esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Apparatus  
A commercial thermal desorption unit (TDU, TDS-2, Gerstel, Mulheim and der Ruhr, North Rhine-Westphalia, Germany) equipped with a CIS-4 (cooled injection system, Gerstel) was used for the programmed thermal desorption (from 40 \({}^{\circ}\)C to 300 \({}^{\circ}\)C, held for 10 min and at 60 \({}^{\circ}\)C/min) under helium flow at 75 mL/min. After desorption (splitless for 2 min), the sulphur volatiles were cryofocused in the CIS-4 system employing liquid N\({}_{2}\) at \(-140\)\({}^{\circ}\)C. After cryofocusing and for the analysis by GC-MS, the CIS-4 was programmed from \(-140\)\({}^{\circ}\)C to 300 \({}^{\circ}\)C (held for 5 min) at 10 \({}^{\circ}\)C/s.  
Capillary GC-MS analyses in the electron impact mode (EI+ at 70 eV) were performed using an Agilent 6890 GC-5973N MS system (Agilent, Little Falls, DE, USA). The mass detector worked in a range from 30 to 400 amu.  
A DB-Wax capillary column (J&W Scientific, Folsom, CA, USA), 60 m \(\times\) 0.25 mm I.D., with 0.25 \(\upmu\)m coating was used. The program of the GC oven was as follows: Temperature held at 35 \({}^{\circ}\)C for 10 min, and then ramped up to 100 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min. After that, the oven temperature was elevated to 210 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min and kept for 40 min. Helium was employed as carrier gas at a flow rate of 1.0 mL/min.  
The MS signal identification was performed by analogy with mass spectra (with a minimum of 90% similarity) from commercial standards and those from Wiley 7N Edition Library [22] and confirmed by their retention times. Each volatile sulphur compound was quantified using selective ion monitoring mode (Table 2). Quantitative data were obtained by measuring the molecular ion area in relation to that of the internal standard (T).  
For calibration, six levels of concentration, covering the expected concentration range, were tested in triplicate for each sulphur volatile compound.
# Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
## 3 Results
### Comparison with COSMIC Electron Density Data and IRI-2016  
During the testing, the study compared the performance effect of EDG-DNN with IRI-2016.  
Figure 6 verifies the prediction effect of the electron density of the EDG-DNN mod-el on 20 March 2012 and 20 September 2012. The first row is the comparison of the electron density distribution of the COSMIC trajectories and the prediction effects of the EDG-DNN model and the IRI-2016 model, expressed in red, blue, and green, respectively. The second row is the distribution of the difference between the EDG-DNN and IRI models and the COSMIC electron density trajectories, expressed in blue and green, respectively. The IRI-2016 tends to underestimate the electron density in the ionospheric region below 300 km and slightly overestimate the electron density in the ionospheric region above 300 km at the vernal equinox, while the error between the EDG-DNN and COSMIC electron densities remains small and stable. The EDG-DNN and IRI models have root mean square errors of 0.587 and 0.993, respectively. The prediction effects of the EDG-DNN model and the IRI-2016 model are similar during the autumnal equinox, and the errors of the EDG-DNN model and the IRI-2016 model are kept within a limited range with those of the COSMIC electron density trajectories. The IRI-2016 model, on the other hand, has some individual electron density prediction mistakes. The EDG-DNN model and the IRI model have root mean square errors of 0.453 and 0.471, respectively. As a result, in most circumstances, EDG-DNN's prediction performance is comparable to that of COSMIC electron density trajectories and better than that of the IRI-2016 model.  
The average _RMSE_ and _MAD_ for EDG-DNN and IRI-2016 at various latitudes around the world are shown in Table 1. EDG-DNN performs better at midlatitudes than at high latitudes because of fewer electron density profiles distributed at high latitudes, allowing the model to obtain relatively less information through learning, and probably because of  
Figure 6: Row 1: SZA-DNN and IRI-2016 predict electron density and COSMIC trajectory electron density distribution. Row 2: Distribution of electron density difference of EDG-DNN, IRI-2016, and COSMIC trajectories.  
the more complex ionospheric motion at high latitudes. The average _RMSE_ and _MAD_ of EDG-DNN at different latitudes are always lower than those of IRI.
# DMSO Improves the Ski-Slope Effect in Direct PCR
## 1 Introduction  
First developed by [PERSON] in 1985, DNA fingerprinting has evolved rapidly since the 1990s and found applications in domains such as criminal investigations, individual identification, and paternity tests [1, 2, 3]. The development of scientific investigation techniques leading to a high success rate of DNA extraction from human tissue samples--including crime scene samples--and improvements in short tandem repeat (STR) amplification have further resulted in more reliable DNA profiling results. In addition, more efficient direct polymerase chain reaction (PCR) amplification techniques for obtaining DNA profiles from samples have been recently developed [4, 5, 6, 7]. The direct PCR method, known as colony PCR, has been widely used for rapid detection of gene cloning and transfection in the field of microbiology since the early 1990s [8, 9]. This method directly amplifies DNA from samples and has the advantage of minimizing DNA loss during the extraction process. [PERSON] et al. [10] reported that 20-70% of sample DNA was lost during the DNA extraction process, with a possibility of contamination with foreign DNA. Direct PCR circumvents intermediate stages such as DNA extraction and quantification, making it a rapid and labor-effective method for successfully obtaining DNA profiles from samples [11, 12]. This advantage has led to the continuous development of the direct PCR method since the mid-2000s and has recently been applied to contact evidence from crime scenes [4, 13, 14, 15]. Additionally, [PERSON] et al. [16] reported a rapid and cost-effectivemodification of direct PCR involving the use of direct PCR buffer in reference buccal samples. However, since direct PCR amplifies the target gene without DNA extraction, purification, or quantification from the sample, it can lead to non-specific amplification and cause stutters, ski-slope effect, split peaks, allele imbalance, and allele drop-in/out due to the state of the sample and various PCR inhibitors [17; 18]. For this reason, continuing research resulting in refinement of techniques to successfully obtain DNA profiles through direct PCR is warranted.  
Even though PCR is one of the most important techniques to obtain a DNA profile, the poor condition of the samples to be amplified can lead to abnormal or insufficient amplification of the target sequences. Thus, many studies have focused on these aspects for further improvement [19]. Particularly, various organic additives such as dimethyl sulfoxide (DMSO) have been reported to improve the amplification yield and specificity of PCR [19; 20; 21; 22] and are referred to as PCR additives or enhancers. In this study, we confirmed the applicability of the additive DMSO in reducing the ski-slope effect, which is an issue arising due to non-specific amplification in direct PCR, observed especially when using Prep-n-Go buffer. We aimed to develop an improved experimental method for direct PCR that can be applied in forensics to successfully obtain DNA profiles from various types of evidence at crime scenes.
## 2 Theory--Analytical and Numerical Models
### Numerical Model for Acoustic Attenuation  
While the analytical model works well for a simple centralised acoustic source, it cannot model a non-axisymmetric acoustic source. Thus, a numerical model was developed. A finite element analysis (FEA) approach was chosen to compute the amplitude distribution of a propagating acoustic wave at increasing distances along the water pipe. Since acoustic wave propagation is a dynamic problem, the governing equation of motion for an FEA model involving guided acoustic wave propagation along a hollow cylinder is [26; 27]  
\[[M]\ddot{u}+[C]\dot{u}+[k]u=[F] \tag{4}\]  
where \([M]\) is a diagonal lumped mass matrix whose values are determined by the material density of the cylinder, \([k]\) is the static stiffness matrix whose values are determined by the Young's modulus and Poisson's ratio of the cylinder, \([C]\) is the viscous damping matrix which is determined by the Rayleigh damping of the cylinder, \([F]\) is the external acoustic excitation force applied to the hollow cylinder and \(\ddot{u}\), \(\dot{u}\) and \(u\) are the cylinder acceleration(m/s), velocity (m/s) and displacement (m), respectively. For the FEA model, wave propagation occurs along the cylinder when the initial equilibrium condition is disturbed by the application of force or displacement constraints. To solve Equation (4), and hence compute the dynamic response of the cylinder, numerical calculations in the time domain were employed using ABAQUS.  
For the FEA, a two-dimensional (2-D) cross-section of the water pipe waveguide was constructed and subsequently extruded into a three-dimensional (3-D) cylinder. Following the geometry extrusion, the pipe was meshed into finite elements using first-order hexahedral elements with reduced integration (C3D8R). Infinite element (CIN3D8) meshing was also applied to the pipe edges to prevent acoustic wave reflections at the edges. From the convergence analysis, the optimum mesh density comprising individual mesh lengths of 1 = 0.009 m and a mesh density comprising individual mesh lengths of 1 = 0.02 m at distances beyond 1 m from the acoustic source were selected. Acoustic excitation was also applied to the pipe in the radial and circumferential directions (Figure 1) based on the mechanical alignment of the acoustic source as described in Section 3.  
Figure 1 shows the mesh for the numerical modelling demonstrating that both the soil medium using the AC3D8R (eight-node linear acoustic brick) elements and the pipe can be modelled. To save computational resources, the soil medium was only added to the relevant section of the pipe model (where acoustic measurements were to be taken).
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 2 Materials and Methods
### Experimental Elements
#### 2.2.4. Experimental Procedures  
The experiments were conducted individually in a controlled laboratory setting, adhering to a standardized procedure, with participants instructed to wash their scalp prior to the experiment to minimize impedance [63]. Initially, participants, equipped with the necessary sensors, were asked to complete a pretest involving eye movement and electrical brain stimulation by remaining seated and viewing four warm-up photos as directed by the experimenter. Upon meeting the pre-experiment standards, participants, under the experimenter's guidance, proceeded to view a series of 12 stimulus photos (A1-A12) sequentially, following the stimulus presentation protocol of 20 s of viewing, followed by a 10 s rest period, to ensure the consistency of experimental variables. The collected EOG and EEG signals were transmitted and stored in a laptop via signal amplifiers. Throughout the experiment, efforts were made to maintain a constant environment, ensuring that only the stimuli presented on the monitor influenced the participant's responses, thereby ensuring a high correlation between the results and the stimulus conditions. Upon completing the physiological portion of the experiment, participants rested briefly, removed the physiological sensors, and then filled out both the Beauty Assessment Scale and the SAM Scale questionnaires. After questionnaire completion, participants were thanked, and the experiment concluded (as shown in Figure 6). The total duration of the experiment was approximately 23-28 min.  
Figure 5: Self-assessment Scale (SAM). Used to score the emotional dimension of valence (first line) and arousal (second line).  
Figure 6: Experimental program diagram. From experimental preparation (5–6 min) to physiological data collection (6 min), to the completion of two subjective questionnaires (10–15 min).
###### Abstract
[44] used PPG to process accent conversion, and the results showed a 20% improvement in speech quality. [PERSON] et al. [45] applied PPG to achieve cross-lingual voice conversion; the results showed effectiveness in intralingual and cross-lingual voice conversion between English and Mandarin speakers. More recently, in our previous study, the PPG was used to assist the gated CNN-based voice conversion model to convert dysarthric to normal speech [46], and the results showed that the PPG speech feature can benefit the voice conversion system for the dysarthric patient speech conversion task. Following the success of PPG features in previous dysarthric patient speech signal processing tasks, the first purpose of this study is to propose a hybrid system, called CNN-PPG, which is a CNN model with PPG features which could be used to improve the SCR accuracy for severe dysarthric patients. It should be noted that the goal of the proposed CNN-PPG in this study is to achieve high accuracy and stable recognition performance. Therefore, the concept of the personalized SCR system is also adopted in this study. The second purpose of this study is to compare the proposed CNN-PPG system with two classical systems (CNN model with MFCC features and ASR-based system) to ensure the benefits of this proposed system in this task. The third purpose of this study is to study the relation between the number of parameters and accuracy in these three systems, which can help us to reduce the cost of implementation in the future. The rest of the article is organized as follows. Sections 2 and 3 presents the method and experimental results. Finally, Section 4 summarizes our findings.
## 5 Conclusions  
This study demonstrates that audible sound stimuli can significantly alter _S. cerevisiae_ growth and the volatile compounds it produces. The growth rate of yeast in fermentations exposed to sound was increased, with a 28.9% higher specific growth rate in fermentations exposed to high-frequency (10 kHz) sound compared to silence. Furthermore, the volatile profile of the resulting fermions was modulated by the sound frequency applied. Of particular interest was the alteration of levels of aroma compounds produced in the synthetic liquid medium by the different frequencies of sound. The levels of citrus compounds, such as limonene (orange) and isocyclocitral (leafy citrus), were increased by both sound treatments, whereas sweet fruit compounds, such as ethyl hexanoate (banana) and ethyloctaonate (apricot), decreased. The production of benzoin (vanilla) was elevated by the application of low-frequency sound, yet decreased by high-frequency treatment. Overall, these results suggest that specific audible sound stimuli could be employed by beverage fermentation industries to increase productivity and achieve desired aroma profiles, thus providing a novel avenue with which to diversify product styles.  
The following are available online at [[https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)]([https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)) 3390/metabo11090605/s1, Table S1: Potentially common aroma compounds (found in fermented beverages) amongst the putatively identified compounds.  
Conceptualisation, A.H., A.R.D.G., A.J. and S.G.V.-B.; methodology, A.H., M.A.L. and S.G.V.-B.; validation, A.H., M.A.L. and S.G.V.-B.; investigation, A.H.; resources, S.G.V.-B.; data curation, A.H.; writing--original draft preparation, A.H.; writing--review and editing, ALL; supervision, S.G.V.-B. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the New Zealand Ministry of Business, Innovation and Employment's Endeavour Fund (UOAX1713).  
Not applicable.  
The data presented in this study are available in article and Supplementary Materials.  
We would like to thank [PERSON] and the University of Auckland MaSH hub for their support with statistical analyses and GC-MS work.  
The authors declare no conflict of interest.  
We identified potentially common aroma compounds (found in fermented beverages) amongst the putatively identified compounds (Table S1; \(p<0.05\)). Of the 346 putatively identified compounds, 24 were identified as aroma compounds (Table A1; [32]).  
Appendix B The LOOCV of the LDA showed a 100% accuracy for identifying fermentations from different conditions. Most aroma compounds showed a high correlation with this discrimination, with the R-squared value \(\geq\) 0.8 for all, bar three, compounds.
## 3 Justification of Assumptions
However, both these calculations assume the antenna is in Free Space. In real-world applications, the antenna is always near \"something\", i.e., the Earth, its mounting structure, a radome or cover, etc. These parasitic bodies change the calculated radiation patterns of antennas [18]. In the case evaluated here, the antenna is mounted inside an airframe, and so the airframe deforms the radiation patterns. The general approach of this paper to accept the E- and H-plane radiation patterns arbitrarily helps the model to be useful in real-world applications. However, due to the lack of information about real GNSS antennas, an algorithm to generate arbitrary radiation patterns has been developed and will be introduced in Section 4.2.
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 3 Overview of MPTCP Congestion Control Algorithms  
The multipath congestion control algorithms such as LIA, OLIA, and wVegas aim at the fair and efficient distribution of network resources among the competing flows. The multipath congestion control is mostly applied at the congestion avoidance phase, whereas the other phases (slow start, fast retransmit, and fast recovery) remain the same as the standard TCP algorithm. Cubic is a TCP congestion control algorithm that runs separately on individual subflows when used with MPTCP. The following is a brief description of these algorithms.
## 3 Experimental Section  
### Strains Employed  
The bacteria, plasmids (Figure 1A) and phages employed are listed in Table 10. \begin{table}
\begin{tabular}{l l l} \hline
**Bacterial strains** & **Characteristics or genotype** & **Source/Ref.’; Hayes lab \#a** \\ \hline
594 & F\({}^{\prime}\)_lac_-3350 _galK2 galT22 rpslL179_ & [97], SH lab; B10 \\  & IN(_rmD-rmE_)1; see [97]; called & \\  & R594 & \\ \hline TC600 & _supE_, Pm\({}^{+}\) & SH lab, B8 \\ \hline Ymel & _supF_, Pm\({}^{+}\) & SH lab, B71 \\ \hline DE407 & _lexA3_[Ind ] _malB_::Tn9_ & Tet\({}^{\rm R}\) & [PERSON]; B142 \\  & _sulaA211 sfta11_, UV\({}^{\rm S}\) & \\ \hline FC40 (=SMR624) & \(\Delta\)(_srlR-recA_)306::Tn10_ Tet\({}^{\rm R}\) UV\({}^{\rm S}\) & [PERSON] [98]; Y921 \\ \hline AB2834 _aroE_ & _grpD55, thi_ txs\({}^{\rm R}\)\(\lambda^{\rm R}\) at 42 °C from & [PERSON] [32,33]; NB83 \\  & K552 & \\ \hline W3874 _malB5_ & _dnaB grpA80 lac\({}^{\rm-}\)_Str\({}^{\rm R}\)\(\lambda^{\rm R}\) at 42 °C & [32], NB81 \\ \hline W3350 _dnaB- grpD55_ & _grpD55 malF3089::Tn10_ Tet\({}^{\rm R}\)\(\lambda^{\rm R}\) at 42°C, \(\lambda\)_rep_P22\({}^{\rm S}\) & [33], NB15 \\ \hline
594 _dnaB-grpD55_ & _grpD55_ allele _malF3089::Tn10_; & [34], NB295 \\  & Tet\({}^{\rm R}\), \(\lambda^{\rm R}\) at 42°C, \(\lambda\)_rep_P22\({}^{\rm S}\) & \\ \hline
594 _lexA3_[Ind] & LexA repressor induction defective & CE, NB293 \\ _malB_::Tn9 & & \\ \hline
594 \(\Delta\)(_srlR-recA_)306::Tn10_ & & \\ \hline W3350 & _F lac’3350 galK2 galT22 rpslL179_ & SH lab, B12 \\  & IN(_rmD-rmE_)1 & \\ \hline Y836 & SA500(\(\lambda bio275 cI\)[Ts]857 \(\Delta\)431) & [78,82], NY1049 \\  & _his’_ & \\ \hline
594::_nadA_::Tn10_ [\(\
eg cIII\)-_ren\(]^{\lambda}\) & Tn10 [zbh29 at 16.8 min] _bio\({}^{+}\)_ & [PERSON], SH lab, NY1057 \\  & transductant = 594 bio275 (\(\lambda cIII\)-_cl_[Ts]857-O-P-_ren_) \(\Delta\)431 & \\ \hline Y836 _P_::kan (Bib11t) & SA500 (\(\lambda bio275 cI\)[Ts]857 \(O^{+}\)Pr::kan \(\Delta\)431) _his’_ Kan\({}^{\rm R}\) & SH, NY1153 \\ \hline
594(\(\lambda cI857\)Sam7) & \(\lambda\) lysogen defective for cell lysis & [PERSON], SH lab, Y1163 \\ \hline
594(\(\lambda cI857\)Sam7)[pcIpR- \(P\)-timm] & as above with transformed plasmid & SH lab, P509 \\ \hline
594 _clpP_::kan & _clpP_, Kan\({}^{\rm R}\) from SG22159 & [PERSON]. [PERSON]; [99], NB276 \\
**Plasmids** & **Transformed into strain 594** & **Source/Ref.’; Hayes lab \#a** \\ \hline pUC19 & Wild type Amp\({}^{\rm R}\) (New England Biolabs) & NP188 \\ \hline pClpR-_P_-timm & _Bam_HI-_Cla_l PCR fragment from & CH, P466 \\  & \(\lambda\)cl857, replacing D-CAP in P459 & \\  & with \(\lambda\) bp’s 39582-40280 & \\ \hline pClpR-_::kan-timm & PCR _Bam_HI-_Cla_l fragment from & KM, P510 \\  & Y836 _P_::kan (Bib11t) strain & \\  & NY1153 & \\ \hline \end{tabular}
\end{table} 
Table 10: Bacteria, plasmids and phages employed.
_Applied_  
_sciences_  
_Article_  
**Peroxynitrite Production Induced by LPS and X-ray Treatment Enhances Cellular Incorporation of Porphyrin in Mouse RAW264 Macrophages**  
**[PERSON]**  
Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka,  
Kagoshima 890-8544, Japan; [EMAIL_ADDRESS]; Tel.: +81-99-275-6272  
**Abstract:** Photodynamic therapy (PDT) is a minimally invasive cancer therapy that combines the accumulation of photosensizers such as porphyrins in cancer cells with laser irradiation. I have previously reported that mitochondarially derived reactive oxygen species (ROS) regulate the expression of a porphyrin transporter, heme carrier protein 1 (HCP1), and increase porphyrin accumulation in cancer cells. Tumors that contain activated macrophages, referred to as tumor-associated macrophages (TAMs), have been reported to have increased malignancy. TAMs produce nitric oxide (NO), via the expression of inducible NO synthase (iNOS), and the highly reactive nitrogen species, peroxynritrite, which is produced by the reaction of NO with superoxide. Here, I examined the relationship between peroxynritrite, HCP1 expression, and intracellular porphyrin uptake in the murine macrophage cell line RAW264. RAW264 cells were activated by lipopolysaccharide (LPS) treatment which resulted in increased iNOS expression and NO production. Additional X-ray irradiation resulted in the generation of ROS and the subsequent generation of peroxynritrite. Importantly, LPS and X-ray co-treatment significantly enhanced HCP1 expression and porphyrin accumulation in cells, suggesting that the peroxynritrite upregulates the porphyrin transporter, HCP1. Therefore, TAMs may be effectively targeted with PDT, and tumor progression may be suppressed in general by agents that target the activation of macrophages.  
peroxynitrite; nitric oxide; reactive oxygen species; porphyrin; HCP1
# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 3 Experimentation and Results
### Experimental Environment and Dataset  
This experiment was designed to be conducted on a high-performance server platform equipped with an Intel (R) Core (TM) i9-12900 KF CPU, 32 GB RAM (Intel, Santa Clara, CA, USA), and NVIDIA GeForce RTX3080 ti (Nvidia, Santa Clara, CA, USA). The experiment used the SWINySEG dataset made publicly available by the National University of Singapore [15] and the TCDD dataset made publicly available by Tianjin Normal University [22].  
Figure 4: Main components of optimized CAM and SAM in Im-CSAM.  
The SWINySEG dataset includes 6768 daytime and nighttime cloud images along with their corresponding binary label maps, while the TCDD dataset consists of 1874 ground cloud images and their corresponding cloud masks. In this experiment, both datasets were divided, with 90% used for the training set and 10% for the test set.