esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Main Objectives and Scoop  
The main objective of the current paper is to use artificial intelligence (AI) techniques in the load-slip behavior prediction of channel shear connectors embedded in normal and high-strength concrete (HSC). For this purpose, an artificial neural network (ANN) is developed whose weights and biases are determined by the use of particle swarm optimization (PSO) technique. Along with the ANN-PSO model, another ANN model is also developed and tuned by a conventional backpropagation (BP) algorithm. To generate the required data for the ANN-based models, several push-out tests were conducted. Channel connectors with different dimensions were embedded into normal strength concrete and HSC with two different compressive strengths. Consequently, a dataset containing 1010 data points was gathered. In addition, the modes of failure and load-slip diagrams of the channel connectors in normal concrete and HSC have been determined and discussed. Finally, the load-slip behavior of channel connectors is predicted by both of the models, and the performance of the hybrid ANN-PSO model is compared with that of the ANN-BP model.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 6 System Model
### LTE Module and ICIC on ns-3  
We provide our proof-of-concept results using an LTE system model. The LTE module on ns-3 has two main components: the LTE model and the Evolved Packet Core (EPC) model. The former includes the E-UTRAN protocol stack, i.e., the RRC, PDCP, RLC, MAC, and PHY layers. These entities reside within the UE and the eNB nodes. The EPC model includes the core network functionalities, allowing end-to-end IP connectivity. All protocols and entities reside within the MME, S-GW, and P-GW nodes and partially within the eNB nodes. Figure 5 shows the LTE-EPC protocol stack for the data plane on ns-3 [40]. The only relevant simplification is the combination of the S-GW and P-GW functionalities into one node.  
There are currently seven ICIC algorithms implemented in the LTE module, described in [41]. The FFR algorithms act on scheduling, commanded by the MAC layer [40]. The algorithm is consulted and, depending on its rules for bandwidth allocation, it may allow (or not) the scheduling of a UE to a certain Resource Block Group (RBG). These algorithms have three main parameters that can be configured, as described below.  
The power level of a sub-band is defined through a power offset between the Reference Signal (RS) and the Physical Downlink Shared Channel (PDSCH). Each sub-band has a variable that defines this offset in decibels. For example, the Strict FR has _CenterPowerOffset_ and _EdgePowerOffset_.  
Resource allocation in LTE is done through RBGs, but its size in number of RBs depends on the system bandwidth [25]. For example, for a system bandwidth of 100 RBs, each RBG has four RBs [25]. Hence, each sub-band has a variable that defines the number of available RBGs for DL and Uplink (UL), separately.  
Section 3.1 introduced the RSRQ threshold, which determines user allocation on sub-bands. On LTE, RSRQ is measured in decibels and mapped into integer values before being reported [42]. On Release 8, these values range from 0 to 34. Hence, the variable _RsrqThreshold_ only assumes values within this range.  
Figure 5: LTE-EPC data plane protocol stack (taken from the ns-3 documentation [40]).
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 6 Results and Discussion
### Fire Challenges
At \(t=60\), when the fire starts to affect path 2, the only scheme that could handle this challenge was \(\text{MPResiSDN}(k=3)\) because it has a third path to use to deliver data. No overhead is generated by \(\text{MPResiSDN}(k=3)\) at this point because it works with only one path. \(\text{MPResiSDN}(k=2)\) and \(\text{MPResiSDN}(k=1)\) fail completely, and Spanning Tree continues to fail because it takes an exceptionally long time to find the new shortest path. Finding a new path becomes increasingly difficult for Spanning Tree due to rapid changes in network topology. In this case, obviously no overhead is generated as no data is being delivered. Figure 12(d) shows the CDFs of the delays incurred by each of the four schemes when evaluated under the fire challenge. As can be seen, the delay results here are like the delay results of the normal operation case. The delay values that are produced by all schemes during the entire time interval are almost constant. They primarily vary between 5 and 6 ms. As has been noted, regardless of the type of scheme or the challenge status, the delay values are not affected. In conclusion, using alternative paths definitely helped handle the fire challenge. Moreover, it is evident that increasing the number of alternative paths played a major role in increasing the resilience of routing schemes against the fire challenge. Differing from the case under no challenge, the generated overhead is not worthless.
## 2 Background
### Tree-Based Models  
Tree-based models are a common ML approach for analyzing network data, generally outperforming deep learning models on tabular data [28]. They are considered more  
Figure 1: The paper contributes to the challenge of evasions in network data by evaluating learning with genetically optimized adversarial samples.  
trustworthy than many other ML models due to their inherent interpretability [29]. Unlike deep learning models, which can be complex and opaque, tree-based models can be visualized as decision trees, making their decision-making process easier to understand. This transparency helps to build trust and accountability. Research studies, such as [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43], have explored the explainability and reliability of tree-based models. These studies have shown that, while tree-based models offer a degree of transparency when compared to deep learning models, such models are not immune to the challenges that can lead to less trustworthy ML models. Tree-based models, such as RF and XGBoost, have emerged as the preferred approach for data-driven, context-aware computing applications [36]. Their ability to handle tabular data effectively has contributed to their widespread adoption [15]. This prominence is particularly significant given the critical nature of many domains that rely on these models.  
RF represents an ensemble technique grounded in bagging (bootstrap aggregating), wherein the algorithm generates a \"forest\" composed of autonomous decision trees. Each tree is developed from a random selection of the dataset, thereby adopting range among the trees and allay overfitting by stopping any individual tree from exerting an unbalanced effect over the model. The ultimate prediction is derived by averaging the outputs of all trees for regression tasks or by executing a majority vote for classification. This parallel configuration of RF renders it relatively straightforward, resilient against overfitting, and efficient for a wide array of tasks. However, due to the independent nature of each tree, RF is deficient in a mechanism to exploit intricate patterns that may arise from the interactions among trees.  
XGBoost is a tree-based algorithm that utilizes boosting, where trees are constructed sequentially as opposed to concurrently. This sequential methodology allows each subsequent tree to concentrate on the errors or residuals produced by the ensemble of preceding trees, thereby enhancing accuracy with each successive iteration. This gradient boosting paradigm is directed by gradient descent, which thoroughly adjusts each tree's parameters to reduce errors and optimize predictions. Furthermore, XGBoost integrates advanced regularization techniques to avert overfitting, rendering it exceptionally accurate and computationally efficient for extensive datasets. However, due to its sequential characteristics, XGBoost may present a higher computational burden and greater complexity in parameter tuning compared to RF.
## 2 Theory
### Scattering Model of Dirty Particles  
Dirty particles above the surface of an optical element generally have equivalent diameters between tens of nanometers and tens of micrometers. The sample sizes of the dirty particles in the present study are smaller than the visible wavelengths. Therefore, these particles can be utilized to simplify the problem into a free-space particle scattering scenario using the two-interaction model [24] as proposed by [PERSON] [10] and [PERSON] [25], as shown in Figure 3.  
When the incident light irradiates the target surface at wavelength \(\lambda\) and angle of incidence \(\theta_{i}\), the pBRDF expression for a particle of radius \(a\) at a distance \(d\) above the smooth surface in the direction of the scattering zenith angle \(\theta_{s}\) and the relative azimuth angle \(\phi_{s}\) is as follows:  
\[\mathrm{pBRDF}_{part}=\frac{16\pi^{4}}{\lambda^{4}}\left(\frac{{n_{1}}^{2}-1}{{ n_{1}}^{2}+2}\right)^{2}\frac{a^{6}}{\cos\theta_{i}\cos\theta_{s}}\frac{NF}{A}M_{ij} \tag{8}\]  
where \(N/A\) represents the density of particle scatterers in the irradiated region. The structure factor \(F\) depends on the correlation between scatterers and equals 1 for random and uncorrelated examples.
# A Deep Learning-Based Course Recommender System for Sustainable Development in Education
## 3 DECOR: A Deep Learning-Based Course Recommender System
### Problem Formulation  
This study collects information from user behavior features and course attribute features that express users' interests and preferences in the online education domain. Let define \(U\) and \(V\) be the sets of users and courses and the binary user-course matrix \(Y\in R^{[U]\times[V]}\). Where \(Y\) indicates whether the user \(u\) takes the course \(v\) and if the user \(u\) has a taking record for course \(v\), then \(Y=1\); otherwise, \(Y=0\). The goal of recommendations for implicit feedback is to generate a course list base on the user's preference.  
\[Y_{uv}=\begin{cases}1,&\text{if user $u$ takes course $v$}\\ 0,&\text{otherwise.}\end{cases} \tag{1}\]  
To recommend a personalized course for users on the online website, it should consider additional information such as subject, job, and language skills. As discussed above, additional information can help improve the recommendation performance. Therefore, we define the collected dataset as follows. Suppose we are given a set of user behavior features \(\mathcal{U}=(U,J,\,C,L)\), where \(U=\left\{u_{1},u_{2},\cdots,u_{|U|}\right\}\) indicates the set of users,\(J=\left\{j_{1},\,j_{2},\,\cdots,\,j_{|I|}\right\}\) indicates the set of jobs, \(C=\left\{c_{1},c_{2},\cdots,c_{|C|}\right\}\) indicates the set of certificates and \(L=\left\{l_{1},l_{2}\cdots,l_{|L|}\right\}\) indicates the set of language skills. Here, \(|U|\), \(|J|\), \(|C|\) and \(|L|\), indicate the numbers of users, jobs, certificates, and language skills, respectively. \(\mathcal{U}\) indicates the set of user behavior features, where each feature is indicated as a specific instance (\(u\), \(j\), \(c\), \(l\)), \(u\in U\), \(j\in J\), \(c\in C\), and \(l\in L\); \(u\) indicates a user, \(j\) indicates a job, \(c\) indicates a certificate, and \(l\) indicates language skills. We also define the course attribute feature as \(\mathcal{C}=(V,\,M)\), where \(V=\left\{v_{1},v_{2},\cdots,v_{|V|}\right\}\) indicates the set of courses and \(M=\left\{m_{1},m_{2},\cdots,m_{|M|}\right\}\) indicates the set of subjects. Here, \(|V|\) and \(|M|\) indicate the numbers of courses and subjects, respectively. Additionally, \(\mathcal{C}\) indicates the set of course attribute features and is indicated as \((v,\,m)\); \(v\in V\) and \(m\in M\), where \(v\) indicates the course and \(m\) indicates the subject, respectively. The DECOR model utilizes a user behavior feature \(\mathcal{U}\), and a course attribute feature \(\mathcal{C}\) to predict the probability that user \(u\) will interact with the course \(v\). Therefore, this model can be defined as a predictive function \(\hat{y}_{uv}=\mathcal{F}(u,v;\,\Theta,\mathcal{U},\,C)\), where \(\Theta\) indicates the model parameters of function \(\mathcal{F}\).
## 1 Introduction
In addition, after 26-41 days of storage, a low level (0.3-20 cfu/g) of _Listeria innocua_ was observed in cold-smoked salmon treated at 900 MPa, although no _L. innocua_ was detected after five days [14]. However, _L. innocua_ was observed to be inactivated significantly (\(p<0.05\)) even after 60 s at 500 MPa [14]. [PERSON] and [PERSON] [15] observed that HPP treatment at 250 MPa for 20 min at a temperature of 9 \({}^{\circ}\)C did not inactivate _L. monocytogenes_ in cold-smoked salmon samples but increased the lag-phase time of _Listeria_. The lag-phase time of _L. innocua_ in cold-smoked salmon samples was also extended by HPP treatment above 600 MPa [14]. The extension of microbe lag-phase time also contributes to a longer shelf life of a food product even if the microbe is not totally destroyed. According to [PERSON] et al. [16], HPP treatment at a pressure over 414 MPa for 5 min reduced tested _Listeria_ strains by more than four logarithmic times in minced rainbow trout. In addition, sodium chloride (NaCl, 1% and 3%) enhanced the reduction of _Listeria_ at higher pressure treatments (ca 500 MPa) [16]. A synergistic antimicrobial effect of high hydrostatic pressure (HHP) treatment and liquid smoke was also shown to reduce different _Listeria_ strains, whereas NaCl had no significant effect for most of the samples [17]. However, the antimicrobial effect of NaCl also depends on the target microbe, as different _Listeria_ strains react differently to NaCl [17]. [PERSON] et al. [18] observed that smoke (phenol concentration 82 ppm) and NaCl (2.93%), together with high-pressure treatment (300 MPa for 15 min), prevented the growth of _L. monocytogenes_ effectively in cold-smoked dolphinfish fillets during the storage time. RTE food products are not heated before consumption, causing a risk of _Listeria_ infection if raw material has already been contaminated or the final product has been contaminated during manufacturing. Moreover, cold-smoked fish products are prepared within a temperature range that is below the inactivation temperature of pathogens such as _Listeria_. Thus, HPP treatment could enhance the microbial safety and extend the shelf life of RTE products. The aim of this study was to assess the effectiveness of different HPP treatment pressures, 200, 400, and 600 MPa, on the inactivation of _L. monocytogenes_ in cold-smoked and warm-smoked rainbow trout RTE fish products.
# A Review on Internet of Things for Defense and Public Safety
## 4 Operational Requirements
### Mobility Capabilities  
Mobility is another challenge for the IoT implementations because most of the services are expected to be delivered to users on the move. Service interruption can occur when the devices transfer data from one gateway to another. To support service continuity, [PERSON] et al. [93] propose a resource mobility scheme that supports two modes: caching and tunneling. These methods allow applications to access IoT data when resources become temporarily unavailable. The evaluation results show a reduction of service loss in mobility scenarios of 30%.  
The huge number of smart devices in IoT systems also requires efficient mechanisms for mobility management (the components and their needs are illustrated in Figure 9). For instance, a feasible approach for M2M communications is presented in [94]. In the scheme presented, group mobility is managed by a leader based on the similarity of their mobility patterns.  
Various solutions support mobility for vehicle-to-vehicle networking (Internet of Vehicles (IoV) solutions). An example of a group mobility mechanism for MANET is presented in [95], where the authors were inspired by the mobility of a flock of birds flying in a formation.
## 3 Results and Discussion  
### Chitosan Conversion from Chitin to Chitosan  
Chitosan is the product that results from the removal of the acetyl group from chitin. The amine group that is produced upon removal of the acetyl group is an important functional group that gives chitosan a variety of activities [32; 33]. When the degree of deacetylation is greater than 65%, the resulting product is soluble in acidic solutions. The FTIR spectra of chitin and chitosan are shown in Figure 2. The peaks at 3450 cm\({}^{-1}\) for chitin and chitosan were attributed to the stretching vibration and the intermolecular hydrogen bonding of the -NH\({}_{2}\) and -OH groups. The peak at 1660 cm\({}^{-1}\) in the spectra of chitin corresponded to the stretching vibration between the C=O and the N-H groups, and the peak at 1625 cm\({}^{-1}\) corresponded to the hydrogen bond resulting from the C=O and hydroxyl-methyl groups. In addition, the peaks at 1555 and 1315 cm\({}^{-1}\) corresponded to N-H and C-N groups, respectively. These results showed that the acetyl group was present in the chitin [34; 35].  
Figure 1: Measuring system for the moisture retention of emulsion.  
Figure 2: FTIR (Fourier Transform Infrared) spectra for chitin and chitosan.  
As shown in Figure 2, the peak at 1595 cm\({}^{-1}\) corresponded to the -NH\({}_{2}\) group, meaning that chitin was converted to chitosan, while the peak at 1555 cm\({}^{-1}\) disappeared. Since the amine group was produced by the removal of the acetyll group, a hydrophobic group, such as the N-octyl contained in aldehydes, can be used to prepare chitosan derivatives. As shown in Figure 3, the absorption bands at 2912 and 2855 cm\({}^{-1}\), corresponding to the stretching vibration of the -CH group, indicated that the alkylation process was conducted to modify chitosan by octaldehyde [36; 37]. The appearance of absorption bands at 1245 and 1212 cm\({}^{-1}\) was attributed to the presence of a sulfate group [38; 39]. The peak at 3450 cm\({}^{-1}\) in Figure 2, corresponding to the stretching vibration and the intermolecular hydrogen bonding of the -NH\({}_{2}\) and -OH groups in the chitosan, was shifted to 3280 cm\({}^{-1}\) in Figure 3 because of the conjugation in the sulfate group [36; 37]. Similarly, the peak at 1082 cm\({}^{-1}\) in Figure 2, corresponding to the stretching vibration of C-O in the chitosan, was shifted to 985 cm\({}^{-1}\). These results confirmed that the chitosan was modified as an amphoteric chitosan. Figure 3 shows the FTIR spectra of the modified chitosan and chitin, and the structure of the chitosan and the modified chitosan are shown in Figure 4. Unlike those of the modified chitosan, the absorption bands at 2912 and 2855 cm\({}^{-1}\), which resulted from the stretching vibration of the -CH group, were insignificant for chitin because no deacetylation process was conducted. However, the absorption bands at 3280 cm\({}^{-1}\) and 985 cm\({}^{-1}\), which corresponded to the stretching vibrations of -OH and C-O groups, indicated the presence of a sulfate group in chitin.  
Figure 3: FTIR spectra for the modified chitosan and the modified chitin.
## 1 Introduction  
Giant cell tumor (GCT) is a relatively uncommon, locally aggressive primary bone neoplasm characterized by the proliferation of three different cell populations: the giant cell tumor stromal cells, which constitute the neoplastic cells, and the mononuclear histiocytic cell and multinucleated giant cell (osteoclast-like) fractions that are secondarily recruited and comprise the non-neoplastic cell population. In this case, tumoral development is slow, but the recurrence rate can be as high as 40-50% [1].  
The main incidence of GCTs is between 25-40 years, and it is slightly more frequent in females with a ratio of 1.3:1 [2]. GCT represents about 4-5% of primary bone tumors and about 20% of benign bone tumors, with high incidence appearing in South and East Asia, where the incidence can go up to 20%. This type of tumor usually occurs in skeletally mature patients and very rarely occurs in patients with open physis [3; 4].  
The usual site of proliferation is the epiphyseal or metaphyseal region of the long bones, and half of the cases are located in the vicinity of the knee joint: distal femur, proximal tibia and fibula. Other frequent locations are the distal radius (11%); proximal humerus (4%); and flat bones (15%), especially the pelvis, sacrum and vertebral bodies. It is rare in the scapula, ribs, skull, mandibula and small bones (head and foot). Multicentricity of GCT is rarely encountered, and the differential diagnosis of a secondary bone tumor can be challenging in this specific situation [5].  
Although classified as a benign tumor, GCT has been observed to metastasize mostly to the lungs (5% of cases) and in rare instances (1-3%) can transform into the malignant sarcoma phenotype.
## 2 Related Work
Their study found that ensemble learning, mainly stacking models, offered significant improvements in accuracy, F1-score, and Matthew's correlation coefficient (MCC) compared to individual ML models. Like the approach by [PERSON] et al. [23], [PERSON] et al. [5] did not incorporate feature reduction techniques or apply hyperparameter tuning to optimize the performance of their ML models. Similarly, the study in [4] utilized MQTTset for binary classification. The authors employed feature selection methods to identify the most relevant features, enhancing the detection efficiency of an IDS. With the Pearson correlation coefficient (PCC) feature selection technique, they identified the top ten relevant features, significantly improving detection accuracy while reducing computational complexity. They evaluated multiple ML models, including Decision Tree, Random Forest, KNN, AdaBoost, and XGBoost, achieving high performance compared to previous studies utilizing the same dataset. As previously discussed, our study extends existing research by introducing SMDT, a feature selection method that leverages statistical central moments to identify significant features distinguishing between legitimate and malicious traffic. By focusing on these statistical moments, SMDT aims to reduce feature dimensionality while maintaining high detection accuracy. This approach addresses the limitations of traditional feature selection methods, particularly in resource-constrained IoT devices, by ensuring efficient performance without compromising resource availability. Table 1 summarizes the previous studies utilizing the MQTTset dataset. Figure 1 presents the proposed method's framework for feature selection and classification. This framework serves as the basis for the implementation discussed in the subsequent sections. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Study** & **Year** & **Classific-cation** & **ML Method** & **DL Method** & **Evaluation Metrics** & **FS Method** & **Retained Features** & **Hyperpara-meter Tuning** & **Cross-Validation** \\ \hline
[23] & 2020 & Multi-class & RF, NB, DT, GB, MLP & DNN & Accuracy, F1-score & ✗ & 33 & ✗ & ✗ \\ \hline
[5] & 2023 & Binary & DT, RF, NB, MLP, Bagging, Adaboost, AkoBoost, HGBoost, XGBoost, stacking & ✗ & Accuracy, F1-score, MCC & ✗ & 33 & ✗ & ✗ \\ \hline
[4] & 2024 & Binary & DT, KNN, RF, Adaboost, XGBoost & AkoBoost & AkoBoost & Accuracy, Precision, Recall, F1-score, ROC & 10 & ✓ & ✓ \\ \hline This study & 2025 & Binary and Multi-class & RF, LR, DT, GB, XGBoost, MLP & DNN & Accuracy, Precision, Recall, F1-score, ROC-AUC & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the studies that utilized MQTset. Figure 1: Overall implementation framework.