esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
#### Sensory Evaluation  
The results of youtiao (control) showed higher-ranking mean of 90 panelists for color (1.97), flavor (2.11), and overall acceptability (1.92). It indicates the panelists prefer the youtiao with the addition of potassium aluminum sulfate, using a traditional frying method. The ranking of the flavor of the youtiao control was significantly better than that of youtiao fried in the electrostatic fryer (\(p\) < 0.05). However, the color and appearance of youtiao with potassium aluminum sulfate addition was better than if potassium aluminum sulfate was not added, regardless of the frying method (Table 4). This may be because the youtiao is subjectively darker in appearance without the addition of potassium aluminum sulfate. The texture and flavor of traditional deep-fried youtiao is better than those of electrostatic frying (Table 4). The overall acceptability of the traditional frying youtiao was better than that of youtiao with no potassium aluminum sulfate addition and when fried in the electrostatic fryer (Table 4).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Treatments** & **Acrylamide (ppb)** & **Reducing Sugar (\%)** \\ \hline Control & 421.61 \(\pm\) 48.63 \({}^{\text{b}}\) & 11.5 \(\pm\) 1.15 \({}^{\text{b}}\) \\ TnA & 2095.20 \(\pm\) 133.46 \({}^{\text{a}}\) & 13.99 \(\pm\) 3.52 \({}^{\text{ab}}\) \\ \hline EA & 667.37 \(\pm\) 42.98 \({}^{\text{b}}\) & 12.04 \(\pm\) 1.17 \({}^{\text{ab}}\) \\ EnA & 1147.13 \(\pm\) 676.84 \({}^{\text{ab}}\) & 11.77 \(\pm\) 2.21 \({}^{\text{ab}}\) \\ \hline VA & 1084.98 \(\pm\) 0.0 \({}^{\text{ab}}\) & 13.33 \(\pm\) 0.0 \({}^{\text{ab}}\) \\ VnA & 1530.26 \(\pm\) 0.0 \({}^{\text{ab}}\) & 14.52 \(\pm\) 0.0 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} \({}^{\text{*}}\) Expressed as mean \(\pm\) standard deviation (VA \(n\) = 2; VnA \(n\) = 2); Values followed by the different letter \({}^{\text{a,b}}\) within each column are significantly different (\(n\) = 4; \(p\) < 0.05). Control: Traditional frying + potassium aluminum sulfate; TnA: Traditional frying without adding potassium aluminum sulfate; EA: Electrostatic frying + potassium aluminum sulfate; EnA: Electrostatic frying without adding potassium aluminum sulfate; VA: Vacuum frying + potassium aluminum sulfate; VnA: Vacuum frying without adding potassium aluminum sulfate.  
\end{table}
Table 3: Comparison of different frying methods for acrylamide and reducing sugar content \({}^{\text{*}}\) of fried bread youtiao.
# Mobile Data-Mule Optimal Path Planning for Wireless Sensor Networks
## 6 Conclusions and Topics for Further Research  
The data harvesting problem over a WSN using several MoEls acting as data mules is considered in this article. The problem is formulated as a Mobile Element Scheduling scheme, along with a MILP-optimization problem. Both account for the extraction of the routes to be travelled by the data mules within the network. The optimization problem is formulated based on individual time, distance, and energy constraints for both the static and the mobile nodes. With this iterative implementation, the objectives of continuous collection of sensor measurements and the data delivery to the base station in a periodic mode are achieved. Simulation studies are used to validate the advocated MES-scheme/MILP optimization problem.  
A possible extension is the reformulation of the MILP problem, which will allow the static nodes to be visited more than once in a route by different MoEls. Furthermore, rather than using straight line segments, different types of paths can be employed (Dubins paths or Bezier curves). In this case, non-holonomic vehicles can be used as MoEls involving their kinodynamic equations in the MILP formulation.  
Conceptualization, O.T. and A.T.; methodology, O.T. and A.T.; software, O.T.; investigation, O.T. and A.T.; data curation, O.T.; writing--original draft preparation, O.T. and A.T.; writing--review and editing, O.T. and A.T.; visualization, O.T.; supervision, A.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
Figure 5: Mobile Element Scheduling Routes with 3 MoEls and 13 Nodes—No priorities assigned.
## 5 Performance Evaluation
### Evaluation Setup  
The system considered in our evaluation study is composed of a remote cloud center, an MEC server and several local processors. For simplicity, each processor is presumed to have an infinite number of available positions. An application, which is composed of multiple dependent tasks, needs to be finished before its deadline. To verify the practicability of the application, we consider three kinds of applications with different DAG topologies: tree, workflow, and random topologies.  
To understand the merits of IDCP, three baseline algorithms are adopted to make a comparison, namely:* Infocom-2018: an efficient offloading algorithm for multicore-based mobile devices proposed in [8], which can minimize the energy consumption while satisfying the completion time constraints of multiple applications;
* TC-2017: an online dynamic-resilience scheduling algorithm called Adaptive Scheduling Algorithm (ASA) proposed in [36], which realistically deals with the dynamic properties of multiprocessor platforms in several ways;
* TPDS-2012: an offloading algorithm for multiprocessor-embedded systems proposed in [32], which is an online scheduling methodology for task graphs with communication edges.  
Table 1 lists the four algorithms evaluated in our simulations.  
The finish time of the application, referred to as application latency, is the only performance metric that interests us. It is defined as the EST value of the exit node, which represents the earliest time when the application can finish. Indubitably, a lower value of the application latency indicates higher performance of the algorithm.  
First, we fix the other settings and only vary the number of nodes in range (30, 70), which indicates the scale of the application. The results are represented graphically in Figure 5a-c. Second, the number of nodes is set at 50 while the number of local processors varies in the range [3; 15]. Figure 6a-c depicts the corresponding results. Finally, the number of nodes and local processors are fixed at 50 and 10 respectively, with the delay factor varies between 1 and 5. The delay factor controls the deterioration of the network. The results are presented in Figure 7a-c.
# Impact of CoAP and MQTT on NB-IoT System Performance +
## 2 Performance Evaluation
### Simulation Scenario  
The simulated scenario consists of 7 base stations with 3 sectors i.e. cells each, creating a network of 21 hexagonal cells. UEs are created uniformly across the simulation area, each connecting to the base station which controls that particular cell.  
In the simulations the UEs transmit a packets of size 25 bytes or 400 bytes in uplink, the transfer protocol headers for CoAP or MQTT are assumed to be included in this payload. The arrival rate of UEs to the system is varied to show the impact of system load on the performance.  
The arrival rate of 1 MTC request per second per cell corresponds to 21 devices per second in the system, which is around 75,600 devices transmitting data in the system in one hour. A load of 2 MTC requests per second thus corresponds to \(\sim\)150,000 requests in the system per hour, and a load of 4 MTC requests per second per cell to \(\sim\)300,000 requests in the system per hour respectively. These loads may seem high as NB-IoT has been design target was to support 50,000 devices per cell [15], however, the objective is to test the limits of the system.
## 4 Performance
### Variation of Time Synchronization  
#### 4.3.1 Reception Ratio  
Because many applications in wireless sensor networks require the simultaneous sensing of data, much recent research has addressed the subject of time synchronization. A time synchronization protocol attempts to precisely synchronize the clocks of all sensor nodes. If sensor nodes are synchronized precisely, they will sense and send data simultaneously, which can in turn cause network congestion and packet loss. In the case of images, many packets need to be sent for a single image, so that much more congestion can take place and many more packets can be lost. In this simulation, we varied the difference in the clocks of the sensor nodes in a range from 0 s to 40 s. A clock difference of  
Figure 13: Energy Consumption. RAIT consumed more energy than SRI and MintRoute, however a large amount of energy was wasted with SRI and MintRoute.  
0 s means that all sensor nodes are perfectly synchronized, and a clock difference of more than 1 s means that each sensor node begins to work and to sense randomly within the range of clock difference. 20 sensor nodes take images and send them to a sink node.  
Figure 14: Number of received packets (A) and number of received images (B) with variations in time synchronization.  
sink nodes when all sensor nodes were perfectly synchronized (0 s). In this case, all of the 20 image sensor nodes simultaneously tried to send an image to a sink node, which highly increased the probability of congestion, leading to the dropping of packets in the relaying nodes. As a consequence, just 38% of the packets were received by the sink node. As the range of difference between the clocks of the sensor nodes was increased, there was an increase in the number of received packets, but when the difference between clocks exceeded 10 s, only 66% of the sent packets arrived at the sink nodes, regardless of the clock difference. In this case, even though the sensor nodes tried to send an image with a difference of 40 s in the case of little congestion among the leaf nodes, packets from several sensor nodes could cause congestion near the sink nodes. If the sensor nodes are precisely synchronized, congestion can take place around the leaf nodes of a tree while congestion can still be taking place around the sink nodes. In Figure 14 (B), with MintRoute, very few images arrived at the sink nodes when the sensor nodes were precisely synchronized. When the clock difference was 0 s, only 12 out of 200 sent images were received by a sink node, which was just 6%. In this case, we could say that most of the images were lost. Even if the clock differences were increased, the ratios of received images would be less than 40%.  
With RAIT, on the other hand, all of the sent packets arrived at a sink node, for a 100% delivery rate. With this result, we would say that RAIT can prevent the congestion that took place both around the leaf nodes and around the sink nodes in order to provide completely reliable image transfer.
# Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery
## 4 Results
### 3D Beach Topography Comparisons
Note that the maps of the DEMs were rotated 110\({}^{\text{o}}\) in order to present them horizontally. Figure 7 show the statistical evaluation of the comparison between the Pleiades and LiDAR elevations extracted over the 266 cross-shore transects (Figure 6). Data from the beach face, containing natural morphological changes (not related to the method), were removed from this statistical comparison. The error distribution indicates that differences between the LiDAR and Pleiades DEMs over the full domain are normally distributed with a mean difference of -0.015 m and RMS error of 0.48 m (LiDAR DEM is slightly higher). The 1:1 comparison (Figure 7) shows that Pleiades and LiDAR beach topographies are highly correlated (slope = 1.01 and CC = 0.99). Figure 5: Example of the errors observed on the dune face, over the Pleiades orthophotomap; the presence of shadows on the dune face (**right image**) coincides with the area where the largest differences (>0.5 m in red, left image) were observed (**left image**). Figure 6: Results of the RMS error, slope and shadow calculations for the 266 cross-shore transects. The second panel (from the top) presents the RMS error of the difference between Pleiades (PL1A) and LiDAR topographic elevations, computed for the dune face and back of the dune; the bottom panel shows the dune face and back of the dune slope (bottom panel) and the shadow size (length of the shadow over each transect).
## 3 Datasets and Methods
### Methodology
#### 3.2.2 Dimensionality Reduction-Principal Component Analysis  
Since all the convolutional-related features of an image that are extracted in Section 3.2.1 can be extensive in number, dimensionality reduction is applied on these features. This is performed to retain only the most significant features so that the model does not learn from insignificant features that reduce accuracy. It also helps to lessen storage space and multicollinearity [25].  
PCA utilizes orthogonal transformation [26] to reduce a covariance matrix of features into a set of uncorrelated features, which correspond to the maximum eigenvalues and at the same time retaining trends and patterns such as patterns that are capable of identifying different hand gestures.  
The set of uncorrelated features obtained after applying PCA are referred to as principal components which are linear combinations of the original data containing as much compressed information as possible in decreasing order, i.e., the first component has maximum information, the second component has the second maximum information and so on.
## 4 Results and Discussion
### Comparison with Keyword-Matching Baseline  
Each of the four previously mentioned keyword extraction methods had an accuracy rating of roughly up to 70% with varying degrees of success. The results were influenced by the keyword list, the filtering methods, and the presence of vulgar word variations or misspellings. We could locate specific keywords within a text quickly by using these straightforward, effective keyword-matching techniques. However, the results have shown these methods also had problems with keyword variations and the need for frequent keyword list updating remained. Another problem not solvable by simple keyword-matching methods was the use of language that is context-sensitive. This shows that the ability of keyword-matching methods to handle complex linguistic structures or comprehend the context and meaning of the text is limited.  
On the other hand, machine learning and deep learning methods leverage algorithms and models trained on labeled datasets to classify text segments as vulgar or non-vulgar. The results are typically measured using metrics such as accuracy, precision, recall, and F1-score. Machine learning and deep learning approaches achieved a high accuracy of approximately 84-91%, or roughly twenty percentage points above the keyword-matching baselines, which shows such algorithms can learn patterns and features from the data more effectively. They are also capable of capturing complex linguistic nuances, identifying context-sensitive vulgar words, and handling variations. A disadvantage of these methods is that they require significant amounts of labeled training data and computational resources for training and inference, which means that unless the training data are constantly updated, their performance will also degrade with time. However, this could be to some extent mitigated by (1) applying the automatic vulgar term extraction method proposed in this paper to update and expand the vulgar term lexicon of keyword-matching baselines and (2) using those baselines to collect potential vulgar and non-vulgar sentence candidates. This would allow for initial information triage [89] and ensure more efficiency in the vulgar remark data collection and annotation process.
## 2 Concept of Unit Cell  
The two-port network as shown in Figure 1 is analyzed to figure out the overall antenna prototype. The cylinder of defined radius, r, which varies from 0.5 mm to 3.5 mm is supported by the base of 7.5 mm \(\times\) 7.5 mm \(\times\) 1.5 mm. The length and breadth of the base is slightly less than (\(\lambda/3\)), whereas thickness (0.06\(\lambda\)) is comparatively thin. Two parallel plates are kept above and bellow this structure so we have considered the perfect electrical conductor metal plate above and bellow the cylindrical unit cell whose dimension is 7.5 mm \(\times\) 7.5 mm \(\times\) 1 mm. The designed frequency of 12 GHz (\(\lambda=25\) mm) is considered. The reflection coefficient is denoted by sigma (\(\sigma\)), whereas the transmission coefficient is indicated by tau (\(\tau\)). The different permittivity values are generated by using one material that is based on the radius of cylindrical rods. The change in relative radius of these cylindrical rods generates respective transmission and reflection coefficient values, which are used to find permittivity. A calculation or a library from Computer Simulation Technology (CST) software were used where one simply finds the relativity permittivity.  
Figure 1: Cylindrical unit cell with perfect electrical conductor.
# Effect of Shot Peening on the Evolution of Scale on T91 Steel Exposed to Steam
## 2 Materials and Methods  
T91 boiler tube steels were purchased from \"IIJIN Steel\" with outer diameter of 50.8 mm and wall thickness of 6.5 mm. The as-received tubes were cut into 15 \(\times\) 10 \(\times\) 2 mm\({}^{3}\) pieces and mechanically polished to #2000 grit-finish before ultrasonic shot peening (SP) and subsequent steam exposure. Steam was generated by heating distilled water to \(\sim\) 650 \({}^{\circ}\)C; it was then blown across the sample surface for 100, 200, 400, 600, 800, and 1000 h. further details of the steam exposure are available in reference [10]. The shot peening conditions and specimen list are presented in Table 1.  
The chemical composition of the T91 tubes was measured by optical emission spectrometry (OES, mode ARL-3460), and is given in Table 2. The morphology, elemental distribution, and phase distribution of the oxidation scale were determined by SEM (JSM-6510, equipped with INCA EDS, operated at an accelerating voltage of 20 kV), FE-SEM (EBSD, TESCAN MIRA II LMH equipped with TSL OIM components with the depth beam scan mode), XRD (Rigaku SmartLab, operated at a tube voltage and current of 45 kV and 200 mA), and TEM (Philip CM200-FEG, operated at an accelerating voltage of 200 kV). Specimens for SEM/EBSD analysis were prepared by mechanical polishing with #2000 grit and subsequent final polishing with a 0.05 \(\upmu\)m colloidal silica suspension for 1 h. Specimens for TEM observation were prepared by Struers TenuPol-5 twin jet polishing system. The microhardness was measured by a Vickers hardness tester (Future-Tech Model FM-7E) on the polished cross section with 1 \(\upmu\)m metallographic polishing cloth. Then the hardness of each depth was tested for five times on the load of 200 gf.
# Changes in Elderberry (_Sambucus nigra_ L.) Juice Concentrate Polyphenols during Storage
###### Abstract  
Elderberry (_Sambucus nigra_ L.) juice concentrate is highly rich in polyphenols, particularly anthocyanins and flavonoids, which have been associated with a wide range of health-promoting properties. Phenolic compounds, in particular anthocyanins, are unstable and may change during storage, which might influence the product color quality and its potential health effects. The aim of this study was to evaluate the changes in the polyphenols profile of elderberry juice concentrate produced at an industrial scale during seven months of storage at 5 \({}^{\circ}\)C and at room temperature. The total phenolic content, the total monomeric anthocyanins, the percent polymeric color, and the ABTS\({}^{\bullet\bullet}\) scavenging activity were monitored over time. In addition, the profile and content of the main individual phenolic compounds were also assessed by HPLC-DAD. The results show that cyanidin-3-O-sambubioside, cyanidin-3-O-glucose, cyanidin-3-O-sambubioside-5  
have been identified as cyanidin-3-O-glucoside, cyanidin-3-O-sambubioside, cyanidin-3,5-O-diglucoside, and cyanidin-3-O-sambubioside-5-O-glucose[8, 9, 10]. Phenolic acids such as chlorogenic acid and the flavonoids quercetin-3-O-glucose and rutin have also been detected in elderberry juice [11, 12].  
During storage, the anthocyanins in berry juices may undergo several reactions to form more stable compounds, which typically involve oxidation, polymerization, co-pigmentation with other phenolic compounds, and cleavage reactions [13, 14, 15]. The cleavage of anthocyanins results in colorless compounds, polymerization is accompanied by browning, and co-pigmentation might result in various colored compounds [16]. The transformation of these compounds influences not only the product color quality and consumer acceptance [17], but also the potential health effects [18].  
The main objective of the present work was the evaluation of the effect of storage on the polyphenolic composition of elderberry juice concentrate and the possible consequences on its color pigments and antioxidant capacity. Two elderberry juice concentrates produced at an industrial scale were monitored during seven months of storage at 5 \({}^{\circ}\)C and at room temperature.
## 2 Experimental Setup  
FERMI is a seeded FEL, where the harmonics of an ultraviolet seed laser of frequency \(\omega_{\text{UV}}\) are produced using the high-gain harmonic generation (HGHG) scheme [10]. The seed laser itself is created by frequency tripling the pulses from an NIR laser of central frequency \(\omega_{\text{NIR}}\) (\(\omega_{\text{UV}}=3\omega_{\text{NIR}}\)). The harmonics generated using the technique of HGHG are phase-locked to the seed laser and possess very stable spectral properties on a shot-to-shot basis and a high degree of coherence, both transverse and longitudinal [10; 25]. FERMI is equipped with six undulators (U\({}_{i}\), \(i=1-6\)); a phase shifter (PS\({}_{i}\), \(i=1-5\)) is situated between each pair of consecutive undulators. Each PS is a magnetic chicane used to match the phase between the radiation emitted by the undulators upstream and downstream of it by delaying the electron bunch. The harmonics are focused in the experimental chamber using a Kirkpatrick-Baez (KB) mirror setup. The NIR and the XUV are propagated collinearly using a drilled mirror, and are recombined after the KB mirror setup. A detailed experimental scheme has been presented elsewhere [23; 26].  
Our study used a harmonic comb consisting of either three (H\({}_{7}\), H\({}_{8}\), and H\({}_{9}\)) or four (H\({}_{7}\), H\({}_{8}\), H\({}_{9}\), and H\({}_{10}\)) harmonics, where H\({}_{q}\) is the \(q\)th harmonic of the seed laser. The undulator configuration for generating three and four harmonics is shown in Sections 3 and 4. In the three-harmonic case, each harmonic is generated using two adjacent undulators with the phase difference between the generated harmonics controlled using the phase shifter PS\({}_{2}\) or PS\({}_{4}\). In the four-harmonic case, each undulator generated a harmonic, and the last two undulators were left open, not generating any radiation. For the phase manipulation, phase shifters PS\({}_{2}\) and PS\({}_{3}\) were utilized. The RABBITT scheme generally used to characterize the APTs cannot be directly implemented at FERMI due to a delay jitter of \(\pm 3\) fs between the XUV and NIR fields that is much higher than that required to observe the sideband oscillations [16]. As the delay randomly varies on a shot-to-shot basis, a method independent of the delay stability between the XUV and the NIR fields is required.