esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Grazer Exclusion on Carbon Cycling in Created Freshwater Wetlands
## 2 Methods
### Gas Fluxes  
We measured CO\({}_{2}\) fluxes using the static chamber method [53]. Measurements were made during the peak growing season (June-July) and at the beginning of plant senescence (late August-September) in both 2017 and 2018. We measured fluxes in two of the four experimental blocks (eight pairs of caged and uncaged plots). The chamber was constructed from a PVC frame that covered a 1 m\({}^{2}\) area and was adjustable in height (1 to 1.7 m) depending on the height of the plant canopy. The top was fitted with a clear polycarbonate panel that covered roughly one-third of the area, and the remainder of the top and the sides were covered with clear polyethylene greenhouse film. The chamber fit over the permanently installed PVC corner posts used to mark the plot and during measurements the plastic film was rolled down into the water and secured at the sediment surface with a chain to prevent lateral exchange of water or gases. The polycarbonate top and clear plastic film used to construct the chamber allowed approximately 67% of photosynthetically active radiation to pass through. For dark measurements, we covered the chamber with an opaque tarp.  
The polycarbonate top was fitted with a small radiative panel that attached to two gas tight bulkheads so that chilled water could be circulated continuously through the chamber while it was sealed. We continuously monitored temperature throughout the sampling periods both inside and outside the chamber, and maintained internal temperatures within 5 \({}^{\circ}\)C of external temperature [53] by adjusting the chilled water circulation. Two additional bulkheads connected the inflow and outflow of an infrared gas analyzer (LI-820) and air was continuosly circulated from the chamber, through the analyzer and back to the chamber using a small air pump. We measured changes in headspace CO\({}_{2}\) in both the light and the dark over approximately a 10 min period.  
Fluxes were calculated from the slope of the first 5 min period after chamber closure and calculated based on the headspace of the chamber above the water. We estimated gross primary productivity (GPP) by subtracting the dark measurement (ecosystem respiration, ER) from the light measurement (net ecosystem exchange, NEE) for each plot. We estimated summer C budget values for GPP and ER from chamber fluxes, assuming a 24 h period for respiration and a 12 h period for photosynthesis. Because submerged plants may directly take up dissolved inorganic carbon (DIC) released from heterotrophs in the sediments and water column, which was not reflected in our measured changes of CO\({}_{2}\) in the chamber headspace, we have likely underestimated overall ER and GPP.
# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 3 Materials and Methods
### YOLO Family
#### 3.5.1 YOLOv8  
YOLOv8 [15] was developed with the aim of reducing the number of parameters while maintaining or even improving detection accuracy compared to previous versions. Its main improvements include:  
* The YOLOv8 Backbone uses CSPDarknet53+, an improved version of CSPDarknet, which incorporates additional convolutional layers and optimized CSP blocks. These improvements provide more efficient feature extraction and increase detection accuracy.
* YOLOv8 neck combines FPN (Feature Pyramid Network) with PANet (Path Aggregation Network), introducing SE (Squeeze-and-Excitation) blocks to improve the model's focus on essential features. The SE blocks apply a channel-attention mechanism, dynamically recalibrating the feature maps to highlight the most relevant information.
* The YOLOv8 detection head benefits from the refined feature maps generated by the enhanced backbone and neck, resulting in superior accuracy, especially for the detection of small and partially occluded objects.  
YOLOv8 offers five variants, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. Each variant adjusts the depth and width of the net to balance speed and precision, allowing for greater flexibility of use according to the needs of the target.  
Figure 5: Operation of head and NMS in YOLO.
# Feasibility of Optical Flow Field Measurements of the Coolant in a Grinding Machine
## 3 Experimental Setup and Signal Processing  
### Grinding Machine with Optical Access  
The measurements are conducted in a surface grinding machine of the type Micro-Cut A8 CNC manufactured by the company ELB. The MWF is supplied in the \(x\)-direction of the used coordinate system through a nozzle with a specific profile according to [PERSON] et al. [28; 29] (see Figure 3a) onto the grinding wheel surface. The distance between the nozzle orifice and the point of impact on the grinding wheel is 120 mm. The used metal working fluid is _CUT MAX 902-10_ with a density of 0.82 kg/m\({}^{3}\) and dynamic viscosity of 11 mm\({}^{2}\)/s at a temperature of 40 \({}^{\circ}\)C. The MWF has not been diluted and is a visually transparent yellow-golden liquid with a refractive index number of 1.44575 \(\pm\) 0.00018. The used volume flow is measured with an oval wheel meter with an accuracy of \(\pm\)0.5 L/min.  
For the MWF supply, two different orifice sizes were used with a common width of 20 mm and the used coordinate system has its origin at the centre of the nozzle orifice. For investigations focusing on the free jet, a nozzle with an orifice height \(h=2.3\) mm is used. For MWF flow measurements with the grinding wheel, the nozzle height \(h=0.9\) mm is applied to achieve higher flow velocities (relevant for industrial applications) for the same volume flows. The used experimental parameters for the measurements focussing on the supply flow can be seen in Table 1 and for the flow-tool interaction in Table 2. The used grinding wheel has a radius of 200 mm and rotates with a circumferential velocity of 25 m/s. To provide access for the optical components into the grinding chamber, the protective acrylic glass window that closes the grinding chamber is removed and replaced with a transparent foil. Viewing windows are cut into the foil to achieve an undisturbed observation of the volume flows. Work safety is ensured by an external protective housing, which additionally grants laser safety.  
For investigating solely the MWF supply flow at first, an interaction with the grinding wheel is prevented by moving it out of plane in \(z\)-direction. In order to determine the top view of the MWF free jet flow, the nozzle is rotated by 90\({}^{\circ}\) instead of reassembling the optical measurement setup. Finally, the nozzle is placed in its original position and the MWF flow interacting with the rotating wheel is studied.
## 4 DC-DC Converter Topologies for LEV Charging  
Design and analysis of power electronic converters is an integral aspect of LEV chargers. In this respect, researchers have proposed novel DC-DC converter topologies with optimized active and passive component count [17; 18; 19; 20]. Furthermore, researchers have also leveraged soft-switching (zero voltage and/or current switching) of power electronic switches in recent works to reduce switching losses [21; 22; 23; 24]. These converters may be used as part of two-stage or single-stage on-board/off-board chargers.  
Irrespective of whether they are on-board, off-board or wireless, LEV chargers have evolved from fundamental DC-DC converter topologies. These include buck, boost, buck-boost, Single-ended primary inductor (SEPIC), Cuk and Zeta converters [25]. Super-lift Luo converters are also an option for when a high-current output is required [26; 27].  
Isolated DC-DC converters are preferred for chargers with higher power ratings. They provide galvanic isolation between the input and output stage by using a high frequency transformer. Isolated DC-DC converters can include single switch topologies (Flyback and forward converters) and multiple switch topologies (Push-pull, half-bridge, full, bridge, multilevel) [25]. Single switch topologies have the same characteristics as buck-boost converter [28]. In multiple switch topologies, bidirectional excitation of transformer is achieved by alternate operation of switches [29]. However, they have limitations such as transformer core saturation [30], and current stresses on primary-side switches due to discontinuous mode operation. Furthermore, half-bridge converters may be forced to  
Figure 4: (a) Methodology workflow for this review study (b) Hierarchy for segregation of collected articles.  
operate at low switching frequencies at low input voltage conditions, leading to increased current ripples [31]. Multilevel converters are also a multi-switch option for high-gain applications. They also ensure reduced electromagnetic interference (EMI) due to reduced voltage jump [32]. Recently, researchers have proposed modified topologies such as bridgeless and interleaved topologies to achieve one or more of the following: improved charging efficiency, reduced voltage/current ripples, reduced size of passive components, and reduced component count. Furthermore, efficiency of charging may be enhanced by the introduction of soft-switching mechanisms using resonant converter topologies. Such topologies employ resonant capacitors in addition to the transformer leakage inductance to create resonance condition for switching. This can help in the reduction in switching losses at high frequency operation.  
However, the most important evaluation for LEV charger design is its efficiency and charging time. Most LEVs take approximately 3-4 h to achieve full charge [4]. Since LEVs have immense potential in public transportation, improving charging time is of immense importance.
## 3 Chassis Strains for 3-HP Production
### Escherichia coli  
_E. coli_ is a model organism with the advantages of a clear genetic background, easy modification, broad substrate spectrum, simple growth conditions, and mature means of genetic manipulation, which have made it the most widely used host organism in biotechnology. However, _E. coli_ is less tolerant to 3-HP than other microorganism, growing normally only in neutral basic salt media with less than 18 g/L 3-HP, and failing to grow at all when the 3-HP concentration is higher than 45 g/L [33]. A highly 3-HP-tolerant strain of _E. coli_ was developed through adaptive laboratory evolution (ALE), and after the reversion of mutation in glycerol kinase GlpK, the strain produced a significantly higher 3-HP titer (63.05 g/L) than the parental strain (49.09 g/L) in bioreactor culture. Further genomic analysis revealed that mutations in the _yieP_ gene confer 3-HP tolerance in _E. coli_[33]. To date, three 3-HP synthesis pathways have been successfully constructed in _E. coli_. There is no endogenous pathway in _E. coli_ that catalyzes the conversion of glycerol to 3-HPA, and _E. coli_ cannot naturally produce coenzyme B\({}_{12}\). Therefore, the production of 3-HP via the glycerol pathway in _E. coli_ requires the introduction of heterologous GDHt and the exogenous addition of expensive coenzyme B\({}_{12}\), which undoubtedly affects the economics of the production process. By contrast, the production of 3-HP via the malonyl-CoA pathway and the \(\beta\)-alanine pathway does not require the involvement of coenzyme B\({}_{12}\), which provides a promising alternative for the production of 3-HP when using _E. coli_ as the chassis cell [57]. Nevertheless, the highest 3-HP titers reported in engineered _E. coli_ via the \(\beta\)-alanine pathway (31.1 g/L) [38] and the malonyl-CoA pathway (52 g/L) [36] were much lower than that obtained via the glycerol pathway (76.2 g/L) [31]. In the future, the factors limiting 3-HP production in _E. coli_ via the remaining two pathways, such as the imbalance or low activity of key enzymes in the production pathway and a tightly regulated accumulation of metabolic intermediates, need to be further addressed. In addition, the study that obtained a titer of 76.2 g/L clearly showed that the titer, yield, and productivity were significantly dependent on the culture conditions, including dissolved oxygen levels and feeding strategies [31]. It is an important task in metabolic engineering for the fermentation condition to be optimized for each engineered strain, as the optimal condition for the selected strain is not guaranteed to be the best for another engineered strain.
## 3 Results  
As reported in Table 1, regarding the static test, in group 1, the failure load was 2227 N (SD 58.2 N). The samples of group 2 failed at 2490 N (SD 95.8 N).  
The fatigue test, as reported in Table 2, shows that all samples withstood pressure above the average chewing force of 130 N [21]. Only one sample of group 1 (8.3%) survived the entire test, with group 1 samples failing at an average of 130,000 cycles at 1053 N (SD 162.5 N). In group 2, 9 samples (75%) finished the test at 155,000 load cycles without failure (Figure 7).  
Moreover, it can be noted that the fracture mode differs between the two different groups: in group 1, all samples showed complete destruction of the crown with total debonding of the NCR material from the titanium abutment (Figure 8a). Conversely, all customized abutments' and crowns' fracturing was partial in all cases and did not affect the zirconia abutment (Figure 8b).  
\begin{table}
\begin{tabular}{c c c} \hline
**Group** & **Failure Load (Mean [N])** & **Failure Load (SD [N])** \\ \hline
1 & 2227 & 58.2 \\
2 & 2490 & 95.8 \\ \hline \end{tabular}
\end{table}
Table 1: Strength calculated from static testing (n = 3).  
Figure 7: Fatigue test results. Cumulative failure as a function of the cycles and the load.  
The means, standard deviations, and minimum and maximum fracture loads for the two groups from the static test are listed in Table 1. The samples in group 2 presented higher failure (2490 N) loads when compared to the samples in group 1 (2227 N).  
Descriptive statistics and the one-way ANOVA test (significance of 0.05) were used to determine the effect of the failure loads among the two groups (Figure 9). To test whether the fatigue response of the crown was significantly affected by the geometry of the two proposed abutments, one-way analysis of variance was performed for both groups on the number of survival samples at varying load values. The validity of the test was tested by [PERSON]'s test, ensuring the assumption of homogeneity of variance (\(p\)-value 0.008).  
The \(p\)-value for the F-statistic of the ANOVA test was 0.0459 for the survival load values and 0.0034 for the survival cyclic numbers (smaller than the significance level of 0.05), confirming that the fatigue resistance of the crowns with different abutment architectures is not the same.  
Figure 8: Fracture modes of the two groups: (**a**) group 1, with debonding of the NCR material from the titanium abutment, and (**b**) group 2, with partial fracture of the NCR material without failure of the zirconia abutment.  
Figure 9: Boxplots of the fracture loads (number of cyclic loadings).
## 5 ML Intersection with 6G Enabling Technologies
### ML and IRS Convergence for 6G Wireless Networks  
The integration of ML with IRS has been undertaken by several researchers in an effort to improve the KPI of the future 6G network. This convergence has been reported to yield enhanced quality of service (QoS) and seriously mitigate transmission losses in NLOS scenarios [38; 39; 168]. The performance of a classical LS estimator-based method was enhanced when a DNN-based approach was integrated into its implementation [169]. The authors reported a pilot overhead reduction of 35% using the DNN approach as compared to the conventional least-square (LS)-based approach. The DNN assisted a direct mapping that connects the received pilot signals to the optimum phases and beamformer. Similar success was achieved using CNN for reconfigurable phase optimization applications in wireless networks [170]. The study showed that models could learn the mapping dynamics in an RIS-embedded environment and produce better achievable rates while using less than 2% of the training Rx locations. A CNN-based approach has been developed to improve the sensing quality of an IRS-enabled network [171]. The proposed approach utilized the full characteristics of ROI, which can help replace missing pixels and eliminate false pixels. The results proved that the new approach significantly improved the sensing quality. In an online configurable indoor communication application, DNN was integrated with RIS to achieve an increase in throughput at the user end [172]. The study revealed that the closer the distance to the user location, the better the throughput of the communication. Similar findings have been reported for large intelligent surface-assisted applications in multi-path channel scenarios [173] where deep learning integration has enhanced achievable training overhead. In a different study, [PERSON] et al. [174] integrate DL, RIS, and a massive MIMO framework for channel estimation applications. The authors found that the DL-based approaches outperformed the existing LS methods and showed better normalized-mean-square-error (NMSE) values. DRL-based approaches have been proposed for solving wireless communication problems that involve a joint IRS phase reconfiguration and transit beamforming optimization problems [175; 176], and for overcoming the high dimensionality challenges of the precoders and the IRS phase shift matrix [177]. A summarized table of all the applications of ML and IRS technologies for enabled 6G future networks is given in Table 8.
# Parameter Design for Inter-Satellite Laser Link Acquisition Under Weak Light Conditions
## 1 Introduction
However, this theoretical derivation process was based on the specific position of the receiving satellite, limiting its broader practical application. [PERSON] et al. (2017) geometrically derived the effect of jitter on the acquisition probability, assuming the acquisition failure probability is the ratio of the scan lost region to the overall scan area. This model, however, did not account for the fact that acquisition can be accomplished through different scanning trajectories and is not applicable in cases where the laser power is low. [PERSON] et al. (2017) investigated a multi-scan link acquisition model based on the signal-to-noise (SNR), but this model did not consider the energy accumulation during the beam scanning process. The process of establishing inter-satellite laser links for space-based GW detection has also been the focus of several studies. The LISA mission validated its acquisition scheme early through simulation ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2017), but it differs from current approaches. [PERSON] et al. (2017) investigated a method for the laser spot location to achieve the required accuracy of space-based GW detection under weak light conditions. [PERSON] et al. (2017) realized on-ground verification of laser link constriction for LISA-like space-based GW detection. For the design of the acquisition parameters, [PERSON] et al. (2016); [PERSON] et al. (2016); [PERSON] et al. (2016) conducted an in-depth study on the impact of jitter correlation on the acquisition process and presented a new model to calculate the acquisition failure probability. However, this series of studies was more applicable to a scheme that uses a steering mirror to move the scanning beam over a wide speed range and does not account for acquisition thresholds in the actual space-based GW detection process. To the best of our knowledge, research in laser link acquisition using the telescope pointing structure has lacked comprehensive considerations. In this paper, we first focus on the key parameters that significantly influence acquisition probability in space-based GW detection. We analyze the effect of each parameter in a comprehensive manner and derive an analytical expression for acquisition failure probability. After that, we implement a simulation to mimic the real acquisition process. The essay is organized in the following way. In Section 2, factors affecting the acquisition process are listed and analyzed in depth. The analytical expression for the acquisition failure probability is derived in Section 3. Section 4 discusses the selection of the acquisition camera integration time, while Section 5 covers the implementation of a Monte Carlo simulation to model the real acquisition process. The final simulation results are presented in Section 6, and conclusions are drawn in Section 7.
# Hyperspectral Python: HypPy
## 2 Design and Implementation
### Tools
#### 2.4.1 Band Math Tool  
Multispectral and hyperspectral images can be regarded as a set of spectral bands, and functions are often created to process such data in a band-by-band fashion. The band math tool implements this.  
An example of such a band math operation is the calculation of the NDVI, in which the difference between a near-infrared band and a red band is normalized by dividing by the sum of the near-infrared band and the red band. For the NDVI, usually for the red band, a range of 400 nm to 700 nm is used, and for infrared, the range is 700 nm to 1100 nm. In the following example, for red, we pick a band around 675 nm, and for infrared, a band around 1000 nm is chosen. In HypPy, the NDVI of an AVIRISNG image can be calculated using one of the following expressions: (1) or (2).  
\[(i1[59]-i1[124])/(i1[59]+i1[124]), \tag{1}\] \[(i1(675.0)-i1(1000.0))/(i1(675.0)+i1(1000.0)). \tag{2}\]  
In band math, the sequence of image file names is linked to variable \(i1\), \(i2\) and so on. In the example of the NDVI, only one input image is needed, which is linked to the variable \(i1\). Images are numbered starting at 1, and band numbers are numbered starting at 0. In the example, band 59 is the band with a center wavelength of 676.9 nm and band 124 has a center wavelength of 1002.4 nm. Please note that, in this example, the BBL is used, and the first seven bands, which are marked as bad, are not present.  
In band math, it is also possible, and even preferred, to use wavelengths instead of band numbers. The band with the nearest central wavelength will be automatically selected. Note that, in band math, when wavelengths are used rather than band numbers, these wavelengths must be enclosed by round brackets, not square brackets. In Python,  
Figure 5: Plot window showing the spectrum Monazite_REE_GDS947_Calif_ASDFRb_AREF. This sample is known to contain Ce, La, Nd, and Th. Notice the Nd absorption features at 740 nm and 800 nm.  
this is implemented using a 'call' on the image class. Python objects can be designed to behave like lists or iterators, having an index between square brackets or a function with a parameter list between round brackets. Figure 6 shows an example of a band ratio.  
HypPy uses the Python eval() function to execute expressions like the NDVI example above. Band math expressions are basically Python expressions. HypPy sets up the variables \(i1\), \(i2\), and so on. These are HypPy image objects, which can be called to return bands of a certain wavelength. Linking the image variables to image files is an essential step in the use of band math expressions. Note that the syntax used in band math differs from the one used in spectral math. Band math uses image objects, while spectral math uses spectrum objects; see below.