esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Ultra-Fast Gas Chromatography Analysis  
The ultra-fast gas chromatography (UFGC) analysis (mod. Heracles II, Alpha MOS, Toulouse, France) coupled with an Odorscanner headspace autosampler (mod. HS 100, CTC Analytics, Zwingen, Switzerland) to automate sampling and injection was used. The Heracles II was equipped with two metal columns of different polarities working in parallel mode: a non-polarcolumn (MXT-5: 5% diphenyl, 95% methylpolysiloxane), and a mid-polar column (MXT-1701: 14% cyanopropylphenyl, 86% methylpolysiloxane), both 10 m long and 0.18 mm in diameter, coupled to two flame ionization detectors (FID1 and FID2). Therefore, two chromatograms are obtained simultaneously, allowing a well-defined identification of the chemical compounds. The instrument is operated through AlphaSoft 12.4, software that can be used within the additionally AroChemBase module (Alpha MOS, Toulouse, France). The data collected can be analyzed like that of a conventional chromatograph, using Kovats retention index (RI) values and Alpha MOS' AroChemBase neural network to identify specific compounds. Every single compound, detected using the FID detector, can be identified by Alpha MOS' AroChemBase software by comparing the retention indices of a sample's peaks to those of known standards to give a list of compounds that could be responsible for each particular peak; the software calculates how different an unknown compound is from the nearest standard. A standard mixture of C6-C16 n-alkanes (from n-hexane to n-hexadecane) is used to calibrate the system, to allow retention time conversion into KI or RI. In addition to classical chromatography functionalities, the software for the instrument management and control provides chemometrics data processing tools to perform multivariate statistical analysis, such as sample fingerprint analysis and comparison, qualitative and quantitative models, quality control charts.  
The analytical parameters optimized for this analysis as follows: an aliquot of each sample (1.5 g) was placed in a 10 mL glass vial, sealed with magnetic plugs. The vials were placed in the Heracles' auto sampler and left to equilibrate for approximately 20 min at 60 \({}^{\circ}\)C before beginning headspace analysis. Syringe pierced the silicone septum of the magnetic plug and for each sample, approximately, sampled 2 mL the headspace delivered at 125 \(\upmu\)L/s by the autosampler to the injector at 200 \({}^{\circ}\)C. The 2 mL headspace aliquot was, before the chromatographic separation, adsorbed on a TENAX absorbent trap maintained at 50 \({}^{\circ}\)C for 20 s, while the carrier gas (H2) flowed (flow rate: 1 ml/min) through it in order to concentrate the analytes and to remove excess air and moisture.  
Subsequently, desorption was obtained by increasing the temperature of the trap up to 250 \({}^{\circ}\)C in 93 s and the sample was injected. The thermal program started at 50 \({}^{\circ}\)C (held for 2 s) and increased up to 80 \({}^{\circ}\)C at 1 \({}^{\circ}\)C s\({}^{-1}\); to 250 \({}^{\circ}\)C at 3 \({}^{\circ}\)C s\({}^{-1}\) and held for 21 s. The total separation time was 110 s.
## 3 Simulation Test Analysis of Sorting and Conveying Process
### Analysis of Simulation Results  
Figure 7 shows that when the inclination angle of the set cane plate is small, the sugarcane falls on the set cane plate, there is a certain offset, and the sugarcane slides down slowly. When the distance between neighbors is larger, the sugarcane at the bottom end of the set cane plate is sorted by the sorting organization; when it is smaller, the amount of sugarcane sorted by the sorting organization is smaller, usually consisting of 1-2 roots. The number of sorted canes is often two to three canes because the cane displacement during the downward sliding operation is minimal, and the distance between adjacent canes is appropriate when the collecting plate's inclination angle is 35\({}^{\circ}\). As the cane collecting plate's inclination angle increases, the cane's gravitational force increases as well. This causes the cane to collide with the plate and then slide rapidly to the bottom. The conveying process becomes less continuous and less orderly, which leads to uneven sorting by the sorting organization.  
Figure 8 shows the simulation results for different sprocket speeds of the sorting system. As seen in Figure 8, the higher the sprocket wheel's rotational speed, the faster the sorting mechanism's sorting speed is, and the fewer equivalent sorting hook claws there are each time, even leading to the occurrence of empty hooks. Conversely, when the sprocket's rotational speed decreases, the frequency of sorting decreases as well. A certain amount of sugarcane may accumulate at the bottom of the cane collection plate, increasing the number of cases where two or three canes need to be sorted. However, if the sprocket's rotational speed is too slow, it is easy for the sorting mechanism to fail to grasp the sugarcane in time, causing it to pile up at the bottom of the cane collection plate, which reduces the sorting effect.  
Figure 7: Process of simulating various case collection plate inclination angles. (**a**) Represents the sorting of sugarcane at \(\alpha=25^{\circ}\); (**b**) represents the sorting of sugarcane at \(\alpha=30^{\circ}\); (**c**) represents the sorting of sugarcane at \(\alpha=35^{\circ}\); (**d**) represents the sorting of sugarcane at \(\alpha=40^{\circ}\); and (**e**) represents the sorting of sugarcane at \(\alpha=45^{\circ}\).
# Cybersecurity Challenges in the Maritime Sector
## 3 Cyberattacks on the Ship Automation Systems
### Radio Detection and Ranging  
Although radar signals are more difficult to interrupt than satellites, they are still susceptible to interference and DDoS attacks from cyberattacks. In the event of a cyberattack, radar can provide false information about nearby objects due to false echoes caused by external radar waves. This incorrect information can cause ship collision accidents.  
This incorrect information can cause the ship to collide with an object. It is important to note that while radar and other frequencies in the electromagnetic spectrum are susceptible to noise-based interference or more advanced spoofing attacks, the mechanisms to achieve the same effect vary significantly between systems [44].
# Haystack Fires in Australia: Causes and Considerations for Preventative Management
## 5 Monitoring of Agricultural Parameters
### Conductivity Sensors  
Conductivity is the measure of a material's ability to allow electric current to pass without transferring its energy into heat. This measure varies with moisture because the cellulosic fibre which makes up most of the hay is an insulator. When moisture is added, it increases the conductivity through the transfer of ions into solution and saturation of the fibre matrix [64]. This conductivity is measured in Siemens, which incorporates the surface area of the electrode, distance between the electrodes and the resistance met by current passing from one electrode to the other.  
Electric conductivity sensors can be arranged on the same plane or separated entirely, providing diverse potential configurations. The current must overcome the resistance, requiring the electric signal to be developed for the distance between electrodes and the material resistance range [68]. The resistance of the material can vary with density, plant type, ambient temperature and the source of moisture. Various contact types can be used for electrical signal transmission, including pins, probes and electrodes. Previous research has questioned the accuracy of conductance-type sensors tested with a square baler [69].
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Manual Fruit Count
#### 2.3.1 Sample Size and Variance
For Simple Random Sampling (SRS), a 90% confidence interval with a maximal error of 10% of the mean, the required finite population adjusted sample sizes derived using Equations (3) and (4) were between 20 and 156 trees (Table 3). Observe that as \(n\) / \(N\to 0\), the difference between \(n\) and \(n^{*}\) is small, but at larger \(n\)/ \(N\), \(n\) is an overly conservative prediction of sample size (Equations (3) and (4)). An orchard with high intrinsic variability, sometimes referred to as 'biological variability', will provide samples with high within-sample variability, even for precise estimators. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{\(N\) **(\# Trees)**} & **Average (\#** & \multirow{2}{*}{\(SD\) **(\# Fruit/Tree)**} & \multicolumn{2}{c}{\(n\)**} & \multirow{2}{*}{\(n^{*}\) **(\# Trees)**} & \multirow{2}{*}{\(n^{*}\)**(\# Trees)**} \\  & & & & & **Equation (3)** & \\ \hline
1 & 469 & 88 & 82 & 234 & 156 & 33 \\
2 & 486 & 259 & 102 & 42 & 38 & 8 \\
3 & 1017 & 240 & 160 & 120 & 107 & 11 \\
4 & 1100 & 80 & 34 & 49 & 47 & 4 \\
5 & 224 & 59 & 36 & 100 & 69 & 31 \\
6 & 1205 & 97 & 65 & 121 & 110 & 9 \\
7 & 1091 & 201 & 55 & 20 & 20 & 2 \\
8 & 1818 & 106 & 51 & 62 & 60 & 3 \\
9 & 1176 & 77 & 61 & 169 & 148 & 13 \\
10 & 1115 & 85 & 40 & 60 & 57 & 5 \\ \hline \hline \end{tabular} Only half of the orchards required a sample size of 5% or less of the trees.  
\end{table} 
Table 3: Calculated sample sizes \(n\) (Equation (3)) and adjusted sample size \(n^{*}\) (Equation (4)) required for SRS with a 90% confidence and a PE of 10% based on an estimated \(SD\) from a preliminary sample taken from the population of \(N\) trees. Data from [PERSON] et al. [16]. # represents number. To illustrate this, we can decompose the estimator variance into terms expressing the 'intrinsic' (or 'biological') variability, \(SD^{2}_{bio}\) and the sampling variance \(SE^{2}\):  
\[Var=SD^{2}_{obs}=SD^{2}_{bio}+SE^{2} \tag{5}\]  
where \(SD^{2}_{obs}\) is the observed variance as calculated from the sample, and the \(SE\) is the standard error. The biological variance imposes a lower bound on the observed total variance.
## 4 Prediction Model for Time-Dependent Capacity of Uplift Piles  
In cohesive soils, the uplift bearing capacity of suction piles is primarily composed of the cohesive force cc and the frictional force FF formed at the pile-soil interface. It is assumed that under the action of uplift loads, the side friction develops stably and sufficiently and the pile-soil interface conforms to the friction law. For the purpose of this study, a differential element \(dz\) of the pile body is considered, with the coordinate at the pile top being (0, 0) and the coordinate at the pile bottom being (0, \(L\)), as illustrated in Figure 6.  
For normally consolidated soil, during the interaction between the pile and the soil, the normal pressure acting on the infinitesimal surface at a depth of \(z\) is:  
\[\sigma=k_{0}\gamma z \tag{3}\]  
In the equation, \(\sigma\) represents the normal stress on the pile's side surface. For normally consolidated soils, the coefficient of at-rest earth pressure \(k_{0}\) is given by \(k_{0}=1\,-\,\sin(\delta)\), where \(\delta\approx\phi\) and \(\phi\) is the angle of internal friction of the soil. \(\gamma\) denotes the effective unit weight of the soil, and \(z\) indicates the depth of the calculation point. Under ultimate equilibrium conditions, experiments have shown that the failure mode of the model pile in uplift is characterized by the pile-soil contact interface acting as the failure surface, and the frictional resistance on the side of the infinitesimal element is given by:  
\[F=\mu\pi D\sigma dz \tag{4}\]  
In the equation, \(\mu\) is the pile-soil friction coefficient, taken as \(\mu=\tan(\phi)\); \(D\) is the pile diameter.  
The lateral resistance acting on the infinitesimal element is:  
\[f=\pi D(c+\mu k_{0}\gamma z)dz \tag{5}\]  
Figure 6: Calculation and analysis diagram of uplift pile.  
By integrating the lateral resistance along the pile body, the calculation formula for the uplift bearing capacity in uniform soil can be obtained:  
\[U=\int_{0}^{L}\pi D(c+\mu k_{0}\gamma z)dz \tag{6}\]  
By integrating, we can obtain:  
\[U=\pi D\bigg{(}cL+\frac{1}{2}\mu k_{0}\gamma L^{2}\bigg{)} \tag{7}\]  
In the formula, \(L\) represents the pile length and \(c\) denotes the cohesion of the soil.  
Since the internal friction angle \(\phi\) and cohesion \(c\) are both functions of time and vary with the duration of rest, incorporating the functional relationships of cohesion and internal friction angle with respect to time yields a prediction formula for the limit bearing capacity against uplift considering aging effects, which is as follows:  
\[U=\pi D\bigg{\{}c(t)L+\frac{1}{2}tan\varphi(t)[1-sin\varphi(t)]\gamma L^{2} \bigg{\}} \tag{8}\]
## 8 Future Work  
The integrity verification framework proposed in this research primarily focuses on the integrity verification of datasets composed of files. However, object-based data transfer systems (OBDTS) can also be used to transfer other types of data, such as streams and databases. Extending the framework to support these other data types would make it more versatile and applicable to a wider range of use cases. Furthermore, it would be beneficial to explore further optimizations to improve the efficiency and scalability of the CRBF-based integrity verification framework. This could be done by exploring techniques such as distributed computing or hardware acceleration to handle larger datasets and higher data transfer rates effectively.  
The current study only evaluates the performance of the framework on a small number of workloads. It would be interesting to evaluate the performance of the framework on a wider range of workloads, including both synthetic and real-world workloads. By addressing these future research directions, the proposed CRBF-based integrity verification framework can be further enhanced, extended, and adapted to meet the evolving needs of object-based data transfer systems, ultimately contributing to the advancement of data integrity and reliability in modern computing environments.  
Conceptualization, P.K. and P.H.; Data curation, P.K.; Formal analysis, P.K., P.H. and T.-S.C.; Funding acquisition, T.-S.C.; Investigation, P.K. and P.H.; Methodology, P.K.; Project administration, T.-S.C.; Resources, T.-S.C.; Software, P.K.; Supervision, T.-S.C.; Validation, P.K.; Writing--original draft, P.K.; Writing--review & editing, P.K., P.H. and T.-S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00255968) grant and the ITRC (Information Technology Research Center) support program (IITP-2021-0-02051), funded by the Korea government (MSIT).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CRBF & Cross-Referencing Bloom Filter \\ PFS & Parallel File System \\ LADS & Layout-Aware Data Scheduling \\ OST & Object Storage Target \\ OSS & Object Storage Server \\ OBDTS & Object-based Big Data Transfer Systems \\ TPBF & Two-Phase Bloom Filter \\ DLBF & Data- and Layout-Aware Bloom Filter \\ DSBF & Dataset Bloom Filter \\ MDBF & Metadata Bloom Filter \\ OLCF & Oak Ridge Leadership Computing Facility \\ \end{tabular}
## 1 Introduction
### Research Contribution  
The key contributions of this paper are summarized as follows:  
* We conduct field measurements using wireless laptops and cards to collect a rich dataset in a large shopping mall parking lot in Auckland. To this end, we performed an extensive field measurement campaign to evaluate and validate the system's performance.
* We identify and measure the key parameters including received signal strengths, packet send/receipt, and response times between two experimental vehicles equipped with 802.11n cards. These field data form a rich data set to be used by system planners to predict the system's performance in a similar parking lot elsewhere. We also evaluate and validate the system's performance through various field experiments.
* We develop simulation models using OPNET Modeler to study the system's performance. To this end, we design five practical simulation scenarios to analyze and validate the system's performance.
* We analyze and find the best fit model that closely matches the field data to the propagation models. We consider and analyze five well-known propagation models (Free Space, Shadowing Path Loss, Egli, Hata, and COST 231). We have selected these models due to their popularity and relevance to our study.  
This paper is organized as follows. The background information and related work are presented in Section 2. The research design, including setting up the field trial measurements, is presented in Section 3. Section 4 presents the measurement results and validation. The simulation results are discussed in Section 5. The practical implications for the system design and deployment are discussed in Section 6. Finally, a brief discussion in Section 7 ends the paper. Table 1 lists the abbreviations used in this paper.
## 4 Experiments Analysis and Discussion
### Ablation Study  
TSNN-ESTD utilizes iTransformer as a base predictor and trains short-term and long-term forecasting models concurrently. TSNN-ESTD mainly includes three components, MLP-based encoder-only architecture, LSTM-based encoder-decoder architecture, and an ensemble strategy for short-term bias prediction.  
Figure 4: Performance of different short-term time step parameter S.  
Figure 5: Performance of different weight composition.  
To further examine the effectiveness of each component, Table 4 presents the results of ablation experiments of the long-term forecasting with 336 time steps. iTransformer is the original benchmark model, and TSNN-ESTD is the final model presented in this paper. TSNN-ESTD w/o ensemble removes the short-term forecast correction and only uses the output results of two long-term forecast models. TSNN-ESTD w/o LSTM and ESTD w/o MLP only use short-term prediction models and the MLP-based encoder-only architecture or the LSTM-based encoder-decoder architecture for bias correction, respectively. The experiments indicate that each component plays a positive role in the overall performance improvement.
## 3 Results
### Raman Measurements
However, such definitions are challenging to use in practice due to the difficulty in evaluating these complex quantities. Plotting the 1358 cm\({}^{-1}\) Raman peak intensity against concentration and fitting a curve to the data points, an allometric exponential relationship (\(I=aC^{b}\)) can be noted (Figure 4). The approximation is slightly inaccurate around the higher concentration points, due to the reasons described previously. The obtained approximation curves can be used as calibration curves, by which an unknown concentration of Rhodamine B could be deduced, performing a SERS measurement on one of these substrates and relating the obtained  
Figure 3: Comparison of Rhodamine B SERS spectra measured on the substrates (A, B, and C) at concentrations of (**a**) \(10^{-8}\) M and (**b**) \(10^{-12}\) M. The incident laser wavelength is 532 nm @ 2.55 mW, 1 s exposure. 1358 cm\({}^{-1}\) peak intensity to the concentration. It can be noted from this exponential relationship that the enhanced Raman intensity is not proportional to the concentration of the analyte (as is the case for conventional Raman spectra). This points to the fact that SERS is a non-linear surface phenomenon; the change in intensity (\(\frac{dI}{dc}=abC^{b-1}\)) is larger in the range of higher concentrations but becomes smaller with diminishing analyte concentrations. At lower concentrations, the molecules on average get closer and closer to the active surface and hotspots, which are the main contributors to the enhanced Raman signal, and thus the signal diminishes at a slower rate. This again shows the limitations of the commonly used AEF as a reliable metric for the characterisation of SERS substrates. In Figure 5, surface enhancement maps are presented in which the intensity ratio of the 1649 cm\({}^{-1}\) Raman maximum to the background intensity (taken as intensity at position 1680 cm\({}^{-1}\)) is calculated and plotted. Each map consists of \(\sim\)2500 points, at which the Raman spectrum has been measured and the ratio calculated. These maps give an idea of the enhancement uniformity of the substrates, clearly showing the locations of hotspots as well as locations of weak enhancement. However, the enhancement distribution is also highly dependent on the way the analyte dries after being deposited on the surface, and this drying is an uncontrolled and somewhat random process. Nevertheless, it can be seen that substrate B is the one yielding the most homogeneous signal-to-background intensity ratio along its surface.
## 2 Background and Related Work
### Study of European Portuguese Nasal Vowels  
The European Portuguese (EP) inventory includes five nasal vowels and three nasal diphthongs [20]. However, it is debated whether the vowels are phonemic nasal or simply contextually nasalized. For example, for Brazilian Portuguese, [PERSON] [21] found a synchronous coordination of the nasal vowel with the preceding consonant, while [PERSON] [22] reported a very late alignment of nasal and oral gestures, arguing against the phonemic status of the nasal vowels. In an RT-MRI study with 12 participants from southeastern Brazil, [PERSON] et al. [23] found or-pharyngeal differences between nasal and oral vowel counterparts /a \(\bar{\mathrm{a}}\)/, \(\bar{\mathrm{i}}\) i/, and \(\mathrm{/u}\)\(\bar{\mathrm{u}}\)/, and nasal coda emergence, arguing that nasal vowels are underlyningly /\(\bar{\mathrm{V}}\)/ rather than /VN/ sequences in BP.  
The study of nasality towards a richer description of its unfolding over time is an important step towards a greater understanding of speech production, with a potential impact in several application areas, such as speech therapy, and it is paramount to evolving articulatory-based speech synthesis [24; 25].  
In this regard, our team has studied the static and dynamic patterns of European Portuguese nasal vowels, resorting to technologies ranging from EMA [7; 8; 9; 26] to RT-MRI [10; 11; 27]. To expand this line of work, further improvements are still required regarding how oral and nasal gestures are synchronized in EP nasal vowels production by moving beyond the limitations of the previously analyzed data, mostly due to a restricted number of speakers (between 1 and 3) and partial information regarding the tract, for EMA, or reduced temporal resolution (14 Hz), for RT-MRI. The way velar aperture varies over time and how it coordinates with other articulators [28] is paramount for their characterization. This dynamic behavior of nasal vowels, starting with a dominant oral segment and ending with a dominant nasal configuration [29], is relevant for their perception [30], and hence understanding it is also important for improving, for instance, articulatory synthesis models. Recent work considering a novel RT-MRI dataset [1; 3] acquired at 50 Hz for 11 speakers presented first results for the synchronization of the oral gestures [31], analyzing the data for each speaker individually. However, such rich sets of data open additional possibilities to expand our knowledge on the dynamic nature of nasal vowels, particularly by going beyond a speaker-by-speaker analysis. To this end, two important challenges need to be addressed. First, the considered data need to provide a characterization of the whole vocal tract configuration, and at the same time, support higher time resolutions, a key aspect for studying dynamic sounds such as nasal vowels. Second, since previous research was often restricted to a small number of speakers, the exploration of data providing a wider diversity is paramount to moving research towards more generalizable findings and normative data.