esa-sceva/satcom-chunk-collection huggingface dataset:
# Land Suitability Mapping Using Geochemical and Spatial Analysis Methods
## 3 Materials and Methods
### Land Evaluation Suitability  
Cadmium and Co concentrations in the soil of the study area were compared against corresponding median values for European topsoil given by the Forum of European Geological Surveys (FOREGS) [58]. Land evaluation suitability in the study area was performed by adopting guidelines for soil concentration of Cd and Co established by Environmental Protection Agency (EPA) [59], Canadian Council of Ministers of the Environment (CCME) [60] and Department of Environment and Conservation (DEC) [61] (Table 1). Soil quality criteria for agricultural land use are applied uniformly for different soil types and crops, not fully considering the effects of soil properties on Cd and Co uptake via soil-plant transfer [62].
# Spectrum Based Power Management for Congested IoT Networks
## 3 System Model  
In any conventional IoT communication environment there are three major actors: IoT sensor or sender, coordinator (CN), and destination. Spectrum and power management problem is common at a IoT sensor level. Unlike other works, in our proposal we try to solve these two issues by solving problems created due to congestion. As we understood from other existing studies the main reason for high power consumption is due to difficulty to access channel which is due to bad spectrum management.  
We proposed use of FREDEVPLA board in between the coordinator and destination. Its functionalities are: (1), carefully follow and manage spectrum related parameters of overall network. (2), Decide which coordinator can do energy scan and decide when to do scanning. (3), Decide when to change communication channel of the coordinator. The memory capacity of FREDEVPLA allows us to save all the connected CN and its child's nodes information in a form of table called child table.  
As seen in Figure 3, the first step that the FREDEVPLA does is checking the type of data received from any of its coordinators. If the data is DisAssociation Request (DisAss_Req) the FREDEVPLA will delete the mentioned device from its child table list. However, if the received data is Association Request (Ass_Req) it will update its child table list accordingly. If none of the above, the data is forwarded to its destination or saved for future use.  
We assume an IoT sensor is distributed randomly to collect specific data such as temperature, light, diabetic sugar levels, A1C etc. The IoT sensor collects the data and transmits it through the CN to its destination. A FREDEVPLA will receive the data packets from various sources of CNs and FREDEVPLA software will then determine whether the data packet should forwarded to the destination or processed by itself. Most of the data packets sent from IoT CN to FREDEVPLA are processed by FREDEVPLA itself. If the data packet is targeted for another IoT device then the IoT CN will send it directly to its destination.
## 6 Numerical Examples
### Linear Verification  
The first example is a linear test system of the form (3) and generated using the inverse Lyapunov procedure [87], in a variant that enforces state-space symmetric systems [88]. For state-space symmetric systems, \(A=A\tau\), \(B=C\tau\), all system Gramians are symmetric and equal [89]. The system is configured to have \(N=\dim(x(t))=256\) states and \(\dim(u(t))=\dim(y(t))=4\) inputs and outputs. For the computation of the reduced order model, the empirical linear cross Gramian with an impulse input \(u_{i}(t)=\delta(t)\) and a zero initial state \(x_{0,i}=0\) is used, while for the trajectory simulation the default SSP32 (Section 5.5) integrator is utilized.  
To quantify the quality of the resulting reduced order models, the error between the original full order model output and the reduced order model's output is compared in the time-domain \(\mathcal{L}_{2}\)-norm,  
\[\|y-\tilde{y}\|_{\mathcal{L}_{2}}=\sqrt{\int_{0}^{\infty}\|y(t)-\tilde{y}(t) \|_{2}^{2}\,\mathrm{d}t}.\]  
In addition, the balanced truncation upper bound is assessed [41]:  
\[\|y-\tilde{y}\|_{\mathcal{L}_{2}}\leq 2\|u\|_{\mathcal{L}_{2}}\sum_{k=n+1}^{N} \sigma_{k},\]  
for a reduced model of order \(n\). Instead of impulse input, zero-centered, unit-variance Gaussian noise is used as input time series for the evaluation.  
Figure 2 shows the reduced order model's relative \(\mathcal{L}_{2}\)-norm model reduction error, as well as the upper bound for increasing reduced orders. The error evolves correctly tightly below the bound, until the numerical accuracy (double-precision floating point arithmetic) is reached.  
Figure 1: Sample code for the computation of the empirical cross Gramian of a non-minimal fourth order system, see Section 5.6.
# Study of 5G-NR-MIMO Links in the Presence of an Interferer
## 4 Numerical Results
### Smart Jammer  
Next, we simulated the case described in Section 3.2. The received symbols are presented in (16). Figure 8 shows the transmitted and received symbols for the 4, 8, 16-PSK modulations without AWGN, sending 1000 bits, and SIR = 10 dB. It can be seen that the received symbols are replicated according to the modulation order of the interferer, i.e., for a 4-PSK modulation, around each symbol transmitted, four symbols will be received in a 4-PSK constellation. For an 8-PSK modulation, around each symbol eight symbols will be received in an 8-PSK constellation, and, in the same way, will be accepted for other modulations. This phenomenon corresponds to (16) in which, in addition to the transmitted signal, the interference is obtained multiplied by a real number.  
The BER performances as a function of SIR are shown in Figure 9. For M = 2, 4, 8, 16-PSK constellation orders, SNR = 15 dB, with \(10^{5}\) transmitted bits, and \(\Delta\)SIR = 0.2 dB.  
Figure 8: A constellation of transmitted symbols (blue) and received symbols in the presence of the smart jammer (orange), for (**a**) 4–PSK, (**b**) 8–PSK, (**c**) 16–PSK modulations, no AWGN, and SIR = 10 dB.  
For low SIR values, in the graphs of 4, 8, 16-PSK modulations, a BER greater than 0.5 is obtained. This can be explained by Figure 8c. In this graph, it can be seen that the received symbols are next to the neighboring symbols and, therefore, will inevitably be incorrectly demodulated. As a result, the probability of the resulting error is greater than 0.5. This phenomenon does not occur in 2-PSK modulation because, in this modulation, the symbols have no neighbors on either side but only on one side. In addition, for low SIR values, the graphs are horizontal. This phenomenon also occurs because of symbol duplication. The SIR value affects the distance between the transmitted symbols and those received. When SIRs are low, the received symbols are more distant and therefore located in their neighbor's symbols; however, there is a particular value of SIR in which they will no longer slide to their neighbors. As a result, the BER performance will improve sharply and noticeably, as indicated in Figure 9.
## 4 Experimental Results  
The effectiveness of the presented approach is assessed using several experiments on synthetic and real-world images. All the experiments were conducted on a general-purpose personal computer with an Intel Core i5-3570 CPU and 4 GB of DDR3 memory. We implemented all the approaches in Matlab, using C mex functions under Microsoft Windows operating system environments.
## 2 Results  
### Identification of SmMTPSLs in the _S. moellendorffii_ Genome  
In the previous search of the draft genome sequence of _S. moellendorffii_, eight putative full-length _SmMTPSL_ genes were identified, which were named as _SmMTPSLS_, _-15_, _-19_, _-23_, _-33_, _-37_, _-46_, and _-47_[12]. In this study, the eight full-length _SmMTPSL_ genes were successfully extracted from _S. moellendorffii_ plants. The eight _SmMTPSL_ genes were annotated to contain principally zero or one intron. Their ORF sequences encoded 366, 348, 368, 356, 372, 372, 323, and 366 deduced amino acids, respectively. The calculated molecular weight of proteins encoded by these _SmMTPSL_ genes ranged 37.46-42.58 kDa (Table S1). Proteins encoded by _SmMTPSL_ genes were considerably smaller than typical plant TPSs mainly as a result of their lack of \(\beta\) and \(\gamma\) domains [17].  
Multiple sequence alignment showed that the amino acid sequence of eight SmMTPSLs contained two features that are highly conserved among known TPSs. One was the Asp-rich 'DDxxD' motif, the other was the 'NSD/DTE' motif (Figure 1). The two conserved motifs were both involved in metal-dependent ionization of the isoprenyl diphosphate substrate [6]. The average identity and similarity of the eight _SmMTPSL_ genes was 47% and 61%, respectively.
# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
## 6 Parameter-Setting of the Optimization Technique
Equation (37) formulates a multi-objective optimization problem based on the Hybrid BESSA with the objective of minimizing errors and extracting these unknown parameters necessary for the modeling of PV cells. By studying the obtained results, it was found that the Hybrid BESSA was capable of correctly formulating the PV panel model when compared to the BES technique. \[\epsilon_{\mathrm{I}}=\int\Big{(}I_{SC\_ref}-I_{SC}\Big{)}^{2}dt \tag{31}\]  
\[\epsilon_{\mathrm{V}}=\int\Big{(}V_{OC\_ref}-V_{OC}\Big{)}^{2}dt \tag{32}\]  
\[\epsilon_{\mathrm{P}}=\int\Big{(}P_{MPP\_ref}-I_{MPP}\Big{)}^{2}dt \tag{33}\]  
Figure 9: Block diagram depicting the parameter extraction technique used by the proposed Hybrid BESSA technique. Equations (34)-(36) represent the minimization of the ISE of I\({}_{\rm sc}\), the minimization of the ISE of V\({}_{\rm oc}\), and the minimization of the ISE of P\({}_{\rm MPP}\). \[y_{1}=\min(\epsilon_{\rm I}) \tag{34}\]  
Figure 5:0  
Figure 5:1  
Figure 5:2  
\(\omega_{1},\ \omega_{2},\ \rm{and}\ \omega_{3}\) are declared as weighting factors, and their values are selected in such a way as to cause each term to compete with the others in the course of the optimization search process. This normalizes all three objectives (i.e., \(y_{1},y_{2},\) and \(y_{3}\)) in one uniform scale.
# Advanced and Potential Methods for Extraction of Bioactive Compounds from Avocado Peel--A Review
## 3 Research Trends in Extraction of Bioactive Compounds from Avocado Peel  
Avocado is a source of nutrition and various bioactive compounds. The compounds may exhibit bioactivity, which may be beneficial for human health. The potential bioactivity of avocado lies not only in its flesh but also in its seeds and peels. Figure 1 illustrates the potential bioactivity of avocado, including antioxidant, antimicrobial, anticancer, and anti-inflammatory activity. It is widely known that the bioactivity of each part is determined by its components inside the flesh, seed, peel, or extract [4].  
Flesh is the main product of avocado. However, nowadays, great attention is given to the bioactive compounds in the avocado's residue, including its peels, which are about 13% of the fruit's weight. As shown in Figure 2, the research trends regarding avocado peel extraction have increased significantly in the past ten years. In 2014, the publications on the topic of avocado peel extraction were about 811 articles, and in 2023, it increased to almost three times that, at 3080 publications. Various methods have been applied by scientists to extract bioactive compounds from avocado peels. It includes microwave-assisted extraction (MAE), ultrasound-assisted extraction (UAE), enzyme-assisted extraction (EAE), pressurized liquid extraction (PLE), supercritical fluid extraction (SFE), natural deep eutectic solvents (NaDESs) extraction, and three phase partitioning (TPP).
## 4 Experiment
### Quantitative Evaluation  
Tables 4 and 5 enumerate key properties of the reconstructed model, including the number of points, actual and extracted basic elements, and runtime. For the statistical analysis of geometric errors, the reconstructed results with real data did not contain ground-truth data, so we used the distance from each original point cloud to its corresponding model plane as geometric error. The summary is presented in Table 6 and Figure 23.  
In Table 4, for the benchmark data, 42 doors and 8 windows were correctly detected: 20 doors and 1 window on the first floor and 22 doors and 7 windows on the second floor. The closed doors cannot be detected. The detection failure for the other windows was due to high sparsity and noise in the wall point clouds. However, all rooms and corridors were correctly segmented and had no under-segmentation or over-segmentation. The Shenzhen corridor dataset had high levels of noise and sparsity due to multiple reflections of moving objects and refraction from the glass wall. However, the structured information of pillars and doors were accurately extracted. For the Xiamen corridor dataset with high-quality point clouds, all openings were correctly detected. For the Xiamen parking lot dataset, 18 pillars were correctly detected. In terms of time efficiency, the reconstruction of the four real-world datasets required little runtime (see Table 5), with only the room segmentation taking relatively more processing time. The results indicate our proposed methodology has high modelling efficiency with wide-ranging applications in different indoor scenes.  
The summary of Euclidean distance deviation and diagram are shown in Table 6 and Figure 23. The reconstruction accuracy from the Xiamen corridor was highest, having 75.83% of point distance deviation within the 0.05 m range. The two floors from the benchmark showed comparable results with 51.50% (1 st floor) and 52.31% (2 nd floor) of deviations coming from the 0.05 m range. For the Shenzhen Corridor and Xiamen parking lot, the percentage of deviation within 0.05 m reached 25.10% and 32.82% respectively. This indicates that when using our approach, the reconstruction quality is heavily dependent on the quality of point clouds. Nevertheless, our method shows it can provide reliable and accurate reconstruction of indoor scenes within the 0.10 m range without the need for manual intervention.