esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of a Microbial Cocktail Used as a Starter Culture on Cocoa Fermentation and Chocolate Flavor
###### Abstract  
Chocolate production suffered a vast impact with the emergence of the \"witches' broom\" disease in cocoa plants. To recover cocoa production, many disease-resistant hybrid plants have been developed. However, some different cocoa hybrids produce cocoa beans that generate chocolate with variable quality. Fermentation of cocoa beans is a microbiological process that can be applied for the production of chocolate flavor precursors, leading to overcoming the problem of variable chocolate quality. The aim of this work was to use a cocktail of microorganisms as a starter culture on the fermentation of the ripe cocoa pods from PH15 cocoa hybrid, and evaluate its influence on the microbial communities present on the fermentative process on the compounds involved during the fermentation, and to perform the chocolate sensorial characterization. According to the results obtained, different volatile compounds were identified in fermented beans and in the chocolate produced. Bitterness was the dominant taste found in non-inoculated chocolate, while chocolate made with inoculated beans showed bitter, sweet, and cocoa tastes. 2,3-Butanediol and 2,3-dimethylpyrazine were considered as volatile compounds making the difference on the flavor of both chocolates. _Saccharomyces cerevisiae_ UFLA CCMA 0200, _Lactobacillus plantarum_ CCMA 0238, and _Acetobacter pasteurianus_ CCMA 0241 are proposed as starter cultures for cocoa fermentation.  
chocolate quality; GC-MS; sensory analysis; culture-independent analysis; starter culture
# Sampling Rate Impact on Precise Point Positioning with a Low-Cost GNSS Receiver
###### Abstract  
Nowadays, with the invention of low-cost GNSS receivers with modern characteristics, it is common to investigate and apply new methodologies and solutions with different receivers of this nature. Based on this fact, the performance of the solution obtained from the low-cost GNSS receiver is evaluated compared to a geodetic grade GNSS receiver at different sampling frequencies for the PPP-static and PPP-kinematic modes. For this, the original RINEX observation files were analyzed and decimated into different sampling rates as 0.1, 0.2, 1, 5, 15 and 30 s with TEQC software. All RINEX files were submitted to the Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP) online service for processing with static and kinematic modes. The PPP-derived coordinates from the low-cost GNSS receiver were compared with the geodetic receiver to evaluate the obtained solution. The results reveal that the behavior of all studied sampling rates from the low-cost GNSS receiver are constant in achieved positioning. In addition, the achieved precision shows that it is recommendable to use a high sampling rate to obtain a cm level in PPP-static mode by using a low-cost GNSS receiver, this mode being the most accurate and potential alternative for structural health monitoring studies, mapping and positioning in urban areas.  
low-cost GNSS receiver; PPP; GNSS high rate +
Footnote †: journal: Applied  
Article
## 2 Materials and Methods
### Methodology
#### 2.3.3 Factor Analysis  
To measure villages' two attributes, which we mentioned in the introduction section, _accessibility_ and _potential_, three indices are dimensionally reduced using factor analysis (Figure 3). First, non-weighted multiple centrality indices are reduced as the Comprehensive Transport Index to evaluate the transportation accessibility. Second, because the _potential_ is a comprehensive evaluation of accessibility and socio-economic condition, the Village Development Index, downscaled by population, per capita income, area, and area growth, was used to measure socio-economic condition and served as the weight of village nodes. However, for 1964, due to a lack of data, the Village Development Index was directly normalized by the area of the residential patch. Finally, the Transport Development Index was extracted from the centrality indices weighted by the Village Development Index to examine the potential.  
Figure 3: Factor analysis structure of multi-indices.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Abbreviation** \\ \hline Global Reach & GR \\ Global Betweenness & GB \\ Global Closeness & GC \\ Global Gravity & GG \\ Global Straightness & GS \\
5 min (minutes) Local Reach & CR I \\
5 min Local Betweenness & CB I \\
5 min Local Closeness & CC I \\
5 min Local Gravity & CG I \\
10 min (minutes) Local Reach & CR II \\
10 min Local Betweenness & CB II \\
10 min Local Closeness & CC II \\
10 min Local Gravity & CG II \\ Local Straightness & CS \\ \hline \hline \end{tabular}
\end{table}
Table 3: Indices to be calculated and their abbreviations.
## 3 Simulation Experiments and Analysis  
### Simulation Experiment  
This paper utilized the publicly available RadioML dataset provided by DeepSig Inc., Arlington, VA, USA, specifically version RadioML2018.01a. The dataset consists of 24 different types of digital and analog modulation signals, including OOK, 8 ASK, QPSK, 16 PSK, 16 APSK, 64 APSK, 16 QAM, 64 QAM, 256 QAM, AM-SSB-SC, 128 QAM, AM-SSB-WC, FM, OQPSK, GMSK, 128 APSK, BPSK, 4 ASK, 8 PSK, 32 PSK, 32 APSK, 32 QAM, AM-DSB-SC, and AM-DSB-WC. The dataset covers a SNR ratio range of \(-20\) to \(30\) dB. Each signal in the dataset contains both the in-phase I and quadrature Q components, with a length of \(1024\). The specific structure of the dataset is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Layer** & **Type** & **Output Shape** \\ \hline Input & & \(128\times 19\times 19\) \\ \hline \multirow{4}{*}{1} & Conv2d & \(64\times 17\times 17\) \\  & BatchNorm2d & \(64\times 17\times 17\) \\  & ReLU & \(64\times 17\times 17\) \\  & MaxPool2d & \(64\times 8\times 8\) \\ \hline \multirow{4}{*}{2} & Conv2d & \(64\times 6\times 6\) \\  & BatchNorm2d & \(64\times 6\times 6\) \\  & ReLU & \(64\times 6\times 6\) \\  & MaxPool2d & \(64\times 3\times 3\) \\ \hline
3 & Linear & 8 \\ \hline
4 & Linear & 1 \\ \hline \end{tabular}
\end{table}
Table 2: Structure of the relation metric module.  
Figure 3: RadioML.2018.01a dataset structure. In the waveform plot, the red line represents the I channel of the signal, and the blue line represents the Q channel.  
In order to ensure the representativeness of experimental results, experiments were conducted to divide the dataset of 24 different signal categories into training and testing sets using a ratio of 17:7. The dataset was partitioned randomly, taking into account various modulation schemes. Moreover, due to the inherent characteristics of meta-learning, the labels assigned to the training and testing sets were entirely separate. Specific details regarding the dataset partitioning are provided in Table 3.  
The simulation experiments were conducted with SNRs ranging from \(-10\) dB to 18 dB in steps of 2 dB, resulting in a total of 15 different SNR values. For each SNR, 100 random samples were extracted for each signal class. The extracted signal features using TSSG were then fed into the relation metric network for classification training.  
The experiments in this paper were based on the N-way K-shot setting. In this setting, N represents the number of classes and K represents the number of samples per class. In the meta-learning network, the extracted features had a dimensionality of 64, the learning rate was set to 0.001, and the Adam [30] optimization algorithm was employed.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 4 Design of the Framework
### PDoS Severity Score Based on Combined Criteria
#### 4.9.3 Class 3 (Significant)  
Class 3 has the following characteristics:  
* Impact Effect: Slow or Fast;
* DoS Effect Duration: Permanent;
* Recovery Action: More complex measures, potentially involving component replacements.  
Class 3 in the spectrum of permanent denial-of-service (PDoS) attacks denotes a significantly higher level of threat compared to its lower-tier counterparts. This category is defined by attacks that deliver a slow or fast impact effect with permanent damage, necessitating advanced recovery methods often involving component replacements. Examples of Class 3 threats would include the CIH/Chernobyl Virus and MBRLock, which exhibit characteristics such as bootloader corruption, leading to substantial system disruption. Recovery from such attacks extends beyond simple resets or software reinstalls, demanding more resources and technical expertise.  
The implications of Class 3 attacks are noteworthy in their capacity to cause lasting damage to systems, which may result in prolonged operational downtime or the permanent loss of critical data. The severity of these threats underscores the need for robust security measures and advanced preparedness plans. Class 3 PDoS attacks represent a sophisticated and dangerous class of malware, requiring immediate and specialized attention to mitigate and recover from their impacts. In the broader context of the PDoS threat landscape, these attacks highlight the evolving nature of cyber threats and the need for the continual adaptation of cybersecurity strategies.
## 2 Materials and Methods
### Simulated Annealing Wavelength Selection
After every 100 iterations, the state was returned to the state with the lowest error found thus far to ensure that the algorithm does a thorough search in the area of the state space, which is presumably near the global minimum. The algorithm ended its search upon reaching the maximum number of iterations, or upon reaching 300 consecutive iterations with no change in the average prediction error greater than \(1\times 10^{-10}\), whichever came first. Wavelength selections were conducted for numbers of wavelengths \(k=3\), 5, 7, 11, 15, 20, 40, and 60 (the total number of wavelengths to select from in the 520-600 nm range was 81). To prevent the unnecessary selection of adjacent wavelengths for low values of \(k\), a validity check was added to the simulated annealing algorithm, whereby a candidate selection was declared invalid if the separation between any pair of wavelengths was less than 2 nm and \(k<20\).
## 2 Methods
### Experimental Setup for SD-OCT  
To benchmark the performance of the proposed method in terms of imaging speed, data volume, and lateral resolution, a 1301 nm SD-OCT system was built, as shown in Figure 3.  
Figure 3: Schematic diagram of the SD-OCT system. \(\mathrm{L}_{1}\): focused lens; PC: fiber polarization controller; \(\mathrm{M}_{1}\): mirror.  
A super luminescent diode (SLD1021S, Thorlabs, Newton, NJ, USA) provided the broadband illumination. The bandwidth of the source was 85 nm, with a 6 mW output power. The light passing through the circulator was divided by a 90:10 fiber-optic coupler (TW1300R2A1, Thorlabs, Newton, NJ, USA) into sample light and reference light for interference measurements. The reference arm contained a piezo-stage (P-752.1 CD, Physik Instrumente GmbH & Co. KG, Karlsruhe, Germany) to achieve phase shifting, so as to remove conjugated images and fundamental frequency noise. In the sample arm, the focused lens had a focal length of 19 mm, a spot size of approximately 10 \(\upmu\)m, and a DOF of approximately 193 \(\upmu\)m. The focused lens was an achromatic lens. The reference light reflected from the mirror interfered with the backscattered light of the sample arm, and the interference signals were collected by a spectrometer with a spectral resolution of approximately 0.13 nm at 1300 nm wavelength. In the spectrometer, a 1200 line/mm transmission grating was used for splitting, and a linear charge-coupled device (CCD) camera operating at a center wavelength of approximately 1300 nm (SU-LDV-512 LX-1.7T1-0500, Goodrich, 512 pixels, pixel size 50 \(\upmu\)m \(\times\) 50 \(\upmu\)m, 14-bits) was used to acquire spectra. The spectrometer had a spectral range of 1262.2 nm to 1338.4 nm. The sample was scanned in the \(x\)-direction using a one-dimension galvanometer scanner (TSH8310, Sunny Technology, Beijing, China) to obtain a 2D image. The rotating axis of the scanning mirror was at the focus of the focused lens to form a telecentric optical path. The interval between two lateral scanning points was 10 \(\upmu\)m. Further details about the system configuration can be found in Table 1.  
The theoretical maximum imaging depth is the imaging depth calculated using the resolution of the spectrometer at the center wavelength, without considering the attenuation of the tissues.
# Social Internet of Vehicles for Smart Cities
## 5 SIoV Security Issues
### SloV Threats
#### 5.1.3 Malware  
Malware, such as viruses or worms, is usually introduced through outside unit software and firmware updates. Malware can infect vehicles and even allow remote adversaries to take control of individual vehicles. Remote access Trojans, paired with the advanced communication facilities that VANETs bring to the SloV, can gain control of the internal CAN bus and disrupt essential services. Remote attacks of this form have been widely demonstrated and have shown in dummy tests to risk the safety of drivers and passengers [94]. Other research on malware targeting vehicles has shown that the spread of malware can be achieved through vulnerabilities in the computers used to maintain and diagnose vehicles during servicing. This has wider implications than just the infection of a single vehicle, because the spread of malicious software is taking place through a trusted service platform, potentially affecting an entire product line. As this is already possible with today's technologies, the potential for malware to spread through the SloV is even higher, for example when a vehicle's social component receives software or firmware updates from another unknown vehicle.  
Existing work proposed the person authentication system [95] to ensure only the authorized user can use the vehicle. [PERSON] proposed a cloud-assisted vehicle malware defence framework to address the malware challenges [96]. A key challenge is the maintenance of up-to-date patches and signature files. While cloud-assisted frameworks can centralize some of these efforts and make the problem more manageable, the roll-out of automated patch management in a large-scale SloV is a formidable challenge, given the heterogeneous nature of current vehicles. Other considerations are that the sophistication of advanced persistent threats (APT) is beyond what manufacturers andinfrastructure providers are prepared to or can afford to defend against. This means that the defence of individual vehicles will be left to individuals, which based on experience with currently-available ICT infrastructures, is open to a wide array of threats, including masquerading and social engineering attacks on the members of the SIoV.
## 3 Research Model and Hypothesis Development
### Environmental Factors  
This subsection will outline hypotheses for the environmental factors, specifically focusing on supplier support.  
Supplier Support  
Qualified suppliers play a critical role in supporting firms through the implementation of Industry 4.0 technologies by offering robust technical support and training [95]. Particularly for firms lacking high-level technical competence internally, external suppliers can compensate for this deficiency [107]. Research underscores the importance of close cooperation with suppliers, emphasizing that competent suppliers possess the necessary capabilities and experience in technology implementation, thereby lowering the knowledge and skill barriers for adopting firms [91].  
Training is another vital aspect of supplier support. Employees require training in the necessary skills to operate Industry 4.0 technologies effectively [47]. Additionally, training helps employees feel more comfortable with the new technologies, reducing resistance and enhancing the learning process [97]. Well-trained employees are better equipped to implement and utilize new technologies to their fullest potential. Therefore, high-quality training programs and technical competence make a supplier indispensable.  
It is evident that the lack of qualified and experienced suppliers poses a significant barrier to the successful implementation of Industry 4.0, particularly for firms in developing economies [23]. Therefore, this paper hypothesizes the following:  
**Hypothesis 5 (H5)**.: _Supplier support positively affects the implementation success of Industry 4.0 technologies._
## 3 Results  
### Characterization of Poly(S/DVB)HIPEs  
Typically, the structure of polyHIPEs are mesoporous polymers with interconnecting pores or windows [23]. Figure 2 provides information about the morphology of poly(S/DVB)HIPE using SEM micrographs with a magnification of \(\times\)500, prepared by a three component surfactant (SPAN80, DDBSS, and CTAB) and difference amounts of styrene and divinylbenzene in the emulsion systems. The pore size diameters of poly(S/DVB)HIPE were found to decrease cell size when increasing the amount of divinylbenzene. For 20 vol%, 80 vol%, and 100 vol% DVB, the average pore diameters of poly(S/DVB)HIPE structure were 79.4 \(\upmu\)m, 58.8 \(\upmu\)m, and 41.2 \(\upmu\)m, respectively. As a results, the decrease of the average pore diameters of poly(S/DVB)HIPE were affected by the crosslinking comonomer (DVB). The poly(S/DVB)HIPE system was incorporated more easily when the system increased the divinylbenzene content, which the crosslink network formed earlier [24].  
Surface areas of poly(S/DVB)HIPEs prepared by using three difference ratios of S/DVB i.e., 80:20, 20:80, and 0:100 were measured by an Autosorb-1 MP machine. The result showed that poly(S/DVB)HIPEs had a surface area between 22 and 363 m\({}^{2}\)/g. The surface area of poly(S/DVB)HIPEs tended to increase with the increased amount of DVB in the system, as shown in Table 1, due to the ability of a high degree of crosslinking (DVB), making the structure more stable [25].  
Figure 2: Scanning electron micrographs of different ratio S/DVB of poly(S/DVB)HIPE: (a) 80:20, (b) 20:80, and (c) 0:100.  
The mechanical properties of poly(S/DVB)HIPE were studied using the LLOYD universal testing machine. In Table 1, information on compressive strength and Young's modulus of poly(S/DVB)HIPE were given. When increasing the divinylbenzene content, the compressive strength of the polyHIPE was found to increase from 0.13 to 0.30 MPa. Young's modulus of sample was increased from 1.79 to 5.41 MPa. The highest compressive strength and Young's modulus of poly(S/DVB)HIPE was a S/DVB ratio of 0:100, mainly because a large amount of crosslinking gave a stronger structure, so when increasing the amount of DVB in the system, poly(S/DVB)HIPE became more stable than others [25].  
Thermal properties of poly(S/DVB)HIPE were investigated by TG analysis. The decomposition temperature (T\({}_{\rm d}\)) and residue yield of poly(S/DVB)HIPEs were also listed in Table 1. The result showed that T\({}_{\rm d}\) and the residue yield of the samples increased with the increased concentration of divinylbenzene. Due to the high concentration of DVB, it led to a high degree of crosslinking, so the structure of poly(S/DVB)HIPE became more stable [25]. Generally, material used for CO\({}_{2}\) capture are exposed to temperatures in excess of 300 \({}^{\circ}\)C, so the poly(S/DVB)HIPEs prepared in this study would be suitable for this application [26].
# A Frame-to-Frame Scan Matching Algorithm for 2D Lidar Based on Attention
## 6 Experiments  
In order to verify the effectiveness of the ASM algorithm, experiments were conducted in four typical indoor scenarios, including offices, office buildings, libraries, and shopping malls. Cartographer [31] software was used to build 2D maps of the four test environments and are shown in Figure 5.  
All four of the environments shown in Figure 5 have a back-shaped structure. The robot starts from the origin and makes a circle around the environment and then returns to the vicinity of its origin. These types of environments are very suitable for comparing accuracy with end-to-end error metrics.  
In order to compare the presented method with existing methods, four representative methods: ICP [3], PL-ICP [4], iCSM [20], and IMLS-ICP [9] were selected. The ICP is implemented in the Point Cloud Library (PCL) [32], the PL-ICP method uses the author's own open-source code, the iCSM method uses the implementation in the cartographer [31] software with covariance matrix degradation analysis, and the IMLS-ICP method is not open source, and therefore, a 2D version was implemented according to the methodology  
Figure 5: Maps of the test environment. (**a**) Office map; (**b**) office building map; (**c**) library map; (**d**) shopping mall map.  
outlined in the paper in which it was published. The test platform used in this paper is shown in Figure 6.  
The test platform shown in Figure 6 is a differentially driven wheeled robot and is a prototype of the delivery robot created by Yuefan Innovation. The robot is equipped with a wheeled odometer and a low-cost 2D TOF lidar sensor. The update frequency of the wheel odometer is 100 Hz, the update frequency of the lidar data is 10 Hz, and the field of view is 250 degrees. The wheel odometer pose is used as the initial pose, and the scan matching algorithm is further optimized based on the initial pose. All programs run on an Rockchip ARM Core RK3399 process at 1.5 Ghz with 2 GB of memory.  
Since it is impossible to obtain the true value of the robot's trajectory in these four datasets, the end-to-end error was used to compare the accuracy. When the robot starts from the origin position, moves around the environment, and then returns to the origin position, the spatial displacement of the same object in the environment is selected as the cumulative error of the frame-to-frame matching algorithm.  
To ensure that the experimental results are not affected by different batches of experimental data. Before the start of the experiment, the robot was controlled to move in the four environments, and the data of the sensors carried by the robot were collected to form four datasets. All experiments are carried out on these datasets.
# Landslide-Tunnel Interactions and Control Countermeasures under an Orthogonal System
## 3 Numerical Simulation of Tunnels and Landslides
### Numerical Modeling  
To fully consider the influence of landslides on tunnels, the finite difference software FLAC(3D) 6.0 is used to establish a three-dimensional numerical model for simulation analysis. The tunnel in the model is located on the sliding surface. The three-dimensional size of the model is 180 m \(\times\) 30 m \(\times\) 100 m; the height of the left side is 30 m; and the height of the right side is 100 m. The sliding mass is mainly composed of limestone and sandstone, mostly in a weak weathering state, and the cracks are highly developed. The surrounding rock grade of the tunnel is grade V, and the integrity is poor. The lining is made of C25 concrete, and the radial excavation length is 30 m. The boundary conditions of the model are fixed at the bottom of the model, and the normal constraints are taken on its front and rear boundaries and its left and right boundaries. The soil is modeled using the Mohr-Coulomb constitutive model, while the surrounding rock and supports are simulated using solid elements. The material parameters of the model are shown in Table 1. A schematic diagram of the model is shown in Figure 5.  
Determining the initial stress state is a critical step before tunnel excavation, as it affects the stress redistribution in the surrounding rock and the stability of the tunnel. Firstly, the boundary conditions of the model are defined, including the size of the model, the position and direction of the geological layer, and any possible sliding surface. The initial stress is determined by applying self-weight stress to the model. The initial stress equilibrium of the model is shown in Figure 6. Under the influence of self-weight, the vertical stress of the model is evenly distributed from top to bottom, and the whole model reaches a state of equilibrium.