esa-sceva/satcom-chunk-collection huggingface dataset:
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 8 Performance Evaluations
### Impact of Network Size and Density on Popular Messages  
Figure 8 summarises our findings for our metrics (delivery ratio, delivery delay and network overhead) for Keetchi and Epidemic for increasing number of nodes (Figure 8, left) and decreasing cache size (Figure 8, right), for popular messages only. We first focus on network size and popular messages.  
When the number of nodes in the network increases, without increasing the area, the number of contacts increases exponentially and thus also the communication opportunities. This does not influence the work of Keetchi significantly, as it leverages the broadcast advantage. Of course, it does influence what we called the \"significant neighbourhood change\" (refer to Section 5) and forces Keetchi to send more popular messages than unpopular ones. This effect can be seen in Figure 8e, where the overhead of Keetchi increases very slowly with increasing number of nodes.  
While Keetchi masters well larger number of nodes, this scenario puts Epidemic under stress. With more communication opportunities, Epidemic attempts to synchronise its cache with each and every node it encounters, resulting in very high overhead. Notably, the delivery ratio for both protocols in this scenario for popular data items stays almost the same and very high.  
In terms of delay, Keetchi exhibits a slight disadvantage compared to Epidemic. The data still reach most of the nodes, but, due to its randomness, it may not select certain popular data. We believe that the quality of service does not suffer dramatically, as we define the application scenario as delay tolerant and since also Epidemic exhibits delays in the order of hours. Furthermore, it depends on which metric is used in the quality of service calculation--many users would naturally prefer higher delays instead of high energy costs (represented here by overhead). However, it remains one of our main research questions for the future to better understand the dynamics of the delay and to improve Keetchi.  
_Take-home message._ With increasing number of nodes, Keetchi delivers the same amount of data as Epidemic at a significantly lower cost.
# A 100 Gb/s Quad-Lane SerDes Receiver with a PI-Based Quarter-Rate All-Digital CDR
## 2 Proposed Multi-Lane SerDes Receiver Architecture
### Proposed CTLE  
Figure 7 shows the block diagram of the proposed CTLE, which consists of a near-ground (NG) receiver [35; 36], an adaptive CTLE, and a power detector [37]. High-speed small-swing differential input signals (RX\({}_{\text{IN}}\), RX\({}_{\text{INb}}\)) passing through the parasitic RLC input network (including package/bond wire/PAD and electrostatic discharge (ESD) devices) are applied as inputs to the NG receiver.  
The NG receiver adopts a dual gain-path common-gate amplifier with feed-forward capacitors (C\({}_{\text{FF}}\)) that boost high-frequency gain. The NG receiver also utilizes an adaptive bias generator (ABG) to compensate for the input common-mode variation and to improve the channel impedance matching performance. The input terminal of the NG receiver provides the impedance matching function for the channel termination, which has the advantage of not requiring additional channel termination devices. If the amplitude of the high-speed serial input signal changes, the characteristics of the NG receiver  
Figure 6: Block diagram of the E/L detector and the bang-bang PD.  
Figure 7: Block diagram of the proposed CTLE.  
and impedance matching are affected. The ABG using common-mode feedback can reduce the current mismatch problem in the receiver input stage according to the V\({}_{\text{CM}}\) level changes, which results in improved impedance matching performances. The PMOS active inductor loads of the NG receiver provide efficient high-frequency compensation with less area and power consumption than the passive inductor loads.  
The proposed adaptive CTLE is a two-stage differential pair amplifier with active inductor loads. It is used to compensate for the additional high-frequency gain at about 12.5 GHz to generate open-eye data (CDR\({}_{\text{IN}}\), CDR\({}_{\text{INb}}\)). The power detector [37] using the spectrum balancing technique creates a control voltage (V\({}_{\text{CTRL}}\)) that can adaptively adjust the gain of the CTLE. The power detector consists of a high-pass filter (HPF), a low-pass filter (LPF), a rectifier, and a voltage-to-current converter. Figure 8 shows the simulated frequency response of the proposed CTLE for different V\({}_{\text{CTRL}}\) voltages. The proposed CTLE achieves a maximum gain boosting characteristic of about 19.7 dB at a Nyquist frequency of 12.5 GHz.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 3 Proposed Method
### Gait Feature Extraction Module
#### 3.1.2. Vector Representation of Gait  
To construct a more compact and comprehensive vector representation of gait, we used the same pipeline as in GraphGait [13]. At the core of our model lies the ResGCN network, as introduced by [PERSON] et al. in [40] and adapted for our specific context. The ResGCN network processes feature tensors (\(\mathbf{G}^{i}\)), which are the graph representation of gait information (see Section 3.1.1), to obtain a vector representation (\(\mathbf{z}_{\text{gait}}^{i}\)). \(\phi^{\text{gait}}\) the function that transforms \(\mathbf{G}^{i}\) into \(\mathbf{z}_{\text{gait}}^{i}\).  
The structural design of each ResGCN block blends advanced graph convolution layers with traditional 2D temporal convolution layers. This combination allows for the processing of both spatial and temporal features. In the architecture, a residual connection is integrated to maintain the fidelity of the input data as it progresses through the network layers. Additionally, there is an optional bottleneck structure designed to optimize the processing of features. This network comprises multiple ResGCN blocks sequenced in such a way that each block enhances the output of its predecessor.  
After these ResGCN blocks are processed, the data undergo an average pooling operation. This step aggregates the features across the entire network, distilling them into a more manageable form. Following this pooling, a fully connected layer takes over, transforming the pooled features into a final, comprehensive feature vector representing gait.  
The network is trained with the supervised contrastive (SupCon) loss function proposed by [PERSON] et al. in [44] to effectively differentiate between gait patterns.  
For each individual (\(i\)), ResGCN converts the gait graph (\(\mathbf{G}^{i}\)) into a vector (\(\mathbf{z}_{\text{gait}}^{i}\)) that captures the person's gait characteristics over \(T\) frames in a compact and analyzable form, as expressed by the following equation:  
\[\mathbf{z}_{\text{gait}}^{i}=\phi^{\text{gait}}(\mathbf{G}^{i}) \tag{2}\]  
This equation represents the transformation of the gait graph data into a vector that captures an individual's gait patterns, enabling effective gait-based person Re-ID.
## 3 Results and Discussion
### Impedance Spectroscopy Results
However, after 4 h of dispersion, the impedance of CNTs0.3-IV (Figure 2d) did not reach the impedance of the specimens with filler content above the percolation threshold. CNTs0.5-I also showed a minor drop of the impedance until the third hour of mixing, followed by an increase after the fourth hour of dispersion by almost half an order of magnitude (Figure 2d). This denoted the structural degradation of the MWCNTs due to extensive dispersion process. Both ternary systems revealed an almost linear amelioration of their electrical properties in the course of dispersion time, with CNTs0.5_CB2-I being slightly more conductive than CNTs0.5_CB1-I during the progress of dispersion (Figure 2). CNTs0.5_CB2-IV exhibited optimum electrical properties with a magnitude of impedance of about \(5\times 10^{4}\) Ohms, and the transition from Ohmic to non-Ohmic behavior was identified at almost \(10^{4}\) Hz (Figure 2d). In conclusion, synergy effects of MWCNTs and CB offered improved electrical properties compared to the binary systems.
## 3 Materials and Methods
### Defense Integration
#### 3.4.2 Machine Learning-Based Anomaly Detection  
In addition to traditional filtering methods, we implemented a comprehensive anomaly detection system using the EfficientAd model from the anomalib library. This system provides an additional layer of security by learning normal sensor behavior patterns and detecting deviations that may indicate attacks.  
The anomaly detection system operates on RGB and DVS sensor data, providing anomaly scores that can be used to identify potential attacks even when traditional filtering methods may not be sufficient. The system is trained exclusively on normal sensor data, allowing it to detect novel attack patterns without requiring prior knowledge of specific attack types.  
These defense mechanisms work in conjunction to provide comprehensive protection against the implemented attack vectors while maintaining real-time performance requirements for autonomous driving applications.
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## Abbreviations  
R Range to the observation point  
\(\theta_{i}\) Incident angle  
\(\theta_{r}\) Refracted angle  
\(\theta\) Observation angle from vertical laser beam axis  
\(\varphi\) Observation angle from horizontal axis  
\(t_{r}\) Optical transmissivity of the water  
\(\beta\) Thermal expansion coefficient  
\(C_{p}\) Specific heat of the water medium  
\(\mu\) Optical absorption coefficient  
\(a\) Laser beam radius  
\(\tau\) Laser pulse duration  
\(T\) Laser pulse repetition period  
\(k\) Acoustic wave number  
\(\omega\) Angular frequency of acoustic wave  
\(c\) Speed of acoustic wave
## 4 Conclusions  
The presented study showed that both soluble fibres, (SDF N) and inulin (SDF I), used in a mixture with OSA-starch, influenced the properties of emulsion and obtained powder. Emulsions reconstituted from powders had a slightly different microstructure than fresh samples, with the best particle size distribution profile of the reconstituted emulsion compared to the original emulsion occurring in the case of the sample containing 15% SDF N and 5% OSA-starch. SEM images showed that powders containing SDF N had a higher number of fine particles than those containing SDF I. However, the composition of the coating materials had no significant effect on the surface oil content, which remained extremely low. Juices with powders containing the same amounts of OSA-starch and SDF I or SDF N, were characterized by the most distinct colour compared to the control sample. The highest concentration of SDF I and the lowest concentration of OSA-starch resulted in the smallest changes in colour, however the colour change was still visible to the observer. The fortified juices were stable during storage, but greater differences were observed in the content of volatile components of the obtained juices. The added powders reduced the volatility of aroma compounds typical of apple juice but introduced propanal and hexanal, especially the powders with the highest OSA-starch share. The comparison of SDF N and SDF I showed that the impact of both of these preparations on the properties and functionality of linseed oil capsules in apple juice was practically the same. These results show that it is possible to add nanocapsules of omega-3-rich oils to apple juice to increase its nutritional value, but pilot-scale studies are needed.  
Conceptualization, D.O. and M.T.; methodology, D.O., M.T., S.C. and P.B.; formal analysis, D.O., M.T., P.B., S.C. and B.P.; investigation, D.O., M.T., P.B., S.C. and I.K.; resources, D.O.; data curation, D.O., M.T., S.C. and I.K.; writing--original draft preparation, D.O., M.T., S.C. and I.K.; writing--review and editing, D.O., M.T., S.C. and I.K.; visualization, M.T., P.B. and S.C.; supervision, I.K.; project administration, D.O.; funding acquisition, M.T. and I.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to thank [PERSON] for technical assistance during the spray drying process and SEM images of obtained powders.  
The authors declare no conflicts of interest.
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 4 Results
### Ground Level Field Trial  
To select a reliable telecommunications service provider for the drone drive test, first a ground-level drive test was conducted to evaluate the performance of four available telecommunication service providers in terms of \(RSRP\) and data rate. \(RSRP\) is a key measurement parameter indicating the received signal power level in an LTE network and can be calculated as:  
\[RSRP=RSSI-10\log_{10}(12\times N) \tag{9}\]  
where, \(N\) is the number of resource blocks of the E-UTRA carrier \(RSSI\) measurement bandwidth and \(RSSI\) is the receive signal strength indicator. The \(RSSI\) is measured over the entire bandwidth and is the average of the total power (including intracell power, interference, and noise) observed within Orthogonal Frequency Division Multiplexing (OFDM) symbols that contain reference symbols.  
Figure 11 shows the drive test results in terms of cumulative percent of \(RSRP\). As results show, Maxis provides the most reliable coverage, wherein 90% of the time, \(RSRP\) is above \(-75\) dBm and in 100% it lies above \(-90\) dBm.  
On the other hand, Figure 12 shows the data rate results of four service providers in both uplink and downlink. Based on the result, Maxis provides the highest throughput value with an average of 6451 kbps in uplink and 41,332 kbps in downlink. Therefore, based on the \(RSRP\) and uplink throughput values, the Maxis network is carried up for further evaluation.
## 7 Real-World Applications and Challenges  
The integration of the Elliott wave principle with AI-driven multi-agent systems has demonstrated promising results in stock market forecasting, as evidenced by the experiments conducted using the ElliottAgents platform. The ability to accurately recognize complex wave patterns and generate actionable insights has the potential to significantly impact various real-world applications, particularly in the domain of financial analysis and trading. However, the deployment of such systems in practice poses distinct challenges that warrant further investigation.
## 2 Panel/Viscous Vortex Particle Aerodynamic Model
### Hybrid Method  
In the present method, almost all the wake panels that one would use in a normal panel method are replaced by vortex particles. At each time step, a new row of wake panels are emitted from the trailing edge, which is used to define the scalar potential jump, and their strength is determined to satisfy the Kutta condition. A second row of wake panels is generated by convecting the previous time step first row with the local velocity and by keeping their strength unchanged and is prepared to be converted into vortex particles at next time step, as depicted in Figure 1. Thus, there are always two rows of wake panels after the first time step, with the remaining portion of the wake represented by vortex particles.  
The conversion of doublet wake panels to vortex particles follows [PERSON]'s equivalence principle for which the velocity induced at any point by a dipole with strength \(\mu\) is equivalent to a surface vorticity \(\gamma=\mathbf{n}\times\
abla\mu\) and a boundary line vortex \(\mu\). For the low-order panel method, the dipole strength is constant within each panel; thus, the surface vorticity is zero and the dipole is equivalent to a vortex ring around the perimeter of the panel. The wake panel is transformed into a vortex particle in such a way that the zero and first moments of vorticity are conserved, as illustrated in Figure 1. It is noted that, in the present work, only three edges of the wake panel are integrated into the vortex particle while the fourth edge, which is adjacent and parallel to the second row wake panels' trailing edge, is treated as a vortex filament. The remaining vortex line can be regarded as modeling the circulation jump at the edges of the wake panel of the last row or as canceling out the circulations on the vortex particles so that [PERSON]'s circulation theorem is met [15; 19].  
When referring to the particle velocity, the contribution from panels must be accounted for. This can be evaluated by the theory of basic singularities. Meanwhile, the stretching effect from panels to particle can be considered by differentiating the velocity field. The particles also have an influence on each panel. In a traditional source-doublet combination panel method, the particles influence is added into the freestream conditions to solve for the source strength for each panel:  
\[-\sigma_{i}=\mathbf{n}_{i}\cdot(\mathbf{u}_{B}+\sum_{j=1}^{N}\mathbf{u}_{i,j}), \tag{17}\]  
where \(\sigma_{i}\) and \(\mathbf{n}_{i}\) are the source strength and normal vector of the \(i\)th panel, respectively, and \(\mathbf{u}_{i,j}\) is the induced velocity of particle \(j\) to panel \(i\).  
Figure 1: A description of the conversion of the wake doublet panel to vortex particles.
## 3 Results and Discussion
### Droplet Impact on Horizontally Moving Micro-Pillar Surfaces
#### 3.2.3 Maximum Spreading Diameter  
[PERSON] and [PERSON] [55] reported that when a droplet slides on an inclined superhydrophobic surface, the adhesion caused by solid-liquid contact leads to droplet stretching, thereby increasing its maximum spreading diameter. Additionally, they emphasized the dynamic pressure (\(P_{d}=\rho v_{n}^{2}\)) during impact, and based on these considerations, they derived the formula for the maximum spreading diameter of a droplet impacting an inclined superhydrophobic surface, that is,  
\[D_{\text{max}}^{*}=D_{0}^{*}+c\cdot tan(AOI)We_{n}^{1/2}(\rho v_{n}^{2}), \tag{14}\]  
where \(c\) is the adhesion coefficient, \(AOI\) is the angle of incidence, and \(D_{\text{max}}^{*}\) is the dimensionless maximum diameter during droplet impact on a moving surface, which is defined as \(D_{\text{max}}^{*}=D_{\text{max}}/D_{0}\). Similarly, [PERSON] et al. [54] combined extensive experimental data to summarize a model for droplet impact on horizontally moving grooved surfaces, that is,  
\[D_{\text{max}}^{*}=D_{0}^{*}+c\cdot v_{t}^{2}/v_{n}We_{n}^{1/2}(\rho v_{n}^{2}), \tag{15}\]  
where the term \(c\cdot v_{t}^{2}/v_{n}We_{n}^{1/2}(\rho v_{n}^{2})\) represents the additional contribution from the moving wall. However, since the scale of the droplet and surface in this study is on the micron order, which is much smaller than millimeter-sized droplets, the proportional relationship of the internal forces differs significantly. The Laplace stress at the micrometer level cannot be neglected because the smaller the droplet diameter, the greater this force [61]. Moreover, the micro-pillar structure is distinguished from the grooved surfaces in that the array of pillars allows the fluid to pass through the spaces between pillars. According to the data obtained in this study, it can be concluded that when micrometer-sized droplets impact horizontally moving micro-pillar structured surfaces, the dimensionless maximum spreading diameter is proportional to \(We_{t}\), as shown in Figure 13a. Based on this relationship, a new model is proposed for the maximum spreading diameter of micrometer-sized droplets impacting horizontally moving micro-pillar surfaces, that is,  
\[D_{\text{max}}^{*}=D_{0}^{*}+a\cdot We_{t}We_{n}^{b}. \tag{16}\]  
Figure 12: Variations in dissipated energy and kinetic energy during droplet impact on a moving surface with \(We_{t}=4\).  
This formula incorporates the dynamic pressure and Laplace stress during droplet impact and is expressed using dimensionless parameters. Fitting the model based on the summary of a large number of simulation, the factors can be determined as \(D_{\max}^{*}=D_{0}^{*}+0.014\cdot We_{t}We_{t}We_{n}^{-0.45}\). To validate the rationality of this formula, more cases with \(We_{n}\) = 5.4 are simulated and the results are shown in Figure 13b, which exhibits a good robustness of the proposed predictive correlation.
## 5 Conclusions  
This article proposed a technical solution that allows spectral coexistence between terrestrial fixed links and the downlink of Earth exploration LEO satellites. The solution maximizes throughput in interfering conditions while maintaining the mission BER requirement within the specified value for the interference plus noise levels during the satellite pass transmitting the image data telemetry. To achieve the target, we developed a strategy that combined the underlying CR exploitation with the adaptive MODCOD technique. The method adjusts the MODCOD to receive error-free data images based on the SINR per antenna angle. There is a numeric evaluation of the quality of the images given by the specified BER of \(10^{-7}\). In our emulation, we analyze qualitatively the impact of the drop in signal-to-noise ratio on image processing.  
Figure 14: Power spectrum of the unmodulated WFI signal (\(C\)) and the interference (\(I\)).  
Figure 15: WFI moving window with processing failures.  
**Author Contributions:** Conceptualization, R.A., L.d.S., W.S. and M.S.; methodology, R.A. and L.d.S.; validation, R.A., L.d.S. and W.S.; formal analysis, R.A.; investigation, R.A.; resources, R.A. and L.d.S.; data curation, R.A.; writing--original draft preparation, R.A.; writing--review and editing, R.A., L.d.S., W.S. and M.S.; visualization, R.A.; supervision, L.d.S., W.S. and M.S.; and project administration, R.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** We would like to express our sincere gratitude to the National Institute for Space Research (INPE), the Amazon Program, for their support and contribution to our research project. We extend our appreciation to the researchers and scientists at this Research Institute for their collaboration and provision of essential resources.  
**Conflicts of Interest:** The authors declare no conflict of interest.