esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Relationship Analysis  
Artificial neural networks (ANN) are propelled in the way shown in Figure 10. An artificial neural network (ANN) technique was deployed to analyse the strength behaviour of the different brick bond combinations and mortar ratios. Compressive strength (MPa) and flexural strength (MPa) are two major parameters known as decision-making indicators for the strength of a structure. These two variables were predicted in terms of cement, sand, water, the mortar ratio, the bond type, the cement/sand ratio, the water/binder ratio and the water/sand ratio. A relationship analysis of compressive and flexural strength and these variables provided a significant combination relation analysis. The data description for this study is shown in Table 8.
## 2 Theoretical Analysis  
### FWM Process  
FWM was found in the first decade of the laser epoch, and it has rapidly developed in the last twenty years. FWM is a third-order optical parametric process, in which four waves interact with each other through third-order optical nonlinearity [73]. Three waves form a nonlinear polarization at the frequency of the fourth wave during the FWM process. The wave functions of the four waves can be expressed as:  
\[E_{j}(r,t)=A_{j}(r)\exp[\mathrm{i}(k_{j}\bullet r-\omega_{j}t)]\ \mathrm{(j=1, 2, 3, 4)} \tag{1}\]  
where, \(\omega_{j}\) and \(k_{j}\) are frequencies and wave vectors of the four waves, and \(A_{j}(r)=\left|A_{j}(r)\right|\exp[\mathrm{i}\phi(r)]\) is the complex amplitude.  
There are two possible roadmaps of the FWM process that satisfy the conservation of photon energies and momenta. The phase-matching condition or conservation of photon energies and momenta can be written as: (i) \(\omega_{4}=\omega_{1}+\omega_{2}+\omega_{3}\), \(k_{4}=k_{1}+k_{2}+k_{3}\); (ii) \(\omega_{4}+\omega_{3}=\omega_{1}+\omega_{2}\), \(k_{4}+k_{3}=k_{1}+k_{2}\).  
The case (i) involves THG and third-order sum frequency generation. We are more interested in the FWM in case (ii), where the nonlinear polarization at frequency \(\omega_{4}\) can be written as:  
\[P^{(3)}(\omega_{4})=3e_{\omega}\chi_{\mathrm{eff}}^{(3)}E_{1}(\omega_{1})E_{2 }(\omega_{2})E_{3}^{*}(\omega_{3})\exp[\mathrm{i}(\Delta k\bullet r)] \tag{2}\]  
where \(\chi_{\mathrm{eff}}^{(3)}\) is the effective third-order nonlinear optical susceptibility, and \(\Delta k=k_{1}+k_{2}+k_{3}-k_{4}\) is the wave vector phase-mismatching in the process. By solving the coupled-wave equations for FWM shown as follows:  
\[\frac{\mathrm{d}E_{4}(\omega_{4})}{\mathrm{d}r}=\frac{\mathrm{i}\omega_{4}}{ 2e_{\omega}cn(\omega_{4})}P^{(3)}(\omega_{4})\exp[-\mathrm{i}\,\Delta k \bullet r] \tag{3}\]  
we can get the optical field, \(E_{4}(\omega_{4})\), of the newly generated signal.
## 4 Understanding Centralised Identity Systems
### Federated Identity: Connecting Multiple Domains  
CIMSs prove to be efficient in single-domain systems. However, in cases where multiple organizations are involved, an enhanced version of the CIMS extends to Federated Identity Management (FIM). In FIM, each organization has its own IdP to share resources and authenticate users across domains (see Figure 4). This model allows interoperability between organizations while retaining the centralised nature of identity management [12; 19].  
A trust broker helps to manage trust relationship between organizations. Each organization's IdP registers themselves with the trust broker, which maintains metadata about each entity. The trust broker ensures that organizations registered with it trust each other's authentication token. Some common protocols that are used in federated systems are SAML (Security Assertion Markup Language), OpenID, and OAuth [13].  
Figure 4: Federated identity management.  
Figure 3: Sequence diagram of CIMS.  
This approach helps build scalable systems and provides easy access for users. However, it also introduces an additional layer of complexity in trust management. The metadata need to be dynamically updated between organizations to ensure that this relationship remains secure without affecting any connected user or their data [12; 19].
# Deep Morphological Anomaly Detection Based on Angular Margin Loss
## 4 Proposed Method
### Morphological Image Processing  
In digital image processing, a mathematical morphology transformation is a mechanism for extracting image components useful for representing and describing the shape of the regions, such as boundaries, skeletons, and convex hulls [30]. The proposed deep anomaly detection learns the morphological features by three representative morphological transformations: erosion, dilation, and morphological gradient.
## 4 Simulation Results
### Numerical Simulation Results  
In this section, we set up two simulation scenarios. A formation tracking simulation was conducted in the first scenario without external disturbances. In the second scenario, we tested the disturbance rejection by introducing considerable disturbances. All of the simulations were conducted by using MATLAB 2015a on a Windows 7 equipped with an Inter core i7 CPU at 3.6 GHz and 8 GB of RAM.
## 3 Results and Discussion  
### Validation of CFD Model  
The results of the CFD analysis of the wind turbine blade without a VG were compared with experimental BEMT data. The BEMT-based analysis was carried out using GH Bladed, which is commercial code used for wind turbine design. Figures 4 and 5 shows the computational grid system of the blades in the simulation, and Table 3 provides the number of elements in the computational grid system used for the simulation. Figure 6 and Table 4 present the results of the CFD analysis of the blade and the GH Bladed design values. These results are confirmed to be consistent within a margin of error of 0.24% at a wind speed of 7.5 m/s and 2.36% at a rated speed of 10 m/s.  
Figure 4: Grid for the simulation of a VG mounted blade.
# A Survey on Hardware Accelerators for Large Language Models
## 3 FPGA-Based Accelerators
### FPGA Npe  
In 2021, [PERSON] et al. presented an FPGA acceleration for language models called NPE [12]. The NPE architecture consists of an instruction control unit (ICU), a memory read unit (MRU), a memory write unit (MWU), a matrix multiply unit (MMU), and a nonlinear vector unit (NVU).  
The MMU computation consists of five stages: data selection, inner product, adder tree reduction, accumulation, and quantization. Data selection loads the necessary matrix operands from the input buffers and rearranges them as needed. The matrix multiplication is implemented using an array of PEs, where each PE performs an inner product using an array of multipliers followed by an adder tree. The MMU implementation features 128 PEs, each equipped with 16 multiply-accumulate units (for a total of 2048 multipliers). In our implementation, these multiply-accumulate units are mapped to the FPGA's DSP slices.  
The key novel component of NPE's design is the nonlinear vector unit (NVU) that handles high-throughput nonlinear function computation with minimal resource overhead. The NVU is a data-parallel vector load/store architecture that performs arithmetic and logical operations on multiple elements in each clock cycle.  
NPE was implemented on a Xilinx Zynq Z-7100 FPGA board clocked at 200 MHz and is compared with other frameworks like FTRANS, as well as implementations on CPUs and GPUs. Although it does not achieve significant speedup compared to other computing platforms, its main advantage lies in energy efficiency. NPE achieves approximately 4\(\times\) better energy efficiency compared to a CPU (i7-8700k) and 6\(\times\) compared to a GPU (RTX 5000).
## 4 Methodology
### Fuzzy Modules for Each Group of Predictors
#### 4.5.1 FuzzyDecision Module  
The _FuzzyDecison_ module (Figure 6) consists of a cascade fuzzy inference system with four input predictors and their corresponding membership functions used in the fuzzification process:  
* _videoLength_: Assessed over a range from 0 to 22 min, divided into fuzzy sets labeled '_low_', '_middle_', and '_high_'. These sets are defined by trapezoidal membership functions which categorize the video length into intervals reflecting short, moderate, and long durations.
* _decisionTime_: Ranges from 0 to 6 s and categorized into '_short_', '_moderate_', and '_long_' also using trapezoidal membership functions to indicate quick, average, and slow-decision times.
* _correctDecision_: Measured on a scale, categorized into '_do nothing_', '_evade_', and '_brake_' where each category is defined to reflect the accuracy and appropriateness of the decisions made.
* _decisionMade_: This involves the actual decision made by the driver or system, categorized similarly into '_do nothing_', '_evade_', and '_brake_'.  
For example, a combination of '_low_' video length and '_long_' decision time led to a '_low_' outcome, indicating a potentially rushed or uninformed decision. Conversely, a '_brake_' correct decision and a '_brake_' decision made result in a '_high_' outcome, suggesting a well-informed and appropriately executed action.  
In _Casade 1_ of the _FuzzyDecision_ module, the inputs are combined in two pairs--_videoLength_ and _decisionTime_ pair, and _correctDecision_ and _decisionMade_ pair.  
Once each input is fuzzified, the rules are applied. There are nine rules for each input pair. Table 4 shows the rules for combining fuzzified inputs, e.g., combining _videoLength_ and _decisionTime_ (as Parameter 1 and Parameter 2) and combining _correctDecision_ and _decisionMade_.  
Those rules are used in fuzzy logic inference sub-systems 1 and 2 (Figure 6) to process subsets of inputs and generate interim outcomes after the defuzzification process.  
Each sub-system generates a crisp outcome: _crispDecisionSub1_ and _crispDecisionSub2_. In _Casade 2_, within inference sub-system three, each outcome undergoes fuzzification, followed by the application of nine rules. This results in a _fuzzifiedDecision_, and after the defuzzification process, we obtain the final _crispDecision_ output from the _FuzzyDecision_ module. The rules are the same as those in Table 4.  
Figure 6: Block diagram of the _FuzzyDecision_ module.
## 3 Proposed System Methodology  
This section elaborates our proposed methodology for multi-person tracking and crowd behavior detection. We propose a robust multi-person tracking system based on a particles force model and human crowd behavior detection system using an improvedentropy classifier with spatio-temporal and particles gradient motion descriptors. In the proposed system, the first step is the preprocessing of extracted video frames from a static camera. Secondly, object detection is transacted using multi-level thresholding, morphological operations, and labeling. Thirdly, for human silhouette extraction, a distance algorithm is applied, and non-human filtering is performed on all extracted labeled objects. At this stage, we administered our work into two streams: the first was for multi-person counting and tracking, where we first performed a human silhouette verification step by converting extracted objects into particles and a robust particles force model was introduced for human silhouette verification. In the next step, after verification of human silhouettes, as all verified human silhouettes are a collection of particles, by treating each group of particles as a cluster we performed labeling and cluster estimation using a K-nearest neighbors searching algorithm for multi-person counting. After that, for multi-person tracking, the position of each detected human silhouette was then located and locked by assigning an integer ID for temporally fixing each human silhouette in the full video, and detected fixed humans were tracked using a Jaccard Similarity Index. However, in the second facet, for crowd behavior detection, the extracted foreground objects were passed through a feature extraction step and multiple distinguishable global and local features were extracted from every frame. After that, all the extracted features were standardized using the bat optimization algorithm. Lastly, in the classification phase, an improved entropy classifier was proposed for detection of crowd behavior. Figure 1 depicts the synoptic schematics of our proposed system.
## 1 Introduction
Additionally, existing commercial UAVs face challenges related to carrying a variety of sensors while having limited load capacity and endurance. Therefore, there is a need to design a high-performance, multi-functional, and reliable UAV inspection system specifically tailored for pipeline inspections. With the development of drone and vision computer technology, drones equipped with lightweight sensor devices will become a more mainstream option for the inspection of pipe networks. In view of this, this paper's objective is to propose a real-time 3D reconstruction system for UAVs based on RTAB-Map, which collects data through RGB-D cameras and generates 3D point clouds inside the pipeline through real-time processing by on-board computers that are then remotely viewed, in real time, at the other end. The main contributions are as follows: (i) A real-time 3D reconstruction platform for UAVs with RTAB-Map is proposed to deal with harsh scenarios such as urban underground pipelines that are difficult for people to reach. (ii) The validation compares the reconstruction effect of mainstream visual SLAM algorithms in the underground pipe scenario, showing that the RTAB-Map method has better robustness in this scenario. (iii) Experiments are carried out in real sewage pipes, and the results show that the system can carry out 3D reconstruction in real time in the underground pipe scene, and the reconstruction accuracy is also guaranteed. The rest of this paper is organized as follows: Section 2 describes the methodology and framework. This section mainly introduces the hardware architecture of the UAV system and the RTAB-Map method. Section 3 describes the experiments and results and also includes a comparison of trajectories and point cloud between RTAB-Map, ORB-SLAM, and Manhattan-SLAM. Section 4 provides an analysis and discussion of the experimental results. Finally, Section 5 summarizes the main conclusions of this research and discusses its limitations.
## 2 Materials and Methods
### Soil Sampling and Field Measurements  
The rhizosphere and bulk soil samples were collected in November (dry season) and April (wet season) (Figure 1). The rhizosphere soil (composed of particles directly attached to the roots) were collected in triplicates and were carefully separated from plant roots (_B. maritima, S. virginicus_ and _B. portulacoides_). The bulk soil samples were separately collected in triplicate for each species outside the patches, within one meter from the board of the vegetation patch (Figure 2). The samples were collected during both seasons (dry and wet), accounting for a total of 36 samples. Bulk soil samples were collected from 0-20 cm soil depth, stored in plastic bags, and transported to the laboratory under refrigeration (approximately 4 \({}^{\circ}\)C). Each sampling site was georeferenced using a portable GPS and the coordinates of each site were used for the measurement of the distance to the nearby river using geoprocessing techniques. At the laboratory, the soil adhered to the roots were removed for analysis by gently shaking and brushing the soil-rhizosphere system [37; 38; 39; 40]. Subsequently, the samples of bulk and rhizospheric soil were air-dried, crushed, and 2-mm sieved, whereas subsamples were frozen for posterior determination of iron forms by a sequential extraction procedure.  
Redox potential (Eh) was measured in situ by using a portable Eh-pH electrode (Hanna, model HI98121). The Eh values (mV) were obtained after equilibrating the electrodes for 2 min and the final readings were corrected by adding the potential (+244 mV) of a calomel reference electrode.
# Towards Lunar In-Situ Resource Utilization Based Subtractive Manufacturing
## 4 Discussion and Conclusions  
This paper addresses an innovative approach to subtractive processing based on in situ resource utilization. Based on a review of the available resources on the Moon, a comprehensive literature search on the state of the art in in-space manufacturing processes was presented. Moreover, a research gap in the field of subtractive processing was identified. On this basis, water jet cutting was selected as a versatile cutting technique and explored for its applicability on the Moon. The main purpose of the investigations was a proof of concept with a focus on the use of local resources and space-representative materials. In summary, it is concluded that no contradictions were identified about the line of argument that led to the selected method using the selected system technology and materials, although further research is needed. From a technical point of view, it is concluded that:  
* It is possible to perform an abrasive water jet cutting and removal process with regolith simulant on the tested materials;
* LS-M100, the sieved regolith simulant made of basalt representing the material of the mare regions, has a higher suitability as an abrasive due to the higher erosion rates that could be achieved;
* In order to enable the regolith simulant to be conveyed, the abrasive must be sieved, and small particle adhesions must be removed;
* For the regolith particle fractions of 45-63 \(\upmu\)m, 63-90 \(\upmu\)m and 90-125 \(\upmu\)m, the experimentally determined water jet pressure for stable particle transport was between 200 and 300 MPa;
* It is possible to cut and kerf the aluminum material in the investigated test field, whereby the generated geometries are similar to the geometries generated by means of common abrasives with regard to their topography;
* It is possible to machine the investigated lithium materials with the selected test parameters. As with the Al material, typical cutting and kerf geometries are produced for the abrasive water jet technology;
* Uniformly deep kerf can be created with the regolith simulant;
* In the case of lithium silicate materials, due to the brittleness of the materials, the machining strategy must be adapted, for example, via pressure ramps, in order to prevent breakouts.
# A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks
## 4 Experimental Arrangement  
### Experimental Specim  
In this work, we chose polymethyl methacrylate (PMMA) as the research object. As shown in Figure 6, the flat bottom hole defects with three different depths were manufactured on the one side of specimen. To verify the adaptability of proposed method, two different diameters were set in manufacturing the defect. The thermal parameters are listed in Table 1.
# Reliability Modelling and Evaluation for LTD System Based on Load-Sharing Model
## 2 Reliability Model of a Linear Transformer Driver (LTD) System
### LTD System Description
Then, the failure will affect the voltages in other modules within the same level, where the voltage redistribution process is subject to the load-sharing mechanism.
###### Abstract  
The structural health of fiber-optic communication networks has become increasingly important due to their widespread deployment and reliance in interconnected cities. We demonstrate a smart upgrade of a communication system employing a dual-polarization-state polarization shift keying (2-PolSK) modulation format to enable distributed vibration monitoring. Sensing can be conducted without hardware changes or occupying additional communication bandwidth. Experimental results demonstrate that forward transmission-based distributed vibration sensing can coexist with PolSK data transmission without significant deterioration in performance. This proof-of-concept study achieved a sensitivity of 0.4141 \(\mu\)V/\(\mu\)\(\epsilon\) with a limit of detection (LoD) of 563 \(\mathrm{p}\epsilon/\mathrm{Hz}^{1/2}\)@100 Hz. The single-span sensing distance can reach up to 121 km (no optical amplification) with a positioning accuracy as small as 874 m. The transmission rate is 300 Mb/s, the Q\({}_{dB}\) is 16.78 dB, and the corresponding BER is 5.202 \(\times\) 10\({}^{-12}\). For demonstration purposes, the tested vibration frequency range is between 100 and 200 Hz.  
distributed sensing; vibration sensing; fiber-optic sensor; forward transmission; PolSK; communication-sensing integration; long-distance +
Footnote †: journal: Sensors  
0
Footnote †: journal: Sensors
## 5 Evaluation
### Clustering Interference  
To reduce inter-cluster interference, each cluster in LEACH communicates using TDMA and direct-sequence spread spectrum (DSSS). Each cluster uses a unique spreading code; all the nodes in the cluster transmit their data to the cluster head using this spreading code and the cluster head filters all received energy using this spreading code. This is known as _transmitter-based code assignment_[27], since all transmitters within the cluster use the same code. In this paper, clustering interference is defined as the mean number of sinks a node senses. Wen a node wants to select a sink as its cluster head, the less sink node it hears, the less energy it needs to consume. Figure 17 shows that by using our algorithm, less sensor nodes need to decide which sink they should belong to. The reason is that since all sinks are broadcasting in a more smaller radio range by using MSCWSNs-PC, then less sensor nodes can receive the broadcast packets.
## 5 Time Series Prediction Using LSTM and ARIMA
### Arima  
The autoregressive integrated moving average (ARIMA) [35] model is used for time series analysis and forecasting future points in the series, or for better understanding of the data. Some of the advantages of the ARIMA model are that it uses an online learning environment, sample sizes are storage cost- independent, and parameter estimation can be performed online in a scalable and efficient way.  
On the downside, the ARIMA model has a subjective process, the reliability of the selected model can depend on the skill and experience of the predictor, there are a number of restrictions on the parameters, and from the class of possible models. Determining the right model can be difficult.
###### Abstract  
Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.  
feature fusion; sensitive IMFs selection; fault diagnosis; EEMD; SVM +
Footnote †: journal: Applied Sciences