esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 2 Background
### Deep-Learning-Based IDS  
Learning approaches are the most effective way to produce IDS models because of their capability to automate the process, design a viable detection system, and reduce the amount of work required from individuals [19]. For example, ML and DL are self-learning anomaly-based IDS (AIDS). Lately, many researchers are considering DL techniques more efficient than ML techniques, especially when involving a significant volume of data. For example, nowadays, it is not difficult to find DL techniques utilized by researchers in various fields, such as speech recognition, visual information processing, and autonomous driving [9; 20]. AIDS is scalable in nature due to its ability to integrate new detection techniques, including, but not limited to, DL techniques, making it a powerful tool in detecting new and unknown attacks, such as ICMPv6 flooding DDoS attacks.  
ML-based intrusion detection is an outdated method for detecting abnormal network behavior, and it has received many criticisms for low throughput and high false-positive rates. According to [PERSON]'s [9] survey of intrusion detection methods, traditional ML-based detection techniques lag behind deep networks. The DL algorithm used to train the layers of hierarchical networks was based on how the human brain learns. Therefore, it is greedy and susceptible, similar to how the brain learns. Since the discovery of deep networks, DL principles have been used to develop other techniques [21]. Therefore, this research paper utilizes the three most commonly employed algorithms in IDS (recurrent neural networks (RNNs), long short-term memory networks (LSTM), and gated recurrent unit (GRU)-based DL techniques) for detecting ICMPv6 flooding DDoS attacks. The following sections discuss these algorithms in more detail.
# Green Strategies for Molecularly Imprinted Polymer Development
## Green Strategies in MIP Development
### Ionic Liquids (ILs) and Deep Eutectic Solvents (DES)
#### 2.2.5 Deep Eutectic Solvents (DESs)  
DESs are commonly designated as fourth generation ILs, which are basically mixtures of salts, that are obtained by the complexation between a hydrogen acceptor, such as nontoxic quaternary ammonium salt, choline chloride, and naturally-derived uncharged hydrogen bond donors, as alcohols, carboxylic acids, amides, amines, urea, and glycerol. DES can also be made by compounds that are non-ionic, which is obviously an advantage over the conventional ILs. The main advantages are low cost, biodegradability, no need of additional purification, and the low toxicity of anions and cations used [76; 77].  
[PERSON] et al. [78] reported the use of a DES as an auxiliary solvent to enhance the affinity and selectivity of MIPs towards chlorogenic acid (CA). DES was synthetized using choline chloride (organic cation) and glycerol (inorganic) (1:2 molar ratio). DES-NIPs and NIP and MIP without DES were prepared for comparison at identical conditions. The DES-MIPs exhibited the most promising SPE recoveries of CA, 72.56% compared with DES-NIPs (64.79%), MIPs (69.34%), and NIPs (60.08%), respectively, for the purification of chlorogenic acid from honeysuckle.  
In addition, [PERSON] and co-workers [79] explored the application of magnetic DES as a new type of functional monomer in molecular imprinted technology. DES was synthetized using choline chloride and methacrylate acid (1:2 molar ratio), and the magnetic ability was provided by Fe\({}_{3}\)O\({}_{4}\). These affinity materials were designed to separate proteins using BHb as template molecule. The magnetic DES-MIPs could adsorb 184.10 mg\(\cdot\)g\({}^{-1}\) of BHb compared with analogue molecules such as Lys (101.97 mg\(\cdot\)g\({}^{-1}\)), BSA (87.29 mg\(\cdot\)g\({}^{-1}\)), and ovalbumin (18.44 mg\(\cdot\)g\({}^{-1}\)), respectively.
## 3 Materials and Methods
### M-G Encoder  
The M-G encoder can integrate information within and across different granularities. In the constructed heterogeneous hypergraph, the target node can be a neighbor to nodes with different granularities. Obviously, nodes within the same granularity contain the same semantic information, while nodes between different granularities contain different semantic information.  
Information aggregation within a granularity. For target nodes, information aggregation within a granularity focuses on the importance of neighboring nodes. \(v^{id}\in R^{d}\) is the target node embedded with the granularity item id. We denote a target node with its neighboring nodes of a specific granularity (\(t\)) as \(N_{id}^{t}\). First assume that the target formed by the neighboring nodes of a specific granularity (\(p\)) is \(N_{id}^{p}\), as in Equations (7) and (8), where \(v_{i}^{p}\in N_{id}^{t}\), \(u_{t}^{T}\in R^{d}\) is the attention vector. For different granularity embeddings for each target node, the granularity information is extracted by applying different attention vectors (\(u_{t}^{T}\)) for better model learning. \(\theta_{id}^{p}\in R^{d}\) is the granularity embedding of \(v^{id}\) with respect to the granularity \(p\). Regarding the target node item id, the embedding of category information \(\theta_{id}^{c}\) is similar to the embedding of price information.  
\[\theta_{id}^{p}=\sum_{i}\alpha_{i}v_{i}^{p} \tag{7}\]  
\[\alpha_{i}=\frac{\exp(u_{t}v_{i}^{p})}{\sum_{\alpha_{i}^{p}\in N_{id}^{p}} \exp(u_{t}v_{i}^{p})} \tag{8}\]  
Inter-granularity information aggregation. For the target node, inter-granularity information aggregation focuses on the impact of different granularities. Information with different granularity provides different semantic information to the target node, we merge the learned category information \(\theta_{id}^{c}\) and price information \(\theta_{id}^{p}\), which is then propagated to the target node, as in Equations (9) and (10), where \(W_{id}\in R^{d*3d}\), \(W_{id}^{p}\) and \(W_{id}^{c}\in R^{d*d}\) are learnable parameters, \([;]\) is a cascade, and \(\sigma\) is a sigmoid function. \(A\in R^{d}\) is an embedding of the target node whose semantics are enriched by its neighboring nodes.  
\[\omega=\sigma(W_{id}[v^{id};\theta_{id}^{p};\theta_{id}^{c}]+W_{id}^{p}\theta _{id}^{p}+W_{id}^{c}\theta_{id}^{c}) \tag{9}\]  
\[A=v^{id}+\omega*\theta_{id}^{p}+(1-\omega)*\theta_{id}^{c} \tag{10}\]  
By using intra- and inter-granularity aggregation mechanisms, the embeddings of all item id nodes are updated: \(\gamma_{i}=f_{b}(v^{id},\theta_{id}^{p},\theta_{id}^{c})+avg(N_{id}^{id})\), where \(f_{b}\) is the aggregation mechanism, \(N_{id}^{id}\) consists of id nodes adjacent to \(v^{id}\), and \(avg(N_{id}^{id})\) computes the average id embedding in \(N_{id}^{id}\).
## 4 Vibration Signal Analysis
### Comparison of EMD and EEMD Method  
The modal frequencies of the harvester under operating conditions identified by the SSI algorithm are shown in Table 3. The modal frequencies of the corn harvester are mainly distributed between 21 and 158 Hz.  
Figure 8: The frequency of harvester under different mass values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order** & **Modal Value (Hz)** & **Order** & **Modal Value (Hz)** \\ \hline
1 & 21.924 & 9 & 98.090 \\
2 & 29.578 & 10 & 109.345 \\
3 & 39.247 & 11 & 117.355 \\
4 & 45.229 & 12 & 122.489 \\
5 & 54.385 & 13 & 132.451 \\
6 & 59.303 & 14 & 141.396 \\
7 & 64.123 & 15 & 150.583 \\
8 & 77.930 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modal values identified by the SSI method.  
The 15 main frequencies of the 16 test points extracted from the EMD and EEMD results are compared with the modal frequencies calculated by SSI, as shown in Figure 9. The EMD and EEMD lost some frequencies between 100 and 140 Hz (order 9-13 of modal frequencies). Frequency components such as 24 Hz, 26 Hz, 37 Hz, 75 Hz, 82 Hz, and 94 Hz exist in the EMD and EEMD results, but do not exist in modal frequencies. These errors may be caused by the coupling system.  
Using the method of least squares to fit the modal frequencies identified by the SSI algorithm, the main frequencies were extracted from the EMD result and the EEMD result for six times, respectively. Finally, the frequency fitting curves of the three methods are obtained, as shown in Figure 10.  
Figure 9: Comparison of the main frequencies extracted from the EMD and EEMD results and modal frequencies identified by SSI: **(a)** Comparison of EMD and SSI. **(b)** Comparison of EEMD and SSI.  
The fitting equation of modal frequencies identified by the SSI algorithm is:  
\(y_{1}=3.22\times 10^{-4}\times x^{6}-1.28\times 10^{-2}\times x^{5}+1.77\times x ^{4}-0.93\times x^{3}+1.19\times x^{2}+10.54\times x+10.33\)  
The fitting equation of main frequency extracted by EMD is:  
\(y_{2}=-\)\(5.38\times 10^{-4}\times x^{6}+2.35\times 10^{-2}\times x^{5}-0.39\times x^{4}+3.13 \times x^{3}-12.29\times x^{2}+27.04\times x+4.87\)  
The fitting equation of main frequency extracted by EEMD is:  
\(y_{3}=-\)\(4.68\times 10^{-5}\times x^{6}+2.42\times 10^{-4}\times x^{5}+2.51\times 10^{-2} \times x^{4}-0.38\times x^{3}+2.20\times x^{2}-0.49\times x+20.61\)  
Using the Euler distance, we evaluated the fitting degrees of the three curves. The value of the Euler distance between the EMD fitting equation and the SSI algorithm fitting equation is 446.7883, and the value of the Euler distance between the EEMD fitting equation and the SSI algorithm fitting equation is 417.2845. According to the principle that the smaller Euler distance, the better the correlation between two curves, the comparison shows that the EEMD fitting equation fits the SSI algorithm fitting equation better. Therefore, EEMD is more suitable for estimating the modal frequency of harvesters. This method can be used to estimate the modal frequency of mass time-varying conditions, such as harvesters, seeders and fertilizer applicators.
# Research on Sentiment Analysis of Online Public Opinion Based on RoBERTa-BiLSTM-Attention Model
## 5 Results
### Sentiment Evolution Analysis  
Figure 4 shows the full-cycle evolution of public opinion in different emotional categories, with the y-axis showing the percentage of that category on the same day. The trend shows the percentage change in the public's negative, neutral, and positive sentiment tendencies from 1 June 2023 to 25 June 2023. The overall analysis shows that netizens' emotions fluctuated greatly, and their emotional reflections were strong and variable. Among them, the overall trend of negative sentiment fluctuated down, positive sentiment fluctuated up, and neutral sentiment fluctuated slightly up.  
This study also analysed the trend of sentiment evolution based on important event points in the development of public opinion, identifying five important time periods: A, B, C, D, and E (orange blocks in the graph). In stage A (3-4 June), the proportion of negative sentiment increased significantly from 35% to 44% and the proportion of positive sentiment decreased from 36% to 19%. At this time, the statement issued by the school and the Municipal Supervisory Bureau was contrary to the facts and did not persuade the public but caused the spread of negative public opinion, which resulted in a significant increase in the proportion of negative sentiment. In time period B (6-8 June), positive sentiment rose from 22.18% to 23.1% and then dropped back to 22.51%, showing a slight increase in the percentage, but it was not long-lasting. This was because an animal expert made a statement on 6 June saying that the foreign substance was a rat's head rather than a duck's neck, which supported the public's stance and suppressed the spread of negative public opinion to a certain extent, but not for a long time. In period C (10-12 June), the proportion of neutral sentiment increased from 36.41% to 44.08% and the proportion of positive sentiment increased slightly from 21.66% to 22.43%. This was because the establishment of the joint investigation team prevented negative public opinion from spreading in an uncontrolled manner, but the proportion of positive sentiment was not high, indicating that announcements of regulatory developments could calm the public, but an announcement of the investigation results was needed to resolve the negative public opinion. In period D (16-17 June), there was a small peak in the proportion of negative sentiment and a small trough in the proportion of positive sentiment. This may have been due to the climb in negative public opinion caused by the long period of time it took for the survey results to be announced. However, after the results were announced, the proportion of negative sentiment continued to fall and the proportion of positive sentiment continued to rise, indicating that the authoritative survey results prevented the spread of negative public opinion. In period E (20-21 June), the secondary opinion (food safety issues in other universities) caused negative sentiment to rise again to 44%, but this only lasted for one day and had a small impact.
### Overview of Road Network Pricing for Travel Demand Management  
In the context of travel demand management, road network pricing refers to wide-ranging strategies aimed at managing private vehicle travel, reducing road congestion, and enhancing performance of the road network (Table 2). These strategies are primarily aimed at managing travel demand on public roads by discouraging the use of low occupancy private vehicles, influencing travel behaviour, and shifting travellers away from private vehicles and towards more sustainable modes of transport. They are normally deployed at a number of spatial levels at a certain road facility, cordon or zone, or with distance-based pricing. Similarly, they can be deployed at a number of temporal levels, including strategies for fixed pricing, time-of-day pricing, or dynamic pricing, as shown in Table 2. These strategies, when considered as part of a holistic approach, would result in positive impacts, including reduced congestion and emissions [14; 15].  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Strategy** & **Description** \\ \hline \multirow{4}{*}{**Road pricing**} & Strategies for managing travel demand and reforming transport taxation by charging directly for use of public roads rather than relying on vehicle registration fees or fuel excise. These strategies aim to provide equitable and transparent costing of trips such that the costs are directly related to the amount of travel [16]. \\ \cline{2-3}  & Congestion pricing (also known as congestion charging) aims to reduce traffic congestion in specific areas such as central business districts. Users who drive into these locations during certain times of the day (e.g., peak hours) are charged either a fixed fee or a dynamic charge according to the time of day or congestion levels [17]. \\ \hline \multirow{4}{*}{**Parking pricing**} & Parking pricing refers to a number of strategies for managing the demand for parking in congested locations through static or variable charges according to available supply and expected demand [18]. \\ \cline{2-3}  & Similar to congestion pricing, but the charges are triggered by degree of pollution in the area, rather than according to the degree of traffic congestion. In this scheme, low-emission vehicles such as electric or hybrid vehicles would be exempt to encourage uptake of low carbon/low emission vehicles [19]. \\ \hline \multirow{2}{*}{**High emitting vehicle pricing**} & This type of pricing targets vehicles that have high emissions (e.g., freight vehicles) particularly when they enter congested or polluted areas. The charges applied to these vehicles would be similar to congestion pricing [20]. \\ \hline \multirow{2}{*}{**Freight pricing**} & This type of road pricing applies directly to freight vehicles and could include charges per entry into a certain area, or entry to a road facility or bridge, or can be applied per kilometre of travel [21]. \\ \cline{2-3}  & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Strategies for road network pricing for travel demand management.
# Capturing a Space Target Using a Flexible Space Robot
###### Abstract  
Capturing space targets by space robots is significant for on-orbit service and is a challenging research topic nowadays. This paper focuses on the dynamics and control of capturing a non-cooperative space target by a space robot with a long flexible manipulator. Firstly, the dynamic equation of the flexible space robot is given. The Hertz contact model is used to describe the contact force between the robot and the target. Secondly, an active compliance controller is designed to reduce the capture impact on the robot. Finally, the capture impact on the whole system is analyzed in detail in four scenarios: the combination of two kinds of movement forms and two kinds of relative positions of the robot and the target before capturing. Simulation results indicate that the capturing operation may cause complicated dynamic behaviors such as the vibration of elastic links and the continuous collision of the target. Moreover, the results show that the control method effectively offsets the capture impact on the space robot system. In general, this work lays a theoretical foundation for further study of the dynamic phenomena of the capture process.  
flexible space robot; space target; capture; impact; control +
Footnote †: journal:  
ccepted: 27 December 2021  
Published: 19 January 2022  
**Keywords:** flexible space robot; space target; capture; impact; control
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 3 Proposed Method
### Object Association
#### 3.2.2 Triplet-Based Association Network with Attention
In the tracking process, feature vectors are extracted from the image information of the objects using the embedding network trained with the triplet loss. To compare the feature vectors of existing tracks with those of the objects in the current frame, the feature vectors of existing tracks were used as arithmetic means because the existing tracks consist of images extracted from multiple frames, some of which may have occlusions or unclear appearances. To take comprehensive information into account, the arithmetic mean of the feature vectors extracted from the images on the track was adopted. This approach can also be interpreted in terms of the characteristics of metric learning because the arithmetic mean of a particular class vector represents the information that best represents that class. The parameter \(\bar{r}_{i}\) is computed as follows:  
\[\bar{r}_{i}=\frac{1}{L}\sum_{k=1}^{L}r_{i}^{k}, \tag{5}\]  
where \(L\) is the number of times a track has been detected and \(r_{i}^{k}\) is a feature vector extracted from the detected image. Subsequently, we computed the detected results and L2-norm distance in the current frame. Figure 9 shows the process of computing an object's appearance distance as follows:  
Figure 6.0
## 4 Results and Discussion
### Intercalibration of Turbidimeters  
For the intercalibration analysis, the turbidity data, averaged from three consecutive measurements (one every ten minutes), from the fixed stations were compared with the mean turbidity measured in the 1-2 m depth-range from CTD probe profiles, acquired in their proximity. A total of 29 and 19 match-ups, respectively, were considered for TRP and SN. The correlations were statistically significant with a coefficient of determination R\({}^{2}\) of 0.98 and 0.96 for the instruments located at TRP and SN, respectively (Figure 2). A good correlation was found throughout the whole sampled range, even if most of the data pairs were localized in the lower part of the calibration range. Suspended sediments in the water column of the Lido inlet are, in fact, typically low and values above 20 FNU are quite uncommon in calm meteo-marine conditions.
## 3 Our Proposed Method
### Region Contrast via LTRSH Comparison  
After applying the above procedure, we obtain the region contrast via the LTSCH comparison. For a superpixel, we first define its contrast with the others, as in Equation (3).  
\[\mathit{CP}_{\mathit{SP}}\left(\mathit{SP}_{1},\mathit{SP}_{2}\right)=\left( \sum_{i=1}^{n1}\sum_{j=1}^{n2}f(t_{i,j})f(t_{2,j})D(t_{i,j},t_{2,j})\right)+ \left(\sum_{i=1}^{n1}\sum_{j=1}^{n2}f(c_{i,j})f(c_{2,j})D(c_{1,j},c_{2,j})\right) \tag{3}\]  
where \(\mathit{CP}_{\mathit{SP}}\left(\mathit{SP}_{1},\mathit{SP}_{2}\right)\) is the contrast between superpixel \(\mathit{SP}_{1}\) and \(\mathit{SP}_{2}\), \(f(t_{m_{i}})\) is the probability of the \(i\)-th color \(t_{m_{i}}\) among all \(N_{m}\) colors, which represent the local texture information in the superpixels \(\mathit{SP}_{k},k=\left\{1,2\right\}\), and \(D(t_{m_{i}},t_{n_{i}})\) is the distance between that two color categories. \(f(c_{m_{i}})\) and \(D(c_{m_{i}},c_{n_{i}})\) are the same as RC [8,9] method. Then, we further incorporate the superpixel's spatial information to weight the region contrast. In this way, the region saliency is expressed as Equation (4).  
\[\mathit{Sal}_{R}(\mathit{SP}_{i})=\sum_{\mathit{S}_{i}\
eq\mathit{SP}_{j}} \exp\left(-\frac{D_{s}\left(\mathit{SP}_{i},\mathit{SP}_{j}\right)}{\sigma^{2} }\right)\mathit{N}(\mathit{SP}_{j})\mathit{CP}_{\mathit{SP}_{i}}\left(\mathit{ SP}_{i},\mathit{SP}_{j}\right) \tag{4}\]  
In Equation (4), \(D_{s}(\mathit{SP}_{i},\mathit{SP}_{j})\) is the spatial information of the superpixels, which is normalized to between 0 and 1, and \(\sigma^{2}\) is given as 0.4. Here, \(\mathit{N}(\mathit{SP}_{j})\) is the number of pixels in the superpixel \(\mathit{SP}_{j}\) region, which can be used to emphasize the region's contrast compared to the larger region.
## 4 Design Basis  
The first step is to know the existing conditions in three locations, considering the most relevant data for the study. That includes: section drawings and characteristics of the breakwaters where the wave energy converters will be installed, bathymetry, geology and geotechnics, seismicity, metocean conditions as waves and tides, etc. As can be seen in Figure 5, Punta Langosteira is a rubble mound breakwater with concrete blocks in the armor layer around 150 tons [9], with the top of the non-overtopping crown wall at +25.00 m. Torres is a rubble mound breakwater with concrete pieces in the armor layer around 90 tons, with the top of the non-overtopping crown wall at +17.50 m. Norte is a vertical breakwater with 32 \(\times\) 52 \(\times\) 32 m caissons, with the top of the crown wall at the same height as Torres breakwater. Punta Lucero is a rubble mound breakwater with concrete blocks in the armor layer around 150 tons, with the top of the non-overtopping crown wall at +21.00 m [10]. The rest of characteristics of the four breakwaters can be seen in Table 1.  
Figure 5: _Cont_. Regarding the water depths in the breakwaters, it is 39 m in Punta Langosteira, 22 m in Torres, 30 m in Norte and 33 m in Punta Lucero. In terms of geology and geotechnics, Punta Langosteira is in a granite terrain, with a density of 2.6 t/m\({}^{3}\), compressive strength of 10,000 t/m\({}^{2}\) and deformation modulus of 20,000 MPa; Torres and Norte is a in a quartzite soil, with a density of 2.6 t/m\({}^{3}\), compressive strength of 10,000 t/m\({}^{2}\) and deformation modulus of 20,000 MPa; Punta Lucero is in a soil composed of sand above a cayuela rock (limestone type), with a density of 2.4 t/m\({}^{3}\), compressive strength of 5000 t/m\({}^{2}\) and deformation modulus of 10,000 MPa. Seismic analysis has been carried out according to the NCSE02 (Spanish Seismic Building Code 2002). The North coastal facade presents a seismic acceleration lower than 0.04 g (g is gravity acceleration) (see Figure 6). According to that Technical Standard, normal or special seismic considerations have to be taken into account in the structural design when seismic acceleration