esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 4 Results
### Comparison to Prior Arts
#### 4.3.2 Quantitative Results  
To the best of our knowledge, in the field of video editing, there is still a lack of widely recognized metrics to comprehensively evaluate the performance of different methods [37; 46]. However, in order to compare the advantages and disadvantages of the method we proposed with other methods as objectively as possible, in combination with previous studies, we have comprehensively considered several key metrics, including user study metrics: Motion Alignment (MA), Appearance Alignment (AA), Editing Quality (EQ), Temporal Consistency (TC). Motion Alignment (MA) serves to assess the congruence of motion in edited videos with respect to the motion intent encapsulated in the reference image, focusing on the conformity of object movements to predefined editing objectives. Appearance Alignment (AA) quantifies the degree of visual correspondence between edited videos and the reference image, encompassing attributes such as color consistency, texture fidelity, and object-shape preservation. Editing Quality (EQ) provides a holistic assessment of visual integrity in edited outputs, evaluating parameters including sharpness, absence of artifacts, and perceptual naturalness. Temporal Consistency (TC) measures the spatiotemporal coherence of edits across frame sequences, ensuring seamless transitions and the absence of abrupt or unnatural visual discrepancies over time.  
These metrics are crucial as they cover different aspects of video editing quality, from the alignment of motion and appearance to the overall editing quality and temporal consistency. Through comparison, we have found that our results surpass those of other methods in local video editing.  
To ensure a robust evaluation, we conducted a user study with 50 participants, each evaluating 20 videos. For each case, the participants viewed the source video and the reference image before being presented with four edited videos in random order. They rated each video on a Likert scale from 1 to 5 for each metric.The scores were computed by aggregating participant ratings and normalizing them to a 100-point scale for better comparability.  
The results are shown in Table 1. Our method achieved the best performance in all aspects of the human evaluation. It also obtained the best results in other perceptual index evaluations. These outcomes prove that our proposed method has superior editing capabilities.
## 3 Results and Discussion
### Thermal Exposure Test at 180 \({}^{\circ}\)C and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parametric** & **60 \({}^{\circ}\)C, 4 h** & **180 \({}^{\circ}\)C, 2 days** & **180 \({}^{\circ}\)C, 5 days** \\ \hline Initial temperature of first endothermic peak (\({}^{\circ}\)C) & 129.04 & 129.45 & 128.84 \\ First endothermic peak (\({}^{\circ}\)C) & 131.15 & 131.35 & 131.55 \\ Initial temperature of second endothermic peak (\({}^{\circ}\)C) & 334.95 & 335.12 & 335.35 \\ Second endothermic peak (\({}^{\circ}\)C) & 336.02 & 336.06 & 336.23 \\ Initial temperature of exothermic peak (\({}^{\circ}\)C) & 405.34 & 406.37 & 404.33 \\ Exothermic peak (\({}^{\circ}\)C) & 431.28 & 424.77 & 428.51 \\ Decomposition heat E (Jg-1) & 3447 & 3454 & 3367 \\ Energy loss rate 1 (\%) & 0 & 1.01 & 3.26 \\ \hline \hline \end{tabular} \({}^{1}\) Energy loss rate: (E\({}_{60^{\circ}\)C,4h}\)-E\({}_{180^{\circ}\)C, 4h}\)/E\({}_{60^{\circ}\)C, 4h}.  
\end{table} 
Table 5: DSC parameter comparison of 4#BPN sample (\({}^{\circ}\)C) (heating rate 5 \({}^{\circ}\)C/min). As shown in Table 6, from the comparison of the weight loss ratios of BPN samples in the 180 \({}^{\circ}\)C test for different times, after five days, the weight loss ratio of 1# BPN without binder is approximately 0.1%, that of 4#BPN containing fluororubber binder is approximately 1%, and that of 5# BPN containing unsaturated polyester is approximately 2%. Combined with the analysis of component content, the change of KNO\({}_{3}\) and boron in 1#BPN without binder in the 5-day 180 \({}^{\circ}\)C test is 0.5%, i.e., almost no change. Thus, it can be judged to some extent that the stability of BPN in which the main components are KNO\({}_{3}\) and boron is well under 180 \({}^{\circ}\)C for two days and five days. The reason for the component variation of 4#BPN and 5#BPN is the binder (i.e., 4#BPN contains 3% fluororubber and 5#BPN contains 5.6% unsaturated polyester). P-t curve data of BPN samples in 180 \({}^{\circ}\)C test is given in Table 8.  
It can be seen from the data in Table 8 that the peak pressure of the three kinds of BPN decreases with increasing time of the 180 \({}^{\circ}\)C temperature test. The thermal analysis of the single component shows that boron and KNO\({}_{3}\) are relatively stable at 180 \({}^{\circ}\)C, and the peak pressure attenuation of the 1#BPN sample without binder is 0.95% (i.e., <1%), which also supports this argument. It is also known that the binder has a great impact on the high temperature stability of BPN. The high temperature stability of the fluororubber binder is better than that of unsaturated polyester.
## 2 Materials and Methods
### Electrophysiological Data
#### 2.4.1 General Pre-Processing of Electrophysiological Data
Then, EEG data were re-referenced relative to the average of earlobe electrodes (A1 and A2). Next, the sampling rate was down-sampled to 1000 Hz based on spline interpolation. Moreover, in the case of ERPs at the distractor singleton array onset, continuous EEG data were epoched from \(-\)1500 ms to +1501 ms and baseline-corrected with a 200 ms period before distractor array onset. Next, an artifact rejection-automatic inspection function was applied three times. Epochs within the time interval from \(-\)200 ms to 350 ms post-distractor-singleton onset, with eye blinks (exceeding \(\pm\)60 \(\mu\)V at the FP1,FPz, and FP2 channels), eye movements (exceeding \(\pm\)30 \(\upmu\)V in the HEOG channels), and movement-related artifacts (exceeding \(\pm\)80 \(\upmu\)V in all other channels), were rejected.
# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 2 Materials and Methods
### Methods
#### 2.2.4 Explainable Artificial Intelligence
\[\phi_{i}(v)=\sum_{S\subseteq N\setminus\{i\}}\frac{|S|!(|N|-|S|-1)!}{|N|!}[v(S \cup\{i\})-v(S)] \tag{8}\]  
where  
* \(\phi_{i}(v)\) is the Shapley value for feature \(i\);
* \(N\) is the set of all features;
* \(S\) is a subset of features, with the exclusion of feature \(i\);
* \(|S|\) is the number of features within subset \(S\);
* \(|N|\) is the total number of features;
* \(v(S)\) is the prediction model evaluated with the features in subset \(S\);
* \(v(S\cup\{i\})\) is the prediction model evaluated with the features in \(S\) plus feature \(i\). The formula calculates the average marginal contribution of feature \(i\) across all possible combinations of features. It does so by summing over all subsets of features \(S\) that do not include \(i\), computing the difference in the model's prediction with and without feature \(i\) in each subset. The factorial terms are used for weighting these differences to ensure a fair distribution of contributions among the features. In the context of SHAP in machine learning, this formula is adapted to calculate the contribution of each feature to the prediction of a machine learning model for a specific instance. The SHAP method applies this formula to understand how each feature value changes the prediction from what it would be if that feature were absent (or at its baseline value). The strength of this approach lies in its equitable distribution of predictive contributions among feature values. This allows for the assessment of importance ranging from local (individual sample-based) to global (overall model) explanations, leveraging the additive property of SHAP. The SHAP values offer an intuitive understanding of feature importance by quantifying the contribution of each feature to the prediction. Positive SHAP values indicate that a feature increases the prediction, while negative values suggest the opposite. The SHAP value's magnitude represents the contribution's strength, allowing for comparing the relative importance of different features. This interpretability of SHAP values enhances the transparency and trustworthiness of machine learning models.
## 4 Conclusions  
This paper investigates the potential of various regression-based equations, RF, M5P, and stochastic M5P based models in predicting the relative energy dissipation (%) of the cascade spillway. Three input parameters and one output parameter were spillway slope (\(\alpha\)), step number (\(N\)), rate of spillway height to critical depth (\(H_{dam}\)/\(d_{c}\)), and relative energy dissipation of stepped spillway (\(\Delta E\)/\(E_{max}\)), respectively. Experimental data were used to evaluate the models' exactness. From the comparison of performance evaluation parameters, it was found that the stochastic M5P based model works better (CC = 0.9993, RMSE = 0.8246%) in comparison to other aforementioned regression and soft computing-based models. Another major conclusion was that the RF-based model works better than the M5P and various equation-based models. The stochastic approach significantly improved the performance of the traditional M5P algorithm, with an increase in RMSE by 60%. Sensitivity investigation concludes that spillway slope in degrees (\(\alpha\)) is the most influential input variable in predicting the relative energy dissipation (%) of the spillway in comparison to other input variables used in this study.  
The study revealed that a more reliable energy dissipation predictor model can be built based on the stochastic M5P model and experimental data. In other words, using such studies, the design efficiency can be improved due to time saving and low operational costs. The main limitations of the proposed model are the fact that it has a black box structure and is highly based on used data. In case of lacking 3D modeling methods which are able to perform as well as or better than the black box, the M5P can be recommended. In the present study, the data driven methods were only compared with linear and nonlinear regression methods. In future works, the proposed methods may be compared with 3D modeling methods and their accuracies can be assessed by considering their advantages and disadvantages.  
M.N.; analyzed experimental results and wrote the paper. PS.; used the predictor models and analyzed their results. F.S.; carried out experiments and contributed in the results analysis. O.K.; reviewed and revised the paper and also contributed to the models' results analysis. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 11: Scatter plot of predicted relative energy dissipation using the stochastic M5P versus results of Equation (21) by [PERSON] [29].
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 2 Mission
### Power Budget Analysis  
The power that is generated by four body-mounted solar panels has been determined for multiple mission scenarios using the power simulation tool from the Virtual Satellite (VIRSAT) software [8]. These panels cover the whole surface of the CubeSat except the rails and standoffs According to typical solar panels [9; 10] and the dimensions of the satellite we assume an area of 0.021 m\({}^{2}\) with solar cells and an effectivity of about 29%.  
The power budget has been calculated for a 550 km sun-synchronous orbit in order to cover a wide spectrum of the possible launch opportunities for InnoCube. In this orbit the time of sunlight is 3585 s, the time of eclipse is 2120 s according to a Systems Tool Kit (STK) analysis. The worst case 24 h operational scenario is as follows:* Electrical Power System (EPS), Receiver (Rx), Beacon und onboard computer (OBC) always ON;
* Transmitter (Tx) 50 min within 24 h for 10 min ON and battery heater 75 min ON;
* attitude determination and control system (ADCS) low power Mode ON + 10 min high power mode and 120 min medium power mode;
* GNSS Payload 150 min in ON and Wall#E charging in max power mode for 15 h per 24 h;
* Operational average temperature of the solar panel 50 \({}^{\circ}\)C.  
The simulations and calculations of the power budget shows (Figure 2) that the system is power positive and the batteries have enough margin and can always be fully charged. The average generated power with the assumptions above is about 67 Wh in 24 h while the satellite needs 54 Wh in the described worst case scenario. The maximum power needed is about 15 Wh while in a specific ADCS mode. Therefore, the average depth of discharge (DoD) for a battery with about 77 Wh capacity [11] is <5% with the maximum DoD < 10%. This should have a positive effect on the battery degradation and lifetime.
## 4 Discussion  
Table 1 provides a comparison of various S/C-band GaN power amplifiers proposed in the literature [29; 30; 31]. The \(\mathrm{PAE_{peak}}\) denotes the maximum PAE achieved at saturated output power. We have included [28] in the table due to the resemblance of its continuous mode design to that of this study. It is noteworthy to assess the performance against our CoB wire-bond assembly outcomes. Despite [28] being a one-stage design, our split design retains a smaller GaN chip area compared to [28], while still maintaining commendable output performance. Although our work entails a two-stage PA design, we have included [29; 30] for comparison purposes. Furthermore, the GaN chip areas in our designs are also smaller than those in [29; 31].  
The proposed quasi-MMIC PAs are designed in two stages while utilizing the minimum GaN chip area. This design approach optimizes the use of expensive GaN 0.25 \(\upmu\)m chips, reducing costs significantly. Furthermore, considering that GaAs IPD chips cost about 1/4 of GaN 0.25 \(\upmu\)m chips, the proposed quasi-MMIC PAs are not only highly efficient but also cost-effective.  
The combination of superior performance and cost effectiveness makes the proposed PAs stand out as a compelling solution. They provide excellent power efficiency, output power, bandwidth, and power gain, making them well-suited for S/C-band applications. Overall, the proposed PAs demonstrate a well-balanced approach, maintaining good performance while also considering cost considerations, which makes them a highly competitive choice compared to other alternatives discussed in the literature.  
Figure 16: Plot of the measured PSD and ACLR: (**a**) the split CoB wire-bond assembly PA; (**b**) the stack CoB wire-bond assembly PA.
## 2 Results and Discussion
### Correlation of weather fluctuation with CME:  
To conclude these considerations let us look at most impressive demonstration of the influence of solar activity on the lower layers of our atmosphere. The new concepts concerning solar-terrestrial relationship research take into account the primary processes of the whole Sun-Earth system. Cyclic changes of the general atmosphere circulation are of prime interest as are the transformation and recurrence of circulation forms, which characterize planetary wave dynamics. The changes of the atmospheric pressure in geomagnetically and electronically excited cases (including the solar activity effect) in comparison to the variations in geomagnetically and electronically quiet cases.  
Solar-atmospheric interactions were observed more in the Northern India. The Delhi, Haryana, Meghalaya Mizorum stretch of India received more than 100% of the normal rainfall during the monsoon period of 2003.  
The activation of the energetically active regions occurs after the geomagnetic disturbances. The corresponding regions are Delhi-Haryana-U.P-Bihar-Bengal-Orissa-M.P-Meghalaya-Mizoram with high temperature contrasts and a vertical wind shift together with baroclinic instability.  
The basic disturbances appear after the geomagnetic ones in the form of the planetary wave structures with wave numbers from 3 to 4. This corresponds to the result about the selective properties of the baroclinic instability and fits the earlier obtained results on the increase of kinetic energy of such waves after the geomagnetic disturbances. Recurrence of drought after 12 years in various parts of the Earth due to irregular rain fall is due to changes in the ionosphere triggered by the change in the Electron flux during earth directed CME.  
One may conclude that the helio-geomagnetic activity comes out as an agent promoting permission of instability mechanisms in the atmosphere, liberation of useful potential energy and generation of vertical kinetic energy. The disturbances arisen in energetically active zones would be transported along the main zonal flow generating planetary waves.
# Laser Transmission Characteristics of Seawater for Underwater Wireless Optical Communication
## 2 Method  
In this study, we analyzed the transmission characteristics of an underwater laser based on the three-dimensional Monte Carlo simulation program by providing the typical parameters of the emitted laser and the optical properties of three typical waters (i.e., clear water, coastal water, and turbid harbor water). The abbreviations and symbols used are listed in Table 1, the majority of which are the parameters in the marine optics area [37]. The simulations were conducted using MATLAB R2019b.  
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Symbols** & **Descriptions** & **Unit** \\ \hline \(d\) & Offset relative to the beam center on the receiving plane & m \\ \(r\) & Transmission distance & m \\ \(a\) & Absorption coefficient & m\({}^{-1}\) \\ \(b\) & Scattering coefficient & m\({}^{-1}\) \\ \(c\) & Attenuation coefficient, \(c=a+b\) & m\({}^{-1}\) \\ \(\tau\) & Optical path length, \(\tau=cr\) & \\ \(\xi\) & Uniform random variable distributed between 0 and 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of symbols.
## 8 cAMP Signaling in the Kinetoplasids: Small GTPases Control Host Cell Invasion
_Leishmania donovani_ specifically upregulates the expression of Rab5a by degrading c-Jun via their metalloprotease gp63 to downregulate the expression of miR-494 in THP-1 differentiated human macrophages [105]. In _Leishmania_ species, downstream effector proteins of a cAMP-regulated pathway are present. However, it is not clear whether cAMP activates the EPAC pathway to phosphorylate Rap-1. More than10 stage-regulated adenylate cyclases were identified in _Leishmania_ species. Previously, two receptor adenylate cyclases from _L. donovani_ (LdracA and LdracB) were analyzed which form a part of a cluster of five similar genes [106]. They were observed to be developmentally regulated and highly expressed in promastigotes. Four different leishmaniasis phosphodiesterase [107] have been recently cloned (PDEA, PDEB1 and PDEB2, PDEC and PDED); PDEB and PDEC are predominantly membrane bound whereas PDEA and PDED are cytosolic. These PDEs might be a controlling factor for the differentiation of the parasites as the cytosolic PDE activity decreased during stage differentiation whereas the membrane-bound PDE activity remained unaltered. Phosphodiesterase A (PDEA) belongs to the type I diesterases and the protein structurally differs in parts from its mammalian counterpart although the catalytic chains A and B are conserved. Thus inhibition by high concentrations of the mammalian inhibitors dipyramidol and triquinsin [108] can be explained but might not be useful in antiparasitic treatment. However, it remains an interesting observation that a PDEA knockout mutant decreases the peroxide concentration and triggers an antioxidant defense in the parasite. Therefore, PDEA might be an attractive target controlling the antioxidant machinery in the parasite.
## 1 Introduction
### Related Works  
There have been several related works in this area with different parameters. The paper [16] investigates the task offloading and resource management for an IRS-assisted D2D cooperative computing system. Here, the convex optimization theory and semi-definite relaxation method are utilized to provide the optimal solution. On the other hand, an IRS-aided D2D offloading system is studied in [17], where an IRS is employed to assist in the offloading of computations from a group of intensive users to a group of idle users. A mixed-integer stochastic successive convex approximation scheme is proposed to tackle this problem. The physical layer security and data transmission for underlay D2D networks have been investigated in [18], where a combination of IRS and a full-duplex jamming receiver is considered for robustness and security enhancements. Another application of IRS in D2D communications is presented in [19], where a double deep Q-network (DDQN) structure is applied to optimize the transmit power and channel assignment for the D2D pairs, the RIS position, and the phase shift to maximize the sum rate of the D2D network. An IRS-assisted single cell uplink D2D network has been studied in [20], where the problem of maximizing the total system rate is formulated by jointly optimizing the transmission power of all links and the discrete phase shifts of the surface; then, the optimization problem is decomposed into two sub-problems and solved iteratively. Another IRS-empowered underlay D2D network is presented in [10], where the spectrum efficiency (SE) and the energy efficiency of the network are maximized by jointly optimizing the resource reuse indicators, the transmit power, the IRS's passive beamforming, and the BS's receive beamforming. An efficient relative-channel-strength-based user-pairing scheme is proposed to determine the resource reuse indicators, and then other variables are jointly optimized by iterative algorithms. In [21], the IRS is introduced into a D2D communication system to improve the throughput of the D2D network, where the block coordinate descent algorithm and a semidefinite relaxation technique were utilized to optimize the beamforming vector, power allocation, and phase shift matrix. A two-timescale IRS-aided D2D ergodic rate maximization problem is studied in [22] subject to a given outage probability constrained by a cellular link QoS requirement. Here, the optimization problem is decoupled into two sub-problems and then solved iteratively with closed-form expressions.