esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
High power electromagnetics (HPEMs) pose a potential threatening risk to the wireless communication system, especially according to the main coupling path of the RF front-end channel. SPICE modeling of the responses coupled on the RF channel is crucial for the EM risk assessment, which helps us learn more about how the pulse conducts on the RF channel. A simplified linear RF channel with pyramid horn antenna is taken as an example by the selection of the key electronic modules of the actual wireless system. This paper proposes a system-level SPICE circuit model for the simplified RF channel according to the hybrid methods of the antenna electromagnetic simulation and SPICE modeling of the RF circuit. The equivalent circuits of the horn antenna illuminated by HPEMs are established with the Vector Fitting method based on [PERSON] and [PERSON] theorems. The short current response as the excitation files for the SPICE models are obtained by the commercial electromagnetic simulation of the horn antenna illuminated by Multiple HPEM environments. Equivalent circuits of a micro-strip bandpass filter are also derived with \(\pi\) type circuit structure based on the measured admittance data. Then we analyze the HPEM risk faced by the RF channel by considering multiple HPEM environments. The norm theory is utilized to analyze the waveform characteristics from electric fields of HPEMs to the responses of the RF channel. The ratios of the responses versus electric field for each norm are computed and the EM risk degree is ranked based on those results. The results demonstrate that high power microwave is the highest threatening risk for the linear RF channel compared to the other two HPEMs such as ultra-wide band, high altitude electromagnetic pulse. Finally, the flowchart of EM risk assessment is presented based on a previous analysis, which will benefit the EMC design in engineering.  
high power electromagnetics; wireless communication system; pyramid horn antenna; SPICE model; equivalent circuit; vector fitting; micro-strip; vector norm +
Footnote †: journal: Physics Letters A
## 1 Introduction
[22] optimized the significant process parameters of FDM 3D printer parts to increase their dimensional accuracy by using hybrid tools such as ANN and GA-ANN. In ref. [27], the authors performed a characterization of the influence of five manufacturing parameters on the tensile strength (UTS) and tensile modulus (E) of a part, and data were later used for ANN construction. The resulting methodology was used to create two load-bearing components with satisfactory performance after only a few iterations. On the other hand, the study by [PERSON] and [PERSON] [28] proposed an ANN model to determine the relationship between five input parameters and three output responses, namely, the roughness of the top, bottom and side surfaces of the component. [PERSON] et al. [29] proposed a new class of design of experimental techniques for an integrated second-order definitive screening design (DSD) and an ANN to design experiments for evaluating and predicting the effects of six important operating variables. By determining the optimal manufacturing conditions to achieve better dimensional accuracies for cylindrical parts, this study significantly reduces the time and number of complex experiments. Moreover, the results indicate potential in the integration of second-order DSD with ANN for additive manufacturing applications. In summary, a review of the current research shows that the process parameters of FDM have a significant impact on the tensile strength of printed parts. However, most of these studies were conducted for commonly used FDM materials (PLA, ABS, PA, etc.). A lack of sufficiently reliable models was observed for the prediction of mechanical properties of FDM printed parts of the new composite polymers, as well as the insufficient research of these materials. Therefore, the objective of this study was to investigate the influence of the FDM process parameters on the tensile strength and predict the tensile strength of new polymer composites using RSM and ANN methods based on varying values of the FDM process parameters. The RSM method was applied because this method, with a not-fully experimental design, gives the right answers and offers proper analysis of the interaction and significance of the influential process parameters on the output response. ANN was adopted since it ensures arbitrary levels of adjustment to the underlying model. Thus, this study focuses on developing a reliable model for the prediction of the tensile strength of FDM printed parts with a short carbon fiber reinforced polyamide composite since this material is one of the new and insufficiently researched FDM materials.
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## 5 Evaluation with DTN Protocols
After that, the LTP drops the bundle. Additionally, the protocol was configured to not resume interrupted transmissions and instead drop the affected bundle, as the interest was in linking the benefits of the improved bundle transmission time estimation in terms of the bundle drop rate, which drove the network's throughput. Routing was provided by CGR, either by using the original estimation method for the service time (i.e., Method 1) or the proposed approach (Method 4). It is important to note that CGR does not directly attempt to minimize the end-to-end bundle response times. Instead, the graph traversal method used by CGR has the approximated effect of finding the shortest contact path but with capacity considerations.
# Induction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller
## 1 Introduction  
The effects of environmental pollution on air quality demands the utilization of electric vehicle (EV) technology by introducing renewable energy sources in the place of traditional fossil fuel. In recent years, EVs have started being considered an ideal replacement for traditional vehicles due to their better operational design, efficiency, and cheaper maintenance cost [1]. The latest advancements in EVs have shown better mobility and reduced pollution in urban areas. The speed controller plays a vital role in the smooth operation of EVs, and thus researchers at industrial and academic levels have focused on the development of speed control for domestic and commercial applications. Energy management and reliable performance are key factors that need intensive analysis [2]. The EV controller should acquire desired speed with lower steady-state error and power consumption. The control system is vastly time-dependent, nonlinear, and unpredictable in terms of changing road dimensions, load parameters, and external perturbations. Therefore, the development of a speed controller that eradicates external perturbations and sustains road uncertainties with reduced control action has become the core issue to be addressed [3].  
Traditional PID controllers are used in many industrial applications due to their robustness and ease-of-tuning mechanism. However, they do not perform effectively at frequently varying speeds. The emergence of artificial intelligence with PID controllers further improved PID controller performance with an effective tuning mechanism [4]. Fuzzy logic controllers (FLCs) in EV systems are capable of transforming human perception into controller design and are suitable for the management of nonlinear control parameters desired in speed control operation [5]. The precision of FLC depends on fuzzy rules developed to form a logical relation between input and output parameters.  
IM multivariable parameters, nonlinear behavior, and complex mathematical models create difficulties in variable speed operations [6]. Adjustable speed controllers have been devised to handle variable speed operations [7]. The vector control technique is suitable for the variable speed drive operation of induction motor. The phasor form of indirect vector control is presented in \"Figure 1\". The speed sensor is used for the continuous monitoring of desired speed operation. The speed control system needs suitable arrangements to negate electrical noise. AC drive operation for variable speed requires more composite control structure leading to intelligent and smart applications. For the control of the electric drive system against abrupt changes of reference points and variations due to environmental effects, the P-I controller is suitable as a closed-loop control method. Fuzzy control is another approach that regulates the variations as per defined rules [8]. IM serves as a pillar for a wide range of speed applications ranging from small to large horsepower applications [9].
## 3 Results and Discussion
### Effects of Elastic Supports  
Various conditions for elastic support can be evaluated according to the change in spring values. However, the spring values required to provide a clamped condition were examined. The rotational spring or translational spring, in the absence of axial loading and concentrated mass, increased from \(\exp(0)\) to \(\exp(25)\); the results are presented in Figure 3. The properties of the beam were considered as follows:  
**Example 1.**_E = 200 GPa, \(\rho\) = 7850 kg/m\({}^{3}\), \(h_{0}\) = 0.01 m, \(b_{0}\) = 0.03 m, and L = 0.8 m._  
The first three nondimensional natural frequencies \(\left(\overline{\omega}_{\rm i}=\sqrt{\rm m_{0}\omega_{\rm i}^{2}L^{4}/E_{\rm I 0}}\right)\) were examined. The spring values required for convergence to the clamped condition differed according to the order of natural frequency. \(K_{W}\) = 9.745 \(\times\) 10\({}^{9}\) (=exp(23)) N/m and \(K_{\Phi}\) = 1.319 \(\times\) 10\({}^{9}\) (=exp(21)) Nm/rad were required for the analysis up to the first third of the natural frequency. The results presented in Figure 3 are obtained by increasing only one spring when \(c\) = 0. \(K_{\Phi}\) increased only the rotational spring value, and \(K_{W}\) increased only the translational spring value. The number of terms in the power series required to calculate the natural frequency was analyzed in detail in a previous study [49].  
The results obtained here and in previous studies were compared [49]. The properties employed to compare the results are as follows: \(c\) = 0, \(E\) = 69.79 GPa, \(\rho\) = 2600 kg/m\({}^{3}\), \(h_{0}\) = 0.025 m, \(b_{0}\) = 0.05 m, \(L\) = 0.996 m, \(K_{W}\) = 26.5 MN/m, and \(K_{\Phi}\) = 150 kNm/rad. To demonstrate the accuracy of the proposed method, the elastic supports attached at \(\overline{x}\) = 0 were examined, and the effect of the elastic support attached at \(\overline{x}\) = 1 was analyzed. The comparison results are presented in Table 1 and have good agreement for \(n\) = 1 and  
Figure 3: Variation of nondimensional natural frequency with respect to increase of spring value.  
Figure 2: Beam types utilized to consider various conditions: (**a**) Type A, (**b**) Type B, (**c**) Type C, and (**d**) Type D.  
\(n=2\). As the comparison results were for uniform beams, the results for \(n=1\) and \(n=2\) were the same.
## 3 Results and Discussion
### Phenolics Content and Antioxidant Capacity  
Chokeberry pomace is known to be abundant in phenolic compounds, especially anthocyanins, which have antioxidant properties and may provide various health benefits [46]. The content of TPC and AA of raw materials and enriched breads is presented in Table 6. CP contained the highest phenolics content with the strongest antioxidant activity. Most phenolic compounds are primarily found in the fruit skin. As a result, a considerable amount of these compounds are retained in the CP even after the extraction of juice from the fruit [47]. [PERSON] et al. [48] found about 63 mg GAE g DM\({}^{-1}\) of TPC in chokeberry pomace. This effect was visible in the enrichment of bread. With the addition of 1% CP, the TPC doubled; with 6% addition, it was about 4.5 times higher compared to the control sample (Table 6). The relationship between the percentage of CP and TPC in bread samples was linear (r = 0.987, \(p\) = 0.001). Consequently, antioxidant capacity of enriched bread increased (decrease in EC\({}_{50}\) index). Both ABTS and DPPH antiradical activity increased with the percentage of CP in the bread recipe. However, the relations were non-linear, and the highest decrease in EC\({}_{50}\) was observed when wheat flour was replaced with 1% of CP from 552.47 to 202.33 mg DM mL\({}^{-1}\) and from 194.37 to 115.89 mg DM mL\({}^{-1}\), for ABTS and DPPH, respectively. Other authors also found that CP significantly increased TPC and antioxidant capacity of short bread cookies [49]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**TPC [mg GAE g DM\({}^{-1}\)]**} & \multicolumn{2}{c}{**EC\({}_{50}\)DPPH**} & \multicolumn{2}{c}{**EC\({}_{50}\)DARTS**} \\  & & **[mg DM mL\({}^{-1}\)]** & **[mg DM mL\({}^{-1}\)]** \\ \hline CP & 54.04 [B] & 43.33 [A] & 26.07 [A] \\ WF & 0.52 [A] & 585.60 [B] & 190.25 [B] \\ \hline C & 0.46 \(\pm\) 0.03 [a] & 552.47 \(\pm\) 24.93 [d] & 194.37 \(\pm\) 3.30 [f] \\ CP1 & 0.86 \(\pm\) 0.05 [b] & 202.33 \(\pm\) 32.40 [c] & 115.89 \(\pm\) 5.18 [e] \\ CP2 & 1.03 \(\pm\) 0.05 [bc] & 187.03 \(\pm\) 11.10 [c] & 90.24 \(\pm\) 4.01 [d] \\ CP3 & 1.25 \(\pm\) 0.08 [cd] & 140.42 \(\pm\) 4.04 [b] & 62.64 \(\pm\) 1.62 [c] \\ CP4 & 1.34 \(\pm\) 0.06 [e] & 110.73 \(\pm\) 4.36 [ab] & 51.67 \(\pm\) 1.28 [b] \\ CP5 & 1.76 \(\pm\) 0.06 [e] & 88.84 \(\pm\) 8.08 [a] & 38.65 \(\pm\) 1.52 [a] \\ CP6 & 2.07 \(\pm\) 0.18 [f] & 75.31 \(\pm\) 1.81 [a] & 35.62 \(\pm\) 2.01 [a] \\ \hline \hline \end{tabular}  
* CP—chokeberry pomace, WF—wheat flour, C—control bread, CP1, CP2, CP3, CP4, CP5, CP6—bread enriched with 1, 2, 3, 4, 5, and 6% of chokeberry powder, respectively; the values designated by the different letters [A], [B] or [a] are significantly different (\(p\) < 0.05).  
\end{table} 
Table 6: Phenolics content and antioxidant capacity of raw materials, control, and enriched bread samples.
## 3 Method
### Basic Theory and Techniques for GRU
#### 3.2.2 Limitations of GRU Method in Failure Prediction  
As a common recurrent neural network, the GRU is often used for timing processing tasks, and although the GRU performs well in many sequence modeling tasks, there are still some limitations in fault prediction:  
(1) Long-term dependence: Although the GRU solves the problem of gradient disappearance or explosion in the traditional circulating neuron through the gating mechanism, there are still limitations in the modeling of long-term dependence. In failure prediction, some failures may be caused by complex factors over a long period of time in the past, and the GRU may not be able to effectively capture this long-term dependence.  
(2) Difficulty in parameter adjustment: Many parameters in the GRU model need to be adjusted, including learning rate, regularization, etc. In fault prediction, the selection of these parameters may be affected by factors such as data quality, sample size, and fault type, so adjustments need to be made, increasing the difficulty of modeling.
## 2 System Model
### Coded Cooperative WSN Based on MC-CDMA  
It can be seen from Figure 1 that the distance to the sink node is different for sensors with different positions in the considered WSN. The channel conditions of sensors that are close to the sink node, such as \(P_{l}\), are usually better; on the contrary, the channel conditions of sensors that are far from the sink node, such as \(S_{l}\), are usually poor. In order to improve the wireless transmission performance of sensors with poor channel conditions, the sensors whose average channel gain exceeds a threshold \(\eta\) are elected to be the alternative group of cooperative sensors. \(K\) source sensors with poor channel conditions select their cooperative sensors from the alternative group, respectively, to form \(K\) pairs of partner sensors. For example, the source sensor \(S_{l}\) and cooperative sensor \(P_{l}\) form a pair of partner sensors as shown in Figure 1[24]. Figure 2a,b show the block diagrams of the transmitter and receiver of sensor nodes based on MC-CDMA in an underground coal mine.  
Figure 3 illustrates the time-frequency coded cooperative transmission of sensors in two phases. In phase 1, signals \(\mathbf{T_{S_{l}}},\mathbf{T_{S_{2}}},\cdots,\mathbf{T_{S_{K}}}\) are transmitted by source sensors \(S_{l},S_{2},\cdots,S_{K}\) after their time-frequency coding frames are modulated by MC-CDMA. The signals in phase 1 received by the cooperative sensors and sink node are processed by MC-CDMA demodulation, multi-sensor detection and time-frequency decoding. In phases 2, cooperative sensors assist source sensors to transmit  
Figure 2: Proposed transmitter and receiver based on MC-CDMA.  
signals. Signals \(\mathbf{T}_{\mathbf{P}_{p}},\mathbf{T}_{\mathbf{P}_{p}},\cdots,\mathbf{T}_{\mathbf{P}_{k}}\) are transmitted by cooperative sensors after the detected original information of source sensors is recoded and modulated by MC-CDMA. The signals in phase 2 received by the sink node are processed by MC-CDMA demodulation, multi-sensor detection and time-frequency decoding. Finally, the channel coding frames detected in phase 1 and phase 2 are combined by the sink node to obtain the estimated channel coding frames of the \(K\) source sensors.
# An Online Multicriteria--Spatial Decision Support System for Public Services Planning
## 2 Preliminary Remarks and Basic Systematisation
### Background and Goals of the Study  
Since 2017, the Federal State of Lower Saxony has been developing and operating the Digitaler Daseinsvorsorgeatlas Niedersachsen (Digital Atlas of the Provision of Public Services in Lower Saxony, or DVAN for short). Therefore, the data of the relevant infrastructure types had to be collected from numerous public administrations of the municipalities and the state of Lower Saxony and integrated into a harmonized data base. A major goal of the DVAN is to make these infrastructure data more usable for planners in regional and state administration. It is a web application with simple spatial analysis tools. Unique to the tool is a rather large dataset consisting of a variety of infrastructure data from different service areas. Due to the legal protection of the data (and in some cases due to a licensing obligation), access to the application is restricted for planners of the Lower Saxony authorities. The provided basic functionalities of the web application include a calculation of infrastructure supply degrees in a given area and simple accessibility analyses. In a cooperation with the Dresden University of Technology, this has been extended to a decision support system including spatial multi-criteria analyses.  
The aim is to provide the planners with an overall picture of the supply situation with the help of spatial analysis methods with adjustable parameters. Despite the high complexity of parameter selection and modelling that spatial multi-criteria analyses entail, the user should be able to select the analysis area and set decisive model parameters via a simple and straightforward user interface.  
In summary, the contributions of this paper are as follows:  
* The further development and use of a web-based MC-SDSS in spatial planning (services of general interest);
* The implementation of a web-based online GIS solution with DSS functionality;
* The presentation of a technical solution and a user interface as a result of the application-orientated approach.
# Opportunistic Weather Sensing by Smart City Wireless Communication Networks
###### Abstract  
This paper presents how the concept of opportunistic integrated sensing and communication (ISAC), focusing on weather sensing, is incorporated into wireless smart cities' networks. The concept, first introduced in 2006, utilized standard signal level measurements from wireless backhaul cellular networks for rain monitoring. Since then, it has expanded to include technologies like satellite communication and smart cities' networks. Opportunistic ISAC (OISAC) for weather involves transforming communication networks into virtual sensors by interpreting the signal attenuation caused by environmental factors, such as rain. These virtual sensors form the sensing layer of an IoT system, with built-in connectivity. In this paper, we present the recent advancements in the field, emphasizing the potential of current and future smart cities' wireless networks for accurate rainfall monitoring. We also demonstrate a test case in the city of Rehovot in Israel, where high spatiotemporal resolution rain maps produced via the OISAC paradigm significantly outperform the spatial resolution achieved by modern weather radars. We also discuss the challenges and opportunities in applying this concept.  
smart city; ISAC; opportunistic ISAC; weather monitoring +
Footnote †: journal: _Sensors_
## 2 Materials and Methods
### Radiomics Analysis and Model Development  
Following feature extraction and selection, a random forest (RF) machine learning model was developed and training was performed using the artificial intelligence platform ChatGPT version 4.0. The dataset was randomly divided into training and testing sets with an 80:20 ratio. To enhance robustness and reduce variability, a 5-fold cross-validation was performed on the training set to validate model performance and optimize hyperparameters. The dataset presented an unbalanced distribution between patients with and without lymph node involvement, and this class imbalance was preserved in both the training and test sets to reflect real-world clinical conditions. During model development, data augmentation techniques were employed to mitigate this issue. Model performance was evaluated using metrics such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Sensitivity and specificity were also assessed across individual imaging sequences and their combinations (Table 2).  
[PERSON]'s test was employed to compare the highest AUCs obtained from analyses of the nodule, whole gland, and PPAT. Statistical significance was set at \(p<0.05\).  
Data augmentation was simulated using boosting techniques, including XGBoost and CatBoost. These techniques informed the development of the RF model, enhancing predictions and identifying the most significant features for lymph node invasion prediction.  
Feature importance analysis, based on boosting algorithms and RF modeling, identified key predictive variables. This analysis improved the model's interpretability and overall prediction accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Seq** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1-Score** & **Specificity** & **Sensitivity** \\ \hline ADC & 78\% & 0.722 & 1.000 & 0.333 & 0.500 & 100 & 33 \\ DWI & 67\% & 0.83 & 0.50 & 0.33 & 0.40 & 100 & 33 \\ T2 nod & 78\% & 0.78 & 0.50 & 0.33 & 0.40 & 100 & 33 \\ all nodule & 88 & 50 & 87 & 1 & 93 & 100 & 50 \\ seq & 78\% & 0.86 & 1.000 & 0.33 & 0.50 & 100 & 33 \\ T2 gland & 78\% & 0.972 & 1.000 & 0.333 & 0.500 & 100 & 33 \\ ALL mask & 88.89\% & 1 & 1.000 & 0.67 & 0.80 & 100 & 100 \\ \hline \end{tabular}
\end{table}
Table 2: The performances for the different MRI sequences of prostatic nodules, for the whole gland, and for the PPAT reported in terms of accuracy, AUC, Recall, and F1.