esa-sceva/satcom-chunk-collection huggingface dataset:
# Extending UWOC System Applications through Photon Transmission Dynamics Study in Harbor Waters
## 2 Materials and Methods
### Selection of Appropriate Scattering Phase Function
In this study, this function is denoted as \(\beta(\varphi)\), the \(\varphi\) refers to the newly acquired scattering angle in the photon motion coordinate system following a scattering event, it has nothing to do with \(\varphi_{0}\) and the z axis in Figure 1. Instead, \(\varphi\) is changing in the photon motion coordinate system, which is constantly changing with each scattering event and is determined by Equation (14). The relationship between the volume scattering phase function and the scattering coefficient \(b(\lambda)\) is articulated in Equation (3) [46]. Here, \(\tilde{\beta}(\varphi,\lambda)\) represents the scattering phase function, which plays a crucial role in determining how light is redistributed after interacting with particles in seawater. \[ \begin{cases}1=2\pi\int_{0}^{\pi}\tilde{\beta}(\varphi,\lambda)sin\varphi d \varphi\\ \tilde{\beta}(\varphi,\lambda)=\frac{\beta(\varphi,\lambda)}{b(\lambda)} \end{cases}  \tag{3}\]  
To date, several scattering phase functions have been proposed to study the light transmission characteristics in underwater channels. Notable among these are the Henyey-Greenstein Function (HG) [46], the two-term Henyey-Greenstein Function (TTHG) [47], Sahu and Shanmugam Function (SS) [46], and the Fournier-Forand Function (FF) [48; 49]. The FF scattering phase function, in particular, has been shown to align well with the characteristics of Petzold Harbor water, as indicated in the relevant literature [38]. Further supporting the use of the FF function, simulation results have demonstrated its similarity to the measured data of Petzold Harbor water, especially when specific parameters are applied: a slope (\(\mu\)) of 3.5835 and a seawater refractive index (n) of 1.13 [15]. Given these findings, this paper adopts the FF scattering phase function for its simulation analysis, and the scattering angle \(\varphi\) is generated according to the Equation (4), ensuring a direct and unbiased determination of the angle. The expression for the FF scattering phase function is provided in Equation (4). In this expression, \(\psi\) represents the azimuth angle of the photons, and \(\varphi\) denotes their scattering angle. The variables \(v\) and \(\delta\) are intermediate variables without direct physical significance, with \(\delta_{180}\) representing the value of \(\delta\) when the scattering angle \(\varphi\) equals to \(180^{\circ}\). \[ \begin{cases}\tilde{\beta}_{FF}(\varphi)=\frac{1}{4\pi(1-\delta)^{2}\beta^{ \circ}}\Big{\{}v(1-\delta)-(1-\delta^{v})+[\delta(1-\delta^{v})-v(1-\delta)] sin^{-2}\big{(}\frac{\varphi}{2}\big{)}\Big{\}}+\frac{1-\delta^{v}_{180}}{16 \pi(\delta_{180}-1)\delta^{v}_{180}}\big{(}3 cos^{2}\psi-1\big{)}\\ \delta=\frac{4}{3(n-1)^{2}}sin^{2}\big{(}\frac{\varphi}{2}\big{)}\end{cases}  \tag{4}\]
# Experimental and Numerical Evaluation of Calcium-Silicate-Based Mineral Foam for Blast Mitigation
## 4 Results from Simulations
### Validation of the Blast Loading  
Figure 17 shows the pressure-time histories from the experiments in comparison to the results of the numerical simulation (only shown for Gauge 1). Without the sacrificial cladding (Figure 16a), the computed reflected peak over-pressure and impulse are 8 MPa and 241 Pa.s, respectively. When the foam is added, the computed reflected peak over-pressure and impulse are 17.6 MPa and 353 Pa.s, respectively. The relative difference in the peak reflected over-pressures between the finite element model and the average measured data are equal to 3.6 and 3.4% without and with the protective foam, respectively. Similarly, the relative differences in the reflected impulses are 8.4 and 7.3%.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multirow{2}{*}{\
\begin{tabular}{c} **Foam** \\ **Mass (g)** \\ \end{tabular} } & \multirow{2}{*}{\(M\)**(g)**} & \multirow{2}{*}{\(R\)**(mm)**} & \multirow{2}{*}{**OPD\({}_{\text{Num}}\) (mm)**} & \multirow{2}{*}{**OPD\({}_{\text{Exp}}\) (mm)**} & \multirow{2}{*}{
\begin{tabular}{c} **6\({}_{\text{Num}}\)** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\(\delta_{\text{Exp}}\)} & \multicolumn{3}{c}{**Internal Energy (J)**} \\ \cline{5-10}  & & & & & & **(mm)** & **FS** & **Foam** & **BP** \\ \hline WF & 0 & & 250 & 20.1 & \(21\pm 0.7\) & 17.6 & 19.8 \(\pm\) 0.6 & 0 & 0 & 48.8 \\ A & 594 & & & 11.3 & \(11.6\pm 0.1\) & 8.2 & \(8.9\pm 0.3\) & 0.9 & 74 & 15.8 \\ B & 507 & & & 10.7 & \(10.9\pm 0.4\) & 7.1 & \(6.1\pm 0.4\) & 2.5 & 75.3 & 12.4 \\ C & 446 & 25 & & 9.2 & \(10.6\pm 0.2\) & 5.5 & \(5.1\pm 1.1\) & 4 & 77.6 & 9.2 \\ D & 404 & & & 12.8 & - & 10 & - & 9.2 & 59.7 & 16.5 \\ E & 330 & & & 13 & - & 10.4 & - & 10.6 & 56.4 & 17.1 \\ F & 211 & & & 14.4 & - & 12.3 & - & 18.7 & 48.4 & 17.9 \\ \hline WF & 0 & & 250 & 25.7 & - & 24.3 & - & 0 & 0 & 82.7 \\ A & 594 & & & 14.4 & - & 11.1 & - & 1.4 & 98.8 & 26.4 \\ B & 507 & & & 12.4 & - & 10.2 & - & 4.2 & 108 & 15.1 \\ C & 446 & 35 & & 10.5 & - & 8.2 & - & 7.3 & 111 & 10.1 \\ D & 404 & & & 15.6 & - & 13.4 & - & 17.4 & 84.1 & 26.3 \\ E & 330 & & & 16 & - & 14.1 & - & 18.4 & 78.1 & 30.2 \\ F & 211 & & & 18 & - & 16.5 & - & 29.8 & 69.3 & 29.7 \\ \hline WF & 0 & & 250 & 30.9 & - & 29.9 & - & 0 & 0 & 123 \\ A & 594 & & & 18.2 & - & 16.3 & - & 3.7 & 190 & 46.2 \\ B & 507 & & & 15.1 & - & 13.1 & - & 13.2 & 215 & 27.1 \\ C & 446 & 50 & & 12.4 & - & 10.5 & - & 20 & 216
## 2 Analytical Strategies for Authentication and Quality Control
### Sensor Arrays  
Sensor arrays comprise a series of electronic sensors, such as electronic noses, capable of detecting certain categories of compounds [58]. The interaction between sensors and analytes occurs mostly through physical adsorption producing an electrical signal that enables molecular recognition through comparison with a pre-existing database [59]. They do not provide information on specific components but give quick general information on the sample [60]. There are many types of sensor arrays used in spirits assessment, including mass-sensitive quartz crystal microbalances [61], photonic crystals [62], UV-vis, fluorescence and cataluminescence [63; 64; 65]. Many devices, however, have a low chemical specificity, which makes it difficult to detect small differences between very similar compounds [66]. An effective discrimination between 14 spirits, including brandy, was achieved with a colorimetric sensor array made up of 36 different classes of indicators (pH, acid-base, redox and containing acid and amine nucleophiles specific for aldehydes and ketones) and solvatochromic dyes [66]. Color variations in the sensor before and after exposure to the vapors of the spirits enabled a color difference pattern to be obtained, allowing the 14 spirits to be correctly classified according to their alcohol percentage and brand with high accuracy (>99%). An optoelectronic tongue sensor array consisting of a negatively charged, positively charged and two neutral poly(para-aryleneethynylene)s (PAE) at different pH values (3, 7 and 13) was applied for the discrimination of 37 brandy-like alcoholic beverages, including the GI German Weinbrand, aged for different times and a number of \"fake\" brandies consisting of mixtures of various brandies with ethanol, caramel-colored clear spirits and blends of different brandies [67]. The discriminating power of the assay was compared to that obtained by measuring alcohol, density, extract and different volatile compounds by GC-MS, showing that the sensor was able to discriminate between different brandies with 99% accuracy and between identical brandies from heterogeneous batches with higher discriminating power than that obtained by GC-MS. Given the scarcity of studies, further research may be needed to improve the application of sensor arrays for the authentication of distillates.
# Blockchain-Based Secured Access Control in an IoT System
## 3 The Use of Blockchain in IoT  
A blockchain (a chain of linked blocks) is a distributed ledger with a list of linked data records or blocks, which are linked and secured by cryptographic hashes. Each block contains a set of new data records or transactions, as well as the hash value of the previous block, along with a timestamp that verifies the transactions at the time of the creation of the block, making the modification of records difficult, as they are dependent on prior records, as shown in Figure 1. Blockchain is characterized by the fact that data cannot be modified because they are copied and stored in a distributed and dependent manner [10].  
Blockchain is a highly transparent access control technology that provides end-to-end decentralized security and reduces the risk of human error. It provides solid protection against hacker attacks and can be of ultimate importance to access control systems. It was introduced in 2008 as a core framework of Bitcoin and it allows data to be recorded, stored and updated in a distributed way [11]. Blockchain is a decentralized approach that maintains the integrity of data by using a consensus mechanism. A third-party intermediary is not required and trust is developed through a public ledger stored in a decentralized manner in order to ensure secure distributed transactions in a trustless environment. The blockchain protocol is a potential candidate that may bring some evolutionary changes to traditional IT security technologies due to its decentralized infrastructure and anonymity maintenance [12].  
Traditional security measures and access control mechanisms require a centralized trusted entity that ultimately compromises end-to-end security properties. Applying a centralized access control mechanism to an ever-growing number of IoT devices can complicate the process of trust management and affect the scalability of the system. Using blockchain can help the development of a fair authorization framework to address the abovementioned IoT access control challenges. The Ethereum blockchain is used to authenticate the server introduced by Auth0 [3] by using the challenge-response method. This model uses the decentralized approach of a blockchain but is also dependent on a third-party authentication server. This centralized infrastructure is one of the major drawbacks (single point of failure; single point of trust) of this method. [PERSON] et al. [3] proposed a blockchain-based IoT access control and secure authentication mechanism for users to connect securely to IoT devices by providing accountability (achieved through tamperproof records) and availability.  
While a public blockchain brings numerous benefits, it is not suitable for resource-constrained IoT devices due to its high bandwidth overheads, delays and utilization of a large amount of computation resources [11]. Therefore, to address these challenges, our proposed solution uses a private blockchain, which provides light and decentralized IoT access control security.  
Figure 1: The components and basic architecture of the blockchain.
# Mechanical and Fiber-Bridging Behavior of Slag-Based Composite with High Tensile Ductility
## 2 Materials and Methods  
### Materials and Mixture Proportion  
This study was designed from our previous study, which considered the workability of mixtures; Table 1 lists the investigated materials and the mixture proportion [8]. Ground-granulated blast furnace slag (GS) was used a source material. Chemical compositions of the GS were obtained by X-ray fluorescence analysis and are shown in Table 2. GS has a high amount of calcium oxide and silicon dioxide of over 71%; these are the main minerals that compose the calcium silicate hydrate (C-S-H) structure in the hydration reaction with water. The Blaine fineness and specific gravity of the GS were 4 320 cm\({}^{2}\)/g and 2.92, respectively. Powders of Ca(OH)\({}_{2}\) and Na\({}_{2}\)SO\({}_{4}\), with purities over than 95%, were used to activate the GS. For uniform fiber distribution, a polycarbonate-based superplasticizer (SP) and viscosity modifying admixture (VMA) were used to proper fluidity and viscosity of matrix. One previous study reported that fiber distribution depended on the plastic viscosity of fresh mixtures, and that there is an optimal range of this viscosity for good fiber distribution [14]. In order to obtain highly tensile strength and ductility of the composite, PE fiber with high aspect ratio (1500) and high strength was adopted as a reinforcing fiber. The diameter and length of the fiber were 12 \(\upmu\)m and 18 mm, respectively. The tensile strength and elastic modulus of the fiber were 2700 MPa and 88 GPa, respectively.
## 3 Results and Discussion
### Evaluation of Flexural Performance  
Flexural performance is an important parameter in evaluating the effect of fibers on the post-peak behavior of FRCM. To obtain the flexural performance properties of FRCM, various methods have been developed, and intensive studies have been carried out [33; 34; 35]. Therefore, in order to evaluate the flexural performance of the specimen in this study, tests were performed using the third-point loading method according to the test method specified in ASTM C 1609/C 1609/M and Korean regulation KS F 2566. Table 5 summarizes the flexural performance test data with parameters such as \(P_{600}\), \(P_{150}\), \(f_{600}\), \(f_{150}\), \(T_{600}\), and \(T_{150}\), and Figure 9 shows the flexural toughness measurement results of MFRCM and HyFRCM at L/600 and L/150. As shown in Table 5, in the case of MFRCM containing only CF, the flexural toughness could not be measured at \(T_{150}\), and the average flexural toughness at \(T_{600}\) was the lowest 4.51 J. On the other hand, in the case of HyFRCM containing 0.5% SF and 0.5% CF, the average flexural toughness at \(T_{600}\) and \(T_{150}\) was the highest 8.11 J and 32.7 J, respectively, and the flexural toughness significantly improved the post-cracking, indicating that it had the highest load-carrying capacity. It is judged that the flexural toughness is improved as the SF controls the macro-cracks relatively, while the CF controls the micro-cracks.
## Detection, Composition and Treatment of Volatile Organic Compounds from Waste Treatment Plants  
**[PERSON], [PERSON] * and [PERSON]**  
Composting Research Group, Department of Chemical Engineering, Universitat Aut'noma de Barcelona, 08193-Bellaterra (Cerdanyola del Vall'es), Barcelona, Spain;  
E-Mails: [EMAIL_ADDRESS] (X.F.); [EMAIL_ADDRESS] (A.S.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-935-814-480; Fax: +34-935-812-013.  
_Received: 23 February 2011; in revised form: 18 March 2011 / Accepted: 1 April 2011 / Published: 6 April 2011_  
**Abstract:** Environmental policies at the European and global level support the diversion of wastes from landfills for their treatment in different facilities. Organic waste is mainly treated or valorized through composting, anaerobic digestion or a combination of both treatments. Thus, there are an increasing number of waste treatment plants using this type of biological treatment. During waste handling and biological decomposition steps a number of gaseous compounds are generated or removed from the organic matrix and emitted. Different families of Volatile Organic Compounds (VOC) can be found in these emissions. Many of these compounds are also sources of odor nuisance. In fact, odors are the main source of complaints and social impacts of any waste treatment plant. This work presents a summary of the main types of VOC emitted in organic waste treatment facilities and the methods used to detect and quantify these compounds, together with the treatment methods applied to gaseous emissions commonly used in composting and anaerobic digestion facilities.  
**Keywords:** volatile organic compounds (VOC); organic waste; waste treatment plants; composting, anaerobic digestion; gaseous emissions; odor
## 5 Discussion on Comparing the Velocity and Acceleration Feedback  
In this section, the discussion on comparing the velocity and acceleration feedback control.  
Figure 16 shows that the frequency response comparison curve of time-delayed velocity feedback and time-delayed acceleration feedback at \(p=-0.05\), \(\tau=\pi\), and case 1 is the time-delayed velocity feedback; case 2 is the time-delayed acceleration feedback. Obviously, when \(p=-0.05,\tau=\pi\), with acceleration feedback, the primary resonance response of the nonlinear beam increases. From one aspect, the selection of control parameters is very important.  
Figure 16: Comparison of frequency response curves of primary resonances response between the time delayed velocity feedback and the time delayed acceleration feedback.  
Figure 15: Amplitude of the response as a function of amplitude of the excitation for several detunings with acceleration feedback.  
On the other hand, Figure 7 provides a way for us to choose reasonable control parameters. The fixed control gain value can select the optimal control parameter according to the response curve of the peak value and the time delay. Figure 17 shows the relationship between the external excitation amplitude and the response amplitude. It can be seen that there are multiple values in the interval [0.015,0.025]. Under the same excitation amplitude, the response amplitude under the time delayed velocity feedback is slightly higher than the time delayed acceleration feedback.  
It can be seen from Figure 18, different from the comparison result of the primary resonance response, when selects the same control parameter value in the subharmonic resonance response, i.e., the control gain and the time delay, the control effect under the velocity feedback control is better than the acceleration feedback control. The interval of resonance also shifts.
## 3 Results and Discussion
### Experimental Results of Distance and Time  
When the robot was climbing in the upward movement, the actual data on distance versus time and speed versus time were taken. The experiment results in various speeds: Low speed, which takes 20 s, Medium speed which takes 16 s, and Fast speed which takes 8 s to climb up the tower. The 3540 kg payload that the robot can hold without any problem at the various speeds shown in Figures 18-20 is included in the climbing process.  
Figure 19: Distance and Time: (**a**) Medium Speed (MS) at 16 s, (**b**) distance vs. time in MS and (**c**) distance vs. time in MS with load.  
Figure 17: Payload capacity: (**a**) Body Frame 1 and (**b**) Body Frame 2.  
Based on Figures 18a, 19a and 20a, its distance versus time results during the indoor experiment are as follows: the different durations of time are 20, 16, and 8 s to climb straight up to the 1200 mm wind turbine tower height and approximately 1000 mm distance traveled since the robot has a height of 200 mm. The distance sensor is connected to the robot's body frame and can detect the robot's distance in the tower. The distance was displayed on the user monitor in terms of centimeters, which is then converted to millimeters. The first climbing speed was Low at 20 s; there was no large disparity between the test results of \"without load\" and \"with load at 3540 kg,\" as shown in Figure 18b,c, the two graphs of distance vs. time in LS. This means that the robot can handle the movement going straight up the tower surface, and in a second there are two quick variations on its constant speed. The second climbing speed was Medium at 16 s; there is no large disparity between the test results of \"without load\" and \"with load at 3540 kg\", as shown in Figure 19b,c, the two graphs of distance vs. time in MS. Still, the robot can handle the movement going straight up the tower surface at a smooth and steady speed for almost the entire duration. The third climbing speed was Fast at 8 s; there is no large disparity between the test results of \"without load\" and \"with load at 3540 kg\", as shown in Figure 20b,c, the two graphs of distance vs. time in FS. The robot can handle the movement going straight up the tower surface, and every second there is change on its constant speed. From the test results of the robot climbing in the three various speeds the robot can suffice to climb straight up movement without breaking or slipping onto the ground. Its downward movement was also the same time and speed to roll out to the wind turbine tower mock-up. The actual tests of the climbing robot without and with payload are shown in Figures 21 and 22.  
Figure 21: Climbing robot without payload.  
Figure 20: Distance and Time: (**a**) Fast Speed (FS) at 8 s, (**b**) distance vs. time in FS and (**c**) distance vs. time in FS with load.
## 6 Secure Image Monitoring
### Processing of Image Contents  
The content of images retrieved from a monitored field may have different significances for applications. Additionally, such contents may require different security protections. For example, the face of a person in an image may be removed or protected in the transmitted image, when his/her identity must to be hidden. Additionally, other parts of sensed images may also have privacy requirements. As the relevance of parts of images may be identified, security mechanisms should be optimized to protect that data.  
Actually, relevance processing brings new challenges for image security in wireless sensor networks, fostering investigations in this area.  
In general, it may be more important to be able to observe the behavior of a person than knowing the actual identity. This is achieved by identification and obfuscation of personally identifiable information [13]. As in some cases, such information may be required, as when a is was violated, this personal information may be of interest, but it must be protected and only revealed when necessary.  
The work in [77] proposes mechanisms for automatic people identification and human body obscuring with preserved structure and motion information from video. Algorithms are used to identify relevant data, which can be removed, disabled or protected using cryptography. Other works have also investigated face detection by visual sensors [78; 79], which may also be considered for WISN.