esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### SigFox  
Similar to LoRa, the SigFox is a low-power wide area network (LPWAN) technology and provides a complete end-to-end connectivity solution based on ultra-narrowband (UNB) technologies with a coverage of more than 1000 m [10]. The SigFox coverage area ranges from 30 to 50 km in rural areas, and from 3 to 10 km in urban areas, while it is susceptible to interference from other networks existing in the unlicensed ISM band. It can be offered as a cellular network variation, which enables mobile devices to connect to different BSs with UNB. Similar to LoRa, SigFox is suitable for the IoT, due to its long battery life, low device cost, high network capacity, and long range. The low power consumption is achieved by utilizing the duty cycles; thus, a wireless node wakes up and transmits, then waits, listens for a short duration, and returns to sleep mode [93].  
In fact, SigFox is an Internet protocol with no time synchronization requirements. In more detail, the SigFox BSs detect, demodulate, and send the message to the SigFox cloud, a client-server backend that can be accessed by an end user or through application program interfaces (APIs), which are programmed to handle specific data from the SigFox cloud data server. The network messages are sent multiple times on random frequency channels aiming to improve the likelihood of successful reception. Similarly, the end-devices' messages are received by multiple BSs, which provides the opportunity for applying the angulation or lateration localization techniques. Furthermore, the RSS measurements can easily be collected, since they are included in the network messages. The \"Atlas Native\" [94] is the first system that offers the SigFox location service, solely based on the SigFox network location, aiming to provide the geographic coordinates (latitude/longitude). It consists of a SigFox cloud platform, which receives the different frame replicas, which are then transferred to the geomodule, and the node's location is estimated.
## 3 Background and Theory
### Hjorth Features  
The fourth feature extraction approach involves Hjorth parameters [29; 30], classified as time-domain features. These statistical metrics characterize time-domain signals through three key aspects: activity, mobility, and complexity. Together, they offer meaningful insights into the signal's energy, frequency composition, and degree of irregularity.  
Activity Parameter A: quantifies the variance of the EEG signal and is calculated using (13):  
\[A=\text{Var}(x(t))=\frac{1}{N}{\sum_{i=1}^{N}}(x_{i}-\overline{x})^{2} \tag{13}\]  
where the following apply:  
* \(x_{i}\) represents the signal samples;
* \(\overline{x}\) represents the mean value of the signal;
* \(N\) denotes the total number of samples.  
Mobility Parameter M: measures the standard deviation of the first derivative of the EEG signal normalized by the activity. It is given by (14):  
\[M=\sqrt{\frac{\text{Var}(\Delta x(t))}{A}}=\sqrt{\frac{\frac{1}{N-1}{\sum_{i=1 }^{N-1}}{(\Delta x_{i})}^{2}}{A}} \tag{14}\]  
where the following apply:  
* \(\Delta x(t)\) denotes the first derivative of \(x(t)\);
* \(\Delta x_{i}=x_{i+1}-x_{i}\) represents the first differences between consecutive signal samples.  
Complexity Parameter C: the ratio of the mobility of the first derivative of the signal to the mobility of the signal itself, expressed as (15):  
\[C=\frac{M_{\Delta x(t)}}{M}=\frac{\sqrt{\frac{\text{Var}(\Delta^{2}x(t))}{ \text{Var}(\Delta x(t))}}}{M} \tag{15}\]  
where the following apply:  
* \(\Delta^{2}x(t)\) represents the second derivative of \(x(t)\);
* \(\Delta^{2}x_{i}=\Delta x_{i+1}-\Delta x_{i}\) represents the second differences in the signal.
## 4 FSOPSC with Two VVV Sets
### Comparison with Previous Methods  
The proposed controller using two VVV sets (from now on termed C3) will be compared with the help of performance maps. An optimal value for \(\lambda_{sc}\) will be used for each point of operation. Figure 7 shows that C3 outperforms both C1 and C2. Furthermore, from Table 3 one can see that C3 retains the positive traits of C2 with respect to the minimum and maximum values of \(e_{\alpha-\beta}\) and a reduced mean value for said \(e_{\alpha-\beta}\). The switching frequency remains below the limits required by the objectives.  
The proposed C3 controller is now compared to the C1 controller using the standard VVV set (set 1). Figure 10 presents the trajectories for mechanical speed, stator currents on the \(d-q\) axes, and stator phase currents for a particular operating point that falls in \(\Omega_{2}\). The plot on the left corresponds to the standard C1 controller, and the plot on the right  
Figure 8: The space between the lines is region \(\Omega_{2}\), where C2 outperforms C1 according to the selected criteria.  
Figure 9: Diagram of the proposed FSWPSC with two VVV constellations.  
corresponds to C3. An improvement in current regulation is observed when using the proposal.  
Regarding dynamic performance, Figure 11 shows the experimental results for a speed ramp test. It can be seen that the proposed scheme is able to deal with transient states. During the test, the reference speed is changed from 200 (rpm) to 400 (rpm). The tracking of the reference speed is satisfactory (see the upper plot in Figure 11), while stator phase currents perfectly evolve in the expected sinusoidal states.  
Figure 10: Comparison with previous methods in terms of trajectories of control variables for several speeds. In all cases the the standard C1 controller is on the (**left**) and the proposed C3 on the (**right**).
## 2 Methods and Materials
### Methods
#### 2.1.2 FDMAS Beamforming  
For FDMAS beamforming, as proposed by [11], each pixel is reconstructed by multiplying the compounded delayed samples \(s_{n}(x,z)\) in pairs. To keep the pixel dimensionality in volts, the signed square root is initially applied:  
\[r_{FDMAS}(x,z)=\sum_{n=1}^{N-1}\sum_{n^{\prime}=n+1}^{N}sign(s_{n}(x,z)\cdot s _{n^{\prime}}(x,z))\times\sqrt{|s_{n}(x,z)\cdot s_{n^{\prime}}(x,z)|}. \tag{7}\]  
The value of the pixel computed by FDMAS reflects an autocorrelation process for the receive aperture. In this way, FDMAS rejects noise and incoherent echoes with greater efficiency than DAS. Note that the multiplication of signals with the same polarity leads to the loss of the sign information. Therefore, if the RF signals \(s_{n}(x,z)\) oscillate at \(f_{0}\) (along the depth dimension), the spectrum of the image \(r_{FDMAS}(x,z)\) is split between the DC component and \(2f_{0}\). To retrieve narrow-band image lines before envelop detection, the RF image needs to be band-pass filtered at \(2f_{0}\) along the depth dimension.
# P-Wave Reflection Approximation of a Thin Bed and Its Application
## 2 Theory
### Inversion of Thin-Bed Elastic Parameters and Thickness
#### 2.2.1 Extraction of Complex Reflection Coefficients  
Because thin-bed reflection coefficients are complex valued and frequency dependent, we construct complex seismic trace and analyze it in the frequency domain. The conventional AVA seismic trace \(\mathbf{s}^{real}(\theta,t)\) of a thin bed is viewed as the real component of thin-bed complex trace. The imaginary component \(\mathbf{s}^{im}(\theta,t)\) can be calculated through Hilbert transform [25; 26; 27]:  
\[\mathbf{s}^{im}(\theta,t)=\mathbf{s}^{real}(\theta,t)\ast\frac{1}{\pi t} \tag{6}\]  
where the symbol \(\ast\) is time convolution.  
The complex AVA trace \(\mathbf{s}(\theta,t)\) in the time domain is:  
\[\mathbf{s}(\theta,t)=\mathbf{s}^{real}(\theta,t)+\mathbf{j}^{im}(\theta,t), \tag{7}\]where \(j\) is the imaginary term. \(\mathbf{s}(\theta\),\(t)\) in the frequency domain can be obtained through Fourier transform:  
\[\mathbf{S}(\theta,f)=\mathrm{FFT}[\mathbf{s}(\theta,t)]. \tag{8}\]  
If the seismic wavelet \(\mathbf{w}(\theta\),\(t)\) in the time domain is known, we can calculate its value in the frequency domain through Fourier transform:  
\[\mathbf{W}(\theta,f)=\mathrm{FFT}[\mathbf{w}(\theta,t)]. \tag{9}\]  
Thus, the reflection coefficients of a thin bed in the frequency domain can be derived by:  
\[\mathbf{R}^{obs}(\theta,f)=\mathbf{S}(\theta,f)/\mathbf{W}(\theta,f), \tag{10}\]  
where \(\mathbf{R}^{obs}(\theta f)\) is the observed reflection coefficient matrix of an AVA dataset at different frequencies.  
If the seismic wavelet is not known, [PERSON] et al. [28] gave a method to estimate the reflection coefficient from seismic data for a single-interface case. We can extend [PERSON]'s method to estimate \(\mathbf{R}^{obs}\) by extracting its real and imaginary components from those of AVA traces, respectively.
# A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
## 3 Security Threats for IoT Systems
### Routing-Specific Threats
This may result in disrupting legitimate traffic as well as consuming resources available to the victim node. _Buffer reservation attack:_ The buffer reservation attack is closely linked to the fragment duplication attack and may be caused as a consequence of a successful fragment duplication attack. The buffer reservation attack also targets the vulnerability in the fragmentation mechanism employed by 6 LoWPAN networks. As identified by [36], it leverages the fact that the recipient of a fragmented packet is unable to determine if all fragments will be received correctly. Therefore, a recipient node reserves a buffer space based on the information provided in the 6 LoWPAN header with any additional fragments discarded. Taking advantage of this setting, a malicious node can send a victim a single _FRAG1_ to reserve arbitrary buffer space thereby consuming scarce memory of the resource-constrained node. _Sybil and clone ID attack:_ Sybil and Clone ID attacks are similar in that the objective of the attacker is to use spoofed logical identities within a network without deploying physical devices. In particular, for a Clone ID attack, an attacker aims to use a victim's logical identity within the network whereas in Sybil attack, the attacker aims to assume multiple logical identities within a network without deploying physical nodes. These logical identities may not be currently present in the network. A number of existing efforts, such as [PERSON] et al. [30; 34] have identified these attacks for IoT and historically for wireless sensor networks.
## 3 Fiber-Reinforced Concrete (FRC)
### Natural-Fiber-Reinforced Concrete (NFRC)
#### 3.1.2 Durability of Bio Fiber-Reinforced Concrete  
Degradation of natural fibers are investigated by treatment in aging environments [108; 109; 110; 111; 112; 113; 114]. Pretreatment of natural fibers is a well-documented method to enhance the degradation resistance. Silane coating [115; 116], out-of-autoclave method [117], hornification [118], sodium silicate [119], potassium silicate [119], alkaline treatment [115; 120], and coating fibers with bacterial nano-cellulose [121] were used to enhance the durability and mechanical characteristics of NFRC by creating protective layers on the fiber surface or enhancing cellulose structure of natural fiber.  
The other method for increasing the durability of natural fibers is use of appropriate SCMs in the concrete matrix. The lignin quantity of natural fiber has a main role in the sensitivity of NFRC to natural weathering. This is caused by the more susceptibility of hemicelluloses and lignin to chemical deterioration and alkaline environment of cement. The findings show that by combining the calcined clay minerals, alkalinity of pore solution is decreased which can lead to mitigating fiber deterioration. Both alkali hydrolysis and mineralization of natural fiber can be alleviated significantly by this technique [6]. Different studies agree that decreasing the alkalinity of the matrix using SCMs as cement replacement can prevent the chemical attack to lignocellulosic fibers in the matrix [6; 7; 110; 111; 122; 123; 124; 125].  
The rate of natural fiber deterioration in cementitious materials could be reduced by using some SCMs such as nano-calcined clay [7]. [PERSON] et al. stated that the substitution of cement with 1 wt.% nano-calcined clay results in not only enhancing the microstructure but also facilitating the pozzolanic activity which results in stronger bonds between the matrix and the surface of treated hemp fibers [7]. Furthermore, initial flexural strength as well as durability of NFRC are improved by the coupled replacement of metakaolin and montmorillonite, due to modifying the mineralization and alkaline degradation of the fibers. For example, the degradation of sisal fibers was moderated most considerably at high cement substitution level (about 50%) [6]. Using short-length natural fibers and the effects of cement and SCMs on fiber degradation is well documented [6; 7; 75; 111]. Moreover, incorporating a pozzolanic substance into the matrix results in a considerable reduction in capillary absorption and chloride penetration. Overall, the use of appropriate amount and quality of SCMs can enhance the mechanical and durability properties of FRC [84]. Table 2 shows the effect of SCMs on the mechanical performance of natural fiber cementitious composites. Future studies need to be conducted on the adjustment of the mix design, assessing the long-term performance and standardization of the natural-fiber-reinforced LC\({}^{3}\) concrete.
# An Image Segmentation Method Based on Improved Regularized Level Set Model
## 4 Experimental Results
### Segmentation of Single-Objective Images  
In this subsection, the goal is to test the effectiveness of our method in terms of the segmentation of the single-objective images. The DRLSE model [27] can maintain the regularity of the level set function, particularly the desirable signed distance property in the vicinity of the zero level set. The LBF model [49], as a region-based ACM for image segmentation in a variational level set framework, effectively uses local image information to segment the intensity-inhomogeneous images. The LIF model [50] has less computational complexity than the LBF model and does not need reinitialization for image segmentation. The LSACM [51] includes two-phase level set models and yields closed-form solutions for the estimated parameters in the distribution. These representatives ACM algorithms are four state-of-the-art level set methods published recently for image segmentation. They show improvements over the classical ACM and are specially selected based on the level set method for comparison experiments. The chosen parameters for the four models can be found in [27; 49; 51; 50]. The experimental results are demonstrated in Figure 3, and the original images shown in Figure 3a can be found in [52; 74]. Following the experimental techniques for image segmentation in [26; 27; 28; 50; 51] in which the boundaries of objects were labeled in red, we used the same techniques to mark the boundaries of the target regions in this paper.  
As shown in Figure 3, the Row 3, and Row 4 images are two real-world images, and the other images are synthetic images. Some unnecessary objects are obtained by the DRLSE and the LBF models in the second and third columns, and the corresponding experimental results are illustrated in  
Figure 2: The segmentation results of a blood vessel using different parameter values: (**a**) The original image (left) and the segmentation results of our method with \(\
u\) = 0.15 and different \(\sigma\); (**b**) The original image (left) and the segmentation results of our method with \(\sigma\) = 2.6 and different \(\
u\).  
Figure 3b,c, while the LIF model cannot deal with the second and third images in the fourth column of Figure 3. The reason for the segmentation failure is that the LIF model relies on global information, and so it cannot segment images with a weak boundary. The segmentation results are ideal for the fourth image in the fifth column, and the experimental results are demonstrated in Figure 3e. As seen from Figure 3f, the IRLS-IS model succeeds in segmenting the images. It follows that perfect segmentation is primarily due to the external energy term, which can efficiently solve this defect of the weak boundary and obtain good segmentation results. This outcome states that the IRLS-IS algorithm efficiently completes the segmentation of single-objective images with a weak boundary.
## 2 Materials and Methods
### Events Reports Scoring  
Event reports were submitted to three independent judges who had no prior knowledge of the hypotheses formulated. The three judges were all trained psychologists, experienced in interview administration with adequate knowledge of the subject matter. Interrater reliability was >80% for all dimensions considered. Judges then resolved their scoring discrepancies, and the reconciled versions were used in the data analysis.  
The verbal reports were transcribed verbatim by a trained research assistant. The memories from birth to the kindergarten entrance were coded in the first infantile amnesia epoch, whereas the memories recalled during the kindergarten period were considered in the second epoch.  
For the analysis of the different types of events, a scoring grid was prepared in which the events collected were codified according to the presence of the following aspects:  
**Perceptual-Visual Fragment (PVF):** The participant remembers only an image (visual, auditory, smell, or taste) without a narrative organization (e.g., \"I remember the orange light in my room in the afternoon.\").  
**General Semantic Memory (GSM):** Generic situations in which the participant refers to a general memory without any contextual reference or exact space-time (e.g., \"I never wanted to walk, I just needed to be held.\").  
**Episodic Fragment (EpF):** The participant reports a fragment of an incomplete event without specifying contextual details or narrative context but only a behavior as a disconnected memory (e.g., \"I was playing in the countryside making perfumes, a mixture of mint, water, and soap.\").  
**Repeated Episode (REp):** The memory refers to a typical complete event in the life of the participant without reliving and without a specific time-space contextualized memory (e.g., \"My brother, who was a little older than me, went out to go to kindergarten and I was drying on the sofa because I wanted to go too.\").  
**Single Episodic Event (SEpE):** The participant describes a personal recollection of a complete event (e.g., \"In kindergarten I fell and broke my forehead. I remember that my mother came to pick me up at the hospital and I told her: 'Mum I was good, I didn't cry.\" The teachers and my sister were there. I fell when I was alone. I got very scared.\").  
Each memory may present one or more of the aspects listed above. For example, a memory may present Perceptual Visual Fragment (PVF) referred together with a Single Episodic Event (SEpE: e.g., I remember a loud noise and then I remember sitting in my room playing with a teddy bear and my mother came in and told me to put on my shoes that we had to leave right away).
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Experimental Setup
#### 3.1.4 Evaluation Metrics  
We employed five standard metrics to evaluate forecasting performance:  
* Mean Absolute Error (MAE): \(\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}i|\).
* Mean Absolute Percentage Error (MAPE): \(\frac{100\%}{n}\sum i=1^{n}|\frac{y_{i}-\hat{y}i}{y_{i}}|\).
* Root Mean Square Error (RMSE): \(\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}i)^{2}}\).
* Mean Square Percentage Error (MSPE): \(\frac{100\%}{n}\sum_{i=1}^{n}(\frac{y_{i}-\hat{y}i}{y_{i}})^{2}\).
* Coefficient of Determination (\(R^{2}\)): \(1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}i)^{2}}{\sum_{i=1}^{n}(y_{i}-\hat{y})^{2}}\).
# Design of Multifunctional Mesosphere-Ionosphere Sounding System and Preliminary Results
## 3 Prototype Device  
In order to verify the correctness of the scheme described in this paper and the value of engineering practice, we have designed and developed a prototype device. The finished product shown in Figure 12a shows three 2 kW power amplifier units. For every unit, the main components, i.e., the monitoring module, power supply module and linear power amplifier, are placed independently in the frame of three layers of the same movable cabinet, which is convenient for disassembly, reorganization and troubleshooting. In Figure 12b, the transmitting excitation source and multi-channel receiver are integrated into the same casing. Each module, especially the receiving channel, is shielded and isolated by an aluminium alloy frame. The connecting cables of each channel are strictly cut to the same length.  
Figure 11: The pattern of the signal antenna in HF band with the impedance of 50 \(\Omega\): (**a**) 3 MHz; (**b**) 5 MHz; (**c**) 15 MHz; (**d**) 20 MHz. Although the main lobe splits as the frequency increases, it is not important for ionospheric sounding. The gain of full working band is more than 4 dB and the pointing direction is always upward.  
The local oscillator (LO) and calibration reference signals are generated by the separate single-channel DDS devices and fed into each channel by power splitters to maintain the consistency.
## 3 Results
### Detection of Pro-Inflammatory Cytokine Expression  
The ELISA test was conducted to determine the expression levels of the pro-inflammatory cytokines (IL-1\(\beta\), IL-6, TNF-\(\alpha\), MMP-8 and TIMP-1) in HGnF cells that were cultured under various treatment conditions (control, blue@m oxygen fluid at 0.6, 1.2 and 2.4%, and CHX). The control group exhibited a baseline level of cytokine expression (Figure 7, Table 1).  
Figure 6: Cellular oxidative stress levels in gingival fibroblasts exposed with blue@m oxygen fluid at different concentrations for 24 h. (**A**) The mean fluorescence intensities of the control and treatment groups were analyzed using NIS-Elements AR software. Data are presented as the mean of 8 measurements \(\pm\) SD. Phi (\(\phi\)) indicates statistically significant difference at \(p<0.05\), and (\(\phi\phi\)) \(p<0.001\). (**B**) Representative images observed by CLSM. Scale bar: 50 μm, magnification: 20\(\times\).  
Pro-inflammatory cytokine production was suppressed in a concentration-dependent manner as a consequence of exposure to blue(r)m oxygen fluid. A moderate decrease in IL-1\(\beta\), IL-6, TNF-\(\alpha\) and TIMP-1 levels was observed at 0.6% blue(r)m oxygen fluid in comparison to the control. This suppression became more apparent at 1.2% blue(r)m oxygen fluid, and a further substantial decrease was observed at 2.4% blue(r)m oxygen fluid. TNF-\(\alpha\) and TIMP-1 exhibited the most significant decrease among the cytokines assessed in response to rising blue(r)m oxygen fluid concentrations, with IL-1\(\beta\) and IL-6 following suit. Conversely, the concentration of blue(r)m oxygen fluid exposure resulted in an increase in MMP-8 levels.  
On the other hand, CHX exposure significantly increased the expression levels of all measured cytokines relative to the control and that of the different blue(r)m oxygen fluid concentrations. The upregulation of cytokine production following CHX exposure was statistically significant (\(p<0.01\)) across all markers, except IL-1\(\beta\) (\(p<0.05\)). Overall, these findings demonstrate that oxygen fluid exerts a dose-dependent anti-inflammatory effect on HGnF cells, whereas chlorhexidine promotes a pro-inflammatory response.