esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Formulation
### Full-Duplex Single-Antenna Energy Harvesting  
In the energy harvesting phase, the relay node uses a single antenna for the energy harvesting, and the signal received by the relay node R can be expressed as  
\[y_{r2}=h_{1}\sqrt{P_{s}}x_{2}+n_{r}. \tag{12}\]  
The energy received by the relay node R can be expressed as  
\[E=\eta\alpha TP_{s}|h_{1}|^{2}. \tag{13}\]Therefore, the transmission power of the relay node can be expressed as  
\[P\frac{\eta\alpha TP_{s}|h_{1}|^{2}}{(1-\alpha)T}\frac{\eta\alpha P_{s}|h_{1}|^{2 }}{(1-\alpha)}. \tag{14}\]  
In the information transmission phase, the single-antenna energy harvesting system and the dual-antenna energy harvesting system are identical for the relay node regarding the full-duplex information transmission performance, in which one antenna is used to receive signals from the source node and the other is used for decoding. Therefore, signal-to-interference-plus-noise ratio (SINR) can be expressed as follows in regard to the single-antenna energy harvesting system  
\[\text{SINR}=min\left\{\frac{(1-\alpha)}{\eta\alpha\big{|}f\big{|}^{2}},\frac{ \eta P_{s}|h_{1}|^{2}\big{|}g\big{|}^{2}\alpha}{N_{0}(1-\alpha)}\right\}. \tag{15}\]  
The objective function of energy efficiency can be expressed as  
\[\text{max}\xi=\frac{(1-\alpha)log_{2}\bigg{(}1+min\left\{\frac{(1-\alpha)}{ \eta\alpha\big{|}f\big{|}^{2}},\frac{\eta P_{s}|h_{1}|^{2}\big{|}g\big{|}^{2} \alpha}{N_{0}(1-\alpha)}\right\}\bigg{)}}{\beta P_{s}+P_{c}}. \tag{16}\]
## 1 Introduction
### 1.1 EU Regulatory Environment  
Directive (European Union (EU)) 2024/3019 of 27 November 2024 concerning urban wastewater treatment [5] sets the new binding objective for the energy neutrality of municipal WWTPs. Member States shall ensure that, at the national level, the total annual energy from renewable sources, as defined in Article 2(1) of Directive (EU) 2018/2001 [6] generated by urban WWTPs treating a load of 10,000 population equivalent (PE) and above,is equivalent to at least 70% of the total annual energy used by such plants by 31 December 2040, and 100% of the total annual energy used by such plants by 31 December 2045 [5].  
The energy neutrality of WWTPs will also help to reduce the EU energy dependence, one of the objectives expressed in the European Commission (EC) \"Repower EU\" Plan [7]. It is also in line with Directive (EU) 2018/844 [8] and with Directive (EU) 2018/2001 [6], in which urban wastewater treatment sites are qualified as 'go-to' areas for renewables, which means a location that is recognized as particularly suitable for the installation of plants to produce energy from renewable sources [5].  
Special attention must be paid to identify and utilize the potential for biogas production from sludge, the better use of the available surfaces in urban WWTPs for solar energy production, waste heat recovery and use in WWTPs or in district heating systems [5], and producing other renewable energy sources, especially focusing on green hydrogen.  
The EC Delegated Regulation setting out detailed rules for the production of renewable liquid and gaseous transport fuels of non-biological origin [9] sets the rules for determining when electricity used for the production of green hydrogen can be considered fully renewable. Renewable hydrogen producers like municipal WWTPs are encouraged by this EU Regulation to produce the amount of renewable electricity required for the production of green hydrogen in renewable electricity generation facilities they own themselves [9].  
The Hydrogen strategy for climate-neutral Europe, adopted in July 2020 by the EC [10], defines a strategic objective to install at least 40 GW of renewable hydrogen electrolyzers by 2030 and produce up to 10 million tons of renewable hydrogen in the EU.  
The 'Repower EU' Plan [7] invites the immediate acceleration of clean energy transition and the diversification of the EU gas supply, and thus to minimize the impact of rising energy prices. Therefore, the European biogas and biomethane sectors are committed to delivering 35 billion m\({}^{3}\) of sustainable biomethane by 2030, supporting the EU in achieving climate goals and energy security. The integration of biomethane and green hydrogen in Municipal wastewater treatment plants will contribute to achieving these ambitious EU goals.
## 2 Results
### Identification of Transcription Factors  
Transcription factors (TFs) are important for the growth, development, and reproduction of organisms [50]. Understanding the metabolic response of TFs to the treatments with blue light and SA helps further investigate the molecular mechanism regulating both the astaxanthin and lipid synthesis. Plants improve their tolerance to a variety of stresses with the involvement of TFs, which in turn are affected by a variety of molecular factors [51]. In our study, a total of 54 TF families were identified in _H. pluvialis_ based on the transcripts assembled de novo using iTAK. The top 10 TF families included C2H2 with 94 unigenes, TRAF (90 unigenes), SNF2 (75 unigenes), C3H (67 unigenes), GNAT (59 unigenes), SET (54 unigenes), MYB (48 unigenes), zn-clus (46 unigenes), MYB-related (44 unigenes), and HMG (42 unigenes). Results of the comparative transcriptomic analysis of the control and the experimental groups of _H. pluvialis_ treated with blue light irradiation revealed a total of 503 putative genes assigned in 47 families encoding TFs in response to blue light exposure (Table S4). Compared with white light, the expression levels of genes encoding two TF families (i.e., SNF2 and zn-clus) were significantly increased on day 2 of the treatment with blue light irradiation.  
Varied compositions of the dominant TFs were also revealed under different treatments of _H. pluvialis_ (Table 2). For example, there was a lack of GNAT and MYB families of TFs on day 2 and day 7 after the treatment with blue light, respectively. On day 7 with the addition of SA, the TFs of the jumonji family were not identified. Studies have shown that these TFs play important roles in various biological processes. For example, zinc finger proteins (e.g., C2H2) regulate various cellular processes [52], TRAF induces the activation of several kinase cascades--eventually leading to the activation of signal transduction pathways and regulation of various cellular processes [53], and SNF2 regulates the structure of chromatin and participates in biological processes, such as cell differentiation and immune response [54]. As the potential regulators in the astaxanthin synthesis of _H. pluvialis_ under blue light, the functions of these families of TFs identified in our study warrant further investigations based on combined genetic, molecular, and biochemical approaches.
## 2 Host Directed Therapy
### Inhibition of M. tuberculosis Induced Inflammation and Host Cell Death
#### 2.4.4 TNF and TNF-Mediated Signaling  
Further downstream of intracellular mediators or regulators of cell death, inflammation and cytokine release, there are more direct targets amenable to therapeutic interventions. These include the cytokines themselves. Biologicals targeting TNF\(\alpha\) such as infliximab and adalimumab (monoclonal antibodies) or etanercept (TNF receptor fusion protein) may be used to limit exacerbated pathology and improve antibiotic activity. These substances are restricted for use in combination with antibiotics (adjuvant treatment) since TNF\(\alpha\) is essential for protective immunity and granuloma integrity. Monotherapy with infliximab and other anti-TNF antibodies led to reactivation of latent TB [90].  
However, when combined with anti-TB drugs, TNF neutralization enhanced _M. tuberculosis_ clearance and reduced lung pathology [91]. A clinical study performed with adjuvant etanercept in patients with pulmonary TB and HIV showed a trend towards improved outcome when the TNF blocker was added to the antibiotic regimen [92].  
TNF signaling is also the main target of other HDT candidates such as thalidomide, phosphodiesterase inhibitors or Janus kinase (JAK) inhibitors. Thalidomide has potent anti-inflammatory properties which led to successful application of the drug in cases where anti-TB or HIV treatment triggered hyperinflammatory syndromes such as paradoxical reactions or immune reconstitution syndrome (IRIS). A general application for adjuvant treatment approaches may be hampered due to side effects as seen in a study performed with children suffering from TB meningitis [93]. Phosphodiesterase (PDE) inhibitors seem to be more promising for broad application in TB patients. PDEs degrade cyclic AMP (cAMP), a second messenger negatively regulating TNF levels. Decreased levels of cAMP stimulate TNF\(\alpha\) secretion, thus making PDE inhibitors interesting HDT targets. Among the five PDE subtypes, targeting PDE4 seems to be the most promising option in TB with adjuvant use of inhibitors leading to improved outcome in several animal models [94; 95]. A phase II clinical trial with the PDE4 inhibitor CC-11050 is ongoing (Table 1).
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 2 Materials and Methods
### Feature Extraction
#### 2.4.4 Visible Satellite CNR vary ratio  
Visible satellite CNR vary ratio is also an essential feature in time sequence. Figure 8(a) shows the variation trend of GPS-12 CNR with different devices when IO transition under the same scenario. Figure 8(b) shows the trend of all visible satellite CNR using Mate 9. The variation trend of satellite CNR is close to that of the number of satellites in Figure 8 when IO transition. It is worth noting that the satellite CNR varies widely over time in both outdoor and indoor scenarios. It is not advisable to extract features by relying on the variation trend of single satellite CNR.  
Figure 8: The changes in the number of visible satellites under different devices when switching between indoor and outdoor scenarios.  
Figure 9: The variation of satellite CNR when an indoor and outdoor transition in the same scenario.  
To avoid the influence mentioned above of the variation trend of single satellite CNR, we mined and utilized the variation trend of all visible satellites as features in time sequence. We defined a function \(f_{(x,y)}\) that represents the variation of the satellite CNR in two moments as follows:  
\[f_{(x,y)}=sign\Big{(}cnr_{i}^{x}-cnr_{i}^{y}\Big{)}\Big{|}i=0,1,\cdots,n \tag{10}\]  
where \(cnr_{i}^{x}\) denotes the satellite CNR at current time t, \(cnr_{i}^{y}\) denotes the satellite CNR at time _t-k_. \(k\) denotes the window size (e.g., 1, 2, 3). In the experiment, we compared the influence of the feature under different window size on indoor and outdoor detection.  
We extracted the variation ratio of all visible satellite in \(\mathbf{G_{u}}\) as features. We defined the formulas for the descending ratio \(\mathrm{P(down)}\), rising ratio \(\mathrm{P(up)}\) and flat ratio \(\mathrm{P(hold)}\) of all visible satellite CNR at different times as Equations (11), (13) and (14), respectively.  
\[\mathrm{P(down)}=\frac{n-\sum\limits_{i=1}^{n}sign\Big{(}f_{(x,y)}+1\Big{)}}{n} \tag{11}\]  
\[\mathrm{P_{1}}=\frac{\sum\limits_{i=1}^{n}abs\Big{(}sign\Big{(}f_{(x,y)}\Big{)} \Big{)}}{n} \tag{12}\]  
\[\mathrm{P(up)}=\mathrm{P_{1}}-\mathrm{P(down)} \tag{13}\]  
\[\mathrm{P(hold)}=1-\mathrm{P(down)}-\mathrm{P(up)} \tag{14}\]
# In-flight Wind Field Identification and Prediction of Parafoil Systems
## 6 Conclusions  
In this paper, we studied a combination method of wind field identification and prediction for autonomous homing of parafoil systems. First, a wind field identification method was studied to identify the ambient wind field according to the flight dynamics of parafoil systems in windy environments. And then, based on atmospheric motion, an average wind field model was proposed to predict the lower-altitude wind field based on the identification results. At last, simulations were conducted to show the effectiveness of the proposed methods. The results show that the average wind field identification method can precisely identify the wind field. Meanwhile, the average wind field prediction model can reasonably predict the average wind field variations at different attitudes. From the practical points of view, the proposed methods provide critical wind information for precise autonomous homing of parafoil systems without velocity measurement sensors that could be difficult to install.  
For future work, we will further validate the proposed methods in fight tests of real parafoil systems. Also, we will consider more practical environmental factors, e.g., high-frequency wind fluctuations, and use a general computation intelligence aided design framework for design.  
[PERSON] and [PERSON] conceived and designed the research; [PERSON] designed and performed the simulation experiment; [PERSON] contributed to the theory studies; All authors wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Anhui Province, China (no.1808085 MF183), the University Natural Science Research Key Project Foundation of Anhui Province, China (no. KJ2016A169), the Talents Program Foundation of Anhui Science and Technology University, China (no. ZRC2014444), the National Natural Science Foundation of China (no. 61973172) and the Academy of Finland (no. 315660). The authors would like to express their thanks to [PERSON] and [PERSON] for their help with data preparation for this study.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Total Phenolics, Anthocyanins and Antioxidant Activity Determinations
#### 2.5.2 Total Polyphenol Content Analysis  
Total polyphenol content (TPC) of samples was determined using Folin-Ciocalteau reagent's method [16]. Briefly, 0.2 mL of extract prepared as described in Section 2.5.1 was mixed with 0.4 mL of Folin-Ciocalteau reagent, 4 mL of redistilled water and 2 mL of 15% sodium carbonate. After 60 min of solution incubation in a dark place at the ambient temperature, the absorbance determinations were taken with a UV-1650C spectrophotometer (Schimadzu, Kyoto, Japan) at 765 nm against mixed reagents. A five-point standard curve was prepared based on 59 mM stock solution of gallic acid in 80% methanol (y = 0.3938 + 0.107, R\({}^{2}\) = 0.9995). The results were expressed as mg gallic acid equivalents (GAE) per 100 g of confure sample.
## 5 Conclusions  
In this study, the FairMOT model was used to detect and track underwater fish using Multiple Object Tracking (MOT) technology. The performance of two backbone models, DLA-34 and YOLOv5s, was evaluated and compared.  
Figure 8: ID detection comparison for FairMOT-DLA-34 and FairMOT-YOLOv5s.  
Figure 7: Comparison of test results between FairMOT-DLA-34 and FairMOT-YOLOv5s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **IDF1** & **MOTA** & **MOTP** & **IDSW** \\ \hline FairMOT-DLA-34 & 11.0\% & 44.1\% & 0.393 & 1 \\ FairMOT-YOLOv5s & 14.6\% & 43.8\% & 0.400 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test results of FairMOT.  
The experimental results showed that the FairMOT-DLA-34 model performed well in IDF1, MOTA, and MOTP metrics, but had relatively high error rates in ID switches (IDSW). In contrast, the FairMOT-YOLOv5s model showed relatively better performance in IDF1 and IDSW and satisfactory results in MOTA and MOTP. Especially, the FairMOT-YOLOv5s model showed superior performance in detection accuracy and ID tracking.  
Comparing the performance of the two models through video samples, the FairMOT-YOLOv5s model showed better performance in accurately detecting and tracking fish IDs in the same frame. In contrast, the FairMOT-DLA-34 model showed relatively lower performance in fish detection and ID tracking in specific frames.  
Through this study, the strengths and weaknesses of the two backbone models of the FairMOT model for underwater fish detection and tracking were clearly identified. The FairMOT-YOLOv5s model is more suitable when ID tracking performance is important, while the FairMOT-DLA-34 model may be advantageous when considering detection accuracy and overall tracking performance.  
Future research can contribute to the development of more efficient and accurate underwater fish detection and tracking systems by conducting additional experiments in various environments and comparing them with other backbone models.  
S.-H.L. and M.-H.O.: writing--original draft, data curation, software, and visualization. S.-H.L.: writing--review and editing. M.-H.O.: conceptualization, validation, writing--review and editing, and project administration. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2022-0-01000, Development of 5G Edge Computing SW for Flexible Healthcare to support Mobile Customized Medical Services).  
Not applicable.  
All data, models, and codes generated or used during the study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
# Metrics for Polyphonic Sound Event Detection
## 4 Metrics for Polyphonic Evaluation  
### Precision, Recall and F-Score  
Precision (\(P\)) and Recall (\(R\)) were introduced in [36] for information retrieval purposes, together with the \(F\)-score derived as a measure of _effectiveness_ of retrieval. They are defined as:  
\[P=\frac{TP}{TP+FP},\quad R=\frac{TP}{TP+FN} \tag{1}\]  
Precision and Recall are the preferred metrics in information retrieval, but are used also in classification under the names _positive prediction value_ and _sensitivity_, respectively. Adapting these metrics to polyphonic data is straightforward based on the definitions of the intermediate statistics. \(F\)-score is calculated based on \(P\) and \(R\):  
\[F=\frac{2\cdot P\cdot R}{P+R} \tag{2}\]  
or, alternatively, based on the intermediate measures:  
\[F=\frac{2\cdot TP}{2\cdot TP+FP+FN} \tag{3}\]  
Segment-based \(P\), \(R\) and \(F\) are calculated based on the segment-based intermediate statistics, using instance-based averaging or class-based averaging. The calculation is illustrated in Figure 4, panel A. Event-based \(P\), \(R\) and \(F\) are calculated the same way from the event-based intermediate statistics. The calculation is illustrated in Figure 5, panel A. Similarly defined event-based \(P\), \(R\), \(F\) are also used for evaluating polyphonic music transcription, with events being the musical notes [21].  
The advantage of using \(F\)-score for evaluating sound event detection performance is that it is widely known and easy to understand. Its most important drawback is that the choice of averaging is especially important. Because the magnitude of F-score is mostly determined by the number of true positives, in instance-based averaging large classes dominate small classes. In class-based averaging it is necessary to ensure presence of all classes in the test data, to avoid cases when recall is undefined (when \(TP+FN=0\)). Any dataset of real-world recordings will most likely have unbalanced event classes, so the choice of averaging will be always an important factor.
## 5 Conclusions  
According to the several seismic intervals in this research, the identified gas accumulation is shown by bright spot anomalies. One advanced method is by correlating seismic amplitude anomalies with AVO analysis. The AVO result shows that the gas in the target area is classified as AVO class three. It shows a decrease in amplitude and an increasing angle, moreover, it has a negative intercept and gradient. SQp and SQs are proposed as new AVO attributes to distinguish facies and fluid, furthermore, it is successfully generated. These attributes compare to Scaled Poisson's Ratio attribute. Through numerical simulation and application in a field northern Malay basin, SQp and SQs demonstrates a better performance than Scaled Poisson's Ratio attribute in facies and hydrocarbon prediction. By comparing new attributes to Scaled Poisson's Ratio, the new attributes are less ambiguous in facies and hydrocarbon prediction. The proposed attributes can predict facies and the presence of possible hydrocarbon sand effectively, which can reduce exploration risk. As the new attributes, SQp and SQs values are validated with logs data; therefore, it shows the regularity of the changing. The well log shows that the gas sand areas have high resistivity and low acoustic impedance. The SQp AVO attribute provides the best separation of facies, while the SQs AVO attribute is a suitable indicator for fluid identification. The proposed AVO attributes methods are suitable to be used as other options in facies and fluid determinations. Moreover, the new technique is potentially examined in any field and to extend the application in other cases, e.g., the carbonate reservoir.  
Conceptualization, T.K.R.; methodology, T.K.R. and M.H.; software, T.K.R.; validation, T.K.R., M.H., L.A.L., and Z.A.R.; formal analysis, T.K.R.; investigation, T.K.R., M.H., L.A.L., and Z.A.R.; resources, T.K.R. and M.H.; data curation, T.K.R.; writing--original draft preparation, T.K.R.; writing--review and editing, T.K.R., M.H., L.A.L., and Z.A.R.; visualization, T.K.R. and L.A.L.; supervision, M.H. and L.A.L. All authors have read and agreed to the published version of the manuscript. This research was funded by UTP fundamental research grant with grant number 015 LCO-224. The authors would like to thank PETRONAS Malaysia for providing the data for this study. We would like to express our appreciation to Centre of Seismic Imaging and Geoscience department Universiti Teknologi PETRONAS colleagues for supporting us throughout the project. Huge gratitude to UTP fundamental research grant with cost centre 015 LCO-224 for granting this research. We acknowledge to CGG Company for providing Hampson Russell software licensing, Schlumberger Company for providing Petrel software licensing, and Ikon Science for providing Rokdoc software licensing. The authors declare no conflict of interest.