esa-sceva/satcom-chunk-collection huggingface dataset:
## Nomenclature  
The nomenclature of this study is based upon the following principles and introduced here in concise form. A (relative) position vector is denoted as follows:  
\[\left(\vec{\mathbf{r}}^{AB}\right)_{C}=\left(\vec{\mathbf{r}}^{B}\right)_{C}- \left(\vec{\mathbf{r}}^{A}\right)_{C}\]  
Here, \(A\) and \(B\) are the points, while \(C\) is the coordinate frame. A velocity is consequently given by  
\[\left(\frac{\mathrm{d}}{\mathrm{d}t}\right)^{E}\left(\vec{\mathbf{r}}^{AB} \right)_{C}=\left(\dot{\vec{\mathbf{r}}}_{D}^{AB}\right)_{C}^{E}=\left(\vec{ \mathbf{v}}_{D}^{AB}\right)_{C}^{E}\]  
Here, \(D\) denotes the kind of the velocity, e.g., kinematic \(K\). Furthermore, the superscript \(E\) describes the coordinate frame with respect to whom the time derivative is taken. Then, an acceleration is  
\[\left(\frac{\mathrm{d}}{\mathrm{d}t}\right)^{F}\left(\vec{\mathbf{v}}_{D}^{AB }\right)_{C}^{E}=\left(\dot{\vec{\mathbf{v}}}_{D}^{AB}\right)_{C}^{EF}=\left( \dot{\vec{\mathbf{a}}}_{D}^{AB}\right)_{C}^{EF}\]  
The superscript \(F\) depicts the coordinate frame with respect to whom the second time derivative is taken.  
After this overview on vectors, the following listing summarizes further symbols and indices used in this study.  
\begin{tabular}{l l} \(\vec{\mathbf{\ddot{x}}}\) & Linear acceleration vector \\ \(\Delta\,a_{x,T}\), \(\Delta\,a_{y,T}\), \(\Delta\,a_{z,T}\) & Trajectory acceleration deviation in three axes \\ \(\vec{\mathbf{\ddot{x}}}\) & Rotation acceleration vector \\ \(\alpha_{K}\) & Kinematic angle of attack \\ \(\mu_{K}\), \(\gamma_{K}\), \(\chi_{K}\) & Kinematic trajectory angles \\ \(\Delta\,\mathbf{\ddot{T}}\) & Specific force vector \\ \(\Delta\,\mathbf{f}_{\mathrm{x}}\), \(\Delta\,\mathbf{f}_{\mathrm{y}}\), \(\Delta\,\mathbf{f}_{\mathrm{z}}\) & Specific forces in three axes \\ \(G_{\mathrm{LOES}}\) (\(s\)) & Actuator transfer function \\ \(\delta\) & Gravitational acceleration \\ \(\vec{J}\) & Jerk vector \\ \(\mathbf{K}\) & Controller gain matrix \\ \(m\) & Mass \\ \(\vec{v}\) & Nonlinear dynamic inversion pseudocontrol \\ \(\vec{\boldsymbol{\omega}}\) & Rotational velocity vector \\ \(\omega_{0}\) & Natural frequency \\ \(\phi\), \(\theta\), \(\psi\) & Euler angles \\ \(\vec{\mathbf{\ddot{r}}}\) & Position vector \\ \(\Delta\,x_{T}\), \(\Delta\,y_{T}\), \(\Delta\,z_{T}\) & Trajectory deviation in three axes \\ \(x_{N}\), \(y_{N}\), \(z_{N}\) & Aircraft position in navigation frame (generally: North-East-Down) \\ \(s\) & Laplace transform variable \\ \(\vec{\mathbf{\ddot{u}}}\) & Nonlinear dynamic inversion control vector \\ \(\Delta\,u_{T}\), \(\Delta\,v_{T}\), \(\Delta\,w_{T}\) & Trajectory velocity deviation in three axes \\ \(\mathbf{V}_{K}\) & Kinematic velocity \\ \(\vec{\mathbf{\ddot{v}}}\) & Linear velocity vector \\ \(\zeta\) & Relative damping \\ \(Z_{\mathrm{z}}\) & Lift force derivative w.r.t. angle of attack \\ \(F\) & Trajectory foot point \\ \(K\) & Kinematic quantity/frame \\ \(N\) & Navigation/inertial frame \\ \(O\) & Aircraft-/Trajectory-fixed North-East-down Frame \\ \(R\) & Aircraft reference point \\ \(T\) & Trajectory frame \\ \end{tabular}
## 2 Materials and Methods
### Quality Control  
For internal quality assurance, standard reference material (SRM) \"Montana Soil 2710\", \"San Joaquin Soil 2709\" and \"Buffalo River Sediment 8704\" of National Institute of Standards & Technology (NIST) were processed and analyzed under the same conditions used for samples. The data of the analysis of SRM as listed in Table 3.  
Pseudo-total concentration of Pb was conducted using the quality controls established by the Standard Methods (3020B) of APHA-AWWA-WPCF [39], including blank, fortified and duplicates. The samples used for the determination of metal concentration in the geochemical fraction extracts was determined by triplicate.  
Descriptive statistical tests, including average, standard deviation and relative standard deviation (RSD) were applied to obtained data. The chosen significance level was \(\alpha=0.05\) (equivalent to 5%). All analyses were performed with the statistical package MINITAB v.15.
## 3 Results and Discussion
### Adsorption Isotherm of PTSA  
The adsorption isotherms of PTSA on resins at 303 K are depicted in Figure 6. The adsorption amounts of all the resins increased with the rise of initial PTSA concentration. To further evaluate adsorption behavior, the data were analyzed using the [PERSON] (Equation (5)) and [PERSON] (Equation (6)) equilibrium models.  
\[Q_{e}=\frac{K_{L}\times Q_{m}\times C_{e}}{1+K_{L}\times C_{e}} \tag{5}\]  
\[Q_{e}=K_{F}\times C_{e}^{\frac{1}{n}} \tag{6}\]  
where \(C_{e}\) (mmol/L) is the equilibrium concentration of PTSA; \(Q_{e}\) (mmol/g) represents the equilibrium adsorption capacity of the resin; and \(K_{L}\) (L/mmol), \(Q_{m}\) (mmol/g), \(K_{F}\) (L/g), and \(n\) are the characteristic constants. As listed in Table 4, the adsorption isotherms were well described by both the Langmuir and Freundlich models due to the high values of correlation coefficients (\(R^{2}\)). The value of n was greater than unity, indicating that the PTSA was favorably adsorbed by the five resins. Thus, all the results denoted that the adsorption process may occur on heterogeneous surfaces, and that the adsorption is not restricted to monolayer formation [8]. Both \(Q_{m}\) and \(K_{F}\), which are relative to the adsorption capacity in Langmuir and Freundlich models, respectively, decreased in the order of SD5 \(>\) SD4 \(>\) SD3 \(>\) SD2 \(>\) SD1. This was consistent with the result in Section 3.3, when there was no sulfate competition system.  
Figure 5: Adsorption kinetics of PTSA onto resins at 303 K.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Adsorbents**} & \multirow{3}{*}{\(Q_{e,exp}\)} & \multicolumn{3}{c}{**Pseudo-First-Order**} & \multicolumn{3}{c}{**Pseudo-Second-Order**} \\ \cline{3-8}  & & \(Q_{e,cal}\) & \(k_{1}\) & & \(Q_{e,cal}\) & \(k_{2}\) & \\ \cline{2-8}  & **(mmol/g)** & **(mmol/g)** & **(l/min)** & \(R^{2}\) & **(mmol/g)** & **(g/mmol-min)** & \(R^{2}\) \\ \hline SD1 & 2.8419 & 2.6114 & 0.0184 & 0.9976 & 3.0208 & 0.0073 & 0.9888 \\ SD2 & 3.0869 & 2.8045 & 0.0195 & 0.9883 & 3.2218 & 0.0075 & 0.9952 \\ SD3 & 3.2943 & 2.9373 & 0.0202 & 0.9819 & 3.3630 & 0.0075 & 0.9974 \\ SD4 & 3.8598 & 3.4670 & 0.0157 & 0.9828 & 4.0619 & 0.0046 & 0.9975 \\ SD5 & 4.3795 & 4.1325 & 0.0123 & 0.9838 & 4.5570 & 0.0028 & 0.9975 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Kinetic data for the adsorption of PTSA onto resins.
## 4 UbiPriSEQ: The Proposed Framework
### UbiPriSEQ: Architectural Overview  
UbiPriSEQ is built on a three-layered model of ubiquitous environments comprising **IoT Devices Layer (dLayer)**, **FRAN Layer (fLayer)**, and **Core Layer (cLayer)**. Figure 1 provides its overview, and Figure 2 accentuates its two functional components, Device Module (DevM) and Network Module (NetM).  
**dLayer**[17; 25; 26; 27; 28] comprises devices (smartphones and other smart \"things\" in IoT) that produce or consume data or take certain actions. **fLayer** consists of a macro base station and several micro base stations along with several fog nodes. FRAN (Fog Radio Access Networks) is a paradigm for 5G networks to provide high spectral and energy efficiency [23]. The idea behind FRAN is to take full advantage of local radio signal processing, cooperative radio resource management, and distributed storing capabilities in edge devices, which can decrease the heavy burden on fronthaul and avoid large-scale radio signal processing in the centralized Baseband Unit (BBU) pool [24]. The fog nodes near the micro base stations can locally store and process the data. Both the fog nodes and the micro base stations have caches; however, the data stored in them are not the same. Fog caches have more locally relevant data than the micro base station caches. **cLayer**[22] consists of the BBU pool connected through the wider Internet with clouds and HPC clusters, dealing with larger, delay-tolerant, applications that require large compute and storage resources. DevM executes in the dLayer on all devices and NetM executes in both fLayer (not in all fog nodes, rather centralized) and cLayer (see Figures 1 and 2). The two modules continuously learn from each other and adaptively optimize the ubiquitous environment.
###### Abstract  
As core algorithms of geographic computing, overlay analysis algorithms typically have computation-intensive and data-intensive characteristics. It is highly important to optimize overlay analysis algorithms by parallelizing the vector polygons after reasonable data division. To address the problem of unbalanced data partitioning in the task decomposition process for parallel polygon overlay analysis and calculation, this paper presents a data partitioning method based on shape complexity index optimization, which achieves data equalization among multicore parallel computing tasks. Taking the intersection operator and difference operator of the Vatti algorithm as examples, six polygon shape indexes are selected to construct the shape complexity model, and the vector data are divided in accordance with the calculated shape complexity results. Finally, multicore parallelism is achieved based on OpenMP. The experimental results show that when a data set with a large amount of data is used, the effect of the multicore parallel execution of the Vatti algorithm's intersection operator and difference operator based on shape complexity division is clearly improved. With 16 threads, compared with the serial algorithm, speedups of 29 times and 32 times can be obtained. Compared with the traditional multicore parallel algorithm based on polygon number division, the speed can be improved by 33% and 29%, and the load balancing index is reduced. For a data set with a small amount of data, the acceleration effect of this method is similar to that of traditional methods involving multicore parallelism.  
overlay analysis; shape complexity; data partitioning; parallel computing; acceleration ratio; load balancing; OpenMP
# Online Distributed User Association for Heterogeneous Radio Access Network
## 6 Performance Evaluation
### Normalized Rate  
Another performance metric for evaluation of the network performance is considering the normalized data rate of the UEs. The normalized minimum user rate, \(\widehat{\beta}_{min}\) for different schemes in the uniform UE deployment scenario is shown in Figure 7(a). \(\widetilde{\beta}_{min}\) for _max-SINR_ is about 0.30, while _Prob. scheme 1_ and _Prob. scheme 2_ show improved performance. _Prob. scheme 1_ shows a 70% improvement, while a similar trend can be seen for _Prob. schemes 2. \(d\)-choices 1_ and _d-choices 2_ show good results, and the minimum UE rate is higher, as compared to _max-SINR_. This gain in minimum user rate ensures that our scheme performs well for the cell edge UE, which mainly suffers with low data rate. All our proposed schemes perform better than _BS-centric_ and _max-SINR_, while _Prob. scheme 1_ achieves the highest minimum user rate. A similar trend can be seen in non-uniform deployment, as shown in Figure 7(b). _Prob. scheme 1_ continues to perform well in non-uniform deployment. The _max-SINR_ scheme, on the other hand, continues to perform poorly. The metric \"normalized sum rate\", denoted as \(\widetilde{\beta}_{sum}\), tells about the overall throughput of the network. In the case of traditional _max-SINR_ schemes, the UEs near to the BSs receive higher signal strength. As can be seen in Figure 8(a), our proposed methods for uniform deployment perform better than the _max-SINR_. On the other hand, for the non-uniform deployment, as shown in Figure 8(b), the throughput of _max-SINR_ performs slightly better. However, the _max-SINR_ can achieve a higher sum user rate while ignoring the user fairness.  
Figure 7: The cumulative distribution function (CDF) of spectrum efficiency for different association schemes in different deployment scenarios.
## Results and Discussion
### Mechanical Testing
#### 3.1.2 Tensile Tests  
At room temperature, the tensile properties of the as-built specimens were comparable to the manufacturer's reference as-built state [11], with the exception of a UTS in the vertical (Z-axis) direction (which had a 9% lower value). The HT1 state exhibited a 26% higher UTS compared to the as-built state and a 3.6% higher UTS compared to the HT2 state (Figure 4). The HT1 state specimens exhibited the highest yield strength, with an 8.4% increase over the as-built state and 4.1% over the HT2 state. The investigation of the anisotropy revealed differences in tensile performance between directions: 17.4% for the as-built, 8.7% for the HT1, and 12.4% for the HT2 states. All states exhibited better tensile performance in the horizontal (X-axis) direction. The HT2 state showed no improvement in tensile properties over the HT1 state at room temperature. Compared to the manufacturer's heat-treated state (Table 3), both the HT1 and HT2 states exhibited worse tensile performance, averaging 13.5% and 16.7% lower tensile performance, respectively.  
Figure 4: Inconel 939 Rm and Rp0.2 values for as-built, HT1, HT2 states at room temperature.  
Figure 3: Inconel 939 hardness for as-built, HT1, and HT2 states.  
At an elevated temperature of 700 \({}^{\circ}\)C, the specimens tested in the vertical (Z-axis) direction exhibited better tensile performance for all material states and parameters with the exception of yield strength for the as-built state (Figure 5). This behavior (contrary to the expectations for LPBF materials) aligns with the manufacturer's data [11] and findings by [PERSON] et al. (2022) [14]. The HT1 state exhibited worse tensile performance at an elevated temperature, with the exception of the UTS in the vertical (Z-axis) direction. The UTS in the vertical (Z-axis) direction for HT1 exhibited a 3.9% increase compared to the as-built state. The HT1 yield strength and UTS in the horizontal (X-axis) direction were 15.8% and 21.5% lower compared to the as-built state. The HT2 state experienced similar yield strength and UTS in the horizontal (X-axis) direction compared to the HT1 state. In the vertical (Z-axis) direction, the yield strength was 4.7%, and the UTS was 15.8% lower compared to the HT1 state. Relative to the manufacturer's data [11] (Table 3), in the heat-treated state, the HT1 state had 16% and 20% lower R0.2 and UTS, while the HT2 state had 17% and 29% lower R0.2 and UTS. The as-built state at an elevated temperature exhibited a 35% increase in yield strength and a 7% increase in UTS compared to [PERSON] et al. (2023) [15].
## 3 Results  
The application of the THz time-domain ellipsometer will be shown for two samples, which are commonly used in the automotive industry. Typical automotive multi-layer systems consist of different functional layers (see Figure 2). The first layer on a metal substrate is usually a cathodic electrodeposition coat (E-coat) for corrosion protection. On a plastic substrate, the first layer is a primer as an adherence agent. The second layer is called the base coat (BC). It is responsible for the visual appearance and color. The last, topmost layer is a clear coat (CC) for protection against mechanical damage and UV exposure. In order to demonstrate a first application, we use the ellipsometer for material characterization of a typical automotive multi-layer system of a car body paint on a metal substrate. A second application scenario will focus on the characterization of a PP substrate, which shows a strong anisotropy. The substrate dispersion will be determined by using THz TDS measurements in reflection and transmission geometry as well as THz TDS ellipsometry. Subsequently, the complete stack of an automotive car body paint consisting of multiple layers on this PP substrate will be measured with a THz TDS reflectometer.  
Figure 2: Model of a three-layer system on a PP/metal substrate. Transmission and backside reflection (dashed lines) only occur for the transparent PP substrate.
# Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize
## 2 Materials and Methods
### Sensors on the UAS Data Ferry and Node Stations  
#### 2.3.1 UAS Data Ferry Sensors  
RF 450 radio (Campbell Scientific, Logan, UT, USA) is a frequency-hopping, spread-spectrum radio that operates within the 902 to 928 MHz license-free bands designed specifically to work along with Campbell Scientific dataloggers. The radio has a maximum link throughput of 115.2 kbps. The RF 450 radio offers the advantage of high data transfer speeds along with a low current drain. Wireless network communication over long distances (13-60 miles) could be achieved with this radio based on the antenna and the line-of-sight when no objects interfere with the signal.  
In a point-to-multipoint network (multi-point network), the transceiver designated as a primary radio can simultaneously communicate with secondary radios. A multi-point network functions with the primary radio (on the UAS in this study) broadcasting its messages to all secondary radios (radios on the node stations); the secondary radios respond to the primary radio when data from the datalogger are received at the data port. A multi-point network was used to collect data from one-to-many dataloggers and report back to one central site.  
Figure 3: Location of stationary sensor node stations and the trajectory of UAS for four replications during the 2020 growing season. There were three replications of UAS flights that were conducted over the node stations at a height of: (i) 31 m; (ii) 61 m; and (iii) 122 m.  
Figure 4: Location of stationary sensor node stations and the trajectory of UAS for five replications during the 2021 growing season: (**a**) before the collapse of the station at point ‘B’, and (**b**) after the installation of the station at point ‘F’. There were three replications of UAS flights: (i) over the station; (ii) 38 m from the station; and (iii) 76 m from the station.  
A 900 MHz, dipole, omnidirectional antenna (Product: 15970, Campbell Scientific, Logan, UT: an indoor antenna with a gain value of 1 dBd [decibels relative to a dipole antenna]) was attached to the secondary RF 450 radios at each node station and housed within the datalogger enclosure located within the canopy.  
A 900 MHz 0 dBd 1/2 wave omnidirectional antenna (Product: 14204, Campbell Scientific, Logan, UT, USA) was attached to the primary RF 450 radio mounted on the UAS. The primary antenna has a center frequency of 916 MHz and is equipped with an articulating base that allows the antenna to tilt 90 degrees and rotate 360 degrees, giving flexibility to orient in different directions based on the platform where the antenna is installed (in this study, that is the UAS).
# Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams
## 5 Conclusions  
A model of a working IP network has been created in which both voice and video streams were successfully exchanged.  
From this study, it was found that in a VoIP network in which the VoIP server is an Asterisk Free PBX with the \"Responsive Firewall\" feature enabled, TCP DoS attacks do not affect the voice and video streams or the parameters of these streams. The measured average jitter values (at an average of about 1.1 ms for voice traffic and at an average of about 0.7 ms for video traffic) and packet losses (both measured to be about 0%) are far from the maximum allowable levels.  
The results for the round-trip delay also prove that the different attacks do not affect Asterisk's performance. As can be seen from the graphs, during the entire period of both studies, there were no loss of measurement packets, although it was expected that ping would not be possible during the attacks.  
The study shows that the Responsive Firewall feature successfully protects the system from the various TCP DoS attacks. Furthermore, it neutralized the impact of these attacks on Asterisk's performance. This could be seen very well in the CPU-utilization statistic, which is 0.34%.  
This study could be applied to VoIP platforms where the voice and the video traffic are passed/processed through the VoIP server rather than directly between the IP phones. In this way, it will be possible to determine if, how, and in what way the different TCP DoS attacks affect the performance of the systems.  
The APC was funded by South-West University \"Neofit Rilski\".  
Data Availability StatementData are contained within the article.  
The researcher would like to thank the South-West University \"Neofit Rilski\" for the APC.  
The author declares no conflicts of interest.
## 3 Typical Applications of IoT in Smart Agriculture
### Tracking and Tracing  
In order to meet the needs of consumers and increase profit value, in the future, farms need to demonstrate that products offered to the market are clean products and can be tracked and traced conveniently, thereby enhancing the trust of consumers in product safety and health-related issues. In order to solve this problem, a series of tracking- and tracing-based problems for the smart agricultural sector has been proposed, specifically as follows:  
In [96], the authors designed an information system that allows tracking and tracing of agricultural products and foods such as dairy and vegetables, called SISTABENE. This system helps suppliers track the production process and errors arising in the supply chain, and helps end-users trace the origin of food. In [97], the authors proposed a food supply chain traceability system based on blockchain technology. It helps to track and trace agri-food supply chains' production process and trace the origin of agricultural products. This solution has been employed at Shanwei Lvfengyuan Modern Agricultural Development Co., Ltd. (Shanwei, China). Although there are still limitations, the results demonstrate that this solution has successfully supported the tracing of food and agricultural products through QR codes, improving product quality and ensuring the clear traceability of products. In [98; 99], the authors proposed smart agricultural solutions to tracking and tracing agricultural products, thereby allowing consumers to know the product's entire history. These solutions enable tracking and tracing some of the data collected along the supply chain, ensuring that consumers and other stakeholders can identify products' origin, location, and history.