esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic
###### Abstract  
In this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units and are separated from the remote radio heads (RRHs). Based on their radio capacity, the RRHs may form one or many clusters: a single cluster when all RRHs have the same capacity and multi-clusters where RRHs of the same radio capacity are grouped in the same cluster. Each RRH services the so-called multiservice traffic, i.e., calls from many service classes with various radio and computational resource requirements. Calls arrive in the RRHs according to a quasi-random process. This means that new calls are generated by a finite number of mobile users. Arriving calls require simultaneously computational and radio resource units in order to be accepted in the system, i.e., in the serving RRH. If their requirements are met, then these calls are served in the (serving) RRH for a service time which is generally distributed. Otherwise, call blocking occurs. We start with the single-cluster C-RAN and model it as a multiservice loss system, prove that the model has a product form solution, and determine time congestion probabilities via a convolution algorithm whose accuracy is validated with the aid of simulation. Furthermore, the previous model is generalized to include the more complex case of more than one clusters.  
cloud-radio access; cluster; time congestion; quasi-random; product form; convolution 2021 29 August 2021 15 September 2021 15 September 2021
## 2 Bioactive Lipids in Inflammation and Bone Remodelling
### Eicosanoids: Chemistry, Signalling and Pathophysiology  
Eicosanoids are the endogenous lipids that have been studied most thoroughly and for the longest time. They are produced during the whole inflammatory event, where they sustain the activation of the immune system during both acute and chronic inflammation. In addition, eicosanoids control a number of non-pathological processes, such as gastoprotection and kidney blood flow, the impairment of which is at the basis of the undesired side-effects of prolonged use of non-steroidal anti-inflammatory drugs [69]. The synthesis of eicosanoids starts when arachidonic acid (AA) is cleaved from the _sn_-2 position of membrane phospholipids, through the action of phospholipase A\({}_{2}\) (PLA\({}_{2}\)), and then is channeled towards distinct biosynthetic routes: the cyclooxygenase-1/-2 (COX-1/-2)-dependent dioxygenation of AA conveys prostaglandin H\({}_{2}\) (PGH\({}_{2}\)), which in turn is converted into 4 prostaglandamins (i.e., PGI\({}_{2}\)/D\({}_{2}\)/E\({}_{2}\)/F\({}_{2\alpha}\)) or into thromboxane A\({}_{2}\) (TXA\({}_{2}\)), by means of their respective synthases [70]. The other main branch of the so called \"AA cascade\" goes through the catalytic action of 5-, 12- and 15-lipoxygenase (5-, 12- and 15-LOX) isozymes, and yields different leukotrienes (LTs) [71], along with other minor congeners, such as the hydroperoxides (HpETE) and hydroxides (HETE) ofAA, and lipoxins [71; 72]. Eicosanoids act by engaging distinct G protein-coupled receptors (GPCR), as summarized in Figure 2, and act as the main scaffold of the inflammatory network during the immune response [49]. Indeed, they are produced by (and act on) immune cells during the onset of acute and chronic inflammation, when they enhance the response to pathogen- and damage-associated molecular patterns (PAMPs and DAMPs, respectively) and to cytokines, and promote the differentiation, recruitment and activation of pro-inflammatory lymphocytes (e.g., T helper (T\({}_{\text{H}}\))1 and T\({}_{\text{H}}\)17) and monocytes/macrophages [49; 73]. Eicosanoids have also been involved in bone tissue remodelling. In particular, PGE\({}_{2}\) seems to be mainly involved in bone turnover, where it promotes both matrix deposition and resorption, possibly by acting on different receptors of osteoblasts and osteoclasts, in concert with other hormonal signals [61; 62]. The main metabolic pathways leading to eicosanoid synthesis, and the main targets on which microgravity is known to act, are summarised in Figure 2.
# Torsion Test vs. Other Methods to Obtain the Shear Strength of Elastic-Plastic Adhesives
## 2 Materials and Methods
### SLO--Single Lap Offset  
The SLO in compression test (Figure 1a) is adapted from the standard ASTM D905-08 and also described in [21]. The fixture was designed to minimize the substrates' bending and rotation, thus obtaining a parallel relative displacement between them, which generated pure shear (Figure 3). The much higher stiffness of adherend materials (\(E\) moduli: Si4N4 320 GPa, SiC 400 GPa, steel 206 GPa) with respect to the adhesive (\(E\) modulus 4 GPa) contributed to approach the condition of deformable adhesive between stiff adherends, with a consequent, nearly uniform, stress along the bondline. In addition, as the adhesive was ductile, the unavoidable stress peaks at the interface ends could also be compensated for by plasticity when the joint yielded.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Test Type**} \\ \hline
**Adherends** & **SLO (MPa)** & **A4 BP (MPa)** & **THG (MPa)** \\ \hline Si3N4 (\({}^{\circ}\)) & 42.2 \(\pm\) 1.3 & 41.6 \(\pm\) 8.0 & 44.5 \(\pm\) 4.4 (62.6 \(\pm\) 5.0) \\ \hline Sic & 41.6 \(\pm\) 0.9 [20] & 41.5 \(\pm\) 5.0 & 46.5 \(\pm\) 3.7 (62.0 \(\pm\) 4.0) \\ \hline Steel & 40.8 \(\pm\) 4.6 & 39.0 \(\pm\) 3.0 & 52.2 \(\pm\) 4.6 (67.0 \(\pm\) 6.0) \\ \hline \hline \end{tabular} \({}^{\circ}\) engineered surface cohesive failures [19]; all other failures are interfacial.  
\end{table}
Table 1: Results after mechanical tests on Hysol EA9321 joined samples. Torsional (THG) shear strength results obtained by analytical modelling (in grey, in parentheses, are the results obtained by using the maximum of the torsion curve, for discussion purposes only).  
Figure 2: Torsion test apparatus.  
In this perspective, evaluating the shear strength \(\tau_{el}\) as a load-to-area ratio gave not only a conventional value but also an actual material property of the adhesive.  
Tests with these SLO fixtures were performed at a controlled displacement of 0.5 mm/min.
# ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering
## 4 Evaluation
### Evaluating Churn Classifier Performance
#### 4.2.4 Statistical Validation  
To verify that ChurnKB's improvements are not due to random variations, a paired \(t\)-test was conducted. Table 3 presents the results, comparing model performance with and without ChurnKB-derived features. The T-statistic measures the magnitude of performance differences, while the \(p\)-value indicates statistical significance. A larger absolute T-statistic strengthens evidence against random chance, and a smaller \(p\)-value (typically < 0.05) suggests the improvement is unlikely to be random. The results show statistically significant gains across all metrics, confirming that ChurnKB-derived features enhance model performance.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Recall** & **Precision** & **F1 Score** \\ \hline \multicolumn{4}{c}{(A) Scenario One: Numerical Data Only.} \\ \hline Tabnet & 0.5000 & 0.7800 & 0.6094 \\ Gradient Boosting & 0.4970 & 0.7446 & 0.5894 \\ XGBoost & **0.5005** & **0.6830** & **0.5752** \\ MLP & 0.5065 & 0.6190 & 0.5571 \\ LightGBM & 0.4823 & 0.6711 & 0.5567 \\ \hline \multicolumn{4}{c}{(B) Scenario Two: Textual Data Only.} \\ \hline Linear Discriminant & 0.6308 & 0.7966 & 0.7021 \\ Analysis & 0.6410 & 0.7463 & 0.6897 \\ AdaBoost & 0.6229 & 0.7661 & 0.6850 \\ Tabnet & 0.6301 & 0.7188 & 0.6715 \\ XGBoost & **0.6057** & **0.7565** & **0.6698** \\ \hline \multicolumn{4}{c}{(C) Scenario Three: Numerical + Textual Data.} \\ \hline XGBoost & **0.7085** & **0.8946** & **0.7891** \\ LightGBM & 0.7013 & 0.8963 & 0.7842 \\ AdaBoost & 0.7193 & 0.8531 & 0.7754 \\ Tabnet & 06623 & 0.7969 & 0.7234 \\ MLP & 0.6494 & 0.7812 & 0.7092 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model performance comparison across different data scenarios, organized in ascending order, with XGBoost consistently appearing in all scenarios and showing performance improvement from A to C.  
For the purpose of conducting an ablation study and measuring the impact of ChurnKB-enabled features, we considered the results derived from XGBoost, which demonstrated the strongest predictive performance. Table 4 presents the impact of ChurnKB-enhanced features in three experimental setups (A) a baseline model, using only numerical features (without ChurnKB); (B) a ChurnKB-enabled model, using only ChurnKB-derived textual features (lexicon-based churn indicators from ChurnKB); and (C) a full model, combining both numerical and ChurnKB-enhanced features.  
The ablation study confirms that ChurnKB contributes significantly to churn prediction performance. The ChurnKB-derived textual features alone improve the F1 score by +9.4% compared to the baseline. When combined with numerical features, ChurnKB enhances overall performance by +21.3%, demonstrating its effectiveness in feature enrichment.
# A Complete EDA and DL Pipeline for Softwarized 5G Network Intrusion Detection
## 6 Conclusions and Future Works  
The rise of 5G networks is driven by increasing deployments of IoT devices and expanding mobile and fixed broadband subscriptions. This growth supports new applications like AR/VR, smart factories, and autonomous vehicles. Concurrently, the deployment of 5G networks has led to a surge in network-related attacks, due to expanded attack surfaces. Reports have highlighted a significant increase in attacks, with IoT malware incidents rising sharply. Machine learning (ML), particularly deep learning (DL), has emerged as a promising tool for addressing these security challenges in 5G networks. Such systems can efficiently analyze vast amounts of data, enabling effective intrusion detection, without explicit programming. This approach has shown success in various applications and positions ML and DL as key technologies for enhancing security in 5G environments.  
To that end, this work proposed a research initiative that adopted an exploratory data analysis (EDA) and DL pipeline designed for 5G network intrusion detection. This approach aimed to better understand dataset characteristics, implement a DL-based detection pipeline, and evaluate its performance against existing methodologies, thereby contributing to advancements in securing 5G networks. The experimental results using the 5G-NIDD dataset showed that the proposed DL-based models had extremely high intrusion detection and attack identification capabilities (above 99.5% and outperforming other models from the literature), while having a reasonable prediction time. This highlighted their effectiveness and efficiency for such tasks in softwarized 5G environments.  
Several future research directions could be followed to build on this work. One future endeavor would be to investigate the impact of different hyperparameter tuning algorithms on the performance of the different models. This has the potential to further improve the detection effectiveness of the framework. A second endeavor worth exploring would be studying the impact of hybrid feature selection algorithms to reduce the computational  
Figure 16: Recurrent neural network confusion matrix—multi-class classification scenario.  
complexity, and thus the training time, required for such models, without sacrificing their intrusion detection and attack identification capabilities.
## 4 Discussion
### Limitations and Prospects  
There were limitations in this study. The length of the response period may not be accurate enough, since the temporal resolution of the datasets we used were monthly. With regard to the spatial \(r\) of SIF with the four variables, there were insignificant or poor \(r\) values in some areas after adjusting the time series of variables. The potential reason for this was that a single climatic variable change cannot fully explain the vegetation greenness or productivity dynamics. We did not comprehensively utilize P, VPD, ES, and RSM to explore and analyze the response of SIF to a combined indicator or index. In future studies, we will combine multiple climatic factors to generate a composite indictor to explore its relationship with vegetation growth, ecosystem productivity, or carbon exchange. We will also utilize higher temporal and spatial resolution datasets to determine the response period of the ecosystem to climatic factors. We will also extend the study area to larger regions with diverse vegetation types in order to analyze the dynamic response between different vegetation types and climatic factors.
## 5 Materials and Methods
### Preprocessing  
In this stage, we used segmented grayscale images from DMR-IR datasets that were preprocessed by employing cropping, normalization, and resizing. In post-preprocessing these thermal images were transformed from the original 640 \(\times\) 480 dimensions into 56 \(\times\) 56 dimensions, which is the resolution often utilized in CNN applications [25]. In  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Approaches** & \
\begin{tabular}{c} **Imaging** \\ **Modalities** \\ \end{tabular} & **Datasets** & **Results** \\ \hline \multirow{3}{*}{[26]} & \multirow{3}{*}{CNNs+RNNs+AM} & \multirow{3}{*}{Histopathology} & BACH2018 & Acc = 92\% \\  & & & Bioimaging2015 & Acc = 96\% \\  & & & Extended Bioimaging2015 & Acc = 97.5\% \\ \hline \multirow{3}{*}{[27]} & \multirow{3}{*}{CNNs+AM} & \multirow{3}{*}{Histopathology} & \multirow{3}{*}{BreakHis} & Acc = 98.51\% \\  & & & & Sn = 98.70\% \\  & & & & F1-score = 98.28\% \\ \hline \multirow{3}{*}{[28]} & \multirow{3}{*}{CNNs+AM} & \multirow{3}{*}{Histopathology} & BreakHis & Acc = 99.3\% \\  & & & & \\ \hline \multirow{3}{*}{[29]} & \multirow{3}{*}{CNNs+AM} & \multirow{3}{*}{Histopathology} & Peking Union Hospital Benchmark & Acc = 96\% \\  & & & & \\ \hline \multirow{3}{*}{[30]} & \multirow{3}{*}{CNNs+BLSTM+AM} & \multirow{3}{*}{Histopathology} & BreakHis & Acc = 98\% \\  & & & & Acc(4-class) = 85.50\% \\ \hline \multirow{3}{*}{[31]} & \multirow{3}{*}{
\begin{tabular}{c} CNNs+ \\ SE-Attention \\ \end{tabular} } & \multirow{3}{*}{Mammography} & \multirow{3}{*}{New Benchmarking dataset} & Acc = 92.17\% \\ \cline{3-3}  & & & Acc = 92.6\% \\ \cline{3-3}  & & & Sn = 94.7\% \\ \cline{3-3}  & & BreakHis & Sp = 88.9\% \\ \cline{3-3}  & & & F1-score = 94.1\% \\ \cline{3-3}  & & AUC = 0.918 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Model architecture.  
order to minimize overfitting, all images were enhanced in the following stage to provide a more significant size dataset for training  
After the dataset was pre-processed, we divided it into training and testing sets using a stratified 10-fold cross-validation technique. The data were divided into ten roughly equal segments, with one subset used as a roughly equivalent test set.
## 7 Applications in SDN-based VANETs
### Crossing Road Warning  
This component of the programs and applications is utilized to forward the stop signal to the vehicles' drivers if a person on foot is going across the street in the interim. The SDN-based RSUs gather the data from vehicles that are close to the passerby crossing line and confirm if the signal is with a stop sign and the vehicle is moving; it produces admonition information and alarms the vehicle driver to break the vehicle on the grounds that a person on foot is going across the road.
## 2 Related Work
### Learning Algorithm-Based Loss Differentiation  
With the continuous advancement of data mining and machine learning technologies, incorporating time characteristics of packet transmission into machine learning models for loss differentiation can significantly enhance the accuracy of loss differentiation and improve network performance. [PERSON] et al. [20] proposed a packet loss classification technique based on the disparity in RTT measurements between wireless losses and congestion losses, leveraging the estimation capabilities of hidden Markov models for Gaussian components. In [21], the authors introduced a Bayesian packet loss detection mechanism that utilizes end-to-end RTT measurements. The simulation results demonstrate that this Bayesian detector achieved a detection probability exceeding 80% with a false alarm probability below 20%, leading to more than 25% improvement in network performance. The above mechanisms only apply one packet feature and the performance of packet loss differentiation is not optimal.  
Reference [22] constructed a naive Bayes discrimination model based on the statistics of the packet loss ratio of high- and low-priority packets, and packet time interval, so as to capture packet loss state and effectively classify wireless and wired packet loss types. Based on the queue length in the cache, a clustering method based on unsupervised learning was proposed in [23] to distinguish packet loss. [PERSON] et al. [12] extracted state information such as RTT, RTTmin, RTTmax, RTT/RTTmin, RTT/RTTmax, (RTT - RTTmin)/(RTTmax - RTTmin), and CWND/CWNDmax from flows where packet losses are detected at TCP receiversand employ them as inputs to a neural network model to establish a packet loss model. Reference [24] defined various features related to packet losses and employed SVM for distinguishing wired/wireless hybrid networks' packet losses. Reference [25] employed the cuckoo search back-propagation neural network (CSBPNN) algorithm to effectively distinguish packet loss. [PERSON] et al. [26] proposed a reinforcement learning-based loss differentiation (RLLD) algorithm to distinguish TCP packet loss into congestion loss, link error loss and route loss by combining RTT, ACK, and TCP socket information, and proposed a reinforcement learning-based TCP transmission control method (TCP-RLLD). The authors in [10] proposed LDM-satellite, which is a machine learning-based congestion control method capable of end-to-end packet loss discrimination and congestion control by constructing an integrated classifier comprising multiple decision trees along with naive Bayes classifier, while utilizing ACK header flag bits for result feedback. However, this paper does not specify the features of input data packets during the machine learning process.
## 4 Influential Factors in Fluoride Adsorption
### Adsorbent Specific Surface Area and Particle Size
[69] studied a magnetic adsorbent consisting of iron-aluminum oxide nanoparticles anchored on graphene oxide (IAO/GO) for fluoride removal from an aqueous solution. By combining, the advantages of graphene oxide (GO) and IAO, IAO/GO exhibits high adsorption capacity (64.72 mg g\({}^{-1}\)), good acid-alkali stability, super paramagnetism, and good selectivity for fluoride. [PERSON] et al. [70] investigated the flaky Mg-Fe-La trimetal composite as an adsorbent for fluoride removal synthesized by a facile co-precipitation method. The experimental results demonstrated that the adsorbent, with an Mg/Fe/La molar ratio of 25:1:4, obtained the largest adsorption capacity of 112.17 mg g\({}^{-1}\) for fluoride. The BET surface area and average pore diameter of the adsorbent were 178.55 m\({}^{2}\) g\({}^{-1}\) and 31.16 nm, respectively. Upon reviewing the cited studies on fluoride adsorption (Table 2), a notable discrepancy in specific surface area values was observed, attributed to variations in the types of adsorbents employed for defluoridation. However, establishing a direct correlation between specific surface area values and adsorption capacity or fluoride removal efficiency proved challenging within the scope of this literature review. It is noteworthy that, despite the disparity in specific surface area values, it does not emerge as the primary factor influencing adsorption capacity. [PERSON] et al. [1] studied the physicochemical properties and fluoride adsorption capacity of activated alumina (AA) under varying conditions of surface acidity, hydration period, and particle size range. The authors showed that the overall adsorption does not change notably between the two particle size ranges analyzed, but adsorption is much faster when the particles are reduced from 0.5-1.0 mm to 0.125-0.250 mm. At the larger particle size, AA adsorbs about 10% of fluoride from the solution at 5 min, requiring 24 h to remove 90%. In contrast, the thinner AA adsorbs almost 60% fluoride in 5 min and 90% after 40 min. The authors attributed this result to the inverse relationship between diffusion rate and particle size. They further argue that the particle size used in a defluorination filter should be as fine as reasonably possible while maintaining an acceptable flow rate, i.e., to increase the adsorption rate, a relatively fine particle size is recommended. In conclusion, while specific surface area variations among adsorbents were observed, their direct impact on adsorption capacity remains inconclusive. However, the influence of particle size on fluoride adsorption capacity is evident, with practical recommendations provided for optimizing the adsorption process. Further research is warranted to explore the intricate interplay between specific surface area, particle size, and overall adsorption performance in defluoridation applications.
# Towards Environmentally Friendly Accelerometers Based on Bacterial Cellulose
## 3 Modeling  
The transducer is modeled as a mass-spring-damping system for the mechanical part of the device, coupled with an electrical model to implement the composite transduction. Hence, the dynamic behavior is defined by a linear second-order differential equation as follows:  
\[\left\{\begin{array}{rl}m\ddot{x}+d\dot{x}+kx=F(t)\\ V=Gx\end{array}\right. \tag{1}\]  
where \(m\), \(x\), \(\dot{x}\), \(\ddot{x}\), \(d\), \(k\), and \(F(t)\) correspond to the proof mass located at the free end of the BC in beam configuration; the displacement, velocity, and acceleration of the proof mass; the mechanical damping; the elastic constant; and the kinetic excitation of the transducer, i.e., the mass \(m\), multiplied by the applied acceleration (the measurand). The output voltage is represented by the term \(V\). \(G\) is a multiplicative coefficient that models the conversion of the displacement into an output voltage across the two electrodes of the composite. Note that, according to the working conditions investigated in the following, Equation (1) refers to the system working in the proximity of its resonant frequency.  
The model has been implemented by using MATLAB Simulink according to the parameter values given in Table 1.  
Figure 3: (**a**) Schematic of the obtained three-layer sensor; (**b**) a photo of the realized device.  
The value of \(m\) of the proof mass has been directly measured by a scale (see Section 4), \(d\) has been estimated through experiment, and the term \(k\) has been estimated by using the equivalent section method [7] as follows:  
\[k=12\frac{E_{max}I_{n}}{l^{3}} \tag{2}\]  
\(E_{max}\) is the Young's modulus of the BC; \(I_{n}\) is the inertial moment normalized, which is the result of the equivalent section method; and \(l\) is the length of the beam.  
To validate the proposed model, an impulse response of the device has been simulated and compared with the experiments. The experimental validation of the proposed model will be reported in the following section.