esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research Challenges and Opportunities
### Energy Harvesting System Design  
The energy supply of a wireless sensor network is generally provided by batteries which have limited energy [27]. In addition, in marine environment monitoring systems, wireless sensor nodes are often deployed in unapproachable sea surface areas, and they are mostly planned for long-time operation, therefore, it is not convenient to replace the sensor batteries. Moreover, marine sensor nodes (sink nodes) have high energy consumption due to the use of long-range wireless communication protocol (GPRS). In order to reduce system maintenance requirements effectively, there is a clear need to design an energy harvesting system which uses renewable energies source such as solar [4], tidal power [78], or wind energy [79, 80].  
Some energy harvesting devices have been designed and developed to prolong the lifetime of marine environment monitoring systems. For example, [PERSON] _et al._[4] developed a solar energy harvesting device which is composed of two solar panels behind the electronic equipment with an inclination of 45 degrees in an opposite direction. In order that one battery is always being charged while the other is always being discharged, a power management system of a low-power maximum power point tracker (MPPT) circuit was developed and used for wireless sensor networks in [56] and [81].  
To design an advanced energy harvesting system for marine environment monitoring, we should consider the following three aspects: energy harvesting devices, power management system, and energy storage devices.  
* _Energy harvesting devices:_ An energy harvesting device is responsible for harvesting energy from the ambient environment. According to the characteristics of available ambient energies, we should choose appropriate energy harvesting devices and should consider how to install the energy harvesting devices.
* _Power management system:_ A power management system can intelligently manage the batteries to be charged and discharged at separate intervals of time. An ideal power management system can prolong the lifetime of batteries and easily store more energy for the system.
* _Energy storage devices:_ Energy storage devices normally adopt the rechargeable batteries. Usually, the energy capacity of rechargeable batteries is larger than daily system energy consumption and daily harvesting energy in order to store energy and permit the system to supply power even in case of bad weather [56].  
Given the aggressive and hostile marine environment, in order to harvest and use more reliable renewable energies, we can envision a hybrid harvesting energy system for marine environment monitoring in the future, which can use several renewable power sources such as solar, tidal power, seawater generator, and wind energy.
## 3 Material and Methods
### Databases  
Both for calibration of the visual tool and for the study of the indicators and the decision tree, two different databases are used:  
1. Synthetic Power Quality Disturbances--Database #1. This database comprises 1415 synthetic signals operating at 50 Hz, featuring flicker, harmonics, impulsive and oscillatory transients, interruption, notch, spike, sag, swell, harmonics with sag, and harmonics with swell. Signals with different degrees of noise contamination have been also considered. These signals have a duration of 0.2 s and sampling frequency of 3.2 kHz. The authors elaborate in MATLAB, version 9.10.0.1684407, through the tool proposed by [PERSON] et al. [25].
2. Real-life Power Quality Events--Database #2. Two datasets are included within this database. The first one, real-life power quality says [26], encompasses 30 50 Hz signals, which are marked by sag events. The second dataset, real-life power quality transients [27], is composed of 42 50 Hz signals with the presence of impulsive transient. Signals in both datasets have durations of 1 s and sampling frequency of 20 kHz.  
Throughout the next sections, signals with synthetic origin coming from Database #1 have been named S-[Event], while real signals coming from Database #2 use the R-[Event] nomenclature.
# An Effective Algorithm for Finding Shortest Paths in Tubular Spaces
## 6 Discussion
### A Reactive Method  
In this work, we used the same directed graph for [PERSON]'s algorithm and the proposed algorithm for the aim of simplifying the validation and the comparison results. It is important to note that the proposed method does not require knowledge of the entire volume \(\Omega\) to obtain a weighted graph before searching. In fact, the correct direction of the particle can be determined based on the observation in front of it. While using [PERSON]'s  
\begin{table}
\begin{tabular}{l l r r r r r r r r} \hline \hline  & & & \multicolumn{4}{c}{\(N_{\rho}=5\)} & \multicolumn{4}{c}{\(N_{\rho}=20\)} \\ \cline{3-10}  & & \(N_{\theta}=4\) & \(N_{\theta}=16\) & \(N_{\theta}=32\) & \(N_{\theta}=64\) & \(N_{\theta}=4\) & \(N_{\theta}=16\) & \(N_{\theta}=32\) & \(N_{\theta}=256\) \\ \hline \multirow{4}{*}{Tube 6} & LP (cm) & 22.28 & 22.35 & 22.29 & 22.27 & 22.32 & 22.37 & 22.44 & 22.26 \\  & LD (cm) & 25.83 & 25.83 & 25.83 & 23.74 & 25.18 & 25.18 & 25.18 & 22.05 \\  & \(\text{RMSE}^{\text{P}}\) (\%) & 1.05 & 1.34 & 0.77 & 0.71 & 1.13 & 1.70 & 1.72 & 0.89 \\  & \(\text{RMSE}^{\text{D}}\) (\%) & 13.58 & 13.58 & 13.58 & 2.90 & 12.00 & 12.00 & 12.00 & 0.30 \\  & T.P (s) & 0.45 & 0.78 & 1.18 & 1.82 & 0.86 & 1.70 & 2.73 & 17.01 \\  & T.D (s) & 0.16 & 1.54 & 5.70 & 22.00 & 1.66 & 21.38 & 82.90 & 5582.00 \\ \hline \multirow{4}{*}{Tube 7} & LP (cm) & 20.06 & 20.04 & 20.04 & 20.08 & 20.05 & 20.05 & 20.04 & 20.08 \\  & LD (cm) & 20.82 & 20.82 & 20.82 & 20.82 & 20.32 & 20.32 & 20.32 & 20.09 \\ \cline{1-1}  & \(\text{RMSE}^{\text{P}}\) (\%) & 0.62 & 0.14 & 0.30 & 0.73 & 0.55 & 0.17 & 0.20 & 0.59 \\ \cline{1-1}  & \(\text{RMSE}^{\text{D}}\) (\%) & 2.92 & 2.92 & 2.92 & 2.92 & 1.61 & 1.61 & 1.61 & 0.06 \\ \cline{1-1}  & T.P (s) & 0.31 & 0.60 & 0.85 & 1.34 & 0.66 & 1.15 & 1.67 & 7.67 \\ \cline{1-1}  & T.D (s) & 0.15 & 1.52 & 5.58 & 21.82 & 1.63 & 21.89 & 84.34 & 5585.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results between the proposed algorithm and [PERSON]’s algorithm with different granularities of the meshed cross-sections of the tube.  
Figure 11: Canal space with convex and variable cross-sections.  
algorithm, we cannot determine which path is the ESP until visiting all nodes and arcs of the graph; since we need to store all possible paths during the operation, the proposed method enables us to directly generate the motion decision during the movement there by the ESP is gradually traced by the moving path of the VP. Thus, it can be applied as a reactive method for robots that need to explore unknown tubular spaces such as lava tubes on an astronomical object [39] or environments in the absence of GPS signals [40]. In practice, the proposed algorithm should be run together with a given safety boundary constraint to prevent collision with inspection robots.
## 3 Results  
### Simulation Results  
For training purposes, 44 different cases were simulated and classified under one of the three fault locations. To ensure sufficient training within the capabilities of the hardware available, the number of validation checks was set to 44. A graph generated by the training tool to show the progress during neural network training is shown in Figure 10.  
From Figure 9 it can be seen that the training completed 634 epochs before 44 consecutive correct validations were obtained. It is important to note that one epoch in terms of a neural network is when the entire training dataset is passed through the network once. After the dataset is passed forward and backward through the network, the initial weights are updated, and the process runs again. This cycle continues until one of the parameters within the training tool is met. At each of the four (4) fault locations, as shown in Figure 1, eleven (11) fault scenarios were created. These faults are shown in Table 1.  
Aside from the nine faults shown in Table 1, a fault between all three phases and all three phases to ground was simulated, bringing the total number of faults per location to eleven (11). For the phase-to-phase faults, a fault resistance of 0.001 \(\Omega\) was used, and for faults including a ground, the grounding resistance was set to 0.01 \(\Omega\). After a five-level wavelet decomposition was performed on each phase of the transient fault signal, 600,132 data points were obtained per fault. The network was then trained using these data points to classify fault locations. The signals generated from a single-phase-to-ground fault and double-phase-to-ground fault are as shown in Figures 11 and 12, respectively. These are examples of the transient signals used to train the neural network. These two graphs only show the wave shapes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Single-Phase-to-Ground** & **Double-Phase-to-Ground** & **Phase-to-Phase** \\ \hline Phase A-Ground & Phase A-Phase B-Ground & Phase A-Phase C \\ Phase C-Ground & Phase B-Phase C-Ground & Phase B-Phase C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Breakdown of the simulated faults.  
Figure 10: Graph of performance during training.  
Figure 11: Single-phase-to-ground signal.  
Figure 12: Double-phase-to-ground signal.
###### Abstract  
We fabricated surface-enhanced Raman scattering (SERS) substrates using gold nanoparticle (AuNP)-decorated zinc oxide (ZnO) nanorods (NRs). Prior to decoration with AuNPs, ZnO NRs on the glass substrate fabricated using the sol-gel method could enhance the SERS signal for detecting \(10^{-5}\) M rhodamine 6G (R6G). Microscopic analysis revealed that the thermal-annealing process for fabricating the seed layers of ZnO facilitated the growth of ZnO NRs with the highly preferred c-axis (002) orientation. A decrease in the diameter of ZnO NRs occurred because of the use of annealed seek layers further increased the surface-to-volume ratio of ZnO NRs, resulting in an increase in the SERS signal for R6G of \(10^{-5}\) M. To combine the localized surface plasmon resonance (LSPR) mode with the charge transfer (CT) mode, ZnO NRs were decorated with AuNPs through pulsed-laser-induced photolysis (PLIP). However, the preferred vertical (002) orientation of ZnO NRs was prone to the aggregation of AuNPs, which hindered the SERS signal. The experimental results revealed that ZnO NRs with the crystalline structure of horizontal (100) and (101) orientations facilitated the growth of homogeneous, independent and isolated AuNPs which serves as \"hot spots\" for SERS signal of detecting R6G at a low concentration of \(10^{-9}\) M. Comparing to previous fabrication of SERS substrate, our method has advantage to fabricate AuNP-decorated ZnO NR in a short time. Moreover, the optimization of the SERS behaviors for different fabrication conditions of AuNPs using the PLIP method was investigated in detail.  
zinc oxide nanorods; surface-enhanced Raman scattering (SERS); gold nanoparticles; pulsed-laser-induced photolysis (PLIP) +
Footnote †: journal: Applied  
Article
## 6 Proposed Scheme
### Utility Center Registration Process  
The \(UC_{j}\) must register with \(TA\) in order to provide power management services. Figure 7 describes the UC registration process of proposed scheme and the steps of this process are given below.  
**Step 1:**: \(TA\) computes \(RID_{j}=h(ID_{j}||K_{s})\) and retrieves \(\{RID_{i},x_{i}\}\) in secure database. Then, \(TA\) computes \(X_{i}=h(RID_{i}||K_{s}||x_{i})\) and sends \(\{RID_{j},(RID_{i}|i=1,2\ldots,l),X_{i}\}\) to \(UC_{j}\).
**Step 2:**: After receiving the message, \(UC_{j}\) computes \(V_{i}=X_{i}\oplus ID_{j}\) and stores \(\{RID_{j},(RID_{i}|i=1,2\ldots,l),V_{i}\}\) in the database.
# Leveraging Adversarial Samples for Enhanced Classification of Malicious and Evasive PDF Files
## 3 Materials and Methods
### Experimental Setup  
#### 3.2.1 Dataset  
We gathered data from multiple sources including evasive and non-evasive samples. For the non-evasive data, the sources are  
1. The Contagio malware dump dataset [20]
2. The Surrogate dataset used in [18] which contains benign files from Google, and malicious files from VirusTotal [21]. For the evasive data, the sources are
* Public datasets that were the result of different evasion attacks in previous studies: evadeML [16], mimicry [18], reverse mimicry [17], and the parser confusion attack [19]. The common about these datasets is that they only contain evasive samples that are malicious. Even though some of these datasets targeted the evasion of a particular classifier (like evadeML for instance), these datasets can conserve their evasive characteristics when dealing with other models because of the evasion transferability discussed in Section 2.3.
* EvasivePDFMal2022: a real evasive dataset that was used to test the robustness of a malware detection model in [9]. This dataset is not focused on a specific kind of attack like the previous ones. It contains real samples that hold an evasive signature. Moreover, the difference about this dataset is that it contains some samples that are evasive, yet benign, which means that these benign files will trick a classifier into considering them malicious. The kind of attack used to create such samples is not provided by the authors, but such attacks can increase the false positive rate of a malware detection system, which makes it less reliable in practice. For the rest of the paper, these datasets would be referred to respectively as \"evasive dataset\" and \"non-evasive dataset\". The numbers of benign and malicious samples in the datasets are summarized in Table 1.  
Since the gathered data comes from multiple sources, we preprocessed the data to make it consistent. Moreover, we dropped all duplicate samples across the combination of different datasets, and we ended up with an evasive dataset that contains 20,059 samples and a non-evasive dataset that contains 20,647 samples. Next, we divided each of these datasets between training (80%) and validation (20%). For reproducibility of the results, while dividing the original data, we set the random_state parameter to 2, and we stratified it according to the class label (that determines if a PDF file is malicious or not).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Evasive Data** & **Non-Evasive Data** & **Total** \\ \hline Malicious & 15,723 & 9624 & 25,347 \\ Benign & 4336 & 11,023 & 15,359 \\ Total & 20,059 & 20,647 & 40,706 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data Characteristics.
# Feature-Interaction-Enhanced Sequential Transformer for Click-Through Rate Prediction
## 2 Related Work  
### Feature Interaction  
Feature interaction is a technique to extract feature combinations from the individual features, which helps increase the expressive power of the recommendation model.  
Previous works proposed feature interaction through inner product operations, which included a factorization machine (FM) [3] and product-based neural networks (PNNs) [4]. The FM projects the original features into low-dimensional vectors and calculates the inner product to learn second-order feature interactions. The PNN uses a product layer to learn second-order feature interactions and stacks multiple fully connected layers to further extract higher-order feature interactions. Yet, due to the layer depth scalability challenge, both the FM and the product layer cannot learn higher-order feature interactions. Other than explicit inner product operations, Deep Crossing [26] implicitly learns high-order feature crosses through DNNs with residual units, but the model cannot learn low-order linear feature interactions. NFM [27] stacks multiple DNN layers after the FM model and achieves more expressive power in learning than the FM model. AFM [9] combines the FM with an attention network to aggregate the output of the FM.  
Other than the DNN, innovations in feature interaction layer architectures have improved the scalability of feature interaction models and achieved effective higher-order feature interactions. DCN [7] designs a cross network to perform efficient feature interaction in linear complexity and allows for the growth of the stacking layer depth. [PERSON] [28] proposed a novel Compressed Interaction Network (CIN) that combines outer-product and convolution pooling operations to learn explicit high-order feature interactions. Ref. [11] proposed AutoInt, which adopts a residual self-attention network that automatically identifies high-order feature interactions and ensures exponential degree growth. DCN V2 [29] improves the cross network in DCN to learn both point-wise and vector-wise feature interactions. In [30], a self-attention network with a disentangled self-attention operation decouples the unary attention weights from the pairwise ones. FINAL [31] achieves exponential polynomial degree growth through layers of multiplicative interaction.  
Since AutoInt [11] explicitly learns high-order feature combinations through a self-attention mechanism and achieves exponential polynomial degree growth, a few self-attention interacting layers can suffice and provide explainability through the attention scores. Therefore, we adopt the AutoInt model to extract high-order feature interactions.
## 2 Materials and Methods
### Wavelet Transform  
Resting-state hemodynamic oscillations contain valuable information related to physiological activities, especially in the time-frequency domain [25; 30; 31; 32]. Generally, the frequency characteristics of hemodynamic signal could be assessed by the Fourier Transform. However, it is difficult for the Fourier Transform to achieve acceptable time and frequency resolutions simultaneously. Therefore, in this study wavelet transform was introduced to conduct signal decomposition and reconstruction.  
Continuous wavelet transform (CWT) is an approach that can perform the complex transformation of a signal from the time to the time-frequency domain with adjustable window lengths. By scaling (stretching/compressing) the length of the mother wavelet with scale factor \(s\) and translating the scaled wavelet window along the signal with time factor \(t\), CWT could provide acceptable time and frequency resolution for the corresponding frequency component. In this study the Morlet wavelet was selected as the mother wavelet due to its excellent localization in both the time and frequency domain [33]. The definition of wavelet transform is shown as follows:  
\[W(s,t)=\frac{1}{\sqrt{s}}\int_{-\infty}^{+\infty}\psi\Big{(}\frac{u-t}{s}\Big{)} g(u)du \tag{1}\]  
where \(\psi(t)\) is the wavelet, \(g(u)\) is the original time series, and \(W(s,\,t)\) is the complex wavelet coefficient. The absolute value of \(W\) represents the amplitude of the time series while the argument reflects phase information [22; 23]. Detailed description of wavelet transform used in this study could be found in previous studies [25; 27].  
The wavelet transform \(W(s,\,t)\) could be seen as a mapping of the original signal onto the time-scale plane. The signal \(g(u)\) can be reconstructed from \(W(s,\,t)\) by  
\[g(u)=C^{-1}\int_{t}\int_{s}|s|^{-2}\big{|}W(s,\,\,t)\big{|}^{2}ds\,dt \tag{2}\]  
where the constant \(C\) is determined by the shape of the mother wavelet. The reconstruction is only possible when \(0<C<\infty\) which is admissible in this case [25]. This reconstruction of signal from time-scale domain to time domain is called inverse wavelet transform. Thus, the function  
\[e=C^{-1}\frac{\big{|}W(s,\,t)\big{|}^{2}}{|s|^{2}} \tag{3}\]  
can be seen as the energy density of the signal in the time-scale plane, which is often called a scalogram [34].
## 5 Calculating the MUA's Energy Demand
### Annual Energy Demand with TCP Panels and Comfort Conditions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**OP1 MUA 2000**} & \multicolumn{2}{c}{**OP2 TCP Radiant Floor**} \\ \cline{2-5}  & **Summer** & **Winter** & **Summer** & **Winter** \\ \cline{2-5}  & **Wh/m\({}^{2}\)y** & **Wh/m\({}^{2}\)y** & **Wh/m\({}^{2}\)y** & **Wh/m\({}^{2}\)y** \\ \hline Enclosure & & & & \\ Glazing & 15,733 & 20,069 & 8716 & 18,341 \\ Walls & 1689 & 216 & 814 & 251 \\ Floors-ground & \(-\)1506 & 6367 & \(-\)1403 & 5002 \\ Partitions & 0 & 0 & 0 & 0 \\ Rooftops & 5279 & 7115 & 2452 & 4835 \\ Outdoor floors & 310 & 167 & 296 & 138 \\ Infiltration & 12,193 & 21,532 & 6393 & 17,031 \\  & **36,119** & **54,211** & **17,268** & **45,599** \\ \hline Loads & & & & \\ Lighting & 2414 & 2833 & 2414 & 2833 \\ Equipment & 3766 & 2658 & 1219 & 1050 \\ Occupation & 8956 & 6975 & 8956 & 6975 \\ Solar gains & 38,541 & \(-\)25,637 & 38,541 & \(-\)25,637 \\  & **53,671** & **\(-\)13,171** & **51,177** & **\(-\)14,779** \\ \hline  & kWh/m\({}^{2}\)y & kWh/m\({}^{2}\)y & kWh/m\({}^{2}\)y & kWh/m\({}^{2}\)y \\ Primary energy & **89.79** & **41.04** & **67.43** & **30.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculation of energy demands. Current state, radiant floor OP2 and radiating walls OP3.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Registration and Verification Phase
#### 5.2.1 Generating \(uid\)  
The \(uid\) serves as a unique identity identifier for recording \(U_{i}\) as a valid user on the blockchain and is used as the unique ID for updating ledger data on the blockchain. During the user registration phase, patients are required to provide basic information and na \(ID_{R}\) for registration with healthcare institutions. Subsequently, healthcare institutions confirm the patient's \(uid\) with the HMC: if the patient's \(uid\) does not exist, a new \(uid\) is generated by the management center upon request, using a hash function calculation, as shown in Equation (2):  
\[uid=Hash(Enc_{PK}(ID_{R})); \tag{2}\]
## 3 Methodology
### System Model
#### 3.1.4 Route Analysis  
Route analysis is performed to effectively analyze the data and to check the performance and integration of the data. Also, route discovery and analysis take place during this step. To ensure efficient data transmission, all potential routes to the sinks are examined and analyzed exhaustively. This analysis optimizes the routing procedure and reduces energy consumption.