esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Selection of HP Pre-Treatment Conditions for OD of Tomatoes  
The results for the selection of HP pre-treatment conditions in terms of enzyme activity and relative firmness are presented in Figure 4. Pressure treatment time was kept constant for all experiments and equal to 5 min. Increasing treatment pressure up to 400 MPa causes significant (\(p<0.05\)) decrease in firmness, due to the increasing levels of cell disruption caused by the pressure. Enzyme activity for both PME and PG remains unaffected. However, a treatment at 600 MPa, causes a significant (\(p<0.05\)) restoration in firmness, accompanied by complete inactivation of PG and retention of PME activity. As discussed earlier, PG is a lytic enzyme which depolymerizes pectic substances, leading to textural deterioration. Its inactivation leaves PME to de-esterify pectin side groups, leaving them available to be crosslinked via reaction with calcium ions. At the same time, a pressure of 600 MPa causes a maximal degree of cellular disruption.  
Regarding sensorial characteristics, the most salient deterioration is the perception of peel detachment at a treatment pressure of 400 MPa. The intense degree of cellular disruption caused by HP in combination with the action of p  
Figure 4: Residual enzyme activity of PME and PG and relative firmness (compared to control) for untreated and HP treated tomatoes at 100, 200, 400 and 600 MPa for 5 min. Different superscript letters indicate significant differences between means of residual enzyme activity \(\pm\) standard deviation (capital letters for PG and small letters for PME) as calculated by [PERSON]’s multiple range test for a significance level of \(p=0.05\).  
almost complete detachment of the peel, leading to an unacceptable appearance. Both bite hardness and crunchiness decreased with increasing treatment pressure up to 400 MPa but were restored at 600 MPa to levels comparable to those of the untreated sample. This is consistent with the measurements of objective firmness discussed above. Although treatment at 600 MPa causes a high degree of cell disruption, the concomitant inactivation of PG prevents the hydrolysis of pectin and preserves firmness. Based on the intense degradation observed at 400 MPa, treatment at this level was excluded as a viable pre-treatment for OD.
# Dense Pure Tungsten Fabricated by Selective Laser Melting
## 4 Discussion
As plotted in Figure 10b, the temperature variation of spherical powders is similar to that of polyhedral powders, but there is a higher temperature rise. The peak temperature is 764 K after the laser scanning process, and more time (39 s) is taken to reach a uniform isothermal state. The equivalent substrate temperature when the laser is switched off is calculated to be 425 K, which leads to a computed value for the laser absorptivity of spherical powders of 68%. The reasons why spherical powders possess higher laser absorptivity than polyhedral powders are as follows: (i) after spheroidization, the surface area ratio is increased and, therefore, the proportion of surface atoms, which are active for laser absorption, increases significantly; (ii) surface polygons are  
Figure 10: The recorded temperature curves for a substrate pre-deposited with (**a**) polyhedral and (**b**) spherical powders. The red and black lines represent the temperature of the central point and marginal line, respectively. The blue “*” line represents the analytic temperature curve which matches the experiment data very well. absent when using spherical powders and, as a result, diffuse reflection is enhanced; and (iii) with higher packing density, the multiple-scattering absorption probability between particles increases. Additionally, there are more powders to be melted in the dense spherical powder layer, meaning an elongated absorption process. Tungsten possesses high electron and heat conductivities, resulting in a high laser reflection rate. Indeed, a large amount of the input laser energy is reflected during the tungsten SLM process. As our analysis above has shown, spherical powders can absorb more laser energy under the same operating parameters, resulting in a higher molten pool temperature. As is well known, the viscosity of tungsten is inversely proportional to the temperature [26], \(\eta=0.108\exp\left(1.28\times 10^{5}/RT\right)\). After spheroidization of the powders, the viscosity of the tungsten molten pool decreases, improving the wetting speed of the molten pool. It is for this reason that it can be understood why a continuous molten track can be achieved with spherical powders, but not with polyhedral powders. In addition to the effect of decreasing the molten pool's viscosity, the spherical powder layer has a higher packing density, which implies reduced shrinkage, and a smooth surface finishing after laser consolidation. Indeed, subsequent powder layers can only be deposited evenly on a smooth, established surface. For polyhedral powders, as balling cannot be consistently controlled, this causes heritable defects in the following layers. In contrast, for spherical powders, continuous molten tracks may be formed without balling effects, and a smooth surface finishing can be achieved.
# The Study of Thermal Properties of Blackberry, Chokeberry and Raspberry Seeds and Oils
## 3 Results and Discussion
### Study of Oil Obtained from Seeds
#### 3.2.4 TG/DTG of Extracted Oils  
Examples of TG/DTG curves of the studied seed oils are presented in Figure 5. TG curves with calculated derivatives of oils under nitrogen atmosphere show a similar course for all of the studied samples. Three mass losses can be noticed. For chokeberry oil, the temperature range of the first decomposition reaches up to 250 \({}^{\circ}\)C with around 4% of weight loss, the second reaches temperatures of 250-480 \({}^{\circ}\)C with the most significant loss of weight of 94%, and the final loss accounts for over 1%. For blackberry and raspberry oils, temperature ranges are the same, and a first decomposition of around 2.5% and 6% weight of blackberry oil and raspberry oil, respectively, is observed at a temperature of up to 300 \({}^{\circ}\)C. In temperatures of around 300-500 \({}^{\circ}\)C, over 96% of blackberry oil and 93% of raspberry oil is decomposed. A final loss of less than 1% of weight was noticed at over 500 \({}^{\circ}\)C.  
Figure 4: TDSC curves showing oxidation induction times of blackberry, chokeberry, and raspberry seed oils.  
Figure 5: The examples of TG/DTG curves of (**a**) blackberry, (**b**) chokeberry, (**c**) raspberry seed oils under nitrogen atmosphere, and (**a**’) blackberry, (**b**’) chokeberry, (**c**’) raspberry seed oils under an oxygen atmosphere.  
Under an oxygen atmosphere, there are also three noticeable events at the same temperature ranges for all of the oils. First, at 200-380 \({}^{\circ}\)C, the highest mass loss amounts to 47%, 49%, and 57% of raspberry, blackberry, and chokeberry, respectively. The next decomposition occurring at 380-450 \({}^{\circ}\)C contributed to a loss of 20% of chokeberry oil, 27% of blackberry oil, and 29% of raspberry oil weights. The following mass loss amounts to 20-23% within oils.  
The results are in agreement with the study of _Citrullus colocythis_ by [PERSON] et al. [45] carried out in the air atmosphere. The degradation in oil started after reaching a temperature above 286 \({}^{\circ}\)C, and it decomposed in four stages at temperatures: 377.4 \({}^{\circ}\)C, 408.4 \({}^{\circ}\)C, 434.9 \({}^{\circ}\)C, and 559.2 \({}^{\circ}\)C. Moreover, results of the TG study of sunflower oil were shown. Degradation of sunflower oil began at a temperature above 247 \({}^{\circ}\)C and it was also degraded in a four-stage weight-loss with maximum signals at 352.7 \({}^{\circ}\)C, 419.7 \({}^{\circ}\)C, 437.4 \({}^{\circ}\)C, and 541.7 \({}^{\circ}\)C. The temperatures of oil degradation in the following study are lower than for _C. colocythis_ and sunflower oils due to differences in fatty acid composition. _C. colocythis_ and sunflower oils contain more SFA and MUFA than berry seed oils and can be characterized by increased thermal stability.
# Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
## 3 Intelligent Detection Methods for Driver Fatigue and Distraction
### Intelligent Detection Methods Based on Driver's Physiological Characteristics  
Studies indicate that significant changes in physiological characteristics occur when a driver is fatigued or distracted [53]. Monitoring a driver's physiological signals through physiological instrument devices can analyze their attention level, emotional state, and degree of physical fatigue. Common physiological signals include the Electroencephalogram (EEG), Electrocardiogram (ECG), Heart Rate (HR), Electrooculogram (EOG), and Electromyogram (EMG).  
[PERSON] et al. [54] monitored drivers' skin conductivity and blood oxygen saturation using wearable devices, designing a multilayer perceptron model for training to classify and detect drivers' psychological fatigue and drowsiness states. [PERSON] et al. [55], based on EEG signals from drivers' scalps, used electrophysiological source imaging and EEG source localization techniques for signal processing and feature extraction, and established a Support Vector Machine (SVM) classifier to detect whether drivers were in a state of extreme fatigue. [PERSON] et al. [56] employed convolutional neural networks and gated recurrent units to map the relationship between drivers' distraction states and EEG signals in the time domain, verifying the effectiveness of their method through simulation experiments. [PERSON] et al. [57] developed a non-contact vehicle-mounted driver fatigue detection system to collect drivers' biceps femoris electromyographic signals and ECG signals. [PERSON] et al. [58] decomposed EEG signals into wavelet sub-bands to extract nonlinear features beyond the original signal and integrated them with eyelid movement information, using an Extreme Learning Machine (ELM) for state classification.  
The use of full-head EEG monitoring devices often leads to issues such as computational and storage resource wastage and low real-time performance. In light of this, [PERSON] et al. [59] optimized EEG instruments by monitoring the frontal EEG signals of drivers to extract features like the energy, entropy, and frontal EEG asymmetry ratio of EEG signals, proposing a time-series-based ensemble learning method for fatigue and distraction detection.  
Overall, intelligent detection methods based on drivers' physiological characteristics can accurately assess drivers' states by monitoring changes in their physiological states. However, most of these methods use invasive sensors, which may cause discomfort to drivers and have disadvantages such as poor convenience, high cost, and detection accuracy being easily affected by the environment, hindering their application.
# Solid-Phase Microextraction Techniques and Application in Food and Horticultural Crops
## 5 Applications of SPME and HPLC in Agricultural Products
Method development involved comparing the extraction performance of four different SPME fibers: PDMS, PDMS/DVB, CW/TPR, and PA. The CW/TPR polar fiber was selected as the most appropriate for extraction. Good extraction was observed for seven of the fungicides that had a log \(P\) of >2.5. However, here was little extraction observed for the more polar compounds, primicarb (log \(P\) = 1.7) and metalaxyl (log \(P\) = 1.65), while no extraction for acetamiprid (log \(P\) = 0.8) was observed. The experimental variables pH, NaCl concentration, and extraction time were optimized for analyte extraction using a surface response methodology. This resulted in the optimal extraction of samples at pH 8, 18% \(w/v\) NaCl, and a 30 min extraction time (with stirring at 1000 rpm at room temperature). Fiber desorption took place for 10 min in a 60 \(\upmu\)L aliquot of a 90% \(v/v\) aqueous methanol solution. The solution was then injected into the HPLC column for analysis. The developed method was used to determine fungicide concentrations in a crop of lettuce. Fungicides azoxystropin, cyprodinil, fenehexamid, fluidoxonil, folpet, iprodione, and toylfluanid were able to be determined when over a concentration range from 0.8 to 25.6 mg/kg. The method showed good sensitivity, with the lower limit being well below the acceptable residue concentrations for these fungicides. Triazine herbicides are widely used on crops to control weeds. Recently, [PERSON] et al. developed a SPME HPLC-MS method for the analysis of seven triazine herbicides (Figure 8) [52]. This was based on a novel absorbent, consisting of a thin film of a mixture of chromium terephthalate porous solid (MIL-101(Cr)) [53] and chitosan, coated on a cellulose filter paper substrate. Discs with a diameter of 1.5 cm were prepared for herbicide extraction. This type of SPME is referred to as thin-film microextraction (TFME). The advantage of TFME over extraction using a traditional SMPE fiber is that it provides both a thin film together with a relatively large surface area. This enables the good adsorption of an analyte with relatively low extraction and desorption times. The optimal extraction and desorption times for the method were 15 min each. A 5 mL analyte sample was used for the extraction while desorption involved the disk being placed in a tube with 2.5 mL of acetonitrile, which was then evaporated to dryness. The dried extracted sample was then redissolved in 1 mL of methanol before being injected into the HPLC column. A LOD of 0.022 ng/L compared favorably with (or was lower than) the LODs for a number of other different extraction methods. The adsorption of the triazine herbicides from the water sample was thought to be due to \(\pi\)-\(\pi\) interactions between the terphthalic acid groups in the adsorbent layer and the heterocyclic rings in the triazine herbicide molecules.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### AI in Neuro-Ophthalmology  
Over the last 10 years, AI has provided clinicians with new, rapid, precise, and automated methods to diagnose and treat eye diseases, as well as open new avenues for modern eye care [101].  
A few ophthalmologic subspecialties have benefited from AI to a greater extent than others, e.g., automated DL identification of diabetic retinopathy, glaucoma, and age-related macular degeneration.  
On the contrary, subspecialties such as neuro-ophthalmology have lacked major improvements in the detection, treatment, and prognosis based on AI.
# An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis
## 5 Applications
### Analysis of Bearing Inner Ring Fault Data
After obtaining the envelope, more frequency multiplication information can be obtained. The failure information is more intuitive. It can be seen that the fast empirical wavelet transform method is suitable for fault diagnosis of the bearing inner ring.
## 3 Results
### Technologies and Implementation
#### 3.3.1 Communication at the Integration Layer  
The primary objective of the integration layer is to facilitate the trade and flow of information from one system to another system that benefits from the data. This process can be, for example, a machine sending its data to an optimization software that generates new knowledge and new inputs that then flow in the opposite direction to the machinery, optimizing the process. To control the flow of information of different machinery and software mentioned, a publish/subscribe approach was chosen based on a message broker. To implement this approach, a study was performed to test different known software. Several message brokers were analyzed and compared in key points relevant to the Integration process that is conducted.  
Using a publish/subscribe model that uses a topic approach allows the publishing of data by, for example, the MES system, in well-defined topics. This means that different topics are used to write different types of information, for example, distinct topics for sending energy consumption information and receiving production orders. This can be done by communicating directly with the hardware/software tools, using an auxiliary program, or through a service, depending on the configuration of each tool. The publisher can write on several different topics different types of information. These topics can then be subscribed to by multiple subscribers that are notified by the message broker as soon as new information is available. The message broker will then send the new information to all the subscribers of the topic in question. The message broker can communicate using different kinds of data-interchange formats, mainly Avro, JSON, and Protobuf, being able to interchange and auto-format different types of data as needed. This functionality can beparticularly useful when setting up streams or communicating with a different machine that possesses different data formats.  
The topic approach lets the machine, or the software, send data when it is more convenient, without the necessity of waiting for synchronized communication with other entities or causing a bottleneck in the flow of information [40]. These topics can then be subscribed to by any tool (for instance, software of logistics, simulation, energy consumption, or any other) that wants to receive the published data and has permission to do so. This approach also allows easy integration for more machines or optimization software that need to receive or publish data. New tools only need to communicate with the message broker and have knowledge of the structure of the topics and expected data format.
## 4 Results and Discussions
### Engineering Problems
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Variables** & **Terms** & **Design Space** \\ \hline
1 & \(A_{10}^{tip}\) & & [\(-0.167\), \(-0.059\)] \\
2 & \(A_{11}^{tip}\) & & [\(-0.112\), \(0.225\)] \\
3 & \(A_{42}^{tip}\) & & [\(-0.228\), \(0.140\)] \\
4 & \(A_{42}^{tip}\) & & [\(-0.081\), \(0.579\)] \\
5 & \(A_{10}^{tip}\) & & [\(0.123\), \(0.281\)] \\
6 & \(A_{1,1}^{tip}\) & & [\(0.212\), \(0.527\)] \\
7 & \(A_{12}^{tip}\) & & [\(-0.035\), \(0.421\)] \\
8 & \(A_{13}^{tip}\) & & [\(0.048\), \(0.761\)] \\
9 & \(A_{100}^{m1x}\) & & [\(-0.167\), \(-0.059\)] \\
10 & \(A_{111}^{mnx}\) & & [\(-0.112\), \(0.225\)] \\
11 & \(A_{122}^{mnx}\) & & [\(-0.228\), \(0.140\)] \\
12 & \(A_{13}^{mnx}\) & Parametric modeling variables & [\(-0.081\), \(0.579\)] \\
13 & \(A_{10}^{mnx}\) & For the maximum-chord section & [\(0.123\), \(0.281\)] \\
14 & \(A_{11}^{mnx}\) & & [\(0.212\), \(0.527\)] \\
15 & \(A_{122}^{mnx}\) & & [\(-0.035\), \(0.421\)] \\
16 & \(A_{13}^{mnx}\) & & [\(0.048\), \(0.761\)] \\
17 & \(q_{tip}(^{\circ})\) & Mounting angle of blade tip & [5; 20] \\
18 & \(l_{tip}(\text{mm})\) & Chord length of blade tip & [5; 12] \\
19 & \(\varphi_{max}\) (\({}^{\circ}\)) & Mounting angle of maximum-chord section & [15; 30] \\
20 & \(l_{max}(\text{mm})\) & Chord length of maximum-chord section & [12; 30] \\
21 & \(d_{mt}(\text{mm})\) & Position of maximum-chord section & [10; 15] \\
22 & \(\varepsilon(\text{mm})\) & Forward sweep & [0, 0.65] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Variables of rotor blade. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(R^{2}\) & \(5n\) & \(8n\) & \(10n\) \\ \hline PRS & \(0\) & \(0\) & \(0\) \\ RBF-MQ & \(0.824\) & \(0.826\) & \(0.806\) \\ RBF-TPS & \(0.102\) & \(0.042\) & \(0.319\) \\ KRG & \(0.817\) & \(0.835\) & \(0.798\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of \(R^{2}\) of four surrogate models.
## 1 Introduction  
Next generation wireless and cellular communication systems are expected to support a variety of services requiring high data rates, low delays, high availabilities, high reliabilities, and large connection densities [1; 2; 3; 4]. Especially, one of the key requirements for the next generation systems is efficiently supporting a huge number of devices for Internet of Things (IoT) applications [4; 5; 6; 7; 8; 9]. In the future, the prosperity of IoT services can greatly increase the density of devices, which will require massive IoT technologies to support simultaneous random accesses from a large number of devices to a single base station (BS) [4; 5; 6; 7; 8; 9]. For this purpose, one can use non-orthogonal multiple access (NOMA) schemes, in which signals from devices can be superimposed onto the shared resource and distinguished by spreading or interleaving patterns [10; 11; 12; 13; 14; 15; 16; 17]. NOMA schemes can improve the connection density by allowing a greater number of concurrent random accesses compared to other orthogonal schemes.  
Since IoT devices have low transmission power and are often installed in near-shadow areas such as inside-buildings or underground, they typically transmit signals at a very low data rate using repetitions and/or low-rate channel coding. In an uplink NOMA system, data is transmitted at a very low data rate to maintain the required communication coverage, but a large number of users are superimposed on the shared resource, resulting in efficient resource utilization. For low-data-rate transmission, quadrature phase shift keying (QPSK) modulation can be used. QPSK has an advantage over binary phase shift keying (BPSK) in the sense that QPSK allows longer spreading patterns than BPSK, while they have the same bit energy to noise spectral density ratio. In NOMA systems, there is no exact limit on concurrent random accesses, and superimposed signals from many devices can be decoded with the help of interference cancellation techniques. However, preamble or pilot symbols may also be superimposed onto the shared resource to reduce the amount of resources required, and the performance can be degraded due to channel estimation errors, especially when the number of superimposed packets is large [18; 19; 20; 21]. In this paper, we discuss the performance degradation due to channel estimation errors assuming that QPSK modulation is used. We also propose a NOMA scheme robust to channel estimation errors.  
The rest of this paper is organized as follows: Section 2 describes the system model and conventional uplink NOMA scheme. It also addresses the performance degradation due to channel estimation errors. Section 3 proposes a modification to the conventional NOMA scheme to alleviate the performance degradation. Simulation results are shown in Section 4 and conclusions are drawn in Section 5.
# Insights from the Experimentation of Named Data Networks in Mobile Wireless Environments
###### Abstract  
The Information-Centric Network (ICN) paradigm has been touted as one of the candidates for the Internet of the future, where the Named Data Network (NDN) architecture is the one leading the way. Despite the large amount of works published in the literature targeting new implementations of such architecture, covering different network topologies and use cases, there are few NDN implementations in real networks. Moreover, most of these real-world NDN implementations, especially those addressing wireless and wired communication channels, are at a small scale, in laboratory environments. In this work, we evaluate the performance of an NDN-based implementation in a mobile wireless network, as part of a smart city infrastructure, making use of multiple wireless interfaces. We start by showing how we have implemented the NDN stack in current network nodes of the smart city infrastructure, following a hybrid solution where both TCP/IP and NDN paradigms can coexist. The implementation is evaluated in three scenarios, targeting different situations: mobility, the simultaneous use of different wireless interfaces and the network characteristics. The results show that our implementation works properly and insights about the correct NDN parameterization are derived.  
mobile networks; Named Data Networks; performance evaluation; smart city Article