esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### HMI Development  
The system's upper computer uses the SPD-070-Ax, a 7-inch display from Shobo Electronics, to communicate with the control unit through the RS-485 serial port, with default communication parameters of baud rate 9600 b/s, 8-bit data and no checksum. The HMI design and programming are written in the same CODESYS V3.5 SP17 programming environment based on the creation of the HMI Project module. The debugging and downloading of the program are performed via Ethernet connection to the PC. The \(F_{turg}\) for the on-screen parameter setting is sent to the PLC control unit via the CAN bus interface. The design of the HMI is shown in Figure 11, which mainly includes a communication switch, hydraulic switch and compression force range setting. In order to facilitate users to observe the system working condition, the real-time display of the current downforce \(F_{XY}\), the sidewall compaction force \(F_{YS}\) and the downforce quality evaluation index are set at the bottom of the interface.  
Among them, the sidewall compaction force setting range represents the triggering condition of the sowing depth system; the communication switch configures and initializes the parameters of each interface in the way that the control system turns on and off; the hydraulic switch controls the energization of the reversing valve of the main circuit; the current downforce can display the current downforce level of the single unit in real time, and its principle is the actual value of the downforce derived from the conversion of the feedback signal of the pressure-reducing valve; the sidewall compaction force made the monitoring value of the load pin be displayed in real time after filtering and calculating, and the data indicate the actual value of the comprehensive downforce of the gauge wheel on the soil; and the downforce quality index fluctuates and changes in real time with the operation, which reflects the evaluation index of the control effect of the downforce at that moment, and the downforce quality index \(W\) is calculated as follows:  
\[W= \frac{n_{c}}{N_{c}}\times 100\% \tag{16}\]  
where \(W\) is the downforce mass index, %; \(n_{c}\) is the number of samples in the set compaction range; and \(N_{c}\) is the total number of samples per unit time of the compaction force.  
Figure 11: System display interface design.
# Chronotype, Risk and Time Preferences, and Financial Behaviour
## Literature Review
### Conceptual Model and Hypotheses Development
#### 2.2.3 The Moderating Role of Monthly Income in a Second-Stage Moderated Mediation Model (H3 and H4)  
We have proposed that morningness influences the likelihood of delinquent credit card payment through time discounting, and we anticipate the strength of this mediating effect to vary according to the level of income in the path from time preference to delinquent credit card payments.  
The life-cycle model assumes that consumers will maximize the utility from lifetime consumption [68], and [PERSON] [69] proposed that borrowing is intended to change future resources into present resources with the purpose of increasing present consumption. Income is one determinant of outstanding credit card balances [19; 27]. These researchers reported that income is positively associated with the amount of outstanding credit card debt because the purpose of borrowing is to satisfy individuals' consumption demand throughout their lifetime. Therefore, in contrast to high income individuals, low and average income individuals are less likely to hold revolving credit card debt because they might be worried about the amount of such debt, which will add to their financial burden due to compounding interest rates [19]. In addition, low- and average-income people would care about their personal credit over the long term. Therefore, to maintain a good level of personal credit (i.e., changeable utility from future resources into present resources), future-oriented individuals whose income is at low and average levels, are less likely to have delinquent credit card payments when compared with their counterparts. Accordingly, we predict that income moderates the relationship between time perspectives and the probability of delinquent credit card debt and in turn moderates the indirect effect of morning orientation on delinquent credit card payments through the time perspective:  
**Hypothesis 3**.: _Income moderates the relationship between time preference and delinquent credit card payments._  
**Hypothesis 4**.: _The indirect effect of morning-types on delinquent credit card payments through the time perspective, is moderated by income, such that the indirect effect exists for those whose income is at low and average levels._
## 7 Conclusions
For the reproducibility of our results, we have published the code and methods in GitHub ([[https://github.com/elbisbe/XAI_DL_NetworkPacketClassification](https://github.com/elbisbe/XAI_DL_NetworkPacketClassification)]([https://github.com/elbisbe/XAI_DL_NetworkPacketClassification](https://github.com/elbisbe/XAI_DL_NetworkPacketClassification)) accessed on 14 March 2023). Furthermore, the same repository includes tools to preprocess and split datasets to build an appropriate experimental protocol with no biases. **Author Contributions:** Conceptualization, J.E.L.d.V. ; methodology, J.E.L.d.V. ; software, E.L.-B. and V.M.-G.; validation, E.L.-B., V.M.-G. and D.P. ; formal analysis, D.P. ; investigation, E.L.-B. and V.M.-G.; resources, J.E.L.d.V. ; data curation, E.L.-B. and V.M.-G.; writing--original draft preparation, E.L.-B., V.M.-G., D.P. and J.E.L.d.V. ; writing--review and editing, E.L.-B., V.M.-G., D.P. and J.E.L.d.V. ; visualization, E.L.-B., V.M.-G. and D.P. ; supervision, J.E.L.d.V. ; project administration, J.E.L.d.V. ; funding acquisition, J.E.L.d.V. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was partially funded by the Spanish State Research Agency under the project AgileMon (AEI PID2019-104451 RB-C21) and by the Spanish Ministry of Science, Innovation and Universities under the program for the training of university lecturers (Grant number: FPU19/05678). **Data Availability Statement:** The data presented in this study were derived from the following resources available in the public domain: ISCX VPN-nonVPN dataset [33]. The code to process these data is available at GitHub ([[https://github.com/elbisbe/XAI_DL_NetworkPacketClassification](https://github.com/elbisbe/XAI_DL_NetworkPacketClassification)]([https://github.com/elbisbe/XAI_DL_NetworkPacketClassification](https://github.com/elbisbe/XAI_DL_NetworkPacketClassification))) accessed on 14 March 2023. **Conflicts of Interest:** The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l}
1D & one-dimensional \\
2D & two-dimensional \\
3D & three-dimensional \\ ACK & Acknowledgment \\ AI & Artificial Intelligence \\ CNN & convolutional neural network \\ DL & deep learning \\ DNS & Domain Name System \\ DPI & Deep Packet Inspection \\ FPGA & Field Programmable Gate Array \\ FTP & File Transfer Protocol \\ GPU & Graphics Processing Unit \\ GradCAM & gradient-weighted class activation mapping \\ IP & Internet Protocol \\ ISP & Internet Service Provider \\ MDPI & Multidisciplinary Digital Publishing Institute \\ ML & machine learning \\ MTU & Maximum Transmission Unit \\ OTT & Over-The-Top \\ QoS & quality of service \\ ReLU & Rectified Linear Unit \\ RGB & Red, Green, Blue \\ RNN & Recurrent Neural Network \\ SVM & Support Vector Machine \\ SYN & Synchronize \\ TCP & Transport Control Protocol \\ TLS & Transport Layer Security \\ UDP & User Datagram Protocol \\ VPN & Virtual Private Network \\ XAI & eXplainable Artificial Intelligence \\ \end{tabular}
## 4 Event Packet Forwarding Algorithm
### Sink Position Bridges  
The proposed packet forwarding algorithm relies on the sink position, which means that sensor nodes must be aware of the current position of the sink and update it as the sink moves.  
As described in Section 4, the sink broadcasts its position along with a time-to-live (TTL) value. When a sensor node receives this packet, it decreases the TTL by one and checks if it is still greater than zero. If the TTL is positive, the node forwards the packet to its neighboring nodes.  
Since the sink has random mobility, it is possible that an event packet travels a much longer path to reach the sink depending on how the sink moves. Figure 8b shows one example of this path. Sensor node 1 will forward the packet following the sink trail; thus, the path that the event packet will take to reach the sink is \(1\to 2\to 3\to 4\to 5\to 6\to 7\to 8\). However, there is a possible shortest path for this event packet, which is \(1\to 11\to 12\to 13\to 14\).  
In order for the sink to find the shortest path, i.e., bridge, between its previous positions and its current position, the sink keeps track of a certain number of previous positions to estimate the existence of a bridge. As the sink moves, previous positions are stored so that its traveled distance can be estimated. For instance, Figure 8a shows where the sink saves its position, which is conducted every \(x\) meters. When the sink is at \(t12\), it can easily calculate how much it has traveled. For instance, when the sink is at \(t11\), it has traveled \(x\); at \(t10\), it has traveled \(2x\); at \(t9\), is has traveled \(3x\), and so on.  
Once the distance that the sink has traveled is determined, we can compare the Euclidean distance between its current and previous positions. If the ratio of these two distances is less than a predetermined threshold, it indicates that there is a shortest path, and a bridge needs to be created.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Field** & **Type** & **Description** \\ \hline address & string & The address of the sensor node. \\ \hline battery & double & The power remains in the battery of the sensor node. \\ \hline position X & double & Position of the sensor node in the X-axis. \\ \hline position Y & double & Position of the sensor node in the Y-axis. \\ \hline accurate & Boolean & Whether or not the position of the sensor node is accurate. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensor node position packet To create a bridge, the sink sends its position packet with a larger TTL value, which means that this packet will reach the sensor nodes that are close to its previous position. This helps those nodes to update the sink position and find the shortest path.
###### Abstract  
R&D in the area of high-temperature symmetrical electrochemical devices is needed to meet the challenges of hydrogen energy. In the present study, the effect of Fe\({}_{2}\)O\({}_{3}\) and CuO sintering aids on the electrochemical properties of the highly conductive solid electrolyte La\({}_{0.8}\)Sr\({}_{0.2}\)Ga\({}_{0.8}\)Mg\({}_{0.2}\)O\({}_{3-\delta}\) and La\({}_{0.6}\)Sr\({}_{0.4}\)FeO\({}_{3-\delta}\) electrodes for symmetrical solid oxide fuel cells was investigated. It is shown that the use of sintering aids leads to an improvement in grain boundary conductivity and allows us to reduce the sintering temperature to obtain a dense electrolyte with the same level of conductivity. It is shown for the first time that the nature of the sintering aids and the sintering temperature affect the La\({}_{0.6}\)Sr\({}_{0.4}\)FeO\({}_{3-\delta}\) electrode activity differently depending on the gas environment (air or hydrogen). On the basis of the analysis of the impedance spectra by the distribution of relaxation times, assumptions were made about the nature of the rate-determining steps of hydrogen oxidation and oxygen reduction. It is shown that the nature of the rate-determining steps can change depending on the electrode sintering temperature. It was found that among the studied electrodes, La\({}_{0.6}\)Sr\({}_{0.4}\)FeO\({}_{3}-\delta\) with 3 wt.% Fe\({}_{2}\)O\({}_{3}\) sintered at 1050 \({}^{\circ}\)C is optimal in terms of activity in oxidizing and reducing atmospheres.  
sintering aid; solid oxide fuel cell; symmetrical SOFC; LSGM; LSF; DRT; HOR; ORR
# Contrasting Dual Transformer Architectures for Multi-Modal Remote Sensing Image Retrieval
###### Abstract
Recent developments in transformers and its variants have extended its ability to contextualize the information within and across different data modalities through the attention mechanism; therefore, obtaining more representative visual and text features that can help in achieving better text-to-image retrieval. It is worth noting that transformers have been recently introduced in image classification [8], multi-labeling [9], and change detection [10]. Figure 1: Content-based RS image retrieval system: (**a**) single modal, (**b**) cross-modal. In this paper, we propose an efficient text-image retrieval approach based on vision and languages transformers. Our method use an embedding model composed of language and vision transformer encoders for aligning the visual representations of RS images to their related textual representations. We learn the weights of these encoders by optimizing two contrastive losses related to image-to-text and text-to-image classification. Basically, we aim at maximizing the similarity between the image and its corresponding sentence, while minimizing its similarity to unrelated sentences and vice versa. In the experiments, we use four RS benchmark datasets to validate our approach which are the Text-RS, Merced, RSICD, and Sydney datasets composed of images acquired with different sensing platforms. In addition, we propose different transformer-based architectures and introduce different types of transformer architectures. The remainder of the paper is structured as follows: Section 1 discusses related works on single-modal and cross-modal image retrieval; Section 2 introduces the text-image matching methodology; Section 3 presents a detailed experimental analysis on four RS datasets; and finally, Section 4 draws conclusions and forecasts future developments.
## 3 Improvement of the Retinex Algorithm
### Improvement of the Luminance Component V
#### 3.2.1 Fusion Algorithm
The formula for the Retinex algorithm based on bilateral filtering is shown in Equations (13) and (14):  
\[r(x,y)=\log_{2}R(x,y)=\log_{2}S(x,y)-\log_{2}L(x,y) \tag{13}\]  
\[r(x,y)=\sum_{i=0}^{K}\omega_{i}\{\log_{2}S(x,y)-\log_{2}[G_{i}(x,y)S(x,y)]\} \tag{14}\]  
where \(K\) denotes the number of weights, the weight of the \(i\)-th scale; and \(G_{i}(x,y)\) denotes the centre surround function and the inverse logarithmic transformation of Equation (14). An inverse logarithmic transformation was performed to obtain the reflection image \(R(x,y)\). The specific algorithm is shown in Figure 4.
# A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
## 4 Prediction of Local Anode Effect Based on Analysis of Anode Guide Current Fluctuation  
The above method is used to detect the anode current fluctuation in different areas of the aluminum electrolytic cell. If current fluctuation is detected in an anode rod of the electrolytic cell, the current of eight anode rods in the current fluctuation area is tracked and predicted. The anode rods in this area are divided into two groups, non-fluctuation anode rods and fluctuation anode rods, according to the current fluctuation detection results. The ARIMA model is used to track and predict the current of non-fluctuation anode rods in this area, GRU-LSTM hybrid neural network is used to track and predict the current of fluctuating anode rods, and the predicted value \(T_{p}\) at the moment of anode effect is determined from the current tracking results of fluctuation anode rods, and the local anode effect level is determined according to the number of changes from non-fluctuation anode rods to fluctuation anode rods and violently fluctuating guides predicted by the ARIMA model within \(T_{p}\). The flow diagram of local anode effect prediction based on fluctuating anode current detection is shown in Figure 4.  
STEP1: The current sequence data of eight anode rods in one of areas is obtained by dividing the areas according to the electrolytic cell structure. The current sequence is divided by a sliding window. The current sequence fluctuation detection algorithm is used to detect its fluctuation and locate the anode rod where the current fluctuation occurs.  
STEP2: For the fluctuation detection results in STEP1, the GRU-LSTM hybrid neural network is used to track and predict the current of the fluctuating anode rod in this area, and the predicted value of the strong fluctuation current determines the strong fluctuation occurrence time \(T_{p}\) (fluctuating anode rod \(\rightarrow\) strong fluctuating anode rod); the AMIRA model is used to track and predict the current of the non-fluctuating anode rod in this area. According to the prediction results, the number \(N\) of the anode rod changing from non-fluctuating to fluctuating and strong fluctuating in \(T_{p}\) time is determined.  
STEP3: The time \(T_{p}\) in STEP2 is taken as the prediction value of the time when the anode effect occurs, and the local anode effect level is divided according to the number \(N\) of new fluctuation anode rods and strong fluctuation anode rods.
# Electrospun Nanofibers for Integrated Sensing, Storage, and Computing Applications
## 2 Different Types of Electrospun Nanofiber Process and Structures
### Porous Nanofibers by Pretreatment  
Nanofibers prepared by traditional electrospinning methods ought to have a solid structure, but porous fibers can have more advantages due to the dramatically increased specific surface area ratio of the material. In the previous study, there were several ways to generate porous nanofibers. First of all, different material concentrations in the solution can easily affect the morphology and pore formation of nanofibers. It has been proven that varying weight percentages of zeolitic imidazolate framework-8 (ZIF-8) can effectively manipulate the formation of pores in the poly (lactic acid) (PLA)/ZIF-8 nanofibers. Increasing concentrations of ZIF-8 (0, 1, 2, 3, 5 wt% based on the weight of PLA) were added to the precursor solution to produce porous fibers with rising pore size from 0.018 um\({}^{2}\) to  
Figure 1: Schematic setup image for electrospinning method. Reprinted with permission from Reference [9]. Copyright 2017 American Chemical Society.  
Figure 2: SEM images of as-spun composite InTiO nanowires at different scales: (**a**) 2 μm, (**b**) 100 nm. Reprinted with permission from Reference [32]. Copyright 2019 IOP Publishing Ltd.  
0.080 \(\upmu\)m\({}^{2}\) as exhibited in Figure 3a-e [33]. Besides, selectively removing one of the components from the as-spun fibers is another way to the formation of the porous structure. The calcination process in the fabrication of nanofibers will result in the removal of the organic components. When adding a highly volatile solvent into the spinning solution, pores would appear on the surface of nanofibers after the annealing process. This proper treatment of selective removal of the sacrificial components in the fiber is considered to be the most straightforward approach to generating nanofibers with pores, and several experiments have clarified the contributions of the solvent with volatility such as dichloromethane (DCM) to the porous nanofibers [9]. Finally, electrospinning the nanofibers directly into a cryogenic liquid causes polymer solvent phase separation, providing another route for porous structure formation. Polystyrene (PS), Poly (acrylonitrile) (PAN), and Poly (vinylidene fluoride) (PVDF) nanofibers with pores made by rapidly cooling the fibers before complete solidification can be seen in Figure 4f-h [34].
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 6 Comparison and Evaluation
### Computation Overhead
According to Figure 7c,d, we can see that the computation time of PPDB is only a little higher than that of scheme [23] at the AC side. However, the computation time of PPDB at the SP side is far less than that of [27]. The reason is: in PPDB, when the number of SMs is \(n\), SP needs to execute \(2T_{B}+T_{C-EIGamal}\), while in [27], SP needs to execute \(2(n+1)T_{B}+T_{C-EIGamal}\). When \(n=100\), the computation time of [27] at the SP side is about 70 times that of PPDB. From the above analysis, it is easy to see that PPDB has good performance. We also compared the computation time of scheme [23] with our scheme in the Billing phase. In our simulations, we set the number of SMs and time slots to \(n=10,20,30,\cdots,100\), \(t=10,20,30,\cdots,100\), respectively. Figure 8 shows the comparison results. It is not difficult to find that the computation time required for scheme [23] is much higher than that of PPDB. When \(n=100\) and \(t=100\), PPDB is about 40 times more efficient than [23]. Figure 6: Comparison of computation time for batch verification and single verification by FN. Figure 8: Comparison of computation time between [PERSON] et al. [23] and our PPDB in Billing phase. Figure 7: Comparison of computation time of [PERSON] et al. [23], [PERSON] et al. [27] and our PPDB at each side.
# Experimental Study on Horizontal Pipeline Transportation Characteristics of Coarse Particle
## 4 Results and Discussion
### The Influence of Particle Diameter on the Velocity Distribution of Coarse Particle Groups  
As shown in Figure 12, it can be observed that for coarse particle groups of the same size, an increase in water flow velocity leads to a continuous increase in both the overall velocity and the maximum velocity of the coarse particles. Simultaneously, the particle groups with higher velocities near the centerline of the pipeline become more pronounced. Notably, the 0.9 mm coarse particle exhibits a unique behavior: as the water flow velocity increases, the maximum velocity of the coarse particles shifts vertically, a phenomenon not observed in the two larger coarse particle groups.  
Figure 11: Suspension motion of heterogeneous flow (**a**) d = 0.9 mm; (**b**) d = 1.5 mm; (**c**) d = 2.5 mm.  
Figure 12: Velocity distribution for different particle sizes.
## 2 Stable Flows
### Algorithms to Find Stable Flows
A similar argument can applied to the end vertex of the blocking walk: there must be a dominated edge ending at the same vertex and having nonzero value. The unsaturated edge can not be a refused one, since we always refuse the worst edges. The argument above shows that the blocking walk must start with a refused edge and end with a not fully proposed one. This means that along the walk, there has to be at least one refused edge, \(uv\), and an at least partly unexamined one, \(vw\). This implies that vertex \(v\) refused flow, although it has not filled up its outgoing quota, a contradiction. **Corollary 2.6**. : _If \(c\) is rational, both algorithms return a rational stable flow._  
Proof. : Any instance with a rational capacity vector can be transformed to an instance with an integral capacity vector by multiplying all capacities with their smallest common denominator. A simple network setting can illustrate how large capacities may cause a long running time. Note that this example is the flow extension of the allocation problem described by [PERSON] and [PERSON] [2]. In the example above, \(S=\{s,t\}\), and \(N\) is an arbitrary large number. After saturating \(sv\) and \(su\), the simultaneous push algorithm saturates \(uw\) and \(vz\) and proposes along \(vw\) with one unit. This offer will be accepted by \(w\), forcing it to refuse one flow unit along \(uw\). Thus, \(u\) has to submit an offer to \(z\), which needs to accept it and reject a flow unit from \(v\). An alternating cycle of new offers and refusals will be made along \(v,w,u,z\), as long as there is any flow to refuse along \(uw\) and \(vz\). This means, in total, \(N\) augmentations are along the cycle.