esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Open Issues and Research Directions  
Although researchers have shown decent efforts in improving the security of CPS, they are still in their infancy and several open issues are remaining for further exploration. Challenges faced by CPS and corresponding research opportunities are highlighted here:  
* Delay in encryption and decryption process cause network latency;
* Weak scheme for user authentication and lack of multi-factor verification in devices;
* Lack of firewall protection;
* Insufficient Intrusion detection techniques;
* Need of cipher algorithms for CPS security;
* Strong user authentication;
* Data availability and verified backups.  
Some potential research opportunities are discussed as under:  
* Many studies have been conducted for attack detection, but there is a need to consider real-time monitoring of CPS security. To employ real-time CPS security, the complexity of predictive models should be reduced to avoid data transmission delay.
* A resilient design of a CPS system for recovery after sensor attacks and software faults needs to be devised.
* Artificial intelligent-based models require sufficient data for training, so there is a need to generate a dataset for training and learning of malicious behaviors.
## 1 Introduction  
Twenty-four hour dietary recalls provide metrics of estimated food intake, necessary for studies that link nutrient intake to diseases or other health-related outcomes. The multiple pass 24-h recall method used for estimating food intake has been proven to be a reliable method for the estimation of nutritional intakes in individuals with obesity [1, 2]. Usually, several 24-h recalls are needed, in order to provide sufficient time to capture intra-individual intake variations. The recommended number of 24-h recalls varies, depending on the outcome of the study, ranging from a minimum of two (for the comparison of protein and potassium intake between European countries [3]) to a maximum of 10-15 days when assessing comprehensive diets across a six month period [4]. [PERSON] et al.1 [5] suggested a maximum of eight 24-h recalls in an overweight and obese population, in order to reduce random errors.  
Several authors have underpinned that the simple averaging of several 24-h dietary intakes is not a suitable method to account for systematic errors in the design of the study. This method is useful only to account for random errors in this type of assessment. Therefore, through a carefully planned design, some sources of known bias on the measurement of intake such as gender, age, body mass index (BMI), day of the week, or rural or urban location can be accounted for [6; 7].  
Underreporting of food intakes has been acknowledged as a source of bias and has been associated with higher BMI or body fat percent, feminine gender, and social desirability [8; 9; 10]. Even when dietary restraint practices were related to lower energy and fat reporting, this did not modify the accuracy of the method [11]. Several studies support the idea that attentional bias toward food cues is increased in individuals with obesity [12]. Interventional studies have proven that this attentional bias in individuals with obesity can be altered in both directions [13; 14].  
Little is known about whether the order of the subsequent recalls could be a specific factor to account for when assessing bias, or whether the order of recalls would induce behavior changes through alterations of attentional bias to the interviewed individuals. The purpose of this study was to evaluate the consistency of reported energy, macro-, and micronutrient intakes in a series of four 24-h recalls in adults with obesity.
## 6 Numerical Results
### Outage Probability  
In this subsection, we formulate the probability of an outage event to occur in the system. When the total power available at the transmitter is limited, the integer forcing precoding may fail, depending on the total power requirement of the precoder. We define the outage probability, \(P_{out}\), as the probability when the integer forcing precoding requires more power than the power available at the transmitter. Let the total power available at the transmitter be given by \(\gamma_{a}\). This power, \(\gamma_{a}\), is the maximum power that the transmitter can use to precode. The power required by the precoded signal is given by \(\gamma_{w}\). Therefore, if the precoder is such that it needs more power to precode than the available power, \(\gamma_{a}\), we have a case of outage, as:  
\[P_{out}=\Pr(\gamma_{w}>\gamma_{a})\]  
The distribution of the above probability depends on the distribution of the channel fading coefficients, because IFP is designed as \(w_{l}=h_{l}^{z}/h_{l}\). The outage probability for a Gaussian channel with varying available power has been shown in Figure 4. The outage probability decreases with increasing available power. When \(\gamma_{a}>4\), the outage probability goes to zero, because \(\gamma_{w}<4\), as shown in Theorem 1.  
Figure 4 indicates that as the channel variance increases, the outage probability decreases. This is due to the fact that with increasing channel gain, \(h_{l}\), the power required by the precoder decreases. Hence, the higher the instantaneous channel gain, the better is the outage performance of IFP.  
Figure 3: Compute and forward (CF) with the integer forcing precoder (IFP) compared to Time Division Multiplexing (TDM) performance at the destination.
# MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization
## 1 Introduction  
Transportation plays a crucial role in people's work and life, influencing the development of society and economy. Roads serve as the foundation for road traffic vehicles, with pavements being a critical component of road infrastructure. Therefore, it is essential to ensure that their performance meets usage requirements, guaranteeing the safety of vehicle operation and the travel experience of users. Due to the limitations of the full life cycle, pavement performance degrades during use. Road maintenance is the primary method to improve pavement performance [1; 2; 3], and timely and effective maintenance isof significant importance in maintaining good pavement conditions. Road maintenance planning involves numerous issues, including maintenance targets, maintenance timing, maintenance approaches, costs, impacts of maintenance, and expected maintenance effectiveness. In fact, road maintenance decision-making is not only a focus for researchers but also of interest to road management authorities.  
The climatic characteristics of high-altitude areas, such as low temperatures, frequent rain and snow, and intense ultraviolet radiation, accelerate pavement aging and induce various pavement diseases, severely affecting pavement lifespan [4; 5]. Additionally, the climate in high-altitude regions influences pavement maintenance, particularly the planning of preventive pavement maintenance. For example, some road segments may be challenging to maintain due to accumulated snow. Therefore, studying pavement maintenance decisions in the areas and determining scientifically reasonable maintenance plans can not only help extend the pavement lifespan but also contribute to ensuring road service levels, thereby providing robust support for social and economic activities in such regions.
## 2 Numerical Model
### Model Geometry  
The broadband horn antenna that was utilized had initially been designed using computer-aided design (CAD) software, specifically AutoCAD 2022. The dimensions of the antenna's aperture were 590 mm \(\times\) 560 mm, while its height measured 660 mm. The antenna was connected to a rectangular waveguide that had dimensions of 36 mm \(\times\) 62 mm and a length of 150 mm. Figure 1 illustrates the dimensions and design of the antenna under consideration. The aforementioned geometry was imported into the computational modeling software COMSOL. The geometry of the antenna enabled the stimulation of the specimen with a larger surface area, which is useful for the structures found in the civil engineering field. The dimensions of the aperture allow for heating samples with an almost 1 m width (with the inclination direction of the antenna). The antenna used in the present system had a gain of 21.67 dB in its main direction. The radiation pattern measurements were made in the far field by a sensor (placed at 25 m from the aperture) in the vertical and horizontal directions. The radiation pattern (H-plane and E-plane) of the antenna was considered in a polar axis system [29].  
A block measuring 400 mm \(\times\) 400 mm \(\times\) 45 mm was utilized to model the test sample, which was a CFRP-reinforced concrete slab. The composite reinforcement was represented as a bilayer structure that comprised an epoxy adhesive layer measuring 1 mm in thickness and a layer of CFRP composite with a thickness of 1 mm. The defect in the form of a lack of adhesive was represented as a void space within the layer of epoxy adhesive. This type of defect can be caused by mechanical actions during the service life of civil engineering structures reinforced with CFRP. The dimensions of the defect were 100 mm \(\times\) 100 mm and 1 mm in height. Three cases were simulated, namely, defect-free, centrally located defect, and off-center defect. Figure 2 displays all the dimensions and sample designs.  
The specimen was positioned at a distance of 200 mm from the central point of the antenna aperture and was rotated at an angle of 45\({}^{\circ}\) with respect to the antenna. Figure 3a shows the full geometry of the model. In order to minimize the calculation time and the necessary computational resources, the simulation covered only 1/2 of the model presented in Figure 3a. The division of the model is illustrated in Figure 3b.  
Figure 1: Geometry of utilized horn antenna with dimensions (dimensions in mm).
## 3 Results and Discussion
### Validation of the Measured and Simulated Values  
The values of air velocity, using five ridge openings and air speed of 0.1 and 1.0 m s\({}^{-1}\), gave a fair correlation of measured versus simulated values. Values of R\({}^{2}\) and the regression equation are shown in Table 4. Good correlations were observed between measured and simulated values of air temperature, using five ridges opening and air speed of 0.1 and 1.0 m s \({}^{-1}\). In both conditions, for the linear and angular coefficients significant differences were detected (\(t\) test, \(p\)-value \(>\) 0.05).  
Figure 7: Computational grid mesh detail of the scale model: (**A**) sidewall and compost pack area, (**B**) external structure, (**C**) barn model with roof and (**D**) the volume mesh.  
A good relationship was observed between measured and simulated values using air speed of 0.1 m s\({}^{-1}\) and four wind directions tested. The adjusted equations of air velocity and temperature had the lowest R\({}^{2}\) of 0.559 and 0.627, respectively (Table 4). Overall, the wind directions performed similarly with the exception of East to West, which produced the largest values of air velocity and temperature measured. In this treatment, there was a positive and linear relationship between the average of measured and simulated air velocity (slope = 0.8239, R\({}^{2}\) = 0.559) and temperature (slope = 0.4663, R\({}^{2}\) = 0.627). The results show a good correlation between measured and simulated air velocity at four wind directions tested (Table 4). These were demonstrated by the correlation coefficients (R\({}^{2}\)). In both conditions, these equations showed the linear and angular coefficients significant (\(t\) test, \(p\)-value \(>\) 0.05).  
Given that the absolute average errors calculated between air velocities and temperature measurements and simulated values were very close to or less than 0.21 m s\({}^{-1}\) and 1.53 \({}^{\circ}\)C, respectively, the accuracy values of the anemometer (\(\pm\)0.01 m s\({}^{-1}\)) and thermometer (\(\pm\)1.0 \({}^{\circ}\)C) at that magnitude of these errors would have little influence on the final result for the calculation of the variables studied. Thus, the computer models can be considered suitable for the proposed use.
## 4 Case Study: Two 3 DOF Manipulators with a Periodically Moving Obstacle on a Line
### Simulation
For comparison, the paths computed by PRM and TD3 are also presented. Hence, there are three generated paths in Figure 12. The red lines are the resulted paths by the PRM  
Figure 11: Path generation using the trained actor DNN. Figure 6:0 Initial and goal positions in the workspace. points graph), the blue lines by TD3 with HER, and the green lines by the proposed method path. As seen in Figure 12, the proposed SAC-based method generates the smoothest and shortest path. To analyze quantitatively, 100 simulations with the arbitrary initial point and goal point are carried out using PRM, TD3 with HER, and the proposed method simultaneously. Then, the lengths of all 100 generated paths are given in Figure 6:1 As seen in Figure 13, the proposed SAC-based path planning generates the shortest path. Since, inevitably, PRM and TD3 with HER deal with the moving obstacle in a conservative manner, the result is quite natural actually. In Table 3, the optimal path by the proposed method is shorter by 14.86% than the path by PRM. If the path by the SAC with HER is compared with the path from TD3 with HER, the path by the proposed method is 17.40% better than that by TD3.
# Integrated Multifunctional Laryngoscope for Medical Diagnosis and Treatment
## 2 Materials and Methods  
### System Design  
Figure 1 shows a schematic of the integrated multifunctional laryngoscope. The white light endoscope and the OCT system were coupled to the probe by two 45\({}^{\circ}\) dichroic mirrors, while the laser ablation treatment system was in a straight line. The endoscope was a common-path structure.  
The OCT unit was a swept-source OCT system with an A-scan rate of 100 kHz (HSL-20-100, Santec Corporation, Japan). The full spectral sweep range was 87 nm centered at 1310 nm, which had  
Figure 1: Schematic of the integrated multifunctional laryngoscope. L#: lens; GRIN: Gradient-index lens; CCD: charge-coupled device; C: circulator.  
lower scattering than at a shorter wavelength [29; 30]. The incident light from the source was coupled into an 80:20 ratio coupler; 80% entered the sample endoscope and 20% entered the reference arm. In the reference arm, the light was reflected by the mirror and propagated back to the interferometer. The collimated beam in the sample arm was scanned by a two-dimensional galvanometer (GVS002, Thorlabs, Newton, NJ, USA) and focused with an achromatic doublet L5. The beam was guided to the probe by a customized dichroic mirror and then focused on the sample with a working distance of 15 mm. The OCT interference spectrum was acquired by using a balanced detector (PBD430c, Thorlabs, Newton, NJ, USA) and a high-speed digitizer (ATS9360, Alazar Technologies, Pointe-Claire, QC, Canada ).  
The white light imaging unit consisted of the probe, the proximal objective, and the camera. Illumination light from a white LED was transmitted to the sample through fiber optic bundles. The reflected light from the sample surface was deflected using a customized dichroic mirror that passed the proximal objective onto the CMOS (Complementary Metal Oxide Semiconductor) sensor (CatchBEST Optoelectronic Equipment, Beijing, China) with \(1024\times 1248\) pixels.  
For the laser ablation unit, the medical ablation laser (Leonardo Dual 45, Biolitec biomedical technology, Jena, Germany ) output beam was guided to the system via a multimode fiber and then coupled to the probe by a doublet L3, as shown in Figure 1. The ablation laser had dual wavelengths at 980 and 1470 nm, and the maximal output power was 45 W. The ratio of power between the two wavelengths could be adjusted for different kinds of applications.
# Network Coding for Efficient Video Multicast in Device-to-Device Communications
## 4 System Evaluation  
To evaluate the performance of the proposed scheme, we experimented in a simulated scenario and a real scenario, respectively. First of all, we evaluate the feasibility of the scheme on the two aspects of network coding efficiency and delay distribution. Further, we analyze the performance with the SDR and Peak Signal to Noise Ratio (PSNR) and compare the performance with the traditional replication-based scheme and the RLNC-based scheme.  
The experimental parameter settings are listed in Table 1:  
Because the D2D communication module hasn't been implemented in commercial mobile devices at the current stage, we used the WiFi Direct technology to implement device-to-device communication between different devices. In the experiment, the video transmission is conducted in the form of a UDP multicast by adopting the IEEE 802.11g protocol. The experimental scenarios for this scheme are shown in Figure 2.
## 4 Results and Discussion  
### Results  
Figure 4 depicts true stress-strain curves at different temperatures. The variations in stress are considered to be largely dependent on variations in temperature and strain rate. The peak stress decreases with increases in temperature, while the peak stress increases with increases in strain rate. Maximum elongation was observed at 430 \({}^{\circ}\)C and 0.003/s, while minimum elongation was observed at 290 \({}^{\circ}\)C and 0.03/s.  
Figure 5 shows a comparison between the measured stress and the stress predicted by the Arrhenius equation at 290 \({}^{\circ}\)C, 360 \({}^{\circ}\)C, 430 \({}^{\circ}\)C, and 500 \({}^{\circ}\)C. The results show that the Arrhenius equation has low prediction accuracy. Specifically, it was observed that the prediction accuracy was significantly low at 290 \({}^{\circ}\)C. The MAE--defined as \(MAE=1/\text{n}\sum_{\text{k}=1}^{\text{n}}(|y_{\text{k}}\,-\,\bar{y}_{\text{k} }|)\)--of the predicted data was 4.515, while the RMSE--defined as \(RMSE=\sqrt{1/\text{n}\sum_{\text{k}=1}^{\text{n}}(|y_{\text{k}}\,-\,\bar{y}_{ \text{k}}|)^{2}}\)--of the predicted data was 6.904.  
To evaluate the predictive performance of the proposed LCRMs, data samples of strain, strain rate, and stress were extracted at temperatures of 290 \({}^{\circ}\)C, 360 \({}^{\circ}\)C, 430 \({}^{\circ}\)C, and 500 \({}^{\circ}\)C from 5005 aluminum alloy; therefore, four log-log regression models were created to predict the stress of aluminum alloy relative to the temperature. Each regression model had different properties to capture the overall stress dynamics, and the parameters were estimated by a vector consisting of the strain and strain rate of aluminum alloy obtained  
Figure 3: Schematic of the proposed multiple models with switching used to predict the stress of 5005 aluminum alloy.  
from 348 samples. After optimizing the parameters, a set of multiple models was tested with a series of 87 new data samples. For comparison with the proposed modeling scheme, a Neural Network (NN)--a representative nonlinear global model--was employed, and the selected number of neurons in the hidden layer of the NN was 20.  
Figure 4: True stress–strain curves at (a) 290 \({}^{\circ}\)C, (b) 360 \({}^{\circ}\)C, (c) 430 \({}^{\circ}\)C, and (d) 500 \({}^{\circ}\)C.  
Figure 6 shows the comparison between the measured stress and the stress predicted by the Neural network at 290 \({}^{\circ}\)C, 360 \({}^{\circ}\)C, 430 \({}^{\circ}\)C, and 500 \({}^{\circ}\)C. The accuracy of the predictions made by the Neural network were substantially improved compared to those made by the Arrhenius equation. Furthermore, Figure 7 depicts the comparison between the measured stress and the stress predicted by LCRMs at 290 \({}^{\circ}\)C, 360 \({}^{\circ}\)C, 430 \({}^{\circ}\)C, and 500 \({}^{\circ}\)C. The prediction made by the LCRMs is shown to predict the flow stress with the best accuracy.
# Evolution towards Smart and Software-Defined Internet of Things
## 3 Smart or Cognitive IoT Architecture
### Intelligent Low Power Interface  
The intelligent low power plan is in charge of short-distance communication. Near Field Communication (NFC) is a technology that enables IoT devices to communicate within millimeters. By placing two NFC-enabled IoT devices in close vicinity, data can be transmitted in seconds. It employs RFID technology and magnetic field fluctuations to send data between two NFC-enabled devices. NFC employs the same 13.56 MHz frequency as high-frequency RFID in active and passive mode operation. In active mode, both devices create magnetic fields, while in passive mode one device generates the field while the other employs load modulation to send data. Batteries benefit from passive mode to save power. The proximity between devices is useful for safe transactions such as payments. Finally, unlike RFID, NFC allows for two-way communication. Consequently, practically every smartphone today has NFC [52].
# Do We Get What We Need from Clinical Acoustic Voice Measurements?
## 2 Background: Current State of the Art and Challenges in Clinical Acoustic Measurements
### Do We Know Enough about Normal Voice Function?  
Patient characteristics such as gender, age, voice training, and voice use level have been shown to influence vocal function characteristics, including vocal maps, jitter, shimmer, HNR, and CPPS [42; 44; 51; 52; 53; 54; 55]. Age effects may be attenuated by voice training and appear less distinct in elderly individuals with a good general physical condition [56; 57]. Thus, in the clinical application, we rely on an understanding of how and to what extent our target measures will be affected by these factors to understand pathological voice function. To date, it is unclear for most of these characteristics how we should take them into account when making a clinical decision. Moreover, further physiologic influencing factors, including heartbeat and motor neuron activity, have been shown to affect acoustic perturbation [58]. From a clinical viewpoint, this substantially reduces the usefulness of acoustic voice quality measures for determining the extent of functional restriction.  
Given all of these circumstances, there is a need for far more baseline data for Voice Range Profiles and so-called voice quality measures, including technically well-researched normative values for gender and age, at least, to better understand the normal biological variation in voice production within and between individuals. This is the basis for an objective description of voice function restrictions to derive patient-specific treatment goals, and to reliably measure treatment effects.