esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Processing on Volatile Organic Compounds Formation of Meat--Review
## 3 Non-Thermal Processing
### Ozone  
Ozone and electrolyzed water (EW) treatments are some of the newest preservation technologies used in the meat industry. A relatively low level of aqueous ozone and electrolyzed water can be used as an economically convenient, safe, and environmentally friendly way to preserve meat products at the final stage of the slaughtering process. These systems were studied in [52] research, and then the EW method was closely examined by [PERSON] et al. [53]  
The object of the research [52] was to extend the shelf life of beefsteaks. Methods embracing aqueous ozone containing 6 mg/L and electrolyzed water at 100 mg/L of free chloride were investigated. GCMS analysis of VOCs profile was carried out before the treatment and after 1 and 15 days of storage at 4 \({}^{\circ}\)C. Meat samples were extracted using NaCl solution and internal standard, then exposed to SPME fiber. Helium at flow rate of 1 mL/min was used. The identification of VOCs was confirmed by comparison with retention indices of pure standards. VOCs, for which standards were not available, were identified using the mass spectra and retention indices from the literature. Thirty-two volatile substances were detected, of which alcohols, aldehydes, and ketones were the most abundant. It was stated that amounts of volatile compounds varied during storage time, but were not affected by different treatments.  
[PERSON] et al.'s [53] study focused on the impact of different dosages of electrolyzed water on the spoilage profile of meat. Preliminary studies showed that lower concentrations of EW (25 and 50 mg/L) were not efficient at reducing microbial spoilage. Only EW at 100 mg/l of free chloride was efficient, so it was utilized in the study. Based on previous studies, 11 VOCs were quantified in the headspace. The analysis was conducted using the procedure used in a previous study [52]. The results showed increasing concentrations during the storage. The VOCs concentration was not affected by the control and experimental treatments, nor by different production runs.
## 4 Experimental Section
### Ablation Study  
To evaluate the effectiveness of the WGAN-GP model, ablation experiments were conducted by comparing it with a modified model where only the gradient penalty (GP) was added. This experiment aimed to specifically assess the impact of each component on the model's performance and to highlight the advantages of the WGAN-GP architecture compared to conventional methods. The results yielded the following insights and challenges:  
Firstly, when the Gradient Penalty (GP) was added to a standard GAN model alone, it helped control the gradient of the Critic, improving the stability of the training process. However, it did not fundamentally resolve the existing issues inherent in traditional methods. This approach still presented challenges for the Critic's learning process, and it was evident that using GP alone was insufficient to completely overcome these limitations. Therefore, in the WGAN-GP model, both the Wasserstein distance and Gradient Penalty are utilized simultaneously to address these problems, ensuring stable gradients and continuous feedback during the training process.  
Additionally, an analysis of the causes of color inconsistency revealed that the model struggled to achieve stable training, leading to occurrences of color inconsistency and degradation in image quality. In the WGAN-GP experiments, color consistency was a critical factor, and using a simple GP addition without the Wasserstein distance resulted in the Critic failing to provide adequate feedback, producing irregular or inconsistent color outcomes. These findings clearly illustrate the rationale for using the Wasserstein distance in the WGAN-GP model, demonstrating its superiority in delivering stable and consistent performance compared to traditional methods.  
These ablation experiment results support the conclusion that the design of the WGAN-GP model offers superior training stability and image quality compared to conventional GAN-based approaches.
Sensors_**2015**, _15_, 14016-14044; doi:10.3390/s150614016  
\begin{tabular}{c}  \\ _Seriesors_ \\
**ISSN 1424-8220** \\ www.mdpi.com/journal/sensors \\ _Article_ \\ \end{tabular}  
On Designing Thermal-Aware Localized QoS Routing Protocol for _in-vivo_ Sensor Nodes in Wireless Body Area Networks  
**[PERSON] * and [PERSON]**  
Department of Information Technology, Faculty of Computing and Information Technology,  
King AbdulAziz University, Jeddah-21589, Saudi Arabia; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +966-567-734-785.  
Academic Editor: [PERSON]  
_Received: 21 April 2015 / Accepted: 5 June 2015 / Published: 15 June 2015_  
**Abstract:** In this paper, we address the thermal rise and Quality-of-Service (QoS) provisioning issue for an intra-body Wireless Body Area Network (WBAN) having _in-vivo_ sensor nodes. We propose a thermal-aware QoS routing protocol, called TLQoS, that facilitates the system in achieving desired QoS in terms of delay and reliability for diverse traffic types, as well as avoids the formation of highly heated nodes known as hotspot(s), and keeps the temperature rise along the network to an acceptable level. TLQoS exploits modular architecture wherein different modules perform integrated operations in providing multiple QoS service with lower temperature rise. To address the challenges of highly dynamic wireless environment inside the human body. TLQoS implements potential-based localized routing that requires only local neighborhood information. TLQoS avoids routing loop formation as well as reduces the number of hop traversal exploiting hybrid potential, and tuning a configurable parameter. We perform extensive simulations of TLQoS, and the results show that TLQoS has significant performance improvements over state-of-the-art approaches.  
**Keywords:** localized routing; wireless body area networks; thermal-aware; _in-vivo_ sensors
## 2 Background and Related Work of LPWAN
### Background
As the amount of information transmission is reduced, the power consumption of IoT devices can be greatly reduced. For example, applications such as electricity meters, water meters, and street lamp controls are suitable for Sigfox because the flow rate reporting frequency is less than once per hour. In addition, Sigfox also provides SigfoxCloud cloud system integration services, which can reduce the complexity of users developing programs and accessing the data. At present, Sigfox has promoted the deployment of base stations in 32 countries around the world, including France, the United Kingdom, Spain, Germany, the Netherlands, and the United States. Weightless: The Weightless Technology and Standards Organization was founded by Dr. [PERSON] in 2012. The initial board members included Neul, ARM, Cable & Wireless, CSR, and other companies. Dr. [PERSON] serves as the CEO of the Weightless SIG Standards Organization, and concurrently serves as the co-founder and CTO of Neul. The Weightless standard was released in April 2013. In response to the needs of the IoT communication market, in accordance with the goal of city-level networking, the Weightless Internet-of-things wireless communication standard was created. Weightless has three different architectures: Weightless-N, Weightless-P, and Weightless-W. Weightless-W is the earliest developed version, which mainly uses TV white space (TVWS) to transmit data. However, due to the significant differences in the development process of TVWS released by various countries, its development is limited. The Weightless-N version adopts a sub-GHz license-free frequency band; the transmission rate is 30-100 kbit/s and the transmission distance is about 5 km. Weightless-W and Weightless-N only support one-way transmission, while the latest version, Weightless-P, supports two-way communication, and its transmission rate and transmission distance are about 100 kbit/s and up to 2 km, respectively. As shown in Table 1, under the same conditions, LoRa has the best network signal coverage, but in terms of the number of supported nodes and spectrum efficiency, Weightless performs better than LoRa and Sigfox.
## Related Methodology
### Radial Basis Function Neural Networks  
The RBF neural network is a forward network model with good performance [42], global approximation, and is free from the local minima problems. In this paper, the RBF neural work is used to estimate the parameter of polynomial regression.  
It has three layers: an input layer, a hidden layer with a non-linear RBF activation function, and a linear output layer, which is a two-layer feed-forward neural network.  
The network output \(y\) is a vector with \(m\) components, determined in terms of the \(n\) components of the input vector \(x\) by the following formula:  
\[y_{i}=\sum_{i=1}^{N_{h}}w_{ij}\varphi_{j}(x)+\theta_{wi},\,I=1,2,\ldots,m; \tag{8}\]  
where \(\varphi_{j}\) are the radial-basis functions, and \(N_{h}\) is the number of hidden-layer neurons. The hidden-layer-to-outputs interconnection weights are given by \(w_{ij}\). The threshold offset is denoted by \(\theta_{wi}\).  
Generally, the hidden neuron of an RBF network employs a Gaussian form for the activation function, which is given as:  
\[\Phi_{j}(x)=exp\left(-\frac{||x-C_{i}||}{2\sigma_{i}^{2}}\right), \tag{9}\]where \(C_{i}\) are centers, and the \(\sigma_{i}\) are widths or variances. For simplicity, the centers and variances are pre-defined and fixed. The above equation can be transformed to the matrix form below:  
\[Y=W^{T}\Psi(x), \tag{10}\]  
where \(\Psi(x)=\left[\phi_{1},\varphi_{2}\ldots,\varphi_{N_{h}}\right]^{T}\) and \(W\) is the weights matrix.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 2 Background
### Remote Monitoring Applications in VANETs
#### 2.1.3 Mobile Surveillance  
Remote monitoring in VANETs also has scope in mobile surveillance applications. Cameras can be mounted on top of buses, police vehicles and other government vehicles to collect the video surveillance data and transfer it to a monitoring center such as a police headquarter [32]. An embedded wireless video surveillance system for vehicles was created in [33] using 802.11-based WLANs and CDMA connectivity solutions. The video surveillance data was then accessible through the Internet. In [34], the performance of IEEE 802.11p vehicular video surveillance system was analyzed, and a mechanism for adaptive target video bit rate selection was proposed.
# Double-Peaked Mid-Infrared Generation Based on Intracavity Difference Frequency Generation
## 3 Results and Discussion
This results in a shortening of each peak and a decrease in the intensity away from the phase-matching interval. In summary, the double-peaked mid-infrared generation with a primary peak of 3695-3747 nm is in the phase-matching condition. The FWHM of each peak varies in the range of 20-30 nm, and the double-peaked intensities are similar. In contrast, the double-peaked idler beams generated outside this range are in a state of phase-mismatched. With the tuning of the polarization period, the central wavelength of the double-peaked mid-infrared generation gradually moves toward the long wavelength, and the FWHM of the spectrum decreases. Meanwhile, the FWHM of the primary and secondary peaks decreases, and the secondary peak in the long wavelength gradually lowers. The secondary peak decreases when the double-peaked mid-infrared generation is tuned in the short wavelength under phase-matching.
## 2 Related Work
### Key Considerations for Efficient and Secure SM in IIoT Environment  
An SM system based on DT requires six key considerations as follows:  
* _Scalability_: There are many heterogeneous nodes in the IIoT, and the number of nodes in the network may increase with the work progress and even exceed the network load capacity. Therefore, we need to constantly expand the data types and data processing platform, even if the increasing amount of data should not cause too much impact on the operation of the whole system. A complete network system should be resistant to this problem [27].
* _Security and Privacy_: SM aims to collect large amounts of data and interact with them in various production environments, forming a complete industrial chain. Among them are the product's private information such as ID, location, working status, performance indicators, etc. Hackers may try to obtain this information for personal benefit. Therefore, protecting these data is a crucial requirement [28].
* _Confidentiality_: Private data in the industrial chain can be directly linked to commercial interests. The loss or exposure of these sensitive data will often bring economic losses or administrative risks [29].
* _Integrity_: Any unauthorized third party's operation or data modification is a critical challenge in IIoT [30].
* _Availability_: Many IIoT devices are based on real-time communication connections and operations. In distributed networks, even if one node (server or device) fails, processes on other nodes should be able to continue [31].
# Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods
## 2 Materials and Methods
### Methodology Overview
#### 2.3.2 Methodology B  
We considered the methodology proposed by [PERSON] and co-authors [4], 2024, which, for brevity, we call Methodology B. Experiments were conducted using three different missing data handling techniques. First, the missing values were replaced with the mean value of the corresponding characteristic. Second, Multivariate Imputation by Chained Equations (MICE) was performed, which uses regression models to predict missing values based on information from other variables. Third, age group-based imputation was used to replace missing BMI values with the mean value for the respective age group (4 groups: 0-20, 21-40, 41-60, and 61-80). Methodology B uses the SMOTE technique as a data balancing tool, which increases the representation of the minority class. The paper emphasizes that an incorrect application of SMOTE can lead to overfitting, which requires careful management of method parameters. The paper evaluates SMOTE's performance by comparing the model's performance on balanced and unbalanced datasets. This also includes a consideration of potential drawbacks of the method, such as the risks of overfitting and data reduction by reducing examples from the majority class. The methodology relies on basic and advanced models such as logistic regression, neural networks, TabNet, and gradient-boosting methods (XGBoost, CatBoost, etc.) for classification analysis. The dataset is divided into training and testing data using a 70:30 ratio to evaluate the results. Then, k-fold cross-validation is used to evaluate models. The results are based on various performance metrics. An advantage of the classification analysis is that the authors presented the elements of confusion matrices for the models used, through which the necessary metrics can be calculated.
## 6 Computation Overhead Analysis
### Proposed TCA Method  
For the TCA method in single-zone communication, signature generation requires one hash computation, mapping to a point in the group \(G_{1}\) and one scalar multiplication over group \(G_{1}\), where \(\sigma_{i}=sk_{i}\cdot H(m)\). Hence, the signature generation time can be represented by TM2P + TMG1 = 0.479 + 0.135 = 0.614 ms. Signature verification requires two pairing operations to check the validity of the bilinear pairing operation, where \(e\left(\begin{array}{c}g_{222 ID},\sigma_{i}\end{array}\right)=e(pk_{i},\ H(m))\). Thus, the signature verification time is two pairing operations and one HashAndMapTo operation = 2 TP + TM2P = 4.9 + 0.135 = 5.035 ms. For the case of TCA, both the signature generation and verification overhead is constant, irrespective of the number of destination zones. The total computation time due to security overhead is the combination of signature generation time and signature verification time. For the TCA method, the computation overhead is TM2P + TMG1 +2 TP+TM2P = 0.479 + 0.135 + 4.9 + 0.135 = 5.649 ms; (the cryptographic operations used in the computation time calculations are defined in Table 4).
## 2 Materials and Methods
### Comparison IMERG-Rain Gauges  
From an operational point of view, the first step was the comparison procedure between the IMERG data and the rain gauge data of the three weather stations under examination. The comparison is based on a technique previously used by some researchers and is called point-to-pixel [5; 21]; this technique takes the exact coordinates of some rain gauges and allows the acquisition of the rain gauge pixel data through the use of ArcGis 10.8 software. Then, depending on the chosen time interval, in our case the daily one, the gridded data corresponding to the rain gauge are extracted. A subtraction was carried out between the satellite and rain gauge observations, and the daily average difference was evaluated on an annual scale and for each month. Furthermore, using the snow level sensor located at each weather station studied, transforming snow cm into water equivalents, the data were compared with the rain gauge values and IMERG data. In addition, the difference in precipitation between IMERG values and rain gauge values was taken into account in relation to the daily average wind speed in m/s. The classes of average daily speeds were obtained by searching for a numerosity congruent with the significance level chosen for the survey, i.e., 95%. The methods to assess the appropriate sample size were the \"sample size for one sample, continuous outcome\" [22] and, to compare IMERG data with rain gauge data, the \"sample size for matched samples, continuous outcome\" [23]. Then, in order to investigate the discrepancies between the different rain gauges, an assessment of the prevailing winds and their speeds day-by-day was carried out, evaluating the precipitation for each class of values in order to identify any problems in rain detection. Subsequently, data from 27 validated weather stations with an altitude below 1400 m were interpolated. The method chosen was backward multiple linear regression [24], with annual precipitation as the dependent variable and five independent variables such as slope, exposure, altitude, distance from the sea and distance from the main ridge line [25]. This multiple regression model allowed the elimination of the independent variables that do not have significance (95%) in explaining variance and therefore do not improve the model, the F-test was decisive for the exclusion. The results were correlated with the IMERG satellite data to assess the difference between the interpolation and the value obtained from the IMERG analysis. Finally, a percentage increase was evaluated to make the rain gauge or IMERG data more reliable.
## 3 Hybrid Computing Architecture of MSD Algorithm  
The MSD algorithm is divided into two integral components: correlation calculation and symbol decision. The correlation calculation primarily comprises two fundamental computational units: complex multiplication and complex addition. Meanwhile, symbol decision involves three essential computational units: computation of the modulus, determination of the maximum value, and the subtraction of maximum values.  
For the correlation calculation, this study employs SC to reduce the complexity of implementation. Stochastic multipliers and adders exhibit lower resource consumption than binary multipliers and adders; however, compared to stochastic adders, stochastic multipliers are susceptible to the correlation of sequences. Therefore, when applying stochastic multipliers, it is necessary to ensure that the input sequences are independent. Given that complex multiplication constitutes the initial step of the MSD algorithm, the random bit sequences are directly generated by the SNGs and do not exhibit excessively high correlation. Therefore, this limitation can be ignored in the SC-based correlation calculation.  
However, when utilizing SC for correlation calculation, aside from the challenge of sequence correlation, there arises a problem of data scaling. As the data range in SC is inherently limited to 0 to 1, it is imperative to maintain this range within appropriate bounds throughout the entire computation process. Stochastic multipliers, due to their inherent computational characteristics, naturally avoid this issue. Conversely, when executing stochastic addition, the use of a suitable scaling factor becomes necessary to prevent potential overflow. Both excessively large and small scaling factors can result in indistinguishable differences among various computational outcomes. Therefore, this study proposes a flexible scaling adder circuit that can select an appropriate scaling factor based on different input conditions, which can ensure that the computational results do not overflow.  
When performing symbol decision in the MSD algorithm, the first step is calculating the modulus of the correlation results. If SC is adopted for this process, it requires two uncorrelated random sequences with identical probability. If a new sequence is generated based on the outcomes of the correlation calculation, it can result in higher correlation between the two sequences and larger computational errors. Therefore, binary computing is adopted to complete the symbol decision process. Therefore, in the various computational stages of the MSD algorithm, this study adopts two distinct computational paradigms--SC and binary computing--based on their respective characteristics. A hybrid computing scheme is designed to implement the hardware circuit for the entire MSD algorithm.
## 3 Results
### Protein Content in Wheat Malts
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Growing Season** & **Cultivar** & **Control** & **Variant I** & **Variant II** & **Variant III** \\ \hline \multirow{4}{*}{2020/21} & Eliver & 9.0 ABa & 9.9 BCb & 9.9 ABb & 10.5 Ac \\ \cline{2-6}  & Lawina & 9.0 ABa & 9.2 Aaab & 9.6 Abc & 10.9 Ac \\ \cline{2-6}  & Gimantis & 9.1 BCa & 9.3 Aa & 9.8 ABb & 10.6 Ac \\ \cline{2-6}  & Rockefeller & 9.2 BCa & 9.8 Bbc & 10.3 Bcd & 10.4 Ad \\ \hline \multirow{4}{*}{2021/22} & Eliver & 9.1 BCa & 9.6 ABb & 10.3 Bc & 10.9 Ad \\ \cline{2-6}  & Lawina & 8.9 Aa & 10.5 Db & 11.2 Dc & 11.5 Bc \\ \cline{2-6}  & Gimantis & 9.2 BCa & 10.0 BCDb & 10.2 Bb & 11.0 ABc \\ \cline{2-6}  & Rockefeller & 9.5 Ca & 10.4 CDb & 10.3 Bb & 11.1 ABc \\ \hline \multirow{4}{*}{2022/23} & Eliver & 8.6 Aa & 10.4 CDb & 10.4 BCb & 11.0 ABc \\ \cline{2-6}  & Lawina & 8.8 ABa & 10.5 Db & 10.9 CDb & 10.7 Ab \\ \cline{2-6}  & Gimantis & 9.2 Ba & 9.5 ABa & 10.0 Ab & 10.6 Ac \\ \cline{2-6}  & Rockefeller & 9.0 ABa & 9.3 Aa & 10.3 Bb & 10.8 Ab \\ \hline \multirow{4}{*}{Yearly Average} & \multicolumn{4}{c}{2020/21} & \multicolumn{4}{c}{9.8 \({}^{1}\) \(\pm\) 0.6} \\ \cline{2-6}  & \multicolumn{4}{c}{2021/22} & \multicolumn{4}{c}{10.2 \({}^{1}\) \(\pm\) 0.8} \\ \cline{2-6}  & \multicolumn{4}{c}{2022/23} & \multicolumn{4}{c}{10.0 \({}^{1}\) \(\pm\) 0.8} \\ \hline \multirow{4}{*}{Cultivar Average} & \multicolumn{4}{c}{Eliver} & 10.0 \({}^{\text{A}}\) \(\pm\) 0.8} \\ \cline{2-6}  & Lawina & 10.1 \({}^{\text{A}}\) \(\pm\) 1.0} \\ \cline{2-6}  & Gimantis & 9.9 \({}^{\text{A}}\) \(\pm\) 0.6} \\ \cline{2-6}  & Rockefeller & 10.0 \({}^{\text{A}}\) \(\pm\) 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Total protein content (% d. m.) of wheat malts from wheat grain obtained from field experiments in the study years analyzed. Figure 2: Relationship between the analyzed winter wheat cultivar and the level of nitrogen fertilization (A—statistical differences between cultivars; a—statistical differences at the analyzed levels of nitrogen fertilization; and \(p\) < 0.05).