esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 2 Background
### Related Work
#### 2.1.1 Trust-Based AODV Protocol Approaches  
In [4], routing tables of nodes contain trust value for each path. Therefore, if there are more entries for some destination with the smallest hop count, the more trusted path is chosen. The trust of a path is calculated as a product of the node trusts along the path.  
Authors in [15] proposed a trust-based scheme for AODV which uses a friendship mechanism. Nodes keep lists of friends and their friendship values, calculated over some features. Route Request (RREQ) and Route Reply (RREP) messages are rejected based on trust values of previous and next hops. Furthermore, a friendship of the route is evaluated, and the route is registered if it is more friendly than the existing one.  
The method proposed in [5] changes the original neighbor table entries and routing table entries to keep information about trust. Neighbor trust values are used to handle RREQ and RREP messages from those neighbors, while route trusts are used to select routes.  
Authors in [16] have a slightly different approach to incorporate trust. Each node has its trust table, and when receiving a packet, it checks the trust value of the source. If it is untrusted, then the packet is dropped.  
In [6], the path trust value is computed during the spread of RREQ and RREP messages. Each node on the way adds the trust value of the next hop while the control message is spread over the network. The destination node computes the average and that is considered as the trust of the path.  
In [7], when a node receives a RREQ message, it checks the trust of the source. If the source is untrusted, the RREQ message is ignored. Otherwise, the trust of the neighbors is calculated, and RREQ is forwarded only to the trusted ones. RREP messages are only forwarded.
## 4 Results  
The results of the SRD fault are presented in Figure 3. The SRD fault was simulated by varying the resistance R4 up to ten times its original value. Figure 3a shows how the current of the faulty string changes as the degradation process caused by resistor R4 continues. This means that the differential voltages \(V_{a}\) and \(V_{f}\) are separating as the resistor R4 is increasing; see Figure 3b. The relative percentage difference calculated with Equation (6) is presented in Figure 3c. In this case, it is shown that there is a power loss by the faulty module concerning the non-faulty modules in the same string; the arrow shows the degradation process. The array's output power is shown in Figure 3d, and it goes down according to the increment of the serial resistance R4; also, the degradation process is indicated with the arrow.  
Table 5 shows how the resistor value affects the voltage at the maximum power-point (\(Vmpp\)), the maximum power point (\(Pmax\)), and the total power loss (TPL). The TPL indicates how much power is lost in the whole PV array with respect to an expected non-faulty value of 1600 W, as indicated in Equation (8). Additionally, the table shows the value of voltage for the affected (\(V_{a}\)) and faulty (\(V_{f}\)) PV modules as well as the relative percentage difference (\(\Delta_{V}\)) between them. The last column in the table shows the module power loss (MPL) by the faulty module with respect to its maximum power as reported in the datasheet (200 W).  
The graphical results for the following six fault types are presented in the Appendix A, each figure belongs to one fault type, and it contains the same four charts explained before for the SRD fault. In the Appendix A, the Figure A1 presents OD fault, and the results were obtained moving I4 values randomly. Figure A2 presents CHD fault; this simulation changes the values for R8 and I4 randomly and separately. Figure A3 presents PID\({}^{1}\) fault; here, the elements R8 and I4 move randomly but proportionally. Next, Figure A4 presents PID\({}^{2}\) fault; for this simulation, R8, I4, and the parameter \(\eta\) in diode D4 move freely and randomly. Further, the results for MC fault are presented in Figure A5, here I4, and the parameter \(I_{s}\) in the diode D4 move randomly but in a proportional way. Finally, Figure A6 shows the results for LID fault when the parameter \(I_{s}\) moves in the diode D4. It is interesting to see that all fault types present similar patterns and behaviors in all the charts.
## 3 Numerical Simulation (FE)
### Heat Source Model  
For the analysis of transient temperature field in numerical simulation, the widely used heat source models are mainly divided into surface heat source and bulk heat source models. The surface heat source model is that the laser beam loads energy on the surface of the powder layer in the form of heat flux, and the heat is transferred to the inside of the powder layer and the substrate in the form of heat conduction. Common surface heat source models include Gaussian surface heat source models and ray tracing models. At present, in the numerical simulation of the temperature field of parts formed by SLM, Gaussian surface heat source is often used as a high-energy laser beam for calculation and analysis [30].  
The model formula of Gaussian heat source is as follows:  
\[q(r)=\frac{2 AP}{\pi a^{2}}\exp\left(-2\frac{r^{2}}{a^{2}}\right) \tag{11}\]  
where \(P\) is the laser power (W), \(A\) is the absorption rate of the laser beam by the material, \(r\) is the distance from any point in the laser spot coverage area to the spot center (m) and \(a\) is the laser spot radius (m).
# Enterprise Security for the Internet of Things (IoT): Lightweight Bootstrapping with EAP-NOOB
## 8 Evaluation
### Evaluation of the Message Size  
The size of each message is a crucial concern for resource-limited IoT devices. Whereas, for example, smart light bulbs are not constrained by the available power, many battery operated smart home and automation devices need to carefully consider the number of bytes sent and received over the radio interface. Figure 8 shows the number of bytes for each message type in our implementation. The overhead of each protocol layer is shown separately. The UDP, IPv6, MAC, and physical layers add a constant overhead of 64 bytes to all messages.  
The messages that are necessary for bootstrapping a device with EAP-NOOB range from 74 to 329 bytes in size, which is arguably a significant amount of data, as can be seen in Figure 8. However, any protocol that provides strong security properties, such as perfect forward secrecy and cryptographic agility, without assuming any pre-configured credentials must at least exchange fresh public keys, nonces, and lists of supported cryptographic algorithms. Therefore, the sizes of the EAP-NOOB messages are justifiably large. Moreover, since the bootstrapping step is performed only once during the initial device setup, consuming additional resources for strong security is reasonable. Nevertheless, the overhead of EAP-NOOB could be further reduced by minor tweaks, such as using the more light-weight Concise Binary Object Representation (CBOR) [64] for messages instead of the JavaScript Object Notation (JSON) currently described in the specification. Finally, it is worth noting that the overhead of EAP itself is rather small, only 4-5 bytes per packet. Given this low overhead and the benefits of EAP discussed in this paper thus far, it becomes evident that using the EAP framework for device bootstrapping is a reasonable choice.  
Figure 8: Message size of full nimble out-of-band authentication for Extensible Authentication Protocol (EAP-NOOB) exchange.
# Influence of Material on the Density of a Point Cloud Created Using a Structured-Light 3D Scanner
## 2 Literature Review and Research Questions
### Research Questions  
As assumed from the literature review, research in the field of structured-light 3D scanning data acquisition and processing is ongoing, but it is focused on the semi-automation of data processing and the modeling of imperfectly scanned parts of objects. This article aims to answer the following research questions. RQ1: To what extent is the density of the point cloud predictable during building object reconnaissance? RQ2: How can the material surface be easily treated for higher point cloud density? RQ3: Can the data acquisition process become efficient with knowledge of the appropriate surface treatment? Answering these questions can increase the overall density of the point cloud of the scanned objects, especially for the problematic parts of these objects, and can also possibly increase the efficiency of the data acquisition process.
###### Abstract
[12] performed experiments with mineral oils and synthetic esters on disc-type windings for both natural and directed cooling modes to liquid-based transformers and found out that the synthetic ester gave more uniform flow distributions when in directed mode, while it developed lower flow rates (and, therefore, higher temperatures) in natural flow. Combined experimental and numerical analyses by [PERSON] et al. [13] and computations by [PERSON] et al. [14] inside disc-type windings showed that the operating conditions, such as loading and inlet flow rate, and the winding geometry affect the relative thermal performance of mineral oils and natural esters. The scarcity and sparseness of these results suggest that the behavior of these cooling media is far from being completely understood, since it depends also on the specific configuration and not only on the fluid itself. In this work, a CFD analysis is brought forward to assess the heat transfer performance of different coolants in a disc-type winding configuration, representative of a typical power transformer winding geometry. The reference case is taken from a previous study in the literature [15; 16], for which experimental and numerical data are available. Simulations are carried out on an axisymmetric domain using a previously validated open-source, conjugate heat transfer Finite Volume solver [17]. Heat generation in the copper and the presence of insulating paper around the windings are taken into account. Four different oils are considered, with different composition and origin: (i) the mineral oil used in reference works [15; 16]; (ii) another mineral oil (Nytro Taurus), widely established among power transformer manufacturers [7]; (iii) a synthetic ester oil (Midel 7131); and (iv) a natural ester oil (Midel 1215) [9]. The influence of temperature-dependent thermophysical properties on the overall flow, on the average and maximum temperature of the copper and on the location of the hot spots is discussed, highlighting the strength and weaknesses of each of the four oils as transformer coolants.
## 3 The Selection Parameters of the MPPT Algorithms
### Sensed Parameters  
Sensed parameters are the physical quantities that need to be measured in order to implement the MPPT algorithm. Common parameters include the output voltage and current of the PV module, solar radiation, and temperature used to track the maximum operating point. The type of parameters sensed affects the algorithm's complexity and response time.
## 5 Conclusions and Future Directions  
The proposed Round Robin with Adaptive Priority Scheduling (RRAPS) algorithm combines two well-established scheduling techniques: priority scheduling and Round Robin scheduling. The algorithm prioritizes the execution of processes by assigning a priority to each one. When a higher-priority process arrives in the ready queue, it is placed at the front of the queue to be executed as soon as possible. This ensures that essential tasks are completed promptly and meet necessary deadlines. The algorithm also uses dynamic time quantum scheduling to determine the sequence of process execution and to adjust the amount of time allocated to each process based on their needs and the current workload. The mean burst time of all processes in the ready queue is used to calculate the dynamic time quantum. This allows the system to respond to changing conditions and maintain high levels of efficiency. This approach is essential because it adapts to different scenarios and RRPAS workloads, giving a chance to the process that requires more time to finish while ensuring fair access to the CPU by all processes in the RQ. The RRAPS method combines a dynamic time quantum and priorities to balance fairness and efficiency. It prioritizes critical processes while also ensuring that all processes receive equal CPU time, making it an improved scheduling method. The major drawback of the RRAPS algorithm is the potential for starvation, where a process with a low priority may continue to run for an extended duration, causing the starvation of processes with a higher priority.  
In the near future, the RRAPS algorithm has potential for further developments, such as integration with other scheduling algorithms, such as Multi-Level Feedback Queue (MLFQ) or Earliest Deadline First (EDF), to enhance performance and adaptability to handle scheduling in multi-core or distributed systems, where processes can execute simultaneously on different nodes. This will involve developing a mechanism for coordinating the scheduling of processes across other cores or nodes. The RRAPS can be applied in real-time embedded and multimedia systems to ensure fast response time and predictability.  
Conceptualization, M.I., Z.U. and I.A.K.; Data curation, M.I., Z.U. and S.A.; Formal analysis, H.S. and M.H.; Investigation, Z.U., I.A.K., M.A.S. and A.M.; Methodology, M.I., H.S. and M.H.; Project administration, M.A.S. and S.A.; Resources, I.A.K., S.A. and A.M.; Software, M.I., Z.U. and I.A.K.; Validation, M.I., H.S., M.H. and A.M.; Visualization, Z.U. and S.A.; Supervision, I.A.K., Writing--original draft, M.I., I.A.K., Z.U. and S.A.; Writing--review & editing, H.S., M.H., M.A.S. and A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Proof-of-Concept Control Measurements  
In a further step, we created two synthetic ECG signals of common cardiac pathologies: ST segment depressions indicating myocardial ischemia on the one hand, and Long-QT syndrome (LQTS), a type of hereditary or acquired heart rhythm disorder and a relevant predisposing risk factor for arrhythmias. Again, a Gaussian jitter of 0.1-0.3 mV was overlaid to simulate artifacts. As performed with the initial physiologic ECG trace, the RF-based Boruta algorithm [25] was applied in the same way as described above, to determine which components of the modified ECGs were most important for detecting differences between the modified and the baseline ECG. By applying these control measurements, we tried to exclude the potential bias posed by the algorithm learning features other than the ones we initially modified, and aimed to demonstrate that the actual shape of the ECG only plays a minor role regarding the ability of the individual machine learning algorithms to detect subtle ECG changes despite artifacts.
# BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions
## 4 Experimental Results and Discussion  
### Experimental Platform  
To verify the effectiveness of the proposed algorithm, a test bench is implemented in a laboratory environment, as shown in Figure 2. Differing from other traditional test-rig, the proposed test bench selects two asynchronous induction motors as the driving source and the load, respectively. One motor is controlled under speed mode as the power source while the other is controlled under torque mode as the generator. A high-performance motion control system is utilized to realize the dynamic characteristics with common DC bus. Then a roller bearing fixed in a gearbox is selected for experiments. An IEPE accelerometer is fixed on the gearbox for vibration signal acquisition. The IEPE accelerometer is wired to a data acquisition card (NI 9234), and the software is programmed by LabVIEW. The details of the IEPE accelerometer are listed in Table 2. The sampling frequency is 5 kHz while the sampling point number is 5000. In addition, a torque tachometer is utilized to condition the kinetic characteristics of the test bench.  
Then, four different rolling bearings are processed by artificial damage to simulate different wearing status. In this work, the artificial damage is set in the outer races with a small slit cut. In Figure 3, the sectional dimension of the slit is set as 0.5 mm \(\times\) 0.5 mm, 1 mm \(\times\) 0.5 mm, and 1.5 mm \(\times\) 0.5 mm, respectively. Meanwhile, the depth of the slit cut is the same, which is 14 mm. These four different rolling bearings are labeled as HF, LF, MF, and FF, which represents little fault, medium fault, high fault, and fault-free. The details of the defected bearings are listed in Table 3. In particular, the rotation speed is set at 1000 and 1500 rpm. Meanwhile, the torque of the load motor is set at 0 and 1 N/m.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Technical** & **Measuring** & **Frequency Response** & **Sensitivity** & **Constant Current** \\
**Indicator** & **Range** & **Characteristic** & & **Source Excitation** \\ \hline Values & \(-\)50\(\rightarrow\)\(+\)50 g & 0.2 Hz\(\sim\)20 kHz & 93.7 mV/g & 2\(\sim\)10 mA \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the IEPE accelerometer.
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 1 Introduction
The proposed data-driven fault analysis method only relied on operation data of DTU devices instead of human experience. Thus, it was objective rather than subjective. 3. The fault analysis method was oriented towards DTU devices, which is more specific than other general methods for the whole system. To the best of our knowledge, this was the first method that investigated the fault of DTU devices. 4. A series of simulations and applications demonstrated that the proposed method can effectively reduce DTU fault incidence and shorten fault elimination time. The rest of this paper is organized as follows. Section 2 discusses the fault analysis of DTU devices. Section 3 illustrates the Eclat algorithm and evaluates three different association rule mining algorithms. Section 4 proposes a DTU fault analysis method based on Eclat algorithm. Section 5 describes the implementation of this method on realistic DTU fault databases and analyzes the outcome. Section 6 is the application of this proposed fault analysis method. Section 7 is the conclusion.
# The Effects of L-Citrulline and Malic Acid on Substrate Utilisation and Lactate Elimination
## 1 Introduction
Having said that, acute malic acid supplementation may indeed be beneficial since, in an animal model, mice improved their swimming times until exhaustion [12]. The reviewed literature [9; 10; 11; 12] clearly suggests that L-citrulline and malic acid can decrease RER and lactate values and an athlete's perceived exertion, hence enhancing sports performance. The RER value is used to describe an individual's respiratory exchange ratio. It can be measured using a gas exchange analyser, which measures the oxygen-to-carbon-dioxide ratio in the inhaled and exhaled air. This ratio enables us to draw conclusions regarding an individual's substrate utilisation [16; 17]. During exercise, the human body must catch up with the increased demand for ATP by the muscles which can be produced primarily from carbohydrates and fat but also from amino acids. When the intensity is increasing, less fatty acids can be metabolised as they can only be broken down under aerobic conditions. Therefore, the ratio of carbohydrates to fatty acids metabolised shifts to a higher amount of carbohydrates which increases the RER value. The usual resting RER value of 0.8 can increase up to 1.2-1.3 during maximal effort [18]. The RPE (rate of perceived exertion) is a subjective value that is used in sports science to measure an individual's perception of exhaustion [19; 20]. The most well-known RPE scale is the Borg scale [19] where 6 indicates no effort and 20 indicates maximal effort. The RPExHR(heart rate) value is the arithmetical product of the subjective RPE and the objective HR. Combining HRwith the RPE may give a more accurate picture of an individual's state of fatigue under a given load [21]. The aims of this study were to investigate the possible benefits of citrulline as well as malic acid supplementation on aerobic endurance. In order to clarify the often-contradictory results and quantify the possible performance-enhancing benefits of separate citrullineand malic acid supplementation, we focused on measuring RER values and blood lactate levels to gain insight into an athlete's metabolic response to the ingested supplements. Furthermore, we wanted to obtain a comprehensive picture of how the participant's perception of fatigue changed after supplementation; thus, we decided to record RPE values throughout the experiment and to use the subjective RPE rating combined with objective HR data (RPExHR to characterise the participant's sense of exhaustion. Based on the literature on this topic we hypothesised the following:  
1. L-citrulline supplementation will significantly decrease blood lactate levels during exercise;
2. L-citrulline supplementation will significantly decrease RER values during exercise;
3. L-citrulline and malic acid supplementation will significantly decrease RPE.
# Finite Element Simulation of HELICOIL(r) Inserts
## 6 Simulation Model for the HELICOIL(r) Insert
### Cohesive Zone Model
#### 6.4.1 Bilinear Model of Cohesive Zone  
The bilinear model is the simplest multi-linear model. For composite structures, it is described below (in a simplified manner):  
\[\begin{split}&\textbf{IF }0\leq u\leq u_{c}\textbf{ THEN }t=\frac{2G_{c}u}{u_{m}u_{c}},\\ &\textbf{ELSE IF }u_{c}<u\leq u_{m}\textbf{ THEN }t=\frac{2G_{c}u}{u_{m}}\frac{u_{m}-u}{u_{m}-u_{c}},\\ &\textbf{ELSE }t=0,\end{split} \tag{44}\]  
where \(t\), \(G_{c}\), \(u_{c}\), and \(u_{m}\) are the cohesive traction, cohesive energy, displacement at maximum cohesive traction, and displacement at the end of debonding, respectively. The parameters of the model are \(G_{c}\), \(u_{c}\), and \(u_{m}\).  
We assumed that the HELICOIL(r) insert can be replaced in a similar way, and the bilinear curve was used as an initial estimate for a multi-linear curve. The set of points **P** for the bilinear model was as follows:  
\[\textbf{P}=\{[u_{0},p_{0}],[u_{1},p_{1}],[u_{2},p_{2}]\}=\{[0,0],[u_{c},S_{c}],[u_{m},S_{m}]\}, \tag{45}\]  
where \(S_{c}\) and \(S_{m}\) are shear stresses and \(0<u_{c}<u_{m}\) are displacements. In Section 4, the points \([u_{i},p_{i}]\), parameters \(p_{i}\), and position of the parameters \(u_{i}\) are introduced. These correspond to the shear stress \(S_{i}\) and displacements \(u_{i}\) in the application of the HELICOIL(r) insert. Initial values were estimated from measured data (\(F_{k}^{\text{EXP}}\)-Force, \(u_{k}^{\text{EXP}}\)-displacement, and \(t_{k}\)-Time); the situation is shown in Figure 10.  
By comparing both curves, we determined \(t_{c}\approx 16\) [s], \(t_{m}\approx 27\) [s], and \(F_{c}^{*}\approx 3400\) [N], where \(F_{c}^{*}\) is the maximum force. From our simulation model (see Figure 9), we identified the area of the interface as \(A=\pi\cdot d\cdot l\approx 110\), where \(l=7\) is the length of the HELICOIL(r) insert. The measured data were related to the entire  
Figure 10: Schematic for the bilinear model of the cohesive zone and estimation of the initial parameter values for the bilinear model.  
is to address the behaviour of the cohesive zone; therefore, the data calculated according to the formula (42) were used. From \(t_{c}\), \(t_{m}\), and Table 3 we could estimate the values of \(u_{c}=0.456\) [mm] and \(u_{m}\approx 1.572\) [mm] (maximum). Regarding the solution time 20 [s], the value \(u_{m}\approx 0.92\) [mm] for the time (Table 3) was used. Furthermore, \(S_{c}=F_{c}^{*}/A\approx 31\) [MPa]. The values of the initial parameter set estimate are given in Table 7.