esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Chirality Determined from Mueller Matrices
## 4 Results
### Structural Optical Activity in Cellulose Nanocrystals
#### 4.5.3 Method III  
The chirality parameter \(\kappa=\kappa_{\rm Re}+{\rm i}\kappa_{\rm Im}\) and the refractive indices \(n_{x^{\prime}}\) and \(n_{y^{\prime}}\) in Equation (12) were fitted in a single layer model with film thickness of 20 \(\upmu\)m and with dispersion models for the optical functions. Two Gaussian resonances were used for \(\kappa\) according to Equation (16) with the best-fit parameter values as shown in Table 4. Linear anisotropy was fitted using Cauchy models for \(n_{x}\) and for \(n_{y}\) similar to that in Equation (15), but with only three terms.  
The cellulose material had low absorption, and linear dichroism was not included in the fit. The best-fit Cauchy parameters found were \(A_{x^{\prime}}=1.57650\pm 0.00002\), \(A_{y^{\prime}}=1.57608\pm 0.00002\), \(B_{x^{\prime}}=B_{y^{\prime}}=0.0034\pm 0.0003\)\(\upmu\)m\({}^{2}\), and \(C_{x^{\prime}}=C_{y^{\prime}}=-0.00034\pm 0.00001\)\(\upmu\)m\({}^{4}\). Due to the small birefringence, the \(B_{j}\) and \(C_{j}\) terms were set equal for the \(x^{\prime}\) and \(y^{\prime}\) directions. The linear birefringence found was, thus, wavelength independent and equal to \(\Delta n_{x^{\prime}y^{\prime}}=A_{x^{\prime}}-A_{y^{\prime}}=0.00042\). The origin of the linear birefringence may be density or orientation effects leading to a small in-plane linear anisotropy.  
The reference axes \(x\) and \(y\) of the instrument are not aligned with the sample reference axes \(x^{\prime}\) and \(y^{\prime}\), and a Euler rotation angle \(\phi\) is included in the fit. A value \(\phi=23.7^{\circ}\) was found, which is in accordance with the \(\pi/8\) estimate found using method II. The best model fit is shown in Figure 13, and Figure 15 presents the model chirality parameter \(\kappa\) of the CNC film.
# Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors
## 3 Results and Discussion
### Radiometric Capability Evaluation of Huan Jing 1/Charge-Coupled Device (H)-1/CCD
 } & \multirow{2}{*}{**Standard**} \\ \hline \multirow{8}{*}{HJ-1A/CCD2} & Blue & \(-6.73\times 10^{-5}\) & 0.1586 & 0.1392 & 0.1489 & 0.1695 & 0.0295 & 0.0069 \\  & Green & \(-5.59\times 10^{-5}\) & 0.1700 & 0.1559 & 0.1629 & 0.1797 & 0.0137 & 0.0052 \\  & Red & \(-4.26\times 10^{-5}\) & 0.2049 & 0.1935 & 0.2011 & 0.2058 & \(-\)0.0172 & 0.0057 \\  & NIR & \(-4.96\times 10^{-5}\) & 0.2399 & 0.2196 & 0.2300 & 0.2299 & \(-\)0.0441 & 0.0063 \\ \hline \multirow{8}{*}{HJ-1A/CCD2} & Blue & \(3.56\times 10^{-5}\) & 0.1775 & 0.1552 & 0.1651 & 0.1879 & 0.0479 & 0.0071 \\  & Green & \(6.67\times 10^{-5}\) & 0.1849 & 0.1637 & 0.1741 & 0.1920 & 0.0260 & 0.0071 \\  & Red & \(4.89\times 10^{-5}\) & 0.2249 & 0.1984 & 0.2133 & 0.2183 & \(-\)0.0047 & 0.0078 \\  & NIR & \(6.77\times 10^{-5}\) & 0.2436 & 0.2165 & 0.2316 & 0.2315 & \(-\)0.0425 & 0.0079 \\ \hline \multirow{8}{*}{2011} & Blue & \(4.52\times 10^{-5}\) & 0.2171 & 0.1737 & 0.1909 & 0.2173 & 0.0773 & 0.0109 \\  & Green & \(2.80\times 10^{-5}\) & 0.2178 & 0.1791 & 0.2026 & 0.2235 & 0.0593 & 0.0087 \\ \cline{1-1}  & Red & \(5.74\times 10^{-5}\) & 0.2606 & 0.2131 & 0.2409 & 0.2465 & 0.0235 & 0.0132 \\ \cline{1-1}  & NIR & \(5.23\times 10^{-5}\) & 0.2799 & 0.2154 & 0.2549 & 0.2548 & \(-\)0.0192 & 0.0173 \\ \hline \multirow{8}{*}{2012} & Blue & \(-7.56\times 10^{-5}\) & 0.2083 & 0.1694 & 0.1934 & 0.2201 & 0.0801 & 0.0107 \\ \cline{1-1}  & Green & \(-4.75\times 10^{-
## 4 Discussion  
### Discussion of the Number of Sites  
We used 17 sites in our study, and the time span of all sites is 2001-2014 (Table 1). The entire dataset contains more than 50,000 samples on a daily scale, which are large enough for establishing the ML-based method. As we know, the size of the sample we used is larger than some existing publications. For example, [PERSON] et al. [31] used nine stations in the arid region of Northwest China during the period 2002-2016. [PERSON] et al. [57] evaluated ET in the eddy covariance flux observations at 14 Chinese flux tower sites during the period 2003-2017, and each site has at least 3 years of reliable data. [PERSON] et al. [58] only used the daily meteorological records of seven weather stations in Iran for 10 years (2008-2017). Therefore, our data are enough to train a machine learning model. Our study is mainly for cropland. There are currently limited open-access cropland sites, but our sites cover the current main farming areas. These areas cover different climate types. Therefore, our model has wide applicability. There is currently a lack of stations in tropical regions. When applied in this climate region, the model needs to be further tested.
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 5 Analysis and Discussion  
### Complexity Analysis  
The inference speed of the model is approximately 7 fps on the DanceTrack dataset at full resolution on an A5000 GPU. Compared with the baseline model MOTR, which reports an inference speed of 7.5 fps, the main differences are as follows:  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **HOTA\(\uparrow\)** & **DetA\(\uparrow\)** & **AssA\(\uparrow\)** & **MOTA\(\uparrow\)** & **IDF1\(\uparrow\)** \\ \hline _track-by-detection models_ & & & & & \\ CenterTrack [36] & 41.8 & 78.1 & 22.6 & 86.8 & 35.7 \\ TraDes [37] & 43.3 & 74.5 & 25.4 & 86.2 & 41.2 \\ ByteTrack [4] & 47.7 & 71.0 & 32.1 & 89.6 & 53.9 \\ GTR [37] & 48.0 & 72.5 & 31.9 & 84.7 & 50.3 \\ QDTrack [38] & 54.2 & 80.1 & 36.8 & 87.7 & 50.4 \\ OC-SORT [39] & 55.1 & 80.3 & 38.3 & **92.0** & 54.6 \\ C-BioU Tracker [40] & 60.6 & **81.3** & 45.4 & 91.6 & 61.6 \\ \hline _track-by-attention models_ & & & & & \\ TransTrack [9] & 45.5 & 75.9 & 27.5 & 88.4 & 45.2 \\ MOTR [11] & 54.2 & 73.5 & 40.2 & 79.7 & 51.5 \\ STDFormer-EMPH [41] & 57.8 & 80.5 & 41.7 & 91.7 & 60.5 \\ FusionTrack (Ours) & **65.3** & 75.0 & **57.5** & 90.1 & **73.3** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance reported on the DanceTrack [13] test set. The best results under each metric are marked in bold font.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SG-MLQF** & **QPM** & **HOTA\(\uparrow\)** & **MOTA\(\uparrow\)** & **IDF1\(\uparrow\)** & **DetA\(\uparrow\)** & **AssA\(\uparrow\)** \\ \hline  & & 56.3 & 90.5 & 59.0 & 74.6 & 43.4 \\ ✓ & & 59.2 & 91.1 & 64.0 & 75.1 & 47.3 \\  & ✓ & 56.8 & 90.6 & 59.9 & 74.6 & 43.9 \\ ✓ & ✓ & 59.5 & 91.2 & 63.7 & 74.9 & 48.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance reported on DanceTrack validation set with different components enabled.  
1. The object queries and track queries are computed separately. This doubles the parameter of FFNs in each layer, while keeping the computation complexity unchanged. Let \(N_{L}\) be the number of decoder layers and \(C\) be the feature channel; this adds \(4N_{L}C^{2}\) parameters to the model.
2. The score-guided multi-level query fuser performs attention in a different way than the TAN module in MOTR. Let \(N_{t}\) be the number of tracklets in a frame, the computation complexity of the score-guided multi-level query fuser and TAN is \(O(N_{t}(N_{L}+2)^{2}C^{2})\) and \(O(N_{t}^{2}C^{2})\), respectively. In practice, our method requires slightly more computation in the DanceTrack dataset. This is negligible since both modules make few contributions in a model that performs the \(O(N_{q}^{2}C^{2})\) attention calculation in the decoder layers.
3. FusionTrack requires a post-process for the prediction outputs. Let \(N_{q}\) be the number of object queries, and the computation complexity of the post-process be \(O(N_{q}N_{t})\). This is also negligible for the same reason as the score-guided multi-level query fuser.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Application Level
#### 3.6.1 Secure User Authentication  
The first line of defense at the application level is, of course, the use of secure user authentication. Applying strong password policies, multi-factor authentication, and the like can ensure that only authorized users are allowed to access applications, thus safeguarding IoT devices from malware infections and other security threats. In doing so, systems verify that only authorized users are allowed to access specific data or IoT device functions. This authentication technique prevents unauthorized access and operations and improves system security.  
Figure 7: A list of sample secure coding libraries, frameworks, and patches, which enable developers to build robust and secure IoT systems.  
To improve security, an increasing number of IoT systems adopt multi-factor authentication, or MFA [76]--a mechanism that combines passwords, biometrics, and other authentication methods. This security approach greatly enhance the level of security, making it difficult for intruders to gain access through simple password guessing or stealing.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 1 Introduction
### Background and Previous Work  
This paper extends the results from our conference paper [11]. In this subsection, we give the background on queueing systems used for EDI-type messages, and review some related literature.
## 3 Results
### MERRA-2 GDB (SSR Trends) and Its Evaluation
#### 3.2.2 The MERRA-2 GDB on Global, Hemispherical and Regional Scales
The MERRA-2-based time series for Europe, India and East Asia enable to assess the inter-decadal changes of SSR occurring over these three world regions characterized by significant anthropogenic activities. The obtained results reveal that over Europe (continental areas) a brightening occurred from 1980 to 2000, in agreement with other studies ([22] and references therein), which reversed to weak dimming beyond 2000, which rather agrees with [106] who suggested a tendency of stabilization of SSR since the early 21 st century. Over India, a considerable brightening took place from 1980 to 1985, followed by a dimming till 2010 (in agreement with [107]) and a subsequent brightening till 2019. In East Asia, a brightening occurred from 1980 to 2000, in agreement with [26], which was succeeded by a slight dimming through to 2014. This slight dimming is in line with the reported decline of the rate of increase of SSR in the 2000s by [24] and has been followed by a brightening after 2014. Figure 13: Timeseries (January 1980–December 2017) of deseasonalized anomalies of SSR for three selected world areas with homogeneous trends (see red rectangles in Figure 11), namely, Europe (**a**), East Asia (**b**) and India (**c**). For every area, results are given based on GEBA stations (red dotted lines), corresponding MERRA-2 pixels (including the stations, green dot-dashed lines) and all MERRA-2 pixels of the areas (blue lines). Moreover, the linearly fitted lines to each timeseries are also displayed with similar colors, while blue line displaying the 4 th-order polynomial fit applied to the time series of MERRA-2 SSR for the entire world areas are also shown.
## Appendix E Performance Metrics of the CHT Behavior
### P metric  
The average values for P metric are shown in Table A13.  
### P metric  
The P metric values for each algorithm with the CHTs and the related average values per CHTs for the three study cases.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Study Case 1**} & \multicolumn{4}{c}{**Study Case 2**} & \multicolumn{4}{c}{**Study Case 3**} \\  & **FR** & **SR** & **\(\mathbf{e}\)C** & **PF** & **FR** & **SR** & **\(\mathbf{e}\)C** & **PF** & **FR** & **SR** & **\(\mathbf{e}\)C** & **PF** \\ \hline DER1B & 0.067 & 0 & 0.033 & 0 & 1 & 0.87 & 0.33 & 1 & 0.57 & 0 & 0.17 & 0.6 \\ DER1E & 0.17 & 0 & 0.3 & 0.13 & 1 & 0.93 & 0.033 & 0.97 & 0.17 & 0 & 0.067 & 0.2 \\ DER1B & 0 & 0 & 0 & 0 & 0.6 & 0.3 & 0.17 & 0.43 & 0.13 & 0 & 0 & 0.1 \\ DER1E & 0.17 & 0 & 0.067 & 0 & 0.7 & 0.1 & 0.27 & 0.73 & 0.17 & 0 & 0.033 & 0 \\ DECR & 0.1 & 0 & 0 & 0.1 & 1 & 1 & 0 & 0.97 & 0.2 & 0.1 & 0.1 & 0 \\ DECB & 0.033 & 0 & 0 & 0.13 & 0.77 & 0.53 & 0 & 0.73 & 0 & 0 & 0 & 0 \\ DECR1B & 0 & 0 & 0 & 0 & 0.97 & 1 & 0 & 1 & 0 & 0.1 & 0.1 & 0 \\ DECR1E & 0 & 0.1 & 0 & 0.067 & 0.97 & 0.1 & 0 & 1 & 0 & 0 & 0 & 0 \\ GA & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.033 & 0 & 0 \\ PSO & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.27 & 0 & 0 & 0 & 0 \\ \hline Mean & \(\mathbf{0.053}\) & 0.01 & 0.04 & 0.043 & 0.7 & 0.48 & 0.08 & \(\mathbf{0.71}\) & \(\mathbf{0.12}\) & 0.023 & 0.047 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table A12: FP metric values for each algorithm with the CHTs, and the related average values per CHTs for the three study cases.
## 2 System Model and Problem Formulation
Based on the system models, the MSC maximization (MSCM) problem is formulated as  
Figure 2:5 Figure 2:6 Figure 2:7 (8b) Figure 2:8 (8c) Figure 2:9 (8d) \[y_{m} = \underbrace{\mathbf{g}_{m}^{H}\mathbf{v}_{m}s_{m}}_{\text{ desired signal}}+\underbrace{\mathbf{g}_{m}^{H}\sum_{i=1,i\
eq m}^{M}(\mathbf{v}_{i}s_{p,i})}_{ \text{intra-tier interference}}+\underbrace{\mathbf{h}_{m}^{H}(\sum_{k=1}^{K} \mathbf{w}_{k}s_{s,k}+\mathbf{z})}_{\text{inter-tier interference}}+n_{m},m=1,...,M,\]0where \(\bar{R}^{c}\) denotes the secrecy capacity requirement of the HP-SN\({}_{k}\), \(\bar{E}^{c}\) and \(\bar{E}^{e}\) mean the harvested power requirement of the HP-SN\({}_{k}\) and the LP-SN\({}_{l}\), respectively, \(P\) represents the total transmit power at the femtocell sink node, \(P_{1}\) indicates the transmit power about the information signal part at the femtocell sink node, \(P_{2}\) stands for the total transmit power at the macrocell sink node. The constraints (8a) guarantees that the minimum secrecy rate should be achieved at the HP-SN\({}_{k}\). The constraints and (8b) and (8c) guarantee that the minimum harvested power at the HP-SN\({}_{k}\) and the LP-SN\({}_{l}\) are no less than \(\bar{E}^{c}\) and \(\bar{E}^{e}\), respectively. The constraint (8d) limits the transmit power. Obviously, it is seen that problem (8) is non-convex due to the existence of coupling among variables \(\rho_{k}\) and \(\mathbf{w}_{k}\), which is difficult to solved directly.
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 6 Future Directions and Challenges
### Energy Efficiency and Real-Time Processing  
Another important challenge is the need to improve the energy efficiency of VLC systems, especially for use in portable or battery-powered healthcare devices. As real-time patient monitoring becomes increasingly critical in e-health systems, VLC-based solutions must be designed to support low-latency, energy-efficient communication [77]. Future research should focus on developing new coding and modulation schemes that minimize power consumption without compromising data transmission quality or speed. Furthermore, real-time processing capabilities must be enhanced, to ensure that data from multiple medical devices can be processed quickly and accurately, with minimal delay, in applications such as telemedicine or remote patient monitoring.  
We can see that VLC presents exciting opportunities for revolutionizing wireless communication in healthcare; however, significant research efforts are still required to overcome challenges related to channel coding, resource allocation, environmental impact,  
Figure 5: A schematic diagram of VLC-RF hybrid system for remote health monitoring.  
and energy efficiency. Advanced coding techniques and hybrid VLC-RF systems offer promising solutions for enhancing system performance in complex healthcare environments. By addressing these challenges, future VLC systems can provide reliable, efficient, and safe communication in critical healthcare applications.
## 4 Beyond Memory-Restricted Scenario
To our knowledge, the problem of determining an exact upper bound on the information available to [PERSON] conducting her measurement after the privacy amplification stage remains open due to the difficulty of calculating the accessible information for an ensemble of states of a high-dimensional space [33]. At the moment, this fact limits the applicability of the proposed method in the case of no assumptions made about the eavesdropper's quantum memory storage time. Nevertheless, future research may discover ways of calculating the value that are sufficiently easy to be practically implemented. Currently, it is known that the above-mentioned value is upper bounded by the Holevo quantity and lower bounded by additive accessible information (which is much easier to calculate than the exact value of information available to [PERSON] due to a significantly lower dimensionality of the problem)--in both cases, we are to take the influence of the privacy amplification into account, i.e., to subtract the corresponding number of bits as if we worked with classical data. Note that in contrast to the case of error-correction data encryption, using the one-time pad for encrypting communication related to the privacy amplification procedure would not necessarily guarantee a gain in the secret key generation rate, as it consumes a relatively large additional amount of the pre-distributed key because of the large number of hash functions in the family, e.g., a large bit string is needed to specify the Toeplitz matrix [34].
# Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN
## 4 Damage Identification of ASCE Benchmark Structure [41]
### Damage Detection Results of Decision--Level Fusion Model  
Structural damage detection using the decision-level fusion model in this paper means that the damage decision results of feature level fusion model were used as the local decision results and eventually the fusion decision results are obtained by decision-level fusion calculation. According to the fusion decision calculation method described in Section 3.4, for a test sample \(A\), the results of data fusion decision is the largest of the total weight among the four damage patterns, namely, \(out=\underset{j=1,2,...4}{\max}F_{j}\) and, here \(F_{j}=\sum\limits_{i=1}^{4}w_{i,j}\times f_{i,j}\),which is the total weight of the damage pattern \(j\). since the correct recognition rate of RCPN network model in the training process is 100%, then, \(w_{i,j}\) was set as 1/3 for all of them. Decision-level data fusion was carried out for each test sample one by one until all damage detection samples were fused. At this time, the output fusion results were the final damage identification results. The damage identification results of RCPN decision-level fusion model were shown in Table 4.  
As can be seen from the identification results in Table 4, when the noise level is 30%, the IA of the three decision-level fusion methods are all more than 96%, and the total average IA is 96.9%. The identification results of decision-level RCPN model are satisfactory. When the noise level is 50%, the average IA of the three decision-level fusion methods is 90.25%, 91.75% and 90%, respectively, and the total average IA is 90.7%. When the noise level is 70%, among the three fusion methods, the average IA of the second fusion method is the highest, reaching 85.75%. This is mainly because these two classifiers had obtained better recognition results in feature-level fusion. Therefore, in the calculation of decision-level fusion, the damage recognition results of feature-level fusion were taken as the local decision results to obtain better recognition results. On the whole, even when the noise level is up to 70%, the decision-level fusion damage recognition model still had good anti-noise performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Noise Level** & **Decision-Level Fusion** & \multicolumn{4}{c}{**IA of Damage Patterns (\%)**} & \multicolumn{2}{c}{**Average**} & \multicolumn{1}{c}{**Total**} \\ \cline{3-6} \cline{6-It can also be seen from Table 4 that with the increase of noise level, the identification accuracy of decision-level fusion model decreases. Contrary to that, it can be found from the results that, under the same noise level, the decision results obtained by different fusion methods are very close with little difference, which also indicates that the structural damage model based RCPN is insensitive to the feature input vectors and has good robustness.
Sensors_**2009**, \(9\), 4918-4940; doi:10.3390/s90604918  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
CoCMA: Energy-Efficient Coverage Control in Cluster-Based Wireless Sensor Networks Using a Memetic Algorithm  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3 and [PERSON] 2**  
1 Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei 106, Taiwan; E-Mails: [EMAIL_ADDRESS] (C.C.); [EMAIL_ADDRESS] (C.C.); [EMAIL_ADDRESS] (T.L.)  
2 Department of Electrical Engineering, National Taipei University of Technology, Taipei 106, Taiwan; E-Mails: [EMAIL_ADDRESS] (C.T.); [EMAIL_ADDRESS] (Y.W.)  
3 Department of Entomology, National Taiwan University, Taipei 106, Taiwan; E-Mail: [EMAIL_ADDRESS] (E.Y.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-2-3366-5341; Fax: +886-2-2362-7620  
_Received: 20 May 2009; in revised form: 14 June 2009/ Accepted: 19 June 2009/ Published: 22 June 2009_  
**Abstract:** Deployment of wireless sensor networks (WSNs) has drawn much attention in recent years. Given the limited energy for sensor nodes, it is critical to implement WSNs with energy efficiency designs. Sensing coverage in networks, on the other hand, may degrade gradually over time after WSNs are activated. For mission-critical applications, therefore, energy-efficient coverage control should be taken into consideration to support the quality of service (QoS) of WSNs. Usually, coverage-controlling strategies present some challenging problems: (1) resolving the conflicts while determining which nodes should be turned off to conserve energy; (2) designing an optimal wake-up scheme that avoids awakening more nodes than necessary. In this paper, we implement an energy-efficient coverage control in cluster-based WSNs using a Memetic Algorithm (MA)-based approach, entitled CoCMA, to resolve the challenging problems. The CoCMA contains two optimization strategies: a MA-based schedule for sensor nodes and a wake-up scheme, which are responsible to prolong the network lifetime while maintaining coverage preservation. The MA-based schedule is applied to a given WSN to avoid unnecessary energy consumption caused by the redundant nodes. During the network operation, the wake-up scheme awakens sleeping sensor nodes to recover coverage hole caused by dead nodes. The performance evaluation of the proposed CoCMA was conducted on a cluster-based WSN (CWSN) under either a random or a uniform deployment of sensor nodes. Simulation results show that the performance yielded by the combination of MA and wake-up scheme is better than that in some existing approaches. Furthermore, CoCMA is able to activate fewer sensor nodes to monitor the required sensing area.  
wireless sensor network; sensing coverage; energy efficiency; memetic algorithm