esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 3 Results and Discussion
### Correction of TRMM Products  
#### 3.2.1 Calibration and Validation  
The regression machine learning by the ANN model mentioned above was carried out. In the process, we have a total of 20 gauges with 16 years of data sets, of which 224 sets of data are used for the model training, and the remaining 96 sets are used for model testing. The result of a comparison of output and target is shown in Figure 5.  
It can be seen from Figure 5a,c that the training and testing are very good with the determinant coefficients 0.90, 0.91 and 0.88, 0.90, respectively. There was also improvement with the determinant coefficients 0.89, 0.91 of corrected TRMM precipitation compared with the original TRMM determinant coefficients of 0.61, 0.70. It also can be seen that the values of original 3B42 RT and 3B43 are all mostly above the 1:1 line when gauged precipitation was less than 100 mm, while a lot of the values of original TRMM precipitation are under the 1:1 line when gauged precipitation was more than 200 mm. This indicates that the original TRMM precipitation in the LGPA was overestimated and the TRMM precipitation in the HGPA was underestimated. On the whole, the dispersion of TRMM precipitation points on both sides of the 1:1 line is large and uneven, the R\({}^{2}\) is only 0.61 and 0.70, respectively. Fortunately, the 3B42 RT and 3B43 precipitation improved significantly both in the LGPA and HGPA. They closely dispersed on the both sides of the 1:1 line after the correction by the ANN model. The R  
Figure 5: The calibration and validation of TRMM precipitation by artificial neural networks (ANN). (**a**) Training and testing for 3B42 RT; (**b**) correction for 3B42 RT; (**c**) training and testing for 3B43; (**d**) correction for 3B43.  
increased to 0.89 and 0.91, respectively. It was proven that the fusion model based on an ANN is effective for the correction of TRMM products.
## 2 System Description
### Direction-Finding Accuracy  
For two array elements spaced by a distance \(d\), as shown in Figure 11, the time difference \(\tau\) of long-wave signals arriving at the elements is related to the angle of arrival \(\alpha\) as follows:  
\[\alpha=\cos^{-1}(\frac{\tau\tau_{d}}{d}), \tag{14}\]  
Figure 10: Time-frequency transmission principle diagram of Beidou co-viewing.  
where \(c\) is the speed of light. By calculating the time difference \(\tau_{d}\), the direction cosine \(cos\alpha\) of the arriving long-wave signal can be determined.  
By taking the partial derivatives of the parameters with respect to the errors, the error propagation function is derived as follows:  
\[\sigma_{\alpha}=\frac{\tau\sigma_{c}+c\sigma_{\tau}+\frac{\tau_{\mathrm{T}}}{d} \sigma_{d}}{\sqrt{d^{2}-(c\tau_{d})^{2}}}, \tag{15}\]  
where \(\sigma_{\alpha}\) represents the direction finding error, and \(\sigma_{\tau}\) denotes the time difference error, which contributes the system's inherent error. The baseline length error \(\sigma_{d}\) can be neglected under precise measurement conditions, and \(\sigma_{c}\) represents the velocity error caused by the propagation of electromagnetic waves in the ionosphere-ground waveguide model. In traditional systems, \(\sigma_{c}\) is often neglected or set to a specific value of the speed of light (e.g., 0.9922\(c\)), and we have  
\[\tau=\frac{d\cdot cos\alpha}{c}, \tag{16}\]  
By substituting Equation (16) into Equation (15), neglecting \(\sigma_{c}\) and \(\sigma_{d}\), and treating \(\sigma_{\tau}\) as a fixed value, \(\sigma_{\alpha}\) becomes a function of  
\[\sigma_{\alpha}=\frac{c\cdot\sigma_{\tau}}{d\cdot\sin\alpha}(0<\alpha<180^{ \circ}), \tag{17}\]  
From the above equation, it is evident that for a fixed system, without considering errors in the speed of light, both the speed of light \(c\) and the baseline length \(d\) are fixed values. The direction finding error \(\sigma_{\alpha}\) is determined by the incident angle \(\alpha\) at which the signal arrives at the array elements and the time difference error \(\sigma_{\tau}\). The time difference error includes both the receiver synchronization error and the propagation error: the receiver synchronization error is 10 ns, and the propagation error is typically assumed to be 1 \(\upmu\)s per 100 km. Therefore, for a site spacing of 5 km, the propagation error is approximately 50 ns. Considering other uncontrollable factors, with a total time difference error of 100 ns, the distribution of \(\sigma_{\alpha}\) with \(\alpha\) is shown in Figure 12. The longer the baseline length, the better the direction finding error \(\sigma_{\alpha}\). Considering site and terrain constraints, the baseline length is set to approximately 5.18 km. At this baseline length, when the signal incident angle  
Figure 11: Schematic diagram of the one-dimensional interferometric measurement principle.  
is between \(9.9^{\circ}\) and \(170.1^{\circ}\), the direction finding error is less than \(2^{\circ}\), which is within the acceptable range.
## 4 Payload and Subsystem Architectures and Design
### Platform Transceiver  
The selected UHF uplink/UHF downlink transceiver is a full-duplex communication system for nanosatellite TT&C applications. This transceiver can operate in commercial bands of the UHF frequency spectrum. It is low-power, low-mass, and highly configurable, offering the flexibility of changing data rates and frequencies in flight. This space-qualified transceiver was designed and tailored for nanosatellite missions and cross-compatible with  
Figure 14: VHF uplink block diagram used as payload.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline
**Power Supply** & 5 V DC \\ \hline
**Power Consumption** & \textless{}1 W (receiver only during full load) \\ \hline
**Mass** & 55 g \\ \hline
**Dimensions** & \(90\times 96\times 15\) mm \\ \hline
**First Receiver Frequency Range** & 156.025–162.025 MHz \\ \hline
**Second Receiver Frequency Range** & 144–146 MHz \\ \hline
**First and Second Receiver Modulation** & Gaussian Minimum Shift Keying (GMSK) \\ \hline
**First and Second Downlink Data Rate** & 9600 bps \\ \hline
**Channel Bandwidth** & RSSI of 100 kHz \\ \hline
**Operating Temperature Range** & \(-30\) to +70 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specifications of the AIS receiver.  
other subsystems, such as onboard computers and antenna systems. The specifications of this type of transceiver are presented in Table 6.  
From the above table, the technical configuration based on the block diagram of the selected transceiver is shown in Figure 15.  
The downlink of mission data and detailed telemetry data are handled by the UHF band transmitter at a maximum of 500 kbps. The transceiver selected for the nanosatellite can be provided by the ISIS manufacturer and will be its communication system bus [45]. These functions are enabled by an omnidirectional whip antenna and dipoles antenna mounted on the bottom of the nanosatellite, such as the antenna provided by ISIS [46].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline
**Power Supply** & 6.5-20 V DC \\ \hline
**Power Consumption** & 4 W (transmitter on), 0.48 W (receiver only) \\ \hline
**Mass** & 75 g \\ \hline
**Dimensions** & \(90\times 96\times 15\) mm \\ \hline
**Transmitter Frequency Range** & 267–273 MHz \\ \hline
**Transmitter Power** & 27 dBm \\ \hline
**Transmitter Modulation Scheme** & Binary Phase Shift Keying (BPSK) with G3 RUH scrambling \\ \hline
**Transmitter Data Rate** & 1200, 2400, 4800, and 9600 bps \\ \hline
**Receiver Frequency Range** & 312–322 MHz \\ \hline
**Receiver Sensitivity** & \(-104\) dBm Sensitivity for BER 1E-5 \\ \hline
**Receiver Modulation Scheme** & Frequency Shift Keying (FSK) with G3 RUH scrambling \\ \hline
**Downlink Data Rate** & 9600 bps \\ \hline
**Protocol** & AX.25 or HDLC \\ \hline
**Operating Temperature Range** & \(-20\) to \(+60\)\({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 6: Specifications of the selected UHF Uplink/UHF downlink transceiver.  
Figure 15: UHF uplink/UHF downlink transceiver block diagram.
# miRNA-Gene Association Prediction Method Based on Deep Learning Model
## 2 Materials and Methods  
### Dataset Construction  
Training deep learning models requires a large quantity of data. This is particularly important for biological research. miRNAs and genes are difficult to observe directly, and there are limitations to understanding their characteristics and properties through wet lab experiments. Therefore, computational methods have been adopted, and most of them have used open datasets to construct the datasets. However, open datasets cannot be used in their current state, and researchers must combine and extract data to suit their specific research purposes. In this study, using a deep learning model that predicts the association between human miRNAs and genes, we propose that the best way to improve a model's performance is to train it with a large amount of association data; therefore, we built a large number of datasets through the following process.  
Three datasets were used to construct the data for this study. (a) First, datasets with a proven positive relationship between human miRNAs and genes were obtained from the miRNABase [6]. The miRNA-gene interactions containing data on 502,652 miRNA-gene pairs. We filtered out duplicate pairs, after which 380,634 pairs remained. However, we cannot utilize all these relationship datasets unless we have comprehensive biological information for every miRNA and gene element within the dataset. Thus, to make use of a large number of datasets, we decided to use nucleotide sequence information, which is the most widely available among any other biological data. Details of the datasets are discussed in the subsections that follow. (b) Second, miRBase [7] was chosen as the miRNA sequence database, and (c) biomaRt [8] was chosen as the gene sequence database. miRBase contains the sequence and annotation information of miRNAs. It also provides extensive information on miRNAs, including genomic coordinates and deep sequencing expression data. biomaRt is an open-gene dataset developed by the European Bioinformatics Institute. The main advantage of biomaRt is that users can easily extract gene sequence information without any programming knowledge or complex database structure and can segment the data according to user-specified criteria. The gene sequence information was acquired using the biomaRt R package (Version 2.58.0). Pairs containing miRNAs and genes for which no sequence information exists in miRBase and biomaRt were excluded from the 380,634 pairs. This process resulted in 2656 miRNA and 14,319 gene sequences, and 358,864 positive relationship datasets (Table 2). Therefore, we built the largest number of positive datasets among miRNA-gene association deep learning model studies.
# Optimization of High-Performance Computing Job Scheduling Based on Offline Reinforcement Learning
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.2.2 Scheduling Performance and Generalization Ability  
To evaluate the scheduling performance and generalization ability of the proposed algorithm, we tested two different optimization objectives: average job waiting time and bounded slowdown. Additionally, a hybrid dataset was created by collecting 2000 expert-guided optimal trajectories and 500 RL \"learning\" trajectories from the Lublin-256 dataset. This hybrid dataset was used to train our offline model, which was then applied to schedule unseen workloads from CTC-SP2, HPC2N, and SDSC-BLUE.  
As shown in Figures 7 and 8, each scheduling algorithm was tested 10 times, with each test sampling the same sequence of 1024 jobs for online scheduling. The box plots illustrate the scheduling performance: the red line represents the median, the box indicates the 25 th to 75 th percentiles, and the orange dots denote the average values for the data.  
(a) Scheduling on AVGbsld.  
As shown in Figure 7, our method demonstrates consistent performance with smaller boxplots under the CTC-SP2, HPC2N, and SDSC-BLUE workloads, indicating stability in the AVGbsld metric. The corresponding Table 2 presents the average performance across different workloads. In the CTC-SP2 workload, our method achieved the highest improvement, with a 23.54% increase compared to F1 [11] and a 41.44% improvement over DRL [22].  
(b) Scheduling on AVGwt  
Similarly, our method shows stable performance in the AVGwt metric across four workloads, as indicated by smaller boxplots. Table 3 reports the average values for the AVGwt metric under different workloads. In the Lublin-256 workload, our method outperforms SJF by 60.55%. Compared to DRL [22], our method achieved a maximum improvement of 21.07% on the HPC2N workload.  
Figure 6: The training curves on different historical datasets.  
Figure 8: Performance comparison on four representative workloads in AVGwt.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Wordload** & **FCFS** & **WFP** & **UNI** & **SJF** & **F1** & **RL** & **Ours** \\ \hline CTC-SP2 & 43.19 & 23.73 & 104.09 & 63.30 & 24.69 & 30.42 & 17.04 \\ HPC2N & 20.70 & 13.15 & 26.63 & 23.84 & 10.67 & 14.86 & 9.91 \\ Lublin-256 & 257.28 & 111.01 & 256.44 & 88.76 & 67.73 & 70.09 & 63.14 \\ SDSC-BLUE & 38.44 & 39.85 & 47.56 & 42.24 & 33.38 & 28.47 & 25.52 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scheduling Mean Value on AVGbsld.  
Figure 7: Performance comparison on four representative workloads in AVGbsld.
# A Fast Calibration Method for Sensors of Atmospheric Detection System
## 3 System Control Method  
### MAC PID Controller Design Method  
Figure 3 shows the structure of the PID control system, where \(T^{*}(t)\) represents the discrete temperature setting signal input, \(e^{*}(t)\) represents the discrete control signal input to the PID controller, \(c^{*}(t)\) represents the discrete temperature control signal output from the PID controller to the thermostat, and \(T^{\prime}(t)\) represents the continuous temperature change of the temperature sensor. In the process of parameter setting of the PID control method, only the steady-state time and overshoot of the final output \(T^{\prime}(t)\) are considered, and the controller output is ignored. This will cause the signal \(c^{*}(t)\) output from the controller to the thermostat to have a larger amplitude, or to change direction too quickly, eventually leading to damage to the thermostat.  
Based on the original PID, the input temperature limit \(T_{s}\) and the minimum switching time \(t_{s}\) of the working state are added as the main standard; the parameters are time-adjusted automatically according to the prescribed procedure; the adaptive parameter adjustment process of PID parameters is achieved; the long-term stability of the thermostat  
Figure 3: PID control system structure.  
is guaranteed; and the rapidity and stability of \(T^{\prime}(t)\) is also guaranteed, which improves the reliability of the system.  
In the process of ethanol temperature adjustment, we limit the change of setting temperature and avoid damage to the instrument. The input temperature change limit value is \(T_{s}\). The temperature setting limit criterion function can be expressed as  
\[f_{limit}(t)=\left\{\begin{array}{ll}1&|c^{*}(t)-T^{*}(t)|\leq T_{s}\\ 0&|c^{*}(t)-T^{*}(t)|>T_{s}\end{array}\right.. \tag{12}\]  
Heating and cooling of the converter is completed by switching between the compressor and the heating resistor. Switching in a shorter time can cause damage to the compressor; so, it is necessary to limit the switching time of the working state. The minimum switching time of the working state is \(t_{s}\). The switching time limit criterion can be expressed as  
\[f_{switch}(t)=\left\{\begin{array}{ll}1&\prod\limits_{j=1}^{t_{s}/\Delta t }\frac{c^{*}[t-(j-1)\Delta t]-c^{*}(t-j\Delta t)}{\Delta t}\geq 0\\ 0&\prod\limits_{j=1}^{t_{s}/\Delta t}\frac{c^{*}[t-(j-1)\Delta t]-c^{*}(t-j \Delta t)}{\Delta t}<0\end{array}\right.,t\in(t_{s},+\infty). \tag{13}\]  
where \(\Delta t\) is the sampling time of the discrete control system.  
Based on the adjustment time and overshoot criterion designed by the PID controller, temperature setting criteria and switching time limit criterion were added, and adaptive adjustments were made based on the set expected performance indicator parameters, as shown in Algorithm 1.
# Mapping Recent Shoreline Changes Spanning the Lateral Collapse of Anak Krakatau Volcano, Indonesia
###### Abstract  
We use satellite imagery to investigate the shoreline changes associated with volcanic activity in 2018-2019 at Anak Krakatau, Indonesia, spanning a major lateral collapse and period of regrowth through explosive activity. The shoreline changes have been analyzed and validated through the adaptation of an existing methodology based on Sentinel-2 multispectral imagery and developed on Google Earth Engine. This work tests the results of this method in a highly dynamic volcanic environment and validates them with manually digitized shorelines. The analysis shows that the size of the Anak Krakatau Island increased from 2.84 km\({}^{2}\) to 3.19 km\({}^{2}\) during 15 May 2018-1 November 2019 despite the loss of area in the 22 December 2018 lateral collapse. The lateral collapse reduced the island area to ~1.5 km\({}^{2}\) but this was followed by a rapid increase in area in the first two months of 2019, reaching up to 3.27 km\({}^{2}\). This was followed by a period of little change as volcanic activity declined and then by a net decrease from May 2019 to 1 November 2019 that resulted from erosion on the SW side of the island. This history of post-collapse eruptive regrowth and coastal erosion derived from the shoreline changes illuminates the potential for satellite-based automated shoreline mapping to provide databases for monitoring remote island volcanoes.  
Sentinel-2; shoreline; Google Earth Engine; cloud computing; explosive volcanism; tsunami hazards; Anak Krakatau
# Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
## 1 Introduction
### Space Management in Smart Working Adoption
These measures also assist in reducing reliance on fossil fuels and promoting energy security, while fostering technological development, job creation, and regional development, particularly in isolated, rural, and off-grid areas [48]. It is of the utmost importance to consider the greenhouse gas emissions generated by buildings throughout their life cycle and to integrate emission-reduction measures into national building renovation plans by adopting circular economy strategies. The field of intelligent energy systems for buildings has been a significant area of research for several years, driven by the need to reduce carbon dioxide emissions and enhance energy efficiency. The International Energy Agency (IEA) has identified building energy efficiency as a critical factor in limiting CO2 emissions [49; 50]. The primary goal is to conserve energy while ensuring a healthy and comfortable environment for occupants. Therefore, it is essential to align the operation of various energy systems within a building with the occupants' usage patterns and to gather precise information on the interaction between occupants and these systems. Indoor occupancy data are becoming increasingly vital for the development of intelligent applications.
# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 3 Descriptive Statistics
### Issues in Microbiological Counts  
#### 3.7.1 Negative Counts  
During an estimation of microbial counts, one of the issues that microbiologists encounter when conducting research is how to deal with observations that are not quantifiable by the method or by observations, due to reporting units which will result in negative counts. At first, negative counts are not intuitive for people because one thinks, \"How is it possible to be able to count something and at the same time get a negative value?\" These types of issues are very common in microbiology, especially when changing reporting units for the counts, which dramatically affects the final result. For example, an experiment was conducted in which beef carcasses were swabbed with a 25 mL pre-hydrated swab on a 100 cm\({}^{2}\) area, and aerobic counts on a plate were analyzed. It was decided to plate directly from the swab, as it is known that counts will be very low, and the average results obtained from the plate were 2 CFU/mL. Then, after some literature review, it was observed that swabbed areas are normally reported as Log CFU/cm\({}^{2}\). The conversion is calculated below.  
\[\frac{2\;\text{CFU}}{\text{mL}}*\frac{25\;\text{mL}}{swab}*\frac{1 swap}{100 \;\text{cm}^{2}}=\frac{0.5\;\text{CFU}}{\text{cm}^{2}}=-0.30\frac{\text{Log CFU}}{\text{cm}^{2}}\]  
As mentioned before, the final units to express results will have an impact on positive or negative value of the result. To overcome this issue, changing reporting units will solve the problem. If the final reporting units for the last example were Log CFU/mL, the final result would be 0.30 Log CFU/mL. Furthermore, the use of negative values will directly affect the calculation of the mean; however, another problem arises when dealing with negative values. Usually, researchers will report zeros in their data sets in samples that were not quantifiable, meaning that these samples had lower counts than the ones that researchers were actually able to count. For those cases, another set of solutions can be used to express the data in the best possible way, as explained below.
# Neural Network DPD for Agramizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance
## 3 Comparison with Volterra Method
### Modelling Approach
#### 3.1.2 Generalized Memory Polynomial Model  
The GMP method was implemented for VCSEL-based RoF in [15; 17; 18] and DFB-based links in [6; 10]. The GMP model is evinces:  
\[\begin{array}{l}\hat{x}(n)=\sum_{k=0}^{K_{a}-1}\sum_{q=0}^{Q_{a}-1}c_{kq}x(n -q)|x(n-q)|^{k}+\sum_{k=1}^{K_{b}}\sum_{q=0}^{Q_{b}-1}\sum_{r=1}^{R_{b}}d_{kpq} \\ x(n-q)|x(n-q-r)|^{k}+\sum_{k=1}^{K_{c}}\sum_{q=0}^{Q_{c}-1}\sum_{r=1}^{R_{c}}e_ {kqr}x(n-q)|x(n-q+r)|^{k}\end{array} \tag{7}\]  
In Equation (7), \(x(n)\) and \(\hat{x}(n)\) represent the input and output, respectively, while \(c_{kq}\), \(d_{kpq}\) and \(e_{kpq}\) symbolize signal and envelope coefficients, signal and lagging envelope coefficients and signal leading envelope coefficients, respectively. \(K_{a}\), \(K_{b}\) and \(K_{c}\) are the orders of nonlinearity; \(Q_{a}\), \(Q_{b}\) and \(Q_{c}\) represent the depths of memory; \(R_{c}\) shows the leading and \(R_{b}\) symbolizes the lagging delay tap lengths, respectively.