esa-sceva/satcom-chunk-collection huggingface dataset:
# Considerations for Developing Robot-Assisted Crisis De-Escalation Practices
## 4 Integrating the De-Escalation Process into Human-Robot Interactions
### Actions to Support De-Escalation
#### 4.3.1 Verbal Communication Principles  
As outlined in Figure 1, there are numerous verbal communication principles that crisis responders are advised to incorporate throughout de-escalation interventions, regardless of the specific strategies implemented at that moment. These include active listening, paraphrasing, using open questions and clear, concise, and decisive language, demonstrating perspective-taking, and showing empathy and concern. Robots engaged in de-escalation require the ability to understand and respond to open-ended input from agitated individuals, who may be uncooperative and deviate from a predictable conversational flow. Continued advancement in voice-enabled chatbots, harnessing natural language processing technology, will be crucial in developing robots that can utilize sophisticated receptive and expressive language skills to effectively employ these verbal de-escalation strategies (see [PERSON] et al. [26] for a recent example of a model for an interview chatbot capable of active listening). Regarding empathy, [PERSON] and colleagues [27] found that a robot designed to maintain the flow of conversation by asking related vs. unrelated follow-up questions to further understand a person's feelings was perceived as more empathetic than one that did not use questioning. This finding suggests that humans may be just as sensitive to the nuances that convey empathy and understanding (and related constructs) in robot-human interactions as they are in human-human interactions.  
Some research suggests that design choices regarding verbal communication may be more complicated than simply using state-of-the-art AI voice technology. For example, [PERSON] and colleagues [28] conducted an experiment in which they manipulated a robot's perceived emotional intelligence, gender, and communication method (voice vs. text) to explore the impact on trust. Unexpectedly, they found that participants reported greater trust in the robot when it communicated through text rather than voice format. They hypothesized that this effect may have been due to participants' expectation that the robot would have a more expressive and human-like voice and the violation of this expectation subsequently resulted in reduced trust.
## 2 Materials and Methods
### Eddy Covariance Measurements  
Continuous Eddy Covariance (EC) measurements of CO\({}_{2}\), water vapor, and sensible heat fluxes were performed at five EC towers (Figure 1), one of which is a permanent FLUXNET EC site (IT_MBo, Tower A in Figure 1). The other four temporarily installed EC towers were located within a maximum distance of one kilometer from the IT_MBo tower and were operational three days before the flight campaign, and maintained for two weeks after the flight campaign (8-9 July 2011). All EC systems consisted of the same open-path infrared gas analyzer (Li-7500, Li-COR Inc., Lincoln, NE, USA) and a 3-D ultrasonic anemometer (Gill R3, Gill Instruments Ltd., Lymington, UK or YOUNG Model 81,000 V, R. M. Young Company, Traverse City, MI, USA), mounted at a height from 1.30-2.50 m. A preliminary footprint analysis demonstrated that in most cases for all the five sites, in unstable conditions, 70% of the total CO\({}_{2}\) flux originated within 30 m from the EC tower. Incoming total and diffuse PAR (BF3H, Delta-T Devices Ltd., Cambridge, UK) were measured at the permanent IT_MBo tower (Tower A; Figure 1) and assumed (in cloud-free conditions) to be representative of all the sites. Radiation variables were recorded at one-minute intervals and averaged over 30 minutes. Half-hourly measurements of NEE (\(\upmu\)mol CO\({}_{2}\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) and photosynthetically active radiation (PAR, \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) were averaged and matched to the time of hyperspectral data flight acquisitions (Figure 1).
# Atomic Receiver by Utilizing Multiple Radio-Frequency Coupling at Rydberg States of Rubidium
###### Abstract  
Rydberg atoms have been extensively utilized in microwave measurement with high sensitivity, which has great potential in the field of communication. In this study, we discuss the digital communication based on a Rydberg atomic receiver under simultaneously coupling by resonant and near detuning microwaves. In addition, we verify the feasibility of the Rydberg atom-based frequency division multiplexing (FDM) in microwave communication. We demonstrate the principle and performance of the atom-based FDM receiver by applying amplitude modulation (AM) and frequency modulation (FM), respectively. To demonstrate the actual communication performance at different data transfer rates, we consider monochromatic images as an example. The experimental results show that the maximum acceptable data transfer rate of both AM and FM is about 200 kbps, whereas their maximum bit error rates (BER) is less than 5%. When compared with the traditional electronic receiver, this atomic receiver, which is compatible with FDM, has numerous advantages, such as small size, low power consumption, and high sensitivity. Furthermore, this receiver has a strong ability of anti-electromagnetic interference, and the signals transmitted do not interfere with each other in different channels.  
Keywords:Rydberg atoms; microwave communication; frequency-division multiplexing; antennas +
Footnote †: journal: Applied Sciences
# A Simple Relationship to Estimate Parameters of the Optimum Compaction Point
## 2 Materials and Methods
### Method Used  
The approach used is described in Figure 1. The first step, to establish the equations for the estimation of the maximum dry unit weight and the optimum water content, was to evaluate the correlation between the different used parameters. In this evaluation, \(w_{L}\), \(w_{P}\), \(I_{P}\), \(\gamma_{dmax}\), \(w_{Opt}\) and fine-grained content were selected. The XLSTAT [20] was used to establish a matrix correlation between the different selected parameters. The used formula for the correlation calculation is presented in Equation (1):  
\[r=\frac{\sum_{i=1}^{n}(xi-xm)^{2}(yi-ym)^{2}}{\left(\sum_{i=1}^{n}(xi-xm)^{2} \right)^{\frac{1}{2}}(\sum_{i=1}^{n}(yi-ym)^{2})^{2})\ ^{1/2}} \tag{1}\]  
In Equation (1), \(xm\) is the mean value of the \(x_{i}\) variable values and \(ym\) is the mean value of the \(yi\) variable values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(w_{L}\) **(\%)** & \(w_{P}\) **(\%)** & \(I_{P}\) **(\%)** & \(w_{Opt}\) **(\%)** & \(\gamma_{dmax}\) **(kN/m3)** \\ \hline _Minimum_ & 21 & 28 & 7 & 9.9 & 13.4 \\ _Maximum_ & 101 & 172 & 71 & 31.6 & 20.4 \\ _Mean value_ & 44.9 & 71.0 & 26.4 & 18.5 & 16.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical parameters of used soils.  
Figure 1: Steps to determine the proposed equation used in the prediction of the \(\gamma_{dmax}\) and \(w_{Opt}\).  
A value of \(r\) greater than about 0.56 usually indicates a strong correlation between two parameters.  
After the evaluation of the parameter's correlations, the Lab Fit Software [21] was used to establish the mathematical equations that best relate the various parameters (based on the considerations described above). To simplify the formulations, the number of correlated parameters was limited to two and this selection was based on the higher correlation between the selected parameters.  
An identical approach, with correlation analyses, was used by [22; 23; 24; 25; 26; 27; 28; 29] to establish links between analyzed parameters.
## 2 Materials and Methods
### Initial Dataset and Exploratory Analysis
Figure 4: Theoretical power curve (red) and observations form the SCADA system (blue). Figure 3: Actual power as a function of wind speed (**left**) and the theoretical power (**right**). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric/Parameter** & **Real Power, kW** & **Wind Speed, m/s** & **Theoretical Power, kW** & **Wind Direction**\({}^{\circ}\) \\ \hline Samples number & 46,976 & 46,976 & 46,976 & 46,976 \\ Average value & 1406 & 7.709 & 1546 & 123.7 \\ Standard deviation & 1309 & 4.269 & 1371 & 92.72 \\ Minimum value & 0.000 & 0.000 & 0.000 & 0.000 \\
25 th percentile value & 168.5 & 4.467 & 215.0 & 50.00 \\
50 th percentile value & 991.1 & 7.294 & 1154 & 73.50 \\
75 th percentile value & 2613 & 10.48 & 3057 & 201.4 \\ Maximum value & 3618.7 & 25.21 & 3600 & 360.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical evaluation of the initial data results. Figure 5: Distribution and density plots of the real power (**a**), wind speed (**b**), theoretical power (**c**), wind direction (**d**). Figure 6: Wind direction scatter plot.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 3 Vulnerability Analysis, Discovery, Detection, and Mitigation
### Research on Vulnerability Detection
#### 3.3.1 Network Scanning Method  
The network scanning method detects known vulnerability by sending probe packets with payload to services of online IoT devices. It is more versatile in the security field. With the development of IoT security, special topics for IoT devices of network scanning emerge.  
**Technical requirements:** The ability to know vulnerability information such as Proof of Concept (PoC).  
[PERSON] et al. [70] scan existing embedded devices on the Internet to discover a list of devices with weak password and other types of vulnerabilities. After 2013, search engines such as Shodan [71], Censys [72], and Zoomeye [73] emerge, which identify and detect weak passwords, backdoor, and known vulnerability. However, it is difficult to find most security vulnerabilities only by external scanning. In addition, there are ethical issues with unauthorized analysis of devices on the Internet. Thus, the vulnerability scanning is typically performed in laboratories and intranets. The advantage of this method is that the detection from the service layer does not need to consider the structure of the device. It is fast, effective, and suitable for large-scale testing. The current commercial vulnerability detection systems are mainly based on this method.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 4 Cislunar Dynamics (Constellation Scenario)
### Constellation Modelling
#### 4.1.2 Walker Constellations  
In the other portion of the constellation, a well-defined mixed geometry walker pattern has been used as given in [29], and using the \(J_{2}\) perturbation as defined by [9; 30; 31], the following equations for orbital elements under \(J_{2}\) are rewritten as below:  
\[\hat{\Omega}=\frac{r*P_{h}*\sin\theta}{h*\sin i} \tag{7}\]  
\[\dot{\omega}=-\frac{\eta}{ane}\bigg{\{}P_{r}\cos f-P_{s}\bigg{(}1+\frac{r}{p} \bigg{)}\sin f\bigg{\}}-\frac{r\cot i}{a^{2}n\eta}P_{h}\sin\theta \tag{8}\]  
\[\dot{M}=n+\frac{\eta}{ane}\Bigg{\{}\begin{array}{rl}P_{r}\bigg{(}\cos f- \frac{2\pi}{p}\bigg{)}-\\ P_{s}\bigg{(}1+\frac{r}{p}\bigg{)}\sin f\end{array}\Bigg{\}} \tag{9}\]  
\[\begin{array}{rl}n=\sqrt{\frac{\mu}{a^{3}}};\eta=\sqrt{1-e^{2}};\\ p=a(1-e^{2});h=\sqrt{\mu p};\\ r=\frac{p}{1+e\cos f};\end{array} \tag{10}\]  
where \(a\) is the semi-major axis; \(e\) is the eccentricity of the orbit; \(\mu\) is the Gravitational parameter of the Earth; \(f\) is the true anomaly; the magnitude of the position vector is \(r=p/(1+e\cos f)\); just to add, \(P_{h},P_{s},P_{r}\) are the components of the perturbation force.
# Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex
## 3 Proposed Method  
This paper, firstly, in the HSV space, adopts the improved multi-scale guided filtering algorithm for the brightness component to extract the illumination component, which can accurately retain the edge details while avoiding halo artifacts. Secondly, the extracted illumination component adopts the Weber-Fechner algorithm to perform adaptive brightness correction, and the reflection component is processed using a fusion method of contrast limited adaptive histogram equalization and improved guided filter to prevent excessive enhancement and image distortion; then, the principal component analysis is performed to fuse the two components. Thirdly, to prevent the color distortion, an improved adaptive stretching method is used to balance the saturation component. Finally, the three components are fused and converted back to the RGB space. The framework is presented in Figure 3.  
Figure 1: Retinex model.  
Figure 2: Retinex algorithm.
## 4 Proposed Efficient Monitoring System (EMS)
### The Node Categorization Component
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Value of \(j\)** & **Type of Node** & **Reputation Value (\(\gamma\))** \\ \hline \(-1.0\) & Selfish & \(\gamma<10\) \\ \(-0.5\) & Doubtful & \(10\leq\gamma\leq 40\) \\ \(0.0\) & Normal & \(40<\gamma\leq 50\) \\ \(0.5\) & Partially Cooperative & \(50<\gamma\leq 75\) \\ \(1.0\) & Fully Cooperative & \(\gamma>75\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Value of constant \(j\) according to classification of node in EMS. Figure 2: Overall Structure of Proposed Scheme. Figure 3: Flowchart of the Categorization Component. ```
1:INPUT: Number of Nodes \(n\) OUTPUT: Nodes Classification
2:for\(i=1:n\)do
3: Compute \(CVM\) of node
4:\(CVM_{i}=\eta\times\Delta i\)
5:if Node relayed packages for a node \(m\)then
6:\(y=\Sigma_{m=1}^{N}(TRP_{m}-TPP_{m})\)
7:\(\eta_{1}=\frac{y-y_{min}}{y_{max}-y_{min}}\)
8:else
9:if Node discard packages for a node \(m\)then
10:\(z=\Sigma_{m=1}^{N}(TRP_{m}- TDP_{m})\)
11:\(\eta_{2}=\frac{z-z_{min}}{z_{max}-z_{min}}\)
12:endif
13:endif
14:endfor
15:for all\(j\in n\)do
16: Compute reputation value \(\gamma\) of each node
17:if\(\gamma<10\)then
18: Node =='Selfish'
19:else
20:if\(10\leq\gamma\leq 40\)then
21: Node=='Doubftul'
22:else
23:if\(40<\gamma\leq 50\)then
24: Node=='Normal'
25:else
26:if\(50<\gamma\leq 75\)then
27: Node=='Partially Cooperative'
28:else
29: Node=='Fully Cooperative'
30:endif
31:endif
32:endif
33:endfor
34:End
```  
**Algorithm 1** Algorithm for Categorization Component.
# A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches
## 3 Deep Learning Based MQA Approaches
### Recurrent Neural-Network-Based Models
#### 3.3.1 Long Short Time Memory (LSTM)  
LSTM is an artificial RNN architecture used in the field of deep learning [12]. Unlike normal feedforward neural networks, LSTM has feedback connections that enable it not only to process single datapoints (such as images) but also entire sequences of data (such as speech or video).  
An LSTM model is proposed in [62] to classify relations from clinical notes. The model was tested on the i2b2/VA relation classification challenge dataset, and the results showed that, with only word embedding feature and no manual feature engineering, they achieved a micro-averaged \(f\)-measure of 0.661 to classify medical problem-treatment relations, 0.683 for medical problem-medical problem relations, and 0.800 for medical problem-test relations.  
Moreover, a multiple positional sentence representation with Long-Short-Term Memory (MV-LSTM) and MatchPyramid were proposed in [61], to generate two hidden states to reflect the meaning of the whole sentence from the two directions for each word, as demonstrated in Figure 11, where MV-LSTM is a basic matching model that has a steady performance, and MatchPyramid is a model usually used for text matching.  
A SeaReader model that uses LSTM networks to model the context representation of text was proposed in [29]. This reading comprehension model uses the Attention to model information flow between questions and documents, and across several documents. Information from various documents is merged to make the last prediction. The model in [29] will be introduced in detail as follows:  
Figure 11: Architecture of Long Short Time Memory.  
Figure 10: RNN architecture for sentence embedding [63].  
The matching matrix is computed as the dot-product of the context embeddings of the question and every document, as shown in Equation (11)  
\[M_{n}(i,j)=S(i)\odot D_{n}(j) \tag{11}\]  
where \(S\) denotes the statement and \(D\) denotes the document.  
Then, in the path of question-centric, the column-wise attention is performed on the matching matrix as shown in Equation (12)  
\[\alpha_{n}(i,j)=\text{softmax}(M_{n}(i,1),\dots,M_{n}(i,L_{D}))(j) \tag{12}\]  
Each word \(S(i)\) in question-answer gets a summarization read \(R_{n}^{Q}(i)\) of related information in the document  
\[R_{n}^{Q}(i)=\sum_{j=1}^{L_{D}}\alpha_{n}(i,j)D_{n}(j) \tag{13}\]  
Moreover, in the document-centric path, the row-wise attention is performed to read related information in the question. Finally, the cross-document attention is performed on attention reads of all the documents.
# Analysis of Plasma Reactor Interaction with the Power Grid Depending on the Power Supply Design
## 4 Measurement System  
In order to carry out tests on the cooperation of the plasma reactor with different types of power supply systems, a test platform was built consisting of two circuits: (1) the electricity supply and (2) the plasma gas supply.  
A schematic diagram of the system for measuring electrical quantities is shown in Figure 6. Measurements were made at the inputs and outputs of the power supplies under study. Currents of all phases, phase-to-phase voltages, harmonic content in currents and voltages, active, reactive and apparent powers, and power factor were measured. The measuring system was supplied from a three-phase 400 V grid via a 23.4 kVA ATS-FAZ3-23 autotransformer. The autotransformer allows regulation of the supply voltage in a range from 0 to 450 V. The measuring system consisted of Merazet LM-3 voltmeters, Mezaret LE-3 ammeters, Mezaret LW-1 wattmeters, Tektronix DPO3054 oscilloscope, Fluke Norma 5000 power analyser, Tektronix P6015A and TCP0030 measuring probes.  
An important part of the test platform is the system for supplying the plasma reactor with plasma-generating gases. The test installation consisted of four cylinders of technical gases containing argon, helium, nitrogen, and air (Figure 7). The type of plasma-generating gas introduced into the discharge chamber of the GlidArc reactor determines its performance characteristics [29]. Cylinder gas was fed to the gas inflow nozzle of the plasma reactor through Bronkhorst's EL-FLOW mass flowmeters and flow controllers: the F-202 AV-M20-AAD-44-V with a flow rate of up to 15 m\({}^{3}\)/h, the F-201 AV-50K-AAD-44-V with a flow  
Figure 6: Measuring system for testing the cooperation of a plasma reactor with different types of power supplies, AT—autotransformer, V—voltmeter, A—ammeter, W—wattmeter, OS—oscilloscope, PA—power analyser, PR—plasma reactor, T–reactor working electrodes power circuit, IS—ignition electrodes power circuit.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Power Supply Type** & **TPS** & **ITPS** & **IFCTPS** & **PCS** \\ \hline Ignition system & electronic & transformer & transformer & electronic \\ \hline Supply voltage & 230 V & – & – & 230 V \\ \hline Supply current & 250 mA & – & – & 250 mA \\ \hline Output voltage & 15 kV & 10 kV & 7.6 kV & 15 kV \\ \hline Output current & 40 mA & 200 mA & 350 mA & 40 mA \\ \hline Output frequency & 20 kHz & 150 Hz & 50 Hz & 20 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the reactor ignition electrode supply circuits.  
rate of up to 2 m\({}^{3}\)/h, and the F-201 CV-1K0-AAD-44-V with a flow rate of up to 0.06 m\({}^{3}\)/h. These regulators allow precise mixing of the gases and control of their flow through the plasmotron nozzle. The volumetric flow rate of gases entering the plasmotron nozzle was measured during the study.
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c} \hline
**Index Name** & **Acronym** & **Formula** \\ \hline Difference vegetation (veg.) index & DVI & NIR \(-\) Red \\ \hline Normalized difference veg. index & NDVI & (NIR \(-\) Red)/(NIR \(+\) Red) \\ \hline Near-infrared reflectance of veg. & NIRv & NIR \(\times\) (NIR \(-\) Red)/(NIR \(+\) Red) \\ \hline Simple ratio & SR & NIR/Red \\ \hline Enhanced veg. index & EVI & \(2.5\times\) (NIR \(-\) Red)/(NIR \(+\) 6 \(\times\) Red \(-\) 7.5 \(\times\) Blue \(+\) 1) \\ \hline
2-band Enhanced veg. index & EVI2 & \(2.5\times\) (NIR \(-\) Red)/(NIR \(+\) 2.4 \(\times\) Red \(+\) 1) \\ \hline \end{tabular}
\end{table} 
Table 1: Spectral index formulas. All indices computed from simulated SuperDove spectra. using scikit-learn v0.24.2 (sklearn.feature_selection.mutual_info_regression). MI uses the Kullback-Leibler divergence of the marginal distributions of two variables to quantify the amount of information that can be obtained about one variable by observing the other variable [33; 34; 35; 36]. Unlike \(\rho\), MI does not require parametric statistical assumptions (or even real-valued variables), so should provide a more accurate estimate of the strength of non-Gaussian and nonlinear relationships. A linear regression estimation for the NIRv:Fv was conducted in the R computing environment (v4.1.1) using base package 'lm'. Negative VI values were excluded when estimating slope. Analytic procedure was as follows:  
1. Download AVIRIS-ng radiance data from JPL data hub. 2. Apply ISOFIT atmospheric correction. 3. Compute spectral mixture fractions. 1. Identify endmembers. 2. Apply linear mixture model. 4. Compute simulated multispectral VIs. 1. Convolve AVIRIS-ng spectra with SuperDove spectral response function. 2. Apply spectral index formulas. 5. Characterize relationships between (3) and (4). 1. Parameteric (Pearson correlation coefficient). 2. Nonparametric (mutual information).
## 1 Introduction  
The traffic on internet services has been increased exponentially in the last few years, and it is expected that the data rate demands will touch the zettabyte digit between 2022 to 2025 [1, 2]. Therefore, a developed approach is required to support growing needs like propagation range and multimedia based data transmissions. Passive optical networks (PONs) are considered fruitful solutions for transmitting high capacity data with minimum bit error rate (BER). Furthermore, the installation of PONs is acceptable to the entire world for increasing speed and capacity to the users [3, 4]. However, the wired based PONs contain extra issues like nonlinear impairments (NILs) while transmitting high bandwidth data over fiber for long range, which badly disturbs the system flexibility [5]. On the other side, the physical-less based communication network (CN) presents accurate probability within the flexible environment; however, it fails to accommodate a considerable number of users with the high bandwidth capacity of long range transmission [6]. Thus, to overcome on NLIs and support above mentioned demands, the new approach named radio over fiber (RoF) links is developed, which combines the physical and physical-less setups of CNs [7; 8; 9; 10]. Moreover, the wavelength division multiplexing (WDM) technology and advanced modulation schemes have developed the fidelity many times of the RoF system. This advanced methodology of CN minimizes the NLIs, and the system performance increases in terms of transmission range, number of users and capacity. In contrast, the RoF system uses the concept of electrical to optical (E/O) and visual to electrical (O/E) conversion procedures, which make space for the peak to average power ratio impairments (PAPRIs) [11; 12]. Therefore, this paper investigates the novel WDM based RoF technology which aims to suppress the NLIs and PAPRIs.