esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Due to the influence of the atmospheric environment and pointing errors, the performance of free space optical communication is greatly limited. In this paper, we propose a parallel multi-hop hybrid free space optical (FSO)/radio frequency (RF) system to improve the system performance. The FSO sub-link and RF sub-link are modeled by Gamma-Gamma turbulence with pointing errors and Nakagami-m distributions, respectively. Based on the selective combination scheme, the probability density function (PDF) and cumulative distribution function (CDF) of the output signal-to-noise ratio (SNR) of the hybrid FSO/RF one-hop or direct link are obtained. Then, the PDF and CDF of the output SNR of the parallel multi-hop hybrid system are derived with the decoded forward (DF) protocol considered. Finally, the expressions of the average bit error rate (ABER) and outage probability are derived for the parallel multi-hop hybrid system, the hybrid FSO/RF direct link, and the FSO-only direct link. The results show that the parallel multi-hop hybrid system can effectively mitigate the negative impact of atmospheric turbulence and pointing errors and can significantly improve the system performance.  
multi-hop parallel hybrid FSO/RF system; gamma-gamma turbulence; nakagami-m fading; pointing errors; average bit error rate; outage probability
## 2 Materials and Methods
### Synthesis of Compounds
The product was purified by flash chromatography; pale yellow solid; yield: 91%; 1H NMR (400 MHz, DMSO-\(d_{6}\)), \(\delta\) (ppm): 7.52 (d, 1H, J = 16 Hz, =CHC\({}_{\rm ar}\)), 7.42 (d, 2H, J = 8.7 Hz, H\({}_{\rm ar}\)), 7.07 (d, 1H, J = 2.0 Hz, H\({}_{\rm ar}\)), 7.02 (dd, 1H, J = 8.2 Hz, 2.0 Hz, H\({}_{\rm ar}\)), 6.91 (d, 2H, J = 8.7 Hz, H\({}_{\rm ar}\)), 6.76 (d, 1H, J = 8.2 Hz, H\({}_{\rm ar}\)), 6.67 (d, 1H, J = 8.2 Hz, H\({}_{\rm ar}\)), 6.67 (d, 1H, J = 16 Hz, CH\({}_{\rm ar}\)CH=CH), 6.33-6.24 (m, 2H, =CHCOO + CH\({}_{\rm Z}\)CH=CH), 5.02 (s, 2H, OCH\({}_{\rm Z}\)), 3.76 (s, 3H, OCH\({}_{\rm J}\)). 13C NMR (101 MHz, DMSO-\(d_{6}\)), \(\delta\) (ppm): 166.85, 159.62, 148.98, 146.05, 145.81, 133.56, 129.06, 128.31, 125.93, 121.96, 121.90, 116.19, 115.23, 114.55, 114.25, 64.96, 55.58 (1H and 13C NMR spectra were included in the Supplementary Materials). (E)-3-(3,4-Dihydroxyphenyl)-1-phenylprop-2-en-1-one (**42**) was synthesized according to the procedure that we previously reported [28]. The product was purified by flash chromatography; yellow solid; yield: 99%; 1H NMR (400 MHz, DMSO-\(d_{6}\)), \(\delta\) (ppm): 9.45 (br s, 2H, OH), 8.10 (d, 2H, J = 7.6 Hz, H\({}_{\rm ar}\)), 7.67-7.54 (m, 5H, CHCHCO + H\({}_{\rm ar}\)), 7.27 (s, 1H, Har), 7.19 (d, 1H, J = 8.1 Hz, H\({}_{\rm ar}\)), 6.82 (d, 1H, J = 8.1 Hz, H\({}_{\rm ar}\)). 13C NMR (101 MHz, DMSO-\(d_{6}\)), \(\delta\) (ppm): 189.45, 149.25, 146.08, 145.45, 138.49, 133.23, 129.19, 128.76, 126.71, 122.69, 118.87, 116.22, 116.01 (1H and 13C NMR spectra were included in the Supplementary Materials). (E)-1-(3,4-Dihydroxyphenyl)-5-phenyl-1-en-3-one (**44**) was synthesized according to the procedure that we previously reported [28]. The product was purified by flash chromatography; yellow solid; yield: 99%; 1H NMR (400 MHz, DMSO-\(d_{6}\)), \(\delta\) (ppm): 9.45 (br s, 2H, OH), 8.10 (d, 2H, J = 7.6 Hz, H\({}_{\rm ar}\)), 6.67-7.54 (m, 5H, CHCHCO + H\({}_{\rm ar}\)), 7.27 (s, 1H, Har), 7.19 (d, 1H, J = 8.1 Hz, H\H\({}_{\rm ar}\)), 6.64 (d, 1H, J = 16.2 Hz, =CHCO), 2.99-2.97 (m, 4H, CH\({}_{2}\)CH\({}_{2}\)). \({}^{13}\)C NMR (101 MHz, Acetone-d\({}_{6}\)), 8 (ppm): 198.13, 147.78, 145.31, 142.52, 141.79, 128.37, 128.27, 127.03, 125.79, 123.52, 121.89, 115.43, 114.30, 41.68, 29.95 (\({}^{1}\)H and \({}^{13}\)C NMR spectra were included in the Supplementary Materials).
## 2 Evaluation Method
### Measurement
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Weather Condition**} \\ \cline{2-5}
**Test Case** & \begin{tabular}{c} **Weather Type** \\ **(Clear, Fog. Rain)** \\ \end{tabular} & \begin{tabular}{c} **Level** \\ **(Visibility Distance 40 m, 100 m, 200 m;** \\ **Rainfall 20 mm/h, 50 mm/h)** \\ \end{tabular} & \
\begin{tabular}{c} **Target Position** \\ **(5 m, 12.5 m, 25 m)** \\ \end{tabular} &
\begin{tabular}{c} **Color Temperature** \\ **(3000 K, 6000 K)** \\ \end{tabular} \\ \hline \hline \multirow{2}{*}{\(1\)} & Clear & - & 5 m & 3000 K \\ \cline{2-5}  & Clear & - & 5 m & 6000 K \\ \hline \multirow{2}{*}{\(2\)} & Clear & - & 12.5 m & 3000 K \\ \cline{2-5}  & Clear & - & 12.5 m & 6000 K \\ \cline{2-5}  & Clear & - & 25 m & 3000 K \\ \cline{2-5}  & Clear & - & 25 m & 6000 K \\ \hline \multirow{2}{*}{\(4\)} & Fog & 200 m & 5 m & 3000 K \\ \cline{2-5}  & Fog & 200 m & 5 m & 6000 K \\ \hline \multirow{2}{*}{\(5\)} & Fog & 200 m & 12.5 m & 3000 K \\ \cline{2-5}  & Fog & 200 m & 12.5 m & 6000 K \\ \cline{2-5}  & Fog & 200 m & 25 m & 3000 K \\ \hline \multirow{2}{*}{\(6\)} & Fog & 200 m & 25 m & 3000 K \\ \cline{2-5}  & Fog & 200 m & 25 m & 6000 K \\ \cline{2-5}  & Fog & 100 m & 5 m & 3000 K \\ \hline \multirow{2}{*}{\(7\)} & Fog & 100 m & 5 m & 6000 K \\ \cline{2-5}  & Fog & 100 m & 12.5 m & 3000 K \\ \hline \multirow{2}{*}{\(8\)} & Fog & 100 m & 12.5 m & 6000 K \\ \cline{2-5}  & Fog & 100 m & 25 m & 3000 K \\ \hline \multirow{2}{*}{\(9\)} & Fog & 100 m & 25 m & 6000 K \\ \cline{2-5}  & Fog & 40 m & 5 m & 3000 K \\ \hline \multirow{2}{*}{\(10\)} & Fog & 40 m & 5 m & 6000 K \\ \cline{2-5}  & Fog & 40 m & 12.5 m & 3000 K \\ \hline \multirow{2}{*}{\(11\)} & Fog & 40 m & 12.5 m & 6000 K \\ \cline{2-5}  & Fog & 40 m & 25 m & 3000 K \\ \cline{2-5}  & Fog & 40 m & 25 m & 6000 K \\ \hline \multirow{2}{*}{\(12\)} & Fog & 20 mm/h & 5 m & 3000 K \\ \cline{2-5}  & Rain & 20 mm/h & 5 m & 6000 K \\ \cline{2-5}  & Rain & 20 mm/h & 12.5 m & 3000 K \\ \hline \multirow{2}{*}{\(13\)} & Rain & 20 mm/h & 12.5 m & 6000 K \\ \cline{2-5}  & Rain & 20 mm/h & 25 m & 3000 K \\ \hline \multirow{2}{*}{\(14\)} & Rain & 20 mm/h & 25 m & 6000 K \\ \cline{2-5}  & Rain & 50 mm/h & 5 m & 6000 K \\ \hline \multirow{2}{*}{\(15\)} & Rain & 50 mm/h & 12.5 m & 3000 K \\ \cline{2-5}  & Rain & 50 mm/h & 12.5 m & 6000 K \\ \hline \multirow{2}{*}{\(16\)} & Rain & 50 mm/h & 25 m & 3000 K \\ \cline{2-5}  & Rain & 50 mm/h & 25 m & 6000 K \\ \hline \multirow{2}{*}{\(17\)} & Rain & 50 mm/h & 25 m & 6000 K \\ \cline{2-5}  & Rain & 50 mm/h & 25 m & 3000 K \\ \hline \multirow{2}{*}{\(18\)} & Rain & 50 mm/h & 25 m & 3000 K \\ \cline{2-5}  & Rain & 50 mm/h & 25 m & 6000 K \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test cases considering weather conditions.
## 4 CKC-Based Taxonomy on Security Threats to SF and PA
### The Taxonomy
#### 4.2.2 Threats Related to the Weaponization Stage  
Attacks on SF and PA use a variety of techniques to evade detection in the weaponization stage. Most of these techniques try to prevent the attack to be revealed by security mechanisms operating in IoT device level or in network level. Thus, we classify cyber-threats related to the weaponization stage as follows.  
* **IoT Device Level Evasion:** It has been observed that defence in IoT device level mainly relies on anti-viruses and end-point security solutions embedded in or installed on devices, which detect well-known virus signatures or anomalies in behaviors. Well-designed attacks on SF and PA try to hide themselves from these mechanisms. As an example, one may refer to following technique or heap spraying, which may successfully embed a malicious code inside an application even in the case the anti-virus includes the related signature [90,91,92,93,94].
* **Network Level Evasion:** Firewalls and Intrusion Detection/Prevention Systems (IDSs/IPSs) are the most common tools for in-network protection [95,96]. A key point to be reiterated here is that there is no completely secure solution, including firewalls and IDS/IPS. Although these tools can detect a malicious executable file, they may be unable to detect a malicious file attached to an email. Thus, they are not considered as completely-secure mechanisms in SF and PA. To evade these mechanisms, SF and PA attacks depend on a wide range of techniques, among which we can mention illegal use of well-known protocols (HTTPS, DNS, HTTP, etc.) [37] or ports (53, 80, 443, etc.) [37] as well as network spoofing [89].
## 5 Discussions
As the ground plane size increases, impedance bandwidth and total efficiency increase too. However, enlarging the ground plane beyond the 120 mm \(\times\) 60 mm does not necessarily imply better results at the given frequency bands of operation. It has been shown that there is an optimum length of around 0.39A to maximise bandwidth [25] because the fundamental mode can be excited on the conductive portion of the ground plane.
## 3 Results
### Assessing the Potential for Estimating Tr from SIF and Environmental Factors  
The linear model based on the empirical relationship between SIF and Tr had R\({}^{2}\) values of 0.33-0.39 for the half-hour scale and 0.33-0.56 for the daily scale (Table 2). The accuracy of the linear model was relatively poor, especially at the half-hour scale. We established random forest regression models using SIF and environmental factors as input variables, with Tr as the target variable. The results indicated that species and temporal scales can alter the importance ranking of factors. In all regressions, the IncNodePurity of SIF, PAR, Ta, and SM was consistently high (Figure 9a-c). With the aggregation of the temporal scale, the importance ranking of SIF and SM moved up, while the importance ranking of PAR and Ta moved down. VPD had a higher importance for poplar, while Ws had a higher importance for arborvitae. To refine the model, we sequentially eliminated the least important environmental factors based on their IncNodePurity and re-ran the regressions, continuing this process until only SIF was left. When using only SIF in the random forest regression, the variance explained ratio ranged from 15.4% to 41.55%. After incorporating all environmental factors, the variance explained ratio increased to between 53.91% and 72.27%. As the number of factors increased, the percent of variance showed an increasing trend (Figure 9d-f). Based on RMSE, R\({}^{2}\), and the slope of zero-intercept linear regression between observations and predictions (k\({}_{0}\)), appropriately reducing less-important envi  
Figure 8: The impact of key environmental factors on the Pearson correlation coefficient between SIF and Tr (SIF–Tr, red) and the slope of zero-intercept linear regression (Tr/SIF, blue) in Chinese cork oak (PAR, (a); Ta, (d); VPD, (g); SM, (j)), poplar (PAR, (b); Ta, (e); VPD, (h); SM, (k)), and arborvitae (PAR, (c); Ta, (f); VPD, (i); SM, (l)) plantations.  
ronmental factors will not adversely affect the model's performance. Compared with the linear model, combining SIF with 2 to 4 important environmental factors can significantly enhance the accuracy of Tr estimation.
# Probabilistic Sensitivity Amplification Control for Lower Extremity Exoskeleton
## 3 Evolving Learning of the Sensitivity Factor  
The sensitivity amplification control proposed in [9] aims to improve the control performance without directly measuring the interaction information with the pilot. Thus, by inserting a sensitivity factor \(\alpha\) in dynamic equation:  
\[\mathbf{\tau}=\hat{\mathbf{P}}(\mathbf{\theta})+(1-\mathbf{\alpha^{-1}})[\hat{\mathbf{M}}(\mathbf{ \theta})\tilde{\mathbf{\theta}}+\hat{\mathbf{C}}(\mathbf{\theta},\hat{\mathbf{\theta}})\hat\mathbf{{\theta}}], \tag{13}\]  
The interaction between the pilot and the exoskeleton is given:  
\[\mathbf{d}=\mathbf{\tau}-\mathbf{P}(\mathbf{\theta})-(1-\mathbf{\alpha^{-1}})[\mathbf{M}(\mathbf{\theta}) \tilde{\mathbf{\theta}}-\mathbf{C}(\mathbf{\theta},\hat{\mathbf{\theta}})\hat{\mathbf{\theta}}], \tag{14}\]  
With perfect estimation \(\hat{\mathbf{M}}=M,\hat{\mathbf{C}}=\mathbf{C}\) and \(\hat{\mathbf{P}}=\mathbf{P}\) and sufficient large \(\alpha\). The interaction \(\mathbf{d}\) can approach zero. Nevertheless, the sensitivity factor should not be selected with a sufficiently large parameter due to the weak robustness. Moreover, in an actual situation, the interaction varies owing to stochastic environment factors. Therefore, choosing a fixed sensitivity factor is not a good option. An improvement has been made in [27], where the sensitivity factor is learned with Qlearning. Compared with the classic SAC, the interaction force has been reduced. However, the learning algorithm mainly copes with the discrete region of interest. In addition, the optimization of the constraint domain of the sensitivity factor is not addressed in the reference.  
In this paper, the sensitivity factor is evolved in real time in a continuous constraint domain. By applying \((1+1)\) covariance matrix adaptation evolution strategy (\((1+1)\)-CMA-ES) [28], the sensitivity factor seeks the optimal value owing to tracking error penalty.
## 5 Discussion
### Causes of Pit Slope Deformation and Failure
#### 5.1.5 Precipitation and Freeze-Thaw Cycles  
The arid and water scarcity in the study area is not favorable for plant growth, and the vegetation cover is extremely low (Figure 1). As outlined in Section 2.1, rainwater and ice meltwater follow the drainage path toward the southern side of the eastern pit slope in the Wolong Coal Mine (Figure 1). Under rainfall conditions, the topsoil is prone to runoff due to its poor water retention, and water seepage will further affect slope stability.  
Freeze-thaw cycles are important triggers for the mass movement [48]. According to Section 2.3, the temperature in the study area is highly variable throughout the year, with peaks at 37 \({}^{\circ}\)C and extreme lows at \(-28\) \({}^{\circ}\)C. The intense freeze-thaw cycles experienced by the rock mass of the pit slope have resulted in continued structural deterioration and reduced stability of the rock mass. Notably, the study area experiences significant temperature variations, with a pronounced impact from freeze-thaw cycles. Over numerous freezing and thawing cycles, the micro-fissures of rock masses are persistently subjected to microscopic freezing and swelling, leading to continuous damage. Concurrently, the fractures with the rock mass progressively deteriorate, resulting in a reduction in the slope body's stability (Figure 30b,c).
## 2 Materials and Methods
### Measurement of Airflows Downwind the Building  
In order to investigate airflow characteristics and to predict air pollutant dispersion properties downwind the building, air velocities at three vertical planes (Planes 1-3, Figure 3) downwind the 1:50 scaled pig building model were measured with the LDA. Streamwise and vertical velocity components were measured simultaneously. Mean and instantaneous air velocities were recorded to obtain average and turbulent airflow information. Plane 1 and Plane 2 were parallel to the building sidewalls at separation distances of 0.2 m (corresponded to 10 m in full-scale) and 1.0 m (corresponding to 50 m in full-scale) downwind the building model, respectively. Both Plane 1 and Plane 2 had a dimension of \(Y\times Z\) = 0.8 m (width) \(\times\) 0.2 m (height), each consisting of 200 velocity measurement points with 10 points in each vertical line and 20 points in each horizontal line. Plane 3 was positioned along the symmetry plane of the scaled model with a dimension of \(X\times Z\) = 0.8 m (length) \(\times\) 0.2 m (height), including 90 measurement positions distributed at 9 vertical lines. In this study, \(X\), \(Y\) and \(Z\) denoted the axes of the coordinate system, which were aligned with the streamwise, spanwise and vertical wind directions, respectively. The origin of the coordinate system was located at the centre of the downstream edge of the scaled model at the wind tunnel floor. The airflow measurement positions and the definition of the coordinate system are depicted in Figure 3. According to the wind profile measurement results (described in Section 3.1), a stable and fully developed turbulent flow that represents a farmland terrain boundary layer could be obtained when the free stream wind tunnel speed (\(U_{inlet}\)) was at least 8 m\(\cdot\)s\({}^{-1}\). Therefore, all airflow measurements downwind the scaled model were performed at \(U_{inlet}\) of 8 m\(\cdot\)s\({}^{-1}\).
# A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context
## 4 Node Architecture
### LoRa Transmission Module
As it can be seen in Figure 1, four signals are reported:  
* MISO (Master Input Slave Output) is used by the slave to transfer data towards the master;
* MOSI (Master Output Slave Input) is used by the master to transfer data towards the slave;
* CS (Chip Select) is used by the master to select the slave to communicate with;
* SCK (Serial Clock) is used by the master to synchronize the communication. Such signals are exploited to fulfill the data transmission of the sensed trash level: the microcontroller selects the LoRa module via CS and it starts the clock signal via SCK; the data to be transmitted is sent through the MOSI so that the module is able to transmit it and when the transmission is over the module notices it to the microcontroller via the MISO.
## 5 6 GCIoHE System Paradigm
### The 6G Enabled Tactile Internet End-to-End Architecture
#### 5.2.5 End-to-End Latency  
End-to-end latency of less than 1 ms remains the main challenge related to the system realization of a Tactile Internet. As the human reaction time is of the order of 1 ms, the P2M communication faces no cyber issues; therefore, it is a vital requirement for a reliable Tactile Internet system. The following are the factors that affect the end-to-end delay: (i) propagation delay; (ii) transmission delay; (iii) queuing delay; (iv) coding and decoding process; (v) routing process; and (vi) protocol stack optimizations. The Tactile Internet must handle the tactile data in the same manner as the conventional audio/visual information. Hence, the communication between smart sensors or devices for healthcare requires encoding mechanisms which provide transmission of tactile data over packet-switched networks.
# A Review of Plant Disease Detection Systems for Farming Applications
## 2 Literature Review: Precision Agriculture Research Developments
### Shape Features
Using the same assumption of an image partitioned into pixels in the above Section, the following are the textural features used for feature extraction in plant leaves:  
* Entropy (Entr)--this is a measure of the complexity and uniformity of a texture of a leaf image [68]: \[C=\frac{4\pi A}{P}\]0 (12)
* Contrast (Con)--this is a measure of how clear the features are in a leaf image; it is also referred to as the moment of inertia [69; 70]: \[C=\frac{4\pi A}{P}\]1 (13)
* Energy (En)--this is a measure of the degree of uniformity of a gray image. It is also called the second moment [69]:
* Correlation (Cor)--this is a measure of whether there is a similar element in a sample picture that corresponds to the re-occurrence of a similar matrix within a large array of pixels [68]. \[C=\frac{4\pi A}{P}\]2 (14) \[C=\frac{4\pi A}{P}\]3 (15)where: \[C=\frac{4\pi A}{P}\]4 \[C=\frac{4\pi A}{P}\]5 \[C=\frac{4\pi A}{P}\]6 \[C=\frac{4\pi A}{P}\]7
* Difference moment inverse (DMI)--this is a measure of the degree of homogeneity in an image [69]: \[C=\frac{4\pi A}{P}\]8 (16)  
Other textural features include the maximum probability, which is the highest response to correlation; the standard deviation and/or variance, which is the aggregate texture observed in a leaf picture; and the average illuminance, which is the average light distribution across the leaf when an image was captured [66; 68; 69; 70]. The selection of a particular color, shape, or textural feature strictly depends on the application of the system being designed.
# Structuring Reference Architectures for the Industrial Internet of Things
## 5 Considered Reference Architectures
### X-Road  
Estonia's X-Road data exchange framework and the underlying X-tee integration [26] layer are certainly one of the most successful, if not the most successful, data sharing approaches for governance data at the moment. It has become the central infrastructure for information access for nearly the majority of Estonian government services and enables the digital information exchange between the government and its citizen. The target-oriented frameworks like x-Tee define selected, very specific requirements of connectivity (see Figure 8). IIRA covers a broad range of topics while RAMI4.0 focuses more on aspects regarding physical entities (Things').  
The main components are so-called Security Server, which act as gateways between the local databases and the other x-road components. The communication itself utilizes SOAP multipart messages, combining the data payload with security-related metadata. Service endpoints and offered RPC methods are described with WSDL files, supporting the integration process.  
X-Road is, in contrast to other outlined approaches, not a fully decentralized environment. Central server provide identity information and enable the maintenance and control of the network as a whole. As its purpose is the exchange of government-related data, controlling the network by an official agency is therefore a strict requirement. At the same time, this characteristics hampers its applicability for other scenarios where equal partners need to communicate. In addition, the technical specifications regarding the used protocols and security roles are rather strict and inflexible, suitable for frequent and stable data exchange but less for ad-hoc communication or automated network configurations. Furthermore, the used set of technologies and description patterns hamper an automated integration and require human developers for providing interoperability and maintenance.