esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Enabling Technologies
#### 3.1.2 Recognition  
Recognition describes the possibility for a CPS to see obstacles while moving in space, and to recognize its surroundings and what it could manipulate at the same time. All of these actions require some _vision_ capabilities to be provided to an otherwise blind system. Vision is only one way to give senses to robots--together with touch, hearing and equilibrium (particularly regarding the change between stasis and acceleration)--and make them more similar to humans. CV is the interdisciplinary field dealing with supplying high-level comprehension to a computer via digital images and videos. Born in the 1960s [22], CV finds a key development with the introduction of deep learning (DL) and neural networks.  
Sight can be obtained via traditional cameras, sonars, lasers or RFID. However, the more up-to-date strategy for a chance at recognizing interactive objects is using a classifier based on convolutional neural networks (CNNs). The most desirable way to implement this technology is using cameras (to give the neural network a field-of-view to evaluate the presence and position of objects--in this case study, grape clusters), whereas obstacle recognition and avoidance can also be solely based on sonars and lasers.  
Recognition represents a fundamental requirement to introduce a cobot in an agricultural company, as it allows the CPS to acquire the following two separate but complementary functions:  
**Interactive objects recognition:**: being able to discern the class of an object and, therefore, to determine whether the CPS can interact with it via manipulation (the object can be picked, actions can be performed with or on it) or with movement (the object is a charging base, the object represents the goal coordinates of the mobile base and so on);
**Obstacle recognition:**: to execute manipulation or movement trajectories, the CPS understands that certain portions of space must be avoided not to incur into collisions.
# Study of Global Navigation Satellite System Receivers' Accuracy for Unmanned Vehicles
## 5 Conclusions  
High-precision GNSS navigation is introduced more frequently in the civilian life due to the higher requirements for position accuracy. Nevertheless, of the achieved accuracy in sub-meter or sub-centimeter range, GNSS receivers still cannot send position values with update rates which exceed 10 Hz due to the very complex computation task of the correlation processes. Also, GNSS signals may be completely lost in lots of situations, such as tunnels, underground garages, etc. In these cases, autonomous vehicles cannot calculate their position. The integration of GNSS receivers with an inertial measurement unit (IMU) may overcome this navigation problem for a limited period of time, as IMU systems may calculate the spatial object position and its speed and movement direction. However, the position calculation is a relative process, which leads to unlimited error accumulation, but the position refresh rate may exceed 400 Hz. The integration of both navigation systems may significantly reduce their drawbacks.  
The possible applications of the current study may be directed toward intelligent transport systems to evaluate the position accuracy of standalone GNSS receivers or to integrated IMU/GNSS systems to evaluate the parameters of the Kalman filter in unmanned vehicles. The current research results may be used to solve important problems when designing different types of Kalman filter, such as incomplete a priori knowledge of the process noise covariance matrix and measurement noise covariance.  
Conceptualization, R.M.; Methodology, R.M. and R.Y.; Software, R.M.; Validation, R.Y. and T.B.; Investigation, P.Z.P.; Resources, P.Z.P.; Data curation, T.B.; Writing--original draft, R.M. and P.Z.P.; Writing--review & editing, P.Z.P.; Supervision, P.Z.P.; Project administration, P.Z.P.; Funding acquisition, P.Z.P. All authors have read and agreed to the published version of the manuscript.  
This study is financed by the European Union--NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, project No. BG-RRP-2.004-0005.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 4 Joint Message-Passing and Convex Optimization Framework for UAV Scheduling
### Computational Complexity  
The computational complexity of the proposed algorithm can be separately discussed in the two separable problems, i.e., (i) MWIS-based UAV scheduling via message-passing and (ii) joint charging matching and wireless energy transfer. The first problem is originally NP-hard, because it is pure MWIS. Message-passing is used, which works in polynomial-time, in order to relax the NP-hard computational complexity. In addition, the second problem is originally formulated in the form of non-convex, however, the problem is converted into convex based on Theorems 1 and 2. Therefore, this problem also works in polynomial-time. Finally our proposed framework works in polynomial-time because two separable and sequential problems are all with polynomial-time operations.
## 5 Numerical Results and Simulation
Hence, SUs' success probability can be improved under the hybrid spectrum access scheme. The rate of PUs' transmission can also affect SUs' success probability in  
Figure 4: Outage probability with an increase in transmission data rate (\(v_{p}=v_{s}=1\)). Figure 3: Conventional service completion rate in the primary network under hybrid mode (\(\theta_{p}=9.5\) dB\(\beta_{p}=v_{s}=1\)). both the overlay and underlay models as shown in Figure 5. On channels with a more dominant presence of PUs, SUs do not get enough access to transmit, hence the reason for lower delivery rate in such a scenario. With an improved detection of PU signal, the QoS constraints of PUs can be met, while the average delivery rate of SUs reduces owing to the transmission constraints when at least one PU is present on the channel. The hybrid spectrum access model shows improved performance over the overlay mode and can continue to produce a remarkable higher average delivery rate for secondary transmissions even when \(p_{d}=1\) as shown in Figure 6.  
As expected, the network throughput in the primary network, as presented in Figure 7 under the selected simulation parameters decreases with an increase in the distance between the tagged primary transmitter-receiver pair since the transmit power of PT is fixed, while the effect of interference can also influence the network performance, especially in the underlay and hybrid modes. Similar relationships were observed in the secondary network as shown in Figure 8. Furthermore, lower network throughput is observed in the overlay mode for SUs as shown in Figure 8 since SUs have limited access to transmit. As expected, SUs benefit more when transmitting under the hybrid spectrum access mode. As previously mentioned, the analysis of the conditional average AoI depends on the conditional success probability--a parameter that is very difficult to obtain analytically. As a result, its b-th moment was obtained. Using this conditional success probability analysis, the average AoI was investigated in both primary and secondary networks. Figure 9 shows the mean AoI for the hybrid, overlay and underlay modes. The packet is timelier from the tagged PR's perspective in the overlay mode, since PTs hold exclusive access to channel usage with no interference from STs. Generally, the probability of re-transmission (a parameter that is proportional to the AoI) increases with an increase in \(\theta_{Tt}\) provided that all other parameters remain unchanged. The AoI experience at the tagged SR reflects fewer channel access opportunities for STs due to PTs' transmissions in the overlay mode. As a result, the AoI is higher at the tagged SR in the overlay model as presented in Figure 10. The hybrid model provides timely packets at the intending receiver from the tagged SR's perspective. Figure 8: Network throughput in the secondary network, (\(\theta_{s}=9.5\) dB, \(v_{p}=v_{s}=0.5\)). Figure 7: Network throughput in the primary network, (\(\theta_{p}=9.5\) dB, \(v_{p}=v_{s}=0.5\)).
## 3 Results and Discussion
### CO Emissions
The 18.8 millisecond (ms) available for the intake stroke at 1600 rpm is reduced to 8.3 ms at 3600 rpm. As a result of this reduction in available time, the combustion efficiency declines from its optimum value. The lower speeds increase the AFR and render the mixture within the combustion chamber leaner. Similarly, with all fuels and at any speed the CO emissions from the exhaust are augmented when the engine operates under higher loads, a trend that has also been reported in the literature [44; 49; 50]. As is generally true for ICEs, increasing the engine load initially results in a richer mix (with lower AFR). However, the increase in CO emissions only continues up to an engine loading of 75%, after which the trend reverses. At full or extremely high loading, the combustion temperature increases, leading to complete combustion and, ultimately, a decrease in CO emissions [49]. It was also found that blending the fossil-based diesel with the DSO biodiesel resulted in a reduction of CO emissions in proportion to the fraction of DSO biodiesel in the blend. Once again, this finding is in full agreement with the literature [47; 48]. In these cases, the presence of fuel-bound oxygen in the DSO biodiesel provided additional oxidant for the fuel in the combustion chamber and boosted complete oxidation to aid in the formation CO\({}_{2}\)[40; 51]. Another contributing factor to the reduction in CO emissions was the higher levels of cetane number (CN) (62) in the DSO biodiesel, which shortened the ignition delay period and minimized the likelihood of the formation of local fuel-rich regions within the cylinder that could serve as a CO incubator [52; 53]. It was also found that, for all blends and at all loads, the reduction in CO emissions through DSO biodiesel addition increased with engine speed up to mid-range. This trend can be attributed to the inherent oxygen in the DSO biodiesel, which enhanced the need for the oxidant, and the fact that, up to this speed range, the high viscosity of the biodiesel did not clearly affect the combustion process. The observed peak reductions in CO emissions under full loading at 3000 rpm were 0.0046, 0.01, 0.024, and 0.024 vol.% for blends B5, B10, B15, and B20, respectively. In the high-speed range, however, the effects of biodiesel viscosity negated the emission advantages of the intermediate speed range. At higher speeds, in which the combustion process could operate under a very short time interval, the higher viscosities of the DSO biodiesel induced a relapse in the droplet atomization, fuel evaporation, and homogeneous distribution within the cylinder, reducing the combustion efficiency. That is, at higher speeds, the adverse effect of the high viscosity of  
Figure 5: Schematic of a typical stabilized combustion jet in a diesel engine [46]. DSO biodiesel superseded a significant portion of the positive effect gained from the inherent oxygen contained by the biodiesel. As a result of this rebound, the CO emissions of the DSO biodiesel blends approached that of fossil-based diesel at higher speeds.
## 3 Results and Discussion
### Optimization of ETL Thickness  
ETL is the first layer in the PSC through which light passes, and its thickness has a significant impact on efficiency. To find out the effect of ETL on PSC, the thickness was changed from 50 to 200 nm for the simulation.  
Figure 8 exhibits the current density-voltage (_J_\(-\)_V_) graphs for different ETL (TiO\({}_{2}\)) thicknesses, and Figure 9 shows the effects of ETL thickness on _V\({}_{oc}\)_, _J\({}_{sc}\)_, _FF_, and _PCE_. As shown in Figure 9d, _PCE_ increases with ETL thickness until the thickness of the ETL layer reaches 100 nm and decreases afterwards. The maximum _PCE_ of 19.30% is observed at an ETL thickness of 100 nm. An overly thin ETL leads to leakage current and device failure due to discontinuity. Therefore, as the thickness increases up to 100 nm, the efficiency also increases. After reaching the maximum of 19.30% at 100 nm, the _PCE_ efficiency decreases with an increasing ETL thickness because, similar to the HTL, the charging resistance and series resistance rapidly increase with the ETL thickness, resulting in a decrease in _PCE_ due to absorption loss [69; 70].  
Figure 8: Current Density-Voltage (_J_\(-\)_V_) curves for various TiO\({}_{2}\) thicknesses.  
Figure 9: _Cont._
# A Stock Prediction Method Based on Deep Reinforcement Learning and Sentiment Analysis
## 3 Preliminaries
### Sentiment Analysis  
Natural language processing (NLP) is one of the hottest research areas at the moment, aiming to develop applications or services that can understand human language. An important branch of NLP is sentiment analysis, also known as opinion mining, which is used to analyze the emotions, opinions, attitudes, etc. expressed by people in textual messages. The development and rapid start of sentiment analysis was made possible by the rapid growth of social media on the web, such as online shopping software, short video software, WeChat Version 8.0.50, Weibo Version 14.9.1, etc., which was the first time in the history of mankind that there was such a huge amount of digital volume in the form of records.  
Sentiment analysis is usually divided into two steps: feature extraction and sentiment classification. Sentiment analysis techniques are mainly classified into three types, which are DL based methods, machine learning-based methods and rule-based methods. Sentiment analysis methods based on DL utilize deep neural network models such as CNN, RNN, etc. to learn semantic information in text sequences; machine learning-based sentiment analysis methods aim at autonomously deducing the correlation between the text content and its emotional meanings through the training of the model, and the commonly used methods include Support Vector Machine, Decision Tree, Naive Bayes, etc.; rule-based sentiment analysis methods are manually defined sentiment lexicon and grammar rules. SnowNLP is an official Python class library inspired by TextBlob and written to facilitate the processing of Chinese, using Bayesian machine learning methods to train the text. The output is between 0 and 1; the closer the number is to 1 indicates that the text sentiment is more positive. This paper uses SnowNLP's sentiment analysis module to process the stock bar posts in the dataset.
## 2 Related Work & Background
### Dynamic Data Acquisition
#### 2.2.2 Summary  
Edge computing leverages native and standardized communication protocols such as OPC UA and MQTT to ensure efficient data processing and transmission. The use of native protocols can significantly enhance performance, particularly in reducing latency and improving real-time responsiveness [62].  
For legacy systems that do not support modern protocols, edge computing gateways can act as protocol converters, enabling their integration into modern IIoT architectures [63]. These gateways allow older industrial systems to be part of a connected and intelligent network without requiring complete hardware replacement [64].  
The adaptability of edge computing enables the development of customized solutions that cater to both legacy systems and modern data processing requirements [65]. This leads to optimized data processing and improved overall efficiency in industrial environments.  
In summary, edge computing, by combining native and standardized protocols, provides a flexible and high-performance solution that accommodates both modern and legacy systems, facilitating a seamless transition into digital transformation.
## 4 Probability of Chip Error in Non-Coherent O-QPSK
### Useful and Interfering Symbols are Asynchronous  
The condition in which interfering symbols can be asynchronous with respect to useful symbols increases the number of possible cases to be considered for the evaluation of \(\Theta_{R}(\omega t)\). In particular, the interferer signal can change its turning direction at a random instance, \(t_{t}\), and the corresponding random angle is \(\phi_{t}=\omega t_{t}\). Consequently, the received signal during a chip interval can be written as:  
\[R(\omega t)=\begin{cases}\sqrt{C}e^{j(b_{C}\omega t+\phi_{c})}+\sqrt{I}e^{j(b_ {I_{1}}\omega t+\phi_{I})}&\omega t=[0,\phi_{t}]\\ \sqrt{C}e^{j(b_{C}\omega t+\phi_{c})}+\sqrt{I}e^{j(b_{I_{2}}\omega t+d)}& \omega t=(\phi_{t},\pi/2]\end{cases} \tag{30}\]  
where \(b_{C}\) defines the turning direction of the useful chip, while \(b_{I_{1}}\) and \(b_{I_{2}}\) are the turning directions of the sequential chips of the interferer. In Equation (30), at the angle \(\phi_{t}\), the interferer starts transmitting another chip. We denote this asynchronous interferer case probability as \(P_{\mathit{mix}}\). The occurrences of \(P_{\mathit{eq}}\), \(P_{\mathit{dif}}\) (which are given in Section 4.1) and \(P_{\mathit{mix}}\) for the different combinations of \(b_{C}\), \(b_{I_{1}}\) and \(b_{I_{2}}\) are shown in Table 2.  
By considering these occurrences, the chip error probability can be evaluated as:  
\[P_{c}=1/4P_{eq}+1/4P_{dif}+1/2P_{mix} \tag{31}\]  
To obtain \(P_{mix}\), we consider the case of \(b_{C}=+1\), \(b_{I_{1}}=+1\), \(b_{I_{2}}=-1\), and \(d=\phi_{I}+2\phi_{t}\), which is mathematically identical to the all other \(P_{mix}\) cases in Table 2. Following the same approach followed in Section 4.1, we can write the decision rule as:  
\[\frac{\sqrt{C}-\sqrt{I}\cos(\phi_{I}+2\phi_{t})}{\sqrt{I}\sin(\phi_{I}+2\phi_{t })}\gtrapprox\frac{s}{e}\frac{\sqrt{I}\sin(\phi_{I})}{\sqrt{C}+\sqrt{I}\cos( \phi_{I})} \tag{32}\]  
which can be simplified to:  
\[C+\sqrt{CI}(\cos(\phi_{I})-\cos(\phi_{I}+2\phi_{t}))-I\cos(2\phi_{t})\gtrapprox \frac{s/e}{e/s}0 \tag{33}\]  
where the sign in the inequality depends on the sign of denominators in Equation (32). Equation (33) can be rearranged as:  
\[C/I+\sqrt{C/I}(\cos(\phi_{I})-\cos(\phi_{I}+2\phi_{t}))-\cos(2\phi_{t})\gtrapprox \frac{s/e}{e/s}0 \tag{34}\]  
\(P_{mix}\) is obtained by using the test in Equation (34) considering the different signs of the denominators in Equation (32) and the definition domain of the \(\arctan\) function.
## 3 Results and Discussions
### Ultraviolet-Visible Spectroscopy (UV-Vis)  
The UV-Vis spectrograms of GO, GO-Ag, GO-Ag-APTES, GO-ZnO and GO-ZnO-APTES powders are shown in Figure 6.  
The UV-VIS spectra of the GO-based powder materials from Figure 6 contains absorption bands at 222 nm (GO), 223 nm (GO-Ag), 232 nm (GO-Ag-APTES), 224 nm (GO-ZnO and GO-ZnO-APTES) corresponding to the \(\pi\)-\(\pi\)\({}^{*}\) electronic transitions of the aromatic C-C bonds. In the cases where the recorded absorption peaks are lower as compared to the values reported in the literature (230 nm, 232 nm, 238 nm) [52; 53], it could be assumed that there was a decrease in the delocalized electrons and thus more energy was required for the electronic transition, suggesting that the samples were oxidized with several functional groups on the basal planes. This assumption was based on the direct proportionality between the maximum intensity and the degree of oxidation [54]. The absorption peaks at around 300 nm, namely at 348 nm (GO), 315 nm (GO-Ag), 318 nm (GO-Ag-APTES) are associated to the n-\(\pi\)\({}^{*}\) transitions of the C=O bonds from the carbonyl group [55]. According to the literature [56; 57], the absorption peak of ZnO appears around at 365 nm. In our study, a close value to this wavelength was noticed in the spectra of both GO functionalised with ZnO samples (blue lines in Figure 6), which also confirms the presence of ZnO on the GO powder.
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
###### Abstract  
1U CubeSats often use the 430-MHz band for communication due to their size and power limitations, and half-wavelength dipole antennas are employed. A 430-MHz-band dipole antenna requires a deployable structure for a 1U CubeSat. However, a 1U CubeSat has a small volume margin for redundant systems, so the antenna deployment system can be a single point of failure. In this paper, the 1U CubeSat structure itself was used as an antenna. As a sub-mission of the BIRDS-4 project, three 1U CubeSats (GuaraniSat-1, Maya-2, and Tsuru) demonstrated this antenna structure. The results of the ground tests showed a maximum gain of \(-5.7\) dBi with the flight model. These satellites were deployed from the International Space Station on 14 March 2021. Radio signals were alternately transmitted from the dipole antenna and the structure antenna onboard Tsuru for on-orbit demonstration on 15 December 2021, and the received signal strength on the ground was compared using RTL-SDR, SDR and several codes. The ground station was able to receive both dipole and structure CW signals. The received power strength indicates that a gain of \(-8.1\) dBi is being demonstrated with the structure antenna.  
Keywords:1U CubeSat; non-deployable antenna; on-orbit experiment +
Footnote †: journal: Electronics Letters
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 6 Conclusions  
This paper designed and implemented an area measuring instrument based on an STM32 microcontroller and a high-precision BeiDou positioning chip, which solves the accuracy problem of traditional area measuring tools in weak signal environment.  
In terms of hardware, we introduced a high-performance BeiDou positioning chip, which still has a high accuracy and stability of measurement in complex environments. In the software design, we used a FreeRTOS real-time operating system to achieve efficient scheduling and management of tasks, and ensure the real-time and response speed of the system. In addition, the application of dynamic memory management technology overcomes the limitations of traditional static memory allocation and improves the flexibility and resource utilization of the system.  
The experimental results show that the average relative error of the measuring instrument in open terrain is 0.37%, and the maximum relative error in complex environments, such as urban canyons, is 2.72%, which is lower than the 5% threshold specified in the global navigation satellite system (GNSS) receiver calibration specification. Compared with traditional GPS equipment, it still maintains high precision and high stability under weak signal conditions, showing better environmental adaptability. In summary, the area measuring instrument designed in this paper has achieved results in terms of technological innovation and experimental verification, and provides for land and resource planning, agricultural management, and surveying and mapping exploration.  
X.W.: conceptualization, methodology, software, investigation, formal analysis, writing--original draft; B.N.: validation, supervision, writing--review and editing; G.H.: conceptualization, resources, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement:Data are available upon request from the authors.  
The authors declare no conflicts of interest.