esa-sceva/satcom-chunk-collection huggingface dataset:
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 5 LoRaCog Protocol
### LoRaCog Gateway  
The GW will have CR capability; it will scan the spectrum continuously for holes and identify the possible channels to be used in the licensed spectrum. In addition, it is able to communicate on different spectrum channels supported by required hardware. The spectrum sensing is assigned to the GWs and not to the EDs, since the former are usually main-powered. Thus, there is no energy constraint related to this additional operation. Moreover, the GWs are installed at high altitude, providing them with good communication conditions with PUs. This kind of installation is not usually ensured for the EDs.  
The sensing results will be forwarded from the GWs to the NS, where the latter will decide which channels to be forwarded to EDs and when to match the RX2 window, respectively. Each GW will receive its related instructions to be forwarded to the EDs.  
The transmission from GW should use a default channel which is known and fixed for all EDs. Since the purpose of this protocol is to reduce unlicensed spectrum congestion, the transmission will be done using the lowest SF (SF7) to reduce collision risk with uplinks from EDs. The decision to use the lowest SF will achieve better efficiency for spectrum and ensure the parallel functioning of both LoRaCog and LoRaWAN due to the below:  
* Far EDs will most likely not receive the frame, and hence, they will operate normally using the normal LoRaWAN mechanism; that is, ED can choose channels allocated and permitted by regional parameters. This will reduce the possibility of collision and ensure fair spectrum usage.  
Figure 6: LoRaCog Class A Operation.  
* The nearby EDs will switch to available licensed (cognitive) spectrum to use the holes indicated by GW as they are in the range. The time over the air (OTA) will be less in this case, which is a safety measure not to interrupt any PU that might show up.
# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 1 Introduction
In this paper, we focus on the AI-based techniques specifically for link modeling and monitoring in optical networks. In addition, we discuss and summarize the advantages and challenges for adopting the AI-based modeling and monitoring methods in the future EON. This paper is organized as follows. * In Section 2, we firstly introduce the background and challenges for modeling the QoT and impairments in EON's. The potentials of applying ML to estimate network performance are also discussed. Then, we review many previous works on ML-based modeling techniques. * In Section 3, we firstly review various previous works on ML-based monitoring techniques. Afterwards, the monitoring techniques specifically for failure management are elaborated. * In Section 4, the use cases for AI-based modeling and monitoring techniques are discussed. * In Section 5, we provide a lookout for the future of utilizing ML methods in EON by discussing both the challenges and opportunities. * In Section 6, a conclusion for this paper is provided.
## 3 Numerical Examples
### Influences of Various Construction Factors of the Unit Cell on the Band Structures  
In what follows, the effects of various construction factors of the unit cell (with fixed length 2 m) on the band structures, including the geometrical and material parameters of the constituent beams and the unit-cell configuration, are studied on the bi-coupled periodic beams with the Ternary-I unit cell. The referred geometrical parameters to all the constituent beams are \(A=0.1\) m\({}^{2}\) and \(I=2.083\ \times\ 10^{-3}\) m\({}^{4}\). During the research on the influences of geometrical and material parameters, only the considered parameters of the concrete beam in the Ternary-I unit cell are altered, while the other parameters of this beam and all parameters of the other constituent beams remain unchanged. The degree of alteration is represented by the ratio of these parameters defined by the updated value of the varied beam divided by the original value of the previous beam. These ratios are specified during our study. Except for the shape and position of the band structures, particularly emphasized are the effects of the geometrical and material parameters on the newly found novel dispersion property, i.e., the frequencies corresponding to the Brillouin zone boundaries may not be the demarcation between pass-bands and stop-bands for bending waves in bi-coupled periodic multi-component beams.
# TOAR: Toward Resisting AS-Level Adversary Correlation Attacks Optimal Anonymous Routing
## 5 Anonymity Analysis  
In this section, we employ the anonymity analysis method proposed by [PERSON] and [PERSON] [21] for correlation attacks to assess the anonymity of TOAR.  
Assuming the length of the anonymous transmission path is \(n\), with \(c\) compromised nodes, the probability that a forwarding node is an effective node is \(p=1-\frac{c}{n}\). The probability that the sender \(S\) and the receiver \(R\) are correctly associated by the adversary through the information collected from compromised nodes is denoted as \(d\). According to the anonymity analysis method proposed by [21; 22], \(d\) can be calculated as follows:  
\[d=\frac{(1-p)^{2}}{\big{(}1-p^{np-1}\big{)}(1-p^{np})} \tag{17}\]  
\(d\) can serve as the anonymity metric for anonymous communication systems. The smaller the value of d, the stronger the system's anonymity, and vice versa.  
For TOAR, after the execution of the two-stage routing algorithm, there is at most one compromised node controlled by the adversary on the anonymous transmission path. That is \(c=1\); then, \(p=1-\frac{1}{n}\). Therefore, the probability \(d\) can be simplified as:  
\[d=\frac{\big{(}\frac{1}{n}\big{)}^{2}}{\big{(}1-(1-\frac{1}{n})^{n-1}\big{)}( 1-\big{(}1-\frac{1}{n}\big{)}^{n}\big{)}} \tag{18}\]  
The probability of TOAR is evaluated based on Equation (18). From Figure 6, we can see that \(d\) decreases with the increase in \(n\), which results in stronger anonymity of TOAR. With the gradual increase in \(n\), the probability \(d\) is decreased, and its value is far less than 1. When \(n=5\), especially, the probability \(d\) is already close to 0.1; when \(n>15\), \(d\) approaches 0, nearing the level of absolute anonymity, which proves the excellent anonymous feature of TOAR.  
Figure 5: Process of route negotiation and route confirmation.
## 4 Material and Method
### Spatial Transformer  
In this subsection, we elaborate on the component of our transformer network. The spatial transformer consists of the spatial positional embedding layer--the longitude and latitude of the pick-up and drop-off points. The spatial transformer block is fused with the liner and scaled dot product of the weighted sum. The spatial-temporal embedding layer learns to incorporate the spatial-temporal positional information, for instance location, clusters, neurons, etc. Therefore, in the transformer feed-forward structures, there are no  
Figure 3: The working of the encoder cell.  
convolutional or recurrent operations. The spatial position of a trajectory, distance, and time determine the spatial dependencies of the previous trajectory rides that should be utilized to predict the future. As the next taxi destination prediction, the transformer injects the prior positional embedding as input sequences. For the learning of our network, we adopt the learnable spatial embedding positions in the embedding layer.  
Updating the grid, the input features are projected into the high-dimensionally latent subspace. These subspaces are learnable mappings. The learnable parameters are realized in the feed-forward neural networks. In the single head attention, three subspaces are obtained for each trajectory. The names of the three subspaces are (Q, K, V). In our model, we feed the query layer and the value. The linear layer gets the value through the vector. In our problem, we estimate the error rate in the distance in terms of kilometers, queries, and keys to undergo the scaled dot product to produce a score of the error in kilometers. The mathematical equations are formulated as follows:  
\[[S^{Q}=P^{S}T_{s}^{d},S^{K}=P^{S}T_{s}^{k},S^{V}=P^{S}T_{s}^{v}] \tag{7}\]  
where \(T_{s}^{d},T_{s}^{k},T_{s}^{v}\) are the weighted matrices of spatial transformer and \(S^{Q},S^{K},S^{V}\) are the spatial subspaces. \(P^{S}\) represents the spatial input features. The spatial scaled dot product of the three latent high-dimensional subspace is  
\[S^{S}=SoftMax(\frac{S^{Q}(S^{K})^{T}}{\sqrt{d_{S}^{K}}})\times S^{V} \tag{8}\]  
The spatial weighted matrices and subspaces are \(T_{s}^{d}.S^{Q},T_{s}^{k}.S^{K},T_{s}^{v}.S^{V}=W^{S}\).
# Design Methodology and Application Dynamics of Compact Quasi-Zero Stiffness Isolators
## 3 Compact Design of QZS Isolation
### Magnetic Type  
Magnetic QZS isolation has many unique advantages. In terms of structural design, it can reach a wider range of zero stiffness areas through reasonable construction of space layout and can also be used to design a multi-directional vibration isolation platform. The magnetic negative stiffness element has the advantages of non-contact characteristics, a compact structure, and a higher degree of design freedom. These advantages make the magnetic QZS isolation in the vibration isolation application have greater potential and value, whether in response to a complex vibration environment or to meet the diverse needs of vibration isolation, and can play a positive and effective role. The more common is the magnetic spring based on permanent magnets and electromagnetic coils.
# Risk Assessment and Source Apportionment of Heavy Metals in Soils from Handan City
## 3 Results and Discussion
### Risk Assessment for Heavy Metals  
#### 3.3.1 Pollution Index and Geoaccumulation Index  
The _PI_ were calculated through Equation (1), as shown in Figure 3a. These heavy metals in Handan city had different levels of pollution compared to the soil background in Hebei. The _PI_ values of the heavy metals in soil were ranged from 0.14 (Cr) to 25.42 (Cd). It should be noted that the _PI_ value of Cd in 95% of soil samples sites was classified as class IV, indicating that the contamination of Cd was serious in almost the entire study area. As shown in Figure 3a, the _PI_ value of Mn in 77% of the sampling points was grouped as class I, and 23% obeyed class II, indicating no pollution in most sampling sites. According to the _PI_ values, Cr, Cu, Ni, Pb and Zn belonged to the pollution II group in most sampling sites.  
The _Igco_ classification of heavy metals is shown in Figure 3b. Compared with the pollution index, the _Igco_ assessment was divided into seven, more detailed levels. The mean _Igco_ of the heavy metals in soil were arranged in the following order: Cd (2.47) > Zn (0.31) > Cu (0.01) > Ni (\(-\)0.16) > Pb (\(-\)0.25) > Cr (\(-\)0.48) > Mn (\(-\)0.65). Except for Cd, the _Igco_ of the other six heavy metals ranged from class I to class IV, and the _Igco_ values of most sampling points were in the range of class I-class II, showing that heavy metal pollution in soil was between uncontaminated and moderately contaminated conditions. The _Igco_ values of Cd in 95% of soil samples belonged to class IV, which showed that the pollution of Cd was serious compared with other metals, ranging from moderate contamination to heavy contamination. The results of _Igco_ had more non-pollution points than those of the pollution index. Considering the high Cd contamination levels in urban areas, it is necessary to identify the sources and carry out quantitative assessments of risks.
## 6 Experiments
### Application 2: 3D Object Localization
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Scene No.** & **ICOS** & **RANSAC (1000)** & **RANSAC (1 min)** \\ \hline _Scene-01_, \(s=1\) & \(E_{R}=\mathbf{0.151}\) & \(E_{R}=65.867\) & \(E_{R}=0.662\) \\ \(N=341,90.91\%\) & \(E_{t}=\mathbf{0.005}\) & \(E_{t}=1.309\) & \(E_{t}=0.014\) \\  & \(t=\mathbf{0.060}\) & \(t=0.903\) & \(t=6.245\) \\ \hline _Scene-03_, \(s=1\) & \(E_{R}=\mathbf{0.337}\) & \(E_{R}=128.182\) & \(E_{R}=0.924\) \\ \(N=316,94.62\%\) & \(E_{t}=\mathbf{0.008}\) & \(E_{t}=1.278\) & \(E_{t}=0.2018\) \\  & \(t=\mathbf{0.198}\) & \(t=0.851\) & \(t=27.688\) \\ \hline _Scene-09_, \(s=1\) & \(E_{R}=\mathbf{0.058}\) & \(E_{R}=0.910\) & \(E_{R}=0.397\) \\ \(N=579,86.18\%\) & \(E_{t}=\mathbf{0.001}\) & \(E_{t}=0.022\) & \(E_{t}=0.010\) \\  & \(t=\mathbf{0.043}\) & \(t=1.514\) & \(t=3.013\) \\ \hline _Scene-12_, \(s=1\) & \(E_{R}=\mathbf{0.068}\) & \(E_{R}=165.928\) & \(E_{R}=0.191\) \\ \(N=639,89.98\%\) & \(E_{t}=\mathbf{0.001}\) & \(E_{t}=1.747\) & \(E_{t}=0.004\) \\  & \(t=\mathbf{0.047}\) & \(t=1.645\) & \(t=8.537\) \\ \hline _Scene-01_, \(s\in(1,5)\) & \(E_{R}=\mathbf{0.037}\) & \(E_{R}=101.892\) & \(E_{R}=151.913\) \\ \(N=579,94.99\%\) & \(E_{t}=\mathbf{0.005}\) & \(E_{t}=5.779\) & \(E_{t}=5.782\) \\  & \(E_{s}=\mathbf{0.002}\) & \(E_{s}=3.428\) & \(E_{s}=3.429\) \\  & \(t=\mathbf{0.069}\) & \(t=1.508\) & \(t=60.001\) \\ \hline _Scene-03_, \(s\in(1,5)\) & \(E_{R}=\mathbf{0.139}\) & \(E_{R}=60.219\) & \(E_{R}=176.511\) \\ \(N=345,91.88\%\) & \(E_{t}=\mathbf{0.010}\) & \(E_{t}=4.718\) & \(E_{t}=4.720\) \\  & \(t=\mathbf{0.009}\) & \(E_{s}=3.552\) & \(E_{s}=3.553\) \\  & \(t=\mathbf{0.060}\) & \(t=0.936\) & \(t=9.052\) \\ \hline _Scene-09_, \(s\in(1,5)\) & \(E_{R}=\mathbf{0.045}\) & \(E_{R}=0.065\) & \(E_{R}=0.096\) \\ \(N=619,80.94\%\) & \(E_{t}=\mathbf{0.001}\) & \(E_{t}=0.004\) & \(E_{t}=0.007\) \\  & \(E_{s}=\mathbf{0.001}\) & \(E_{s}=\mathbf{0.001}\) & \(E_{s}=0.002\) \\  & \(t=\mathbf{0.041}\) & \(t=1.264\) & \(t=1.237\) \\ \hline _Scene-12_, \(s\in(1,5)\) & \(E_{R}=\mathbf{0.008}\) & \(E_{R}=128.166\) & \(E_{R}=0.079\) \\ \(N=631,89.54\%\) & \(E_{t}=\mathbf{0.001}\) & \(E_{t}=7.151\) & \(E_{t}=0.005\) \\  & \(E_{s}=\mathbf{0.001}\) & \(E_{s}=3.417\) & \(E_{s}=\mathbf{0.001}\) \\  & \(t=\mathbf{0.052}\) & \(t=1.613\) & \(t=7.560\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative object localization results on cap and table. Figure 11: Qualitative results of object localization (_cereal box_) using ICOS, RANSAC (1000), and RANSAC (1 min). From left to right, we show the correspondences matched by FPFH (inliers are in green lines, and outliers are in red lines) and the registration (reprojection) results with ICOS, RANSAC (1000), and RANSAC (1 min), respectively. Figure 12: Qualitative results of object localization (_cap, table_) using ICOS, RANSAC (1000), and RANSAC (1 min). From left to right, we show the correspondences matched by FPFH (inliers are in green lines and outliers are in red lines) and the registration (reprojection) results with ICOS, RANSAC (1000), and RANSAC (1 min), respectively.
## 3 Results and Discussion
### Biological Activity
 } & Raw & 10.3 \(\pm\) 0.37 \({}^{\mathrm{b}}\) & 6.28 \(\pm\) 0.58 \({}^{\mathrm{c}}\) & n.d. & 16.6 \(\pm\) 0.47 \({}^{\mathrm{d}}\) & n.d. & n.d. & n.d. & n.d. \\  & SV 90 \({}^{\circ}\)C & 8.49 \(\pm\) 0.79 \({}^{\circ}\) & n.d. & 6.25 \(\pm\) 1.15 \({}^{\mathrm{c}}\) & 14.7 \(\pm\) 0.97 \({}^{\mathrm{a}}\) & n.d. & 0.84 \(\pm\) 0.01 \({}^{\mathrm{b}}\) & n.d. & 0.84 \(\pm\) 0.01 \({}^{\mathrm{c}}\) \\  & ST85 \({}^{\circ}\)C & 12.8 \(\pm\) 0.89 \({}^{\mathrm{a}}\) & 27.7 \(\pm\) 0.38 \({}^{\mathrm{a}}\) & 106.4 \(\pm\) 0.41 \({}^{\mathrm{a}}\) & 51.1 \(\pm\) 0.56 \({}^{\mathrm{a}}\) & n.d. & n.d. & 4.41 \(\pm\) 0.75 \({}^{\mathrm{a}}\) & 4.41 \(\pm\) 0.75 \({}^{\mathrm{b}}\) \\  & SV 90 \({}^{\circ}\)C & 9.88 \(\pm\) 0.56 \({}^{\mathrm{b}}\)C & 5.46 \(\pm\) 0.70 \({}^{\mathrm{c}}\) & 9.13 \(\pm\) 0.36 \({}^{\mathrm{a}}\) & 24.5 \(\pm\) 0.54 \({}^{\mathrm{c}}\) & n.d. & n.d. & n.d. & n.d. \\  & Steaming & 11.1 \(\pm\) 1.05 \({}^{\mathrm{b}}\) & 17.2 \(\pm\) 1.32 \({}^{\mathrm{b}}\) & 8.12 \(\pm\) 0.70 \({}^{\mathrm{b}}\) & 36.4 \(\pm\) 1.02 \({}^{\mathrm{b}}\) & n.d. & 10.5 \(\pm\) 0.20 \({}^{\mathrm{a}}\) & 0.89 \(\pm\) 0.17 \({}^{\mathrm{b}}\) & 11.4 \(\pm\) 0.10 \({}^{\mathrm{a}}\) \\ \hline \multirow{8}{*}{ \begin{tabular}{c} Green pepper \\ \end{tabular}  } & Raw & 42.7 \(\pm\) 1.88 \({}^{\mathrm{d}}\) & n.d. & 6.95 \(\pm\)vegetables in their raw form was not statistically significantly lower than in their forms subjected to SV processes at all temperatures (red cabbage) or higher in the SV 85 \({}^{\circ}\)C, SV 90 \({}^{\circ}\)C and steaming processes (green pepper). The lowest decrease or the highest increase in the antioxidant potential value was found in vegetables such as beetroot, red pepper, green pepper and kale after the sous-vide at process 80 \({}^{\circ}\)C, respectively, 3.43, 17.0, 7.06, 7.61 mM Trolox/g freeze-dried vegetables vs. 5.81, 18.8, 5.79, 8.55 mM Trolox/g freeze-dried vegetables their raw form (\(p\leq 0.05\)). In contrast, the SV 90 \({}^{\circ}\)C process turned out to be the most beneficial despite the statistically insignificant decrease in the antioxidant potential value of red cabbage (7.39 mM Trolox/g freeze-dried vegetable) versus its raw form (7.87 mM Trolox/g freeze-dried vegetable). Other studies on the antioxidant potential determined by the DPPH radical scavenging method indicate that sous-vide thermal processing is more beneficial in preserving these capabilities compared to other technological processes. The antioxidant potential is influenced not only by the vacuum sealing of the product, but also by the use of an appropriate temperature and duration of the process [34; 35].
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 3 Results  
### Calibration Phase  
The processing chain is calibrated on the outskirts of Antananarivo by comparing results with those obtained from two optical images (pre- and post-flood).  
Figure 3 shows surface water detected from Sentinel-2 optical imagery with automatic thresholding of the normalised difference water index (NDWI). Permanent water bodies are filtered out by processing another Sentinel-2 image acquired on 18 September 2017 (dry season) in the same fashion. This date was chosen because a Sentinel-1 image acquired the same day will be used as the reference radar image when processing radar data.  
As the calibration is small enough, it was easy to variate the threshold. Maximum precision is obtained with a \(-\)0.35 threshold.  
Figure 4 shows flood detection results obtained using a single polarisation (VV and then VH) and both polarisations at the same time.  
We note that VV polarisation is sensitive to the irregularity of water surfaces caused by wind [3]. That is why this polarisation does not detect flooding better in the relatively open northwestern part of the calibration area. On the other hand, the effectiveness of VV polarisation in the agricultural fields is due to the sensitivity of VH to the double bounce phenomenon that occurs when vegetation still emerges from water. These two polarisations are then complementary, minimising false negative flood detection. Both polarisations will therefore be used henceforth.  
Figure 4: Detection of floods in the Antananarivo plain using different polarisations of the Sentinel-1 image (VV only, VH only and both VV and VH). The post-flood image was acquired on 13 March 2018 and the reference image on 18 September 2017.  
Table 1 shows how processing chain results match according to the type of filter used with respect to the result obtained with two Sentinel-2 optical images.  
The best match is obtained with the refined Lee filter on a single image. We note that accuracy is slightly reduced when using multidate filters. This may be because different nonpermanent features (temporary water bodies) are in different places and have different shapes in the multidate imagery.  
After generating 100 random points for each predicted class (flood/non-flood), the real state of each pixel was verified manually (reference). The confusion matrix is presented in Table 2, indicating an overall accuracy of 85%. A significantly improved accuracy is achieved for the predicted non-flood class (user's accuracy = 92%) compared to the predicted flood class (user's accuracy = 78%) thanks to the utilisation of the HAND index. The majority of the reference non-flooded areas are effectively identified as non-flood-prone regions through the HAND thresholding (producer's accuracy = 81%).
## 3 Materials and Methods
### PreferSAGE-SCF
#### 3.3.5 SCF and Final Recommendation Results  
Slow-acting Collaborative Filtering (SCF) is a collaborative filtering integration method with limited influence designed in this paper. Its main body is a conventional collaborative filtering algorithm, with the main difference being the integration method, which combines misaligned results to give it a slow-acting effect. For the PreferSAGE model in this paper, SCF plays a role in optimizing the results. Compared to the rigid proportional merging of results, this method does help further improve various indicators. This slow-acting effect can also to some extent solve the problem of newly added item nodes not being recommended by a collaborative filtering algorithm, enabling the inductive feature of PreferSAGE to be effectively utilized.  
From the experimental results of various non-neural network-based collaborative filtering algorithms, we can see that the effectiveness of these algorithms is often weaker than that of neural network-based algorithms. Meanwhile, the hit items of neural network algorithms are usually concentrated in the head, and the density of hit items decreases as they rank lower. After the experiments, it was found that if the calculation results of neural network algorithms are directly combined with the results of collaborative filtering algorithms, the performance is not ideal in many cases, and it is also difficult to adjust parameters. If we can find a critical point in the middle place and make the results of the collaborative filtering algorithm work from here, we should be able to improve the model's performance to a certain extent. The method used in this paper's experiments is to manually adjust the position of this critical point after observing the results. After finding this critical point, the first n items of PreferSAGE are retained as the first n items of the prediction results. Then, the first \(k-n\) items of the PreferSAGE prediction results are fused with the collaborative filtering results of user similarity using the weighted voting method, and the first \(k-n\) items from the fusion result are selected and added to the prediction results to generate the final prediction result. A schematic diagram of the fusion process is shown in Figure 4.  
The collaborative filtering method used in this paper's experiments is based on user collaborative filtering using the cosine similarity method to calculate the similarity. Based on the calculation results, we obtained the highest rated m historical access items from n users with the highest similarity between users, attached these items with user similarity, and returned them to the current user as collaborative filtering results. The formula is as follows:  
\[cos(\theta)=\frac{\sum_{k=1}^{n}x_{1k}x_{2k}}{\sqrt{\sum_{k=1}^{n}x_{1k}^{2} \sqrt{\sum_{k=1}^{n}x_{2k}^{2}}}} \tag{4}\]