esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Adsorbent-Desiccant Based on Aluminium Oxide
## 2 Methods of Obtainment of Aluminium Oxide Used in Industry
### Methods of AO Otainment
#### 2.2.2 Acid Technology  
1. Dissolution of gibbsite in acid: \[\text{Al(OH)}_{3}+3\text{HNO}_{3}\rightarrow\text{Al(NO}_{3})_{3}+3\text{H}_{2 }\text{O};\] 2. Precipitation with alkali or ammonia water: \[\text{Al(NO}_{3})_{3}+3\text{NaOH}\rightarrow\text{Al(OH)}_{3}+3\text{NaNO}_{ 3},\] \[\text{Al(NO}_{3})_{3}+3\text{NH}_{4}\text{OH}\rightarrow\text{AlOOH}+3\text{NH}_{4} \text{NO}_{3}+\text{H}_{2}\text{O}.\]  
The aluminium hydroxide precipitate is filtered, rinsed on a filter press and moulded into granules, which are later dried and calcined.  
Acid technology also makes it possible to obtain a wide range of products depending on the conditions of the process. Precipitation of aluminium hydroxide at low temperatures and pH > 10 contributes to the formation of bayerite, while precipitation at pH = 7-9 and elevated temperature contributes to the formation of pseudoboehmite. Precipitation is carried out from acidic solutions of aluminium salts (sulphate, nitrate, chloride) with solutions of bases (ammonia, ammonium carbonate). However, this method is more expensive, since in this case, three moles of acid and, respectively, three moles of alkali are consumed per mole of the obtained product. On the other hand, when using ammonia as a precipitator, the sodium impurity in the oxide is excluded.  
Aluminium hydroxides of the pseudoboehmite type (Al2O3 1.5H2O) and, much less often, of the bayerite type (Al(OH)3), are usually obtained from aluminium-containing solutions using aluminate and acid technologies of \"deposition.\" Sodium aluminate, as a raw material for obtaining \(\upeta\)-Al2O3, is undesirable. When obtaining bayerite (pH > 10), the precipitate is always heavily contaminated with the sodium admixture (0.2-0.5% Na2O).  
The deposition conditions--the temperature, pH, time and the temperature of the subsequent aging of the precipitate--have a direct impact on the properties of the resulting product: The chemical and phase composition, porous structure and dispersion [17]. To obtain aluminium oxide, hydroxides are subjected to calcination. Adsorbents-desiccants based on \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) are most frequently obtained through the precipitation technology. A common drawback of precipitation methods is the high consumption of reagents and a significant number of chemically contaminated effluents [24].  
Some companies have made significant advances in the development of alumina obtaining methods based on acidic process. Canadian company Orbite Aluminae Inc. presented technological methods of using HCl for the production of aluminium oxide and a number of by-products, such as silica, hematite and rare earth metals with HCl regeneration, thus increasing the profitability of alumina production [33].
# Assessment of Frost Depth and Frost Heave in Romania
## 3 Frost Depth Prediction  
Frost depth is a function of the material type, soil thermal properties, soil water content, and climatic conditions such as temperature, wind speed, precipitation, and solar radiation, whether those factors are acting individually or as a whole.  
The depth to which the ground freezes depends on the following factors: the duration and intensity of winter frosts, soil moisture, the presence and type of vegetation, and the thickness of snow cover. The soil freezing regime is also closely linked to local relief and microclimate features, which cause considerable variation within small areas.  
In the case of forests, the frost depth is much lower than in open fields because of the protective role of vegetation and the stability of the snow cover in winter. The thicker the snow cover, the less frost penetrates the soil; the moist soils freeze less than dry soils because the latent heat from freezing water slows the spread of frost to lower depths. In sandy soils, the depth of frost is greater than in clay soils, which have better heat conductivity, and the marshlands and marshy soils freeze the least.  
Existing frost depth prediction models can be classified into numerical, analytical, semi-empirical, and empirical models. Some models require as inputs various thermal and hydraulic properties of soil and different meteorological data [5].
## 2 Results
### Analysis of the Key Genes Involved in Volatile Organic Compound (VOC) Biosynthetic Pathways  
In most flowering plants, VOCs are divided into several classes, including terpenoids, benzenes/phenylpropanes, and fatty acid derivatives. In the 32 VOCs identified in _A. 'Mystral'_, 13 of these were monoterepenes while six were benzenes/phenylpropanes. To further characterize the regulation of VOCs at the molecular level, the transcript levels of key genes involved in monoterepene and phenylpropane biosynthesis were assessed (Figure 3). In total, 10 related genes were retrieved from NCBI and our transcriptome database (unpublished), including six key genes in the MEP pathway (_AaDXS_, _AaDXR_, _AaCMK_, _AaMDS_, _AaHDS_, and _AaTPS_) and five key genes in the phenylpropane biosynthesis or shikimate pathways (_AaDAHPS_, _AaEPSPS_, _AaADT2_, _AaPAL1_, and _AaPAL2_). In _A._ 'Alabama', most VOC biosynthesis-related genes showed similar expression patterns in all three parts of the inflorescence, except for _AaPAL1_. The expression level of _AaPAL1_ in the middle part of the spadix (MS) was ten times higher than in the top part of the spadix (TS) and the base part of the spadix (BS). Unlike in _A._ 'Alabama', most VOC biosynthesis-related genes showed higher expression in the TS of _A. 'Mystral'_. Compared with the TS of _A._ 'Alabama', the expression level of _AaDXS_--the first enzyme in the MEP pathway--increased at least 80-fold in all three parts of the _A._ 'Mystral'_ spadix. _AaTPS_, the key enzyme responsible for production of the major monoterepenes, showed a 52-fold increase in the BS of _A._ 'Mystral'_. The expression levels of _AaDXR_ and _AaHDS_ were much higher in all three parts of the _A._ 'Mystral'_ spadix. Compared with the TS of _A._ 'Alabama', _AaCMK_ and _AaMDS_ exhibited higher expression both in the TS and in the MS of _A._ 'Mystral'_. _AaDAHPS_ and _AaEPSPS_ were the key enzymes in the shikimate pathway assessed in this study, and the expression of _AaDAHPS_ was higher in _A._ 'Mystral'_ spadix than in _A._ 'Alabama' spadix, while the expression level of _AaEPSPS_ was not significantly different between the two cultivars. Both _AaPAL_ and _AaADT_, the key enzymes in the phenylpropane biosynthesis pathway, showed higher expression in _A._ 'Mystral'_ than in _A._ 'Alabama' spadix. These results indicated that the expression levels of VOC biosynthetic pathway-related genes were significantly different between _A._ 'Mystral'_ and _A._ 'Alabama', and _AaDXS_, _AaTPS_, and _AaPAL_ might be the key functional genes in the biosynthesis of VOCs. Figure 2: Phenotype of _A._ ‘Alabama’ and _A._ ‘Mystral’. S1, spathe folding stage; S2, pistillate emerge stage; S3, spadix fully extended stage.
# L2-CalSat: A Calibration Satellite for Ultra-Sensitive CMB Polarization Space Missions
However, it is expected to be a very competitive solution for scientific applications that can be adapted to those constraints, as the calibration of ultra-sensitive CMB-Sats operating at L2, like LiteBIRD [15; 16], PICO [17] or CORE [18] (see also the microwave spectro-polarimetry white paper for the \"Voyage 2050\" long-term planning of the ESA science programme [19]). Although the calibration satellite concept can be adapted to the characteristics of any of those missions, the CubeSat designed in this work takes into account the main characteristics of a LiteBIRD-like small aperture CMB space mission. The proposed calibration satellite has been conceived to travel as a piggyback integrated on  
Figure 1: Artist’s view of two satellites in formation flying. Nearby to the left, L2-CalSat, the proposed calibration system hosted by a 6U CubeSat, while to the right and in the distance a space satellite for CMB polarization measurements is visible. the service platform of the main ship. It would be deployed once in the L2 orbit, to start the communication with the CMB-Sat for the formation flying and calibration activities. Furthermore, the small weight and cost of the CubeSats would allow them to be added to the main mission without having a great impact, and also to be conceived as science enhancements of the CMB polarization mission without jeopardizing it. According to the scanning strategy followed by the CMB telescope, the influence of the relative position between the two satellites in the calibration process has been studied. The relative motion of the spacecrafts has been considered with a simplified dynamic model. Based on the calibration requirements, the subsystems of L2-CalSat have been sized and preliminary designed to evaluate its feasibility. The design has been carried out under the principle of minimizing the impact on the main spacecraft architecture. The paper is organised as follows. In Section 2 the calibration requirements of CMB polarization missions are analysed. Section 3 is dedicated to the impact of the proposed calibration satellite on different systematic errors. In Section 4 L2-CalSat is described while Section 5 is dedicated to its operation at L2. Finally, some conclusions are presented in Section 6.
## 2 Hybrid Real-Time Scheduler in the MIROS
### Related Works about the Hybrid Scheduling in the WSN OSes  
Currently, the hybrid scheduling structure has been implemented in several WSN OSes in order to address the different kinds of WSN challenges.  
In TinyOS, the TOST Thread [13] is developed for the purpose of combining the _ease_ of the threaded programming model with the _efficiency_ of a fully event-driven OS. TOSThread is achieved in the TinyOS application level (Figure 3a). It supports the application logic to be implemented in the user-level preemptive threads, and supports the lengthy computation program to be developed inside the TinyOS application.  
In Contiki [4], the hybrid scheduling structure is also implemented (Figure 3b). The same as the TOSThread in the TinyOS, the multithreaded scheduler in the Contiki is also built on top of the event-driven scheduler. However, differing from TOSThread, the multithreading in the Contiki is not used expressly for the user application. Instead, it is applied optionally in the low-level system, and serves the Contiki _processes_ that need the preemption support (e.g., the lengthy computation task).  
LIMOS [14] is another hybrid WSN OS. The LIMOS multithreaded scheduler is built on top of the event-driven scheduler, as well. However, as opposed to the former two mechanisms, the scheduling model in the LIMOS can be configured in terms of the run-time contexts: (1) In the case that the thread number inside each process is configured to be 1, LIMOS runs in the pure event-driven scheduling model. (2) In the case that the process number in the system is configured to be 1, LIMOS runs in the pure multithreaded scheduling model. As a result, LIMOS becomes customizable and context aware, and can thus adapt flexibly to different application contexts.  
The hybrid scheduling structure in the MIROS is different from the mechanisms above in that the MIROS multithreaded scheduler is implemented in parallel with the event-driven scheduler (Figure 1), and the design purpose of this hybrid scheduling is to achieve the RTOS with low data memory  
Figure 3: (**a**) Hybrid scheduling structure in the TinyOS TOSThread. (**b**) Hybrid scheduling structure in the Contiki multithreading.  
consumption. A key drawback of the hybrid scheduling structure in the TinyOS, Contiki and LIMOS is that these OSes are still not real-time ones even if the hybrid scheduler is implemented. This is because the event-driven scheduling is still used in the native scheduling layer of these OSes. For example, if the Contiki _thread 1-2_ is executing and the _thread 3-1_ needs to be executed quickly (Figure 3b), the preemption from the _thread 3-1_ to the _thread 1-2_ cannot be achieved immediately. This is because all the Contiki _processes_ are still scheduled by the event-driven scheduler, and the _process 3_ can be executed only after the _process 1_ runs to completion.
# Optimization of Airflow Organization in Bidirectional Air Supply Data Centers in China
## 4 Airflow Organization Optimization
### Analysis of the Optimized Data Center Temperature Distribution
A significant reduction in exhaust air temperatures from high-power racks is achieved, indicating improved server cooling efficiency. Case7 achieves complete physical segregation between cold and hot air streams, resulting in clearly defined thermal zones across the server room cross-section. Measurement data indicate that the average rack intake temperature remains consistently near the supply air temperature of 19 \({}^{\circ}\)C, with maximum intake temperatures staying at approximately 20 \({}^{\circ}\)C, maintaining a 7 \({}^{\circ}\)C safety margin below ASHRAE's recommended upper limit of 27 \({}^{\circ}\)C. Under these conditions, the air conditioning setpoint temperature can be safely elevated to reduce cooling energy consumption while maintaining equipment cooling reliability.
## 2 Methods
### CNN-BiGRU-Attention Hybrid Neural Network Model Based on NGO Optimisation Algorithm  
While CNN, BiGRU, Attention mechanism [41] and NGO algorithms have been utilised in various domains, this study represents the first instance of integrating these methods for predicting rockburst intensity. Furthermore, specific enhancements and optimisations were implemented to address potential limitations.  
Given the sensitivity of the original CNN-BiGRU-Attention model to initial parameter settings and its susceptibility to local optimal solutions during training, we propose a hybrid neural network model that combines CNN-BiGRU-Attention with the NGO optimisation algorithm.  
This approach leverages CNN's feature extraction capabilities, BiGRU's proficiency in time series modelling and the Attention mechanism's ability to weigh important features with the aim of enhancing accuracy and robustness in rockburst intensity prediction. The Northern Goshawk Optimization Algorithm is employed for optimising hyperparameters (such as the size of CNN's convolutional kernel, number of GRU units and learning rate), as well as weight initialisation. With its strong global search capabilities and adaptability to complex prediction scenarios related to rockbursts, this optimisation algorithm effectively improves the classification prediction results' accuracy and reliability. The model structure is shown in Figure 6:Specific operation steps:  
* Data preprocessing: firstly, a sample library for data preprocessing and data partitioning must be established; the data set comprises 80% training data and 20% test data.
* Hyper-parameter optimisation: the objective function is defined as the prediction error, and the NGO optimisation algorithm is employed to optimise the hyper-parameters of the model.
* Data prediction: once the training package has been incorporated into the training model, the latter will undergo a hyperparameter search conducted by the NGO. Once this process has reached its conclusion, the model parameters will be established. At this juncture, the data will then be fed into the CNN-BiGRU-Attention for the prediction of rockburst intensity levels.
* Model evaluation: the accuracy, precision, recall and F1 score were employed as indicators to assess the precision of the model's predictions and to facilitate comparisons and analyses with other prediction models.
###### Abstract
We present a comparative evaluation of the presented method with the base learning methods used in the study. In our current study, we aim to answer the following research questions, which were not answered by previous studies:  
1. RQ1--Available studies on CD in SDP are mainly focused on showing the proneness of defect data to concept drift. Therefore, there is a need to explore how significantly the concept drift points in numbers are present in defect data used for SDP. 2. RQ2--Studies in other domains of data analysis proved that CD adaptation is required to prevent performance degradation of the prediction model. No previous studies in SDP ever used paired learner for CD adaptation in SDP. Therefore, how would the proposed approach adapt to the CD discovered in SDP? In this work, we provide a procedure for concept-drift detection and adaptation in SDP along with a comparative analysis of the number of drift points discovered to answer question 1. In order to answer question 2, we provide an algorithmic procedure to explain the CD adaptation procedure and a comprehensive comparative performance analysis of the results as proof by implementing the window-based approach. The remainder of this paper is organized into the following sections. The background of the study is presented in Section 2. Section 3 presents the synopsis of the related studies. A detailed description of the proposed approach is presented in Section 4. The experimental settings are presented in Section 5. Section 6 presents the experimentation results and discussion. The comparative analysis results are provided in Section 7. Section 8 describes the threats to validity. Finally, Section 9 concludes the paper.
## 4 Results and Discussion
### Hoods at Both the Entrance and Exit of the Tunnel  
The mentioned study shows that an oblique enlarged hood with opening holes installed at the entrance or exit can alleviate the MPW emitted from the tunnel. However, the geometric parameters of the hood located at different positions have varying effects on the MPW due to their mitigation mechanisms, especially the cross-section area and the number of holes.  
The cross-section area of the hood is limited by local terrain and construction cost, and a hood with a small cross-section \(A_{\text{h}}=155\) m\({}^{2}\) is acceptable in engineering. However, the alleviation effects of the hood can be optimized by adjusting the number of holes at a reasonable cost. The pressure gradient at RH point when the same hood with various numbers of holes is installed at both the entrance and exit of the tunnel is shown in Figure 17a. The pressure gradient in the exit hood decreases compared to that in the tunnel influenced by the reflected expansion wave, which is more obvious as the number of holes increases. Due to the relief effect of the opening holes, the amplitude of the MPW emitted from the hood is alleviated further, as shown in Figure 17b. In contrast to that in Figure 11, the amplitude of the MPW decreases at first, and then increases as the number of holes increases. The amplitude of the MPW decreases to its lowest when \(n=16\), with \((\Delta p_{\text{m}})_{\text{max}}=17.12\) Pa, which drops by 86.85% compared to that without a hood.  
In fact, the optimized hood can satisfy the alleviation requirements of the MPW at higher speeds. The maximum values of pressure gradient at RP point and the MPW at RM point are listed in Table 3. It can be seen that the pressure gradient in the tunnel and the MPW outside the hood increases rapidly with the train's speed. The amplitude of the MPW can be reduced to 42.33 Pa by the optimized hood when the train passes through the tunnel at a speed of 500 km/h, which still satisfies the specification.  
Figure 16: The effects of number of holes at the exit hood on tunnel aerodynamics: (**a**) Pressure gradient at RH point; (**b**) MPW at RM point.  
Figure 17: The effects of number of holes on tunnel aerodynamics: (**a**) Pressure gradient at RH point; (**b**) MPW at RM point.
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 3 Sensing System
### Sensing Technologies
#### 3.1.3 Optical Sensors
The authors of \"Drone Detection in Long-Range Surveillance\" [52] worked on a previous iteration of the same dataset, with quite good results in the detection of small objects. They applied a Faster R-CNN network with various backbones and showed that ResNet-101 had the best results. In [53], a deep-learning-based detection method was adopted, termed YOLOv2, whose training is performed using an artificial dataset obtained by mixing images of real birds and drones, each with a different background. The obtained results demonstrated that the diversity and the scale of the dataset have a positive impact on the detection and tracking processes. The size, weight, required power and cost of the cameras is such that their use on drones does not find particular impediments and certainly makes it possible to use them as a sensing system for C-UAS for all operations of detection, identification and tracking.
## 6 Conclusions  
In this paper, the impact of variable heat sources/sinks on a Darcy-Forchheimer fluid flow with an hh reaction and thermal stratification on a moving thin needle is examined. MATLAB bvp4c was used to solve the dimensionless equations governing the problem. The foremost findings of this study are as follows:  
* Higher values of \(F_{r}\) result in the decline of velocity distribution as well as the thickness of the boundary layer.
* Increments of \(S\) and Pr diminish the thermal boundary layer and temperature field.
* The temperature field increases for non-uniform heat sources \(C,D>0\) as they respond to heat generators, while \(C,D<0\) signifies variable heat sinks that absorb heat and affirm the decline of \(\theta(\zeta)\) and boundary layer thickness.
* The concentration distribution is lessened with upgraded values of \(K\) and \(K_{\mathrm{s}}\) that are the homogeneous and heterogeneous parameters,
* The concentration profile increases for larger estimates of the Schmidt number.  
Conceptualization, M.R.; methodology, N.S.; software, S.K.; validation, S.K. and Y.R.; formal analysis, N.S.; investigation, Y.N.; writing--original draft preparation, N.S.; writing--review and editing, Y.N.; supervision, M.R.; project administration, M.R.; funding acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2017R1D1A3B03028309) and also supported by the Soonchunhyang University Research Fund.  
The authors have no conflict of interest regarding this publication.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(n\) & \multicolumn{3}{c}{\(f^{\prime}\left(n\right)\)} \\ \cline{2-4}  & [38] & [45] & **Present** \\ \hline
0.10 & 01.2888 & 01.2888171 & 01.2888188 \\
0.010 & 08.4924 & 08.4924360 & 08.4924389 \\
0.0010 & 062.1637 & 062.163672 & 062.163677 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of \(f^{\prime\prime}\left(n\right)\) for varied estimates of \(n\) done by [PERSON] et al. [38] and [PERSON] et al. [45].
## 2 Sensors
### Light Detection and Ranging (LiDAR)
#### 2.4.3 LiDAR-SLAM  
Similar to the cameras, LiDARs could be employed to find the trajectory of an MMS. One approach is to sequentially register LiDAR point clouds (see e.g., [108]) to form a robust path of the moving platform. LiDAR SLAM, despite being extensively successful, suffers from a lack of preserving global accuracy over long baselines; however, this approach could be considered as a valuable alternative localization solution under specific circumstances, e.g., when a GNSS signal is temporarily lost for a short period of time or in situations where no other localization method exists at all. In such scenarios, SLAM approaches are useful. In a more comprehensive scenario, a combination of GNSS, IMU, LiDAR, and cameras could be used for a complex multi-sensorial location awareness.