esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Internet of Things (Iot)
#### 2.3.1 General Description  
According to Press [46], Forbes estimates that approximately 87% of all people until 2014 had never heard of the term Internet of things, although, it is not a new concept and has its roots in the late 1990s. Many inventions and advancements made IoT possible as we know it today, these technologies include but are not limited to: Barcodes, scanners, radio frequency identification (RFID) tags and receivers, the Advances Research Project Agency Network (ARPAnet), then the Internet, etc. Connected cars also fall in this category, as they are capable of exchanging data with other vehicles, infrastructure elements and other devices. The problems currently faced by highways, freeways and urban areas can be addressed using technology-based solutions like the Internet of things and big data. These technologies mainly involve the Internet and GPS (global positioning system) enabled devices, to provide valuable information from several data points, in combination with cameras, sensors, radio frequencies and other tools using similar technologies [47; 48; 49].  
Over two billion people around the world use the Internet for daily tasks, from sending and receiving emails to controlling house appliances. As more and more people join the network of global information and communication infrastructure, another big leap forward is coming, connected to the use of the Internet as a universal podium for letting machines and smart objects interconnect, dialogue, calculate and coordinate [6]. This idea of physical objects being connected to the Internet at an unparalleled rate brings society one-step closer to the realization of the idea of the Internet of things (IoT). This technology will transform traditional objects into smart objects by exploiting its underlying technologies, such as, permeating and persistent computing, communication, sensor networks and Internet protocols [50]. IoT has incredible potential of transforming the future, where almost every consumer device, from cars to a coffee mug, may connect through the Internet. This will lay the foundation of data points, which would lead to a better society by providing enormous quantities of valuable sensory data for analytics and other uses [51].
## Materials and Methods
### Validation Study  
Validation study of the ICP-OES method was conducted to determine essential analytical parameters concerning the quality of the method and technique. Those parameters are linearity, instrument detection limit (IDL), method detection limit (MDL), accuracy, and precision. Aim of the validation process was to fulfill the requirements given by the method [19]. The calibration standard solutions of lead, cadmium, arsenic, copper, iron, zinc, manganese, nickel, and chromium were in a concentration range of 0.02-1.00 mg/L, potassium and sodium in a concentration range of 0.50-10.00 mg/L, and calcium and magnesium in a concentration range of 0.50-6.00 mg/L. Standard solutions were used to create the calibration curve, which described the dependence of intensity on concentration of the standard compound and to determine the linearity for each element.  
Precision, accuracy, IDL, and MDL were determined using commercially available bottled water (Rosa, Serbia) as a blank sample. Spiking of blank samples was performed at two concentration levels (minimal and maximal), S\({}_{1}\) and S\({}_{2}\), while analyses were performed in tenfold. From the obtained results IDL and MDL were calculated using the equations given in standard method [19]:  
\[\text{IDL}=3\;(\text{S}), \tag{1}\]  
\[\text{MDL}=(\text{t})\;(\text{S}), \tag{2}\]  
where S is standard deviation of the measurements and \(t\) is Student's \(t\) factor for the confidence level of 99% for standard deviation determined for N-1 degrees of freedom (3.143 for seven replicates). Accuracy was determined applying the blank sample, while the precision was determined form the repeatability and reproducibility of the method.
## 1 Introduction
Therefore, the need for a CT image captioning model that can deal with the characteristics of a CT scan is high. At this time, 3D-CNNs (3D convolutional neural networks) are known for their ability to extract feature vectors from sequential images. As a result, they have been utilized in studies related to CT scans that involve artificial intelligence techniques. [PERSON] et al. proposed an ICH prognosis prediction model using a custom 3D-CNN model and feed-forward network [16]. [PERSON] et al. proposed a stroke classification model for brain CT scans using a 3D-CNN model [17]. They achieved a 14.28% higher F1 score compared to the state-of-the-art stroke classification model at that time. [PERSON] et al. proposed a segmentation model for organs-at-risk (OARs) in the head and neck utilizing a 3D-CNN model [18]. They showed a performance that was on par with the state-of-the-art methods at that time, even with limited training data. [PERSON] et al. proposed an automatic brain tumor detection model for CT and MRI images utilizing a 3D AlexNet model and a wireframe model [19]. This showed a high level of accuracy on the RSNA-MCCAI. These studies highlight the significant performance of 3D-CNN-based models in extracting meaningful information from CT scans. Based on that, we propose an ICH-related CT scan captioning model that is based on the 3D-CNN model with a language model. Our goal is to generate corresponding reports of the given CT scan, which consists of normal and ICH CT scans. The proposed method utilizes the 3D-CNN model as an encoder and distilGPT-2, one of the language models, as a decoder. In addition, we present experimental results for combinations of models converted from ResNet-50 [20], EfficientNet-B5 [21], DenseNet-201 [22], and ConvNeXt-S [23] to 3D-CNN structures with distilGPT-2, as well as the result of utilizing penalty applied loss function to prevent the generation of specific sentences. This paper is organized as follows. The proposed method, including utilized models and the loss functions with penalty for CT scan captioning, is outlined in Section 2. Section 3 analyzes the experimental results. This study is summarized in Section 4.
# Time Series Forecasting of Thermal Systems Dispatch in Legal Amazon Using Machine Learning
## 3 Proposed Method
### Parameters  
For the hyperparameters evaluation, the linear (LIN) radial basis function (RBF) [72], and polynomial (POLY) kernel functions are considered. The sequential minimal optimization (SMO), iterative single data algorithm (ISDA) [73], and L1 soft-margin minimization by quadratic programming (L1 QP) solver are evaluated [74]. Besides the error measures, the total time (training and testing) for each experiment is presented.  
The L1 QP involves solving the optimization task in its entirety using traditional optimization methods, such as the interior-point method [75]. The Wolfe dual problem that is evaluated in the optimization process is calculated by the following equation:  
\[\max_{\alpha} \sum_{i=1}^{m}\alpha_{i}-\frac{1}{2}\sum_{i=1}^{m}\sum_{j=1}^{m} \alpha_{i}\alpha_{j}y_{i}y_{j}\mathbf{x}_{i},\mathbf{x}_{j}\] (1a) s.t. \[: 0\leq\alpha_{i}\leq C,\forall i=1,\ldots,m \tag{1b}\] \[\sum_{i=1}^{m}\alpha_{i}y_{i}=0 \tag{1c}\]  
where \(\alpha_{i}\) is the dual variable that lies within the box.  
SMO improves the computational efficiency by solving a simpler sequence of optimization problems. The SMO algorithm works on the dual form of the problem, solving the dual problem for two points at a time, thereby updating two dual variables at each step [48]. Instead of solving the complete optimization problem, ISDA processes one data point at a time. This makes its functionality similar to SMO, but with the key difference of updating  
Figure 4: Boosting ensemble model architecture.  
Figure 5: Bagging ensemble model architecture.  
only one multiplier at a time. To determine which multiplier to update, ISDA selects the point that most significantly violates the Karush-Kuhn-Tucker (KKT) conditions [76].  
The kernel functions (\(K\)) applied in the support vector regression, which are the weak learners used in the ensemble learning methods evaluated here are as follows: the linear (LIN) (2), radial basis function (RBF) (3), and polynomial (POLY) (4). It should be noted that the selection of the transformation to be applied is determined by the type of data and is often performed by experimentation [74]. In this paper, these functions are evaluated to obtain the best application of the compared methods.  
\[K(x_{j},x_{k})=x_{j}^{\prime}\,x_{k}. \tag{2}\]  
\[K(x_{j},x_{k})=\exp(-\left\|x_{j}-x_{k}\right\|^{2}). \tag{3}\]  
\[K(x_{j},x_{k})=\left(1+x_{j}^{\prime}\,x_{k}\right)^{q}. \tag{4}\]
# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
## 3 PRNet Framework  
In this section, the architecture and implementation of a 3D object detection network based on point-region fusion are detailed. As shown in Figure 1, this network employs PointNet++ as its backbone for processing point cloud data, incorporating 4 set abstraction (SA) and 4 feature propagation (FP) modules. For image data, a straightforward convolutional neural network serves as the backbone, generating 4 feature maps of varying sizes. The initial 4 Point-Region Fusion (PRF) modules merge features from point clouds and images. Subsequently, IF-Fusion integrates the image feature maps, which are then combined with the final FP module through a PRF fusion module. The resultant point cloud features are utilized to segment the point cloud. Foreground points identified in this segmentation are used to generate 3D proposal boxes, which are refined to yield the final detection outcomes.
## 6 Conclusions  
In this paper, a new methodology is presented to distinguish the effect of rail corrugation on the vehicle response from the other sources of excitation. This is achieved with the prior knowledge of their impact on the vehicle accelerations: modes of vibrations and resonances are constant in the frequency domain, constructive singularities have an impulsive effect, wheel defects are constant in the wavelength domain and their excitation lasts for the entire acquisition, etc. However, open issues remain for the detection of rail corrugation when combined with one or more other excitation sources and for the exclusion of other sources with unknown effects on vehicle accelerations. The goal of the present work was to investigate the possibility to detect corrugation when the only available data are axle box accelerations and acoustic measurements acquired on in-service vehicles provided with onboard monitoring systems. Labelled data are required to improve the current approach and to investigate these open points. Accelerations acquired over the same section, with and without corrugation, should be compared to separate the specific excitation of interest. Once the methodology is fully validated, it can be simplified and the spectrogram analysis automatised for application in practical settings. With these additional data, further approaches such as machine learning classification and other deep learning techniques can be implemented.  
Conceptualisation, B.L. and J.F.; methodology, A.D.R. and B.L.; data analysis, A.D.R.; MBD simulations, G.M.; writing--original draft preparation, A.D.R. and G.M.; writing--review and editing, B.L.; project administration, J.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The project Metrics was funded by the program \"Mobilitat der Zukunft\" of the Austrian Federal Ministry for Climate Action (BMK). The publication was written at Virtual Vehicle Research GmbH in Graz and partially funded within the COMET K2 Competence Centers for Excellent Technologies from the Austrian Federal Ministry for Climate Action (BMK), the Austrian Federal Ministry for Labour and Economy (BMAW), the Province of Styria (Dept. 12) and the Styrian Business Promotion Agency (SFG). The Austrian Research Promotion Agency (FFG) has been authorised for the programme management.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** The authors gratefully acknowledge the supporting industrial partner Siemens Mobility Austria GmbH.  
**Conflicts of Interest:** Authors [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company Virtual Vehicle Research GmbH who provided technical support and partially funded this work.
## 4 Case Analysis
### Analysis of the Optimization Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Variable**} & **Practical** & **Optimal Results of** & **Optimal Results of** \\  & **Conditions** & **the Original Process** & **the New Process** \\ \hline BFG used by the pulverized coal—gas mixed & 299.01 & 396.42 & 100.80 \\ combustion boiler power generators (m\({}^{3}\)/t steel) & - & - & 200.28 \\ BFG used by the CPP system (m\({}^{3}\)/t steel) & - & - & 67.39 \\ BFG used by the power boiler (m\({}^{3}\)/t steel) & 215.70 & 76.56 & 73.89 \\ BFG used by the heating furnace (m\({}^{3}\)/t steel) & 31.24 & 126.38 & 157.00 \\ BFG emitted (m\({}^{3}\)/t steel) & 53.41 & 0.00 & 0.00 \\ LDG used by the pulverized coal—gas mixed & 0.00 & 42.45 & 0.00 \\ combustion boiler power generators (m\({}^{3}\)/t steel) & - & - & 0.00 \\ LDG used by the CPP system (m\({}^{3}\)/t steel) & - & - & 42.96 \\ LDG used by the power boiler (m\({}^{3}\)/t steel) & 0.00 & 1.58 & 2.48 \\ LDG used by the heating furnace (m\({}^{3}\)/t steel) & 27.34 & 5.02 & 3.59 \\ LDG stored in the gas holder (m\({}^{3}\)/t steel) & 16.05 & 0.00 & 0.00 \\ LDG emitted (m\({}^{3}\)/t steel) & 5.66 & 0.00 & 0.00 \\ COG used by the pulverized coal—gas mixed & 45.29 & 26.46 & 0.00 \\ combustion boiler power generators (m\({}^{3}\)/t steel) & - & - & 0.00 \\ COG used by the CCPP (m\({}^{3}\)/t steel) & - & - & 0.00 \\ COG used by the CPP system (m\({}^{3}\)/t steel) & 0.00 & 33.90 & 0.00 \\ COG used by the heating furnace (m\({}^{3}\)/t steel) & 44.79 & 37.67 & 0.00 \\ COG used by hydrogen production (m\({}^{3}\)/t steel) & - & - & 0.00 \\ COG used by urea production (m\({}^{3}\)/t steel) & - & - & 98.03 \\ COG used by methanol production (m\({}^{3}\)/t steel) & - & - & 0.00 \\ COG emitted (m\({}^{3}\)/t steel) & 7.95 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between the model optimization results and practical conditions of byproduct gas distribution. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable**} & **Practical** & **Optimal Results of** & **Optimal Results of** \\  & **Conditions** & **the Original Process** & **the New Process** \\ \hline Purchased steam coal (kg/t steel) & 126.29 & 91.53 & 204.50 \\ Pollution costs (CNY/t steel) & 4.00 & 2.90 & 6.49 \\ Economic benefits of the energy & \multirow{2}{*}{\(-\)28.16} & \multirow{2}{*}{\(-\)12.39} & \multirow{2}{*}{152.10} \\ system (CNY/t steel) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the model optimization results and practical conditions of the economic benefits of the energy system.
# Edge and Cloud Computing in Smart Cities
## 5 Challenges, Open Issues, and Future Research Directions
### Architectural Complexity and System Integration  
The integration of edge and cloud computing in smart cities presents significant architectural challenges due to the heterogeneous nature of computing, networking, and storage resources across different tiers. The multi-tier architecture involves frequent inter-layer interactions, requiring efficient coordination to maintain performance, security, and reliability. The complexity arises from diverse hardware and software environments, interoperability constraints, and the need for dynamic adaptation to fluctuating workloads and network conditions [166; 167].  
One of the primary challenges is system-wide interoperability, as edge devices, edge servers, and cloud platforms operate on different frameworks, communication protocols, and computing models. The lack of standardized architectures complicates seamless integration, making cross-platform orchestration a critical issue. Existing solutions, such as containerized microservices and service mesh architectures, attempt to address this by providing a unified framework for managing distributed workloads. However, ensuring consistency in service execution, API compatibility, and latency-aware decision-making remains an open challenge [168; 169; 170].  
Moreover, another significant concern is real-time orchestration across tiers. Edge nodes handle time-sensitive tasks closer to end-users, while cloud servers provide long-term data storage and large-scale computation. The challenge lies in determining the optimal placement of services dynamically to minimize network congestion and maximize responsiveness. While AI-driven orchestration frameworks have been explored, their scalability and adaptability to highly dynamic environments require further investigation [171; 172].  
Future research should focus on developing standardized orchestration frameworks to ensure seamless system integration across heterogeneous edge-cloud environments. AI-driven workload balancing can dynamically optimize resource allocation and cross-layer interactions, reducing system complexity. Additionally, cross-layer optimization strategies must be explored to enhance adaptability in real-time applications while ensuring efficient communication across software and hardware layers [173; 174; 175].
## 3 Link Level Simulation Model for Interference Evaluation in 5G UDN with LAB
### SIR Dependence on the Terrestrial Separation of Two gNB-UE Links in 5G UDN with LAB
#### 3.1.3 Evaluation of SIR Dependence on the Terrestrial Separation  
The map of SIR values has the local maxima, the number and position of which is determined by the shape of the antenna array pattern. Peaks occur, when the AA pattern of the serving gNB (SOI) is directed with maximum gain at the UE and the neighbor gNB (SNOI) is directed with minimum gain at the UE. The SIR values decrease as the UE moves away from the serving gNB and approaches the neighboring gNB. At the same time, there is no dependence of the SIR values on the selected terrestrial separation \(\Delta y\) along y-axis.
## 2 Spatial Time Domain Reflectometry
### Spatial Time Domain Reflectometry Post-Analysis  
Once the capacitance profile can be inversely determined using this algorithm, the permittivity profile of the surrounding soil along the sensor can be calculated in accordance with the capacitance model characterizing the cable sensor, as shown in Figure 2c. Before calculating the permittivity of the soil (\(\varepsilon_{m}\)), the three capacitance values \(C_{1}\), \(C_{2}\) and \(C_{3}\) ought to be experimentally calibrated. This calibration has been completed previously (see [17; 18]), and the permittivity is calculated based on the capacitance model shown in Figure 2c as:  
\[C(\varepsilon_{m})=\frac{\varepsilon_{m}C_{1}\cdot C_{2}}{\varepsilon_{m} \cdot C_{1}+C_{2}}+C_{3} \tag{7}\]  
where \(C_{1}\) (14.8 pF/m), \(C_{2}\) (323 pF/m) and \(C_{3}\) (3.4 pF/m) are parameters of the capacitance model, \(C\) is the total capacitance of the soil and coating isolation, \(\varepsilon_{m}\) is relative permittivity [17]. Eventually, the soil moisture profile can be calculated by the empirical models (Equation (2)) or the phase-mixing model (Equation (3)), which needs specific calibration using a Vector Network Analyzer (VNA) with the open-ended coaxial cell [45; 46; 47].  
Instead of the reconstruction of the capacitance profile along the sensor, the travel time determined using the tangent method along one TDR trace can also be used to calculate the mean capacitance using:  
\[C(\varepsilon_{m})=\frac{1}{L}\cdot\left(\frac{t}{2L_{p}}\right)^{2} \tag{8}\]  
where \(C\) is the total capacitance for the entire measuring zone, \(\varepsilon_{m}\) is relative permittivity, \(L\) is inductance (constant \(L_{0}\) = 756 nH/m), \(t\) is travel time and \(L_{p}\) is the length of the sensor. Equation (8) is useful to validate the mean moisture content for the total measuring volume. The mean moisture content later can be determined using Equation (8) for soil permittivity with [PERSON]'s model for the moisture content calculation.
# FPGA Implementation of a Higher SFDR Upper DDFS Based on Non-Uniform Piecewise Linear Approximation
## 1 Introduction
Later, [PERSON] [21] proved the SFDR upper bound of DDFS based on first-order and higher-order polynomial approximation, where the SFDR upper bound of first-order linear interpolation is the same as Equation (2). The SFDR upper bounds of second and third order polynomial interpolation are shown in Equation (3) and Equation (4) respectively. \[SFDR=16s^{2}+1 \tag{2}\]  
\[SFDR=256s^{3}-96s^{2}+24s-1 \tag{3}\]  
\[SFDR=\frac{5120s^{4}+768s^{2}+5}{3} \tag{4}\]where \(s\) is the number of segments. However, [PERSON] [23] pointed out that the above SFDR upper bound is only true if the polynomial is complete, and analyzed the harmonics generated in the case of even fourth-order polynomial. The polynomial approximation methods mentioned above are all based on uniform segmentation, while there are few works based on non-uniform segmentation, and we only found [24]. No matter what method is used to design PAC, three indicators are the main considerations of the designer: MAE, MSE and SFDR, which indirectly reflect the noise in the signal. The first work proposing the idea of improving sFDR by reducing MAE is [29]. In this paper, we propose a high SFDR DDFS. The characteristic of this DDFS is using a non-uniform PWL approximation method to convert phase into amplitude, rather than the traditional uniform PWL approximation or LUT.We call the method PWLMMAE (Piecewise Linear Minimize Maximum Absolute Error) and will describe it in detail in the next section. This method can calculate the piecewise points, the slope and intercept of each segment, satisfying the preset MAE. Moreover, this method is hardware friendly. Based on this method, We first implemented a set of DDFS under different MAE to study the impact of MAE on SFDR, and then evaluated its hardware overhead. We also investigate the relationship betwween the number of segments in the PWL approximation of \(\sin\) function and SFDR and compare it with Equation (2). Finally, we implemented multi-core DDFS using a time interleaving scheme to increase the frequency of the output sine signal. In summary, the main contributions of this paper are as follows:  
* Proposing a non-uniform PWL approximation method with MAE controlled. * Implementing a set of DDFS under different MAE based on the above method on FPGA to study the the impact of MAE on SFDR. * our proposed DDFS breaks through the SFDR theoretical upper bound of DDFS based on the piecewise linear approximation method represented in Equation (2). * Implementing multi-core DDFS which can achieve 3.9 GHz sampling rate and 114 dB SFDR. The organization of this paper is as follows. The Section 2 introduces the non-uniform piecewise linear approximation method we proposed, and the Section 3 designs the hardware architecture of single-core and multi-core DDFS based on this method. The Section 4 presents the experimental results, including the approximation of sine function by our PWL approximation method, SFDR of DDFS under different MAE and the performance of multi-core DDFS. Finally, the Section 5 summarizes this paper.