esa-sceva/satcom-chunk-collection huggingface dataset:
# A Portable Optical Sensor for Microplastic Detection: Development and Calibration
## 1 Introduction
[18] effectively used reflectance-FTIR spectroscopic imaging to detect polyethylene and polypropylene microplastics in marine salt samples, demonstrating its potential for high-throughput environmental monitoring. However, FTIR encounters limitations, including difficulty in detecting particles below a specific size threshold and dependence on controlled laboratory settings. Furthermore, alternative techniques to FTIR and Raman spectroscopy are needed to determine the presence of microplastics in aquatic ecosystems [19]. Researchers actively develop alternative techniques to overcome the limitations of traditional methods for detecting microplastics. Biomonitoring methods, such as using aquatic masses like _Fontinalis antipyretic_, have the potential to identify MPs and mesoplastics in freshwater ecosystems. Single-molecule tools also enable precise detection and quantification of microplastics across various environments. In 2022, [PERSON] and [PERSON] investigated the use of _Fontinalis antipyretic_ as a biomonitor for MPs and mesoplastics. They compared how native moss samples and moss clones retained fluorescent polystyrene particles under laboratory conditions and in field settings near wastewater treatment plants [20]. Recent advances in single-molecule tools, such as atomic force microscopy (AFM), provide a more detailed understanding of the physicochemical properties of aquatic microorganisms at the nanoscale, thereby aiding biodiversity preservation and ecosystem understanding [21]. Additionally, membrane filters that support the samples should not introduce background interference during spectrum acquisition [8,22]. Despite their potential, these methods also face challenges in scalability and integration into fieldwork. The ability to detect and quantify microplastics is imperative for effective environmental management. Conventional methods, such as density separation, filtration, and visual sorting, have proven to be effective. However, they are often time-consuming, underscoring the necessity for more expeditious approaches. The proposed prototype offers researchers a practical way to detect microplastics and confirm their presence in samples. It provides a preliminary analysis by measuring light attenuation and generating color spectra to help researchers identify contaminated samples. This approach simplifies sample processing and ensures that only relevant samples are sent for advanced techniques, such as Raman spectroscopy. As a result, it saves time and reduces costs while improving resource allocation. The findings presented in this paper offer a comprehensive description of the device's design, calibration, and methodology. Additionally, the paper discusses the prototype's performance during beta testing with commercial microplastics, highlighting its sensitivity and reliability.
## 5 Conclusions  
The evolution of remote sensing is introducing new techniques for acquiring more rapid and accurate data, and this innovation has resulted in easier workflows that different end-users can take advantage of. Although multiple techniques are available, examinations of the appropriate method according to disaster type are missing. In this study, we compared three different conventional remote sensing classification methodologies using high-resolution imagery to identify windthrow and landslides using a simple workflow.  
The high temporal and spatial resolution of the imagery played an important role in land cover change detection, in addition to the methodologies applied to identify windthrow and landslides. This result was confirmed by the fact that all three conventional methods, namely, the NDVI filtering method, the SAM method, and the SVM method, combined with the high-resolution imagery were suitable for windthrow and landslide detection, judging from kappa values that had a \"substantial level of agreement\" and an \"almost perfect level of agreement\". One aspect to consider in further studies is the use of different-resolution datasets.  
For windthrow, the SAM method performed best due to its high capacity to identify damage even if the spectral signatures of the classes were similar. The NDVI filtering method, although not fully suitable for windthrow detection, performed best in landslide detection. The capability of the NDVI to distinguish different amounts of vegetation facilitated the distinction between areas with and without vegetation.  
Even though the SVM method had lower performance than the other methods for windthrow and landslide detection, it delivered satisfactory results. The usage of different kernel types could potentially improve its performance.  
The following are available online at SI: [[https://code.earthengine.google.com/](https://code.earthengine.google.com/)]([https://code.earthengine.google.com/](https://code.earthengine.google.com/)) Za247@bce549e6546 ec176611233 bd66 and S2: [[https://code.earthengine.google.com/bb530](https://code.earthengine.google.com/bb530) ba42 aaf9b0 ef3b67b8 dfed9318]([https://code.earthengine.google.com/bb530](https://code.earthengine.google.com/bb530) ba42 aaf9b0 ef3b67b8 dfed9318)  
Conceptualization, F.F., J.M. and N.Y.; methodology, F.F.; validation, N.Y.; formal analysis, F.F.; resources, M.K.; writing--original draft preparation, F.F.; writing--review and editing, F.F., J.M. and N.Y.; supervision, J.M.; project administration, J.M. All authors have read and agreed to the published version of the manuscript.  
This research received JSPS KAKENHI Grant Number JP17H01516 and TOUGOU Grant Number JPMXD0717935498.  
The authors would like to acknowledge Planet Labs Inc. for data providing.  
The authors declare no conflict of interest.
## 2 The FCS and WFCS Algorithms
### WFCS Algorithm
#### 2.2.2 The Adjustment Strategies  
(1) Adjustment of \(\omega_{k}\)  
Let  
\[\Delta_{k}=\sum_{i=1}^{c}\sum_{j=1}^{n}\mu_{y}^{m}\left(\left\|x_{jk}-a_{ik} \right\|^{2}-\eta_{i}\left\|a_{ik}-\overline{X_{k}}\right\|^{2}\right) \tag{16}\]  
then Equation (14) can be written as:\[\omega_{k}=\frac{\Delta_{k}^{\frac{1}{1-\alpha}}}{\sum_{t=1}^{s}\Delta_{k}^{\frac{1 }{1-\alpha}}} \tag{17}\]  
If the value of \(\Delta_{k}\) is zero, this means that the \(k\)th feature has exactly the same effect on all clusters then \(\omega_{k}\) should be zero.  
Here, \(\Delta_{k}\) is the grand fuzzy distance between data points and crisp kernels on the \(k\)th feature. Hence, \(\Delta_{k}\) is non-negative when distribution of data points is balance and so is \(\omega_{k}\). On the contrary, \(\Delta_{k}\) is negative when distribution of data points is imbalance and \(\omega_{k}\) could be negative. Consequently, we have to make some adjustment. Here, \(\Delta_{p}\in\left\{\Delta_{k}\mid\Delta_{k}\
eq 0,k=1,...,s\right\}\). Therefore, the projection function may be expressed as:  
\[\Delta_{p}=P\left(\Delta_{p}\right)=\Delta_{p}-\min\left(\Delta_{t}\right)+ \min_{\Delta_{p}>0}\left(\Delta_{q}\right) \tag{18}\]  
where \(t=1,...,s\) and \(p=1,...,s\).  
After the adjustment, the feature weighting can be given by Equation (14).  
(2) Adjustment of \(\mu_{y}\)  
Let  
\[\Delta_{y}=\sum_{k=1}^{s}\omega_{k}^{\alpha}\left(\|x_{jk}-a_{ik}\|^{2}-\eta_{ i}\|a_{ik}-\overline{X_{k}}\|^{2}\right) \tag{19}\]  
then Equation (15) can be presented as:  
\[\mu_{y}=\frac{\Delta_{y}^{\frac{1}{1-\alpha}}}{\sum_{t=1}^{c}\Delta_{y}^{\frac {1}{1-\alpha}}} \tag{20}\]  
If \(x_{j}\) falls on the \(i\)th crisp boundary, \(\Delta_{y}=0\). Accordingly, the membership value of \(x_{j}\) is infinite. The fact is that the membership value of \(x_{j}\) is fuzzier than that of data point in the crisp kernel. Furthermore, the membership value of \(x_{j}\) is greater than that of data point lying outside crisp kernel. Based on the discussions above, we have the projection function as Equation (21):  
\[\Delta=P\left(\Delta\right)=\Delta+\min_{\Delta_{y}>0}(\Delta_{\alpha_{y}}) \tag{21}\]  
where \(\Delta\in\left\{\Delta_{y}\mid\Delta_{y}\geq 0,i=1,...,c,j=1,...,n\right\},\)\(t=1,...,c\) and \(q=1,...,n\).  
After the adjustment, \(\mu_{y}\) can be given by Equation (15).
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 4 Evaluation
### Simulation
#### 4.1.2 Simulation Results  
Table 2 comprehensively presents the RMS (root mean square), MAX (maximum) value, and SD (standard deviation) of the absolute result values as the outcome of the simulation verification, comparing the results for each tracker. The graphs for each performance indicator are shown in Figure 8.  
Figure 6: Reference path for simulation.  
Figure 7: Simulation architecture based on MATLAB.  
Based on the RMS results of the lateral deviation and heading angle error, it was confirmed that the proposed path tracker, unlike the conventional 2 WS and conventional 4 WS path trackers, reflects the vehicle's turning radius and the curvature of the reference path, thereby improving the path tracker's response performance and demonstrating excellent path-tracking capabilities. Regarding the RMS of the side slip angle, it was observed that the rear-wheel steering reduced the side-slip angle compared to the other path trackers. The RMS of the yaw rate showed higher values than those of the conventional 2 WS path tracker. However, since the environment is primarily low speed, this is not expected to significantly impact vehicle stability. Additionally, similar to the RMS, MAX and SD also tend to decrease in most cases, compared to other path trackers. This demonstrates the higher consistency of the proposed path tracker's performance.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 4 Discussion
### Selection of Predictors and Models  
To predict carbon flux, this study used two remote sensing variables and six meteorological parameters and compared the performance of three different machine learning algorithms. After exploring the relationship between variables, the results showed that remote sensing variables can strongly impact on the carbon flux variations. Therefore, selecting and incorporating more predictors, especially remote-sensing-based variables related to vegetation, is crucial for machine learning based modelling of carbon fluxes. Moreover, the deep-learning based CNN model was used in this study. According to the results, the overall performance of the deep learning model was better than that of conventional machine learning methods. Therefore, we suggest that other deep learning based models such as ConvLSTM (Convolutional Long Short-Term Memory) [54] should be explored, and the stability and suitability of the modelling for carbon flux prediction should be improved.  
This study also found that LAI is the dominant factor for the prediction of carbon flux variables. Now, studies suggest that the relation between LAI and GPP is not so consistent in different land cover types, with a strong relationship in arid areas and a weak relationship in humid areas [55]. Therefore, further investigation is needed to see how GPP can respond to variables of greenness, such as LAI and NDVI, in different vegetation types. Recent studies also showed that the increasing trend of CO\({}_{2}\) concentration has a positive effect on GPP trend [56], and, therefore, its incorporation in the proposed machine learning modelling can be investigated in future studies.  
Figure 10: GPP maps of the global and an oceanic climate area from different products, FAPAR maps, and LAI maps on 10 July 2010. The resolution is 1 km for GCFD, 0.5 degree for FLUXCOM, and 0.1 degree for [PERSON] et al.  
In addition, the spatial and temporal scales of the remote sensing data, meteorological data, and carbon flux data used in this study were incompatible. In order to match the time scale of remote sensing data, the 10-day average of the other two types of data were calculated, which may result in bias if the original time series contains missing values. On the other hand, the spatial scale of the remote sensing data is 1 km, while the resolution is 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) for the meteorological data. So, spatial interpolating was conducted before carbon flux estimation. In this process, the nearest value was used when interpolating the ERA5-Land data into a 1 km scale. This may reduce some detailed information and produce uncertainty in the 1 km carbon flux dataset.
# Semi-Automated Mapping of German Study Data Concepts to an English Common Data Model
## 2 Materials and Methods
### Methods  
#### 2.2.1 Mapping to OMOP Concepts  
The mapping pipeline involves a series of sequential tasks. Initially, medical abbreviations in the German input terms are replaced by using a dictionary of 874 common German medical abbreviations and their long names [20]. This is followed by translation to English using HuggingFace transformer version 4.25.1 with model 'Helsinki-NLP/opus-mt-de-en' [21]. The translated data are then subjected to the removal of stop words and punctuation, lowercase conversion, and lemmatization via spaCy version 3.3.1 using model 'en_core_web_lg' to generate keywords [22]. Thereafter, the Scikit-learn library version 0.24.2 [23] is utilized to generate TF-IDF vectors for keywords and OMOP SNOMED CT (and LOINC) concepts, including their synonyms. The vectors are then used to compute the cosine similarity between words, identifying the top similar concepts for each variable.
## 3 System Architecture, Software Services and Deployment
### Cloud-Based Services  
The deployed framework supports cutting edge features that ensure full compliance with the pillars of fog computing, namely efficiency, security and augmented reliability. In particular, the framework has integrated subsystems for identity management, rapid access to historic events, real-time telemetry support. along with the inherent capability of issuing asynchronous notifications, all bundled with specialized interfaces used by authorized users and external services alike to get access to the available services.
## 4 Related Work  
In recent years, the security of network transmission has gained critical importance due to the increasing complexity and variability of network environments. Particularly in the field of heterogeneous networks, researchers have been actively exploring methods to enhance the security and reliability of network transmission. This section reviews related work from the perspectives of multi-objective optimization and multipath transmission.
## 3 Materials and Methods
### Features
#### 3.3.8 Payload Data  
**Feature feasibility:** Payload data can be used to detect misconfigured systems or malicious actions. Payload abnormalities are a good sign for detection. The usage of default user credentials should be avoided in an ICS network. This enables the detection of systems trying out default credentials as a first line of attack. Detection also exposes poorly configured legitimate systems.  
**Our data:** Simple string detection discovered no flagrant default usage of most typical usernames and passwords in our traffic, but more work is required for a meaningful assessment of the feature. Analysis however detected both confidential data and data useful for attacker for identifying both network and SCADA hardware.
## 2 Materials and Methods
### Research Methods  
The current study is a retrospective study. According to treatment protocol in OMFS at Baruch Padeh MC, every orthognathic patient gets a lateral cephalometric X-ray at most 1 month prior to surgery, another one in the first 48 h postoperative and another one 6 months postoperative. All x-rays were performed by the same X-ray machine.  
The analysis was divided into three categories: surgeries involving the maxilla, surgeries involving the mandible, and surgeries involving the chin. We checked only horizontal movement and checked it regardless of surgery type performed--as a methodological assumption--in order to get larger study groups and allow us to obtain individual independent and dependent variables.  
All measurements were performed by the same researcher (F.D.) on those lateral cephalometric X-Rays, using measuring tools (on screen digitization) of Dolphin Ceph Tracing 11.95 (Dolphin Imaging & Management Solutions, Chatsworth, CA, USA). The software enables measuring the accurate distance between two points on an image in millimeters after calibration with \"on image\" ruler.  
The following data were collected for all study participants: age, sex, medical condition, operated jaw/area, and operational movement (advancement, setback). Figure 1 shows the measurements conducted for all patients: upper lip thickness (A-A'), lower lip thickness (B-B'), and chin ST thickness (Pog-Pog').  
The measurements of hard and soft tissue operational changes were performed by superimposition of preoperative and 6 months postoperative lateral cephalometric X-Rays. The superimposition was performed by aligning constant hard tissue points (not affected by surgery)--Nasion (N), Sella Turcica (S), and S-N line.  
Changes of hard tissue were measured by the following points:  
* Patients after maxillary surgery--A postop-A preop distance;
* Patients after mandibular surgery--B postop-B preop distance;
* Patients after chin surgery--Pog postop-Pog preop distance;
* Soft tissue Changes were measured in the same way: A' postop-A' preop distance; B' postop-B' preop distance; and Pog' postop-Pog' preop distance;
* Preoperative soft tissue thickness was measured on preoperative X-ray (A-A', B-B', Pog-Pog'--Figure 1).  
All measurements were made independently of the surgery type performed and were taken for every area that was intentionally horizontally moved during surgery.  
The relation between postoperative hard and soft tissue movement was calculated by division of soft tissue movement to relevant hard tissue movement--\(\frac{X^{\prime}_{\text{postop}}-X^{\prime}_{\text{prep}}}{X_{\text{ postop}}-X_{\text{postop}}}\) (X = A/B/Pog).
# Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics
## 2 Related Work
### Biometrics-Based Mobile User Authentication  
Biometrics have been widely used in the user authentication of mobile devices. The Apple Touch ID utilizes fingerprints to unlock the user's device and authenticate Apple Pay. However, it was shown in [18] that fingerprints can be easily forged. Face recognition is used by Apple Face ID and Android Face Unlock to unlock the screen, which is susceptible to attacks, such as using a photo or video of the authorized user.  
Besides the traditional biometrics like the fingerprint and face, behavioral biometrics have also been used for user authentication of mobile devices. In [19], keystroke dynamics were combined with the PIN as multifactor authentication for the mobile user. Data from the motion sensors, such as the accelerometer, gyroscope, and rotation was included to improve the performance of authentication. [PERSON] et al. [20] proposed a scheme that uses the gait pattern from the accelerometer and keystroke dynamics of text input for continuous authentication. The scheme was proved to be secure against the zero-effort attack and minimal-effort mimicking attack. SwipeVLock is a screen unlock mechanism proposed in [21] based on swipe dynamics. Touch features, such as the coordinates of location, touch pressure, touch size, touch time, and touch speed were used to model the swipe behavior.  
Recently, the use of physiological biometrics for mobile user authentication has increased significantly. [PERSON] et al. [22] proposed a privacy-preserving ECG-based authentication scheme that utilizes the ECG signal acquired by a medical IoT for ECG monitoring to authenticate the patient. A mobile EEG-based biometric authentication system was developed in [23] that combines EEG recordings with other techniques, such as nearfield communication (NFC) and face recognition. [PERSON] et al. [24] proposed a framework to enhance the pattern-based authentication with EEG signals. The Hidden Markov Model (HMM) was applied to model the EEG signals recorded during pattern drawing. The Support Vector Machine (SVM) classifier was then used to verify the authenticity of the drawn pattern. In the studies of [25; 26], sEMG signals generated from a list of gestures were used for mobile user authentication. A list of gestures is used as the password, which is combined with the features extracted from the sEMG signals to defend against shoulder-surfing attacks.  
In this paper, we propose to enhance the security of pattern unlock with sEMG-based biometrics. To the best of our knowledge, the proposed scheme is the first work that incorporates sEMG-based biometrics with pattern unlock for mobile user authentication.
# Spatial Variation of Water Chemistry in Aries River Catchment, Western Romania
## 2 Methodology
### Interpolation Methods Used for Representation of HPI, HEI and WQI  
In order to assess the polluted areas, several interpolation methods from ArcGis 10.1 spatial analyst were used: IDW, Ordinary Kriging (OK) and Empirical Bayesian Kriging (EBK). From these methods, only the EBK method was appropriate to represent the contaminated areas regarding the number and the distribution of the sampling points in the Aries catchment. EBK was reported to have good results with small datasets regarding the contaminated areas [44]. However, in order to reduce the bias, the EBK was Appl. only to the mainstream and to the floodplain area around the Aries River (and few tributaries) for determining the polluted areas.