esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
In order to use the full range of the informative data and to eliminate noise, using a data processing method is essential for a robust calibration model. While the LIBS has been used for normalizing data treated with processing method in most cases, other spectroscopy methods such as NIR spectroscopy have been employed for several data processing methods to predict qualitative and quantitative content analysis.  
In this study, Savitzkye-Golay (SG) smoothing and the multiplicative scatter correction (MSC) methods were used for the analysis of the concentration in the blended coal samples. The SG smoothing method uses linear least squares and fits sub-sets of adjacent data with a certain order of polynomial. The SG smoothing method can eliminate spectral noise effectively. It is important to properly adjust polynomial order and the number of smoothing points when using this method. Among this, the number of smoothing points in determining the degree of smoothing is very significant. If the number of smoothing points is too small, a calculation error will occur, resulting in poor model accuracy. Too many smoothing points will cause the spectral data to become too flat and less accurate. Therefore, it is important to test and decide what mode will produce the best results by changing the number of points [20]. Seven points and a third-order polynomial in the SG smoothing method were selected as a proper mode to avoid either calculation error or excessive smoothing. The MSC is another effective data processing method. This is used for the correction of non-uniform particle sizes, gap between particles, and uneven flatness of the sample surface. This method can modify the spectrum of each sample so that all samples have the same scattering signal regarded as an ideal spectrum [20].
# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
## 3 Diagnosing Failures in Medical Equipment
### Structure of the CT Device  
CT scanners consist of three main components: the gantry, X-ray tube, and detector arrays. The gantry, a large rotating ring, houses the X-ray tube and detectors, rotating around the patient to capture multiple images. The X-ray tube emits X-rays that pass through the patient's body. The detector array on the opposite side of the gantry captures these X-rays, which are converted into electrical signals that the system processes to reconstruct 3D images of the body's internal structures. The operator controls key operational parameters of the CT, such as tube voltage, current, and rotation speed, by selecting specific scan protocols, ensuring detailed images suited to diagnostic needs. Continuous monitoring of critical performance metrics is essential to ensure CT systems operate efficiently and safely. These include the following:  
* Image Quality Parameters: Resolution and noise levels must be consistently monitored to ensure diagnostic accuracy.
* Dose Parameters: Monitoring radiation exposure to patients is crucial for safety.
* Operational Parameters: Metrics like X-ray tube voltage and current, gantry rotation time, and table movement are critical for system longevity and performance.  
Sensors embedded within the CT system monitor these parameters in real time. For example, voltage and current sensors in the high-voltage generator track the X-ray tube's input, preventing image quality issues and reducing wear on the equipment. Temperature sensors monitor both the cooling system and the X-ray tube to avoid overheating, which can cause irreversible damage.  
Real-time monitoring [16] can prevent failures, reduce downtime, and ensure accurate diagnostics by continuously tracking these factors.
## 4 Results
### Preliminary Phase
#### 4.1.1 Comparison of In Situ Parameter Thresholds  
We developed linear regression models to relate in situ observations to Sentinel-2 indices. Our initial models showed weak R\({}^{2}\) values when we included all available data regardless of the parameter used, e.g., Figure 3a illustrates the scatterplot (R\({}^{2}<0.2\)) for the model using the relationship between Chl-a and the two-band ratio (2 BDA) built from red-edge and red data. We experimented with different thresholds and found that model fit improved when we limited in situ observations of Chl-a to the points above 8 \(\mu\)g/L, which aligns with the trophic index used by NYSDEC for eutrophic lakes [60]. Figure 3b shows the improvement in the 2 BDA model fit by focusing on Chl-a points above 8 \(\mu\)g/L (R\({}^{2}>0.5\)). We found this threshold improved the model fit regardless of the index used for Chl-a models and we also achieved similar improvement in the phycocyanin models when these were also limited to field points with observations above 8 \(\mu\)g/L. We used this threshold for all subsequent analyses.  
We used Sentinel-2 data acquired on the date of the in situ sampling as well as one and two days before and after the in situ sampling to consider the effect this time separation  
Figure 3: Example of scatterplots between Chl-a and Sentinel-2 based 2 BDA (RE\({}_{1}\): band 5 and R: band 4) index with temporally matched in situ data and satellite data for (a) all in situ data, (b) in situ data above 8 \(\mu\)g/L.  
may have on the correlation between in situ and satellite data. This analysis was conducted using 2019 data and aimed to determine if an expanded time window could be used to mitigate any potential insufficiency in the number of concurrent data matches. Figure 4 shows the change in R\({}^{2}\) with expansion of the time window, using the 2 BDA-Chl-a model as an example. The best model was the concurrent data collection, but using a one-day time window had a relatively small reduction in R\({}^{2}\) (from 0.51 to 0.45) and provided a substantially larger dataset. The models developed by expanding the time window out to two days resulted in an R\({}^{2}\) of 0.24. This trend in model fit (similar results for concurrent and the one-day window with weaker results for the two-day window) held true for the different indices; hence, we excluded the two-day window from subsequent analyses.
## 2 Numerical Modelling Using Plaxis 2D
### Material Input Parameters
#### 2.3.3 Reinforcement Materials' Input Parameters  
The geosynthetic reinforcements used for the RSF and GRS RW are modelled as geogrids in the Plaxis 2D program with an elastic property and axial stiffness (\(EA\)) of 1600 kN/m. The \(EA\) of the geotextile was approximated to be the tensile strength (\(T\) = 80 kN/m) divided by the strain at 5%. The steel rods used for Case 1A-C are also modeled as geogrid elements with an elastic property and \(EA\) of 2300 kN/m. For the steel bar reinforcement, the \(EA\) is computed as the product of the elastic modulus of steel (\(E\) = 200 GPa) and the cross-sectional area has a diameter of 13 mm.
## 2 Materials and Methods
### Datasets Used
VSM indicates Volumetric Soil Moisture content. Figure 4: Calculated FTSW of the four seasons for Colinas do Douro from 2018 to 2021. (**a**) FTSW in 2018 season, (**b**) FTSW in 2019 season, (**c**) FTSW in 2020 season, and (**d**) FTSW in 2021 season. validation purposes, only data from one treatment--soil tillage in the inter-row combined with herbicide under the vines--was used, which is comparable to the tillage method used at Colinas do Douro. Soil moisture was assessed with a portable capacitance probe (Diviner 20001: Sentek Environmental Technologies, King Town, Australia). Readings were made periodically between bud break and harvest in 12 access tubes (3 per replication), placed along the row between two contiguous vines, at increments of 0.1 m from the soil surface to a depth of 1.0 m. For each access tube, soil moisture data were computed for an 80 cm depth, and the average was used as the input for the test dataset. The total average soil available water, up to an 80 cm depth, was 124 mm, calculated as the difference between the field capacity and the lowest soil moisture value reached at the end of the season of the driest year (2003), before the first rainfall. For more details, see [49]. Similar to Colinas do Douro, the difference between the soil water content at field capacity and the minimum soil water content was used to determine the TTSW for each access tube, and the amount of ASW was calculated as the difference between the soil water content on the day of measurements and the minimum soil water content. The ratio of the ASW to the TTSW was used to calculate the FTSW [48]. Figure 5 show the FTSW calculated from the soil water content data for Quinta de Pancas. As the daily ETo value was missing from the Quinta de Pancas dataset, the data were estimated by the Hargreaves-Samani method [50; 51]. This method estimates the ETo using only the observed maximum and minimum temperatures and the estimation of the extraterrestrial radiation, expressed by  
\[\text{ETo}=0.0135\times k_{Rs}\times 0.408R_{a}\times(\text{Temp}_{\text{avg}}+17. 8)\times(\text{Temp}_{\text{max}}-\text{Temp}_{\text{min}})^{1/2} \tag{10}\]  
where ETo is the reference evapotranspiration (mm day\({}^{-1}\)); \(R_{a}\) is the extraterrestrial radiation (MJ m\({}^{-2}\) day\({}^{-1}\)); 0.0135 is a factor for conversion from the U.S. to the international  
Figure 5: Calculated FTSW for Quinta de Pancas from 2002 to 2004. (**a**) FTSW in 2002 season, (**b**) FTSW in the 2003 season, and (**c**) FTSW in 2004 season. system of units; \(k_{Rs}\) is the radiation adjustment coefficient (\({}^{\circ}\)C\({}^{-0.5}\)). The empirical coefficient \(k_{Rs}\) was originally taken to be 0.17 \({}^{\circ}\)C\({}^{-0.5}\)[50].
## 3 Results
### Mercury Removal Performance of NbSs
#### 3.3.3 Microbial Dynamics and MeHg Risk in NbS Soils  
Alongside vegetation components, microbial communities were also found to influence Hg transformation processes in NbSs. Particularly, sulfate-reducing bacteria (SRB), such as _Desulfovirio_ spp., were noted as significant drivers of MeHg formation under anoxic and sulfate-rich conditions [29; 52]. Other groups such as iron-reducing bacteria, methanogens, and fermentative anaerobes also showed contribution to Hg methylation, especially in organic-rich substrates where redox gradients are pronounced [53]. The methylation process is highly sensitive to biogeochemical factors like sulfate, organic carbon, and redox potential, making it a dynamic function of both microbial composition and environmental conditions. Conversely, seasonal and spatial heterogeneity in microbial activity can shift wetlands from MeHg sinks to sources, as demonstrated in [PERSON] et al. [54], where higher summer microbial activity correlated with increased MeHg production.  
The structure and function of microbial communities involved in Hg cycling were observed to be strongly influenced by NbS design parameters. Substrate amendments such as biochar or iron compounds, vegetation type, and oxygen availability have been shown to alter microbial assemblages and their metabolic pathways. For instance, [PERSON] et al. [55] reported that aerated wetlands amended with biochar promoted the growth  
Figure 5: Alluvial diagram showing the relationship among NbS type, plant species, and mercury uptake level, categorized by concentration ranges: very high (>100 mg/kg), high (20–100 mg/kg), medium (2–20 mg/kg), and low (<2 mg/kg).  
of Hg-detoxifying bacteria such as _Hydrogenophaga_, _Arenimonas_, and _Lysobacter_, which corresponded with lower MeHg levels. Similarly, [PERSON] et al. [56] found that manipulating redox conditions in vertical flow wetlands suppressed MeHg formation while maintaining total Hg removal. Moreover, [PERSON] et al. [57] further emphasized that aeration disrupts anaerobic niches required for SRB and thus serves as a potential control strategy for MeHg suppression. These findings collectively highlighted the importance of integrating microbial considerations into NbS design, as microbial-mediated Hg transformations are not only biologically driven but also highly sensitive to system configuration and operational dynamics.
###### Abstract  
In this paper, we propose a novel design of a two-element array antenna on an electrical thick substrate with an extremely narrow array distance. The proposed array consists of a rectangular ring patch printed on a thick substrate and a monopole wire in the center of the substrate. Each element has a modal difference in the radiation pattern, causing high isolation and low correlation between the array elements. From the measurement results, the monopole and patch elements exhibit reflection coefficients of \(-10\) dB and \(-10.7\) dB with peak gains of \(3.8\) dBi and \(6.1\) dBi, respectively, at \(1.6\) GHz. The mutual coupling between the two elements is \(-20.7\) dB. For modal analysis of the antenna pattern, spherical mode decomposition is performed on the radiation patterns of the two elements, and low envelope correlation coefficient levels below \(16\%\) are maintained. We also investigate the antijamming performance using a power inversion algorithm in a practical pattern nulling application; a null depth of \(-47.7\) dB and a null width of \(33.2^{\circ}\) are obtained when the interference signal arrives at the elevation angle of \(45^{\circ}\).  
compact array antenna; interference mitigation; modal difference; low mutual coupling +
Footnote †: journal: Applied Sciences
###### Abstract  
Fringe projection technologies have been widely used for three-dimensional (3D) shape measurement. One of the critical issues is absolute phase recovery, especially for measuring multiple isolated objects. This paper proposes a method for absolute phase retrieval using only one coded pattern. A total of four patterns including one coded pattern and three phase-shift patterns are projected, captured, and processed. The wrapped phase, as well as average intensity and intensity modulation, are calculated from three phase-shift patterns. A code word encrypted into the coded pattern can be calculated using the average intensity and intensity modulation. Based on geometric constraints of fringe projection system, the minimum fringe order map can be created, upon which the fringe order can be calculated from the code word. Compared with the conventional method, the measurement depth range is significantly improved. Finally, the wrapped phase can be unwrapped for absolute phase map. Since only four patterns are required, the proposed method is suitable for real-time measurement. Simulations and experiments have been conducted, and their results have verified the proposed method.  
Keywords:absolute phase retrieval; phase-shift; fringe order; geometric constraints +
Footnote †: journal: Applied Sciences
## 7 Limitations and Prospects  
In this paper, FLAC3D numerical simulation and SBAS-InSAR technology were used to analyze the dynamic evolution law of ground settlement amount and range under repeated mining in multiple coal seams. It can be found that the surface deformation results of the mining area obtained using the FLAC3D numerical simulation were highly consistent with those obtained by the SBAS-InSAR technology, which provides a reliable means for the surface deformation prediction in mining areas. At the same time, this paper also had certain limitations. For example, the SBAS-InSAR technology was used to monitor the surface deformation of the mining area, which can be considered to have uniform or linear characteristics. However, it can be found in the monitoring of the ground subsidence of the mining area that sudden collapse may occur, resulting in failure to obtain the surface deformation results of the collapse area, thus limiting the sequential InSAR. The traditional D-InSAR technique can be used to monitor the surface deformation characteristics before and after the collapse. Therefore, how to combine time series InSAR and D-InSAR to monitor the land subsidence of mining areas is also a future research focus.
## 5 Discussion
### Analysis of Individual Components (Ablation Study)  
To analyze the individual contribution of the main components of the proposed model (CNN, LSTM, and TransformerConv), additional experiments were conducted using only specific modules. In particular, we evaluated a model focused solely on temporal dependencies (LSTM) and one focused solely on spatial relationships (GraphSAGE). The results show that the LSTM model achieved an RMSE of 0.0305 and a correlation of 0.8939, while the GraphSAGE model obtained an RMSE of 0.0294 and a correlation of 0.8997. These results indicate that both temporal and spatial relationships individually contribute significant predictive value. However, the ordered integration of both dimensions--first capturing temporal dependencies via CNN1D+LSTM and then spatial relationships via TransformerConv--improves predictive capability, as reflected in the superior performance of the CNN1D+LSTM+TransformerConv hybrid model (RMSE of 0.0169 and correlation of 0.9601). This provides functional evidence of the relevance of each component in urban noise prediction.
## 4 Case Study of Interworking of QKD Networks and the Role of SDN Orchestrator
### Implementation Results of SDN Controller and SDN Orchestrator for KOREN
#### 4.2.3 Data Model of SDN Orchestrator for Interoperable KMS  
When two networks separated by a specific QKD vendor are interconnected, a new data model is required. The parameters of a QKD application to support an interoperable KMS are described in Table 2. The data model used here is based on the parameters specified in ETSI GS QKD 015 [6]. As per the current ETSI GS QKD 015 data model, only \"client\" and \"internal\" exist; thus, it is not possible to address the use cases for inter-device and inter-network integration. Therefore, a new data model called \"multi-segment\" has been defined to support multi-vendor and inter-network integration, as described in Table 2. In other words, a new application type called \"multi segment\" in YANG for interworking cases is defined, and this application type is only set when interworking is necessary.  
If we refer to the two SD-QKD nodes of \"service link A for network A\" in Figure 6 as node A and node B, respectively, the YANG data model between the two nodes can be represented in Figure 7 as an example.  
To invoke the key delivery process for integrating heterogeneous KMSs, the necessary information is passed as parameters in \"rpc:app-registration-ms\", and the app_type is  
\begin{table}
\begin{tabular}{c c c c} \hline
**Name** & **Type** & **Details** & **Description** \\ \hline app\_qos & container & None & Requested quality of service. \\ \hline qos/ max\_bandwidth & uint32 & None & Maximum bandwidth (in bits per second) allowed for this specific application. Exceeding this value will raise an error from the local key store to the application. This value might be internally configured (or by an admin) with a default value. \\ \hline qos/ min\_bandwidth & uint32 & None & This value is an optional QoS parameter that enables a minimum key rate (in bits per second) for the application. \\ \hline qos/ jitter & uint32 & None & This value allows for the specification of the maximum jitter (in milliseconds) provided by the key delivery API for applications requiring fast rekeying. This value can be coordinated with the other QoS to provide a wide enough QoS definition. \\ \hline qos/ ttl & uint32 & None & This value is used to specify the maximum time (in seconds) that a key could be kept in the key store for a given application without being used. \\ \hline backing\_qkdn\_id & leaf-list: ietf-yang-types: uuid & None & Unique ID of the key association link which provides QKD keys to these applications. \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 7: An example of the YANG data model within one QKD network for an interoperable KMS.  
defined as MS (multi-segment) to provide MS information. In the figure, \"extkeys\" refers to the transmission of keys at the border node between QKD network A and QKD network B for the interoperable KMS described in clause 3.
## 3 Statistical Analysis
### Hourly Values
#### 3.1.6 Sky Conditions Classification  
As it was indicated earlier, weather conditions can be classified into four categories according to the values of clearness index, i.e., (i) cloudy sky (\(k_{t}\leq 0.35\)); (ii) partially cloudy with predominance of diffuse component (0.35 \(<\)\(k_{t}\)\(\leq\) 0.55); (iii) partially cloudy with predominance of direct component of the solar radiation (0.55 \(<\)\(k_{t}\)\(\leq\) 0.65); and (iv) clear sky (\(k_{t}\)\(>\) 0.65). Figure 19: (a) diurnal variation of the average hourly values of \(R_{ns}\) for each month of the year; and (b) monthly variability of net shortwave irradiances (W/m\({}^{2}\)). Symbol * indicates an outlier. Figure 18: Cumulative frequency distribution (CDF) of shortwave solar radiation components: (a) direct normal (\(B_{n}\)); (b) direct horizontal (\(B\)); (c) diffuse (\(D\)); and (d) global (\(G\)) irradiances (W/m\({}^{2}\)). Table 6 presents the statistical values of all radiation components considering the prevailing weather conditions as classified by the clearness index. In general, the hourly fractions of _UVT_ component to global solar irradiance, namely _UVT_/_G_, increase as sky conditions changed from clear to overcast; by contrast, the fraction (_NIR_/_G_) decreases with sky conditions changing from clear to overcast, while the fraction of _GPAR_/_G_ remains relatively constant. The maximum hourly values of \(G\), _UVT_, GPAR and NIR can be interpreted as the amplitude of the diurnal evolution, and they increase as the cloudy cover decreases. The monthly statistics of the shortwave radiation components for the four sky conditions are presented in Table 7. The monthly means of the global irradiances under clear sky conditions range between 460 and 760 W/m\({}^{2}\), while under cloudy conditions they range between 100 and 180 W/m\({}^{2}\). High values are recorded for direct irradiance under clear sky conditions, in contrast to the very low direct irradiance under cloudy conditions. The global irradiance under different sky conditions can be estimated from the equation:  
\[G=a*m^{b} \tag{31}\]  
where \(m\) is the optical air mass which is estimated by the [PERSON] and [PERSON] (1989) [44] formula:  
\[m=(p/p_{0})/[\cos\theta_{z}+0.50572*(96.07995-\theta_{z})^{-1.6364}] \tag{32}\]  
\[(p/p_{0})=\exp(-z/8435.2) \tag{33}\]  
where \(\theta_{z}\) is the solar zenith angle in degrees and \(z\) is the elevation of the station in m. Figure 20 shows the result of Equation (31) for the four sky conditions. The estimated parameters \(a\) and \(b\) are shown on the graph. Figure 20: Relationship of global irradiance in W/m\({}^{2}\) and optical air mass (m).
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 5 Signal Technologies  
The communication platform used in WSN systems is crucial in assessing that the accuracy of elderly care systems is between 0.5 m and 1 m. The value of the update rate must be at least 5 s. The classification can be made according to the main medium used to determine the position. The technologies used are radiofrequency signals, ultrasound, infrared, optical signals, and inertial measurements.
## 2 Key Steps in a FRB Search Pipeline
### Rejection of Radio Frequency Interference
#### 2.1.1 Static Channel Mask  
Any radio telescope has a number of persistent, local RFI sources. Some of these sources are always emitting at the same radio frequency with little time variability. In these situations, it is sensible to pre-emptively mask out the relevant frequencies from the telescope data as they will be completely unusable. Hence, astronomers can generate a static frequency channel mask for these known sources of RFI that are always present in the data. One of the main drawbacks of a static mask is that it cannot account for the time variability of RFI, or for the corruption of a new region of the frequency band. Then, the mask needs to be updated. Hence, more sentient RFI flagging algorithms are used for telescopes around the world nowadays.