esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A
When initializing the [PERSON] algorithm, it selects an  arbitrary node, while [PERSON] chooses the node with the  fewest edges. B. [PERSON] starts from an arbitrary node, while [PERSON] startsfrom the set of edges ordered from lowest to highest cost. C. [PERSON] is more efficient in the case of a very sparse graph, while [PERSON] is more efficient if the graph is very dense. D. None of the above. Second recommendation  
Which of the following statements is true regarding the cost of some algorithms and their efficiency? A. [PERSON]'s algorithm has a cost that is in 0(n log n). B. [PERSON]'s algorithm, when the adjacency list is combined with the use of a heap, is more efficient when the graph is dense because this way the cost is in 0(n log n). C. The cost of the Quicksort algorithm in the worst case is of order 0(n log n). D. The function that creates a heap from a collection of values, if the Sink procedure is used, can have a linear cost. Third recommendation  
Given the undirected graph in the figure, indicate the order in which the nodes would be selected (become part of the tree) when applying [PERSON]'s algorithm starting from node A: A. A, F, E, C, B, D B. A, B, C, E, D, F C. A, C, D, B, F, E D. None of the above
# Effects of Vertical Glottal Duct Length on Intraglottal Pressures in the Convergent Glottis
## 3 Results
### Exit Coefficient  
The exit coefficient represents the effect of intraglottal pressure recovery (increase) from the glottal exit to the hypopharyngeal cavity for a given transglottal pressure [29; 45]. It provides a nondimensional quantitative measure of how the pressures in the glottis differ from the lossless Bernoulli condition.  
The exit coefficient is calculated as follows:  
\[k_{ex}=2A_{ex}2\frac{P_{ex}}{\rho U_{g}^{2}} \tag{3}\]  
where \(A_{ex}\) is the glottal area at the glottal minimal diameter location (just prior to the exit rounding) and \(P_{ex}\) is the pressure recovery (increase) from the glottal minimal diameter location to the supraglottal region (pharynx) (assumed to be atmospheric or zero pressure in this study). \(U_{g}\) and \(\rho\) have the same meaning as for the entrance loss coefficient.  
Figure 5 shows the exit coefficient values versus Reynolds number for all of the cases in this study. The exit coefficient value is relatively small (between 0.04 and 0.36) and tended to decrease as the glottal minimal diameter increased. For example, \(k_{ex}\) decreased from 0.295 to 0.141 and then to 0.126 as the minimal diameter increased from 0.01 cm to 0.04 cm and then to 0.16 cm, respectively, for the condition of the 0.308 cm duct length, \(-5^{\circ}\) glottal angle, and 500 Pa transglottal pressure. \(k_{ex}\) also tended to decrease as the transglottal pressure increased for the smallest diameter, but not for the larger diameters. There was essentially no relation between \(k_{ex}\) and the length of the vertical glottal duct. These results (\(k_{ex}\) being inversely related to the minimal diameter and transglottal pressure) are consistent with results of [PERSON] et al. [44] and [PERSON] et al. [40]. These results suggest that the pressure recovery from the glottal exit is relatively independent of the vertical glottal duct length and convergent glottal angle and more dependent on minimal diameter and transglottal pressure.
# Smooth Interpolation Design with Consideration of Corner Tolerance Constraints for Robotics
## 5 Results of Simulations and Experiments
### Experimental Comparison of VPO and ADAI  
The simulation results discussed in the previous sections clearly indicate that the _OVLP_ parameter affects the cycle time and corner tolerance. In this section, experiments were conducted for the HIWIN robot RT605 using real trajectories; the results are shown in Figure 17. Trajectories were obtained using the VPO method and the conventional interpolation method (ADAI). The feed rate, tolerance, and maximum acceleration of the TCP were the same for both algorithms. For the ADAI method with a time constant of 500 ms and the VPO method with the acceleration time set to 0.25 s and _OVLP_ to 74%, the contour and tracking errors in the \(x\), \(y\), and \(z\) directions were almost identical (Figure 17,c). However, the cycle time of the VPO method was only 8.98 s, 10.6% less than that of the  
Figure 16: Corner tolerances in the CTC method. (**a**) Trajectory. (**b**) Corner A. (**c**) Corner B. (**d**) Corner C. (**e**) Corner D. (**f**) Corner E.  
Figure 15: Simulation results for the joint profile. (**a**) Position and angular velocity. (**b**) Angular acceleration and angular jerk.  
ADAI method. However, if the acceleration time and _OVLP_ parameter in the VPO method were adjusted to 0.35 s and 58% (to maintain a similar cycle time), the contour error [25] at point A decreased from 0.1841 mm to 0.1566 mm, a reduction of approximately 15.22% (Figure 17e,f). This result indicates that the trajectory planning of the VPO algorithm is excellent. Table 5 reveals that the cycle time of the VPO method was 9.19-12.30% lower than that of the ADAI method. For Cases A, B, and C, the cycle times of the VPO method were approximately 8.98, 9.18, and 6.42 s, respectively. Table 6 reveals that the contour error in the VPO method was 6-15% lower than that of the ADAI method. For Cases D, E, and F, the maximum contour errors of the VPO method were approximately 0.1566, 0.1693, and 0.1883 mm, respectively. The statistical results presented in Tables 5 and 6 demonstrate that the VPO method outperforms ADAI in terms of both feasibility and performance.
## 3 Theoretical Analysis and Simulation Model of Temperature Error
### Solar Radiation  
When the temperature sensor conducts in situ measurements in near space, the Pt thin film will receive direct solar radiation and scattered radiation from the surrounding environment, which can transfer heat energy.  
The formula for calculating the radiation heat flux of an object is:  
\[Q_{B}=A_{3}\Big{\{}\sigma\varepsilon(T_{m}^{4}-T_{n}^{4})+I_{s}(1-\varepsilon )\Big{\}} \tag{10}\]  
where \(A_{3}\) is the irradiated surface area; \(\sigma\) is the Stephen-Boltzmann constant, which is \(5.67\times 10^{-8}\)W/(m\({}^{2}\cdot\)K\({}^{4}\)), \(\varepsilon\) is the emissivity of the object, and its value is 0-1; \(T_{m}\) is the object's surface temperature; \(T_{n}\) is the temperature of the air; and \(I_{s}\) is solar radiation intensity.  
Thus, the amount of heat gain in the Pt thin film due to radiation heat is expressed as Equation (11):  
\[Q_{b}=Q_{B}-Q_{1}-Q_{2}=A_{3}\Big{\{}\sigma\varepsilon(T_{m}^{4}-T_{n}^{4})+I_ {s}(1-\varepsilon)\Big{\}}-A_{1}\lambda_{1}\cdot\Delta T_{1}-A_{2}\lambda_{2} \cdot\Delta T_{2} \tag{11}\]  
Then, the value of the temperature increase in the Pt thin film is:  
\[T_{b}=\frac{Q_{b}}{cm} \tag{12}\]  
where \(T_{b}\) is the temperature rise of the Pt thin film due to Joule heat, and \(Q_{b}\) is the thermal increment of the Pt thin film due to Joule heat.  
In the design, we can modify the sensor's surface emissivity to minimize the impact of radiation on the temperature sensor. The surface emissivity of the silicon substrate and the silicon dioxide insulation layer is above 0.5. The material surface was coated with radiation protection, and the coating structure is shown in Figure 1. In order to analyze the influence of different anti-radiation coatings on the radiation temperature rise, we studied the effect of coatings on sensor temperature. Copper, aluminum, and silver were selected as radiation-proof materials, and their surface emissivity was 0.03, 0.02, and 0.01, respectively. The atmospheric temperature was set at 233.15 K; the initial temperature of the sensor was 238.15 K; the ambient air pressure was 50 Pa; the wind speed was 100 m/s; and the solar radiation was set to 500 W/m\({}^{2}\), 1000 W/m\({}^{2}\), 1500 W/m\({}^{2}\), and 2000 W/m\({}^{2}\) to study the effect of radiation magnitude on the temperature rise in the temperature sensor. Similarly, different air pressures affect the Pt thin film heat dissipation, which further affects the temperature error generated by radiation. Therefore, the temperature rise in the sensor under different air pressure conditions of \(5\times 10^{0}\) Pa, \(5\times 10^{1}\) Pa, \(5\times 10^{2}\) Pa, \(5\times 10^{3}\) Pa, \(5\times 10^{4}\) Pa, and \(5\times 10^{5}\) Pa was studied.
## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction  
Developing automated approaches to quantify quality attributes would represent an important step towards monitoring, controlling and enhancing meat quality in the industrial production context. Spectroscopy, a multidisciplinary field combining physics and chemistry, has promising potential to provide rapid estimation of the qualitative and quantitative attributes of meat and meat product quality [13]. The electromagnetic spectrum can be classified according to specific wavelength ranges into \(\gamma\)-rays, X-rays, ultraviolet, visible light, infrared (IR), microwave, and radio waves [98]. Four wavelength regions have been identified as the most promising for meat applications: ultraviolet, visible (Vis), near-infrared (NIR) and mid-infrared (MIR) [14]. The American Society of Testing and Materials (ASTM) has established specific wavelength ranges for these regions: ultraviolet (100-400 nm), visible (400-750 nm), near-infrared (NIR) (780-2500 nm), and mid-infrared (MIR) (2500-25,000 nm). [PERSON] et al. (2021) [14] concluded that the majority of studies assessing these regions for predictive value in terms of meat quality have reported the suitability of visible and NIR spectroscopy for meat quality applications in terms of the predictive performance, but the speed, lower initial investment costs, and simpler equipment setup were also relevant factors. Apart from these methods, Fourier transform IR spectroscopy (FT-IR) and Raman spectroscopy can be also used for meat applications [99]. Recently, digital imaging, also termed machine vision or computer vision (CV), has been used as a method for the analysis of meat quality, focusing on the colour, size, shape and surface texture features [100; 101; 102; 103]. Nevertheless, the CV approach is recognised as ineffective in representing the characteristics of the internal components of meat samples [104].  
The combination of spectroscopy and digital imaging is called hyperspectral imaging (HSI) [105] and facilitates the simultaneous acquisition of spatial and spectral data from the target object within a single system [106]. Among the aforementioned optical sensing techniques, NIR spectroscopy, Raman spectroscopy and near-infrared hyperspectral imaging (NIR-HSI) have proven the most relevant for meat quality attributes and therefore will be described in more detail in the following sections.
## Abbreviations  
Indices:  
s  
Index for scenario  
t  
Index for time period  
Sets:  
Sets of scenarios  
T  
Sets of time periods  
Parameters:  
\(\pi(s)\)  
The probability of scenario s  
\(P_{WT}(t,s)\)  
The output power of WT  
\(P_{PV}(t,s)\)  
The output power of PV  
\(C_{WT}(t,s)\)  
The operation cost of WT  
\(C_{PV}(t,s)\)  
The operation cost of PV  
\(C_{DE}(t,s)\)  
The fuel cost of DE  
\(C_{GRD}(t,s)\)  
The selling/buying electricity cost  
\(C_{ESS}(t,s)\)  
The operation cost of ESS  
\(a,b,c\)  
The cost coefficients of DE  
\(k_{WT}\)  
The generation cost coefficient of WT  
\(k_{PV}\)  
The generation cost coefficient of PV  
\(k_{GRID}(t)\)  
The electricity price during time period t  
\(k_{DE}\)  
The operation and maintenance cost coefficient of DE  
\(SOC_{t}\)  
The SOC value of ESS at time t  
\(SOC_{min}\)  
The minimum SOC of ESS  
\(SOC_{max}\)  
The SOC of ESS at the end of the day  
\(SOC_{0}\)  
The SOC of ESS at the beginning of the day  
\(\eta_{ch}\)  
The charging efficiency of ESS, which is set as 0.9 in this paper  
\(\eta_{dis}\)  
The discharging efficiency of ESS, which is set as 0.9 in this paper  
\(C_{ESS}\)  
The capacity of ESS  
\(J\)  
The total number of pollutant types  
\(c_{j}\)  
The treatment cost of the \(j_{th}\) pollutant, yuan/kg.  
\(\alpha_{DE,j}\)  
The pollutant emission factor of DE, g/kW.  
\(\alpha_{GRID,j}\)  
The pollutant emission factor of the main grid, g/kW.  
\(P_{DE,min}\)  
The minimum output power of DE, kW  
\(P_{DE,max}\)  
The maximum output power of DE, kW  
\(R_{up}\)  
The maximum ramp up rate of DE, kW/h  
\(R_{down}\)  
The maximum ramp down rate of DE, kW/h\(P_{GRID,max}\) The maximum exchange power between the microgrid and the main grid.  
\(\omega_{1},\omega_{2}\) Weight coefficient, which are set as 0.5 and 0.5 respectively in this paper  
Variables:  
\(P_{DE}(t,s)\) The output power of DE  
\(P_{dh}(t,s)\) The charging power of ESS  
\(P_{Bis}(t,s)\) The discharging power of ESS  
\(P_{GRID}(t,s)\) The exchanged power with the main grid  
\(u_{ch}(t,s)\) Equals 1 if the battery is charging during period t and 0 otherwise  
\(u_{dis}(t,s)\) Equals 1 if the battery is discharging during period t and 0 otherwise
## 1 Introduction  
With the rapid technological progress of the past few years, artificial intelligence (AI) is increasingly being put to use in medical research. Often equated with human-like robots by the general public, AI is ultimately any system that adapts its performance based on its perception of the environment. This includes advanced statistics such as machine learning (ML), which allows a variety of variables and their relationship to one another to be analyzed through complex mathematical algorithms, as well as the quantification of the quality of a statistical model [1; 2; 3; 4]. When it comes to psychiatric research though, statistical analyses are usually conducted using null hypothesis significance tests (NHSTs) or simple linear regressions. This results in the following certain limitations: (I) mainly linear relationships can be determined, and, with NHSTs, it is not even possible to examine the relationships between the variables themselves; (II) in order to avoid alpha error accumulation, only a limited number of variables can be analyzed; and (III) the research question must be precisely defined and rather constrained, as it can only be determined whether a (null) hypothesis is true or not. However, this approach does not accommodate psychiatric syndromes with their complex and often highly interdependent multifactorial relationships. The genesis of psychiatric diseases and pathological behavioral disorders is by no means a linear process influenced by only single, independent factors. This is especially true for the generally under-researched field of forensic psychiatry, where the interplay of psychopathology, offending, and aggression has yet to be comprehensively understood. Consequently, to investigate such phenomena, modern statistical methodssuch as ML are necessary and already applied in psychiatric research areas regarding pharmaceuticals or neuroimaging [5; 6; 7; 8; 9]. The following analysis should serve as an example of the practical use of ML in the field of forensic psychiatry, specifically aggression and schizophrenia spectrum disorders (SSDs). Factors linked to aggressive behavior outside the clinical setting have recently been evaluated by means of ML and include higher PANSS scores as well as younger age at SSD diagnosis [10; 11; 12]. For this study, we chose an explorative approach, as aggression is considered to be a multifactorial, complex phenomenon, mediated through a broad variety of parameters from different domains. This study now aims to determine the most predictive factors of aggression within the institutional setting, based on a unique group of forensic offenders with SSD, (objective I) and to quantify the performance of the calculated model (objective II).
## 4 Experiments and Results
### Results  
For the target CNC machine detection, the contrastive loss of the model is plotted in Figure 8. It can be observed that the loss for training and validation achieved 0.127 and 0.112, respectively.  
Table 2 compares our proposed method with three milestone methods in object detection: Faster-RCNN, SSD, and YOLO. Two hundred frame images from the recorded video were used for the validation. The accuracy of the proposed method is shown in the equation.  
\[\text{Accuracy}=\frac{N}{T} \tag{8}\]  
where \(N\) is the number of correctly detected objects, and \(T\) represents the total number of images used.  
For the HMI detection, the accuracy of the proposed method is shown in Figure 9. The model's accuracy for training and testing can reach 66.3% and 61.9%, respectively. The performance is acceptable considering the size of the dataset. Different parameter values,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy** \\ \hline Faster-RCNN & 0.67 \\ SSD & 0.54 \\ YOLO & 0.53 \\
**The Proposed Method (this paper)** & **0.78** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of accuracy with benchmark methods (the target CNC machine detection).  
Figure 8: Contrastive loss for the target CNC machine detection.  
Figure 7: Text recognition examples for recognizing the information on HMI display. (**a**) Basic instructions recognition; (**b**) G-code recognition.  
such as the batch size of training data, the learning rate of the model, and the activation function, have been applied, but they achieved limited benefit. Therefore, increasing the training dataset size would be better to improve the models' performance.  
One thousand text samples from the HMI display were adopted to validate the proposed working status recognition method. The benchmark method RNN was compared with the proposed method in this study to estimate the performance of the proposed method. The results for both methods are presented in Table 3. It can be concluded that the proposed method improves the recognition performance in precision without the loss of efficiency.
## 3 tRNA Core Modifications and Their Impact on Translation and Stress Adaptation
### tRNA Core Modifications and Their Relation with Stress Adaptation Mechanisms
#### 3.3.2 Gm18 Regulates the Immune-Stimulatory Effect of tRNAs  
The Gm18 modification is present in both bacteria and eukaryotes [14]. In _E. coli_, this modification was recently shown to be involved in a stress adaptation mechanism to antibiotic treatment in relation to the sensing of bacteria by the immune system of the human host (Table 2) [123]. Bacterial tRNAs containing Gm18 exhibit a reduced immunostimulatory effect in humans [151]. Whereas unmodified tRNAs trigger the activation of the human Toll-like receptor 7 (TRL7) [152], Gm18-modified tRNAs act as TRL7 antagonists and inhibit the immune response [153; 154]. In _E. coli_, the gene encoding the G18 methyltransferase, i.e., _trmH_, is part of an operon activated during the stringent response, and levels of Gm18 are thus expected to increase in certain stress conditions, for instance antibiotic treatment [155]. An increase in the Gm18 level in some tRNAs was indeed observed upon mild antibiotic treatment in _E. coli_[123]. In addition, and in line with previous reports, the stress-induced increase in tRNA Gm18 levels led to a reduced immune-stimulatory effect of the bacterial tRNAs [123]. Thus, Gm18 is a tRNA core modification that responds to stress with implications outside protein translation and appears as a key modification to escape the immune system of the host.
# GNSS Spoofing Detection Based on Wavelets and Machine Learning
###### Abstract  
Global Navigation Satellite Systems (GNSSs) are widely used for positioning, timing, and navigation services. Such widespread usage makes them exposed to various threats including malicious attacks such as spoofing attacks. The availability of low-cost devices such as software-defined radios enhances the viability of performing such attacks. Efficient spoofing detection is of essential importance for the mitigation of such attacks. Although various methods have been proposed for that purpose it is still an important research topic. In this paper, we investigate the spoofing detection method based on the integrated usage of discrete wavelet transform (DWT) and machine learning (ML) techniques and propose efficient solutions. A series of experiments using different wavelets and machine learning techniques for Global Positioning System (GPS) and Galileo systems are performed. Moreover, the impact of the usage of different types of training data are explored. Following the computational complexity analysis, the potential for complexity reduction is investigated and computationally efficient solutions proposed. The obtained results show the efficacy of the proposed approach.  
Keywords:discrete wavelet transform; global navigation satellite system; spoofing; detection; computational complexity; machine learning
## 2 Broadband System
### Fixed-Line Broadband System
#### 2.1.1 Hybrid Fiber Coax Network  
There has been an extensive deployment of communication media by cable TV providers. The network mainly comprises coaxial cables which are normally deployed between a central point (optical node) and homes. In addition, a number of optical nodes are connected to a head-end through optical fibers. Therefore, the cable TV operator's HFC network is a combination of both coaxial cable and fiber. Note that HFC network bandwidth limitations are mainly as a result of the employed coaxial cable [105].  
The HFC MAC mechanism could be based on the Multimedia Cable Network System Partners Limited (MCNS) or IEEE 802.14 standards [106; 107; 108]. Furthermore, DOCSIS of the MCNS is the de facto standard in the cable industry [107]. For instance, in the United States, a very huge number of broadband subscribers that are about 40% more than fiber and DSL subscribers are being served by the cable companies [109; 110]. The trend can be mainly attributed to a notable relentless evolution in cable technology as presented in Table 1.  
In 2013, the Cable Television Laboratories, Inc. (CableLabs) announced an updated DOCSIS specifications (DOCSIS 3.1) [109]. Moreover, in 2017, CableLabs, completed its Full Duplex DOCSIS 3.1 specification with improved capacity and support for symmetric multi-gigabit services [111]. The evolution has brought considerable enhancement that has changed the signal from single-carrier QAM (SC-QAM) to orthogonal frequency division multiplexing (OFDM). In addition, higher-order modulations up to 4096 QAM have been adopted with optional support for 8192 and 16384 QAM. Furthermore, like Long-Term Evolution (LTE) networks in which carrier aggregation (CA) is supported, recent DOCSIS specifications support channel bonding [110]. The enhancements enable support for mobile backhaul/fronthaul (MBH/MFH), ultra-high resolution videos (4K/8K), and other virtual reality and IoT-enabled emerging applications. Consequently, with cable technology evolution, broadband capabilities, significant flexibility, and ability to support a massive number of users, the HFC networks are expected to continuously dominate the broadband access market [109; 112]. Nevertheless, as illustrated in Figure 2, cable TV broadband services depend on shared network infrastructure. Therefore, the network dependence on the number of subscribers sharing the head-end connection limits the effective bandwidth that can be delivered [16; 113].  
Figure 2: A typical HFC architecture.