esa-sceva/satcom-chunk-collection huggingface dataset:
# Polyhydroxyalkanoates (PHAs): Biopolymers for Biofuel and Biorefineies
## 1 Introduction  
Polyhydroxyalkanoates (PHAs) are bacterial polyesters synthesized by several prokaryotic microorganisms under unbalanced nutrition conditions, e.g., carbon and energy storage conditions. [PERSON] first discovered these thermoplastics in 1926, and since then, PHAs have attracted significant commercial and research interest from the eco-friendly polymer market due to their favorable properties such as high biocompatibility, excellent biodegradability, diverse chemical structure, and manufacturing from renewable carbon resources [1; 2; 3; 4; 5].  
Bio-based polyesters are considered alternates for petrochemical plastics. Moreover, among the various biopolymers, PHAs symbolize a sustainable future alternative for fossil fuel-based plastics. PHAs have mostly been synthesized by microbial cultures grown on renewable materials in clean environments [6; 7; 8; 9]. Fossil fuels are energy recourses that fulfill most of the world's energy requirements. However, their production and use cause severe health and environmental problems including global warming and pollution. Consequently, plant and animal-based fuels (also termed as biofuels), such as biogas, biodiesel, and many others, have been introduced as alternatives to fossil fuels. Despite the advantages of biofuels, such as being renewable, environmentally friendly, easy to source, and reducing the dependency on foreign oil, there are several drawbacks to using biofuels including high cost, and other factors discussed in the fuel vs. food debate. Therefore, it is imperative to produce novel biofuels while also developing suitable manufacturing processes that ease the aforementioned problems. Polyhydroxyalkanoates (PHAs) arestructurally diverse microbial polyesters synthesized by numerous prokaryotic microorganisms. PHAs are naturally synthesized bio-based materials. Since PHAs are biocompatible, biosensorable, and biodegradable; hence, they have no negative effect on the environment. When PHAs-based products are left in the environment, they degraded into CO\({}_{2}\), H\({}_{2}\)O, and CH\({}_{4}\), which facilitate the natural cycle of circulatory and renewability. In addition, the spent PHAs can undergo hydrolysis and produce optically pure building blocks which can be used as synthons for pharmaceuticals, organic synthesis, and fragrances [10]. Moreover, the structural diversity of PHAs allows them to readily undergo methyl esterification and to be used as biofuels, which further extends the application value of PHAs.
## 4 Lunar Antenna Types
### Reflector Antennas  
The progress of antenna theory has come from the success of space exploration. Because of the necessity to communicate across long distances as on the lunar surface, specialized antennas were required to broadcast and receive signals that had to traverse millions of kilometers. Reflector antennas have been used in a variety of applications throughout history because, among other antenna designs, they give the highest gain, largest bandwidth, and finest angular resolutions at the lowest cost. A reflector antenna's principal function is to confine or radiate the majority of the electromagnetic energy passing through its aperture onto a focal plane or distant field for communication or energy transfer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Operating Frequency (MHz)** & **Bandwidth Fractional (\%)** & **Gain (dBi)** & \(\mathbf{S_{11}}\) (dB at Centre Frequency)** \\ \hline
[44] & 2050 & 6.3 & n/a & \(-24\) \\ \hline
[44] & 2300 and 16,800 & 0.5 and 3 & n/a & \(-29\) and \(-27\) \\ \hline
[50] & 1000–4750 & 130.43\% & 2–8 & \(-15\)–(\(-17.5\)) \\ \hline
[18] & 2101.2 and 2266 & 0.004 & 6 & \(<\)\(-15\) dB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between planar lunar antennas in terms of performance.  
Figure 8: All-metal circular patch antenna [18].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Antenna Type** & **Antenna Name** & **Ref** & **Frequency Band** & **Mission/Research** & **Application** \\ \hline \multirow{4}{*}{Planar} & CMMA (patch) & [44] & S band & Research & Omnidirectional antenna \\ \cline{2-5}  & fHCFA (patch) & [44] & S and Ku bands & Research &
\begin{tabular}{c} Lunar surface network \\ communications and planetary \\ exploratory missions \\ \end{tabular} \\ \cline{2-5}  & Vivaldi (array, slot) & [50] & L and S bands & Mission & Lunar regolith penetrating radar \\ \cline{2-5}  & All-Metal Circular & [18] & S band & Mission & Moon–Earth direct communication \\ \cline{2-5}  & Patch Antenna & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between planar lunar antennas in terms of application.  
Conic sections, parabolas, ellipses, hyperbolas, and spheres are examples of typical reflector antenna designs. Reflector antennas are employed in a variety of applications, including satellite communication, radio astronomy, radar, remote sensing, medical, and military applications [55; 56].
# The Effects of L-Citrulline and Malic Acid on Substrate Utilisation and Lactate Elimination
###### Abstract  
Endurance athletes often aim to improve their aerobic metabolism. The aim of this pilot study was to examine if malic acid and L-citrulline supplementation can improve aerobic metabolism and lactate elimination. Nine young (23.9 \(\pm\) 1.9 years) recreational male athletes participated in this study. Following a standardised breakfast and a body composition analysis (InBody720), 6000 mg of citruline and 3000 mg of malic acid or a placebo of 300 mL of water were consumed on three separate days in a cross-over design using a double-blind method. Sixty minutes after the supplementation, participants completed a ramp bicycle spiroergometer protocol (35 W/3 min) until reaching a respiratory exchange ratio (RER) of 1.1, followed by a 9 min active recovery. Cadence, heart rate (HR), rate of perceived exertion (RPE), respiratory parameters and lactate levels were registered. The RPExHR value was calculated to accurately characterise exhaustion. During the exercise protocol, citruline supplementation induced significantly lower RER values at 70-105-140 W compared to malic acid and the placebo, respectively. There was no difference in lactate levels neither during rest nor at RER 1.1. RPExHR rate values were significantly lower after malic acid supplementation compared to placebo at 175 and 210 W. Power at RER 1.1 was higher after malic acid (+4 W) and citruline (+5 W) supplementation. Although the supplementation failed to decrease lactate levels, lower RER and RPE values may indicate a performance-enhancing benefit.  
RER; RPE; citruline; malic acid +
Footnote †: journal: Applied Sciences
# Gamification in IT Service Management: A Systematic Mapping Study
## 4 Results
### RQ1. What Issues of ITSM Process Implementation Have Been Addressed through Gamification?
#### Motivation and Engagement
As the previous works, [P13] also highlighted the importance of motivating and involving service desk operators in their tasks for improving service quality. The authors offered a general vision about how gamification could positively influence those aspects. Finally, [PERSON] [P09] stressed the usefulness of gamifying the actions of both agents and customers of service desks. In addition to motivating service desk agents, gamification could encourage service desk customers to collaborate and help each other to resolve their issues.
## 4 Results and Discussion
### Moisture Distribution  
The study of moisture distribution aims to examine the water distribution patterns at various initial particle packed bed temperatures using an inlet velocity of \(\
u_{0}\) = 0.8 m/s and a ventilation temperature of T = 308.15 K. Figure 15 illustrates the water distribution in the packed bed at different heights \(Z\) = 10 mm and \(-\)10 mm relative to the center point of the bed after 3 h of drying. The uniformity of axial drying in the packed bed is excellent. As the drying time increases, the moisture gradient in the packed bed decreases.  
Figure 14: Average temperature of particles in packed beds at different times.  
Figure 15: _Cont._Figure 16 illustrates the moisture distribution at different times on the Y = X plane. During the drying process, water is transferred from the interior of the soybean to the surface, which is contrary to the heat transfer process. The convective mass transfer near the particle surface is significant, while the diffusion of water decelerates as it moves towards the central region. After drying for 2 h, there is still high moisture inside the soybean. After drying for 3 h, the moisture content in the packed bed decreases significantly. The drying effect is effective.  
Figure 15: The moisture distribution at the Z = 10 mm and \(-\)10 mm plane after 3 h of drying at different particle temperature: (**a**) 288 K; (**b**) 283 K; (**c**) 278 K.  
Figure 16: The moisture distribution at different times at the Y = X plane at different particle temperature: (**a**) 288 K; (**b**) 283 K; (**c**) 278 K.  
Figure 17 illustrates the variation of moisture content over time in a stacked bed at different particle temperatures. It is evident that when the particle temperature reaches 288 K, the moisture content of soybean particles changes the fastest. This phenomenon further illustrates the perspective that an increase in particle temperature will enhance the water transfer rate in the packed bed. Due to the consideration of the interaction between particles and fluid media, as well as the relative flow mass transfer and solid-phase water diffusion processes, it is expected that the particle temperature in the range of 283 K to 288 K can improve the water transfer rate. Experimental research has shown that it supports the research results [30; 31; 32].
## 2 Data and Method
### Data  
#### 2.2.1 AVHRR GIMMS 15-Day Composite NDVI Dataset  
We used the continental NDVI dataset at 8 km resolution for the period 1984-2006 produced by the Global Inventory Monitoring and Modeling Studies (GIMMS). The dataset was derived from imagery obtained from the AVHRR instrument onboard the NOAA satellite series 7, 9, 11, 14, 16 and 17. It contains channel 1 (0.58-0.68 um) and channel 2 (0.73-1.1 um) reflectance, channel 4 (10.3-11.3 um) and channel 5 (11.5-12.5 um) brightness temperatures, solar and view zenith angles, and the day of compositing. These channels and associated data correspond to the maximum NDVI value during a 15-day compositing period. The NDVI is expressed on a scale between \(-1\) and \(+1\). GIMMS NDVI values range between \(-0.2\) and \(0.1\) for snow, inland water bodies, deserts, and exposed soils, and increases from approximately 0.1-0.7 for increasing amounts of vegetation. The GIMMS dataset includes calibration using desert targets, atmospheric correction for stratospheric aerosol, and normalization for temporal changes in solar zenith angle [21].
# The Utilization of Artificial Neural Network Equalizer in Optical Camera Communications +
## 4 Experimental Setup
6  Calibrate \(\mathbf{z}^{Q}\) with respect to the averaged DC value \(\mathbf{z}_{\text{cal}}^{Q}=\mathbf{z}/\bar{\mathbf{z}}_{DC}\)
7  Find the frame with full packet inclusion using Algorithm 2
8  Resample \(\mathbf{z}_{\text{cal}}^{Q}\) with respect to the packet length
9  Locate the start of each packet in the frame
10  Synchronize both Tx and Rx signal using a correlation algorithm
11  return \(\mathbf{z}_{\text{cal}}^{Q}\) signal
13 end
```  
**Algorithm 1** Signal Extraction Algorithm  
Next, to ensure that a full packet was captured by the IS, Algorithm 2 was applied to select the optimum frame (i.e., including both pre- and post-ambles) for each Qth Tx packet per \(f_{\text{Tx}}\), in which \(10\times\mathbf{P}_{U\times V\times 3}^{Q}\) are captured at Qth Tx packet to maintain the synchronization between both the Tx and the Rx. A resampling process was then applied to resize the signal length based on the packet size observed in pixels. Next, a correlation algorithm was used to maintain the synchronization between the transmitted Qth Tx packet and received \(\mathbf{z}_{\text{cal}}^{Q}\) signals, where a filtered version of \(z_{\text{cal}}^{Q}\) was simulated based on the encoded Qth packet using a moving average filter. Note, the window size of the filter was set to \(n_{\text{samp}}\) since it provided an optimal match compared with the observed signal. Next, \(\mathbf{z_{\text{cal}}}\) in the vector form was applied to an MLP equalizer using an array of tapped-delay lines as previously described. The MLP used here included single input, hidden, and output layers. All the key system parameters are listed in Table 2.  
Figure 5: An example of the received \(Q\)th Tx packet signal at a \(T_{\text{exp}}\) of 2 ms: (**a**) without DC gain normalization, and (**b**) with normalization.
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 4 Software Defined Network (SDN)
### Multi-Tenancy in SDN Controllers
In addition, moving the controller to the cloud has other obvious advantages of removing hardware maintenance downtime and decreasing recovery time in case of failure. In their proposed architecture, reported in Figure 5 the NFV infrastructure is comprised of an optical transport network interconnecting distributed data-centres, providing storage, compute and network hardware resources. On top of the physical infrastructure, they have deployed the NFV virtualization layer, which virtualizes storage, compute and network resources of NFV infrastructure. Above the virtualization layer, they have deployed various Virtual Network Functions (VNF) managers to manage the VNF life-cycle. They have proposed a VNF manager, called vSDN controller manager, which requests the cloud controller the creation of VMs and installation of OS images with compiled Floodlight or OpenDayLight SDN controllers. Finally, on the top, there is the orchestrator for multi-tenant SDN-enabled transport networks. This work was further extended in [60], where the authors combined NFV and optical network virtualization, implementing an on-demand OpenFlow-controlled virtual optical networks (VON). Every tenant SDN controller runs on the cloud to control the deployed VON. The users of virtual optical networks can create, modify and delete virtual network slices dynamically in response to application demands. The users' SDN controllers can control the allocated virtual network resources independently. This is achieved by the Optical Network Hypervisor (ONH) which slices the transport network into multiple virtual optical networks and represents an abstract topology of each virtual optical network to corresponding users' SDN controllers. Moreover, the SDN controller allows controlling virtual optical networks remotely through well-defined interfaces. Their implementation was deployed in the ADRENALINE testbed, practically demonstrating how the NFV orchestrator can provide multi-tenancy on top of the heterogeneous transport networks.
## 5 Analysis of Experimental Results
### Effects of Heating Regime on the Detectability of Delaminations  
Figure 12 depicts the SNR for all cases of different depths. On the graph, eight lines represent the mean values of SNR corresponding to the eight depths under ten heating regimes from 2 to 40 min. Generally, as the heating time increases, SNR climbs. The larger heating time means that more heat energy is provided to heat the specimen. With the same depth, the strength of volume concentrated heat produced in a longer heating time will be higher than in a shorter regime.  
For the depths from 1 to 4 cm, the heat source energy significantly affects the thermal contrast, even when the specimen is stimulated by a short heating time (i.e., from 2 to 5 min). In particular,  
Figure 11: SNR during cooling time for the depth from (1 to 4) cm in the case of 30-min heating.  
Figure 10: SNR for delaminations with different depths.  
in the case of 4 cm depth, the SNR increases by approximately 8 dB within only 3 min from 2 to 5 min heating time, as shown in Figure 12. However, the SNR value increases only approximately 12.5 dB as the specimen heats up from 5 to 30 min. In addition, over a 15 min heating time, the SNR rises steadily at slow speed; for example, when the heating time goes up from 20 to 30 min for delamination at 2 cm depth, SNR increases from just 19.1 to 19.7 dB. In addition, for the depth from 5 to 8 cm, when the heating time increases from 15 to 20 min, SNR values go up quickly, but only delamination at 5 cm depth can be detected, while the others are still undetectable. From the heating time of over 20 min, delaminations at 5 and 6 cm depth show the same tendency, whereas delaminations at 7 and 8 cm depth do not show any tendency, and they are difficult to recognize by the naked eye. As the specimens continue heating up to 40 min, the 7 cm depth delamination shows better contrast with the SNR of about 1.8 dB, while the SNR value of delamination at 8 cm depth is still in the negative. The reason might be because the delamination at 8 cm depth has its WTDR of 1.25, which is usually reported as being unable to be detected [3; 6; 17; 19].  
Figure 13 shows an example of recorded SNR values for the 2 cm of defect depth in both heating and cooling time. Six heating regimes from 2 to 15 min are shown. Figure 13 also illustrates the effects of heating time on the detectability of defects. The same tendency can be observed in all SNR curves that the SNR climbs, even though the heat source is already turned off, and reaches the maximum value, before slumping.  
Figure 12: The SNR for delaminations under different heating times.  
Figure 13: The SNR during both cooling time and heating time for 2 cm of depth under 2 to 15 min heating.
# Microstructure, Magnetic Properties, and Application of FINEMET-Type Alloys with Co Addition
## 2 Materials and Methods  
Amorphous Fe\({}_{83-x}\)Co\({}_{x}\)Nb\({}_{3}\)B\({}_{13}\)Cu\({}_{1}\) (x = 6, 25, 58 at.%) alloys were prepared in a 10 mm wide and 25 \(\upmu\)m thick ribbon form using rapid solidification on a cooper quenching wheel. To obtain the nanocrystalline structure, samples were subjected to isothermal annealing for 10 min at 750 K in a vacuum of 1.33 \(\times\) 10\({}^{-3}\) Pa. The annealing temperature was determined by differential scanning calorimetry (DSC) using NETSCH STA 449 F1 Jupiter (NETZSCH Analysing & Testing, Ahlden, Germany) set at a heating rate of 20 K/min.  
The microstructures of the as-quenched ribbons and nanocrystalline samples were examined by X-ray diffractometry, Mossbauer spectroscopy, and transmission electron microscopy. A Bruker-AXS, type D8 Advanced, X-ray diffractometer with CoK\(\alpha\)1 radiation (1.78897 A) was used. Transmission Mossbauer spectra were recorded at room temperature using a conventional constant acceleration spectrometer with a \({}^{57}\)Co(Rh) source of 50 mCi radioactivity. The spectra were analyzed using a Normos package (written by [PERSON]) according to the procedure developed in [24]. Observations using a classical electron and high-resolution microscope, type JEM 3010, were carried out for samples in the form of discs with a diameter of 3 mm cut from strips and ion-polished to a thickness of about 100 nm.  
Magnetic properties were studied using a hysteresisgraph (AMH-50K-S, Laboratorio ElletroFisico Engineering, Nerviano, Italy) for core samples with an outer diameter of 20 mm and an inner diameter of 15 mm fabricated by winding the ribbons into toroidal cores. Using the results obtained, such as maximum induction and core losses, the basic parameters for the choke cores were calculated using RALE design software (RALE RDS, version 16.81).
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 3 The NDMR Approach
### Control Messages  
NDMR uses control messages to discover and establish two node-disjoint paths between source and destination nodes in a wireless network. Figure 2 shows the structure of the control messages.  
NDMR uses PREQ and PREP messages for path discovery and path failure (PFLR) messages for path maintenance.  
* _Path request message_: A PREQ message is broadcast by the source node when it initiates a new path discovery process. The PREQ contains the following data field: + _Node_list_: the list of nodes through which the PREQ has been passed.
* _Path reply message_: A PREP message is used to report a path discovered. The PREP contains the following data field: + _Discovered_path_: the path discovered between the source and destination.
* _Secondary path request message_: A secondary path request (SPREQ) message is used to discover the second path. The SPREQ contains the following data fields: + _Node_list_: the list of nodes through which the SPREQ has been passed. + _First path_: the first path discovered and established between the source and destination.  
The _sequence number_ associated with the _source address_ is used to uniquely identify each control message and detect duplicates of the message. To prevent control messages from being duplicated and circulated in wireless networks, intermediate nodes discard duplicates of the control messages.
# Risk-Quantification Method for Car-Following Behavior Considering Driving-Style Propensity
## 5 Experimental Analysis
### Risk Level Classification  
To apply CFR to car-following risk prediction, a risk class classification is required. The Fuzzy C-Means (FCM) clustering method determines each sample's degree of membership through iterative calculations, addressing the ambiguity in sample categorization to achieve objective classification. This membership degree enhances the precision in sample classification, offering a more nuanced and adaptable clustering outcome than the K-means algorithm. Consequently, FCM was selected for delineating risk states. Post-clustering, classification thresholds are established by averaging the minimum and maximum CFR values across adjacent categories.  
The implementation of the FCM clustering algorithm was carried out in Python. Drawing on findings from prior studies [24], setting the number of categories to four yields a more logical categorization. Inputting the number of clusters and real-time CFR data for 632 vehicle groups undergoing the heeling process into the FCM algorithm facilitated the derivation of thresholds for the four defined heeling risk categories, detailed in Table 4. The identified clustering centers were 0.3514, 0.5765, 0.7624, and 0.9418, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Thresholds** \\ \hline High Risk (IV) & CFR \(>\) 0.8569 \\ Medium Risk (III) & 0.6741 \(<\) CFR \(\leq\) 0.8569 \\ Low risk (II) & 0.4635 \(<\) CFR \(\leq\) 0.6741 \\ Safe (I) & CFR \(<\) 0.4635 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Risk classification.  
Level I represents the safest category, characterized by a CFR below 0.4635. Vehicles in this category are deemed to have minimal risk, emphasizing the importance of maintaining a safe following distance.  
Level II is designated for low-risk vehicles, with a CFR ranging from 0.4635 to 0.6741. Drivers in this category should be particularly mindful of the spacing between their vehicle and others on the road, ensuring adequate distance is maintained to prevent potential collisions.  
Level III corresponds to a medium-risk status, where the CFR falls between 0.6741 and 0.8569. In this category, heightened vigilance is recommended, with drivers advised to closely monitor the driving behaviors of vehicles both ahead and behind and to make prompt adjustments to their driving as necessary to enhance safety.  
Level IV is indicative of highly unsafe following practices, with a CFR exceeding 0.8569. Vehicles in this category are at significant risk, necessitating a state of readiness to respond effectively to unforeseen events and situations on the road.