esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives
## 5 Ray Tracing Algorithms
The root of the octree represents the entire volume; see Figure 13.  
Research on the use of octree-based approaches can be found in [69; 70; 71; 72]. [PERSON] et al. present, in [73], an approach based on an octree volume format and a traversal method that allows the faster ray tracing of compressed scalar volume data. The method is particularly  
Figure 12: Application of kd-trees in ray tracing based on [63]: (**a**) conventional approach, (**b**) exploiting property of kd-trees to skip large empty regions. Figure 13: Application of octrees based on [69]: (**a**) active cells in black, (**b**) corresponding octree. suitable for large volume data, whereas GPU volume renderers are preferable for smaller volume data, according to the authors. In [74], a further development of the procedure is described. In [75], authors [PERSON] and [PERSON] develop a ray tracing isosurface algorithm for spectral/\(hp\) element methods, which are higher-order finite element methods. The method can be used to both quantify and minimize the visualization error that occurs. The intersection point between the ray and the isosurface is determined on the basis of analytical methods. The advantage of this method is that the polynomial approximation can be adapted to the true solution as precisely as required. In addition, the convergence speed is higher than with other classical low-order methods, such as marching cubes algorithms. To process unstructured data sets, [PERSON] and [PERSON] present an approach in [76] that is based on the use of partial differential equations (PDE). The advantage of the method is that no resampling of the data or mesh generation is required. The authors use the information regarding the neighborhood relationships to estimate the gradients and mean curvature at each individual sampling point using a four-dimensional least-square fitting approach, as these are necessary for the use of the PDE-based method. In [77], [PERSON] and [PERSON] propose a method of determining the isosurface that incorporates a NURBS-based isogeometric analysis. The main aspects of the novel approach include a ray generation scheme that only generates rays that tend to contribute to the subsequent image creation. In addition, there is a method to determine all intersection points of the rays with the curved surface and a solution approach for memory management. In the course of validating the method, the authors find that the approach that they present is both faster and more accurate than GPU-based ray casting approaches. Further research work in the field of ray tracing can be found in [78; 79; 80]. Table 4 provides a condensed overview of the ray tracing methods described in the literature.
## 2 Materials and Methods
### Universal GNSS Channel Design Decisions
#### 2.1.4 Correlation  
In order to provide the feedback to the carrier and code NCOs, several feedback signals are required to compute the error to be compensated for. For the code, a Non-coherent Early Minus Late (NEML) discriminator requires three correlators, _i.e._, E, P and L code replicas on both the phase (I) and quadrature (Q) branches as illustrated in Figure 5. With the current implementation based on 4 16-bit addressable registers (as opposed to dedicated RAM blocks with improved delay resolution), the different code replica offsets may belong to \(P\pm 32/f_{S}\) samples, thus achieving a correlator spacing \(\Delta=\pm\delta\) slightly larger than \(\pm\)% chip for a 1.023 Michip/s spreading code. The resulting 6 correlators are deployed for both components of a signal.  
Figure 4: Spreading Codes Module (BPSK _vs._ TMBPSK Overhead).  
To reduce the correlator number, the Delay Lock Loop (DLL) discriminator could only involve in-phase (carrier and eventually sub-carrier phases) measurements, thus requiring a lock on the Phase Lock Loop (PLL) (and eventually Sub-carrier Lock Loop or SLL). Such a coherent approach may not be as robust as its non-coherent equivalent [36].  
[PERSON], [PERSON] and [PERSON] [37] specify that an SLL provides more precise (due to higher rate), but ambiguous (periodic clock signal) measurements compared to the DLL based on the primary code Pseudo-Random Noise (PRN). Both these estimates may be combined as:  
\[\hat{\tau}^{+}=\hat{\tau}^{*}+round\left(\frac{\hat{\tau}-\tau^{*}}{T_{s}} \right)T_{s} \tag{8}\]  
where:  
* is the combined delay estimate;
* is the sub-carrier delay estimate;
* is the code delay estimate;
* is the sub-carrier half-period.  
To keep the correlator count as low as possible, the sub-carriers are weighted \((\alpha,\beta)\) and summed, _i.e._, SC2 + SC1, prior correlation, avoiding an extra loop. Also, only Prompt (P) and Differential (\(\mathrm{D}=\mathrm{E}-\mathrm{L}\)) instances are used to implement the NEML sub-carriers discriminator. Note that combining the sub-carriers also simplifies the discriminator, which then becomes identical as the Dual Estimator (DE), rather than the Triple Estimator (TE) extension for MBOC [25], with the same performances.  
Having higher chipping rates requires greater accumulation registers. Multiplication and accumulation are performed through a DSP48 slices available in the Xilinx XC4 VSX55-10 FF1148 Field Programmable Gate Array (FPGA). Hence, the number of bits for these operations is not critical, as long as it remains below \(48-\log_{2}\left(60,000\right)\approx\ 32\), assuming the integration of 60,000 samples in 1 ms.  
Coherent integration provides better post-correlation Signal to Noise Ratios (SNR) than non-coherent ones, where navigation bit (or secondary chip) removal introduces squaring losses [38]. The navigation data period limits the coherent integration time, thus imposing a lower limit on the sensitivity of an unaided, stand-alone receiver.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 1 Introduction
In Reference [4], a survey of multicarrier communications about prototype filters, lattice structures and the implementation aspects is reported by [PERSON] et al., and this work provides four classes of filters according to the design criteria--energy concentration, rapid decay, spectrum nulling and channel/hardware characteristics. In Reference [54], the methods of prototype filter design for cosine modulated filter banks (CMFBs) with nearly perfect reconstruction (NPR) are reviewed by [PERSON] et al., and these methods are categorized into nonlinear optimization methods [55; 56; 57; 58; 59; 60; 61; 62; 63], spectral factorization methods [64], linear search methods [65; 66; 67; 68; 69; 70], interpolated finite impulse response (IFIR) methods [71; 72; 73], and frequency response masking (FRM) methods [74; 75; 76; 77; 58; 78; 79], and so forth. The authors of Reference [80] provide the basic introduction of the FIR filter, some relevant works on FIR and various factors which effect the performance of FIR filter in communication systems. In this paper, we pay more attention to the literature from more recent years and provide a survey on various FIR filter design methods in multicarrier systems. Compared with the existing surveys in References [4; 54; 80], this paper provides an in-depth introduction to advanced filter design methods, which are not categorized based on the design criteria. In addition, this work can serve as an extension of the survey in Reference [54], which is dedicated to the aspect of CMFBs. Specifically, we firstly introduce the FIR filter design criteria, that is, the objective to be optimized. The evaluation criteria and the implementation methods are summarized in canonical form. Subsequently, the design methods of realizing the objectives are divided into three categories: frequency sampling methods, windowing based methods and optimization based methods. Lastly, the reviewed filter design methods are analyzed for multicarrier modulation schemes in terms of power spectral density (PSD) [81; 82], bit error rate (BER) [83; 84; 85], spectral efficiency, latency, computational complexity, and so forth, which are important measurements to characterize the performance of modulation waveforms. We expect that this survey can provide a reference on how to select multicarrier modulation schemes and their corresponding prototype filters in future wireless communication systems. The remainder of this overview paper is organized as follows. Section 2 introduces the concepts of different multicarrier modulations and the evaluation criteria of FIR filter design. In Section 3, the classification of FIR prototype filter design methods and corresponding implementation procedures are described in details. The discussions of different filter design methods in multicarrier modulation systems in terms of PSD and BER performances are presented in Section 4. Finally, Section 5 concludes the paper.
## 2 Literature Review
### Condition-Based Maintenance  
In recent decades, maintenance strategies have evolved significantly. Initially, corrective maintenance aimed to reduce costs but increased downtime. After that, preventive maintenance followed, scheduling interventions at the cost of not fully optimizing equipment lifespan. Consequently, predictive maintenance (PM) emerged, using sensor data to monitor equipment and predict and prevent imminent failures, thereby extending equipment lifespan [6]. One of the most recent advancements in this evolution is Condition-Based Maintenance (CBM), which enables continuous or intermittent monitoring of asset conditions without operational interruption [2]. CBM predominantly relies on predictive maintenance techniques, analyzing component conditions such as temperature and vibrations to schedule interventions. This approach enhances decision-making and improves operational metrics. However, the feasibility of CBM can vary in different functional scenarios due to the higher implementation costs of monitoring tools [1]. Consequently, numerous studies have explored this field.  
In a comprehensive review by the authors of Ref. [1], the advantages and disadvantages of CBM are explored. The study focuses on CBM's core components: data acquisition, processing, and decision-making. While CBM offers benefits such as saving operator time and transforming data into valuable insights, it also presents challenges. These include higher initial costs, uncertainties due to device failures, and complexities in real-world scenarios like imperfect inspections [6].  
In a study by Ref. [7], CBM is compared with Time-Based Maintenance (TBM), revealing that the deterioration process behavior significantly influences CBM's cost-benefit performance, and practical factors like planning time and uncertainty play a crucial role in their relative performance, suggesting that these factors should be considered when applying either approach.  
Multiple other studies have been conducted about this topic, improving CBM policies. In Ref. [8], a novel approach termed Condition-Based Maintenance and Production (CBMP) was proposed, integrating both CBM and Condition-Based Production (CBP). The authors' findings demonstrated that CBMP, alongside CBP and CBM individually, surpassed traditional static maintenance strategies by effectively balancing production and maintenance costs.  
Furthermore, ref. [9] advanced the field by introducing a multi-component CBM-based policy. This policy accommodated the challenges associated with monitoring limited system components and investigated the interplay between predictive model accuracy and the economic benefits in intricate multi-component systems.  
As evident from recent advancements, leveraging data collection allows CBM to craft efficient maintenance strategies. Moreover, when combined with robust models like PHM, CBM can significantly elevate the development of predictive maintenance policies.
# Design and Implementation of a Smart Intercom System through Web Services on Web of Things
## 7 Conclusions  
In this study, a remotely controlled smart intercom system based on an embedded system is designed and integrated with the chatbot function of an instant messaging platform. The embedded system is the intercom host and the chatbot server of this system. This system does not require a handset in the home, but only the instant messaging software of a smartphone, without the need to install an app. This can reduce the cost of codes and wiring between the interior and exterior of the system.  
The instant messaging software of the smartphone replaces the handset, and the chatbot function is triggered through the messaging API of the instant messaging platform. The resident can use the existing instant messaging software of the smartphone to click on one of \"Open Door\", \"Watch\", \"Alarm\", and \"Busy\" menus to remotely control the WoT devices through the web services.  
Residents can receive system notifications through their smartphones, whether they are at home or not, without installing new apps, and they can communicate with visitors. Visitors cannot confirm if a resident is at home by pressing a button on the intercom, which promotes home security. For security reasons, the system uses a cathode lock that cannot be tampered with from the outside and will not unlock in the event of a power failure. The system is designed to install a buzzer as an alarm to keep outsiders away.  
All kinds of web services used in this system are executed within the closed system to meet the security requirements. The smart intercom is available for visitors and receives an instant messaging platform to drive various chatbot functions which smooth the operation of various devices, such as the touch panel, the camera, the buzzer, and the relay, through web services.  
This study combines an embedded system with an instant messaging platform to build a chatbot server that controls door locks, cameras, buzzers, and related devices through the web services on the WoT, making the system significantly safer and more convenient than the traditional intercom systems.  
Conceptualization, H.-Y.H., C.-H.H. and Y.-Y.F.; methodology, H.-Y.H. and Y.-Y.F.; software, H.-Y.H. and C.-A.L.; validation, C.-H.H., H.-Y.H., C.-A.L. and Y.-Y.F.; writing--original draft preparation, H.-Y.H. and C.-A.L.; writing--review and editing, C.-H.H. and Y.-Y.F.; supervision, Y.-Y.F.; funding acquisition, Y.-Y.F. All authors have read and agreed to the published version of the manuscript.  
This work was partially sponsored by the Ministry of Science and Technology (Taiwan) under Grants MOST 110-2221-E-030-001 and MOST 111-2221-E-030-010.  
Not applicable.  
The authors declare no conflict of interest.
# Phosphorus Distribution in Soils from Australian Dairy and Beef Rearing Pastoral Systems
###### Abstract  
The influence of soil type and management practices on P distribution in soils from Australian dairy and beef rearing pastoral systems has been investigated by chemical measurements and phosphorus-31 (\({}^{31}\)P) nuclear magnetic resonance (NMR) spectroscopy. The amount and forms of P within the soil profile varied with soil type, with the acidic red Ferrosols containing relatively high orthophosphate concentrations, averaging 72.2% compared with 66.8% for Dermosols, under similar management conditions. Soil from Sodosol sites which received less fertiliser P addition had the lowest orthophosphate concentration with only 57.6%. In contrast, relatively high proportions of organic P were found in soil samples from unfertilised Sodosol sites. On average, soil from Sodosol sites contained 37.5% organic P (combined monoester P and diester P), while those from Dermosol and Ferrosol sites contained 31.7% and 25.8%, respectively. Of these, the highest monoester phosphate proportions of 44.6% (site M3) and 46.4% (site M4) were found in Sodosol sites with no recent P inputs, but the highest proportion of diester phosphate (5.7%) was found in an unclassified grey sandy loam Dermosol. The higher organic P concentrations in soil from Sodosol sites may be associated with more regular moisture input from both rainfall and boarder-check (flood) irrigation. The highest level of pyrophosphate (8.5%) was also found in a grey/yellow Sodosol. Overall, the results indicate that P speciation in the Australian soils is strongly influenced by soil type, fertiliser P application rate and timing, and moisture variations.  
Phosphorus; \({}^{31}\)P NMR spectroscopy; soil; organic P; cultivation; irrigation +
Footnote †: journal: Applied Sci. 2016
# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 1 Introduction
* Comprehensive performance evaluation: The effectiveness of MIPART was validated using diverse performance metrics, including accuracy, sensitivity, precision, the PRC area, and the ROC area. Additionally, non-parametric statistical tests, such as Friedman aligned ranks and Wilcoxon signed-rank tests, confirmed the statistical significance of MIPART's superior performance compared to traditional and state-of-the-art techniques. * Advancing the field of multiple-instance classification: By proposing an effective method, this study contributes to advancing multi-instance learning and provides a foundation for further research and development in handling complex multi-instance datasets. * Versatility and efficacy for diverse applications: MIPART demonstrates its potential to address various multi-instance learning problems across different domains, as evidenced by its performance on 12 datasets from diverse fields, highlighting its versatility and practical significance. * Advancements in understanding multi-instance learning methodologies: The integration of Bagging, PART, and SimpleMI in MIPART provides valuable insights into improving model robustness and accuracy, offering a framework for exploring new methodologies in multi-instance learning. The paper is structured as follows: Section 2 reviews related work in multi-instance learning. Section 3 introduces the background on multiple-instance classification and explains the proposed MIPART method. Section 4 describes the datasets and presents the experimental results. Section 5 compares MIPART with state-of-the-art methods, emphasizing its advantages. Finally, Section 6 summarizes the findings and outlines future research directions.
# Large Scale Implementations for Twitter Sentiment Classification
## 4 Sentiment Analysis Classification Framework
### Algorithmic Description
#### 4.4.2 Feature Vector Construction  
In this step, we build the feature vectors \(F_{T}\) and \(F_{TT}\) needed for the subsequent distance computation process. To achieve this, we combine all features of a tweet into one single vector. Moreover, \(\forall tt\in TT\) we generate a list (_training_) of tweets in \(T\) that share at least one word/n-gram/pattern feature. The Map and Reduce functions are outlined in the following Algorithm 2.  
```
1:Input: Features \(F\) from tweets
2:function\(\text{MAP}(\text{k1},\text{v1})\)
3:\(f=\text{getFeature}(\text{v1});t\_list=\text{getTweetList}(\text{v1})\);
4:\(test=training=List\{t\}\);
5:forall\(i\in t\) list do
6:output(\(t\_id,<f,t.weight>\));
7:if\(t\_class\
eq NULL\)then
8:\(training\_add(\text{newRecord}(t\_id,t.class))\);
9:else
10:\(\text{\itest\_add}(\text{newRecord}(t.t\_id,t.class))\);
11:endif
12:endfor
13:forall\(i\in test\)do
14:output(\(t\_id,training\));
15:endfor
16:endfunction
17:functionReduce(\(k2,\text{v2}\))
18:\(features=training=List\{\}\);
19:forall\(v\in v2\)do
20:if\(v\) instanceOf List then
21:\(training\_addAll(v)\);
22:else
23:\(features.add(v)\);
24:endif
25:endfor
26:if\(training\_size()>0\)then
27:\(\text{\it output}(k2,<training,features>)\);
28:else
29:\(\text{\it output}(k2,features)\);
30:endif
31:endfunction
```  
**Algorithm 2**MapReduce Job 2  
Initially, the _Map_ function separates \(\forall f\in F\), the tweets that contain \(f\) into two lists--_training_ and _test_, respectively. In addition, \(\forall f\in F\) it outputs a key-value record, where the key is the tweet id that contains \(f\) and the value consists of \(f\) and weight of \(f\). Next, \(\forall v\in test\) generates a record where the key is the id of \(v\) and the value is the _training_ list. The _Reduce_ function gathers key-value pairs with the same key and build \(F_{T}\) and \(F_{TT}\). For each tweet \(t\in T\) (\(tt\in TT\)), it outputs a record where key is the id of \(t\) (\(tt\)) and the value is its feature vector (feature vector together with the _training_ list).
# Scattering or Pushing for Object Singulation in Cluttered Environment: Case Study with Soma Cube
## 4 Parameter Estimation and Simulator Based Dynamic Manipulation
### Coefficient of Restitution  
In order to calculate the coefficient of restitutions (COR) between object and manipulator and among objects, we have recorded several videos. To find COR between objects and manipulator, several videos are recorded by colliding each object with robot manipulator. In this case, the colliding velocity of the manipulator is known, and the velocity of the object is calculated by processing the recorded videos. the coefficient of restitutions between each object and robot manipulator is calculated using following equation  
\[\mathbf{v_{a}}^{\prime}-\mathbf{v_{a}}=-\dot{\mathbf{F}}_{ext}/m_{a}=-(1+e)\mathbf{v_{I}}, \tag{24}\]  
where \(\mathbf{v_{a}}\) and \(\mathbf{v_{a}^{\prime}}\) are the velocities of object before and after collision, respectively. \(\mathbf{v_{I}}\) is the velocity of the robot manipulator which is known, and \(\mathbf{v_{a}}=0\) because the object is initially at rest and \(\mathbf{v_{a}^{\prime}}\) is calculated from videos for each object. In order to calculate the coefficient of restitution among objects the following equation is used.  
\[\mathbf{e}=\frac{\mathbf{v_{a}}^{\prime}-\mathbf{v_{b}}^{\prime}}{\mathbf{v_{b}}-\mathbf{v_{a}}}, \tag{25}\]  
where \(\mathbf{v_{a}}\) and \(\mathbf{v_{b}}\) are the velocities of objects \"a\" and \"b\" before collision and \(\mathbf{v_{a}^{\prime}}\) and \(\mathbf{v_{b}^{\prime}}\) are the velocities of objects \"a\" and \"b\" after collision.  
In this case, the videos are recorded by making the collision of two objects with robot manipulators.  
The velocity of the objects before and after collision is calculated by processing the record videos.  
Figure 5 shows the experimental results: coefficient of friction \(\mu\) (o-T) between soma block and the table surface, the coefficient of restitution between objects e (o-o), and the coefficient of friction between object and manipulator e(o-m), respectively. The mass ratio among 7 objects of the soma puzzle was also estimated and its value ranges from 0.95 and 1.05.
# Counter-Terrorism Video Analysis Using Hash-Based Algorithms
## 2 Overview of the Method  
This section gives an overview of the frames comparison chain, which is summarized in Figure 1. Implementation details are postponed until Section 6. This method is based on hash functions whose output will only keep the most relevant information about the original image [9]. Similar features have been used in recent years [10]. The basic idea is that images can be encoded so that only their most unique features are stored. This is achieved by causing a big information loss to the images and by making both images follow certain criteria. For better invariance to considerable changes in the original image, it is also useful to omit certain differences in the obtained hashes [11]. This can be done by establishing an appropriate threshold for the distance between the two hashes.