esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Blind Source Separation Techniques
### Complexity Pursuit Algorithm (CP)
#### 2.3.1 Complexity and Predictability  
The complexity pursuit method seeks a weight vector, which provides a set of orthogonal projections of mixed signals to minimize the complexity of each extracted signal. A simple complexity measure can be formulated as predictability. If each value of a signal is easily predicted by the previous signal value, the signal has low complexity. On the contrary, if the successive value of a signal is independent and cannot be predicted, such a signal has high complexity.  
The measure of temporal predictability \(F\left(\mathbf{w}_{i},\ \mathbf{x}\right)\) is widely used to estimate the complexity of signals, specifically the signal \(\mathbf{y}_{i}\) that is extracted using a given weight vector \(\mathbf{w}_{i}\), where \(\mathbf{y}_{i}\)= \(\mathbf{w}_{i}^{\mathrm{T}}\mathbf{x}\). In order to identify source signals that are more easily predictable, a comparison is made between the predictability of a specific source signal, \(\mathbf{q}_{i}\), and that of a mixture of signals. If a source signal is comparatively easy to predict, then the optimal value of the weight vector, \(\mathbf{w}_{i}\), that maximizes the predictability of the extracted signal, \(\mathbf{y}_{i}\), should correspond to the generation of that specific source signal (\(\mathbf{y}_{i}=\mathbf{q}_{i}\)). It is noteworthy that maximum predictability of the signal corresponds to minimum complexity and vice versa. Through the measure of temporal predictability, this study aims to identify the weight vectors which generate signals with relatively lower levels of complexity and to explore their potential applications in signal processing.  
Figure 1: The relationship between system responses and modal responses (in free vibration).
## 5 Discussion
### Controlling Factors of RCPs
#### 5.3.1 Tectonic Effect  
Numbers of studies have shown that the influence of tectonics on the formation and evolution of RCPs is mainly reflected in the palaeotopography before platform development and the activities during the platform growth period [2; 27; 30]. Early intense tectonic movement (such as subduction, collision, compression, etc.) formed some structural high terrain, including fault uplift, palaeuplift and buried palaeohill, which were conducive to the initial formation of RCPs. The late stable tectonic environment was very important for the stability of RCPs [5; 31]. The Liyue Movement, which occurred in the Early-Late Cretaceous period, was a relatively important tectonic movement in the early Wan'an Basin [32; 33; 34; 35]. It caused the pre-Cenozoic stratum to suffer strong denudation and folding, and a large number of intermediate-acid igneous rocks intrusive and eruption, forming the basement of Wan'an Basin. Subsequently, under the influence of the Wan'an strike-slipfault in the eastern part of the basin, the most important Xiwei Movement occurred in the Middle and Late Eocene [36]. The fault was active from the late Eocene to the end of Oligocene, and the tectonic blocks formed by the fault created a higher topography for the initial development of RCPs in the Early Miocene. In addition, the uplift-sag pattern controlled by faults limited the spatial distribution of late RCPs in Figures 2 and 10.  
In addition, these high-angle normal faults also determine that the slope at the edge of the carbonate platform is dominated by steep slopes in Figures 6 and 7. Since the Early Miocene, the faulting activity in the basin has basically stopped, and the subsequent relatively slow tectonic subsidence provided stable favorable conditions for the mass development of RCPs. In the early Middle Miocene, although the expansion of the South China Sea had ceased, the Nansha block continued to move southeastward and collided with the Borneo block, resulting in the compression of the southern part of the South China Sea and forming an important Wan'an Movement in the Nansha Sea area [20; 21]. The compressed movement reached its maximum at the end of the Middle Miocene and caused the overall uplift of the sedimentary strata in the Nansha Sea area and suffered large-scale denudation.  
For the Wan'an Basin, the early developed carbonate rocks were exposed and denuded, forming an important T\({}_{3}\) unconformity in Figure 3. The denuded carbonate clasts were mixed with mudstone and redeposited, forming a large mixed sedimentary zone throughout the late Middle Miocene in Figure 10C. Since the Late Miocene, the evolution of the basin has entered the phase of rapid subsidence, and the RCPs have developed again. With rapid subsidence in Figure 11, RCPs rapidly migrated to higher ground. Vertical coral reefs are identified only from the high points of the early platforms. Since the Pliocene, with the acceleration of tectonic subsidence, the whole region is in a semi-bathyal environment, which is not conducive to the growth of RCPs.
# Neurorotrophic Factors in Glaucoma and Innovative Delivery Systems
## 2 Neuroprotection: Insights on Biochemical Pathways and Treatment Opportunities
### Neurotrophic Factors Rationale for Use in Glaucoma Treatment and Preliminary Results  
NTFs are secreted peptides regulating neuronal survival and function, which include the family of NTs, the glial cell-derived neurotrophic factor (GDNF) family, and the neuroopoietic cytokine family.  
NTs belong to a small family of pleiotropic molecules that are indispensable for regulating neuronal development, survival, and differentiation and promoting synaptic plasticity and axonal regeneration [12]. Currently, four NTs have been isolated: nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3), and neurotrophin-4/5 (NT-4/5). NTs and their receptors modulate multiple signaling pathways through the activation of two types of transmembrane glycoproteins: the tropomyosin receptor kinases (TrkA, TrkB, and TrkC) and the low-affinity neurotrophin receptor p75\({}^{\text{NTR}}\). Specifically, NGF and BDNF bind with high affinity to TrkA and TrkB, respectively. NT-3 preferentially binds to TrkC, but it may bind with low affinity to both TrkA and TrkB depending on cellular context. NT-4/5 predominantly acts through TrkB. The Trk activation induces signaling cascades including the Ras/ERK (extracellular signal-regulated kinase) protein kinase pathway with stimulation of mitogen-activated protein (MAP) kinases, the phosphatidylinositol-3-kinase (PI-3 kinase)/Akt kinase pathway, and phospholipase C (PLC)-\(\gamma\)1. On the other hand, immature precursor forms of NTs (proNTs) are able to bind and activate p75\({}^{\text{NTR}}\) with different functional outcomes in terms of apoptosis or cell survival in dependence on the concurrent expression of Trk receptors. Therefore, a delicate balance between the relative percentage of pro- and mature NTs and/or the interaction between Trk and p75\({}^{\text{NTR}}\) receptor availability determines cellular homeostasis in the nervous system [13; 14].  
The role of NTs in the maintenance and survival of retinal cells during several degenerative diseases including glaucoma has been clearly demonstrated [23; 24; 25; 26]. Specifically, various evidence showed that the blockage of axoplasmic flow at the lamina cribrosa in glaucoma would markedly compromise axonal long-range retrograde transportation via endosome neurotrophic signaling from the lateral geniculate nucleus in the CNS to ganglion cell bodies [10; 11]. The decrease in neuronal trophic support, in turn, triggers programmed cell death in RGCs, as shown after experimental axotomy of the optic nerve in animals [27]. Furthermore, as a result of degenerative changes in the RGCs and optic nerve head, local production of NTs in the retina is markedly reduced, contributing to disease progression (Figure 1) [28; 29; 30].  
In recent decades, scientific efforts have been strongly directed at investigating the potential use of neurotrophic agents in promoting RGCs survival in glaucoma.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 3 Surface Preparation and Its Impact on Debonding
### Behaviour of IRP System in 12.7 Mm (0.5 inch) Discontinuous Host Pipe  
#### 3.1.1 Deflection Performance  
The findings depicted in Figure 19 establish a direct relationship between adhesive strength and the load-bearing capacity of a 12.7 mm (0.5 inch) discontinuous IRP system, highlighting the influence of adhesive strength, which is attributable to surface preparation processes. A higher adhesive strength leads to a better capacity to bear loads because of the higher composite action between the host pipe and the IRP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Load kN (kip)**} & \multirow{2}{*}{**Locations inch**} & \multicolumn{2}{c}{**Deflection mm (inch)**} & \multirow{2}{*}{**Difference(\%)**} \\ \cline{3-4}  & & **FEA (Debonding)** & & \\ \hline \multirow{2}{*}{21.4 (4.8)} & 1.7065 & 0.897 (0.035) & 0.975 (0.038) & 8 \\  & 23.3005 & 0.526 (0.021) & 0.617 (0.024) & 15 \\ \multirow{2}{*}{69.1 (15.5)} & 1.7065 & 4.992 (0.197) & 4.925 (0.194) & 1 \\  & 23.3005 & 2.622 (0.103) & 2.855 (0.112) & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of FEA incorporating the debonding mechanism and experimental results for the 12.7 mm (0.5 inch) discontinuous IRP system.  
Figure 18: Validation of the load-displacement relationship in the 12.7 mm (0.5 inch) discontinuous IRP system incorporating the debonding mechanism.  
Table 7 provides a performance comparison of various adhesive strength levels for a 12.7 mm (0.5 inch) discontinuous IRP system subjected to a 71.2 kN (16 kip) force to explore this relationship further. At the maximum loading of 71.2 kN (16 kip), the unclean surface with a 5 MPa (0.7 ksi) adhesive strength exhibits a deflection of over 310%, while the ablation preparation with a 40 MPa (5.8 ksi) adhesive strength deflects less than 23% compared with the benchmark adhesive strength of 20 MPa (2.9 ksi). The higher the adhesive strength, the lower the likelihood of debonding between the IRP and the host pipe. Consequently, the IRP system could experience a higher level of stress under lateral deformation compared with the system using a lower shear strength adhesive.
# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 3 Results and Discussion
### Exploratory Analysis of the Data  
The exploration data used in this study come from unpublished exploration reports of gold prospecting by Centamin plc in the Quartz Ridge region of the eastern desert. It includes details on the lithology and geographic location of boreholes and Au assay values. This region is a valuable and virgin resource for exploration because it has never been mined. The information is based on 523 exploratory boreholes, with an average drilling interval of 20 m. In total, 27,505 samples from the boreholes were obtained as a result, which were not arranged in a regular grid. The samples, which come from various rock types, were taken at various intervals. Reverse circulation (RC) and diamond drilling (DD) drill holes, which are used in mineral prospecting for gathering samples from deep underground locations, were used to collect data over a number of years.  
Figure 2: Depicts a geological map of the area of the Quartz Ridge Prospect. The territory that was investigated is denoted by the dark rectangle in the image at the middle.  
Overall, this exploration data offers essential knowledge about the regional geology and mineral potential of Quartz Ridge. Machine learning and statistical methods can be used to analyze these data to reveal information about the distribution of mineral grades and the likelihood of economic mineralization in the region.
## 3 Technical Background
### AxCache [3:0] and AxUser [4:0] Signals  
The _AxCache[3:0]_ and _AxUser[4:0]_ signals (for each signal \(x=R\) for read or \(x=W\) for write) of the ACP control the coherency access of a request. The _AxUser[4:0]_ signal is composed of the following bits, Shared bit _AxUser[0]_ and the _AxUser[4:1]_ bits, which control the write strategy (write-back, write-through, etc.) adopted by the request. The  
Figure 5: Memory hierarchy in the Xilinx Zynq-70110 SoC-FPGA.  
_AxCache[3:0]_ signal also controls the write strategy adopted by the request. It is composed of the following bits: in-buffer bit _AxCache[0]_, cacheable bit _AxCache[1]_, read allocate bit _AxCache[2]_ and write allocate bit _AxCache[3]_.  
According to Xilinx recommendations [13], _AxUser[0]_ and _AxCache[1]_ bits must be set for a coherency request. Other import signals are the protection signals _ARPort[2:0]_ and _AWPort [2:0]_. These signals are important for the security of the system and are detailed in the following section.
# A Novel Plug-in Board for Remote Insect Monitoring
## 2 Materials and Methods
### Sensing Device Timing and Data Capture Hardware and Software  
Before the device's characteristics are investigated, the principles of the moth-counting software are defined. According to these, the sensing device turns on every day during the observation period at 10:00 o'clock for sufficient light and a lower daily temperature. At the beginning of the process, the software initializes the system components (used modules) and synchronizes the RTC and system time from the GNSS's NMEA messages. Timestamps are generated from the RTC time. Setting the time accurately is important for tracking the date of capture. In the next step, the Raspberry Pi Zero takes an image of insects on sticky paper and applies the insect-counting algorithm. Depending on the operating mode, the number of detected insects and the timestamp will be transmitted to the server side, and the captured image may also be sent through the LTE network. At the end of the process, the software sets up the next \"wake up\" time and initiates the shutdown of the Raspberry. A simplified flow chart of the controller software can be seen in Figure 9.  
Figure 7: Schematic of the signal delay circuit.  
Figure 8: Sensing device case with transparent cover. The battery is placed behind the printed circuit board (not visible).  
Figure 9: Flowchart of the software.
# Vehicle Rollover Warning and Control Based on Attitude Detection and Fuzzy PID
## 3 Selection of the Control Strategy  
The flow of early warning-based differential braking anti-rollover control is shown in Figure 4. First, the actual parameters of the car model are obtained and the lateral load transfer rate LTR value is calculated in real time, and the anti-rollover control system works when \(\left.\right|LTR\) \(\left.\right|\geq\) 0.8. The control system takes the lateral load transfer rate (\(LTR\) value) as the control variable, and the PID or fuzzy-PID controller decides the size of the target wheel cylinder pressure (braking torque) according to the deviation value between the actual \(LTR\) value and the ideal \(LTR\) value, when the deviation is negative, the output wheel cylinder pressure is positive; at this time, the left front brake works, while the right front brake does not work; when the deviation is positive, the output wheel cylinder pressure is negative, at this time, the left front wheel brake does not work, while the right front wheel brakeworks, after which the braking force is distributed and the braking torque of the target wheel cylinder is input into the car model to achieve closed-loop control.
## 8 Highly Branched Isoprenoids in _Haslea_  
Terpenoids, also known as isoprenoids, are a large and remarkably diverse group of secondary metabolites. With more than 60,000 out of the approx. 300,000 natural products reported to date in the Dictionary of Natural Products, terpenoids represent one of the most often encountered chemical classes [140]. Terpenoids have been exploited throughout human history and are still extensively used as constituents in fragrances, in the food industry as supplements and pigments, and in the pharmaceutical sector [141].  
Diatoms produce a variety of terpenoids (Figure 9) including sterols and carotenoids, the latter including fucoxanthin, diadinoxanthin, and diatoxanthin [142; 143]. In addition to these well-known terpenoids, a few diatom species belonging to the genera _Rhizosolenia_, _Pleurosigma_, _Navicula_, _Berkeley_ and _Haslea_ synthesize highly branched isoprenoids (HBIs) [75; 144; 145]. HBIs are branched hydrocarbons with 20, 25 or 30 carbon atoms. They can be saturated or unsaturated with one to six double bonds [146]. Even though most HBIs are acyclic, there are a few reports of cyclic derivatives [147; 148]. Other functionalized structures of HBIs, such as epoxides and alcohols, have also been reported [149]. HBIs have been found in marine sediments from various locations around the globe. They were first found in marine sediments in Peru, Antarctica, the Gulf of Suez, the North Sea, the north Atlantic Ocean, later in Australia, and in crude oil from Utah, USA; more recently, they have been found in sediments of the western and eastern Mediterranean basins [150; 151]. The biosynthesis of HBIs has been shown to take place in different species of the genus _Haslea_, including _H. ostrearia_, _H. criciegera_, _H. pseudostreamia_ and _H. saltstonica_[152]. The biogenic source of HBIs was first reported in 1994 by [PERSON] and co-workers, who identified seven C\({}_{25}\) and five C\({}_{30}\) HBIs in laboratory cultures of _H. ostrearia_ and _Rhizosolenia sefiegera_, respectively [144]. A few years later, [PERSON] and co-workers identified three additional C\({}_{25}\) HBIs with three, four and five double bonds from _H. ostrearia_[153], and new C\({}_{25}\) and _n_-C\({}_{27}\) HBIs alkenes in _R. sefiegera_[154].
## 5 Discussion and Open Research Issues
### QoS in I2O WBANs
#### 5.1.3 QoS Requirements for I2O WBANs
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **QoS Issues** & **QoS Metric** & **QoS Requirements** \\ \hline \multirow{4}{*}{Application} & \(\bullet\) Total system lifetime & \(\bullet\) Communication distance & \(\bullet\) Maximum system lifetime \\  & \(\bullet\) Data fusion ability & \(\bullet\) Resource allocation & \(\bullet\) Transmission reliability \\  & \(\bullet\) Error tolerant & \(\bullet\) Available working time & \(\bullet\) Transmission reliability \\  & \(\bullet\) Error tolerant & \(\bullet\) Fault awareness & \(\bullet\) Security \\ \hline \multirow{4}{*}{Transport} & \(\bullet\) Link reliability & \(\bullet\) Delay & \(\bullet\) Minimum transmission latency \\  & \(\bullet\) Latency & \(\bullet\) Jitter (delay variance) & \(\bullet\) Minimum energy consumption \\  & \(\bullet\) Packet loss & \(\bullet\) Buffer & \(\bullet\) Decrease packet loss \\  & \(\bullet\) Transmission corruption & \(\bullet\) Error packet ratio & \(\bullet\) Suitable data rates awareness \\  & \(\bullet\) Energy efficient strategy & \(\bullet\) Fault resistance \\ \hline \multirow{4}{*}{Network} & \(\bullet\) Path latency & \(\bullet\) Channel latency & \(\bullet\) Minimise latency \\  & \(\bullet\) Routing & \(\bullet\) Traffic strategy & \(\bullet\) Mobility support \\  & \(\bullet\) Mobility capability & \(\bullet\) System robustness & \(\bullet\) Routing fault tolerance \\  & \(\bullet\) Faults tolerant & \(\bullet\) Energy control (lower overhead) \\ \hline \multirow{4}{*}{MAC} & \(\bullet\) Throughput & \(\bullet\) Data rate & \\  & \(\bullet\) Delay & \(\bullet\) Throughput & \(\bullet\) Improve reliability \\  & \(\bullet\) Packets delivery & \(\bullet\) Collision probability & \(\bullet\) Error control (channel coding) \\  & \(\bullet\) Collision management & \(\bullet\) Interference immunity & \(\bullet\) Minimise data collision \\  & \(\bullet\) Faults tolerant & \(\bullet\) Bandwidth utilisation & \(\bullet\) Decrease interference \\  & \(\bullet\) Power management & \(\bullet\) link fault tolerant & \\ \hline \multirow{4}{*}{Physical} & \(\bullet\) Topology & \(\bullet\) Capacity & \(\bullet\) Efficient topology \\  & \(\bullet\) Physical standards (such as interface) & \(\bullet\) Data rate & \(\bullet\) Data rate \\ \cline{1-1}  & \(\bullet\) Bandwidth management & \(\bullet\) SNR & \(\bullet\) Priority information guarantee \\ \cline{1-1}  & \(\bullet\) Tx(s), Rx(s) faults tolerant & \(\bullet\) Power attenuation & \(\bullet\) Channel allocation strategy \\ \hline \hline \end{tabular}
\end{table} 
Table 12. Important QoS Parameters of WBAN Service Systems [4,21].
## 2 Results  
### In Vitro Assay of VOC Production  
In Figure 1, the in vitro effects of VOCs from bioantagonists _G. cerinus_ and _H. osmophila_ on the pathogens _Botrytis cinerea_, _Aspergillus tubingensis_, _Penicillium expansum_ and _Rhizopus stolonifer_, as determined by double plate test (Scheme 1a), are shown. The VOCs emitted by the bioantagonists were shown to effectively inhibit mycelial growth of all pathogens evaluated. Indeed, inhibitory effects were greater in the PUCV-VBL biocontrol than those of each BCAs separately, demonstrating a synergistic effect among all the VOCs.  
Figure 1: Effect of VOCs of bioantagonists _G. cerinus_, _H. osmophila_ and PUCV-VBL biological consortium on mycelial growth of pathogen _B. cinerea_, _A. tubingensis_, _P. expansum_ and _R. stolonifer_ under in vitro conditions, established by means of a double plate assay and through fungal colony diameter graph (mm) measurement, conducted using image analysis with the Image \(\mathrm{J}^{\mathrm{\SIUnitSymbolOhm}}\) progam. 1: Control without biocontrol, 2: VOCs of _G. cerinus_, 3: VOCs of _H. osmophila_, 4: VOCs of PUCV-VBL biological consortium. [PERSON]’s multiple comparisons test \({}^{*}\)_p_ < 0.05; \({}^{**}\)_p_ < 0.01; \({}^{***}\)_p_ < 0.001.  
_Botrytis cinerea_ was most affected, at 86% inhibition mycelial growth (Table 1). _Aspergillus tubingensis_ was significantly inhibited as well, achieving 52%. This is likely due to _H. osmophila_ VOCs preferentially inhibiting _A. tubingensis_ sporulation instead of mycelial growth. _Penicillium expansum_ also showed significant inhibition (68%), and similar to _A. tubingensis_, _H. osmophila_ VOCs inhibited sporulation of _P. expansum_. While mycelia of _Rhizopus stolonifer_ was the least inhibited (35%), it still had significant differences concerning control.  
\begin{table}
\begin{tabular}{c c c c} \hline  & _G. cerinus_ & _H. osmophila_ & **PUCV-VBL** \\ \hline _B. cinerea_ & 32 \(\pm\) 15 1  & 39 \(\pm\) 26 1  & 86 \(\pm\) 4 2  \\ _A. tubingensis_ & 36 \(\pm\) 2 1  & 53 \(\pm\) 16 1  & 52 \(\pm\) 1 1  \\ _P. expansum_ & 50 \(\pm\) 13 1  & 68 \(\pm\) 1  & 55 \(\pm\) 2 1  \\ _R. stolonifer_ & 18 \(\pm\) 3  & 29 \(\pm\) 8  & 35 \(\pm\) 7  \\ \hline \end{tabular}
\end{table}
Table 1: Mycelial growth inhibition (percentage) determined by in vitro assay by VOCs emitted by _G. cerinus_, _H. osmophila_ and PUCV–VBL biological consortium. Means in rows followed by the same letters are not significantly different according to [PERSON]’s test (\(p\) = 0.05).