esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Movement Noise Detection and Cancellation Performance  
Figure 12a shows a measured SDPPG signal with movement noise and Figure 12b,c show \(A_{6}\) and \(D_{3}\) of wavelet transform results, respectively. The algorithm extracts time slots of the pulse waves from the peaks (marked with asterisks) of Figure 12b, and detects movement noise from the power of the detail components of Figure 12d during a slot period. To determine whether movement noise is included in the period, the algorithm computes the \(SNS\) of the detail component of level 3 and compares it with a threshold. If the \(SNS\) is greater than 1.5, the SDPPG signal in the period contains movement noise; otherwise, the SDPPG signal during the period has no noise. Detection is performed at all channels.  
As shown in Figure 12c, when an SDPPG signal is distorted by movement noise, the detail component surges according to the noise level. Hence, movement noise can be detected.  
After detecting movement noise in each period, the algorithm selects the best quality SDPPG signal among channels. If only one channel is unaffected by movement noise, the algorithm selects the signal of that channel. If there is more than one channel that has noise, the channel signal where \(SNS\) is closest to 1 is chosen.  
Figure 12: Noise analysis: (**a**) measured signal; (**b**) \(A_{6}\) components and peaks; (**c**) \(D_{3}\) components; (**d**) Power of \(D_{3}\).  
Figure 13 shows an example of removing movement noise by the proposed algorithm in various environments. In cases when only one of two channels has movement noise in the time period between 15 and 35 s, as shown in the Figure 13, the noise is removed at the output signals. In cases when all the channels simultaneously have movement noise in the time period between 45 and 60 s, the noise is reduced, but not entirely removed, by selecting the better signal. When two signals are decided to be clean in the time period between 0 and 15 s, the algorithm selects the better signal among them.  
We evaluated the performance of our proposed algorithm in terms of accuracy, sensitivity, and specificity (Sensitivity, specificity, and accuracy are used to check the effectiveness of diagnostic test to correctly identify the patients and healthy persons [20]. In our paper, these terms are used to evaluate the performance of our proposed algorithm to correctly identify the noise and noise free time period.). The accuracy is the ratio of the correctly detected noise and noise free period to the total time period. The sensitivity is the ratio of the correctly detected noise period to the total noise period while the ratio of the correctly detected noise free period to total noise free period is define as specificity.
# AMR-Assisted Order Picking: Models for Picker-to-Parts Systems in a Two-Blocks Warehouse
## 5 Numerical Experiments
### Details of the Case Study  
Figures 4 and 5 show the distances of the two-blocks layout with separated and shared handover locations, respectively.  
In both the configurations, each sub-corridor consists of 30 houses, 15 on the left and 15 on the right. As can be seen in the figures, the houses are numbered from 1 to 30, starting with those closest to the handover location associated with the aisle. The warehouse consists of 7 handover locations and 14 sub-aisles, which are numbered clockwise. The dimensions and parameters of that warehouse, as well as the standard operator and AMR activity times, are as follows, and represent an instance of the general layout depicted in Figure 4:  
* Distance between two close handover locations (meters): 10
* Distance between the depot and the leftmost handover location (meters): 2
* Distance between two adjacent storage locations (houses) (meters): 2  
Figure 4: Distances in the two-blocks layout with separated handover locations (meters).  
Figure 5: Distances in the two-blocks layout with shared handover locations (meters).  
* Distance between each handover location and the first storage location of the corridor (meters): 3  
The picking strategy used by the aisle operator, it is the same as that proposed in [21]: the picker, starts from the handover location of his aisle, begins by picking the products stored in the farthest house among those containing items of the lot he has been assigned, and then continues picking moving towards the nearest products in his return route, always picking from both the right and left houses.  
The following data are related to operator and AMR activity time and speed:  
* Unit picking time (seconds): 8
* Unitary loading/unloading time (time required to load/unload a unit from the cart, seconds): 4
* Walking time of the operator between two adjacent storage locations (seconds): 3
* Traveling time of the AMR between two consecutive handover locations (seconds): 15
* Traveling time of the AMR from the storage location to the first handover location (seconds): 3
* Traveling time of the operator from the handover location to the first storage location (seconds): 4.5  
We neglect for simplicity (and consistently with [21]) the time taken by the operator to walk from the handover location in his aisle to the first storage location. The distance between the handover location and the first house of each sub-corridor measures, 0.66 m and 1.5 m, for the separated and shared locations.
## 5 Experiments
### Experiment Settings  
#### 5.1.1 Datasets  
We evaluate the proposed GLARA model on two publicly available benchmark datasets commonly used in knowledge-aware recommendation research:  
* Last.FM: This dataset contains user listening records for music tracks, along with associated artist and tag metadata. We follow previous work [ref] by extracting 2000 users, 8302 items, and 23,355 interactions, supplemented by a domain-specific knowledge graph built from music-related entities (e.g., genre, singer, and album).
* MovieLens-1M (ML-1M): This widely used dataset contains approximately 1 million user movie ratings. We binarize the interactions by retaining only ratings \(\geq\) 4 as positive feedback. The dataset includes 6040 users, 3706 movies, and 996,314 interactions. To construct the knowledge graph, we align movie entities with external sources such as IMDb and DBpedia, integrating side information like genres, directors, actors, and production companies.  
Consistent with previous research, we converted the logged data into user-item pairs as active interaction data and used Microsoft Satori to match the headers of all the triples in each dataset with the item IDs to construct a knowledge graph. The basic statistics of the two datasets are shown in Table 1.  
All datasets are split into training (80%), validation (10%), and test (10%) sets using the leave-one-out strategy, where for each user, the most recent interaction is held out for testing.
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
## 3 User-Based Integrity with LEO-PNT Formulation (Option 2)
### User Equations
#### 3.4.1 Basic LEO Barrier Formulation
The objective of the barrier is to evaluate the covariance involved in the MEO position error estimation process, and the performance of the LEO barrier is measured with the sigma monitor \(\sigma_{mon,i}\), which is the projection of the \(\hat{\mathbf{x}}_{i}-\mathbf{x}_{i}\) covariance on the user in direction \(\mathbf{u}_{i}\):  
\[\sigma_{mon,i}^{2}=\mathbf{u}_{i}^{T}\mathbf{\mathrm{E}}\Big{[}(\hat{\mathbf{x}}_{i}-\mathbf{ x}_{i})(\hat{\mathbf{x}}_{i}-\mathbf{x}_{i})^{T}\Big{]}\mathbf{u}_{i}=\mathbf{u}_{i}^{T} \Big{(}\mathbf{C}_{i}^{T}\mathbf{W}_{L_{i}}\mathbf{C}_{i}\Big{)}^{-1}\mathbf{u}_{i} \tag{4}\]  
The Basic LEO Barrier in Equation (4) requires a large amount of LoS to provide a low-covariance matrix and an accurate estimation of \(\hat{\mathbf{x}}_{i}-\mathbf{x}_{i}\). Usually, this is overcome by using time filtering, and the most popular options are **WLSQ with a priori information** and **the Kalman filter**. Since we are aiming for a low TTA, we opt for the WLSQ with a priori information, where the a priori covariance matrix remains constant over time and allows us to better ensure a detectability time. The state vector \(\mathbf{x}_{i}\) from epoch \(t\) is here renamed as \(\mathbf{\Delta}\mathbf{x}_{t}\). At epoch \(t\), we start with the state vector from the previous epoch \(t-1\), \(\mathbf{\Delta}\mathbf{x}_{t-1}\). The a priori covariance matrix of \(\mathbf{\Delta}\mathbf{X}_{t-1}\) is \(\mathbf{P}_{0}^{-1}\). In this case, it can be demonstrated that  
\[ \begin{split}\hat{\mathbf{x}}_{i}&=\mathbf{\Delta}\mathbf{X}_{t }=\Big{(}\mathbf{C}_{i}^{T}\mathbf{W}_{L_{i}}\mathbf{C}_{i}+\mathbf{P}_{0}\mathbf{\mathrm{\Delta} }\mathbf{X}_{t-1}\Big{)}\\ \sigma_{mon,i}^{2}&=\mathbf{u}_{i}^{T}\mathbf{\mathrm{E}} \Big{[}(\hat{\mathbf{x}}_{i}-\mathbf{x}_{i})(\hat{\mathbf{x}}_{i}-\mathbf{x}_{i})^{T}\Big{]} \mathbf{u}_{i}=\mathbf{u}_{i}^{T}\Big{(}\mathbf{C}_{i}^{T}\mathbf{W}_{L_{i}}\mathbf{C}_{i}+\mathbf{P}_{ 0}\Big{)}^{-1}\mathbf{u}_{i}\end{split}  \tag{5}\]  
Figure 1: Geometry and notation used for equations. The detectability of the barrier is not compromised significantly as long as the following are true:  
* \(\mathbf{P_{0}}\) is \"smaller\" than \(W_{L_{i}}\) in terms of their diagonal elements;
* The LEO constellation's altitude is low enough that the user-MEO geometry is like the LEO-MEO geometry.
# Dynamic Parameters Identification of Serial Robot Based on Dual Quaternion
## 6 Conclusions  
This paper studied the problem of parameter identification of a serial robot dynamics model using dual quaternion, and shows the dynamic parameters identification method of load and linkages of a serial robot based on dual quaternion, which has improved the completeness of robot model parameters and has a lower cost compared with the RBFNN-based method. The experiments were conducted on a dual-arm robot SDA5F with two serial construct arms. The experiments used two six-dimensional force sensors, which collected the data in order to derive the formula of the dynamic parameters identification method. The main contributions of this paper can be summarized as follows.  
Based on the dynamic analysis model of rigid body established, a dynamic parameters identification model of load has been derived using the load force and torque information and dual quaternion algebra.  
Furthermore, the method of identifying dynamic parameters of linkages of serial robot has been established. The procedure used the data obtained from the six-dimensional force sensor, so that the model can include more complete information of the robot system.  
The dynamic parameters of load and linkages of a serial robot have been identified through a combination of procedures of the method. This method is useful for different platforms of serial robot systems.  
The theoretical results of the dynamic parameters of load and linkages were obtained. The effectiveness of the identification method proposed in this paper was verified by the results of the simulation and the experiment.  
Methodology, G.L.; Software, D.L.; Validation, X.Y.; Writing--original draft, W.C.; Supervision, H.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangxi Science and Technology Base and Talent Project of China, (Grant No. 2022 AC21285), and Project of Improving Scientific Research Basic Ability of Young and Middle-aged Teachers in Guangxi Universities (Grant No. 2023 KY0206).  
Not applicable.  
Figure 9: Average elapse in ten iterations of dynamic parameters identification for dual-arm robot SDA5F.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
# Analytical Computation of the Spatial Resolution in GNSS-R and Experimental Validation at L1 and L5
## 5 Conclusions  
In this study, the GNSS-R response to a step function in the reflectivity was derived analytically using known principles of electromagnetism. However, the evaluation of the Fresnel integrals has to be performed numerically. Analytical/numerical predictions match well with the experimental results at L1 and L5, acquired during an airborne experiment using the MIR (Microwave Interferometric Reflectometer) instrument over a water reservoir south of Melbourne and in a transition from the ocean into Port Phillip Bay, in terms of width and ringing, if incoherent integration times multiplied by the speed of the aircraft are shorter than the width of the Fresnel zone. Larger incoherent integration times blur the step response and degrade the achievable spatial resolution. Considering the size of the first Fresnel zone (tens of meters from a plane, to a few hundred meters from a spacecraft) the analysis presented is applicable to most coastlines.  
Conceptualization, A.C.; methodology, A.C.; software, J.F.M.-M. and A.C.; validation, A.C.; formal analysis, A.C.; investigation, A.C.; resources, A.C. and J.F.M.-M.; data curation, J.F.M.-M.; writing--original draft preparation, A.C.; writing--review and editing, A.C. and J.F.M.-M.; visualization, A.C. and J.F.M.-M.; supervision, A.C.; project administration, A.C.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript. This work was funded by the Spanish MCIU and EU ERDF project (RTI2018-099008-B-C21/AE1/10.13039/501100011033) \"Sensing with pioneering opportunistic techniques\" and grant to \"CommSensLab-UPC\" Excellence Research Unit Maria de Maeztu (MINECO grant MDM-2016-600).  
The authors would like to express their gratitude to [PERSON] and [PERSON] who flew and operated the MIR instrument during the field experiment, and to [PERSON], [PERSON], and [PERSON] for their support during the execution of the campaign.  
The authors declare no conflict of interest.
## 1 Introduction  
Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a positive-strand RNA virus. The worldwide pandemic caused by this virus, which began in early 2020, has severely restricted economic and social activities around the world. The genome of SARS-CoV-2 shares approximately 80% identity with that of SARS-CoV, the virus causing SARS, which was identified in 2003 [1, 2]. In both cases, spike glycoproteins (S proteins) on the surface of the virus recognize human cell receptors and mediate membrane fusion between the virus and human cells [3, 4, 5]. The S protein is cleaved into the S1 and S2 subunits during viral infection. The S1 subunit contains the receptor-binding domain (RBD) [6, 7].  
Human angiotensin-converting enzyme type 2 (hACE2) is the receptor of SARS-CoV on the human cell surface; as such, hACE2 was promptly identified as the receptor of SARS-CoV-2 [1, 6, 7]. ACE2 is a type I membrane protein containing a single transmembrane helix and a \(\sim\)40-residue cytoplasmic segment that is expressed in the lungs, heart, kidneys, and intestine [8, 9]. The S protein of SARS-CoV-2 interacts with hACE2 with a dissociation constant (\(K_{\text{d}}\)) of several tens of nano-molars, based on surface plasmon resonance (SPR) analysis; this is \(\sim\)10- to 20-fold higher than that of SARS-CoV [5, 10, 11]. Neutralizing antibodies, which bind to the S protein and prevent its interaction with ACE2, are useful for preventing viral infection [12, 13, 14]. Vaccines, which ideally produce neutralizing antibodies against the S protein, are an extremely useful way to produce immunity for the prevention of SARS-CoV-2 [15; 16; 17]. Current treatment strategies for COVID-19 are mainly focused on chemical medicines, including various neutralizing antibodies [13; 18; 19]. Thus, it is important to characterize inhibitory chemicals such as antibodies and drugs to prevent SARS-CoV-2 infection [20].  
Fluorescence correlation spectroscopy (FCS) and its advanced method using two-color fluorescence, fluorescence cross-correlation spectroscopy (FCCS), have been used for biomolecular interactions with single-molecule sensitivity [21; 22; 23; 24; 25]. The most important feature of FCS/FCCS is a solution measurement system that does not use a solid-liquid interface, as in SPR. Based on these characteristics, screening for interaction inhibitors has been conducted. This approach also benefits from the requirement of only a small amount of liquid and a short measurement time [23]. These features suggest that FCCS has potential as a screening method for interaction inhibition; however, the interaction between S protein and ACE2 has not been demonstrated using FCCS. In this study, we analyzed the interaction between the fluorescent protein-tagged recombinant ACE2 and the S1 subunit expressed in mouse culture cells using FCCS.
# An Overview of Fog Data Analytics for IoT Applications
## 3 Data Analytics
### Current Trends in Data Analytics  
Data analytics has many opportunities and a large scope for improvisation considering its vast applications in the IoT and fog paradigm. Some of the current trends in data analytics include:  
* Continuous intelligence;
* Graph analytics;
* Commercial AI/ML
* Conversational AI analytics/ NLP;
* Augmented data analytics;
* Automatic data and content management;
* Persistent memory servers.  
We will look at a couple of them in detail below.  
1. Augmented Data Analytics This is considered as the future of data analytics. The main motivation for a company to perform data analytics is insight generation from data. In the present scenario, the industry has a shortage of data scientists/analysts, and the need is increasing even more. The McKinsey Global Institute estimated that the U.S. economy could be short of around 250,000 data scientists by 2024. Even if the gap gets filled somehow, data scientists are not business experts; they can perform all tasks independently and must be under the constant scrutiny of business analysts. Thus, augmented data analytics is emerging to overcome all of the barriers because it reduces a company's dependenceon data scientists by automatically generating insights. It does so with the help of complex and advanced machine learning and artificial intelligence algorithms.
2. Persistent/In-memory storage Persistent storage is also one of the emerging trends, and will help foster data analytics even more. As the data generated are increasing exponentially, there is a current need for better ways to store these data so that they can be accessed rapidly with fewer latency issues. Persistent memory (PM) combines the byte-addressability of DRAMs and the non-volatility of disks and flashes. PM can be supported either through direct DAX or block access. The use of PM can be performed in three ways. Firstly, the applications can use it as an external or augmented storage entity and are not concerned about the non-volatility. In this case, the applications do not need any changes. Secondly, the applications can use its non-volatility property against DRAMs. Here, the applications themselves need to be modified to use the persistence property of PM. In the third case, the applications may use just PM instead of flash or drives.
## 3 Results  
### X-ray Phase-Contrast CT Results  
Analysis of the data that have been published previously [32; 37; 47] demonstrated that the propagation-based PCT technique offers superior soft tissue and tumor visibility compared to the standard absorption-based CT technique. The overall radiological quality evaluation from our studies comparing PCT and standard absorption-based CT are published in [PERSON] et al. [47] for 12 samples and in [PERSON] et al. [37] for another 14 samples. PCT images from a variety of mastectomy samples were evaluated and scored by medical imaging specialists and practicing radiologists. The scoring was conducted to evaluate the overall radiological image quality [37; 47] for two sample-to-detector distances, 0.19 and 6 m, and varying X-ray energies at certain MGD values. The previous studies show that the PCT images obtained positive scores, which means that the PCT images show higher image quality than the absorption-based CT. On the contrary, a negative score means that the absorption-based CT is better. Additionally, the advanced step of phase retrieval (TIE-Hom algorithm) in the CT reconstruction process has been shown to increase the SNR in the resultant 3D PCT images.  
Figure 2 shows the difference between the reconstructed slices from absorption-based CT collected within the contact regime (0.19 m sample-to-detector distance) and the propagation-based PCT slices collected at a 6 m sample-to-detector distance for data taken at 32 keV and 4 mGy MGD. We can see that PCT using TIE-Hom phase retrieval shows better SNR compared to the absorption-based CT. All data presented here was acquired using the Hamamatsu detector. Figure 3 shows the reconstructed slices illustrating the effect of the X-ray energy variation in PCT. The mastectomy sample contained more dense glandular tissue (white color) and less dense adipose tissue (gray color). The glandular tissue yields higher contrast at 34 keV, but the adipose tissue has better contrast using lower energies such as 32 keV. The effect of the MGD variation on the PCT image quality is shown in Figure 4. The highest dose of 8 mGy produced the best results. However, in a clinical setting the lowest achievable dose while maintaining an acceptable image quality is typically required.  
Figure 2: Comparison of absorption-contrast CT at 0.19 m sample-detector distance (top row) with PCT using TIE-Hom phase-retrieval images at a 6 m sample-detector distance (bottom row) of a breast tissue sample. The PCT images exhibit a better signal-to-noise ratio compared to the contact image.
# De Novo Transcriptome Identifies Olfactory Genes in _Diachassimomorpha Ionigicaudata_ (Ashmead)
## 2 Materials and Methods
### Likely Protein Identification and Annotation  
We next extracted the likely proteins from the assembled transcriptome using TransDecoder. Then, the likely proteins were searched against the UniProtKB/Swiss-Prot database to identify known proteins, functional PFAM domains were identified using HMMER [26], signal peptides were predicted using SignalP [27], and transmembrane domains were predicted using TMHMM Sever v2.0 [28]. The EggNOG database v4.1 [29] was searched against to identify proteins in EuKaryotic Orthologous Groups (KOG), Clusters of Orthologous Groups (COGs), and non-supervised orthologous groups (NOGs). All the annotation for the assembled genes and likely proteins were subjected to the Trinotate v3.1.1 ([[http://trinotate.github.io](http://trinotate.github.io)]([http://trinotate.github.io](http://trinotate.github.io))) for combination.  
Based on the gene annotation and likely protein annotation, we obtained the Unigenes, which were annotated into olfactory gene families, such as OBP, OR, IR, GR, and SNMP, using their names as key words. For the CSP transcripts, we aligned the Unigenes to all the CSP transcripts from NCBI GenBank. Hits with an e-value \(>1\times 10^{-5}\) were filtered.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
## 4 Process Flow of the Proposed Architecture  
Our architecture is designed to provide a secure, energy-efficient, scalable, and trusted data sharing environment for cement manufacture, based on secure P2P data transmission between users, sensors, actuators and machines. All network participants must be registered in advance with the blockchain network, as described below.