esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Total C Export by Harvest Per Year--Temporal Trends and Spatial Variability  
For the estimation of the C export over the entire growing season, the samples collected at the four harvest dates were used (from monitoring program in 2014). While the relationship between terrain position/prevalent soil type and exported carbon by the fourth harvest shows a weak trend, the dependency becomes more obvious when the development of dry phytomass over the entire growing season is taken into account. The amounts generally decrease continuously from the first to the fourth harvest date. However, the differences between soils become obvious over time (Figure 19). The strongest decline can be observed for the extremely eroded soil (plot 7) hereafter referred to as C1. Whereas soils with additional water supply, either by groundwater (plots 9 and 10) or lateral water fluxes in 1.5m depth (plot 12, without manipulation), showed the lowest decline over time (C3). The non-eroded soils at the plateau (plots 1, 4, 5) and the manipulated plot 11 behaved intermediate (C2).  
The averaged export of total carbon for the fourth harvest (27 August 2014) ranges between 124 g\(\cdot\)m\({}^{-2}\) from C1 and 161 g\(\cdot\)m\({}^{-2}\) from C3 (Table 4). With 156 g\(\cdot\)m\({}^{-2}\), C2 cannot be distinguished clearly from C3. Multiplying these values with the case specific factors calculated from the summed phytomass divided by the phytomass from the fourth harvest, the total exported carbon per year ranges between 624 g\(\cdot\)m\({}^{-2}\) from C1 and 718 g\(\cdot\)m\({}^{-2}\) from C2, which is slightly more than the 697 g\(\cdot\)m\({}^{-2}\) from C3. This is caused by the higher mean dry phytomass estimated from UAV imagery for the plots belonging to C2 (363 g\(\cdot\)m\({}^{-2}\)_vs._ 316 g\(\cdot\)m\({}^{-2}\)).  
Figure 18: Spatial distribution of total exported carbon by harvest within the eight plots of lucerne at the CarboZALF experimental area.  
Altogether, the differences are relatively small between the groups due to weather conditions in 2014, which were almost optimal for plant growth. Nevertheless, differences in site properties (terrain and soil) are known to result in quite different growth conditions [43; 44] and seasonal and intra-annual changes in weather conditions affect the within-field variability of phytomass production [45; 46]. [PERSON] _et al._[47] reported higher within-field variation of crop yield in dryer years, which was spatially associated with soil properties. The effect was less pronounced in wetter years with adequate water supply, which was most recently confirmed in a study by [PERSON] _et al._[48]. In addition, soil related within-field variability was found to be more pronounced and visible (beginning senescence) at the end of the growing season [44; 45]. As a consequence, mapping of small-scale variability of crop characteristics and finally C-export is most effective in a narrow time window. UAV-based remote sensing meets all requirements for this purpose and helps to reduce time consuming and expensive ground-based measurement campaigns.
###### Abstract  
This study used a discrete-element analysis to predict the excavation performance of a 7.73 m-diameter earth pressure balance (EPB) shield tunnel boring machine (TBM). The simulation mainly predicted several excavation performance indicators for the machine, under different operating conditions. The number of particles in the chamber and the chamber pressure varied, as the operating conditions changed during the simulated TBM excavation. The results showed that the compressive force, torque, and driving power acting on the TBM cutterhead varied with its rotation speed, increasing as the cutterhead rotation speed rose. The overall compressive force acting on all of the disc cutters and their impact wear increased linearly as the cutterhead rotation accelerated. The position of a disc cutter on the cutterhead had a particularly strong influence, with higher compressive forces experienced by the cutters closer to the center. In contrast, the gauge disc cutters at the transition zone of the cutterhead showed more wear than those elsewhere. The muck discharge rate and the driving power of the screw conveyor rose with increasing screw conveyor and cutterhead rotation speeds. Finally, this study suggests optimal operation conditions, based on pressure balance and operational management of the TBM.  
EPB shield TBM; excavation performance; discrete element method; operating condition IEEEexample:BSTcontrol
## 7 Results of Simulations
### Impact of the Sample Quantity on the ED Performance
Since higher Tx power is generally used for transmission of modulations with a lower constellation order and vice versa, in combined MA and RA system designs this type of transmission can obtain PU detection with a smaller sample quantity. The impact of the sample quantity on the detection probability (\(P_{d_{i}}\)) for MA systems is shown in Figure 15a-d. The results were obtained for the fixed false alarm probability (\(P_{fa_{i}}\) = 0.1), the DT factor \(\rho\) =0.1, and transmission at four distinct Tx powers (100 mW, 1 W, 10 W, 15 W). Analyses were also performed for channels with a constant NU variation equivalent to 2% of the average noise level. The results obtained showed that a lower detection probability is accomplished for a lower SNR, and a lower sample quantity for any OFDM modulation (Figure 15a-d). Since a lower SNR is a direct result of the impact of higher noise on signal transmission at a specific Tx power, the detection probability will decrease as the level of SNR decreases (Figure 15a-d). However, Figure 15a-d indicate the presence of the SNR threshold below which the detection probability cannot be guaranteed (\(P_{d_{i}}\) < 100%). The value of the threshold will be larger for signals sampled with a larger sample quantity or transmitted with a higher Tx power. Therefore, increasing the detection probability is related to the trade-off between the Tx power of the PU in MA systems and the sample quantity used during ED by SU. According to this trade-off, for a higher Tx power and lower sampl  
Figure 15: Influence of SNR and sample quantity on the detection probability in combined RA and MA systems transmitting at: (**a**) 0.1 W; (**b**) 1 W; (**c**) 10 W and (**d**) 15 W.  
to obtain better detection probabilities when compared to the PU transmission of signals with a low Tx power detected using an increased sample quantity. This confirms the importance of the PU Tx power level in the ED performance of any OFDM based system (MA, RA, or combined MA and RA).
## 1 Introduction
The results of this study showed that the specific absorption rate of the human tissue showed an increasing trend due to the resonance phenomenon and high response field in the vehicle. Some researchers have investigated the exposure level of the electromagnetic environment generated by vehicles' wireless charging systems to the human body, and the results of these studies show that the exposure level is within the ICNIRP limits [24; 25; 26; 27]. [PERSON] and [PERSON] [28] studied the influence of vehicle bodies with different materials on the electromagnetic exposure dose of passengers in a PEV and found that the aluminum alloy body could reduce the magnetic flux density over the space occupied by the driver's body inside the vehicle by 99.76%. In response to the complexity of the electromagnetic environment inside electric vehicles and to prevent potential health risks, many researchers have studied the use of protective materials [29; 30; 31] to minimize the impact of this electromagnetic environment on the human bodies of passengers. Thus far, in the context of studying human electromagnetic dosimetry, some studies have used approximate simple human models [32; 33], but more studies have used an anatomical real human model based on high-resolution MRI scanning [34; 35; 36]. In order to better understand the effects of electromagnetic exposure in pure electric vehicles on real human tissues and organs, this study incorporated a real human anatomy model with detailed organ and tissue characteristics [37; 38]. Comsol Multiphysics 6.0 software, based on the finite element method (FEM), was used; the inverter in the PEV was considered to be the electromagnetic exposure source. The magnetic flux density (B-field) over the space occupied by the driver's body, as well as the electric field (E-field) and the induced current density (J) in different tissues and organs of the driver's body, were calculated by the FEM. In this paper the B-field and the E-field are compared with the exposure limits stipulated by the ICNIRP to assess the exposure level to the driver's body in the electromagnetic environment of the inverter. This study could help to further clarify the human exposure limit for the electromagnetic environment generated by PEV inverters, as well as the effects that the electromagnetic environment of PEVs have on different tissues and organs of the human body, and effectively supplement studies on electromagnetic exposure in the context of PEVs.
## 3 Task Planning Model and Solving Algorithm
### Fitness calculation
#### 6.1.6 Checking constraints and eliminating conflicting tasks  
The solution generated by the GA-SA algorithm may not necessarily satisfy the constraint conditions of satellite observation. The generated solution must be checked for constraints to determine whether these tasks conflict with each other. If any one of the constraints is not satisfied, the current solution needs to be modified to eliminate the conflicting tasks. This paper carries out two conflict checks to improve the stability of the algorithm.
## 5 Application of the Proposed Criteria  
The advantage of using the mathematical method proposed in this paper is to improve the accuracy of predicting the mechanical properties and regularities of UD composite materials under different loading modes. This method allows engineers to save time and raw materials when designing composite structures, thus reducing production costs. In order to utilize the proposed model properly, it is essential to initially compute the coefficients for a specific material enabling the failure envelope to be predicted with greater precision under various strain rates.  
Researchers across Western nations have developed various simulation methodologies to examine the impacts of space debris. A primary focus has been the establishment of computational techniques for modeling the behavior of hypersonic spacecraft during collision events to conduct impact dynamics analyses on composite structures. Due to experimental limitations, specifically the launch speed capacity of experimental equipment on Earth, impact testing of protective structures with projectile velocities exceeding 8 km/s has been infrequently conducted. As a result, the high-speed zone ballistic limit equations, based on experimental data, may not accurately reflect the protective capability of these structures within this high-speed environment. Consequently, scientists generally simulate the operational conditions of the protective structures within the high-speed zone, such as during impacts, to obtain their ballistic limits. The data thus generated enable the prediction of the point at which the material would fail. Numerical simulations for determining the ballistic limit of protective structures in the high-speed zone are commonly employed, utilizing existing technologies and theories to propose differing simulation methods.  
In most high-speed simulations (speeds not exceeding 1 km/s), the Johnson-Cook mathematical model is employed to simulate the effects of strain rate. However, this approach is no longer viable for hypervelocity impacts in space, where velocities surpass  
Figure 7: Dependence of coefficients \(m\), \(n\), \(\alpha\) on strain rate (lines—approximation results; points—data from Table 2).  
8 km/s. The work in this article can be applied in the strength calculation and collision simulation of hypersonic spacecraft collision avoidance structures, integrating genetic mechanics and simulation by integrating the proposed MPFE in the design of spacecraft protection structures. Specifically, the principles from the simulation of hypersonic impacts will be improved by substituting the change in strain rate of the material during impact into the calculation module in order to improve the accuracy of the software simulation.
## 2 Preliminaries
### Internet of Medical Things (IoMT)  
With the thriving of the IoT [48], and massive related applications in different areas [49], the IoT shows a lot of potential for benefiting healthcare [50]. The IoMT is the IoT version that only focuses on medical applications [30]. The IoMT defines physical space as any device capable of retrieving data from the human body and any device used to monitor, control, and manage bodily functions. This technology plays an essential role in chronic disease management, remote patient monitoring, telemedicine, and hospital automation, addressing key challenges in healthcare, such as rising costs, staff shortages, and inefficiencies in traditional medical workflows. Wearable health trackers, smart implants, and remote monitoring devices enable continuous tracking of vital signs, facilitating early disease detection and timely interventions. For instance, IoMT-powered ECG monitors can detect irregular heart rhythms in real time, reducing the risk of sudden cardiac arrest. Additionally, smart infusion pumps, connected insulin delivery systems, and AI-driven diagnostic imaging help streamline hospital operations and minimize human errors. The integration of IoMT with AI and DL further enhances predictive analytics, personalized treatment plans, and automated alerts for high-risk patients. With the expansion of 5G networks and edge computing, the IoMT is becoming even more robust, enabling faster data transmission and reducing latency in critical care applications. As healthcare shifts toward preventive, personalized, and decentralized models, the IoMT serves as the backbone for next-generation smart healthcare systems, enhancing accessibility and quality of care while reducing hospital readmissions and operational costs. The IoMT can improve diagnostic accuracy, enhance surgery operation precision [51], and track patients, vital to further monitoring [52]. In general, the IoMT is a combination of various MCPSs to satisfy a specific purpose [34].
# DLI-Based DP-QPSK Reception Scheme for Short-Range Optical Communication
###### Abstract  
The unacceptably high cost of digital coherent receivers for short-range optical communication has in recent years fueled the study of alternative transmission schemes that are simultaneously highly performing and cost-effective. However, the proposed solutions (e.g., Kramers-Kronig receiver) usually require the cooperation of a transmitting end, which is unachievable in the context of non-cooperative communication. In this work, we mainly studied a dual-polarization quadrature phase-shift-keying (DP-QPSK) non-coherent reception scheme based on a delay-line interferometer (DLI) and measures to improve the reception performance. A data recovery algorithm was proposed for DLI-based DP-QPSK demodulation. The simulation results demonstrated that the algorithm could accurately recover the raw data from the transmitter. Moreover, decreasing the differential delay of the DLI could effectively increase the chromatic dispersion tolerance of the receiver and the optimal delay scaled inversely with the chromatic dispersion. It was found that a DLI-based DP-QPSK reception scheme is a better choice for short-range, non-cooperative communication with less severe transmission impairments.  
Keywords:dual-polarization quadrature phase-shift keying; non-coherent detection; delay line interferometer; recovery algorithm; free spectral range optimization +
Footnote †: journal: Applied
## 4 Experimental Results and Analysis
### Dataset Description  
For evaluating the performance of the DL methodologies considered in this study, we used the publicly available dataset IoT-Botnet 2020 [71]. This dataset is available in comma-separated values (CSV) format and is adopted from Pcap files of the BoT-IoT dataset [72] by generating many more network and flow-based attributes. A detailed description of the number of records for Benign and Anomaly labels in the original dataset and this study is given in Table 1. The original dataset contains the samples of different types of attacks such as Denial of Service, Distributed Denial of Service, Reconnaissance, and information theft attacks. We selected the benign samples from the original dataset, while for the anomaly class, we considered the random samples from each anomaly class for fair model evaluation. The original dataset contains a total of 85 features of different data types such as integer, float, and categorial, as detailed in Table 2. Among those 85 features, we perform experiments to find out the performance of different DL algorithms, considering the 80, 32, 16, 8 best numerical features and 5 best categorical-numerical features calculated using the mutual information (MI). The MI is an important concept in the information theory, which provides the average reduction in uncertainty of one random variable provided the information of other variables. Mathematically, MI is given as [73]:  
\[I(U;V)=\sum_{u\in U}\sum_{v\in V}p(u,v)\log\frac{p(u,v)}{p(u)p(v)} \tag{4}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**IoT-Botnet Dataset**} & \multicolumn{2}{c}{**This Study**} \\ \cline{2-5}  & _No. of Records_ & _No. of Records_ & _Train [75\%]_ & _Test [25\%]_ \\ \hline Benign & 97197 & 97197 & 72,907 & 24,290 \\ Anomaly & 1843192 & 300520 & 225,380 & 75,140 \\ \hline Total Samples & 1940389 & 397717 & 298,287 & 99,430 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: IoT-Botnet 2020 Dataset Distribution.
## 3 Research Methodology
### Initial Dataset  
Our initial dataset consists of three distinct components:  
1. Historical demand data: Our dataset comprises historical demand data, which is also our target variable, covering a 76-month period from January 2016 to February 2022. It includes daily demand amounts for over 330 products across 3 main product categories: fruits (A), fresh meat (B), and soft drinks (C). In total, the dataset spans more than 6 years, resulting in over 5.2 million available records for training and testing. The data are extracted from a sales transaction dataset from a supermarket located in Austria;
2. Internal data: This component includes data that results from business decisions, such as pricing and promotions. Key components of these internal data include pricing  
Figure 1: Demand forecasting process.  
information and promotional activities. This dataset includes daily price values for each product;
3. External data: In addition to internal factors, our dataset incorporates external variables that are beyond the direct control of the retail company. These external variables encompass various factors, including calendar-related data, weather conditions, and COVID-19-related data. Calendar-related data provide information for each day, including the month of the year, week of the year, day of the week, day of the month, and any special day or event. Weather data include temperature in Celsius, wind speed in m/s, amount of precipitation in mm, and precipitation type (no precipitation, rain, snow, rain-snow). COVID-19-related data include information about the type of lockdown, including no lockdown and lockdown.
## 4 Experimental Evaluation  
The experimental evaluation was performed considering a power system consisting of 18 generating units, the thermal characteristics of which are listed in Table 1.  
The hourly, net demand (load minus RES) for a representative week (5 weekdays) in summer is presented in Figure 1.  
The simulations were realized with the aid of MATLAB (MATLAB R2020, MathWorks) on a computer with an Intel-Core i7-4510U CPU @2.6 GHz, a 64-bit operating system (Windows 10) and 6 GB of memory. Utilizing the program developed based on the Lagrange relaxation approach, the parameters considered regard 120 hourly intervals, a 3500 MW peak load, a 5% spinning reserve requirement, 0.01 and 0.002 for the \(\alpha\) and \(\beta\) update coefficients, respectively, a 0.01 gap error, a \(10^{-9}\) tolerance for power balance and a maximum number of iterations of 350.
# Speech Emotion Recognition through Hybrid Features and Convolutional Neural Network
## 3 Materials and Methods
### Data Pre-Processing  
The pre-processing of voice is an extremely important stage in systems that cannot tolerate background noise or silence. Such systems include SER and speech recognition, which require efficient extraction of features from audio files, where the majority of the spoken part comprises emotion-related characteristics. To achieve this objective, this study utilized silence removal and pre-emphasis techniques.  
By increasing the power of high frequencies in speech signals and leaving low frequencies unchanged, the pre-emphasis technique can enhance the signal-to-noise ratio. This is achieved through the use of a high-pass filter called finite impulse response (FIR), which enhances the high-frequency energy. Essentially, pre-emphasis enhances the high-frequency components of speech signals using Equation (1).  
\[H(z)=1-\alpha z^{-1},\alpha=[1,-0.97] \tag{1}\]  
The use of FIR in pre-emphasis results in changes to the distribution of energy across the frequencies, as well as alterations to the overall energy level, which can have a significant  
Figure 3: The waveforms representing each emotion in the EMO-DB dataset.  
influence on the features associated to energy. In contrast, signal normalization utilizes Equation (2) to ensure that voice signals are comparable, regardless of any variations in magnitude.  
\[S_{Ni}=\frac{S_{i}-\mu}{\sigma} \tag{2}\]  
The \(S_{i}\) denotes the ith part of signal, while the mean and standard deviation of the signal are represented by \(\mu\) and \(\sigma\), respectively. \(S_{Ni}\) refers to the normalized ith part of the signal.
# Development of Practical Design Approaches for Water Distribution Systems
## 2 Practical Design of Water Distribution Systems
### Multi-Objective Optimization Framework for Water Distribution Systems Design
#### 2.2.2 Hydraulic Constraints  
Hydraulic constraints such as boundary nodal pressure and pipe velocity are applied to aid in the practical design of WDSs. If each nodal pressure or the pipe velocity at each pipe does not satisfy the minimum requirement, then a large penalty value such as Equation (3) is added to each of the objective function values. The main purpose of constant \(\alpha\) included in the penalty function is to impose the penalty in proportion to the extent of deviation from hydraulic constraints. The constant \(\beta\) of penalty function plays a role to eliminate the design with negligible deviation from hydraulic constraints by imposing the penalty. Constants \(\alpha\) and \(\beta\) are variously applied depending on the average design cost of WDSs. Approximately \(10^{7}\), \(10^{8}\) are used.  
\[\textit{Penalty}=\left\{\begin{array}{c}\alpha\\ v_{j}-v_{min}\end{array}\right.\begin{array}{c}\textit{l}_{i}-h_{min}\; \textit{or}\;h_{max}\\ \textit{v}_{j}-v_{min}\;\textit{or}\;v_{max}\end{array}\right.\begin{array}{c }\textit{l}+\beta\;\text{if, pressure constraint}\\ \textit{v}\;\text{velocity constraint}\end{array}\right., \tag{3}\]  
where _h\({}_{i}\)_ is the pressure head at node \(i\) (m), _v\({}_{j}\)_ is the water velocity at pipe \(j\) (m/s), _h\({}_{min}\)_ and _h\({}_{max}\)_ are the minimum and maximum pressure heads (m), respectively, _v\({}_{min}\)_ and _v\({}_{max}\)_ are the minimum and maximum water velocity (m/s), respectively, and \(\alpha\) and \(\beta\) are the penalty constants.