esa-sceva/satcom-chunk-collection huggingface dataset:
# The Removal of Crystal Violet from Textile Wastewater Using Palm Kernel Shell-Derived Biochar
###### Abstract  
In this study, we explored the adsorption potential of biochar derived from palm kernel shell (BC-PKS) as an affordable adsorbent for the removal of crystal violet from wastewater. Kinetics, equilibrium, and thermodynamics studies were carried out to evaluate the adsorption of crystal violet onto BC-PKS. The kinetics adsorption process followed the pseudo-second-order model, indicating that the rate of adsorption is principally controlled by chemisorption. The adsorption equilibrium data were better fitted by the Langmuir isotherm model with a determination coefficient of 0.954 and a maximum adsorption of 24.45 mg/g. Thermodynamics studies found the adsorption of crystal violet by BC-PKS to be endothermic with increasing randomness at the BC-PKS/crystal violet interface. The percentage removal and adsorption capacity increased with the pH of the solution, as the negative charges on the biochar surface at high pH enhance the electrostatic attraction between crystal violet molecules and BC-PKS. Increasing the BC-PKS dosage from 0.1 to 1.0 g increased percent removal and decreased the adsorption capacity of crystal violet onto BC-PKS. Therefore, biochar from agricultural by-products, i.e., palm kernel shell, can be cost-effective adsorbents for the removal of crystal violet from textile wastewater.  
biochar; adsorption; crystal violet; palm kernel shell; agricultural byproduct +
Footnote †: journal: Applied Physics Letters  
1
## 3 Experimental Results
### On-Sun Tests  
Several on-sun tests have been conducted in order to investigate the thermal performance of the heat exchanger at high temperatures, i.e., higher than those that can be obtained in the steady-state tests. In the on-sun tests, the heat exchanger uses hot particles to further heat up the recuperated air of the T100 unit. The particles, whose temperatures are higher than 500 \({}^{\circ}\)C, are allowed to flow through the PHR, where they are irradiated by the heliostat field, thus, raising their temperature to around 650 \({}^{\circ}\)C. The hot particles then transfer some of the collected thermal energy to the T100's air while flowing through the heat exchanger. Figure 8 shows a typical example of such tests. The heat exchanger kept supplying thermal energy to the T100's air for around five hours. However, it is obvious that the temperature of particles entering the heat exchanger keeps changing with time as a result of the variable incident solar flux on the PHR during the test period. Due to such variation of air and particle temperatures, it is not possible to assess the heat exchanger performance. The thermal mass of the heat exchanger needs a certain amount of time to reach a thermal equilibrium which gets more complicated when the particle inlet temperature varies with time. For the particle inlet temperature to get steady, a buffer tank needs to be installed upstream of the heat exchanger, just after the PHR to receive the hot particles leaving the PHR. Along with that, the incident solar flux on the PHR and/or the particle flow rate should be adjusted/controlled. The incident solar flux can be controlled by adjusting the number of running heliostats, and a particle mass flow control system can be installed upstream the PHR. Nevertheless, it is evident that the heat exchanger is able to transfer some of the collected solar energy to the T100's air. The area enclosed by the air lines (shown in Figure 8) qualitatively represents the amount of energy added during this test, i.e., solar contribution.  
Figure 7: Air pressure across the heat exchanger.
## 4 Sensing-Oriented Issues and Solutions
### Materials and Manufacturing Technologies
The other effective operating parameters on the film formation are surface temperatures, surface energy of the substrate, surface tension, and viscosity of the ink [126]. Furthermore, the evaporation, the film homogeneity, the electrical properties, all rely heavily on ink formulation [117]. The increasing numbers of research articles and demonstrations of printed sensors and electronics in a number of applications reflects the keen interest of researchers to fulfill the promise of large area electronics on flexible substrates through cost-effective printing technologies. Reference [127] investigated for the first time inkjet-printed UHF and microwave circuits fabricated on paper substrates as an approach that aims for a system-level solution for fast and ultra-low-cost mass production. Reference [128] introduced printed electronics through flexible substrates and low-cost fabrication with huge potential for the future integrated smart sensing and network application. Reference [129] demonstrated a prototype printable chipless RFID, which can be easily transferred to plastic, paper, and other material substrates, making it suitable for mass deployment for low-cost items. Reference [58] presented a chipless RFID tag sensor that potential to be printed on flexible laminates for ultra-low cost ubiquitous sensing. Reference [130] discussed new materials and technologies towards emerging flexible sensors, e.g., printing technologies to support low-cost electronic devices for multisensory and monitoring. The more detailed challenges in printable electronics from materials, technologies, and perspective applications including printed antennas and RFID tags for sensors and integrated smart systems can be found in [131]. Some potential trends are going to be discussed in Section 5.3.
# Towards Explainable Pedestrian Behavior Prediction:
## 5 Experimental Results and Discussion
### Experiment 3--Randomness  
The objective of this experiment was to determine _whether incorporating human reasoning in the selection of videos for fuzzy rule generation could enhance the performance_ of the pedestrian behavior predictor or if random selection would be sufficient to achieve accurate results. To evaluate this hypothesis, we compared the results against the **J8K** and **P14K** configurations from Experiment 1--dataset quantity.  
As outlined in the data distribution (See Section 4.1), before selecting input data for the fuzzy rule mining process, videos were systematically ranked based on their quality and relevance to pedestrian behavior prediction. This experiment introduced six additional configurations, each containing the same number of records as **J8K** (8000 records) and **P14K** (14,000 records) but with randomly selected videos contributing to the training set used for mining the fuzzy rules.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multirow{2}{*}{**Rules**} & \multicolumn{2}{c}{\(J\)\(A\)\(A\)\(D\)\({}_{all}\)} & \multicolumn{2}{c}{\(J\)\(A\)\(D\)\({}_{beh}\)} & \multicolumn{2}{c}{\(PI\)\(E\)\({}_{all}\)} & \multicolumn{2}{c}{\(PI\)\(E\)\({}_{beh}\)} \\ \cline{3-10}  & & **A** & **F** & **A** & **F** & **A** & **F** & **A** & **F** \\ \hline J14K & **58** & **0.72** & **0.75** & **0.72** & **0.76** & 0.73 & 0.50 & 0.73 & 0.52 \\ J14 Kn & 28 & 0.68 & 0.70 & 0.67 & 0.71 & 0.67 & 0.43 & 0.67 & 0.47 \\ J30 Kn & 29 & 0.70 & 0.73 & 0.68 & 0.73 & 0.66 & 0.45 & 0.68 & 0.48 \\ \hline P80K & **36** & 0.58 & 0.54 & 0.56 & 0.54 & **0.81** & **0.54** & **0.81** & **0.57** \\ P80 Kn & 79 & 0.60 & 0.55 & 0.58 & 0.55 & 0.81 & 0.51 & 0.81 & 0.53 \\ J160 Kn & 69 & 0.55 & 0.39 & 0.53 & 0.39 & 0.85 & 0.50 & 0.83 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment 2—data preprocessing results. Accuracy (A) and F1 score. Bold values indicate the best performance for each evaluation metric across the different configurations.  
According to the results presented in Table 5, random data selection can enhance the performance of the explainable predictor within its own dataset, taking into account both accuracy and F1 score. Notably, the **J8 KR3** and **P14 KR1** configurations achieved the best results for JAAD and PIE, respectively. However, the process of analyzing and systematically ranking videos based on relevance, quality, and contextual suitability proved to be beneficial. This structured approach not only improved the predictor's overall performance but also contributed to its generalization capabilities across different datasets.
## 4 The 5G Use Cases and Spectrum Requirements
### Key 5G Applications
With drone services powered by 5G, one can access high-resolution photos and films for security, surveillance, and other filming purposes. * Augmented Reality (AR) and Virtual Reality (VR): with the deployment of 5G, the way games are being played currently will witness a complete transformation, as 5G has been designed with outstanding features for HD gaming. Additionally, virtual reality has emerged as the newest innovation in the tech sector. Virtual reality and its variants will undoubtedly become increasingly visible as 5G connection technology develops. Beyond gaming, 5G will make it possible to experience virtual reality and sports events. * Agriculture Industry Use Cases: Despite being the oldest sector in the economy, the agricultural sector of any economy will also benefit tremendously from 5G services. Security of the sector will be improved by installing sensors for surveillance purposes and providing vital crop information, such as the requirement for water, insect control, disease prevention, etc., that will further enhance the timely and healthy germination of the agricultural produce. Additionally, the health of animals, such as cows and sheep, can be easily monitored onsite and remotely with 5G IoT devices. * Support for Artificial Intelligence (AI): Large companies with a vast amount of information will have to use AI to process their massive data, and these processes will certainly be accelerated with 5G. Furthermore, when additional sensors are deployed to smart cities, data from these smart sensors must be relayed to allow them to be deployed when necessary. Cellular sensors are in high demand for metering applications, traffic and parking sensors, city lighting, and other uses. * Autonomous Vehicles: In the current 5G era, people will have the opportunity to drive autonomous vehicles, a brand-new technology. This can be achieved only with the speed and low latency services that will be guaranteed once the 5G technology is launched. One primary application of autonomous vehicles is the Vehicle-to-Everything (V2X) network, which allows 5G users to connect any of their desired devices to their vehicles.
## 4 Results and Analysis
### Regionalization Results  
In ANSYS Fluent, numerical data from the temperature cloud map were extracted, including spatial coordinates (x, y, z) and corresponding temperature values. At each specified height, a set of 26,000 data points was obtained. The temperature values were used as the primary feature for clustering analysis. An example of such data is presented in Table 6.  
The temperature variations observed in the cloud map are closely related to spatial position. The Euclidean distance was employed to quantify the linear distance between data points, thereby facilitating the identification of the temperature distribution. Accordingly, the Euclidean distance was chosen as the metric for temperature segmentation, with a threshold set to 200. The resulting clustering is illustrated in Figure 8.  
The clustering dendrogram illustrates the sample grouping based on Euclidean distances. A distance threshold of 200 (red dashed line) was applied to truncate the dendrogram, resulting in the identification of three distinct clusters. But, in the actual material board, the same category was distributed on both sides of the center. So, we divided the material board into six regions; you can divide the temperature region on the material board, the results of the division shown in Figures 9 and 10.  
The results indicate that the temperature fields in the upper, middle, and lower layers exhibited pronounced gradient distributions, with the upper layer registering relatively higher temperatures. Areas near the air inlet displayed elevated temperatures and steep gradients due to the influence of wind speed. Notably, the regions of local temperature fluctuation corresponded closely with the hierarchical clustering divisions. Therefore, the extracted spatial location features, combined with image features, were integrated into the final feature set used as input for the prediction model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material Board**} & \multicolumn{2}{c}{**Position and Temperature on Different Material Plates**} \\ \cline{2-4}  & **X [m]** & **Z [m]** & **Temperature [K]** \\ \hline \multirow{3}{*}{Y = 0.8} & \(-1.26\times 10^{-1}\) & \(-2.41\times 10^{-1}\) & \(2.97\times 10^{2}\) \\  & \(-1.25\times 10^{-1}\) & \(-2.42\times 10^{-1}\) & \(2.97\times 10^{2}\) \\  & \(-1.26\times 10^{-1}\) & \(-2.42\times 10^{-1}\) & \(2.97\times 10^{2}\) \\ \hline \multirow{3}{*}{Y = 0.535} & \(-1.12\times 10^{-2}\) & \(-2.50\times 10^{-1}\) & \(2.88\times 10^{2}\) \\  & \(-1.12\times 10^{-2}\) & \(-2.50\times 10^{-1}\) & \(2.88\times 10^{2}\) \\ \hline \multirow{3}{*}{Y = 0.35} & \(1.40\times 10^{-1}\) & \(-2.50\times 10^{-1}\) & \(2.88\times 10^{2}\) \\  & \(1.43\times 10^{-1}\) & \(-2.50\times 10^{-1}\) & \(2.88\times 10^{2}\) \\ \cline{1-1}  & \(1.42\times 10^{-1}\) & \(-2.48\times 10^{-1}\) & \(2.89\times 10^{2}\
# A Review on 5G Sub-6 GHz Base Station Antenna Design Challenges
## 3 Challenges in BSA Design
### Size and Cost  
Operators are keen to maximize the performance per unit area in the tower space. Therefore, they require compact antennas with better overall performance at a low cost. This requires the designs to be compact and low in cost. The miniaturizations are often achieved by having interspersed multiband arrays [4]. A comparison of different arrangement schemes for high-frequency elements and low-frequency elements is performed [2]. As shown in Figure 14, side-by-side schemes, up-and-down coaxial schemes, and embed  
Figure 13: Components of a \(4\times 4\) Tx/Rx dual polar mMIMO configuration.  
ded schemes were considered. Although the up-and-down scheme is simpler, cable losses increase due to the increased length of the main feed line. The embedded scheme has the advantage of compactness as two antennas are located within a single band antenna volume. However, embedded schemes exhibit challenges of isolation and pattern distortions. A single radiating element for both the low and high bands is used in[72; 73] to reduce the number of required radiating elements and footprint. However, the required vertical element spacing for optimum elevation patterns is hard to achieve in this approach. Hence, the grating lobe levels are high. The dual broadband planer BSA configuration is followed in [32], where high band elements are nested inside lower band elements to achieve compactness.  
The lower cost in the antenna production is achieved by using low-cost, simple, printed antennas. The printed circuit board technologies reduces antenna assembly times. Another low-cost choice for enabling 5G BSA is 3D printing, which facilitates complex designs. Three-dimensional printing is an effective manufacturing method for designing MIMO antenna prototypes to reduce the cost. In [33], \(\pm 45\) dual-polarized antenna is fabricated using 3D printing technology while achieving a wide bandwidth. Although printed antennas have a low-cost advantage, they can exhibit higher dielectric losses compared to die-cast radiating elements. Die-casting can be cost-effective if the entire element is made out from a cast reducing the assembly time. In addition to the cost incurred on radiating elements, the other RF components, such as phase shifters, may incur higher costs. A wiper phase shifter [74] can be a cost-effective solution as it is very compact but may have other disadvantages, such as limited control for null-filling in down tilts and the use of a large number of cables. Other types of phase shifters such as [75; 76] can overcome some of these limitations while reducing fabrication costs.
## 1 Introduction
Through various experiments, the performances of predicted inference latency is compared and analyzed according to the object detection algorithms and communication protocols. The main contributions of the paper are summarized as follows. 1. A system with AI technology was constructed using an object detection service as an AI service, and the performance of the object detection model on this system was studied from the perspective of the inference latency of the entire system, deviating from the inference accuracy;
2. When performing object detection, we reveal that there is no correlation between image size and inference latency, which presents a problem--that inference latency prediction is required for scheduling purposes;
3. We propose an approach for inference latency prediction during object detection in edge computing. The proposed approaches predict the inference latency between devices and object detection algorithms by using the statistical information on inference latency;
4. By exploiting the predicted inference latency, the client may effectively determine whether to perform the object detection task locally or remotely. By exploiting this scheduling phase, the overall performance of AI services could be improved;
5. Through simulation results, we demonstrate that the performances of the proposed approaches were compared and analyzed according to the object detection algorithms (RFCN and SSD-MobileNet) in consideration of two communication protocols (gRPC and REST). The rest of the paper is organized as follows. Section 2 presents related works on AI services with the optimization of edge computing. In Section 3, we present the system model. Section 4 presents the proposed inference latency prediction approaches. Simulation results are presented in Section 5, and Section 6 concludes the paper.
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
## 3 Real-Time Health Assessment Algorithm
### Star Swarm Mission Effectiveness Assessment  
Since the errors between the actual and the expected values of mission-based swarm telemetry data can reflect the degree of mission accomplishment, this paper adopts the hierarchical analysis method to construct a reasonable swarm effectiveness assessment model according to the principles of completeness, hierarchy, and feasibility. To reduce the computational complexity of the dimensionless processing and avoid taking up more satellite resources, the input data are selected as the three-axis position, three-axis attitude, and energy consumption with the expected errors. The steps are as follows.  
Step 1: A reasonable model of the effectiveness evaluation index system is established, and the structure of the system is shown in Figure 1.  
In the figure, \(E\) denotes the swarm effectiveness, the primary indicator \(E_{i}(i=1,2,\cdots,N)\) denotes the effectiveness of satellite \(i\), and the secondary indicator \(E_{ij}(i=1,2,\cdots,N;j=1,2,\cdots,7)\) denotes the status of indicator \(j\) of satellite \(i\).  
Step 2: Data are obtained with respect to the error between the measured and expected values of the metric.  
Step 3: The entropy weighting method [19] is used to determine the weights of each index.  
The entropy weight method in the objective assignment method is used to determine the weights of each index, which can reduce the interference of subjective factors. Assuming that the normalized value of each error \(E_{ij}\) of the secondary index is \(z_{ij}\), the value of the secondary index weight \(p_{ij}\) is as follows [19].  
\[p_{ij}=\frac{z_{ij}}{\sum\limits_{i=1}^{7}z_{ij}} \tag{17}\]  
Figure 1: Star Swarm Effectiveness Assessment Model.  
The primary indicator weights are obtained from the following steps.  
\[r_{i}=-\frac{1}{\ln 7}\sum\limits_{j=1}^{7}p_{ij}\ln(p_{ij}) \tag{18}\]  
\[z_{i}=1-r_{i} \tag{19}\]  
\[\omega_{i}=\frac{z_{i}}{\sum\limits_{i=1}^{N}z_{i}} \tag{20}\]  
where \(r_{i}\) denotes the information entropy of each secondary indicator, \(z_{i}\) denotes the information utility value, and \(\omega_{i}\) is the primary indicator weight value.  
Step 4: The combined swarm effectiveness is calculated.  
\[E_{swarm}=\sum\limits_{i=1}^{N}\omega_{i}\sum\limits_{i=1}^{N}\sum\limits_{j=1 }^{7}p_{ij}\times z_{ij} \tag{21}\]
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 5 Evaluation
### Mersenne Twister PRNG
#### Idq Qrng  
The Quantis PCIe-240M device generates random data using elementary quantum optical processes. It employs a multi-mode LED to illuminate a CMOS image sensor. The number of photoelectrons stored in each pixel follows a Poissonian distribution, providing a source of quantum randomness. After digitization by an analog-to-digital converter (ADC), only the bits that remain unaffected by non-quantum noise are retained. Post-processing techniques can be applied if necessary, although Quantis entropy sources are exceptionally reliable and pass most statistical tests.  
The Quantis device incorporates an auto-calibration function that controls the optical power of the LED and the exposure time of the CMOS image sensor. This ensures that the produced random bits are unpredictable and independent of environmental and operating conditions. The auto-calibration process guarantees high randomness, contributing to the device's security and suitability for applications requiring strong cryptographic properties.
## 3 Materials and Methods
### Experimental Setting
#### 3.2.1 Datasets  
This study evaluates the performance of the EduDCM framework using two datasets designed for educational dialogue classification. The first dataset, TalkMoves [23], is a dataset containing 10 pedagogical categories and is used to evaluate the model's generalization capabilities across diverse and complex data. The second dataset, Educational Dialogue Talk Dataset (EduTalk), was constructed based on the EduDCM framework and comprises 5200 dialogue utterances categorized into the same 10 pedagogical categories as TalkMoves. EduTalk integrates both simulated and real classroom interactions, providing a foundation for evaluating EduDCM's ability to generate high-quality annotations in low-resource educational contexts.
## 3 Performance Evaluation of the Proposed SAR Processor
### Hardware Resource Utilisation
The proposed system is suitable as a near-real-time implemetation due to the total processing time of 1.38 s. It also achieved a shorter processing time compared to the previously developed system. The power consumption is also relatively lower compared to that of other implementations.
## 2 Mathematical Formulation of the 2L-VRPSC
### Stochastic Programming Model of the 2L-VRPSC  
The Monte Carlo method is adopted in this study to simulate customer uncertainties, and a scenario reduction approach is applied to generate a reasonable number of scenarios with good tractability and fidelity, as well as diverse characteristics. Then a stochastic programming model is developed based on the obtained scenarios.