esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Foundations
### CGS Based LDA  
The objective of LDA training is to learn the topic-word distribution \(\phi_{k}\) for each topic \(k\). It can be utilized to infer the transcription-topic distribution \(\theta_{m}\) for any new transcription \(d_{m}\). The CGS method generates topic samples alternatively for all the words in \(D\) and then conducts Bayesian estimation for the topic-word distribution based on the generated topic samples.  
Starting with random initialization of topic assignments \(\mathbf{z}\) to values \(1,2,\ldots,K\), in the CGS method, topic assignments are sampled from the whole conditional distribution in each iteration, which is defined as follows:  
\[p_{k}=p(z_{i}=k|\mathbf{z}_{-i},\mathbf{w})\propto\frac{N_{k,m}^{-i}+\beta_{n} }{N_{k}^{-i}+\beta}\frac{N_{m,k}^{-i}+\alpha_{k}}{N_{m}^{-i}+\alpha}, \tag{3}\]  
where the notation \(N^{-i}\) is a count that does not include the current assignment of \(z_{i}\). \(N_{k,m}\) is the number of assignments of word \(n\) to topic \(k\). \(N_{m,k}\) is the number of assignments of topic \(k\) in transcription \(m\). \(N_{k}\) is the total number of assignments of topic \(k\). \(N_{m}\) is the size of transcription \(m\). \(\alpha=\sum_{k}^{K}a_{k}\) and \(\beta=\sum_{m}^{N}\beta_{n}\). This full conditional distribution can be interpreted as the product of the probability of the word \(n\) under topic \(k\) and the probability of topic \(k\) under the current topic distribution for transcription \(m\).  
For a single step \(s\), \(\Phi\) and \(\Theta\) are estimated from the counts of topic assignments and Dirichlet parameters by their conditional posterior means:  
\[\hat{\phi}_{k,n}^{s}=E(\phi_{k,n}^{s}|\mathbf{z}^{s},\mathbf{\beta})=\frac{N_{k,n }^{s}+\beta_{n}^{s}}{N_{k}^{s}+\beta^{s}};k=1\ldots K,n=1\ldots N, \tag{4}\]  
\[\hat{\phi}_{m,k}^{s}=E(\theta_{m,k}^{s}|\mathbf{z}^{s},\mathbf{\alpha})=\frac{N_{m,k}^{s}+a_{k}^{s}}{N_{m}^{s}+\alpha^{s}};m=1\ldots M,k=1\ldots K. \tag{5}\]  
which are the predictive distributions over new words and new topics conditioned on \(\mathbf{w}\) and \(\mathbf{z}\).  
Summarizing, the three main steps of CGS algorithm are as follows:  
* Initialization. In the beginning, each word \(w\in D\) is randomly assigned with a topic \(k\), and the word-count information is counted, namely the number of times that word \(n\) has been assigned with the topic \(k\), and the number of times that topic \(k\) has been assigned to a word of the document \(d_{m}\).
* Burm-in. In each iteration, the topic assignment for each word \(w\in D\) is updated alternatively by sampling from a multinomial distribution \(\mathbf{P}=[p_{1},\ldots,p_{k},\ldots,p_{K}]\). After the given \(T\) iterations, the burn-in process stops and the topic samples \(\mathbf{z}\) can be obtained.
* Estimation. The topic-word distribution \(\phi_{k}\) for each topic \(k\) is estimated based on the topic samples \(\mathbf{z}\) and the words \(\mathbf{w}\in D\).
# An Improved Model-Free Adaptive Nonlinear Control and Its Automatic Application
## 5 Conclusions  
Based on the MFAC method, this study proposes a new nonlinear MFAC-OER method. The convergence and stability of the new control method are rigorously proven. The efficacy and advantages of the proposed method are demonstrated through a transfer function model of the circulating fluidized bed boiler drum level and on-site testing in the laboratory platform. Experimental results show that the proposed MFAC-OER method is significantly better than the MFAC and PID methods under different operating conditions, as follows:  
1. Compared to the MFAC and PID methods, the tracking speed is improved by 2-3 times, enhancing the fast response characteristic of the MFAC strategy.
2. Compared to the MFAC and PID methods, the MAE and MSE are approximately half of that of the MFAC method and much smaller than the PID method. This indicates higher accuracy and better stability. Even under varying operating conditions, the MFAC-OER method can still respond more quickly to feedback and better track the desired boiler drum level.
3. The feedwater flow rate maintained by the MFAC-OER method remains within a reasonable working range without significant fluctuations. It can quickly respond to changes in the operating conditions.  
The MFAC-OER method is proven to be effective in the actual SISO system through the boiler drum water level transfer function model. In future work, two main factors will be considered: the method is applied to the actual system of MISO or MIMO.  
Conceptualization, J.X. and F.X.; methodology, J.X. and Z.S.; software, Z.S.; validation, J.X. and F.X.; formal analysis, Z.S.; resources, J.X. and Y.W.; writing--original draft preparation, J.X. and F.X.; writing--review and editing, J.X. and F.X.; supervision, Z.S.; funding acquisition, Y.W. and Z.S.; experimental assistance: Y.W. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Three control methods’ system water supply input curves: (**a**) PID water supply input curve; (**b**) MFAC water supply input curve; and (**c**) MFAC-OER water supply input curve.  
**Funding:** This research was funded by Zhejiang Province Basic Public Welfare Research Project (LGC22E050006).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The (DATA TYPE) data used to support the findings of this study are included within the article.  
**Conflicts of Interest:** The authors declared no conflict of interest.
## 2 Materials and Methods
### Human ERas Has No Effect on ARG1 Enzymatic Activity  
We next aimed to investigate the impact of ERas on ARG1 enzyme activity. Therefore, we measured the urea production with and without 10 uM ERas _h_sNex at increasing concentrations of purified ARG1 after one hour (Figure 4A), as well as a full time-dependent cycle of L-arginine conversion into L-ornithine and urea with 100 nM ARG1, 2 mM L-arginine and five times excess ERas _hs_Nex or _hs_FL (0.5 uM) in HPLC at pH 9 (Figure 4B). In  
Figure 4: Functional analysis of ERas-ARG1 interaction. (**A**) Arginase activity assay of 20, 40 or 100 nM ARG1, with or without 10 uM _hs_Nex, measured via colorimetric urea assay (n = 3). (**B**) Arginase activity was measured with 2 mM L-arginine, 100 nM ARG1 and 500 nM ERas _hs_Nex or _hs_FL at pH = 9.0 by HPLC. (**C**) Liposome sedimentation assay of 0.2 uM ARG1, immobilized on liposomes together with GST-_h_sNex and incubated for 30 min at room temperature (RT). After sedimentation with 20,000\(\times\)_g_ for 30 min, immunoblotting was used to show protein binding on liposomes and protein-protein interactions by using monoclonal antibodies against ARG1 and GST. (**D**) Ratio of supernatant to pellet from liposome assay indicating ARG1 binding to liposomes with and without the addition of GST-_hs_Nex. (**E**) Arginase activity measured with 2 mM L-arginine, 100 nM ARG1 without, or in the presence of liposomes at pH = 7.5 in HPLC. (**F**) qRT-PCR analysis of _Arg1_, _ERas_, _i_Nos_, _Cat1_, _Cat2a_, _Cat2b_ and _Cat3_ in HSCs at day 0, 1, 4, and 8. Gene expression was normalized to the expression of the housekeeping gene _Hprt_1. All error bars represent S.D.  
both experiments, no change in the velocity of enzymatic ARG1 activity could be detected. Furthermore, we measured fixed ARG1 concentration with increasing concentrations of ERas _hs_FL and _hs_Nex (Supplementary Figure S7) and the Michaelis-Menten constant of the enzymatic reaction of ARG1 (6.5 \(\pm\) 0.99 mM) and ARG1 + ERas _hs_Nex (7.79 \(\pm\) 0.33 mM) with concentrations between 0.25 and 10 or 5 mM, respectively (Supplementary Figure S8), and could also not detect any differences. The arginase activity in HSCs was determined by colorimetric urea assay using TCL of HSCs at day 0, 1, 4 and 8 (Supplementary Figure S9). The ARG1 activity in cell lysates was highest in quiescent (d0) HSC lysates, following the protein expression data in Figure 2B.
## 3 Methodology  
In this study, three most common methods of damage assessment including elevation difference, coherence difference and texture difference have been used. In addition, machine-learning algorithms including RDF and SVM have been used to classify the information obtained from these methods. By using these methods in analyzing LIDAR and SAR images, valuable information can be obtained. Moreover, three computer programs including ENVI v.5.3, ArcGIS v.10.7.1 and XLSTAT v.2020 were used for buildings damage assessment. ENVI and ArcGIS, which are powerful tools for analyzing maps and other geographic information, were used to analyze SAR and LIDAR data. In addition, XLSTAT, which is an efficient tool for statistical data analysis, was used for machine learning.
## 5 ESTCube-Luna Preliminary Mission Requirements and Platform Design Solutions
### On-Board Computer (OBC)  
The ESTCube-LuNa OBC is designed to command platform subsystems and payloads during nominal operations. The OBC receives and distributes mission control commands via COM and collects and sends housekeeping, telemetry and experiment data. Since the ESTCube-LuNa mission is limited in the link between the spinning nanospacecraft and ground stations, the OBC must be able to operate the platform and payloads autonomously.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Part** & **Power (mW)** \\ \hline E-sail & 2000 \\ \hline Tether camera & 1000 \\ \hline Langmuir probe & 4000 \\ \hline HSCOM transmission & 7000 \\ \hline HSCOM reception & 1000 \\ \hline Reaction wheels & 6000 \\ \hline Two attitude thrusters & 2500 \\ \hline Triangulated celestial navigation & 4500 \\ \hline On-board computer & 600 \\ \hline Electrical power system & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The main power consumers of ESTCube-LuNa.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Mass (g)** \\ \hline Structure and wings & 4000 \\ \hline E-sail & 2200 \\ \hline Tether camera & 500 \\ \hline Langmuir probes & 400 \\ \hline Avionics & 1000 \\ \hline HSCOM & 1000 \\ \hline RW & 700 \\ \hline TNC & 1000 \\ \hline Solar cells & 300 \\ \hline Attitude thrusters & 200 \\ \hline
**Sum:** & 11,300 \\ \hline
**Total with 30\% margin:** & 14,690 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The preliminary mass budget of ESTCube-LuNa.  
The on-board storage must accommodate weeks of experiment data and employ lossless compression to reduce the downlink data budget. The EPS serves as a backup captain during safe modes, when all manoeuvres and experiments are paused until a connection with the ground is established. The OBC's real-time clock (RTC) synchronisation also relies on the EPS's battery-backed RTC.  
The plan is to interface the payloads via two cubesat space protocol (CSP) buses. An ARM architecture-based rad-hard microcontroller will deliver the high performance required to handle tasks and communication between all subsystems and payloads.
###### Abstract  
Space-division multiplexing over few-mode fibers is a promising solution to increase the capacity of the future generation of optical transmission systems. Mode-dependent loss (MDL) is known to have a detrimental impact on the capacity of few-mode fiber systems. In the presence of MDL, spatial modes experience different attenuations which results in capacity reduction. In this work, we propose a digital signal processing solution and an optical solution to mitigate the impact of MDL and improve the channel capacity. First, we show that statistical channel state information can be used for a better power allocation for spatial modes instead of equal launch power to increase the system capacity. Afterwards, we propose a deterministic mode scrambling strategy to efficiently reduces the impact of MDL and improves few-mode fiber systems capacity. This scrambling strategy can be efficiently combined with the optimal power allocation to further enhance the capacity. Through numerical simulations of the average and outage capacities, we show that the proposed techniques bring significant capacity gains.  
Keywords:optical communications; space-division multiplexing; few-mode fibers; mode-dependent loss; multiple-input multiple-output (MIMO); channel state information; mode scrambling +
Footnote †: journal: Applied Sciences
# Solanesol Biosynthesis in Plants
## 3 Biosynthetic Pathway of Solanesol  
The biosynthetic pathway of terpenoids generally involves three stages: the generation of the C5 isopentenyl diphosphate (IPP) precursor and its double-bond isomer dimethylallyl diphosphate (DMAPP), the generation of direct precursors (geranyl diphosphate (GPP), farnesyl diphosphate (FPP), geranyl geranyl diphosphate (GGPP), etc.), and the biosynthesis and modification of terpen via oxidation-reduction, acylation, and glycosylation (Figure 2) [8]. The first two stages of this pathway are well understood; however, because of the relatively simple structure of solanesol, studies on the third stage of biosynthesis remain very scarce.  
Figure 2: The solanesol biosynthetic pathway in plastids of higher plants. Enzymes involved in the solanesol biosynthetic pathway: DXS, 1-deoxy-d-xyglucose 5-phosphate synthase; DXR, 1-deoxy-d-xylulose 5-phosphate reductioisomerase; IspD, 2-C-methyl-d-erythnitol 4-phosphate crytidylyltransferase; IspE, 4-diphosphocytidyl-2-C-methyl-d-erythnitol kinase; IspF, 2-C-methyl-d-erythnitol 2,4-cyclodiphosphate synthase; IspG, 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate synthase; IspH, 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate reductase; IPI, isopentenyl diphosphate isomerase; GPS, geranyl diphosphate synthase; FPS, farnesyl diphosphate synthase; GGPP, geranyl geranyl diphosphate synthase; SPS, solanesyl diphosphate synthase.
## 4 Discussion
### Socio-Economic Determinants  
We found significant correlation of suicide mortality with unemployment rate, reduction in GDP, and low disposable income. In the region of Attica where a higher rise in suicide rate was observed, unemployment recorded a higher increase among all Greek regions, and was found to be the main determinant of the elevated rate. In the same region, the suicide rate was also associated with change in per capita GDP and average DHI. In the second larger region, Central Macedonia, the increase by 33.4% was found to be significantly associated only with unemployment rate. It is known from many studies that these parameters are among the strongest social determinants of suicide [4; 6] and the results of the present study were consistent with them. A systematic review of the association betweenunemployment and suicide found that unemployment might increase the risk of suicide by 250% within the first five years, the effects remaining even after that period, but at a lower level [22]. However, that correlation with the socio-economic variables cannot explain all regional differences observed in our study. The regression model we applied could only explain 17.2% of the overall regional variation. This finding means that other not-investigated factors determine suicide patterns, underlining a need for further research. As stated above, these factors may include cultural, geophysical, genetic, or mental determinants.  
A remarkable methodological finding of our study was that the variables \"change (%) in per capita GDP\" and \"average disposable household income\" were found to be more predictive than the \"per capita GDP\" itself. This is likely to be because the change in income is more strongly perceived by the population than the GDP itself. The per capita GDP is mainly related to the overall national economy, and probably the real economic conditions of the population are not necessarily reflected in GDP.
## 2 Materials and Methods
### Characterization of HIFU System  
The evaluation of the HIFU system was carried out in terms of electrical output voltage before applying for the HIFU transducer. An oscilloscope (InfiniVision MSOX2024A, Keysight Technologies, Chandler, AZ, USA) was used to pick up the output signals from the HIFU system at a sampling rate of 2 G/s. The acoustic pressure signals generated from the HIFU transducer were captured by a hydrophone (0.2-mm needle, Precision Acoustics Ltd., Dorchester, UK) before being displayed on the oscilloscope (Figure 3a). The acoustic pressure was calculated as the equation below during the HIFU activation [36]:  
\[I=\frac{P^{2}}{\rho c}, \tag{2}\]  
where \(I\) is the acoustic pressure waveform (W/cm\({}^{2}\)), \(P\) is the measured hydrophone voltage (Pa), \(\rho\) is the water density (997 kg/m\({}^{3}\)), and \(c\) is the sound velocity (1480 m/s) in the water at 20 \({}^{\circ}\)C. To protect  
Figure 2: HIFU system touch screen interface.  
the needle hydrophone from high power pressure during the measurements, only a low voltage level from the HIFU circuit (\(\pm 9\) V) was tested. A manual, three-axis stage with a spatial resolution of 0.1 \(\upmu\)m drives the hydrophone to detect acoustic signals to attain corresponding pressure values.  
To illustrate the applicability of a certain HIFU system in the experimental environment, the thermal effect of this system was also considered, or, in other words, the ability to heat the target sample to a certain temperature was firmly confirmed. The chicken breast tissues (\(40\times 40\times 20\) mm\({}^{3}\)) were used to check the maximum temperature of our HIFU system. Figure 3b indicates how to set up the HIFU ex vivo experiment. The HIFU transducer was delicately calibrated to focus the beam at the 2 mm away from the top surface of the chicken breast tissue. The HIFU transducer and half of the chicken breast tissue were laid underwater during the experiment. The thermocouple was attached under the surface of the tissue and 1 mm away from the treatment axis to avoid potential acoustic absorption caused by the thermocouple. In addition, to observe the maximum temperature and the temperature distribution during the experiment with the HIFU system, the IR thermal camera (FLIR i5, FLIR Systems Inc., Portland, OR, USA) was used to capture the surface (A-A) temperature image every 10 s interval. The HIFU circuit was turned on until the focused area reached the highest temperature, and thermal data was collected from two different sources.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 1 Introduction
[4], the Enhanced Distributed Channel Access (EDCA) mechanism was proposed as an improvement to the widely adopted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. This enhancement increases the likelihood of successful message transmission for high-priority traffic directed toward the coordinating access point.
## 2 Materials and Methods
### Validation for DEGs by qRT-PCR  
For qRT-PCR, first-strand cDNA was done from total RNA using Superscript IV First-Strand cDNA Synthesis SuperMix Kit (Invitrogen, Carlsbad, CA, USA) following manufacturer's protocol. Quantitative real-time PCR was performed using SYBR Green Master Mix in the QuantStudio 12k Flex Real-Time PCR system (Life Technologies, Carlsbad, CA, USA) to validate DEGs. Two micrograms of the sample (concentration) were added to 5 \(\upmu\)L of Applied Biosystems SYBR Green Master Mix and primers at a concentration of 0.4 \(\upmu\)M. The amplification cycle consisted of the following: initial denaturation at 50 \({}^{\circ}\)C for 5 min and 95 \({}^{\circ}\)C for 2 min followed by 45 cycles of 95 \({}^{\circ}\)C for 15 s and 60 \({}^{\circ}\)C for 60 s. Each sample was run in triplicates. Specific qRT-PCR primers for six target genes were designed using an online tool Prime-Blast ([[http://www.ncbi.nlm.nih.gov/tools/primer-blast](http://www.ncbi.nlm.nih.gov/tools/primer-blast)]([http://www.ncbi.nlm.nih.gov/tools/primer-blast](http://www.ncbi.nlm.nih.gov/tools/primer-blast))) (Table S1). Each sample was run in triplicates. The 18S rRNA and elongation factor 1-\(\alpha\) (PGSC0003 DMG400020772,ef1\(\alpha\)), [35] were used as the reference genes for normalization, and the mock-treated samples used as calibrators. The comparative 2\({}^{-\Delta\Delta CI}\) method was used to determine the relative fold change according to Schmittgen and Livak [36]. Despite the two techniques (RNA-seq and qRT-PCR) being different, the expression patterns of selected genes upon nematode infestation was consistent between the two procedures (Figure S1).
# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 3 Material and Method
### Deep Learning Architectures
#### 3.3.1 Convolutional Neural Networks  
CNNs are generally used in computer vision applications and are widely used in deep learning studies such as face recognition and image recognition. Especially in the field of image processing, the most used model is CNN models. CNN outperforms previous models in image processing tasks through its effective image inference capabilities [34]. CNNs use more than one convolutional layer for feature extraction and use a combined set of operations such as pooling layer and activation layer for inference [35]. Convolution layer is the main feature of CNNs. The advantage of CNN-based architectures is that it does not require manual feature extraction [36].
# The Effect of Custom Insoles on Muscle Activity in Diabetic Individuals with Neuropathy
## 2 Materials and Methods
### Acquisition Set Up  
Subjects walked while wearing (CI) and not wearing (NoCI) the CI at their preferred walking speed at the orthotics manufacturer site (Figure 1); a minimum of three walking trials per subject were collected through seven video cameras (GoProHero 7), plantar pressure insoles (Novel PedarX system, 99 sensors per insoles, 20.000 sensors/second), and an sEMG system (FREE EMG1000, 1000 Hz, BTS). The electrical activity of four muscles was simultaneously acquired bilaterally: Rectus Femoris (RF), Tibialis Anterior (TA), Medius Gluteus (MG), Gastrocnemius Lateralis (GL). After appropriately cleaning and preparing the skin, sEMG electrodes were placed according to the guidelines reported in [32] (Figure 2). The sensors (Hydrogel/Ag/AgCl, Kendall Arbo H124 SG, Covidien, Dublin, Ireland) were 24 mm in diameter and positioned 1 cm apart. Retroreflective markers were applied bilaterally on calcaneus, lateral and medial malleolus, first, second and fifth metatarsal heads, tibial tuberosity, and the head of the fibula. The same evaluators recorded the data (the first author recorded the sEMG signals, the third author recorded the video and pressure data) of all participants. The present study was approved by the Ethics Committee of the Padua University Clinics (N\({}^{*}\)1001P). Each subject signed the informed consent form.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Age (Mean \(\pm\) st.dev)** & **Weight (Mean \(\pm\) st.dev)** & **Height (Mean \(\pm\) st.dev)** & **BMI (Mean \(\pm\) st.dev)** & **Risk Classification [3]** & **Years of Disease** \\ \hline \hline PN & 61.7 (\(\pm\)12) & 93 (\(\pm\)20) * & 1.73 (0.07) & 30 (\(\pm\)5) * & 80\% & 10\% & 10\% & 27.6 (\(\pm\)13) \\ \hline C & 61.2 (\(\pm\)5.07) & 70 (\(\pm\)17) & 1.68 (12) & 24 (\(\pm\)3) & N.a & N.a \\ \hline \hline \end{tabular}
\end{table}
Table 1: Anamnestic data: PN = Peripheral Neuropathy, C = healthy controls, Risk classification according to [3], N.a = not available, * = \(p\) < 0.05.  
Figure 1: Example of acquisition set up.
## 3 System Architecture and Service Process
### System Services
#### 3.2.4 Condition Notification Service  
The condition notification service is a service to prevent dangerous conditions beforehand by notifying users in real time about changes in the outdoors environment and taking some measure. Figure 5 shows the operating process of the condition notification service.  
The data sensed by the environmental sensors is sent to the sensor manager. The sensor manager extracts sensing values from the received data to store them into the database. The Web server monitors the stored sensing values periodically to notify users that an event is ocurred if they are over or below some set reference value.