esa-sceva/satcom-chunk-collection huggingface dataset:
# A Broadband Active Microwave Monolithically Integrated Circuit Balun in Graphene Technology
## 3 Results
### Balun Circuit Characterization  
First functionality tests have been performed thanks to time domain measurement using a RF source set at 100 MHz and \(-\)10 dBm. One single RF probes is used at the input while a differential probe is used at the output.  
Two other RF probes are used for convenience to apply the DC bias. \(+\)2 V and \(-\)2 V are applied on VDD and VSS, respectively. Also, \(+\)2 V is applied on the input thanks to a bias tee. A photography of the balun under measurement conditions is shown in Figure 3a.  
Scope measurements are presented in Figure 3b, highlighting the functionality of the circuit with 180\({}^{\circ}\) phase shift between the two outputs. In that first measurement setup, the measurement is not calibrated and does not allow high-frequency measurement. In the following, we discuss high-frequency measurements carried out with a 4-port PNAX vector network analyzer (VNA) which can obtain calibrated data at the probe level. In order to perform the differential measurement with the VNA, the intermediate frequency is set to 100 Hz and the input power is fixed to \(-\)10 dBm.  
The setup is calibrated using a calibration kit dedicated to differential probes. Measurements reveal that the two output signals have a phase difference of 180\({}^{\circ}\) up to the GHz range (see Figure 4a). This difference decreases to 170\({}^{\circ}\) at 6 GHz. The output signals are attenuated of about 20 dB with an amplitude imbalance lower than 0.5 dB below 6 GHz (see Figure 4b). The bias current is 40 mA giving a power consumption of 160 mW. The strong attenuation of 20 dB is directly correlated to the transistors' performances and especially to the high output conductance gDS, a specific weakness of graphene devices. Also, the power consumption can be optimized by reducing gate width to obtain the optimal tradeoff between power consumption and output impedance of the balun.  
Figure 2: **(a)** Schematic circuit diagram of the balun and **(b)** photography of the balun highlighting its layout. Scale bar is 180 \(\upmu\)m.
## 4 Simulation Results and Analysis
### Stress Analysis of the Surrounding Rock Induced by Mining Activities
#### 4.2.2 Stress Evolution of the Silstone Interlayer  
After the mining of the 43 upper01 working face, the stress of the top and floor strata is shown in Figure 8a,b. As mining continued, the extent of the mined-out area increased, and the stress of the surrounding rock near the edge of the mined-out area showed a fluctuating rise, with edge stress reaching between 57 and 60 MPa. The stress at the floor edge remained in the range of 45 to 55 MPa. Within a small distance from the edge of the mined-out area, the surrounding rock stress sharply decreased below the initial level, with top and floor stress dropping to approximately 10 MPa. Thereafter, the stress of the top and floor strata remained close to the initial level. Overall, the final mining stress exhibited distinct stress zoning characteristics: an elevated stress zone, a reduced stress zone, and an initial rock stress zone.  
Figure 8: Stress evolution in immediate roof and floor with 43 upper01 working face excavation: (**a**) Stress evolution of the roof, and (**b**) Stress evolution of siltstone interlayer.  
After mining at the 43\({}_{\text{upper}}\)02 working face, the stress of the top and floor strata followed a similar trend, as shown in Figure 9a,b. After mining of the 43\({}_{\text{upper}}\)02 working face, the stress in the top and floor strata remained at a near-zero level, while the previous top and floor stress in the 43\({}_{\text{upper}}\)01 working face transitioned to a non-uniform multi-peak stress state, with maximum stress levels of 70 MPa at the roof and 47 MPa at the floor. The mining of the 3\({}_{\text{lower}}\) coal seam, influenced by initial mining activities, caused the top and floor strata to exhibit nearly identical characteristics, apart from differing stress levels.  
As shown in Figure 10a,b, prior to the mining of the 3\({}_{\text{lower}}\) coal seam, the siltstone interlayer did not display the initial rock stress level observed in the 3\({}_{\text{upper}}\) coal seam, nor the stress zonation characteristic seen after mining. Instead, the originally non-uniform multi-peak stress state became more chaotic, with a significant increase in the number of stress peak points. It is worth noting that, although the stress in the interbedded rock layer after secondary mining exhibited a chaotic multi-peak state, the overall stress range remained almost consistent with the pre-mining state, with only localized stress values showing a marked increase.
# Scalability of Mach Number Effects on Noise Emitted by Side-by-Side Propellers
## 3 Numerical Results
This could be due to the phase shifts of the dipole signals coming from the two propellers of the counter-rotating configuration, which produce a destructive interference even at small observation distances. Figure 9 shows the same results for the co-rotating configuration. In this case, the differences between near- and far-field predictions are greater, although the overall trend is the same as the counter-rotating configuration, in terms of both tip-clearance and tip Mach number effects. In this case, the phase shifts of the dipole signals seem to be such that the dipole contributions do not cancel out each other in the near field. Figure 7: Co-rotating propellers, OASPL directivity pattern on rotor disk for different TCs and different phase shifts (black line \(\gamma=0^{\circ}\), blue line \(\gamma=12^{\circ}\), red line \(\gamma=24^{\circ}\), green line \(\gamma=36^{\circ}\)); upper \(M_{t}=0.52\), lower \(M_{t}=0.7\). Figure 1:0 Counter-rotating configuration, difference between far-field and near-field OASPL for the defined TCs and for \(M_{t}=0.52\) (upper) and \(M_{t}=0.7\) (lower). Figure 1:1 Co-rotating configuration, difference between far-field and near-field OASPL for the defined TCs and for \(M_{t}=0.52\) (upper) and \(M_{t}=0.7\) (lower). Finally, Figures 10 and 11 show, respectively for the counter-rotating and co-rotating case, the comparison between near-field and far-field directivity patterns evaluated on the circular array of microphones located on the rotor disk plane, for different phase shifts and tip Mach numbers. For the sake of conciseness, only results for the phase angles \(\gamma=0^{\circ}\) and \(\gamma=36^{\circ}\) and \(\mathrm{TC}=0.05d\) and \(\mathrm{TC}=d\) are shown (the conclusions that are drawn in these cases are valid also for all the other configurations examined). Note that, also in this case, for comparison purposes, the OASPLs have been scaled by the ratio between the hemisphere radii. These results prove that the directivity patterns of the near-field and far-field predictions are in excellent agreement for the counter-rotating case, whereas some discrepancies appear in the co-rotating configuration. Indeed, in this latter case an angular shift between them is present, due to the non-vanishing dipole contribution in the near-field noise discussed above.
## 2 Background  
### Multi-Objective Optimization Problem (MOP)  
MOP consists of finding a set of decision vectors which optimize the objective vector, whose elements represent an ensemble of conflicting objective functions.  
Mathematically, an MOP (for minimization case) is described as below [25]:  
\[\begin{array}{l}\min F(x)=[f_{1}(x),f_{2}(x),\cdots,f_{m}(x)]^{\text{T}}\\ \text{s.t.}g_{j}(x)\leq 0,j=1,2\cdots,p\end{array} \tag{1}\]  
where \(x=[x_{1},x_{2},\ldots,x_{n}]^{\text{T}}\in X\) is the decision vector made up of \(n\) decision variables, \(X\) represents the decision space, \(f_{i}(x),i=1,2,3,\ldots,m\) signifies the objective function, \(m\) denotes the number of objective functions and \(g_{j}(x)\) indicates the constraint function. Since several objective functions are simultaneously carried out by multi-objective optimization, the concept of dominance is used to compare the solutions.  
In the minimization problem, a decision vector \(u\in X\) dominates another vector \(v\in X\) (symbolized by \(u\prec v\)), only if:  
\[f_{i}(u)\leq f_{i}(v),\forall i\in\{1,2,\cdots,m\} \tag{2}\]  
The solution that is not dominated by other feasible ones is called non-dominated or the Pareto solution. Furthermore, the set of non-dominated solutions PS is known as the Pareto set and is defined as:  
\[PS=\{u\in X,\exists v\in X,u\prec v\} \tag{3}\]  
The image of the Pareto set PS by the objective vector represents the Pareto front (commonly known as Pareto front PF).
Materials_**2012**, \(5\), 1084-1113; doi:10.3390/ma5061084  
_Materials_  
**ISSN 1996-1944**  
www.mdpi.com/journal/materials  
_Article_  
**Glass Fiber Reinforced Polypropylene Mechanical Properties Enhancement by Adhesion Improvement**  
**[PERSON] and [PERSON] ***  
Planta Piloto de Ingenieria Quimica, PLAPIQUI (UNS-CONICET), Cno. La Carrindanga Km. 7, Bahia Blanca 8000, Argentina  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +54-291-4861700; Fax: +54-291-4871600.  
_Received: 10 April 2012 / Accepted: 26 May 2012 / Published: 18 June 2012_  
**Abstract:** Glass fibers (GF) are the reinforcement agent most used in polypropylene (PP) based composites, as they have good balance between properties and costs. However, their final properties are mainly determined by the strength and stability of the polymer-fiber interphase. Fibers do not act as an effective reinforcing material when the adhesion is weak. Also, the adhesion between phases can be easily degraded in aggressive environmental conditions such as high temperatures and/or elevated moisture, and by the stress fields to which the material may be exposed. Many efforts have been done to improve polymer-glass fiber adhesion by compatibility enhancement. The most used techniques include modifications in glass surface, polymer matrix and/or both. However, the results obtained do not show a good costs/properties improvement relationship. The aim of this work is to perform an accurate analysis regarding methods for GF/PP adhesion improvement and to propose a new route based on PP _in-situ_ polymerization onto fibers. This route involves the modification of fibers with an aluminum alkyl and hydroxy-a-olefin and from there to enable the growth of the PP chains using direct metallocenic copolymerization. The adhesion improvements were further proved by fragmentation test, as well as by mechanical properties measurements. The strength and toughness increases three times and the interfacial strength duplicates in PP/GF composites prepared with _in-situ_ polymerized fibers.  
**Keywords:** glass fiber/polyppropylene composites; _in-situ_ polymerization; improvement adhesion; mechanical properties
# UAV Image Multi-Labeling with Data-Efficient Transformers
## 4 Experimental Results
### Results
#### 4.4.2 Experiment 2: The Role of Encoder's Layers
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Number of Layers** & **Sp** & **Re** & **Pr** & **F1** & **F2** & **Avg** & **mAP** & **HL** & **RL** &
\begin{tabular}{c} **Time** \\ **(Seconds)** \\ \end{tabular} \\ \hline
1 & 96.25 & 69.14 & 76.14 & 72.47 & 74.63 & 82.69 & 57.20 & 7.75 & 28.14 & 731 \\
2 & 95.93 & 73.15 & 75.96 & 74.40 & 75.17 & 83.54 & 59.33 & 7.43 & 24.65 & 1022 \\
3 & 96.04 & 73.95 & 76.41 & 75.16 & 75.91 & 85.00 & 60.35 & 7.21 & 23.98 & 1288 \\
4 & 95.92 & 71.60 & 75.22 & 73.37 & 74.47 & 83.76 & 58.05 & 7.67 & 26.76 & 1673 \\
5 & 96.80 & 73.11 & 79.82 & 76.32 & 78.38 & 84.95 & 62.33 & 6.70 & 24.66 & 2019 \\
6 & 95.78 & 76.17 & 75.78 & 75.97 & 75.86 & 85.98 & 61.24 & 7.11 & 23.00 & 2353 \\
7 & 96.89 & 74.59 & 80.58 & 77.47 & 79.31 & 85.74 & 63.85 & 6.40 & 23.53 & 2556 \\
8 & 96.41 & 77.96 & 78.97 & 78.46 & 78.76 & 87.18 & 64.81 & 6.32 & 21.54 & 2835 \\
9 & 97.12 & 77.98 & 82.40 & 80.13 & 81.48 & 87.55 & 67.50 & 5.71 & 20.87 & 3036 \\
10 & 97.14 & 80.54 & 82.99 & 81.75 & 82.49 & 88.84 & 69.71 & 5.31 & 18.67 & 3347 \\
11 & 97.49 & 78.88 & 84.50 & 81.59 & 83.31 & 88.19 & 69.77 & 5.25 & 18.85 & 3769 \\
12 & 97.91 & 76.77 & 86.43 & 81.32 & 84.31 & 87.34 & 69.79 & 5.20 & 15.42 & 4156 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the Civezzano multi-label dataset. Figure 6: Attention heat maps generated by the first, sixth, and last layers of the encoder for classifying different samples from Trento multi-label datasets.
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
## 5 Conclusions  
This paper investigated a low-altitude sensing model based on ISAC. First, we discussed the 3D sensing capabilities of a single base station. Subsequently, considering the differences between low-altitude sensing and traditional communication networks, we designed a single base station 3D sensing model. Furthermore, we proposed a multi-BS cellular-like topology to achieve a continuous and seamless low-altitude sensing network. Finally, we conducted actual tests that validated the practical performance. In this paper, all results were based on the first commercial ISAC network. We utilized mainstream equipment vendors to build the test environment and analyze the collected data. The single base station sensing model was verified to effectively replicate the sensing range of a single ISAC base station. Meanwhile, the multi-BS topology effectively mitigated sensing blind spot issues. The research innovatively proposed a mathematical model for qualitatively evaluating the sensing range, which exhibits universality and applicability. It can be extended to various networked sensing designs. This is crucial for the future development of wide-area 3D sensing networks.  
Conceptualization, X.L.; methodology, X.L. and X.D.; software, X.L.; validation, X.L. and X.D.; formal analysis, X.L.; investigation, X.L.; resources, X.L.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, X.D., W.X., W.W., J.Y. and W.-Y.D.; visualization, X.L. and W.W.; supervision, W.X., J.Y. and W.W.; project administration, W.X. and J.Y.; funding acquisition, W.X. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by National Science and Technology Major Project-Mobile Information Networks under Grands 2024 ZD1300200.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
This work is supported in part by National Science and Technology Major Project-Mobile Information Networks under Grands 2024 ZD1300200.  
The authors declare no conflicts of interest.
## 4 Simulation Experiments
### Analysis and Results
#### 4.2.3 Orthogonal Analysis Results  
The results of the orthogonal analysis are presented in Table 3. The orthogonal analysis evaluates the optimum ratio of the average vehicle delay in the coordinated through direction as the target variable using the range method. The analysis considers the larger-is-better criterion for the best level and the smaller-is-better criterion for the worst level. This provides the range (extreme values), reflecting the importance of each factor in influencing the experimental results. It also yields the best and worst levels within the proposed range of factors.  
Based on the results of Table 2, for the L-S cycle transition, the importance of each factor in influencing the experimental results, from most to least significant, is as follows: cycle length difference > inflow traffic volume > transition segment length > desired speed. The optimization effect of the proposed coordinated control method is at its best when the inflow traffic volume is 2000 pcu/h, the transition segment length is 1240 m, the desired speed is 65 km/h, and the cycle length difference is 10 s. Conversely, the optimization effect is at its worst when the inflow traffic volume is 2500 pcu/h, the transition segment length is 440 m, the desired speed is 60 km/h, and the cycle length difference is 40 s. These two combinations can be denoted as (2000, 1240, 65, 10) and (2500, 440, 60, 40). When the traffic volume is 3000 pcu/h, the transition segment length is 440 m, the desired speed is 70 km/h, and the cycle length difference is 50 s, the optimization effect of the proposed coordinated control method is at its best. Conversely, when the traffic volume is 1500 pcu/h, the transition segment length is 1040 m, the desired speed is 50 km/h, and the cycle length difference is 30 s, the optimization effect is at its worst. These two combinations can be denoted as (3000, 440, 70, 50) and (1500, 1040, 50, 30). However, in practical applications,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Transition Type**} & \multirow{2}{*}{**Analysis Indicators**} & \multicolumn{4}{c}{**Influencing Factor**} \\ \cline{3-6}  & & **Inflow Traffic Volume (pcu/h)** & **Transition Section Length (m)** & **Desired Speed (km/h)** & **Difference in Cycle Lengths (s)** \\ \hline L-S cycle & Best level & 2000 & 1240 & 65 & 10 \\  & Worst level & 2500 & 440 & 60 & 40 \\ S-L cycle & Best level & 3000 & 440 & 70 & 50 \\  & Worst level & 1500 & 1040 & 50 & 30 \\ \hline L-S cycle & \(R\) & 0.17 & 0.13 & 0.06 & 0.38 \\ S-L cycle & \(R\) & 0.36 & 0.11 & 0.22 & 0.14 \\ \hline \hline \end{tabular}  
* Ratio of the average delay (s) of the coordinated through direction before and after optimizing the signal timing.  
\end{table}
Table 3: Analysis of the orthogonal experimental range for the coordinated through direction and the corresponding optimal ratio of average delay \({}^{\text{a}}\).  
considering safety factors, speeds exceeding 60 km per hour should be carefully considered in conjunction with the actual situation.
## 3 Results and Discussion  
### TEM Imaging and FT-IR Analysis  
Representative TEM images of Fe\({}_{3}\)O\({}_{4}\) nanoparticles are shown in Figure 2a. Fe\({}_{3}\)O\({}_{4}\) magnetic particles were found to have a mean diameter of \(\sim\)20 nm and to be rod-like in shape. As shown in Figure 2b, the TEM image indicated that the synthesized P\(p\)PD polymers formed nanofibrous mats, which were amorphous as determined by their electron diffraction patterns. Similar results have been reported for polyaniline/Fe\({}_{3}\)O\({}_{4}\) nanocomposite [24].  
Figure 2: TEM (transmission electron microscopy) and electron diffraction patterns of (**a**) Fe\({}_{3}\)O\({}_{4}\); (**b**) P\(p\)PD; and (**c**) P\(p\)PD/Fe\({}_{3}\)O\({}_{4}\).  
The P_p_PD fibers were observed to be randomly cross-linked, and the magnetic particles were aggregated with the fibers (Figure 2c). The TEM image of the P_p_PD/Fe_\({}_{3}\)O_\({}_{4}\) composites was similar to that of the P_p_PD fibers, but small light dots were observed on the composite, which were absent from the image of P_p_PD fibers, suggesting that Fe_\({}_{3}\)O_\({}_{4}\) magnetic particles were embedded into the P_p_PD nanofibrous mats. It has been reported that there is a strong interaction between the quinoid rings of P_p_PD and Fe_\({}_{3}\)O_\({}_{4}\) nanoparticles that enhances the formation of P_p_PD/Fe_\({}_{3}\)O_\({}_{4}\) nanostructures [25]. The P_p_PD/Fe_\({}_{3}\)O_\({}_{4}\) composites could be efficiently separated from the solution using a magnet.  
The FT-IR spectra of the nanocomposites are shown in Figure 3. As seen in Figure 3a,b, a broad peak at around 3413 cm\({}^{-1}\) corresponded to the N-H stretching vibration of the secondary amine group in the polymer chain [24]. The peak at 1613 cm\({}^{-1}\) was attributed to the stretching vibrations of benzenoid rings, and the bands at 1355 cm\({}^{-1}\) and 1230 cm\({}^{-1}\) corresponded to C-N stretching vibration with aromatic conjugation [26]. Besides the above bands, a band at 572 cm\({}^{-1}\), which was attributed to Fe_\({}_{3}\)O_\({}_{4}\), was clearly observed in Figure 3a,c, indicating that Fe_\({}_{3}\)O_\({}_{4}\) magnetic nanoparticles existed in the newly synthesized nanocomposite [27]. In brief, these absorption bands confirmed the successful synthesis of the P_p_PD/Fe_\({}_{3}\)O_\({}_{4}\) composite, in agreement with the findings of TEM imaging.
## 4 Concluding Remarks and Discussion
They are presumed to be driven by a unified thermodynamic regime that stimulates the concurrent evolution of two warm cloud layers within the limited space in the low troposphere. It is noteworthy that the warm-warm double layer system that was examined in this study does not exactly correspond to the double layer warm clouds within the boundary layers, which are associated with deeper and cumulus-coupled boundary layers ([PERSON], 2016). Therefore the physical regime for maintaining the double layer structure within boundary layer cannot be directly used to explain the formation of a warm-warm double layer system, mostly of which are above boundary layer. More studies are still needed to reveal the underlying factors that result in these distinct forms of warm clouds. There is evidence of regional dependence for cloud altitude and thickness, but they are unlike that of the occurrences of each kind of cloud layers. A clear poleward gradient exists for all of them. In the ITCZ and warm pool regions, all of the warm cloud layers tend to reach higher altitudes despite the relative few occurrences therein. For single-layer warm clouds, there are extensive regions with cloud layer thickness above 1440 m. In contrast, there is no region with a thickness exceeding 720 m for the upper layer warm clouds in the warm-warm double layer system. The present study conducted a preliminary examination on oceanic warm clouds while taking into account those embedded in the multilayered structures. It is believed that such knowledge on cloud overlapping is critical for fully understanding the distribution of warm clouds in three-dimensional space. The results that were derived in this study could help in validating the cloud results of numerical models, which are indeed three-dimensional in nature. They could also be used to improve the estimation of cloud radiative forcing, since it is affected by cloud occurrences and especially their vertical structures. It should be pointed out that solid explanations for the above cloud features could not be presented by only using these satellite data themselves. Specific studies are required to reveal the underlying physical regimes that induce these features of warm clouds, which may utilize various data sources, including those from ground site observations, reanalysis fields, and numerical simulations. In particular, for the warm-warm double layer clouds, besides the geometric features, their microphysical and optical properties need to be further clarified. Moreover the terrestrial warm clouds would also be investigated in future work to help form a complete characterization of global warm clouds. [PERSON] performed the data analysis and participated in the writing of manuscript. [PERSON] designed the study and wrote the manuscript. [PERSON] helped the data preparation and participated in the discussion on results. This work was funded by the National Natural Science Foundation of China (41575019 and 41875030). We would like to thank the NASA CloudSat team for sharing CPR product data in the CloudSat Data Processing Center.