esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Collection Equipment and Tools  
Both the lower-back and leg regions were selected as the locations of interest, and the participants in our pilot study reported the prominent presence of fatigue in the legs. Thus, the muscle groups to be studied included the left/right erector spinae longissimus (LES, RES) and the bicep femoris muscles (LBF, RBF). Both these muscle groups are known to contract the most during trunk flexion, as they are responsible for pulling the weight of the upper torso and torso to ensure a stable posture. Four Trigno Wireless sensors (Delsys, Natick, MA, USA, 1200 Hz) for measuring muscle activity using surface Electromyography (EMG) were placed on the four muscles. We followed the protocols provided by SENIAM [27] for placing the sensors on the respective muscle groups using double-sided tapes. To segment the data into different portions, as well as to detect trunk movement, an optoelectronic motion capture system (VICON, Hauppauge, NY, USA, 100 Hz) was  
Figure 1: A schematic showing the overall study sessions and their respective durations.  
used. This included 20 reflective markers placed on the upper body (3 on the upper-back, 2 on the middle back, and 3 on the hip) and lower body (3 on each leg and 3 on each foot) of each participant. Marker placement locations were determined based on guidelines provided by VICON [28]. For understanding the impacts on balance, participants stood upon two floor-embedded force plates (AMTI OR6-6 platform, Watertown, MA, USA, 1000 Hz) with each foot on one force plate. All three systems were time-synced using NEXUS v1.7.1 software (VICON, Hauppauge, NY, USA). Lastly, the perceived fatigue levels in the back and legs were obtained using ratings of exertion (RPEs) on the Borg RPE CR-10 scale [29; 30].
## 5 Conclusions
Considering the entire period of the measurements, the share of TC in PM\({}_{2.5}\) from Szczecin and Trzebinia was generally much higher in relation to data from the majority of the European urban stations. Relatively high values of TC/PM\({}_{2.5}\) ratio were also recorded at the measurement stations in Marseille (France), Prague (Czech Republic), Athens (Greece) and Bern (Switzerland). The total share of the secondary inorganic ions in PM\({}_{2.5}\) from Szczecin and Trzebinia was generally lower compared to data registered at many stations located in the northern and central parts of the Europe (e.g., Menen (Belgium), Birmingham (UK), Saint-Omer and Dunkerque (France) and Basel (Switzerland). Finally, it was found that the measurement stations in Poland stand out from the other European stations with lower or comparable contribution of SIA in PM\({}_{2.5}\) mass and relatively high share of total carbon. The strong dominance of carbonaceous aerosol over SIA results from the structure of energy consumption in the country, with the dominance of fossil fuels, especially coal and lignite and the wide use of biomass in low-efficiency furnaces. This probably results from the combustion conditions favouring emissions of substances rich in organic carbon. It is also worth to note that the seasonal variation of the SIA share in PM\({}_{2.5}\) from Szczecin and Trzebinia is definitely less pronounced and almost negligible in case of rural background station in Zloty Potok. While analysing the data recorded at other European stations it could be easily observe that if seasonal differences occur, they are more visible in the case of rural background stations. B.B. : conceptualization, methodology, formal analysis, resources, data curation, visualization, investigation, writing--original draft preparation, writing--review and editing, project administration; K.W.-R.: methodology, formal analysis, writing--review and editing, English correction; N.Z.--formal analysis, writing--original draft preparation; K.K. : writing--review & editing, project administration, supervision, funding acquisition; K.J.-R.: writing--review and editing, project administration, supervision. **Funding:** This work was supported by the Polish National Science Centre under PRELUDIUM funding scheme 2 nd edition, Project no. 2011/03/N/ST10/05542 (_Investigations of chemical composition of fine particles and assessment of secondary inorganic aerosol contribution at urban and regional background sites_, 2012-2015). Data analysis was supported by the Institute of Environmental Engineering of the Polish Academy of Sciences basic (statutory)research project no. A1-104/2015 (_The influence of local and synoptic factors on the physicochemical composition of aerosols and selected gaseous substances_, 2015-2017). Review materials presented in this work is a part of the doctoral dissertation of the corresponding author (Analysis of the seasonal variability and origin of the secondary inorganic aerosol in the fine particulate matter from selected background stations in Poland, under preparation). The authors declare no conflict of interest.
# A Haar Wavelet Decision Feedback Channel Estimation Method in OFDM Systems
## 1 Introduction
However, this semi-blind estimation algorithm is not applicable in MIMO-OFDM systems, since the signal model in MIMO-OFDM systems is quite different from that in MIMO systems. This paper proposes a Haar wavelet decision feedback (DF) channel estimation method applied in wavelet-OFDM system. It has high spectrum and data transmission efficiency while suppressing the AWGN existing at the estimated channel coefficients in time-domain effectively. It can improve the system BER and normalized MSE (NMSE) performance and is easy to implement in the system hardware. Simulation results verify that the proposed Haar wavelet method is superior to the conventional LS, discrete Fourier transform (DFT), and DF channel estimation methods in terms of BER and NMSE performance under multipath fading channel conditions. The remainder of this paper is organized as follows. In Section 2, the system model is presented. Section 3 introduces three conventional channel estimation methods. Section 4 illustrates the proposed Haar wavelet decision feedback channel estimation method. The experimental results and comparison analysis are presented in Section 5. Conclusions and future work are presented in Section 6.
# Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
## 4 Experimental Study
### Visualization Analysis  
To further analyze the feature extraction capability of the MBCNN-BiLSTM bearing RUL prediction model designed in this paper, the output of different hidden layers of the model was visualized and analyzed by dimensionality reduction using the t-SNE algorithm. Taking B1_3 as an example, the input layer, fully connected layer 1, GAM layer and fully connected layer 2 features were selected for visualization and analysis, and the results are displayed in Figure 16.  
Figure 14: The RMSE values of the four models in different noises.  
Figure 15: The Score values of the four models in different noises.  
According to Figure 16, it is clear that the bearing degradation features are from disorder to order, which fully indicates that the proposed model has great feature extraction ability, and the bearing degradation trend can be better obtained by feature extraction of the final fully connected layer.
## 5 Results
### Evaluation of EmoSDS
#### 5.1.2 Response Generation Quality  
EmoSDS was trained on three datasets for evaluation: StyleTalk, DailyTalk, and EmoSC. Table 2 presents the BLEU, BERTScore, and ROUGE-L scores across the datasets. EmoSDS trained on StyleTalk outperformed Spoken-LLM by 5.6% in BLEU and 11.3% in BERTScore and achieved more than twice the ROUGE-L score. On DailyTalk, EmoSDS maintained high text generation quality with the BLEU (8.3%) and BERTScore (89.9%) and more than doubled the ROUGE-L score of StyleTalker (16.1%). EmoSDS trained on EmoSC achieved the highest performance, with BLEU (10.4%), ROUGE-L (40.5%), and the highest Resp. F1 (48.6%), indicating superior emotional adaptability. The consistently high performance across all metrics highlights the effectiveness of EmoSDS in generating structurally coherent and emotionally aligned responses. These results suggest that our proposed method enhances both linguistic coherence and emotional appropriateness, leading to more contextually relevant and emotionally expressive responses.
## 7 Review of Security and Privacy-Preserving Schemes in VANETs
### Identity-Based Encryption Schemes with Pseudonyms  
Identity-based encryption (IBE) was first proposed in [29]. Identity-based cryptography allows the public key of an entity to be derived from its public identity information such as name, email address, or VIN, _etc_. Anybody who wants to send a message to a user can use the recipient's identity to get their public key and encrypt the message using this key, which can only be decrypted by the private key of the intended recipient. A private key generator (PKG) is used to generate and distribute private keys for users (through a secure channel) and also to distribute the public key. IBE is highly suited to a dynamic _ad hoc_ network like VANETs, as it allows nodes that have not met before to start communicating with each other quickly and safely.  
The authors in [15,30,31] have used the idea of IBE in VANETs. The proposed architecture in [31] consists of four entities, two TAs which are trace authority (TRA), a PKG, an RSU at the roadside, and mobile OBUs on vehicles. Privacy in IBE schemes is achieved by using pseudonyms that the user can request from the TRA. The TRA is responsible for registration of RSUs and OBUs, and can reveal the actual identity of the signing OBU. The PKG is responsible for generating and assigning private keys for OBUs and RSUs. Self-generating pseudonyms (PNs) are suggested for privacy preservation. For authentication, a pool of PNs is preloaded into a vehicle for different regional trusted authorities (RTAs). Users from different regions can authenticate each other via RTAs. RTAs are responsible for generating cryptographic key materials for the RSUs and the vehicles in the region and deliver them over secure channels. The users use their self-generated pseudonyms as identifiers instead of real-world identities. Similarly, the idea of IBE is used in [30] to improve the performance and reduce the processing time of RSU when it is verifying signatures for a large number of users.  
Identity-based cryptography seems to be a good candidate for security and privacy in VANETs but has its own limitation and challenges, especially with revocation, which is still a major problem and is open for research. Secure channels for secret key distribution and heavy computation costs are other factors to consider.
# A Web-Based BIM-AR Quality Management System for Structural Elements
## 2 The Proposed Framework for BIM-AR Integration for Quality Control
### Data Needed for Quality Control  
In the proposed BIM-AR QC procedure, the information needed for the quality management of structural elements is represented by three types of data that must be collected, exchanged, and synchronized to have a 4D BIM quality model:  
* model of physical objects involved in the construction processes such as column and beam components and equipment employed, including the geometric data and the data on materials and other specifications; an example of generic physical objects that can be modeled is presented in Figure 3, in the case of a steel and cast-on-site reinforced concrete structure;
* work schedule data based on the project tasks, their relationship, and their time schedule, where inspection lots and their parameters can be related to these tasks to give them a time dimension;
* QC data, including definition of inspection lots and relevant quality parameters serving as a checklist to be controlled for each element, acceptance criteria, decisions, and instructions to the persons in charge in case of rework.  
All these data can be divided into three categories, based on when they need to be used, namely: (a) before, (b) during, and (c) after execution. The aforementioned categorization of the data for QC purposes is quite basic to apply in real-life projects, but structured enough to comprise the most relevant aspects and verify the proposed framework.  
According to European standard 13670:2009 [35] on the execution of concrete structures, quality parameters must be controlled for material, process, and geometry. The used material must be consistent with the required specification as well as the compliance of the finished element. The related activities and preventive measures must follow specific guidelines, and defined tolerances are admitted in terms of geometry of both simple and composite structural elements. As an example, Figure 4 shows how activities for the execution of a concrete structure are divided into the three phases; in each phase, quality parameters are controlled for material, process, and geometry. In Table 1 of  
Figure 2: The proposed BIM–AR QC workflow.  
Appendix A, all the quality parameters for the control of the material of a concrete structure are listed, with the corresponding inspection lots, related activity, relevant standards, and execution phase.
## 4 Performance Analysis of SU over Rayleigh Fading Channel  
In this section, the SNR, SINR and BER of SU over a Rayleigh fading channel will be analyzed in detail. First, we will look into the no-collision case and then at the collision case. We consider four different modulation schemes: 4-QAM, 16-QAM, 64-QAM, and 256-QAM for BER simulations in this work. Figures 3 and 4 show the simulation and theoretical SNR and BER plots for Equations (19) and (20) for the no-collision scenario, respectively.  
Here, the peak power is assumed to be \(p\) for both PU and SU. Intuitively, one can observe in Figure 3 that the higher peak power results in higher SNR as compared to less peak power in comparison with the IT. It is also well known that the higher the modulation order, the higher the BER [21]. This phenomenon can be easily observed in Figure 4. On close observation, it can be seen that the saturation of BER curves at the IT (10 dB), given by \(q\). This is because of the limitation imposed by PU network via IT on the SUs transmit power.  
Figure 4: BER of M-QAM for no-collision case with IT (\(q=10\) dB).  
Figure 3: Theoretical and simulation plot of SNR in no-collision case with a) \(p=10\) dB and \(q=1\) dB (i.e., \(p>q\)) and b) \(q=10\) dB and \(p=1\) dB (i.e., \(q>p\)).  
In the next scenario, we will look into the case of SINR and BER for collision case. As in the previous case, as the order of modulation M-QAM increases, the BER also increases, i.e., the value of BER is lowest for M = 4, and the highest for M = 256. Figures 5 and 6 show the simulation and theoretical plots for SINR from Equation (12) and BER from Equation (13) for the collision case.  
In Figure 5, one can observe the effect of collisions that degrade the SINR even though the peak power is higher that IT, while Figure 6 follows the same intuition of no-collision case, that is, the higher the modulation order, higher the BER. The saturation of BER curves at IT can also be observed, as in the case of no-collision. On the other hand, Figure 7 shows the mean BER for 16-QAM at different IT values of 10 dB, 20 dB, and 30 dB, and the saturation effect of it on mean BER. Furthermore, an increase in IT (\(q\)) will result in a lower BER as it relaxes the peak power constraint on SU transmission.  
In addition, the mean BER given in Equation (8) for different modulation schemes at a given IT is plotted in Figure 8. For this simulation, SNR is varied from \(-30\) and 40 dB, while \(q\) is set at 17 dB. The total number of available carriers were kept at 100, out of which 60 are reserved for the PU, and 20 carriers are reserved for the SU. A total of 1000 iterations were performed in the simulation. The same intuition as in the cases of no-collision and collision cases will apply here too, that is, the higher the modulation order, higher the mean BER.  
Figure 5: Theoretical and simulation plot of signal to interference and noise ratio (SINR) in collision case with a) \(p=10\) dB and \(q=1\) dB (i.e., \(p>q\)) and b) \(p=1\) dB and \(q=10\) dB (i.e., \(q>p\)).  
Figure 6: Bit error rate (BER) for M-quadrature amplitude modulation (M-QAM) for collision case with \(q=17\) dB.
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 5 Swarm Based Solution
### Cluster Head Selection Problem  
The suggested solution is offered in two stages. When sensor nodes are placed and turned on, the first phase starts. All nodes in a homogeneous network have the same initial energy. Sensor nodes communicate with their neighbors by sending \"hello\" messages that include node location, ID, and energy remaining. After exchanging messages, nodes generate neighbor tables. The sensor node with the most neighbors is the first cluster head. In a heterogeneous network, the first cluster head is based on node residual energy. Advanced and super nodes are given priority to become cluster heads since they have greater energy.  
The ACO algorithm eliminates repetitive rotation and prevents sensor nodes from transmitting a lot of control signals during the second step, cluster head selection.  
During each time interval \(t\), the sink node broadcasts an advertisement message to trigger cluster head rotation. Then, each cluster head broadcasts an ant across neighboring nodes to start head rotation. Each neighbor set receives one ant, which ACO designates as the cluster head. The node with the highest probability is chosen to serve as the cluster's new head after it scans its neighbors. Each of the \(k\) ants chooses the head node according to a probabilistic rule. Ant k's likelihood of selecting node \(x\) as the cluster head is:  
\[P_{x}^{k}(t)=\frac{\left[\tau_{x}^{2}(t)\right]^{\theta_{1}}\left[\eta_{x}^{2} (t)\right]^{\theta_{2}}\left[\lambda_{x}^{2}(t)\right]^{\theta_{3}}}{\sum \limits_{l\in NM_{x}}\left[\tau_{l}^{2}(t)\right]^{\theta_{1}}\left[\eta_{l}^{2 }(t)\right]^{\theta_{2}}\left[\lambda_{l}^{2}(t)\right]^{\theta_{3}}} \tag{93}\]  
where \(\tau_{x}^{2}(t)\) is the pheromone value of node \(x\) at the time \(t\), \(\eta_{x}^{2}(t)\) and \(\lambda_{x}^{2}(t)\) are the heuristic information, \(\theta_{1}\), \(\theta_{2}\), and \(\theta_{3}\) are the control variables that affect the pheromone value and the heuristic information parameters, respectively. A flowchart of the proposed cluster head selection approach is shown in Figure 4.
# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 2 Related Work
### Neural Machine Translation (NMT) Architecture
#### 2.1.2 RNN Based Model with Attention Mechanism  
To address the aforementioned problem, [PERSON] et al. [5] introduce an attention mechanism in the RNN encoder-decoder architecture. In modified RNN models with an encoder-decoder attention mechanism, given the input sequence \((x_{1},\dots,x_{t})\), all hidden states in the encoder phase are stored in a matrix \(H=[h_{1},\dots,h_{t}]\). Then, in step \(i\) of the decoder phase, the hidden state (denoted as \(s_{i}\) to identify) is updated as well, while a weighted sum \(c_{i}\) of encoder hidden states is calculated to represent the information that is important for the output in this step. Formally, given the decoder hidden state \(s_{i}\) (information of output status), the normalized weight for the encoder hidden state \(h_{j}\) is as Equation (4):  
\[e_{i,j}=\frac{\exp(a(s_{i},h_{j}))}{\sum_{j=1}^{l}\exp(a(s_{i},h_{j}))},j=1, \dots,I \tag{4}\]  
where \(a(s_{i},h_{j})\) can simply be dot product of \(s_{i}\) and \(h_{j}\) in practice.  
Then \(c_{i}\) is, as Equation (5):  
\[c_{i}=\sum_{j=1}^{l}e_{i,j}h_{j} \tag{5}\]  
Then, \(c_{i}\) can be used to generate output \(y_{i}\). Notice that in each \(h_{j}\), the information from the corresponding input \(x_{j}\) and its neighbor is expected to be predominant, and a high value of \(e_{i,j}\) makes \(c_{i}\) contain more information from \(h_{j}\). Therefore, weight \(e_{i,j}\) indicates the attention of the decoder on input \(x_{j}\). In general, RNN models with an encoder-decoder attention mechanism can focus on important parts of input information in the transduction task, which improves performance [5; 6; 7; 56; 57; 58; 59; 60; 61]. However, RNN models have an auto-regressive workflow in both training and inference; that is, the hidden state \(h_{i}\) has a dependency on \(h_{i-1}\) from the last step. Therefore, the input and output have to be processed step-by-step and thus cannot be accelerated by parallel computation. Moreover, the encoder-decoder attention mechanism works in the decoder phase, while context information is neglected when encoding the input sequence.
# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
## 2 Materials and Methods
### Methods
#### 2.3.4 The Influence Mechanism of Characteristic Variables on Soil Moisture  
(1) Topography affects the runoff and accumulation of water. Steep slopes may result in rapid drainage, while sunken areas may pond, causing uneven soil moisture distribution [10; 31; 32].  
(2) Vegetation cover affects the evaporation of soil moisture and the absorption of water by the roots, and deep-rooted plants can generally absorb groundwater more efficiently [33].  
(3) High temperatures increase evaporation, which reduces soil moisture.  
(4) The water content in the soil affects the electromagnetic properties of the soil, especially in the microwave band. Because the nodal properties of water are higher than the dielectric properties of soil, as the water content of the soil increases, the dielectric constant of the soil increases, thus affecting the scattering properties [34].
# Enhanced Osteoblast Adhesion and Proliferation on Vacuum Plasma-Treated Implant Surface
## 3 Results
### Enhancement of Protein Adsorption by Plasma Treatment  
Once an implant is inserted into the body, ECM proteins in body fluid, such as fibrinogen, FN, and collagen, will be adsorbed to the implant surface, forming a primary adsorption layer. Then, cells will adhere to this layer to migrate and proliferate [26; 27; 28]. Therefore, protein adsorption efficiency is an important indicator that demonstrates the biocompatibility and the osseointegration efficiency of the implant. Among various ECM proteins, FN is known to contribute to forming focal adhesion of cells and assisting cell adhesion. Therefore, to compare the amount of protein adsorbed on different implant surfaces, SLA, CaSLA, and PCaSLA were prepared and immersed in fibronectin solution (50 \(\upmu\)g/mL) for 2 h. The amount of protein adsorbed to the implant surfaces was quantified by micro-BCA assay kit, as shown in Figure 4A.  
Figure 4: (**A**). the amount of adsorbed fibronectin, (**B**). the amount of adherent cells on SLA, CaSLA, and PCaSLA measured by CCK-8 assay after 2 h of culture, and (**C**). the amount of proliferated cells on SLA, CaSLA, and PCaSLA measured by CCK-8 assay at day 3 and day 7. [*, \(p<0.05\); ***, \(p<0.001\); n.s., not significant].  
Figure 4A demonstrates that FN adsorption rate on CaSLA increased by 49.2% compared to the one on SLA. This result indicates that calcium coating on the SLA surface enhanced the protein adsorption efficiency. FN adsorption rate after plasma treatment (PCaSLA) improved the FN adsorption rate by 46.3% compared to the one on CaSLA. When SLA was coated with calcium and subsequently treated with plasma, it was shown to have much better protein adsorption efficiency than SLA without any treatment.