esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Related Agricultural Policies Affect the TCF  
The changing trend of TCF was affected by the related national and local agricultural policies. The TCF had experienced roughly stable, slowly decreasing, and rapidly increasing periods, which were related to the periods 1995-1999 before the GFGP, 2000-2007 during the GFGP, and 2008-2016 after the GFGP. From 1995 to 1999, the TCF was roughly stable, because the food security should have been guaranteed by intensifying agricultural land use or expanding cropland area [25; 26]. From 2000 to 2005, the TCF decreased, because the ecological security should have been guaranteed mainly by reducing cropland at high risk of degradation, and the agricultural inputs were relatively lower  
Figure 8: Change in ratio of carbon intensity in Qinghai province to that in China over 1995–2016.  
Figure 6: Change in carbon intensity in Qinghai province over 1995–2016.  
Figure 7: Change in relative carbon intensity in Qinghai province and China over 1995–2016.  
over 2000-2005 of the initial implementation period of GFGP in the study area, which was obviously different from other GFGP implementation areas in western China [34].  
The TCF slightly increased between 2006 and 2007, probably because some contingency policies were implemented to keep the balance between the food security and ecological security in the late implementation period of the GFGP period [35]. In order to control the increasing TCF and to maintain the agricultural production, China adopted many specific low-carbon agricultural techniques [36]; however, the overall agricultural energy efficiency was low, especially in undeveloped western China [37]. In addition, the environmental pollution induced by higher agrochemical inputs has not attracted wide public attention because this type of concealed and dispersed pollution is difficult to detect [38]. The TCF in the study area rapidly increased between 2008 and 2016, and the CF proportion by energy input during that period was relatively higher. This was because that the cropland and orchard area had slightly increased and all agricultural inputs had generally increased after the implementation period of GFGP. The changes in agricultural policies and associated agricultural land use structure and input structure strongly affected the changing trend of TCF for agricultural production in the study area, which were also evident in China [3], Jordan [39], the Indo-Gangetic Plains [40], and the South American Chaco [41].  
As a part of driver and amplifier of the global climate change, Qinghai province should control its relatively lower but increasing TCF. Based on the changing trends for CV and CA in Qinghai province over 1995-2016 (Figure 6), the input structure and intensity of agrochemical and energy during 2005-2007 may be comparatively reasonable to hold a balance between higher carbon efficiency and lower CA in the study area under certain natural and economic conditions. In order to limit the increasing TCF, it is necessary to control the application amount and use efficiency of N fertilizer, diesel, and plastic film.
###### Abstract  
In this research, the moisture susceptibility of a nanoclay-modified asphalt concrete (AC) mixture containing plastic film (in flakes) collected as urban waste was evaluated with specimens subjected to the tecnico accelerated ageing (TEAGE) procedure. The TEAGE procedure attempts to simulate--in a laboratory setting--the effect of field ageing by applying watering/drying cycles and ultraviolet radiation. For comparison purposes, three AC mixtures were considered, one for control, without plastic and nanoclay, a mixture with only plastic, and a mixture with both plastic and nanoclay. Furthermore, only half of the specimens were subjected to the ageing procedure. The plastic was added to the mixture using the dry process, and the nanoclay was blended with the bitumen before mixture preparation. The moisture susceptibility was evaluated, using a total of 48 Marshall specimens, by the indirect tensile strength ratio (ITSR). From the results of this study, the nanoclay-modified AC mixture containing plastic film presented slightly higher indirect tensile strength (ITS) values, lower moisture susceptibility, and enhanced ageing resistance. These slight improvements can be justified by the reduced air voids content of the samples and consequently they must be seen as conservative. Nevertheless, the modification of AC mixtures with flakes of plastic and nanoclay can be a viable solution for the recycling of plastic film collected as urban waste, being an eco-friendly alternative to disposal in landfills.  
Keywords: ageing; plastic film; urban waste; nanoclay; moisture; indirect tensile strength +
Footnote †: journal: Applied Soft. 2019, 9, 3738; doi:10.3390/app9183738
## 3 Building Blocks of the Overall DFE Architecture
### FIR5 (Channel Selection Filter)  
FIR5 (channel selection filter or CSF) at the final stage rejects adjacent channel interference including the inband blocker and the narrowband blocker, which can be much stronger than the desired signal. This imposes the highest performance on FIR5, which does not conduct decimation. The left half of Figure 14 shows the input of FIR5 (CSF) and the right half shows the output of FIR5 in case of LTE (20 MHz + 20 MHz). At the input of FIR5, carrier 1 was located around the origin (center frequency) and carrier 2 was in the middle of the graph together with the inband blocker that was more than 20-30 dB larger, than carrier 1. After passing through FIR5 (a low-pass filter), the inband blocker became similar or smaller than carrier 1 and also carrier 2 became considerably smaller by over 40 dB. The inband blocker lost its adverse influence after it passed through the fast Fourier transform (FFT) processor in the OFDM receiver.  
The filter coefficients were obtained over all the 10 cases or frequency modes and for carrier 1 and carrier 2. The coefficients for both carrier 1 and carrier 2 show symmetry and the numbers of coefficients were 31, 30, 31, 32, 27, and 29 and 32, 43, 32, a d32 for cases 1-6 and 10-13, respectively, concerning carrier 1, and the number of coefficients are 32, 32, 32, and 32 for cases 10-13, respectively, as to carrier 2.  
Figure 13: FIR4 input (**left**) and output (**right**) signals in the frequency domain.
## 3 Simulation Environment  
For testing purposes, a full simulation environment was created prior to software development using CopppeliaSim. The simulation included all of the necessary elements to ensure a close resemblance to the real track described above used in the RoboCup Portuguese Open.  
Figure 1 shows the full environment, containing:  
* The car (Figure 2), the main agent, which only uses primitive shapes and contains all of the necessary actuators and sensors. A cuboid of 60 \(\times\) 30 \(\times\) 2 cm is used for the base and four cylinders with 15 cm radius and 3 cm height for the wheels; these are connected to the base through joints. The back wheels use one joint each to work as motors, while the front wheels use two joints each; one works as a slipping joint and the other is responsible for steering and is perpendicular to the floor.
* The track, consisting of a digitally created image with a black background and white lines, two continuous white lines delimiting the track, a dashed line in the middle to separate the lanes, a zebra crossing, and two parking areas.
* A traffic light placed over the zebra crossing, with two screens (25 \(\times\) 25 cm CoppeliaSim planes) facing opposite directions in order to be seen from both directions.
* Obstacles, consisting of 40 \(\times\) 40 \(\times\) 60 cm green cuboids randomly placed on the track.  
Figure 1: Full view of the environment and its components.  
Figure 2: Basic car model used in simulation.  
To gather the necessary track information from the simulation environment, a vision sensor (camera) is located in the car's front side, tilted slightly downwards for a better view of the track, with a perspective angle of 75 degrees and a resolution of 512 \(\times\) 256 pixels. This camera collects the images to be interpreted. The car contains other sensors that are not used for neural network purposes.
## 7 Results of Experiments
### Feature Pre-Selection  
As part of feature selection using the SFFS method, the authors generated a different number of significant features from the perspective of the analyzed experiment for each of the subsystems. As a consequence, the size of individual feature vector matrices for speech signal models was reduced. The feature vector of the phonation system contains 5 vectors out of 20 features after selection. The feature vector of the articulation system contains 9 out of 15 features after vector selection. And the prosody system contains two vectors after selection. The classification effectiveness for subset 2 is shown collectively in Table 4. A general improvement of classifier quality can be observed when selecting features under each of the speech signal modeling techniques defined as stand-alone systems. The dominant processes with the highest classifier effectiveness are still phonation and articulation. The operating efficiency of a system based on the prosody process was also increased when using the SVM network.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 7 Conclusions
### Ethical Considerations  
The application of machine learning models in financial markets raises several ethical concerns that must be acknowledged:  
* Market Manipulation Risks: Predictive models, if misused, could exacerbate volatility or unfairly influence market behavior. Implementing safeguards and monitoring mechanisms is essential to ensure responsible use of automated trading strategies.
* Data Privacy: Sentiment analysis relies on financial news, which may inadvertently include private or sensitive information. Compliance with data protection regulations, such as GDPR, is essential to maintain ethical standards.
* Algorithmic Bias: Models trained on historical data may reflect or amplify existing biases in financial markets, potentially leading to unfair advantages or reinforcing systemic inequalities. Regular audits of model performance and fairness are necessary to mitigate these risks.
* Accessibility: Advanced AI models in trading may disproportionately benefit institutional investors, widening the gap between large firms and individual investors. Developing tools that enhance accessibility and transparency for smaller investors can address this concern.  
Addressing these ethical considerations requires transparency in model development, adherence to regulatory frameworks, and regular monitoring to prevent unintended consequences.
# An Integrated Metabolomics Study of Glucosinolate Metabolism in Different Brassicaceae Genera
## 4 Materials and Methods
### Untargeted Metabolomics  
#### Analysis of Non-volatile Metabolites  
Sample Preparation  
100 mg of freeze-dried powder samples were treated with 3 mL of 70% methanol containing 20 \(\upmu\)g/mL FMOC-glycine as internal standard for 10 min. The extract was centrifuged at 15,000\(\times\)\(g\) for 15 min, and filtered through a 0.22 \(\upmu\)m nylon filter. 100 \(\upmu\)L and 10 \(\upmu\)L of the filtrate were used for LC/MS and GC-MS analysis, respectively. For GC-MS analysis, the 10 \(\upmu\)L filtrate was dried under nitrogen, and then derivatized with methoxyamine (50 \(\upmu\)g/mL in pyridine), with subsequent trimethylsilylation by MSTFA.  
The stability of the system was validated using a pooled quality control (QC) sample, which was prepared by pooling 10 \(\upmu\)L from each sample. The QC sample was analyzed at the beginning, the end, and randomly throughout the analytical runs.  
Instrumental Analysis  
LC-MS analysis was performed on an Agilent 1290 UHPLC system (Waldbronn, Germany) coupled to a 6540-quadrupole time-of-flight (Q-ToF) mass detector (Agilent, Santa Clara, CA, USA), which was equipped with an electrospray ionization source. The samples were analyzed in both positive and negative ion modes. The elution was conducted at 35 \({}^{\circ}\)C on a Rapid Resolution HT ZORBAX SB-C18 Column (2.1 \(\times\) 50 mm, 1.8 mm, Agilent, Santa Clara, CA, USA) at a flow rate of 0.4 mL/min, with a gradient of 95% solution A (0.1% formic acid in water)-5% solution B (0.1% formic acid in acetonitrile) to 55% solution A-45% solution B over a period of 9 min, followed by 45-100% solution B over a period of 9 min, and subsequently 100% B for 2 min. The autosampler was cooled at 4 \({}^{\circ}\)C, and 5 \(\upmu\)L of the extract was injected. The following parameters of the mass spectrometer were used for the LC-MS analysis: Ion spray voltage, 4000 V; heated capillary temperature, 350 \({}^{\circ}\)C; drying gas flow, 12.0 L/min; nebulizer, 50 psi; collision energy, 10 V, 20 V, or 40 V.  
GC-MS analysis was conducted on an Agilent 7890A Series Gas Chromatograph System coupled to an Agilent 7200 Q-ToF mass detector (GC-Q-TOF/MS) and an Agilent 7683B Series Injector kept at 250 \({}^{\circ}\)C. The elution was conducted on a fused silica capillary column HP-5 MSI (30 m \(\times\) 0.25 mm i.d., 0.25 mm film thickness). 1 mL of each sample was injected in the splitless mode for the individual analysis. The following parameters of the mass spectrometer were used for the GC-MS analysis: Helium, 1 mL/min; oven temperature, 70 \({}^{\circ}\)C for 1 min, 70 \({}^{\circ}\)C to 250 \({}^{\circ}\)C at an incremental rate of 10 \({}^{\circ}\)C/min, 250 \({}^{\circ}\)C to 300 \({}^{\circ}\)C at an incremental rate of 25 \({}^{\circ}\)C/min, and then held at 300 \({}^{\circ}\)C for 6 min; transfer line temperature, 280 \({}^{\circ}\)C; electron energy, 70 eV; full scan monitoring, _m/z_ 50 to _m/z_ 550; ion source temperature, 230 \({}^{\circ}\)C; quadrupole temperature, 150 \({}^{\circ}\)C.
# Self-Cleaning Solar Mirror Coatings: From the Laboratory Scale to Prototype Field Tests
## 5 Conclusions  
This work proposes a cheap and robust process for depositing, by means of magnetron sputtering, an auxetic transparent aluminum nitride-based self-cleaning coating onto solar mirrors with dimensions of 50 \(\times\) 40 cm, facing the problem of scaling from lab to pre-industrial scale encountered in previous studies on the promising self-cleaning performance of auxetic materials. Prototypes of the produced self-cleaning solar mirrors were positioned in a demonstrative Fresnel plant located in ENEA, Casaccia (Rome) Research Center, and tested on-field to study their performance. They have shown stability in external conditions: no degradation occurred during the test campaign. Moreover, it is possible to wash them, restoring the initial reflectance affected by soiling. The test campaign will be extended for a longer period.  
By addressing the issue of washing mirrors with a smaller amount of water with respect to glass utilized for back surface mirrors, these coatings contribute to the overall efficiency and viability of solar mirror applications in the pursuit of sustainable and clean energy solutions.  
Conceptualization, A.C.; methodology, A.C. and E.G.; software, E.G.; validation, A.C., E.G. and G.C.; formal analysis, E.G.; investigation, G.V. and G.C.; resources, A.C.; data curation, E.G.; writing--original draft preparation, A.C.; writing--review and editing, A.C.; visualization, G.V.; supervision, A.C.; project administration, A.C.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript.  
Funding: This work was funded by the Italian Ministry of Environment and Energy Security through the \"National Electric System Research\" program--Project 1.9 \"CSP/CST technology\", 2022-2024 implementation plan.  
Not applicable.  
**Data Availability Statement:** Further data on the measurement campaign will be published on the ENEA site.  
We acknowledge [PERSON], [PERSON] and [PERSON] for giving us the opportunity to install self-cleaning solar mirrors in the demonstrative ENEASHIP plant.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Adsorption Tests
#### 3.2.1 Organic Dyes Removal
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **MB** & **EB** & **BB** & **Ph** & **CP** & **BPA** \\ \hline \multirow{4}{*}{SCGs-AC} & Model & Langmuir & Freundlich & Freundlich & Freundlich & Freundlich & [PERSON] \\  & Equations & y = 0.0056 & y = 1.6429 & y = 10.04 x & y = 2.5548 & y = 2.1638 & y = 0.0045 \\  & Parameters & x + 0.0025 & x \(-\) 1.5483 & \(-\) 40.232 & x \(-\) 4.9972 & x \(-\) 3.1231 & x + 0.0005 \\  & Parameters & Q\({}_{0}\) = 179 & n = 0.609 & n = 0.100 & n = 0.391 & n = 0.462 & Q\({}_{0}\) = 222 \\  & k\({}_{\rm L}\) = 2.24 & k\({}_{\rm F}\) = 0.213 & k\({}_{\rm F}\) = 0.007 & k\({}_{\rm F}\) = 0.047 & k\({}_{\rm F}\) = 0.044 & k\({}_{\rm L}\) = 9.00 \\  & R\({}^{2}\) & 0.9894 & 0.9731 & 0.9021 & 0.9129 & 0.9029 & 0.9658 \\ \hline \multirow{4}{*}{C1-AC} & Model & Langmuir & Langmuir & Langmuir & Freundlich & Freundlich & [PERSON] \\  & Equations & y = 0.0036 & y = 0.0062 & y = 0.0039 & y = 3.4639 & y = 1.2778 & y = 0.0079 \\  & Parameters & x + 0.0006 & x + 0.0132 & x + 0.0449 & x \(-\) 10.379 & x + 0.399 & x + 0.0026 \\  & Parameters & Q\({}_{0}\) = 278 & Q\({}_{0}\) = 161 & Q\({}_{0}\) = 256 & n = 0.289 & n = 0.783 & Q\({}_{0}\) = 127 \\  & k\({}_{\rm L}\) = 6.00 & k\({}_{\rm L}\) = 0.470 & k\({}_{\rm L}\) = 0.087 & k\({}_{\rm F}\) = 0.19 & k\({}_{\rm L}\) = 1.49 & k\({}_{\rm L}\) = 3.04 \\  & R\({}^{2}\) & 0.9972 & 0.9609 & 0.9152 & 0.9118 & 0.9099 & 0.9956 \\ \hline \multirow{4}{*}{C2-AC} & Model & Langmuir & Langmuir & Langmuir & Freundlich & Freundlich & Langmuir \\  & Equations & y = 0.0085 & y = 0.0119 & y = 0.0129 & y = 1.6268 & y = 0.8865 & y = 0.0032 \\  & & x + 0.0036 & x + 0.0127 & x + 0.096 & x \(-\) 1.2875 & x + 2.3257 & x + 9.10\({}^{5}\) \\ \cline{1-1}  & Parameters & Q\({}_{0}\) = 118 & Q\({}_{0}\) = 84.0 & Q\({}_{0}\) = 77.5 & n = 0.615 & n = 2.015 & k\({}_{\rm L}\) = 313 \\ \cline{1-1}  & R\({}^{2}\) & k\({}_{\rm L}\) = 2.36 & k\({}_{\rm L}\) = 0.672 & k\({}_{\rm L}\) = 0.134 & k\({}_{\rm F}\) = 0.276 & k\({}_{\rm F}\) = 10.2 & k\({}_{\rm L}\) = 35.6 \\ \cline{1-1}  & R\({}^{2}\) & 0.9952 & 0.9617 & 0.9701 & 0.9067 & 0.9640 & 0.9929 \\ \hline \hline \end{tabular}  
\end{table} 
Table 5: Details of PACs adsorption isotherms for organic dyes and for phenolic compounds.\({}^{1}\)
## 6 Showcase Results
### Redundancy in the Grid-Like Urban Scenario  
In this scenario, we focused on the detection redundancy achievable via V2X services. The interpretation of the redundancy score is explained in Figure 2. The measurement results were evaluated for the various sensor setups, and penetrations are depicted in Figure 12. This diagram represents the sensor setups with different colors and the penetration values with different locations on the penetration axis. As we can see, the radar can only detect a few vehicles compared to the V2X-extended perception cases. The detections of the radar gradually grow together with the ratio of the equipped vehicles. In the second sensor setup, we can see that the number of detections is much larger than in the radar case. In this case, we can also observe a small amount of redundant object detections. However, the degree of redundancy does not exceed the value of 2. In the combined radar, CAM, and CPM sensor setup, we can see that the number of detections is even higher than in the CAM case. In rare situations, the number of redundant detections can also reach the level of 11, which means that the receiver vehicle could obtain information about an object from 11 different observations. The gap between the CAM and the CPM use case redundancy results is similarly massive as the gap between those and when only the radar sensor was enabled. The amount of the objects in the CPMs also depend on the applied redundancy mitigation techniques. The gap between the CAM and the CPM results would probably be even greater with a more dense traffic environment.  
In Figure 13, the total number of object detections is represented. This diagram highlights the sensor deployment schemes with different colors, and different groups represent the different penetration values on the horizontal axis. Generally, we can see that the number of detected objects in such ADAS applications grows with the higher number of equipped vehicles. The number of objects detected by the CPMs usually exceeds the number of objects detected by CAMs. In the case of 20%, the number of objects detected by the CAMs exceeds the CPM case. This is likely due to an unfortunate setup with the random assignment of the equipped vehicles.  
Figure 12: The redundancy score of the different scenarios.
## 3 Method  
On 14 August 2020, a search of two scientific databases, Web of Science and Scopus, was done for articles containing \"Change Detection\" OR \"Land Cover Change\" with \"Landsat\" and \"Time Series\" in the title, abstract or keywords, from 1970 to 2020. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [61] proposed method was followed (see Figure 4). After removing duplicate results, 643 articles were screened and 490 titles were selected for the systematic review. Landsat data have been used in several scientific fields and in some of them the data only used as an ancillary data or for validation purposes. Articles from non-remote sensing sources that did not use change detection methods or Landsat images as the primary data were excluded from the database. In addition, review papers and articles written in non-English languages were excluded.  
Figure 4: PRISMA flow diagram for database article selection.  
The database was built to include 39 attributes in total, which are filled with numeric, Boolean, and string data types containing quantitative, qualitative, and categorical characteristics of the research describing article information, study area and application, data acquisition, preprocessing steps, change detection method, and evaluation results (Table 1).