esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 1 Introduction
### Related Work
When the UE speed is 500 km/h, adding _DMRS_ symbols shows a better block error rate (_BLER_). In my previous study, which solved the Doppler shift by adding _DMRS_ symbols, only _BLER_ performance analysis was performed according to the number of iterations [28]. In previous studies, beam coverage size reduction is a proposed method to solve the residual Doppler shift problem. However, it does not address link performance degradation issues. This study proposes a minimum beam coverage size that can compensate for the residual Doppler shift calculated through 3D satellite orbits. In addition, link performance is improved by adding _DMRS_ symbols, and the results are analyzed by defining various performance indicators.
form that represents non-standard language and is afterward normalized into the standard form. Manual transcription of speech data is highly time-consuming. However, it continues to represent the most reliable way to provide exact speech transcriptions, which are needed, for example, to build speech recognition systems. Automatic language normalization could at least help to speed up the whole process of speech database creation.  
For major world languages such as English, Spanish, and Chinese, many linguistic resources have been developed, making them dominant in language technology development and the most commercially attractive. To ensure that smaller languages are not endangered, significant efforts have recently been made to develop language technologies for them. Nevertheless, the extent of linguistic resources available for these languages remains limited compared to major languages. This shortage is particularly evident in the area of speech resources. For these reasons, they are called low-resource languages.  
The paper presents a study of the normalization of Slovenian speech. Slovenian belongs to the group of low-resource highly inflected languages, so one of the problems addressed by the study is the scarcity of data. We analyze the use of three different types of basic units, which cope with the problem of data scarcity to different extents: words, subwords, and characters. Three different approaches for building normalization models were used, inspired by the machine translation research. After selecting the most suitable modeling unit and best-performing model, we were also interested in the errors that occur in best-performing models and their extent.  
Previous research used standard evaluation metrics like word error rate, character error rate, or ChrF, as described in the remainder of the paper. To our knowledge, there is no tool for a detailed inspection or categorization of different errors during automatic normalization. Therefore, we also present a tool specifically developed for this purpose, which includes statistical tests.  
The remainder of this paper is organized as follows: Section 2 presents the characteristics of the Slovenian spoken language and Section 3 defines the normalization. Section 4 reviews previous research carried out in normalization. The motivation for using different modeling units is given in Section 5. The theoretical background of sequence-to-sequence models is briefly presented in Section 6, and the evaluation metrics are presented in Section 7. Section 7.2 describes how deeper insight into errors was obtained using the developed tool and how statistical tests were implemented. Section 8 details the experiments and discusses the results. Section 9 concludes the paper.
# Adaptive Navigation in Collaborative Robots: A Reinforcement Learning and Sensor Fusion Approach
## 3 Methodology
### Navigation Algorithm  
Figure 3 shows the algorithm for navigation that was used in this study in our test vehicle. The algorithm begins by obtaining the vehicle's current GPS coordinates and the desired destination coordinates. The GPS module supplies the vehicle's current location, and the microcontroller processes this information to calculate both the required bearing angle, which indicates the direction to the destination and the distance between the current position and the destination.  
Next, the vehicle's current heading angle is measured by the magnetometer. The microcontroller then determines the heading error by comparing this current heading with the calculated bearing angle.  
The vehicle's movement is guided by the heading error and the distance to the destination. If the heading error falls within a specified tolerance of \(\pm 5\) degrees and the distance to the destination is less than 10 m, the vehicle will stop. If the heading error is outside the tolerance, the vehicle's motor actuators adjust its direction by turning left or right to correct the heading. If the heading is correct but the distance to the destination exceeds 10 m, the vehicle moves forward.  
Figure 3: Navigation algorithm flow diagram.
###### Abstract  
The aim of this study was to investigate the reliability of the tennis-specific change of direction (77 COD) test with vertical jump tests (Squat Jump and Countermovement Jump) in adolescent tennis players. A total of 58 athletes (31 girls, 27 boys) actively playing tennis, with an average age of 10.57 \(\pm\) 2.54 years, participated in the study. Tests and retests were completed within 2 weeks. Bland-Altman (B&A) plots were established and Pearson's correlation analysis was conducted on the 77 COD, CMJ, and SJ tests. Simple linear regression analysis (enter method) was used to determine the significant predictors of the 77 COD variable from the SJ-CMJ variables. The SJ and CMJ were found to be highly negatively correlated with the 77 COD test (\(-0.72\leq\mathbf{r}\leq-0.74\)). The test-retest reliability of the 77 COD test was found to be very high, with an intraclass correlation coefficient (ICC) of 0.95. Our findings showed that the 77 COD test is suitable for determining COD ability in adolescent tennis players. In addition, vertical jump heights are an informative and practical performance indicator for 77 COD. In conclusion, the 77 COD test is a COD test that can be used in coaching practices without the need for complex testing materials.  
tennis; reliability; change of direction +
Footnote †: journal: Medical Sciences
## 3 Methods
### Self-Supervised Feature Representation  
Self-supervised feature representation focuses on learning generalizable features from unlabeled data. The SimCLR framework, as contrastive learning, is used to construct the Self-Supervised Classification Learning (SSCL) branch. The SSCL branch leverages data augmentation techniques to generate positive and negative sample pairs. As shown in Figure 4, positive pairs are obtained by performing different augmentation transformations on the same input sample, while negative pairs are formed by combining representations of different samples within the same batch. Each pair of augmented views (denoted as \(x_{i}\), \(x_{j}\)) is processed by the encoder \(f(\cdot)\) to generate the corresponding feature representations \(h_{i}\), \(h_{j}\), respectively. Subsequently, a projection head, consisting of a fully connected layer and a nonlinear activation function, is introduced to map the feature representation to a higher-dimensional representation space. This mapping function \(g(\cdot)\) produces the projected representations \(z_{i}=g(h_{i}),z_{j}=g(h_{j})\). The ultimate goal is to maximize the similarity of thepositive sample pairs, while minimizing the similarity score between the negative sample pairs. The similarity scores is calculated using the InfoNCE loss, as shown in Formula (6).  
\[L^{ij}_{unsupervised}=-\log\frac{\exp(\text{sim}\big{(}z_{i},z_{j}\big{)}/\tau)}{ \sum_{k=1}^{2N}1_{[k\
eq i]}\exp(\text{sim}(z_{i},z_{k})/\tau)} \tag{6}\]  
where \(sim(\dots)\) represents the cosine similarity between the two vectors. \(\tau\) is the temperature scalar used as a hyperparameter to adjust the smoothness of the similarity. This unsupervised framework utilizes a large amount of unlabeled data to learn general representations and applies them to smaller models for specific tasks.
# A Continuous Music Recommendation Method Considering Emotional Change
## 5 Continuous Recommendation of Music Reflecting Emotional Changes
### Calculate the Similarity Between Music Emotion Vector and User Emotion Vector  
This section describes the method for calculating the similarity between music and user emotion vectors. By calculating the similarity between the user emotion vector and the music emotion vector each time, a content recommendation list was created. Various similarity calculation methods can be used to calculate the similarity between two vectors. In this study, we used the cosine similarity calculation formula based on the vector space model, which calculates the similarity between two vectors. The similarity value is between 0 and 1; the closer the similarity value is to 1, the more similar it is judged to be between the user's emotional state and the music's emotions. To calculate the similarity between the music emotion vector and the user emotion vector each time, Equation (18) was used.  
\[\cos(\overrightarrow{V_{m}},\overrightarrow{V_{u}^{t}})=\frac{\sum_{i=1}^{N} \left(V_{m,i}\times V_{u,i}^{t}\right)}{\sqrt{\sum_{i=1}^{N}\left(V_{m,i} \right)^{2}}\times\sqrt{\sum_{i=1}^{N}\left(V_{u,i}^{t}\right)^{2}}} \tag{18}\]  
\(\overrightarrow{V_{m}}\) is the emotion vector of music \(m\), \(\overrightarrow{V_{u}^{t}}\) is the emotion vector of the user \(u\) at each time, \(V_{m,i}\) is the value of the emotion vector of music \(m\), \(V_{u,i}^{t}\) represents the value of the emotion vector of the user \(u\) at each time, and \(N\) represents the number of emotion vector elements.
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Design Requirements
#### 3.2.4 Conceptual Specification of Smart Leggings  
To develop smart leggings, high-quality polyamide, polyester, LYCRA(r) and graphene-coated fabrics can be used to ensure breathability, a perfect fit, muscle control, UV protection, quick drying, stretchable, bacteriostatic properties, etc. [80]. The leggings read muscle activity and detect force while performing a physical activity, including running, cycling, climbing and other sports. The leggings acquire muscle signals surrounding the knee joint, which vibrates, and lights up during the recording of muscle activity, one that is prone to injury. A small Bluetooth hardware unit embedded into the leggings can send measurement data in real time to a user's smartphone. Once the data are received by the app, they are analysed using advanced AI algorithms to take further necessary actions. The leggings may send smart notifications to the user's phone to alert them to their health condition and level of fitness. A mobile app that uses inputs of the user's physical attributes and muscle data from the leggings should recommend a rest time. An online dashboard that archives any video data of the user and correlates them with the muscle data is presented in [81]. A conceptual technical specification for future smart leggings is presented in Table 2
## 5 The Evolution towards a Global Smart Grid Deployment: Challenges and Perspectives
### Evolution and Adaptability of MGs towards a SG Scenario
#### 5.2.1 New End Users and Equipment Connected to the Grid  
A rapid evolution of the number of actors implied in the power grid is expected, mainly smart devices and electric/hybrid vehicles. In the near future, plenty of smart devices will be part of the power grid, connected through communication networks: smart meters, smart sensor nodes, smart data collectors, renewable energy sources. As a consequence, it will be necessary to implement scalable communication architectures to deal with the large quantity of data generated by those devices [105]. One of the proposed solutions is to use a Meter Data Management System (MDMS) which stores and processes data received thru the Data Aggregator Units (DAUs). Each DAU collects measurement data from the nearness smart meters and acts as a temporary buffer. The Smart Grid should be able to propose reliable protocols with advanced issues as self-configuration possibility and reinforced security.  
The number of electric and Plug-in Hybrid Electric Vehicles (PHEV) is expected to rise with the aim of progressively decreasing the fossil fuel consumption. In the absence of an adapted control strategy, the simultaneous charging of an important number of vehicles during the peak consumption periods can affect the grid operation [106]. Fortunately, those vehicles can have an important place to take in the management of the Smart Grids by their own storage capacity. Batteries can be charged during the off-peak hours and can supply energy to the grid during peak hours [107; 108]. The load scheduling can be envisaged by means of attractive electricity prices and allowing users to decide the quantity of energy to buy or to sell. By their intrinsic storage capability, electric vehicles can provide additional services to the grid such as participation on the voltage and frequency regulation, peak power diminution and reactive power injection or absorption, thereby increasing the grid efficiency and stability, and reducing at the same time the power system operating cost [109].
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 3 Reviewing Relevant Technologies and DT Components
### Technologies Involved in DTs
#### 3.1.5 Distributed Ledger Technologies and Blockchain
* The distributed architecture of blockchain ensures that information is stored and verified by multiple nodes in the network, thus ensuring integrity and continuity of data and reducing dependency on a central authority.
## 5 Conclusions  
In this paper, we proposed the flared type ferrite IPM model as an alternative to the basic spoke type ferrite IPM model. As a result of the experimental results of the proposed flared IPM model, the efficiency was increase by 0.9% and torque ripple was reduced by 66.2% compared to the basic spoke type ferrite IPM PMSM motor. Therefore, the comparison using the results of FEM analysis and the prototype experiment showed that the proposed flared IPM model is an effective substitute for the basic spoke type IPM model requiring the same output. In particular, the proposed flared type IPM model has lower torque ripple and higher efficiency than the basic spoke type IPM model. The proposed flared type IPM model also has a low nodal force ripple, giving it an advantage for acoustic noise and vibration. Through the analysis and experimental verification, we can confirm  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Unit**} & \multicolumn{2}{c}{**Basic Model**} & \multicolumn{2}{c}{**Proposed Model**} & \multirow{2}{*}{**Rate**} \\ \cline{3-4} \cline{6-6}  & & **Analysis** & & & **Analysis** & **Experiment** \\ \hline Average torque & Nm & 0.72 & 0.72 & 0.72 & - \\ Torque ripple & Nm & 0.068 (9.4\%) & 0.016 (2.2\%) & 0.023 (3.2\%) & \(\mathbf{\
abla}66.2\%\) \\ Output power & W & 303.7 & 302.5 & 304.1 & - \\ Efficiency & \% & 92.7 & 93.8 & 93.6 & \(\mathbf{\Delta}0.9\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of the basic model and the prototype model.  
Figure 17: Prototype of the proposed model; (**a**) stator assembly; (**b**) rotor assembly; (**c**) magnet arrangement for flared shape.  
Figure 18: Experimental characteristics at rated load test (at 4020 rpm) and enlarged torque ripple waveform.  
that the proposed flared type IPM model is an excellent alternative possibility for applications that require low cost, high output, and low noise and vibration. Forward, we will conduct a noise vibration experiment for the prototype to verify the improvement effect and optimize the proposed flared type IPM model.  
Conceptualization, K.-Y.Y.; methodology, K.-Y.Y.; software, K.-Y.Y.; validation, K.-Y.Y. and S.-W.B.; writing--original draft preparation, K.-Y.Y.; writing--review and editing, K.-Y.Y.; and S.-W.B. funding acquisition, S.-W.B. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (no. 2017R1C1B5075525); this study was also supported by a 2017 research fund from Honam University.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### Antioxidant Activity of Scepe-A, Scepe-B and Scepe-C  
#### 2.4.1 DPPH\(\bullet\)  
Savenging Activity  
DPPH is a relatively stable organic radical that can be scavenged by accepting a proton-donating substance (H\({}^{+}\)), which reduces the absorbance at 517 nm because the solution color changes from deep purple to yellow [19]. As shown in Figure 6A, Scepe-A, Scepe-B and Scepe-C showed dose-dependent anti-DPPH\(\bullet\) activity, with EC\({}_{50}\) values of 2.43, 2.43, and 1.99 mg/mL, respectively, and Scepe-C exhibited the highest radical-scavenging activity among all samples, except the positive control of ascorbic acid. The EC\({}_{50}\) of Scepe-C was lower than that of Pro-Ser-Tyr-Val (PSVV) (17.0 mg/mL) [28], Thr-Thr-Ala-Asn-Ile-Glu-Asp-Arg-Arg (TTANIEDRR) (2.503 mg/mL) [26], Phe-Leu-Asn-Glu-Phe-Leu-His-Val (FLNEFLHV) (4.950 mg/mL) [29], Trp-Glu-Gly-Pro-Lys (WEGPK) (4.438 mg/mL), Gly-Val-Pro-Leu-Thr (GVPLT) (4.541 mg/mL) [5], Gly-Phe-Gly-Pro-Leu (GFGPL) (2.249 mg/mL), Val-Gly-Gly-Arg-Pro (VGGRP) (2.937 mg/mL) [30], FIMGPY (2.60 mg/mL), GPAGDY (3.48 mg/mL), IVAGPQ (3.93 mg/mL) [11], WDR(3.63 mg/mL), PYFNK (4.11 mg/mL) and LDK (3.06 mg/mL) [19; 20] from the protein hydrolyses of loach, blue mussel, salmon, blueinf leatherjacket, grass carp skin, skate (_Raja porosa_) cartilage and scalloped hammerhead muscle, but it was higher than that of Gly-Ser-Gln (GSQ) (0.61 mg/mL) [31], Pro-Ile-Ile-Val-Tyr-Trp-Lys (PIIVYWK) (0.713 mg/mL), Phe-Ser-Val-Val-Pro-Ser-Pro-Lys (FSVVPSPK) (0.937 mg/mL) [29], Pro-Tyr-Ser-Phe-Lys (PYSFK) (1.575 mg/mL) [30], His-Phe-Gly-Asp-Pro-Phe-His (HFGDPFH) (0.20 mg/mL) [32], Phe-Leu-Pro-Phe (FLPF) (0.789 mg/mL), Leu-Pro-Phe (LPF) (0.777 mg/mL) and Leu-Leu-Pro-Phe (  
Figure 5: Mass spectrograms of Scepe-A, Scepe-B and Scepe-C.  
(LLPF) (1.084 mg/mL) [33] from the protein hydrolysates of Chinese leek, blue mussel, grass carp skin, mussel sauce and corn gluten meal. Therefore, the present results suggested that SCPE-A, SCPE-B and SCPE-C were DPPH inhibitors and primary antioxidants that reacted with free radicals.
# The Effect of Processing Methods on Phytochemical Composition in Bergamot Juice
## 3 Results
### In Vitro Antioxidant Activity  
The antioxidant activity of the BJ juices obtained using 3 different juice extraction methods were estimated by DPPH radical scavenging assay (Figure 3), ascorbic acid was used as the reference standard. The antioxidant activity of debittered bergamot juice (dBJ) was also evaluated in order to find a possible correlation between the free radical scavenging activity (RSA) of fresh processed BJ and its' content in phytochemicals.  
Ascorbic acid, at a concentration of 20 \(\upmu\)g/mL, exhibited a percentage inhibition of 51.1% and a calculated EC\({}_{50}\) value of 21.25 \(\upmu\)g/mL.  
After 40 min, screw press (Ep-3) BJ showed the highest DPPH radical scavenging activity, (EC\({}_{50}\) was 9.35 \(\upmu\)g/mL, \(p<0.0001\)). The EC\({}_{50}\) of hand squeezed (Ep-1) BJ was significatively lower than FMC juice (Ep-2) (\(p<0.005\)). Debittered bergamot juice (dBJ) showed an EC\({}_{50}\) value of 29 \(\upmu\)g/mL, significantly higher than the other juice (\(p<0.0001\)). It is mainly due to the L-ascorbic acid content, as previously reported in Table 2.  
A very strong antioxidant activity (AAI \(>2.0\)) was observed only in screw press (Ep-3) BJ, while the AAI of FMC juice (Ep-2) was significatively higher than Ep-1 (hand squeezed) and debittered bergamot juice (dBJ) (\(p>0.05\)). The present results demonstrate stronger activity of EP-3 BJ than that of the standard antioxidant ascorbic acid (\(p>0.0001\)).  
Moreover, the BJ debittered with exchange resin showed significantly lower (\(p<0.0001\)) total antioxidant activity due to minor content of flavanone glycosides and limonoid aglycones (Table 2, Figure 3). The exchange resin process led to a significant decrease (\(p<0.005\)) in the total concentration of these compounds compared with samples of fresh juice, while no effects were observed on L-ascorbic acid, which ranged from 387 to 402 mg/L, and \(N\),\(N\)-dimethyl-\(L\)-proline (Table 2).  
Correlations between the antioxidant activity index (AAI) and the content of flavanone glycosides, ascorbic acid, \(N\),\(N\)-dimethyl-\(L\)-proline (ProBet), and limonoid aglycones were calculated using the Pearson test in order to find out if parameters were statistically correlated (Figure 4). Strong positive correlations (\(r>0.95\)) were determined for all compounds with the same exception: only a high negative correlation was observed for ascorbic acid content and AAI.  
Results in Figures 3 and 4 indicated that the antioxidant activity index of BJ was mainly due to ascorbic acid content and increased together with the flavanone glycosides and limonoid aglycones content according to processing of juice extraction.  
Figure 3: Antioxidant activity expressed as EC\({}_{50}\) (**A**) and antioxidant activity index (AAI) (**B**) of bergamot juice obtained using 3 different juice extraction methods: hand squeezing (Ep-1); FMC (Ep-2); screw press of peeled fruits (Ep-3); and in debittered bergamot juice (dBJ). Asc: ascorbic acid (positive control). Error bar represent standard deviation (\(n=3\)).
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
## 3 Analysis of the Delay Performance
### SNC in the SNR Domain
#### 3.3.2 Mellin Transform over the Service Process in the SNR Domain  
Equation (24) also shows that deriving the closed-form expression for the Mellin transform over the service process in the SNR domain at user \(k\) is a key component when analyzing the UB-QDVP, which motivates the following Theorem 2.  
Theorem 3.2: _The Mellin transform over the service process in the SNR domain of user \(k\) at one time slot is_  
\[\mathcal{M}_{\varphi_{k}}(1-s)\leq\mathcal{I}_{k}\Big{(}s,\varepsilon_{k}^{d} \Big{)}+\mathcal{G}_{k}\Big{(}\gamma_{0},\varepsilon_{k}^{d}\Big{)}, \tag{27}\]  
_where \(\mathcal{I}_{k}\Big{(}s,\varepsilon_{k}^{d}\Big{)}=\left(1-\varepsilon_{k}^{ d}\right)M_{\gamma_{k}}\Big{(}-\frac{s}{\text{ln}^{2}}\Big{)}\) and \(\mathcal{G}_{k}\Big{(}\gamma_{0},\varepsilon_{k}^{d}\Big{)}=\varepsilon_{k}^ {d}+\left(1-\varepsilon_{k}^{d}\right)\)F\({}_{\gamma_{k}}(\gamma_{0})\). \(\varepsilon_{k}^{d}\) is the decoding error probability. \(\gamma_{0}\) is the upper bound of the PPSNR when the maximum achievable data rate of user \(k\) is \(0\)._  
Proof: Please refer to Appendix C.  
Combining and extending Theorem 3.2, Equations (24) and (26) yield the following corollary.  
Corollary 2: _The approximate UB-QDVP of the kth user is_  
\[\varepsilon_{k}^{v,\text{UB}}\cong\frac{\left(\mathcal{I}_{k}\Big{(}s, \varepsilon_{k}^{d}\Big{)}+\mathcal{G}_{k}\Big{(}\gamma_{0},\varepsilon_{k}^ {d}\Big{)}\right)^{t_{\text{T}}}}{1-e^{\lambda s}\big{(}\mathcal{I}_{k}\big{(} s,\varepsilon_{k}^{d}\big{)}+\mathcal{G}_{k}\big{(}\gamma_{0},\varepsilon_{k}^ {d}\big{)}\big{)}}. \tag{28}\]  
Proof: Please refer to Appendix D.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data
#### 3.4.11 Northeast China AGB Map  
[PERSON] et al. [97] produced this map at 500 m resolution for the year 2005 by combining field biomass data, GLAS data and MODIS data using the Random Forest model. Both bootstrap resampling and cross-validation techniques were used to quantify the uncertainties of estimated biomass sourced from sampling and modelling. An uncertainty map was also provided along with the AGB map.