esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Particle Filter  
Particle filter is a recursive Bayesian filter by Monte Carlo (MC) simulations which generalize the traditional Kalman filter [21]. It consists of two steps of prediction and update for estimating the posterior distribution of state. In particle filter framework, the posterior probability density function (PDF) is estimated with a set of samples with associated weights. If the number of samples is large enough, MC characterization estimates the true posterior pdf well to achieve the optimal Bayesian estimate. In most practical applications, the kinetic model of maneuvering target is nonlinear and pdf of target state is non-Gaussian. Particle filter can deal with it well because it can estimate the state pdf regardless of the underlying distribution.  
Concretely, affine transform parameters are used as target state \(x_{t}\) at \(t\) frame, and \(y_{1:t-1}=\left\{y_{1},y_{2},\cdots,y_{t-1}\right\}\) denotes the available observations up to \(t-1\) frame. The predicting distribution given all available observations can be computed recursively as  
\[p(x_{t}|y_{1:t-1})=\int p(x_{t}|x_{t-1})p(x_{t-1}|y_{1:t-1})dx_{t-1} \tag{1}\]  
After observation \(y_{t}\) is available at \(t\) frame, the probability of target state is updated with the Bayesian principle  
\[p(x_{t}|y_{1:t})=\frac{p(y_{t}|x_{t})p(x_{t}|y_{1:t-1})}{p(y_{t}|y_{1:t-1})} \tag{2}\]  
where \(p(y_{t}|x_{t})\) denotes the observation probability.  
Then the posterior pdf is estimated by \(N\) samples \(X_{t}=\{x_{t}^{1},x_{t}^{2},\cdots,x_{t}^{N}\}\) with associated weights \(W_{t}=\{w_{t}^{1},w_{t}^{2},\cdots,w_{t}^{N}\}\). The samples are generated by sequential importance distribution \(q(x_{t}|y_{1:t},x_{1:t-1})\) and weights are updated as  
\[w_{t}^{i}=w_{t-1}^{i}\frac{p(y_{t}|x_{t}^{i})p(x_{t}^{i}|x_{t-1}^{i})}{q(x_{t} |x_{1:t-1},y_{1:t})} \tag{3}\]  
For bootstrap filter, \(q(x_{t}|x_{1:t-1},y_{1:t})=p(x_{t}|x_{t-1})\) and the weight has a simple form of \(w_{t}^{i}\propto w_{t-1}^{i}p(y_{t}|x_{t}^{i})\).  
However, the variance of the weights will increase over time thus most particles only have negligible weight after several iterations. In other words, most computational cost is spent on updating weights of those particles which are useless to estimate the posterior probability. To avoid such degeneracy phenomenon, resampling is performed according to weights and a set of new samples with equal weights are obtained in each frame. The number that \(x_{t}^{i}\) appears in the new set is equal to \(N\times p(y_{t}^{i}|x_{t}^{i})\).
all operational elements are used to achieve a seamless link between each other through autonomous cooperative control, thus achieving autonomous coordination. In the process of cooperative control, the battlefield situation is changing rapidly, so the advanced combat communication system must be able to accurately and completely grasp the battlefield situation [1], perceive the battlefield, and integrate all information into an information network so as to obtain high-precision and overall dynamic combat situation information [2; 3] and carry out effective coordination. In the combat system, it is necessary to dynamically establish a fire aiming data link between the sensor and the shooter within the same battle echelon, and organically integrate the information perception and combat strikes so as to strike the target accurately. Because of the intelligence of various entities in an information-based combat, they can share the battlefield situation and become intelligent entities, leading to the formation of self-organizing cooperative behavior on the information-based battlefield. In the study of this kind of autonomous cooperative method and technology, the information between various combat elements and functional units is effectively integrated so that interconnection and interoperability can be realized among each other.  
Based on the combat network's requirement of self-organizing [4; 5] cooperative control tasks and capabilities, this paper proposes the concept of the \"awareness combat network\", which integrates information perception and combat strikes organically. An awareness combat network is a very complex network. One of the key technologies is employed to establish a high-speed real-time dynamic routing protocol for the awareness combat network with dynamic network typology which is suitable for cooperative control requirements.
## 3 Nutritional, Anti-Nutritional and Health Beneficial Constituents of Lima Beans
### Germination  
Germination has been defined as a controlled bioprocessing step, normally adopted to alter inherent food grain composition for desirable characteristics, including the biosynthesis of health-promoting compounds [114]. During this process, the inherent physical attributes of protein, including its composition, structure, conformation and amino acid sequence, are affected and have been linked to an increase in the protein content of the germinated lima bean, noted in a study conducted by [PERSON] and [PERSON] [59]. The changes or mechanisms occurring during the fermentation or germination of lima beans have been determined to be similar, with slight modifications in their mechanisms.  
According to [PERSON] et al. [16], a reduction in fat, protein, fiber and ash was observed in lima beans after germination, with an increase in the carbohydrate content. However, compared to other food processing methods (roasting, fermentation, cooking), germination produces the lowest carbohydrate content [16]. The high carbohydrate content in the processed lima bean samples could be due to processing methods that soften the cellulose, causing starch granules to break down and make the starch readily available [115]. The lower carbohydrate content in germinated samples might be due to the breaking down of carbohydrates in the seed into simple sugars as the embryo is the bean's major energy source for growth [16].  
The minerals zinc, sodium, phosphorous, iron, calcium, potassium and magnesium increase with germination [16]. Likewise, all the processing methods discussed, including germination, significantly reduce the antimutrient content in lima beans. Tannins and other polyphenols in legumes may be lessened during germination, and this could be a result of the formation of polyphenol complexes with proteins, and gradual oligosaccharidesadization [116]. Germination is reported to have the lowest reduction effect (among tested processing methods) on trypsin inhibitors in the lima bean. This gives an indication that heat treatment, specifically moist heat, is more efficient at reducing trypsin inhibitors to acceptable minimal levels [16]. It should be noted that these processing methods can be combined or used singly during the preparation of lima beans.
# ALReg: Registration of 3D Point Clouds Using Active Learning
## 2 Related Work
### Active Learning  
Active learning [57] is a method to use fewer training labels for a machine learning approach. Although it has been heavily associated with deep learning, it was studied before the deep learning era as well. In [58], an active learning method to select samples from a class-imbalance dataset for Support Vector Machines (SVM) [59] is presented. Similar data-selection methods for SVM have been successfully applied to areas like text classification [60; 61] and image retrieval [62].  
Here, we will mainly focus on active learning strategies for deep learning and we follow the taxonomy from [27], discuss the active learning methods according to the acquisition functions they use: uncertainty-based, diversity-based and hybrid methods.  
In general, maximum entropy sampling and its variances are widely used as the acquisition functions to score uncertainty. BALD (Bayesian Active Learning by Disagreement) [63], using an information-theoretic approach, in addition to uncertainty, measures a selected sample's impact on the network's uncertainty. AL-DL (Active Learning in Deep Learning) [64] uses confidence scoring, margin sampling, and entropy scoring for image classification. In [65], new acquisition functions focusing on samples with maximum variation ratio and mean standard deviation are presented. In CoreGCN (Core Graph Convolutional Network) [66], a graph convolutional network is formed where labeled and unlabeled samples are represented as nodes, and the cross-entropy between each labeled and unlabeled node is minimized. Then, samples are selected according to their uncertainty values. In QUIRE (QUerying Informative and Representative Examples) [67], representativeness and informativeness measures are calculated using network uncertainties.  
In [68], a diversity-based active learning mechanism that is inspired by a core-set selection is presented. Using the k-center algorithm on training samples, most representative points are selected according to their position in the feature space.  
Hybrid approaches, like BADGE (Batch Active learning by Diverse Gradient Embeddings) [69], create hypothetical labels for unlabeled data using the predictions of neural network and represent their representation in gradient space where large gradients indicate uncertain samples, then they cluster the feature representations of those samples to select a diverse set. In ALBL (Active learning by learning) [70], a multi-armed bandit selects one of the active learning algorithms according to an important-weighted accuracy score. For many general vision tasks like semantic segmentation [71; 72; 73], object detection [74] and classification [75] hybrid approaches are used.
## 4 Results and Analysis
### Deblur of Simulation Results  
We used FEKO to build a system model and simulated the imaging of ten dipole sources (4 GHz) placed in a circle shape on the object plane. The model structure and the degraded pictures are shown in Figure 7.  
As shown in Figure 8, the row above is the step result of the log-polar transformation algorithm. It can be seen that the log-polar transformation basically eliminates the spatial variation characteristics of the blur, and the number and position of the electromagnetic radiation source are consistent with the real conditions. The row below is the result of applying other algorithms to Figure 8a. It can be seen that due to the large blur and the obvious spatial variation characteristics, the direct super-resolution (SR) algorithm and segmentation SR algorithm cannot satisfy the space-invariant hypothesis which results in some speckle noise and false signals. In addition, the only polar transformation method can recover part of the blur, leaving the noise caused by the radial variant blur.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 4 Results
### Hierarchical Decision Tree
#### 4.1.2 The Decision Tree for Vegetation Division  
The vegetation extracted by above decision tree were further divided into forests, shrubs, grasslands, wetlands (covered with herbaceous and woody vegetation) and croplands by the decision tree shown in Figure 7. Obvious differences between the herbaceous vegetation (including withered grasslands and harvest croplands) and the woody vegetation (including shrubs and forests) existing in spectral reflectance of green band of leaf-on satellite images. Therefore, the feature of HJ_B2_Mean was used to distinguish them. In leaf-on season, the NDVI values (HJ_NDVI_Mean) between croplands and grasslands have differences, which were chosen to separate grasslands and croplands. The standard deviation of red and mid-infrared bands (TM_B3_Std and TM_B5_Std) was applied to reduce the commissions between grasslands and croplands because cropland objects usually have a higher degree of dispersion of spectral reflectance than grassland objects. Using the absorption feature of forest canopy in the red band (HJ_B3_Mean), the forests were extracted from the woody vegetation. The wetlands have relatively low reflectance in the near-infrared band (HJ_B4_Mean) due to the absorption feature of water, which was adopted to distinguish between wetlands and shrubs. The forests confused with the shrubs were extracted again by the spectral reflectance of blue band in leaf-off satellite images (TM_B1_Mean), and the grasslands confused with the croplands were extracted again by the spectral reflectance of mid-infrared band in leaf-off satellite images (TM_B7_Mean). Above all, each satellite image played a unique role and the multi-temporal satellite images contributed to effectively discriminating between each vegetation type.
## 2 Methodology
### Influence of Input Parameters on Bearing Temperature  
The selection of the datasets is critical for implementing the proposed temperature prediction framework. Furthermore, determining input parameters for the bearing thermal model necessitates physical analysis of temperature-influencing factors. Leveraging the DT system for bearing temperature monitoring, we conducted multi-condition transient thermal tests using bearing lubrication test bench within the PEL and DAL. The resultant temperature variations at reference nodes--outer surface temperature (Tor)of the outer ring and inner surface temperature (Tir) of the inner ring--versus operational inputs (rotational speeds and radial loads) are presented in Figure 4.  
In Figure 4a, the radial load on the bearing is set at 2650 N\(\cdot\)m. When the rotational speed is held constant for specific durations, the temperature at the reference node exhibits a continuous rise over time under each constant speed. Furthermore, each switch to a higher speed resulted in a further temperature increase. The bearing consistently failed to reach thermally stable state under unchanged heat dissipation conditions, indicating intensified frictional losses. This demonstrates that rotational speed is one of the factors influencing bearing temperature.  
In Figure 4b, the bearing rotational speed is fixed at 6000 rpm. Higher radial load leads to increased bearing temperatures. Notably, when the radial load applied to double (5500 N\(\cdot\)m), the steady-state temperatures on the inner surface of the inner ring and the outer surface of the outer ring increased by nearly 20 \({}^{\circ}\)C and 15 \({}^{\circ}\)C, respectively, compared to the 2650 N\(\cdot\)m condition. This demonstrates that radial load is also one of the factors influencing bearing temperature.  
The above physical analysis clarified the influencing factors of bearing temperature and determined the input parameters of the bearing thermal model.
## 2 Related Research
### ADL Measurement  
A study to detect dementia symptoms in elderly adults living alone using a machine-to-machine (M2M) and IoT platform was proposed in [30]. The authors collected user behavior data by installing sensor devices in the home. Furthermore, they conducted questionnaires about user habits and personalities. The platform analyzed both sets of data to determine the presence of dementia. However, since the installed sensors and wires discomted the patients, this platform may be challenging to apply to a real house. A watch-type device was proposed in [31] to provide activity-monitoring and healthcare for dementia patients. The device, which includes a GPS, accelerometer, and illumination sensor, measures the patient's activities and steps but does not analyze the flow of the activities to detect whether those activities were appropriate. An integrated system was presented in [32] to collect, record, and transmit data from multiple sensors placed in the homes of dementia patients using cloud applications. This system can identify the patient's forgetfulness and dangerous situations in real-time. However, this system did not consider inter-device interaction, collaboration, and system scalability. A context-aware reminder system for daily activities was proposed in [33]. This system helps elderly adults with dementia complete several ADL without the help of a caregiver. This system can obtain elderly adults' tool usage information using a wireless sensor node. An automated functional and behavioral health assessment of elderly adults with dementia was proposed in [34]. The authors used signal processing on wearable sensor data streams and machine learning algorithms to assess cognitive impairment and its correlation with functional health decline. Currently, B-ADL/I-ADL tests used in hospitals are conducted only through questionnaires, and this paper aims to measure these ADL tests more realistically. The system of this paper is designed based on B-ADL/I-ADL test elements [35].
## 1 Introduction
### Study Area  
The CGID is located approximately between 36\({}^{\circ}\)04\({}^{\prime}\)41\({}^{\prime\prime}\) S and 36\({}^{\circ}\)38\({}^{\prime}\)26\({}^{\prime\prime}\) S latitude, and between 144\({}^{\circ}\)46\({}^{\prime}\)37\({}^{\prime\prime}\) E and 145\({}^{\circ}\)23\({}^{\prime}\)41\({}^{\prime\prime}\) E longitude in the northern part of Victoria, Australia (Figure 1). It is spread across the council jurisdiction of the Greater Shepparton City. It covers an area of approximately 1,900 sq. km., of which a large part is irrigated. This area is an alluvial plain, dominated by fluviatile sedimentation since the Early Tertiary period. The sediment deposits vary from approximately 50 m to 125 m in depth. The well-drained soils are red-brown, mainly fine-sandy loans. The poorly drained soils are grey heavy soils.  
The climate is temperate, and the region is relatively dry with an average annual rainfall between 200 mm and 400 mm. In general, most of the rainfall is received during winter (June to August). Summers (December to February) are usually dry. The average maximum temperature ranges between 12\({}^{\circ}\) and 15 \({}^{\circ}\)C in winter, increasing to between 27\({}^{\circ}\) and 30 \({}^{\circ}\)C in summer (December to February). The average minimum temperature varies from 3\({}^{\circ}\) to 6 \({}^{\circ}\)C in winter and from 12\({}^{\circ}\) to 15 \({}^{\circ}\)C in summer (Figure 2). Victoria's largest river, the Goulburn, flows on the eastern boundary before it joins Australia's largest river, the Murray, in the north. The flat terrain is covered by a network of irrigation channels. The irrigation systems used in the region include micro-irrigation, conventional sprinklers, flooding and furrowing. The main industries are daiying, horticulture, cropping and grazing.  
Figure 1: The study area. (**a**) CGID location in Australia, and (**b**) extent of CGID (in yellow) with the background of Landsat-8 satellite image bands 5/4/3 (displayed as a false colour composite image).
# A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
## 6 Implementation and Evaluation
### Storage Overhead  
The storage overhead refers to the authentication-related data information stored in the smart grid device. The consumption incurred by other information stored in the smart grid device is out of the scope of our discussion. Since we use prime256v1 elliptic curve to generate public-private key pairs, both the public and private keys are 256 bits in size, which is 32 bytes. In our proposed authentication mechanism, the certificate consists of an identity, a public key, a timestamp, and a signature. The identity is 128 bits and globally unique, the public key is 256 bits, the timestamp is 64 bits, and the signature is 72 bits maximum. Therefore, the size of the certificate is 520 bits. Moreover, the hash value of the certificate is saved in the blockchain and the certificates are saved in the off-chain database, which can reduce the storage pressure of the blockchain, improve the consensus efficiency, and also prevent the certificate from not being modified.
# Ocean Currents Reconstruction from a Combination of
## 3 Results
### Effective Depth of the Optimal Currents  
As discussed in Section 2, the PIT09 method has been implemented relying on an optically weighted Chl dataset. This is obtained via Equation (2) integrating contributions from several layers of the water column, i.e., from the sea-surface to the light penetration depth \(Z_{\text{P}}\)[45]. Maximum penetration depths can reach 30 m in the Mediterranean area; therefore, we evaluate whether a vertically integrated \"near surface\" oceanic tracer can impact the effective depth of the optimally reconstructed currents. To this scope, we compare the PI of the optimal currents (Equation (5)) using several modelled PHY currents  
Figure 11: Kinetic energy power spectral density of the Synthetic Altimeter Currents (SAC, red), OPtimal currents (OPC, blue) and CMEMS PHY modelled currents (light blue). The spectra are computed in the WEST and EAST areas of the Mediterranean, depicted by the yellow and green borders, respectively.  
as ground truth, selecting outputs at 1, 3, 5, 7, and 10 m depth. In Figure 12, the PI is presented for both the zonal and meridional flows (blue and red lines, respectively). The figure suggests that the OPC is closer to the PHY surface layer currents, i.e., the 1 m depth output. In that case, we obtain the highest basin-scale averaged PI, around 45%, while, for larger depths, the PI decreases monotonically, reaching \(\simeq\)20% at 10 m depth (eventually becoming negative for larger depths, not shown). The monotonic decrease of the PI, following Equations (5) and (6), indicates that for larger depths, the OPC RMSD increases due to the OPC deviation from the PHY modelled currents.  
The effective depth of the OPC is also evaluated as a function of space. This is achieved computing the OPC improvements with respect to the 1, 3, 5, 7, 10 m PHY currents in \(1^{\circ}\times 1^{\circ}\) boxes. The results are reported in Figure 13 only for the zonal OPC (the behaviour of the meridional currents is analogous). In the figure, the colours stand for the depth at which we record the maximum PI of the OPC, evaluated via Equation (5) and based on one year statistics (2017). More than 90% of the basin exhibits maximum PIs when the surface layer (1 m) PHY currents are considered as a benchmark. Very rarely, and mostly in coastal zones, some PIs are larger at 3 or 5 m depths. Nevertheless, in these cases, the PI differences between the surface and the deeper layers never exceed 0.4%. We concluded that our optimal reconstruction mainly yields information on the sea-surface circulation.  
Figure 12: Maximum OPC basin scale averaged PI over the year 2017, seen as a function of depth.  
Figure 13: Depth of maximum OPC PI over the year 2017, represented in \(1^{\circ}\times 1^{\circ}\) boxes.