esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 3 Related Work
### USRP/GNU Radio and RP3 Testbeds
#### 3.2.1. USRP/GNU Radio without RP3 Testbeds  
In [15; 28; 31; 32; 33; 34; 35], traditional testbeds are used in their investigations. In general, a testbed consists of USRP/GNU radio units (i.e., BSs and nodes) that exchange control messages (e.g., channel sensing outcomes and available routes) with a single processing unit such as a PC (i.e., CC) via a Gigabit switch [15]. Control messages are transmitted in the wired medium; while data packets are transmitted in the wireless medium [36]. Hence, the effects and the incurred delay of control message exchange to network performance (e.g., end-to-end delay), which may affect the fulfilment of the network requirements (e.g., the delay constraint imposed by IEEE [37]), are not considered in the investigations.  
As shown in Table 2, our testbed extends existing testbeds to provide separate processing units (i.e., RP3), has a higher number of nodes (i.e., 9), has the highest number of D2D links (i.e., 8), and considers heterogeneous network entities. Although the characteristics of our testbed are similar to that in [27], the main difference between these two testbeds is that the source node receives and follows route selection made by the base station since a single processing unit is used in [27], while the source node makes route selection independently based on the available routes given by the base station in our testbed that applies USRP/GNU radio units with RP3. In addition, in our testbed, the effects of control message exchange over wireless medium to the end-to-end delay performance is considered in our proposed route selection mechanism.
# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
## 4 Experiments
### Detection Technique  
Energy detection is the initial method for detecting PUE attacks and releasing the channel for the possible PU/PUE. The following steps outline the energy detection process:Signal collection: SDR receivers collect signals over a specified bandwidth.  
Energy measurement: the received signal's energy is measured using the energy detection algorithm, which involves computing the signal's power in a given time window.  
Threshold comparison: the measured energy is compared against a threshold to determine if a PU signal is present or an attack is occurring.  
A PU is moved in the environment to define the threshold, and the energy data are saved for the DNN's learning process.  
All users are in a grid of 10 \(\times\) 10 m. The FC is located in the center of the grid, and the 3 SUs are at the corners of the downside and in the center. The PU is randomly distributed in the environment, as are the PUE attacks.  
For the DNN's learning process, a PU is moved for all the parts of the grid, and the energy is calculated. These data calculate the threshold to recognize a device transmitting at some frequency in UL and DL. Figure 5 shows an example of the user's distribution on the grid.  
Authentication Detection  
In the learning process, for authentication detection, the SU attempts to connect to the PU's base stations and obtain accurate data on the stations in the zone. A database with these data is generated and shared with the FC and all the SUs in the zone.  
Then, in the detection, when a possible PU is detected for some of the SUs, the SU tries to connect with it. If it is a PU, the data are compared with the database, saved, and shared with the FC. If the SU authentication process is invalid, it is marked as a PUE and shared with the FC.  
After the detections are achieved, the DNN algorithm takes data from all the SUs. Based on the energy and authentication results, the PUE attack presence in the network is estimated.  
The whole process is achieved when one PUE attack and four PUE attackers are in the network, and the results are compared.
# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 3 Preliminary  
CSI is leveraged in this study, and we will give a brief introduction of the background knowledge in this section.  
The wireless signals propagate through multiple paths from the transmitter to the receiver in a typical indoor scenario. As a result, the received signal is the superposition of the signals from LOS path and several reflection paths. OFDM framework is the basis of 802.11 n wireless networks, in which our system works. In this framework, the wireless channel can be described by a ChannelImpulse Response (CIR) in the time domain. Under the assumption of time-invariant, CIR can be expressed as:  
\[h(\tau)=\sum_{i=1}^{N}\alpha_{i}e^{-j\theta_{i}}\delta(\tau-\tau_{i})+n(\tau), \tag{1}\]  
where \(\alpha_{i}\), \(\theta_{i}\), and \(\tau_{i}\) denote the amplitude, phase and time delay of the signal from \(i^{th}\) path, respectively; \(N\) is the total number of paths; \(n(\tau)\) is complex Gaussian white noise; and \(\delta(\tau)\) is the Dirac delta function.  
Nevertheless, precise CIR can be extracted only from dedicated devices rather than commodity infrastructures. To overcome this limitation, Channel Frequency Response (CFR) can be extracted from frequency domain, which can model the wireless channel. CFR contains amplitude\(-\)frequency response and phase\(-\)frequency response. Under the assumption of infinite bandwidth, CIR is equivalent to CFR, and CFR can be transformed by Fast Fourier Transform (FFT) from CIR: [20]  
\[H=FFT(h(\tau)). \tag{2}\]  
We can obtain CFRs in the format of CSI:  
\[H=[H(f_{1}),H(f_{2}),\ldots,H(f_{N})], \tag{3}\]  
where \(N\) is the number of subcarriers in the wireless network.  
The CSI is composed of amplitude and phase of a subcarrier:  
\[H(f_{k})=\|H(f_{k})\|e^{j\sin(\angle H)}, \tag{4}\]  
where \(f_{k}\) is the central frequency of the subcarrier, and \(\angle H\) represents its phase. Thus, a group of CSIs, \(H(f_{k})\), \((k=1,\ldots,K)\), denote \(K\) sampled CFRs in subcarrier level.
## 3 Results and Discussion  
### Proximate Analysis  
Results of the proximate analysis of the fourteen microgreens are shown in Table 3. The total carbohydrate content of the Brassicaceae, black sesame, morning glory, and roselle microgreens was 3.02 g 100 g\({}^{-1}\) on average. On the other hand, Fabaceae microgreens (except green pea) showed a relatively higher carbohydrate content, with mung bean the highest at 7.16 g 100 g\({}^{-1}\) followed by buckwheat at 4.90 g 100 g\({}^{-1}\). Grain legumes are known to have a high carbohydrate content of up to 65% [19].  
The total protein content of the studied microgreens ranged from 1.75 and 1.76 g 100 g\({}^{-1}\) (buckwheat and morning glory microgreens, respectively) to 6.47 g 100 g\({}^{-1}\) (lentil microgreens). Lentils are rich in protein, approximately 16% albumins, 70% globulins, 11% glutelins, and 3% prolamins, and contain low levels of sulfur-containing amino acids [20,21]. A similar range of protein was found in fenugreek microgreens by [PERSON] et al. (2020) [5], but our study obtained a higher protein content in radial and roselle microgreens than the one published.  
As expected, the lentil and mung bean microgreens had higher total calories (53.43 and 50.08 g 100 g\({}^{-1}\), respectively) due to their high carbohydrate and protein contents (Table 3). On the other hand, the other studied microgreens had a relatively low caloric content of 27.62 g 100 g\({}^{-1}\) on average.  
Ash and total fat contents of the microgreens were low. A study on six genotypes of microgreens including _Brassica oleracea_ L. var _italica_ also reported that the lipid content of the microgreens is insignificant [22]. The microgreens contained a high moisture content ranging from 86.57 g 100 g\({}^{-1}\) to 94.67 g 100 g\({}^{-1}\).
## 2 Experimental Approach
### Data Post-Processing  
All the images were calibrated utilizing a dot plate with a dimension of 105 mm (length) \(\times\) 105 mm (width). A 12 \(\times\) 12 dots array was designed on the plate, the dot diameter was 3.2 mm, and the spacing between the dots was 15 mm. After calibration, the dimension of the field of view was determined, which was \(\sim\)90 mm \(\times\) 90 mm from the burner outlet, corresponding to a resolution of 112 \(\upmu\)m/pixel.  
The obtained chemiluminescence images were averaged over 100,000 shots, and then a two-dimensional (2D) median non-linear filter was applied for noise reduction (5 \(\times\) 5-pixel filter size). The inverse Abel transform was used to obtain a mean 2D flame shape.  
For PLIF data processing, the background was first subtracted from every instantaneous raw image. Second, the inhomogeneity of the laser sheet intensity was considered. During the experiments, a beam splitter was placed in the incident path to reflect a small portion of the incident laser sheet into a cuvette filled with ethanol, which was mixed with a tiny amount of Rhodamine 6G dye. The cuvette was placed at a specific position where the distances from the beam splitter to the burner outlet and the cuvette were identical. The fluorescence from the Rhodamine 6G/ethanol dye solution was recorded using another high-speed camera (Phantom v2012, Vision Research, Wayne, NJ, USA), allowing the temporal shot-to-shot correction for the PLIF signals. An example of the PLIF image recorded from the dye in the cuvette is shown in Figure 4a, and the intensity profile is plotted in Figure 4b by integrating the PLIF signal along the \(x\)-direction. Then, the background-subtracted PLIF images were corrected by the corresponding single-shot fluorescence intensity profile. Two images before and after correction are displayed and compared in Figure 4c,d, and the influence of the intensity inhomogeneity of the laser sheet can be relieved.
## 3 Results and Discussion  
### Stretch Forming  
#### 3.1.1 Forming Force and Strain Evolution of the Mono-Materials  
The stretch-forming behaviour of the steel sheet TS275 and the organosheets RG and RGUD were analysed at different forming temperatures. The first results about the stretch forming of TS275 at RT and 235 \({}^{\circ}\)C are illustrated in Figure 10. As can be seen, with increasing temperature, the force, drawing depth (Figure 10a) and the strain evolution (Figure 10b) of TS275 decreased, which correlated with its tensile properties (Figure 3).  
Figure 8: (**a**) Forming limit curve (FLC) and (**b**) specimen geometries: green arrow refers to the rolling direction (RD) of the steel sheets.  
Figure 9: Illustration of the time-dependent method for FLC-determination [45].  
Moreover, the stretch forming experiments of organosheets RGUD at RT were carried out for two thicknesses (0.5 and 2.0 mm), as shown in Figure 11. The four DIC images, in ascending order, shown in Figure 11b correspond to the red points (1)-(4) in the force-depth curve in Figure 11a. As can be observed, with increasing thickness of the organosheet at RT, the drawing force increased. However, at a drawing depth of approximately 8 mm, a first force drop appears, indicating the start of fibre cracking along the warp direction due to the less cracking resistance in werf direction with lower fibre content, giving a limit in formability of the organosheet. With further punch displacement, the force increased again (see the green arrow from point (1)-(3)), which was due to the resistance of the fibres in the wrap direction in the organosheet at RT. This central crack propagated, as shown in Figure 11b, until reaching complete failure at point 4.  
Under forming conditions above the melting point of the PA6, a high drawing depth up to 40 mm of both types of the organosheet RG and RGUD with a thickness of 0.5 mm was achieved (Figure 12). The maximum drawing force was reduced to approximately 2 kN. Moreover, no remarkable difference between RGUD and RG was observed. However, a distortion of the organosheet in the die edge area was detected (marked by yellow arrows),  
Figure 11: (**a**) Force evolution of RGUD under stretch-forming conditions at RT; (**b**) DIC images of the crack propagation.  
Figure 10: (**a**) Force and (**b**) strain evolution of TS275 under stretch forming at RT and 235 °C.  
which was due to the hindered flowability of the fibres due to the existing draw beads in die/blank holder region.
## 5 Discussion
Although external antennas and choke coils are also used to improve the positioning performance of smartphones, they may not be the best choice for practical applications. We expect that manufacturers will continuously improve the quality of GNSS modules in smartphones, while we continue to study optimisation algorithms for positioning. In future research, we can make full use of the advantages of smartphones equipped with multiple sensors, in order to make up for the shortcomings of a single GNSS through integration of GNSS/IMU and other sensors. As they do not cause changes in coordinates during deformation monitoring that are too large, coordinate or baseline constraints may be used to improve the positioning effect.
## 5 Results and Discussion
### Numerical Analysis
#### 5.2.2 Time Performance  
Because the objective is to maximize coverage, we compared the Gurobi and 3 SH methods because they achieved greater coverage than the greedy method. As shown in Table 3, the average time performances of the Gurobi was 4946.23 s, 3 SH 26.46 s, and MCGRH 0.87 s. Although the experiments were conducted on different computers, it can still be observed that the MCGRH is substantially faster than the other methods. Furthermore, given that the hardware specifications of the computer used to test the MCGRH were lower than those of the machine used to test the Gurobi and 3 SH methods, this again attests to the remarkable time performance of the MCGRH. In fact, the MCGRH runs extremely rapidly (less than 1 s on average). Again, we performed the Wilcoxon signed-rank test using SPSS to compare the time performance of MCGRH and each of the two other methods. The results are as follows:  
1. Comparison of MCGRH and Gurobi.
* Null hypothesis: The population distributions of the Gurobi and MCGRH are identical with respect to time performance.
* Results: The ranks of all pairs were negative (22 negative and 0 positive), indicating that MCGRH outperformed Gurobi in terms of time performance. In addition, \(p\)-value = 0.001, where 0.001 < 0.05, indicating that the null hypothesis is rejected.  
2. Comparison of MCGRH and 3 SH.
* Null hypothesis: The population distributions of the 3 SH and MCGRH are identical with respect to time performance.
* Results: The ranks of all pairs were negative (22 negative ranks and 0 positive ranks), indicating that MCGRH outperformed 3 SH in terms of time performance. In addition, \(p\)-value = 0.001, where 0.001 < 0.05; therefore, the null hypothesis was rejected.
# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 3 Remote Metering Life Prediction Method
### SOC and Remaining Time Calculation Using the Predicted Discharge Capacity  
The SOC indicates the remaining battery capacity relative to the rated capacity. The SOC value ranges from 0% to 100%, with 100% representing a fully charged battery and 0% representing a fully discharged battery. To estimate the SOC of the MSD primary battery, we use the discharge capacity predicted by the C-SDFormer. Equation (9) shows the \(SOC_{t}\) calculation process.  
\[SOC_{t}=100\%-\frac{C_{released}}{C_{rated}}\times 100\%, \tag{9}\]  
where \(SOC_{t}\) is the SOC at time t, \(C_{released}\) is the discharge capacity, and \(C_{rated}\) is the rated capacity as specified by the manufacturer.  
In addition, to enhance the practical relevance of our approach, we converted the predicted SOC into an estimated remaining time until complete battery discharge. Measuring current in real-world settings is challenging. Therefore, we defined \(I_{avg}\) as the daily average current consumption derived from the predicted SOC based on the device's power consumption profile data. This profile was estimated in real time because it varies with each device's operating conditions, such as the number of retransmissions determined by the communication environment and the ambient temperature. The estimated remaining time, \(T_{remaining}\), is then calculated using:  
\[T_{remaining}=\frac{C_{rated}-C_{released}}{I_{avg}}, \tag{10}\]  
where \(T_{remaining}\) represents the estimated remaining operational time until full discharge, measured in days, and \(I_{avg}\) is the daily average of \(C_{released}\). This transformation enables the model to provide users with a practical, time-based prediction of battery life, thereby further improving the applicability of our battery management strategy.
## 5 Conclusions  
This paper proposes a baseband signal preprocessing model, which has two pre-filters and uses state feedback values to control the generation of local carrier and code. Compared with the conventional approach based on a single-filter structure, this model limits the impact of code phase error on carrier tracking, and hence enables the receiver to achieve better navigation precision. Furthermore, this model can ensure the stationarity of the estimation of input carrier frequency, even in the case of local carrier frequencies with wide fluctuation. In fact, compared with a popular error-state pre-filter, even pre-filter-S proposed in this paper performs better in the tracking domain due to the benefits of state feedback.  
To further promote the tracking and navigation performance, a specific ultra-tight GPS/INS integration is designed on the basis of the new preprocessing model. Since the pseudorange rate estimation error in each channel has been treated as the additional state variable, the proposed  
Figure 21: GPS/INS (tight integration, traditional navigation filter) velocity estimation errors.  
Figure 20: GPS/INS (tight integration, traditional navigation filter) position estimation errors.  
navigation filter ensures the estimation of INS errors will not be contaminated. Moreover, in this integration, although the corrected inertial navigation information is used to estimate the carrier frequency, the tracking channel still aims to make the phase error return to 0 in the shortest amount of time. The results of the semi-physical simulation based on a telemetered missile trajectory indicate that navigation solutions with higher accuracy can be achieved. Meanwhile, because tracking is assisted by external navigation information, the error contained in the estimated Doppler shift can be reduced substantially.  
The authors wish to acknowledge the financial support of the National Natural Science Foundation of China (Grant No. 61153002).  
[PERSON] provided insights in formulating the ideas and wrote the paper. He also performed the main part of the experiments. [PERSON] offered the experimental data telemetered in a missile test, and critically reviewed the paper. [PERSON] contributed greatly to experimental data evaluation. All authors read, revised and approved the final manuscript.  
The authors declare no conflict of interest.
# Solid-Phase Microextraction Techniques and Application in Food and Horticultural Crops
## 2 Optimizing SMPE Analyte Extraction
### Effect of Extraction Efficiency on pH  
pH can also have a significant effect on extraction efficiency for acidic and basic analytes in a sample [29]. Weakly acidic and basic analytes will be more effectively extracted in their non-ionized (less polar) form by a SPME fiber. Therefore, to optimize extraction, lower pHs are favored for weakly acidic analytes, while higher pHs are favored for weakly basic analytes. If both weakly acid and basic analytes are present in a sample, a compromise needs to be made on sample pH in order to maximize extraction of all of the different types of analytes present.  
[PERSON] used direct SPME to develop a method to analyze a number of endocrine-disrupting chemicals leaching from plastic baby milk bottles [30]. In method development, a 65 \(\upmu\)m polydimethylsiloxane/divinylbenzene (PDMS/DVB)-coated fiber was chosen to be optimal for the extraction of endocrine-disrupting chemicals 2,4,6-trichlorophenol, 4-tert-octylphenol, 4-octylphenol, 4-nonylphenol, and bisphenol A (Figure 4). Extraction efficiency for bisphenol A was higher by a factor of around 1.6 at pH 2 compared to pH 8, while for 2,4,6-trichlorophenol it was higher by a factor of around 2. However, for 4-tert-octylphenol, 4-octylphenol and 4-nonylphenol, extraction efficiency was essentially unchanged over a pH range of 2-8.
## 2 Main Result
### Robustness Lyapunov Analysis for Error System
#### 2.5.7 Alternative \(\Delta L\) Bound  
As detailed in the previous section, the bound on \(\Delta L\) depends on the assumption outlined in Equation (43). Consider the case where the adaptive law is considered without the feedback filter (\(aL=0\)), as detailed in Equation (56), and the Equation (43) constraint is removed. Applying Sylvester's Inequality to \(\gamma_{y}^{-1}\)  
\[\lambda_{\text{min}}(\gamma_{y}^{-1})\leq\gamma_{y}^{-1}\leq\lambda_{\text{ max}}(\gamma_{y}^{-1}), \tag{66}\]  
Therefore, \(V_{L}\) can be alternatively bounded:  
\[\frac{\lambda_{\text{min}}(\gamma_{y}^{-1})}{2}\|\Delta L\|_{F}^{2}\leq V_{L} =\frac{1}{2}\operatorname{tr}(\Delta L\gamma_{y}^{-1}\Delta L^{\dagger}) \leq\frac{\lambda_{\text{max}}(\gamma_{y}^{-1})}{2}\|\Delta L\|_{F}^{2}. \tag{67}\]  
Meaning, the lower bound of \(V_{L}^{\frac{1}{2}}\) can be expressed as follows:  
\[\frac{\sqrt{\lambda_{\text{min}}(\gamma_{y}^{-1})}}{\sqrt{2}}\|\Delta L\|_{F} \leq V_{L}^{\frac{1}{2}}. \tag{68}\]  
Using the result from Equation (68) to lower bound Equation (51):  
\[\frac{\sqrt{\lambda_{\text{min}}(\gamma_{y}^{-1})}}{\sqrt{2}}\|\Delta L\|_{F} \leq V_{L}^{\frac{1}{2}}\leq V_{eL}(\tau)^{\frac{1}{2}}\leq e^{-\sigma\tau}V_ {eL}(0)^{\frac{1}{2}}+\frac{\sqrt{2}(\lambda_{\text{max}}(P_{x})^{\frac{1}{2} }+1)M_{\infty}}{2a}(1-e^{-a\tau}). \tag{69}\]  
Solving and taking the _supremum_ of \(\|\Delta L\|\) from Equation (69) as time approaches infinity results in the following:  
\[\limsup_{t\to\infty}\|\Delta L\|_{F}=\frac{\sqrt{\lambda_{\text{max}}(P_{x})} M_{\varphi}}{\sqrt{\lambda_{\text{min}}(\gamma_{y}^{-1})a}}\equiv R_{**}. \tag{70}\]  
There are several bounds for \(\|L\|\) as depicted in Equations (64), (65), and (70); however, they are all dependent on the assumptions and constraints of the control problem.