esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Adaptive Ensemble Square Root Kalman Filter
### Ensemble Square Root Kalman Filter  
In EnSRF, the calculation of the analytic state variables is divided into two parts [22], one is the update of the mean value of the analytic state set, the other is the update of the mean deviation of the analytic state set sample  
\[\overline{X}^{a}=\overline{X}^{f}+K(\overline{Z}-H\overline{X}^{f}) \tag{23}\]  
\[X^{aa}=X^{ff}+\overline{K}(Z^{\prime}-HX^{\prime f}) \tag{24}\]  
where \(\overline{X}^{a}\) represents the mean of analysis state set, \(X^{\prime a}\) indicates the mean deviation of analysis state set samples, \(\overline{X}^{f}\) denotes the mean deviation of prediction state set, \(X^{\prime f}\) is the mean deviation of prediction state set samples, \(\overline{Z}\) is the observation value obtained by PMU, \(Z^{\prime}\) is the random observation deviation obeying the probability distribution of observation error, \(K\) is the gain of traditional Kalman filter, \(\overline{K}\) is the mean deviation gain of the update set.  
In EnKF, when all sets of samples are updated with the same observation value and the same gain [23], i.e., \(Z^{\prime}=0\) and \(\overline{K}=K\), the covariance of the analysis state set can be expressed as (25). In the traditional Kalman filter, the covariance expression of state analysis error is shown in (26). As can be seen from (25) and (26), the value of \(P^{a}\) in EnKF lacks the term \(KRK^{T}\), so EnKF has the problem of systematic underestimation  
\[P^{a}=(I-KH)P^{f}(I-KH)^{T} \tag{25}\]  
\[P^{a}=(I-KH)P^{f}(I-KH)^{T}+KRK^{T}=(I-KH)P^{f} \tag{26}\]  
EnSRF eliminates the problem of systematic underestimation of EnKF without disturbing the observed values [22; 23; 24]. In EnSRF, (24) can be reformulated as  
\[X^{\prime a}=X^{\prime f}-\overline{K}HX^{\prime f}=\left(I-\overline{K}H \right)X^{\prime f}\]  
\(\overline{K}\) is substituted for (25) and the analysis error covariance of the set \(\overline{K}\) needs to satisfy (26). Furthermore, if \(\overline{K}=\alpha K\) and \(\alpha\) is a constant, one can further derive  
\[(I-\overline{K}H)P^{f}(I-\overline{K}H)^{T}=(I-\overline{K}H)P^{f} \tag{27}\]  
\[\frac{HP^{f}H^{T}}{HP^{f}H^{T}+R}\overline{K}\overline{K}K^{T}-K\overline{K}K ^{T}+KK^{T}=0 \tag{28}\]\[\frac{HP^{f}H^{T}}{HP^{f}H^{T}+R}\alpha^{2}-2\alpha+1=0 \tag{29}\] \[\alpha=(1+\sqrt{\frac{R}{HP^{f}H^{T}+R}})^{-1} \tag{30}\]  
The traditional Kalman filtering algorithm is a linear unbiased minimum variance estimation algorithm under standard conditions. Kalman filter can obtain better estimation results when the dynamic system model and noise statistics are known. However, in the practical application, it is difficult to obtain accurate system model as well as accurate noise statistical characteristics, which will affect the accuracy of Kalman filter estimation results.
## 4 LBV of SAFs
### LBV of Synthetic Fuels
The LBV test results of S-8 fuel near 400 K from four representative studies are depicted in Figure 4. Despite similar temperature and pressure conditions during measurement, significant variations in LBV are evident. [PERSON] et al. reported an LBV\(\_\)max of approximately 57 cm/s, notably lower than the 60-66 cm/s range observed by others [53]. Additionally, [PERSON]'s LBV\(\_\)max occurred at an equivalence ratio skewed towards rich conditions, reaching 1.3 compared to others, which were nearer to 1.1. Consequently, [PERSON]'s LBV values under lean conditions were significantly lower than the others but exhibited higher values under rich conditions. [PERSON] et al. recorded the highest LBV of the studies, peaking at 66 cm/s [33], surpassing [PERSON] et al. (63 cm/s) and [PERSON] et al. [5] (61 cm/s). Under lean conditions, LBV data from [PERSON], [PERSON], and [PERSON] were comparable. Apart from differences and errors in testing methods and processes, one of the primary sources of variation in S-8 fuel LBV curves is the differing compositions of fuels used by different researchers. [PERSON] et al., [PERSON] et al., and [PERSON] et al. utilized S-8 fuels with different average molecule compositions of C\({}_{10}\)H\({}_{22.7}\), C\({}_{11.8}\)H\({}_{25.6}\), and C\({}_{11.8}\)H\({}_{25.5}\), respectively [54]. Compared to S-8 fuel, LBV test results for Jet A exhibited smaller discrepancies among scholars, with an LBV\(\_\)max of around 58-62 cm/s under the same experimental conditions. LBV trends with the equivalence ratio were highly consistent, and the LBV\(\_\)max at different temperatures corresponded to equivalence ratios of 1.05-1.1, as shown in Figure 2.  
Figure 4: Illustration of the LBV of S-8 at atmospheric pressure (P = 1 atm) under around 400 K. Reference cited from [PERSON] (2012) [33], [PERSON] (2011) [5], [PERSON] (2011) [54], [PERSON] (2020) [53].
## 3 Results
### Impact Absorption Test Results  
At 5.0 m/s, PLA and HIC values were similar, regardless of impact point and shell type (Figure 5). The similarity between PLA and HIC values was even more noticeable when considering the standard deviations due to their similar range of values. While PLA values were between 120 and 140 g, regardless of the impact point and the shell stiffness, the HIC value was slightly higher for both shell groups when the helmet was dropped on the P point. Regardless of the slight magnitude differences observed between both shell  
Figure 4: Mean and standard deviation (SD) of the intrusion measured in the penetration tests for each helmet model. The standard deviation of H4 was zero (3 sites were tested in this case).  
groups, different impact locations showed different trends between the two groups. While PLA and HIC values were higher for the hard shell group in the B and P impact points, they were lower in the X point.  
Figure 6 includes the mean and the SD of the PLA and HIC for each impact point at 8.2 m/s. One of the helmets (H7) of the soft shell group had a higher acceleration peak when testing B point (see Table A5 in Appendix A), causing the SD to be larger than in the other impact locations. Regardless of this helmet, the values measured for the hard shell helmets at 8.2 m/s resulted in more repeatable results and therefore reduced SD values. This effect was particularly true for HIC at the P location of the helmet. Again, different impact locations showed different trends. However, at this impact velocity, the results showed the opposite of what was observed at 5 m/s. In this case, while PLA and HIC values were lower for the hard shell group in the B and P impact points, they were higher in the X point.  
Figure 5: Mean and standard deviation (SD) of the peak resultant linear acceleration (PLA) and head injury criterion (HIC) for each shell group and each impact point in the impact absorption tests at 5 m/s.  
Figure 6: Mean and standard deviation (SD) of the peak resultant linear acceleration (PLA) and head injury criterion (HIC) for each shell group and each impact point in the impact absorption tests at 8.2 m/s.  
In view of the observed results at the two impact velocities and for the different impact locations, we cannot conclude that helmets with stiffer shells result in higher acceleration or HIC (and therefore higher risk of injury) than those with less stiff shells.
# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 6 Performance Comparison and Analysis
### Simulation Results in the Dolphin Pods Routing Scenario
Moreover, if the source node does not find the forwarder in the forwarding direction, then it could select a node from the suppressed region and therefore the scheme came up with utmost reduction of average packets dropped by a nodes in the network (refer to Figure 13c). The logic not only causes the reduction of the packets dropped number, but it also significantly improved the energy consumption. The result shown in the Figure 13a,b for alive nodes number against the number of simulation rounds. It has been noticed that, as number of rounds increases, the number of alive nodes reduces. We further elaborate the above-mentioned trend in Figure 13c that the number of packets drop reduces with the increase in network density. This is due to the fact that there are more numbers of alternative forwarders readily available in the dense networks compared to sparse networks. Consequently, the number of packets dropped reduces. The other strong reason is that, in a sparse network, the packets may not reach the distinct neighbors due to high bit error rate or degraded link quality, and hence the packets dropped. Conversely, in dense networks, enough nodes are placed in close vicinity of the source node, which causes reduction of packets being dropped. However, the hops count value to reach the destination increased and may degrade end-to-end delay. Figure 13: (**a**) number of alive nodes vs. rounds; (**b**) number of alive nodes vs. rounds; (**c**) number of packets dropped with suppressed vs. number of nodes; (**d**) number of packets dropped without suppressed vs. number of nodes. Simulation results using arbitrary values in SET3.
## 4 Experiments and Numerical Results
### Experimental Setup  
#### 4.1.1 Datasets  
We utilized a diverse set of four datasets, as outlined in Table 2. These datasets were specifically chosen due to their distinct scales, enabling a comprehensive demonstration of the model's capabilities across various scenarios.  
FB15K-237 [46] is a subset of the widely used Freebase [47]. Its entities consist of those mentioned more frequently than 100 times in FreeBase entities. To avoid information leakage caused by test data in the training set, FB15K-237 filters the datasets. Similarly, WN18 RR is a subset of WordNet that avoids the same issue by selecting entities exclusively from WordNet. Evaluating models on both FB15K-237 and WN18 RR could be beneficial for comparing the performance comprehensively, which are widely used in link prediction tasks.  
The CoDEx-M and CoDEx-L datasets are subsets derived from CoDEx [48], a collection of KG completion datasets obtained from Wikidata and Wikipedia. CoDEx provides a broader and more diverse range of content, resulting in richer relations and a more balanced distribution of relation types. Furthermore, there are distinct characteristics that differentiate CoDEx-M from CoDEx-L in terms of relation types. CoDEx-M consists of a higher proportion of symmetric relations, while CoDEx-L contains a larger number of composition relations. Evaluating models on both the CoDEx-M and CoDEx-L datasets offers valuable insights into their performance in challenging and complex scenarios.
# A Channel Allocation Mechanism for Cellular Networks
## 2 Related Work
### Centralized DCA Mechanism  
In the centralized DCA mechanism, the channels in cells are allocated by the central pool in PCS for temporary use, and the channels return to the central pool when the use is over. The difference between these mechanisms is the specific cost functions adopted for selection the candidate channel for allocation. Generally, the factors of cost functions are First Available (FA) and Locally Optimized Dynamic Assignment (LODA). In the centralized DCA mechanism, the FA is the simplest strategy to select the candidate channel for use. In FA, the first available channel within the reuse distance \(\sigma\) selected during the channel search is assigned to the cell. In LODA, the selected cost function is based on the future blocking probability adjacent to the cell where a call is initiated.
## 1 Introduction
developed an irrigation system that could control the quantity of irrigation water [5]. The system adjusted the amount of irrigation water according to the moisture level of the soil. Drip irrigation was applied to reduce evaporative loss. However, in a cultivated field, the soil moisture at the same depth but in different locations does not remain at a constant level. Uneven ground and variations in soil density cause the irrigated water to be distributed unevenly. Hence, this paper proposed a Soil Moisture Forecasting (SMF) model and implemented a novel Constant-moisture Automatic Irrigation System (CAIS). The SMF model collected soil moisture from different depth layers, distinct distances from the watering point, and various irrigation intervals to infer the probable soil moisture after a specific period. The CAIS used information which were fed back from the SMF model to dynamically adjust the irrigation interval and maintain the soil moisture at a constant level.
# Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds +
## 3 Use Case  
The injection molding process is known for its high productivity rate; however, as the number of working cycles increases, the degradation of the IMM and its components tends to increase as well. In this work, we studied a practical use case at the facilities of OLI, a company known for its expertise in the production of injected thermoplastic parts, in Aveiro, Portugal. One of the most relevant maintenance issues of the enterprise is related to production stops due to mechanical failures of injection models. When these problems occur, the mold must be repaired, as it is an expensive component with no spares.  
It is important to note that proactive actions have a cost, in that technicians that need to inspect the IMM and decide on the actions to be taken. Possible mechanical failuresof molds differ in severity; there are failures that are easily solved with quick corrective actions as well as others that imply long unavailability times and expensive repairs. With this consideration, and taking into account that CM is the only strategy currently applied by this enterprise, the objective of our work was to reduce the unavailability of injection molds due to mechanical failure and consequently reduce the costs of maintenance, which we sought to achieve both through proactive maintenance and by selecting the correct maintenance strategy in each case, i.e., failures that can be easily repaired do not justify proactive maintenance, while more severe ones can be mitigated.  
The enterprise has several IMMs equipped with built-in sensors to monitor the injection process, following the standards of EUROMAP 63 [52], which is a protocol used in the plastics industry for data exchange between IMMs and peripheral devices. These sensors collect a total of 123 parameters; however, only a few of these present any abnormal behavior in the periods between mold failures according to the experts and engineers at the enterprise that monitors the data. For this reason, only ten signals were considered for analysis in this work, represented in Table 1. Although the majority of IMMs are monitored by sensors, tracking the degradation patterns of injection molds is not straightforward, as they are used in different IMMs, requiring analysis of a given mold to account for working cycles involving different IMMs. Furthermore, each mold may have different working settings, such as temperature, pressure, and cycle time, because the produced parts may be made from different thermoplastic materials. Additionally, each mold may have multiple cavities, allowing for production of parts with different geometries, and they may produce identical parts in each cavity or unique part with different geometries. All of these factors make the forecasting of mechanical failures in molds a challenging task.
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
## 3 Statistical Characteristic of Zero-Bias
### Standard Deviation of Zero-Bias
According to Equation (13), the standard deviation of the zero-bias of the code phase discriminator is:  
\[\sigma_{S^{c}}=\frac{\sqrt{F_{c}T_{coh}\cdot\big{|}\overline{\varepsilon}_{s} -2\cdot round(\overline{\varepsilon}_{s}/2)\big{|}}}{\left(1+2d_{0}^{c} \right)\overline{F}_{s}T_{coh}}. \tag{18}\]
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 2 Preliminaries
### Background
#### 2.1.2 WiFi Preamble Detection  
The WiFi signal is detected by WiFi preamble detection, which is designed to detect the WiFi signal and trigger the subsequent WiFi decoding process. In Figure 3, a WiFi preamble consists of ten repeated short training symbols (STS) and two repeated long training symbols (LTS). The duration of STS and LTS are 0.8 \(\upmu\)s and 3.2 \(\upmu\)s, respectively. Essentially, if ten repeated signals are detected, this signal is determined to be the WiFi preamble.  
The most common applied WiFi preamble detection algorithm is the Schmidl-Cox algorithm, which has two main functions: WiFi preamble detection and carrier frequency offset estimation, as shown in Figure 4. Sampling rate of a WiFi device is 20 M/s and duration between two consecutive samples is 0.05 \(\upmu\)s. \(S(k)\) denotes amplitude of the \(k_{th}\) sample. As illustrated in Figure 3, duration of a short training symbol is 0.8 \(\upmu\)s, which also equals to the duration of 16 samples [16]. Hence, \(S(k)=S(k+16)\) if \(S(k)\) and \(S(k+16)\) are sampled on WiFi preamble.  
Essentially, Schmidl-Cox computes the phase offset between \(S(k)\) and \(S(k+16)\). If the received signal is WiFi preamble, \(\angle(S(k),S(k+16))=0\). In practice, even though WiFi sender and WiFi receiver are working on same channel, center frequency of sender and receiver are not the same due to drift of oscillators. Then, \(\angle(S(k),S(k+16))\) is a bit higher than zero.
## 3 Results and Discussion
### Electrical Properties of Hybrid GST-Based Resistive Switching Memory (ReRAM)  
ReRAM is a possible novel memory device for future data storage systems; therefore, we characterized the electrical properties of the fabricated device to make it suitable for storing data. The GST-based ReRAM showed resistive switching behavior (Figure 6a). The current-voltage relation of the Ag/GST/Al\({}_{2}\)O\({}_{3}\)/Pt device was obtained with direct current voltage applied to the Ag electrode while the Pt electrode was grounded. Triggering the device by applying forming voltage caused the formation of two conductive filaments (CF), one composed of Ag within the GST layer and one composed of oxygen vacancies within the Al\({}_{2}\)O\({}_{3}\) layer. After the CFs had formed, the insulating layers changed to their low-resistance state (LRS). We applied a compliance current of \(10^{-4}\)\(A\) to prevent permanent breakdown of the device. In contrast, a change to the polarity of the applied voltage caused the CF to rupture, so insulating layers changed to their high-resistance state (HRS) (Figure 6a).  
Figure 6: (a) Semi-logarithmic \(I\)–\(V\) characteristics of the resistive switching memory device (ReRAM) with bilayer insulator film for 100 cycles. (b) Double-logarithmic plot for Ag/GST/Al\({}_{2}\)O\({}_{3}\)/Pt/SiO\({}_{2}\) in the positive and negative sweep with Ohmic model. (c) Data retention characteristics of the low-resistance state (LRS) and high-resistance state (HRS) under continuous readout voltages for the as-fabricated ReRAM device.  
A repeated cyclic test was performed for more than 100 cycles; results confirmed that switching in the device was reproducible. Measurements of 100 cycles demonstrated that bipolar switching did not affect the electrical properties of the device (Figure 6a). A semilogarithmic \(I\)-\(V\) curve (Figure 6a) suggested that the changes in conductivity were consequences of formation and rupture of CFs.  
Double-logarithmic plots of the \(I\)-\(V\) curves during positive and negative voltage sweeps confirmed the construction of CFs inside the insulator layer. Furthermore, the equations of the fitted curves were Ohmic for both LRS and HRS (\(I\)\(\propto\)\(V\)) (Figure 6b). Fitting the curves to the results of positive and negative biases indicated that Ohmic conduction was the dominant phenomenon at both low and high voltages [6].  
To determine the reliability of the fabricated device, the data retention test was performed. We examined the ReRAM device with a reading voltage of 0.2 V. For both LRS and HRS states, the stored data were retained without any noticeable change for \(>\)4000 s. The \(I_{\text{ON}}/I_{\text{OFF}}\) (ON and OFF current) ratio (\(\sim\)10\({}^{6}\)) was high; this result confirms that the memory window was wide (Figure 6c).  
These results prove that this memory device fulfills the requirements of nonvolatile memory.
## 4 Results and Discussions
### Descriptive Statistical Analysis
#### 4.1.2 Descriptive Statistical Analysis of Experiment 2  
The nonparametric statistics for each traffic scenario in Experiment 2 are shown in Table 5. It is found that there is a significant correlation between the blind spot locations and the time buffer values under the three traffic scenarios of expressway (\(p\) = 0.014), left lane change (\(p\) = 0.012), and high traffic (\(p\) = 0.003), which indicates that the blind spot locations have a relatively large effect on the cognitive load of the drivers in these three traffic scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \(p\)-Value of & \(p\)-Value of & \multirow{2}{*}{**Vehicle Type**} & \multirow{2}{*}{**Veh-Mean**} & \multirow{2}{*}{**Veh-Std.**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Std.**} \\ \cline{5-8}  & & Veh-Mean & & & 1 & 1.363 & 0.556 & \\ \hline City Roads & 0.471 & 0.501 & 2 & 1.368 & 0.665 & 1.379 & 0.577 \\ \cline{3-8}  & & & 3 & 1.406 & 0.511 & & \\ \hline Expressway & 0.026 & 0.041 & 1 & 1.522 & 0.438 & & \\ \cline{3-8}  & & & 2 & 1.828 & 0.214 & 1.685 & 0.326 \\ \cline{3-8}  & & & 3 & 1.705 & 0.326 & & \\ \hline Right Lane Changes & 0.282 & 0.366 & 1 & 1.343 & 0.561 & & \\ \cline{3-8}  & & & 2 & 1.389 & 0.634 & 1.410 & 0.557 \\ \cline{3-8}  & & & 3 & 1.497 & 0.476 & & \\ \hline Left Lane Changes & 0.309 & 0.506 & 1 & 1.508 & 0.459 & & \\ \cline{3-8}  & & & 2 & 1.798 & 0.26 & 1.594 & 0.383 \\ \cline{3-8}  & & & 3 & 1.477 & 0.43 & & \\ \hline Medium Traffic Flow & 0.014 & 0.02 & 1 & 1.538 & 0.421 & & \\ \cline{3-8}  & & & 2 & 1.77 & 0.278 & 1.500 & 0.465 \\ \cline{3-8}  & & & 3 & 1.191 & 0.697 & & \\ \hline High Traffic Flow & 0.096 & 0.226 & 1 & 1.156 & 0.699 & & \\ \cline{3-8}  & & & 2 & 1.278 & 0.739 & 1.347 & 0.602 \\ \cline{3-8}  & & & 3 & 1.606 & 0.367 & & \\ \hline \end{tabular}
\end{table}
Table 4: Nonparametric statistical results for different traffic scenarios in Experiment 1.  
By comparing the mean and standard deviation of the time buffer values in different traffic environments, it is found that the blind spot image at position 3 has the smallest mean and largest standard deviation of the time buffer value, which is considered to have the largest cognitive load on the driver. The cognitive load is smallest when the blind spot image is at position 2 and is slightly higher at position 1. This pattern only shows anomalies under medium traffic, with high probability caused by a poor correlation.