esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Parameter Impact Analysis
#### 4.2.2 Different Image Block Sizes on Building Extraction  
Using image blocks as input and CNN to learn depth features shows strong recognition ability, but the ability has certain defects in space segmentation. Over-smoothing of the boundary often occurs in the classification results, and it is easy to cause smaller targets to be ignored. Although a certain degree of excessive smoothing of the edges can be understood and accepted. Additionally, the use of the minimum adaptive map for CNN can slightly slow down the smoothing phenomenon [42]. However, we find that the use of small image block sizes during building extraction is not the best for building extraction, so we test the size of the image blocks.  
In this paper, we use three different types of PolSAR data. For different data, we select different-sized samples in the same position as the input of CNN. Here we will discuss the effect of different speckle sizes on building extraction, and the results are not superpixel-constrained. The building extraction evaluation index F1 size under different sizes is shown in Figure 12.  
We have found that a large image block size will lead to the occurrence of serious over-smoothing, resulting in poor extraction of the building. Too small a pattern will also  
Figure 11: Sample selection and building extraction results. (**a**) is the samples from the true surface map; (**b**) is the result of building extraction using (**a**) samples; (**c**) consists of positive and negative samples (dark green); and (**d**) is the result of building extraction using (**c**) samples.  
Figure 12: Accuracy of building extraction results under different image block sizes.  
lead to a decrease in extraction accuracy. It can be seen from the figure that, under the three-image data, the scale has an outstanding building extraction result of 25 \(\times\) 25 pixels to 29 \(\times\) 29 pixels. The size of the sample spots under GF-3 and RADASAT-2 images is suitable for 29 \(\times\) 29 pixels. The size of the sample spots under ESAR images is 25 \(\times\) 25 pixels. At this scale, the boundary smoothing phenomenon has a good balance with the building extraction accuracy.
# Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
## 3 Results
### User Interfaces  
AI-based DTs in healthcare are characterized by user interfaces that are highly intuitive and visually engaging, designed to present complex data in an easily understandable format. They often include 3D representations of the human body, where clinicians can interact with different organs or systems, gaining real-time insight into the patient's condition. Interactive dashboards display a wealth of information, from vital signs to predictive analytics, using clear graphs, tables, and color-coded alerts to highlight critical issues. Interfaces are typically responsive, adapting to different devices such as tablets or desktops, ensuring accessibility in a variety of clinical environments. Advanced features can include simulation controls that allow healthcare providers to predict the outcomes of potential treatments by manipulating variables in the digital twin. Natural language processing capabilities are often integrated, allowing users to search the system using conversational language, making the technology more accessible to non-specialists. The interfaces also emphasize data privacy and security, with multi-layered authentication and encrypted data streams to protect patient information. Collaboration tools are embedded, allowing multiple healthcare providers to simultaneously view and interact with the DT, supporting a collaborative approach to patient care. Additionally, these interfaces are designed to be highly configurable, allowing clinicians to tailor the display to their specific needs, whether focusing on a specific organ system or integrating a specific patient history. They also offer real-time updates and notifications, informing users of any significant changes in a patient's condition or new insights generated by the AI algorithms. Finally, the interfaces often include educational modules, providing training and support to healthcare providers, ensuring they can effectively use the advanced features of the digital twin [61; 62].
## 2 Materials and Methods
### Tolerance Assays of Yeast Strains
#### 2.5.7 Analysis of Volatile Aroma Compounds in Sea Buckthorn Fruit Wine  
The determination of volatile aroma compounds in different fruit wines was performed using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), based on the method described by [PERSON] et al. (2025) [PERSON] et al. (2025) [PERSON] et al. (2025), with modifications for optimization.  
Sample Preparation: The sample pretreatment was conducted using the headspace solid-phase microextraction method. A 5 mL wine sample was mixed with 1 g of sodium chloride and 50 \(\upmu\)L of internal standard (3-octanol) in a 15 mL headspace vial. After vortex mixing, the vial was kept in a refrigerator at 4 \({}^{\circ}\)C overnight. The sample vials were processed using a TriPlus RSH Autosampler-SPME system with a 50/30 \(\upmu\)m DVB/CAR/PDMS fiber. The extraction conditions were set as follows: adsorption at 60.0 \({}^{\circ}\)C for 30 min, followed by a desorption period of 5 min.  
GC Analysis Conditions: GC-MS analyses were carried out on a TRACE 1310 gas chromatograph coupled with an ISQ-LT mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). A DB-WAX column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) was used for separation. The injector temperature was 250 \({}^{\circ}\)C, and helium was used as the carrier gas at a constant flow rate of 1.2 mL/min. The injection volume was 1 \(\upmu\)L in split mode with a split ratio of 40:1. The oven temperature program was as follows: initial temperature 40 \({}^{\circ}\)C for 3 min; increased at 6 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C (held for 2 min); then increased at 10 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C (held for 6 min).  
MS Detection Conditions: Mass spectrometry (MS) was conducted using an electron impact (EI) ionization source at an electron energy of 70 eV. The ion source temperature was set at 200 \({}^{\circ}\)C, and the interface temperature was maintained at 250 \({}^{\circ}\)C. The mass scan range was set between 33.00 and 450.00 amu.  
Identification and Quantification of Volatile Compounds: Volatile compounds were presumptively identified by comparing their mass spectra with entries in the NIST Mass Spectral Library (NIST 17) using Thermo Xcalibur (version 4.1) software. A similarity threshold of 85% was applied for tentative identification. As no authentic reference standards were injected and retention indices were not calculated, all identifications are considered presumptive. Quantification was performed using the internal standard method, with 3-octanol as the internal standard. The concentration of each compound was calculated based on the ratio of its peak area to that of the internal standard, assuming similar response factors for compounds of similar chemical nature. Results were expressed as micrograms per liter (\(\upmu\)g/L) of wine. Each sample was analyzed in triplicate.
# Free Space Optic Receiver with Strongly Overlapped Photodetectors' Field of View
## 1 Introduction
### Parallel Amplifier Topology
Equation (3) can be shortened to:  
\[\frac{1}{N}\sum_{m=1}^{N}(X_{n}+Y_{m})^{2}=X_{n}^{2}+\frac{1}{N}\sum_{m=1}^{N}Y _{m}^{2}=X_{n}^{2}+\overline{Y^{2}}. \tag{4}\]  
The mean power value of the sum of the two whole sequences can be found in the same manner:  
\[\overline{(X+Y)^{2}}=\frac{1}{N}\sum_{n=1}^{N}\left(X_{n}^{2}+\overline{Y^{2} }\right)=\overline{X^{2}}+\overline{Y^{2}}. \tag{5}\]  
Equation (5) may be generalized for an arbitrary count of the \(u_{nA1}\), \(u_{nA2,2}\)..., \(u_{nA\text{s}}\) sequences, which are voltage realizations of noise powers \(p_{nA1}\), \(p_{nA2}\),..., \(p_{nA\text{s}}\). For \(p_{nA1}=p_{nA2}=\ldots=p_{nA\text{s}}\) conformity, Equation (5) can be rewritten as:  
\[\overline{(u_{nA1}+u_{nA2}+\ldots+u_{nA\text{s}})^{2}}=\overline{su_{nA}^{2}} =sp_{nA}. \tag{6}\]  
After this, the SNR in the photodetector's electrical part, in Equation (2), takes the form:  
\[SNR_{out}=\frac{s^{2}p_{\text{s}}K_{21}^{2}}{s^{2}p_{\text{n}}K_{21}^{2}+sp_{ nA}}\ \ \ [/]. \tag{7}\]  
Due to the \(s^{2}\) component, \(p_{nA}\) may be eliminated more easily. As result, \(SNR_{out}\) will be almost equal to \(SNR_{in}\) in Equation (1), which is desirable.
## 3 Workflow and Validity Analysis of HDE Algorithm
### Validity Analysis of HDE Algorithm  
In this section, the validity constraints of the HDE algorithm proposed in Section 2 are discussed in detail. First, the conditions for satisfying the approximation assumptions in the algorithm's derivation are analyzed. Then, we derive a model for the motion construction error under the approximation assumptions, equating the motion construction error to the slant-range error and deriving its effect on the deceptive scene imaging.
# Computer-Aided Design and Additive Manufacturing for Automotive Prototypes: A Review
## 2 Material and Methods
### Essential Features of Current Research
#### 2.3.7 Challenges in Adopting AM Techniques  
Adopting additive manufacturing (AM) techniques in the automotive industry presents several challenges. One significant challenge is the need for new design methodologies that fully exploit AM's capabilities. Traditional design principles must be adapted to accommodate the geometric freedom and ability to create complex internal structures inherent to AM [47]. This requires designers to deeply understand both the manufacturing process and material properties, which can be a steep learning curve.  
Another challenge is integrating AM within existing manufacturing workflows. The extended Design for Manufacturing and Assembly (DFMA) process, which includes considerations for AM, often necessitates a comprehensive re-design of products to realize AM's full benefits [48]. This re-design process can be resource-intensive and requires significant investments in time and capital. Additionally, practical challenges related to the material properties and consistency of AM-produced parts exist. Variability in mechanical properties, such as tensile strength and thermal expansion, can affect the reliability of automotive components produced using AM [47]. Achieving consistent quality in large-scale production remains a significant hurdle due to these variations.  
Economic factors also play a critical role in the adoption of AM. The high cost of AM equipment and the need for specialized labor to operate and maintain these technologies can be prohibitive for some manufacturers [48]. This makes it essential to develop cost-effective solutions and training programs to facilitate wider adoption. Lastly, the scalability of AM processes is limited by the build volume of current machines, restricting the size of components that can be produced. This limitation requires manufacturers to carefully consider the feasibility of AM for different types of automotive parts and to explore hybrid manufacturing approaches that combine AM with traditional methods [48].  
These challenges underscore the need for ongoing research and development to address the obstacles associated with integrating AM into the automotive industry. By overcoming these hurdles, manufacturers can fully leverage the advantages of AM, including improved design flexibility, reduced material waste, and enhanced product performance.
## 4 Experimental Results and Analysis  
### ZWNet Training  
For this experiment, ZWNet is implemented using PaddlePaddle ([[https://github.com/PaddlePaddle/Paddle](https://github.com/PaddlePaddle/Paddle)]([https://github.com/PaddlePaddle/Paddle](https://github.com/PaddlePaddle/Paddle)) (accessed on 30 July 2023)) and executed on the Paddle AI Studio ([[https://aistudio.baidu.com](https://aistudio.baidu.com)]([https://aistudio.baidu.com](https://aistudio.baidu.com)) (accessed on 30 July 2023)) with cloud computation powered by Nvidia A100. The optimizer employed is Adamax, and a Step Decay strategy is applied to the learning rate. The initial learning rate is set to 0.0001, with a gamma factor of 0.8. The learning rate is adjusted for each epoch.  
The training dataset comprises a selection of images from mini-ImageNet ([[https://www.kaggle.com/datasets/arjunashok33/minimagenet](https://www.kaggle.com/datasets/arjunashok33/minimagenet)]([https://www.kaggle.com/datasets/arjunashok33/minimagenet](https://www.kaggle.com/datasets/arjunashok33/minimagenet)) (accessed on 30 July 2023)) and additional images collected from the internet. The images are selected to train ZWNet with varied image features, aiming to improve its generalization ability. The training dataset consists of 2000 images; a subset of these images is displayed in Figure 8 for visualization.  
We employed data augmentation as a preprocessing step to expand the training dataset and enhance ZWNet's robustness. The image processing methods used in data augmentation are detailed in Table 1. These methods encompass a mix of techniques, and following data augmentation, the number of training images increased to 100,000.  
Figure 8: A few examples of training datasets. (**a**) Car. (**b**) Lake. (**c**) Panda. (**d**) Koala.  
When training [PERSON], two principles were adhered to, as mentioned in Section 3.5. First, an image and its augmented versions were assigned the same image identifier. Second, different images (including their augmented versions) were allocated different identifiers. We used an auto-incrementing number as the image identifier for simplicity.  
To assess ZWNet's performance, we employed a test dataset consisting of one hundred images. Four of these test images are presented in Figure 9 for visualization. It is noted that none of these images are in the training dataset.  
The training process involved updating ZWNet using the training data for each epoch and then evaluating the loss with the test images. If the loss on the test data no longer decreased or even began to increase, the training was terminated. Consequently, the test data was solely used to verify whether the training process was sufficient and did not impact the updating of ZWNet. Furthermore, once successfully trained, ZWNet remained stable and deployable. It could process arbitrary images for zero-watermark service without the need for retraining or adjustments. The changes in loss during the training stage are illustrated in Figure 10.
## 5 Experimental Verification
### The Improvement of the \(Tp\) with EOB  
In Figure 12, the closed-loop bode responses of the position with or without EOB were described. When the EOB was introduced, the amplitude of the position controller should drop a little to guarantee enough margins. Although the bandwidth of the system with EOB was decreased by about 2 Hz, it would make no difference, because the performance below 1 Hz was adopted for tracking.  
Figure 11: The closed-loop bode response of the velocity.  
Figure 10: The open-loop bode response of the velocity.  
The error suppression bode response of the MTOS was presented in Figure 13, which only provided the target signal. Three methods were listed overall, the basic VPDC method, the direct feedforward method and the EBO method based on the VPDC. Obviously, the direct feedforward method based on sensors fusion with the predictive way had the best error suppression performance. However, the performance of the EBO method was close to the direct feedforward, which signified that the previous analysis was right and the proposed way was approximately equivalent to the direct feedforward method. Moreover, compared with the direct feedforward, the EBO method did not need an additional position sensor to measure the angular of the platform and cost a smaller amount of computation. Below 2 Hz, the system with EOB would have a better ability of error suppression than the pure VPDC without EOB; actually, it would be valid for both tracking and anti-disturbance. With the decrease of the frequency, the promotion of ability would be more apparent and could even reach \(-20\) dB under 0.1 Hz. In the frequency band between 2 Hz and 18 Hz, the performance of the pure VPDC was a little better than the system with EBO, as shown in Figure 7, and the performance could be accepted. In Figure 14, the time-domain residual error was presented with the given target signal of different frequencies. In the frequency domain below 1 Hz, the EBO can make a big difference.  
Figure 12: The closed-loop bode responses of the position.  
Figure 13: The error suppression response of the MOTS.
## Appendix A Full Description of the SWH Model
### Calculation of Resistances  
We used the same approach as [PERSON] and [PERSON] [9] to calculate the three aerodynamic resistances, i.e., \(r_{\rm ac}\), \(r_{\rm as}\) and \(r_{\rm aa}\). Soil surface resistance \(r_{\rm ss}\) was estimated as the function of soil water content [23]:  
\[r_{\rm ss}=b_{1}(\frac{SWC_{\rm s}}{SWC})^{b_{2}}+b_{3} \tag{16}\]  
where \(SWC\) and \(SWC_{\rm s}\) are the soil water content and saturated water content in the surface soil (m\({}^{3}\cdot\)m\({}^{-3}\)), and \(b_{1}\) (s\(\cdot\)m\({}^{-1}\)), \(b_{2}\) and \(b_{3}\) (s\(\cdot\)m\({}^{-1}\)) are empirical constants.  
The Ball-Berry model [21] is used to calculate the canopy stomatal resistance, \(r_{\rm sc}\):  
\[r_{\rm sc}=\frac{1}{g_{0}+\frac{a_{1}p_{\rm s}h_{\rm s}}{c_{\rm s}}} \tag{17}\]  
where \(p_{n}\) is a key driving variable to estimate \(r_{\rm sc}\). We used the gross primary productivity (GPP) estimated from the measurements of eddy covariance systems in our previous study [14]. For the purpose of regional scale applications, we re-estimated GPP with a satellite-based light-use efficiency model:  
\[\rm GPP=\varepsilon\times\rm PAR\times\rm FPAR \tag{18}\]  
where PAR is the incident photosynthetically-active radiation (umol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) and FPAR is the fraction of PAR being absorbed by the canopy. FPAR is estimated as a function of NDVI (FPAR = 1.24 NDVI \(-\) 0.168; [PERSON] et al. [25]). \(\varepsilon\) is the light use efficiency (umol CO\({}_{2}\) umol\({}^{-1}\) PPFD) and is down-regulated by air temperature, soil water moisture and the vapor pressure deficit (\(VPD\)):  
\[\varepsilon=\varepsilon_{\rm max}\times f(T)\times f(VPD) \tag{19}\]  
\[f(\rm T)=\frac{(T-T_{\rm min})(T-T_{\rm max})}{(T-T_{\rm min})(T-T_{\rm max})- \left(T-T_{\rm opt}\right)^{2}} \tag{20}\]  
\[f(VPD)=\frac{VPD_{\rm max}-VPD}{VPD_{\rm max}} \tag{21}\]where \(\varepsilon_{\max}\) is the apparent quantum yield or maximum light-use efficiency and \(f(T),f(W)\) and \(f(VPD)\) are the downward regulation scalars for the effects of temperature and \(VPD\) on light-use efficiency of vegetation, respectively. \(T_{\min}\), \(T_{\max}\) and \(T_{\mathrm{opt}}\) are the minimum, maximum and optimum air temperature (\({}^{\circ}\)C) for photosynthetic activity, respectively. If the air temperature falls below \(T_{\min}\) or increases above \(T_{\max},f(T)\) is set to zero. In this study, \(T_{\min}\), \(T_{\mathrm{opt}}\) and \(T_{\max}\) were set to 0, 20 and 40 \({}^{\circ}\)C, respectively. If the estimated \(f(VPD)\) was less than zero, \(f(VPD)\) was set to zero [27]\(VPD_{\max}\) was set to 3.5 kPa.
## 4 Performance Analysis and Discussion
### Simulation Environment  
The simulation environment has 200 sensor nodes deployed in a 2,000 m \(\times\) 2,000 m area. A two-ray ground is adopted as the radio propagation model and an omni-directional antenna with unity gain. Each data packet size has 64 bytes, query packet and the others are 36 bytes. The source node generates one data packet per second. Each simulation time lasts for 200 seconds. The power consumptions of the nodes for transmitting, receiving and idling are approximately 660 mW, 359 mW and 35 mW respectively.  
The performance of the RRM mechanism was measured and compared with two existing well-known protocols, AODV and DSR, using the packet delivery ratio, reliability of delivery, control overhead, average latency, throughput and system lifetime.  
* Packet delivery ratio refers to the ratio of the number of data packets received by the destination to the number sent by the source.
* Reliability of delivery refers to the reliability of path between the destination and the source.
* Routing overhead refers to the total number of packets generated during discovering routing paths.
* Average latency refers to the average time between the time when a source transmits a packet and the time when a sink receives the packet.  
Figure 14: DSR operations.  
* Throughput refers to the number of bytes passing through a data communication system per period of time.
* System lifetime refers to the time that the network is alive.
## 4 Discussion
Miniscrews are simpler, smaller, less invasive, more economical, and do not require a long interval between placement and force application, since loading can occur immediately after placement. Their main advantage is their ability to move specific teeth without involving other teeth or using inter arch mechanics [34]. However, the stability of the miniscrew is influenced by their insertion sites; it has been described [36] that the failure rate for miniscrews inserted in the interradicular space is higher than that in the retromolar area. In this case series, in which miniscrews were located in the retromolar area, none of them suffered loss of mechanical anchorage until the end of the straightening of the inclined molars. Due to numerous advantages and according to a systematic review of the literature, miniscrews seem to constitute a reliable solution for treating tipped or impacted molars [37]. The main clinical involvement of the use of miniscrews in the retromolar area is the considerable improvement in the position of the second molar present after the absence of the first molar, increasing the restorable distal mean prosthetic space and handling correct prosthetic spaces for the replacement of the molar absent by dental implants. In addition, it is possible to avoid the retention of food and dental plaque that the open space can generate when the second molar is poorly positioned, and the prosthesis cannot seal it. All these improvements can be obtained in a short time (2-3 months usually) without the need to associate with orthodontic treatments in those patients who do not require it. Patients are also more satisfied with the more invisible treatment compared to conventional methods. The present clinical series suffered certain limitations. A larger sample size would have been preferable in order to amplify the quantity of data for analysis. Moreover, post-treatment follow-up would make it possible to assess the medium- to long-term prognosis of the treatment. This is a preliminary study to analyze the behavior of miniscrews on which it is intended to increase the sample and the follow-up time in the future. More studies of the use of miniscrews for straightening molars and producing adequate prosthetic space to replace missing teeth are needed.