esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of a Three-Dimensional-Printed Titanium Implant in Human Tissues: Case Study
###### Abstract  
A titanium alloy implant of appropriate pore size can potentially enhance osseointegration and soft tissue integration. However, the human clinical application of such implants has not been reported. Here, we present a case of limb salvage surgery for a bone tumor using customized three-dimensional (3D)-printed Ti6 Al4V radius and ulna implants. The patient presented with local recurrence at the proximal junction of the ulna and underwent a re-wide excision. Single forearm bone surgery was performed using another 3D-printed implant after resection of the recurrent tumor with an ulnar implant. Host osseointegration and soft tissue integration of the retrieved implant were quantified through histological evaluation. The total tissue integration rates of the implant at the proximal and distal bone junctions were 45.96% and 15.03%, respectively. The mesh structure enhanced bone integration by up to 10.81% in the proximal and by up to 8.91% in the distal bone junction. Furthermore, the soft tissue adhesion rates of the implant shaft were 59.50% and 50.26% in the axial and longitudinal cuts, respectively. No area was left unoccupied throughout the shaft of the implant. Overall, these results indicate that the 3D-printed Ti6 Al4V titanium alloy implant with a rough surface has considerable tissue integration ability.  
Keywords:three-dimensional printing; bone tumor; implant; tissue integration; titanium; human Article
# An Efficient Distalization Technique Using Coil Springs and Mini Screws--A Finite Element Analysis
## 1 Introduction
Moreover, the posterior teeth will rotate and tip distally when buccal distalization forces are applied. This rotation and tipping causes frictional resistance between the archwire and the bracket, which might affect the balance of the force system. Furthermore, although [PERSON] et al. [9] reported that the tooth movement pattern caused by the conventional grouped distalization was employed, they only investigated the initial displacement of teeth in the PDL. A previous study [10] suggested that the force system generated by a multibracket appliance drastically changed over time due to tooth movement caused by bone remodeling. Thus, their results might be insufficient to understand the actual behavior of the conventional grouped distalization. The objective of the present study was to clarify whether our proposed technique improves force distribution over posterior teeth and supports efficient distalization, using the finite element method (FEM). The FEM is broadly used to evaluate force systems generated by orthodontic appliances because of its reproducibility [10; 11; 12] and capability  
Figure 1: An overview of the mechanics that we propose for distalizing molars efficiently. Each tooth will theoretically receive a distalization force of 1 N.  
of visualizing force and stress distribution easily. We also employed an analysis method that considers the bone remodeling process to reveal more realistic behavior of both our proposed technique and the conventional grouped distalization method.
## 2 Materials and Methods
### Experimental Design  
Experimental design included 4 groups (\(n=6\) per group) treated as follows: (i) control (vehicle-treated), (ii) diabetic (STZ-treated), (iii) diabetic plus _L. regularis_ 150 mg/kg/day (Lr150 + STZ \(-\) treated) and (iv) diabetic plus _L. regularis_ 300 mg/kg/day (Lr300 + STZ \(-\) treated). _L. regularis_ extract was suspended in 0.25% carboxymethyl cellulose sodium (CMC) solution and given once per day orally by gavage for 4 weeks, beginning one week after STZ injection. An equal volume of CMC solution was used as a vehicle for control and STZ-treated animals. Animals were anesthetized through intraperitoneal injection of a ketamine (92 mg/kg, Hikma Pharmaceuticals, Amman, Jordan) and xylazine (10 mg/kg, Bayer, Turkey) mixture followed by the collection of blood samples through cardiac puncture. The collected samples were centrifuged at 1800 g for 10 min. The obtained serum samples were separated and stored at \(-\)20 \({}^{\circ}\)C. Animals were euthanized, and the kidneys were dissected. A cross-section of the harvested kidneys was fixed in 10% neutral buffer formalin (NBF) (pH 7.4) for histopathological analysis, and the remaining harvest was dipped in liquid nitrogen for one minute and stored at \(-\)80 \({}^{\circ}\)C.
# Cross-Lingual Named Entity Recognition Based on Attention and Adversarial Training
## 3 Model  
In order to improve the cross-lingual migration capability of the model, this paper proposes a cross-lingual named entity recognition model based on an attention mechanism and adversarial training. The sequence representation is first obtained through the encoding layer, and the key information in the text is extracted using the attention mechanism. Then, the bilingual word vector is further aligned through the adversarial training layer and passed through the CRF to the output layer. Finally, the labels are output.  
The model in this paper contains an embedding layer, a double attention layer, an adversarial training layer, and an output layer, and its structure is shown in Figure 2. The embedding layer consist of M-Bert and source word embedding and target word embedding. The double attention layer consist of LSTM and attention. The adversarial training layer consist of a Word discriminator that will calculate discriminator loss and generator loss. Finally, the CRF layer will output all results.  
Figure 1: Diagram of the calculation process of attention mechanism.  
Figure 2: Model Structure.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 4 Commercial UWB Modules and Chips for IPSs
### B-UWB-MOD1  
The B-UWB-MOD1 (STMicroelectronics, Geneva, Switzerland) is an ultra-wideband module designed for implementing high-precision indoor positioning and location systems. It is an ultra-compact surface-mounted module that is designed to work with UWB frequencies from 3.25 to 4.75 GHz and support the UWB channels 1, 2, 3, and 4 with bandwidths of 500 MHz and 1 GHz. It can cover ranging distances of up to 600 m in LoS conditions with 10 cm distance measurement accuracy and can provide an adjustable sample rate of up to 250 per second. The data can be acquired using the UART communication bus. This module is currently used as the core element of the commercial evaluation kit B-UWB-MEK1 (STMicroelectronics, Geneva, Switzerland), which is used for the prototyping, evaluation, and demonstration of indoor location systems [41].
## 2 Materials and Methods
STL files, grouped by sequence numbers into D, C, and DC, were aligned and registered globally using the align and global registration commands, allowing for the transfer and alignment of the D and C STL models onto the DC STL model. Following the transfer and alignment processes of the D and C scans, the gap between D and C was modeled based on their resulting positions. The marginal and internal regions were segmented using design commands on the modeled internal structure, and volume calculations were performed within 3-matic 18.0 (Materialise, Leuven, Belgium) software (Figure 2). Figure 1: (**a**) Actual photo (crown, die), (**b**) STL data (crown, die, and cemented crown). Figure 2: The evaluation of marginal and internal adaptation using a triple-scan protocol. (**a**) Image of the cemented crown, (**b**) gap volume between the scanned crown and die, (**c**) marginal gap volume, and (**d**) internal gap volume. The effect of the production technique on the marginal and internal gap values of the crowns was analyzed using a statistical package program (IBM SPSS Statistics for Windows, v20.0; IBM Corp.; Armonk, NY, USA). The data distribution of the marginal and internal gap values (mm\({}^{3}\)) was tested with the Shapiro-Wilk test. The data were normally distributed, while the values were analyzed with a one-way analysis of variance (ANOVA). The variances were also tested with the Levene test, and they were found to be homogenous (\(p\) = 0.550 for marginal gap and \(p\) = 0.101 for internal gap values). Pairwise comparisons were performed with Tukey HSD tests (\(\alpha\) = 0.05).
## 4 Examined Distributed Consensus Gossip-Based Algorithms  
In this section, we introduce all the distributed consensus gossip-based algorithms chosen for evaluation in our research presented in this paper. We analyze five frequently cited approaches primarily proposed for estimating arithmetic mean, namely we examine these algorithms:  
\(\square\) _Randomized gossip algorithm (RG)_: see Section 4.1.  
\(\square\) _Geographic gossip algorithm (GG)_: see Section 4.2.  
\(\square\) _Broadcast gossip algorithm (BG)_: see Section 4.3.  
\(\square\) _Push-Sum protocol (PS)_: see Section 4.4.  
\(\square\) _Push-Pull protocol (PP)_: see Section 4.5.  
Furthermore, as stated above, we compare these five algorithms for not very frequently applied functionality--distributed network size estimation. In each algorithm, the agents of MAS initiate their inner state (or the variable sum in the case of \(PS\)) with, e.g., locally measured information in the case of distributed averaging or distributed summing. The vector gathering the inner states (representing arithmetic mean estimates) of all the agents at a time instance \(k\) is labeled as \(\mathbf{x}(k)\), the inner state of \(v_{i}\) at a time instance \(k\) is labeled as \(x_{i}(k)\), and \(x_{i}(0)\) is a label of the initial inner state of \(v_{i}\). However, in the case of distributed  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Graph Parameter** & **Numerical Value** \\ \hline Graph order & 30 \\ Median degree & 25.21 \\ Max degree & 29 \\ Min degree & 16.17 \\ Diameter & 2 \\ Graph size & 371.43 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Graph parameters of 100 RGGs used in our experiments.  
Figure 4: Example of random geometric graphs used in our experiments.  
network size estimation, exactly one of the agents has to be selected as the leader--for example, the agent at which the query is inserted [47]. The initial inner state of the leader is equal to \"1\", while all other agents set their initial inner state to \"0\". In our experiments, either the best-connected or the worst-connected agent is chosen as the leader in order to identify the impact of the leader selection on the performance of the algorithms. Thus, the inner states of all the agents approach \(\frac{1}{n}\) (\(n\) is equaled to 200 in our experiments) as the number of time instances is increased. Thus, the agent can easily estimate the network size at each time instance as follows:  
\[NSE_{i}(k)=\frac{1}{x_{i}(k)} \tag{2}\]  
Thus, in our experiments, \(NSE_{i}\) approaches 200 and \(x_{i}(k)\)\(1/200\). The vector gathering all the estimates at a \(k\)th time instance is labeled as \(\mathbf{NSE}(k)\). We refer to the states/the estimates after the consensus among all the agents in MAS is achieved as the final inner states/the final estimates. Figure 5 shows an example of how the inner states/the network size estimates differ before and after any examined algorithm for network size estimation is executed.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 7 Algorithms and Protocols
### Obstacle Avoidance  
The obstacle avoidance method allows drones to avoid colliding with e.g., people, infrastructure, vegetation, the inspection environment, etc. The obstacle avoidance algorithm uses an Red-Green-Blue-Depth (RGBD) camera for obstacle detection (Figure 10).  
The depth estimation of the sensed environment is used to generate a collision-free path for the drone based on a guided gradient-based trajectory optimization [47] with the Fast-Planner software implementation [48]. Specifically, the path planning method uses path-guided trajectory optimization, which consists of two phases and represents trajectories as B-splines. First, it generates an intermediate smooth B-spline trajectory close to a guiding path by optimizing the distance between the trajectory and the guiding path. The geometric paths are generated by using sampling-based topological path searching to find collision-free paths to the goal. Second, the intermediate B-spline trajectory is further optimized by involving smoothness cost, dynamic feasibility cost, and collision cost. The collision cost is constructed from gradients of an Euclidean Signed Distance Field (ESDF), which is converted from the RGBD camera depth estimation [47]. Different costs are combined as a weighted summation and the combined cost function shapes the motion of the drone.  
Figure 8: Overview of the inspection path planner processing method [46].  
Figure 10: Example of the use of RGBD camera for obstacle detection. The camera senses the trees observed with the front camera (to the **left**) and outputs point cloud data relative to the camera frame (to the **right**).  
Figure 9: Point cloud of a bridge target for inspection based on a drone preflight test.
## 2 Islanding Detection Methods
### Computational Intelligent Based Methods
#### 2.6.1 Artificial Neural Network Method (ANN)  
In this procedure, the significant features from the measurement data are taken out and used to identify variations in the power system parameters. This approach performs accurately and effectively for multi-DGs. However, the biggest drawback of this approach is the lengthy data processing time [42].
## 2 General  
Apart from 5-trifluoromethyl-1,3-cyclohexanedione that was discovered by our group [13], the reagents and solvents in the appropriate grades were purchased and used without further purification. Melting points were done on Mel-temp LL and were uncorrected. IR spectra were recorded on a Perkin Elmer Spectrum One FT spectrometer. \({}^{1}\)H and \({}^{13}\)C were recorded in CDCl\({}_{3}\) on Oxford NMR (300 MHz) instrument using TMS as internal standard. The elemental analysis was carried out on Perkin Elmer 2400 Elemental Analysis (C-H-N). The single crystal X-ray diffraction of **4d** was performed on the Rigaku XtaLAB Mini.  
**General Procedure for synthesis of compounds 4a-u**: 5-(trifluoromethyl)-1,3-cyclohexanedione (1 mmol), acetoacetate ester (1 mmol) aldehyde (1 mmol), ammonium acetate (1 mmol) were dissolved in 2 mL of TFE and stirred at 50 \({}^{\circ}\)C. The reaction progress was monitored by TLC and TFE was removed at the end of the reaction by distillation. The crude product was purified by recrystallization from ethanol with a few drops of water to afford pure fluorinated hexahydroquinoline derivatives.  
**General Procedure for synthesis of compounds 5a-b**: A mixture of 5-(trifluoromethylomethyl)-1,3-cyclohexanedione (2 mmol), aldehyde (1 mmol), ammonium acetate (1 mmol), were dissolved in 2 mL of TFE and stirred at 50 \({}^{\circ}\)C for 50 min. The progress of the reaction was monitored by TLC. The crude product was purified by recrystallization from ethanol to afford pure fluorinated acidinedone **5a-b**.  
The spectroscopic data of selected compounds are shown below.  
**4-(4-chlorophenylophenyl)-2-methyl-5-oxo-7-trifluoromethyl-1,4,5,6,7,8-hexahydroquinoline-3-carboxylic acid ethyl ester (4d)**: mp = 258-259 \({}^{\circ}\)C, \({}^{1}\)H NMR 300 MHz, CDCl\({}_{3}\) : \(\delta\) = 1.28 (t, J = 7.2 Hz, 3H), 1.65 (s, 3H), 2.55-2.80 (m, 1H), 4.05 (q, J = 7.2, 2H), 5.01 (s, 1H), 6.60 (s, 1H, NH), 6.77 (d, J = 8.4 Hz, 2H), 7.28 (d, J = 8.4 Hz, 2H), \({}^{13}\)C NMR (300 MHz CDCl\({}_{3}\)): \(\delta\) = 14.2, 19.1, 19.6, 25.9, 32.8, 41.4, 61.7, 102.3, 112.0, 128.7, 130.5, 131.2, 137.5, 142.2, 150.7, 167.7, 198.8; IR (KBr, cm\({}^{-1}\)) 3287, 3209, 3088, 2950, 1708, 1610; MS m/z: 75 (5%), 303 (100%), 413 (M\({}^{+}\), 55%); Anal. Calc for C\({}_{20}\)H\({}_{19}\)CIF\({}_{3}\)NO\({}_{3}\): C, 58.05; H, 4.63; N, 3.38%. Found: C, 57.02; H, 4.60; N, 3.40%.  
**9-(4-fluorophenyl)-3,6-bis(trifluoromethyl)-3,4,6,7,9,10-hexahhydroacridine-1,8(2H,5H)-dione (5a):** mp \(>\) 300 (decomposes), \({}^{1}\)H NMR 300 MHz, CDCl\({}_{3}\)): \(\delta\) = 1.36-2.81 (m, 8H, cyclohexyl-Hs), 2.81 (m, 4H), 2.99 (m, 2H, CH next to CF\({}_{3}\)), 4.82 (s, 1H, CH), 7.10-7.21 (m, J = 8.0, 4H, Ar-H), 8.53 (s, 1H, NH); \({}^{13}\)C NMR (300 MHz CDCl\({}_{3}\)): \(\delta\) = 19.8, 25.8, 33.0, 111.9, 115.2, 130.7, 137.5, 140.0, 149.3, 160, 198.9; IR(Nujol, cm\({}^{-1}\)): 3436 (NH), 1653 (O=C-C=C-NH). MS m/z: 75(5%), 353 (100%), 447(M\({}^{+}\), 50%). Calc for C\({}_{21}\)H\({}_{16}\)F\({}_{3}\)NO\({}_{2}\): C, 56.38; H, 3.61; N, 3.13%. Found: C, 56.35; H, 3.66; N, 3.15%.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture  
The following text highlights the MM mechanisms tailored with 5G architecture. We segregate the section into the following areas: (i) Design aspects of 5G-related MM mechanisms, (ii) Research contribution (non-3 GPP) for MM strategy in 5 G network, and (iii) MM mechanisms and 6G network.
## 4 Discussion
### Attacking Phase Characteristics  
The attack duration was significantly affected by both the initial pressure and initial penetrative actions, as previously discussed. These findings highlight the complex interplay between various factors that determine the duration of an attack. Although early penetration can effectively create chances to score [15], it may also trigger rapid transitions -- either by leading to a successful offensive action or by resulting in possession loss when the attempt fails.  
The analysis revealed that the majority of the final attempts during the knockout phase of the UEFA Youth League originated from open play (56.7%), with 43.3% stemming from set plays. Previous research on senior tournaments reported that the majority of goals are scored from open play, typically exceeding 65% [25]. However, the proportion of final attempts from set pieces in the UEFA Youth League (43.3%) was notably higher than the typical range observed in senior soccer, with research indicating that over 65% of goals typically result from open play situations [25]. This finding may partially be attributed to the unique characteristics of the knockout phase. As proposed by [PERSON] et al. [40] in their analysis of Champions League men's matches, the increased number of yellow cards and technical indicators observed in the knock-out stage suggests a greater emphasis on defensive actions and a more proactive approach to preventing scoring opportunities. Teams may be more inclined to commit fouls to disrupt opponents' attacks and maintain defensive solidity, potentially leading to more set-play situations.  
Previous studies, such as [PERSON] et al. [21], reported that senior and U20 teams demonstrated a greater propensity for counter-attacks, and [PERSON] et al. [22] observed that the first team exhibited a more elaborate approach, whereas the U18 team favored a direct style within an English club context. In contrast, our findings in the UEFA Youth League revealed that organized attacks were associated with a higher frequency of final attempts compared to counter-attacks, suggesting a patient build-up play and structured possession. The UEFA Youth League knock-out stage features highly competitive matches between top European clubs, demanding a high level of technical skill, tactical awareness, and the ability to break down well-organized defenses. These findings collectively suggest that the attacking styles employed at the highest levels of youth competition may differ from those observed in younger age groups or in less competitive environments.
# Highly Flowable Nano TiO2/Porous Organic Polymer (POP) Supports for Efficient Metallocene Catalysts
## 3 Results and Discussion
### Ethylene Polymerization and Kinetic Curve of the Supported Metallocene Catalysts
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Cat.**} & **Zr** & **Al/Zr** & **Cat** & **Yield** & **Activity** & **Productivity** & **Bulk** \\  & **(umol/g)** & **Moal** & **(mg)** & **(g)** & **(kg PE/mol.Zr.bar. h)** & **(g PE/g cat)** & **Density** \\ \hline Zr/MAO@POP-1 & 42.0 & 114 & 130 & 44.9 & 4112 & 345 & 0.26 \\ Zr/MAO@POP-4 & 43.5 & 112 & 135 & 37.4 & 3184 & 277 & 0.22 \\ Zr/MAO@POP-6 & 43.2 & 117 & 165 & 55.3 & 3879 & 335 & 0.30 \\ Zr/MAO@POP-7 & 40.6 & 123 & 140 & 54.5 & 4794 & 389 & 0.24 \\ Zr/MAO@silica **4** & 47.3 & 103 & 132 & 24.2 & 1940 & 183 & 0.36 \\ \hline \hline \end{tabular}  
* Slurry polymerization condition: 4 Bar ethylene pressure in 800 mL reactor of stainless steel, 300 mL hexane, 80 \({}^{\circ}\)C, 3 mL (10 wt% in hexane) TEAL (scavenger), polymerization time: 30 min. ** Silica gel-supported commercial metallocene catalyst.  
\end{table} 
Table 4: Ethylene polymerization results (cataly: (n-BuCp)\({}_{2}\)ZrCl\({}_{2}\)/MAO supported on nano TiO\({}_{2}\)-doped POP Particles *. Figure 8: **(a)** Kinetic curves of ethylene polymerization. 0.83 g Zr/MAO@Silica gel was evaluated at 80 \({}^{\circ}\)C in 10 bar ethylene pressure; 0.67 g Zr/MAO@POP-7 was evaluated at 80 \({}^{\circ}\)C in 6 bar ethylene pressure. **(b)** GPC curves of two mPEs from the two catalysts. catalyst (2908 kg PE/mol Zr.bar.h), partly due to a similar analogue to homogeneous polymerization, and the bulk density of mPE-2 from the Zr/MAO/POP-7 catalyst was closer to inorganic counterparts. From Figure 8b, we can see that the average molecular weight of mPE-1 (Mw = 170,300 g/mol) was less than that of mPE-2 (Mw = 211,100), and the molecular weight distribution of the mPE-2 (PDI = 2.71) was narrower than that of mPE-1 (PDI = 2.90) prepared from the silica gel-based metallocene catalyst.
## 2 Materials and Methods
### Shelf-Life Determination  
OD-treated tomato and spinach-based RTE salad products were subjected to shelf-life determination. Approximately 100 g of each product was packed into polyethylene-polpropylene sachets and stored in incubators at constant temperatures 4, 8, 12 and 20 \({}^{\circ}\)C. At regular time intervals, samples were withdrawn and analyzed in terms of microbial growth and sensory quality loss described in Section 2.5. Microbial growth versus storage time for each storage temperature was mathematically modeled using the Gompertz model [39]. The evolution of the sensory total acceptance versus storage time for each storage temperature was mathematically modeled using a simple zero order equation of the form \(S=S_{0}-k_{s}\ t\), where \(S\) is the sensory attribute at time \(t\), \(S_{0}\) is the sensory attribute at time zero and \(k_{s}\) the deterioration rate constant. The dependence of the rate constant and lag phase parameters of the Gompertz equation, as well as the dependence of the sensory deterioration rate constant from storage temperature, was mathematically modeled using the Arrhenius equation. Combining the mathematical models used, the following expressions for the shelf life were derived [25]:  
\[t(SL_{MG})=\frac{logN_{l}-logN_{0}}{\mu_{m}(T)}+\lambda(T) \tag{3}\]  
\[t(SL_{s})=\frac{S_{0}-S_{L}}{k_{s_{T\!off}}\cdot\exp\left(-\frac{E_{a}}{R} \cdot\left(\frac{1}{T}-\frac{1}{T_{\!off}}\right)\right)} \tag{4}\]  
where \(t_{SL_{s}}\) is the shelf life determined in terms of sensory deterioration, \(t_{SL_{MG}}\) is the shelf life determined in terms of microbial growth, \(S_{0}\) is the initial sensory score, \(S_{L}\) is the acceptance limit of the sensory score, \(logN_{l}\) the acceptable maximum microbial load limit of the product, \(logN_{0}\) the initial microbial load of the product and \(\mu_{m}(T)\), \(\lambda(T)\) the dependence of the rate constant and the lag phase parameter from storage temperature through the Arrhenius equation, \(R\) is the universal gas constant, and \(E_{a}\) and \(k_{n\!off}\) the activation energy and rate constants at the reference temperature, respectively, for each measured parameter. Since different processing conditions were expected to lead to different shelf-life estimations based on the two parameters, both values were calculated. The parameter leading to the lowest shelf life estimate at each storage temperature was considered as the limiting parameter [25].