esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Several bone biological processes after tooth extraction have been described in the literature. All events related to the bundle-bone resorption during alveolar bone healing promote a clinical bone volume shrinkage and higher buccal bone resorption. All clinical evidence shows an unfavorable crown-implant ratio and several esthetic and bio-mechanical complications that could represent the potential failure of final dental implant rehabilitation. In the last 15 years, several studies have been carried out to understand the bone loss process after tooth extraction and to promote surgical procedures and autologous or heterologous biomaterials to reduce the volume decrease after tooth extraction and support bone regeneration at the end of the bone healing period [1, 2, 3]. Therefore, initially, guided bone regeneration (GBR) and, recently, alveolar socket preservation (ASP) procedures, were applied to decrease bone loss and promote alveolar preservation, obtaining adequate horizontal/height bone volume, simple implant placement, favorable crown-implant prosthetic ratio and predictable prognosis of rehabilitation [4, 5, 6, 7]. To evaluate the ideal biomaterial in ASP, several autologous and heterologous materials with biocompatibility, osteogenesis, osseoinduction, and different osseoconduction characteristics have been used [8]. Reabsorbable or non-reabsorbable membranes used in the GBR technique are crucial in the bone regeneration and avoid the alveolar socket space colonization by fibroblasts that have a greater cellular turn-over than osteoblast cells. In 2011, [PERSON] et al., in a systematic review, observed better results in ASP cases compared with spontaneous bone healing, concluding that ASP is useful to reduce bone loss but is unable to prevent it [9]. In 2014, [PERSON] et al. showed that non-reabsorbed heterologous particles surrounded by bone in a post-extraction socket after healing were able to occupy the space (osseoconduction), promote bone growth and prevent further bone resorption [10]. In 1967, [PERSON] showed the demineralized dentin matrix osteoinductive potential, promoting the tooth as autologous bio-material for bone reparation and regeneration [11; 12]. In 2015, [PERSON] showed a similar chemical composition for the hydroxyapatite inorganic portion of bone (95%) and dentin (65%) as well as the Col-1 protein and other secondary molecules [13].  
The aim of the present study was to assess, in compromised wall socket sites, the clinical, histological and histomorphometrical values for a novel, minimally invasive flapless ASP technique using three reabsorbable membranes, compared to classic open-flap elevation ASP procedures covered with a single reabsorbable membrane.
# Precision Calibration and Linearity Assessment of Thin Film Force-Sensing Resistors
###### Abstract  
In this study, we thoroughly analyzed the linearity and repeatability of force-sensing resistor (FSR) sensors through static load tests to ensure their reliability. The novelty of this research lies in its comprehensive evaluation and direct comparison of two widely used FSR sensors, i.e., Flexiforce A201-1 and Interlink FSR-402, under various loading conditions by employing a robust calibration methodology. This study provides detailed insights into the sensors' performances, offering practical calibration equations that enhance measurement precision and reliability, which have not been extensively documented in previous studies. Our results demonstrate that the linearity of thin film FSR sensors is highly accurate, closely resembling a straight line. We employed MI Class weights, applying loads ranging from 20 g to 300 g. The resistance of the FSR sensors, which varies with the applied load, was measured using a voltage divider circuit and an analog-to-digital converter of a microcontroller. MATLAB was used to calculate the average output voltage for each applied load and fixed resistance. Additionally, we examined the relationships among load, FSR sensor resistance, and conductivity. Our research indicates that with precise calibration, thin film FSR sensors can be highly reliable for force measurement applications.  
force-sensitive resistor; Flexiforce; Interlink; precision calibration; linearity assessment +
Footnote †: journal: Applied
###### Abstract  
Water quality and pollution is the main environmental concern for ports and adjacent coastal waters. Therefore, the development of Port Environmental Management systems often relies on water pollution monitoring. Computer vision is a powerful and versatile tool for an exhaustive and systematic monitoring task. An investigation has been conducted at the Port of Palma de Mallorca (Spain) to assess the feasibility and evaluate the main opportunities and difficulties of the implementation of water pollution monitoring based on computer vision. Experiments on surface slicks and marine litter identification based on random image sets have been conducted. The reliability and development requirements of the method have been evaluated, concluding that computer vision is suitable for these monitoring tasks. Several computer vision techniques based on convolutional neural networks were assessed, finding that Image Classification is the most adequate for marine pollution monitoring tasks due to its high accuracy rates and low training requirements. Image set size for initial training and the possibility to improve accuracy through retraining with increased image sets were considered due to the difficulty in obtaining port spill images. Thus, we have found that progressive implementation can not only offer functional monitoring systems in a shorter time frame but also reduce the total development cost for a system with the same accuracy level.  
computer vision; marine litter; marine pollution; monitoring technologies; port water quality +
Footnote †: journal: _aciences_  
_Article_  
Use of Neural Networks and Computer Vision for Spill and Waste Detection in Port Waters: An Application in the Port of Palma (MaJorca, Spain)
## 4 Results
### System Latency  
In the proposed system equipped with an NVIDIA RTX 3050 Ti GPU with an Intel i5 processor (12 cores) and 32 GB of RAM, the system's latency demonstrates notable variation depending on the hardware setup. When utilizing the GPU for inference, it achieves up to 72 frames per second (fps), and when solely relying on the CPU, it maintains 43 fps. This performance improvement is due to the ONNX optimization of the YOLOv8 model and the proposed fall detection model. When the system's resources are limited to using only four CPU cores, it still attains a reasonable speed of 14 fps. This performance demonstrates the system's ability to sustain real-time processing capabilities across a variety of hardware  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Accuracy [\%]** & **Precision [\%]** & **Sensitivity [\%]** & **Specificity [\%]** & **F1-Score** \\ \hline LE2I & 98.86 & 90.02 & 91.11 & 99.35 & 0.905 \\ URFD & 96.23 & 97.18 & 92.56 & 98.40 & 0.948 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Frame-wise evaluation results for the LE2I and URFD datasets using the proposed fall detection method.  
Figure 13: Time-series plot of the average pose velocity and its temporal variation: (**a**) original time series, (**b**) with 25-frame shift to the right, and (**c**) with 25-frame shift to the left.  
setups, emphasizing its adaptability and potential applicability in end-user scenarios that involve low-cost hardware. Additionally, by employing a smaller YOLOv8 model like YOLOv8-nano, the system may achieve even faster speeds, further enhancing its accessibility and performance in budget-friendly hardware setups. The proposed system architecture may also scale effectively to manage feeds from multiple cameras simultaneously to address different room and blind spot issues without sacrificing processing efficiency. This scalability ensures comprehensive monitoring in various scenarios, ensuring that no blind spots are left unwatched.
# An Autonomous Thermal Camera System for Monitoring Fumarole Activity
## 2 The Thermal Camera System
### Software  
The Sleepy-Pi has a custom code created with Arduino IDE 1.0.5 [20] that reads the battery voltage and boots the Raspberry Pi at hourly intervals. The Raspberry Pi has a GNU/Linux operating system (Raspbian 9.8, codename \"Stretch\" [21]). An initialisation script written in Bash [22] is launched automatically when booting the Raspberry Pi. This script calls other shell scripts that are wrappers around programs needed to bring up the network connection to the camera, operate the camera (upload settings, trigger a NUC, capture and download an image), read the GPS and synchronize time, read the multiple sensors on the camera and in the control unit, perform data management, and perform a controlled shutdown after measurements have been taken. The camera itself is operated with the \"Aravis\" library [23] that allows communication with the camera over the GenICam interface. A custom-made Python [24] script is used to set camera parameters and receive the captured frames. A link to the developed scripts is in the \"Data Availability Statement\" below.  
Figure 3: The two observation towers deployed in Olkaria, with a photo of the hardware deployed in the field (**top**) and the thermal IR image recorded by the camera (**bottom**). On the left is the installation at the “OI Njorowa” gorge, consisting of a thermal camera only. On the right is the installation at the “Oldbutot” lava field, consisting of a thermal camera, a timelapse camera, and a meteostation.
## 3 Materials and Methods
### Evaluation Metrics
#### 3.5.5 Coefficient of Determination (R\({}^{2}\))  
R\({}^{2}\) represents the proportion of variance in the dependent variable that is predictable from the independent variables.  
The Coefficient of Determination is given by:  
\[R^{2}=1-\frac{RSS}{TSS}\]  
R\({}^{2}\), or the coefficient of determination, is a widely used metric in regression analysis. It measures the proportion of the variance in the dependent variable that is predictable from the independent variables. However, it has limitations, such as being biased statistics and providing invalid results in the presence of measurement errors. Therefore, while R-squared is a valuable metric, it should be interpreted cautiously and in conjunction with other criteria [38]. It is widely used in regression and forecasting models to indicate how well the model explains the variability in the data. A value closer to 1 indicates a better fit.  
R\({}^{2}\) is not always suitable for time series forecasting due to the complex nature of time series data and the potential for model uncertainty. [PERSON] [39] highlights the need for hybrid models that can capture both linear and nonlinear components in time series data, suggesting that a single metric like R\({}^{2}\) may not adequately capture the predictive performance. [PERSON] [40] further emphasizes the importance of considering model uncertainty in time series analysis, which can affect the accuracy of forecasts. [PERSON] [41] underscores the need for robust and efficient forecasting methods, indicating that R\({}^{2}\) may not be the most suitable metric in all cases. Lastly, [PERSON] [42] discusses the impact of estimation error on forecast mean squared errors, which can also affect the reliability of R\({}^{2}\) in time series forecasting.  
The limitations mentioned regarding the R\({}^{2}\) metric, such as potential bias and sensitivity to measurement errors, are intended to highlight practical issues related to real-world model interpretation and data quality considerations. Specifically, R\({}^{2}\) can be influenced by factors such as model specification, omitted variables, and data inaccuracies, rather than being inherently flawed mathematically. Therefore, R\({}^{2}\) values should always be interpreted with careful attention to these practical and contextual limitations, especially in complex forecasting scenarios.
# Ultrasonic Non-Destructive Testing of Accelerated Carbonation Cured-Eco-Bricks
## 4 Discussion of Results
### UNDT (Ultrasonic Pulse Velocity Test) and DT (Compression Test)  
#### 4.1.1 UNDT (Ultrasonic Pulse Velocity Test)  
Figure 5 below shows a comparison of the P-wave velocities at different compression pressures and curing durations for the 0% TRC samples. In this result, three sets of samples were considered. The first were the samples that were wrapped and cured in a humid environment (HC), the second were the samples that were tested after the carbonation durations with no water curing afterward (NWC), and the third were the samples that were hydrated for 72 h in water (WC) after carbonation. All the samples cured with CO\({}_{2}\) gas (NWC and WC) had improved strength compared to the HC samples. This highlights carbonation as a preferred curing approach. Additionally, analyzing the ACC samples (NWC and WC) shows that the WC samples had improved strengths compared to the NWC samples. This shows that post-carbonation hydration is essential to restore the water lost in the samples during pre-conditioning and carbonation, thereby enhancing the secondary hydration of unreacted hydraulic cementitious pastes [61]. Furthermore, the samples with maximum velocities were those compressed with 1.9 MPa. This again shows that to obtain the optimum strength for carbonated samples, the samples should be compressed with a steady pressure of no less than 1.9 MPa, as recommended by research [60; 68].
## 8 Empirical Findings
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables and Items** & **Directions** & **Variables and Items** & **Estimate** & **SE** & **CR** & **P** \\ \hline CBTE & \(<\)— & EL & 0.40 & 0.16 & 2.39 & 0.017 ** \\ CBTE & \(<\)— & GPPP & 0.41 & 0.08 & 5.02 & *** \\ CBTE & \(<\)— & ECB & 0.64 & 0.09 & 7.12 & *** \\ CBTE & \(<\)— & GPB & \(-\)0.24 & 0.16 & \(-\)1.50 & 0.132 \\ EL2 & \(<\)— & EL & 1.00 & & & \\ EL3 & \(<\)— & EL & 0.88 & 0.15 & 5.67 & *** \\ EL4 & \(<\)— & EL & 1.26 & 0.17 & 7.05 & *** \\ EL5 & \(<\)— & EL & 1.04 & 0.16 & 6.21 & *** \\ GPB5 & \(<\)— & GPB & 1.00 & & & \\ GPB4 & \(<\)— & GPB & 1.15 & 0.14 & 8.18 & *** \\ GPB3 & \(<\)— & GPB & 0.75 & 0.11 & 6.46 & *** \\ GPB2 & \(<\)— & GPB & 1.11 & 0.13 & 8.38 & *** \\ GPB1 & \(<\)— & GPB & 0.97 & 0.13 & 7.47 & *** \\ GPPP5 & \(<\)— & GPPP & 1.00 & & & \\ GPPP4 & \(<\)— & GPPP & 1.06 & 0.14 & 7.57 & *** \\ GPPP3 & \(<\)— & GPPP & 0.96 & 0.13 & 6.98 & *** \\ GPPP2 & \(<\)— & GPPP & 1.01 & 0.13 & 7.43 & *** \\ GPPP1 & \(<\)— & GPPP & 0.84 & 0.13 & 6.30 & *** \\ ECB5 & \(<\)— & ECB & 1.00 & & & \\ ECB4 & \(<\)— & ECB & 1.12 & 0.09 & 11.93 & *** \\ ECB3 & \(<\)— & ECB & 1.32 & 0.10 & 12.57 & *** \\ ECB2 & \(<\)— & ECB & 1.24 & 0.09 & 12.65 & *** \\ ECB1 & \(<\)— & ECB & 1.15 & 0.09 & 11.68 & *** \\ EL1 & \(<\)— & EL & 1.12 & 0.16 & 6.82 & *** \\ CBTE2 & \(<\)— & CBTE & 1.10 & 0.13 & 8.37 & *** \\ CBTE5 & \(<\)— & CBTE & 0.25 & 0.09 & 2.69 & 0.007 ** \\ CBTE3 & \(<\)— & CBTE & 1.08 & 0.13 & 8.10 & *** \\ CBTE4 & \(<\)— & CBTE & 0.53 & 0.09 & 5.88 & *** \\ \hline \hline \end{tabular}  
* \(p<0.001\), ** \(p<0.05\).  
\end{table} 
Table 2: Regression weights for Group 1—default model.
## 1 Introduction
found 8-10 dB for silk fabric/natural rubber latex/CNT composites with a thickness of 2-8 mm [21]. An improved CNT distribution on a cotton woven fabric was reached by polymerizing polyaniline (PAni) on the CNTs, resulting in a 23 dB shielding effectiveness for a single-layer fabric [22]. While such coatings are often used in the textile industry, metal or carbon fibers can be included in varns and fabrics. Carbon fiber laminates were stitched with copper, titanium, and Kevlar metallic and non-conductive threads formed to composites by vacuum-assisted infusion, resulting in EMI shielding effectiveness values of 40-47 dB in the X band [23]. Interestingly, while copper threads resulted in slightly larger shielding values, the shielding effectiveness was generally improved in the compact fiber arrangement of the stitched multi-layer composites. Combining carbon fiber waste with nylon fibers to create a needle-punched nonwoven, [PERSON] et al. found EMI shielding values around 25-80 dB for different carbon fiber fractions and sample thicknesses [24]. Adding a polymer, as a binder, to recycled carbon fiber waste, [PERSON] et al. reached 30-70 dB shielding, depending on the sample thickness and the polymeric binder [25]. Finally, intrinsically conductive polymers can be used as a coating. Applying one-five layers of PAni or PPy on a woven poly(acrylonitrile) (PAN) fabric using a special inkjet printing process, [PERSON] et al. reached an EMI shielding effectiveness of 5-22 dB for PAni and 2.25-7 dB for PPy [26]. [PERSON] et al. combined PPy with silver nanoparticles and poly(3,4-ethylenedioxythiophene):polystyrenesulfonic acid (PEDOT:PSS), leading to a shielding effectiveness of 40 dB [27]. A similar value was reached by [PERSON] et al., who tested varying PPy concentrations dip-coated on a cotton fabric and the subsequent in situpolymerization of poly(N-isopropylacrylamide) (PNIPAAm) on the PPy-coated fabric [28]. However, these coatings require relatively complicated preparation of varns or fabrics if adequate shielding effectiveness values are to be reached [12; 13]. In this paper, we report investigations of commercially available woven fabrics as well as conductive and magnetic varns, which were processed by hand-crocheting, and commercially available conductive coatings on textile fabrics. The broad variety of different textile and 3D-printed samples allows for a better comparison of specimens with strongly differing thickness, porosity, and material composition, thus enabling quite general statements about the influence of these parameters on the EMI shielding properties of laminar objects. The applied measurement test set was dedicated to the X frequency band. Validation was carried out by means of measurements on standard aluminum foil for food applications.
# Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments
## 4 System Validation
### Experimental Comparison between Passive and Active Technologies
#### 4.1.1 Equipment  
(a) Passive components  
The passive tag selected for the experiments is the Class 1 Gen 2 UHF of Confidex Ironside, which employs EPC Global 1G2 ISO 18000-6C as air-interface protocol. This tag was selected due to some features such as a low weight (22 g), enough read range on metal surfaces (up to 9 m), ruggedized package (for outdoor applications), and IP68 classification. Moreover, the tag dimensions (\(51.5\times 47.5\times 10\) mm\({}^{3}\)) seem convenient for its applicability in metal surfaces, due to larger tags usually presenting a better performance in near metal environments [41], without being large enough to interfere in the visualization of the traffic sign. In addition, a critical feature of this tag is its EPC memory capacity (96-240 bits), which is necessary in order to employ this identifier for inventory and location purposes as explained in Section 2.2.1.  
The selected RFID reader is the R4300P ION of CAEN RFID. Its frequency range is 902-928 MHz (FCC part 15) and 865.5-867.6 MHz (ETSI EN 302-208), using the same air-interface protocol (EPC Global 1G2 ISO 18000-6C) and a RF power up to 32 dBm (1.6 W). Besides, we use a linearly polarized antenna connected to the RFID reader, with a gain of 8 dBi, a frequency range of 860-970 MHz, VSWR (voltage standing wave ratio) \(<1.5\):1, and a beamwidth of 65-69\({}^{\circ}\).  
(b) Active components  
The selected active tags are the Hussar Slim (HKRAT-NT02) and Garrison Rugged (HKRAT-RT02 and HKRAT-ZT02) models. Both of them use a replaceable lithium battery and transmits at 2.4 GHz. RF output is 0 dBm, presenting low power consumption (16-18 \(\upmu\)A, 3 V). These active tags differ mainly in their application; HKRAT-NT02 is oriented to tracking and inventory applications, whereas Garrison tags find applications in harsh environments, such as metal and other non-RF-friendly items. Therefore, Garrison tags have a higher ruggedization level (meeting the IP65 standard), although they still provide a compact size (78.8 \(\times\) 33.6 \(\times\) 10 mm\({}^{2}\)). Additionally, the rugged tags have a longer lifetime (around three years without replacing the battery), so these tags could mean a reliable solution for this application, in the case of opting for active technologies.  
The RFID reader is the model EMWF, from the manufacturer Empress, working at the 2.4 GHz band with a proprietary communication standard, as usual for RFID active equipment. This reader has a long range, about 30-50 m outdoors, a RF output power of 0 dBm, and a sensitivity of \(-\)85 dBm with an installed omnidirectional antenna without gain. Employing a GFSK modulation, this reader is able to identify more than 100 tags/readings simultaneously without collision, and transmit the collected data through the standards such as 802.11 b/g or TCP/IP.
## 4 Performance Evaluation
### Simulation Setup
#### 4.2.1 m-Nakagami Parameter Variable  
The results presented in Figure 3 show the inter-node distance estimation NRMSE by varying the value of the \(m\)-Nakagami parameter while \(\gamma=3\) and \(\sigma=4 dB\). NRMSE is calculated based on Equation (20). The blue boxplots show the results of 100 runs conducted using the parameters listed in Table 3. They show how the data error is extended, and red  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters [Unit]** & **Values (s)** \\ \hline \(\sqrt{A}[m]\): Square length & 100 \\ \hline \(N\): Sensor node cardinality & 150 \\ \hline \(P_{threshold}\): Threshold power [dBm] & –90 \\ \hline \(PL(d_{0})\): Free space path loss at \(d_{0}\) [dB] & –45 \\ \hline \(P_{l}\): Transmitting power [dBm] & 0 \\ \hline \(\eta\): Number of runs & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: WSN simulation parameter setup.  
lines show the median of the error. A significant relationship is identified, as shown in Figure 3, between the Nakagami shape parameter \(m\) and the localization error. Notably, the results showed that as \(m\) increases from 1 to 5, the median localization error progressively decreases from 5.5% to 4% of the range. This improvement aligns with the theory that a higher \(m\) indicates a signal less prone to fading, resulting in greater accuracy. However, a key observation is that the most significant changes occur at \(m=1\), where the model exhibits the most multipath fading. Beyond \(m=5\), improvements become marginal, with the error slightly increasing to 4.01% for \(m=6\). This indicates a plateau in performance gains, influenced by other limiting factors such as the path loss exponent or shadowing. Additionally, it is important to note that, for \(m=1\), the error increases by 1.5% from its ideal level observed at \(m=5\).
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 2 System Model  
This section describes the system model, which includes the channel model and the structure of the received signals, the ZF detector description, and the imperfect channel estimation criterion used.
## 3 Materials and Methods
### Headspace Solid-Phase Microextraction (HS-SPME)  
Headspace solid-phase microextraction (HS-SPME) was performed with a manual SPME holder using two fibres covered with PDMS/DVB (Polydimethylsiloxane/Divinylbenzene) or DVB/CAR/PDMS (Divinylbenzene/Carboxen/Polydimethylsiloxane) obtained from Supelco Co. (Bellefonte, PA, USA). The fibres were conditioned prior to the extraction according to the instructions by Supelco Co. For HS-SPME, prepared samples (1 g) were placed separately in 5 mL glass vials and hermetically sealed with PTFE/silicone septa. The vials were maintained in a water bath at 60 \({}^{\circ}\)C during equilibration (15 min) and HS-SPME (45 min). After the sampling, the SPME fibre was withdrawn into the needle, removed from the vial, and inserted into the injector (250 \({}^{\circ}\)C) of GC-FID and GC-MS for 6 min where the extracted volatiles were thermally desorbed directly to the GC column. The procedure was similar as in previous paper [32]. HS-SPME was done in triplicate for each alga.