esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Community-Based Event Delivery Protocol
### _The implementation of queue management scheme_  
Each sensor will maintain a events record list coming from three sources.  
a) After the sensor acquires data from its sensing unit, it creates an event record and inserts the record into the data queue.  
b) When the sensor receives an event record from other sensors, it may insert the record into its data queue.  
Figure 4: Pseudo-code of updating the survival time.  
Figure 5: The arrangement of events in the data queue (where the number in the front represents event successful delivery time while the latter number represents event survival time).  
c) After the sensor sends out an event record, it may insert the record again if this record is created by the source sensor node, because this record is not guaranteed to be delivered to all subscribers that have interest in it.  
Our queue management scheme is based on both the successful delivery time and the survival time of events (as shown in Figure 5) and we believe that the event with smaller successful delivery time is more important and should be transmitted with a higher priority. This is done by arranging the events in the queue with a decreasing order of their successful delivery time. Furthermore, for events with the same successful delivery time, priority should be given to those events that have smaller survival time. An event is dropped at the following two occasions. First, once the successful delivery time of an event is lager than a value, for example \(\alpha\), the event is dropped. Second, once the survival time of an event in the process of updating is larger than the network's delay tolerant threshold (the maximum delay value of events), the event is dropped. This is to reduce network energy consumption, given that the event either has been delivered to all its interested subscribers with a high probability or has been invalid in our application. Meanwhile, if the data queue of a sensor is full when an event arrives, no more events will be accepted.
## 3 Experiment and Results
### Data Preprocessing  
Following data acquisition, image preprocessing is performed through a two-step procedure:  
* Demultiplexing the raw images into distinct polarization-direction components via the polarization unit array.
* Calculating light intensity values and DoP using Equation (5).  
As shown in Figure 6, the light intensity maps under different scenarios are presented  
As shown in Figure 7, the DoP maps under different scenarios are presented  
To further analyze the light intensity and polarization information, we compare the grayscale histograms derived from the intensity maps and DoP maps. As shown in Figure 8, displaying intensity histograms across different scenarios, it can be observed that under foggy conditions, the light intensity values typically exhibit multi-peak distributions with uneven grayscale allocation, leading to suboptimal performance in direct threshold segmentation.  
As illustrated in Figure 9, analysis of grayscale histograms for the DoP across diverse scenarios reveals that polarization maps consistently exhibit distinct bimodal distributions.  
Figure 8: DoP maps in different scenarios.  
Figure 6: Intensity maps in different scenarios.  
Figure 7: DoP maps in different scenarios.  
This indicates that polarization information, compared to light intensity data, enables more effective discrimination between background and target regions. In foggy conditions, this phenomenon manifests as a clear separation between haze-affected areas and haze-free areas.
## 4 Phase II: Proposed Point-to-Point Relay-to-Destination Transmission
### Decoding Original Signals at the Destination  
In order to detect the original source signals, the destination node requires collecting \(L\) independent linear combinations. After \(L\) transmissions, the destination obtains \(L\) linear combinations of \(L\) messages from \(L\) users. Using these linear combinations, the destination decodes the original source messages using the decoder given in Equation (10). In order to design the decoder, we rewrite the \(L\) linear combinations as (for each dimension):  
\[\underbrace{\left[\begin{array}{c}\hat{v}_{D}^{1}\\ \vdots\\ \hat{v}_{D}^{L}\end{array}\right]}_{\mathbf{v}}=\underbrace{\left[\begin{array} []{ccc}a_{1}^{1}&\ldots&a_{L}^{1}\\ \vdots&\ldots&\vdots\\ a_{1}^{L}&\ldots&a_{L}^{L}\end{array}\right]}_{\mathbf{A}}\underbrace{\left[ \begin{array}{c}s_{1}\\ \vdots\\ s_{L}\end{array}\right]}_{\mathbf{s}}\]  
Here, \(\hat{v}_{D}^{l}\) denotes the \(l\)-th linear combination element estimated at the destination. Furthermore, \(a_{k}^{l}\) denotes the linear coefficient of the \(k\)th source signal in the \(l\)th linear combination. The matrix, \(\mathbf{A}\), contains the integer linear coefficients of the linear combinations. If \(\mathbf{A}\) is full rank, \(\mathbf{s}\) is obtained by inverting \(\mathbf{A}\), such that:  
\[\hat{\mathbf{s}}=\mathcal{D}_{D2}(\mathbf{v})=\mathbf{A}^{-1}\mathbf{v}\]  
where \(\hat{\mathbf{s}}\) is the estimate of the original source signals. It is assumed that, since the channel conditions are highly variant (typically in vehicular networks), the channel fades independently in each transmission. Consequently, the matrix formed by the rounded channel coefficients is full rank with high probability. As proposed in our scheme, the transmitter chooses the integer coefficients closest to the channel coefficients; therefore, \(\mathbf{A}\) is full rank with high probability.
# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
## 3 Transmission Line Monitoring Using WSN in Smart Grids
### Scenarios
By setting up a direct link to the base station on each pole, the delay will be minimized and the workload among relays will be balanced. Nevertheless, such solution requires the base station to be within sensor's radio range (so they both can hear each other). Although pretty small time delays speak in a favor of this scenario, there exists just another disadvantage that prevents from using it in practice. This approach is expensive in terms of equipment cost and extra energy consumption of the direct cellular wireless links. _Scenarios 3 and 4_ In order to find a compromise between the cost and time delays, we decided to select only some relay nodes to establish direct links to the base station. Relays that are not directly connected to the base station should send their information to cluster heads which have a direct link to base station established. It is thus obvious, that the positions of direct links would influence time delays. To find the optimal arrangement of direct base station connections, we proposed to analyze Scenarios 3 and 4, and examine how the number and the position of direct base station connections affect time delays. Consider, for instance, Scenario 3, where the number of direct links is equal to 12. Here, relay from node 1 and 3 both send their information to node 2, which has the direct link and will be able to send the data to the substation. Then, nodes from pole 4 and 6 send data to node 5 (which is responsible for communicating with the base station), nodes 7 and 9 send the data to relay on pole 8, which communicates with one of the available substations and can transmit the data using its direct link), and so forth (Figures 6 and 7). Figure 5: WSN architecture deployed on the example transmission grid, where \(\eta=1\). Figure 6: WSN architecture deployed on the example transmission grid, where \(\eta=2\). _Scenario 5_ The last but not least, Scenario 5 examines the performance and time delays when there is only one substation and the measured data from every pole need to traverse the entire power line in order to reach the substation. From the performance point of view, it does not make a sense to consider the number of hops to be greater than 23--the half of the line. The scenario with \(\eta\) equal to 45 is considered only as an example, in order to show that the approach, where the number of hops is greater than the half the length of the line, is inefficient and should not be used. Prepared scenarios, together with factors significant in our analyzes, are summarized in Table 3.
# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 4 Potential-Field-Based UAV Movement Modeling  
Potential fields have been used to guide autonomous agents [14; 25] in complex environments and in real-time. This section briefly explains how potential fields guide UAVs. Assume that, initially, \(N\) UAVs are placed near the center of the AOI and are hovering at 100 m altitude with zero horizontal speed (\(sp\)). The maximum speed of a UAV is fixed to 15 m/s, with an acceleration of 0.1 \(\mathrm{m}/\mathrm{s}^{2}\). Assume that a UAV (\(k\)th) can communicate with \(n\) neighbors directly and experience an attractive (\(P_{a}\)) and repulsive (\(P_{r}\)) potential field based on the distance from each of its neighbors. The vector sum of these potential fields (\(P_{k}\)) is given by Equation (4), which provides a heading or direction for the \(k\)th UAV (\(\in(0,2\pi)\)) to move along.  
\[\vec{P}_{k} = \vec{P}_{a}+\vec{P}_{r}\] \[\vec{P}_{k} = \sum\limits_{i=0,i\
eq k}^{n}\vec{p_{aki}}+\sum\limits_{i=0,i\
eq k }^{n}\vec{p_{ki}} \tag{4}\]  
In the literature, potential fields of the form \(c*d^{e}\) have been used to control the movement of autonomous agents [40], where coefficient (\(c\)) and exponent (\(e\)) are optimizable parameters that determine the field effect. In Equation (4), both \(P_{a}\) and \(P_{r}\) can be substituted by the form of \(cd^{e}\) as shown by Equation (5).  
\[\vec{P}_{k} = \sum\limits_{i=0,i\
eq k}^{n}\vec{a_{ki}}c_{a}d_{ki}^{e_{a}}+\sum \limits_{i=0,i\
eq k}^{n}\vec{a_{ik}}c_{r}d_{ki}^{e_{r}} \tag{5}\]  
In the above equation, \(\vec{a_{ki}}\) is a unit vector pointing from the \(k\)th UAV to the \(i\)th neighbor and vice-versa for \(\vec{a_{ik}}\), \(d_{ki}=d_{ik}\) is the distance between the \(k\)th and \(i\)th UAVs, \(c_{a},c_{r}\) are potential field coefficients, and \(e_{a},e_{r}\) are potential field exponents. The genetic algorithm optimizes these coefficients and exponents to control each UAV's movement, and \(\vec{P}_{k}\)'s direction specifies the \(k\)th UAV's heading to move along. In our model, UAVs change their current heading to this new heading (computed using Equation (5)) instantaneously with velocity (\(\overrightarrow{v}\)) and position (\(\overrightarrow{p}\)) computed using Equation (6).  
\[\overrightarrow{v} = (sp\times cos(heading),100,sp\times sin(heading)) \tag{6}\] \[\overrightarrow{p} = \overrightarrow{p}+\overrightarrow{v}\times\Delta t\]  
Since UAVs move under the influence of potential fields, their movement comes to a halt when the resultant field value (\(P_{k}\)) reaches zero or falls below a threshold value. So far, the background of the problem, the fitness criteria, training, and testing scenarios, and the UAV's movement model have been discussed. The next section presents the proposed network deployment algorithm in two phases to deploy on-demand wireless networks.
## 5 Conclusions and Future Work  
In this contribution, an efficient transceiver architecture of the two-dimensional packing FTN signaling based on the DFT block transmission, i.e., DBT-MC-FTN scheme has been proposed. Then, a soft SIC detector has been utilized to eliminate the inherent ISI and ICI. The BER performances over the AWGN and fading channels showed that the proposed SIC detector can eliminate the mixed interference effectively as the number of iterations increases, making the proposed DBT-MC-FTN signaling an efficient method to improve the spectral efficiency. In addition, based on the characteristics of the realization structure, two pilot-aided channel estimation schemes for the DBT-MC-FTN system have been developed. The MSE simulation results demonstrated the feasibility of the proposed two channel estimation schemes.  
Note that, in this paper, the channel-induced ISI and the manually introduced ISI and ICI due to time-frequency packing are dealt with separately. When the soft SIC detector at the receiver is employed to cancel the inherent ISI/ICI, the residual interference will be mixed into the ISI which is caused by the previous LS channel equalization step, resulting in certain performance loss. Therefore, to further improve the system performance, joint detection schemes to tackle the mixed interference should be explored in the future. Additionally, in terms of the channel estimation scheme, conventional linear and DFT interpolation methods have been utilized to derive the complete CIR. However, the estimation accuracy needs to be further improved with more advanced interpolation methods, which are also our future work.  
Conceptualization, Y.P. and M.L.; methodology, Y.P. and M.L.; software, Y.P.; formal analysis, Y.P.; investigation, Y.P. and M.L.; resources, Y.P.; data curation, Y.P.; project administration, Y.P., writing--original draft preparation, Y.P. and M.L.; writing--review and editing, Y.P.; supervision, M.L. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Shanghai Technical Standard Project under Grant No.18 DZ2203900, Shanghai Excellent Academic Leader Program under Grant No.18 XD1404100, and National Key R&D Project of China under Grant 2019 YFB1802703.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AWGN & additive white Gaussian noise \\ BCJR & Bahl-Cocke-Jelinek-Raviv \\ BER & bit error rate \\ CE & channel estimation \\ CIR & channel impulse response \\ CP & cyclic prefix \\ CS & cyclic suffix \\ \end{tabular}
## 4 Surface-Enhanced Infrared Absorption (SEIRA) Spectroscopy
### Preparation of SEIRA Substrates  
Enhancement of SEIRA depends on the size, shape and particle density of the selected material. In most cases a metal-underlay configuration is used. The supporting substrates can be divided into IR transparent materials (Si, Ge, CaF\({}_{2}\), BaF\({}_{2}\), KBr, ZnSe, ZnS, sapphire, MgO) and non-transparent materials including glass, glassy carbon, polymers, and metals. The materials of the first group enable measurement in transmission and/or reflection mode, respectively, while the others are only adequate for reflection measurements. SEIRA active metal islands are commonly prepared by high vacuum evaporation of the metal onto a supporting substrate. As an alternative, electrochemical deposition can be applied. Applying this method, the substrate on which the metal is to be deposited must have high electrical conductivity. Coinage metals Ag and Au, which show high enhancement factors in SERS, have also been the most widely used metals in SEIRA experiments. However, theoretical SEIRA models predict enhancement on transition metals being as strong as on coinage metals [77]. In fact, while Ag and Au island films lead in practice to the greatest intensity enhancement, other metals, e.g., the platinum-group metals show the modest enhancement accompanied with maximal band asymmetry.
## Case Study
### Case I: Distribution Network Information Interaction
The name of the power data (such as the current of phase A) and the specific value are the most critical information fields. It is also the core content required in the advanced application of the distribution network; the time tag option can be used in situations where information synchronization is required; In the wide-area distribution network information exchange, the unit option is to identify the specific numerical value. However, in order to save the storage space of the intelligent distribution network system, this label can be ignored in the case where the power data unit is easily clarified. After the introduction of the virtual plane layer, it will accept the power data processed by the underlying layer and process it in a unified data format. Then the plane provides it to advanced applications in the power distribution network. For advanced applications, power data with a uniform format can be directly used without concern for the underlying data processing, avoiding the cumbersome conversion process between multiple protocols. At the same time, since the virtual plane layer contains the power data warehouse, it has a power data collection function. Power data can be provided to multiple programs simultaneously in virtual plane.
## 5 Conclusions  
This research is a contribution to rice mapping and monitoring methods based on multi-temporal SAR data. We presented a new rule-based algorithm, based on agronomically relevant rules and parameters that classified rice area with consistently high accuracy (>85%) across different rice environments and crop management practices (wet season and dry season, irrigated and rainfed, direct seeded and transplanted, short- to long-duration varieties). This is the first time that such diversity in rice systems has been assessed with the same mapping approach. The study demonstrates that regularly acquired X-band HH SAR imagery is suitable for rice crop monitoring across the major rice environments of South and Southeast Asia. Future studies should consider a broader range of SAR wavelengths, polarizations and acquisition angles.  
The study used the largest number of sites (13), images (127) and field data observations (228 monitored fields, 1922 monitoring visits and 1334 map validation points) of which we are aware. The study demonstrates that this new rice mapping algorithm is robust, that the parameters can be suitably tuned using local knowledge and field observations and that large-scale rice monitoring is feasible. Future studies should consider mobile devices for field data collection to reduce the costs across larger areas. The study presented the concept of agronomically relevant temporal feature dynamics for rice and provided their respective values for X-band HH SAR with acquisition angles between 39\({}^{\circ}\) and 48\({}^{\circ}\). The research suggests that further temporal feature descriptors should be developed to improve the guidance for parameter selection.  
One limitation highlighted by the research is the need for ancillary information on land use/land cover to improve the classification in areas where the temporal signature for rice is similar to that of wetlands, water tanks or other areas with comparable temporal dynamics in both water and vegetation cover. Better access to such data and the development of an open-access library of signatures would improve rice mapping with SAR. This study extracted rice area information only from the temporal series, but in the future, the method should be adapted to extract further rice crop parameters from the temporal signature, such as flooding dates, crop establishment dates, crop stage identification and SAR-derived parameters for input to crop growth simulation models, thus providing spatial and temporal information on both rice area and production for food security applications.
# Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm
## 6 Results and Discussion
### Comparison with Genetic Algorithm-Based Protocols
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Throughput (Mbps)/Protocol**} \\ \hline
**Node** & **GA-AODV** & **LEACH-GA** & **GA-AODV** \\ \hline
100 & 2.45 & 2.33 & 2.05 \\
150 & 2.61 & 2.45 & 2.19 \\
200 & 2.91 & 2.65 & 2.34 \\
250 & 3.03 & 2.83 & 2.56 \\
300 & 3.22 & 2.98 & 2.68 \\
350 & 3.54 & 3.21 & 2.88 \\ Sum & 17.76 & 16.45 & 14.7 \\ Gain \% & & 7.96 & 20.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Throughput values for Figure 6.  
Figure 8: E2E vs. mobility speed. Figure 7: Packet delivery ratio vs. No of Nodes. In Figure 9, GA-AODV needs to consume more energy as it is vulnerable to topological change and link failure. Additionally, LEACH-GA requires energy to adapt to the new topology. Having alternative routes helps GA-AODDV to be more adaptable to the topological change. Therefore, it needs to produce less routing overhead and this reduces the energy consumption degradation. The network topology changes more often when the mobility speed is higher. Therefore, the performance of the network drops in terms of throughput and packet delivery ratio, as it is depicted in Figures 10 and 11. In this regard, GA-AODV shows adaptability where having more reliable routes are based on the least consumed energy at their nodes and also utilization of the GA mechanism.
## 6 Imaging Application  
ATR shows promise for use in an imaging mode to get precise absorption information without the inevitable noise induced by Fresnel refraction under general specular reflections [75; 33]. Researchshows THz-ATR imaging methods could ideally monitor and analyze liquid and biological samples. A typical THz-ATR imaging system is shown in Figure 6. The raster scanning method, point-by-point scanning by moving the prism in \(y\) or \(z\) direction, is used for obtaining image data of the samples.  
[PERSON] et al. [77] presented a THz-ATR imaging technique, using the relative differential spectral phase of two orthogonal polarizations. This system showed advantages on the subwavelength ability in longitudinal resolution. [PERSON] et al. [78] used THz-ATR technology to study three most used skin-hydrants in commercial moisturizers and their interaction with the skin of a subject. Images of the forearm were obtained taking THz spectra images in sequential times. The results showed that the THz amplitude clearly decreased for all three substances between application and first measurement. Lanolin and hyaluronic acid approximately maintained the amplitude of the THz signal and glycerin slowly started to recover the reference state of the skin. [PERSON] et al. [33] demonstrated an attenuated TIR imaging system. The surface information of the sample can be obtained by two-dimensionally scanning. The effective imaging area, image resolution, and polarization dependence of contrast enhancement and stability improvement were discussed in detail. The THz-ATR image information of distilled water (Figure 7a), solid agar (Figure 7b), and porcine tissue (Figure 7c) were obtained, showing high sensitivity for the proposed method. While comparing the results in Figure 7a, the image obtained by the \(p\)-polarized THz (right one) shows better contrasts than the elliptically polarized one (left one), thus indicating that polarization is one of the key factors for the quality of the image. In their following research [76], detailed analysis of the optimization for vertically scanning THz-ATR imaging has been presented. The optimum prism design was determined, considering the TIR condition, effective ATR imaging area, utilization of the prism, output heights of the reflected beams, and spatial resolution. They also found that the \(p\)-polarized waves can contribute to achieve minimum error.  
Figure 6: The setup of the vertically scanning imaging system. Reproduced with permission from [76], Copyright © 2018, The Optical Society.
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 2 Reference Assumption PLL Features  
Generally, GNSS applications suffer from some technical problems in harsh environments. For instance, indoor localization is trying to invent high-sensitivity receivers to receive weak signals, while missile guidance requires a better capacity to bear high-dynamics. As a result, the technologies aimed at solving high-sensitivity and high-dynamics are developed separately. However, space navigation with GNSS in the domain of SSV is influenced by the problem of realizing simultaneous high-sensitivity and high-dynamics, especially when the space vehicles are making orbital maneuvers. Theoretically, it is much more difficult to settle both of the two problems which interact with each other. To evaluate the performance of a current onboard GNSS receiver convincingly, we must establish a unified standard loop named SSV RRAM first.