esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper, we present a theoretical study on the maximum specific loss power in the admissible biological limit (P\({}_{\mathrm{MM}}\)) for CoFe\({}_{2}\)O\({}_{4}\) ferrimagnetic nanoparticles, as a possible candidate in alternative and non-invasive cancer therapy by superparamagnetic hyperthermia. The heating time of the nanoparticles (\(\Delta\)t\({}_{\mathrm{0}}\)) at the optimum temperature of approx. 43 \({}^{\circ}\)C for the efficient destruction of tumor cells in a short period of time, was also studied. We found the maximum specific loss power P\({}_{\mathrm{SM}}\) (as a result of superparamagnetic relaxation in CoFe\({}_{2}\)O\({}_{4}\) nanoparticles) for very small diameters of the nanoparticles (D\({}_{\mathrm{0}}\)), situated in the range of 5.88-6.67 nm, and with the limit frequencies (f\({}_{\mathrm{I}}\)) in the very wide range of values of 83-1000 kHz, respectively. Additionally, the optimal heating temperature (T\({}_{\mathrm{0}}\)) of 43 \({}^{\circ}\)C was obtained for a very wide range of values of the magnetic field H, of 5-60 kA/m, and the corresponding optimal heating times (\(\Delta\)t\({}_{\mathrm{0}}\)) were found in very short time intervals in the range of -0.3-44 s, depending on the volume packing fraction (\(\epsilon\)) of the nanoparticles. The obtained results, as well as the very wide range of values for the amplitude H and the frequency f of the external alternating magnetic field for which superparamagnetic hyperthermia can be obtained, which are great practical benefits in the case of hyperthermia, demonstrate that CoFe\({}_{2}\)O\({}_{4}\) nanoparticles can be successfully used in the therapy of cancer by superparamagnetic hyperthermia. In addition, the very small size of magnetic nanoparticles (only a few nm) will lead to two major benefits in cancer therapy via superparamagnetic hyperthermia, namely: (i) the possibility of intracellular therapy which is much more effective due to the ability to destroy tumor cells from within and (ii) the reduced cell toxicity.  
c pacs: 16 May 2021  
10 June 2021  
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**[PERSON]**  
Department of Physics, Physics Faculty, West University of Timisoara, Bv. V. Piravan No. 4, 300223 Timisoara, Romania; costica.caizer@e-uvr.tro
## 2 Materials and Methods  
### Sample Preparation  
Mortar samples were made using wollastonite, EAF slag, distilled water, NaOH pellets, and crushed stones of granite origin. Untreated slag was obtained from a local steel manufacturer, while highly pure NaOH and wollastonite were obtained from a local chemical supplier. The binder materials were sieved to a size of less than 45 \(\upmu\)m and scanned with an Epsilon model X-ray fluorescence (XRF) instrument (PANalytical, Almelo, The Netherlands). Table 1 lists the XRF results in which the untreated slag was found to have a relatively higher amount of Fe2O3. The loss in ignition (LOI) of these binder materials was found to be lower, indicating a lesser presence of organic species in them. Samples were heated to 1000 \({}^{\circ}\)C in a hot air oven to obtain information on such losses. The binder materials were scanned using a Malvern Panalytical model XRD at a rate of 0.02\({}^{\circ}\) 20/min for a 5-55\({}^{\circ}\) 20 range to obtain mineralogical information. Figure 1 shows the XRD results. The main X-ray peaks indicate the main presence of CaSiO3 and SiO2 compounds in wollastonite and EAF slag. Figure 1 also shows the particle size distribution of these raw materials, which was determined using a laser particle analyzer (Cilas, Orleans, France). The EAF slag particles were found to be coarser than that of wollastonite. The SEM image in Figure 1 reveals the angular morphology. The slag sample was mounted on a metal stub using a carbon adhesive, gold coated for 80 s, and scanned with the JSM-6610A SEM operated (JEOL, Tokyo, Japan) at higher voltage. The specific gravity and water absorption of the crushed aggregates were determined according to EN 1097-6 [33] and were 2.75 and 0.48% by weight. In this study, wollastonite mortar mixtures containing 0%, 20%, 40%, and 60% EAF slag were labeled WS0, WS20, WS40, and WS60. The liquid-to-powder ratio of these mixes was maintained at 0.25, while the aggregate-to-powder ratio was 3. The liquid used was a 0.3 molar concentrated NaOH solution. To prepare the mortar samples, the ingredients were mixed in a high-speed stand mixer for 3 min, and the fresh mix was loaded into iron molds of different sizes in two equal layers. The samples were demolded after 24 h and stored in a carbonation chamber for up to 7 days. The pressure and temperature of CO2 within this chamber were 1 MPa and 90 \({}^{\circ}\)C, and this environment is explained in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Sample (\%)** & **CaO** & **SiO2** & **Al2O3** & **Fe2O3** & **Na2O** & **MgO** & **TiO2** & **MnO** & **SO3** & **Cr3O5** & **LOI** \\ \hline Wollastonite & 39.89 & 47.12 & 3.91 & 2.76 & 0.23 & 1.46 & 0.21 & - & - & - & 4.42 \\ EAF slag & 36.02 & 12.86 & 7.52 & 31.07 & 0.13 & 1.73 & 0.63 & 2.68 & 0.32 & 2.46 & 4.58 \\ \hline \end{tabular}
\end{table}
Table 1: Oxide composition of raw materials.
# Limitations and Performance Analysis of Spherical Sector Harmonics for Sound Field Processing
## 4 Limitations on Radial Extrapolation
### Interior Case
#### 4.2.2 Point Source  
We further investigate the sound field extrapolation performance in the interior case with a unit strength point source, with the Cartesian coordinates \((0,0,-2)\) m. The experimental setup, including settings of \(\mathbb{M}\) and the microphone placement, is maintained as described in Section 4.2.1. We estimate the sound field for various inner concentric sector regions with \(R_{T}=R_{M}\) and \(0.6R_{M}\). The results, shown in Figure 8, demonstrate that, consistent with the findings in the preceding subsections, the accurate estimation of the sector sound field is achievable when \(R_{T}=R_{M}\). However, significant errors arise when \(R_{T}\
eq R_{M}\).  
Continuing our investigation, we conduct additional simulations to analyze the extrapolation error (\(\epsilon\)) using various setups while maintaining a point source instead of a plane wave. The results are depicted in Figure 9, which reveals the following: (i) the results are similar to those for a plane wave in Figure 6; (ii) the impact of frequency and the size of \(\mathbb{T}\) on \(\epsilon\) remains less regular; (iii) compared to Figure 6, the trajectories of \(\epsilon\) in Figure 9 are more concentrated across different \(\mathbb{T}\) setups. This suggests that the size of \(\mathbb{T}\) has a relatively minor effect on the extrapolation accuracy with point sources, in contrast to plane waves.  
In conclusion, the findings from both the exterior and interior case simulations underscore the limitations of conducting radial extrapolation with SSHs. While accurate extrapolation is challenging when \(R_{T}\
eq R_{M}\), (18) shows potential for estimating the extrapolation in the exterior case, especially under specific conditions involving low frequencies, large sector regions, and small extrapolation distances.  
Figure 8: Spherical sector sound field radial extrapolation for the interior case with a point source, where \(\theta_{M}=120^{\circ}\), \(R_{M}=1\) m, \(f=300\) Hz, \(Q=100\): (**a**) true sound field with \(R_{T}=1\) m; (**b**) estimated sound field with \(R_{T}=1\) m; (**c**) true sound field with \(R_{T}=0.6\) m; (**d**) estimated sound field with \(R_{T}=0.6\) m.  
Figure 9: _Cont._
## 3 Results
### Flexural Strength  
Figure 6 shows that the development of the flexural strength of polysulfide polymer concrete is also dependent on the curing temperature, as seen in the case of compressive strength. However, the development of flexural strength was found to follow a different pattern than that of compressive strength. Specifically, while compressive strength decreased steadily with an increase in curing temperature, flexural strength decreased gradually up to 20 \({}^{\circ}\)C and then decreased rapidly above 20 \({}^{\circ}\)C. These results imply that there exists a specific temperature range in which the development of flexural strength occurs. Furthermore, in the case of the polysulfide polymer concrete used in this study, the flexural strength of the specimen cured at \(-10\)\({}^{\circ}\)C was the highest among all of the specimens cured at various temperatures. This is similar to the result obtained for the compressive strength; that is, the sub-zero temperature had a negative effect on the initial flexural strength by decreasing the rate of the chemical reactions, but ultimately had a positive effect on the final flexural strength.  
Within 7 days, the specimens cured at temperatures higher than 40 \({}^{\circ}\)C exhibited a flexural strength lower than 14 MPa (as shown in Figure 6), which is the minimum flexural strength recommended in the ACI 548.5 guideline [38]. The reason for this low strength can be inferred as follows: at a curing temperature higher than 40 \({}^{\circ}\)C, chemical reaction progresses very quickly at first and not only forms chemical products with a poorer physical structure, but this rapid progress then delays subsequent  
Figure 5: Effect of curing temperature on average compressive strength.  
chemical reaction. As a result, the polymer concrete becomes porous, riddled with many small holes, and its compressive and flexural strengths decrease significantly. Therefore, if polysulfide polymer concrete that has been cured at temperatures higher than 40 \({}^{\circ}\)C is used in existing or new buildings, its low flexural strength may cause problems.  
Figure 7 shows the load-displacement relationships of all of the specimens subjected to flexural strength tests at various curing temperatures. The beams cured at \(-10\)\({}^{\circ}\)C and 5 \({}^{\circ}\)C exhibited a linear load-displacement relationship; after the maximum load was applied, these beams underwent brittle fractures, similar to the type of behavior observed in over-reinforced concrete beams.  
In contrast, the specimens cured at 20 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C exhibited ductile behavior, similar to the behavior of under-reinforced concrete. As a result, the specimens cured at a higher temperature showed a significantly larger displacement than those cured at a lower temperature under the same load. Therefore, the use of a higher curing temperature (above 20 \({}^{\circ}\)C) is recommended for improving the ductility of polysulfide polymer concrete.
## 5 Conclusions  
This study proposed a solution to elaborately monitor cultural landscapes at heritage sites. In particular, a semi-automatic workflow was customized to extract land-cover changes from VHR remote-sensing images. Superior to traditional methods based on manually interpretation, the developed workflow can monitor delicate changes and greatly improve the efficiency with the accuracy around 85%. The object-based two-level workflow combines unsupervised CVA and supervised classification algorithms, which do not require a high-level technique skill to use for non-remote-sensing researchers. This semi-automatic workflow also demonstrates flexibility and good generalization. It can be improved by simply replacing advanced algorithms at each step and even evolves into an automatic workflow by introducing deep-learning techniques. Moreover, we proposed an indicator of the interference degree based on the monitoring result to quantify the amount of changed elements caused by human activities and natural factors. This indicator well reflects different preservation statuses of three diverse CHS in our case study. It can be periodically observed as a complementary indicator to evaluate the status of CHS and thus offer assistant decision-making in cultural-heritage preservation. This study demonstrates a practical effective technological solution to generate the first-hand global CHS data at the fine monitoring scale so as to evaluate Target 11.4 for the sustainable development of the world CHS.  
Conceptualization, F.C.; writing, Y.T. and F.C.; data curation, W.Y., Y.D. and H.W.; visualization, W.Y., H.W. and Y.T.; investigation, W.Y. and F.C.; methodology, Y.T. and F.C.; funding acquisition, L.J., Z.S. and F.C.; supervision, L.J. and F.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2019 YFC1520800; the Strategic Priority Research Program of the Chinese Academy of Sciences, grant number XDA19030502; the National Key Research and Development Program of China, grant number 2017 YFE0134400; and the National Natural Science Foundation of China, grant number 41972308, 42071312, 42171291.  
**Acknowledgments:** The authors thank three anonymous reviewers for providing helpful comments and suggestions to improve the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Formulating the Expression of the Total Number of Transmissions and Receptions
### Formulating \(N_{r}^{p}\)
#### 4.2.4 Calculating \(N_{r4}^{q}\) and \(N_{r5}^{q}\)  
As illustrated in Figure 8, the two black circles denote the communication range of two transmitting nodes, \(\mathbf{n}_{r1}^{q}\) and \(\mathbf{n}_{r2}^{q}\), in the \(q\) forwarding. The red circle denotes the communication range of transmission node \(n_{t}^{q-1}\) in the \(q-1\) forwarding. The calculation of \(N_{r4}^{q}\) and \(N_{r5}^{q}\) is similar to that of \(N_{r2}^{q}\) and \(N_{r3}^{q}\):  
\[N_{r4}^{q} =C_{N_{t}^{q}}^{1}C_{N_{t}^{q-1}}^{1}p^{\prime}{}_{L}\times C_{N_{t }^{q}-1}^{1}\frac{\overline{S}_{8}}{\pi r_{t}^{2}}\times N\frac{\overline{S}_{9 }}{S}, \tag{55}\] \[N_{r5}^{q} =C_{N_{t}^{q}}^{1}C_{N_{t}^{q-1}}^{1}p_{L}\times C_{N_{t}^{q-1}}^ {1}\frac{\overline{S}_{10}}{\pi r_{t}^{2}}\times N\frac{\overline{S}_{11}}{S}, \tag{56}\]  
where \(S_{8}\) and \(S_{10}\) denote the area of the black region and \(S_{9}\) and \(S_{11}\) denote the area of the shadow region. Compared with Figures 7 and 8, we have the following:  
\[\left\{\begin{array}{l}\overline{S}_{8}=\overline{S}_{4},\overline{S}_{9}= \overline{S}_{5}\\ \overline{S}_{10}=\overline{S}_{6},\overline{S}_{11}=\overline{S}_{7}\end{array} \right.. \tag{57}\]  
Thus, the expressions of \(N_{r4}^{q}\) and \(N_{r5}^{q}\) are:  
\[N_{r4}^{q}=\frac{N_{t}^{q}N_{t}^{q-1}\pi Nr_{t}^{4}\Big{(}N_{t}^{q}-1\Big{)}}{ 6}\Bigg{(}\frac{7\pi}{8}-\frac{3\sqrt{3}}{8}\Bigg{)} \tag{58}\]  
\[N_{r5}^{q}=4N_{t}^{q}N_{t}^{q-1}N\pi r_{t}^{4}\Big{(}N_{t}^{q}-1\Big{)}\Bigg{(} \frac{13}{24}-\frac{\sqrt{3}}{2\pi}\Bigg{)}\Bigg{(}\frac{\pi}{4}-\frac{3\sqrt {3}}{16}\Bigg{)} \tag{59}\]  
In conclusion, by combining Equations (35), (42), (48), (54), (58) and (59), we can obtain the expression of \(N_{r}^{q}\):  
\[\begin{array}{l}N_{r}^{q}=N_{t}^{q}N\frac{3\sqrt{3}}{2}r_{t}^{2}-\frac{C_{N }^{q-1}NN_{t}^{q}}{6}\Big{(}\frac{7\pi}{8}-\frac{3\sqrt{3}}{8}\Big{)}-\frac{C_ {N_{t}^{q-1}}^{2}4r_{t}^{2}NN_{t}^{q}}{\pi}\Big{(}\frac{13}{24}-\frac{\sqrt{3 }}{2\pi}\Big{)}\Big{(}\frac{\pi}{4}-\frac{3\sqrt{3}}{16}\Big{)}\\ -\frac{N_{t}^{q-1}N_{t}^{q-1}\pi Nr_{t}^{q}\Big{(}N_{t}^{q}-1\Big{)}}{6}\Big{(} \frac{7\pi}{8}-\frac{3\sqrt{3}}{8}\Big{)}-4N_{t}^{q}N_{t}^{q-1}N\pi r_{t}^{4} \Big{(}N_{t}^{q}-1\Big{)}\Big{(}\frac{13}{24}-\frac{\sqrt{3}}{2\pi}\Big{)} \Big{(}\frac{\pi}{4}-\frac{3\sqrt{3}}{16}\Big{)}.\end{array} \tag{60}\]
# CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence
## 3 Proposed Method  
In this section, we present a component-aware capsule graph neural network for shape correspondence. In Section 3.1, we introduce the whole network architecture of CA-CGNet. Section 3.2 illustrates the process of turning input shapes into component pairs. The component-aware graph routing is given in Section 3.3. We introduce the functional map of component pair constraints in Section 3.4 and the semantic regularization term in Section 3.5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Routing Methods** & **Outcomes** & **Limitations** \\ \hline CapsGNN [26] (2018) & Dynamic routing & Higher accuracy rate compared to traditional methods & Irrelevant messages from multi-hop neighborhoods has not been restrained \\ \hline Caps-GNN [27] (2020) & Dynamic routing & Higher inference in personalized preference & External knowledge has not been considered \\ \hline HGCN [30] (2021) & Nonlinear function & More effectively capturing the heterogeneous factors under each node. & The over-smoothing issue over graph is ignored \\ \hline CapsGNNEM [28] (2021) & EM routing & Higher graph classification compared to standard methods & Structural information of the graph has not been considered \\ \hline NCGNN [29] (2022) & Dynamic routing & Adaptively identifying a subset of crucial node-level capsules & Unable to preserve structure information of lower-level parts \\ \hline Caps-HAGKT [31] (2022) & Capsule routing & Extracting the latent knowledge structure between levels & Automatic modeling of the complex knowledge structure of the knowledge capsule at same layer is insufficient \\ \hline Ours & Component-aware graph routing & Using component constraints to solve problem of model posture details and low resolution & Performance can be improved with optimizing selection of the number of components \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparative study for Capsule Graph Neural Network in open literature.
# An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks
## 2 Vibration Signal Detections
### Signal Analysis Methods
#### 2.1.3 Other Signal Analysis Methods  
The other signal analysis methods, such as the time-domain method, employing selected parameters like peak values, RMS (Root Mean Square) or mean values, crest factor, PCA (Principal Component Analysis), and kurtosis for vibration frequency detection, entails scrutinizing signal characteristics directly within the time domain [36]. This approach computes various statistical parameters from the raw vibration signal, including peak values (maximum or minimum amplitude), RMS values (a measure of signal magnitude), mean values (average amplitude), crest factor (peak-value-to-RMS-value ratio), PCA (a method for dimensionality reduction and feature extraction), and kurtosis (a gauge of the shape of the probability distribution of a signal's values). By evaluating these parameters, engineers can glean insights into the overall behavior and attributes of the vibration signal, facilitating the identification of anomalies or patterns indicative of specific mechanical faults or operational conditions. This method proves particularly beneficial for detecting alterations in vibration patterns associated with faults like unbalance, misalignment, bearing defects, or rotor eccentricity in rotating machinery.  
Despite its potential, the utilization of the time-domain signal analysis method with selected parameters for vibration frequency detection is relatively limited [37]. Firstly, this method may not consistently capture the complete complexity of vibration signals, particularly when fault signatures are subtle or nonlinear. The selected signal parameters may inadequately represent the underlying characteristics of the vibration signal, resulting in restricted effectiveness for fault detection or diagnosis. Additionally, interpreting these selected parameters often demands significant expertise and experience, as they may not directly elucidate specific fault conditions or mechanical anomalies within the machinery. Engineers may encounter challenges in accurately interpreting results without comprehensively understanding the analyzed system.  
Furthermore, reliance on a predefined set of parameters may constrain the method's flexibility and adaptability across various machinery types or fault scenarios. Vibration signals exhibit considerable variability influenced by machine type, operating conditions, and environmental elements, complicating the development of universally applicable parameters for fault detection. While the time-domain signal analysis method utilizing selected parameters offers valuable insights into machinery vibration characteristics, its limited efficacy, interpretability, and adaptability contribute to its comparatively lower adoption than alternative vibration analysis techniques, such as frequency-domain analysis or machine learning-based approaches.
## 3 Results and Discussion
### Rheological Properties  
In the course of the first storage test, rapid and clear changes in the rheological properties of _P. purpureum_ concentrates were visually noted, with gel formation occurring within a day in the case of concentrate storage. A second storage test was, therefore, conducted to study this systematically.  
Figure 4: (**a**) B-Phycoerythrin (B-PE) levels at the start and (**b**) after storage (both 7 and 14 days) grouped by storage approach. (**c**) B-PE levels after storage (both 7 and 14 days) grouped by storage temperature. Bars that are not marked with a common lowercase letter within one graph are statistically different, \(t_{0}\) data were not included in the statistical analysis of data in graphs (**b**) and (**c**). Because storage time had no significant impact, 7- and 14-days storage data were pooled in graphs (**b**) and (**c**).  
The samples obtained from concentrate storage and dilute culture storage (after centrifugation) had different dm concentrations, initially \(6.4\pm 0.1\%\) and \(12.7\pm 0.1\%\), respectively. Because the biomass concentration [31] and EPS concentration [32] are known to have a large influence on the rheological properties of algae suspensions, our rheology data cannot be used to expose the effect of the storage approach independently from the concentration effect. Hence, this study does not engage with the influence of the storage approach on the rheological properties. Instead, the emphasis is on the changes in the function of time, especially in the case of concentrate storage. After all, when _P. purpureum_ cells can form a gel during concentrate storage, this has a serious impact on later down-stream processing, complicating for instance pumping or mixing of such a solid-like material. On the other hand, when _P. purpureum_ is stored as a dilute culture, this dilute suspension is much more manageable and the rheological properties of the final concentrate can still be adapted by changing the centrifuge settings and adapting the concentration factor.
# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 3 Overview of Fault Detection and Diagnosis (FDD) in DHC Systems
### Leakage Detection in DHC Networks
(2020) [112] have developed ML algorithms to improve postprocessing and satisfactorily make the difference between true leakages and other potential causes.