esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The LVC Converter in Their Traditional Operation  
This section describes the converter under study, also called the low-voltage capacitor (LVC) converter, which in this operation, we call the \"traditional operation\", as introduced in [18]. The topology comprises two capacitors (\(C_{1}\), \(C_{2}\)), two inductors (\(L_{1}\), \(L_{2}\)), and four semiconductor devices. Semiconductor devices in switched mode power supplies are used as open or closed devices (not in their linear mode); in this case, they are two transistors (\(s_{u}\), \(s_{b}\)) and two diodes (\(s_{an}\), \(s_{bn}\)). The converter schematic is depicted in Figure 2.  
The converter has two capacitors that sustain a voltage smaller than the output voltage. This is their main advantage, as introduced in [18], in contrast to other converters in which at least one capacitor sustains the output voltage. The voltage output is the sum of the voltage from the capacitors and the input voltage, as seen in Figure 2.  
\[v_{out}=v_{in}+v_{Ca}+v_{Cb} \tag{1}\]  
Figure 3 shows the same LVC converter with a different way of drawing the schematic. In this case, the draw is on the way of double dual topology. Therefore, the fact that the output voltage is equal to the combination of the capacitor's voltage and the input voltage is more evident in this figure.  
Figure 2: LVC converter topology schematic.  
The traditional operation considers both transistors to have the same switching function, which means both transistors open and close at the same time; this leads to an operation with two equivalent circuits or switching states (in the continuous conduction mode (CCM)); those switching states are shown in Figure 4.
## 4 Simulation Environment
### Performance Evaluation Metrics  
In order to evaluate the performance of VANET in presence of attackers, we implemented following evaluation criteria which can evaluate the MITM attacks in VANET. These are:  
* **End-to-End Delay:** This metric is related to QoS of the network, indicating the delay caused to packet generated by legitimate node to be shared with neighbouring nodes. E2 ED is the difference of packet generation time (\(T_{G}\)) and packet reception time (\(T_{R}\)) which is calculated as follows: \[E2 ED=T_{R}-T_{G}\] (3)
* **Content Delivery Ratio (CDR):** Content delivery ratio shows the amount of messages which are received successfully by the legitimate vehicles [45]. Let \(M_{R}\) are the number of received messages  
\begin{table}
\begin{tabular}{c l l} \hline \hline  & **Parameter** & **Value** \\ \hline \multirow{3}{*}{**Simulation**} & Network Simulator & OMNET++ 5.0 \\  & Traffic Simulator & SUMO 0.25.0 \\  & V2X Simulator & VEINS 4.4 \\ \hline \multirow{6}{*}{**Simulation**} & No. of Vehicles & 100 \\  & No. of RSUs & 5 \\  & No. of Malicious Nodes & 10\%, 20\%, 30\%, 40\%, 50\% \\ \cline{1-1}  & Simulation Area & 2.5 km \(\times\) 2.5 km \\ \cline{1-1}  & Simulation Time & 1000 s \\ \cline{1-1}  & Accident Start time & 75 s \\ \cline{1-1}  & Accident Duration & 50 s \\ \cline{1-1}  & Communication Range & 250 m \\ \cline{1-1}  & Vehicle Maximum Speed & 13.9 km/h \\ \hline \multirow{6}{*}{**Protocols**} & MAC Protocol & IEEE 802.11p \\ \cline{1-1}  & Network Protocol & IEEE 1609.4 (WAVE) \\ \cline{1-1}  & Radio Propagation Model & Simple Path Loss \\ \cline{1-1}  & Data Size & 1024 bits \\ \cline{1-1}  & Header Size & 256 bits \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Details.  
Figure 6: Snapshot of Veins (**a**) Vehicles during accident (**b**) Vehicles after accident.  
and \(M_{PRE}\) are the number of messages which are expected to be received within the network, then CDR is given as: \[CDR=\frac{M_{R}}{M_{PRE}}\] (4) Let '\(N\)' is the total number of vehicles which are transmitting '\(M_{TRANS}\)' messages, then \(M_{PRE}\) is calculated as: \[M_{PRE}=N\times M_{TRANS}\] (5)
* **Packet Loss Ratio (PLR):** Packet loss ratio shows the amount of the messages which are lost due to MITM nodes. Let \(M_{T}\) are the total number of messages, out of which \(M_{L}\) messages are lost, then PLR is given as: \[PLR=\frac{M_{L}}{M_{T}}\] (6) \(M_{T}\) includes messages which are received at both legitimate and malicious nodes. Let \(M_{R}\) is the number of received messages at legitimate nodes and \(M_{L}\) is the amount of messages lost at the MITM nodes, then \(M_{T}\) is given as: \[M_{T}=M_{R}+M_{L}\] (7)
* **Number of Compromised Messages:** This metric indicates the number of messages compromised (either tampered or delayed) from the malicious node.
* **Number of Dropped Messages:** This metric is defined for MITM which is dropping the messages received from legitimate nodes. This metric shows the amount of messages dropped by the attackers in the network.
# A Novel Cross-Layering Power Control Mechanism for AODV +
## 4 DPCM (Density Power Control Mechanism)  
Our objective is to add a power transmission control mechanism based on the detection of neighbors in the AODV protocol by using a cross-layering technique. For this, coordination in the exchange of information between physical and network layers is instrumental. This section details the processes, actions, and implementations that were necessary to achieve cross-layering transmission power control. First, we explain the design of our power control mechanism including its operation and the parameters considered for the calculations performed in the mechanism. After, we show how the routing protocol was modified
## 2 Methodology
### Support Vector Machines  
While linear regression performs well with a dataset that has a linear relationship between its inputs and outputs [44], we need more advanced methods to address datasets with non-linear relationships. SVM is an effective tool for non-linear regression problems, and it is suitable for predicting solar irradiance and energy consumption [45]. It is a machine learning algorithm [46] and typically used to solve regression problems [47]. SVM is mainly based on statistical learning theory, where the goal is to reduce structural risk by ensuring an upper bound of generation error [46].  
SVM uses a kernel \(K\) to map the original space onto a higher dimensional space to find a better hyperplane with a certain margin. The margin is defined as the distance between the hyperplane and the closest \(\mathrm{x}_{i}\) vectors. SVM not only finds the hyperplane, but also seeks to maximize the margin. Let \(\Phi\) be a map from the original space to the higher dimensional space. Then, a kernel is the dot product of pairs of \(\Phi(\mathrm{x}_{i})\) and \(\Phi(\mathrm{x}_{j})\), that is, \(K\big{(}\mathrm{x}_{i},\ \mathrm{x}_{j}\big{)}=\Phi(\mathrm{x}_{i})\cdot\Phi( \mathrm{x}_{j})\). Given the kernel \(K\), \(f\) is defined by  
\[f(\mathrm{x})=\sum_{j=1}^{n}\alpha_{j}k\big{(}\mathrm{x}_{j},\ \mathrm{x}\big{)}+b \tag{2}\]  
for some variables \(\alpha_{i}\) and \(b\). The goal of SVM is to minimize  
\[\lambda\ \left|\left|\sum_{i=1}^{n}\alpha_{i}\Phi(\mathrm{x}_{i})\right| \right|^{2}+\frac{1}{n}\sum_{i=1}^{n}L(y_{i},\ f(\mathrm{x}_{i})) \tag{3}\]  
where \(\lambda\) influences the margin size and \(L\) is a loss function between an output of \(\mathrm{x}_{i}\) and \(y_{i}\). In this paper, we use a popular kernel which is the radial basis function (RBF) kernel [48], that is,  
\[K\big{(}\mathrm{x}_{i},\mathrm{x}_{j}\big{)}=\exp\Big{(}-\gamma\ \big{|}| \mathrm{x}_{i}-\mathrm{x}_{j}||^{2}\Big{)} \tag{4}\]  
for \(\gamma>0\). The RBF kernel results in \(1\) when \(\mathrm{x}_{i}=\mathrm{x}_{j}\) and approaches zero as \(\mathrm{x}_{j}\) moves farther away from \(\mathrm{x}_{i}\).
# 5G-AKA-FS: A 5G Authentication and Key Agreement Protocol for Forward Secrecy
## 4 A 5G-AKA Protocol for Forward Secrecy
### The Initiation Phase: Step 1
#### Step 1.1 (Ue)  
_Inputs._ With HN's public key \(PK_{HN}\), UE executes the followings:  
_The Protocol:_  
1. Generate an ephemeral private-public key pair \((r,R)\) such that \(R=r\cdot G\)
2. With \(PK_{HN}\), compute a ciphertext \(C_{0}=R\) and a key \(k_{UE}=\mathsf{KDF}(r\cdot PK_{HN})\)
3. Parse the shared key \(k_{UE}\) as \(k_{1}||k_{2}\)
4. Compute \(C_{1}=\mathsf{ENC}(k_{1},SUPI)\) and \(C_{2}=\mathsf{MAC}(k_{2},C_{1})\)
5. Set \(SUCI\leftarrow(C_{0},C_{1},C_{2})\)  
_Outputs._ UE sends \((SUCI)\) to SN.
## 2 Experimental Setup  
### Experimental Model  
The experiments were carried out in a multi-functional flume in the State Key Laboratory of Water Environment Simulation, at Beijing Normal University, China. The internal dimensions of the flume are 25.00 m \(\times\) 0.80 m \(\times\) 0.80 m (length \(\times\) width \(\times\) height), whose bottom and side are made of transparent glass. The flow is generated by a complete water circulation system, and the maximum flow rate is 240 L/s.  
The reef monocase model used for this study is 0.06 m \(\times\) 0.06 m \(\times\) 0.044 m in size, 3 mm in thickness, 4 mm in round hole radius, and 4\({}^{\circ}\) in triangle angle, which was scaled by a geometric factor of 1:50 according to the prototype size of the M-shaped reef (Figure 2). The field observation of the daily flow velocity reported in the Liaodong Bay varied between 0.6 and 1.8 m/s, which corresponds to a design flow velocity 0.085-0.257 m/s or flow rate 20-60 L/s range according to the scale factor specified here. The M-shaped reef models were made of plexiglass. Each M-shaped unit reef is composed of 14 reef monocases. In order to avoid drifting and rolling, the reef models were fixed on the bottom of the flume with transparent rubber belt and were located perpendicular to the flow direction.  
The Particle Image Velocimetry (PIV) method was adopted to investigate flow conditions. This is a non-contact two-dimensional flow field measurement technology based on cross-correlation analysis of flow field images, which can produce a quantitative visualization effect of instantaneous flow patterns without disrupting the phenomena under analysis. It is usually used to support the development of phenomenological models for complex flow or numerical simulation verification. It has become the main experimental technique for measuring flow velocity fields [65; 66; 67] and has been widely used in the analysis of flow field around artificial reef models in recent years [30; 31] as well as for other flow applications [68; 69; 70; 71; 72]. The fluid velocity was calculated using an adaptive cross-correlation technique, keeping an interrogation area of 1903.5 \(\times\) 1183.5 p  
Figure 2: Three views and schematic diagram of M-shaped artificial reef prototype.  
of 80 pixel overlap between two adjacent areas. Median correction post-processing was applied to remove erroneous vectors, which removed approximately 2 to 7% vectors from each measurement set. To validate this technique, an Acoustic Doppler Current Velocimetry (ADV) was installed 2 m in front of the test target to measure the inflow velocity and results compared with PIV outputs were within the range of 4% error. With the artificial reef model placed in the middle of the flume, the laser used for the PIV data collection was placed at the bottom of the tank to hit the central axis of the reef model. The images were captured by a CCD (Charge Coupled Device) camera and consequently analyzed using Dynamic Studio software [73]. The velocity maps produced were finally visualized using Tecplot software. A full experimental setup is illustrated in Figure 3.
# A Video-Based Fire Detection Using Deep Learning Models
## 3 Proposed Method
### Majority Voting for Fire Decision  
The decision from LSTM reflects a temporal behavior in a short period like a person's quick glance. As the resulting decision is not stable, we make an ensemble of short-term decisions for a period \(T_{\text{rot}}\) to make the final fire decision. Again, this is similar to human behavior in deciding a fire. The decisions based on short glances are accumulated and combined to make a firm decision on whether it is a fire or not.  
The proposed method combines the majority voting in a time window which contains all the decisions from LSTM. The final fire decision by majority voting is given by:  
\[\begin{array}{c}\text{{Final Decision}}=fire,\\ \text{{if }}N_{fire}>N_{non-fire}\text{ {for }}T_{\text{rot}}\end{array} \tag{3}\]  
where, \(N_{fire}\) and \(N_{non-fire}\) represent the number of fire and non-fire decisions respectively, in the LSTM stage during the time window \(T_{\text{rot}}\). One can use either weighted voting where the more recent decision has a larger weight in the temporal window or simply take the sum of each soft-max output during \(T_{\text{rot}}\).
# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 5 Conclusions  
Results of this study allowed elucidation of the following conclusions:  
1. The high spatial and temporal resolution of the PS constellation of over 200 nanosatellites allows the addition of \"double cropping\" into the legend of LULC maps of the Cerrado biome.
2. The use of textural attributes derived from the GLCM as input parameters in the supervised machine learning classification procedures is highly recommended.
3. The most relevant PS images for identifying double cropping farm management systems are the ones obtained in February.  
The products and the models selected in this work can be used as a powerful public policy to monitor agricultural intensification in the Cerrado biome or in other biomes in an operational way. As the texture attributes showed good performance to identify double-cropped fields, these metrics derived from GLCM can be tested to identify crop-livestock-forest integration systems in Brazil, which have been increasingly adopted in the country. The methodological approach used in this study is a good candidate to be a benchmark to generate consistent and accurate LULC maps of the Brazilian Cerrado with the unprecedented presence of the double cropping in their legends.  
Conceptualization, E.E.S. and E.L.B.; methodology, E.E.S.; validation, E.E.S.; formal analysis, E.E.S.; investigation, E.E.S. and E.L.B.; resources, E.L.B. and E.E.S.; data curation, E.E.S. and T.C.P.; writing--original draft preparation, E.E.S.; writing--review and editing, E.E.S., E.L.B., I.D.S, D.d.C.V., L.E.V., T.C.P. and G.M.B.; visualization, T.C.P.; supervision, E.E.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Sao Paulo Research Foundation (FAPESP), grant # 2019/26222-6 \"Agricultural mapping in the Cerrado via combined use of multisensor images\" ([PERSON]) and by the National Council for Scientific and Technological Development (CNPq)--grant # 406494/ 2018-5 \"Analysis of possibility of mapping abandoned agricultural areas in the Cerrado through Google Earth Engine\" ([PERSON]) and Research Productivity Fellowship of [PERSON] (grant # 303502/2019-3), [PERSON] (302706/2019-4), and [PERSON] (310042/2021-6). This study was also partially financed by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Brazil, Finance Code 001.  
Not applicable.  
The data presented in this study are available upon request to the corresponding author.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Culture Medium and Fermentation Kinetics  
Mono- and co-cultures were carried out in 250 mL Erlenmeyer flasks containing 150 mL of the medium. The first culture medium was semisynthetic medium M2, designed to simulate the basic composition of diluted agave must [26; 27]. This medium contained per liter: glucose, 10 g; fructose, 90 g; yeast extract, 1 g; (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), 2 g; MgSO\({}_{4}\).7H\({}_{2}\)O, 0.4 g;KH\({}_{2}\)PO\({}_{4}\), 5 g. The sugars were separately sterilized in an autoclave. The pH of the medium was adjusted to 5. The second culture medium was the YPD medium modified to contain a fructose/glucose ratio similar to that found in cooked gave juice (9/1) and named YPDA. It contained the following per liter: glucose, 10 g; fructose, 90 g; yeast extract, 10 g; and casein peptone, 20 g. Monocultures of each yeast were carried out by inoculating 1 \(\times\) 10\({}^{6}\) cells/mL. At the same time, binary co-cultures were initiated using 5 \(\times\) 10\({}^{5}\) cells/mL of each yeast. Binary co-cultures were labeled as follows: _S. cerevisiae_ with _T. delbrueckii_; Sc/Td; S._cerevisiae_ with _Z. builli_, Sc/Zb; and _T. delbrueckii_ with _Z. buili_, Td/Zb. Finally, ternary co-cultures were initiated using 3 \(\times\) 10\({}^{5}\) cells/mL of each yeast strain. All fermentations were carried out in triplicate. Samples of culture medium (1 mL) were taken every three hours from each fermentation, which were analyzed in duplicate to determine yeast growth and the concentration of fructose, glucose, and ethanol. Samples were centrifuged at 6720\(\times\)_g_ for 3 min, followed by filtration through a Nylon membrane (0.45 \(\upmu\)m), and stored at \(-\)20 \({}^{\circ}\)C until analysis. The concentrations of residual sugars (glucose and fructose) and ethanol were determined by high-performance liquid chromatography (HPLC). Finally, volatile compounds were identified and semi-quantified after 24 h of fermentation using headspace solid-phase microextraction (HS-SPME) followed by gas chromatography/mass spectrometry (GC/MS) analysis.
## 2 Previous Works and Methods  
Two relatively recent papers discuss the issue of GNSS assumptions parametrically, revealing a progressive understanding of the problem [13,14]. These studies have focused on the calculation of the received power, or \(P_{R}\), at a GNSS receiver's antenna as a means to estimate its performance. To calculate the \(P_{R}\), the calculation of \(h(x)\), as the flight profile, and of \(g(\theta)\), as the radiation pattern of a GNSS receiver's antenna, is required. The altitude of the _Source_ of unintentional interference, or \(h_{TX}\), should also be considered. The final \(P_{R}\) has been reported as  
\[P_{R}(x)=\frac{c^{2}P_{j}}{\left(4\pi f\right)^{2}}\times\frac{g(\theta)}{x^{ 2}+\left(h(x)-h_{TX}\right)^{2}} \tag{1}\]  
where \(f\) is the frequency in hertz, \(c\) is the speed of light in the SI system, and \(P_{j}\) is the power of the transmitted signal [13]. This formulation considers the perspective effect of power distribution in a 2D environment. Four important assumptions are hidden in (1), and are summarized below: As the Path Gain Factor is missing in (1), the Earth has been assumed as ideally absorptive. As far as we know, the surface of the Earth shows both reflective and refractive properties [15,16], especially when the transmitter's or the receiver's altitude is small with respect to the altitude of the other. The reflection from the surface of the Earth causes the fading effect, called multipath fading [17]. The basic formulation of the Path Gain Factor can be found in almost any \"antenna\" and \"propagation\" textbook, such as [18]. To reduce the complexity of the calculations and to provide a better understanding of the underlying method of analysis, many works have modeled the airborne GNSS receiver's flight profile in 2-dimensions [13,14]. While this assumption is true and general enough to perform mathematical modeling of the problem, it introduces some inherent limitations in using the model and its results, which will be discussed in Section 3. One of these limitations is the inability of the model to deal with real-world flight routes. To use this model, a real flight profile must be projected into two dimensions, which requires considerable computational effort. One of the direct consequences of the 2-dimensional flight profile assumption described above is the assumed 2-dimensional radiation pattern of the airborne GNSS antenna. The \(g(\theta)\) value in (1) shows this, where \(\theta\) is the Angle of Arrival (AoA) of the received signal. Even though the real antenna's radiation characteristics are expressed with E-plane and H-plane radiation patterns, earlier studies use one of these patterns without explaining how to choose an appropriate pattern [13,14].
## 3 Results and Discussion
### Solubility of GE  
Solubility of GE was investigated in the presence of different temperature, pH and NaCl, which is important to assess its application in food processing. As shown in Table 1, temperature had a significant effect on the solubility of GE. The solubility increased along with the temperature. It was observed that the solubility was only 0.28 \(\pm\) 0.04 g/100 g at 20 \({}^{\circ}\)C, while 0.91 \(\pm\) 0.06 g/100 g at 100 \({}^{\circ}\)C. Likely, higher temperature led to more active thermal motion of \(\beta\)-glucan and other molecules in GE, thereby weakening the intermolecular interaction. On the other hand, higher temperature increased the collision frequency among \(\beta\)-glucan molecules in GE and water molecules thereby causing a stronger interaction. As a result, wet heating is an effective way for improving solubility of GE in food production.  
The effect of pH on the solubility of GE was also shown in Table 1. The solubility was observably lower in acidic medium than in alkaline or neutral (\(p\) < 0.05). The acid in medium could degrade some of \(\beta\)-glucan in GE, but also H\({}^{+}\) could combine naturally with the OH\({}^{-}\) in \(\beta\)-glucan, which would weaken the electrostatic repulsion among \(\beta\)-glucan molecules. Thus, the molecular aggregation emerged, and a lower solubility of GE was observed under acidic condition.  
Effect of NaCl concentration on the solubility of GE was also shown in Table 1. The solubility decreased as the NaCl concentration increased. NaCl solution with a certain concentration could break surface hydration layer around \(\beta\)-glucan molecules and lead to a decline of GE solubility. However, high-concentration NaCl is not usually adopted in food processing. It was reported that only 0.5% NaCl had been employed to make oat bran bread by using sour dough containing \(\beta\)-glucan [15]. Therefore, the addition of NaCl in conventional food industry could not practically affect the solubility of GE.
# A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks
###### Abstract  
Underwater acoustic sensor networks (UASNs) have become a hot research topic. In UASNs, nodes can be affected by ocean currents and external forces, which could result in sudden link disruption. Therefore, designing a flexible and efficient link disruption restoration mechanism to ensure the network connectivity is a challenge. In the paper, we propose a glider-assisted restoration mechanism which includes link disruption recognition and related link restoring mechanism. In the link disruption recognition mechanism, the cluster heads collect the link disruption information and then schedule gliders acting as relay nodes to restore the disrupted link. Considering the glider's sawtooth motion, we design a relay location optimization algorithm with a consideration of both the glider's trajectory and acoustic channel attenuation model. The utility function is established by minimizing the channel attenuation and the optimal location of glider is solved by a multiplier method. The glider-assisted restoration mechanism can greatly improve the packet delivery rate and reduce the communication energy consumption and it is more general for the restoration of different link disruption scenarios. The simulation results show that glider-assisted restoration mechanism can improve the delivery rate of data packets by 15-33% compared with cooperative opportunistic routing (OVAR), the hop-by-hop vector-based forwarding (HH-VBF) and the vector based forward (VBF) methods, and reduce communication energy consumption by 20-58% for a typical network's setting.  
underwater acoustics sensor networks; glider; sawtooth motion; link-disruption; restoration; network connectivity +
Footnote †: journal: Sensors