esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Fibromyalgiagia syndrome is a chronic pain condition involving altered nociceptive processing, which requires multidisciplinary management. Photobiomodulation therapy (PBMT) has recently emerged as a promising non-pharmacological approach, but its clinical effectiveness and optimal application methods remain unclear. This systematic review evaluated the efficacy of PBMT in managing Fibromyalgiagia symptoms, including pain, physical function, sleep quality, and overall well-being, while comparing localized and whole-body delivery. A systematic review was conducted in accordance with the PRISMA guidelines and previously registered on PROSPERO (CRD42024626368). Literature searches were performed across MEDLINE ((_PubMed_)), PEDro, SPORTDiscus, Scopus, Elsevier (_ScienceDirect_), and Web of Science (WOS), identifying 17 eligible studies (n = 857 participants). PBMT was applied via low-level laser, infrared, or LED-based devices, delivered either locally or to the whole body. The methodological quality of the studies was assessed using the PEDro scale, and the risk of bias was evaluated using the RoB 2.0 tool. PBMT showed significant clinical benefits, including reduced pain intensity, improved physical function, decreased fatigue, and enhanced quality of life. Whole-body PBMT showed greater and more sustained effects than localized applications, likely due to its systemic modulation of nociceptive pathways and autonomic regulation. Improvements were also observed in terms of psychological well-being, sleep quality, and reduced kinesiopobia. In conclusion, PBMT appears to be an effective therapeutic option for Fibromyalgiagia syndrome, with whole-body applications offering superior benefits. However, the variability in treatment parameters and study methodologies underscores the need for standardized protocols and high-quality clinical trials to support its integration into multidisciplinary pain management strategies.  
keywords:fibromyalgiagia; photobiomodulation therapy; chronic pain; central sensitization
## 6 Conclusions  
This research describes the SDN environment that can change the network ecosystem from the hardware-based one to the software-based one while global NRENs are moving their focuses toward software-based future service environments. At present, it is difficult for a certain user to get full network information about end-to-end path. Due to this reason, it is extremely difficult to compute the optimal path to the target destination. Moreover, when an error occurs on the path, the path must be searched again, and the optimal path must be found. However, path information sharing is rare. Allocating optimal paths among the networks and providing connectivity to the users at the end of the network are crucial missions of not only commercial network service providers, but also the NREN.  
SDN is a new network architecture in which network control and data transmission features are separate and programmable. The service providers and the communication service providers can gain network control independence from network equipment vendors by simplifying the network design, implementation and operation. There is no need to understand thousands of protocol standards and details. Instead, the infrastructure layer can be easily controlled simply through the controller.  
This paper proposes an application that calculates optimal paths for data transmission and real-time audio and video transmission, which is a major issue of the network service providers. The SRC application is efficiently designed and applied in the multi-domain network for efficient use of the resources, selection of the optimal path between the domains and optimal establishment of end-to-end connections. The application focuses on the architectural structure design for the selection of the network paths.  
The research presents an optimal path determination model in the SDN network, a new paradigm in the changing network environment, and is expected to help NRENs research activities in the SDN and the newly emerging field and promotes itself as NRENs by being applied to user-friendly services and platforms.
# Contract-Based Incentive Mechanism for Mobile Crowdsourcing Networks
## 2 System Model for Crowdsourcing Incentive Mechanism  
As shown in Figure 1, a MCN includes three basic entities: an SP, end users, and \(N\) MUs. End users first send their requests to the SP for help. Then, the SP divides the service requests into several small crowdsourcing tasks, which are published on the service platform. The MUs are recruited for crowdsourcing tasks by the SP. Once these crowdsourcing tasks are finished, the SP provides the end users with the final service.  
However, because of the selfish characteristics of the SP and MUs, crowdsourcing participants only aim to maximize their own benefits. This paper investigates the incentive mechanism between the above two parties to create mutual benefits. Mobile crowdsourcing is modeled as a labor market. The SP, as the employer, offers the contract to recruit certain MUs for crowdsourcing. The contract is composed of a set of different items regarding the various combinations of the _basic wage_ and _performance bonus_. Each MU, as an employee, chooses one item from the contract when participating in crowdsourcing tasks.  
Moreover, in this context, to characterize the behaviour of crowdsourcing participants regarding their willingness to participate in crowdsourcing tasks, the participants' behaviour can be categorized as risk-averse or risk-neutral [24]. A risk-averse MU does not want to obtain too great a profit by participating in crowdsourcing tasks. A risk-averse SP appreciates higher profit but demands a basic level of service, whereas a risk-neutral participant is an entity whose objective is only to maximize the SP's profit.
# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 2 Related Works  
### Sentiment Analysis  
Sentiment analysis, also known as opinion mining, is a crucial subfield of NLP that focuses on discerning and categorizing opinions expressed in text [13; 14]. Its primary goal is to determine the writer's position toward specific topics or the general polarity of the sentiment of the text. This analysis typically involves categorizing text polarity at various levels: document, sentence, or feature/aspect level, determining whether the expressed opinion is positive, negative, or neutral [3].  
With the advent of deep learning, sentiment analysis has undergone significant advances. Models such as Bidirectional Encoder Representations from Transformers (BERT) and its variants have been extensively employed for nuanced sentiment analysis, enhancing context and semantic understanding [15; 16]. Moreover, transformer-based models like GPT-3 have pushed the boundaries further in generating human-like text, which is advantageous for more intricate sentiment analysis scenarios [17].  
Sentiment analysis finds extensive applications across various domains, from customer service and market research to social media monitoring and political campaigns. It is essential for businesses and organizations to gauge public opinion, conduct market research, monitor the reputation of the brand and the product, and understand customer experiences [1].  
In today's era of advanced NLP technology, sentiment analysis has emerged as a highly focused research area within the field, benefiting from a plethora of readily available high-quality datasets, such as IMDb [18] and SST-2 [19]. This availability has injected significant vitality into research in this direction. However, the \"black box\" nature of many deep learning models used in sentiment analysis poses another major limitation. These models, while powerful, often lack transparency in their decision-making processes, making it difficult for users to understand and trust their predictions.  
Furthermore, sentiment analysis faces challenges in detecting nuances such as sarcasm, irony, and context-dependent meanings. Future research may involve more sophisticated models that understand complex human emotions and incorporate multimodal data (text, images, and videos) to better understand sentiments [20].  
The field of sentiment analysis in NLP continues to be dynamic, with ongoing efforts to enhance the accuracy and versatility of sentiment detection algorithms. As computational models evolve, their ability to discern sentiments from text is expected to become increasingly refined and sophisticated.
## 6 Challenges and Future Trends in LEV Charging
### Fully Renewable-Based Charging System  
Another significant development in the field of LEV charging is the incorporation of renewable energy sources into the charging infrastructure. The main motivation behind this is to alleviate the burden of supply from the AC grid [60]. Works such as [4; 60; 103; 104; 105; 106; 107; 108] have presented topologies that integrate solar PV sources into the EV charging systems. Furthermore, a comprehensive review on this subject is presented by [109], which deals with different aspects of a PV-based EV charger--power electronic converters, energy storage units (ESU), energy management, to name a few. The authors of [110] have also reviewed the performance of grid-connected and standalone PV-based EV chargers. The authors of [4; 60; 103; 104; 105] have considered the charging of LEVs with roof-mounted PV panels for their on-board charger implementation. [PERSON] et al. [104] also considered PV power supply as one of the inputs for the isolated SEPIC converter that they demonstrated for their on-board charger. However, its drawback was that the battery charging process from PV input was solely controlled using MPPT, which did not take the battery current or voltage status into consideration. The work described in [PERSON] et al. [103] has the same drawback.  
However, it is also possible to integrate LEV charging systems with standalone renewable energy sources--particularly solar PV [111]. In this way, the charging process can be made truly sustainable, thus removing any dependence on the AC grid. Furthermore, this type of charging is suitable for areas with limited accessibility to the grid. Consequently, certain research works based on this have been reported in recent years [69; 70]. However, while such works have shown satisfactory performance in terms of charging efficiency, there are still certain aspects in system implementation that require attention:  
* Integration with solar PV will result in an increased number of power conversion stages, on account of the addition of a power converter for PV MPPT. To counteract this, a single power conversion stage can be used, which may be responsible for both maximum power point control and battery charging control. While such a scheme may result in improved efficiency, the complexity of control may also increase.
* Another key challenge in implementing such systems is the continuity of power supply. Supply intermittency may arise from fluctuations in--or lack of--solar irradiation during the day. For this purpose, it is imperative to provide a backup ESU [109; 110]. The sizing of such an ESU must take the sizing of the PV system into consideration. However, this may result in increased maintenance costs.
* Furthermore, there must also be a provision for usage of surplus power in such charging stations when there is no EV available, and the ESU is fully charged. This issue can be partially alleviated if this charging station is to be implemented as a part of an extended DC distribution network, as discussed by [112; 16; 113].
## 3 Experimentation and Analysis
### Fault Classification  
To evaluate the effectiveness of the proposed algorithm for fault classification, the experimental signals were decomposed using VMD with different K-values (3 to 12). The entropy results from the different VMD applications were used as the dataset for an RBF neural network, which was used to identify the health condition of the rail.  
Example results for classification accuracy and the confusion matrix are presented in Figures 12 and 13. The results shown in the figures are for the selected K-value, K = 6.  
The two figures show that, for the selected K-value, both healthy and faulty rails could be classified with over 94% accuracy. This suggested that acoustic impact response signals obtained from different rail conditions could be effectively classified using the proposed IVMD-SVD method. All healthy and all 0.75 mm samples were identified correctly. One 0.1 mm sample (12.5%) was incorrectly identified as healthy. However, this was considered to be due to the small size of the defect, which might also have been too small to diagnose using existing approaches.  
Figure 12: Accuracy of RBF.  
Figure 13: Confusion matrix of RBF.  
The accuracy results for the different K-values (3-12) are shown in Table 2 and Figure 14.  
The figure suggests that it was difficult to classify signals when K = 3, which had only around 60% accuracy. The highest accuracy was achieved when K = 4 to 7 with the absolute greatest accuracy occurring for K = 6.  
Hence, it was demonstrated that the value of K = 6, automatically selected by the proposed IVMD-SVD process, had the greatest effectiveness in decomposition of acoustic impact response signals for use in fault classification. Furthermore, it was demonstrated that underside corrosion of the rail could be effectively diagnosed using the IVMD-SVD method.
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 5 Evaluation of IFC Schema Extension Cases  
### Overview of Extension Cases  
Our corpus of papers includes a total of 64 publications that document the extension of the IFC schema to meet particular domain-specific engineering and management requirements. As per the data in Figure 7, a significant rise in publications was observed from 2013 onwards, and culminated in 2017. This increase is believed to be influenced by the release of IFC 4 in 2013, which highlighted the potential extensions in the infrastructure domain, thus spurring greater interest in exploring necessary extensions for the infrastructure sector in BIM.  
The following sections introduce the trends identified in these extension efforts. Specifically, the studies were classified and evaluated with respect to (1) their domains and sectors, (2) product and process representations, and (3) implementation and validation approaches. Timeline analyses were also selectively performed for these criteria to determine the temporal trends over the defined period.
## 2 Related Works
The number of operations requires a large amount of processing and memory, causing these operations to be limited to small inputs. However, this approach aims to find characteristic vectors by the technique of reducing the data of the inputs that can be associated with the actions [43]  
data from a noise source (Definition 1); the weights are adjusted after the evaluation of the second network, called the discriminator (Definition 2); the total cost function is the sum of both networks (Definition 3). **Definition 1**. : _Let \(n\) be the number of samples, \(D_{1,2}\) be the cost function of the discriminator network, \(G_{1,2}\) be the cost function of the generator network, and \(Z_{1,2}\) be the noise source. The maximization of the cost function of the discriminator network is obtained according to the following expression:_  
\[M_{cf_{D_{1,2}}}=\frac{1}{n}\cdot\sum_{i=0}^{n}log(D_{1,2}(i))+log(1-D_{1,2}(G _{1,2}(Z_{1,2}^{i}))) \tag{5}\]  
**Definition 2**. : _Let \(n\) be the number of samples, \(D_{1,2}\) be the cost function of the discriminator network, \(G_{1,2}\) be the cost function of the generator network, and \(Z_{1,2}\) be the noise source. The minimization of the cost function of the generator network is obtained according to the following expression:_  
\[m_{cf_{G_{1,2}}}=\frac{1}{n}\cdot\sum_{i=0}^{n}-log(D_{1,2}(G_{1,2}(Z^{i}))) \tag{6}\]  
**Definition 3**. : _Let \(D_{1,2}\) be the cost function of the discriminator network, \(G_{1,2}\) be the cost function of the generator network, and \(Z_{1,2}\) be the noise source. The full cost function of a simple GAN architecture is obtained according to the following expression:_  
\[GAN_{cf_{1,2}}=M_{cf_{D_{1,2}}}+m_{cf_{G_{1,2}}} \tag{7}\]  
This approach provides the following solutions for the generation of images, obtaining incredible results: the transformation of an image to another representation of the data in a different domain [23]; generating data to create an image with different machine learning approaches [50]; generating sequences without pretraining data [51]; analyzing the convergence policies during the training [52]. In this proposal, two domains are required: are virtual and real. The GAN has a principal role in connecting both domains, as shown in Figure 3.  
Finally, once the interoperability between two domains, the virtual and authentic environments, has been achieved, it is possible to implement the system on embedded devices by transfer learning, which is used to improve the learner from one domain by transferring information from another related domain [53]. In other words, the original system is changed by a new architecture that learns the same behavior, but in this case with fewer features. Inspired by these contributions, the following procedure was defined to create a dataset in a virtual environment to reduce the development and implementation in low-cost systems. The following section describes the design of the dataset and each approach to generate a path based on the \(A^{*}\) algorithm optimized with the navigation meshes.
## 2 Background
### Input Encoding Methods  
The LSNN we used in this work can work with two types of input encodings: Current (current-driven) or events (events-driven). In the Current-Driven LSNN, the input signal is constant for all the inference time, while in the Events-Driven LSNN, the signal is first encoded as spikes and then provided as input. This section describes some of the state-of-the-art encoding methods and details the algorithm we implemented to encode acceleration signals coming from the MEMS sensor. Considering Figure 2, which shows the frequency shift that we want to detect, we give as input to the network the FFT of the acceleration signal due to the vehicle crossing the viaduct.  
Literature is rich in algorithms to encode a waveform into a stream of spikes. Some of those approaches try to minimise signal reconstruction error, while others focus on emulating biological-plausible behaviours.  
The authors of [29] propose a family of methods that minimise signal reconstruction error. All proposed methodologies are characterised by the presence of two complementary neurons (normally referred to as positive and negative), which expose a contrasting behaviour, that is, when one of the two fires, the other does not.  
The simplest temporal encoding algorithm is called Threshold Based Representation (TBR). In this algorithm, whenever the difference between two consecutive signal samples is higher than a predetermined fixed threshold, then the positive neuron emits a spike. Unfortunately, while being computationally cheap to implement, TBR is known for leading to high reconstruction error [29], even for signals with simple dynamics.  
The Step Forward (SF) method uses a baseline value (initialised as the value of the first signal sample) and a fixed threshold. Suppose the absolute value of the difference between two consecutive samples is higher than the sum of baseline and threshold. In that case, the positive neuron spikes and the baseline is updated, adding the baseline. A variation of this encoding strategy is called Moving Window (MW), where the baseline is updated looking at a moving window of signal samples. The other encoding methods proposed in [29] have not been considered in this work because of their inherent higher computational complexity that is not suitable for the chosen architectural target.  
In [30], the authors describe some biological methods of encoding without considering the reconstruction error. In all these methods, the information is encoded in the reciprocal spikes of several neurons, meaning that proper encoding of the signal depends on the number of neurons adopted. At the time of the first spike, the information is stored in the delay between the start of the stimulus and the neuron's firing. In this method, the first neuron inhibits all the others; therefore, the information is in just one spike. In latency code, the information lies in the time between spikes of different neurons. In Rank-Order Coding (ROC), the information is encoded in the order of the spikes. In this method, every neuron can fire at most once for every sample (representing a single FFT in our case).
## 5 Result and Discussion
### Robustness Test  
In a real-world application, when the quadrotor is flying in an outdoor environment, it is always subjected to an unknown external disturbance such as wind gust [72]. Furthermore, in real implementation on the quadrotor system, feedback data are always noisy and distorted due to the mechanical vibration arising from the sensor [85]. Therefore, to test the controller's robustness under these circumstances, another two separate simulations were conducted to consider the external disturbance and sensor noise.  
In the first test, the quadrotor was assumed to fly in an environment where an external disturbance was present throughout the flight time. The unknown external disturbance force was modeled using a sinusoidal wave function with the amplitude of 1 and the frequency of 8 Hz, as shown in Equation (22) below. Then, the external disturbance modeled was injected into the system for both altitude and attitude subsystem. The plot of the disturbance's model is depicted in Figure 5 below.  
\[\text{d}(\text{t})=1\sin(8\text{t}) \tag{22}\]  
The step response of the system using the proposed IGWO PD2-LQR controller in the presence of unknown external disturbance are shown in Figure 6a-d for the roll, pitch, yaw, and altitude motion, respectively. From these figures, it is observed that the controller was able to control and stabilize the quadrotor at the desired reference altitude and angle without significant degradation in its performance. However, it can still be noticed that the system is attenuating slightly around the nominal point since it was repeatedly subjected to external disturbance. However, if we can see from the altitude and attitude error of the system's response in Figure 7, the error produced is not too large, and the deviation  
In the second test, we assume that the system is corrupted with the noisy signal coming back from the feedback loop sensor. White Gaussian noise with a zero mean value and variance of 0.01 was applied to simulate the sensor noise present in the feedback signal. The generated noise was fed into the feedback loop for both the altitude and attitude subsystems. The plot for the generated sensor noise is presented in Figure 8.  
The step response of the system using the proposed IGWO PD2-LQR controller in the presence of sensor noise are shown in Figure 9a-d for the roll, pitch, yaw, and altitude motions respectively. From these figures, the controller observed that the controller could control and stabilize the quadrotor at the desired reference altitude and angle without significant degradation in its performance. Nonetheless, it still can be noted that the system is attenuating slightly around the nominal point since it was constantly subjected to sensor noise. However, if we can see from the altitude and attitude error of the system's response in Figure 10, the error produced is not too large, and the deviation is only around \(\pm 0.04^{\circ}\) for roll, pitch, and yaw motions, respectively, and \(\pm 0.03\) m for the altitude motion.