esa-sceva/satcom-chunk-collection huggingface dataset:
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
###### Abstract  
Bistatic and multistatic SAR technology, with its multi-dimensional, ultra-wide swath, and high-resolution advantages, is widely used in earth observation, military reconnaissance, deep space exploration, and other fields. The LuTan-1 (LT-1) mission employs two full-polarimetric L-band SAR satellites for the BiSAR system. The bistatic mode introduces phase errors in echo reception paths due to different transmission links, making echo compensation a key factor in ensuring BiSAR performance. This paper proposes a novel bistatic internal calibration strategy that combines ground temperature compensation, in-orbit internal calibration, and pulsed alternate synchronization to achieve echo compensation. Prior to launch, temperature compensation data for the internal calibration system are obtained via temperature experiments. During in-orbit operation, calibration data are acquired by executing the internal calibration pulse sequence and noninterrupted pulsed alternate synchronization. In ground processing, echo compensation is completed based on the above two parts of calibration data. A comprehensive analysis of the entire calibration chain reveals a temperature compensation accuracy of 0.10 dB/1.38\({}^{\circ}\). Additionally, a ground verification system is established to conduct BiSAR experiments, achieving a phase synchronization accuracy of 0.16\({}^{\circ}\). Furthermore, the in-orbit test obtained DSM products with an average error of 1.3 m. This strategy provides a valuable reference for future spaceborne bistatic and multistatic SAR systems.  
b 2024 162965 1.22.212 1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; [EMAIL_ADDRESS] (Y.J.); [EMAIL_ADDRESS] (H.Y.); [EMAIL_ADDRESS] (H.Z.); [EMAIL_ADDRESS] (F.Z.)  
The International Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR. _Remote Sens._**2024**, _16_, 2965. [[https://doi.org/10.3390/rs16162965](https://doi.org/10.3390/rs16162965)]([https://doi.org/10.3390/rs16162965](https://doi.org/10.3390/rs16162965))
# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 4 Discussion
### Datasets
#### 4.4.2 Comparative Experiments on the SHIFT Datasets  
To further compare our method with other methods, we conducted evaluations on the SHIFT [50] datasets. Due to the synthetic nature of the datasets, many nighttime image characteristics present in real datasets, such as glare, noise, illumination, and blur, are not reflected. The experimental results are shown in Table 3. We observe that the UMT [47] and TDD [33] methods both exhibit a decline in performance, likely due to error propagation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AP** & **APs** & **APm** & **API** \\ \hline TDD [33] & 34.6 & 12.1 & 28.3 & 39.1 \\
2 PCNet [41] & 39.7 & 8.3 & 25.0 & 36.6 \\ UMT [47] & 36.2 & 10.8 & 27.4 & 34.6 \\ NUDN (Ours) & 37.4 & 14.1 & 28.8 & 42.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of cross-domain adaptation on the ExDark datasets are reported. The average precision (AP) of various methods is presented, along with the average precision for small objects (APs), medium objects (APm), and large objects (API).  
Although our method has a lower AP performance compared to the 2 PCNet [41] method, we outperform 2 PCNet [41] in APs, APm, and API. The visualization results are shown in Figure 5.  
As illustrated in Figure 5, our method demonstrates superior robustness compared to other methods in the testing phase of the SHIFT dataset, particularly excelling in the detection of small objects. Despite the synthetic nature of the nighttime images in the SHIFT dataset, which lack certain features present in real-world scenarios (such as glare and noise), our method continues to maintain high detection accuracy while reducing false positives. This further validates the adaptability and generalizability of our approach across various scenarios.
## 4 Results
### Migration Vector Decomposition and Intercomparison with GPS Data
It should be noted that (1) the GPS measurements were corrected for background and seasonal trend, as explained in Section 3.3; (2) GPS data were converted into LOS values using Equations (7) and (8). Although there are some slope differences between GPS and InSAR deformations, concurrent migration trends in the ascending and descending modes were obvious in both GPS and InSAR (Figure 7). Considering that GPS migration is mostly in the E-W direction and that InSAR observations are far more sensitive in E-W rather than N-S directional migration, we concluded that migrations along corresponding areas are mostly governed by E-W directional deformation. In addition, our proposed PS algorithms to suppress error terms worked well based on the trends of GPS and InSAR observations, which demonstrated good agreement, except for some differences in migration speeds presented in Figure 7b,d. It is worth noting that the entire southern Korean peninsula has a peculiar drift to the eastern side of up to few tens of mm/year ([PERSON] et al., 2015; [PERSON] et al., 2015) since 2010 Tohoku earthquake. Therefore, the migration slope difference on WOLS (Figure 7b,d) can be naturally explained by the referencing differences between InSAR which is referenced on the local domain of this study and GPS, which is referenced from nine International GNSS service (IGS) Stations and consequently influenced by the migration of the southern Korean peninsula. We found that 25 mm/year is the optimal drift value to minimize the slope difference between GPS and InSAR measurements. Figure 6: Horizontal (**a**) and vertical (**b**) displacement by decomposition. Total cumulative values during in June 2015 to January 2018 and the temporal evolutions in major deformation units (see Table 4 for details). Note the locations of P1 and P3 and the Ulsan fault line along with Unit 1. Figure 7: Ascending mode LOS deformations compared to ULSN (**a**) and WOLS GPS data (**b**) and descending mode LOS deformations compared to ULSN (**c**) and WOLS GPS data (**d**) are presented. Total cumulative values during January 2016 to July 2018 were analyzed. All GPS and InSAR observations are referenced to 01 March 2017 for seasonal variation processing. Note the seasonal correction in WOLS is only applied to Up-Down GPS observation as seasonal correction on the E–W GPS component produced a highly scattered pattern. The systematic migration speed differences in (**b**) and (**d**) were explained by the referencing issue and peculiar drift of southern Korean peninsula.
# Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture
## 3 Proposed IoT-Enabled Healthcare Architecture
### Mathematical Analysis  
The mathematical analysis stated in [36] is considered the base of this mathematical analysis. Suppose that the healthcare data arrives to the priority queues using Poisson distribution with rate equal to \(\lambda_{x}\), where '\(x\)' is the data request index per unit time (second). The total number of requests equals '\(T\)', so the overall arrival rate \(\lambda\) for all requests can be determined using Equation (1).  
\[\lambda=\sum_{x=1}^{T}\lambda_{x} \tag{1}\]  
For simplicity, the data, which comes to the priority queues, is subject to the processing rule \"first come--first served\". The prioritize receives the data from the healthcare cluster with probability \(P_{1}\) and the users receive the data after processing with probability \(P_{2}\). The M/M/1 queue model is used for the system gateways with finite buffer. The M/M/C queue model is used for the computing centers. The processing time in each queue of the prioritize is determined by \(1/\mu_{P}\). The processing time of computing centers is determined by \(1/\mu_{C}\), where \(\mu\) is the service rate. For each private computing center, the M/M/C/K queue is used with processing time equal to \(1/\mu_{C}\), where \(1<i<y\) and y in the number of computing centers. The probability of data staying in the prioritize is determined using Equation (2), where \(1-P\) is the probability that the healthcare data is forwarded to a prioritize \(P_{j}\) in a specific organization \(O_{i}\) and \(P^{m}\) is the probability that the data is served in a specific computing center where m is the total data in a specific node in the healthcare system.  
\[O_{i}P_{j}(\pi_{m})=O_{i}P_{j}((1-P)\ P^{m}) \tag{2}\]  
The mean of healthcare data in execution requests, \(MN_{P_{j}}\), is determined using Equation (3). The number of healthcare data waiting requests for a specific prioritize in a specific  
Figure 5: Simple view of the proposed healthcare architecture workflow.  
organization \(DN_{P_{j}}\) is determined using Equation (4). The utilization of each prioritize, \(U_{P_{j}}\), is determined using Equation (5)  
\[MN_{P_{j}}=O_{i}(\ (1-P)\sum_{m=1}^{\infty}mP^{m}) \tag{3}\]  
\[DN_{P_{j}}=O_{i}P_{j}(\sum_{m=1}^{\infty}m\pi\pi_{m+1}) \tag{4}\]  
\[U_{P_{j}}=O_{i}P_{j}(1-\pi_{0}) \tag{5}\]  
The mean response time, \(MR_{P_{j}}\), is determined using Equation (6), and the mean waiting time, \(MW_{P_{j}}\), is determined using Equation (7).  
\[MR_{P_{j}}=\frac{O_{i}(\ (1-P)\sum_{m=1}^{\infty}mP^{m})}{\lambda} \tag{6}\]  
\[MW_{P_{j}}=\frac{O_{i}P_{j}(\sum_{m=1}^{\infty}m\pi_{m+1})}{\lambda} \tag{7}\]  
The number of satellites and HAPs that are required to cover the entire earth is determined using Equations (8) and (9), where \(r_{earth}\) is the earth radius, \(E\) is the elevation angle, and \(h\) is the height of antenna.  
\[N_{SH}=\left\lceil\frac{4*\pi}{(1-\cos(\theta))*3\sqrt{3}}\right\rceil \tag{8}\]  
\[\theta=\left[\cos^{-1}*\left(\frac{r_{earth}*\cos(E)}{r_{earth}+h}\right) \right]-(E) \tag{9}\]
## 5 Results and Discussions
### Proposed Structure with FD Pre-Compensator (LQR-FDC): Fault-Free and Fault Flight Conditions  
As explained earlier, the main problem of a non-minimum phase system that occurs when designing a feedback tracking controller is achieving internal stability. In this paper, the internal stability problem is assessed by replacing the fractional-order integral pre-compensator with a fractional-order derivative pre-compensator in the proposed structure. The LQR-FDC control structure produced favorable step responses even in the cases of fault as depicted in Figure 6. However, considering the open-loop step response of the augmented dynamics that was explained earlier in Figure 3b, the open-loop step response still exhibits non-minimum phase dynamics, which may lead to an internal instability problem for some states of the dynamics. This can be appreciated if the results shown in Table 1 for the integral pre-compensator are compared with the derivative compensator. It can be concluded that the LQR-FDC highly penalizes the pitch angle state compared with the other states, which may also lead to the internal stability problem.  
Figure 4: Pitch angle step responses to various controllers.  
Figure 5: Pitch angle step response with (**a**) 50% fault and (**b**) 80% fault.  
Figure 6: Pitch angle step response with and without faults.
## 5 Results and Discussion
### Comparisons with Euclidean Representations  
We conclude the present analysis with a discussion of a key question. Specifically, we inquire as to whether the efficacy of the Euclidean representation systematically underperforms compared to that of the geometrically constrained approach. To address this, we repeated our experiments for all datasets and the \(F_{intra}\), \(\omega^{-0\%RF}\), \(\omega^{-100\%RF}\) experimental protocols and the common pole approach. But in this case, the \(\Psi^{I,Enc}_{\mathbf{X},\mathbf{Y}}\) Euclidean difference vector formation lacks the geometrical constraints of the SPD manifold, so the Euclidean-based \(\Psi^{I,Euc}_{\mathbf{X},\mathbf{Y}}\) DV is provided simply by the vector space matrix subtraction along with the Dichotomy Transform, which is eventually reduced to the following:  
\[\Psi^{I,Enc}_{\mathbf{X},\mathbf{Y}}=|(\mathbf{I}-\mathbf{X})-(\mathbf{I}-\mathbf{\gamma})|=|\mathbf{Y}- \mathbf{X}|=|\mathbf{X}-\mathbf{Y}| \tag{14}\]  
followed by a corresponding \(vec_{\textit{cue}}(\cdot)\) vector operator of Equation (13), which finally provides local \(v_{\textit{Enc}}\) representation. Figure 13 directly compares, in terms of the average \(EER^{user}_{SF}\), the verification efficiency of the corresponding SPD and Euclidean vector space experiments. The performance of the SPD approach is indisputably superior to the Euclidean one with a minor exception of the \(D_{2}\) (MCYT) dataset, at the \({}_{LFWI}\mathcal{E}_{D_{2}}(A,100,SV)\) experiment, and for values of the segment index greater than twelve (\(a\geq 12\)). This systematically observed advantage of considering data in the SPD manifold compared to the hitherto view of them as elements of a vector space can be evidence for assuming SPD representations in the form of covariance matrices as a flexible image representation tactic capable of blending multiple image modalities while compactly capturing their second-order statistics. Finally, the performance of the proposed approach is compared to the existing literature. To this end, both direct results (i.e., experiments were performed by the authors) along with a comparative summary are presented in Table 9. We choose to report only WI-SV cases that clearly and explicitly stated the number of reference samples (\(|\mathbb{N}_{REF}|\)) during the implementation of the testing stage. The inspection of the contents of Table 9 is quite interesting. They strongly indicate that modeling the handwritten signature images as entities that lie into matrix manifolds yields low verification error rates, which can be considered competent for other state-of-the-art, data-driven approaches. Figure 13: Direct comparisons, in terms of the average \(EER_{SF}^{user}\) (%), for the Euclidean (EUC) \(\psi_{X,Y}^{L,Euc}\) and the SPD \(\Psi_{X,Y}^{I}\) approach under the \(5\times 2\)\(F_{intra}\) protocol, local LFW1/TFW1, 0%RF, 100%RF, and all \(D_{i}\) datasets.
# LEO-Based Satellite Constellation for Moving Target Detection
## 2 Materials and Methods
### Clutter Suppression Based on DFR Design  
In view of the aforementioned pre-compensation strategy, the multi-source fusion process is performed in this section through setting the cell interval in DFR domain. On one hand, for the LEO-based satellites with different orbit elements and incident angles, although the DFR difference of clutter echoes can be corrected by employing the range and Doppler history of the cell where the beam center is pointed, the target velocity vector may still cause the dispersion of the target fusion energy in DFR domain. Therefore, there exists a theoretical lower bound for the DFR cell, where the multi-source echoes can be entirely integrated in the same cell. On the other hand, for the sake of the resolving ability in DFR domain, the echoes related to the target and the observation scene should be separated with an interval greater than the DFR cell, which possesses a theoretical upper bound accordingly.  
Multiplying \(S_{k}(f_{\tau},\eta)\) by \(H_{k}(f_{\tau},\eta)\), the compensated signal in range-DFR domain [10] can be represented as \(S^{\prime}_{k}(R,f_{dr})\). Let the cell size of DFR be \(\Delta_{fdr}\), and the parameter selection technique of \(\Delta_{fdr}\) should meet the following restrictions to achieve accurate information fusion as well as the effective clutter suppression.  
(i) The DFR deviation of the target between any two satellites should be less than \(\Delta_{fdr}/2\), that is,  
\[\left|f_{dr2,m}-f_{dr2,n}\right|<\Delta_{fdr}/2 \tag{8}\]  
In Equation (8), \(f_{dr2,m}\) and \(f_{dr2,n}\) are DFRs of the \(m\)th satellite and the \(n\)th satellite, respectively. \(m\) and \(n\) satisfy \(1\leq m,n\leq K,m\
eq n\). According to Equation (4), Equation (8) can be described as:  
\[\left|\begin{array}{c}\frac{\pi}{4}\left(\mathbf{P}_{T,n}-\mathbf{P}_{T,2} \right)^{-3}\cdot\left[\left(\mathbf{P}_{T,n}-\mathbf{P}_{T,2}  
\[S^{\sigma}(x,y)=\frac{1}{K}\sum_{k=1}^{K}\left|S^{\sigma}_{k}(x,y)\right| \tag{11}\]  
Step 2 Weight calculation of the contrast ratio based on the sliding window.  
Assume that the sliding window is composed of \(M*M\) cells, in which the central position is represented as \((x_{0},y_{0})\), and the edge region is described as \((x_{0}+i,y_{0}+j)\). The weight of the contrast ratio in respect of the sliding window center is indicated by:  
\[\eta(x_{0},y_{0})=\frac{|S^{\sigma}  
Figure 3 demonstrates the flow chart of multi-source fusion CFAR detection algorithm with regard of the LEO-based satellite constellation.
## 3 Results
### Artificial Ageing Protocol (AAP)
#### 3.1.3 Gas Chromatography Coupled with Mass Spectrometry (GC-MS)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Amino Acids** & **P1 aCa(A)** & **P2 vCa(A)** & \
\begin{tabular}{c} **P1 aCa(A)** \\ **Aged** \\ \end{tabular} &
\begin{tabular}{c} **P2 vCa(A)** \\ **Aged** \\ \end{tabular} \\ \cline{2-5}  & & \multicolumn{3}{c}{\(\mu\)g/100 \(\mu\)g} \\ \hline ALA & 3.6 & 1.6 & 2.6 & 2.7 \\ \hline GLY & 10.8 & 4.1 & 4.1 & 5.8 \\ \hline LEU & 0.5 & 0.1 & 0.8 & 0.6 \\ \hline PRO & 6.7 & 2.0 & 3.0 & 3.8 \\ \hline HPRO & 6.4 & 2.9 & 5.3 & 3.9 \\ \hline ASP & 0.5 & 0.3 & 0.2 & 0.9 \\ \hline GLU & 2.4 & 0.8 & 0.9 & 1.3 \\ \hline PHE & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline TOT & 30.9 & 11.7 & 16.8 & 19.0 \\ \hline MED & 22.0 & & 22.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of GC–MS results of animal glue amino acids before and after ageing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Monosaccharides** & \begin{tabular}{c} **P1 aGa(A)** \\ \end{tabular} & \begin{tabular}{c} **P2 vGa(A)** \\ \end{tabular} & \
\begin{tabular}{c} **P1 aGa(A) Aged** \\ \end{tabular} &
\begin{tabular}{c} **P2 vGa(A) Aged** \\ \end{tabular} \\ \cline{2-5}  & & \multicolumn{3}{c}{\(\mu\)g/100 \(\mu\)g} \\ \hline Arabinose & 0.1 & 0.4 & 0.1 & 0.2 \\ \hline Rhamose & 0.1 & 0.0 & 0.1 & 0.1 \\ \hline Galactose & 1.2 & 1.6 & 1.2 & 1.6 \\ \hline Glucose & 0.0 & 0.1 & 0.0 & 0.0 \\ \hline TOT & 1.4 & 2.1 & 1.4 & 1.9 \\ \hline MED & 1.6 & & 1.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of GC–MS results of Arabic gum monosaccharides before and after ageing. age and polymerization within drying oils, indicating progressive ageing and chemical transformation. This chemical evolution influences not only the mechanical properties but also the optical characteristics of the paint layer, potentially contributing to embrittlement and color changes observed in aged samples [43] (Table 6). These results complement FTIR findings that show new hydroxyl group bands and reductions in carbonyl and alkane groups, collectively confirming oxidative polymerization [36,43]. The integrated data thus provide a comprehensive picture of the degradation pathways affecting linseed oil binders under ageing conditions.
# Applications of Natural Language Processing Tools in Orthopaedic Surgery: A Scoping Review
## 3 Results
### Use for Orthopaedic Diagnoses  
Five studies explored the role of NLP in assisting orthopaedic clinicians with formulating and identifying diagnoses. [PERSON] et al. trained NLP algorithms to interpret meniscal tears in magnetic resonance imaging (MRI) scan reports and later applied the same algorithm to arthroscopy reports [10]. For MRI reports, the algorithm had an F1 score of 0.93-0.94 for medial meniscus tears and an F1 score of 0.86-0.88 for lateral meniscus tears. With respect to scope reports, it had an F1 score of 0.97 and 0.99, respectively. [PERSON] et al. also combined scope and MRI reports to identify if NLP was able to identify mismatch between the two reports, resulting in a sensitivity of 79% and specificity 87%. [PERSON] et al. also used machine learning and deep learning NLP models on radiology reports--specifically orthopaedic trauma X-rays [11]. They compared various available models and identified the Bidirectional Encoder Representations from Transformers (BERT) as the most accurate, with an accuracy of 96% and an F1 score of 0.95. The radiology reports were in Dutch and therefore NLP proved to be effective in the non-English written language.  
[PERSON] et al. successfully used NLP algorithms to identify bony metastases in bone scintigraphy reports of patients undergoing surgery for bone metastases [12]. They found that the NLP algorithm had a sensitivity of 0.94 and specificity of 0.82, a positive predictive value of 0.97 and an F1 score of 0.96. Similarly, [PERSON] et al. used an NLP algorithm to identify periprosthetic femoral fractures post total hip arthroplasties and compared its results to a diagnosis made by orthopaedic surgeons using chart and radiological review [13]. The algorithm was applied to operative and consult notes and had a sensitivity of 100% and specificity of 99.8% for identifying periprosthetic femoral fractures. With respect to identifying the correct Vancouver classification, it demonstrated a sensitivity of 78.6% and a specificity of 94.8%. Lastly, [PERSON] et al. used an NLP system trained to identify lumbar spine imaging findings from both X-ray and MRI reports obtained from patients with lower back pain presentations [14]. In their study, four spine experts annotated the presence of 26 findings of which the NLP systems were able to achieve a high inter-rater agreement for 25 of 26 findings with a sensitivity of over 0.94 and an AUC of over 0.90, indicating the NLP system performed excellently when benchmarked to reference standard annotation by spinal experts.
## 6 Results
### Results for HFSPDM  
The frequencies used for HFSPDM follow relation of \(F3=2\times F2=4\times F1\). Figure 17 shows the obtained signal in terms of column sum for each frame. In order to provide ease of understanding and representation, the duration for each frequency is extended to multiple frames, and a repeated sequence of four 2-bit pairs is transmitted. A sliding window of size 5 is used to smoothen the obtained signal, which is then further processed to identify the frequencies. Figure 18 shows the filtered signal. Three levels can be identified from the signal; the lowermost level shows frames with stripe widths of 22 pixels approximately, the second level at a width of around 45 pixels, and the third level at a width of 80 pixels approximately. Each of these levels represents three distinct frequencies. The number of frames containing frequencies 2 and 3 between successive frequency 1 frames is identified for decoding. The final bit pair is decoded based on the percentage of frequency 2 frames. Figure 18 shows repeated decoding of bit pattern 00, 01, 10 and 11.  
Figure 16: BFSOOK BER vs. Distance.  
Figure 15: BFSOOK max widths per Frame and Thresholding Window.  
An overall comparison of all three implemented techniques, i.e., OOK, BFSOOK, and HFSPDM, is shown in Figure 19. The best results for each technique are compared. In general, bit rate should be kept constant for comparing BERs. However, OOK operates at very high bit rates compared to BFSOOK and HFSPDM, thus, the ratio of BER to bit rate is used as metrics to evaluate. Hence, the lower the value better will be the performance of the technique. It was observed that BFSOOK provided consistent BER/bps close to 0 for frequency 200 Hz while our proposed HFSPDM technique has shown BER/bps closer to 0.04. However, it was also observed that in HFSPDM, the number of frames required to transmit the same amount of data as that in BFSOOK was considerably lesser but more than OOK. This is because HFSPDM represents 2 bits per 5 frames on average, whereas BFSOOK represents 1 bit per 3 frames. The bit to frame ratio is much higher in OOK which is of order 20 bits-per-frame; however, HFSPDM gives better BER for longer distances than OOK based on standard implementation using low-cost devices. Table 3 shows the relative performance of HFSPDM in comparison with OOK and BFSOOK.  
Figure 17: HFSPDM Signal.  
Figure 18: Filtered HFSPDM Signal.