esa-sceva/satcom-chunk-collection huggingface dataset:
## Quantification of Polyfunctional Thiols in Wine by HS-SPME-GC-MS Following Extractive Alkylation  
**[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 2, [PERSON] 2, [PERSON] 2 and [PERSON] 1,*  
* [1] Department of Food Science, Cornell University, Stocking Hall, 411 Tower Road, Ithaca, NY 14853, USA; E-Mails: lem248(c)cornell.edu (L.E.M.); ir45(c)cornell.edu (I.R.)
* [2] E. & J. Gallo Winery, 600 Yosemite Boulevard, Modesto, CA 95354, USA; E-Mails: [PERSON]DRESS] (B.S.P.); [PERSON]] (N.L.); [PERSON]DDRESS] (H.F.); [PERSON]ESS] (M.T.C.)
* [*] Author to whom correspondence should be addressed; E-Mail: gls9(c)cornell.edu; Tel.: +1-607-255-2335.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 30 May 2015 / Accepted: 30 June 2015 / Published: 6 July 2015_  
Analyses of key odorous polyfunctional volatile thiols in wines (3-mercaptoohexanol (3-MH), 3-mercaptoethanolate (3-MHA), and 4-mercapto-4-methyl-2-pentanone (4-MMP)) are challenging due to their high reactivity and ultra-trace concentrations, especially when using conventional gas-chromatography electron impact mass spectrometry (GC-EI-MS). We describe a method in which thiols are converted to pentafluorobenzyl (PFB) derivatives by extractive alkylation and the organic layer is evaporated prior to headspace solid phase microextraction (HS-SPME) and GC-EI-MS analysis. Optimal parameters were determined by response surface area modeling. The addition of NaCl solution to the dried SPME vials prior to extraction resulted in up to less than fivefold improvement in detection limits. Using 40 mL wine samples, limits of detection for 4-MMP, 3-MH, and 3-MHA were 0.9 ng/L, 1 ng/L, and 17 ng/L, respectively. Good recovery (90%-109%) and precision (5%-11% RSD) were achieved in wine matrices. The new method was used to survey polyfunctional thiol concentrations in 61 commercial California and New York State wines produced from _V. vinifera_ (Riesling, Gewurztraminer, Cabernet Sauvignon, Sauvignon blanc and non-varietal rose wines), _V. labruscana_ (Niagara), and _Vitis_ spp. (Cayuga White). Mean 4-MMP concentrations in New York Niagara (17 ng/L)were not significantly different from concentrations in Sauvignon blanc, but were significantly higher than 4-MMP in other varietal wines.  
keywords: wine; polyfunctional thiols; extractive alkylation; _Vitis labruscana_
## 3 Materials and Methods
### Performance Measures  
The proposed approach was compared with other task scheduling approaches using a number of performance measures, which are briefly described below:  
1. Total number of VMs: The total number of VMs used by a given approach to process all the tasks in the task set.
2. Degree of Imbalance (DoI): Used to provide a measure of the variation in the relative finishing times of the used VMs, expressed as follows: \[\text{DoI}=\frac{MaximumFinishTime_{VM}-MinimumFinishTime_{VM}}{AverageFinishTime_{VM}}\]
3. Makespan: The final time to complete all the tasks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Detail** \\ \hline Processor & Intel® CoreTM 15-3337U \\ Main memory & 8 GB \\ Operating system & Debian 12.8 \\ Integrated development environment & Eclipse 4.34.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardware and software details of the system used in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Detail** \\ \hline No. of processing elements & 2 [34] \\ Processor clock frequency & 2800 MHz [35; 36] \\ Main memory & 4 GB [34] \\ VM cost per second (USD) & 0.07948 [37] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of parameters of the used VMs.  
Total cost: Sum of the total cost incurred by all VMs, expressed as follows: \[\text{TotalCost}=\sum_{j=1}^{m}(FinishTime_{v_{j}}*v_{j_{rate}})\]
5. Average completion time of various tasks: The average finishing times of a subset of tasks among the complete task set. This metric has is obtained for high- and low-priority subsets of the task set.
6. Average waiting time of various tasks: Average waiting time of a subset of tasks computed for-high priority, low-priority, and all tasks. The average waiting time of a single task is expressed as follows: \[\text{WaitingTime}_{t_{i}}=(ExecutionStartTime_{t_{i}}-SubmissionTime_{t_{i}})\]
7. Total time under SLA violation: This is the time spent beyond the deadline of high-priority tasks.
8. Total SLA violation count: The total number of tasks that miss their SLA deadlines.
9. Total high-priority SLA violation count: The total number of high-priority tasks that miss their SLA deadlines.
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 3 Related Work
### Mental Models of Encryption  
The cybersecurity research community has endorsed using secure communication methods to protect confidential information. [PERSON] et al. [7] explored users' knowledge, experience, and perception of different communication tools. They found that many participants did not understand the fundamental concept of end-to-end encryption, which decreased their motivation to adopt secure tools. They identified several inaccurate mental models that underpinned participants' reasoning and decision making. [PERSON] et al. [6] conducted a study by interviewing a sample of users from an activist organization whose tasks required secrecy. The participants had different levels of technical sophistication and involvement with confidential information. They explored users' decisions about whether and when to encrypt emails and hypothesized that the organization's employees would have a strong motivation to encrypt emails. They found that the participants perceived only \"paranoid people\" or \"people up to no good\" would use encryption. Furthermore, a group of researchers [36] identified four mental models of encryption as a problem that illustrated how users perceived encryption.  
[PERSON] and [PERSON] [37] found that non-adoption of secure encryption tools is due to usability issues, such as users having great difficulty using email encryption software. On the contrary, [PERSON] et al. [34] found that the non-adoption of secure encryption tools might not be entirely due to usability issues. Their results showed several fundamental issues, such as misaligned incentives, incomplete threat models, and insufficient understanding of encryption. They also mentioned that just expanding the availability and usability of encryption functionality will not be enough to increase the adoption of end-to-end encryption. They suggested that creating comprehensive end user mental models related to email protection could increase adoption. [PERSON] et al. (2018) explored users' mental models of the \"HTTPS\" protocol. They found that end users often mistake encryption for authentication, significantly undervalue the security advantages of HTTPS, and neglect security indicators. Recently, users began using Gmail's Confidential Mode (GCM) to share confidential emails, believing it encrypts them [PERSON] et al. (2018). While GCM does not encrypt email content, it does ensure confidentiality by using built-in access controls. We designed a video message to highlight the importance of using end-to-end encrypted email before our participants explored Virtru's security features.
###### Abstract  
Snow in the mountainous watersheds of the Tarm River Basin is the primary source of water for western China. The Snow Cover Daily L3 Global 500-m Grid (MOD10A1) remote sensing dataset has proven extremely valuable for monitoring the changing snow cover patterns over large spatial areas; however, inherent uncertainty associated with large sensor zenith angles (SZAs) has called its reliability into question. Comparative analysis that utilized a paired-date difference method for parameters such as snow cover frequency, snow cover percentage, and normalized difference snow index (NDSI) has shown that overestimation of snow cover in the Tarm River Basin correlates with high values of SZA. Hence, such overestimation was associated with an increase in the NDSI, attributable to the change in reflectance between Band 4 and Band 6 imagery. A maximum threshold value of SZA of 22.37\({}^{\circ}\) was used alongside a multiday refilling method to modify the MOD10A1 dataset to produce a new daily snow cover map of the Tarm River Basin, spanning a 10-year period. A comparison of benchmark results of snow cover classification produced by the HJ-1A/B satellite revealed an increase in the overall accuracy of up to 4%, confirming the usefulness of our modified MOD10A1 data.  
MOD10A1; sensor zenith angles (SZA); snow cover overestimation; Tarm River Basin; HJ-1A/B Article
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 3 Proposed Model  
The methodology of this paper has the following steps:  
* Step 1: Introduce the IHEPC dataset, the theoretical background of the proposed model, and the model evaluation measurements.  
In this step, a glimpse of the dataset is given, and an integrated DNN with a multi-layered LSTM model is introduced. This obtains trends and time-based evidence from the dataset used. The model evaluation measurements used in the experiment are listed below.  
* Step 2: Practical application This step is divided into three parts:
* Preprocess the IHEPC dataset, such as resolving the issues of missing values and outliers, variable normalization, and data resampling.  
The IHEPC dataset contains several missing values and outliers that can be attributed to metering errors, weather conditions, and even uncharacteristic customer consumption trends. The atypical trends and redundancies in the data result in poor predictive performance and ambiguous predictions. To resolve this issue, the data are preprocessed.
* Train the desired model, a DNN model or an LSTM model, with the dataset and draw graphs to summarize the results.
* Compare the results of the proposed model with those of the developed DNN and LSTM models to check for improvements. The proposed model is also compared with other models in related research, such as the CNN multilayered bidirectional LSTM, CNN-BiGRU, GRU, CNN-ESN, R-CNN with ML-LSTM, linear regression models, LSTM, bidirectional LSTM, multiple LSTM, and WT multiple LSTM models.
# The Numerical Study for the Effect of Stiffness Matching on Wheel-Rail Curve Squeal Noise
## 3 Results
### Establishment and Verification of Complex Modal Analysis Model for Wheel-Rail System
#### 3.1.2 Verification of Complex Modal Analysis Model for Wheel-Rail System  
The primary objective of this research paper is to investigate the occurrence of wheel-rail curve squeal noise resulting from the lateral creep state between the wheel and rail. In this context, the creep force that arises due to lateral creep between the wheel and rail fundamentally represents sliding friction. Consequently, the central challenge and focal point of this model lie in accurately simulating the behavior of sliding friction. To verify  
Figure 2: FEA model of the wheel–rail system and its parts: (**a**) wheel; (**b**) rail; (**c**) sleeper; (**d**) track plate; (**e**) wheel–rail system.  
the correctness of the sliding friction behavior within the model, we incorporate the output of the friction force into the ABAQUS 2022 history output, for instance, when the vertical load acting on the wheel is set at 90 kN and the friction coefficient is 0.4.  
Figure 3 illustrates that upon completion of the analysis step loading, the transverse friction reaches its maximum value, closely approximating the theoretically expected friction value of 36 kN. This observation attests to the wheel-rail contact model's adherence to Coulomb friction characteristics, wherein the transverse friction is equal to the product of the normal load and the friction coefficient.
## 1 Introduction  
The Internet of Things (IoT)-based applications and services include sensor networks, healthcare systems, transportation, smart industry, communication systems, smart cities, and manufacturing [1]. The Industrial Internet of Things (IIoT) has been proposed to dramatically enhance qualities of traditional industries, break regional limitations to achieve remote monitoring, perform autonomous production, and provide real-time information to users [2, 3, 4]. The Internet of Thing (IoT) will deliver about 85% of all IoT devices in healthcare by 2025 [1]. According to Tractia, an intelligent organization, annual earnings in this sector using blockchain technologies would reach USD 9 billion by 2025 [2]. IoT devices are widely used in healthcare to give real-time services to patients and physicians [3]. IoMT-based medical device applications include medical institutions and businesses. However, as the number of internet-connected medical devices (IoMT) increases, greater volumes and inconsistency of data will be generated. With centralized cloud-based characteristics, handling significant data traffic in IoT (IoMT) has now become a severe problem and reason for concern [4]. As a result, patient safety and confidentiality concerns have grown while data collection, data ownership, location privacy, etc., will be at risk. By copying data and changing the identification of healthcare equipment, intruders and hackers can easily target the 5G-enabled IoMT network. IoMT-Cloud currently has a single point of failure, malicious attacks, and privacy leaks, as shown in Figure 1. To ensure network security and secure PHR transmission, data transfer between IoMT and Cloud requires trust, device identification, and user authentication (UA). With the traditional Central Cloud service, however, due to the round-the-clock networking of nodes in this IoT network, it is vulnerable to various security issues, such as message tampering, eavesdropping, and denial-of-service attacks [5]. In the industrial industry, this raises major security issues as the misuse of data can result in the incorrect diagnosis and can cause life-threatening scenarios for the patients under observation [6; 7].
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
## 6 Choosing DD Grid Parameters for Different Use Cases
This requirement results from the discussion presented in Section 3, see also Table 1. Nevertheless, case A is included in our example for possible use with other pilot configurations. We are making the following assumptions:  
* the carrier frequency equals 70 GHz, as before;
* BW of the signal equals 100 MHz;
* the signal is critically sampled, i.e., \(f_{s}=100\) MHz. We start by defining the maximum Doppler value based on (21): \(f_{d}=14\) kHz. Now, the maximum value of \(M\) could be defined based on (22). After rounding the obtained value down to the power of 2, the maximum \(M\) equal to 2048 is obtained. For \(f_{s}=100\) MHz, the size of the resolution element in the range direction is 3 m. In the calculation of \(\Delta t_{element}\), the maximum velocity of the use case considered should be used. In use case B \(\Delta t_{element}=50\) ms. To define the number of samples in one RP-OTFS frame, we assume that the time duration of the OTFS frame in (23) should be at least ten times smaller than \(\Delta t_{element}\). Thus, the number of samples in one OTFS frame in use case B is equal to 500,000. Rounding this value down to the nearest power of two, one obtains \(MN=262\),144. The final step is to choose concrete values of \(M\) and \(N\). Based on considerations about computational complexity, discussed before, and on the maximum value \(M\) calculated in the example, we obtain \(M=2048\), \(N=128\). The values of the OTFS parameters, together with the resolution values, calculated for the three considered use cases A, B and C, are presented in Table 2.
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 2 System Model  
As illustrated in Figure 1, the CSS problem can be decomposed into three key stages: compressive sampling, signal reconstruction, and WSS. Each of these stages is elaborated on in detail in this section.
# Statistical Pass-By for Unattended Road Traffic Noise Measurement in an Urban Environment
###### Abstract  
Low-noise surfaces have become a common mitigation action in the last decade, so much so that different methods for feature extraction have been established to evaluate their efficacy. Among these, the Close Proximity Index (CPX) evaluates the noise emissions by means of multiple runs at different speeds performed with a vehicle equipped with a reference tire and with acoustic sensors close to the wheel. However, signals acquired with CPX make it source oriented, and the analysis does not consider the real traffic flow of the studied site for a receiver-oriented approach. These aspects are remedied by Statistical Pass-By (SPB), a method based on sensor feature extraction with live detection of events; noise and speed acquisitions are performed at the roadside in real case scenarios. Unfortunately, the specific SPB requirements for its measurement setup do not allow an evaluation in urban context unless a special setup is used, but this may alter the acoustical context in which the measurement was performed. The present paper illustrates the testing and validation of a method named Urban Pass-By (U-SPB), developed during the LIFE NEREiDE project. U-SPB originates from standard SPB, exploits unattended measurements and develops an in-lab feature detection and extraction procedure. The U-SPB extends the evaluation in terms of before/after data comparison of the efficiency of low-noise laying in an urban context while combining the estimation of long-term noise levels and traffic parameters for other environmental noise purposes, such as noise mapping and action planning.  
SPB method; sound pass-by; low-noise surfaces; noise modeling; road traffic noise; unattended noise measurement procedure; traffic measurements; noise emission; environmental noise; sound +
Footnote †: journal: _Sensors_
# Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network
## 5 Discussion  
The experimental in this study is operated on a Windows 10 64-bit PC with 4.00 GHz Intel(R) Core(TM) i7-4790K CPU and a 16 GB memory. The software used in our experiment is Jupyter 6.1.1 and Python 3.6. The key parameters of four benchmark models are shown in Table 5.  
Where n_neighbors mean the number of nearest neighbors; depth denotes the depth of trees; learning_rate is used for reducing the gradient step, which affects the overall time of training: The smaller the value, the more iterations are required for training; loss_function represents the a certain metric during model training; C limits the importance of each point; gamma controls the width of the Gaussian kernel; kernel means kernel function; alpha means regularization strength; and random_state means the seed of the pseudo random number generator to use when shuffling the data while random_seed is the same with random_state.  
The proposed forecasting models in this study are evaluated by comparing with four other predictors: SVM, CATBOOST, KNN, and BAGGING (the average result of SVM, CATBOOST, and KNN into an ensemble learning). SVM could deal with overfitting problem and have good generalization performance because SVM can construct a mapping from one dimensional input vector into high-dimensional space by the use of reproducing kernels. Furthermore, the SVM is also slow in the test phase due to the high algorithm complexity and needs a large memory capacity to calculate. CATBOOST uses an efficient gradient modification of ordered boosting to overcome the problem of target leakage, and it performs well in small datasets, but training a CATBOOST model requires a great deal of time and compute memory. KNN is suitable for small datasets but it is usually hysteretic in time series. BAGGING is a combination of KNN, SVM, and CATBOOST, and outperforms each individual method.
# Consistency Models of NoSQL Databases
## 3 Consistency Models  
In the past, almost all architectures used in databases systems were strong consistent. In these cases, most architectures would have a single database instance only responding to a few hundred clients. Nowadays, many systems are accessed by hundreds of thousands of clients, so there was a mandatory requirement to system's architectures that scale. However, considering the CAP theorem, high-availability and consistency do conflict on distributed systems when subject to a network partition event. The majority of the projects that have been experiencing such high-traffic have chosen to adopt high-availability over a strong consistent architecture by relaxing the consistency level.  
There are two perspectives on consistency, the _data-centric consistency_ and the _client-centric consistency_, as illustrated in Figure 1. Data-centric consistency is the consistency analyzed from the replicas' point of view. Client-centric consistency is the consistency analyzed from the clients' point of view [13].  
For both perspectives, there are two dimensions, staleness and ordering. Staleness measures how far from the latest version the return data is. Ordering describes what operations order has been taken in the replica in a data-centric point of view, and, in a client-centric perspective, what order is shown to clients [13]. Figure 2 extends this taxonomy and illustrates how consistency models can be classified by client-centric and data-centric perspectives, and by staleness and ordering dimensions. Under the data-centric perspective we can find two dimensions: _Models for Specifying Consistency_ that describe the consistency models that allow measuring and specifying the consistency levels that are tolerable to the application (e.g., Continuous Consistency Model); _Models of Consistent Ordering of Operations_ that describe the consistency models that specify what ordering of operations are ensured at the replicas (e.g., Sequential Consistency and Causal Consistency). On the client-centric perspective are also defined two dimensions, _Eventual Consistency_ and _Client Consistency Guarantees_. The Eventual Consistency dimension states that all replicas will gradually become consistent if no update operation occurs (e.g., Eventual Consistency Model). The client Consistency Guarantees defines that each client process must ensure some level of consistency while accessing the data value on different replicas (e.g., Monotonic Writes Model, Monotonic Reads Model, Read your Writes Model, and Write Follow Reads).  
Figure 1: Data-centric and Client-centric consistencies [13].  
Figure 2: Consistency Models based on Reference [14].  
In the next sections, we will review the main consistency models implemented in storage systems: Strong consistency, weak consistency, eventual consistency, causal consistency, read-your-writes consistency, session consistency, monotonic reads consistency, and monotonic writes consistency.
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 2 Materials
### Reference Data  
The Land Parcel Identification System (LPIS) contains the field polygons and the farmer-declared crop type for each polygon [19]. In this work, we use the regional LPIS data of 2018. LPIS was offered by INTIA, a public company that is part of Spain's Department of Rural Development, Environment, and Local Administration, being, also, the paying agency for the Navarre district, and responsible for all CAP compliance inspections in the area. Figure 2 illustrates the onset and duration of the principal growth stages for the major crops of the study area, along with the corresponding Sentinel-1 and Sentinel-2 acquisitions. The LPIS polygons were used for generating a field-based feature space, as well as for training the SVM classifier and evaluating the quality of the generated classification models using the crop type declarations.