esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
### Optimization of the Parameters for HS-SPME  
The different factors, including different fibers (PDMS, PDMS/CAR, DVB/CAR/PDMS, and PDMS/DVB), sample amount (0.8, 0.9, 1.0, 1.1, and 1.2 g), extraction time (5, 10, 15, 20, and 25 min), and temperature (80, 90, 100, 110, and 120 \({}^{\circ}\)C), were investigated and optimized to extract volatile components from _S. doederleinii_. The detailed discussion was as follows.  
Different types of fibers have different adsorption and retention capacities and are suitable for different analytes [22]. In this paper, four fibers (PDMS, CAR/PDMS, DVB/CAR/PDMS, and PDMS/DVB) were applied to extract the volatiles from _S. doederleinii_. In gas chromatography, the sample extracted by PDMS/DVB fiber showed the largest peak area. As shown in Figure 1A, the total number of peaks obtained by PDMS/DVB fiber was also more than that obtained by the other three fibers, which proved that PDMS/DVB fiber had good adsorption and retention capacities for the extraction of volatile components from _S. doederleinii_. Therefore, PDMS/DVB was identified as the optimal fiber in this study.  
Temperature is an important parameter for the SPME extraction process. Since extraction by this method is an exothermic process, it affects the diffusion rate of the analytes into the fiber coating [23]. One gram samples were extracted with 65 \(\upmu\)m PDMS/DVB fiber for 20 min. Volatile components were detected at 80, 90, 100, 110, and 120 \({}^{\circ}\)C, respectively. Each sample was repeated three times. As can be seen in Figure 1B, when the temperature reached 100 \({}^{\circ}\)C, the total peak area and the number of the volatile oil were the highest. Therefore, 100 \({}^{\circ}\)C was the optimal extraction temperature.  
The time that the fiber is in contact with the sample has a significant effect on the chromatographic peak areas of the volatile oils [24]. The extraction time was optimized under the conditions of 65 \(\upmu\)m PDMS/DVB fiber, 1.0 g sample amount, and 60 \({}^{\circ}\)C, and the total peak areas of the volatiles were analyzed at 5, 10, 15, 20 and 25 min (see Figure 1C). The results showed that the total peak area and the number of the volatile oil were the highest at 20 min. Therefore, the optimal extraction time was set to 20 min.  
After screening the effects of PDMS/DVB fiber, extraction temperature, and time, the influence of sample amount on the extraction efficiency of the volatile oils is shown in Figure 1D. The extraction time was 20 min, and the extraction temperature was set at 100 \({}^{\circ}\)C. Under these conditions, different sample amounts were analyzed and optimized. As shown in Figure 1D, the sample amount of 1.0 g had a significant effect on the extraction efficiency of volatile oil. Therefore, it was appropriate to select 1.0 g as the sample amount.  
Figure 1: Effects of extraction parameters on the volatiles of _Selagintella doederleinii_. (**A**) solid phase microextraction (SPME) fiber; (**B**) extraction temperature; (**C**) extraction time; (**D**) sample amount.
# ITOC: An Improved Trie-Based Algorithm for Online Packet Classification
### Summary and Limitations of Prior Art  
The vast majority of work on packet classification can be divided into two major categories: the architectural and the algorithmic. Architectural approaches are based on special hardware platforms, such as Ternary Content Addressable Memory (TCAM) [4; 5; 6; 7]. The approaches inevitably have the limitations brought by the hardware platforms as well. Generally, the approaches based on TCAM are expensive, area-inefficient, and power-hungry, which are also the inherent limitations of TCAM hardware.  
The performance of algorithmic approaches depends on their algorithms, while each algorithm has its limitations. For instance, the decomposition methods, represented by Bit Vector (BV) [8; 9; 10], lack scalability, and only perform well in small sets. The partition methods [11; 12; 13; 14], represented by decision-tree, lack of flexibility as the decision-tree is designed to serve static data. Some other methods, such as Tuple Space Search (TSS) [15] and trie methods [16; 17], also have their shortcomings.  
Since the trade-off among classification speed, memory footprint, and update complexity is inevitable, a good online packet classification method should give consideration to both fast lookup and fast update.In online packet classifications, the lookup performance is determined not only by the lookup speed of algorithms but also by the update speed. For example, when a flow modifying control message is sent to an SDN switch, the switch should stop forwarding and update its flow tables. As shown in Figure 1, the update requests will interrupt the processing progress of lookup requests, and as a result, the number of lookup requests the switch could finish will be less. If the update process takes too much time, the lookup requests will accumulate and the overall performance of the switch will be affected.
## 2 Experimental Procedures
### Density and Absorption Capacity  
The physical properties of the converter slag before and after the reaction with SCD were measured by density (skeletal and apparent density) and absorption capacity. The skeletal density was measured using AccuPyc II 1340 Gas Pycnometer (Micromerities, Norcross, GA, USA). Prior to the measurements, the apparatus was calibrated with two NIST traceable steel ball standards. The 3.5 g of converter slag samples were placed in a 10 cm\({}^{3}\) chamber and 1.345 bar helium was introduced. The mass of the specimen was divided by the average of 10 volume runs to calculate the skeletal density of the specimens.  
Apparent density and absorption capacity of the converter slag were measured according to ASTM C 128 Standard Test Method for Relative Density (Specific Gravity) and Absorption of Fine Aggregate [31]. The absorption capacity of converter slag was calculated by measuring the amount of water required to meet saturated surface-dry (SSD) condition from oven dry(OD) condition. The apparent density was measured using a pycnometer, and the measurement method is as shown in Equation (1).  
\[\frac{W_{od}}{W_{od}+W_{pyc}-W^{*}{}_{pyc}} \tag{1}\]  
where \(W_{od}\) is mass of oven dry specimen (g), \(W_{pyc}\) is mass of pycnometer filled with water to calibration mark (g), and \(W^{*}{}_{pyc}\) is mass of pycnometer filled with specimen and water to calibration mark (g).
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 2 Materials and Methods
### Data  
#### 2.2.1 PlanetScope Imagery  
We selected four cloud-free PlanetScope image datasets acquired in the snow-free summer months of 2020 (1 June 2020; 5 July 2020; 5 August 2020; 18 September 2020). PlanetScope is a satellite constellation consisting of over 150 CubeSats (Doves) flying in a near-polar orbit at 475 km altitude [33], which allows for observing the entire Earth's land surface on a daily basis [34]. The constellation has been continuously recording data since 2016 and has been enlarged by additional Dove generations with enhanced sensor systems (1 st generation: PS2, 2 nd generation: PS2.SD and 3 rd generation: PSB.SD). PlanetScope imagery is acquired at a spatial resolution of 3.7 m and three (Blue, wavelength \(\lambda\) = 455-515 nm; Green \(\lambda\) = 500-90 nm; Red \(\lambda\) = 590-70 nm) or four bands (additional near infrared (NIR) \(\lambda\) = 780-860 nm) depending on the generation [33].  
For this study, we used the PlanetScope PS2 (Blue, Green, Red and NIR) orthorectified and atmospherically corrected \"_Analytic Ortho Scene_\" surface reflectance product (resampled to 3 m) [33]. Further pre-processing and analysis steps were performed using the open-source software R [35] and QGIS [36]. First, for each time step the individual scenes of each date were mosaicked by averaging the spectral information of overlapping areas. Second, remaining patches of snow and cloud covered areas (present in scenes acquired on 1 June 2020 and 18 September 2020) were masked using PlanetScope's data product mask (UDM2) [33] and an additional threshold based on overall brightness [37; 38]. We then filled these data gaps with the information from the closest observations in time.
## 4 Security Analysis
### Informal analysis
#### 4.2.8 Drone Capture Attacks  
When a UAV is captured, attackers can obtain the communication and key information stored in the UAV and then simulate it to establish a session with the UAV or the GCS. In the proposed protocol, even if attackers obtain the master key, the session key, and the authentication key, they cannot establish a session with the UAV or the GCS. Because the entire UAV network conducts keys refresh when the drone is captured, the unidirectional nature of the hash function and the random nature of the keys make it impossible for attackers to calculate the session key and the authentication key for the next phase of communication. Therefore, the scheme is resistant to drone capture attacks.
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
## 3 Proposed Approach
### Deep Convolutional Autoencoder (DCAE)  
A Convolutional Autoencoder is a variant of an AE. The encoder part is implemented as a CNN [18] rather than a basic neural network, and the decoder part is implemented as a flipped CNN (i.e., deconvolutional layers instead of convolutional layers). In a CAE, the encoder is a multi-layer CNN (i.e., a deep CNN), and the decoder consists of a flipped deep CNN. Deep CNNs are very efficient at extracting features from multi-scale perspectives, from the bottom layer up to the top layer of the network. Therefore, DCAE [28] leverages the ability of the AE to attain a strong, relevant feature representation of the training data.  
A Deep CNN mainly consists of convolution layers stacked over each other. A convolution layer applies a convolution operation between a filter called a \"kernel\" and its input. The kernel size is usually 3 \(\times\) 3 and its weights are learned during the training process. Starting from the upper left of the input image (or a feature map from a previous layer), the kernel swipes through the whole input image, moving one position at a time (i.e., stride) and the convolution operation is repeated until the whole input image is covered. The result of this process is called a \"feature map\". There are multiple kernels for each convolution layer, and all are randomly initialized. Therefore, a convolution layer outputs multiple feature maps. When a Graphics Processing Unit (GPU) is used, all of the aforementioned computations are executed simultaneously. Once feature maps are computed, each element in the feature maps is passed through the ReLU [29]. The ReLU is a piece-wise linear function that outputs the same input if it is positive and zero otherwise.  
In order to downsample the feature maps, a pooling layer comes after a convolution layer in DCAE and many other CNN architectures. This downsampling is accomplished using either maximum or average pooling. Max-pooling sets the output value as the maximum value of the values covered by the pooling filter, whereas average-pooling calculates and outputs the average value. Similar to the convolution kernel in the convolution layer, the pooling filter moves one stride at a time and swipes through the whole feature map. The size of the pooling filter is usually 2 \(\times\) 2. This results in reducing the length and width of feature maps by half.
# An Optical Interference Suppression Scheme for TCSPC Flash LiDAR Imagers
## 3 Related Work
The basic idea of CDMA for LiDAR is that the light signal produced by each camera is shaped according to a specific digital or analog modulation scheme that allows the emitter to recognize its own signal over noise and interference. Detection and recognition are often performed by correlating the measured (noisy) signal with a specific correlation target function. The use of pseudorandom noise in i-ToF [24] falls into this broad category, as do phase-shift keying (PSK) modulation in TCSPC LiDARs [25] and the use of optical orthogonal codes [26; 27]. Related research has been published on the topic of the identification and authentication of a LiDAR signal as one's own by means of side-channel fingerprinting and advanced encryption standard (AES) encryption [28]. While unsuitable for solving the multi-camera interference problem _per se_, these techniques are important steps towards comprehensive security of the LiDAR channel.
## 6 Analysis of Results
### End-To-End Delay  
End-to-End delay is the time taken by a packet in traversing a distance from the source node to the centralized station. It includes transmission delay, propagation delay, and the necessary processing time for the sensor node to forward a packet. The result of the end-to-end delay is plotted by varying the number of nodes and the transmission range. The end-to-end delay of EESEVBF is lower than that of ESEVBF, as shown in Table 4 and Figures 20-22. There are two reasons for this:  
* When a node forwards a packet, the next forwarder is selected based on the holding time algorithm. ESEVBF protocol only considers the advance distance covered in the 1 st hop from the source to the forwarder in selecting the next PFN and not the one in the second hop of the respective PFN. On the other hand, the proposed protocol extends the holding time up to the second hop of the source node, i.e., the holding time mechanism of the forwarder node includes the distance from the edge of the transmission range of the source node to the forwarder, the distance of the source node to the second hop forwarder, the distance between the sink and next forwarder, and the distance from the virtual pipeline.
* The EESEVBF uses a container packet in which the nodes exchange their holding times at both the first and second hops. The nodes found from the holding time of each other set their priorities at the first and second hop, then only the one with the highest priority at the second hop transmits the packet within no time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline _Network Size_ & **200** & **300** & **400** & **450** \\ \hline \(T_{r}=500m\) & 4.52 & 16.3 & 50.1 & 45.9 \\
550 m & 4.43 & 18.3 & 37.9 & 35.5 \\
600 m & 21.2 & 43.3 & 27.1 & 30.7 \\
650 m & 20.3 & 26.6 & 24.4 & 23.2 \\
700 m & 30.2 & 25.1 & 15.4 & 11.3 \\
750 m & 21.1 & 16.7 & 15.9 & 11.6 \\
800 m & 23.3 & 12.4 & 9.50 & 7.39 \\
850 m & 18.2 & 6.34 & 5.53 & 4.90 \\
900 m & 12.2 & 5.80 & 4.08 & 4.44 \\ \hline \multicolumn{5}{c}{**\% Improvement**} & **17.1\%** & **18.7\%** & **20.2\%** & **19.5\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall end-to-end delay improvement of EESEVBF compared to ESEVBF.  
Figure 20: Number of Nodes vs. End-to-End delay.
## 3 Results and Discussion
### Linear Model of the Relative Performance Index and the Yield of Electron Transport  
The correlation between the logarithms of the relative yield of electron transport (ET\({}_{0}\)/ABS, \(\phi_{\text{ED}}\)) and performance index at the absorption basis (PI\({}_{\text{abs}}\)) represents the capacity of energy utilization and the overall performance of the plant [34,105,106]. The linear relationship between logarithms indicates that any change in \(\phi_{\text{EO}}\) would induce a change in relative PI\({}_{\text{abs}}\) in plants exposed to drought and after recovery [50]. To emphasize the reactions of each variety, the data were normalized to their corresponding control. Our results revealed that the seedlings after recovery generally had a better vitality than those after the drought stress (Figure 5). The linear relation showed the strongest relationships in kale (R\({}_{\text{d}}\)\({}^{2}\) = 0.983, R\({}_{\text{r}}\)\({}^{2}\) = 0.959; Figure 5c) compared to Chinese and white cabbage (Figure 5a,b). It is proposed that, generally, tolerant cultivars show positive values, while the susceptible ones exhibit lower values [87,105,107]. However, consistency between treatments was also identified as a major factor in the determination of the overall performance of the plant [34]. White cabbage was shown to be the variety with the lowest consistency (Figure 5b), which was also supported by the formation of positive L and K bands (Figure 1g,h), suggesting a considerable sensitivity to drought. Moreover, the absolute value of the slope between \(\phi_{\text{EO}}\) and the log PI\({}_{\text{abs}}\) can be used to quantify the tolerance to the drought of certain _Brassica_ variety [70]. Based on that, compared to Chinese cabbage and kale, white cabbage, with the lowest absolute slope values, could be signified as the most sensitive to drought.  
Figure 4: Difference in the driving forces (\(\Delta\)DF) of three _Brassica_ seedlings after exposure to drought and subsequent recovery. Stacked columns represent differences in DFs in treated seedlings minus the corresponding control separately for each variety. Each DF is calculated by summing up their partial driving forces.  
Figure 5: Linear model between logarithms of relative ET\({}_{0}\)/ABS and PI\({}_{\text{ABS}}\) in three _Brassica_ seedlings: Chinese cabbage (**a**), white cabbage (**b**) and kale (**c**), subjected to drought (filled circles) and subsequent recovery (empty circles) relative to corresponding controls.
## 3 Results and Discussion
### Acidogenic Fermentation of Glucose  
The experimental results obtained when fermenting glucose are presented in Figure 2. In this Figure, the glucose consumption and the biomass growth, as well as the bio-hydrogen, carbon dioxide and SCFA generation are shown. As shown in Figure 2, the glucose was completely consumed after about 16.5 h. In this experiment the trend was almost linear during the first 8 h, with an average degradation rate of 0.55 g L\({}^{-1}\) h\({}^{-1}\); after that, the fermentation rate decreased. The decrease in the fermentation rate could be due to the low substrate concentration, near the half-saturation coefficient value, which limited the mass transfer and therefore the fermentation rate. Once the acidogenic fermentation of the glucose was complete, the average hydrogen yield was 370 mL g\({}^{-1}\) glucose consumed, measured at STP. The maximum hydrogen production rate when fermenting glucose was obtained during the first two hours, with a value of 250 mL of hydrogen h\({}^{-1}\). The cumulative hydrogen production was about 3.33 L measured at STP, whereas the cumulative carbon dioxide production was 2.68 L measured at STP. These results are in accordance with those reported in the literature [31].  
The glucose fermentation presented a biomass growth yield of 0.11 g TSS g\({}^{-1}\) glucose consumed. Regarding the production of SCFAs, the acetate presented the highest production rate at the beginning of the experiment. However, lactate was the SCFA presenting the highest final concentration--28.9 mmol, a value slightly higher than that obtained by the acetate, at 27.5 mmol.
# Accelerated Testing and Reliability of FDM-Based Structural Electronics
## 1 Introduction
Another reason for this phenomenon may be design error and local exceeding of safe temperature caused by too-high current density in the circuit. This leads to open-circuit situations and can increase the probability of metal migration [13]. Another phenomenon that may cause damage to the electronic system is the short circuit of neighbor traces by tin whiskers or dendrites. This problem is caused by the natural tendency of tin-rich alloys to grow filamentary crystals (whiskers) on the surface of the metal. Whiskers are electrically conductive and mechanically strong and grow faster towards high voltage gradients. Other factors that affect the growth rate of whiskers are humidity and operating temperature, solder composition, contamination, etc. [14,15]. The next mechanism that can lead to the failure of an electric device is the formation of voids. These are nonconductive cavities in the solder joints, and their size can reach up to tens of percent of joint size. There are many reasons for their formation, such as evaporation of gases from the solder paste flux, temperature shrinkage, or differences in diffusion coefficients between solder and pads. The formation of voids leads to increased resistance of the joint and an increase in the current density flowing through the solder. This can cause local overheating and further damage to the circuit [15,16]. Figure 1: Example electronic device made traditionally (**A**) and as structured electronics (**B**). On the one hand, many of the mechanisms outlined above are reduced or even eliminated in structured electronics. On the other hand, different phenomena may be enhanced and, furthermore, others not previously observed in conventional systems may occur in new technology approaches (such as additive manufacturing). Because of the layered structure of SE, delamination is a common phenomenon that occurs not only in additively manufactured electronics but in many 3D printed parts [17]. In contrast, the occurrence of whiskers and dendrites in SE should be limited for two reasons. First, in this technique, the conductive material is based on silver, and second, each conductive trace is usually covered with an insulating layer. Because soldering is replaced by bonding with electrically conductive materials, void creation should also be significantly reduced. Nevertheless, additively manufactured objects usually have lower reliability than traditionally manufactured ones [18]. This is caused by the technological thermal stresses or porosity of the fabricated structures. To sum up, the reliability of structural electronics is difficult to predict, so in this work, we investigated how it is comparable with currently available technological solutions. Furthermore, the mechanisms that lead to the destruction of such devices during their operation were investigated.
## 4 CT Algorithm
### The Design of CT
#### 4.2.1 Phase 1: DAS Slot Allocation  
The algorithm in this phase starts from the base station, and each node stores its potential parent node, the number of hops between the node and the base station, and the allocated time slot information. When a node receives a set of \"non-empty\" messages, it will select one of the senders as its parent  
Figure 2: CT algorithm.  
node, add the remaining nodes as potential parents, and update its own time slot to be less than the time slots of all neighbor nodes until the whole network time slot allocation is completed.  
When a node broadcasts its own state information, the information includes its one-hop neighbors, so each node can store the information of its own two-hop neighbors when receiving the state information of its neighbors. Such two-hop node information can cause each node to check whether its own time slot conflicts with the time slot of its two-hop neighbors and be used to resolve the conflict. The process at this phase is shown in Algorithm 2. The algorithm of phase 1 is described as follows:  
1. Initialize the slot of Sink node to the total number of nodes, so that the whole network has sufficient slot allocation and the slots allocated by each node are different.
2. Nodes in the network continuously broadcast messages until there is no slot conflict between each node and its two-hop neighbor nodes. If the slot of the receiving node has not been determined but the slot of the sending node has been determined, the sending nodes become all potential parent nodes of the receiving node. In addition, among the one-hop neighbor nodes of the sending node, the nodes that are not allocated by the slot may be potential competitors of the receiving node, that is, they may be assigned to the same slot.
3. Detect slot conflict and update slot. If the slot is updated from parent, and the slot of the current node is greater than or equal to the slot of parent node, the slot value will be updated.