esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 BDS-3 SAIM Monitoring Method  
### Current Monitoring Methods  
BDS-3 SAIM monitors the health status of navigation signals and satellite clocks by continuously monitoring its own broadcast signals from navigation satellites. SAIM includes downlink navigation signal quality monitoring and time-frequency stability monitoring. Among these, downlink navigation signal quality monitoring involves monitoring of the signal power, the pseudo-range, code phase consistency and the correlation value. Time-frequency stability monitoring involves monitoring of the frequency hopping and phase hopping of the satellite's atomic clocks. Finally, the integrity information of the navigation signal is generated by integrating the monitoring information of the satellite's atomic clock and navigation signal [7].  
The downlink navigation signal quality monitoring function of SAIM is to identify the abnormal situation of the carrier to noise density of the user receiver caused by the decrease of the navigation signal power through power monitoring; through pseudo-range monitoring, identify the abnormal jump of the pseudo-range observed by the user. Moreover, due to the different paths used for generating the frequency signal of the pseudo-range and carrier phase on the satellite, if any path produces an anomaly, the pseudo-range and carrier phase will be inconsistent, which would affect the high-precision positioning of users. Therefore, such anomalies can be identified by monitoring the code phase consistency. Correlation peak monitoring [18] uses three pairs of narrow correlators to obtain three pairs of correlation values, monitor the symmetry of the signal correlation peak and realize the monitoring of the signal's pseudo-range deviation. At present, the code phase consistency monitoring and correlation peak monitoring algorithms are still in the on-orbit test phase and have not yet actually been involved in monitoring the integrity of the system. The time-frequency stability monitoring function of SAIM monitors the stability of the satellite's atomic clock by monitoring the frequency and phase jump of the satellite clock.  
Due to the absence of propagation and environmental errors and noise, SAIM monitoring data usually fluctuate around a certain mean. If the navigation signal is abnormal, resulting in an abnormal jump in some monitoring data that exceeds the alarm threshold, SAIM will issue an alarm for that navigation signal. SAIM alarm modes consist of the message integrity alarm mode and the non-standard code integrity alarm mode. In the message integrity alarm mode, if SAIM detects an anomaly, the satellite will set the signal integrity flag ('SIF') parameter in the signal navigation telegram as '1' independently. After rectification of the anomaly has been confirmed by the ground system, the 'SIF' parameter will be reset to '0'. In non-standard code alarm mode, if SAIM detects an anomaly, the satellite will independently switch the spread spectrum code of the signal to the non-standard code, so that the user cannot receive the navigation signal. The current SAIM used on BDS-3 satellites is show in Figure 1.
# RSB: Robust Successive Binarization for Change Detection in Bitemporal Hyperspectral Images
## 3 Numerical Experiments
### Preliminary Discussion  
In Figures 5-8, the image \(\widetilde{I}_{i}\) is shown, for \(i=1,\ldots,6\), for all the considered datasets, by varying the adopted error function \(f_{i}\) with \(i=1,\ldots,6\). After this first step, for the _Herniston_ dataset, it is clear that \(f_{1}\) and \(f_{2}\) are producing some relevant noise around the changed areas, while \(f_{3}\) and \(f_{4}\) produce a better output but also the river silhouette appears at the bottom of the images. Regarding \(f_{5}\) and \(f_{6}\), these two functions produce the best output. Although there are pixels that could be wrongly classified if \(f_{1}\), \(f_{2}\), \(f_{3}\), and \(f_{4}\) were considered separately; after step 7 of the proposed algorithm in Section 2, the wrong detections would be completely discarded.  
Regarding the _USA_ dataset, the functions that produce the best output, according to the given ground truth, are \(f_{1}\) and \(f_{2}\). All the others fully highlight the river at the bottom of the image. In particular, the worse output is given by \(f_{3}\) and \(f_{4}\), while the function \(f_{5}\) produces a less visible river shape and some interesting details in the upper part of the image, and the function \(f_{6}\) yields a too clean output in the upper part of the image and a still visible river shape at the bottom of it.  
Regarding the _River_ dataset, all the assayed functions produce a reasonable output. Only \(f_{6}\) gives a very dark image as the \(\rho\) values for the considered spectral row vectors are found to be very small.  
Finally, in the _Bay Area_ dataset, after suitably scaling each spectral vector of \(I_{2}\) within the same range of values of the corresponding spectral vectors in \(I_{1}\), for the error functions \(f_{i},i=1,\ldots,4\), a gamma correction with \(\gamma=0.2\) to enhance brightness was necessary to improve the frequency ranges.
## 3 Materials and Methods
### EMI Shielding Measurements  
The measurements of scattering parameters were performed using the waveguide variant of the transmission/reflection method [31; 32; 33; 34] by means of the test set shown in Figure 2. The test set consists of two sections (W) of the WR-90 waveguide that are both 250 mm in length and two broadband SMA/WR-90 transitions (T). The sample under investigation is placed in a fixture (S) shown in Figure 2b and connected to ports 1 and 2 of the WR-90 waveguide. Therefore, it is placed directly between the flanges of the waveguides (W) at the symmetry plane of the test set. The applied waveguides operate in the X band range of frequencies with the fundamental mode TE\({}_{10}\).  
The scattering parameters of interest can be determined with the vector network analyzer (VNA) directly connected to ports SMA1 and SMA2 of the test set. Since the application of the VNA without additional instrumentation for measurements of materials with high shielding effectiveness showed a strongly increased noise level, the following measures were applied: narrowing the intermediate frequencies (IF) bandwidth of the VNA, averaging the measurement results, and increasing the meter signal source power to the maximum. Additionally, two 35 dB cascaded multistage amplifiers (Amplica XM564302) operating in the frequency range from 6 to 12.4 GHz were applied. Due to the large number of stages, the amplifiers are treated as unilateral. Figure 3 shows the setup, including the amplifiers connected to the output port of the waveguide part of the test set.  
\begin{table}
\begin{tabular}{c c c} \hline
**Name** & **Description** & **Micrograph** \\ \hline Magnetic PLA & 3 layers, FDM-printed & \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: (a) Transmitting/reflecting waveguide measurement test set with the fabric sample inserted to be characterized; (b) fabric fixture.  
In Figure 4, the signal flow graph of the whole system is presented. Due to the unilaterality of the amplifier, \(S_{\rm 21 AM}=0\) is assumed. Using the measured parameters of the \(S_{\rm TA}\) matrix to characterize the whole system, the scattering matrix \(S_{\rm TS}\) of the waveguide test set can be determined as  
\[S_{\rm 1 ITS}=S_{\rm 22 TS}\approx S_{\rm 1 ITA}, \tag{13}\]  
\[S_{\rm 2 ITS}=S_{\rm 12 TS}\approx\frac{S_{\rm 2 ITA}}{S_{\rm 21 AM}}(1-S_{\rm 11 AM}S_{ \rm 1 ITA}), \tag{14}\]  
where \(S_{\rm AM}\) is the scattering matrix of the amplifier.  
High standard \(SE\) measurements impose numerous quality requirements on the experimental setup. Since the entire signal path must be electromagnetically tight, precision coaxial connectors, semi-rigid cables, precise waveguide-coaxial transitions, precise waveguide sections, and fabric fixture are required. For waveguides, the most important elements are the quality of the flange surface and tight flange-waveguide and flange-flange connections.
## 2 Related Work  
### UWB Localization  
UWB localization is a technology that performs precise location estimation using ultra-wideband. UWB localization generally uses the Time of Arrival (ToA) [23; 24; 25] and Angle of Arrival (AoA) [26; 27; 28] to perform distance and angle-based positioning, so the positioning principle is the same as other wireless communication positioning systems. However, UWB technology has the characteristic of enabling more-accurate positioning by utilizing a highly wide bandwidth and transmitting pulse signals with a fairly short width [16; 29].  
UWB positioning has the following three major strengths compared to other systems. First, unlike packet-type messages used in other RF-based positioning, the UWB system uses pulse signals with a very short time width, so it performs accurate positioning based on a high time resolution [30; 31]. Second, the UWB positioning system has a relatively weak degree of multipath interference [32; 33]. Therefore, it has the strengths of robustness in environments with many obstacles and high noise levels. Since the UWB system communicates with very short pulse signals of tens of picoseconds to several nanoseconds, it is possible to distinguish between signals even if there is only a slight difference in the path distance between direct and reflected waves. In theory, in a UWB system using pulses with a width of 500 ps, if the signal path difference is more than 15 cm, the two pulse signals can be distinguished and do not cause mutual interference. Third, the UWB system utilizes ultra-wideband and has a low energy level, so it has good signal penetration. Therefore, it has the advantage of being strong against signal decline even in an environment with many obstacles, smooth connection between devices, and high-speed data transmission.  
However, the UWB positioning system does not have only advantages. Designing and implementing a positioning system can be more expensive and complex compared to RFID,Bluetooth, and other sensor-based networks [34; 35]. Additionally, technical regulations and standardization are different in many countries, which can cause problems with frequency spectrum management [28; 36]. In terms of security, it shows weaknesses like other wireless-communication-based positioning systems [37; 38]. In particular, malicious users can spoof UWB signals to forge or alter data, which can seriously impair positioning performance. Therefore, in order to apply UWB positioning to large-scale applications, the cost, regulatory, and security aspects must be considered.
# Comparison Study of the PSO and SBPSO on Universal Robot Trajectory Planning
###### Abstract  
Industrial robots were modified over the years. The benefit of robots is making production systems more efficient. Most methods of controlling robots have some limitations, such as stopping the robots. The robot stops by various reasons, such as collisions. The goal of this study is to study the comparison of improving the Artificial Potential Field (APF) by the traditional Particle Swarm Optimization (PSO) algorithm and the Serendipity-Based PSO (SBPSO) algorithm to control the path of a universal robot UR5 with collision avoidance. Already, the metaheuristic algorithm kinds deal with a premature convergence. This paper presents a new approach, which depends on the concept of serendipity and premature convergence applied to the path of the universal manipulator UR5 and also compares it with traditional the PSO. The features of the SBPSO algorithm prototype are formalized in this paper using the concept of serendipity in two dimensions: intelligence and chance. The results showed that the SBPSO is more efficient and has better convergence behavior than the traditional PSO for controlling the trajectory planning of the URS manipulator with obstacle avoidance.  
Particle Swarm Optimization (PSO); Serendipity-Based PSO (SBPSO); Artificial Potential Field (APF); serendipity; premature convergence; scout particles Article
## 3 Results
### Emission Characteristics  
#### 3.3.1 CO emissions  
Although NOx and smoke are the main harmful emissions emitted from diesel engines, with the gradual strengthening of automobile exhaust emission regulations, CO and HC emissions have also received extensive attention. Therefore, in this study, the impacts of different concentrations of bioethanol on CO, HC, NOx, and PM emissions from a CRDI diesel engine under various loads were compared and investigated. Figure 7 shows the CO emissions for all test fuels according to different engine speeds. CO emission is mainly caused by incomplete combustion of fuel caused by insufficient oxygen. As shown in Figure 7, it can be clearly seen that CO of all test fuels shows a decreasing trend with the increase of engine load. At 60 Nm, the CO of D100, D95E5, D90E10, and D85E15 is decreased by 33.48%, 37.33%, 36.12%, and 40.76% compared with that at 30 Nm, respectively. At 90 Nm, the CO of D100, D95E5, D90E10, and D85E15 is decreased by 8.48%,  
Figure 5: Maximum heat release rate for all test fuels according to various engine loads.  
Figure 6: Maximum heat release rate for all test fuels according to various engine loads.  
28.57%, 31.67%, and 31.09% compared with that at 30 Nm, respectively. This is because the increase of load leads to the increase of temperature and pressure in the cylinder, which is conducive to the oxidation reaction of CO [27]. On the other hand, the addition of ethanol also reduces CO emissions as a whole, compared with diesel fuel. Especially under high load, the reduction of CO is the largest. For example, at 90 Nm, the CO of D95E5, D90E10, and D85E15 is decreased by 28.24%, 30.09%, and 24.07% compared with that of D100, respectively. This is because more air and fuel enter the cylinder under higher load, resulting in higher combustion temperature and accelerating the oxidation of CO. In addition, ethanol is an oxygenated fuel, which further promotes complete combustion [33].
## 3 Results and Discussion
### Degree of Hydrolysis and Peptide Length  
The degree of hydrolysis refers to the percentage of hydrolyzed peptide bonds and depends on several factors, such as the initial amount of protein, the type of enzyme used in the hydrolysis, the duration of the hydrolysis, temperature and pH. For all of the by-product mussel hydrolysis in this study, the degree of hydrolysis was between 2.41% and 7.55% \(\pm\) 0.6% as assayed using the pH stat method, shown in Figure 1. For samples KHN18, MBF19 and KHM19, the degree of hydrolysis values were positively correlated with hydrolysate yields. For other samples no such correlation was observed, which could be a result of these samples containing byssus threads or empty broken shells that were separated from the final hydrolysate product and did not contribute to the final yield. A lower degree of hydrolysis is often associated with production of larger peptides having more than seven amino acids, whereas a greater degree of hydrolysis leads to smaller di- and tri-peptides [31]. Similar results were reported by other researchers using Protamex(r) and hydrolysis of bone extract. Protamex(r) leads to a lower degree of hydrolysis and peptides with higher molecular weights compared to other proteases such as Flavourzyme(r)[32]. In the 3-kDa fraction generated following filtration in this work, about 57% of the peptides that were characterised consisted of seven amino acids, whereas the remaining 43% were larger peptides with more than seven amino acids and up to 16 amino acids in length. No di- or tri-peptides were detected in the sequenced samples due to the mass tolerance setting and m/z range selected in the MS method.
## 3 Wind Tunnel Experimental Results  
Aeroelastic testing was carried out at the DICCA laboratory at the University of Genoa, Italy. The DICCA wind tunnel is a closed-loop subsonic circuit tunnel with a cross-section of 1.70 (width) \(\times\) 1.35 (height) m. A total of three different wind angles were investigated, 0\({}^{\circ}\) (along wind), 45\({}^{\circ}\) and 90\({}^{\circ}\) (across wind), according to a suburban Terrain I velocity profile. Correction factors were used to take blockage effects into account. The factors ranged between 1.028 (for 0\({}^{\circ}\) and 90\({}^{\circ}\)) and 1.04 (for 45\({}^{\circ}\)), [23] and were applied to the velocity and acceleration scale factors. Nine accelerometers monitored acceleration signals. Six accelerometers were located on six floors at z/H = 0.17, 0.33, 0.5, 0.67, 0.83; one accelerometer was linked to the wind tunnel basement; and two accelerometers were located on the top floor of the model. A total of thirteen different velocities were tested, ranging from 5.4 to 112 m/s at the building top at prototype scale.  
Accelerations were measured at a sampling frequency of 1000 Hz for a time length of 180 s. At the prototype scale, this corresponds to a time step of 0.05 s and a time length of 9000 s.  
The entire process, to investigate the variability of magnitudes such as singular values or eigenvectors, was subdivided into fifteen 10 min long recording sessions [24, 25, 26, 27, 28, 29]. Figure 2 shows two examples of processes with \(U\) = 49.0 m/s, along and across wind, subdivided in sub-processes. The along versus across wind acceleration point cloud graph shown in Figure 3 shows that the difference between sub-processes (in the 10 min records) is relevant because it changes significantly as for example with T [0; 9000] (Figure 3c), T [2400; 3000] (Figure 3d), T [7200; 7800] (Figure 3e) and T [840; 9000] (Figure 3f) [24, 25, 26, 27, 28, 29].  
Figure 2: Ten minute accelerometric signal records at the building top with \(U\) = 49.0 m/s, along wind (0\({}^{\circ}\)) (**a**) and across wind (90\({}^{\circ}\)) (**b**).
## 3 Results and Discussion
### The Behavior of VOCs in the Food Samples After Irradiation
#### 3.2.1 Aldehydes  
It was found that aldehydes, which are considered to be among the main substances responsible for the specific odor of meat, fish, and poultry products [42], constitute the majority of VOCs identified in food samples. While acetaldehyde was detected in all food samples, hexanal, 2,3-butanedione, and ethanol were found in animal products.  
The concentration of acetaldehyde, formed by the decomposition of various fatty acids and alcohols often present in meat products [42; 43], in irradiated beef and poultry samples decreased with an increase in the irradiation dose. This can be due to the decomposition of acetaldehyde under the action of accelerated electrons during e-beam irradiation. The content of acetaldehyde in salmon samples increased nonlinearly with the higher dose,  
Figure 4: Chromatograms of beef (**A**), turkey (**B**), fish (**C**), and chicken (**D**) samples exposed to a dose of 1 kGy.  
which is a sign of intensive oxidation of fatty acids that salmon is rich in. It should be noted that acetaldehyde was not detected in non-irradiated potato samples but was found in irradiated potato samples, which can be an indicator of carbohydrate decomposition.  
Hexanal, characterized by a strong grassy odor, is formed by the oxidation of unsaturated fatty acids in meat, but can also be derived from the hydrolysis of triglycerides or the degradation of amino acids [42]. In all samples of meat products, an increase in hexanal content with irradiation dose was observed, which may indicate intensive oxidative processes of fatty acids caused by irradiation. In potato samples, this compound was absent, which is due to the low fat content in plant products.  
The compound 3-methylbutanal, with an unpleasant but low-intensity odor, is an oxidation product of the amino acid isoleucine, which is contained in the amino acid sequence of proteins [44]. This compound was detected in non-irradiated and irradiated beef, salmon, and potato samples, and increased with a higher irradiation dose, which is a sign of protein oxidation.
## 5 Performance Evaluation
### Simulation Results: Different Parameters' Effects on Replication Attack Detection  
The DT method evaluates key factors recognized as true positive, false positive and false negative. The DT based approach involves three major security parameters; true positive, false positive and false negative. Furthermore, it also shows the impact of other performance parameters such as detection accuracy, communication overhead, memory overhead, detection rate, energy, effect of lambda, network life time, detection time, delay impact and comparison of SPRT (centralized approach) and XED (distributed approach) in detail. The outputs show the average value for 20 simulation runs, and it uses the average or stabilized value to analyze the replication attacks performance. The parameters used for detection of clones to evaluate the hybrid process are defined and discussed as follows.
## 4 Our Proposed Routing Algorithm
### Route Maintenance Phase  
If there were a transmission link failure in sensor network, the data communication in the corresponding transmission link will be truncated, causing network partition and isolated nodes. In other words, transmission link breaks have an important influence on data communication. Many factors can cause a link break. First, if any one sensor node hardware facility in the transmission link is damaged, it will cause the disruption in the corresponding transmission link. Second, the residual energy of each sensor node reduces gradually. The energy of a sensor node in the transmission link may be not enough to forward data packets, thus its corresponding transmission link will also be truncated, causing a certain area to be unreachable.  
Therefore, considering the residual energy of each sensor nodes during the routing process is quite necessary to alleviate the transmission link break problem. In addition, other alternative forwarding sensor nodes need to be elected to replace the damaged sensor node or sensor node with insufficient energy to ensure complete data communication. Before data transmission in each round, sensor nodes will transmit a message including their ID, residual energy and status to mobile sink nodes.  
The flowchart to find substitute forwarding sensor nodes is illustrated in Figure 5. If the residual energy of sensor node \(s_{i}\) is not sufficient for the next data transmission, it will send a REJECT_MSG to its peripheral sensor node, and will not participate in the next data forwarding process. During the data communication process, if a sensor node \(s_{i}\) in the transmission link has already failed, an adjacent sensor node near \(s_{i}\) with the highest residual energy will be elected to act as the substitute forwarding sensor node. Thus we can ensure all sensor nodes in each transmission link can work properly and have enough energy to forward monitored data.