esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Existence of Solutions  
**Theorem 1**: _Let the system (8) hold for \(t\geq t_{0}\), and let initial data \(x(t)=x_{0}(t)\), \(y(t)=y_{0}(t)\) be given for \(t\in[t_{0}-\tau,t_{0}]\), \(\tau>0\), with \(x_{0},y_{0}\) Lipschitz continuous. Then there is a unique solution to (8) in \([t_{0},\infty)\). Moreover, if \(x_{0},y_{0}\) are non-negative, the solution is also non-negative._  
**Proof.** The proof follows from basic principles of DDE theory, _cf._[19; 24; 25]. We provide here a sketch of the proof steps. For simplicity, we shall denote the right-hand side of the system (8) by \(f(u,v)\), where \(u=(x(t),y(t))\) and \(v=(x(t-\tau),y(t-\tau))\).  
Local existenceFor the construction of a local (maximal) solution on an interval \([t_{0},t_{0}+\Delta)\), \(\Delta>0\), it is sufficient to guarantee Lipschitz continuity of the initial data, as well as of \(f\) with respect to both arguments, _cf._[25] (Thm. 2.2.1). It is easy to verify that the right-hand side of (8) is continuously differentiable with respect to the delayed, as well as to the non-delayed argument, and that the partial derivatives are bounded (computation not shown).  
Non-negativityPreservation of positivity is due to the fact that the delay only appears in the positive feedback term. Indeed, if for some \(\bar{t}>t_{0}\), \(x(\bar{t})=0\) then \(\dot{x}(\bar{t})=\alpha>0\), and \(x(t)\) remains non-negative. With this result it follows that also \(y\) stays non-negative. If for some \(\bar{t}>t_{0}\), \(y(\bar{t})=0\), then \(\dot{y}(\bar{t})=\rho\frac{x(\bar{t}-\tau)^{m}}{y_{th}^{m}+x(\bar{t}-\tau)^{m} }\geq 0\).  
Global existenceWe show that the maximal solution is bounded. This follows with estimates on the right-hand side. Observe that  
\[\dot{y}(t) =\rho\frac{x(t-\tau)^{m}}{y_{th}^{m}+x(t-\tau)^{m}}-\omega y(t)\] \[\leq\rho-\omega y(t)\]  
hence for all \(t\geq t_{0}\) we have \(0\leq y(t)\leq\dot{y}\), where \(\dot{y}:=\left(y_{0}(t_{0})-\frac{\rho}{\omega}\right)e^{-\omega t}+\frac{ \rho}{\omega}\).  
Similarly,  
\[\dot{x}(t) =\alpha-\gamma x(t)-\delta x(t)y(t)+\beta\frac{x(t)^{n}}{x_{th}^{ n}+x(t)^{n}}\] \[\leq(\alpha+\beta)-(\gamma+\delta y(t))x(t)\] \[\leq(\alpha+\beta)-\gamma x(t)\]  
Thus for all \(t\geq t_{0}\) we have \(0\leq x(t)\leq\dot{x}\), with \(\dot{x}:=\left(x_{0}(t_{0})-\frac{\alpha+\beta}{\gamma}\right)e^{-\gamma t}+ \frac{\alpha+\beta}{\gamma}\). The maximal solution is bounded, hence it exists on \([t_{0},\infty)\), _cf._[25] (Thm. 2.2.2).
# A Dosimetric Analysis of Reduction Cardiac Dose with Lead Shielding in Breast Cancer Radiotherapy
## 4 Discussion
It needs further investigation in the clinical situation when considering put the technique into practice. The lead shields can potentially reduce the skin surface dose of the surrounding organs; however, the reproducibility of the lead shields for each treatment fraction of radiotherapy is a non-negligible issue due to the breathing movement. The use of lead shields in conjunction with deep inspiration breath-hold irradiation may be a useful solution to tackle the issue. Several studies have pointed out that the DIBH technique of radiotherapy on the left breast can reduce the irradiated heart volume and cardiac dose [25; 26; 27; 28; 29]. The combination of the two techniques is expected to further reduce the cardiac dose. On the other hand, the materials of the lead shields can be further optimized to ensure the fitness between the lead clothing and the body surface, thereby reducing the reproducibility of the lead shields for each treatment. Our study has several limitations. First, OAR doses were correlated with the patient's chest wall thickness, thoracic angle, respiratory motion, tumor location, and operation method. However, only the heart and the lung doses were discussed in this study, whereas the above related variables were not included in the discussion. Second, due to the limited geometry of the anthropomorphic breast phantom and the treatment plan design, the study only investigated dose changes in patients undergoing breast-conserving surgery, while those who received total mastectomy were not discussed in the study.
## 6 Forecasting Results and Discussion  
The real data sets included load profiles of 56 households and 44 commercial buildings for the year 2018 with a one-hour resolution, and thus, contained a total of 28,876 daily consumption. After the outlier detection process, 22,505 daily consumption can be used to generate our proposed deep learning model.  
The input of the prediction model is the historical energy consumption values of 100 customers in Turkey collected by the smart meters of the customers. The output of the proposed short-term load forecasting is precise future energy consumption prediction for one step ahead. The output of the forecasting results can deliver insights for utility company operators and building managers to modify their physical systems according to the predicted results.  
To evaluate the forecasting performance results, we utilized 10% of the test dataset to examine the model after training and validation with 70% and 20% of the dataset, respectively. The conventional metrics used to evaluate the prediction models are utilized  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Setting** \\ \hline CNN-1D filter size & 64 \\ CNN-1D Kernel size & 3 \\ BLSTM 1s hidden layer & 50 hidden neurons \\ BLSTM 2 nd hidden layer & 25 hidden neurons \\ Optimizer & ReLU \\ Loss function & Mean square error (MSE) \\ Learning rate & 0.001 \\ Batch size & 120 \\ Epoch & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings of the proposed model.  
to evaluate the forecasting in our experiments. Traditional metrics such as the root-mean-squared error (RMSE); the coefficient of variation (CV) of RMSE, known as the normalized root-mean-squared error (NRMSE); MAE; and MAPE, are defined as follows (17) to (20):  
RMSE \[= \sqrt{\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\widehat{y}_{i})^{2}}\] (17) NRMSE \[= \frac{RMSE}{\overline{y}}\times 100\%\] (18) MAE \[= \frac{1}{m}\sum_{i=1}^{m}|y_{i}-\widehat{y}_{i}|\] (19) MAPE \[= \frac{1}{m}\sum_{i=1}^{m}\frac{|y_{i}-\widehat{y}_{i}|}{y_{i}} \times 100\%\] (20)  
where \(m\) represents the total number of data points in the time series, \(\overline{y}\) represents the average of the actual values, \(y_{i}\) is the actually measured time series in the original scale of the dataset, and \(\widehat{y}_{i}\) is the predicted output of the time series.  
For the forecasting results section, several different evaluation criteria are utilized to evaluate the prediction performance models of residential and commercial buildings. The first criterion is directly bookmarking the average prediction metrics to conventional prediction models. Then, we compare the average prediction performance between residential buildings and commercial buildings within the clean data period (April- August). Third, we examine the preprocessing effects on our proposed prediction model for residential and commercial buildings with and without outliers. Lastly, we examine the time resolution effects on our proposed prediction model.
## 2 Materials and Methods
### SPME-GC-MS Analysis of vFFA  
To examine the effects of different PCI_Lip mutants on the aroma of the cheeses, the vFFAs of the cheeses were analyzed by headspace SPME-GC-MS. Therefore, 3 g of cheese was transferred into a 20 mL headspace vial. After the addition of 2 mL HCl (2 M), the mixture was carefully homogenized via a vortex-mixer. The samples were incubated for 10 min at 55 \({}^{\circ}\)C (250 rpm agitation rate) by means of an MPS 2 XL multipurpose sampler (GERSTEL, Muhlheim an der Ruhr, Germany) and subsequently extracted for 40 min (55 \({}^{\circ}\)C, 250 rpm) using a polydimethylsiloxane (PDMS)/divinylbenzene (DVB) SPME fiber (1 cm \(\times\) 65 \(\upmu\)m, Supelco, Steinheim, Germany). The analytes were desorbed in the GC inlet (SPME liner, 0.75 mm inner diameter, Supelco) at 250 \({}^{\circ}\)C for 90 s. After desorption, the SPME fiber was baked out for 5 min at 250 \({}^{\circ}\)C. For GC analysis, an Agilent (Waldbronn, Germany) 7890A gas chromatograph, equipped with a split/splitless (S/SL, split ratio 5:1) inlet and a polar VF-WAXms column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m film thickness, Agilent; temperature program: 40 \({}^{\circ}\)C (3 min), 5 \({}^{\circ}\)C min\({}^{-1}\) to 240 \({}^{\circ}\)C (12 min); carrier gas: helium at 1.2 mL min\({}^{-1}\) (constant)) was used. The GC system was connected to an Agilent 5975C mass spectrometer (transferline temperature: 250 \({}^{\circ}\)C, source temperature: 230 \({}^{\circ}\)C, quadrupole temperature: 150 \({}^{\circ}\)C, ionization energy: 70 eV, collision gas: nitrogen at 2.25 mL min\({}^{-1}\)quencher gas: helium at 2.25 mL min\({}^{-1}\), ms scan \(m/z\) 33-300). For substance identification, obtained mass spectra and calculated retention indices according to [PERSON] and [PERSON] [30] were compared to those of authentic standards (C2:0, C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, and C18:0 (40 mg L\({}^{-1}\) in \(n\)-hexane) and the National Institute of Standards and Technology (NIST) MS Search (2011).
## 3 Proposed Method
### Proposed Post-Processing Optimization Method
Donating the mapping function as \(\phi(\cdot)\) and width of the kernel as \(\sigma\), the radial basis function is:  
\[K\big{(}o_{i},o_{j}\big{)}=\phi(o_{i})\cdot\phi\big{(}o_{j}\big{)}=exp\Bigg{(} -\frac{\big{(}\left|o_{i}-o_{j}\right|\big{)}^{2}}{2\sigma^{2}}\Bigg{)} \tag{3}\]  
Figure 3: The proposed optimization workflow. Blue arrows describe the baseline defect detection processes that directly generate outputs from the baseline segmentation model. Orange arrows present workflows of the devised post-processing method, which filters out false alarms from candidate positive patches. Green arrows draw the sample synthesis and model training process of the SVM classification model depicted in Section 3.3. During the testing stage, \(o_{n}\) is automatically generated from positive patches identified on candidate masks by the segmentation model. The classification model assigns a false alarm probability score to each candidate patch. If the false alarm probability score of a positive patch exceeds the threshold of 0.5, it is classified as a false positive, triggering subsequent post-processing steps at both pixel and image levels. Pixels associated with this patch undergo multiplication by a coefficient, \(c_{p}\), to attenuate their impact on the anomaly map, thereby aiding in the reduction in false alarms in the segmentation mask. Simultaneously, the image-level prediction score for this image decreases proportionally to the area ratio of this patch relative to the entire image.
## 2 Topological Graph and Fault Analysis
### GSC Modeling  
Although various power switching components can be used to match a full scale power converter, this paper only focuses on the GSC with the back to back dual pulse width modulation (PWM) structure, as it is the most used structure in the wind turbine industry. Figure 1 shows the topological graph of the GSC.  
In the Figure 1, the DC-Link is mainly consisted of shunt capacitors (C1 and C2), whose two-terminal provides a stable direct current (DC) voltage to GSC. The GSC is mainly composed of 6 IGBT modules. Every two IGBT modules compose phase A, B and C, respectively. Through PWM control strategy, the DC voltage is transformed into sinusoidal alternating current with required equivalent frequency and amplitude. Afterward, the harmonic and peak are suppressed and filtered by the filter, and the power is fed to the grid via the transformer.  
For the full scale power converter of PMSG wind turbine, the key of grid connection is the GSC control strategy, which generally needs to meet two basic principles: first, to maintain the stability of DC-Link voltage. The second is to realize the control of output phase current. The relationship between the output power of the full scale converter and the wind speed can be expressed as follows:  
\[P_{\text{GSC}}\approx\frac{1}{2}\rho Av^{3}C_{\text{p}}k \tag{1}\]  
where \(P_{\text{GSC}}\) is the output power of GSC, \(\rho\) is the air density in kg/m\({}^{3}\), \(A\) is the sweep area of the blades in m\({}^{2}\), \(v\) is the wind speed in m/s, \(C_{\text{p}}\) is the power coefficient of the blade, \(k\) is the power conversion coefficient.  
When the control strategy of the GSC and the load parameters are certain, the effective value of the output line voltage \(U_{AB/BC/CA}\) and the phase current \(I_{A/B/C}\) can be calculated by the DC-Link voltage:  
\[U_{AB/BC/CA}=\sqrt{\frac{1}{2\pi}\int_{0}^{2\pi}u_{AB/BC/CA}^{2}d\omega t}=0.8 16U_{d}, \tag{2}\]  
\[I_{A/B/C}=\frac{P_{GSC}}{\sqrt{3}U_{AB/BC/CA}\cos\varphi}, \tag{3}\]  
where \(u_{AB/BC/CA}\) is the output line voltage of GSC; \(U_{d}\) is the DC-Link voltage; \(\cos\varphi\) is power factor of a phase load. In the actual operation of wind turbine, the voltage \(U_{\text{d}}\), \(U_{AB/BC/CA}\) are constant by the function of control strategy of converter. As can be observed from (1) to (3), phase current \(I_{A/B/C}\) of GSC is nearly a cubic function of the wind speed \(v\) when the other parameters are invariant. It can be  
Figure 1: The topological graph of the grid-side converter (GSC).  
stated that the phase current is random and unstable. Thus, it is difficult to diagnose the open-circuit fault, which is also one of the most significant differences between the wind turbine converter and other converters.
# Measuring the Lense-Thirring Orbital Precession and the Neutron Star Moment of Inertia with Pulsars
## 3 Evolution of Binary Pulsars  
In Figure 4, we plot the spin period (\(P\), horizontal axis) and the spin period derivative (\(\dot{P}\), vertical axis) for most known radio pulsars (grey dots). These appear in two main groups: The more numerous, which we will call the \"normal\" pulsars, form a large clump around \(P=1\) s and \(\dot{P}=10^{-15}\) s s\({}^{-1}\). The second group appears in the lower left, with significantly smaller values of \(P\) and \(\dot{P}\), and are known as the \"recycled\" pulsars.  
Figure 4: \(P\)–\(P\) diagram of known pulsars plotted in log–log scale. Pulsars in the upper right with spin period \(\sim\)\(10^{-1}\)–\(10^{1}\) s are known as normal pulsars, whereas pulsars in the bottom left with spin period of milliseconds are called millisecond pulsars (MSPs). Pulsars in binary systems are marked with a red circle, and the DNS systems are highlighted in blue. Binary pulsars detailed in this article are marked by black arrows with their name labelled. The data were taken from the ATNF Pulsar Catalogue version 2.0 [60].  
The latter group are known as \"recycled\" because, unlike the normal pulsars, they were spun up (and their magnetic fields degraded) by the accretion of mass from a stellar companion after they formed and died. The dots with red circles indicate the pulsars that are in binary systems: in the lower left corner, about 80% of pulsars are in binaries, while very few \"normal\" pulsars are in binaries.
# Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems
## 2 System Model
### System Model
Then, the combined signals at the receiver can be modeled as  
\[\mathbf{y}=\mathbf{W}^{H}\mathbf{H}\left(\mathbf{F}\mathbf{s}+\mathbf{e}_{I} \right)+\mathbf{W}^{H}\mathbf{n} \tag{2}\]
## 6 Result and Discussion
### Comparison with Existing Baseline Models  
We carried out a thorough comparison with current baseline models that are frequently used in the field of medical picture segmentation to assess the efficacy of our suggested retinal vascular segmentation model. These baseline models stand for cutting-edge methods or proven architectures for segmenting retinal vessels in images. We evaluate the progress made by integrating multi-scale feature fusion, contextual understanding, and attention mechanisms by contrasting our suggested model's performance with these baseline models.  
Table 2 presents the performance metrics of our proposed model compared to existing baseline models on the CHASEDB1, DRIVE, HRF, LES-AV, IOSTAR, and STARE datasets. The existing baseline models include ResU-Net, Attention U-Net, and DeepLabv3+, which have been extensively studied and benchmarked for segmentation tasks.  
Using the six datasets, our suggested approach routinely outperforms the baseline segmentation models that are currently in use in terms of all performance parameters. Specifically, our model's combination of attention mechanisms, contextual understanding, and multi-scale feature fusion shows notable gains in the Mean IoU, Recall, Precision, and F1 Score measures.  
On the CHASEDB1 dataset, our proposed model achieves a Mean IoU of 0.8047, while Rest-Net and Attention U-Net achieve Mean IoUs of 0.7853 and 0.7872, respectively. Similarly, our model outperforms ResU-Net (0.7871) and Attention U-Net (0.7746) on the DRIVE dataset, with a Mean IoU of 0.8027. These findings demonstrate the efficiency of our suggested methodology in precisely classifying retinal vessels and its potential to raise the standard of ophthalmology diagnostics.  
For the HRF dataset, our model achieves a Mean IoU of 0.7713, which is higher than the 0.7655 and 0.7662 achieved by Rest-Net and Attention U-Net, respectively. On the LES-AV dataset, our model records a Mean IoU of 0.8194, surpassing the 0.8153 and 0.7996 achieved by ResU-Net and Attention U-Net.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **Mean IOU** & **Recall** & **F1 Score** & **Dice** \\ \hline Baseline + CBAM & 0.8026 & 0.7173 & 0.7794 & 0.7794 \\ Baseline + Transformer + JPU & 0.7972 & 0.6985 & 0.7716 & 0.7716 \\ Baseline + Transformer & 0.7805 & 0.6526 & 0.7476 & 0.7476 \\ Baseline + JPU & 0.7925 & 0.6891 & 0.7652 & 0.7652 \\ Baseline + CBAM + Transformer & 0.8036 & 0.7212 & 0.7808 & 0.7808 \\ Baseline + CBAM + JPU & 0.7869 & 0.6737 & 0.7571 & 0.7571 \\ Baseline + CBAM + Transformer + JPU & 0.8047 & 0.7254 & 0.7824 & 0.7824 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of Ablation Study.  
In the IOSTAR dataset, the proposed model achieves a Mean IoU of 0.8069, outperforming the 0.7977 and 0.7987 by ResU-Net and Attention U-Net, respectively. Finally, on the STARE dataset, our model achieves a Mean IoU of 0.8313, higher than 0.8194 by ResU-Net and 0.8249 by Attention U-Net.
# A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins
###### Abstract  
The histogram watermark, which performs watermark embedding by slightly modifying the histogram of the original image, has been a hot research topic in information hiding technology due to the superiority of its pixel modification during the watermark embedding process, which is independent of the pixel position. This property makes the histogram-based watermark strong resistant to geometric attacks, such as cropping attack, crossed attack, rotation attack, etc. In this paper, we propose a large capacity histogram-based robust watermarking algorithm based on three consecutive bins for the first time. In our scheme, we divide the shape of three consecutive bins into eight cases. According to these cases, we embed Information Number 0, 1, 2, 3, 4, 5, 6, or 7, respectively. The embedded information capacity reaches one bit per bin (bpb), and the amount of embedded information is equal to 200% of the previous existing algorithms. Experimental results show that the new algorithm not only has a large capacity of embedding information, but also has strong robustness to geometric attacks, as well as common image processing operations.  
Keywords:robust watermarking; histogram; three consecutive bins; large capacity +
Footnote †: journal: Applied Sciences
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Elevation (\({}^{\circ}\))**} & \multicolumn{3}{c}{**SR (\%)**} & \multicolumn{3}{c}{**Correctly Fixed Solution (cm)**} & \multicolumn{3}{c}{**Float Solution (m)**} \\ \cline{2-9}  & **LCM** & **TCM** & **Imp. 1** & **LCM** & **TCM** & **Imp.** & **LCM** & **TCM** & **Imp.** \\ \hline
0 & 100 & 100 & - & 2.45 & 2.37 & 3.27\% & 0.27 & 0.25 & 7.41\% \\
5 & 100 & 100 & - & 2.45 & 2.37 & 3.27\% & 0.27 & 0.26 & 3.70\% \\
10 & 100 & 100 & - & 2.45 & 2.37 & 3.27\% & 0.28 & 0.27 & 3.57\% \\
15 & 100 & 100 & - & 2.52 & 2.46 & 2.38\% & 0.32 & 0.30 & 6.25\% \\
20 & 99.89 & 100 & 0.11\% & 2.57 & 2.52 & 1.95\% & 0.38 & 0.35 & 7.89\% \\
25 & 99.72 & 100 & 0.28\% & 2.60 & 2.58 & 7.69\% & 0.46 & 0.41 & 10.87\% \\
30 & 98.71 & 100 & 1.31\% & 2.68 & 2.70 & -0.77\% & 0.58 & 0.50 & 13.79\% \\
35 & 96.80 & 99.13 & 2.41\% & 2.89 & 2.91 & -0.69\% & 0.72 & 0.59 & 18.06\% \\
40 & 90.59 & 97.47 & 7.60\% & 3.54 & 3.45 & 2.5\% & 0.90 & 0.78 & 13.33\% \\
45 & 75.20 & 92.16 & 22.55\% & 4.36 & 4.31 & 11.47\% & 1.37 & 1.15 & 16.06\% \\
50 & 53.62 & 80.20 & 49.57\% & 4.66 & 4.60 & 12.87\% & 1.86 & 1.49 & 19.89\% \\ \hline \hline \end{tabular}  
* \({}^{1}\) Imp., the improvements in the SR and positioning accuracy of TCM RTK with respect to LCM RTK.  
\end{table} 
Table 5: The SR, accuracy of both the correctly fixed and float solutions of LCM and TCM RTK, and the corresponding improvement in TCM RTK with respect to LCM RTK. obviously when the conventional RTK (LCM RTK) is applied. A 90.6% SR can be achieved in LCM RTK with a cutoff elevation angle of 40\({}^{\circ}\). However, with the ISB ignored in the inter-system difference observations from identical-type receivers, TCM RTK can achieve a higher SR of 96.5% and a higher positioning accuracy than LCM RTK. The significant increase in SR demonstrates the advantages of TCM RTK at a high cutoff elevation angle: with fewer pivot satellites chosen, more measurements can be used to achieve higher performance, and one satellite is enough in a mixed constellation to enhance the model. Assessing the performance of LCM and TCM RTK under different cutoff elevation angles in 5\({}^{\circ}\) intervals from 0 to 50\({}^{\circ}\), we found that the improvement in performance with TCM RTK becomes even more obvious with respect to LCM RTK as the cutoff elevation angle increases. In addition, the improvement provided by TCM RTK validated that it is correct to ignore the ISBs between identical types of receivers and the ISB-corrected model TCM RTK logically improves RTK positioning.