esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Evaluation of Weighted Fusion Methodology
#### 3.3.5 Provincial Evaluation Analysis  
Figure 15 delineates radar charts, visualizing the three primary evaluation indicators for each province across the three assessment periods. Several important patterns emerge from this analysis:  
1. Forecast Reliability : Jiangsu consistently demonstrated the highest forecast reliability across all three periods, followed by Zhejiang. Shanghai showed the lowest reliability in June and August but improved significantly in December.
2. Provincial Impact: Jiangsu maintained the highest provincial impact on the overall grid due to its substantial industrial load. This impact remained relatively stable across seasons, whereas other provinces showed more significant seasonal variations.
3. Forecasting Complexity: Anhui consistently presented the lowest forecasting complexity, while Shanghai demonstrated the highest complexity during summer months, reflecting its sensitivity to cooling demand in its predominantly urban environment.  
Figure 14: December 2024 scatter comparison.  
Figure 13: December 2024 hourly error (The yellow shadow indicates the peak area).  
These evaluation patterns directly informed the time-varying weights assigned by our PCA-based methodology, allowing the system to adaptively emphasize more reliable provincial forecasts while accounting for their relative impact on the overall grid.
# Experimental Study on Strength of Polypropylene Fiber Reinforced Cemented Silt Soil
## 4 Study on Macro and Micro Mechanism of Polypropylene Fiber Improving Cement-Soil Strength  
### Failure Mode of Fiber Cement Soil under Macroscopic Condition  
The failure morphology of fiber cement soil with different fiber content at the same age was shown in Figure 10. The cement dosage is 15%, fiber length was 9 mm. It intuitively showed that under the same conditions, with the increase in the fiber content, the compressive strength increased, the crack width at failure decreased, and the crack form also changed. With the increase in the fiber content, the crack form also developed from a through crack to a small oblique crack. When the fiber content was 0%, the failure of the sample presented obvious brittle failure, showing a penetrating vertical crack with a width of more than 3 mm. In the process of failure, small pieces of samples and soil chips fall out. When the fiber content was 0.1% and 0.2%, with the increase in the fiber content, brittle failure was also significantly improved, with irregular oblique cracks that had a width between 1-3 mm, and the strength was also improved. When the fiber content was 0.4%, the fracture was greatly improved, which was not an obvious oblique crack. The crack width was less than 1 mm, and the strength was the highest under the same condition because, with the addition of polypropylene fiber, the interfacial bonding force between the fiber and the soil particles can be generated, which can resist the deformation caused by specimen failure and improve the toughness of fiber cement soil. The macro-cracks were caused by the poor tensile strength of cement soil. To improve the tensile strength, fiber was added, which can delay the continuous development of cracks through its own unique toughness. With the increase in the fiber content, the fiber bound the cracks together and  
Figure 9: The measured value and curve fitting diagram of fiber cement soil under confining pressure. (**a**) fiber content 0%; (**b**) fiber content 0.1%; (**c**) fiber content 0.2%; (**d**) fiber content 0.4%.  
redistributed the stress through the interface between the fiber and the cement soil, and then generated new microcracks to improve the strength of the solidified body [24; 25; 26; 47].
## 4 Radiation Pressure-Driven PhC Cavity Sensors
### Sensing Applications Based on Radiation Pressure
#### 4.2.2 Force Sensing
Through optomechanical modulation of coupling between the two PCN cavities in the unresolved side-band regime, optical force generated by photons inside the PCN cavities leads to rotation of the nanobeam. The mechanical rotation of optomechanical nanobeam modulated two PCN cavities dispersively in an antisymmetric way, as shown in Figure 21b. The nanobeam rotated counter-clockwise under the blue-shifted pump pulse and rotated clockwise under the red-shifted pump pulse. Finally, a multi-cavity optomechanical torsion sensor was demonstrated with a very high detection sensitivity of the torque moment determined to be 9.7 \(\times 10^{-21}\) N-m/Hz\({}^{1/2}\) and could be potentially utilized in some reported torsional magnetometer [128,129] and accelerometer [130]. Figure 19: PCN nanocavity torque sensors: (**a**) photonic crystal split-beam nanocavity [124]; (**b**) photonic crystal paddle optomechanical cavity [126]. Figure 20: Split-beam PhC nanocavity for magnetic sensing (inset: mechanical profile of moving nanobeam under applied magnetic field Hz) [70].
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 3 Results
### Study Areas and Data  
#### 3.1.1 Study Areas  
The main experimental area is the Xinfa reservoir (see Figure 4a), located in Inner Mongolia, China. It is used for flood control, water supply, the comprehensive utilization of power generation, and aquaculture irrigation. The total storage capacity of the reservoir is 38.08 million cubic meters, including 23.31 million cubic meters of flood-control storage and 5.26 million cubic meters of regulation storage. The standard return period of design flood is 30 years, and the corresponding peak flow is 303  m\({}^{3}\)/s. At 15:30 (UTC/GMT +8) on 18 July 2021, the Xinfa reservoir collapsed, causing the downstream highway to be washed away. Figure 4(a1,a2) show the Sentinel-1 SAR images acquired before and after the flood, respectively. Another study area is the Zhujiang River in Guangzhou province (see Figure 4b), where dense river branches and fishponds are distributed. Both study areas are challenging and can prove the superiority of the proposed method. In this paper, the comprehensive processing results of study area 1 and only the final classification results of study area 2 are presented.
## 2 Research Significance  
EP concrete (or EP mortar) is widely employed in the repair of existing Portland cement concrete structures, more so than in the construction of precast products. This is due to its physical, mechanical, and chemical properties, as well as cost. The most crucial consideration when selecting a repair material is its compatibility with the material employed in the existing structure [20]. Basic elements associated with compatibility include the modulus of elasticity, setting shrinkage, and thermal expansion. A stress-strain relationship occurs when loads are applied; this relationship is related to the modulus of elasticity. Setting shrinkage begins during the curing process immediately after casting PC; thermal strain occurs repeatedly with any temperature change. These properties are closely related to the compatibility between the parent and repair concretes. If the materials are incompatible, the adhesive layer debonds, degrading the durability of the repair.  
In previous studies of PC, researchers simultaneously carried out experiments regarding the modulus of elasticity and setting shrinkage [20], and thermal expansion and modulus of elasticity [21; 22]. Other studies [5; 18] examined the modulus of elasticity, setting shrinkage, and thermal expansion individually. However, it is difficult to find a study that simultaneously considered all three basic elements.  
Therefore, the novelty of this study is that the deformability characteristics of bisphenol A-type EP-based polymer concrete (i.e., the modulus of elasticity, setting shrinkage, and thermal expansion) were simultaneously investigated when different types of hardener and filler were employed. The results of this study can serve as a reference for the basic data necessary to establish compatibility and dimensional stability when using EP polymer concrete as a repair material or in precast products.
## 3 Results
### HSV-1 Induces the Expression and Post-Translational Modifications of FoxO Protein Family Members
The transcriptome analysis showed an increase in _FoxO1_ and _FoxO3_ mRNAs (Figure 3D) early in infection (4 h.p.i. ), however, the results were somewhat inconclusive for the _FoxO4_ transcript due to the low number of the reads (not shown). The transcriptional activation of the _FoxO_ family was additionally confirmed in a time-course experiment (Figure 3E) using RT-qPCR for _FoxO1_ and _FoxO3_. Similar to the sequencing results, we observed an increase early after infection and a sharp drop in mRNA levels after 8 h.p.i. To further investigate whether the FoxO family induction is limited to HSV-1 infection or if it might represent a conserved host-response mechanism to infection with other herpesviruses, we studied the expression of FoxO members in cells infected with murine cytomegalovirus (MCMV). In brief, primary mouse embryonic fibroblasts (MEFs) were infected with the MCMV strain C3X, and samples for protein and RNA analysis were collected at different times post-infection. Interestingly, we observed a strong upregulation of all of the tested members of the FoxO protein family (Figure 4A) during the time course of MCMV infection. Moreover, the analysis of RNA confirmed the transcriptional activation of _FoxO_ genes (Figure 4B), similar to HSV-1. It is important to note that although we observed an apparent increase in the phosphorylation of FoxO proteins in contrast to the HSV-1 infected human cells, we did not observe an obvious size shift (Figure 3B). At this point, we cannot explain this difference, but it might be related to the specificity of the antibodies or different activities of the virus and/or cellular kinases. Taken together, these results led us to the hypothesis that FoxO family members might be involved in intrinsic antiviral response and triggered in response to virus infection. For example, it has been found that FoxO1 promotes the degradation of IRF3 and limits _IRF7_ transcription [69], while FoxO3 has been found to target _IRF7_, and thus, negatively regulate virus-induced type I interferon (IFN) expression [70; 71; 72]. To test this hypothesis, we analyzed the expression of FoxO1 and FoxO3 in response to the immunositulants poly (I:C) and IFN-\(\beta\), which mimic viral infection and elicit an antiviral response, respectively (Figure 4C). Indeed, the stimulation of RPE1 cells with poly (I:C) resulted in the gradual induction of both the FoxO protein levels, with a peak protein expression at 6 h post-stimulation. On the other hand, stimulation with IFN resulted in a sharp, but very transient, increase in FoxO1 and FoxO3 immediately after stimulation. The observed drop in the FoxO protein levels 3 h or 6 h after stimulation with IFN-\(\beta\) is in agreement with the work of [PERSON] et al., showing that IFN-I limits the transcription of FoxO3, which is responsible for targeting _IRF7_, and thus increases the transcription of _IRF7_ for the maximum antiviral response [70]. Regardless, the results indicate that FoxO family proteins are induced as a result of an immune response to viral infection.
# Mixed-Degree Cubature \(H_{\infty}\) Information Filter-Based Visual-Inertial Odometry
###### Abstract  
Visual-inertial odometry is an effective system for mobile robot navigation. This article presents an egomotion estimation method for a dual-sensor system consisting of a camera and an inertial measurement unit (IMU) based on the cubature information filter and \(H_{\infty}\) filter. The intensity of the image was used as the measurement directly. The measurements from the two sensors were fused with a hybrid information filter in a tightly coupled way. The hybrid filter used the third-degree spherical-radial cubature rule in the time-update phase and the fifth-degree spherical simplex-radial cubature rule in the measurement-update phase for numerical stability. The robust \(H_{\infty}\) filter was combined into the measurement-update phase of the cubature information filter framework for robustness toward non-Gaussian noises in the intensity measurements. The algorithm was evaluated on a common public dataset and compared to other visual navigation systems in terms of absolute and relative accuracy.  
Keywords:visual-inertial odometry; cubature information filter; navigation; IMU; RGBD camera +
Footnote †: journal: Applied Sciences
# Application of the Parabola Method in Nonconvex Optimization
## 2 Univariate Optimization: Parabola Method
### The Golden Section Method  
Step 0. Choose the accuracy of the algorithm \(\varepsilon>0\). Set \(\gamma=\frac{\sqrt{5}-1}{2},\alpha_{0}=\alpha,\beta_{0}=\beta\), \(\lambda_{0}=\alpha_{0}+(1-\gamma)(\beta_{0}-\alpha_{0}),\mu_{0}=\alpha_{0}+ \gamma(\beta_{0}-\alpha_{0})\). Evaluate \(f(\lambda_{0})\) and \(f(\mu_{0})\). Set \(k=0\).  
Step 1. If \(\beta_{k}-\alpha_{k}\leq\varepsilon\), stop: \(x^{*}\) is \(\varepsilon\)-optimal point. Otherwise, if \(f(\lambda_{k})>f(\mu_{k})\), go to Step 2; else, go to Step 3.  
Step 2. Set \(\alpha_{k+1}=\lambda_{k},\beta_{k+1}=\beta_{k},\lambda_{k+1}=\mu_{k},\mu_{k+1 }=\alpha_{k+1}+\gamma(\beta_{k+1}-\alpha_{k+1})\). Evaluate \(f(\mu_{k+1})\). Go to Step 4.  
Step 3. Set \(\alpha_{k+1}=\alpha_{k},\beta_{k+1}=\mu_{k},\mu_{k+1}=\lambda_{k},\lambda_{k+1 }=\alpha_{k+1}+(1-\gamma)(\beta_{k+1}-\alpha_{k+1})\). Evaluate \(f(\lambda_{k+1})\). Go to Step 4.  
Step 4. Increase \(k\to k+1\), and go to Step 1.
# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
## 3 Proposed Model
### Innovation #3: Environment Awareness  
Recent work has shown that data augmentation during the knowledge distillation process can improve the performance of compressed models in mismatched domain scenarios without compromising the model's size [18]. However, there are still limitations on how much robustness a model can acquire from the distillation process. The latest system described in [17], for example, showed some sensitivity to varying environmental conditions. Here, we propose a third innovation to overcome this issue, namely the use of environment awareness.  
More specifically, different compressed models are obtained, each optimized for a distinct environmental condition (e.g., high signal to noise or highly reverberant room). During inference, the best model is selected and used for ASR. This hierarchical approach allows for each compressed model to act as an expert for a given environment scenario. While increasing the number of models used reduces the overall compression gains seen with distillation, here we explore the use of only two models, thus still achieving some compression relative to the original teacher model. Moreover, as only one model is used during inference, the gains in inference time are not affected by relying on two models. Figure 3a,b depicts the two models we explore here: one that characterizes the noise levels in the environment, and another that characterizes the reverberation levels, respectively.  
As seen from the figures, for real-time inference, a noise/reverberation level selector is needed. As we assume that access to clean reference signals are not available, a \"blind\" measure is needed. Here, we explore with a signal-to-noise ratio (SNR) estimator called Waveform Amplitude Distribution Analysis (WADA), described in [28]. For the reverberation time (RT60) estimation, we rely on the speech-to-reverberation modulation energy ratio (SRMR) metric, followed by a mapping from the SRMR metric to the reverberation time (RT60) via a support vector regressor, as described in [29].  
Figure 2: Block diagram of the adapted RobustDistiller pipeline without the enhancement head.
## 4 Discussion  
This is the first study assessing the intra- and inter-examiner reproducibility of Kinovea for measuring the hip flexion angle during the SLR test. In summary, the results obtained in this study demonstrate that using the Kinovea software to measure the hip flexion angle during the SLR test based on plain video recordings is highly reliable for both intra- and inter-examiner assessments. Compared to other 2D analysis methods, Kinovea stands out as a free, open-source, and user-friendly tool, offering significant advantages for clinical applications. Additionally, the software allows for the freezing of frames at precise moments of joint ROM for an accurate measurement [27; 28] and provides more control in confusing factors during the maneuver (e.g., knee flexion compensations) compared with other tools (e.g., goniometer or inclinometer). Previous studies have analyzed the reproducibility of Kinovea for the measurement of other regions and activities, but none have conducted a study of its use for the measurement of SLR-associated ROM in LBP populations. Evidence on the reliability of goniometric measurement has shown that data may vary between different joints and actions, even between active and passive maneuvers, in relation to the complexity of movements and functional and structural differences [29]. This study provides innovative information on a useful tool to be used in combination with the SLR test, in populations with and without LBP, given its already studied applicability for the flexibility assessment [17]. Regarding the patients with LBP, having a reliable tool such as Kinovea for measuring the ROM achieved in hip flexion during the SLR test allows professionals to gather important information on adaptive neural movement, which has a major relationship with the presence of both lumbar and radicular symptoms [18]. Furthermore, beyond lumbar pathology and flexibility problems, the Kinovea software could be used in combination with the SLR test for the evaluation of other lower limb conditions such as hamstring injuries. Its use could facilitate the follow-up of subjects by both physiotherapists and sport professionals (in the case of athletes) as the relationships between ROM, time since injury, and time to return to play have been established [19; 20]. Our results are consistent with those of other studies on the use of Kinovea in other structures such as the shoulder, both in healthy adults [25] and in female volleyball players [30], lower limb during gait analysis [4] or running [5], or the cervical spine [6] among others, showing good ICC values and postulating itself as a feasible alternative for the evaluation of ROM in different joints and movements. However, as already stated in the evidence on goniometric evaluation [29], it is essential to have precise control of the measurement procedures, carry out standardized protocols, and determining specific locations for the markers [4].
# Comparison of Hysteresis Based PWM Schemes \(\Delta\Sigma\)-PWM and Direct Torque Control
## 4 Simulation Results
### Comparison of Flux Space Vector Trajectory  
DTC utilizes the flux space vector as trajectory. Figure 10 compares those trajectories. It is worth to mention that both figures do not present zero vectors and the procedure of stopping the flux space vector for decreasing torque is not visible within Figure (b)b.  
First of all, both PWM schemes ensure that the flux space vector stays on an almost circular curve. The trajectory of DTC shows a flux ripple with a magnitude that corresponds to the limits of the flux controller (\(\Delta|\underline{\Psi}|/\Psi_{0}=\pm 0.5\,\%\)). In contrast, the tolerance range of \(\Delta\Sigma\)-PWM is \(\Delta|\underline{\Psi}|/\Psi_{0}=0.37\,\%\) and corresponds to the required limits as well. The hysteresis parameter as well as the ripple of physical quantities are inversely proportional to the switching frequency. Because the average switching frequency of \(\Delta\Sigma\)-PWM is higher, more switching events per electric rotation are triggered. The tolerance in the flux space vector is smaller for \(\Delta\Sigma\)-PWM.  
Secondly, DTC switching events near the sector boundaries are rare. For explanation, the following descriptions refer to the second sector. The control uses the switching states (010) and (000) to increase the magnitude of the flux space vector. In contrast, the states (110) and (111) are used for reduction. The impact of voltage vector depends on the actual position of the flux space vector. The perpendicular component is responsible for accelerating and the parallel component for changing the magnitude of \(\underline{\Psi}\). However, the coupling between both components is dependent on the actual position of the flux space vector. Within one sector, the magnitude increasing state will change to the accelerating  
Figure 10: Trajectory of flux space vector.  
state. Thus, the change of the magnitude of the flux space vector by this switching state will be faster at the beginning of the sector. In contrast, the behavior of the accelerating switching state reverses. Just before reaching a sector boundary a certain switching state must be held for a relatively long time. After that, the control has to hold the increasing state for a long time. According to Table 1, the voltage vectors exchange their roles at sector boundaries. Thus, the control uses one switching state and the corresponding zero vector for a long time only.  
During the transition from sector two to sector three, the voltage vectors (110) and (111) are used most frequently. Hence the control changes the switching state of the first inverter half bridge only. In the meantime, the maximum negative voltage is applied in the first phase of the PMSM. Consequently, the minimum of \(u_{s1}\) will be reached. When entering sector three, the duty cycle of switching state (100) increases according to Table 1.