esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area, Dataset, and Methods
### Methods
#### 2.3.2 The Continuous Triangular Model  
The continuous triangular model (CTM) is an extension of the triangular model (TM). The TM, initially introduced by [PERSON], is a 2D representation of time intervals [46]. In the TM, any time interval I, which starts from t\({}_{1}\) and ends at t\({}_{2}\), is represented by a corresponding point P, the intersection point of two straight lines L\({}_{1}\) and L\({}_{2}\), with L\({}_{1}\) passing through t\({}_{1}\) and L\({}_{2}\) passing thought t\({}_{2}\) (see Figure 2). However, the TM is limited in its ability to represent time intervals continuously. To overcome this limitation, the TM was extended to the CTM [47].  
The CTM can represent continuous temporal data, e.g., attribute values during all time intervals. Attribute values during any time interval can be calculated using specific algebra operators (such as mean, maximum, and summation) based on the attribute values at the finest timestamps within the time interval. The attribute values between two neighboring timestamps can be calculated using interpolation [48; 49]. As a result, the continuous fieldof the CTM can be displayed as an image through color coding, where each color denotes the attribute value during the corresponding time interval [48; 49].  
Figure 3 presents a simple example of representing temporal data (i.e., speed data) using the traditional linear method (Figure 3a) and the CTM method (Figure 3b). Note that the part of curve marked by the dotted red rectangle in Figure 3a is represented by the dotted red triangular in Figure 3b. In Figure 3b, one can observe that every single point in the CTM has a specific color corresponding to a speed value. Since each point in the CTM represents a specific time interval, the CTM has the unique property of simultaneously displaying the dataset at all temporal resolutions. Therefore, the CTM can exhibit more abundant information compared to the traditional linear representation, as it displays information at all temporal scales (from the minimum temporal scale to the maximum temporal scale) simultaneously in the same figure.  
Given the advantage of the CTM, this paper uses the CTM to analyze trip data to reveal potentially interesting temporal patterns of intra-urban human mobility that might be difficult to uncover with other methods.  
Figure 3: Illustration representing temporal data using the CTM: (**a**) the linear representation of temporal data and (**b**) the CTM representation of the temporal data (from reference [48]).  
Figure 2: Illustration of the TM (from reference [49]).
# Seismic Exploration Methods for Structural Studies and for Active Fault Characterization: A Review
## 4 Seismic Attributes
### Curvature  
The curvature attribute is a horizon-based seismic attribute that measures the curvature of the seismic reflection events. As the previous statement suggests, curvature is calculated directly from a selected seismic horizon, and typically, it requires good data quality and the presence of a prominent impedance contrast in the horizon of interest [89]. Curvature obtained from horizons picked on noisy data can indeed lead to misleading results. The concept of curvature involves assessing the degree of bending of a surface at a particular point using volumetric techniques [102]. After picking and filtering a horizon on a seismic volume, a mathematical quadratic surface is fitted to the picks within a user-defined aperture. Subsequently, various measures of curvature are analytically computed based on the coefficients of the quadratic surface. Therefore, instead of analyzing variations in seismic amplitude, this attribute focuses on the geometric distortion. [PERSON] [102] presented the application of various curvature attributes. Among them, according to [PERSON] and [PERSON] [89], the most-positive and most-negative curvature measures seem the most straightforward in establishing direct connections with geologic structural and stratigraphic features commonly encountered in the field, providing helpful information to coherence and other attributes (Figure 7 see also [PERSON] et al. [103] and references therein).  
To conclude, this attribute can effectively identify small-scale faults and fractures that may not be detectable with other attributes (e.g., Sigismondi and Soldo, [104]). For instance, [PERSON] [105] successfully correlated fractures measured on an outcrop to curvature values.
# Fluctuation-Enhanced Sensing (FES): A Promising Sensing Technique
## 5 Practical Sources of Errors
### Measurement Circuitry Problems: Noise, Bandwidth  
Performing sensible low frequency noise measurements (LFNM) is never an easy task. The very instrumentation required for performing noise measurements introduce noise that may mask the noise coming from the Device Under Test (DUT), thus reducing the sensitivity that can be obtained. Since in the field of FES we are mainly interested in the low frequency noise generated by the DUT, it is generally the flicker component of the noise introduced by the instrumentation that sets the limit to the sensitivity that can be obtained. It must also be noted that LFNM measurement set-ups are extremely sensitive to interferences coming from the environment. Electro-magnetic interferences (EMI) at frequencies much higher than those one is interested in may translate into what could appear as additional low frequency noise components because of rectifying effects due to the active components in the measurement chain [76]. Even moderate air movement, in the immediate proximity of active components, as caused by unrestricted convection motion due to power dissipation, may result in apparent increase in the noise. Assuming that all causes of interferences are removed by proper shielding and positioning of the components, the ultimate sensitivity is set by the intrinsic noise introduced by the DUT bias systems, by the preamplifiers that detect and amplify the noise signal across the DUT and, possibly, by other auxiliary systems such as DUT temperature control systems. While standard instrumentation exists that can be used for laboratory set ups and even wafer level noise testing [77; 78; 79], depending on the DUT characteristics, dedicated instrumentation may be a better choice in order to maximize sensitivity [80] and, in the specific case of FES applications, portability. The most important problems to be faced and possible circuitry solutions will be discussed in Section 6.3.
# The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems
###### Abstract  
End-to-end learning in optical communication systems is a promising technique to solve difficult communication problems, especially for peak to average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems. The less complex, highly adaptive hardware and advantages in the analysis of unknown or complex channels make deep learning a valid tool to improve system performance. In this paper, we propose an autoencoder network combined with extended selected mapping methods (ESLM-AE) to reduce the PAPR for the DC-biased optical OFDM system and to minimize the bit error rate (BER). The constellation mapping/de-mapping of the transmitted symbols and the phase factor of each subcarrier are acquired and optimized adaptively by training the autoencoder with a combined loss function. In the loss function, both the PAPR and BER performance are taken into account. The simulation results show that a significant PAPR reduction of more than 10 dB has been achieved by using the ESLM-AE scheme in terms of the complementary cumulative distribution function. Furthermore, the proposed scheme exhibits better BER performance compared to the standard PAPR reduction methods.  
orthogonal frequency division multiplexing; autoencoder; end-to-end learning; peak-to-average power ratio +
Footnote †: journal: Applied Sciences
## 2 General Concept of Two-Port Interconnection  
This work introduces the way of analyzing behavior of the resulting response of a sum of lossless integrator and differentiator where each of them has integer as well as fractional-order character (Figure 1). We will also observe the impact of the gain variation (designated as \(A_{1}\) and \(A_{2}\) in Figure 1) on the features of each path. We decided to study four particular cases: (a) integer-order integrator + fractional-order integrator, (b) integer-order differentiator + fractional-order differentiator, (c) integer-order integrator + fractional-order differentiator, (d) integer-order differentiator + fractional-order integrator.  
The transfer of the generalized system has form:  
\[K_{\text{C}}(s)\ =\ A_{1}\text{:}H(s)+A_{2}\text{:}G(s) \tag{1}\]  
where partial transfers \(H\)(s) and \(G\)(s) are as follows:  
\[H_{\text{int\_C}}(s)\ =\ \frac{1}{\tau_{1}s} \tag{2}\]  
\[G_{\text{int\_CPE}}(s)\ =\ \frac{1}{\tau_{2}s^{a}} \tag{3}\]  
\[H_{diff\_C}(s)\ =\ \tau_{1}s \tag{4}\]  
\[G_{diff\_CPE}(s)\ =\ \tau_{2}s^{a} \tag{5}\]  
\[H_{diff\_C}(s)\ =\ \tau_{1}s \tag{6}\]  
\[G_{\text{int\_CPE}}(s)\ =\ \frac{1}{\tau_{2}s^{a}} \tag{7}\]  
\[H_{\text{int\_C}}(s)\ =\ \frac{1}{\tau_{1}s} \tag{8}\]  
\[G_{diff\_CPE}(s)\ =\ \tau_{2}s^{a} \tag{9}\]  
where the particular index of \(H\)(s) and \(G\)(s) indicates the character of the accumulating device used in a particular two-port, and \(\tau_{1,2}\) are initial values of the time constants.  
Commercially-available, active devices can be used for purposes of practical verification of the concept from Figure 1[29]. Variable gain amplifiers (VGAs) providing control of \(A_{1}\) and \(A_{2}\) gains will be based on VCA810 devices [30], the standard concept of the integrator and differentiator topology will utilize the LT1364 operational amplifier [31], and the sum operation is easily achievable by an AD8130 element [32]. In order to obtain fractional-order integrator or differentiator topology, a fractional-order element is required. The Constant Phase Element (CPE) is one of the possibilities described in the next section. The following subsections describe each of the four considered topologies, together with the most significant simulation and experimental results.
###### Abstract  
Low agricultural output and a decline in plant-available nutrient content in soil pose significant challenges to developing countries. To test the hypothesis that poultry litter and its biochar improve soil quality, nutrient status, and plant growth, a greenhouse pot experiment with sandy-loam soil was conducted. Selected application rates of poultry litter (0, 15, 30, 60, and 120 g/kg) and its biochar pyrolyzed at 350 \({}^{\circ}\)C and 600 \({}^{\circ}\)C were used. With the addition of organic amendments, Jatropha plant height, leaf number, and stem diameter improved significantly, as did soil fertility indicators (pH, organic matter content, cation exchange capacity, and plant-available nutrients). When compared to the control, increased application rates ranging from 60 g/kg to 120 g/kg significantly improved soil properties and plant growth. PL (Poultry litter)- and BC350 (Biochar produced at 350 \({}^{\circ}\)C)-treated soil outperformed other organic amendments in terms of soil quality, nutrient status, and plant growth. Soil pH, CEC, and OM were found to be positively correlated with available plant nutrients, with PL-treated soils having higher levels of plant available nutrients. Because the properties of the feedstocks complement each other, combined organic amendments improved studied parameters, particularly PLBC600 (Poultry litter mixed with biochar produced at 600 \({}^{\circ}\)C) compared to BC600 (Biochar produced at 600 \({}^{\circ}\)C). Increased application rates of pure and combined feedstock effectively increased soil fertility and Jatropha growth; however, lower temperature biochar is recommended for use as a soil organic amendment.  
biochar; nutrient availability; poultry litter; _Jatropha curcas L.; soil fertility_
# Fabrication of Alumina-Doped Optical Fiber Preforms by an MCVD-Metal Chelate Doping Method
## 3 Results and Discussion
(**b**) shows the opaque regions in the core due to the presence of Al-rich aluminosilicate crystallites. Figure 9a-c shows contour plots of the Al distribution for fibers F1, F2, and F3, respectively, that were drawn from preforms P1, P2, and P3, respectively. In general, the Al distributions for all fibers show the same pattern, with the highest Al content being located at the center of the core area. The maximum Al concentrations for fibers F1, F2, and F3 were 1.7, 4.3, and 3.2 wt.%, respectively. This corresponds to alumina concentrations of 1.9, 4.9, and 3.7 mole% for F1, F2, and F3, respectively. In the current study, the fabrication process efficiency was measured by dividing the alumina content obtained from EDX analyses of fibers by that derived theoretically. For the fabricated fibers F1 and F2, the process efficiency values were found to be 11 and 21%, respectively. These values indicate that the  
Figure 8: **(a)** A contour plot of the Al concentration in the core region of preform P3. The EDX point identification measurements were performed for \(10\times 10\) grid points covering an area of \(2.0\times 2.0\) mm. The sample for EDX analysis was cut from the edge of the preform adjacent to the outlet. **(b)** The EDX spectrum measured at the point with the highest Al concentration (9.9 wt.%). The inset table lists the wt.% of Al, Si, and O.  
Figure 7: **(a)** The refractive index profile and **(b)** the longitudinal variation in the refractive index difference (\(\Delta\)n) for preform P3. \(\mathrm{T_{Al}}\)\({}^{\circ}\)C is a critical factor; an increase in \(\mathrm{T_{Al}}\)\({}^{\circ}\)C of 10 \({}^{\circ}\)C resulted in a 3.3-fold increase in \(\mathrm{Al_{2}O_{3}}\) content. The relationship between \(\Delta\)n (obtained from RIP) and \(\mathrm{Al_{2}O_{3}}\) mol.% (obtained from EDX analysis) is plotted in Figure 10. As can be observed from the figure, the refractive index of silica increased by 0.0025 per mol% of \(\mathrm{Al_{2}O_{3}}\). This is in line with the results obtained by [PERSON] et al. (2015), where the authors used an MCVD vapor phase technique with \(\mathrm{AlCl_{3}}\) as the precursor. Figure 9: Contour plots of Al distribution across the core of fibers (**a**) F1, (**b**) F2, and (**c**) F3. The highest Al concentrations detected in F1, F2, and F3 were 1.7, 4.3, and 3.2 wt.%, respectively. The corresponding concentrations of \(\mathrm{Al_{2}O_{3}}\) were 1.9, 4.9, and 3.7 mole% for fibers F1, F2, and F3, respectively.
# A Fair Cooperative MAC Protocol in IEEE 802.11 WLAN
## 4 Performance Analysis  
In this section, the performance of the FC-MAC protocol is analysed in terms of network throughput, time fairness, and network lifetime. We assume that the network is in saturation conditions and all nodes always have packets for transmission.
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 5 Information Receiver for TDS-OFDM-NOMA
### Frame Synchronization and Coarse Doppler Scaling Factor Estimation  
The time synchronization and Doppler scaling Factor (DSF) estimation have a lot of attention on underwater research spatially in early underwater research work. The most common way for synchronization and channel estimation is to exploit an embedded repetitive pattern in the transmitted preamble as reported in [24; 27; 28], and we use the same way for DSF estimation and time synchronization. The corresponding P-tap window correlation can be written as:  
\[\left(\hat{\mathcal{P}}_{i}\hat{a}_{i}\right)=\arg\max_{\overline{\hat{\mathcal{ P}}_{i}}}\frac{\sum_{p=a}^{a+p-1}z_{i,p}^{a}z_{i,p+p}^{a}}{\left\langle\sum_{p=a}^{a+p-1 }\left|z_{i,p}\right|^{2}\sum_{p=a}^{a+p-1}\left|z_{i,p+p}\right|^{2}}\right. \tag{39}\]  
where \(\hat{a}_{i}\) is the received preamble starting time. \(\hat{\mathcal{P}}_{i}\) is an integer representing the coarse estimation of the initial tap delay, \(\beta_{i,0}\), discussed in detail in [24; 27; 28], and the estimated initial tap delay can be written as follows:  
\[\overline{\beta}_{i,0}=\frac{\alpha N-\hat{\mathcal{P}}_{i}}{\hat{\mathcal{P }}_{i}} \tag{40}\]  
The estimated initial tap delay in (40) offers the frame time synchronization. The DSF estimation is limited by integer parameter \(\alpha\). The estimated Doppler shift of the UWA channel can be written as:  
\[\in_{i,0}=\frac{\beta_{i,0}-\overline{\beta}_{i,0}}{1+\overline{\beta}_{i,0}} \times\frac{F_{c}}{B} \tag{41}\]
# Modeling and Analysis of Noise Emission Using Data from Flight Simulators
###### Abstract  
The main goal of this article is to examine the impact of various factors, including the aircraft take-off weight and configuration, on the noise and gaseous emissions. The analysis was based on trajectory data obtained from the X-Plane \(11\) simulator and a MATLAB noise engine created in accordance with ECAC.CEAC (European Civil Aviation Conference) Doc 29 recommendations. It allowed us to present various noise metrics in the form of noise maps and conduct a general analysis of how certain factors, _e.g._, flap settings, impact the noise and gaseous emissions. The study indicates that, from the \"global\" perspective (i.e., the entire airport vicinity), the NADP1 procedure shows better noise reduction, while the NADP2 procedure exhibits lower \(CO_{2}\) emissions. Conversely, from the perspective of individual observers (i.e., the local perspective), it may (but not necessarily) be the case that the NADP2 procedure allows for achieving both minimal noise and \(CO_{2}\) emissions compared to the NADP1 procedure. For example, reducing take-off thrust may reduce the SEL \(>85\) [dB] area in NADP2 by twice as much as in NADPH1, but at the cost of almost four times less \(CO_{2}\) emission reductions. The study may be further developed to find an algorithm for designating individual noise-abatement procedure parameters that will account for various factors in order to minimize the aforementioned emissions.  
n pacs: **Keywords:** noise emissions; noise modeling; airplane; aircraft; aviation; flight simulation
## 5 Simulation Experiment
### Analysis of AER  
Figure 9 depicts that a lower SINR can result in a lower AER. After 400 experiments, the AER is stabilized at around 0.4923% when \(k=-3\) and \(\rho=10\). The reason for the high AER is that detailed simulation in the signal generation time is not performed in the initial stage of the simulation experiment. However, a random noise generation source and a simple random signal generation source are employed, and the subsequent signal processing is used. Furthermore, the signal that needs to be analyzed is not sufficiently pre-processed, resulting in a higher AER.  
Similarly, as the number of experiments grows, the training signals are assessed and constantly received at the receiver, allowing the optimal threshold for detecting attacks with rich experience to be determined. Finally, the optimal threshold stabilizes at a particular stage, and the AER remains essentially constant. Figure 10 depicts a comparison of AER based on the two algorithms. It can be observed from the figure that AER based on the SARSA algorithm is lower than that of Q-learning, implying that the SARSA algorithm  
Figure 8: The comparison of MDR based on SARSA and Q-learning.  
Figure 9: When \(k=-3\), the AER at \(\rho=20\) and \(\rho=10\) are tested separately. The Q table is updated \(20\) times per round as the number of experimental rounds increases.  
detects legal and illegal signals at the receiving end. Hence, we can summarize that the SARSA-based method can improve detection accuracy while reducing the risk of impersonation attacks.  
In summary, we compared the result of our proposed SARSA algorithm with the traditional Q-learning technique. It was found that the proposed method outperforms the traditional algorithms in terms of FAR, MDR, and AER. For instance, the SARSA-based algorithm achieves a 3-5% lower FAR as compared to the traditional algorithm after 200 rounds of experiments. Furthermore, the algorithm detection accuracy of SARSA is approximately 2-3% higher than the traditional Q-learning-based technique. Consequently, the AER based on the SARSA approach is lower than that based on the Q-learning technique. This is due to the conservative nature of SARSA that finds a suitable threshold via a continuous trial-and-error process. Therefore, the SARSA algorithm has a lower chance of misinterpreting the training signal from legal or illegal users that results in a lower rejection.  
We conducted a series of simulation experiments that included receiver utility, receiver's test threshold, analysis of MDR, analysis of FAR, and analysis of AER. Compared with traditional reinforcement learning, we can draw the following conclusions. The suggested scheme can avoid impersonation attacks in a dynamic environment. The experimental results show that our proposed scheme has a higher detection accuracy and lower average error rate, effectively preventing the impersonation attack compared to the traditional reinforcement learning approach.
# Detection of Household Furniture Storage Space in Depth Images
## 3 Methods
### Empty Storage Volume Detection Algorithm
#### 3.1.2 Detection of Empty Storage Volume Candidates
In general, the storage volume candidates generated by the described procedure are not necessarily completely supported by occupied voxels, as shown in Figure 2(b). Thus, the algorithm must determine the supporting surface below each storage volume candidate and crop the storage volume candidate to fit its supporting surface. To find the supporting surface of a storage volume candidate, the algorithm observes a layer of voxels in the occupancy grid directly below the storage volume candidate, referred to as the _supporting layer_. Only occupied voxels in the supporting layer can be used as supporting surfaces. Given the initial assumption that the storage volumes are cuboids, the supporting surfaces of these volumes are expected to be rectangular. Therefore, the problem of detecting supporting surface can be approached analogously to obtain free space cores. The supporting layer can essentially be regarded as a 2D grid, where each cell of this grid represents one voxel. The cells are assigned binary values: \"1\" if the cell represents an occupied voxel below the storage volume candidate, or \"0\" otherwise. This representation allows detection of rectangular supporting planes. A storage volume candidate can only be supported by one supporting surface. Therefore, it is necessary to check whether its supporting layer consists of a single connected component. If the supporting layer has multiple connected components, this storage volume candidate is eliminated from further process. The remaining storage volume candidates must be adjusted to the sizes and shapes of their supporting surfaces. To obtain a supporting surface that can be used as a storage surface, the algorithm performs a 2D CDT in the supporting layer. As the result, each cell of the supporting layer is assigned its occupancy distance. By identifying cells corresponding to occupancy distance local maxima and grouping them into connected subsets, _storage surface cores_ are obtained. Finally, the storage surface core is dilated by the occupancy distance \(\delta^{\prime}\) of its cells, creating a bounding box of the usable _storage surface_. By adding the height of its storage volume candidate to the storage surface, the algorithm constructs a bounding box of an empty volume, as shown in Figure 4. If this bounding box is the largest empty storage volume candidate for a particular coordinate axis, it is classified as the storage volume candidate for that axis. The described process of storage volume candidate detection for each axis is summarized in Algorithm 2. After proposing three empty storage volume candidates for a given FSC (one for each axis), the algorithm classifies the largest of them as a storage volume.