esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Wireless sensor networks (WSNs) have emerged as a key technology for a broad spectrum of applications, ranging from weather forecasting [1] or complex industrial plant monitoring [2] to military surveillance [3]. These types of cyber-physical systems are prone to various malicious attacks which theoretically originate from three different causes: (i) the limited power, communication and computational resources of the nodes; (ii) the unattended and hostile environments where they are often deployed; and (iii) the open nature of the wireless transmission medium. In order to cope with security related issues, besides already traditional approaches like message encryption or node authentication, a convenient solution arises: equipping the sensor nodes with directional antennas.  
Usually, sensor nodes employ omnidirectional antennas for wireless communication due to a variety of reasons including their small size, low cost, ease of deployment, simplified transmission-related protocols, _etc._ With the advancements of smart antenna technology, the omnidirectional antennas may either be replaced by directional ones or can work in tandem with them on the same modes. The advantages brought by directional antennas to WSN nodes can be seen not only in increased quality of transmissions, optimization of energy usage, decreased number of hops due to longer transmission range, but also from the security point of view.  
Directional antennas can mitigate the malicious attack risks in WSNs in two ways: (a) directly, by being immune to attacks launched from outside their narrow radiation region; or (b) indirectly based on node position verification--here a node equipped with directional antenna, using the received signal's direction of arrival to compute the position of a sender node (in conjunction with other trusted nodes or beacons), can identify malicious nodes by checking their position against a trusted list. By using these two lines of defense against hostile attacks, the nodes equipped with directional antennas may identify, mitigate or even eliminate security risks when speaking about eavesdropping, jamming, wormhole attacks or Sybil attacks. From this perspective, this paper aims to survey the current state of the art in the field and to identify the major research challenges and perspectives.  
The remainder of the paper is organized as follows: Section 2 presents a brief comparison of the two antenna types--omnidirectional and directional, while Section 3 is devoted to small directional antenna prototypes that can equip sensor nodes. The state of the art in coping with security attacks using directional antennas is surveyed in Section 4. Section 5 reveals the research challenges and opportunities of employing directional antennas to reduce security risks, while conclusions are drawn in Section 6.
# Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model
## 3 Proposed Methodology
### Intelligent Prediction of Stuck Pipe Probability Based on LSTM
#### 3.2.2 Selection of Characteristic Parameters for Stuck Pipe  
Based on the mudlog data and the analysis of the factors affecting the stuck, we use 15 parameters: bit depth, hook tension, stand pipe pressure, torque, weight on bit, rpm, total pump stroke, inlet/outlet temperature, Inlet/outlet conductance, inlet/outlet density, inlet/outlet flow.
# Creation of One- and Two-Dimensional Copper and Zinc Oxides Semiconductor Structures
###### Abstract  
The most effective methods for the synthesis of nanostructured copper and zinc oxides, which have unique properties and potential applications in a variety of fields including electronics, photonics, sensors, and energy conversion, are analyzed. Special attention is paid to laser-based methods for synthesizing oxide nanostructures, with an emphasis on the importance of controlling power density distribution to influence the quality and properties of the nanomaterials. The great significance of wavefront shaping techniques for controlling laser-initiated processes is highlighted, which enable precise control over the phase and amplitude of light waves to achieve desired outcomes in optics and laser-assisted formation of one- and two-dimensional structures of oxide semiconductor materials. Diffractive computer optics is presented as a powerful tool for precise beam control. The significance of laser-induced thermochemical processes for creating and improving the properties of ZnO and CuO-based nanomaterials is discussed. The presented analysis shows that the synthesis of nanocomposites based on ZnO and CuO using pulse-periodic laser treatment, coupled with precise laser beam control using free-form diffractive optics, presents novel opportunities for applications in optoelectronics, sensor technology, electronics and portable energy sources manufacturing, and various other fields.  
nanostructured; zinc oxide; copper oxide; laser-based methods; wavefront shaping techniques; diffractive computer optics; laser-induced thermochemical processes +
Footnote †: journal: Applied Sciences
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 2 Signal Design Requirements
### High-Precision Measurement Requirement  
To achieve centimeter-level positioning accuracy, it is essentially based on carrier phase measurement. After fixing the carrier phase integer ambiguity, a PPP positioningsolution is achieved. The measurement accuracy of carrier phase has a decisive impact on the final positioning accuracy.  
To accelerate the convergence speed of PPP, LEO navigation augmentation signals must support continuous high-precision carrier measurement, and the carrier phase measurement accuracy is equivalent to GNSS signals so that the contribution or weight value of LEO navigation signals in the positioning solution is sufficient to unleash the potential for accelerating convergence.  
Time division pulse signals such as STL broadcasted by Iridium Next are difficult to support high-precision carrier phase measurement. Using continuous direct sequence spread spectrum signals similar to GNSS signals can better meet the needs of high-precision carrier phase measurement.
# Application of Wavelet Transform and Fractal Analysis for
## 4 Conclusions
This in turn leads to a fast diagnosis suggestion that is computed and available for the gastroenterologist. As it was the first attempt to use wavelet transforms with fractal analysis in the case of GERD diagnostics, it is understandable that by adding and using impedance measurements the procedure can be perfected so that the results are better. The first step would be to include more patients and more data. Moreover, further research can be performed to determine and produce a computer system capable of distinguishing between the GERD-correlated diseases and complications (known or unknown for the patient at the time of the measurement). A very important step, however, will be the application of the developed methods and approaches to pH-metry with impedance. This approach will allow the checking of whether it will be possible to find similar relationships in the esophageal impedance waveforms, which would lead to the possibility of developing one of the first effective methods of computer-aided esophageal impedance analysis in the diagnosis of GERD (including non-acid reflux). It is worth emphasising that the research carried out and described in this work is one of the first pilot studies. The authors, undertaking the implementation of the issue, intended to test the thesis stated at the beginning. If the research assumptions had been confirmed, the authors would have planned to continue the research, with a wider scope, using the methods proven and described in this work, with the use of the appropriate modifications, to improve the research quality indicators--sensitivity and specificity. Only then can formal actions be taken to develop a medical device/system supporting the diagnosis of reflux diseases and to apply for legalisation, certification, and admission of the device for use in health facilities. Conceptualization, P.M.T., G.R. and M.J.; methodology, P.M.T., G.R. and L.D. ; software, L.D. and J.S. ; validation, P.M.T. and M.D. ; formal analysis, G.R. ; investigation, P.M.T., G.R. and L.D. ; resources, M.J.; data curation, P.M.T. and J.S. ; writing, P.M.T. and G.R. ; writing--review and editing, P.M.T., L.D. and G.R. ; visualization, L.D. ; supervision, G.R. ; project administration, P.M.T. and G.R. ; funding acquisition, G.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Patient consent was waived due to the fact that the data collected from the participants were obtained as a standard diagnostic/medical procedure and only after that used in research. Moreover, every participant agreeing to be diagnosed/treated in a university/teaching hospital is informed that their measurements can be used in research, providing that no identifying data are connected with the test results and therefore used. Data sharing not applicable. The authors declare no conflict of interest.
## 3 Results and Discussion
### XRD Results
#### 3.2.1 XRD for the FSW Sample  
In Figure 4a, the FSW sample contains original phases of raw fly ash (e.g., quartz, mullite, hematite, and magnesiorrite) that have been often observed in alkali-activated fly ash binders [11; 41; 42].  
As reaction products, the FSW sample produced geopolymer, which manifests itself in the form of an amorphous hump at 28-35\({}^{\circ}\) in XRD [12; 13; 43], with several crystalline zeolite phases. At 7 days, besides geopolymeric gel, zeolite Y (Al\({}_{71}\)Na\({}_{71}\)O\({}_{384}\)Si\({}_{121}\)) and Cl-sodalite (Al\({}_{6}\)Cl\({}_{2}\)Na\({}_{8}\)O\({}_{24}\)Si\({}_{6}\)) are only visible. At 28 days, while zeolite Y was more synthesized, zeolite X (Al\({}_{92}\)Ca\({}_{32}\)K\({}_{28}\)O\({}_{384}\)Si\({}_{100}\)) and Cl-chabazite (Al\({}_{2}\)Ca\({}_{1}\)Cl\({}_{2}\)O\({}_{8}\)Si\({}_{2}\)) are newly grown.  
Zeolites X and Y are very similar zeolites that belong to faujasite. Their main difference is simply the Si/Al ratio [44]. It is worth noting that [PERSON] et al. synthesized zeolites from NaOH-activated fly ash using seawater within the temperature range of 35 to 60 \({}^{\circ}\)C [21]; in their study, zeolite X was  
Figure 4: XRD patterns of (**a**) raw fly ash and FSW, and (**b**) raw GGBFS and SSW samples at 7 and 28 days; dashed line ‘S’ is the location of the (0 1 1) basal reflection of Cl-sodalite; dashed line ‘C’ is the location of the (0 0 3) basal reflection of Cl-Chabazite.  
observed when the samples were prepared with seawater at 35 \({}^{\circ}\)C, while it was not found in the samples prepared with a distilled water at the identical temperature. This result implies that zeolite X is a more stable phase in seawater than in distilled water at the atmospheric temperature. Similarly, in the present study, the active formation of zeolite X was observed along with zeolite Y. However, zeolites X and Y were not likely to contribute to the chemical Cl-binding because they contain no chlorine in their chemical compositions.  
In the FSW sample, the Cl-bearing phases were Cl-chabazite and Cl-sodalite, as shown in Figure 4a. These phases are the resultant zeolites after binding the chloride. Note that geopolymeric gel resembles zeolites in terms of structure on the nanometer scale [13; 31], and thus the presence of Cl-chabazite and Cl-sodalite may indicate that a portion of the geopolymeric gel in the FSW sample is very similar to them in terms of structure and composition. Therefore, these types of geopolymeric gels are likely to chemically hold a portion of the chloride in the FSW sample. However, given that the XRD peaks for zeolites X and Y are much larger than those of Cl-chabazite and Cl-sodalite, the zeolite X and Y types of geopolymer gel seem to have a more significant portion in the FSW sample. This may explain the limited degree of chloride binding in the FSW sample as shown in Figure 3.
## 2 Methods
### Case 2: Low-Latitude Region
#### 3.2.3 Mitigation of Ionospheric Effects on SAR Interferometry  
To mitigate the ionospheric effect, the proposed method is applied to the data from Thailand. Figure 11 shows the HH-polarized original and corrected interferogram. Figure 11a, displaying the original interferogram, indicates that main phase contribution is from the ionospheric error based on the presented long-wavelength characteristics. Figure 11b shows the residual-ionosphere-corrected interferogram, which is generated by subtracting the fitted residual ionospheric phase in (11) from Figure 11a. It is found that most long-wavelength signals have been successfully removed. However, the residual long-wavelength signal still remains, which can be attributed to the ionospheric phase error. Figure 11c conveys the interferogram generated by subtracting the ionospheric phase map from the original interferogram. As observed in Figure 11c, most of the long-wavelength signal remains, which is mainly from the residual ionospheric phase error. Figure 11d shows the interferogram that  
Figure 10: FR angles and VTEC maps. (**a**,**b**) FR angles on 26 March 2010 and 29 March 2011, respectively; (**c**,**d**) VTEC maps on 26 March 2010 and 29 March 2011, respectively.  
is jointly corrected by the least square method from (11). It can be clearly seen that almost all the long-wavelength signals have been successfully removed from Figure 11a, demonstrating the reliability of the proposed method. The residual phase in Figure 11d may be from the turbulent tropospheric phase delay and topographic phase error.
## 6 Simulation Setup and Results
### Baseline  
With DSS techniques applied in satellite-terrestrial deployments, entities are able to move out of initially allocated slots. However, for benchmarking purposes, a baseline scenario without DSS is defined. For this reference scenario, all DSS mechanisms are disabled for the GSO and NGSO links, and static spectrum usage is assigned. Therefore, in the baseline, satellite-terrestrial systems keep initial spectrum allocation status throughout the simulation, in which all the involved entities, including GSO-SAT, NGSO-SAT, ESIMs,  
Figure 4: Simulation scenario on Earth’s surface.  
Figure 5: Initial spectrum allocation status.  
and terrestrial networks, operate in the same frequency bands. As motivated in Section 4, a basic MF-TDMA MAC protocol is considered for both downlink and uplink, running in all satellite-related entities. However, for the baseline scheme, restrictions are imposed on the MF-TDMA MAC scheme to comply with the current DVB-RCS2 satellite standard (TDM in downlink and MF-TDMA in uplink).
## 4 Impact of Network Latency on Teleoperation
### CAV Teleoperation Requirements  
Considering various domains, most of the literature on teleoperation uses mobile robots with or without manipulators. Comparatively, there is less research conducted on CAV teleoperation [8; 12]. Despite the similarities of challenges faced by teleoperated robots and CAVs, the latter operate in significantly more complex environments, encountering far greater challenges [7]. For example, CAVs have higher stakes and operate in adverse weather conditions, long distances from remote stations, at higher speeds, and involve ethical decision making. Hence, more research is recommended in this area. The network requirements of CAVs are different from those of teleoperation in other domains. For example, compared to robotic teleoperations, CAVs involve realistic, uncertain, long-distance environments with usually higher vehicle speeds. The requirements of CAV teleoperation are shown in Table 4. Most studies provided requirements for latency thresholds and data rates, as the most critical QoS parameters for CAV teleoperation are the uplink data rates and the downlink latency [100]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Scenarios** & **Latency Thresholds** & **Data Rates** \\ \hline
[101] & At low speed with a multi-camera system, low visual quality & 600 ms & Up to 2 Mpbs \\ \hline
[65] & Commercial NSA 5G measurements in three different locations: cross-border trial site (between Portugal and Spain), test site, and laboratory & RTT minimum is 45 ms; <50 ms is infrequent; minimum of 80–100 ms & N/A \\ \hline
[90] & Dataset of 78 h and 5200 km of driving in different areas of Germany over LTE networks & 250 ms, 300 ms impossible to control & Steering commands 0.25 Mbps; 1–3 Mbps for one stream; UL: 3 Mbps and DL: 0.25 Mbps \\ \hline
[102] & Multi-operator switching to improve LTE coverage & 100 ms & UL: 3 Mbps \\ \hline
[22] & Lane changes, sharp turns, lead vehicle brake, curve, roadblock, highway & Three studies—delays of up to 300 ms, other studies 400–3000 m; avg. 120.3 ms in WLAN; median of \(\sim\)55.14 ms; 96\% delays 250 ms; 5G 60–260 ms & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CAV teleoperation requirements. The effect of latency thresholds can be classified as less impact, acceptable, difficult, and impossible thresholds. As mentioned earlier, using different wireless technologies for teleoperation can induce different latency thresholds. Summarising Table 4, it is evident that the downlink latency threshold is relatively lower than the uplink. By combining all the overall latency thresholds, it can be concluded that less than 170 ms has less impact on performance, and between 250 ms and 300 ms has an acceptable effect, with the average latency threshold of 170 ms.
## 3 Space--Time Dynamic Analysis Method Based on DCM  
### Dynamic Equation and Initial and Boundary Conditions  
A one-dimensional Timoshenko shear beam is selected as the analysis object. The dynamic governing equation under general dynamic load \(F(x,t)\) is  
\[EI\frac{\partial^{4}y}{\partial x^{4}}+\rho\frac{\partial^{2}y}{\partial t^{2} }-\rho\zeta\frac{\partial^{4}y}{\partial x^{2}\partial t^{2}}=F(x,t) \tag{3}\]  
where \(x\) and \(t\) represent the coordinates of space and time, respectively. \(y\) represents lateral displacement. The range of \(x\) is \([0,L]\), and the range of \(t\) is \([0,T]\). \(L\) is beam length, \(T\) is external load duration, \(EI\) is distributed stiffness. \(\rho\) is the distribution mass, and \(\zeta\) is the shear influence parameters, of which the dimension is the quadratic of length.  
For the convenience of discretizing the equation, let \(\xi=x/L\), \(\tau=t/T\), dimensionless Equation (1) into  
\[\frac{EI}{L^{4}}\frac{\partial^{4}y}{\partial\xi^{4}}+\frac{\rho}{T^{2}}\frac{ \partial^{2}y}{\partial\tau^{2}}-\frac{\rho\zeta}{T^{2}L^{2}}\frac{\partial^{ 4}y}{\partial\xi^{2}\partial\tau^{2}}=F(L\xi,T\tau) \tag{4}\]  
Assuming that both ends of the beam are simply supported, the boundary conditions are  
\[(y)_{\xi=0,1}=0 \tag{5}\]  
\[\left(\frac{\partial^{2}y}{\partial\xi^{2}}\right)_{\xi=0,1}=0 \tag{6}\]  
The initial condition is  
\[(y)_{\tau=0}=y_{0} \tag{7}\]  
\[\left(\frac{\partial y}{\partial\tau}\right)_{\tau=0}=\dot{y}_{0} \tag{8}\]
## 7 Experimental Results
### Periods' Histogram  
As discussed in the earlier sections, the primary rationale behind this work is that, in most techniques, the periods are limited to certain numbers and thus a period with some specific value can't participate. It is not acceptable in the IoT context in general and Industry 4.0 specifically where almost all objects will be doing some critical manufacturing activities. Harmonization and related approaches accept task sets with period values in the harmonic series. That being said, tasks with period values not falling in the series will not be permitted. To prove that the proposed system accepts a task with every integer value of period, 10 tasks are generated 20 times. The period range is kept constant between 1 and 10. The histogram of the period for different approaches is presented in Figure 12. For harmonization, the histogram is not balanced. For instance, value 2 is repeated 75 times and value 7 does not appear even once. On the other hand, the optimal threshold and static threshold performs identical, but an optimal threshold outperforms a static threshold in terms of overhead.
## 4 Results and Discussion
### Experiment Environment
#### 4.1.3 Ablation Test  
Ablation test results are shown in Table 5. In this experiment, we compared the proposed framework with the basic YOLOv5 base model, the YOLOv5 model with a modified detection loss, and the full framework with a modified loss and domain adaptation procedure. The performance significantly rises as the proposed module is applied. In the human detection task, small-sized objects are less likely to appear. Therefore, the performance improvement is rather small when adding only the Wasserstein loss. However, in the vehicle detection task, the mAP value rises 14.6%, showing the small object detection ability of the proposed method. Additionally, in both cases, the domain adaptation module provides a significant improvement in performance. This shows the ability of the model to extract domain-invariant features, yielding the robustness of the model along with high accuracy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **mAP** \\ \hline YOLOv5 & 39.3 \\ Faster-RCNN & 21.7 \\ YOLO-Z & 26.7 \\ Proposed & 57.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Benchmarks on vehicle detection task.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **mAP** \\ \hline YOLOv5 & 41.3 \\ Faster-RCNN & 31.5 \\ YOLO-Z & 32.4 \\ Proposed & 64.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Benchmarks on human detection task.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Task** & **mAP** \\ \hline Base Only & Human Detection & 41.3 \\ Base + WL & Human Detection & 43.7 \\ Base + WL + DA & Human Detection & 64.7 \\ Base Only & Vehicle Detection & 39.3 \\ Base + WL & Vehicle Detection & 45.9 \\ Base + WL + DA & Vehicle Detection & 57.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation test results of the proposed model. WL denotes usage of Wasserstein loss, and DA denotes usage of domain adaptation.
# A Metric for Secrecy-Energy Efficiency Tradeoff Evaluation in 3 GPP Cellular Networks
## 2 System Model
### SINR and Capacity Determination  
The cellular system that has been described in the previous paragraph has been implemented in MATLAB (version R2016a, academic use, The MathWorks, Inc., Natick, MA, USA) simulation environment, providing the SINR values in the square playground which is depicted in Figure 1. The SINR values permit achieving the capacity in all of the playground areas.  
Figure 1: SINR geographical distribution for \(P=20\) W.