esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Resource-Aware Federated Profiling Model
 \tag{2}\]  
\[\text{s.t. }EWP_{Te}<T_{delay} \tag{3}\]  
This utility function is used for MCDM modeling problem, where the edge quality profile attributes are normalized to be in the same interval scale and prioritized according to the weights given by domain knowledge experts depending on the application type and its requirements. To further explain the problem, the MCDM model is formalized as follows: Let A be a set of edge quality profile attributes, where  
\(A=\{C_{e},\beta_{e},S_{e},\mu_{e},DFR_{e},Reputation_{e}\}\) in the case of static profiling, and  
\(A=\{C_{u},E_{e},P_{e},\mu_{u},T_{train},DFR_{e},Reputation_{e}\}\) when dynamic profiling is considered. Let \(n\) be the number of quality attributes used for resource profiling, and \(A^{\prime}=\{a^{\prime}_{1},a^{\prime}_{2},\ldots,a^{\prime}_{n}\}\) is the set of normalized values measured for each of the quality attributes included in \(A\). Let \(W=\{w_{1},w_{2},\ldots,w_{n}\}\) be the set of weights given to each of the corresponding quality attribute in \(A\) to afford flexible prioritization for each attribute according to the applications requirements and preferences. \[EWP_{e}=\sum_{i=1}^{n}w_{i}*a^{\prime}_{i},\text{where}\sum_{i=1}^{n}w_{i}=1 \tag{4}\]  
If the status of the edge node given by \(EWP_{e}\) satisfies the federated profiling requirement and the time constraint is satisfied, the edge is selected. The \(Delay_{e}\) factor should be kept to a minimum for time-sensitive applications. **Example of an Edge Workload Profile (EWP)**  
A sample of the Edge Workload Profile which captures the static and dynamic characteristics of an edge node is presented in Figure 3.  
Dynamic parameters, such as CPU utilization, network statistics, and battery information are collected by the EWPM's resource monitoring agent; static parameters are kept up to date via the nodes' resource configuration files; and both sets of data are used to optimize the workloads. For the purpose of determining a node's reputation, the EWP keeps track of the total number of training requests as well as the number of requests that have been successfully completed. The FWP module at the federation server further parses the EWP received from all the edges.
# Multi-Objective Resource-Constrained Scheduling in Large and Repetitive Construction Projects
## 4 Case Study
Some indicative results of such kind are presented further below. The numbers in bold in Table 3 indicate the absolute best values of the examined parameters among all optimization tests in each project length/resource availability case. The results in Table 3 show that all criteria end up with considerable resource allocation improvement in comparison to the initial settings. All criteria but RIO eventually converge to the given project duration deadline. An overview of the table results shows the following:  
* Criteria STD, RLE, and MaxR aim mainly at resource leveling and overallocation reduction. The RLE and STD provide comparable outputs; in some cases, interestingly, a slightly better value of RLE is obtained when using the STD criterion rather than the RLE itself (and vice versa). Although the results are comparable, the main advantage of the RLE output is that it can be easily valuated in pure cost terms. Instead, the STD value does not have a direct cost representation of the resource allocation inefficiency. The MaxR criterion provides a coarser picture of the optimization output as it accounts only for the peak resource deviation without considering the distribution of individual resources in detail. However, this criterion occasionally ends up with the best RLE and/or STD outputs among other criteria;
* The RLE criterion appears to be more representative of the financial loss than the MaxR since it accounts for any single-resource excess above the availability threshold over the project duration. Instead, the MaxR criterion indicates the maximum excess
# Modeling and Simulation of IGCC Considering Pressure and Flow Distribution of Gasifier
## 2 Description of Shell Gasifier and IGCC Power Plant  
The Buggenum IGCC plant and its Shell gasifier are chosen to be the representative research objects. Pressurized coal, oxygen and, if necessary, steam are the main reactants and enter the gasifier through pairs of opposed burners. The raw gas (mainly H\({}_{2}\) and CO) leaves the reactor at near-gasification temperature, and is subsequently quenched by a cool recycled gas. Then the convective cooler cools the raw gas and generates superheated steam. After leaving the syngas cooler, the cooled gas passes through the bag filter where about 98% of the fly slag is removed. Part of the cleaned gas is recycled to be used as quench gas [8].  
In order to show the pressure and flow distribution characteristics for the Shell gasifier, the model consists of 10 compartments and is shown in Figure 1. Each compartment has unique parameters. For models of earlier works, which will be called no-pressure models in the following text, only the chemical reactions and energy equations are considered. Thus, the pressure and mass flow rate are not state parameters, which means that they are only related to the input parameters, while the inertias are ignored. However, in this paper, the pressure and flow distribution of the gasifier would be added into the new model and solved by the state equations through the volume-resistance method.  
The layout of the IGCC system is shown in Figure 2. The gasification island is introduced in the above. A remodeled gas turbine is the core of the power island, and a double pressure with reheating (2 PR) heat recovery steam generator (HRSG) recovers the heat from the exhaust gas of this gas turbine. As mentioned above, the waste heat from the cooled gas is also recovered to generate the superheated steam.  
The main steam from the high-pressure evaporator is mixed with the steam from one of the convective coolers of the gasification island, and is heated in superheater to enter the steam turbine. Then, the exhaust steam of the high-pressure cylinder, mixed with steam from the other syngas cooler, is heated in the reheater and expanded in the intermediate-pressure cylinder. At last, the low-pressure steam system generates the low-pressure steam, which is mixed with the exhaust steam of the intermediate-pressure cylinder and finally enters into the low-pressure cylinder.
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 4 Security Analysis
### Informal Security Analysis
#### 4.1.7 Stolen-Verifier Attacks  
In the proposed protocol, \(\mathcal{CSP}\) maintains a database to store the identities \(\{ID_{k},ID_{f}\}\) of \(\mathcal{U}_{k}\) and \(\mathcal{FN}_{f}\), respectively. However, the real identities of \(\mathcal{U}_{k}\) and \(\mathcal{FN}_{f}\) are encrypted using the private key \(x\) of \(\mathcal{CSP}\). Since \(x\) is the private key of \(\mathcal{CSP}\), which is only known to \(\mathcal{CSP}\), \(\mathcal{A}_{att}\) cannot extract \(\{ID_{k},ID_{f}\}\). Hence, our protocol resists stolen verifier attacks.
## 3 Research Methodology
### Speed Controller Subsystem  
Super Twisting Sliding Mode Controller  
The conflict between system chattering and attaining speed is resolved by the high-order sliding mode method. The super twisting algorithm, in contrast to other high-order sliding mode algorithms, does not require the sliding mode surface and its derivative to settle to zero instantaneously, preventing the need for complicated noise management law construction.  
The ST-SMC, created specifically for systems with a relative degree of one, is a workable substitute for the traditional first-order SMC without compromising tracking performance or chattering. This method describes a direction that resembles one of its twisting algorithms and converges in a finite amount of time with the appropriate parameter choices.  
The standard PI controller shown in Figure 10 is a non-linear variant of the ST method. The ST-SMC is more robust to the output amount of noise and potential estimate errors, making it particularly well suited for practical application. Two terms make up the ST-SMC control law \(u(t)\). The second term is the sliding variable continuous function that is available and is only present during the reaching phase, whereas the first component is described by the discontinuity of the derivative concerning time. The control algorithm is defined by the following control law [32].  
\[u(t)=U_{1}(t)\dotplus U_{2}(t) \tag{12}\]  
\[\dot{u}_{1}(t)=\left\{\begin{matrix}-u&\text{if }|u|>1\\ -Wsign(\sigma)&\text{if }|u|\leq 1\end{matrix}\right. \tag{13}\]  
\[u_{2}(t)=\left\{\begin{matrix}-\lambda|\sigma_{0}|\psi sign(\sigma)&\text{if }| \sigma|>\sigma_{0}\\ -\lambda|\sigma_{0}|\psi sign(\sigma)&\text{if }|\sigma|\leq\sigma_{0}\end{matrix}\right. \tag{14}\]  
where \(u\) is the boundary control value, \(\sigma_{0}\) is the boundary layer surrounding the sliding surface \(\sigma\), and \(W\), \(\lambda\), and \(\rho\) are control gains. In our controller, \(u\) is the output that controls the acceleration, and the inputs of the controller are \(\sigma\) and the error or distance between the sliding surface and our current value. The proposed speed controller in the system based on the ST-SMC accelerates the vehicle in normal conditions to its desired velocity  
Figure 9: Block diagram of PID-based braking control.  
and when braking is applied. Under this condition, the speed controller decelerates the vehicle using a throttle to support the EBS.
###### Abstract
[20] applied an ANN model combined with a soil conservation service (SCS) curve number (CN) to investigate the effects of land use and/or land cover on daily runoff. Their model produces highly accurate estimations. [PERSON], et al. [21] suggested a coupled model that combines the HEC-HMS model with the ANN model. They forecasted a fifteen-minutes event and continuous runoff simulation, and the results indicated that the coupled model outperformed the single model. In addition, some studies have shown the importance of selecting input data for data-driven models based on methods such as the Boruta algorithm and/or cross-correlation analyses and support vector machine recursive feature elimination (SVM-RFE). They found that the selected variables could improve the results of the data-driven models [11; 22]. To the best of our knowledge, several researchers have prioritized combining model-driven models and data-driven models. However, few of them applied combined model-driven models with ELM, SVR, and particularly LSTM for forecasting daily river runoff to present the best ML model. In the present study, our objectives were (i) to compare the potential of ML models with a hydrological model for simulating river runoff on a daily scale, (ii) to investigate the accuracy of the results if tropical rainfall measuring mission (TRMM) satellite data are applied in ungagged regions, and (iii) to predict 1-day-ahead river runoff with data-driven models and combine data-driven models with a hydrological model. The assumption of this study was that TRMM provides reliable satellite data for use in ungauged areas. Therefore, daily TRMM satellite data were used as inputs for the models instead of gauge precipitation data. Daily data were used because most of the studies were conducted for events and used hourly data. To the best of our knowledge, the proposed model, which is a combination of the LSTM model and HEC-HMS model and uses the SVM-RFE method to select input data, is a new model for simulating and predicting daily river runoff in the Voshmgir basin. Figure 1 shows the Voshmgir basin, which is located in the north of Iran.
## 4 Experiments
### Experimental Results
#### 4.3.2 Comparison of Kvasir-SEG Dataset  
The experimental results obtained by employing MSLUnet alongside various segmentation models on the Kvasir-SEG dataset are presented in Table 3. The highlighted section of the table indicates the optimal experimental results. An analysis of Table 3 reveals that MSLUnet outperforms other models across three evaluation metrics: Mean Intersection over Union (mIoU), Dice coefficient, and Specificity. Notably, the proposed method increases the Dice coefficient by 0.6%, the mIoU by 1.2%, and the specificity by 0.7%, outperforming the advanced GCASCADE [45] network.  
To better illustrate the comparative analysis, a visual comparison of segmentation results on the Kvasir-SEG dataset is depicted in Figure 6. The figure shows that most networks effectively segment small and distinct target regions. However, MSLUnet demonstrates su  
Figure 5: Qualitative comparison results of U-Net, U-Net++, Attention U-Net, Res-U-Net++, SwinU-Net, ConvUNeXt, CMUNeXt, AAU-Net, LM-Net, and MSLUNet on the BUSI dataset. The blue boxes represent our designed MSLUnet networks, while the red boxes highlight the segmentation regions where the MSLUnet networks outperform other models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Architecture** & \begin{tabular}{c} **mIoU** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Dice** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Specificity** \\ **(\%)** \\ \end{tabular} \\ \hline UNet [8] & 87.3 & 86.6 & 94.2 & 94.2 & 86.0 \\ UNet++ [9] & 90.8 & 90.0 & 95.1 & 95.0 & 87.2 \\ Attention\_Unet [11] & 88.9 & 89.5 & 95.2 & 95.1 & 87.4 \\ ResUnet++ [25] & 88.3 & 88.0 & 92.7 & 94.5 & 83.3 \\ SwinUnet [24] & 82.5 & 84.6 & 91.8 & 93.2 & 82.8 \\ ConvUNeXt [15] & 90.7 & 89.6 & 94.3 & **95.4** & 86.6 \\ CMUNeXt [43] & 90.9 & 91.2 & 94.9 & 95.3 & 87.0 \\ GCASCADE [45] & 89.9 & 92.6 & 95.2 & 94.6 & 87.7 \\ ULite [29] & 90.4 & 88.4 & 95.2 & 94.4 & 86.8 \\ LM-Net [28] & 89.2 & 91.5 & 95.0 & 95.3 & 87.3 \\ MSLUnet (our) & **91.1** & **93.2** & **95.6** & 94.9 & **88.4** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Presents a comparison of experimental results between MSLUnet and other segmentation models on the Kvasir-SEG dataset.  
perior capability in accurately delineating targets within endoscopic images of colon polyps, which are known for their intricate shapes, compared to other segmentation networks.
## 1 Introduction  
Systematic estimation of production time is essential for small and medium-sized enterprises (SMEs) to reach their full potential and maintain their market position. It enables efficient planning, resource optimization, and workforce management in manufacturing processes. The current progress of each production process can be monitored to identify problems or delays quickly. This allows for rapid response and corrective action to be taken. Overall visibility into the production process increases, reducing the risk of delivery delays.  
Many methods for processing data and estimating production time were developed during the 20 th century [1; 2; 3; 4]. Two major trends in developing time estimation methods emerged in the 20 th century. On the one hand, Predetermined Motion Time Systems (PTSs) were grown. PTSs are based on motion studies of manual work and time measurement units (TMUs). These methods are mainly developed under the Methods-Time Measurement Association, known as the Methods-Time Measurement (MTM). On the other hand, time studies based on direct measurement of work time in production were developed. The flagship organization for this approach is the REFA (Reichsausschuss fur Arbeitszeitermittlung, renamed to Verband fur Arbeitsstudien und Betriebsorganisation e.V.) [3,4].  
Production time estimation is based on motion data, time data, and specific data. These are current or historical data. Data are processed by PMTS or by mathematical methods, including descriptive analytics (DA), predictive analytics (PA), or simulation. DAs use descriptive statistics to describe the data they are [5]. The data from the technological systems are almost processed by the PAs [6]. PAs are statistical and probabilistic learning methods, fuzzy systems, optimization, topology, etc., applied by algorithms within Machine Learning (ML) [7].
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
## 2 Classification and Synthesis of Nanocellulose
### Bacterial Nanocellulose (BNC)  
Bacterial nanocellulose (BNC) is manufactured through the bottom-up technique (glucose molecules are metabolized to produce nanocellulose), whereas the other types of nanocellulose, such as CNC and CNF, are prepared through a top-down approach (cellulose molecules of higher dimensions are broken down to produce nanocellulose) [51]. BNC is produced as floccus, which then emerges into fiber and interweaves to create pellicles. Species of bacteria such as _Acetobacter_, _Azotobacter_, _Pseudomonas_, and _Sarcina ventriculi_ are capable of producing BNC. The fermentation of glycerol and glucose contained in natural or synthetic media (in which the bacteria is cultivated) results in BNC production. The mechanical characteristics, crystallinity, and shape of the produced BNC are influenced by cultivation conditions such as oxygen delivery, pH variations, etc. [35].  
BNC is biodegradable and commonly manufactured in the form of 10-100 nm diameter fibers, with a crystallinity range within 75-96% [52; 53]. Several approaches have been used to calculate the mechanical characteristics of the single fiber, with [PERSON]'s moduli ranging from 78 to 114 GPa, which is higher than CNF and CNC [54; 55].  
Figure 2 depicts the desirable properties such as lightweight, high flexible nature, and non-toxicity of various forms of nanocellulose derived from the cellulose, which makes them potential candidates in the biomedical field.
# Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM
###### Abstract  
Due to transistor shrinking, intermittent faults are a major concern in current digital systems. This work presents an adaptive fault tolerance mechanism based on error correction codes (ECC), able to modify its behavior when the error conditions change without increasing the redundancy. As a case example, we have designed a mechanism that can detect intermittent faults and swap from an initial generic ECC to a specific ECC capable of tolerating one intermittent fault. We have inserted the mechanism in the memory system of a 32-bit RISC processor and validated it by using VHDL simulation-based fault injection. We have used two (39, 32) codes: a single error correction-double error detection (SEC-DED) and a code developed by our research group, called EPB3932, capable of correcting single errors and double and triple adjacent errors that include a bit previously tagged as error-prone. The results of injecting transient, intermittent, and combinations of intermittent and transient faults show that the proposed mechanism works properly. As an example, the percentage of failures and latent errors is 0% when injecting a triple adjacent fault after an intermittent stuck-at fault. We have synthesized the adaptive fault tolerance mechanism proposed in two types of FPGAs: non-reconfigurable and partially reconfigurable. In both cases, the overhead introduced is affordable in terms of hardware, time and power consumption.  
fault tolerance; error control codes; fault injection; hardware description languages; logic circuits; FPGA +
Footnote †: journal: Electronics Electronics
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 3 Experiments and Results
### Stop Position Accuracy Measurement  
The purpose of path tracking is to follow a reference trajectory to a specified destination, so the accuracy of the position of the vehicle when it stops is of equal importance. In this experiment, the vehicle starts from the same starting point every time, drives one lap of the test field automatically, and returns to the starting point, as is shown in Figure 10.  
As no position sensor was used in this experiment, the accuracy of the stop position was obtained manually using a ruler, as is shown in Figure 11.  
The stop position error is displayed in Table 2. Three different speeds were tested in this experiment, and three sets of experiments were conducted for each speed.
## 4 Results and Discussion
### Characterization Results of Coupler and Integrated Passive Component Block  
A comparison of simulated and measured results of the coupled-line coupler covering the 24.25-40 GHz frequency range is shown in Figure 15a. The maximum measured insertion loss of the coupler in the entire frequency range is 0.4 dB, the return loss is better than 20 dB and the coupling is about 20 dB with small variation.  
Figure 13: Comparison of simulated and measured results of hairpin diplexers (**a**) for bands n257 and n260 and (**b**) for bands n258 and n260.  
Similarly, for the fabricated integrated passive component block in Figure 10, only two paths are measured: the band n257 filter in the diplexer to the antenna port, and the band n257 filter to the coupled port of the coupler. All additional line lengths to accommodate ease of testing are de-embedded to obtain the results shown in Figure 15b.
## 2 Channel Model and Signal-to-Noise Ratio
### Signal to Noise Ratio for Angle-Diversity Receivers
Replacing Equation (29) in Equation (28), the SNR using MRC can be calculated as the sum of each SNR of the \(j\)-th receiving elements, which is given by:  
\[SNR_{MRC}=\frac{\left(\sum\limits_{j=1}^{J}\left(k_{0}\cdot\frac{RP_{S,j}}{ \sigma_{j}^{2}}\right)RP_{S,j}\right)^{2}}{\sum\limits_{j=1}^{J}\left(k_{0} \cdot\frac{RP_{S,j}}{\sigma_{j}^{2}}\right)^{2}\sigma_{j}^{2}}=\sum\limits_{j= 1}^{J}\frac{\left(RP_{S,j}\right)^{2}}{\sigma_{j}^{2}}=\sum\limits_{j=1}^{J}SNR _{j} \tag{30}\]  
The SB method chooses the branch or receiving element with the highest SNR from among all branches. This technique can often improve SNR since it separates the signal from ambient light noise, but the gains are not as large as those achieved using MRC. Simulations have shown that SB requires 1-2 dB more transmitter optical power than MRC [27]. Also, in [11], it was shown that a reduction in multipath delay spread occurs when directional narrow FOV receivers are employed in the branches, making SB a suitable technique for high bit-rate systems. On the other hand, SB is not simpler to employ than MRC, since it requires estimating the SNR at each diversity branch. The SNR using SB is given by:  
\[SNR_{SB}=\underset{j}{max}\left(\frac{\left(\frac{\left(RPs_{j}\right)^{2}}{ \sigma_{j}^{2}}\right)}{\sigma_{j}^{2}}\right),\ \ \ 1\leqslant j\leqslant J \tag{31}\]  
EGC corresponds to MRC, but without attempting to weigh the signals; that is, the weights of all the received signals \(I_{j}(t)\) are equal to a constant. This technique increases the receiver FOV but is unable to separate the signal from ambient noise. Moreover, using EGC can result in an increase in multipath distortion, making it unsuitable for very high bit rate links. The main advantage of EGC is that it avoids the need to estimate the SNR [7]. The SNR using EGC is not dependent on the constant weight value, and its expression is given by:  
\[SNR_{EGC}=\frac{\left(\sum\limits_{j=1}^{J}wRPs_{j}\right)^{2}}{\sum\limits_{ j=1}^{J}w^{2}\sigma_{j}^{2}}=\frac{\left(\sum\limits_{j=1}^{J}RPs_{j}\right)^{2}}{ \sum\limits_{j=1}^{J}\sigma_{j}^{2}} \tag{32}\]