esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 LiCSAR Applications for Measuring Tectonic and Volcanic Deformations
### LiCSAR for Volcanic Applications
#### 4.2.1 Use of Basic Interferometric Products for Volcanic Studies  
Interferograms of a short temporal baseline in the LiCSAR database can be used to detect strong volcanic deformation related to shallow magma intrusions, such as the March 2017 intrusion at Cerro Azul (Galapagos) and the January 2017 dyke intrusion at Erta Ale (Ethiopia) (Figure 12). The interferogram at Cerro Azul shows two lobes of displacements: \(\sim\)11 cm of subsidence in the North and \(\sim\)14 cm of uplift in the South (Figure 12a).  
In addition, coherence products can be used to map the emplacement of new volcanic products during an eruption (Figure 13). The loss of coherence (black) in the central area is an indication of the  
Figure 11: Line of sight (LOS) velocity map and time series for frame 014A_04939_131313, which corresponds to a region around the North Anatolian Fault (NAF). (**a**) Velocity map with active faults [67]. Black square is the reference area used for the velocity field determination. Yellow star is the location of the time series shown in (**b**). (**b**) Displacement time series from the location corresponding to the yellow star in (**a**) relative to the reference area. The best-fit linear and annual seasonal trends are overlain on the time series. The sizes of the spatial and temporal filters are 2 km and 25 days, respectively.  
Figure 12: Descending wrapped interferograms generated by the LiCSAR system showing ground deformation associated with (**a**) the March 2017 intrusion at Cerro Azul and (**b**) the January 2017 intrusion at Erta Ale.  
fresh lava flow emplaced during the 2017 Erta Ale eruption. The production of time series of coherence is useful for tracking flow propagation (e.g., lava initially flows to the Northeast before flowing to the Southwest on June 2017) and to derive the cumulative flow area [72].
# Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway
## 5 Radio Frequency (RF) and Infrared Wake-Up Mechanisms and Implementation
### Infrared Wake-Up Implementation  
To achieve a comparative setup to justify the proposed RF method, the infrared wake-up mechanism was also implemented in this endeavor. Infrared is a commonly used solution for simple wireless communication at short ranges [48]. There are two basic components in infrared wireless communication: emitter and receiver. The emitter first transmits the coded data, generated by the micro-controller, to the receiver. When the receiver reads the IR signal, it decodes the signal into digital data and then passes the information to its following component, i.e., the sensor node. In our implementation we integrated a 950 nm IR LED into our UAV and a TSOP38238 IR Receiver Module on the sensor board. Figure 6b shows these modules. The emitter was connected to the UAV with the MCU's PWM-capable I/O pin, and the receiver was connected to the MCU's regular digital I/O pin on the sensor board.  
To achieve the wake-up function, the emitter on the UAV sent out the wake-up signal to the receiver only when the signal matched the code that was stored in the MCU (on the sensor board). The sensor board then turned on its sensors and the XBee communication module. In this setup, the sensors and the XBee module could be turned off until the MCU received the wake-up signal. However, the sensor board MCU always had to stay on in order to check whether the infrared signal matched the specific pattern.
## 3 Results of Experiments and Discussion
### MBN Measurements
Corrosion attack results in the growth of _FWHM,_ which is compensated by the decreasing evolution along with the tensile stresses. The effective values drop down with the higher extent of corrosion damage. However, the response with respect to the tensile stress is asymmetric in RD and TD due to realignment of the DWs into RD. Finally, _PP_ tends to increase with the corrosion attack as well as the tensile stress and this parameter only exhibits the systematic behavior in RD as well as TD. On the other hand, MBN-extracted parameters as well as their combination provide no exclusive values on which the pure contribution of corrosion and tensile stress can be distinguished.
# Feedback Stabilization of First Order Neutral Delay Systems Using the Lambert W Function
## 3 Main Results
### Oscillation and Non-Oscillation Criteria
_If \(a=-1\), then every solution of Equation (3) oscillates._  
**Proof. ** If \(a=-1\), then \(f(\lambda)\geq 0\) for all \(\mathrm{Re}(\lambda)\). As \(g(\lambda)<0\) for all \(\mathrm{Re}(\lambda)\), they do not intersect at any point. This implies that \(p(\lambda)\) has no real roots. Another proof can be formulated as follows, if \(a=-1\), the Equation (4) becomes:  
\[p(\lambda)=\lambda-\lambda e^{-\tau_{1}\lambda}+be^{-\tau_{2}\lambda}=\lambda \Big{(}1-e^{-\tau_{1}\lambda}\Big{)}+be^{-\tau_{2}\lambda}. \tag{24}\]  
For all \(\mathrm{Re}(\lambda)\), \(p(\lambda)\geq b>0\). **Theorem 3. **_If \(a<-1\) and_  
\[2-W(-ea^{-1})+ae^{W(-\alpha^{-1})-1}+\tau_{1}b(-a)^{-\frac{\tau_{2}}{\tau_{1} }}>0, \tag{25}\]  
_then every solution of Equation (3) oscillates._  
**Proof. ** The proof is similar to that of Theorem 1. The maximum value of \(g(\lambda)\) on the interval \(\Big{(}0,\frac{1}{\tau_{1}}\ln(-a)\Big{)}\) is \(g(\frac{1}{\tau_{1}}\ln(-a))\) as \(g(\lambda)\) is a concave function. If \(f(\lambda_{m})\) is greater than \(g(\frac{1}{\tau_{1}}\ln(-a))\), thenthey do not intersect at any point. We proceed as in the proof of Theorem 1 to immediately obtain the following result:  
\[f(\lambda_{m})>g(\frac{1}{\tau_{1}}\ln(-a))\Leftrightarrow\ \frac{1}{\tau_{1}} \Big{(}2-W(-ea^{-1})+ae^{W(-ea^{-1})-1}\Big{)}-be^{-\frac{\tau_{2}}{\tau_{1}}\ln (-a)}\]  
\[\gamma W(-ea^{-1})=2W(-ea^{-1})-W^{2}(-ea^{-1})+aW(-ea^{-1})e^{W(-ea^{-1})}e^ {-1}. \tag{16}\]0  
\(\Box\)  
**Theorem 4**: _Assume that \(-1<a<0\). If_  
\[\gamma W(-ea^{-1})=2W(-ea^{-1})-W^{2}(-ea^{-1})+aW(-ea^{-1})e^{W(-ea^{-1})}e^ {-1}. \tag{16}\]1  
_then Equation (3) has two negative real roots._  
**Proof. ** First, we determine the maximal number of real roots of the quasi-polynomial function (Equation (4)). From the Polya-Szego bound (see Theorem A1), setting \(\xi=\theta=0\) gives a bound on the number of real roots for (4). Here, \(d_{1}=2\) and \(d_{2}=1\), therefore, the maximal number of real roots is 2. Next, two functions \(f(\lambda)\) and \(g(\lambda)\) intersect in the third quadrant of the complex plane if Equation (26) is satisfied. As \(g(\lambda)\) is a strictly increasing function, and \(f(\lambda)\) strictly decreases on the interval \(\Big{(}\frac{1}{\tau_{1}}\ln(-a),\lambda_{m}\Big{)}\) and strictly increases on the interval \((\lambda_{m},0)\), then \(g(\lambda)\) intersects two points of \(f(\lambda)\) if \(f(\lambda_{m})<g(\lambda_{m})\). This completes the proof. \(\Box\)  
**Theorem 5**: _Assume that \(a<-1\). If_  
\[\gamma W(-ea^{-1})=2W(-ea^{-1})-W^{2}(-ea^{-1})+aW(-ea^{-1})e^{W(-ea^{-1})}e^ {-1}. \tag{16}\]2  
_then Equation (3) has two positive real roots._  
**Proof. ** The proof is similar to that derived for Theorem 4. \(\Box\)
## 5 Drift Support Analysis  
Contemporary forms of strengthening for mine drift include shotcrete support, bolt support, shotcrete mesh support, metal support, and combinations of these supporting methods. The decision on which support method to adopt is dependent on the quality of the rock mass in the drift, drift size, site construction conditions, etc.  
In deep hard-rock drifts, rock bolt supports are the most commonly used method to strengthen the surrounding rock of excavation. The bolts used as standard mainly include resin bolts and pipe seam bolts. Bolt-support mechanisms are based on static load, which strengthens the surrounding rock, preventing rock burst and other dynamic damage, often leading to high deformation and a loss of anchoring force. Resin anchor bolts, for example, are widely used because of their high prestress, high anchoring force, good bearing capacity, and improvements to the shear resistance of the surrounding rock. According to the mechanical mechanism of bolt support, different geological conditions and surrounding rock conditions are considered to select the corresponding bolt-support parameters.
# Machine Learning-Assisted Secure Random Communication System
## 5 Reception
### Demo Transmission
Finally, in Figure 7, we vary N while \(\alpha\) and \(\Delta\beta\) remain constant to analyze the potential difference in the retrieval process at the T and LR. Figure 7 (left diagram) shows the random carriers \(S_{\alpha=1.1}\) (\(\beta_{=0.8}\), \(\gamma=1\), \(\mu=0\)) for binary '1' and \(S_{\alpha=1.1}\) (\(\beta_{=-0.8}\), \(\gamma=1\), \(\mu=0\)) for binary '0', with N = 15. However, we selected N = 25, as shown in Figure 7 (right diagram). In conclusion, increasing N yields a better prediction for \(\hat{\beta}\). A better prediction helps achieve better BER rates, which is also confirmed in the next section of our paper. Figure 7: Left diagram: Binary bits and transmitted noise sequence (R) at T (top two), received noise sequence (\(\gamma\)), classified beta \(\beta_{C}\), and corresponding retrieved binary bits at LR (bottom two); \(\alpha=1.1\), \(\Delta\beta=1.6\), \(N=15\). Right diagram: Binary bits and transmitted noise sequence (R) at T (top two), received noise sequence (\(\gamma\)), classified beta \(\beta_{C}\), and corresponding retrieved binary bits at LR (bottom two); \(\alpha=1.1\), \(\Delta\beta=1.6\), \(N=25\). The successful demo transmission reflects the ability of ML-RCSs to securely convey binary information. Moreover, the trend observed in Figures 5-7 was also observed for many bits in the proposed ML-RCS. The next section focuses on this issue.
## 4 LNSSCL Algorithm
### Dynamically Adjusting Particle Control Capacity Increment  
In the Stochastic Competitive Learning algorithm, the control capacity of particles is quantified as the proportion of node visits, thereby the number of times a particle visits a node determines its control capacity over that node. When particle \(k\) visits node \(v_{i}\), the equation for the change in the particle's visit count to that node is given by:  
\[Nu_{i}^{(k)}(t+1)=Nu_{i}^{(k)}(t)+1 \tag{14}\]  
According to Equation (14), it can be observed that the increment of particle control capacity remains constant at 1. This would lead to particles having the same level of competitive increment during the walking process. This uniform competitive increment among different particles could potentially result in similar community sizes controlled by different particles. Consequently, this might lead to instances where representative particles of smaller communities erroneously compete for nodes at the boundaries of larger communities. However, many real-life complex systems, represented as complex networks, often encompass communities of varying sizes. The constant increment in particle control capacity could potentially yield suboptimal results in the final community detection outcome. Figure 2 depicts the possible encroachment of a small community into a large community node when the particle control capacity increment is constant. The size of community 1 in the figure is actually larger than that of community 2. However, because of the constant particle control capacity increment, it makes it possible for the range of communities controlled by each particle to converge to the same size. This then causes nodes that should belong to community 2 to be misclassified to community 2.  
Addressing the aforementioned issues, an improvement is made to the particle control capacity increment in order to enhance the effectiveness of discovering communities of varying sizes within the network. The enhanced particle control capacity increment is dynamically adjusted based on the current control range of the particle, and the specific equation is provided below:  
\[Nu_{i}^{(k)}(t+1)=Nu_{i}^{(k)}(t)+1+\frac{\left|C^{(k)}(t)\right|}{\left|V \right|} \tag{15}\]  
where \(\left|C^{(k)}(t)\right|\) represents the current control range of particle \(k\), which indicates the number of nodes currently under the control of particle \(k\). From Equation (15), it can be observed that the particle's control capacity increment is positively correlated with its current control range. This relationship can unveil community structures of different sizes within the network and prevent particles from erroneously encroaching upon nodes located at the boundaries of communities.
# RFID RSS Fingerprinting System for Wearable Human Activity Recognition
## 2 RSSI Model
### Model Development  
To develop an activity database using the RSSI of passive tags for various positioning, the RSSI model was formulated. Since the patient localisation was RFID-based, with a direct line-of-sight (LOS) between the tags and the RFID reader, a free-space propagation was assumed. Therefore, the RSSI model is expressed in the form:  
\[RSSI(P)=a\log\delta+b \tag{1}\]  
where \(\delta\) is the distance between the tags and the reader, \(a\) and \(b\) are the model parameters and RSSI (P) is the signal strength indicator measured as the tags RSSI.  
Equation (1) considers the effect of multipath as noise, which is influenced by various indoor parameters surrounding the tag and the reader. Moreover, the measurements were of the type \(\left\{\left(RSSI_{i,t},\log\delta_{i,t}\right)\right\}\). Therefore, the objective was to dynamically obtain an RSSI model for each tag, \(i\), attached to the body of the patient at a time, \(t\). Therefore, taking Equation (1) as a linear function of RSSI and \(\log\)\(\delta\), the activity database was constructed by performing simple linear least square regression as:  
\[a=\frac{\sum_{t}RSSI_{i,t}\log\delta_{i,t}-\overline{RSSI_{i}}\sum_{t}\log \delta_{i,t}}{\sum_{t}\left(\log\delta_{i,t}\right)^{2}-\overline{\log}\delta \sum_{t}\log\delta_{i,t}} \tag{2}\]  
\[b=\overline{RSSI_{i}}-\left(a_{i}\overline{\delta_{i}}\right) \tag{3}\]  
where \(\overline{RSSI_{i}}\) and \(\overline{\delta_{i}}\) represents the mean of RSSI and \(\delta_{i,t}\) respectively.  
An RSS fingerprinting technique using Euclidean distance was employed to develop the offline and online activity database for patient tracking. The Euclidean distance was employed since the  
Figure 1: (**a**) Front and (**b**) back view of tag placement on target in a standing position.  
Figure 2: Six basic activities of daily living patient positions to reflect daily activity.  
obtained data varies on more than one feature, i.e., the RSS and location. The error in the model was computed by comparing the position of the tags at each location on the patient using their RSS and the location predicted by the RSSI model. The error in the RSSI model is expressed in the form:  
\[RSSI_{(error)}=\frac{1}{N}\sum_{t=1}^{M}e_{i,t}^{2} \tag{4}\]  
where  
\[e_{i,t}^{2}=\frac{(RSSI_{i,t}-b_{i})}{a_{i}}-\|X_{t}-Y_{i}\| \tag{5}\]  
From Equation (5), \(X_{t}\) represents the patient location at time t and \(Y_{t}\) represents the distinct location of each RFID tag.
###### Abstract  
The limitation of enzymatic saccharification of lignocellulosiculose is attributed to the nonproductive adsorption between lignin and cellulase. This study aims to investigate the effects of ozone pretreatment on the physical structure and chemical properties of milled wood lignin (MWL). The objective is to reduce the non-productive adsorption of cellulase on lignin. The structure-activity relationship between the physical structure of MWL and the occurrence of nonproductive adsorption was analysed using two-dimensional heteronuclear single quantum coherence-nuclear magnetic resonance (HSQC-NMR) and phosphorus-31 nuclear magnetic resonance spectrum (\({}^{31}\)P-NMR), etc. The results indicate that ozone pretreatment resulted in a decrease in the phenolic hydroxyl content and S/G ratio, an increase in the carboxyl content, and a negative zeta potential of MWL. The maximum adsorption capacity decreased from 25.77 mg/g to 10.09 mg/g, the Langmuir constant decreased from 13.86 mL/mg to 10.11 mL/mg, and the binding strength decreased from 357.14 mL/g to 102.04 mL/g, as determined by Langmuir isothermal adsorption. This suggests that ozone pretreatment resulted in a reduction in the hydrophobicity of lignin and a weakening of the electrostatic attraction between lignin and cellulase, thereby effectively reducing the non-productive adsorption of cellulase on lignin. This study provides an environmentally friendly pretreatment technique and comprehensively analyses the structural changes of ozone-treated MWL. These findings contribute to a deeper understanding of the interaction between lignin and cellulase.  
Keywords:non-productive adsorption; ozone pretreatment; milled wood lignin +
Footnote †: journal: Applied Sciences
## 4 Parameter Setting and Analysis  
To evaluate the accuracy of the proposed method, the obtained extraction results were compared with manually extracted road surface results, and the following three metrics (Equations (7)-(9)) were introduced to assess the quality of the extraction results:  
\[\text{Recall}= \frac{TP}{TP+FN}\times 100\% \tag{7}\] \[\text{Precision}= \frac{TP}{TP+FP}\times 100\%\] (8) \[F1= 2\times\frac{\text{Recall}\times\text{Precision}}{\text{Recall}+ \text{Precision}}\times 100\% \tag{9}\]  
where \(TP\) is the number of correctly detected road surface points; \(FN\) is the number of road surface points that were not detected; and \(FP\) represents the number of incorrectly detected road surface points.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Indicator Parameters** & **Performance Specifications** \\ \hline Gyro input range (max) & \(\pm\)490\({}^{\circ}\)/s \\ \hline Gyro bias stability (25 \({}^{\circ}\)C) & \(\leq\)0.05\({}^{\circ}\)/h(typical) \\ \hline Gyro scale factor (maximum input range, 25 \({}^{\circ}\)C) & \(\leq\)50 ppm \\ \hline Gyro angle random walk (25 \({}^{\circ}\)C) & 0.012\({}^{\circ}\)/\(\surd\)h (\(\leq\)0.7\({}^{\circ}\)/h/\(\surd\)h) \\ \hline Accelerometer range (max) & \(\pm\)10 g \\ \hline Accelerometer bias stability & \(<\)0.05 mg \\ (constant temperature) & \(<\)0.12 mg/\(\surd\)h(0.23 ft/s/h) \\ \hline Accelerometer velocity walks randomly & \(\leq\)0.12 mg/\(\surd\)h(0.23 ft/s/h) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter information of IMU-KVH1750 IMU inertial navigation technology.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Configuration** \\ \hline Processor & AMD Ryzen 5 5600H (AMD, Santa Clara, CA, USA) \\ \hline Graphics card & NVIDIA GeForce GTX 1650 (NVIDIA, Santa Clara, CA, USA) \\ \hline Run memory & 8 GB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental operating environment.
# Releasing the Full Potential of Cannabis through Biotechnology
## 4 Overcoming Limitations in Cannabis Gene Editing
### Gene Editing Strategies  
Recent advances in plant gene-editing tools extended the target range and increased the tissue specificity of delivering CRISPR reagents. The CAS12 and CAS\(\Phi\) nucleases were reported to install all possible mutations in target sites precisely [60]. However, these tool kits were applied to plant species with well-developed transformation systems, such as Arabidopsis, tobacco, wheat, and rice. By far, most reports of genetic engineering in Cannabis concerned transient expression, which means foreign DNA was delivered into the plant cells successfully. However, these cells failed to develop into embryogenic callus, which could be one of the main barriers to increase the regeneration rate in Cannabis. An alternative approach is to deliver the developmental regulators into plant cells and induce meristem formation. These regulating genes showed increased transformation frequencies in monocots and dicots, most likely by promoting the transition between stem cells and embryogenic calli [58].  
An approach by coexpressing developmental regulators and genome editing components might be useful for genome manipulation and regeneration, which has been reported to be effective in several monocot and dicot species such as wheat, rice, and maize [53]. The transformation of BABY BOOM (BBM) and WUSCHEL (WUS) into immature maize embryos promoted the germination of seedlings without a callus phase. However, expressions of BBM and WUS demonstrated negative pleiotropic effects on plant growth while improving plant regeneration efficiencies. Therefore, these regulators had to be excised from generation lines [47]. To overcome these shortcomings, additional developmental genes were proposed to improve the regeneration efficiency. In _N. benthamiana_, overexpression of isopentenyl transferase (IPT) and shoot meristemless (STM) induced the regeneration of shoots [57]. The transcription factors grf-interacting factor (GIF) and growth-regulating factor (GRF) were demonstrated to form a functional transcriptional complex, which performed well under various transformation protocols by regulating the transition between stem cells, and to boost genetic transformation in various crop species [58; 61].  
To increase regeneration efficiency, the Cannabis genes homologous to _WUS2_, _STM_, _IPT_, _GRF4_, and _GIF1_, were co-delivered with gene editing reagents into hypocotyls [44]. The -regeneration efficiency of the _CsGRF3_-_CsGIF1_ chimera performed the best among all the regulators, which constitutes the first report of successful and stable genetic transformation with _CsPDS1_ editing. Although impressive work was presented, the gains were still modest. The expression of gene regulators only increased regeneration 1.7-fold, which was still below 0.2%. Further research is necessary to increase the regeneration efficiency by estimating new developmental regulators robust to induce Cannabis roots and shoots from callus.
## 1 Introduction
Subsequently, it can be concluded that AVQI, with CPPS as its main contributor, is a very promising tool to measure overall voice quality and record voice therapy outcome in practice. A current limitation of AVQI and CPPS is, however, the lack of normative data. Diagnostic thresholds to differentiate between normophonic and dysphonic voices have been proposed for mostly treatment-seeking subjects using receiver operating characteristics (ROC) curve analysis (Table 1). However, studies investigating the performance of AVQI in strictly normophonic subjects are scarce. Such normative data are valuable to contribute to the interpretation of dysphonic voice samples and thus increase the clinical utility of AVQI and CPPS. Therefore, the primary purpose of the present study was to acquire a normative data set for AVQI and its main contributor, CPPS, in Praat and to evaluate the influence of gender. The second aim was to evaluate the ability of AVQI and CPPS to distinguish the true normophonic cohort from the subclinical slight dysphonia cohort. Finally, their ability to differentiate between the several sub-levels of subclinical dysphonia was assessed.