esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Volatile Organic Compounds Emitted from Tuma Cheeses  
Table 4 shows the VOC profiles generated from cheese samples with and without OEOs' addition. Twenty-one compounds were detected in control cheeses: seven acids, five ketones, four aldehydes, and four alcohols.  
The main class found in cheese samples was free fatty acids (FFA). Hexanoic acid showed the highest values, followed by butanoic and acetic acid. Butanoic and hexanoic acids may generally result from the lipolysis of milk fat due to the action of the lamb rennet used for curdling, and also, in part, due to the activity of raw milk lipoprotein lipase [73; 74]. Acetic acid can originate from different processes, including the oxidation of lactose by lactic acid bacteria under anaerobic conditions and carbohydrate catabolism by lactic acid bacteria [75]. Other odor-active compounds, such as alcohols (1-butanol-3-methyl1 (isoamyl alcohol) and aldehydes (hexenal and heptanal), were also revealed. Globally, the volatile composition detected in control cheeses reflects the volatile profile of cheeses produced from sheep's milk, as observed in many studies [18; 76]. SPME-GC-MS analysis clearly showed the effect of OEOs' addition, as both experimental products (ECPO100 and ECPO200) were characterized by components characteristic of the control cheese, such as hexanoic, butyric, and acetic acids and hexenal, heptanal, and isoamyl alcohol. However, the major volatile fraction was constituted by the typical compounds detected in OEOs, including, first of all, carvacrol. Furthermore, compounds belonging to the monoterpene hydrocarbons class, such as \(\gamma\)-terpinene, myrcene, \(\beta\)-pinene, p-cymene, and \(\alpha\)-terpinene, and oxygenated monoterpenes, such as linalool, thymol, and \(\beta\)-caryophyllene, were also detected. Our results showed that independently of the amount of OEOs added, generally, no significant differences were found for VOCs emitted from cheese, but the percentage of carvacrol significantly increased. A similar effect was observed by [PERSON] et al. [69] on the VOC profiles of cheeses processed with different concentrations of citrus EOs. These results revealed that a small addition of OEOs (100 \(\mu\)L/L of OEO) significantly influenced the cheese's flavor profile, demonstrating that OEO compounds are easily transferred to cheese.
# Research Status of and Trends in 3D Geological Property Modeling Methods: A Review
## 5 Conclusions  
This review article presents a systematic analysis of two main aspects, namely grid model generation and property interpolation, in 3D property modeling. The following conclusions are drawn:  
1. Although there are various studies on the grid forms for the numerical simulation of reservoirs, orthogonal hexahedral grids are still the most suitable for simulation and computation in the current application field.
2. Most geological phenomena are nonstationary, to simulate various types of reservoirs; how to improve geostatistics by adding geological constraints and reducing the limitation of stationarity is the main development trend.
3. Multiple-point geostatistics is a developing trend in 3D properties modeling and is more accurate than traditional geostatistics; some limitations still exist for different approaches.
4. The multiscale problem of multiple-point geostatistical results has always been a research hotspot in this field.
5. As a new method, deep learning algorithms and application verification are still in the stage of rapid development, and new research results continue to appear.  
**Author Contributions:** Conceptualization, Y.L. and X.Z.; methodology, Y.L. and X.Z.; software, Y.L.; validation, Y.L. and W.G.; formal analysis, Y.L. and W.G.; investigation, Y.L. and L.K.; resources, Y.L. and R.Y.; data curation, Y.L. and Y.S.; writing--original draft preparation, Y.L. and X.Z.; writing--review and editing, Y.L., W.G., L.K., J.G., R.Y., Y.S. and M.P.; visualization, Y.L. and W.G.; supervision, Y.L. and X.Z.; project administration, Y.L. and X.Z.; funding acquisition, Y.L., X.Z., W.G. and Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The APC was supported by the funding of Major National Science and Technology Projects of China (2016 ZX05037-006-005) and project of R&D Department of Petrochina (No. 2021 DJ2005).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are included within the article.  
**Acknowledgments:** The work was supported by the funding of Major National Science and Technology Projects of China (2016 ZX05037-006-005) and project of R & D Department of Petrochina (No. 2021 DJ2005). We express our sincere thanks for American Journal Expert (AJE) for their linguistic assistance.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
From an industrial perspective, however, more attractive is the deposition of iron-based hard alloys, for instance, those close to the FeNiCoCrAl1.3 Mo0.5 alloy described in ref. [17]. However, a combination of the laser and microplasma processes was required for the deposition of this alloy, which markedly lowered the adaptability-to-manufacture and increased the cost. The relevance of this work lies in the development of an additive microplasma process involving the deposition of powder filler materials from higher-strength alloyed steel to manufacture 3D objects. Ref. [18] performed a comparison of additive deposition technologies, demonstrating that it is rational to apply the SLM technology to achieve the highest 3D product shape accuracy and to minimize its surface roughness. If maximal productivity is required, WAAM and PAW technologies at more than 100-150 A currents are suitable. Thus, microplasma powder additive deposition represents a certain compromise between shaping accuracy (SLM technology) and productivity (WAAM and PAW). This makes this technology quite attractive for industrial use [19]. However, for the development of the processes of microplasma additive powder deposition, it is necessary to take into account the significant thermal impact of the microplasma energy source on the produced 3D object. Here, in addition to the possibility of predicting the formation of the required structures of 3D object metal, it is necessary to be able to predict the susceptibility to residual deformations of its walls and to assess the level of residual stresses in the deposited metal. The absence of such a prediction procedure hinders the application of 3D printing of finished products by the method of additive microplasmapowder deposition. This work aimed at the development of the respective procedure and studying the effectiveness of its application in the case of 3D printing with the powder of higher-strength alloyed steel. The objective of this work lies in the investigation of the regularities of the influence of the technological parameters of additive microplasma deposition of spatial objects, using powder filler materials from higher-strength alloyed steel, on the formation of the microstructure and stress-strain state (SSS) of the material of 3D products and the creation, based on these approaches, to the automatic 3D printing of spatial metal products with a predicted structure and stressed state. The following tasks were solved to reach the defined objective:  
1. Investigations of the regularities of the influence of the technological procedures of layer-by-layer deposition on the peculiarities of the formation of the structure and properties of 3D metal objects were performed;
2. The prediction of the stress-strain state of the produced 3D metal objects was carried out;
3. The development of basic technological procedures of additive microplasma deposition, aimed at minimizing the stress-strain state of the produced 3D objects, was carried out.
# Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology
## 1 Introduction
### Energy Efficiency in the Context of IoT Systems  
IoT systems consist of a large number of devices that are often located in remote locations and operate autonomously. As a result, energy efficiency is a key factor determining their performance and durability. Reducing power consumption can significantly extend battery life, which is especially important for devices that do not have constant access to the power grid. This not only reduces the cost of battery replacement but also helps reduce the overall operating costs by saving electricity. In addition, energy-efficient IoT systems contribute to reducing greenhouse gas emissions, thereby having a positive impact on the environment. Reducing power consumption also reduces component heating, increasing their reliability, and enables the creation of more scalable and complex IoT networks.  
The energy efficiency of IoT systems is influenced by a number of factors (Figure 1). Hardware plays an important role: the choice of processors, memory, and radio modules, as well as the optimization of component operating modes, can significantly reduce energy consumption. The use of energy-efficient sensors is also an important aspect. System software must ensure efficient data processing, the optimization of operating systems, and intelligent power management. The choice of appropriate communication protocols, such as LoRaWAN (Cycleo, Grenoble, France) or Bluetooth LE (Bluetooth Special Interest Group, Kirkland, WA, USA), contributes to the efficient use of energy, while optimizing the data transmission frequency further helps to reduce energy costs. The network infrastructure also plays an important role: the correct network topology and the use of repeaters ensure an increase in transmission range and a reduction in the load on individual devices.  
A number of strategies are used to improve the energy efficiency of IoT systems. Software optimization involves reducing the frequency of sensor polling, using sleep modes, and simplifying the data processing algorithms. The choice of energy-efficient components, such as low-power microcontrollers and sensors with minimal power consumption, also contributes to reducing energy costs. The optimization of communication protocols involves the use of adaptive data transfer rates and reducing packet sizes. It is worth noting the possibility of using alternative energy sources, such as solar panels and thermal generators, thereby providing a significant increase in the autonomy of IoT systems.  
Figure 1: Optimization of energy consumption in IoT systems.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 3 Method
### The CTC Alignment Algorithm
#### 3.3.3 CTC-Forced Alignment  
The matrix \(\boldsymbol{\alpha}\) in Equation (15) is computed over all possible paths \(\boldsymbol{\pi}\in\mathcal{M}^{-1}(\mathbf{I})\), because it is used to compute the CTC loss (see Equation (17) and Equation (13)). In the CTC-forced alignment scenario, the labelling \(\mathbf{I}\) is given, and we search for an optimal \(\boldsymbol{\pi}^{*}\in\mathcal{L}_{\epsilon}^{T}\), maximising the probability \(p(\mathbf{I}|\mathbf{x})\), as in the textbook DTW scenario.  
Figure 9: CTC path-permitted transitionsThe Equation (15) is thus slightly modified, so that only the predecessor path with the maximal probability is considered at each step, and becomes:  
\[\alpha(i,t)=\begin{cases}\max\begin{cases}\alpha(i,t-1)&p(a_{i},t) \quad\text{ if }a_{i}=\epsilon\text{ or }a_{i}=a_{i-2}\\ \alpha(i-1,t-1)&\\ \alpha(i,t-1)&\\ \alpha(i-1,t-1)&p(a_{i},t)\quad\text{otherwise}\\ \alpha(i-2,t-1)&\\ \end{cases}\\ \forall(i,t)\in\{3:2|\mathbf{l}|+1\}\times\{2:T\}\end{cases} \tag{18}\]  
The probability \(p(\mathbf{l}|\mathbf{x})=\alpha(|\mathbf{a}|-1,T)+\alpha(|\mathbf{a}|,T)\) can then be computed recursively using Equations (16) and (18), accumulating at each step the probability of the most probable predecessor path (forward pass). The optimal path \(\boldsymbol{\pi}^{*}\) can then be traced back using the sequence of optimal transitions retained in the forward pass (backward pass).  
Similarly to the textbook DTW algorithm, the computation of the matrix \(\boldsymbol{\alpha}\) in a CTC context given by Equation (18) during the forward pass has a computational and memory complexity that scales quadratically in \(\mathcal{O}(|\mathbf{a}||\mathbf{b}|)\), i.e., \(\mathcal{O}(|\mathbf{l}|T)\).
###### Abstract  
In this work, we propose a combined method to implement both modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation, a method based on density-based spatial clustering of applications with a noise (DBSCAN) algorithm. The proposed method can automatically extract the cluster number and density information of samples. The cluster number can be used for MFI and the density information combined with a fourth-order polynomial fitting can correctly estimate OSNR. We verify the feasibility of the method through simulation and conceptual proof experiments. The results show that the MFI can achieve 100% accuracy when the OSNR values are higher than the 7% forward error correction (FEC) thresholds for five commonly used modulation formats (MFs) like polarization division multiplexing (PDM)-QPSK, PDM-16 QAM, PDM-32 QAM, and PDM-64 QAM. Mean absolute OSNR estimation errors are not higher than 1 dB for different signals. There is no additional hardware required, so the proposed method has the ability to be integrated into existing optical performance monitoring systems without burden. Furthermore, the proposed method has the potential to be used in bit-error ratio (BER) calculation, linear, or nonlinear impairments monitoring. We believe that our multifunctional and simple method would be favorable to a future elastic optical network (EON).  
coherent optical communications; clustering algorithm; modulation format identification; OSNR estimation
# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
###### Abstract  
Accurate, ubiquitous and reliable navigation can make transportation systems (road, rail, air and marine) more efficient, safer and more sustainable by enabling path planning, route optimization and fuel economy optimization. However, accurate navigation in urban contexts has always been a challenging task due to significant chances of signal blockage and multipath and non-line-of-sight (NLOS) signal reception. This paper presents a detailed study on environmental context detection using GNSS signals and its utilization in mitigating multipath effects by devising a context-aware navigation (CAN) algorithm that detects and characterizes the working environment of a GNSS receiver and applies the desired mitigation strategy accordingly. The CAN algorithm utilizes GNSS measurement variables to categorize the environment into standard, degraded and highly degraded classes and then updates the receiver's tracking-loop parameters based on the inferred environment. This allows the receiver to adaptively mitigate the effects of multipath/NLOS, which inherently depend upon the type of environment. To validate the functionality and potential of the proposed CAN algorithm, a detailed study on the performance of a multi-GNSS receiver in the quad-constellation mode, i.e., GPS, BeiDou, Galileo and GLONASS, is conducted in this research by traversing an instrumented vehicle around an urban city and acquiring respective GNSS signals in different environments. The performance of a CAN-enabled GNSS receiver is compared with a standard receiver using fundamental quality indicators of GNSS. The experimental results show that the proposed CAN algorithm is a good contributor for improving GNSS performance by anticipating the potential degradation and initiating an adaptive mitigation strategy. The CAN-enabled GNSS receiver achieved a lane-level accuracy of less than 2 m for 53% of the total experimental time-slot in a highly degraded environment, which was previously only 32% when not using the proposed CAN.
## 2 Study Area and Data  
### Study Area  
The CLP (33 deg43'7''-41 deg16'7'' N and 100 deg54'7''-11 deg33'7'' E) is situated in north-central China and covers an area of nearly 640,000 km2 [40]. The region is characterized by four main types of landforms: Mountain regions, loes tableland regions, loes hilly-gullly regions, and river valley regions [41]. In 1999, the Chinese government implemented the GGP to improve the EE of CLP, with Shaanxi province serving as the pilot region [42].  
The study area (33 deg7''-36 deg4' N and 105 deg7''-110 deg6' E) is located in a typical CLP region in Shaanxi province, comprising the Huanglong coal base, the Xi'an-centered Guanzhong plain urban agglomeration, and the GGP (including GGP regions and hill-closing and afforestation regions), as shown in Figure 1[43]. The loess tableland and loess hilly-gullly regions are mostly located north of the Weihe River, while the river valley plains and mountainous regions are mostly situated south of the Weihe River. The Huanglong coal base is located north of the Weihe River [44], while the Guanzhong plain urban agglomeration is located south of the Weihe River. Figure 1 also illustrates the distribution of landforms in the study area.  
Figure 1: Study area overview: (**a**) Study area; (**b**) distribution of coal mining, GGP, urbanization regions in the study area; (**c**) landform classification of the study area; (**d**) location of the study area in China.  
Due to the abundant coal resources in CLP, coal mining has a significant impact on the EE. The Huanglong coal base is located in central Shaanxi province and is one of the 14 large coal bases that China is focused on developing. The landform of the Huanglong coal base is complex, with elevations ranging from about 400-1850 m. The main landforms in the area are loess hilly-guilly. However, the resistance to deformation of the loess layer is very small, and coal mining can lead to ground deformation and collapse, as shown in Figure 2. Large-scale mining operations in the Huanglong coal base began in the early 1980s, and as a result of mining and other human projects, the EE became poor in the early 21 st century.  
The China Western Development strategy, which began in 2000, coincided with the implementation of the GGP and has resulted in the rapid expansion of the Guanzhong plain urban agglomeration. The extensive urban construction has had a negative impact on the EE in the surrounding area. The urban agglomeration is centered in Xi'an, Shaanxi Province, which is situated in the river valley plain region.
## 3 Methodology
### TRFM Trajectory Prediction Model
#### 3.3.4 Normalization  
In this paper, Min-Max Normalization is used for various dimensions of the model input data. This normalization method is applied to normalize four trajectory point features: the course over ground (\(cos_{i}\)), speed over ground (\(sog_{i}\)), relative longitude (\(\Delta lon_{i}\)), and relative latitude (\(\Delta lat_{i}\)). The specific formula is as follows:  
\[X^{*}=\frac{X-\min}{max-min} \tag{29}\]  
Here, \(max\) represents the maximum value in the sample data, \(min\) represents the minimum value in the sample data, \(X\) is the original data, and \(X^{*}\) is the normalized data.
Sensors_**2012**, _12_, 13088-13108; doi:10.3390/s121013088  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**On Increasing Network Lifetime in Body Area Networks Using Global Routing with Energy Consumption Balancing**  
**[PERSON] *, [PERSON] and [PERSON]**  
Communications Laboratory, Rochester Institute of Technology, 79 Lomb Memorial Drive,  
Rochester, NY 14623, USA; E-Mails: [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (N.A.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-585-475-6452; Fax: +1-585-475-5845.  
_Received: 2 July 2012; in revised form: 12 September 2012 / Accepted: 13 September 2012 / Published: 26 September 2012_  
**Abstract:** Global routing protocols in wireless body area networks are considered. Global routing is augmented with a novel link cost function designed to balance energy consumption across the network. The result is a substantial increase in network lifetime at the expense of a marginal increase in energy per bit. Network maintenance requirements are reduced as well, since balancing energy consumption means all batteries need to be serviced at the same time and less frequently. The proposed routing protocol is evaluated using a hardware experimental setup comprising multiple nodes and an access point. The setup is used to assess network architectures, including an on-body access point and an off-body access point with varying number of antennas. Real-time experiments are conducted in indoor environments to assess performance gains. In addition, the setup is used to record channel attenuation data which are then processed in extensive computer simulations providing insight on the effect of protocol parameters on performance. Results demonstrate efficient balancing of energy consumption across all nodes, an average increase of up to 40% in network lifetime corresponding to a modest average increase of 0.4 dB in energy per bit, and a cutoff effect on required transmission power to achieve reliable connectivity.  
**Keywords:** wireless body area networks; routing protocols; network lifetime
# Bit Independence Criterion Extended to Stream Ciphers
## 2 Preliminaries
### Comparison between SAC and BIC  
The SAC [13] verifies whether each output bit changes approximately half of the time by changing an input bit. Using the SAC matrix \(H^{i}\), it is said that \(f\) satisfies the SAC if for all \(i\) and every avalanche variable \(v^{i}_{:j}\), with \(1\leq j\leq m\) and \(1\leq i\leq n\), \(HW(v^{i}_{:j})\) is binomial distributed with parameters \(n=l\) and \(p=\frac{1}{2}\), i.e., \(v^{i}_{:j}\sim\ B\left(l,\frac{1}{2}\right)\), where \(HW(\cdot)\) is the Hamming weight. On the other hand, the BIC [13] measures the degree of independence between each pair \(v^{i}_{:j}\), \(v^{i}_{:k}\) of avalanche variables. Thus, the two criteria measure a different characteristic from the effect produced on the output bits changing an input bit; the SAC verifies uniformity in the distribution of each output bit, while the BIC measures the degree of independence between the output bits.  
In [30], a new method to assess the correlation between statistical randomness tests based on mutual information was presented, using some test statistics and \(p\)-values of the tests. This tool can be used to determine the degree of correlation between these two statistical tests. In [29], an assessment of the independence between these two tests through absolute correlation coefficient is given, concluding that these tests are quite uncorrelated.