esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Characteristics of the Proposed Multimodal Emotion Detection System
### Representation of the Detected Emotions  
As stated in Section 2.1, there are two approximations to classify the detected emotions: the discrete approach and the use of an affective space. In our approach, we have adopted the first option for the following reasons:* To simplify the implementation of new interaction dialogs: Since the dialog system uses the perceived user emotion and this dialog is specified by an XML file [48] (which follows the VoiceXML [49] standard and is interpreted by the dialog manager, that is based on the slot filling tool [50], it is much easier to write sentences, such as: \"_if the user is happy, I offer to play a game\"_ instead of: \"_if the activation level is \(\geqslant\)50 and the evaluation level is \(\geqslant\)90, then I offer to play a game\"._
* To simplify the unification of information given by the different channels used to detect the user emotion.  
The selected set of emotions is the following: _neutral_, _happiness_, _sadness_ and _surprise_. The reasons for selecting these four emotions are the following:  
* A small set of emotions is required, since the success rate in emotion detection decreases as the number of emotions increases.
* For the HRI, these emotions have been considered to be the most relevant ones in order to personalize the dialog.
* These are emotions that are easily distinguished by the different channels used. Some of the emotions proposed by [PERSON] have not been considered (fear, disgust and anger), since the considered visual tools have difficulties in detecting them.  
Figure 3: Multimodal emotion detection system.  
Moreover, neutral emotion has been considered, since, as other authors say [51], frequently, the percentage of neutral talk is much higher than the emotional one. Therefore, in the automatic emotion detection area, the neutral state is considered as another emotion.
# Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction
## 4 Experimental Setup
### Datasets  
In the area of PdM, a well-established benchmark called the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) [36] dataset has been considered for many years as the de facto standard benchmark for RUL prediction. Since the dataset became publicly available on the NASA's data repository in 2008, it has been widely used to develop and evaluate a multitude of RUL prediction models. In the CMAPSS dataset, the data  
Figure 2: Illustration of the Symbolic Fourier Approximation process. \(X_{i}\) denotes a selected Fourier coefficient, and \(o_{i}\) represents each output symbol from the SFA.  
consist of the run-to-failure degradation trajectories of aircraft engines, which however are solely based on MATLAB simulations, without considering real flight conditions.  
In 2021, the NASA released the new CMAPSS (N-CMAPSS) [37] dataset. One major difference with respect to the previous dataset is that each time series consists of millions of samples, reflecting data acquired under real flight conditions. Thus, the dataset is significantly larger and it is more realistic. In the following, we briefly present the details of the two datasets.
# An Investigation of Alternatives to Transform Protein Sequence Databases to a Columnar Index Schema
## 2 Background
### Real-Time Analysis of Mass Spectrometry Data  
As introduced above, protein identification is not usually streaming-based and needs all the experimental data at once. In order to circumvent waiting hours until the measurement is finished, we need to adapt the algorithm to the FAST data architecture [19]. Figure 3 shows the general cloud architecture of the real-time diagnostic platform [20]. Each measured experimental spectrum is streamed directly from the device during the measurement (Step 1 in Figure 3). For the identification, the protein data are already digested and indexed in a distributed DBMS, using a column-oriented index on the charge and the mass of the peptides (Step 2 in Figure 3). For each incoming spectrum, the similarity with every suitable candidate from the database is calculated (Step 3 in Figure 3), and the best results get validated (Step 4 in Figure 3). For each spectrum, we need to query only suitable candidates. Since the mass spectrometer measures the peptide's total mass (notably, the mass spectrometer produces mass-to-charge ratios, which we then use to calculate the masses of the peptides), we can use the measuring accuracy from the device specifications to generate a range query over the total mass of the peptide in the DBMS. For example, the mass of a peptide is 125.2941 Daltons (Da), and the measuring accuracy is 0.001 Da. The mass of the candidates should be between 125.2931 and 125.2951. The peptide ion charge is also known during the measurement, which should be considered in our DBMS and precalculated. This group of indices is necessary to reduce the number of candidates for each spectrum to a minimum.
## 3 Experimental Setup and Numerical Models  
### Experimental Apparatus: Test Case  
The test case used to validate the CFD model is from [12] and is briefly presented. Figure 3 shows the measurement apparatus used to obtain the experimental data, consisting of a plenum designed to dampen pressure oscillations, followed by a duct, which shrinks up to the slot width B.  
The jet from the slot impacts an aluminum plate, an excellent conductor, so as to suppress the temperature differences on its sides; this plate is heated electrically, while the back and sides are thermally insulated. Its temperature is controlled at 36 [\({}^{\circ}\)F] higher than the outlet nozzle temperature, low enough to be able to neglect the radiation heat losses. The convective heat exchange is therefore obtained from the interaction between a cooling air flow and an isothermal hot surface. A transducer mounted on the plate, sliding on the latter, allows the local heat exchange coefficient calculation. The measurements were made for different slot openings, equal to 1/4, 1/8 and 1/16 inch, with a slot length fixed to 6 inches. In addition to the single slot, tests were conducted on rows of slots, arranged in different ways: two slots (placed at a distance of 4 inches) or three slots (separated by 2 inches).
# Optimal Interplanetary Transfer of Solar Wind Ion Focusing Thruster-Based Spacecraft
## Conflicts of Interest: The authors declare no conflict of interest. Abbreviations  
### Abbreviations  
Notation  
\(A\)  
SWIFT cone base area [m\({}^{2}\)]  
\(a_{D_{\odot}}\)  
design parameter; see Equation (16) [mm/s\({}^{2}\)]  
\(a\)  
propulsive acceleration vector [mm/s\({}^{2}\)]  
\(a_{\rm max}\)  
maximum propulsive acceleration magnitude [mm/s\({}^{2}\)]  
\(a_{r}\)  
radial component of \(a\) [mm/s\({}^{2}\)]  
\(a_{\theta}\)  
transverse component of \(a\) [mm/s\({}^{2}\)]  
\(D\)  
radial thrust (\"drag\") [N]  
\(d\)  
spacing between circular conducting wires [m]  
\(e\)  
elementary charge [C]  
\(H\)  
SWIFT cone height [m]  
\(\mathcal{H}\)  
Hamiltonian  
\(k\)  
dimensionless design parameter; see Equation (11)  
\(k^{\star}\)  
critical value of \(k\); see Equation (37)  
\(\hat{\tau}_{r}\)  
radial unit vector  
\(\hat{\tau}_{\theta}\)  
circumferential unit vector  
\(J\)  
performance index  
\(L_{s}\)  
total length of structural elements [m]  
\(L_{w}\)  
total length of conducting wires [m]  
total spacecraft mass [kg]  
\(m_{b}\)  
spacecraft main body mass [kg]  
\(m_{e}\)  
electron mass [kg]  
\(m_{p}\)  
proton mass [kg]  
SWIFT power generation system mass [kg]  
\(m_{s}\)  
SWIFT structure mass [kg]  
\(m_{w}\)  
SWIFT wires total mass [kg]  
\(N_{c}\)  
number of circular conducting wires  
\(N_{e}\)  
number of straight conducting wires  
\(N_{s}\)  
number of radial support booms  
\(n_{\rm sw}\)  
solar wind density [particles/m\({}^{3}\)]  
\(n_{\oplus}\)  
value of \(n_{\rm sw}\) at 1 au from the Sun  
\(O\)  
Sun's center of mass  
\(P\)  
total power required by the SWIFT [W]  
\(P_{\rm ex}\)  
power required by ion acceleration [W]  
\(P_{\rm inv}\)  
power required by wire grid [W]  
\(R\)  
SWIFT cone base radius [m]  
\(r\)  
Sun-spacecraft radial distance [au]  
\(r\)  
spacecraft position vector [au]  
\(r_{w}\)  
conducting wire radius [m]  
\(r_{\oplus}\)  
reference distance [1 au]  
\(T\)  
steerable thrust [N]  
\(t\)  
time [days]  
\(\mathcal{T}\)  
polar reference frame  
\(\mathbf{v}\)  
spacecraft velocity vector [km/s]  
\(\hat{v}_{r}\)  
radial component of the spacecraft velocity [km/s]  
\(v_{\rm sw}\)  
solar wind velocity [km/s]  
\(v_{\rm ex}\)  
exhaust velocity [km/s]  
\(\hat{v}_{\rm p}\)  
transverse component of the spacecraft velocity [km/s]  
\(\alpha\)  
thrust angle [deg]  
\(\alpha^{\star}\)  
optimal thrust angle [deg]  
\(\Delta\alpha\)  
contingency value [deg]
# Joint Resource Allocation in Secure OFDMA-Based Networks Taking a Base Station as a Two-Way Relay
## 5 Conclusions and Future Research  
We have set up the mathematical model to maximize the total secrecy rate when paired and normal users coexists in one cell. We take into account the joint optimization problem consisting of subcarrier pairing, subcarrier assignment and power allocation. The optimization problem belongs to mixed integer programming, which is very complicated. We solve this problem in a dual domain and put forward the equal power allocation method to reduce the calculation complexity. Simulation results show that the proposed scheme can improve the secrecy rate compared to conventional schemes, and the secrecy spectral efficiency tends to be stable at a high signal-to-noise ratio, because the mean value of the secrecy spectral efficiency is a constant for fixed power at the base station. As the user power reaches infinity, the average secrecy spectral efficiency expressed as Equation (15) can be worked out according to channel statistical characteristics. Recently, related issues in cooperative communication, such as wireless resource allocation, relay selection, physical-layer security, energy harvesting and the relationship between nodes, have triggered a great deal of research interest. Based on these research topics, we further investigate some key technologies of cooperative communication. In the future, we will take into account the possibility of an eavesdropper with multiple antennas. However, the perfect channel state information only makes sense in a slowly changing environment. In actual wireless links, due to a series of uncertain factors such as channel estimation error, quantization error and feedback delay, it is difficult to obtain full channel state information. Therefore, it is very important to establish the optimization model of partial channel status information for the application of the actual system in our future research.  
The authors would like to express their acknowledgement for the support from the Natural Science Foundation of Shandong Province in China (ZR2015 FL028), Science and Technology Plan Project of the Educational Department of Shandong Province of China (No. J16L112) and project of the 13 th five-year planning of education science in Shandong Province (Joint resource allocation research in OFDMA relay network based on convex optimization).  
[PERSON] conceived and designed the experiments; [PERSON] analyzed the data and wrote the paper; [PERSON] performed the experiments; and [PERSON] provided some suggestions and revised the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks
## 2 Literature Review
### Routing Protocol for Low-Power and Lossy Networks (RPL)  
RPL is an IPv6 routing protocol that has been used in IoT and standardized by IETF in 2012. It is mainly designed to operate on energy-constrained devices that use low power, low-cost communication technologies, and less memory. Figure 1 illustrates the main components and terminology used in RPL. The RPL protocol arranges the nodes (sensors) into a tree topology called a destination-oriented directed acyclic graph (DODAG). The tree root (sometimes named a border) is a non-constrained node that initiates and orchestrates the tree construction. All nodes on each DODAG are rooted at a single root. A specific objective function is used to determine how a DODAG is structured based on specific application needs. It uses some metrics and constraints to compute some parameters such as the rank of the node (based on the distance to the root) and the preferred parent of the node. For example, the hop count metric can be used to compute the rank.  
In order to exchange messages between the nodes themselves and with the root (in an ad hoc manner), RPL uses some ICMPv6-based control messages. The main control message used is the DODAG Information Object (DIO). It is used by the root to initiate and maintain a DODAG tree and by other nodes to join this tree and to keep track of its RANK (position in relation to the DODAG root). For DODAG consistency purposes, each node will inspect the next received DIOs for a different DODAG version or different RANK number than the previous ones. To search for new DODAG or maintain an existing one, if the node does not receive any DIO message within a specific time, it will multicast a DODAG Information Solicitation (DIS) message. This will solicit a DODAG DIO message from an RPL node. RPL also supports downward traffic routes by using a Destination Advertisement Object (DAO) control message, which is a unicast message that is used to propagate destination information upward along the DODAG.
## 5 Evaluations
### Informal Evaluation on Visualization System
#### 5.2.2 User Experiment  
To demonstrate the usability and utility of our system, we conducted a system evaluation with recruited users. First, below four tasks are designed based on the three datasets:  
1. Identify changes in network scale (increase, decrease, or unchanged).
2. Determine if there have been significant changes in network structure.
3. Identify whether specific structures have been preserved.
4. Provide the previous layout position of specific nodes.  
Then, 15 volunteers were recruited, including 10 undergraduate students and 5 graduate students. We first demonstrated our system to them, and then they were asked to  
Figure 14: Similar star-shaped network structures.  
Figure 15: The network structure comparison for time step 5 (**left**) and 6 (**right**).  
complete the four tasks using our system. After the volunteers completed the tasks, they were invited to rate the system for its usability, readability, interpretability, and effectiveness on a scale ranging from 0 (strongly disagree) to 5 (strongly agree). These ratings were used to evaluate the system's performance in assisting users in completing the tasks.  
The statistical results, as shown in Figure 16, indicate that the users generally had a high acceptance level for the visualization system. The system are helpful in completing routine analysis tasks and enabling users to understand and explore the data in a straightforward manner. Through the system's interactive operations and highlighting features, users could rapidly grasp changes in the network's structure, facilitating exploration of the network dynamics.  
While the visualization system is helpful to users in understanding network changes and capturing relatively stable structures, there were instances where the effectiveness of the system received low ratings from two volunteers, scoring 0 and 1, respectively. Through user interviews, we found that these two users struggled when dealing with large-scale dataset (the email dataset) or when the network underwent significant structure changes. In such scenarios, users found it challenging to track changes, especially for multiple structural changes within the network, resulting in lower effectiveness ratings. This inspired us a direction for future optimization efforts, where we will explore potential technologies to enhance the comparative analysis of large-scale networks and the multi-focus tracking.
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 1 Introduction
### Pictorial Planner Object Based Localization
Another new technique is for filtering out weak matched feature point (FP) pairs. Two filters are designed to inspect the matching result produced by FLANN and can greatly reduce the number of useful matched FP pairs before we use them in the homography calculation. These filters can drastically decrease the FP matching output (e.g., from 1656 pairs to 48 pairs in an example in Section 5.3). This results in a big saving of the average pose homography calculation time (from 30 ms to 1 ms, or about 95% improvement in our experiment). Both new techniques can significantly improve state-of-the-art object matching technologies. The rest of this paper is organized as follows: Related works on visual based localization are presented in Section 2. Section 3 shows the design of our planar surface based 3D object localization framework and the main components in it. Based on this framework, in Section 4, we present the ArPico method which converts framed pictures into ArUco-like markers for localization. Section 5 shows the design of PicPose. It first detects pre-learned picture from camera view using deep learning approach, and then extract feature points from the picture to produce the camera pose. We have built a moving robot that uses ArPico and PicPose for localization. The performance data collected from our prototype and VBL study are presented in Section 6. Section 7 concludes the paper.
## 3 Results and Discussion
### The Test Results
When the sputtering power is small, the grain shape of the Cu foil is irregular and the grain size is small. The number of grain boundaries increases greatly and the grain boundary resistance increases. With the continuous improvement of the sputtering power and rate, the grain size of the sputtered Cu foil becomes larger, the range of grain boundaries becomes smaller, and the resistivity becomes smaller, which is more conductive to energy conversion and to obtaining enough sublimation heat or melting heat. Driven by the narrow pulse high energy, the electric explosion produces more plasma, the ability for cutting and pasting the flyer becomes stronger, and the speed of the flyer becomes higher. Under the same driving voltage, the density and the uniformity of the grain size of the Cu film have a great influence on the velocity of the flyer. The higher density of the Cu film and greater uniformity of the grain size will result in a higher velocity of the flyer, which is more beneficial to energy conversion. Figure 9 shows the flyer velocity test results of Cu foils with different grain sizes, after an electric explosion and under different driving voltages. It can be seen from Figure 9 that in the Cu foil samples with the same grain size, the flyer speed is proportional to the driving voltage, namely, with an increase in the driving voltage, the flyer speed will rise. When the bridge foil explodes, the resistance increases sharply due to the vaporization of the materials. Thus, the voltage reaches the peak value, and then the vaporized material ionizes and produces plasma under the induced voltage. Since the conductivity of plasma leads to a sharp drop in voltage, with the increase of the driving voltage, more Joule heat is generated in the unit of time of exploding foil, resulting in the acceleration of the phase transformation rate, which improves the induction voltage, enhances the plasma output performance, and finally greatly improves the flyer speed.
## 3 Result and Discussion  
### Determination of Actual Corrosion Rate  
After failure of the column, the corroded reinforcing steel bars and stirrups in the column were removed to observe the condition of the corroded surfaces. It was found that the stirrups were severely corroded and deformed. As shown in Figure 6a, the stirrups were nearly uniformly corroded under the condition of accelerated corrosion by current. The main reinforcing steel bars were also slightly corroded because they lacked protection. The main reinforcing steel bars were insulated from the stirrups and the corrosion of the main reinforcing steel bars was not affected by the electric current; however, chloride ions could still reach the surface of the main reinforcing steel bars through the concrete pores during the dry and wet cycle, which led to natural corrosion of the main reinforcing steel bars. The corrosion of the main reinforcing steel bars indicated a certain non-uniformity under the conditions of natural corrosion, since the corrosion near the concrete cover was more serious while corrosion far away from the concrete cover was relatively light. After pickling, we could see that the surface was covered with small pits, but no big pits, as shown in Figure 6b. The corrosion morphology of the stirrups and main reinforcing steel bars was treated as uniform for the convenience of calculation. By observing the surface corrosion of the stirrups and main reinforcing steel bars, it was found that the degree of corrosion of the same section was different and the corrosion of steel bars on the side of the concrete cover was more serious. In addition, the corrosion was non-uniform along the length of the steel bars, as some sections had serious corrosion while some sections had mild corrosion. Due to the non-uniformity of the steel corrosion, the average corrosion rate calculated by the weight loss method was used to indicate the degree of corrosion of the steel bars. The stirrups and main reinforcing steel bars were cleaned with 12% hydrochloric acid. The average corrosion rate results are shown in Table 2. \(\rho_{\text{mr,th}}\) and \(\rho_{\text{vc,th}}\) are the theoretical corrosion rates of the main reinforcing steel bars and stirrups, respectively; and \(\rho_{\text{mr,ex}}\) and \(\rho_{\text{vc,ex}}\) are the average measured corrosion rates of the main reinforcing steel bars and stirrups, respectively. As can be seen from Table 2, the measured corrosion rate of each column stirrup was close to the theoretical corrosion rate, and the corrosion rate of the main reinforcing steel bars was less than 3%. It was previously shown in ref. [27] that when the corrosion rate of the main reinforcing steel bars was less than 3%, the effect on the mechanical properties of the test column was not significant. Therefore, the effect of main reinforcing steel bar corrosion on the mechanical properties of the test column could be ignored.  
Figure 6: Detailed drawing of partial samples corroded inside the concrete. (**a**) Partial samples of corroded stirrups. (**b**) Partial samples of corroded main reinforcing steel bars.