esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Developing SNEP Model in AVISPA
### Model Validation and Analysis  
Upon completion of the AVISPA model development using HLPSL, we use SPAN (Security Protocol Animator) [14], which symbolically executes an HLPSL protocol specification. SPAN is very useful while writing HLPSL specifications, since it gives better understanding and confirms if the specification is executable. We use SPAN's protocol simulation that builds message sequence charts (MSC) corresponding to our HLPSL specifications. During protocol simulation, no intruder's role has been introduced. Figures 11 and 12 show the MSC of the AVISPA models that we have built to validate node to node key agreement and counter exchange protocols with strong freshness. The figures confirm that the number of messages exchanged while the models are executed are equal to the messages we have modeled.  
Figure 10: session and environment roles modeled in the node to node key agreement protocol with strong freshness.  
AVISPA supports Dolev-Yao channels [15] (denoted by channel (dy) in the roles) for message transfer. In this model, the adversary or intruder has full control over message transfer. She can overhear, intercept and synthesize any message, and her actions are only limited by the constraints of the cryptographic methods used. Next, we introduce an active intruder and verify the model using all four back-ends of AVISPA to find an attack if any exists. AVISPA cannot simultaneously detect multiple attacks during a single run. Therefore, we verify the security goals one-by-one. The first two back-ends, On-the-Fly Model-Checker OFMC and Constraint Logic-based Attack Searcher CL-AtSe have reported SAFE for BOUNDED_NUMBER_OF_SESSIONS. The other two, SAT-based Model-Checker SATMC and Tree Automata based Automatic Approximations for the Analysis of Security Protocols TA4 SP, have reported NOT_SUPPORTED and produced INCONCLUSIVE results. Figure 13 shows the validation output of OFMC for node to node key agreement with strong freshness. Due to the complexity of the model, we have to run OFMC with a bounded depth. Finally, we can conclude that the AVISPA models that we have developed are free from the attacks that AVISPA is able to find. Hence, the security goals of the two models that we have mentioned earlier have been validated.  
Figure 11: Protocol simulation for node to node key agreement with strong freshness.  
Figure 12: Protocol simulation for the counter exchange protocol with strong freshness.
# Ultra-High-Performance Concrete (UHPC) Piles and Splicing Options
###### Abstract  
Conventional driven piles are made from steel, concrete, timber, or composite materials. These piling options have limitations with respect to corrosion, durability, driveablity, and performance. Ultra-High-Performance Concrete (UHPC) pile is a new alternative that has already been adopted by various state Departments of Transportation in the United States for addressing the limitations that exist with conventional piles. UHPC piles are made of a cementitious composite material mixture that possesses exceptional properties such as higher strength, low capillary porosity, and high resistance to corrosion, making them a suitable option for use as a deep foundation. For several reasons, it is necessary to cast piles with a shorter length and splice them at the site to reach the desired lengths. These reasons include shipping limitations, unpredictable soil condition, reducing transportation costs, construction time, and damage during installation. This study aims to explore and summarize the currently available options for connecting UHPC pile segments. Accordingly, after a brief introduction on driven piles, this paper investigates various splicing systems that can be used for UHPC piles through reviewing previous research studies and field applications. The applicable splices are then compared based on several criteria such as capacity, durability, cost, and ease of application.  
UHPC piles; pile splices; driveability; corrosion; durability; deep foundation +
Footnote †: journal: Applied Sciences
# Chitosan Nanocomposite Coatings for Food, Paints, and Water Treatment Applications
## 1 Introduction  
Millions of tons of crustaceans such as shells of crabs, shrimps, lobsters, and krill are produced each year and consumed as protein-rich seafood worldwide. The shells of the crustaceans and other non-edible parts that constitute about half of the body mass, and which are a prominent source of chitin and protein, are generally discarded as waste. Though chitin is the second largest natural polysaccharide on earth, after cellulose, it is not widely used for fabrication of products or as a food commodity due its insolubility in many commonly used solvents [1; 2]. Chitosan (CH) can be obtained commercially from crustaceans' wastes and the cell walls of some fungi by the de-acetylation of chitin [3; 4; 5]. The United States Food and Drug Administration (USFDA) has recommended chitosan a GRAS (Generally Recognized as Safe) material, which has led to the application of chitosan in food, agriculture, and biomedicine.  
Chitosan is a biopolymer, also known as \"hydrocolloids\". Although most hydrocolloids are neutral or negatively charged at acidic pH, chitosan is charged positively due to the presence of highly reactive amino groups. Chitosan is a linear polysaccharide composed of N-acetyl, D-glucosamine,and D-glucosamine amine units [6]. Chitosan is not extensively available in nature and is thus usually derived from chitin by the partial deacetylation in alkaline solutions at elevated temperatures. Chitosan has attracted attention for food and paint applications owing to its superior characteristic properties, such as degradability, solubility in weak acids, pH-sensitivity, film-forming property, biocompatibility, non-antigenic, absence of toxicity, and low-cost [7; 8; 9]. Moreover, because of its natural origin and multiple possible applications, like preparation of biodegradable films, blends, coatings, composites, nanocomposites, etc., it has attracted attention of both the scientific community and various industries, particularly the food and paint industries (Figure 1). This review mainly focuses on the physicochemical properties of chitosan, the development of composite materials, and its various applications in water treatment, paints, and food industries.
# A Snapshot on the Occurrence and Risk Assessment of Organic Pollutants in an Urban River
## 3 Results and Discussion
### CECs Monitoring in Sediment Lixivivates
Similarly to surface water, the most ubiquitous compounds found in sediments were caffeine and BPA. It is not surprising that BPA is the most abundant compound found in the river. Indeed, it is one of the most widely produced plasticizers used massively in the formulation of polycarbonate and epoxy resins. Polycarbonate is used in reusable plastics, food packages, drinking water bottles, dental sealants, and in many other products. Epoxy resins are generally coated in food contact surfactants and electrical coils [8; 82]. BPA release probably originated from the degradation of the polymer structure of plastics in the environment and the wastewaters [83; 84]. Reference [85] verified that BPA was found in higher concentrations in suspended matter than in sediments because of the affinity of BPA for small organic particles. Reference [84] corroborated the results obtained by [85], observing that BPA is mostly present in water and suspended solids (52%), soil (25%) and sediments (23%). This behavior might be due to its moderate hydrophobicity (log K\({}_{\rm{ow}}\) = 3.32: Table S6). The results obtained for F1 are in line with these assumptions. In F2 and F3, the higher levels of BPA associated with sediments, and eventually to suspended solids, can be explained by the gradual increasing predominance of fine particles in the riverbed (Figure 1). Caffeine was also found in F1, F2, and in F3. This compound is considered an indicator of anthropogenic disturbance because it is present in all ecosystem compartments (water, sediment, and biota) including in rainwater [9; 86]. As expected, in the Fervenca river, it was detected both in water and sediments, with increasing concentrations in water from F1 to F3. Nevertheless, in F1 and F2 the caffeine concentration was higher in sediments than in water. The ability of the studied compounds to sorb to the sediments is influenced by their octanol-water partitioning coefficient (K\({}_{\rm{ow}}\): Table S6): The higher log K\({}_{\rm{ow}}\) values, the greater is the tendency for a given compound to be sorbed into the sediment [60; 87]. However, this correlation is not always consistent with the concentrations in sediments for all the detected compounds. As shown by [78; 79], for small rivers, the ratio of the measured concentrations between water and sediment is not only influenced by the solubility of CECs but also by the polar and ionic nature of the compounds [60; 80; 81; 87]. Indeed, this ratio depends on other local characteristics such as: sediment mineral composition and grain size [81]; temporal contamination fluctuations [78], river bed morphology [88; 89]; the degree of shading in the riverbed promoted by riparian vegetation [90], which influences photochemical degradation rates of many CECs [91]; and the river ecosystem complexity and associated biodiversity [4; 92; 93; 94]. These characteristics have an influence on the CECs' sorption and desorption behavior and ultimately on water and sediment concentrations' ratio.
## 3 Results
### Power Consumption and Carbon Emissions  
Figure 4 displays the energy consumed by the whole cultivation setup as well as the one consumed only by the lighting system. The two curves exhibit a linear trend, allowing for extracting the daily energy consumption. Each day, the cultivation setup consumed 0.662 kWh of electricity, of which 0.244 were dedicated to powering the LED. Using this last value and the estimated power to photon efficiency of the light source (21.6%), it was possible to compute the microalgae photosynthetic efficiency, Equation (5). For this process, it was established at 4.34 \(\pm\) 0.68%.  
Using electrical power consumption, it is possible to compute the carbon emissions associated with the process. To do so, the European carbon intensity of electricity production was used. As of 2021, it reached 275 gCO\({}_{2}\)/kWhe [28]. Thereof, the total carbon dioxide emissions can be evaluated at 182 gCO\({}_{2}\)/day Equation (7), while the ones associated with the lighting system are around 67.1 gCO\({}_{2}\)/day.
## 3 Results
### Evaluation of Classification Accuracy Based on XGBoost for Different Altitude Tree Species
#### 3.4.2. Altitude 1800 m  
As shown in Table 11, in the 1800 m altitude region, the classification accuracy of coniferous species was also higher than that of broad-leaved species, with a user accuracy (UA) range of 94.78% (_Abies fargesii_) to 98.21% (_Pinus armandii_), while the UA range of broad-leaved species was 57.14% (_Carpinus cordata_) to 86.84% (_Cyclobalanopsis glauca_), and the UA precision of the mixed species category \"other species\" was 85.71%. This was mainly due to the misclassification of _Carpinus cordata_, _Betula albosinensis_, and _Cyclobalanopsis glauca_ with \"other species.\" The classification results for this altitude level are shown in Figure 6.
## 5 Mathematical Modeling and the Control of Attitude Dynamics
### Controller Design
#### 5.4.3 Yaw-Rate Controller
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Performance**} & \multicolumn{3}{c}{**Roll Response**} & \multicolumn{3}{c}{**Pitch Response**} & \multicolumn{3}{c}{**Yaw Rate Response**} \\ \cline{2-7}  & **Without** & **With** & **Without** & **With** & **Without** & **With** \\  & **Controller** & **Controller** & **Controller** & **Controller** & **Controller** & **Controller** \\ \hline Settling time & 104 s & 0.0811 s & 14.9 s & 0.09 s & 0.398 s & 0.006 s \\ Rise time & 58.4 s & 0.0244 s & 8.32 s & 0.13s & 0.223 s & 0.0029 s \\ Overshoot & zero & 11\% & zero & 0.4\% & zero & zero \\ Steady-state error & zero & zero & zero & zero & 79.9\% & zero \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of octocopter responses with and without controller.
## 3 Three Methods for Calculating Satellite Repeat Shift TTime
### Aspect Repeat Time Method  
For the static receiver on the ground, what we are most interested in is the repeat period of the satellite in the same vector direction of the receiver to satellite. Assume that the unit vector of the receiver to satellites is \(x_{0}(t_{0})\) on the epoch \(t_{0}\) in the reference day for the first time it is observed, and the other vector is \(x_{1}(t_{1})\) on the epoch \(t_{1}\) in the subsequent day for the second time it is observed after a repetition period. It is noted that, the epochs \(t_{0}\) and \(t_{1}\) are counted from zero for every solar day. If the angle between the two unit vectors \(x_{0}(t_{0})\) and \(x_{1}(t_{1})\) reaches the minimum value, the satellite repeat shift time can be determined:  
\[TS=F_{t}(t_{1}-t_{0}) \tag{14}\]  
where \(F_{t}\) is the sampling of the observations and \(t_{1}\) is the epoch of the second time it is observed. The process of the calculation involves the following steps and is presented in Figure 1:  
1) Estimate the receiver position, and the satellite position is also estimated by the precise satellite ephemeris on the given reference epoch \(t_{0}\);  
2) Calculate the unit vector of the receiver to satellite at the epoch t\({}_{0}\), and express it as \(x_{0}(t_{0})\);  
3) Search the epoch \(t_{1}\), which has a similar unit vector \(x_{1}(t_{1})\) to the initial vector \(x_{0}(t_{0})\) during the repeat period; and  
4) Determine the satellite repeat shift time of this epoch by Equation (14).
## 2 Background
### Motivation  
This paper intends to propose a solution that can address two configurable parameters of a BCH decoder. First, the solution should be scalable across different \(GF\) fields, i.e., it should be able to support different \(GF\) field extensions (\(GF(2^{m})\)). Second, the solution should be able to scale across different  
Figure 1: Area-efficient syndrome generator.  
bit errors \(t\). Different configurable BCH decoder solutions have been proposed [20; 24], but they lack support for both configurable parameters of the BCH decoders. Inspired by the attempt to solve BCH decoders for multiple \(GF\) dimensions in [20], we propose an alternate hybrid approach to have a flexible solution. In [20], a hardware solution was proposed to support multiple BCH codes; however, the circuit area increases in order to support multiple \(GF\) dimensions because of the dual-mBCH decoders' requirement. We have proposed a method to share the hardware logic between the BCH encoder and BCH syndrome generator by modifying the encoding method in [14]. In this paper, we extend our previous work by using a programmable modified syndrome generator algorithm and GPU to have a decoder that works with multiple \(GF\) dimensions.
## 2 Security Requirement and S-Box Design Methods
### S-Box Properties
#### 2.1.7 Balancedness  
An \(n\)-variable Boolean function \(f\) is said to be balanced if \(wt(f)=2^{n-1}\), where \(wt(f)\) is the Hamming weight of \(f\) for the \(n\)-variable. An S-box is called balanced if every value in \(\mathbb{F}_{2^{n}}\) occurs once. This implies that the function is bijective (also known as the permutation function).
# Self-Adaptive Differential Evolution with Gauss Distribution for Optimal Mechanism Design
## 5 Numerical Simulation
### Modified Differential Evolution for Real-World Problem
#### 5.4.4 Optimization Problem of Gear Reducer  
Gear reducer is widely applied in machinery, it is used to reduce the speed and increase the torque taken by the motor. This research applied the modified differential evolution to two optimization problems, single stage and two-stage planetary spur gear reducer. Problem 1: Single stage spur gear reducer  
This problem is described in [26] and shown in Figure 26, it contains seven design variables, including the face width, \(x_{1}\); the gear module, \(x_{2}\); the teeth number on pinion gear, \(x_{3}\); the length and diameter of the shaft I, \(x_{4}\) and \(x_{5}\), respectively; and the length and diameter of the shaft II, \(x_{6}\) and \(x_{7}\), respectively. The target is the minimal weight of speed reducer box expressed in Equation (38). This problem is bounded by 11 constraint functions as Equations (39)-(49). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Design Variables** & **CDE** & **CPSO** & **CGA** & **EOBLDE** & **G-ISADE** \\ \hline \(x_{1}\) & 0.051609 & 0.051728 & 0.051480 & 0.051689 & 0.051897 \\ \(x_{2}\) & 0.354714 & 0.357644 & 0.351661 & 0.356718 & 0.361748 \\ \(x_{3}\) & 11.410831 & 11.244543 & 11.632201 & 11.288947 & 11.00000 \\ \(g_{1}\) & \(-0.000039\) & \(-0.000845\) & \(-0.002080\) & \(-2.220\times 10^{-16}\) & \(-10^{-15}\) \\ \(g_{2}\) & \(-0.000183\) & \(-1.2600\times 10^{-5}\) & \(-0.000110\) & \(-1.1102\times 10^{-16}\) & 0.00000 \\ \(g_{3}\) & \(-4.048627\) & \(-4.051300\) & \(-4.026318\) & \(-4.053785\) & \(-4.063608\) \\ \(g_{4}\) & \(-0.729118\) & \(-0.727090\) & \(-4.026318\) & \(-0.727728\) & \(-0.724236\) \\ \hline \(f(x)\) & 0.0126702 & 0.0126747 & 0.0127048 & 0.012665 & **0.012662** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Best solution for the compression spring problem. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Best** & **Mean** & **Worst** & **Standard Deviation** \\ \hline CDE & 0.012670 & 0.012703 & 0.012790 & \(2.70\times 10^{-5}\) \\ CPSO & 0.012674 & 0.012730 & 0.012924 & \(5.19\times 10^{-5}\) \\ CGA & 0.012704 & 0.012769 & 0.012822 & \(3.93\times 10^{-5}\) \\ EOBLDE & 0.012665 & 0.012669 & 0.012713 & \(9.01\times 10^{-6}\) \\ G-ISADE & 0.012662 & 0.0126845 & 0.0128108 & \(3.23\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical results for compression spring problem. Design variable: \(x_{1}\) = [2.6, 3.6]; \(x_{2}\) = [0.7, 0.8]; \(x_{3}\) = [17, 28], \(x_{3}\) is an integer; \(x_{4}\) = [7.3, 8.3]; \(x_{5}\) = [7.3, 8.3]; \(x_{6}\) = [2.9, 3.9]; \(x_{7}\) = [5, 5.5].
## 2 Materials and Methods
### Conventionally and LPBF-Manufactured 316L Materials  
Conventionally manufactured austenitic stainless steel AISI 316L (1.4404) sheet metal (\(20\times 500\times 500\) mm) from Thyssenkrupp Schulte GmbH, Dusseldorf, Germany, was used for comparison with the additively manufactured material. The chemical composition is given in Table 1. Laser powder bed fusion by an SLM280 HL (SLM Solutions Group AG, Lubeck, Germany) machine was chosen in terms of the commercial 316L material. The model pressure vessels and fatigue testing specimens were manufactured by Rolf Lenk Werkzeug u. Maschinenbau GmbH in Ahrensburg, Germany. The process parameters are given in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Fe** & **C** & **Si** & **Mn** & **P** & **S** & **Cr** & **Ni** & **Mo** & **N** & **Cu** \\ \hline
316L sheet metal & Balance & 0.015 & 0.470 & 1.26 & 0.027 & 0.001 & 16.8 & 10.10 & 2.14 & 0.04 & 0.064 \\
316L powder & Balance & 0.02 & 0.67 & 1.16 & 0.022 & 0.006 & 17.13 & 10.92 & 2.33 & 0.1 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of the austenitic 316L sheet metal and powder materials (wt.-%).  
Figure 1: Technical drawing of the experimental pressure vessel model [20].  
316L powder material (particle size distribution: 10-45 \(\upmu\)m, spherical morphology; supplied by SLM Solutions Group AG, Lubeck, Germany) was used for manufacturing the semi-finished samples (Figure 2a). The chemical analysis of the powder material is displayed in Table 1.  
The final compact tension (CT) specimen geometry (Figure 2b) was produced by electrical discharge machining (EDM) from the semi-finished samples produced by LPBF (Figure 2a). A CT specimen geometry with a width of W = 40 mm, thickness of B = 10 mm, and an initial notch depth of a\({}_{0}\) = 10 mm in accordance with the ASTM testing standard [21] was applied in the experimental program. The same geometry was used for the conventionally produced CT specimens.
## 1 Introduction  
Phenolic compounds are introduced into the body by consuming plant food. In the digestive tract, they are released from the food matrix in their natural chemical form [1, 2, 3, 4, 5]. They can also degrade due to the environment of the digestive system, with the low pH in the stomach and a higher pH in the small intestine. Those that are absorbed undergo biotransformation, which creates many different metabolites that are finally found in the bloodstream [6, 7] or excreted with urine [6, 7]. Studies conducted on humans identified many such phenolic compound metabolites in plasma and urine after the consumption of plant food, fruit juices, or tea [8, 9, 10, 11]. Natural phenolic compounds that are not absorbed in the small intestine can reach the colon, where they can be transformed by the gut microbiota into different catabolites. Many catabolites are identified in human urine and plasma [9, 10]. Catabolites that are not absorbed in the colon are eliminated from the body with feces [6, 7].  
Although phenolic compounds are intensively metabolized and catabolized, they still appear in the digestive system in their natural form, in which they are present in fruit. Moreover, their concentrations in natural forms are relatively high. For this reason, the potential beneficial actions of native phenolic compounds in the digestive system are being intensively studied. Studies have been investigating the effects of phenolic compounds in the mouth and on the teeth [12, 13], in the stomach [14, 15], or in the small intestine [16, 17].  
Many similar effects are described in the review paper [18]. Natural phenolic compounds have the potential to inhibit harmful lipid peroxidation in the stomach, to help in the healing of gastric ulcers, or to inhibit _Helicobacter pylori_ infection [18]. In the small intestine, phenolic compounds showed the potential to maintain the proper intestinal barrier function or to help regulate postprandial glucose levels [18]. The fate of phenolic compounds and the rate of their release might help in understanding those beneficial effects.  
Up until now, the fate of phenolic compounds in the digestive tract is still not completely known. Degradation products are not completely identified, and the effects of other food components on the release of phenolic compounds are not completely known. Furthermore, the digestive process is a process in which the release of compounds from the food matrix happens in time. The rate at which the phenolic compounds are released is not well known. The release of phenolic compounds in the digestive tract can be studied with chemical kinetics.  
The aim of this paper was to study the simulated in vitro gastrointestinal digestion of apples in different time periods and to model the results of this process (concentrations of released phenolic compounds vs. time) with non-linear regression, using equations of first- and second-order kinetics. Those equations were modified to fit the release of phenolic compounds in gastric and intestinal digestion. The modification of these equations is developed and applied here.