esa-sceva/satcom-chunk-collection huggingface dataset:
# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 4 Applications of Machine Learning in XRD Data Analysis
### Defects and Substituent Concentration Detection  
Determination of substituent concentrations in [Sm\({}_{1-y}\)Zr\({}_{y}\)]Fe\({}_{12-x}\)Ti\({}_{x}\) crystal structures was performed by [PERSON] et al. [110] using a dynamic time-warping (DTW) analysis of simulated XRD patterns coupled with the Ward linkage method for clustering based on Euclidian distances between pairs of time series. The method had an accuracy of approximately 96% distinguishing different Sm/Zr substitution concentrations. The method is less suitable for distinguishing between XRD patterns for Fe/Ti substitutions, with a success rate of only 33%. While this issue can be mitigated by performing the clustering on a different dissimilarity measure, such as DTW weighted by the magnetization per unit volume of the sample, these data (i.e., magnetization) will not always be available for experimental XRD patterns. The authors of the work state that their algorithm is applicable to other systems where atomic substitutions within a phase must be tuned.  
In a different publication by [PERSON] et al. [111] an autoencoder was used to compress XRD patterns to two dimensions. The hidden layers used ReLU activation functions, while the final layers used tanh (hyperbolic tangent) for the encoder and a linear function for the decoder. Although the features learned by the autoencoder algorithm do not have physical significance in general, in this case, they appear to be related to the composition of the samples, which is justifiable through the connection among atomic substitutions, lattice constants, and XRD peak shifts. Clustering of the feature space was performed, using local information (linear interpolation) instead of unsupervised ML algorithms, such as k-means, since the different groups did not form simply connected regions within the feature space. The authors applied the autoencoder to assess the significance of XRD peaks by removing peaks and measuring the shift observed on the feature space. Additionally, the feature space could also be used to simulate the XRD patterns of small concentration changes by conducting an interpolation on the feature space instead of performing more costly computer simulations or experiments.  
For the determination of defects and substituent concentration detection, ML techniques have the advantage of providing the results in fewer steps compared with the conventional, time-consuming approaches. However, clustering models show a good accuracy in distinguishing substituent concentrations only in cases where there is a substantial difference between the element regularly placed on a certain crystallographic site and the substituent.
# Study on the Design and Cutting Performance of a Revolving Cycloid Milling Cutter
###### Abstract  
Problems such as low machining efficiency, severe tool wear and difficulty in safeguarding surface quality always exist in the machining process of titanium alloy with ball-end milling cutters. To address these issues, the design and manufacture of a revolving cycloid milling cutter for titanium alloy processing were studied in this paper. Firstly, the mathematical model of the revolving cycloid milling cutter contour surface was established. The parametric equation of an orthogonal helix cutting edge curve of a revolving cycloid milling cutter is presented. Then, the bottom boundary curve of the rake face is introduced. The five-axis grinding trajectory equation of revolving cycloid milling cutter rake face was derived based on the edge curve equation and coordinate transformation. Next, fabricating the revolving cycloid milling cutter and detecting the grinding accuracy of tool profile and geometric angle were performed. At last, a contrast test regarding the performance of the revolving cycloid milling cutter and the ball-end milling cutter in cutting titanium alloy TC11 was carried out. According to the test results, in comparison to the ball-end milling cutter, the revolving cycloid milling cutter had a smaller ratio of the axial force to the tangential force. Moreover, its flank face wore more slowly and evenly. As a result, a good surface processing quality can be maintained even under larger wear conditions, demonstrating an outstanding cutting performance.  
revolving cycloid milling cutter; cutting tool design; tool fabrication; cutting performance; titanium alloy
# A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
## 2 Related Work
### Routing Protocols Based on Data
In [45], the authors investigated the Direct Diffusion Routing Algorithm (DDRA) in detail. This routing algorithm was based on the direct diffusion and the clustering selection scheme to analyze the network topology and energy level of nodes. Finally, the analysis results demonstrated that the DDRA could improve the network delay and delivery rate in a fixed-size scenario. [PERSON] et al. [46] presented a review on energy-efficient data centric routing protocols for WSN. The related issues of the protocols were discussed, i.e., fault tolerance, scalability, production cost, operating environment, and so on. Finally, the authors gave the comparison results of six data-centric routing protocols in mobility, power usage, scalability and multi-path aspects.
# Power Control Method for Energy Efficient Buffer-Aided Relay Systems
## 2 RN System Model  
Consider an RN system as shown in Figure 1, in which SN transmits data to DN through RN. RN is located at the fixed position \(d_{sr}\) km apart from SN. Each node has a single antenna. DN could be either an UAV in the air or ground user, whose direct link to SN is blocked due to the obstacles between SN and DN. The channel of the first link between SN and RN is denoted by \(h_{sr}=\sqrt{\rho_{sr}}\tilde{h}_{sr}\). Here, \(\rho_{sr}\) is the large-scale fading between SN and RN, and small-scale fading \(\tilde{h}_{sr}\) is independent and identically distributed (i.i.d.) random variables with \(\mathcal{CN}(0,1)\) distribution, i.e., Rayleigh fading channels. Similarly, the channel of the second link is modeled as \(h_{rd}=\sqrt{\rho_{rd}}\tilde{h}_{rd}\), where \(\rho_{rd}\) is the large-scale fading between RN and DN and \(\tilde{h}_{rd}\sim\mathcal{CN}(0,1)\) is the small-scale fading.  
At time \(t\), instantaneous achievable rates of the first and second links are written as follows.  
\[R_{s}(t) = \frac{1}{2}\log_{2}\left(1+\gamma_{sr}(t)\right)=\frac{1}{2}\log _{2}\left(1+\frac{P_{s}\rho_{sr}|h_{sr}(t)|^{2}}{\sigma_{r}^{2}}\right), \tag{1}\] \[R_{r}(t) = \frac{1}{2}\log_{2}\left(1+\gamma_{rd}(t)\right)=\frac{1}{2}\log _{2}\left(1+\frac{P_{r}\rho_{rd}|h_{rd}(t)|^{2}}{\sigma_{d}^{2}}\right), \tag{2}\]  
where \(\gamma_{sr}(t)\) and \(\gamma_{rd}(t)\) are the instantaneous signal-to-noise ratios (SNRs) at RN and DN, respectively; \(P_{s}\) and \(P_{r}\) are the transmit power of SN and RN, respectively; and \(\sigma_{r}^{2}\) and \(\sigma_{d}^{2}\) are the variances of the additive white Gaussian noise (AWGN) at the RN and DN, respectively; without loss of generality, the average power of the symbols transmitted from SN and RN is assumed to be one.  
When the first link is better than the second link, i.e., \(\gamma_{sr}\geq\gamma_{rd}\) and equivalently \(R_{r}(t)\geq R_{s}(t)\), no matter how much information is delivered from SN to RN, the RN can forward no greater than \(R_{r}(t)\). On the other hand, even if \(\rho_{sr}<\rho_{rd}\), the RN cannot forward more information than what RN received, i.e., information causality [1; 21]. Thus, the instantaneous achievable rate of the overall link at time \(t\) is written as follows [2; 3]:  
\[R(t)=\min\{R_{s}(t),R_{r}(t)\}. \tag{3}\]  
Using (3), the average achievable rate for \(T\) seconds is obtained as  
\[R(P_{r})=\frac{1}{T}\sum_{t=1}^{T}R(t), \tag{4}\]  
where note that the average achievable rate is a function of the transmit power of RN, i.e., \(P_{r}\), which will be designed later.
## 2 Materials and Methods  
### Physical Properties of the Soil  
The non-cohesive soil, used for these tests, was the silty sand from a road construction site in Pisa, Italy. The specific gravity (\(G_{S}\)) of the soil was 2.73. Based on the sieve analysis, we did the soil classification according to Eurocode 7, and we recognized the tested soil as sandy silt/silty sand (saSi/siSa). The results of the sieve analysis are shown in Figure 1. According to the value of C\({}_{C}\) and C\({}_{U}\), the shape of the grading curve could be classified as medium-graded. The compaction test was performed with the application of Proctor's method with modified effort (2700 kN-mm\({}^{-3}\)). The applied method consisted of 25 blows of a hammer on each of the five soil layers in a 1 dm\({}^{3}\) mold according to ASTM D1557-12e1. We performed the tests for seven different moisture contents, which were equal to 4.6%, 6.1%, 8.1%, 9.4%, 10.8%, 12.9%, and 14.8%.  
The results of the test showed maximal dry unit density \(\rho_{ds}\) equal to 1.89 g\(\cdot\)cm\({}^{-3}\), in optimum water content equal to 12.2%. Table 1 presents the results of the Proctor test with a corresponding degree of saturation and void ratio data. Figure 2 presents the optimal moisture content curve. We evaluated the bulk density with the use of the tapping fork test. The minimal bulk density \(\rho_{d\ min}\) was equal to 1.19 g\(\cdot\)cm\({}^{-3}\), and the maximum bulk density \(\rho_{d\ max}\) was equal to 1.57 g\(\cdot\)cm\({}^{-3}\). The Proctor test showed that the maximal bulk density was equal to 1.89 g\(\cdot\)cm\({}^{-3}\), and this value was taken for further calculations. \(e_{max}\) and \(e_{min}\) were equal to 1.288 and 0.444, respectively.  
Figure 1: Grain-size distribution curves of tested soil (blue dots, dry sieve analysis and sedimentation analysis; orange dots, wet sieve analysis).
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 4 System Design  
### System Overview  
The DNCL algorithm proposed in this paper is an RFID localization method based on the Toeplitz rank recovery DOA estimation combined with NMDS. The DOA estimation was performed by the phase difference received by the reader antenna moving scanning to obtain the angle characteristics of the tags, and, using the linear relationship of constructing the tag angle distribution profile moving with the antenna position, we can derive their distance characteristics. The NMDS algorithm was introduced according to the rank order of different tags' characteristics combined with the reference tags. DNCL can obtain the 3D spatial absolute coordinates of the passive tags on the shelves or racks shown in Figure 1 since the position of the reference tags is known, and then achieve multi-target cooperative localization. Figure 3 depicts the DNCL algorithm as a flow chart.
## 4 Optimization Results and Discussion
Mutual coupling between ports for the initial (found to be \(<\) -15 dB) and optimized antennas (found to be \(<\) -12 dB) are shown in Figure 11. Higher isolation was observed for ports 1 to 2 and 3 to 4 due to the identical structure of elements. The peak values of the isolation (within the obtained band) were -22 dB and -21.24 dB for the initial and optimized antennas, respectively, at 10.3 GHz. Thus, the isolation was sufficiently good with the optimized antenna. Table 4 represent the difference between initial and optimized antenna parameters. Figure 8.0 Peak gain in dB for initial and optimized antenna designs.
# Temperature Regulation of Hot Vapor Preservation Treatment of Litch Based on PSO-Fuzzy PID
###### Abstract  
In this paper, a control method was designed based on particle swarm optimization (PSO)-fuzzy PID (PSO-Fuzzy PID). In order to address the issues of significant nonlinearity and extensive hysteresis in the vapor temperature of litchi hot vapor preservation treatment equipment, as well as the inadequacy of traditional control schemes, a PSO-Fuzzy PID controller was developed through the integration of particle swarm optimization and fuzzy logic algorithms. This controller incorporates a scale factor and quantization factor, building upon the foundation of the Fuzzy PID controller. Additionally, a transfer function model was established for both the heating and disturbance links, utilizing a combination of test data modeling methods and relevant theory. Thirdly, a system simulation model was built to compare and verify PID, Fuzzy PID, and PSO-Fuzzy PID control methods, which were carried out on the MATLAB/Simulink simulation platform. Finally, the litchi load test was conducted to further verify the control performance of the PSO-Fuzzy PID method, where the litchi varied was \"Jinggang Hongruou\". The simulation results showed that the overshoot of the optimized control system was reduced by 93.5% and 42.9% compared with the traditional PID control and Fuzzy PID, and the regulation time was shortened by 46% and 31%, which indicates a better control effect. The test results showed that PSO-Fuzzy PID showed better system stability and accuracy than the fuzzy PID control method in different processing temperature and processing time for litchi's hot vapor treatment. The maximum temperature difference decreased by 0.56 \({}^{\circ}\)C on average and the interference adjustment time was reduced by 7.5 s on average. PSO-Fuzzy PID has a good control effect on the temperature of litchi hot vapor treatment, which can provide a reference for model building and temperature control of other similar fruit and vegetable heat treatment equipment.  
litchi; preservation treatment; vapor; temperature control; PID control; particle swarm optimization algorithm; fuzzy-PID +
Footnote †: journal: Applied sciences
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 2 Materials and Methods
### Mapping Procedure  
For the purposes of the investigation, the geometric extent of the airflow was estimated to be analogous to the average horizontal corneal diameter size of 12 mm [30]. Furthermore, to simulate a curved mapping surface, the average corneal radius of 7.8 mm [31] with an eccentricity of 0.5 [32] was used.  
**CorvisST**: To record the maximum pressure, Map 1 was used (resolution 1 mm). Map 2 (resolution 0.6 mm) was utilised to assess the spatial and temporal distribution in depth. The respective mapping grid was placed on the CorvisST screen and the sensor or aperture opening were, respectively, aligned according to the measurement point on the grid.  
Starting centrally, the CorvisST was moved horizontally and/or vertically to align to the next measurement point until for each point a pressure value was obtained. The pressure sensor was moved backwards/forwards to change the distance between nozzle and tube opening, accounting for the corneal curvature. Therefore, the pressure readings were taken on a curved surface.  
At each measurement point, the CorvisST airflow was triggered and recorded ten times consecutively, and the mean was used to obtain the pressure value at the specific point.  
**ORA:** To assess the spatial and temporal distribution of the ORA air-puff, the ORA device was kept in a static position. The pressure sensor was moved to align the nozzle with the pressure sensor opening for each measurement point. The pressure sensor was moved horizontally and vertically in 1 mm steps, starting centrally. To account for corneal curvature change, the pressure sensor was also moved to change the distance between nozzle and sensor. At each point, the ORA airflow was initiated ten times using the demo puff mode, and the average was used as the value for each position.
# All-in-Focus Three-Dimensional Reconstruction Based on Edge Matching for Artificial Compound Eye
## 3 Overview of Edge Matching and Propagation  
The workflow of the proposed method is given in Figure 5, and our method mainly consists of the following five processing modules: (1) image correction; (2) edge depth estimation; (3) edge depth propagation; (4) inconsistency check and depth refinement; and (5) all-in-focus reconstruction.  
The five modules are briefly explained below, and the following section will give each module in detail:  
1. Image correction. Because the ACE image suffers distortion, the first step is to rectify sub-images. This will greatly reduce the complexity of the following depth estimation.
2. Edge depth estimation. The inputs to the edge depth estimation module consist of a reference image and four adjacent images, which have been rectified already. The main purpose of this module is to obtain a consistent sparse edge depth map of the reference image.
3. Edge depth propagation. The purpose of this module is mainly to propagate the edge depth to the whole image. Associated with over-segmentation results and four neighbor images, this matching cost of the segmented area is much more robust to obtain the correct depth.
4. Inconsistency checking and refinement. The main purpose of this module is to identify segmented areas which have unreliable depth. Instead of the left-right check used in many stereo matching algorithms, matching consistency with four neighboring images is used.
5. All-in-focus reconstruction. Finally, based on the refined depth map of each sub-image, the sub-image arrays are rendered to reconstruct the all-in-focus image, and the depth maps are also merged into a whole depth map.
# Design and Simulation of On-Orbit Assembly System Based on Insect-Inspired Transportation
## 3 Kinematics Model of Robot Moving Process
After establishing the kinematic connection between the robot's components, it is necessary to plan the motion characteristics of each joint and the motion trajectory of the robot to ensure normal motion. Initially, the trajectory planning of the mobile components is conducted, with a focus on the moving legs during the single-leg support phase. The allowable rotation direction of each joint is predetermined to determine the robot's trajectory, ensuring the stable movement of the robot. To analyze the swing leg, firstly, the following four basic parameters are agreed upon: step size \(S_{L}\), single-leg support period time \(T_{S}\), the highest distance \(H_{m}\) of the swing leg, and the corresponding horizontal distance \(S_{m}\) when moving to the highest point. Other constraints used to design the trajectory of the swing leg are the repeatability of the gait and the impact of the leg when it falls. Constraint 1: When the robot swings its leg to lift and fall, the end of the sole of the foot is located in the horizontal plane. The constraint equation can be expressed as:  
\[z_{a}(0)=0,z_{a}(T_{s})=0 \tag{10}\]  
Constraint 2: The maximum height of the robot raised by one leg cannot be too high \(H_{m}\) to prevent possible collision. At this time, the vertical velocity is 0. The constraint equation can be expressed as:  
\[ \begin{array}{l}x_{a}(T_{m})=S_{m}\\ z_{a}(T_{m})=H_{m}\\ \dot{z}_{a}(T_{m})=0\end{array}  \tag{11}\]  
where \(T_{m}\) is the time of movement when the leg is lifted to the highest point. Constraint 3: The robot's horizontal movement distance during a single-leg support phase is defined as the step size \(S_{L}\). To ensure a smooth transition between gait phases, the velocity of the robot's position during the two-leg support phase and the one-leg support phase must be zero. Therefore, when switching from the two-leg support phase to the one-leg support phase, the initial velocity of the swing leg in the horizontal and vertical directions must be zero. This can be mathematically represented by the constraint equation:  
\[x_{a}(0)=-\frac{S_{L}}{2},x_{a}(T_{s})=\frac{S_{L}}{2} \tag{12}\]  
\[\dot{x}_{a}(0)=0,\dot{z}_{a}(0)=0 \tag{13}\]  
Constraint 4: In order to reduce the impact of the swing leg landing, the horizontal and vertical speeds of the swing leg landing should be 0. \[\dot{x}_{a}(T_{s})=0,\dot{z}_{a}(T_{s})=0 \tag{14}\]  
The three-order polynomial of the motion trajectory of the swing leg in the horizontal direction and the fifth-order polynomial in the vertical direction can be obtained by 11 simultaneous constraint equations satisfying the above four constraint conditions. The equations are as follows:  
\[\left\{ \begin{array}{l}x_{a}(t)=a_{0}+a_{1}t+a_{2}t^{2}+a_{3}t^{3}\\ z_{a}(t)=b_{0}+b_{1}t+b_{2}t^{2}+b_{3}t^{3}+b_{4}t^{4}+b_{5}t^{5}\end{array}  \right.\quad 0\leq t\leq T_{s} \tag{15}\]
# Scalable and Reliable Data Center Networks by Combining Source Routing and Automatic Labelling
###### Abstract  
Today, most user services are based on cloud computing, which leverages data center networks (DCNs) to efficiently route its communications. These networks process high volumes of traffic and require exhaustive failure management. Furthermore, expanding these networks is usually costly due to their constraint designs. In this article, we present enhanced Torii (eTorii), an automatic, scalable, reliable and flexible multipath routing protocol that aims to accomplish the demanding requirements of DCNs. We prove that eTorii is, by definition, applicable to a wide range of DCNs or any other type of hierarchical network and able to route with minimum forwarding table size and capable of rerouting around failed links on-the-fly with almost zero cost. A proof of concept of the eTorii protocol has been implemented using the Ryu SDN controller and the Mininet framework. Its evaluation shows that eTorii balances the load and preserves high-bandwidth utilization. Thus, it optimizes the use of DCN resources in comparison to other approaches, such as Equal-Cost Multi-Path (ECMP).  
cloud computing; SDN; data center networks; Ethernet; source routing; automatic labelling +
Footnote †: journal: Network  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-000000-000000-0000000-0000000000000000000000000000000000000000000000  
same time, it can be deployed in network topologies of different types and sizes, guaranteeing flexibility. It also follows a plug-and-play approach, achieving the auto-configuration feature, as it leverages the automatic labelling protocol for this purpose. Finally, the fourth requirement is accomplished thanks to its fast recovery, almost immediate (zero delay) in some scenarios, when some network elements fail.  
The structure of this manuscript is as follows: First, we relate our proposal with the state of the art in Section 2. Second, Section 3 describes the main features of eTorii and its application to DCNs. Afterwards, Section 4 elaborates on the proof-of-concept of eTorii, implemented in the Ryu SDN controller, which is evaluated in Section 5. Finally, Section 6 recapitulates and concludes the work.
# Research Status and Development Trend of Wastewater Treatment Technology and Its Low Carbonization
## 2 Conventional Wastewater Treatment Technology  
This section examines conventional urban sewage treatment technology, and takes the activated sludge removal process as an example to discuss and analyze.  
The traditional activated sludge removal process is to supply oxygen to activated sludge in sewage through external aeration equipment, and convert organic matter in sewage (40-50%) into CO\({}_{2}\) through activated sludge, and the remaining organic matter (50-60%) into residual sludge, which is difficult to be degraded by microorganisms [20]. From long-term engineering practice and various studies, it has been found that, depending on the change of water quality, the characteristics of microbial metabolic activity, operation management, technical economics, discharge requirements, etc., a variety of operation modes and pool types have been developed. The main types are as follows: pushed-flow activated sludge method, completely mixed activated sludge method, adsorption-regeneration activated sludge method, delayed aerated activated sludge method, pure-oxygen-aerated activated sludge method, sequential-batch-reactor activated sludge method (SBR), etc. Their removal rates of biological oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS) and other pollution indicators in municipal sewage, as well as their advantages and disadvantages, are shown in Table 1 below: