esa-sceva/satcom-chunk-collection huggingface dataset:
## Abstract  
The development of process parameters for electron beam powder bed fusion (PBF-EB) is usually made with simple geometries and uniform scan lengths. The transfer to complex parts with various scan lengths can be achieved by adapting beam parameters such as beam power and scan speed. Under ideal conditions, this adaption results in a constant energy input into the powder bed despite of the local scan length. However, numerous PBF-EB machines show deviations from the ideal situation because the beam diameter is subject to significant changes if the beam power is changed. This study aims to demonstrate typical scaling issues when applying process parameters to scan lengths up to 45 mm using a fourth generation \(\gamma\)-TiAl alloy. Line energy, area energy, return time, and lateral velocity are kept constant during the additive manufacturing process by adjusting beam power and beam velocity to various scan lengths. Samples produced in this way are examined by light microscopy regarding lateral melt pool extension, melt pool depth, porosity, and microstructure. The process-induced aluminum evaporation is measured by electron probe microanalysis. The experiments reveal undesired changes in melt pool geometry, gas porosity, and aluminum evaporation by increasing the beam power. In detail, beam widening is identified as the reason for the change in melt pool dimensions and microstructure. This finding is supported by numerical calculations from a semi-analytic heat conduction model. This study demonstrates that in-depth knowledge of the electron beam diameter is required to thoroughly control the PBF-EB process, especially when scaling process parameters from simply shaped geometries to complex parts with various scan lengths.  
Electron beam powder bed fusion; electron beam; beam diameter; additive manufacturing; titanium aluminide; microstructure; evaporation Article
Simultaneously, non-cooperative game theory is introduced to the cluster-head selection, which can acquire the relatively optimal set of the cluster-heads and ensure the energy consumption of the whole network balance as much as possible. (2) Both intra-cluster and the inter-cluster topology construction are key sub-processes to find the optimum relay nodes to perform the forwarding task. It would help to reduce some part of nodes burden and make the network higher energy-efficiency. The rest of the paper is organized as follows. In Section 2, we describe the related work on topology control techniques for UWSN. The system models which include network model and energy consumption model of UWSN are presented in Section 3. Moreover, in Section 4, the algorithm TCEB, which covers cluster-head selection, intra-cluster topology construction and inter-cluster topology construction, is proposed and analyzed in detail. The performance of TCEB is evaluated in Section 5. Finally, we conclude this paper in Section 6.
## 2 Methods
### Multifractal Detrended Fluctuation Analysis (MFDFA)  
MFDFA is an extension of the conventional DFA and consists of five steps. The first three steps are identical to the DFA procedure. Let us consider \(x(i)\) of length \(N_{\text{max}}\) and construct the profile by integrating \(x(i)-\langle x\rangle\), as shown in Equations (1) and (2). After dividing the integrated series into \(N_{n}\) (\(=N_{\text{max}}-n+1\)) overlapping boxes of equal size \(n\), the authors calculated the detrended fluctuation function \(F_{n}^{2}(v)\) as done in Equation (3). Then, the authors averaged over all boxes to obtain the \(q\)-th order fluctuation function as follows:  
\[F_{q}(n)=\left\{\frac{1}{N_{n}}\sum_{v=1}^{N_{n}}\left[F_{n}^{2}(v)\right]^{q /2}\right\}^{1/q}, \tag{6}\]  
where the index variable \(q\) can take any real value in general. For \(q=2\), the standard DFA was retrieved. Lastly, the authors examined the scaling behavior of the generalized \(q\)-dependent fluctuation functions by analyzing double-logarithmic plots of \(F_{q}(n)\) versus \(n\) for each index value \(q\). If the \(x(i)\) series featured a long-range power-law correlation, the authors could find a power-law relationship between them, such as  
\[F_{q}(n)\sim n^{h(q)}. \tag{7}\]  
Here, the authors called the exponent \(h(q)\) a generalized Hurst exponent since \(h(2)\) was identical to the well-known Hurst exponent \(H\)[26]. For positive values of \(q\), \(h(q)\) describes the scaling behavior of the boxes with large fluctuations because the boxes \(\
u\) with large variance \(F_{n}^{2}(\
u)\) contribute more to the average \(F_{q}(n)\). On the contrary, for negative values of \(q\), \(h(q)\) describes the scaling behavior of the boxes with small fluctuations, since the boxes \(\
u\) with small variance \(F_{n}^{2}(\
u)\) dominate the average \(F_{q}(n)\).
## 3 Results
### Quasi-Static Test Results  
Table 2 summarizes the results of the tensile tests and the micro-hardness measurements. The average tensile strength, \(\sigma_{u}\), the elongation to failure, \(\varepsilon_{f}\), and the Vickers hardness are reported in Table 2.  
The tensile strength obtained in the present paper (line 1 in Table 2) is 24% larger than that obtained by testing wrought AlSi10 Mg specimens [35]. The average \(\sigma_{u}\) and \(\varepsilon_{f}\) values are slightly smaller (with a difference of about 30 MPa and 0.7%) than the corresponding values obtained in the literature for the AlSi10 Mg alloy [22]. The difference may actually be considered as negligible, since, in [22], mechanical tests were performed on machined samples, thus reducing the influence of surface defects on final fracture conditions. This hypothesis is further confirmed by yield stresses being very similar in [22] and in the present work (about 260 MPa). On the contrary, the average Vickers hardness is significantly larger than that found in [22], presumably due to the different heat transfer generated during the SLM processes. In detail, even if similar process conditions are used in the current work and in [22], the scanning strategies are different, and the power distribution of the laser beam is not reported. This is consistent with different width/penetration ratios of the liquid pools in the present work and in [22]. Therefore, it can be affirmed that the reduced overheating that occurred in the present work originated higher microhardness values. This is in good agreement with previous studies, in which different energy densities [36] and different emission modes [37] induced significant variations of the microhardness of AlSi10 Mg SLMed samples.
# Aluminum-Loaded Bifunctional Resins for Efficient Fluoride Removal from Aqueous Solutions
## 4 Conclusions  
This study employed S957, which contains a phosphoric-sulfonic acid difunctional group, as the carrier of aluminum to achieve deep defluorination. Al-S957 has a maximum fluoride adsorption capacity of 15.49 mg/g at 298 K. The adsorption is a spontaneous endothermic process that occurs rapidly within 2 h. The process was minimally affected by pH, demonstrating consistent performance across a broad pH range. Even in the presence of competing anions and organics, it maintained high fluoride selectivity. Notably, under identical conditions, Al-D001 exhibited poor aluminum-binding stability, while Al-D412 was significantly affected by organic interference. Al-S957 has been found to efficiently remove fluoride through a ligand exchange mechanism. Furthermore, Al-S957 also can remove fluoride efficiently from actual photovoltaic wastewater and retain high removal efficiency after multiple adsorption-regeneration cycles. These results demonstrate that Al-S957, with superior adsorption selectivity and strong resistance to organic interference, has excellent fluoride removal potential in complex water.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15147829/sl](https://www.mdpi.com/article/10.3390/app15147829/sl)]([https://www.mdpi.com/article/10.3390/app15147829/sl](https://www.mdpi.com/article/10.3390/app15147829/sl)). Figure S1: The pH\({}_{\text{pac}}\) of Al-S957. Figure S2: Leakage of aluminum in the presence of chloride ions in different molar. Figure S3: The linear relationship between ln\(K_{\text{L}}\) and 1/\(T\)(K\({}^{-1}\)) for the adsorption of fluoride by Al-S957 at different temperatures. Figure S4: Column adsorption of Al-D412 and Al-D001 with photovoltaic wastewater. Table S1: Basic information about S957, D412, and D001. Table S2: Adsorption kinetics fitting parameters of Al-S957. Table S3: Adsorption isotherm fitting parameters of Al-S957. Table S4: Adsorption thermodynamic fitting parameters of Al-S957. Table S5: Cost of preparing Al-S957 for treating 1 L of actual photovoltaic wastewater.  
Figure 9: Column adsorption of Al-S957 by using photovoltaic wastewater.  
**Author Contributions:** Conceptualization, Y.S.; data curation, M.M.; formal analysis, M.M.; funding acquisition, Y.S.; investigation, M.M.; supervision, Y.G.; visualization, M.M. and R.W.; writing--original draft, M.M.; writing--review and editing, Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China (No. 51578131) and National Key Research and Development Program of China (2019 YFD1100205).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Methodology
### Noise Pattern Identification
For a given noise signal dataset \(D\), the \(\varepsilon\)-neighborhood of a point \(p\) is defined as:  
\[N_{\varepsilon}(p)=\{q\in D|dist(p,q)\leq\varepsilon\} \tag{1}\]  
where \(dist(p,q)\) represents the distance between signals \(p\) and \(q\), which is calculated by sound pressure level indicators, main frequency components, time accumulation of energy, and events exceeding specific sound pressure levels. The pseudocode for the noise pattern identification DBSCAN algorithm is shown in Algorithm 1. ```
Input: Dataset (D), neighborhood radius (\(\varepsilon\)), minimum points (minPts) Output: Clusters of different noise patterns
1: clusterID = 0
2: for each unvisited signal Sig in dataset D do
3: mark Sig as visited
4: Neighconfigals = regionQuery(Sig, \(\varepsilon\))
5: if size of(NeighborSignals)! minPts then
6: mark Sig as noise
7: else
8: clusterID = clusterID + 1
9: expandCluster(Sig, NeighborSignals, clusterID, \(\varepsilon\), minPts)
10: end if
11: end for
12: function expandCluster(Sig, NeighborSignals, clusterID, \(\varepsilon\), minPts)
13: add Sig to cluster clusterID
14: for each signal Sig' in NeighborSignals do
15: if Sig is not visited then
16: mark Sig's as visited
17: NeighborSignals' = regionQuery(Sig', \(\varepsilon\))
18: if sizeof(NeighborSignals') \(\geq\) minPts then
19: NeighborSignals \(\pm\) NeighborSignals'
20: end if
21: end if
22: if Sig' is not yet part of any cluster then
23: add Sig' to cluster clusterID
24: end if
25: end for
26: end function
27: function regionQuery(Sig, \(\varepsilon\))
28: NeighorSignals = []
29: for each signal Sig' in dataset D do
30: if signalDistance(Sig, Sig') \(\leq\varepsilon\) and signalSimilarity(Sig, Sig') then
31: NeighborSignals.add(Sig')
32: end if
33: end for
34: return NeighborSignals
35: end function
36: function signalDistance(Sig, Sig')
37: return sqrt((Sig, \(-\) Sig', \(x\)))\({}^{2}\) + (Sig, \(y\) - Sig', \(y\))\({}^{2}\) + (Sig,frequency \(-\) Sig', frequency)\({}^{2}\))
38: end function
39: function signalSimilarity(Sig, Sig')
40: return four types of similarity metrics
41: end function
42: function soundPressureLevelSimilarity(Sig, Sig')
43: return abs(Sig, \(-\) Sig', \(\cdot\)SPL) \(\leq\)\(\varepsilon\)\(\_\)SPL
44: end function
45: function mainFrequencyComponentSimilarity(Sig, Sig')
46: return abs(Sig, \(-\) Sig', \(\cdot\)MF) \(\leq\)\(\varepsilon\)\(\_\)MF
47: end function
48: function timeAccumulationOfEnergySimilarity(Sig, Sig')
49: return abs(Sig,TE \(-\) Sig',TE) \(\leq\)\(\varepsilon\)\(\_\)TE
50: end function
51: function eventsExceedingSpecificSoundPressureLevelSimilarity(Sig, Sig')
52: return abs(Sig,NE \(-\) Sig',NE) \(\leq\)\(\varepsilon\)\(\_\)NE
53: end function
```  
**Algorithm 1** Pseudocode for the noise pattern identification DBSCAN algorithm
# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 2 Review of Medical Imaging Techniques for BC Detection
### Medical Imaging Techniques for BC Diagnosis
#### 2.1.5 Thermography  
Another medical imaging model is represented by breast thermography. It is based on the increased quantity of heat produced by the malignant cells which may indicate significant anomalies at the breast level. Thermal imaging is considered a non-invasive and non-contact technique and is efficient in early detection of BC. Additionally, it was observed that this imaging modality can help in advancing the diagnosis of BC 8-10 years earlier than other medical imaging techniques. However, it only alerts the patient regarding certain changes, but the proper diagnosis is efficiently performed afterwards.  
Nevertheless, in most cases, BC is often detected after the symptoms appear which delays the adoption of the proper treatment. This may lead to reaching an advanced phase and increasing the risk of BC degeneration. Thus, a series of strategies and studies have been continuously developed in order to detect BC early.
# Analyzing the SAR in Human Head Tissues under Different Exposure Scenarios
## 3 Results and Discussion
### SAR Simulations for SAM  
In order to determine the excitation signal levels for simulations, the SAR values that were measured at the laboratory were used as a reference. In the simulations, SAM with relative permeability and conductivity values approximately equal to those of the phantom fluid in the laboratory and a monopole antenna were used. Taking into account the plastic surrounding the antenna used in the laboratory, a distance of 10 mm was maintained between the antenna and the SAM in the simulations. Furthermore, the antenna location and angle in the simulations were adjusted to match those in the laboratory using the reference lines of the SAM models. In simulations, different excitation signal levels were set according to measured SAR values and were indexed. Simulation results for 900 MHz and 1800 MHz are also given in Figure 4. It can be clearly seen from Figure 4 that measured and simulated SAR values follow each other closely, ensuring accuracy.  
In order to visualize the SAR distribution in the head region a sample color scale image is shown in Figure 5. The sample images are given for simulations performed for moderate doses of exposure at 900 MHz and 1800 MHz respectively. It can be observed from the figures that the highest SAR values are obtained around the radiation source, and the values decrease significantly by distance from the source.  
Figure 4: SAR laboratory and simulation results for 900 MHz and 1800 MHz.  
Figure 5: SAR 10g distribution over SAM, **(a)** 900 MHz and **(b)** 1800 MHz.
## 1 Introduction and Related Research
That means that each time the number of the resources changes, the simulation should be rebuilt and redeployed again. In other words, the research does not put into consideration the adaption to the fluctuation in the number of the operational resources as a new dimension of configurability in the cooperative workcell. Moreover, the main motive of the research is to distribute the tasks among the available operational resources, and it does not schedule the whole existing group of tasks in the cooperative workcell. The research in [5] addresses the physical safety problem in a cooperative workcell, the research introduces a novel approach to achieve the safety during the worker-industrial robot cooperation. The solution methodology suggested to estimate the worker task execution time. This estimation was based on an offline analysis of the worker and the industrial robot tasks in a simulation environment. From this analysis, the researchers were able to calculate the safety zone where the industrial robot is permitted to move. From a theoretical point of view, the research approach is well-explained. However, from the technical point of view, the article does not provide enough details to explain how to implement the solution over a real industrial case scenario. In our research, we address the scheduling problem which is another different subject than the physical safety in a cooperative robot. However, the idea of analyzing the worker task execution time in a cooperative manufacturing scenario seems to be not only useful for scheduling, but also for the physical safety. In Section 2 of this article, we are going to explain the research case-study and formulate the research questions. In Section 3, we are going to introduce the research fundamentals which are needed to implement the solution. Section 4 will describe the solution implementation over the previously mentioned case-study. Finally, Section 5 will summarize and discuss the research to derive the conclusion and the future work.
## 3 Results
### SVM Model
#### 3.3.2 Models' Accuracy Evaluation  
In this stage, SVM models' predictions with SA improvement are compared to reference values both for cooling and heating energy demand. Starting with the cooling period, the load profile for SVM models with SA for a complete working day from the test subset is presented in Figure 19.  
Figure 19 shows that SVM models with SA are very accurate in forecasting cooling energy demand, as the projected load profile is very similar to the reference. Moreover, as stated in the feature selection stage, the improvement in the load profile matching in Figure 19, when compared to the load profile without SA, in Figure 13, is also noticeable.  
In Figure 20, the load profile of forecasted values based on SVM models with SA for the heating period is presented and compared to the reference values.  
Just as it had in the cooling period (Figure 19), SA proved to be a major improvement to SVM models in the heating period, with the load profile for the predicted values almost identical to the reference with only one major variation occurring at 7:00.  
Figure 17: Error metrics for cooling energy demand using SVM+SA.  
Figure 18: Error metrics for heating energy demand using SVM+SA.
## 3 Results and Discussion
### X-ray Diffraction Analysis  
The XRD patterns of the mild steel panels after subjection in WSS for 96 h in the presence or absence of the evaluated corrosion inhibitors are depicted in Figure 13. The collected XRD spectra reveal that the exposure of the mild steel panels to WSS + Na\({}_{2}\)HPO\({}_{4}\) leads to the detection of XRD diffraction peaks corresponding to [65-4899 Fe iron], [30-0662 Fe\({}_{3}\)(PO\({}_{4}\))\({}_{2}\)*8H\({}_{2}\)O vivianite, syn], [01-074-5846 Fe\({}_{0.96}\)P\({}_{0.04}\) iron phosphide], goethite [\(\alpha\)-FeO(OH)], and lepidocrotic [\(\gamma\)-FeO(OH)] [73] (Figure 13b). Moreover, the same diffraction peaks are detected via the exposure of mild steel panels to WSS in the presence of Na\({}_{2}\)HPO\({}_{4}\) and MBT (Figure 13d). Furthermore, the exposure of mild steel panels to WSS in the absence or presence of MBT leads to the verification of XRD diffraction peaks that are attributed to [65-4899 Fe Iron], goethite [\(\alpha\)-FeO(OH)], and lepidocrotic [\(\gamma\)-FeO(OH)] (Figure 13a,c).  
The mild steel specimen surfaces after subjection in 3.5 wt % NaCl for 96 h were also analyzed by XRD to determine the crystal structure (Figure 14). XRD diffraction peaks corresponding to [65-4899 Fe iron], [30-0662 Fe\({}_{3}\)(PO\({}_{4}\))\({}_{2}\)*8H\({}_{2}\)O vivianite, syn], [01-074-5846 Fe\({}_{0.96}\)P\({}_{0.04}\) iron phosphide], goethite [\(\alpha\)-FeO(OH)], and lepidocrotic [\(\gamma\)-FeO(OH)] are ascertained after the exposure of mild steel panels to 3.5 wt % NaCl in the presence of either Na\({}_{2}\)HPO\({}_{4}\) (Figure 14b) or Na\({}_{2}\)HPO\({}_{4}\) along with MBT (Figure 14d) [73]. Finally, the submission of the mild steel panels in 3.5 wt % NaCl in the absence or presence of MBT leads to the verification of XRD diffraction peaks that are attributed to [65-4899 Fe iron], goethite [\(\alpha\)-FeO(OH)], and lepidocrotic [\(\gamma\)-FeO(OH)] (Figure 14a,c). According to the literature [74], vivianite is a common and complicated breakdown product of the interaction of phosphate, iron, and H\({}_{2}\)O.
## 1 Introduction  
The National Institute of Information and Communications Technology (NICT) of Japan is one of the pioneers in the field of space optical communications. Since the 1990s, NICT has carried out some of the most significant in-orbit demonstrations, paving the way for the generalized establishment of this technology in the communication networks [1]. Developing universal free-space laser communication (lasercom) terminals to fit the needs of all kinds of platforms is one of the current strategic plans of NICT. Within this endeavor, a series of versatile miniaturized terminals are presently being designed and developed, with the goal of demonstrating them in several different scenarios onboard several different platforms [2].  
CubeSats in low-Earth orbit (LEO) is not only one of these, but conceivably the most important one from the miniaturization standpoint, because it pushes the size, weight and power (SWAP) requirements to the limit [3]. Following the path of the NICT's SOTA mission [4], the miniaturized lasercom terminal CubeSOTA was conceived to integrate in an ultra-small terminal all the capabilities of high-end space optical communication systems [5]. CubeSOTA aims at providing a high datarate communication solution for LEO satellites requiring the transmission of large volumes of data from orbit. Earth-observation satellites are a typical example of these users, who do not require receiving high-speed communications in the satellite, but only need to download all the imaging data acquired by the spacecraft sensors.  
A key part of this communication system is a high-power optical amplifier [6; 7] which can provide enough gain to the transmitted signals to be able to close the link on the ground's receiver with the smallest impact on the CubeSat's platform in terms of size, weight and power, and the capability to survive the space environment [8]. This manuscript describes the development of a miniaturized 2-W space-grade 2-stage erbium-doped fiber amplifier (EDFA) compatible with the CubeSat form factor (Figure 1). Performance results under realistic conditions, based on the LEO-orbit environment as well as its full space qualification and extensive test campaign, are presented.