esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### \({}^{1}\)HMRS Spectroscopy in Soldiers--The Comparison between the Brain Hemispheres  
A comparison of the relative concentrations of metabolites between the hemispheres for the frontal and occipital lobes, separately, was only conducted in the group of soldiers who preferred a right shooting position (n = 52). The means with standard deviations and 95% confidence intervals of relative concentrations of particular metabolites within the right and left frontal and occipital lobes of soldiers preferring the right shooting position, as well as the \(p\) values obtained for their comparison, are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Location** & **Metabolite** & \multicolumn{2}{c}{**Right Side**} & \multicolumn{2}{c}{**Left Side**} \\  & & **Mean \(\pm\) SD** & **95\% CI** & **Mean \(\pm\) SD** & **95\% CI** & \(p\) Value** \\ \hline \multirow{4}{*}{**Frontal lobes**} & NAA/Cr & 2.009 \(\pm\) 0.407 & 1.896–2.122 & 1.936 \(\pm\) 0.435 & 1.815–2.057 & 0.240 \\  & Cho/Cr & 0.926 \(\pm\) 0.151 & 0.885–0.968 & 0.950 \(\pm\) 0.201 & 0.894–1.006 & 0.514 \\  & ml/Cr & 2.446 \(\pm\) 1.277 & 2.099–2.848 & 0.845 \(\pm\) 0.251 & 0.783–0.927 & \textless{}0.001 \\  & NAA/Cr & 1.875 \(\pm\) 0.279 & 1.797–1.953 & 2.056 \(\pm\) 0.332 & 1.963–2.148 & 0.003 \\ \multirow{4}{*}{**Occipital lobes**} & Cho/Cr & 0.773 \(\pm\) 0.112 & 0.737–0.805 & 0.781 \(\pm\) 0.122 & 0.745–0.818 & 0.640 \\  & ml/Cr & 0.805 \(\pm\) 0.113 & 0.774–0.837 & 0.831 \(\pm\) 0.127 & 0.796–0.867 & 0.230 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison of the relative concentrations of metabolites in the right and left frontal and occipital lobes of soldiers preferring a right shooting position.  
Figure 1: Exemplary \({}^{1}\)HMRS spectra obtained from volumes of interest in the brains of (**A**) a healthy control and (**B**) a soldier.  
In the frontal lobes, the relative mean concentration of the mI/Cr (\(p\) < 0.001) ratio was significantly higher on the right side than on the left side--in this case the difference was particularly pronounced (2.446 \(\pm\) 1.277 on the right side, 0.845 \(\pm\) 0.251 on the left side). Furthermore, in the occipital lobes, the NAA/Cr (\(p\) = 0.003) ratio was markedly lower on the right side, in comparison to the left side. There was no statistically significant difference in the relative concentrations of the remaining metabolites, either in the frontal or occipital lobes.
# Unsupervised Image Segmentation on 2D Echocardiogram
###### Abstract  
Echocardiography is a widely used, non-invasive imaging technique for diagnosing and monitoring heart conditions. However, accurate segmentation of cardiac structures, particularly the left ventricle, remains a complex task due to the inherent variability and noise in echocardiographic images. Current supervised models have achieved state-of-the-art results but are highly dependent on large, annotated datasets, which are costly and time-consuming to obtain and depend on the quality of the annotated data. These limitations motivate the need for unsupervised methods that can generalize across different image conditions without relying on annotated data. In this study, we propose an unsupervised approach for segmenting 2D echocardiographic images. By combining customized objective functions with convolutional neural networks (CNNs), our method effectively segments cardiac structures, addressing the challenges posed by low-resolution and gray-scale images. Our approach leverages techniques traditionally used outside of medical imaging, optimizing feature extraction through CNNs in a data-driven manner and with a new and smaller network design. Another key contribution of this work is the introduction of a post-processing algorithm that refines the segmentation to isolate the left ventricle in both diastolic and systolic positions, enabling the calculation of the ejection fraction (EF). This calculation serves as a benchmark for evaluating the performance of our unsupervised method. Our results demonstrate the potential of unsupervised learning to improve echocardiographic analysis by overcoming the limitations of supervised approaches, particularly in settings where labeled data are scarce or unavailable.  
unsupervised learning; image segmentation; echocardiography; convolutional neural networks; medical imaging 2024
## 2 Related Works  
### DL-Based Channel Estimation  
Accurate channel estimation is the basis for choosing the correct choice coding scheme in AMC. In the terrestrial audio and video transmission scenario, [PERSON] et al. [25] use LSTM networks to predict the packet loss rate at the next moment and dynamically adjust the code rate of the Reed-Solomon codes [34]. Reed-Solomon codes are a type of cascade code that can recover the contents of lost packets based on the packets before and after Nevertheless, it cannot accurately recover the contents of lost packets even after the communication quality has deteriorated significantly. This inter-packet FEC can save the time needed to retransmit automatic repeat-request messages. The paper demonstrates that their proposed method can recover more packets at different packet loss rates then a fixed redundancy rate coding scheme at lower redundancy rates using two metrics: the number of successfully recovered packets and the redundancy rate.  
Wireless channels are more complicated to estimate compared to wired channels. [PERSON] et al. [26] use LSTM to predict the channel state and dynamically allocate transmitter power. This article is a non-orthogonal multiple access [35] scenario for multi-user communication, where adaptive coding is achieved by dynamically adjusting the pilot symbols for each user, assigning power to each user, and sending information from the base station. The advantage of using LSTM networks for channel estimation is that the historical information in the channel is used.  
Rain loss has a significant impact on the satellite-to-ground channel. [PERSON] et al. [36] used different coding schemes for different rainfall and different atmospheric conditions in Germany. They demonstrated through simulation that their method could serve more users than the original AMC method under weather conditions.
## 1 Introduction
The quality of the reconstructed image was not good due to the loss of recover bits according to compress the bitmaps. They employed an inpainting technique to improve the quality of the recovered image. For the tampered block, the most important thing to recover depends on how to exactly find the tampered location. In this paper, we propose a fragile watermark technique based on self-embedding using AMBTC to restore the tampered cover image. To improve the quality of the cover image, it uses Optimal Pixel Adjustment Process (OPAP) [34] to encode self-embedded data (Watermark). OPAP is introduced to optimize the error in the DH process using LSB replacement, and it is a coding method with excellent performance. In this scheme, we used checksum for authentication of every block and embeded authentication bits in every block to detect forgery. Although the checksum is a simple method, it guarantees relatively accurate performance in detecting whether or not it is a tampered block through threshold comparison. This improves the accuracy of tamper detection and localization. This scheme has several advantages: (1) high accuracy of tampered detection; (2) the quality of the recovered marked image is guaranteed by the use of high-quality compression bits generated by AMBTC; (3) the quality of marked images is guaranteed because the original image encoded with AMBTC is hidden in the cover-image using OPAP; (4) recovering bits for a block are concealed at a far distance from the current block to prepare for cropping attacks. Experimental results show that the proposed scheme allows high-quality recovery up to a modulation rate of 45%. The rest of this paper is organized as follows. Section 2 briefly introduces watermark technology. Section 3 presents a review of current and related work. Section 4 introduces the proposed self-embedding watermarking scheme. Section 5 explains the experimental results, and Section 6 provides the conclusions and future work.
## Materials and Methods
### GPS Sensor  
GPS is a satellite-based radio navigation system owned by the US government and operated by the United States Space Force. GPS technology enables the use of GPS devices as navigation and orientation tools as well as instruments used to capture traveled routes [18; 19]. It has been used in several sectors such as travel mode detection [20] physical activity detection [18], and for informing classifiers for detecting physical activities' types [18; 19; 21].  
The GPS data has composite types of parameters that are used to allocate a specific object relying on the earth. GPS data include latitude, longitude, date, time, horizontal dilution of precision, vertical dilution of precision, number of satellites, altitude, and instantaneous speed [18].  
The latitude and longitude parameters captured by the GPS allow the localization of the affected building. In order to alleviate the problem of locating victims in the vertical direction, the sensor node is labeled based on the room number along with the building's floor number indicating the deployed microcontroller.
# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
## 3 Proposed Method  
### NTN Simulator and Data Description  
The simulator was designed to simulate the communication between satellites and UE through service links. It was developed using the C-sharp Net Framework and STK 11.3 engines. The STK engine is software for satellite orbit generation and communication analysis between satellites and UEs. The satellite is an LEO satellite with an altitude of 300 km and operates at a speed of 7.56 km/s.  
The LEO satellite operated by the simulator owns three cells, and the diameter of each cell is set at 250 km; in addition, 62,908 users were placed randomly in the three cells. Due to the distinctive features of non-terrestrial networks, such as high mobility, wide coverage areas, and constrained network resources, handover procedures might be more complicated in an NTN. Additionally, the 3 GPP study on NR support for non-terrestrial networks did not yet include a dataset for handover; as a result, in this study, we developed an NTN simulator for handover to design and simulate the behavior of user equipment and satellite in non-terrestrial communication networks. As shown in Figure 3, we designed an intra-satellite handover scenario between overlapped satellite beams; the satellite and UE are connected directly through a service link. Note that, here the \(C1_{UE},C2_{UE}\), and \(C3_{UE}\) denote different clusters and each cluster contains many users.
###### Abstract
In [21], using fast-setting cement in the amount of 400 kg/m\({}^{3}\), silica fum in the amount of 40 kg/m\({}^{3}\) and steel fiber 60 mm in length in the amount of up to 50 kg/m\({}^{3}\), concrete was obtained with a compressive strength at the age of 3 days of up to 56.8 MPa. The maximum flexural strength at 28 days was 7.29 MPa. Basalt stone is used as a coarse aggregate, which has a high natural strength, in contrast to the widespread crushed stone. It should be noted that the works [18; 19; 20; 21] did not study shrinkage deformations, which can significantly affect the properties of the repair material in the pavement. The authors [22] studied fiber-reinforced concrete with CEM II/A-S 42.5 cement in the amount of 360 kg/m\({}^{3}\) and silica fum in the amount of 40 kg/m\({}^{3}\). The amount of steel anchor fiber with fiber lengths of 16 and 30 mm was varied in the range of 0 to 50 kg/m\({}^{3}\). Steel-fiber-reinforced concretes with a strength of 90-100 MPa were obtained, however, there is no information on the effect of the composition on the early flexural strength. At the same time, it was found that an increase in the amount of fiber and its length have a positive effect on a decrease in shrinkage at different times of hardening. The positive effect of steel anchor fiber on the reduction of shrinkage deformations was also established in [23], but it is important to note that the studies were carried out with only one fiber dosage of 20 kg/m\({}^{3}\). A large amount of experimental data [24; 25] confirms the importance of determining the threshold for the effectiveness of dispersed reinforcement and modifiers, the excess of the rational amount of which already negatively affects the physical and mechanical properties of fiber-reinforced concrete and their durability. For example, [26] shows the positive effect of steel fiber, the amount of which varied from 0-62 kg/m\({}^{3}\), on the frost resistance of concrete. Thus, fiber-reinforced concretes with steel fibers and modifiers have been widely studied. However, complex studies of the properties of fiber-reinforced concrete for the rigid road and airfield pavements repair, in particular when using full depth repair technology. There is also not enough research on the combined effect of anchor steel fiber and hardening accelerators on concrete properties for the rigid pavements repair. For such materials, an important indicator is early flexural strength, frost resistance, abrasion and shrinkage. Such is a motivation for the methodology presented in the paper.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 5 Experimental Results and Discussion
### Emitter Identification Accuracy
The validity of the proposed algorithm was verified again. At low SNR, the accuracy of baseline 1 decreases dramatically, whereas the other algorithms maintain their accuracies. Baseline 2, baseline 3, and the proposed method work well when applied to the SS signal, even at low SNRs. However, the proposed method outperforms the baselines, and the ensemble approaches outperform the other algorithms at all SNRs. These findings imply that a deep learning-based classifier at baselines 2 and 3 can learn the differences in the SFs for RF fingerprinting, but our proposed algorithm (i.e., using the spectrogram and DIN classifier) with the ensemble approach is more effective than the baselines. The confusion matrix of the ensemble approach based on the proposed method is presented in Table 4. The confusion matrix is a specific metric for a classifier that can represent the relationship of each emitter. This matrix can be obtained by simply counting the results of the test samples with their true label information. The rows of the matrix indicate the true emitter IDs, and the columns indicate the predicted emitter IDs. The diagonal terms in the confusion matrix represent the correct classification result cases, and the off-diagonal terms represent the incorrect classification result cases. Thus, Table 4 shows that our ensemble approach based on the proposed method can identify the FH emitters with more than 94.6% accuracy without confusion between emitters.
## 3 Results and Discussion
### Dynamics and Source of Fungi during SSF of PFPT  
In raw material, the dominant fungi were classified as no_rank_Fungi (65.39%). When the water content of tea leaves was increased, the fungi belonging to _Aspergillus_ were multiplied to dominate throughout the SSF process. They accounted for 94.08%, 74.79%, 79.27%, 93.76%, and 95.79% of the total fungal community at day 7, 14, 21, 28, and 35, respectively. Additionally, fungi belonging to _Rluizomucor_ multiplied and accounted for 3.73% at day 7, and increased to 24.54% and 20.63% at day 14 and 21, respectively, and then decreased to 3.07% at day 35 (Figure 5a). The differences in the dominant fungi between the raw material and fermented samples suggested a large shift in the fungal communities, which was corroborated by the separation of fungal communities in the raw material (day 0) from other fermenting samples (day 7, 14, 21, 28, and 35) in the PCA analysis (Figure 5b). The similarity in the dominant fungi in each fermented tea leaf sample suggests that fungal communities did not change significantly during the SSF of PFPT, and this was corroborated by the groupings of the fermented samples (day 7, 14, 21, 28, and 35) in the PCA analysis (Figure 5b). Fungi belonging to _Aspergillus_ were identified as dominant throughout the SSF process of PFPT, and this corroborated previous reports that the major fungi involved in the SSF of PFPT were _A. niger_ and _B. adeninivorans_[8], or _A. niger_, _S. cerevisiae_, and _P. glabrum_[11], and several _Aspergillus_ spp. [1]. Comparisons of major fungi (>1%) at the genus level showed that they came from the raw material, suggesting that the raw materials were responsible for the active fungi. To our knowledge, this is the first work to survey the sources of the bacteria and fungi in the SSF of PFPT.  
Figure 4: Comparisons of major bacteria (>1%) at the genus level between day 0 and 7 (**a**); day 7 and 14 (**b**); day 14 and 21 (**c**); day 21 and 28 (**d**); day 28 and 35 (**e**); and day 0 and 35 (**f**) during the SSF of PFPT.
## 3 Analysis of Factors Affecting Water Inflow
### Permeability Coefficient  
Applying the single-factor variable method, the permeability coefficient of the surrounding rock exhibits a range of 0.01 m/d to 0.15 m/d, and the remaining influencing factors are held constant. Figure 4 illustrates the correlation between the permeability coefficient and the tunnel's water inflow. Whether it is a single-hole tunnel or a main tunnel with three parallel holes, the water inflow exhibits a linear increase in response to the rising permeability coefficient. The water inflow in the main tunnel under the three-hole parallel working condition is comparatively lower than that in the single-hole tunnel under the same conditions. Additionally, the rate at which water inflow increases with changes in the permeability coefficient is also relatively lower in the main tunnel.
###### Abstract  
Efficient and seamless railway operations depend on the systematic and well-coordinated maintenance of both rolling stock and infrastructure. However, track maintenance, or 'trackwork', can cause substantial delays if not properly aligned with train schedules. This study comprehensively investigates how trackwork influences train operations in Sweden. It involves an in-depth analysis of an extensive dataset comprising over 225,000 recorded instances of planned trackwork and approximately 32.5 million train passages throughout the year 2017. Multiple logistic and negative binomial regression models showed that train running time delay occurrence is higher in the sections with scheduled trackwork. Trains passing through trackwork are 1.43 times more likely to experience delays compared to trains that do not pass through scheduled trackwork. The likelihood of an opportunity for the train delay recovery passing the section with scheduled trackwork is reduced by 11%. Additionally, the frequency of train delay increase is 16% higher, and delayed recovery is 4% lower in relation to trackwork. With the number of trackwork set to increase over the coming years, these results bring attention to train scheduling and the performance of trackwork.  
railway infrastructure maintenance; train delays analysis; trackwork scheduling; Swedish railway system; regression analysis +
Footnote †: journal:  
There is a growing body of research focusing on identifying the factors behind train delays, with notable contributions from various studies [3; 5; 15; 16; 17; 18]. Among the methodologies employed, statistical regression models, machine learning, neural networks, and hybrid approaches are frequently used to analyse and predict railway operation delays [18]. Research in this domain utilises a diverse array of predictors for train delays, including train and infrastructure faults, maintenance activities (such as servicing or trackwork), speed limitations, human errors, network control actions, weather conditions, and other causative factors [10; 16; 18; 19; 20; 21]. Delay prediction models often incorporate parameters like the distance a train covers, its length, running time margins, the railway capacity, and passenger volumes [5]. Furthermore, delays are also linked to railway infrastructure failures, inadequate maintenance, and imposed speed reductions [22; 23; 24; 25; 26]. Previous research indicates that factors such as ongoing construction, maintenance, speed restrictions, and the state of the infrastructure can contribute to reduced train punctuality [5; 10; 15]. Despite these insights, the specific impact of trackwork on train delays remains under-explored. This study endeavours to fill this knowledge gap, aiming to provide a detailed understanding of how trackwork specifically affects train delays.
# Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm
## 5 Conclusions  
For the solution of high-dimensional complex optimization problems, many improved DE algorithms still have many deficiencies in solution accuracy, speed, and so on. For this reason, this paper uses the advantages of the standard differential evolution (DE) mutation strategy to improve and combine mutation strategies based on chaotic mapping theory. The control parameters are adaptively weighted, and Gaussian perturbation strategies are adopted in the later iterations to prevent prematureness and jumping out of local optima, thus constituting an adaptive dynamic disturbance strategy differential evolution algorithm (ADDSDE). In order to verify the feasibility, effectiveness and optimization of the algorithm, five standard tests were selected for performance testing and compared with the DE algorithm, the SADE algorithm and the CAPSO algorithm. Finally, the simulation results show that when the algorithm is applied to high-dimensional and complex optimization problems, it can still quickly converge to the theoretical optimal value. It has a strong ability of global exploration and jumping out of the local optimal. Besides, the algorithm is stable and has a certain reference value and promotion value, which provide assistance in solving high-dimensionaland complex problems in engineering and information science.  
Figure 1: Convergence curve of each algorithm: (**a**) Function f1 = Sphere when D = 30; (**b**) Function f2 = Rosenbrock when D = 30; (**c**) Function f3 = Rastrigin when D = 30; (**d**) Function f4 = Griewank when D = 30; (**e**) Function f5 = Ackley when D = 30; (**f**) Function f1 = Sphere when D = 50; (**g**) Function f2 = Rosenbrock when D = 50; (**h**) Function f3 = Rastrigin when D = 50; (**i**) Function f4 = Griewank when D = 50; and (**j**) Function f5 = Ackley when D = 50.  
**Author Contributions:** [PERSON] designed the main idea, the chaotic mapping strategy and the adaptive adjustment strategies of the manuscript. [PERSON] designed the weighted dynamic mutation Strategy and disturbance mutation strategy of the manuscript. [PERSON] implemented the algorithm and analyzed the experimental results. [PERSON] revised the manuscript and perfected the language of the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was fundedby the National Social Science Foundation of China (No. 15 CGL001).  
**Conflicts of Interest:** The authors declare no conflict of interest.