esa-sceva/satcom-chunk-collection huggingface dataset:
# A Trust-Based Predictive Model for Mobile Ad Hoc Network in Internet of Things +
## 5 Security Analysis of the Protocol  
From the simulation results presented in the previous section, it can be seen that the proposed protocol outperforms the similar routing schemes. In this section, we discuss the attack model for the  
Figure 8: Throughput vs. number of malicious nodes.  
Figure 7: Packet delivery ratio vs. number of malicious nodes.  
proposed protocol. The trust model runs in real time and the malicious (i.e., dropped or falsely injected packets) can be detected on-the-fly and isolated as a _blacklisted_ node.
* _Address Spoofing_: In this attack, the attacker tries to spoof an address (that is IP and ID) of a victim node. Using a one-way secure hash function the present scheme can prevent the attacker from stealing the ID/IP of a node. Moreover, the attacker cannot generate the signature of the victim node. This is because an attacker may know the public key, but it is difficult to know the private key of the victim's node.
* _Routing Table Overflow and resource consumption attacks_: In the proposed model, trust is calculated real time and nodes are being monitored for their functionality in order to calculate trust. Thus, any compromised or malicious attacker generating overflow or resource consumption can be detected.
* _Byzantine/Blackhole/DoS Attack_: During node monitoring, if a node finds another neighbor sending packets to a particular node repeatedly, dropping packets, or holding the packet more for than a certain interval, a _Warning_ message is generated and temporarily restricts other good nodes from communicating with the suspicious node until the next review comes. In this way, Byzantine/blackhole/DoS attacks can be detected and prevented.
* _Sleep Derivation Attack_: In a similar manner, generating unnecessary traffic (e.g., seeking some information repeatedly in a small interval of time) can be identified and a _Warning_ message is generated. Unless it behaves properly, any request from that node will not be entertained. Thus, sleep deprivation attacks may be identified and mitigated.  
In the proposed scheme, nodes need a certain time for bootstrapping to be a member of a cluster. It should be noted that this bootstrapping time is a vulnerable time for the presented protocol. As it is a quantitative model, unless some behaviors are recorded, trust cannot be established. Thus, the proposed scheme requires _vulnerable time_ to detect any malicious activities.
## 5 Case Study Implementation of the Decision-Support Framework
### Traffic Characteristics  
The historical traffic volume data was obtained from traffic counting stations. Passenger vehicles were classified into passenger cars and passenger buses, accounting for 75% and 25%, respectively. Truck vehicles were classified into light-duty trucks and heavy-duty trucks, accounting for 55% and 45%, respectively.
## 2 Materials and Methods
### Machine Learning Models for Estimation of Sailing Losses
#### 2.6.2 Gradient Boosting Models  
Gradient boosting is a machine learning technique that builds an ensemble of weak learners, typically decision trees, in a sequential manner in which each new tree corrects  
Figure 5: Five-fold cross validation.  
Figure 6: Cross-validation RMSE obtained for each number of neighbours whilst performing grid search for the kNN model.  
Figure 7: Estimating SL in kNN when the number of neighbours is set to three.  
Gradient Boosting LightGBM Model  
LightGBM is a gradient boosting algorithm developed by Microsoft [28]. To improve the scalability and efficiency of a base gradient boosting algorithm, LightGBM implements techniques such as Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). These techniques, along with leaf-wise tree growth and the use of efficient data storage formats, allow LightGBM to achieve similar performances to other gradient boosting algorithms with higher speeds. This algorithm has been found to be robust for making predictions whilst working with meteorological data such as predicting PM\({}_{2.5}\)[29].  
Grid search and five-fold cross-validation were used to determine the parameters for lightgbm implementation in python. These parameters are presented in Table 6. The number of trees that the algorithm grows (n_estimators) is arbitrarily set to 80, reduced from the default value of 100 to prevent overfitting.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline n\_estimators & Number of trees & 80 \\ num\_leaves & Number of leaves that tree will grow & 16 \\ max\_depth & Maximum depth per tree & 6 \\ learning\_rate & Step size at each iteration & 0.1 \\ feature\_fraction & Random subset of features on each tree & 0.725 \\ lambda\_11 & L1 regularisation & 0.4 \\ lambda\_12 & L2 regularisation & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters for LightGBM model.  
Figure 8: Gradient boosting algorithm.  
CatBoost is a gradient boosting algorithm developed by Yandex [30]. Its main objective is to handle categorical variables during the training phase using a developed algorithm, rather than addressing them in the data preprocessing phase, as is common. Although the database used in this article for the regression task of predicting soiling losses does not contain any categorical variables, CatBoost has been employed for its ability to deliver good results with default parameters and its built-in early stopping system to prevent overfitting. The parameters for this model are listed in Table 7. Using the built-in overfitting detector based on early stopping, the model was trained by growing trees until an iteration failed to produce an improvement greater than the od_pval threshold. As shown in Figure 9, no additional trees were grown after iteration 90.
## 4 Conclusions
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15095009/s1](https://www.mdpi.com/article/10.3390/app15095009/s1)]([https://www.mdpi.com/article/10.3390/app15095009/s1](https://www.mdpi.com/article/10.3390/app15095009/s1)), Figure S1. Extracted ion chromatograms (exact mass \(\pm\) 5 ppm) of a water sample from Danube River. From the top to the bottom: TIC (total ions current); \(m/z\) = 332.14 ciprofloxacin; \(m/z\) = 461.15 oxytteracycline; \(m/z\) = 366.10 amoxicillin; \(m/z\) = 367.22 drospireno; \(m/z\) = 425.18 pravastatin; \(m/z\) = 229.12 bisphenol A; Figure S2. The precursor ion \(m/z\) = 297.0555 (imazalli) and ion fragments with \(m/z\) = 255.00 and \(m/z\) = 158.97 at the retention time (RT) of 6.87 min. The chromatogram was extracted with a mass tolerance of 5 ppm; Figure S3. Cumulative CECs concentrations in surface water in the Danube-Black Sea target area, calculated (a) without BPA; (b) with BPA; Table S1. Obtained results for physico-chemical parameters of water, with analytical errors; Table S2. Obtained results for metals in water, with analytical errors; Table S3. Random Forest correlations; Table S4. Total coliforms--CECs regression analysis. **Author Contributions:** Conceptualization, A.E., L.T. and G.E.B. ; Data curation, A.E., L.T., C.L.C. and A.M.V. ; Formal analysis, A.B., A.M.V. and D.S.-O. ; Funding acquisition, A.E. ; Investigation, A.E., L.T., C.L.C., A.B., C.D., G.E.B., A.M.V., D.S.-O. and E.E. ; Methodology, A.E., L.T., C.L.C., A.B., C.D., G.E.B., A.M.V. and E.E. ; Project administration, A.E. and L.T. ; Resources, A.E. and L.T. ; Software, A.E. and C.L.C. ; Supervision, A.E. and G.E.B. ; Validation, C.D., D.S.-O. and E.E. ; Visualization, C.L.C. and A.B. ; Writing--original draft, A.E., L.T., C.L.C., A.B., C.D. and G.E.B. ; Writing--review and editing, A.E., L.T., C.L.C., A.M.V., D.S.-O. and E.E. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was performed in the frame of the project with code BSB 27-MONITOX (2018-2021), financed through the Joint Operational Programme Black Sea Basin 2014-2020 of European Union. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. **Acknowledgments:** We acknowledge the technical support given by the DDNI and INPOLDE (UDJG) research teams during the sampling and analytical investigations. **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Analysis
### Method Accuracy Validation
#### 3.3.2 Quality Accuracy Verification  
Figure 12 displays the ovality comparative precision validation for the SILSS and the Leica Pegasus Two Ultimate scanner. The results reveal variations in ovality measurements, with differences generally remaining below 1 mm. This indicates a reasonable agreement between the two systems, supporting the capability of the SILSS to achieve reliable ovality measurements for tunnel deformation monitoring.  
Figure 11: Shield ring point cloud displacement. Difference between point cloud displacement measured by the proposed method and the Leica Pegasus Two Ultimate scanner system.
## 6 The Design and Application of MESs
### Microbial Remediation Cells  
Another challenge throughout urban systems as well as developing nations is environmental contamination, including soil and groundwater contamination. Microbial remediation cells (MRCs)present a solution to such contamination, without the costly or energy-intensive methods currently in use [1]. MRCs act as MESs and are placed at sites of contamination to enrich and stimulate the activity of microorganisms, including EAMs, capable of degrading pollutants. MRCs set up for the degradation of diesel have demonstrated removal rates of up to 82% of contaminants [132]. The role of the electrode in MRCs varies based on the type of contaminant being targeted, with MRCs focused on petroleum or diesel utilising it as an acceptor [132; 133], and those focused on the oxidation of uranium, perchlorate or chromium utilising the electrode as a donor [134; 135; 136].
# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 4 Experimental Studies
### Dataset Description
#### 4.1.12 Westeast Dataset  
The westeast dataset contains 20 train samples, with 10 labeled as positive (representing westbound trains) and 10 labeled as negative (representing eastbound trains). Each sample in the dataset is described by 24 features. The primary objective of the dataset is to classify trains based on their direction, either west or east. This classification is essential in transportation management, helping to automate the monitoring of train movements and ensuring timely and accurate transportation operations.
# Time Shared Optical Network (TSON): A Programmable Network Edge Solution for Multi-Access Support
###### Abstract  
The time shared optical network (TSON) has been proposed as a dynamic optical transport network solution to provide high bandwidth and low latency connectivity in support of 5G technology and beyond. This work reviews the TSON evolution stages developed in the framework of the U.K. national project Towards Ultimate Convergence of All Networks (TOUCAN). The details of the TSON architecture and its various development phases are discussed, and the performance of its latest implementation is evaluated through relevant demonstration activities across the Smart Internet Lab's 5G (5 GUK) test network.  
TSON; programmable; 5G; B5G; optical; fibre; SDN; edge node
# REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs
## Results
### Performance Analysis
#### 5.2.2 Experimental Analysis
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Process Type**} & \multicolumn{4}{c}{**Number of Process**} \\ \cline{2-6}  & \(SN\) & \(CH\) & \(LS\) & **Running Time** & **Storage** & **Energy** \\ \hline SHA1 hash & 1 & 1 & Many & 0.05529 & 160 bits & 0.008464 \\ BLAKE2 bp hash & 1 & 1 & Many & 0.040606 & 512 bits & 0.006216 \\ Keys generation & 2 & 2 & 2 & 0.000859 & 256 bits & 0.000132 \\ Point multiplication & 2 & 2 & Many & 0.000543 & - & 0.000083 \\ ECDSA-SHA1 signature & 1 & 1 & - & 0.072838 & 256 bits & 0.011151 \\ ECDSA-SHA1 verification & - & - & Many & 0.073103 & - & 0.011191 \\ ECDSA-BLAKE2 bp signature & 1 & 1 & - & 0.050046 & 256 bits & 0.007662 \\ ECDSA-BLAKE2 bp verification & - & - & Many & 0.052076 & - & 0.007972 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: REISCH’s computational processes. Figure 19: Minimum execution time of hash functions with different data sizes. Figure 17: Comparison of SHA and BLAKE2 with 1 MB data. Figure 18: Execution time of ECDSA-SHA1 and ECDSA-BLAKE2 bp with 1 MB data. Figure 22: Minimum execution time of ECDSA algorithms with different data sizes. Figure 23: Maximum execution time of ECDSA algorithms with different data sizes. Figure 20: Maximum execution time of hash functions with different data sizes. Figure 21: Average execution time of hash functions with different data sizes. Figure 22: Minimum execution time of ECDSA algorithms with different data sizes. We computed message complexity which is the number of messages transmitted between network entities. For each round, \(SN\) and \(CH\) send one message while \(CH\) and \(LS\) receive a set of aggregated messages. Thus, the message complexity with modified algorithms for \(SN\)s is (156,972), \(CH\)s is (8313), and \(LS\) is (165,285), while with original algorithms for \(SN\)s is (142,541), \(CH\)s is (7572), and \(LS\) is (150,113). Message overhead is to calculate the message size between network entities. In each round, the message overhead of \(SN\) is (1024 + 32) bytes while \(CH\) is (15,360 + 32) bytes. Figure 25 demonstrates that REISCH-ECDSA-BLAKE2 bp is better than REISCH-ECDSA-SHA1 in terms of alive \(SN\)s, namely, HWSN will have a longer life span to collect patient data when it uses REISCH-ECDSA-BLAKE2 bp. We noticed that REISCH with the modified algorithm (ECDSA-BLAKE2 bp) has more alive \(SN\)s by 24% than the original algorithm (ECDSA-SHA1). Furthermore, the first \(SN\) dies when using the modified algorithm in round 322, while in the original algorithm is in round 295.
# Field Data Forecasting Using LSTM and Bi-LSTM Approaches
## 2 Materials and Methods
### Data Collection (Study Area)  
Our data were collected using a testbed at Innovative Village in Pa Daet Sub-district, Mueang, Chiang Mai, Thailand (GPS coordinates: 18.7453356, 98.9801823). The sensors are installed in the greenhouse and include a soil sensor, an air indoor sensor, and an outdoor weather station (see Figure 2). The data collection list and proposals are explained in Table 1. Every five minutes, data are collected and stored on a Google Cloud IoT database.  
1. _Soil sensor:_ used to monitor the real-time soil moisture, soil temperature, soil pH, and soil electrical conductivity (EC) which impact crops growth and health;
2. _Air indoor sensor:_ used to monitor the real-time air temperature, relative humidity, UV index, and light intensity, which help to control the crops environment and maintain it as suitable to crop production inside the greenhouse;
3. _Outdoor weather station:_ used to monitor the weather parameters outside the greenhouse comprising air temperature, relative humidity, UV, light intensity, rainfall or precipitation, and wind speed, which also impact the environment inside the greenhouse  
Figure 1: Methodology of the proposed soil moisture forecasting model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Data Field** & **Purpose** \\ \hline
1 & Soil Moisture & The historical collected soil moisture value will be used for retraining the proposed forecasting model. \\ \hline
2 & Soil Temperature & The historical collected soil temperature value will be used to train/retrain the proposed model. And the real-time soil temperature value will be used to predict the future value of soil moisture. \\ \hline
3 & Indoor: Air Temperature & The air indoor temperature indicates the air temperature inside the greenhouse. \\ \hline
4 & Indoor: Relative Humidity & The indoor relative humidity indicates the air moisture inside the greenhouse that helps in making a decision for irrigation. \\ \hline
5 & Indoor: Light Intensity & The indoor light intensity indicates the temperature and relative humidity inside the greenhouse. \\ \hline
6 & Indoor: UV index & The UV index value impacts the temperature and relative humidity inside the greenhouse. \\ \hline
7 & Outdoor: Air Temperature & The air outdoor temperature indicates the air temperature outside the greenhouse. \\ \hline
8 & Outdoor: Relative Humidity & The outdoor relative humidity indicates the air moisture outside the greenhouse. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Collected data and their purpose.  
Figure 2: Multiple sensors installed for data collection (1 set of soil moisture sensors, 1 set of air indoor sensors, 1 weather station, and 1 set of water sensors).
# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 2 Materials and Methods
### Machine Learning Regression Algorithm  
It is common in machine learning to test different algorithms to find the one that best meets the needs of the user. In this specific study, the variable to be predicted was wheat yield at the end of the season. The auxiliary variables used were 14 NDVI images obtained from each of the S2 images, and the elevation obtained from the DEM. All variables were introduced into the model at the same time. With this inquiry in mind, the predictive ability of 15 different regression algorithms was analyzed using the Caret package [27]. The algorithms used were as follows: (1) Simple linear model (LM MDT), with DEM as the independent variable, (2) Artificial Neural Networks (NNET), (3) Partial Least Squares (PLS), (4) Linear Regression with Forward Selection (LeapForward), (5) Linear Model (LM), (6) General Linear Model (GLM), (7) Lasso (Lasso), (8) Bagged CART (Treebag), (9) Bagged EARTH (BagEarth), (10) Bayesian Regularized Neural Networks (Brnn), (11) Gaussian Process (Gaussian), (12) Boosted Tree (BstTree), (13) Support Vector Machines with Radial Basis Function Kernel (SvmRadial), (14) k-Nearest Neighbors (KNN), and (15) Random Forest (RF). Once the pre-processed dataset was standardized, it was used to train all the algorithms. The cross-validation (\(n=10\)) resampling technique was used to confirm the stability of each algorithm. The hyperparameters of the different algorithms were adjusted using the Caret package implemented in R.  
Seventy percent of the data (2641) were used to train all the algorithms. The accuracy of each model was evaluated using the root mean square error (RMSE) and the coefficient of determination (R2). The five models that obtained the best results in the training phase were selected for evaluation of their predictive capability with the test dataset (the remaining 30% of the dataset). This dataset includes the same variables as the training dataset, but the yield variable was used to validate the model.  
Once the algorithm with the best result was chosen, it was validated using the \"leave one out\" technique. In this case, the technique consists of training the model using all the data from all the plots except one. Then, the algorithm was validated using information from the remaining plot. This process was repeated for all the plots (13 times).
# Proactive Scheduling and Routing of MRP-Based Production with Constrained Resources
## 6 Computational Experiments
### Optimization Under Different Objectives (Q2-Q4)  
In the second phase, we applied the model to extended scheduling scenarios aligned with questions Q2-Q4:  
* Q2: Minimize total production cost (Cost_1 + Cost_2).
* Q3: Minimize the number of execution periods (Cou_1).
* Q4: Assess feasibility and optimize performance under resource unavailability. Several scenario variants were introduced:
* Q2A: Minimize cost under a time constraint (Cou_1 \(\leq\) 54).
* Q3A: Minimize time under a cost constraint (Cost_1 + Cost_2 \(\leq\) 4800).
* Q4A: Minimize cost under the unavailability of resource W1.
* Q4B: Minimize time under the unavailability of resource W1.
* Q4C: Minimize time under the unavailability of additional resource R2.
* Q4D: Minimize cost under the unavailability of additional resource R1.
* Q4E: Minimize cost under the unavailability of additional resource R2.  
Key insights (Table 5) include the following:  
* Cost optimization (Q2) yielded the lowest cost (4745 units), while time optimization (Q3) shortened execution to 48 periods--nearly half the baseline duration.
* Q3A satisfied both constraints with only a modest cost increase, demonstrating the model's flexibility.
* Q4D was the only infeasible case, revealing the unavailability of resource R1. This resource turned out to be critical for fulfilling product A, which requires R1 for the critical \"polishing\" step (Table A3). Since no alternative resources or routing options exist (Table A6), the model correctly flagged infeasibility, highlighting its ability to identify critical resource dependencies.  
These results confirm that the model can enhance the feasibility and cost-efficiency of MRP-driven systems, as well as improve responsiveness. Furthermore, they confirm the model's ability to explore trade-offs between cost and time and its capacity to proactively support a \"what-if\" analysis for risk-aware planning.
_Appl. Sci._**2015**, \(5\), 1164-1183; doi:10.3390/app5041164  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Contribution of TEMPO-Oxidized Cellulose Gel in the Formation of Flower-Like Zinc Oxide Superstructures: Characterization of the TOCgel/ZnO Composite Films  
**[PERSON] ***, [PERSON] and Claude [PERSON]**  
Lignocellulosic Materials Research Center, University of Quebec at Trois-Rivieres, des Forges avenue 3351, 500 Trois-Rivieres, QC G9A-5H7, Canada; E-Mails: [PERSON]ESS] (C.M.); [PERSON] (C.D.)  
*** Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel.: +1-819-376-5011; Fax: +1-819-376-5148.  
Academic Editor: [PERSON]  
_Received: 15 October 2015 / Accepted: 3 November 2015 / Published: 13 November 2015_  
**Abstract:** In the present paper, we report on a simple and new approach for the synthesis of hierarchical flower-like zinc oxide superstructures ZnO (FL) in the presence of the TEMPO-oxidized cellulose gel (TOCgel) through a room temperature sol-gel process in aqueous medium. Resulting composite films based on TOCgel and ZnO were investigated by several techniques including scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA) and mechanical tests. SEM images demonstrated the formation of well-shaped flower-like ZnO superstructures within the fibrous structure of the TOCgel with a uniform diameter (\(\sim\)5 \(\upmu\)m). FTIR and XPS results clearly confirmed the formation of such ZnO structures. We suggested that the carboxylate groups of TOCgel fibers act as capping agents and promote the construction of such flower-like ZnO via a nucleation-growth process. A proposed mechanism based on the oriented attachment-driven growth was discussed in order to explain the formation of ZnO (FL). The photocatalytic activity of the TOCgel/ZnO composite in the degradation of methylene blue (MB) under UV irradiation was clearly confirmed. Finally, mechanical tests demonstrated that the former TOCgel/ZnO film maintained a good flexibility (bent up to \(\sim\)120\({}^{\circ}\)) without losing its photocatalytic activity.  
**Keywords:** TEMPO-oxidized cellulose gel; flower-like ZnO; crystal growth; sol-gel; composite