esa-sceva/satcom-chunk-collection huggingface dataset:
# Proactive Scheduling and Routing of MRP-Based Production with Constrained Resources
## 6 Computational Experiments
### Initial Case Study--Order Fulfillment and Baseline Comparison  
In the first phase, we analyzed two customer orders: one for 100 units of product **A** and another for 150 units of product **F**. The primary objective was to verify feasibility (Q1): Can the system fulfill customer orders with the available resources?  
We compared the following two methods:  
* Baseline Approach--using standard MRP II/ERP with manual scheduling.
* Proposed MILP Approach--using the model (1)-(18) with integrated PT/PR procedures. As shown in Table 5, the MILP-based method yielded the following results:
* A cost reduction of approximately 15% (from 6100 to 5600 units).
* A shorter execution time by eight periods (23% reduction).  
\begin{table}
\begin{tabular}{|l l l l l|} \hline \hline \multicolumn{1}{|c|}{**Q**} & **Constraints** & **Objective Function** & **Cost\_1** & **Cost\_2** & **Cou\_1** \\ \hline Q1 & MRPII/ERP & — & 3800 & 3900 & 80 \\ Q1 & 1–12 & — & 2840 & 2760 & 72 \\ Q2 & 1–14 & Min (Cost\_1 + Cost\_2) & **2310** & **2435** & 64 \\ Q2A & 1.12,15–18 & Min(Cost\_1 + Cost\_2), & 2480 & **2435** & 50 \\ Q3 & 1.12,15–18 & Min Cou\_1 & 2480 & **2435** & **48** \\ Q3A & 1.12,15–18 & Min Cou\_1, Cost\_1 + Cost\_2 \(\leq\) 4800 & **2310** & **2435** & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the first and second stage experiments.  
Figure 6: Pseudocode for PR procedure.  
This demonstrates that the model not only ensures feasibility but also improves schedule efficiency compared to traditional approaches.
## 2 Connected Cities and Its Development  
Several survey papers thoroughly inscribe the development of IoT and its application in smart cities. The work on connectivity protocol and standard procedures is given in [16]. This work explains the use of a developed model by the Internet Engineering Task Force (IETF). The IETF has enabled the IEEE 802.15.4 stack to connect with standard IPv4- and IPv6-based networks because IP-based networking is widely used. Low-power and lossy network routing (RLP) and constrained application protocol (CoAP) are discussed in this work.  
IoT must provide high-level security for secure data and information exchange. Various security protocols are analyzed in [17]. These protocols authenticate the data and processes and defend the integrity, as well as confidentiality, of the IoT data. The work relates IoT security to only the MAC layer of the IEEE 802.15.4 standards because physical layers are using the ASM band of 2.4 GHz with 16 channels and are provided with a direct sequence spread spectrum (DSSS) for overcoming channel congestion and improving the signal-to-noise Ratio (SNR). On the other hand, the MAC layer starts from a 32-bit message integrity code (MIC) data authenticity up to 128-bit data authenticity and encryption. The physical frame for IEEE 802.15.4 is 128 bytes and can be four types of frames defined by MAC--data frame, acknowledgement frame, beacon frame, or MAC command frame.  
IoT Market and Governing Technologies Overview  
The IoT market is widespread and is continuously expanding because of its core feature of autonomous control. IoT services can be used in the energy sector, health care, education, transportation, military, aerospace, agriculture, environmental studies, etc. [18]. These separate yet common-to-man systems must be unified for maximum output, as well as to be in the reach of beneficiaries. Therefore, regardless of the application of IoT in a unique environment (module), the basic architecture of the service follows the same standard protocol stack.  
The contribution of each stack level is given in Table 1.
# Adsorption of Emerging Pollutant by Pecan Shell-Based Biosorbent
## 3 Results and Discussion
### Biosorption Kinetics  
The batch adsorption experiments initially determined the equilibrium time, where a fixed CPX concentration was used and the adsorption time varied from 5 to 360 min. Figure 3 shows the experimental results obtained for (A) % of removal and (B) amount adsorbed.  
It took approximately 240 min of contact for the system to reach an equilibrium. The removal percentage of the initial CPX concentration in this period was approximately 49%; this corresponds to \(\pm\)2.30 mg of CPX per g of PSB. The adsorption speed is faster at the beginning of the process, while it gradually slows down at the end of the experiment until reaching an equilibrium. This adsorption behavior is due to the large number of empty and available active sites for adsorption at the beginning of the experiment. With the results of the equilibrium time, it was possible to create a mathematical modeling for the most frequently used kinetic models: pseudo-first order; pseudo-second order; and Elovich, which is calculated as expressed in Table 1. Figure 4 shows the linear graphs of each model, prepared in accordance with Avci (2020); Table 2 shows the calculated results.  
As Table 2 shows, the pseudo-second order model best fits the experimental data, for it has the highest coefficient of correlation (R\({}^{2}\) = 0.9915). In addition, the calculated adsorption capacity (\(q_{e}\) = 2.25 mg g\({}^{-1}\)) is close to the experimental adsorption capacity (\(q_{e}\) = 2.30 mg g\({}^{-1}\)). The better fit to the experimental data by a second order model indicates that the adsorption is driven by a chemical-type reaction rate (chemisorption); this  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kinetics Model** & \multicolumn{3}{c}{**Kinetics Model Parameter and Regression Coefficient**} \\ \hline Pseudo-first order & \(k_{1}\) (h\({}^{-1}\)) & \(q_{e}\) (mg g\({}^{-1}\)) & \(R^{2}\) \\ \cline{2-4}  & 0.0155 & 2.19 & 0.8553 \\ \hline Pseudo-second order & \(k_{2}\) (g mg h\({}^{-1}\)) & \(q_{e}\) (mg g\({}^{-1}\)) & \(R^{2}\) \\ \cline{2-4}  & 0.0082 & 2.25 & 0.9915 \\ \hline \multirow{2}{*}{Elovich} & \(\beta\) & \(\alpha\) & \(R^{2}\) \\ \cline{2-4}  & 2.05 & 0.15 & 0.9391 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetics model parameters and regression coefficients for ciprofloxacin the adsorption by biosorbent made of pecan shells.  
Figure 3: Equilibrium time for the CPX antibiotic. (**A**) the percentage of removal as a function of time; and (**B**) the amount adsorbed as a function of time.  
suggesting that the sharing and the exchange of electrons on the surface of the biosorbent for the adsorption of ciprofloxacin is the mechanism that drives the process [10].
## 3 Previous Wind Tunnel Studies on NVLBs
### The Flow Studies
#### 3.1.4 The Effect of Obstacles Surrounding an NVLB  
[PERSON] et al. [52] investigated the effect of vegetative environmental buffers (VEB), such as trees and shelterbetts, on the mitigation of odours from swine barns using both wind tunnel experiments and field monitoring studies. The wind tunnel experiments revealed that the animal barns themselves had the most significant impact on the flow patterns. Hence, the previously held assumptions that VEB are the main contributors to lowering the odours around a swine facility were incorrect. However, the authors outlined that more research is needed in order to understand the effects of VEB better further downwind and how VEB intercept and hold odours emitted from livestock buildings.
## 3 Results
### Situational Awareness  
#### 3.3.1 Task Performance  
The descriptive statistics and analysis of variance for the task performance measures of the correct rate and response time are shown in Figure 5. Regarding accuracy, the training group remained essentially unchanged [M\({}_{1}\) = 87.50, SD\({}_{1}\) = 5.89; M\({}_{2}\) = 88.00, SD\({}_{2}\) = 3.44]; the control group also did not change significantly [M\({}_{1}\) = 87.73, SD\({}_{1}\) = 3.44, M\({}_{2}\) = 82.27, SD\({}_{2}\) = 5.64]. Regarding the reaction time, the training group's reaction time decreased after training [M\({}_{1}\) = 82.27, SD\({}_{1}\) = 26.52; M\({}_{2}\) = 72.92, SD\({}_{2}\) = 19.72], and there was no significant change in the control group [M\({}_{1}\) = 79.12, SD\({}_{1}\) = 24.57, M\({}_{2}\) = 83.46, SD\({}_{2}\) = 12.89].  
Figure 4: Comparison of working memory capacity before and after training. (**a**) Verbal working memory capacity; (**b**) spatial working memory capacity.  
Before training, the independent-samples \(t\)-test found no statistical difference between the training group and the control group in terms of the correctness and response time before training [correctness: T(19) = \(-\)0.109, \(p\) = 0.914; response time: T(19) = 0.731, \(p\) = 0.474]. A 2 (pre-test, post-test) \(\times\) 2 (training group, control group) repeated-measures ANOVA was conducted for all task performance indicators; for the indicator of correctness, the main effects of the grouping, the time of measurement and the interaction between the grouping and time were not significant [\(p>0.05\)]. Regarding the reaction time, the main effect of the grouping was not significant [F(1, 19) = 0.018, \(p\) = 0.895, \(\eta^{2}\) = 0.001], the main effect of the time of measurement was not significant [F(1, 19) = 2.830, \(p\) = 0.109, \(\eta^{2}\) = 0.130], and the interaction between the grouping and time was significant [F(1, 19) = 9.846, \(p\) = 0.005 \(<\) 0.01, \(\eta^{2}\) = 0.341]. Further simple effects analyses showed that there was no significant difference between the training group and the control group before and after the training. There was also no significant difference in correctness [\(p>0.05\)]; however, the reaction time of the training group was significantly reduced after training compared to that before training [F(1, 19) = 11.088, \(p\) = 0.004 \(<\) 0.01, \(\eta^{2}\) = 0.369], while the reaction time of the control group was not significantly different before and after training [\(p>0.05\)].
## 5 Experiments
### Sensitivity Test  
In this experiment, we measured the sensitivity of GeneDiff to the parameters. In particular, we considered the number of training epochs and the dimensions of the vectors. We also measured the impact of the abstraction rules on clone detection. By default, GeneDiff utilizes the abstraction rules, its representation vector dimension is 100, and the number of training epochs is 50.  
To better illustrate the impacts of each parameter on clone detection, we used the control variable method in the experiment. Figure 10a shows the impacts of the dimensions of the representation vectors on the AUC-ROC, the average training time per function, and the average search time per function. We note that the AUC-ROC of GeneDiff seriously degrades when the representation vector dimension is low. The distinctions between instructions are not sufficiently obvious when the vector dimension is too low. When the vector dimension is increased, the AUC-ROC increase slightly. However, the average training time per function and the average search time per function increase sharply. After comprehensive testing, we believe that a reasonable dimension for the representation vectors is 100.  
Figure 10b shows the impacts of the training epoch on the AUC-ROC, the average training time per function, and the average search time per function. As the number of training epochs increases, the average training time per function increases linearly, while the average search time per function shows almost no fluctuations. When the number of training epochs is less than 50, the AUC-ROC increases rapidly as the number of training epochs increase; when the training epoch number exceeds 50, the AUC-ROC tends to remain stable.  
We also evaluated the impacts of the abstraction rules on clone detection. The abstraction rules can mitigate the binary differences of different ISAs, which makes GeneDiff more resilient to cross-architecture differences. As shown in Table 5, using the abstraction rules significantly increases the AUC-ROC and reduces both the training and search time. Using the abstraction rules also reduces the size of the vocabulary and mitigates OOV issues in query mode. Due to a lack of sufficient context, the semantics of words that occur at very low frequencies are difficult to represent accurately, which may increase the convergence time.  
Through the sensitivity test, we can confirm the reasonable parameters of GeneDiff, which are a representation vector dimension of 100 and 50 training epochs.  
Figure 9: Two examples of detection efficiency: sysinfo in binutils-2.32, libssl in OpenSSL-1.0.1f. (**a**) sysinfo; (**b**) libssl.
## 2 Climbing Scaffold Status Monitoring Method
### Climbing Scaffold Status Warning Model
#### 2.3.2 External Climbing Frame Status Warning  
The climbing scaffold status warning model is an integral part of the external climbing formwork state monitoring system (Figure 2). It processes real-time BeiDou/GNSS displacement data, enabling automated anomaly detection and risk assessment. The model workflow consists of the following steps:  
1. Data Input: This step receives filtered displacement data (E, N, and U components) from the GNSS monitoring system.
2. CUSUM Calculation: This step continuously computes positive and negative cumulative deviations to detect scaffold movement trends.
3. Threshold-Based Alerting: If the CUSUM statistic exceeds a predefined threshold, an alert is triggered.
4. Risk Notification: Warning messages are sent to the construction monitoring platform, enabling timely safety intervention.  
To ensure adaptability in varying construction conditions, the system implements multi-level warnings based on the deviation magnitude: Level 1 Warning: minor displacement anomaly detected, requiring observation; Level 2 Warning: significant deviation observed, requiring further monitoring; and Level 3 Warning: large abnormal displacement, requiring immediate safety intervention.  
By dynamically adjusting thresholds based on historical data and real-time trends, the system effectively minimizes false alarms while providing a timely risk assessment. This approach ensures the safe and stable operation of climbing scaffolds in super-high-rise buildings.  
Figure 2: Overall process diagram of external climbing formwork state monitoring based on BeiDou/GNSS technology.
## 2 Advanced Oxidation Processes
### Sonochemical Methods
#### 3.1.1 Parabens Abatement by Photolysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pollutant** & \multicolumn{2}{c}{**Test**} & **Results and** & **Reference** \\  & \multicolumn{2}{c}{**Conditions**} & **Conclusions** & **Reference** \\ \hline Bisphenol A, Bisphenol B, & \(\bullet\) & Sun radiation in a pilot plant. & \(\bullet\) & Degradation of about 30\%, 35\%, \\ Diamylphthalate, & \(\bullet\) & 0.3 mg/L of each pollutant in & 55\%, 50\%, 30\%, and 30\% for MP, \\ Butylbenzylphthalate, MP, & real wastewater effluent. & EP, BA, BB, BP, and DP, & [3] \\   and EP & & respectively, in 240 min. & \\ \hline MP & \(\bullet\) & UVA radiation & & \\  & \(\bullet\) & 10 mg/L of MP in deionized & \(\bullet\) & Degradation \(<\)5\% in 120 min. & [7] \\  & & water. & & \\ \hline BeP & \(\bullet\) & UVA radiation. & & \\  & \(\bullet\) & 10 mg/L of BeP in deionized & \(\bullet\) & Degradation of 10\% in 120 min. & [32] \\  & & water. & & \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & \(\bullet\) & 10 mg/L each paraben in & & 60\% for BeP in 2 min. About \\  & & & & 95\% for all in 120 min. \\ MP, EP, PP, BuP, BeP, and & & distilled water. & \(\bullet\) & Mixture degradation of about \\ p-HBA & \(\bullet\) & UVC radiation. & & 20\%, 30\%, 30\%, and 20\% in \\  & & & & 120 min (2 h) for MP, EP, PP, and \\  & & & & BuP and 80\% BeP removal in \\  & & & 20 min. Total removal in \\  & & & 480 min. & \\ \hline MP, EP, and PP & \(\bullet\) & Parabens mixture solution of & \(\bullet\) & Degradation of 0\% using UVA \\  & & 1 mg/L each in ultrapure water. & & and 16\% achieved by solar light \\  & \(\bullet\) & UVA and Solar radiation. & & in 60 min. \\ \hline  & \(\bullet\) & Paraben mixture solution of & & \\  & 10 mg/L each in ultrapure & \(\bullet\) & Degradation: \(<\) 4\% in 180 min. & [91] \\ MP, EP, PP, BuP, and BeP & \(\bullet\) & UVA radiation. & & \\ \hline EP & \(\bullet\) & Solar radiation & \(\bullet\) & Degradation of 0.46\% at lab \\  & \(\bullet\) & Simulated solar radiation in the & & scale in 2 h. \\  & & laboratory experiments & \(\bullet\) & At pilot scale was 1.5\% and \\  & & & 2.72\% in 2 h and 6 h. & \\ \hline  & & & \(\bullet\) & Deionized water: removal of \\  & & & 97–99\% and 98–99\% for PP and BuP, and the paraben mixture & \\  & \(\bullet\) & PP and BuP in demineralized & had 96–99\% and 97–99\% for PP \\ PP and BuP & \(\bullet\) & each. & & River water: 97\% and \(>\)99\% for \\  & \(\bullet\) & UV-Vis radiation. & & PP and BuP, while for the \\  & & & mixture of parabens was 90–99\% and 92–99\% for PP and BuP, respectively. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parabens removal by photolysis.
## 6 Protocol Comparison and Security Analysis  
In this section, we compare our proposed protocol with two other authentication protocols in terms of computing overhead and key security against replay attacks, denial of service attacks and impersonation attacks.
## 3 Results

Table 2: Relevant to the Stromboli campaign: main processing parameters. Figure 11: Relevant to the Stromboli campaign: MLC amplitude SAR image of the area where the CRs were deployed. The 8 CRs are highlighted by circles. The green circle represents the CR analyzed in Figure 12.  
Figure 12: Relevant to the Stromboli campaign: azimuth (left panel) and range (right panel) cut of the SLC amplitude image shown in Figure 11 in correspondence with the CR highlighted by the green circle. model dated to 2012 and characterized by a geometric resolution of 50 cm and a vertical error of 15 cm [78]. From the eight obtained interferograms (one for each straight track of each circuit in Figure 10), we obtained the corresponding DEMs, which were then properly mosaicked to achieve the final DEM reported in Figure 15.  
Figure 14: Relevant to the Stromboli campaign: single-pass interferograms. The red lines pictorially represent the corresponding acquisition flight tracks. Processing parameters are summarized in the right column of Table 2.  
Figure 13: Relevant to the Stromboli campaign: calibrated amplitude of MLC SAR images. The red lines pictorially represent the corresponding acquisition flight tracks. Processing parameters are summarized in Table 2.  
Note that all images reported in Figures 13-15 are geocoded and superimposed over a Google Earth image. Some further considerations are now in order. First, we stress that the processing of the SAR data was limited to the area covered by the available external DEM. Furthermore, we remark that the InSAR fringes observable in Figure 14 on the crater terrace of Stromboli and on the area known as Sciara del Fuoco [77] provide information about the topographic differences measured by the MIPS sensor and the external DEM used during the SAR focusing procedure. It is recalled that this external DEM is dated to 2012; therefore, the abovementioned fringes are due to the cumulated topographic variations induced by the volcanic activities that occurred in the past 10 years. Finally, we observe that, according to the Cramer-Rao bound reported in [79], the generated InSAR DEM is characterized by a height error with standard deviation on the order of 1.5 m.
# Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems
## 3 Cascaded Fuzzy Systems Based on Greenshields Models for Vehicle Speed and Travel-Time Prediction
### Inference Rule and Defuzzification  
To establish the fuzzy inference relationship between inputs and outputs, a fuzzy rule base grounded in Greenshields theory is developed to capture the relationships among traffic flow, density, and vehicle speed, thereby reflecting real traffic conditions. In this study, T-norms are primarily used to perform fuzzy set logic operations, supporting the construction of fuzzy rules and inference processes to assess the similarity between fuzzy sets. Within the fuzzy rules, the intersection of fuzzy sets is represented by the minimum value, while the union operation expresses the degree of inclusiveness between two fuzzy sets using the maximum value. Two separate fuzzy rule sets are formulated for congested and non-congested conditions, as detailed in Tables 7 and 8. The tabular representation of these rule sets helps ensure consistency, prevents contradictions, and avoids illogical rules that would not align with real-world highway traffic conditions, thereby supporting accurate vehicle speed prediction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Vehicle Flow** & \multicolumn{2}{c}{**Traffic Density**} & \multicolumn{2}{c}{**Vehicle Speed**} \\ \hline If & EL & OR & EH & Then & ES \\ If & EL & AND & VH & Then & ES \\ If & EL & OR & H & Then & VS \\ If & VL & OR & EH & Then & ES \\ If & VL & AND & VH & Then & VS \\ If & VL & OR & H & Then & S \\ If & L & AND & EH & Then & VS \\ If & L & OR & VH & Then & VS \\ If & L & AND & H & Then & S \\ If & Sp & OR & EH & Then & VS \\ If & Sp & AND & VH & Then & S \\ If & Sp & OR & H & Then & S \\ If & QL & OR & VH & Then & S \\ If & QL & OR & H & Then & S \\ If & QL & AND & De & Then & S \\ If & ML & OR & VH & Then & S \\ If & ML & OR & H & Then & S \\ If & ML & AND & De & Then & St \\ If & M & AND & VH & Then & S \\ If & M & AND & H & Then & St \\ If & M & AND & De & Then & St \\ If & MH & OR & H & Then & St \\ If & MH & AND & Dense & Then & St \\ If & MH & OR & QH & Then & QS \\ If & QH & AND & H & Then & St \\ If & QH & AND & De & Then & QS \\ If & QH & AND & QH & Then & QS \\ If & De & AND & De & Then & QS \\ If & De & OR & QH & Then & QS \\ If & De & OR & MH & Then & QS \\ If & H & AND & De & Then & MS \\ If & H & OR & QH & Then & MS \\ If & H & OR & MH & Then & MS \\ If & VH & OR & QH & Then & MS \\ If & VH & AND & MH & Then & MS \\ If & VH & OR & M & Then & M \\ If & EH & OR & QH & Then & MS \\ If & EH & OR & MH & Then & M \\ If & EH & OR & M & Then & M \\ \hline \end{tabular}
\end{table}
Table 7: Fuzzy logic rule set in congested mode.  
In the final stage of the Greenshields-based fuzzy logic system, defuzzification converts the fuzzy outputs into specific numerical values for further analysis and application. The centroid method is employed in the defuzzification process to calculate the center of the fuzzy result region, yielding a definitive and interpretable vehicle speed value.