esa-sceva/satcom-chunk-collection huggingface dataset:
# The Summertime Diurnal Cycle of Precipitation Derived from IMERG
## 3 Results
### Variations in the Diurnal Cycle of Accumulation across the Seasons  
The amplitude and phase of the 24 h harmonic representing the diurnal cycle of accumulation for each meteorological season are shown in Figure 8. Considering seasonal variations in the diurnal cycle is particularly important in the tropics, where summertime is not well defined.  
Over land, seasonal variations in diurnal amplitude are generally larger in the mid-latitudes than the tropics. The tropics tend to have strong amplitudes across all seasons, with some seasonal trends in the north of South America and central Africa. Over the ocean, diurnal amplitudes tend to be weaker across all seasons, though strong diurnal variations are found across the North Atlantic Ocean. In general, summertime diurnal amplitudes in the mid-latitudes generally exceed those in the wintertime.  
The time of maximum diurnal accumulation tends to show little seasonal variation. Accumulation generally peaks in the late afternoon and evening over land across all seasons, however some morning and early afternoon peaks can be found in the northern mid-latitudes and Australia outside of the summer season. Over the ocean, accumulation peak times are quite noisy across the seasons, however accumulation generally peaks in the early to mid morning in the tropics. Times of maximum accumulation in the mid-latitude oceans are generally noisiest in wintertime.  
Figure 7: Fitting errors on the harmonic parameters plotted in Figure 6. (**a**) The normalised fitting error on the amplitude of the 24 h harmonic; (**b**) the fitting error on the phase of the 24 h harmonic; (**c**) the normalised fitting error on the amplitude of the 12 h harmonic; (**d**) the fitting error on the phase of the 12 h harmonic. The caps on the colourbars represent the condition under which a parameter is considered unreliable.
## 5 Simulation Analysis
### Simulation of Power Control Strategy in Isolate Island Operation
The system active power flow simulation waveform is shown in Figure 17.  
As can be seen from Figure 17a that at the initial moment of the system, the output power of the DC side photovoltaic #1 reaches a maximum value of 18 kW, and the actual output power of the AC side photovoltaic #2 is lower than the theoretical value, which is about 29 kW. The sum of the active power of the DC load and the AC load reaches 70 kW, that is, \(P_{\text{island}}^{\text{net}}<0\). So, the DC side battery #1 is \(P_{\text{dc}}^{\text{bat}}-U_{\text{dc}}\) droop control. At the same time, the active power deficit exceeds the rated power of AC side battery #2, so battery #2 maintains the rated power output of 10 kW, and the AC side is still unbalanced. As shown in Figure 17b, about 11 kW of active power is transmitted from the DC side to the AC side through the interlinking converter. At this time, in order to balance the power balance of the DC sub-microgrid and the AC sub-microgrid, the DC side battery #1 power output is about 14 kW. Figure 17: System simulation curve of active power flow in case 2 in island operation. (**a**) is the overall power flow of AC/DC sub-microgrid in island mode; (**b**) is the transmission power of interlinking converters in island mode. At 3 s, the active power of the AC load increases to 55 kW, \(P_{\text{island}}^{\text{net}}<0\) remains unchanged, and the active power deficit increases. However, the DC side battery #1 and the AC side battery #2 still do not reach their rated output power, so they can continue to maintain the system balance. At this time, the DC side battery #1 output power is about 19 kW. At 6 s, the active power of the AC load continues to increase by 5 kW and the DC side battery #1 reaches the rated output power is still not enough to maintain the system balance. Therefore, it is necessary to cut off the secondary load of 10 kW on the DC side. The load power on the DC side is reduced to 10 kW, and the DC side battery #1 output power is about 14 kW. Meanwhile, according to Figure 17b, the power entering the AC sub-microgrid is about 21 kW. The DC bus voltage in this mode is shown in Figure 18.  
As can be seen from Figure 17a that at 3 s, the active power of the AC load increases, and the DC side battery #1 will not be discharged to the power balance state immediately. Therefore, at 3 s in Figure 18, the DC bus will drop to about 620 V and then rise back to the rated voltage of 650 V, the system power balance. At 6 s, the DC bus voltage will decrease due to the continuous increase of AC load active power. However, when the whole AC/DC microgrid system is overloaded, the secondary load of 10 kW on the DC side will be cut off. At this time, because the output of DC side battery #1 will not change immediately, the DC bus voltage will rise to 705 V, and then it will drop to the rated voltage. In the whole process, the deviation of the DC bus voltage is within \(\pm\)10%, which meets the requirements of allowable deviation of power quality supply voltage [26].
## 3 Research Method
### Data Quality Control and Preprocessing  
Prior to statistical modeling and exploratory analysis, a comprehensive data quality control process was conducted to ensure the reliability and consistency of the dataset. The dataset, sourced from the UCL rehabilitation program for COPD patients, was first examined for completeness. All variables were found to have 100% validity, with no missing values identified, thereby eliminating the need for imputation techniques.  
Outlier detection was performed using both statistical summaries and visual inspection through density plots for continuous variables (e.g., age, FEV1, Forced Vital Capacity (FVC), six-minute walking test distance (MWT), pack history, and St. George's Respiratory Questionnaire (SGRQ) score). These plots facilitated the identification of potential anomalies or skewness in the data distribution. For example, pack history displayed a right-skewed distribution, which informed our choice of transformation methods in subsequent analysis. However, no extreme outliers or implausible values requiring removal were detected.  
Logical and clinical consistency checks were also applied. Continuous measures were reviewed to ensure they fell within physiologically plausible ranges, and categorical variables were confirmed to be properly coded and internally consistent.  
Together, these data-cleaning steps ensured a high-quality analytical dataset, supporting the robustness of the descriptive and inferential analyses that follow.  
As the classification strategy was influenced by data distribution characteristics and model performance constraints, a comprehensive rationale for adopting a binary classification approach over a multi-class model is presented in the Section 5.
# Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection
## 3 Material and Methods
### Differential Privacy  
Differential privacy (DP), a widely used privacy-preserving method, was initially proposed by Dwork in 2006 [36]. It aims to present a solution for statistical attacks targeting databases. Differential privacy is a resilient method that protects private information by introducing random noises to query responses. It ensures that the distribution of noisy results closely fits to the distribution of the actual results [37]. The fundamental premise of differential privacy is that statistical information should not be significantly impacted by the existence or absence of any record in a database. Thus, it protects private information against attackers with a background knowledge [9].  
\((\varepsilon,\ \delta)\)-Differential Privacy: the mechanism \(M\) is considered to satisfy \((\varepsilon,\ \delta)\)-differential privacy if it meets the constraint stated in Equation (1), for every output \(S\), among neighboring datasets \(D_{1}\) and \(D_{2}\). The parameters \(\varepsilon\) and \(\delta\) established the degree of privacy. \(\varepsilon\) is the upper level of impact that an individual's record can have on public information, whereas \(\delta\) denotes the probability that this upper level does not hold true:  
\[\Pr[M(D_{1})\in S\leq e^{\varepsilon}*\Pr[M(D_{2})\in S]+\delta \tag{1}\]  
For a dataset, \(D\), the mechanism \(M\) that answers query \(f\) with Gaussian noise is constructed as in Equation (2):  
\[M(D)=f(D)+normal(0,\sigma^{2}I) \tag{2}\]
## 5 Bridge Modal Analysis  
Modal analysis is used to determine the undamped free-vibration mode shapes and frequencies of structures. These natural modes provide an excellent insight into structural behavior and can also be used as the basis for response-spectrum analyses (RSA) of bridges. Table 1 shows the translational and rotational modes obtained from an eigenvalue analysis of the model RC arch bridge in the current study. The bolded values indicate the dominant modes in each direction. The data in Table 1 confirm that the translational mode in the transverse direction is dominant in the first mode, which is associated with the maximum value of the rotational mode about the longitudinal axis. This means that the first natural (fundamental) mode of the bridge structure is a coupled mode consisting of both a transverse mode and a rotational mode. The rotational mode about the vertical axis is extracted from the third vibrational mode. The dominant mode shape and modal frequencies for all translational and rotational directions are shown in Figure 8.  
Figure 7: Moment-curvature relation.  
The number of modes considered in the analysis must be sufficient to achieve at least a 90% mass participation in the longitudinal and torsional directions of the bridge [19, 20, 21]. A minimum of 1850 modes were therefore considered for the response-spectrum (RSA) and time-history (THA) analyses in this investigation, as shown in Table 1. However, due to space limitations, only the first 15 modes of vibration plus modes 61 and 441, which had the highest mass participation in the vertical translation and transversal rotation, respectively, and the last mode, which satisfied the 90% minimum modal participation, are presented here. To provide a better understanding, the participation of various modes of vibration resulting from an eigenvalue analysis are drawn in terms of their mass fractions in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & & & & & & \\ \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**Period**} & \multicolumn{4}{c}{**Translational Masses (\%)**} & \multicolumn{4}{c}{**Rotational Masses (\%)**} \\ \cline{3-8}  & & **L** & **T** & **V** & **L** & **T** & **V** \\ \hline
1 & 2.34 & \
# Exploring ISAC: Information-Theoretic Insights
## 5 Network ISAC with Sensing Distortion
### Multi-Access ISAC: Collaborative Sensing and Suboptimality of Symbolwise Estimators  
This section reviews information-theoretic models for ISAC over multi-access channels (MAC). The first information-theoretic ISAC MAC scheme was proposed in [65] based on [PERSON] coding scheme for data communication [95]. [PERSON]'s scheme is again based on a block Markov strategy where in each block, the Txs not only send fresh data but also update information pertaining to the previous block. Again, the update information can be sent in a collaborative way, which renders the communication more efficient.  
In [67; 68], an improved collaborative ISAC scheme was proposed where the two Txs not only cooperate for the purpose of data transmission but also for the purpose of exchanging sensing information from one Tx to the others to allow the system to improve its sensing performance. More specifically, in [67; 68], the two Txs exchange sensing information and data in each block, where the exchanged data is then retransmitted in the next block to improve the decoding performance at the Rx. Recently, in [66], the authors proposed a further improvement where the common update information sent by the two Txs not only consists of data but also includes sensing information, allowing the Rx to obtain better state information and thus improve the system's decoding performance. (In the discussed models, the Rx has no sensing task, which, however, could easily be included.)
## 2 Deflecting Cavity Design
### Particle Tracking Simulation
The energy spread can be ignored after DC field and is increased differently with different charge due to Table 3 when leaving the deflecting cavity. To further improve the temporal resolution as the initial charge is 50 fC and the solenoid is off, by introducing a 100-\(\upmu\)m-gap horizontal slit placed 127 mm before the entrance of deflecting cavity the resolution can be improved to 190 fs. As the vertical size of the electron bunch becomes smaller after passing through the slit, the bunch hitting on the screen produces a narrower beam spot with cavity off and is kicked up with cavity on. Figure 6 indicates the bunch length measurement result at the screen with cavity off and on. In addition, the vertical RMS size are 0.18 mm and 5.84 mm, respectively. Simulation shows the resolution of the measurement system can reach to 190 fs with 50 kV deflecting voltage and the bunch length is 6.30 ps which is consistent with the actual value of 6.28 ps. Due to the exist of slit, the \(\sigma_{y,0}\) becomes small and a better resolution has been achieved, but the charge of electron bunch becomes lower so CCDs with higher sensitivity are required. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Bunch** & **Solenoid Focus** & **Bunch-Length** & **Simulated Value** & **Energy** & **Energy Spread** \\
**Charge/FC** & **Intensity/Gauss** & **Actual Value/ps** & **(Resolution)/ps** & **Increase/keV** & **Energy Spread** \\ \hline
50 & 214 & 6.28 & 6.31 (0.59) & 1.5 & 1.32\% \\
100 & 215 & 6.50 & 6.55 (0.60) & 1.5 & 1.37\% \\
200 & 218 & 6.93 & 6.99 (0.65) & 1.5 & 1.42\% \\
500 & 224 & 8.10 & 8.17 (0.88) & 1.5 & 1.86\% \\ \hline \end{tabular}
\end{table} 
Table 3: The results of bunch length simulation with SCE under 50 kV deflecting voltage.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 2 AIoT in PV Systems: Background
### Artificial Intelligence Techniques
#### 2.1.3 Generative AI
These models iteratively enhance image quality, making them highly effective for tasks that require generating realistic images from scratch [30]. In the context of photovoltaic (PV) system performance optimization, diffusion models can simulate environmental conditions, such as cloud cover and temperature fluctuations, to predict their impact on energy generation. Additionally, these models can generate high-resolution simulations of weather patterns, which can support energy forecasting and grid integration planning. Generative AI has the potential to revolutionize photovoltaic (PV) system monitoring by enhancing predictive capabilities, optimizing energy management strategies, and reducing operational risks. However, challenges such as high computational demands, data bias, and issues with interpretability must be addressed to ensure the reliable, ethical, and effective deployment of GenAI-driven solutions in solar energy management. Figure 8: GAN architecture. Figure 9: Transformer architecture.
## 2 Materials and Methods
### Methods
#### 2.3.4 Total Flavonoid Content (TFC)  
TFC was determined with spectrophotometry using quercetin as the reference standard, described by [PERSON] et al. [34] with some modifications [35]. The extract sample (1.0 mL) was mixed with 1 mL of a 2% AlCl\({}_{3}\cdot\)6H\({}_{2}\)O solution (in methanol). The mixture was made up to 10 mL with distilled water. After 10 min of incubation of the mixture at room temperature in the dark, the absorbance was measured at 430 nm. A calibration curve was created based on the quercetin and the results were expressed as mg quercetin equivalent per 100 g of dry mass (mg QE/100 g dry mass) [35]. The following calibration curve was used to determine the total flavonoids content.  
\[TFC=0.092A-0.032\qquad\qquad\left(R^{2}=0.998\right) \tag{2}\]  
where: TFC--total flavonoids content [10\({}^{-6}\) g(QE)/mL], A--absorbance [dim].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Set**} & **Factor 1** & **Factor 2** & **Factor 3** & **Factor 1** & **Factor 2** & **Factor 3** \\  & **A:** & **B: Fraction** & **C: Process** & **A:** & **B: Fraction** & **C: Process** \\  & **Temperature** & **Size** & **Time** & **Temperature** & **Size** & **Time** \\  & [\({}^{\circ}\)**C]** & **[mm]** & **[min]** & **Coded** & **Coded** & **Coded** \\ \hline
1 & 110 & 1.4 & 10 & \(-\)1 & 0 & \(-\)1 \\
2 & 140 & 1.4 & 30 & 0 & 0 & 0 \\
3 & 170 & 1.4 & 50 & 1 & 0 & 1 \\
4 & 170 & 1.9 & 30 & 1 & 1 & 0 \\
5 & 140 & 0.9 & 10 & 0 & \(-\)1 & \(-\)1 \\
6 & 140 & 1.9 & 10 & 0 & 1 & \(-\)1 \\
7 & 140 & 1.9 & 50 & 0 & 1 & 1 \\
8 & 110 & 1.9 & 30 & \(-\)1 & 1 & 0 \\
9 & 140 & 1.4 & 30 & 0 & 0 & 0 \\
10 & 140 & 0.9 & 50 & 0 & \(-\)1 & 1 \\
11 & 110 & 1.4 & 50 & \(-\)1 & 0 & 1 \\
12 & 170 & 0.9 & 30 & 1 & \(-\)1 & 0 \\
13 & 110 & 0.9 & 30 & \(-\)1 & \(-\)1 & 0 \\
14 & 140 & 1.4 & 30 & 0 & 0 & 0 \\
15 & 170 & 1.4 & 10 & 1 & 0 & \(-\)1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design of Experiment (DoE)—coded and real values of process parameters.
## 5 Results
### The Proposed Mechanisms
#### 5.3.4 The Impact of Cache Size  
We argue, based on previous results, that NDN has the best performance against LFBL due to the more efficient use of in-network caching. To verify this hypothesis we conducted experiments considering a scenario with two consumers requesting contents, and we varied the cache size of consumers. The reason for considering two consumers lies in the fact that, for LFBL, the higher the number of consumers is, the higher the competition for different flows is. Therefore, with two consumers, we tried to minimize interflow interference. We varied the cache size of nodes from a content of 10,000 to 50. We initially used the same cache size as that of previous simulations, 10,000. To reduce that cache size, we observed the mean number of PIT entries during previous experiments and thus, set the cache size to 200. The idea was to choose a starting value for the cache size limit, and thus decrease this size by half. The other cache size values were contents of 100 and 50.  
In Figure 10, it can be observed that the PIF02 mechanism delivered more contents than the other mechanisms until the cache size reached 100, because more contents were retrieved from the caches of the nearest nodes. If the cache size was less than 100, PIF02 started to deliver fewer content than all the other mechanisms. The reason for that resulted from the combination of the PIF02 forwarding criteria and fewer cache hits from near neighbors. Namely, fewer neighbor nodes had popular contents in  
Figure 9: Content packets delivered, average delivery delay and overhead for 2, 10, 20 and 30 consumers requesting popular content at 50 interests/s.  
their small cache sizes. Therefore, this reduced the probability of receiving content from neighbors. Hence, this restriction promoted a smaller average delivery delay for PIF02 with a drawback of less content delivery. This behavior occurred because the farthest nodes used less shared cache from their neighbors.  
Figure 10 also shows that LFBL outperformed the default NDN and our protocols for a cache size equal to 50 kB with few consumers. However, it is worth mentioning that this extreme cache limitation was only induced to prove our hypothesis. For example, in our configuration, each content was 1 KB and thus, a cache that stored 50 contents only required 50 KB of memory space. This cache is not a hard requirement for current network devices.
# Radiator Enablers for Wireless Communication Evolution
## 2 Challenges, Enabling Technologies, and Antenna Developments
### Antenna Evolution to Provide New Degrees of Freedom
#### 2.2.2 The RIS Technology  
One of the prominent relative systems is the reconfigurable (reflecting) intelligent surfaces. Again, the paradigm is to use an antenna array with many radiating elements that now should work not as a transmitter nor as a receiver but rather as a relay to assist NLOS links. Thus, one can envision a reflect array that properly manipulates not the communicating nodes but the channel in between. The notable difference between the reflect array and RIS is that the latter is armed with controlling reconfigurable lumped loads attached to each one of the reflecting elements. This technology translates and inherits the multi-beamforming response from the communicating node to the channel (see Equation (2), parameter H). Indeed, with proper tuning, multiple beams can be shaped to illuminate the intended user(s), providing the power needed for reliable communication. In this way, the system is armed with extra degrees of freedom stemming from channel control.  
But this is not the whole story. Currently, such systems are conceived as narrowband and working to assist and not to define and lead the communication paradigm. Nevertheless, there is a hidden advantage. These arrays can be made extremely large to cover, for example, the external walls of multi-storied buildings. In this case, the multiplexing gain (spatial filtering in the angular domain) is equipped with an extra dimension. A large array means that the near-far boundary (Fraunhofer distance) is pushed from the antenna outwards and the users are entering the near field area. For example, for a 1 m \(\times\) 1 m antenna array operating at 28 GHz, the near field area extends to 373 m, covering a microcell (ref. [23]). Thus, near-field communications become viable, and the extra dimension, or extra degree of freedom of depth or Tx-Rx distance is now relevant, again increasing both the capacity and user density. Thus, in scenarios where near-field or short-range communication is intended, RIS can be deployed to enhance the propagation of electromagnetic waves, boosting communication reliability in challenging environments such as crowded or obstructed spaces.
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 4 An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling  
In this section, we present an efficient heuristic for satellite integrated scheduling. First, we introduce a fast evaluation mechanism where constraint violation can be identified and evaluated with O(1) complexity. Then, we propose a local search heuristic to solve the EOSIS problem. The fast evaluation mechanism serves as a core component to guarantee the efficiency of the algorithm.