esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Future Challenges
### Blockchain and IoT Integration Strategies  
Integrated blockchain-IoT architecture can be classified into three parts. The basic IoT model shown in Figure 9 is the simplest form of operation in which devices communicate with one another across a local area network. Since it does not directly utilize the complex computer methods of blockchain technology, this approach is typically the fastest to execute. Currently, blockchain's primary practical application is in the recording of digital exchanges and conversations. This architecture is best for uses in which a lesser level of security and faster communication are not crucial.  
_IoT-Blockchain Model:_ Blockchain is the sole means by which IoT gadgets can exchange data with one another. We can use the security properties of blockchain to protect IoT systems by implementing the architecture shown in Figure 9. Blockchain is used to capture and keep all of the IoT system's data and transactions, which can ensure the immutability of the data.  
_Fog/Cloud Computing and Distributed Computing:_ Many issues with the resource constraints of IoT devices have been alleviated thanks to advancements in cloud and fog computing [142], which form the basis of the IoT-blockchain model. Computing tasks such as hashing, encryption, decryption, etc., can be offloaded from IoT devices and onto fog and cloud infrastructure. Since blockchain necessitates a great deal of computing power, electricity, and related resources to function, the integration of fog/cloud computing with IoT technology becomes more relevant. IoT devices in the hybrid approach can either talk to blockchain nodes directly or to fog/cloud nodes indirectly. This allows us to take advantage of models combining the IoT with blockchain technology.  
Figure 8: Issues associated with the integration of IoT and blockchain.
# An Intelligent Control Method for Servo Motor Based on Reinforcement Learning
## 4 The Proposed Intelligent Control Method
### The Structure of the Control Method
#### 4.1.2 Structure and Loss Function of Strategy Network  
As shown in Figure 5, the inputs of the strategy network are the motor state \(s_{t}\) and target \(g_{t}\), and its output is the duty ratio after normalization, which is denoted as \(\pi(s_{t},g_{t})\). Therefore, the objective function of the strategy network can be expressed as Equation (6).  
\[J(\phi)=E_{s_{t}\sim\mathcal{D}}[\log\pi(f(\varepsilon_{t};s_{t},g_{t}))-Q(s_{ t},f(\varepsilon_{t};s_{t},g_{t}))] \tag{6}\]  
where \(\varepsilon_{t}\) is noise sampled from a standard normal distribution. Equation (6) represents the update objective function of the policy network, which adjusts the update objective of the proposed control algorithm based on the structure and input-output of the value network and policy network, while the training method remains basically the same.  
Figure 4: The structure of the value network.  
In order to use the gradient descent method to train the policy network, it is necessary to make the random policy output of the network derivable. Therefore, the algorithm uses the reparameterization technique to calculate the output of the strategy network. The strategy network itself outputs two real values, \(\mu\) and \(\sigma\), corresponding to the mean and standard deviation of the normal distribution, respectively. The value of the output action \(a_{t}\) can be calculated via Equation (7).  
\[a_{t}=f(\epsilon_{t};s_{t},g_{t})=\epsilon_{t}\times\sigma(s_{t},g_{t})+\mu(s_ {t},g_{t}) \tag{7}\]
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 5 Multiple Input Multiple Output Technology
### Future Research Trends  
A UAV antenna tracking is an emerging technology with great potential to address the needs of hobbyist and professionals. As the technology gains extensive visibility, it has become necessary to conduct further studies on the following.  
1. [label=_._, ref=_._]
2. _Energy Efficiency:_ Generally, UAVs suffer from the problem of reduced size, weight, and power (SWaP). Energy efficiency is crucial to UAV antenna tracking, especially when mechanical beam steering tracking is involved [164; 165]. Energy efficiency will also become critical if MU-MIMO is adopted as the core of UAV antenna tracking. Hence, more system modelling and energy efficiency is needed.
3. _Wireless Channel Model:_ There is a need for adequate UAV wireless channel modelling, considering the fact that UAV is generally regarded as a LoS communication system. This assumption might not be feasible considering that there are lots UAV hobbist in crowded urban areas [166; 167]. Secondly, the transmission range of UAVs varies according to the equipped transceiver pairs. It is plausible that the dynamic channel model will be develop to cater for this variation.
4. _Performance of UAV Tracking Antenna in Poor Channel:_ Studies on the performance of UAV tracking antenna in poor channels are yet to be fully comprehended [168]. Most significantly, this study is imperative to avoid crashing the UAVs when the  
Figure 17: A four-sector beam-forming antenna for UAV tracking.  
UAV is in critical mission. Therefore, a model must be developed to analyze this performance, in the absence of first person view (FPV) module. _Machine Learning:_ Machine learning is an evolving technology that can enhance the UAV tracking antenna system in terms of reduction in beam mis-alignment and bean weight prediction. Machine learning algorithms are divided into supervised learning, unsupervised learning (classification), and reinforced learning [169; 170]. Towards this trend, machine learning beam tracking and weight optimization for mmwave multi-UAV links has been proposed [171]. _Steering Beam Alignment Error:_ Unlike other mobile nodes, UAV tracking must address the issue of beam alignment resulting from angular Doppler as the UAV moves from one place to the next. Steering beam alignment is not only used for the mechanical beam steering approach. This can also be experienced by the electronic beam steering resulting from poor channel modelling and incomplete channel state information. Hence, this opens up a great research area.
# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
## 4 Hybrid Sensor Fusion Algorithm Overview
### Object Classification and Road Segmentation using Deep Learning
#### 4.1.2 Proposed Fully Convolutional Network (FCNx) Architecture  
The architecture consists of two main parts: an encoder and a decoder. First, we have the encoder; it is based on the VGG16 architecture [34], with its fully connected layers replaced with convolutional layers. The purpose of the encoder is to extract features and spatial information from our four-channel RGBD input image. It uses a series of convolutional layers to extract features and max-pooling layers to reduce the size of the feature maps. Second, we have a decoder, which is based on the FCN8 architecture [23]. Its purpose is to rebuild the prediction of pixel classes back to the original image size while maintaining low-level and high-level information integrity. It uses transposed convolutional layers to upsample the output of the last convolutional layer of the encoder [24]. Also, it uses skip  
Figure 7: Early fusion of LiDAR depth image and camera image as an input to a DNN pipeline.  
convolutional layers to combine the finer low-level features from encoder layers with the coarser high-level features of transposed convolutional (upsampled) layers.  
In our proposed network, which builds on the above shown in Figure 8, we combine the encoder output from layer four with the upsampled layer seven. This combination is an element-wise addition. This combined feature map is then added to the output of the third layer skip connection. In the skip connection of the layer three output, we use a second convolutional layer to further extract features from layer three output. The addition of this convolutional layer adds some features that would be extracted in layer four. Including some basic layer four level feature maps will help the layer three skip connection to represent a combined feature map of layer three and layer four. This combined feature map is then added to the upsampled layer nine, which itself represents a combined feature map of layer seven and layer four. This addition is shown to give a better accuracy and lower Cross-Entropy loss compared to the base VGG16-FCN8 architecture. The better performance can be explained by the fact that having some similar layer four feature maps can help better align the extracted features when performing the last addition. We will refer to our proposed architecture as FCNx.  
The purpose of the FCNx applied to camera and LiDAR fused raw data is to segment the road image into free navigation space area and non-driveable area. The output can be further processed and sent through a plug and play detector network (YOLO [33; 35; 36], SSD [37]) for object detection and localization depending on the situation.
## 6 Conclusions  
To solve the problem that safety and mobility of AVs are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance in unanticipated obstacle scenarios at intersections, an obstacle avoidance motion planning method based on APF is proposed in this study. The core of this method lies in the construction of compliance-oriented potential fields and violation cost potential fields. Intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. The PVCI is designed according to theories of autonomous driving ethics. Violation cost potential fields are constructed by truncating compliance-oriented potential fields according to PVCI. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with MPC for trajectory optimization and tracking. Simulation tests are performed in the MATLAB/Simulink environment. Results indicate that, in unexpected obstacle scenarios, the proposed algorithm can plan trajectories with relatively lower penalties, achieving obstacle avoidance with a minimal degree of violation, thus ensuring both vehicle safety and mobility.  
The algorithm will be further optimized to meet the requirements of on-road testing. Future research will focus on further exploration to more accurately identify conflict moments and strive for a reasonable balance between driving demands. A more precise description of non-compliant behaviors to better assess violation costs will be established. More complex scenarios will be considered to enhance the adaptability of the motion planning method.  
Conceptualization, methodology, investigation, and software, R.M. and W.Y.; writing--original draft preparation, R.M.; validation and writing--review and editing, Z.L., W.Y. and R.M.; resource, C.W., G.Z., W.Z. and M.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2022 YFB2503003) and the National Natural Science Foundation of China (No. 52072215 and No. U1964203).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenarios** & **Obstructed Delay Time (s)** & **Violation Type** & **Occupancy of Adjacent Lane (m)** \\ \hline Scenario 1.1 & \(\backslash\) & \(\backslash\) & 0 \\ Scenario.1.2 & 148.25 & Crossing the double solid yellow line & 2.12 \\ Scenario.1.3 & 148.68 & Crossing the solid white line & 1.78 \\ Scenario.2 & 151.35 & Crossing the solid white line & 1.83 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Vehicle performance in simulation testing.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to all the simulation data being generated during the study on a local computer.  
The authors would like to appreciate the support from the Joint Laboratory for Internet of Vehicles and the Ministry of Education--China Mobile Communications Corporation.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
It is also interesting to note that this temperature is close to considered polymorphic transitions of both pure compounds. A major step to be mentioned is also the potential use of the IRT method of the detection of transitions that could be associated to the limits of the solid solubility domain. A metatectic transition, not fully validated with DSC measurements (as mentioned previously), is also found in the right part of the phase diagram. Considering the phase \begin{table}
\begin{tabular}{c c c} \hline \hline  & **This Work (IRT)** & **Literature** \\ \hline \multirow{3}{*}{Eutectic point (molar \%PA)} & \multirow{3}{*}{67\%} & 66.6\% [13] \\  & & 66.6\% [14] \\  & & 70\% [15] \\ \hline \multirow{3}{*}{\(T_{eutectic}\) (\({}^{\circ}\)C)} & \multirow{3}{*}{55.12} & 52.3 [13] \\  & & 52 [14] \\  & & 55.75 [15] \\ \hline \(T_{perihetic}\) (\({}^{\circ}\)C) & 57.37 & 57.05 [15] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between experimental and literature properties of the PA–SA binary mixture. diagrams of other fatty acids' binary systems, such a transition could however be expected. Further works are therefore needed to confirm the results obtained with the IRT method (validation of the solid solubility domain detection and of the polymorphic transitions detection). Additionally, and as mentioned previously, efforts are expected to automatize the transitions detection and the sample preparation in order to increase the IRT method accuracy. In addition, the adjusted thermodynamic model is able to highlight the effect of the peritectic transition on the liquidus line, providing a good fit with IRT data. However, the equilibrium constant value giving the best approach is still tricky to find. Moreover, depending on the properties chosen for the pure components and on the applied cooling/heating rates, completely different diagrams may be obtained for the same binary system. Another limitation to the modeling of solid-liquid equilibrium for such systems is the occurrence of a metatectic transition and of solid solutions. Indeed, as the model assumes immiscibility of the components in the solid phase, it is in contradiction with the presence of a solid solubility domain. Although its shape and its location are coherent with the experimental data, a noticeable deviation occurs in the 0.3-0.5 molar fraction range. This deviation seems to be growing and coincides with the increase of the solid miscibility of both compounds. Essentially, the problem could be treated in the same way as described in Section 2.5.2 but by considering, this time, a solid solution or metatectic compound formed from a reaction with a variable stoichiometry. If this solid solubility domain can in fact be determined using the IRT method, then the stoichiometry of the metatectic compound can be known and the solid-liquid equilibrium can hence be computed optimally by adjusting the value of the Gibbs-free energy of reaction.
# Phytochemical Screening and Antioxidant Activity of Selected Estonian _Galium_ Species
## 3 Materials and Methods
### Phytochemical Screening and Quantification  
The 1 mg/mL standard solutions of asperuloside, neochlorogenic acid, chlorogenic acid, rutin, and quercetin were prepared in ethanol, and diluted to create calibration curves for the quantitative HPLC-DAD analyses. The calibration curves were constructed based on the UV signal at 254 nm (slit 4 nm), using the ratio of the peak area of a standard compound to the peak area of the internal standard bicalutamide (2 g/L ethanolic solution was added at a final concentration of 40 mg/L into all the samples). All standard compounds exhibited a linear range between 5 to 250 mg/L. Other phytochemicals detected on the chromatograms were also quantitatively determined using these calibration curves, by matching these compounds with a standard compound based on the similarities in the respective UV spectra. The DAD spectra were measured from the analyses of 50% ethanol extracts in the range of 200 to 400 nm. These selections and respective results are given in Table 3 (all measured in triplicate and given as mean mg per L of extract \(\pm\) standard deviation), and all UV spectra as Figures S2-S29 in the Supplementary Materials.  
Before the HPLC-DAD-MS/MS analyses, the extracts were centrifuged, the sample then diluted in ultrapure water two-fold, and spiked with the internal standard bicalutamide at a final concentration of 40 mg/L. The sample volume injected was 5 mL. HPLC-DAD-MS/MS analyses were conducted using an Agilent 1260 Infinity II instrument (Agilent Technologies, Inc., USA) with an Agilent Poroshell 120 EC-C18 column, particle size 2.7 \(\upmu\)m, measurements 4.6 mm \(\times\) 100 mm (Agilent Technologies, Inc., USA) thermostated at 28 \({}^{\circ}\)C. The mobile phase consisted of ultrapure water (A) and acetonitrile (B), both acidified with 0.1% (\(v/v\)) formic acid. The elution procedure was a linear gradient increasing from 5% to 50% B (0-20 min), then from 50% to 95% B (20-25 min), isocratic 95% B (25-30 min), a linear gradient decreasing from 95% to 5% B (30-30.01 min), and isocratic 5% B (30.01-35 min). The flow rate was kept at 0.6 mL/min. The column was coupled with an Infinity 1260 DAD (Agilent Technologies, Inc., USA), the chromatograms were recorded at a UV absorbance wavelength of 254 nm (slit 4 nm), and DAD spectra in the range of 200 to 400 nm. Following the DAD analysis, the sample was analysed using the LC/MSD Trap XCT mass spectrometer (Agilent Technologies, Inc., USA) equipped with an electrospray ionization source. The mass spectra were recorded in negative-ion mode, in the \(m/z\) range from 100 to 1000. Nitrogen was used as the nebulizing and drying gas, and helium served as the collision gas. The MS/MS fragmentation patterns (generated using the automatic Agilent software MS/MS settings) were used to identify compounds for which there were no standard compounds available.
# Design of Novel Fiber Optical Flexible Routing System
## 3 Numerical Experiments and Results  
In this study, we used OptiSystem software [30] to verify and evaluate the performance of the proposed optical routing system. OptiSystem is an optical communication system simulation software package for designing, testing and optimizing virtually any type of optical link in a wide range of optical network physical layers, from analog video broadcast systems to intercontinental backbone networks. The experimental setup is shown in Figure 2, in which the block diagram of the proposed routing system is shown in Figure 1. The red connection points of A, B, C, D, and so on, in Figure 2, correspond to the connection points in Figure 1. During the simulation, we used optical spectrum analyzers (OSAs) to measure static performance to verify the feasibility of its function and evaluate the performance of the optical interleaver, OXC, and ROADM functionalities.  
In this study, the insertion loss of the C-band of the various components of the proposed router, AWG, WDM, SW and OC were set to 2.2 dB, 1.5 dB, 1 dB and 0.7 dB, respectively. We used eight  
Figure 2: Experimental setup for the demonstration of the proposed optical routing system.  
distributed feedback (DFB) lasers as eight independent channels with an input power of \(-10\) dBm per channel. The channel spacing in the experiment was 0.8 nm (100 GHz), depending on the channel spacing of the AWG used. The wavelength of the signal belongs to the C-band and ranges from 1547.6 nm to 1553.2 nm. At present, all optical switches sold on the market have switching speeds of less than 10 milliseconds. Therefore, we used it as a research benchmark for future experiments to test dynamic performance. Since the OptiSystem software does not have the function of setting the switching speed, the switching time of this static performance study was set to the default value of zero by the software. Figure 3 shows the spectrum of the eight DFB laser sources.
# Failure Detection for Semantic Segmentation on Road Scenes Using Deep Learning
## 3 Experimental Results
### Experimental Results on the Rain/Haze Dataset
#### 3.4.2 Qualitative Results  
Examples of images detected as failure cases are presented in Figure 13. These images are also detected as failure cases when there are few road areas or many obstacles and buildings.
## 3 Results
### RQ1: Linguistic Competence  
In order to answer RQ1, the analysis focused on those aspects that permit assessing students' language proficiency according to the parameters set by the CEFR for level A1.2 [51]. Especially interesting for the analysis were (1) the correct use of specific vocabulary and expressions related to the field of leisure time and everyday situations (e.g., expressions used to plan to go with another person to the cinema or a concert; expressions used to ask for and express an opinion of a film or a music group; expressions used to ask for and indicate the time, day and place of an appointment and jointly perform a certain activity, etc.) and (2) the type and complexity of the syntactic structures that students used (noun and verbal phrases, adjectival phrases, interjective phrases, etc.). Considering that _Terminkalender_ required students to negotiate and agree amongst peers on a series of activities to be carried out jointly (expressing preferences, disagreements or even the lack of availability regarding a certain activity, place or time slot), the analysis especially focused on linguistic aspects such as the following: the correct use of adjectives and adverbs, personal pronouns, local, temporal and conceptual prepositions that are used in combination with personal pronouns (e.g., mit mir, mit uns, etc. ); the use of interjections and idiomatic expressions (e.g., Neeel, Keine Ahunug!, Tut mir leid!, Alles klar!, Cool!, Perfekt!, etc. ); and finally, the type and complexity of the structures used (sentences and questions with a single word, sentences and more complex questions). Table 1 shows descriptive statistics and parametric/nonparametric independent test results that help to answer RQ1. The results revealed significant differences between the ExpGr and ContrGr. The most remarkable result is that the ContrGr employed a greater variety of words/ expressions in 7 of 11 linguistic aspects considered, with no significant difference between both groups for the remaining four aspects. Thus, the Mann-Whitney test showed that the following linguistic aspects were significantly higher for the ContrGr than for the ExpGr: a variety of interrogative pronouns employed by the learners to require a piece of information (D_IP), number of different adjectives (D_AD), ways of greeting or saying farewell to a person (D_GOF), number of different idioms (D_ID), diversity of affirmative expressions (D_AE), variety of expressions of denial/doubt (D_EOD) and the total of different expressions used to ask for and indicate the time (D_TI).