esa-sceva/satcom-chunk-collection huggingface dataset:
## 3. Results and Discussion
### Molecular Weight of FPs  
The molecular weight of polysaccharide is closely related to the biological activity, and the drying process will cause the difference in molecular weight of polysaccharide. The polydispersity index (Mw/Mn) is used to measure the breadth of molecular weight distribution of the polymer, and the larger the value, the wider the molecular weight distribution. The molecular weight and Mw/Mn results of the four FPs detected by HPSEC-MALLS-RI are shown in Table 3. The difference in drying methods resulted in the difference in the weight average molecular weight (Mw) of the four FPs, which ranged from \(8.473\times 10^{4}\) to \(1.79\times 10^{5}\) Da. FPH has the largest Mw (\(1.798\times 10^{5}\) Da), followed by FPV (\(1.66\times 10^{5}\) Da) and FPF (\(1.457\times 10^{5}\) Da) and FPFI has the smallest Mw (\(8.473\times 10^{4}\) Da). The Mp of FPF and FPFI was not significantly different and smaller than that of FPH and FPV, indicating that the relatively major chain segments of FPF and FPFI were shorter. The Mw/Mn size of the four FPs is FPF > FPFI > FPF > FPV. The molecular weight distribution of FPF and FPFI is wider than that of FPH and FPV.  
Our study was somewhat different from the Mw of fig polysaccharide reported by [PERSON] et al. [24] at \(8.28\times 10^{4}\) Da to \(1.21\times 10^{5}\) Da. The main reasons for the difference in Mw were the treatment methods before extraction of fig polysaccharide and the difference in fig origin and variety. The formation of the protein-polysaccharide complex and the change in temperature are important factors affecting molecular weight [16]. The protein content of FPH is the highest among the four FPs. With the long-term drying of hot air, the structure of FPH is destroyed and aggregation occurs, and protein-polysaccharide complexes are formed, so FPH has the largest Mw. [PERSON] et al. [38] reported that hot air drying of _Inonotus obliquus_ polysaccharide disrupted the hydration layer and structure of the polysaccharides and induced aggregation, resulting in elevated molecular weights of the polysaccharide. It is inferred that the Mw/Mn of FPH and FPV is small and the molecular weight distribution is narrow due to the phenomenon of heat aggregation and surface hardening. [PERSON] et al. [39] observed lower Mw/Mn ratios and narrower molecular weight distributions in loquat leaf polysaccharides processed by HD and vacuum drying VD compared to FD. These trends align with our experimental findings.
# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 4 Description of Experimental _I-V_ Curves Database
### Generation of Training Set and Validation Set for a Degraded PV Panel  
Although the NREL database collects \(I\)-\(V\) curves of PV panels operating in outdoor conditions, there are no indications concerning the degraded \(I\)-\(V\) curves. In this paper, a selection of \(I\)-\(V\) curve is performed by analyzing the \(I\)-\(V\) curve shape, slopes and operation conditions in order to take from the NREL database only the healthy \(I\)-\(V\) curves. On the other hand, experimental degraded \(I\)-\(V\) curves are not easily detectable because of the difficulty of reproducing the large variety of degraded conditions and the long time the measurement process takes for registering these kinds of phenomena. For these reasons, the \(I\)-\(V\) degraded curves are reproduced artificially by still using the single diode model, where variations on the SDM parameters are fixed and know a priori. In this way, it is easy to generate enough \(I\)-\(V\) curves that are useful for the ANN training process. This is a similar approach already adopted for emulating PV faults and mismatched operating conditions in others' fault identification methods [10; 12; 34].  
The degraded \(I\)-\(V\) curves' database have in common with the healthy \(I\)-\(V\) curves database the same environmental conditions. The new database is generated by applying the pseudo code shown in Table 3. \(N_{healthy}\) is the number of experimental \(I\)-\(V\) curve taken from the healthy database.  
It is worth noting that a degradation effect is applied separately to each parameter; thus, the database containing the degraded \(I\)-\(V\) curves is five times larger than the healthy database. Moreover, to introduce a detectable \(I\)-\(V\) curve deformation, the applied parameters degradation is randomly chosen, according to the boundaries shown in Table 4. Such boundaries are chosen by taking into account that the sensitivity of the \(I\)-\(V\) curve with respect to each SDM parameter is strongly different, as highlighted in Section 2.1.  
An example of degraded \(I\)-\(V\) curve obtained artificially by starting from a healthy, experimental \(I\)-\(V\) curve is reported in Figure 4. The figure also shows the only three points that are used by the ANN to estimate the SDM parameters.  
\begin{table}
\begin{tabular}{l} for \(n=1:N_{healthy}\) \\ load irradiance and temperature \(G,T\) \\ load \(I\)-\(V\) curve as vectors \(\mathbf{V_{pv}},\mathbf{I_{pv}}\) \\ load the vector \(\mathbf{p}\) of healthy SDM parameters \\ for \(k=1:5\) \\ assign a random variation (\(\alpha_{k}\in[\alpha_{k,min},\alpha_{k,max}]\)) to \\ the k-th parameter: \(\mathbf{p}(k)^{deg}=\alpha_{k}\cdot\mathbf{p}(k)\) \\ calculate the \(I\)-\(V\) degraded curve by using Equation (1) \\ \(\mathbf{I_{pv}}^{deg}=f(\mathbf{V_{pv}},\mathbf{p}^{deg})\) \\ save \(G,T\), \(\mathbf{I_{pv}}^{deg},\mathbf{V_{pv}}\) and \(\mathbf{p}^{deg}\) in the degraded database \\ end \\ \end{tabular}
\end{table}
Table 3: Pseudo code for generating degraded \(I\)-\(V\) curves.
## 3 Proposed TCN Model Architecture
### Dataset Generation  
We consider the RIS channel for wireless communication, and the direct communication channel is not taken into account. Random data bits are generated and transmitted through the channel with AWGN. The parameters are listed in Table 1. QPSK and BPSK are adopted along with orthogonal frequency division multiplexing (OFDM). In the OFDM, the subcarrier length is 128, and the cyclic prefix is 32. The binary data elements are transformed into OFDM symbol and the symbol is assigned to a corresponding label. We consider two complex data sequences for training. One is before adding noise and the second is after adding noise. Each sequence has a real and imaginary part that is separated and assigned with a label. The data symbol for QPSK and BPSK before noise can be expressed as follows:  
\[\xi=\mathfrak{R}_{\epsilon}+\mathfrak{I}_{m}. \tag{9}\]  
Again, the data symbol after adding noise can be expressed as:  
\[\xi_{n}=\mathfrak{R}_{\epsilon}+\mathfrak{I}_{m}. \tag{10}\]  
Figure 3: (**a**) Example of TCN with different dilation factors 2, 3, and 4 with kernel size 3, (**b**) different layers are present in the TCN block.  
Figure 2: Example TCN with 4 layers where the number of input features is 5 and output is also 5.  
Thus, one label can be mapped as the following values:  
\[\text{Y}=[\xi\;\xi_{n}]. \tag{11}\]  
For QPSK, each transmitting antenna can have 4 unique symbols. Thus, for two antennas that can represent \(4^{2}=16\) combinations, each can express different labels. In addition, two user data are considered simultaneously which makes the input feature as follows:  
\[\text{Y}_{in}=[\xi\;\xi_{n}\;\xi\;\xi_{n}]. \tag{12}\]  
Similarly, for BPSK, each transmitting antenna can have 2 unique symbols. Thus, for two antennas that can represent \(2^{2}=4\) combinations, each can express different labels. Two user data can be expressed as follows:  
\[\text{Y}_{in}=[\xi\;\xi_{n}]. \tag{13}\]
## 3 Problem Statement and Results
### Instantaneous Lateral Distance Maximization  
In this part, we focus on analyzing the properties of the trajectories when we use the instantaneous lateral distance maximization as the optimization target (\(J_{D3}\) and \(J_{D4}\)). As we can infer from Figures 6 and 7, in this case the main objective consists of updating the lateral position as soon as possible in order to meet the requirements set by the functionals.  
Figure 4: Trajectory evolution of functional \(J_{D1}\) for different \(t_{f}\).  
Figure 5: Trajectory evolution of functional \(J_{D2}\) for different \(t_{f}\).  
This implies using alternatively the two highest (absolute) values of acceleration during the trajectory, which naturally makes the vehicle build a more sensitive path that can be obviously more affected by additional disturbances (_i.e._, bad weather conditions). This is explained by assuming that the vehicle will try to adapt its lateral position instantaneously in order to maximize the lateral distance. Thus, it is possible that if there is a sudden change in the width of the road, or another car gets too close, the trajectory is modified very harshly, so it could undoubtedly end in a lateral collision. With \(J_{D1}\) and \(J_{D2}\) there could effectively be lateral collisions, but they are less prone to disturbances, because lateral distance is maximized regarding only the final lateral positions.  
Figure 6: Position, speed and acceleration \(J_{D3}\) (blue) and \(J_{D4}\) (red) (\(t_{f}=10\) s).  
Figure 7: Position, speed and acceleration \(J_{D3}\) (blue) and \(J_{D4}\) (red) (\(t_{f}=2\) s).  
Evaluating the results for the same scenario (see Table 1) for \(J_{D3}\) and \(J_{D4}\), we can see that for longer time intervals (Figure 8) until reaching the final position, a vehicle focus on achieving the final lateral position very quickly (thus the rapid changes in acceleration, which oscillate between the two extreme values although there is enough time to make a smoother maneuver). Besides, it is surprising that for both functionals the final lateral speed reached at the end of the path is null, and both functionals provide very similar behaviors. On the other hand, if we analyze Figure 9, we see that for very short time intervals, the evolution of mobility is very similar to what we saw for the functionals \(J_{D1}\) and \(J_{D2}\).  
Figure 8: Trajectory evolution of functional \(J_{D3}\) for different \(t_{f}\).  
Figure 9: Trajectory evolution of functional \(J_{D4}\) for different \(t_{f}\).
## 4 Discussion
It cannot be determined if the difference in aroma is due to the variety or winemaking practices for the Marechal Foch, Marechal Foch/Frontenac blend, or Vincent wines. Figure 11: A principal components analysis of wine aroma of seven wines from three different Iowa wineries (**A**) and variables (**B**). Numbered variables refer to peak numbers in Table 2. Internal standard (17) 3-nonanone is not included in (**B**) because there was no variation between the samples. Figure 10: Overlay of total compound chromatograms of seven selected Iowa red wines using the optimized method. Wines included: two bottles of Marechal Foch, one bottle of St. Croix, two bottles of Frontenac, one bottle of Vincent, and one bottle of Marechal Foch/Frontenac blend. Conditions: 2 min pre-sampling desorption of 50/30 μm DVB/CAR/PDMS SPME fiber; 4 mL wine sample in a 10 mL threaded glass amber vial with PTFE/silicone septa; 10 min incubation at 50 \({}^{\circ}\)C; 10 min extraction time at 50 \({}^{\circ}\)C; agitation speed 500 rpm; 2 min desorption time into GC inlet. An automated headspace SPME method, coupled with GCMS-olfactometry was developed to characterize wine aroma. This method was applied to characterize 36 aroma compounds present in seven Iowa red wines from three different wineries. The interactions between the experimental factors were not considered in this research. A multivariate experimental design aimed to determine the main factors followed by a response surface methodology [25] could yield better results. Although a distinct varietal aroma character was not 'pinpointed' for these Iowa red wines, there were notable differences in the aroma profile by grape variety and by the winery. Linking aroma compounds to grape variety and winemaking practices continues to be important in producing high quality Iowa wines. The authors acknowledge funding provided through the Iowa Department of Agricultural and Land Stewardship (07-1273; Project title: Development of a rapid screening method for chemical and aroma analyses of Iowa wines: transresveratrol and aroma-enhancing compounds). [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON], [PERSON], and [PERSON] wrote the paper. The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{6}{c}{**Location Based Routing Protocols for VANETs**} \\ \cline{2-11}  & **GSR [(34)]** & **GPCR [(33)]** & **GPSR+ [(9)]** & **A-STAR [(40)]** & **CryTAR [(31)]** & **E-GytTAR [(2)]** & **TFOR [(1)]** & **DCSR [(14)]** & **E-G-GYARD [(14)]** \\ \hline
**Dynamic Junction Selection** & No & No & No & No & Yes & Yes & Yes & Yes \\
**Static Junction Selection** & Yes & - & No & Yes & No & No & No & No & No \\
**Scenario** & City & City & City & City & City & City & City & City & City \\
**Hop Count** & Single hop & Single hop & Two hops & Single hop & Single hop & Single hop & Single hop & Single hop & Single hop \\
**Realistic Mobility Flows** & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline
**Local Optimum** & Fall back on & Right hand & Perimeter & Reconstruct & Carryry and & Carry and & Carry and & Carry and & Carry and \\
**Handling Strategy** & greedy mode & rule & mode & anchor path & forward & forward & forward & forward & forward \\ \hline
**Map Required** & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\
**GPS Required** & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\
**Prediction Based** & No & No & Yes & No & Yes & Yes & Yes & No & Yes \\
**Location Service Required** & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\
**Traffic Aware** & No & No & No & No & Yes & Yes & Yes & No & Yes \\ \hline \hline \end{tabular}  
* VANETs, Vehicular Ad-hoc Networks CSR, Geographic Source Routing Protocol; GPCR, Greedy Performance Coordinate Routing Protocol; CSRR+, Greedy Parameter Stades Routing Junction + Protocol; A-STAR, Anchor-based Street and Traffic Aware Routing Protocol; E-GyTAR, Enhanced Greedy Traffic Aware Routing Protocol; TFOR, Traffic Flow Oriented Routing Protocol; DCSR, Directional Greedy Source Routing Protocol; E-GyTARD, Enhanced Greedy Traffic Aware Routing Protocol-Directional.  
\end{table} 
Table 1: The comparative analysis of position-based routing protocols.
# Foot-Mounted Inertial Measurement Units-Based Device for Ankle Rehabilitation
## 2 Materials and Methods  
### Inertial Sensor  
#### 2.1.1 Inertial Measurement Units  
Inertial measurement units (IMUs) have been widely used in mobile robot navigation [31; 32; 33], and can be used to make portable rehabilitation devices. IMU devices measure acceleration, angular rates, and magnetic field vectors in their own three-dimensional local coordinate system, and, with proper calibration, represent an orthonormal base that is aligned with the outer sensor casing [34]. Some commercial devices provide the orientation estimation of this sensor for a global fixed coordinate system. They do this by using algorithms that provide orientation information in the form of quaternion, rotation matrix, or Euler angles. These algorithms employ a strap-down integration of the angular rates to obtain a first estimate of the orientation. Also, the angular position drifts in the inclination part of the IMU's orientation, which is eliminated using the assumption that the measured acceleration is dominated by gravitational acceleration (i.e., for low-acceleration ranges) [34].  
Ankle movement can be described as a three-dimensional object with three degrees of freedom. The basic movements used in ankle rehabilitation (abduction, adduction, plantar flexion, dorsiflexion, inversion, and evasion) are shown on the foot anatomical reference frame in Figure 1. This work is focused on ankle movements that can be described with two degrees of freedom: dorsiflexion, plantarflexion, inversion, and evasion. Dorsiflexion and plantarflexion correspond to the angular movement denominated as pitch, which is performed around the \"Y\" axis, with normal ranges of 20\({}^{\circ}\) and 50\({}^{\circ}\), respectively. Inversion and aversion relate to the angular movement denominated as roll, which is performed around the \"X\" axis, with normal ranges of 45\({}^{\circ}\) and 20\({}^{\circ}\), respectively. For this work, the main interest lies in the active free stage of rehabilitation, which is the stage where the patient makes the movements without any opposing force [1].  
A fundamental problem in IMU-based human motion analysis is that the IMU's local coordinate axes are not aligned with any physiologically meaningful axis. However, a common approach is to do this via calibration postures and/or calibration movements. Some authors make the patient stand with vertical, straight legs for a few seconds, and use the acceleration measured during that time interval to determine the local coordinates of the segment's longitudinal axis [34].
## 3 Results and Discussion
### Monitoring the Volatile Compound Profiles in Seaweed Fermentation
#### 3.2.2 Laminaria
1-Octen-3-one (metallic and mushroom) [29] and \(\beta\)-ionone were identified in the fermented and unfermented groups. Notably, 3-octanone, described as herb, butter, and resin notes [30], was generated in the fermented _Laminaria_ sp. suspension, and 3-hydroxy-2-butanone (sweet), also known as acetoin, was generated in the BS-fermented group. \(\beta\)-ionone, which was responsible for the rose aroma [31] and sweet note in green tea [32], was enhanced during SC, LA, LB, or LC fermentation. Linear, branched, or volatile unsaturated aldehydes, which were identified in brown seaweeds, may contribute to seaweed-like or seafood-like odors [11; 22]. Our data indicated that the total aldehyde content was decreased in the _Laminaria_ sp. suspension fermented with BS, SC, and LA (Table 2). [PERSON] et al. [33] have reported that yeast fermentation can improve the odor of tiger puffer skin gelatin and reduce its volatile aldehydes. Heptanal (dry fish notes), hexanal (fishy notes) [22; 34], and furfural (burnt note) were reduced in the fermented _Laminaria_ sp. suspension, which is consistent with the observation in the fermented _Ulva_ sp. (Table 1), indicating the potential application of fermentation in removing off-odors from seaweeds. The major alcohol compound detected in the unfermented _Laminaria_ sp. is 1-octen-3-ol (529.91 \(\upmu\)g/L, Table 2), which is described as fishy and grassy notes [34].
# Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image +
## 3 Implementation of Gimbal Control through PV Image Analysis  
### Gimbal Control with PWM Signal  
Manual and automatic signals for gimbal control are transmitted via GCS. For automatic signals, control values are determined by image processing, and for manual signals, control values are determined by external radio controls that are transmitted by a X8R receiver. The control value for the gimbal will be determined in the GCS with the combination of both manual and automatic input, with the priory of manual input signal. This result, with the format of the PWM signal, will be sent to the GCB from the GCS. According to the PWM signal, the GCB sends out control signals to each of its motors. The PWM is a cycle control method that controls the width of the pulse. Regularly, an output voltage value is maintained with high or low voltage values to produce a square wave output between 0 V and 3.3 V.  
Figure 7: Gimbal control system (GCS).  
Figure 8 shows an example of the PWM. \(T\) refers to the cycle period, and \(t_{on}\) is the duration of high voltage. The duty-cycle is calculated using Equation (1).  
\[\text{DutyCycle}=\frac{t_{on}}{T}\times 100\% \tag{1}\]  
The cycle period is 18 ms in our system, and the \(t_{on}\) range is between 0.7 ms and 2.3 ms. When \(T\) is 1.5 ms, the motor will be stopped, when it is 0.7 to 1.3 ms, the motor will be run as clockwise, and when it is 1.7 to 2.3 ms, the motor will be run as counterclockwise. Therefore, the duty cycle value is operated between 3 and 12. For the safety of the gimbal, the duty cycle is limited from 6.3% to 10.5%. Therefore, the PWM value is converted to a value between 64 and 108 by setting the cycle period value \(T\) (18 ms) to 1024.
# Routing Protocols for Delay Tolerant Networks:
## 3 Performance Evaluation
### Evaluation Using Real-World Mobility Traces
#### 3.3.1 Simple Protocols  
The main parameter that affects the performance of simple protocols is the meeting opportunity, namely the probability of meeting another node. An indirect indication of the meeting opportunity is given by observing the performance of Direct-Delivery and recalling that in this protocol, a message is directly delivered to the intended destination. As a consequence, the observed metrics only depend on the source-destination meeting opportunity.  
We observe that although the Inter-Contact Time distribution (ICT) is similar for the real traces and the synthetic model (see Figure 5), the meeting opportunity on real traces is higher than the one in  
Figure 10: Performance of advanced protocols in a real-world outdoor setting.  
Figure 9: Performance of single technique protocols in a real-world outdoor setting.  
synthetic traces. Indeed, the delivery ratio of Direct-Delivery is higher (Figure 5b _vs._5a), and the delay is lower (Figure 5d _vs._5c). Similar arguments apply also to First-Contact, where a single copy of the message is forwarded in a multi-hop fashion.  
The higher meeting opportunity of real traces is confirmed by the fact that the overhead generated by Epidemic is significantly higher for real traces. However, a higher overhead does not provide a higher delivery ratio. In Epidemic, the message is spread to all of the encountered nodes; even if in the synthetic case the meeting opportunity is lower, the number of copies of the messages is sufficient to achieve a delivery ratio comparable to the one observed in real traces. Clearly, the delivery ratio is higher when a bigger buffer is used.  
Regarding the outdoor mobility traces, by examining the performance of the Direct-Delivery protocol, it is evident that the network is much sparser than the indoor cases, and the inter-contact time is much longer. The delivery ratio of the Direct-Delivery protocol is as low as 10% throughout the execution of the experiment. In [48], the mobility trace is analyzed using statistical techniques, graph theory and network analysis. Their analysis indicates that the nodes are forming groups based on their mobility patterns, that is the group of vehicles that share many common encounters, but not meeting often. In this sense, the connectivity graph is partitioned and remains partitioned during the execution of the experiment, thus making direct delivery impossible.  
As indicated in [5], it is therefore evident that the only approach to improve the performance of the network is to relay messages through the other nodes of the network. As we observe, the Epidemic protocol achieves much higher success rates that go above 50% as the message is spread to all of the encountered nodes, and eventually, one copy reaches the destination. Clearly, this approach speeds-up the delivery of the nodes (_i.e._, reduces the delivery delay), while as each message is copied multiple times, the overhead of the protocol is also higher.