esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Machine Learning  
In addition to these, machine learning (ML) is also expected to be necessary for 5G networks and the emerging use cases. Broadly speaking, an ML algorithm can analyse huge volumes of data, detect anomalies, predict future scenarios, and quickly adapt to fluctuating environments. Such functionalities allow ML to improve and automate network management within the network environment. In fact, there are a number of use cases where ML can be useful such as power-saving, fault management, maintenance, operation, power control, network configuration, QoS prediction, and throughput and performance of coverage.  
There are three main categories into which ML algorithms can be divided:  
1. Supervised Learning: supervised learning is useful for classification or prediction of the tasks based on a labeled dataset. In general, supervised learning has been beneficial for applications that can create large amounts of data, as the number of instances directly influences the algorithm robustness [18]. Some example application areas of supervised algorithms can be predicting network demand, coverage area, or energy availability to dynamically allocate the resources and maximize the network performance.
2. Unsupervised Learning: in unsupervised learning, the data used to train the algorithm is not labelled. Thus, given the data, it tries to discover subgroups with similar characteristics without any guidance. This technique is especially useful to detect patterns and relationships that may not be clearly visible in the dataset [18]. As an example, the unsupervised learning technique can be useful when detecting abnormality or fault in wireless networks, capturing correlations within the data traffic, or clustering fog nodes in heterogeneous edge networks.
3. Reinforcement learning: reinforcement learning (RL) algorithms are especially useful in stochastic environments under uncertainty. In an uncertain environment, the system dynamics can be modelled using a Markov decision process (MDP). Then, the RL algorithm is used to find the optimal policy by trying possible actions and learning from the feedback in a given state [18]. Some application areas of RL are user scheduling, resource allocation, channel allocation, and handover decisions.  
Considering the necessity of automation of network functions for design, deployment, control, and management of the networks, [PERSON]. [19] elaborated a comprehensive overview on possible ML techniques for the network functions. Details are presented in Table 1.  
In addition to these, ML algorithms have also demonstrated significant improvements in enhancing the communication reliability for various applications such as radio resource allocation, physical security, signal decoding, and channel estimation [20; 21]. Such applications require high computational power as well as accurate and efficient estimation, where ML plays a critical role. In this respect, recent studies also showed that Bidirectional LSTM, KNN, and Random Forest algorithms in particular can outperform traditional methods in terms of estimation performance and computational complexity.
## 3 Results and Discussion
### Stress Relaxation Characteristics of Machine-Harvested Seed Cotton
#### 3.2.3 Effect of Compression Density on Stress Relaxation Characteristics  
The high density compression of seed cotton increases the required compression force [14; 17; 18; 23]. Figure 6a shows the stress relaxation characteristics curves of machine-harvested seed cotton compressed to different compression densities. The trends of stress relaxation characteristics curves of machine-harvested seed cotton were basically similar at each compression density. Figure 6b,c show the curves of stress relaxation characteristics of parameters \(E_{e}\), \(T_{1}\), and \(\varepsilon\) with the compression density of machine-harvested seed cotton under different compression densities, \(T_{1}\) decreases with the increase of compression density, and the lowest point appears when the compression density is 400 kg/m\({}^{3}\). The main reason is that the compression force of machine-harvested seed cotton at high density is large, and the stress exists transient drop, stress relaxation rate is fast; thus \(T_{1}\) has the minimum value [36; 41]. Moreover, the maximum value of \(E_{e}\) and the minimum value of \(\varepsilon\) were obtained at the compression density of 400 kg/m\({}^{3}\). This is due to the fact that the higher the compression density is, the higher the compression force and the greater the corresponding stress are [23; 32; 35], while the stress relaxation process itself is a long process, and the rate of stress decrease becomes slower, in which there is a higher \(E_{e}\) at high compression density. Meanwhile, with the increase of compression density, the seed cotton pressure drops, while its equilibrium stress time \(T_{2}\) is also larger, which means that the seed cotton under high compression density reaches the equilibrium modulus \(E_{e}\) rapidly, and there is more time for stress homogenization, thus there is smaller \(\varepsilon\) under high compression density, indicating a better stress relaxation effect.  
Figure 6: Stress relaxation characteristics at different compression densities: (**a**) Stress relaxation curve; (**b**) Equilibrium elastic modulus \(E_{e}\) and rapid decay time \(T_{1}\), (**c**) Residual stress ratio \(\varepsilon\).
## 3 Results and Discussion
### Potentiodynamic Polarization
#### 3.3.2 Oxide or Passive Layer Breakdown Mechanism and Pit Morphology  
Figure 5a,b shows the surface morphology of conventional rebar and DP rebar after oxide or passive layer breakdown during anodic polarization in SCPS containing NaCl. The surface morphologies showing pitting on the surface seemed different, although the electrolyte composition used was the same. We observed cracks beside the pits formed by corrosion. In addition, we observed cracks initiating oxide or passive layer breakthrough at other places. Hydrogen absorption at the metal surface might be possible. Water molecules or hydrogen ions might get reduced at the metal surface and seep through the broken oxide or passive layer, facilitating hydrogen absorption, which might form hydrides [34; 38]. This generates tensile stress, leading to crack initiation in the oxide or passive layer. Cracking inhibits repassivation and promotes active dissolution. Anodic dissolution and crack propagation are responsible for the growth of pits in the vertical (depth) and lateral directions, respectively.  
We also observed unprompted passivity for DP rebar in alkaline solution with Cl\({}^{-}\) ions, which was stable under anodic polarization. Cl\({}^{-}\) ions showed a stronger effect, and alkaline layers revealed a  
Figure 5: Surface morphology of (**a**) conventional rebar and (**b**) DP rebar.  
greater steadiness for DP rebar. High Cl\({}^{-}\) ion concentrations in a strongly alkaline solution can induce breakdown of DP rebar passivity.
## 4 Discussion
### Remote Sensing Monitoring of Walnut Planting in the Research Area  
Based on the extracted walnut planting information in the research area from 2017 to 2021, this study compares the planting areas of adjacent years with the NDVI difference method. Taking Landsat TM and DEM images as data sources, [PERSON] et al. [35] used the NDVI threshold method to extract the spatial information of rice, wheat, and maize in the Jiangsu Province by building different NDVI threshold vegetation models. To summarize, many mixed pixels in the walnut planting area are extracted by this method. In order to extract more accurate planting areas of walnuts and achieve increasingly accurate  
Figure 7: Average temperature and weekly average sunshine from May to September 2017–2020.  
monitoring with respect to walnut growth, this study extracts walnut planting areas and monitors walnut growth using the random forest algorithm, combined with the NDVI difference method, and adopts S-G filtering to correct the time series curves of NDVI and EVI, so as to reduce monitoring errors and improve accuracy. It can be observed from Figure 5 that the number of pixels in the NDVI increment is inclined toward the positive value range, indicating that growth is better than the previous year.  
Numerous previous studies have shown that GEE can be effectively applied to crop classification and area monitoring [36; 37; 38]. In this study, walnuts were extracted from 2017 to 2021 in Ganquan Town, and the GEE cloud platform was used to quickly map the walnut planting area. From Figure 5, it can be observed that, although NDVI increased in the \(-0.3\) range in 2018 compared to 2017, the overall increase was concentrated within the 0.6 to 0.7 range. In 2019, compared to 2018, the NDVI increase was concentrated within the 0.1 to 0.4 range. In 2020, compared to 2019, the NDVI increase was concentrated within the 0.5 to 0.6 range. In 2021, compared to 2020, the increase in NDVI was concentrated within the 0.6 to 0.7 range. This means that the overall growth of walnuts in the current year is better than the previous year.
# Towards NGGM: Laser Tracking Instrument for the Next Generation of Gravity Missions
## 3 LTI Units
### Laser Head Unit  
The laser head unit for the NGGM LTI was the reference laser unit (RLU) from TESAT Spacecom. The RLU is a space-qualified, fiber-coupled, low-noise, single-frequency, solid-state laser system with control electronics. It provided 25 mW of polarized optical output power at 1064 nm in a single-mode polarization, maintaining fiber to the optical bench, and was connected to the cavity via a second fiber output. A dedicated telemetry and telecommand (TM/TC) interface facilitated control of the system.  
It is routinely used in the European data relay system (EDRS), and also in the LRI on GRACE-FO, and has demonstrated excellent performance and reliability throughout many missions, including LISA pathfinder [31]. Figure 5 shows the RLU.  
The laser was based on a non-planar ring oscillator (NPRO) and was internally partially redundant with respect to the pump laser modules. It achieved the required frequency noise performance when locked to the cavity/laser stabilization unit. The TRL of the RLU with respect to NGGM was assessed with TRL 6, formally limited by the increased mechanical launch loads expected for the NGGM compared to the heritage missions.
###### Abstract  
The geochemical data from surface soils are often neglected or questioned when prospecting for ore deposits within active mining districts due to the \"background noise\" produced by anthropogenic pollution derived from mining activity. Large datasets on a national and international scale offer interesting possibilities to discover prospective zones. In the present work, data from the Geochemical Atlas of Castilla-La Mancha were treated in an area with an intense history of mining Hg, Pb, Zn, and Sb: the Almaden and Guadalmez synclines and the Alcuda Valley. The sampling grid was densified to adapt it to the scale of the main geological formations, and a cluster analysis was carried out to establish the relationships between the variables and a factor analysis to distinguish between geogenic and metallogenic factors. The results showed very high concentrations of some elements of prospective interest in local background areas: Hg (51 mg kg\({}^{-1}\)), Pb (1190 mg kg\({}^{-1}\)), and Sb (45 mg kg\({}^{-1}\)), with high variation coefficients. Cluster analysis unveiled a relationship between most of the local ore-forming metals (As, Sb, Hg, Pb, and Ag) with a clear correlation between Hg and organic matter, suggesting a notable contribution of Hg in soil. The factor analysis highlighted five factors, three geogenic and two ore-forming elements. Despite Hg being the main candidate to form a separate factor, its aerial deposition and a large number of outliers in the Almaden syncline contributed to integrating the element into a geogenic factor. Instead, factors F4 (Pb and Zn) and F5 (As and Sb) delimited the prospective areas in both synclines far from the known and the exploited mines. Some of these areas coincided with discovered mineralized zones, specifically in the area SE of a derelict Sb mine, confirming the usefulness of these datasets and statistical tools in areas with recent mining activity.  
geochemical prospecting; mercury; antimony; lead; soil +
Footnote †: journal: Applied Sciences
# A Review on SIW and Its Applications to Microwave Components
## 1 Introduction  
The electromagnetic (EM) spectrum is becoming overcrowded and is heavily crammed with a variety of wireless signals and other communication and sensing circuits and devices [1]. Electromagnetic waves of frequencies varying from 300 MHz up to 300 GHz stay classified as microwaves. This frequency span matches the free space wavelengths of 1 m to 1 mm, in that order. Electromagnetic waves of frequencies varying from 30 GHz to 300 GHz stay classified as millimetre-waves due to having wavelengths that lay directly over 1 mm and directly under 10 mm. The RF band falls somewhere beneath the microwave range, though the border in the middle of the radio frequency and microwave bands is subjective and changes based on the method established for developing the band [2].  
SIW transmission line [3, 4, 5, 6, 7, 8, 9, 10] is basically a dielectric-filled waveguide implemented by two lines of conducting posts (also known as vias) implanted within a dielectric substrate, and electrically connecting the top and the bottom conducting walls [11]. The structural evolution of the SIW technology is shown in Figure 1[3]. The evolution shows how the conventional rectangular waveguide presented in Figure 1a was modified by occupying the airspace with a dielectric material of dielectric constant, \(\epsilon_{x}\), as shown in Figure 1b. The structure was further modified by using metallic posts to imitate/replace the two side walls as shown in Figure 1c, to form the SIW. This paradigm shifting technology can basically be generalised as a planar transmission line that portrays waveguide characteristics, as it builds the traditional waveguide on a section of printed circuit board, substituting its metallic side-walls with two rows of metallised vias [12]. The substrate-integrated waveguide retains the benefits of a microstrip, including compactness and ease of integration, while also retaining some of the waveguide attributes, including minimal radiation loss, elevated unloaded Q-factor, and the elevated power processing capacity [13]. The very importantadvantage of the SIW transmission line technology is the opportunity to combine various devices (both active and passive) on a single substrate [14].
# Effects of Caprolactam Wastewater on Algal Growth and Nutrients Removal by _Arthrospira platensis_
## 3 Results and Discussion
### Investigation of Factors Inhibiting the Growth of _A. platensis_
#### 3.2.3 Effect of Heat-Sensitive and Volatile Chemical Matters in WCP on the Growth of _A. platensis_  
The results presented in Sections 3.2.1 and 3.2.2 showed that four organic compounds and peroxides in WCP were not the main factors affecting the growth of _A. platensis_; some unknown components must have existed and caused growth inhibition. If the unknown components that negatively impact the growth of _A. platensis_ are heat-sensitive, then heat treatment of WCP will relieve the growth inhibition of algae. Therefore, heat-treated WCP was prepared by boiling mixed WCP at 100 \({}^{\circ}\)C for 30 min as described in Section 2, and then the effect of heat-treated WCP on algal growth was tested.  
_Arthrospira platensis_ was cultivated with heat-treated WCP with concentrations of 12.5%, 25.0%, and 50.0%. As shown in Figure 6, heat-treated WCP exhibited significant inhibitory effects on _Arthrospira_ growth, and the inhibition increased as the heat-treated WCP concentration increased. Compared with that of the control, the biomass concentration of _A. platensis_ was decreased by 9.9%, 13.6%, and 29.9% at 12.5%, 25.0%, and 50.0% heat-treated WCP (Figure 6), respectively.  
Figure 5: Effects of different concentrations of reduced caprolactam wastewater (RWCP) on the growth of _Arthrospira platensis_. (**A**) Growth curves of _Arthrospira platensis_, (**B**) final biomass concentration, (**C**) growth inhibition rate (%). Error bars represent the standard deviation (\(n=3\)). Data with different letters indicate significant differences (\(p<0.05\)) between the control and treatments.  
The growth inhibition rates of _A. platensis_ by WCP, reduced WCP, and heat-treated WCP and the contents of the main organic matters (CHN, CHL, and CHO) in the three types of WCP are summarized in Table 2. The inhibition rates of _A. platensis_ by heat-treated WCP were similar to those by WCP and reduced WCP, thereby suggesting that heat treatment of WCP did not relieve the negative effect of WCP. Furthermore, the chemical analysis results showed that CHN, CHL, and CHO could not be detected in the heat-treated WCP. These results confirmed that the four organic compounds, peroxides, and other heat-sensitive and volatile chemicals in WCP were not the main factors that affected the growth of _A. platensis_. The inhibitory effect of WCP on _A. platensis_ might have been the result of the combined influence of multiple factors rather than just one substance.  
Before the main inhibitory factors were clarified, it was difficult to completely eliminate the inhibitory effect of WCP. Nevertheless, optimizing the WCP supply mode is another way to minimize the inhibitory effect and improve the efficiency of N and P removal in WCP by _A. platensis_.
## 5 Conclusions  
We proposed an integrated model based on label attention networks that simultaneously performed MA and NER. The proposed model comprised two layers of Bi-LSTM-LAN that were closely associated with each other. The lower layer performed MA, whereas the upper layer performed NER. To optimize  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & **Average Memory Usage** & **Average Prediction Time** \\  & **(Megabyte)** & **(Millisecond)** \\ \hline MANE & 4276 & 4.8 \\ MorpheNE & 4946 & 8.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparisons of memory consumption and prediction time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Size of Dataset** & **Parameters** & **F1-Score** \\ \hline
50\% of the morpheme dataset + the NE dataset & Static & 0.8476 \\  & Fine-tuned & 0.8497 \\ \hline
70\% of the morpheme dataset + the NE dataset & Static & 0.8531 \\  & Fine-tuned & 0.8554 \\ \hline
90\% of the morpheme dataset + the NE dataset & Static & 0.8583 \\  & Fine-tuned & 0.8597 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performances of NER according to different training data sizes.  
the weighting parameters of the proposed model, we used a two-phase training scheme: in the first phase, the lower layer was trained for MA, whereas in the second phase, all layers were trained for NER. In our experiments using public datasets, the proposed model outperformed all of the previous state-of-the-art models in Korean. Moreover, the proposed integrated model demonstrated greater performances than the independent MA (i.e., the lower layer) and the independent NER models (i.e., the upper layer). Based on these experiments, we conclude that the proposed model can effectively reduce the error propagation problem caused by a pipeline architecture. Moreover, we conclude that the proposed model can provide important feedback information from the upper layer (the NER model) to the lower layer (the MA model).  
Conceptualization, H.K. ([PERSON]); methodology, H.K. ([PERSON]); software, H.K. ([PERSON]); validation, H.K. ([PERSON]); formal analysis, H.K. ([PERSON]); investigation, H.K. ([PERSON]); resources, H.K. ([PERSON]); data curation, H.K. ([PERSON]); writing--original draft preparation, H.K. ([PERSON]); writing--review and editing, H.K. ([PERSON]); visualization, H.K. ([PERSON]); supervision, H.K. ([PERSON]); project administration, H.K. ([PERSON]); funding acquisition, H.K. ([PERSON]). Both authors have read and agree to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant, funded by the Korean government (MSIP) (No. 2016R1A2B4007732). This work was also supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2013-0-00131, Development of Knowledge Evolutionary WiseQA Platform Technology for Human Knowledge Augmented Services).  
We thank the members of the NLP laboratory at Kangwon National University and Konkuk University for their technical support.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Optimization of Analytical Protocols  
To verify the effectiveness of the analytical methods developed for the analysis of PAH and PCB occurrence and partition in water, sediment and biota, extraction yields, methods detection (MDL) and quantitation limits (MQL) were assessed.  
For each matrix, the extraction recoveries were determined using surrogates and were identified in the following ranges, water: from 52% (Ind-d12) to 88% (BaA-d12) for PAHs and from 54% (\({}^{13}\)C\({}_{12}\)-PCB118) to 77% (\({}^{13}\)C\({}_{12}\)-PCB28) for PCBs, with RSD% lower than 10% for all the analytes; sediment: from 54% (Ind-d12) to 96% (BaA-d12) for PAHs and from 56% (13C12-PCB52) to 86% (13C12-PCB28) for PCBs, with RSD% lower than 7% for all the analytes; biota: from 80% (DBA-d14) to 93% (BaA-d12) for PAHs and from 79% (\({}^{13}\)C\({}_{12}\)-PCB28) to 93% (\({}^{13}\)C\({}_{12}\)-PCB118) for PCBs, with RSD% lower than 8% for all the analytes. Extraction yields are reported in Figures S2-S4 of Supplementary Materials for both PAHs and PCBs in the three matrices.  
The results here obtained for all the matrices are in the same range (or higher) than other studies devoted to the analysis of PAHs and PCBs in marine compartments [25, 34, 35]. Differently from the procedures typically applied to sediments, the proposed QuEChenERS protocol is green and sustainable since it does not require high volumes of organic solvents. Furthermore, the method does not require a final evaporation step (which increases the risk of losing target analytes) [25].  
MDL and MQL for water, sediment and biota are reported in Tables S1 and S2, together with regulation limits (if present). MQL were in the following ranges, water: from 4.6 ng/L (NaPh) to 27 ng/L (DBA) for PAHs and from 6 ng/L (PCB11) to 32 ng/L (PCB123) for PCBs; sediment: from 3 \(\mu\)g/Kg (DBA) to 20 \(\mu\)g/Kg (AcPY) for PAHs and from 1 \(\mu\)g/Kg (PCB153) to 3 \(\mu\)g/Kg (PCB123) for PCBs; biota: from 10 \(\mu\)g/Kg (BaP and Ind) to 21 \(\mu\)g/Kg(BP) for PAHs and from 15 ug/Kg (PCB138) to 35 ug/Kg (PCB189) for PCBs. These values fully satisfy the limits fixed by the Italian and European regulations (previously described).
## 2 Smart Homes
### Home Application Areas  
The smart home has been of interest to researchers over the last 30 years. Several studied this topic which has branched out into a wide variety of applications. According to the literature, the smart home will enable the management and control of different areas of a residence [3]. Four distinctive general functional areas of service can be classified, which are [10; 11; 12; 13]: (a) Energy Efficiency and Management; (b) Health Care; (c) Entertainment; and (d) Security.