esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Generation of Fast and Low-Memory Smart Digital/Geometrical Beamforming MIMO Antenna
## 4 Numerical Evaluations
### Beamforming to a Desired Direction  
The proposed eight-element dipole antenna configuration from our previous single beam work [3], shown in Figure 5, is used as the transmitter and is optimized to form a beam in arbitrarily selected directions. This antenna can steer the beam to any direction (from \(0^{\circ}\) to \(360^{\circ}\)). These outcomes are already validated in our previous work [3] and will not be repeated herein considering space and similarity concerns. However, the radiation patterns are tested to steer the beam towards random angles \(0^{\circ}\), \(5^{\circ}\), \(15^{\circ}\), \(27^{\circ}\), \(42^{\circ}\), \(55^{\circ}\), \(73^{\circ}\), \(88^{\circ}\), \(135^{\circ}\), \(152^{\circ}\), \(177^{\circ}\), \(213^{\circ}\), \(245^{\circ}\), \(260^{\circ}\), \(305^{\circ}\), and \(340^{\circ}\) by optimizing the phase delay coefficients, as shown in Figure 6.  
Figure 5: Proposed eight-element dipole antenna.  
Figure 8: Azimuth view of the formed narrow beam.  
Figure 10: The same beam is shown at an angle for a better view of the side lobes. The side lobes have negligible energy.  
Figure 9: Vertical view of the formed narrow beam.  
Seven different space vectors occur in an eight-element antenna structure. The beam is guided in the desired direction by the contributions of phase shifters from each space vector. Eight phase delay shifters, combined with proper dipole placement positions, are sufficient to precisely guide the beam toward any desired angle between 0\({}^{\circ}\) and 360\({}^{\circ}\). The shape of the beam is very narrow and highly focused. Moreover, the energy wastage is negligible due to the negligible side lobes, which illustrates that this antenna is highly energy efficient.
## 3 Results and Discussion
### Total Sulfhydryl Content of Actomyosin  
The change in total sulfhydryl content of actomyosin is shown in Figure 4. The control group showed 42.4% decrease in total sulfhydryl content of actomyosin by the second freeze-thaw cycle, and 52.7% decrease after six freeze-thaw cycles. While the total sulfhydryl contents of SuSo, PH-2, PH-4, PH-6, AH-2, AH-4, and AH-6 groups decreased rapidly over the first two freeze-thaw cycles, and then decreased slowly, by 38.4, 37.2, 44.6, 47.2, 42.9, 48.5 and 50.9%, respectively, after six freeze-thaw cycles. PH, SuSo, AH-2 and AH-4 groups exhibited noticeably slower decreases in the total sulfhydryl contents than that of the control group over six freeze-thaw cycles (\(P<0.05\)), with PH-2 and SuSo groups showing the slowest reduction (\(P<0.05\)). In unwashed fish mince, Amur sturgeon skin gelatin hydrolysate demonstrated protective effect toward the oxidation of the sulfhydryl groups induced by freeze-thawing [30]. [PERSON] et al. also found that gelatin hydrolysate could prevent sulfhydryl groups in surimi from oxidation after repeated freeze-thaw treatments [9]. Similarly, PH-2 can prevent myosin sulfhydryl groups (especially sulfhydryl groups in protein head region) from being oxidized by temperature abuse during freeze-thaw treatments and retard the decreases in Ca\({}^{2+}\)-ATPase activity (Figure 3).  
Figure 3: Ca\({}^{2+}\)-ATPase activity of actomyosin from different surimi samples during freeze-thaw cycles. SuSo (surimi added with sucrose-sorbitol blend); PH-2, PH-4 and PH-6 (surimi with addition of 2, 4, and 6 g of the hydrolysate prepared by Protamex hydrolysis, respectively); AH-2, AH-4 and AH-6 (surimi with addition of 2, 4, and 6 g of the hydrolysate prepared by Alcalase hydrolysis, respectively); and control (surimi without cryoprotectant). The reported data represent mean values from three replications. Bars represent standard deviations.
# Cortical Connectivity Response to Hyperventilation in Focal Epilepsy: A Stereo-EEG Study
## 5 Discussion
One could speculate that HV promotes a conductive environment that facilitates seizure propagation in focal epilepsy, but such an effect is not sufficient to trigger the seizure onset. In our experience during long-term EEG and SEEG monitoring, we have rarely observed seizures during such a procedure in the case of focal epilepsy: in some cases, seizures developed only after several HV sessions or minutes after the activation maneuver was carried out. This may suggest that the transition from a state of increased connectivity to a state with relatively decreased connectivity facilitates the focal desynchronization of epileptogenic sites and the consecutive rise in ictal discharges. It is noteworthy that the EZ, namely, the cortex area responsible for seizure generation, responds differently to the HV maneuver compared to healthy cortical and subcortical regions. A past study demonstrated that, during HV, the mean decreases in cerebral blood flow were 20.9% and 10.8%, in epileptic and non-epileptic temporal cortical regions, respectively [5]. The authors found a linear dependency between the blood flow reduction and the interval between seizures, concluding that the EZ is more susceptible to hypoperfusion and particularly vulnerable to ischemia. Even if HV exerts a global connectivity effect, different pathological and physiological areas may respond in a personalized fashion. Therefore, studying how HV affects the cortical sites within the EZ may enable the definition of new biomarkers for better EZ localization. Our study has several limitations. First, the sample size was limited and heterogeneous in terms of etiology and explored cortical areas, though most patients had a temporal EZ and exploration focused mainly on temporal structures. Further validation of our findings over a broader population with uniform characteristics represents an essential step for continuing this research and its effectiveness from a clinical point of view. Secondly, we did not systematically assess the relative increase or decrease in IED frequency during N-REM sleep, wakefulness, and HV periods, which could be either a cause or effect of the observed connectivity changes. Additionally, due to the small sample size, we did not separately analyze patients in whom HV induced IED versus those in whom the maneuver had no effect. From a methodological point of view, we considered only the PTE algorithm for estimating cortical connectivity, while extending the analysis by considering frequency-dependent metrics may enable discerning connectivity patterns according to specific frequency bands. We did not assess connectivity changes within the EZ compared to propagation zones, unaffected zones, and contralateral healthy control areas. Lastly, SEEG explorations are only conducted in people with drug-resistant epilepsy, as the invasive nature of the procedure precludes the inclusion of healthy control subjects.
# Ultrasonic Attenuation Characteristics of Glass-Fiber-Reinforced Polymer Hull Structure
## 6 Discussion  
### Effects of \(Gc\) and Thickness Variation  
Further statistical analysis was performed on the ultrasonic test results obtained using the \(Gc\) and thickness variation groups. The attenuation coefficient exhibited predictable levels of variation with the \(Gc\) and thickness. However, in the case of attenuation variation, there was also a significant difference between the two groups (Figures 6a and 9a).  
Both types of variation increased the attenuation, but when the \(Gc\) was varied, the irregularity of the wave was greater. This characteristic is thought to result from the fact that, although the ultrasonic wave speed increases upon increasing the laminate density, the E-glass fiber chop interferes with the wave simultaneously.  
The increments in attenuation for the two groups were also different. The plates in the thickness variation group produced a larger change than the plates in the \(Gc\) variation group. The correlation analysis results obtained for each group also supported this finding. The Pearson correlation analysis revealed a larger correlation in the thickness variation group than in the \(Gc\) variation group, indicating that the plate thickness had a considerably higher correlation with the attenuation than the \(Gc\) (Figure 11). In particular, the analysis of the thickness variation group (Figure 11b) confirmed that the \(Gc\) had an almost negligible effect (\(\rho\) = -0.092), whereas the thickness had a highly significant effect (\(\rho\) = 0.948) on attenuation.  
Figure 10: Ultrasonic wave attenuation coefficient as a function of the thickness. (**a**) Regression analysis results. (**b**) Residual analysis results.  
According to the analysis results (Figure 12), thickness variation had a slightly greater impact on the generation of voids than _Gc_ variation. For a more detailed investigation, further statistical analyses were performed. The population mean T-test for the mean void volumes in the _Gc_ and thickness variation group plates yielded a significance level of 0.078. Thus, there was a difference in the generation of voids between the two groups, but this difference was not significant. This is because the _Gc_ values of the hull plate fabricated in this study were 32.28-43.83%, which did not indicate a high _Gc_ region but rather a normal _Gc_ region; thus, a high ratio of void generation was not observed. In fact, thickness variation had a greater effect on void generation than _Gc_ variation. An average void volume of 1.8% was generated in the thickest plate (18.34 mm) According to an earlier study [34], hull plates with 50-60% _Gc_ contain up to 3-6% voids, and the thickness of these hull plates is less than half that of the plates analyzed in this study. Therefore, inner defect volumes equal to approximately 1% of the plate volumes in both the variation groups were considered normal [23].
## 3 Materials and Methods  
### Accident Risk Assessment Framework  
Figure 1 presents the flow chart of the risk assessment framework developed for traffic accident hazard identification in the present research. The framework starts by defining the boundaries of the study area. Then, accidents' data for the years (2017-2019) were collected from relevant agencies and authorities. The subsequent step imports the data to ArcMap and identifies the actual accident locations on the area's map. The road under study was divided into segments of an equal length (2000 m approx.) followed by an estimation of the risk index for each hotspot. The framework culminates at a comparison between the situations before and after the installation of speed cameras to evaluate the effectiveness of accident control measures adopted in the past.
# Numerical Investigations of Pile Group Foundations under Different Pile Length Conditions
## 4 Settlement Analysis
### Cap Settlement  
Table 2 presents the settlement of the cap in the Y-direction following the application of a 100 kN static uniform axial load, with observation points set at intervals of 5 m. The table highlights that the smallest settlement of the cap occurs near y = 306 m, which corresponds to the middle section without backfill. In the no-backfill section, settlements increase as one moves closer to the edge of the cap. This same trend is observed in the backfill section as well. However, the settlement at the boundary of the backfilled section is more than double compared to the boundary of the no-backfill section.  
Several factors contribute to the settlement of the cap, including the weight of the cap itself, foundation conditions, actual bearing capacity of the pile foundation, negative frictional forces acting on the pile body, lateral deformation of the weak soil layer exerting forward thrust on the pile, and the characteristics of the soil surrounding the cap. The simulated results reveal a significant difference in settlement at both ends of the cap. This discrepancy primarily arises because the soil around the cap was not backfilled and compacted during construction, resulting in minimal binding force on the bearing platform.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline Location (m) & 276 & 281 & 286 & 291 & 296 & 301 & 306 & 311 & 316 & 321 \\ \hline Settlement (mm) & 1040 & 260 & 81 & 30 & 15 & 11 & 6 & \(-25\) & \(-322\) & \(-1947\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The settlement of pile cap.  
Figure 3: Pile group settlement.  
Additionally, one side of the cap is backfilled while the other side lacks backfill. The action of the backfill counteracts a substantial portion of the axial load, leading to an upward displacement trend in that particular section of the cap. Simultaneously, the no-backfill section experiences a downward displacement process. As a result, the cap as a whole undergoes forward movement. From this perspective, it is evident that backfilling the cap pit before construction significantly reduces the forward movement of the bridge platform.
# Safety Analysis of Secondary Lining of Yulinzi Tunnel Based on Field Monitoring
## 4 Analysis of Test Results
### Variation Law of Section Internal Force
#### 4.3.2 Time-Varying Law of Axial Force of the Secondary Lining  
We calculated the axial force and bending moment values of the secondary lining structure according to Equations (5) and (6) based on the field-measured inner and outer reinforcement strain values of the selected seven measuring points and drew relevant charts. Figure 6 is the time-varying diagram of the axial force at the section of the seven representative measuring points selected above, and Figure 13 is the end of the monitoring of all measuring points. The following results are obtained from the comprehensive analysis of Figures 13 and 14.  
From the slope of the curves in Figures 13 and 14, it can be observed that the axial force and bending moment at the right arch foot (D7) in the secondary lining increased at the fastest rate, reaching 625 kN\(\cdot\)m and 4635 kN, respectively, on the 90 th day. The axial force at the vault (D1) increased at the slowest rate, only reaching 484 kN by the 90 th day of monitoring. The bending moment increase rate at the left arch shoulder (D11) was the slowest, only increasing to 194 kN\(\cdot\)m on the 90 th day of monitoring.  
It is worth mentioning that, compared with the other two areas, the internal force of the arch shoulder area could maintain a nearly stable state in the later stage, because it  
Figure 14: Time-varying bending moment of section ZK280+325.  
Figure 13: Time-varying axial force at section ZK280+325.  
mainly bore the role of load transmission in the structure. Therefore, under the surrounding rock pressure, the growth rate of the positional internal force was not large.  
Through the comprehensive analysis and comparison of the above figures, it can be seen that the axial force and bending moment of the secondary lining of the tunnel show an overall growth trend over time during the monitoring time, and the internal force growth trend of each monitoring section is the same. In the early stage of monitoring, the internal force growth of each section showed a rapid increase. In the first three months of monitoring, the axial force and bending moment of all the measuring point sections could reach at least 65% of the final value at the end of the monitoring. After that, as time went by, the growth rate of the internal force of each section gradually slowed down, showing the characteristics of \"fast first and then slow\" as a whole.
###### Abstract
[17] also found that skeletal muscle and human umbilical cord-derived decellularized hydrogels can repair peripheral arterial disease in a rat hind limb ischemia model, and the former reconstructed muscle tissue was closer to natural healthy skeletal muscle. Several studies showed that protein composition found in decellularized matrices derived from specific tissue has specific biological function on the target tissue. As previously shown that DNM-G preserved critical biological factors unique of peripheral nerve which facilitated myelination. Proteins found in DNM-G only, such as collagen IV \(\alpha\)1, collagen IV \(\alpha\)2, and collagen V \(\alpha\)2, show a positive correlation with regulation for myelination [14]. There are 22 and 16 specific ECM matrisome proteins for the decellularized spinal cord matrix (DSCM) and decellularized peripheral nerve matrix, respectively. The DSCM specific glycoprotein and secreted factor, tenascin family, and fibroblast growth factor 2, are proved to be able to promote the NSFCs proliferation [15]. For regeneration of complex tissues, a study from [PERSON] et al. [18] showed that specific proteins were found in scaffolds derived from Growth plate ECM, such as chondro sulfate proteoglycan 4 and angiopoietin like 2, which are believed to play a role in angiogenesis; and C-Type lectin Domain Containing 11A, Matrix Metallopeptidase 13 and S100 Calcium-Binding Protein A10, which can promote osteogenesis. In contrast, specific proteins were found in scaffolds derived from articular cartilage ECM, such as Gremlin 1, Frizzled Related Protein, and Transforming Growth Factor Beta 1, which can inhibit hypertrophy and promote chondrogenesis. Therefore, the comparison of pDSIS-gel to pDMYO-gels in order to determine tissue-specific advantages for myocardial injury repair in animals, the relationship between the protein composition in pDMYO and potential tissue-specific biological functions needs to be defined. To this end, this study investigated the micromorphology, mechanical strength, composition, effect on cardiac-related cell behavior in vitro, and cardiac function in rat acute myocardial infarction models in vivo of these two gels. The ultimate aim was to correlate the differential effects exerted by these two gels on cell.
## 5 Discussion  
In this paper, we evaluated the SAITI-DCGAN (spectral and spatial attention improved thermal image deep convolutional generative adversarial network) using self-collected infrared thermal images. Our assessment included both qualitative analysis and quantitative metrics such as the inception score (IS) and Frechet inception distance (FID). The results indicate that our model generated more realistic and higher-quality defect images compared to the traditional GAN and DCGAN models, because of the spectral-spatial attention mechanism that enhanced the feature extraction and image fidelity.  
While the trained discriminator can extract useful features for defect detection via transfer learning, its primary role in distinguishing real from generated images limits its fine-grained recognition capabilities. For optimal defect detection, we recommend using the generated images for data augmentation and employing specialized detection models for precise identification.  
Future work will focus on refining the attention mechanisms to improve the image realism, integrating multi-source data for richer training sets, and developing customized algorithms for specific defect types. This approach aims to enhance the application of infrared thermal imaging in non-destructive testing.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 3 Locking Detection, Assessment, and Resolution
### The Idea and Algorithm of a Posteriori Phenomenon Detection
#### 3.1.2 Check on Bending-Dominance of the Solution  
Check on bending-dominance of the solution to the global problems of the type (6) was proposed by [PERSON] [32]. Such a check is necessary as the locking phenomena are present only in the bending-dominated problems. In the membrane-dominated problems the phenomena do not appear. In addition, in the case of the mixed dominance, the locking may appear.
## 2 Data and Method
### Model Fitting and Evaluation  
The hourly forecast data in August 2019 were used as independent assessment data to evaluate the performance of post-correction techniques. Two AutoML models were fitted, respectively, by the data from August in 2018, i.e., the same month to the evaluation period in the previous year and the data from July 2019, i.e., the previous month of the evaluation period; they were then compared.  
For comparison, we used three other popular post-processing techniques for the bias-correction of forecasts in August 2019. First, a moving error post-processing method was used as the baseline model. Moreover, other MOS models of single linear regression (SLR), multiple linear regression (MLR), and random forest (RF) were also adopted for comparison. Furthermore, the confidence level that the AutoML model had a better performance than other models was tested and evaluated with the bootstrap method. In this method, around 250 data items were sampled from the evaluation data in August 2019 in 200 iterations. The sampling data in each iteration were evaluated with statistical metrics.  
To evaluate post-correction performance, statistical metrics were reported, including coefficient of determination (R-square), mean error (ME), mean absolute error (MAE), normalized mean error (NME), and root mean square error (RMSE). Statistical metrics were  
Figure 3: Stacking ensemble Auto-ML model framework.  
defined in Equations (3)-(7), where \(y_{i}\) and \(\hat{y}_{i}\) were, respectively, observation and forecast data items with N, and \(\overline{y}\) was the average of N observations.  
\[R^{2}=1-\frac{\sum_{i}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i}(y_{i}-\overline{y})^{2}} \tag{3}\]  
\[ME=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i}) \tag{4}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y}_{i}| \tag{5}\]  
\[NME=\frac{1}{N}\sum_{i=1}^{N}\frac{y_{i}-\hat{y}_{i}}{y_{i}} \tag{6}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}} \tag{7}\]
## 4 Experimental Results and Discussion
### Result Analysis
incorporated a forgetting factor and filtering algorithm for lithium-ion battery health state estimation, mitigating particle degradation during the filtering process; however, this led to increased time complexity [34]. The Issa-lstm model enhanced predictive performance by optimizing model parameters, yet it overlooked the issue of noise interference in the capacity sequence [35].
# Enabling Decision Making with the Modified Causal Forest: Policy Trees for Treatment Assignment
## 2 Framework
```
1:Input:\(\{(X_{i},\hat{\Theta}_{i}(j))\}_{i=1}^{n}\), \(L\)
2:if\(L=1\)then
3:Return \((\max_{j\in\{1,\ldots,d\}}\sum_{i}\hat{\Theta}_{i}(j),\operatorname*{argmax}_ {j\in\{1,\ldots,d\}}\sum_{i}\hat{\Theta}_{i}(j))\)
4:else
5: Initialise reward \(\mathcal{R}\leftarrow-\infty\), and empty tree \(\mathcal{T}\leftarrow\emptyset\) for all \(m=1,\ldots,p_{1}+p_{2}\)
6:for\(m\gets 1,2,\ldots,p_{1}+p_{2}\)do
7:for sorted values of ordered or unique categories of continuous \(m\)-th features do
8: reward_left, tree_left \(\leftarrow\) Tree-Search\((set_{L},L-1)\)
9: reward_right, tree_right \(\leftarrow\) Tree-Search\((set_{T},L-1)\)
10:if reward_left \(+\) reward_right \(>\mathcal{R}\)then
11:\(\mathcal{R}\leftarrow\) reward_left \(+\) reward_right
12:\(\mathcal{T}\leftarrow\) Tree-search\((m,\text{splitting value, tree\_left},\text{tree\_right})\)
13:endif
14:endfor
15:endfor
16:Return \((\mathcal{R},\mathcal{T})\)
17:endif
```  
**Algorithm 1** Policy Tree  
Besides the policy tree, the OptimalPolicy() class provides a best-score method to create an assignment rule. This method simply assigns units to the treatment with the highest estimated potential outcome. Although computationally cheap, this best-score method lacks clear interpretability for the allocation rules, which may make it difficult for policymakers to adopt it. To address the issue of interpretability, one could analyse individuals' characteristics retrospectively. For example, the evaluate() method of the OptimalPolicy() class shows the distribution of features for each treatment. This information helps to understand the relationship between characteristics and assigned treatments.