esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### SKAB Dataset
#### 3.2.4 Effectiveness in Various Scenario Settings  
The experimental results mentioned above were obtained based on the premise that the categories for testing and training were distinct. It has been demonstrated that after learning, GOPML was capable of identifying faults through the use of initial parameter values and a limited number of adjustment steps when faced with new categories. The remarkable adaptability of GOPML to new categories underscored the method's reliability in practical industrial scenarios. Moreover, to mitigate the influence of random division of data categories, three different random partitions on the SKAB dataset were also conducted. Subsequent experiments were carried out across these varied data divisions (Datasets A to C). The experiments executed under these divisions are summarized in Table 10 and Figure 10. These results show that GOPML maintained a balanced performance across different divisions, suggesting that regardless of how the fault categories were distributed, the learning ability of GOPML was consistently effective.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**3-Way Accuracy**} & \multicolumn{2}{c}{**8-Way Accuracy**} \\  & **5-Shot** & **6-Shot** & **5-Shot** & **6-Shot** \\ \hline Dataset A & 91.50 & 92.03 & 80.78 & 81.30 \\ Dataset B & 91.32 & 91.89 & 80.61 & 81.18 \\ Dataset C & 91.43 & 91.93 & 80.76 & 81.26 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Fault classification results in different scenarios on the SKAB dataset.  
Figure 9: Comparative accuracy in the inner loop for different task configurations.
## 6 Atmospheric Gaseous Losses Profile for Aircraft-Stratospheric Basestation (SB) Link
Therefore, for aircraft-SB communications link, the overall atmospheric attenuation, \(L_{\alpha,w}\), can be written by:  
\[L_{\alpha,w}=\sum_{n=1}^{N}(l_{o}+l_{w})\rho_{n} \tag{15}\]  
where \(l_{o}\) and \(l_{w}\) are the Oxygen and water vapor specific gaseous attenuation in dB/km respectively. The \(n\)th layer thickness, \(\delta_{n}\), can be calculated according to the following equation [27]:  
\[\delta_{n}=0.0001e^{(n-1)/100}\ \text{km} \tag{16}\]  
Figure 8 displays the layer thickness variation with \(n\) along with the altitude of the segment. This figure is useful for determining the range of values for \(n\) between the aircraft and SB as will be explained below. Figure 8: Atmospheric layer altitude and thickness variation with layer number. Figure 7: Layered air model for atmospheric attenuation calculation [13]. If we start with \(\beta_{1}\) given by:  
\[\beta_{1}=90-\alpha_{1} \tag{17}\]  
then, we can calculate the incidence and refraction angles using Snell's Law in polar form as follows:  
\[\beta_{n}=\sin^{-1}\!\left(\frac{\eta_{1}R_{1}}{\eta_{n}R_{n}}\sin(\beta_{1})\right) \tag{18}\]  
and,  
\[\gamma_{n}=\sin^{-1}\!\left(\frac{\eta_{1}R_{1}}{\eta_{n}R_{n+1}}\sin(\beta_{1 })\right) \tag{19}\]  
The corresponding \(n\)th layer path segment can be calculated using the following formula [27]:  
\[\rho_{n}=-R_{n}\cos(\beta_{n})+\sqrt{{R_{n}}^{2}\cos(\beta_{n})^{2}+2R_{n} \delta_{n}+{\delta_{n}}^{2}} \tag{20}\]  
where \(R_{n}\) is the radial distance from the Earth's center to the bottom boundary of the \(n\)th layer and \(\eta_{n}\) is the refractive index of the \(n\)th layer. The layer thicknesses in Equation (16) are used to calculate the atmospheric path loss of the whole atmosphere as suggested in [14], however, for the aircraft-SB communication link, the signal passes through specific slant range that will not span all the atmosphere and the therefore not all the steps provided in Equation (16) will be used. For example, to reach an altitude of 100 km, the value of \(N\) should be 992, while the link stops at the SB height at approximately 20 km. Also, the starting step at which \(n=1\) is used only when the aircraft is on the ground level. Denoting the starting value of \(n\) by \(N_{p}\) at an aircraft altitude \(h_{p}\) should achieve the following equation:  
\[h_{p}=\sum_{n=1}^{N_{p}}\delta_{n} \tag{21}\]  
For SB, it will be \(N_{s}\), which is found by achieving the following equation:  
\[L_{\alpha,w}=\sum_{n=1}^{N}(l_{o}+l_{w})\rho_{n} \tag{15}\]0  
Then, the slant path atmospheric attenuation between aircraft and SB can be calculated from the following equation:  
\[L_{\alpha,w}=\sum_{n=1}^{N}(l_{o}+l_{w})\rho_{n} \tag{15}\]1  
Therefore, each segment has its corresponding specific atmospheric attenuation and length, and the overall atmospheric attenuation can be calculated using Equation (23). According to the link path between an aircraft and SB, there are two main scenarios that could be followed as discussed in the following sections.
## 5 Experiments and Results
### Comparison Results
#### 5.3.1 Results of Comparison of Value-Based Algorithms
We point out that GHQ is a group parameter-sharing method in which only the agents from the same group share parameters. Such an implementation naturally avoids the combination of action semantic conflict with global parameter sharing, and therefore achieves good performance against other baseline algorithms. Furthermore, G-QMIX applies global parameter sharing while distinguishing different action semantics, which brings better convergence speed and better scalability. (3) _Hard: We double all units of both sides simultaneously._ In Figure 5b,f, the number of all entities is doubled. Theoretically, the optimal policies of maps in Figure 5b,f are similar using the \"divide and conquer\" method. However, most of the baseline algorithms fail to deal with the complex combination of scalability and heterogeneity. On the one hand, as we have analyzed before, G-QMIX, RODE, GHQ, and QMIX can achieve a _WR_ greater than 0.5 with relatively low variance in Figure 5b. Nevertheless, on the other hand, only G-QMIX and GHQ are capable of achieving significant _WR_ in Figure 5f. G-QMIX not only wins GHQ with higher _WR_ and lower variance, but also reaches _WR_ 0.6, which is very close to its performance in Figure 5b. This result indicates the superiority of G-QMIX in dealing with heterogeneous scalability problems thanks to the introduction of GAPO methods. In summary, G-QMIX outperforms other baseline algorithms with higher _WR_ and lower variance because G-QMIX increases the cooperation between agents and solves the action semantic conflict problem. Due to the higher computing speed of the CGP loss against the IGMI loss, G-QMIX performs better than GHQ with faster convergence speed and a better-converged result.
## 3 Transactive DR Based on Flexibility Potential Evaluation
### Solution Procedures  
Figure 3 illustrates the flowchart of the proposed transactive DR framework. Once the pre-dispatch references are obtained from (40)-(62) and the flexibility potential evaluation is performed via (1)-(39), cost-optimal real-time DR can be implemented for prosumer aggregators. All the schemes are coded on the YALMIP toolbox of MATLAB 2020b and solved using the CPLEX V12.10.0 solver.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 5 Performance Evaluation
### Additional Performance Enhancements  
The analysis described din the previous sections showed that the positioning accuracy was largely improved by (a) using as much information as possible, as highlighted by the performance loss suffered by strategies using less information and (b) by performing additional preprocessing on the data, as highlighted by the positive effect of data interpolation. Correspondingly, in this section, two approaches to further improve accuracy are introduced.
## Appendix L Description of Mechanisms for Vehicles
### Secured Event-Information Sharing [628]  
As per the proposed system flow outlined by the authors [628] (Figure 2), a central 'cloud server' is responsible for the admission of new entities to the network and their authentication. Therefore, the proposed protocol can be considered to be designed exclusively for permissioned networks, and not to provide Sybil attack resistance.
## 2 Materials and Methods
### Computational Details  
All density functional calculations were conducted using the Gaussian16 package [19]. Three-dimensional models of the PCzPO and PNoFPO D-A moiety were built using Avogadro molecular editing software [20] and preoptimised using molecular mechanics forcefield (MMFF) [21]. Exchange and correlation were treated using the B3 LYP hybrid functional, composed of 20% exact Hartree-Fock exchange [22]. Geometry optimisations were performed using the 6-31 g(d) basis set. Frequency analyses were performed to confirm that the optimised structures correspond to the true minimum in the potential energy surface. Single point energy and frequency calculations were then performed using the 6-311 g(d,p) basis set to calculate the Gibbs free energies. These computational approaches have been extensively used to study polymer photocatalysts and are shown to consistently reproduce experimental orbital energies at a reasonable computational cost [15; 23; 24; 25; 26]. The average proton binding free energies (\(\Delta\)G\({}_{\text{H+}}\)) were calculated as follows  
\[\Delta\)G\({}_{\text{H+}}=\)G\({}_{\text{H+/Polymer}}-\)G\({}_{\text{Polymer}}-\)(1/2)G\({}_{\text{H}_{2}}\) (1)  
where G\({}_{\text{H+/Polymer}}\), G\({}_{\text{Polymer}}\), and G\({}_{\text{H}_{2}}\) are the calculated Gibbs free energies of the protonated D-A moiety, the bare D-A moiety and the free hydrogen molecule, respectively. Finally, atomic charge distributions were calculated using the Mulliken population analysis.
## 2 Materials and Methods
### Proposed Classification Schemes
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layers** & **Input Size** & **Kernel Size** & **Stride** & **Output Size** \\ \hline Input & 224 \(\times\) 224 \(\times\) 6 & & & \\ Conv 1 & 224 \(\times\) 224 \(\times\) 6 & 7 \(\times\) 7 \(\times\) 64 & 3 \(\times\) 3 & 98 \(\times\) 98 \(\times\) 64 \\ Relu 1 & 98 \(\times\) 98 \(\times\) 64 & & & 98 \(\times\) 98 \(\times\) 64 \\ Maxpool 1 & 98 \(\times\) 98 \(\times\) 64 & 2 \(\times\) 2 \(\times\) 64 & 2 \(\times\) 2 & 49 \(\times\) 49 \(\times\) 64 \\ Conv 2 & 49 \(\times\) 49 \(\times\) 64 & 5 \(\times\) 5 \(\times\) 128 & 2 \(\times\) 2 & 23 \(\times\) 23 \(\times\) 128 \\ Relu 2 & 23 \(\times\) 23 \(\times\) 128 & & & 23 \(\times\) 23 \(\times\) 128 \\ Maxpool 2 & 23 \(\times\) 23 \(\times\) 128 & 2 \(\times\) 2 \(\times\) 128 & 2 \(\times\) 2 & 12 \(\times\) 12 \(\times\) 128 \\ Conv 3 & 12 \(\times\) 12 \(\times\) 128 & 3 \(\times\) 3 \(\times\) 128 & 1 \(\times\) 1 & 10 \(\times\) 10 \(\times\) 128 \\ Relu 3 & 10 \(\times\) 10 \(\times\) 128 & & & 10 \(\times\) 10 \(\times\) 128 \\ Maxpool 3 & 10 \(\times\) 10 \(\times\) 128 & 2 \(\times\) 2 \(\times\) 128 & 2 \(\times\) 2 & 5 \(\times\) 5 \(\times\) 128 \\ Global Avg. Pool & 5 \(\times\) 5 \(\times\) 128 & & & 1 \(\times\) 1 \(\times\) 128 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The Proposed CNN network configuration. Figure 8: The proposed classification schemes. For training, a ratio of 90-10% is used where 10% of the un-augmented database images (i.e., only original database images) are randomly selected for blind testing, and the remaining 90% (i.e., remaining original database images and their corresponding augmented images) are used for training so that there is no overlap between the testing and training samples. If the test data are selected from augmented data, the accuracy can be significantly biased due to leakage and can be higher than the real test. As there are not many tests or original data, it is more appropriate to separate the testing images from the whole database for testing purposes and the rest of the database is utilized to generate training data with augmentation. A 10-fold cross-validation scheme is also employed along with an exhaustive grid search method using the average validation accuracy as a metric to determine the hyper-parameters of the neural network. This network employs the Adam optimization technique [42] and a batch size and learning rate of 64 and 0.01, respectively. The training data are applied to the CNN network through 40,000 iterations. The performance of the proposed method is measured using the performance indices like accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), etc. Later, the confusion matrices are obtained by measuring true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN), respectively, where positive stands for a malignant tumor, and negative stands for a benign tumor. The results are discussed in Section 3.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 4 Source Anonymity-Based Lightweight Secure AODV Protocol
### Source Anonymity-Based Lightweight Secure AODV Protocol
#### 4.1.3 Random Delayed Transmitting Scheme  
In a sense, the point of using the RSA digital signature and CRC is to guarantee the data cannot be tampered with. Although the CRC is simple, and its security strength is not as high as RAS, when compared with the RAS digital signature (1024 bytes), CRC (4 bytes) has certain advantages in the computation and transmitted energy consumption, especially for resource-constrained nodes. Then, how to guarantee the integrity of CRC becomes a critical issue. In this sub-section, we give the random delayed transmitting scheme to solve the above problem, and use a checksum for the CRC code to guarantee its integrity to a certain extent.  
In order to illustrate the proposed algorithm, the notations we have introduced and will introduce later are summarized in Table 1.  
Briefly, the proposed scheme mainly involves two parts: generating the CRC and random delayed transmitting based on given random out-of-order space (\(R_{r}\)). The random out-of-order space (\(R_{r}\)) is generated by a Knuth-Durstenfeld Shuffle algorithm in fog node, and is described in Algorithm 2. Although the number of loops is executed more frequently during the generation of primes, the work only needs to be performed once in this algorithm.  
In the SAL-SAODV, the structure of a typical data packet is shown in Figure 3. Meanwhile, we claim that intermediate nodes only constitute forward packets.  
where, we define \(R_{r}\) as a set, which includes the overall sequence randomly aligned 0 to 15. In theory, there are 16 possibilities for this permutation. Each aligned sequence has a strong randomness  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Definition** \\ \hline _S\_ID_ & Source node ID \\ _D\_ID_ & Destination node ID \\ _BSN_ & Block sequence number \\ _Len_ & Length of original packet \\ _Payload_ & Payload of packet \\ _CHK[BSN]_ & Checknum for CRC \\ _CRC[BSN]_ & Cyclic Redundancy Check for original packet \\ _P\_buffer_ & Transmitting/Receiving packets buffer \\ _C\_buffer_ & Transmitting/Receiving CRCs buffer \\ _Tx\_packet_ & Transmitted packet (involved: packet, _CHK_ and _CRC_) \\ _Rx\_packet_ & Received packet (involved: packet, _CHK_ and _CRC_) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of notations.  
Figure 3: Structure of a Typical Data Packet in SAL-SAODV protocols.  
to randomize the order in which each packet matches the check information. By the following formula, a specific out-of-order sequence's index is obtained.  
\[\textit{Index}=P_{s}\bmod P_{d} \tag{2}\]  
where, \(P_{s}\) is the source node's false ID and \(P_{d}\) is the destination node's false ID. These false IDs are generated by using Algorithm 1. A typical example is shown in Figure 4.
## Case Study  
### Background  
The deep foundation pit project of a subway station considered in this Section is located in Chaple District, Fuzhou, China. It is a two-story, double-column, and three-span station with a length of 293 m and a width of 21.7 m. The total construction area of the station is 17,861.2 m\({}^{2}\). The station was constructed with the cut-and-cover method. The retaining structure is a 800 mm-thick continuous wall. The layout of the monitoring points is shown in Figure 4a, where the black dots labeled with \"QCX\" are the monitored locations. Figure 4b presents a photo from the in-situ construction, where the reinforced concrete supports are used at the top of the excavation and steel supports are used elsewhere.  
The CX-06B inclinometer was used in this project to measure the deformation of retaining walls. The CX-06B inclinometer is a geotechnical instrument equipped with a high-precision accelerometer. It has been widely used in the measurements of lateral displacements for building foundations, mines, foundation pit excavation, and underground structures. The major technical features of CX-06B are as summarized in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Description** \\ \hline Resolution & 0.02 mm/500 mm \\ Measurement range & \(-\)50\({}^{\circ}\)\(\sim\)+50\({}^{\circ}\) \\ Accuracy & \(\pm\)4 mm/15 m \\ Minimum stepover & 500 mm \\ Surrounding temperature & \(-\)10 \({}^{\circ}\)C\(\sim\)+50 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features of the inclinometer used in the project.  
Figure 4: Foundation pit excavation of a subway station. **(a)** Layout. **(b)**_In-situ_ photo.
## 3 Four-Port MIMO Antenna
### Results and Discussion
#### 3.2.6 Channel Capacity Loss  
Channel capacity losses also happen as a result of correlation losses in MIMO antennas. It is a crucial component of every MIMO antenna. The value of CCL is observed for the proposed ultra-wideband MIMO antenna at about 0.01 bps/Hz over the operational bandwidth of 26.5-42.7 GHz, as shown in Figure 16. The value of CCL in an ideal situation is in the vicinity of 0.5 bps/Hz. The Formula (6) to compute CCL is shown below [45; 46].  
\[C_{Loss}=-log_{2}\det\left(\propto^{R}\right) \tag{6}\]  
Figure 14: ECC of the proposed MIMO antenna.  
Figure 15: Comparison among predicted and measured DG.
# A Practical Group Symbol and Group Name Approach (PSNA) according to EN ISO 14688-2:2018
## 2 Standards  
EN ISO 14688-2:2018 [21] and ASTM D2487-17e1 [25] are very well-known standards; they are summarized here with their tables and identification characteristics to provide a better understanding of the group symbol and group name approach proposed in the present study. In order to use these two well-known standards, a useful symbolization and naming approach was proposed.
## 1 Introduction
The prism P1 is designed to guide the incident beam into the basin filled with water samples, while the role of the prism P2 is to guide the direct light outside the basin. Without P2, the specular reflection of the direct light onto the boundaries of the basin could induce a sharp increase in the VSF at a backward angle (i.e., typically from 150\({}^{\circ}\)). To avoid the direct light reflected back to the basin, an air gap was constructed inside the P2. However, during our practice, the manufacture of the P2 is not easy because it is easily broken when creating the air gap. Additionally, polishing the surface of the air gap is another challenge as limited space. The roughness of the air gap surface can cause undesirable diffuse stray light back to the basin and thus overestimation of VSFs in backward direction near 180\({}^{\circ}\). At present, measurements of total attenuation coefficient, absorption and scattering coefficient of suspended particles, and absorption coefficient of CDOM had been carried out in the East China Sea (ESC), but the VSF had not been measured and studied yet. The main reason was the lack of suitable instruments. The angle range and angle resolution of the rentable instrument are limited. The instrument developed by [PERSON] et al. [24] has only seven measurement angles, covering the angles from 20\({}^{\circ}\)-160\({}^{\circ}\). [PERSON] et al. [25] developed a laboratory prototype suitable for measuring the scattered light of submicron particles (0.1-0.8 \(\upmu\)m), but the accuracy for larger beads (such as 11 \(\upmu\)m) was limited and forward (near 0\({}^{\circ}\)) and backward VSF (near 180\({}^{\circ}\)) measurement accuracy were unclear. [PERSON] al. [26] manufactured the three-dimensional VSF instrument covering the angles from 18\({}^{\circ}\)-160\({}^{\circ}\). The commercial instrument LISST-VSF could only reach 150\({}^{\circ}\) at backward direction [27]. The aim of the study is to establish a benchtop VSF instrument called VSFLab and study natural features of VSF from 1\({}^{\circ}\) to 178.5\({}^{\circ}\) in the coastal region of the ECS. The VSFlab instrument is established based on the principle of the double periscopic optical system and rotating detector. A simple design prism combing a single prism and a neutral density (ND) filter, which is easier to be fabricated with considerable efficiency in the stray light reduction, was designed and used. For confirming measurement accuracy, a detailed calibration procedure accounting for the rigorous validation works and comparison with LISST-VSF and POLVSM were carried out through the measurements in DUKE standard beads samples. The features of VSF in the ESC were analyzed by comparison with VSFs in other coastal regions. The \(\chi_{p}\) factor derived from VSF was used to study the relationship between the shape of the particulate VSF in the backward direction and particle characteristics (i.e., diameter and so on).
## 4 Discussion  
One major limitation of the NPP dataset used in this study is the lack of its validation in the region of interest (India). Several studies over the past 10 years have addressed the issue of field validation of LAI and NPP estimates from satellite data. In general, these studies conclude that while LAI can be estimated to within 0.5 LAI units for many biomes (needle leaf forests are an exception), the NPP estimates can deviate from observations by as much as 50%. Observed NPP data are rare, mostly concentrated in mid and high-latitude ecosystems. However, the logic behind NPP estimation is quite robust based on radiation absorption by vegetation canopies. Though data for flux towers is being used to validate NPP estimates, we believe it is still not a direct validation, as flux towers only measure net ecosystem exchange and not NPP. Robust validation of global NPP estimates is challenging because, as in the case of field observations, towers are also located mostly in mid and high-latitude ecosystems. For example, in the case of India, we only know of two towers established recently. The data collected from these towers is still being diagnosed and not yet released to the science community. We intend to use these data once they become available. Furthermore, our analysis here aims to address the interannual variability and trends in NPP and the underlying causes. While the absolute magnitudes are important, the errors associated with them would likely be uniform through the time series.  
Another limitation relates to our statistical analysis and evaluation of correlations which cannot establish firm casual relationships. Furthermore, the R\({}^{2}\) values and correlations obtained are also small in many cases. This indicates the interplay of multiple variables that exert influence on the terrestrial carbon cycle. Additional sources of uncertainty may arise from the resolution of the data: since the terrestrial ecosystems are very heterogeneous and patchy, 25-km resolution for the NPP product is insufficient to characterize the spatial variations accurately. It is believed that global land observations at the scale of 1 km would be needed to better understand the terrestrial carbon dynamics. Therefore, our conclusions on attribution of trends and variability require careful observation and modeling evaluation in the future. Specifically, multiple global and regional scale modeling based on detailed process representation and their validation using multiple observations are needed for reaching robust conclusions.