esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation and Results
### Definition of Proposed Features
Finally, we apply a Wilcoxon rank-sum test [67], which allows determining whether such extracted features are statistically significant (\(p\)-value \(<0.05\)) under the hypothesis that the median value of normal class data differs from the median value of the acidotic class data. The previous operation was performed independently for the complete signal (\(E_{CS}\)), during decelerations (\(E_{DD}\)), and during resting periods (\(E_{DR}\)). Table 1 presents the results, whose corresponding box plots are shown in Figure 8. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & \begin{tabular}{c} **Normal** \\ **Cases** \\ \end{tabular} & \
\begin{tabular}{c} **Acidotic** \\ **Cases** \\ \end{tabular} &
\begin{tabular}{c} **Significance** \\ **(_p-Value_)** \\ \end{tabular} \\ \hline \(E_{CS\mu}\) & \(5.09[3.14-7.66]\) & \(3.50[2.14-4.56]\) & \(0.0213\) \\ \(E_{CS\sigma}\) & \(2.06[1.18-3.78]\) & \(1.03[0.74-2.42]\) & \(0.0079\) \\ \hline \(E_{DD\mu}\) & \(4.87[2.85-8.35]\) & \(2.67[1.52-4.22]\) & \(0.0016\) \\ \(E_{DD\sigma}\) & \(1.69[0.95-3.03]\) & \(0.72[0.39-1.64]\) & \(0.0015\) \\ \hline \(E_{DR\mu}\) & \(4.76[2.98-7.31]\) & \(3.63[1.90-5.31]\) & \(0.0708\) \\ \(E_{DR\sigma}\) & \(1.77[1.01-3.42]\) & \(1.08[0.82-2.11]\) & \(0.0277\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the statistical test for the extracted spectral features for the complete signal (CS), during decelerations (DD), and during resting (DR) periods, whose values correspond to the 50 th[25 th–75 th] percentiles of the data, respectively. Figure 7: Representative cases: (**a**) raw FHR signal of recording number \(1189\), \(\mathrm{pH}=7.36\), \(\mathrm{BDecf}=0.43\); (**b**) raw FHR signal of recording number \(2011\), \(\mathrm{pH}=7.01\), \(\mathrm{BDecf}=12.10\); (**c**,**d**) preprocessed FHR signal (blue), PBL (magenta), floating-line (red), and deceleration episodes (black); (**e**,**f**) detrended FHR signal; (**g**,**h**) \(\mathrm{IMF}_{6}\); (**i**,**j**) TV-AR spectrum computed from \(\mathrm{IMF}_{6}\); (**k**,**l**) spectral energy (\(E\)) from (**i**,**j**). As can be observed in the last column of Table 1, most of the features we tested showed that the median values corresponding to the normal class were significantly higher (\(p\)-value \(<\) 0.05) compared with the acidotic class.
## 8 Evaluation
### Workload and Traces  
We generated synthetic data that mimics the Azure 2019 Serverless Traces Dataset for simulation purposes. This dataset includes invocation patterns, temporal features,and noise, as described in Section 5.3. To validate our model using real-world serverless functions, we used FunctionBench [34] and Ensure [26] to obtain the workload of four applications: Image Resizing (IR), Email Generation (EG), Prime Number computation (PN), and Matrix Multiplication (MM). These applications were selected based on their runtime and memory requirements. The first two, IR and EG, are short-lived applications, while PN and MM are long-running and resource-intensive.
# Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
###### Abstract  
The failure of wind turbine blades is a major concern in the wind power industry due to the resulting high cost. It is, therefore, crucial to develop methods to monitor the integrity of wind turbine blades. Different methods are available to detect subsurface damage but most require close proximity between the sensor and the blade. Thermography, as a non-contact method, may avoid this problem. Both passive and active pulsed and step heating and cooling thermography techniques were investigated for different purposes. A section of a severely damaged blade and a small \"plate\" cut from the undamaged laminate section of the blade with holes of varying diameter and depth drilled from the rear to provide \"known\" defects were monitored. The raw thermal images captured by both active and passive thermography demonstrated that image processing was required to improve the quality of the thermal data. Different image processing algorithms were used to increase the thermal contrasts of subsurface defects in thermal images obtained by active thermography. A method called \"Step Phase and Amplitude Thermography\", which applies a transform-based algorithm to step heating and cooling data was used. This method was also applied, for the first time, to the passive thermography results. The outcomes of the image processing on both active and passive thermography indicated that the techniques employed could considerably increase the quality of the images and the visibility of internal defects. The signal-to-noise ratio of raw and processed images was calculated to quantitatively show that image processing methods considerably improve the ratios.  
Keywords:thernography; wind turbine blades; defects; image processing; condition monitoring +
Footnote †: journal: Applied Physics Letters
## 2 Model
Different aspects of a cubic anharmonic oscillator have been investigated, including the behavior of the resonance eigenvalues of a cubic anharmonic oscillator with a real nonlinearity strength \(\alpha\)[36], the periodic motion of a cubic anharmonic oscillator with a real parameter \(\alpha\)[37], the real spectrum of a cubic anharmonic oscillator with a complex nonlinearity strength \(\alpha\)[38], the ground-state energy of a cubic anharmonic oscillator with a complex parameter \(\alpha\)[39; 40], the eigenenergies and eigenfunctions of a cubic anharmonic oscillator with a real parameter \(\alpha\) or a complex parameter \(\alpha\)[41], and the Stark effect in a cubic anharmonic oscillator with a real parameter \(\alpha\) or a complex parameter \(\alpha\)[42]. For simplicity, here we consider the case that the parameter \(\alpha\) is real and positive. The equations for the time evolutions of the expectation values of the system operators can be found from the Heisenberg equations of motion, which are given by  
\[ \begin{array}{rcl}\tfrac{d}{dt}\langle q\rangle&=&\tfrac{\langle p\rangle}{m},\\ \tfrac{d}{dt}\langle p\rangle&=&-m\omega_{m}^{2}\langle q\rangle-\alpha\langle q \rangle\langle q\rangle-\hbar g\langle c^{\dagger}\rangle\langle c\rangle- \gamma_{m}\langle p\rangle,\\ \tfrac{d}{dt}\langle c\rangle&=&-[\kappa+i(\omega_{0}-\omega_{c}+g\langle q \rangle)]\langle c\rangle+\sqrt{\eta\kappa}\varepsilon_{c}+\sqrt{\eta\kappa} \varepsilon_{p}e^{-i\delta t},\end{array}  \tag{2}\]  
where we have included the optical and mechanical dissipations, we have used the factorization assumption \(\langle ab\rangle=\langle a\rangle\langle b\rangle\), and \(\gamma_{m}\) is the mechanical damping rate. It is worth mentioning that this factorization assumption is not valid when both the coupling and probe fields are weak [43; 44; 45]. When the power of the probe field is much smaller than that of the coupling field, in the limit where \(t\rightarrow\infty\), the steady state solution of Equation (2) can be truncated to second order in \(\varepsilon_{p}\)  
\[\langle s\rangle=s_{0}+s_{1+}e^{-i\delta t}+s_{1-}e^{i\delta t}+s_{2+}e^{-2i \delta t}+s_{2-}e^{2i\delta t}, \tag{3}\]where \(s=q,p,c\), \(s_{n+}\) and \(s_{n-}\) (\(n=1,2\)) indicate the \(n\)th-order upper and lower sidebands, respectively. In addition, \(c_{n+}\) and \(c_{n-}\) (\(n=1,2\)) correspond to the \(n\)th-order anti-Stokes and Stokes fields, respectively. The solution has five components oscillating at the frequencies \(\omega_{c}\), \(\omega_{p}\), \(2\omega_{c}-\omega_{p}\), \(2\omega_{p}-\omega_{c}\), \(3\omega_{c}-2\omega_{p}\), respectively.
# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 2 Data and Method
### Model Prediction  
During model prediction, the mask network obtains the most accurate candidate bounding box and a single-channel 28 \(\times\) 28 feature map from the first ROI Align layer. These are then resized to match the predicted target candidate box dimensions and overlaid onto the corresponding region of the original multi-meteorological-element visualization map to generate the mask. A threshold is subsequently applied to convert this mask into a binary image--specifically, areas with predicted values exceeding the threshold (set at 0.5 in this study) are classified as frontal zones, while the remaining regions are designated as background (non-frontal). This process yields the frontal category information, bounding box data, and mask output for the identified region at a specific time. Building on this, the model's frontal line extraction module further refines the results by performing two operations within the mask area: first, selecting the maximum temperature gradient value per row, followed by a curve-fitting operation to ultimately derive the final frontal identification results.
# Gene Editing with CRISPR/Cas Methodology and Thyroid Cancer: Where Are We?
## 4 Gene Editing with CRISPR/Cas in Thyroid Cancer Research
### Targeting Coding Genes in Thyroid Cancer
#### 4.1.3 Cell Metabolism, Drug Resistance and Apoptosis  
The investigation of thyroid-cancer-cell metabolism by unraveling its metabolic components may contribute to the identification of new therapeutical targets. Moreover, CRISPR/Cas9 can provide a potent tool to target specific genes involved in drug specificity or resistance, and improve cancer treatment.  
Drug screening focusing on transcriptional dysregulation revealed ATC cells' responsiveness to THZ1 inhibitor, leading to strong reduction in cell viability and apoptosis [56]. The main target of THZ1 is CDK7, a cell cycle progression that phosphorylates several substrates including RNA polymerase II activation sites. High levels of CDK7 correlated with poorer prognosis and Ki67 levels in ATC, and targeting _CDK7_ with CRISPR/Cas9 resulted in reduction of cell viability, colony formation, G2/M cell cycle arrest and cell apoptosis [56]. Similarly, ATC responds to the treatment with THZ531, an inhibitor that blocks the activity of CDK12 (cyclin dependent kinase 12) in controlling gene transcription [57]. Furthermore, THZ531 reduced CDK12-mediated phosphorylation of RNA pol II and transcriptional elongation, and induced cell growth arrest, apoptosis, and reduction in colony formation in ATC cells. This effect was mimicked by the disruption of the _CDK12_ gene using CRISPR/Cas9 [57].  
In the same context of kinase inhibitors, the treatment of ATC cells with lestaurtinib, an inhibitor of JAK2, resulted in cell cycle arrest and reduction of cell migration [58]. Indeed,CRISPRR/Cas9-mediated gene editing of _JAK2_ in anaplastic thyroid cancer cells reduced cell proliferation but treatment with lestaurtinib resulted in incremental inhibition, indicating additional target genes or pathways [58].  
One interesting gene involved in cell metabolism is the intraflagellar transport 88 (IFT88) gene that participates in primary cilia assembly, a sensory organelle that sends signals from the cell membrane to the control cell metabolism. Loss of _IFT88_ gene function using CRISPR/Cas9 resulted in metabolism dysregulation by altering mitochondria function (oxidative phosphorylation) and dynamics (fragmentation) in thyroid cancer cells. Moreover, _IFT88_ gene editing reduced cell proliferation, migration, and invasion in ATC cell line [59]  
In hypoxia conditions, NAD(P)H oxidase 4 (NOX4), a mitochondria NOX, is responsible for generating mitochondrial ROS to stabilize HIF1 aIfa and increase glycolysis and cell growth [60]. The activation of NOX4 is dependent on p22 phox, and the depletion of p22 phox with CRISPR/Cas9 gene editing leads to a reduction in NOX4 levels. Consequently, mitochondrial ROS generation reduced and destabilized HIF1a-induced glycolysis in hypoxia condition necessary for PTC cell line proliferation [60].
# Hybrid Chirp Signal Design for Improved Long-Range (LoRa) Communications
## 6 Conclusions  
In this paper, we have proposed novel modulation and demodulation schemes for LoRa communications. The modulation scheme is designed based on up- and down-chirp  
Figure 4: Performance comparison of different LoRa signals in the presence of interference. (**a**) Interference signal \(\mathcal{S}=6\); (**b**) interference signal \(\mathcal{S}=8\); (**c**) interference signal \(\mathcal{S}=10\).  
Figure 3: Performance comparison between the proposed and traditional LoRa modulation schemes with different spreading factors.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Des.**} & **Int.** & \(\mathcal{S}=6\) & \(\mathcal{S}=7\) & \(\mathcal{S}=8\) & \(\mathcal{S}=9\) & \(\mathcal{S}=10  
signals. An FFT-based demodulation scheme is proposed to detect the proposed modulated symbols. For further enhancement, a hybrid demodulation scheme is also presented, which is comprised of FFT- and correlation-based operations. With and without interference, the simulation results show that the proposed schemes exhibit better bit error rate performance compared to the conventional LoRa modulation scheme.  
**Author Contributions:** Formal analysis, M.N.-A.-R. and M.O.K.; Investigation, A.M.; Methodology, M.O.K., A.M. and D.P.; Software, A.M. and X.L.; Supervision, D.P. and H.V.P.; Validation, X.L.; Writing-original draft, M.N.-A.-R.; Writing- review and editing, D.P. and H.V.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This publication has emanated from research conducted in part with the financial support of Science Foundation Ireland under Grant number 16/RC/3918. The work of [PERSON] was supported in part by the U.S. National Science Foundation under Grant CCF-1908308.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Simulation and Design of Three 5G Antennas
## 5 Design of an Eight-Element MIMO Array Antenna for Mobile Terminals with a Metal Frame
### Optimization of the Parameters for the Eight-Element MIMO Array Antenna
#### 5.2.2 Layout of the \"Ring-Slot\" Unit Modules  
Three layouts of the \"ring-slot\" unit modules are considered, as shown in Figure 43: Figure 43a: Layout 1, where all the slot antennas (Ant2, 4, 6, 8) have their L-shaped feed branches pointing in the same direction. Module 1 (Ant1, Ant2) and Module 2 (Ant3, Ant4), placed along one side, are axisymmetric to Modules 3 (Ant5, Ant6) and 4 (Ant7, Ant8) on the other side of the antenna ground plane. Figure 43b: Layout 2, where the L-shaped feed branches all point toward the interior of the antenna ground plane, and the four \"ring-slot\" modules are placed in a central symmetry arrangement. Figure 43c: Layout 3, where the L-shaped feed branches all point toward the exterior of the antenna ground plane, and the four \"ring-slot\" modules are also placed in a centrally symmetric manner.  
As shown in Figure 44, the simulated S parameters for the array antenna are compared for three different layouts of the antenna units. From Figure 44a, it can be observed that the first layout with L-shaped feed branches placed in the same direction results in lower isolation between the array antenna elements, which is not conducive to achieving the desired high gain for MIMO array antennas. Figure 44b shows that the second layout with L-shaped feed branches directed inward has a narrower bandwidth and insufficient isolation. Figure 44c demonstrates that the third layout with L-shaped feed branches directed outward has higher isolation compared with the first layout and can completely cover the LTE band 42 frequency range when compared with the second layout. Therefore, the layout in which L-shaped feed branches are directed outward is chosen as the final antenna array configuration.  
Figure 42: Current distribution comparison before and after adding the T-type decoupling structure. (**a**) Before loading and (**b**) after loading.  
Figure 43: Three layout options for the “ring-slot” antenna modules. (**a**) Layout 1: L-shaped feed branches placed in the same direction; (**b**) Layout 2: L-shaped feed branches all directed inward; and (**c**) Layout 3: L-shaped feed branches all directed outward.
# Analyzing LLAMA3 Performance on Classification Task Using LoRA and QLoRA Techniques
## 2 Methodology
### Hardware, Time, and Memory  
All the experiments were conducted on a single A100 GPU with 40 GB of memory (Nvidia, Santa Clara, CA, USA). As shown in Table 1, the time taken for all the LoRA experiments at the all-layer and attention-layer settings was approximately 30 min. LoRA reduces the number of trainable parameters significantly and the GPU memory requirement by 3\(\times\) times. In contrast, the time required for all the quantized QLoRA experiments was around 5 h, approximately 10\(\times\) times longer than that for the LoRA. However, in QLoRA, the parameters are quantized and stored in 4-bit memory settings, which makes them more storage efficient and requires less memory. Because of the quantization in QLoRA, dequantization is needed during matrix multiplication, requiring additional computation time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Hardware**} & **Time Taken for Fine Tuning** & **Memory** \\ \hline \multirow{2}{*}{LoRA (All-Linear and Attention Layers)} & 1 \(\times\) A100 & \multirow{2}{*}{30 min} & \multirow{2}{*}{16 GB} \\  & 40 GB & & \\ \hline \multirow{2}{*}{QLoRA (All-Linear and Attention Layers)} & 1 \(\times\) A100 & \multirow{2}{*}{5 h} & \multirow{2}{*}{6 GB} \\  & 40 GB & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training times and memory consumed for LoRA and QLoRA models.
## 4 Results
### Mapping and Area Estimate for Smallholder Maize Farms  
The maps for the maize planted areas produced by the SVM and Xgboost algorithms are depicted in Figure 7. The classification maps reveal the spatial distribution of the smallholder maize farms in our study area. It can be seen that most farmers that planted maize during the 2018/2019 season are from the south eastern part of Makhuduthamaga. These observations are consistent with both maps that were produced by the two algorithms. Visual inspection reveals no obvious disagreement between the two maps as predicted by the SVM and Xgboost algorithms.  
Figure 5: Permutation importance scores for the Principal Component Analysis (PCA)-derived images used in the analysis. PCA 3 for the VH polarization is the most important variable in our study. The same results were obtained for the two estimators.  
Figure 6: Examples of the PCA images for different polarizations derived from Sentinel-1 datasets. The PCA VH polarization composite seems to visually enhance smallholder farms compared to other polarizations.  
The unbiased proportional areas were generated. The SVM algorithm estimated the planted maize class to be 7073.558 \(\pm\) 0.01 ha and the non-planted class was estimated to be 33420.96 \(\pm\) 0.01 ha. Meanwhile, the Xgboost estimated the planted maize class area to be 7303.32 \(\pm\) 0.180 ha and the non-planted class was estimated to be 33191.2 \(\pm\) 0.820 ha. It is worth noting that the SVM algorithm has better error margins (0.01 ha) for both classes compared to the Xgboost algorithm, which has error margins of 0.18 and 0.82 ha for the planted maize class and non-planted areas, respectively. The areas for the 18 smallholder farms (Figure 8) compared well with those generated by the classification models. The SVM classifier had a better fit (R = 0.89) in comparison with the Xgboost algorithm (R = 0.84). The linear model was an ideal fit for the data. The positive relationship was significant at a 95% level (\(p<0.5\)).  
Figure 7: Planted maize crop maps produced by the SVM (**a**) and Xgboost (**b**) algorithms. Insert maps for SVM and Xgboost are represented by (**c**) and (**d**), respectively.
## 3 Experiments  
### Metrics Modification  
1. Faithfulness metric. The faithfulness metric allows you to determine the degree of correspondence between the explanation of the importance of each individual feature and is determined by the Pearson sample correlation coefficient between the weights of the feature and its approximate contribution to the change in the model prediction when it is removed or fixed.  
A schematic representation of the iteration of the algorithm is shown in Figure 2.  
The values of the Pearson correlation coefficient, by definition, are in the range from \(-1.0\) to \(1.0\), the faithfulness metric can take values from \(0\) to \(1\). Values close to \(1\) indicate the correctness of the distribution of weights, and close to \(0\) indicate that the influence of features on the prediction does not match the set of weights. It is important to note that this metric does not consider the correctness of each weight, but considers them in aggregate, which in the general case is not always the main indicator of the correctness of an explanation.  
Figure 1: Schematic representation of the method.  
Figure 2: Scheme of the algorithm for calculating the faithfulness metric.  
2. Monotonicity metric. The monotonicity metric is based on this concept and determines the correctness of a sequence of features, ordered by increasing their weight, obtained by XAI methods. If monotonicity is not observed, then the XAI method allowed a distortion of feature priorities: a feature with less influence received more weight, or vice versa. This metric also cannot assess the accuracy of the weight value of each feature, but it can assess the correctness of the distribution of weights between features. A schematic representation of the iteration of the algorithm is shown in Figure 3.  
The main difference between the faithfulness metric and the monotonicity metric is that to calculate the faithfulness metric, subsets of features are considered, from which each of the features is iteratively removed. At the same time, the monotonicity metric considers the cumulative effect of adding features, and therefore can be more reliable if they are highly correlated.
3. Incompleteness metric. The Incompleteness metric determines the effect of noise perturbations of each feature on model predictions by calculating the difference between the weights and the difference between the original prediction and the predictions given by the noisy feature sets.
# The Accumulation Characteristics of Different Heavy Metals in Sea Rice
## 3 Results and Analysis
### Cu Accumulation Characteristics of HH12
#### 3.2.2 Cu Content and Cu Accumulation in Different HH12 Parts  
With an increase in Cu concentration, the root Cu content of HH12 increased significantly (Figure 3a), and a significant difference was observed at different Cu concentrations (\(p<0.01\)). At 300 mg/kg Cu, the root Cu content was 1063.47 mg/kg, which was 1100% higher than that in the control group. Compared with the control group, the Cu content in the shoots of Cu-treated HH12 was significantly higher (\(p<0.05\)). When the Cu concentration was <200 mg/kg, the spike of HH12 showed no significant difference in Cu content compared with that in the control group (\(p>0.05\)). However, the Cu content of the spike under the 300 mg/kg Cu concentration treatment was significantly lower than that under the 200 mg/kg Cu concentration treatment. The Cu content in the shoot and spike of HH12 were 82.86 and 46.97 mg/kg, respectively, which increased by 140.06 and 36.51%, respectively, compared with that in the control group. The Cu content in the shoot did not exceed the critical content standard of the Cu hyperaccumulator (1000 mg/kg) for Cu. HH12 treated with Cu experienced a significant rise (\(p<0.05\)) in Cu accumulation relative to the blank control (Figure 3b). Cu accumulation was 1526.41 ug at 300 mg/kg Cu, which was significantly higher than that with the other treatments, and it increased by 260.29% compared with that in the control group.