esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Finite Element Analysis
### Finite Element Type and Mesh Size  
In this study, the Shell181 element available in the ANSYS library was used to model the steel tube of Warren CHS tubular trusses since this element is suitable for analyzing thin to moderately thick shell structures. The Shell181 element is a 4-node element with six degrees of freedom at each node: translations in the x, y, and z axes. In addition, Shell181 is well-suited for linear, larger rotation, and large strain nonlinear applications.  
The solid element has been used by many researchers for finite element analysis of concrete-filled tubular trusses. In this study, the Solid65 element available in the ANSYS library was used to model the concrete infill since this element is used for the 3-D modeling of solids without reinforcing bars, and the solid is capable of cracking in tension and crushing in compression. The element Solid65 is  
Figure 12: Bilinear stress–strain curve of steel tube.  
Figure 13: Stress–strain model used for confined concrete.  
defined by eight nodes with three degrees of freedom at each node: translations in the nodal x, y, and z directions.  
In order to guarantee the mesh quality, a sub-zone mesh generation method was used during the finite element modeling. In this method, the entire Warren CHS tubular truss was divided into two different zones. One is the mesh dense zone at every joint, meshed by free meshing. The other is the mesh loose zone between tubular joints, meshed by mapped meshing. The convergence studies were carried out to obtain the optimum finite element mesh size. It was found that the smart size of 10 was appropriate for the mesh dense zone. The ratio of the long side to short side of the mesh element of about 16 could achieve accurate results with minimum computational time. The typical finite element mesh of a Warren CHS tubular truss is shown in Figure 14.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 5 Discussion  
The remote sensing and ML methods discussed above were evaluated in this study for lithological classification of the well-exposed Khyber area having negligible plant cover. Irrational geological conclusions might result from human error coupled with inaccessibility to challenging terrain. Remote sensing can help explorers to identify specific regions of interest by geological mapping.
## 2 Materials and Methods
### Retrieval Methodology
#### 2.2.1 The Forward Model
Utilizing the least-squares method to minimize the residual between the forward model and the satellite spectrum allows the inversion of \(\omega\) and \(\mathrm{F_{s}^{TOA}}\), thereby retrieving the intensity information of the SIF signal.
## 5 Results and Discussion
### Performance Units
#### 5.2.4 Average End-to-End Delay (\(T_{avg}\))  
It is defined as the time taken in forwarding a packet from the starting hop to accepting the packet at the target hop. Figure 9 shows the comparison of the MAMC-MAC protocol with IEEE 802.11p and WSD. Here, the WSD scheme is compared for recognizing delivery delay as a stringent QoS requirement. It is observed that a delay arises due to an increase in the number of nodes. MAMC-MAC performs moderately better in terms of delay experienced.  
Figure 8: Packet loss ratio comparison.  
Figure 7: Throughput comparison.
# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
###### Abstract  
The rapid expansion of video conferencing and remote works due to the COVID-19 pandemic has resulted in a massive volume of video data to be analyzed in order to understand the audience engagement. However, analyzing this data efficiently, particularly in real-time, poses a scalability challenge as online events can involve hundreds of people and last for hours. Existing solutions, especially open-sourced contributions, usually require dedicated and expensive hardware, and are designed as centralized cloud systems. Additionally, they may also require users to stream their video to remote servers, which raises privacy concerns. This paper introduces scalable and efficient computer vision algorithms for analyzing face orientation and eye blink in real-time on edge devices, including Android, iOS, and Raspberry Pi. An example solution is presented for proctoring online meetings, workplaces, and exams. It analyzes audiences on their own devices, thus addressing scalability and privacy issues, and runs at up to 30 fps on a Raspberry Pi. The proposed face orientation detection algorithm is extremely simple, efficient, and able to estimate the head pose in two degrees of freedom, horizontal and vertical. The proposed Eye Aspect Ratio (EAR) with simple adaptive threshold demonstrated a significant improvement in terms of false positives and overall accuracy compared to the existing constant threshold method. Additionally, the algorithms are implemented and open sourced as a toolkit with modular, cross-platform MediaPipe Calculators and Graphs so that users can easily create custom solutions for a variety of purposes and devices.  
android; blink; cross-platform; edge computing; eye aspect ratio; face orientation; head pose; iOS; MediaPipe; open source; proctoring; Raspberry Pi; real-time; toolkit; video conference
## 3 Results and Discussion
### Thermal Properties of A-A' Copolymers  
The thermal stability of polymers is an important parameter when applied to electronic devices. It was studied for all the A-A' copolymers by thermogravimetric analysis, under nitrogen between 0 \({}^{\circ}\)C and 600 \({}^{\circ}\)C, with a heating rate of 10 \({}^{\circ}\)C min\({}^{-1}\) (Figure 6).  
All copolymers were stable up to at least 350 \({}^{\circ}\)C, except P(DPP-DAP), which exhibited two steps of degradation starting at 250 \({}^{\circ}\)C. For the latter, the first step of degradation  
Figure 5: HOMO-LUMO energy levels of the copolymers (A-A’) based on DPP and (D-A) based on DTS determined by cyclic voltammetry.  
Figure 6: TGA of copolymers P(DPP-DPPeH), P(DPP-DPPC12), P(DPP-DAP), and P(DPP-BTPBF) with a heating ramp of 10 \({}^{\circ}\)C min\({}^{-1}\) up to 600 \({}^{\circ}\)C under N\({}_{2}\) flux.  
occurred from 250 \({}^{\circ}\)C to 400 \({}^{\circ}\)C with a weight loss of around 20%. The second weight loss of an additional 30% finished at 500 \({}^{\circ}\)C. This early degradation was also observed with P(DTS-DAP) (see SI, Figure S12) and can be related to a weaker stability of the ethylhexyl1 chains when connected to the sulfur atom as in the DAP unit. The weight loss of around 20% is in agreement with the theoretical weight percentage of 21% expected for both ethylhexyl chains in the repeating unit constituted of DPP and DAP. For the copolymers P(DPP-DPPeH), P(DPP-DPPC12), and P(DPP-BTPBF), the thermograms showed one step of degradation, with a weight loss of 60%, 70%, and 59%, respectively. These variations can be roughly related to the molar mass of the alkyl chains present on the DPpeH, DPPC12, and BTPBF units.  
For comparison, the corresponding D-A' copolymers exhibited a similar degradation temperature around 400 \({}^{\circ}\)C, except for P(DTS-DAP) that also presented a lower degradation temperature of 200 \({}^{\circ}\)C, with an observed weight loss of around 20%, in agreement with an expected one of 23% in the repeating unit DTS-DAP (see SI, Figures S12 and S13). The thermal properties of P(DPP-DPPEH), P(DPP-DPPC12), P(DPP-DAP), and P(DPP-BTPBF) copolymers were also examined by differential scanning calorimetry (DSC). The DSC thermograms for copolymers are shown in the SI, Figures S14-S17 for P(DPP-DPPEH), P(DPP-DPPC12), P(DPP-DAP), and P(DPP-BTPBF), respectively. For all copolymers, DSC thermograms did not show any clear thermal transition up to 300 \({}^{\circ}\)C, even if a slight difference was observed between the first and second cycle. The absence of a melting point suggests that these polymers either tend to form an amorphous phase or that the melting temperature is above 300 \({}^{\circ}\)C. The size of the crystal domains may also be too small to be detected.
# Pre-Flight SAOCOM-1A SAR Performance Assessment by Outdoor Campaign
###### Abstract  
In the present paper, we describe the design, execution, and the results of an outdoor experimental campaign involving the Engineering Model of the first of the two Argentinean L-band Synthetic Aperture Radars (SARs) of the Satellite Argentino de Observacion con Microondas (SAOCOM) mission, SAOCOM-1A. The experiment's main objectives were to test the end-to-end SAR operation and to assess the instrument amplitude and phase stability as well as the far-field antenna pattern, through the illumination of a moving target placed several kilometers away from the SAR. The campaign was carried out in Bariloche, Argentina, during June 2016. The experiment was successful, demonstrating an end-to-end readiness of the SAOCOM-SAR functionality in realistic conditions. The results showed an excellent SAR signal quality in terms of amplitude and phase stability.  
SAR; pre-flight testing; SAR performance +
Footnote †: journal: Remote Sens. **2017**, \(9\), 729; doi:10.3390/rs9070729
# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 7 Discussion  
This study highlights the importance of integrating a cyclical approach into EMRs to improve clinical data management and enhance the effectiveness of CDSS. Traditional EMRs primarily rely on independent, event-driven data entry, often neglecting the iterative nature of treatment plans. Our findings address this critical research gap by demonstrating how treatment cycles impact medical decision-making and the quality of predictive analytics within CDSS.  
The current study's findings align with existing studies investigating the role of structured data organization in EMRs. [PERSON] et al. [66] found that longitudinal data structuring in EMRs enhances predictive analytics for chronic diseases, particularly diabetes management. However, their approach focused on temporal event sequences rather than explicitly modeling treatment cycles. Similarly, [PERSON] et al. [67] examined the benefits of structured data entry in EMRs, finding that predefined templates improve consistency in medical documentation and facilitate better AI-driven decision-making. While their study reinforced the value of standardization, it did not address the need for a cyclical framework. Moreover, [PERSON] et al. [68] investigated the effectiveness of repeated intervention tracking in EMRs and found that tracking sequential medical interventions improved treatment predictions. However, their study primarily analyzed repeat hospital admissions rather than structured treatment cycles within a single disease management framework. Our research provides a more granular approach by demonstrating how each treatment cycle uniquely contributes to the overall effectiveness of medical interventions.  
A quantitative analysis of the proposed cyclical framework confirms that incorporating treatment cycles significantly enhances the precision of clinical assessments. Specifically, we observed that 14.96% of the variance in post-treatment glucose levels and 37.31% of the variance in treatment success rates could be attributed to the cyclical structure of treatments. These findings suggest that ignoring treatment cycles leads to substantial information loss, making capturing patterns in patient responses challenging over time. Without a cycle-based framework, crucial insights regarding treatment efficacy and disease progression may be overlooked, ultimately reducing the accuracy of CDSS recommendations. Although the potential benefits are evident, implementing this model in real-world practice requires addressing several critical considerations. Key topics include practical implementation strategies, usability impacts, ethical concerns, and opportunities for future research.
## 3 Results and Discussions
### Optimization of Dispersive Solid-Phase Extraction Procedure
#### 3.2.3 Selection of Adsorption and Desorption Volume  
The adsorption volume (V\({}_{\text{adsorption}}\)) and desorption volume (V\({}_{\text{desorption}}\)) of the d-SPE method were selected based on the requirements for compatibility with the sample preparation technique and the ICP-AES system. Therefore, the sample volume was set to 50 mL, which is the lowest quantity after the sample digestion that can be employed for pH adjustment. Similarly, the desorption volume was set to 2.5 mL, which is the lowest quantity required for the injection into the used ICP-AES instrument.
## 2 The Q-M Interferometer Scheme and Theoretical Analysis
\begin{array}{ll}Path_{1}:&(\overset{\leftarrow}{s_{b}}\cdot QR_{\lambda/4 }\cdot\overset{\rightarrow}{s_{b}})\cdot e^{i\phi}\cdot C\cdot(\overset{ \leftarrow}{l_{a}}\cdot QR_{\lambda/4}\cdot\overset{\rightarrow}{l_{a}})\\ &=e^{i\tilde{p}_{b}}\cdot QR_{\lambda/4}\cdot e^{i\phi}\cdot C\cdot e^{i\alpha _{a}}\cdot QR_{\lambda/4}\\ &\overset{\leftarrow}{l_{b}}\cdot QR_{\lambda/4}\cdot\overset{\rightarrow}{ l_{b}})e^{i\varphi}\cdot e^{i\phi}\cdot C\cdot(\overset{\leftarrow}{s_{a}}\cdot QR_{ \lambda/4}\cdot\overset{\rightarrow}{s_{a}})\\ &=e^{i\alpha_{b}}\cdot QR_{\lambda/4}\cdot e^{i\phi}\cdot C\cdot e^{i\tilde{ p}_{a}}\cdot QR_{\lambda/4}\cdot e^{i\varphi}\end{array}  \tag{3}\]  
where \(C\) is the operator of quantum channel and can represent arbitrary birefringence resulting from quantum channel, \(l_{i}\) and \(s_{i}\) (\(i=a\), \(b\)) represent the operators of the long and short PM optical fiber in the arms of the Q-M interferometer, \(\alpha_{i}\) and \(\beta_{i}\) are the phase caused by the interferometer's long and short arms, respectively, \(\phi\) is the phase of transmission fiber, and \(\varphi\) is the phase shift from the phase shifter in [PERSON]'s interferometer. Supposing that the input Jones vector is \(E_{in}\) on [PERSON]'s side, the output of [PERSON]'s interferometer can be written as  
\[E_{out}=\frac{1}{4}\big{[}e^{i(\alpha_{a}+\beta_{b}+\phi)}+e^{i(\alpha_{b}+ \beta_{a}+\varphi+\phi)}\big{]}\cdot QR_{\lambda/4}\cdot C\cdot QR_{\lambda/4 }\cdot E_{in} \tag{4}\]  
where the factor 1/4 originates from the PMCs of [PERSON]'s and [PERSON]'s interferometers. Since \(S_{i}\), \(L_{i}\), and \(C\) are unitary, the interference output power can be expressed as  
\[ \begin{array}{ll}P_{out}&=E_{out}^{+}\cdot E_{out}\\ &=E_{in}^{+}[\frac{1}{4}(e^{i(\alpha_{a}+\beta_{b}+\phi)}+e^{i(\alpha_{b}+ \beta_{a}+\varphi+\phi)})QR_{\lambda/4}\cdot C\cdot QR_{\lambda/4}]^{+}\cdot[ \frac{1}{4}(e^{i(\alpha_{a}+\beta_{b}+\phi)}+e^{i(\alpha_{b}+\beta_{a}+\varphi+ \phi)})QR_{1/4}\cdot C\cdot QR_{\lambda/4}]E_{in}\\ &=\frac{P_{in}}{8}[1+\cos(\Delta\alpha+\Delta\beta-\varphi)]\end{array}  \tag{5}\]where \(\Delta\alpha=\alpha_{a}-\alpha_{b}\), \(\Delta\beta=\beta_{a}-\beta_{b}\). This means that the interference output \(P_{out}\) is independent of any polarization perturbation in the whole QKD system, especially that caused by the quantum channel. In an ideal case, \(\Delta\alpha\) and \(\Delta\beta\) are invariable; hence, interference fringe is only modulated by the phase shifter in the interferometer. In the real case, the phase drifting of \(\Delta\alpha\) and \(\Delta\beta\) caused by the fluctuation of temperature or environmental vibration can be solved by active compensation, such as controlling the phase shifter in real time.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 5 Future Trends
### Standardization and Regulatory Compliance  
Due to the early stages of DT implementation for PVs, existing DTs are not designed to meet any specific solar industry standard. However, standardization of DTs will be required to implement DTs for distributed systems, smart homes/cities, and connected cyber-physical systems. This will ensure regulatory compliance with interconnected energy systems as well as the required cybersecurity at all levels. As the global digitalization age continues into industry 5.0, cybersecurity of interconnected systems will become more critical than ever to ensure reliability [150].  
Furthermore, standardization will ensure transparency in reporting, interoperability (integration with software/hardware from different manufacturers), and insurance claims where necessary.
## 6 Conclusions  
The appropriate extraction of characteristics depends on the techniques used and the image itself. However, the extracted features should be robust or constant in terms of scale, translation, illumination, and orientation and partially consistent in terms of noises and artifacts. This study proposes an efficient model for the detection of breast cancer in mammogram images. The main contribution of this study is the development of a new model for feature extraction in mammogram images. The M-FD model is proposed as an improvement of the FD technique. The EWBFEM model based on WT is applied to ROIs for noise reduction before feature extraction. The GA is used to select relevant extracted features from ROIs. Extracted texture features can capture the essential differences amongst classes of objects (benign or malignant). The selected features are then fed to the trained ANN to capture the essential differences between benign and malignant samples. A series of experiments were conducted using four different databases. The first experiment was conducted using a single dataset for training and testing. The required training time is 237-277 s, and 0.3 s is required for testing each case. The second experiment was performed using one dataset for training and another dataset for testing. The proposed model required 324-367 s to train the proposed model and 0.3 s to test one case.  
The performance evaluation in this study is efficient but still can be improved in the future. A suitable or strong threshold value can be used to remove noise and preserve the quality of the image with increasing PSNR. Furthermore, the proposed model can be used along with different deep learning methods to provide better performance for the classification of breast cancer on mammogram images. Overall, the continuous development of systems is very important to improve and contribute to the research on breast cancer, create algorithms that can aid experts, and reduce the examination time subjectively. Therefore, the proposed method shows great potential and will be investigated further.  
Conceptualization and methodology, D.A.Z. and D.A.L; supervision M.A.M. and H.H.; validation D.Q.Z. and N.A.Z., writing--original draft preparation D.A.Z. and D.A.I.; writing--review and editing, R.D. and R.M.; funding acquisition, R.D. and R.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This authors would like to thank ERAWAN-ResearchHPC for giving us a high-performance computer for computation. This research work was partially supported by Chiang Mai University.  
The authors declare no conflict of interest.
# On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
## 4 Results and Discussion
### Comparison and Analysis of Apple Fruit Target Detection Algorithms  
The IOU value of the article algorithm is calculated by manually labeling the correct apple position, and the IOU standard deviation is 0.14, and the average value is 0.85. And the calculated accuracy rate of the detection result is 95.66% (accuracy rate = the number of correct test images of the detection result/the number of test sets), which is 5.31% higher than the accuracy of the initial detection result based on the fusion-improved saliency detection. The article algorithm and the detection results of [17], the CenterNet used in [24], the detection results of the DeepSnake algorithm, the detection results of [19], the detection results of [20] and the detection results of [21] are shown in Table 3. It can be seen that the article algorithm combines common image processing algorithms and deep learning algorithms to detect the target fruit, and it can detect the target fruit more accurately with fewer training samples. Although the method based on deep learning can detect the target fruit more accurately, it requires a large number of data sets to train the model. The accuracy of the algorithm depends on the quality of the model, and it takes a lot of time and cost to train the model. Table 4 shows the detection accuracy obtained by testing for overlapping fruits and obscured branches and leaves. From the results, it can be seen that for apple fruits under natural light, the algorithm in this paper can effectively separate overlapping fruits, and can detect the target fruits more accurately in the case of branches and leaves occlusion, poor lighting conditions, etc., and achieve rapid and accurate target labeling. If the number of apples in the picture is more than one, the apple fruit with the largest area in the picture, that is, the apple fruit located closer to the lens, is determined as the target. This also satisfies the actual demand in the picking operation of the apple picking robot.  
The processing time of the proposed algorithm was evaluated on a self-built 1036 test data set. The resolution of the smallest image contained in the data set is 188 \(\times\) 186 pixels, and the resolution of the largest picture is 2048 \(\times\) 1536 pixels. It takes an average of 0.42 s to run the algorithm code for each picture, which can realize fast processing.
## 3 Results and Discussion  
### Isolation of a Bloom-Forming Cyanobacterium Responsible for Geosmin Production  
Two _A. urcanica_ strains that exude an earthy/musty odor were obtained from Dianchi and Erhai lakes, and chemical analysis showed geosmin as their main volatile component (Figure S1). The features of geosmin production in _A. urcanica_ strains were studied (Figure 1). Strains CHAB 1432 and 2155 similarly showed the following pattern in geosmin production: A slight decrease in rapid growth period (0-6 days), a rapid increase in stagnant period (approximately 14 days) and a significant decrease in the last period of cultivation. The maximum total geosmin production of these two strains were 2.20 \(\times\) 10\({}^{-5}\) ng\(\cdot\)cell\({}^{-1}\) and 2.60 \(\times\) 10\({}^{-5}\) ng\(\cdot\)cell\({}^{-1}\), and the total production was more than 1.20 \(\times\) 10\({}^{-5}\) ng\(\cdot\)cell\({}^{-1}\) in the entire growth cycle.  
Among documented cyanobacterial geosmin/2-methylisoborneol (MIB) producers, more than 13 planktonic species were identified [2, 7]. These planktonic taxa can form unsightly or highly visible surface blooms, and numerous T & O episodes have been attributed to bloom-forming genera, such as _Anabaena_, _Aphanizomenon_ and _Planktonrix_[4, 20, 21, 22]. _A. urcanica_ has been historically implicated as the main source of geosmin in a reservoir in Japan [21]. Results of the chemical analysis in the present study showed _A. urcanica_ as a geosmin producer and probably the principal producer of geosmin in Dianchi and Erhai lakes. On the basis of the total geosmin productivity determined in these experiments (1.20-2.60 \(\times\) 10\({}^{-5}\) ng\(\cdot\)cell\({}^{-1}\) ) and the sensory threshold of geosmin, water bodies are at risk for T & O events when the abundance of geosmin-producing cyanobacteria is greater than 5 \(\times\) 10\({}^{5}\) cells/L (10 ng\(\cdot\)L\({}^{-1}\) total geosmin according to the average productivity).