esa-sceva/satcom-chunk-collection huggingface dataset:
# Compact Midwave Imaging System: Results from an Airborne Demonstration
## 5 Conclusions
The cryocooler used by CMIS has significant flight heritage, but it introduces vibrations as well as a limited lifetime. The current state of the art for TE coolers is an operating temperature of about 180 K, which is about 30\({}^{\circ}\) warmer than that needed by CMIS. TE coolers have the advantage of no moving parts, plus no significant concerns about lifetime. As HOT detectors and TE coolers continue to improve, it may become possible to eliminate cryocoolers from the CMIS design. This paper provided strong evidence that a low-cost constellation of CubeSats is capable of providing stereo observations needed for accurate, precise retrievals of global CTHs and AMVs. Despite the challenges associated with turbulence and course corrections for an aircraft, the CMIS instrument demonstrated excellent accuracy for CTHs and AMVs with standard deviations of 20.2 m and 0.23 m s\({}^{-1}\), respectively, for idealized collections against stationary terrain. For a case which the Gulfstream-III underflow both GOES and Aeolus, CMIS provided reasonable results. The success of the aircraft retrievals suggests that even better results would be achieved from space. Conceptualization, M.A.K. and D.L.W. ; Methodology, M.A.K., J.L.C. and A.C.G. ; Software, J.L.C. and I.P. ; Validation, J.L.C., D.L.W. and A.C.G. ; Formal analysis, J.L.C., A.C.G. and R.T.M. ; Investigation, M.A.K. and A.C.G. ; Resources, M.A.K. ; Data curation, I.P. and R.T.M. ; Writing--original draft preparation, M.A.K., J.L.C. and D.L.W. ; Writing--review and editing, M.A.K., J.L.C., A.C.G. and D.L.W. ; Visualization, J.L.C., D.L.W., I.P. and R.T.M. ; Supervision, M.A.K. ; Project administration, M.A.K. ; Funding acquisition, M.A.K. All authors have read and agreed to the published version of the manuscript. This work was supported by NASA under the Earth Science Technology Office (ESTO) Instrument Incubator Program grant award NNX17 AG65G and the ESTO Advanced Information Systems Technology (AIST) program award AIST-18-0082. Data Availability Statement: Data from the case studies can be made available upon request to the corresponding author. The authors are grateful to the engineering team led by [PERSON] and [PERSON] of the Johns Hopkins University/Applied Physics Laboratory for the design, build and integration of the CMIS instrument, and to [PERSON], [PERSON], [PERSON] and [PERSON] from NASA LaRC, for their critical assistance and support to plan and conduct the airborne demonstration. The authors thank two anonymous reviewers for their constructive comments, which resulted in an improved final manuscript. The authors declare no conflict of interest.
## 2 Materials and Methods
### Methodology for Building and Improving Defect Detection Systems
#### 2.1.3 Stage 3  
During the image processing in the previous stage, representations of places were collected, and the following were observed:  
* _NNDiff_ > 50% and the area was not a blue stain;
* _NNDiff_ < -50% and the area was a blue stain;
* _NNDiff_ > -10% and _NNDiff_ < 10% and the area was a defect.  
The places in the third category were areas that the network could not classify into one or another group at all.According to the percentages of testing results (95%, 3%, 91%, 4%), there were fewer and fewer such (unspecified) places. The network worked better; until about 8000 samples were collected from each side of the board, they were scanned, and they constituted 50% of the input set.  
At this stage, the final training of the neural network was performed. The sample size was 78,000 (62,000 + 16,000). Examples of learning outcomes are as follows:  
1. Accuracy: 92%; Loss: 13%; Epoch: 10.
2. Accuracy: 95%; Loss: 8%; Epoch: 15.
3. Accuracy: 84%; Loss: 34%; Epoch: 20.  
A model trained for 15 epochs was selected for further work.  
At this stage, work with the prepared set was completed. The network model and parameters were sent to production (to the production process), where testing took place under the following production conditions:  
1. A total of 8 h of scanner (model) operation;
2. Material with an average of 10% blue stain (every 10 th board had more than 30% of its length covered with blue stain);
3. A total of 8224 m of material was scanned.  
The total production of boards was as follows:  
1. 5387 boards, 550 mm long;
2. 3412 boards, 610 mm long;
3. 6491 finger-joint boards (180-400 mm)--class 1;
4. 3561 finger-joint boards (180-400 mm)--class 2 (blue stain contained).  
In these groups, the following were collected:25 boards, 550 m long, which had a blue stain but were not detected by the scanner (clean); b 13 boards, 610 mm long, on which there was no blue stain, but were wrongly classified (blue stain); c 48 pieces of finger-jointed strips (180-400 mm) with a blue stain, but were classified as class 1 (clean); d 28 pieces of finger-jointed strips (180-400 mm) that did not have a blue stain, but were classified as class 2 (blue). The system errors for these groups were as follows: a 25/5387 = 0.4%, b 13/3412 = 0.1%, c 48/6491 = 0.7%, d 28/3561 = 0.7%.
## 2 System Model and Problem Formulation
### Problem Formulation  
This paper mainly focuses on maximizing the average throughput of the considered large-scale multi-cell multi-user system subject to QoS of all users by jointly optimizing the transmit power and bandwidth allocation of all the users. Denote the average throughput of all the users by \(\overline{TH}\); then, the joint resource allocation problem is formulated as follows:  
\[\begin{split}&\text{P1}:\max_{\mathbf{P_{l_{m,n}}},B_{i,l_{m,n}}} \overline{TH}\triangleq\frac{1}{K}\sum_{m=1}^{M}\sum_{n=1}^{N}TH_{l_{m,n}}\\ \text{s.t.}&\text{C1}:P_{\min}\leq p_{l_{m,n}}\leq P _{\max}\,\forall l_{m,n}\in\mathcal{L}_{m},m\in\mathcal{M},\\ &\text{C2}:\sum_{i=1}^{3}B_{i}X_{i}\leq W,\\ &\text{C3}:TH_{l_{m,n}}\geq TH^{\text{th}},\forall l_{m,n}\in \mathcal{L}_{m},m\in\mathcal{M},\end{split} \tag{5}\]  
where \(P_{\min}\) and \(P_{\max}\) are the minimum and maximum transmit power of each user, respectively. Constraint C1 limits the transmit power budget per user; C2 indicates that the allocated bandwidth cannot exceed the total bandwidth of the system; and C3 ensures the QoS of each user. \(TH^{\text{th}}\) denotes the required minimum throughput. Note that \(p_{l_{m,n}}\) and \(B_{i,l_{m,n}}\) are the decision variables associated with user \(l_{m,n}\), where \(p_{l_{m,n}}\) is the allocated power of the user \(l_{m,n}\), and \(B_{i,l_{m,n}}\) denotes the bandwidth assigned to the user \(l_{m,n}\) of width \(i\). This paper aims at obtaining better throughput by jointly optimizing the two variables.  
Problem P1 is non-convex; it is difficult to solve using traditional methods due to the high computational complexity. Furthermore, owing to the intricacy of the co-channel interference relationship in large-scale systems and the interaction between users in different cells, it is challenging to find the effective solution for joint transmission power and bandwidth allocation directly. To tackle these challenges, we proposed the JPRL method, which is excellent for the multi-cell multi-user system. In the proposed method, the MADDQN algorithm was used to allocate the bandwidth, and the P-MADDPG algorithm was developed to optimize the transmit power.
## 4 Conclusions  
In summary, the series of MIL-88A metal-organic frameworks were prepared under different conditions: temperature (70 and 120 \({}^{\circ}\)C) and solvent (distilled water and DMF). It was found that synthesis factors strongly influence materials' physicochemical properties and sorption abilities. The highest surface area and pore volume exhibited MIL-88A-3 synthesized in DMF at a lower temperature, while the smallest textural parameters, MIL-88A-1, prepared in distilled water at 70 \({}^{\circ}\)C. The materials synthesized in water (MIL-88A-1 and MIL-88A-2) were more thermally stable than samples obtained in DMF. MIL-88A-1 and MIL-88A-2 samples indicated rod-like morphology, while MIL-88A-3 and MIL-88A-4 particles were characterized by spherical or diamond shapes, respectively. Diclofenac sodium adsorption onto metal-organic framework surfaces occurred mainly via hydrogen bonding and metal coordination, as well as electrostatic interactions. The highest amount of drug was adsorbed onto MIL-88A-1 (2021 mg/g) with the smallest specific surface area and pore volume, which implies, that the adsorption course is mainly influenced by the surface moieties. The drug was desorbed from the MIL-88A carriers to a phosphate buffer of pH 6.8 in two stages. At first, the drug was released quickly from the outer surface of MIL-88A materials, followed by a gradual and controlled diffusion of its molecules from inside the pores. The outcomes of this study confirm the potential of MIL-88A carriers to be used as drug delivery platforms in anti-migraine therapy.  
A.G.--Investigation, Visualization, Data curation, Writing--Original Draft Preparation; J.G.--Conceptualization, Supervision, Methodology, Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy reasons.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Comparison  
Similar segmentation approaches used in the literature include fuzzy c-means [2,3] and watershed segmentation [5]. These techniques were compared in accuracy and runtime, using the following experimental setup:  
* WaveCluster was run with an 85% density threshold and the 2.2 biorthogonal wavelet.
* WaveCluster and watershed segmentation were evaluated by considering a cluster a true positive if and only if at least half of the cluster fell within the bounds of a ground truth ROI.
* Na \(\mathrm{\SIUnitSymbolMicro\secondSymbolMicro}\)e watershed segmentation is prone to severe oversegmentation. In practice, watershed segmentation requires an h-minima transform as a preprocessing step. The h-minima transform removes all minima in the image of depth less than \(h\). \(h=0.075\) was chosen empirically.
* Fuzzy c-means was run with \(\mathrm{c}=5\). Since each pixel can have multiple cluster memberships, a cluster was considered a true positive if at least 50% of the _sum_ of cluster weights came from pixels within the ground truth ROI.
* All methods were run with the morphological post-filtering described above. This particularly improved performance on the watershed segmentation approach.  
Comparative results on the ROI detection task are shown in Table 1:  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **WaveCluster** & **Fuzzy 5-means** & **Watershed** \\ \hline Sensitivity: & 81\% & 23\%/78\% & 33\% \\ False Positives: & 0.96/image & N/A & 1.17/image \\ Average Runtime: & 5.2 s/image & 325 s/image & 7.3 s/image \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of sensitivity, false positive rate, and runtime for the WaveCluster, fuzzy c-means (c = 5), and Watershed segmentation approaches.  
Figure 5: Runtime of WaveCluster as a function of image size.  
While fuzzy c-means performed excellently at detecting dense tissue (the application originally proposed in [2]), correctly stratifying regions of the breast by density and discovering a significant cluster containing an ROI in 78% of positive cases, it was found unsuitable for differentiating between normal and abnormal density because it did not incorporate locality. This is shown in Figure 6, and is also visible in Figure 1 (as k-means). When compared using a similar 50% (weighted) coverage metric to the metric used to assess other algorithms, it only correctly isolated 23% of true positive ROIs. This could be corrected in many cases by adding the thresholding and connected component steps inherent in WaveCluster to the fuzzy c-means approach, imposing an additional partitioning on the existing clusters. However, since this is not part of the original fuzzy c-means approach, it was not performed. Fuzzy c-means was also orders of magnitude slower than other approaches, due to its nature as an iterative algorithm with superlinear computational complexity.
## 3 Results and Discussion
### Selectivity of CO\({}_{2}\)  
Photocatalysis allows for the transformation of many VOCs into CO\({}_{2}\) and H\({}_{2}\)O. The classic reaction of this chemical mechanism can be summarized as follows:  
\[\text{VOCs}+\text{O}_{2}\frac{\text{TiO}_{2}-\text{UV}}{\text{by}}-\text{ products}(\text{ads})\frac{\text{TiO}_{2}-\text{UV}}{\text{CO}_{2}}+\text{H}_{2}\text{O}\]  
The selectivity is determined by the following formula [44]:  
\[\text{CO}_{\text{X}}\text{selectivity}=\frac{[\text{CO}_{\text{X}}]_{\text{in }}-[\text{CO}_{\text{X}}]_{\text{out}}}{\text{Nc}\cdot\text{RE}(\%)\cdot[ \text{VOC}]_{\text{in}}}\cdot 10^{4} \tag{16}\]  
where Nc is the number of carbon atoms in the pollutant, RE(%) is the removal efficiency, x = 1 for CO and x = 2 for CO\({}_{2}\), and \([\text{CO}_{\text{X}}]_{\text{in}}\) and \([\text{CO}_{\text{X}}]_{\text{out}}\) are, respectively, the concentrations in ppm of CO\({}_{\text{X}}\) at the inlet and outlet of the reactor, and \([\text{VOC}]_{\text{in}}\) is the inlet concentration of the VOC in ppm.  
3.3.1 The CO\({}_{\text{X}}\) Selectivity with a Variation in the Flow Rate and VOC Concentration  
Figures 5 and 6 display the selectivity of the VOCs, showing similar variations in the CO\({}_{2}\) selectivity of CB and CN. The CO\({}_{2}\) selectivity decreased with an increasing flow rate and concentration. The CO\({}_{2}\) selectivity of CB varied from 49% to 15% for a concentration of 90 mg\(\cdot\)m\({}^{-3}\), when the flow rate was increased from 1 to 9 m\({}^{3}\)/h. For a flow rate of 3 m\({}^{3}\)/h, the CO\({}_{2}\) selectivity of CB varied from 59% to 30%, when the concentration was increased from 30 to 150 mg\(\cdot\)m\({}^{-3}\).  
For CN, the CO\({}_{2}\) selectivity varied from 60% to 21% for a concentration of 130 mg\(\cdot\)m\({}^{-3}\) when the flow rate increased from 1 to 9 m\({}^{3}\)/h. For a flow rate of 3 m\({}^{3}\)/h, the CO\({}_{2}\) selectivity of CB varied from 65% to 40% when the concentration increased from 44 to 216 mg\(\cdot\)m\({}^{-3}\).  
Indeed, during the photocatalytic mechanism, the number of reactive species photo-generated and the active sites of the support remained constant. Thus, a small fraction was mineralized into CO\({}_{2}\) for high concentrations of pollutants.  
The improvement in CO\({}_{2}\) selectivity was noticeable for small concentrations of pollutants injected at the inlet of the reactor, because the availability of the active sites of the catalyst remained greater, so the compounds were easily converted into CO\({}_{2}\). Several studies [19; 43; 44; 45], are unanimous regarding the fact that increasing the initial concentration and flow rate decreases the CO\({}_{2}\) selectivity during the photocatalytic process.  
The products of photocatalytic degradation are generally CO\({}_{2}\) and H\({}_{2}\)O. However, the identification of CO at the reactor outlet has often been reported [45]. This point was found in this study only regarding the photocatalytic degradation of CN. Accordingly, the following figure shows the small amount of CO released.
## 3 Results and Discussion
### X ray Fluorescence (XRF)  
The results of the study of the chemical composition of the samples are listed in Table 3. The high values of SiO\({}_{2}\) in the most altered andesite samples are highlighted, such as PAAZ-01 (65.17%) and PAAZ-02 (64.49%), respectively, although they are also significant in the case of samples PAAZ-04 (62.4%) and PAAZ-03 (61.2%). In samples of unaltered andesites (UPA-01 and UPA-02), the SiO\({}_{2}\) contents are markedly lower than in the altered samples, which leads us to conclude that the high percentages of SiO\({}_{2}\) are a direct consequence of the processes of zeolitization and smectization caused by hydrothermal activity [27; 28; 29]. Through these processes, it is deduced that the contents of alkaline compounds (Na\({}_{2}\)O and K\({}_{2}\)O) experienced an increase with respect to the alkaline earth (CaO and MgO), as shown in Table 3 for the groups of altered and unaltered samples. On this same criterion, the high values of loss on ignition (LOI) in the altered samples, specifically PAAZ-01, PAAZ-02 and PAAZ-04, compared to the practically insignificant values detected in the unaltered samples, are also highlighted. The high values of SiO\({}_{2}\), the predominance of alkaline compounds and the LOI are factors that establish their nature as pozzolans of the altered andesites studied in this work. These three factors have been mentioned before by several authors, such as [28; 30], in their research on other types of pozzolanic materials.
## 3 Results
### Mathematical Modeling
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cultivar**} & \multirow{2}{*}{
\begin{tabular}{c} **Drying Method** \\ **and Temperature** \\ \end{tabular} } & \multicolumn{2}{c}{**Model Name and**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{\(\mathbf{\chi^{2}}\)} & \multirow{2}{*}{\(\mathbf{R^{2}}\)} \\ \cline{3-3} \cline{5-6}  & CD 50 \({}^{\circ}\)C & Logarithmic [18] & 0.0025 & \(4.91\times 10^{-6}\) & 0.999 \\ \cline{2-6}  & CD 60 \({}^{\circ}\)C & Midilli et al. [20] & 0.0019 & \(1.82\times 10^{-6}\) & 0.999 \\ \cline{2-6}  & CD 70 \({}^{\circ}\)C & Two-factor [23] & 0.0018 & \(6.51\times 10^{-6}\) & 0.999 \\ \cline{2-6}  & CD 80–50 \({}^{\circ}\)C & Midilli et al. [20] & 0.0071 & \(2.64\times 10^{-5}\) & 0.999 \\ \cline{2-6}  & CD 80–60 \({}^{\circ}\)C & Midilli et al. [20] & 0.0067 & \(2.45\times 10^{-5}\) & 0.999 \\ \cline{2-6}  & VD 50 \({}^{\circ}\)C & Wang [21] & 0.0110 & \(9.44\times 10^{-5}\) & 0.998 \\ \cline{2-6}  & VD 60 \({}^{\circ}\)C & Wang [21] & 0.0055 & \(1.51\times 10^{-5}\) & 0.999 \\ \cline{2-6}  & VD 70 \({}^{\circ}\)C & Midilli et al. [20] & 0.0170 & \(1.37\times 10^{-4}\) & 0.997 \\ \cline{2-6}  & VD 80–50 \({}^{\circ}\)C & Midilli et al. [20] & 0.0118 & \(7.29\times 10^{-5}\) & 0.998 \\ \cline{2-6}  & VD 80–60 \({}^{\circ}\)C & Midilli et al. [20] & 0.0118 & \(7.29\times 10^{-5}\) & 0.998 \\ \hline \multirow{2}{*}{Weiki} & CD 50 \({}^{\circ}\)C & Midilli et al. [20] & 0.0033 & \(5.33\times 10^{-5}\) & 0.999 \\ \cline{2-6}  & CD 60 \({}^{\circ}\)C & Midilli et al. [20] & 0.0048 & \(1.21\times 10^{-5}\) & 0.999 \\ \cline{2-6}  & CD 70 \({}^{\circ}\)C & Midilli et al. [20] & 0.0030 & \(6.51\times 10^{-6}\) & 0.999 \\ \cline{2-6}  & CD 80-50 \({}^{\circ}\)C & Two-factor [23] & 0.0066 & \(1.85\times 10^{-5}\) & 0.999 \\ \cline{2-6}  & CD 80–60 \({}^{\circ}\)C & Two-factor [23] & 0.0056 & \(1.26\times 10^{-5}\) & 0.999 \\ \cline{2-6}  & VD 50 \({}^{\circ}\)C & Wang [21] & 0.0078 & \(2.93\times 10^{-5}\) & 0.999 \\ \cline{2-6}  & VD 60\({}^{\circ}\)[PERSON] & Midilli et al. [20] & 0.0138 & \(6.51\times 10^{-6}\) & 0.997 \\ \cline{2-6}  & VD 70 \({}^{\circ}\)[PERSON] et al. [20] & 0.0061 & \(2.66\times 10^{-5}\) & 0.999 \\ \cline{2-6}  & VD 80–50 \({}^{\circ}\)[PERSON] et al. [20] & 0.0104 & \(4.52\times 10^{-5}\) & 0.998 \\ \hline VD 80–60 \({}^{\circ}\)[PERSON] et al. [20] & 0.0128 & \(6.58\times 10^{-5}\) & 0.998 \\ \hline \end{tabular} CD, convective drying and VD, vacuum drying. The number adjacent to the drying method abbreviation signifies the applied temperature.  
\end{table} 
Table 3: Statistical analysis of evaluated best fitted mathematical models for tested methods of drying.
# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 4 Experimental Results  
### Datasets and Experimental Setting  
#### 4.1.1 Datasets Configuration  
The experimental dataset consists of 5800 images sourced from video clips recorded in a substation environment. To ensure diversity, the clips captured were selected to reflect various environmental conditions. Each dataset category contains a sufficient number of samples to address class imbalance. Standard image processing techniques, such as cropping, scaling, and data augmentation--including random rotation, translation, flipping, and color adjustment--were employed to enhance diversity and prevent model overfitting. Before partitioning, each image was manually labeled. The dataset was then randomly divided into training, validation, and test sets in an 8:1:1 ratio to maintain the integrity of model training and evaluation. The training set was used for model training, the validation set for hyperparameter tuning and model selection, and the test set for evaluating final model performance.
###### Abstract  
To address the adverse effects of Tuned Inertia Dampers (TIDs) on track slab vibrations while controlling high-frequency rail vibrations, a hybrid Finite Element-Statistical Energy Analysis (FE-SEA) method is developed for modeling the vehicle-track-bridge coupled system. Short-wavelength track irregularities are introduced as high-frequency excitation, and the accuracy and efficiency of this method are validated by comparison with the traditional finite element method (FEM). A vibration control model for track-bridge structures incorporating TIDs is designed, and the effects of the TID's inertance, stiffness, and damping coefficients on the vertical acceleration responses of the rail and track slab are investigated in detail. The study reveals that although TIDs effectively reduce rail vibrations, they may induce adverse effects on track slab vibrations. Using the vibration acceleration amplitudes of both the rail and track slab as dual control objectives, a multi-objective optimization model is established, and the TID's optimal parameters are determined using a multi-objective genetic algorithm. The results show that the optimized TID parameters reduce rail acceleration amplitudes by 16.43% and improve the control efficiency by 12.45%, while also addressing the negative effects on track slab vibration. The track slab's vibration acceleration is reduced by 5.47%, and the vertical displacement and acceleration of the vehicle body are reduced by 14.22% and 47.5%, respectively, thereby enhancing passenger comfort. This study provides new insights and theoretical guidance for vibration control analysis in vehicle-track-bridge coupled systems.  
F 2025 15 8675 1 State Key Laboratory of Safety and Resilience of Civil Engineering in Mountain Area, East China Jiaotong University, Nanchang 330013, China  
English Teaching and Research Section, School of Education, Nanchang Institute of Science & Technology, Nanchang 330108, China  
Correspondence: [PERSON]DDRESS]  
FE-SEA method; vehicle-track-bridge system; Tuned Inertia Damper (TID); vibration control; multi-objective optimization