esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Study of the Performance of a Crossed Rib Diffuser in Room Acoustic Control
## 3 The Experiment Using a Small Meeting Room
### Comparisons of Room Acoustic Parameters
#### 3.2.2 Early Decay Time \(Edt\)  
Figure 11a,b shows a comparison of spatial averaged \(EDT\)s measured in each case and reductions in \(EDT\) relative to case 0 normalized with a just noticeable difference (JND) for each case. Here, we used 5% as the JND, which is from ISO3382-1 [29]. The normalized difference was used as the measure to present a magnitude of change in the acoustic parameter before and after the installation of the diffusers. We note that whether the normalized differences and perceptibility of difference have a linear relation or not is unclear. Cases 1-6 show shorter \(EDT\) at all frequency bands than case 0, as in the \(T_{20}\) results. Case 6, with CRD-A installed onto two surfaces, presents the shortest \(EDT\) values at all frequencies. Case 6 showed reduced \(EDT\) compared to that of case 0--more than 4.9 and 10.6 JND, respectively, at 500 Hz and 1 kHz, indicating that CRD-A has potential for greatly improving reverberation in reflective small meeting rooms. Although case 5, in which the CRD was installed onto two surfaces, showed a longer \(EDT\) than case 3 at frequencies higher than 500 Hz, case 5 reduced \(EDT\) by 2.2 and 4.6 JND at 500 Hz and 1 kHz in comparison with case 0, respectively. This result presents that CRD without a sound absorbing structure can also improve reverberation in the room more than JND. Additionally, case 5 showed a shorter \(EDT\) of more than 1 JND compared to case 4 at frequencies higher than 250 Hz, indicating that CRD has better performance than the 1D periodic diffuser with same installation area for broadband reverberation control.  
Figure 10: Spatially averaged reverberation time, \(T_{20}\)s, in a small meeting room for cases 0–6.
## 5 Experimental Results
### Results of the Navigation Process After Compensating for Gyro Drift  
After the calibration process, the gyro bias can be compensated to resolve the navigation-resolving process. In order to verify the effectiveness of the compensation method, we conduct several experiments to analyze the improvement of navigation accuracy. In our test, the IMU was put on a rotational turntable (as shown in Figure 9). The initial longitude and latitude of the IMU was acquired from the GNSS, which was also used as the initial parameters for the IMU to conduct its initial alignment. In our experiment, the RINS setup is working for a time of 6 h, and both the conventional navigation algorithm and the compensated method are conducted and compared.  
Figure 8: Diagram of our gyro drift calibration method.  
Our RINS comprises three gyros, three accelerometers and a turntable. The gyro we use is the laser ring gyroscope with a zero-bias stability better than \(0.01^{\circ}/\mathrm{h}\); the bias stability of the accelerometer is better than \(5~{}\upmu\mathrm{g}\). In the calibration process, the turntable rotates with a speed of \(360^{\circ}/\mathrm{min}\), and its rotation angle output was compared with the resolved azimuth angle using the conventional navigation algorithm to calibrate the gyro bias.  
The azimuth error comparison result is show in Figure 10. The blue line shows the difference between the navigation-resolving azimuth results and the turntable rotational output angle. Since the turntable has a high angle measurement accuracy (better than arcsecs) and this error does not diverge or accumulate with time, the difference can be regarded as the azimuth errors of the navigation algorithm. The gyro bias could be calibrated by fitting this curve with Equation (23) (as shown in the red line). From the least square fitting results, the gyro bias in our system could be \(0.036^{\circ}/\mathrm{h}\).  
In order to verify the improvement of navigation accuracy using the compensated gyro bias, the gyro bias was compensated in the navigation process, and the improvement of the navigation is analyzed. The results are shown in Figures 10-12. Three independent experiments were conducted. The longitude, latitude and position error results were given. As shown in Figure 11, the blue line shows the navigation results resolved from the measured gyro angle, while the red line represents the results calculated after compensating for gyro bias. From the results shown in Figures 11-13, we could see that this compensation method could improve the navigation accuracy from \(7~{}\mathrm{km}\) to \(1~{}\mathrm{km}\) over a period of \(6~{}\mathrm{h}\).  
Figure 10: Calculated azimuth error and the fitting curve.  
Figure 9: The experimental setup of a one-axis IMU on a rotational turntable.
# Plant Exosomal Vesicles: Perspective Information Nanocarriers in Biomedicine
## 8 Methods for Loading Biomolecules into Nanovesicles  
For a long time, the presence in plant cells of a molecular mechanism that regulates the specific composition of the exosomes remained an unresolved issue. It was found that the profile of miRNAs in the nanovesicles is strikingly different from the profile of all miRNAs in the cells [44], indicating the selective nature of their transport. It turned out that the TET proteins fraction, associated with nanovesicles, contains several RNA-binding proteins: argonuate (AGO1), helicases, and annexins. Moreover, AGO1 is the only representative of this family of proteins that is secreted by nanovesicles and binds precisely to the exosomal, rather than cellular, miRNAs [82]. On the contrary, the annexins demonstrate a nonspecific binding to miRNAs, but play an important role in their stabilization in the exosomes. The ESCRT complex is simultaneously involved in the loading of intraluminal vesicles with both ubiquitylated proteins, which are further broken down by lysosomes (Figure 1, 4b), and those secreted as a part of the exosomes (Figure 1, 4a). However, the exact mechanism of protein selection for one or another pathway still remains not fully understood [27]. An ESCRT-independent transport pathway has so far been described for a relatively small number of proteins and is likely to be involved in certain highly specialized processes [31] (Figure 3).  
Currently, the largest number of studies is devoted to the use of native exosomes containing such biomolecules as proteins, miRNAs, and secondary metabolites inherent in certain plant species [62]. At the same time, the therapeutic potential of native nanovesicles is determined mainly by the plant species used for the isolation [26]. It should be noted  
Figure 3: Methods for loading biomolecules into nanovesicles.  
that there is no information in the literature about any adverse reaction on the part of the recipient organism induced by the introduction of plant nanovesicles that indicates their safety and biocompatibility.  
The composition of exosomes can also be artificially altered by introducing both high- and low-molecular-weight target molecules (Figure 3). There are two main methods of loading nanovesicles: active and passive. Active loading includes such methods as sonication, extrusion, and freeze and thaw cycles that temporarily disrupt the nanovesicle membrane, allowing various compounds to diffuse inwards, after which the membrane is repaired [100]. Passive loading is an incubation process during which the loading takes place. The incubation is carried out by two methods: incubation of nanovesicles with a compound [101], and incubation with donor cells of target molecules [102].
# THz Rectennas and Their Design Rules
## 5 Power Level
### Received Wave Bandwidth
#### 5.2.2 Impact from the Rectifier Device  
This impact would quantitatively affect the \(\eta_{j}\) as stated earlier. A good example might be attributed to [PERSON] and [PERSON] [66] where they studied the effect of the rectified wave bandwidth on the rectification efficiency starting from a monochromatic light, all the way to a wideband wave representing the black body radiation at sun temperature. The diode model is a simplified piecewise-linear diode model with zero reverse leakage current and was solved using the harmonic balance method. They found out that the efficiency can reach 100% for monochromatic light if the correct DC voltage is reached. However, the efficiency drops to 40% when a wideband signal is applied to the circuit. This number reminds us of the 44% Shockley-Queisser limit [9] calculated for p-n  
Figure 10: Equal time mutual coherence function (EMCF) function curve for the quasi-monochromatic light for wavelengths \(\lambda=$1.3\,\mathrm{\SIUnitSymbolMicro m}$,$0.9\,\mathrm{\SIUnitSymbolMicro m }$,$0.5\,\mathrm{\SIUnitSymbolMicro m}$\) and for solar black body radiation (Adapted with permission from [34], [PERSON], 2013).  
junction solar cells. Again, this study assumes a perfectly received wideband signal at the antenna side disregarding any of the aforementioned issues noted in the earlier section.  
In fact, the findings for both impacts suggest that splitting the received wideband signal into narrower sub-bands will not only increase the wave coupling efficiency with the antenna \(\eta_{a}\), but will also increase the diode efficiency \(\eta_{j}\) as denoted in (1). In practice, thermophotovoltaics (TPV) have been around for many decades [89] and have recently become a hot research area [90; 91; 92]. Broadly speaking, they re-emit the absorbed radiation at different frequencies and bandwidths. [PERSON] et al. [92] suggest using them with rectennas and propose many design prototypes. Some trials have taken place to commercialize their application [89; 93].
## 3 System Modelling and Analysis
### System Composition and Description  
Figure 2 intuitively shows all slot states of the system. A gray grid indicates an active user that has a packet to send, while colorless grids refer to those users that have no packet to send. Obviously, if the number of gray grids in a slot is more than one, this slot will collide. If a user's packet can be correctly received, the user will be free at the beginning of the next slot and send a newly arrived packet with the probability \(p_{f}\), so \(p_{f}\) is the probability that a packet is sent for the first time. If a user's packet conflicts, the user is backlogged at the beginning of the next slot and retransmits a replica of this conflicting packet with the probability \(p_{r}\), so \(p_{r}\) is the probability that a packet is retransmitted.  
Note that the use of signature codes brings an overhead on the system, and it is necessary to calculate the optimal length of the signature code word. Different users are provided with different signature codes. \(S_{m}\) represents the signature code of user \(m\). The length of the signature code \(L\) should ensure that a sum of up to \(K\) out of \(M\) signature codes are uniquely decodable. According to [10], \(L\) can be expressed as  
\[L=\left|\log_{2}\left(M^{K}-1\right)\right|+1\approx\lceil K\log_{2}M\rceil. \tag{2}\]  
From Equation (2), we can see that \(L\) is proportional to the maximum number of detectable conflicting users \(K\). The larger \(K\) is, the longer the signature code will be. Thus, the greater the overhead that will be brought to the system. If \(K\) is too large, it will result in an unreasonable length ratio between the information data and the signature code. For example, most machine-to-machine (M2M) communications transmit small scale information data, which is suitable for the case where \(K\) is small. However, the packet loss rate will be high, if \(K\) is too small, resulting in low transmission efficiency. In order to reduce the system overhead and the number of retransmissions as much as possible while improving system transmission efficiency, this paper considers recovering packet conflicts where there are no more than three users based on the discussion in Section 2.2. If there are more than three conflicting users, the receiver directly discards the conflicting packets and requests that the sender retransmits.
## 3 Results and Discussion
### Gas Permeability  
Figure 6 shows the relationship between the gas permeability and moisture content. It is noted that the gas permeability of the soils was measured immediately after drying for various times. It is observed the gas permeability of compacted clay increased with the rise in moisture. The relationship of GCL K\({}_{\rm p}\) and moisture content can be reflected in three stages in Figure 6. The first trend in stage I represents K\({}_{\rm p}\) decreasing sharply with increasing moisture content ranging from 4% to 30%. The second trend in stage II shows K\({}_{\rm p}\) very slightly decreasing with the increasing moisture content of 30-50%. The third trend in stage III represents K\({}_{\rm p}\) decreasing sharply with the increasing moisture content of 50-100%. According to the comparison between this study and geosynthetic clay liners in previous studies, as shown in Figure 6, the gas permeability of both CCC and GCL decreases with the rise in moisture content. In addition, the test results of [PERSON] et al. [11] showed that when the moisture content of the GCL was 5-100%, the gas permeability at 20 kPa overburden pressure varied from 10\({}^{-13}\) m\({}^{2}\) to 10\({}^{-16}\) m\({}^{2}\), much lower than that of compacted clay. Only when the ZP content reached 1.0% was the gas permeability of ZP-modified compacted clay lower than that of the GCL, with moisture content in the range of 20% to 28%.  
Figure 5: Water loss percentage of the compacted clay mixed with different contents of ZP.  
The CCC sample total drying time is 17.5 h. The CCC's final state after drying can be compared to unveil the barrier performance under different ZP contents. Figure 7 shows the influence of ZP content on the gas permeability of the compacted clay after 17.5 h of drying. It can be observed that the moisture content of the clay enhanced as the content of ZP increased, while the gas permeability K\({}_{\rm P}\) gradually decreased. The moisture content and gas permeability showed an apparent negative correlation, meaning that gas permeability decreased with the rise in moisture content [11; 12; 13]. When the ZP content was 1.0%, the gas permeability of the clay after drying was 1.12 \(\times\) 10\({}^{-11}\) m\({}^{2}\), which was 97% lower than that of the ZP admixture content of 0.2%. As proved, ZP can increase the moisture content of clay after drying, thereby reducing its gas permeability. When the PAM content is 1%, the gas barrier performance of clay can be effectively improved by about one order of magnitude.  
Figure 6: Relationship between the gas permeability and moisture content of compacted clay after ZP modification [11; 13; 60].  
Figure 7: Influence of ZP content on gas permeability of the compacted clay.
## 4 Discussion
### The Impact of Human Activities on Vegetation Change  
The results indicate that the overall NDVI trend in the Aksu region has increased significantly, consistent with previous research that highlights the focus on ecological conservation and agricultural development in Xinjiang. Particularly, the \"Kekeya Greening Project [59; 60],\" recognized by the United Nations as one of the \"Global 500 Best Practices\", exemplifies Aksu's active exploration of paths for ecological civilization construction and economic development under the guidance of the \"Two Mountains\" theory. This model provides valuable insights for green development in other ecologically fragile regions.  
To isolate the effects of artificial projects from global warming and increased CO\({}_{2}\) concentrations, this study employs a proxy analysis using nighttime light intensity as an indicator of human activity. Regions with significant nighttime light intensity changes, indicative of urbanization and infrastructure development, were compared to areas with minimal human intervention. While the impact of climate factors such as temperature and precipitation was included in the correlation analysis, the dominance of human-driven projects is inferred from the spatial correspondence between increased nighttime light and NDVI trends. Future studies could enhance this approach by incorporating land-use data and higher-resolution remote sensing imagery to quantitatively separate these effects.
## 4 Results
### Results of the Analyses of a Single Factor's Effects
#### 4.1.4 Analysis of the Contrast Ratio  
After analyzing the results of the camera rotation, the influence of the contrast ratio between the foreground object and the background was measured. The authors considered five contrast ratio values: 1.5:1, 3:1, 7:1, 14:1 and 21:1, and their test numbers were 1066, 167, 1121, 164, 191, respectively. See Table 6 for the numerical average rates. Similar to the previous cases, the regression coefficients were computed by logistic regression and the test statistics (testing their zero values), moreover the appropriate \(p\)-values can be seen in Table 6.  
Comparing the results to the 1.5:1 contrast ratio, the 7:1, 14:1 contrast ratios produce significantly worse results in the probabilities, even the contrast ratio of 21:1 is on the 0.05 level. Therefore, the authors grouped the contrast ratios into two different groups: IMP_C, which contains 1.5:1 and 3:1, and the other, NO_C, which contains 7:1, 14:1 and 21:1. IMP_C has 1233 tests and NO_C has 1476, as seen in Table 7. For checking the equality of the probability of correct answers by logistic regression analysis, see Table 7.  
According to \(p\)-value = \(2.56\times 10^{-6}\), the IMP_C contrast ratio group gives a significantly better probability of correct answers than the NO_C group. This means that the bright scenes give better results.
# SDN-Based Routing for Backhauling in Ultra-Dense Networks
## 6 Proposed Approach
### Control-Flow Routing  
For the control traffic the bandwidth-constrained Dijkstra algorithm is applied using as edges' cost the inverse of the _residual capacity_, i.e., the edge's capacity minus the traffic demand already allocated on the edge. We define a _residual capacity_ matrix \(\mathbf{R}\), whose generic element is \(r_{e_{i,j}}\), and whose dimension is \(VxV\). This matrix is initialized at the edges' capacity value, i.e., \(\mathbf{R}(0)=\mathbf{C}\).  
The SDN controller performs the path search successively for all control flows that must be transmitted in the network. In particular, for each flow, the algorithm finds the path that has the minimum cost and can support the flow's bandwidth demand. Then the residual capacity is updated by removing from each edge that belongs to the selected path, the capacity demand of the allocated control flow. More in detail, at the \(s\)-th iteration the \(s\)-th control flow is selected. The selected flow is characterized by the source-cell \(v\), the destination-cell \(w\), and the traffic demand \(d^{C}_{v,w}\). The Dijkstra algorithm is used to determine the path from cell \(v\) to cell \(w\), with the minimum cost and sufficient capacity, i.e., \(L^{C}_{v,w}=\{e_{v,i},e_{i,j},...,e_{k,w}\}\). Hence, the residual capacity of edges belonging to the path \(L^{C}_{v,w}\) is updated as: \(r_{e_{i,j}}(s)=r_{e_{i,j}}(s-1)-d^{C}_{v,w}\) with \(e_{i,j}\in L^{C}_{v,w}\). Moreover, the capacity allocated to the control flow is \(a^{C}_{v,w}=d^{C}_{v,w}\).
# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
## 5 Experimental Evaluation
### Impact of Identification Delay  
There are two sources of time delay for PEiD. One is the sampling time that happens in the initial step of the algorithm. The other is the transition time caused by channel state changes. The former can be ignored since it no longer occurs after the window is loaded. However, the latter can impact the performance of real-time LOS/NLOS identification. It counts because most packets remaining in the window belong to the NLOS path at the moment of LOS blocking. It takes time to update them with the new samples. When LOS samples coexist with NLOS ones in the window, the identification result of the model is ambiguous. Increasing the window length will lengthen the time of ambiguous results.  
To confirm the impact of this problem on PEiD, we record the identification result at a fixed time after the channel state change. For each sample, the time of walking out of the room is regarded as the channel state transition point. Keeping the window length at 95, we test the performance with different time shifts and calculate the average identification accuracy. The results are shown in Figure 12.  
The results depicted in Figure 12 reveal that the identification rate remains below 30% when the sampling time is short. However, as the delay surpasses 150 ms, the situation begins to improve. Specifically, when the delay is approximately 235 ms, corresponding to the average delay in a static situation, the identification accuracy can reach 95%. The complete identification of a sample requires a time delay of around 328 ms.  
We believed such a latency is insignificant and acceptable for many in-home applications like gesture recognition and fall detection to determine the channel state. However, for applications with higher real-time requirements, such as trajectory tracking, our approach may still face challenges to maintain desired performance.
## 2 Results and Discussion
### Optimization of Headspace-SPME Parameters
#### 2.4.2. Extraction Temperature  
Temperature plays a crucial role in the partition of the analytes in liquid phase--vapor phase--fiber coating equilibria, and therefore greatly affects the sensitivity of the technique [34]. Actually, temperature can have opposite effects: in headspace sampling, higher temperatures promote analytes to move from the aqueous phase to the headspace, thus increasing the mass transfer rates into the fiber [35]; on the other hand, higher temperatures can decrease the distribution coefficient of the analyte from the fiber coating material back again to the headspace, decreasing the amount extracted [35]. In this work, the influence of extraction temperature in the interval between 30 \({}^{\circ}\)C and 50 \({}^{\circ}\)C was studied. The results obtained are shown in Figure 5b, where an increase in the total peak area is observed until 45 \({}^{\circ}\)C, followed by a marked decrease for higher temperatures, in agreement with previous works [35; 36]. Therefore, and since no differences in behavior were detected for individual compounds, a temperature of 45 \({}^{\circ}\)C was taken as the optimal extraction temperature.  
Figure 5: Effects of the different extraction conditions on the efficiency of pD14 towards standard solutions. Extraction time (**a**) (\(T\) = 40 \({}^{\circ}\)C, 2.5 wt % of NaCl, 200 rpm, concentration: 50 mg:L\({}^{-1}\)); extraction temperature (**b**) (\(t\) = 15 min, 2.5 wt % of NaCl, 200 rpm, concentration 20: mg:L\({}^{-1}\)) and salt concentration (**c**) (\(T\) = 45 \({}^{\circ}\)C, \(t\) = 15 min, 200 rpm, concentration: 2 mg:L\({}^{-1}\)).
## 3 Results and Discussion
### Unrestrained Concentric Dent-Crack Defects
#### 3.1.3 Effect of Dent Depth on Burst Pressure  
Increasing the dent depth from 0% to 6% OD while keeping the crack length and the crack depth constant did not change the burst pressure if the dent was formed at zero pressure (Figure 8a). But, where denting of the pipe was accomplished with the pipe already under pressure, increasing the dent depths at constant crack length and crack depth reduced the burst pressure, as shown in Figure 8b,c. The rate of drop in the burst pressure with dent depth increased with crack length and denting pressure.  
Dent-crack defects having dents \(>\) 4% OD deep, formed at denting pressure \(>\) 0.3 P\({}_{y}\) with a crack of length \(>\) 100 mm and depth \(\geq\) 0.5 t were likely to burst at pressures lower than MAOP of the pipe. All dents formed at 0.5 P\({}_{y}\) denting pressure and having crack of lengths \(>\) 100 mm inside them did not sustain the MAOP of the pipe.  
Figure 7: Variation of burst pressure of specimens with 0.5 t deep crack inside dents of different depths formed at: (**a**) Denting pressure = 0, (**b**) Denting pressure = 0.3 P\({}_{y}\); (**c**) Denting pressure = 0.5 P\({}_{y}\).