esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 3 Results
### Tuning of Thresholding Filter  
From the previous phase, it seems that every model has learned its task, but their output is not yet a binary mask. To achieve this, a thresholding operation has to be performed to select a suitable value of \(p_{min}\). Its tuning is conducted in a range of values from 0.01 to 0.9 and different models over the 300 images test set.  
For every \(p_{min}\) and model, the values (over the whole test set) of Precision, Recall and F1 are computed and reported in Figure 5. It can be seen (Figure 6) that the network with 128 filters performs better in terms of output mask. The value around 69% for the F1 index means a minimized number of discrepancies between the BOSS algorithm's prediction and the mask. This value is the highest one if the best achieved F1 values are considered among all the networks. Because of this, a model with 128 filters and \(p_{min}=0.04\) was chosen for the BOSS algorithm design.  
Precision behaviour for low \(p_{min}\) values is due to an increasing number of FPs because as the \(p_{min}\) decreases, the number of FPs becomes greater if compared to TPs. For higher \(p_{min}\), the number of FPs tends to 0, while TPs tend to a finite value, and the 100% Precision is achieved. This maximum value of Precision still does not mean that the prediction is good.  
A similar discussion can be conducted for the Recall: as the \(p_{min}\) decreases, the risk of predicting FNs decreases with respect to TPs. This is why Recall grows. For higher \(p_{min}\), the increasing number of FNs causes the Recall value to fall towards 0%.  
A test with a 256 initial filters model was conducted, but performances do not show relevant improvements with respect to the 128 initial filters model, while the required storage memory and computational time increment is not negligible (and also undesired). This is the reason why they are not reported in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Image size & 512 \\ Learning Rate & \(10^{-4}\) \\ Regularization Factor & \(10^{-5}\) \\ Dropout Rate & 0.25 \\ Kernel Size & 3 \\ Kernel initializer & _’he\_normal’_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: U-Net configuration parameters.
## 4 Experimental Results
### Dense Model Performance Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Air\_conditioner & 0.75 & 0.97 & 0.84 & 195 \\ Car\_horn & 0.97 & 0.86 & 0.91 & 91 \\ Children\_playing & 0.69 & 0.68 & 0.68 & 205 \\ Dog\_bark & 0.76 & 0.77 & 0.76 & 182 \\ Drilling & 0.93 & 0.86 & 0.89 & 202 \\ Engine\_idling & 0.91 & 0.94 & 0.93 & 216 \\ Gun\_shot & 0.96 & 0.63 & 0.76 & 87 \\ Jackhammer & 0.89 & 0.93 & 0.91 & 187 \\ Siren & 0.96 & 0.90 & 0.93 & 199 \\ Street music & 0.78 & 0.75 & 0.77 & 183 \\ Accuracy & 0.84 & 0.84 & 0.84 & 1747 \\ Macro avg & 0.86 & 0.83 & 0.84 & 1747 \\ Weighted avg & 0.85 & 0.84 & 0.84 & 1747 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification performance of Dense model on UrbanSound8K dataset. the confusion in categories such as children playing and dog bark suggests that these sounds share overlapping characteristics, which complicates their classification. The lower recall in the gunshot category, despite a high precision score, indicates that while the model is good at identifying gunshots when they occur, it misses a significant number of gunshots, reflecting a need for further refinement in this area. Overall, Dense layers performed well in classifying urban sounds within Urban-Sound8K dataset. Most categories were classified correctly, with some minor confusions. Notably, the \"children playing\" category exhibited more confusion with other categories. These confusions indicate that certain sounds may share similar characteristics with others, making them more challenging for the model to distinguish. While the model's overall performance is positive, further improvements could be made to enhance accuracy in specific categories.
## 4 Analysis of the Existing Datasets
### UMN Dataset  
The UMN Dataset [84] is a collection of 11 short videos depicting a panicking crowd's abnormal movements. The dataset includes three distinct scenes, comprising two outdoor and one indoor environment. Each video in the dataset concludes with a sudden escape of individuals, depicted as walking in normal directions. It is important to note that the ground truth, or the labeled information, is only available at the frame level.  
The UMN dataset is significant in abnormal behavior detection as it provides a unique opportunity to study the dynamics of panicking crowds. The dataset is particularly useful for developing anomalous actions, such as walking on grass and vehicles moving on the sidewalk. Items are essential for designing surveillance systems that identify and respond to potential safety hazards in crowded spaces.  
In conclusion, the UMN dataset is a valuable resource for researchers and practitioners in abnormal behavior detection. The dataset provides a realistic representation of panicking crowds and can be used to test and improve the performance of abnormal behavior detection algorithms in crowded environments. Figure 5 shows examples of both normal and abnormal frames.  
Figure 4: Examples of abnormal frames from the UCSD dataset are presented. Abnormal frames are shown in the first row from Ped1, and abnormal frames are shown in the second row from Ped2.
## 3 Discussion
### Participant's Perspectives on Political Efforts to Address AMR  
The quantitative findings discussed above were reflected by the open-ended question responses that led to a better understanding of current regulations and the perspectives of politicians and stakeholders. HIC stakeholders and politicians emphasized the need for a more holistic approach integrating One Health activities. There was particular mention of addressing AMR in the field of animal husbandry, food safety, and wastewater management among HIC participants. Participants from LMICs emphasized capacity building and awareness campaigns. Interestingly, many participants working at a local governmental level mentioned that local authorities were not in the position to contribute to the AMR problem. This is a misconception, given that community-based interventions have previously been shown to reduce AMR prevalence and antimicrobial consumption [25; 26]. Notably, the participants unanimously agreed that the main challenges to mitigate the AMR burden include the promotion of knowledge and awareness of the general public and stakeholders. There was a consensus on stronger regulations for antimicrobial use in all sectors among all stakeholders and civil service workers. Overall, these results provided novel insights that regional politicians demonstrated a lack of belief in regional interventions that could help mitigate the AMR burden even though political interventions had proved successful previously [27].  
There is also a significant knowledge gap on the economic burden that AMR will cause in the future. The financial cost of AMR in the European Union alone is estimated at EUR 1.1 billion per year [28]. One representative from an LMIC shared an interesting perspective that the regulation of access to antimicrobials is not feasible since it would have a big impact on the economy of the country. The rapid emergence of AMR challenges the treatment for infections with multidrug-resistant pathogens, leaving last-resort therapeutics as the last treatment option. Antimicrobial consumption data from Vietnam has previously shown that the purchase of last-resort drugs accounted for a relatively high proportion of health budgets [29]. According to our survey responses, LMIC politicians and stakeholders might not be aware of the financial burden AMR might cause in the near future, which should, therefore, be implemented as part of awareness and knowledge campaigns.
# Acquisition of Weak BDS Signal in the Lunar Environment
## 3 Results  
### Observation Results  
In this section, the lunar probe receptor characteristics with a combination of main and side lobe signals are evaluated. Based on the simulation scenario and setup described above, we analyzed the number of signals received by the lunar vehicle, the received signal power, and the carrier-to-noise ratio when using single GNSS and combined GNSS systems, respectively. The performances of the signals from the main and side lobe were also compared. The receiver sensitivity was set to 15 dB-Hz or 10 dB-Hz, and the number of visible GNSS satellites and the geometric dilution of precision (GDOP) values were determined based on these two thresholds.
###### Abstract  
Digital-based artificial neural network (ANN) machine learning is harnessed to reduce fiber nonlinearities, for the first time in ultra-spectrally-efficient optical fast orthogonal frequency division multiplexed (Fast-OFDM) signals. The proposed ANN design is of low computational load and is compared to the benchmark inverse Volterra-series transfer function (IVSTF)-based nonlinearity compensator. The two aforementioned schemes are compared for long-haul single-mode-fiber-based links at 9.69 Gb/s direct-detected optical Fast-OFDM signals. It is shown that an 80 km extension in transmission-reach is feasible when using ANN compared to IVSTF. This occurs because ANN can tackle stochastic nonlinear impairments, such as parametric noise amplification. Using ANN, the dynamic parameters requirements of the sub-ranging quantizers can also be relaxed compared to linear equalization, such as the reduction of the optimum clipping ratio and quantization bits by 2 dB and 2-bits, respectively, and by 2 dB and 2 bits when compared to the IVTSF equalizer.  
optical Fast-OFDM; neural networks; nonlinearity compensation; optical fiber communications 2019 9 4038; doi:10.3390/app9194038
## 4 Discussion
Moreover, the decrease in the concentration of petroleum hydrocarbons in the soil may be due to the transfer of contaminants from the soil to the plant [85; 86; 87]. This assumption is confirmed by the results of chromatographic analyses of the root and aboveground parts of _Echinacea purpurea_ (stem + leaves). The experiments conducted showed the presence of petroleum hydrocarbons (TPHs and PAHs) in the roots and shoots of purple coneflower after the experiments. It is likely that during the treatment process of the studied soils, some of the analyzed contaminants were adsorbed on the root surface [20; 88], and some were accumulated in the above-surface part of the plant via phytoextraction [18; 87; 89]. It should also be noted that some plants--for the proper conduct of the process of photosynthesis--use the ability to retain hydrocarbons in the tissues of the roots. This helps provide the energy needed for survival and is considered a self-defense strategy [87]. In addition, some contaminants can undergo phytoevaporation, which involves moving the contaminants into a volatile state [12; 90]. It is also extremely important that \(\gamma\)-PGA was used as an additive to the microbial consortia in the study. Systems inoculated with \(\gamma\)-PGA-enhanced microbial consortia showed the highest \(\%\) biodegradation of TPHs and PAHs, as well as the highest biomass production. \(\gamma\)-PGA provides an easily digestible source of carbon and energy for micro-organisms and _Echinacea purpurea_, which significantly affects the growth of the plant and the development of root zone micro-organisms, providing greater efficiency in the treatment of contaminated land. Analyzing the effectiveness of the phytoremediation processes carried out in the systems studied, one cannot but mention the effect of the concentration and age of the contaminant on the efficiency of the treatment process. Remediation of aged soils (drilling waste) occurs much more slowly than soils contaminated by oil spills. This is related to the stronger adsorption of contaminants on the surface and in the porous space of aged soil [17; 91]. Moreover, the study showed that the process of cleaning the soil from the tested contaminants proceeded at different rates depending on the type and dose of the toxicant. It was found that in soil containing a higher diversity of contaminants, the cleaning process was slower compared to soil from an oil spill site. Toxicological tests carried out using bioindicators belonging to three taxonomic groups (bacteria, crustaceans, plants) confirmed the effectiveness of ongoing phytoremediation of soils contaminated with petroleum hydrocarbons in systems supported by inoculation with microbial consortia and microbial consortia with \(\gamma\)-PGA.
# A Review: Using Multiple Templates for Molecular Imprinted Polymer: Is It Good?
## 3 A Review of MT-MIPs That Have Been Created
### Template
#### 3.1.2 Organic Compounds
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Monomer**} & \multirow{2}{*}{**Template**} & \multicolumn{2}{c}{**Qmax (mg/g)**} & \multicolumn{2}{c}{**IF**} & \multirow{2}{*}{**Reference**} \\ \cline{3-4} \cline{6-7}  & & **ST-MIP** & & **MT-MIP** & & **MT-MIP** \\ \hline \multirow{2}{*}{2-VP} & \multirow{2}{*}{Diclofenac} & \multirow{2}{*}{324.8} & 5.643 & \multirow{2}{*}{7.185} & 1.195 & 113,116; 119 \\ \cline{5-7}  & & & - & 5.2 & \\ \hline
2-VP & Fenoprofen & 38.8 & 3.655 & 1.9 & 1.174 & [116; 121] \\ \hline MAA & Cephalexin & - & 0.0026 & 14.7 & 3.4 & [75; 120] \\ \hline \hline \end{tabular}  
2-VP: 2-vinylpyridine; 4-VP: 4-vinylpyridine; IF; imprinting factor; MAA: methacrylic acid; MT-MIP, multi-template molecular imprinted polymer; Qmax: maximum adsorption capacity; ST-MIP, single-template molecular imprinted polymer; VP: vinyl pyridine.  
\end{table} 
Table 4: Comparison of single-template and multi-template molecularly imprinted polymers generated using drug templates. activities of dencichine. Even though extraction and purification of individual bioactive components from traditional Chinese medicines and natural products are tough, they is necessary for drug development and pharmaceutical analysis. SPE with MIPs could be particularly useful to extract compounds due to high selectivity, mechanical/chemical stability, and inexpensive preparation. Indeed, this technique has been used to selectively extract dencichine from _P. notoginseng_[132]. As shown in Table 5, the most widely used functional monomer when using organic compounds as templates is MAA. It is preferred because it interacts with neutral or basic target molecules. In fact, MAA can develop hydrogen and ionic bonds with basic molecules and operate as an H\({}^{+}\) donor and acceptor with strong dipole-dipole interactions [79]. AM and 4-VP have also been used as functional monomers. The most widely used crosslinker is EGDMA, which can increase resistance to heat, solvents, and abrasion in the copolymerisation process [133]. In addition, EGDMA can form polymers with good flexibility; EGDMA creates a space between the polymer chains that facilitates analyte molecule accessibility and attachment to the functional groups of EGDMA. The hydrophilicity and polarity of EGDMA provide it with a high affinity for the aqueous phase, and it does not exhibit steric constraints when used as an adsorbent [134].
# Improved Acoustic Emission Tomography Algorithm Based on Lasso Regression
## 7 Conclusions  
This study proposed a new method of AE tomography based on Lasso regression, named LASSO algorithm. The proposed theory was validated by both simulations and experiments and compared with the traditional SIRT algorithm. The results are summarized as follows:  
(1) Simulations were carried out by using the newly proposed theory for three models. The LASSO algorithm can obtain relatively acceptable identified results for structures with approximately less than 30% of the damage area in the model. Further, the velocity distribution of typical concrete damage types suitable for non-destructive testing of the hole, central concentration, and edge deterioration were identified with the conventional SIRT algorithm. LASSO could obtain a relatively accurate description of the damage range, and there was little impact in the healthy area with few shadows, thus obtaining accurate results. It was proven that the Lasso regression algorithm is feasible in AE tomography for velocity distribution.  
(2) In comparison with the SIRT in the experiment, the Lasso regression algorithm obtained acceptable results of velocity distribution with fewer event data, and the approximate range of defects could be reconstructed. At the same time, the results of the velocity distribution with premium quality were more precise and distinguishable than the result of the SIRT, because the shadows in reconstructed image became invisible.  
Conceptualization, X.Q. and Y.K.; methodology, X.Q. and Y.K.; validation, X.Q., Y.K., K.O. and K.N.; formal analysis, X.Q.; data curation, X.Q.; writing--original draft preparation, X.Q.; writing--review and editing, Y.K., K.O. and K.N.; visualization, X.Q.; supervision, Y.K., K.O. and K.N. All authors have read and agreed to the published version of the manuscript.  
The authors declare no conflict of interest.
## 5 Guidance and Control Algorithm  
### Rendezvous  
The ideal goal of DRSK is to achieve zero relative velocity at a specific distance. However, orbit control errors result from the orbital prediction, ADC, and thrust errors when a real satellite is in operation. After DRSK is complete, it has a random relative distance and relative velocity within the specified range. To prevent the deputy from drifting away and settling in the projected circular orbit (PCO), it is necessary to perform rendezvous after station-keeping is complete.  
If DRSK satisfies the requirements, the deputy is located within the relative position of 1 km (3\(\sigma\)) in the positive along-track direction and has a relative velocity of less than 0.12 m/s in a random direction. Because the magnitude of the relative velocity is obtained by multiplying the relative distance by the orbital angular velocity (\(\omega=\sqrt{\mu/a^{3}}\)), where \(\mu\) is the gravitational standard constant and \(a\) is the semi-major axis, the reference trajectory is set as a PCO with a relative orbital radius of \(1/\omega\) times the initial relative speed. Rendezvous is usually performed by fuel-optimal control based on the HCW equation. In this study, sliding mode control was additionally applied to control the errors from the nonlinear dynamics and disturbances. When operating a satellite, the relative orbit state is obtained from the DGPS algorithm, utilizing the GPS raw data from the chief. Therefore, a strategy is required to complement these errors in the guidance trajectory.
## 3 Results and Discussion
### Determination of Relevant Visual Information  
#### 3.2.1 Participant Demographics  
Twenty-nine experienced male sprayer operators participated in this phase of the research, 73% of which were from Manitoba, 17% from Saskatchewan, and 10% from Alberta. Among these 29 operators, 26 of them completed the study online, while the remaining operators completed the experiment in person. Approximately 50% of the respondents were within the age bracket of 41-65 and self-reported to have more than 6 years of experience operating a sprayer. Almost 30% of respondents were between the ages of 16 and 25, with two-thirds of these individuals having less than 2 years of experience operating a sprayer (Figure 5). Two of the participants reported that they had not operated a sprayer within the 12-month period prior to participating in this study. Only 10% of respondents were custom applicators, with the remaining respondents being farm owners (and therefore spraying only their own crops). Self-propelled sprayers were operated by 88% of respondents, with the remainder of the respondents using pull-type sprayers. All but one participant used auto-steer navigation technology when spraying.
# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 5 Sustainable/Green Computing in Fog/Edge
### Energy Sustainability  
With IoT services pervading all aspects of our lives, energy-constrained IoT devices spark concern while considering sustainability. The massive number IoT sensors and actuators deployed necessitate a continuous and persistent power supply. As the IoT node size reduces, the size of the battery also decreases. In light of the current trend to enhance IoT device functionality, formulating sustainable solutions for confronting power constraints is essential [158].  
Numerous research efforts have been oriented towards energy harvesting for self-sufficient IoT functioning, alongside tackling IoT security issues. The energy consumed by digital and smart gadgets has become concerning. Energy harvesting from renewable energy sources can power a myriad of IoT sensors [159; 160]. With IoT sensors having a battery that lasts for a limited time, frequent charging or replacement is not viable at all times. Hence, energy harvesting from renewable energy such as kinetic, solar, thermal, etc., seems plausible [161]. Moreover, energy harvesting this issue can be handled by deploying an efficient data transmission policy [162], with almost 80% of a sensor's energy being depleted on data transmission. Even though efforts for enriching the energy efficiency of IoT systems are underway, they hardly match the proliferating pace of IoT services/dependence [158].