esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Aroma Profile of Nut Products After and Before Roasting Conditions
### Almonds  
In raw almonds, different family of compounds such as alcohols, aldehydes, benzaldehyde, organic acid, terpenes, sulfur compounds and some pyrazines have been detected [66]. The cultivar of almonds can significantly influence the quantity and quality of volatile compounds. For instance, the _Ferragnes_ cultivar exhibits a total VOC content of 19.9 ug/g, while the _Bonita_ cultivar has a VOC content of approximately 110 ug/g. Furthermore, the predominant compounds in _Ferragnes_ are alcohols, whereas aldehydes are more prevalent in _Bonita_. However, certain compounds, such as benzyl alcohol (6.74 ug/g), 1-octanol (0.9 ug/g), phenylethyl alcohol, hexanal (0.69 ug/g), nonanal, 3-methyl-1-butanol (18.43 ug/g), and benzaldehyde, are commonly found across all cultivars [73]. Another study, conducted by [PERSON] et al. (2017) on raw almonds, identified vanillin (0.83 ug/g), acetic acid (0.137 ug/g), and trace amounts of 2-acetyl-1-pyrroline [64].  
Similarly to hazelnuts, the roasting process significantly alters the profile of compounds responsible for the aroma and flavor of almonds. [PERSON] et al., after roasting almonds at 165 \({}^{\circ}\)C for 15 min, identified several molecules absent in raw almonds. For example, the Maillard reaction and other non-enzymatic reactions can trigger the synthesis or enhancement of compounds such as hexanal (1.650 ug/g), phenylacetaldehyde (0.571 ug/g), 3-methylbutanal (0.319 ug/g), and HDMF (1.180 ug/g) [64]. Another study reported the VOCs found in roasted almonds, which included hexanal (6.39 ug/g), benzaldehyde (240 ug/g), limonene (3.39 ug/g), benzyl alcohol (1.09 ug/g), 1-pentanol (4.59 ug/g), and 3-methyl-1-butanol (13.79 ug/g) [73]. Furthermore, [PERSON] et al. (2019) identified 2-pentylfuran, 5-methylfurral, furfural, 2,3-butanedione, 2,3,5-trimethylpyrazine, 2,6-dimethyl-3-ethylpyrazine, 2-ethylpyrazine, 1H-pyrrole, 2-acetylpyrrole, methional, and other compounds [66].  
[PERSON] et al. (2014) reported that lipid decomposition during almond roasting at 138 \({}^{\circ}\)C, ranging from light roasting (28 min) to dark roasting (38 min), increased the emission of several volatile compounds. These compounds were detected using HS-SPME GC/MS analysis. The authors observed significant increases in various compounds in roasted almonds compared to raw almonds. For example, butanal levels increased from 19 ng/g in raw almonds to 40 ng/g in dark-roasted almonds. Other notable increases included 2,3-butanedione (from 8 to 226 ng/g), 3-methylbutanal (from 32 to 4268 ng/g), furfural (from 0 to 460 ng/g), hexanal (from 422 to 1140 ng/g), and 1-(methylthio)-2-propanol (from 12 to 325 ng/g). Furthermore, the study highlighted a significant rise in pyrazine compounds during roasting. The authors identified seven classes of pyrazines, including 2-methylpyrazine (increased to 26 ng/g), 2,6-dimethylpyrazine (from 0 to 4 ng/g), trimethylpyrazine (from 0 to 6 ng/g), and 2,5-dimethylpyrazine (from 11 to 66 ng/g) [65].
# Development of Ground Special Vehicle PHM with Case-Based Reason Model
## 4 Application Validation and Analysis  
### Applicaiton Experiment Test  
With the development of the application, function, and performance of the CBR reason model and optimization process must be evaluated to check if practical engineering requirement is satisfied. In this section, the application experiment is carried out by adopting historical data from GSV. Stored cases have been checked to reduce redundancy and to verify data validation. In the paper, the optimization method of even weight allocation CBR model (EW-CBR) and correlation coefficient weight allocation CBR model (CW-CBR) is also employed to make a comparison with the developed SA weight allocation CBR model (SA-CBR), and to check if the module functions properly.  
In Table 3, \(A_{i}(i=1,2,3,4,5)\) represents a subsystem or a combination of the subsystems, and the feature attribute includes the numerical value from the sensor (including eight pressure parameters, four temperature parameters, eight flow parameters, four voltage parameters, and four current parameters) and Discrete quantities (relay open/closed state, valve closed/closed state, communication identification state, gear switch, status indicator). For example, if the health status is judged from the hydraulic subsystem, only the feature attributes corresponding to the hydraulic subsystem are used. If the health status is comprehensively considered from the hydraulic subsystem and the temperature control subsystem, the corresponding feature attributes of both the hydraulic subsystem and the temperature control subsystem are used, the reasoning result will show if the system is normal or abnormal. Accordingly, in Table 4, the reasoning result will show the degree of health performance in the probability of (0, 1). The subset \(B_{i}(i=1,2,3,4,5)\) has the same meaning as in Table 3. The case name denotes the subsystem or combination of the subsystem, in which H denotes hydraulic subsystem, T denotes temperature control subsystem, F denotes fixed aiming subsystem, P denotes power supply and distribution subsystem, Fc denotes fuel control subsystem, E denotes electric control subsystem, C denotes communication subsystem.  
The basic process is as follows:  
1. Given initial weights, take one of the 5 subsets as the target case and the remaining four subsets as the source case, and calculate the similarity in turn.
2. Determine the number of matching cases according to the similarity threshold and k-nearest neighbor. If it is met, go to step 3, if it does not match, go to step 4.
3. Verify the case reuse result. If it meets the requirements, and the result is correct, go to Step 5. Otherwise, continue to step 4.
4. Use SA-CBR, EW-CBR, and CW-CBR to optimize weight distribution as described in Sections 4.1.1-4.1.3, and return to step 3.
5. Save the case, output the result, return to step 1, repeat five times.
# LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites
###### Abstract  
An autonomous robot that can monitor a construction site should be able to be can contextually detect its surrounding environment by recognizing objects and making decisions based on its observation. Pixel-wise semantic segmentation in real-time is vital to building an autonomous and mobile robot. However, the learning models' size and high memory usage associated with real-time segmentation are the main challenges for mobile robotics systems that have limited computing resources. To overcome these challenges, this paper presents an efficient semantic segmentation method named LNSNet (lightweight navigable space segmentation network) that can run on embedded platforms to determine navigable space in real-time. The core of model architecture is a new block based on separable convolution which compresses the parameters of present residual block meanwhile maintaining the accuracy and performance. LNSNet is faster, has fewer parameters and less model size, while provides similar accuracy compared to existing models. A new pixel-level annotated dataset for real-time and mobile navigable space segmentation in construction environments has been constructed for the proposed method. The results demonstrate the effectiveness and efficiency that are necessary for the future development of the autonomous robotics systems.  
Keywords:efficient real-time segmentation; embedded platform; autonomous navigation in construction; autonomous data collection +
Footnote †: journal: Computer Vision and Pattern Recognition
## 3 Oxidative Stress Biomarkers  
There are numerous markers that assess the intensity of OS as well as degree of oxidative damage caused by it.  
Free radicals cause damage to many cell components and disrupt cellular metabolism. The first element exposed to free radicals is the cell membrane, therefore the earliest symptom of OS is increased concentration of peroxidation products, mainly phospholipids, membrane glycolipids and cholesterol [33]. Polyunsaturated fatty acids, i.e., those containing two or more double bonds, classified as omega\(-\)3 (n\(-\)3) and omega\(-\)6 (n\(-\)6) fatty acids, are the most susceptible to peroxidation [34]. The process of lipid peroxidation consists in the oxidation of polyunsaturated fatty acid radicals and can occur either without the participation of enzymes or result from enzymatic reactions [35] (Figure 1). It should be emphasized that both mechanisms of lipid peroxidation may take place simultaneously. The enzymatic process starts when large amounts of lipid peroxides are supplied by nonenzymatic lipid peroxidation. In turn, reactions catalyzed by lipoxygenases may initiate nonenzymatic processes (Figure 2).  
Figure 1: Abbreviation: L—carbon-centered lipid radical, LO—lipid alkoxyl radical, LOO—lipid peroxy radical, LOOH—lipid hydroperoxide. Free radical peroxidation of lipids according to [36] as modified by the authors.  
High ROS concentrations lead to oxidative modifications to proteins. Both single amino acid residues and an entire polypeptide chain may undergo oxidative modifications. Oxidation of amino acids results in their modification, breaking of the polypeptide chain and formation of cross-links within one or more polypeptide chains [34] (Figure 3). Oxidative damage to proteins leads to the loss of their biological activity and function, with all consequences for the cell.  
Figure 3: Abbreviation: R—rest of the fatty acid. Oxidation of the polypeptide chain at the \(\alpha\)-carbon according to [38] as modified by the authors.  
Figure 2: Abbreviation: AA—arachidonic acid. Diagram of isoprostane formation according to [37] as modified by the authors.  
Nucleic acids are more resistant to free radicals than fats and proteins. Therefore, they are not often transformed into free radicals. Moreover, damage to the genetic material is quickly repaired. Oxidative DNA modifications are mainly caused by \(\bullet\)OH, while H\({}_{2}\)O\({}_{2}\) and O\({}_{2}\bullet\) do not reveal this ability [35] (Figure 4).  
In some cases, the concentration of oxidatively modified molecules (the only reliable oxidative stress markers) may be reduced or even unchanged compared to the control due to their elimination by specific repair systems. Such a result may erroneously suggest a normal redox state of the tested material. Therefore, an oxidative stress index (OSI) was introduced. OSI enables the assessment of the relationship between total oxidant status (TOS) and total antioxidant status (TAS), and is a recognized golden standard in assessing oxidative stress [39; 40; 41].
## 3 Results
### Optimization Results
#### 3.2.1 Optimization with Genetic Algorithm  
Objective Function \(J_{1}\)  
The first scenario of the optimization set a goal to minimize the model area and keep the first natural frequency above 140 Hz (level of the frequency derived from the domain screening) as reported in Equations (14) and (15). The history of the change in natural frequency of the first system mode is presented in t Figure 17.  
Change history for the model area presented in Figure 18.  
The algorithm converged after 1280 evaluations (13 iterations) to propose three candidates (the top three solutions) reported in detail in Table 5. The maximum number of iterations were not reached since the convergence stability percentage criterion was fulfilled earlier.  
Figure 16: Interpretation of the convergence diagram (history chart).  
Figure 17: Convergence history for first mode natural frequency.  
Figure 18: Convergence history for model area.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 1 Introduction
These improvements not only result in safe and efficient roadways but also in other environmentally sustainable development initiatives. V2V is expected to advance even faster in the next few years and be further entwined with the other ITS applications, further unlocking the opportunities V2V has for changing the nature of city transportation systems to be better, safer, cleaner, and more efficient [16; 17]. The major contributions of this study are stated as follows:  
* We present a review that contributes to this dynamic field by providing a comprehensive synthesis by critically analyzing the state of the art and aiming to identify trends, challenges, and emerging research areas of ITSs;
* Additionally, we seek to offer insights into the transformative potential of intelligent hybrid V2V communication systems in enhancing sustainable road cooperation, safety, and overall transportation efficiency;
* With this contribution, we aim to guide future research and developments in connected and cooperative vehicle systems for the ITS;
* We analyze and deliberate on the most advanced, sustainable V2V communication architectures;
* We thoroughly examine the advantages of V2V communication architectures, their contributions, and their limitations for ITS network environments. The remaining portions of the article are grouped into the following categories: Section 2 outlines the rationale and motivation behind utilizing multiple V2V communication systems. Section 3 presents a comprehensive comparison of the latest relevant studies. Section 4 focuses solely on V2V communication methods. Section 5 presents a concise overview of the survey on certain communication architectures. Section 6 provides future research prospects and directions. Section 7 finally presents the conclusion.
## 3 Results and Discussion
### Ni\({}^{2+}\) Adsorption on Sand and Soil Colloids
#### 3.2.3 Isothermal Adsorption of Ni\({}^{2+}\)  
Both Langmuir and Freundlich models were used to fit the adsorption isotherms of Ni\({}^{2+}\) on quartz sand and soil colloids (Figure 4), and the related parameters are shown in Table 2. It was obvious that the adsorption capacity of quartz sand and soil colloids for Ni\({}^{2+}\) increased with the increase of initial concentrations of Ni\({}^{2+}\) at the range of 0-2 mg\(\cdot\)L\({}^{-1}\). Both Langmuir and Freundlich models fit well with the adsorption isotherms of Ni\({}^{2+}\) on quartz sand and soil colloids since the correlation coefficients (R\({}^{2}\)) were larger than 0.95. In addition, both models fit better for Ni\({}^{2+}\) adsorption on sand than that on soil colloids, which may be because the composition of soil colloids is more complex than quartz sand.  
According to the parameters shown in Table 2, the \(Q_{m}\) values fitted by the Langmuir model for Ni\({}^{2+}\) adsorption by soil colloids were larger than the Ni\({}^{2+}\) adsorption on the sand. This result indicated that the adsorption capacity of Ni\({}^{2+}\) on soil colloids was much stronger than that of Ni\({}^{2+}\) adsorption on the sand, which is consistent with the previous study [43; 44]. Moreover, it was also reported that soil colloids had a large specific surface area and could be bound with exchangeable cations to a greater extent than sand. Thus, Ni\({}^{2+}\) could not only be adsorbed on quartz sand, it could be adsorbed much more on soil colloids. The Freundlich model assumes that the sorption of Ni\({}^{2+}\) occurs on a heterogeneous surface. The \(K_{f}\) value of Ni\({}^{2+}\) adsorption by soil colloids is approximately 45 times higher than that on the sand. It may be because soil colloids have greater specific surface areas  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Materials**} & \multicolumn{3}{c}{**Langmuir Equation**} & \multicolumn{3}{c}{**Freundlich Equation**} \\ \cline{2-7}  & \(Q_{m}\) & \(b\) & \(\mathbf{R^{2}}\) & \(K_{f}\) & \(n\) & \(\mathbf{R^{2}}\) \\ \hline Sand & 0.1973 & 1.3259 & 0.9976 & 0.1503 & 1.2077 & 0.9993 \\ Soil colloid & 57.1429 & 0.0938 & 0.9911 & 6.8391 & 0.7874 & 0.9604 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of Langmuir and Freundlich models for Ni\({}^{2+}\) adsorption.  
Figure 4: Langmuir model for Ni\({}^{2+}\) adsorption on (**a**) sand and (**b**) soil colloids; Freundlich model of Ni\({}^{2+}\) adsorption on (**c**) sand and (**d**) soil colloids.  
and CEC than sand, resulting in a better adsorption capacity for Ni\({}^{2+}\). As a result, it can be speculated that heavy metal ions could be adsorbed on the colloids or fixed on the solid particles in the soil environment to reduce heavy metal contamination in groundwater.
## 3 Results
### Data Acquisition and Preprocessing  
Spectral reflectance endmembers were collected from the pre-fire (3 March 2013) and post-fire (2 March 2015) Landsat images after Dark Object subtraction was applied to them. The fire occurred on 11 February 2015 and the post-fire images were selected based on close proximity to the incident and availability. Figure 5 shows spectral reflectance curve of healthy green vegetation has a significant minimum of reflectance in the visible portion of the electromagnetic spectrum resulting from the pigments in plant leaves. Reflectance increases dramatically in the near infrared (0.7-0.9 \(\upmu\)m). Scorched or charred vegetation can be detected at a significantly lower reflectance in the infrared. Charred soil shows a much lower reflectance than uncharred soil cover.
## 4 Evaluation  
### Test Setup  
The Wi-PoShardware platform combines several unique features with centimeter-precision localization. The performance of the localization system is evaluated on ranging accuracy and power consumption. First, the receiver sensitivity, link margin and packet receive ratio are measured for the Wi-PoShardware platform. Next, the accuracy of the system is presented. The additional sub-GHz backbone communication offers supreme reductions in power consumption at the anchor nodes, compared to solutions that use UWB for communication between the nodes. In all experiments, we measured at channel 1 with a 110 kbps data rate, 64 MHz PRF, and preamble of 1536 symbols. The transmit gain is 12.5 dB, as this is the default value of the Decawave DW1000 transceiver for the chosen channel and PRF. If other parameters are used, it will explicitly be stated.  
To calculate the distance between the 2 nodes, asymmetric two way ranging (TWR) [27] is used, as this cancels out clock drift in sender and receiver as well as different reply times between messages [12]. With this protocol (Figure 5), we can use the same poll message for multiple anchor nodes. During 1 superframe, the tag will sequentially range with four different anchors, where every anchor gets a specific timeslot in the superframe appointed for ranging with the tag node. At the start of the superframe, the tag sends a sub-GHz beacon with the anchors he desires to range with and uponreception, the anchor nodes will allocate time slots for transmit and receive messages with the tag. Then, the tag will send an UWB poll message and every anchor will answer in his dedicated timeslot with a response message. The tag will send a final message to the anchor node. After receiving this final message, the distance between anchor and tag is calculated at the anchor node and reported over the sub-GHz backbone to a server and to the tag node. Every ranging requires three consecutive UWB messages to be transmitted and received successfully. When a packet is not correctly received, no retransmissions will be sent. An in depth study of the protocol can be found in Reference [28].  
The DW1000 timestamps the packet when it enters the differential input. The time for the signal to propagate between reception at the antenna, and entering the DW1000 is compensated with the _Antenna Delay_ value. This value contributes to a constant ranging error, and it is required to be calibrated for each device for optimal results.
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
###### Abstract  
Augmented (AR) and Mixed Reality (MR) technologies are enablers of the Industry 4.0 paradigm and are spreading at high speed in production. Main applications include design, training, and assembly guidance. The latter is a pressing concern, because assembly is the process that accounts for the biggest portion of total cost within production. Teaching and guiding operators to assemble with minimal effort and error rates is pivotal. This work presents the development of a comprehensive MR application for guiding novice operators in following simple assembly instructions. The app follows innovative programming logic and component tracking in a dynamic environment, providing an immersive experience that includes different guidance aids. The application was tested by experienced and novice users, data were drawn from the performed experiments, and a questionnaire was submitted to collect the users' perception. Results indicate that the MR application was easy to follow and even gave confidence to inexperienced subjects. The guidance support was perceived as useful by the users, though at times invasive in the field of view. Further development effort is required to draw from this work a complete and usable architecture for MR application in assembly, but this research forms the basis to achieve better, more consistent instructions for assembly guidance based on component tracking.  
assembly; assembly instructions; assembly guidance; augmented reality; mixed reality; unity; Vuforia 2023
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 5 Future Perspectives
### Expansion and Curation of Databases  
One of the most impactful advancements in the future of MALDI-TOF MS will be the continued expansion and refinement of microbial reference databases. A global effort to systematically catalog foodborne pathogens, spoilage organisms, and beneficial microbes--across diverse geographies, processing environments, and food types--will greatly enhance identification accuracy and coverage. Open-access, peer-reviewed databases with strain-level spectral data and associated metadata (e.g., isolation source, geographic origin, genomic background) are expected to emerge as central resources [12; 81; 105; 136].  
Additionally, advances in machine learning and bioinformatics will facilitate automated spectral matching and classification beyond current rule-based algorithms, allowing more nuanced discrimination among closely related strains and subspecies.