esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Multi-Level Lexical Features Extraction Module
#### 3.1.1 Acquisition of POS Tags  
The CoNLL04 [25] dataset was used as a basis for extracting and analyzing the part-of-speech (POS) tags highly associated with named entities, and a POS tag table was determined for POS tag embedding based on the analysis results. The number of corresponding words, entities, and related entities for different POS tags in the CoNLL04 dataset were extracted using the NLTK toolkit [26], as shown in Table 1.  
It can be observed from the statistical results in Table 1 and the case depicted in Figure 2, that nouns or nouns with modifiers are mostly present in named entities and those with relationships. The POS tags, such as proper nouns singular (NNP), proper nouns plural (NNPS), nouns singular or plural (NN) or nouns plural (NNS), etc., are often observed. Therefore, it can be concluded that the lexical feature could be crucial prior knowledge for entity and relationship extraction tasks. Forty-five types of POS tags were used in the enhanced span embedding in this work, such as NNP, NNPS, NN, NNS and others.  
To incorporate POS tags into span representation embedding, the NLTK tool is used to extract the POS tags of word tokens and spans, and then the POS tags are converted into embedding representation. The POS tag of a token or a span is defined by:  
\[\mathrm{P_{t_{i}}}=\mathrm{POS(t_{i})},\mathrm{P_{s_{i}}}=\mathrm{POS(s_{i})} \tag{1}\]  
POS is the POS tag recognition function provided by the NLTK toolbox. \(\mathrm{P_{t_{i}}}\) is the POS tag of the ith word token \(\mathrm{t_{i}}\). \(\mathrm{P_{s_{i}}}\) is the POS tag of the ith span \(\mathrm{s_{i}}\), which is the combination of word tokens in the span:  
\[\mathrm{s_{i}}=\left\{\mathrm{t_{i}},\mathrm{t_{i+1}},\ldots,\mathrm{t_{i+m-1}}\right\} \tag{2}\]  
These POS tags are then converted into two types of lexical feature embeddings at span-level and token-level, then transformed into coarse-grained and fine-grained POS embeddings, respectively, where they are used to enhance the span embedding at two levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of** & **Meaning of** & **Word Tokens** & **Entities with** & **Entities with** & **Relations** \\ \hline NNP and NNPS & Proper noun & 3045 & 1125 & 868 \\ JJ & Adjective & 308 & 96 & 73 \\ IN & conjunctions & 113 & 17 & 10 \\ VB & Verb & 123 & 40 & 26 \\ RB & Adverb & 37 & 26 & 18 \\ DT & Determiner & 73 & 16 & 9 \\ NN and NNS & Noun & 5061 & 3575 & 2914 \\ CD & Cardinal number & 503 & 253 & 10 \\ Others & Other parts of speech & 279 & 15 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The numbers of word tokens, entities and entities with relations related to different POS tags in the CoNLL04 dataset.  
Figure 2: An example of lexical properties. The line above the sentence shows the POS annotations of each word in the sentence. In the sentence, the entities were highlighted in blue. The relation was highlighted in green. As can be seen, the POS tags of words in most entities were NNP or NN.
## 4 Simulations and Analysis  
Control signals being transmitted from sea surface to underwater in shallow sea is one of the most commonly used applications of UWA communication. This article focuses on this application and analyses the factors in the estimation of UWA channel. There are two scenarios of shallow sea illustrated in Figure 5a with a range of 2 km from the southeast of Meizhou island in the East China Sea, which are selected to simulate on the proposed platform. Scenario A is located between 119.6590\({}^{\circ}\) E, 24.8385\({}^{\circ}\) N and 119.6689\({}^{\circ}\) E, 24.8541\({}^{\circ}\) N with an approximate flat sea bottom at the depth of about 70 m. Scenario B is located between 119.5708\({}^{\circ}\) E, 24.3453\({}^{\circ}\) N and 119.5609\({}^{\circ}\) E, 24.3297\({}^{\circ}\) N where the depth of sea bottom increases from 50 m to 80 m. The time of databases is set at noon on June 22. According to WOA2013, with an increase of sea depth, the temperature gradually decreases from 27.9 \({}^{\circ}\)C to 16.7 \({}^{\circ}\)C and the salinity slowly increases from 33.5 psu to 34.2 psu. The SSP calculated by temperature and salinity is shown in Figure 5b, varying from 1513 m/s to 1540 m/s. In addition, the average SWH can also be found in Aviso+ with a value of 1.29 m.  
Three simulations are set to model channels in different receiving depth, sea bottom boundaries, and sea surface boundaries, respectively, and the influence on channel estimation performance is analysed afterwards. The detailed simulation parameters are tabulated in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline
**Parameter Class** & **Parameters** & **Values** \\ \hline \multirow{6}{*}{UWA Channel Modelling} & Transmitting Depth & 10 m \\  & Receiving Depth & 10 m, 30 m, 50 m, 70 m \\  & Significant Wave Heittht & 0 m (flat), 0.5 m, 1.29 m (measured), 2 m \\  & Sea Bottom Boundaries & flat, rising, falling, protruding, sagged \\  & Signal Frequency & 10,000 Hz \\  & Number of Beams & 1000 \\ \hline \multirow{6}{*}{OFDM Channel Estimation} & Modulation Scheme & 16 QAM \\  & Symbol Order & Grey \\  & Algorithm & LS, LMMSE \\ \cline{1-1}  & Number of Carriers & 2048 \\ \cline{1-1}  & Size of FFT & 2048 \\ \cline{1-1}  & Length of Cyclic Prefix & 512 \\ \cline{1-1}  & Pilot Interval & 8 \\ \cline{1-1}  & Number of Symbols per frame & 64 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of simulations.  
Figure 5: (**a**) Geographical location of two scenarios in simulations. (**b**) sound speed profile (SSP) curve generated from WOA2013.
# Improving Ship Maneuvering Safety with Augmented Virtuality Navigation Information Displays
## 3 Results
### Verification of Hypotheses on the Normality of the Random Variable Distribution  
After the analysis of the values of the test statistics obtained from the performed tests of the normality of the distribution of random variables of the primary criterion, in the independent groups of Scenarios from 1 to 5b, there was no basis for the rejection of the hypotheses on the normality of the distribution of the tested random variables.  
In all cases, the W test statistics (Shapiro-Wilk test) of the random variables of the primary criterion, as well as the random variables of the secondary criterion in Scenario 1 (container ship berthing) and Scenario 2 (general cargo ship berthing) turned out insignificant (\(p>0.05\)); therefore, the hypotheses on the normality of distribution was accepted.  
The Comparison of the Random Variables Obtained from the Series of Tests for All Variants of Simulation Scenarios  
The first stage of the analysis of the obtained results consisted in the comparison of the arithmetic means of distributions of all random variables: \(V_{1}\), \(T_{1}\), \(V_{2}\), \(T_{2}\), \(X_{3}\), \(X_{4}\), \(D_{5a}\), \(D_{5b}\) in independent groups (Table 8), obtained during all simulation passages of the ships.  
It should be noted that for Scenarios 1 and 2, the measured parameter--resultant speed--has relatively small values, which may vary from the actual operational parameters of the given ship. This is due to the assumption that the simulation experiment requires the navigator to achieve the lowest possible value of resultant speed while performing the maneuver in the possibly shortest time. The assumption was aimed to eliminate the situation where the navigator would adopt a subjective, higher level of the resultant speed, that in his judgement would be safe for a given ship (depending on the navigator's habits). That, in some way, specified the reference level for the analyzed random variables. This is important for statistical analysis of the measurement results and enables the assessment of the usefulness of the system during maneuvering.
# Punching Shear Strength of Reinforced Concrete Flat Plates with GFRP Vertical Grids
## 2 Shear Strength Equations
### ACI Building Code (ACI 318-19)  
If there is no shear reinforcement, in ACI 318 the critical section of the flat plate is considered to be located 1/2 of the effective depth (d) away from the column. On the other hand, if shear reinforcement is present the critical section is supposed to be on the  
Figure 1: The component of the glass fiber reinforced polymer (GFRP) vertical grid.  
perimeter, d/2 away from the outermost shear reinforcement. Figure 2a indicates the critical section of the standard of ACI 318.  
The equation provided by ACI 318 is the sum of the shear strengths of the concrete and the shear reinforcement by Equation (1), which can be replaced with Equation (2). The shear strength of the shear reinforcement is calculated by considering the vertical strips of the GFRP vertical grid as a steel stirrup. The amount of shear reinforcement on the vertical components of the GFRP plate arranged on the four faces of the column is calculated by Equation (3).  
\[V_{c,ACI}=0.083\Big{(}2+\frac{6\mu d}{b_{0}}\Big{)}\lambda_{s}\lambda\sqrt{f_{ c}^{b}}b_{0}d\text{ or }0.33\lambda_{s}\lambda\sqrt{f_{c}^{b}}b_{0}d\text{ or } \tag{2}\]  
\[\begin{split} V_{f,ACI}=nA_{f}f_{fu}.\end{split} \tag{3}\]  
\(V_{c,ACI}\): the nominal shear strength provided by concrete (ACI 318) (kN).  
\(V_{f,ACI}\): the nominal shear strength (ACI 318) (kN).  
\(b_{0}\): the perimeter of the critical section (mm).  
\(d\): the effective depth of the slab (mm).  
\(s\): the spacing of the shear reinforcement (mm).  
\(\lambda\): the modification factor to reflect the reduced mechanical properties of lightweight concrete.  
\(\lambda_{s}\): the size effect factor.  
\(A_{f}\): the sectional area of the vertical strip of the GFRP grid (mm\({}^{2}\)).  
\(n\): the number of vertical components of the vertical grid.  
\(f_{fu}\): the tensile strength of the GFRP (MPa).  
\(t_{f}\): the thickness of the vertical grid (mm).  
\(w_{f}\): the width of the vertical grid (mm).
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
## 4 Localization Schemes and MAC Considerations
### Two-Way Ranging
#### Disadvantages  
* More energy consumption.
* More complex implementation, e.g., selecting the best anchors set, roaming management.
* More messages required for localization.  
Table 5 summarizes and compares the two approaches in terms of anchors synchronization, energy overhead, MAC protocol flexibility and estimated position availability.  
Figure 2: Two Way ranging Scheme.
## 3 Results
### Ionic Conductivity  
The ionic conductivity of all prepared electrolytes was measured by EIS technique and is reported through the Arrhenius plots from 80 to 20 \({}^{\circ}\)C (Figure 4). The curvature of these plots indicates that the temperature dependence of conductivity can be expressed by Vogel-Fulcher-Tamman relation, which also describes the temperature dependence of viscosity in viscous material. The ionic conductivity value of **ZN**-0 is \(2.6\times 10^{-4}\) S cm\({}^{-1}\) at 30 \({}^{\circ}\)C, whereas the ionic conductivities of **ZN**-\(x\) (\(x\) = 0.05, 0.10 and 0.15) at the same temperature are \(2.2\times 10^{-4}\) S cm\({}^{-1}\), \(1.6\times 10^{-4}\) S cm\({}^{-1}\) and \(1.4\times 10^{-4}\) S cm\({}^{-1}\), respectively. The addition of **ZN** causes a slight decrease of ionic conductivity as its content increases. This drop in ionic conductivity after adding the zwitterion was already observed for PEG-based electrolytes [20; 21] and ionic liquid based electrolytes [23; 25]. Indeed, the electrostatic interaction of zwitterion with LiTFSI and PEGDM causes an increase in the viscosity of the electrolytes and, consequently, decreases their ionic conductivity [29]. However, the presented electrolytes possess good ionic conductivity at 30 \({}^{\circ}\)C which is higher than those obtained for similar electrolyte systems reported previously using 3-(1-(2-(2-cyanoethoxy)ethyl)-1H-imidazol-3-ium- 3-yl)propane-1-sulfonate zwitterion as electrolyte additive [21].  
Figure 4: Arrhenius plots of ionic conductivity of LiTFSI/poly(ethylene glycol)dimethyl ether (PEGDM) mixtures with (**ZN**-0.05, 0.10 and 0.15) and without (**ZN**-0) **ZN** zwitterion measured by electrochemical impedance spectroscopy (EIS) technique using stainless steel (SS)/Glass fiber/SS coin cell.  
Figure 3: \({}^{7}\)Li NMR spectra of **ZN**-0 and **ZN**-0.1 electrolytes.
## 5 Conclusions  
The output voltage of inverters is affected by nonlinear factors such as dead time and voltage drops, which inject low-order harmonics, causing fifth and seventh harmonic distortions in the stator current and inducing periodic torque ripple. This significantly impacts the control precision of PMSM. To address this issue, this paper proposes a control  
Figure 21: Harmonic analysis results for PI, QPR-PI, and QPR-SMC control methods at different speeds: (**a**) Suppression results at low speed; (**b**) Suppression results at medium speed; (**c**) Suppression results at high speed.  
strategy based on dynamic harmonic compensation sliding mode control. This method improves the harmonic suppression performance of the motor drive system, yielding effective results, and leads to the following conclusions:  
* The resonant frequency of the proportional resonant controller can be designed and adjusted as needed, allowing for selective suppression of harmonics at specific frequencies without causing distortion at other frequencies.
* Traditional QPR-PI controllers have limited effectiveness in dealing with nonlinear disturbances such as dead time and voltage drops, which introduce harmonics, thereby affecting current harmonic suppression. Compared to the traditional QPR-PI feedforward compensation method, the QPR-SMC method results in lower harmonic content and provides better current harmonic suppression, making the stabilized current waveform closely resemble the ideal sinusoidal waveform.
* To verify the correctness of the proposed scheme, a corresponding test platform was established, and comparative experiments were conducted. The results show that this method can adaptively cancel the sixth harmonic component in the stator voltage in the d-q axis system, successfully reducing the fifth and seventh harmonic components in the three-phase current, and significantly mitigating motor torque ripple.  
[PERSON] and [PERSON] designed the proposed control strategy and experiments. [PERSON] conducted the experiments and organized the data. [PERSON] and [PERSON] wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 52277068 and in part by the Key Research and Development Program of the Zhejiang Science and Technology Department under Grants 2024C01230 and 2023C01159.  
Not applicable.  
Data Availability StatementData is contained within the article.  
This research was partially supported by the National Natural Science Foundation of China and the Key Research and Development Program of the Zhejiang Science and Technology Department. We express our sincere gratitude for their support, which was crucial for the successful completion of this study.  
The authors declare no conflicts of interest.
# Influence of the Menstrual Cycle on Performance in CrossFit(r)
###### Abstract  
The purpose of this study was to compare the fast maximal dynamic strength, maximal dynamic strength, and muscular endurance changes between the follicular phase (FP) and luteal phase (LP) in the menstrual cycle (MC). Fourteen healthy resistance-trained CrossFit(r) athletes and enumorrheic women without oral contraception participated in this study (age: 29.64 \(\pm\) 3.95 years; height 1.62 \(\pm\) 0.04 m and mass: 60.43 \(\pm\) 6.56 kg). A double cross-sectional and descriptive study was developed to evaluate strength, power, and muscular endurance performance in the FP (days 5 to 7) and LP (days 20 to 23). In each assessment, the subjects performed three countermovement jumps (CMJs) for assessing fast maximal dynamic strength using My Jump 2, one-repetition maximum (1-RM) on deep squat for maximal dynamic strength, and the Karen(r) protocol for muscular endurance. The data were analyzed using a paired sample t-test to determine whether there were any significant differences between FP and LP for all the assessments and the significance was set at \(p\leq 0.05\). Results revealed no significant differences between the FP and LP in performance: (a) fast maximal dynamic strength (FP: 0.27 \(\pm\) 0.04 m; LP: 0.29 \(\pm\) 0.04 m; t = 0.98; \(p\) = 0.34; ES = 0.26); (b) maximal dynamic strength (FP: 88.14 \(\pm\) 12.60 kg; LP: 87.07 \(\pm\) 12.51 kg; t = 0.90; \(p\) = 0.38; ES = 0.24); and (c) muscular endurance (FP: 459.43 \(\pm\) 92.71 s; LP: 456.93 \(\pm\) 110.68 s; t = 0.27; \(p\) = 0.78; ES = 0.07). CrossFit(r) performance is not altered during the MC in CrossFit(r) trained athletes. However, monitoring the MC and its symptoms could be used to adapt the training prescription to the needs and preferences of the athletes.  
Keywords:follicular phase; luteal phase; training; strength; female; response +
Footnote †: journal: Applied Sciences
## 5 Experimental Hardware Realization
### Experimental Current Response in the d-q Axis  
On the basis of the experimental speed variation presented in Figure 16a, Figure 17 displays the experimental results for the _d-q_ current responses. For example, if the speed setting was adjusted from 0.25 to 0.35 p.u-speed, the current reference \({i_{q}}^{*}\) increased from 0.0122 to 0.0129 p.u-current. As the motor speed decreased--for example, from 0.55 to 0.45 p.u--the current decreased from 0.0141 to 0.0134 p.u. The current \({i_{q}}\) can generally track the current reference \({i_{q}}^{*}\). A spike in \({i_{q}}^{*}\) and \({i_{q}}\) was observed in response to the stepped speed adjustment. The experimental results indicated that the current \({i_{d}}\) trace was close to zero relative to the reference \({i_{d}}^{*}\).
## 6 Results and Discussion
### RQ2: Does MPPEngine Produce More Patches for Bugs Compared to jGenProg? In this research question, we analyzed the number of patches generated for each bug by both the approaches and compared the results to find which has better repair efficiency. Complete results of all the 27 bugs with patches are tabulated in Table 3.  
As shown, the Bug column represents the name/ids of the bugs from the Defects4j dataset. Column 2 shows approach names, and column 3 is for the patches produced for each bug through both the approaches. The TC Count column shows the total count of executed test cases to produce the patches. The difference between the generated patch count of jGenProg and MPPEngine is available in column 5, and the approach that generated more patches is mentioned in the last column. Results displayed in Table 3 shows that, for few bugs, the MPPEngine executes more test cases than jGenProg in the given time. However, in most of such cases, MPPEngine produced more patches as it could validate more program variants by eliminating invalid patches earlier. For example, in the given 3 h, Chart 3 executed 192,418 test cases through jGenProg and produced 9 patches where MPPEngine executed 1,387,003 test cases and produced 32 patches. Even though our approach takes more test cases, it produces more test suite adequate patches. Here, it has been proved that within the given time, MPPEngine validates more program variants and produces equal or more patches compared to jGenProg. As a result, MPPEngine produced patches for Chart 12 (1), Math 20 (34), Math 28 (142), Math 32 (6), Math 49 (26), and Math 50 (112) but no patch produced by \begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Cost** & **jGenProg** & **MPPEngine** \\ \hline Min & 4.80 s & 0.86 s \\ Median & 30.96 s & 19.53 s \\ Max & 792.47 s & 119.75 s \\ Std.DEV & 153.86 s & 34.29 s \\ Average & 79.27 s & 33.70 s \\ \hline
**Time Reduction** & \multicolumn{2}{c}{57.50\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time taken to validate a program variant by jGenProg and MPPEngine. jGenProg. However, for Chart 14, jGenProg produces 7 patches where MPPEngine produced none in the given time.
###### Abstract  
Fourier transform spectroscopy with bandpass sampling (BPS-FTS) increases the interferogram sampling step and reduces sampling frequency, thereby reducing system implementation difficulty. However, this technology has only been introduced in principle and lacks analysis of the actual system error. In this paper, we present a theoretical analysis of the optical system diffraction effect, derive the bandpass spectrum bandwidth and interferogram sampling step under the influence of the diffraction effect, and conduct modeling and a simulation analysis with and without the diffraction effect. Through simulation, we found that when the diffraction effect was considered, the relative deviation of the recovered spectrum from the input spectrum was 1.30%, and the spectral similarity was 0.9669. However, without considering the diffraction effect, the relative deviation of the recovered spectrum from the input spectrum was 6.23%, and the spectral similarity was only \(-\)0.0056. The simulation results demonstrate that the diffraction effect has a significant influence on BPS-FTS interferogram sampling and recovery spectra. The results of this study have reference significance for their technical realization of BPS-FTS.  
FTS bandpass sampling; the diffraction effect +
Footnote †: journal: Applied Sciences
# Experimental Evaluation of the Packet Reception Performance of LoRa
## 6 Future Work  
At present, the practical application of LoRa has achieved remarkable results in many fields, such as manufacturing, agriculture, home furnishing, transportation, car networking, and medical treatment, resulting in an increasing density and scale of networked sensor deployments. However, LoRa networks usually use a star topology, with thousands of LoRa nodes connected to a single LoRa gateway. For example, in a smart factory, multiple sensor nodes send the collected information to an LoRa gateway. Such an aggregated network structure will cause serious data packet collisions, resulting in a large amount of data packet loss and a drop in throughput. The failure of communication also increases the delay, and energy is wasted. Furthermore, if network scheduling is used for information transmission, it will bring about the problem of hidden terminals, which will also cause collisions. Since LoRa is the long-distance transmission, this problem is even more obvious. Meantime, the network scheduling scheme will undoubtedly increase the complexity of the network and consume more energy of the nodes. Last but not least, some applications require concurrent transmission, that is, multiple points instantaneously transmit messages to the gateway. For example, when a poisonous gas leak triggers an alarm in a factory, all poisonous gas detection nodes in the factory are required to immediately upload current air status information. Therefore, research on the concurrent transmission is also necessary. In the future, we expect to evaluate the LoRa performance in concurrent transmission scenarios. In addition, we hope to find a solution to achieve multi-packet decoding, which can ultimately increase the throughput of the LoRa network and reduce latency.