esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Olive mill wastewater (OMW) is a highly relevant agricultural waste stream in the Mediterranean area. More than 30 million m\({}^{3}\) arise each season worldwide. Tunisia has a share of about 8% [1]. The challenges related to the management of OMW were one of the drivers for the conversion of olive mills to more efficient 2-phase decanters that separate the oil from a wet pomace instead of OMW and dry solids. However, millions of cubic meters of OMW arise every season in olive oil-producing countries. They are responsible for vast amounts of GHG emissions, contamination of surface and groundwater, and smelly nuances [2; 3; 4].  
The anaerobic (co-)digestion of OMW has been researched since the 1980s, however with limited industrial applications [5; 6; 7]. Much like in the case of cow and swine manure in Central Europe, anaerobic digestion is a suitable vaporization path for this important waste stream. The technology is fully developed and optimized on an industrial scale.  
There are several reasons why, with the exception of a few large-scale plants in Italy, Spain, and Greece, there is no large-scale implementation of anaerobic technology for the valorization of OMW as the main or co-substrate in agricultural biogas digesters: (1) The seasonal and uneven occurrence of OMW over the year; (2) the weather dependent properties of the substrate; (3) the potential inhibition of the biodegradation by high molecular weight polyphenols or potassium and with its high organic load (COD can exceed 100 kg\(\cdot\)m\({}^{-3}\)); and (4) the risk of over-acidification due to the low initial pH. The general feasibility of the process is well-researched and documented. To be a viable substrate and energy source, the challenges of process stability, yield, co-fermentation, and digestate utilization need further investigation [8]. Only an in-depth understanding of inhibition effects, optimal operating parameters, maximum biogas potentials, and holistic digestate concepts can give plant operators and planners the necessary certainty that large-scale plants will continue to operate stably when OMW is added.  
OMW carries enormous potential regarding nutrients, water, and energetic valorization. Cow slurry is now an incremental part of the nutrient supply for agriculture in Germany. Around 3000 biogas plants produced 4.0 TWh of electricity in 2016 [9]. A similar scenario with OMW in the Mediterranean is conceivable if the process control and inhibition issues are well understood and managed.  
The effect of a newly developed polyphenol extraction process for selective removal of low molecular weight polyphenols inhibiting anaerobic degradation was investigated in biomethane potential batches and an anaerobic continuous fixed bed digester. All OMW-derived waste streams of the process (sediment, ultrafiltration retentate, swim layer, flushing water, and depolymerhenolized OMW), as well as a slurry of organic market waste, were investigated. The phytotoxic properties of the resulting digestate were quantified using the germination index.
# Novel Reliable and Energy-Efficient Routing Protocols for Wireless Sensor Networks
## 7 Conclusions  
In this paper, we introduced a protocol which maximizes the minimum residual energy subject to a predefined reliability constraint. We optimized the energy balancing of WSNs by a new protocol (OREBP) to achieve uniform energy consumption by maximizing an entropy-like measure. As demonstrated by the simulation results, the proposed protocols improve the energy efficiency of WSNs by maximizing the life span, subject to a reliability constraint. They also improve the energy consumption distribution among the nodes of the network. The performance of the proposed protocols is compared to the performance of PEGASIS. As shown, the proposed protocols still have a high degree of complexity. We defined four performance parameters: life span, data loss percentage, the maximum number of hops, and the complexity. The user has the choice to control the performance by striking an optimal trade-off among these parameters. To achieve this requires a solution of a complicated optimization problem.  
Conceptualization, M.A. and J.L.; Methodology, M.A. and J.L.; Software, M.A.; Validation, M.A. and J.L.; Formal Analysis, J.L.; Investigation, M.A.; Resources, M.A.; Data Curation, M.A.; Writing-Original Draft Preparation, M.A.; Writing-Review & Editing, J.L.; Visualization, M.A.; Supervision, J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The research reported in this paper was supported by the Higher Education Excellence Program of the Ministry of Human Capacities in the frame of Artificial Intelligence research area of Budapest University of Technology and Economics (BME FIKP-MI/SC).  
We declare that no conflict of interest.
## 3 Results
### Metamodel Selection
#### 3.4.2 Comparative Experiments with Different Models  
To verify the superiority of the ensemble model proposed in this paper, it was compared with the TCN (Temporal Convolutional Network) and ConvLSTM (Convolutional LSTM) models through comparative experiments. These two models are extensively used and studied in the field of time series prediction, particularly excelling in modeling sequential data. TCN is renowned for its parallelism and capability in modeling long-term dependencies [38; 39]. ConvLSTM combines the strengths of CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory), making it well suited for time series prediction tasks [40; 41]. Since the prediction of a building heat load is also a time series  
Figure 11: The predictive results of the three basic models in residential group four.  
Figure 10: The predictive results of the three basic models in office group one.  
prediction task, these two models can be considered as appropriate benchmark models. The RMSE and MAE values of each model's predictive results across the eight groups are shown in Table 7. The residential group is abbreviated as Rg and the office group is abbreviated as Og.  
By observing and analyzing Table 7, it can be seen that the ConvLSTM model performs the worst among all residential groups, but its performance is relatively acceptable in office groups, even better than the TCN model. Particularly in office group one, the ConvLSTM model's performance is slightly inferior to that of the proposed model. In contrast, the TCN model performs better than ConvLSTM in residential groups but still falls short of the proposed model in most groups. The proposed model's predictive results, in terms of RMSE and MAE, are superior to both TCN and ConvLSTM across all eight groups. In residential groups, the model performs best in group four, while in office groups, the best performance is observed in group one.  
The average RMSE of the ConvLSTM model is 38.918, with an average MAE of 30.757. For the TCN model, the average RMSE is 35.022, with an average MAE of 27.034. In comparison, the proposed model has an average RMSE of 22.744 and an average MAE of 17.282. This indicates a reduction in the average RMSE and MAE by 35.1% and 36.1%, respectively, compared to TCN, and a reduction by 41.6% and 43.8%, respectively, compared to ConvLSTM. This outcome underscores the significant advantage of the proposed ensemble model in predictive performance. This superiority may stem from the model's ability to effectively integrate the strengths of different models and accurately capture the complex temporal patterns of the heat load.  
In summary, the proposed ensemble model demonstrates outstanding performances across various aspects, providing an effective and robust solution for short-term building heat load single-step prediction problems.
## 4 Experimental Results
### Comparison of Proposed CNN Model with Other Models  
To compare the proposed method with the current common methods, experiments of identical strategies but using DTM and SVM were conducted, with the Mel spectrogram as the input image. The SVM process was performed with the LIBSVM toolbox [41], with RBF as the kernel function and a kernel function with a parameter coefficient g of \(2^{-27}\), and a penalty factor coefficient c of \(2^{6}\)[22]. The DTM utilized the TreeBagger function, and NumTrees is set to 50 [42]. These recognition results are shown in Figure 8.  
In Figure 8, the symbols of \(1^{\#}\), \(2^{\#}\), \(3^{\#}\), \(4^{\#}\), \(5^{\#}\), \(6^{\#}\) denote six different pipelines as shown in Table 1, respectively. This figure highlights that the recognition accuracies of the DTM with ponding volume of six pipelines are between 88.25% and 94.67%, the recognition accuracies of the SVM between 90.89% and 96.89%, and the recognition accuracies of the CNN between 98.33% and 99.44%. This proves that the CNN recognition model is more stable and has a higher accuracy than the other two models.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 5 Results and Discussion
### Comparison with Other Research and Future Research  
Recent advancements in lung nodule classification have predominantly centered on deep learning techniques, encompassing ensemble models, transfer learning, attention mechanisms, and capsule networks. In our study, simple CNNs were constructed as the baseline, to concentrate on exploring various metrics and data augmentation strategies. Leading entries on the LUNA16 leaderboard have achieved recall above 90% at 1/8 FPs per scan. In our research, the top performance of combination obtained more than 96% at 1/5 FPs, far outperforming the State-of-the-Art (SOTA) model. Our research also delved into the practical challenges of implementing these models in real-world clinical settings, where inconsistencies in imaging protocols and equipment variability can adversely affect performance. The insights from this study aim to inform ongoing developments in diagnostic technologies, ensuring they are sufficiently robust for use across varied healthcare scenarios, thereby improving patient outcomes through the early and precise identification of critical conditions such as lung nodules. Our future research aims to expand investigation into data augmentation techniques and their synergistic effects, while also deepening our understanding of the interplay between different performance metrics within the framework of SOTA architectures and broader application contexts. This ongoing research is anticipated to enhance the proficiency of deep learning models in medical imaging, thereby revolutionizing diagnostic methodologies and improving the effectiveness of medical treatments.
###### Abstract  
This paper introduces a novel algorithm for designing a low-pass filter (LPF) and a microstrip Wilkinson power divider (WPD) using a neural network surrogate model. The proposed algorithm is applicable to various microwave devices, enhancing their performance and frequency response. Desirable output parameters can be achieved for the designed LPF and WPD by using the proposed algorithm. The proposed artificial neural network (ANN) surrogate model is employed to calculate the dimensions of the LPF and WPD, resulting in their efficient design. The LPF and WPD designs incorporate open stubs, stepped impedances, triangular-shaped resonators, and meandered lines to achieve optimal performance. The compact LPF occupies a size of only 0.15 \(\mathrm{Ag}\times 0.081\) kg, and exhibits a sharp response within the transmission band, with a sharpness parameter of approximately 185 \(\mathrm{dB/GHz}\). The designed WPD, operating at 1.5 GHz, exhibits outstanding harmonics suppression from 2 GHz to 20 GHz, with attenuation levels exceeding 20 dB. The WPD successfully suppresses 12 unwanted harmonics (2 nd to 13 th). The obtained results demonstrate that the proposed design algorithm effectively accomplishes the LPF and WPD designs, exhibiting desirable parameters such as operating frequency and high-frequency harmonics suppression. The WPD demonstrates a low insertion loss of 0.1 dB (S\({}_{21}\) = 0.1 dB), input and output return losses exceeding 30 dB (S\({}_{11}\) = \(-\)35 dB, S\({}_{22}\) = \(-\)30 dB), and an output ports isolation of more than 32 dB (S\({}_{23}\) = \(-\)32 dB), making it suitable for integration into modern communication systems.  
design algorithm; low-pass filter; power divider; neural network +
Footnote †: journal: _arxiv
# Quantification of Liver Fibrosis--A Comparative Study
## 4 Results and Discussion
### Results from Earlier Works Employing DIA and HAI Systems
The current group continued their research in reference [29] and concluded that in the univariate analysis the first clinical decompensation was associated with a fibrosis rate of increase based on CPA (\(p\) < 0.001), with an Ishak stage fibrosis rate of increase (\(p\) = 0.001), with advanced donor age (\(p\) = 0.007) and with histological hepatitis C (\(p\) = 0.013). Moreover, the only factor associated with clinical decompensation was the rate of fibrosis increase according to CPA (\(p\) = 0.001). New studies evaluated the relationship between the CPA measured by microscopy image analysis, Ishak score and HVPG. [PERSON] et al. [30] showed that the CPA was a better histological correlate with HVPG (r = 0.62, \(p\) < 0.001) than the Ishak stage, it had a greater numerical change when HVPG was low and resulted in further quantitation of fibrosis in cirrhosis. Respectively in a later work [31], Ishak 5 and 6 stages of CPA correlated with HVPG (\(p\) = 0.04), but due to the wide range of output values, a greater sensitivity for distinguishing \"early\" from \"late\" severe fibrosis/cirrhosis was indicated. Besides, CPA was a unique and independent predictor of HVPG \(\geq\) 10 mm of mercury (mmHg), with the endpoint being the clinical liver decompensation and CPA providing better discrimination than HVPG or Ishak stage. [PERSON] et al. [32] found that the CPA was associated with future clinical decompensation (\(p\) = 0.017). Notably, CPA increased significantly across the Laennec scoring system, with mean values in cirrhosis stages carrying a correlation value of \(p<0.001\). This difference was also present in the Kumar classification with a correlation of \(p=0.003\). To conclude, CPA positively correlated with the septal width (r = 0.622, \(p<0.001\)) and negatively with the size of nodules (r = \(-0.620\), \(p<0.001\)), while there was no correlation with the number of nodules. More importantly, CPA's AUROC when predicting clinical decompression was at 0.909. In [PERSON] et al. [33], CPA Sirius red (CPAs) along with CPA trichrome (CPAt) were well correlated with the METAVIR stage, with CPAs showing a better performance and correlating with seven of the eight total serum markers (\(p<0.001\)). Later, [PERSON] et al. [34] found a good correlation between the CPA stage values and the METAVIR fibrosis grading system (\(p<0.001\)). In conclusion, both research works presented similar sensitivity values, with CPA presenting a significantly higher predictive ability for the HCC development than the METAVIR stage.
# Phase Therapy: Consider the Past, Embrace the Future
## 3 Strategies for Phage Therapy
### Phage Engineering  
Genetic engineering technology has greatly promoted life science research and application, including phage therapy. In addition to the phage cocktail, another solution to overcome the narrow lysis spectrum of a phage and inhibit the bacterial resistance is phage genetic engineering. Phage engineering can increase phage therapeutic potential, by expanding or changing lysis spectrum or/and delivering exogenous genes and proteins. One of the most common methods is to engineer genes encoding receptor-binding proteins (RBPs) in the tail fibers or spikes of phages to obtain a broad spectrum [74]. In a previous study, the chimeric phage is obtained by homologous recombination of the anti-receptor gene orf18 of _Streptococcus thermophilus_ phage DT1 and siphovirus MD4, and this chimeric phage acquired the host range of phage MD4 [75]. [PERSON] et al. constructed a chimeric _E. coli_ phage T2 with broader lysis spectrum by embedding the long tail fiber genes of phage IP008 into phage T2 through homologous recombination [76]. In 2015, [PERSON] et al. established a yeast-based high-throughput platform to change the phage host range through the modular swapping of phage tail components, and the obtained synthetic phages were able to effectively kill their new target bacteria for bacterial population editing [41]. In combination with bioinformatics analysis and the RBP crystal structure analysis, [PERSON] et al. constructed a synthetic phage library containing random sequence RBP and screened a polyvalent phage with an extended host range [77]. Meanwhile, [PERSON] et al. identified host-range-determining regions (HRDRs) of the T3 phage and synthesized the phages through the site-directed mutagenesis of these regions to alter host range and suppress bacterial resistance [78]. All these make it possible for us to exploit ordinary phages to construct novel phages that can killspecific pathogens. [PERSON] and [PERSON] engineered _E. coli_ phage T7, as a phage carries to express the enzyme dispersin B capable of degrading the extracellular polymers of bacterial biofilm, and this engineered enzymatic phage reduces the bacterial biofilm cell number by 2 orders of magnitude, compared with the wild-type phage [31]. The \(\lambda\) phage-based vaccine is capable of inducing antigen-specific cellular and humoral immune responses [35]. Engineered M13 phages with two functional peptides are able to significantly reduce _Chlamydia trachomatis_ infection in both HeLa and primary endocervical cells [32]. A successful case of using a modified phage cocktail for curing a 15-year-old patient with cystic fibrosis with a diffused _M. abscessus_ infection was reported [13]. Furthermore, phages have been used to deliver toxins [79], chloramphenicol [80], photosensitizing agents [81] and functional genes (such as CRISPR-Cas, see the next section for details) for therapeutic purposes.
## 2 Materials and Methods
### Contact Angle  
Contact angle (\(\theta\)) is the angle from the solid-liquid interface through the liquid interior to the gas-liquid interface at the junction of solid, liquid, and gas [29]. Contact angle is an important parameter for characterizing a surface and predicting its behavior in applications involving wettability and adhesion [30,31]. The surface is hydrophilic when \(\theta\) is lower  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trade Name** & **Registration Holder** & **Main Ingredient** & **Type** & \begin{tabular}{c} **Possible** \\ **Properties** \\ \end{tabular} \\ \hline \multirow{2}{*}{Silwet L-77} & \begin{tabular}{c} Momentive, \\ USA \\ Lion Brand, \\ Singapore \\ \end{tabular} & \begin{tabular}{c} Polyethoxylated \\ heptamethyl trisloxane \\ Coconut oil \\ diethanolamide \\ Fatty alcohol \\ polyoxyethylene ether \\ \end{tabular} & \
\begin{tabular}{c} Organosilcone \\ surfactant \\ Nonionic \\ surfactant \\ Emulsifiable \\ oil \\ \end{tabular} &
\begin{tabular}{c} Spreader \\ Detergent \\ Detergent \\ Penetrant \\ Anti-drift \\ additive \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of selected adjuvants listed.  
than 90\({}^{\circ}\); otherwise, it is hydrophobic [32]. Here, the contact angle formed by a pesticide droplet with an additive on the surface of tea was measured from side images of the droplet profile, which was monitored as a function of time using the JC2000D3K contact measuring instrument (POWEREACH, Shanghai, China). The instrument, experimental sample, and measurement method are shown in Figure 2. Before the measurement, the tea leaves were cut into strips of 5 mm \(\times\) 300 mm (avoiding the main veins) and fixed flat on the glass slide to make them shorter in the optical path direction, which was necessary to avoid light blocking caused by uneven samples.  
The microsyringe was placed above the leaf sample, and a sufficient volume of the liquid droplet (3-5 \(\upmu\)L) was formed at its tip by adjusting the control knob. The sample platform was then slowly raised, and the measurement started once the droplet was gently deposited on the leaf sample. After droplet deposition, the images were captured by a charge-coupled device (CCD) camera at 0, 5, 15, 30, 60, and 90 s. JC2000D software was used to calculate contact angles through the five-point fitting method (Figure 2). The five points included two endpoints (a, b) on the contact line and three points (b, c, d) on the arc line, ensuring that the baseline coincided with the contact line.  
Each measurement was repeated three times by setting other droplets at different positions on the same strip of leaf sample. The specific dripping positions are marked in the upper right corner of Figure 2. The contact angle values of the 18 mixture samples (four adjuvants at four concentrations, bifenthrin diluent, and pure water) on tea leaves at two growth stages (fresh bud and young leaf) were obtained. Each mixture sample had 18 contact angle values on each surface, including six recording time points (0, 5, 15, 30, 60, and 90 s) for three repetitions.
## 2 Materials and Methods
### Statistical Analyses  
All statistical analyses were performed using SPSS (version 25.0; SPSS, Inc., Chicago, IL, USA) and were expressed as means with standard deviations (\(\pm\)SD). Moreover, the 95% confidence intervals for mean values and relative differences (i.e., in percentages) between baseline (BA) and post-intervention were also calculated. Statistical significance was set at \(p<0.05\). The Shapiro-Wilk, Mauchly's, and [PERSON]'s tests were used in order to verify the normality, sphericity, and variance homogeneity of the sample data, respectively. In order to compare symmetry index values between the unilateral and bilateral groups, the non-parametric Mann-Whitney U test was used, while the Wilcoxon signed-rank test was used to assess the impact of training (pre- vs. post-training). The independent \(t\)-tests were used to compare baseline values in relative peak torque and relative peak power of knee extension and flexion. A two-way ANOVA (2 [unilateral; bilateral training] \(\times\) 2 [pre; post-training]) was used to examine the changes in relative peak torque and peak power of the dominant and non-dominant knee extension and flexion. When a significant main effect or interaction was found, post-hoc tests with Bonferroni correction were used to analyze the pairwise comparisons. The magnitude of mean differences was expressed with standardized effect sizes. Thresholds for qualitative descriptors of Hedges g were interpreted as \(\leq\)0.20 \"small\", 0.21-0.79 \"medium\", and \(>\)0.80 as \"large\".
## 4 Result and Discussion  
The investigational results of the planned cluster-forming technique in the WSN to improve the QoS and energy parameters are analyzed here. The proposed system was simulated using the NS2 simulator. A 1000 \(\times\) 1000 m region with 100 nodes with a transfer limit of 250 m for the simulation was considered. The proposed method was simulated within 50 s. Here, the nodes were clustered and the cluster head was selected by the planned method. This section discusses the effectiveness of the planned strategy for several metrics such as packet delay, delivery rate, energy consumption, efficiency, and network longevity. The simulated measurement of our planned strategy is shown in Table 3. In this simulation, the IEEE 802.11 wireless protocol was used.