esa-sceva/satcom-chunk-collection huggingface dataset:
# Local Ecological Knowledge (LEK) Can Guide Decision-Making in Inland Fisheries Management
## 3 Results
### Fishers' Perspectives  
Non-compliance with fishing regulations was identified as the most significant issue affecting the fishery in the Polyphytos Reservoir, as reported by 25 individuals (67.5%) (Figure 4a). Furthermore, the lack of law enforcement and basic fishing infrastructure were also considered substantial problems.  
Other significant problems from the fishers' perspective included: (i) the absence of an ichthyologist in the responsible department of the region, (ii) the lack of comprehensive lake management, (iii) non-conductance of stockings, (iv) a large number of seasonal fishers (primarily those who fish only in the spring or solely for crayfish), and (v) a significant number of anglers fishing in the reservoir without adhering to the legislation. Furthermore, a small number of fishers (16.2%) reported that problems arise from fluctuations in water levels, mainly when fish have deposited their eggs in the coastal zone, affecting the stocks. They also mentioned the high cost of fuel, which increases the expenses of their activities, as well as the absence of subsidies and compensations for fishers, especially following the collapse of the crayfish stock. Other issues are related to false catch reports (some do not issue invoices), the increase in lake temperature, which they believe has impacted fish stocks, the absence of maximum allowable discharge quantities (they request the implementation of discharge limits) leading to overfishing, and the lack of processing units (manufacturing/industrial) that would add value to their fishing products.  
Figure 3: Vessel catches per season based on the statements of the interviewed professional fishers in Polyphytos Reservoir. The boxplots show the median (horizontal line), the 50% of the values (box), the highest and lowest values (outer lines), and the outlier values (dots). Number of vessels: 19.  
Regarding the stocks in the Polypytos Reservoir, most fishers (35 individuals, 91.9%) stated that crayfish and common carp are overfished (Figure 4b). However, some believe that common carp get overfished only during the spring season. In response to the question of how much they believe the catches have decreased compared to 25 years ago, most fishers answered that the reduction percentage was 0-25% and 50-75% (30% of the respondents in both cases) (Figure 5a). Fishers mentioned overfishing as the primary cause of the reduction (25 fishers, 67.6%) (Figure 5b). However, 56% of the fishers attribute this reduction in water temperature to the decrease in the lake's water renewal time due to the construction of the upstream Ilarionas Dam (resulting in the construction of the homonymous reservoir) in the upper part of the Aliakmonas River basin and climate change (Figure 5b).  
Figure 4: The main (**a**) problems faced by commercial fishers, (**b**) overexploited stocks according to professional fishers in Polypytos Reservoir.
## 1 Introduction  
In recent years, enzyme assisted extraction (EAE) has been widely used for the extraction of target compounds from different materials [1; 2; 3]. Compared with conventional methods, EAE improved the time efficiency, reduced the solvent consumption and operated at milder extraction temperature [4; 5]. The positive effect of EAE is that the cell walls catalyzed by enzyme became thin and disorganized under optimum experimental conditions, then the intracellular components were released more completely, leading to the improved yield [6].  
Some studies showed that EAE was an excellent method to extract polysaccharides from plants. [PERSON], et al. reported that the yield of polysaccharides from _Tricholoma mongolicum_ Imai (TMIPs) was 24.01% by enzyme-assisted extraction [7]. [PERSON], [PERSON], & [PERSON] also obtained higher yield of polysaccharides from the fruit of _Cornus officinalis_ than that of UAE and HWE [8]. In addition, [PERSON], [PERSON], & [PERSON] proved that the polysaccharides from _Astragalus membranacus_ extracted by enzyme assisted extraction had better antioxidant activity (about 2 times higher) than none enzyme treated ones [2]. Moreover, EAE has been used to separate the polysaccharides from _Malva sylvestris_[9], _Lentinus edodes_[10], _Panax ginseng_[11], and _Polygonatum odoratum_[12], all these results further confirmed that EAE could be widely applied to the polysaccharide field.  
In our earlier work, hot-water extraction (HWE) and ultrasound-assisted extraction (UAE) have been employed for the polysaccharides from pumpkin seeds [13; 14]. After separation by two-step column chromatography, two purified polysaccharides (PSP-I and PSP-1) were obtained from pumpkin seeds, respectively [14; 15]. PSP-1 extracted with UAE had a lower molecular weight of 3728 g/mol than PSP-I of 21,100 g/mol from HWE. This result showed using diverse methods obtained different polysaccharides from the same materials, matching well with the reported literature [16]. Therefore, it is necessary to develop the new extraction method for the novel polysaccharide.  
The present work studied the application of enzyme-assisted extraction for the polysaccharides from naked pumpkin seeds. The influences of various parameters like extraction time, extraction temperature, enzyme concentration, and pH were investigated. The novel purified polysaccharide (PSPE) was gained and its structural character was determined by GC, FT-IR, and NMR.
## 3 VLC for Underwater Communication
### Configurations of the Optical Link
#### 3.2.1 Point-to-Point Line-of-Sight (LOS)  
Point-to-point line-of-sight requires the receiver to align precisely with the transmitter. Narrow-divergence light sources like lasers are used. It provides benefits such as higher bandwidth, lower latency, and enhanced security due to the narrow beamwidth of the optical signal [24]. However, the effectiveness of LOS is affected by water turbidity.
## 4 Results and Discussion  
The paper presents the results of suspended-sediment measurements that were collected over a period of 13 years (2005-2018) from four monitoring sites: Sturmov potok, Ptuj, Studenska brv Maribor, and Zgornji Duplek during a high-water event. (Figure 1) During the measurements with different measurement techniques, some experience was gained in order to propose the optimal technique for the monitoring and management of suspended sediments on the River Drava. The hydrological data were obtained from Dravske elektrane (DEM)--the operator of the hydroelectric power plants on the River Drava--who provide data on discharge; at the Ptuj measuring point, the discharge and water level were measured by the Slovenian Environment Agency site. Different measurement methods were used to determine the suspended-sediment concentration, depending on the available equipment and the conditions at the measurement sites. The suspended-sediment concentration was determined by analysing the collected water samples. A variety of water samplers were used, from the simple water samplers used under severe conditions, such as floods, to the depth-integrated samplers described and shown in the previous chapter. However, to obtain the most representative results of suspended-sediment dynamics in a cross-section, the basic measuring technique was supplemented with the acoustic method. The results presented are divided into three parts: First, the dynamics of suspended sediment at selected sites at mean discharge of the River Drava are presented. In addition, a case study is presented where a combination of direct and indirect methods was applied. Finally, the results of a high-water event in October 2018 are presented.
# Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
## 3 Pickup Vehicle Scheduling Model
### Variable Definitions  
To depict the PVSP-MSS effectively, the parameters and decision variables involved in the optimization model are defined below.  
(1) _Parameters_  
\(n:\): Total number of vehicles.  
\(m:\): Total number of steel logistics park yards.  
\(I:\): Set of vehicles, where \(I=\{1,2,\ldots,n\}\).  
\(M:\): Set of yards in the steel logistics park, where \(M=\{1,2,\ldots,m\}\).  
\(i,i^{\prime}:\): Index of vehicles, where \(i,i^{\prime}\in\{1,\ldots,n\}\).  
\(n_{i}:\): Number of operations of vehicles \(i\).  
Figure 2: Number of pickup vehicles.  
\[\begin{array}{ll}J_{i}:&\text{Set of operations of vehicle $i$, where $J_{i}=\{1,2,\ldots,n_{i}\}$.}\\ j,j^{\prime}:&\text{Index of operations of vehicles $i$, where $j,j^{\prime}\in\{1,\ldots,n_{i}\}$.}\\ k,k^{\prime}:&\text{Index of yards, where $k,k^{\prime}\in\{1,\ldots,m\}$.}\\ O_{ij}:&\text{The $j$ operation of vehicle $i$.}\\ U_{ij}:&\text{Optional set of yards for $j$ operations of vehicle $i$.}\\ S_{ij}:&\text{Start time of operation $O_{i,j}$.}\\ t_{i,j,k}:&\text{Pickup time of operation $O_{i,j}$ at yard $k$.}\\ C_{ij}:&\text{Completion time of operation $O_{i,j}$.}\\ C_{\max}:&\text{Makespan of steel logistics park.}\\ SC_{\max}:&\text{Maximum makespan of all vehicles.}\\ L:&\text{A large number for maintaining the consistency of the inequality.}\\ \end{array}\]  
(2) _Decision Variables_  
\[\begin{array}{ll}X_{i,j,k}:&\text{Takes a value of $1$ if the operation $O_{i,j}$ is processed at yard $k$ and $0$}\\ y_{i,j,p,q,k}:&\text{Takes a value of $1$ if the operation $O_{i,j}$ takes precedence over the operation $O_{p,q}$ at yard $k$ and $0$ otherwise.}\\ \end{array}\]
# Analyzing and Discovering Spatial Algorithm Complexity Vulnerabilities in Recursion
## 4 Methodology
### Model-Based Vulnerable Call Chain Search
#### 4.3.2 Unsafe Recursion Termination Conditions  
The termination condition is the key part of the recursive algorithm, which determines when the recursion stops and returns the result. In recursive algorithms, the termination condition is usually a conditional statement. When the condition is met, the recursion will no longer continue to execute, but start to return results or perform other operations. The existence of termination conditions is to prevent recursion from entering an infinite loop and ensure that recursion ends eventually.  
In general, termination conditions have two important characteristics:  
* The termination condition must be attainable. That is to say, in the process of recursion, after a series of recursive calls, the termination condition should be met finally.
* The termination condition should not include a recursive call, otherwise the recursion will not end. The termination condition should be a explicit case without further recursion.  
To match and detect whether there is a limit on the number of recursive levels, software developers commonly use two methods to record and judge the recursion depth:  
* Define a class attribute in the class corresponding to the recursive method to record the recursion depth. Each time the recursive method proceeds to the next level, increment this class attribute to update the recursion depth. Before executing the next recursive method, compare this class attribute with a constant representing the maximum depth. The recursive method can proceed to the next level if the depth is less than this constant value.
* Include a variable to record the recursion depth as a parameter in the recursive method. Each time the method recurses to the next level, increment this depth variable and pass it to the next level of recursion. Before executing the next recursive method, compare this variable with a constant representing the maximum depth. The recursive method can proceed to the next level if it is less than this constant value.  
The common features of these two methods are: (1) There is a variable that records the depth of recursion, which increases in synchronism with each recursion level. (2) This variable is compared with a constant value, and the next level of recursion is only allowed if the variable is less than this constant value. Hence, the logic for judgment is as follows, and the algorithm pseudocode is shown in Algorithm 2.  
* Identify the variable in the recursive method that has '+1' increment (Line 7).
* Determine whether this variable is a formal parameter of the recursive method or comes directly or indirectly from a class attribute (Line 7).
* Check if this variable is compared with a constant value, and ensure that the call to the next level of recursion is within the judgment logic that checks for the variable being less than this constant value (Line 5).
## 3 Methodology
### South Africa Vehicular Traffic Flow  
Note that for this research study, the period of the day is divided as:  
1. _00:00:00-04:59:59 (Off-peak)_
2. _05:00:00-09:59:59 (On-peak)_
3. _10:00:00-14:59:59 (On-peak)_
4. _15:00:00-19:59:59 (On-peak)_
5. _20:00:00-23:59:59 (Off-peak)_  
For clarity and understanding of the vehicular traffic volume of each roadsites, the dates and days the traffic datasets were collected are from 15 July 2019 (Monday) to 25 July 2019 (Thursday). Figure A1 (these figures can be found in the Appendix A section, they are numbered A-G) shows the traffic volume of vehicles at each roadsites considering the period of the day. The traffic volume tables for each roadsites can be found in the Appendix A section. They are named Tables 2, A1 and A3-A7. The traffic flow system depends on the traffic volume at a certain period of the day. For example, in each of the roadsites, the period between (00:00:00-04:59:59) is when the traffic flow is gradually building up. This is a period between midnight and the early morning when people are just starting to their place of work. Between 05:00:00-09:59:59 and 10:00:00-14:59:59 is the on-peak period, this when the traffic volume attains the maximum number of vehicles on the road. This is usually when traffic congestion occurs due to high road traffic density (large vehicle occupancy on the road). The traffic congestion can be either recurrent (this occurs when the route is heavily used because of its network connectivity) and non-recurrent (this might be due to road accidents or unforeseeable circumstances on the road). The exception among these vehicular traffic flows is that during the non-working days, which are Saturday and Sunday, from critical observation, between the period of 00:00:00-04:59:59 and 05:00:00-09:59:59, there is a low traffic volume due to people not going to place of work at this period of the day. During these days (Saturday and Sunday),  
Figure 6: Video cameras attached to a traffic signal light.  
the traffic flow only started to build up in the afternoon, around 15:00:00 and 19:59:59. When the traffic flow is high, the traffic density will increase, which means that the traffic volume will be high, i.e., high traffic volume means there are many vehicles on the road. The traffic flow periods have shed light on one thing, to understand traffic flow, it is crucial that we first have clarity on the traffic volume, traffic density, and the period of the day (off-peak and on-peak).
# Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach
###### Abstract  
Modern complex systems include products and services that comprise many interconnected pieces of integrated hardware and software, which are expected to serve humans interacting with them. As technology advances, expectations of a smooth, flawless system operation grow. Model-based systems engineering, an approach based on conceptual models, copes with this challenge. Models help construct formal system representations, visualize them, understand the design, simulate the system, and discover design flaws early on. Modeling tools can benefit tremendously from querying capabilities that enable gaining deep insights into system aspects that direct model observations do not reveal. Querying mechanisms can unveil and explain cause-and-effect phenomena, identify central components, and estimate impacts or risks associated with changes. Being connected networks of system elements, models can be effectively represented as graphs, to which queries are applied. Capitalizing on established graph-theoretic algorithms to solve a large variety of problems can elevate the modeling experience to new levels. To utilize this rich set of capabilities, one must convert the model into a graph and store it in a graph database with no significant loss of information. Applying the appropriate algorithms and translating the query response back to the original intelligible and meaningful diagrammatic and textual model representation is most valuable. We present and demonstrate a querying approach of converting Object-Process Methodology (OPM) ISO 19450 models into graphs, storing them in a Neo4J graph database, and performing queries that answer complex questions on various system aspects, providing key insights into the modeled system or phenomenon and helping to improve the system design.  
Object-Process Methodology (OPM); OPM ISO 19450; model-based system engineering (MBSE); conceptual modeling; graph databases; query languages +
Footnote †: journal: Applied Sciences
# A Nonlinear Optimization Design Algorithm for Nearly Linear-Phase 2D IIR Digital Filters
## 7 Conclusions  
A new framework for a nonlinear optimization design method for nearly linear-phase 2D IIR digital filters with separable denominators is presented. The method is used to solve the design problem, expressed as a nonlinear constrained optimization problem. In our implementation, we distinguish two kinds of constraints: soft and hard constraints. Meeting the soft constraints is not compulsory and, therefore, not decisive in rejecting the filter. The hard constraints, however, must be satisfied to accept the filter. The designed filters are evaluated with three quality factors, namely passband amplitude and group delay quality factors, and a new quality factor for the stopband magnitude response. compared with other 2D IIR filters designed with other methods, the results show that the framework produces very good results for different filter specifications.  
Evaluating the resulted filters, this method improves the linearity of the phase in the passband with magnitude improvements, generally without significant deterioration in the magnitude response. The method is also flexible enough to be used after any other 2D IIR design algorithm by taking the resulting filter as the initial filter and running this method to obtain possible further improvements.  
In future work, we intend to improve our nonlinear optimization method by directly optimizing the quality factors instead of the group delay deviations only.  
Conceptualization, A.O. and P.A.; methodology, A.O., P.A. and D.S.; software, A.O., P.A. and B.M.; validation, A.O., P.A. and D.S.; formal analysis, A.O., P.A. and D.S.; investigation, A.O., P.A. and D.S.; resources, A.O., P.A. and B.M.; data curation, A.O. and P.A.; writing--original draft preparation, A.O.; writing--review and editing, A.O., P.A. and D.S.; visualization, A.O., P.A. and D.S.; supervision, P.A. and D.S.; project administration, A.O., P.A. and D.S. All authors have read and agreed to the published version of the manuscript. Funding Funding provided by NSERC. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Proposed Method** & **Method in [8]** \\ \hline Orders & (4,4) & (4,4) \\ \(H_{\text{min}}\) & 0.809 & 0.633 \\ \(H_{\text{max}}\) & 1.191 & 1.167 \\ \(Q_{h}\) & 19.100 & 29.667 \\ \(H_{\text{max}}^{\text{r}}\) & 0.855 & 0.798 \\ \(H_{\text{max}}^{\text{s}}\) & 0.324 & 0.268 \\ \(Q_{s}\) & 0.668 & 0.734 \\ \(\tau_{\text{min}}\) & 1.822 & 1.828 \\ \(\tau_{\text{max}}\) & 5.073 & 6.737 \\ \(Q_{\text{T}}\) & 47.150 & 57.315 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results for bandpass filter.
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Applications of the Car-Following Models
#### 3.3.1 Calibrating the Car-Following Model
Considering that the calibration process of car-following models is an essential solving process for optimization problems, [PERSON] et al. discussed the impacts of data filtering, the correlation between parameters, sampling technique, and objective function on robust calibration, and derived the confidence interval near the minimum [126]. Calibration is a necessary step before applying the car-following model. The performance of the model is greatly affected by the performance of the calibrating method. In the aforementioned works, the vital compositions, including the algorithm, model itself, data set, and evaluating method in the whole calibration process of the car-following model were discussed. The research results can provide a reference for improving aspects of calibration performance, such as the calibrating speed. The calibrated model can be utilized in simulating traffic flow and then analyzing the characteristics of traffic flow, as well as other applications such as evaluating energy consumption and emission.
## 3 Elastographic Methods
### Two-Dimensional Shear Wave Elastography (2D-SWE)
#### 3.3.1 Technical Success Rates of Liver Stiffness Estimates  
Five studies on healthy subjects and/or patients with chronic liver diseases have reported results on the technical success rate of 2D-SWE in pediatric patients. In two studies on, respectively, 96 and 88 subjects, no technical failure was observed [72,73]. In another study on NASH pediatric patients, 2D-SWE was feasible in 68/69 (99%) of them [12]. In a large series, the success rates of LSMs in the study group and the control group were 96.4% (244/253) and 100% (40/40), respectively [73]. The main reasons for failure were crying and body movements. No technical failure was observed in a free-breathing status [72]. A more recent study evaluated the stability index (SI) of 2D-SWE acquisitions as a quality indicator of measurements [74]. Using an SI \(<\) 90% as an indicator of unreliable measurement, failure to obtain reliable 2D-SWE measurements was observed in five of 29 patients (17%) in the free-breathing group and in two of 29 patients (7%) in the breath-holding group. Comparison of the mean elasticity value revealed no significant difference between free breathing and breath-holding (6.31 \(\pm\) 3.98 kPa vs. 6.47 \(\pm\) 4.09 kPa, \(p\) = 0.354, \(n\) = 29) [74].  
Hepatic 2D-SWE performed with free breathing yielded results similar to the breath-hold condition. With a substantially lower time requirement, which could be further reduced by lowering the number of acquisitions, it was concluded that the free-breathing technique may be suitable for infants and less cooperative children not capable of breath-holding [13].