esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Range Measurement
#### 3.3.1 Range-Free Scheme  
In this part, we discuss the range-free scheme where the range between the nodes is not required without the synchronization requirement. On the contrary, the range measurement method such as TOA, TDOA needs the synchronization requirement. Thus, we introduce some papers that are asynchronous based on a range-free scheme. In [39; 40], the range-free scheme employs an AUV to periodically broadcast a message block via four directional acoustic beams. Both AUV position and a directional dependent marker are contained in the message block. The directional dependent marker is used to identify the respective transmit beam. As showed in Figure 8, the angles between the beams and the AUV body are fixed. The node receives the message and using the two different successive  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Ref.** & **Spatial Coverage** & **Coverage** & **Localization Time** & **Accuracy** & **Computational Complexity** & **Energy Consumption** \\ \hline
[31] & 2D-network and anchor-based & Large & Medium & Low & Medium & Medium \\
[32] & 2D-network and anchor-based & Large & Medium & High & Low & Medium \\
[33] & 3D-network and anchor-free & Large & Medium & Average & Low & Low \\
[34] & 3D-network and anchor-based & Large & Medium & Average & Medium & Medium \\
[35] & 3D-network and anchor-based & Large & Medium & High & Medium & Low \\
[36] & 3D-network and anchor-based & Large & Medium & Average & Medium & Medium \\
[37] & 3D-network and anchor-based & Large & Medium & Average & Medium & Large \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of localization algorithms for using spatial coverage.  
beams can obtain the location of the AUV at two different time instants. Then, utilizing the two estimated positions can obtain the position of the node.  
Considering that many applications do not require accurate position information, the authors in [41] present an efficient Area Localization Scheme (ALS). This scheme localizes the sensor within a certain area. Hence, this scheme is simple because no range measurement needs to be made in the sensor node, and the complex calculation deals with powerful sinks. The anchor nodes broadcast beacon signals and the acoustic signals can be sent at varying power levels. The sensor nodes passively listen to the signals and record the information. According to the fact that the signal levels from the same anchor node are different at different locations of the sensor node. The sensor node measures its signal coordinates and stores this information. The sensor node forwards the stored information to the sinks when required. Then, the sink utilizes the information collected from the sensor nodes that can localize the area where the sensor node is located.
# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
## 4 Experimental Results
### Conduct Simulation Experiments on Semiconductor Manufacturing Datasets  
#### 4.2.1 Experimental Data Set  
The data set we use for simulation experiments is Secom. Secom data set first appeared in [27], which is the data collected by various types of sensors in the semiconductor production process. The data set is composed of 591 attributes and signals. A total of 1567 tests (104 of which failed) are recorded, and each test belongs to a production entity. By analyzing the data and finding out the correlation between test failure and test attributes, engineers can first understand the cause of the failure, and then understand which attribute has the greatest impact on the failure. Nearly 7% of the data in Secom data set is lost. These data points have either never been measured, or somehow lost and never entered the final record. However, not all recorded information is useful for data timeliness evaluation. From the perspective of data timeliness evaluation, we only care about the timeseries of Secom dataset. The time series of Secom dataset is shown in Table 4.  
If there are many extreme outlier(s) in the data, the calculated value of \(AARD\) is likely to exceed one [22]. The Secom dataset in this paper has \(TP=1463,FP=104\), and \(AARD=0.4819\) is calculated. It can be seen that the degree of outlier of the dataset is relatively small. It is feasible for this model to use this dataset as an anomaly monitoring simulation experiment.
## 2 Materials and Methods
### Data Analysis  
#### 2.2.1 Preprocessing  
Two distinct preprocessing procedures were performed on the data, depending on the analysis. In both cases, EEG data were first preprocessed using pyPREP [38], a Python implementation of the PREP pipeline [39] that provides a standardized EEG preprocessing procedure. After initial preprocessing with the PREP pipeline, the data were further preprocessed with different methods for each experiment. All discussed filters are finite impulse response filters with a design of a one-pass, zero-phase, non-causal bandpass and  
Figure 3: Locations of the sensors with colors indicating which subsets they belong to. Empty circles indicate that they are only used in the full subset.  
use a windowed time-domain design (firwin). The windowing method uses a Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation. Filter design choices were based on the recommendations made in [40].  
For the data quality assessment, independent component analysis (ICA) [41] was performed to ensure that only brain-related activity was present in the signal. Before performing ICA, the data were filtered between 1 and 100 Hz, as recommended by [42]. For this filter, the lower transition bandwidth was 1 Hz with a \(-6\) dB cutoff frequency and the upper transition bandwidth was 25 Hz with a \(-6\) dB cutoff frequency. The filter length was 1651 samples (i.e., 3.3 s).  
A total of 10 ICA components were extracted using the infomax method [41] and then labeled using the ICLabel ML pipeline [43] to identify the components related to brain activity. The ICA solution was then applied to the unfiltered signal to only include components that ICLabel classified as brain activity. Finally, the signal was filtered between 1 and 40 Hz to eliminate low-frequency drifts and high-frequency muscle activity, respectively. This filter had a lower transition bandwidth of 0.1 Hz and an upper transition bandwidth of 10 Hz with a \(-6\) dB cutoff frequency in both cases. The length of this filter was 16,501 samples or 33 s.  
To train the ML models, the clean data resulting from the PREP pipeline were filtered with a highpass frequency of 8 Hz and a lowpass frequency of 30 Hz, as these frequencies contain most information related to MI [44]. For this filter, the length was of 825 samples resulting in a time window of 1.6 s. The data were subsequently split into epochs of 2 s before the movement initiation cue and 2 s after the cue. The epochs were then resampled to 250 Hz. The epochs of individual runs of the same type and session for the same participant were merged to obtain individual datasets that were then used to train and evaluate the decoding pipeline.
# A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis
## 2 Background
### Fault Tolerance Techniques for Memory  
There are a variety of ways to improve the reliability of a memory system. Basic single bit error detection without correction can be accomplished using parity checking. More sophisticated error detection exploits check summing techniques such as CRCs or MD5 signatures. When error correction is desired, different error-correcting codes (ECC) such as Hamming or Reed-Solomon codes can correct single and multiple bit errors. Another way to implement a fault tolerant memory subsystem is to make multiple copies of the data. These techniques are also orthogonal and can be used together [23].  
One way is to implement the memory as a Redundant Array of Independent Disks (RAID) array [24; 25], which implements redundancy by distributing a piece of data across multiple disks (or in this case, FPGA memories) in such a way that any one (or two, in the case of RAID level 6) disks (or memories) could fail without corrupting the stored data. These fault tolerance techniques can be used with FPGA external memory or with the FPGA's internal Block RAM (BRAM).  
However, the above method incurs large area and power overheads with high memory density requirements. Power hungry bit-cells management only protecting the crucial data is used [12; 13; 14]. Other approaches that extend generic instruction sets with approximation features and specialized hardware units exist [15; 16; 17]. However, these methods are still based on redundancy which yields nontrivial area and power overheads.
# Applications of Water Jet Cutting Technology in Agricultural Engineering: A Review
## 3 Applications of Water Jet Cutting Technology on Different Agricultural Materials
### Meats  
As microbial cross-contamination cannot be avoided entirely by blade cutting and the cost of disposable knives is high, water jet cutting technology is gradually applied in meat cutting and trimming. Moreover, it is essential to improve the cutting ability for cutting off meat with bone and control meat loss to guarantee economic benefits.
# Integration of Organic Waste for Soil Stabilization through MICP
## 3 Results
### Properties of MICP-Treated Soil Samples with Waste Additives
#### 3.2.1 Effect of MICP Treatment and Additives on Compressive Strength of Soil Samples  
Treatment of soil samples with MICP led to a significant increase in the compressive strength in each case. The soil sample with the addition of shredded wastepaper showed the highest compressive strength value (Figure 6B), which was 2.1 times higher than that for the soil sample without additives (Figure 6A). The lowest mechanical compressive strength was detected in the soil sample with the addition of sawdust. Interestingly, treating soil samples only with a CS (urea + calcium chloride) without the addition of the bacterial mixture also led to an increase in compressive strength compared to control samples treated with water, except for the sample with sawdust additive (Figure 6D).
# Evaluating GNSS Receiver Resilience: A Study on Simulation Environment Repeatability
###### Abstract  
Global navigation satellite systems (GNSSs), with their ubiquitous coverage, have become a cornerstone of modern position, navigation, and timing (PNT) services. While their spread spectrum communication offers inherent, albeit partial, resilience against interference, GNSSs remain a prime target for malicious actors seeking to disrupt or degrade precise location and time synchronization. Jamming mitigation has been an active research area for over three decades. Despite diverse research efforts, a key weakness in the literature is the absence of rigorous, methodologically sound testing of proposed mitigation techniques in a controlled laboratory environment. This work addresses this deficiency by exploring the challenges of evaluating GNSS receiver performance and response under interference and by proposing a more robust methodological framework for result interpretation. We present a custom simulation environment that enables repeated, controlled measurements of GNSS receiver behavior under various jamming attacks, revealing discrepancies between expected performance and real-world observations. Using three low-cost receivers as a case study, we demonstrate the inherent uncertainty in the results, the unpredictable behavior of the receivers' embedded software, and appropriate statistical analysis practices. A key contribution of this work is a publicly available dataset of extensive GNSS receiver response measurements acquired under controlled interference conditions using an advanced signal generation and a comprehensive satellite constellation simulator.  
GNSS; navigation; jamming; interference; GNSS simulator Article
# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
## 2 Signal Model and Problem Statement  
The classification of wireless signals can be generally formulated as an n-class classification problem, where \(n\) is the number of different modulation schemes. In this study, only digital modulation schemes are considered, these are BPSK, 4 ASk, QPSK, 8 PSK, QAM16, QAM64.  
A general description of a wireless communication system is given as a system that transmits information from one point to another point via a wireless medium. The received baseband signal at the output of the matched filter can be expressed as:  
\[r=\sum\
olimits_{k\in\mathbb{Z}}s_{m}(k)\cdot h(t-kT),\ \ \ m=1,\ldots,M,\ \ 1\leq t\leq T \tag{1}\]  
where, \(s_{m}\) is the _i.i.d_ symbol stream carried by constellation \(m\in M\), and \(T\) is the period of the received signal \(r\). The physical link between each transmitter and receiver is classically modeled as a delayed tapped channel model, expressed as:  
\[h(t)=A(t)\cdot e^{i(2\pi f_{c}+\varphi)}\cdot\delta(t-\tau) \tag{2}\]  
where, \(v(t)\) stands for additive white Gaussian noise (AWGN) with unknown variance \(\sigma_{v}^{2}\). The received signal whose modulation type and symbol energy are unknown is pre-processed blindly by assuming to be drawn from a minimum-energy constellation, which is true in the case of almost all the modulation types. The discrete-time samples \(r(n)\), at the output of the pre-processing unit are given as:  
\[r(n)=\sum\
olimits_{u=1}^{U}y_{u}(n)+v(n) \tag{3}\]where, \(A(t)\) is the amplitude, \(f_{c}\) represents the carrier frequency, \(\varphi\) is the phase and \(\tau\) is the delay. We can rewrite the received multipath signal at the receiver as follows:  
\[r(t)=\sum_{k=0}^{L-1}r(t-k)\cdot h(k)+v(t) \tag{4}\]  
where, \(r_{u}(n)\) is the \(u^{th}\) component of the signal.  
\[r_{u}(n)=A(t)\cdot e^{i(2\pi f_{rc}n+\varphi)}\sum\
olimits_{k\in\mathbb{Z}}s _{u}(k)\cdot h(nT_{s}-kT-\tau) \tag{5}\]  
\(T_{s}\) denotes the sampling period and \(f_{rc}\) is the residual carrier frequency offset (CFO) for the received signal \(u\).  
Convolutional Neural Networks (CNN), Support Vector Machine (SVM) and Neural-Induced Support Vector Machine (NSVM)  
In this section, the Convolutional Neural Networks are briefly described first, followed by the Support Vector Machine details. The focus is to inspect their internal structures to provide insights into their respective strengths and weaknesses on the present modulation classification task. This analysis will lead to proposing NSVM, which combines the strengths of the two methods. For example, SVMs can produce good decision surfaces if the input representation is reasonably well-behaved, but, with their fixed architecture, they cannot learn to extract relevant features so as to handle complicated invariances in the input. Conversely, Convolutional Neural Networks can learn invariant local features that are appropriate for wireless signal classification, but the top layers seem to produce suboptimal classification surfaces.
## 4 Conclusions  
The development of composite technology based on natural and biodegradable materials may be one of the answers to the ever-increasing problem of plastic waste disposal. A major challenge is to develop materials with properties similar to those of conventional polymers at a competitive price.  
In this work, two types of composites based on a PLA with different contents of flax or wool fibres were obtained. The effect of the type of fibre on PLA degradation was investigated. For samples with flax added, composites with better properties than pure PLA were obtained, the better the greater the fibre addition. Flax fibres accelerated the simulated degradation in the environment and slowed the fast degradation of pure PLA under UV exposure. Wool fibres in small amounts did not significantly change the properties of PLA, and high filler addition resulted in lower mechanical properties. Wool fibres significantly accelerated degradation by composting, mainly due to their high hygroscopicity, which allowed water transport and hydrolysis of the polymer.  
Given the latest trends associated with a circular economy, more and more attention is paid to materials that are of natural origin and do not burden the environment. Sheep's wool fibres have been present in our civilisation for thousands of years and are characterised by unique functional properties. The amount of waste, mainly packaging, is constantly increasing and the main problem is the decomposition time, which is inadequate for the duration of use. Wool accelerates the degradation of polymer matrix materials and can serve as a factor in controlling and determining the lifetime of a biocomposite that contains sheep's wool. Due to the hygroscopic nature of washed wool, water can be transported deep into the material, and, thus, hydrolysis of the matrix occurs throughout the composite, accelerating the degradation of the entire material. Natural fibres, through their structure and properties, improved PLA degradation. The use of low-cost natural fibres or even a waste product such as sheep wool further reduces the price and increases the attractiveness and competitiveness of such a material on the market.  
Conceptualisation, E.S. and P.S.; methodology, E.S., P.S. and K.P.; software and validation, E.S. and P.S.; formal analysis, E.M., P.S. and K.P.; investigation, E.S.; resources, E.M., P.S. and K.P.; data curation, E.S. and P.S.; writing--original draft preparation, E.S. and P.S.; writing--review and editing, K.P. and E.M.; visualisation, E.S., E.M. and K.P.; supervision, K.P. and P.S.; project administration and funding acquisition, K.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported from the subsidy of the Ministry of Education and Science for the AGH University of Krakow (Project No. 16.16.160.557).  
Not applicable.  
The data used to support the findings of this study are included within the article. Additional data are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
## 2 Coronary Artery Calcium Score
### Prognostic Value of CAC and Risk Assessment
#### 2.3.3. Role of CAC in Specific Subgroups  
It is essential to mention the role of CCTA and CAC in some specific subgroups of patients at higher risk of developing ischemic events, such as those with diabetes, older age, and chronic renal insufficiency.  
Patients with diabetes more frequently develop calcified and extensive CAD, presenting as silent ischemia, and greater plaque progression in terms of disease load and ad verse plaque characteristics than patients without diabetes. Therefore, [PERSON] et al. reported higher mortality in diabetic patients than in nondiabetic patients for any degree of CAC [45]. Accordingly, a recent meta-analysis reported that the rise in CAC score is substantially linked to an elevated risk for all-cause mortality and/or fatal and non-fatal CV events in asymptomatic diabetics [70]. In addition, evaluation of CAC may help identify diabetic subjects who are more likely to have inducible ischemia and may be utilized as a strategy for preselecting people before functional imaging [45].  
Few studies have examined the predictive significance of CAC in the elderly (>75 years). A recent study found that women are more at risk than men for cardiovascular and all-cause mortality, with CAC scores and percentiles being highly predictive of these outcomes among older persons. In contrast, relatively low-risk older individuals have low coronary artery calcium scores of 0 to 9 or 25 th percentile [71].  
Dialysis patients experience a faster progression of CAC, which is correlated with age, the length of dialysis, the etiology of chronic renal failure, changes in mineral metabolism, and the use and dosage of calcium-based phosphate binders. According to reports, the level of CAC in dialysis patients is related to cardiovascular outcomes [72]. However, further information is required to determine the potential practical application of the CAC score for further risk categorization of this subgroup [39].
# Impact of Iterative Bilateral Filtering on the Noise Power Spectrum of Computed Tomography Images
## 1 Introduction  
Despite its popular applications in medical imaging, computed tomography (CT) provides a relatively high-radiation dose for patients that can increase the probability of malignancy [1, 2] in leukemia and brain cancer [1, 3]. Various efforts were made to overcome this problem by following the \"as low as reasonably achievable\" (ALARA) principle [4]. The two areas of study that are most frequently explored include reducing patient doses as effectively as possible [5, 6, 7] and improving image quality for diagnostic purposes [8, 9].  
Image quality can be determined by selecting appropriate input parameters before scanning, such as tube current, tube voltage, rotation time, pitch, reconstruction type, reconstruction filter, and so on [10, 11, 12], and it can be enhanced at the post-processing stage. Radiographers or medical physicists select the pre-scanning input parameters and can perform noise reduction to get an improved image during post-scanning (i.e., after scanning the patient or phantom). Filtering the image using a noise reduction algorithm can not only suppress image noise but also allows patients to be exposed to a lower dose. However, although these filters can suppress noise, they tend to produce blurriness in the images [13]. Many linear denoising algorithms have been developed, such as the Wiener [14] and Gaussian filters [15].  
Non-linear filters, such as the bilateral filter, non-local mean filter, total variation filter, and wavelet filter [16], can be used to overcome the shortcomings of linear filters. The bilateral filter uses two weights in the process, namely distance weighting (\(\sigma_{d}\)) and intensity weighting (\(\sigma_{r}\)) [17]. Its behavior in filtering images based on these weights results in less-noisy images while preserving edges [18], making it one of the most widely used noise-reducing filters [19].  
There have been many studies of the various aspects of the bilateral filter, including theoretical studies [20], determination of optimal parameters [21; 22], modification [23; 24], and acceleration of the process [25; 26]. However, to our knowledge, there are no studies characterizing the bilateral filter in the context of iterative filtering. Since bilateral filters can be iterated [27; 28], it is necessary to explore the impact of iteration on noise texture, because noise texture is an important parameter of image quality [29; 30; 31]. This study does not introduce a new perspective on the bilateral filter, but rather it investigated the effects of iteration on bilateral filtering of CT images and analyzed its impact on the noise texture. The study aims to provide a better understanding of the nature of bilateral filters and preferences for implementing them in CT images.