esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Data Detection Techniques for 5G Massive MIMO Systems
###### Abstract  
Massive multiple-input multiple-output (MIMO) is a backbone technology in the fifth-generation (5G) and beyond 5G (B5G) networks. It enhances performance gain, energy efficiency, and spectral efficiency. Unfortunately, a massive number of antennas need sophisticated processing to detect the transmitted signal. Although a detector based on the maximum likelihood (ML) is optimal, it incurs a high computational complexity, and hence, it is not hardware-friendly. In addition, the conventional linear detectors, such as the minimum mean square error (MMSE), include a matrix inversion, which causes a high computational complexity. As an alternative solution, approximate message passing (AMP) algorithm is proposed for data detection in massive MIMO uplink (UL) detectors. Although the AMP algorithm is converging extremely fast, the convergence is not guaranteed. A good initialization influences the convergence rate and affects the performance substantially together and the complexity. In this paper, we exploit several free-matrix-inversion methods, namely, the successive over-relaxation (SOR), the Gauss-Seidel (GS), and the Jacobi (JA), to initialize the AMP-based massive MIMO UL detector. In other words, hybrid detectors are proposed based on AMP, JA, SOR, and GS with an efficient initialization. Numerical results show that proposed detectors achieve a significant performance enhancement and a large reduction in the computational complexity.  
5G; massive MIMO; detection; approximate message passing; successive over relaxation; Gauss-Seidel Jacobi; [PERSON] +
Footnote †: journal: _arXiv preprint arXiv:1907.03815v1_
# A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
## 4 Experimental Analysis
### Experimental Results and Analysis  
In this paper, four classes of bearing operating conditions data sets, including normal (NS), inner race fault (IF), outer race fault (OF), and ball fault (BF) were applied, respectively. Each piece of the data set consisted of 12,000 points. The working speed, fault size, load and sampling frequency were set to 1730 r/min, 0.007 in, 3 HP, 12 kHz, respectively.  
Figure 2: The architecture of the proposed condition monitoring strategy.
# High-Reliability Wireless Pressure Measurement System Based on FEC Algorithm
## 3 Key Hardware Circuit Design
### Wireless Communication Module  
This system uses a wireless serial port based on LoRa spread spectrum modulation technology to support data transmission between the front-end and back-end devices. This module is based on the SX1278 chip, adopting half -duplex communication and SPI communication control. It uses hardware verification and hardware spread spectrum coding, and the frequency modulation mechanism can be customized [25]. Different working modes can be configured to meet the application requirements in various scenarios. Moreover, it has low power consumption, which is sufficient for the real-time transmission of data from the front-end sensors to be measured during an engine ignition test. An interface circuit is designed for this module, which is directly connected to the main-control FPGA through a 3.3 V IO port. The electrical connection of the interface is shown in Figure 5. The pin definitions of the chip are shown in Table 1.  
To enhance the reliability of wireless communication, this system employs Reed-Solomon (RS) coding to implement the FEC algorithm. RS codes are widely used in the field of wireless communication due to their strong error correction capabilities and mature hardware implementation solutions. The system opts for the RS(255,223) coding scheme, where each data block contains 223 bytes of valid data and 32 bytes of check code, capable  
Figure 4: Schematic diagram of the analog-to-digital module circuit design.  
Figure 5: Connection between FPGA and the wireless module.  
of correcting up to 16 bytes of errors. This coding scheme ensures high error correction capability while balancing the rational utilization of FPGA resources. Table 2 shows the FPGA resource occupancy and allocation, with LUT mainly used for lookup table methods and state machine control, DSP for accelerating polynomial operations, and BRAM for storing generator polynomials and intermediate calculation results.
## 2 Theoretical Background of Applied Method
### k-Nearest Neighbors  
The \(k\)-nearest neighbors algorithm (\(k\)-NN) is a non-parametric supervised learning method developed by [PERSON] and [PERSON] in [70] and later extended in [71]. In \(k\)-NN, the input consists of the \(k\)-closest training examples in a dataset [52; 72]. [PERSON] and [PERSON] [73] first applied this method for oxygen volume flow prediction at the endpoint of the BOS process.  
In classification and regression, the technique of weight assignment is used on the contributions of the neighbors so that the nearer neighbors contribute more to the average than the more distant ones. The algorithm's training phase consists only of storing the feature vectors and class labels or property values of the training samples. In regression, the label assigned to a query point is computed based on the mean of the labels of its nearest neighbors.  
Figure 4: The proposal of a NN for the prediction of process variables in the BOS.  
The problem in regression is to predict labels \(\mathbf{y}^{\prime}\) for new patterns \(\mathbf{x}^{\prime}\) based on a set of \(N\) observations, i.e., labeled patterns \((\mathbf{x_{1}},\mathbf{y_{1}}),...,(\mathbf{x_{N}},\mathbf{y_{N}})\). In BOS modeling, the patterns can be extracted from data tensor (1) or (2) depending on the chosen modeling approach. The goal is to learn regression function \(\hat{f}\) based on this training set. For an unknown pattern \(\mathbf{x}^{\prime}\), \(k\)-NN regression computes the mean of the function values of its \(k\)-nearest neighbors [74].  
\[y_{T/C}=\hat{f}(\mathbf{x}^{\prime})=\frac{1}{k}\sum_{i\in\mathcal{N}_{k}( \mathbf{x}^{\prime})}\mathbf{y}_{i}, \tag{19}\]  
where \(y_{T/C}\) represents the output variable that is predicted (i.e., temperature or carbon content in the melt), and \(\mathcal{N}_{K}(\mathbf{x}^{\prime})\) is the set containing the indices of the \(k\)-nearest neighbors of \(\mathbf{x}^{\prime}\).  
The averaging is based on the assumption of the location of the functions in the data and label space. In local neighborhoods of \(\mathbf{x}_{i}\), patterns \(\mathbf{x}^{\prime}\) are expected to have similar continuous labels \(\hat{f}(\mathbf{x}^{\prime})\) such as \(\mathbf{y}_{i}\). Hence, for the unknown \(\hat{f}(\mathbf{x}^{\prime})\), the label must be similar to the labels of the closest patterns, which is modeled by the average of the label of the \(k\)-nearest patterns. The neighborhood size \(k\) is an important parameter affecting the regression model's performance. This paper applied an open-source implementation of \(k\)-NN proposed by [PERSON] [75] to the BOS process. In the working setup of \(k\)-NN modeling in this study, the number of neighbors was set to 5. In the settings, a Euclidean distance metric as the most used was applied. It is a measure of the actual straight-line distance between two points in Euclidean space.
## 3 RF Structure Design
As shown in Figure 10, \(E_{sp}\) is located at the end of the electrodes and has a value of 56.82 MV/m (the electric field values shown in Figure 10 are not normalized, and the actual \(E_{sp}\) value of the cavity given by the normalized calculation in post-processing is 56.82 MV/m), which is slightly higher than the dynamics design result. This deviation may be attributed to structural differences between the actual electrode configuration and the dynamics design for ease of fabrication. Nevertheless, the electric field remains within the safe range. The \(B_{sp}\), which is located at the vane's undercut, is 27.46 mT, significantly below the upper limit of 80 mT. The 3D EM field simulation results of this SC RFQ are consistent with the dynamics design results, which verifies the safety and feasibility of the dynamics design. The actual construction of the SC RFQ can be carried out in accordance with this validated structure afterward.
## 4 Discussion
Therefore, it is evident that this tool is very useful in plant protection. It can provide an initial signal to the plant protection specialists or farmers to start with the control measures, aiming to prevent significant spread of the disease. By remote sensing, a specialist or a farmer is given the opportunity to timely identify problematic areas in the field, inspect them and determine the actual condition of the crop, detect the beginning of the disease infestation, and decide when it is the proper time to apply fungicides. Since copper is a preventive fungicide, it is crucial to apply it timely. In this regard, the vegetation index serves as an additional tool for the specialists to be able to analyze larger areas. Satellite imaging has the potential to become an integrated part of pest management practice since it enables both the prevention and timely suppression of diseases. There are many attempts to reduce the usage of pesticide through modelling as a prediction of a pest's attack [31]. These models consider many factors for achieving optimum timing and rates of pesticide applications within integrated pest management principles [32; 33].
# Energy Efficient Routing Protocol for an IoT-Based WSN System to Detect Forest Fires
## 3 Proposed Model
### Setup Phase
It can be seen in Figure 2 that the ordinary sensor nodes SN-29 and SN-32 in cluster 7 (CL-7) transmit their data to CH-31 in the first slot and second slot respectively in the intra-cluster communication period. The other two slots (_coloured grey_) in the intra-cluster communication are left unused. Similarly, SN-18 in CL-4 sends data to CH-17 in the first slot of the intra-cluster communication period (_three unused slots shown in grey_). Furthermore, SN-4 and SN-5 in CL-1 send data to CH-3 in the first slot and second slot respectively in the intra-cluster communication period (_two unused slots shown in grey_). The data collected by CH-31 are sent to CH-17 in the first slot of the inter-cluster communication period. The data collected by CH-17 are sent to CH-3 in the second slot of the inter-cluster communication period. The data collected by CH-3 are eventually sent to the BS in the third slot of the inter-cluster communication period. The fourth and fifth slots (shown in grey) are used by CH-9 and CH-12 respectively to send data to the BS. These slots are not unused, but they have been shown in grey as they are not used in the multi-hop transmission from CH-31 to the BS. Figure 3 shows the flowchart of the setup phase of the proposed model and Algorithm 1 shows the steps in the setup phase of the proposed model. Figure 1: The network architecture in the Proposed Model. Figure 2: Intra-cluster and Inter-cluster communication periods in a frame. ```
1:Input: Sensor nodes deployed in target area
2:Output: Formation of clusters in target area
3: N= number of CHs/grids
4: n= number of cluster nodes
5: m= number of nodes in the target area
6: i = 0, j = 0, k = 0, l = 0, c = 0
7: for i = 1 to m
8: each node sends INF message to BS
9: end for
10: for i = 1 to N
11: for j = 1 to n
12: if (RE > UT)
13: node can participate in CH selection procedure
14: c = c + 1
15: else
16: node will become an ordinary node
17: end if
18: end for
19: end for
20: for i = 1 to N
21: for k = 1 to c
22: if (RE = max(RE))
23: node appointed as CH
24: else
25: node will become ordinary node
26: end if
27: end for
28: end for
29: for i = 1 to m
30: each node receives CH selection message from BS
31: end for
32: for i = 1 to n\(-\)1
33: each ordinary node sends JOIN_REQ message to its CH
34: end for
35: for i = 1 to m
36: each node receives frame information message from BS
37: end for
38: for i = 1 to m
39: each node receives schedule message from BS
40: end for
41: for i = 1 to n\(-\)1
42: each ordinary node receives schedule message from its CH
43: end for
```  
**Algorithm 1**Pseudo-code for Setup-phase
# Communication Technologies for Smart Grid: A Comprehensive Survey
###### Abstract  
With the ongoing trends in the energy sector such as vehicular electrification and renewable energy, the Smart Grid (SG) is clearly playing a more and more important role in the electric power system industry. One essential feature of the SG is the information flow over high-speed, reliable, and secure data communication networks in order to manage the complex power systems effectively and intelligently. SGs utilize bidirectional communication to function whereas traditional power grids mainly only use one-way communication. The communication requirements and suitable techniques differ depending on the specific environment and scenario. In this paper, we provide a comprehensive and up-to-date survey on the communication technologies used in the SG, including the communication requirements, physical layer technologies, network architectures, and research challenges. This survey aims to help the readers identify the potential research problems in the continued research on the topic of SG communications.  
review; survey; smart grid; smart grid technologies; smart grid communication; wireless communications; wired communication; smart grid security +
Footnote †: journal: Sensors  
2021  
218087[[https://doi.org/10.3390/21238087](https://doi.org/10.3390/21238087)]([https://doi.org/10.3390/21238087](https://doi.org/10.3390/21238087))
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 3 Proposed Sensors
### Horizon Navigation
However, the handling of errors in a fused Extended Kalman Filter (EKF) is problematic, as it is challenging to map the measurement noise to the Cholesky space geometry. The non-linear case is used in this work. The performance of the horizon estimation algorithm is illustrated in Figure 4 for a changing altitude above the lunar surface. For this evaluation, a star tracker with a precision of 10 arc-sec is treated, as was described and proposed in Section 3.1. The horizon sensor will have the same resolution and sensor shape, with 1024 px in both horizontal and vertical directions. The field-of-view size is, however, 40\({}^{\circ}\) to encapsulate more of the lunar arc. Figure 3: Ellipsoid as seen by the spacecraft in the camera sensor body frame. In Figure 4, the ELFO orbital altitude range is indicated from 3026 km to 10,210 km. The transition between a hyperbolic and elliptical projection is also highlighted, and it does not fall within the orbital altitude range of the ELFO, occurring at 707 km. From Figure 4, altitudes beyond the ELFO operating range appear unsuitable for this application using horizon-based navigation, moving towards accuracies of more than 3 km. Within the operating range, performance varies between approximately 650 m and 3 km. It is suspected that this is caused by changes to the visible length of the lunar limb, with a constant camera resolution, at various distances from the surface. At altitudes below 300 km, the horizon approach plateaus as meaningful curvature information is no longer measurable. Results below 100 km have been omitted for this reason. Visibility of the Moon as it changes from day to night will affect the performance of optical navigation. The visible lunar limb arc will predominantly stay the same; however, the length visible to the spacecraft will depend on the camera's attitude. For the purposes of this study, the lit limb arc will be assumed to always be half the lunar disc's circumference. Except at full and new Moon, this will always be the case. However, this makes an assumption that the camera's attitude will be placed to ensure that the maximum arc is visible.
## 2 Theoretical Background  
Demand forecasting is an essential aspect of supply chain management because it helps businesses anticipate and meet consumer demands, especially in retail and the food industry. It enables businesses to optimize inventory levels, pricing, and promotions. Traditional regression models are commonly used for demand forecasting, but they often provide unstable estimations [7]. However, recent studies have shown that machine learning techniques, such as long short-term memory (LSTM) networks, can improve the accuracy of demand forecasting by learning non-linear relationships and time-series-specific information [8]. Despite the limited empirical data available, some mathematical models, like seasonal ARIMA, ARIMA with Fourier terms, ETS, and TBATS, have proven to be efficient in forecasting demand [9].  
Additionally, demand forecasting in the automotive sector has greatly benefited from artificial intelligence algorithms, such as random forest, multiple linear regression, k-nearest neighbors, extreme gradient boosting, and support vector machines [10]. Notably, another study proposed a SARIMA-LSTM-BP model combination to predict new energy vehicle demand, demonstrating better predictions than traditional econometric and deep learning models [11]. Another article developed an improved model using deep learning methods aimed at enhancing supply chain management, thereby increasing accuracy in product delivery [12].  
In the context of blood supply chain management, a smart-platform-based approach was proposed using machine learning and time-series forecasting models to reduce uncertainty around blood requirements, balancing collection and distribution [13]. The effectiveness of different machine learning methods, such as random forest, gradient boosting, and XGBoost, was analyzed to forecast demand, with XGBoost proving to be the most accurate in predictions [14]. Lastly, a study explored how artificial intelligence (AI) and machine learning (ML) can be integrated to manage and respond to energy demand in real time, emphasizing their potential to improve energy usage and balance power supply across different consumers [15].
## 4 Case Study
### Extraction of Information Related to Lane-Changing Behavior  
The vehicle trajectory data including only one-time lane changes were extracted from the basic data set. The three stages of lane-changing behavior were calibrated. The preparation, execution, and adjustment stages of the vehicle lane-changing process are represented by these three-time nodes. A new data set was created from the vehicle speed, average acceleration, headway, spacing, and duration at each stage. Table 1 displays the extracted data.  
Figure 4: Diagram of vehicle trajectory data smoothing (acceleration as an example).  
Figure 3: I-80 highway segment lane layout diagram.  
As a result, 455 vehicles that only changed lanes once and 18 variables linked to the lane-changing procedure were extracted to assess the influencing factors of longitudinal driving distance during lane changes.
# A Comprehensive Literature Review on Hydrogen Tanks: Storage, Safety, and Structural Integrity
## 1 Introduction  
### General Background  
Hydrogen is currently regarded as a highly promising alternative to fossil fuels for the mitigation of emissions, encompassing both pollutants and agents that contribute to climate change, particularly in applications involving road vehicles and numerous other fields. It can be considered a flexible energy carrier that addresses several essential energy supply and use issues. It is primarily produced from fossil fuels like coal and natural gas, resulting in substantial annual CO\({}_{2}\) emissions, already increasing year after year [1] (Figure 1). In order to facilitate an energy transition, it is imperative that clean hydrogen be made available. The generation of clean hydrogen from renewable or nuclear energy sources orfossil fuels with carbon capture technology has the potential to significantly contribute to reducing carbon emissions across a range of sectors. This encompasses challenging sectors such as long-haul transportation, chemical production, and iron and steel manufacturing, where emission reductions have been challenging to achieve. The introduction of hydrogen-powered vehicles could enhance air quality and contribute to energy security. Furthermore, hydrogen has the potential to facilitate the integration of fluctuating renewable energy sources into the electricity grid, serving as one of the few options for long-term energy storage spanning days, weeks, or months [2; 3].  
Hydrogen is a promising yet challenging energy carrier due to its unique features. On the positive side, hydrogen boasts an impressive Higher Heating Value (HHV) of approximately 140 MJ/kg, the highest among commonly occurring materials, which makes it an enticing candidate for efficient energy storage and transportation applications. This high energy content means that a relatively small mass of hydrogen can deliver substantial energy output (Table 1). Moreover, this value is roughly three times that of conventional fuels such as gasoline and diesel [4; 5]. However, the flip side of this equation is hydrogen's low volumetric density, which poses logistical obstacles. At standard conditions, it has a density of about 0.08988 kg/m\({}^{3}\) (the lowest density of all elements), necessitating large storage volumes for significant quantities of the gas: this makes storing large amounts of hydrogen gas challenging, because it requires more space compared to denser fuels like gasoline or diesel. Despite its volumetric limitations, hydrogen's impressive specific energy underscores its pivotal role in advancing sustainable energy solutions [6].  
Figure 1: Global CO\({}_{2}\) emissions from energy combustion and industrial processes, from 1940 to 2022. The first plot shows the increasing CO\({}_{2}\) trend, underlining the values (in Gt CO\({}_{2}\) eq.) for every ten years and the last analyzed year (2022), while the second bar chart shows the percentage increase/decrease year by year in (CO\({}_{2}\)) emissions. Adapted from [1].
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 3 Results  
### Characteristics of Spectral Changes in Different Forest Stand Ages  
#### 3.1.1 Seasonal Variation Patterns of Canopy Spectra  
Figure 3 shows the seasonal variation patterns of vegetation red edge (\(B_{5}\), \(B_{6}\), \(B_{7}\) and \(B_{8A}\)), NIR (\(B_{8}\)) and short-wave infrared (\(B_{11}\)) bands in Sentinel-2 data, and it is found that all bands except for the thermal infrared band (\(B_{11}\)) have significant seasonal changes. The S-G filtering algorithm is a polynomial smoothing algorithm based on the least square principle in the time domain; it can eliminate spectral noise to the greatest extent and ensure the real information of the spectrum is obtained. The results show that the S-G algorithm could smooth the spectral data and effectively extract the development process of the forest plant canopy, especially in the red edge (\(B_{5}\), \(B_{6}\), \(B_{7}\) and \(B_{8A}\)), _NIR (\(B_{8}\))_ and _SWIR (\(B_{11}\))_ bands of vegetation. However, the seasonal variation in the canopy \(B_{11}\) is weak because the canopy thermal infrared data are mainly affected by other factors such as weather.  
Figure 3: Seasonal variation patterns of canopy spectra (the blue dots represent the reflectance of Sentinel data during the year, and the red lines are fitted curves). (**(a)** represents the seasonal pattern of Red edge(\(B_{5}\)), (**(b)** represents the seasonal pattern of Red edge(\(B_{6}\)), (**c**) represents the seasonal pattern of Red edge(\(B_{7}\)), (**d**) represents the seasonal pattern of NIR(\(B_{8}\)), (**e**) represents the seasonal pattern of Red edge(\(B_{8A}\)), (**f**) represents the seasonal pattern of NIR(\(B_{11}\))).