esa-sceva/satcom-chunk-collection huggingface dataset:
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 3 Methodology
### 2D Rasterization  
To enable template matching of the point cloud, the point-cloud representation needs to be transformed into a two-dimensional map for every rotational candidate. After applying the rotation to the point cloud, a grid \(g\) over the \(XY\) plane is created with resolution \(r\). Each pixel on the resulting image represents the number of points contained within a grid cell. Figure 2(a) shows a floor plan of the BIM model, and Figure 2(b) shows a corresponding recorded point cloud. The resulting image is then split into three categories: _Wall_, _Floor_, and _No Observation_. This is done through thresholding, where each category is assigned a value of 0, greater than 0, and lower than 0:  
\[g_{i,j}=\begin{cases}\beta&\text{if }c_{i,j}\geq t_{w},\\ \beta-1&\text{if }t_{w}>c_{i,j}\geq t_{f},\\ 0&\text{else}.\end{cases} \tag{2}\]  
The pixel map \(c_{i,j}\) contains the number of points of which the \(xy\) coordinates correspond to the grid cell \(g_{i,j}\). The values \(t_{w}\) and \(t_{f}\) are the thresholds of vertices needed in a cell to be recognized as a wall or floor, respectively. In the resulting image, a wall pixel has a positive value; a floor pixel a negative value; and when no observation has been made, the pixel is set to 0, as seen in Figure 2(c). The parameter \(\beta\in[0,1]\) balances floor and wall pixels, giving higher importance to either observed floors or walls. During template matching, walls in the point cloud correlate positively with walls in the model while floors in the point cloud correlate negatively with walls in the model. Cells in which the point cloud shows no observation are set to zero and have no impact on the template matching. This rewards correct matching of walls while punishes missing walls where an observation has been made.  
To improve wall matching, we also perform binary dilation on all wall pixels. This dilation creates a fast approximation of the distance calculation done in Section 3.1, improving robustness. The distance \(\delta\) in pixel of the dilation should roughly match the distance cutoff of the inverse distance map. Without the dilation, large empty spaces would be treated favorably as the amount of wall pixels to floor pixels differs. The dilation is then added to the grid map, resulting in the final grid map as the weighted sum of both maps:  
\[g_{i,j}=c_{i,j}+\alpha\,s_{i,j}. \tag{3}\]  
The factor \(\alpha\) weighs the two maps and should be between 0 and 1. Figure 2(d) shows an example of a grid map \(g\).
# Single-Shot Compressed Imaging via Random Phase Modulation
## 2 Fundamentals of Compressed Sensing  
In 2006, Compressed Sensing (CS) was firstly proposed by [PERSON] and [PERSON] et al. Compared to the traditional Shannon-Nyquist sampling theorem, CS provides a new sampling framework for band-limited signals. As we all know, most signals in the real world are sparse or compressible in some bases or dictionary, which means that they may have a concise representation by selecting the appropriate transform domain or dictionary.  
The success of CS relies on two properties: sparsity and incoherence. Sparsity is the inherent property of those signals. The information contained in the signal can be represented only with the help of a few significant components, as compared to the total length of the signal. Incoherence provides a guideline for the design of the measurement matrix, and performance guarantees for successful recovery of the signal from fewer measurements.  
CS involves three major foundations: sparse representation, random measurement matrix, and nonlinear recovery algorithm.
## 8 Discussion
### Challenges  
In every research work, there are always challenges. In our literature study, we found several challenges relating to different domains. Here, we mention some of the challenges that are most common in the domains of affinity analysis, biblioming, and technology mining:  
**Scalability:** Scalability refers to a system's capacity of adapting to new demands at a larger scale in the future. According to the reviewed literature, many of the proposed methods for affinity analysis, biblioming, and technology mining systems operate on limited scales, and the validity of these approaches is only guaranteed within a small range. Scalability is a crucial consideration; however, the offered approaches were employed mainly in constrained settings.  
**Mobility:** Mobility refers to the ability of a system or a model to be accessed from anywhere at any time. Due to the rapid growth of information technology, mobility is a significant factor that has received little attention in the literature.  
**Systematic Comparison:** The majority of the literature reported on model performances based solely on the achieved results with a few relevant metrics, rather than a model's efficiency combined with quality metrics. Most of the studies lacked systematic comparisons, making it difficult to determine the applicability of any system in that domain.
# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 2 Data and Methodology
### Methodology
#### 2.3.4 LSTM Model Construction  
In time series analysis, LSTM networks are a variant of RNNs capable of capturing long-term dependencies. They effectively address gradient vanishing/explosion issues in standard RNNs for long-term dependency problems, particularly suitable for non-linear sequence data with periodic, trending, or complex dynamic characteristics. For periodic component displacement prediction, LSTM dynamically regulates memory cell states through gating mechanisms, extracting short-term correlations between adjacent measurement points while capturing long-term seasonal hydrological cycle patterns and enabling precise predictions of future periodic variations. Its basic principle can be found in Appendix B.  
The LSTM architecture resembles GRU but adopts a dual-layer design with added Dropout layers to prevent overfitting.  
A two-layer LSTM architecture was employed to progressively abstract temporal patterns, balancing model capacity and overfitting risk. The first LSTM layer used 32 hidden units, with 'OutputMode' set to'sequence', which outputs the entire sequence of hidden states to retain intermediate time step information for further processing. A subsequent Dropout layer with a rate of 0.2 was added to mitigate overfitting. The second LSTM layer used 16 hidden units and set 'OutputMode' to 'last', outputting only the hidden state at the final time step to focus on global temporal features, which is suitable for single-step prediction tasks.  
The specific parameter settings of the LSTM model are shown in Table 2.  
The model was trained using the trainNetwork function, employing the previously constructed LSTM network architecture (Input Layer + LSTM Layers + ReLU Activation Layer + Fully Connected Layer + Regression Layer) to learn from the data.
## 3 Results and Discussion  
### Geopolymer Characterization  
The granulated GP (Figure 1a) had a Si:Al molar ratio (SAR) of 1.79 and comprised mainly of 45.5 wt.% SiO\({}_{2}\), 22.4 wt.% Al\({}_{2}\)O\({}_{3}\), 15.6 wt.% K\({}_{2}\)O, 13.6 wt.% LOI, 1.1 wt.% Fe\({}_{2}\)O\({}_{3}\), 1.0 wt.% Na\({}_{2}\)O and 0.8 wt.% TiO\({}_{2}\), which is typical for conventional geopolymers made of metakaolin [46]. The CuO content of GP was determined to be 55 ppm only. The mineralogy (Figure 1b) was dominated by an amorphous phase (95 wt.%) due to the non-crystalline aluminosilicate structure of conventional geopolymers [33], in addition to quartz (4 wt.%) and anatase (1 wt.%) that are inherited from the metakaolin raw material. The FTIR spectrum of GP (Figure 1c) revealed a broad absorption at 3385 cm\({}^{-1}\) and a more intense IR band at 1641 cm\({}^{-1}\), which are attributed to OH stretching vibrations and H-O-H bending vibrations of interlayer adsorbed water and structurally bound water molecules in geopolymers [47]. The weak adsorption at 1381 cm\({}^{-1}\) indicates the presence of minor amounts of potassium carbonate (K\({}_{2}\)CO\({}_{3}\)) in GP, which probably formed by the interaction of potassium hydroxide (KOH), water and atmospheric carbon dioxide (CO\({}_{2}\)) during geopolymer preparation [48]. Additional IR bands at 972 cm\({}^{-1}\), 881 cm\({}^{-1}\) and 684 cm\({}^{-1}\), respectively, can be assigned to asymmetric stretching vibrations of Si-O-Al and stretching vibrations of Si-O in Si-OH groups [48; 49].  
The GP grains had an average size between 100-200 \(\upmu\)m, an external SSA value of 2.18 m\({}^{2}\)/g and appeared as particle agglomerates (Figure 1d), which is typical for metakaolin-based geopolymer granulates [50]. The GP microstructure was largely homogeneous, as indicated by the uniform distribution of a porous aluminosilicate matrix (Figure 1e), which barely contained unnected quartz grains and anatase crystals. SEM-EDX analyses confirmed the potassium-aluminum-silicate-hydrate (K-A-S-H) composition of the geopolymer matrix, as evident from SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) ratios of \(\sim\)2.0 and Al\({}_{2}\)O\({}_{3}\)/K\({}_{2}\)O ratios of \(\sim\)1.4. According to [PERSON] and [PERSON] [51], geopolymers with high Si/Al ratios have a more  
Figure 1: Optical appearance, mineralogical composition and microstructural features of granulated pristine GP used for the Cu\({}^{2+}\) immobilization studies. (**a**) Photograph; (**b**) XRD pattern; (**c**) FTIR spectrum; (**d,_e_) SE images of GP.  
uniform, finer and more smoothly connected microstructure than those with low Si/Al ratios, rendering the GP product studied here suitable for water treatment applications.
## 1 Introduction
proposed an improved layered control strategy to increase coordination ability in the original cooperative control theory characteristics and solved a series of problems that could not be solved by traditional linear methods. In Reference [14], [PERSON] proposed a new distributed hierarchical control architecture, which can help to recover the frequency faster and more accurately during the primary frequency control, effectively suppress the interference in the system, improve the transient dynamics of the geographic system, and has good robustness. However, none of the above methods has made deep research on distributed generation unit control. In summary, for the actual operation of the microgrid, only relying on the self-regulation of the lower converter cannot meet the needs of all operating conditions, so it is necessary to further improve the overall control strategy of the AC/DC hybrid microgrid. In view of the above analysis, this paper takes the AC/DC hybrid microgrid of parallel interlinking converters as the research object. Based on the hierarchical control, respectively, it studies the control strategy of the lower converter and the coordinated control strategy of the overall operating power in the AC/DC hybrid microgrid. For the lower converter, this paper sets one or more control strategies for each unit to meet the needs of different situations. For the higher central controller, this paper analyzes the power balance relationship in the AC/DC hybrid microgrid under different operating conditions and designs the power coordination control strategy of AC/DC hybrid microgrid under grid-connected and isolated island conditions. Since the droop control model in the paper is a concept under steady-state, there is no dynamic. In this paper, the transient state with a large jump is not considered for the time being. So, this paper mainly focuses on the study of switching control under the stable state of the system. Finally, the simulation model of the overall AC/DC hybrid microgrid was built in Matlab/Simulink, and different experimental situations are set to verify the effectiveness of the designed power control strategy. The rest of the article is shown below. Section 2 introduces the topology and control framework of the AC/DC hybrid microgrid studied in this paper. Section 3 establishes each unit model and analyzes the control strategy of the lower converter. Section 4 describes the power coordination control strategy of the AC/DC hybrid microgrid designed in this paper under the condition of grid-connection and isolated island. Section 5 describes related experiments and analyzes the experimental results. Finally, Section 6 concludes.
# An Interoperable UMLS Terminology Service Using FHIR
## 5 UMLS FHIR Terminology Service: Design and Implementation
### Implementation: FHIR Extensions and Profiled ConceptMap  
The HAPI library [13], an open-source implementation of the FHIR specifications in Java, allows users to implement the extensions and profiled ConceptMap. The HAPI library defines Java classes for every resource, data types, and enumerations for the valuesets defined by the FHIR. For example, the ConceptMap Java class in the HAPI library corresponds to the FHIR ConceptMap resource. The elements of the resource are programmed as class attributes. HAPI also supports data validation for the modeled FHIR resources to ensure resource confirmation to the FHIR specification. Figure 7 shows the profiled ConceptMap as a UML class diagram. The semantic type extension (Figure 6) is translated as a semanticType attribute, and is added to both SourceElement and TargetElement, as both concepts and atoms have an assigned semantic type(s). The attributes mappingLabel (mapping label extension, Figure 6) and mappingType (mapping type extension, Figure 6) are added to TargetElement to capture the mapping (UMLS relationship) type and label. The mappingType attribute is bound to the UMLSRelationTypeCode valueset. Following HAPI documentation for implementing extensions  
Figure 6: Three FHIR extensions for ConceptMap to enable an interoperable representation of UMLS knowledge.  
and profiles [35], the profiled ConceptMap (Figure 7) is represented by creating a new class called UMLSConceptMap, which extends the resource ConceptMap. The UMLSRelationTypeCode is a Java enumeration with the same name. The source code is available at [[https://github.com/rishikantha/umlsfhir](https://github.com/rishikantha/umlsfhir)]([https://github.com/rishikantha/umlsfhir](https://github.com/rishikantha/umlsfhir)).
## 6 Performance Evaluation
### Directory Activation Experiment
Therefore, since the active file mode change mechanism does not change the files to the online mode, all test files are changed to the local mode from the start of the experiment and this mode is maintained. Conversely, if the _USRT_ value is 0 (usrt-0), only one file can be changed to the local mode because the active file mode change mechanism terminates the mode change operation when the storage usage exceeds 0% when changing the file to the local mode. Furthermore, if it determines that the directory is inactive, it changes all files to the online mode so that the storage usage is below 0%. During the directory activation period, as the file access progresses, the local storage usage increases when the online mode files are accessed. The larger the _USRT_ value, the smaller the number of online mode files, so the storage usage becomes high. That is, from the viewpoint of the local storage usage, the smaller the _USRT_ value, the better the performance. Figure 18 measures the number of online mode file accesses according to changes in the _USRT_. As we can see from the graph, in terms of file access cost, the higher the _USRT_ value, the better the performance. If the _USRT_ value is large, more local storage can be used, so the number of online mode file accesses decreases as the proportion of the online mode files decreases. Therefore, as with _DSLAT_ and _LMT_, users of the active file mode change mechanism can adjust the _USRT_ value according to the important factors between the local storage usage and the file access cost. The _USRT_ can control the upper and lower limits of the local storage usage but cannot control when storage usage increases or decreases as do the _DSLAT_ and _LMT_. Figure 16: Change of the number of online mode file accesses according to _LMT_ in directory activation scenario. Figure 17: Change of local storage usage of active file mode change mechanism according to _USRT_ in directory activation scenario. (**a**) usrt-0. (**b**) usrt-0.1. (**c**) usrt-0.2. (**d**) usrt-0.3. (**e**) usrt-0.4. (**f**) usrt-0.5. (**g**) usrt-0.6. (**h**) usrt-0.7. (**i**) usrt-0.8. (**j**) usrt-0.9. (**k**) usrt-1.0. Figure 18: Change of the number of online mode file accesses according to _USRT_ in directory activation scenario.
# A Novel RFID Authentication Protocol Based on Reconfigurable RRAM PUF
## 5 Experimental Results Analysis
### Protocol Characteristics Analysis
#### 5.2.2 Security Analysis
Assuming that an attacker intercepts or tampers with Y and Z, resulting in protocol failure, the reader can retrieve the tag and restore the key in the next authentication, regardless of whether the tag updates the key K or not. (4) Machine learning attack  
At present, most strong PUFs generate a large number of incentive response pairs through repetitive hardware units. Attacker scan infer their correlation through machine learning and create PUF models. In the related literature, an attacker only needs to collect one thousand CRPs to create a model with an accuracy rate of 90% [11]. In the traditional encryption method, the Hash function is generally used to encrypt the PUF response to avoid the attacker getting CRPs. However, even the simplified Hash module requires at least 1700 gates to implement. In this protocol, combined with an error correction algorithm, the response chain generated by PUF is used to encrypt each other, that is, X = R \(\oplus\) PUF(R). The reader can recover R by using RS and error correction code H, and subsequent authentication messages have R participation, but the attacker cannot infer R only from H. Therefore, the protocol can resist machine learning attacks.
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 4 Discussion
Properly integrating Transformer models with CNNs could further elevate image reconstruction accuracy, especially in scenarios with limited sample sizes. This integration might bolster the network's ability to capture comprehensive global features crucial for defect detection. Investigating the synergy between Transformer models and CNNs could be instrumental in advancing defect detection methodologies.
## 4 Numerical Simulation  
In this section, MSK and GMSK are employed to verify the performance of PF-SCFDE, and the simulations are performed over the Rayleigh fading and typical channels, respectively. The performance is also compared with SCFDE. The data frame length of the simulated signal was 1024 bits, the CP length was 256 bits, the signal center frequency was 6 kHz, the bandwidth was 3 kHz, and the transmission rate was 2 kbps. The added noise was bandpass filtered additive white Gaussian noise. The channel estimation used the OMP algorithm in the simulation.  
Both MSK and GMSK belong to the binary CPM, with a modulation index of \(1/2\). In addition, we used a GMSK signal with BT = 0.3 and a truncated pulse length \(L\) = 3. With Laurent decomposition, the transfer functions \(g_{n}\) of MSK and GMSK can be expressed as follows:  
\[\begin{split} g_{MSK}&:\left\{1,\sqrt{2}/\pi,0, \ldots,0\right\}\\ g_{GMSK}&:\left\{0.996,0.7255,0.0802,9.249\times 10^{-4}, 0,\ldots,0\right\}\end{split} \tag{26}\]
## 5 Data Products, Processing and Distribution
### Data Processing System  
An automated data processing system is key to releasing standardized products in a timely manner. Data processing begins with the GPS-INS and scanning LiDAR data, since some of the products are used in processing the imaging spectrometer and thermal data. Workflows for the scanning LiDAR and spectrometer data have been developed as illustrated in Figures 9 and 10. Manual pre-processing of L0 data with vendor supplied software is the first step in the workflows, followed by automated data processing algorithms that have been custom coded in the IDL-ENVI (Interactive Data Language and Environment for Visualizing Images) scientific programing language (Exelis Visual Information Solutions, Boulder, CO, USA). The following sections describe the specific data processing steps.
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 3 Performability Evaluation Method Based on Phased Fault Trees
### System Details
#### 4.1.4 Mission Equipment Unit  
The mission equipment unit comprises a range of sensors and control units that are utilized to execute the task. Various sensors are used to perceive the exterior surroundings of the AUV. These sensors include the sonar sensor, camera and control computers. Additionally, the system incorporates a control unit responsible for executing several functions, such as automated control. Sensor malfunctions can result in amplified outputs, zero signals, constant signals, intermittent signals, jumps, drifts, fixed deviations, and more [85]. They can also introduce errors into the system's closed-loop motion control [86]. When detecting the surroundings and gathering information about the terrain, sonar, camera, and light failures may happen [87]. Furthermore, there is a chance that the sensors that the AUV uses to determine its position internally will encounter issues such as DVL failure, altimeter failure, inertial navigation failure [88], depth sensor noise [82], depth sensor failure [80], ADCP spontaneous restarts [77], etc.
###### Abstract  
Free-space optical (FSO) communication promises to bring fibre-like speeds to data transmissions between ground, sky and space. This is becoming more important in light of the increasing volume of data collected by aircraft and spacecraft. The University of Western Australia (UWA) is commissioning optical ground stations to support FSO communications payloads. We propose retroreflected laser links to drones as a useful step towards further ground-to-sky and ground-to-space FSO communications demonstrations. In this paper, we describe the operation of a hardware testbed for a high photon efficiency optical communication physical layer. This testbed was deployed over a slanted free space link to a drone to verify sub-systems required in communication between the ground station and a spacecraft in cis-Lunar space. Accomplishing this verification of the telescope pointing systems and communications systems would have otherwise been much harder or impossible without using a retroreflected drone link.  
applications of drones; free-space optical communication; pulse position modulation +
Footnote †: journal: Journal of Optics and Photonics
## 4 Experimental Results  
The results from this study's model have been compared to models employed by previous researchers, enabling the evaluation of different models based on established metrics. The SER system used in this study was scrutinized via speaker-independent experiments conducted on five datasets. The data were partitioned using a percentage-based stratification approach, with 75% of the data being used to train the SER models and the remaining 25% being reserved for testing. As illustrated in Figures 7-15 that show the performance of the proposed transformer model. The model was trained using a feature set comprising 192 features from the five datasets. In this study, we evaluate the performance of our ensemble model across five different datasets. The results are summarized in Tables 3-7, which provide insights into the ensemble model's performance on each dataset. Table 8 presents the accuracy of the all model across the five datasets.