esa-sceva/satcom-chunk-collection huggingface dataset:
# On Cross-Layer Interactions for Congestion Control in the Internet
## 5 Performance Evaluation
### Access Link, Mixed TCPs
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**PIE**} & \multicolumn{2}{c}{**CoDel**} \\ \hline  & **QS (SD)** & **DR** & **QS (SD)** & **DR** & **QS (SD)** & **DR** \\ \hline UC & 741 (114) & 0.11 & 38 (29) & 0.03 & 35 (24) & 0.03 \\ \hline MC & 856 (33) & 0.87 & 58 (24) & 0.91 & 89 (32) & 0.82 \\ \hline HC & 893 (8) & 12.11 & 44 (21) & 15.84 & 632 (281) & 11.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Mean queue length (QS) and standard deviation (SD) of queue length [pkts] and packet drop ratio (DR) [%] in the Access Link scenario with mixed TCPs, UC—uncongested, MC—mildly congested, HC—heavily congested network. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DT**} & \multicolumn{4}{c}{**PIE**} & \multicolumn{2}{c}{**CoDel**} \\ \hline  & & **AvgThr (SD)** & **BD [\%]** & **JI** & **AvgThr (SD)** & **BD [\%]** & **JI** & **AvgThr (SD)** & **BD [\%]** & **JI** \\ \hline \multirow{4}{*}{UC} & Nreno & 16.24 (16.28) & 52 & 0.60 & 11.84 (11.48) & 38 & 0.61 & 10.93 (10.27) & 35 & 0.63 \\ \cline{2-10}  & Cubic & 14.07 (5.99) & 45 & 0.89 & 13.81 (3.60) & 44 & 0.96 & 15.51 (2.66) & 50 & 0.98 \\ \cline{2-10}  & C-TCP & 0.90 (0.33) & 3 & 0.92 & 5.58 (2.96) & 18 & 0.84 & 4.77 (3.63) & 15 & 0.72 \\ \cline{2-10}  & All & 10.40 (11.26) & 100 & 0.49 & 10.41 (7.23) & 100 & 0.70 & 10.40 (7.30) & 100 & 0.70 \\ \hline \multirow{4}{*}{MC} & Nreno & 1.17 (0.95) & 37 & 0.61 & 1.47 (1.17) & 46 & 0.62 & 1.42 (1.04) & 45 & 0.66 \\ \cline{2-10}  & Cubic & 1.67 (0.52) & 53 & 0.91 & 1.01 (0.21) & 32 & 0.96 & 1.07 (0.21) & 34 & 0.96 \\ \cline{2-10}  & C-TCP & 0.33 (0.10) & 10 & 0.92 & 0.70 (0.41) & 22 & 0.75 & 0.68 (0.36) & 21 & 0.79 \\ \cline{2-10}  & All & 1.06 (0.83) & 100 & 0.62 & 1.06 (0.79) & 100 & 0.65 & 1.06 (0.71) & 100 & 0.69 \\ \hline \multirow{4}{*}{HC} & Nreno & 0.17 (0.10) & 54 & 0.76 & 0.12 (0.05) & 38 & 0.86 & 0.12 (0.05) & 37 & 0.86 \\ \cline{2-10}  & Cubic & 0.09 (0.03) & 30 & 0.89 & 0.09 (0.02) & 28 & 0.94 & 0.10 (0.02) & 33 & 0.95 \\ \cline{1-1} \cline{2-10}  & C-TCP & 0.05 (0.01) & 16 & 0.97 & 0.11 (0.04) & 33 & 0.90 & 0.09 (0.02) & 30 & 0.94 \\ \cline{1-1} \cline{2-10}  & All & 0.11 (0.08) & 100 & 0.65 & 0.11 (0.04) & 100 & 0.88 & 0.11 (0.04) & 100 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The summary results in the Access Link scenario with mixed TCPs, UC—uncongested, MC—mildly congested, HC—eavily congested network. * C-TCP, combined with DT, provides very high inter-fairness even in the presence of connections using more aggressive TCP variants. * PIE is the only buffer management strategy that provides acceptable results in terms of fairness, and queue stability in all load scenarios, however, at the cost of a higher drop ratio.
# Performance Evaluation of Directional Antennas in ZigBee Networks under NLOS Propagation Conditions
## 5 Conclusions  
A study was carried out to evaluate the performance of directional antennas operating under NLOS conditions. As the work context was ZigBee networks, the power consumption of the transmission system must be low. Measurements with XBee radios operating at 2.4 GHz aimed to obtain the RSSI and PDR quality parameters. Three locations allowed the testing of different situations of NLOS propagation and Wi-Fi interference. We compared the performance of a directional antenna of 9.5 dBi with an omnidirectional antenna of 2 dBi. The transmission systems operated at the same EIRP. Although the directional antenna receiver had an additional 7.5 dB gain, the RSSI and PDR results were virtually the same for greater distances to the transmitter. To help understand this result, we applied a deconvolution technique to extract the received signal as a function of the azimuth angle. The results showed that the system with omnidirectional antennas performs better when receiving high-signal components from various angles. Directional antennas may also not prevent interference. The directional antenna received power from directions with high Wi-Fi interference in many measurement positions. Therefore, the directional antenna did not improve the SINR. The disadvantage of the omnidirectional antennas was to require a higher transmit power for the same range. The difference in current consumption between the highest and lowest power levels of the modules employed in the systems was less than three milliamperes. However, the study also demonstrated that the control circuits of directional antennas typically consume more than three milliamperes.  
Directional antennas continue to be a good solution for increasing range in low-attenuation environments. Therefore, future work will investigate the performance implications of combining directional and omnidirectional antennas in ZigBee networks.  
Conceptualization, J.A.A.; methodology, J.A.A.; software, J.A.A. and F.E.S.; validation, J.A.A. and F.E.S.; formal analysis, J.A.A.; investigation, J.A.A.; resources, J.A.A. and F.E.S.; data curation, J.A.A.; writing--original draft preparation, J.A.A.; writing--review and editing, J.A.A. and F.E.S.; supervision, J.A.A.; project administration, J.A.A.; funding acquisition, J.A.A. All authors have read and agreed to the published version of the manuscript.  
This research has been partially supported by the Center for Research in Mathematics and Applications (CIMA) related with the Statistics, Stochastic Processes and Applications (SSPA) group, through Project UIDB/04674/2020 of FCT--Fundacao para a Ciencia e a Tecnologia, Portugal.  
The authors declare no conflict of interest.
# Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation
###### Abstract  
Malignant melanoma is among the fastest increasing malignancies in many countries. Due to its propensity to metastasize and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. In non-Caucasian populations, melanomas are frequently located in acral volar areas and their dermoscopic appearance differs from the non-acral ones. Although lesion segmentation is a natural preliminary step towards its further analysis, so far virtually no acral skin lesion segmentation method has been proposed. Our goal was to develop an effective segmentation algorithm dedicated for acral lesions. We obtain a superpixel oversegmentation of a lesion image by performing clustering in a joint color-spatial 5d space defined by coordinates of CIELAB color space and spatial coordinates of the image. We then construct a region adjacency graph based on this superpixel representation. We obtain the ultimate segmentation result by performing a hierarchical region merging. The proposed segmentation method has been tested on 134 color dermoscopic images of different types of acral melanocytic lesions (including melanoma) from various sources. It achieved an average Dice index value of 0.85, accuracy 0.91, precision 0.89, sensitivity 0.87, and specificity 0.88. Experimental results suggest the effectiveness of the proposed method, which would help improve the accuracy of other diagnostic algorithms for acral melanoma detection. The results also suggest that the computational approach towards lesion segmentation yields more stable output than manual segmentation by dermatologists.  
acral malignant melanoma; region adjacency graph; hierarchical merging; segmentation; dermoscopy +
Footnote †: journal: Medical Imaging
## 3 Processing and Extraction  
Bioactive compounds may be isolated from pomaces by physical techniques, such as cold pressing, or chemical techniques, i.e., extraction, and extracts as well as oils may be products of these processes. Conventional methods of extraction may, however, require extended energy intake and use of organic solvents in large quantities. To help to reduce the environmental and financial impact of extraction processes caused by both high energy and organic solvent consumption, non-conventional extraction methods have been proposed. Particular novel extraction methods may lead to the obtention of extracts or oils with improved properties. A scheme for the procedures of extraction of bioactive compounds and oils from berry fruit by-products is presented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Source of** & **Cholesterol** & **Campesterol** & **Stigmasterol** & \begin{tabular}{c} **\(\beta\)-** \\ **Stitosterol** \\ \end{tabular} & \begin{tabular}{c} **D5-** \\ **Avenasterol** \\ \end{tabular} & \begin{tabular}{c} **D7-** \\ **Stigmasterol** \\ \end{tabular} & \begin{tabular}{c} **D7-** \\ **Stigmasterol** \\ \end{tabular} & \
\begin{tabular}{c} **D7.25-** \\ **Stigmasterol** \\ \end{tabular} &
\begin{tabular}{c} **Reference** \\ **Reference** \\ \end{tabular} \\ \hline Chokeberry & 2.95 & 5.5 & 3.85 & 81.8 & 1.85 & 1.8 & 1.8 & [38] \\ \hline Blackberry & 0.33 & 5.3–7.03 & 1.8–4.87 & 77.77–84.7 & 3.02–7.0 & 1.41 & - & [37,59] \\ \hline Raspberry & 0.43 & 4.5–4.51 & 0.84–1.2 & 79.6–83.95 & 5.35–7.2 & 1.24 & - & [37,59] \\ \hline Blackcurrant & 2.5 & 1.25 & 4.9 & 86.6 & 1.3 & 0.85 & 1.4 & [38] \\ \hline Blackcurrant & 0.31 & 8.14 & 0.42 & 81.09 & 3.10 & 1.92 & - & [59] \\ \hline Redcurrant & 0.36 & 10.01 & 0.24 & 87.58 & 0.36 & - & - & [59] \\ \hline Blueberry & 0.24 & 3.4–4.63 & 0.3–0.37 & 66.5–82.85 & 2.14–13.8 & 3.97 & - & [37,59] \\ \hline Strawberry & - & 5.4 & 2.3 & 71.1 & 8.7 & - & - & [37] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sterol composition of lipid fraction of berry pomaces (%).  
Figure 2: A scheme for the extraction procedures for bioactive compounds and oils from berry fruit by-products.
# Compact SOI Dual-Mode (De)multiplexer Based on the Level Set Method
## 3 Results  
### Simulation  
The final optimized structure is shown in Figure 5a, with a footprint of 9.4 \(\upmu\)m \(\times\) 2.9 \(\upmu\)m. Figure 5b,c displays the simulations of two modes injected into the device, respectively. The simulations demonstrated that the input TE\({}_{0}\) mode energy was predominantly guided to output 1 port, while the TE\({}_{1}\) mode energy was directed to output 2 port. These results align well with our expectations.  
As depicted in Figure 6, the IL of TE\({}_{0}\) mode was less than 0.28 dB within the wavelength range of 1500-1600 nm, and the IL of TE\({}_{1}\) mode was less than 0.35 dB. At the central wavelength of 1550 nm, the ILs were measured at 0.16 dB for TE\({}_{0}\) mode and 0.14 dB for TE\({}_{1}\) mode. It was observed that the TE\({}_{0}\) mode light exhibited a lower IL at shorter  
Figure 4: The initial structure (**a**) and the optimized structure (**b**) surface in level set method. (**c**) The value of FOM as the function of iteration numbers.  
Figure 5: (**a**) Optimized structure of demultiplexer. (**b**) E\({}_{y}\) field distribution of the device with a TE\({}_{0}\) input. (**c**) Distribution of E\({}_{y}\) field in the device with a TE\({}_{1}\) input.  
wavelengths, while TE\({}_{1}\) mode light demonstrated the opposite trend at longer wavelengths. Consequently, these two modes achieved a balance at the central wavelength with the highest FOM values; in other words, the device exhibited minimal insertion loss for TE\({}_{0}\) and TE\({}_{1}\) input overall at 1550 nm. Additionally, the energy coupled to the output 1 port of TE\({}_{1}\) mode and output 2 port of TE\({}_{0}\) mode was characterized by CT, both measuring less than \(-\)23 dB.  
Basically, the result of the simulation demonstrated that the device exhibited low IL fluctuation and low CT in the C-band and thus can support wavelength multiplexed signals in the C-band.
# QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing
## 2 Literature Review
### VANET Models  
An intelligent transportation system (ITS) is an advanced communication system amongst vehicles on the roadside to exchange information. Its advanced type is VANET, which can connect thousands of wireless nodes (Vehicles). VANET is an advanced type of ad-hoc network, which is used by ITS currently. In [3], the author proposed the VANET architecture-based mobile ad-hoc network (MANET). The proposed model is used for vehicles by using wireless technology to decrease energy consumption while using dynamic topology. Similarly, in [9], the author proposed safety and non-safety-based VANET architecture. In the proposed model, the author sent safety messages first and then non-safety messages to increase QoS. In VANET, vehicles are directly connected to share necessary information.
## 4 Results and Discussion
### Signal Image Conversion  
The size of the GAF images is square related to the length of the time series. When the sampling length is too long, it can lead to an increase in the size of the GAF image, thereby increasing the time and difficulty of model training. If the sampling length is too short, it cannot effectively reflect fault information. This article selects a time-series length of 200 for a single sample and converts it into a two-dimensional image using the Gram angle field. Figure 7 shows the GASF and GADF images when the iron core is loose at different points.  
In Figure 7, it can be seen that the different types of GAF two-dimensional images have significant pixel differences. Using this as ResNet input can further enhance the network's feature extraction ability and improve classification and recognition accuracy.  
Figure 7: GASF and GADF images of iron-core loosening at different points. (**a**) GASF under normal operation; (**b**) GADF under normal operation; (**c**) GASF with loose yoke in phase A; (**d**) GADF with loose yoke in phase A; (**e**) GASF with loose lower yoke in phase A; (**f**) GADF with loose lower yoke in phase A.
## 6 Experimental Results and Analysis
### PDR Package Delivery Ratio  
In this subsection, we present the PDR results for uplink communications, i.e., the end nodes send 100 packets to the gateway with a payload length of 16 bytes (node 1) and 8 bytes (node 2) with a frequency of 2000 ms, at various distances with different SF configurations, including SF = 7, 9 and 12. Figure 11a illustrates the PDR for node 1 and Figure 11b the result for node 2. It can be visualized that the PDR decreases with increasing payload length, demonstrating an impact of packet length on frame reception. Being in the SF configuration in 12 with a PDR in the range of 42-47% with PL of 16 bytes in positions D, F and G. Furthermore, it is determined that the PDR success rate is different for each end node depending on the test environment; therefore, in position A, B, C, E and H, a better PDR is obtained than in positions D, F and G. The effect is due to the fact that positions D, F and G are located in areas with slopes of about 15%, surrounded by eucalyptus trees, vegetation and no line of sight (NLoS), thus generating more interference, more path loss and noise to the LoRa radio communication signal.  
Figure 11: PDR: **(a)** Nodes 1, **(b)** Node 2.  
In contrast to the results obtained in [46], the tests carried out in a forested area at 90 m, there was no communication for the transmission powers tested (7, 13 and 14 dBm); in comparison with the present work, communication was achieved for this type of orography; obtaining a PDR of 42-47% in uplinks and downlinks a PDR greater than 87%, for forested areas.  
Node 3 was set up for downlink communications, i.e., the gateway sends 100 packets with a payload length of 4 bytes to the end node. Figure 12 shows the PDR of node 3. It is determined that the PDR with SF of 7, 9 and 12 at positions D, F and G maintains an acceptable success rate, since they are the locations with the highest interference, a PDR of no less than 87% is displayed. It is important to note that the objective of the tests is to check the coverage of the LoRa physical layer using different propagation factors.
## 2 System Overview
### Mobile Node: Aerial Robot as a Mobile WSN Node  
The employed aerial platform to carry out this mission is a mini vertical takeoff and landing aircraft, denoted as quadrotor. Unlike helicopters, quadrotors provide simple assembly and maintenance due to their non-complex mechanical control linkages for rotor actuation. The vehicle control is done through variation of the speed of four fixed rotors. Recently, quadrotor manufacturers have been able to slightly increase payload capacity while maintaining a great maneuverability. Furthermore, due to the small dimensions and lightweight of the platform, safety and legislative issues are out of discussion. The small quadrotor employed in the experiments has the following characteristics:  
* Width: 0.63 m
* Dimensions: 40 \(\times\)40 \(\times\)10 cm
* Total weight: 550 grams
* Payload: 200 grams
* Spektrum DX7 SE 2.4 GHz remote control
* X-Bee 2.4 GHz data link
* LiPo 11.1 V 2100 mAh
* Maximum height: 500 m
* The four rotors are made up of X-BL-52s brushless motors and their controllers.
* Sensing capacity: GPS, IMU, 3D-MAG, pressure sensor.
* The mainframe is made up of a power board and an autopilot card.  
Position estimation is based on GPS that allows performing position control at 1 Hz, whereas attitude control is based on an Attitude and Heading Reference System (AHRS), which runs at 58.8 Hz. The sensor navigation data from the quadrotor can be received via wireless connection (Data link X-Bee at 2.4 GHz). The control commands can be sent to the vehicle using the R/C transmission radio control or the Data link X-Bee in write-mode. The operator is able to enter in the control loop at any time by using the radio control or through a Graphical Operator Interface (GOI) developed for the assessment of the mission proposed. The mission can also be manually aborted using the GOI shown in Figure 6. In this case two procedures can be followed: (i) the quadrotor will remain hovering to wait for operator commands or (ii) the quadrotor will return to the home-point (beginning of the mission) autonomously.  
Figure 6: Aerial vehicle experimental setup. **(a)** Mobile base-station, **(b)** Quadrotor graphical operator interface, **(c)** Quadrotor, **(d)** Data link, **(e)** Emergency pilot remote control X-Bee.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 2 Historical Review
### Characteristics of Research Output
#### 2.1.3 Geographic Patterns  
There has been a large geographic bias of the cities being studied (Figure 2). The selected literature has focused predominantly on Asia (62%). North America had the next largest number of studies (24%), which was followed by Europe (15%). There has been relatively little SUHI research in Africa, South and Central America, and Oceania, with <37 publications, and these cities were mostly included in global-scale studies rather than having a particular focus. Studies on all continents increased dramatically through time, especially after 2010. North America dominated the SUHI literature before 2000 while Asia became the major focus thereafter. A country-based analysis indicates that China was the most actively-studied country of SUHI research (213), which was followed by the United States (106) and India (38). The high fraction of studies in China is due to the large number of Chinese researchers publishing on the subject. About 91% (194) of the studies on Chinese cities are led by Chinese scientists. Furthermore, a city-based analysis demonstrates that the SUHI was studied in more than 1400 cities worldwide, particularly in Chinese cities such as Beijing (72), Shanghai (51), Nanjing (35), Guangzhou (33), and Wuhan (32). The top 20 cities were nearly all in China. Indianapolis (22) and Phoenix (21) were the most active research sites in the United States. Paris (11) and Berlin (11) were the most active research sites in Europe, and Sao Paulo (5), Lagos (5), and Sydney (4) were the most active sites in South America, Africa, and Oceania, respectively.  
Figure 1: Number of studies in each year and the mean number of publications in each five-year interval. Note that the final year (2018) has data only for eight months (January–August).
# Detection of Software Security Weakness Using Cross-Language Source Code Representation (CLaSCoRe)
## 3 The Proposed Architecture for the Cross-Language Source Code Representation
### Clustering Tokens from Multiple Languages, Based on Semantic Similarities  
The word vectors are used to develop clusters of tokens from different programming languages, all tokens in a cluster being replaced in the next phases with the cluster identification number. As a result, the lexical and semantical differences between programming languages are reduced and the code representation is less dependent on the language.  
We propose the K-Means clustering algorithm for the word embedding vectors. Based on our previous research, other clustering algorithms, like Hierarchical Agglomerative Clustering (HAC), behave in a similar way.  
The word vectors resulted from the NIST dataset corpus, and the 920 tokens vocabulary are grouped in two main clusters, which can be observed visually in Figure 2. The elbow score, calculated and presented in Figure 3 using the KElbowVisualizer [33], suggests a number of 17 clusters, each containing an average of 54 tokens. This may lead to a very abstract and generic representation of the source code, and being unable to preserve the security weaknesses patterns. We have chosen to apply iterative clustering and obtain clusters with 10 tokens or less, balancing the desired independence on the programming language with an acceptable level of abstraction that still captures the security vulnerability patterns. We have generated with this approach 167 clusters of tokens. Examples of tokens' clusters are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Cluster**} & \multirow{2}{*}{**Token**} & \multicolumn{3}{c}{**Token Found in:**} & \multirow{2}{*}{**Cluster**} & \multirow{2}{*}{**Token**} & \multicolumn{2}{c}{**Token Found in:**} \\ \cline{3-4} \cline{6-9}  & & **C/C++** & & & & **C/C++** & **Java** & **C\#** \\ \hline \multirow{4}{*}{65} & args & & & x & & command & x & \\  & assign & & x & & & executable & x & \\ \cline{1-1}  & connect & x & x & & & program & x & \\ \cline{1-1}  & pass & & x & & & run & x & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Examples of clusters containing mixed tokens from C/C++, Java, and C# languages.  
Figure 2: Bi-dimensional view of NIST dataset tokens’ word vectors.