esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background and Hypotheses
### Perceived Plaffulness  
Users not only expect technology benefits but also a playful experience. Perceived playfulness is defined as the psychological state of playfulness or flow, which was gained from enjoyment or pleasurable experiences when an individual is doing the activity itself instead of extrinsic rewards [10]. [PERSON] and [PERSON] suggested that a user will become engaged in a website if they experience the effects of flow [30]. [PERSON] and [PERSON] extended TAM by including an intrinsic motivation factor, perceived playfulness, as an intrinsic motivational factor to affect users' acceptance of the World Wide Web [8]. Playfulness is an important factor to motivate users to utilize a new system [31]. Perceived playfulness had a significant positive relationship with attitude toward using [8]. They concluded that it is important for developers to include intrinsic and extrinsic motivational factors in user interface design, thus helping to improve usability.  
Perceived playfulness is derived from the user's subjective evaluation of their interaction with technology and is represented through subdimensions: Concentration, enjoyment, and curiosity [32]. Researchers examined two social networking sites (SNSs) and found that providing playful services for users that will keep them engaged without losing their interest. In their study, the results show that there is a positive association between perceived playfulness and a user's attitude toward using the technology [32].  
[PERSON] et. studied the effect of perceived enjoyment and usefulness on usage in a field study of Finnish computer users and found strong relationships between perceived usefulness and usage, but weak, insignificant links between enjoyment and usage [33]. In contrast, [PERSON] et al. noted that perceived enjoyment correlates positively with the frequency of Internet usage and daily Internet usage [34]. They explained that perceived fun and perceived playfulness are similar to the concept of perceived enjoyment. In this study, theseterms are handled the same and are used interchangeably. Prior research has studied the impact of PU or PEOU on perceived enjoyment, similar to perceived playfulness in our model. Therefore, we hypothesize the following:  
**H6**.: _Switch user's perceived playfulness has a significant effect on the perceived usefulness. [PP\(\rightarrow\)PU]_
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Posture-Based Routing
#### 3.1.3 Comparison and Analysis  
As shown in Table 2, the above two protocols are all modeled in the dynamic WBAN environment. The difference is that the MHRP [19] protocol monitors the heart condition, under the condition of human body movement, which uses two identical and independent sets of nodes to form a fault-tolerant system. The method is novel, but its function is too single, which achieves a reliable transmission by redundant nodes, the relay node only has the function of receiving and forwarding, to balance the energy consumption of the acquisition node. It cannot fully consider the human comfort, which can bring inconvenience to people's normal life. NCMD [19] takes full account of the uncertainty of the topological connections in dynamic environments, uses the opportunity to establish connections  
Figure 5: Overall framework of the NCMD protocol.  
in a targeted manner, moreover, it minimizes the cost of network management and improves the energy efficiency. By contrast, NCMD is more adaptable to the environment of dynamic WBAN.
## 5 Conclusions  
In this paper, we have discussed an underdetermined mixed blind signal separation method with the application of TF overlapped communication signals. The classical SCA methods to solve the UBSS problem require high sparsity of the signal. For TF overlapped signals, the classical SCA methods cannot be applied to such problems. To tackle this issue, we propose a novel method for separating TF overlapped signals based on the SCA algorithm. The core idea of the method is to construct a reasonable TF soft mask matrix by designing a contribution degree function. In the proposed method, we utilize the KDE algorithm to obtain the angle probability density distribution, followed by which the source number is estimated by detecting the peak points of the distribution. Then the contribution degree function is designed to generate the contribution degree, based on which the TF soft mask matrix is constructed to obtain the reconstructed TF signal. The sparsity requirement of the algorithm is reduced by constructing a reasonable TF soft mask matrix, which makes the separation algorithm suitable for separating TF overlapped signals. Compared with the classical SCA algorithm, the most significant contribution of the proposed algorithm is to expand the application scope of the SCA algorithm, which no longer requires the signal to have sparsity in the TF domain. In addition, the source estimation process prior to separation enables it to adapt to the case in which the number of sources is unknown. The algorithm's anti-aliasing ability and anti-noise ability are simulated and verified by simulation, which demonstrates that the proposed method enables to overcome the poor performance of blind separation for TF aliasing communication signals by classical separation algorithms.  
Conceptualization, H.M. and X.Z.; methodology, H.M. and X.Z.; writing--original draft preparation, H.M.; writing--review and editing, X.Z., L.Y., X.W. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  
Not applicable.  
Figure 16: (**a**) The SDR of different modulation signals under different SNR; (**b**) The BER of different modulation signals under different SNR. The 1 st and 2 nd comparison algorithms are given in literatures [8,10], respectively.
# Coreference Resolution for Improving Performance Measures of Classification Tasks
## 3 Methodology
### The Techniques Used to Create the Input Vectors
#### 3.3.2 Word2 Vec  
A weak point of the previous approaches is the neglecting of the semantics of a text [33]. Word2 Vec was introduced to overcome this. The Word2 Vec model uses a two-layer neural network and was created in 2013 from the need to capture the semantic similarity of words [34]. A corpus--a set of text--is needed for the training process itself (learning neural networks).  
The output of the trained Word2 Vec models are word vectors containing a large cluster of data, where individual elements (words) are represented by their position in a multidimensional space. Logical and mathematical operations can be performed with the mentioned vectors, and, thus, they can simulate semantic or lexical relations between words. Formula (4) gives the most common example of a trained Word2 Vec vector and determines that the difference between the words \"father\" and \"man\" is equal to the difference between the words \"mother\" and \"woman\". The mentioned relations are language-independent.  
\[\
u_{\text{father}}-\
u_{\text{man}}=\
u_{\text{mother}}-\
u_{\text{woman}}, \tag{4}\]  
Word2 vec can utilize one of two possible model architectures to produce these distributed representations of words:  
* CBOW (continuous bag of words)--the algorithm predicts the missing word depending on the other words in the document;
* Skip-gram--opposite of CBOW. It receives one word for the input to predict the surrounding window of context words. Both algorithms work as a neural network, which is trained by a standard procedure. The architecture is shown in Figure 5. The implementation of both algorithms is also used by the Deeplearning4j [35] and Gensim [36] tools.  
Figure 5: Model architecture of CBOW and Skip-gram [22].
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
###### Abstract  
In many structures, undesired noise and vibrations generated by external sources represent a huge problem in terms of structural damage and comfort. Active vibration absorbers can be used to dynamically suppress vibrations, by increasing the damping of the system. A wireless smart active damper has been developed to perform this task and some automated functionalities have been implemented to perform the identification of the structure on which it is mounted on. The sharing of information between wireless sensors represents one of the most interesting features of this kind of control system. In this work, a procedure to estimate the nondimensional damping and modal amplitude for each wireless sensor location and each vibration mode is studied. Then, the information obtained by each sensor in the identification phase are used to implement a coordinated control strategy, which is based on a modified version of the Efficient Modal Control (EMC). Such control strategy implements the low level Selective Negative Derivative Feedback control law and modulates the control gains of each actuator and controlled mode pair in order to get an effective vibration reduction. The tuning procedure represents the next step of the algorithm, in which the evaluation of the introduced damping and the maximum applicable gains are derived; finally, the proposed solution is validated with experimental results on a simply-supported beam.  
vibration control; inertial actuators; wireless sensors; control algorithms; automation Article
## 6 Conclusions and Future Direction  
In this study, a novel similarity measure has been suggested by exploiting the information of item genre to cover user preference similarities, even for users who having a very few available ratings. The study's principal contribution is to produce user likenesses using preferred item genre information, as users generally like specific genres. Hence, two classes of user neighbors were determined to learn user relationships. The first class was the initial neighbor group, which contains user relationships according to every genre separately, who share their preferences with active users in a specific genre, based on average user preferences. The second class is the final neighbors group, which includes users who share their preferences in all genres with the active user.  
Furthermore, we suggested two evaluation metrics to calculate the algorithm accuracy: the level of predicted ratings and the users' reliability. We compared the UGSM to two traditional similarity measures: PCC and COS, in terms of MAE, level of predicted ratings, and user reliability to confirm UGSM performance. In addition, it was compared with modern collaborative filtering algorithms that introduced user similarity measures. Although the sparsity level was different in both tested datasets, UGSM performed stably and delivered better results than traditional similarity measures with smaller neighborhood sizes in regular user experiments and with users with few ratings.  
Generally, in recommender systems, a similarity measure is used among users with a sufficient number of ratings; and another action is used between users who did not cast an adequate number of ratings. However, in this paper, the proposed model (UGSM) delivered accurate results for both user types at the same time.  
Future studies plan to integrate the item genre into the item-based collaborative filtering algorithm to improve the item similarity measure and solve the cold-start problem. Besides, examining how user characteristics can improve the accuracy of a user-based CF algorithm.  
Conceptualization, J.A.-S. and C.K.; methodology, J.A.-S.; software, J.A.-S.; validation, J.A.-S.; formal analysis, J.A.-S. and C.K.; investigation, J.A.-S.; data curation, J.A.-S.; writing--original draft preparation, J.A.-S.; writing--review and editing, J.A.-S. and C.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: MAE for (**a**) ML-20M dataset and (**b**) R2-Yahoo music dataset. The \(X\)-axis is the number of ratings and the \(Y\)-axis is the MAE value.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 3 Polyaniline-Based Electrodes
### 0D Nanostructures  
PANI-based nanoparticles, which are often available as pristine nanoparticles rather than functional coating materials, can be employed to improve binding attractions with the respective biomolecules. [PERSON] et al. synthesized hollow carbon spheres coated with needle-like polyaniline to produce biosensor electrodes for the detection of malathion, an organophosphate insecticide (Figure 4a) [97]. Briefly, hollow carbon spheres were synthesized using the SiO\({}_{2}\) template method, after which PANI was coated on the surface via in situ oxidative polymerization of the aniline within the hollow carbon sphere. Acetylcholine esterase was then immobilized on the PANI's surface by the amide group in the chain. As reported by [PERSON] et al. Au nanoparticles were attached to PANI's surface to enhance binding attractions with the HBV binding aptamer biomolecules [98]. Conversely, [PERSON] et al. fabricated lutetium phthalocyanine containing silica-PANI conducting nanobeads without the need to etch the core particle (Figure 4b) [99]. In this case, the SiO\({}_{2}\) core was vital for attaching glucose oxidase bioreceptors by introducing lutetium phthalocyanine. Thus, PANI served to interact with glucose oxidase and form thin films, thereby enabling glucose sensing.  
Figure 4: (**a**) Hollow carbon spheres coated with needle-like PANI-based pesticide biosensors [97]. Copyright 2018, Elsevier. (**b**) Lutetium phthalocyanine incorporated silica–PANI nanobeads for use as glucose electrochemical sensors [99]. Copyright 2018, Elsevier.
# Polar Cap Patches Scaling Properties: Insights from Swarm Data
## 2 Data and Methods
### Swarm Data
#### 2.1.2 The PCP Flag
This asymmetry reflects both in the total number of PCPs being higher in the Northern hemisphere than in the Southern one and also in the PCPs spatial distribution, as shown in Figure 2, indicating quite a different dependence on quasi-dipole (QD) magnetic latitude and magnetic local time (MLT) between the two hemispheres. Figure 1: Time distribution of all PCPs identified between 16 July 2014 and 28 February 2018 from Swarm A. **Left** side refers to the Northern hemisphere while **right** side to the Southern one. Blue solid line represents the monthly sunspot number. Figure 2: Space distribution of all PCPs identified between 16 July 2014 and 28 February 2018 from Swarm A PCP flag as a function of QD-latitude (\(\lambda_{\text{QD}}\)) and magnetic local time (MLT). **Left** side refers to the Northern hemisphere while **right** side to the Southern one. Circles are drawn at QD-latitudes of \(70^{\circ}\), \(75^{\circ}\), \(80^{\circ}\), and \(85^{\circ}\). Since we are going to perform a scale analysis, it is crucial to have an idea of the order of magnitude of the spatial extension of PCPs under consideration. An estimate can be obtained by multiplying the temporal length, \(\Delta\)t, of their observations by the orbital velocity of Swarm A. This is clearly an approximation since it assumes that PCPs are not traveling. Figure 3 displays: in blue, the cumulative distribution function (CDF) and, in red, the probability density function (PDF) of the sizes of the PCPs identified by the Swarm PCP flag. More than 90% out of the 32,382 identified PCPs have a dimension \(\Delta\)x in the range 50 \(\div\) 1350 km, as expected. To have an idea of the corresponding error we can consider that Swarm A has a speed of \(\sim\)7.6 km/s and PCPs have a maximum speed of \(\sim\)1 km/s (i.e., the maximum value for plasma drift in the F-region) [5]. Based on simple kinematic considerations, we expect the maximum error in our estimate of PCP size to be \(\sim\)11% when the spacecraft and the PCP move in opposite directions, which is the case with the greatest relative speed between the satellite and the PCP. Since we are assuming that PCP size is proportional to the time needed by the satellite to cross the patch, the error made in case of a patch that is not fixed in space can be estimated by the ratio of the relative speed between the spacecraft and the patch to the speed of the spacecraft. In practice, this ratio corresponds to that of the time needed to the spacecraft to completely cross the patch in the case they move in opposite directions to that needed to cross the patch in the case the patch is fixed in space. Actually, because we are dealing with a large dataset that most likely includes all possible configurations of satellite-PCP relative motion, the error associated with our PCP size estimate is likely much lower than 11%.
# Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
## 6 Experimental Results
### Three Lanes Two Arms  
The experimental results are presented in Table 12 for an environment where two arms had three outgoing lanes and two arms had four outgoing lanes, which had six environments. In the proposed method, there is an average for all six environments; the values of WTAVG, WTLAVG, and WTCAVG can be reduced by 81.66%, 9.22%, and 66.21%  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Lane**} & \multicolumn{3}{c}{**Method**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Traditional**} & \begin{tabular}{c} **Original DQN** \\ **with Fuzzy Logic**[29] \\ \end{tabular} & \begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} \\ \hline E-3 & 66.51 & 17.32 & 13.73 \\ \hline N-3 & 59.48 & 17.79 & 14.53 \\ \hline S-3 & 56.59 & 18.18 & 15.09 \\ \hline W-3 & 88.50 & 18.05 & 14.36 \\ \hline \multicolumn{4}{c}{**Average total waiting time for vehicles to make a left turn (s)**} \\ \hline \multirow{2}{*}{**Lane**} & \multicolumn{3}{c}{**Method**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Traditional**} & \begin{tabular}{c} **Original DQN** \\ **with Fuzzy logic**[29] \\ \end{tabular} & \begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} \\ \hline E-3 & 5.05 & 6.93 & 4.07 \\ \hline N-3 & 5.02 & 6.96 & 4.09 \\ \hline S-3 & 5.00 & 7.23 & 4.70 \\ \hline W-3 & 5.01 & 7.37 & 4.26 \\ \hline \multicolumn{4}{c}{**Average total waiting time per car (s)**} \\ \hline \multirow{2}{*}{**Lane**} & \multicolumn{3}{c}{**Method**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Traditional**} & \
\begin{tabular}{c} **Original DQN** \\ **with Fuzzy logic**[29] \\ \end{tabular} &
\begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} \\ \hline E-3 & 11.07 & 4.84 & 3.85 \\ \hline N-3 & 10.61 & 4.82 & 3.90 \\ \hline S-3 & 10.60 & 4.90 & 3.96 \\ \hline W-3 & 11.73 & 4.89 & 3.91 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The experimental results in a three-lanes and one-arm environment.  
when compared with the traditional method, and 20.23%, 45.14%, and 21.35% when compared with the original DQN with fuzzy logic method, respectively.  
It is noted that the results are consistent with Section 6.4 when using the traditional method, which yielded the worst overall waiting time and better results than the original DQN with the fuzzy logic method compared to vehicles turning left. However, the proposed method yielded better results than all the other methods when comparing all the metrics.
# A Relative Coordinate-Based Topology Shaping Method for UAV Swarm with Low Computational Complexity
## 1 Introduction
The simulation results have demonstrated the effectiveness of this algorithm. The LAPJV algorithm significantly reduces the computational complexity by an average of 94% in the case of 1000 UAV nodes while achieving the same minimum global energy consumption as other algorithms. The rest of this paper is organized as follows. In Section 2, the system model of topology shaping for the UAV swarm system is presented. In Section 3, the initial and target topology coordinate systems are constructed based on the inter-distance of nodes without relying on any external localization information. In Section 4, the corresponding relationship of the UAVs from the initial topology to the target topology is derived through optimal coordinate mapping. Numerical simulations and results are shown in Section 5, followed by conclusions and discussions presented in Section 6.
## 2 Datasets
### Non-AMP Generation Methods  
Given the scarcity of annotated negative AMPs, it is necessary to curate or generate negative AMPs on the basis of certain rational criteria. Here, we outline several commonly utilised methods for generating non-AMP datasets.  
**Normal generation method**  
Several methodologies involve downloading peptide sequences from UniProt and filtering out those annotated as AMPs or predicted with high AMP likelihood. For example, [PERSON] et al. and [PERSON] et al. excluded sequences labelled AMPs, membrane proteins, toxins, secretory proteins, or those associated with defensive, antibiotic, anticancer, antiviral, and antifungal functions [93; 94]. Subsequently, redundant sequences were eliminated, and entries containing non-standard amino acids (B, J, O, U, X, Z) were discarded, with the remaining sequences forming the negative dataset. However, constructing balanced classifiers requires aligning the size and length distributions of the negative dataset with those of the AMP dataset. This poses a challenge, as modern AMP repositories now exceed 30,000 entries, making it difficult to achieve parity at the dataset scale and structural consistency. This challenge has prompted researchers to pursue innovative approaches for generating non-AMP datasets to address the imbalance in scale and structural alignment.  
**Random generation method**  
[PERSON] et al. introduced a randomised negative dataset generation approach in 2022 [74]. For every sequence within the positive dataset, a corresponding negative sequence of identical length was created by substituting each residue in the template with a randomly selected natural amino acid. This method operates under the assumption that randomly generated sequences exhibit an extremely low probability of retaining biological activity akin to the original functional peptides.  
**Shuffle generation method**  
[PERSON] et al. introduced a shuffled negative dataset generation strategy in 2022 [74], which replaces residue mutation with amino acid shuffling within positive peptide sequences to construct negative counterparts. Specifically, instead of altering individual residues, they randomised the order of amino acids in functional peptides from the positive dataset to generate nonfunctional analogues. This approach similarly assumes that scrambled sequences exhibit a negligible likelihood of retaining the biological activity of their original templates, thereby serving as reliable negative dataset entries for model training and validation.