esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this article, a reversely variable circuitry was proposed for an outdoor FTHX in an ASHP to enhance its overall energy performance. The effect of the circuit number on the FTHX performance in both evaporator and condenser modes was studied numerically, so as to find the optimal circuit number for either mode. The reversely variable circuitry was then proposed conceptually for the FTHX to change the refrigerant flowpath in different direction and satisfied the contrasting requirements as regards the circuit number of the evaporator and the condenser. Finally, the reversely variable FTHXs were compared with the reversely fixed ones experimentally, showing the energy-saving potential of the formers. Our detailed conclusions are as follows.  
1. The evaporator capacity first increased and then decreased as the circuit number increased, exhibiting the highest 2.61 kW with the optimal four-circuit scheme due to the trade-off between reduced overall heat transfer coefficient and increased temperature difference.
2. The condenser capacity decreased monotonously from 4.76 kW to 3.21 kW as the circuit number increased from one to six, because of the greatly reduced overall heat transfer coefficient.
3. The reversely variable circuitry was achieved with the combination of single-way valves and distributors, and exhibited four circuits in the evaporator mode and 1.5 circuits in the condenser mode, so as to better satisfy the respective needs in circuit number for both modes.
4. The reversely variable FTHX led to a better overall energy performance of the ASHP, with a 6.1% higher heating capacity than the four-circuit reversely fixed FTHX and a 3.9% larger cooling capacity than the 1.5-circuit FTHX.  
Conceptualization, D.H. and F.W.; Methodology, R.Z.; Software, R.Z.; Validation, Y.S. and Z.W.; Formal analysis, R.Z.; Investigation, R.Z., F.W. and Y.S.; Resources, D.H. and F.W.; Data curation, Z.W. and Y.S.; Writing--original draft preparation, R.Z.; Writing--review and editing, Z.W. and Y.S.; Supervision, D.H.; Project administration, F.W.; Funding acquisition, R.Z. and D.H. All authors have read and agreed to the published version of the manuscript.  
The present work was supported by the National Natural Science Foundation of China (No. 51806161) and Major Science and technology innovation engineering projects of Shandong Province (No. 2019 JZZY020813).  
Not applicable.  
The authors declare no conflict of interest.
## Data Used in this Study
### Raster Data
#### 2.1.2 FluxCOM GPP  
In this study, we used the FLUXCOM GPP product (website: [[https://www.bgc-jena.mpg.de/geodb/projects/DataDnld.php](https://www.bgc-jena.mpg.de/geodb/projects/DataDnld.php)]([https://www.bgc-jena.mpg.de/geodb/projects/DataDnld.php](https://www.bgc-jena.mpg.de/geodb/projects/DataDnld.php))) to derive vegetation productivity variation information during drought conditions. FLUXCOM GPP, which is spatially-upscaled from GPP estimations by eddy-covariance flux tower measurements using three machine learning (ML) methods [44; 45], is one of the state-of-the-art GPP products at regional to global scales [45; 46]. Spatially-continuous GPP is estimated using established relationships between flux tower GPP and a variety of variables, including remotely sensed VIs and climate and meteorological variables. GPP estimations based on the three ML approaches were averaged prior to analyses. In this study, FLUXCOM GPP data covering Victoria from 1982 to 2013 were used. The spatiotemporal resolution of the mean GPP data is identical to that of the SIF data.
## 2 Materials and Methods
### Adaptive Second-Order Tistable Stochastic Resonance Method Based on CACO  
The parameter selection of the stochastic resonance method determines its ability to enhance weak signal characteristics. Therefore, the chaotic ACO with global optimization performance was introduced to optimize the parameters of STSR in order to obtain the best stochastic resonance method. That is, the objective function of optimization parameters for CACO is set as the output SNR, which can reflect the advantages and disadvantages of the system output. The parameters \(a\), \(b\), \(V\), \(r\), and \(c\) of the STSR system were adjusted adaptively by using the CACO algorithm, and the optimal STSR method was obtained. The flow of adaptive STSR method is shown in Figure 1.  
The process of the adaptive STSR method based on the CACO algorithm is described as follows:  
* **Step 1.** Signal preprocessing. For the simulation data, the simulation signal is first passed through a high-pass filter, and then the variable-scale processing is carried out.  
This processing ensures that the signal is input into the stochastic resonance system in order to meet the condition that the frequency signal is less than 1 Hz. As for the bearing vibration signal, it needs to be transformed into an envelope signal before the signal is processed.
* **Step 2.** The parameters \(a\), \(b\), \(V\), \(r\) and \(c\) are optimized by using the CACO algorithm.
* **Step 3.** The optimal parameters are input into the stochastic resonance system to extract fault features, and then the fault diagnosis is completed.
# The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin:
## 2 Materials and Methods  
### Study Area  
This study had three domains with different scales and grid cell resolutions. The northern parts of the Indus River consist of high mountains, including the HKHK mountain ranges, whereas its southern parts consist of flat plains. The inner domain (d03) covered the whole Indus Basin (Figure 1), but this study is focused on the Upper Indus Basin (UIB). The climate in the northern parts (UIB) is largely influenced by western disturbances, and this region receives most of the precipitation (i.e., up to 2000 mm) in the winter season [28; 29]. However, the precipitation in the Lower Indus Basin (southern parts) is driven by the monsoon precipitation in the summer season, which ranges from 100 to 500 mm [30; 31]. In the UIB, the mean monthly temperature varies from 2 to 49 \({}^{\circ}\)C, whereas in the Lower Indus Basin, the mean monthly temperature varies from 14 to 44 \({}^{\circ}\)C [30]. The elevation of this study region varies from 0 to 8550 m above sea level, which has been derived from a 90 m resolution digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) project. The UIB is a high altitude and complex mountainous area wherein the Karakoram Mountain Ranges and the Hindu Kush and Himalayan Mountains are located. Typically, three major factors may influence the climate over this region: the winter westerlies, the summer monsoon, and a high-pressure system formed over high Asia [32]. Most of the precipitation over this region occurs in winter and spring and is influenced by winter westerly flow [33].  
Figure 1: Configuration of the WRF model domain and topography. The study has three nested domains (d01–d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km grid cell resolution, respectively. The elevation of the study region was derived from a 90 m resolution SRTM DEM.
# Privacy Auditing in Differential Private Machine Learning:
## 2 Preliminaries
### Noise Injecting  
In the ML pipeline, there are multiple stages at which we can insert noise (perturbation) to achieve DP: (1) on the training data (the input level), (2) during training, (3) on the trained model, or (4) on the model outputs [90].  
**Input perturbation.** At the input level, we distinguish between _central_ or _local settings_[91]. Input perturbation is the simplest method to ensure that an algorithm satisfies the DP. It refers to the introduction of random noise into the data (into the input of the algorithm) itself. If a dataset is \(D=\left\{\mathbf{x}_{1},\mathbf{x}_{2},\ldots\mathbf{x}_{n}\right\}\) and each record \(\mathbf{x}_{i}\) is a \(d\)-dimensional vector, then a differential private \(\mathbf{x}\) is denoted as \(\widehat{\mathbf{x}}=\mathbf{x}+noise\), where _noise_ is a random \(d\)-dimensional vector.  
**Output perturbation.** Another common approach is output perturbation [92], which obtains DP by adding random noise is introduced to the intermediate output or the final model output [50]. By intermediate output, we mean the middle layers of the neural networks, while the final model output implies the optimal weight obtained by minimizing the loss function. The differential private layer is denoted as \(\widehat{h}=h+noise\), where \(h\) represents the hidden layers in a neural network.  
**Objective perturbation**. In objective perturbation, random noise is introduced to the underlying objective function of the machine-learning algorithm [50]. As the gradient is dependent on the privacy-sensitive data, randomization is introduced at each step of the gradient descend. We can imagine that the utility of the model changes slightly when the noise is added to the objective function. A differential private objective function is represented as \(\widehat{L}(D,w)=L(D,w)+noise\).  
**Gradient perturbation.** In gradient perturbation [19], noise is introduced into the gradients during the training process to solve the optimal model parameter using gradient descent methods. The differential private gradient descent is \(w_{t+1}=w_{t}+\eta_{t}(xv_{t}+\
abla L(x_{i},w_{t})+noise)\), where \(\lambda\) is a regularization parameter, and \(\eta\) is a learning rate.  
Each option provides privacy protection at different stages of the ML development process, with privacy protection being weakest when the DP is introduced at the prediction level and strongest when it is introduced at the input level. Keeping the input data private in different ways means that any model trained on that data will also have DP guarantees. If you introduce DP during training, only that particular model will have DP guarantees. DP at the prediction level means that only the model's predictions are protected, but the model itself is not differentially private. Note that if perturbations (noise) are added to data to protect privacy, the magnitude of this noise is often controlled using norms (also called scaling perturbations with norms).
# Attenuating Oxidative Damage with Macelignan in Glutamate-Induced HT22 Hippocampal Cells
## 3 Results
### Macelignan Decreased Glutamate-Induced ROS Generation in HT22 Cells  
The effect of macelignan on glutamate-induced oxidative damage was investigated by detecting ROS levels in HT22 cells. Following glutamate treatment, ROS levels were noticeably higher (2.68-fold) than in the control cells, as seen in Figure 3a. Nonetheless, the treatment of macelignan at 5 uM (1.92-fold) and 10 uM (1.46-fold) markedly decreased the ROS levels. Following NAC treatment, a notable reduction in ROS generation was observed (Figure 3b). Moreover, the glutamate-induced ROS levels, which were recovered by macelignan, were confirmed by fluorescence imaging (20\(\times\) magnification; Figure 3c).  
Figure 2: Effect of macelignan on cytotoxicity in HT22 cells. (**a**) Cell viability of Mac (1, 5, 10, and 20 uM) pretreatment in glutamate (5 mM)-treated HT22 cells; (**b**) cell viability of NAC (the positive control at 0.5, 1, and 2 mM) pretreatment in glutamate-treated HT22 cells; (**c**) morphology of HT22 cells after exposure to glutamate for 24 h. Scale bar, 50 u\(\mu\)m. Value represents the mean \(\pm\) S.D. (\(n=3\)). # \(p<0.05\) compared to the untreated cells, * \(p<0.05\) compared to glutamate-treated cells (ANOVA, Tukey’s test). Mac, macelignan; NAC, N-acetylcysteine; CTL, control; Glu, glutamate.
## 3 Methodology
### SDR Module
#### 3.3.2 Detecting Algorithm Using Autoencoders
Anomaly Detection: If the percentage in any given segment exceeds a second detection threshold, an anomaly is considered to be present in that segment of the analyzed frequency. 6. Detection Confirmation: The process is repeated when \(N\) new samples of the same frequency are received. If an anomaly is detected in the same segment of the frequency in this second iteration, the presence of a UAV is confirmed. Figure 4 graphically illustrates step 2, \"Reconstruction Error Calculation\", from the previously described process, while Figure 5 shows the steps of \"Storing Results\", \"Segment Analysis\", and \"Anomaly Detection\". Figure 3: Examples of the performance of the autoencoder-based detection algorithm: (**a**) shows a situation of no detection as the autoencoder can reconstruct the input signal properly, (**b**) shows a detection in the part surrounded by the red circle, because the reconstruction error there is very high, as the autoencoder has not been able to reconstruct the input signal, indicating the possible presence of a UAV. Thus, autoencoders are utilized to identify the presence of potential intrusive UAVs by analyzing and comparing the RF signals received from each band with the training data of the band obtained in the absence of UAVs. This approach ensures that the system does not require the manual creation of a complex database, wherein UAVs would need to be flown in the installation area in order for UAV signals to be captured for the database. Moreover, with this approach using autoencoders, the algorithm is capable of identifying all types of UAVs. Figure 4: Reconstruction Error Calculation process performed with each sample received. Figure 5: Steps of Storing Results, Segment Analysis, and Anomaly Detection in the autoencoder-based detection process. The architecture of the developed autoencoders can be seen in Figure 6 and is composed of seven layers, where the input layer has 2048 values, corresponding to the length of the power samples, and the dimensionality is reduced to only 32 neurons. Each layer has a ReLU activation function, except for the output layer, which uses a linear activation function. For the training process, the input data are passed through a Savitzky-Golay (Savgol) filter, and the model is trained for 400 epochs using the Adam optimizer with an adaptive learning rate of 0.001 that varies based on the model's validation performance. The model's loss function is the Mean Absolute Error (MAE), which evaluates accuracy by measuring the average difference between the autoencoder outputs and the original inputs. The MAE is suitable for this model as it provides a direct measure of the magnitude of the prediction error in the same units as the original data, facilitating the use of a threshold for detection.
# Aspects of Cyber Security in Autonomous and Connected Vehicles
## 4 Autonomous and Connected Vehicle
### Safety Systems  
Vehicle safety systems are classified into two types: active safety systems (ASS) and passive safety systems (PSS). The ASSs are designed to prevent accidents by giving support to the driver. The ASS mainly includes three important things to work sensors for sensing the environment, processors for processing, and actuators to perform an action to prevent undesired events [25]. The ADAS features are the best examples of ASS. According to the national highway traffic safety administration, ADAS includes five levels of autonomous safety features from level 0 to level 4 [26]. A few of ADAS' features are lane departure warnings, an anti-lock braking system (ABS), adaptive cruise control, and so on. Lane departure warning is invented to avoid road accidents while changing lanes this system displays the lane changing in formation and alerts while changing lanes [27]. ABS is mostly used nowadays to prevent accidents. Input for this system is a wheel speed sensor with the help of the collected data it will stop the wheel from locking during the breaking process [28]. Adaptive cruise control is one of the popular systems designed to maintain a certain distance from the vehicle ahead, stay at the limit speed, and automatically change the speed if the driver is not alert [29].  
A PSS uses the same components as ASS to work but PSS deals after an accident to reduce the risk of injury [30]. This system includes airbags system, seat belts, etc. The airbag system works with the help of crash sensors. When a crash sensor detects a reduction in speed or crashes it will send the signal to the airbag module to start operating then with the help of some chemical reaction nitrogen gas will fill in the airbags to inflate them within 0.05 s. The airbag will stay inflated for 0.1 s and deflate in 0.3 s. Seat belts are designed to keep the passengers in a stable position and avoid collision with things ahead [31].
# Study of the Effect of Continuous Thermal Effects on the Shear Stability of Magnetheological Grease
## 2 Materials and Methods
### Rheological Property Testing  
The rotational rheometer Physica MCR-301 from [PERSON], Berlin, Germany (Figure 1), was used to conduct experiments on the rheological properties of magnetorheological grease in rotational shear mode and oscillatory shear mode. The test rotor of the rheometer was PP20/MRD, and the clearance between the rotor and the test platform was 1 mm; the magnetic field control module was PS-MRD, and the temperature control module was VISCOTHERMVT2.  
We conducted magnetic field scanning tests, thixotropy tests, and apparent viscosity tests using rotary shear mode. The specific test parameters were as follows: for the magnetic field scanning test, the shear rate was set to 10 s\({}^{-1}\), and the magnetic induction strength ranged linearly from 0 mT to 1100 mT (1 A current in the rheometer equaled 220 mT magnetic induction strength); in the thixotropy test, the shear rate initially increased from 0.01 s\({}^{-1}\) to 100 s\({}^{-1}\) and then decreased from 100 s\({}^{-1}\) to 0.01 s\({}^{-1}\); and for the apparent viscosity test, the shear rate was fixed at 100 s\({}^{-1}\).  
In the oscillatory shear mode, experiments were conducted to examine the effects of frequency and strain amplitude. The frequency scanning test involved controlling the shear frequency from 1 rad/s to 100 rad/s with a fixed strain amplitude of 0.1%. Conversely, the strain scanning test maintained a constant shear frequency of 1 Hz while varying the strain amplitude from 0.01% to 100%. The experiments were conducted under different magnetic induction strengths (55, 110, 220, and 440 mT) and temperatures (20, 40, 60, and 80 \({}^{\circ}\)C).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Properties** & & **Details** & \\ \hline Numbering & 00\#MRG & 0\#MRG & 1\#MRG \\ Types of thickeners & & Lithium 12–hydroxystearate & \\ Base oil & & Mineral oils & \\ Consistency grade & 00 & 0 & 1 \\ Working taper/0.1 mm & 400–430 & 355–385 & 310–340 \\ Appearance at room & Fluid & Semi–fluid & Soft–solid \\ temperature & & & \\ \hline \end{tabular}
\end{table}
Table 1: Main elements of magnetorheological grease (MRG) in different continuous phases.  
Figure 1: Rotational rheometer.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 4 Research Challenges
### Ultra-Dense and Heterogeneous Networks  
The 6G networks will be typified by a massive density of small cells, millimeter-wave and terahertz bands, and the convergence of terrestrial, UAVs, and satellite networks. The high base station density will result in high-frequency handovers, causing increased signaling overhead and computational complexity. Seamless connectivity in such heterogeneous networks requires advanced mobility management techniques that can optimize HO decisions in terms of balancing network load and interference. Moreover, excessive handovers will result in unnecessary HO events (ping-pong effect), reducing network efficiency. Highly intelligent algorithms capable of predicting and optimizing HO decisions for ultra-dense heterogeneous networks are required to maintain QoS and eliminate unnecessary HO triggers [8].
# MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain
## 2 MIMO LS-SVR-Based Vibration Response Prediction under Unknown Uncorrelated Loads
### Application Scopes of the Proposed Methods  
The system in this paper needs to satisfy the following conditions:  
1. This method can only be used in frequency domain, but not in time domain.
2. The structure must be linear time invariant. Otherwise, it is impossible to convert the data from time domain to frequency domain.
3. The locations of multiple load points must be unchanged; the loads applied at each load point are stationary with stable random excitation.
4. The history data of the measured multi-points' response and the unmeasured multi-points' response are needed. The locations of response points must be known. Because the location is different, the response will change accordingly. The number of measured multi-points must be greater than or equal to the number of loads, which means \(n_{1}>m\).
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## Appendix A Proof of Theorem 1  
Proof.: Consider the optimal subcarrier and power allocation matrices, denoted as \(\mathbf{\rho}^{opt}\) and \(\mathbf{P}^{opt}\), respectively, for (8). In this context, \(\hat{\text{EE}}\) represents the energy efficiency defined as \(\hat{\text{EE}}=\frac{\sum_{k\in\mathcal{K}}\sum_{n\in N}\rho_{k_{n}}^{opt}r_{k,n}}{\xi\sum_{k\in\mathcal{K}}\sum_{n\in N}\mathbf{P}_{k_{n}}^{opt}+P_{c}}\).  
For any fixed values of \(\mathbf{\rho}\in\varrho\) and \(\mathbf{P}\in\mathcal{P}\), we can express the energy efficiency, denoted as EE, as follows:  
\[\begin{split}\text{EE}&=\frac{\sum_{n\in N}\sum_{k= 1}^{K}\rho_{k,n}r_{k,n}}{\sum_{n\in N}\sum_{k=1}^{K}(\xi p_{k,n}+\alpha_{k, \text{RIS}}P_{c})}\\ &\geq\min_{k\in\mathcal{K}}\frac{\sum_{n\in N}\rho_{k,n}r_{k,n}} {\sum_{n\in N}(\xi p_{k,n}+\alpha_{k,\text{RIS}}P_{c})}.\end{split} \tag{10}\]  
Here, \(\mathbf{\alpha}_{k,\text{RIS}}\in\alpha_{k,\text{RIS}}\), and equality holds if and only if:  
\[\frac{\sum_{n\in N}\rho_{1,n}r_{1,n}}{\sum_{n\in N}\zeta p_{1,n}+\alpha_{1, \text{RIS}}P_{c}}=\cdots=\frac{\sum_{n\in N}\rho_{K,n}r_{K,n}}{\sum_{n\in N} \zeta p_{K,n}+\alpha_{K,\text{RIS}}P_{c}}. \tag{11}\]  
This equality condition implies that \(\alpha_{k,\text{RIS}}\) can be expressed as \(\alpha_{k,\text{RIS}}=\frac{\sum_{n\in N}\zeta p_{k,n}r_{k,n}}{\text{EE}P_{c}}- \frac{\sum_{n\in N}\zeta p_{k,n}}{P_{c}}\).  
Furthermore, it is evident that:  
\[\hat{\text{EE}}=\max_{\rho\in\varrho,\mathbf{P}\in\mathcal{P}}\text{EE}\geq\max_{ \mathbf{\rho}\in\varrho,\mathbf{P}\in\mathcal{P}}\left\{\min_{k\in\mathcal{K}}\frac{ \sum_{n\in\mathcal{N}}\rho_{k,n}r_{k,n}}{\sum_{n\in\mathcal{N}}\zeta p_{k,n} \alpha_{k,\text{RIS}}P_{c}}.\right\} \tag{12}\]  
In this equation, \(\mathbf{\alpha}_{K,\text{RIS}}\in\alpha_{k,\text{RIS}}\). Therefore, the right-hand side (RHS) cannot exceed the left-hand side (LHS) for any \(\mathbf{\alpha}_{K,\text{RIS}}\in\alpha_{K,\text{RIS}}\). Additionally, equality always holds if the RHS uses the same \(\mathbf{\rho}\) and \(\mathbf{P}\) as \(\mathbf{\rho}^{opt}\) and \(\mathbf{P}^{opt}\) that achieve the LHS. This requires setting \(\{\alpha_{k,\text{RIS}}\}\) such that \(\alpha_{k,\text{RIS}}=\alpha_{k,\text{RIS}}^{opt}\triangleq\frac{\sum_{n\in \mathcal{N}}\rho_{k,n}^{opt}r_{k,n}^{opt}}{\text{EE}P_{c}}-\frac{\sum_{n\in N} \zeta p_{k,n}^{opt}}{P_{c}}\). It becomes apparent that through a straightforward substitution of \(\alpha_{k,\text{RIS}}^{opt}\) in the expression for EE, \(\hat{\text{EE}}=\frac{\sum_{k\in\mathcal{K}}\sum_{n\in N}\rho_{k,n}^{opt}r_{ k,n}^{opt}}{\xi\sum_{k\in\mathcal{K}}\sum_{n\in N}\rho_{k,n}^{opt}+P_{c}}\); we can draw the conclusion that the sum of the optimized \(\alpha_{k,\text{RIS}}^{opt}\) values for all \(k\) in \(\mathcal{K}\) equals 1.  
This concludes the proof.