esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Carbon steel and bentonite are materials selected as engineered barriers for high-level radioactive waste confinement. Their long-term interaction must be evaluated to confirm the barrier's stability. Three laboratory experiments of the carbon steel--Mg-bentonite interaction were conducted for 1, 6, and 22 months under a hydrothermal gradient. Changes in bentonite's water content, specific surface area, and cation exchange capacity were measured. Mineralogy was studied by X-ray diffraction and scanning electron microscopy. The redistribution of aqueous species and the redox state of iron were determined across the bentonite columns. Results indicated water saturation after 22 months. The specific surface area of bentonite was reduced near contact with the steel, while the cation exchange capacity mostly decreased at 3-6 mm from the steel interface. The corrosion rate decreased with time and bentonite enriched in Fe in the first 1.5 mm from the steel contact. The formation of new Fe-bearing minerals, such as di-tri ferri-sudoite, magnetite, hematite, maghemite, lepidocrocite, siderite and ankerite was observed. Aqueous species redistributed in the porewater of bentonite with decreasing concentrations of Fe and Cl as a function of time and increasing concentrations of Na, Ca and SO\({}_{4}\) after 22 months. This occurs under conditions where the bentonite is saturated with Mg, which conditioned the formation and nature of iron clay minerals with time.  
Mg-montmorillonite; carbon steel; bentonite; hydrothermal gradient +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
Before the research commencement, approval was obtained from Saveetha Dental College and Hospitals, Chennai, Tamil Nadu, India (SRB: SDC/ENDO-1703/20/456). Prior to extraction, patient consent was obtained. PRILE guidelines (Figure 1) were followed for reporting the study [17]. Sample size was based on a previous study, which assessed canal cleanliness using an automated irrigation device [16]. A C* power, version 3.1 (Heinrich Heine University, Universitatsstrasse 1, 40225 Dusseldorf, Germany) was used. A total sample size of 21 was obtained (7 per group) at an effect size of 0.98 and alpha set at 0.05 and power at 95%.  
Twenty-one freshly extracted single-rooted teeth were collected. The inclusion criteria for the specific teeth were curvatures < 5 degrees, fully matured apex, patent root canals, and teeth undergoing extraction for therapeutic or periodontal reasons. The exclusion criteria for the study were teeth with severe calcifications, teeth with restorations, root caries, and signs of fracture or root resorption.  
Immediately after the extraction, the extracted tooth surface was curetted to remove the accumulated debris and soft tissue. Following this, each tooth specimen was ruled out for extra canals using digital radiography taken at multiple angulations. The specimens were then stored in physiological saline at 4 \({}^{\circ}\)C until experimentation [16]. All the specimens were then dipped in molten wax forming an approximate 0.2-0.3 mm layer to mimic the periodontal ligament. Alveolar bone was simulated by dipping in wax 1 mm to CEJ. Following this, the molds were dipped in clear self-cure acrylic resin and the wax was removed. Finally, elastomeric impression material was used to fill the mold space [16].  
Figure 1: PRILE 2021 flow chart.  
After specimen confirmation, the teeth were decorated 2 mm coronal to CEJ using a diamond disc to obtain uniform standard length. The access cavity was then prepared by an endodontist who was not involved in the study. Endo-access bur (Dentsply Mallefer, Ballagues, Switzerland) was used to initiate the access and Endo-Z (Dentsply Mallefer, Ballagues, Switzerland) was used to refine the prepared access walls. Following this, a manual glide path was established with standard #10 hand K-file (Dentsply Mallefer, Ballagues, Switzerland). Estimated working length was kept 1 mm short from the visible file tip which was seen through the apical foramen. Once the access cavity was prepared, the teeth were randomly allocated to the second endodontist who was not involved in the study. The instructions on the entire instrumentation and irrigation protocol were provided to the operator, who was blinded until the end of the study. Experimental sample allocation was then carried out [16]:  
* Group I: Manual irrigation group (MI) (n = 7).
* Group II: Automated root canal irrigation (AI) (n = 7).
* Group III: Passive ultrasonic irrigation (PUI) (n = 7).
# On the Spectral Efficiency for Distributed Massive MIMO Systems
## 6 Conclusions  
In the current work, an analysis of the performance of D-MIMO systems under different conditions was carried out. Several MIMO scenarios have been proposed. On the frequency side, several bands were studied, including the sub-6 GHz and the mmWave band. On the scenario side, both simulated and real measurements were analyzed for a wide variety of environments. Rural and urban scenarios were emulated, and channels from anechoic and reverberation chambers were acquired.  
The analysis of all these measurements was focused on the balance power and the correlation between propagation channels for distributed MIMO systems. These systems are expected to be fundamental in the future deployment of mobile networks due to the large number of antennas which allows high spectral efficiencies. In this study, it was found that D-MIMO provides high data rates compared with common MIMO systems. However, it is fundamental to perform an accurate deployment analysis in order to find decorrelated and balanced propagation channels in terms of power. Throughout the study, we observed how the combination of both properties takes full advantage of the benefits of the propagation channel, significantly increasing the spectral efficiency on the physical layer of the communication channels. The network operation following design criteria such as those seen in this study is fundamental for the multi-connectivity expected in D-MIMO systems. An optimal choice of such criteria showed network performances of 45.6% (22 TX \(\times\) 5 RX D-MIMO) and 118.3% (66 TX \(\times\) 25 RX D-MIMO) better than non-optimal cases in terms of spectral efficiency for similar D-MIMO systems but with different power allocation criteria.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON], [PERSON] and [PERSON]; formal analysis and investigation, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; writing--original draft preparation, [PERSON], [PERSON], [PERSON] and [PERSON]; writing--review and editing, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
Figure 8: (**a**) Spectral efficiency (D-MIMO 66 \(\times\) 25) for a parametric sweep of \(Att_{1}\) and \(Att_{2}\) and (**b**) spectral efficiency for the anechoic MIMO (blue line), unbalanced D-MIMO (red line) and balanced MIMO (black line).  
**Funding:** This work was supported in part by the Spanish Government under Project PID2020-112545 RB-C54, Project RTI2018-102002-A-I00 and Project TIN2016-75097-P, in part by \"Junta de Andalucia\" under Project B-TIC-402-UGR18, Project A-TIC-608-UGR20 and Project P18.RT.4830, in part by \"Junta de Extremadura\" under Project IB18003 and in part by the predoctoral grant FPU19/01251.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the constructive comments and help of [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Data and Examples
### Analysis of Multivariate Traffic Parameter Characteristics
#### 4.2.1 Time-Varying Characteristics of Headway  
As shown in Figure 4, vehicles travel from detection point 6269 downstream, passing through detection point 6271 and exiting the road section at detection point 6273. The main traffic flow within the analysis object enters from detection point 6269. Therefore, in this section, we analyze the headway characteristics of each lane at the position of detection point 6269. The headway scatters are shown in Figure 5, where the y axis is the headway and the x axis is the series number of records.  
Figure 4: Typical RFID base station distribution in the selected section.  
Figure 3: Distribution of RFID base stations on urban roads in Nanjing.  
An analysis of Figure 5 reveals that the total number of valid records for the three lanes of Section 6269 exceeded that of 6800. The number of vehicles passing through Lane 2 exceeded that of Lane 1 and Lane 3, indicating that the main driving requirement was concentrated in Lane 2, i.e., the middle lane, which aligns with actual driving habits. This is because the lanes at the entrance and exit are easily impacted by intersections, so most drivers choose to drive in the middle lane.  
Regarding the distribution characteristics of headway, some abnormal values are exhibited in each lane; these values appeared during the early morning hours when vehicle movement was relatively low. The large headway between vehicles passing through the section is consistent with the actual conditions. For other periods, the headway is mainly distributed within 200 s. Due to the influence of the signal intersection cycle time, the generated headway time series also exhibits obvious periodic variation characteristics. However, due to the traffic density changes and the impact of vehicles arriving at adjacent intersections, the headway of vehicles passing through each cycle also exhibits complex variation patterns.  
Figure 5: _Cont._
## Channel Model
### The Speed of Acoustic Waves  
The underwater channel is highly unpredictable and possesses properties that affect the speed of acoustic waves during underwater communications. The speed of acoustic waves varies in response to the characteristics of sea water as the medium. The salinity \(S\) in ppt (parts per thousand), temperature \(T\) in \({}^{\circ}\)C and depth \(D\) in meters all affect the speed of acoustic waves in sea water. Mathematically, it is modeled by the following [28]:  
\[\begin{array}{rcl}c&=&1449+4.591T-5.304\times 10^{-2}T^{2}\\ &&+2.374\times 10^{-4}T^{3}+1.34(S-35)\\ &&+1.63\times 10^{-2}D+1.675\times 10^{-7}D\\ &&+1.025\times 10^{-2}T(S-35)-7.139\times 10^{-3}TD^{3}\end{array} \tag{10}\]  
where \(c\) is the speed of acoustic waves in water and the other parameters have the same description as above. Contrary to the higher speed of radio waves, the slower speed of acoustic waves results in inherent delay in underwater communications. The temperature, salinity and depth limitations for the above equation to be applicable are 0 \({}^{\circ}\)C \(<T\leq\) 30 \({}^{\circ}\)C, 30-40 ppt and 0 m \(\leq D\leq\) 8000 m, respectively. The proposed scheme meets all of these conditions while performing routing. The proposed network has a depth of 1000 m that leads to variations in the speed of acoustic waves during protocol operation. Consequently, the above relation is used to measure the speed of acoustic waves during data transmission and delay measurement.
## 2 Data and Methodology
### Methodology
#### 2.3.5 Accuracy Verification  
The validation samples for the WCM were obtained from field-collected samples and randomly selected points. To ensure objectivity in accuracy validation and to minimize subjectivity in sample selection, the visual validation samples for the GDM were generated from random points within the study area. The methodology used to determine the attributes of these random points was consistent with the approach used for determining the attributes of visual samples in the training set (see Section 2.2.1). The details of the GDM validation points, regarded as ground truth data, are presented in Table 2.  
All classification results underwent validation by comparing the confusion matrix with the samples, a method of accuracy validation conducted at the pixel level and commonly employed in accuracy assessment. Classification accuracy was evaluated by comparing the validation predictions with the selected validation points and conducting a consistency test.
# Perspective on Double Pulsed Gas Metal Arc Welding
###### Abstract  
Aluminum alloy welding suffers from problems such as solidification cracking and hydrogen-induced porosity, which are sufficiently severe to limit its potential applications. Because mitigated porosity incidence and solidification cracking are observed in aluminum welds using double pulsed gas metal arc welding (DP-GMAW), a comprehensive review of the mechanism is necessary, but absent from the literature. The oscillation of arc force and droplet pressure causes a weld pool stir effect. The expansion and shrinkage of the weld pool cause unusual remelting and resolidification of the previously solidified metal. DP-GMAW has an increased solidification growth rate and cooling rate, compared with conventional pulsed welding at same heat input. Both numerical and experimental results reveal the remarkable concept that refined microstructure in the fusion zone is obtained by using DP-GMAW. The mechanism of microstructural refinement is revealed as a weld pool stir effect and increased cooling rate. Hydrogen bubbles easily float out and then release from the weld pool originated from the weld pool stir effect. Reduced solidification cracking is achieved due to the refined solidification structure that originated from the increased cooling rate. The advantages, evolution process, and future trend of DP-GMAW are discussed.  
double pulse welding; weld pool stir; solidification parameters; microstructural refinement; solidification cracking susceptibility +
Footnote †: journal: Applied Physics Letters B
# Dynamic RACH Partition for Massive Access of Differentiated M2M Services
## 3 System Model  
This section firstly describes a typical M2M traffic model defined in 3 GPP. Then, it briefly reviews the random access (RA) procedures in LTE-A. At last, the categories for M2M devices are specifically addressed.
## 4 Databases and Evaluation Parameters
### Evaluation Parameters  
Two parameters were used to analyze the improvement of the McCann Retinex algorithm: peak signal-to-noise ratio (PSNR) and contrast. For the segmentation process, sensitivity, specificity, accuracy, and area under the curve (AUC) were used. These parameters are discussed in more detail below.
# A Hand Motor Skills Rehabilitation for the Injured Implemented on a Social Robot
## 3 Architecture of HaReS, the Hand Rehabilitation System
### Rehabilitation Exercises
#### 3.4.1 Copy Pose  
In this exercise, the patient must copy a static hand pose for a configurable amount of time. The goal hand poses are recorded using the Leap Motion device by the therapists. The same device is used for processing the patient's hand pose whilst performing this exercise. A tolerance threshold is applied in order to consider that a pose is correct.  
As explained before, the Leap Motion controller provides the tridimensional position of each joint in a hand, taking advantage of this to measure the users' performance. In this case, we computed the mean euclidean distance to each fingertip from the user's pose to the goal pose. In order to make the system tolerant to different users, the hands were normalized so that they all had the same size. This is also adequate for measurements, as it enables a global framework of comparison that is not user-dependent.  
An image depicting a subject executing this exercise can be seen on the leftmost image of Figure 4.
## 3 Results
### Von Mises Stress in Peri-Inplant Bone  
The von Mises stress in the peri-implant bone was measured in the skull model with the midpalatal suture (Table 2). The difference between the bicortical model (2213.15 MPa) and the monocortical model (2787.58 MPa) was 22.97%, the difference between the bicortical deepening model (2105.60 MPa) and the monocortical model (2787.58 MPa) was 27.87%, the difference between the bicortical model (2213.15 MPa) and the monocortical deepening model (2646.48 MPa) was 17.83%, and that between the bicortical deepening model (2105.60 MPa) and the monocortical deepening model (2646.48 MPa) was 22.76%. The difference between the monocortical deepening model (2646.48 MPa) and the monocortical model (2787.58 MPa) was 5.19%, and that between the bicortical deepening model (2105.60 MPa) and the bicortical model (2213.15 MPa) was 4.98%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Miniscrew** & **Monocortical** & **Monocortical** & **Bicortical** & **Bicortical** \\
**Implant** & **Monocortical** & **Deeping** & **Bicortical** & **Deeping** \\ \hline Stainless steel & 2787.58 & 2646.48 & 2213.15 & 2105.60 \\ Titanium alloy & 2398.63 & 2205.90 & 1967.08 & 1756.59 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean von Mises stress (MPa) of the peri-implant bones for the skull. Model with midpalatal suture for all 4 anchorage models.  
Figure 4: (**a**) The cross-section of cranium was constrained as boundary condition (triangle arrowheads); (**b**) palatal bone-borne force application was indicated with red arrows; 0.25 mm displacement of each driving step was applied at the expansion screw of the SME to simulate expansion treatment; (**c**) the mesh model of the skull with midpalatal suture treated by SME expansion system.  
The mean von Mises stress at the implant-bone interface of titanium alloy miniscerews was also calculated for each anchorage model. The difference between the bicortical model (1967.08 MPa) and the monocortical model (2398.63 MPa) was 19.77%, the difference between the bicortical deepening model (1756.59 MPa) and the monocortical model (2398.63 MPa) was 30.90%, the difference between the bicortical model (1967.08 MPa) and the monocortical deepening model (2205.90 MPa) was 11.45%, and that between the bicortical deepening model (1756.59 MPa) and the monocortical deepening model (2205.90 MPa) was 22.68%. The difference between the monocortical deepening model (2205.90 MPa) and the monocortical model (2398.63 MPa) was 8.37%, and that between the bicortical deepening model (1756.59 MPa) and the bicortical model (1967.08 MPa) was 7.31%.  
The von Mises stress was obviously lower in the two bicortical anchorage models compared to the two monocortical anchorage models. The implant-bone interface of stainless steel miniscerews had greater stress compared to that of titanium alloy miniscerews. In all models, the von Mises stress was localized at the implant-bone interface surrounding the initial cortical bone layers (Figure 5a-h).  
Figure 5: Cont.
# Nucleation Mechanism and Rupture Dynamics of Laboratory Earthquakes at Different Loading Rates
## 3 Results
### Effect of Loading Rate on the Features of Stick-Slip Events
#### 3.2.3 Stress Drop  
The shear stress drop is closely related to the energy release of earthquakes and is one of the most important parameters of source process. Figure 9a describes the shear stress drop as a function of loading rate. With the increase in the loading rate, the shear stress drop decreases, and the relationship between the shear stress drop and the loading stress is fitted as \(\Delta\tau\) = 0.294 \({\rm V_{L}}^{-0.291}\). This trend is generally consistent with previous studies [20]. Figure 9b describes the friction drop \(\Delta\mu\) as a function of loading rate, and the curve can be expressed as \(\Delta\mu\) = 0.101 \({\rm V_{L}}^{-0.281}\) by fitting the data. For friction coefficient drop, it is observed that a lower loading rate guarantees a higher friction coefficient drop by analyzing both the macroscopic and local data [33], which is consistent with our result.  
Figure 7: (**a**) Friction coefficient–time curve defining the static friction (\(\mu^{p}\)), the minimum friction (\(\mu^{\text{min}}\)), and the friction drop (\(\Delta\mu\)). (**b**) Static friction (\(\mu^{p}\)) as a function of loading rate. The light color symbols represent the actual value. The highlight color symbols are the mean values with a 95% confidence interval. The dotted line is the fitting curve based on the mean values at different loading rates.
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 6 Conclusions  
This paper focuses on the challenging occlusion problem of MOT. The proposed method performs NMS later to avoid discarding low-scoring boxes that are useful for tracking, and to sequentially match high-scoring and low-scoring boxes. Bicubic interpolation is used to improve the quality of low-scoring boxes, which effectively improves the matching accuracy of low-scoring boxes. At the same time, by performing unsupervised pre-training on the person ReID network, the ability of the ReID network to reduce the domain gap is improved. The experimental results verify that the method proposed in this paper can effectively solve the occlusion problem while obtaining good tracking results.  
**Author Contributions:** Resources, Q.Z.; data curation, H.Z.; writing--original draft preparation, T.W.; writing--review and editing, T.W.; supervision, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Science Foundation of Shandong Province under Grant No. ZR2020 MF005.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 4: Visualization of tracking results on MOT17 and MOT20 test datasets. Boxes of the same color and numbers in the upper left corner of the box indicate the same tracking target. The method proposed in this paper has visible and effective effects on object density and occlusion.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to Data not being completely sorted out.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Review on the Performance of Linear and Mixed Integer Two-Stage Stochastic Programming Software
## 4 Benders Decomposition Enhancement Strategies  
The application of BD often leads to slow convergence, long computational times, and excessive use of memory resources, particularly for the case when the MILP master problem has poor LP relaxation [27; 28; 29]. Major BD disadvantages include time-consumingiterations, poor feasibility and optimality cuts, ineffective initial iterations, primal solutions that behave erratically, slow convergence at the end of the algorithm (tailing-off effect), and upper bounds that remain stuck in successive iterations due to second-stage degeneracy [26; 30].  
Various strategies have been proposed to accelerate the convergence of the standard BD method. Enhancement strategies are mainly split into two categories: reducing the cost of each iteration, or reducing the number of iterations [26; 31].