esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 4 Results
### Simulation Results
#### 4.1.1 Fixed Temperature Setpoint  
Under those considerations, initially, the behavior of the proposed model is illustrated under the following situations: no heater control, \(T_{set}=273\) K, \(T_{set}=278\) K, and \(T_{set}=283\) K.  
Figure 10: Normalized values for battery voltage (\(V_{B}\)), power input (\(P_{int}\)), average temperature of PV panels, and average irradiance over the course of an orbit (sunlight).  
Figure 9: Temperature of the photovoltaic panels during the 8 th orbit.  
As can be seen, in the case with no heater (no control) in Figure 13, despite the CubeSat attaining the highest energy levels, the temperature reaches 265 K in eclipse regions, which tends to impact its lifespan and functioning. The most intensive use of the heater in the defined setpoints occurs in the eclipse regions. Typically in these regions, the satellite's energy balance is negative, so tasks with high energy consumption should be avoided in those regions. As expected, a higher setpoint requires more energy from the system and the temperature is higher.  
Figure 14: Energy levels (\(L\)) for different battery temperature setpoints.  
Figure 13: Battery temperature (\(T_{\beta}\)) for different battery temperature setpoints.  
keeps the satellite at lower energy levels (L) (Figure 14). In comparison, the cases with \(T_{set}=273\) K, \(T_{set}=278\) K and \(T_{set}=283\) K have the heater on during the eclipse for 520, 916, and 1305 s, respectively.  
For instance, consider the balance of energy shown in Figure 15, which zooms in on the satellite entering into an eclipse. Initially, the energy balance is close to 0 and constant as the heater is not activated, and CubeSat is in energy-saving mode \(L=1\). However, as the battery's temperature outside the eclipse was kept higher by the case with \(T_{set}=283\) K, the activation of the heater suffers a delay, and its use is reduced when the satellite is within the eclipse. Maintaining a higher temperature outside the eclipse keeps the battery at a lower voltage and state levels as a negative point. Thus, a trade-off must be found between maintaining the battery temperature and its use.
# Novel 3U Stand-Alone CubeSat Architecture for Autonomous Near Earth Asteroid Fly-By
## 3 Autonomous Navigation
Combining these two considerations with the ephemeris uncertainty propagation, which depends on the initial uncertainty and on the ToF, it is possible to define a navigation position requirement at the end of the cruise. For Eros, a 2-body problem propagation of around 300 days (typical ToF) leads into a 3\(\sigma\) uncertainty in the order of few thousands kilometers, which translates into a 3\(\sigma\) navigation requirement of maximum 2000 km, if a \(\Delta V\) budget below 5 m/s is available; if a larger budget is available, and possibly also more days prior to fly-by, this requirement could be less stringent. Finally, (c) shows the accuracy of the navigation filter (Extended Kalman Filter) in the last 60 days of the cruise (assumed as a Hohmann transfer), which meets the requirement. Both Jupiter and Mars are observed with a tracking rate of one observation/hour for all the duration of the cruise, with a line-of-sight measurement accuracy of 10 arcsec (as it corresponds to micro star tracker accuracy). The assumed initial position and velocity 3\(\sigma\) uncertainties are respectively 10\({}^{5}\) km and 0.1 km/s, accordingly to previous works [17]. It is necessary to remark that both navigation performance and requirements will depend on the specific target and mission design, but in general, this navigation technique appears suitable. Moreover, as reported in Reference [21], the time to correct the trajectory towards the asteroid can be further reduced, as the asteroid should be observed sufficiently to compute accurately its trajectory and to plan the maneuver. Again, this will depend on the particular target, but it is important to be taken into account for further navigation analysis. The third navigation phase is widely investigated nowadays and it is strongly linked to the scientific phase, since it can use the same instrumentation (visible camera). It also influences the quality of the scientific return. Relative navigation around an asteroid is challenging due to the various uncertainties of the particular target, from its actual dimension and shape, to its actual mass and rotation characteristics. So, it is important that the navigation system (and strategy) is robust to that uncertainties. Many techniques have been investigated or are under current investigation, from more traditional center-of-brightness and geometric centering [11; 22], to brand-new deep-learning and neural network based [23; 24]. Indeed, the fast growth in neural-networks based navigation will characterize the next years of small bodies exploration. A traditional approach based on geometrical features and surface landmarks appears more suitable for this application, since, as of today, deep-learning based approaches are still under investigation; however, in the near future, when their high performances and robustness will be proved, they will be optimal for this application.
## 4 Results and Discussion
### Modeling Results
#### 4.2.2 The Results of the Classification of the Mechanical Strength Level
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Tree Species**} & \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Cross-Validation Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{3-6}  & & **ACCCV** & **F1 CV** & **ACCP** & **F1P** \\ \hline \multirow{4}{*}{larch} & WT + MSC, CARS + LLE, PLS-DA & 0.7931 & 0.7752 & 0.7777 & 0.7498 \\  & WT + MSC, CARS + LLE, SVM & 0.8276 & 0.8142 & 0.8333 & 0.8267 \\  & WT + MSC, CARS + LLE, RF & 0.8540 & 0.8379 & 0.8333 & 0.8267 \\  & WT + MSC, DenseNet & 0.8342 & 0.8328 & 0.8540 & 0.8454 \\  & **WT + MSC, SE-DenseNet** & **0.8611** & **0.8601** & **0.8889** & **0.8831** \\ \hline \multirow{4}{*}{hemlock} & WT + MSC, CARS + LLE, PLS-DA & 0.7688 & 0.7624 & 0.7543 & 0.7499 \\  & WT + MSC, CARS + LLE, SVM & 0.7854 & 0.7745 & 0.7637 & 0.7591 \\  & WT + MSC, CARS + LLE, RF & 0.8065 & 0.8012 & 0.7965 & 0.7825 \\  & WT + MSC, DenseNet & 0.7854 & 0.7729 & 0.7965 & 0.7876 \\  & **WT + MSC, SE-DenseNet** & **0.8276** & **0.8201** & **0.8108** & **0.8016** \\ \hline \multirow{4}{*}{mongolica} & WT + MSC, CARS + LLE, PLS-DA & 0.7462 & 0.7387 & 0.7354 & 0.7321 \\  & WT + MSC, CARS + LLE, SVM & 0.7688 & 0.7539 & 0.7428 & 0.7456 \\ \cline{1-1}  & WT + MSC, CARS + LLE, RF & 0.8067 & 0.7976 & 0.8142 & 0.8078 \\ \cline{1-1}  & WT + MSC, DenseNet & 0.7928 & 0.7863 & 0.8142 & 0.8046 \\ \cline{1-1}  & **WT + MSC, SE-DenseNet** & **0.8214** & **0.8159** & **0.8571** & **0.8381** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the results of the classification methods of strength level. Figure 16: SE-DenseNet for three coniferous wood’s mechanical strength binary classification: (**a**) Cross-validation set ROC curve for larch; (**b**) Cross-validation set ROC curve for hem block; (**c**) Cross-validation set ROC curve for Mongolia; (**d**) Test set confusion matrix for larch; (**e**) Test set confusion matrix for hem block; (**f**) Test set confusion matrix for monoglica.
# Analysis of Hydrological Characteristics of Blue Nile Basin, Nishe Watershed
## 2 Materials and Methods
### Model Calibration and Validation  
Calibration is used to determine the appropriate parameter in modeling and a method that is used to compare the observed and simulated streamflow through parameter evaluation. Model calibration is necessary for preliminary testing of the model and observed data. The two types of calibration are automatic and manual where the automatic calibration is powerful, fast, less subjective, and labor-saving and reduces the uncertainty that often characterizes manual calibration. Manual calibration is time-consuming, very subjective and it is hard to acquire global optimality whereas the automatic calibration offers many advantages over the manual approach [16].  
The process of validation is to analyze the efficiency of the calibrated parameters. For this watershed, the sensitivity, uncertainty, model calibration, and validation were analyzed using streamflow data by utilizing the four algorithms of SWAT-CUP: SUFI-2, GLUE, ParaSol, and PSO. These methods have been selected because they are widely used for parameter uncertainty analysis. The streamflow data from the period of 1987-1999 used for calibration and 2000-2008 for validation. The calibration and also validation of the watershed was performed at three selected sub-basins (upstream, downstream, and at the middle) since it represents the other sub-basins. The years from 1985-1986 were used to initialize the model.  
The time period for calibration and validation years was determined by the length of the observed data record. When the observed record is not sufficient for an equal split, the length of the data may be different in such a way that the calibration period is sufficiently long. After the validation phases, if the model achieves a satisfactory performance, it becomes possible to perform model simulations according to different scenarios [36].  
Inadequate model calibration measurement uncertainty in streamflow data and short streamflow records can also result in weak SWAT hydrologic predictions [37]. Most of the SWAT studies contain both calibration and validation, while others performed only calibration due to a lack of observed data [38]. The streamflow data play a critical role for calibration and validation processes. Therefore, the parameters of the SWAT model are calibrated by historical data and are assumed to have extrapolative ability even under future LULC scenarios. Similarly, the calibration of hydrological models is subject to the type of rainfall data source [16].
# Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
## 5 Experimental Results
### Datasets
#### 5.1.1 Ardizzone  
This dataset is composed of medium-sized images, most of them with a size of either \(1000\times 700\) or \(700\times 1000\) pixels. The Ardizzone dataset is divided into several subsets: Subset D0 contains 50 images with simple tampering that only involves basic translation transformations; subsets D1 and D2 contain tampered images with additional rotations and scaling transformations; and subset D3 contains the corresponding original images of the tampered images in D0.
# DCS-ST for Classification of Breast Cancer Histopathology Images with Limited Annotations
## 3 Method
### Training and Evaluation  
The model is trained using the cross-entropy loss:  
\[L=-\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}y_{i,c}\log(p_{i,c}),\]  
where \(N\) is the batch size, \(C=4\) is the number of classes, \(y_{i,c}\) is the ground-truth label, and \(p_{i,c}\) is the predicted probability. For semi-supervised training, a weighted loss incorporates pseudo-labels, as detailed in Algorithm 1.  
```
1:Labeled dataset \(D_{\text{labeled}}=\{(x_{i},y_{i})\}_{i=1}^{N_{l}}\), Unlabeled dataset \(D_{\text{unlabeled}}=\{x_{u_{j}}\}_{j=1}^{N_{u}}\), Test dataset \(D_{\text{test}}\), Epochs \(E\), Threshold \(\tau=0.9\)
2:Initialize Enhanced Swin Transformer parameters \(\theta\), optimizer, and scheduler
3:for epoch \(e=1,\dots,E\)do
4:if\(e<2\)then\(\triangleright\) Initial training on labeled data only
5: Train model on \(D_{\text{labeled}}\), update parameters \(\theta\)
6:else\(\triangleright\) Semi-supervised learning phase
7: Initialize PseudoLabels \(\leftarrow\)\(\emptyset\)
8: Initialize PseudoImages \(\leftarrow\)\(\emptyset\)
9: Generate pseudo-labels:
10:for all\(x_{u}\in D_{\text{unlabeled}}\)do
11: Predict class and confidence for \(x_{u}\)
12:if confidence \(>\tau\)then
13: Add \((x_{u},y_{u})\) to PseudoLabels
14:endif
15:endif
16:endif
17:if PseudoLabels not empty then
18: Train model on pseudo-labeled data using weighted loss
19:endif
20: Update learning rate scheduler
21:endfor
22:Testing: Compute predictions on \(D_{\text{test}}\) and evaluate metrics
23:return Classification metrics and trained model
```  
**Algorithm 2** Enhanced Swin Transformer with semi-supervised learning  
The model on pseudo-labeled data using weighted loss is shown in Figure 2.
# CFD Based Non-Dimensional Characterization of Energy Dissipation Due to Verticle Slosh
## 6 Non-Dimensional Property Parameter Space  
The intended aim of the Protospace experiment [3] was to provide a Froude scaled aircraft wing with a geometry scaling of \(\lambda=0.2\) while employing water whereas the full-scale model makes use of cold kerosene. The calculated non-dimensional values for the Froude scaled Protospace (employing kerosene), actual Protospace (as achieved in the experimental campaign employing water) and the full-scale model are given in Table 5. Accordingly, the range of the non-dimensional parameter space for which to build the scaling-laws was chosen to contain both the Protospace and full-sized model non-dimensional values, as presented in Table 6. Note that the liquid-solid contact angle lower limit allowed by the software was 30\({}^{\circ}\) and thus does not contain the 0\({}^{\circ}\) value for kerosene. It was however found that (see next section) the key importance of contact angle lies in the initial meniscus, i.e., if the contact angle is 90\({}^{\circ}\) or not. This is as the \(We\) numbers are high while a 90\({}^{\circ}\) contact angle results in the absence of Rayleigh-Taylor instability and therefore no violent slosh. Further, turbulence was found to be a minor contributor to energy dissipation (the major being liquid impact) and hence \(Re=\infty\) here implies the inviscid limit, i.e., \(\mu=0\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Quantity** & **Factor** \\ \hline Length & \(\lambda\) \\ Time & \(\lambda^{1/2}\) \\ Mass & \(\lambda^{3}\) \\ Velocity & \(\lambda^{1/2}\) \\ Acceleration & \(\lambda^{0}=1\) \\ Viscosity & \(\lambda^{1.5}\) \\ Surface Tension & \(\lambda^{2}\) \\ Force & \(\lambda^{3}\) \\ Energy & \(\lambda^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scaling Rules.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\mathbf{f}\) [Hz] & \(\mathbf{Fr}\) \\ \hline Required & 3.35 & 1.65 \\ Achieved & 7.0 & 3.44 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Protospace Froude Similarity Parameters.  
The CFD simulations to follow employ a single compartment tank undergoing a damped sinusoidal vertical acceleration where the vertical displacement is defined by,  
\[y(t)=Ae^{-\xi\omega t}(Bcos(\omega t)+Csin(\omega t)) \tag{27}\]  
where,  
\[\begin{split}\omega&=2\pi f\\ a_{1}&=\left(\frac{f}{\gamma}\right)^{2}a_{0}\\ A&=\frac{a_{1}}{(\xi^{2}\omega^{2}-\omega^{2})^{2}+(- 2\xi\omega^{2})^{2}}\\ B&=\xi^{2}\omega^{2}-\omega^{2}\\ C&=-2\xi\omega^{2}\end{split} \tag{28}\]  
allowing an approximate acceleration definition such that,  
\[a(t)=a_{1}\;e^{-\xi\omega t}\;cos(\omega t)=\tilde{y}(t) \tag{29}\]  
Finally, as per the Protospace experiment \(a_{0}=200\) m \(\cdot\) s\({}^{-2}\) and \(\xi=0.06\) for the non-dimensional number sensitivity study (next section). However, in computing the non-dimensional scaling graphs (later section) we set \(\xi=0.0\) so as to focus on quantifying slosh induced damping.
# Metamaterial Integrated Folded Dipole Antenna with Low SAR for 4G, 5G and NB-IoT Applications
###### Abstract  
The fast growth of wireless technology for mobile communication devices requires broad bandwidth, high data rate facilities and compact device size. The solution to the next generation user equipment is high data rate 4G/5G technologies. In this research, wideband antenna design was analyzed and evaluated for 4G, 5G and NB-IoT applications. CST microwave studio was used for simulations and investigations of the performance parameters. The antenna was designed as a folded dipole with a tunable bandwidth and resonates for 5G NR n78, NR-IoT bands B1, B2, and B25, and eleven TDD LTE frequency bands with a bandwidth percentage and minimum scattering loss of 69.02% and \(-\)42 dB respectively. Additionally, the designed antenna is small (\(35\times 48\times 1.62\) mm\({}^{3}\)) and planar in structure and can be easily integrated with radio equipment. The antenna design was also investigated for SAR minimization and gain enhancement using metamaterial integration. For all operating frequency bands, the antenna design results in a considerable gain improvement. The metamaterial was shown to be an excellent absorber of radiation, particularly in high frequency regions. This research also included a SAR examination with and without metamaterial integration. SAR values were found to be significantly reduced throughout all operating bands. The results were validated by fabricating the design prototype on FR-4 substrate for 4G, 5G and IoT bands. The antenna will be possibly used for communication in high data rate applications.  
4G; 5G; FDD; IoT; LTE; NR; TDD
## 3 Unique Contributions of Ka-Band Information in AltiKa Data
Also the approachability towards the coast has been studied using the SARAL/AltiKa based ISRO Space Applications Center (SAC) coastal product and the Coastal and Hydrology Altimetry product (PISTACH) product from Jason-2. Figure 15a shows the 20-Hz SWH from Jason-2 towards the coast and Figure 15b shows 40-Hz SWH from SARAL/AltiKa. Clearly the SARAL/AltiKa based coastal product provides more valid data in proximity to the coast that will help to improve the coastal models.
## 2 Materials and Methods  
### Overview  
One study focused on the background of CWR implementation in the Seoul urban rail system, status of track improvement projects, and the cumulative tonnage passing through the tracks. Figure 1 illustrates the installation status of a subway CWR.  
Owing to the nature of urban railways, the incidence of CWR damage and breakage due to environmental factors such as frequent train operation, accumulated passing tonnage, and climate change is gradually increasing. This study was designed to analyze the characteristics of CWR breaks around urban railway ventilation openings. The cause analysis was performed for each location based on the history of CWR breakage in urban railways.  
Standard-length rails were used when Seoul Metro Line 1 opened on 15 August 1974. However, in 1995, CWRs were introduced as part of a track improvement project to address the aging infrastructure. The first implementation of CWRs in the Seoul urban rail system began with the construction of Line 2. Subsequently, \(\sim\)60% of all track sections were built using CWRs on Lines 3 and 4. Among the total 277 km of the mainline track across Seoul Metro Lines 1-4, 42% (120.3 km) included curved sections, with 65% (78.5 km) having a sharp curvature radius of 600 m or less (R \(\leq\) 600 m).  
Table 1 lists the cumulative rail tonnage, which indicates that the annual cumulative tonnage for Seoul Metro Lines 1-8 reached a maximum of 37 million tons (with an average of 24 million tons), indicating a high frequency of train operations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{3}{c}{**Cumulative Tonnage (ton)**} & \multicolumn{1}{c}{**Annual Tonnage**} \\ \cline{2-5}  & **Average** & **Maximum** & **Minimum** & **(ton)** \\ \hline Line 1 & 353,564,757 & 1,166,542,899 & 46,534,472 & 37,985,936 \\ Line 2 & 429,770,051 & 1,409,051,143 & 43,191,733 & 35,412,833 \\ Line 3 & 308,694,055 & 1,009,647,703 & 24,793,729 & 25,783,785 \\ Line 4 & 371,380,412 & 1,116,506,539 & 25,250,378 & 30,590,571 \\ Line 5 & 543,161,448 & 714,882,321 & 15,016,651 & 17,210,184 \\ Line 6 & 398,654,404 & 424,788,449 & 117,362,310 & 16,346,463 \\ Line 7 & 538,070,364 & 624,131,400 & 202,664,753 & 21,690,988 \\ Line 8 & 336,416,626 & 369,261,145 & 291,078,661 & 11,304,729 \\ Average & 409,964,015 & 854,351,450 & 95,736,586 & 24,540,686 \\ \hline \hline \end{tabular}  
* As of December 2023, based on mainline data (excluding branch lines).  
\end{table}
Table 1: Cumulative rail tonnage.  
Figure 1: Installation status of subway CWR: (**a**) rail continuous welding; (**b**) area around underground ventilation shafts; (**c**) CWR weld fracture.
## 1 Introduction
However, the simultaneous incorporation of rubber granules and basalt fibers into concrete results in microstructural and performance differences compared to cases where they are individually incorporated into concrete. Therefore, the existing theoretical studies on the bonding properties of these two concrete types and rebar can hardly be applied. There is limited research on the bonding properties between RBFC and ribbed rebar. Therefore, it is essential to analyze the bonding properties between RBFC and rebar to establish a theoretical basis for the promotion and application of RBFC in engineering structures. Various forms of bond-slip constitutive models have been proposed by scholars for the bond-slip between rebar and composite concrete. The dominant expressions are the BEP four-segment model [27], the three-segment model that improves the BEP [28], and the continuous curve model proposed by [PERSON] [29; 30]. However, the expressions for the micro-slip segment during the early stage of loading are vague and do not explicitly represent this stage. Moreover, the damage process in the uplift section is actually a gradual transformation from an elastic section to an elastic-plastic section, and the uplift section needs to be delineated more carefully. Consequently, a precise, brief, and effective bond-slip constitutive model based on the actual damage process of RBFC and rebar must be developed. In this study, 54 specimens from 18 sets of center pull-out tests of RBFC were used to investigate the effects of rebar diameters, bonding lengths, and concrete types on the bonding properties between RBFC and rebar. The process of bond-slip failure between RBFC and rebar was better elucidated. By analyzing and exploring various bond stress-slip curve sets and combining them with experimental data, a semi-theoretical and semi-empirical bond-slip constitutive model of RBFC with rebar was obtained.
## 6 Results and Discussion
### Effectiveness Analysis Using Key Sensitivity  
The key sensitivity of the obtained optimum key in the ABC-ROA-based privacy preservation system for three datasets with various existing meta-heuristic algorithms at various percentage levels is shown in Figure 9. The key sensitivity of the proposed system is noticed as a lower value while increasing the percentage of the key for all the three datasets. From the analysis, the developed model shows key sensitivity improvements of 11%, 15%, 20%, and 19% to heuristic algorithms such as HHO, EF-HHO, BCO, and ROA, respectively. In the given graph analysis, it shows the equivalence performance. Based on the key sensitivity value, the ROA algorithm is not effective to secure the data in the blockchain. At learning percentage 30, the key sensitivity of the existing EF-HHO algorithm secures second-best performance. As a result, the suggested blockchain-based privacy preservation model executes more effectively than other heuristic algorithms.  
Figure 8: Convergence analysis on developed blockchain-based privacy preservation model in terms (a) dataset 1, (b) dataset 2, and (c) dataset 3.