esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Factors Controlling Pore Development in Different Lithofacies
#### 5.1.2 Mineral Composition  
The degree of pore development in shales is controlled not only by the TOC content but also by mineral composition [51; 52; 53]. The silicate mineral (e.g., quartz and feldspar) content displays a positive correlation with the PV and SSA of microscopes and mesopores and a negative correlation with the PV and SSA of macropores (Figure 13). Compared with other lithofacies, the PV and SSA of siliceous shales with high quartz content are relatively high. Previous studies suggested that the precipitation of abundant microcrystalline quartz sourced either from biogenic silica or clay conversion can form a rigid framework that is resistant to overlying mechanical compaction [40; 44]. [PERSON] et al. (2019) [5] and [PERSON] et al.  
(2021) [54] showed that authigenic microcrystalline quartz aggregates are very common in WF-LMX shales and are mainly of biological origin. A large number of authigenic quartz aggregates can be found in SEM and CL images (Figure 11C-F). Organic matter can be observed to coexist with authigenic quartz in the WF-LMX shales, forming an organic matter network. Abundant sponge-like materials, rounded to sub-rounded pores, were developed within the organic matter (Figure 6). A rigid quartz framework provided by quartz cementation has the potential to preserve pores (both organic pores and interparticle pores) during the compaction process [55].  
The PV and SSA of micropores and mesopores display negative correlations with the clay mineral contents in siliceous and argillaceous lithofacies (Figure 14), and the PV and SSA of macropores have no correlations with clay mineral contents (Figure 14). The above-mentioned correlations indicate that the abundance of micropores and mesopores decreases with increased clay mineral contents. This may be due to the fact that shale samples  
Figure 12: The correlation between TOC content and pore volume (micropore, mesopore, macropore), surface area (micropore, mesopore, macropore) in the Wufeng–Longmaxi shales.  
Figure 13: The correlation between silicate mineral (quartz + feldspar) content and pore volume (micropore, mesopore, macropore), surface area (micropore, mesopore, macropore) in the Wufeng–Longmaxi shales.  
with high clay mineral content typically have low TOC content and low silicate minerals. Therefore, the abundance of organic pores is low simply due to the low TOC content. In addition, without the support of rigid quartz framework, some primary interparticle pores and organic matter pores are difficult to preserve due to mechanical compaction. Although SEM images show the development of some shrinkage fractures associated with clay minerals (Figure 7H), their contribution to porosity is limited or still questionable because some micro-fractures associated with clay minerals may be generated during the processes of sample preparation and experiments. Meanwhile, due to the release of pressure from the in situ reservoir conditions to the surface, the clay minerals in the shale samples dehydate and form some artificial shrinkage cracks.
# A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution
## 2 Problem Formulation  
Add a valid time [b, e] to each fact of the knowledge graph to obtain the temporal knowledge graph (TKG), where TKG = \(<\)F, C, Q\(>\), C is the timing constraint condition, Q is the query condition of the knowledge graph, and FQ is the set of all the facts involved in the query condition Q, FQ \(\subseteq\) F. The timing fact is expressed as the probability that f = \(<\)s, \(p\), \(o\), \(w\), \([b,e]\)\(>\), f \(\in\) F, and \(w\) is the probability of the fact being true. Here is [PERSON]'s example of his career:  
1. \(<\)[PERSON], retirement from, NSA,1.0, [2014, now]\(>\)
2. \(<\)[PERSON], served as, the 1 st Commander of the United States Cyber Command, 1.0, [2010, 2014]\(<\)[PERSON], served as, the 16 th Director of the National Security Agency, 1.0, [2005, 2014]\(>\)
4. \(<\)[PERSON], served as, the Deputy Chief of Staff G-2 in U.S. Army, 0.9, [2003, 2005]\(>\)
5. \(<\)[PERSON], served as, the Commanding General of the U.S. Army Intelligence and Security Command, 0.7, [2001, 2003]\(>\)  
If you want to check out [PERSON]'s job positions from 2010 to 2014, from the above facts we know that although there is overlap between facts (2) and (3), there is no conflict because [PERSON] served two positions in 2010-2014. This query-based conflict resolution is described by the following method.  
For TKG, a query Q based on time limit C is given, and the query result F\({}_{\text{Q}}\) is returned, but F\({}_{\text{Q}}\) will contain some conflicting facts. The goal of this article is to perform conflict resolutions in F\({}_{\text{Q}}\), thus returning accurate query results. Therefore, it is necessary to maximize the sum of the consistency fact weights to minimize the actual conflict, as shown in the following condition (1):  
\[\max\sum_{f\in F_{\text{Q},C}}w(f)\quad\text{s.t.}\;\forall f_{\text{Q},C}\in F _{\text{Q},C}\;\text{Confidence}(C,f_{\text{Q},C})>\theta. \tag{1}\]  
Here, Confidence (\(C,f_{\text{Q},C}\)) represents the confidence of the fact \(f_{\text{Q},C}\) under the timing limit C. When this confidence is greater than threshold \(\theta\), this fact is added to the set of query results. Therefore, the evolution goal of the knowledge graph is to maximize the sum of the weights for the facts of the query results and resolve the factual conflicts of the query results, ensuring the consistency of knowledge during evolution.
# Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
## 1 Introduction
Based on the results of composing statistical methods, we identify anomaly PQP and create a supervised dataset to predict future PQP. The second objective is to predict future PQP by applying LSTM, a well-known deep learning model. We compared the LSTM model with the other state-of-art models, such as autoregressive integrated moving average (ARIMA), Random Forest (RF), and artificial neural network (ANN). The experimental results show that the proposed LSTM model outperforms other state-of-art methods in predicting PQP by at least 6%. To verify product quality improvement, the proposed method is exemplified in a real case study of a smart manufacturing system in the automotive industry. The rest of this paper is organized as follows. Section 2 discusses related studies on the prediction and estimation of anomalous detection in manufacturing. Section 3 provides in-depth details of the proposed PQP and presents PQP predictions based on the LSTM neural network, Softmax activation algorithm, and evaluation metrics. Section 4 covers implementation results of LSTM prediction model comparing to those of other machine learning and deep learning models Finally, the paper is concluded in Section 5.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 6 Conclusions  
This paper has presented an MEC in an NFV integrated environment, on which Mobile Edge applications can run. The proposed architecture can overcome some known management problems typically encountered in the integration of an MEC architecture with an NFV framework.  
To demonstrate the capabilities offered by the system, an ME app for CPU-intensive immersive video (named i-EVS) services has been described, combined with a related end-user app.  
The virtualization infrastructure hosting the ME apps includes Graphical Processor Units, hence also high CPU-intensive tasks can be performed. Experimental results which show the effectiveness of the proposed solutions have been presented and briefly discussed.  
Conceptualization, G.C., C.M., D.M., F.G. and P.P.; Investigation, G.C. and P.P.; Supervision, P.P.; Writing--original draft, C.M. and P.P.; Writing--review and editing, G.C., C.M., P.P., F.G. and D.M.  
This research was funded by the EU Horizon 2020 5G Essence project (GA No. 761592).  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Bending Test at 16 mm
#### 3.3.3 Shape Recovery  
For the recovery of shape, the curves of the first cycle do not vary compared to the trial with a single cycle (Figure 14). The curves linked to successive cycles have a first descending phase and a subsequent increasing phase. This behavior is particularly evident for the lozenge, since the roller does not show such a phenomenon. Close recovery cycles are not well tolerated by the structure, as a thermal stimulus is provided at short intervals. Therefore, once immersed in the tank and due to the pressure of water, structures tend to rise. Once removed from the tank, they have recovered their original form. This is related not only to the limited density of the structure but also to the poor thermal stability of PLA [56]. The values recorded with the measurement made at the end of the third recovery cycle indicate that the lozenge is the structure which can recover most of its original shape at an angle of 0.7\({}^{\circ}\). Visual analysis of the midpoint, by means of a microscope, showed a constant but increasingly minor recovery from the first to the third cycle. To support this statement, in Figure 15, the images of the structures are shown under examination pre- and post recovery for each cycle.  
The lozenge and roller show very similar trends for the two replicas, reaching a nearly total recovery of the bending angle. This result confirms the good replicability of the two chosen structures. For the roller, the angle measured at the end of the recovery of the third cycle is 2.8\({}^{\circ}\) and 2.4\({}^{\circ}\), respectively, despite the temporal proximity with which the cycles were performed. For the analysis of the recovery of the midpoint, the geometry that showed a better recovery is roller, with a final value of 7.65 mm thickness.  
Figure 13: Lozenge and Roller spring back bending test at 16 mm.
## 5 Discussion
### Limitations  
Our studies have some limitations that affect the generalizability of the results. Although our studies included participants from various backgrounds, the lack of gender balance in the first study and the inclusion of healthy individuals in both studies could potentially affect the external validity. However, our analysis showed that demographic factors did not systematically influence participants' ratings, which indicates that the observed effects are not due to the sample population. Nevertheless, a more patient-centered focus could provide deeper insights into the specific needs and perceptions of individuals with orthopedic conditions. While some results did not reach statistical significance, they indicate notable trends that suggest potential effects worth further investigation. Furthermore, the results are limited to the specific stimuli and orthotic categories that were used in the study. Additionally, the AI-generated designs, which rely on individual prompts or selected pictures of orthopedic products, cannot be consistently reproduced with identical inputs. This inconsistency requires multiple iterations of prompts to achieve the desired outcomes.  
Another notable limitation is that AI-generated designs do not adhere to any medical guidelines or standards, as they are primarily visual concepts rather than functional prototypes. While the prompts and images were adjusted to the medical functions and specific requirements of the selected orthosis categories, DALL-E image generation is not specialized for medical functions and does not take manufacturing constraints or biocompatibility into account. The results are limited to the perspective of end users and do not consider technical aspects that are relevant from an expert perspective, such as material feasibility or regulatory compliance.  
The generative AI tool DALL-E was limited to its current version during both studies. Furthermore, biases in the training datasets of AI models could reinforce stereotypes (e.g., related to ethnicity, gender, or other factors) [73]. These factors are critical as they could influence the social acceptance of the designs, particularly if they fail to address the needs and preferences of various user groups or if they misrepresent them. Therefore, any application of such AI-generated designs in real-world medical products must carefully consider and comply with strict regulatory standards to ensure their safety and effectiveness.
## 3 Experimental Methods and Numerical Verification
### Digital Image Correlation (DIC)  
Digital image correlation is a well-established method for displacement, strains, and shape measurements [39]. Two-dimensional DIC (with one digital camera) and 3D DIC (with two or more digital cameras) are widely used and accepted in the field of experimental mechanics [40; 41].  
A straightforward measurement procedure requires acquiring a set of images of the object under investigation, which is subjected to any kind of load. One of the images is selected as the reference image (in most cases it is the image acquired before the load is applied) and the remaining images are subjected to correlation analysis. The reference image is divided into small sub-images or subsets. The subsets are subsequently matched against similar subsets in images acquired in different load states. Repeating the procedure for all subsets of the reference image yields displacement maps, which, in following steps, can be used for the calculation of strains. To allow the application of the DIC method, the object under investigation has to exhibit a random texture (speckle pattern) on its surface. In most cases, the speckle pattern is introduced on an object surface by spraying on paint [31].
## 1 Introduction
[PERSON] proposed a special point constraint processing method for the multiple dispersion curves produced by interference, fitting each multiple dispersion curve separately to produce a series of special dispersion points, and then fitting these special dispersion points with polynomial fitting to generate the ultimate dispersion curve [20]. [PERSON] found that in observed microtremor signal spectra, association of a given zero with a particular zero crossing of the zero-order Bessel curve may be difficult because noise in the spectrum can cause missed or extra zero crossings. To allow for this, he created a technique for estimating phase velocity to produce dispersion curves of superior quality by artificially adding the zero point [21]. Multiple dispersion curves will be obtained from two-station calculations when using SI to extract dispersion curves from a station array. These multiple dispersion curves need to be integrated into a single dispersion curve, which is thought of as a uniform response below the coverage of the station array. The superimposed averaging approach used in engineering may quickly obtain the final mean dispersion curve. The dispersion information of various spacing dispersion curves cannot, however, be fully utilized by this method, and there are certain mistakes. The zero poles of the coherence coefficients are generally less in small-scale exploration, and the results' quality of special point constraint method are not always guaranteed. The zero-point add method depends more on the operator's empirical understanding of spectrum analysis and may introduce additional errors if the zero-point location is incorrectly added. Thus, it is crucial to research a better extraction and processing technique for dispersion curves. This paper studied the processing method of dispersion curves extracted using SI. Based on the correlation between station pair spacing and the frequency components of the gathered signals, the depth hierarchy of the concentrated dispersion curves was established, and a hierarchical frequency fusion criterion was proposed. The dependable frequency was utilized to establish the fusion boundary; then, station pair dispersion curves at various distances were fused to produce the final dispersion curve. A microtremor survey experiment was carried out in Xiamen City. The microtremor data were processed and analyzed using this method and a superimposed averaging approach. Compared to the superimposed averaging approach, the result of the hierarchical frequency fusion method more precisely achieved delicate depiction of the S-wave velocity structure. The accuracy and reliability of the method were verified by drilling results, and they can provide certain engineering significance for similar microtremor survey projects.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 3 Design of the Protocol Stack
### Design of Transport Plane
#### 3.2.4 AOS Frame Switching Scheme  
In the CCSDS framework, the AOS Space Data Link Protocol uses a fixed-length PDU (protocol data unit), called an AOS frame, to transmit data. In the traditional scheme, it is impossible to switch the AOS frame directly in an intermediate node without the sign to tell the node where to forward the frame on the current layer. Therefore, even intermediate nodes still have to unpack the AOS frame to obtain the traffic and give it to an upper-layer to perform further processing. After the procession from the upper-layer, the traffic needs to be packed again and forwarded to the specified optical ISL. Data unpacking and packing approaches consume the energy and resources of the node, especially the satellite. In addition, the traditional scheme increases the node processing delay of the AOS frame because the frame cannot be forwarded until the whole procession has been completed. In order to solve these problems, we need to learn from the mature technologies used in terrestrial networks. As we used optical communication to transmit data, the technology of optical packet switching [13] can be considered. However, there are still some processes, such as photoelectric conversion of control messages and processing of the optical signal, that are difficult to implement in the optical packet switching on satellites. As a result, considering the limitations of satellites and the demands of space-based optical backbone network, we used the multiprotocol label switching (MPLS) network [14] as a reference. So, we added a label field in the AOS frame (Figure 2) to carry labels being used to forward AOS frames according to LFIB (label forwarding information base), which includes information such as InLabel, OutPort and OutLabel.  
Figure 9 shows the diagram of the AOS frame switching scheme. As can be seen, when an AOS frame arrives at an intermediate node, the node reads the label (if A) located in the label field and queries it in the InLabel column of the LFIB. Having found it, the node then reads the corresponding value of the OutPort (3) and OutLabel (B). Then, the value of the label in the label field is replaced by that of the OutLabel (B), which can be used as the InLabel in the next node, and the AOS frame is then forwarded to the port specified by the value of the OutPort (3). It is clear that the AOS frame switching scheme can switch AOS frames directly in intermediate nodes, which can avoid unnecessary processes, such as unpacking, upper-layer processing and repacking for service traffic to reduce the energy and resource consumption of the intermediate node and decrease the node processing delay of the AOS frame. Furthermore, we can see that only the labels without any information from services are needed to transmit the services. That is to say, the AOS frame switching scheme could provide transparent transmission for services, no matter what type they are.
# Improving Landslide Recognition on UAV Data through Transfer Learning
## 1 Introduction
In addition, the deep learning algorithms often have complex structures, large model parameters and a long training time, which have high requirements for computing resources and hardware equipment. In this paper, we proposed a transfer learning approach, which can share knowledge from the source domain to the target domain [34]. The transfer learning method was based on similar characteristics shared between the source and the target domains [35; 36]. However, there are few source domain datasets available for landslide detection. In this work, the Places365 dataset [37], the UC Merced land use dataset [38], and the RSSCN7 dataset [39] were selected as the source domain for feature transferring. By fine-tune existing model parameters and using a small number of field samples, the computing resources and training time could be greatly reduced. The efficiency of landslide extraction is improved, and the workload of field manual interpretation is reduced [40]. The organization of this work is as follows: Section 2 introduces the study area, and the process of the image acquisition is also elaborated. Section 3 focusses on the research methodology, and we introduce the principles and methods of the transfer learning model. The data processing and experimental results are given in Section 4. Finally, we discuss the overall achievements and summarize the full text.
## 2 Theories, Simulation, and Experiment Methods
### Numerical Simulation  
In this paper, a numerical simulation model is constructed using a commercial program, COMSOL Multiphysics, which provides the pressure acoustics. To solve acoustic problems, the program models the propagation of sound pressure waves in the frequency domain. The governing equation is a modified Helmholtz equation [14].  
\[\
abla\cdot\left(-\frac{\
abla p}{\rho}\right)-\frac{\omega^{2}p}{\rho c^{2}}= 0\, \tag{3}\]  
where \(p\) is the acoustic pressure, \(\rho\) is the density, \(c\) is the speed of sound, and \(\omega\) is the angular frequency. This equation assumes that reactive damping prevails in the low-frequency range.  
For boundary conditions, the numerical model uses sound hard boundary conditions at the solid boundaries, imposing that the normal velocity at the boundary is zero, and incoming and outgoing plane waves are specified at the inlet and outlet, respectively.  
The transmission loss, \(TL\), is the measure of the attenuation of the acoustic energy in a system and gives the damping as a function of frequency.  
\[TL=10 log_{10}\frac{P_{in}}{P_{out}}=10 log_{10}\frac{\int_{A_{in}}(p_{0}^{2}/2 \rho c)dA}{\int_{A_{out}}(|p_{e}|^{2}/2\rho c)dA}\, \tag{4}\]  
where \(P_{in}\) and \(P_{out}\) are the acoustic powers at the inlet and the outlet, respectively. Those variables are dependent on the pressures at the inlet and the outlet.
# Removal of Chromium from Synthetic Wastewater Using Modified Maghemite Nanoparticles
###### Abstract  
The main aim of this study was to ascertain the removal of chromium ions from synthetic wastewater using modified maghemite nanoparticles (MNPs). Commercially available maghemite nanoparticles (NPs) (\(<\) 50 nm) were modified using sodium dodecyl sulfate (SDS). The MNPs were characterized using a powder X-ray diffractometer (XRD) and Fourier transform infrared spectrophotometer (FTIR). Statistically designed batch experiments were carried out to evaluate the effects of various parameters, namely the pH of the solution, initial chromium concentration, and adsorbent dose, on the chromium removal efficiency (RE). The results from this study showed that the adsorbent dose had a synergistic effect on chromium RE, while pH and initial chromium concentration had antagonistic effects on the RE. An optimal chromium RE of 95.8% was obtained at pH = 2.6, adsorbent dose = 5 g/L, and initial chromium concentration = 20 mg/L. The analysis of variance (ANOVA) results showed that the model was significant, with high \(F\) value (\(F\) = 80.07) and low \(P\) value (\(P\) = \(<\) 0.001). Based on the results obtained from the experiments, the MNPs could be exploited as an efficient adsorbent for chromium removal from wastewater.  
chromium removal; maghemite nanoparticles; SDS; FTIR; XRD +
Footnote †: journal: Applied Physics Letters  
1