esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Analysis
For an imbalanced classification problem with multiple classes, _precision_ is computed as the ratio of the sum of true positives across all classes to the sum of true positives and false positives across all classes. In an imbalanced classification, the distribution of data across the known class is biased. It is extremely challenging in predictive modeling as most of the machine learning methods are developed considering the assumption that there are equal number of data in each class. Thus imbalanced classification is a problem when there is unequal class distribution in training data. Many real life classification problems such as spam and fraud detection involve imbalanced distribution. Equations (5) and (6) represent _Recall_ and _Precision_ respectively. \[\text{Recall}\ =\ \frac{TP}{TP+FN}, \tag{5}\]  
\[\text{Precision}\ =\ \frac{TP}{TP+FP}, \tag{6}\]  
where _TP_ or True Positive is when the sample is correctly classified as positive, _FP_ or False Positive is when the sample is incorrectly classified as positive and _FN_ or False Negative is when the sample is incorrectly classified as negative. _F-measure_ is a parameter which decides the image accuracy. Equation (7) computes F-measure as:  
\[F\_measure\ =\ \frac{2.\text{Precision}.\text{Recall}}{\text{Precision}+ \text{Recall}}. \tag{7}\]  
A high value of recall and precision guarantees a high _F-measure. Precision, recall_ and _F-measure_ are applicable mainly in pattern recognition and classification. The significance of the confusion matrix lies in the fact that it can point out the errors made by the classifier. ROC or Receiver Operating Characteristic curve is a graph representing the classification performance. Area Under Curve (AUC) is a measure of the two-dimensional area under ROC curve. ROC is plotted between True Positive Rate (TPR) and False Positive Rate (FPR). TPR is the ratio of the positive samples that are correctly considered as positive w.r.t all positive samples. FPR is the ratio of negative samples which are misclassified as positive w.r.t all negative samples. AUC ranges between 0 and 1 and closer the value to 1, better is the classification performance. Similar to ROC curve is precision-recall curve which is focused towards the performance of the minority class. It can analyze a single classifier but challenging for classifier comparisons. Precision-Recall AUC can also be computed and is useful for imbalanced classification.
# A Sun-Tracking Algorithm for Satellite-Borne Spectrometers Based on the Orbital Motion Model
## 5 Conclusions  
The stability and precision of a spectrometer used for sun tracking for on-orbit work are extremely important. In this paper, coordinate transformation matrices are used to analyze the matrix transformation relationship of the spectrometer system, and the analytical relationship between the angular position of the turntable and the sun vector in the orbit system were obtained. Then, the target motion equation for sun tracking was acquired. Based on certain equations proposed above, an on-axis tracking control algorithm was derived and the target position and velocity were predicted and filtered, along with the simulation and ground tests. The results show that the RMS of the sun tracking error of the spectrometer was reduced from 5.72\({}^{\prime\prime}\) to 1.43\({}^{\prime\prime}\) after using the control algorithm proposed in this paper, which improves the accuracy of sun tracking and provides theoretical support for improving the on-orbit performance of the spectrometer system.  
Conceptualization, X.G.; methodology, X.G.; software, X.G. and X.Q.; validation, X.G. and X.C.; formal analysis, X.G.; investigation, X.G.; resources, C.Y.; data curation, X.G.; writing--original draft preparation, X.G.; writing--review and editing, B.H.; visualization, X.G.; supervision, C.Y. and Y.G.; project administration, C.Y. and Y.G.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China, grant number 12103053.  
Not applicable.  
The authors declare no conflict of interest.
# A Review: Thermal Stability of Methylammonium Lead Halide Based Perovskite Solar Cells
## 5 Other Strategies to Improve Thermal Stability
Although FAPbI3 is more thermally stable than MAPbI3, the structural instability of FAPbI3 in the presence of moisture limits its use in perovskite solar devices ([PERSON] et al., 2018). Replacing FAI by MAI or MABr results in FA1\({}_{\rm 1-x}\)MA\({}_{\rm x}\)PbI3 or FA1\({}_{\rm 1-x}\)MA\({}_{\rm x}\)PbI3\({}_{\rm -y}\)Br\({}_{\rm y}\) perovskites, with a stabilized black perovskite phase ([PERSON] et al., 2016; [PERSON] et al., 2016). [PERSON] et al. achieved 3D black FA-based perovskite by adding a smaller MA cation compared to FA, resulting in no phase transition in the 25-150 \({}^{\circ}\)C temperature range ([PERSON] et al., 2018). Such stability improvement was ascribed to the larger dipole moment of MA, which increases the Coulomb interactions within the structure ([PERSON] et al., 2018). However, since the unstable nature of the MA cation can hinder long-term stability, inorganic cations, such as cesium, have attracted on-going research attention due to their established sustainability at higher temperatures ([PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2020). For example, CsPbI3 based perovskite forms in a cubic structure with a band gap of 1.73 eV ([PERSON] et al., 2018; [PERSON] et al., 2019), but crystallizes into a photo-inactive yellow phase when exposed to air. In contrast, CsPbBr3 is less sensitive to moisture ([PERSON] et al., 2018; [PERSON] et al., 2019), but does not have a favorable bandgap for single-junction solar cell applications ([PERSON] et al., 2019). Since inorganic cesium lead halide perovskites (CsPbX3, X = Cl, Br, I) have demonstrated advantageous thermal stability, efforts have been made to develop mixed cation perovskites with simultaneous high efficiency and good thermal stability ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2021). Several groups have studied Cs/FA mixtures, showing enhanced stability results in light, moisture, and heat compared to the pure material (FAPbI3). [PERSON] et al. have also fabricated solar cells using quadruple-cation perovskites (MA/FA/Cs/Rb), revealing improved reproducibility and stability ([PERSON] et al., 2019). [PERSON] et al. tested devices annealed for 500 h at 85 \({}^{\circ}\)C under continuous illumination in a nitrogen atmosphere, and the current-voltage characteristics of the best performance solar cells and the external quantum efficiency (EQE) electroluminescence (EL) are shown in Figure 12a-c, where the compounded stress test exceeds industrial standards ([PERSON] et al., 2019). They showed that the device retained 95% of its initial performance when annealed at 85 \({}^{\circ}\)C for 500 h, as shown in Figure 12d.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Environmental Factors
#### 3.4.1 Temperature  
The temperature during electrode processing and device operation can significantly influence sticking in MXene electrodes. Higher temperatures can promote the mobility of MXene flakes and increase the likelihood of their agglomeration. This enhanced mobility enables the flakes to come into close contact and form strong inter-particle adhesion, leading to sticking. Conversely, lower temperatures tend to reduce the mobility of the flakes, minimizing agglomeration and sticking. Therefore, careful control of the processing and operating temperature is crucial to prevent sticking and maintain optimal electrode performance.  
For example, the authors in [42] have found that the differences in the sheet length, width, and thickness across samples at temperatures ranging from 20 \({}^{\circ}\)C to 60 \({}^{\circ}\)C were less than 2 A, which aligns with the uncertainty range of experimental errors. These findings indicate that the structure of the Ti\({}_{3}\)C\({}_{2}\) nanosheets is not significantly impacted within this temperature range. Nonetheless, the study presents valuable insights into the lamellar architecture present in the sample sets. The Ti\({}_{3}\)C\({}_{2}\) nanosheets are characterized by a multilayer lamellar morphology, comprising layers of transition metal carbides with interstack gaps. As such, the Small-Angle Neutron Scattering (SANS) data, suggesting a hard block thickness or crystalline thickness of 11.4-11.8 A (equivalent to 1.14-1.18 nm), indicates the thickness of a single layer of Ti\({}_{3}\)C\({}_{2}\). Additionally, the soft block thickness of 20.3-21.5 A (approximately 2.03-2.15 nm) can be interpreted as interstacking layer gaps within the Ti\({}_{3}\)C\({}_{2}\) samples, which is evidence for a lack of sticking.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 4 Data Security Sharing Modeling
### Constraints for Secure Data Sharing
#### 4.2.2 Efficiency (Real-Time) Constraints  
The delay from data request to completed sharing must not exceed the maximum allowed value:  
\[\forall D_{j},\ \tau_{j}\leq\tau_{max}\]  
where \(\tau_{j}\) is the transmission delay of data \(D_{j}\), and \(\tau_{max}\) is the system's maximum allowed delay.
## 3 Results  
### Temperature Distribution Along the Antenna Length  
The distribution of temperature difference between absolute temperature and ambient temperature \(T_{amb}\) = 293.15 K is presented in Figure 6. Hot junction corresponds to \(x\)-coordinate at 500 \(\mu\)m. The \(Bi_{88}Sb_{12}\) layer is heated up to a higher temperature in comparison with the \(Sb\) layer. Temperature in the \(Bi_{88}Sb_{12}\) layer is higher than in the hot junction. The impact of heating in the \(Cu\) layer is almost negligible. The results are presented for different \(h_{port}\) values. The same picture of temperature distribution along the antenna length was true for all simulation cases (with different power input and frequencies).  
\begin{table}
\begin{tabular}{c c c} \hline
**Frequency, THz** & **Re** & **Im** \\ \hline
0.1 & \(-\)6219.67 & \(2.70\times 10^{2}\) \\
0.2 & \(-\)6219.42 & \(1.35\times 10^{2}\) \\
0.3 & \(-\)6218.99 & \(9.00\times 10\) \\
0.4 & \(-\)6218.39 & \(6.75\times 10\) \\
0.5 & \(-\)6217.62 & \(5.40\times 10\) \\
0.6 & \(-\)6216.68 & \(4.50\times 10\) \\
0.7 & \(-\)6215.58 & \(3.86\times 10\) \\
0.8 & \(-\)6214.3 & \(3.37\times 10\) \\ \hline \end{tabular}
\end{table}
Table 2: Calculated relative permittivity of copper.  
Figure 6: Example of temperature distribution along the x-axis (antenna length). Copper films correspond to the x-coordinates: 0–100 \(\mu\)m, \(Bi_{88}Sb_{12}\) to 100–500 \(\mu\)m, \(Sb\) to 500–800 \(\mu\)m; hot junction corresponds to the x-coordinate at 500 \(\mu\)m.
## 3 Results and Discussion
### Vertical Displacements
From this point of view, PCD measurements seem to be sufficiently valuable for evaluating the vertical displacements adjacent to a deep excavation site. If the vertical displacement of the ground adjacent to the construction site is measured at the levelling error level, it is also possible to perform displacement detection using PCD and apply the level measurement to the area where displacements occur.
# A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data
## 2 Research Question on the Placement of Cellular Tower(s)
Thus, 2D data and rough land use and landcover data significantly preclude the estimation of detail and accurate signal strength for a place. Developing countries have a large proportion of unplanned cities, offering a very mixed land use and land cover and often creates a significant challenge in terms of the accurate estimation of signal strength for a place from a cellular service provider. Thus, it is required to come up with a technique that can model or compute accurate signal strength for a user location from a cell phone tower. The model should also be able to estimate the best location for setting up a cellular tower for a place or estimate numbers and locations of possible cellular towers for an area to provide good signal strength to every user location. The existing GIS software suffers in terms of its ability to accommodate huge terrain data or extract buildings and other obstructions in 3D automatically, which prevents direct transmission of a signal from the cellular tower(s) to different user locations. There is thus a requirement to use 3D terrain data of high precision, which can find out a technique to extract all obstructions between any pair of the cellular tower and user locations, determine losses in transmission at its every route of transmission, and can estimate an optimization algorithm that can work with all the spatial attributes (transmission paths, distances, transmission losses, etc.) and non-spatial attributes (number of users at a building, population density, etc.) to determine the signal strength and optimum location(s) for the setting-up of cellular tower(s). LiDAR data offers very precise 3D data in point cloud form. It is required to establish a novel cellular tower locating optimization technique using LiDAR data, which will overcome challenges of accommodating precise 3D data and enable highly accurate optimization in terms of offering the best location(s) for the setting-up of cellular tower(s) and giving the best estimate of signal strength for any surrounding areas.
# Effect of Systemic Zoledronic Acid Dosing Regimens on Bone Regeneration in Osteoporotic Rats
## 1 Introduction
The proven effectiveness of anti-osteoporotic drugs in increasing the biomechanical properties of bone tissue suggests a possible role of these molecules in promoting and/or accelerating bone healing. The use of anti-osteoporotic drugs has been reported as an approach to favor early bone healing in bone defect regeneration in osteopororotic conditions [37]. A meta-analysis assessed the data available from preclinical studies to quantify literature evidence of anti-osteoporotic drug efficacy, in adjunction with bone grafting, in bone regeneration, in healthy and osteopororotic conditions. It showed an increase in overall histological bone area percentage and concluded that using anti-osteoporotic drugs in adjunction with bone grafting stimulated bone regeneration [38]. BPs are the preferred pharmacotherapy for osteoporosis treatment. ZA is a new-generation intravenous BP that counteracts osteoporosis on two fronts by decreasing bone resorption and encouraging bone anabolism. BPs reduce bone turnover, largely by decreasing the activation frequency and the recruitment of osteoclast precursors [39], but they also reduce the amount of bone removed during bone remodeling by increasing apoptosis [40]. The effect of BPs on bone regeneration in osteopororotic animal models has already been evaluated in multiple studies. [PERSON] et al. studied the impact of systemic alendronate treatment on autologous bone graft in a vehicle for spinal fixation in ovariectomized (OVX) rats [41]. They showed BPs to inhibit endochondral ossification and to reduce graft as well as newly formed bone resorption in osteopororotic rats compared to untreated animals due to the suppression of osteocl activity [41]. Using an osteopororotic sheep model, [PERSON] et al. reported that local BP treatment of calcium phosphate-grafted vertebral bone defects counteracted symptoms of osteoporosis by promoting new bone formation and increasing bone area [15]. Accordingly, we hypothesized that an initial treatment of an osteopororotic condition before bone defect preparation and bone substitute installation could enhance bone regeneration compared to a treatment started after this procedure. Therefore, the aim of the present study was to evaluate bone regeneration in bone defects grafted with a bone substitute material in an osteopororotic rat model treated with intravenous ZA using three different treatment regimens (pre-bone grafting, post-bone grafting, and pre- and post-bonegrafing) compared to non-treated controls. The obtained data can be used as a prelude for clinical steps focused on the regeneration of maxillofacial bone defects.
## 6 Case Analysis and Result
### Feature Selection and Feature Reconstruction
The selected 12 variables included: the seven-day historical load at each sampling point (\(L_{t,1}\), \(L_{t,2}\), \(L_{t,3}\), \(L_{t,4}\), \(L_{t,5}\), \(L_{t,6}\), \(L_{t,7}\)), the temperature on the current day (\(T_{t}\)), the temperature on the three previous days (\(T_{t,1}\), \(T_{t,2}\), \(T_{t,3}\)), and the solar irradiance on the current day (\(S_{t}\)). It is important to note that the solar irradiance is only considered as an influencing factor during daylight hours since it has a value of zero during nighttime. When calculating the average MIC value, only the periods with non-zero solar irradiance are taken into account. However, for the convenience of the model input during the prediction, the solar irradiance for the entire day was included as an element in the original feature set. This ensured that the original feature set remained the same for different types of days, with only varying degrees of influence at different time intervals. The input weights for different features and the redefined input feature set for each sampling point of different date types are shown in the following figure. In Figure 8, Ld1-Ld7 represent the historical load data of the previous 1-7 days at different sampling points; Td0 represents the temperature at the current day of different sampling points; Td1-Td3 represent the historical temperature data of the previous 1-3 days at different sampling points; Sd0 represents the irradiance at the current day of different sampling points. As can be seen from Figure 8, in the first type of load, which was the medium temperature day, the main influencing factor was the historical load value, and the influence of the irradiance was concentrated in the noon period. Since there was no cooling and heating load on the first type of load day, the influence of the temperature was not obvious. For the third type of load, namely, the low-temperature days, the influence of the irradiance was higher than that of the first type of load days, in addition to the more significant effect of the temperature. On high-temperature days, it was mainly due to the influence of the temperature and the historical load, and the influence of the irradiance was relatively low. This was because the load at noon on high-temperature days tended to be stable due to the large increase in the refrigeration load and the access to the distributed photovoltaic. Figure 8: The weights of different features at different times: (**a**) the weight coefficient of each input feature at different times in the first type of day (medium-temperature day); (**b**) the weight coefficient of each input feature at different times in the second type of day (high-temperature day); (**c**) The weight coefficient of each input feature at different times in the third type of day (low temperature day).
## 3 Definition and Theory Analysis  
RECN is the cross-layer congestion notification scheme, based on the frame transmission efficiency \(R_{FTE}\) and using the standard ECN mechanism. RECN works at the MAC layer and adjusts the sending rate of the transport protocol automatically and transparently. In this scheme, the frame transmission efficiency is the key point, being put as the new congestion metric. Thus, in this section, we give the definition and analysis of the frame transmission efficiency to demonstrate the relationship between the frame transmission efficiency and the link layer congestion.
# INS/Vision Integrated Navigation System Based on a Navigation Cell Model of the [PERSON]
In our model, the camera is employed for environmental perception as the eyes of animals, and the INS is used for velocity and position measurement as the path integrator based on the speed cell model. The place cell nodes are established by storing feature scene pictures in the system and associating them with corresponding accurate position information. When the carrier moves, the images captured by the camera are matched with the stored feature scene pictures. When the match is successful, the place cell node is triggered, and its corresponding accurate place information is fed back to the INS to correct the accumulated position error. At the same time, the INS adjusts the path integration mechanism to reduce the error of the path integrator. 2. The INS/vision brain-like navigation system is built on an STM32 single-chip computer and LattePanda (a kind of card-type computer). The complete circuit board includes the main controller, namely STM32, and two main functional components, which are the INS and the camera based on LattePanda. The main board is composed of an ADC analog-to-digital conversion chip, Bluetooth module, various voltage regulating circuits, and so on. STM32 and LattePanda communicate through the serial port. The hardware system can realize real-time information processing of the proposed brain-like navigation system. The paper is organized as follows: Section 2 briefly describes the concept of speed cells and place cells, and proposes the INS/vision brain-like navigation model. Section 3 introduces the hardware design. Experiment and comparison results are shown in Section 4. The paper ends with a conclusion in Section 5.