esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Data Preprocessing  
#### 3.1.1 Farmland Parcel Maps  
Parcel-based crop mapping requires known farmland parcel geometries that are accessible in most regions of Europe [32] (including the Dijon study area). In the absence of geometry data (as in the Zhaosu study area), farmland parcel maps were generated from high-spatial-resolution images (the GF-1 images used for Zhaosu) using the method detailed in our previous study [33]. First, roads, waterlines, and terrain lines derived from DEMs were used to spatially split the GF-1 images of study areas into multiple subareas. Then, in each subarea, the trained (by manually labeled samples of parcel boundaries) boundary-semantic-fusion convolutional neural network (BSNet) [33] was utilized to automatically generate binary raster maps of parcel boundaries. Finally, automatic postprocessing (including the vectorization of binary parcel boundaries, topology checks on parcel geometries, and the removal of small polygons) and the manual correction of parcel polygons were applied to generate precise farmland parcel maps.
## 5 Conclusions and Outlook  
The work highlighted in this review demonstrates the greater potential and the POC applicability of SERS spectroscopy as a bioanalytical technique for both qualitative and quantitative infectious disease diagnostics using biomarkers ranging from nucleic acids to proteins to metabolites. Intrinsic SERS enables the monitoring of the molecular structural changes of biomarkers during disease progression, and Raman labels- and molecular recognition elements-assisted indirect SERS detection of biomarkers offer versatile POC diagnostic potentials. Enabled by the excellent optical properties of plasmonic nanomaterials, the high sensitivity, selectivity, and multiplexing capability of SERS-based bioassays, collectively, offer characteristics of a powerful diagnostic tool that can be integrated into field-transferable devices for early and rapid infectious disease detection.  
While many of the SERS-based techniques demonstrate excellent and versatile biomarker detection capabilities, it is crucial to reconsider the sensory characteristics and practical needs for their clinical and/or POC applications. In order for SERS-based sensing platforms to reach the front line of the POC detection, technique standardization and validation is highly important. The performance of the SERS-based diagnostic assays mainly depends on the quality of plasmonic nanomaterials and the nano-bio functional interface. Therefore, we have to address two main aspects of the nanoparticles-based SERS diagnostic systems to further advance them for POC diagnosis; (i) both the SERS signal stability and the SERS enhancement factor depend on the quality of the plasmonic NPs. Therefore, it is vital to ensure the batch-to-batch consistency and reproducibility of plasmonic nanoparticles-based SERS substrates for reliable and quantitative SERS-based biomarker detection. (ii) In addition, the careful fabrication of the functional nano-bio interface is critical to obtain high diagnostic specificity and accuracy. Thus, synthetic approaches for stable and reproducible surface functionalization of SERS substrates with molecular recognition elements and surface passivation ligands are needed. It will help eliminating non-specific biological interactions hence reducing SERS spectral interference and ensuring colloidal stability for stable SERS signal acquisition. An appreciable amount of effort is currently being invested by the research community to investigate these fundamental aspects of the nanoparticle-based SERS diagnostic assays [7; 17; 70; 73; 125; 126]. Combining the current exponential improvement in both fundamental chemistry and device engineering, the widespread use of SERS-based diagnostics for infectious diseases at the POC and clinical setting is only a very few years away from us.  
Writing--original draft preparation, L.H. and A.G.; Conceptualization, writing--review and editing, A.S.D.S.I.  
This work was funded by University of North Carolina at Charlotte.  
The authors declare no conflict of interest.
## 4 Demonstration
### Demonstration of Advantages
The results show that when \(0.5\sigma\) falls below the A1 line (when \(x_{ij}\in[0,\;0.5]\)) or \(0.5\sigma\) falls below the A3 line (when \(x_{ij}\in[0.5,\;1]\)), \(min(R(a_{P}|[x]),\;R(a_{B}|[x]),\;R(a_{N}|[x]))=R(a_{B}|[x])\), and the smaller the \(\sigma\) value (i.e., the more insufficient the information obtained or the cost for undesirable decision results is high), the larger the dominant value and dominant range (\(x_{ij}\) interval corresponding to the dominant value) of decision \(a_{B}\), so it is more flexible and reasonable to make three decisions (i.e., \(POS(C)\), \(NEG(C)\), \(BND(C)\)) than to make two decisions (i.e., \(POS(C)\), \(NEG(C)\)). To sum up, the proposed approach has two advantages. First, the proposed approach uses DL ontology to automatically generate attribute values, which is more intelligent and convenient than the traditional ways of obtaining attribute values. Second, it is based on multi-attribute three-way decisions, which are more flexible and reasonable than the traditional multi-attribute two-way decisions, especially when the information obtained for decision making is insufficient. When the information obtained is sufficient, by assigning \(\sigma=0.5\), the multi-attribute three-way decisions will transform into multi-attribute two-way decisions. Figure 8: Loss value functions for A1, A2, and A3.
# New Isolated Autochthonous Strains of _S. cerevisiae_ for
###### Abstract  
Many commercial strains of the _Saccharomyces cerevisiae_ species are used around the world in the wine industry, while the use of native yeast strains is highly recommended for their role in shaping specific, _terroir_-associated wine characteristics. In recent years, in Poland, an increase in the number of registered vineyards has been observed, and Polish wines are becoming more recognizable among consumers. In the fermentation process, apart from ethyl alcohol, numerous microbial metabolites are formed. These compounds shape the wine bouquet or become precursors for the creation of new products that affect the sensory characteristics and quality of the wine. The aim of this work was to study the effect of the grapevine varieties and newly isolated native _S. cerevisiae_ yeast strains on the content of selected wine fermentation metabolites. Two vine varieties--Regent and Seyval blanc were used. A total of 16 different yeast strains of the _S. cerevisiae_ species were used for fermentation: nine newly isolated from vine fruit and seven commercial cultures. The obtained wines differed in terms of the content of analyzed oenological characteristics and the differences depended both on the raw material (vine variety) as well as the source of isolation and origin of the yeast strain used (commercial vs. native). Generally, red wines characterized a higher content of tested analytes than white wines, regardless of the yeast strain used. The red wines are produced with the use of native yeast strains characterized by higher content of amyl alcohols and esters.  
wine; _Saccharomyces cerevisiae_; biodiversity; aroma compounds; volatile compounds +
Footnote †: journal: Applied Sciences
## 4 Example Analysis  
### Basic Parameters of Power Grid  
A case study of a city power grid in 2019 was carried out to verify the effects of different penetrance on the benefits of the multi-energy coordinated control strategy, as well as the effects of different energy structures on the benefits. The power supply capacity proportion of the power grid is shown in Table 1.  
Figure 2: Flow chart of a solving multi-energy coordination problem based on the alternating direction multiplier algorithm.  
New energy output and new energy penetration rate are shown in Figure 3. The new energy penetration rate (total new energy generation installed capacity/total installed generation capacity) is 18.4%.  
Output analysis of each type of unit is shown in Figure 6.  
As is shown in Figure 4, thermal power units are the main power units of the power grid, and their output accounts for more than half of the total load. The regular changes in load are mainly borne by thermal power units. After 8:00 a.m., the load gradually climbs to the peak and stays at a high level. At this time, all eight thermal power units are started up, and all of them reach the rated output at 9:00 a.m. Considering the flexibility of hydropower generation, the hydropower units cannot always maintain a high output level; hydropower closely follows the load change after 10:00 a.m. Therefore, its main function is to respond to the immediate change of load after the thermal power units reach the rated output, and according to the calculation results each hydropower plant has completed the power limit during the dispatching period, which is determined by the economic optimization as the objective function of the model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Power Source Type** &
\begin{tabular}{c} **Wind** \\ **Power** \\ \end{tabular} & **PV** & **Coal** & **Gas** & **Hydraulic** & **Total** \\ \hline Capacity (MW) & 976.0 & 700.0 & 2474.0 & 497.6 & 800.0 & 5447.6 \\ \hline The percentage & 17.92\% & 12.85\% & 45.41\% & 9.13\% & 14.69\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power supply capacity ratio of the power grid.  
Figure 3: Urban power grid load and new energy prediction curve.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 2 Related Work
### Leveraging the Acoustic Wave by Smartphone  
Leveraging the acoustic wave via smartphones for localization has been well addressed at present. Most of them leverage the low speed of the acoustic wave compared to wireless signals, such as the mechanism of TOA (time-of-arrival) and TDOA (time-difference-of-arrival) [23; 24; 25]. [PERSON] et al. [26] address the fundamental problem of distinguishing between a driver and passenger using a mobile phone. This acoustic approach has the phone send a series of customized high frequency beeps via the car stereo. Beepbeep [27], which leverages COTS (Commercial off-the-shelf) Mobile Devices, is a pure software-based solution and uses only a speaker, a microphone, and some forms of device-to-device communication. It detects the distance between two smartphones with high accuracy. More importantly, this scheme is extremely lightweight. Inspired by the idea of relative positions of peer devices nearby as unique physical constraints on the possible location of a smartphone, [PERSON] et al. [28] propose a peer assisted localization approach that leverages much more accurate distance estimate through acoustic ranging. [PERSON] et al. [29] raise an acoustic ranging based localization scheme. The basic idea of this scheme is to perform localization using acoustic ranging with respect to multiple landmarks in known locations. Furthermore, [PERSON] et al. [30] present a solution for achieving high speed 3D continuous localization for phone-to-phone scenarios. Their basic approach uses acoustic cues based on time-of-arrival and power level. Besides, there are other locaization technologies leveraging acoustic signals, e.g., [31; 32; 33].
## 11 Limitations  
Human health hazards, including radiation exposure and physiological impacts such as bone and muscle loss, cardiovascular issues, and psychological challenges, limit space missions. There are technological constraints in developing reliable life support, propulsion, and navigation systems. Cost and financial limits make ambitious missions difficult. Resource management, optimizing energy use, and limiting the impact on the environment such as space debris are all examples of sustainability restrictions.  
Environmental monitoring and sustainability research are critical. Developing tools for precise tracking and mitigation of space debris, assessing the environmental impact of space activities, and applying sustainable practices for resource management and energy consumption are vital to the mission's sustainability over time. Planetary science and astrobiology research is critical for mission planning. Further research into the origins of life, conditions that can be inhabited in our solar system, and the search for extraterrestrial life will inform future mission targets and techniques, while limiting the risk of contamination. International collaboration is required to pool resources, knowledge, and financing for space missions and to make the supply chain for space missions more effective.
# An Abstraction Technique for Verifying Shared-Memory Concurrency +
## 3 Formalisation
### Programs
#### 3.2.9 Operational Semantics
: _Any command \(C\) is locked if \(\mathsf{locked}(C)\) holds, where \(\mathsf{locked}\subset\mathsf{Cmd}\) is defined as follows, by structural recursion on \(C\):_  
\[\mathsf{locked}(C)\triangleq \begin{cases}\mathsf{true}&\text{if }\ C=\mathtt{ inatom}\,C^{\prime}\\ \mathsf{locked}(C_{1})&\text{if }\ C=C_{1};C_{2}\\ \mathsf{locked}(C_{1})\vee\mathsf{locked}(C_{2})&\text{if }\ C=C_{1}\parallel C_{2}\\ \mathsf{locked}(C^{\prime})&\text{if }\ C=\mathtt{inact}\,C^{\prime}\\ \mathsf{false}&\text{otherwise}\end{cases} \]  
The rules \(\leadsto\)-par-l and \(\leadsto\)-par-r for parallel composition allow a thread to make an execution step only if the other thread is not locked, thereby preventing thread interference while executing atomic programs. One might ask whether this handling of locks could not potentially lead to deadlock scenarios, for example by encountering configurations \((C_{1}\parallel C_{2},h,s)\) during runtime for which both \(\mathsf{locked}(C_{1})\) and \(\mathsf{locked}(C_{2})\) hold. However, we will later see and prove that no such deadlocks can be reached, given that one starts with an initial configuration that contains a user program. Furthermore, the specification-only language constructs do not affect the state of the program (not the heap nor the store) and are essentially handled as if they were comments. Notice however, that commands of the form action_do\(C\) are first reduced to inact\(C\) before \(C\) is being executed. This is done for technical reasons, as this makes it more convenient to later establish a simulation relation between execution steps of programs and processes. The semantics of programs has the following preservation properties. **Lemma 3**. : _Program execution preserves basicality and wellformedness:_  
1. _If_ \(\mathsf{basic}(C)\) _and_ \((C,h,s)\leadsto(C^{\prime},h^{\prime},s^{\prime})\)_, then_ \(\mathsf{basic}(C^{\prime})\)_._
2. _If_ \(\mathsf{wf}(C)\) _and_ \((C,h,s)\leadsto(C^{\prime},h^{\prime},s^{\prime})\)_, then_ \(\mathsf{wf}(C^{\prime})\)_._
## 4 Discussion
### Environmental Observations and Future Market Conjunctures  
The selection of feature extractors (CNN vs. MLP) and lookback periods (16 vs. 28 periods) plays a crucial role in the agent's ability to forecast market trends and adjust portfolio allocations accordingly. CNNs, known for their capacity to capture localized patterns in sequential data, demonstrated superior performance in identifying shifts in volatility and market trends. For instance, the _DQN-CNN-16_ configuration, despite not using technical indicators, achieved Sharpe and Sortino ratios identical to the market portfolio (0.8 and 1.12, respectively) while also capturing an excess of 12% in annualized returns. This performance highlights the CNN's ability to effectively recognize upward trends and maintain a favorable return-to-risk ratio. The elevated Sortino ratio, in particular, suggests that CNN-based agents capitalized on volatile growth periods while successfully minimizing downside risk. In comparison, the EWP, with a Sharpe ratio of 0.69 and Sortino ratio of 0.94, exhibited a more conservative profile, which CNN-based agents often outperformed in more dynamic market conditions.  
Figure 5: Performance comparison of RL agent portfolios and baseline models.  
In contrast, MLP-based agents, particularly those using shorter lookback periods (e.g., 16 periods), struggled to effectively anticipate market downturns. For example, _PPO-MLP-16_ experienced higher maximum drawdowns, reflecting its difficulty in adjusting to rapid market declines. These MLP-based agents frequently underperformed relative to the EWP in risk-adjusted returns, as indicated by their lower Sharpe ratios and comparable Sortino ratios. For instance, _DDPG-MLP-16_ yielded a Sharpe ratio of 0.66 and a Sortino ratio of 0.91, showing moderate performance but an inability to capitalize on bullish periods as effectively as CNN-based models. The market portfolio itself often outperformed these MLP configurations during periods of heightened volatility.  
In summary, CNNs--especially with longer lookback periods--were more adept at recognizing and reacting to periods of volatility, allowing for higher allocations to riskier, high-reward assets like \({}^{\Lambda}\)IXIC and \({}^{\Lambda}\)GSPC. On the other hand, MLPs, particularly those with shorter lookback windows, exhibited more conservative behavior but were less effective at adapting to shifting market conditions. Overall, CNN-based models delivered stronger risk-return performance, as evidenced by superior Sharpe and Sortino ratios, outperforming MLP configurations and baseline portfolios in many cases.
# Confirmation of Final Bolt Tightening via Deep Learning-Based Image Processing
## 3 Overview of Proposed Method  
Our proposed bolt tightening judgment system consists of four modules. The modules are a module for extracting bolts from input images, a module for extracting markers from extracted images, a module for making a tightening judgment from extracted markers, and a module for generating the output of the judgment results (Figure 1). Since some bolts are difficult to detect when photographed with a digital camera depending on the shooting angle, this study uses a video camera as input data to capture a single bolt from multiple angles.
In addition, a research group at the Institute of Automation, Chinese Academy of Sciences, proposed the Informer model for timing prediction based on Transformer. Despite the model's poor parallel processing capabilities, difficulty in stabilizing training, and high computational requirements, it validates the potential value of the Transformer-like model in capturing individual long-term dependencies between the outputs and inputs of long-series time series. Based on the aforementioned information, the objective of this study was to construct a water quality prediction model using a Linear model approach. The modeling process of a Linear model is efficient, as it does not require complex computations, and remains fast even when dealing with large datasets. The proposed model aims to address the aforementioned issues, and it was tested using the Huangyang Reservoir as a case study. Furthermore, it is anticipated that the model can be extrapolated to encompass the entire country. An extensive analysis was conducted on the water quality of the Huangyang Reservoir, which included the calculation of three key water quality monitoring indicators: DO, Turbidity, and pH. Next, to enable real-time predictions of the three water quality indicators, we employed three deep learning techniques for comparison--ARIMA, LSTM, and Informer--to construct an optimal model capable of accurately predicting water quality. A Linear-based model was chosen as the baseline model for comparison with the deep learning model. Calculations were subsequently conducted using data collected from 1 January 2022, at 0:00, to 12 July 2023, at 10:00, encompassing a duration of approximately one and a half years. The comparison of model prediction accuracy was based on the evaluation metrics of mean absolute error (MAE) and mean square error (MSE).
## 4 Multi-Objective Evolutionary Algorithm for the PP Problem
### Position Update Operator  
This operator aims to improve a path by utilizing consecutive rotation points. A path (\(p=[S=p_{0},p_{1},p_{2},...,p_{n},p_{n+1}=T]\)) contains a starting point (\(S\)), a target point (\(T\)), and \(n\) rotation points. Based on the position and velocity of rotation point (\(p_{i}\)), a new rotation point (\(p_{i}^{new}\)) can be acquired with Equation (7): \(i=1,2,...,n\). The general mechanism of the position update operator is presented in Figure 7.  
Figure 5: Smoothness operator.  
Figure 6: Shortness operator.  
\[\begin{array}{l}v^{f}=p_{i-1}-p_{i}\\ v^{l}=p_{i+1}-p_{i}\\ v=r_{1}\times v^{f}+r_{2}\times v^{l}\\ p_{i}^{new}=p_{i}+v.\end{array} \tag{7}\]  
The generated velocity (\(v\)) should satisfy the constraint \([Min\_v,Max\_v]\). In other words, the vector of \(v\) should be located in the rectangular region generated by the minimum velocity (\(Min\_v\)) and maximum velocity (\(Max\_v\)). Furthermore, the newly generated rotation point (\(p_{i}^{new}\)) should be in the free workspace. If point (\(p_{i}^{new}\)) is not feasible, its new value is adjusted with Equation (8) until point (\(p_{i}^{new}\)) is feasible. Since new segments may collide with obstacles, we should examine the feasibility of the two new segments (\(p_{i-1}^{new}p_{i}^{new}\)) and (\(p_{i}^{new}p_{i+1}\)). If there is a collision, the position of rotation point (\(p_{i}^{new}\)) needs to be adjusted with Equation (8). In this operator, \(r_{1}\), \(r_{2}\), and \(r_{3}\) are random numbers ranging from 0 to 1:  
\[p_{i}^{new}=r_{3}\times p_{i}+(1-r_{3})\times p_{i}^{new}. \tag{8}\]
###### Abstract  
As the number of mobile users and video traffics grow explosively, the data rate demands increase tremendously. To improve the spectral efficiency, the spectrum are reused inter cell or intra cell, such as the ultra dense network with multi-cell or the cellular network with Device-to-Device communications, where the co-channel interferences are brought and needs to be suppressed. According to the time-frequency energy sensing to the communication signals, the desired signal and the interference signal have different energy concentration areas on the time frequency plane, which provide opportunities to suppress the co-channel interference with time varying filter. This paper analyzes the time-frequency distributions of the Gaussian pulse shaping signals, discusses the effect of the analyzing window length on the time-frequency resolution, exploits the equivalence between the time frequency analysis at the baseband and at the radio front end, and finally reveals the advantages of the proposed masking threshold constrained time varying filter based co-channel interference mitigation method. The pass region for the linear time varying filter is generated according to the time-varying energy characteristics of the I/Q separated 4-QAM pulse shaping signals, where the optimum masking threshold is obtained by the optimum-BER criterion. The proposed co-channel interference suppression method is evaluated in aspect of BER performance, and simulation results show that the proposed method outperforms existing methods with low-pass or band-pass filters.  
Keywords:time-frequency energy sensing; STFT; Choi-Williams distribution; time-varying filter; co-channel interference +
Footnote †: journal: Sensors