esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Quality Control and Outlier Detection  
Data post-processing was conducted in three stages: (i) data quality assessment, rejecting low-quality data, data associated with sensor malfunction and visibly inconsistent data; (ii) the data were subjected to a robust outlier detection algorithm, as proposed in the literature [15]; and (iii) due to the low turbulence conditions at night, all night flow data were rejected if the friction velocity (u*) was below a critical threshold (m/s); (iv) due to the low turbulence conditions at night, all night flow data were rejected if the friction velocity (u*) was below a critical threshold (0.22 m/s) according to the method suggested in the literature [17; 66].  
The estimation of the u*-threshold was based on the moving point test (MPT) algorithm on nighttime data, as described by the literature [67]. To eliminate spurious oscillations in the CO\({}_{2}\) and energy fluxes values, an algorithm based on median analysis (moving median) was used to identify spurious oscillations. The method consists of separating the series of values into a smooth part and a residual part, after which the discrepant values are manually removed.  
The gaps arising from the exclusion of spurious data during the screening process were filled using the marginal distribution sampling (MDS) gap-filling algorithm, which takes into account the covariation of fluxes with meteorological variables and also the temporal auto-correlation of fluxes [68]. In this algorithm, actions are taken under the following conditions: (i) if flow data are missing but meteorological data are available (air temperature and vapor pressure deficit--VPD), the gap is filled with the average value considering similar meteorological conditions in a 7-day window; (ii) if only incident solar radiation data are available, the gap is filled with the average value considering similar meteorological conditions in a 7-day window; (iii) if no meteorological data are available, the gap is filled with the average value of the last hour, thus considering the diurnal variation of each variable. If there are still data gaps after applying the algorithm, the same procedures are performed but consider longer time windows. The gap-filling method was performed using an online tool from the Max Planck Institute for Biogeochemistry ([[http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/](http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/)]([http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/](http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/)), accessed on 27 January 2023).  
The hyperbolic relationship pattern (Michaelis-Menten model) between NEE and photosynthetically active radiation (PAR, or photosynthetic photon flux density (PPFD)) involves coefficients that represent the maximum photosynthetic efficiency of the canopy (a2), its yield (a3) and the average nocturnal respiration of the ecosystem (a1) as the intercept of the curve [17], and this allows for the correction of NEE daytime data failures, such that:  
\[NEE=a1+(a^{2}\times PAR/a^{3}+PAR) \tag{9}\]
# Long-Term Effects in Structures: Background and Recent Developments
## 2 Motivation
### Concrete Dams in the USA  
In the design and construction of the first modern concrete dams, relatively little attention had been paid to the materials and their behaviour over time. However, even the engineers at the time were aware of certain shortcomings and attempted to solve them. The works of [PERSON] [13] from 1918 on concrete mixture and determination of water-cement factor are likely among the earliest in this field. Understanding the processes created by hydration of cement was encouraged in the late 1920s, when engineers tried to understand the phenomenon of hydration heat that creates unwanted cracks in massive concrete dams.  
The design and construction of the Hoover Dam in the 1930s (Figure 6), the largest dam in the world at that time, with an extremely large amount of concrete, led to advancement in concrete mix design, transportation, installation and cooling of concrete. Strict controls of the installed concrete enabled the reduction in the water-cement factor, which led to significantly better quality of the concrete. Low hydration heat concrete, originally developed for the Hoover Dam, also showed very high resistance to the sulphate environment. At the same time, these successes encouraged researchers into further development of the concrete resistant to all types of degradation [14; 15].  
In the years that followed, various concrete mixes with different additives were proposed for concrete dams. Some concrete dams met expectations, and some did not. Comparing the results on individual dams has proven highly beneficial in designing durable and resistant concrete mixture. For example, concrete in direct contact with water at the Arrowrock Dam (Boise River, ID, USA) and the Lahontan Dam (Carson River, NV, USA) required significant repairs after 20 years of use, with concrete on the overflows proving particularly sensitive. In contrast, the Elephant Butte Dam (Elephant Butte, NM, USA) remained completely undamaged (Figure 7) [14; 15].  
Figure 5: (**a,b**) The Koror-Babeldaob Bridge collapse [11].  
One extreme example is the American Falls Dam, built in 1927, which exhibited significant signs of degradation and had to be replaced in 1977.  
Systematic studies of the physical properties of dams, considering chemical changes in the concrete over time, have provided invaluable insights into estimating changes in compressive strength and modulus of elasticity of the concrete in contact with water.  
Various improvements in the concrete durability over time are evident: from the first concrete with a reduced water-cement factor, through concrete with additives such as pozzolani, to the concrete with the addition of polymer and silicate dust (silica fume) (Figure 8) [14]. This development continues to this day.  
Figure 8: Timeline of various improvements in concrete durability [12].  
Figure 6: The Hoover Dam on Colorado river, USA [16].
# A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
## 2 Background of the Selected Earthquakes
### Choice of Building's Damage Inducing Parameters  
It is necessary to gather certain primary information beforehand, to begin with, every RVS process. Several experiments have investigated the usefulness of building characteristics as input variables for evaluating seismic susceptibility [18; 50; 51]. In line with FEMA154 [5], many of the methods as mentioned earlier contained parameters and exhibited that the most useful inputs are namely, (i) system type, (ii) vertical irregularity, (iii) plan irregularity, (iv) year of construction, and (v) quality of construction. [PERSON] et al. [52] considered more criteria for the seismic risk assessment of buildings. Concerning the characteristics of the affected structures and the tremendous amount of current building stocks, the following eight criteria were formulated for research purposes. The criteria considered are No. of story, total floor area, column area, concrete wall area in X and Y directions, masonry wall area in X and Y directions, and captive columns. These parameters also form the basis of the Priority Index (PI), as proposed by [PERSON] and [PERSON] [53]. These parameters have been selected because the effect of these has been examined and adjusted to assess structures, not meeting code requirements. The parameters can also be applied to different regions as they have been tested in areas with different construction practices. Furthermore, the parameters are easily collected by visual inspection, which leads to a lowered vagueness for investigators, thereby saving time. The eight parameters have been presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Earthquake** & **Year** & **Moment Magnitude (\(M_{w}\))** & **PGA (g)** & **PGV (cm/s)** \\ \hline Ecuador [47] & 2016 & 7.8 & 0.4 & 82 \\ Haiti [39] & 2010 & 7 & 0.4 & 40 \\ Nepal [48] & 2015 & 7.3 & 0.2 & 20 \\ S. Korea [49] & 2017 & 5.4 & 0.2 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the selected earthquakes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Parameter** & **Unit** & **Type** \\ \hline \(\alpha_{1}\) & No. of story & N & Quantitative \\ \(\alpha_{2}\) & Total Floor Area & m\({}^{2}\) & Quantitative \\ \(\alpha_{3}\) & Column Area & m\({}^{2}\) & Quantitative \\ \(\alpha_{4}\) & Concrete Wall Area (Y) & m\({}^{2}\) & Quantitative \\ \(\alpha_{5}\) & Concrete Wall Area (X) & m\({}^{2}\) & Quantitative \\ \(\alpha_{6}\) & Masonry Wall Area (Y) & m\({}^{2}\) & Quantitative \\ \(\alpha_{7}\) & Masonry Wall Area (X) & m\({}^{2}\) & Quantitative \\ \(\alpha_{8}\) & Captive Columns & \(N\) (exist = 1, absent = 0) & Dummy \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for earthquake hazard safety assessment [16].
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
## 5 Interference Reduction  
If \(\ell\) or/and \(\kappa\) are not integers, i.e., the given propagation path (or target in radar case) contains fractional reflection components, then it follows from Equation (6) (or (12) in radar case) that such reflections generate oscillations in both delay (sinc component in the equations) and Doppler (\(\xi_{N}\) component in the equations) directions. The oscillating components propagate throughout the whole DD grid and cannot be directly estimated by RP-OTFS' pilots. In this section, we will discuss methods of reducing this effect.  
Figure 8: The system model based processing diagram.
## 3 Results and Discussion
### Determination of pH and Water Activity of Protein Extracts  
The water activity (a\({}_{\mathrm{w}}\)) values of the protein extracts generated from _Spirulina_ sp. and _Isochrysis_ sp. were determined to be 0.431 \(\pm\) 0.01 and 0.389 \(\pm\) 0.03, respectively, when measured at 22.4 \({}^{\circ}\)C \(\pm\) 0.78 \({}^{\circ}\)C. The water activity values observed for the protein extracts would not permit the growth of microbes (yeasts, molds, spoilage bacteria or fungi), as they are below 0.45. The protein extracts can therefore be considered stable and should not support microbial growth. The pH values of the _Spirulina_ sp. protein extract and _Isochrysis_ sp. protein extract were determined to be 4.25 \(\pm\) 0.00 and 4.29 \(\pm\) 0.05, respectively. Both of these physicochemical characteristics are important for the storage of any food product before further use in industry. Foods with a high a\({}_{\mathrm{w}}\) value show rapid deterioration due to biological and chemical changes, due to the fact that a\({}_{\mathrm{w}}\) influences microbial growth, lipid oxidation and enzymatic activities in foods [26; 27]. The pH values of both protein extracts are comparable to those of protein extracts reported previously from the brown macroalga _Himanthalia elongata_[9], which has a pH value of 3.99 \(\pm\) 0.02. The water activities identified for the _Spirulina_ sp. and _Isochrysis_ sp. protein extracts were similar to those of sliced almonds, which have a reported a\({}_{\mathrm{w}}\) of 0.476 at 20 \({}^{\circ}\)C [19].
###### Abstract  
Low Earth Orbit (LEO) satellites can be used for remote sensing and gravity field recovery, while precise orbit determination (POD) is vital for LEO satellite applications. However, there are some systematic errors when using the LEO satellite orbits released by different agencies in multi-satellite-based applications, e.g., Swarm and Gravity Recovery and Climate Experiment-Follow-On (GRACE-FO), as different GNSS precise orbit and clock products are used as well as processing strategies and software. In this paper, we performed undifferenced kinematic PODs for Swarm and GRACE-FO satellites simultaneously over a total of 14 days by using consistent International Global Navigation Satellite System (GNSS) Service (IGS) precise orbit and clock products. The processing strategy based on an undifferenced ionosphere-free combination and a least squares method was applied for Swarm and GRACE-FO satellites. Furthermore, the quality control for the kinematic orbits was adopted to mitigate abrupt position offsets. Moreover, the accuracy of the kinematic orbits solution was evaluated by carrier phase residual analysis and Satellite Laser Ranging (SLR) observations, as well as comparison with official orbits. The results show that the kinematic orbits solution is better than 4 cm, according to the SLR validation. With quality control, the accuracy of the kinematic orbit solution is improved by 2.49 % for the Swarm-C satellite and 6.98 % for the GRACE-D satellite when compared with their precise orbits. By analyzing the accuracy of the undifferenced kinematic orbit solution, the reliability of the LEO orbit determination is presented in terms of processing strategies and quality control procedures.  
LEO satellite; kinematic precise orbit determination; GNSS; Swarm; GRACE-FO +
Footnote †: journal: Physics Letters B  
1  
[PERSON]  
1 School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China; [PERSON]ADDRESS]  
2 Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China; [EMAIL_ADDRESS]  
3 School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China  
4 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 45400, China  
5 School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China  
* Correspondence: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel.: +86-21-34775292
# Short-Range Transportation Using Unmanned Aerial Vehicles (UAVs) during Disasters in Japan
## 5 Conclusions  
This experiment suggests that UAVs can be used during disasters, especially when traffic is blocked (as mentioned above) in both urban and suburban areas, in affected areas where people's survival is threatened by medicine and medical supply shortages, and in areas where there is a need to secure food after the disaster. Automatic UAV flights that pilots are capable of visually monitoring are less susceptible to communication infrastructure issues. Although fully automated UAVs are effective during peacetime, there use is not completely feasible during disasters. Considering UAVs' practical uses, including transporting medical resources, food, and so on over short distances, the use of UAVs under visual observation may become an important response during emergency situations.  
Figure 2: Model of UAV-assisted transport for a disaster.  
There are several limitations to this study. The biggest problem is that the trial was conducted in non-disaster conditions. While we do not wish for a disaster, we would like to demonstrate the effectiveness of transporting medical resources by UAVs by actually dispatching these in a disaster zone. Moreover, the trials were conducted under conditions of low winds and little or no rain. There is no legal limit to flying based on wind speed, but due to the capacity of the UAV aircraft, it was unable to fly at a wind speeds of 10 m/s or greater; therefore, the effectiveness of its use in actual disasters may be viewed somewhat negatively. Along these lines, this study did not clarify the weather conditions under which UAV transport is effective. Additionally, there are no other existing trials against which to compare our trial.  
Conceptualization, F.Y., H.F., Y.H.; methodology, F.Y., K.Y.; validation, N.H., M.M.; investigation, K.Y.; writing, F.Y.; project administration, F.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
We would like to thank [PERSON] for providing us with the airfield. The authors declare no conflict of interest.
# Machine Learning Methods with Decision Forests for Parkinson's Detection
## 3 Decision Forests
### Systematically Developed Forest (SysFor)
#### 3.2.2 Building the Decision Forest  
Once the number of good attributes is in hand, the SysFor decision forest is built upon, which proved to be fast and more accurate in the classification process. Based on the descending order of gain ratio score, the attributes are picked up one by one to build the C4.5 trees in the SysFor decision forests. If the attribute is categorical, the underlying data is partitioned based on unique values (categories) of that attribute. On the other hand, for numerical attributes the dataset is partitioned into two horizontal segments based on the split points achieved for these attributes. Finally, C4.5 decision trees are created as ensemble on those partitions. For a set of data segments \(S\), if \(D=\big{\{}D_{1},D_{2},\ \ldots\ldots,\ D_{j}\big{\}}\) and \(A=\big{\{}A_{1},A_{2},\ \ldots\ldots,\ A_{j}\big{\}}\) represents the number of instances and good attributes of \(j=1,\ j=2,\ \ldots,\ j=|S|\) segments, respectively, the number of trees in the forest can be estimated as:  
\[T=\frac{\sum_{j=1}^{|S|}\big{(}|A_{j}|\times|D_{j}|\big{)}}{\sum_{j=1}^{|S|}D _{j}} \tag{7}\]  
Using the prediction results of \(T\) number of trees in the forest, SysFor applies the voting scheme to reach the final decision about the unlabeled incoming instances.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 2 Related Work
### Contextual Information Exploration  
Contextual information denotes the environmental or background attributes of pixels or localized regions, enabling computational models to interpret spatial positioning, dimensional characteristics, and morphological properties of objects, along with their interdependencies within an image. This global semantic framework transcends reliance on isolated local features by integrating structural cues that reflect holistic scene composition. For instance, the context-aware detection network (CAD-Net) [29] innovatively combines macro-level scene context with micro-level object relationships through a dual-stream architecture. Its integrated spatial-scale attention module dynamically prioritizes informative regions while optimizing feature resolution across scales, thereby refining detection robustness. Concurrently, [PERSON] et al. [30] devised a context-aware pixel aggregation (CPA) strategy, employing multi-scale convolutional operations to adaptively characterize variably sized objects. Their complementary context-aware feature aggregation (CFA) framework further enhances semantic coherence through graph-based relational modeling. [PERSON] et al. [31] advanced this paradigm with a dynamic context exploration (DCE) framework, which synergizes three operational layers: (1) adaptive local context retrieval via environmental scanning, (2) relational feature optimization through object interaction mapping, and (3) global feature enrichment for cross-scale consistency. Parallel innovations include [PERSON] et al.'s study [32], which proposed a novel scene context detection network (SCDNet), disentangling scene-object interactions via dedicated feature decoders. Building upon these advancements, our proposed spatial context awareness module (SCAM) introduces a dual-branch architecture that hierarchically integrates global scene semantics with localized spatial dependencies, effectively suppressing background interference while amplifying discriminative object features.
## 5 Results
### Ablation and Sensitivity Analysis  
#### 5.3.1 Ablation Study  
As shown in Table 5, removing either the trend smoothness loss or the event sparsity loss leads to a decrease in the model's performance on the classification task. Removing the trend smoothness loss resulted in a 3.62 percentage point decrease in accuracy, a 5.04 percentage point decrease in F1 score, and a 0.6 percentage point decrease in AUC. Removing the event sparsity loss resulted in a 4.59 percentage point decrease in accuracy, a 6.04 percentage point decrease in F1 score, and a 1.3 percentage point decrease in AUC.  
These results indicate that both temporal consistency constraints we introduced contribute to improving the model's performance, with the event sparsity constraint contributing more significantly.
## 3 Experimental Results
### Experimental Data  
The tests were performed in different working conditions to provide adequate data points for evaluation of the off-design performance of the system. To this end, the system was run at different flow rates as far as the system boundaries were met. The top boundary represents whether the available power in the evaporator insufficient to superheat the working fluid or the electric generator's maximum rotation speed is reached. The low boundaries are related to a minimum superheating degree of 2 K and the low electric power produced at low flow rates, which is below the minimum electric load, thus causing unstable operation of the system. Instead of creating a matrix of the experiments, which would make the work exhaustive and time-consuming, the data were taken for each heat source and sink conditions such that the system boundaries were met and thus most possible working conditions were tested. Figure 2 presents some important parameters of the experimental data representing the range of the experimental database.
## 3 Our Proposed Method
### Discretization  
Boolean discretization, which uses only two states (0 for off and 1 for on) is very popular, as it can speed up the computational process and save on running time. However, the conversion from real-valued expression data to Boolean data often reduces the accuracy of the inference system due to information loss and representative simplicity. In this regard, it is often appropriate to convert the real-valued expression of certain genes into a higher-level discretized expression values. Hence, MIDNI employs a hybrid approach for data discretization considering both two- and three-level discretizations. To determine the optimal level of discretization, we used the validity index [10; 11; 24]. The validity index is defined as  
\[Validity=\frac{Intra}{Inter}, \tag{7}\]  
where  
\[Intra=\frac{1}{T}\sum_{i=1}^{C}\sum_{x\in C_{i}}\parallel x-z_{i}\parallel^{2}\text { and}\]  
\[Inter=\min_{i\
eq j\in\{1,2,\cdots,k\}}\parallel z_{i}-z_{j}\parallel^{2}.\]  
In the above equations, \(T\) is the number of time steps, \(C\) is the number of clusters, and \(z_{i}\) denotes the center of the \(i\)th cluster \(C_{i}\). The optimal number of clusters is chosen as \(C\) to minimize the validity index. The expression values of a gene are classified into two or three classes by K-means clustering, then converted to the discrete values corresponding to the cluster.  
Figure 1: Framework of the MIDNI algorithm. A time series gene expression dataset (the columns denote genes as \(v_{1}\), \(v_{2}\),..., \(v_{N}\) and the rows denote time steps as \(t_{1}\), \(t_{2}\),..., \(t_{N}\)) is used as input. The validity index is used to determine the most suitable number of clusters for each gene. Subsequently, the discretized expression dataset generated by the K-means method is used as input to an inference algorithm consisting of MIFS and SWAP subroutines. Finally, the regulatory network is reconstructed based on the obtained regulatory genes and rules.