esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Gender** & **T11** & **T11/T12** & **T12** & **T12/L1** & **L1** & **L1/L2** & **Total** \\ \hline [PERSON] et al. & Male & - & - & 34 (31.50\%) & 28 (25.90\%) & 45 (41.70\%) & 1 (0.90\%) & 108 (100\%) \\ \cline{2-8} (2024) [16] & Female & - & 3 (3.90\%) & 33 (42.90\%) & 16 (20.80\%) & 23 (29.90\%) & 2 (2.60\%) & 77 (100\%) \\ \hline [PERSON] et al. & Male & - & - & 23 (33.81\%) & 10 (14.70\%) & 35 (51.46\%) & - & 68 (100\%) \\ \cline{2-8} (2021) [17] & Female & - & - & 14 (35.88\%) & 11 (28.20\%) & 14 (35.89\%) & - & 39 (100\%) \\ \hline [PERSON] et al. & Male & - & 2 (1.46\%) & 58 (42.34\%) & 35 (25.55\%) & 41 (29.93\%) & 1 (0.73\%) & 137 (100\%) \\ \cline{2-8} (2021) [18] & Female & - & 5 (4.95\%) & 63 (62.37\%) & 22 (21.78\%) & 11 (10.89\%) & - & 101 (100\%) \\ \hline \multirow{2}{*}{Present study} & Male & 4 (4.40\%) & - & 65 (71.40\%) & - & 22 (24.20\%) & - & 91 (100\%) \\ \cline{2-8}  & Female & 2 (8.00\%) & - & 20 (80.00\%) & - & 3 (12.00\%) & - & 25 (100\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The vertebral level at which the CT originates as found in 4 studies with respect to gender. the common hepatic was reported to take origin from the superior mesenteric artery, while in the other five, the common hepatic was observed to be a direct branch of the abdominal aorta [5]. In the current study, in the two cases in which the gastrolgenic trunk was present, the common hepatic artery took origin from the superior mesenteric artery. A comparison of the branching pattern of CT found in four studies is shown in Table 4.  
Abnormal variations in the blood vessels have several important clinical implications. In general, and oncological, surgeries, it may result in intraoperative complications or may even require modifications in the surgical technique [6]. Several complications can take place when surgeons need to perform a pancreaticoduodenectomy [27], and there is a report of a rare anomaly of the CT and HA that complicated pancreaticoduodenectomy [28]. The authors of the same study stressed the recognition of the variations pre- and postoperatively in order to check the adverse outcomes of complex hepatopancreatobiliary surgical procedures [28]. Interestingly, the distance of the CT from other ventral branches may vary according to gender [29]. Also, the diameter measurement with regard to the plane and position of the CT has much significance as values may alter [29]. Anatomical knowledge of the variations in the CT is important for foregut perfusion and essential for surgical procedures such as pancreaticoduodenectomy, reconstruction of the arteries, mesenteric revascularization, and transarterial chemoembolization [30]. A three-dimensional reconstruction of the contrast enhances the CT and is useful in post-mortem studies [31]. In the present study, we presented a 3D reconstruction for the better anatomic evaluation of the arteries.
# Smart Embedded System for Skin Cancer Classification
## 4 Results
### Performance of the Accelerator  
The models were quantized and mapped in a ZYNQ UltraScale+ MPSoC ZCU104 platform using Vitis-AI.  
The first implementation was with a single model without model reduction, ResNet18-F-F-F. The system was executed with different DPU configurations running at 300 MHz. The area occupation and the throughput (frames per second--FPS) are reported in Table 4 for different DPU configurations. The DPU reference indicates the number of operations per second (OPSs).  
As expected, smaller DPUs require fewer resources but are also slower. The throughput reduces proportionally with the number of parallel operations.  
All dual models were tested in software to measure the accuracy and to later be compared with the onboard results. The results show a small range of results between 86.4% and 87.1%, with the mean being 86.8%. Therefore, the accuracy degradation caused by quantization is very small.  
Considering that the application is to be run in an embedded system, the following experiments considered only the hardware solution with a B512 DPU.  
All dual models using the ResNet50 as the more accurate model were generated. Dual models with the same accuracy as or higher accuracy than ResNet50 after quantization were considered to run and test on the board. All dual models were compiled using the developed flow and run on the board. A script was written to automatically run all dual models serially in the DPU and to save the results. The synthesis tool reports a power ranging from 3 to 4 W for the whole FPGA. An extra 1 W was considered for the access to the external memory of the board. Thus, a total maximum of 5 W is reported for the system. The comparison metrics were the accuracy, the throughput and the energy per frame (see Table 5).  
From the results, it is possible to identify two solutions with an accuracy higher than ResNet50 and a higher performance. The best dual model, with an accuracy of 88%, includes the architecture ResNet18-S-F-F. With an accuracy of 87%, the best dual model (ResNet18-S-F-VS/ResNet50) achieves a performance of 13.5 FPS (speedup of \(\times\)4.3).  
As far as we know, there are no previous FPGA implementations for the classification of the HAM10000 dataset. There are a few FPGA implementations for skin cancer identification, with only two classes (cancer/no cancer) using an SVM classifier [35].  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**DPU** & **LUT** & **FF** & **DSP** & **BRAM** & **URAM** & **FPS** \\ \hline B4096 & 54533 & 99952 & 528 & 254 & 1 & 220 \\ B2304 & 46718 & 72314 & 304 & 166 & 1 & 121 \\ B1024 & 38696 & 51848 & 144 & 102 & 1 & 51 \\ B512 & 33374 & 39540 & 80 & 70 & 1 & 24 \\ \hline \end{tabular}
\end{table}
Table 4: Occupation of resources and throughput for different architectures of the DPU running the inference of ResNet18-F-F-F.
# Exploring Efficient Neural Architectures for Linguistic-Acoustic Mapping in Text-To-Speech +
## 7 Conclusions  
In this work, we present two competitive and fast acoustic model replacements for our MUSA-RNN TTS baseline. The first proposal, QLAD, is a quasi-recurrent neural network that moves the computational burden of learnable feedback connections to feed-forward ones, while maintaining a simpler recurrent pooling. This allows a comparable modeling of temporal dynamics to that of LSTMs for the speech synthesis task. SALAD is based on the Transformer network, where self-attention modules build a global reasoning within the sequence of linguistic tokens to come up with the acoustic outcomes. In this case, positioning codes ensure the ordered processing in substitution of the ordered injection of features that RNN has intrinsic to its topology. Both systems converge to comparative amounts of distortion compared to the RNN baseline. However, only QLAD reaches the same level of subjective naturalness. On the other hand, both QLAD and SALAD are more efficient than LSTMs in large utterance generation regimes, in both CPU and GPU contexts. More specifically, with the proposed QLAD acoustic model, we achieve a 11.2 times speedup on CPU and 3.3 times on GPU with respect to the LSTM based model. Based on this evidence, we can conclude that QLAD is a high quality and effective replacement for the LSTM based linguistic-acoustic decoder, even surpassing the performance and speed of a non-recurrent model such as SALAD.  
Conceptualization, S.P., J.S. and A.B.; Methodology, S.P. and J.S.; Software, S.P.; Data curation, S.P. and A.B.; Visualization, S.P.; Validation, S.P.; Investigation, S.P. and J.S.; Supervision, S.P., J.S. and A.B.; Funding acquisition, A.B.; Resources, A.B.; Writing-original draft preparation, S.P. and J.S.; Writing-review and editing, S.P., J.S. and A.B.;  
This work was supported by the Spanish Ministerio de Economia y Competitividad and European Regional Development Fund, contract TEC2015-69266-P (MINECO/FEDER, UE).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Max. CPU Latency [s]** & **Max. GPU Latency [s]** \\ \hline Big RNN & 23.52 & 1.2 \\ Big QLAD & 2.09 & 0.36 \\ Big SALAD & 8.03 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum inference latency with linear fit for RNN and QLAD models, and quadratic fit for SALAD. All measurements are taken at 45 s utterances.  
Figure 3: Inference time for the three different studied models: (**Left**) CPU speed curves; and (**Right**) GPU speed curves.  
**Acknowledgments:** We deeply thank the participants of the subjective evaluation.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Experimental Validation  
### Implementation Data  
This section validates the performance of the OMKHES-MS technique using benchmark test images. The results are assessed under distinct such as Peak Signal Noise Ratio (PSNR), Maximum Difference (MD), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Normalized Cross-Correlation (NCC), Average Difference (AD), maximum difference (MD), Normalized absolute Error (NAE), and Structural Content (SC). For enhanced performance, the values of PSNR, SC, and NCC should be as high as possible whereas MSE, RMSE, AD, NAE, and MD should be as low as possible.  
Figure 4 shows the sample set of benchmark images used as secret images and the respective image histograms are offered in Figure 5.  
Figure 4: Sample set of Secret Images.  
Figure 5: Histogram of Secret Images.
## 5 Simulation Results
### Unbalancing, LG Fault, Feeder Impedance, and Load Variation  
In this case study, the unbalancing load condition is discussed. Therefore, the DGs should participate in the unbalancing with the algorithm below:  
1. If the unbalancing is procurable only with one DG, the DGs must supply the unbalanced current from low capacity to high capacity one by one.
2. If there are any single phase DGs which could have supplied the defective phases, the corresponded DG must procure the unbalanced load located at this power line.
3. If there are grid-connected operation modes, since the DGs cannot supply the unbalanced current, the up-stream network participates in the condition.  
According to the management algorithm above, this case study is conducted with a three-phase unbalancing load. From the beginning to \(t=2\) s, a three-phase unbalanced load is connected to the system while at \(t=1\) s, the feeder impedance is changed as similar to case study 3 (Section 5.3).  
Figure 15: Third case study results: (**a**), active power of load; (**b**), reactive power of load; (**c**), network frequency; (**d**), load voltage.  
At \(t=2\) s to \(t=3\) s, the unbalanced load is disconnected from the grid and the balanced system is obtained. Meanwhile, at \(t=4\) s to \(t=5\) s, a single-phase fault at phase A happens in order to examine the proposed controller stability. Then, at \(t=4\) s, the fault is cleared and the system is restored to its balanced condition. Figure 12 shows the \(dq\) voltage and current of the DG and the active and reactive power sharing. As represented in Figure 16d, the frequency is stable in all contingencies.  
Figure 16: (**a**) \(dq\) voltage of DG output terminal; (**b**) \(dq\) generated current of DG; (**c**) generated active power; (**d**) generated reactive power; (**e**) system frequency.
# Decentralized Documentation of Maritime Traffic Incidents to Support Conflict Resolution
## 5 Application and Evaluation  
In the following, we present a proof-of-concept prototype to show the applicability of the architectural framework and the negotiation algorithm that was introduced in Section 4. For this purpose, we first describe how the approach was prototypically implemented. Then, an exemplary use-case is presented in which the approach is practically applied under real conditions in a maritime testbed infrastructure. Finally, the results of the application are measured and discussed with respect to the system's performance.
## 6 Experimental Verification  
### Experimental Platform  
An experiment was conducted to verify the correctness and effectiveness of the proposed model. A four-pole permanent magnet synchronous motor was used for experimental verification. The PMSM parameters used in this experiment are shown in Table 1.  
The experimental platform shown in Figure 6 includes PMSM, load motor, motor controller unit (MCU), two PCs, signal conversion device (Kvaser), data collection device (Vector VX1060), speed sensor, and torque sensor. The MCU contains a controller and an inverter. The core of the control border is an Infineon DSP microprocessor (Infineon TC1782). Two PCs were used in this experiment. The first one was used to control the load motor, which provides various load torque for PMSMs. The second one was used to send out a CAN control signal to the MCU, which also collected data through VX1060 and then fed back to the second PC.  
The temperatures of the critical positions (the stator, rotor, and end cap) were collected for the purpose of identifying parameters of the state equation. In order to facilitate the temperature acquisition, the motor was modified by embedding K-type temperature sensors. The sensors located in the middle of the stator slot and winding end were applied to measure the stator temperature. In general, since the winding temperature is higher than the temperature in the middle of the stator slot, the winding temperature was regarded as the stator thermal node temperature and the middle temperature of stator slot was used as a reference. The average temperature in the inner casing and the outer casing was obtained to represent the temperature of the casing thermal node, which is  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline rated power & 42 Kw \\ rated torque & 100 Nm \\ rated speed & 4000 rpm \\ inductance in \(d\)-axis & 0.1425 mH \\ inductance in \(q\)-axis & 0.3359 mH \\ phase resistance & 0.016 \(\Omega\) \\ poles & 4 \\ permanent magnet flux & 0.0566 Wb \\ \hline \end{tabular}
\end{table}
Table 1: Model parameters of PMSM.  
Figure 6: Test bench for the temperature measurement.  
neither an input quantity nor a state variable and is the middle variable in the state equation. The acquisition of this temperature node was used to verify the accuracy of the temperature estimation. The temperature in the bearing outer ring was sampled to simulate the end cap node temperature. The distribution of the temperature sensor is shown in Figure 7a. It was sampled by a dedicated temperature acquisition device, as shown in Figure 7b. For the sampling of the rotor temperature, a wireless transmitting and receiving device were used to transmit the internal temperature signal to the external receiving device, as shown in Figure 7c. The coolant temperature was collected by the MCU under real vehicle conditions.
## 3 Results
### Estrogen Concentrations in Water Samples  
This study conducted the sampling campaign from 2011/01 to 2012/02 at six sites (WL1 to WL6). Samples were taken 14 times at sampling sites (WL1 and WL4) and other sites 13 times. In total, 80 samples were collected, in which five samples failed to be extracted; estrogen concentrations could not be detected. Therefore, 75 valid samples were analyzed for estrogen concentrations. In this study, the detection rate indicated that each estrogen concentration was greater than the statistical quantity of MDLs, and the non-detectable concentrations were ignored. Table 2 lists the estrogens' statistical summaries, including concentration range, detection rate, and \(p\)-value for the normality test. Figure 2 shows the average concentration and median for each species. The normality test had the same results for both test methods. The estrogen concentrations are not in the normal distribution (\(p<0.05\)) for E2, DES, EE2, E1-3G and E2-3G. The E1, E3, E1-3S and E2-3S concentration had a normal distribution.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **T** & **pH** & **EC** & **DO** & **NH3-N** & **SS** & **TS** & **DOC** \\ \hline  & \({}^{\circ}\)C & & **\#S/cm** & **mg/L** & **mg/L** & **mg/L** & **mg/L** & **mg/L** \\ \hline Mean & 27.9 & 7.3 & 698 & 2.3 & 7.3 & 106 & 815 & 9.3 \\ SD & 3.3 & 0.3 & 248 & 1.3 & 5.3 & 68 & 473 & 5.9 \\ Max & 33.5 & 8.3 & 1719 & 7.6 & 24.8 & 343 & 2867 & 22.6 \\ Min & 20.0 & 6.8 & 14 & 0.0 & 1.5 & 25 & 441 & 2.3 \\ Median & 29.0 & 7.3 & 725 & 2.1 & 5.8 & 86 & 660 & 7.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Water quality parameters of samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compounds** & **Concentration** & **Detection Rate** & **\(p\)-Value \({}^{*}\)** & \(p\)-Value \({}^{**}\) \\  & **Range (ng/L)** & **(\%)** & **\(p\)-Value \({}^{**}\)** & **\(p\)-Value \({}^{**}\)** \\ \hline E1 & 2.0–145.6 & 100 & 0.169 & 0.200 \\ E2 & ND–13.2 & 84 & 0.003 & \textless{}0.001 \\ E3 & ND–34.0 & 95 & 0.083 & 0.200 \\ DES & ND–10.0 & 63 & \textless{}0.001 & \textless{}0.001 \\ EE2 & ND–4.4 & 15 & 0.001 & \textless{}0.001 \\ E1-3S & ND–13.2 & 76 &
## 6 Localization Systems
### Position Measurement on the Playing Court by Video Analysis  
Another process of player position and motion acquisition (tracking) in the court is made by video match analysis [207; 208]. This computer-aided system takes place in individual and team sports analysis using video recordings of the match. An excellent example of this technology for football-specific performance analyses is TRACAB's tracking systems. In particular, the \"Gen4\" consists of two multi-camera units placed in two locations on either side of the halfway line. The \"Gen5\" comprises two stereo camera pairs on each side of the field and two monoculars behind the goal areas. Recent studies on twenty football players in the total distance traveled showed only trivial deviations compared to the reference system (Vicon MoCap system) [209].  
Other studies in the football field compared the agreement of the movement demands data during a match (distance, average, and maximum speed) between an optical tracking  
Figure 9: The RTK NTRIP system block scheme.  
system and a GNSS device. The results of this study showed that the measurement variation between the two methods was minimal and promising [210].
# APT-Attack Detection Based on Multi-Stage Autoencoders
###### Abstract
Anomaly detection is also utilized in application areas such as video-processing [4], network monitoring and intrusion detection [5; 6; 7], cyber-physical systems [2] or monitoring industrial control systems [8]. In recent years, however, the scientific community has increasingly focused on anomaly detection methods in cybersecurity [9; 10; 11; 12]. This is especially the case for intrusion detection for APT attacks [1; 13]. This paper builds upon previous works from [14; 15], where a novel two-stage approach for anomaly detection, relying on autoencoders, was introduced. In this work, we additionally investigated several anomaly-detection methods with an emphasis on filtering methods and performance evaluation on two datasets. We analyse neural networks, including standard and variational autoencoders, as well as their combinations and a support vector machine. Each autoencoder is trained with benign or malicious data during an unsupervised learning process. The corresponding algorithms are used to investigate several known malware attacks against networks. Eventually, their authentic imprints in the datasets are identified according to distinctive evaluation metrics. The reminder of the paper is structured as follows. Section 2 discusses related work on ML-based anomaly detection in multiple domains. Section 3 provides an overview of the applied methodology, including datasets, APT attacks of interest and evaluation metrics. Subsequently, Section 4 discusses the proposed anomaly-detection framework, i.e., the main contribution of this paper, in detail. Finally, Section 7 elaborates on the experimental results and provides final remarks for future research.
# Antimicrobial Volatiles of the Insect Pathogen _Metarhizium brunneum_
###### Abstract  
Fungal volatile organic compounds (VOCs) represent promising candidates for biopesticd fumigants to control crop pests and pathogens. Herein, VOCs produced using three strains of the entomogenic fungus _Metarhizium brunneum_ were identified via GC-MS and screened for antimicrobial activity. The VOC profiles varied with fungal strain, development state (mycelium, spores) and culture conditions. Selected VOCs were screened against a range of rhizosphere and non-rhizosphere microbes, including three Gram-negative bacteria (_Escherichia coli, Pontan agglomerans, Pseudomonas aeruginosa_), five Gram-positive bacteria (_Micrococcus luteus, Staphylococcus aureus, Bacillus subtilis, B. megaterium, B. thuringiensis_), two yeasts (_Candida albicans_, _Candida glabtabrata_) and three plant pathogenic fungi (_Pythium ultimum, Botrytis cinerea, Fusarium garmentarum_). Miches differed in their sensitivity to the test compounds, with 1-octen-3-ol and isovaleric acid showing broad-spectrum antimicrobial activity. Yeasts and bacteria were inhibited by the same VOCs. Cryo-SEM showed that both yeasts and bacteria underwent some form of \"autolysis\", where all components of the cell, including the cell wall, disintegrated with little evidence of their presence in the clear, inhibition zone. The oomycete (_P. ultimum_) and ascomycete fungi (_F. graninumarum, B. cinerea_) were sensitive to a wider range of VOCs than the bacteria, suggesting that eukaryotic microbes are the main competitors to _M. brunneum_ in the rhizosphere. The ability to alter the VOC profile in response to nutritional cues may assist _M. brunneum_ to survive among the roots of a wide range of plant species. Our VOC studies provided new insights as to how _M. brunneum_ may protect plants from pathogenic microbes and correspondingly promote healthy growth.  
Metarhizium brunneum_; entomopathogenicogenic fungi; antimicrobial compounds; volatile organic compounds; plant pathogens