions are generated and executed on the same host.
# NetSel-RF: A Model for Network Selection Based on Multi-Criteria and Supervised Learning
## 3 NetSel-RF Model
### Methodology  
We followed the Cross Industry Standard Process for Data Mining methodology (CRISP-DM) [27] to build our classification model. The aim was to optimize the network selection by choosing the best AP available in Wi-Fi networks. We carried out three CRISP-DM steps, namely data interpretation, data preparation, and modeling. Data interpretation generates, gathers, and defines the initial dataset that includes the set of data and the set of features related to the selection of the best AP to perform a hand-off. Data preparation covers all activities necessary to construct the final dataset from the  
Figure 1: Motivation Scenario.  
initial dataset; data preparation tasks include attribute selection as well as the transformation and cleaning of data. Modeling refers to the assessment of several supervised ML algorithms to choose the one that best foresees the AP to make the handover decision. The selection metrics are the true positive rate (TPR) [28], false positive rate (FPR) [29], Matthews correlation coefficient (MCC) [24], and time of classification (\(T_{C}\)) [28].
## 3 Results
### Conclusions  
The use of MEMS technology in metasurfaces allows for subtle changes in the resonant frequency value specified for each eSRR geometry. Such behavior can be utilized to extend the possibilities of the evaluation of thin dielectric layers. It is possible to observe not just the change of one resonant frequency caused by change of dielectric parameters of MUT, but it is possible to observe this effect for many resonant frequencies (from tenability range of the metasurface sensor). This could be more beneficial in case of strongly dispersive materials. The resonant frequency is not sensitive to all changes in the geometry of the unit cell, but the analyzed metasurface responds very sensitively to a change in the relative permittivity coefficient. The parallel application of two methods (MEMS technology and additional dielectric layer) allows for the more sensitive control of resonant frequency. This is a very desirable feature because the methods can be combined with each other as needed. Furthermore, approximation-based formulas were derived that enable the simple evaluation of MUT permittivity and, in consequence, permittivity-related inhomogeneities.  
In further work, other types of structural elements will be designed and validated in order to increase the tunability range of the metasurface resonator, and more analysis will be performed in order to evaluate sensitivity for various kinds of inhomogeneities and for variations of measuring conditions, such as the distance of the MS sensor and material under test.  
Conceptualization, P.L.; methodology, P.L. and P.G.; simulations, P.G.; validation, P.L.; investigation, P.G.; writing--original draft preparation, P.G.; writing--review and editing, P.L.; supervision, P.L.; project administration, P.L.; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.  
This work was done within the project co-financed by the Polish National Agency for Academic Exchange (NAWA, Poland) and German Academic Exchange Service (DAAD, Germany), under the grant no. PPN/BDE/2021/1/00012/U/0001. The APC was funded by the Research Fund of the Faculty of Electrical Engineering (West Pomeranian University of Technology, Szczecin, Poland).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experimental Results
### Results of Applying the Detection Model  
In this experiment, the data presented in Table 1 were applied to the digital document forgery detection models proposed in [7; 22; 28] to compare the results based on the input data. To ensure reliable statistics, 5-fold cross-validation was performed on the training data, while bootstrap cross-validation with 1000 iterations was applied to the test data, and the confidence interval was set to 95%. Across all models, the performance was compared based on the language combinations used in training and testing. Consistently, the highest accuracy and F1-score were observed when both the training and testing data were in English. This was followed by the case where both were in Korean. In contrast, when the training and testing data were in different languages, the models showed relatively lower performance. As shown in Table 6, these results suggest that the visual differences between Korean and English can affect model performance, highlighting the importance of using a unified language dataset when developing digital document forgery detection models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{\multirow{2}{*}{**Type of Data**}} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-5}  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\  & **(5-Fold CV, 95\% CD)** & **(5-Fold CV, 95\% CD)** & **(95\% CI)** & **(95\% CI)** \\ \hline \multirow{5}{*}{[28]} & Training—English & 0.65 & 0.64 & 0.76 & 0.74 \\  & Test—English & (0.63–0.66) & (0.63–0.66) & (0.72–0.79) & (0.71–0.78) \\ \cline{1-1}  & Training—Korean & 0.64 & 0.64 & 0.70 & 0.70 \\ \cline{1-1}  & Test—Korean & (0.62–0.67) & (0.61–0.67) & (0.66–0.74) & (0.66–0.74) \\ \cline{1-1}  & Training—English & 0.65 & 0.64 & 0.60 & 0.58 \\ \cline{1-1}  & Test—Korean & (0.63–0.66) & (0.63–0.66) & (0.55–0.64) & (0.54–0.62) \\ \cline{1-1}  & Training—Korean & 0.64 & 0.64 & 0.50 & 0.33 \\ \cline{1-1}  & Test—English & (0.62–0.67) & (0.61–0.67) & (0.43–0.56) & (0.30–0.36) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of applying the detection models when the language types of the training and testing data are different.  
Figure 11: The difference in matching distances between English and Korean using the SIFT algorithm.
# Real-Time HIFU Treatment Monitoring Using Pulse Inversion Ultrasonic Imaging
## 1 Introduction
Because the two sets of the signals received by the imaging transducer contain HIFU interferences in the same phase and imaging signals in \(0^{\circ}\) and \(180^{\circ}\) phases, the HIFU interferences can be eliminated after subtracting the two sets of the received signals. In this case, the amplitude of the imaging signal becomes doubled. Additionally, the frequency spectrum of an imaging transducer can be fully used for imaging because no HIFU interference exists in theory. Furthermore, the proposed method is relatively easy to implement because a general ultrasound imaging system has a circuitry for pulse inversion for tissue harmonic imaging. However, the best performance of the proposed method can be achieved when the target is stationary, which is not the case in practice. In the paper, therefore, we demonstrate how to implement the proposed method in a commercial ultrasound scanner and evaluate its imaging performance. In addition, the effect of the target motion on the performance of the proposed HIFU interference rejection is presented.
## 3 Experimental Data and Evaluation of Results  
### Source of Experimental Data  
In order to verify the performance of the proposed method, synthetic data and real data were used for experiments.
# RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks
## 4 Experimentation
### Data Set and Data Pre-Processing  
We use two new real traffic datasets, namely the CICIDS 2017 dataset [24] and the Bot-IoT dataset [38] for the experiments. Tables 2 and 3 summarizes the statistics of attacks in Training and Test in both datasets. Both datasets satisfy the eleven indispensable characteristics of a valid IDS dataset, namely Anonymity, Attack Diversity, Complete Capture, Complete Interaction, Complete Network Configuration, Available Protocols, Complete Traffic, Feature Set, Metadata, Heterogeneity, and Labelling [39].  
The Bot-IoT dataset contains more than 72,000,000 records devised on 74 files, each row having 46 features. We use the version proposed by [PERSON] et al. [40], which is a version of training and testing with 5% of the entire dataset.  
The CSV version of CICIDS 2017 contains 2,830,743 rows divided in 8 files, each row having 79 features. Each row of CICIDS 2017 is labelled as Benign or one of fourteen types of attack.  
In order to create a training and test subset, we concatenate the 8 files in one file containing a unique table that contains all benign and attacks rows. Then, we remove all rows that have the feature \"Flow Packets/s\" equal to 'Infinity' or 'NaN'. After that, we remove identical rows, namely Bwd PSH Flags,Bwd URG Flags, Fwd Avg Bytes/Bulk, Fwd Avg Packets/Bulk, Fwd Avg Bulk/Rate, Bwd Avg Bytes/Bulk, Bwd Avg Packets/Bulk, Bwd Avg Bulk/Rate and Fwd Avg Bytes/Bulk.  
After the elimination of these features, we extract the training and test subsets based on the distribution described in Table 2. In each subset we tried to include rows that contain all the attacks but the same row cannot appear in both subsets. For the training subset, we select the first rows of each type. Then, for the test subset, we select randomly some rows after the suppression of the training subset rows.  
Finally, each value \(x_{i}\) of the feature j is normalized based on the following equation:  
\[\overline{x_{i}(j)}=\frac{x_{i}(j)-min(x(j))}{max(x(j))-min(x(j))} \tag{1}\]
# Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
## 2 Materials and Methods
### Lue Gpp
Because the LUE equation estimate GPP from multiple satellite data, for example, NDVI, FPAR, and GLEAM soil water, it is considered satellite-observed GPP.
# Automatic Short Text Summarization Techniques in Social Media Platforms
## 5 Approaches to Summarization
### Hybrid Approaches:  
Hybrid approaches in short text summarization combine elements from both extractive and abstractive summarization techniques. These approaches aim to leverage the strengths of both methods to achieve more accurate and coherent summaries. Here are some common hybrid approaches used in short text summarization:  
* Extract-then-abstract: This approach involves first applying an extractive summarization technique to select important sentences or phrases from the original short text. Then, an abstractive summarization model is used to rephrase and refine the extracted content into a more coherent and concise summary. This way, the final summary benefits from the factual accuracy of extractive methods and the fluency of abstractive methods.
* Abstractive pre-processing: In this approach, the original short text undergoes abstractive pre-processing before being fed into an extractive summarization model. The pre-processing step involves paraphrasing and rephrasing the text to enhance its coherence and readability. The extractive model then selects sentences from the pre-processed text to form the final summary. By improving the input text's quality, this approach aims to generate more coherent and informative summaries.
* Extract-then-cluster-then-abstract: This approach combines extractive summarization with clustering techniques. First, important sentences are extracted from the original text. Then, these sentences are clustered based on their similarity, grouping similar content together. Finally, an abstractive summar rization model is applied to each cluster to generate concise and coherent summaries for each group of related sentences. This approach helps in handling overlapping information and ensures that important content is not repeated in the final summary.
* Reinforcement learning: Hybrid approaches can also involve the use of reinforcement learning to combine extractive and abstractive summarization [28]. The model learns to select important sentences through extractive summarization and generates new sentences using abstractive techniques. Reinforcement learning is used to optimize the summary's quality by rewarding the model for generating accurate and informative summaries while penalizing for errors. Hybrid approaches in short text summarization aim to address the limitations of individual extractive and abstractive methods and create more effective and informative summaries. These approaches require careful design and optimization to strike the right balance between factual accuracy and fluency while maintaining the summary's coherence and relevance to the original text. Ongoing research in this area continues to explore innovative ways to combine these techniques and enhance the overall performance of short text summarization models.
# Research on an ID-PCA Early Fault Detection Method for Rolling Bearings
## 3 Improved Deep Principal Component Analysis Method  
Aiming at the problem that the deep principal component analysis method cannot directly use the vibration signal to detect the early fault of rolling bearing, an Improved Deep Principal Component Analysis (ID-PCA) method is proposed in this paper. This method establishes a matrix through the time-domain characteristics of the vibration signal and then deeply decomposes the newly established matrix through the deep principal component analysis method so as to extract the weak components of the vibration signal. The fault characteristics of the rolling bearing can be detected at an early stage. By increasing the order of the ID-PCA method, extremely weak fault information can be detected, thereby realizing early fault detection.
# Experimental Evaluation of the Packet Reception Performance of LoRa
## 3 LoRa
The Coding Rate (CR) is the FEC rate used by LoRa modems, which provides protection against burst interference, and it can be set to 4/5, 4/6, 4/7, or 4/8 [29; 30; 31]. The results of the different combinations of BW and SF are shown in Table 1 using the SX1272 LoRa Calculator [32] published by Semtech. SF means the number of chips sent per symbol. As the SF value increases, the time-on-air also increases. At the same time, the probability that LoRa signals can be detected and received increases, and the transmission distance increases. BW determines the size of the chirp rate. As the BW value increases, the data rate will increase. However, as the time-on-air is reduced, the sensitivity will be affected. The size of the CR is related to the redundant information in the data packet. Therefore, a smaller value of CR is more resistant to interference, i.e., a packet transmitted with a code rate of 4/8 will be more tolerant to interference than a signal transmitted with a code rate of 4/5. Therefore, after theoretical analysis, we already know that SF, BW, and CR will affect the performance of LoRa.