esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Medical Image Segmentation with Vision Transformer
## 4 Experiments and Discussions
### Skin Lesions Dataset Results  
To validate the effectiveness of the algorithm across different medical image modalities, we utilized the ISIC2016 and ISIC2018 skin lesion image datasets. The 2016 dataset contained 900 images, which we divided into 600 for training, 200 for testing, and 100 for validation. The 2018 dataset had 5000 images, which we split into 3500 training images, 1000 testing images, and 500 validation images.  
As shown in Figures 5 and 6, our network demonstrates superior lesion segmentation compared to the baseline algorithm. The segmentation maps from our network align closely with the ground truth data and contain significantly fewer erroneous segmented regions than the baselines. In contrast, U-Net and Attention U-Net yield very unsatisfactory  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Dice** & **Sen** & **Spec** & \(S_{\mathbf{\alpha}}\) & \(E_{\mathbf{\phi}}^{mean}\) & **MAE** \\ \hline U-Net [2] & 0.574 & 0.561 & 0.949 & 0.706 & 0.744 & 0.105 \\ Atte-U-Net [36] & 0.582 & 0.508 & 0.971 & 0.684 & 0.762 & 0.096 \\ Inf-Net [60] & 0.647 & 0.709 & 0.910 & 0.737 & 0.827 & 0.103 \\ \hline Ours & 0.695 & 0.721 & 0.939 & 0.778 & 0.854 & 0.083 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative results of infection region on CT dataset.  
Figure 4: CT infection segmentation results for visual comparison: Ronneberger [2], [PERSON] [36], [PERSON] [60].  
segmentation results on this task. While Inf-Net delivers a comparable performance to our network, our approach still produces slightly better delineation of edge details in the infection regions. Overall, these results highlight the effectiveness of our network for accurate lesion segmentation in the ISIC2016 dataset.  
The results in Tables 2 and 3 demonstrate that our proposed model achieves the best performance across all evaluation metrics compared to the baseline algorithms. Our model also outperforms Inf-Net on multiple indicators, which can be attributed to the addition of the transformer module in our architecture.  
Figure 5: ISIC2016 infection segmentation results for visual comparison: [PERSON] [2], [PERSON] [36], [PERSON] [60].  
Figure 6: ISIC2018 infection segmentation results for visual comparison: [PERSON] [2], [PERSON] [36], [PERSON] [60].  
In summary, our model consistently surpasses the baseline algorithms on most metrics for all the datasets. The use of additional unlabeled data and refined attention mechanisms enables our model to more accurately identify infection regions. The quantitative results validate the superior infection segmentation capability of our proposed model across different imaging modalities.
# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 4 Experiments and Evaluation
### Threshold Value Method  
Figure 5 shows the probability density function (PDF) of the three GNSS-R observables under the circumstance with or without cyanobacteria bloom occurrence. We manually selected a threshold in each PDF to make the classification accuracy relatively the same in each class, and the thresholds are marked by red lines in the PDFs. In the PDF of PN, we expect that the power from points with cyanobacteria bloom coverage is mostly concentrated in the PN values between 10 and 20, as the coherent power of reflected GNSS signals is less spread when the surface is smooth, similar to specular reflection. As we can see in Figure 5a and Table 1, the threshold we selected is 17.5, which generally agrees with our expectation. While in the PDF of SR, the situation is the opposite, the stronger the coherent reflection, the higher the reflectivity. So, the conclusion is readily drawn that the cyanobacteria bloom points tend to show the character of high SR values. Actually, Figure 5b also proves this tendency, and the threshold is set as 0.08. The last GNSS-R observable employed in this paper is SNR; its tendency is similar to SR, but the data distribution of it is more even relative to PN and SR, and the threshold is set as 12.1 dB (Figure 5c). The results of the threshold value classification via PDF are recorded in Table 1. In order to indicate the classification accuracy, we employed the performance indexes including the true positive (TP) rate (TPR), true negative (TN) rate (TNR) and OA. TP and TN refer to the two correctly classified situations, and OA is calculated as follows:  
\[OA=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
These three performance indexes range from 0 to 1, and the closer they are to 1, the better the results. In this method, TPR, TNR and OA are mostly better than 65%, which shows the ability to distinguish cyanobacteria bloom from the clean water surface using GNSS-R observables. However, the accuracy of it is still unsatisfactory, the probable reason being that there are some situations where the surface is calm due to low wind speed instead of actual cyanobacteria bloom, or a strong wind suddenly blows and roughens the lake surface but the cyanobacteria bloom is not blown away. To better fix these mistakes, a scheme is designed to combine GNSS-R data and meteorological data with the help of the ML method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PN** & **SR** & **SNR** \\ \hline Threshold & 17.5 & 0.08 & 12.1 \\ TNR & 69.6\% & 66.8\% & 64.5\% \\ TPR & 67.9\% & 67.0\% & 63.5\% \\ OA & 0.68 & 0.67 & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of threshold value method for three GNSS-R observables.  
Figure 5: PDFs of three GNSS-R observables with cyanobacteria bloom covered or not, red lines and numbers refer to the classification threshold.
# A Cooperative MAC Protocol for a M2M Heterogeneous Area Network
## 5 M2M Heterogeneous Area Network
Additionally, the transmission power of end devices is limited such that packets can only be received by the local cluster heads. Hence, by using alternating carrier frequencies and limiting transmission power, inter-cluster interference can be avoided. The red and green dotted circles show the transmission distances of 802.15.4 end devices and the black dotted circles show the transmission distance of the 802.11 transmitter in the MFDRR. It can be easily seen that the 802.11 transmitter in the MFDRR will interfere with all 802.15.4 end devices while transmitting WLAN packets.
# Circularly Polarized Broadband Printed Antenna for Wireless Applications
###### Abstract  
A simple, compact sickle-shaped printed antenna with a slotted ground plane is designed and developed for broadband circularly polarized (CP) radiation. The sickle-shaped radiator with a tapered feed line and circular slotted square ground plane are utilized to realize the wideband CP radiation feature. With optimized dimensions of 0.293 \(\times\) 0.293 \(\times\) 0.012\(\lambda\) at 2.22 GHz frequency for the realized antenna parameters, the measured results display that the antenna has a 10 dB impedance bandwidth of 7.70 GHz (126.85%; 2.22-9.92 GHz) and a 3 dB axial ratio (AR) bandwidth of 2.64 GHz (73.33%; 2.28-4.92 GHz). The measurement agrees well with simulation, which proves an excellent circularly polarized property. For verification, the mechanism of band improvement and circular polarization are presented, and the parametric study is carried out. Since, the proposed antenna is a simple design structure with broad impedance and AR bandwidth, which is a desirable feature as a candidate for various wireless communication systems. Because of the easy printed structure and scaling the dimension with broadband CP characteristics, the realized antenna does incorporate in a number of CP wireless communication applications.  
broadband; circular polarization (CP); compact; planar; sickle-shaped; wireless applications Article
## 2 Materials and Methods
### Soil Bin Experiments  
In order to validate the FE model for the interaction between soil and a single shovel, the simulated draught forces are compared with those obtained by a soil bin tester (TCC-3, Bona Science & Technology Co. Ltd., Beijing, China) in agricultural machinery laboratory at Jilin University, Jilin Province, China. The soil bin is 40 m \(\times\) 2.5 m \(\times\) 1.5 m (length \(\times\) width \(\times\) depth, the effective travel is 30 m) and it is filled with black soil. The experimental setup corresponds to that of the simulations for the interaction between soil and a single shovel. As shown in Figure 6b, the force sensors consist of an upper pull rod sensor (BK-1-LG, China Academy of Aerospace Aerodynamics, Beijing, China) and two lower suspension pin sensors (BK-5-XG, China Academy of Aerospace Aerodynamics, Beijing, China), whose maximum range are all 15 kN. Figure 6c shows the connecting shank. By adjusting the screwing length of the double-end stud (8) in the joint bearing (7), the lower shank (10) will rotate around the rotating shaft (9) and then the mounting angle of the potato digging shovel will be changed. The cutting depth is controlled by the vertical elevator on the soil bin test vehicle, while the cutting speed is controlled by the operation panel of the vehicle. The resistance signals are recorded and exported by the data acquisition and processing system on the soil bin test vehicle. Figure 6d,e demonstrates the DZ and YS used in the soil bin experiments, respectively.  
When an experiment begins, the soil in the soil bin is first treated by rotary tillage, repression and water spray, in succession. Then, the volumetric water content (VWC) of soil is measured by a soil moisture meter (Field ScoutTM TDR 300, Spectrum Technologies, Inc., Plainfield, IL, USA), and the cone index of soil is measured by a soil compaction meter with a \(\frac{1}{2}^{\prime\prime}\) diameter cone tip (Field ScoutTM SC 900, Spectrum Technologies, Inc., Plainfield, IL, USA). These two indicators within the depth of 0.30 m need to be controlled in the range of 30-40% and 400-500 kPa, respectively. In addition, the experimental values of the draught force are acquired when the cutting process reaches a steady state, and each experiment is repeated three times.  
Figure 6: Soil bin experiments for a single potato digging shovel. (**a**) Soil bin experiments system, (**b**) test system, (**c**) connecting shank, (**d**) DZ, (**e**) YS, (1) soil bin, (2) tractor, (3) mounting frame, (4) upper pull rod sensor, (5) lower suspension pin sensor, (6) upper shank, (7) joint bearing, (8) double-end stud, (9) rotating shaft, (10) lower shank and (11) potato digging shovel.
# Content Management Based on Content Popularity Ranking in Information-Centric Networks
## 4 Performance Analysis
### Content Delivery Time
The number of client nodes a server node can handle was increased from 5 client nodes per server node up to 50 client nodes per server node to evaluate the performance of the proposed schemes for content caching and cache removal under a high traffic load. The servers operating on the host-based Internet architecture always needed more time to deliver the requested content, were always behind the server nodes operating on the CCN-based architectures in terms of CDT, and were unable to catch up with the server nodes throughout the experiment. At the point where each server was responding to a maximum of 12 client nodes, the server nodes operating on the three different architectures: the host-based Internet architecture, the CCN architecture incorporating LCE and LRU schemes, and the CCN architecture incorporating the proposed CPR-based schemes for content caching and cache removal, required approximately 6.33 s, 5.76 s, and 3.6 s, on average, respectively, to deliver each requested content to the client nodes. The difference in performance increased even further when the traffic load increased, as each server had to handle up to 50 clients at a time. The server nodes needed approximately 24.89 s, 19.93 s, and 15.02 s on average, respectively, while operating on the three different architectures mentioned before in order to deliver each requested piece of content to the client nodes. Therefore, the content delivery times can be reduced significantly using the proposed CPR-based schemes for content caching and cache removal when the cache size of the server nodes is fixed but the traffic load is increasing.
# A Miniaturized and Fast System for Thin Film Thickness Measurement
###### Abstract  
Transparent films are significant industrial components that are widely used in modern optics, microelectronics, optical engineering, and other related fields. There is an urgent need for the fast and stable thickness measurement of industrial films at the micron-grade. This paper built a miniaturized and low-cost film thickness measurement system based on confocal spectral imaging and the principle of thin-film spectral interference. The reflection interference spectrum was analyzed to extract the phase term introduced by the film thickness from the full spectrum information, where local spectral noise can be better corrected. An efficient and robust film thickness calculation algorithm was realized without any calibrating sample. The micron-grade thickness measurement system had an industrial property with a measurement range of up to 75 \(\upmu\)m with a measurement uncertainty of 0.1 \(\upmu\)m, presenting a good performance in single-layer film thickness measurement with high efficiency.  
thin-film interference thickness measurement; reflection interference spectrum; non-uniform Fourier transform +
Footnote †: journal: Applied Sciences
## 5 Muscular Disease and Histone Modifications  
Facioscapulohumeral dystrophy (FSHD) is an autosomal dominant muscular dystrophy characterized by progressive wasting of facial, shoulder, and upper arm muscles [52]. FSHD1 (>95% of all cases) is caused by monoallelic partial deletion of D4Z4 repeat sequences at the subtelomeric region of chromosome \(\mathbf{4_{q}}\)[53]. D4Z4 is a 3.3 kb macrosatellite repeat that contains an open reading frame for the double-homeobox transcription factor DUX4 retrogene, in which the transcription of DUX4 gene is strongly repressed in most adult tissues [54]. There are only 1-10 D4Z4 repeats in the contracted allele in FSHD1, in contrast to 11-150 copies in the intact allele. FSHD2 (<5% of all cases) is more rare form that exhibits no D4Z4 repeat contraction [55]. D4Z4 repeats contain transcriptionally repressive heterochromatin harboring DNA hypermethylation and H3K9 me3 together with H3K27 me3 [56; 57]. The loss of DNA methylation and H3K9 me3 at D4Z4 is the hallmark of both FSHD1 and FSHD2, strongly suggesting that FSHD is an epigenetic abnormality disease associated with the impairment of heterochromatin at D4Z4. [PERSON] et al. [58] also reported that H3K9 acetylation along with reciprocal loss of H3K27 me3 at contracted D4Z4 was key epigenetic events that result in DUX4 expression. [PERSON] at al. [59] found that the _SMCHD1_ gene is mutated in >80% of FSHD2 cases. [PERSON] et al. [52] also found that the suppression of H3K9 me3 results in displacement of SMCHD1 at D4Z4 and increases DUX4 expression in myoblasts. These observations indicate that epigenetic abnormalities possibly trigger a subtype of muscular dystrophy.
## 1 Introduction
Techniques such as electrochemical biosensors, SERS-based Raman spectroscopy with machine learning, molecularly imprinted polymers, MALDI-TOF MS, and microfluidic PCR platforms enable rapid, specific, and often cultivation-free identification of pathogens. These approaches are increasingly applied in clinical, environmental, and food safety settings, representing a shift toward real-time, point-of-care microbiological diagnostics [12; 13; 14; 15; 16]. As a rapid microbial detection technique, thermography could overcome the limitations of conventional methods for assessing the microbiological quality of food [17]. First and foremost, it is a non-destructive method that can be used to test samples in a non-invasive, non-contact manner, reducing the risk of food contamination and offering the possibility of immediate temperature measurement. The method comprises short processing times to obtain thermal images, no harmful radiation during measurements, ease of use, and low-complexity instrumentation. Unlike other methods, thermal imaging allowssimultaneous monitoring of a large area (many samples) and temperature changes during real-time measurement [18]. The infrared spectroscopy method requires tiny amounts of sample. A few milligrams are often sufficient for a single measurement. It is non-invasive, meaning it does not destroy the sample. It does not require specialized sample preparation; the sample only needs to be mixed with a carrier made of potassium bromide, forming a pallet. It is rapid. The entire measurement takes, at most, 5 min. It does not require specific chemical reagents, hence it is environmentally friendly. It can be used both where the final product inspection is performed (inline) and on a mobile production line, i.e., inflow (online). The results obtained are accurate and reproducible [19; 20; 21]. This study aimed to develop a method for monitoring the microbiological quality of raw poultry meat (chicken fillets) in real-time using a thermal imaging camera with an uncooled microbolometer detector and Fourier-transformed infrared spectroscopy (FT-IR). This study assumed that the effect of the development of contaminating microflora would be not only an increase in temperature but also changes in the sample's emissivity related to the secretion of metabolites and the consumption of nutrients from the tested material.
# Fuzzy Multi-Criteria Based Trust Management in Heterogeneous Federated Future Internet Testbeds
## 4 Evaluation  
The operation of the proposed reputation algorithm has been tested and evaluated in a real future Internet federation of testbeds in the context of the HELNETproject. The HELNET project [29] provides a federation of heterogeneous testbeds aiming to facilitate and promote test-driven research for the future Internet. The federation offers experimentation services in the fields of 4G/3G communications, WiFi networking, software-defined networking and software-defined radios. For the evaluation of the proposed reputation-based trust framework introduced here, the wireless NETMODE [30] and NITOS [31] testbeds are used in particular for demonstration purposes. Figure 2 illustrates the high-level architecture of the HELNET reputation service. The core of the reputation service is the reputation computation engine, where the two proposed reputation systems are developed using the Ruby on Rails MVC. The experimenters submit their evaluation on the portal of federation, while the reputation computation engine collects SLA and monitoring data from the testbeds through different APIs. The testbed management module is responsible for the administrative tasks of the reputation service. Finally, the reputation score of all testbeds and the credibility value of all users are stored in the reputation service repository.  
Initially, we demonstrate the evaluation of the proposed reputation algorithm for the two federated wireless testbeds with four KPIs. Three QoE KPIs are also utilized, i.e., document readability (K1), support satisfaction (K2) and operability (K3) focus on non-technical aspects, while node availability (K4) is the QoS KPI that measures the average availability of all reserved wireless nodes during an experiment. The user submits his/her rating of the reputation service, and in the following, we show the step-by-step computation of the experiment's reputation score, using the modified fuzzy VIKOR and the credibility value using Algorithm 1. In the second use case, one hundred users are assumed to have conducted two thousand experiments on the NETMODE testbed. This dataset contains a mix of random, honest and malicious ratings. This scenario demonstrates the key role of the credibility mechanism and the effect of the \(\alpha\) parameter in Step 5. Finally, our proposed solution is compared against an existing reputation-based trust framework, named FTUE [8], which is designed for the federated facilities of FEDAFIRE. As mentioned before, FTUE uses only crisp QoS and QoE KPIs  
Figure 2: HELNET reputation service.  
to infer the reputation of a testbed and the experimenter's credibility. In the following subsections, a higher score translates to better reputation for both reputation systems. Consequently, since the corresponding reputation values are obtained as a combination of the perceived users' experience and the usage of technical KPIs, we argue that a high reputation score is a strong indication that the testbed is better as a whole.
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
## 2 Materials and Methods
### Mathematical Modeling of the ISD
#### 2.3.1 Energy Balance of the Cover for the Heating Area  
Figure 4 shows the energy equilibration of a control volume in the heating area. The energy balance of the cover is given as:  
\[T_{cov}=\frac{\alpha_{cov}\cdot(1+\tau_{cov}\cdot\rho_{abs})\cdot I_{t}+h_{w} \cdot T_{amb}}{h_{w}+h_{c,cov-air}+h_{r,cov-sky}+h_{r,abs-cov}}+\frac{h_{c,cov- air}\cdot T_{air}+h_{r,cov-sky}\cdot T_{sky}+h_{r,abs-cov}}{h_{w}+h_{c,cov-air}+h_{r,cov- sky}+h_{r,abs-cov}} \tag{4}\]  
where \(T_{cov}\) is the temperature of the cover, \(T_{amb}\) is the temperature of the ambient conditions, \(T_{air}\) is the temperature of the air, \(T_{sky}\) is the temperature of the sky, \(T_{abs}\) is the temperature of the absorber, \(h_{w}\), \(h_{c,cov-air}\), \(h_{c,abs-air}\) are the convection heat transfer coefficients between the cover and the air outside of the heating area due to wind, the cover and the air inside the heating area, and the surface of the heating area and the air inside, respectively. Additionally, \(h_{r,abs-air}\) and \(h_{r,cov-air}\) are the radiative transfer coefficients between the cover and the air outside of the heating area, and the absorber and the cover, respectively. \(\alpha_{cov}\) is the absorbance of the cover, \(\tau_{cov}\) is the transmittance of the cover, \(\rho_{abs}\) is the reflectance of the absorber and \(I_{t}\) is the instantaneous solar radiation. Complementary information on the heat transfer coefficients and how to determine them are provided in Appendix A.
## 3 Results and Discussions
### Optimization of the \(InGaP/GaAs\) DjSCs with Buffer and New Top BSF Layers
#### 3.3.3 Impact of Tunnel Junction Material on the Performance of DJSCs
Typically, the source photocurrent is the highest, followed by the available photocurrent, while the cathode current is the lowest. Measuring these currents helps assess the incident photon rate (via the source photocurrent) and the photo-absorption rate (via the available photocurrent). Whereas the cathode current represents the solar cell's output when an anodic voltage is applied. These three components related to the top \(InGaP\) solar cell are shown in Figure 12.  
The upper cell, which incorporates the \(InGaP\) material with a bandgap measuring 1.9 eV, plays the main role of photon absorber in the ultraviolet and visible part of the spectrum. It is remarkable that, in the visible spectrum, this upper layer shows a notable increase in cathode current generation, as illustrated in Figure 12.  
In contrast, the lower \(GaAs\) cell, with a bandgap of 1.42 eV, is designed to capture photons with energies corresponding primarily to the near-infrared light spectrum. The cathode current resulting from this particular configuration is again shown in Figure 13. In this context, \(GaP\) and \(GaAs\) cells prove to be very efficient in absorbing photons with energies close to their bandgap. They are carefully selected based on regions where solar spectra (AM1.5G) display high photon intensity, allowing optimal use of sunlight for electrical energy generation.