esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 7 Staggered Transition towards a Smart WMS
### Scalability  
Scalability refers to the system's ability to be mapped onto an industrial infrastructure with a huge volume and size while still being able to work efficiently. To manage this, we used the MQTT protocol instead of the conventionally used HTTP/s, which was explained in detail in Sections 6 and 6.3. For the case study, the data that were transmitted in the system consisted of various sensor readings and, most importantly, the IDs on the goods. The articles were packed in the cartons, and each carton was tagged with a unique RFID, which was linked to the sales order of that particular order. At a certain time, the several scanning stations were working at the same time and transmitting the data. MQTT being light weight helped with the transmission of the data, as it has a very small payload. In Table 2, the data show the latency in the transmission of the payload bytes. To deal with the load of the system, more than one broker was added at a few places to enhance the system horizontally. This divided the load where there was network congestion and kept the transmission running at a smooth rate.
## 10 Conclusions  
We model the UT NDE data analysis task probabilistically as the NDM and ODM and evaluate the potential to provide decision support and automation to UT NDE data analysis using their CSDI- and DDPM-based implementations. We demonstrate the veracity and utility of the NDM and ODM by way of their ability to explain the variance in CSDI and DDPM performance across a variety of uniquely partitioned UT NDE datasets. We show that the NDM and ODM provide a basis for understanding the behavior of their CSDI and DDPM based implementations. And thus provide a basis for a technical justification needed to support the use of diffusion based methods in safety critical contexts in practice. We train the CSDI model on various datasets to learn the NDM and sample from it to obtain estimates of peak amplitude response time of flight. We sample estimates from trained CSDI models and find their accuracy, against ground truth, to be on par with manual analysis. The unsupervised training procedure does not rely on dataset labelling or annotation, and the accuracy of sampled estimates depends only on learning  
Figure 7: Opportunities for improvement: wig, wag, and support. The plot shows detail of the CSDI prediction fit, and support from \(\mathcal{X}_{0.95}^{n}\). the distribution of a single dataset. In this way, the approach may be used offline on a per-inspection basis, with no data annotation, and without recourse to out of distribution generalization. We suggest various means to improve results and confidence therein. We also suggest an iterative human-in-the-loop training verification process that may act, in lieu of the availability of ground truth, as a means for fault detection and remediation. The method improves greatly upon prior work where results rely on data annotation, pre-processing, brittle heuristics, and out-of-distribution generalization. And the probabilistic model-based explainability provides a basis for interface with regulatory bodies seeking some justification for usage of novel methods in safety-critical contexts. [PERSON] completed the experiments, wrote the majority of the paper. [PERSON] provided reviews and direction for the research. All authors have read and agreed to the published version of the manuscript. Thanks to Bruce Power LLP for providing funding for this research. Please contact the first author regarding access to the data. There is no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} ADAPT & Automated Data Analysis of Pressure Tubes \\ DBSCAN & Decibel Scan \\ CANDU & Canada Deuterium Uranium Nuclear reactor \\ CSDI & Conditional Score-Based Diffusion \\ DDPM & Denoising Diffusion Probabilistic Model \\ DPM & Diffusion Probabilistic Model \\ MAE & Mean Average Error \\ MABE & Maximum Absolute Error \\ MAPE & Mean Average Percentage Error \\ MSE & Mean Squared Error \\ NDE & Non-Destructive Evaluation \\ NDM & Nominal Data Model \\ ODM & Observed Data Model \\ PT & Pressure Tube \\ SGF & Savitzky-Golay Filter \\ TOF & Time Of Flight \\ UT & Ultrasonic, Ultrasound \\ VAE & Variational AutoEncoder \\ \end{tabular}
## 5 The Use of AI for the Creation of SRE
### Federated Learning
FL architectures that minimize centralized data dependencies have been proposed in order to protect user data during beam reflection and channel estimation [54, 55]. The aforementioned approaches handle the problem of preserving the user's data privacy, which is a fundamental requirement in 6G, by using small datasets to train local models that are sent to the central server to create a global model, while they ensure that private data remains localized on devices. Other applications of FL in RIS-aided networks include channel estimation and spectrum optimization [56, 57, 58, 59]. In [56], FL was employed to reduce the fading channels effect, resulting in lower training loss and higher test accuracy. A similar problem has been tackled in [57] by using Federated Spectrum Learning (FSL), while a novel Federated Deep Residual Learning Neural Network (FDReLNet) has been introduced in [58] to predict channel state information in RIS-aided networks. A significant reduction in transmission overhead has been observed due to the application of FL [58, 59] and the employment of FDReLNet has led to better spectrum prediction accuracy and enhanced system utility, albeit slower \begin{table}
\begin{tabular}{c c c c} \hline
**Ref.** & **Problem Description** & **Algorithm Used** & **Achievement** \\ \hline [PERSON] et al., 2022 [59] & Great transmission overhead in MIMO and RIS-aided MIMO systems & FL & Transmission overhead 16 times lower when compared to Centralized Learning \\ \hline [PERSON] et al., 2022 [60] & Users sum rate optimization in a RIS-enhanced NOMA & FL-DDPG & Improvements in sum rate, training time, and system reliability \\  & wireless network & & \\ \hline [PERSON] et al., 2021 [61] & Minimization of the energy consumption of devices in a RIS-aided multi-antenna BS system & FL & Energy consumption reduced by 12\% compared to FL without the presence of RIS \\ \hline [PERSON] et al., 2023 [45] & Model training on multi-RIS. Poor performance of heterogeneous communication and OoD environments & FL combined with Nash game theory & Up to 15\% better performance in OoD Environments \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont_. convergence has been observed, compared to centralized learning approaches [57]. Improvements in users' sum rate, training time and system reliability have been reported in [60] by employing FL-DDPG in a RIS-enhanced non-orthogonal multiple access (NOMA) network, whereas FL has also been used to minimize the energy consumption of users connected to a RIS-aided multi-antenna BS [61]. The studies presented so far underline FL's versatility in addressing various use cases in RIS-aided wireless systems, like privacy-preserving, communication efficiency, and scalability. Nonetheless, further innovations, such as hybrid architecture, stronger security models, and more rapidly convergent algorithms, may be accommodated in the combination of 6G wireless technologies and AI, which is enabled by FL.
## 1 Introduction  
Mankind involves global navigation satellite systems (GNSS) in different economic activities: autonomous agriculture, transport monitoring, unmanned vehicle transportation, spacecraft navigation, surveying, and much more [1]. Scientists also use GNSS to monitor the state of the environment: sounding the ionosphere [2] and the atmosphere [3], tectonic plate movements [4], natural hazards monitoring [5], etc.  
Because most GNSS users still apply single-frequency equipment [6], it is important to compensate for errors caused by the ionosphere. Different methods have been used for this purpose (as recommended by interface control documents of each system). They are first-principles models [7; 8], empirical models [9; 10], and assimilative models [11; 12].  
The accuracy and precision of the models should be studied to answer which models are better. To evaluate the models' accuracy, we can use two criteria: how accurate the coordinates are after the models' corrections (positioning domain) and how accurately the models can estimate the ionospheric total electron content (TEC) (TEC domain). Global ionospheric maps (GIM) [13] provided a huge archive of TEC data, while IGS [14] and other GNSS communities provided a huge archive of GNSS observations.  
This current paper compares nine ionosphere models--Klobuchar, NeQuickG, BDGIM, GLONASS, IRI-2016, IRI-2012, IRI-Plas, NeQuick2, GEMTEC--in the TEC domain and the positioning domain based on a data set spanning the period 2014-2020 (entire data set covered 2000-2020, but not all the models can be calculated for the period). The following main scientific questions were addressed: \"How accurately do the models calculate TEC?\" and \"How precisely can we estimate the receivers' coordinates using the models to correct for TEC in single-frequency positioning?\"
# Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions
## 2 Path Planning Algorithms Under Sparse Map Conditions
### Adaptive Heuristic Function A* Path Planning Algorithm Based on Sparse Maps
If the current node is within a penalty area, its magnitude is directly proportional to the elevation of the current node and the maximum gradient of the current node's elevation on the X-Y plane and inversely proportional to the average elevation of all points in the known sparse map. This function can reflect the ruggedness of the area around a node within the penalty zone and the difference between the elevation of the penalty area and the average elevation of the map. In parts of the map not considered penalty areas, the obstacle distance cost function is reduced to zero. Through this approach, the intelligent agent can avoid areas around obstacles as much as possible during path planning but can choose to proceed along flatter routes near obstacles when no better paths are available. The pseudocode of the improved A algorithm incorporating these modifications is provided in Algorithm 1.
# Empirical Trials on Unmanned Agriculture in Open-Field Farming: Ridge Forming
###### Abstract  
The decreasing rural population and migration to urban areas for high-tech opportunities have weakened the agricultural labor force. While data technology has been adopted in protected agriculture, numerous challenges remain in field agriculture. In this study, we focus on one of the fundamental steps of field operations, i.e., ridge forming, specifically in unmanned agriculture. We compared the performance of a conventional tractor with an implement to that of a ridge-forming robot. The operation data were collected using an acquisition system, and a comparison between the results of both methods was conducted. Additionally, we analyzed the linearity of autonomous driving and the expenses associated with the selected operation. Our findings indicate that the developed robot for ridge forming caused less torque damage and achieved a more accurate target soil depth, with a linearity performance showing a distance error of only 0.267 m. Furthermore, it eliminated the need for hiring an operator and significantly reduced fuel consumption, which accounts for 50.81% of the operational expenses. These results suggest that field operations can be effectively replaced by autonomous systems, and further research on unmanned agriculture is warranted.  
field robot; replaceability; unmanned agriculture +
Footnote †: journal:  
Article Empirical Trials on Unmanned Agriculture in Open-Field Farming: Ridge Forming: Ridge Forming  
[PERSON]  
1 Department of Bio-Industrial Mechanical Engineering, College of Agriculture and Life Sciences, Kyungpook National University, Daehak-ro 80, Bukgu, Daegu 41566, Republic of Korea; [EMAIL_ADDRESS] (S.K.); [EMAIL_ADDRESS] (Y.K.); [EMAIL_ADDRESS] (H.P.); [EMAIL_ADDRESS] (S.J.); [EMAIL_ADDRESS] (Y.H)
## 2 Flexible Electronics Based on GnPs  
Most electronic devices are based on rigid inorganic components. These conventional materials present drawbacks in light of the rise of applications that require flexibility, such as artificial electronic skin, wearable and compliant electronics, and portable energy harvesting devices [48]. The combination of the mechanical properties of polymers and conductive nanofillers is promising as a way of creating flexible and compliant conductive materials. In particular, investigation into polymers combined with silver nanoflakes showed encouraging results in flexible electronics [49]. However, nano-silver's high cost limits its large-scale production [50]. Figure 2: Schematic of the manufacture of GnPs starting from natural graphite. The typical black powder obtained after liquid phase exfoliation and solvent evaporation is constituted by a mixture of single and few layer graphene and nanostructured graphite. In such a context, carbon-based conductive nanofillers, and in particular, graphene nanoplatelets, gained increased attention as materials for flexible electronics due to their flexibility and low sheet resistance, i.e., that can reach the order of \(\Omega\)/sq [43]. Different approaches were developed (see Table 1). One method consists of fabricating freestanding GnP-based materials. [PERSON] et al. [36] fabricated a flexible and light-weight self-standing graphene nanoplatelets paper, reaching the remarkable electrical conductivity of \(\sigma\approx 2\times 10^{6}\) S/m. This binder-free porous film was bent without ruptures, as shown in Figure 3. It was impregnated with both thermoset and thermoplastic polymers to increase its mechanical properties. After this impregnation procedure, the GnP paper displayed a reduced electrical conductivity (\(\sigma\approx 7\times 10^{5}\) S/m). Coupling with carbon fibers diminished its sheet
# Novel Real-Time Compensation Method for Machine Tool's Ball Screw Thermal Error
## 4 Parameter Identification of Simplified Model and Application of Compensation  
### Simplification of Parameter Expressions  
Most parameters (e.g., material properties) required for the screw's thermal error prediction model could be determined from the screw and machine manuals (Table 1), but some required calculation. The convection heat transfer coefficient \(h\), the heat flow from the fixed-end bearing \(Q_{B}\), and the heat flow from the ball screw nut \(Q_{N}\) are difficult to determine through theoretical calculation. However, since these three parameters are positively correlated with the feed speed [15], they could be simplified into the following expressions:  
\[h=k_{1}v+k_{2} \tag{17}\]  
\[Q_{B}=k_{3}v \tag{18}\]  
\[Q_{N}=k_{4}v \tag{19}\]  
Here, \(k_{1}\), \(k_{2}\), \(k_{3}\), and \(k_{4}\) are parameters to be identified, and \(v\) is the feed speed.
# Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution
## 2 Data Sources  
The typhoon path and intensity information used in this paper came from the CMA-STI Best Track Dataset for Tropical Cyclones over the western North Pacific compiled by the China Meteorological Administration (CMA) and Shanghai Typhoon Institute (STI). The detailed specification of the CMA-STI Best Track Dataset can be referred to in the work of [PERSON] et al. [30] and the dataset can be obtained from the website (www.typhoon.gov.cn, accessed on 1 May 2021). Based on the screening principle that the typhoon center enters within 500 km of Ningbo meteorological station and the disaster loss information is complete, 25 typhoons affecting Ningbo area during 2014-2019 were selected for analysis, and the specific path information is shown in Figure 1. According to the distance from the typhoon landing site to Ningbo, typhoon paths can be divided into three types: Type I, with landing sites in coastal areas of Zhejiang such as Ningbo and Taizhou; Type II, with landing sites in adjacent provinces and cities such as Fujian and Shanghai; and Type III, with landing sites in more distant areas such as Guangdong or without landing but whose central path entered within 500 km of Ningbo meteorological station.  
The Ningbo policy rural housing insurance data used in this paper were provided by the Ningbo branch of the People's Insurance Company (Group) of China Limited. The data include underwriting and claims data. Underwriting data include policy number, number of insured households, underwriting address, insurance amount per household, total insurance premium, etc.; claims data include policy number, date of insurance, reason for insurance, address of insurance, loss of insurance subject, actual compensation amount, etc. The policy number corresponded to each other in the two types of data, and the insurance address detailed the village, town, or street where the accident was located, which can be used to assess the typhoon losses in rural housing in each township.
## 4 Results and Analysis
### Data Preparation
As we know the concept of scaling in distributions, we multiply constant with standard deviation \(\sigma\) as depicted in Table 9. It can be observed that the value of the probability density function decreases as we move right to the \(\mu\). By using these observations we defined lower and upper bound using gamma distribution, for already generated need-based packages. From \(\mu\) till \(\mu+\sigma\), we can set affordable premium amounts for each package in each category. However, if we move to or greater than \(\mu+2\sigma\), then there is the chance of monetary losses. From generated gamma distribution, we can define lower bound and upper bound for each group of every category. Deciding when to move from lower to upper bound is based on attributes like age, gender and marital status which are defining needs of the employees. Table 18 is showing lower and upper bounds for setting premium amounts for each group of all categories. Employers/organizations can decide on the affordable premium amount within defined bounds exceeding these amounts can result in financial losses. Figure 14: Out of pocket employees in original data. Figure 15: Number of Out of Pocket employees before and after Optimization. The total amount is computed by first using the mean \(\mu\) as the premium amount for each group in each category. Then \(\mu+\sigma\) as the premium amount for each group of each category. After that \(\mu+2\sigma\) is computed for each group of all categories and used as a premium amount. Table 19 depicts the total amount computed using gamma distribution for categories A, B, C, D, E, F and G. The last three columns in Table 19, are probability density functions for \(\mu+\sigma\), \(\mu+2\sigma\) and \(\mu+3\sigma\). Category B, D and F, we can see currently allocated medical amount is less than the need of the employees who are in these categories. The proposed methodology solves the real-life problem of insurance package design. The insurance packages are designed by learning from historical medical records. This data-driven analysis provides an easy way to compute out of pocket employees and to define lower/upper bound for the premium amount for all groups of every category. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Category** & **Group No** & \(\mu\) & \(\sigma\) & \(\mu+\sigma\) & \(\mu+2\sigma\) & \(\mu+3\sigma\) & \(P(\mu+\sigma)\)  
After analyzing each category, we compute the overall amount by moving to the right on the probability distribution curve as depicted in Table 19. The organization can adjust the amount of their medical benefit by using our proposed methodology. The need of the employees can be changed, so our model enables employers to move an employee from one group to another.
# Partial Discharge Wideband Full-Band High-Gain Resonant Cavity UHF Sensor Research
## 2 Principle of Antenna
### The Basic Principle of Fabry-Perot Resonant Cavity Antenna
usually, the maximum radiation direction of the Fabry-Perot resonant cavity antenna is \(\alpha=0^{\circ}\), and \(S\) is maximum when \(\varphi_{1}+\varphi_{2}-(4\pi/\lambda)l=2n\pi\), \(n=0,1,2,\ldots\) are satisfied, and its maximum value is  
\[S_{\max}=\frac{1+p}{1-p}f^{2}(0) \tag{11}\]  
It can be seen that the transmitted power coefficient of the resonant cavity antenna is strongly related to the reflection coefficient amplitude \(p\) of the partially reflected surface [21]. The greater the amplitude of the reflection coefficient of the partially reflective cladding, the greater the transmission power coefficient of the resonant cavity antenna, and the higher the gain. Therefore, it is necessary to improve the gain of the antenna through the reasonable design of part of the reflective cladding so that the transmitted wave of the feed antenna through the upper part of the reflective plate is continuously transmitted and superimposed in the same phase.
## 4 Experiments
### Pedestrian Hazardous Crossing Intention Recognition and Prediction Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Prediction Horizon** & \multicolumn{2}{c}{**Acc.**} & **Rec.** & **F1.** & **Pre.** \\ \hline \multirow{4}{*}{0.1 s} & Min & 0.963 & 0.966 & 0.960 & 0.965 \\  & Max & 0.984 & 0.994 & 0.988 & 0.985 \\  & Ave. & 0.974 & 0.975 & 0.978 & 0.976 \\ \hline \multirow{4}{*}{0.5 s} & Min & 0.960 & 0.940 & 0.963 & 0.960 \\  & Max & 0.975 & 0.977 & 0.977 & 0.988 \\  & Ave. & 0.968 & 0.967 & 0.971 & 0.975 \\ \hline \multirow{4}{*}{1 s} & Min & 0.828 & 0.857 & 0.828 & 0.762 \\  & Max & 0.944 & 0.929 & 0.950 & 0.977 \\  & Ave. & 0.889 & 0.888 & 0.898 & 0.911 \\ \hline \end{tabular}
\end{table} 
Table 7: Prediction results for different time horizons. Prediction Accuracy (Acc. ), Recall (Rec. ), F1-score (F1. ), Precision (Pre.). Min, Max, and Average (Ave.) results. Figure 13: Pedestrian hazardous crossing intention prediction experiment: (**a**) single person does not have a hazardous crossing intention, (**b**) single person has hazardous crossing intention, (**c**) pedestrians have hazardous crossing intention, and (**d**) pedestrians do not have hazardous crossing intention.
## 7 System Performance Optimization
### Packet Loss Rate Calculation
#### 7.3.1 Packet Loss Rate (QoS)  
For real-time applications, such as Internet telephony or video conferencing, the quality of service (QoS) will degrade when the packet loss is excessive [45]. However, the real-time applications are not sensitive to packet loss, and a packet loss rate of 1%\(\sim\)3% is acceptable in most cases [46].  
In public places, people often surf the Internet, watch online videos, chat online or play games using public WiFi, so the packet loss rate of the network service for normal users should be less than 3%, and the network service for normal users will not be affected.
## 2 Deep Neural Networks for Inverse Kinematics
### Unsupervised Learning
#### 2.2.1 Generative Adversarial Networks  
Generative Adversarial Networks (GAN) consist of two interlinked neural networks: the generative model captures the data distribution, and the discriminative model tries to differentiate between genuine and generated data [61]. The objective of the generative model is to maximize the probability of the discriminative model making a mistake. In [62], four types of GAN are compared to the performance of a Fully Connected Neural Network for both IK and inverse dynamics, achieving similar performances in every case. The idea is to approximate the real model globally using a limited real-world dataset, which is augmented with fake data generated using GANs. As happened with other DNNs, the performance is lower when the number of DOF grows. To solve this problem, ref. [63] uses GANs to generate initial solutions to the IK problem that are later refined using an iterative numeric approach. In large workspaces, GANs have also been shown to perform better than MLP [43].