esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Strategy Creation for UAV Path Planning Based on Q-Learning
### The System State Transition of UAV Path Planning  
The state transition of the system depends on the action strategies for the UAV under the control; the motion of the UAV must be considered accordingly during the problem solving process. Let us suppose that all the sensors are fixed up to the airframe and there are no distortions of them; then, the state updating of the sensors can be regarded as the status update after the UAV takes the action strategy.  
If the state of UAV at time \(k\) is given by \(s_{k}=(p_{k}\ \ q_{k}\ \ V_{k}\ \ \theta_{k})\), where \((p_{k}\ \ q_{k})\) represents the position coordinates, \(V_{k}\) denotes the instant velocity, and \(\theta_{k}\) is the heading angle of the UAV at time \(k\), then, the state updating of the UAV can be described as  
\[s_{k+1}=\psi(s_{k},u_{k}), \tag{2}\]  
where \(\psi\) is a mapping function that constructs the correlation between the control variables and the movement of the UAV, and can be collected by a group of simple equations that govern the UAV kinematics motion. For the sake of establishing the relationships between the control actions and the movements of the UAV, we choose the control actions as follows:  
\[u_{k}=(a_{k},\ \ \phi_{k}). \tag{3}\]  
The control action \(u_{k}\) can be deduced from the kinematics motion equations of the UAV, and the speed and the heading angle of the UAV are updated according to the flight mechanics theories as follows:  
\[V_{k+1}=[V_{k}+a_{k}T]_{V_{\min}}^{V_{\max}} \tag{4}\]  
\[\theta_{k+1}=\theta_{k}+\frac{gT\tan\phi_{k}}{V_{k}}. \tag{5}\]  
Specifically,  
\[[v]_{V_{\min}}^{V_{\max}}=\max\{V_{\min},\ \ \min(V_{\max},\ \ v)\}, \tag{6}\]  
where \(V_{\min}\) and \(V_{\max}\) denote the minimum and maximum limits on the speed of the UAV, respectively; \(g\) is the acceleration of gravity; and \(T\) is the length of the planning time step.  
The position coordinates of the UAV are updated as follows:  
\[p_{k+1}=p_{k}+V_{k}T\cos\theta_{k} \tag{7}\]  
\[q_{k+1}=q_{k}+V_{k}T\sin\theta_{k}. \tag{8}\]
# Application of Elliptic Jerk Motion Profile to Cartesian Space Position Control of a Serial Robot
## 5 Multibody Model of the Manipulator  
A multibody model of the RRFbR arm has been developed using Simscape MultibodyTM by MathWorks, neglecting friction in joints. The Cartesian space position control has been implemented using both the KD and the KDHD algorithms discussed in Section 3. Table 1 collects the geometrical and mass parameters of the manipulator.  
Regarding the control parameters, for the KD controller: K\({}_{KD}\) is diagonal with \(k_{KD,x}=k_{KD,y}=k_{KD,z}=8\times 10^{3}\) N/m, and \(k_{KD,\theta}=5\times 10^{2}\) Nm/rad; D\({}_{KD}\) is diagonal with \(d_{KD,x}=d_{KD,y}=d_{KD,z}=5\times 10^{3}\) Ns/m, and \(d_{KD,\theta}=7.5\) Nms/rad.  
Figure 5: Kinematic comparison of motion profiles in terms of dimensionless jerk \(j_{\rm ad}\), dimensionless acceleration \(a_{\rm ad}\), dimensionless velocity \(v_{\rm ad}\), and dimensionless position \(s_{\rm ad}\); elliptic jerk (EJ, red), sinusoidal jerk (SJ, blue), modified sinusoidal jerk (MSJ, cyan), cycloidal (CY, black), trapezoidal acceleration (TA, green), trapezoidal velocity (TV, magenta).  
For the KDHD controller, the stiffness matrix \(\mathbf{K}_{KDHD}\) is equal to \(\mathbf{K}_{KD}\), all the values of the \(\mathbf{D}_{KDHD}\) matrix are halved with respect to \(\mathbf{D}_{KD}\), and this diminution is compensated by the half-order damping, introduced by the \(\mathbf{HD}_{KDHD}\) matrix with \(hd_{KDHD,x}=hd_{KDHD,y}\) = \(hd_{KDHD,z}\) = 22,500 \(\mathrm{Ns}^{1/2}\)/m, and \(hd_{KDHD,\theta}\) = 2.4 \(\times\) 10\({}^{4}\)\(\mathrm{Nms}^{1/2}\)/rad. These values have been obtained using the tuning criteria discussed in [17]. For the half-derivative digital filter of the KDHD controller, \(T_{s}\) = 0.005 s and \(n\) = 10; the same sampling time is used also for the KD controller.
# Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
###### Abstract  
The demand for high-capacity communication systems has grown exponentially in recent decades, constituting a technological field in constant change. Data transmission at high rates, reaching tens of Gb/s, and over distances that can reach hundreds of kilometers, still faces barriers to improvement, such as distortions in the transmitted signals. Such distortions include chromatic dispersion, which causes a broadening of the transmitted pulse. Therefore, the development of solutions for the adequate recovery of such signals distorted by the complex dynamics of the transmission channel currently constitutes an open problem since, despite the existence of well-known and efficient equalization techniques, these have limitations in terms of processing time, hardware complexity, and especially energy consumption. In this scenario, this paper discusses the emergence of photonic neural networks as a promising alternative for equalizing optical communication signals. Thus, this review focuses on the applications, challenges, and opportunities of implementing integrated photonic neural networks for the scenario of optical signal equalization. The main work carried out, ongoing investigations, and possibilities for new research directions are also addressed. From this review, it can be concluded that perceptron photonic neural networks perform slightly better in equalizing signals transmitted over greater distances than reservoir computing photonic neural networks, but with signals at lower data rates. It is important to emphasize that photonics research has been growing exponentially in recent years, so it is beyond the scope of this review to address all existing applications of integrated photonic neural networks.  
Keywords:artificial intelligence; equalization; integrated circuits; literature review; optical communications; photonic neural networks +
Footnote †: journal: _photonics_
## 4 Techniques Used in Academia to Improve Performance Metrics
### Mechanisms to Improve Reliability
#### 4.1.1 Diversity  
When the wireless channel is in a deep fade, any communication scheme will suffer from error. In case of narrowband fading, by passing the information symbols through multiple signal paths, multiple independently faded replicas of data symbols are received at the receiver. This is a potential way to improve the performance of the communication over fading channels, provided one of the signal paths is strong [14]. This mechanism is called diversity. The common diversity methods that are widely utilized in wireless communication systems are spatial diversity (or antenna diversity), cooperative diversity, temporal diversity, and frequency diversity.
## 4 Results and Discussion
### Transformer Condition Monitoring
#### 4.8.1 Voltage and Current  
Voltage and current are critical parameters of any electrical transformer; monitoring and analyzing the trends and behavior of these parameters can reveal a great deal of information about the transformer during operation. The relationship between these two parameters is inversely proportional to each other for both primary and secondary circuits [28]. Whenever the primary voltage steps up the current, the primary side of the transformer should step down, and the same relationship applies to secondary voltage and current as well [33]. Voltage and current parameters are critical and can be utilized to monitor the loading of transformers, thus minimizing overloading and power theft. These parameters can also be utilized to calculate power and efficiency and to perform diagnostic tests such as the transformer voltage ratio test, no-load losses, and load losses, etc. Monitoring these parameters using the IoT with transformers in service can determine developing issues with interturn insulation failure. There are various IoT sensors available in the market that can be utilized to gather real-time data of these two critical parameters of the transformer, with the ACS712 IoT current sensor and the ZMCT103C current module being widely used sensors in IoT-based condition monitoring systems. The Zmpt101B voltage sensor is the most preferred sensor for transformer condition monitoring due to this sensor being rated at a maximum of 250 Vac. Various voltage sensors are rated at a maximum of 25 V, hence the preference for the Zmpt101B voltage sensor by most researchers.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 5 Experimental Results
### RQ1: How Effective Is CIB? How Much Improvement Can It Obtain Compared with the Baselines?
With respect to AGF (see Table 5), we notice that, on average, CIB obtains the best MCC as 0.196 across all datasets, which is closely followed by SMOTE and MAHAKIL. The AGF of CIB ranges from 0.033 (on dataset MC1) to 0.356 (on MC2) across all 11 datasets. According to the results of Wilcoxon signed-rank test (see the row 'Win/Tie/Lose') and the background of table cells, we notice that on most datasets, our CIB significantly outperforms AdaC2, AdaBoost, and None with large effect size on 11 datasets. Moreover, CIB has similar performance to SMOTE, MAHAKIL, and RUS. With respect to MCC (see Table 6), we can see that, on average, CIB obtains the best MCC as 0.285, which improves the performance over the baselines by at least 3.7% (see AdaC2 [45]). The MCC of CIB ranges from 0.137 to 0.559 across all 11 datasets. According to the results of 'Win/Tie/Lose' and the background of table cells, we notice that CIB always significantly outperforms MAHAKIL, SMOTE, and significantly performs worse than the baselines on, at most, two datasets. With respect to AUC (see Table 7), we can see that CIB achieves the biggest AUC as 0.769 on average, which improves the performance over the baselines by at least 1.2% (see AdaBoost [46]). The AUC of CIB ranges from 0.672 to 0.923 across all 11 datasets. According to the results of 'Win/Tie/Lose' and the background of table cells, we notice that our CIB always significantly outperforms MAHAKIL, SMOTE, RUS, and None with large effect size on each of 11 datasets. On at least half of 11 datasets, CIB significantly outperforms the remaining two baselines, i.e., AdaC2 and AdaBoost, with non-negligible effect size and has no significant differences with them on four out of 11 datasets.
## 2 Materials and Methods
### Research Station  
MSS pre-treatment with SCO\({}_{2}\) was performed in glass, open, continuous-stirred reactors (JLT 6, VELP Scientifica, Milano, Italy). The reactors were first filled with raw MSS having a temperature of 20 \({}^{\circ}\)C MSS, which was then amended with SCO\({}_{2}\). The mixture was stirred at 50 rpm for 20 min. The samples were left until the SCO\({}_{2}\) was fully sublimated out, then, after reaching 20 \({}^{\circ}\)C, they were subjected to AD.  
The AD process was conducted in continuous-stirred, continuous-flow anaerobic reactors (Gunt CE 702, Hamburg, Germany) (Figure 1) [24]. Total/active volume was 30 dm\({}^{3}\)/20 dm\({}^{3}\), respectively. The digestion process was run at 38 \(\pm\) 1 \({}^{\circ}\)C. The reactor was fitted with detectors, a temperature stabilization system, a stirring system with a four-blade mechanical agitator rotating at 100 rpm, a feedstock in/digestate out pump, and a biogas outflow meter. Feedstock input and digestate discharge occurred daily. The OLR and HRT values used depended on the variant of the experiment. Each variant was continued for three times the hydraulic retention time. Analyses were conducted every 4 days.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Unit** & **MSS (S1)** & **MSS (S2)** & **AS** \\ \hline pH & - & & & \\ Total solids (TS) & [\%] & 4.96 \(\pm\) 0.68 & 4.94 \(\pm\) 0.92 & 2.24 \(\pm\) 0.55 \\ Volatile solids (VS) & [\%TS] & 84.91 \(\pm\) 1.81 & 84.70 \(\pm\) 1.98 & 67.22 \(\pm\) 1.66 \\ Mineral solids (MS) & [\%TS] & 15.09 \(\pm\) 1.81 & 15.30 \(\pm\) 1.98 & 32.78 \(\pm\) 1.66 \\ Total carbon (TC) & [mg/gTS] & 585 \(\pm\) 10 & 583 \(\pm\) 12 & 330 \(\pm\) 12 \\ Total organic carbon (TOC) & [mg/gTS] & 571 \(\pm\) 15 & 568 \(\pm\) 19 & 308 \(\pm\) 9 \\ Total nitrogen (TN) & [mg/gTS] & 91.51 \(\pm\) 6.43 & 90.22 \(\pm\) 5.5 & 34.6 \(\pm\) 3.8 \\ C/N ratio & - & 6.39 \(\pm\) 1.56 & 6.46 \(\pm\) 2.18 & 9.54 \(\pm\) 3.16 \\ Total phosphorus (TP) & [mg/gTS] & 3.5 \(\pm\) 1.1 & 3.4 \(\pm\) 1.2 & 1.6 \(\pm\) 0.2 \\ Soluble COD & [mg/dm\({}^{3}\)] & 115 \(\pm\) 10 & 383 \(\pm\) 14 & 1100 \(\pm\) 16 \\ Soluble TOC & [mg/dm\({}^{3}\)] & 34.30 \(\pm\) 3.21 & 127.96 \(\pm\) 2.25 & 325 \(\pm\) 11 \\ Soluble P-PO\({}_{4}\)\({}^{3-}\) & [mg/dm\({}^{3}\)] & 60.33 \(\pm\) 2.43 & 78.21 \(\pm\) 4.16 & 65.23 \(\pm\) 2.13 \\ Soluble N-NH\({}_{4}\)\({}^{+}\) & [mg/dm\({}^{3}\)] & 83.25 \(\pm\) 10.51 & 263 \(\pm\) 14 & 99.44 \(\pm\) 3.18 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of the sludge types used in the experiment.
## 5 Experimental Results and Analysis
### Evaluation of Real-Time Requirements  
In this section, we conduct the experiments of time consumption to evaluate the real-time performance. The United States (US) Department of Transportation illustrated that the highest priority vehicle safety services, such as over-taking and collision warnings, should meet the real-time requirements of 10 to 100 ms [54]. Furthermore, for V2X safety applications scenarios including autonomous driving, 10 to 50 ms time consumption is required [55]. Thus, the time consumption of vehicle anomaly detection methods should require a latency of 10 ms at the utmost for real-time requirements.  
Table 9 shows the detection time of our proposed model. The average time required for performing an anomaly detection of CWTS generation and CNN detection is 6.08 ms and 1.87 ms, respectively. The detection time of the CWT-CNN model is 7.98 ms overall. Thus, the proposed model satisfies the vehicular security timeliness requirements (10 ms).
## 1 Introduction  
Endodontic diseases involve microbial infections organized in biofilm structures [1]. These microbial communities are embedded in self-produced extracellular polymeric substances (EPSs) that attach to surfaces and form three-dimensional matrix structures, making the bacteria more resistant to antimicrobial agents [1; 2; 3; 4; 5]. Therefore, an effective root canal disinfection procedure requires the use of an irrigation solution with the ability to disrupt the biofilm matrix along with having a broad antimicrobial spectrum [5].  
Sodium hypochlorite (NaOCl) is the most widely used irrigation solution in endodontics [6], and its antibacterial and tissue dissolution effectiveness depends on its pH, among other factors [7; 8; 9]. At pH 12, the regular type of NaOCl solution, the hypochlorite ion (OCl-) predominates in solution. At this pH, tissue dissolution of NaOCl is maximized and it has the capability to disrupt and reduce the matrix of biofilm-related matter [8; 9; 10; 11; 12].  
On the other hand, when NaOCl is acidified to pH 6.5, hypochlorous acid (HOCl) is mostly present [13; 14; 12], and the bactericidal ability of NaOCl is maximized [9; 10; 12; 14; 15].  
This study was designed with the rational of sequential use of NaOCl at two different pH: first disrupting the protective biofilm matrix, using NaOCl at pH 12, in order to allow subsequent better penetration of the more bactericidal acidified NaOCl (pH 6.5).  
The aim of this study was to determine the bactericidal efficacy of sequential use of NaOCl at pH 12 followed by acidified NaOCl at pH 6.5, as compared to either NaOCl at pH 12 or NaOCl at pH 6.5 alone. The null hypothesis was that there will be no difference between such sequential use and the bactericidal effect of NaOCl at either of the above pH levels alone.
## 3 Network Architecture Description  
The network architecture is schematically outlined in Figure 1. For the sake of clarity, such a network is fully modular and scalable to be employed in the most diverse application scenarios. These features foster the network usability even in harsh and critic contexts. The network is made of three stacked layers: Sensor Nodes, Forwarding and the Cloud. As its name suggests, the Sensor Nodes Layer includes the network sensor nodes. In particular, they are of a twofold species depending on the communication technology upon which they rely: LoRaWAN Nodes and NB-IoT Nodes. The Forwarding Layer is in charge of forwarding data from the Sensor Nodes Layer to the Cloud Layer and viceversa. To this end, such Layer is composed of LoRaWAN-NB-IoT Gateways, which are de facto multi-protocol Gateways, and Long-Term Evolution (LTE) base stations, named evolved Node B (eNodeB), which are in charge of running the NB-IoT network. Finally, the Cloud Layer is responsible for collecting and storing data as well as for arranging downlinks towards the sensor nodes and for allowing users to interact with the whole system. Each of the layers components are described in detail later on.  
This network allows for bidirectional transmissions. Indeed, the devices within the Sensor Nodes Layer can be equipped with sensors and/or actuators. Therefore, users may issue commands to be executed by the nodes on the basis of what they sense within the deployment environment. Moreover, such commands may be also automatically emitted by the Cloud itself (e.g., whenever a physical phenomenon exceeds a certain threshold). In addition, NB-IoT Nodes are capable of setting up a peer-to-peer subnetwork to directly exchange data between them without involving either the users or the Cloud. Given the network functioning scheme, an edge-to-cloud connectivity framework is set up along with a fog computing paradigm due to the fact that data are sparsely processed throughout all of the network layers.  
Finally, three different communication protocols are involved. The first one is the well-known LoRa modulation, which provides LoRaWAN Nodes with wireless connectivity as the LoRaWAN standard prescribes. Second, the Message Queue Telemetry Transport (MQTT) protocol over NB-IoT is involved. It is exploited by the NB-IoT Nodes to communicate towards eNodeBs. Finally, the MQTT protocol over LTE is concerned, by which eNodeBs forward and receive data to and from the Cloud Layer.
## 5 Discussion
### Relationships between TOC Content and Fractal Dimensions of Shale Pores  
The fractal dimensions (D1 and D2) show a clear negative correlation with the total organic carbon content, indicating that the fractal dimension decreases significantly with the increase in the TOC content (Figure 10a). This phenomenon is not consistent with the findings of previous studies on the Qingshankou and the Nenjiang Formations [46; 50; 51; 65; 78], which are discussed below.  
Figure 9: Relationships between mineral compositions and fractal dimensions of K\({}_{2}\)n\({}^{1+2}\) and K\({}_{2}\)q shale. The K\({}_{2}\)q compared to [78].  
For the shales of the Qingshankou Formation within a depth range of 500-2300 m and a T\({}_{\rm max}\) range of 427-460 \({}^{\circ}\)C, [PERSON] et al. found that D1 and D2 decreased rapidly with increasing TOC content when the TOC content was less than 2.5-3.0 wt.% [51]. This is interpreted as the result of hydrocarbon generation and expansion in low-maturity shale to create more uniform mesopores, which further lead to lower D1 and D2 and a higher average pore size [51]. When the TOC value is greater than 2.5 to 3.0%, D1 and D2 increase with the increase in the TOC content [51]. This indicates that the organic pores may collapse due to higher TOC levels, burial, and thermal maturation, leading to more complex and smaller pores.  
For the shales of the Nenjiang Formation, previous studies have indicated a positive correlation between the fractal dimensions and the organic matter content [49; 51]. However, in the present study, we found a negative correlation between the fractal dimensions and the organic matter content (Figure 10). One possible explanation is that previous studies have selected discontinuous samples from multiple cores and/or outcrop sections. The analysis of our 16 continuously distributed shale samples from the SK-1(S) core indicates that, although the T\({}_{\rm max}\) is low, the limited maturation process may still cause expansion of hydrocarbons to some extent, thus increasing the pore sizes of the K\({}_{2}\)n\({}^{1+2}\) shale and decreasing the fractal dimensions (Figure 10b). The results of the study by [PERSON] et al. support our explanation (Figure 10).
## 2 Methods
### SBAS-InSAR  
Assuming \(j\)th interferogram, the two images are formed at \(t_{B}\) and \(t_{A}\) moments in the azimuth with range coordinates of \((x,i)\).  
\[\delta\varphi_{j}(x,i)=\varphi(t_{B},x,i)-\varphi(t_{A},x,i)\approx\frac{4\pi} {\lambda}[d(t_{B},x,i)-d(t_{A},x,i)] \tag{1}\]  
where \(\lambda\) is the wavelength; \(d(t_{B},x,i)\) and \((t_{A},x,i)\) are the cumulative deformation of the line of sight at time \(t_{B}\) and \(t_{A}\) relative to the initial time \(t_{0}\), respectively; \(\delta\varphi_{j}(x,i)\) marks the differential interference phase of the jth interferogram in \((x,i)\) coordinates; and \(\varphi(t_{B},x,i)\) and \(\varphi(t_{A},x,i)\) are the phase values at time \(t_{B}\) and \(t_{A}\).  
According to the principle of the spatiotemporal baseline, \(N\) scene data can obtain \(N(N+1)/2\) interference pairs in order to avoid the incoherence of interference image pairs caused by loose spatiotemporal baseline limitation, the reduction in redundant observations, and the amplification of random errors caused by a small spatiotemporal baseline limitation.  
Thus, SBAS-InSAR technology can form multiple small baseline combinations of short time baselines and short spatial baselines, and the image data of the short time and space baselines are used for interference processing, which can improve the quality of the interferogram to a certain extent, reduce the influence of speckle noise, and increase the utilization rate of effective data. In SBAS technology, not only the main image and other images are registered, but also areas between other images. The independent image data caused by the long baseline are connected to form a short baseline image collection, and the image collection is processed via differential interference to obtain the coherent coefficient map, the filtered interference map, and the phase de-entanglement map. The singular value decomposition method is used to jointly solve and remove the residual terrain phase between the image collections, obtain the land surface deformation information, and improve the accuracy of land surface deformation monitoring [24].