esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Software-Defined Networks to Mitigate Private VLAN Attacks
## 4 Comparative Performance Analysis
### Experimentation  
To analyse the performance of the two mitigation strategies, a variable bandwidth of legitimate and malicious traffic between endpoints and their gateway was injected into the network. This traffic used UDP as the transport protocol and had to be processed by the ACL configured in the gateway or on the flow tables of the switches, depending on the strategy for detecting PVLAN attacks. UDP packets did not exceed 1500 bytes to adjust to the standard Ethernet frame size. The legitimate traffic used a fixed bandwidth of one percent of the available bandwidth on the uplinks in the switches located at the access level in each scenario. Therefore, the legitimate traffic used 1 Mbps in the first scenario and 10 Mbps in the second scenario and was generated from one member located in each community.  
Malicious traffic was generated from endpoints located in isolated ports in both scenarios. In the first scenario, one attacker was emulated in each of the three LAN  
Figure 5: Scenarios of the emulated corporate network with PVLAN segmentation. Switch port key: community 1 (blue), community 2 (yellow), isolated (red), promiscuous (green), and trunks (dotted).  
segments, and the aggregate malicious traffic was progressively increased in a balanced manner by 15 Mbps (5 Mbps per attacker) between tests. In the second scenario, two attackers were emulated in \(S1\) and two attackers in S3 switches, and the aggregate malicious traffic was progressively increased in a balanced manner by 400 Mbps (100 Mbps per attacker) between tests.  
During the tests, the corporate network supported an aggregated background traffic of 8 Gbps exclusively at the access level in both scenarios. This traffic simulated a multimedia e-learning activity on the corporate network via multicast communications. It should be noted that the multicast traffic involved four members of the two communities, so this traffic was neither received nor sent through isolated ports. In addition, both scenarios were configured so multicast traffic was not sent through the promiscuous ports of the switches, so the network gateway (Figure 5a) or the aggregation switch (Figure 5b) never processed this type of traffic. The _ipref_ traffic injector was used [40] to generate the legitimate, and the malicious UDP traffic processed by the mitigation strategies and the multicast background traffic that emulated the underlying e-learning activity.  
Finally, several tests were performed in the router and the switches in both scenarios in order to measure several performance parameters such as CPU, memory usage, and power consumption. The round-trip delay time and packet loss were measured in a non-attacker user connected to an isolated port. Tests had a duration of five minutes and were repeated three times. Table 1 summarises the settings used during the experimentation.
# In Vivo Evaluation of Decellularized Human Tooth Scaffold for Dental Tissue Regeneration
## 4 Discussion
Although some of the mechanisms behind tooth development have not yet been identified, scaffolds can play an important role in tooth regeneration in addition to stem cells and growth factors [73]. The decellularized human tooth scaffolds used in this study, maintained structure with remaining collagen from both pulp and PDL scaffolds. Both types of scaffolds were recellularized when cultured in vivo after being inoculated with DPSCs and PDLSCs. These stem cells expressed genes related to pulp and PDL regeneration. Mesenchymal stem cells applied to decellularized human tooth scaffolds have the potential to open a novel approach to pulp and PDL regeneration through proliferation and differentiation. Therefore, a single scaffold could contribute to regenerate both PDL and pulp by recellularization of PDLSCs and DPSCs. This study has limitations in that it may show structural limitations due to the different area of PDL and pulp chamber size for each scaffold. The results exceeded the structural and functional limitations seen in scaffolds such as tissue substitutes, bioactive substances, and synthetic scaffolds used in the existing dental tissue regeneration studies. However, the process of restoring vitality of teeth by using damaged natural teeth as a scaffold still has many factors that can affect the results, such as the stem cell inoculation method and the degree of tooth damage, and further research is needed.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 5 Hydrogen Applications in End-Use Sectors
### Decarbonizing the Transport Sector  
In the road transportation sector, fuel cell electric vehicles (FCEVs) can expand the scope of electric mobility to high duty cycle segments, due to their driving range and refueling time being similar to conventional vehicles. Long-range and high-utilisation vehicles, where today's batteries face limitations, can find in hydrogen a green alternative to fossil fuels [99]. According to the IRENA and the Hydrogen Council, FCEVs and battery electric vehicles will collaborate in decarbonizing the road transport sector [99; 100].  
In addition to the possibility of being used in FCEVs, hydrogen can also be used to produce synthetic hydrocarbons, like methane, methanol, or diesel/petrol/jet fuel [1]. These fuels can be used to supply internal combustion engine vehicles instead of using fossil fuels, generating lower GHG emissions [101]. Hydrogen can, thus, work as a bridging technology between today's scenario, where the majority of the world's vehicle fleet is composed of internal combustion vehicles with a lifespan that can reach up to 15 years and which, consequently, needs to find an alternative to fossil fuels, and an ideal scenario where the entire world fleet would be composed by electric vehicles.  
When considering rail and maritime transportation, hydrogen can have a big impact on lowering these sectors' emissions. Hydrogen can be used to replace diesel trains on non-electrified lines. Fuel cells can be integrated into short-distance ships, such as ferries and shuttles, and liquefied hydrogen is seen as a potential option to reduce greenhouse gas emissions for long-distance ships [1]. In addition, hydrogen-based fuels, such as ammonia and methanol, can also significantly contribute to decarbonizing the maritime sector [102].  
In the aviation sector, fuel cells are being considered as a potential application for some propeller-driven regional aircraft and unmanned aerial vehicles [103]. For jet aircraft, which are difficult to electrify due to their high energy density demands, sustainable aviation fuels (SAFs) offer a promising pathway to decarbonize the sector. SAFs can replace conventional aviation fuels while significantly reducing GHG emissions. SAFs include both biofuels and power-to-liquid fuels [104]. While biofuels present challenges relatedto biomass availability, power-to-liquid fuels are produced using a mixture of CO2 and H2. The produced e-fuels present greater volumetric density than H2, remaining in the liquid state under standard conditions, and can be used as a drop-in fuel in existing aircraft engines [105].
## 2 Results and Discussion
### Signal Processing and Chemometric Workflow
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Single-Note Model Performance**} & \multicolumn{3}{c}{**Multi-Note Model Performance**} \\ \hline _Sensory Note_ & _Model Factors_ & _R2 val_ & _RMSEV_ & _RMSEP_ & _Model Factors_ & _R2 val_ & _RMSEV_ & _RMSEP_ \\ \hline _Acid_ & 3 & 0.663 & 1.129 & 0.946 & 3 & 0.856 & 0.726 & 1.192 \\ _Bitter_ & 4 & 0.817 & 1.142 & 1.063 & 4 & 0.936 & 0.626 & 1.315 \\ _Woody_ & 4 & 0.669 & 1.570 & 1.725 & 4 & 0.884 & 1.003 & 2.306 \\ _Flourery_ & 4 & 0.746 & 1.038 & 1.345 & 4 & 0.907 & 0.651 & 1.964 \\ _Fruity_ & 4 & 0.661 & 1.026 & 1.499 & 2 & 0.790 & 0.785 & 1.598 \\ _Spicy_ & 1 & 0.792 & 0.963 & 1.209 & 3 & 0.784 & 0.977 & 1.194 \\ _Nutty_ & 6 & 0.544 & 1.506 & 1.661 & 4 & 0.893 & 0.864 & 1.891 \\ _Aroma intensity_ & 1 & 0.557 & 0.936 & 1.296 & 4 & 0.764 & 0.627 & 1.642 \\ _Overall quality_ & 4 & 0.556 & 0.936 & 2.120 & 4 & 0.756 & 0.726 & 2.239 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multi-note model performance summary. Figure 7: Comparison of the measured sensory profiles (from the panel) and predicted sensory profiles, from the developed model, of a selection of external test set samples. Sensory and chemical data were pre-processed using pareto scaling.
###### Abstract  
3D heritage documentation has seen a surge in the past decade due to developments in reality-based 3D recording techniques. Several methods such as photogrammetry and laser scanning are becoming ubiquitous amongst architects, archaeologists, surveyors, and conservators. The main result of these methods is a 3D representation of the object in the form of point clouds. However, a solely geometric point cloud is often insufficient for further analysis, monitoring, and model predicting of the heritage object. The semantic annotation of point clouds remains an interesting research topic since traditionally it requires manual labeling and therefore a lot of time and resources. This paper proposes an automated pipeline to segment and classify multi-scalar point clouds in the case of heritage object. This is done in order to perform multi-level segmentation from the scale of a historical neighborhood up until that of architectural elements, specifically pillars and beams. The proposed workflow involves an algorithmic approach in the form of a toolbox which includes various functions covering the semantic segmentation of large point clouds into smaller, more manageable and semantically labeled clusters. The first part of the workflow will explain the segmentation and semantic labeling of heritage complexes into individual buildings, while a second part will discuss the use of the same toolbox to segment the resulting buildings further into architectural elements. The toolbox was tested on several historical buildings and showed promising results. The ultimate intention of the project is to help the manual point cloud labeling, especially when confronted with the large training data requirements of machine learning-based algorithms.  
heritage; 3D documentation; point cloud; automation; segmentation; classification; GIS 10.3390/s20082161 [1][[https://doi.org/10.3390/s20082161](https://doi.org/10.3390/s20082161)]([https://doi.org/10.3390/s20082161](https://doi.org/10.3390/s20082161))
# Security Analysis of the MQTT-SN Protocol for the Internet of Things
## 5 MQTT-SN Attacks and Performance Evaluation
### Disconnect Wave  
Disconnect wave is a DoS attack that exploits a big failing in the MQTT-SN protocol. The specification of MQTT-SN indicates that each client has a unique _Id_, so if a new client tries to register this _Id_ again, the latter gains the _Id_ and ejects the previously created connection. Once we know the _Id_, the attack can be carried out immediately. The _Id_ field has a variable length between 1 and 23 characters, and it is sent by the client when making the connection. This means that if a bad security policy is used by the client application, it is possible to guess the _Id_. In our application, this _Id_ is a device counter to ensure that it is unique.  
Figure 10 shows the format of the _connect_ packets used to steal the _Id_ of legitimate clients and removethem from the system. In this case, the _client Id_ must be modified depending on the targeted client.  
As can be seen in Figure 10(a), the malicious device connects to the _Id_s of legitimate devices to eject them. To achieve this, it iterates over the _Id_s of the legitimate devices. If guessing this _Id_ is not trivial, it is possible to generate an MitM scheme to know the identifiers of the legitimate devices. Figure 10(b) shows the result of the attack, and as we can see, the legitimate devices are expelled.  
Evaluation of the Disconnect Wave Attack  
As is shown in Figure 11(a), the broker receives a flood of connect commands, but it does not send any publish packets. The attacker sends connect packets iterating over the _Id_ to expel legitimate devices whose _Id_ matches those of these packets. Therefore, we have many _connect ack_ packets and zero _publish message_ packets, which can ultimately completely override the network.  
Even if the attacker performs this attack successfully, as we can see in Figure 11(b), legitimate devices keep sending messages. This attack can be stopped with a rules-based IDS, which detects that the network is flooded with connect packets. This attack is an innovation introduced in this work, so it can be difficult to find rules if you want to use a rule-based threat detector, as is the case with other novel attacks due to the lack of knowledge about them.  
Figure 11: MQTT-SN disconnect wave scheme. (**a**) MQTT-SN disconnect wave, implementation of the attack. (**b**) MQTT-SN disconnect wave, result of the attack.  
Figure 10: MQTT-SN connect packet format.
## 5 Variation in Squalene Content of Oil from Unconventional Plant Sources  
Among the studied oil sources squalene was determined only in 14 kinds of seed oils (Table 6). The lowest content (only traces) was found in strawberry oil, and the highest in Japanese quince (up to 1497.3 mg/100 g). In one study [180] squalene content was even higher and was 22.72% of total oil, but this result is highly uncertain and should be confirmed in other studies. Relatively high squalene contents were also noted in basil, cherry, pear, and plum seed oils. Unfortunately, these results are often variable. For example, in oils from Japanese quince seeds, the presented results are in the range from 0.07 or 67 mg/100 g (various units in Table and text) to 1497.3 mg/100 g (interestingly, extreme results are obtained by the same analytical method). None of the tested oils (except the questionable one from cucumber seeds) were as rich in squalene as amaranth oil.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Species** & **Content** & **Ref.** \\ \hline \hline \multicolumn{3}{l}{Fruits} \\ \hline \hline \multirow{2}{*}{apple} & 1.0–34.0 & [97] \\  & 26.0 & [92] \\ \hline \hline \multirow{2}{*}{apricot} & 12.6–43.9 & [171] \\  & 9.0 & [189] \\ \hline \hline \multicolumn{3}{l}{blackberry} & 17.0 & [197] \\ \hline \hline \multicolumn{3}{l}{blueberry} & 178.1 & [197] \\ \hline \hline \multirow{2}{*}{cherry} & 60.9–127.7 & [93] \\  & 65.8–102.8 & [96] \\ \hline \hline \multirow{3}{*}{Japanese quince} & 33.0 & [92] \\  & 1032.0–1433.0 & [143] \\  & 0.07 or 67.0 *** & [99] \\  & 1035.9–1497.3 & [177] \\ \hline \hline \multicolumn{3}{l}{pear} & 25.5–40.8 & [95] \\ \hline \hline \multicolumn{3}{l}{plum} & 26.2–80.4 & [94] \\ \multicolumn{3}{l}{raspberry} & 8.4 & [197] \\ \hline \hline \multicolumn{3}{l}{strawberry} & trace & [197] \\ \hline \hline \multicolumn{3}{l}{Vegetables} \\ \hline \hline \multicolumn{3}{l}{broccoli} & 41.9 * & [209] \\ \hline \hline \multirow{2}{*}{cabbage} & 202.7 * & [209] \\  & 26.8 * & [209] \\ \hline \hline \multirow{2}{*}{cucumber} & 22.7 & [180] \\  & 89.1 * & [209] \\ \hline \hline \multirow{2}{*}{adish} & 64.8 * & [209] \\  & 75 * & [209] \\ \hline \hline \multirow{2}{*}{tomato} & 300 ** & [125] \\  & 10.2 & [101] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Squalene content of oils from unconventional plant sources (mg/100 g of oil).
## 3 Results and Discussion  
### SEM Surface Analysis  
The surface and cross-section of the uncoated paper and PHBV-coated paper samples obtained in step 1 were analyzed by means of scanning electron microscopy (SEM). The uncoated paper sheets show a typical fiber network and a flat surface due to the calendering treatment (Figure 1a); PHBV coatings (Figure 1a-d) determined the filling of void space of the network; however, the surface was characterized by the presence of lumps, whose dimensions and density seemed to increase both in double layer coatings and in single layer coating of 80 \(\upmu\)m at 50 mm/s (Figure 1c,d).  
Other authors [23; 31] characterized PHBV paper coatings by SEM, but the different techniques, polymers and base support used do not allow a reliable comparison of results.  
A second set of tests was conducted by changing the coating preparation conditions, obtaining an improvement of the solubilization. The PHBV solutions appeared more homogeneous and transparent compared to the ones obtained in the first set; PCL completely solubilized in chloroform and the solutions were more transparent and homogeneous than PHBV ones.  
The new coated samples obtained were also observed by SEM and a significant improvement of surface homogeneity was noticed, especially in samples with 10% and  
Figure 1: (**a**) Uncoated paper (calendered side) (UCP); (**b**) PHBV 80 \(\upmu\)m single layer at 80 mm/s (8080); (**c**) PHBV 80 \(\upmu\)m double layer at 80 mm/s (804); and (**d**) 80 \(\upmu\)m single layer at 50 mm/s (80850). See Table 1 for coating conditions.  
20% PEG. The comparison of PHBV-coated paper samples obtained in the first and second step allows to assume the effectiveness of the optimized solubilization strategy and the role of PEG in improving coating distribution and spreadability (Figure 2a,c).  
PCL coatings show a characteristic surface, more regular and homogeneous compared to PHBV ones, although several discontinuities in the coatings were observed, but it is not clear if they are pores or simple depressions in the coating. The presence of pores in PCL coatings (of the same type used in this work, but dissolved in ethylacetate at 10%) on paperboard, was observed in a previous study too [32] and this structural characteristic was related to a lack of interaction between coating and base support. The addition of plasticizer seemed to progressively mitigate this phenomenon (Figure 2d-f). In both PHBV with PEG at 10% and PCL with PEG at 10% and 20% coatings (shown at 1500\(\times\) magnification for this reason), a surface presenting bubbles (PHBV) and sponge-like structure (PCL) was observed. The fast evaporation of the solvent in combination with the plasticizer could justify this particular structure (Figure 2b,e).  
The SEM images of the cross section in Figure 3, demonstrate that coating layer and base paper are not clearly distinguishable.  
Figure 2: **(a)** PHBV without PEG; **(b)** PHBV with PEG 10%; **(c)** PHBV with PEG 20%; **(d)** PCL without PEG; **(e)** PCL with PEG 10%; and **(f)** PCL with PEG 20%.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 2 Methods
### Construction of the Micro-Capsule Networks
#### 2.2.4 Integrated Optimization Algorithm of Micro-Capsule Networks  
To enhance the detection accuracy of epileptic EEG signals using the micro-capsule network model, these signals are designated as low-level routing nodes, while the overall objective is designated as a high-level routing node. After optimizing the weights, the model links the low-level routing nodes to their corresponding high-level routing nodes [27]. This process is implemented through a dynamic routing protocol.  
To improve the epileptic recognition rate, the proposed model can further optimize and detect different epileptic tasks through a dynamic routing protocol. The corresponding parameters and formulas are detailed in the literature [27; 35], and the routing algorithm from the following aspects:  
(1) The input EEG eigenvector \(u\) is multiplied by a different weight matrix \(W\) to generate the output of capsule networks \(U_{j|i}\); (2) calculate all coupling coefficients \(C_{ij}\) based on softmax function; (3) the EEG eigenvector \(U_{j|i}\) is multiplied by coupling coefficient \(C_{ij}\) to obtain the weighted vector. The vector \(s_{j}\) can be obtained by summing weighted vectors of all capsule neurons in the next layer; (4) the square compression activation function is used to compress weighted sum \(s_{j}\) to obtain updated capsule neural unit; (5) after several iterations, the output vector of high-level capsule networks can be calculated and the corresponding weight \(B_{ij}\) can be updated;(6) after weight \(B_{ij}\) is updated, the algorithm returns to start calculation \(C_{ij}\) and repeats \(r\) times until the training model is obtained; (7) the individual margin loss function \(L_{k}\) is used to calculate the margin loss value of each category, and total loss value \(Loss\) is obtained by the cumulative method. If the total loss value \(Loss\) is less than threshold \(L_{th}\), the output layer corresponds to the detection of epileptic seizures. Otherwise, the algorithm continues to optimize the weight coefficient \(W\).
## 4 Discussion
### Impact of Human Activities on the MODEI  
#### 4.2.1 Changes in the Grazing Pressure Index  
For Fuyun County, grassland is the primary ecosystem type in this region, and grazing is the most basic utilization mode of grassland. It is also a major factor among human activities disturbing grassland [66]. Therefore, our study analyzed the grazing pressure index of grassland from 2001 to 2018.  
From 2001 to 2008, the average values of the GPI in summer pasture, spring and autumn pasture, and winter pasture were 1.13, 2.90, and 1.74 from 2001 to 2008 and 0.93, 2.95, and 2.03 between 2009 and 2018, respectively. The GPI of summer pastures, spring and autumn pastures, and winter pastures showed an upward trend from 2001 to 2008 and reached the maximum in 2008 (Figure 12). As shown in Figure 12c, since 2008, the grassland GPI of summer pastures has shown a fluctuating downward trend. Therefore, the improvement of the MODEL in summer grassland may be related to the fluctuating decrease in the GPI after 2008. In addition, the improvement of the MODEL in summer pasture was possibly related to the grassland ecological protection engineering measures implemented in recent years [67]. From 2009-2018, the slopes of the GPI of spring and  
Figure 11: Spatial distribution map of the changes in average temperature and annual precipitation from 2008 to 2018.  
autumn pastures and winter pastures were positive, showing an increasing trend, especially in spring and autumn pastures. Therefore, the rising grazing pressure may be the main reason for the deterioration of the ecological quality in spring and autumn pastures and winter pastures. [PERSON] et al. (2018) also pointed out that grazing was the main driving force of grassland changes caused by human activities.  
The statistical results in Table 6 show that the area where the MODEI of spring and autumn pastures declined reached 4225.22 km\({}^{2}\), and the area where the MODEI of winter pastures decreased was 1305.86 km\({}^{2}\). The area of MODEI decline of winter pasture was much smaller than that of spring and autumn pasture. The reasons are as follows: firstly, the utilization period of spring and autumn pastures is the crucial period for forage germination in spring and seed set in autumn, and the excessive utilization intensity affects the growth and reproduction of herbage. Secondly, Figure 12c shows that although the GPI of winter pasture showed an upward trend from 2009 to 2018, the trend was not apparent. In addition, a survey of herders showed that after 2010, the number of herders grazing in winter decreased, and most herders implemented captive breeding in winter ([PERSON] et al., 2018). This measure also controlled the deterioration of the ecological quality of winter pastures.