esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Apple Juice Type on Fermentation Process and Volatile Compounds of Hard Apple Cider
## 2 Materials and Methods
### Volatile Compounds Determination  
Volatile compounds in samples were determined on an Agilent 7890B gas chromatograph (GC) with an Agilent 5977A mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). Sample preparation included transferring 5 mL of the sample in a 20 mL vial, adding 1 g of NaCl and 5 \(\mu\)L of internal standard (myrtenol, 0.5 mg/L). The vial was closed and placed in agitator, where the sample was stirred at 300 rpm and heated at 40 \({}^{\circ}\)C. In the vial's headspace, an SPME (solid-phase microextraction) fiber was placed with a PDMS/DVB (polydimethylsiloxane/divinylbenzene) sorbent for 45 min. After the extraction and adsorption of the volatile compounds, the SPME fiber was transferred into the GC injection port where desorption occurred at 250 \({}^{\circ}\)C for 7 min in splitless mode. In the GC oven, a HP-5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) was placed. The temperature of the oven was 40 \({}^{\circ}\)C for 10 min, and then it was raised to 120 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, then to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min. The carrier gas was helium 5.0 (purity 99.999%) with the flow of 1 mL/min. The mass spectrometer (MS) conditions were as follows: MS Source temperature was 230 \({}^{\circ}\)C, MS Quad temperature was 150 \({}^{\circ}\)C, \(m/z\) was from 40 to 400, and the ionization energy was 70 eV. The obtained peaks of compounds on the chromatogram were identified according to the retention time, retention index, and mass spectra of compounds, and it was compared to the NIST (National Institute of Standards and Technology, Gaithersburg, MD, USA) and Wiley mass spectral databases. For retention index calculation, the C7-C30 saturated alkanes standards were analyzed under the same GC/MS conditions. The quantification of compounds was made using the semi-quantitative method, with the use of the internal standard (IS). For concentration calculation purposes of the detected compounds, the relation index and response factor with IS was determined by injecting the known concentration of a standard. The exceptions were 2-ethylhexanoic acid, stearic acid, isobutanol, methionol, 2-nonanone, methone, methyl eugenol, butyl acetate, and 2,4-di-t-butylphenol, whose concentrations were determined tentatively. Changes in volatile compounds were followed up for three weeks after fermentation was finished (after fermentation, after 1 week of maturation, after 2 weeks of maturation, and after 3 weeks of maturation).
# Designing an Interactive Visual Analytics System for
## 6 Case Studies
### Case Study: Performing a Comparative Analysis on Multiple Regions
#### 6.2.3 Baseline Climatology Comparison  
Baseline climatology comparison supports identifying precipitation patterns for each location by performing historical monthly precipitation averages across multiple years. We performed a monthly precipitation analysis to conduct an analysis on the findings from the  
Figure 16: Extreme event analysis with the annual PCA analysis visualization. It shows all annual precipitation events plotted with PC1 and PC2 on a floating window, where several selected annual precipitation events represent details of measured precipitation features.  
PCA analysis results (see Section 6.2.2) to identify precipitation patterns that significantly deviate from expected seasonal norms.  
Figure 17A shows visualizations of monthly average precipitation from 1972 to 2022. By evaluating over 50 years of precipitation data in the five stations, we found that the most noticeable precipitation events mainly occurred in Houston. Figure 17B shows the monthly average precipitation in 1977. In that year, none of the stations recorded noticeable precipitation events. However, Los Angeles showed the highest precipitation peak. Although the average precipitation amount was not high, a tropical cyclone, Hurricane Doreen, impacted Southern California, including Los Angeles, in 1977 [96]. It was recognized as an unusual event in Southern California during the normally dry summer months. In 2013, two anomalous precipitation events were observed in Houston and Washington, DC. Houston showed the highest average precipitation in June. Although no major catastrophic event happened in Houston in 2013, an unusually persistent upper-level low-pressure system stalled over the area and produced heavy rainfall from thunderstorms in June. Heavy rainfall happens in the case of conditions of atmospheric instability, abundant moisture, and slow storm movement [97]. Using this unique event, scientists conducted a simulation study to investigate thunderstorm formation mechanisms by modeling the June 2013 conditions [98]. In October 2013, Washington, D.C. experienced a similar anomaly when a slow-moving frontal system produced several days of heavy rainfall. By evaluating the actual precipitation data with the station-specific analysis interface, we found that it ranged from 0.02 to 3.45 inches and averaged 0.34 \(\pm\) 0.65 inches from 9 to 13 October. Notable intense precipitation occurred on 7 October (3.45 inches in a single hour), 10 October (over 3 inches during the morning hours), and 11 October (peak intensity of 3.4 inches between 7 and 8 p.m.). Due to these heavy rainfall events during that period, Washington, DC, appeared as the location with the second-highest monthly average precipitation in 2013 [94].
## 3 Methodology and Solution
### Multi Mitigation Using the Chain of Notch Filters  
In order to remove the interference, a chain of notch filters is utilized. A notch filter is a narrow stop band filter that removes a very small section of the original signal at a very specific frequency band. The role of the notch filter is to remove the interference while  
Figure 7: Utilized mechanism for extracting key parameters of interferences.  
keeping as much as possible of the valuable data. The transfer function of the IIR M-order notch filter can be expressed as [33]:  
\[H(z)=\ \frac{\prod_{i=1}^{M-1}\bigl{(}1-e^{i\omega_{i}}z^{-1}\bigr{)}}{\prod_{i=0}^ {M-1}\bigl{(}1-re^{i\omega_{i}}z^{-1}\bigr{)}}, \tag{11}\]  
where \(\omega_{i}\) represents the center frequency of each notch and the pole radius \(r\) (\(0<r<1\)) controls the bandwidth of each notch. One important note about DVB or other communication signals is that, information is carried in two dimensional In-phase and Quadrature components. It means that these signals can be considered as complex signals. By replacing \(M\) with 1, the Equation (11) can be simplified as the first order complex notch filter:  
\[H(z)=\ \frac{1-e^{i\omega_{0}}z^{-1}}{1-re^{i\omega_{0}}z^{-1}}, \tag{12}\]  
where \(w_{0}\) is the center frequency of the notch filter. As it can be seen, each notch filter is controlled by two parameters: \(w_{0}\) as the center frequency and \(r\) that determines the gap bandwidth (Figure 8). Using this notch filter design, paired with the detector presented above, the interference mitigation system can adaptively track and remove narrowband interferences from the original signal.
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
## 2 Asteria
### Operations
The downlink receiver bandwidth was increased by a factor of 10 to reduce the loss of lock. 2. The uplink signal from the ground station was reduced by approximately 9 dB in order to avoid receiver saturation. 3. The spacecraft pointing system was commanded to point the antenna at the ground station to limit the loss of contact due to spacecraft rotation. Results showed that all three fixes improved the length and quality of the telecommunication passes. Specifically, passes duration moved from 2-3 min to 8 min and the Eb/N0 at high data rates moved from an initial value of 10 dB to 18 dB at high data rates (21 dB after the ground station LNA replacement in August 2018).
## 2 Materials in Model and Methods  
### Porous Materials under Investigation  
Materials with pores are now widely used in material engineering, medicine, and biochemistry, among other fields. Some of the resulting unusual properties have attracted increasing attention in engineering. In medicine, stainless steel, Co-Cr alloys, titanium, and its alloys are the main implant materials used to manufacture artificial joints, bone plates and artificial tooth roots [6; 7; 8; 9; 37]. These materials have different pore shapes and sizes, overlapping patterns(distribution modes). Hence, this study aims to elaborate a computational strategy to generate porous models close to the real microstructures, which can lead to a more accurate estimate of the effective elastic moduli of porous materials [15; 16; 17; 18], by taking account of the variation of pore sizes, shapes and the statistics information obtained experimentally.  
Titanium and its alloy have been widely used in artificial hip joints, dental implants, and in artificial verteb joints [12] because of their excellent mechanical properties and good biocompatibility. However, modulus of bones is usually within 10-30 GPa [12] whereas the modulus for titanium and its alloy is typically between 55-117 GPa [12]. This suggests that porous materials are needed to reduce the modulus for better integration with natural bones. Titanium is a poisonous metal that will cause irreversible harm to the human body if it is used as a long term substitute for bones.  
Cobalt-chromium (Co-Cr) alloys are the most common alternative for metallic restoration in dental laboratory [38]. They are relatively inexpensive compared to noble alloys and have excellent mechanical properties including a high Young's modulus, a high corrosion resistance, and a high strength [39; 40]. However, these alloys have the highest melting point range, which makes the finishing and other subsequent manufacturing processes difficult as well [39; 41].  
Porous tantalum is an attractive biomaterial for several clinical applications including hip and knee arthroplasty due to its excellent biocompatibility and good biomaterial properties [42]. In recent years, tantalum and its alloy have been received close attention because they do not cause known disease in humans and are not listed as a possible carcinogen. Additionally, porous tantalum forms a self-passivating surface oxide layer which can help the growth of bones and soft tissue [42].  
The three materials presented above have different properties and largely summarize most of the possible pore characteristics in porous materials. They represent different pore shapes, different pore sizes, and different distribution modes.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 2 Materials and Methods
### Feature Extraction
#### 2.3.2 Binaural Cues  
In order to extract the binaural cues, the signals were first band-pass filtered using a 42-channel gammatone filter bank with the lowest and the highest center frequencies of the filters being equal to 50 Hz and 16 kHz, respectively. Then, the inner hair-cell envelope of the bandpass filtered signals was extracted using the method consisting in half-wave rectification of the signals followed by their low-pass filtering with a cut-off frequency being equal to 1000 Hz, as proposed by [PERSON] [47]. Next, the rate-maps were calculated. The rate-maps constituted a representation of auditory nerve firing rates [48]. The obtained rate-maps formed a basis for calculating the binaural cues (ILD, ITD, and IC). In addition to the above features, the standard delta metrics were also computed.  
The binaural cues were estimated using the publicly available software package developed as an auditory front-end of the TWOEARS system [17]. The same software was also utilized to calculate the spectral features described in the next subsection.  
In summary, the following binaural cues were derived:  
1. means and standard deviations of the ILD values calculated separately for each of the 42 gammatone filters (\(2\times 42\) features),
2. means and standard deviations of the ITD values calculated separately for each of the 42 gammatone filters (\(2\times 42\) features),
3. means and standard deviations of the IC values calculated separately for each of the 42 gammatone filters (\(2\times 42\) features),  
The above procedure yielded 252 metrics (\(3\times 2\times 42\) features). In addition, in a similar way, the delta values were calculated, which doubled the number of metrics. Hence, in total 504 binaural features were produced.
# Accurate Phase Calibration of Multistatic Imaging System for Medical and Industrial Applications
## 6 Conclusions  
The phase calibration in a multistatic system has been developed. Since no system reconfiguration is required, the system can be calibrated automatically, and the phase calibration can be performed in a short time. The phase calibration was performed by  
Figure 9: Reconstructed image of a metal column by simple back-projection of waves at 6.5 GHz. Height interval 12.5 mm; range 0 mm to 75 mm.  
analyzing the reflection coefficient of the antenna in the time domain. The difference in electrical length of each port was determined. In the phase-calibrated system, the phase difference between the ports was found to be approximately 1/45 of the wavelength. An image reconstruction experiment was performed on the calibrated system using a simple inverse projection of the waves. The image reconstructed at 6.5 GHz showed the location and shape of the metal rod, which is one-fourth the diameter of the wavelength and beyond the diffraction limit.  
Conceptualization, H.T.; methodology, H.T.; software, H.T.; validation, H.T.; formal analysis, H.T.; investigation, H.T.; resources, H.T.; data curation, H.T.; writing--original draft preparation, H.T.; writing--review and editing, S.Y. and M.R.A.; visualization, H.T.; supervision, S.Y.; project administration, S.Y.; funding acquisition, S.Y. and M.R.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.
# Chaos Based Frequency Modulation for Joint Monostatic and Bistatic Radar-Communication Systems
## 6 Monostatic Radar Signal Processing
### Signature Analysis for the Monostatic Radar Configuration  
As mentioned earlier, in the presence of a complex target, the received is the summation of reflections from multiple point reflectors. Each of these reflections is delayed and Doppler-shifted depending on the location of the reflector. Hence, when the received signal is processed through the matched filter with impulse response \(s(t)\), the resultant is the signature analysis. Note that for the bistatic radar, the matched filter response is \(s_{rec}(t)\). Figure 16 shows the signature analysis of the same complex target. In addition, the entropy of the Lorenz and Burke-Shaw CBFM waveforms is close to 2. The positive entropy value signifies the presence of self-noise and a rise in the noise-floor of the signature. However, all the hotspots are distinguishable from each other.
## Materials and Methods
The second model inversion creates the date-by-date displacements, which is then used to create the geocoded output, which is displacement along the Line of Sight (LOS). The interferometric workflow is based on [PERSON] et al. 's methodology, for a mathematical account of the algorithms used see [PERSON] [11; 12]. Transport infrastructure data was freely sourced from the UK Ordnance Survey Meridian 2 dataset, which provides a frequently updated and accurate network of principle infrastructure types across Great Britain. The data provides information on the location and extent of motorways, A-roads, B-roads, Unclassified roads and Railways at the nominal viewing scale of 1:50,000. Data is provided in a GIS ready format, with road classification provided as attributes. Road types were categorised into minor roads (Unclassified and B-roads) and major roads (A-roads and Motorways), to simplify the analysis. From these categorisations, a buffer distance of 2 m from the center line of infrastructure was created for minor roads, 3 m for major roads and railways, and 4 m for motorways. This buffer zone was then used to subset the PSI output. This ensured that only PSI points which are in close proximity to the infrastructure were analysed, which reduces the number of PS points returned from pavements (sidewalks), surrounding buildings and vegetation and adjacent unconsolidated ground. By analysing the PSI output in a GIS environment, each PS point was attributed with information pertaining to the major soil group, which permitted contrasting the information about ground deformation with major soil groups. This study uses Cranfield University's National Soil Map [21]. The 1:250,000 scale soil map contains information related to soil properties including ground movement potential (based on the laboratory assessment of volume change potential of soils, categorized from \"low\" to \"high\"), texture, drainage, fertility, land cover, and habitats. The major soil groups included in this study were Brown soils, Ground-water Gley soils, Lithomorphic soils, Pelosols, Podozolic soils, Surface-water Gley soils, and Peat soils--see Table 2 below for their descriptions. Validation of the PSI outputs was undertaken over the closed Meldon quarry site (50.716084N, \(-\)4.026326W--see Figure 1. Meldon Quarry has been disused since 2007 [24], and comprises a mixture of metamorphic and igneous rocks. To this extent, the geology is highly stable and so presents itself as
## 4 PSI-CA Protocol Constructed Based on DH Key Exchange Mechanism
### Summary of This Chapter  
This chapter explores the PSI-CA Protocol built on the Diffie-Hellman (DH) key-exchange mechanism, detailing its processes and experimental analysis. Initially, the protocol employs a DH mechanism for securing data exchanges between two parties, outlined in specific stages: data encryption by the receiver, computation and further encryption by the sender, followed by decryption and intersection cardinality computation by the receiver.  
The experimental analysis section provides a practical examination of the protocol's runtime across varying dataset sizes, demonstrating that imbalances significantly affect  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cardinality of Dataset** & **Cardinality of Dataset** & **Protocol Runtime** \\
**from Participant One** & **from Participant Two** & **(Seconds)** \\ \hline \hline \(2^{10}\) & \(2^{15}\) & 1.8095 \\ \(2^{10}\) & \(2^{17}\) & 7.3003 \\ \(2^{10}\) & \(2^{20}\) & 56.1277 \\ \(2^{10}\) & \(2^{25}\) & 1859.9520 \\ \(2^{15}\) & \(2^{15}\) & 5.2207 \\ \(2^{15}\) & \(2^{17}\) & 10.0672 \\ \(2^{15}\) & \(2^{20}\) & 63.4835 \\ \(2^{15}\) & \(2^{25}\) & 1886.3966 \\ \(2^{17}\) & \(2^{17}\) & 20.7044 \\ \(2^{17}\) & \(2^{20}\) & 71.9252 \\ \(2^{17}\) & \(2^{25}\) & 1977.5657 \\ \(2^{20}\) & \(2^{20}\) & 170.3074 \\ \(2^{20}\) & \(2^{25}\) & 2054.2694 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Runtime of the PSI-CA protocol constructed based on the DH key-exchange mechanism.  
efficiency. As the dataset sizes diverge, particularly when a smaller dataset is compared with a rapidly increasing larger dataset, the protocol's runtime escalates dramatically.  
Therefore, there is an urgent need to design a new protocol to alleviate the adverse effects of dataset size imbalance on the performance of the PSI-CA protocol.
## 2 Theoretical Basis
### Ride quality index (RQI)  
Driving comfort is the most direct reflection of road service level and also the subjective feeling of road users towards the public service level. The driving comfort index is one of the important evaluation indicators for road maintenance quality, which is mainly affected by road smoothness. The current \"Highway Performance Evaluation Standard\" (JTG H20-2007) [11] uses the ride quality index (RQI) for evaluation, and the international roughness index (IRI) is measured using a continuous roughness meter. The ride quality index is calculated according to Formula (3):  
\[RQI=\frac{100}{1+a_{0}e^{(a_{1}\cdot IRI)}} \tag{3}\]  
where \(a_{0}\), \(a_{1}\)--model parameters, using 0.026 and 0.65, respectively.
# Optimized Parameter Search Approach for Weight Modification Attack Targeting Deep Learning Models
## 3 Methodology
### Selection of the Most-Influential Weights  
The main challenge of selecting the weights to be modified is that they can also affect the prediction of other input sets (different from the selected ones), making the model attack evident. Therefore, optimizing the chosen modifications in the parameters is necessary to carry out this attack efficiently.  
There are several ways to perform this selection, such as centering the search to a specific layer [18] or by analyzing the sensibility of the weights in the model [21]. In this work, following the evasion attack approach, parameters are chosen by how the gradient of the input data influences predictions. This gradient indicates how important a feature of the input data is, i.e., it can be considered a metric to select the position of the most-relevant data of the input layer (input neuron). In this regard, it should be noted that each input neuron is usually assigned more than one weight by the next layer. Therefore, once the input neurons have been chosen, the attacker must set the upper limit quantity indicating the maximum number of weights to be selected for pursuing the attack. Moreover, a metric is necessary to select this quantity from the assigned parameters. In this work, this subset of parameters is generated according to the absolute values of the weights, taking the highest ones.  
In summary, this approach starts by detecting the most-relevant input neurons of the input layer through the gradient. Among the weights assigned to those features, the ones with the highest absolute values are defined as the most influential ones. This subset is used for generating the attack.