esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Islanding Detection Methods
### Computational Intelligent Based Methods
#### 2.6.2 Probabilistic Neural Network Method (PNN)  
The probabilistic neural network has been suggested as a way to enhance ANN performance because to the calculation time restriction of ANN. The Bayesian classifier-based approach uses four layers: an input layer, a pattern layer, a summation layer, and an output layer with a feed-forward mechanism [43]. While employing those layers and carrying out their functions, learning is not necessary. This technique can be trusted for detecting islands.
## 6 Conclusions  
This paper summarizes the historical and current trends of Ge-on-Si photodetectors development, which is essential for Si EPIC integration. Various electrical structures (PIN, MSM, and avalanche) and optical coupling schemes (normal incidence, resonant cavity enhancement and waveguide integration) have been adopted in Ge photodetectors, demonstrating high responsivity approaching 100% quantum efficiency and high speed operation at around 40 Gb/s. With practical Si photonics EPIC around the corner, higher speed, easier integration with CMOS fabrication and novel approaches such as plasmonics-enabled nano-scale detector will become the main focuses of research in the near future.
## 3 Context Modeling
### Adjustment of Categorization Schemata
A CA application needs to know whether a required contextual parameter can be pulled on demand, retrieved at specific time ticks, captured only on specific scenarios or retrieved from a repository. From an implementation point of view, the availability of contextual data is validated with the use of this schema of time-driven, event-driven, on demand and archived data.
# Embedded AI-Based Digi-Healthcare
## 5 Results and Discussion
### Comparative Analysis of ML Models
#### 5.1.3 Machine Learning Metrics  
Different performance metrics are used to evaluate different Machine Learning Algorithms. For this purpose, precision, recall and F1 scores of ML models are important in evaluating these models.  
1. **Precision:** Precision is a measure of the ratio between the True Positives and all the Positives.
2. **Recall:** The recall is the measure of an ML model correctly identifying True Positives.
3. **F1 score:** 1. F1-score acts as a trade-off between precision and recall. More accurately, it is the harmonic mean of precision and recall.  
Figure 10 shows the values of these performance metrics of the applied models: Again, according to these performance metrics, it is seen that KNNs performs the best while Neural Networks performs the worst. Random Forest also seems to have exceptional metric scores.
## 2 Computational Methods
The number of \(k\) points included in the calculations depends on the particular crystal structure, but we generally use \(\sim\)1000 \(k\) points or more for one atom/cell calculations and fewer for cells with many atoms. Each energy eigenvalue is broadened with a Gaussian having a width of 20 mRy. Second, we employ a full-potential linear augmented plane-wave (FPLAPW) method as implemented in FlapwMBPT (many body perturbation theory) [57]. This approach has many similarities with the FPLMTO method introduced above. As in the FPLMTO method, the space is divided into non-overlapping MT spheres and an interstitial region. The electronic density and effective potential are expanded in spherical harmonics in the MT spheres and in plane waves in the interstitial region (exactly as in the FPLMTO method). One conceptional difference between FLAPW and the other two methods is that orbital polarization is not applied in the FLAPW calculations. For this reason, Th, Pa, and U were studied with FLAPW, but not Np and Pu where orbital polarization is more important. Another conceptional difference is that FlapwMBPT allows for a complete inclusion of relativistic effects through the four-spinor Dirac equation. Particularly important is that the spin-orbit interaction is here formulated exactly. Other technical differences between FLAPW and FPLMTO are mostly related to the use of different basis functions in the interstitial region (plane-waves and Hankel functions, respectively) and in the augmentation of them with the functions inside the MT spheres. Details of the implementation of the FLAPW method can be found in [58]. Specific values of the different cut-offs used in the FLAPW calculations are summarized in Table 1. The crystal-structure parameters given in Table 1 are obtained from structural relaxations using the FPLMTO method.
## 4 Discussion  
### Removal of Organic Matter  
In general, the combined VFF-HFF system achieved efficient removal of organic matter (91 \(\pm\) 11%) over the entire period of operation (126 weeks). The biochar used in the filter units was characterized by large surface area (170-200 m\({}^{2}\)/g) and high porosity, which provided a suitable surface for biofilm attachment. Removal of organic matter in the VFF was initially low and increased with time (Figure 2). This can be explained by wash-out of carbon dust from the biochar at the beginning of operation. The long-term removal of organic matter was 93 \(\pm\) 3%.  
Despite its shallow depth (30 cm), the VFF removed 87% of COD, i.e., most of the organic matter removal in the system, and did not show any sign of clogging or failure. Biodegradation of the organic matter under the aerobic conditions in the VFF led to this high removal of COD. It should be pointed out the incoming wastewater was distributed over the whole area of the VFF and that the filter received raw wastewater on an intermittent basis (four to six times per day). The uniform distribution of the wastewater probably led to better utilization of the infiltration area and the intermittent loading gave the biofilm on the biochar particles the chance to consume the applied organic matter before the next loading, which prevented accumulation of organics and hence clogging. In the HFF, only a minor proportion of the COD was removed. We believe that the COD removed in the HFF was consumed by denitriying bacteria, which reduced the NO\({}_{3}\)-N to nitrogen gas. According to [PERSON] [25], the amount of COD required to reduce nitrate to nitrogen is about 2.85 mg O\({}_{2}\) mg\({}^{-1}\) N. The effluent of VFF contained on average 29 mg NO\({}_{3}\)-N L\({}^{-1}\), which required about 83 mg COD L\({}^{-1}\) to be available for denitrification. The effluent of the VFF and HFF contained 61 \(\pm\) 21 and 30 \(\pm\) 12 mg COD L\({}^{-1}\), respectively. Increasing the HLR from 23 to 31 to 39 L m\({}^{-2}\) day\({}^{-1}\) did not influence the efficiency of the system in terms of COD removal. Removal of organic matter in filter media is achieved by adsorption and biological degradation by active biofilm and adsorption [26; 27]. The combination of processes in the VFF-HFF system provided back-up capacity for the system to maintain stable removal of organic matter despite the changes in loading conditions. In addition, the HFF worked as a buffering zone and used its capacity (biofilm) to degrade additional organic matter which escaped from the VFF without treatment.  
Figure 6: **(A)** Boxplots of _Escherichia coli_ concentration in influent and effluent of the vertical flow filter (VFF) and horizontal flow filter (HFF). The middle line (\(-\)) of the box is the mean, the cross (\(+\)) is the median, the box is the confidence interval and the whiskers are the non-outlier range. (**B**) Concentration of _Salmonella_ spp. in influent (blue) and effluent of the VFF (red) and HFF (green) following spiking of the wastewater with _Salmonella_ spp.
# Triaxial Test of Coarse-Grained Soils Reinforced with One Layer of Geogrid
## 2 Experimental Setup  
### Soil  
For this study, six soil types were selected--three gravelly and three sandy soils from different locations in Lithuania. The determination of the particle size distribution was obtained using sieve analysis according to LST EN ISO 17892-4:2017 [49]. Particleslarger than 14 mm were removed before the analysis to avoid interference during triaxial compression testing.  
These soils were classified according to LST 1331:2022 [50]--the primary Lithuanian road construction classification. LST EN ISO 14688-2:2018 [51] and the Unified Soil Classification System (USCS) were also included to provide a broader perspective. The particle size distribution is illustrated in Figure 1, with the average particle size d\({}_{50}\) values ranging from 0.19 to 1.05 for sandy soils and 1.23 to 6.13 for gravelly soils. These values, particularly the uniformity coefficient (C\({}_{\mathrm{U}}\)), were used in our subsequent analysis. The average particle size (d\({}_{50}\)) and the uniformity coefficient (C\({}_{\mathrm{U}}\)) are detailed in Table 1.  
To ensure uniform testing conditions for the coarse soils, each sample needed to be prepared by recompacting it. To achieve consistent compaction, it was decided to compact the samples to their optimal water content, to reach their optimal density. The Proctor compaction test was conducted according to LST EN 13286-2:2013 [52] to determine the optimal water content and density. The results of these tests are presented in Table 2. It was established that gravel samples should be prepared with water content ranging from 3.0% to 7.5%, resulting in a density between 1.90 and 2.23 g/cm\({}^{3}\). For sandy samples, the determined optimal water content range was between 8.0% and 14.3%, leading to a density range between 1.86 and 2.16 g/cm\({}^{3}\). The density of the prepared soil samples was utilized in the subsequent analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Sample** \\ **No.** \\ \end{tabular} } & \multicolumn{6}{c}{**Soil Classification System**} & \multirow{2}{*}{**d\({}_{10}\)**} & \multirow{2}{*}{**d\({}_{30}\)**} & \multirow{2}{*}{**d\({}_{50}\)**} & \multirow{2}{*}{**d\({}_{60}\)**} & \multirow{2}{*}{**C\({}_{\mathrm{U}}\)**} & \multirow{2}{*}{**C\({}_{\mathrm{C}}\)**} \\ \cline{2-2} \cline{6-9}  & **LST** & & & & & & & & \\
**1331:2022** & & & & & & & & \\ \hline \hline \(1\) & ZB & GrP & GrP & 1.95 & 3.62 & 6.13 & 7.94 & 4.08 & 0.85 \\ \(2\) & ZG & GrM & GrP & 0.17 & 0.51 & 1.23 & 2.10 & 12.22 & 0.72 \\ \(3\) & ZP & GrP & GrP & 1.54 & 2.64 & 4.31 & 5.37 & 3.48 & 0.84 \\ \(4\) & SB & SaU & SaP & 0.09 & 0.15 & 0.19 & 0.21 & 2.25 & 1.08 \\ \(5\) & SG & SaM & SaP & 0.23 & 0.44 & 1.05 & 1.69 & 7.42 & 0.51 \\ \(6\) & SP & SaU & SaP & 0.14 & 0.20 & 0.28 & 0.35 & 2.45 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of soils based on grading.  
Figure 1: Particle size distribution curves (sample numbering coincides with the numbering given in Table 1).
## 4 Materials and Methods
### Predictability of OTP Extraction Function Values  
If the values of the extracted data are all duplicated as shown above, the OTP range will become a set of elements in the form of \"\(arr[i]||arr[i]||arr[i]\)\", where \(0\times 00\leq arr[i]\leq 0\times\) FF and \(0\leq i\leq 15\). If we call this reduced set \(V\), then \(|V|=256\). Even if we convert the elements of this set into decimal numbers using only the lower 6 digits, there will be no collision pairs, and the set will still have 256 elements. As a result, \(|V|\) shows a decreased rate of about 99% compared to [0, 999,999], and the range is rapidly reduced, making it easy  
Figure 3: Vulnerabilities in the improved dynamic extraction algorithm.  
Figure 2: Type of extraction algorithms: static, dynamic, and improved.  
to predict the range of possible random numbers that can be output. Ref. [1] also analyzes how frequently this phenomenon occurs.  
As indicated in Figure 4, Ref. [1] conducted experiments on a single continuous random number sequence, applying three extraction algorithms handled in the TTA standard, converting them to decimal numbers, and outputting only up to a maximum of six digits. For example, if the OTP's three-byte data 0 xE7812B are converted to decimal form, the resulting \(15,171,883\) is outputted up to two digits, which is 83, and the possible range is \([0,10^{2}-1]\). Alternatively, if it is outputted up to four digits, it becomes 1883, and the possible range is \([0,10^{4}-1]\). Table 1 shows the experimental group, which applies an improved dynamic extraction algorithm to the sequence to demonstrate predictability, and the control group, which applies the other two algorithms to the sequence. The random variable \(X\) represents the frequency of occurrence of each 'element' within the range of decimal numbers outputted; that is, the frequency. If all elements have an equal probability of occurrence, each element within the range should be distributed evenly at \(E(X)\). However, by looking at \(MAX(X)\) in Table 1, we can see that the value of the experimental group, which is the 'improved dynamic', is significantly different from that of the control group. Threshold H is a value obtained experimentally in Ref. [1] and can clearly classify the experimental and control groups. While the samples of the control group appear somewhat similar to the expected value \(E(X)\), the samples of the experimental group show a significant difference from the expected value. By using the improved algorithm, the probability of OTP results being mapped to 256 numbers is increased by \(\frac{1}{16}\)[1]. As evidence of this, the number of 'elements exceeding the threshold' (i.e., \(\#(X>H)\)) is exactly 256, and none of the remaining elements of the control group exceed threshold \(H\). Therefore, this algorithm is vulnerable to prediction attacks by attackers due to the increased predictability of certain numbers appearing with a high probability.
## 3 Experiment
### Settings
For the performance comparison, nine methods, which are differentiated in the way of utilizing user feedback, were implemented. The methods are grouped into two categories, ones that utilize only ordinal or binary ratings, denoted by \(O\) or \(B\), and ones that exploit both ratings including the aforementioned TMF and the proposed method. Specifically, there are three types of TMF denoted by \(\text{TMF}(1,1)\), \(\text{TMF}(1,0)\), and \(\text{TMF}(0,1)\), which respectively indicate TMF with a parameter pair (\(w_{l}\),\(w_{d}\)) of (1,1), (1,0), and (0,1). In addition, four types of the proposed method, \(F(c_{o})\), \(F(c_{b})\), \(F(c_{d})\), and \(F(c_{l})\), which utilize feedback rating matrices constructed by using the four functions, \(c_{o}(o_{i,j},b_{i,j})\), \(c_{b}(o_{i,j},b_{i,j})\), \(c_{l}(o_{i,j},b_{i,j})\), and \(c_{d}(o_{i,j},b_{i,j})\), respectively, were implemented. The numbers of non-null elements in the feedback rating matrices utilized for \(O\) and \(B\) were 1326 and 202, respectively, and those of TMFs and Fs were the same--1348. Note that we only adopted the basic approach of MF and tried to focus on the comparison of feedback rating incorporation approach. For the optimal parameter selection, we considered two parameters--\(\lambda\) and \(k\)--in the proposed method. \(\lambda\) is a regularization parameter which helps prevent overfitting as shown in Equation (11) and \(k\) is the dimension of the latent space for performing MF. We investigated the effects of \(\lambda\) and \(k\) on the performances of the proposed method. Experiments were conducted on the validation set with diverse \(\lambda\)s, 0.2, 0.02, and 0.002, and diverse \(k\)s ranging from 10 to 90 at intervals of 10. According to the results, we determined the optimal values of parameters for the rest of the experiments. For sentiment analysis, we employed a lexicon-based approach [30], where the sentiment score of a review text is determined as the average of the sentiment scores of the words composing the review text. The sentiment score of a word is assigned according to a lexicon of words, where words are annotated with sentiment scores between \(-2\) (negative) to 2 (positive). Thus, when the sentiment score of a review text is close to 2, it means the review is positive, while when it is close to \(-2\), it indicates the review is negative. The publicly-available deep learning based morphological analyzer for Korean called Khaiiii [31] was utilized to extract the morpheme tags for the words in review texts. Among 23 morphemes, only the words corresponding to the four morphemes that have lexical meanings such as noun, adjective, positive copulas, and negative copulas were used. Additionally, the publicly-available sentiment lexicon for Korean named KOSAC (Korean sentiment analysis corpus) [32] was employed to assign the sentiment scores of the words in review texts.
# Evaluating the Efficacy of a Passive Exoskeleton for Enhancing Ergonomics in Manufacturing
## 2 Methods
### Survey Components and Analysis
#### 2.3.2 Survey  
In addition to the Borg scale, the participants completed a detailed survey assessing several dimensions of the exoskeleton's usability and impact. We used a validated survey questionnaire used in a study on user perception of exoskeletons in various tasks [43]. The complete questionnaire used in the study is shown in Appendix A, Table A1. The survey included sections on ease of use, practical utility, user attitudes, trust, and overall experience. Each aspect was rated on a scale from 0 to 10. The purpose of this detailed survey was to provide a comprehensive assessment of the exoskeleton from multiple perspectives, ensuring a thorough understanding of its usability and impact and areas for improvement. The detailed feedback from the participants offers valuable insights for further development and optimization of exoskeleton technologies in occupational settings.  
Figure 2: Procedure setup and task performance in the lab; **(a)** front and back view of the BExo; **(b)** tasks completed by the participants.  
Ease of use: The participants rated cognitive effort, movement freedom, physical effort, and overall ease of use. This comprehensive evaluation covered mental demand, autonomy of movement, physical exertion, and user-friendliness.  
Usability: The participants assessed posture improvement, fatigue level, perceived inefficiency, movement constraint, and overall helpfulness. These evaluations provide a holistic view of the exoskeleton's impact on physical alignment, user fatigue, and task efficiency.  
Attitudes and trust: The participants rated the exoskeleton's trustworthiness, ergonomic impact, general sentiment, comfort, and perceived innovation. Understanding these attitudes helps gauge user confidence in and acceptance of the technology.  
Additional feedback: The participants were also asked to provide their ratings on usage likelihood, perceived power augmentation, training requirements, and impact on autonomy. These ratings offer insights into practical considerations and user expectations of the exoskeleton.  
The survey results were analyzed using descriptive statistical methods to quantify and interpret the intensity of perceived discomfort and physical exertion and the overall effectiveness of the exoskeleton while completing manual material handling tasks. The analysis aimed to provide critical insights for ergonomic improvements and enhance occupational health and safety.
## 2 Materials and Methods
### Data Acquisition  
#### 2.2.1 Eddy Covariance and Meteorological Data  
In order to monitor CO\({}_{2}\) and H\({}_{2}\)O fluxes between the grassland and the atmosphere, an eddy covariance tower was installed as reported in Figure 1 in 2008 [36], providing continuous measurements of NEE and water exchanges between the grassland and the atmosphere. The eddy covariance system consists of an open path infrared gas (CO\({}_{2}\) and H\({}_{2}\)O) analyzer (LI-7500, Li-Cor, Lincoln, NE, USA) and a sonic anemometer (WindMaster 1590-PK, GILL, Hampshire, UK), installed at a height of 2 m. Flux data were recorded at 10 Hz and then averaged half-hourly, for an estimated footprint of 195 m using the model after [37]. The applied methodology for preparing the flux data was based on the Euroflux protocol [38] with the Webb Pearman Leuning correction [39] and method 4 of Burba correction [40]. All post processing elaborations and frequency response corrections have been performed using EdiRe Data software [41], and quality assessment and quality check analysis (QA/QC) were conducted according to [42]. The weather station provides measurements of environmental variables such as soil temperature and soil water content, both at a depth of 10 cm, incoming short-wave radiation (Rg), air temperature (Tair), humidity, soil heat flux and precipitation (P). All these measurements were taken at 0.1 Hz and then averaged at half-hour time step [36]. Since PAR can be estimated as a constant fraction of shortwave radiation [43], in this study, we adopted Rg values instead of PAR.  
Tair and Rg data were gap-filled based on data from a nearby meteorological station in Koper (at a distance of 15 km from the tower). NEE data were partitioned into GPP and R\({}_{\text{eco}}\) according to [44] using daytime data-based estimates, considering temperature sensitivity of respiration and VPD limitation of GPP.  
Figure 1: Study area, a karst grassland.
Sensors_**2009**, \(9\), 1768-1793; doi:10.3390/s90301768  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling**  
**[PERSON] 1,2 and [PERSON] 1,3,4**  
1 State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; E-Mail: [EMAIL_ADDRESS]  
2 Graduate University of Chinese Academy of Sciences, Beijing, 100049, China  
3 TRIO/LSIIT(UMR7005 CNRS)/ENSPS, Bld Sebastien Brant, BP10413, 67412 Illkirch, France; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]  
_Received: 18 December 2008; in revised form: 7 March 2009 / Accepted: 12 March 2009 / Published: 13 March 2009_  
**Abstract**: With the development of quantitative remote sensing, scale issues have attracted more and more the attention of scientists. Research is now suffering from a severe scale discrepancy between data sources and the models used. Consequently, both data interpretation and model application become difficult due to these scale issues. Therefore, effectively scaling remotely sensed information at different scales has already become one of the most important research focuses of remote sensing. The aim of this paper is to demonstrate scale issues from the points of view of analysis, processing and modeling and to provide technical assistance when facing scale issues in remote sensing. The definition of scale and relevant terminologies are given in the first part of this paper. Then, the main causes of scale effects and the scaling effects on measurements, retrieval models and products are reviewed and discussed. Ways to describe the scale threshold and scale domain are briefly discussed. Finally, the general scaling methods, in particular up-scaling methods, are compared and summarized in detail.  
**Keywords**: Scale effects; Scaling; Scale domain; Scale threshold; Remote Sensing