esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Model Validation
#### 3.1.2 The Eccentric Case  
In order to show that the analytical model in this paper can accurately and effectively reflect the phenomenon of eccentricity, the spatiotemporal radial and tangential distributions of the air-gap flux density under the conditions of \(\varepsilon_{\mathrm{S}}=50\%\), and \(\varepsilon_{\mathrm{D}}=50\%\) were calculated. Figure 5 shows the spatiotemporal distributions of radial and tangential components of the air-gap flux density. Equation (29) was also used to analyze the model error. The calculation results show that the _RMSPEs_ of the spatial radial and tangential components are 1.02% and 0.97%, respectively. Additionally, _RMSPEs_ of the temporal radial and tangential components are 1.11% and 1.12%, respectively. Therefore, the analytical model of the air-gap magnetic field distribution with mixed eccentricity is consistent with the FEA model, which verifies the accuracy and effectiveness of the proposed modelling method.  
In addition, from the spatial diagram of the radial components, the peak values of the magnetic flux density of the air-gap are mainly affected by the magnetic bridge of the rotor and are close to 2T. Especially in the case of mixed eccentricity, the tightest of the air-gap  
Figure 5: The spatial and temporal distributions of air-gap flux density with mixed eccentricity. (**a**) The spatial distribution of radial flux density; (**b**) The spatial distribution of tangential flux density; (**c**) The temporal distribution of radial flux density; (**d**) The temporal distribution of tangential flux density.  
can reach a saturation state. Once saturated, the maximum magnetic density reaches 2.07T, which is consistent with the results of finite element analysis.
# An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO
## 5 Bound DNA Storage Constraint Coding
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**n\backslash d** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
4 & \(32\)\({}^{a}\) & \(11\)\({}^{a}\) & & & & & & & \\  & \(32\)\({}^{K}\) & \(12\)\({}^{K}\) & \(4\)\({}^{K}\) & & & & & & \\  & \(68\)\({}^{a}\) & \(17\)\({}^{a}\) & \(7\)\({}^{a}\) & \(2\)\({}^{a}\) & & & & & \\  & \(68\)\({}^{K}\) & \(20\)\({}^{K}\) & \(8\)\({}^{K}\) & \(3\)\({}^{K}\) & & & & & \\  & \(216\)\({}^{a}\) & \(44\)\({}^{a}\) & \(16\)\({}^{a}\) & \(6\)\({}^{a}\) & \(4\)\({}^{a}\) & & & & \\  & \(216\)\({}^{K}\) & \(55\)\({}^{K}\) & \(23\)\({}^{K}\) & \(8\)\({}^{K}\) & \(4\)\({}^{K}\) & & & \\  & \(528\)\({}^{a}\) & \(110\)\({}^{a}\) & \(36\)\({}^{a}\) & \(11\)\({}^{a}\) & \(4\)\({}^{a}\) & \(2\)\({}^{a}\) & & & \\  & \(528\)\({}^{K}\) & \(121\)\({}^{K}\) & \(42\)\({}^{K}\) & \(14\)\({}^{K}\) & \(7\)\({}^{K}\) & \(3\)\({}^{K}\) & & \\  & \(1704\)\({}^{a}\) & \(289\)\({}^{a}\) & \(86\)\({}^{a}\) & \(29\)\({}^{a}\) & \(9\)\({}^{a}\) & \(4\)\({}^{a}\) & \(4\)\({}^{a}\) & \\  & \(1694\)\({}^{K}\) & \(339\)\({}^{K}\) & \(108\)\({}^{K}\) & \(35\)\({}^{K}\) & \(13\)\({}^{K}\) & \(5\)\({}^{K}\) & \(4\)\({}^{K}\) & \\  & \(4336\)\({}^{a}\) & \(662\)\({}^{a}\) & \(199\)\({}^{a}\) & \(59\)\({}^{a}\) & \(15\)\({}^{a}\) & \(8\)\({}^{a}\) & \(4\)\({}^{a}\) & \(4\)\({}^{a}\) & \\  & \(4310\)\({}^{K}\) & \(705\)\({}^{K}\) & \(216\)\({}^{K}\) & \(69\)\({}^{K}\) & \(22\)\({}^{K}\) & \(11\)\({}^{K}\) & \(4\)\({}^{K}\) & \(3\)\({}^{K}\) & \\  & \(13,688\)\({}^{a}\) & \(1810\)\({}^{a}\) & \(525\)\({}^{a}\) & \(141\)\({}^{a}\) & \(43\)\({}^{a}\) & \(7\)\({}^{a}\) & \(5\)\({}^{a}\) & \(4\)\({}^{a}\) & \(4\)\({}^{a}\) \\  & \(13,410\)\({}^{K}\) & \(1796\)\({}^{K}\) & \(546\)\({}^{K}\) & \(148\)\({}^{K}\) & \(51\)\({}^{K}\) & \(20\)\({}^{K}\) & \(9\)\({}^{K}\) & \(4\)\({}^{K}\) & \(4\)\({}^{K}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Lower bounds for A\({}^{\mathrm{GC,NL}}\) (\(n,d,w\)). (\(K\) represents our work, \(a\) represents [PERSON]’s work). From the perspective of the coding rate, this was an important factor for measuring the transmission rate in DNA storage. The improvement of the lower bound directly leads to an increase in the coding rate. The code rate is defined as \(R=log_{4}\ M\!/n\)[54], where \(n\) is the length of the DNA code and m is the number of DNA code sets. For example, in the literature [26], for \(n=9\) and \(d=4\), \(R=log_{4}\ 199/9\approx 0.42\). Using our method, when \(n=8\) and \(d=4\), it also reached \(0.42\). In the case of the same code rate, short sequences can achieve the same storage performance as long sequences. A comparison with long DNA sequences, shows that short sequences are easier to synthesize, less expensive, and more stable, which means that the betterment of the lower bound is of great significance for further enhancing the storage performance.
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 4 Problem Solution  
### Subchannel Assignment Based on Dynamic Hypergraph Coloring  
Given UAV trajectory design and power control, problem (25a) transforms into  
\[\max_{l}\sum_{u=1}^{U}\sum_{i=1}^{M}\sum_{j=1}^{N}\Bigl{(}R_{i}^{u}+R_{i,D_{j}}^{ u}\Bigr{)} \tag{26a}\] \[P_{i}^{u}+\sum_{D_{j}\in\mathcal{D}_{i}}I_{i,D\Upsilon_{j}}^{u}P_{ i,D_{j}}^{u}\leq P\] (26b) \[I_{i,D\Upsilon_{j}}^{u}\in\Bigl{\{}0,1\Bigr{\}}\] (26c) \[\sum_{c_{i}\in\mathcal{C}_{u}}\sum_{D_{j}\in\mathcal{D}_{i}}I_{i,D\Upsilon_{j}}^{u}\leq\xi_{u}\] (26d) \[\sum_{u\in\mathcal{U}}\sum_{C_{i}\in\mathcal{C}_{u}}I_{i,D \Upsilon_{j}}^{u}\leq 1 \tag{26e}\]  
It can be seen that the above problem still has integer constraints and problem (26a) is still a non-convex problem. In this section, the problem is solved using a dynamic-hypergraph-based coloring method.  
In general, edges in a traditional graph can only connect two vertices, and when a cellular user and a D2D user are used as vertices to construct an interference graph A using a traditional graph, only strong interference between communication links can be modeled. Since multiple D2D links are allowed to multiplex the channel resources of a cellular user simultaneously, there may be cumulative interference that affects the link quality, and the conventional graph cannot model such interference. In order to consider both independent and cumulative interference in the system, the literature [28] introduces the hypergraph model, which avoids interference by constructing a hypergraph structure containing nodes and hyperedges (representing interference relationships between multiple nodes), and it colors the nodes to ensure that neighboring nodes use different resources (e.g., subchannels).  
A hypergraph \(H_{g}(t)\) is characterized by a bipartite group \(\bigl{(}V_{g}(t),E_{g}(t)\bigr{)}\), where \(V_{g}(t)=\bigl{\{}\
u_{1}(t),\
u_{2}(t),\cdots,\
u_{n}(t)\bigr{\}}\) is a finite set of vertices and \(E_{g}(t)=\Bigl{\{}e_{1}(t),e_{2}(t),\cdots,e_{m}(t)\Bigr{\}}\) is the set of edges of the hypergraph \(H_{g}(t)\). Each edge \(e_{i}(t)\) is determined by a finite element in \(V_{g}(t)\) and satisfies \(e_{i}(t)\
eq\emptyset(i=1,2,\cdots,m)\), \(2\leq|e_{i}(t)|\leq n\). Where the edges of \(|e_{i}(t)|=2\) are called ordinary edges, the edges of \(|e_{i}(t)|>2\) are collectively called hyperedges, the edges of \(|e_{i}(t)|=Q\) are called \(Q\)-element edges, and the hypergraph \(H_{g}(t)\) is also called a \(\max|e_{i}(t)|(i=1,\cdots,m)\)-element hypergraph.  
The hypergraph theory is used to solve the subchannel assignment problem in two steps. The first step is to construct a dynamic hypergraph, and the second step is to color the dynamic hypergraph.
# Spatiotemporal Modeling of the Smart City Residents' Activity with Multi-Agent Systems
## 5 Spatiotemporal Modeling of the Warsaw Area, Poland  
With an appropriately calibrated model and its implementation in the form of a multi-agent system, the authors attempted to conduct research and simulation on real data. The agglomeration of Warsaw in Poland, with its 1,754,000 inhabitants, was the test object (Figure 11). Spatial data used in the study come from the general geographic database, which contains data at the level of accuracy that is equivalent to analogue maps with a scale of 1:250,000. This study was up-to-date in 2016. As with the model city, the distribution of residents was modeled in the form of a dot distribution map (Figure 12), where each of 1754 dots (agents) represents 1000 inhabitants. The data contain a set of characteristics defining the demographic, social, and cultural features of individual residents (Table 2).  
The research [51; 52] is examining the level of trust of Poles, both in public institutions and each other. It indicates the direct relationship between trust, civic activity, and the level of education. It turns out that \"trust increases civic activity only after reaching or exceeding the threshold of secondary education.\". In addition, in 2015, at the request of the City Hall of Warsaw, a study was conducted on the quality of life of the residents of Warsaw districts, in which a set of questions was devoted to trust. Also, although the level of trust in friends and family remains at a level close to 90%, confidence in politicians (17%), journalists (34%), and local authorities in Warsaw (35%) still remains very low.  
Figure 10: Changes in the level of mutual social trust in a model city through a simulated time of 10 years (20 positive leaders in the “Old Factory” attractor); iteration 0, 1200, 2400, 3650.  
Analytical experiments were carried out for Warsaw by simulating long-term social processes for three selected test areas (Figure 12):  
* The central area of the capital (controversy regarding the spatial development of Parade Square);
* The so-called Mordor (with the problem of extreme street congestion during the day and its forlornness at night);
* The so-called Miasteczko Wilanow (socially associated with a \"ghetto\" for young and wealthy residents born outside of Warsaw, who have a low level of identification with the city).
###### Abstract  
Prior studies in Wireless Sensor Network (WSN) optimization mostly concentrate on maximizing network coverage and minimizing network energy consumption. However, there are other factors that could affect the WSN Quality of Service (QoS). In this paper, four objective functions that affect WSN QoS, namely end-to-end delay, end-to-end latency, network throughput and energy efficiency are studied. Optimal value of packet payload size that is able to minimize the end-to-end delay and end-to-end latency, while also maximizing the network throughput and energy efficiency is sought. To do this, a smart grid application case study together with a WSN QoS model is used to find the optimal value of the packet payload size. Our proposed method, named Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP), along with other three state-of-the-art multi-objective optimization algorithms known as OMOPSO, NSGA-II and SPEA2, are utilized in this study. Different packet payload sizes are supplied to the algorithms and their optimal value is derived. From the experiments, the knee point and the intersection point of all the obtained Pareto fronts for all the algorithms show that the optimal packet payload size that manages the trade-offs between the four objective functions is equal to 45 bytes. The results also show that the performance of our proposed MOSFET method is highly competitive and found to have the best average value compared to the other three algorithms. Furthermore, the overall performance of MOSFET on four objective functions outperformed OMOPSO, NSGA-II and SPEA2 by 3%, 6% and 51%, respectively.  
network modeling; smart grid; multi-objective optimization; quality of services (QoS); optimality +
Footnote †: journal:  
_Article_
# A Simulation Study of Global Evapotranspiration Components Using the Community Land Model
###### Abstract  
The Qian atmospheric forcing dataset is used to drive the Community Land Model, version 4.0 and 4.5 (CLM4 and CLM4.5) in off-line simulation tests. Based on flux network (FLUXNET) data and reanalysis data provided by National Oceanic and Atmospheric Administration (NOAA) and Climate Forecast System Reanalysis (CFSR), the simulation results of CLM4.5 on the global evaporation of intercepted water from the vegetation canopy (Ec), vegetation transpiration (Et), evaporation of soil (Es) and latent heat flux are evaluated. Subsequently, the improvement in the simulation results of CLM4.5 compared with CLM4.0 is tested and analyzed. The results show that the simulated spatial distribution of Ec, Et and latent heat flux in CLM4.5 are closer to the reanalysis data than Es. The simulated annual means of Et, Es and latent heat flux in CLM4.5 are larger than the reanalysis data, but Ec is smaller. The spatial distribution of the simulation bias of latent heat flux in CLM4.5 is mainly determined by the bias distribution of Es. There is a significant difference in the simulation of Et, Es and latent heat flux between CLM4.5 and CLM4.0. These differences are mainly present near the equator and in the middle and high latitudes in the Northern Hemisphere. In general, compared with CLM4.0, the simulation bias of Et, Es, and latent heat flux have been reduced in CLM4.5, and the simulated means are more consistent with the reanalysis data. Although there is a significant improvement in the simulation of the spatial distribution of Et and Es in CLM4.5 compared with CLM4.0, the ability of CLM4.5 to simulate the spatial distribution of global latent heat flux shows little improvement relative to CLM4.0.  
land surface process; numerical simulation; CLM; evapotranspiration 1
# An Adaptive and Secure Holographic Image Watermarking Scheme
## 4 The Proposed Watermarking Method
### Watermark Extraction  
(1) First, the watermarked image is decomposed by 1-level SFLCT to achieve the low frequency sub-band _SL'_. Then _SL'_ is splitted into non-overlapping blocks of \(z\times z\) pixels.  
(2) Using the stored the \(x\)-coordinates and the \(y\)-coordinates, all the embedded blocks can be obtained.  
(3) Apply Schur decomposition to all obtained blocks.  
(4) The watermark signal _WM\({}_{i}\)_' can be extracted by use of the following formula.  
\[WM_{i}^{\prime}=\left\{\begin{array}{ll}0,&\text{if mod}[S_{i}^{\prime}(1,1),q]<0. 5q\\ 1,&\text{otherwise}\end{array}\right., \tag{15}\]  
where \(S_{i}\)' be the \(S\) matrix of the \(i^{\text{th}}\) obtained block after Schur decomposition,  
(5) By utilizing the inverse zigzag algorithm, the hologram watermark _CW'_ can be achieved.  
(6) With the private keys _SX_(1), _SY_(1), _SZ_, \(\alpha\), \(\beta\), \(\gamma\), \(t1\), \(t2\) and \(t3\), the extracted hologram _CW'_ is reconstructed in term of reconstruction process in Section 3.2.  
Figure 1 delineates the flowchart of the proposed method. In Figure 1,b are the diagrams of the watermark embedding process and the watermark extraction process, respectively.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AGC & automatic gain control \\ ASK & amplitude shift keying \\ BJT & bipolar junction transistor \\ CMOS & complementary metal-oxide semiconductor \\ COTS & commercial of the shelf \\ FSK & frequency shift keying \\ GBWP & gain bandwidth product \\ IoT & Internet of Things \\ ITU & International Telecommunication Union \\ LED & light-emitting diode \\ LF & low frequency \\ LNA & low noise amplifier \\ MCU & main microcontroller unit \\ OA & operational amplifier \\ OOK & on-off-keying \\ PCB & printed circuit board \\ PER & package error rate \\ RF & radio frequency \\ RSSI & received signal strength indicator \\ SAW & surface acoustic wave \\ SoA & State of the Art \\ TIA & transimpedance amplifier \\ TSS & tangential signal sensitivity \\ VGA & variable gain amplifier \\ WSN & wireless sensor network \\ WuPt & wake-up packet \\ WuRx & wake-up receiver \\ WuTx & wake-up transmitter \\ \end{tabular}
## 1 Introduction
To emphasize the dangers of contaminants as well as the necessity of cleaning technologies and restoration algorithms, it is essential to develop reliable performance evaluation methods that measure the effect of contaminants on the performance of camera sensor systems. [PERSON] et al. [33] were the first to develop a method to evaluate object detection performance in a controlled virtual simulation environment by replicating dust contamination conditions. The study validated object detection performance with variables such as dust concentration, color, and object type. However, since both the contamination and the driving environments were simulated, the study does not fully capture the complexity and variability of real-world conditions. [PERSON] et al. [34] evaluated image quality degradation on contaminated camera sensors and proposed self-cleaning capabilities through hydrophobic coatings with UV durability. The study demonstrated that image quality is degraded by droplets, but it did not examine how such degradation affects the performance of advanced camera systems. [PERSON] et al. [35] investigated the object detection performance under artificially created driving conditions in the rain. However, the object detection performance was evaluated using only one stop sign, so additional evaluation with datasets containing various objects is needed. [PERSON] et al. [36] analyzed the impact of droplet contamination, which is frequently generated by weather, on the real-time object detection performance of an autonomous driving system. They demonstrated that droplet contamination degrades object recognition performance, negatively impacting autonomous driving. However, the droplet contamination was simulated and does not reflect the characteristics of real-world contamination. This paper addresses the limitations of previous studies by applying real-world contaminants directly to camera sensors and systematically analyzing their impact on system performance. Specifically, we experimentally evaluate the impact of droplet contamination on camera lens surface in terms of image quality and the performance of object detection algorithms. We also propose a method to quantitatively evaluate the degradation of image quality and object detection performance caused by contamination. We compared system performance under contaminated and uncontaminated conditions by incorporating various factors such as lens surface type, contamination level, droplet volume, and object size in the image. The evaluation results not only demonstrate the performance degradation caused by droplet contamination on camera systems that use AI-based image analysis algorithms but also clearly identify the limitations of object detection algorithms, providing insights for potential improvements.
# Communication Protocol Design for IoT-Enabled Energy Management in a Smart Microgrid
## 5 Numerical Simulation Results
### Error Performance at Different HEMSs  
Figure 2(a)-d illustrate the comparison of the proposed scheme and the direct DF scheme for the error probability at all the HEMSs when they decode the energy mismatch information of HEMS 6. The results are demonstrated for the following two different cases: when the channel gains for all channel coefficients are the same (Figure 2(a),b) and when the channel gain for the channel between HEMS 1 and 2 drops to 50 (Figure 2(c),d). From Figure 2(a),b, it is clear that the error probability at the HEMSs is greater in the proposed scheme in comparison with the error probability when direct DF is considered and all channels are in good conditions. It can be seen that the BER is the highest at HEMS 1 for both schemes, since the message from HEMS 6 has to pass through all the intermediate HEMSs. The SNR penalties used to achieve the same error performance as the direct decode\(-\)and\(-\)forward scheme are 2 dB, 2 dB, 4 dB, 5 dB and 2 dB at HEMS 1, 2, 3, 4 and 5, respectively. This indicates that the error performance at the middle HEMSs is more significantly affected, as compared to the other HEMSs, when the proposed scheme is considered. This represents the worst case error performance for the proposed scheme for a given channel gain condition.  
Figure 2(c),d show the error performance when the channel gain for channels between HEMS 1 and 2 drops to 50. From the results, we can obtain that the error performance at HEMS 1 for direct DF worsens significantly. In addition, the error performance at HEMS 5 with direct DF degrades more compared to the error performance with the  
Figure 2: Error performance comparison for the proposed scheme and direct decode\(-\)and\(-\)forward when HEMS 6 is decoded by all other HEMS under different channel conditions.  
proposed scheme. The reason is that the error accumulates at each stage when direct DF is used. On the other hand, with the proposed scheme, some HEMSs will be able to decode the message correctly, even if some of the other HEMSs suffer poor channel conditions. The SNR penalties used to achieve the same error performance as the direct decode\(-\)and\(-\)forward scheme are 2 dB, 2 dB, 1.8 dB and 0.8 dB at HEMSs 1, 2, 3 and 4, respectively. Thus, the difference in error performance at the middle HEMSs becomes smaller when the channel gains are not equal and the channel conditions are poor during the first stage. Note that it is possible to develop an adaptive strategy that switches between direct DF and the proposed communication based on the scenarios when the channel conditions are equally good for the HEMSs and when channel conditions are worse for some HEMSs. However, this requires a detailed analysis of the thresholds used for switching between communication schemes and is beyond the scope of this paper.
## 5 Security Analysis
### Key Space Analysis  
For the purpose of analyses, we take \(q=1\) as an example in our communication scheme. If a third party intercepts the signals of transmission channel such as \(s^{\prime}(t),z_{1}(t)\) and \(z_{2}(t)\), as \(s^{\prime}(t)=\dot{H}+Dg(y_{1},z_{1},z_{2})+A\varepsilon\), the message signal \(H\) cannot be decrypted without the private keys such as \(D,z_{1},z_{2}\), the function \(g\) and \(A\varepsilon(t)\). In particular, when the amplitude of \(\dot{H}\) is much smaller than that of \(Dg(y_{1},z_{1},z_{2})\) or \(A,s^{\prime}(t)\approx Dg(y_{1},z_{1},z_{2})+A\varepsilon\), it is impossible to decrypt the message signal \(H\) using only the signals of transmission channel. Therefore, for private keys \(D\), \(z_{1}\), and \(z_{2}\), the key space of our algorithm is infinite.  
Figure 10: The traditional chaotic masking process with noise \(A=50\) and the same control strength.  
Figure 9: The diagram of state variables \(\mathbf{x}_{1}\) and \(\mathbf{y}_{1}\) with noise \(A=50\).
## 3 Results and Discussion
### Model Development and Statistical Analysis
#### 3.2.2 Statistical Analysis
The predicted R\({}^{2}\) of 0.9677 agrees reasonably with the adjusted R\({}^{2}\) of 0.9943, i.e., the difference is <0.2. This indicates a high correlation between the observed and predicted values. The coefficient of variations (C.V. %) recorded a low value of 5.86, indicating a high precision of the developed model. These findings indicate that the regression model offers an excellent description of the correlation between the independent variables and the response (hydraulic conductivity).