esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Forward and Inversion Theory
#### 2.2.2 Inversion Theory
The iterative process is summarized as follows:  
For \(i=0,1,\cdots,Iter_{\text{max}}\), calculate:  
\[\mathbf{q}_{i}=\mathbf{G}\mathbf{p}_{i}, \tag{13}\]  
\[a_{i+1}=\frac{\left\|\mathbf{g}_{i}\right\|^{2}}{\left\|\mathbf{q}_{i}\right\| ^{2}}, \tag{14}\]  
\[\Delta\mathbf{m}_{i+1}=\Delta\mathbf{m}_{i}+a_{i+1}\mathbf{p}_{i}, \tag{15}\]  
\[\mathbf{r}_{i+1}=\mathbf{r}_{i}-a_{i+1}\mathbf{q}_{i}, \tag{16}\]  
\[\mathbf{g}_{i+1}=\mathbf{G}^{\text{T}}\mathbf{r}_{i+1}, \tag{17}\]  
If \(\left\|\mathbf{g}_{i+1}\right\|^{2}/\left\|\mathbf{g}_{0}\right\|^{2}\leq\varepsilon\), the iteration terminates; otherwise, proceed to the next step:  
\[\beta_{i+1}=\frac{\left\|\mathbf{g}_{i+1}\right\|^{2}}{\left\|\mathbf{g}_{i} \right\|^{2}}, \tag{18}\]  
\[\mathbf{p}_{i+1}=\mathbf{g}_{i+1}+\beta_{i+1}\mathbf{p}_{i}, \tag{19}\]  
where \(\alpha_{i+1}\) and \(\beta_{i+1}\) are the correction factors for the solution vector \(\Delta\mathbf{m}\) and the conjugate direction vector \(\mathbf{p}\), respectively. During the inversion process, the maximum number of iterations is set to \(Iter_{\text{max}}=1000\), and the conjugate gradient iteration termination error is set to \(\varepsilon=10^{-6}\). To improve the efficiency of the inversion calculation, the for loop in the conjugate gradient algorithm is parallelized using OpenMP [51]. By solving Equation (12) using the above method, the model parameter correction \(\Delta\mathbf{m}\) can be obtained and substituted into the following equation:  
\[\mathbf{m}_{1}=\mathbf{m}_{0}+\Delta\mathbf{m}, \tag{20}\]  
Then, the new model parameter vector \(\mathbf{m}_{1}\) can be obtained. After several iterations, the process continues until the iteration count and the average root mean squared error (RMS) satisfy the termination conditions. The formula for calculating the RMS is as follows:  
\[RMS=\sqrt{\Delta\mathbf{d}^{\text{T}}\Delta\mathbf{d}/N}, \tag{21}\]
# Quadrotor Stabilization and Tracking Using Nonlinear Surface Sliding Mode Control and Observer
## 5 Sliding Mode Control-Based Tracking System
### Position Control  
The control is performed successively by the time-scale separation between the rotational and translational movements. The altitude and attitude control is executed by \(u_{1},u_{2},u_{3}\), and \(u_{4}\) for the quadrotor stabilization. After the attitude of quadrotor becomes stable, the quadrotor is moved to the target position by \(u_{x}\) and \(u_{y}\). In Equation (8), we can see that motions in the \(x\)- and \(y\)- axes depend on \(u_{4}\). For a quadrotor system, the control input \(u_{4}\) also affects motion in the \(z\)-axis. As the desired angle \((\phi_{d},\theta_{d})\) is influenced by the position control input, we also consider the control inputs \(u_{x}\) and \(u_{y}\) for the tracking system. Position control inputs \(u_{x}\) and \(u_{y}\) can be obtained by SMC from Equation (8), and these inputs are composed of the desired angle \((\phi_{d},\theta_{d})\) which tracks the path in the \(x-y\) plane. The control algorithm calculates the desired angle \((\phi_{d},\theta_{d})\) for quadrotor tracking to the desired x and y positions within minimal time. We can obtain the inputs \(u_{x}\) and \(u_{y}\) following the design procedures of attitude and altitude control inputs for \(u_{1}\) through \(u_{4}\).  
\[\begin{split} u_{x}&=\frac{m}{u_{4}}\Big{[}\dot{x}_ {10d}-\frac{1}{\lambda_{x}}(x_{10d})-\alpha_{x}\dot{q}_{x}\big{(}\tilde{x}_{5 }+\lambda_{x}\dot{x}\tilde{x}_{5}\big{)}\\ &\quad\quad\times\big{\{}\gamma_{5}(\sigma_{x})-\gamma_{5}^{2}( \sigma_{x})\big{\}}+\mu_{9}sat(\sigma_{x})+\mu_{10}\alpha_{x}\big{]},\\ u_{y}&=\frac{m}{u_{4}}\Big{[}\dot{x}_{12d}-\frac{1}{ \lambda_{y}}(x_{12}-x_{12d})-\alpha_{y}q_{y}\big{(}\tilde{x}_{6}+\lambda_{y} \dot{x}_{6}\big{)}\\ &\quad\quad\times\big{\{}\gamma_{6}(\sigma_{y})-\gamma_{6}^{2}( \sigma_{y})\big{\}}+\mu_{11}sat(\sigma_{y})+\mu_{12}\dot{\sigma}_{y}\big{]}. \end{split} \tag{19}\]  
From Equations (4) and (19), we can obtain the desired angle \((\phi_{d},\theta_{d})\) as follows:  
\[\begin{split}\phi_{d}&=\sin^{-1}\big{(}u_{x}\sin \psi-u_{y}\cos\psi\big{)},\\ \theta_{d}&=\sin^{-1}\left[\frac{u_{x}\cos\psi+u_{y} \sin\psi}{\sqrt{1-\big{(}u_{x}\sin\psi-u_{y}\cos\psi\big{)}^{2}}}\right]. \end{split} \tag{20}\]
## 3 Results  
Figure 2 displays the pattern of the cumulative explained variance versus the number of PCs. According to Figure 2, 15 components (out of 40 analyzed shapes) are sufficient to cover over 90% of the total variance in the case of the mandible, while only 9 components are able to cover over 90% of the variance in the case of the proximal femur.  
The regression of each mandible versus the respective PCs has resulted in a root mean square error below 1.13 mm, while the same error has resulted to be below 1.09 mm for the proximal femurs.  
The different PCs describing the main shape variations with reference to the average shape are illustrated in Figures 3 and 4 for the mandible and for the femur, respectively. The respective animations are reported as Supplementary Materials.  
Figure 3: The first 15 mandible principal components (PCs). The average mandible shape is represented in blue, while the deformations along the \(i\)th PC applying a weight equal to \(\pm\sqrt{\lambda_{i}}\) are reported in green/brown.  
Figure 2: Cumulative explained variance versus the number of principal components for the mandible (**a**) and for the proximal femur (**b**).  
Following the definition of the SSM (Equation (6)), each \(i\)th PC contributes to determine the geometry of each bone sample with a certain weight \(W_{i}\). Hence, the frequency distributions of these weights among the whole analyzed cohort can be considered. Figure 4(a) shows them with reference to the mandible's dataset. It is evident that the normality of distributions is quite questionable. For this reason, the authors chose to develop a stochastic model based on empirical cumulative frequency distributions (Figure 4(b)).  
Figure 4: The first nine proximal femurs principal components (PCs). The average femur shape is represented in blue, while the deformations along the \(i\)th PC applying a weight equal to \(\pm\sqrt{\lambda_{i}}\) are reported in green/brown.  
Figure 5: Empirical Frequency (**a**) and cumulative frequency (**b**) distributions of weights for each principal component, with reference to the 40 mandibles set.
## 2 Preliminaries
### Denial of Service (DoS) Attacks  
Denial of service (DoS) attacks are a type of cyberattack that attempts to make a machine or network resource unavailable to its intended users. While DoS attacks have been around since the 1970s, they have grown in prevalence and severity in recent years. In 2013, internet security experts estimated that 15% of all websites were suffering from a DoS attack on any given day. The FBI reported that there were more than 3 million active denial of service attacks per month in 2016. The most common DoS attack vector targets web servers by flooding them with unwanted requests, making them unable to deliver the intended content. Such attacks are typically difficult to detect and prevent since they can be orchestrated remotely by an attacker over the Internet. The attacker often has no need to identify the server he or she is attacking; all that is needed is a high level of bandwidth or computing power, which may be acquired over the Internet anonymously. The impacts and effectiveness of DoS attacks vary. Some DoS attacks can be merely inconvenient, while others can prevent affected systems from performing useful work for hours or even days. Advanced persistent DoS (APDoS) attacks can be intended to permanently destroy dataor computer resources. The DoS attack can affect multi-agent systems by producing some packet dropout during their communication. In this article, the packet dropout produced by DoS attacks and the communication's time-varying delays are considered for the formation control of multiple UAVs systems. The packet drops produced by DoS attacks are modeled by parameter \(m_{i}(k)\), This parameter specifies whether a packet is lost or not.  
\[m_{i}(k)=\left\{\begin{array}{cc}0&\text{if}\quad p_{i}(k)\;\;\text{and}\;v _{i}(k)\;\text{are received}\\ 1&\text{if}\quad p_{i}(k)\;\;\text{and}\;v_{i}(k)\;\text{are lost}\end{array}\right. \tag{5}\]  
where \(p_{i}(k)\) and \(v_{i}(k)\) indicate the sampled position and the sampled velocity of agent \(i\), respectively. We assume the time-varying delays are bounded by \(\tau_{d_{max}}\) and \(\tau_{d_{min}}\) with bounded sampling by \(h_{max}\) and \(h_{min}\) and the number of subsequent packet dropouts are bounded by \(\overline{\delta}\), which means  
\[\sum_{v=k-\overline{\delta}}^{k}m_{i}(v)\leq\overline{\delta} \tag{6}\]  
Then, the admissible sequences can be described as follows:  
\[\wp_{i}=\left\{\begin{array}{cc}\left(s_{i}(k),\tau_{d_{i}}(k),m_{i}(k) \right)_{k\in N}\lvert h_{min}\leq s_{i}(k+1)\\ -s_{i}(k)\leq h_{max},\\ s_{i}(0)=0,\tau_{d_{min}}\leq\tau_{d_{i}}(k)\leq\tau_{d_{max}},\sum_{v=k- \overline{\delta}}^{k}m_{i}(v)\\ \leq\overline{\delta}\end{array}\right. \tag{7}\]  
where \(s_{i}(k)\) represents the sampling instants:  
\[s_{i}(k)=\sum_{k=0}^{m}h_{i}(k)\quad\;m\geq 1\;\;s_{0}=0 \tag{8}\]  
which are having the same distance in time made by \(h_{i}(k)>0\), and this is denoted to sampling intervals.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 3 Classification of Energy Management Techniques for Prolonging WSN's Lifetime
### Energy Consumption Analysis of WSN
#### 3.1.1 Analysis of the Energy Consumption of a Sensor Node
The sensor node's transmission energy model is given from [52] as:  
\[E_{total}=\sum_{k}\Big{(}E_{Active,k}+E_{Sleep,\ k}\Big{)} \tag{1}\]2  
where \(P_{WUT,k}\) and \(T_{WUT,k}\) represent the starting power and starting time of the transceiver, \(P_{TX}\) is the power of the transceiver in the transmitting mode, \(L\) is the length of the packet transmitted/received, \(R\) is the data rate, \(P_{RX}\times A\times d^{n}\) is the power sent to the antenna of the transmitting node, in which \(P_{RX}\) is the power received by the antenna of the receiving node and delivered to the low noise amplifier (LNA), \(A\) is determined by characteristics of the transmitting and receiving antennas, \(n\) is the path loss exponent, which depends on the nature of the clutter type, and \(\eta\) is the drain efficiency of the power amplifier (PA). The sensor node's reception energy model is given from [52] as:  
\[E_{total}=\sum_{k}\Big{(}E_{Active,k}+E_{Sleep,\ k}\Big{)} \tag{1}\]3  
where \(P_{RX,k}\) is the power of the transceiver in the reception mode, and \(E_{dec}\) is the energy consumed in decoding a single bit. Combing Equations (11) and (12), the energy consumed by the communication is given by  
\[E_{total}=\sum_{k}\Big{(}E_{Active,k}+E_{Sleep,\ k}\Big{)} \tag{1}\]4  
The energy consumption of the communication unit for digital signal processing in an active state depends on the number of received and transmitted bits and the amount of energy needed for coding and decoding packets [53]. This energy consumption analysis is focused at the node level, making the modeling applicable to both traditional WSNs and LWSNs. After modeling the energy consumption of a wireless sensor node, the following subsection will present the parameters that influence the energy consumption of a WSN and also the sources of energy wastage in the WSN.
# A Review of Optical Neural Networks
## 3 Development Processes and Categories
### Fiber-Based Optical Neural Networks  
The human brain is complex, with 10\({}^{11}\) neurons and 10\({}^{15}\) synapses occupying a minimal space and consuming very little power. The average power consumption of a human brain is only 15 W [69]. In addition, the human brain is thought to be the fastest and most intelligent processing system in existence. The human brain relies on an interconnected network of organic biological microfibers, also known as neurons, to propagate information through the body. Using electrical action potentials, these signals are processed via different spatiotemporal principles [70].  
Optical fibers have been widely used in communication, sensors, and illumination since the 1970s. Optical fibers are used as a transmission medium to transmit information via light. Optical fibers work similarly to biological neurons, where the neurons are activated by the input information and recognize it. The following section focuses on applying and developing optical fibers in neural networks.
## 3 Networks and Protocols of Communication
### Network and Communications Protocols for IoT Applications
#### 3.3.1 Message Queuing Telemetry Transport  
The arrival of IoT has allowed most electronic devices to collect, process, and monitor data in real-time. MQTT protocol is one of the most popular protocols for data sharing because it is lightweight and easy to implement. MQTT is suitable for systems where a lot of real-time data is shared. There are two elements of the message sent via Protocol MQTT, they include topic and message. The data is sent from an editor to those who subscribe to a topic published by [36].  
The central controller that distributes the message is called the MQTT broker. The MQTT broker forwards, filters, and prioritizes publishing requests from publishers to subscribers. To communicate via the MQTT protocol, the editor (or data generator, e.g., sensor system) should define the two elements described above (message and topic) for the MQTT broker. The content of the message is the string data that the editor wishes to share with subscribers through the MQTT broker. Meanwhile, the topic is a string used by the broker to filter and decide what each subscriber should receive [36].
## 4 Results and Discussion
### Parametric Studies
#### 4.2.3 Effects of Sleeper Stiffness
A good pattern is distinct for most, but the highest values of the parameter exhibit unlike behavior; the maximum bending moment is higher than the expected. It is likely that the irregularities applied in the simulation contribute to these variations, changing the location impact forces. Figure 12: Effect of modulus: (**a**) Variations in dynamic factor which correspond to different moduli of elasticity; (**b**) contact forces for minimum and maximum modulus of elasticity. Figure 13: Longitudinal bending moment distribution with reference to various moduli of elasticity. Apart from concrete, the common materials in sleeper design are timber, steel, and composite materials such as Fibre-reinforced Foamed Urethane (FFU). In practice, the choice of materials can cause some drawbacks. For instance, timber sleepers are vulnerable to biological and mechanical degradation. A comparison for various materials is presented in Figure 15. The maximum positive and negative bending moments have common behavior with other simulations. For instance, the asymmetrical properties still exist on the sleepers, such as the sleeper at the closure panel. The discrepancies in bending moment distributions is more perceptible for the sleeper at closure and crossing panels. Aside from the common properties, the highest bending moment takes place when the concrete sleeper is preferred. The magnitude of the maximum positive bending moment is 17 kNm at the section under the crossing nose. Regarding only the sleepers under the crossing nose, soft timber and FFU sleepers could be used interchangeably with those of hard timber and steel since they generally show similar bending moment distributions in general. It seems that steel could be a better choice for the sleepers under the crossing nose since they have better bending moment distributions. Nevertheless, steel sleepers are expensive, require extra care during maintenance, and are subject to corrosion. FFU could be an alternative to concrete sleepers/bearers. Specifically, it has been reported that FFU provides good mechanical properties and works well in high frequency regions [53; 54; 55]. Figure 14: Bending moment distributions of specific sleepers with reference to different moduli of elasticity: (**a**) sleeper at the entrance; (**b**) sleeper under the switch blades; (**c**) sleeper at the closure panel; (**d**) sleeper under the crossing nose; (**e**) the longest sleeper; (**f**) the shortest sleeper.
## 3 Proposed Methodology
### Multi-Level Compression Based on JPEG  
To achieve data compression, we use JPEG compression as the base compression algorithm. To meet the requirements of multi-level compression, we use downsampling, continuously taking the average on a 2 \(\times\) 2 grid, and adjust the parameters of JPEG to control the compression performance and ratio.  
JPEG, as the most well-known and widely used image format, provides the ability to compress images from 0% (heavy compression) to 100% (lossless compression), greatly satisfying our need for variable compression ratios. JPEG compression uses predictive coding (DPCM), discrete cosine transform (DCT), and entropy coding in a joint manner to reduce spatial and statistical redundancy. However, since JPEG is typically used for image data and our research focuses on scientific data, which mainly exist in floating-point format, preprocessing is required before the JPEG compression algorithm is applied. We normalize the scientific data to the range of 0-255 using min-max normalization to better adapt to the JPEG compression algorithm. Specifically, we use the following calculation formula:  
\[x=\frac{s-s_{min}}{s_{max}-s_{min}}\times(new\_max-new\_min)+new\_min \tag{1}\]  
In the above formula, \(s\) represents the original data, \(s_{\max}\) and \(s_{\min}\) are the maximum and minimum values of the original data, and \(new\_max\) and \(new\_min\) are the maximum and minimum values of the target range, respectively. To adapt to the JPEG compression algorithm, we set \(new\_max\) and \(new\_min\) to 0 and 255, respectively. This way, the data will be mapped to a range suitable for JPEG compression. For the decompression operation of the data, we will perform the corresponding reverse normalization, using the following formula:  
\[s=\frac{x-new\_min}{new\_max-new\_min}\times(s_{max}-s_{min})+s_{min} \tag{2}\]  
JPEG compression uses an 8 \(\times\) 8 block-based approach, applying discrete cosine transform (DCT) coefficients to 8 \(\times\) 8 blocks of the input image. Subsequently, the DCT coefficients are quantized, and a rounding function is applied. For encoding, the quantized coefficients undergo entropy coding, with different compression quality factors Q (quality) corresponding to different quantization tables. Here, Q ranges from 0 to 100 as an integer, where lower Q values indicate more information loss. It is worth noting that the quantization of DCT coefficients is the primary cause of image information loss.  
Therefore, for a given input matrix, the process of JPEG compression and decompression is described as follows:  
\[e_{i}=f(X) \tag{3}\]  
\[d_{i}=\widetilde{f}(e_{i}) \tag{4}\]  
where \(f\) represents the JPEG compression operation, \(\widetilde{f}\) denotes the JPEG decompression operation, and \(d_{i}\) is the data after decompression.  
Figure 1: Methodology framework.
## 3 TCM Protocol
### The Topology Establishment Phase  
Currently most of the clustering algorithms such as the LEACH-M and ALM just improve the selection of cluster heads on the basis of LEACH. These algorithms do not consider certain factors like energy, distance, position and mobility which can likely influence the clustering results. Meanwhile, the cluster head selection algorithm itself has the feature of randomness, so it cannot ensure the stability of the number of cluster heads and a balanced distribution. This may cause more energy consumption and isolated nodes. To solve these problems, we put forward an efficient clustering algorithm support for mobile nodes. On the basis of the algorithm described in [19], we consider the factors like energy  
2. Assign node \(n_{i}\) to the closest cluster head, calculate the fitness values of each particle \(P(P\ =\ 1,2,\cdots\ S)\) using Equations (1)-(4).
3. Select the global optimal extremum \(G(t)\) of the population and the individual optimal extremum \(P_{i}(t)\) of each particle.
4. Update the position and velocity of particles.
5. Mapping the position of particle to the actual cluster head.
6. Repeat steps (1) to (5) until the maximum iteration is reached or the required coverage rate is satisfied.  
The clustering process is shown in Figure 3. After clustering, the network is in the topology maintenance phase. Sensor nodes will transmit the data to cluster heads by a single-hop transmission mode.
# Flexible Rectenna on an Eco-Friendly Substrate for Application in Next-Generation IoT Devices
## 3 Results and Discussion
### Design, Fabrication, and Testing of the Rectifier  
We designed and implemented a single-series rectifier circuit based on NSVR351 SDSA3 Schottky diode to harvest energy from ambient RF fields in target bandwidths. We adopted a single-series diode configuration due to its balance of low complexity, reduced cost, and reliable RF-to-DC conversion performance [40]. After that, we design a matching network to achieve maximum output DC power [41]. To find the final values of the matching network elements, we adopt an iterative optimization process that combines electromagnetic simulations with prototype measurements [25]. This methodology allows the design to meet performance requirements and reduce the influences of the parasitic effects of the SMD components used in the circuit. The schematic of the circuit and a photograph of the fabricated prototype are depicted in Figure 8.  
We evaluated the rectifier RF-to-DC conversion efficiency (\(\eta_{\text{rectifier}}\)), connecting the fabricated prototype to the AS108 signal generator, as shown in Figure 9. We measured the DC voltage across four different load resistances (51 \(\Omega\), 510 \(\Omega\), 1000 \(\Omega\), and 4999 \(\Omega\)) as a function of frequency, with RF input power levels ranging from \(-15\) dBm to \(+15\) dBm. The corresponding RF-to-DC conversion efficiency values, calculated using the formula (1) provided below, are illustrated in Figure 10.  
\[\eta_{\text{rectifier}}=(\text{P}_{\text{DC}}/\text{P}_{\text{in}})\times 100, \tag{1}\]  
where \(\text{P}_{\text{in}}\) is the input RF power from the AS108 signal generator in W, and \(\text{P}_{\text{DC}}\) is the output DC power calculated from (2):  
\[\text{P}_{\text{DC}}=\text{V}^{2}_{\text{DC}}/\text{R}_{\text{L}}, \tag{2}\]  
where \(\text{V}_{\text{DC}}\) is measured DC voltage, and \(\text{R}_{\text{L}}\) is the load resistance in \(\Omega\).  
Figure 8: Rectifier: (**a**) schematic of the circuit; (**b**) photograph of the fabricated prototype. The main circuit parameters are as follows: L1 = 11 nH, L2 = 33 nH, C1 = 1 pF, C2 = 100 pF.  
Figure 9: Photograph of the test setup and fabricated rectifier circuit prototype.  
Figure 10 shows that the rectifier RF-to-DC conversion efficiency increases with the input RF power level. Furthermore, within the frequency range of 2 to 2.6 GHz, the efficiency exhibits an increasing trend with frequency. Beyond 2.6 GHz, a decrease in efficiency is observed with increasing frequency. Figure 10b also shows that the rectifiers achieve a maximum RF-to-DC conversion efficiency of 45.49% at 2.5 GHz for an RF input power of 15 dBm and a load resistance of 510 \(\Omega\). As observed, the conversion efficiency exceeded 40%, 30%, 20%, and 5% across the targeted frequency band (2.1-2.7 GHz) at input power levels of 15 dBm, 10 dBm, 0 dBm, and \(-\)5 dBm, respectively.