esa-sceva/satcom-chunk-collection huggingface dataset:
# Life Cycle Analysis Challenges through Building Rating Schemes within the European Framework
## 2 Materials and Methods
### Mapping Procedure and Gap Analysis  
The applied mapping procedure has been performed with a spreadsheet to identify how the Level(s) indicators align and conform to the indicators within the BRS. It calculates the percentage \"degree of compliance\" for the LCA indicator and others within a detailed quantitative conformity assessment. It also gives a rough overview of compliance through a qualitative conformity assessment of all Level(s) indicators. This feeds the gap analysis between Level(s) and the BRS. In addition, it shows compliance with Level(s) at the three levels. For the quantitative procedure, a deep dive into the Level(s) indicators was necessary to break them down into their \"methodological aspects\" (building scope, system boundaries, and reference standard, etc.). The official report on \"How to make performance assessments using Level(s)--Part 3 for office and residential building\" [24] and its related spreadsheet \"Level(s) common reporting format for all indicators and tools, release v1.3\" are the reference for this procedure. For the qualitative procedure, the approach was a brief set of questions sent to the BRS managers. For each item, the choice is \"compliant\" or \"non-compliant\", and each \"compliant\" is awarded with 1 point and then added to the spreadsheet. Table 3 shows the \"degree of compliance\" (in %), calculated as the ratio of the achieved points to total possible points, following each one of the headings referenced in the above-mentioned reports. The degree of compliance is calculated for all three levels and sets up the gap analysis.  
Finally, for the qualitative assessment, the level to be considered is chosen, and a subjective input is provided as \"similar\", with \"deviation\" or \"missing\", with the possibility to add a comment. The levels of knowledge of the building assessment, the BRS used, and Level(s) are high. A wider mapping procedure was performed in the research project (32 items); however, in this article, the 3 indicators related to LCA analysis have been compared: Indicator 1 \"Use-stage energy performance\", Indicator 2 \"Life cycle Global Warming Potential\", and Indicator 3 \"Life Cycle Costs\".
## 4 PMSM Speed Control System Description  
The proposed hardware control system for PMSM speed control using the FPGA is shown in Figure 5. The XILINX ARTIX-7 (XC7A100T, Digilent Inc., Pullman, WA, USA) is used to control the speed of the PMSM. The program algorithm is written in the Very High-Speed integrated circuits hardware Description Language (VHDL), which is a hardware description language that describes the behavior of an electronic circuit or system, from which the physical circuit or system can then be implemented [27]. A multiplier, an adder and subtraction are used for the calculations.  
Vector control is the PMSM control method used for variable-speed control systems. The control blocks, which include the PI-P controller as a speed controller and two PI controllers required for current control, \(dq\) (direct-axis, quadrature-axis) and inverse \(dq\) coordinate transformations, receive the speed commands. Then, a PWM pulse generator is produced for inverter switching. The speed controller is the 2 DOF PI-P. An incremental pulse encoder mounted on the rotor axis of the PMSM generates a series of pulses to detect and calculate the rotor position and the motor angular speed \(\omega_{m}\). Sensors measure the phase currents, and a 12-bit analog-to-digital (AD) converter converts these phase currents into digital values. To maximize the switching frequency, PMSM speed control is implemented using a high-performance FPGA-based digital hardware controller. The FPGA is used for torque and speed control in some cases, because fast processing operation is obtained when using its hardware processing ability.  
PMSM speed control has two control loops, such as the speed control loop and minor current loop, as shown in Figure 5. The speed control loop is the control loop of speed command and measured speed. The current control loop is the control loop of plant input current \(i_{q}\)' and \(q\)-axis current \(i_{q}\). The error between speed command and measured speed is amplified by the controller, then moves to plant input current \(i_{q}\)' in the current loop. Plant input current \(i_{q}\)' is amplified by the controller that determines the magnitude and directions of torque that causes the speed of motor \(\omega_{m}\) to be close to speed command \(\omega_{m}\)'. The magnitude of the minor current loop influences the magnitude of the speed control loop. If there are errors in the minor current loop, then there are errors in the speed control loop.  
Figure 5: Block diagram of the PMSM (permanent magnet synchronous motor) speed control.
## 1 Introduction
Overall, the above discussions motivate us to propose a new method in this article to optimize stability-weakest parameters to improve the stability of the multi-converter grid-tied system. The major contributions and innovations of this research are reflected in the following three aspects:  
1. Proposing a stability evaluation function for a multi-converter grid-tied system based on the Generalized Nyquist Criterion (GNC) and the Gerschgorin disc theorem, which provides an intuitive quantitative indicator for estimating system stability. By considering the distance between the fastest intersection of the Gerschgorin disc with the real axis and the point \((-1,\,\mathrm{j0})\), the stability evaluation function in this paper can overcome the misjudgment in parameter optimization direction caused by the change in the Gerschgorin disc's radius due to parameter variation. 2. By using the derivative operation among the control parameters of VSCs and the stability evaluation function, numerical results that characterize the impact of VSCs control parameters on system stability are obtained. The parameters with large absolute sensitivity values are identified as stability-weakest parameters. 3. Developing a stability-weakest parameters-based optimization method by iterative calculation, which can calculate the specific control parameter optimization value to improve the system stability. It can compensate for the theoretical shortcomings of traditional methods, i.e., that parameter optimization can only be attempted step by step and lacks targeted theoretical guidance for the optimization of VSCs control parameters. The rest of this article is organized as follows. Section 2 deduces the impedance model of the multi-converter grid-tied system. Section 3 uses the GNC and the Gerschgorin disc theorem to establish a stability evaluation function for the system. Additionally, it proposes a method of parameter optimization based on the parameter sensitivity analysis. Section 4 elaborates on the simulation results to validate the effectiveness of the proposed method. Finally, Section 5 summarizes the conclusions and contributions of this article.
# A Review of the Influence of Steel Furnace Slag Type on the Properties of Cementitious Composites
###### Abstract  
The type of steel furnace slag (SFS), including electric arc furnace (EAF) slag, basic oxygen furnace (BOF) slag, ladle metallurgy furnace (LMF) slag, and argon oxygen decarburization (AOD) slag, can significantly affect the composite properties when used as an aggregate or as a supplementary cementitious material in bound applications, such as concretes, mortars, alkali-activated materials, and stabilized soils. This review seeks to collate the findings from the literature to express the variability in material properties and to attempt to explain the source(s) of the variability. It was found that SFS composition and properties can be highly variable, including different compositions on the exterior and interior of a given SFS particle, which can affect bonding conditions and be one source of variability on composite properties. A suite of tests is proposed to better assess a given SFS stock for potential use in bound applications; at a minimum, the SFS should be evaluated for free CaO content, expansion potential, mineralogical composition, cementitious composite mechanical properties, and chemical composition with secondary tests, including cementitious composite durability properties, microstructural characterization, and free MgO content.  
steel furnace slag; electric arc furnace slag; basic oxygen furnace slag; ladle metallurgy furnace slag; argon oxygen decarburization slag; concrete
# Geotechnical Behaviour of Fly Ash-Bentonite Used in Layers
## 1 Introduction  
Around 70-75% of the electricity is generated by coal-based thermal power plants in India. Indian coal is of poor quality and contains almost 30-45% of ash [1, 2]. Fly ash is also considered a hazardous water pollutant due to the presence of toxic heavy metals [3]. According to estimates from the Fly Ash Utilization Program (FAUP), the coal supplied to thermal power plants in the amount of 686.34 million tonnes in 2020-2021, which contains a significant amount of ash, had a fly ash generation of 232.56 million tonnes in 2020-2021 [4]. The Ministry of Power, Govt. of India estimates 1800 million tonnes of coal use every year and 600 million tonnes of fly ash generated by 2031-2032. Due to persistent efforts by the Fly Ash Mission in India and some other agencies, the utilization of fly ash has improved from a meagree 3% in 1994 to 92% in 2020, which is nowhere near its target of 100% fly ash utilization [5]. Various authors have successfully stabilized expensive soil by blending it with fly ash [6, 7, 8]. Fly ash has also been modified with lime, gypsum, and fibre [9, 10].  
Bentonite is an efficient clay substance for creating low-permeability barriers. Bentonite is a valuable material because of its great plasticity, strong dry-bonding strength, high shear and compressive strength, and low permeability [11, 12, 13]. Many researchershave carried out experimental investigations on fly ash mixed with bentonite [14; 15; 16]. The suitability of fly ash as a hydraulic barrier as well as the use of bentonite were investigated to improve the geotechnical qualities of fly ash [14]. It was established that 70% by weight of fly ash can be blended with bentonite to meet the requirements of compacted landfill liners [15; 17].  
The permeability of the compacted fly ash deposits is moderate. Fly ash is not suitable for use in seepage barriers such as liners. To lower the permeability of fly ash, clay-like admixtures should be used [2; 18]. The utilization of a large quantity of fly ash means proper mixing of the fly ash with other materials may not be significantly achieved. To overcome this problem, the layered system has been adopted in the present study. When two layers of different materials interact at their interfaces, the deformation behaviour of the composite is influenced by the inherent properties of the materials. The effect of fly ash on bentonite ratio and the number of layers was studied.
## 4 Discussion
### Limitations  
The results of this study should be evaluated considering certain limitations related to the sample characteristics and the methodology of the study, which may affect the generalizability of the findings. The participants in our study were young, asymptomatic, and physically active individuals, factors that could potentially limit the applicability of the results to older individuals, those with symptoms, and less active populations. Previous studies have indicated that beyond the adaptations expected from repetitive loading due to foot misalignment, older individuals are likely to exhibit muscle atrophy in the arch-supporting muscles such as the abductor hallucis and flexor hallucis brevis muscles [76]. These muscles may also be less developed in inactive individuals compared to the more developed muscles typically found in healthy, athletically active individuals [17; 77; 78], such as the participants in our study, affecting foot stability and, consequently, the ability to maintain an undisturbed gait. Additionally, the presence of symptoms can affect gait kinematics and the related parameters, even if the symptoms are manifested more centrally (e.g., lower back pain) [49]. Furthermore, participants were assessed barefoot, which may alter the results, as footwear is a significant variable that influences gait mechanics in individuals with hyperpromation [25]. The classification of hyperpromation is another limitation, as inconsistencies exist among tests regarding the identification of affected individuals [79]. Finally, our findings should be interpreted within the context of the specific technique used for gait analysis, as different methods may produce varying results [80]. To enhance the generalizability of findings, future research should include diverse populations, such as older adults, symptomatic individuals, and those with varying activity levels with various degrees of hyperpromation, employ multiple assessment methods to accurately identify hyperpromation of the feet, and implement standardized methodologies alongside advanced technologies, such as wearable sensors and machine learning algorithms, under various footwear conditions.
# TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
### Novelty and Contributions of the Study  
This study introduces a groundbreaking approach to emotion recognition with the novel Cumulative Attribute-Weighted Graph Neural Network (CA-WGNN) model. Diverging from the conventional focus on primary emotions such as joy, sadness, and fear, our research employs the Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Carnegie Mellon University Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSI) datasets. This approach is a significant deviation from existing literature, marking our investigation as the first to apply the CA-WGNN model to these specific datasets, thereby addressing a notable gap in the field.  
The innovative edge of our study lies in the CA-WGNN model's ability to capture nuanced emotional states, a facet often overlooked in prior research. By broadening the horizon of emotion recognition beyond traditional categories, we introduce a novel perspective to the domain. The following points encapsulate the core contributions of our study:  
* Employing the CA-WGNN model to analyze textual, audio, and video data, using the CMU-MOSI and IEMOCAP databases as trimodal inputs, enhancing the accuracy and scope of emotion recognition (ER).
* Introducing a unique methodology for trimodal emotion identification through the CA-WGNN model, integrating auditory, visual, and textual data.
* Innovating by combining data from multiple modalities through a weighted graph structure within the CA-WGNN model is a significant advancement in multimodal affective computing.  
The rest of the paper is organized into sections. Section 2 provides a literature review. Section 3 describes the methods and proposed approach. Performance analysis and discussions are presented in Section 4 and Section 5, respectively. The conclusion of the paper is in Section 6.
# Carbon and Nitrogen Pools and Fluxes in Adjacent Mature Norway Spruce and European Beech Forests
## 2 Materials and Methods
### Biomass Measurements and Litterfall Collection  
In the spruce stand, two surveys of tree biomass were conducted in 1994 (2500 m\({}^{2}\)) and 2013 (1000 m\({}^{2}\)). Diameter at breast high (DBH, in cm), tree height and number of tree individuals were measured. Furthermore, in 1994, eleven trees were cut at the spruce site, and different parts of tree biomass were measured to obtain site-specific biomass functions:  
\[\text{Stem (kg DW)}=0.3397\times\text{(DBH)}^{2}-1.786\times\text{(DBH)}+10.22 \text{ (}R^{2}=0.99\text{)} \tag{1}\]  
\[\text{Bark (kg DW)}=0.018\times\text{(DBH)}^{2}-0.0947\times\text{(DBH)}+0.5417 \text{ (}R^{2}=0.99\text{)} \tag{2}\]  
\[\text{Branches (kg DW)}=2.53\times\text{(DBH)}-29.331\text{ (}R^{2}=0.88\text{)} \tag{3}\]  
\[\text{Needles (kg DW)}=1.55\text{ (DBH)}-16.08\text{ (}R^{2}=0.87\text{)} \tag{4}\]  
Estimation of root biomass was based on equation from [37]:  
\[\text{LN roots (kg DW)}=-8.35+4.57\times\text{LN(DBH)}-0.33\times\text{LN(DBH)}^{2}+0.2 8\times\text{LN(Age)}\text{ (}R^{2}=0.97\text{)}\]  
Annual aboveground net primary production (ANPP) of different biomass components was calculated for the period 1994 to 2013 from the above equations based on DBH growth measurement.  
A biomass survey was conducted in the beech stand in 2013 only, and DBH, tree height and number of tree individuals were measured. Biomass increments for the last 20 years were calculated based on ring width series. The diameters were multiplied by a constant of 1.096 to account for bark thickness and water loss [38]. Total tree biomass was calculated according to changes in DBH measurements using models developed by [39].  
Litterfall amount has been measured since 2013, based on 5 frame collectors (0.5 m\({}^{2}\)) placed randomly within each site. Litterfall was collected bimonthly and divided into needles/leaves, twigs and fruiting parts.  
All biomass parts were sampled for total C and N contents. Samples were dried (60 \({}^{\circ}\)C), homogenized and analyzed using NCS Flash 2000 (Thermo Scientific, MA, USA). Data on N concentration in roots were obtained from [PERSON] (2000) [PERSON] (2000).
## 4 Network-Reconfiguration Optimization Model
### Optimization Variables  
In network-reconfiguration optimization, the state of each switch and the specific compensation capacity of each reactive power compensation device are treated as 0-1-type and integer-type variables, respectively.
## 6 Conclusions  
In this paper, the current status of QZSS was introduced, and the carrier phase DD models of GPS/QZSS and BDS/QZSS were deduced for baseline resolution. A week of simulation on availability, PDOP, ADOP, and the Bsrt simulation was carried out for GPS/QZSS and BDS/QZSS based in the HKLT station. That QZSS brings about promotion of the NOS and ADOP can make up for the single GPS's sustainability, reliability, and ambiguity resolution success rate especially under difficult conditions of high mask angles. Two cases of different baseline data were used to present the improvement in GPS and BDS SFSE baseline resolution when using QZSS data. Six indicators were introduced to exhibit the experimental results and two major conclusions were drawn: (1) BDS performs better than GPS in most indicators, excluding PDOP because BDS has more and higher-elevation satellites in the Asia-Pacific region; (2) The systems augmented by QZSS not only achieve a better performance in visibility and PDOP, but also achieve a great improvement in SFSE carrier phase DD ambiguity resolution such as ADOP and success rate. Moreover, the characteristics of ISBs between overlapping (GPS L1 and QZSS L1) and different frequencies (BDS B1 and QZSS L1) will be researched in future work.  
[PERSON] and [PERSON] proposed the concept; [PERSON] designed the methods; [PERSON] provided the software; [PERSON], [PERSON] and [PERSON] made the validation; [PERSON] gave the formal analysis; [PERSON] and [PERSON] carried out the investigation; [PERSON] wrote the original draft; [PERSON] made the revisions and editing; [PERSON] undertook the funding acquisition.  
This research project was jointly supported by National Natural Science Foundation of China (No. 41504032), a Project Funded by Science Foundation of Jiangsu Province (No. BK20150175), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYLX16_0542), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and International Cooperation Project of Jiangsu Province Government Policy Guidance Plan (B2Z017056). The author thanks the Hong Kong Geodetic Survey Products & Services, the International GNSS Monitoring and Assessment Service (iGMAS) and the International GNSS Service (IGS) for the data and products.  
The authors declare no conflict of interest.
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 4 Implementation and Experimental Results  
Our experiment was conducted in two main phases. In the first phase, our proposed framework components were deployed and installed on different network strata. This ensured the completeness and integrity of the framework since all different components were communicating through the proposed flow seamlessly. In the second phase, we studied the latency improvement of different websites after manually offloading their real static resources in the fog nodes. In this phase, we measured the latency improvements from the Fog Browser component. The experiment was conducted on and only considered the static resources hosted on cloud providers' servers. In our experiment, we relied on the cloud-based CDN used by the studied websites for their static resource. This allowed us to run our experiment and compare our latency improvement against different cloud CDN providers.
## 3 Results
### Latent Space Disentanglement  
The disentanglement properties of the trained model were assessed visually by inspecting latent graphical representations of the learnt latent space, such as PCA and tSNE. The embedding of the whole latent space using tSNE (Figure 4) shows clear clustering of the data according to morphology and staining.  
The subspaces \(z_{s}\) and \(z_{m}\) were visualized using PCA (Figure 5) and label related colouring to emphasize on the distribution of identically stained images. The subspace used for stain prediction by the classifier has a tight label related coupling while the structural embedding in the complementary part indicates loose stain related clustering, but morphologically similar image patches are grouped together.