esa-sceva/satcom-chunk-collection huggingface dataset:
# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
###### Abstract
The FPGA offers an advantage over fixed application-specific integrated circuit implementations due to its re-programmability, allowing hardware designs to be upgraded or re-purposed after deployment. It also provides dynamic re-programmability, where the function changes at run time in response to the requirements of the application, and supports partial reconfiguration, where only parts of the hardware are modified at run time [23]. In this article, we present a trade-off analysis of four FPGA hardware designs for a co-processor in an SDR architecture, which enables us to classify low and high CQI based on ML models and the eurecom elasticimon 5G 2019 dataset [24]. The dataset uses 4G/5G statistics and monitoring data. The four FPGA architectures are implemented the ML models, namely, Decision Trees, Multi-layer Perceptron (MLP), Logistic Regression, and Support Vector Machines (SVM). The CQI classification makes it possible to sense the communication environments and react according to user's necessities since hardware components are susceptible to noise and radiation, reflecting a low CQI. The design and development of the FPGA hardware architectures have several challenges focused on research for satisfying different requirements such as low energy consumption, high performance, small hardware resources, and high efficiency since the initial designs are hardly applied directly. We can summarize the main contributions of the paper as follows:* A hardware architecture proposed to be part of the components of a radio system. Its output can help change functions or algorithms for the modules and processors of the system itself to detect high or low quality in the communication channel, allowing it to sense the environment and adapt to its conditions. To the best of our knowledge, no studies have investigated using the eurecom elasticmon 5G 2019 dataset in this manner;
* Design and development of FPGA hardware architectures to implement four ML algorithms that classify low and high CQI: (a) two Decision Trees with depths of five and six; (b) a three-layer MLP with a configuration of 5-5-1, using an ReLU activation function in the first two layers and a Sigmoid activation function in the third layer; (c) a Logistic Regression architecture which comprises three modules; and (d) an SVM architecture consisting of two modules;
* A trade-off analysis of the FPGA hardware implementations for a co-processor in an SDR architecture comparing models based on four different ML algorithms in terms of accuracy, objectives, platforms, latency, Hardware Resources, Frequency, Throughput, and Efficiency. The paper is structured as follows: Section 2 presents several studies of Decision Trees, Neural Networks, Logistic Regression, and SVM. Section 3 gives an overview of the used ML Algorithms. Section 4 describes our Machine Learning architectures and the shared basic modules. Section 5 shows resources comparison of the developed ML architectures and with related works. Finally, we conclude in Section 6.
# Understanding Balance Control in the Context of Riding a Personal Mobility Device
## 4 Discussion
### Performance and Pattern of the Targeted Weight Shifting
#### 4.1.2 Effects of Target Direction on the Weight-Shifting Performance  
The results showed that the COP movement was also affected by the target directions, and the movement strategies could be classified into three groups as follows: (1) N direction, (2) E and W directions, and (3) NE and NW directions. In terms of MT, the total MT was not significantly affected by the target direction. However, the MT\({}_{\text{PMS}}\) values in the E and W directions were significantly larger than those in the other directions. Regarding fluency measures, the COP movement was the most fluent when the target was located in the N direction, followed by the E and W directions and the NE and NW directions. However, the ability to control the COP movement direction was not significantly different between the cases when the target was located in the N direction and when it was in the E and W directions. In terms of accuracy, the participants showed the best performance mostly when the target was located in the N direction while the performances in the remaining four directions were not significantly different from each other.  
In summary, the COP movement in the N direction was relatively slow before it reached a target but showed better accuracy of aiming, and its movement pattern was closest to the ideal path along the on-axis. The COP movements in the E and W directions were faster than those in the other directions, but they fluctuated more than the movement in the N direction and were less accurate. The COP movements in the NE and NW directions showed the worst performance in terms of the time, fluency, and accuracy.  
The different patterns of the COP movement by the target direction implied that postural control strategies can be implemented differently according to the weight-shifting directions. In previous studies, an inverted pendulum model had been extensively investigated to explain postural control of the human body [36; 39; 40; 41]. In the model, an ankle strategy is applied in the AP direction while a hip strategy dominates in the ML direction during standing still [9]. The characteristics of each strategy are obviously different in terms of precise control and force application [42; 43].  
In this regard, the accuracy in the N direction was higher than those in the other directions due to the ankle strategy used to control posture. The diagonal weight shifts (i.e., in the NE and NW directions) can be interpreted as a combination of both the ankle and hip strategies but it seems that it is mostly affected by the hip strategy. The COP movement pattern of the diagonal weight shifts was similar to that of the lateral weight shifts (i.e., in the E and W directions) rather than that of the anterior weight shift (i.e., in the N direction) in terms of movement fluency and accuracy.
# Machine Learning Techniques for Non-Terrestrial Networks
## 7 Conclusions and Open Research Issues  
In this paper, we proposed a review of NTNs from the services and architectures point of view. In the first part, we detailed how NTNs work and the services that these networks can provide. We summarized the required data rates for each service in eMMB, mMTC and uRLLC categories of 5G, as well as the suited NTEs for each of them. Afterward, we detailed the most important architectures and its protocol stacks considering the transparent NTN architecture, the regenerative architecture and eventually the on-board distributed unit architecture.  
Due to the heterogeneity of NTNs, these networks introduce several challenges compared to terrestrial networks, which can be solved or mitigated at least by the joint use of ML techniques. In the last two sections, we carried out an analysis of the most promising applications of ML, referring to the services mentioned in Section 4, and afterwards organizing them by protocol stack layers.  
Considering the interest of NTN and its integration with terrestrial NR networks, the performance should be accurately investigated in order to tailor the most suitable integration point based on a given service (e.g., collecting IoT sensor data in wide areas or providing dual connectivity to a moving platform) in terms of peak and average data rate, latency and channel reliability. Moreover, the location of the analytics platform within the integrated network can be further investigated considering the availability of big data, the interoperability of different types of sensors (or in general UEs) connecting to two different mobile network operators and the connectivity to a given NTE.  
Conceptualization, R.G.; methodology, R.G.; software, E.I.; validation, E.I.; formal analysis, R.G.; investigation, R.G.; resources, R.G. and E.I.; data curation, R.G. and E.I.; writing--original draft preparation, R.G. and E.I.; writing--review and editing, R.G. and E.I.; visualization, R.G.; supervision, R.G.; project administration, R.G.; funding acquisition, R.G. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**NTN Layer** & **ML Applications** & **Target NTE** \\ \hline Physical Layer & Constellation modulation schemes detection, pathloss prediction & UAV, HAPS, LEO \\  & Resource allocation, beams dynamic footprint & UAV, HAPS, LEO \\ Link and Medium Access & size adjustment, dynamic spectrum allocation & UAV, HAPS, LEO \\ Control Layers & between NTEs, traffic prediction, NTE selection & UAV, HAPS, LEO \\  & based on network congestion & UAV, HAPS \\ Network Layer & ML powered routing protocol & HAPS, LEO \\ Application Layer & Traffic classification, RTT estimation for throughput improvement & \\ \hline \hline \end{tabular}
\end{table}
Table 5: ML applications for layers in the NTN protocol stack.
## 3 Results
### Kinetic Parameters
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Direction**} & \multicolumn{2}{c}{**Side**} & \multicolumn{2}{c}{**Mean (N/Kg)**} & \multicolumn{2}{c}{**Std. Error**} & \multicolumn{2}{c}{**95\% Confidence Interval**} & \multicolumn{1}{c}{**Sig.**} \\ \hline \multirow{10}{*}{**Direction**} & \multirow{3}{*}{Left} & \multirow{3}{*}{Medial} & After CLS & 0.93 & 0.04 & 0.86 & 1.00 & 0.015 \({}^{*}\) \\  & & & Before CLS & 0.87 & 0.04 & 0.79 & 0.95 & \\ \cline{3-8}  & & \multirow{2}{*}{Left} & \multirow{2}{*}{Lateral} & After CLS & \(-0.50\) & 0.04 & \(-0.58\) & \(-0.42\) & \(p<0.001\) *** \\  & & & Before CLS & \(-0.38\) & 0.02 & \(-0.43\) & \(-0.33\) & \\ \cline{3-8}  & & \multirow{2}{*}{RoF} & After CLS & 1.43 & 0.06 & 1.32 & 1.54 & \(p<0.001\) *** \\  & & & Before CLS & 1.25 & 0.04 & 1.16 & 1.34 & \\ \cline{3-8}  & & \multirow{2}{*}{Right} & \multirow{2}{*}{Medial} & After CLS & 0.34 & 0.03 & 0.28 & 0.40 & 0.006 \({}^{**}\) \\  & & & Before CLS & 0.30 & 0.03 & 0.25 & 0.35 & \\ \cline{3-8}  & & \multirow{2}{*}{Left} & \multirow{2}{*}{Lateral} & After CLS & \(-1.06\) & 0.04 & \(-1.14\) & \(-0.98\) & \(p<0.001\) *** \\  & & & Before CLS & \(-0.99\) & 0.04 & \(-1.07\) & \(-0.91\) & \\ \cline{3-8}  & & \multirow{2}{*}{RoF} & After CLS & 1.39 & 0.04 & 1.31 & 1.48 & \(p<0.001\) *** \\  & & & Before CLS & 1.29 & 0.04 & 1.21 & 1.38 & \\ \hline \multirow{10}{*}{**Direction**} & \multirow{3}{*}{Left} & \multirow{3}{*}{Lateral} & \multirow{3}{*}{Lateral} & After CLS & 2.18 & 0.02 & 2.14 & 2.21 & \(p<0.001\) *** \\  & & & Before CLS & 2.11 & 0.02 & 2.08 & 2.14 & \\ \cline{3-8}  & & \multirow{2}{*}{Lateral} & \multirow{2}{*}{Lateral} & After CLS & \(-9.15\) & 0.06 & \(-9.27\) & \(-9.04\) & \(p<0.001\) *** \\  & & & Before CLS & \(-8.97\) & 0.05 & \(-9.07\) & \(-8.88\) & \\ \cline{3-8}  & & \multirow{2}{*}{RoF} & After CLS & 11.33 & 0.07 & 11.20 & 11.46 & \(p<0.001\) *** \\  & & & Before CLS & 11.08 & 0.06 & 10.97 & 11.19 & \\ \cline{3-8}  & & \multirow{2}{*}{Tension} & \multirow{2}{*}{Lateral} & After CLS & 2.15 & 0.02 & 2.11 & 2.19 & \(p<0.001\)*** \\  & & & Before CLS & 2.07 & 0.02 & 2.04 & 2.09 & \\ \cline{3-8}  & & \multirow{2}{*}{Right} & \multirow{2}{*}{Compression} & After CLS & \(-9.14\) & 0.06 & \(-9.25\) & \(-9.03\) & 0.008 \({}^{**}\) \\  & & & Before CLS & \(-8.98\) & 0.05 & \(-9.09\) & \(-8.87\) & \\ \cline{3-8}  & & & \multirow{2}{*}{RoF} & After CLS & 11.29 & 0.07 & 11.16 & 11.42 & \(p<0.001\) *** \\  & & & Before CLS & 11.05 & 0.06 & 10.92 & 11.17 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Direction**} & \multicolumn{2}{c}{**Side**} & \multicolumn{2}{c}{**Mean (N/Kg)**} & \multicolumn{2}{c}{**Std. Error**} & \multicolumn{2}{c}{**95\% Confidence Interval**} & \multicolumn{1}{c}{**Sig.
# Systematic Quantification of Cell Confluence in Human Normal Oral Fibroblasts
## 4 Discussion  
The measurement of cell growth is the most fundamental technique to be learned by new students who are interested in working in molecular and cell biology labs. Except for routine passages, adherent cell cultures are usually treated with experimental agents before they are trypsinized for counting cell numbers. Therefore, the adequate and accurate determination of cell confluence can greatly influence the results of experiments. For example, different levels of cell confluence represent specific phases of growth curves, which may differentially respond to the same agents [27]. An inaccurate determination of cell confluence would lead to poor experimental results and meaningless repeats and troubleshooting. Our analysis demonstrated that inexperienced students made more diverse decisions on cell confluence than did experienced researchers and the algorithm. A closer correlation was also observed between experienced researchers and the algorithm. In a busy lab, experienced researchers may overlook the guidelines on confluence determination for novice students for various reasons. Therefore, our algorithm would save time for experienced researchers and prevent consumable supplies being wasted due to mistaken confluence causing confused data.  
[PERSON] et al. proposed a non-invasive, non-destructive, and label-free method using ImageJfreeware bundled area fraction (AF) output analysis [28]. In our method, we proposed a scheme and designed a program on the Matlab platform by directly measuring the cell shapes and unoccupied space using hundreds of cell imaging files for testing. In this method, we built a regression model based on morphologic operations, an image enhancement method, and a quadratic regression. This algorithm could precisely recognize similar types of cells with distinct morphology and confluence. In previous work, the free software ImageJ was used to adjust parameters, and all operations had to be performed manually. For example, \"image analysis\" included eight steps that had to be operated non-automatically. This may be not suitable for processing sets of images. In this study, we were able to write the source codes with image processing techniques. This algorithm and its code are fully automatic for processing files by batch. We are able to modify the program if the images have different situations such as different illuminations and focal lengths, and even if the cell morphology is different from the shape of the fibroblasts. Our scheme established a robust and user-friendly code that has better potential for multiple applications.  
We used human oral fibroblasts in the current study. As the primary cells will enter replicative senescence, it may be possible to use this program to assess the growth of oral cells and their responses to compounds against oral ulcers. We also expect to execute the high-throughput analysis of malignant cells treated with therapeutic agents to determine the efficacy of drugs using this Confluence-Viewer program.
## 3 Hardware Design
### Data Acquisition Module Design
#### 3.2.2 Design of Six-Axis Acceleration Gyroscope Acquisition Module  
The design uses an InvenSense(r) (San Jose, CA, USA)'s MPU6050 chip, which integrates a three-axis acceleration and three-axis gyroscope acquisition module, communicates with the SMT32 through the IIC communication protocol, simultaneously outputs the operating data of the three-axis acceleration and three-axis gyroscope, and utilizes the DMP module (Digital Motion Processor) inside the MPU6050 chip to solve the sensor data, transmit the posture data to the microcontroller, and reduce the computing power of the master controller. The peripheral circuit design of the MPU6050 chip is shown in Figure 5. The following notations are used: SDA--data signal; SCL--clock signal; C3, C4, C5 and C6 are filter capacitors; R5 and R6 are pull-up resistors; R7 is a pull-down resistor.  
In this system, the MPU6050 chip is used to measure the elevator car posture, including rotation around the \(x\)-axis (roll angle), rotation around the \(y\)-axis (pitch angle), and rotation around the \(z\)-axis (heading angle yaw) [27; 28]. Due to the use of the three-axis acceleration  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average** & **(Statistics)** & **Variance (Statistics)** \\  & **Value** & **Standard Deviation** & \\ \hline Use of temperature compensation & 0.4371 & 0.0835 & 0.0069 \\ Temperature compensation not used & 0.5531 & 0.1491 & 0.0222 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Actual height sensor readings.  
Figure 4: Comparison of height sensor readings.  
Figure 5: MPU6050 peripheral circuit design.  
data alone for the posture solution, it is not possible to calculate the heading angle. And the use of the three-axis gyroscope data to calculate the car posture alone, there is a need to integrate the angular velocity over time, and the data results gradually shift with the change in time; therefore, there is a need for the complementary fusion of the two calculated results to ultimately obtain accurate data.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 3 Methodology
### Proposed Protocol Design
#### 3.2.2 Server Authentication  
The protocol begins with the client sending a ping (authentication) message to the server, \"_Authenticate me_\". The server sends its signed certificate \(Cert_{S}\) to the client. The client verifies the certificate with \(k_{pub,S}\) using ECDSA to validate the server's authenticity Upon successful validation, the client generates a nonce/coin value (\(r_{i}\)) that will be utilized in the protocol to prevent replay attacks. The client then derives a shared secret using ECDH from \(k_{pr,C_{i}}\) and \(k_{pub,S}\). The client then generates a shared local key (\(k_{local}\)) by applying SHA-3 to the shared secret.  
\[\text{Shared Secret}=ECDH(K_{pr,C_{i}},K_{pub,S})\]  
\[k_{local}=\text{SHA-3}(\text{Shared Secret})\]  
Afterwards, the client encrypts its certificate \(Cert_{C_{i}}\) along with the nonce \(r_{i}\) using \(k_{local}\) to generate the encrypted message \(m^{r}_{C_{i}}\) using AES-256 in cipher block chaining (CBC) mode. The client then generates an HMAC \(h_{m}=HMAC_{k_{local}}(m^{r}_{C_{i}})\) of the encrypted message. The encrypted message \(m^{r}_{C_{i}}\) and the corresponding HMAC \(h_{m}\) are transmitted to the server.
## 5 Results and Discussion
### C-2: Construction of the FBN Using Expert Elicitation (FPr) Only  
This subsection addresses the construction of the FBN for the likelihood of TEs using the FPr for all twenty-six BEs. Figure 8 shows the results obtained using FPr. The IEs have low failure probabilities compared to case C-1. Figure 9 compares the failure probabilities for cases C-1 and C-2. By comparing risk variables, notable differences emerge between CPr and FPr. Specifically, the failure probabilities for the equipment conditions and environmental factors are 23% and 3% greater, respectively, for CPr than for FPr. Conversely, the failure probabilities for weather conditions, operational parameters, and cybersecurity are 8%, 2%, and 8% lower for CPr than for FPr. Despite the difference in CPr and FPr IEs, the failure probability for TE remains the same for both cases. It is crucial to carefully distinguish that the failure of the power grid remains consistent regardless of whether historical failure data are used or expert elicitation is employed.  
CPr are based on historical failure data, which may lack important updates or safety and maintenance measures compared to expert elicitation. For instance, the probability of failure in cybersecurity is 0% when historical data are used. This finding seems implausible in real-world scenarios, given the increasing likelihood of cyberattacks due to technological advancements. Similarly, the significance of SCADA failure is given 12% weight when assessed by experts, compared to 1% extracted from RTO data, a critical BE that threatens the integrity of power grids. Such discrepancies highlight the importance of incorporating  
Figure 7: Power grid shutdown scenario modeled using the FBN.  
expert insights to capture nuanced risk factors and potential threats more accurately. The results of our fuzzy-aggregation approach offer valuable insights for informed maintenance and design decisions in power grid infrastructure. By identifying areas at higher risk of failure due to weather-related events, aging infrastructure, or environmental threats, the model helps prioritize maintenance resources and targeted interventions. For instance, regions prone to wildfires or extreme storms can benefit from strengthening grid infrastructure, while regions with aging equipment may require upgrades or replacement. The model's output can also enhance design strategies for new power grids, ensuring their resilience in the face of local environmental risks.  
Figure 8: Modeled FBN using expert elicitation (FPr) only.  
Figure 9: Comparison of failure probabilities of the IEs using CPr and FPr.
# Catalytic Advanced Oxidation Processes for Sulfamethoxazole Degradation
###### Abstract  
The degradation of sulfamethoxazole (SMX) by several advanced oxidation processes (AOPs) is carried out in the presence of different catalysts. The catalysts used consisted of carbon nanotubes (CNT), titanium dioxide (TiO\({}_{2}\)), a composite of carbon nanotubes and titanium dioxide (TiO\({}_{2}\)/CNT), and iron supported on carbon nanotubes (Fe/CNT). SMX removal was evaluated by catalytic ozonation, photocatalysts, catalytic oxidation with hydrogen peroxide, and combinations of these processes. The evolution of the SMX concentration during reaction time, the mineralization degree, the toxicity of the treated solution, and the formation of organic intermediates and ions were monitored. Ozonation catalyzed by Fe/CNT and CNT and photocatalytic ozonation in the presence of CNT presented the fastest degradation of SMX, whereas photocatalytic ozonation with CNT showed the best results in terms of organic matter removal (92% of total organic carbon (TOC) depletion). Total mineralization of the solution and almost complete reduction of toxicity was only achieved in the photocatalytic ozonation with H\({}_{2}\)O\({}_{2}\) and Fe/CNT catalysts. The compound 3-amino-5-methylisoxazole was one of the first intermediates formed during SMX degradation. p-Benzoquinone was only formed in photocatalysis. Oxalic and oxamic acids were also detected and in most of the catalytic processes they appeared in small amounts. Ion concentrations increased with the reaction time.  
catalytic ozonation; photocatalysis; hydrogen peroxide; emerging pollutants; carbon nanotubes; titanium dioxide +
Footnote †: journal: Applied Materials