esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods  
### Plants and Headspace Collection of Volatiles  
Eighteen-month-old _V. corymbosum_ cv. Legacy plants, 50-70 cm in height, were obtained from a commercial nursery in La Union County (40\({}^{\circ}\)17\({}^{\prime}\)43\({}^{\prime}\) S 73\({}^{\circ}\)04\({}^{\prime}\)56\({}^{\prime}\) O) in the south of Chile. The plants were contained in 2.26 L plastic pots (12 cm diameter and 20 cm deep) with a mix of 2:1:2 peat, perlite, and oil palm fiber, and 5.5 pH. Plants were maintained under greenhouse conditions and suitably watered every two days for three months before the bioassay.  
The headspace technique was used to collect root VOCs from uprooting _V. corymbosum_ plants, as described in [PERSON] et al. [5], with the following modifications. The root was gently cleaned and washed under tap water. Instead of isolating the root portion of the plant through a hole in a lab bench, we individually attached the plant to a universal stand using tweezers to enclose the roots in a 900 mL glass chamber (9.0 cm i.d. \(\times\) 14.5 cm height). One end of the glass chamber was fully open to introduce the roots. Once the roots were enclosed, the aerial part was separated into a two-piece Plexiglas guillotine, which, once assembled, allowed a circular central opening for the aerial part and closure of the chamber. The other end had a small, elongated opening (0.8 cm i.d. \(\times\) 2.0 cm long), where the solid-phase microextraction (SPME) fiber was inserted to sample the root volatiles (Figure 3). A manual SPME holder with polydimethylsiloxane/divinylbenzene (PDMS/DVB; df 65 \(\upmu\)m, needle size 24 ga, Supelco) fiber was exposed to the root headspace for 30 min at 20 \(\pm\) 2 \({}^{\circ}\)C [66; 67; 68]. We sampled an empty chamber as a control under the same described conditions. We randomly assigned thirty-six plants to two groups of eighteen plants each. Each plant in one group was infested with five third-instar larvae of _A. superciliosus_. The larvae fed from the root for five days, and later, the larvae and substrate were carefully removed, as described above. The other group of plants remained uninfested for the same time, and their roots were cleaned using tap water. All root volatile collections were performed without substrate or larvae. In addition, using the same technique (HS-SPME), we collected the volatiles emitted only by the _A. superciliosus_ larvae, and we found none of these larval compounds in the _V. corymbosum_ root volatiles.
## 3 Numerical Results
### Code Profiling  
The model was coded in Fortran and compiled by GCC 8.2.0 using a double precision for all variables. The time spent by each routine was obtained at runtime by the performance analysis tool \"GProf\" [37]. As was expected, the solution of the in-step linear system \((I-hA)x=b\), where \(h\) is the time step, by the _LU_ decomposition was the most demanding routine of the code accounting for 95% of the runtime; of that, 85% was devoted to the floating-point operations \(\times\), \(\div\) and \(\pm\), 5% on the cycle loops and 5% on the variable assignment.  
The listing of the LU routine is presented in Table 1; the arithmetic complexity was evaluated in percent for a linear system of dimension \(n=9\). The algorithm operated over \(n^{2}+n+2\) floating-point variables contained in input matrix \(A\), input vector \(b\), input parameter \(h\) and additional parameter \(S\) used for the summation. The entries of the matrix and the vector were changed during the algorithm run. At the end of the execution, vector \(b\) contained the final result and matrix \(A\) consisted of the lower and upper triangular matrices of the input matrix; the diagonal elements of \(A\) referred to matrix \(U\), assuming the diagonal entries of \(L\) were filled with one.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 2 Material  
### Test Site  
The study area was selected in a collaboration with the Finnish Forest Centre. It is a forested landscape in Southern Finland in which a severe windstorm damage occurred on 12 August 2017. The fastest speed of the wind in the inland was near 30 m/s and on the sea outside the capital 32 m/s [35]. The area reaches from Helsinki capital region towards the towns Kouvola and Ampeenranta east and northeast of Helsinki region (Figure 1). The area of the forestry land in the study area, covered by Sentinel-1 scenes, is 830,000 ha. Forestry land includes three land categories: (1) forest land; (2) poorly productive forest land; and (3) unproductive land [36]. The two commonest stand level dominant tree species are Norway spruce (_Pica abies_ Karst. L.) and Scots pine (_Pinus sylvestris_ L.) (see also Tables 1 and 2).
## 6 Simulation Results
### Performance Evaluation Using Different Step-Size Values  
First, we analyzed the former case by choosing different step-size values for the two MISO filters. We chose the same projection order \(K_{1}=K_{2}=4\) for both the MISO filters, and we varied the step-size values. In particular, we took into account the range of values \(\left[0.001,1\right]\) for \(\mu_{2}\), which represents the larger step size, and we set \(\mu_{1}=\mu_{2}/q\), where \(q\) is a proportionality factor. The parameter setting for the adaptive combination was characterized by: \(\mu_{\mathrm{c}}=0.5\), \(a_{k}\left[0\right]=0\) and \(r_{k}\left[0\right]=1\) for \(k=0,\ldots,K_{\mathrm{max}}-1\).  
The results are shown in Figure 2 with different settings of the factor \(q\), i.e., 4, 10, 25, and 100. These results prove the validity of the derived theoretical EMSE (59), as the measured EMSE values approached the theoretical ones. It is worth noting, in Figure 2, the effect of the combination, especially for small step-size values and high \(q\).  
We also evaluated the convergence performance of the combined MISO scheme. The scenario was similar to the previous experiment, but in this case, we considered \(P=8\) unknown channels, each one formed by \(M=10\) independent random values between \(-1\) and one. The input signal was again generated by using a first-order autoregressive model, and its length was \(L=\) 40,000. In order to study the ability of the combined filtering scheme to react to abrupt changes in the scenario, the coefficients of all the unknown channels were randomly reassigned at time instant \(n=L/2\). Again, we considered additive noise with \(\sigma_{v}^{2}=0.01\) at the output of the unknown MISO system. We chose a small step-size value \(\mu_{1}=0.001\) for the first MISO filter and a larger one \(\mu_{2}=0.1\) for the second MISO filter, while we considered the same projection order \(K_{1}=K_{2}=2\) for both filters. Performance was evaluated in terms of the measured EMSE, averaged over 10,000 independent runs. We compared the adaptive combined MISO filter with the corresponding individual filters. Results are depicted in Figure 3, where it is worth noting that the MISO filter with \(\mu_{1}\) shows a slow convergence rate, but a good precision at steady state, while the MISO filter with \(\mu_{2}\) provides a faster convergence rate, but lower precision. The combined MISO filter was capable of exploiting the advantages of both the individual MISO filters, thus showing a fast convergence rate and good precision at steady state. Moreover, a performance gain due to the cross-EMSE was visible when the mixing parameter was changing its value from zero to one. In Figure 3, the evolution of the current projection of the mixing parameter, i.e., for \(k=0\), is also depicted to highlight the changing behavior of the combined MISO filter.  
Figure 2: Steady-state theoretical and measured excess mean squared error (EMSE) for a combined MISO filter using different step-size values, according to \(\mu_{1}=\mu_{2}/q\), with (**a**) \(q=4\), (**b**) \(q=10\), (**c**) \(q=25\), and (**d**) \(q=100\).
# Extraction of Novel Bioactive Peptides from Fish Protein Hydrolysates by Enzymatic Reactions
## 5 Characterization of Novel Bioactive Peptides
### Purification and Identification of Peptides Using One-Dimensional Separation Systems
#### 5.1.1 Membrane Fractionation (Ultrafiltration and Nanofiltration)  
The process of protein ultrafiltration (UF) involves using a pressure-driven membrane to concentrate or purify proteins in aqueous solutions [99]. The typical pore sizes of UF membranes are in the range of 10-500 A, and the rate of filtration of components depends on their response to the given pressure driving force. In separating peptides from non-hydrolyzed proteins, the UF method can be considered, and UF membranes such as molecular weight cut-off (MWCO) with a size of 20 kDa have been studied to retain proteins effectively [100]. [PERSON] et al. [101] compared various UF membranes for fractionating a fish protein hydrolysate, and the authors suggested that using UF membranes can improve the bioactivity of the tested peptides with sizes smaller than 7 kDa by fractionating some specific-molecular-weight peptide classifications. Recently, modifications have been made in the use of UF membranes, such as in the work of [PERSON] et al. [102], who used a multilayer UF membrane to fractionate tilapia byproduct protein hydrolysates and observed that peptide selectivity could be improved using 5/5 multilayer membranes. Likewise, [PERSON] et al. [103] investigated the fractionation of protein hydrolysates from fish waste by using UF and were able to separate peptides into four fractions (<3, 3-10, 10-30, and >30 kDa), which showed good antimicrobial and antioxidant properties.  
The use of nanofiltration (NF) has been widely utilized in the field of biological research, with a focus on the combination of nanoparticles and biological compounds such as proteins to form a more functional and hybrid system [104]. Some researchers emphasized that the challenge for fractionation and purification is separating these hybrid  
Figure 3: Schematic diagram of isolation and purification of peptides from fish waste and byproducts.  
materials from unbound free biomolecules. Some work has been published on combining UF and NF, especially in the field of biochemistry. Fish fillet hydrolyses were fractionated by [PERSON] et al. [105] using UF coupled with NF, showing that fractions with a size of 3000 Da had the highest yield. Furthermore, the authors suggested that the refinement of methods should be conducted via purification in diafiltration mode to achieve the most active fractions. Meanwhile, [PERSON] et al. [106] investigated the effect of UF and NF on fish protein hydrolyses produced from the processing industry and observed that the successive fractionation of peptides on UF and NF membranes was able to select concentrations of selected sizes of peptides. It is interesting to note that the combination of multiple membranes to classify peptides according to size could significantly improve the selectivity of the filtration system used and would yield high-quality peptides with improved bioactivities.
## 5 Conclusions  
A new and low complexity angle of arrival method, which estimates the AOAs of the received signals directly from the received data without the need to construct the CM, is proposed in this paper. The reduction in complexity of the proposed method has been demonstrated mathematically and then explained in terms of the number of computational operations. The reduction of complexity has been achieved through avoiding constructing the CM and computing the matrix inverse or applying eigenvalue decomposition. The PDDA was implemented with two types of sensor arrays: uniform linear sensor array and circular sensor array. Many simulation examples illustrate the performance estimation of the new algorithm in comparison with well-assessed state-of-the-art AOA estimation methods over a wide range of scenarios. Results show that the new method is suitable for a single or low number of snapshots, has a lower computational cost than existing techniques, and works well with correlated signals.  
This work is partially supported by the financial support from higher committee for education development (HCED) in Iraq and the European Union's Horizon 2020 research and innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424.  
[PERSON] raised the idea and wrote the paper; [PERSON] modified and analysed results; [PERSON] performed discussion of the results and comparison with other works; [PERSON] helped to model the proposed algorithm mathematically; [PERSON] assisted to develop the simulation codes and contributed in the paper writing; [PERSON] contributed to model the direction of arrival for different sensor array geometry; [PERSON] revised the manuscript and corrected the English.  
The authors declare no conflict of interest.
## 2 Mathematical Modelling  
### Problem Description Based on Directed Acyclic Graph  
For DRT based on reservations, only the bus stations and available departure times are fixed. At the same time, passengers can choose the appropriate departure time, departure station, and final station according to their needs. A defined time point will stop reservations for a certain time period. The system will generate orders and vehicle scheduling plans based on this information. Figure 1 shows the reservation-based DRT scheduling process. After the passengers make a reservation, the system filters the orders that meet the service requirements based on the capacity conditions and adds them to the order pool. Once the reservation time closes, the pre-scheduling system generates all the departure tasks within that time period, including departure time, departure station, travel path, final station, vehicle size, and vehicle number. After the departure tasks are determined, each will be assigned to the corresponding vehicle before departure according to the vehicle location distribution status and availability provided by the big data center, which ultimately forms the overall vehicle scheduling plan.  
If two or more departure tasks require the same vehicle size and the departure and arrival times of the individual tasks, they do not conflict with each other. These departure tasks can be accomplished with a single vehicle. The number of vehicles required to complete all the tasks will be significantly reduced. Figure 2 shows a series of tasks during a time slot. There are 7 departure tasks in the figure, forming a total of 3 vehicle chains, which means that only 3 vehicles need to be called to complete the 7 vehicle tasks.  
Figure 1: The reservation-based DRT scheduling process.  
The above problem can be formulated as a network in terms of a directed acyclic graph. In graph theory, a directed graph is called a Directed Acyclic Graph (DAG) if it is impossible to return to any vertex in a directed graph by passing through several edges. Each departure task (including departure time, departure station, travel path, final station, and vehicle size) is abstracted as a node, and the conditions of time, space, and vehicle succession between departure tasks are transformed into the connecting edges between different nodes. Suppose two departure tasks can meet the conditions for articulated scheduling under certain constraints. In that case, they are said to be compatible and matchable, shown in Figure 3a, and they can be transformed into the form in Figure 3b. With minimum vehicle size as the single optimization objective, the vehicle scheduling problem can be transformed into a simple problem of finding the least disjoint coverage path in a DAR.
# The Cyclically Resolvable Steiner Triple Systems of Order 57
## 1 Introduction
If the KTS(\(v\)) is point-cyclic, fast encoding might be possible too by using the quasi-cyclic structure of the remaining matrix. A KTS(\(v\)) exists if and only if \(v\equiv 3\) (mod 6), \(v\geq 3\)[11]. KTS(\(v\))s are classified up to \(v=15\)[12; 13]. The full classification of KTS(21)s is out of reach now. One of the most recent results on KTS(21)s presents over 13 million KTS(21)s with subsystems STS(7)s and STS(9)s [14]. Constructions of KTS(\(v\))s for \(v\geq 21\) have been considered by many authors [14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. General constructions of point-cyclic KTS(\(v\))s are considered in [24; 25] and point-cyclic KTS(\(v\))s with small parameters have been classified up to \(v=45\) (Table 1). Before the present work \(v=57\) was the smallest open case. The big number of cyclic STS(\(v\))s and possible parallel classes make it much more difficult to consider \(v=57\) than any smaller \(v\). The present paper solves this problem (Table 1). A KTS(57) with sub-STS(27) can be obtained from the infinite class of Dukes and Lamken of KTS(\(2u+3\)) with sub-STS(\(u\))[16]. The existence of a KTS(57) with sub-KTS(15) is proved in [20]. There are 2,353,310 cyclic STS(57)s [2; 26]. Using them we construct all point-cyclic KTS(57)s and study their automorphism groups and some important configurations. Our investigation is computer-aided. We use two different construction algorithms implemented respectively by the two authors in C++. The paper consists of 5 sections. The action of the cyclic automorphism group of order \(v\) on a point-cyclic resolution of an STS(57) is considered in Section 2 and the construction methods are explained in Section 3. Section 4 presents the main results, and a discussion on their impact follows in Section 5.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 3 Proposed Scheme
### Registration Phase
#### 3.2.2 Cloud Server Registration  
A cloud server \(S_{j}\) sends its identity \(SID_{j}\) and a pseudo-identity \(PSID_{j}\) to \(CS\) by a secure channel. Then, \(CS\) uses the secret key \(x\) to compute \(A_{j}=h(PSID_{j}||ID_{CS}||x)\) and \(B_{j}=h(SID_{j}||x)\), stores \(SID_{j}\) in its database, and sends (\(A_{j}\), \(B_{j}\), \(ID_{cs}\)) to \(S_{j}\) by a secure channel. When \(S_{j}\) receives these parameters, \(S_{j}\) stores (\(A_{j}\), \(B_{j}\), \(SID_{j}\), \(SPID_{j}\), \(ID_{cs}\)) in its memory. The flowchart of the cloud server registration phase is shown in Figure 2.
## 2 Materials and Methods
### Module One: Rapid Redundant Feature Generation (RRG)  
In most frameworks for the segmentation of remote sensing images, leveraging existing convolutional architectures as feature extractors leads to the creation of numerous redundant feature channels. Diminishing these channels could result in the loss of crucial information, such as edges and textures, negatively impacting segmentation accuracy. On the other hand, retaining these redundant features escalates computational complexity, which could hinder the model's efficient deployment on embedded or edge computing platforms. To overcome these challenges, this paper introduces a module for Rapid Redundant Feature Channel Generation, executed through RRG. This module is designed to swiftly generate feature maps, aiming to preserve accuracy with minimal loss while significantly increasing the generation speed of feature maps, thereby effectively minimizing computational complexity.
# Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES
## 4 Evaluation Setup of ID-Based Authentication
### Experimental Setup on \(n\)-Round AES  
In the experiment, we used ALTERA CycloneIV (FPGA) on Terasic DE0-nano (FPGA board) [6] as the prover device. A 1000-round AES modified from the 128-bit AES [7] was used as the calculation to generate side-channel information. The AES implementation uses a 128-bit data path and the composite-field S-box, which runs at 50 MHz on the DE0-nano board. The side-channel information was measured as the electro-magnetic radiation near the FPGA by the EM probe (Langer-EMV RF-U 5-2). The signal captured by the probe was recorded using an oscilloscope (Agilent Technology DSO7032A), which recorded at 1 GSa/s. Each measurement included about 21,000 samples. A photo of the experimental setup is shown in Figure 5. Note that, the measurement of side-channel information can be performed without modifying the hardware, but the probe still needs to be close to the FPGA to ensure the quality of measurements is sufficient [8; 9].  
On the verifier side, we used a normal PC to process the data. The correlation calculation was performed with both non-profile leakage models and leakage profiles.
###### Abstract  
The particle size distribution of tailing sand will affect its strength, deformation and mechanical properties, thus affecting the overall safety and stability of the tailing dam. The main purpose of this study is to obtain the gradation of sand samples with the best shear strength and seepage effect by studying the influence of particle size on the mechanical properties of saturated iron tailing sand. The tailings were sampled from Yangjiawan tailing reservoir of Chengchao Iron Mine, and the moisture content test, particle size distribution analysis and scanning electron microscope test (SEM) were carried out on the samples. It is measured that the average moisture content of the tailings is 12%, and the gradation is generally poor. SEM was used to observe the microstructure of the three kinds of particles, and it was found that these structures were more likely to be damaged under load, which was not conducive to the improvement of the strength of tailings. Through the consolidation undrained test of five particle size groups, it is concluded that the larger the particle size is, the greater the shear strength will be. The Cu indicates the width of the distribution range of the grading curve, the Cc indicates the distribution pattern of the grading curve, and they are both used to indicate the particle grading of sand. Then, by changing the sample gradation, the coefficient of uniformity (Cu) and the coefficient of curvature (Cc) of the gradation parameters are controlled to remain unchanged, respectively. By changing another parameter, it is found that there is a close relationship between the coefficient of uniformity and the coefficient of curvature and the shear strength through the triaxial shear action, three kinds of tailing sand gradation with better strengths are obtained. Based on the study of strength characteristics, the permeability coefficients of three kinds of tailing sands are calculated by using the seepage failure test. The test results show that when the coefficient of uniformity is 5.5 and the coefficient of curvature is 1, the permeability coefficient is 1.699 \(\times\) 10\({}^{-3}\) cm/s, and the shear strength and permeability of the sample are the best.  
Keywords:tailing sand; particle size; shear strength; seepage characteristics +
Footnote †: journal: