esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A novel active transmitter detection scheme in spatial modulation that consists of a transmitted signal pre-distortion technique and a received power-based statistical detection method is proposed to be incorporated with a signal space diversity technique. Experimental results have shown that the proposed transmitter detection scheme can effectively recover the power-based spatial bits from the numerous-level-based CAP waveforms, thus realizing an improved system data rate brought by SM which is ~7.5 Gbit/s. Meanwhile, SSD can be offered with about 0.61 dB SNR improvement. In addition, improved BER performance with an increasing constellation rotation angle is verified when the channel gain difference ratio equals to 0.5. Furthermore, it is indicated that by using our transmitter detection scheme, a tradeoff between SM and SSD performances is expected with a decreasing channel gain difference, which is different from the ideal SSD performance without considering SM. As for the effectiveness of the pre-distortion technique, the BER improvement can be significant by selecting an appropriate suppress ratio without introducing much nonlinearity. The proposed active transmitter detection scheme provides a practical solution to implement power-based SM that incorporates SSD for pursuing a better system BER with enhanced system throughput.  
Conceptualization and methodology, T.S., C.L. and A.N.; experimental validation, T.S.; writing--original draft preparation with review and editing, T.S., C.L. and A.N.; funding acquisition, C.L. and A.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by an Australian Research Council Discovery Project Grant, DP170100268.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Electrophysiological Processing  
The majority of offline analyses were conducted using BrainVision Analyzer Professional Edition (v. 2.1.0.327; BrainVision LLC, Morrisville, NC, USA). To mirror past work which examined highly similar ERP and alpha attenuation outcomes [19], our across-participant analyses focused on electrode site Pz and also a \"pooled\" occipitoparietal signal, which was the average of sites Pz, P4, Oz, PO7, and PO8. Within-participant examinations of alpha attenuation retained data from each of the 5 recording occipitoparietal recording electrodes. Offline analyses were conducted on calibration recordings only; EEG data recorded during copy phrase were only used online for real-time target classification and letter selection. One participant had consistently poor contact at site Oz, so that site was removed from the pooled signal and all other analyses for that individual.
# Upgrading of the L-P Band Cryogenic Receiver of the Sardinia Radio Telescope: A Feasibility Study
## 4 Results and Discussion
### New Filters Requirements for the P-Band Channel
If the scientific community has the need to use the L-P receiver also at ambient temperature, it is possible to replace the HTS BPF with a new cryogenic BPF in accordance with the features calculated with the Matlab software and shown in Figure 14 (red curve).
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 3 Experimental Results and Comparisons of Various Algorithms
### Cross-Dataset Performance Evaluation for the Proposed-I Algorithm  
To validate the robustness and effectiveness of the proposed XTTS-based data augmentation method, as well as its generalization capability across diverse languages, we conducted extensive performance evaluations on multiple datasets, including the Qualcomm English dataset (4 classes) [19], the ASR-SCKwsptSC Mandarin dataset (18 classes) [20], the RHS Minnan dataset (16 classes) [4], the Arabic Speech Command dataset (16 classes) [21], and the Google Speech Command dataset (16 classes) [10]. All datasets consisted of real speech samples and were standardized to 70 samples per class, aligning with the benchmark of the RHS dataset used in this study. The proposed augmentation method significantly improved model accuracy across these linguistically diverse datasets, demonstrating consistent and superior performance, as detailed in Table 8. These findings not only highlight the robustness and cross-lingual generalization capability of the proposed method, but also confirm its efficiency and applicability in low-resource speech recognition scenarios while maintaining computational efficiency.
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 3 Microbial Safety of Meat and Meat Products
### Pathogens in Meat  
The predominant pathogen microorganisms are _Salmonella_, _L. monocytogenes_, _B. cereus_, _C. botulinum_, _E. coli_, _Cl. perfringens_ and _S. aureus_, while pathogenic spore formers are predominately _B. cereus_ and _Cl. perfringens_.
# UAV Support for Mission Critical Services
## VMCS Concept
### VMCS Orchestration  
The proposed VMCS orchestration is hierarchical with Cloud-based orchestration at the highest level, Edge-based orchestration and the middle level and Fog orchestration based at the lowest level. The orchestration architecture is redundant--all orchestrators should be able to work independently (i.e., the VMCS may have the Edge orchestrator only); however, in the case they are all present, they should cooperate to achieve their goal. This is illustrated in Figure 5.
# Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
## 4 Assessing the Level of Digital Twinning (LoDT)
### Digital Twinning Level of Road and Rail Case Studies  
#### 4.4.1 Context  
Infraestruturas de Portugal (IP) is the biggest transport infrastructure manager in Portugal and is responsible for the construction, operation, and maintenance of road and railway networks. This organisation manages almost 15.000 km of national roads and more than 3.000 km of railway networks [102], distributed by multiple asset groups (road pavements, rail track, switches and crossings, power equipment, engineering structures, etc.). In 2023, IP established 50 innovation challenges [103], one of which related to the creation of digital twins of road and rail infrastructure. While IP recognizes DTs as innovative solutions, their full meaning and potential impacts remain incompletely understood. To bridge this gap, a series of case studies were undertaken to first validate the UNI-TWIN model and subsequently communicate, both internally and externally, how the various dimensions of digital twinning are embedded in IP's innovation projects.
# Electromechanical Behavior of Afyonkarahisar Clay Under Varying Stress and Moisture Conditions
## 2 Materials and Methods  
### Afyonkarahisar Clay Characterization  
The clay soil used in this study was collected from the center of Afyonkarahisar, Turkey, at a depth of approximately 1-1.5 m. The collection method could be classified as disturbed soil. The clay soil was classified as high-plasticity clay (CH) according to the Unified Soil Classification System (USCS) [18], ISO 14688-1 [19], and ISO 14688-2 [20]. The properties of the clay include a specific gravity of 2.62, a unit weight of 16.5 kN/m\({}^{3}\), a shrinkage limit of 5.8%, a maximum dry density of 1.58 g/cm\({}^{3}\), and an optimum water content of 21.9%. The liquid limit (LL) is 48.6%, and the plastic limit (PL) is 23%, resulting in a plasticity index (PI) of 25.6%. According to the USCS, soils with a PI greater than 7 and a liquid limit above 50% are classified as high-plasticity clay (CH). While the liquid limit in this case is just below the 50% threshold, the high PI value strongly supports the classification as CH, as the plasticity index is a key determinant in this classification. The PI indicates that the soil is more prone to significant volume changes with moisture variations, which is characteristic of high-plasticity clays.  
Soil identification tests were carried out in accordance with several internationally recognized standards. The Unified Soil Classification System (USCS) [18] is a widely used system in geotechnical engineering for categorizing soils based on their particle size distribution and Arterberg limits (liquid limit, plastic limit, and plasticity index). According to USCS, a soil with a liquid limit greater than 50% and a plasticity index greater than 7 is classified as CH (high-plasticity clay), which is consistent with the high PI value observed in this sample.  
Additionally, ISO 14688-1 [19] and ISO 14688-2 [20] provide international standards for the classification and identification of soils. ISO 14688-1 specifies the principles for identifying soil types based on their physical properties, including the limits for liquid and plasticity indices, while ISO 14688-2 focuses on the detailed classification criteria, ensuring consistent soil identification worldwide.  
The soil tests were also conducted in compliance with several standards, such as Turkish Standard TS 1900 [21], ASTM D 854 [22] for determining specific gravity, ASTM D 422 [23] for grain size analysis, ASTM D 4318 [24] for liquid and plastic limits, and ASTM D 2216 [25] for determining the moisture content of soils. These standards are essential for ensuring the accuracy and reliability of soil classification, providing a standardized approach to assessing the physical properties that influence soil behavior in geotechnical applications.
# One-Dimensional Modeling of the Pressure Loss in Concrete Pumping and Experimental Verification
## 4 Experimental Measurements of the Concrete Pumping Pressure Loss  
The concrete pumping process causes circumferential and axial deformation of the pumping pipe. The strain is the external manifestation of the effect between the concrete and the inner wall of the pipeline, reflecting the stress variation law under the interaction between concrete and the inner wall of the pipeline. Based on the strain data monitored under field conditions, according to the generalized Hooke law, the circumferential stress \(\sigma_{\theta}\) and axial stress \(\sigma_{Z}\) in the pumping pipeline wall are  
\[\sigma_{\theta}=\frac{E}{1-\
u^{2}}(\varepsilon_{\theta}+\
u\varepsilon_{Z}) \tag{28}\]  
\[\sigma_{z}=\frac{E}{1-\
u^{2}}(\varepsilon_{Z}+\
u\varepsilon_{\theta}) \tag{29}\]  
where \(E\) is the modulus of elasticity, \(\varepsilon_{\theta}\) is the circumferential strain, \(\varepsilon_{Z}\) is the axial strain, and \(\
u\) is Poisson's ratio.  
The wall thickness of the pumping pipe is set to \(h\), and the inner wall of the pipeline is subjected to a homogeneous pressure \(p\). According to the mechanics of materials, the circumferential stress \(\sigma_{\theta}\) at any point along the pipe diameter is  
\[\sigma_{\theta}=\frac{pa^{2}}{b^{2}-a^{2}}\bigg{(}1+\frac{b^{2}}{4r^{2}} \bigg{)} \tag{30}\]  
Along the concrete pumping direction, a pumping pipe section of length \(l\) is selected, axial and circumferential strain gauges are arranged at cross-flow sections 1 and 2, and the axial and circumferential strains are measured as \(\varepsilon_{\theta 1}\), \(\varepsilon_{Z1}\), \(\varepsilon_{\theta 2}\), and \(\varepsilon_{Z2}\). As shown in Figure 4, the pressure difference \(\Delta p_{\mathrm{n}}\) between cross-flow sections 1 and 2 is  
\[\Delta p_{n}=\frac{E(b+a)h[(\varepsilon_{\theta 2}-\varepsilon_{\theta 1})+\
u( \varepsilon_{z2}-\varepsilon_{z1})]}{a^{2}(1-\
u^{2})(1+b^{2}/4r^{2})} \tag{31}\]  
Figure 4: Layout of the measurement points and stress distribution.  
According to the Concrete Pumping Construction Technical Regulations (JGJ/T10-2011) [29], the conversion coefficient of the circumferential pressure and axial pressure in the pumping pipe is 0.9. The circumferential pressure \(p_{n}\) calculated by Equation (31) needs to be converted into an axial pressure, and the axial pressure \(\Delta p_{e}\) can be expressed as follows:  
\[\Delta p_{e}=\frac{\Delta p_{n}}{0.9} \tag{32}\]
# On the Benefits of Listen before Talk Scheme for NB-Fi Networks
## 7 Numerical Results  
We simulate the scenario described in Section 6 to evaluate the dependence of PLR, the average delay per successfully transmitted packet, throughput, and the normalized sensor's energy consumption \(E\) per a successfully transmitted packet on the load. In the simulation, we assume that sensors operate independently, have equal capabilities and parameters, such as the transmission power of 14 dBm, and use the same data rate assigned by the BS. We assume that a transmitted frame can be damaged only by a collision with other frames. Thus, a frame is received only if the SINR during the frame transmission exceeds 7 dB for the whole duration of the frame, otherwise, it is lost. We assume that the sensors are static, and the channel is static and flat as well, i.e., we neglect the frequency selectivity.  
We measure throughput as the ratio of the number of successful packets sent by the sensors during the experiment to the experiment time. Furthermore, we calculate \(E\) as the total energy consumed by the sensors during the simulation according to the model described in Section 6 divided by the number of successfully delivered frames. We calculate the delay of frame transmission as the time interval between the frame generation at the sensor and the successful delivery of the frame to the BS. Please note that the delay is calculated only for the successful frames. The average delay is obtained by averaging this value over all sensors in the network and all their delivered frames. For each value of \(\lambda\), we perform 100 runs of the experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Notation** & **Value** \\ \hline Current in receiving state & \(I_{rx}\) & 17 mA \\ Current in transmitting state & \(I_{tx}\) & 50 mA \\ Current consumed by the & \(I_{s}\) & 3 mA \\ power source & \(V\) & 3.3 V \\ Power consumed for & \(P_{tx}\) & 175 mW \\ transmission & & \\ Power consumed for sensing & \(P_{rx}\) & 66 mW \\ and reception & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensor energy consumption [34].
## 2 Materials and Methods  
### Patients and Imaging Protocol  
The dataset used in this study corresponds to a retrospective review that includes 224 patients (age 32-85, average age 64.5, 65% men and 55% CAD), whose SPECT MPI scans were issued by the Nuclear Medicine Department of the Diagnostic Medical Center \"Diagnostiko-Iatriki A.E.\", Larisa, Greece. The dataset consists of images from patients who had undergone stress and rest SPECT MPI on suspicion of ischemia or infarction concerning the prediction of CAD between June 2013 and June 2017. The participant patients went through invasive coronary angiography (ICA) 40 days after MPI.  
The set of stress and rest images collected from the records of 224 patients constitute the dataset used in this retrospective study. The dataset includes eight patients with infarction, 142 patients with ischemia, and eight patients with both infarction and ischemia, while the remaining (61 patients) were normal. Indicative image samples are illustrated in A 1-day stress-rest injection protocol was used for Tc-99m tetrofosmin SPECT imaging. Patients underwent either symptom-limited Bruce protocol treadmill exercise testing (n = 154 [69%]) or pharmacologic stress (n = 69 [31%]) with radiotracer injection at peak exercise or during maximal hyperemia, respectively.  
Figure 1: Image samples of SPECT MPI (from our dataset) (Labels: (**a**) ischemia, (**b**) infarction, (**c**) healthy).  
Within 20 min after an injection of 7 to 9 mCi 99 mTc-tetrofosmin, stress SPECT images were collected due to either an effort test or pharmacological stress with dipyridamole. In the case of the effort test, a treadmill test was performed. The Bruce protocol was employed, and when at least 85% of the age-predicted maximum heart rate was achieved, a 99 mTc-tetrofosmin injection was provided to the patient, whereas the exercise stopped 1 min later. Rest imaging followed 40 min after a dose of 21-27 mCi 99 mTc-tetrofosmin had been injected. The data collected from SPECT system came from 32 projections regarding a period of 30 s for the stress and 30 s for the rest SPECT MPI. The rest of the configurations regarded a 140 keV photopeak, a 180-degree arc and a 64 \(\times\) 64 matrix.
# Application Layer Protocol Identification Method Based on ResNet
## 4 Experimental Results and Discussion
### Experimental Results and Analysis
#### 4.4.5 Test Summary  
From the data acquired from training and testing the five datasets with the models, it can be concluded that Group A achieves the highest scores in various metrics during training and testing due to more accurate separators. However, upon calculation, it was found that Group A's metrics were only 0.218% higher on average compared to the \"our\" group, indicating a minimal difference in performance. Additionally, in Section 4.4.2, the feature extraction results of Groups A and \"our\" were almost identical, with the \"our\" group demonstrating a 3.06% reduction in extraction time compared to Group A.  
In conclusion, the separators and application-layer protocol recognition method proposed in this paper are deemed the optimal solution under acceptable model performance loss.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & \multicolumn{2}{c}{**HTTP**} & \multicolumn{2}{c}{**TLS**} \\ \cline{2-5}  & **Data Block** & **Frequency** & **Data Block** & **Frequency** \\ \hline
1 & HTTP & 679 & \(\backslash\)x17\(\backslash\)x03\(\backslash\)x03 & 165 \\
2 & GMT & 646 & \(\backslash\)x06\(\backslash\)t & 83 \\
3 & application & 644 & com & 75 \\
4 & 2017 & 639 & \(\backslash\)x16\(\backslash\)x03\(\backslash\)x03 & 65 \\
5 & com & 613 & \(\backslash\)x01\(\backslash\)x01\(\backslash\)x16\(\backslash\)x03\(\backslash\)x03 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of feature data blocks for HTTP and TLS.  
Figure 9: This image displays the confusion matrix of the test set on ResNet50.
## 5 Network Function Virtualization (NFV)

Table 3: _Cont._* NFV optimizes end users provisioning resources with high QoS and ensures the performance of virtual network functions (VNFs), e.g., low latency. * NFV has higher flexibility than traditional networks because network operators use new services over the same hardware platform and can dynamically deliver services to customers based on their demands via NFV performance gradation. Software and hardware do various functions because of their separation. * NFV decreases energy consumption by merging the network devices. * NFV does not need additional hardware since the network operator can activate the software over virtual resources, e.g., VMs, and containers. * NFV increases security since the main architecture components of NFV, e.g., virtual infrastructure manager (VIM), can be embedded with security methods. The NFV infrastructure includes hardware and software resources that link VNFs via the virtualization layer. Examples of the virtualization layer are the hypervisor and the virtualization solutions of containers like Docker. NFV contains the following three main components [53; 54]:  
1. Virtual network function (VNF): VNF is responsible for the software's functionality to execute network operations specified by the infrastructure of the NFVI on one or multiple VMs. 2. Network function virtualization infrastructure (NFVI): The NFVI has software and hardware resources to manage and support carrier networks, processing, virtual storage, and VNFs. 3. NFV management and orchestration (NFV-MANO): NFV-MANO provides an architectural framework between services of interfaces of VNFs and NFVI and orchestrates their respective sub-components. Deploying NFV technology in smart city networks offers significant benefits contributing to efficient operation, scalability, and adaptability of urban ecosystems. Table 4 provides some of the main benefits of this deployment [49; 50; 51; 52].
## 6 Discussion  
Under real-world conditions, this study tested the fault region presence and type detection algorithms for the emulated electrical substation with an IB-PV array farm. The foremost significant contribution of this study, beyond other studies in the literature, was employing the ML algorithm, which was trained based on the simulation data created with a completely distinct substation model. In the literature, many studies on ML-based fault detection algorithms were tested considering the same substation parameters used for training operations. Please note that data generation is a lengthy process, and it is not always possible to obtain enough data to train a model. However, it was shown in this study that we used a training model from another substation and applied to the distribution system testbed explained in Section 2.  
The designed model from the 46 kV substation simulation environment consisted of three main processes: finding the fault region in the data, checking the presence of the fault, and defining the type of fault. The first part of the model was created using an envelope detector-based fault region detection algorithm that was improved from the previous study to achieve higher precision. This part was investigated using data collected from various relays and meters. Because the relays and power-meter hardware components may vary, test data were recorded at different sample rates. However, these variations were effectively avoided in the algorithm's performance by adjusting threshold values based on the relays and meters in the envelope detector-based fault region detection algorithm.  
Figure 16: Total processing time of ML-based classification layers by considering different ML methods and feature extraction techniques.  
The second part of the model separated the fault signal from the effect of switching and the motor in the power grid systems. This part was examined using various feature extraction methods and ML algorithms. Because the DT and RFC had tree structures trained based on the other substation, their prediction accuracy was 0% with various feature extraction methods except PSD and WT. However, the 100% estimation performance was achieved with SVM and CNN ML algorithms, but the raw data structure could not provide valuable features for CNN algorithms. For this reason, the ML-based structure in the proposed model was verified with various ML algorithms and feature extraction methods. The third part of the model found specific fault types of the test data. This part was also analyzed using different ML and feature extraction methods. The results revealed that the prediction accuracy for fault type detection was decreasing because the ML models were trained considering different substation parameters. The final results suggest that the specific fault type detection model should be improved to perform better under real-world conditions.