esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Experimental Procedure
### Acoustic Emission Recording  
The AE monitoring is conducted by means of multiple sensors. Acoustic Emission is recorded using two or four sensors (\(\upmu\)80 or PicoHF). Each sensor is connected to a preamplifier (gain 40 dB, type 20 H) and AE signals are recorded by a PCI-2 acquisition system (Physical Acoustics Corporation, Princeton, USA). Each AE signal waveform is digitized and recorded. The acquisition threshold is set to 35 dB or 45 dB and the acquisition parameters are equal to 25 \(\upmu\)s, 50 \(\upmu\)s and 1000 \(\upmu\)s for the peak definition time (PDT), the hit definition time (HDT)and the hit lockout time (HLT).  
Depending on the type of test to be performed and the configuration of the test bench, it is not always possible to position the sensors in the same place. Instrumented tensile tests using four similar sensors are conducted on ceramic matrix composites, at room temperature. For the CMC composites, four sensors \(\upmu\)80 are applied on the same side (Figure 1a) in order to examine the influence of the sensor position on AE signals. A first pair of sensors noted \(\upmu\)80 P1-P2 is positioned inside the grips, separated by a distance of 190 mm (Figure 1a). A second pair of sensors noted \(\upmu\)80 P3-P4, separated by a distance of 80 mm, is placed at the edge of the useful zone. For the static fatigue tests, two resonant \(\upmu\)80 sensors are fixed on the specimen inside the grips, directly on the specimens 190 mm apart (\(\upmu\)80 P1-P2). Another type of setup can be used in specific configurations for testing at higher temperatures or in hostile environments. It consists of using waveguides between the specimen and the sensors. In the same way as before, an instrumented test with 4 sensors makes it possible to compare the results. The 2 sensors, \(\upmu\)80 P3-P4, positioned near the useful area are placed directly on the sample surface, the other two sensors noted \(\upmu\)80 P1-P2 are fixed on waveguides (Figure 1b).  
In order to investigate the effects of the sensors, tensile tests are conducted on organic composites (OMC) with two kinds of sensors (\(\upmu\)80 sensors and PicoHF sensors) located at the same position on each face of the specimen 200 mm apart, denoted \(\upmu\)80 P3-P4 and picoHF P3-P4. These two sensors display a good sensitivity in different frequency range, 200 to 900 kHz for \(\upmu\)80 sensor and 500-1850 kHz for Pico HF sensor (Physical Acoustics data, Princeton, USA). Thus, using them both for tensile testing is interesting for investigating the effect of the sensor on AE descriptors. In all cases, medium viscosity vacuum grease is used as coupling agent.  
The AE wave velocities are measured before the tests by calculating the difference in time of arrival on each sensor of several pencil lead breaks, generated at well-known positions. The velocity is found equal to 10,000 m/s for the CMC composite (threshold is equal to 45 dB). The average wave speed is evaluated to 4020 m/s in PA6.6 composite (threshold = 35 dB) and to 3500 m/s in Vinylester composite (threshold is equal to 45 dB).
## 5 Summary and Conclusions  
The existence of water in the active layer is a crucial factor in the process of heat transfer in global climate change and in geotechnical problems. In this study, the variation of water content in the active layer was characterized by the application of an advanced device as part of an in situ testing method.  
Penetration-type TDR (PTDR), which can determine the dielectric constant of a material from the measurement of the travel time of electromagnetic waves, was developed for estimating the water content along the depth in the active layer. The PTDR comprised a PTDR module, connecting rods, and a guide with a hammer. The PTDR module, comprising three electrodes and a thermocouple, was designed as a tapered rectangle to improve the contact between the electrodes and soil. A guided hammer was mounted on the top of the PTDR for penetration into the soil. The co-axial cable soldered into the electrodes was connected to the TDR unit for detecting the reflection signals in the time domain.  
Using the soils sampled at Ny-Alesund, the dielectric constants measured from PTDR were calibrated with the volumetric water content. For estimation of the volumetric water content from the dielectric constant measured by the PTDR, a relationship was established from the specimens prepared with different water contents. It was also found that temperature had a minor effect on the travel time determined in the time domain within a range of the measured subsurface temperature of the active layer.  
Field application tests using PTDR were performed at three different positions of the Ny-Alesund site. As the PTDR penetrated the ground, electromagnetic waves and temperature were measured at each depth. The test results of the PTDR showed that the volumetric water content at the three different positions generally increased with an increase in depth. To compare with the water content profiles estimated from PTDR, an electrical resistivity survey was carried out at the same site. Although the variation in resistivity profiles depended on the position, the resistivity values decreased with an increase in depth. The relationship between the water content and resistivity was obtained from regression analysis, and the values of the coefficient of determination and standard deviation showed a consistency among the PTDR results. With its good mobility, PTDR can be effectively used to evaluate the water content distribution in the active layer.  
[PERSON], [PERSON], and [PERSON] designed the PTDR; [PERSON], [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2017R1A2B3008466).  
The authors declare no conflict of interest.
## 2 System Design
### Signal Model of Two Links  
The signal of the I branches in two links can be described as three parts - transmission data, PN code, and carrier [17]. The carrier frequencies of the two links are on the S-band and C-band. The transmitting data frames in the uplink are modulated by the PN code in both branches, with a PN code rate of 10.23 MHz and a PN code length of 10,230 chips. The transmitting data frames in I the branch of downlink are modulated similar to those of the uplink, with a PN code rate of 20.46 MHz and a PN code length of 20,460 chips. Thus, the symbol period can be calculated as 1 ms.  
Hence, we set the data frame length of I branches of two links are 1000 bits, and each prime frame is divided into five subframes with a length of 200 bits. So when the transmission data frame period of I branch is 1 s, the lasting time of each subframe is 0.2 s. The data game structure in our system is similar to the CCSDS proximity-1 Version 3 transmission structure. One transmission frame consists of the synchronization code, frame guide, and data area. Furthermore, the front part of data area can be defined as a measuring duty section containing the information about the receiving satellite (opposite satellite) and the remainder of the data area is used to save the transmitting satellite information (local satellite) called a data segment. Figure 3 shows the detailed data structure of each subframe.  
Figure 3: Inter-satellite transmission subframe structure.  
Figure 2: System model of simultaneous ranging and communication in Master/Slave mode.  
The local pseudo-range (LPR) is acquired when the local transmission is synchronized. Then, the dual-way measurement between two satellites is attained by embedding LPR into the duty segment continuously. More details about data transmission are presented in Table 1.  
The above subframe structures are employed in both the MS and SS, and data frames are sent periodically between the two. After spreading, the mixture signal is modulated to the carrier, and the Q branch in the downlink is adopted by the non-spreading spectrum BPSK.
## 3 Results and Discussion
### Experiments on the Common Benchmark
#### 3.2.2 Evaluation on the TC-128 Dataset  
Unlike OTB, the TC-128 dataset consists of 128 color video sequences which offer more intricate and demanding tracking tasks. To demonstrate the versatility of our algorithm, we ran further tests on the TC-128 dataset. We conducted quantitative comparisons between our trackers and several state-of-the-art trackers, including SiamFC [36], SiamMASK [40], SiamFC++ [41], SiamRPN++ [42], SiamGAT [43], Ocean [46], HCFT [47], and RMIT [48]. The tracking results are illustrated in Table 2. Our approach ranks first in success and precision rate, with a precision score of 77.9% and a success rate AUC score of 58%, respectively. In comparison with the baseline method SiamMask, we have a success rate of 4% and a precision rate of 5.4% higher, respectively.  
Our tracker outperforms the memory tracker RMIT in both success rate and precision. While RMIT also utilizes target appearance information and leverages memory content about the target through a memory residual branch, our approach differs in terms of the content stored in the memory. Instead, we store mask information relevant to the historical frames to capture the robust target representation. As a result, both our algorithm and RMIT demonstrate superior performance compared to other trackers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tracker** & **Success Rate** & **Precision** \\ \hline Ours & 0.580 & 0.779 \\ SiamRPN++ & 0.577 & 0.775 \\ SiamFC++ & 0.566 & 0.763 \\ SiamGAT & 0.559 & 0.753 \\ Ocean & 0.557 & 0.752 \\ RMIT & 0.551 & 0.761 \\ SiamMASK & 0.540 & 0.725 \\ HCFT & 0.495 & 0.692 \\ SiamFC & 0.489 & 0.672 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of experimental results on the TC-128 dataset.  
Figure 4: Success and precision plots on OTB100.  
Figure 5: Success and precision plots of four attributes challenges from OTB100.
# Detecting the Absence of Lung Sliding in Lung Ultrasound Using Deep Learning
## 2 Materials and Methods
### Automated M-Mode Classification
#### 2.2.3 M-Mode Classification  
The M-mode image is a 2D slice across the temporal dimension in the ultrasound video, as shown in Figure 3. For the classification of this type of image, we used ResNet 18 [17]. Input dimensions were \(3\times H\times N\), where \(H\) is the image height, \(N\) is the number of frames, and the number 3 is the number of channels. The greyscale input images were stacked three times to simulate three-channel RGB images. The model is 18 layers deep. First, the input is passed to the convolutional layer, batch normalisation, and ReLU activation, followed by the max-pooling layer. Second, these features were forwarded to a series of residual blocks. Features from the last residual block were flattened by adaptive average pooling. Average pooling features were fully connected to the network output. Finally, the binary prediction was made using a softmax layer. The loss was calculated as a weighted cross-entropy, where the weights represent the class distribution.  
All the presented models were pre-trained on the ImageNet database and then fine-tuned for a maximum of 15 epochs, with early stopping when the validation loss did not improve in five consecutive epochs. We used the Adam optimisation algorithm with the learning rate set to 0.001. The dataset was split into four stratified folds with patient-wise split. We always used three out of four folds for the training and validation of the neural network. The training and validation datasets follow the patient-wise split, and the ratio between them is 1.5:1. To ensure validity of our methodology, we present the results from the fourth fold, which was not used in training or validation.  
For data augmentation, we used random erasing [18] and random affine transformations (rotation, translation, and scaling), see Figure 5. The probability of the transforms was set equally to 0.5. Rotation ranged between \([-10,10]\) degrees and horizontal translation between \([-0.1,0.1]\) of the image height; vertical translation ranged between \([-0.1,0.1]\) of the image width, and size scales ranged from 0.9 to 1.1 of the original size. Random erasing removed rectangular areas (pixels values set to 0) between \([0.05,0.33]\) of the image area.  
Figure 4: Centroid shifts calculated for the pleura, lung, and rib. Upper axis: The histogram represents number of correct and incorrect predictions of classifiers based on the distance from the centroids (Hit: Correct classification; Miss: Incorrect classification). The yellow vertical line depicts the range of a possible shift for the centroid. Lower axis: Probability of correct classification based on the distance from the centroids within selected range. The yellow line represents the resulting shift for the tissue.
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 4 Experiments and Evaluation
### Evaluation Metrics
#### 4.2.2 Robustness  
Environmental variations, such as weather, illumination and water condition, often interfere with the effect of waterline detection. For instance, in some scenarios, waterline detection obtains ideal recall, whereas its precision demonstrates the opposite. The reason is that many pixels irrelevant to the waterline have been mistakenly detected as the ground truth. Thus, we need to test the robustness of waterline detection against environmental noises so that the capacity of resisting environmental disturbances to a certain waterline detection approach can be better analyzed. To this end, we define _FP_-irrelevance metrics to quantify the robustness of waterline detection against environmental noises in an estimated image-map, wherein _FP_ counts the number of all false positives, i.e., how many irrelevant pixels caused by noises are selected in an image-map, and irrelevance measures the overall deviation trend of pixel-level distances between those irrelevant pixels and the ground truth, which actually characterizes the statistical distribution on the distances of irrelevant pixels related to the ground truth in an estimated image-map. Formally, _FP_ and irrelevance are defined as follows:  
\[FP=\text{Card.of}\left\{e_{i}|\min\left\{\forall_{j}dist\big{(}e_{i},a_{j} \big{)}\right\}>\lambda,\text{and}\;i,j\in N\right\} \tag{5}\]  
\[irrelevance=SK\text{ of }\left\{d_{i}|d_{i}=\min\left\{\forall_{j}\text{ dist}\big{(}e_{i},a_{j}\big{)}\right\}>\lambda,\text{and}\;i,j\in N\right\} \tag{6}\]  
where _min_{*} denotes the minimum of all elements in a finite set *}, \(SK\) refers to the asymmetry coefficient of the skewness distribution on the pixel-level distance between the wrongly estimated waterline and the ground truth, and the set \(\{e_{i}\}\) in Equation (5) actually represents a finite set consisting of all irrelevant pixels in an estimated image-map. Moreover, \(e_{i}a_{i}\), \(\lambda\), and \(\text{dist}(e_{i},a_{j})\) are similar to the ones in Equations (3) and (4).  
As far as an evaluated waterline detection approach is concerned, in the case of the same _FP_, if the distance distribution presents positive skewness and higher irrelevance is obtained, we consider those wrongly estimated pixels to be more convergent to ground truth, and further its robustness is deemed to be better. In other words, in this case, the evaluated approach enables the impact from environmental disturbances on waterline detection effect to be shrunk as far as possible into the area around ground truth, where its estimation error gets smaller. Correspondingly, its capability to withstand environmental noises manifests more robust.
## 3 Adaptive Data Fusion Method Based on ADCNN for Fault Diagnosis
### Fault Classification  
The fault classification displays the results of the prediction and diagnosis of rotating machinery. The experimental case in this paper predicts the fault classification of the dental cutter. ADCNN outputs the malfunction probability distribution of the four classifications through Softmax. There are four classification results: (1) normal, (2) breaking, (3) wear, and (4) tipping.
# Joint Transmit Waveform and Receive Mismatched Filter Design to Suppress Range Sidelobe
## 5 Conclusions  
In this letter, a novel NLFM-CPM radar-communication integrated waveform was designed, enabling flexible modulation of communication signals while achieving radar detection, thus meeting both communication and detection requirements. The time-domain echo signals were discretized, and the received data were rearranged to design filter coefficients based on a weighted time-domain correlation matrix. For the minimum ISL criterion, the waveform and filter were jointly optimized, with constraints on the mainlobe width and pulse compression levels. To solve the SOCP problem, the ADMM algorithm was employed, transforming the SOCP problem into a k-iteration alternating problem by introducing Lagrange multipliers. Through iterative updates of the transmitted signal and filter, an optimal solution for minimizing ISL was obtained. During the iterations, a  
Figure 10: Comparison of the recognition capability of conventional matched filters and EWMF filters for weak targets under different waveform conditions.  
reference sidelobe template based on environmental design was introduced, guiding the filter coefficients to converge towards the reference template and achieving the minimum sidelobe level for the EWMF method. The LPG caused by mismatched filtering was analyzed, and its lower bound was derived using the Young inequality. Finally, the algorithm's complexity was analyzed, and its performance, along with the impact of communication modulation parameters, was validated through experimental simulation data. The results demonstrated that the EWMF algorithm effectively reduced NRSI and improved SINR for weak targets. In our forthcoming research, since it is also necessary to balance the main target signal gain and sidelobe suppression of ambiguous signals in multi-target detection, we plan to incorporate range ambiguity factors into the EWMF design of the integrated waveform.  
Conceptualization, H.W. and H.T.; methodology, H.W.; software, H.W.; validation, H.W., H.T. and T.Z.; formal analysis, H.W.; investigation, H.W.; resources, H.W.; data curation, H.W.; writing--original draft preparation, H.W.; writing--review and editing, H.W., H.T., T.Z. and W.L.; visualization, H.W.; supervision, H.T.; project administration, H.T.; funding acquisition, H.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by National Key Laboratory Foundation under grant number 6142411332103 and 6142411332105, in part by Shanghai Aerospace Science and technology Innovation Foundation under grant number SAST2021, in part by National Natural Science Foundation of China under grant number 62301404, and in part by Basic Research Program of Shannxi Province under grant number 202203021212204.  
The data generated or analyzed during this study are not publicly available due to privacy restrictions. Requests for access to data can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Plant-Derived Bioactive Compounds for Rhabdomyosarcoma Therapy In Vitro: A Systematic Review
## 3 Results
### Plant Extracts and Isolated Bioactive Compounds That Induce Cell Death In Vitro through Apoptosis
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Raw Material** & **Extraction Method** & **Isolated Compound(s)** & **Cell Line** & \multicolumn{2}{c}{**ICu Value of the Functional**} & \multicolumn{1}{c}{**Mechanism of Action**} \\  & & & & & **Related Compounds** & \\ \hline Essential old from the serial parts & & & BD & \(\text{DJ73.05}\pm 4.46\) g/ml. & Multiple  
The ethanolic extract of the leaves of _Ficus carica_, in combination with chemotherapy (doxorubicin hydrochloride) and photodynamic therapy, obtained an IC\({}_{50}\) value of 0.15 \(\upmu\)g/mL in RD, so the association of these three therapeutic options in vitro was better than monotherapy with the chemotherapeutic agents [19]. The same results were obtained with the ethanolic extract of rhizomes from _Rheum ribes_ in combination with the Newcastle Disease Virus. The result was better than the use of the extract alone, obtaining an IC\({}_{50}\) value of 0.00978 mg/mL. Cell death was produced by the activation of caspases 3 and 9 and by the expression of the virus surface antigen in the tumor cell line RD [22]. Turmeric is one of the most researched plants for its possible antitumor properties [57]. It has also been tested in RMS, both in the alveolar type (RH30 cell line) and in the embryonic type (RD cell line). Its extract was obtained using supercritical CO\({}_{2}\). The IC\({}_{50}\) value of this extract is between 7133 and 7501 \(\upmu\)g/mL, respectively; therefore, it was more effective in alveolar RMS. However, with the combination of Cyclophosphamide and Vinblastine, the IC\({}_{50}\) value was between 0.004 and 0.045 \(\upmu\)g/mL, making it more effective in the embryonic RMS cell line. Turmeric acts by inducing apoptosis and regulating the expression of the intrinsic pathway of genes such as Bcl-2, BAX, BAK, and p53. In addition, it has an anti-inflammatory role by inhibiting the expression of genes associated with inflammation, such as NF-kB [26]. Another extract that produces tumor cell death by regulating the intrinsic pathway of apoptosis is _Crocus sativus_, commonly called \"saffron\". A methanolic extraction of these compounds was tested in two cell lines: one from RMS (TE-671) and another from glioblastoma (A-172). The isolated compounds were crocins (a type of carotenoid) and a crocin ester derivative (DMCRT). The result was statistically significant for treated versus untreated cells. The IC\({}_{50}\) value of both bioactive components was similar in the TE-671 cell line using crocins (1.02 mg/mL) and DMCRT (1.27 mg/mL). However, the IC\({}_{50}\) of the crocin extract in the A-172 cell line was higher (1.72 mg/mL) [33].
## 3 Overall Design Architecture
### Real-Time and Reliable Transport Layer Protocol for Wysn  
For transport protocol of WMSN reliability, low delay and real-time services are the aspects involved. The proposed protocol in transport layer is aimed to meet the expectation of the concerned aspects by using information which is provided by upper layer. Transport layer protocol forwards data packets without using retransmission techniques while it takes account of reliability. In addition, this protocol provides inter-layer command messages that are used in other layers to achieve real-time transmission. Section 5 explains the proposed transport layer protocol in detail.