esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study  
In order to verify the effectiveness and reliability of the proposed method, an industrial park composed of three transformers is selected as the case study, as depicted in Figure 2. The loss of each transformer is set to 0.02. The industrial park consists of three microgrids with transformers. Each micro-grid includes an MT, an ES, a PV power generation device, and a load. The selected cases are general which can not only avoid the contingency of the results of a single system but also verify the advantages between inter-connected microgrids. At the same time, the uncertainty planning method selected in this paper requires multiple systems to verify the effectiveness of the proposed method. The equipment parameters within the industrial park are presented in Table 1 and the PV and load profiles for typical days are illustrated in Figure 3 [29]. As the industrial park is located in the same area and shares common environmental conditions, the PV outputs of the internal microgrids are identical. According to the time-of-use electricity price description of China's Jiangsu Province, the electricity price varies based on different time intervals: from 0:00 to 8:00, the price is 0.37 CNY/kW; from 8:00 to 12:00 and 17:00 to 21:00, the price is 1.3612 CNY/kW; and from 12:00 to 17:00 and 21:00 to 24:00, the price is 0.82 CNY/kW. The time interval considered for analysis is 15 min.  
Figure 3: PV and load curves for industrial parks on typical days.  
Figure 2: Industrial park with three transformers.
## 3 Materials and Methods
### The Proposed End-to-End Solution Integrating CRF-RNN with CNNs for PCa Detection
#### 3.2.1 Data Preprocessing  
Considering that the images were collected in different conditions (e.g., different scanners and acquisition configurations), an intermediate data preprocessing step was deemed necessary to ensure reliable data properties. The available images were characterized by having a different resolution in the 3D space (i.e., anisotropic voxels). Therefore, isotropic cubic interpolation was used on every image to achieve a resolution of 1.0 mm\({}^{3}\).  
An image registration procedure was also necessary because several factors can contribute to making the individual studies not comparable (i.e, patient movement, different machine configurations) [61]. In image registration, a set of transformations \(\tau\) is applied on one image (i.e., the moving image) so that its landmarks/features or pixel intensities overlap onto another reference (i.e., the fixed image), maximizing the matching of the pair. Only affine transformations (i.e., rotation, translation, and shearing) were considered. Mutual Information Criteria [62] were used to measure the quality of the registration. T2w was set as the fixed image, while PDw and ADC were used as moving images. The image interpolation and registration were performed by using the Diffusion Imaging Python (DIPy) package [63].  
To extract the lesion information, a 64 \(\times\) 64-pixel Region of Interest (RoI) was centered on the PCa coordinates. Z-score normalization was then employed to transform all the pixel values of each MRI slice to a common scale with zero mean and unitary standard deviation. The normalized RoI patches extracted from the T2w, ADC, and PDw MRI sequences were concatenated as a 64 \(\times\) 64 \(\times\) 3 image to serve as the model inputs, as illustrated in Figure 1.  
At the end of this procedure, considering the 204 subjects with CS annotations, 335 lesions were collected, corresponding to 20.5% of the lesions analyzed.
### Contribution of the Present Work  
Having justified our work through the review of the advances in the literature and the limitations encountered, we present the CEEMDAN-CNN-ATT-LSTM model, a hybrid deep learning architecture designed to predict short- and medium-term irradiance. Our model combines an encoder-decoder structure, a CNN, an LSTM, an attention mechanism (ATT), and a complete empirical ensemble modal decomposition with adaptive noise (CEEMDAN). Unlike other architectures, our approach offers a hybrid model for irradiance prediction in a harsh climate and at high altitude to facilitate the integration of solar generation into the power grid, which is a major challenge for solar power distributors. In addition, our CEEMDAN-CNN-ATT-LSTM model was evaluated using a historical dataset reflecting real meteorological conditions. This study can contribute to the exploitation of PV potential in difficult areas and provides guidance for the future development of models to ensure the efficient integration of solar power plants into the power grid.  
The highlights of this research are described below:  
* A hybrid CEEMDAN-CNN-ATT-LSTM deep learning architecture to forecast irradiance is presented; this model implements an attention mechanism and two neural networks--a CNN and an LSTM--with multivariate input, to improve the forecasting capability for 1 h, 6 h, and 12 h in advance.
* CEEMDAN is used to increase the irradiance forecasting accuracy, proving effective in the two existing climatic seasons.
* The proposed methodology addresses the volatility and randomness of irradiance in a high altitude area in the south of Ecuador, characterized by a difficult climate, offering a robust solution for integrating solar energy into the power grid.
* Our model is analyzed and compared to existing forecasting base models, demonstrating the superiority and benefits of the proposed hybrid approach.  
The remainder of this study is stuctured as folllows: Section 2 describes the methodology, the data, and the architecture developed, while also detailing the evaluation metrics used to measure the performance of the models. Section 3 presents the case study and the results. Finally, Section 4 concludes the study.
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 3 Ground Microgravity Analogues  
### Bed Rest  
Bed rest is used to simulate microgravity-associated inactivity in humans, in a normal gravitational environment. Horizontal beds were initially used, but later, 6\({}^{\circ}\) head-down tilt (HDT) bed rest was introduced by Russian scientists. HDT better simulates the head-ward fluids shifts in Space, where blood is redistributed from the legs to the head (Figure 2a) and, at present, HDT bed rest is the most common analogue for the simulation of microgravity. Long-duration bed rest normally uses volunteers and is employed to simulate the effects of microgravity on various physiological systems, mainly for bone, muscles, and cardiovascular, as well as to test countermeasures for microgravity environment.  
The duration of bed rest experiments may vary from minutes to months (up to 12 months), depending on the objectives of the study (Table 2) [42]. Healthy individuals are monitored 24 h per day and subjected to standardized conditions, like diet, stress, physical activity, fluid intakes, light/dark circadian cycles, etc. Bed rest is generally used for studying cardiovascular deconditioning, exercise capacity impairment, and musculoskeletal disorders [43; 44]. Bed rest is also widely used to test countermeasures for musculoskeletal and cardiac atrophy, and orthostatic intolerance. Recently, focusing on long-duration missions, such as to Mars, bed rest has been used for investigating psychological behaviour and changes in the sleep-wake cycle.  
Bed rest is not effective at compensating the effects of gravity on internal organs, but it remains the most effective analogue for whole body physiology research [42]. As bed rest is performed in hospitals, it is associated with medium-high costs (Table 2). However, researchers have frequent opportunities to apply for utilization of this microgravity analogue.
## 2 Materials and Methods  
### Clinical Chemistry Analysis Reports  
The goal was to identify laboratory parameters relevant to the patient-focused scenario. After discussions, consensus was reached on a core set of laboratory tests that included the following: Red Blood Cells (RBCs), Hemoglobin (HB), Hematocrit (HCT), MCV (Mean Corpuscular Volume), MCH (Mean Corpuscular Hemoglobin), MCHC (Mean Corpuscular Hemoglobin Concentration), White Blood Cells (WBCs), complete blood count (CBC) with differential (leukocyte subsets), gamma-glutamyl transferase (GGT), glucose, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total bilirubin. In addition to this core set, a second group of tests was identified, including ferritin, prostate-specific antigen (PSA), thyroid-stimulating hormone (TSH), free thyroxine (FT4), alkaline phosphatase, activated partial thromboplast time (aPTT),thrombin time (PT), and glycated hemoglobin (HbA1c); eGFR (Glomerular Filtration Rate); BUN (Blood Urea Nitrogen); protein electrophoresis (Total Proteins, Albumin, Alpha 1, Alpha 2, Beta 1, Beta 2, Gamma, A/G Ratio).  
The analyses were performed across three different laboratories, each utilizing distinct instruments for clinical chemistry, hematology, coagulation, and protein profile testing:  
Laboratory Cerba Healthcare Italia in Milan employed the cobas(r) 8000 (Roche Diagnostics, Basel, Switzerland) modular analyzer series for clinical chemistry and the Liaison(r) XL (DiaSorin SpA, Saluggia VC, Italy) for immunoassay testing; hematology and protein electrophoresis were performed by DXH690T (Beckman Coulter, Inc., Brea, CA, USA) and Sebia Capillarsy 3 system (distributed through Menarini, Italy), respectively. Laboratory Biolab in Cutrofiano (Lecce) utilized the cobas(r) 6000 (Roche Diagnostics, Basel, Switzerland) modular analyzer series for clinical chemistry and the Architect ci16200 (Abbott Diagnostics, Chicago, IL, USA) for specialized immunoassay testing, Sebia Capillarys 2 FlexPiercing analyzer system (distributed through Menarini, Italy) for protein electrophoresis and Alinity I (Abbott Diagnostics, Chicago, IL, USA) for hematology. Laboratory Mater Gratiae Biolab in Squinzano (Lecce) utilized the Roche cobas(r) 6000 (Roche Diagnostics, Basel, Switzerland) modular analyzer series for clinical chemistry Liason(r) XL (DiaSorin SpA, Saluggia VC, Italy) for specialized immunoassay testing, the Sebia Capill arrays 2 Flex Piercing analyzer system (distributed through Menarini, Italy) for protein electrophoresis and BC-5000 (Mindray, Guangdong, China) for hematology.  
This heterogeneity in analytical platforms provided an opportunity to assess the AI system's robustness in handling results from different manufacturers' reference ranges and measurement units [2; 16].
# Software Design and Experimental Evaluation of a Reduced AES for IoT Applications
## 4 Results and Evaluation
### Processing Time  
The time efficiency of LAES and its counterparts were measured using three metrics, as shown in Figures 7-9. Figure 7 shows the effective (total) encryption time achieved by LEAS and its counterparts. It illustrates that the encryption time of LEAS increases modestly with file size, but in contrast, it increases exponentially for Present and AES. This behavior is attributable to Present having a large number of rounds (\(n=31\)). While Celfia demonstrates a lower encryption time than Present, the lowest encryption time was achieved by LEAS, with an average difference of 79.7%, 95.0%, and 97.4% lower than Celfia. Present, and AES, respectively. These outcomes also conform to the outcomes displayed in Figure 8, indicating the required encryption time for a single block.  
Figure 9 shows the encryption time for a single round achieved by LEAS, Present, Celfia, and AES. LEAS evidently outperforms both Present and Celfia regardless of file size, with an average difference of 79.8% and 82.7% lower (respectively). This is due to the deceleration propagated from the key generation process in Present, and the bit permutation method it uses. Celfia also shows relatively slower round encryption time compared to LAES due to its internal structure, which is based on a generalized Feistel structure, with four data lines having two 32-bit F-functions per round (comprising two different 8-bit S-boxes), followed by a diffusion matrix multiplication [35]. This elaborate process slows down Celfia compared to LAES.  
Figure 7: Effective (total) encryption time.
## 2 Main Subject
### Proposed Sensor Fusion Technique for Active Driving Assistance
This is often used to calculate estimates by fusing sensor data based on data that are difficult to measure precisely due to a lot of noise from each sensor and supplementing the shortcomings of each sensor [45]. Therefore, EKF is used in this study to remove noise included in the data and estimate the result of fusing the sensors. The sensor fusion algorithm presented in this study estimates the position and direction of the wheelchair by fusing the amount of real-time position data, \(\Delta X_{En}\) and \(\Delta Y_{En}\), previously calculated by the encoder sensor; the direction, \(\theta_{t}\), calculated by fusing the angle data measured from the IMU and UWB sensors using a complementary filter; and the positions \(X_{UWB}\) and \(Y_{UWB}\) calculated from the UWB sensor. Equation (9) is a step of predicting the position and direction of time t in the system model A for considering a wheeled mobile robot system and calculates the predicted value \(\hat{x}_{\overline{t}}\) of time t based on the estimated value \(\hat{x}_{t-1}\) of time \(t-1\). Equation (10) is a process of obtaining \(P_{t}\), which is an error covariance prediction value of time t, based on the system noise Q and system model A based on the error covariance \(P_{t-1}\) estimated at time \(t-1\). Equation (11) is a process of calculating the Kalman gain \(K_{t}\), a coefficient that determines how much sensor measurements are to be reflected based on the measurement matrix H, sensor noise R, and error covariance P. Equation (12) is the final estimation value and calculates an estimated value by multiplying the value measured with the UWB and IMU sensors by the Kalman gain K. Equation (13) is a process of estimating and reflecting the current error covariance based on the calculated Kalman gain and prediction error covariance. \[A= \begin{bmatrix}1&0&dt&0\\ 0&1&0&dt\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix} ,\ \hat{x}_{\overline{t}}=A\hat{x}_{t-1} \tag{9}\]  
\[Q= \begin{bmatrix}0.01&0&0&0\\ 0&0.01&0&0\\ 0&0&10&0\\ 0&0&0&10\end{bmatrix} ,\ \ P_{t}=A_{t}P_{t-1}A_{t}^{T}+Q \tag{10}\]  
\[R= \begin{bmatrix}10&0&0&0\\ 0&10&0&0\\ 0&0&0.01&0\\ 0&0&0&0.01\end{bmatrix} ,\ K_{t}=P_{\overline{t}}H_{t}^{T}\Big{(}H_{t}P_{ \overline{t}}H_{t}^{T}+R\Big{)}^{-1} \tag{11}\]\[z= \begin{bmatrix}X_{\text{L}IWB}\\ Y_{\text{L}IWB}\\ \Delta X_{\text{E}N}\\ \Delta Y_{\text{E}N}\end{bmatrix} ,\;\hat{x}_{t}=\hat{x}_{\overline{t}}+K_{t}(z _{t}-h(\hat{x}_{\overline{t}})) \tag{12}\]  
\[P_{t}=(1-K_{t}H)P_{\overline{t}} \tag{13}\]
## 3 Results
### The Impact of High-Hydrostatic-Pressure Treatment on the Polyphenol Content of Marselan Red Wine
#### 3.1.2 Effects of High-Hydrostatic-Pressure Treatment on Various Phenolic Substances
However, when the treatment time was 20 min, the total amount of non-flavonoid compounds significantly increased to 99.45 mg/L at 300 MPa, an increase of 13.05 mg/L compared to the control. Moreover, under the same treatment conditions, resveratrol  
Figure 1: The effect of different HHP treatments on polyphenol content. As the treatment duration remains constant (10 min, 20 min, 30 min), polyphenic compounds vary with changes in treatment pressure. and the content of hydroxycinnamic acid compounds, such as ferulic acid, substantially increased, reaching 2 to 3.5 times that of the control group. Additionally, at a pressure of 100 MPa with a treatment duration of 20 min, the concentration of hydroxybenzoic acid in the wine sample peaked at 72.45 mg/L. Conversely, when the treatment duration was fixed at 10 min and 30 min, the total non-flavonoid content gradually decreased as pressure increased to 600 MPa, reaching lows of 61.34 mg/L and 72.17 mg/L, respectively. This decline was primarily due to a significant reduction in ferulic acid content, which decreased by 50% in the 10 min treatment group, while in the 30 min group, it was markedly lower, almost to trace levels. As shown in Figure 2, the total quantity of flavonoid compounds increased and then decreased as the treatment time increased when the pressure was set between 100 and 300 MPa, reaching a peak at 20 min. Increasing the pressure to 400 MPa stabilized the compound concentration over time. Specifically, at pressures of 100 and 200 MPa, the concentrations of phenolic compounds, such as protocatechuic acid, apigenin, isorhametin, and quercetin, decreased after high-pressure processing, with only minor changes observed in other compounds. At 300 MPa, only protocatechuic acid and cynaroside decreased after processing, whereas most other compounds initially increased and then decreased. At this pressure, the concentrations of cianidanol, epicatechin, and rutin increased by 51.20 mg/L, 16.83 mg/L, and 9.37 mg/L, respectively, after 20 min compared to the control. At 400 MPa, the concentrations of cianidanol and rutin peaked at 30 min, increasing by 31.40 mg/L and 8.37 mg/L, respectively, compared to the control. However, a further increase in the pressure to 500 MPa resulted in no significant differences between the treatment groups.
# CubeSat Mission Scheduling Method Considering Operational Reliability
## 6 Conclusions  
In conclusion, based on the traditional task priority scheduling model, this paper improves the state-of-the-art scheduling of CubeSats by adding a step-by-step start-up design, analyzing and proposing indicators related to the reliability of CubeSats, and establishing an optimization model considering task value and reliability comprehensively.  
The scheduling results show that the CubeSat scheduling method based on task priority can obtain the highest sum of task priorities and achieve the highest task value, but it lacks a reasonable evaluation of the CubeSat's operating status and reliability and cannot achieve comprehensive optimization. The optimal CubeSat scheduling method considering operational reliability has certain optimization effects in terms of start-up reliability, state of charge, and task start-up times. From the results, the scheduling method improves the CubeSat energy backup level, reduces the surge current caused by mission start-up, makes reasonable arrangements for the mission start-up timing, and optimizes both the energy backup and start-up impact levels. Therefore, it can reduce the unreliable factors that may occur during operation and better optimize the operation of the CubeSat.  
Now, the research in this paper still has some limitations, which are reflected in the following aspects. First, the scheduling model is now solved by the BB method, and when the scale of the solved model is too large, there will be a long solving time and difficulties in solving. Second, this paper uses the AHP in the process of selecting the weight coefficients, which is not a novel method and has a certain degree of subjectivity. This can be improved in the future by considering the use of more novel methods. In future research, we can further improve this scheduling model from two aspects. First, we can obtain a more refined CubeSat scheduling plan and optimize the CubeSat missions second by second, so this also puts forward higher requirements for the prediction accuracy of PV power; second, the data transmission problem between the CubeSat and the ground station can be added to the scheduling model, and the memory capacity of the CubeSat can be used as the allocation resource and constraint condition so as to schedule the information resources of the CubeSat better.  
J.Z.: Conceptualization, Methodology, Writing. C.H.: Methodology, Data curation, Writing. Y.Z.: Data curation, Validation. X.Q.: Data curation. X.Y.: Data curation. All authors have read and agreed to the published version of the manuscript.
# A Gaussian Process Method with Uncertainty Quantification for Air Quality Monitoring
## 4 Experiments and Analysis
### Impacts of Noise Level on ELBO and UBML  
Figure 7 shows the results from **Experiment 2**. According to our theoretical results, the impact of \(s_{f}\) on the uncertainty should become greater as \(s_{f}\) increases. This is verified by the results shown in Figure 7b,d. Our theoretical results also suggest that the variation of \(s_{f}\) would not affect the prediction accuracy. We can see from Figure 7a,c that when \(s_{f}\) is smaller, it does affect the prediction accuracy, but when it exceeds a certain value, the impacts become negligible. Considering the Neumann series approximation, we would say that the experimental results comply with the theoretical conclusion.  
The results of **Experiment 3** are shown in Figure 8. We can see that when \(l\) is smaller, both RMSE and the uncertainty bounds change rapidly. While after it exceeds certain values, both converge. This again complies with our theoretical conclusions and simulation results. We should also notice from Figures 7 and 8 that the increment of \(s_{f}\) tends to increase the uncertainty, whereas the increment of \(l\) tends to decrease the uncertainty. Taking both into consideration, an optimised uncertainty bound can be obtained.  
We also conduct an experiment to demonstrate how the noise level \(\sigma_{n}^{2}\) affects the ELBO and UBML. In our experiment, we set \(\sigma_{n}^{2}\) to vary from 0.5 to 4.5. The results are shown in Figure 9. To make the results distinguishable, we set the vertical axes to \(\log(-ELBO/UBML)\). To make the logrithm work, we reverse the signs of both ELBO and UBML. This is the reason why ELBO is 'greater' than UBML in Figure 9. The full GPs model is trained by setting \(\sigma_{n}^{2}\) to \(\{1,7,13,19,25,31,37,43,49\}\) to obtain 9 sets of hyperparameters. For each set of them, we then set \(\sigma_{n}^{2}\) to vary from 0.5 to 4.5. The darker the colour in Figure 9, the smaller \(\sigma_{n}^{2}\) is for model training. We can see that generally, greater \(\sigma_{n}^{2}\) can slow down the convergence speed of both ELBO and UBML, while training a model. When the model is trained, the increment of \(\sigma_{n}^{2}\) can lower down UBML, which is the maximum that ELBO can reach. This implies that the increment of \(\sigma_{n}^{2}\) can cause the failure of a sparse GPs model, as ELBO is deeply related to determine a sparse GPs model. Nevertheless, the experimental results again comply with our theoretical conclusions.
# Passive Intermodulation at Contacts of Rough Conductors
## 2 Electrical Nonlinearities of Conductor Joints
### Nonlinearity of MIM Contacts
At voltages in the range -0.4 V-5 V, the slope of resistivity-voltage curves rapidly changed, similar to Figure 3, and exhibited the essentially _nonlinear_ behaviour of the MIM junction. Thus, the MIM nonlinearity evidently represents a source of PIM that has not been considered in the literature, yet. The effects of conductor surface roughness, thickness and conductivity of the insulating layer on the MIM junction resistance are still debated in the literature.