esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Joint and Separable Reversible Data Hiding in Encrypted Images with High Payload
## 4 The Proposed Separable Data Hiding System
### Image Restoration  
In the image restoration phase, we consider two cases: (1) the receiver only has the encryption key; and (2) the receiver has both the data hiding and encryption keys.  
When the receiver has the encryption key but does not know the data-hiding key, undoubtedly, he cannot extract the embedded data without data-hiding key. However, the original image content can be roughly restored. To do this, receivers first generate a random sequence, \(R_{i,jk}\) according to the encryption key using a standard stream cipher. Then, a bitwise XOR of \(I^{\prime\prime}\) and \(R\) is performed to decrypt the encrypted image. Since some most significant bits or second-most significant bits are modified in the separable method, it introduces salt-and-pepper noise on the directly decrypted image. Then, a median filtering is applied to the directly decrypted image to suppress the noise, and a filtered decrypted image is obtained.  
When the receiver has both the data hiding and encryption keys, he can extract the embedded bits and resort the encrypted image to the original version. The detailed steps are as follows.  
Step 1: With the aid of data-hiding key, pixels with hidden bits are selected and by fetching the \(t\) th bit of the corresponding chosen pixel, embedded bits are extracted. By using the data-hiding key, the extracted bits are inverse permuted to get the actual information bits.  
Step 2: A random sequence, \(R_{i,jk}\) is obtained according to the encryption key using a standard stream cipher. These bits are utilized to decrypt the encrypted image and directly decrypted image, \(D\) is obtained. In the directly decrypted image, only the \(t\) th bits of the \(Q\) specific pixels may differ from the original.  
Step 3: From the directly decrypted image, the receiver pseudo-randomly chooses \(Q\) pixels by using the data-hiding key. Let \(D(1)\), \(D(2)\),..., \(D(Q)\), be the \(Q\) selected pixels in the directly decrypted image.  
Step 4: By setting the \(t\) th bit as \(0\) and \(1\), two possible values of \(D(d)\) are achieved. Let \(D_{0}(d)\) and \(D_{1}(d)\) be the two possible values of \(D(d)\), respectively.  
Step 5: The fluctuation function of \(D_{0}(d)\) and \(D_{1}(d)\) are calculated as follows  
\[f_{0}(d) =\Big{\lvert}D_{0(i,j)}(d)-D_{1,j+1}(d)\Big{\rvert}+\Big{\lvert}D _{0(i,j)}(d)-D_{i,j-1}(d)\Big{\rvert}+\Big{\lvert}D_{0(i,j)}(d)-D_{i+1,j}(d) \Big{\rvert}+\Big{\lvert}D_{0(i,j)}(d)-D_{i-1,j}(d)\Big{\rvert} \tag{15}\] \[f_{1}(d) =\Big{\lvert}D_{1(i,j)}(d)-D_{1,j+1}(d)\Big{\rvert}+\Big{\lvert} D_{1(i,j)}(d)-D_{i,j-1}(d)\Big{\rvert}+\Big{\lvert}D_{1(i,j)}(d)-D_{i+1,j}(d) \Big{\rvert}+\Big{\lvert}D_{1(i,j)}(d)-D_{i-1,j}(d)\Big{\rvert} \tag{16}\]By comparing \(f_{0}(d)\) and \(f_{1}(d)\), image restoration can be performed. If \(f_{0}(d)<f_{1}(d)\), then \(D_{0}(d)\) will be the original pixel. Otherwise, \(D_{1}(d)\) will be the original pixel. When \(Q\) original pixels are obtained, the original image is restored.
# Efficient Retrieval of Music Recordings Using Graph-Based Index Structures
## 3 Graph-Based Nearest Neighbor Search
### HNSW Graphs
Similar to the number \(v^{\prime}\) of intermediate neighbor candidates in the search procedure, we denote the number of intermediate candidates for inserting edges by \(M^{\prime}\in\mathbb{N}\). We select \(M\) database items among the \(M^{\prime}\) candidates for inserting edges. In their paper [25], the authors propose two options for this selection. As a first option, they select the nearest neighbors to \(\mathbf{x}\) by exhaustive search. As a second option, the authors propose a heuristic to create connections to \(\mathbf{x}\) from diverse directions, which is beneficial for highly clustered data (see the original paper [25] for more details). In our experiments, we use the second option. If any of the nodes turn out to have more edges than a predefined maximum (set to \(2M\) for the bottom layer and \(M\) for all other layers), the nodes' surplus edges with the highest distance are removed. Figure 4: Illustration of HNSW graph. In summary, we have various important parameters for the construction and search procedure of an HNSW graph. Beyond the distance function \(d\), these parameters are \(\
u\) (number of neighbors to search for), \(\
u^{\prime}\) (number of neighbor candidates during search), \(M\) (minimum number of edges for each node), and \(M^{\prime}\) (number of edge candidates during construction). The authors recommend a range of \(M\in[5:48]\), where higher \(M\) values imply better search results and higher memory consumption. Furthermore, they recommend increasing \(M\) for higher dimensionalities \(K\). In our experiments described in the next section, we fix the squared Euclidean distance (see Equation (2)) as distance function \(d\) as well as the default parameter settings of \(\
u^{\prime}=M^{\prime}=100\) and \(M=5\). As we will see later, a fine-tuning of these parameters is not necessary for obtaining good results in our application.
# Cybersecurity of Microgrid: State-of-the-Art Review and Possible Directions of Future Research
## 2 The Microgrid Architecture
It also performs communication with the Supervisory Control and Data Acquisition (SCADA) system in larger management applications. (C) Enclave 3: the distribution system Distribution systems refers to transmission and distribution technologies, specifically line frequency AC, high frequency AC and DC technologies, whose main role is to transmit and distribute electricity in microgrid systems. (D) Enclave 4: control and communication system Control and communication system in microgrid architecture refers to technologies that handle the output data from microgrid and deliver them for further analysis by different applications, and microgrid controls and management. Two communication media, i.e., wired and wireless, support the communication technologies for power control and protection. Microgrid controls and management includes:* Microgrid Central Controller (MGCC) The MGCC facilitates communication between the Distribution Management System (DMS) and the microgrid to detect and control blackout procedures. The MGCC was also introduced to improve the voltage profile and handle tripping problems. * Supervisory Control And Data Acquisition (SCADA) In a microgrid application, SCADA as a computer-based application plays an important role in acquiring data, and monitoring and controlling operations, including the adjustment of signal alarms. It also enhances the safety, reliability, and economic benefits of the microgrid, and reduces the burden on the dispatchers. Moreover, it employs the automation and modernisation of electrical power dispatch to improve the efficacy and level of information of the system [15].
# Even Dispersion Design for a Compact Linear Loudspeaker Array with Adaptive Genetic Algorithm
## 2 Excitation Parameters Optimization in Narrowband Case
### Theoretical Basis  
The SPL at an observation position \(\mathbf{x}=[x,y]\) in a line is given as  
\[P(\mathbf{x},k)=\int_{\frac{-l}{2}}^{\frac{l}{2}}D(\mathbf{x}_{0},k)G_{2D}(\mathbf{x},\mathbf{x} _{0},k)\,dx_{0}, \tag{1}\]  
where \(l\) is the length of the line array. \(k\) is the wave number that relates to the frequency \(f\). \(\mathbf{x}_{0}=[x,0]\) refers to one position of the line array. \(D(\mathbf{x}_{0},k)\) denotes the sound driving function at this position, which can be given by  
\[D(\mathbf{x}_{0},k)=A(\mathbf{x}_{0},k)\text{exp}(-j\alpha(\mathbf{x}_{0},k)). \tag{2}\]  
\(G_{2D}(\mathbf{x},\mathbf{x}_{0},k)\) is the two dimensional free-field Green's function defined in [36]  
\[G_{2D}(\mathbf{x},\mathbf{x}_{0},k)=\frac{\text{exp}(-jk|\mathbf{x}-\mathbf{x}_{0}|)}{4\pi|\mathbf{ x}-\mathbf{x}_{0}|}. \tag{3}\]  
When the observation position is far away from the center of the line loudspeaker array with a fixed distance \(r=|\mathbf{x}-\mathbf{x}_{0}|\), the sound pressure at angle \(\theta\) can be reduced to [37]  
\[\widetilde{P}(\theta,k)=\frac{1}{r}\sum_{n=1}^{N}D(\mathbf{x}_{0,n},k)\text{exp}( -jknd_{k}\text{sin}(\theta)), \tag{4}\]  
where \(N\) is the number of loudspeakers. \(\mathbf{x}_{0,n}=[x_{n},0]\) is the postion of the \(n\)th loudspeaker. \(d_{k}\) is the interspace of the two adjacent loudspeakers which could be different in different frequencies. \(\theta\) is illustrated in Figure 1. The parameters that need to be optimized are the magnitudes \(A(\mathbf{x}_{0},k)\) and the phases \(\alpha(\mathbf{x}_{0},k)\) in \(D(\mathbf{x}_{0},k)\).  
The directivity is defined as  
\[R(\theta,k)=20\text{log}_{10}\frac{|\widetilde{P}(\theta,k)|}{\text{max}(| \widetilde{P}(\theta,k)|)}. \tag{5}\]
## 2 Related Works and Problem Statement
### An Overview and Operation of WirelessHART  
In this section, we describe the overview and operation process of WirelessHART. Figure 1 shows an example of a WirelessHART topology and its resource allocation.  
The network consists of field devices, a gateway, and a network manager. The field devices are physically deployed sensors that sense information from a device or environment and transmit the sensed data to the gateway. The gateway performs the role of connecting the WirelessHART network with the external backbone network. It is the endpoint of the data collected from each field device.  
The network manager configures and manages networks for low-delay and high-reliable data transmission required by industrial environments. To achieve this goal, the manager constructs a graph of the network topology and allocates resources based on device information collected through the gateway. Especially, by exploiting TSCH in the resource allocation process, transmission collisions and interference that could occur in the data transmission process of each device are prevented. In addition, it adjusts the transmission path and time when network changes such as addition or removal of devices  
Figure 1: An example of WirelessHART network and resource allocation.  
occur. That is, the network manager performs the core functions of the WirelessHART network and is specifically aimed at responding to changes in the network. Typically, there is the join process to involve newly added devices in the network and the neighbor discovery process to deal with topology changes caused by device location changes and environmental changes.  
The neighbor discovery process [16; 17; 18] is exploited to address changes in the location of devices which have already participated in the network through the join process, or when the quality of communication between devices has changed due to environmental changes. To accomplish this process, field devices transmit discovery messages for each period determined by the network manager, which functions similarly to a keep-alive message while also searching for new paths. In other words, the discovery message is exploited to periodically check whether the neighborhood near it operates normally when there is no change in the network topology. However, if the network changes occur, some field devices receive a discovery message from a new field device or fail to receive a discovery message from the existing field devices. In this case, the field devices report the detected changes to the network manager. The network manager, which receives the report, performs graph reconstruction and resource reallocation according to the changed network topology. Finally, it informs each field device of the information recalculated for communication in the changed topology.
## 5 Heuristic Scheduling Algorithm  
We try to assign a callback \(\tau_{i}\) from a set \(\Delta\) to a unique executor \(e_{i}\) of a set \(\xi\). The problem consists of finding a feasible assignment using a minimal number of executors while meeting the timing requirements. The system is declared schedulable if and only if any executor \(e_{j}\) of \(\xi\) is schedulable and satisfies the deadline constraint.  
To achieve this, we propose the following algorithm that assigns \(n\) callbacks from \(\Delta\) to \(m\) executors of \(\xi\) (Algorithm 1).  
```
Input:\(\Delta=\{\tau_{1},\tau_{2},\ldots,\tau_{n}\}\) Output:\(\xi=\{e_{1},e_{2},\ldots,e_{m}\}\) // \(\xi=\emptyset\) ; // \(j=1\) ; // Starting from the lowest priority while\(\Delta\
eq\emptyset\)do \(\Pi_{j}=Assignment(\Delta)\) ; // Algo. 2 if\(\Pi_{j}\
eq\emptyset\)then \([\Lambda_{j},e_{j}]=CreateExecutor(\Pi_{j})\) ; // Algo. 3 \(\xi=\xi\cup\{e_{j}\}\) ; // \(\Delta=\Delta\backslash\Lambda_{j}\) ; // Remove \(\Lambda_{j}\) from \(\Delta\) \(j=j+1\); else \(\xi=\emptyset\) ; // Set empty output break ; // System is not scdedulable!
1
2 end if
```  
**Algorithm 1**Assigning callbacks to executors.  
To establish the output list of executors \(\xi\), the algorithm invokes two other algorithms in its main loop. The first algorithm, 'Assignment' (Algorithm 2), establishes a subset of callbacks \(\Pi_{j}\) to be assigned to the same executor \(e_{j}\) by starting from the lowest prior  
Figure 4: Example of assigning callbacks to the executor \(e_{1}\). \(\pi_{1}=LCM(10,15,15,30)=30\). \(T_{1}=GCD(10,15,15,30)=5\). \(D_{1}=\min\{8,10,12,19\}=8\). \(Number\ of\ frames\ N=30/5=6\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(s^{th}\) Frame} & \multicolumn{4}{c}{\(f_{i}(s)\)} & \multicolumn{4}{c}{\(h_{i}(s)\)} & \multirow{2}{*}{\(C_{1,s}\)} \\ \cline{2-2} \cline{5-10}  & \(\tau_{1}\) & \(\tau_{2}\) & \(\tau_{3}\) & \(\tau_{4}\) & \(\tau_{1}\) & \(\tau_{2}\) & \(\tau_{3}\) & \(\tau_{4}\) \\ \hline
4 & 4 & 4 & 3 & 5 & 1 & 1 & 0 & 0 & 2 \\ \hline
5 & 4 & 4 & 3 & 5 & 0 & 0 & 0 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: An example of computing executor model parameters.  
ity level (j = 1). In the case of not empty \(\Pi_{j}\), a second algorithm, called 'CreateExecutor' (Algorithm 3), is called to define the subset of callbacks \(\Lambda_{i}\) from \(\Pi_{j}\) to be assigned to the executor \(e_{j}\) and determine its parameters. By iteration, the set of executors \(\xi=\{1,\ldots,j,\ldots,m\}\) is constructed until all callbacks (\(\Delta=\emptyset\)) are assigned to the executors. If, in any iteration, Algorithm 2 does not find a feasible subset \(\Pi_{j}\), then the entire system is declared unschedulable. In the following, we detail Algorithms 2 and 3 used by the main Algorithm 1.
## 1 Introduction  
The construction of embankments over very soft to soft clay, such as reclamation projects, is on the rise due to the scarcity of suitable land for infrastructure and other developments. The development of port infrastructure located along the coast often requires reclamation over the sea. One such development is the reclamation project in the District Manyar, Gresik region of East Java. The seabed in this area is predominantly composed of soft clay. Research on embankment construction on soft soil, utilizing both empirical methods and the finite element method (FEM), has been extensively discussed, including [1, 2, 3, 4]. The embankment on soft soil leads to various stability issues, one of which is significant settlement with a low bearing capacity [3, 4, 5]. Soil improvement is implemented to mitigate stability issues of embankments on soft soil, for example, using prefabricated vertical drains (PVD) to accelerate the consolidation process [3]. PVD can also be combined with vacuum preloading [6, 7, 8]. PVD is often preferred because of its low cost and ease of installation. PVD combined with soil preloading or vacuum preloading can improve the shear strength of low-lying area. Gained soil strength remains even after the preloading is removed [9]. Reinforcement can also be carried out to enhance the bearing capacity of soft soil layers, including geotextile reinforcement [10, 11], pile reinforcement [12, 13], and bamboo reinforcement [14]. A comparison of the various soft soil reinforcement method is explained in Table 1.  
According to one of the previous studies on embankments in the Sidoarjo Mud Dam [3], untreated soft soil tends to experience significant settlement and long consolidation times. The largest settlement is located directly beneath the 12-m-high dam crest, which reaches 7 m. Settlement has persisted for several decades. One part of the reclamation, which involves the construction of a causeway, intersects with a gas pipeline at a depth of 5 m from the seabed. The embankment construction will undoubtedly impact the surrounding area, prompting research on the effects of river channel excavation on underground tunnels [18]. A buried gas pipe is highly sensitive to ground deformation due to its linear structure. Load from embankment filling above the pipe can cause settlement and lateral displacement on the soil around the pipe. This can lead to deformation of the buried gas pipeline and potentially severe damage to the connections between pipe segments, ultimately resulting in breakage. Figure 1 shows a schematic of how the load from embankment filling can affect the buried gas pipeline.
## 4 Sequential Signature Transmission Scheme for Sensor Data
Considering that \(i_{1}\), \(i_{2}\), \(i_{3}\) and \(i_{4}\) are 4, 12, 18, and 30, respectively, the sensor node uses the hash values \(H(sk_{4})\), \(H(sk_{12})\), \(H(sk_{18})\), and \(H(sk_{30})\) as signatures \(s_{1},s_{2},s_{3},s_{4}\); at the same time, they are sent to the remote state estimation system together with the data message \(data_{k+T}\); the message is actually a certain kind of monitoring data or alarm data. After receiving the signature and message, the remote state estimation system directly calculates \(f(s_{j})\) and verifies whether a certain public key \(pk_{i_{j}}\) (\(1\leq j\leq 4\)) in the set matches \(f(s_{j})\). In actual scenarios, in order to meet the required security level, we choose a large number \(n\), such as \(log_{2}\)\(n\), which is at least 160 bits. Then, in the case of point-to-point communication, when receiving the signature and monitoring data from the sensor node, the corresponding remote state estimation system calculates \(h\), generates a substring \(h_{j}\), converts each \(h_{j}\) to \(i_{j}\), and then and checks whether \(f(s_{j})=pk_{ij}\). For point-to-point communication, the security level of the hash string should meet at least (\(l\times log_{2}\)\(k\)) = 128 bits. That is, \(k=2^{8}\) = 256, \(l\) = 16 or \(k\) = 2\({}^{10}\) = 1024, \(l\) = 32 can be considered to ensure the standard security level 160-bit hash code. To analyze and verify signature generation and verification, in the communication system scenario based on the DNP3 protocol, the communication between the remote state estimation system and the sensor nodes is realized. Since the default control duration of the DNP3 protocol standard is 250 ms, the channel latency can be reduced to 15 ms at 9600 baud and 32 bytes of data. We consider baud rate B = 100 bit/s, propagation delay D = 150 ms, MTU = 235 bytes, packet size P = 600 bytes. In the SHA1, SHA256, and SHA384 experiments, our scheme works well, and the signature generation and verification times of the tested five packets respectively are (0.048-0.065, 0.091-0.179), (0.099-0.152, 0.116-0.189), and (0.121-0.176, 0.126-0.368) ms. Compared to SHA256 and SHA384, SHA1 takes less time for signature generation and verification. However, SHA384 can provide better security. Thus, it satisfies the transmission of sensing data and alarm messages for SCADA systems, providing an efficient and safe service. Our proposed scheme is efficient and has less signature generation and verification time. Therefore, the proposed scheme is practical, efficient, and reasonably effective, and can be deployed on a real industrial control system network.
# Design of NO\({}_{2}\) Photoacoustic Detection System Based on Finite Element Simulation
## 2 Experimental Principle  
### Theoretical Basis  
The basic process of photoacoustic spectroscopy (PAS) technology is that the gas molecules to be measured will absorb energy from the ground state to the excited state after illumination. Since there is no radiative relaxation phenomenon, the gas to be measured will release heat energy and will produce pressure changes, thereby causing changes in the sound field. This light-heat-sound physical process can be expressed as [25]  
\[H=N\alpha P_{\text{laser}}g \tag{1}\]  
\[\
abla^{2}p-\frac{1}{c_{0}^{2}}\frac{\partial^{2}p}{\partial t^{2}}=-\left[ \frac{\left(\gamma-1\right)^{2}}{c_{0}^{2}}\right]\frac{\partial H}{\partial t} \tag{2}\]  
In this formula, \(H\) represents the thermal power density, \(N\) represents the gas molecular density, \(\alpha\) represents the absorption coefficient of the gas to be measured, \(c\) represents the concentration of the gas to be measured, \(P_{\text{laser}}\) represents the laser power, \(g\) represents the normalized light intensity distribution, \(p\) represents the sound pressure, \(\gamma\) represents the specific heat capacity, and \(c_{0}\) represents the sound velocity of the gas.  
If the concentration of the gas to be measured is relatively low, the relationship between the photoacoustic signal and the concentration of the gas to be measured can be expressed as follows [26]:  
\[S_{\text{PA}}=S_{\text{m}}P_{\text{laser}}C_{\text{cell}}\alpha c \tag{3}\]  
In this formula, \(S_{\text{PA}}\) represents the size of the photoacoustic signal, \(S_{m}\) represents the sensitivity of the microphone, and \(C_{\text{cell}}\) represents the pool constant of the photoacoustic pool.  
It can be seen from Formula (3) that in the case of known \(S_{\text{m}}\), \(P_{\text{laser}}\), \(C_{\text{cell}}\), \(\alpha\) and the concentration value of the measured gas can be obtained by the size of the signal. In addition, because \(S_{\text{PA}}\) is proportional to \(c\), it provides a basis for the subsequent calibration study.  
The quality factor can be calculated by Equation (4):  
\[Q=\frac{f_{0}}{\Delta f} \tag{4}\]  
where \(f_{0}\) is the resonance frequency of the acoustic cavity, and \(\Delta f\) is the line width of the frequency response curve of the acoustic cavity.  
The formula for calculating the resonance frequency of a cylindrical photoacoustic cell with two open ends is as follows [27]:  
\[f_{0}=\frac{w_{j}}{2\pi}=\frac{v}{2L_{eff}} \tag{5}\]  
\[L_{eff}=L_{\text{C}}+\frac{16}{3\pi}R_{c} \tag{6}\]  
where \(v\) represents sound velocity, \(R_{c}\) represents the radius of the resonator, \(L_{\text{C}}\) represents the length of the resonator, and \(L_{eff}\) indicates the corrected length. Resonance frequency, quality factor, and cell constant are also three characteristic indexes to describing the characteristics of a photoacoustic cell.