esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Implementation Challenges  
This section highlights a number of challenges in efficient entropy calculations within the flow recovery process:  
**Language Sensitivity:** Large packet traces may hold millions of packet events (see Figure 5), resulting in hundreds and thousands of flows [5]. Flow recovery is both memory and computationally expensive, and the time to produce an accurate flow dataset with a broad list of useful feature sets (e.g., 100 features) may take hours, depending on the implementation language and efficiency of the design. For example, an interpreted language such as python is likely to be an order of magnitude slower when compared with languages like Go, C, C++, or Rust.  
**Cache Size:** Large packet traces may hold millions of packet events (see Figure 5), resulting in hundreds and thousands of flows [5]. Flow recovery is both memory and computationally expensive and is sensitive to the composition of the packet data and length of the trace. For example, with a short duration trace from a busy internet backbone, there may be tens of thousands of flows that never terminate within the lifetime of the trace--in which case all of these flows will need to be maintained in cache memory until the last packet is processed. Conversely, a longer packet trace from a typical enterprise network may contain many thousands of flows that terminate across the lifetime of the packet trace, and so the cache size will tend to grow to reach a steady state and then gradually shrink.  
**Entropy Calculation:** to avoid multiple processing passes on the entire packet data, flow level information entropy analysis can be implemented within the flow recovery logic. As described earlier, by using the flow tuple as an index to a bidirectional hash table, symbol counts can be updated efficiently on a per packet basis. Each symbol type acts as a unique key to a current counter value. Changes in entropy are, therefore, detectable within the lifespan of the flow, by direction. We include additional measures of entropy variance by flow direction, which can be another useful indicator for major entropy deviations from baselines.  
**Real-time flow recovery:** Flows can be recovered from offline packet datasets and archives. They can also be assembled in real time, using industry standards such as NetFlow [33; 34] and IPFIX [35; 36; 31]. Since our primary interest is in recovering these features from well-known research datasets, we do not implement real-time recovery of payload entropy from live packet captures. Prototyping, however, suggests that using our implementation in a compiled language with controlled memory management (such as Rust or C++) is practical (real-time performance (without packet drop) will depend, to some extent, on how many other features are to be calculated alongside entropy, and the complexity of those calculations). It is also possible to avoid some of the time and memory complexities of flow recovery if we only want to record flow level payload entropy from live packet data by using simpler data structures, although protocol state handling is still required [28].
# Performance Analysis of FSO-UWOC Mixed Dual-Hop Relay System with Decode-and-Forward Protocol
## 3 Statistical Characteristics for End-to-End Instantaneous SNR  
For the FSO-UWOC mixed dual-hop transmission system employing the DF relay scheme, the signal transmitted from the source node S is decoded and re-encoded into a new transmission signal at the relay node R. As a result, the FSO link and the UWOC link are statistically independent. Degradation in the performance of either link can lead to failure of the entire dual-hop system. Consequently, the end-to-end equivalent instantaneous SNR at the destination node D is determined by the minimum instantaneous SNR of the two hops [32]. Specifically, it can be expressed as:  
\[\gamma^{DF}=\text{min}(\gamma_{1},\gamma_{2}) \tag{26}\]
# Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm
## 2 Related Work
### Fingerprint-Algorithm-Based Positioning  
Fingerprint-algorithm-based positioning consists of two phases. In the training (offline) phase, the characteristics of signals are collected and stored in a database for each sample point in the service area. In the positioning (online) phase, the location of an end-device is estimated and provided through a database search.  
It is relatively easy to collect Wi-Fi signal strengths from many access points. Wi-Fi access points are present in most indoor environments, Wi-Fi signals of even private Wi-Fi networks can be freely measured [16]. The service area of Wi-Fi is smaller than that of LoRa, and therefore, it is unsuitable for outdoor positioning. Whereas a Wi-Fi signal can reach distances of only less than 100 m, a LoRa signal can reach distances of a few kilometers.  
Smartphones receive downlink signals from access points for the positioning in Wi-Fi networks as shown in Figure 5a. Figure 5b shows that LoRa uplink signals are transmitted from an end-device to gateways and not vice versa, and therefore, only the owner of the LoRa network can access LoRa signal strength information. As a result, few studies have investigated positioning methods that used the fingerprint of LoRa RSSI.  
[PERSON] et al. constructed RSSI fingerprint data for SigFox and LoRaWAN and compared their positioning errors using a k-nearest neighbors (k-NN) method. As a result, they obtained an average accuracy of 398.4 m using 11 nearest neighbor gateways [17]. Their study used LoRa RSSI data measured at several gateways. However, they did not adopt interpolated fingerprint maps and an inference method for the maps.  
Among various LPWA methods, we focused on LoRa fingerprint-based positioning methods because of their open standard and unlicensed band frequencies. SigFox is another LPWA method that uses unlicensed band frequencies; however, it is a closed network and is not available without permission from a network service provider.  
In our positioning method, every uplink packet has a sequence number, timestamp, and device ID. The firmware of LoRa end-devices and gateways should be modified to send uplink packets and to process them for positioning. End-devices send uplink packets to gateways for positioning, following which RSSI information is accessed at the gateways. The LoRa positioning process is executed on the server because it requires considerable computing power.  
Figure 5: Comparisons of packet flow between Wi-Fi and LoRa positioning methods: (a) downlink packet from AP to smartphone and (b) uplink packet from end-device to LoRa gateways.
# Research and Verification of Trajectory Tracking Control of a Quadrotor Carrying a Load
## 3 Controller Design
### Swing Angle Controller Design  
The swing angle of the hanging load cannot be directly controlled due to its under-driving characteristics. It needs to be converted into a position signal for indirect control by the controller. The swing angle controller is also a neural network adaptive integral backstepping controller. The design process is similar to the above, therefore, we can omit the detailed process and describe the abbreviated process as follows  
The first step is to define the pendulum angle error as:  
\[\left\{\begin{array}{l}e_{13}=\alpha_{d}-\alpha\\ e_{15}=\beta_{d}-\beta\end{array}\right. \tag{48}\]  
The expected intermediate virtual control amount is:  
\[\left\{\begin{array}{l}e_{14}=k_{13}e_{13}+\dot{a}_{d}+\mu\gamma_{7}\gamma- \dot{a}\\ e_{16}=k_{15}e_{15}+\dot{\beta}_{d}+\mu_{8}\gamma_{8}-\dot{\beta}\end{array}\right. \tag{49}\]  
According to the above derivation process, the swing angle control law is:  
\[\left\{\begin{array}{l}u_{a}=\dfrac{\left[\left(1-{k_{13}}^{2}+\mu_{7} \right)e_{13}+(k_{13}+k_{14})e_{14}-k_{13}\mu_{7}\gamma_{7}+\ddot{a}_{d}-\dot{d }_{a}-\frac{2\sin\beta\dot{\beta}\dot{a}}{\cos\beta}\right]}{M\cos\beta}\\ u_{\beta}=\dfrac{\left[\left(1-{k_{15}}^{2}+\mu_{8}\right)e_{15}+(k_{15}+k_{16}) e_{16}-k_{15}\mu_{8}\gamma_{8}+\ddot{\beta}_{d}-\dot{d}_{\beta}+\sin\beta\cos \beta\beta^{2}\right]M\L}{U_{1}}\end{array}\right. \tag{50}\]
## 2 Theoretical Background and Preliminaries
### Off-Line Results  
In this section, the trained ANN has been simulated. The output of the controller was saved and used for off-line simulation. Figure 7 represents the levitation characteristics (input control voltage and output position). Ball position of real system and ANN model are shown. The steady state position for real model is 0.01085 m. It is clearly shown that the ANN model tracks the input in steady state position, (0.0109 m) with about 0.5% error. However, in the transient regions there is considerable offset of about 7% error.  
Figure 7 includes data when the ball is hit to create an external disturbance. The reason ANN response is seen in the present of disturbance is that the input of ANN (control output) is the same as real-model input. However, it is not realistic and the individual control output is used to control the ANN model in the real life experiment. The approximation of error in this noteworthy experiment gives mean square error (MSE) equal to \(2.33\cdot 10^{-7}\). The duration time of simulation is 30 s. The MSE  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Sample rate & 0.001 s \\ Simulation time & 30 s \\ Hidden layer & 12 neurons \\ X, input & Output of controller \\ Delay (external input for control current) & 1 \\ T, target & Output of system (ball position, velocity and coil current) \\ Iteration & 1000 \\ Reference signals & Pulse, Sine, Chirp (1 Hz to 6 Hz) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of the neural network.  
Figure 5: Neural network model with [PERSON]’s filter.  
is calculated as sum of difference between the samples of real ball position and ANN ball position dividing by number of samples (30,001 samples).  
Figure 6: Overview of three parts, hardware, ANN model and Mathematical model.  
Figure 7: Input and output signals to train the NARX model for closed-loop model identification.
## 4 Results
### Algorithm Performance Analysis
#### 4.2.1 Descriptive Statistics  
The samples related to thirty executions of algorithms are analyzed by using descriptive statistics metrics, such as the mean, the media, the standard deviation (std), and the maximum (max) and minimum (min) values of the objective function. In addition, the number of Non-Feasible Solutions (NFS) at the end of the optimization process through the thirty executions is also included to know whether the constraint-handling technique shows some issue to leave the infeasible region. Study case 1:  
Table 5 shows the descriptive statistics of samples for study case 1 using trajectory 1 in the synthesis process. The columns are related to the analyzed algorithm, the descriptive statistics metrics, and the NFS for each sample. Boldface indicates the minimum value for each column. According to the results in Table 5, all metaheuristic algorithms find feasible solutions through the thirty executions (see NFS metric). R1B-NCH, R1B-SR, and R1B-PF obtain the three best solutions in that order (see column \"min\"). R1B-NCH gives the best algorithm performance finding solutions. The obtained solution in R1B-SR and R1B-PF presents an increment of 37.88% and 41.94%, respectively, with respect to R1B-NCH (see column \"min\"). In addition, the mean and minimum results are less in R1B-NCH than other algorithms, and the standard deviation, the median, and maximum values present an average behavior. Table 6 shows the descriptive statistics of the samples for study case 1 using trajectory 2 in the synthesis process. The columns of such tables contain the same terms explained above. In this analysis, the R1B-NCH algorithm reliability is studied under trajectory changes to be followed by the four-bar linkage mechanism. According to the results in Table 6, all metaheuristic algorithms find feasible solutions (see \"NFS\" column). MUMSA-\(e\)C, R1B-FR, and R1B-NCH obtain the three best solutions in that order. In spite of R1B-NCH presenting the third position, the difference with respect to the MUMSA-\(e\)C is minimal at around 2.59% (see \"min\" column). Moreover, the R1B-NCH algorithm has the best mean value, indicating the consistency of results through different executions. The standard deviation, the median, the maximum, and minimum values of the objective function have average performance in the R1B-NCH with respect to other algorithms. Study case 2:  
Table 7 shows the descriptive statistics for study case 2 using trajectory 1 in the synthesis process. The columns of such tables contain the same terms explained above. According to the results in Table 7, all metaheuristic algorithms find feasible solutions except for the PSO-FR and R1B-SR algorithm (see \"NFS\" column). R1B-NCH, MUMSA-FR, and MUMSA-eC obtain the three best solutions (see \"min\" column). R1B-NCH gives the best algorithm performance finding solutions due to the mean, the median, and the min value of the best objective function being less than other algorithms. The standard deviation and the maximum value of the objective function also present a lower value than the average in the proposal.
## 5 Application
### Cyber-Security Techniques and Practices  
An industry's digital system faces threats of all shapes and sizes. Thus, they should be ready to defend, identify and respond to a full range of attacks. The security strategy needs to be able to address the various methods these cybercriminals might employ. The process of the method is depicted in Figure 5.  
Following are fourteen different security practices and techniques:  
1. **Access control (AC)** If the cyberattacker is unable to access your industrial network, then they will do very limited harm. _AC_ limits user access according to their responsibilities, which increases the security, and especially, internal breaches are restricted. \[\left(AC,[0.455,0.575]e^{i[0.305,0.540]\pi},[0.100,0.180]e^{i[0.009,0.270] \pi}\right)\]  
Figure 5: Flowchart of the process for selecting the best cyber-security.  
Anti-malware software (_AMS_) Viruses, Trojans, worms, key-loggers and spyware are all malwares, which are used to infect digital systems. Anti-malware software identifies risky programs and files, then prevents them from spreading.  
\[\left(AMS,[0.560,0.610]e^{i[0.350,0.460]\pi},[0.350,0.390]e^{i[0.300,0.450]\pi}\right)\]  
Anomaly detection (_AD_) Identifying anomalies is a difficult task. Henceforth, anomaly detection engines (ADE) are designed that allow the analysis of an industrial network. This alerts the authorities when breaches occur so that they can respond at the right times.  
\[\left(AD,[0.295,0.385]e^{i[0.390,0.480]\pi},[0.270,0.350]e^{i[0.250,0.330]\pi}\right)\]  
Application security (_AS_) An _AS_ establishes security parameters for any applications that are relevant to industrial security.  
\[\left(AS,[0.250,0.340]e^{i[0.300,0.375]\pi},[0.280,0.400]e^{i[0.220,0.300]\pi}\right)\]  
Data loss prevention (DLP) \(Dlp\) equipment and strategies protect employees and users from ill-use, such as giving away sensitive data.  
\[\left(DLP,[0.360,0.425]e^{i[0.290,0.380]\pi},[0.310,0.385]e^{i[0.200,0.300] \pi}\right)\]
## 3 Results
### Comparison of the Surface Properties after Gas Nitriding and Facing
This relationship cannot be derived from the standard roughness values \(Ra\) and \(Rz\) summarized in Table 4, which reveal higher roughness values for the machining parameter f2. When machining nitrided coatings using CBN-tipped tools, higher \(Vvv\)-values are reached. Machining the coating with the lower feed increases the proportion of the mentioned surface defects strongly. Similar results regarding the machining with different feeds are also shown in [11]. The higher feed results in higher components of the resultant force while machining due to the increased cross-section of the undeformed chip. Simultaneously, however, the tool-workpiece contact area also increases. Hence, there is a changed distribution of these force components, resulting in less pulled-out coating material. Based on the results, an additional surface hardening process in combination with adjusted machining parameters can significantly influence the surface properties of thermal spray coatings. A superior wear behavior of the coatings due to the surface hardening still has to be proven. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Surface State**} & \multirow{2}{*}{**Machining Parameter**} & \multicolumn{2}{c}{**Roughness (\(\mu\)m)**} \\  & & \(Ra\) & \(Rz\) \\ \hline As-sprayed & - & \(14\pm 1.9\) & \(83\pm 11.5\) \\ Finished & f1 & \(1.3\pm 0.1\) & \(7.8\pm 1.1\) \\ Gas nitrided and finished & f1 & \(1.8\pm 0.4\) & \(19\pm 2.7\) \\ Gas nitrided and finished & f2 & \(1.5\pm 0.4\) & \(17\pm 2.4\) \\ Gas nitrided and finished & f3 & \(1.6\pm 0.3\) & \(20\pm 4.2\) \\ Finished and gas nitrided & f1 & \(1.4\pm 0.2\) & \(8\pm 1.8\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Surface roughness of the arc sprayed 17 Cr-steel coatings before and after different post-treatments. Figure 5: Abbott curves of the finished surfaces before and after gas nitriding. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Surface State** & **Machining Parameter** & **Valley Void Volume \(Vvv\) (mL/m\({}^{2}\))** \\ \hline Finished & f1 & 0.296 \\ Gas nitrided and finished & f1 & \(1.19\) \\ Gas nitrided and finished & f2 & \(0.485\) \\ Gas nitrided and finished & f3 & \(0.334\) \\ Finished and gas nitrided & f1 & \(0.279\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Determined valley void volume (\(Vvv\)) after machining.
# Constitutive Characteristics of Rock Damage under Freeze-Thaw Cycles
## 3 Preparation of Samples and Their Associated Physical Characteristics
### Rock Porosity and Saturation Density  
Concurrently, both the quality and wave velocity of the saturated rock samples were measured following each freeze-thaw condition; the values in the figure are the average values of each group of rocks under the same number of FTCs, as shown in Figure 2.  
As depicted in Figure 2, the physical and hydraulic properties of rock samples exhibit variations with the number of FTCs. However, these differences are relatively minor, and the overall trend remains consistent. Initially, at 0 FTC, the porosity of the rock samples stands at 8.78%, suggesting the presence of a significant number of pore structures under natural saturation. As the samples undergo FTC, the water within experiences a phase transition from water to ice, leading to the expansion of existing pores and cracks, as well as the formation of new pores. Consequently, the porosity increases to 10.80% after 40 FTCs, representing a 23.01% increase. Concurrently, the saturated density of the rock samples decreases with the increasing number of FTCs. This decrease is primarily attributed to the loss of mineral particles on the sample surface, resulting in a reduction in the saturated mass. Additionally, the expansion of the rock volume due to the development of cracks caused by the frost heave force contributes to this decrease. Notably, within the first 20 FTCs, the saturation density of the samples decreases significantly, from 2.44 g\(\cdot\)cm\({}^{-3}\) to 2.38 g\(\cdot\)cm\({}^{-3}\). The porosity of the rock samples continues to increase with the increasing number of FTCs, and the growth rate of porosity during the first 20 cycles is significantly higher than that in subsequent cycles. This observation suggests that FTC not only increases the number of pores in the rock but also significantly enhances the openness and connectivity of these pores.
## 3 Results and Discussion
### Effects of EGR Rate
#### 3.2.2 Engine Performance  
Figures 11 and 12 show the effect of increasing the EGR rate on BSFC and BTE at high and low injection pressures. As shown, fuel consumption was generally higher at 450 bar injection pressure, and D100 exhibited a lower BSFC under all conditions due to its lower calorific value. While the injection pressure improved the atomization conditions, it also shortened the penetration length of the fuel, resulting in a slight lack of oxygen in the air, leading to more fuel consumption [23]. It may also be that the oxygen in palm oil improved the combustion quality, making up for the lack of calorific value to some extent. With the increase of EGR rate, the consumption of P50D50 was closer to that of D100. Under each injection condition, the BSFC of the two fuels increased with the increasing EGR rate, while BTE showed the opposite trend. The low oxygen concentration in the exhaust gas and the large specific heat capacity greatly reduced the peak combustion temperature in the cylinder, which may be the main reason for reducing its BTE [35; 37]. In addition, according to the curve shown in Figure 12, EGR had a greater impact on BTE under a low injection pressure. At 300 bar injection pressure, a 20% EGR rate reduced the efficiency of D100 and P50D50 by 2.3% and 3.4%, respectively, but at 450 bar, it only reduced fuel efficiency by 1.9% and 0.9%, respectively.  
As shown in Figure 13, the EGT of all fuels showed a greater temperature rise with the introduction of EGR; especially, when a 20% EGR rate was introduced, the temperature reached the maximum. It is probably because EGR diluted the oxygen entering the cylinder and prolonged the ID period, thereby compressing the combustion time, which resulted in high EGT [38]. This performance is slightly different from that observed in other  
Figure 11: BSFC under different EGR rates at 300 and 450 bar.  
Figure 12: BTE under different EGR rates at 300 and 450 bar.  
studies [35; 39]. It was found that EGT decreased with the growth of EGR rate, mainly because EGR reduced the temperature in the cylinder. Furthermore, it can also be observed that P50D50 was more sensitive to EGR. At 450 bar and 20% EGR rate, the EGT growth rate of P50D50 slowed down, while the EGT growth rate in other conditions was mostly linear.
## 4 Simulation Results
### Case (1): Holographic Optical Tweezers Manipulate Control for Single Cells
Due to the large error between the actual position of the cell and the expected position at the beginning, the number of layers and blocks of NSOFCMNN are adjusted from three layers and three blocks to seven layers and four blocks. When the error continues to converge, the number of network layers is reduced to four layers, and the number of blocks is reduced to two layers, which improves the learning efficiency of the proposed NSOFCMNN until the end of the simulation experiment. The simulation results in this section also demonstrate that the proposed NSOFCMNN can effectively adjust the network structure in real time according to the input data.
# The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array
## 3 Experimental Results
### Comparison Experiment  
In order to verify the effect of system calibration, a comparison experiment were carried out in the same laboratory environment. The direction of the elliptical paraboloid array was adjusted to be parallel to the Y direction of the CMM. Correspondingly, the optical slope sensor was mounted on the quill to move in the Y direction. The measured data were processed with the calibration results and the original design values as reference values in turn. The comparison results in the moving direction are shown in Figure 11. As shown in Figure 11, the error of the measurement results before calibration is less than 100 \(\upmu\)m due to the influence of installation and manufacture. However, the experimental results after system calibration show that the measurement error is controlled within 3 \(\upmu\)m in the moving direction, which satisfies the requirement of the displacement measurement accuracy. Therefore, we can say that the necessity and correctness of system calibration have been proven by the comparison experiment. In fact, the calibration experiment results compensate for the displacement measurement error of the system in a sense.  
Figure 11: Comparison experiment.  
Figure 10: Repeatability of the calibration experiment.