esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Security Analysis
### Formal Proof Under AVISPA Simulation  
\"AVISPA\" is a commonly employed simulation platform for verifying whether authentication protocols provide security against adversarial attacks, such as replay and MITM attacks [50]. In this part, we utilize AVISPA to validate the security of our scheme. The AVISPA simulation uses the \"High-Level Protocol Specification Language (HLPSL)\" to describe the protocol concerning the user, server, and sensor roles. This \"HLPSL\" is converted into an \"intermediate format (IF)\" executing the \"HLPSL2 IF\" compiler. The \"IF\" is analyzed by one or more of AVISPA's four back-ends: \"On-the-Fly Model-Checker (OFMC)\", \"SAT-Based Model Checker(SATMC)\", \"Constraint-Logic-based Attack Searcher (CL-AtSe)\", and \"Tree Automata-based Protocol Analyzer (TA4 SP)\". The results of the analysis are presented in the \"Output Format (OF)\". If no attack is found against the modeled threat, the result is indicated as \"SAFE\", indicating that the protocol security has been formally verified against attacks.  
We employed two back-end models, the \"OFMC\" and \"CL-AtSe\", to simulate the proposed scheme using the AVISPA tool. Due to their support for the algebraic properties of cryptographic functions, including XOR and concatenation, \"OFMC\" and \"CL-AtSe\" are suitable for the formal verification of the proposed scheme. In the HLPSL specification, three roles \(U_{r_{i}}\), \(IoT_{SL_{i}}\), and the \(CS\) are defined, along with corresponding session and environment roles based on these entities. Each session verifies the security of secret parameters and the correctness of the mutual authentication. The simulation results are shown in Figure 7, where both back-ends return the status \"SAFE\", indicating that no attacks were detected. Therefore, the proposed scheme prevents adversary \(\mathcal{A}\) from conducting replay or MITM attacks.
## 3 Results and Discussion
### Effect of Different Sterilization Methods on the Fatty Acid Composition of Red Raspberry Juice  
To better understand the impact of HHP and HTST sterilization treatments on the fatty acid metabolism pathway, the fatty acid contents in three red raspberry juices were determined. As detailed in Table 1, the juice contains four major unsaturated fatty acids, including linolenic and linoleic acids, as well as eight saturated fatty acids, such as lauric acid. Among these, linoleic acid and linolenic acid have been confirmed to be the precursors of C6/C9 aldehyde aroma compounds in fruits and vegetables, including tomato [36], peach [34], and melon [23]. Post-HHP treatment, the levels of linoleic and linolenic acids decreased significantly, likely due to the enzymatic transformation within the unsaturated fatty acid metabolic pathways, whereas vaccenic acid levels remained unchanged. Conversely, HTST treatment led to a significant reduction in the contents of linoleic, linolenic, and palmitoleic acids, with noticeable increases in vaccenic and palmit acids. This shift may result from diminished fatty acid dehydrogenase (FAD) activity due to heat exposure, inhibiting the conversion from saturated to unsaturated fatty acids [37]. Cluster analysis  
Figure 3: The contents of aroma compounds downstream of the fatty acid metabolism pathway, namely, (_Z_)-3-hexenol (**a**), hexanol (**b**), \(\delta\)-decalactone (**c**), and dihydroactinidiolide (**d**), in red raspberry juice after different sterilization treatments. Different lowercase letters (a–c) denote a significant difference (\(p<0.05\)).  
of the fatty acid composition in different raspberry juice treatments (Figure 4) revealed fewer significant changes in fatty acids in HHP-treated juice compared to HTST-treated juice, indicating that HHP sterilization better preserves the original fatty acid profile of the raspberry juice than HTST treatment.
# Cartesian Constrained Stochastic Trajectory Optimization for Motion Planning
###### Abstract  
This paper presents novel extensions of the Stochastic Optimization Motion Planning (STOMP), which considers cartesian path constraints. It potentially has high usage in many autonomous applications with robotic arms, where preservation or minimization of tool-point rotation is required. The original STOMP algorithm is unable to use the cartesian path constraints in a trajectory generation because it works only in robot joint space. Therefore, the designed solution, described in this paper, extends the most important parts of the algorithm to take into account cartesian constraints. The new sampling noise generator generates trajectory samples in cartesian space, while the new cost function evaluates them and minimizes traversed distance and rotation change of the tool-point in the resulting trajectory. These improvements are verified with simple experiments and the solution is compared with the original STOMP. Results of the experiments show that the implementation satisfies the cartesian constraints requirements.  
motion planning; cartesian constraints; robotic arms +
Footnote †: journal: Applied Sciences
# Sulfonation Treatment of Polyether-Ether-Ketone for Dental Implant Uses
## 5 Conclusions  
The ideal sulfonation duration was determined to be 2 min since a porous structure was created very effectively and there was a comparatively low amount of leftover sulfuric acid. The hydrothermal treatment had a greater impact on eliminating the remaining sulfuric acid and had no impact on modifying the morphology of the surface when compared to NaOH immersion or acetone washing. This is due to the fact that the hydrothermal treatment uses higher temperatures. As a result, the hydrothermal treatment of water is recommended for the purpose of treating sulfonated PEEK. A theoretical foundation may be created for the preparation of sulfonated PEEK in order to achieve a potentially advantageous impact by defining the ideal sulfonation period and post-treatment procedure.  
Polyether-ether-ketone and its composites play an important role in the field of oral repair given their excellent physical and chemical properties as well as biological properties. Its machinability enables the accurate manufacturing of various kinds of implants with complex structures. Its excellent properties, such as stable chemical properties, good biosecurity, and elastic modulus, are close to those of human dense bones, and therefore, make it an excellent potential oral implant material. The biological modification methods described above are all aimed at improving PEEK's biological activity. Each modification method has its advantages and disadvantages. Despite considerable experimental studies, modification technologies are still immature and lack sufficient clinical data to prove the clinical efficacy of these modification methods. Therefore, future research should focus on the development of more efficient and practical modification methods and clinical practice, and the exploration of PEEK modification methods to address different branches of stimulatory.  
Conceptualization, H.H.; Methodology, H.H. and I.S.; Resources, H.H.; Writing--original draft, H.H.; Writing--review & editing, I.S.; Supervision, I.S. and F.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
## 1 Introduction  
With the rapid development of mechanical manufacturing, CNC (computer numerical control) machine tools cover a wide range of applications in the mold manufacturing industry, electronic engineering, and the automotive and aerospace industries [1; 2]. Owing to its advantages of being all-purpose with high precision and a large processing scope, it has become a very necessary type of equipment [3]. As is widely known, precision plays a key role in CNC machine tools, and this is a hot topic in the machining automation domain.  
Generally speaking, there are two approaches to enhance accuracy. One is error avoidance, i.e., improving processing techniques, which is effective but costs plenty of resources. The other is error compensation, which is conducted by obtaining the original errors of CNC machine tools by some measuring method [4]. Clearly, as an economical and efficient method, error compensation has attracted the attention of many researchers. The guide rail of machine tools has 6 degrees-of-freedom errors in linear motion [5]. As the most accurate method, laser interferometers are widely applied to identification of the geometric error of the linear guideway [6; 7]. However, laser interferometers have a high price and tedious operation as the cost of their high precision.  
The R-test, invented by [PERSON] et al. [8], was further used to measure the location errors of a guide way by [PERSON] et al. [9]. [PERSON] improved the R-test device to incorporate three displacement sensors designed to simultaneously collect far more information [10]. Another method is to calibrate the errors with standard parts, such as a ball-bar and a ball-plate. For example, [PERSON] realized the determination of the geometric error parameters of CMMs (coordinate measuring machines) using a 1-D artifact (i.e., a 1-D ball-bar) [11]. [PERSON] proposed a classical measuring method, i.e., to conduct ball bar tests in axial, radial, and tangential directions and identify PIGEs (position-independent geometric errors) by analyzing the eccentricities of the circular trajectory [12]; this method has been accepted by many scholars and is included in ISO 10791-6 [13].  
In this article, we propose a systematic method to identify geometric errors in the linear motion of CNC machine tools based on an innovative 1-D artifact. The 1-D artifact is composed of a series of surface mirror targets. Furthermore, each target includes a revolutionary paraboloid and a plane mirror. The former is designed to measure the 2-D displacement and identify the 2-D original location error of machine tools, and the latter is to identify the 2-D original angle error. We can obtain 4 degrees-of-freedom errors by measuring the 1-D artifact using an angle sensor driven by a machine tool in one direction. Then, by rotating the 1-D artifact \(90^{\circ}\), the other 2 degrees-of-freedom errors can be obtained by another measurement. As such, the method can realize the measurement of the geometric errors in the linear motion of machine tools; the result was compared with that obtained by laser interferometer.
## 3 Results  
### Indentation Load-Displacement Curves  
Figure 6 illustrates representative load-displacement curves obtained from cyclic loading-unloading microindentation tests performed under both unconstrained conditions and bidirectional confining pressures. For ease of analysis, the maximum indentation depth (\(h_{\text{max-1}}\)) obtained during the first cycle of loading-unloading was selected for comparison. From Figure 6, it is evident that the value of \(h_{\text{max-1}}\) exceeds the critical threshold (i.e., \(h_{\text{max}}\geq 10\)\(\upmu\)m) at the maximum load of 100 N when considering both bidirectional confining pressure and no confining pressure; this indicates that the mechanical parameters obtained through subsequent analysis of the load-displacement curves can effectively represent the macroscopic mechanical characteristic of the shale.  
As depicted in Figure 6a, no confining pressure results in an indentation depth of \(h_{\text{max-1}}\) as 0.0257 mm (i.e., 25.7 \(\upmu\)m), and Figure 6b-e show that as the confining pressure gradually increases to 20 MPa, the corresponding \(h_{\text{max-1}}\) value decreases progressively from 0.0257 mm without any confining pressure to 0.0163 mm under the condition of 20 MPa, resulting in a reduction of 36.80%. This observation indicates that the shale sample becomes stronger and more resistant to indentation under bidirectional confining pressure, leading to increased difficulty for the indenter to penetrate. These findings highlight that in actual in situ reservoir environments, when considering the influence of confining pressure on enhancing the mechanical properties of shale, the embedment depth of proppant will be considerably smaller compared to scenarios where confining pressure is not taken into  
Figure 5: Representative load–displacement curves of cyclic loading–unloading indentation tests.  
account. This effect will be particularly notable in deep reservoirs characterized by high confining pressures. Figure 6f presents a comprehensive overview of the variation in \(h_{\text{max-1}}\) as a function of confining pressure. Notably, \(h_{\text{max-1}}\) demonstrates an exponential decrease as the confining pressure increases. Moreover, as the confining pressure rises, the curve demonstrates a gradual flattening behavior, indicating that the diminishing effect on indentation depth becomes less significant with higher levels of confining pressure.
## 3 Results and Discussion
### DCF and PARX Adsorption Assays
#### 3.2.3 Adsorption Equilibrium Studies
the PARX molecule has a higher affinity for ACK carbon when compared to CC carbon. It should be highlighted that the maximum adsorption capacities of both DCF and PARX with ACK carbon are quite high and comparable to the ones obtained with commercial carbon. The \(n_{s}\) parameter of the Sips model, considered the heterogeneity factor, presents values different from the unity, confirming the presence of heterogeneous surfaces and a Gaussian distribution of the binding sites energy [52; 53]. For the systems with CC carbon, the \(n_{s}\) parameter has values above 1, indicating a cooperative reaction between the sorption site and n sorbate molecules [54]. The CC-PARX system presented the highest degree of binding site homogeneity, with an \(n_{s}\) parameter value very close to 1. On the other hand, the \(n_{s}\) parameter has values below 1 for the ACK carbon systems, indicating no cooperation in the binding process.
# Studies and Rejection of Intercrystal Crosstalk on FPGA in a High-Energy Photon-Counting System
## 3 Studies of Intercrystal Scatter  
### Proportion and Effect  
Events with different incident and deposition detectors are defined as intercrystal crosstalk. The typical crosstalk interactions occur in multi-Compton scattering, in which the annihilation photon scatters from one scintillation crystal element to another, depositing a portion of its energy in each [28]. Crosstalk is very likely observed in the second and subsequent interactions (see the dotted lines of case 3 in Figure 5). Here, we define the energy deposition event in the incident crystal as the incident event, and the second and subsequent interactions that do not occur in the incident place are defined as crosstalk events. Another instance is when a photon passes through its incident crystal without interacting with it and then completely or partially deposits its energy in neighboring crystals (see the dotted lines of cases 1 and 2 in Figure 5). Simulation findings of more  
Figure 4: Laboratory setup for steel penetration test.  
Figure 3: Simulation model for steel penetration. The thickness of the iron plate is increased in 10 mm increments.  
than 40,000 measured signals reveal that 16.7% of incident photons have experienced intercrystal interactions in the studied high-energy photon-counting system, in which crosstalk occupies 98.0% of all interference events.  
The percentages of various types of events are denoted with different colored areas in Figure 6. When an incident photon undergoes the photoelectric effect within a material, the resulting signal is classified as a non-interfering event if the photon's energy is deposited entirely or partially in the crystal without altering its path. In contrast, signals involving any deviation from the original path are considered interfering events. As can be seen in Figure 6, the proportion of crosstalk (marked in dark blue) in the total signals is fairly stable between 15% and 16%, while its proportion in interference signals (marked in light blue) varies in different models. The proportion of crosstalk in interference tends to be higher with thicker iron plates and narrower collimator widths. For example, the proportion can reach 90.1% in the model of a 10 cm thick iron plate combined with a 5 mm width front collimator (marked as 10 cm Fe\({}_{\text{5 mm}}\)), indicating that crosstalk at this time is the main factor that degrades the image quality. Meanwhile, in the model of 30 cm Fe\({}_{\text{10 mm}}\), the proportion drops to 18.6%, in which crosstalk slightly worsens the image quality.
# Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems
###### Abstract  
The flourishing of Internet of Things (IoT) applications, characterized by vast transmitter populations and the sporadic transmission of small data units, demands innovative solutions for the sharing of the wireless medium. In this context, satellite connectivity is an important enabler for all scenarios in which terminals are under-served by terrestrial communications and are thus fundamental for providing worldwide coverage. In turn, the design of medium access policies that attain efficient use of the scarce spectrum and can cope with flexible yet unpredictable IoT traffic is of the utmost importance. Starting from these remarks, we investigate in this work the coexistence of a quality of service (QoS)-constrained service with IoT traffic in a shared spectrum as alternative to a more traditional orthogonal allocation among the two services, with an eye on satellite applications. Leaning on analytical tools, we provide achievable rate regions, assuming a slotted ALOHA access method for IoT terminals and accounting for practical aspects, such as the transmission of short packets. Interesting trends emerge, showcasing the benefit of an overlay allocation with respect to segregating the resources for the two services.  
machine-type communications; grant-free access; spectrum-sharing; IoT via satellite +
Footnote †: journal: Physics Letters B  
1
Footnote 1: email: {[[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))}
## 4 Case Study  
The Colombo flood detention area is located in the western Province of Sri Lanka. As shown in Figure 3, it consists of three wetlands/marshes: Kotte marsh, Kolonnawa marsh, and Heen marsh. The urban landscape surrounds these marshes. The climate in Colombo is tropical monsoon. The average annual temperature ranges from 26.5 \({}^{\circ}\)C to 28.5 \({}^{\circ}\)C, and it typically receives 2300 mm of precipitation each year. The study area is approximately 1.0-3.0 m above mean sea level. The Metro Colombo Basin covers 105 km\({}^{2}\), and the total wetland coverage is about 20 km\({}^{2}\).  
Figure 3: Selected catchment for the study: Colombo flood detention basin.  
The capacity of the Colombo flood detention basin has decreased by 30% as a result of urbanization [38]. Hence, flooding is a significant threat to Colombo city. Increased surface runoff brought on by rapid development, declining retention areas, and lack of capacities in wetlands can be identified as the leading causes of flooding. Consequently, Colombo district's flood risk can be reduced by putting strategies in place.  
Four water level monitoring stations are located within the basin, Kimbulawala Bridge, Kotte North Canal, Kotte Canal, and Parliament Lake. The data set of each wetland contains daily water levels and the readings of other independent meteorological variables. Wetland water level data were obtained from Sri Lanka Land Development Corporation, while meteorological data were obtained from the Department of Meteorology, Sri Lanka.  
The number of data points recorded for each wetland selected for the study differs. Therefore, considering the number of data points, the train-test data split was decided as presented in Table 3.  
The range of each variable is substantially different from the range of the others. For example, the temperature range between 20 and 35 \({}^{\circ}\)C, the RH value; is between 65 and 95%; the evaporation is between 2 and 3 mm; and the water level is between 0.2 and 0.4 m. This difference in value ranges of each variable will adversely affect the speed of the NN convergence and the time taken to adjust NN hyperparameters. Hence, each variable was normalized to set it between the ranges of 0 and 1, the normalized water level variation, along with the training-testing split presented in Figure 4. In FFNN based prediction model, the water level is used as output, whereas LSTM-based prediction is used as input and output both in the training phase.  
Figure 4: Train–Test data split of (**a**) Kimbulawala, (**b**) Kotte Canal, (**c**) Kotte North, and (**d**) Parliament Lake.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Wetland**} & **Total Number of** & **Training Data** & \multirow{2}{*}{**Testing Data Volume**} \\  & **Data Points** & **Volume** & \\ \hline Kimbulawala Bridge & 1887 & 1500 & 387 \\ Kotte Canal & 1584 & 1200 & 384 \\ Kotte North & 457 & 375 & 82 \\ Parliament Lake & 1126 & 800 & 326 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Train-Test Data Split.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 4 Design
### Phase Mode Excitation of Continuous Circular Aperture  
Unfortunately, the calculated direction vector is not a Vandermonde matrix. In order to leverage MUSIC algorithm to determine the direction of acoustic source, we need to construct a preprocessing matrix for \(A(\vec{\theta})\) via the phase mode excitation technique.  
We consider the case of a continuous circular aperture. The excitation functions are periodic with period \(2\pi\) and can be represented in terms of a Fourier series. An arbitrary excitation function \(w(\gamma)\) is determined by following formula:  
\[w(\gamma)=\sum_{m=-\infty}^{+\infty}c_{m}e^{jm\gamma}, \tag{7}\]  
where the \(m\)th phase mode \(w_{m}(\gamma)=e^{jm\gamma}\) is a spatial harmonic of the array excitation and \(c_{m}\) is the corresponding Fourier series coefficient. As the result of aperture exciting with the \(m\)th phase mode, the normalized far-field pattern is:  
\[f_{m}^{c}(\vec{\theta})=\frac{1}{2\pi}\int_{0}^{2\pi}w_{m}(\gamma)e^{j\beta \cos(\phi-\gamma)}\,d\gamma \tag{8}\]  
where the superscript \(c\) represents the continuous aperture. According to \(w_{m}(\gamma)\), the far-field pattern can be expressed as:  
\[f_{m}^{c}(\vec{\theta})=j^{m}J_{m}(\beta)e^{jm\phi}, \tag{9}\]  
where \(J_{m}(\beta)\) is the Bessel function of the first kind of order \(m\). It contains the information of the elevation angle and the amplitude. Due to the Automatic Gain Control (AGC) we utilize, the amplitude of the acoustic signal is replaced by another one that is close to constant. In addition, we can easily detect that the far-field signal pattern and the excitation function share the same azimuthal angle change \(e^{jm\phi}\).  
However, the number of modes that can be excited is limited. Now, we let \(M\) denote the highest order mode that can be excited by the aperture at a reasonable strength (\(m<M\)). We can say that visible region \(\theta\in[\frac{\pi}{2}]\) translates into \(\beta=\frac{2\pi f_{a}}{c}r\sin\theta\in[0,\frac{2\pi f_{a}r}{c}]\). The mode amplitude \(J_{m}(\beta)\) is small when the Bessel function order \(m\) exceeds its argument \(\beta\). If \(m\geq M\), \(f_{m}^{c}(\vec{\theta})\) will be small over the entire visible region. Thus,  
\[M\approx\left\lfloor\frac{2\pi f_{a}}{c}r\right\rfloor. \tag{10}\]