esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Electromagnetic Seismic Precursors from Swarm Data by Enhanced Martingale Analytics
## 5 Discussion  
The quantitative experiments reveal a notable performance enhancement of our novel approach over the original Martingale framework, especially when considering the r-rank metric. This signifies that our method effectively identifies anomalies of higher importance, based on their rank, without the necessity of pre-defining a threshold. This flexibility stands as a significant advantage over the original framework. Moreover, these results instill confidence in our methodology, demonstrating its ability to enhance sensitivity to complex temporal relationships without relying on a dynamic threshold.  
In our qualitative assessment, we apply both our novel approach and the original Martingale framework to real-world earthquake case studies in Mexico, Greece, and Croatia. Overall, our framework tends to detect more anomalies across all case studies compared to the original framework, particularly when considering data from the Beta satellites, which were excluded from the graphical representation, on which our method does not perform as well.  
However, both methods show inadequacies when compared against manually identified anomalies from ground truth data as evident with Table 3, though the removal of the  
Figure 15: Comparison of Martingale and \(z\)-Martingale values within the \(1000\times 800\) km grid for the Greece case study. The color coding is Alpha (red), Beta (blue), and Charlie (green). **(a)** Martingale value data plotted within the \(1000\times 800\) km grid obtained using the original Swarm satellite data alongside Equation (3). It is observable that this approach detects fewer anomalies compared to the \(z\)-Martingale (**b**) method. **(b)**\(z\)-Martingale value data plotted within the \(1000\times 800\) km grid obtained using the original Swarm satellite data alongside Equation (7). It is observable that the \(z\)-Martingale scores produce more anomaly detections than the regular Martingale method (**a**).  
fixed thresholding is a clear advantage toward \(z\)-Martingale. This raises questions about the reliability and robustness of the Martingale and \(z\)-Martingale methods, suggesting a need for substantial improvement or a shift toward more dependable methodologies. It is interesting that the \(z\)-Martingale is visibly more robust in the detection of anomalies as well as emphasizing/retaining waveform sensitivity.  
Despite the quantitative evidence indicating the capability of our approach in identifying stronger anomalies, it raises concerns when considered alongside the claim that our method is adept at detecting subtle changes. The discrepancy between detecting subtle anomalies statistically while missing obvious ones prompts reflection on the reliability of the hypothesis test method. Furthermore, the ambiguity surrounding the interpretation of these statistical anomalies as false positives underscores the need for a deeper understanding of the physical implications of the detected anomalies.
## 1 Introduction
An algorithm with large blocks and key sizes is not the best option for mobile encryption applications. Due to the above justifications, we propose a new algorithm called LAO-3D, which was developed using an enhanced 3D cipher design to balance its efficiency and security strength. Three contributions can be highlighted in this research. Firstly, the proposed algorithm does not require increasing the block and key sizes of the cipher, which solved the scalability issue highlighted in the previous 3D algorithms. Secondly, the new algorithm provides a better bit permutation for the algorithm. Thirdly, the analyses conducted on the algorithm prove its security strength and performance, which meets the criteria of a lightweight algorithm that require sufficient security strength and is efficient for mobile encryption application. The research presented in this paper is divided into five major sections. Section 1 introduces the scope of this research. Section 2 specifies the 3D cipher design architecture. Section 3 details the proposed LAO-3D block cipher. Section 4 performs the security analysis, performance test, and implementation of the proposed algorithm in a mobile encryption application. Section 5 concludes the overall research work.
## 1 Introduction
A damage index (DI) is based on the output voltages obtained through sensors which are passed through the band pass filter to ensure that they only contain the first natural frequency of the system. Guided wave-based algorithms instead are based on the principle [19; 20; 21; 22; 23; 24] that the propagation of guided waves between the actuator and sensor locations is affected by the presence of damages. Measurements acquisitions consists of broadband signals due to ultrasonic waves propagating in the structure. The damage index method provides information on which sensing paths cross the damage area. All these SHM techniques are able to detect position and extension of damage, by using a reference baseline signal. Nevertheless, it is a common vision that damage detection methods with unknown input force or unknown healthy structural response, are more promising for real engineering applications scenarios, but are usually difficult to be measured. Thus, the identification of a system that works only with the current readout responses is a challenging and (still) open issue [25]. Some interesting approaches involve distributed sensors networks bonded or even embedded into the structural component. In this case, dealing with hundreds or even thousands of sensing points, statistical-based data processing is still demonstrated to be effective and robust [3; 13; 26]. Within this frame, an SHM methodology is presented for BVID damage detection of composite stiffened panels representative for aeronautical applications. The authors focused on a specific feature selection approach. The paper presents a cross-correlation function-based method using distributed responses under backscattering random noise and impulse excitations. Algorithms based on this specific signal filtering method are already proposed and demonstrated to be effective for discrete array of sensors or for dynamic applications [27; 28]. In this study, the cross correlation (X-Cor) functions are applied to spatially shifted distributed strain readout vectors. The cross-correlation function is used to highlight minor changes due to structural failure from backscattering noise with a general improvement of the signal quality. The proposed methodology is applied to investigate the debonding line extension caused by low/medium (45 J) energy impact on 24-ply carbon fiber reinforced plastic (CFRP) stiffened panels. A Rayleigh backscattering technology, providing 5 mm of spatial resolution, is used to identify the strain output vector along a surface bonded fiber optic. The results show a good coherence with respect to the ultrasonic C-scan NDI controls.
# Development and Evaluation of a LiFi-Transceiver Module for TMTC Intra-Satellite Communication +
## 6 Discussion  
We observed the strongest reduction in the maximum eff. data rate with no coding or Hamming code in the worst case in relation to the best case, as compared to the Reed-Solomon code, which may be due to the enhanced correction capabilities. It is also worth mentioning, that by using the _Reed-Solomon code_, in every case where \(BER>0\), it was recognized that the message was faulty. This emphasizes the increase in reliability by using the Reed-Solomon code in relation to the other two configurations. However, the max. eff. data rate without coding with \(BER=0\) is still higher than with coding. This may result from the overhead and thus smaller eff. rate. Nevertheless, when using the faster transmission rate without coding, one could not be sure that errors would not happen.  
The fact that the achievable transmission rate decreases at shorter distances may be the result of saturation due to the higher light signal and thus overloading of the operational amplifier. Reflections could also have a negative influence on the transmission rate. This can be improved by reducing the gain for shorter distances and adjusting it to the specific setup requirements where satellites are an ideal environment due to the static setup.
## 3 Results and Discussion
### Monthly Energy Performance
As an example, the GCHP \(COP_{n}\) in January is 27% higher than that achieved by the ASHP in the same period, while in April this percentage falls to 6%. Another interesting point revealed by Figure 7 is that in April and May the whole air-source-based system works slightly better than the ground-source one, with a COP and an EER value 2.1% and 0.7% higher, respectively.
# Noise Attenuation Based on Wave Vector Characteristics
## 4 Application Results and Discussion
If the absorption attenuation is serious, the apparent period of the valid signal will increase with time. Then, at different times, we can extract several valid signals to estimate time-variant moving window sizes. In addition, the moving time window for the calculation of ground-roll vectors often results in shallow refractions and wide-angle reflections for strong vector amplitudes. Therefore, it is necessary to set a filtering area for the input data when suppressing ground roll. The other moving-time-window size should be calculated based on the high-cutoff frequency of the valid signal to suppress random noise. Similarly, we can determine the corresponding time-variant moving window sizes based on the valid signals extracted at different times. In addition, a small moving time window for random noise attenuation can also lead to large vector amplitudes of shallow refractions and wide-angle reflections. The field application shows that some shallow refractions and wide-angle reflections are filtered, especially on the \(Z\)-component. Nevertheless, this does not affect the applicability of our noise attenuation method. Near the start and end times of a 3C dataset, the filtering effect will be worse owing to the insufficient size of the moving time window. In addition, a similar phenomenon will occur near the start trace and end trace within a receiving line. To make up for this deficiency, we can extend the border of the filtering window used for suppressing ground roll wider. Finally, our noise attenuation method is applicable if the non-ground-roll related coherent noise covers continuous multiple seismic traces, because a large moving trace window is required, which will also greatly damage the valid signal. The proposed method is applicable for the noise attenuation of the land two- or three-component seismic data and the three-component ocean bottom seismic data. To the borehole multi-component seismic data, the suppression of interference waves, such as tube waves and multiples, is one of the main problems. Thus, noise attenuation based on the wave vector characteristics for the borehole multi-component seismic data will require further detailed study.
# A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes
## 3 Non-Destructive Detection Method for Steel Wire Ropes
### Acoustic Emission Detection Method
In this work, the fatigue test of composite cable was conducted, the fractal dimension of AE signal was analyzed, the damage index according to the fractal dimension was established.
## 2 Materials and Methods
### Elemental Analysis  
ICP-MS analyzed twenty-three elements in lichen samples: calcium (Ca), iron (Fe), (magnesium (Mg), manganese (Mn), zinc (Zn), aluminum (Al), silver (Ag), barium (Ba), cobalt (Co), chromium (Cr), copper (Cu), lithium (Li), nickel (Ni), thallium (Tl), vanadium (V), molybdenum (Mo), palladium (Pa), platinum (Pt), antimony (Sb), arsenic (As), lead (Pb), cadmium (Cd), and mercury (Hg). Our previous study described this method [28], and detailed data can be found in Supplementary Material, Tables S1-S4.  
The NexlON(tm) 300S inductively coupled plasma mass spectrometer (PerkinElmer, Inc., Hopkinton, MA, USA) was the platform for elemental analysis; dried lichen sample digestion was performed with 65% HNO\({}_{3}\) and 30% H\({}_{2}\)O\({}_{2}\). [28].  
The obtained results from the ICP-MS analysis were processed with Syngistix Software (PerkinElmer, Inc, Hopkinton, MA, USA) Version 2.3. This determination was done in triplicate, and the mineral concentrations were expressed as the mean (_n_ = 3) \(\pm\) SD [28].  
Figure 1: _U. hurbata_ dried lichen—entire thallus (**a**) and ground—_d_UB (**b**); both separated fractions: medulla–cortex—_mc_UB (**c**) and central cord—_cc_UB (**d**).
# CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation
## 4 Experiments  
### Dataset  
The effectiveness of the CCFNet model is demonstrated by experiments on two different public medical image datasets, such as the Synapse dataset [49] and the Automated Cardiac Diagnosis Challenge dataset [50] (ACDC).  
The Synapse dataset, a public multi-organ segmentation dataset, contains 3779 axially enhanced abdominal clinical CT images from 30 abdominal CT scans. Following the partitioning method of the TransUNet [16] dataset, the dataset is divided into 18 cases for training and 12 cases for validation. The annotations for each image include eight abdominal organs (stomach, spleen, pancreas, liver, left kidney, right kidney, gallbladder, aorta); average HD (Hausdorff distance) and average DSC are used to evaluate CCFNet on this dataset.  
ACDC is a publicly available dataset of cardiac magnetic resonance imaging (MRI) that contains 150 MRI 3D cases gathered from various individuals, with each instance covering the cardiac organ from the bottom to the top of the left ventricle. Following the setup in [16], only 100 well-annotated cases are used in the experiment, and labels for three key parts of the heart are chosen: the left ventricle (LV), right ventricle (RV), and myocardium (MYO).  
Following the partitioning method of the dataset in TransUNet [16], the ACDC dataset is split at a ratio of 7:1:2, with training (1930 axial slices), validation, and test data, and the average DSC is used to evaluate the CCFNet approach on this dataset.
# Numerical and Experimental Characterization of a Coanda-Type Industrial Air Amplifier
## 3 Experimental Setup
### Primary Flow Pressure Line  
The experiments were carried out at the Montegancedo Campus facilities of IDR/UPM. A high-pressure line was installed along the different sections of the facility, fed from a reservoir tank with adjustable output pressure, and maintained by an air compressor (see Figure 3). The reservoir tank has a volume of 300 L, compressing the enclosed air to 9.5 bar. A pressure regulator device equipped with an air filter is located at the outlet of the tank, ensuring constant output pressure during the use of compressed air. Quick-release fittings located along the facility allow for the connection and use of the line for the instrumentation and hardware needed to build and run the experiments for the models during the aerodynamic analyses.  
Constant-diameter fittings and an internal diameter of the piping of 15 mm were used, reducing the length of the tubing to approximately 2 m. Ending the piping, instrumentation was installed to characterize the high-pressure flow feeding the air amplifier:  
* A PFD-202-20 volumetric mass flow measurement device, with a flow rate range from 100 to 2000 NLPM and a diameter section of 15 mm, was chosen to determine the mass flow entering the air amplifier. Following the specifications of the air amplifier manufacturer, a maximum volumetric mass flow of 700-800 NLPM was expected. The PFD-202-20 was designed for clean compressed air, has a resolution of 5 NLPM and a maximum operating pressure of 1 MPa, and allows for cumulative flow rate measurements. Measurements are given in NLPM units.
* A ball valve (with minimal pressure loss when fully opened) was placed downstream from the flow measurement device, ensuring that no disturbances were made on the measurements.  
Figure 3: Air tank (**left**) and details of its outflow pressure regulation control (**right**).  
* A CKD digital pressure sensor (PPX Series) provided static pressure measurements at the high-pressure inlet of the air amplifier.  
In the calculations of the mass flow rate, a constant room and reservoir temperature was assumed. The density of the compressed and entrained flow was thus calculated by assuming air as an ideal gas and only dependent on the local pressure, considering isotropic gas expansions.
## 3 DL for Target Detection
### Two-Stage Detectors  
The two-stage detectors are also called regional proposal-based approaches. In this approach, detection and categorization are achieved by extracting the applicant areas on the attribute map and accomplishing DL. Some frequently used one-stage detectors for target detection through UAVs are explained in the following.  
* In 2015, [PERSON] et al. [54] presented a Faster R-CNN which is another famous target detector. Faster R-CNN symbolizes a \"region-based Convolution neural networks\" which works better on real images. These real images are employed to the area of UAVs images. It can predict locality and classification of numerous bounding boxes at the same time. Its main advantage over other similar models of this algorithm is its high accuracy. In the beginning, the Faster-RCNN algorithm introduces a regional proposal network (RPN) network. In the congruent classification, the target candidates were specified in a similar classification and allocated in similar networks to execute the outstanding detection consequences during training [19]. FPN is an attribute that integrates various levels to make the final regression and classification more efficient during the employment of attributes [55].
* Cascade R-CNN was presented in 2018 which is the repeated form of the Faster R-CNN [56]. It is a cascaded structure that consists of numerous repeated structures and is linked sequentially [19; 57]. This algorithm is composed of three segments which are: (1) feature extraction unit, (2) RPN unit, and (3) multi-stage cascade identification unit. The Cascade R-CNN framework is a multistage augmentation that trains the continuous detectors at various IOU thresholds [56]. For the next phase of training, the boundary frames built by the R-CNN scene are used as input. By utilizing a variety of special repressors, the Cascade R-CNN detects high-quality recognition by eliminating noisy identified boundary frames while maintaining useful, adjacent, and optimistic examples.
## 4 Experimental Evaluation
### Measurements Techniques and Methods
The estimation of SAR can be realized via simulations and measurements [84]. The calculation of SAR should be taken into account during the design procedure to define potential changes in antenna geometry (e.g., an extension of the ground plane [118; 119; 120]),  
Figure 12: Cylindrical Near Field measurement setup. adjust the position and distance of WTA [121] with respect to the body, and constrain the power inserted into antenna input if required to be reduced (with possible cost in the link budget). Power reduction by ground plane extension has been experimentally examined in [122]. Furthermore, controlling the SAR by using an artificial magnetic conductor (AMC) structure in the textile antenna has been presented in [123]. The shielding structure which is slightly larger than the antenna reduced SAR by 90% maintaining the flexibility of the antenna. A lot of research works have successfully presented simulations and measurements of SAR. In [14; 18; 20; 46; 124] simulation models for calculating SAR values are presented. Specifically, in wearable antenna design simulations, a numerical tissue model needs to be applied to evaluate SAR results. Maximum SAR values can be estimated for a specific antenna input power at the frequency of operation. SAR distributions in the surrounding tissues can, also, be generated. In [26] and in [125] measurements of SAR regarding a button antenna and a jacket-like antenna are provided, as well. SAR can be measured by two experimental techniques [126]. The first technique is the electric field probe method and the second one is the thermographic method. The first one is time-consuming and expensive, but with low complexity. On the other hand, the thermographic method is cheaper; still, it requires more components and is not suitable for real RF devices. In general, the measurement setup can include the textile antenna, the tissue phantom, and the SAR measurement probe or the thermographic camera as basic parts.
## 4 Methodology
### Overview of Data Handling and Analysis Strategy
These might be presented as follows:  
* Group 1 (Driving Frequency 0-1): _'low'_: 25%, _'middle'_: 50%, _'high'_: 25%
* Group 2 (Driving Frequency 2-3): _'low'_: 20%, _'middle'_: 40%, _'high'_: 40%  
These results are analyzed to identify patterns or trends, which are then synthesized into weighted factors indicating the relative importance of each driving frequency category on the target outcome. The distributions gained from this analysis are used to define weighted factors that could influence the modeling or predictive analytics part of the study. This approach is indicative of a fuzzy logic or machine learning application, where inputs are weighted according to their relevance and contribution to the outcome variable. Finally, **the distribution metrics are used to derive weight factors for each domain**. These weight factors represent the influence or importance of each segment in predicting the outcome, such as assigning a greater weight to a particular age group that consistently shows a higher percentage in the '_high_' category of SA. Insights from grouped data are further refined by sorting and categorizing into predefined domains, allowing for structured classification based on the data distribution. As an illustration of a predictor after applying weight factors, the _decisionMade_ predictor is shown in Figure 5.
# A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
## 2 Preliminaries
### Dynamic Weight-Adjustment Mechanism
#### 2.3.3 Holiday Travel  
Holiday travelers typically value comfort and efficiency during their journeys. Users tend to prioritize travel comfort over the shortest possible time. The dynamic weight-adjustment mechanism assigns higher weights to total distance and time, ensuring that trips are both comfortable and efficient.