esa-sceva/satcom-chunk-collection huggingface dataset:
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 4 Anti-Collision for RFID Based on TD
### Data Architecture for RFID Anti-Collision Based on TD
#### 4.3.2 Payload  
In this RFID system, when the reader sends the wake-up signal to the tags (for simplicity, we did not include the wake-up signal in the data architecture), tags sequentially transmit the preamble and payload using the specific time slot shown in Figure 3. The payload of this RFID system includes tag  
Figure 3: Data architecture for proposed anti-collision RFID system.  
information such as the identification (ID) number, electronic product code (EPC) number, and other important information [44; 45].  
Using the proposed data architecture, when a specific tag transmits the data to a reader, no other tags transmit any data, as shown in Figure 3. Therefore, there is no interference signal when simultaneously communicating between multiple tags and a reader. While the TD-based anti-collision architecture for RFID in Figure 3 considers four tags and a reader, it may be extended for more tags. As mentioned previously, in the case where some tags can be allocated into the same time slots in a real system, interference from other tags are effectively suppressed, compared to the case with a conventional RFID system without the anti-collision technique.
## 3 Results and Discussion
### Statistical Analysis
Figure 9b shows the correlation between compaction pressure and sintering time, and Figure 9c focuses on the influence of sintering temperature and sintering time on the compressive strength of the composite. From these figures, it was apparent that compressive strength increases significantly with higher sintering temperatures (around 600 \({}^{\circ}\)C) and sintering times between 55 min and 65 min. This suggests a strong interplay between these parameters improving both microhardness and compressive strength. In addition, these maps were a valuable tool for the further optimization of the sintering process. By identifying the exact ranges where maximum compressive strength was achieved, process parameters can be fine-tuned for the most efficient production of high-performance materials. The figures also illustrate how varying sintering time and temperature impact the material's structure, with longer sintering times enhancing densification and the strength of the material, but also potentially leading to undesirable grain growth if overextended. To further improve the credibility of the regression models, future work will focus on validating these models through cross-validation techniques and/or additional experimental data. This validation step was critical to ensure that the models' predictions were reliable and generalizable to other processing conditions.
# A Review of Research on Secure Aggregation for Federated Learning
## 3 Privacy-Preserving Mechanisms  
In this section, privacy-preserving mechanisms such as homomorphic encryption, secure multi-party computation, differential privacy, and blockchain techniques will be introduced, along with privacy-preserving techniques for each privacy-preserving mechanism used for federated learning.
## 2 Materials and Methods
### Analyzed Methods
#### 2.1.2 Maximally Divergent Intervals (MDI)  
Maximally Divergent Intervals (MDI) [10] is a density-based method for offline anomaly detection in multivariate, spatiotemporal data. In this work, we focus on purely temporal data and provide the definitions for this case only. For the original definitions including spatial attributes, please refer to [10]. Given a multivariate time series \(\mathcal{T}\), MDI detects anomalous subsequences by comparing the probability density \(p_{S}\) of a subsequence \(S_{a,b}\subseteq\mathcal{T}\) to the density \(p_{\Omega}\) of the remaining part of the time series \(\Omega(S):=\mathcal{T}\setminus S_{a,b}\) for all subsequences. The distributions are modeled using _Kernel Density Estimation_ or _Multivariate Gaussians_. To measure the degree of deviation \(\mathcal{D}(p_{S},p_{\Omega})\) between \(p_{S}\) and \(p_{\Omega}\), an unbiased version of the _Kullback-Leibler divergence_ is used. The most anomalous subsequence \(\tilde{S}\) is found by solving the underlying optimization problem [10]:  
\[\tilde{S}:=\operatorname*{arg\,max}_{S\subseteq\mathcal{T}}\mathcal{D}(p_{S},p _{\Omega(S)})\]  
MDI locates this most anomalous subsequence \(\tilde{S}\) by scanning all subsequences \(S\subseteq\mathcal{T}\) with a length between \(L_{min}\) and \(L_{max}\) and estimates the divergence \(\mathcal{D}(p_{S},p_{\Omega(S)}\), which is then used as the anomaly score. The parameters \(L_{min}\) and \(L_{max}\) need to be defined in advance. The  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **Mechanism** & **Class** & **Online/Offline** & **Training** & **Multivariate** & **Anomaly Score** \\ \hline
**RRCF** & Isolation Forest & Classical & Online & ✗ & ✓ &
\begin{tabular}{l} Collusive \\ Displacement \\ (KL-/JS) Divergence \\ \end{tabular} \\
**MDI** & Density Estimation & Classical & Offline & ✗ & ✓ &
\begin{tabular}{l} Discord Distance \\ \end{tabular} \\
**AE** & Reconstruction & Deep learning & Offline training & ✓ & ✓ & Reconstruction Loss \\
**GANF** & Density Estimation & Deep learning & Offline training & ✓ & ✓ & Density \\  & & Online inference & & & \\
**TranAD** & Reconstruction & Deep learning & Offline training & ✓ & ✓ & Reconstruction Loss \\  & & & Online inference & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the properties of the anomaly detection methods considered in this comparison.  
top-\(k\) anomalous subsequences are selected by ranking the subsequences by their anomaly score and selecting the top-\(k\) subsequences. To accommodate the application to large-scale data, an interval proposal technique based on [PERSON]'s \(T^{2}\) method [31] is employed, which selects interesting subsequences based on point-wise anomaly scores instead of performing full scans over the entire time series. This preselection method is motivated by the fact that most subsequences are uninteresting for detecting anomalies as these are rare by definition [10]. We selected MDI mainly due to its easily interpretable approach.
## 2 Materials and Methods
### Statistical Analyses  
A preliminary power calculation was conducted using G*POWER 3.1.9.7 (University of Dusseldorf, Dusseldorf, Germany) indicating that a sample of 15 subjects was required to observe an effect size f = 0.35, assuming an alpha level of \(\alpha\) = 0.05 and a power of 1-\(\beta\) = 0.8. Although existing research suggests a strong correlation between VO2 max values derived from various maximal exercise protocols (r = 0.78-0.93) [33; 34], the present study adopted a more cautious approach by selecting an effect size analogous to \(\eta^{2}\)P = 0.14.  
Descriptive statistics are presented as means \(\pm\) standard deviations (SD) and 95% confidence intervals (CI) for all variables. The Shapiro-Wilk test was conducted to confirm data distribution normality and Mauchly's test to validate the assumption of sphericity. A one-way repeated-measures ANOVA was used to evaluate the effect of extra load on the variables of interest. Finally, post hoc analyses with [PERSON]'s adjustment were also conducted to determine the significant differences between the three conditions studied. Effect sizes (ESs) for all pairwise comparisons were also calculated using partial eta squared (\(\eta^{2}\)p) and interpreted as follows: small \(\geq\) 0.01, medium \(\geq\) 0.06, large \(\geq\) 0.14). A statistically significant level was defined as 0.05. Data analysis was performed using SPSS (version 29, SPSS Inc., Chicago, IL, USA).
## 3 Results and Discussion
### Envelope Correlation Coefficient (ECC)  
By applying Equation (1), the ECC evaluates the S-parameters matrix; its methodological background has been discussed in [11]. Substituting this into the above equation, the ECC (\(\rho\)) values were computed and are shown in Figure 12, where the ECC values of antennas 1 to 5 are shown, which indicates that both the simulated and measured ECC values are less than 0.03--as shown in Figure 12. These measured ECC values were obtained from the measured reflection coefficients given by Equation (1).  
\[\rho_{eij}=\frac{|S_{ii}^{*}S_{ij}+S_{j\bar{\mu}}^{*}S_{j\bar{\mu}}|^{2}}{(1-|S_ {ii}|^{2}-|S_{j\bar{\mu}}|^{2})(1-|S_{j\bar{\mu}}|^{2}-|S_{i\bar{\mu}}|^{2})} \tag{1}\]
beyond, where high-speed data transmission, stable connectivity, and optimal spectrum use are critical.  
Figure 11: The 2D radiation patterns of proposed MIMO antenna array at 5.8 GHz.
# Parameter Identification Methods for Hyperelastic and Hyper-Viscoelastic Models
## 4 Conclusions  
In this study, we focused on the parameter identification of the hyperelastic and hyper-viscoelastic materials, and the following conclusions have been made:  
* Based on the pattern search algorithm and the Levenberg Marquardt algorithm, the comprehensive fitting of the multi-type experiment data of hyperelastic models, including uniaxial tension, planar tension, biaxial tension, and simple shear experiment have been realized.
* According to the fitting results of the experiment data from [PERSON] (1944) and the verification results of the numerical simulation, the method of combining the pattern search algorithm and Levenberg Marquardt algorithm proposed in this paper for identifying parameters in the Ogden model is practical and efficient.
* The parameter identification method for the hyper-viscoelastic materials which is characterized by combing the Ogden model with the generalized Maxwell model was developed, and the physical model of the hyper-viscoelastic model was also presented.
* Three groups of relaxation tests and four groups of simple shear tests were conducted in ANSYS and the corresponding experiment data were obtained. After employing the data to determine the parameters, it can be concluded that the proposed analytical model of the hyper-viscoelastic model and the identification method by using a pattern search algorithm is reliable since all the experiment data are fitted very well with extremely low deviations, and the identified results are little affected by the initial values as well as the term considered for Maxwell models.  
Figure 6: Verification of the identified parameters (**a**) relaxation tests (**b**) simple shear tests.  
**Acknowledgments:** This project is supported by the National Science Foundation of China (Grant No.51278104, No.51578151, No.51438002), and the Program for New Century Excellent Talents in University of Ministry of Education (No. NCET-13-0128).Finally, the authors would like to thank the two anonymous reviewers that helped to improve the final version of the article with their comments and suggestions.  
**Author Contributions:** All authors discussed and agreed upon the idea and made scientific contributions. [PERSON] did the mathematical modeling, performed the simulations, and contributed to the writing. [PERSON] and [PERSON] contributed to the revisions and discussion of the results.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 HSI in Fresh-Cut Product Quality Assessment
### Fresh-Cut Green Leafy Vegetables  
#### 4.1.1 Lettuce  
[PERSON] et al. (2015) applied a Vis-NIR (400-1000 nm) HSI device in reflectance to discriminate sound and discolored lettuce areas on both abaxial and adaxial surfaces. A one-way ANOVA was used to determine the optimal wavelengths for discrimination purposes. Two indexes were developed (waveband ratio imaging, RI, and subtraction imaging, SI) to improve classification accuracy. In particular, the best classification performances (prediction accuracy, sensitivity, and specificity \(>\) 99.9%) were achieved by RI between 552 and 701 nm (RI\({}_{552/701}\)) and SI for 557-701 nm (SI\({}_{557-701}\)) [69].  
Again, [PERSON] et al. (2017) proposed an on-line detecting system based on reflectance HSI (Figure 7) (400-1000 nm) to discriminate contaminants (i.e., slugs and worms) from sound lettuce. SI was used to classify slugs, resulting in a classification accuracy of 97.5%, sensitivity of 98.0%, and specificity of 97.0%, while RI was used to discriminate worms, achieving classification accuracy, sensitivity, and specificity rates of 99.5%, 100.0%, and 99.0%, respectively [70].  
Fresh-cut lettuce can be sold in modified atmosphere packaging (MAP), but the tissue decay can begin a few days later after processing. [PERSON] et al. (2015) proposed two lettuce decay indexes (LEDIs) to identify both tissues damaged by cold temperatures and lettuce deterioration (LEDI\({}_{\text{CF}}\), based on the maximum and minimum level of chlorophyll fluorescence, and LEDI\({}_{\text{4}}\), based on reflectance imaging using 677, 744, and 904 nm), achieving about a 97% accuracy in categorizing tissue as being fresh or decayed without removing the MAP [71].  
Instead, [PERSON] et al. (2021) applied reflectance HSI (400-1000 nm) and developed a multivariety model to quantify micro- and macronutrients (i.e., nitrate, calcium, potassium, and soluble solid content (SSC)), pH, and chlorophyll concentration. Two bands were selected as the most informative, 506-601 nm and 634-701 nm, and used to build PLS models with a good prediction capability for all parameters (R\({}^{2}\)p = 0.78-0.99) [51].  
Figure 7: Schematic representation (**a**) and picture (**b**) of the on-line detecting system developed by [PERSON] et al. (2017). Reprinted with permission from Ref. [70]. 2017, Elsevier.  
Concerning safety assessment, [PERSON] et al. (2018) applied fluorescence HSI on romaine lettuce leaves to detect four animal (dairy cattle, pigs, chickens, and sheep) fecal species contaminations. For discriminating feces from the four animal species, single fluorescence wavebands were found, F641 nm, F505 nm, F633 nm, and F645 nm, respectively, for dairy cattle, pig, chicken, and sheep feces, and the ratio between F692 and F668 was used to identify any fecal trace with an accuracy from 80% to 100% according to the fecal dilution level [72].
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
### Background
#### 1.1.1 Two-Way Ranging  
One-way ranging systems such as radars [9] typically measure unresponsive or otherwise-uncooperative targets. Numerous extensions have been developed to refine the performance of this class of systems [10], but the uncooperative nature of the target imposes fundamental limits on the achievable precision [11]. Unlike these systems, two-way ranging (TWR) refers to a class of systems in which both the observer and the target communicate and cooperate to produce ranging estimates [12]. When these technologies were first emerging, radio-frequency (RF) hardware was large, heavy, and expensive, so static, robust, and reliable infrastructure such as a radar installation was a popular solution to the ranging problem [13]. As this hardware becomes more powerful and continues to shrink, however, installing modern TWR electronics on aircraft is significantly more viable [14].  
As low-cost RF hardware became more accessible in the early 2000s, double-sided, two-way ranging became a somewhat more popular research topic [15]. Early theoretical work [16] demonstrated several tractable approaches assuming sufficiently behaved oscillators, closely followed by initial experimental demonstrations [17]. Several algorithmic extensions have since been explored [18] as well as more robust experimental demonstrations [19].  
A fair amount of this early work was focused on traditional RF frequency bands, but the falling cost of higher-frequency equipment has motivated ultra-wideband (UWB) and even optical extensions. Since the precision of these techniques is proportional to the inverse of the signal bandwidth [20], high-frequency extensions can enable significantly better performance. Some studies have developed direct UWB extensions of the previous results [21], while others have explored the implications of integrating these high-frequency techniques with existing passive infrastructure [22]. While these high-frequency extensions enable intrinsically better performance, they also incur additional calibration challenges, which were characterized in [23].  
In parallel, optical two-way ranging techniques have also been explored as high-precision ranging solutions [24]. These high-frequency systems have even better-ranging resolution than RF and UWB systems, but they also tend to have a more limited range and field of view, so they are suitable for different types of applications [25]. Recent studies have explored sophisticated 2D [26] and 3D [27] array processing extensions to further improve this performance. A comprehensive theoretical analysis for this class of systems was presented in [28], and modern studies continue to improve the fidelity of these techniques in real-world demonstrations [29].
## 3 The Proposed Fog-Based MQTT Architecture with an Authentication Scheme
### Multi-Tier MQTT Broker Based on Fog Computing (Mu-TiMB)
#### 3.1.1 Broker Bridging  
To implement the proposed architecture, which requires brokers to communicate with each other for publishing and subscribing to topics, a bridging mechanism needs to be presented by MQTT brokers. When a broker is configured as a bridge, in addition to performing the traditional broker functions, it acts as a client by subscribing to some pre-configured topics from another broker (or multiple brokers) and relaying the PUBLISH message targeted to those topics to the selected brokers. As a consequence, the given topics can be shared among more than one broker. This allows clients to subscribe to topics via a broker different from the one that the publisher sends the message to.  
Since the client can subscribe to or publish messages into topics handled by brokers different from the broker where the subscriber or publisher is connected, as in the second and third communication scenarios, the client needs to determine the broker that is responsible for handling the topic. This can be achieved by several approaches [24], including defining a new MQTT header field that carries the required information or extending some of the current MQTT header fields. In this study, we are focusing on extending the current MQTT header fields as proposed in [24] by exploiting the topic name string to encode both the identifier of brokers and the topic. The advantage of this approach is that MQTT specification and implementations do not need to be modified. In other words, the topic name will use the symbol \"/\" to separate the topic and the identifier of brokers that are responsible for receiving and sending the topic.  
The topic name (TN) is divided into four parts: the topic (T), the identifier of the local broker (LB), the identifier of the aggregation broker (AB), and the identifier of the cloud broker (CB). Therefore, the general structure of the MQTT topic name is introduced as follows: TN = CB/AB/LB/T. In the publishing process, LB, AB, and CB indicate to the brokers where the topic will be published and stored. On the other hand, in the subscription process, LB, AB, and CB indicate to brokers where the topic can be subscribed. This structure of TN is used by all clients to send SUBSCRIBE or PUBLISH messages to their corresponding local brokers. Additionally, it is used by brokers when acting as a client to publish and subscribe topics to/from other brokers.
# BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot
## 3 Results  
### Static Positioning  
In this experiment, the performance of the navigation system was tested under the static state of the spraying robot, and the results of BDS positioning alone and BDS/IMU combined positioning were compared. The test was conducted in the fruit tree Innovation Park of the Horticultural Research Institute of Hunan Academy of Agricultural Sciences, Changsha City, Hunan Province, China in March 2022. During the test, the spraying robot was static, and the system outputted the data of BDS positioning alone and BDS/IMU combined positioning at the same time. The deviation of position and heading angle from their corresponding mean values was used as error to measure the positioning and orientation accuracy. The heading angles and positions obtained from BDS and BDS/IMU integrated navigation system are shown in Figure 10. In BDS positioning, the position error was less than 1.5 cm, the mean values in X and Y direction were 0.413 and 0.574 cm, and the variances were 0.062 and 0.096, respectively. The heading angle error was less than 0.3\({}^{\circ}\), the mean value was 129.409\({}^{\circ}\), and the variance was 0.003223. In BDS/IMU integrated positioning, the position error was less than 1.5 cm, the mean values in the X and Y directions were 0.430 and 0.235 cm, and the variances were 0.060 and 0.087, respectively;  
Figure 9: The path planning for the spraying robot in the citrus orchard.  
The heading angle error was less than 0.25\({}^{\circ}\), the mean value was 129.421\({}^{\circ}\), and the variance was 0.002052.  
It can be seen from Figure 10b that the signal variation in the last part of Figure 10d was caused by the variation in BDS measured heading angle. In the static state, the heading angle directly obtained by BDS itself has large random noise. At the same time, the disturbance of wind on the tree crown in the orchard block and interfere with the satellite signal to varying degrees, resulting in large fluctuations in BDS measured heading angle. Comparing Figure 10b,d, the heading angle measured by the untreated BDS fluctuates greatly. After integrating the BDS and IMU, the error caused by random drift is reduced and the accuracy of heading angle measurement is improved. The mean values of BDS/IMU integrated positioning and BDS positioning were closed, but the variance of integrated navigation was smaller, the positioning data of integrated navigation was relatively centralized, and the system was more stable. In terms of orientation, there is no obvious difference between the two. The BDS receiver with dual antennas can provide higher orientation accuracy under static conditions.
# A Polymer Inclusion Membrane for Sensing Metal Complexation in Natural Waters
###### Abstract  
Metal speciation studies are of great importance in assessing metal bioavailability in aquatic environments. Functionalized membranes are a simple tool to perform metal chemical speciation. In this study, we have prepared and tested a polymer inclusion membrane (PIM) made of the polymer cellulose triacetate (CTA), the extractant di-(2-ethylhexyl) phosphoric acid (D2 EHPA), and the plasticizer 2-nitrophenyl ether (NPOE) as a sensor for Zn and Cu complexation studies. This PIM, incorporated in a device with an 0.01 M HNO\({}_{3}\) receiving solution, is shown to effectively transport free metal ions, and it is demonstrated that the presence of ligands that form stable complexes with divalent metallic ions, such as ethylenediaminetetraacetic acid (EDTA) and humic acid (HA), greatly influences the accumulation of the metals in the receiving phase due to the increasing metal fraction complexed in the feed phase. Moreover, the effect of major ions found in natural waters has been investigated, and it is found that the presence of calcium did not decrease the accumulation of either Zn or Cu. Finally, the PIM sensor has been used successfully to evaluate metal complexation in a river water affected by Zn pollution.  
polymer inclusion membrane; metal complexation; Zn; Cu; river water +
Footnote †: journal: Applied Sciences