esa-sceva/satcom-chunk-collection huggingface dataset:
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 6 Conclusions  
In this manuscript, we derived novel, closed-form, and tractable 2D and 3D CRLBs for position and orientation estimation in a multi-antenna, two-way ranging system. These bounds are specifically formulated as a function of the number of antennas, platform geometry, and geometric dilution of precision. They are also reformulated using different geometric interpretations and plotted as functions of ranging precision and geometric dilution of precision to make these theoretical results more accessible and intuitive to a system design engineer. The performance manifold in Figure 5 also depicts the trade space between ranging precision, positioning precision, and antenna placement, providing a visual reference that does not require any calculations. The proposed CRLBs were compared with several popular position and orientation estimators in a simple MATLAB simulation platform to demonstrate that the bounds are tight, achievable, and consistent with existing work.  
This work offers a closed-form and tractable CRLB for position and orientation estimation, but only for the subset of multi-antenna, two-way ranging systems described in Figure 1. This is the natural conclusion to the original motivation, but possible future extensions include a characterization of different antenna geometries similar to the comparisons in [43] and an extension to an N-dimensional formulation that may enable space-time or other high-dimensional processing techniques.  
Conceptualization, S.S., A.H., and D.W.B.; data curation, S.S.; formal analysis, S.S.; funding acquisition, D.W.B.; investigation, S.S., S.W., and A.H.; methodology, S.S., A.H., and D.W.B.; project administration, D.W.B.; resources, A.H.; software, S.S., and S.W.; supervision, A.H., and D.W.B.; validation, S.S., and S.W.; visualization, S.S., S.W., and A.H.; writing--original draft preparation, S.S., S.W., and A.H.; Writing--review and editing, S.S., A.H., and D.W.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the policies of Arizona State University regarding pending patents and publications.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
2D & Two-dimensional \\
3D & Three-dimensional \\ CRLB & Cramer-Rao lower bound \\ DCM & Direction cosine matrix \\ FIM & Fisher information matrix \\ GDoP & Geometric dilution of precision \\ IRLS & Iteratively reweighted least squares \\ ISNR & Integrated signal-to-noise ratio \\ LS & Least squares \\ ML & Maximum likelihood \\ NLLS & Non-linear least squares \\ OLAE & Optimal linear attitude estimator \\ OLS & Ordinary least squares \\ \end{tabular}
# Optimizing Reinforcement Learning Using a Generative Action-Translator Transformer
## 4 Experimental Validation
### Experimental Results  
We compare ATT with DT and several other key offline reinforcement learning algorithms, including CQL [23], BEAR [24], BRAC [25], AWR [26], and BC [27]. Our ATT model focuses on transformational tasks, DT is centered around generative tasks, and CQL is recognized as a top-performing MDP-based RL algorithm. The experiments are conducted in three Mujoco scenarios. For ATT, we configure both the stacked Encoder and Decoder structures with six layers, set the learning rate to \(1\times 10^{-3}\), and establish the length of receiveable sequences (\(K\)) at the encoder and decoder sides to be 20. The sum of target rewards (\(\hat{R}\)) is determined based on historical trajectory data, with [PERSON] set to 3600, [PERSON] to 12,000, and [PERSON] to 5000. After training for 100\(k\) steps under identical conditions, we compare the effectiveness of various algorithms. To facilitate comparison, we normalize the results using the training results of the SAC algorithm as the expert score and those of a random strategy as the random score. The normalization formula used is as follows:  
\[normalized=100\%\times\frac{score-random}{expert-random} \tag{13}\]  
The results of the experiment are shown in Table 2. The results of the DT are referenced from [11], while the results of CQL, BEAR, BRAC-v, AWR, and BC are referenced from [22].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Environment** & **ATT (Ours)** & **DT** & **CQL** & **BEAR** & **BRAC-v** & **AWR** & **BC** \\ \hline \multirow{3}{*}{Medium-expert} & [PERSON] & 109.8 & 107.6 & **111.0** & 96.3 & 0.8 & 27.1 & 76.9 \\  & [PERSON] & **110.2** & 108.1 & 98.7 & 40.1 & 81.6 & 53.8 & 36.6 \\  & HalfCheetah & **88.9** & 86.8 & 62.4 & 53.4 & 41.9 & 52.7 & 59.5 \\ \hline \multirow{3}{*}{Medium} & [PERSON] & **68.4** & 67.6 & 58.0 & 52.1 & 31.1 & 35.9 & 63.9 \\  & [PERSON] & **82.0** & 74.0 & 79.2 & 59.1 & 81.1 & 17.4 & 77.4 \\  & HalfCheetah & 40.3 & 42.6 & 44.4 & 41.7 & **46.3** & 37.4 & 43.1 \\ \hline \multirow{3}{*}{Medium-replay} & [PERSON] & 79.3 & **82.7** & 48.6 & 33.7 & 0.6 & 28.4 & 27.6 \\  & [PERSON] & **68.7** & 66.6 & 26.7 & 19.2 & 0.9 & 15.5 & 36.9 \\ \cline{1-1}  & HalfCheetah & 36.3 & 36.6 & 46.2 & 38.6 & **47.7** & 40.3 & 4.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of ATT with other offline reinforcement learning algorithms in Mujoco.  
We compare ATT, DT, and CQL in three scenarios of Maze2D to investigate the model's ability to splicing sub-optimal trajectories. The results are shown in Table 3.
## 2 Numerical Model
### Material Model  
#### 2.1.1 Concrete  
In this study, the Winfrith_Concrete model, considering the strain rate and crack width calculation, was utilized to simulate the concrete's response. The Winfrith_Concrete model is derived from the Ottosen plasticity model, which consists of four parameters ([PERSON], 1957). Crack mapping is a crucial feature of the model. The model was deemed appropriate for blast analyses ([PERSON] et al., 2018). The characteristics of the concrete model were taken from [PERSON] et al. (2018) and are presented in Table 1. The fracture energy, also known as the energy needed to open a certain section of a crack's surface, governs the behavior of tensile strain softening. The fracture energy was specified as 65 Nm/m\({}^{2}\), representing concrete grade C30 with a maximum aggregate size of 8 mm ([PERSON] et al., 2018). Figure 4a displays the stress-strain relationship of the Winfrith_Concrete model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Material Model** & **Parameter** & **Value** \\ \hline \multirow{6}{*}{Concrete} & & Mass density (kg/m\({}^{3}\)) & 2500 \\  & & Elastic modulus, \(E\) (GPa) & 24.8 \\  & & Poisson’s ratio & 0.17 \\ \cline{1-1}  & & Uniaxial compressive strength, \(f_{c}\) (MPa) & 27.5 \\ \cline{1-1}  & & Uniaxial tensile strength, \(f_{t}\) (MPa) & 3.2 \\ \cline{1-1}  & & Axial strain at compressive strength, \(\epsilon_{c1}\) (\%) & 0.22 \\ \cline{1-1}  & & Axial strain at tensile strength, \(\epsilon_{c1}\) (\%) & 0.03 \\ \cline{1-1}  & & Ultimate strain value, \(\epsilon_{c}\) (\%) & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model and input parameters for concrete ([PERSON] et al., 2018).  
Figure 3: Finite element model of concrete and steel rebars: (**a**) concrete and (**b**) steel rebars.
## 3 Undifferentiated Cells (Stem Cells)
### Fetal Stem Cells
#### 3.3.3. Chorion Mesenchymal Stem Cells (Ch-MSCs)  
##### 3.3.3.1. General Information  
In humans and other mammals, the chorion is the outermost fetal membrane around the embryo, which exists during pregnancy between the fetus and mother. This tissue is comprised of two layers including an outer sheet (formed by the trophoblast) and an inner sheet (formed by the somatic mesoderm). The somatic mesoderm contains fibroblast-like cells called chorion stromal cells (CSCs). These cells show characteristics of MSCs under appropriate cell culture conditions [142; 143]. They have very close features with human AMCs due to their strong similarity of origin. The differentiation potential of Ch-MSCs into osteogenic, chondrogenic, and adipogenic lineages is significantly higher than MSCs derived from other sources including AM-MSCs [144]. Similar to other fetal-originated stem cells, the isolation of Ch-MSCs faces minimal ethical and legal barriers. The isolation protocol of these cells from early-term, mid-term, and end-term gestational fetal membrane includes a simple procedure in which maternal decidua was mechanically removed and followed by enzymatic digestion of trophoblast layers and reticular stromal layers with collagenase [93; 106; 129; 131].
## 3 Results of Measurements
### Voltage Harmonics during Vehicle Charging
The above-presented measurement results were obtained in similar weather conditions: a sunny spring day with a temperature of about 20 \({}^{\circ}\)C. The on-board charger's parameters were also similar: 3 \(\times\) 400 V AC, 11 kW. All tests indicate that nonlinear load, in this case the charger influences on the voltage and generates higher harmonics. In all cases, the highest values are for the third, fifth, and seventh harmonic. What is important is that we can see all the odd harmonics, and the levels of particular harmonics are different for all phases. It can be seen that the highest THD were registered for the Fiat 500e. Figure 14 presents the harmonics content registered during the charging process, respectively, for the Alfa Romeo Tonale PHEV, the BMW C evolution (BMW Motorrad, Munich, Germany), and the E-Scooter ESA 800 (Electric Scooters Asia, Hong Kong, China). These batteries are charged from a single-phase socket (230 V AC); powers and currents are different for every charger. Figure 14: _Cont._  
Figure 13: THD and individual harmonic content for different electric vehicles: (**a**) for the VW ID.3; (**b**) for the Fiat 500e; (**c**) for the Kia e-NIRO. Results are similar to those obtained for the three phase chargers. It should be noticed that the supply current was equal to 32 A for the Alfa Romeo, 10 A for the BMW, and 2 A for E-Scooter. This difference did not influence the harmonics but it influenced voltage symmetry; the highest current in one phase means lower voltage in that phase. In the case of the Alfa Romeo, that difference was equal to 3 V, while the cross-section of lines was equal to 6 mm\({}^{2}\). Lower cross-sections will cause higher voltage drops and voltage asymmetry.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 2 Related Work  
### Transformer for Point Clouds  
The Transformer [32] and its self-attention mechanism have achieved remarkable achievements in the fields of natural language processing [22] and computer vision [18, 33, 34, 35, 36]. Most point cloud networks use convolution or graph structures like PointNet++ [16], DGCNN [17], ShellNet [37], etc. [38, 39, 40, 41, 42], and the Transformer architecture for point cloud data is less common. Considering that the point cloud is a type of set, the self-attention mechanism is very suitable for dealing with this type of data [18]. Consequently, networks employing Transformer structures for point clouds have demonstrated remarkable performance and achieved noteworthy results. PCT [43] and PointTransformer [18] have a modified Transformer structure to better fit point cloud processing. GPSFormer [42] combines the Transformer structure with graph convolution and achieves good results. Point-BERT [19] and Point-MAE [20] introduce the standard Transformer architecture to process point clouds and achieve advanced results.  
Although the current Transformer-based point cloud processing methods are widely used, these methods still use the traditional scalar neuron structure, which makes it difficult to effectively process vector information with directional characteristics. This paper introduces vector neurons and vectorizes the Transformer architecture, enabling it to directly process vector information and improve the robustness of the network when processing rotated point clouds.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 4 Results
### Results Obtained from the IoT-23 Dataset Using a Unsupervised Machine Learning Approach
#### 4.2.2 Research Analysis According to [47]  
Research [47] has investigated anomaly detection in IoT networks, utilizing the IoT-23 dataset. The methodology encompasses several key steps.  
Step 1: Data Preparation and Processing  
Fifteen scenarios were selected, representing both normal traffic and different types of attacks. Using the Tranalyzer tool, 62 attributes describing the network traffic were  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **ML Approach** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Source** \\ \hline Autoencoder & Unsupervised & 99.2697\% & - & - & - & [46] \\ \hline Autoencoder & Unsupervised & 99\% & 94\% & 92\% & 98\% & [47] \\ \hline Isolation Forest & Unsupervised & - & - & - & 100.00\% & [48] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification report summary on the IoT-23 dataset (own elaboration).  
extracted. The categorical attributes were numerically coded and the total was normalized. Using five measures of attribute importance, the 12 most important attributes were selected, which helped improve model performance and reduce the computation time.  
Step 2: Model utilization  
The Autoencoder model was chosen as the model for anomaly detection. Its task was to learn to represent normal network traffic in order to then identify deviations from this norm. The Autoencoder consisted of an encoder and a decoder. The model was trained on data representing normal traffic, using the loss function'mean squared error' and Adam optimizer. In the learning phase, the model learned a representation of normal traffic, and in the detection phase, it classified new data as normal or anomalous.  
Step 3: Model evaluation  
Common metrics were used to assess the performance of the model. The dataset was split into a 70:30 training and test set, and two experiments were conducted: detecting known attacks and assessing the model's ability to detect zero-day attacks.  
Step 4: Result verification  
The model achieved a high level of accuracy in detecting different types of attacks, although the results varied between attack types and devices. The Autoencoder proved to be more effective than the SVM linear classifier used in previous studies by the authors. Feature selection proved to be crucial in improving model performance. Autoencoder showed great potential in detecting anomalies in IoT network traffic.
# Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays
## 2 Related Works
The method refines on-line the pre-determined calibration parameters through a corneal imaging calibration (CIC) procedure that relies on corneal imaging to obtain correspondence pairs from a pre-calibrated HMD screen with its content reflection on the cornea of the user. In 2016, [PERSON] et al. [19] presented an interesting two phase method in which a depth camera refined on-line the eye-display projection parameters performing the optimization of the hand registration through a genetic algorithm. Again, a first off-line phase was needed to provide a baseline for the subsequent optimization. Finally, in 2017, [PERSON] et al. [20] presented an off-line automated camera based method that resulted in an optimized rendering frustum together with an arbitrary number of distortion maps determined from different viewpoints. Unfortunately, their triangulation approach only worked for OST HMDs with simple optics for which, as stated by the authors on page 57, the \"pixel locations are independent from the eye position\". As anticipated above, all these methods, require an off-line calibration step performed manually or via a camera replacing the user's eye. This off-line calibration, to be effective and accurate, should be performed in a controlled setup for the following two main reasons: to reduce the errors due to human interaction and to ease and speed-up the subsequent prior-to-use calibration refinement done by the user. In line with this, this paper presents a camera based OST calibration method capable of accurately estimating the intrinsic matrix of the eye-display pinhole model, and we provide a detailed and substantiated formulation of each step involved in the calibration procedure.
# From Service Composition to Mashup Editor: A Multiperspective Taxonomy
## 2 Related Concepts  
### Web Services  
Currently, the concept of service is so pervasive that _Science of Services_ is now established as a standalone paradigm [6]. This domain combines the understanding of organizations (enterprises or institutions) and humans with business and technological sciences. In this regard, a service is defined as _\"Any act or performance that one party can offer to another that is essentially intangible\"_[7], in contrast with the physical industry (manufacturing and agriculture). More widely, [PERSON] et al. [8] state that _\"Services are deeds, Processes, and Performance\"_. As a synthesis of existing definitions, we propose the following definition:  
Definition 1: \"A service is an intangible provision, composable, expressed in a perceptible manner, which, in a predetermined operating condition, is a source of value for the consumer and the supplier (service provider)\".  
This concept is much more prevalent in the IT world, where people speak of Web services. Web services are platform-independent software, available in distributed environments such as the Internet. They are mostly used in enterprise contexts for application integration and streamlining B2B software, where they enable developing applications by assembling existing Web services that translate the service-oriented architecture (SOA) philosophy [9]. Indeed, Web services are the most significant achievement of the SOA, in which applications are self-descriptive and low-coupled modules. They are defined by a set of standards that allow us to describe software interfaces and access functions on a network using XML messages [10]. Web-service-related underlying standards and technologies (such as WSDL and UDDI) are exposed in Section 4.1.2. Basically, the World Wide Web Consortium (W3C) ([[http://www.w3.org/TR/ws-gloss/](http://www.w3.org/TR/ws-gloss/)]([http://www.w3.org/TR/ws-gloss/](http://www.w3.org/TR/ws-gloss/)), accessed on 7 March 2020) has defined a Web service as the following:  
Definition 2: \"A Web service is a software system designed to support interoperable machine-to-machine interaction over a network. It has an interface described in a machine-processable format (specifically WSDL). The Web service can be invoked through what is commonly referred to as API (application programming interface) using SOAP messages (typically conveyed using HTTP with an XML serialization) or other Web-related standards such as REST\" (Section 4.1.2).  
The next section introduces the SOA concept that relies on Web services as the basic building blocks in a structured architecture.
## 3 Results and Discussion
### Optimization of IL-UMASDE
#### 3.4.2 Validation of the Optimum Extraction Conditions  
The optimum extraction conditions (independent variables) obtained using the DE software were as follows: 0.75 M [C8 mim]Br as extraction solvent, extraction time 33.2 min, liquid-solid ratio 13.7 mL/g and microwave power 582 W. The predicted yields under the above conditions were 2.475 mg/g for CPT, 0.163 mg/g for HCPT, 0.304 mg/g for VCS-LT, and 0.804 mg/g for EOs. The suitability of the model equations for predicting the response values was confirmed by performing a verification experiment under these optimized conditions. The actual yields of CPT, HCPT, VCS-LT, and EOs were 2.463, 0.164, 0.297, and 0.793 mg/g, respectively. The CPT, HCPT, VCS-LT, and EOs yields obtained using IL-UMASDE were close to the predicted values and showed low deviations (<1.2%), demonstrating the reliability of the RSM models.
## 2 Materials and Methods
### Antioxidant Activity Determinations  
For the ABTS assay, the procedure followed the method described by [PERSON] et al. [12] with minor modifications. To generate ABTS radical cations (ABTS\({}^{\bullet}\)\({}^{\star}\)), a 7 mmol/L ABTS stock solution was mixed with 2.45 mmol/L potassium persulfate and allowed to react in the dark at room temperature for 12-16 h. The resulting ABTS\({}^{\bullet}\)\({}^{\star}\) solution was then diluted with 50% ethanol until it reached an absorbance of 0.70 \(\pm\) 0.02 at 734 nm. For analysis, 5 mg of each lyophilized extract was dissolved in 10 mL of either distilled water or 50% (\(v/v\)) methanol. A volume of 200 \(\upmu\)L of each sample or Trolox standard was added to 3.8 mL of 50% ethanol and 1 mL of the diluted ABTS\({}^{\bullet}\)\({}^{\star}\) solution. After incubation for 45 min at room temperature, absorbance was measured at 734 nm using the previously mentioned BioTek microplate spectrophotometer. Trolox was used as the calibration standard at concentrations ranging from 0.08 to 0.25 mmol/L. The total antioxidant capacity was expressed as \(\upmu\)mol Trolox equivalents antioxidant capacity (TEAC) per g of extract. Each analysis was performed in duplicate, with four independent measurements conducted per replicate. The limit of quantification for this method was 11 \(\upmu\)mol TEAC/g sample.  
The FRAP (ferric-reducing antioxidant power) assay was performed according to the method described by [PERSON] et al. [13], with slight modifications. lyophilized extracts (50 mg) were dissolved in 10 mL of either deionized water or 50% methanol (diluted as necessary). The FRAP reagent was prepared by mixing a 40 mM TPTZ solution, a 20 mM ferric chloride hexahydrate solution, and a 0.3 M sodium acetate buffer (pH 3.6). For the assay, 40 \(\upmu\)L of the sample solution was combined with 200 \(\upmu\)L of acetate buffer and 60 \(\upmu\)L of the FRAP reagent. The mixture was incubated at 37 \({}^{\circ}\)C for 30 min, after which the absorbance was measured at 595 nm using the previously mentioned BioTek microplate spectrophotometer. Quantification was performed using a Trolox calibration curve, and the results were expressed as umol TEAC per g of extract. Each analysis was performed in duplicate, with four independent measurements conducted per replicate. The limit of quantification was established at 1.8 umol TEAC/g sample.