esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The natural ventilation and space heating performance of a combined solar chimney was studied comparatively in this paper. A combined solar chimney has a fine ventilation and space heating effect, which can be utilized in hot summer and cold winter areas. The key findings of the study are summarized below:  
1. The ventilation effect of a combined solar chimney was better than that of a Trombe wall and an inclined-roof solar chimney in the summer. The benefit from the structure and the reasonability of the operation mode suggested that the problem of overheating was negligible for a combined solar chimney.
2. In the summer, the natural ventilation mode of a combined solar chimney (S1 CSC) was appropriate when the ambient temperature ranged from 298 K to 303 K; the anti-overheating mode was feasible when the ambient temperature was higher than 308 K.
3. When the ambient temperature was lower than 273 K, the operation mode of space heating in winter (W1 TW) was suitable with a better heating effect. A preheating mode could be employed to improve the indoor air quality when the ambient temperature was higher than 278 K.  
Conceptualization, H.L. and B.Y.; methodology, H.L., B.Y., Y.W. and Y.Z.; validation, H.L. and B.Y.; formal analysis, P.L., M.Z. and Q.T.; investigation, P.L., M.Z. and Q.T.; data curation, P.L. and Q.T.; writing--original draft preparation, P.L. and Q.T.; writing--review and editing, H.L. and Y.W. All authors have read and agreed to the published version of the manuscript. This research was funded by grants from the National Natural Science Foundation of China (No. 51908527), the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China (No. 18 KJB560009) and The Key Research and Development Projects of Anhui Province, Grant/Award (No. 201904a07020029).  
Not applicable.  
Figure 7: The changing of the outlet air temperature with the ambient temperature.  
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# The Long and Winding Road to Cardiac Regeneration
## 2 Stem Cell Therapy
### Adult Stem Cells
#### 2.1.2 Hematopoietic Stem Cells  
Hematopoietic stem cells (HSCs) are a type of multipotent stem cell that give rise to all blood cell types. While traditionally known for their role in hematopoiesis, recent studies have explored the potential of HSCs in cardiac regeneration [98]. HSCs are derived from the mesoderm during embryonic development and are primarily found in the bone marrow of adult organisms. Their unique ability to self-renew and differentiate into various blood cell lineages has made them attractive candidates for therapeutic interventions, including cardiac repair [99].  
One advantage of using HSCs for cardiac regeneration is their abundant availability. Bone marrow, a rich source of HSCs, can be easily accessed through minimally invasive procedures. Furthermore, HSCs can be expanded in culture, allowing for the generation of large quantities of cells for therapeutic purposes [63]. In addition, HSCs have shown the potential to differentiate into cardiac lineage cells, such as cardiomyocytes, endothelial cells, and smooth muscle cells, which are crucial for repairing damaged cardiac tissue [100].  
Despite these advantages, several limitations exist in the use of HSCs for cardiac regeneration. One challenge is the low efficiency of differentiation into cardiac cells. While HSCs have demonstrated the capacity to differentiate into cardiac lineages, the percentage of cells successfully differentiating into functional cardiomyocytes remains relatively low [101]. Another limitation is the potential for immune rejection when using allogeneicHSCs. Immunosuppressive therapy may be required to prevent rejection, adding complexity to the treatment process [102; 103]. Moreover, the long-term engraftment and survival of HSC-derived cells in the cardiac tissue remain uncertain [104].
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
## 3 UWB Regulations and PHY Considerations
### Low Duty Cycle (LDC)  
In this case, no feedback nor measurements from the environment are available. Thus, a passive mitigation technique must use other sources of knowledge. More precisely, LDC limits the activity of the transmitter. This is formally defined by:  
\[DC=\frac{T_{on}}{T_{on}+T_{off}} \tag{2}\]  
where \(DC\) is the duty cycle, \(T_{on}\) and \(T_{off}\) are the times when the transmitter is on and off, respectively. It is worth mentioning that the timings refer to the entire duration of the UWB pulse frame regardless the pulse repetition frequency (PRF). Besides the limitation imposed by a low duty cycle, i.e., reducing the time spent in transmission by a device, a positive side effect is that a device might be able to increase the transmitted power as it will spend less time in transmission. While low duty cycle could limit the number of transmission per time interval (superframe), the power related aspect can increase the possible coverage of the system and therefore scale it to bigger areas. The LDC restrictions are defined in ECC/DEC/(06)/04, EC DEC 2017/131/EC and EC DEC 2009/343/EC regulations and are listed in Table 4. However, devices belonging to different location tracking categories, have diverse requirements. LT1 systems do not use LDC techniques. LT2 equipment uses a \(DC_{max}=5\%\) per transmitter per second and a \(T_{on\_max}=25\) ms in the band 3.8 GHz-4.8 GHz by default. Moreover, in the same band and for fixed indoor devices, an additional requirement of 1.5% per minute is mandatory (or an alternative mitigation technique that guarantees the same protection). To conclude, LDC is mandatory requirement in the 3.4 GHz-4.8 GHz band for LAES apparatus.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Restrictions** \\ \hline Max transmitter on time \(T_{on\_max}\) & 5 ms \\ Mean transmitter off time \(T_{off\_mean}\) & \(\geq\)38 ms (averaged over 1 s) \\ Sum transmitter off time \(\sum T_{off}\) & \(>\)950 ms per second \\ Sum transmitter on time \(\sum T_{on}\) & \(<\)18 s per hour \\ \hline \hline \end{tabular}
\end{table}
Table 4: Low Duty Cycle (LDC) restrictions for UWB technology.
## 5 Experimental Results  
### Iridium Signal Data Acquisition  
All real Iridium signal data collection was conducted at the Advance Technology Base of the Chinese Academy of Sciences in Beijing, China (40\({}^{\circ}\)04\({}^{\prime}\)15.45\({}^{\prime}\)E, 116\({}^{\circ}\)16\({}^{\circ}\)28.33\({}^{\prime\prime}\)N, 90 m altitude), using a self-constructed reception and processing platform for Iridium signals. The platform was stationary and mounted on the rooftop to minimize signal characteristic variations caused by building obstructions (Figure 8a). As shown in Figure 8b, the platform comprises an Iridium antenna (JCYX008; Zhejiang JC Antenna Co., Ltd., Jiaxing, China), a universal software radio peripheral (USRP B210, Ettus Research, Austin, TX, USA), and a laptop computer (Lenovo ThinkPad X1). The platform enables the acquisition of Iridium signals and the extraction of epoch characteristics for subsequent applications including training and positioning trials.  
Through long-term observation of Iridium signals, we extracted 393,933 epochs with their characteristics from 107 h of real Iridium signal data to construct a 24 h-coverage training set. Additionally, we extracted 80,764 epochs from 28 h of real Iridium signal data to establish a test set for algorithm validation. The specific signal reception windows are illustrated in Figure 9.  
During the reception of training set signals, we deliberately selected diverse observable windows to ensure epoch characteristic variability, thereby enhancing the generalization capability of the trained agent. For the test set, the signals covered nearly all 24 h of the day, enabling comprehensive evaluation of the agent's positioning performance across all time periods.  
Figure 8: The Iridium signal reception and processing platform. (**a**) Platform deployment scenario; (**b**) platform configuration.  
Figure 10 demonstrates that the captured Iridium epochs exhibited wide-ranging characteristic distributions: SNR spanned \(-14\) dB to \(4\) dB, elevation angles covered \(0^{\circ}\) to \(90^{\circ}\), and signals from \(69\) distinct satellites were acquired. These statistics confirm the training set's comprehensive representation of Iridium epoch characteristics across diverse operational conditions, thereby enhancing the trained agent's generalization capability and robustness when processing heterogeneous epoch samples.  
Figure 10: The characteristic statistics of the captured Iridium epsochs. (**a**) SNR; (**b**) Doppler frequency; (**c**) ephemeris age; (**d**) azimuth angle; (**e**) elevation angle; (**f**) satellite-to-receiver distance; (**g**) satellite ID.  
Figure 9: The distribution of signal reception windows of the training set and the test set.
## Appendix B Derivation of Equation (26)  
The \(e_{\mathrm{fin}}\) of the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter is derived using the final value theorem in the \(z\)-domain. Applying a \(z\)-transform to Equations (1)-(4), we obtain:  
\[X_{\mathrm{p}}(z) =X_{\mathrm{s}}(z)/z+TV_{\mathrm{s}}(z)/z, \tag{16}\] \[V_{\mathrm{p}}(z) =V_{\mathrm{s}}(z)/z,\] (17) \[X_{\mathrm{s}}(z) =X_{\mathrm{p}}(z)+\alpha(X_{\mathrm{o}}(z)-X_{\mathrm{p}}(z))+ T\eta(V_{\mathrm{o}}(z)-V_{\mathrm{p}}(z)),\] (18) \[V_{\mathrm{s}}(z) =V_{\mathrm{p}}(z)+(\beta/T)(X_{\mathrm{o}}(z)-X_{\mathrm{p}}(z)) +\theta(V_{\mathrm{o}}(z)-V_{\mathrm{p}}(z)). \tag{19}\]  
By simplifying of these equations, the relationship between \(X_{\mathrm{p}}(z)\) and (\(X_{\mathrm{o}}(z)\), \(V_{\mathrm{o}}(z)\)) is obtained as:  
\[X_{\mathrm{p}}(z)=\frac{(\alpha+\beta)z+\alpha\theta-\alpha-\eta\beta}{f_{ \alpha\beta\eta\theta}(z)}X_{\mathrm{o}}(z)+\frac{(\eta+\theta)z-\eta}{f_{ \alpha\beta\eta\theta}(z)}TV_{\mathrm{o}}(z), \tag{20}\]where:  
\[f_{\alpha\beta\eta\theta}(z)=z^{2}+(\alpha+\beta+\theta-2)z+\alpha\theta-\eta \beta-\alpha-\theta+1. \tag{100}\]  
Because we have assumed that the target has constant acceleration \(a_{\rm c}\) but do not assume the measurement errors,  
\[X_{\rm o}(z) =Z[a_{\rm c}(kT)^{2}/2]=\frac{z(z+1)}{2(z-1)^{3}}a_{\rm c}T^{2}, \tag{101}\] \[V_{\rm o}(z) =Z[a_{\rm c}(kT)]=\frac{z}{(z-1)^{2}}a_{\rm c}T, \tag{102}\]  
where \(Z[\,]\) denotes the \(z\)-transform. With Equations (101), (102) and (102), the predicted error in the \(z\)-domain, \(E_{\rm p}(z)\equiv X_{\rm o}(z)-X_{\rm p}(z)\), is:  
\[E_{\rm p}(z)=\frac{z(z+1)(z^{2}+(\theta-2)z+1-\theta)-2z(z-1)(\eta z+\theta z -\eta)}{2(z-1)^{3}f_{\alpha\beta\eta\theta}(z)}a_{\rm c}T^{2}. \tag{103}\]  
Thus, applying the final value theorem, Equation (26) is derived.
## 3 Results and Discussions  
### BOF Slag Pozzolanic/Reactivity Behavior  
The pozzolanic index (PI) of the BOF slag was 67.29%; therefore, this SS was considered a non-pozzolanic material accordingly to the Brazilian Standard ABNT NBR 12653:2014 [62]. Basic GGBFS, typically a pozzolanic material, has a much higher PI of approximately 95%. In contrast, acidic GGBFS has a lower PI, comparable to the BOF slag, between 65 and 85%. A low pozzolanic behavior of the BOF slag was expected based on the high content of crystalline phases, as highlighted by the XRD (Figure 2).  
The modified R\({}^{3}\) reactivity test results of the BOF slag are summarized in Table 3, where a comparison is made with typical results from other raw materials, i.e., another BOF slag, a ladle furnace steel slag (LFS), GGBFS and limestone [26; 57]. It is possible to see a significant difference in the reactivity of SCMs and even between different samples of BOF slag. The studied BOF slag may be considered to have a low reactivity based on its amount of heat released and calcium hydroxide consumptions, which were 76 J/g SCM and 16 g/100 g SCM, respectively.  
The difference in terms of heat release and calcium consumption of both BOF slag was related to the inherent variability of steel slags. Due to the processing and cooling effects on the chemical and mineralogical compositions, such as the amorphous phases, there was considerable variability in their behavior. The heat released from the LFS and GGBFS were similar and significantly greater than both BOF slag. This may have been due to the more significant amounts of amorphous phases in the formers compared with the present BOF slag [26], which was demonstrated by its crystalline nature (Figure 2). The calcium hydroxide consumption of the LFS and the limestone were negative, which meant that they generated calcium hydroxide possibly because of the reaction of the existing free lime in their composition. GGBFS had the highest calcium hydroxide consumption due to its higher amorphous content compared with the others.
## 3 Results and Discussion
### SEM Observations
#### 3.6.2 Study of Fiber Surface  
In order to investigate the changes of fibers, the SEM images with higher magnification are observed, as shown in Figures 6 and 7. Apparently, the surfaces of the fibers are attached to dyes (Figure 6a, Figure 7a). It can be seen from Figures 6b and 7b that the surface of the outer fibers of denim fabrics after laser bleaching were damaged, and some fibers were broken. Compared with Figures 6b and 7b, it can be found that after etching, the fibers of fabric 1 only have holes on the surface, and the structure has not been damaged. However, for fabric 2, it can be seen that the interior of the fibers is hollow, and the structure has been damaged.  
Figure 5: SEM pictures of different kapok/cotton samples (\(\times\)50): (**a**) Desized, (**b**) laser bleaching, (**c**) enzyme bleaching, (**d**) NaCIO bleaching, (**e**) KMnO\({}_{4}\) bleaching, (**f**) H\({}_{2}\)O\({}_{2}\) bleaching.  
Figure 6: SEM pictures of different treatment of cotton denim fabrics (\(\times\)1500): (**a**) Desized, (**b**) laser bleaching, (**c**) enzyme bleaching, (**d**) NaCIO bleaching, (**e**) KMnO\({}_{4}\) bleaching, (**f**) H\({}_{2}\)O\({}_{2}\) bleaching.  
This is because kapok is a kind of hollow fiber where the internal hollow degree can reach 90%, so after laser etching, the structure of the fiber is seriously damaged, which will eventually affect the decline of the mechanical properties of the fabric. Nevertheless, the internal fibers appear to remain intact and undamaged. In other words, during laser bleaching, only the surface fibers of the outer fibers are damaged, while the inner fibers are not affected [27]. It can also be seen from Figures 6 and 7 that after the other four bleaching treatments, there will be a little peeling or damage on the fiber surface, but this is not as obvious as with the laser treatment. The decomposition and destruction of the surface fiber is helpful to improve the fading effect of the fabrics, so the laser washing water is the best bleaching process.
# Advancing Spanish Speech Emotion Recognition: A Comprehensive Benchmark of Pre-Trained Models
## 3 Materials and Methods
### Embedding Extraction and Mean Pooling  
For each PTM and speech database, embeddings have been extracted sequentially, which are then mean pooled. This transformation condenses the temporal dynamics into a single vector while preserving the essential statistical properties of the features. Formally, let \(\mathbf{E}\in\mathbb{R}^{T\times D}\) be the embedding matrix of an audio sample, where \(T\) is the number of time steps and \(D\) the dimensionality of the feature. The mean pooled embedding \(\mathbf{e}\in\mathbb{R}^{D}\) is computed as:  
\[e=\frac{1}{T}\sum_{t=1}^{T}E[t], \tag{1}\]  
where \(E[t]\in\mathbb{R}^{D}\) corresponds to the feature vector at time step \(t\). This operation compresses the \(T\times D\) sequence into a single \(1\times D\) vector, facilitating compatibility with downstream classifiers. Mean pooling has been consistently employed in SER tasks involving PTM embeddings [52; 54; 80], offering a simple yet effective way to aggregate temporal information. Models like CLAP and TRILLsson are excluded from this step, as they already integrate internal mechanisms for temporal summarization or lack a sequential structure altogether.
## 2 Materials and Methods
### Preparation of Si-Modified rGO@Fe\({}_{3}\)O\({}_{4}\) Nanocomposites  
Si-modified rGO@Fe\({}_{3}\)O\({}_{4}\) nanocomposites were synthesized by the hydrothermal reaction according to literature [35]. First, 90 mg as-obtained Si-modified GO and 900 mg FeCl\({}_{3}\)-6H\({}_{2}\)O were dispersed in 5 mL EG by ultrasonically treated for 0.5 h to obtain a homogeneous solution. Second, the above solution was slowly added into the 5 mL of NaOAc solution (0.2 g\(\cdot\)mL\({}^{-1}\) in EG) at 40 \({}^{\circ}\)C for 0.5 h. Third, the homogeneous mixture was transferred into a 50 mL Teflon lined autoclave and heated at 200 \({}^{\circ}\)C for 10 h. Finally, the as-prepared Si-modified rGO@Fe\({}_{3}\)O\({}_{4}\) was separated with magnet and washed with ethanol and deionized water 5 times. After drying in 50 \({}^{\circ}\)C for 12 h, the Si-modified rGO@Fe\({}_{3}\)O\({}_{4}\) was achieved. According to the silane coupling reagent used, the final products were donated as Si(1)-rGO@Fe\({}_{3}\)O\({}_{4}\), Si(2)-rGO@Fe\({}_{3}\)O\({}_{4}\), respectively. For comparison, rGO@Fe\({}_{3}\)O\({}_{4}\) was also prepared in the existence of Fe\({}^{3+}\) and NaOAc.
# Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
###### Abstract  
Predicting solar power generation is a complex challenge with multiple issues, such as data quality and choice of methods, which are crucial to effectively integrate solar power into power grids and manage photovoltaic plants. This study creates a hybrid methodology to improve the accuracy of short-term power prediction forecasts using a model called Transformer Bi-LSTM (Bidirectional Long Short-Term Memory). This model, which combines elements from the transformer architecture and bidirectional LSTM (Long-Short-Term Memory), is evaluated using two strategies: the first strategy makes a direct prediction using meteorological data, while the second employs a chain of deep learning models based on transfer learning, thus simulating the traditional physical chain model. The proposed approach improves performance and allows you to incorporate physical models to refine forecasts. The results outperform existing methods on metrics such as mean absolute error, specifically by around 24%, which could positively impact power grid operation and solar adoption.  
photovoltaic power prediction; time-series forecasting; transfer learning; deep learning chain model +
Footnote †: journal: _articles_
## 2 Materials and Methods
### Experimental Methods
#### 2.3.2 Test Methods  
The flow of UHPC mixtures over time was measured using the Suttard viscometer according to the ASTM standard specification regarding the flow for use in tests of hydraulic cement [34]. The measurement was conducted for all UHPC mixtures at different intervals, including 0 min (after mixing), 15 min, 30 min, and 45 min.  
To determine the entrapped air content of the UHPC mixture, an air content meter based on ASTM C185 was used [35]. The fresh UHPC mixture was carefully placed into the meter, ensuring minimal disturbance. The meter was then sealed, and pressure was applied according to the device's guidelines. The air content was read directly from the gauge, representing the percentage of air within the mixture.  
The UHPC's compressive strength was tested in accordance with ASTM C39 using 10 \(\times\) 20 cm cylinder samples [36]. The test specimens were measured after a heat curing period of 72 h (5 days) and 28 days. Six samples represented each concrete mix at each age level, and the experimental results were averaged over these six samples.  
The tensile strength in flexure was measured on 5 \(\times\) 10 \(\times\) 50 cm specimens according to ASTM C78 standards at two age levels (5 days and 28 days) [37]. The number of test specimens corresponded to the compressive strength determination experiment.  
A direct tensile experiment was conducted on a \"dog-bone\" shaped specimen, as shown in Figure 2. The test sample, with a length of 50 cm, featured two elongated anchor  
Figure 1: The mixing process of UHPC at the laboratory.  
heads of 10 cm each, securely fastened to a tensile testing machine. The experimental procedure followed standards applicable to other materials, such as ASTM B557M [38], ASTM E8/ESM [39], and ASTM D638 [40]. In addition, the experimental apparatus for this study was established by consulting research studies regarding the approach to measure the tensile strength of concrete by [41; 42].  
The stress-strain relationship curve for UHPC was determined for all mixing proportions by employing LVDT displacement sensors regarding both sides of the sample during the direct tensile experiment.  
To ensure the reliability of the experimental results, each characteristic or stress-strain relationship was tested on three replicate samples.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 5 Methodology  
A structured methodology for conducting post-earthquake fire (PEF) analyses can be established using a performance-based framework, integrating hazard risk assessment, structural response evaluation, damage quantification, and loss estimation. Existing methodologies are largely derived from the performance-based earthquake engineering (PBEE) paradigm, which provides a systematic approach for evaluating structural behavior under sequential and compounding hazards (e.g., earthquake-induced damage followed by fire exposure) [2]. As illustrated in Figure 2, this framework follows a step-by-step process that begins with defining performance objectives and progresses through hazard evaluation, scenario development, and structural response analysis, ultimately concluding with damage assessment and loss quantification.  
The general methodology consists of the following key steps:  
1. Defining performance objectives;
2. Assessing seismic hazards and structural damage resulting from the earthquake;
3. Evaluating the probability of post-earthquake fire (PEF) ignition and subsequent fire spread dynamics;
4. Analyzing the structural response under combined earthquake and fire loading conditions;
5. Assessing damage and quantifying associated losses.
## 2 Methodology
### Meteorological and Hydrological Measurements  
PAR, air temperature, and WTD measurements were used with 30 min timestamps. PAR was measured by BF5 Sunshine Sensor (Delta-T Devices Ltd., Burwell Cambridge, UK) installed on a tower 3 m above the peatland surface, separated 70 m from CR and 120 m from CL sites. The temperature was measured in each sub-site/plot by radiation-sheltered thermohygrometers HygroVue5 (Campbell Sci., Logan, UT, USA) installed 30 cm above the surface [15]. WTD was measured using TD-divers (Royal Eijkelkamp, Giesbeek, The Netherlands) installed at three locations at each site within PVC piezometers permanently fixed to the wooden platforms. These measurements are combined with the peatland surface level oscillation data, using the wooden platforms as the reference point. Further details about the hydrometeorological conditions' measurements are provided by [PERSON] et al. [15].