esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Statistical Analysis of Document Classification  
There are a few statistical analysis methods available to evaluate performance. The Friedman test is one of the more popular multiple comparison methods [28]. The Friedman test was applied for statistical analysis in a study in which the Multivariate Relative Discrimination Criterion (MRDC) method was applied to perform feature selection in text classification. This test was applied to the Precision and Recall values, where the number of methods was \(M\) = 5 and the number of datasets was \(N\) = 3, and the critical value of Fisher distribution with \(M\)--1 degrees of freedom was \(F\) (4, 8) = 3.838 for \(\alpha\) = 0.05. As a result, it was seen that the \(FF\) value was greater than the critical value of 3.838 [29].  
In a different study on text classification, a two-stage feature selection method was proposed involving univariate feature selection and feature clustering. The objective was to first narrow down the search space and then to choose feature sets that were relatively independent. In the study, FS-CHICLUST, SVM, KNN, Decision Tree and Naive Bayes algorithms were used, and when Friedman test was performed, the following results were obtained: Friedman chi-square = 39.9447, df = 4, \(p\) value = 4.444 \(\times\) 108. According to these results, since the \(p\) value is very low, the null hypothesis that the difference in ranks is not significant is rejected, indicating that the FSCHICLUST algorithm demonstrates better performance than the other classifiers [30].
# Parallel Extreme Learning Machines Based on Frequency Multiplexing
## 3 Principle of Operation  
### Introduction  
The working principle of the ELM scheme has been extensively described in our previous work [19]. Here, we recall the fundamental concepts and introduce the novel elements connected to the presence of multiple light sources.  
In an ELM, the hidden layer is obtained by nonlinearly mixing information contained in the input layer. In our photonic implementation, the input layer is encoded in the combs \(E^{\text{in}}\) by the attenuations \(F^{\text{in}}\), and the information contained therein are mixed by PM\({}_{2}\), generating the new set of combs \(E^{\text{hidden}}\) that represent the hidden layer. This mixing is linear and consists in an interference of comb lines (Equation (5)). A quadratic nonlinearity is added in the readout phase (Equations (8) and (9)). The peculiarity of the proposed system consists in the way in which the information mixing works; as expressed by Equation (5), when the set of combs representing the input layer passes through PM\({}_{2}\), interference only happens among lines belonging to the same comb. In other words, input neurons encoded in different combs never get mixed. On the one hand, this may reduce the capability of the network to project the input data onto a higher dimensional space since the mixing of inputs is not complete. On the other hand, this allows for parallel computations that do not interfere with each other to be performed. In the following section (Section 4.2), we show that the limited dimensionality enhancement does not hinder performances when compared to an all-to-all mixing scheme.
## 3 Methods
### Driving Mechanisms of NPP Changes  
#### 3.4.1 Response of NPP to Climatic Factors  
Vegetation NPP is usually influenced by climate elements such as temperature, precipitation, and radiation. Therefore, in this study, temperature, precipitation, and radiation were used as climatological influences on NPP, as well as correlation and significance levels among vegetation NPP, with temperature and precipitation investigated on pixel-by-pixel bases using partial and multiple correlation analyses.  
The second-order partial correlation coefficient allows the correlation between the remaining two variables to be analyzed by excluding the interference of two of the four variables [35]. It is calculated as shown below:  
\[r_{ab,cd}=\frac{r_{ab,c}-r_{ad,c}r_{bd,c}}{\sqrt{(1-r_{ad,c}{}^{2})(1-r_{bd,c}{ }^{2})}} \tag{9}\]  
where \(r_{ab,cd}\) denotes the partial correlation coefficients for variables \(a\) and \(b\) with the constant variables \(c\) and \(d\), and \(r_{ab,c}\), \(r_{ad,c}\), and \(r_{bd,c}\) denote the partial correlation coefficients for variables \(a\) and \(b\), \(a\) and \(d\), and \(b\) and \(d\), respectively.  
A \(t\)-test was used to test the significance of the statistics:  
\[t=\frac{r_{12,34,...,m}}{\sqrt{1-r_{12,32,...,m}^{2}}}\sqrt{n-m-1} \tag{10}\]  
where \(r_{12,34,...,m}\) indicates the bias correlation coefficient, \(n\) indicates the sample size, and \(m\) indicates the quantity of independent variables. In this study, \(p\leq 0.05\) was accepted as statistically meaningful.  
Multiple correlation analysis focuses on the influence of multiple factors on one factor [36]. Assuming that \(y\) and \(z\) are independent variables and \(x\) is the dependent variable, the multiple correlation coefficient between them is calculated as follows:  
\[r_{x,yz}=\sqrt{1-\Big{(}1-r_{xy}^{2}\Big{)}\Big{(}1-r_{xz,y}^{2}\Big{)}} \tag{11}\]  
where \(x\) is the time series NPP, \(y\) represents temperature, and \(z\) represents precipitation.  
An \(F\)-test was conducted as well:  
\[F=\frac{r_{x,yz}^{2}}{1-r_{x,yz}^{2}}\frac{n-k-1}{k} \tag{12}\]  
where \(r_{x,yz}\) is the multiple correlation coefficient, \(n\) is the sample size, and \(k\) is the independent variable number.  
In order to discriminate the impacts of climatic factors on vegetation NPP in different areas, the relative response of climatic factors to NPP was classified into five categories based on previous studies (Table 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Driving Factor** & \(F_{C}\) & \(P_{T}\) & \(P_{P}\) \\ \hline Strong common influence & \(F_{C}\leq F_{0.05}\) & \(P_{T}<P_{0.05}\) & \(P_{P}<P_{0.05}\) \\ Weak common influence & \(F_{C}\leq F_{0.05}\) & \(P_{T}>P_{0.05}\) & \(P_{P}>P_{0.05}\) \\ Temperature influence & \(F_{C}\leq F_{0.05}\) & \(P_{T}<P_{0.05}\) & \(P_{P}>P_{0.05}\) \\ Precipitation influence & \(F_{C}\leq F_{0.05}\) & \(P_{T}>P_{0.05}\) & \(P_{P}>P_{0.05}\) \\ Non-climatic influence & \(F_{C}\geq F_{0.05}\) & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative types of impacts of temperature and precipitation on NPP.
## 3 Results and Discussion
### Detection of Water Toxicity
#### 3.2.1 Fe as a Water Toxicity
For Paramecia, the correlation times at 200\(\times\) PL, 500\(\times\) PL, and 1000\(\times\) PL were observed to be 1.6, 5.2, and 7.5 s, respectively, representing 4.5, 14, and 21.4 times larger than the control, depicted in Figure 5b. At elevated Fe concentrations, Euglena and Paramecia exhibited nearly identical behavior, likely because such high doses of Fe water toxicity could prove fatal to them. These findings indicate that Paramecia was proven to be more sensitive to water toxicity assessments in the case of Fe. Interestingly, unlike Paramecia, no significant change in the correlation time was observed for Euglena when the concentration of Fe increased to 2\(\times\) PL. This suggests that Euglena has a higher tolerance to the pollutant. However, a clear trend emerged with further increments in the concentration of Fe. Figure 4: (a) Cross-correlation function of Paramecia per unit time under different Fe concentrations and (b) the correlation time as a function of varying Fe concentrations (mg/L) for Paramecia and Euglena *, ** indicated the statistical significance of data \(p<0.05\) and \(p<0.01\) (N = 9).
## 3 Experimental Results
### Stability Experiments  
In order to evaluate the stability of the measurement system, experiments were continuously conducted on a single point of measurement. In the actual measurement experiment, the measurement within the range of a single rotating paraboloid could be completed in 4 h and the imaging spot position was sampled 100 times. The Figure 9 gives the deviation curve of the spot position relative to the starting position in the direction of X and Y.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Times** & **1** & **2** & **3** & **4** \\ \hline Value & 0.6817 & 0.6823 & 0.6799 & 0.6799 \\ Standard Error & 0.00105 & 0.00107 & 0.00066 & 0.00069 \\ \hline \end{tabular}
\end{table}
Table 2: Linear fitting parameter.  
Figure 9: Stability of the spot position detection.  
It can be seen from the experimental results that the instability of the spot position in the measurement period was within \(\pm\)0.05 \(\upmu\)m, which had little effect on the total uncertainty of the system and could be ignored.
# A Survey of Resource Allocation Techniques for Cellular Network's Operation in the Unlicensed Band
## 9 5G New Radio  
For the upcoming 5G new radio (NR), 3 GPP has approved the operation in the existing 5 GHz unlicensed band and the new 'greenfield' 6 GHz unlicensed band. Five key scenarios for 5G NR in the unlicensed band (also termed as NR-U) are [91; 92; 93; 94]:  
* Carrier Aggregation within gNodeB,
* Dual connectivity across eNodeBs,
* LTE anchor in licensed spectrum,
* 5G NR anchor in licensed spectrum,
* Uplink only in licensed spectrum,
* Downlink only in unlicensed spectrum with the stand-alone operation.  
The licensed-assisted access NR-U (LAA NR-U) scenario aggregates an unlicensed spectrum with licensed spectrum, which enables operators to boost the network performance speed and capacity. It has been demonstrated in [95; 96; 97; 98; 99; 100] that 5G NR boosts the network speed and capacity. Furthermore, key issues like handover, antenna designing, and spectrum management along with possible solutions for 5G NR have been discussed in [95; 96; 97; 98; 100]. Note that the LAA NR-U supports both NR and LTE in a licensed spectrum combined with NR-U in unlicensed spectrum. The LAA NR-U can be deployed by a macro-cell using licensed spectrum, and a local small-cell using an unlicensed spectrum or by using carrier aggregation in a small-cell (supporting both licensed and unlicensed spectrum). The 5G NR operating in the unlicensed spectrum will broaden the 5G vision to expand and transform industries. These industries are dedicated to a specific application such as industrial Internet-of-Things (IoT) and mobile broadband for enterprises.  
Network slicing is one of the key enabling technologies for 5G NR due to its ability to customize and'slice' a common resource to support diverse services and verticals [101]. An inter-operator network slicing framework for multiple mobile network operators (MNOs) has been considered in [101] in both licensed and unlicensed bands. Cellular vehicle-to-everything (V2X) communications is another key technology considered in 5G NR [102]. In [102], the coexistence problem of cellular vehicle-to-everything (V2X) users and vehicular ad hoc network (VANET) users over the unlicensed spectrum have been considered. However, detailed studies for resource allocation of LAA NR-U are needed. The ability to operate 5G NR stand-alone in an unlicensed spectrum is the next important scenario. A possible candidate technology for 5G NR's stand-alone cellular operation in an unlicensed spectrum is called MulteFire and discussed in the following section. In [37], an experimental testbed for indoor co-existence of LTE-U and Wi-Fi has been considered. Coverage, capacity, clear channel assessment energy detection threshold for LTE-U along with supplemental downlink and cell sleeping have been investigated to verify the fair coexistence between LAA and Wi-Fi in the unlicensed spectrum. A virtual coalition formation game approach has been used in [103] to solve the unlicensed band selection problem in 5G.
# Loop-Type Field Probe to Measure Human Body Exposure to 5G Millimeter-Wave Base Stations
## 5 Conclusions  
This paper proposes the field probe to measure PD at the mmWave. The proposed probe has a structure to simultaneously measure the E- and H-fields, so PD can be measured even in the radiative near-field region. The measurement results and the comparison with the simulation results show that the proposed probe measures PD in the radiative near-field region with a maximum error less than 1.3 dB. The proposed probe can be used to evaluate human exposure to EMF generated by a 5G mmWave base station. Unlike other probes, the proposed probe measures PD even in the radiative near-field region; hence, the EMF exposure can be evaluated at a short distance from the base station, where such a short distance could increase PD beyond the limitation.  
Figure 10: Measured power density sensitivity of the probe array with frequency.  
**Author Contributions:** Conceptualization, C.-H.H. and J.-H.H.; validation, Y.S.L.; investigation, D.K., C.-H.H. and J.-H.H.; writing--original draft preparation, D.K. and [PERSON]; writing--review and editing, Y.S.L., [PERSON] and H.-D.C.; visualization, D.K.; funding acquisition, H.-D.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2019-0-00102, A Study on Public Health and Safety in a Complex EMF Environment).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Results and Discussion
### Comparison of Modulus of Elasticity  
The tests on the modulus of elasticity (MOE) were also carried out at 30-, 60-, and 120-day intervals for the ambient and marine conditioned specimens, along with the 28-day testing. The MOE test results of the conditioned specimens also reveal that the marine exposure conditions favoured the test specimens under short-term exposure, as shown in Table 8 and Figure 9 below.  
Generally, super-workable geopolymer concrete such as SGC has lower modulus of elasticity values than the regular workable concrete of the same grade. This can be observed from the results of the ambient exposed specimens, whose MOE was reported in the range of 15-17 GPa for a 40 MPa strength SGC specimen. After three months of ambient exposure, the MOE values climbed by 13%, but remained lower than the modulus of elasticity values of conventional concrete. This growth concurs with the increase in compressive strength over the exposure period. Similar studies have confirmed these trends in geopolymer concrete by various researchers [49; 50; 51], reporting lower modulus of elasticity for geopolymer/alkali-activated concretes [3; 8; 49; 52; 53]. However, under marine exposure, the modulus of elasticity values increased with one month of exposure, with the test specimens reporting 23.73 GPa versus 15 GPa of the ambient exposed specimens. After a three-month marine exposure, there was a 10% increase from the first month MOE of the marine specimens and a 50% increase compared to the three-month ambient exposed specimen. This can be strongly attributed to the decrease in porosity of the SGC  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{Days of Exposure} & \multicolumn{3}{c}{Modulus of Elasticity, GPa} & \multicolumn{2}{c}{Percentage change in weight between 28-day value and ambient conditioned samples (\%)} & Percentage change in weight between 28-day value and ambient and 28-day value and 28-day value conditioned samples (\%) & Percentage change in weight between 28-day value and 28-day value and 28-day value conditioned samples (\%) & Percentage change in  
specimens under marine environments due to better hydration of the slag species in the SCGC mix. The Young's modulus values improve for geopolymer concrete materials with lower porosity values. Moreover, most of the existing literature on geopolymer concrete has investigated its performance under elevated curing temperatures where the water evaporates, leading to higher porosity and lower densities. However, in the current study, ambient cured concrete was further subjected to marine water exposure, leading to the lower porosity of SCGC specimens and better MOE values [49]. This again confirms the importance of various curing and exposure conditions for the strength development of geopolymer concrete mixes.
## 3 Results and Discussion
### Odor Reduction Using Surface Foam-Covering
#### 3.4.2 Closed Soil Reactor System  
Figure 3 shows changes in NH\({}_{3}\) concentration from the NH\({}_{3}\) soil in the closed reactor. The initial NH\({}_{3}\) concentration in the closed reactor remained constant at 1000 ppm for 3 h, and then decreased to 975 ppm and 900 ppm in 5 h and 10 h, respectively. The NH\({}_{3}\) concentration in the reactor was 625 ppm at 72 h. After spraying foam (\"foam alone\") in the reactor, in stage 1, the initial 1000 ppm NH\({}_{3}\) concentration was reduced to 30, 40, and 90 ppm in 1 h, 3 h, and 5 h, respectively, in the closed condition. In stage 2, after foam disintegrated, NH\({}_{3}\) concentration increased to 525 ppm in 10 h. Finally, NH\({}_{3}\) concentration was reduced to 325 ppm in 72 h. However, after adding bio-foam, the initial concentration was reduced to 20, 50, and 75 ppm in 1, 3, and 5 h, respectively, in stage 1 of foam application. The NH\({}_{3}\) concentration was increased to 350 ppm (10 h) in stage 2 after foam disintegration. The final NH\({}_{3}\) concentration was 30 ppm in 72 h. Thus, the bio-foam significantly reduced NH\({}_{3}\) concentration in the closed system.  
The apparent difference in NH\({}_{3}\) concentration in the two different reactor systems (open and closed conditions) in all methods clearly demonstrated that the closed system prevented the release of NH\({}_{3}\) gas from the reactor. After the foam breakage period, the  
Figure 2: Change in NH\({}_{3}\) concentration from NH\({}_{3}\)-contaminated soil with time in the open reactor; control (no treatment), foam alone (application of surfactant foam only), and bio-foam (surfactant foam containing bacteria).  
NH\({}_{3}\) gas concentration gradually decreased in \"control\" and \"foam alone\" reactors up to 72 h, whereas in the \"bio-foam\" reactor, the concentration sharply decreased within 24-48 h, although a sudden increase in NH\({}_{3}\) gas was observed because of its re-emission from all of the foam sprayed reactors. The final concentrations of NH\({}_{3}\) gas emitted from the bio-foam reactors were similar to the background NH\({}_{3}\) concentration (background NH\({}_{3}\) near open soil reactors = 10 ppm; near closed reactors = 5 ppm). The results of this study showed that the odor-degrading, bacteria-loaded foam spraying method substantially reduced malodors from the emitting source. This implies that the sharp and pungent odor-producing ammonia can be degraded efficiently within 48 h using the bacterial-foam spraying method.
# Cost-Aware IoT Extension of DISSECT-CF
###### Abstract  
In the age of the Internet of Things (IoT), more and more sensors, actuators and smart devices get connected to the network. Application providers often combine this connectivity with novel scenarios involving cloud computing. Before implementing changes in these large-scale systems, an in-depth analysis is often required to identify governance models, bottleneck situations, costs and unexpected behaviours. Distributed systems simulators help in such analysis, but they are often problematic to apply in this newly emerging domain. For example, most simulators are either too detailed (e.g., need extensive knowledge on networking), or not extensible enough to support the new scenarios. To overcome these issues, we discuss our IoT cost analysis oriented extension of DISCreate event baSed Energy Consumption simulaTor for Clouds and Federations (DISSECT-CF). Thus, we present an in-depth analysis of IoT and cloud related pricing models of the most widely used commercial providers. Then, we show how the fundamental properties (e.g., data production frequency) of IoT entities could be linked to the identified pricing models. To allow the adoption of unforeseen scenarios and pricing schemes, we present a declarative modelling language to describe these links. Finally, we validate our extensions by analysing the effects of various identified pricing models through five scenarios coming from the field of weather forecasting.  
cloud computing; Internet of Things; pricing; infrastructure as a service; DISSECT-CF; cloud simulator +
Footnote †: journal: Future Internet
## 2 Related Works  
Very few scientific studies have been published in the field of HOA gait classification using ML techniques. [PERSON] et al. [23] extensively investigated ML applications utilizing HOA gait information. A Support Vector Machine (SVM) with a linear kernel was applied on kinematic trajectories to differentiate gaits between healthy and HOA patients. The overall reported accuracy is at 88%, with the sagittal hip angle providing the highest discriminating result at 85%, while the rest of the considered kinematic trajectories, from the knees and feet, were registered below 80%. Like other shallow networks, considerable work is completed with feature extraction that includes manual handcrafting. This is considered to be the traditional ML model's major disadvantage.  
A decade after the publication of the first study in this field, [PERSON] et al. [24] designed a DL architecture through the transfer learning method on kinematic trajectories for the classification of HOA and healthy gaits. Transfer learning is a DL methodology that utilizes a pre-trained network from a different domain that is re-used for faster training but maintains performance. In the presented investigation, a state-of-the-art image-based CNN was used and re-purposed for the gait classification problem. A novel tiling method is also employed to exploit both the operated and non-operated limb in one image. The result showed a 97% G-Mean score using the sagittal hip angle, while the rest of the considered kinematic parameters are upward of 90% in accuracy. An additional step is required to transform the gait cycle into an image via a continuous wavelet transform scalogram. Thus, the method requires more computational resources.  
[PERSON] et al. [25] used IMU sensors to acquire gait kinematics and STP for an SVM model with a Gaussian RBF Kernel to discriminate impaired and non-impaired gaits after THA. Healthy and post-THA subjects were used for the classification and, conspicuously, without HOA gait information. It was found by their study that kinematic parameters are more significant with 97% validation accuracy as compared to STP with 87%.  
Overall, there is a clear research gap in the investigation of healthy, HOA, and post-THA gaits in one protocol as a multi-classification problem. Table 1 summarizes the related research on HOA gait analysis based on ML techniques. Whilst exploratory CNN work has been recently conducted by [PERSON] et al. [24], there is a research gap in the comprehensive investigation of HOA and THA research utilizing DL methodologies.  
Generally, the contribution of this paper is as follows:  
* HLSTM-CNN model is presented and tested for the multi-classification of healthy, HOA, and post-THA gaits.
* Investigation of the most relevant kinematic parameters that can best discriminate the multi-class problem.
* Performance evaluation and comparison of the proposed HLSTM-CNN model with methodologies published in the literature.
* Examination of the multi-classification model and its applicability in determining the success and failure of THA operation.