esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification
## 2 Materials and Methods
### Wheat Grain Counting Method  
#### 2.3.1 Image Feature Extraction and Selection  
All wheat spikelets were extracted separately based on the YOLOv8-seg instance segmentation model. As shown in Figure 6, it was difficult to directly segment the grains from wheat spikelets, because the dense structure and different morphology of grains made it hard for even experts to tell which region belongs to which grain. However, it was possible to empirically distinguish the number of grains in the spikelet. Therefore, the classification method was applied to the counting of grains by extracting image features as input. A single spikelet of the specific variety and growth period generally contained 0-3 wheat grains, and the spikelets with different amounts of wheat grains had differences in shape, color, and texture.  
The shape features included width-length ratio (WLR), area (Are), perimeter (Per), roundness (Rou), rectangularity (Rec), convex hull area ratio (CHAR), convex hull perimeter ratio (CHPR), and 7 Hu invariant moments (H1-H7). The color features included 7 average values and 2 variances, involving the average values of the red component (Rc), green component (Gc), blue component (Bc), hue component (Hc), saturation component (Sc), brightness component (Ic), G-R (PDmr), and G-B (PDmb), and the variances of G-R (PDvr) and G-B (PDvb) [22]. The texture features included 12 two-dimensional Discrete Wavelet Transform features, 6 Gray Level Co-occurrence Matrix features, and 10 uniform rotation-invariant features of Local Binary Patterns. The two-dimensional Discrete Wavelet Transform features consisted of low-frequency textures (FEA1, FEA2, and FEA3), diagonal textures (FED1, FED2, and FED3), horizontal textures (FEH1, FEH2, and FEH3), and vertical textures (FEV1, FEV2, and FEV3) at the first, second, and third levels [23]. The Gray Level Co-occurrence Matrix features consisted of contrast (Con), dissimilarity (Dis), homogeneity (Hom), angular second moment (ASM), energy (Ene), and correlation (Cor) [24]. The uniform rotation-invariant features of Local Binary Patterns consisted of bin0, bin1, bin2, bin3, bin4, bin5, bin6, bin7, bin8, and bin9 [25].  
In this study, different feature selection methods were further used to obtain the most important features from all 52 spikelet image features, including chi-square (CS) test, F-test, mutual information entropy (MIE), L1 regularization (L1), random forest (RF), and extremely randomized trees (ERT). After feature selection, the spikelet image features lost  
Figure 6: Examples of spikelets with different numbers of grains.  
a little effective information, but it could enhance the interpretability of the model and improve the classification efficiency, which had important research and application value in actual agricultural production.
## 3 Results
### Biomarkers Analysis
#### 3.3.6. Triglyceride (TG) Levels  
The estimated marginal means of triglyceride (TG) levels at baseline (t1), 1 h (t2), 2 h (t3), and 4 h (t4) are presented in Table 9 and Figure 7. Baseline TG concentrations were comparable among groups: CTRL (82.083 \(\pm\) 8.442 mg/dL), OLT (78.000 \(\pm\) 7.576 mg/dL), and OLB (75.083 \(\pm\) 6.034 mg/dL).  
Across all groups, TG levels increased markedly at 1 h, peaking at 2 h with CTRL reaching 147.042 \(\pm\) 15.595 mg/dL, OLT 145.208 \(\pm\) 16.805 mg/dL, and OLB 139.000 \(\pm\) 12.866 mg/dL. By 4 h, TG levels declined but remained elevated compared to baseline, with values of 110.583 \(\pm\) 13.913 mg/dL (CTRL), 102.042 \(\pm\) 11.991 mg/dL (OLT), and 97.667 \(\pm\) 9.833 mg/dL (OLB).  
No significant group-by-time interaction was observed (\(p\) = 0.975), indicating similar temporal TG profiles across groups. The 95% confidence intervals overlapped substantially, supporting the absence of group-specific effects (e.g., t3: CTRL [114.78, 179.30], OLT [110.44, 179.97], OLB [112.39, 165.62]).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **TG** & **TG** & **TG** & **TG** & **95\% Confidence Interval** & \(p\) **Value** \\  & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **Time** & **Lower** & **Upper** \\  & **at Baseline (t1)** & **at 1 h (t2)** & **at 2 h (t3)** & **at 4 h (t4)** & **Time** & **Bound** & **Bound** \\ \hline CTRL & 82.083 \(\pm\) 8.442 & 139.197 \(\pm\) 10.365 & 147.042 \(\pm\) 15.595 & 110.583 \(\pm\) 13.913 & t1 & 64.619 & 99.547 & \\  & & & & & t2 & 117.73 & 160.61 & \\  & & & & & t3 & 114.78 & 179.30 & \\  & & & & & t4 & 81.803 & 139.36 & \\ \hline OLT & 78.000 \(\pm\) 7.576 & 136.333 \(\pm\) 11.982 & 145.208 \(\pm\) 16.805 & 102.042 \(\pm\) 11.991 & t3 & 62.328 & 93.672 & \\  & & & & & t2 & 111.55 & 161.12 & \\  & & 11.982 & 16.805 & 11.991 & t3 & 110.44 & 179.97 & \\  & & & & & t4 & 77.237 & 126.85 & \\ \hline OLB & 75.083 \(\pm\) 6.034 & 129.000 \(\pm\) 8.327 & 139.000 \(\pm\) 12.866 & 97.667 \(\pm\) 9.833 & t2 & 111.78 & 146.23 & \\  & & & & t3 & 112.39 & 165.62 & \\  & & & & t4 & 77.326 & 118.00 & \\ \hline  & & & & & & \\ \end{tabular} Values are presented as mean \(\pm\) standard error (SE). TG: serum triglyceride concentration (mg/dL). t1: baseline, t2: 1 h, t3: 2 h, t4: 4 h postprandialally. CTRL: Control meal without olive leaf supplementation. OLT: Meal supplemented with olive leaf tea. OLB: Meal supplemented with olive leaf-enriched biscuit. Statistical analysis was performed using repeated measures (ANOVA) to assess the interaction effect (group \(\times\) time).  
\end{table}
Table 9: Postprandial changes in triglyceride (TG) levels.  
Figure 7: Postprandial triglyceride concentrations (Mean \(\pm\) SE) over time (baseline, 1 h, 2 h, 4 h) in control (CTRL), olive leaf tea (OLT), and olive leaf biscuit (OLB) groups.  
Overall, the data indicate a transient increase in TG levels peaking around 2 h post-intervention, followed by a gradual decrease toward baseline values, consistent across all study groups.
# Smart Manufacturing Scheduling Approaches--Systematic Review and Future Directions
## 4 Main Findings
### Existing Approaches
In addition to this, some problems arise when unrealistic assumptions are made since manufacturing environments are very dynamic and do not rely on static features. These assumptions can rapidly lead to unfeasible scheduling solutions which can be costly to the companies [27]. The most common assumptions are: all jobs and machines are available at starting time and release times are not considered; each machine can only execute one specific operation, which is not always true since some machines may have the flexibility to perform more than one operation, by changing tools, for example; the processing time of an operation is unchangeable, but processing times may change according to the conditions in a specific moment; machines never break and they are always available; setup times of any operation are sequence-independent and are included in the processing time, which is not always true and may compromise the entire schedule; pre-emption is not allowed; storage buffers are unlimited. Aligned with this problem, the authors of [27] state that \"... the intersection among three or more sets of constraints for any shop layout would return an empty set of references most of the time\". This means that only a few studies contemplate more than three constraints. From Table 1, it is possible to observe that only about a third of the research studies considered more than three of the identified requirements in the same approach, as demonstrated in Figure 4.  
Figure 4: Number of requirements considered per study. It is also possible to observe that the most adopted requirements are flexibility, related to the ability of the shopfloor to adapt and adopt different features, and due dates, related with the date when each job shall be completed. After this comes the consideration of dynamic environments, where the arrival of new jobs, job cancellations or machine breakdowns during the execution process are considered and a reschedule may be necessary. Finally, the last consideration is tardiness, which evaluates the extent to which jobs are delayed from the initial deadline. All the other requirements are less expressive, being adopted in less than 20% of the articles. This is a substantially small number considering all the requirements and constraints that can be found in manufacturing systems. Additionally, almost half of the studies considered two or three constraints. On the other side, two of the studied articles did not consider even one of the requirements mentioned in this study, as they only tried to optimize the objectives without any kind of limitation.
## 2 Materials and Methods
### Bacterial Culture and Growth Conditions, and UV-C LED Irradiation Through the Optical Fiber System  
The _E. faecalis_ (ATCC 29212) strain used in this study was obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). The bacteria were cultured in Brain Heart Infusion (BHI; Becton, Dickinson and Company, Sparks, MD, USA) broth at 37 \({}^{\circ}\)C under aerobic conditions for 24 h. After incubation, bacterial cells were harvested via centrifugation (8000\(\times\)_g_ for 10 min) and re-suspended in sterile phosphate-buffered saline (PBS) to obtain a suspension with optical densities (ODs) of 0.4-0.5 at 600 nm, measured using a spectrophotometer. The bacterial concentration was standardized to 10\({}^{8}\) CFU/mL using the CFU counting method to ensure uniform inoculum sizes across all experimental groups.  
Aliquots of 0.1 mL were used in the experiments. Bacterial suspensions (0.1 mL each) were poured into 96-well plates and irradiated with the developed laboratory-manufactured 270 nm UV-C-emitting prototype device with a double-lens optical system for different durations (10, 30, 60, 90, 120, and 180 s). The fiber tip was positioned 3 mm above the bottom of the plate for irradiation. Additionally, _E. faecalis_ suspension (10 uL) was placed in a 96-well plate and subjected to irradiation under each condition. Following UV irradiation, the cultures were transferred to fresh broth and incubated at 37 \({}^{\circ}\)C under 5% CO\({}_{2}\) for 6 h. Microbial growth over time was measured using a microplate reader at 600 nm. Incubation of the strain with fresh broth was used as a control. To ensure the reliability of the results, the experiments were repeated three times.
## 4 Results
### Geophysical Data
#### 4.1.1 MASW Survey
The pairs g1-t1 and g2-t2 show similar 300 \(<\) VS \(<\) 600 m s\({}^{-1}\), thus limiting identifications of the upper boundary of t1 and t2 forming aquifers regarding the lower boundary of g1 and g2 forming aquitards. The lowermost part of t1 and t2 and the uppermost part of WB are cemented and show similar VS \(<\) 900 m s\({}^{-1}\), thus preventing its boundary definition. As described in next Section 4.1.2, radargrams help to disambiguate these boundaries. MASW maps (Figure 4) show how spatial VS continuity is interrupted by NW-SE normal and NE-SW strike-slip fault systems associated to active tectonics. The former is a first-order fault system determining the accommodation space for the Quaternary sedimentation whereas the latter is a second-order one that compartmentalizes the bedrock, thus inducing interruptions of some geological formations such as t1 and t2. The MASW maps identify geological boundaries of special hydrogeological interest, such as the (1) bedrock bathymetry at the 0-m (Figure 4c), \(-\)10-m (Figure 4d), and \(-\)20-m (Figure 4e) elevation maps; (2) NSUS-SFUS boundary at the 0-m (Figure 4c) and \(-\)10-m (Figure 4d) elevation maps; and (3) WB extension at the 10-m elevation map (Figure 4b).
## 2 Materials and Methods
### Methods  
The experimental setup used in this study is shown below (Figure 3). The experimental conditions in this study simulated the reservoir conditions for UHS with a hydrogen storage pressure of 10.34 MPa at a temperature of 50 \({}^{\circ}\)C. The pressure and temperature conditions were estimated by referring to studies in hydrogen storage that mention the optimum operating conditions [36; 37]. The cement specimens underwent exposure to hydrogen gas within a core holder apparatus utilizing an Ametek Chandler Engineering 2-cylinder Quizix pump (Chandler Engineering, Broken Arrow, OK, USA), with constant pressure maintained via pressure ramp cycling scheduler software. Pressure ramping occurred for 5 h at a rate of 2.06 MPa/hr, followed by a 28-day constant pressure set at 10.34 MPa. Subsequently, pressure reduction was executed over 5 h using the scheduler. The pressure of the system was monitored using a data acquisition system consisting of the Omega Digital Transducer Application software (version 2.3.1.306) and pressure transducers. This pressure cycling protocol was repeated for three cycles spanning 28 days each. Before each exposure cycle, a vacuum pump was employed to remove the air in the core holder. After the exposure cycles, the samples were placed inside air-tight containers to eliminate the possibility of reacting with the moisture in the air.  
Computed tomography was employed to identify any pre-existing cracks or deformations within the specimens. The Ceretom(r) NL3000 transportable small-bore CT scanner (Neurologica, Danvers, MA, USA), which has a maximum slice thickness resolution of 1.25 mm, is used to acquire the images. Because the CT scanner has eight detector rows, it may acquire eight picture slices simultaneously during a complete rotation. The scanner runs on tube voltages between 100 and 140 kV and currents between 1 and 7 mA. The scanner has a maximum scanning range of 64 cm and a rotation time range of 2 to 6 s. The thickness of each acquired picture slice is 1.25 mm. The image acquisition was performed before and after each pressure cycle, and the images were analyzed using ImageJ software to detect any cracks in the cement. Following each exposure cycle, a comprehensive analysis was conducted, encompassing computed tomography, scanning electron microscopy coupled with energy dispersive spectroscopy (SEM-EDS), evaluation of porosity and permeability, and measurement of ultrasonic velocities in the samples to calculate the dynamic elastic properties.
# A Ku-Band GaAs Multifunction Transmitter and Receiver Chipset
###### Abstract  
This paper presents a Ku-band monolithic multifunction transmitter and receiver chipset fabricated in 0.25-\(\upmu\) GaAs pseudomorphic high-electron mobility transistor technology. The chipset achieves a high level of integration, including a 4-bit 360\({}^{\circ}\) digital phase shifter, 5-bit 15.5-dB digital attenuator, amplifier and 9-bit digital serial-to-parallel converter for digital circuit control. Since the multifunction chipset includes a medium power amplifier and a low-noise amplifier, it features high P1 dB and low noise figures over the full Ku-band frequencies. The multifunction transmitter shows a peak gain of 16.5 dB with output P1 dB of 19.2 dBm at 15 GHz. The multifunction receiver shows a peak gain of 17.3 dB with noise figure of 2.5 dB at 15 GHz. The attenuation range is 15.5 dB with a step of 0.5 dB and the phase shift range is 360\({}^{\circ}\) with a step of 22.5\({}^{\circ}\). Each chip area of the transmitter and receiver is 4.2 \(\times\) 2.8 mm\({}^{2}\).  
multifunction; transmitter; receiver; attenuator; phase shifter; medium power amplifier; low-noise amplifier; serial-to-parallel converter +
Footnote †: journal: Electronics  
Article
## 2 Analytical Approach
These are given in Equation (11) and the final expression for the current density is obtained as in Equation (12). \[ \begin{cases}H_{y}\left(0\right)=0\\ \oint{H_{y}\cdot d\vec{l}}=I_{p}\cdot\left(k-1\right)\\ I_{p}=\int{\int{\vec{l}_{z}\cdot d\vec{S_{p}}}}\end{cases}  \tag{11}\]  
\[J_{z,k}\left(x\right)=a\frac{I_{p}}{w_{p}}\left\{\frac{k\cosh\left(ax\right)- \left(k-1\right)\cosh\left[a\left(x-h_{p}\right)\right]}{\sinh\left(ah_{p} \right)}\right\} \tag{12}\]  
Equation (12) considers both skin and proximity effects, and it can also be found in [6]. The current density module along the conductors inside the slot is shown in Figure 5 at an operating frequency of 1000 Hz. This behavior [6] has also been validated by means of finite elements analyses (FEA).
## 3 Experiment  
### Terahertz Channel Model  
[PERSON] et al., based on scattering theory and ray-tracing technique, proposed a deterministic large-scale fading model suitable for terahertz frequency bands. It deterministically models the line-of-sight loss as the sum of free space path loss and molecular absorption loss [17]:  
\[A(f,d)=A_{spread}(f,d)+A_{abs}(f,d) \tag{5}\]  
where \(A(f,d)\) represents terahertz deterministic large-scale fading, \(A_{spread}(f,d)\) represents free space path loss, and \(A_{abs}(f,d)\) represents molecular absorption loss.  
Considering that the inter-satellite terahertz communication scenario is located in space, the molecular absorption loss is very small, so only the free space path loss is considered for the inter-satellite terahertz channel modeling.  
Free space path loss is a classical model of wireless channel transmission, which plays an important role in satellite communication simulation. In the case of an ideal omnidirectional antenna, the free space path loss can be expressed as the ratio of the transmitted and received power of the antenna.  
\[\frac{P_{t}}{P_{r}}=\frac{\left(4\pi fd\right)^{2}}{c^{2}} \tag{6}\]  
The logarithmic form is:  
\[\begin{array}{l}L_{dB}=10\lg\left(\frac{P_{t}}{P_{r}}\right)\\ =10\lg\left(\frac{\left(4\pi fd\right)^{2}}{c^{2}}\right)\\ =20\left|\lg\left(\frac{4\pi f}{c}\right)+\lg(f)+\lg(d)\right|\\ \approx 92.44+20\lg(f)+20\lg(d)\end{array} \tag{7}\]  
where, \(P_{t}\) is the transmitting power, in the unit \(w\), \(P_{r}\) is the receiving power, in the unit \(w\), \(f\) is the signal frequency, in the unit GHz, \(d\) is the distance, in the unit km. when calculating the final value of \(L_{dB}\), units shall not be included.
## 3 Results
### Canopy Height Model (CHM) and Tree Detection  
The height normalised point clouds were rasterised to a CHM with a resolution of 0.1 m, and subjected to the individual tree detection. The 'tree_detection' function in the lidR package with the LMF algorithm was adjusted to detect mangrove trees above the breast height. The individual trees derived from the algorithm can be observed in Figure 8.  
The root mean square error (RMSE) analysis of the individual tree detection based on fieldwork data and visual inspection is described in detail in Appendix B, Table A2. The RMSE value for tree location was 0.23 m on average. All three validators provided similar RMSE\({}_{\text{r}}\) with values ranging 0.15, 0.28, and 0.25 m, while the maximum RMSE\({}_{\text{r}}\) was 0.48 m. As observed, the number of the detected trees for the dense mangrove forest was underestimated, whereas it was more accurate in the sparse mangrove forest.  
The CHM-derived height demonstrated that the mangroves' median height is 3.5 m in the southern delta while the northern delta with its older mangroves had a median height of 4.2 m. As shown in Figure 9, the northern delta had bi-modal tree height distribution. The distribution is likely correlated to the mangrove planting activities in 2016. Corresponding rectangular shaped mangrove areas can be clearly seen in Figure 7a.  
Figure 6: Point clouds processing: (**a**) raw point clouds, (**b**) the thinned 20% elevation percentile points, (**c**) high noise-classified points, (**d**) cleaned photogrammetry point clouds, (**e**) ground points classification (brown colour represents ground points and grey represents non-ground), (**f**) height normalisation.  
Figure 8: The detected individual tree as represented here as the red dots derived from UAV Structure from Motion (SfM) photogrammetry in (**A**) northern delta and (**B**) southern delta.  
Figure 7: (**a**) Digital Surface Model (DSM) and Digital Terrain Model (DTM) of the northern delta and (**b**) DSM and DTM of the southern delta.
# Kirchhoff Migration for Identifying Unknown Targets Surrounded by Random Scatterers
## 5 Further Result: Imaging of Extended Dielectric Targets in an Inhomogeneous Medium
Unfortunately, it is impossible to identify outline shape of all \(\mathbf{A}_{m}\) through the subspace migration with small and large \(N\)._  
**Example 7** (Comparison of imaging performances). : _For the final example, let us apply various non-iterative techniques such as MUSIC, direct sampling method, and factorization method for imaging extended targets and compare the imaging performances. In Figure 8, imaging results via subspace migration (map of \(\mathfrak{F}_{\mathrm{SUB}}(\mathbf{x})\)), MUSIC (map of \(\mathfrak{F}_{\mathrm{MUSIC}}(\mathbf{x})\)), direct sampling method (map of \(\mathfrak{F}_{\mathrm{DSM}}(\mathbf{x})\)), and factorization method (map of \(\mathfrak{F}_{\mathrm{FM}}(\mathbf{x})\)) are exhibited with the same simulation configuration of Figure 6._  
_First, let us compare the imaging results via subspace migration and MUSIC algorithm with different selection of nonzero singular values. For this, different from the Figure 6e, \(N_{0}=10\) and \(N_{0}=37\) nonzero singular values are selected to define \(\mathfrak{F}_{\mathrm{SUB}}(\mathbf{x})\). However, for any selection of \(N_{0}\), an outline shape of inhomogeneities \(\mathbf{A}_{m}\) cannot be retrieved. Thus, we can examine that the result via Kirchhoff migration is better than the one via subspace migration and MUSIC for imaging of arbitrary shaped inhomogeneities embedded in a random medium._  
_Next, let us consider the imaging result via direct sampling method with a fixed incident direction \(\mathbf{\theta}_{1}\). Based on the Figure 8c, we can easily examine that the result via direct sampling method is poorer than the one via Kirchhoff migration. Notice that Kirchhoff migration and direct sampling method are identical when the number of incident fields becomes sufficiently large (see Section 5, [24]) so that total number of incident fields must be large enough to obtain a good result via direct sampling method._  
_Figure 8c shows the imaging result via factorization method. It is interesting to observe that similar to the imaging result via Kirchhoff migration, the outline shapes of inhomogeneities are also successfully retrieved. Hence, factorization method can be regarded as an appropriate imaging technique in random medium._  
Figure 6. (Example 5) Imaging results for extended targets embedded in an inhomogeneous medium.