esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Extension to Multiple SWIPT AF Relaying Systems: Performance Analysis
### Approximation SWIPT Relay to AF Relay
#### 4.5.4 Approximated Channel Capacity  
The channel capacity in [PERSON]'s sense is a fundamental performance metric, representing the maximum achievable data rate under which reliable communication is possible. For the direct SD link without relaying, the normalized channel capacity is given by  
\[\begin{split} C_{\text{sd}}=E[\log_{2}(1+\gamma_{0})]& =\int_{0}^{\infty}\log_{2}(1+\gamma)f_{\gamma_{0}}(\gamma)d\gamma \\ &=\frac{1}{\ln(2)}\exp\biggl{(}\frac{1}{\gamma_{0}}\biggr{)}E_{1} \biggl{(}\frac{1}{\gamma_{0}}\biggr{)}.\end{split} \tag{46}\]  
For \(\gamma_{tot}\in\{\gamma_{\text{id}},\gamma_{\text{cb}}\}\), the average channel capacity for the relaying system is expressed as  
\[\begin{split} C&=\frac{1}{R+1}E[\log_{2}(1+\gamma _{tot})]\\ &=\frac{1}{R+1}\int_{0}^{\infty}\log_{2}(1+\gamma)f_{\gamma_{tot} }(\gamma)d\gamma\end{split} \tag{47}\]  
where the factor \(\frac{1}{R+1}\) accounts for the use of \((R+1)\) orthogonal time slots or subchannels required for cooperative transmission. The approximated average channel capacity of the indirect link, using the PDF from (39), can be expressed as  
\[\begin{split} C_{\text{id}}&=\frac{1}{R+1}E[\log_{ 2}(1+\gamma_{\text{id}})]\\ &\approx\frac{1}{R+1}\int_{0}^{\infty}\log_{2}(1+\gamma)f_{\gamma _{\text{id}}^{\text{AF}}}(\gamma)d\gamma\\ &=\frac{1}{(R+1)\ln(2)}\sum_{r=1}^{R}\pi_{\text{id}},\exp\biggl{(} \frac{1}{\hat{\gamma}_{r}^{\text{min}}}\biggr{)}E_{1}\biggl{(}\frac{1}{\hat{ \gamma}_{r}^{\text{min}}}\biggr{)}=C_{\text{id}}^{\text{AF-App}}.\end{split} \tag{48}\]Similarly, the approximated average capacity of the combined link is given, from (39) and (47), by  
\[\begin{split} C_{\text{cb}}&=\frac{1}{R+1}E[\log_{2}(1 +\gamma_{\text{cb}})]\\ &\approx\frac{1}{R+1}\int_{0}^{\infty}\log_{2}(1+\gamma)f_{\gamma _{\text{cb}}^{\text{AF}}}(\gamma)d\gamma\\ &=\frac{1}{(R+1)\ln(2)}\sum_{r=0}^{R}\pi_{\text{cb}},\exp\biggl{(} \frac{1}{\bar{\gamma}_{r}^{\text{min}}}\biggr{)}E_{1}\biggl{(}\frac{1}{\bar{ \gamma}_{r}^{\text{min}}}\biggr{)}=C_{\text{cb}}^{\text{AF-App}}.\end{split} \tag{49}\]  
The above expressions for \(C_{\text{id}}^{\text{AF-App}}\) and \(C_{\text{cb}}^{\text{AF-App}}\) provide tractable estimates of the average achievable rates under multiple SWIPT AF relaying. The accuracy of these approximations will be validated in Section 5.2.3.
# Classification of Imbalanced Data Represented as Binary Features
## 2 Materials and Methods
### Classification Methods
#### 2.4.2 Decision Tree C4.5  
C4.5 is a famous algorithm of decision tree classification, which can be used to make a decision based on a certain set of data, either univariate or multivariate. This classifier is a flowchart-like tree structure where attributes or features are represented by internal nodes, decision rules are represented by branches, and the outcome is represented by a leaf node. Certain training algorithms are applied to a training dataset to automatically create the decision tree.  
C4.5 is a type of decision tree which employs the gain ratio as a splitting criterion. C4.5 stops its splitting process when the number of samples to be split falls below a specified threshold. The least reliable branches are pruned using error-based pruning. In the case of class imbalance, the decision tree may need to generate a lot of tests to differentiate between minority and majority classes. The split action may be terminated before the branches for forecasting minority classes are recognized in some learning procedures. Other learning procedures may prune the branches for forecasting minority classes as they are prone to overfitting. The reason for this is that accurately predicting a small number of samples from minority classes yields insufficient success to considerably reduce the error rate, compared to the error rate generated by overfitting. Because the pruning in the decision tree is mostly based on forecasting error, certain branches that predict small classes are likely to be deleted, and the new leaf node will be labeled with a dominating class.  
Compared to other types of decision tree, such as ID3, C5.0, and CART, C4.5 is said to be less affected by class imbalance and performs noticeably better than MLP (multi-layer perceptron), SVM, _k_-NN, and Naive Bayes on medium datasets with high ratio of class imbalance [32].
## 4 System Architecture  
This system uses low-power image-sensors (Cyclops cameras [9]) on a tiered wireless sensor network system called Tenet. The Tenet system includes all necessities for basic wireless sensor network programming: drivers, routing protocol, flow control, end-to-end reliability, a two-tier network hierarchy and a simple scripting language for easy programming of applications. These properties, in addition to Tenet's extensibility, make Tenet an ideal choice for our deployments.  
Tenet collects images and environmental data from every sensor node and stores them on the local server at James Reserve. Tenet applications run on a local server, and one or more Stargate(s) act as Tenet-masters, which relay commands to and data from the Cyclops nodes. A back-end server retrieves the data from the local server via the Internet and archives and processes the data. These components together give a complete, real-time, end-to-end system. These factors together enabled us to reach our goal of increasing the temporal resolution of data, providing near real-time monitoring, which saved time and labor for the biologist, allowing a larger spatial area to be covered with sensors.
## 5 Findings
### Transfer Learning  
Transfer learning means the extrapolation of information between different devices. It facilitates training with more data. Figure 8 illustrates this. Hypothetically, this improves the model performance, especially for operation ranges that are seldom used. By using all subfleet data, the SOC-vs-temperature region of available data is enlarged, which enhances the extrapolation power of the model.  
Figure 7: Evaluation of the selected sections (one color per device). The upper plots show the RMSE-vs-time diagram and the corresponding sections-vs-time histogram of the training sections. The lower plots show the same for the validation sections. (See also the remark concerning the change of sampling rate at the end of the section above).  
We examine the validity of our hypothesis with the following strategy:  
1. We use training data from all devices and evaluate the model on validation sections of a given device.
2. Only training data from the device selected in approach 1. is used and evaluated on the same validation sections.
3. We compare the results of 1. and 2. for each validation section.  
The difference between approaches 1. and 2. is merely the additional training data--and thus information--provided by the other devices of the fleet. The validation is performed on the same validation sections in step 3.  
The first proof-of-concept experiment shows promising results: The accuracy on most validation sections is increased by incorporating information from other fleet vehicles (see Figure 9).  
Figure 8: Available data for each vehicle individually (dashed lines) and for the whole fleet (blue line).  
Figure 9: Comparison of RMSE of validation sections after training with all fleet data (approach 1.) versus training with data from a selected device (approach 2.). The errors could be reduced for most sections.  
The effect is assumed to be even greater in more inhomogeneous fleets, e.g., in a passenger car fleet.
## 4 Analysis of the Loading Status of an Example Container Ship (Results and Discussion)
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Frame No.**} & \multicolumn{4}{c}{**Shear Forces**} & \multicolumn{4}{c}{**Bending Moments**} & \multicolumn{4}{c}{**Torsion**} \\ \cline{2-10}  & & & & & & **Sea** & & & \\ \cline{2-10}  & **Existing** & **Limits** & **L**imits** & **Existing** & **Limits** & **rel. [\%]** & & & **rel.** \\  & **[kN]** & **[kN]** & **rel. [\%]** & **[kN]** & **rel. [\%]** & & & **[\%]** \\ \hline
35 & 25,627 & 29,850 & 86 & 345,864 & 454,300 & 76 & max. & Limit & \\
52 & 30,560 & 35,000 & * 87 & 723,082 & 1,021,200 & 71 & (kNm) & (kNm) & \\
77 & 8741 & 27,850 & 31 & 1,111,843 & 1,313,300 & 85 & 41,852 & 79,900 & 52 \\
94 & 8460 & 50,900 & 17 & 1,214,300 & 1,882,500 & 65 & Area of T. & Limit & \\
110 & 5749 & 50,900 & 11 & 1,262,429 & 1,891,500 & 67 & (kNm\({}^{2}\)) & (kNm\({}^{2}\)) & \\
131 & \(-\)2466 & \(-\)50,900 & 5 & 1,227,304 & 1,891,500 & 65 & 2,466,056 & 7,801,000 & 32 \\
148 & \(-\)8441 & \(-\)50,850 & 17 & 1,099,119 & 1,809,600 & 61 & & & \\
168 & \(-\)17,299 & \(-\)50,200 & 34 & 825,336 & 1,271,900 & 65 & & & \\
185 & \(-\)17,065 & \(-\)47,650 & 36 & 524,069 & 805,400 & 65 & & & \\
204 & \(-\)7958 & \(-\)23,300 & 34 & 241,033 & 420,200 & 57 & & & \\
222 & 7552 & 18,900 & 40 & 144,324 & 148,000 & * 98 & & \\ \hline \hline \end{tabular}  
* marks max. percentage.  
\end{table} 
Table 6: Shearing forces and bending moments for sea conditions acting on the frames of a container ship loaded with 10 t and 10.5 t containers. Authors’ elaboration. The problem of the containers' weight was the reason for the SOLAS amendment, making weighing of the containers compulsory before loading. However, the problem still exists, as shown by the accident of CMA CGM G. Washington, losing 137 containers in the North Pacific Ocean in 2018. The investigation identified several factors that affected the container stow on the deck: acceleration forces exceeding the structural strength of containers during the large rolls, parametric rolling not recognized by the crew, structural failure of containers, transport of 53 ft containers, having different limits than ISO containers (which were exceeded), inaccurate declared weight of some containers, and the final cargo bayplan not being amended to reflect weights measured in port. Force limits were not exceeded in this case, although one stack in bay 18 was 100% [24]. The post-accident comparison showed that the total terminal weight for the 2469 containers (30,394 t) was 736 t (2.4%) lower than the total declared verified gross mass and 65% of the containers' VGMs were within \(\pm\)5% of the terminal weights. Of the remaining 35%, 10% were over 1 t lighter than their declared VGM, with the largest variance being 8 t, and 2% were over 1 t heavier than their declared VGM, with the largest variance being 16 t [24].
# Quad-Port Multiservice Diversity Antenna for Automotive Applications
## 2 Antenna Design
### Equivalent Circuit  
The antenna mechanism is studied physically by means of an equivalent circuit [30]. The equivalent circuit is derived from the impedance characteristics of the antenna. These resonances can be represented by the \(R\), \(L\), and \(C\) components. Two peak impedance points (5.124 GHz and 8.625 GHz) are chosen from Figure 4 (measured) and a corresponding circuit is derived.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimension** & **Value (mm)** & **Dimension** & **Value (mm)** \\ \hline \(L7\) & 6.7 & S10 & 4.8 \\ \hline \(L8\) & 6.5 & S11 & 2.35 \\ \hline \(L9\) & 4.3 & S12 & 4.8 \\ \hline \(S1\) & 12.3 & S13 & 2.1 \\ \hline \(S2\) & 8.15 & S14 & 5.4 \\ \hline \(S3\) & 24.5 & S15 & 14.75 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 4: Reflection coefficients of the proposed antenna element.  
Figure 3: Reflection coefficients of the evolution steps.  
The impedance characteristics determine the type of resonant circuit. When the impedance moves from low (negative) to high (positive), a series resonance circuit is drawn, and when the impedance moves from high (positive) to low (negative), a parallel resonance circuit is drawn [31]. The real and imaginary curves (shown in Figure 5) can be used to draw the _RLC_ equivalent of the antenna. The equivalent circuit of the antenna element and the corresponding result is shown in Figure 6, and the corresponding _RLC_ parameters are given in Table 2. The 1.5 GHz and 2.45 GHz frequencies are contributed by two parallel resonant circuits, and the UWB is supported by two series resonant circuits.  
Figure 5: Impedance characteristics of the antenna element.  
Figure 6: Equivalent circuit of the antenna (**a**) model, (**b**) S-parameters.
## 3 Results and Discussion
### Biomass Production, Sugars and Nitrogen Consumption
Also, between X\({}_{\text{max}}\) values in agitated cultures of M-20 (16.15-18.16 g/L) and M-25 (16.27-18.10 g/L) presented almost no or small growth, dependent on species (Table 2a-c). Similarly, [PERSON] et al. [13] indicated a significant reduction in X production and TS consumption at C/N = 50 compared to the trials with C/N = 20 for static cultures of _M. elata_ in molasses substrates. In our cultures, although notable X production ocurred in M-25, a significant portion of substrate remained unconsumed, as TS assimilation reached 59.0-80.3%. As it turns out, the increase in the initial concentration of total sugars in the molasses substrates, from 22 g/L (C/N = 20; M-20) to 41 g/L (C/N = 25; M-25) did not push the species to consume higher percentages of sugars. The trend of decreasing substrate consumption (up to 25.2%) in increasing initial sugars concentration could be attributed to possible substrate inhibition due to increased sugar quantities--high osmotic pressure of the culture substrate that resulted in metabolic shift and partial utilization of the assimilated sugars for requirements of energy maintenance. A similar trend has been also reported by [PERSON] et al. [17] in glucose-based shake-flask cultures of _Volvariella volvaca_, in glucose agitated cultures of _Coprimus comatus_[51], in fermentations of _Ganoderma lucidum_ in glucose or lactose in shake-flasks and batch bioreactors [25; 52], during _Tuber siense_ fermentation in a sucrose-based medium under agitation [53], as well as in other higher or lower fungi cultivated on several agro-industrial residues or renewable resources in shake-flask experiments [54]. Besides, the high biomass yield on sugars consumed Y\({}_{\text{X/S}}\) (g/g) values achieved by three _Morchela_ species, (e.g., \(\geq\)0.5 g/g and in some cases up to 0.8 g/g) indicate the potentiality for bio-valorization and de-pollution of sugar-rich residues or wastes for the production of nutritious fungal biomass. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**(a) _M.
# New Treatment Addressing the Pathogenesis of Psoriasis
## 2 Pathogenesis of Psoriasis
### IL23/IL17 Axis  
IL17 is produced by Th17, Tc17, innate lymphoid cells, and \(\tau\delta\)T cells in the skin [47; 48]. IL23 expressed by dermal DCs drives IL17 production [49]. IL17 is produced, as well as TNF, IL26, and IL29 (IFN\(\lambda\)1), under specific situations such as psoriasis autantigens and/or certain environmental stimuli (e.g., trauma or infection) [50]. These cytokine signals create a feed forward inflammatory response in keratinocytes by activating CCAAT enhancer-binding protein (C/EBP) \(\beta\) or \(\delta\), STAT1, and nuclear factor kB [49]. This feed-forward response accelerates the development of psoriasis [49]. IL17 collaborates with TNF to potentiate IL17-induced transcription of several proinflammatory genes (e.g., TNF, IL1\(\beta\), IL6, and IL8) [51]. They activate mDCs and promote the differentiation of Th17 cells in the skin and draining lymph nodes [52].  
IL17 regulates epidermal hyperplasia indirectly by activating STAT3 and promotes keratinocytes to induce IL19, IL36 [50]. IL22, which is mainly produced by Th17 and possibly IL20, also activates STAT3 and induces epidermal hyperplasia (Figure 1) [53]. In the upper spinous and granular layers of the epidermis of psoriatic lesions, IL17-induced transcription factors (e.g., C/EBP\(\beta\) or C/EBP\(\delta\)) and keratinocyte-derived gene products, including S100A7/8/9, hBD2, lipocalin-2, and CCL20 are increased [49, 54]. IL17 in psoriasis has the ability to recruit neutrophils and macrophages by inducing keratinocytes to produce CXCL1, CXCL2, CXCL3, CXCL5, and CXCL8 (i.e., IL8) [46]. IL17A, IL22, and TNF also stimulate CCL20 expression in keratinocytes [55]. CCL20 attracts CCR6+ cells such as mDCs and Th17 cells and sustains the inflammatory response through a positive chemotactic feedback loop [55]. Keratinocytes in psoriasis produce platelet-derived growth factor, angiopoietin-2, and vascular endothelial growth factor, and these growth factors result in erythematosus skin lesions [56].
# Efficient Multifocal Structured Illumination Microscopy Utilizing a Spatial Light Modulator
## 3 Results
### Characterization of Resolution Enhancement with Microspheres  
To validate the spatial resolution improvement of the MSIM system, we imaged 1 um-diameter orange-labeled microspheres (FL-PS-O-01, Big goose Technology, Tianjin, China) with an excitation wavelength of 532 nm and emission wavelength of 581 nm. Having extracted the parameters of the illumination patterns accurately from the nine raw images, the proposed reconstruction procedures were successively performed.  
Images of three separate microspheres by WF and MSIM are shown in Figure 5c,b, respectively. Figure 5c shows a decrease in the lateral full width at half maximum (FWHM) from \(1.776\pm 0.053\) (WF) to \(1.181\pm 0.0599\) um (MSIM), which was calculated by repeated fits with the thirteen different microspheres. The MSIM nearly improves the spatial resolution by a factor of nearly 1.504 compared with the conventional WF microscopy under current configurations. According to the period (12 pixels) of the illumination patterns illuminating on the sample and the practical resolution (7 pixels corresponding to the FWHM of WF in Figure 5c), the theoretical spatial resolution of the samples, could be enhanced by a factor of 1.58 at most.
# Atomic Spectroscopy-Based Analysis of Heavy Metals in Seaweed Species
## 2 Atomic Spectroscopy-Based Heavy Metal Analysis Methods for Seaweed
### Atomic Emission Spectroscopy
#### 2.3.1 Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES)
After cooling, the samples were diluted to 25 mL with Milli-Q water, and the concentrations of Cu, Mn, and Zn were measured with ICP-OES. The microwave digestion method as well as ICP-OES analysis were validated by analyzing NIES-09 CRM: measured values were found to agree well with certified values. Statistical analysis was performed on the measured values of Cu, Mn, and Zn and various other elements measured by HG-AFS and ICP-MS in order to classify the seaweed samples, though with mixed outcomes. For example, LDA (linear discriminant analysis) correctly classified brown, red, and green seaweeds, while SIMCA (soft independent modeling of class analogy) was only able to classify red seaweeds with satisfactory results. The authors also concluded that different metal elements offer varying discrimination powers for distinguishing among brown, red, and green seaweeds: based on the classification approach used in this paper, Ca, Cu, and Mg concentrations have the highest discriminating capabilities, while Zn concentration has poor discriminating power. The fates, transportation, and impacts of three recognized _Sagrassum_ (brown seaweed) genotypes from seven sampling stations along a 3600 km transect extending from the middle of the Atlantic Ocean (open sea) to Guadeloupe were studied by [PERSON] et al. [78]. The concentrations of As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, Sr, V, and Zn were assessed using ICP-OES. Quantities of 70-80 mg of each dried powdered sample were mineralized for 3 h at 100 \({}^{\circ}\)C in plastic tubes closed with 1 mL of HNO\({}_{3}\) and diluted with 5 mL DI water upon completion of the process. DOLT-5, TORT-3, and IAEA-413 were used as certified reference materials. The elements Co, Pb, Se, and Sr were below the limits of detection (LODs). As and Hg were the most and the least abundant elements, respectively. The study confirmed the high As content of _Sargassum_ at a large spatial scale. The authors emphasized the potential use of _Sargassum_ as a construction material and as a source of bioenergy [79; 80] rather than as a fertilizer component in order to avoid environmental and health issues. The studied brown seaweeds could also cause environmental and health problems by discharging contaminants into the coastal marine environment.
# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 5 Discussion
### Batch Size Setting  
For small training datasets such as ESC-10, we used the entire dataset to train and test, while for the large datasets such as ESC-50 and Urbansound8k, we used the batch mode to train and test. For training, each group of batches is constructed by randomly selecting training samples without repetition. In order to find the optimal batch size, we compared the model accuracy with different batch sizes. We evaluated the candidate batch sizes of 50, 100, 150, 200, and 250, as suggested in prior works [18; 19; 20], and conducted experiments on the ESC-50 dataset using the fusion model. The experimental results showed that the batch size of 200 achieved the best results and was used in all of our experiments.
## 2 Materials and Methods
### Antiviral Compounds  
The half-maximal effective concentration (EC\({}_{50}\)) or half-maximal cytotoxic concentration (CC\({}_{50}\)) of Z-Tyr-Ala-CHN\({}_{2}\) was compared across cell lines and coronaviruses with other antiviral agents. These compounds were PF-0083523 (intravenous M\({}^{\rm pro}\) inhibitor; Pfizer, New York, NY, USA), nirmatrelvir/PF-07321332 (oral M\({}^{\rm pro}\) inhibitor; Pfizer, New York, NY, USA), apilimod (Merck, Darmstadt, Germany), K777 (BOC Sciences, New York, NY, USA), remdesivir (Merck, Darmstadt, Germany), bprefeldin A (Santa Cruz Biotechnology, Dallas, TX, USA), and molnupiravir (Bio-Connect BV, Huissen, The Netherlands). Compounds were dissolved in 100% dimethyl sulfoxide (DMSO), stored at room temperature, and eventually diluted to a defined dosage in experimental medium. The final in-assay DMSO concentration did not exceed 0.4% (\(v/v\)).  
Antibodies, Cellular Counterstains, and Staining Buffers for Immunofluorescence and LysoTracker Assays  
Viral S protein was detected by interaction with anti-spike S1 monoclonal antibody (anti-rabbit), purchased from Sino Biological (Houston, TX, USA), used at 1:1000 or 1:2000 dilution. Primary anti-double-stranded RNA (dsRNA) monoclonal antibody J2 (anti-mouse) was purchased from SCICONS (Jena Bioscience, Jena, Germany) and diluted to 1:2000 or 1:2500 in staining buffer. Polyclonal immunoglobulin G (IgG) secondary antibodies, conjugated to Alexa Fluor 488 (goat anti-mouse) or Alexa Fluor 568 (goat anti-rabbit) were purchased from Invitrogen (Waltham, MA, USA) and were used at dilutions of 1:500 or 1:800. Hoechst 33,342 for nucleus identification and HCS CellMask(tm) Deep Red Stain for visualization of the entire cell structure were purchased from Invitrogen (Waltham, MA, USA). These stains were used at a dilution of 1:1000 or 1:2800 and 1:10,000, respectively. Buffers used for permeabilization, blocking, and staining were prepared in a sterile solution of 1% bovine serum albumin (BSA, Sigma, St Louis, MO, USA) in Dulbecco's phosphate buffered saline (DPBS; Invitrogen, Waltham, MA, USA). Goat serum (Sigma, St Louis, MO, USA) at a concentration of 5 % (\(v/v\)) was used for blocking, and Triton X-100 (Sigma, St Louis, MO, USA) was used at 0.1 % (\(v/v\)) as a permeabilizing agent.