esa-sceva/satcom-chunk-collection huggingface dataset:
# Quasi-Uniform Density Non-Solid Infill Strategy for Axisymmetric Non-Planar Additive Manufacturing
## 3 Results and Discussion
### Path Planning Examples with the 2-Opt Nearest Neighbor Algorithm  
Several final point distributions obtained by the above optimization were further employed to illustrate infill path planning through the geodesic 2-opt nearest neighbor heuristic. In Figures 10-12, the results show smoothly connected Hamiltonian paths across all distributed points for \(\Omega_{1}\), \(\Omega_{2}\), and \(\Omega_{3}\) (GBNS seed), respectively.  
In all the examples presented, the trajectory starting point was a slice polygon vertex (the bottom-right corner in \(\Omega_{1}\) and \(\Omega_{3}\) and the bottom vertex in \(\Omega_{2}\)). However, the starting point can be chosen according to other criteria. The present infill algorithm is to be embedded within a complete slicing algorithm, which includes walls and infill. Usually, this implies that the infill phase is done after the wall phase. In this kind of situation, the infill start point can be located close to the wall end point, shortening build cycle times. Other situations may include slice polygons without vertices. In any case, the algorithm can be initialized from any point, without the risk of generating an unsuitable trajectory.  
Additionally, this path planning strategy could be extended and refined to address functionality requirements such as optimizing fatigue performance [10; 59], optimizing internal support creation [25], reducing build cycle time [60], or increasing mechanical strength [16]. Other research lines may include the definition of functionally graded structures through non-uniform infill densities [61] or incorporating machine dynamics onto the path planning [54], optimizing the overall build efficiency for robot-based NPAM processes.  
Figure 10: Geodesic path planning results on the barrel-shaped build platform, domain \(\Omega_{1}\), both in the embedded map (right-hand side) and on the platform.
## 1 Introduction
Specifically, the basic model of GSDM will be described at first, then integrated by CIM to finish the whole structure building. What is more, the theoretical analysis of the average bit error rate (ABER) at both Bob and Eve are exhibited for clearer performance comparison. Finally, the simulation results are presented to investigate the benefits of the proposed scheme, even if Eve is equipped with the identical distributed receivers as Bob's, which supports the theoretical analysis. The rest of this paper is organized as follows. Section 2 describes the system model used in this paper thoroughly, which is constructed by GSDM in conjunction with CIM. In Section 3, the theoretical analysis of ABER at both Bob and Eve are derived for further comparison. Our simulation results and performance analysis are presented in Section 4 and we conclude our discourse in Section 5 finally. _Notation:_ In this paper, \((\cdot)^{H}\) and \(\|\cdot\|\) represent the conjugate transpose of a matrix and the Frobenius norm, respectively. What is more, \(\mathcal{R}(\cdot)\) indicates the real operators and \(\mathcal{CN}\) denotes circularly symmetric complex Gaussian distribution.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Chemical Properties and Their Characterization of MNPs
#### 3.2.7 X-ray Diffraction (XRD)  
XRD is employed to analyze the arrangement of atoms in the magnetic core, providing insights into their crystalline structure. It offers information regarding crystal arrangement, the distance between their lattices, and the dimensions of the crystallites. These factors are crucial in determining the magnetic behavior of these substances.
## 3 Results and Discussion
### Development of an Intensive LMO Management Scheme  
Therefore, an intensive investigation and management system for LMO re-collection sites is necessary. In 2014, the NIE attempted to establish and apply a priority management scheme for LMO re-collection areas in the natural environments of South Korea.  
The general LMO monitoring process from 2009 to 2013 was conducted in three steps: (1) survey and sampling based on the results of the previous year, (2) LMO detection and data analysis using event-specific PCR and sequencing analysis, and (3) post-management at the collection site (Figure 2A). To establish and apply a priority management scheme, priority management areas of the total LMO re-collection sites were defined as sites where LMOs were re-collected twice or more in the last five years (Figure 2B). Priority management areas have been assigned annually since 2014. As shown in Table 1, LMOs were consistently found at the same sites, indicating the need for intensive surveys and post-management.  
Figure 2 shows the differences between the general environmental LMO monitoring process and the priority management survey. Firstly, a priority management area was  
\begin{table}
\begin{tabular}{c c c c c c c}  & **2009** & **2010** & **2011** & **2012** & **2013** & **Total** \\ \hline LMO collection sites & 8 (19) a  & 10 (12) & 10 (19) & 19 (42) & 18 (21) & 65 (113) \\ LMO re-collection sites & 5 (14) b  & 3 (3) & 4 (11) & 8 (18) & 5 (6) & 25 (52) \\ \hline \end{tabular}
\end{table}
Table 1: The number of living modified organism (LMO) collection sites and LMO plants in the same places from 2009 to 2013.  
Figure 2: Novel management scheme for priority management areas. (**A**) Process of general LMO monitoring. (**B**) Novel survey and management system for priority management areas (PMA) or LMO re-collection sites.  
assigned where LMOs had been collected twice or more in the last five years. Secondly, while the general LMO monitoring survey was conducted within a 100 m radius of the site, the priority management survey was performed within a 1 km radius. Thirdly, LM crops and closely related plants were collected and analyzed. Fourthly, while general environmental LMO monitoring was conducted twice a year, priority management areas were surveyed more than four times yearly. According to the newly established scheme, intensive surveys and post-management of the priority management areas were conducted after selecting priority management areas in 2014.
## 5 Discussion
### State-Of-The-Art Industry 4.0 in the Construction Industry
#### 5.1.1 Cluster Technology  
The first cluster includes a wide range of technologies available in Industry 4.0: internet, automatic equipment, Internet of Things, augmented reality technology, and sensors. This cluster completes the cyber-planning-physical ecosystem by integrating physical machineries and devices, non-physical technologies, and BIM to accommodate BIM functionalities to improve construction activities during different phases. The application of these technologies in the construction industry could not possibly be realized without the digitization of data from BIM as the collaboration medium. For example, design automation combining the BIM model with advanced simulation tools and genetic algorithms was able to mass-customize housing construction [54]; the use of BIM in the navigation core provided an augmented reality navigation system to navigate pathways in building [47]; the use of Bluetooth Low Energy (BLE) Beacons, Extended Markup Language (XML) formatted informative BIM, and BIM model on the Unity platform provided workers with relevant information in real-time based on their current position on the construction site through augmented reality [59]; and BIM, IoT, sensors, data computing, and advanced analytics tools were used to simulate, re-simulate, and map the simulation for steel bridge performance monitoring as the core enabling the technology system [51]. Automated real-time construction technologies would help the construction industry to improve the productivity and quality of a project throughout its lifecycle. As such, the availability of the Internet and Internet of Things enables the creation of a cyber domain to support these smart technologies in the physical domain with the support of BIM in the planning domain.
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
###### Abstract  
The number of older people needing healthcare is a growing global phenomenon. The assistance in long-term care comprises a complex of medical, nursing, rehabilitation, and social assistance services. The cost is substantial, but technology can help reduce spending by ensuring efficient health services and improving the quality of life. Advances in artificial intelligence, wireless communication systems, and nanotechnology allow the creation of intelligent home care systems avoiding hospitalization with evident cost containment. They are capable of ensuring functions of recognition of activities, monitoring of vital functions, and tracking. However, it is essential to also have information on location in order to be able to promptly intervene in case of unforeseen events or assist people in carrying out activities in order to avoid incorrect behavior. In addition, the automatic detection of physical activities performed by human subjects is identified as human activity recognition (HAR). This work presents an overview of the positioning system as part of an integrated HAR system. Lastly, this study contains each technology's concepts, features, accuracy, advantages, and limitations. With this work, we want to highlight the relationship between HAR and the indoor positioning system (IPS), which is poorly documented in the literature.  
human activity recognition; indoor localization; human tracking; signal measurement; positioning algorithms; GPS; IPS +
Footnote †: journal: Sensors
# Consistency Models of NoSQL Databases
## 5 NoSQL Consistency Implementations
### Neo4j  
Neo4J is considered the most popular graph database worldwide, used in areas, such as health, government, automotive production, military area and others. Neo4j favors strong consistency and availability. Neo4j has clustering features in its Enterprise Edition. These features are capable of providing a fault tolerant platform, reading scale up and Causal Consistency model.  
A cluster is composed of two types of nodes, core servers and read replicas. Core Servers' primary responsibility is to ensure data durability. Once the majority of the Core Servers set has accepted a given transaction, the client will be acknowledged of the commit. In order to calculate the number of Core Servers required to tolerate F failed servers, Neo4j states that the number of Core Servers needed is \(2\)F + 1. In a real situation where occurs a certain number of failed Core Servers greater than F, the cluster will become read-only to preserve data safety, because the minimum requirements to achieve write consensus has been compromised.  
Figure 9 shows Neo4j Cluster Architecture. Read replicas' main responsibility is to ease the load from read requests. They asynchronously reflect the changes consented by the majority of the Core Servers set. As the Read Replicas do not change data states, they can view as disposable servers, which means that their arrival or departure will only decrease or increase query latency respectively, but it will never compromise data availability [40].  
Neo4j in a single node architecture is strongly consistent. In Neo4j Enterprise Edition, the cluster ensures causal consistency. As we previously mentioned, causal consistency guarantees that reading data previously written from the same client will be consistent. However, we have eventual consistency when reading data that was changed by other clients, because there is a millisecond time window which the latest data has not been propagated yet [40]. Neo4j is located on the CA quadrant by providing consistency and availability.
## 3 HIBAS Implementation for Control of a Foraging System with Deviating Motor Speeds
### Hormone Interaction with Motor Speed
#### 3.2.1 Demand  
'Demand', as a new feature to the virtual hormone system, required the development of a novel formula accounting for: a target number of items to be collected (to be specified before deployment), the allotted time to collect said items, the current collection rate throughout the experiment. Following this, Equation (2) was created:  
\[D(t)=\frac{I_{T}}{t_{T}}-\frac{I_{c}+1}{t} \tag{2}\]  
In this equation \(D(t)\) represents the demand function, \(I_{T}\) is the total number of items desired by the end of the allotted time period, \(t_{T}\) is the end time for the allotted period, \(I_{c}\) is the current number of stored items and \(t\) is the current time step. Decentralisation is required to remain'swarm like' during the experiment, therefore the 'demand value' is only accessible to individual robots in the nest. The value is updated as they leave and used as their stimuli throughout their next period of exploration.  
Equation (2) models the demand value to fluctuate as items were collected without incurring an exponential increase near the end of the experiment should the swarm only be a few items away from the target collection. By setting the demand as the difference between the required average rate of collection and the current rate of collection, the hormone value and speed could increase with repeated failure to meet target collection rates. This meant that speed would only slightly deviate from the optimal speed of travel. Gradual incrementation in this manner prevented an inefficient burst of speed late in the experiment to compensate for a lack of items collected.  
With a function for demand in place, the two Hormone equations were produced (Return Hormone and Speed Hormone, shown respectively in Equations (3) and (4)) to regulate the speeds and behaviours of each robot in the swarm. The hormones produced in these experiments were designed in the same format as [5,7] with \(\lambda\) representing decay and \(\gamma\) representing the coefficient of stimuli.
# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 4 Context-Aware Navigation (CAN)
### Mitigating Multipath/NLOS Effects in Different Environmental Contexts
This is further elaborated in a clearer way by using a pie-chart in Figure 10, which shows the number of occurrences of RMS errors greater than 2 m and less than 2 m. The pie-chart shows the overall performance of the multi-constellation and multi-frequency GNSS receiver in dynamic mode with and without using CAN. The RMS error was found to be less than 2 m for 53% of the total experimental period with CAN and 32% without using the proposed CAN in the P3 observation window. This improvement in the positioning accuracy demonstrates the efficacy of CAN in degraded and highly degraded environments. We concluded that the CAN method is effective for mitigating the multipath effects in order to improve the positioning accuracy and availability in the case of degraded and highly degraded environments without using any aiding devices or additional hardware.
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Integration between Sensor and Textile
#### 3.6.4 Integration at the Garment Level  
The Austria-based company QUS Sports is in the process of producing smart textiles (shirt and bra) that can track body fitness. Intelligent micro-EMG sensors are placed in a QUS shirt, recording all relevant data via Bluetooth. Monitoring of the heart rate, breathing rate, calorie consumption, acceleration, g-force, geo-data, sleep and regeneration for sports-training is performed by the smart clothing [143].  
An embroidery technique can be used to develop a conductive fabric. MIT proposed an approach of stitching patterns that can be defined circuit traces, component connection pads, or sensing surfaces designed with traditional CAD tools for circuit layout [144]. They developed a musical jacket comprising a fabric keypad, a MIDI synthesizer, speakers behind speaker grills in the pockets, fabric buses visible inside the jacket and a fabric keypad with the circuit board placed behind it (Figure 18).
## 2 Related Works
[13] employed both convolutional and recurrent neural networks to process the time and frequency domain information of PPG signals, amalgamating the extracted features to estimate systolic and diastolic blood pressure. Their tests on a dataset of 510 subjects from the MIMIC III database resulted in mean absolute errors of 9.43 mmHg and 6.88 mmHg for systolic and diastolic blood pressure estimations, respectively. However, these estimations fell short of the precision standards set by the Advancement of Medical Instrumentation (AAMI) and British Hypertension (BHI). In another study, [PERSON] et al. [14] extracted PPG and ambulatory blood pressure signal records from 4000 patients in the MIMIC-III database. They evaluated the performance of four deep learning models--AlexNet, ResNet, [PERSON]'s proposed model, and BiLSTM--in continuous blood pressure estimation tasks. Notably, AlexNet and ResNet are based on the CNN architecture, BiLSTM leverages the RNN architecture, and [PERSON]'s model integrates both CNN and RNN elements. Their findings indicated that none of these models met the blood pressure measurement accuracy requirements outlined by the Association for the AAMI [15] or the British Hypertension Society (BHS) [16]. Consequently, these models are not yet ready for clinical use, unlike traditional cuff-based blood pressure monitoring devices.
## 3 Pathway Metabolic Engineering Strategies to Improve Glycerol-Based Bioprocesses
### Lipids  
Given that SCOs can serve as functional oils and as feedstock for the biodiesel industry, SCO production by _Y. lipolytica_ takes the lion's share of the research conducted. Engineering strategies of _Y. lipolytica_ to improve lipid production focus on enhancing the lipogenic pathway flux, shutting down lipolysis and \(\beta\)-oxidation, and increasing NADPH supply [85; 86]. However, the identification of gene targets is challenging due to the complexity of lipid synthesis that is subject to multiple layers of regulation. The study of [PERSON] et al., demonstrated that the overall kinetics of lipid synthesis is susceptible to a plethora of factors that should be properly balanced to fully optimize the SCO biosynthesis. Working with glucose and acetate as a sole carbon source, they examined whether the overexpression of 44 native genes involved in glycerolipid synthesis, FA synthesis, central carbon metabolism, NADPH generation, regulation, and metabolite transport enhances lipid production in _Y. lipolytica_. Their results showed that a diverse subset of genes was effective at individually influencing lipid production, sometimes in a substrate-dependent manner. In the case of glucose, the most productive strain overexpressed the diacylglycerol acyltransferase (DGAT) _DGA2_ gene, increasing lipid titer, cellular content, and yield by 236, 165, and 246%, respectively, compared to the control strain. On acetate, the acylglycerol-phosphate acyltransferase (AGPAT) _SLC1_ gene was overexpressed, leading to increased lipid titer, cellular content, and yield by 99, 91, and 151%, respectively. Increased lipogenesis was attributed to the overexpression of genes encoding enzymes that directly catalyze lipid synthesis, and genes such as the G3P dehydrogenase (GPDH) _GPD1_ that produces glycerol head groups and the 6-phosphogluconolactone (6 PGL) _SOL3_ from the oxidative PPP that increases NADPH availability for FA synthesis [47].
## 3 Datasets and Methods Used in SATIDS
### Modified Artificial Fish Swarm Algorithm (AFSA)  
AFSA is one of the best swarm intelligence algorithms for optimization. Fish schooling and swarm intelligence inspired the development of population-based evolutionary computing techniques. Random AFSA solves optimization issues based on the movement and intelligence of swarms in the process of finding food. In order to solve the optimization challenge, random and parallel searching algorithms are used to mimic fish behavior such as following and swarming in order to find the best possible solution for the entire population as in Figure 2. In AFSA, fish have three behaviors in eating: preying, swarming, and following [24].  
The three possible behaviors of an AF are described in the following:
* Preying behavior: A fish's basic activity is to prey on other fish in order to relocate to an area where there is more food available. This behavior is modeled in AFSA using an AF's current position and the area immediately surrounding it. \[\overrightarrow{x_{v}}=\overrightarrow{x_{i}^{(t)}}+(v.u(0,1).\overrightarrow{ \frac{u}{\|u\|}})\] (9) where \(\overrightarrow{x_{i}}^{(t)}\): the current i-th position of AF \(\overrightarrow{x_{v}}\): the random i-th position of AF \(v\): visual length \(u(0,1)\): random number \(\overrightarrow{\rightarrow}\): vector of uniformly distributed in \((-1,1)\) \(\overrightarrow{\frac{u}{\|u\|}}\): Unit vector
* Swarming behavior: To avoid danger and crowds, fish gather together in groups in nature. It is a common feature in AFSA that fish swarms behave this way. According to the next equation \[\overrightarrow{x_{s,i}}=\overrightarrow{x_{i}^{(t)}}+(s.u(0,1).\overrightarrow{ \frac{\overrightarrow{x_{c}}-\overrightarrow{x_{i}^{(t)}}}{\overrightarrow {\overrightarrow{\overrightarrow{\  
As the number of artificial fish (AFs) increases, AFSA's convergence speed and accuracy are improved. However, as the number of AFs rise, so does the algorithm's complexity. As the number of iterations increases, so does the effect of an AF's fitness function value on the ultimate optimal outcome in AFSA, regardless of whether that value is too large or too small. As a result, the algorithm complexity is increased by the use of these weak fish. For maximum or minimum optimization, if a weak AF's fitness function value exceeds or falls below a defined threshold, it will be swallowed in the AFSA computations [25; 26].
## 3 Discussion
At 60 \({}^{\circ}\)C styrene migration from GPPS reaches approx. 4000 \(\upmu\)g/kg after 10 days and the corresponding weight increase is 45%. GPPS exposed to temperatures of 50 \({}^{\circ}\)C is affected by stress-cracking [23], which lead to an overestimation of the migration under the actual condition of use. The weight increase of GPPS after contact with isooctane at 20 \({}^{\circ}\)C and 40 \({}^{\circ}\)C was below 1%; migration of styrene after 1 day and 10 days at 40 \({}^{\circ}\)C underestimates levels found into milk, cream and olive oil tested at 10 days 40 \({}^{\circ}\)C.  
Testing both HIPS and GPPS with 95% ethanol at the condition 10 days at 40 \({}^{\circ}\)C largely overestimates the real migration into milk, cream and olive oil. While testing with 95% ethanol at the condition 1 day at 40 \({}^{\circ}\)C styrene migration fits to the real foods. With 50% ethanol, after 10 days contact at 60 \({}^{\circ}\)C or 40 \({}^{\circ}\)C both the GPPS and HIPS polymers are slightly swollen (weight increase < 1.1%). Contrary, after 60 days contact with milk, cream and oil at 20 \({}^{\circ}\)C or after 10 days 40 \({}^{\circ}\)C, the weight increase was below 0.4%. The migration values in this food simulant at 10 days/60 \({}^{\circ}\)C are higher (especially in case of GPPS) compared to the real food tested at 10 days/40 \({}^{\circ}\)C. Testing both polymers with 50% ethanol at the condition 10 days/20 \({}^{\circ}\)C underestimates the real migration into foods. In case of HIPS, migration levels at 10 days/40 \({}^{\circ}\)C fit the styrene migration at the same condition in cream and olive oil but slightly overestimate that in milk. For GPPS, although a measurable weight increase was observed at the condition 10 days/40\({}^{\circ}\)C, migration of styrene remained below the detection limit. In conclusion, contact conditions for migration testing of 10 days at 60 \({}^{\circ}\)C using isooctane, 95 % ethanol and 50 % ethanol are too severe for both GPPS and HIPS. Other combination of time/temperature conditions should be applied. These findings are in good agreement with previously reported results for other polymers, such as for acrylonitrile