esa-sceva/satcom-chunk-collection huggingface dataset:
# Brain Implantable End-Fire Antenna with Enhanced Gain and Bandwidth
## 2 Antenna Design
However, the proposed design gradually distributed these currents to the center tapered slot and three vertical slots, making the electrical length longer (miniaturization) and main radiating source stronger (improved gain). The similarity in current distributions at 3, 4, and 5 GHz imply that a stable radiation characteristic can be maintained over a broad bandwidth. Figure 4: Comparisons of surface current densities of conventional and proposed Vivaldi antennas. Figure 3: Geometry of the proposed Vivaldi antenna with vertical slots. 3-5 GHz; an average of \(-13\) dBi realized gain can be achieved. It is worth noting that the realized gain of a conventional Vivaldi is low, about 6 GHz, despite the \(S_{11}\) being low, implying its antenna impedance matching and radiation performances have a discrepancy. In contrast, the proposed Vivaldi's resonant frequency and the frequency exhibiting high realized gain are matched. Altogether, the vertical installation of the end-fire antenna and optimization of its geometry improved the realized gain by approximately 3 dB and 2 dB (total of 5 dB) relative to horizontally installed broadside antennas, respectively. Figure 6 shows parametric studies of \(S_{11}\) by altering the slot's geometry. The width of slot (s) can be used to tune the antenna's resonant frequency (see Figure 6a). Here, \(s\) between each slot is set to be the same to make the optimization process concise. The wider \(s\) shifted the resonant frequency toward the lower end, resulting in antenna miniaturization by introducing more slot inductance than capacitance. Slot length (\(l_{1}\)) was another parameter for adjusting the resonant frequency (see Figure 6b). The longer \(l_{1}\) provided more inductance without altering the capacitance. Therefore, the resonant frequency shifted left with increasing \(l_{1}\). We note that \(l_{1}\) is the length of the first slot. The second and third slots were longer. Their lengths were determined by the Vivaldi's exponential curvature profile at the middle. In particular, \(l_{1}\) = 2.4 mm, \(l_{2}\) = 2.8 mm, and \(l_{3}\) = 3.4 mm. Table 2 lists the final antenna's geometrical parameters. These values were obtained for the target frequency range, 3-5 GHz, with the given antenna space, 12 mm \(\times\) 7 mm; however, the \(s\) and \(l\) parametric optimizations can be applied for any frequency band and antenna size. Figure 5: Comparisons of (**a**) \(S_{11}\) and (**b**) realized gain of conventional and proposed Vivaldi antennas. Figure 6: Parametric studies of \(S_{11}\) by varying (**a**) slot width \(s\) and (**b**) slot length \(l_{1}\).
## 3 Results and Discussion
The remaining percentage of MNS nanoparticles in the gastrointestinal tract (GIT) was significantly higher than that of the sitagliptin suspension (\(p<0.05\)). The results indicated that polymer was incorporated in the nanoparticles due to their chemical components and the readily swollen large mass polymers in PBS, providing a large adhesive surface on the nanoparticle for the maximum interaction with the mucin, which prolonged the residence time of the nanoparticles in the GIT. Also, perhaps hydrogen bonding is a key part of the adhesion [21; 22]. The albumin polymers adhere better in an acidic environment and are in a highly protonated condition, possibly due to the presence of a large number of carboxylic groups to form hydrogen bonds. MNS showed percentage of drug release at 30 min is 12%, 1 h--32%, 4 h--64%, 6 h--78%, 8 h--87%, and 12 h--97% (Figure 8). In comparison with sitagliptin nanoparticles, the amount of drug that was released at 30 min was 54%, and at 1 h the total drug released was 98%. At the same time, MNS nanoparticles showed 32% drug release, indicating a significant amount of sitagliptin was adsorbed on or incorporated at the surface of the nanoparticles. This may be due to the drug's \"burst effect\" and allows the nanoparticle preparation to have a fast and effective effect on the patients in clinical practice. Figure 8: Percentage drug retained in the stomach after oral administration of formulation and pure drug. Figure 7: In vitro release studies of sitagliptin nanoparticles.
## 4 Development and Validation of the Platform
### Platform Performance Results
#### 4.2.2 Evaluation of 5G RAN and Core  
While RAN simulators are instrumental in facilitating initial performance evaluations, they sometimes overlook some real-world performance factors, such as network constraints and variability. To complement the core network tests, additional real, non-simulated evaluations were conducted to assess the full performance of the Edge Site 1 network. These assessments, which included tests on the n40 and n78 frequency bands, measured the maximum throughput experienced by each user, taking into account the characteristics of the radio channel, physical RAN, and core network.  
The radio access network at Edge Site 1 is configured with a 3/7 uplink/downlink slot ratio TDD pattern to prioritize the uplink, which better supports the real-time demands of applications like Digital Twins. The n40 band uses a 20 MHz bandwidth with 256 QAM modulation and MIMO 2 \(\times\) 2 on the downlink, while the n78 band is configured with a 100 MHz bandwidth, 64 QAM modulation, and MIMO 2 \(\times\) 2 on the downlink, enabling high data throughput and low latency across the IoT, Edge, and Cloud layers.  
All network measurements were taken using a 5G-enabled smartphone directly connected to the radio access network, performing _ipperf_ tests over a fixed duration to gauge throughput accurately. This approach provided a comprehensive view of the overall performance under real-world conditions, accurately reflecting the user experience across the entire network infrastructure.  
It is also important to note that this network setup is entirely experimental, with specific configurations tailored to support research and development, rather than a commercial deployment.  
To analyze the results of these platform tests, we compared the measured throughput values to theoretical values calculated using a standardized formula from 3 GPP, specifically tailored for 5G New Radio (NR) throughput estimations [53]. This formula provides an estimate of the maximum achievable throughput for both user equipment (UE) and cell capacity, taking into account various factors, like the modulation scheme, resource block allocation, and overhead reduction.  
These tests, as shown in Table 2 and represented graphically in Figure 5, saw a maximum downlink of 552 Mb/s and uplink of 87.3 Mb/s for the n78 indoor band, while the n40 outdoor band achieved a downlink of 120 Mb/s and an uplink of 29 Mb/s. These measured values, being close to the theoretical limits, demonstrate the platform's robust performance under real-world conditions, effectively handling network constraints and channel variability with minimal deviation from ideal calculations.  
These preliminary tests demonstrated the architecture's scalability and high data throughput, which are required for real-time applications like Digital Twins, while highlighting areas for improvement, such as optimizing resource usage and reducing latency during peak loads. Further iterations will enhance the orchestration mechanisms for seamless operation across the IoT-Edge-Cloud continuum.
## 1 Introduction  
Nowadays, among the concerns that must be dealt with on a global level are toxic and carcinogenic environmental pollutants. A large portion of toxic contaminants are dyes, used for dyeing textiles and other industrial purposes. Many physical, chemical and biophysical processes have been studied for their ability to remove environmental pollutants [1]. In particular, the development of new technologies for the easy decolorization or remediation of these types of compounds is of current interest.  
Dyes used in the textile industry include a wide range of chemical compounds, based mainly on substituted aromatic structures linked by azo groups [2]; therefore textile dye wastewater is based not only in color removal but also in the degradation, mineralization and total removal. A large variety of techniques has been used including chemical methods such as coagulation [3; 4; 5], flocculation [6; 7; 8] or precipitation [9; 10], and new photocatalytic processes such as the Fenton reaction [11; 12] are being implemented and optimized; biological with aerobic and anaerobic microbial degradation [13; 14; 15], and the use of pure enzymes [16; 17] have been investigated to overcome physical methods such as membrane-filtration processes. Nano-filtration [18; 19; 20] reverse osmosis [21], electro-dialysis [22; 23; 24] and sorption techniques [25; 26; 27; 28; 29; 30] are the most conventional methods due to higher production costs and regeneration difficulties. There is a trend towards developing sustainable water treatment materials and processes [31; 32] and consequently, the possibilities of the use of new synthetic material--with low cost and easy availability--are desirable to be evaluated for wastewater management.  
Functionalized mesoporous materials are interesting adsorbents in comparison with other commercial DVB or styrene base adsorbents because of their attractive properties, such as a high level of heavy metal removal capacity, large surface area, narrow pore size distribution and the specificity of the chelating agents inserted in the network. An emerging group, the synthesized materials are pillared clay structures (PILCs) and similar pillared porous phosphate heterostructures (PPH) [33; 34]. These materials can be specifically designed for their use for Hg(II) and Ni(II) remediation from the electrochemical industry [35], or catalytic applications [36; 37; 38; 39; 40].  
We investigate herein, for the first time to the best of our knowledge, the use of PPH modified with functional phenyl groups as a new class of adsorbents for dyes from wastewater. These solids are synthesized from different combinations of silicon precursors and surfactant along with various combinations of condensation processes of alkoxides. The effect of the adsorbate was studied in order to achieve optimal conditions for the adsorption of the azo dye Acid blue (AB113), proving to be an appropriate and cost effective alternative to remove it from wastewater.
# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 5 Discussion and Conclusions
### Limitations and Future Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline Moreno-Sanchez (2023) [74] & Cardiovascular medicine & The authors mentioned that explainability is important in their specific application of heart failure survival prediction to facilitate healthcare professionals’ understanding and interpretation of the model’s outcomes. \\ \hline Wongburi et al. (2022) [150] & Wastewater treatment & The authors mentioned that complex models like RNNs offer high accuracy, they can be challenging to interpret. Therefore, explainability was crucial to understand why the algorithm made certain predictions in the context of predicting the Sludge Volume Index in a Wastewater Treatment Plant. \\ \hline Obayya et al. (2022) [79] & Diabetic retinopathy grading and classification & The authors mentioned that explainability is essential in healthcare settings, especially in diagnosing diseases like diabetic retinopathy, to provide transparent and interpretable insights into the decision-making process of the AI model. \\ \hline Heistrene et al. (2023) [333] & Electricity price forecasting & To identify whether or not the model prediction at a given instance is trustworthy. \\ \hline Azam et al. (2023) [125] & Automating the skull stripping from brain magnetic resonance (MR) images & They need visualizations to detect/segment the brain from non-brain tissue. \\ \hline Ribeiro et al. (2022) [334] & Detection of abnormal screw tightening processes & For an interactive visualization tool that provides explainable artificial intelligence (XAI) knowledge for the human operators, helping them to better identify the angle–torque regions associated with screw tightening failures. \\ \hline Zinonos et al. (2022) [144] & Grape leaf diseases Identification & To visualize the decisions of CNN’s output layer. \\ \hline Neupane et al. (2022) [184] & Intrusion detection/cybersecurity & For example, to enable users to locate malicious instructions. \\ \hline Aslam et al. (2022) [151] & Prediction of undesirable events in oil wells & To enable surveillance engineers to interpret black box models to understand the causes of abnormalities. \\ \hline Pisoni et al. (2021) [225] & Explanations for art & To provide improved accessibility to museums and cultural heritage sites. \\ \hline Blomerus et al. (2022) [335] & Synthetic aperture radar target detection & To furnish the user with additional information about classification decisions. \\ \hline Estitvill-Castro et al. (2022) [336] & Human-in-the-loop machine learning & Humans’ (domain experts or not) tend to not trust ML systems if they do not provide explanations. Sufficient explanations are important because they show correlations between features and therefore help understand the model. \\ \hline \end{tabular}
\end{table} 
Table 1: Cont.
# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 2 Network Based Systems
### Range Based
#### 2.1.1 Time
Unlike WiFi, UWB systems are not worldwide deployed and thus any IPS based on UWB will have additional expenses due to the infrastructure cost. Nevertheless, this problem will be circumvented with the deployment of the Fifth Generation (5G) of cellular networks around the world, which will also employ signals with large bandwidth and centimeter level ranging accuracy [71]. Time based localization methods are susceptible to errors produced for inaccuracies in the clocks or errors in the time estimation. Take into account that for a signal traveling at light speed 1 us of error corresponds to an approximate distance error of 300 m. Furthermore, Non Line of Sight (NLOS)  
Figure 2: Lateration method concept. conditions produce a positive bias in the distance estimation. Therefore time based methods must include methods for detecting NLOS conditions increasing the complexity of the algorithms. In general, time based systems obtain highly accurate position estimations and can be scaled to large areas by adding new nodes to the network. The scalability to multiple users is limited by the possible collisions when multiple users coexist in the same area. Thus, collision avoidance mechanism must be implemented increasing the overall complexity of the algorithm. Finally, the cost of the system is determined by the network technology and the number of nodes required.
# Indoor Carrier Phase Positioning Technology Based on OFDM System
## 3 Positioning Algorithm
### Extended Kalman Filter
\right] \tag{37}\]  
\[\frac{\partial\mathbf{h}_{p}^{ij}}{\partial\mathbf{x}}|_{x(k+1|k)}=\frac{ \partial\mathbf{h}_{p}^{ij}}{\partial\mathbf{x}}|_{x(k+1|k)}-\frac{\partial \mathbf{h}_{p}^{ij}}{\partial\mathbf{x}}|_{x(k+1|k)};(i=1,\ldots,m;i\
eq j) \tag{38}\]  
\[\frac{\partial\mathbf{h}_{p}^{ij}}{\partial\mathbf{x}}|_{x(k+1|k)}=\frac{ \partial\mathbf{h}_{p}^{ij}}{\partial\mathbf{x}}|_{x(k+1|k)}-\frac{\partial \mathbf{h}_{p}^{ij}}{\partial\mathbf{x}}|_{x(k+1|k)};(i=1,\ldots,m;i\
eq j) \tag{39}\]  
\[\frac{\partial\mathbf{h}_{p}^{ij}}{\partial\mathbf{x}}|_{x(k+1|k)}=\frac{ \partial\mathbf{h}_{p}^{ij}}{\partial\mathbf{x}}|_{x(k+1|k)}=\frac{x(k+1|k)- x_{i}}{\sqrt{(x(k+1|k)-x_{i})^{2}+(y(k+1|k)-y_{i})^{2}}} \tag{40}\]  
\[\frac{\partial\mathbf{h}_{p}^{ij}}{\partial\mathbf{y}}|_{y(k+1|k)}=\lambda\frac {\partial\mathbf{h}_{p}^{ij}}{\partial\mathbf{y}}|_{y(k+1|k)}=\frac{y(k+1|k)- y_{i}}{\sqrt{(x(k+1|k)-x_{i})^{2}+(y(k+1|k)-y_{i})^{2}}} \tag{41}\]  
\[\frac{\partial\mathbf{h}_{p}^{ij}}{\partial\mathbf{h}_{p}^{ij}}=-1 \tag{42}\]  
Given the state and measurement equations in previous sections, the EKF algorithm can be applied to calculate the estimate of \(\mathbf{x}(k+1)\) based on the SD measurements. EKF algorithm [31; 32] includes the following time-update and measurement update equations. Furthermore, the time-update equation are:  
\[\mathbf{x}(k+1|k) =\mathbf{F}(k)\mathbf{x}(k|k); \tag{43}\] \[\mathbf{P}(k+1|k) =\mathbf{F}(k)\mathbf{P}(k|k)\mathbf{F}^{T}(k)+\mathbf{Q}(k); \tag{44}\]  
where \(\mathbf{x}(k|k)\) and \(\mathbf{P}(k|k)\) are, respectively, the estimated state vector and its covariance matrix at the epoch \(t=t_{k}\). \(\mathbf{x}(k+1|k)\) and \(\mathbf{P}(k+1|k)\) represent, respectively, the predicted state vector and its covariance matrix at the epoch \(t=t_{k+1}\), based on \(\mathbf{x}(k|k)\) and \(\mathbf{P}(k|k)\). The matrixes \(\mathbf{F}(k)\) and \(\mathbf{Q}(k)\) are defined in Equation (44). Furthermore, the measurement update equation are:  
\[ \begin{array}{l}\mathbf{K}(k+1)=\mathbf{P}(k+1|k)\mathbf{H}(\mathbf{x}(k+1 |k))\left[\mathbf{H}(\mathbf{x}(k+1|k))\mathbf{P}(k+1|k)\mathbf{H}^{T}( \mathbf{x}(k+1|k))+\mathbf{R}(k)\right]^{-1};\\ \mathbf{x}(k+1|k+1)=\mathbf{x}(k+1|k)\mathbf{K}(k+1)\mathbf{Z}(k+1)-\mathbf{ h}(\mathbf{x}(k+1|k));\\ \mathbf{P}(k+1|k+1)=[\mathbf{I}-\mathbf{K}(k+1)\mathbf{H}(\mathbf{x}(k+1|k))] \mathbf{P}(k+1|k);\end{array}  \tag{45}\]  
\(\mathbf{H}(\mathbf{x}(k+1|k))\) is the Jacobian matrix given by Equation (40), the measurement equation \(\mathbf{h}(\mathbf{x}(k+1|k))\) is defined in Equation (45), and calculated based on the predicted position \((x(k+1|k),y(k+1|k))\) at time \(t=t_{k+1}\).
## 4 Results and Discussion
### Makespan Valuation
#### 4.1.1 Makespan, Onset Rate = 10  
In this unit, we measured the time taken to execute the tasks with 10 as the onset rate. The time taken for time, in microseconds, for various algorithms such as HWACO, QANA, MTF-BPSO, ACO, MIN-MIN and FCFS was 13.78, 15.01, 16.21, 18.8, 15.73, and 32.32, respectively. When the number of tasks was changed to 200, the time taken for the execution for the HWACO, QANA, MTF-BPSO, ACO, MIN-MIN, and FCFS was 25.9, 27.23, 31.09, 34.61, 29.05, and 67, respectively. When the number of tasks was changed to 300, the time taken for the execution for HWACO, QANA, MTF-BPSO, ACO, MIN-MIN, and FCFS was 36.56, 38.31, 46.31, 50.31, 40.02, and 75.5, respectively. Finally, when the number of tasks was changed to 400, the time taken for the execution for HWACO, QANA, MTF-BPSO, ACO, MIN-MIN, and FCFS was 50.4, 52.3, 58.42, 66, 53.1, and 124, respectively. Figure 2 shows the execution time for the tasks by different algorithms when the onset rate = 10.
# Accuracy and Precision of Three-Dimensionally Printed Orthognathic Surgical Splints
### Definition of Accuracy and Precision in 3D Printing  
Three-dimensional printing is an additive manufacturing technique that refers to a process where a 3D object is fabricated by laying down one layer at a time with each successive layer bonding to the preceding layer, and each of these layers can be seen as a thinly sliced cross-section of the object. The main advantages of this technique include its ability to create undercuts, overhangs, and other highly detailed and complex geometry with less material waste.  
Accuracy, or trueness, refers to how close the measurements are to the true value, and it is assessed by measuring the degree of deviation in a measurement of the printed object from the actual dimensions of the virtual object [18]. A printer with a higher accuracy indicates the printer that delivers a result that is closer or equal to the actual dimensions of the digital 3D object. In contrast, precision refers to how close the repeated measurements are to each other and indicates repeatability. A printer with a higher precision correlates to one that produces a more repeatable and consistent print.
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 4 Communication Module
### Firmware  
The Atmel microcontrollers used in the network nodes follow a layered approach compatible with the IEEE 802.15.4 standard. The stack modules PAL, MAC (MAC-API), TAL, and Resources have been modified to the requirements of the grid-connected photovoltaic system. The beacon-enabled mode has been the mechanism chosen of the IEEE 802.15.4 standard to synchronize the bidirectional data transmission between the sensor nodes and the coordinator. The network coordinator is responsible for setting up the wireless network and follows the known negotiation protocol with the launching of beacons to receive the data sent by the sensor nodes, the association requests, and the confirmations by means of acknowledgment (ACK) frames. A slotted Carrier Sense Multiple Access with Collision Detection (CSMA/CA) is the mechanism that provides media access to the sensor nodes, synchronized by means of the beacons of the superframe structure. Nevertheless, sensor nodes also use GTS to avoid time delays in the data transmission and provide a predetermined data monitoring latency. This fact ensures that the controllers of both the boost converters and the inverters can operate stably and in real-time since they receive data at low latency intervals and within a specified time.  
The coordinator (host) node configures the parameters required to create the network. Each sensor node gathers the PV module parameters and broadcasts an association beacon. Then, the coordinator associates a PV module number to the MAC address of each sensor node, and these can now forward the data to the coordinator in a reserved GTS. Next, the data is processed by the algorithms, generating the control signals for the DC/DC converters and the inverters, which return to each sensor node accordingly to track the MPPT and provide the energy from the PV modules to the grid in the correct way. The parameters Beacon Order (BO) and Superframe Order (SO) are defined in the standard and have been set to determine the time between beacons and the time in which the sensor node can transmit, respectively [27].