esa-sceva/satcom-chunk-collection huggingface dataset:
# A 200-MS/s 10-Bit SAR ADC Applied in WLAN Systems
## 3 Circuit Implementation
### SAR-Logic  
According to the previous introduction, the delay \(T_{\mathit{sar}}\) on the SAR logic is a part of the data path in the bit conversion cycle. The traditional SAR logic consists of two stages of DFF, and the delay of the \(T_{\mathit{sar}}\) is shown as the red line in Figure 13a, which can be represented as: \(T_{\mathit{sar}}\) = 2 \(\times\)\(T_{\mathit{cq}}\) + \(T_{\mathit{OR}}\), where \(T_{\mathit{OR}}\) is the delay of the OR gate and \(T_{\mathit{DFF}}\) is the delay of the shift register and the data register.  
To reduce the delay of \(T_{\mathit{sar}}\), the proposed SAR logic uses a window-opening strategy to open the \"window\" signal in advance before each bit of data arrives and complete the \"window\" signal closing operation after data transmission. Figure 14 illustrates the scheme of the proposed SAR logic based on the window-opening strategy. The circuit of the tri-state gate is shown in Figure 15 (left), and the timing block of the window-opening is shown in Figure 15 (right). The bit cycling begins immediately after CLK_SAMPLE pulls down. The first DFF output EN1 of the opening window register level is high, opening the \"Window\" signal of the first tri-state gate. After the comparison process, the resultant signal is directly passed through the 3T structure. At this time, due to the high pulse generated by the CMP_DOWN signal, it will trigger the first DFF of the closing window register level to output a high level of LOCK1, thus locking the \"window\" signal. After the first reset phase of the comparator is completed, the second DFF output EN2 of the opening window register level is high, and the \"window\" signal of the second tri-state gate is opened to wait for the second comparator result. The remaining bit-cycling process is sequentially executed in this way until the last bit triggers the LOCK_RING signal to end the SA phase.  
The \(T_{\mathit{sar}}\) of the proposed SAR logic is based on a window-opening strategy equivalent to a single 3-T cell delay, as shown in Figure 13b.  
Compared with traditional SAR logic, the saved time during the conversion cycle can be expressed as:  
\[T_{\mathit{sar}}=12\times(T_{\mathit{CMP\_RDY}}+2\times T_{\mathit{DFF}}-T_{3-T})\]  
This time is approximately 1.5 ns in the SMIC55 process. By adopting this SAR logic, the conversion speed of the ADC can be greatly improved. However, the 3T cell is a tri-state gate, and during the SA phase, it exhibits a high-impedance state at its output. This necessitates additional risk considerations in circuit design.  
Figure 13: (**a**) Critical path of traditional SAR logic; (**b**) critical path of the 3-T cell.
## 3 Results  
### Consistency between SIF and NDVI  
The seasonal spatial distributions of the PCC values obtained between NDVI and SIF are shown in Figure 2. During the growing season, lower degrees of consistency (PCC values < 0.8) were found in the SC, YGP, part of the SCB, MLRYR, QTP, and ASANC, while higher degrees of consistency were found in the NEP, NCP, LP, part of the SCB, and QTP (Figure 2a). The spatial distributions of the PCC in spring and autumn were similar to that in the growing season (Figure 2b,d). In summer, higher degrees of consistency were found in part of the NEP, NCP, LP, and QTP (Figure 3c). In general, the degrees of consistency in summer were smaller than in other seasons.  
To further analyze the relationship between the SIF and the NDVI, Figure 3 shows the correlations between the NDVI and the SIF obtained for different land-cover types during the growing season, spring, summer, and autumn. Overall, the correlations between the NDVI and the SIF differed significantly among different land-cover types. Figure 3a indicates that, except for the ENF, EBF, CS, and BSV land-cover types, during the growing season, the NDVI and the SIF showed relatively large positive correlations, and the PCC values were basically greater than 0.6. During spring, except for in the ENF, EBF, and BSV land-cover types, the NDVI and SIF showed relatively large positive correlations (Figure 3b). Compared to the springtime correlations, the summertime correl  
Figure 3: Seasonal PCC values obtained between NDVI and SIF for different land-cover types: (**a**) growing season; (**b**) spring; (**c**) summer; and (**d**) autumn.  
Figure 2: Seasonal spatial distributions of the PCC values obtained between NDVI and SIF: (**a**) growing season; (**b**) spring; (**c**) summer; and (**d**) autumn.  
and the SIF were weak, with the PCC values mainly concentrated in the range of 0.4-0.6 (Figure 3c). In autumn, the relationships between the NDVI and the SIF for different land-cover types were similar to those obtained during spring; the correlations between NDVI and SIF in the ENF, EBF, CS, and BSV land-cover types were relatively weak during autumn. In the land-cover types other than the ENF and EBF, the correlations between the NDVI and the SIF revealed larger PCC values (Figure 3d). In general, the correlations between the NDVI and the SIF were stronger in regions comprising other land-cover types compared to the correlations obtained in the ENF, EBF, and BSV regions.
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 1 Introduction
We tested this method on the landslides triggered by the Gorkha earthquake, using a comprehensive independent inventory of triggered landslides produced from manual analysis of optical satellite imagery [2]. Additionally, multiple reports have been published discussing the emergency response effort following the earthquake, allowing identification of how SAR landslide products could have been used if they had been available [5; 6; 7].
## 4 Engineering Example  
Geological and Mining Conditions of the Working Face and Initial Exploration Value Acquisition for Parameter Inversion  
The research mining area is located on the Shaanxi side at the junction of Shaanxi Province and the Inner Mongolia Autonomous Region, and it is under the jurisdiction of Daliuta Town, Shenmu City. A working face is characterized by an average mining depth of 145 m, a coal seam thickness of 4.4 m, a coal seam dip angle ranging from 1 to 3 degrees, a 325 m dip mining width, and a 2430 m strike length. The lithology of the overlying strata on the working face is detailed in Table 3. Additionally, an observation line runs along the surface in the dip direction of the working face, featuring 22 measuring points, as depicted in Line C in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Overlying** & **Layer** & **Stability** & **Delamination** & **Layered Mass** \\
**Strata** & **Thickness/m** & **Coefficient** & **Deformation** & **Density** \\  & & & **Modulus/MPa** & **/(g/cm\({}^{3}\))** \\ \hline Loose layer & 12 & 0.7 & 360 & 1.80 \\ Fine sandstone & 8 & 4.5 & 2721 & 2.56 \\ Medium stone & 14 & 5.0 & 3095 & 2.59 \\ Silty sandstone & 10 & 7.8 & 5571 & 2.62 \\ Fine sandstone & 10 & 4.5 & 2721 & 2.56 \\ Silty sandstone & 8 & 7.8 & 5571 & 2.62 \\ Fine sandstone & 12 & 4.5 & 2721 & 2.56 \\ Medium stone & 14 & 5.0 & 3095 & 2.59 \\ Grit sandstone & 18 & 6.0 & 3900 & 2.63 \\ Silty sandstone & 4 & 7.8 & 5571 & 2.62 \\ \(1^{-2}\) upper coal & 3 & 2.0 & 1100 & 1.44 \\ Fine sandstone & 12 & 4.5 & 2721 & 2.56 \\ Medium stone & 12 & 5.0 & 3095 & 2.59 \\ \(1^{-2}\) coal & 4 & 2.0 & 1100 & 1.44 \\ Fine sandstone & 4 & 4.5 & 2721 & 2.56 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lithology of overlying strata on the working face.  
Figure 5: Inversion results of anti-gross-error interference ability.  
Based on the information provided in Table 3, the comprehensive deformation modulus for the rock strata within the mining area is determined to be 3129.30 MPa, with an average mass density of 2.469 g/cm\({}^{3}\). The similarity criterion for the mining area, as well as the mining subsidence model of the mining area and the closeness degree to various mining subsidence categories, are calculated.  
According to the calculation results of the category closeness degree (Table 4), the mining area exhibits the highest closeness degree with category II-3. Consequently, the initial predicted parameters for mining subsidence in the mining area can be approximated as follows: \(q=0.85\), \(b=0.28\), \(tg\beta=2.00\), and \(S/H=0.155\). Furthermore, considering the 'Regulations for Coal Pillar Retaining and Coal Mining in Buildings, Water Bodies, Railways, and Main Roadways' and the specific mining conditions of the working face, adjustments are made to the deviation of the inflection point and the propagation angle of mining influence. These refinements lead to the final initial exploration values for exploration using the modular vector method, denoted as \(B_{1}\).  
\[B_{1}=\left(q,\theta,tg\beta,S_{1},S_{2},S_{3},S_{4}\right)^{T}=\left(0.85,75^{ \circ},2.00,20,20,15,15\right)^{T} \tag{13}\]
## 2 Materials and Methods
### Implementation of Motion Quantitative Analysis
To consider all of the synergetic and antagonistic participations in each tested window, the \(i\)-th muscle contribution \(y_{j}\) was normalized and presented in the form of participation:  
\[part_{y_{i}}=\frac{y_{i}}{\sum\lvert y_{i}\rvert}. \tag{2}\]  
Significance tests (the second part of approach for assessment of quality of motion) were performed in five arbitrarily chosen ranges for the performed motions: 2% (range 1), 10% (range 2), 38% (range 3), 66% (range 4), and 96% (range 5). The length of each range was equal to the tested window. These arbitrary ranges were chosen to test motor behavior in the initial phase (range 1), acceleration phase (range 2), phase of acceleration-to-middle transition (range 3), phase of middle-to-slowing down transition (range 4), and final phase related to the immobilization (range 5). Due to the fact that kinematic data and muscle activation did not have normal distributions (Shapiro-Wilk test), we used non-parametric tests of significance. To define the influence of the passive manipulator on the natural motions, the statistically significant differences were assessed by applying (threshold of statistical significance was assumed to be \(p\leq 0.01\)): (1) Wilcoxon test between each kinematic data (WJ\({}_{x}\), WJ\({}_{y}\), WJ\({}_{z}\), \(\Delta\)WJ, COM\({}_{x}\), COM\({}_{y}\), COM\({}_{z}\), \(\Delta\)COM) obtained under NC and PMC for each tested subject, i.e., the RS and the DMD, respectively; (2) Mann-Whitney U test between each kinematic data (WJ\({}_{x}\), WJ\({}_{y}\), WJ\({}_{z}\), \(\Delta\)WJ, COM\({}_{x}\), COM\({}_{y}\), COM\({}_{z}\), \(\Delta\)COM) obtained for RS and DMD patients in each tested condition, i.e., under NC and PMC, respectively; (3) Wilcoxon test between each muscle activation (EMG\({}_{1}\), EMG\({}_{2}\), EMG\({}_{3}\), EMG\({}_{4}\)) obtained under NC and PMC for each tested subject, i.e., the RS and the DMD patients, respectively; (4) ANOVA Kruskal-Wallis test between five sets of muscle activations (EMG\({}_{1}\), EMG\({}_{2}\), EMG\({}_{3}\), EMG\({}_{4}\)) obtained under NC and PMC for each tested subject, i.e., the RS and the DMD patients, respectively. All tests of significance were performed in STATISTICA 13.1. Additionally, visual inspections have been conducted to analyze: (1) the normalized mean motion trajectories (Figures 3 and S1 (Supplement S2)) and 3D mean trajectories (Figures S2 and S3 (Supplement S2)); (2) processed EMG amplitude obtained for RS and DMD patients under NC and PMC (Figures 4 and S4 (Supplement S2)). Figure 3: (**a**) Reference subject in **motion1** under natural conditions; (**b**) Reference subject in **motion1** under passive manipulator conditions; (**c**) DMD subject in **motion1** under natural conditions; (**d**) DMD subject in **motion1** under passive manipulator conditions.
# Field Pull-Out Tests of Percussion Driven Earth Anchors (PDEAs)
###### Abstract  
Percussion driven earth anchors (PDEAs) are driven into soils using an installation steel hammer rod. PDEs are relatively easy to install and have gained wide applications recently. The Texas Department of Transportation (TxDOT) planned to use these anchors for slope stability mitigation along the Clear Fork Trinity River at Interstate Highway 20 (IH-20) in Benbrook, Texas. However, there are no straightforward design and construction guidelines for these systems. In addition, the pull-out capacity and failure mechanisms of PDEs in clayey soils have not been thoroughly studied. In this study, three PDEs, Duckbill model 138 IH (DB-138 IH), were installed and tested on the proposed west channel bank slope to acquire the ultimate pull-out capacity. The anchors were embedded to an average depth of 10 feet into the slope bank, predominantly consisting of sandy lean clay (CL) soil. The slope was graded at an average 2:1 to 2.5:1 configuration. After installation, the anchors were subjected to an upward pull-out force using a hydraulic jack system to measure their pull-out capacity. The pull-out load, displacement, and strains were continuously recorded with a load cell, a linear variable differential transformer (LVDT), and a strain gauge, respectively. Pull-out load versus displacement curves were produced and analyzed to determine the behavior of the anchors. An empirical estimation method was then chosen to estimate pull-out capacity based on undrained shear strengths obtained either from laboratory tests or in situ Texas cone penetration (TCP) data. The comparison between estimated and field-obtained pull-out capacities showed that the pull capacity estimated using TCP data resulted in reasonably good agreement with the field-obtained capacity. The field experiment results help us to understand the relationship between the calculated and actual field pull-out resistance when PDEAs are used in clayey soil slopes.  
percussion driven earth anchors; field pull-out test; pull-out capacity estimation method
# Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources
## 1 Introduction
Although the above literature has made certain research on edge computing and knowledge sharing, there are still some problems: (1) In the traditional cloud edge structure, the cloud carries more computing and control tasks. When the amount of data increases rapidly, it will put a lot of pressure on the transmission network, and the centralization of control and storage will increase the dependence of the entire system on the cloud; (2) the existing ontology modeling in the industrial field is mostly based on equipment self-generation, which cannot fully implement the manufacturing process; (3) the generality of the existing learning process knowledge is not strong, and most of them can only learn the same or highly similar process. Therefore, based on the processing environment of multi-variety manufacturing, this paper proposes a cloud-side collaborative manufacturing resource knowledge sharing mechanism, which provides a new solution for the mutual learning and knowledge-sharing of manufacturing resources. The contribution of this article mainly includes the following three aspects. * Adopting an overall communication architecture based on cloud-edge collaboration, and deploying an intelligent manufacturing knowledge sharing system at the edge, realizing the combination of local learning and cloud learning between devices. * Combining ontology to standardize, formalize and abstract the equipment attributes, manufacturing process, environmental restrictions, and equipment action execution effects involved in the field of intelligent manufacturing, and provide a fine-grained decomposition plan for the manufacturing process. * A matching mechanism of knowledge search and reuse is proposed so that the processing experience of manufacturing resources can be reused. The remainder of this article is organized as follows. Section 2 proposes a data communication network architecture based on cloud-edge collaboration. Section 3 defines the manufacturing resources' ontology structure and combines the production process elements to establish a semantic model enabling task decomposition. Section 4 puts forward the similarity matching algorithm for knowledge reuse based on task decomposition. Section 5 verifies the proposed knowledge sharing mechanism's effectiveness in a multi-variety mixed production line prototype platform and discusses the current problems and future research directions. Finally, Section 6 briefly summarizes the work.
## 4 Proposed Model
### Model Validation
#### 4.4.1 Load Balancing
* **Network condition**: A random factor that simulates network fluctuations. Types of Requests Sent from Clients  
Clients send connection requests to the servers using the add_request() method. If the server has the capacity to handle the request, the client connects to the server and is assigned an appropriate cluster. If the client moves within the game, the request is updated using the move() method, which recalculates latency and repositions the client on the most suitable server based on their new location. These methods allow dynamic management of requests and optimize the use of servers.
# Evaluating the Reliability of Powder Bed Fusion for Biomedical Materials: An Experimental Approach
## 5 Conclusions  
This article presents a guideline, useful mainly for new PBF machine users, on how to evaluate the practical characteristics of the PBF parts. The aim is to decrease the manufacturing time and to use a ranking system, to make sure that the mechanical properties are good for personalized medical implants made by PBF, using four types of biocompatible metal powders.  
It is important to note that the mechanical characteristics presented in the tables are not necessarily the maximum achievable values. The primary goal was to produce reliable parts with overall mechanical properties above average. Crucially, these high-quality PBF parts do not require post-processing, an important advantage given that the high temperatures involved in some post-processing methods can induce phase transformations in biocompatible metals. This approach also contributes to time efficiency.  
The values presented represent the average of the obtained results and can be used as such in finite element analyses, as they have been tested by the authors in numerous medical applications. With appropriate process parameters, increased hardness can be expected in the PBF parts. The Ra roughness values obtained ranged from 5 to 50,with surface roughness quality dependent on both the material and the specific process parameter configuration.  
Another key objective of this research was to minimize manufacturing time. Typically, such samples are built vertically or at an angle to reduce residual stress and the risk of damage, although this approach increases production time. In all experiments, specimens were built horizontally (so that Z is minimized), accepting the increased risk in order to minimize manufacturing time.  
This ranking method shows potential for further development with other powder materials and different PBF machines.  
The recommended process parameters for achieving good manufacturability are as follows:  
* Pure Titanium: 120 W, 500 mm/s, 0.12 mm;
* Ti6 Al7 Nb: 70 W, 400 mm/s, 0.12 mm;
* CoCr: 100 W, 500 mm/s, 0.10 mm;
* CoCrWMo: 85 W, 500 mm/s, 0.10 mm.  
Conceptualization, D.V.L., C.C. and N.B.; methodology, D.V.L., C.C. and M.C.D.; investigation, D.V.L. and C.C.; validation, N.B. and M.C.D.; supervision, N.B. and M.C.D.; writing--original draft, D.V.L., C.C., N.B. and M.C.D.; writing--review and editing, D.V.L. and M.C.D.; research funding, D.V.L. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Technical University of Cluj-Napoca, Romania.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
This research was supported by the AM-CIR project (PN-II-RU-TE-2014-4-1157, no. 37/01.10.2015), financed by UEFISCDI under the authority of the Romanian Government.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Numerical Simulation with Varying PCM Encapsulation Material  
Material selection for containing PCM plays an important role in its integration into cold storage. The heat transfer mechanism of charging and discharging is dependent on the heat transfer ability of the encapsulation material. To illustrate this effect, the analysis of a PCM container inside a cold storage unit using 3D Experience software was undertaken, which included two scenarios where heat loads were applied to copper and stainless steel (SS) containers.  
Figure 8: Simulation of temperature distribution profile inside cold room (**a**) without PCM container, (**b**) using copper PCM tray and (**c**) using SS PCM tray.  
Simulations were further performed considering two arrangements. The first case considered the rectangular PCM trays on the opposite and adjacent walls of the cold room, while the second case replicated the experimental setup of the PCM sample test using the tumbler. The simulation according to the first arrangement, which replicated cooling from an evaporator with 3 m/s air velocity, revealed that the PCM container temperature reached a minimum of 7.85 \({}^{\circ}\)C from an initial 27 \({}^{\circ}\)C in the case of copper, as seen in Figure 8b. Similarly, the minimum temperature reached in the case of SS was 9.75 \({}^{\circ}\)C, as shown in Figure 8c. The simulation results thus obtained are indicative of copper's superior thermal conductivity and temperature stability over SS trays in PCM utilization, demonstrating its ability to handle heat loads effectively to maintain lower temperatures.  
In the second arrangement, a model of a copper tumbler was developed to hold 250 mL of PCM, as shown in Figure 9, with the tumbler's enlarged view exhibited, indicating its area of cross-section.  
The result obtained is graphically displayed in Figure 10, wherein the PCM is cooling down from 21 \({}^{\circ}\)C to \(-4\)\({}^{\circ}\)C, which is the phase change temperature of the PCM. The time taken for the cooling process was approximately 8400 s.  
Figure 10: Temperature profile of PCM in copper tumbler model simulated through 3D experience.  
Figure 9: Simulation of tumbler model with enlarged view of the central plane cross-section.