esa-sceva/satcom-chunk-collection huggingface dataset:
# Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
## 1 Introduction  
Wireless sensor networks (WSNs) have revolutionized the process of sensing data in various domains by comprising multiple sensor nodes. WSNs include sensors to detect environmental data and an analog-to-digital converter to convert analog signals into digital data. They also comprise a microcontroller or Raspberry Pi for processing data with the help of the stored machine learning (ML) or optimization algorithms. A communication module, like XBee, is also included for wireless transmission, along with a power source for supplying energy to the node. Therefore, WSNs can be placed in various remote andharsh environments where traditional wired sensors cannot be placed. Hence, WSNs are widely implemented in various fields, which include environmental monitoring, agriculture, healthcare, automation, surveillance, and military defense. The sensors included in WSNs create a distributed network that works to collect and transmit data to the source from its own base station. To achieve this, several wireless communication protocols are incorporated within the WSNs that permit the collection of information to be processed at the base station and enable several decisions to be made. The size, cost effectiveness, and flexible nature of WSNs make them suitable for various applications. However, their energy efficiency, network flexibility, and scalability are all factors that need to be improved in their present form.  
Amongst their other limitations, energy efficiency occupies the highest priority, as it directly affects the overall performance of WSNs. The energy efficiency of WSNs can be improved by utilizing stored energy in an optimized way. Optimization methods are required, as WSNs are implemented with multiple sensors that can drain energy very quickly when all the sensors are in active mode. This can reduce the reliability of the system when it is implemented in the most important applications, such as healthcare and military surveillance, that necessitate continuous operation without any interruptions. Also, the draining of the energy supply from the battery may lead to the degradation of the WSN system, and so it is not suitable for long-term monitoring purposes. This limitation can be addressed by implementing an efficient network algorithm that can provide longer-term operation periods with a constant power supply. An efficient WSN model can also improve the costs spent on the monitoring system and protect the environment by reducing the number of batteries used and replaced.
## 3 Results
### Vitamin D and Clinical Associations in the Studied Population
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Characteristic**} & \multicolumn{2}{c}{**Characteristic Present**} & \multicolumn{2}{c}{**Characteristic Absent**} \\ \hline
**25(OH)D3, ng/mL** & **Mean** & **SD** & **Mean** & **SD** & \(p\) \\ \hline Gender, female & 22.3 & 10.0 & 17.6 & 9.3 & 0.18 \\ Raynaud’s present & 21.6 & 9.7 & 5.6 & - & 1.0 \\ Joint contractures & 20.6 & 10.5 & 23.0 & 6.4 & 0.18 \\ Lung fibrosis (HCT) & 19.5 & 8.4 & 27.2 & 12.8 & 0.032 \\ Esophageal symptoms (dysphagia, reflux) & 22.1 & 8.4 & 19.3 & 12.0 & 0.084 \\ Dyspnea (\(\geq\)NYHA 2) & 19.5 & 8.4 & 23.8 & 11.2 & 0.14 \\ Digital ulcers & 21.3 & 10.3 & 20.9 & 9.0 & 0.97 \\ Joint synovitis & 21.5 & 9.6 & 20.8 & 10.0 & 0.80 \\ Muscle weakness & 20.2 & 8.8 & 22.0 & 10.6 & 0.58 \\ Telangetasia & 21.9 & 12.2 & 20.8 & 7.2 & 0.85 \\ Palpitations & 21.5 & 8.2 & 20.9 & 11.0 & 0.50 \\ Arterial hypertension & 20.1 & 10.5 & 22.4 & 9.0 & 0.14 \\ Intestinal symptoms (diarrhea, bloating, constipation) & 20.8 & 9.0 & 21.3 & 10.2 & 0.99 \\ Gastric symptoms (early satiety, vomiting) & 20.2 & 9.0 & 21.6 & 10.1 & 0.62 \\ Muscle atrophy & 18.6 & 9.7 & 22.0 & 9.7 & 0.18 \\ Restrictive defect (VC \(<\) 80\%) (\(n\) = 45) & 18.9 & 10.0 & 21.1 & 9.5 & 0.47 \\ Proteinuria & 13.7 & 6.2 & 23.0 & 9.9 & 0.0021 \\ Conduction blocks & 16.2 & 9.4 & 21.9 & 9.5 & 0.071 \\ Disastolic dysfunction & 21.3 & 7.3 & 21.0 & 10.1 & 0.85 \\ Systolic dysfunction (contractility) & 16.9 & 8.9 & 22.1 & 9.5 & 0.11 \\ Pulmonary hypertension & 18.3 & 12.0 & 21.8 & 9.3 & 0.25 \\ (echocardiography) & 19.8 & 8.1 & 21.8 & 10.2 & 0.70 \\ CK-elevation friction rubs & 15.5 & 9.5 & 22.1 & 10.0 & 0.15 \\ Renal crisis & 17.9 & - & 21.2 & 9.8 & 1.0 \\ \hline \hline \end{tabular} SD = standard deviation, VC—vital capacity, NYHA—New York Heart Association (NYHA) Functional Classification of heart failure.  
\end{table} 
Table 4: Comparison of serum 25-hydroxyvitamin D3 levels depending on the presence or absence of clinical characteristics of SSc. The Mann–Whitney U test was used. Two-sided \(p\)-values are presented. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **R** & \(p\) \\ \hline Age & \(-\)0.09 & 0.45 \\ Disease duration since [PERSON] onset & 0.13 & 0.28 \\ Disease duration since diagnosis & 0.06 & 0.65 \\ ESR, mm/h & 0.14 & 0.26 \\ CRP, mg/L & \(-\)0.19 & 0.12 \\ Serum creatinine, umol/L & 0.04 & 0.76 \\ eGFR CKD-EPI & 0.02 & 0.87 \\ mRSS & \(-\)0.16 & 0.21 \\ LVEF & 0.07 & 0.59 \\ FVC (\%predicted) (\(n\) = 39) & 0.11 & 0.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlations between the selected clinical parameters (continuous or ordinal) and serum 25-hydroxyvitamin D3 levels. The [PERSON]’s rank sum correlation coefficient (R) is presented, along with \(p\)-values. muscle atrophy and arterial hypertension in dcSSc (both \(p\) = 0.070). Only one out of nine dcSSc patients without vitamin D deficiency suffered from muscle atrophy, compared with six out of eleven in the vitamin D-deficient subgroup.
## 3 Results
### Statistical Analysis and Process Optimization  
The ANOVA results (Table 3) provided comprehensive statistical validation of the parameter effects on surface roughness. The processing environment demonstrated the highest statistical significance with an F-value of 41.29 and a \(p\)-value of 0.000, contributing approximately 50.93% to the total variance. This finding statistically confirms the superior performance of MQL conditions in surface quality enhancement. The number of passes emerged as the second most influential parameter (F-value: 9.68; \(p\)-value: 0.005), contributing about 23.07% to the total variance, with multiple passes consistently producing better surface finish. The burnishing force showed moderate significance (F-value: 4.05; \(p\)-value: 0.052) with a 9.97% contribution to variance, while the feed rate demonstrated relatively lower statistical significance. The dominant influence of the environment aligns with findings by [PERSON] et al. (2013), who emphasized the critical role of cooling-lubrication techniques in titanium machining. The significant contribution of the number of passes (23.07%) supports the work of [PERSON] et al. (2016), who demonstrated the cumulative benefits of multiple processing cycles on surface integrity.  
The regression model demonstrated robust predictive capability with an R-sq value of 87.66%, indicating a strong correlation between process parameters and surface roughness outcomes. The developed regression equation provides a reliable mathematical tool for predicting surface roughness under various parameter combinations, offering valuable guidance for process planning and optimization in industrial applications. This comprehensive statistical analysis supports the optimization of process parameters, with the optimal  
Figure 8: Microscope image and surface roughness profiles of the surface processed (**a**) under MQL conditions (200 N, 0.5 mm/rev, 4 passes, Ra = 0.306 \(\upmu\)m) and (**b**) under dry conditions (50 N, 0.5 mm/rev, 1 pass, Ra = 0.727 \(\upmu\)m).  
combination identified as MQL environment, 200 N burnishing force, 0.5 mm/min feed rate, and four passes, for achieving superior surface quality while maintaining process efficiency.  
\[R_{a}=0.5096+0.0766\ \mathrm{E1}\ -\ 0.0766\ \mathrm{E2}+0.0249\ \mathrm{F1}+0.0229\ \mathrm{F2}\ -\ 0.0479\ \mathrm{F3}\ - \tag{5}\]  
0.0204 Fr1 +0.0238 Fr2 \(-\ 0.0034\ \mathrm{Fr3}+0.0741\ \mathrm{P1}\ -\ 0.0382\ \mathrm{P2}\ -\ 0.0359\ \mathrm{P3}\)
## 2 System Description
### System Architecture  
Surveillance cameras are usually located in places where there are a lot of people and multiple objects. Therefore, surveillance videos usually have high complexity in terms of the number of elements in each frame. To simplify the complex environments captured by the camera, we designed a detection system that is divided into two parts. The first part is called the \"Front End\" and is made up of the YOLO object detection and localization system. YOLO is a real-time object detection and localization system that was trained using a large database called COCO. This database has various types of categories such as persons, cars, animals, etc. The second part of the system is called the Back End, and is made up of the firearms detection model developed in this work. This is shown in Figure 6.  
Firearms are almost exclusively found next to people. Therefore, these areas of an image are the most important regarding detection. YOLO is used to detect, locate, and identify the segments of an image where there are people. These segments are the images that will be the input for the developed firearm detection model. In this way, the firearm detection system will analyze only the segments of the image that are most important, reducing the possibility of obtaining false positives in places where firearms will never be found, discarding a large area of the image that is not important for detection. In this way we will not needlessly analyze multiple places in the image in search of firearm detection. By including this important step, we are greatly reducing the number of false alarms that would otherwise occur due to the complex environments of surveillance videos. This is shown in Figures 7 and 8.  
Figure 6: System architecture.  
Figure 7: Segmentation.  
The firearm detection model was developed through a CNN. The structure of the CNN was chosen after we carried out different tests with multiple configurations. The initial concept was based on the idea of two types of network architectures that have obtained good results in image detection and classification tasks. The first one is VGG Net [12]. This network is characterized by its depth, implementing a large number of layers and small convolutional filters. The second one is ZF Net [13]. This network is characterized by the use of large convolutional filters in its first layer without implementing a large number of these filters. The CNN was programmed using TensorFlow through Custom Estimators, which is a TensorFlow API that simplifies the development of the model.
## 3 Results
### Effect of Downsampling  
The main advantage of slice interpolation is the lower number of segmentations required to reasonably reconstruct a volume, at the price of losing detail at specific zones that might be important in the post-processing. We reconstructed the same geometry described in the previous section but using just 10% and 5% of the available slices (that is, 30 and 15 slices from 300, respectively, equally spaced). Despite significantly reducing the number of slices, our algorithm could reconstruct the bone structures with reasonable accuracy (Figure 4A). Note that a certain degree of detail loss due to downsampling is unavoidable since the features defined in the removed slices cannot be reconstructed (Figure 4B-D).  
Figure 3: Reconstruction of the partial segmentation of a human pelvis: (**A**) reconstruction of the pelvis using im2 mesh (left, orange) and 3D Slicer (right, blue); (**B**) volumetric overlap of the two reconstructions with decreased opacity for better visualisation (same colours). Insets highlight the zones where differences are more noticeable, corresponding with rough areas where the smoothing and triangulation algorithms play a bigger role. Nonetheless, the IoU score attained was 98.6%; (**C**) effect of decimating on the triangulated surface mesh: 5M triangles (left) vs. 50 K (right). Despite visually reducing the smoothness, the IoU score attained (50 k vs. 5 M) was 97.8%; (**D**) volumetric mesh generated with 5 mm objective element size (total number of elements: 72,694), coloured by AR, showing very high quality (>90% of the elements in the 1–2 range); (**E**) value distribution of the AR and AF metrics for mesh quality assessment. Vertical red lines mark the 50, 90, 95 and 99 percentiles, respectively. Additional mesh quality metrics of the pelvis mesh are presented in Appendix A Table A1.  
We computed the IoU metric over the original and downsampled reconstructions of the pelvis to measure the accuracy of our algorithm when the number of slices was reduced. The IoUs obtained were 97.4% and 96% for the 10% and 5% downsampling, respectively.  
This shows both the power and limitations of slice interpolation when dealing with cases with a reduced number of segmented slices or a very coarse slicing, which will ultimately depend on the practical case.
## 3 Experiments and Results
### Healthy Subject  
Figure 3 shows the experimental results acquired by our multi-modal anterior imager on the health subject. Figure 3b shows the UHR-OCT image of the healthy cornea. The best focus of the sample arm was set in the middle of the cornea, so the full cornea could be imaged with the highest sensitivity. In the zoom-in area of the UHR-OCT image (Figure 3c), major layer structures of the cornea, such as the epithelium layer (EP), Bowman's layer (BL), stroma (ST) and endothelium layer (ED), are clearly seen. Figure 3d is the single-shot image of the conjunctival vessel network of the healthy subject. Figure 3e,f shows the segmented and skeletonized vessel network processed from Figure 3d with our customized software. The fractal dimensions of the microvascular network of the healthy subject were calculated to be: mono-fractal D\({}_{\text{box}}\) = 1.51 and multi-fractal D\({}_{\text{0}}\) = 1.52. Figure 3g is a still conjunctival vessel image captured from one representative conjunctival blood flow video clip recorded on the healthy subject. From the zoom-in area shown in Figure 3h, clusters of red blood cells are visible. The registered video clip (Video s1) shows clearly, the movement trajectories of red blood cells, which were used by the software to calculate the hemodynamic results. The calculated quantitative results of the healthy subject are: mean vessel diameter D = 17.84 \(\pm\) 1.52 \(\upmu\)m, cross-sectional area = 251.40 \(\pm\) 44.43\(\upmu\)m\({}^{2}\), axial blood flow velocity V\({}_{\text{a}}\) = 0.26 \(\pm\) 0.07 mm/s, cross-sectional blood flow velocity V\({}_{\text{s}}\) = 0.18 \(\pm\) 0.05 mm/s and blood flow rate Q = 61.82 \(\pm\) 22.29 pl/s.  
Figure 3: Experimental results on the healthy subject with the multi-modal anterior eye imager. (**a**) Appearance image of the healthy subject’s eye; the blue dashed line indicates the UHR-OCT scanning position. (**b**) UHR-OCT image of the health cornea and (**c**) the zoom-in area showing clear corneal layer structures. EP: epithelium layer, BL: Bowman’s layer, ST: stroma, ED: endothelium layer. (**d**) The bulbar conjunctival vessel network image of the healthy subject; the yellow box indicates the location where the representative video clip (Video s1) was taken. (**e**) The segmented vessel network image and (f) the skeletonized vessel network image of the selected region (green box) in (**e**). (**g**) A representative conjunctival vessel image captured from the bulbar conjunctival blood flow video clip (Video s1). (**h**) Zoom-in area of (**g**) showing the resolved clusters of red blood cells (red arrows).
# Measurement of Sub-Surface Microstructures Based on a Developed Ultrasonic Atomic Force Microscopy
## 4 Summary  
In this paper, a CR-AFM based measurement method is developed for detection of subsurface microstructures with micrometric sizes and relatively large buried depths. A simulation model is established by clarifying the relationship between resonance frequency and contact stiffness of the probe-sample system. A new prototype CR-AFM is established for experiments. The experimental results have good agreement with the simulation results. The resonance frequency feedback method used in this study is different from the traditional CR-AFM using contact force as the feedback signal. Owing to the larger buried depth measurement capability proposed in this study, a subsurface grating microstructure with a feature size of 2 \(\upmu\)m, a feature depth of 0.5 \(\upmu\)m and a buried depth of 34 \(\upmu\)m has been successfully measured by the proposed method. The measured buried depth of subsurface microstructure is almost two orders larger than that of the traditional method, demonstrating its potential advantage over the ultrasonic detection technique and the traditional ultrasonic AFM technique.  
In addition, it should be noted that since the scanning method is operated based on the resonance frequency instead of the constant force, it may cause damage to the sample surface, thus reduction of surface damage caused by the scanning would be studied as a future work. It should be also noted that the influence of the film thickness variation on the scanning results is ignored in this study because of the short scanning range, and the uniformity of the film thickness in a large range could generate additional influence on the measurement of subsurface topography, thus elimination of the influence of the film thickness uniformity on subsurface topography measurement associated with a large range scanning would be also investigated in the future work.  
Conceptualization, Y.-L.C.; Data curation, C.W., J.T. and M.D.; Formal analysis, J.C.; Funding acquisition, B.-F.J. and Y.-L.C.; Investigation, B.-F.J.; Methodology, Y.-L.C.; Writing--original draft, Y.W.; Writing--review & editing, Y.-L.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the by the National Natural Science Foundation of China under Grant 51975522, National Key R&D Program of China under Grant 2020 YFB2007600, in part by the Ministry of Industry and Information Technology's Manufacturing High-quality Development Project under Grant TC200H02J. The work of [PERSON] was supported in part by the National Natural Science Foundation of China under Grant 52035013 and Grant U1709206, in part by the Science Fund for Creative Research Groups of National Natural Science Foundation of China under Grant 51821093, and in part by the Zhejiang Provincial Key R&D Program of China under Grant 2018C01065. (Corresponding author: [PERSON].)  
The authors declare no conflict of interest.
## 2 Traceability
### Agriculture Traceability Tools and Technology Solutions  
Agriculture traceability demands a large volume of data that needs to be collected across the supply chain. Very early tracking and traceability systems used workers to record the information on the field and then manually transfer them on handbooks or into a computer system. This method entails risks, such as faulty information recording as well as inefficient resource usage. In the last recent decades, there is a rapid development of automated processes and products, as well as in communication technologies, resulting in the so-called Internet of Things (IoT) paradigm. This rapid evolution of IoT and sensor technology favors the data gathering procedure by offering fast and reliable methods. These methods include technology for product identification, ingredient analysis, transportation, storage, as well as information capture throughout the overall system integration. Methods such as barcodes, QR codes, RFID, wireless sensor networks (WSNs) are the most widespread and well-known among supply chains.  
Due to their attributes, RFID systems provide a safe information and data management system of agri-food for producers, wholesalers, retailers and consumers. RFID technology contributes in agri-food supply chain management by tracing and monitoring of the \"from farm to fork\" route. Once a food safety issue occurs, its source and consequently the solution can rapidly be found [9; 10].  
An alternative or complement to RFID tags are wireless sensors networks (WSNs). A WSN consists of wireless sensors and actuators, such as humidity sensors, temperature sensors, sound sensors, pressure sensors, regulators and more, which communicate via a wireless modem with an external system where the data are stored and rendered available for the stakeholders. Through WSNs, information, such as temperature, humidity, wind speed, plant diseases, insect pests and animal vital signs, can be obtained, processed and sent to a higher level for decision support. WSNs usually consist of many sensor nodes which are battery powered and have low energy consumption.  
Specifically, the sensors in agriculture are usually planted into soil, in order to collect data regarding the plantation conditions [11]. Moreover, WSNs can be combined with more advanced technologies such as GPS and remote sensing to provide extra functionality.  
As the food supply chain is extremely complex and, at the same time, consumer needs and awareness for fresh, safe and quality food are continuously growing, traceability systems offer many advantages, including the decrease in the time needed to recall and withdraw products dangerous for the public health, thus improving consumers' safety and confidence. Furthermore, traceability provides the agriculture supply chain with transparency and reliability, attributes of outmost importance considering the complexity of the food supply chain nowadays.
# Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation
## 2 Preliminaries
### Unscented Kalman Filter  
The UKF provides a suitable alternative for EKF to deal with nonlinear systems. In this paper, we discuss a discrete-time nonlinear system described as  
\[\mathbf{x}(k)=\mathrm{f}\left(k-1,\mathbf{x}(k-1)\right)+\mathbf{q}(k-1), \tag{7}\]  
\[\mathbf{y}(k)=\mathrm{h}\left(k,\mathbf{x}(k)\right)+\mathbf{r}(k), \tag{8}\]  
where \(\mathbf{x}(k)\in\mathbb{R}^{n}\) denotes the \(n\)-dimensional state vector of the system at time step \(k\), \(\mathbf{y}(k)\in\mathbb{R}^{m}\) denotes an \(m\)-dimensional measurement vector. \(\mathrm{f}\) represents a nonlinear system function, and \(\mathrm{h}\) is a nonlinear measurement function and they are assumed to be continuously differentiable. The process noise \(\mathbf{q}(k-1)\) and measurement noise \(\mathbf{r}(k)\) are mutually uncorrelated, with zero mean values and covariance matrices  
\[\mathrm{E}\left[\mathbf{q}(k-1)\mathbf{q}^{T}(k-1)\right]=\mathbf{Q}(k-1), \ \mathrm{E}\left[\mathbf{r}(k)\mathbf{r}^{T}(k)\right]=\mathbf{R}(k). \tag{9}\]  
In general, similar to KF, the UKF includes two steps, namely predict and update: