esa-sceva/satcom-chunk-collection huggingface dataset:
# Citrus Pest Identification Model Based on Improved ShuffleNet
## 2 Materials and Methods
### Improved SCHNet Model
#### 2.3.1 Subsubsection Parametric Linear Rectification Function  
ReLU was selected as the activation function for the convolutional layers in the ShuffleNet structure because of its two primary benefits: overcoming gradient vanishing issues and accelerating training. However, the ReLU has several drawbacks. During training, the ReLU neurons may become inactive when negative values or large gradients pass through them, thereby preventing the corresponding parameters from being updated. To address the limitations of the ReLU, a parametric linear rectifier function (PReLU) was adopted as a replacement. Initially proposed by [PERSON] et al. (2016) in 2016, PReLU introduced a learnable parameter \(\alpha\) as the base of the ReLU. Consequently, when the input is negative, the output becomes \(\alpha\) times the input value instead of simply being 0. This adjustment mitigates the dead neuron problem associated with ReLU, as even negative inputs yield nonzero outputs with a controllable slope. This slope could be learned during training, thereby enhancing the performance of the model. The PReLU activation function is represented by Equation (1).  
\[\text{PReLU}(\chi)=\begin{cases}\chi&,\chi>0\\ \alpha_{i}\chi&,\chi\leq 0\end{cases} \tag{1}\]  
where \(\alpha\) is a random number following a normal distribution between 0 and 1 and i represents a specific channel. When \(\alpha_{i}\) = 0, the PReLU(\(\chi\)) function becomes an ReLU function. When \(\alpha_{i}\) > 0, the PReLU(\(\chi\)) function becomes leaky. When \(\alpha_{i}\) is a variable parameter, PReLU(\(\chi\)) becomes a parametric ReLU function.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 10 Splicing and Termination of Hollow-Core Fibers
### Glass Sleeve Fusion Splicing  
The destruction of HCF cladding during fusion splicing can be avoided, and splice hermeticity can be ensured in the following method invented by the authors (Figure 22).  
First, a short (\(\approx\)0.6-1.0 mm) capillary made of hard borosilicate glass like Pyrex 7740 [109] or other glass with a low thermal expansion coefficient is placed around one of the cleaved fibers to be spliced. Suitable capillaries are available commercially [110]. Next, the fibers are aligned (Figure 22a), later butt-coupled and (optionally) partly fused at a temperature sufficiently low to avoid damage to the cladding (Figure 22b). After this, the capillary is moved to cover the fiber contact location (Figure 22c). In the final step, the electric arc melts this capillary, heating it to approx. 1150 \({}^{\circ}\)C, and making it adhere to the jackets of both fibers (Figure 22d). This temperature is low enough to avoid the deformation of the fine structures in the fused silica fibers. To improve the adhesion between borosilicate and fused silica glasses, and reduce residual strains, fusion shall be followed by annealing the splice at approx. 600 \({}^{\circ}\)C.  
Borosilicate and fused silica glasses have similar Young moduli: 64 GPa and 72 GPa; therefore, the glass cross-section of sleeve shall be similar to the jackets of the fibers. The finished splice will have a diameter small enough (\(\leq\)400 \(\upmu\)m for 250 \(\upmu\)m HCFs) to fit in standard heat-shrinkable fusion protectors designed for fibers in 900 \(\upmu\)m buffers. Arc offset may be required when splicing SMF to HCF of different diameters to optimize the flow of borosilicate glass.
## 2 Methodology
### Data from the Laboratory and Real-World Emission Factors  
The Sustainable Transport Unit (STU) at the JRC, located in Ispra (Italy), routinely measures exhaust emissions of light-duty vehicles (including passenger cars and light-commercial vehicles) in the laboratory and on the road for a variety of purposes using state-of-the-art instrumentation.  
Data from twenty-seven Euro 6d-TEMP and three Euro 6d vehicles were gathered to develop powertrain-specific emission factors for gases and solid particulates for the needs of the present study. All vehicles were tested following the World Harmonised Light-duty Vehicle Test Procedure (WLTP), which is the current type-approval laboratory test procedure for passenger cars and light-commercial vehicles in the European Union (EU) [22]. Emission factors of Euro 6d-TEMP/6d light-duty vehicles have been derived ad-hoc for the study using data gathered by the STU as the 2019 version of the emission inventory guidebook, used within the Emissions Database for Global Atmospheric Research (EDGAR), which did not contain emission factors for such vehicles. More detailed information on the methodology to derive the emission factors in the VELA laboratory is given in Figures S1-S3 of the Electronic Supplement of this paper. Additionally, information about the road traffic emission factors in EDGAR are given in Tables S1 and S2.
## 1 Introduction
The validation against measurements from a CALIOP showed good agreement with an absolute ALH difference of less than 1 km. The study also indicates the possibility for future satellite instruments to better retrieve aerosol height information if they may include wavelengths shorter than 410 nm [10]. There are some existing sensors that already include polarization channels in the single-angle UV band, such as the Ozone Monitoring Instrument (OMI) [19], Particulate Observing Scanning Polarization (POSP), and so on [20]. However, there is a lack of evidence that multi-angle polarization measurements are better for ALH inversion in UV than near UV. More and more multi-angle polarized instruments are being launched in China for remote sensing studies of atmospheric aerosol properties [21; 22; 23; 24]. Since the inversion of the aerosol layer height in the UV band often requires additional constraints, the aerosol model should be assumed in the inversion algorithm in advance, which may introduce errors [25]. Multi-angle polarization provides just enough observational information to improve the accuracy of the inversion. In this paper, we focus on the typical aerosol types in China and further quantitatively investigate the influence of multi-angle polarization measurements on the retrieval of ALH based on these models. The aim of this study is to determine whether the multi-angle polarization measurements of the UV band can provide enough information to retrieve ALH and to investigate how UV multi-angle polarization observations can be used for remote sensing inversion of ALH of different aerosol types. We also demonstrate the influence of aerosol and surface model parameter errors by the degree of freedom for signal (DFS) of ALH retrieval under different conditions (aerosol loads, model types, multi viewing observation and so on). The results of this study could be used to develop the next generation spaceborne polarimetric sensors and corresponding ALH retrieval algorithms.
# A Study of Forest Phenology Prediction Based on GRU Models
## 3 Results and Analysis
### GEI Data Prediction  
The time series dataset was split into training and testing sets using an 80-20 split in the model. The moving average method was used for prediction, which involves selecting a sliding window size. A simple moving average model was used to predict the next value in the time series based on a fixed number of past values. The model was trained by iterating over all data 100 times and minimizing the error between the actual and predicted values during the training period. For the prediction of future 60-day data, the predicted values were obtained in a Jupyter notebook environment, and the results were plotted (Figure 9).  
The model predicts the future results for the next 60 days, and the GRU model performs well as the predicted values are close to the real values. Upon analysis of three preset points, it can be observed that the error between the predicted and real values of GEI for the next 60 days is minimal. Furthermore, the predictive performance of the GRU model demonstrates remarkable alignment with the ground truth data. Based on the forest growth conditions in 2021, the forest was in a dormant period for the next 60 days. The GEI data showed a relatively flat trend with minor fluctuations due to factors such as snowfall. The forest growth trend predicted by the GRU model is consistent with the actual growth patterns, indicating its efficacy in predictive modeling. The minor differences observed in the predicted growth trend for the 60-day period in 2022 suggest that the GRU model has achieved a high degree of accuracy in its predictions.  
Figure 9: The results of the GRU model prediction.
# Rapid and Accurate PPA Prediction for the Template-Based Processor Design Methods
## 5 Discussion  
To meet the demand of processors with different PPA requirements for various scenarios in the AIoT, this paper presents a rapid and accurate PPA prediction method for template-based processor design methods. The two prediction models, the ML-PM and MNL-PM, are built to estimate the PPA for certain parameter designs. The experimental results show that the MNL-PM is better than the ML-PM and both prediction models work well in the instance of the single-core chip template in the Rocket Chip Generator. However, as limitation of the value range is small and the limitation of the single-core chip template whose PPA range is small, the advantage of the MNL-PM is not obvious as shown in Table 4. Therefore, in our further study, we will work on improve the chip template and enlarge the value range of the parameters in the template.
## 2 Technical Background of D&F Co-Operative 5G System Model
Let the received RF composite signal matrix at the user equipment be denoted as \(\mathbf{Y}_{\rm DF}^{2}\)\(\in\mathbb{C}^{\rm N_{R}^{D}\times T}\), which can be further expressed as  
\[\mathbf{Y}_{\rm DF}^{2}=\mathbf{H}^{2}\hat{\mathbf{X}}+\mathbf{N}_{\rm DF}^{ 2}, \tag{5}\]  
where \(\mathbf{H}^{2}\in\mathbb{C}^{\rm N_{R}^{D}\times N_{\rm T}^{R}}\) is the frequency-wireless channel matrix between the D&F relay node and UE to transport \(\mathbf{N}_{\rm r}\times T\) signals, and \(\mathbf{N}_{\rm DF}^{2}\)\(\sim CN(0,\sigma_{\rm N_{\rm DF}^{2}}^{2}\mathbf{I})\) is the AWGN with noise variance in the access link \(\sigma_{\rm N_{\rm DF}^{2}}^{2}\). UE is equipped with FR1-Rx chains and \(\mathbf{N}_{\rm R}^{\rm D}\) antennas. The number of FR1-Rx chains is the same as the number of receiver antennas, as shown in Figure 2. The RF\(\rightarrow\)IF frequency converters at the UE perform downconversion of baseband frequency on the received RF signal \(\mathbf{Y}_{\rm DF}^{2}\). This stage is represented by the \(\mathbf{M}_{\rm DR}\in\mathbb{C}^{\rm N_{R}^{D}\times N_{\rm R}^{D}}\). Therefore, at the UE, baseband matrix \(\mathbf{G}_{\rm BB}\in\mathbb{C}^{\rm N_{d}\times N_{\rm R}^{D}}\) is employed to estimate the desired data symbols matrix being the inverse process to the performed in the base station and can be written as  
\[\hat{\mathbf{S}}=\mathbf{G}_{\rm BB}^{\rm H}\mathbf{M}_{\rm DR}^{\rm H}( \mathbf{H}^{2}\hat{\mathbf{X}}+\mathbf{N}_{\rm DF}^{2})=\hat{\mathbf{S}}+ \mathbf{N}_{\rm D}, \tag{6}\]  
where \(\mathbf{N}_{\rm D}=\mathbf{G}^{\rm H}\mathbf{N}_{\rm DF}^{2}\) and \(\mathbf{G}=\mathbf{G}_{\rm BB}\mathbf{M}_{\rm DR}\). From Figure 2, after the IQ is captured and downconversion of the baseband frequency is performed from the SDR platform hardware interface, the first stage in the \(\mathbf{G}_{\rm BB}\) is the frequency/time offset correction and SSB demodulation. For one-time obtaining the main parameters of the received 5G Signal as cell ID, the UE-node executes the full bandwidth demodulation. In this work, we have implemented the LS channel estimation and MMSE equalization as standardized by the 3 GPP. Finally, it is performed decoding the control, physical, and data channels where \(\hat{\mathbf{S}}\) is obtained. In (6), the first term is the desired signal and the second term denotes the noise in access link at the decode-and-forward (D&F) relay node.
## 3 Metabolic Responses to Different Antibiotic Classes
### Amino Acid Metabolism: Reserve, Signaling, and Persistence  
Amino acids play essential roles in cellular metabolism, serving as energy reserves through their oxidation under nutrient-limiting conditions, acting as signaling molecules that modulate key metabolic pathways, and functioning as precursors for the biosynthesis of a wide array of bioactive compounds, including neurotransmitters, hormones, and nucleotides [92].  
Under antibiotic pressure, bacteria profoundly modulate amino acid metabolism to adapt to stress and promote persistence.  
In particular, arginine and proline activate the urea and carbamoyll phosphate pathways, contributing to the regulation of intracellular pH [93].  
Tryptophan degradation leads to the production of indole, a signaling molecule that influences persister cell formation and the activation of toxin-antitoxin systems [94; 95].  
Finally, glutamine and glutamate support anaperosis of the Krebs cycle and glutathione synthesis, sustaining energy production and defense against oxidative stress [96].  
Biosynthetic pathways and amino acid transport systems are tightly regulated by metabolic circuits that respond to intracellular availability of ATP and NADPH [90; 97].  
These energy metabolites act as key signals, modulating enzymatic activity and gene expression to coordinate amino acid synthesis and import with the cell's energetic and redox status [97].  
Such regulation ensures efficient integration between amino acid metabolism and cellular bioenergetic demands [98].  
Table 6 recapitulates the adaptive modulation of the TCA cycle and glyoxylate shunt as key survival mechanisms during antibiotic stress.  
Figure 5: Energetic and regulatory constraints associated with efflux pump activity in bacterial antibiotic resistance. Efflux systems, regulated in part by quorum sensing signals, depend on the proton motive force (PMF) for active extrusion of toxic compounds, including reactive oxygen species (ROS). The generation of PMF requires metabolic energy, leading to an energy cost that may impact bacterial fitness. NADH produced from nutrient metabolism supports ROS detoxification and energy production. The energetic burden of maintaining efflux activity, particularly under antibiotic pressure, highlights the interplay between redox balance, signal integration, and bacterial survival strategies. Image created using BioRender (accessed via web application, version as of May 2025), GraphPad Prism (version 10.1.2-2024), and PowerPoint (Microsoft 365, version 2404).
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 4 Implementation Details
### Detail Description  
For the \(M\times N\) size image (M and N default to an even number), after db1 wavelet base decomposition, the image becomes half of the original image size (M/2 \(\times\) N/2). In the process of column gradient equalization, to avoid losing image details, the window value is 1 and the one-dimensional vector is adopted. The variance \(\varphi=5\) selected by the experiment. In the process of spatial filtering, the approximate components of wavelet decomposition are taken as the guidance image, the output after the equalization of the column gradient is taken as the input image of the guiding filter, the regularization parameter \(\varepsilon\) is selected as 0.2\({}^{2}\)[28], and the filtering window h is also selected as a small window to avoid image edge blur. In our implementation we set h = 0.3H.
# Dynamic Modeling and Response Analysis of an Optical Machining Robot with Prismatic Joint Clearance
## 2 Modeling of Spatial Prismatic Joint Clearance
### Contact Force Calculation Under Different Contact Modes
#### 2.2.2 Contact Force Calculation for Line Contact  
In the line contact, the contact surfaces have rectangular contact areas, and this interaction is known as cylindrical contact. The contact forces are distributed in a cylinder as described below [19]:  
\[\mathbf{F}(x)=\dfrac{2P}{\pi a^{2}b}\sqrt{a^{2}-x^{2}} \tag{5}\]  
where \(x\) is the perpendicular distance; \(a\) and \(b\) are the width and length of the contact area, respectively; \(P\) denotes the pressure per unit length; \(R\) is the equivalent radius of curvature; and the penetration depth \(\delta\) is as follows [20]:  
\[\delta=\dfrac{P}{\pi E*l}[1-\ln(\dfrac{PR}{\pi E*l^{3}})] \tag{6}\]
## 6 Results and Discussion: Back-Emf
### Summary: SE and DE on Back-Emf of PMSMs and MPMSMs  
In summary, it was found that eccentricity has a low impact on the back-emf of the evaluated slot/pole combinations. In this regard:  
* Back-emf magnitude is not affected by eccentricity, which was observed for all the evaluated slot/pole combinations and can translate into a low impact of eccentricity on the mean torque;
* Slot/pole combinations having a high number of evenly distributed phase groups (see Figure 9) are less likely to develop back-emf unbalance. That is the case for the 18- slot, 12-pole, 24-slot, 20-pole, and 24-slot, 28-pole machines.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{\(\text{Q}_{s}/2p\)ECC} & \multicolumn{8}{c}{**0 mm (Faultless)**} & \multicolumn{8}{c}{**1.00 mm**} \\ \hline  & & \multicolumn{4}{c}{Modular [V]} & \multicolumn{4}{c}{Monolithic [V]} & \multicolumn{4}{c}{Modular [V]} & \multicolumn{4}{c}{Monolithic [V]} \\  & HO & A & B & C & A & B & C & A & B & C & A & B & C \\
18S 12P & 1 st & 247.2 & 247.3 & 247.2 & 263.5 & 263.7 & 263.5 & 247.5 & 247.6 & 247.4 & 263.7 & 263.8 & 263.7 \\  & 3 rd & 21.9 & 21.9 & 21.9 & 24.5 & 24.1 & 24.5 & 21.9 & 22.0 & 22.1 & 24.6 & 24.6 & 24.6 \\  & 1 st & 280.7 & 280.8 & 280.7 & 275.1 & 275.2 & 275.1 & 281.2 & 280.6 & 283.0 & 275.3 & 275.4 & 277.5 \\  & 3 rd & 31.7 & 31.7 & 31.7 & 36.0 & 36.1 & 36.1 & 30.5 & 32.9 & 36.97 & 36.2 & 36.2 & 41.3 \\  & 1 st & 276.7 & 276.8 & 276.7 & 281.7 & 281.7 & 281.7 & 277.4 & 277.4 & 277.3 & 282.3 & 282.3 & 282.2 \\  & 3 rd & 37.9 & 37.8 & 37.9 & 38.6 & 38.6 & 38.6 & 40.3 & 40.4 & 40.4 & 40.4 & 40.4 & 40.5 \\  & 1 st & 283.4 & 283.4 & 283.3 & 283.2 & 283.2 & 283.1 & 284.0 & 285.0 & 282.9 & 283.9 & 284.9 \\  & 3 rd & 28.7 & 28.7 & 28.7 & 29.0 & 29.0 & 29.0 & 29.3 & 30.8 & 32.2 & 29.6 & 31.0 & 32.2 \\  & 1 st & 300.9 & 301.0 & 300.9 & 292.5 & 292.5 & 292.5 & 303.2 & 303.2 & 303.1 & 294.6 & 294.6 & 294.5 \\  & 3 rd & 9.5 & 9.5 & 9.6 & 19.0 & 19.0 & 19.1 & 9.7 & 9.7 & 9.7 & 19.8 & 19.8 & 19.8 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Fundamental component and third-order harmonic of back-emf for different PMSM and MPMSM slot/pole combinations in the presence of SE.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ECC** & **Modular [V]** & **Monolithic [V]** & \(N_{pg}\) \\ \hline
18S 12P & 0.453 & 0.527 & 6 \\
18S 20P & 7.134 & 7.284 & 2 \\
24S 20P & 0.391 & 0.367 & 4 \\
24S 22P & 4.012 & 3.934 & 2 \\
24S 28P & 0.397 & 0.539 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Maximum back-emf unbalance for different PMSM and MPMSM slot/pole combinations in the presence of SE.
# Vision System in Product Quality Control Systems
## 1 Introduction
The result of the conducted control decides whether to refer the semi-finished product to further processing or remove it from the process due to noncompliance with the parameters accepted in the manufacturing documentation. The product's measurement parameters are sent to cooperating machines and master systems and used in the production management process. It is also important to link the parameters describing the product quality with the parameters of the manufacturing process [14; 15; 16]. Increasing the requirements for developing machine functions toward the realization of diagnosis, analysis, and the reporting of parameters describing machine operation force the development of cyber-physical systems [16; 17; 18; 19; 20]. These systems are responsible for process control using sensor networks and production monitoring using the digital network [21]. Additionally, cyber-physical systems enable access to distributed industrial data [22]. Cyber-physical systems should be seen as solutions that integrate sensor networks with machine and robot control systems that continuously use industrial networks to collect and archive production data. It is currently assumed that the quality of product workmanship should be assessed based on recording and evaluating the manufactured product's parameters in the technological operation time. In many cases, quality control carried out after the technological process does not allow for detecting all defects arising from the technological operations [23; 24]. In the context of developing the \"Smart Industry\" concept, the development of the control and measurement capabilities of machines should be connected with introducing new measurement methods on machines currently used only in laboratory control. In many measurement tasks, this involves the development of new hardware solutions and measurement methods, taking into account the specification of the machine workspace. Designers of measurement systems used in machines reach for various technologies, enabling the realization of tasks related to the assessment of product parameters [25]. The paper aimed to indicate the relationship between the possibilities of developing production systems, production management systems, and the development of the capabilities of industrial vision systems. The range of imaging methods, the content of tasks performed by modern vision systems in relation to industrial sectors, and the level of robotization level are indicated. In addition, the development of measurement and diagnostic capabilities is presented based on reports on the market for vision systems observed in recent years, and forecasts for the development of this technology in the coming years.
## 4 Conclusions  
In summary, we have provided evidence that DOT can be utilized as a new approach to monitor the heating process in HIFU therapy. Furthermore, correlation was performed to establish the quantitative relationship between \(\mu^{{}^{\prime}}_{s}\) recovered by DOT and temperature measured by a thermocouple. Extensive ex vivo animal tissue tests (with porcine and chicken breast muscle tissue) were conducted to illustrate the capability of DOT to image \(\mu^{{}^{\prime}}_{s}\) changes in real-time during HIFU therapy. The optical property changes were monitored using a fast DOT imaging system and then reconstructed using a FEM-based DOT algorithm while the temperature was detected and verified by using a thermocouple simultaneously. The temperature-to-\(\mu^{{}^{\prime}}_{s}\) conversion for DOT imaging in the 45 to 60 \({}^{\circ}\)C temperature range was described by a two-degree polynomial and two-term exponential models. The results showed good agreement between the measured and predicted temperature rises, which indicates that this approach based on DOT has the potential to be used for noninvasive, real-time temperature monitoring during HIFU therapy.  
Figure 6: Temperature maps of porcine and chicken breast muscle at 10 min after HIFU treatment based on the polynomial model and exponential model.  
**Author Contributions:** Conceptualization, H.J. and H.Y.; methodology, H.Y.; software, H.J., H.Y. and S.A.; validation, H.Y. and S.A.; formal analysis, H.Y. and S.A.; writing--original draft preparation, H.Y.; writing--review and editing. S.A. and H.J.; visualization, H.Y. and S.A.; project administration, H.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by the University of South Florida.  
**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.