esa-sceva/satcom-chunk-collection huggingface dataset:
# Antibacterialativity of Endodontic Gutta-Percha--A Systematic Review
## 1 Introduction  
Bacteria and their toxins play a significant role in the etiology and progression of pulpal and periapical inflammation [1; 2; 3; 4; 5; 6; 7; 8]. Root canal therapy becomes necessary when the defense mechanisms of the vital dental pulp are compromised, leading to the removal of  
implicated in endodontic treatment failure [5; 9; 21]. Yeasts, such as _Candida albicans_, have also been linked to endodontic failures [5; 6; 21]. Additionally, _Staphylococcus_ spp. is one of the most common microorganisms found in stored gutta-percha cones after improper handling. Therefore, minimizing residual bacterial and fungal contamination is crucial for a successful endodontic approach [20].  
Various materials have been employed in endodontics as root canal fillings, with gutta-percha being one of the most widely used for over a century [2; 5; 11; 23]. It remains the material of choice [5; 8; 11] due to its biocompatibility, cost-effectiveness, long clinical usage, and potential antimicrobial properties, particularly attributed to its zinc oxide (ZnO) component [8]. Gutta-percha endodontic filling points typically consist of approximately 20% gutta-percha (matrix), 66% zinc oxide (filler), 11% heavy metal sulfates (radiopacifier), and 3% waxes and/or resins (plasticizer) [24]. However, the antibacterial effect of gutta-percha is insufficient for disintegrting root canals contaminated with common endodontic pathogens, including _E. faecalis_[5; 18]. Various attempts have been made to enhance the antimicrobial and antifungal efficacy of gutta-percha (GP) points, exclusively used as an inter-appointment adrenal dressing. These efforts involve incorporating substances such as calcium hydroxide, chlorohexdine, tetracycline, iodoform, the combination of olfloxacin-ornidazole, cetylpyridinium chloride (CPC), or nanosized silver particles [2; 3; 4; 5; 6; 10; 11; 13; 14; 15; 16; 17; 18; 21; 25; 26; 27] (Figure 2).  
The main objective of this systematic review was to explore the impact of antimicrobial agents added to gutta-percha cones on their antimicrobial properties. Based on the analysis of articles concerning the use of various ingredients added to gutta-percha to enhance its bactericidal properties, it was concluded that, given the significance of the topic related to endodontic treatment, a systematic review on this subject is warranted. Moreover, a systematic review on this topic has not been published to date. Conducting such a literature review may motivate researchers to pursue additional studies, potentially providing substantial benefits to both endodontists and patients in the future.
## 3 Contextual Received Signal Strength Approach
### Network Model and Overview  
\(N\) nodes are randomly uniformly deployed in a 2D square area \(A\) such that \(A>>\pi R^{2}\), where \(R\) is the communication range of each node. We assume a fully connected network, i.e., no node is isolated. Figure 1 illustrates an example of a sensor network where \(N\) nodes are randomly deployed in a 2D square area. The regular nodes, depicted as blue dots, and a sink node depicted as red square. Unknown nodes are interconnected by dashed lines representing direct or 'one-hop' connections between two neighboring nodes at a particular instant, underscoring the localized structure of the network at that precise moment. This figure challenges traditional models that typically portray coverage areas as well-defined circles with a fixed radius. Instead, it adopts a more realistic approach by acknowledging the variability and irregularities in signal propagation pertinent to sensor networks. This approach more accurately captures the real-world environmental and spatial challenges faced by sensor networks. As a result, the figure not only depicts standard coverage areas but also those under disturbance, shown in cyan, illustrating the complexities and nuances of data transmission and signal propagation in specific sensor network scenarios.  
Figure 1: Network Model.  
Connectivity  
Two nodes (\(i\) and \(j\)) are neighbors at one hop if they are connected; hence, \(C_{ij}\) is a random variable presenting the connectivity context information defined as  
\[C_{ij}=\begin{cases}1,&\text{if }P_{r}(d)\geq P_{threshold}\\ 0,&\text{otherwise}\end{cases}, \tag{4}\]  
where \(i=\{1,\cdots,N\}\), \(j=\{1,\cdots,N\}\), and \(j\
eq i\). \(P_{threshold}\) is the power detection threshold, and \(d\) is the distance separating \(i\) and \(j\). The global connectivity context information matrix \(\mathcal{CI}\) is then defined as  
\[\mathcal{CI}=[C_{ij}]_{N\times N}. \tag{5}\]
# Performance of Linear and Spiral Hashing Algorithms
## 4 Implementations  
We wanted to compare the efficiency of the two data structures. The advantage of LH is its simplicity and elegance and, in comparison with SH, its addressing mechanism that avoids floating-point calculations. The advantage of SH is its better fringe behavior and tail latencies for key-based operations. In fact, we discovered one key (an unsigned int in C++) and one file state where the SH addressing function, when implemented in a straightforward manner with C++ functions, gave a wrong address because of a rounding error. This single instance forced us to include an additional check in the SH addressing mechanism. Because we wanted to compare the relative merits of both data structures, we decided to implement a threaded, in-memory data structure with short records. Alternatives would be a distributed data structure (such as LH* [13]) or, more prominently, an in-storage data structure. Our choice should highlight the relative differences between the two data structures.  
[PERSON] and [PERSON] proposed a structure that allocates buckets in bunches to save memory space [1; 14]. Instead of following their scheme, we relied on the efficiency of the implementations of containers in the C++ standard library. Our buckets are C++ vectors themselves and are organized inside another C++ vector. For concurrency, we used locks. There is a global lock for the file state, which becomes a bottleneck under a heavy load of inserts. Each bucket has an individual lock.  
An LH insert or lookup gains a non-exclusive (also known as a read) lock on the file state. After determining the address of the bucket, the operation gains an exclusive lock on the bucket. It then inserts or retrieves a pointer to the record, depending on the nature of the operation. An insert can trigger a split. The split operation first gains an exclusive lock on the file state. It then acquires another exclusive lock on the bucket to be split. After rehashing the bucket into a new bucket and an old bucket, we replace the old bucket with the new bucket, add the new bucket, delete the old bucket, and release the lock on the file state. The SH inserts and lookups follow the same approach.
## 4 Discussion  
In this study, we applied a water-carbon coupling model (PML-V2) with the use of Sentinel-2 LAI and land use and cover data and with surface meteorological forcing data as the input to estimate urban ET and its components at 10m resolution for the Beijing Sponge City. Our results indicate that the current vegetation coverage for the Beijing Sponge City is still at a low level (only with mean LAI < 1 m\({}^{2}\) m\({}^{-2}\) in June 2018), and the city gains relatively limited benefits from urban ecosystem services.  
Eddy covariance measurements to study evaporation from urban ecosystems [24; 25] generally helped us to understand the combined evaporation from all land cover types, lacking the capability to divide individual contributions from such as impervious surfaces (roofs, roads and plazas, etc.) and vegetated areas (bare soil, forests, grasslands and croplands, etc.). For different land cover classes, by using an advanced water-carbon coupling ET model driven by Sentinel-2 LAI, we find that lakes and rivers have the highest ET (\(\geq\)8 mm d\({}^{-1}\)), followed by mixed forests and croplands (ET is 4-6 mm d\({}^{-1}\) and LAI is 2-3 m\({}^{2}\) m\({}^{-2}\)), where the plant transpiration (Ec) plays the dominant role (>80%), then grasslands have 2-4 mm d\({}^{-1}\) of ET, where the LAI is 1-2 m\({}^{2}\) m\({}^{-2}\), while impervious surfaces have smallest ET (<2.0 mm d\({}^{-1}\)). In most impervious areas, the impervious surface evaporation (Eu) contributes 20-30% of ET, which is larger than the estimate (18%) from previous studies [4]. We have shown that another 40-50% of ET in impervious areas are contributed by plant transpiration (Ec) due to the small fractional area covered by greenbelts with trees and grassland (LAI is 0.5-1.0 m\({}^{2}\) m\({}^{-2}\)). This study did not consider water vapor conversion from human water use activities, which also contributes to the impervious evaporation from building indoor water use [25].
# Multi-Objective Optimization of Three-Stage Turbomachine
## 2 Transfer Matrix Theory
### Transfer Matrix Method  
The TMM shares similarities with methods used in analyzing robotic systems or graphical techniques, where the positional information of a point is transformed using matrices and transferred to another point. Beyond position, the TMM also facilitates the transfer of physical quantities such as force and moment. This is achieved through the use of transfer matrices that operate across state vectors.  
In the implemented rotordynamic solver, axial and torsional motions were excluded to concentrate on bending motion, which is more critical for turbofan engine rotors [28]. To account for the effect of critical speed, gyroscopic effects were incorporated into the model, with bending motion defined along two lateral axes. The Timoshenko beam theory was applied in the rotordynamic solver, using state vectors with nine parameters, as presented in Equation (1). The rotor analyzed belongs to the LP (low-pressure) rotor group and is therefore exposed to considerably lower temperatures than the HP (high-pressure) rotor group. This design feature concentrates the primary thermal effects on the turbine section, while the shaft remains relatively cooler, a favorable condition in turbomachinery design. Consequently, the rotor's stiffness and natural frequency experience only minimal reductions due to thermal expansion [29; 30], making it reasonable to assume that thermal effects may be omitted in the preliminary optimization phase.  
As detailed in Equation (1), X and Y denote lateral vibration freedoms, as illustrated in Figure 2. \(\theta_{y}\) represents shear rotation around the X-axis, while \(\theta_{x}\) represents shear rotation around the Y-axis. \(M_{y}\) and \(M_{x}\) denote shear moments, and \(V_{x}\) and \(V_{y}\) represent shear forces. External loads are introduced via the final unit parameter.  
\[\{Z_{i}\}=\ \begin{bmatrix}X&\theta_{y}&M_{y}-V_{x}-Y&\theta_{x}&M_{x}&V_{y} &1\end{bmatrix}^{T} \tag{1}\]  
The coordinate system used in the CTMM is shown in Figure 2. In the solver created, the Z-axis represents the rotational axis, the X-axis defines the direction of the side, and the Y-axis represents the vertical direction. The leftmost point of the rotor corresponds to the (0, 0, 0) point in this coordinate system.  
In this state vector, the system response at the \(i\)th node is multiplied by the transfer matrix of the \(i\)th element to compute the system response at the \((i+1)\)th node, as described in Equation (2).  
\[\{Z_{i+1}\}=[U_{i}]\{Z_{i}\} \tag{2}\]  
To initiate the process, transfer matrix elements must be developed to accurately represent the characteristics of the desired unit system. Once these element matrices are defined appropriately, they are sequentially multiplied in the order that suits the design configuration to derive the system's transfer matrix. Using this transfer matrix, both the steady-state system response and the examination of mode shapes and natural frequencies can be conducted.
# Plasma-Sprayed Flexible Strain Sensor and Its Applications in Boxing Glove
## 5 Conclusions  
A new fabrication process for flexible sensors; i.e., a plasma spraying method, was proposed in this study. The effects of plasma treatment and pattern parameters on the sensitivity, cyclic stability, and strain range were explored. The fabricated sensor was installed on a boxing glove to test it under real conditions and avoid the previous practice of indirectly reflecting the hitting state of the boxer using a rigid acceleration sensor. The following are the conclusions of this study:  
1. A novel plasma spraying method was proposed to fabricate flexible sensors, achieving high reproducibility of the sensor patterns;
2. The results were compared to those of an untreated sensor. The sensitivity of the flexible strain sensor could be increased from 3.9 to 11.5 when using the plasma treatment (an increase of 195%). However, the strain range was reduced to 10%, making the developed sensor only suitable for small-strain applications;
3. The effects of the pattern parameters on the sensitivity of the flexible sensor were explored. Multiple repeated paths could also increase the sensitivity of the sensor, but all paths had the same sensitivity;
4. A smart boxing glove that could transmit and monitor a striking force of 5-50 kg in real time was developed, showing a recovery time of 100 ms and a sensitivity of up to 1.05 kg;
5. The Wi-Fi data-transmission function of the ESP32 development board was used for real-time data collection and image display, which greatly improved the portability of the smart glove and would not affect the daily and competition use of the glove by boxing enthusiasts and athletes.  
In a future study, we will explore additional types of particles available for spraying, as well as the spraying of ultracomplex patterns. In addition, we will combine our sensor-manufacturing methods with more applications. A kinematic model of the exoskeleton may be the basis for a physical model and the analysis of gait dynamics [27]. Our flexible sensors have a promising future in gathering data more accurately and quickly. The sensors manufactured through our plasma spraying method also have a broad application potential in soft robotics and many other fields.  
Figure 16: Relationship between digital resistance and force.  
**Author Contributions:** Conceptualization, Y.L., Z.J. and Y.Y.; methodology, Z.J.; software, Y.C. and Z.Z.; validation, Y.L. and Y.C.; formal analysis, Z.J.; investigation, Z.J. and R.L.; resources, Y.L. and Z.J.; data curation, Y.C., R.L. and Z.Z.; writing--original draft preparation, Y.C. and Z.Z.; writing--review and editing, Y.Y., R.W. and Z.J.; visualization, Y.Y., R.W.; supervision, Z.J.; project administration, Y.L. and Z.J.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the Hunan Provincial Department of Education Scientific Research Project Youth Project (No. 21B0612) and Beijing Nova Program from Beijing Municipal Science & Technology Commission (Z201100006820146).  
The authors declare no conflict of interest.
# Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal
## 4 Discussion  
### Effect of Reconstruction Parameters and Split Frame Length  
In the TDA features extraction workflow, PSR was applied to obtain the point cloud. The reconstruction parameters of PSR are proved to have a great influence on the ability of the point cloud to characterize the intrinsic properties of the original system. In previous approaches, the reconstruction parameters were mostly selected empirically, lacking theoretical support and generalizability. In this study, we compared the effects of the reconstruction parameters \(m\) and \(\tau\) on the performance of ECG-based VF prediction. In Figure 5, for each split frame length, the distribution of all \(\tau\) values tend to spread gradually from the first to the fifth local minimum, indicating that the MI of the original sequence and its time-delayed sequence are gradually increasing. Therefore, the first local minimum was selected to reduce correlation between the two sequences. From inspecting Figure 6, choosing \(m=6\) as the minimum embedding dimension enabled over 80% of the data to be sufficiently embedded without causing a large computational burden. It can be seen that the choice of reconstruction parameters directly affects the efficacy of the point cloud and the characterization ability of the TDA features, which is an essential part of VF prediction.  
In addition, the effect of the split frame length on the prediction results is discussed, with results demonstrated in Table 4. A trend can be seen in the accuracy results obtained from the classification at different split frame lengths with \(\tau\). For both alternative \(\tau\) values, the accuracy decreases gradually with increasing split frame lengths. This suggests a possibility of a latent selectivity of TDA for the size of the point cloud. It means that for point clouds reconstructed from ECG signals, if the split frame length is short and, thus, the point cloud is small, then, TDA features are more sensitive to the imminent onset of VF. As the split frame lengths become longer, information about VF is gradually obscured.
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 2 Material Characteristic
### Sheet Resistance
#### 2.2.1 ITO Film  
One of the TCOs widely used in the design of transparent antennas is ITO films. OLEDs use ITO as an electrode for its conductive and transparent properties allowing light to go out. Several researchers also use this material in the manufacture of optically transparent antennas [12; 14; 15]. Its high optical transparency makes it an ideal candidate, but its high sheet resistance remains a challenge for the realization of antennas. The sheet resistance is the electrical resistance of a square surface of a material of thickness \"_t_\" [9]. The sheet resistance of the ITO film is given by the following relation:  
\[R_{s}=\frac{1}{\sigma\cdot t} \tag{1}\]  
where \(\sigma\) is the conductivity and \"_t_\" is the thickness of the ITO film.
## 4 Evaluation
### Evaluation Methodology
#### 4.1.1 Simulation Set-Up  
We carry out simulation experiments in the ONE simulator. In order to simulate an OppNet for conveying data between sensors and gateways in a city, users moving according to WDM carry mobile nodes that act as data mules between static sources and static destinations placed in popular locations. User devices are assumed to be smartphones or similar portable devices equipped with GPS technology and a Bluetooth interface capable of transferring data at 2 MBps over 10 m. Each mobile node is limited to a free buffer space of 10 MB for routing-related tasks, since users may not be willing to shed most of their storage space on behalf of the network.  
As shown in Figure 14, the simulation area, which is represented by a 21 KM \(\times\) 11 KM terrain, consists of 32 districts. The simulation is set to run for a duration of 5 days, in order to observe the daily movement routines WDM models in working days. This period entails 1 day for warm-up (to ensure complete generation of encounter and location history), the next 3 days for message generation and routing tasks, and the last day for cool-down (to allow every message delivery attempt).  
To simulate sensors and gateways in a city, a total of 96 static nodes are placed in popular locations to represent 64 sources and 32 destinations, respectively. Specifically, source nodes were located in home locations (representing residential areas) and along roads (for environmental sensors), while destination nodes were located in office and meeting spot locations (to represent work places and shopping malls). A message is generated at each source node to a randomly chosen destination node (to avoid bias) every hour, making a total of 4608 messages for the entire simulation. Considering the type of delay-tolerant applications mentioned in Section 1, the size of messages is uniformly distributed between 10 KB and 15 KB. Each message is assigned a TTL of 24 h, as we opt for deliveries to be done within a day.  
We configure energy consumption for Bluetooth according to the module proposed by [PERSON] et al. [63]. The initial energy of mobile nodes is set to 4800 Joules (which is enough to ensure that they do not run out of energy before the end of the simulation) and 0.08 mW is consumed every second for message transmission (i.e., sending or receiving messages). Since we are only interested in the energy spent due to replication decisions made by different proposals, we consider only energy expended in message transmissions. Hence, other sources of energy expenditure (such as energy consumed from device discovery, device discovery response, and energy consumed in idle state) are set to 0. Table 4 summarises the simulation parameters. For each result, we present the average of 10 trials with different random seeds and the 95% confidence interval.
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 3 System Configuration  
The overall workflow of our system is presented in Figure 1. Our proposed solution consists of the following main steps:  
1. Prepare drone's hardware structure which includes mounting of the dual camera setup.
2. Plan the flight's path automatically with flight planning algorithm and feed it to the drone.
3. The drone flies over the designated path autonomously and the captured images are saved to an SD card along with the GPS information.
4. The saved images are processed using our proposed image processing algorithm on a desktop computer.
5. The locations of the defective solar panels are displayed to the user.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Strengths** & **Weaknesses** \\ \hline [PERSON] et al. [12] & - Detect several PV module’s failure. & - Unable to localize faulty \\ [PERSON] et al. [12] & - Tested on large dataset: 10 polycrystalline silicon modules. & - Unable to localize faulty \\ [PERSON] et al. [15] & - Compute percentage of damage and degradation. & - Lacks testing on large-scale PV power station. \\  & - Localize faulty modules. & - Lacks testing on large-scale PV power station. \\ \hline [PERSON] et al. [16] & - Detection of PV modules and classification of defects. & - Remote controlled \\ [PERSON] et al. [16] & - Identifies significant temperature abnormalities. & - Lacks testing on large-scale PV power station. \\  & - Localize faulty modules. & - Does not specify specific defects. \\ \hline [PERSON] et al. [13] & - Efficient real time diagnostics and inspection. & - Unable to localize faulty PV modules. \\  & - Compute degradation percentage and boundary area. & - Lacks testing on large-scale PV power station. \\ \hline [PERSON] et al. [14] & - Identify the correlation between altitudes of aerial images taken by the UAV and possible defects’ identification. & - Remote controlled - Unable to localize faulty PV modules. \\ \hline [PERSON] et al. [9] & - Information integration from multiple sources. & - Requires geo-referenced data which are not available at all times. \\  & - Panel identification and detection of thermal anomalies. & - Lacks testing on large-scale PV power station. \\ \hline [PERSON] et al. [10] & - Localize faulty modules. & - Remote controlled \\  & - Tested on large scale datasets. & - Remote controlled \\ \hline [PERSON] et al. [11] & - Real time inspection and fault detection - Localize faulty modules. & - Remote controlled \\  & - Tested on Large scale datasets. & - Lacks testing on large-scale PV power station. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between proposed approach and related works in terms of strengths and weaknesses.