esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 2 Wi-Fi Basics
### MAC in Wi-Fi
#### 2.2.2 Enhanced Distributed Channel Access (EDCA)  
Enhanced Distributed Channel Access (EDCA) provides QoS for wireless networks by enhancing DCF in legacy Wi-Fi. It introduces prioritized traffic classes, so-called access categories (ACs), and gives prioritized access to higher-priority packets. The nodes label the data packets in the upper layers. EDCA defines four different ACs: video, voice, best effort, and background traffic. AC-specific inter-frame spaces, called Arbitrary InterFrame Space (AIFS), help to handle packets differently. A high-priority station waits for a shorter AIFS time before decrementing its backoff value. Moreover, EDCA enforces AC-specific min/max values for contention windows. EDCA achieves proportional QoS: higher-priority flows obtain more bandwidth than lower-priority flows. Moreover, EDCA assigns larger Transmission Opportunity (TXOP) values for high-priority flows. In this way, the corresponding stations can send several packets back-to-back to increase their throughput compared to the low-priority stations.
# A Stable Backup Routing Protocol for Wireless Ad Hoc Networks
## 2 Stable Backup Routing Scheme (SBR)
### Backup Route Maintenance
#### 2.3.3 Selecting Backup Route  
It can be seen from the principle of SBR, the upstream node of the broken link may receive multiple BRREPs from different neighbors for a specific destination, which brings the selection problem of backup routes. In SBR, the node compares all the related BRREPs during the stipulated period \(\mathrm{T_{th}}\) and gives DBR highest priority. Concretely, in order to ensure timeliness and stability simultaneously, path type is firstly compared and DBR, EBR, IBR has the priority from highest to lowest. Furthermore, if several usable alternate paths have the same type, the one whose successful rate is the largest wins.  
After choosing backup route, the forwarding route table is then modified appropriately and data transmission will be switched from the main route to the selected backup route.
## 3 Field Tests
### Field Instrumentation  
To verify the effectiveness and observe the performance of the proposed method, the supporting structure and surrounding ground were monitored during excavation and after construction.  
The field monitoring items included the following: (i) A buried earth pressure cell to obtain the load acting on the structure; (ii) a strain gauge installed in the structure unit to measure the stress state of the structure; and (iii) an inclinometer tube to monitor the lateral displacement of the surrounding soil. The distribution and installation of the instruments are shown in Figure 5.  
(1) Distribution and installation of earth pressure cell  
Lateral earth pressure was observed using earth pressure cells. The measuring range of this instrument is from 0 to 200 kPa. As shown in Figure 5, the earth pressure cells were buried 1 m, 3 m, and 5 m underground, respectively. Two monitoring points were distributed along the circumference at each depth; they were labeled Ei1, and Ei3, respectively. The letter, 'i' represents the depth of the earth pressure cell. There are six lateral earth pressure monitoring points in total.  
The earth pressure cells were buried after excavating to the projected depth, and then the supporting structures were assembled with prestress. The holes were dug in the shaft wall, and subsequently, the cells were buried. The axis of the cylindrical earth pressure cell is perpendicular to the excavated shaft wall (see Figure 5b).  
(2) Distribution and installation of strain gauge  
The stress in the structures was observed using strain gauges. The strain gauges were installed 1 m, 3 m, and 5 m underground, respectively. Two monitoring points were distributed along the circumference at each depth; they were labeled Si1 and Si3, respectively. The letter, 'i' represents the depth of the strain gauge, as shown in Figure 5. There are six stress monitoring points in total.  
The strain gauges were installed in the middle of the steel pipe in advance. The supporting structures with strain gauges were assembled after excavating to the preset depth.  
(3) Distribution and installation of the inclinometer  
The lateral displacement was observed using inclinometers. Three inclinometer tubes were installed before excavation and were uniformly distributed along the circumference; they were labeled T1, T2, and T3, respectively. T1 and T3 were 0.25 m from the shaft wall. There is 0.5 m between T2 and the shaft wall. The detailed distribution is shown in Figure 5.  
Figure 5: Distribution and installation of instruments: (**a**) Plan layout of the monitoring points; (**b**) photo of the instrument installation; and (**c**) cross-sectional view of the monitoring points. (The letter, ‘i’ represents the depth of the instruments).
## 3 Results and Discussion
### Proposed Stress Scaling Technique  
Scaling of DEM parameters according to similarity conditions allows increasing the size of the time step constrained by an explicit numerical time integration scheme, which can substantially reduce computing time. It is shown that two models will obey the same governing Equation (1) if they satisfy the major similarity condition for DEM [41]:  
\[\frac{m_{1}l_{1}}{F_{1}t_{1}^{2}}=\frac{m_{2}l_{2}}{F_{2}t_{2}^{2}} \tag{17}\]  
where \(l_{1}\), \(m_{1}\), \(t_{1}\), and \(F_{1}\) and \(l_{2}\), \(m_{2}\), \(t_{2}\), and \(F_{2}\) are the characteristic length, mass, time, and force of models 1 and 2, respectively. For the considered mechanical system, only three dimensions are independent. If \(l_{1}=l_{2}\), \(m_{1}=m_{2}\), \(t_{1}=t_{2}\), and \(F_{sc}=F_{1}/sc\), then  
\[\frac{sc\cdot F_{1}}{sc}=sc\cdot F_{sc}=sc\cdot\sigma_{sc}\cdot A_{sc}=\sigma_ {1}\cdot A_{1}=F_{1} \tag{18}\]  
where \(sc\) is the scaling factor, \(F_{sc}\) is the force magnitude computed with scaled-down values of parameters, \(\sigma_{sc}\) is the stress computed with scaled-down values of parameters, and \(A_{sc}\) is the characteristic area of the scaled model, which is proportional to the square of the characteristic length and equal to \(A_{1}\). Thus, it is necessary to multiply the stresses of the scaled model from the scaling factor in order to get the stresses of the model with the original values of the parameters. In the present research, the values of the elastic moduli and bond breakage parameters (10-11) are scaled down by the scaling factor.  
Figure 5 shows the accuracy of the proposed scaling technique. The curves \"EXP\" and \"DEM\" represent the stress dependency on strain obtained from the experimental uniaxial compression test and that attained from DEM computations with the original values of parameters, respectively. The curve \"DEM (reduced)\" represents the DEM computations with 50 times reduced values of elastic moduli and bond breakage parameters (10-11). The curve \"DEM (scaled)\" is the stress-strain curve of \"DEM (reduced)\", for which the stresses are scaled 50 times. It is worth noting that DEM computations with 50 times reduced values of parameters can be performed with a \(\sqrt{50}=7.07\) times larger time step. Thus, DEM computations with the reduced values of parameters lasted seven times shorter than those with the original values of parameters. When the strain rises from 0.010 to 0.0261, the curves \"DEM\" and \"DEM (scaled)\" almost coincide, which proves the high accuracy of the proposed scaling technique up to the time interval of fast macro crack propagation, leading to the beginning of the sample failure. However, the stress peaks of the curves \"DEM\" and \"DEM (scaled)\" differ by 7.92 MPa, which is 3.2% of the highest stress value obtained in DEM computations with the original values of parameters. Thus, the accuracy of scaling requires further research in the case of fast macro crack propagation and the sample failure.  
Figure 5: The accuracy of the proposed scaling technique.
## 4 The Proposed Scheme: LDAKM-EIoT
### Pre-Deployment Phase  
This phase permitted the trusted authority \((TA)\), a fully trusted entity in the network, to perform the registration of IoT devices, edge nodes, and cloud servers before they were installed in the deployment area. We assumed that \(n_{d}\) number of IoT devices \(D_{i}\) were associated with a particular edge node \(EN\), and the real-time information could be accessed from the IoT devices to \(n_{c}\) number of cloud servers \(CS_{j}\), provided that a mutual authentication and key establishment happened successfully among the IoT devices and cloud servers with the presence of the edge node \(EN\).
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
## 1 Introduction  
Due to global warming and climate change, the monitoring and detection of atmospheric gas concentration has come to be of great value. Although the average background level of methane (CH\({}_{4}\)) (\(\sim\)1.89 ppm) in the earth's atmosphere is roughly 200 times lower than that of CO\({}_{2}\) (\(\sim\)400 ppm), the contribution of CH\({}_{4}\) to the greenhouse effect per mole is 25 times larger than CO\({}_{2}\)[1, 2]. Therefore, a fast, accurate, and precise monitoring of trace greenhouse gas of CH\({}_{4}\) is essential. There are some methods for detecting methane, including chemical processes [3; 4; 5; 6] and optical spectroscopy [7; 8; 9]. Optical spectroscopy for detecting gases is based on the Beer-Lambert law [10; 11; 12], in which the light attenuation is related to the effective length of the sample in an absorbing medium, and to the concentration of absorbing species, respectively. By this theory, the emission wavelength of the narrow-linewidth diode laser is scanned over the target gas absorption line, and tunable diode laser absorption spectroscopy (TDLAS) has become an effective technique for the rapid and online analysis of gas component concentration, due to the advantage of high spectrum resolution [13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Direct detection and wavelength modulation spectroscopy are the most common sensing methods of TDLAS [23; 24]. Comparatively speaking, wavelength/frequency modulation spectroscopy is less vulnerable to the effects of background noise and more suitable for detecting trace gases. Moreover, modulation spectroscopy is widely used for the detection of various gases with the advantage of high signal-to-noise ratio (SNR) [25; 26]. Wavelength modulation spectroscopy (WMS), frequency modulation spectroscopy (FMS), and two-tone frequency modulation spectroscopy (TTFS) are the main techniques in modulation absorption spectroscopy. Since each technique has its strengths and weaknesses, they have also been applied for detecting methane depending on the situation.  
In this review, after introducing methane absorption characteristics and recent progress of tunable diode lasers (TDLs), recent advances in methane detection using modulation spectroscopy are presented.
# Bacterial Indole-3-Acetic Acid Influences Soil Nitrogen Acquisition in Barley and Chickpea
###### Abstract  
Farming of barley and chickpea is nitrogen (N) fertilizer dependent. Using strategies that increase the nitrogen use efficiency (NUE) and its components, nitrogen uptake efficiency (NUpE) and nitrogen utilization efficiency (NUHE) would reduce the N fertilizer application in the soil and its adverse environmental effects. We evaluated the effects of three different strains of diazotroph _Klebsiella_ (_K.p._ SSN1, _K.q._ SGM81, and _K.o._ M5a1) to understand the role of biological nitrogen fixation (BNF) and bacterial indole-3-acetic acid (IAA) on NUE of the plants. A field study revealed that _K.p._ SSN1 results in profound increment of root surface area by eightfold and threefold compared to uninoculated (control) in barley and chickpea, respectively. We measured significant increase in the plant tissue nitrogen, chlorophyll content, protein content, nitrate reductase activity, and nitrate concentration in the inoculated plants (_p_\(\leq\) 0.05). Treated barley and chickpea exhibited higher NUE and the components compared to the control plants (_K.p._ SSN1 \(\geq\) _K.q._ SGM81\(>\)_K.o._ M5a1). Specifically, _K.q._ SGM81 treatment in barley increased NUpE by 72%, while in chickpea, _K.p._ SSN1 increased it by 187%. The substantial improvement in the NUpE and NUE by the auxin producers _K.p._ SSN1 and _K.q._ SGM81 compared with non-auxin producer _K.o._ M5a1 was accompanied by an augmented root architecture suggesting larger contribution of IAA over marginal contribution of BNF in nitrogen acquisition from the soil.  
nitrogen use efficiency; nitrogen uptake efficiency; nitrogen utilization efficiency; _Klebsiella_; indole acetic acid; root architecture 1
# Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator
## 3 System Control
### Acceleration Feedback Enhanced  
When the aerial manipulator works in strong wind, although the \(H\infty\) controller can ensure system stability with proper parameters, the control performance will be seriously reduced by the wind disturbance. To attenuate disturbance of wind and unmolded part of the robotic arm, the acceleration feedback enhanced method is used in the \(H\infty\) controller. Firstly, we used the Kalman Filter to estimate the acceleration and angular acceleration of the hex-rotor, and they are denoted by \(\hat{a}_{b}\) and \(\hat{a}_{b}\), respectively [34]. As we know, velocity and acceleration can form a first order linear system:  
\[\left\{\begin{array}{c}v_{k}\\ a_{k}\end{array}\right\}=\left[\begin{array}{cc}1&\Delta t\\ 0&1\end{array}\right]\left[\begin{array}{c}v_{k-1}\\ a_{k-1}\end{array}\right]+\xi_{k} \tag{27}\] \[\overline{v}_{k}=v_{k}+\eta_{k}\]  
where, \(v_{k}\) is true velocity at time \(t_{k}\). \(\Delta t\) is the time interval, and \(\xi_{k}\) is the process noise which is assumed to be zero mean multivariate normal distribution with covariance \(Q_{k}\), and \(\xi_{k}\sim N(0\),\(Q_{k})\). \(\overline{v}_{k}\) is the measurement velocity at time \(t_{k}\). \(\eta_{k}\) is the observation noise, assumed to be zero mean Gaussian white noise with covariance \(R_{k}\), and \(\eta_{k}\sim N(0\),\(R_{k})\). The critical elements for the Kalman Filter are the covariance of process and observation noise. With the measurement velocity \(\overline{v}_{k}\) and system Equation (27), the acceleration can be estimated by Kalman Filter. For the angular acceleration, it is similar to the linear acceleration.  
Based on Equation (14), the total disturbance in the translational and rotational dynamic can be estimated as follows:  
\[\hat{\Delta}=\left[\begin{array}{c}\hat{\Delta}_{1}\\ \hat{\Delta}_{2}\end{array}\right]=\left[\begin{array}{c}\
u_{1}-\hat{a}_{b }\\ \
u_{2}-\hat{a}_{b}\end{array}\right] \tag{28}\]  
where \(\hat{\Delta}_{1}\) and \(\hat{\Delta}_{2}\) are the estimated disturbance in the translational and rotational dynamic, respectively  
Usually, the estimated acceleration includes high frequency noise because of the vibration, so that estimated disturbance, \(\hat{\Delta}\), cannot be used directly in the controller. The wind disturbance tends to lower frequency. As shown in Figure 3, to eliminate high frequency noise, \(\hat{\Delta}\) is combined with a low pass filter as follow:  
\[\
u_{af}(S)=Q(S)\hat{\Delta}(S) \tag{29}\]  
where \(Q(S)\) is a second order low frequency pass filter. \(\
u_{af}=\left[\begin{array}{cc}\
u_{1 af}&\
u_{2 af}\end{array}\right]^{T}\) is an acceleration feedback enhanced term, and can be added into the controller of Equation (12) and (13) to compensate wind disturbance.
## 4 Discussion
### Effect of MeJA on the Flavonoid and Carotenoid Accumulation in Citrus Juice Sacs In Vitro  
Jasmonic acid is a plant hormone that acts as a signal of plant responses to biotic and abiotic stresses, including the regulation of the bioactive compounds accumulation in plants [32]. It has been reported that MeJA was effective to induce flavonoid accumulation in apples [33], red Chinese pear [34], red raspberry [35], and blackberry [36]. To date, the studies of MeJA-regulated flavonoid accumulation have been mainly focused on anthocyanin and phenolic biosynthesis, while its effects on the flavonoids that are specifically accumulated in the citrus fruit have been largely unknown. In the present study, the results showed that the MeJA treatment significantly decreased the contents of hesperidin, poncirin, and rhoifolin in the juice sacs of citrus fruit, and as a result the total flavonoid content was much lower than that of the control. The gene expression results showed the downregulation of expression of _CitCHS1_, _CitCHS2_, and _CitF3H_ was consistent with the decrease in the flavonoidscontents in the juice sacs. In addition, we found that the MeJA treatment not only inhibited flavonoid accumulation, but also caused a significant decrease of carotenoids in the juice sacs. Indeed, the contents of all-_trans_-violaxanthin, 9-_cis_-violaxanthin, lutein, as well as total carotenoid were much lower than that of the control. Moreover, the expression of _CitHYb_, which is a key gene responsible for xanthophyll biosynthesis, was downregulated by the MeJA treatment. Recently, the roles of jasmonate on carotenoid accumulation has been reported in lettuce, tomato, and maize kernels, in which it was indicated that jasmonate positively regulated the accumulation of lycopene and lutein, and a deficiency of jasmonate led to a great reduction of lycopene in tomato fruit [18; 37; 38]. However, the effect of jasmonate on carotenoids accumulation was dose dependent, i.e., when the concentration of MeJA was higher than 0.5 uM, the carotenoid biosynthesis was inhibited in tomato and maize kernels. In the present study, we treated the juice sacs with 10 uM MeJA and found that the carotenoids content was significantly decreased in the juice sacs. These results were in agreement with previous studies in tomato and maize kernels [38]. In future studies, research on the effects of different concentrations of MeJA on carotenoid biosynthesis is expected, which should contribute to improving the carotenoid accumulation in the juice sacs of citrus fruit.
## 5 Experiments and Discussions
### Hardware Experiments and Analyses
#### 5.3.3 Sensing Experiments and Results  
To test the actual performance of the SCMWC prototype, we conducted a series of experiments. At first, we use the Keysight M8190A Arbitrary Wave Generator and Agilent N9936A to produce a signal consists of two discrete frequency bands: a pure sine waveform at 250 MHz and a quadrature  
Figure 15: Link budget of the hardware system.  
phase shift keying (QPSK) signal at 800 MHz with 5 MHz bandwidth. And both amplitude is around \(-10\) dBm. Figure 16a,b depicts the time-domain and spectrum of both input signal and low rate samples. In Figure 16c, the detected power spectrum is presented. From the three figures above, we can see the accuracy of detected power spectrum is pretty high, which indicates our system can sense two bandlimited signals at 250 MHz and 800 MHz precisely.  
Then, to test the stability, we conducted the following experiments. First using Matlab to generate nine carrier frequencies randomly in every 100 MHz. The frequency support is \(\mathbf{F}=[143,291,318,426,514,613,786,857,954]\) (MHz). Then, we randomly choose two frequencies among \(\mathbf{F}\) to carry out same experiment as above. The total number of times running the experiments is 500. Correct detection is defined as \(\hat{S}_{x}\supseteq S_{x}\) similarly. The result is shown in Figure 16d. As can be seen, during these experiments, the detection accuracy is pretty stable around 90% percent. This demonstrates the possibilities of the proposed SCMWC prototype to be used in actual CR system.
## 3 Results and Discussion
### Results of Inhibition Efficiency Test of IL1 and IL2 as Corrosion Inhibitor by the EIS Method
These data show that the corrosion in the brackish water system occurred quickly due to the significant increase in Icorr/T at a low temperature (25 \({}^{\circ}\)C) as shown in Figure 6a,b. A correlation between the Icorr/T and ln Icorr/T shows that it is indirectly proportional. With the increase in 1000/T values, the ln Icorr/T values decreased. The results also showed that the corrosion reaction occurs easily at higher temperatures. The data of ln (Icorr/T) versus 1/T were also supported by the \(\Delta\)G* values without the addition of ILs which are very high (higher than 70 kJ/mol) as presented in Figure 6c,d, leading to rapid corrosion in the system. To inhibit the corrosion reaction in the system, the Icorr value should be low resulting in the slow oxidation of carbon steel [13; 20; 21]. The addition of IL1 and IL2 increased the hydrophobic properties of the system and inhibited the corrosion reaction between the carbon steel and corrosion agents. These results clearly show that corrosion reactions in the system are inhibited by ILs because the addition of ILs decreases the \(\Delta\)G* values [42]. The maximum corrosion inhibition was achieved with 80 ppm of IL1 and 100 ppm of IL2 at the temperature of 25 \({}^{\circ}\)C as shown in Figures 6c and 6d, respectively. The \(\Delta\)G* decreased after the addition of IL1 and IL2 to 20 kJ/mol and 30 kJ/mol, respectively. The corrosion reaction energy requirement can be decreased up to 40-50 kJ/mol after the addition of IL1 and IL2. When the IL concentration was increased higher than 80 ppm, the performance of IL1 decreased. This is because IL1 tended to saturate and agglomerate at a concentration above 80 ppm. The same phenomenon happened for IL2 when the concentration was increased to higher than 100 ppm. Therefore, the optimum concentration for IL1 is 80 ppm and the optimum concentration for IL2 was 100 ppm at room temperature (25 \({}^{\circ}\)C). The experimental results by the EIS method show that IL1 and IL2 have good corrosion inhibitor activities on carbon steel surfaces under the brackish water condition. These data were validated by the Tafel method as described below.
# Polyp Generalization via Diversifying Style at Feature-Level Space
## 4 Results
### Experimental Results of Kvasir-SEG
#### 4.2.1 Generalizability on CVC-ClinicDB  
The experiment utilized CVC-ClinicDB as the second dataset for training. Table 3 presents the quantitative results of our proposed method alongside various state-of-the-art (SOTA) methods. Our method achieves a Dice coefficient score of 0.881 and a mean IoU of 0.798, slightly surpassing DCRNet, ACSNet and HardNet-MSEG. Notably, there is a substantial performance gap between U-Net, UNet++ and our proposed method. Additionally, SwinE-Net, FRCNet and TransFuse exhibit commendable scores using all metrics, including FM, SM, maxE and MAE, surpassing our proposed method with marginal improvements. However, it is worth noting that these networks are trained on multi-source datasets. Despite being trained on a single-source dataset, our proposed method achieves comparable performance, approaching the performance levels of FRCNet, TransFuse and SwinE-Net.