esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction and Background  
### The Problem of Rollover of Agricultural Machines  
The constantly increasing demand for agricultural products has led to the cultivation also of areas that are non-ideal for mechanization due to the geo-orographic conformation of the land, particularly due to very steep sloping-grounds, albeit with interesting agronomic characteristics in terms of climate and solar radiation. Similarly, the need for farms to remain competitive on the market imposes a growing use of machines worldwide with the aim of: speeding the operations on the crops and increasing the timeliness of plant-protection treatments, reducing the time-to-market from the ripening of the agricultural product to be collected, and, above all, increasing the overall field yield. Extending the use of machines to environments that present objective difficulties for cultivation has led operators to work in conditions with high rollover risk [1; 2; 3; 4; 5; 6; 7; 8] and aggravating loads (e.g., with the machine operating at its limit of carrying capacity or with the center of gravity--CoG--offset from the vehicle median line). Additionally, the same stability problems exist also in other working conditions, generally less dangerous in theory (e.g., agricultural operations carried out on a plane surface), yet still at risk of potentially-critical situations (e.g., shuices, asperity of the soil). Therefore, accidents due to the overturning of machines continue to occur nowadays with high frequency, and often have serious or even fatal consequences for the people involved. Indeed, statistics show that most of the fatal accidents happen when a farm tractor overturns [9; 10; 11; 12; 13; 14; 15], mainly due to the widespread use of these machines, their high CoG, and the absence of suspensions in combination with uneven grounds. In particular, [PERSON] and [PERSON] [16] report that over 50% of fatalities in agricultural accidents per year are due to the rollover of tractors. From a human and social point of view, the cost of these accidents is very high and justifies a continuous research commitment in this domain [17; 18]. For the described reasons, the mechanization of side-slope activities [19] and the dynamic behavior of off-road vehicles in general has been studied since the 1950's, especially with regard to the vehicle-terrain interaction [20], and it is still an open field of investigation. Since then, quite a few publications have been published on this topic, especially in the terramechanics and field robotics literature--such as the work by [PERSON] and [PERSON] [21]--as well as many others addressing tractor stability and the reduction of injuries related to tractor rollovers [17; 22].
###### Abstract  
Most of the rare metal pegmatite deposits in China lie in West, Central and South China, and the Lijiapuzi Nb-Ta deposit is the only pegmatite-type Nb-Ta deposit in Northeast China, therefore a detailed study of the Lijiapuzi deposit is of great importance to the metallogeny and exploration of rare metal deposits in Northeast China. The Nb-Ta bearing pegmatites in Lijiapuzi district are composed of a mircoline wall zone, a muscovite (lepidolite)-albite intermediate zone and a quartz core, and the rare metal mineralization mainly occurs in the intermediate zone and is intimately related to the albitization. The lithogeochemical features indicate that Lijiapuzi pegmatoids are rich in silicon and alkaline, poor in calcium, magnesium, titanium and phosphorus, and belong to peraluminous grannitoid rocks. The enrichment of large ion lithophile elements (LiLEs) and depletion of high field-strength elements (HFSEs), the distinct right-inclined REE pattern with obvious Eu negative anomaly, lower ratio of Nb/Ta and higher ratio of La/Nb and Rb/Sr indicate that the pegmatite was of a crustal origin and formed in relatively extensional tectonic setting of post-orogenesis. The pegmatites in Lijiapuzi area can be divided into two categories, i.e., the NW-trending pegmatites are shorter and thicker, with obvious zoning and localized in the upper part of the near-surface, while the NE-trending pegmatites are longer, thinner, devoid of zoning and localized in the lower part of the district. This probably means that excellent ore prospecting potential exists in the lower part of the Lijiapuzi district.  
Nb-Ta deposit; pegmatite; LCT type; metallogeny; Lijiapuzi deposit Article
# Automated Measurement of Grid Cell Firing Characteristics
## 1 Introduction  
Grid cells in the medial entorhinal cortex of the mammalian brain often fire in spatially periodic fields [1, 2, 3]. These neurons are theorized to be core constituents of the circuit responsible for spatial representation and path integration [4]. However, the exact relationship between the geometric characteristics of grid fields and the computational mechanisms underlying these essential cognitive functions remains under active scientific investigation [5, 6, 7]. Here, we introduce new software, GridMet, to measure grid field sizes, spacings, and angles. GridMet improves on other available software for estimating the spacing and angles between grid fields, such as Opexebo (ppyi.org/project/opexebo/), by including the additional measurement of mean field size and using a different approach to detect grid fields.  
A MATLAB-based toolbox named GridCAT offers automated estimations of grid codes from human functional magnetic resonance imaging (fMRI) data [8]. A strength of GridMet is its ability to characterize firing patterns at the cellular level of electrophysiological recordings. A complementary weakness is that such invasive technology can have limited use in humans but is employed more extensively in other organisms like rodents. GridCAT's reporting on fMRI data has the advantage of suitability for human testing butalso suffers from the limitation of inferior spatial resolution relative to cellular electrophysiology. GridMet's findings can be compared to those observed in GridCAT. For example, grid field differences in invasive rodent recordings assessed by GridMet upon visual stimuli manipulation (e.g., light vs. dark settings) can foster predictions of how grid codes may change under similar circumstances in non-invasive human imaging gauged by GridCAT. Thus, these two software programs can work cooperatively to increase knowledge of grid code behaviors. Future work could include creating a software wrapper for GridCAT inside GridMet, or reimplementing its methods in GridMet, to facilitate comparison of results.  
Opcexebo employs peak firing to identify field positions. However, we have observed that the peak firing does not always occur at the center-of-mass of field firing [9; 10]. GridMet uses clustering and other advanced features to identify grid field positions and shapes, thus accounting for field firing variability that peak firing may overlook. This enhanced identification of firing fields can aid in statistical reporting, such as field spacings, sizes, and orientation angles. The open-source release of GridMet can facilitate grid cell analyses, leading to new findings about spatial cognitive functions.
## 2 Data and Methods  
### Study Area  
The Lanxi urban cluster is located at the westernmost end of the Yellow River Basin, with geographic coordinates ranging from 34\({}^{\circ}\)26\({}^{\prime}\) N to 37\({}^{\circ}\)38\({}^{\prime}\) N and from 98\({}^{\circ}\)55\({}^{\prime}\) E to 105\({}^{\circ}\)55\({}^{\prime}\) E [46] (Figure 1). The Lanxi urban cluster encompasses 39 counties and districts across 9 cities and prefectures, spanning an area of 97,500 km\({}^{2}\). The region features diverse and complex terrain, primarily characterized by mountainous and river valley landscapes, with elevations ranging from 1258 to 5255 m and an average elevation exceeding 2000 m. The Lanxi area is one of the areas in China with the least rainfall, with 58% of the total area receiving an average annual rainfall of less than 300 mm [47]. The Hexi Corridor, which has the lowest annual precipitation, has an annual rainfall of only 40-200 mm [48]. In 2021, grassland predominated as the primary vegetation cover type, accounting for more than 74% of the total area, followed by cultivated land and water bodies, accounting for 13.4% and 3.7% of the total area, respectively. Urban areas cover 0.3% of the total area, totaling 294.3 km\({}^{2}\), but they can have wide impacts on regional vegetation [49]. The population in the area reached 12.19 million, constituting 66.5% of the total permanent population of the two provinces. The eastern part of the Lanxi urban cluster is characterized primarily by a temperate semi-arid climate, whereas the western part is characterized by a continental plateau semi-arid climate. The average annual precipitation is approximately 300 mm. Severe land desertification, desertification, and soil erosion are prevalent in the region, highlighting the critical importance of strengthening ecological environmental protection for the sustainable development of urban clusters [50].
## 2 Materials and Methods
### Quantitative Descriptive Sensory Analysis (QDA)  
QDA was performed according to a previous study with minor modifications [16]. Eight well-trained flavor assessors (4 female and 4 male, 22-30 years) from the College of Food Engineering and Nutritional Science of Shaanxi Normal University participated in this study. All assessors were familiar with the evaluation procedure of QDA, and had professional abilities in evaluating the description and discrimination of juice aromas. Each pomegranate juice (20 mL) sample was placed in a 100 mL glass cup labeled with random triple digits, and evaluated by assessors in the sensory laboratory (at 25 \({}^{\circ}\)C, free of peculiar smells). Prior to sensory analysis, all assessors received 14 days of training to familiarize them with the aroma of the pomegranate juice. Subsequently, they discussed the aroma characteristics of pomegranate juice until they could agree on a common list of aromatic attributes and odor descriptors. Six odor descriptors, including fruity, grassy, woody, floral, earthy, and sweet, were finally adopted. The standard reference details for descriptor definition were presented in our previous study [17]. In addition, \"favorability\" was supplemented as a comprehensive metric for assessing the aroma quality of PJ samples. All samples were rated for odor intensities on a scale of 0-9 (0 = not perceivable, 1 = very weak, 3 = weak, 5 = moderate, 7 = strong, and 9 = extremely strong). Each sample was evaluated by each sensory assessor in triplicate. The average was taken as the final result.
## 2 Communication Middleware Technology Trend for Smart Factory
### MTConnect  
MTConnect [12] is an extensible lightweight protocol developed for the data exchange between manufacturing facilities and applications. It is mainly used for monitoring and data analysis in industrial network environments.  
MTConnect parses and provides manufacturing facility data in XML format (based on HTTP protocol). The interoperability can be greatly improved by exchanging data between different types. In addition, by using the RESTful interface method, the scale scalability and versatility of the interaction can be increased.  
There are three data types that can be expressed by MTConnect: (1) property data of a physical device (model number, serial number, maximum speed, device threshold, and so on); (2) real-time data measured by the device; and (3) real-time data of the device itself (current speed, position, temperature, and so on).  
The MTConnect structure consists of eight basic elements, as shown in Table 1 below.  
In MTConnect, there is an 'agent' that performs the role of real protocol implementation and XML generation. This 'agent' stores and manages data in the form of 'key/value' through a queue. As shown in Figure 1, the agent can manage using unique keys.  
MTConnect's agent processes XML messages in a FIFO method. To identify the data stored in the queue, there are three request types, as shown in Table 2. Table 3 shows MTConnect's response XML main elements of messages.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Element** & **Description** \\ \hline Header & Protocol-related informaion \\ Components & Building blocks of the device \\ Data Items & Description of the data available from the device \\ Streams & A set of samples, events, or conditon for components and devices \\ Assets & Something that is associated with the manufacturing process \\ Samples & A point-in-time measurement of a data item that is continuously changing \\ Events & Discrete changes in state that can have no intermediate value \\ Condition & A piece of information the device provides as an indicator of its health and \\  & ability to function \\ \hline \hline \end{tabular}
\end{table}
Table 1: MTConnect structure.  
Figure 1: Data storage structure of MTConnect agent [13].  
Recently, methodologies for the high reliability of MTConnect based cyber physical systems (CPS) have been developed. They are being used in domains such as manufacturing, defense, and aviation. In particular, the MTConnect Association and OPC Council are promoting MTConnect and OPC-UA Companion standards to ensure the interoperability between the standards maintained by each organization and to expand the scope of the existing manufacturing data exchange standards and implementation technologies. In addition to UA, the use of MTConnect is expected to expand further.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 4 Design
### Resource URL Advertisement  
Resource URL advertisement extends the advertisement process of the underlying architecture (defined in [5]) to include a verification process. Resource URL advertisement in [5] is implemented as follows. Each IoT device _advertises_ its \(Resource_{URL}\) to the edge node where it is attached, and each edge node forwards this advertisement to the SDN network using the _broadcast forwarding identifier_. Therefore, all advertisements are eventually received by all edge nodes. Using these advertisements, edge nodes construct _routing tables_ that map a \(Resource_{URL}\) to a set of (local) IoT devices, as well as to a set of (remote) edge nodes. Therefore, upon receiving a request for a \(Resource_{URL}\), an edge node may forward it to one or more locally attached IoT devices and/or to one or more edge nodes (using the appropriate forwarding identifier). Advertisement messages include the identifier of the edge node that can be used for reaching the corresponding IoT device as well as a nonce used for preventing replay attacks.  
The additional verification step is implemented as follows. Upon receiving an advertisement, an edge node needs to validate it. In order to do this, it uses the registry operationsdefined in [30] to receive the appropriate DID document(s). An edge node may cache these documents. An example of this process is illustrated in Figure 2. In this example, an edge node receives an advertisement for \"smart-city.iot/mall\", then it requests the ICN network to retrieve the DID document for \"did:domain:smart-city.iot\"; in the retrieved document, it performs a closer prefix match with the prefixes included in the assertion list, and selects the appropriate entry. In this example, the entry for \"smart-city.iot/mall\" is of the type URL, so the edge node requests the DID document for that URL and locates the desired key. Finally, the edge node verifies the digital signature of the advertisement using the key extracted from the DID document.If all validations succeed, the edge node updates its routing table accordingly. Therefore, advertisements are handled by edge nodes, and they do not reach IoT devices; hence, IoT devices do not have to perform any DID validation.
# Numerical Simulation-Based Study of Controlled Particle Deposition Technology for Wafer Surfaces
## 3 Results and Discussion
### Simulation Results and Analysis
#### 3.1.1 Nozzle Diameter  
In this study, the impact of nozzle diameter on the spatial distribution characteristics of aerosol particle deposition was systematically analyzed by evaluating five nozzle sizes, as illustrated in Figure 4. To ensure consistency in the number of incident particles, the aerosol volumetric flow rate was kept constant at 0.3 L/min for all nozzle diameters. As a result, the outlet velocity decreased with increasing nozzle diameter.  
Numerical simulation results indicate that the nozzle diameter has a significant influence on the morphology of deposition spots. When the nozzle diameter is small, the higher flow velocity causes inertial forces to dominate the deposition process, resulting in regular circular deposition spots. As the nozzle diameter increases, the flow velocity decreases, leading to a transition where diffusion-driven deposition becomes dominant, causing noticeable changes in spot morphology. When the nozzle diameter reaches 8 mm, the excessively low velocity weakens inertial forces, significantly reducing deposition efficiency and resulting in irregular deposition spot shapes.  
Further analysis reveals that a smaller nozzle diameter generates higher airflow velocity, enhancing turbulent mixing effects and improving deposition uniformity. In contrast, an excessively large nozzle diameter leads to lower airflow velocity, allowing particles to be carried away by the flow rather than being deposited in the target region, ultimately reducing deposition efficiency.  
Based on this analysis and experimental requirements, a nozzle diameter of 4 mm was determined to be optimal, as it ensures effective deposition while meeting the control  
Figure 4: Deposition distribution contours for different nozzle sizes.  
requirements for deposition spot size. At this nozzle diameter, the appearance of a star-shaped pattern in the deposition concentration distribution is attributed to the combined effects of deposition non-uniformity and the simplifications introduced in the simulation boundary conditions.
## 2 Methodology
### Bayesian Binary Search Framework  
In contrast to the uniform prior that is assumed when looking at the classical binary search algorithm from the lens of Bayesian inference, BBS begins with a potentially non-uniform prior distribution, \(P_{learned}(x)\), estimated from data using methods such as Gaussian Process Regression, Bayesian Neural Networks, Quantile Regression, Gaussian Mixture Models, Kernel Density Estimation, or Maximum Likelihood Estimation. This learned prior encapsulates prior knowledge or beliefs about the likely location of the target value.  
The subsequent steps in BBS involve probing the search space based on this learned prior (or the evolving posterior distribution derived from it). Each probe and the resulting observation then lead to an update of the posterior distribution using Bayes' Theorem:  
\[P(x|O_{k},P_{learned})\propto P(O_{k}|x)P_{learned}(x)\]  
or, more generally, at step \(k\):  
\[P(x|O_{k},\ldots,O_{1},P_{learned})\propto P(O_{k}|x)P(x|O_{k-1},\ldots,O_{1}, P_{learned})\]  
This iterative process, starting with a learned prior (Algorithm 1-5), allows BBS to focus its search on regions of higher probability, potentially leading to faster convergence and improved efficiency compared to standard binary search's assumption of a uniform prior. The posterior update in this case is mathematically equivalent to bisecting in probability density space.  
Proof of Posterior Update Equivalence to Bisection in Probability Density Space with a Non-Uniform Prior  
In Bayesian Binary Search (BBS), the prior distribution over the search interval \([a_{k},b_{k}]\) is not necessarily uniform. Let \(P(x)\) be the prior PDF at the \(k\)-th step. The core principle of BBS is to choose a probe location \(p_{k}\) within \([a_{k},b_{k}]\) such that it effectively bisects the probability mass according to the current posterior belief.  
At each step, BBS aims to find a probe location \(p_{k}\) that satisfies\[\int_{a_{k}}^{p_{k}}P(x|\mathcal{D}_{k})dx=\frac{1}{2}\int_{a_{k}}^{b_{k}}P(x| \mathcal{D}_{k})dx\]  
where \(P(x|\mathcal{D}_{k})\) is the posterior PDF after \(k\) probes (or the learned prior at \(k=0\)). Assuming the posterior is properly normalized, \(\int_{a_{k}}^{b_{k}}P(x|\mathcal{D}_{k})dx=1\). Therefore, the probe location \(p_{k}\) is chosen, such that  
\[\int_{a_{k}}^{p_{k}}P(x|\mathcal{D}_{k})dx=\frac{1}{2}\]  
Now, let us consider the two possible outcomes of the probe at \(p_{k}\).
# AR Long-Term Tracking Combining Multi-Attention and Template Updating
## 5 Conclusions  
Our method has a simple tracking framework and abandons any data post-processing and anchor frame operation and has a few parameters. Firstly, the fusion feature is used to extract the last three layers of the network to prevent the network from being too deep. Secondly, to merge the extracted features, a feature fusion network consisting of a dual self-attention module and a cross-attention module is utilized. Among them, the dual attention module adopts the combination of a Non-Local network and channel attention mechanism to enhance the contextual information and reduce computation. The cross-attention mechanism designed by Transformer is used to establish the correlation between long-distance features and effectively combine the contextual information. The tracker's stability is increased in complex surroundings. Finally, the template update module is designed to update the template in real time, avoiding the problem of tracking accuracy degradation caused by the target deformation during long-time tracking while still using the first frame as the template. A large number of experiments show that our method has shown excellent accuracy and real-time performance on multiple benchmarks, achieving a success rate of 69.0% on the OTB100 dataset, which is 15.6% higher than that of SiamFC and 11.4% higher than that of SiamRPN. A success rate of 65.1% is achieved on the LaSOT dataset. Our tracker performs well on both VOT-LT2020 and OxUvA and is second only to STARK. The number of parameters is only 12.659M, and our tracker runs faster than STARK. The network structure is simple, and the robustness shown in various challenges also indicates that our method can meet the requirement for long-term tracking in augmented reality. Future research will focus on further simplifying the network structure to better adapt to mobile augmented reality applications.  
M.G.; methodology, writing--original draft preparation, M.G. and Q.C.; formal analysis, investigation, resources, Q.C.; writing--review and editing, visualization, supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Where data are unavailable due to privacy.  
The authors declare no conflict of interest.
## 3 Proposed System Methodology
### Crowd Behavior Detection  
Understanding that accurate crowd behavior requires robust global and local feature extraction [101; 102; 103], along with a potent decision-making classifier, for crowd behavior detection after applying the distance algorithm (mentioned in Section 3.3), the extracted silhouettes were passed through the feature extraction step and multiple distinguishable global and local features were extracted for every frame. Next, bat optimization was applied for optimal feature extraction and decisions were made by the improved entropy classifier.