esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Classification of Smart Grid Cyberattack
### Data Secrecy Attacks
#### 5.2.4. Traffic Analysis  
A traffic analysis cyberattack is an example of a passive attack that does not alter data but it violates their confidentiality principle. Attackers can sniff and examine the messages to gather important details about the nodes' communication patterns. In a smart grid traffic analysis attack, the traffic is monitored and examined to identify the hosts and devices connected to the network, as well as their IP addresses. An adversary could collect crucial data about the identity, location, and functionality of nodes using network traffic analysis. The traffic patterns of a network's wireless sensor devices can provide a wealth of contextual data, including the location of important nodes. Sensing data, for instance, is sent through relatively set pathways that connect source nodes and sinks. This results in pretty clearly discernible traffic patterns that pinpoint the position of sinks. Traffic analysis attacks can be of various types such as those based on monitoring traffic volumes, traffic rates, and traffic patterns [98; 99].
## 2 Principal of Conversion of Hyperspectral Information for Water Content Prediction  
The main conceptual diagram of this study is shown in Figure 1. First, a hyperspectral camera is attached to the drone and an aerial flight is performed. At this time, the hyperspectral camera can simultaneously measure not only the overall orthophoto but also hyperspectral information (reflectance with wavelength) for each pixel. Afterward, hyperspectral information is substituted into the conversion equation for water content, developed through machine learning, and the water content for each pixel is calculated. This converts the graph of reflectance with wavelength measured in 4 nm increments in the wavelength range of 400-1000 nm into one number (water content). Therefore, each pixel in an orthophoto has latitude, longitude, and water content as a separate data value. Lastly, colors for each water content are additionally specified to visualize the CCM. At this time, color designation could be set in various ways depending on the user.  
Figure 1: Development process of a CCM for water content.
## 3 Results and Discussion
### Modulation of Time Constants  
In Section 3.3, the time constants were measured under constant conditions. The differences between time constants and VRMS in continuous on-state and periodically switched conditions are discussed in this section [61]. As shown in Figure 16, \(\mathrm{V_{GS}}\) dependance on \(\tau_{\mathrm{c}}\) is larger than that of \(\tau_{\mathrm{e}}\), and this suggests that the cycle period (\(\tau_{\mathrm{c}}+\tau_{\mathrm{e}}\)) in on-state is longer than that in off-state because the \(\mathrm{V_{GS}}\) of off-state is smaller than that of on-state. Figure 21a,b show the time constant relationship between continuous on-state and periodically switched conditions. In the periodically switched condition, MOSFETs cycled for 10.6 msec in 700 ms, which was a measurement cycle. Although \(\tau_{\mathrm{e}}\) of almost all samples are the same in both conditions, \(\tau_{\mathrm{c}}\) of some samples in the periodically switched condition is larger than that in continuous on-state. Figure 22 shows (a) histogram of the \(\mathrm{V_{RMS}}\) difference between continuous on-state and periodically off-state (\(\Delta\mathrm{V_{RMS}}\)) and (b) schematic waveform of RTN in continuous on-state and periodically off-state, respectively. Though \(\mathrm{V_{RMS}}\) of 5% samples was increased, that of 95% was not changed or decreased in the periodically switched condition. As a result, \(\mathrm{V_{RMS}}\) decreased in the periodically switched condition, with a few exceptions. This suggests that \(\mathrm{V_{RMS}}\) can be reduced by the modulation of operation conditions, even in the same MOSFET.
## 5 Performance Analysis
### Performance Analysis on Gully Pot Monitoring Application  
The results of the existing Sediment accumulation process [17], EU Water Framework [18], STA and FTIR [19], and Maintenance policy [20] are discussed with the gully pot monitoring dataset. The URL of the dataset is published as [71]. In total, 500 sensor nodes are deployed to conduct the simulation.  
Figure 13 illustrates the _Accuracy_, as defined in Equation (5), for the performance evaluation of four different existing methods using varying numbers of gully pot data points ranging from 100 to 1000. For 200 data points, the _Accuracy_ rates recorded are 87% for the Sediment Accumulation Process, 93% for the EU Water Framework, 74% for STA and FTIR, and 80% for the Maintenance Policy. As the number of data points increases, the _Accuracy_ may rise or fall accordingly. Notably, the EU Water Framework exhibits the highest accuracy among the methods, attributable to its utilization of a basal sediment scour deterministic model. This model effectively predicts sediment behavior at various sites by considering future rainfall predictions and sediment source determination, which enhances its _Accuracy_. Specifically, the _Accuracy_ of the EU Water Framework surpasses that of the Sediment Accumulation Process, STA and FTIR, and Maintenance Policy by 7%, 25%, and 15%, respectively, underscoring its effectiveness in sediment management.  
The overall performance Figure 14 outcomes related to _Accuracy_ for four distinct methods. Among these methods, the EU Water Framework demonstrates superior _Accuracy_, achieving the highest performance in the experiment. The recorded accuracy rates are 89% for the Sediment Accumulation Process, 95% for the EU Water Framework, 76% for STA and FTIR, and 83% for the Maintenance Policy. These results indicate that the EU Water Framework not only outperforms the other methods but also highlights its effectiveness in ensuring accurate assessments in the context of water management.  
Figure 12: Overall performance analysis of cost.
# Efficient On-Off Keying Underwater Acoustic Communication for Seafloor Observation Networks
### Noncoherent UACs  
The MFSK and on-off keying (OOK) are two typical noncoherent modulation schemes. In the present, the MFSK and its variants have been widely used in practice owing to their high robustness. The MFSK has a low spectral efficiency of \(r=\log_{2}\left(M\right)\)/M bit/s/Hz with \(M\) being the modulation level. The spectral efficiency reaches its maximum of \(r=0.5\) at \(M=2\) or 4 and is less than 0.5 when \(M>4\). The low spectral efficiency restricts the data rate [11] as the available bandwidth of a UAC channel is generally limited [12]. In [6], a data rate of 600 bit/s was achieved at a bandwidth of 5120 Hz. The corresponding spectral efficiency is only 0.12 bit/s/Hz. As another example, the frequency-hopping FSK(FH-FSK) was adopted in the WHOI Micromodem [13] and the Bentnos telesonar modem [8]; both have a low data rate of about 80 bit/s. In contrast, the OOK modulation can achieve a higher spectral efficiency up to 1 bit/s/Hz, comparable to the coherent binary phase-shift keying (BPSK) modulation. For the OOK modulation, a carrier with fixed energy is transmitted only when a symbol is 'on.' Thus, it also has the advantage of lower-power consumption per bit, which enables a single-hydrophone receiver [14]. As a result, the OOK modulation is attractive for many applications in sensor networks [15]. However, there are still some challenges limiting the application of an OOK modulation scheme in SONs as follows:  
* It is difficult determine an appropriate decision threshold for demapping [16], especially over the channel near the seafloor. Due to the severe fading and additive noise, the energy of each received symbol is no longer a deterministic value or zero;
* The conventional regular recursive systematic convolutional (RSC) codes have been widely employed as channel coding schemes in UACs. The signal-to-noise (SNR) threshold required for reliable decoding is generally high, which is unsatisfactory for SON applications.
## Appendix B Analysis of Algorithmic Complexity for Sum-Table Methods  
The complexity of calculating one NCC for 3-D block-matching is analyzed in the appendix for the sake of completeness. The analysis of complexity for the 3-D case is an extension to the work done by [PERSON] and [PERSON] in 2D [41]. Under the framework of a 3-D block-matching, one-direction search ranges are \(S_{x},S_{y}\) and \(S_{z}\) for lateral, axial and elevation directions, respectively. Of note, the one-direction search range above was defined as the maximal translation of the center of the tracking kernel along one given direction (e.g., upward or downward). Given a 3-D tracking kernel with a size of \(W_{x}\) [lateral], \(W_{y}\) [axial] and \(W_{z}\) [elevational] samples, we need to get access to a segment of RF data whose size is at least \(M_{x}[lateral]\times M_{y}[axial]\times M_{z}[elevational]\) RF samples in order to complete the calculations for one NCC values (see Equation (1)), where \(M_{x}=2S_{x}+W_{x}+1\), \(M_{y}=2S_{y}+W_{y}+1\) and \(M_{z}=2S_{z}+W_{z}+1\).  
According to the given data, Table 11 shows the complexity for computing a single NCC value for three methods: Standard NCC, [PERSON] method and Luo-Konofagou method. According to Table 11, the algorithmic complexity of the FFT operation depends on the size of the tracking kernel and the corresponding search area. When the search range is small, [PERSON] method has considerably lower algorithmic complexity as compared to the standard NCC method. When the search area becomes larger, the benefit of using [PERSON] method diminishes. In contrast, the algorithmic complexity of Lou-Konofagou method is not dependent on the tracking kernel size and search range.  
The size of the 3-D reference signal \(f\) are \(f_{x}\), \(f_{y}\), and \(f_{z}\) for lateral, axial and elevation directions, respectively. The size of target signal \(g\) along the axial and lateral directions is the same as that of the reference signal \(f\). However, the size of the 3-D target signal \(g\) for elevation direction is \(g_{z}\), which could differ from \(f_{z}\). The required number of operations in terms of setting up the sum tables is shown in Table 12. Recall that the standard NCC method has no need for using the sum-tables.
## 4 Project Examples
### Experimental Analysis  
#### 4.3.1 Model Evaluation Metrics  
The core process of model prediction is to fit the data to build a model, and then predict subsequent data points based on the established model. Fitting is the key link in modeling, and prediction is the final output of this process. This article uses mean squared error (MSE) and mean absolute error (MAE) as evaluation indicators. The calculation formula is as follows:  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{13}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{14}\]  
where \(y_{i}\) is the true value, \(\hat{y}_{i}\) is the predicted value, and n is the number of samples. The smaller the MSE and MAE, the closer the model's predicted value is to the true value.
###### Abstract  
For installing many sensors in a limited space with a limited computing resource, the digitization of the sensor output at the site of sensation has advantages such as a small amount of wiring, low signal interference and high scalability. For this purpose, we have developed a dedicated Complementary Metal-Oxide-Semiconductor (CMOS) Large-Scale Integration (LSI) (referred to as \"sensor platform LSI\") for bus-networked Micro-Electro-Mechanical-Systems (MEMS)-LSI integrated sensors. In this LSI, collision avoidance, adaptation and event-driven functions are simply implemented to relieve data collision and congestion in asynchronous serial bus communication. In this study, we developed a network system with 48 sensor platform LSIs based on Printed Circuit Board (PCB) in a backbone bus topology with the bus length being 2.4 m. We evaluated the serial communication performance when 48 LSIs operated simultaneously with the adaptation function. The number of data packets received from each LSI was almost identical, and the average sampling frequency of 384 capacitance channels (eight for each LSI) was 73.66 Hz.  
robot tactile sense; dedicated CMOS-LSI; LSI network system; data collision avoidance; backbone bus topology; single-ended signaling +
Footnote †: journal: Sensors
## 2 Materials and Methods
### ZnO Nanowire Fabrication on the PVDF Nanofiber Membrane  
Low-temperature (<10 \({}^{\circ}\)C) hydrothermal synthesis technology forms NWs through crystallization of ZnO on the surface of PVDF NFs. Before the hydrothermal synthesis, ZnO nanoparticles (NPs) and ZnO precursor solution were prepared. First, the ZnO NPs were synthesized in ethanol (>99.5%) solution containing 0.030 M sodium hydroxide and 0.010 M zinc acetate at 60 \({}^{\circ}\)C for 2 h. Then, 0.025 M zinc nitrate hexahydrate, 0.025 M hexamethylenetramine, and 0.001 M polyethyleneimine were dissolved in deionized water at 95 \({}^{\circ}\)C for one hour for the preparation of the ZnO precursor solution. All chemicals for ZnO NW fabrication were purchased from Sigma-Aldrich (USA) and did not require additional processes for purification. A ZnO NPs seed layer coating was performed by dripping and drying the ZnO NP-dispersed ethanol solution onto the PVDF NF layer. Finally, this was immersed in the ZnO precursor solution at 80 \({}^{\circ}\)C for 12 h, as shown in Figure 1c. After hydrothermal synthesis, the PVDF NF layer surface was washed with ethanol to clean out debris and was then dried at room temperature for 2 h.
## 4 Optimality Criteria for Load Balancing Problem Solutions
On the other hand, it can be said that the limitation of the flows will be carried out according to their fraction (\(\beta^{k}\)) weighted directly and proportionally relative to the priorities of packet flows (\(PR^{k}\)), and to the intensity of \(k\)th flow packets (\(\lambda^{k}\)). That is, the share of service denials will be lower for those flows with a higher priority and intensity. The peculiarity of optimality criterion (6) applications, which operate under the constraints and conditions (1)-(5), is that load balancing is ensured within the network based on traffic engineering principles. At the same time, traffic limitation at the network edge is carried out within a linear model, considering the intensities and packet flow priorities. As shown in studies [21; 22], with the help of these solutions, it is possible to provide a more intensive limitation of flows that were a source of overload in terms of their intensity, and that had the lowest priority. In the model proposed in this study and named _\"Traffic Engineering routing, Traffic Engineering limitation\"_ (**TER-TEL**), expressions (1)-(5) should be supplemented with the following condition:  
\[v^{k}.\beta^{k}\leq\gamma, \tag{10}\]  
where \(\gamma\) is the additional control variable that characterizes the load limitation threshold entering the network via border routers. The introduction of expression (10) makes it possible to give the process of the load limitation at the network edge a balanced character under TE principles. Furthermore, depending on the chosen expression (8) or (9) for determining the \(v^{k}\) coefficients, it is possible to ensure that the packet flows' parameters are considered, namely, their intensities and priorities. At the same time, it is advisable to choose the following condition as an optimality criterion for the decisions regarding routing and traffic limitation based on the TE principles:  
\[J=w.\gamma+\alpha\rightarrow\mathbf{min}, \tag{11}\]  
where \(w\) is the weighting coefficient (\(w>1\)) that determines how much the load limitation threshold \(\gamma\) is more important than the upper bound of the network links utilization \(\alpha\). Consequently, when using the optimality criterion (11) in the presence of restrictions (1)-(5) and (10), the traffic management process will be balanced both from the point of view of the TE routing within the network, and the application of TE traffic limitation on the network edge.
## 2 Materials and Methods
### Data Sources  
#### 2.2.1 Meteorological Data  
In this study, meteorological data were used to estimate correlations and contributions to carbon fluxes (Table 1). The meteorological data were obtained from 1985 to 2018 at a spatial resolution of 0.1\({}^{\circ}\) from the China meteorological forcing dataset ([[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)), accessed on 10 December 2021) [45; 46; 47]. In this study, the data were calculated and transformed into yearly mean temperature, precipitation, and solar radiation.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Data Type** & **Date Name** & **Spatial Resolution** & **Temporal Resolution** & **Provider** \\ \hline Meteorological data & \begin{tabular}{l} Mean annual temperature, \\ Precipitation, Solar \\ radiation \\ \end{tabular} & 0.1\({}^{\circ}\) & Yearly & \begin{tabular}{l} National Tibetan Plateau \\ Data Center, China. \\ \end{tabular} \\ \hline Vegetation data & LAI & 0.07272\({}^{\circ}\) & \begin{tabular}{l} Every 15/16 days from \\ 1985 to 1999, Every \\ 8 days from 2000 to 2018 \\ \end{tabular} & \begin{tabular}{l} AVHRR and MODIS \\ \end{tabular} \\ \hline \
\begin{tabular}{l} Carbon fluxes \\ stimulated by BEPS \\ \end{tabular} & GPP, NEP & 0.072727\({}^{\circ}\) & Yearly &
\begin{tabular}{l} National Ecosystem \\ Science Data Center, China. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the data used in this study.  
Figure 1: Spatial distribution of elevation (**a**) and vegetation types (**b**) in the TRSR and its subregions.
# Releasing Differentially Private Trajectories with Optimized Data Utility
## 2 Related Work
### Cryptography  
The location privacy protection algorithms based on cryptography mainly exploit encryption techniques to hide actual trajectories from untrusted analyzers. They are usually used in scenarios such as online data releasing. The advantage is providing a provable privacy guarantee. However, people usually need to develop ad hoc encryption protocols for specific application scenarios. In addition, cryptography techniques usually bring about high computation costs and network overhead.  
For example, [PERSON] et al. [18] propose an oblivious-transferring and \(k\)-nearest neighbor query scheme based on the modified Paillier cryptosystem, in which the LBS server cannot link two queries even if they are initiated by individuals at the same location. However, the encryption protocols take a significantly long time to initialize queries and responses, which makes them impractical to be deployed in mobile devices. [PERSON] et al. [17] propose a new encryption notion, order-retrievable encryption, to enable individuals to share their actual locations without leaking any private information to the LBS server. Recently, [PERSON] et al. [5] propose a blockchain-based and privacy-preserving mechanism for the Internet of vehicle networks, which allows drivers to hide their exact locations and takes control of their data during the data communication and voting process.  
Although the cryptography technique can be used to protect location privacy, it cannot be directly compared with the location perturbation and trajectory synthesis techniques because the privacy notions, attacker assumptions and models are different.
## 5 Experiment Results  
In this section, we designed three kinds of experiments to verify the performance of the deep reinforcement learning and heuristic algorithm (DRL-HA): (1) utilizing DRL-HA as a heuristic solving algorithm; (2) testing this algorithm's data distribution sensitivity; (3) utilizing the HCNN as an initial solution of the meta-heuristic algorithm. The algorithm evaluation requires the following indicators:  
\begin{table}
\begin{tabular}{c} \hline \hline
**Actor-HCNN** \\ \hline Global : Q,K,V-dim = 128, Head = 8, Layers = 3, Feed forward = 512, Dropout = 0.1 \\ Encoder : Conv-1D(\(D_{inputsize}\), Filter = 128, kernel size = 1, stride = 1) \\ Conv-1D(\(D_{inputsize}=1\), Filter = 128, kernel size = 1, stride = 1) \\ Decoder : GRU(hidden size = 128, layers = 1, dropout = 0.2) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main parameters of the actor network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Random** & **WF** & **PF** & **PDF** & **PWF** \\ \hline TS & - & TS-W & TS-P & - & - \\ SA & - & SA-W & SA-P & - & - \\ GRILS & - & G-W & G-P & - & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Competitors.  
1. The average task total profits (P). When an algorithm can obtain a better solution in a large number of scenarios, the superiority of high-quality solutions and the stability of the algorithm are verified.
2. The total running time of the algorithm (T). The running time of the algorithm is the required running time for obtaining the scheduling sequence. It fully reflects the timeliness performance of the algorithm for the agile satellite problem (AEOSSP). In addition, as the scale of the problem increases, the time complexities of the different algorithms become different, and the applicable scenarios of the algorithm will also change accordingly. It should be mentioned that the time of the DRL-HA algorithm refers to the time that the trained network runs on the test set. For the following data of T, its unit is second(s).
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Trends of Mass Change Caused by C30**} \\
**2002.08–2016.07** & \multicolumn{3}{c}{**C30 (RL05)**} & \multicolumn{3}{c}{**(RL06)**} \\ \cline{3-5}  & \multicolumn{3}{c}{**[Gt/yr]**} & \multicolumn{3}{c}{**[Gt/yr]**} \\ \cline{3-5}  & \multicolumn{3}{c}{**GRACE**} & \multicolumn{1}{c}{**GRACE**} & \multicolumn{1}{c}{**GSFC**} \\ \hline \multirow{4}{*}{GrIS} & CSR & \(-2.5\pm 0.2\) & \(-1.2\pm 0.2\) & \(-1.4\pm 0.2\) \\  & GFZ & \(-1.4\pm 0.2\) & \(-1.3\pm 0.2\) & \(-1.5\pm 0.2\) \\  & JPL & \(-1.5\pm 0.2\) & \(-1.5\pm 0.2\) & \(-1.6\pm 0.2\) \\ \hline \multirow{4}{*}{AIS} & CSR & \(15.7\pm 1.4\) & \(7.9\pm 1.1\) & \(8.6\pm 1.2\) \\  & GFZ & \(8.9\pm 1.1\) & \(8.4\pm 1.1\) & \(9.5\pm 1.2\) \\ \cline{1-1}  & JPL & \(9.4\pm 1.1\) & \(9.4\pm 1.2\) & \(10.2\pm 1.2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The trend of mass variations caused by C30 from GRACE RL05, RL06, and GSFC over the GrIS and the AIS during the period from August 2002 to July 2016.
## 4 Applications in Photonic Devices
### Fabrication Approaches for Polymer Photonic Devices
Layer-by-layer assembly enables the creation of complex multilayer structures, though interface quality between dissimilar materials requires careful consideration to minimize optical losses [106]. Selective area deposition, using techniques such as microcontact printing or area-selective growth, provides another route to integrating different functional materials within a single device. Post-processing techniques often play a crucial role in optimizing device performance. Thermal annealing can enhance crystallinity and alignment in semicrystalline polymers, improving both optical and electronic properties [107]. Similarly, solvent vapor annealing offers a gentler approach to reorganizing polymer chains and enhancing interchain interactions in conjugated polymer systems [108]. Surface treatments, including plasma processing or self-assembled monolayer deposition, can modify interfacial properties crucial for multi-material integration. Table 1 summarizes the above-mentioned list of fundamental approaches to photonic device fabrication. The selection of appropriate fabrication approaches depends on multiple factors, including resolution requirements, material compatibility, scalability needs, and economic considerations. For high-performance devices, combinations of these techniques are often employed, with solvent-based methods providing the initial structure and post-processing techniques optimizing performance. The continued development of these fabrication approaches, coupled with advances in polymer design, drives progress toward increasingly sophisticated and capable polymer photonic devices.