esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Model Selection  
This section describes how the BSS dataset was split and the methodology that was used in order to select the prediction models that will be included in the predictor agent. Like in Kaggle competitions [17], the data have been split into two datasets; a training dataset and a validation dataset. Figure 8 shows schematically how the available data were employed in the training, selection and validation of the models used. In the upper part of the figure, the green part represents the entire dataset. Like in Kaggle competition, a validation dataset is initially extracted and it is formed from the 20 th to the end of each month, in the diagram it is represented in blue. The rest of the dataset, (those from the 1 st to the 19 th of each month), will be used as training data, represented in violet in the diagram. These data will be one of the inputs of the hyperparameter search technique: GridSearchCV [50]. This technique will use the following as inputs: (1) regression algorithms with their corresponding parameter grid; (2) a scoring function, in order to evaluate the input models, in this case RMSLE and R2; finally; (3) a cross validation method, in this case _TimeSeriesSplit_, a method that is intended specifically for time series and which resembles the usual functioning of a system in production. This method makes it possible to progressively use data from the past for training and use future data for validation, a diagram showing its functioning is situated in the lower part under GridSearchCV, in Figure 8.  
The GridSearch method will make all the possible combinations for each algorithm with the provided grid parameters; this will be done by employing the cross-validation method (Time Series Split) and evaluating the trained methods with the provided scoring functions. As the output of this method, models for each algorithm with the best results will be obtained and these will be evaluated with the validation dataset that had been split at the beginning, on the right-hand side of Figure 8. A Dummy Regressor has been added to the models used and was established as their prediction strategy in order to continually predict the average. The regression algorithms as well as the following parameter girds have been trained using GridSearchCV:  
Figure 8: Data splitting for model training and selection.  
Extra Tree Regressor: learning rate: [0.1, 0.01, 0.001], subsample: [1.0, 0.9, 0.8], max depth: [3; 5; 7], min samples leaf: [1; 3; 5]
* Random Forest Regressor: criterion: [mae, mse], number estimators: [10, 100, 1000], max features: [auto, sqrt, log2]
* Gradient Boosting Regressor: learning rate: [0.1, 0.01, 0.001], subsample: [1.0, 0.9, 0.8], max depth: [3; 5; 7], min samples leaf: [1; 3; 5]  
Once the model is selected, the results of the best selected model are validated with the validation dataset that had been split previously. Additionally, the coefficient of determination R\({}^{2}\) of the models selected for each station has been calculated.
# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 4 MI-Assisted Adaptive Modulation  
In this paper, we consider a drone-user communication with atmospheric disturbances, as shown in Figure 1. The channel is modeled as the Rician MIMO channel, a general analytical model for the Air-to-Ground channel. An ML algorithm called the k-means clustering algorithm is used to find the modulation scheme, providing optimal performance and data rate during atmospheric disturbances.  
```
0: data points \(x\in\{1,2,..,N\}\) and value of K
0: K\(>\)1
1:for\(k\gets 1\) to \(K\)do
2:\(\mu_{k}\leftarrow\) random cluster head location
3:endfor
4:functionLoop(until converged)
5:for\(n\gets 1\) to \(N\)do
6:\(r_{nk}\gets argmin_{k}||\mathbf{x}_{n}-\mathbf{\mu}_{k}||^{2}\)
7:endfor
8:for\(k\gets 1\) to \(K\)do
9:\(\mathbf{\mu}_{k}\leftarrow\) mean(\(r_{nk}\), \(x_{n}\))
10:endfor
11:return cluster head locations
12:endfunction
```  
**Algorithm 1** K-means algorithm.
## 5 Result Analysis
### Result Discussion and Analysis  
Due to changes in the boundary conditions imposed on the heat flow and the different applied air flow velocities, the case studies differ with respect to the temperature fields, which can cause a few hot spots in the server racks. Figure 28 represents the maximum dimensionless temperature predicted in each case study. In this figure, the temperature limit of hot spots in the server racks can be observed. When analyzing the case studies and Figure 28, it is predicted that not all generate hot spots in server racks, especially in the cases with the maximum air flow velocity.  
In this sense, it is possible to reduce the thermal load of the server racks by improving the overall thermal performance of the processing room, without jeopardizing the correct operation of the server  
Figure 28: The maximum dimensionless temperature predicted in each case study for the maximum level and minimum level of air flow velocity.  
Figure 27: The results of the dimensionless velocity vectors for the maximum air flow velocity in the \(x\)–\(y\) plane represented through the planes: (**a**) \(z_{adm}=0\); and (**b**) \(z_{adm}=0.244\).  
racks located in the sensible regions of the data center room and increasing the sustainability of the entire data center. It can also be observed that Case 4 is the one that displays the best behavior and in which there is a lower probability of hot spot occurrence.  
As stated in Section 4, the maximum airflow dimensionless velocity, \(v_{adm}\), is 1, a velocity that is not a common practice in the studied data center. The velocity is set only in cases when the demand for the rack services and operation is greater than usual and the larger part of the racks function at full capacity. By setting the CRAC units to a maximum airflow velocity, the data center room can be successfully cooled, yet this refrigeration is achieved at a high cost since it is accomplished through the consumption of a high quantity of energy. Thus, as an alternative to upgrading the existing CRAC units, which could signify high expenses, a possible and more sustainable resolution can be altering the way the racks are arranged inside the data center room. The purpose is optimizing the existing layout with the aim of efficiently refrigerating the server racks. One of these alterations could be the removal of a few server racks located at the extremity of the line and consequently avoiding hot spots by improving the air flow.
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 2 Internet of Vessels Architecture
### The Technical Architecture of the Networks Layer  
The networks layer is the fundamental infrastructure of the IoV system and represents the fundamental facilities of the data resources integration and management system. In order to meet the various demands of different businesses and avoid the vulnerability of the IoV link, the networks of IoV are constructed in various ways.
# RNA Modification Related Diseases and Sensing Methods
## 2 RNA Modification
### N6-Methyladenosine  
In eukaryotic cells, m6A (Figure 1C) is the most common mRNA modification and accounts for about 80% of mRNA modifications [30]. It is prevalent in the 3\({}^{\prime}\)-UTR and around termination codons. m6A facilitates translation by cap-independent 5\({}^{\prime}\)-UTR [4]. METTL3 is a common m6A writer, reader, and eraser for mRNA [31; 32], while METTL5 is for rRNA [33], and METTL16 is for small nuclear RNA (snRNA) [34]. METTL3 methylates mRNA with m6A and modifies mRNA stability co-transcriptionally [35; 36; 37]. It also regulates heterochromatin in embryonic stem cells [38] and encourages homologous recombination to repair double-strand breaks [39] by regulating DNA-RNA hybrid accumulation. METTL16 [40] controls expression by retaining an intronic enzyme that makes the methyl donor S-adenosylmethionine [41], which is essential to mouse embryonic development. The addition of m6A to 185 rRNA, mediated by METTL5, is required for the translation mechanism [42], and its absence is linked to heart hypertrophy [43] and neurological impairment [44]. In contrast, LINE1 RNA interacts with the m6A reader YTHDC1 in mouse ESCs and early embryos, changing the scaffold function [45]. Another m6A reader, Proline-Rich Coiled-Coil 2A (PRRC2A), plays a role in oligodend and myelination determination [46]. In male germ cells, the ALKBH5 m6A eraser controls translation [47], as well as the stability and splicing of lengthy 3\({}^{\prime}\)UTR mRNAs [48]. FTO Alpha-Ketoglutate Dependent Dioxygenase (FTO) demethylase regulates correct mRNA splicing [49] and snRNA processing [50] by operating on both m6A and N6, 2\({}^{\prime}\)-O-dimethyladenosine (m6 Am) [50].
# Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method
## 5 Experimental Results
### Validation on Typhoon Cases
With a longer lead time, the evaluation indicators of the model based on an FNN changed greatly, while the evaluation indicators of the model based on LSTM remained stable. Figures 9 and 10 show the pressure prediction curves and wind speed prediction curves of the models based on LSTM and the FNN on typhoon Chan-hom (2015). Figure 9a shows the pressure prediction curves of the models with a lead time of 6 h; Figure 9b shows the pressure prediction curves of the models with lead times of 12 and 24 h; Figure 9c shows the pressure prediction curves of the models with lead times of 48 and 72 h; Figure 9d shows the pressure prediction curves of the models with lead times of 96 and 120 h. Figure 10a-d shows the wind speed prediction curves of the models with lead times of 6 h, 12 and 24 h, 48 and 72 h, and 96 and 120 h, respectively. In general, the prediction curves of the LSTM model are closer to the observation curve than those of the FNN model, especially with a lead time of more than 48 h. The uncertainties and prediction curves of the models based on LSTM and FNN on typhoon Soudelor (2015) are similar as these models on Chan-hom (2015), so the discussions about Soudelor (2015) are not shown. As per the results, the model based on LSTM is more stable and has lower uncertainties, so the performance of the model based on LSTM is better. Figure 8: The evaluation indicators of the model based on an FNN and LSTM using the typhoon Chan-hom (2015).
## 5 Research on the Frequency-Domain Characteristics of AE Signals
### Discussion on AE Signal Distribution and Peak Stress Precursor
#### 5.1.1 Frequency-Domain Distribution of AE Signals and the Definition of Prediction Signals
Hence, according to the above results, the failure precursor signal can be considered to be the signal with an AE amplitude greater than 85 dB, a peak frequency greater than 350 kHz, and a frequency centroid greater than 275 kHz; hereinafter, this signal is referred to as the P-signal. Comparison of the stress value corresponding to the P-signals with the peak stress of the granite samples reveals that the stress corresponding to the P-signals under the three confining pressures is 97%, 96%, and 98% of the peak stress values of the granite samples, respectively, and they occur 212, 370, and 393 s ahead of the occurrence of the peak stress in the test.
# The Effect of Space Objects on Ionospheric Observations: Perspective of SYISR
## 4 Assessment of the Effect of Constellation Satellites on SYISR Observations
This implies that \(\sim\)31.47% of the total (\(\sim\)23,720) still affect the ionospheric inversions after the SO-CFAR algorithm. At altitudes below 600 km, the number of space objects with pollution risk is approximately 1336 (5.63% in a total of 23,720). Figure 6: (**a**) Number of satellites that pass through the SYISR with different accumulated days based on the latest StarLink constellation orbits. The red line is the number of satellites that appear at altitudes above 500 km. (**b**) The yearly variations of satellite numbers that pass through the detection region of SYISR over 10 days (the blue bar is the total number, and the white bar is the number at altitudes above 500 km). The orbit information is based on the latest StarLink constellation satellites.
# Helmholtz Coils Based WPT Coupling Analysis of Temporal Interference Electrical Stimulation System
## 2 Principles and Methods Analysis
### Differential Frequency Electrical Stimulation System Based on Temporal Interference
#### 2.3.1 Temporal Interference Generates Low-Frequency Envelope Modulated Magnetic Fields
When the two signals have the same phase, the two vectors are superimposed in the same direction. The post-interference envelope has the largest amplitude, with a value of \(B_{1}+B_{2}\). When the two signals are in opposite phases, the two cancel each other when the vectors are summed. The post-interference envelope has the smallest amplitude, with a value of \(|B_{1}-B_{2}|\). Define the amplitude of the rise of the waveform after interference as the envelope modulation amplitude denoted as \(|B_{AM}|\):  
\[|B_{AM}|=|(B_{1}+B_{2})-(B_{1}-B_{2})|=2\cdot\min\{|B_{1}|,|B_{2}|\} \tag{19}\]  
Modulation depth of the envelope \(m\):  
\[m=\frac{|B_{AM}|}{B_{1}+B_{2}} \tag{20}\]  
As can be seen from the above equation, the envelope modulation amplitude as a function of spatial location is influenced by the flux density amplitude. It is twice the size of the smaller \(B_{1}\) and \(B_{2}\). When and only when the amplitudes of the two magnetic field signals are equal, i.e., \(B_{1}=B_{2}\), does the envelope amplitude of the low-frequency envelope modulated magnetic field become maximum, and the modulation depth become 100%. It has been shown that the coil position and the nature of the excitation together determine the location of the maximum amplitude of the envelope formed by temporal interference [40,41]. This point also makes possible a therapeutic method of electrical stimulation deep in the body based on the temporal interference of the magnetic field superposition.
## 1 Introduction
### Cryogenic System  
The cooling system consists of a cryostat with a cold head operating in a closed helium gas cycle according to the Gifford-McMahon principle [39; 40], a compressor, connectors, cables, cable glands, a vacuum pump, pressure and temperature sensors and a temperature control system. The circuit behaviour was analysed at temperatures ranging from 296.5 K to 83.6 K.  
Figure 6 shows the setup used to measure the IC. To control the temperature, a heating resistor was placed on the same surface as the PCB. As direct heating of the cold head could damage it, a thermal link was made to the surface where all the components were placed. A temperature sensor was placed as close as possible to the IC to ensure that the temperature reading was approximately the same as the IC temperature. The S-parameters of the circuit were measured using a two-port Vectorial Network Analyser (VNA) with a frequency range from 9 kHz to 20 GHz. A temperature control and monitor system was employed to monitor the temperature sensor and to control the heating resistor. Figure 7 shows a photograph of the cryostat before closing it for the tests.  
Figure 8 shows the evolution of pressure inside the cryostat and temperature at the coolest stage, without the elements shown in Figure 7. At point 1, the cryostat remains at a constant pressure level until the vacuum pump is switched on. When this happens, the pressure starts to fall until the pressure in the cryostat reaches the pressure level in the vacuum pump, at about \(10^{-3}\) mbar. The temperature inside the cryostat remains at  
Figure 4: Layout of the implemented PCB.  
Figure 5: (**a**) PCB and (**b**) wire bonds between the IC and the PCB.  
room temperature until the compressor is switched on (point 2). At this point, the cold head starts to work, and the temperature inside the cryostat starts to fall. After three hours, the temperature of the first stage is about 150 K, and the pressure inside the cryostat is lower than in the vacuum pump. At point 3, the valve between the vacuum pump and the cryostat is closed, and the vacuum pump is turned off, causing both the temperature and pressure to drop drastically. At point 4, a stable pressure level (\(10^{-6}\) mbar) and cryogenic temperature (60 K) are reached. Once the measurement is completed, the compressor can be switched off (point 5), causing the pressure and the temperature to rise (point 6).  
Figure 6: Setup used to measure the IC.  
Figure 7: Photograph of the cryostat setup for the test.
## 3 Results and Discussion
### Fire Performance of Wood Parts Coated with PVAc-Bioadditive IFR Adhesive Blends
#### 3.6.1 Morphological Evaluation of Treated and Untreated Wood Pieces by SEM  
Figure 8 shows the study area analyzed by SEM, divided into left (L), severely carbonized area, and right (R), moderately affected area. A transition zone with topographic changes in the surface of the material is observed above the dividing line.  
Figure 8a shows the untreated wood affected by the fire, with the lifting and tearing of tissues, severe exposure of pits in the tracheid, and total damage to its internal structure. The uncoated specimens disintegrated almost completely, showing significant weight loss and deep fractures from the ends toward the center.  
Figure 8: Surface morphology of the samples studied: (**a**) Untreated wood; (**b**) Wood coated with PVAc (standard); (**c**) Wood coated with PVAc-2.5% F2; (**d**) Wood coated with PVAc-12.5% F2.  
Figure 8b shows the wood coated with PVAc adhesive. In area L, severe damage is observed with significant cracks, affecting both the exterior and interior of the sample. In area R, the adhesive layer shows drastic erosion due to the high temperatures, causing internal damage to the wood. Although the adhesive offers some surface protection, in area L, the structural damage is comparable to that of the untreated wood (Figure 8a).  
Both Figure 8a,b evidence an amorphous structure of the carbon generated after combustion, with corridors and open spaces that facilitated heat transfer to the interior due to the high availability of oxygen [103]. Consequently, an uncoated wood produces a porous and light carbon that accelerates the release of CO and CO\({}_{2}\), increasing the spread of fire [104].  
Figure 8c shows wood coated with the PVAc-2.5% F2 adhesive blend. In zone L, the carbonized area presents a fine and homogeneous porosity, without damaged cellular structures, indicating the formation of thermal insulating carbon that stopped combustion. In zone R, a flaky surface attributed to bubbles formed in the coating through the emission of volatile compounds and water vapor during thermal decomposition is observed [102]. This coating generated a kind of carbon foam that slowed down and stopped the advance of the fire.  
Finally, Figure 8d shows the wood coated with the PVAc-12.5% F2 adhesive blend, where areas L and R present similar surfaces. In L, traces of the coating are observed, while in R there is moderate lifting covering the carbonized wood. This sample suffered less damage than the previous ones, forming a compact carbon that limited combustion. The higher efficiency is mainly attributed to the high concentration of Mg(OH)\({}_{2}\) (F2) in the blend, which, when interacting with phosphorus compounds contained in the protein matrix, generated an intumescent and dense carbon blocking heat transfer and suppressing combustion and smoke emission [60; 105].
## 4 Discussion
### Implications for Areas with Coexisting Urbanization and Rural Development  
Based on our findings, several key policy recommendations emerge for sustainable urban development in the Beijing-Tianjin-Hebei region. First, the identification of high vulnerability expansion areas, particularly around Beijing and Tianjin cores, necessitates the implementation of differentiated urban growth management strategies [31]. High-density areas showing positive EVI trends (such as the Temple of Heaven area) demonstrate that well-planned urban intensification combined with ecological restoration can achieve win-win outcomes [32]. These successful models should be replicated in other high-density areas through policies promoting green infrastructure integration and urban ecological restoration.  
Second, the declining ecological quality in low-density areas requires urgent attention to prevent further ecosystem degradation. The 4.2% decrease in low vulnerability areas in northern mountainous regions indicates that current environmental protection measures may be insufficient. Enhanced ecological compensation mechanisms, stricter development controls in ecologically sensitive areas [33], and increased investment in ecosystem restoration are needed to reverse this trend.  
Third, the spatial heterogeneity in urbanization-ecology relationships calls for location-specific management approaches rather than uniform regional policies. The contrasting patterns observed between the airport area (negative correlation) and Temple of Heaven area (positive correlation) suggest that urban development impacts depend heavily on local context, existing ecological conditions, and development practices [34]. Policy frameworks should incorporate these spatial variations through zone-specific regulations and incentive structures.
# AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection
## 3 Foundation
### Pattern Entropy  
The fundamental concept of the proposed algorithm is the underlying PE. It is a metric for quantifying the entropy/complexity of a sequence of network data samples (packets or flows). Considering the dynamic and diverse nature of network traffic, an appropriate definition should include the numbers of attacks and attackers, but also consider the sequential and diverse information of samples.  
Assuming a labeled intrusion data set, the total number of anomaly and normal samples (packets or flows) are X and Y, respectively; the total number of attackers and legitimate users are A and B, respectively; and the types of anomaly and normal samples are represented as P and Q, respectively. Then, the entropy of this data set or its pattern PE can be defined as:  
\[\text{PE}(\text{entire data set})=\left(\text{AP}\right)^{\text{X}}+\left( \text{BQ}\right)^{\text{Y}} \tag{1}\]  
Since every anomaly sample might be launched by any attacker, A represents the entropy of every malicious sample in terms of the possible attackers. Furthermore, every attacker may launch any type of attack; hence, P represents the entropy of every attacker in terms of the possible attacks. Therefore, the term AP should be interpreted as the total entropy of every malicious sample and the term \(\left(\text{AP}\right)^{\text{X}}\) is defined as the PE of malicious pattern due to the presence of x malicious samples. After applying the same method to calculate the PE of normal pattern \(\left(\text{BQ}\right)^{\text{Y}}\), the total PE of the entire data set is expressed by adding the two PEs together. Note, normalization should not be applied because the PE is intended to reflect the complexity variation resulting from factors such as the length of sections, the number of malicious samples, etc.