esa-sceva/satcom-chunk-collection huggingface dataset:
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 3 Results  
### Scenario Configuration  
The scenario simulated with these methods consists of a low moisture sandy soil (with \(\epsilon_{r_{s}}=2.5\) and \(\sigma_{s}=0.0125\) S/m) where a target of 2 cm radius is buried at 25 cm depth (\(x_{tg}=0\) m and \(y_{tg}=-0.25\) m). Both metallic and dielectric targets are considered. If the target is dielectric, it is modeled as trinitrotoluene (TNT) with \(\epsilon_{r_{tg}}=2.9\) and \(\sigma_{tg}=0\) S/m. Simulations are performed between 3.5 and 5.5 GHz at 10 MHz steps considering TE polarization. Although these frequencies are higher than those commonly used in GPR, they have been chosen so that the radar could be light enough to be mounted on board a UAV (as it has been already proved in the prototype shown in [6]). In the ray-tracing simulation, the antenna beamwidth is \(30^{\circ}\) and the results are contaminated with white Gaussian noise, resulting in a signal to noise ratio of 30 dB. It must be noted that the direct signal between the TX and RX has not been included in the simulations, since it is expected to be removed from the received signal in a real implementation of the system (thanks to the fact that it will arrive earlier than the signal coming from the soil reflection and it will likely to be stronger).  
Three different configurations have been simulated:  
* Multimonostatic, where the TX-RX (drone mounted transceiver) is placed at 65 different positions between down-track positions \(x=-0.8\) m and \(x=0.8\) m at \(y=1\) m height. The angle of incidence is \(0^{\circ}\) (i.e., the antennas are aligned perpendicular to the soil surface, with main beam pointing straight down).
* Multistatic, where the TX is placed at a fixed position (on a vehicle, at down-track position \(x=-20\) m and height \(y=2.5\) m) with main beam pointing at an angle of incidence of \(83^{\circ}\) with the nominal ground surface, and the drone-mounted RX is looking downward and is moved to the same positions as in the multimonostatic case.
* Multibistatic, where the vehicle-mounted TX is placed at \(y=2.5\) m height and is moved between down-track positions \(x=-20.8\) m and \(x=-19.2\) m and the drone-mounted RX is moved between the same positions as in the multimonostatic case. Thus, both TX and RX are moved coherently. The angles of incidence are \(83^{\circ}\) for the TX and \(0^{\circ}\) for the RX.  
The positions of the TX-RX in the multimonostatic configuration were set according to those already used in previous experimental work. The positions of the TX-RX and the angle of incidence in the multistatic and multibistatic configurations were found using ray tracing simulations, so as to be able to detect dielectric targets. The performance of all configurations were then verified with FDFD.  
Figure 4: Scheme for estimating the path length (the green dashed line represents the true ray path).
## 5 The Evolution towards a Global Smart Grid Deployment: Challenges and Perspectives
### Data Communication and WSANs
#### 5.1.4 Resource Constraints  
Finally, it is interesting to point to another challenge concerning the application of WSANs to grid management: the limited resources of sensor nodes, which are constrained by energy availability, memory size and processing capacity. The most important resource is the battery energy supply, which is in general limited [98]. Thus, taking into account that periodic replacements of node batteries is an unaffordable task, a WSAN must be designed to maximize its autonomy, including adapted operative systems [99,100] or communication strategies, such as optimized routing techniques [101]. The battery life of nodes can be also increased by applying topology control algorithms [102]. Energy harvesting solutions are other possibilities to increase the energetic autonomy of the sensors [103]. Some of the candidate technologies for harvesting energy applicable to sensor nodes are solar and thermal, vibration-based, and electromagnetic wave and radiofrequency [104].
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 5 Conclusions  
In this study, we proposed a very simple new mathematical model using semi-Markov process theory for zone-based registration using three zones (3Z) and analyzed the performance of a 3Z system using the proposed model. The numerical results for the various situations showed that, when the CMR is small, the 3Z system outperforms 1Z and 2Z systems. On the other hand, when the CMR is very large, the 3Z is not much different from 1Z or 2Z systems or is sometimes inferior to 1Z and 2Z systems. In conclusion, depending on the system situation, the optimal management scheme for zone-based registration will change dynamically, so it is necessary to operate zone-based registration flexibly by considering the system situation.  
Using a simple mathematical model based on semi-Markov process theory, this study can easily support the prompt determination of the optimal management scheme for zone-based registration. These results are directly able to enhance the performance of actual mobile networks and can be implemented quickly and easily. Since no hardware modifications are necessary to implement a 3Z system, it can be easily applied by modifying the 4G/5G networks of the existing 1Z based networks. Noting that a 3Z system outperforms a 2Z system in specific conditions, it is possible to adopt an adaptive scheme to dynamically switch between 1Z, 2Z or 3Z systems according to the current situation.  
Figure 12: Effect of implicit registration by outgoing calls (\(\theta=0.3\)).  
**Author Contributions:** Conceptualization and validation, J.J. and J.H.B.; formal analysis, data curation, J.J.; writing, supervision, project administration, funding acquisition, J.H.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Research Base Construction Fund Support Program funded by Jeonbuk National University in 2019, and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B01014615).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Performance Evaluation of a Full-Duplex Relaying-Enabled Satellite Sensor Network
## 3 Performance Evaluation
### Outage Probability
To this end, by replacing \(x\) with \(\gamma_{th}\) in (24), it is straightforward to calculate the OP of the FD satellite relaying system.
## 4 Conclusions  
In this work, an SP-fed broadband CP circular slot array antenna loaded with an AMC structure was presented. The design method of the antenna and the principle of action of the AMC structure were analyzed and tested in a SATMO microwave anechoic chamber. First, the AR bandwidth is well widened by the introduction of the radiation unit, and the SP feed network is used to connect to form the array. Second, the AMC structure is applied to increase the radiation of the antenna in the +z direction and simultaneously reduce the antenna profile. The measurements prove that our designed antenna realizes impedance bandwidth (2.74-6.22 GHz) of 77.67% and AR bandwidth (3.00-5.90 GHz) of 65.16% with a low profile of 0.11\(\lambda_{0}\) (about 0.11\(\lambda_{0}\) at the center frequency of 4.5 GHz). Our designed SP-fed broadband CP circular slot array antenna with loaded AMC structure can be used in space-constrained environments such as indoor and dense building areas.  
Conceptualization, H.H.; methodology, H.H.; software, H.H.; validation, S.W.; formal analysis, H.G.; investigation, H.G.; data curation, X.M.; writing--original draft preparation, X.M.; writing--review and editing, X.H.; supervision, X.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data are contained within the article.  
The authors declare no conflict of interest.  
Figure 10: The simulated and measured radiation patterns of the proposed antenna at (**a**) 3.0 GHz, (**b**) 4.0 GHz, and (**c**) 5.0 GHz.
## 2 Related Work
### Traffic Classification Methods Based on DL  
DL can learn from large amounts of data and extract features directly, which removes the reliance on expert knowledge. However, DL methods usually have larger time costs than ML methods.  
In order to solve the problem of low efficiency brought by the DL method, [PERSON] et al. [18] proposed an ICS traffic monitoring method based on a two-way simple recursive unit. The two-way structure in the neural network was optimized and the training effect was improved through using skip connections, and achieved a classification accuracy of more than 92%. However, the training time required at least nearly 100 s and increased with the increase in the number of neural network layers, even though the efficiency of the neural network algorithm was optimized. In order to efficiently manage industrial IoT systems, [PERSON] et al. [19] proposed a traffic classification method based on spatiotemporal features, using DL technology to automatically extract spatiotemporal features in data packets and implement traffic classification, which achieved 95% classification accuracy. [PERSON] et al. [20] proposed a tree-structured recurrent neural network for network traffic classification, which achieved 98.98% classification accuracy with a training time of 54.20 s and a classification time of 0.185 milliseconds. [PERSON] et al. [21] proposed a lightweight intelligent intrusion detection system for the industrial Internet of Things using DL algorithms, which achieved 99% classification accuracy through a new predictive model based on sparse evolution training for network monitoring. The training time and predicting time were 61.31 s and 2.36 s, respectively. [PERSON] et al. [22] used an AM-1 DCNN + LSTM DL model to extract features of the ICS traffic, and identified protocols, which achieved accuracy of 93%, but spent 122 min training the model.  
The above DL-based methods required high training and predicting time, which is unacceptable for an ICS that requires high real-time performance.
# Development of Far-UVC-Based Surface Disinfection Prototype for Public Buses
## 5 Conclusions  
In this paper, a prototype of a disinfection system based on 222 nm wavelength far-UVC for public transport was proposed. The specific conclusions are as follows:  
* A far-UVC based surface disinfection prototype for public buses was developed. The far-UVC light angle of the prototype was investigated, and a bus seat irradiation study was carried out in which the irradiances of individual zones were determined by evaluating both the distance between the light source and each surface to be disinfected and the disinfection time to achieve a minimum radiant exposure of 2 mJ/cm\({}^{2}\).
* An energy efficiency assessment model of a far-UVC-based disinfection module was proposed that allows the simulation of the instantaneous electrical energy demand during the disinfection process and can be used for energy consumption optimization and forecasting.
* Three disinfection scenarios were analyzed, focusing on bus zones, disinfection times, and electrical energy demands. It was found that placing the far-UVC light source in a fixed vertical position, 75 cm from the seat and 25 cm from the center of the backrest, satisfies the optimum disinfection time and energy requirements. The disinfection of an entire bus with two lamps per row takes 38.1 min (20.6 min with four lamps) and requires 111.3 Wh of energy (109.8 Wh with four lamps).
* A subsystem that enables the real-time monitoring of occupancy levels in different areas of public buses has been developed using the Yolo v7 passenger detection algorithm. By prioritizing the disinfection process in the most crowded and frequented areas, this subsystem increases the effectiveness of the disinfection process in a bus interior.  
**Author Contributions:** Conceptualization, D.D., M.K., A.T., Z.L., D.S., V.J., A.M., R.D. and J.G.; Data curation, D.D., M.K., A.T. and Z.L.; Formal analysis, D.D., M.K., A.T., Z.L. and J.G.; Funding acquisition, A.M. and R.D.; Investigation, D.D., M.K., A.T. and Z.L.; Methodology, D.D., M.K., A.T. and Z.L.; Project administration, A.M. and R.D.; Resources, D.D., M.K., A.T., Z.L., D.S. and V.J.; Software, D.D., M.K., A.T., Z.L. and D.S.; Supervision, D.D., M.K., A.T., Z.L. and A.M.; Validation, D.D., M.K., A.T., Z.L., D.S., V.J., A.M., R.D. and J.G.; Visualization, D.D., M.K., A.T. and Z.L.; Writing--original draft, D.D., M.K., A.T. and Z.L.; Writing review & editing, D.D., M.K., A.T., Z.L., D.S., V.J., A.M., R.D. and J.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research has received funding from the European Regional Development Fund (project No 13.1.1-LMT-K-718-05-0002) under a grant agreement with the Research Council of Lithuania (LMTLT), funded as the European Union's measure in response to the COVID-19 pandemic.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Synthesis of GO, TiO\({}_{2}\), and rGO/TiO\({}_{2}\)  
The GO was prepared following the previously reported chemical synthesis [16]. Briefly, 3 g of graphite flakes was dispersed in 69 mL of sulfuric acid and 1.5 g of sodium nitrate. After obtaining a homogeneous mixture and a temperature below 5 \({}^{\circ}\)C was achieved in an ice bath, the 9 g of potassium permanganate was added gradually. Afterward, the mixture was heated at 35 \({}^{\circ}\)C for 30 min and milli-Q water was slowly added. Then, the temperature of the mixture was heated up to 98 \({}^{\circ}\)C and held for the next 15 min. At the end of the reaction, the mixture was cooled at room temperature and 420 mL of milli-Q water and 3 mL of hydrogen peroxide (30%) were added. The resultant material was centrifuged (10 min, 3000 rpm) to remove the impurities and isolate the GO nanosheets. To purify the GO nanosheets, the mixture was washed several times with hydrochloric acid (10 wt.%) and milli-Q water until neutral pH was reached.  
TiO\({}_{2}\) colloidal solution was prepared using the following reagents: titanium (IV) isopropoxide as a precursor, \(i\)-propanol as a solvent, acetylacetone as a chelating agent, and nitric acid as a catalyst [32].  
According to the procedure previously reported and the obtained results [16], the selected amount of the synthesised GO suspension (10 wt.%) was added to the TiO\({}_{2}\) sol. To acquire homogeneous dispersion, the suspension was ultrasonicated for 15 min and then transferred to the Teflon-lined autoclave for hydrothermal synthesis (6 h at 180 \({}^{\circ}\)C). Afterward, resulting nanocomposite in the powder form was recovered by centrifuging at 3000 rpm from the supernatant and was washed with the ethanol and milli-Q water. For comparison, the same procedure, as applied for the preparation of rGO/TiO\({}_{2}\), was adopted to prepare pure TiO\({}_{2}\) nanoparticles, but without adding GO. The synthesised pure TiO\({}_{2}\) and rGO/TiO\({}_{2}\) nanocomposite was annealed at 300 \({}^{\circ}\)C with a heating rate of 3 \({}^{\circ}\)C/min and 2 h dwelling time, then cooled naturally to room temperature.
###### Abstract
High muscle stiffness means high muscle tone, and prolonged exposure to these situations is interpreted as a high risk of muscle fatigue or LBP [20]. In previous studies, authors reported increased lumbar muscle tone in patients with CLBP and a 20% greater stiffness of the erector spinae muscles at rest, as compared to that in patients without LBP [21]. High stiffness of the spinal muscles indicates hypertonia due to chronic mechanical overload with or without an inflammatory response [22]. A high muscle stiffness value is a valid factor for predicting the occurrence of LBP, and relevant evidence has mainly been studied in patients with ankylosing spondylitis [23]. However, studies in LLAs with high prevalence of LBP are rare. Intramuscular properties are measured using a variety of methods such as diagnostic ultrasound, magnetic resonance elastography, ultrasonic shear-wave elastography, and electromyography [24]. Although these methods can provide high reliability and quantified data, their high cost and operational complexity can make them difficult to use in simple clinical practice. Several recent studies have investigated intramuscular properties using a handheld pyotonometer. It is a non-invasive device that can quantify stiffness, frequency, and decrement of myofasical tissue, and real-time quantitative measurement of myofasical stiffness can support diagnostics and patient-driven interventions. Studies using a pyotonometer were conducted in patients with ankylosing spondylitis [23; 25], Parkinson's disease [26], and stroke [27], and the reliability was confirmed with an intraclass correlation coefficient of 0.80 or greater [28]. As mentioned above, most previous studies related to LBP in LLAs have focused on the kinematic changes such as trunk and lumbo-pelvic movement asymmetry [5; 6] as well as variations in the trunk-pelvis coordination during walking [14]. Although some studies have reported asymmetry of lumbar muscle activity using surface electromyography [29], to the best of the authors' knowledge, there are few studies on the changes in the intramuscular properties of lumbar muscles in LLAs. Understanding the biomechanical characteristics (i.e., the intramuscular properties) of groups with a high prevalence of LBP will help prevent disease and provide adequate treatment by identifying the potential risk factors. Therefore, this study aims to compare the biomechanical properties of lumbar muscles using a handheld myyotonometer in LLAs and healthy controls. The results of this study will be utilized as basic data for establishing rehabilitation strategies for amputees with LBP.
# Effect of Ionic Liquids on the Isobaric Vapor-Liquid Equilibrium Behavior of Acetone-Chloroform
## 4 Quantum Chemical Calculations  
In order to find the global minimum energy geometry, several initial configurations were optimized and their final energies were compared using the method of quantum chemical calculations. In this work, all initial configurations were optimized at the B3 LYP/6-311++G** level with the Gaussian 09 software package [17].  
After the geometry optimization, the intermolecular energies were calculated with DFT, basis set superposition error (BSSE) correction and zero-point energy (ZPE). In this way, the lowest energy geometry can be approximated as the global optimal geometry. The interaction energy was calculated via Equation (5)  
\[\Delta E(\text{AB})=E(\text{AB})-E(\text{A})-E(\text{B})+\text{BSSE} \tag{5}\]  
where \(E(\text{AB})\) is the energy of the A-B complex and in the case of this work is IL-acetone, IL-chloroform, or the cation-anion complex. The lowest energy geometries are shown in Appendix A, and the interaction energy of those geometries is shown in Table 2. In addition, we can judge whether hydrogen bonds are formed by means of literature [18].  
By experiment, \(\Delta E(\text{acetone + chloroform})>\Delta E(\text{acetone + [MMIM][DMP]})>\Delta E(\text{chloroform + [MMIM][DMP]})>\Delta E(\text{[MMIM]}^{+}+[\text{DMP]}^{-})\). As the most probable structures, the \(\Delta E(\text{acetone + [MMIM][DMP]})\) is weaker than the \(\Delta E(\text{chloroform + [MMIM][DMP]})\). Therefore, it is easier for the ILs to attract chloroform molecules and to have a salting-out effect on acetone. Moreover, because the \(\Delta E(\text{acetone + [EMIM][DEP]})>\Delta E(\text{acetone + [MMIM][DMP]})>\Delta E(\text{chloroform + [EMIM][DEP]})\approx\Delta E(\text{chloroform + [MMIM][DMP]})\), [EMIM][DEP] shows higher extraction efficiency than [MMIM][DMP].
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 5 Performance Analysis
### Bit Error Rate Computation  
We now present and analyze the performance results concerning bit error rate versus the energy per bit to noise power spectral density ratio (EbNo) from the different scenarios described in Section 4.
## 3 Proposed Method
### Overall Architecture
#### 3.2.1 Backbone Module  
Res2 Net-DLA-60 [21] is used as the backbone network, and light-weight ShuffleNet V2 [22] is also chosen as an extra backbone network. For convenience of description, we define the feature map obtained by stage \(n\) as \(\Phi_{n}\). For Res2 Net-DLA-60, we found that using \(\Phi_{4}\), \(\Phi_{5}\), and \(\Phi_{6}\) got the best performance through experiments, which are the feature maps with 1/8, 1/16, and 1/32 times the size of the original image. And we use \(\Phi_{2}\), \(\Phi_{3}\), and \(\Phi_{4}\) in ShuffleNetV2, which are the feature maps with 1/8, 1/16, and 1/32 times the size of the original image. In \(\Phi_{n}\), the larger the number, the higher the feature level, and often the smaller the size. At the same time, because the scale reduction in feature maps is generally a multiple of 2, and the number of stages and the processing methods in different networks are different. In order to maintain consistency in the representations of different networks, we define that, with the increase of the network depth, the feature map whose size is scaled to the n-th power of 2 relative to the size of the input image is \(Rn\), thus \(\Phi_{4}\), \(\Phi_{5}\), and \(\Phi_{6}\) in Res2 Net-DLA-60 correspond to \(R3\), \(R4\), and \(R5\) respectively. And \(\Phi_{2}\), \(\Phi_{3}\), and \(\Phi_{4}\) in ShuffleNetV2 correspond to \(R3\), \(R4\), and \(R5\) respectively. In \(Rn\), the smaller n, the larger the size, and the smaller the receptive field. At the same time, the utilized feature levels are lower, and the detection effect for small objects is better than that for large objects. Reversely, the larger n in \(Rn\), the smaller the size, but the larger the receptive field. At the same time, the utilized feature levels are higher, and the detection effect on large objects is better. However, because of its smaller size, the detection effect on small objects is not ideal, and some certain errors are prone to occur in object location.