esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Adaptive Iterative Extended Kalman Filter Based on Variational Bayesian
## 4 Conclusions  
We have successfully developed an improved adaptive iterative extended Kalman filter based on variational Bayesian for nonlinear situations (VBAIEKF). This innovative  
Figure 5: \(RMSE_{pos}\) and \(RMSE_{vel}\) for different algorithms.  
approach not only enhances the accuracy of state estimation and broadens the applicability of the algorithm in dynamic and complex environments but also addresses the issue of algorithm parameter sensitivity. The foundation of this algorithm is the principle of variational Bayesian, which significantly boosts its capability to handle uncertainties and effectively resolve nonlinear problems. Through extensive simulation experiments, the convergence of the algorithm under various conditions, including different noise levels and outlier scenarios, has been confirmed. Our research results emphasize the algorithm's resilience, demonstrating its ability to maintain stability and accurate estimation in challenging and unpredictable situations. Compared with existing algorithms, the VBAIEKF shows advantages in multiple aspects:  
(1) The VBAIEKF achieves enhanced estimation accuracy, with a 21.81% improvement in position estimation precision and a 22.11% improvement in velocity estimation.  
(2) The VBAIEKF exhibits superior convergence characteristics and adaptability to different noise levels, with a position convergence range of +0.058%\(\sim\)+0.58% and a velocity convergence range of \(-0.17\%\sim\)+0.21%.  
(3) The VBAIEKF demonstrates low sensitivity to various parameters, addressing the impact of parameters on the outcomes.  
(4) The VBAIEKF attains heightened operational efficiency with lower complexity, resulting in a 49.04% increase in convergence speed.  
In future research endeavours, validation of robustness and stability will be pursued. This exploration will unfold on two fronts: one involving theoretical substantiation and the other focusing on the real variability of the experimental process of the experimental process and the treatment of noise anomalies.  
Conceptualization, L.W. and Q.F.; methodology, L.W. and Q.X.; validation, Q.F. and Q.X.; data analysis, L.W. and Q.F.; writing--original draft preparation, L.W.; writing--review and editing, Q.F., L.W., Q.X. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No.62373 067) and the Postgraduate Scientific Research Innovation Project of Hunan Province (no.CX20220921).  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Calculation of Weighted Average  
Given the characteristics of field surveys, both sampling density and study area size play a pivotal role in data reliability. Typically, studies covering larger areas with denser sampling and lower variance yield more results, warranting higher weighting. The weight \(W_{i}\) is computed using the following formula:  
\[W_{i}=A_{i}\times N_{i}/Sd_{i}, \tag{1}\]  
where \(A_{i}\) denotes the study area size, \(N_{i}\) represents the number of soil samples, and \(Sd_{i}\) corresponds to the calculated standard deviation of Cd concentrations in each study.  
The weighted mean concentration \(C\) is determined by the formula:  
\[C=C_{i}\times W_{i}/\sum_{i=1}^{n}/W_{i}, \tag{2}\]  
If weight distribution is skewed, it can disproportionately amplify the influence of a few studies with excessively high weights. A logarithmic transformation is applied to mitigate skewness and approximate a normal weight distribution. The log-transformed weights (\(W_{i}^{*}\)) are as defined Equation (3), and the weighted mean content (\(C\)) is as follows:  
\[W_{i}^{*}=\lg\left(A_{i}\times\frac{N_{i}}{Sd_{i}}\right), \tag{3}\]  
\[C=C_{i}\times W_{i}^{*}/\sum_{i=1}^{n}/W_{i}^{*}, \tag{4}\]  
The log-transformed weight coefficients follow a normal distribution across regions, enhancing representativeness.
## 4 Experiments and Results  
The experiment consisted of assessing the performance of our approach from two aspects: (1) the learning ability of the model in terms of the well-known evaluation metrics (accuracy, precision, recall, and F1-score) to predict the misogyny degree on a test dataset consisting of sentences belonging to the corpus gathered via our proposal, and (2) the recognition ability to determine the presence or absence of misogynistic patterns in a real world dataset that includes sentences which the model has not seen during the learning process.
# Highway Construction Safety Analysis Using Large Language Models
## 3 Database and Methods
### LLM Classification  
The analysis also involved employing the LLM for classification to re-evaluate what was originally coded in the database. To facilitate this process, specific fields were isolated from the original OSHA SIR database, including \"EventTitle\", \"NatureTitle\", \"Part_of_Body_Title\", \"SourceTitle\" \"Hospitalized\", and \"Amputation\".  
By compiling a list of unique entries for each of these fields, the LLM was prompted to determine the most applicable entry for each incident. The following metrics were then used to evaluate the classification of the fields within the OSHA database: accuracy, recall, precision, and F1 Score [39; 40]. These metrics are defined as follows: True Positive (_TP_) indicates when the predicted class matches the actual class and is true in binary classification. True Negative (_TN_) signifies that the predicted class aligns with the actual class and is false in binary classification. False Positive (_FP_) occurs when the predicted class does not match the actual class, predicting true when the actual class is false in binary classification. False Negative (_FN_) corresponds to situations where the predicted class does not match the actual class, predicting false when the actual class is true in binary classification [39; 40].  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{7}\]  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[F1 Score=2\times\frac{(Precision\times Recall)}{(Precision+Recall)} \tag{10}\]  
In scenarios where binary classification was not applicable, such as in cases other than hospitalization and amputation columns, accuracy, recall, and precision can be used to assess the classification capabilities of the LLM. Accuracy provides an overall measure of correctness in the model's predictions. Recall and precision, on the other hand, focus on the model's ability to correctly classify positive instances. To comprehensively evaluate the model's performance, the F1 Score combines precision and recall into a single metric, striking a balance between the two aspects.  
Figure 5a,c demonstrate the edge cases for the number of clusters, four and ten clusters, respectively. Visually, the four clusters are too spread out and are much less centered than the ten clusters, which is key to a centric-based algorithm. Alternatively, the ten clusters appeared to be too fine-grained or too specific. As the number of clusters increases, the convoluted Cluster 1 and 3 in Figure 5a obtain a further distinction, indicating that the incidents in these clusters originally had significant overlap (based purely on the representative embeddings). The six clusters presented in Figure 5b were selected for further analysis. These clusters occupy distinct regions while maintaining minimal overlap between clusters.  
Figure 5: (**a**) Four, (**b**) six, and (**c**) ten clusters identified in highway construction incidents (t—SNE embedding).
## 2 Materials and Methods
### Tested Plants and Laboratory Bioassays  
The bioassay-tested plant seeds, including seeds of _E. crus-galli_, were collected at the experimental farm of Agricultural Genetics Institute, Hanoi, Vietnam (AGI), in 2019. _O. sativa_ seeds of the Khang Dan 18 variety, and commercial seeds of _V. radiata_ were provided by Department of Genetic Engineering, AGI.  
The laboratory bioassay experiments were performed at the laboratory of the Department of Genetic Engineering, AGI, Hanoi, Vietnam. In order to ensure the good quality of the _E. crus-galli_ seeds for bioassays, the unfilled and empty seeds were discarded by floating in tap water. The seeds were then treated with H\({}_{2}\)SO\({}_{4}\) (90%) for 30 s to scarify the seed coats and were rinsed many times with distilled water. The tested seeds were then air-dried and hermetically stored at \(-\)2 \({}^{\circ}\)C to keep the seeds fresh. Before germination tests, the germination percentage of all tested seeds of weeds and cultivated plant species was randomly checked and found to be greater than 90%, respectively [27]. The bioassays were conducted using the method of [PERSON] et al. [28] with some minor modifications [29; 30]. Briefly, agar (5%) was dissolved in distilled water (5 g/L), and then boiled to prepare the growth medium. The powders of _A. pintoti_ were then added to agar medium at various concentrations (50 g/L, 25 g/L, 12.5 g/L, and 6.2 g/L) and mixed thoroughly. The growth media were sterilized by autoclaving at 121 \({}^{\circ}\)C for 20 min, kept cool at 30-35 \({}^{\circ}\)C at room temperature, and then transferred into plastic pots (250 mL).  
Twenty healthy seeds of each tested plant (_E. crus-galli_, _O. sativa_, and _V. radiata_) were sown evenly in the pots, then placed in the growth chamber (25 \({}^{\circ}\)C, light: 9:00 to 17:00 day/night cycle, humidity 70%, 4000 lux). Three replicates of all treatments were conducted using a completely random design, and the distilled water treatment was used as the control.  
After one week, the number of germinated seeds, shoot and root length, and fresh and dry weight of all tested plants were measured and calculated using the following formula [30]:  
\[\text{Inhibition (\%)}=[1-(\text{sample/control})]\times 100\]
# Waste Reduction Methods Used in Construction Companies with Regards to Selected Building Products
## 1 Introduction  
Technological progress, which is an inherent element of the development of society, has both positive and negative effects on the environment. One of the areas of the economy that has a huge impact on environmental changes is the construction industry [1], which is a source of a significant amount of waste. According to Eurostat, in 2018, 27 EU member states generated 2.3 billion tonnes of waste, 35.9% of which was construction waste [2]. In the United Arab Emirates, the latest (2021) government statistics show that in 2016 the amount of collected waste amounted to approx. 35 million tonnes. It has also been noted that since 2015 the amount of collected waste has increased by around 19% due to a significant increase in the amount of construction waste. The construction waste collected in Dubai accounted for about 56% of all waste, in Abu Dhabi for about 28%, in Sharjah for about 8%, and in the remaining Emirates for about 8% of all waste. In 2016, only as a result of construction and demolition works, the share of construction waste in the total amount of waste was about 66%, i.e., about 22 million tonnes [3]. The amount of construction waste can be minimized through its proper management. The effectiveness of construction waste management depends on many things, including the correct identification of the sources and factors that influence the production of waste and the use of waste reduction methods. The most desirable method of reducing construction waste is to minimize its amount by reducing the consumption of construction products that generate waste.  
The aim of the research and analyses presented in the article is to learn about:which waste reduction methods are most often used in the construction industry in relation to: steel, concrete, masonry products, finishing products, and wood;
2. whether the application of individual waste reduction methods depends on the size of the construction company.  
The research and analyses were based on surveys conducted among construction site managers. The research was carried out in companies which, in terms of the number of employees, were classified into five groups: from 1 to 9 employees (1), from 10 to 49 employees (2), from 50 to 99 employees (3), from 100 to 249 employees (4), and 250 employees or more (5). The studies were conducted in the Emirate of Sharjah in the United Arab Emirates (UAE). The analyses were performed with the use of the SPSS 26 statistical software.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Environmental Monitoring and Smart Farming  
#### 3.2.1.  
MoleNet  
The MoleNet [20] sensor node facilitates underground to underground (UG2 UG) and underground to aboveground (UG2 AG) communication for underground soil-monitoring applications. It was developed around the Wattuino Pro Mini, which exploits the Atmega328p microcontroller. The authors utilized an RV8523 real-time clock (RTC) for local time upkeep and removing the MCU from deep sleep mode. It offers the capability of operating in six sleep modes.  
Regarding the MoleNet's communication capabilities, a HopeRF RFM69 CW transceiver was chosen for UG2 UG and UG2 AG communication. At 433 MHz transmission frequency, an UG2 AG transmission range of 80 m was obtained, while a 20 m communication range was yielded by the 868 MHz transceiver. An UG2 UG communication range of 7.5 m is achievable using the transceiver at 433 MHz. The proposed node possesses on-board volumetric water content and temperature sensors for underground deployment.  
A battery life of nearly 5 years is reported by the authors. For power supply regulation, a power-efficient MCP1703 regulator is used. Power consumption is approximately 19 mA during sensing, 56 mA during transmitting and 5 mA in idle states.
# Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications
## 4 Equalization Algorithms
### Standard Equalization Algorithms
Therefore, when averaged over a finite time window (possibly weighted averaging), the effects of ASE noise will tend to diminish and an estimation of the ISI coefficients can be obtained. This principle has been used for phase-noise mitigation [9; 10; 11; 12], polarization rotation cancellation [13; 16], as well as for higher order nonlinear ISI mitigation [20; 21]. We implemented the algorithm described in [21]. For the single polarization case, it can be summarized by the following procedure. \[\epsilon_{n} :=D(s_{n})-s_{n}\] (14) \[H_{l}  
Here \(W_{l,k}\) is a weighting factor (for example, in [21] exponential weighting was used), and \(\alpha_{l}\) is a constant weight function assigned to each filter tap. The parameters \(\alpha_{l}\) and the window weighting functions \(W_{l,k}\) need to be optimized for each specific transmission scenario. One of the main advantages of this algorithm is its relative versatility. While LMS and RLS each have a single parameter to be optimized, the \(W_{l,k}\) and \(\alpha_{l}\) can be different for each filter tap, and so they can be adapted to account for the different properties of the ISI coefficients. However, this algorithm tends to be more computationally expensive than LMS or RLS, as the required window sizes can be quite large. 4. _Kalman filters_ Kalman filters are the most complex equalizers reviewed in this paper. They rely on an auto-regressive model description of the NLIN process. The advantage of this description is that it enables to account for a detailed statistical model of NLIN, utilizing all of the correlations that it entails. The properties of the auto-regressive model of NLIN can either be found heuristically [14], or by using an estimation of the NLIN's covariance matrix and autocorrelation [19; 37]. The description of this algorithm is fairly involved and hence the reader is referred to [19], for the details of the algorithm that we implemented in this paper. Of the methods reviewed in this paper, equalizers based on Kalman filters offer the highest NLIN mitigation capability, but are also the most computationally expensive.
# Freeze-Thaw Pre-Treatment of Cassava Tubers to Improve Efficiency of Mechanical Peeling
## 3 Results and Discussion
### Effect of FTP on the Quality of Cassava Tubers
#### 3.3.2 Scanning Electron Microscopy of Peeled Cassava Tubers  
According to other studies [34; 35], freezing and thawing could form a solid matrix in foods due to syneresis. After the freezing and thawing treatment, ice cells were observed as cavities in the micrograph. The size of the cavity (ice crystal) in micrographs is positively related to the syneresis of starch [30]. In Figure 4, the scanning electron micrographs show a regular alignment of starch granules in both cassava tubers peeled manually and mechanically after FTP. It was determined that the FTP had no negative effect on the structure of the starch granules. No rupture and pores were observed in the starch granule structure due to FTP. In both micrographs, the starch granules had elliptic and oval shapes. The surface of the granules seemed to be smooth without any evidence of imperfections, fissures or pores at \(\times\)200 magnification (Figure 4b,d). However, at higher magnification (\(\times\)2000) a slightly roughened surface could be observed in the starch granules after FTP (Figure 4a,c). Therefore, the structure of starch granules at higher magnification should be further investigated.
## 10 Future and Perspectives
### Ethical and Regulatory Considerations  
As optimization techniques evolve, addressing ethical and regulatory concerns will become increasingly important. The development of fair, transparent, and accountable algorithms will ensure that optimization processes do not inadvertently compromise product safety, consumer trust, or regulatory compliance [92]. Research into creating robust frameworks for evaluating the ethical implications of optimization strategies will be critical to ensuring their acceptance and widespread adoption.  
The future of optimization in the food industry promises a wealth of opportunities driven by technological innovation, sustainability imperatives, and the pursuit of excellence in production systems. By embracing advanced techniques such as AI, hybrid methodologies, and quantum computing while integrating sustainability-focused frameworks, the food industry can address its most pressing challenges and unlock new avenues for growth and innovation. These advancements will not only enhance production efficiency but also contribute to global sustainability goals, positioning the industry as a leader in the transition to a more resilient and sustainable future.
# Federated Distillation Methodology for Label-Based Group Structures
## 2 Related Works
### Federated Distillation  
Federated distillation enables the adaptation of models to suit the computational capacity of a client [5] and minimizes information leakage during the sharing of high-dimensional model parameters [6]. FedMD enables heterogeneous FL among clients with different model structures by distilling knowledge from the server to the clients [5]. By contrast, Federated Group Knowledge Transfer (FedGKT) involves bidirectional distillation between clients and servers [16]. This method transfers the computational load from clients to servers, but raises privacy concerns. FedDF [17] uses unlabeled data to implement distillation while aggregating client-to-server logits across different model architectures. Distillation techniques have also been employed in One-Shot FL methods [18; 19; 20], which compress information from multiple client models into singular models.
## 2 Background  
The benefits of the approach in this paper are that the system is (1) bespoke to the healthcare infrastructure due to its use of a density-based clustering approach rather than following a procedure-based analytics approach. The system framework can understand the unique characteristics of each user's activity rather than a 'one size fits all' approach for the detection of appropriate/inappropriate access to EPR data. (2) The system framework flags up potential patient privacy violations for review to an analyst, and takes feedback from users to continually refine alerts. This aids in preventing alert fatigue. (3) The use of machine learning algorithms enables hidden patterns of data to be detected which current procedure-based solutions cannot detect.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
## 2 Mathematical Background
### Review of Membership-Mappings-Based Privacy-Preserving Transferable Learning
#### 2.5.4 Latent Subspace Transformation Matrices  
For a given subspace dimension \(n_{st}\in\{1,2,\cdots,\min(p_{sr},p_{lg})\}\), the source domain transformation matrix \(V^{+sr}\in\mathbb{R}^{n_{st}\times p_{sr}}\) is defined as with its \(i\)-th row equal to the transpose of the eigenvector corresponding to the \(i\)-th largest eigenvalue of the sample covariance matrix computed on differentially private approximated source samples. The target domain transformation matrix \(V^{lg}\in\mathbb{R}^{n_{st}\times p_{lg}}\) is defined as with its \(i\)-th row equal to the transpose of the eigenvector corresponding to the \(i\)-th largest eigenvalue of the sample covariance matrix computed on target samples.
## 3 Problem Formulation
### Problem Formulation  
The R-VRPTWL is modeled as a directed complete graph \(G=(N,A)\), where \(N=\{0\}\)\(\cup\)\(R\) represents the set of nodes and \(A\) denotes the set of arcs. Here, node \(0\) represents the rescue center, and \(R\) is the set of rescue sites. The set \(A=\{(i,j)\ |\ i,j\in N,\,i\
eq j\}\) contains the arcs connecting the nodes in \(N\), with \(d_{ij}\) indicating the distance between nodes \(i\) and \(j\). Each rescue site \(i\in N\) has a time window \([a_{i},\,b_{i}]\), within which the start service time \(u_{\mathcal{K}}\) of helicopter \(k\) at the rescue site \(i\) must be fulfilled. The \(t_{i}\) denotes the service time at the rescue site \(i\), \(dm_{i}\) represents the demanding material to be delivered at the site \(i\), \(dp_{i}\) is the number of survivors waiting to be rescued at the site \(i\), and \(lt_{i}\) denotes the minimum life strength for survivors at the site \(i\). The set \(H=TH\cup MH\) represents all helicopters, where \(TH=\{1,\ldots,m_{1}\}\) denotes the set of transport helicopters, \(MH=\{m_{1}+1,\ldots,m_{1}+m_{2}\}\) denotes the set of medical helicopters. The transport helicopter has a maximum materiel transport capacity \(cm_{k}\) and a minimum life strength threshold requirement \(tt_{k}\). The medical helicopter has a maximum carrying capacity \(cp_{k}\) and a minimum life strength threshold requirement \(mt_{k}\). Table 1 summarizes the notation used in the R-VRPTWLST model. Decision variable:  
\(x_{ijk}\): if a helicopter \(k\) visits and serves the node \(i\) and travels directly to the node \(j\), \(x_{ijk}=1\); otherwise, \(x_{ijk}=0\);  
Objective:  
\[Min\sum\limits_{i\in N}\sum\limits_{j\in N}\sum\limits_{k\in H}d_{ij}x_{ijk} \tag{1}\]  
Constraints:  
\[\sum\limits_{k\in TH}\sum\limits_{j\in R}x_{ijk}=1\quad\forall i\in N \tag{2}\]  
\[\sum\limits_{k\in MH}\sum\limits_{j\in R}x_{ijk}\leq 1\quad\forall i\in N \tag{3}\]  
\[\sum\limits_{j\in R}x_{0 jk}=1\quad\forall k\in H \tag{4}\]  
\[\sum\limits_{i\in R}x_{i0k}=1\quad\forall k\in H \tag{5}\]  
\[\sum\limits_{i\in N}\sum\limits_{j\in N}dm_{i}x_{ijk}\leq cm_{k}\quad\forall k \in TH \tag{6}\]  
\[\sum\limits_{i\in N}\sum\limits_{j\in N}dp_{i}x_{ijk}\leq cp_{k}\quad\forall k \in TH \tag{7}\]  
\[a_{i}\leq u_{ik}\leq b_{i}\quad\forall i\in N,\forall k\in H \tag{8}\]  
\[lt_{i}e^{-0.037u_{ik}}\geq tt_{k},\forall i\in N,\forall k\in TH \tag{9}\]  
\[lt_{i}e^{-0.037u_{ik}}\geq mt_{k},\forall i\in N,\forall k\in MH \tag{10}\]