esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusion and Perspectives  
In this paper, we evaluated under high data rates the performance of HMC-MAC, a hybrid multi-channel MAC protocol for WSNs. It is based on 3-hop neighborhood channel allocation to avoid interference when data frame acknowledgements are used. We evaluated by simulation the efficiency of HMC-MAC and compared it to other MAC protocols. We considered the packet delivery rate, the number of collisions, frame repetitions, and dropped packets ratio as the main metrics. Results showed how HMC-MAC considerably reduces the number of collisions and frame repetitions compared to the other methods, and this in two different traffic scenarios and different random network topologies. HMC-MAC copes much better than the other protocols when we increase the offered load, this is even more obvious when we use a burst traffic generation.  
In the current version of HMC-MAC, the beacon propagation period is done in a pure TDMA fashion where each node has its own slot for broadcasting the beacon frame. In our future work, we plan on optimizing this period by reducing it using a mix of TDMA and FDMA based on the neighborhood discovery information. Also, we plan on optimizing the frequency of this period in order to limit the control traffic overhead. A trade-off should be found between the clock drift for maintaining synchronization between nodes, the dynamic aspect of the network in order to take into account new nodes joining the network and nodes leaving the network, and the control traffic overhead.  
Managing node failure in the current version of the protocol is not yet considered. One of the enhancements for HMC-MAC would be to consider redundant links to maintain connectivity and network activity in case of a node failure.  
In this paper, we did not optimize the routing protocol in order to achieve load balancing in the network. Hence, we observed a high rate of queue overflow because of the limited storage capacity of the packet queues especially on the nodes located near the sink. In our future works, we will investigate this point and try to achieve a better traffic load balancing to avoid queue overflow near the sink.  
Figure 16: Number of dropped packets for burst traffic.
## 2 Process Design
### Operating Conditions  
The reaction (Equation (1)) was tested at different reactor pressures, ranging from \(1\) to \(20\) atm. The observations show that increasing the reactor pressure adversely affects the rate of conversion [38; 47; 48]. This is explained by recalling that molecular diffusivity is inversely proportional to pressure, i.e., \(D_{M}\sim P^{-1}\)[44]. Hence, the mass transfer decreases as the pressure increases. Notice that the effect of pressure on the metal oxide conversion was investigated by keeping the partial pressure of the gaseous reactant constant, i.e., the percentage of H\({}_{2}\)S in the gas phase was gradually lowered as the pressure was increased such that the effects of a changing reactant concentration could be eliminated from the experiments. Hence, it is more favorable to run the removal process at atmospheric condition rather than pressurized.  
Regarding the operating temperature, despite the common perception that metal oxides are used only at elevated temperatures [7; 28; 49; 50; 51; 52], there are several works that show that certain metal oxides can remove H\({}_{2}\)S to ppm levels even at room temperature [53; 54; 55; 56]. The operating temperature of H\({}_{2}\)S removal with metal oxides can be anywhere between \(25\) and \(1100\)\({}^{\circ}\)C. The reaction (Equation (1)) is  
\begin{table}
\begin{tabular}{c c c c} \hline
**Shape of Pellets** & **A** & **B** & **C** \\ \hline Spheres & 1.740 & 0.390 & 1.140 \\ Cylinders a & 1.703 & 0.373 & 0.611 \\ \hline \end{tabular}  
* Aspect ratio height/diameter = 0.76 to 1.78.  
\end{table}
Table 1: Parameter values of Equation (3) for different pellet shapes [43].  
exothermic which means that at elevated temperature the equilibrium is shifted to the left. This implies that under thermodynamic control the higher the temperature, the higher the outlet concentration of H\({}_{2}\)S, i.e., less efficient removal. However, both the reaction kinetic and diffusion can benefit from elevated temperatures. Depending on the operating temperature, different metal oxides can be suitable for the purpose. This will be further discussed in Section 4.
## 2 Theory and Method
### Modelling of Coupling Errors  
Consider the pitch level as a case in point. When the stroke of the hydraulic cylinders on both sides is \(s_{4}\), if the contour errors on both sides of the mechanism are not equal, it will make the pitch angles on both sides different. The pitch angle of the table is determined by the side of the pitch mechanism that has a lagging motion, and because the table is viewed as a rigid tandem system, the pitch angle when the hydraulic rod is extended is determined by the side with the larger radius of the arcuate slot. Conversely, when the hydraulic rod is retracted, the pitch angle aligns with the side showing the smaller radius of the arcuate slot. The specific principle is illustrated in Figure 5.  
Define the contour error of the inner arc surface of the motion mechanism on both sides of the pitch layer as \(\delta_{41}\) and \(\delta_{42}\), and the actual rotation radius on both sides as  
Figure 5: Influence of surface contour error on pitching mechanism on both sides.  
and \(r_{42}\), and then the difference in contour error can be expressed as \(\Delta r_{4}=\delta_{42}-\delta_{41}\). An approximation of \(r_{4}\) is given by \(r_{4}+0.5\Delta r_{4}\), with \(\Delta{r_{4}}^{2}\) being approximately equal to 0. Ignoring the motion error of the hydraulic cylinder itself, the inverse kinematics model of the pitch and roll layers can be modified as  
\[s_{4}^{\prime}\approx\sqrt{m_{4}^{2}+r_{4}^{2}+r_{4}\Delta r_{4}-(2r_{4}+ \Delta r_{4})m_{4}\cos(\hat{x}_{4}+\theta_{4m})} \tag{7}\]  
\[s_{5}^{\prime}\approx\sqrt{m_{5}^{2}+r_{5}^{2}+r_{5}\Delta r_{5}-(2r_{5}+ \Delta r_{5})m_{5}\cos(\hat{x}_{5}+\theta_{5m})} \tag{8}\]  
Furthermore, the process of pitching motion may induce a coupling error \(\varepsilon_{y}\) in the roll angle, attributable to the disparate contour errors on either side of the mechanism, as depicted in Figure 6. Based on the assumption that the table is a rigid body system, the coupling error of the tumbling angle is determined by the projection in the \(Z\)-direction. Consequently, the model for the coupling error of the tumbling angle can be articulated as  
\[\varepsilon_{y}=\frac{1}{\hat{x}_{4}}\text{arctan}\left(\frac{\Delta r_{4} \cos\hat{x}_{4}}{L_{4}}\right)\times 100\% \tag{9}\]  
where \(L_{4}\) is the horizontal distance between the circular grooves on both sides of the pitch layer. Similarly, the tumbling motion of the table leads to a coupling error \(\varepsilon_{x}\) in the pitch angle, which can be expressed as  
\[\varepsilon_{x}=\frac{1}{\hat{y}_{5}}\text{arctan}\left(\frac{\Delta r_{5} \cos\hat{y}_{5}}{L_{5}}\right)\times 100\% \tag{10}\]  
where \(L_{5}\) is the horizontal distance between the circular grooves on both sides of the roll layer.
## 6 Results and Discussion
### Experimental Analysis of CC over DC with Obstacle  
The following experimental analysis is based on the performance evaluation of DC with CC. A wooden obstacle is placed in the direct path to analyze the performance of the system.  
Figure 8 shows the experimental results of DC and CC with obstacle. Here, significant performance improvement is obtained for CC as compared to DC. From Table 4, it can be seen that average channel capacity gain of CC over DC is 15.97% for BPSK, 16.12% for QPSK, and 17.01% for 8 PSK respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Capacity (Mbits/s/Hz)**} \\ \cline{2-10}
**Tx Antenna Gain (dB)** & \multicolumn{3}{c}{**BPSK**} & \multicolumn{3}{c}{**QPSK**} & \multicolumn{3}{c}{**8 PSK**} \\ \cline{2-10}  & **DC** & **CC** & **Gain (\%)** & **DC** & **CC** & **Gain (\%)** & **DC** & **CC** & **Gain (\%)** \\ \hline
0 & 1.94 & 2.26 & 16.49 & 2.04 & 2.35 & 15.19 & 1.99 & 2.43 & 22.11 \\
5 & 2.04 & 2.36 & 15.68 & 2.07 & 2.44 & 17.87 & 2.08 & 2.49 & 19.71 \\
10 & 2.08 & 2.41 & 15.86 & 2.11 & 2.49 & 18.00 & 2.14 & 2.51 & 17.28 \\
15 & 2.1 & 2.43 & 15.71 & 2.15 & 2.5 & 16.27 & 2.18 & 2.52 & 15.59 \\
20 & 2.13 & 2.48 & 16.43 & 2.21 & 2.53 & 14.47 & 2.21 & 2.57 & 16.28 \\
25 & 2.19 & 2.57 & 17.35 & 2.22 & 2.56 & 15.31 & 2.26 & 2.58 & 14.15 \\
30 & 2.24 & 2.56 & 14.28 & 2.29 & 2.65 & 15.72 & 2.29 & 2.61 & 13.97 \\ \hline \multicolumn{2}{c}{Average Gain} & \multicolumn{3}{c}{15.97} & \multicolumn{3}{c}{16.12} & \multicolumn{3}{c}{17.01} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of DC with CC with obstacle.  
Figure 8: Performance analysis of CC over DC with obstacle.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Capacity (Mbits/s/Hz)**} \\ \cline{2-10}
**Tx Antenna Gain (dB)** & \multicolumn{3}{c}{**BPSK**} & \multicolumn{3}{c}{**QPSK**} & \multicolumn{3}{c}{**8 PSK**} \\ \cline{2-10}  & **DC** & **CC** & **Gain (\%)** & **DC** & **CC** & **Gain (\%)** & **DC** & **CC** & **Gain (\%)** \\ \hline
0 & 2.01 & 2.27 & 12.93 & 2.05 & 2.36 & 15.12 & 2.08 & 2.42 & 16.34 \\
5 & 2.07 & 2.37 & 14.49 & 2.08 & 2.45 & 17.78 & 2.12 & 2.51 & 18.39 \\
10 & 2.08 & 2.42 & 16.34 & 2.16 & 2.5 & 15.74 & 2.17 & 2.52 & 16.12 \\
15 & 2.12 & 2.48 & 16.98 & 2.19 & 2.51 & 14.61 & 2.25 & 2.56 & 13.77 \\
20 & 2.16 & 2.49 & 15.27 & 2.26 & 2.55 & 12.83 & 2.27 & 2.58 & 13.65 \\
25 & 2.26 & 2.55 & 12.83 & 2.29 & 2.59 & 13.10 & 2.31 & 2.6 & 12.55 \\
30 & 2.29 & 2.57 & 12.22 & 2.31 & 2.61 & 12.98 & 2.33 & 2.62 & 12.44 \\ \hline \multicolumn{2}{c}{Average Gain} & \multicolumn{3}{c}{14.44} & \multicolumn{3}{c}{14.59} & \multicolumn{3}{c}{14.75} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of DC with CC without any obstacle.
## 6 Conclusions and Future Work  
Despite the fact that object recognition techniques have seen a significant progress during the last years, content-based video analysis is almost not present in current video portals. In this work, we gave a broad overview of the field and proposed an extensive framework to enable object-based text queries and video scene retrieval in various portals. The presented annotation process starts by the selection of a single keyframe in which the object-of-interest is shown. Positive and negative examples of this object are then selected by interactive annotation with the help of a fast keyframe suggestion. In a further step, an integrated analysis and evaluation takes place to select an appropriate recognition approach for each object automatically. Eventually, an object is automatically annotated in large-scale video collections and the generated metadata is stored in a compact, binary form. Video portals can either integrate the entire framework or selected parts of it in order to improve video retrieval on different levels. An advantage of the proposed framework is the systematic support of improved computer vision techniques that will be proposed in the future. This benefit is inherited from the configurable object recognition infrastructure CORI that can be extended with all kinds of visual features, training and recognition strategies. High processing performance is assured by the support of distributed computer architectures and an intelligent reuse of intermediate results during feature extraction.  
As a proof-of-concept, we developed a simple annotation prototype and performed a case study on two video datasets. In this process, we demonstrated that the framework can be used to efficiently annotate objects in short videos and full-length feature films. The results of automatically selected recognition approaches significantly outperformed the baseline results and further improvements are expected by the planned integration of general and object specific recognition approaches. Furthermore, the presented framework achieved competitive results for example-based video object retrieval on the instance search dataset of TRECVID 2011. As future work, we plan to automatically detect objects as pre-processing step for the interactive object annotation. Moreover, we will improve the region detection approach by the use of image segmentation to capture object boarders more accurately. Finally, we intend to integrate and test the framework in existing video portals.
## 4 Discussion
### GPP, AET, and WUE and Response to the Drought  
Drought intensity, frequency, timing, and duration have a significant impact on carbon and water cycles [95; 46]. During extreme drought (D1) events, our study observed negative  
Figure 11: Intersection map of the sWUE during an extreme drought year and the composite maps of the sGPP and sAET. In this figure, the symbol “+” represents positive standardized anomalies of WUE, GPP, and AET, while the symbol “\(-\)” represents negative standardized anomalies of WUE, GPP, and AET during exceptional drought (D1).  
anomalies in ecosystem GPP and varied responses in WUE. Terrestrial ecosystems in the region were extensively affected by SPEI droughts from 1999 to 2009, with GPP and WUE anomalies correlated with the SPEI-6 monthly timescale. The regional-scale GPP and WUE responses during D1 to D5 drought events ranged from slight to high declines and slight to high increases, respectively. The central part of Iran, under an arid climate pattern, experienced extreme drought episodes (D1) with a significant increase in WUE.  
Hydroclimatic conditions play a crucial role in shaping the spatiotemporal variability of ecosystem WUE [39; 102; 103]. The carbon-water cycle exhibits a robust relationship, where disturbances in solar radiation, temperature, precipitation, and ecosystem dynamics can influence WUE components such as GPP and AET simultaneously. For instance, under hot temperatures, WUE decreases due to a sharp drop in GPP. Precipitation directly affects the variability of ecosystem WUE by immediately influencing evapotranspiration processes and indirectly affecting vegetation carbon uptake through soil moisture regulation [104; 105; 106; 107]. On the other hand, VPD has been found to reduce WUE in various forest types [108; 109; 110; 111].  
During extreme droughts in most parts of the study area, the WUE increased, highlighting the resilience of ecosystems to rapid soil moisture fluctuations, especially in mountainous regions [43; 112]. We observed increased WUE during drought episodes (e.g., 2007-2010) due to decreased GPP and decreased AET in most areas (AET was significantly more and faster affected by drought than GPP; see Figure 5). However, in most northern regions, annual WUE declined due to decreased GPP and decreased AET (Figures 11 and 12). In regions such as northern Iraq and central parts of Turkey, where WUE decreases during severe and extreme drought years, GPP is notably more affected by drought than AET. This leads to diminished WUE, attributed to water and heat stress constraining vegetation growth [19]. Extreme droughts can exacerbate water deficits beyond the capacity of plants' self-regulation (stomatal conductance adjustment and transpiration reduction), leading to vegetation mortality and a broad decline in ecosystem productivity [27; 28; 95]. Nevertheless, in most study areas, ecosystem WUE generally increased under drought stress, as the decrease in GPP was less than that of ET [39; 44; 113; 114].
## 1 Introduction
### Terminology  
This subsection defines some common terminology to be used in the rest of the paper, since different works use different names to refer to the same thing.  
* **Event type**: It refers to a physical phenomenon or to some combination of them, providing semantical meaning to the data it refers to. Therefore, an even type could refer to a single physical phenomenon (e.g., temperature, humidity, _etc._), or to more complex combinations (e.g., an event type 'FIRE' is generated when a sensor measures a temperature higher than 100 \({}^{\circ}\)C, the smoke detector is over 12 ppm and the humidity has been reduced by a 5% in the last 10 measurements).
* **Rendezvous node or home node**: It is the node responsible of storing the data related to a given event type.
* **Producer node**: When a node detects an event, it forwards the information related to that event towards the suitable home node. Then, we name that node as producer.  
* **Consumer node**: It is a node that queries a rendezvous node to retrieve the data related to a given event type.
* **Replica or replication node**: When several nodes play the role of home node for the same data type, we refer to them as replica or replication node.
* **Relay node**: It is a node in the network that does not have any of the previous roles (replica, consumer or producer) so that it is only used to relay messages.  
The rest of this paper is organized as follows: The first DCS proposal, GHT, is discussed in Section 2.. Section 3. covers routing proposals for DCS. Section 4. explains the solutions focused on balancing the storage in DCS systems. The main proposals regarding multi-replication DCS systems are presented in Section 5.. Section 6. briefly describes some interesting works about DCS that are related to other topics different than those presented in previous sections. Our Quadratic Adaptive Replication (QAR) scheme for multi-replication DCS systems is described and compared with other proposals in section 7.. Finally, DCS research challenges are discussed in Section 8. that concludes the paper.
# Hybrid Shielding for Hypervelocity Impact of Orbital Debris on Unmanned Spacecraft
## 3 Results and Discussion
### Honeycomb Core Cell Density  
It was anticipated that the honeycomb core cell density, i.e., the number of cells per unit length across the width of the shield gap, would be a critical parameter influencing the energy absorption efficiency of the shield. In order to test this hypothesis and quantify the influence of the honeycomb core cell density, a number of models were developed using an SPH particle density of 7 particles/mm for the projectile and region of impact, as discussed in Section 2.4.1, and different cell densities. All model parts, including the honeycomb, were set as AL2024-T3 and a finite-element mesh element size of 1 mm was used.  
The results in Figure 13 show how the total kinetic energy of the debris particle was dissipated to increase the honeycomb core cell densities (models C-0 to C-10). Model C-0 is included as it represents the standard Whipple shield, i.e., without a honeycomb core. It can be clearly observed that the kinetic energy of the debris particle significantly decreases when a honeycomb component is introduced, corresponding to an increase in the energy dissipation ranging from a minimum of 42.6% to a maximum of 74.6%, as shown in Table 3. Figure 13 also shows that the change in kinetic energy of the debris particle for cores with more than six cells across the width is minimal. The difference in energy dissipation between model C-6 and model C-10 is only 3.1%. Therefore, for the modelled impact conditions, increasing the core cell density above six cells (model C-6) is not beneficial in terms of energy absorption and will clearly be detrimental to the weight of the structure.  
The simulation frames in Figure 14, showing the numerical model of each honeycomb configuration at time \(t=16.2\) us, further support these observations and conclusions, clearly showing that the dispersion of particles becomes more evident when increasing honeycomb core cell density. There is some visible penetration of rogue SPH particles, which is a known issue with SPH modelling. Common strategies to try to mitigate these nonphysical effects include changing the contact algorithm and/or refining the finite-element mesh of the impacted part. In the present case, however, these two strategies were tested and proved to not decrease rogue particle penetration without significantly increasing the cost of the computation. Additionally, the energy of these rogue particles was estimated to be insignificant (less than 1%) compared to the total energy of the problem. A higher level of fragmentation and damage to the honeycomb shell occurred closer to the front plate, which further supports the results shown in Figure 13. A complete absence of channelling effects is also visible.  
Figure 12: Kinetic energy dissipation for the honeycomb core density, shell thickness, material and impact location models, relative to reference cases (see Table 3).
## 1 Introduction
By using a super-vector framework with MOL, the ILBC system design could be expressed as a linear discrete state-space plant. Then the \(H_{\infty}\) performance and global convergence to equilibrium were guaranteed by an algebraic approach. The main contribution compared with the previous research can be enumerated as follows:  
1. An \(H_{\infty}\)ILBC was proposed for the certain and uncertain DPS to diminish the vibrations in the presence of iteration-varying distributed/boundary disturbances. 2. The dynamics of the vibrating string system in the form of a state-space system were obtained instead of a PDE system. 3. An algebraic approach was employed to confirm that the \(H_{\infty}\)ILBC is globally converging to equilibrium. The organization of this study is given as follows. Section 2 suggests the formulation of the motion equations for the vibrating string. In Section 3, \(H_{\infty}\)ILBC is investigated for mitigating the string's oscillation and compensating the system parameters uncertainties and stability analysis is proofed. In Section 4, simulations are carried out to illustrate \(H_{\infty}\)ILBC-based MOL schemes performance, while Section 5 contains the discussion of the simulations. Finally, conclusion of the proposed is given in Section 6.