esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 4 Energy-Efficiency Model of the Centrifugal Pump
### Analytical Modelling of the Pump Efficiency Characteristics for Variable-Speed Operation
#### 4.1.3 Characteristics of the Existing Pump  
To enable a comparison between simulation results and actual measurements for the existing VSDP, we had to create a simulation model for the existing pump. Initially, we encountered a challenge as only data for 1750 rpm and 1450 rpm were available, which corresponds to a four-pole induction motor [51; 52]. This limited us to two isospeed curves and the corresponding efficiencies.  
Fortunately, we have found that the current successor to the existing pump model, the Etanorm 200-150-250, has identical \(Q\)-\(H\) characteristics at 1750 rpm and 1450 rpm. In addition, this new model includes specifications for operation with six-pole motors and provides isospeed curves at 1160 rpm and 960 rpm. Although the new model has a higher efficiency (\(\eta_{\text{BEP}}\) of 88.2% at 1450 rpm with an impeller diameter of 258 mm), the identical \(Q\)-\(H\) curves at 1750 rpm and 1450 rpm allowed us to use the isospeed curves for 1160 rpm and 960 rpm of the new pump for modelling the existing pump. We proceeded in the same way as before to create the isospeed curve for 560 rpm.  
To create the analytical efficiency model for the existing pump, we first used the discrete efficiency values that were available at 1750 rpm and 1450 rpm. The isoefficiency curves were created as approximate hyperbolas (12) based on points of equal efficiency from the 1750 rpm and 1450 rpm curves intersecting with the coordinate origin. We then determined the coordinates of the points of equal efficiency on the curves for 1160 rpm, 960 rpm, and 560 rpm, using the same method as for the curve of the new pump for 560 rpm. Finally, we determined the functional dependence of efficiency on flow rate for each isospeed curve (11), obtaining efficiency data even below 60%. In this way, we were able to create a comprehensive analytical model for the existing pump that is equivalent to the new pump model and thus suitable for creating the surface-matching model.
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 6 Performance Analysis
### Communication Cost  
Communication cost is defined as the total size of messages contained in VANETs in order to achieve mutual authentication. According to [36; 37], for type A pairing with respect to 80 bits security level, the size of \(p\) is 64 bytes. A point on the group of points \(E(F_{q})\) consists of \(x\) and \(y\) coordinates. This means that the size of each element in \(G_{1}\) is 64 \(\times\) 2 = 128 bytes, whilst that of each element in \(G_{2}\) is 20 \(\times\) 2 = 40 bytes. In addition, the size for a general hash function in \(Z_{q}^{*}\), an expiration, and a timestamp were considered to be 20 bytes, 4 bytes, and 4 bytes, respectively. As the basic configuration message is the same in VANETs, we only considered the size of the signature on the message with the corresponding vehicle's identity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Computational Cost** & **Execution Time (ms)** \\ \hline EAAP & \(7T_{pr}+13T_{pm}+2T_{fp}\) & 37.89 \\ TAAP & \(11T_{pr}+5T_{pm}+10T_{hp}+2T_{ntp}\) & 49.61 \\ LIAP & \(6T_{pm}+3T_{lp}+3T_{ntp}\) & 26.85 \\ PAS & \(T_{pm}+2T_{lp}+2T_{ntp}\) & 12.59 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The computational cost of each scheme.  
In EAAP, the vehicle sends message \(msg\)= \(\{M,sig,\gamma_{k},Cert_{k}\}\) to the verifier, where \(sign\in G_{1},Y_{k}\in G_{2}\), and \(Cert_{k}=\{Y_{k}||E_{i}||DID_{h}||\gamma_{TU}||\gamma_{TV}||c||\lambda||\delta_{1 }||\delta_{2}\}\), \(E_{i}\), \(DID_{\mu}\), \(\gamma_{U}\), \(\gamma_{V}\), \(\lambda\in G_{1}\), \(c\), \(\delta_{1}\), \(\delta_{2}\in Z_{q}^{*}\). Thus, the total of communication cost in EAAP is \(6\times 128+40+3\times 20=868\) bytes. For the authentication protocol in TAAP, the vehicle is requested to transfer message \(M\) and signature \(sign=\{\sigma_{1},\sigma_{2},\sigma_{3},\sigma_{4},\sigma_{5},\sigma_{6}, \sigma_{7},\sigma_{8},\sigma_{9},\sigma_{10},\sigma_{11}\}\), where \(\sigma_{1}\), \(\sigma_{2}\), \(\sigma_{6}\), \(\sigma_{7}\), \(\sigma_{8}\), \(\sigma_{9}\), \(\sigma_{10}\), \(\sigma_{11}\in G_{1}\), \(\sigma_{3}\), \(\sigma_{4}\), \(\sigma_{5}\in G_{2}\). As a result, the communication cost of TAAP is \(8\times 128+3\times 40=1144\)_bytes_. In LIAP, vehicle needs to transfer its pseudonym \(PID=\{PID^{1},PID^{2}\}\in G_{1}\), public key \(PK_{KSU}\in G_{1}\), timestamp \(TS\), and signature \(\sigma\in G_{1}\). Thus, the communication cost of LIAP is \(128\times 4+4=516\) bytes. In PAS, vehicle is requested to send a signature \(Sign_{\
u}=SK_{i}^{b}H_{1}(PS_{\
u}^{i}||TS||N)\) to prove the legitimacy of its identity, where \(Sign_{\
u}\in G_{1}\), \(PS_{\
u}^{b}\in Z_{q}^{*}\), \(TS\) is timestamp, and \(N\in Z_{q}^{*}\). Thus, the total communication cost of PAS is \(128+20+4+20=172\) bytes. The communication costs of each scheme are shown in Table 5.
# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
## 6 Performance Evaluation
### Communication Overhead  
The communication complexity analysis of the C-PBFT consensus algorithm:  
Local consensus is performed first in the region.  
1. In the pre-prepare phase, each group master node broadcasts the message to each node of the committee, and the communication overhead is \(\mathrm{u}-1\) at this time.
2. In the prepare phase, when each RSU node that receives the message sends a verification message to all nodes in the group except its own node, and the overhead is \((\mathrm{u}-1)(\mathrm{u}-1)\) at this time.
3. In the commit phase, in which all nodes receive verification messages from other nodes in the previous phase, the nodes confirm and count the number of messages received, and the overhead is \(\mathrm{u}(\mathrm{u}-1)\) at this time.
4. In the reply phase, each node sends a reply message to the master node, and the communication volume is \(\mathrm{u}-1\).  
The first layer of node consensus is performed, and since there are \(\mathrm{n}\) groups involved in consensus, a total of \(\mathrm{n}\) nodes are involved in the first layer.  
1. In the pre-prepare phase, when the master node in each region receives enough feedback messages, this message is put into the first layer transaction pool, and the first layer master node reads the transaction and broadcasts it to all nodes in the first layer, at this time the communication overhead is \(\mathrm{n}-1\).
2. The communication overhead of the prepare and commit phases is similar to that of the first round of consensus in the bottom layer; then, its communication overhead is \(\mathrm{n}(\mathrm{n}-1)+(\mathrm{n}-1)(\mathrm{n}-1)\).
3. In the reply phase, each regional master node sends a reply message to the master node, and the communication is \(\mathrm{n}-1\).  
Therefore, the number of communications for the improved C-PBFT consensus algorithm to complete a consensus process is the total communication complexity, as shown in Equation (20).  
\[C_{1}=2u^{2}+2n^{2}-2u-n+1 \tag{20}\]  
The number of communications in a consensus process of the original PBFT consensus algorithm is \(C_{2}=2m^{2}n^{2}-2 mn\).  
Figure 6: Detection rate under different trust thresholds.  
Figure 7 shows a comparative analysis of the communication overhead between PBFT and C-PBFT consensus algorithms. It is clear that the communication overhead generated by C-PBFT is significantly lower throughout the blockchain network, and this overhead gradually increases as the number of consensus nodes increases. The reduction of communication overhead is mainly attributed to the reduction of the scale of consensus nodes by the C-PBFT algorithm. A node only needs to send messages to regional committee nodes instead of to all nodes. When the number of network nodes is 180, the communication overhead of PBFT is 43,832, and that of C-PBFT is 20,608 (three groups). This represents a significant 68% reduction in communication overhead.
# An Intelligent Control Method for Servo Motor Based on Reinforcement Learning
## 2 Basic Knowledge  
### Motion Control System  
As shown in Figure 1, a complete servo motor control system includes seven main parts, namely, the man-machine interface, motion controller, driver, actuator, drive mechanism, load, and a feedback part.  
The motor control method works in the motion controller. As a control center, the motion controller closes the feedback loop by monitoring the input and output of the system. The controller receives feedback from the motor and calculates the servo error between the actual and given state. Then, the motor control method generates a control signal to adjust the state of the motor and reduce the error. After that, the driver amplifies these signals to a high-power voltage and current to meet the needs of the motor operation [2].  
Our work mainly focuses on the motor control method, and the goal is to adjust it online to adapt it to changes in operating conditions.  
Figure 1: Structure diagram of servo motor control system.
## 2 Dynamic Nuclear Polarization in Semiconductor Nano-and Microstructures  
The most common ways to polarize nuclear spins are _optical pumping_, _chemical reaction_ or direct transfer of spin angular momentum from electron to nuclear spins called _dynamic nuclear polarization_ (DNP) [9]. There exists a significant difference in thermal polarization between nuclear spins and electron spins. At cryogenic temperatures, while typically less than 1% of nuclear spins are polarized under static magnetic fields of around 10 T, the polarization of electron spins is typically larger than 90% [10]. Thus, DNP is caused by aligning the nuclear spins induced by the alignment of the electron spins [1].  
In Section 2, an overview is given to DNP in semiconductor nano- and microstructures governed by optical pumping (Section 2.1.) along with the microwave irradiation (Section 2.2.). Because of significantly weaker absorption, the latter allows one to achieve much thicker spatial regions of nuclear polarization than the former. DNP by microwave irradiation permits design of the optimal materials (MPs, NPs) and properties (sizes, surface properties) to enhance nuclear polarization for MRI applications [11].  
The presence of imperfections in the crystalline structure, for example, impurities and defects, such as dangling bonds (DBs), in a semiconductor opens up efficient additional--to the coupling with the electrons--channels for relaxation of the nuclear spins (see Section IX C in Reference [12]) polarized due to the optical pumping, what makes DNP by this mechanism less robust with respect to the purity of materials selection as compared to the case of the microwave irradiation.
# RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks
## 3 The Proposed Robust Attack Path Tracing Algorithm
### RAPT Algorithm Design  
The RAPT algorithm is based on periodic logging of statistical information about the SYN packets relayed through the edge routers and comparing the frequency of SYN packets for the current period with the previous period. This information is recorded in certain tables as shown in Table 1 below. The edge router uses this information to deduce and make an informed decision whether to initiate the tracing algorithm or not. A record in the log table will include the following information:  
1. Destination IP address of the packet;
2. Frequency (\(f_{t}\)) and (\(f_{t-1}\)) of received SYN packets during the periods \(t\) and \(t-1\), respectively, noting that \(f_{t}\) will become \(f_{t-1}\) for the next period;
3. Relative change in Frequency (\(\Delta f_{t}\)) of SYN packets;
4. Cumulative mean of Frequency (\(\mu f_{t}\)) in SYN packets.  
It should be noted that packet logging has been used in works such as [36; 37]. Moreover, the observed destination IP addresses are for popular web servers that are prone to attacks for their availability importance. The RAPT algorithm does not consider end hosts or unpopular web servers. When the edge router forwards a SYN packet (or more) towards a destination IP address for the first time, the router will build a new entry for that address and forwards the packet along the routing path to the next router. The logging table does not keep its entries indefinitely, and it follows the Least Frequently Used (LFU) caching policy during each period for evicting entries when it becomes full. LFU was selected because the least used entry means the destination IP address is least targeted. The RAPT algorithm is divided into two parts, the first part is implemented on the edge routers, while the second part is implemented on the core routers.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Destination IP Address** & \(f_{t-1}\) & \(f_{t}\) & \(\Delta f_{t}\) & \(\mu f_{t}\) \\ \hline
142.250.185.132 & 7 & 20 & 1.86 & 84 \\
20.50.169.216 & 126 & 432 & 2.43 & 170 \\
87.236.232.169 & 531 & 274 & \(-\)0.48 & 243 \\
195.26.133.182 & 376 & 248 & \(-\)0.34 & 289 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SYN packet logging table.  
Figure 2: Potential DDoS attacks from six different daemons targeting the same victim.
## 2 Our Proposed Context-Aware Smart Classroom Architecture
### The Context-Aware Smart Classroom Prototype
#### 2.1.2 From a Technology-Rich Classroom to a Smart Classroom  
When a classroom meets the basic requirements listed in the previous subsection, the classroom is ready for most classroom activities, including lectures, discussions, interaction, presentations and debate, supported by technology devices. A technology-rich classroom should have advanced functions or features, such as tele-education, interactive, automation, IoT devices, learning systems, environment control, and classroom management, to be a smart classroom. Technology or equipment needed to achieve smart classroom functions are described as follows:  
1. Device control, as shown in Figure 4 * Doors with controllable locks and access system based on ID cards or biometrics. * Every device in the smart classroom should be remotely controllable, at least ON and OFF. * There should be an all-in-one user interface for people in the classroom to control the devices in the classroom.
2. Advanced interaction experience, as shown in Figure 5 * Besides the main large screen for lecturing, at least one more large screen with interactive function for group discussion and presentation. * The extra-large screens are not only for interaction activities but also better audience experience when the classroom size is large. Therefore, a software or hardware solution for synchronizing contents between large screens is needed. * Wireless display function for projecting contents to large screens from mobile devices
3. Image and audio recording, as shown in Figure 6 * At least two cameras, one points to dais or lectern while the other points to the audience for distance learning. * Software or hardware to record the teacher's computer desktop. * Images from cameras and computer desktop can be recorded or live cast depending on the smart classroom applications such as tele-education, webinar, courseware making, class monitoring, security, attendance management, lecture quality evaluation, and other image-based AI functions.  
Figure 4: Device control: (**a**) an RFID-based door access system and an information board next to the front door. Dynamic classroom schedule’s information is up to date while the paper schedule’s information is outdated; (**b**) Device control user interface on teacher’s computer to control fans, air conditioners, and lights.  
Figure 5: Advanced interaction experience: (**a**) large interactive boards for group discussion. Images can be synchronized on all screens; (**b**) wireless display function for interactive boards.
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 5 Constraints of IPCB Geometry Configuration
### Flight Altitude Constraint  
As discussed above, the flight altitude adjustment of an IPB is achieved by changing its air balloon volume, which cannot change infinitely. The air balloon volume of an IPB can only vary in a feasible range, and so does its flight altitude. During IPCB geometry configuration planning, the flight altitudes of all the IPBs should not go beyond the range.
## 3 Results
### Model Simulation and Comparison
The bifurcation plot confirms the paradox of energy enrichment observed in Figure 4. Furthermore, [PERSON] et al. (2012) and [PERSON] et al. (2013) discussed and formulated models to study a phenomenon known as the \"stoichiometric knife-edge\" in which excess food resource content (in addition to low food resource content) can lead to the collapse of the grazer population [11, 12, 22]. In previous studies, the stoichiometric knife edge effect was incorporated by capping the ingestion rate of phosphorus per each unit of the grazer's biomass per time. Here, we propose to incorporate a mechanism for this observation via a trade-off in energy conversion efficiency using the function \(\alpha(\cdot)\). While the MRL model does not result in a deterministic extinction as \(P\) increases, the grazer population at high \(P\) oscillates dangerously close to 0, which increases the probability of a stochastic extinction event. When comparing the asymptotic bifurcation of the MRL model to the LKE model as \(K\) varies, we find that the MRL model predicts a lower value of \(K\) at which extinction occurs (\(K\approx 1.5\) vs. \(K\approx 1.9\)), and at which oscillatory behaviors arise (\(K\approx 0.25\) vs. \(K\approx 0.55\)). Together, the oscillatory region in the MRL model is roughly twice as large as in the LKE model (0.9 vs. 0.45 unit of \(K\)). Additionally, as \(P\) increases, the LKE model predicts a saturation on the effect of \(P\) as \(K\) becomes the limiting resource (\(P\approx 0.02\)). This means the LKE model does not exhibit the stoichiometric knife-edge effect. The complete bifurcation analysis of the system with respect to each model parameter is provided in Figure A1 in the Appendix A. Figure A1 demonstrates several interesting bifurcations for this model. First, we note that varying a number of parameters can lead towards or away from a stable periodic solution (Hopf bifurcation). Furthermore, for very low values of \(K\), we see that the solution of the system converges to a very high producer-only equilibrium. While this boundary equilibrium is of mathematical interest, it is not within the biologically relevant region \(\Omega\) in Theorem 1. Figure 4: Comparison of model dynamics LKE (dashed curves) vs. the MRL model (solid curves). Red color indicates the producer, and blue indicates the grazer.
# How Hydrogen (H\({}_{2}\)) Can Support Food Security: From Farm to Fork
## 4 H2 in Food Processing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Product** & **Application** & **Process** & **Mechanism** & **Outcome** & **Ref(s)** \\ \hline Apricot (_Prunus armeniaca_ & H2 gas (4\%) & Drying & Increased antioxidant activity & Maintained nutritional profile & [54] \\ L.) & & & & & \\ \hline Beetroot (_Beta vulgaris_) & H2-infused brine (undisclosed concentration) & Fermentation & Termentation-associated microbial content & Reduced biogenic amine formation & [55] \\ \hline Beetroot (_Beta vulgaris_) & H2-enriched solvents (ethanol, methanol, water) & Nutrient extraction & Increased extraction yields: antioxidants, flavonoids and phenolics & Sustainable and efficient extraction process & [56] \\ \hline Lemon (_Citrus lemon L_) & H2-enriched solvents (ethanol, methanol, water) & Nutrient extraction & Increased extraction yields: flavonoid and phenolic & Sustainable and efficient extraction process & [57] \\ \hline Raw butter & HRW & Washing during preparation & Reduced microbial clearance activity; reduced biogenic amine formation; reduced yeast/mould & Improved shelf-life and sensory attributes. Reduced potential for foodborne toxicity & [14; 58] \\ \hline Rice milk & HRW (0.8 mM) & Preparation & Increased essential mineral content (Ca, K, Mg, Na); increased amino acids (Ile, Leu, Met) & Enhanced nutritional profile & [59] \\ \hline Yoghurt & H2 gas infusion into pasteurised milk (1 L/min–10 min) & Fermentation & Increased the acidification capacity: _Streptococcus thermodynamics_ (+10\%); increased the reducing capacity: _Lactobacillus delbruuckii_ (+13.7\%) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The benefits of H2 addition in the processing of food products. Ca—calcium; K—potassium; Mg—magnesium; Na—sodium. Ile—isoleucine; Leu—leucine; Met—methionine. in the preparation of rice milk, an alternative to dairy, replacing pure water with HRW increased essential mineral and amino acid content [59]. The use of 4% H\({}_{2}\) gas in the fruit-drying process has also shown an increased potential to maintain the nutritional and sensorial notes of dried fruits [54]. Here, instead of infusing liquids with H\({}_{2}\) generated on demand, a mixture of gases (typically, CO\({}_{2}\), N\({}_{2}\) and H\({}_{2}\)) into the drying apparatus is regulated via instrumentation. As such apparatuses utilise gas canisters, and since H\({}_{2}\) is explosive above concentrations of 75% (\(v/v\)) [62], increased storage and safety protocols may be required if this mechanism of application is to be exploited in the food preservation industry.
# Random Access Performance of Distributed Sensors Attacked by Unknown Jammers
## 2 System Model
### Power Ramping for Preamble Transmission and Message Transmission  
By using open-loop power estimation, an SN can adjust its initial transmission power based on the received signal strength from the HS [17]. The aim is to let the received power at the HS exceed a predetermined power level. For the initial PT, we assume that all SNs have the same target power level \(\mu L\), recalling that \(\mu\) is an average path power and \(L\) is the number of effective paths. If the initial PT fails, a higher target power level is used for retransmission. We assume that \(\mu L\) is the power increment unit adopted at each of preamble retransmissions as in [22]. Let us denote the number of retransmissions by \(r\), and let \(m_{r}\overset{(\text{def})}{=}r+1\). Then, \(m_{r}\mu L\) represents the target power level in the \(r\)-th preamble retransmission. Hereafter, we denote the average number of preamble retransmissions by \(\overline{m}\), and thus, \(\overline{m}\mu L\) means that the average power level is used in PT. Average jamming power is also represented by \(m_{I}\) (in the unit of power increment step \(\mu L\)), such that \(m_{I}\mu L=P_{I}\), where \(P_{I}\) is the maximum power level of jamming signals.  
After PT is successful, SNs that received AI send MSG with an adjusted power level. Let us denote a power increasing factor for MSG transmission by \(\alpha(\geq 1)\). When an SN receives AI successfully after \(r\) or the \(\bar{m}\)-th preamble retransmission, it sends its MSG using power level \(\alpha m_{r}\mu L\) or \(\alpha\bar{m}\mu L\), respectively.