esa-sceva/satcom-chunk-collection huggingface dataset:
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 2 Binaural Recording and Rendering
### Binaural Rendering
#### 2.2.3 Environment Cues  
Using the static cue, i.e., the HRTF, along to render the binaural signals will have one big problem that the recreated sounds are not well externalized. Dummy head recordings performed in reverberant rooms reveal that reverberation effects are essential for an immersive 3D sensation. For example, the direct-to-reverberant energy ratio enables source distance estimation in the room [70]. Thus, it is important to incorporate environmental scattering cues to achieve good externalization or distance perception [70; 71].  
The environmental scattering is characterized by the room transfer function (RTF) or room impulse response (RIR), a function of the source and receiver positions that includes effects due to reflection at the boundaries, sound absorption, diffraction, and room resonance. The RIR can be separated into two parts, a small number of early strong reflections and very large numbers (hundreds or even thousands) of late weak reverberation. It is believed that the early reflections are helpful for source externalization, thus must be convolved with the appropriate HRTF for the corresponding image source direction [41]. The late reverberation, however, is directionless and thus can be approximated by a generic model for a given room geometry. It is clear that room reverberation has a strong impact on RIR simulation. Physically based reverberation models are widely used in virtual reality to reproduce the acoustics of a given real or virtual space. One example is the image source model for computing the early reflections of RIR in a rectangular room [72]. For a review on different artificial reverberation simulation methods, see, e.g., [73].  
The rendering filter is normally constructed as a Finite-Impulse-Response (FIR) filter, where only the direct path and the first few reflections are computed in real time and the rest of the filter is computed once given the room geometry and boundary. For playback synthesis, simulation of multiple virtual sources at different positons can be performed in parallel, where the audio stream of each source is convolved with the corresponding FIR filter. The convolution can be performed either in the time domain to have a zero-processing lag but with high computational complexity or in the frequency domain for low computational complexity but with unavoidable latency. The synthesized results are mixed together for playback. To further reduce computational complexity, binaural rendering on headphones [74] and for interactive simulations [75] were implemented using a Graphical Processing Unit (GPU).
## 2 Preliminaries  
### Standard Fuzzy C-Means Clustering (FCM) Algorithm  
The standard FCM algorithm attempts to divide a finite number of \(N\) data elements \(X=\{X_{1},X_{2},\ldots,X_{N}\}\) into \(C\) clusters based on some given criteria. Each element \(X_{i}\in X,i=1,2,\ldots,N\), is a vector with \(D\) dimensions. The elements in \(X\) are divided into \(C\) clusters with cluster centers \(V_{1},V_{2},\ldots,V_{C}\) in the centroid set \(V\).  
In FCM algorithm, \(U\) is a matrix that represents the membership of each element into each cluster. Matrix \(U\) has certain characteristics as below:  
* \(U_{ik}\) is the membership grade of an element \(X_{i}\) in the cluster \(k\) with center \(V_{k}\), where \(1\leq i\leq N\); \(1\leq k\leq C\);
* \(0\leq U_{ik}\leq 1\), \(1\leq i\leq N\); \(1\leq k\leq C\) and \(\sum_{j=1}^{C}U_{ij}=1\), for each \(X_{i}\);
* The larger \(U_{ik}\) is, the more element \(X_{i}\) belongs in cluster \(k\).  
An objective function is defined such that the clustering algorithm must minimize the objective function (1):  
\[J(U,V)=\sum_{i=1}^{N}\sum_{k=1}^{C}{U_{ik}}^{m}{D_{ik}}^{2}, \tag{1}\]  
where \({D_{ik}}^{2}=\left|\left|X_{i}-V_{k}\right|\right|^{2}\) is the distance between two vectors \(X_{i}\) and \(V_{k}\), and \(m\) is the fuzzification coefficient of the algorithm.  
**Summary of steps for the standard FCM algorithm:**  
**Input:** the dataset \(X=\{X_{1},X_{2},\ldots,X_{N}\}\).  
**Output:** the partition of \(X\) into \(C\) clusters.  
* Step 1: Initialize value for \(V\), let \(l=0\), set \(\varepsilon>0\) and \(m>1\).
* Step 2: At the \(l-th\) loop, update \(U\) according to the formula: \[U_{ik}=\left(\sum_{i=1}^{C}\left(\frac{D_{ik}}{D_{ij}}\right)^{\frac{2}{m-1}} \right)^{-1}\] (2)
* Step 3: Update \(V\) for the next step \((l+1)\), according to the formula: \[V_{k}=\frac{\sum_{i=1}^{N}{U_{ik}}^{m}X_{i}}{\sum_{i=1}^{N}{U_{ik}}^{m}}\] (3)
* Step 4: If \(\left|\left|V^{(l)}-V^{(l+1)}\right|\right|<\varepsilon\), then go to Step 5; otherwise, let \(l:=l+1\), and return to Step 2.
* Step 5: End.
## 3 Applications of VI-Based Inverters
### HVDC Transmission  
HVDC transmission control can be based on synchronverter technology in synchronverter-based HVDC emulation and energy storage. The control of sending end rectifier (AC to DC converters) emulates the characteristics of an SM. The receiving end of inverter controls acts as SG. The combination of these rectifiers and inverter along with a DC link is known as synchronverter-based HVDC transmission [79]. Synchronverter-based HVDC is researched extensively to integrate frequency droop control and voltage regulation [73].
# Optimization of the Weighted Multi-Facility Location Problem Using MS Excel
### Problem Formulation  
There are \(p\) points where the coordinates of their positions are known, every point has a weight. The objective is to find the positions of the \(c\) centers so that the sum of the distances multiplied by weights (weighted distance) between all the centers and their assigned points will be minimal. Each point will be assigned to exactly one center which is closest to it. Variable \(c\) is an integer greater than or equal to \(1\) and \(p\) is an integer greater than or equal to \(c\).  
A practical example is a company with a number of existing branches which needs to set up a number of centers that will provide services to these branches. Branches have different supply requirements. The locations of the branches are known; the centers will be built as new. The location of these centers and the branches they will serve are not specified.  
The mathematical formulation of this problem is as follows. Let \(P=\left\{P_{1},\ P_{2},\ldots,P_{p}\right\}\) be a set of points (\(p\geq 1\)); the coordinates of these points are known. Let \(C=\left\{C_{1},\ C_{2},\ldots,C_{c}\right\}\) be a set of centers (\(p\geq c\)); the coordinates of these points are not known as they are the subject of the optimization. Let every \(P\) have a weight \(W_{p}\).  
The Euclidean distance between a center and any point in a two-dimensional space is calculated according to Equation (1). In general, any space, number of dimensions and distance function can be used.  
\[d\left(C_{i},P_{j}\right)=\sqrt{\left(P_{x_{j}}-C_{x_{i}}\right)^{2}+\left(P_ {y_{j}}-C_{y_{i}}\right)^{2}}\text{ for }i=1,\ldots,c\text{ and }j=1,..,p \tag{1}\]  
where \(d\left(C_{i},P_{j}\right)\) is the distance between center \(C_{i}\) and point \(P_{j}\), \(C_{x_{i}}\), \(C_{y_{i}}\) are coordinates of the center \(C_{i}\), and \(P_{x_{j}}\), \(P_{y_{j}}\) are coordinates of point \(P_{j}\).  
The objective function of the MFLP-W problem is shown in Equation (2). It corresponds to the sum of weighted distances between all points and their assigned centers which are closest to them.  
\[D=\sum_{i=1}^{c}\min\left(d(C_{i},P_{1})\cdot W_{1},d(C_{i},P_{2})\cdot W_{2},\ldots,d\left(C_{i},P_{p}\right)\cdot W_{p}\right) \tag{2}\]  
The optimization goal is in Equation (3); the aim is to find the locations of \(c\) centers so that the objective function \(D\) is minimal. In the case of two-dimensional space, there are \(2c\) continuous optimization variables.  
\[\begin{array}{c}\text{subject to}\quad\min_{j}P_{x_{j}}\leq C_{x_{i}}\leq \max_{j}P_{x_{j}},\ \ i=1,\ldots,c\\ \min_{j}P_{y_{j}}\leq C_{y_{i}}\leq\max_{j}P_{y_{j}},\ \ i=1,\ldots,c\end{array} \tag{3}\]
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 4 Methodology
### Experimental Approach  
The experimental setup employed a distributed architecture to benchmark the energy consumption of the process running on the DUT (Raspberry Pi). A Windows-based data acquisition system was utilised to record electrical parameters (voltage, current, power, etc.) from a Ruiteng TC66 tester connected inline with the power supply of the Raspberry Pi. The experiment was controlled remotely via network communication between the Raspberry Pi and the Windows machine, with trigger messages being sent for GETREADY, START, and STOP purposes, aligning with the timing of the experiment running on the Pi.  
The key system components included  
* **Target System--Device Under Test--(Raspberry Pi):** This single-board computer executed the software which ran the experiments whose energy consumption was being benchmarked. It also hosted a network client responsible for sending control messages to accurately start and stop the energy acquisition recording.
* **Data Acquisition System (Windows Machine):** This system hosted the data logging software. It was connected to the TC66C energy tester that monitored the electrical utilisation characteristics of the Raspberry Pi. This system also ran a network server to receive control messages to toggle acquisition on and off for the energy data.
* **USB Tester (Ruiteng TC66C):** An external hardware device connected between the Raspberry Pi and its power supply. It accurately measured and reported instantaneous electrical parameters such as voltage, current, and power, and cumulative totals, including the energy consumed.
* **Ethernet Network:** The communication medium facilitating the exchange of control messages between the Raspberry Pi and the Windows machine. This was a dedicated network with UDP messages used, without the latency and additional energy of a TCP handshake.
###### Abstract  
Spectrum-sensing algorithms are one of the effective solutions to the problem of the underwater spectrum resource constraint. However, because the underwater acoustic channel is one of the most complex channels, it has many characteristics, such as a limited communication bandwidth, multipath effect, and ocean noise, all of which render the spectrum detection more difficult. As basic spectrum-sensing algorithms, energy detection algorithms are widely used in underwater acoustic communication and radio. However, most of the existing dual-threshold energy detection methods do not judge the signals with energy values between the thresholds or discard them directly. In this paper, a double-threshold centralized co-operative detection algorithm is proposed to solve this problem. In this algorithm, each sensing user makes a judgment independently, and if the historical energy statistics are between the thresholds, the number of sampling points is increased, and the judgment is made again. In the centralized collaborative sensing algorithm, each sensing user's results are sent to the fusion center, which uses the OR judgment criterion to make decisions. Simulation results show that this algorithm can improve the detection performance and reduce the error rate.  
spectrum sensing; co-operative detection algorithm; underwater acoustic channel Article
## 3 Remote Terminal Unit (RTU)
### Master Routine  
The RTUs' master routine is predominantly controlled by interruptions, this means that the tasks that the system performs are initialized by different types of events. Among the used interruptions can be found: CCP Interruption (Capture-Compare interruption related to the measurement of the frequency of the wind speed sensor), External Interruption (related to measure the amount of rain fall) and the USART interruption (activated when climatologic data is requested to the RTUs).  
The communication model that has been used is a master-slave type; this is, the slave is always in \"listening\" mode and only transmits when it is requested. The master is the central server that generates a request to the BTU, which identifies the destination of the query and requests the remote data from  
Figure 3: Example of destination and source parameters in wireless communication between remote and base units.  
the RTU (which remains in \"listening\" mode). This model reduces significantly the traffic inside the wireless network because the master controls all the transmissions within the network. The flow diagram of the main routine inside the RTUs is illustrated in Figure 4.  
The main routine configures the wireless network at first. This stage consists on setting up: Destination address (DH-DL), Network retries (NR), Power level (PL), Hopping Channel (HP) and Network hops (NH). Then, the MCU enters to listening mode; this is, waits for data on the Tx pin of the USART module. If no data is collected, the main routine will refresh the watchdog timer to keep the MCU in normal mode operation, otherwise an acknowledgement of data received is sent to the base. When the character \"Enter\" (0x0D hexadecimal) is received the program will consequently validate the characters previously stored in the input buffer and if validation occurs, a command interpretation will follow according to the following Table 1.  
Figure 4: Flow diagram of the master routine.  
After the command interpretation, data acquisition is achieved by requesting the values to the corresponding modules (in terms of voltage, frequency or counts) and then converted to physical units in a two-digits format (XX.XX units). Finally weather measurements are sent back to the base and the watchdog refreshed.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 2 A Model for Iterative Steganography
### Message  
The _message_, \(m\), is defined as a vector of \(L\) bits (refer to Equation (3)).  
\[\begin{array}{c}m=[m_{1},\ m_{2},\ \ldots,\ m_{k},...,\ m_{L}]\\ \bigwedge\limits_{k\in\{1..Z\}}m_{k}\in\{0,\ 1\}\\ L\in N,\ m\in\mathcal{M},\mathcal{M}=\{0,\ 1\}^{*}\end{array} \tag{3}\]  
where  
\(L\)--the number of bits in the message, \(m\) (message length);  
\(m_{k}\)--the \(k\)-th bit of the message, \(m\);  
\(\mathcal{M}\)--the set of all messages.  
Let us define \(m(1)\) as the number of \(1\)'s in message \(m\) (refer to Equation (4)).  
\[m(1)=\sum\limits_{k=1}^{L}1_{\{m_{k}=1\}} \tag{4}\]  
Let us define \(m(0)\) as the number of \(0\)'s in message \(m\) (refer to Equation (5)).  
\[m(0)=\sum\limits_{k=1}^{L}1_{\{m_{k}=0\}} \tag{5}\]  
\[m(0)+m(1)=L\]
## 3 System Model
### Energy Consumption Model  
Energy consumption includes four parts: 1 Idle energy consumption: Nodes in the idle state consume little energy and can be ignored. 2 Transmission energy consumption: Sending energy consumption accounts for the main part of energy consumption, which is related to the number of bytes sent. 3 Receiving energy consumption: It is related to the number of bytes received. 4 Control signal energy consumption: Assume that each node is the same.  
If the size of the packet is unchanged and the transmission distance is the same, the energy consumption will be the same. Therefore, if a packet is sent from source to sink, and each hop is the largest distance transmission, and the free space transmission path index is 2, that is, the energy consumption per hop is proportional to the square of distance, then the energy model of each hop transmission is:  
\[E_{hop}=C.D^{\alpha}\to E_{hop}=C.D^{2} \tag{7}\]  
where \(C\) is a constant and \(D\) is a hop distance. \(\alpha\) is the path loss index, which depends on the transmission environment, free space transmission is generally 2. For simplification, set \(C\) to 1 and \(\alpha\) to 2. Packets send energy \(E\) through \(k\) paths \(P_{k}\) from source to target:  
\[E=\sum_{i=1}^{P_{k}}\left(D_{n_{i-1}n_{i}}\right)^{2} \tag{8}\]  
where \(D_{n_{i-1}n_{i}}\) is the transmission distance from \(n_{i-1}\) to \(n_{i}\) hops. Then \(n_{0}\) is the source node and \(n_{P_{k}}\) is the sink node.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Definition** & **Value** \\ \hline T & Propagation delay & 1 \(\mu\)s \\ \(\sigma\) & Time slot & 20 \(\mu\)s \\ \(\text{CW}_{\text{min}}\) & Minimum Competition Window & \\  & Size & 30 \\ \(\text{T}_{\text{PRE}}\) & Physical Layer Synchronization & 128 \(\mu\)s \\ \(\text{T}_{\text{DIFS}}\) & Code Transmission Time & \\ \(\text{T}_{\text{DIFS}}\) & DCF frame interval & 50 \(\mu\)s \\ \(\text{T}_{\text{SIFS}}\) & Minimum Interframe Interval & 10 \(\mu\)s \\ \(\text{T}_{\text{PHY}}\) & Physical Layer Transport Time & 45 \(\mu\)s \\ \(\text{L}_{\text{DATA}}\) & Packet Length & 125 bytes \\ \(\text{L}_{\text{MAC}}\) & MAC Packet Length & 28 bytes \\ \(\text{L}_{\text{ACK}}\) & ACK Packet Length & 14 bytes \\ \(\text{L}_{\text{BITS}}\) & RTS Packet Length & 20 bytes \\ \(\text{L}_{\text{CTS}}\) & CTS Packet Length & 14 bytes \\ \(\text{R}_{\text{DATA}}\) & Data transmission rate & 1 Mbps \\ \(\text{R}_{\text{CTRL}}\) & Control message sending rate & 1 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculating delay symbols and values per hop.
# Synchronization of Analog-Discrete Chaotic Systems for Wireless Sensor Network Design
## 4 Study on Synchronization and Desynchronization Time
### Analog-Discrete and Discrete Simulation and Experimental Setup  
The given study is based on the setup of the first study (Figures 12 and 14), with one key addition that allows the study to be performed: a switch that controls when the synchronization signal is applied. Figure 18 demonstrates the example with analog-discrete synchronization, where the noised synchronization signal is passed to the controlled switch before applying it to the discrete model.  
In case the synchronization occurs in LTspice or MATLAB, only minor functional modifications are required. The only major addition is the discrete-analog synchronization using the hardware prototype demonstrated in Figure 19. The DG419 [38] analog switch circuit is introduced to perform the study. The synchronization signal from the discrete model is passed to the first input \(In_{1}\) of DG419 from the signal generator of ADP3450, while the second input \(In_{2}\) is left open. The switch control input is connected to the second signal generator channel of ADP3450. The generated 2 kHz rectangular control signal has 5 V on high and 0 V on low levels. The switch control signal is recorded on the 3 rd channel of the oscilloscope to assist with getting the start times of the synchronization and desynchronization intervals.  
Figure 18: Chaos oscillator’s discrete model synchronization with LTspice model example with the added switch to estimate synchronization and desynchronization time.  
The case of synchronization acquired in software like in Figure 21, where the synchronization of the discrete model to the hardware the oscillators are kept synchronous for 10\(\theta\); the time between the synchronization is 30\(\theta\). The time before the first synchronization is 50\(\theta\), and the total time is 440\(\theta\). In this case, the start of synchronization and desynchronization are set in software and do not need additional detection. It is important to note that Figures 20 and 21 demonstrate how the synchronization and desynchronization times are measured in simulation and hardware. The results of this study are presented and analyzed in the next subsection.
## 1 Introduction
Joining CS theory with a routing free algorithm would be a worthwhile topic for future work. The main existing problems that limit widespread applications under the real WSN scenario are as follows: (1) lack of a suitable method for a large region covered by a multi-hop network; (2) lack on an effective analysis of sparsity and coherence; and (3) lack of a quantitative analysis of the lifetime of the whole WSN. In this paper we solve all of these issues by jointly employing the hierarchical routing method and compressive sensing in multi-hop networks. The main contributions of this paper are the following:  
* A model combining the hierarchical routing method and compressive sensing (called the HRM_CS) for WSN data acquisition, which includes some variant models, e.g., a model combining the hierarchical routing method and the Bayesian compressed sensing (BCS) based CS recovery algorithm (HRM_CS1), and a model combining the hierarchical routing method and the _L1_-minimization based CS recovery algorithm (HRM_CS2). * A quantitative mathematical analysis of the HRM_CS, which includes signal sparsity analysis, measurement matrix analysis, coherence analysis, and so on. * Design of a HRM_CS framework, being used for the online recovery of large data sets by collecting a small number of readings. * Proof of the effectiveness of our approach for the acquisition and recovery of signals measured in an actual WSN deployment. The paper is structured as follows: in Sections 2 and 3, we present our HRM_CS model for WSN data acquisition and the corresponding analysis in terms of signal sparsity, signal recovery, and routing method. In Section 4, we provide a framework for implementing the HRM_CS. In Section 5, through comparison with standard approaches, we prove the effectiveness of our model. Finally, we present our conclusions in Section 6.