esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 5 Simulation Setup and Implementation  
### Simulation Setup  
The purpose of this sub-section is to present the details of the simulation environment. In this study, we conducted realistic VANET simulations by combining two simulators: the road traffic generator SUMO (Simulation of Urban Mobility) and the network simulator NS-3. SUMO and NS-3 are used to simulate realistic VANET scenarios.  
SUMO is a free, open, and microscopic simulator implemented in C++. It is used to simulate unlimited network size and number of vehicles. It offers the ability to configure vehicle types, traffic lights, vehicle speeds, and multi-lane roads; it also supports the lane-changing model, as well as generating automatic time schedules for traffic lights. SUMO also supports the import formats, such as OpenStreetMap. We executed a set of Python command lines on SUMO in order to generate realistic vehicle trace files that are then used as an input by the network simulator, NS-3. In our study, the simulation zone was extracted from OpenStreetMap, consisting of a map of El Jaddacity, Morocco given in Figure 3. The.osm file generated is interfaced to SUMO to get the mobility:tcl file with details of each node (vehicle), including the number of vehicles, position, speed, and direction.  
In what follows, we explain the various parameters depicted in Table 2 used in performing VANET scenarios to evaluate the proposed detection scheme in the next sections.  
Our simulations are implemented using version 3.29 of the simulation environment NS-3. The 802.11p standard is used on the MAC/PHY sub-layers, and the channels are modeled using the YansWiFiChannel with frisLoss propagation model. The transmission power is fixed to 33 dBm. The simulations run for 100 s with a total of 100 vehicles distributed over the imported simulation zone. Ten source nodes simultaneously generate Constant Bit Rate (CBR) traffic with fixed size packets of 1024 Bytes. Packets are routed using AODV routing protocol. Further, User Datagram Protocol (UDP) is used as the transport layer protocol.
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
###### Abstract  
The trend for dense deployment in future 5G mobile communication networks makes current wired backhaul infeasible owing to the high cost. Millimetre-wave (mm-wave) communication, a promising technique with the capability of providing a multi-gigabit transmission rate, offers a flexible and cost-effective candidate for 5G backhauling. By exploiting highly directional antennas, it becomes practical to cope with explosive traffic demands and to deal with interference problems. Several advancements in physical layer technology, such as hybrid beamforming and full duplexing, bring new challenges and opportunities for mm-wave backhaul. This article introduces a design framework for 5G mm-wave backhaul, including routing, spatial reuse scheduling and physical layer techniques. The associated optimization model, open problems and potential solutions are discussed to fully exploit the throughput gain of the backhaul network. Extensive simulations are conducted to verify the potential benefits of the proposed method for the 5G mm-wave backhaul design.  
millimetre-wave backhaul; 5G; multi-hop routing; scheduling; hybrid beamforming; full duplexing +
Footnote †: journal: Sensors
## 3 Stereolithography: Top-Down and Bottom-Up Approaches  
Stereolithography was patented in 1986 by [PERSON] (US Patent 4,575,330), and the first SLA printers were developed by 3D Systems [58]. It is the first commercially available AM technique, opening the door to a new era in manufacturing. An SLA apparatus is equipped with a laser source usually emitting light in the UV range that is directed by means of lenses to a reservoir filled with a liquid photocurable resin. The light beam is controlled in an XY direction, and a fabrication platform allows vertical movement. Depending on the orientation of the laser and the surface of the photopolymerization occurs, SLA may have two kinds of configurations: (i) top-down and (ii) bottom-up [59; 60].  
In the top-down process, the resin vat is exposed to the laser beam from the top (Figure 1a). When the curing of the first layer is completed, the building platform is moved downward along the \(Z\)-axis, exposing the second layer of the resin to irradiation. The thickness of the printed layer through this technique can be controlled by the intensity of the light source, scanning speed, depth of focus, and exposure time [61]. The printing quality depends on the irradiating parameters (power, speed, laser source), resin chemical composition and operation settings (speed, orientation). Specific drawback refers to the height of the 3D-printed objects, which is limited by the vat depth, as the objects are fabricated facing upward [59]. A larger vat means that more resin is needed and, consequently, the amount of the resin waste rises. Moreover, the printing efficiency in this configuration is reduced due to the extra time needed to level the resin's surface, which is disturbed when the platform moves. Finally, the contact of the cured layers with atmospheric oxygen limits the crosslinking efficiency [61].  
In the bottom-up orientation (Figure 1b), the fabrication platform is dipped into the resin vat, whose bottom surface is transparent and non-adhering. The light is projected from underneath, initiating the photopolymerization of the resin film between the movable platform and the bottom of the vat. After the curing of the first layer, the platform moves up, and photopolymerization continues with a new layer, which is fabricated underneath the previous one. This technique allows the printing of high objects with the use of low resin volumes [61]. Additionally, the cured layers are not exposed to the atmosphere, so the inhibition of the photopolymerization reaction is prevented. Specific advantages of bottom-up techniques refer to smaller layer thickness, which can be accurately controlled by the elevator, higher vertical resolution and surface quality [59; 62]. The common SLA 3D printer models are manufactured by Formlabs, 3D Systems, Full Spectrum Laser, Asiga, etc.  
Figure 1: Schematic illustration of SLA approaches: (**a**) top-down; (**b**) bottom-up.  
SLA is applicable for prototypes of scaffolds for tissue engineering or regenerative medicine that can be fabricated rapidly and with high feature resolution (5-50 \(\upmu\)m).
# Phase-Matching Quantum Key Distribution with Discrete Phase Randomization
## 4 The PM-QKD with Discrete Phase Modulation of Coherent State Sources
### Coherent State with Discrete Phase Randomization  
For the coherent state with discrete phase randomization, the joint state of [PERSON] and Bob of PM-QKD is as follows  
\[|\psi\rangle_{\rm AB}=\sum_{d_{A}=0}^{D-1}\left|\sqrt{\alpha_{\rm A}}e^{i(\pi \kappa_{\rm A}+\frac{2\pi}{D}d_{\rm A})}\right\rangle_{\rm A}\left|\sqrt{ \alpha_{\rm B}}e^{i(\pi\kappa_{\rm B}+\frac{2\pi}{D}d_{\rm B})}\right\rangle_ {\rm B} \tag{13}\]  
where \(\kappa_{\rm A},\kappa_{\rm B}\in\{0,1\}\), \(|d_{\rm A}-d_{\rm B}-d_{\delta}|\) mod \(D=0\) or \(|d_{\rm A}-d_{\rm B}-d_{\delta}|\) mod \(D=D/2\).  
Considering the simple case, \(d_{\delta}=0\), then \(|d_{\rm A}-d_{\rm B}|=0\) or \(|d_{\rm A}-d_{\rm B}|=D/2\). Now, the density matrix can be written as  
\[\rho_{\rm AB}^{D} =\] \[\otimes\left|\sqrt{\alpha_{\rm B}}e^{i(\pi\kappa_{\rm B}+\frac{2 \pi}{D}d_{\rm B})}\right\rangle_{\rm B}\left\langle\sqrt{\alpha_{\rm B}}e^{-i (\pi\kappa_{\rm B}+\frac{2\pi}{D}d_{\rm B})}\right| \tag{14}\] \[=\]  
where \(P_{j|\alpha}^{D}=\sum\limits_{l=0}^{\infty}\frac{e^{-\alpha}d^{l(D+j)}}{(D+j )!}\), \(\left|\lambda_{j|\alpha}\right\rangle_{\rm AB}=\frac{e^{-\alpha/2}}{\sqrt{P_{ j|\alpha}^{D}}}\sum\limits_{l=0}^{\infty}\frac{(\sqrt{\alpha})^{l(D+j)}}{\sqrt{( (D+j)!)}}|lD+j\rangle_{\rm AB}\), with \(\left|lD+j\right\rangle_{\rm AB}=\frac{1}{\sqrt{2^{lD+j}(l(D+j))}}(a^{\dagger} \pm b^{\dagger})^{lD+j}|00\rangle_{\rm AB}\).  
In our security analysis with discrete phase randomization, we modify the final secure key rate Equation (5) to  
\[R_{f}=\frac{2}{D}Q_{\mu}[1-H_{2}(E_{ph}^{D})-fH_{2}(E_{\mu})] \tag{15}\]  
where the upper bound of phase error rate \(E_{ph}^{D}\) comes from the signal state bounded by \(E_{ph}^{D}\leq 1-q_{1|\mu}^{D}\), with \(q_{1|\mu}^{D}=P_{1|\mu}^{D}\frac{Y_{1|\mu}^{D}}{Q_{\mu}}\). The bit error rate \(E_{\mu}\) and the gain \(Q_{\mu}\) remain the same.
## 5 Case Study
### Wind and Smart House Penetration Level
\begin{table}
\begin{tabular}{c c c c} \hline
**Wind Percentage** & **DR Percentage** & **Wind Energy Curtailment (MWh)** & **Load Shedding (MWh)** \\ \hline \multirow{6}{*}{10\%} & 0\% & 29.50 & 95.85 \\  & 5\% & 0 & 42.30 \\  & 10\% & 0 & 23.21 \\  & 15\% & 0 & 7.52 \\  & 20\% & 0 & 0.29 \\ \hline \multirow{6}{*}{20\%} & 0\% & 148.74 & 262.28 \\  & 5\% & 41.87 & 175.20 \\  & 10\% & 3.26 & 148.36 \\  & 15\% & 0 & 130.77 \\  & 20\% & 0 & 108.21 \\ \hline \multirow{6}{*}{30\%} & 0\% & 645.71 & 385.38 \\  & 5\% & 443.40 & 318.88 \\ \cline{1-1}  & 10\% & 329.52 & 290.79 \\ \cline{1-1}  & 15\% & 224.10 & 264.24 \\ \cline{1-1}  & 20\% & 158.40 & 239.70 \\ \hline \end{tabular}
\end{table} 
Table 5: Wind energy curtailment and load shedding energy with different wind and DR percentage. Figure 12: Impact of DR and wind percentage on wind curtailment and load shedding.
## 3 Results and Discussion
### Spectrophotometric Assays in Propolis Residue  
#### 3.2.1 Total Phenolic Content (TPC) and Total Flavonoid Content (TFC)  
Spectrophotometric assays have uncovered a high total phenolic content (TPC) of 16,726.67 \(\pm\) 36.07 mg GAE/kg DW and a total flavonoid content (TFC) of 6802.96 \(\pm\) 32.40 mg RE/kg DW in propolis residue (Table 2); however, TPC and TFC may be affected by matrix interference such as waxes and residual sugars, particularly in these samples.  
According to [32], the chemical composition of propolis residue is like that of raw propolis, considering total phenolic content (TPC) and total flavonoid content (TFC). These findings are lower than those previously reported for this matrix but still surpass the phenolic and flavonoid values observed in various agricultural and industrial by-products.  
For example, our TPC value remains higher, compared to 8000-15,000 mg GAE/kg DW range found in grape seeds by [45] and the 5000-22,000 mg GAE/kg DW in grape pomace reported by [46]. Similarly, the TFC values in our study also exceed the 6000-12,000 mg RE/kg DW reported for Italian grape pomaces by [47], highlighting the unique bioactive potential of propolis residue. Comparatively, [1] analyzed various samples of raw propolis, revealing a TPC range of approximately 20,000-30,000 mg GAE/kg DW, higher than these findings. Also, compared to the values reported by [48], which indicate a TPC range of 8000-15,000 mg GAE/kg DW for various agricultural by-products, these propolis residue samples demonstrated a higher total phenolic content (16,726.67 \(\pm\) 36.07 mg GAE/kg DW). This underscores the superior bioactive potential of propolis residue, highlighting its promise as a valuable source of natural antioxidants.
## Materials and Methods
### Chromatography  
The PAHs and their metabolites were analyzed by gas chromatography (GC) ([PERSON] 2010) with a flame photometric detector. Compounds were separated by using an HP5 column (Agilent), with helium as the carrier gas. The column temperature was kept at 200 \({}^{\circ}\)C for 3 min, then programmed to increase to 270 \({}^{\circ}\)C at 15 \({}^{\circ}\)C min\({}^{-1}\), and finally kept at 270 \({}^{\circ}\)C for 2 min. Before GC analysis, 2,2\({}^{\prime}\)-diphenic and phthalic acids were methylated with CH\({}_{3}\)COCl. Commercial anthracene (RT--4.15 min), 9,10-anthraquinone (RT--5.39 min), phenanthrene (RT--4.08 min), phenanthrene-9,10-quinone (RT--6.86 min), fluorene (RT--5.57 min), 9-fluorenone (RT--4.33 min), 2-carboxybenzaldehyde (RT--7.31 min), 2,2\({}^{\prime}\)-diphenic acid (RT--6.7 min), and phthalic acid (RT--8.99 min) were used as the marker compounds to identify the PAH oxidation products. GC analysis was carried out at the \"Simbioz\" Centre for the Collective Use of Research Equipment in the Field of Physical-Chemical Biology and Nanobiotechnology (Institute of Biochemistry and Physiology of Plants and Microorganisms, Saratov Scientific Centre of the Russian Academy of Sciences (IBPPM RAS)).  
The oil disappearance was measured by adsorption chromatography with gravimetric termination. Oil was extracted three times by 5 mL of chloroform; the extracts were evaporated and applied to a column of Al\({}_{2}\)O\({}_{3}\) (10 mL). The oil was eluted by chloroform and dried to complete evaporation of the solvent and the amount of oil was determined by the gravimetric method. The changes in the fractional composition of the oil were determined by adsorption chromatography on an Al\({}_{2}\)O\({}_{3}\) column, followed by polarimetric and gravimetric analysis. The paraffins (Nd = 1.41-1.43) were eluted with 40 mL of hexane; naphthenes (Nd = 1.45-1.49), with 60 mL of hexane; low-molecular-weight aromatic hydrocarbons (Nd = 1.49-1.53), 100 mL of a mixture of 15% benzene in hexane; high-molecular-weight aromatic hydrocarbons (Nd > 1, 59), 100 mL benzene; and tars (refractive index not determined), 80 mL of ethanol/benzene (1:1). The fractions were dried to complete evaporation of the solvent and analyzed by gravimetric method [27]. The oil used was composed as follows (%): paraffins--51.7, naphthenes--17.0, low-molecular-weight aromatic hydrocarbons--10.3, high-molecular-weight aromatic hydrocarbons--7.3, and tars--13.5.
_applied sciences_  
_Article_  
**Antioxidant Protein Hydrolysates from Hemp Seed Oil**  
**Cake--Optimization of the Process Using Response**  
**Surface Methodology**  
**[PERSON] 1\({}^{\copyright}\)**, **[PERSON] 1, [PERSON] 2\({}^{\copyright}\)**, **[PERSON] 1\({}^{,\star}\)** and **[PERSON] 1**  
1 Institute of Cryobiology and Food Technology, Agricultural Academy, 1407 Sofia, Bulgaria;  
[EMAIL_ADDRESS] (M.D.); [EMAIL_ADDRESS] (S.D.); [EMAIL_ADDRESS] (I.N.)  
2 Department of Mathematics, Physics and Information Technologies, Faculty of Economics, University of  
Food Technologies, 4002 Plovdiv, Bulgaria; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
**Abstract: Hemp seed oil cake, a by-product of hemp seed oil extraction, is characterized by its high protein content and bioactive components, making it a valuable resource for the development of functional products through enzymatic hydrolysis. Hemp seed oil itself is renowned for its rich content of essential fatty acids, vitamins, and antioxidants, contributing to its widespread use in health and wellness products. Consequently, the residual cake presents significant potential for the food, pharmaceutical, and cosmetic industries as a source of high-quality protein ingredients. The optimization of enzymatic hydrolysis conditions is crucial for maximizing the efficiency and quality of the resulting protein hydrolyses. This study aims to optimize the hydrolysis process of hemp seed oil cake with bromelin, focusing on three key factors: enzyme concentration (E/S ratio), temperature, and time, to achieve hydrolyses with superior antioxidant activity. Response Surface Methodology (RSM) was applied using a Box-Behnken design to model and optimize the hydrolysis conditions. The experimental design involved three levels for each factor: 1%, 2%, and 3% for bromelin concentration; 20 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C for temperature; and 60, 120, and 180 min for hydrolysis duration, resulting in 21 experimental runs. The antioxidant activity was assessed via DPPH and ABTS radical scavenging assays (%RSA), and the derived regression models were statistically analyzed and validated. The findings indicate that the optimal conditions for obtaining protein hydrolyses with the highest antioxidant activity are a bromelin concentration of 3.0%, a temperature of 40 \({}^{\circ}\)C, and a hydrolysis time of 60 min.  
_Camabis sativa_ L.; enzymatic hydrolysis; bromelin; antioxidant activity; Box-Behnken design
# Toxic and Environmental Effects of Neonicotinoid Based Insecticides
## 1 Introduction  
Toxic insecticides are widely used in the agricultural industry to control insects. Neonicotinoids are a class of insecticides that were introduced into the farming industry in the past few decades, and they have been in use worldwide for over 20 years [1, 2]. The active ingredients in neonicotinoids include imidalcoprid, clothianidin, thiamethoxam, acetaminophen, sulfoxaflor, and thiacloprid [3, 4] (please refer to Figure 1).  
Insecticides are generally classified as substances that are toxic and used to eliminate insects. For example, disease-carrying insect citrus psyllids (Diaphorina citri and Triroza) carytreae) can be controlled with neonicotinoids [5]. However, neonicotinoids are not used to control mosquitoes (i.e., vectors of human diseases) because these insecticides are slow-acting and not very effective with these nuisance insects. Neonicotinoids are a class of synthetic insecticides that eliminate insects by acting on their nicotinic receptors [6]. They are commonly used to limit pests that compromise crops or to dispatch disease-carrying insects [7]. These insecticides are widely applied in an agricultural context and are especially effective with regard to sap-feeding insects, such as aphids, whiteflies, and planthoppers, as well as insects that chew on plants [8] (Please refer to Figure 2). Imidacloprid, thiacloprid, thiamethoxam, dinotefuran, clothianidin, and acetaminophen are all potent insecticides that fall under the class of neonicotinoids. Neonicotinoids are one of the most widely used insecticides as they were specifically designed to impact the nicotinic acetylcholine receptors (nAChR) of insects, which is why they are generally believed to have relatively low mammalian toxicity [9, 10].  
This manuscript reviews the negative impacts of neonicotinoids on human health and the ecosystem and its effects on the relationship between crop yields and the death rate of natural pollinators, as well as mitigation strategies.
# An Improved Winkler Foundation Modulus for a Beam in a Full Space
## 2 The Formula of the Winkler Foundation Modulus
### The Fitting Expression of the Winkler Foundation Modulus  
A fitting method was adopted by [PERSON] [16] to obtain the Winkler foundation modulus. The detailed process can be divided into three steps. The core goal of [PERSON]'s method is to ensure that the displacements, rotations, bending moments, and shear forces obtained from the two methods have similar overall shapes, but it does not aim to achieve equal maximum bending moments. Since the second derivative of the bending moment is related to displacement, this fitting goal can be achieved by ensuring that the first zero of the bending moment obtained from the two methods is equal. It is worth noting that when \(\kappa\) is small enough, the first zero point of the bending moment derived from Equation (43) is independent of \(\kappa\), which has been proved by [PERSON] et al. [20].  
Figure 5: Displacement coordination of the beam and ground.  
The detailed process can be divided into three steps. In the first step, \(m\) is assumed to be within the range of 0.01 to 1. Based on the assumed \(m\), the first zero point \(\overline{x}_{0}\) of the bending moment from Equation (43) is obtained. In the second step, \(b\xi\) is obtained using Equation (12) based on the given \(\overline{x}_{0}\). In the third step, the relationship function between \(m\) and \(b\xi\) is established through data fitting, and the Winkler foundation modulus can be easily obtained by solving the relationship function. The related data are listed in Table 1.  
Fitting the data in Table 1, the relationship function of \(m\) and \(b\xi\) is obtained as follows:  
\[1.5873\bigg{(}\sqrt[3]{E_{s}b^{4}/\left[C(1-{v_{s}}^{2})E_{b}I_{b}\right]} \bigg{)}^{0.85}=\sqrt[4]{K_{h}b^{4}/\left(E_{b}I_{b}\right)} \tag{47}\]  
and \(\left[C\big{(}1-{v_{s}}^{2}\big{)}\right]^{-0.13}\) approaches 1 for different \(\
u_{s}\). Thus, Equation (37) can be simplified as follows:  
\[K_{h}=4.02\frac{E_{s}}{1-{v_{s}}^{2}}\bigg{(}\frac{E_{s}B^{4}}{E_{b}I}\bigg{)} ^{0.13} \tag{48}\]  
where \(B\) is the width of the beam, having the expression \(2b\).
# VPNFilter Malware Analysis on Cyber Threat in Smart Home Network
## 3 VPNFilter Malware in the Smart Home Network  
Nowadays, the smart home has become very popular with this technology. Note, however, that owners of smart homes are faced with the high probability of hacking, malware attacks, and intrusion of privacy by people with malicious intent. Many of the smart home platforms rely on the home internet gateway to access the cloud to be able to function. In the event that attackers successfully manage to compromise the internet gateway, they may gain complete control of all household devices connected with the platform. [PERSON] et al. [40] suggested that an attack can be initiated when the client accesses an infected website, which automatically executes a Java applet on the code of the device. This script will fingerprint the home internet router's internal IP addresses. The script establishes a reverse socket connection between the client and the attacker wherein the client's IP address provides insight into the internal addressing schema.  
Having complete information related to the router, the attacker takes advantage of the situation wherein most homeowners do not change the manufacturer-set default password and attempt a login query using the default vendor credentials. Upon successful login, the attacker will modify essential configuration settings and gain complete access to the home internet gateway. With the current mode of threat, however, no particular weakness of the home medium is exploited. Relying solely on blocking all unwanted inbound connections creates a false sense of security. A homeowner may accidentally and unknowingly initialize an attack script on internal devices, which in turn provides open access to the entire internal network. The attacker can now control all outbound connections and the home router administration.  
Recently, some researchers from Cisco and Talos reported grave security threats to home internet gateways via a large-scale, advanced persistent threat to SOHO routers known as VPNFilter [41].  
Specially engineered to attack routers, this malware can intercept the user's internet traffic and manipulate the pages visited by the user. It can either steal typed passwords, including those on bank sites, or create fake copies of the page so that the victim does not know that they are being hit. This malware has powerful destructive capability that can leave the infected device unusable, and it can be triggered on individual victim machines. It can also switch off internet access for more than thousands of victims connected within the network worldwide [42]. Once installed in the router, this malware can stop the router from working, collect information from the system that runs through the network, and block network traffic.