esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Study  
Fifteen real time series datasets from the M3 competition with a stationary pattern (five small, medium, and large ones) [33] were used in this study to evaluate the performance of the proposed method. For each one, the augmented Dickey-Fuller test was used to check whether the pattern was stationary. Brief details of these datasets are reported in Table 2.  
\(\alpha\) was set in increments of 0.001 using \(\alpha\in[0,1]\), so there were 1000 sets of conditions for each dataset. The experimental study was conducted via the steps using the R version 3.5.2 [34] to measure the performance of the proposed method compared to the SES method.  
The steps of the experimental study are as follows:  
1. Each time series dataset with a stationary pattern is performed for three initial values by applying methods 1 and 2, and the proposed method are as follows: Method 1: \(F_{1}=y_{1}\) Method 2: \(F_{1}=\left(\frac{1}{6}y_{1}+\frac{1}{6}y_{2}+\frac{1}{6}y_{3}+\frac{1}{6}y_{4 }+\frac{1}{6}y_{5}+\frac{1}{6}y_{6}\right)\) The proposed method: \(F_{1}=\left(\frac{6}{217}y_{1}+\frac{5}{21}y_{2}+\frac{A}{217}y_{3}+\frac{3}{2 1}y_{4}+\frac{2}{217}y_{5}+\frac{1}{21}y_{6}\right)\).
2. For each initial value setting, use the solver in Excel to find the optimal value for \(\alpha\) while minimizing the MSE. Hence, we obtained the optimal value for \(\alpha\) and the MSE value of each initial value setting.
3. For each initial value setting, use a grid search with \(\alpha\) by varying 0.001, 0.002,..., 1.00 and compute the MSE in each \(\alpha\). With these 1000 conditions, we searched the optimal value for \(\alpha\) and obtained the lowest MSE.  
Figure 1: An example of the Solver results pane for the time series of S1 with initial value setting with Method 1.  
The performance of the proposed method compared with the other two initialization methods of the SES method, considering the lowest MSE.
# Optimizing Foreign Language Learning in Virtual Reality:
## 4 VR-CCL Framework for Optimized Language Learning
### Interactive Engagement (Based on Constructivist Learning Theory)
#### 4.1.1. Learner-Content Interaction in VR  
Virtual reality (VR) has revolutionized the pedagogical panorama, especially in linguistics. With its tomes and spoken discourses, the conventional learning alcove has been eclipsed by VR's enveloping, tridimensional realms. In these vistas, the nexus between the scholar and material is not merely amplified; it undergoes metamorphosis.  
Envision an instance where an apprentice, endeavoring to master French, is virtually conveyed to a charming alley in Paris. Meandering through this milieu, every placard, carte du jour, and periodical becomes a conduit to immerse in the dialect. They can virtually handle artifacts, peruse annotations, or even solicit refreshments at a bistro, honing articulation and phraseology in synchrony. Such engagements bestow upon the educational voyage a profound contextuality. Lexemes transform from nebulous concepts to memorized into instrumental conduits for exploration, discourse, and discernment in a virtual cosmos echoing the tangible.  
This profundity of engagement is augmented by the pan-sensory resonance that VR dispenses. An aspirant does not merely visualize the term \"pomme\" at a virtual produce stand; they can 'grasp' the apple, 'eavesdrop' on its accurate enunciation, and even 'perceive' its tactile essence via haptic reverberations. These pan-sensory encounters ascertain that the acquisition process extends beyond the intellect into tangible experiences.  
The VR milieu is malleable. Should a student grapple with the specific lexicon, the setting can orchestrate additional episodes accentuating those terminologies, fortifying assimilation and prowess. In contrast, the milieu can intensify for seasoned scholars, ushering in intricate lexicon and sophisticated syntactical paradigms.  
VR rejuvenates the bond between the scholar and content, rendering it vibrant, modifiable, and profoundly immersive, ensuring that linguistic mastery evolves from a scholastic chore into a palpable, evocative expedition.
## 3 Results and Discussion
When looking at the verification of this study, it can be seen that the results obtained are compatible with the literature in terms of total thermal efficiency. For example, [PERSON] et al. [18] found an overall thermal efficiency for SRC and ORC systems used as a bottoming system in a GT-based triple combined system of 22.6%; [PERSON] et al. [11] for municipal solid waste (MSW) as fuel combustion for power generation, 20.49%; and [PERSON] et al. [32] for a novel combined biomass-based power generation system, 22%.
## 4 Results and Discussion  
This section presents the results of the BILSTM-LFSOA. The design and simulation of the BILSTM-LFSOA method were carried out using Python 3 and Anaconda Navigator V 2.3.1. The system used to perform the yield prediction was configured with 8 GB RAM and an i5 processor with 2.2 GHz. The dataset generated for evaluating this research was divided into 80% for training and 20% for testing. Input features were temperature, humidity, soil moisture, rainfall, wind speed and direction, solar radiation, CO\({}_{2}\), soil pH, and soil nutrients. Similarly, the proposed BILSTM-LFSOA was compared against baseline models such as RNN, RFC, and LSTM in terms of various performance metrics.
# Span-Based Fine-Grained Entity-Relation Extraction via Sub-Prompts Combination
## 3 Methodology  
The overall framework of our approach is shown in Figure 2, in which different pre-trained language models (PLMs) are selected as the core according to the dataset characteristics.  
Given a sentence:  
\[Sent=\{w_{1},w_{2},w_{3},...,w_{n}\},\]  
we will focus on all spans in the sentence:  
\[Span=\{w_{l},w_{i+1},w_{i+2},...,w_{i+l}\},\]  
where \(w\) denotes tokens, \(n\) denotes the sentence length and \(l\) denotes the span width. In this way, we will extract which span is the subject (\(S\)) of a relation (\(R\)) (\(\langle S,R\rangle\)), which span is the object (\(O\)) of a relation (\(R\)) (\(\langle R,O\rangle\)), and which two elements (\(\langle S,R\rangle\) and \(\langle R,O\rangle\)) can form a triple (\(\langle S,R,O\rangle\)).  
For example, the relation \"Adverse-Effect\" in the ADE dataset means \"The effect was caused by the drug.\". This explanation can guide joint entity and relation extraction and decompose the extraction process into:  
1. If \(span_{1}\) is of type \"Effect\" and the sentence indicates that it was caused by a type of \"Drug\", then \(span_{1}\) can be the subject of the relation: \[\langle span_{1},Adverse-Effect\rangle.\]
2. If \(span_{2}\) is of type \"Drug\" and the sentence indicates that it resulted in an \"Effect\", then \(span_{2}\) can be the object of the relation: \[\langle Adverse-Effect,span_{2}\rangle.\]
3. If the sentence expresses that \(span_{1}\) and \(span_{2}\) have a \"Cause\" relation in the sentence, they can form a triple: \[\langle span_{1},Adverse-Effect,span_{2}\rangle.\]  
Based on the above considerations, we propose SSPC, an approach for Span-based Fine-Grained Entity-Relation Extraction with Sub-Prompts Combination. In the following sections, we describe each part of the framework in detail.
# SC-SA: Byte-Oriented Lightweight Stream Ciphers Based on S-Box Substitution
## 2 SC-SA Cipher
### Key Recovery Security  
Assuming the last key stream output is denoted as E\({}_{0}\), the previously output key streams are represented as E\({}_{-1}\), E\({}_{-2}\ldots\)E\({}_{-i}\), where E\({}_{-i}\) represents the data of the key stream output second to last. The 8-byte blocks represented in the Data Group T are denoted as T\({}_{0}\), T\({}_{-1}\ldots\)T\({}_{-7}\),T\({}_{-8}\), where T\({}_{0}\) represents the byte that is about to be dequeued from the T array; and T\({}_{-8}\) represents the byte that is about to be enqueued at the end of the T array. The following is known:  
\[\begin{cases}\text{T}_{0}\oplus\text{T}_{8}+127=\text{E}_{0}\\ \text{T}_{-1}\oplus\text{T}_{7}+127=\text{E}_{-1}\\ \cdots\\ \text{T}_{-6}\oplus\text{T}_{2}+127=\text{E}_{-6}\\ \text{T}_{-7}\oplus\text{T}_{1}+127=\text{E}_{-7}\\ \text{T}_{-8}\oplus\text{T}_{0}+127=\text{E}_{-8}\\ \end{cases}, \tag{7}\]  
T\({}_{i}\) and E\({}_{i}\) XOR in the field GF\({}_{2^{8}}\) yields the following:  
\[\left(\text{T}_{0}\bigoplus\text{T}_{8}+127\right)\bigoplus\left(\text{T}_{-8 }\bigoplus\text{T}_{0}+127\right)=\text{E}_{0}\oplus\text{E}_{-8}, \tag{8}\]  
In the worst-case scenario, if the value of the sum T\({}_{0}\bigoplus\text{T}_{8}+127\), T\({}_{-8}\bigoplus\text{T}_{0}+127\) and T\({}_{-16}+\text{T}_{-8}\)+127 is less than 256--that is, the sum T\({}_{0}\bigoplus\text{T}_{8}\), T\({}_{-8}\bigoplus\text{T}_{0}\) and T\({}_{-16}+\text{T}_{-8}\) is less than or equal to 128, then E\({}^{\prime}_{i}=\text{E}_{i}-127\)--the formula can be simplified as follows.  
\[\begin{cases}\text{T}_{0}+\text{T}_{8}=\text{E}^{\prime}_{0}\\ \text{T}_{-16}+\text{T}_{-8}=\text{E}^{\prime}_{-16}\\ \text{T}_{-8}+\text{T}_{0}=\text{E}^{\prime}_{-8}\\ \end{cases}, \tag{9}\]  
Since E\({}^{\prime}_{0}\), E\({}^{\prime}_{-16}\) and E\({}^{\prime}_{-8}\) are known, the aforementioned formula can be considered to have four unknowns and three equations, with no exact solution.
## 2 Methods
### Data Analysis  
RSJ was divided into three phases: (1) the pre-activation phase defined as 50 ms before touchdown [PERSON] _et al._ (2000); (2) the downward phase defined from touchdown to the maximum knee flexion; (3) the push-off phase defined from the maximum knee flexion to push off [PERSON] _et al._ (2000); [PERSON] _et al._ (2000). Kinematic and kinetic variables were analyzed using the MotionMonitor (Version 8.0, Motion Analysis Corporation) software. A 10 N vertical ground reaction force (vGRF) threshold was used to identify touchdown and push-off from the ground. The kinematic variables were the jump height, touchdown velocity of center of mass (COM), time of downward and push-off phase, peak knee flexion angle, and change in knee angle. The jump height was calculated by the COM displacement from touchdown to the highest position. The horizontal-to-vertical velocity conversion rate (HVCR) was calculated using the following equation:  
\[\text{HVCR}=\frac{H_{vtf}-H_{vtd}}{V_{vtf}-V_{vtd}}\% \tag{1}\]  
Figure 1: Experimental site setting and instrumentation used in this study.  
where \(H_{vtf}\) and \(H_{vtd}\) are the horizontal COM velocities of take-off and touchdown, respectively; \(V_{vtf}\) and \(V_{vtd}\) are the vertical COM velocities of take-off and touchdown, respectively.  
The kinetic variables were the peak horizontal ground reaction force (hGRF), vGRF, moment and powers of the hip, knee, and ankle joint, and eccentric loading rate (ELR) during RSJ. The net internal joint moments were calculated using the inverse dynamics approach. The joint powers were computed by multiplying the respective joint moment by the joint angular velocity. The ELR is a measure of the absorption of landing explosive strength calculated from the average slope of the eccentric loading portion of the vGRF-time curve in the downward phase [16]. The eccentric loading portion was determined from the vGRF exceeding the body mass to the peak vertical vGRF. The hGRF, vGRF, all force, and joint moment data were normalized by each participant's body weight.  
Raw EMG signals were processed using a band-pass filter (20-450 Hz) to remove artifacts and calculate root mean square (RMS) with a 50 ms time interval. The EMG data of each muscle were normalized to a percentage of the highest RMS value of each muscle from trials of RSJ set as the reference voluntary contraction (RVC). The averaged %RVC of each phase of pre-activation, downward, and push-off was used to evaluate the recruited muscle activities [13].
## 3 System and Methodology  
### Hardware Setup  
The staple hardware devices in the vision system include a server with NVIDIA GTX1080 Ti GPU and 3.6 GHz Intel i7-6850K CPU, router, micro UAV(DJI M100), and Percipio RGB-D camera (FM830-I) mounted on the UAV. In addition to being compact, lightweight, and easy to mount and unmount, the Percipio RGB-D camera simultaneously combines structured light and binocular vision to generate depth maps with an accuracy of up to 1 mm. Note that the RGB-D camera also acts as a depth sensor to measure the distance from the UAV to the target. According to the parameters given by Tuyang, the effective range of the Percipio FM830-I RGB-D camera is 0.5-6 m. Figure 2 shows the UAV and Percipio RGB-D camera used in our vision system.
## 9 Conclusions  
In summary, silicon-based polymer-derived ceramics (PDCs), including SiOC, SiCN, SiBCN, and SiOCN, etc., have emerged as promising anode materials for next-generation lithium-ion batteries (LIBs) due to their easy structural design and modulation, chemical stability, etc. These materials overcome the limitations of conventional graphite anodes, such as low theoretical capacity (372 mAh\(\cdot\)g\({}^{-1}\)), and mitigate the severe volume expansion (>300%) associated with pure silicon anodes.  
PDCs synthesized through precursor pyrolysis exhibit unique amorphous/nanocrystalline structures. The synergy between silicon-based glass phases and free carbon significantly boosts lithium-ion storage capacity and cycling stability: SiOC achieves >1300 mAh\(\cdot\)g\({}^{-1}\) reversible capacity through mixed Si-O-C bonding and free carbon networks, while SiCN's nanochannels/active sites enable exceptional kinetics. Free carbon serves as a cornerstone for performance optimization, constructing conductive networks, buffering volume changes, and providing additional Li-storage sites via disordered carbon phases.  
Crucially, PDCs' microstructure and electrochemical properties are governed by pyrolysis temperature, atmosphere, and precursor design. Inert atmospheres retain disordered carbon to enhance capacity, whereas temperatures >1400 \({}^{\circ}\)C increase crystallinity at the expense of active sites. Incorporating conductive frameworks (e.g., CNTs, graphene) or metal nanoparticles (Sn, Si) further optimizes conductivity and mechanical stability, enhancing capacities and cycle life.  
In conclusion, silicon-based polymer-derived ceramics (PDCs), with their design flexibility and tunable properties, hold great potential as high-energy-density anode materials for lithium-ion batteries (LIBs). Their inherent compatibility with solid-state electrolytes--notably in mitigating interfacial resistance and suppressing lithium dendrite formation--positions PDCs as particularly promising candidates for next-generation solid-state batteries. Through multi-scale structural optimization and interdisciplinary innovation, PDCs are poised to play a pivotal role in advancing energy storage technologies beyond traditional LIBs.  
All authors listed have made a substantial contribution to this review and approved it for publication. All authors have read and agreed to the published version of the manuscript.  
This review was supported by the National Natural Science Foundation of China (Nos. 52472082, 52102085 and 22002040) and the National Natural Science Fund for Excellent Young Scholars (Overseas).  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
# A Generic Automated Surface Defect Detection Based on a Bilinear Model
###### Abstract
Finally, the defects in the original input image can be located automatically after processing the heat map with a threshold segmentation method. For the problem of insufficient training samples in actual industrial production, the D-VGG16 is initialized by loading the VGG16 pre-training weights on ImageNet [23] with 1000 classes, and adopt the transfer learning [24] to train the whole network, attaining the target of small samples training. The training of the entire network only uses image-level annotation, and is carried out in an end-to-end manner. The main contributions of this paper are as follows:  
1. The bilinear model for the detect detection tasks was proposed. To the best of our knowledge, this is the first paper that uses the bilinear model for surface defect detection. Moreover, the proposed method has a generalization capability, and can be successfully applied to defective features with texture, shape and color. 2. A D-VGG16 network based upon VGG16 for the feature function of the bilinear model was designed. The Experimental results show that such a network structure for defects detection applications has a higher average precision than that network using VGG16 as the feature function, and is also higher than the known latest methods. 3. The training process of the whole network proposed in this paper has the characteristics of a small sample, end-to-end, and is weakly-supervised. In the training stage, only a few training images of image-level labeled are needed to locate the defects of input images in the prediction stage. The rest of the paper is organized as follows: Section 2 describes in great detail the specific method of the paper, mainly about describing the overall structure of the proposed method. Section 3 presents the details and the results of performing experiments on the datasets, which is followed by the conclusions drawn in Section 4.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 5 Conclusions  
The missing samples in the dataset affect the performance of deep learning models. Therefore, in this paper, a methodology was proposed to extrapolate the missing samples of human activity recognition dataset captures to make deep models better classify the human daily living activities. The proposed methodology utilizes the K-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. Thus, 12 experiments were performed to analyze the effect of data imputation and data normalization, along with different combinations of sensors.  
The proposed methodology, when compared to a non-normalized and non-imputed dataset across all three sensors, reported an improvement of 25.36% in accuracy, 21.58% in precision, 25.36% in recall, and 23.52% in F-measure values over the normalized and imputed dataset across all three sensors. The experimental results revealed that the performance of the implemented model increased with the implementation of the data imputation method.  
Conceptualization, methodology, software, validation, formal analysis, investigation, writing--original draft preparation, writing--review and editing, I.M.P., F.H., N.M.G. and E.Z. All authors have read and agreed to the published version of the manuscript.  
This work is funded by FCT/MEC through national funds and co-funded by FEDER--PT2020 partnership agreement under the project **UIDB/EEA/50008/2020** (_Este trahulho e financialo pela FCT/MEC atraves de fundos nacionais e cofinanciado pel FEDER, no ambito do Aercedo de Parceria PT2020 no ambito do projeto UIDB/EEA/50008/2020_).  
This work is funded by FCT/MEC through national funds and co-funded by FEDER--PT2020 partnership agreement under the project **UIDB/EEA/50008/2020** (_Este trahulho e financialo pel FCT/MEC atraves de fundos nacionais e cofinanciado pelo FEDER, no ambito do Aercedo de Parceria PT2020 no ambito do projeto UIDB/EEA/50008/2020_). This article is based upon work from COST Action IC1303-AAPELE-Architectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226-SHELD-ON-Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). More information in www.cost.eu.  
The authors declare no conflict of interest.
# Evaluation and Prediction of Ecological Benefits in Song-Liao River Basin
###### Abstract  
The evaluation and prediction of ecological benefits are significant for regional resource development planning and path designing. This study established a novel ecological benefits evaluation system by integrating macro-ecosystem structure, Ecosystem service index (ESI), and ecological quality index (EQI). Based on this system, this study evaluated the spatiotemporal characteristics and changing trend of ecological benefits in Song-Liao River Basin (SRB) from 1990 to 2020. The results show that the macro-ecosystem structure in Song-Liao River Basin remains stable, and the ecosystem service and ecological quality generally show a trend of first decline and then increase. The average growth rates of ESI and EQI were 0.6% and 0.4%, respectively, during 1990-2020. The ecological benefits of natural areas with widely distributed forest areas are higher, while those of areas with frequent human activities are lower. The prediction model based on machine learning has achieved good modeling effect, which shows that the ecological benefits of SRB will be on the rise in the future. Based on the evaluation results, we suggest that more environmental protection policies on the basis of maintaining the existing development plan should be promoted to reduce the contradiction between human and nature in the development process. For the abundant natural forests in this area, reasonable forest management should be carried out to improve the carbon-fixation capacity of vegetation, and a Methodology for managing natural forests should be constructed to make full use of the existing carbon sinks. For the new afforestation project being promoted, carbon-sink afforestation projects of CCER (Chinese Certified Emission Reduction) should be promoted to realize the synergy between economic development and environmental protection.  
ecological benefits; macro-ecosystem structure; ecosystem service assessment; ecological quality assessment; Chinese certified emission reduction