esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
used convolutional neural networks (CNNs) to develop a classification model which can evaluate and categorize tools as still suitable to use or not [19]. In that research, he compared results from custom and transfer learning, and the results showed that the model from transfer learning reached 92% accuracy. Another study compared four machine learning algorithms--k-nearest neighbors (kNN), Naive Bayes, linear discriminant analysis, and decision trees--to predict the quality of multi-cavity injection molding products. In this study, the DT was found to be the best method, with a computing time of 8-10 s and an average performance exceeding 90% [20]. A year earlier, another study conducted a comparison of classification tasks and found that the autoencoder (AE) ANN outperformed other methods in terms of accuracy, precision, recall, and F1 score [15]. [PERSON] highlighted that the mechanical properties of plastics and molding parameters exhibit significant nonlinearity, making them challenging to predict [21]. To address this, [PERSON] employed the design of experiments (DOE) method alongside an ANN, achieving accurate predictions of the product's mechanical properties. This combined approach proved effective for predicting other molding outcomes as well. Using data from Moldflow simulations, [PERSON] built a model using the central composite design (CCD) method and performed optimization with differential evolution (DE) to reduce warpage by 33.36% on the \(y\) axis and 11.64% on the \(z\) axis [22]. This proved that using polypropylene and combing backpropagation neural networks (BPNNs) with a genetic algorithm (GA) can optimize injection molding parameters [23]. The result of this approach is a significant reduction in material consumption, short cycle time, and 16% lower energy consumption. In this study, the researchers aim to optimize the weight of the product and the energy consumption of the injection molding machine. Design of experiments (DoE) is employed to provide a reliable dataset for constructing an artificial neural network (ANN) model. The predictive model will then be used to optimize the parameters using differential evolution (DE). The novelty of this research lies in combining an ANN and DE in the field of injection molding in multi-objective cases, a method which has not been extensively explored.
# The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks
## 4 Results
A comparison was made of the anomaly detection effectiveness using the proposed data set with various ANN models and classical machine learning methods. The obtained estimates are presented in Table 5, from which it follows:  
1. The ANN recall and precision metrics, taking into account a slight imbalance in the data, are almost the same. Despite a slight imbalance, the support vector machine (SVM) managed to strike a harmony between precision and recall metrics, but it is still inferior to implemented autoencoders. 2. For the F-measure metric, a similar situation arises since the F-measure is the harmonic mean between the precision and recall. This metric shows that the linear regression method stands out from other methods in that it was unable to cope with the qualitative classification of both normal and anomalous data. 3. Analyzing the AUC-ROC value, it can be seen that the convolutional autoencoder showed the best performance, followed by the proposed ANN with structure 4-3-4. The SVM is only 10% inferior to the convolutional autoencoder due to its complex mathematical implementation and large classification penalties. 4. Despite the convolutional autoencoder superiority, this neural network type is much inferior to other algorithms in training time terms. With larger data and more layers in the decoder and encoder, this figure will increase significantly, so this autoencoder applicability may be reduced. 5. For anomaly detection, SVMs were optimized for comparison with autoencoders. From the obtained results, it is clear that after SVM optimization, its quality characteristics improved, but the training time increased 5 times. Based on the research conducted, it turns out that despite the high-quality indicators of the optimized SVM, it is possible to obtain similar quality indicators from the proposed ANN with faster training.
## 3 Mutual Coupling Suppression using Frequency-Selective Surface
### Ground Slot  
Finally, a 1 mm slot in the ground plane is introduced for further isolation improvement. It can be seen that mutual coupling is significantly suppressed for both MIMO designs. For Design 1, the isolation is now below \(-\)25 dB (cf. Figure 12), whereas, for Design 2, the isolation is below \(-\)28 dB, as reported in Figure 13.  
Figure 11: Transmission coefficient of Design 2 with FSS unit cells and metallic strips.  
Figure 12: Transmission coefficient of Design 1 with FSS unit cells, metallic strips, and ground slot.  
Figure 10: Transmission coefficient of Design 1 with FSS unit cells and metallic strips.  
The CST simulation setups for Designs 1 and 2 are shown in Figure 14. The overall simulated results for all the applied techniques of both designs are shown in Figures 15 and 16, respectively. Furthermore, as shown in Figure 17, using the surface current distributions, the proposed isolation improvement technique suppresses the coupling between the two ports sharing a common ground plane.  
Figure 14: CST simulation setups for (**a**) Design 1, and (**b**) Design 2.  
Figure 15: Transmission coefficients of Design 1: no wall, FSS wall, FSSs and metallic wall, FSSs, metallic wall, and ground slot.
## 2 Proposed Framework and Experimental Methodology  
Large datasets ranging from megabytes to gigabytes were used to assess the encryption and decryption performance. Data files in raw byte format were encrypted without restrictions on file type or extension to provide a broad comparison of hardware performance. This approach allowed the accurate evaluation of computational performance by minimizing the influence of file-specific overhead such as compression and metadata.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 1 Introduction
* Establishing a comprehensive taxonomy of IFA variants that categorizes attacks in multiple dimensions, including source origin, intensity patterns, and target layers, and examining how attack sophistication has evolved from simple flooding to advanced collusive models over the past five years. This taxonomy framework enablessystematic analysis of attack characteristics and facilitates the development of targeted countermeasures for specific threat scenarios. * Performing a critical evaluation of the seven detection and mitigation strategies published between 2013 and 2024, organizing these approaches into five primary categories based on their core methodologies. Our analysis goes beyond simple cataloging by assessing the effectiveness, computational overhead, and deployment feasibility of rate-limiting strategies, PIT management techniques, machine learning approaches, reputation systems, and blockchain-based solutions. The evaluation includes performance comparisons where quantitative data is available and identifies the operational trade-offs inherent in each approach. * Examining domain-specific adaptations of IFA countermeasures for resource-constrained environments, with particular focus on IoT deployments, wireless sensor networks (WSNs), and vehicular networking scenarios. This analysis addresses the practical challenges of implementing security mechanisms within computational and energy limitations while maintaining real-time performance requirements. * Analyzing the evaluation methodologies and performance metrics employed in the reviewed literature, by identifying inconsistencies in benchmarking approaches, and highlighting the need for standardized testing frameworks. Our examination includes the evaluation of simulation environments, experimental parameters, and real-world deployment considerations that affect the practical applicability of the proposed solutions. * Identifying five critical research directions that address current limitations in IFA defense mechanisms. These directions encompass the development of adaptive countermeasures against evolving attack strategies, integration of privacy-preserving detection techniques for encrypted content scenarios, optimization for real-time constraints in edge computing environments, establishment of standardized evaluation frameworks for reproducible research, and design of hybrid defense approaches that combine multiple mitigation strategies for enhanced effectiveness. The remainder of this paper is organized as follows. Section 2 provides background on the NDN architecture and its vulnerability to floods of interest. Section 3 presents a comprehensive taxonomy of the vectors and characteristics of IFAs. Section 4 reviews the latest detection and mitigation strategies. Section 5 examines domain-specific applications and case studies in IoT and vehicular networks. Section 6 identifies open challenges and future research directions. Finally, Section 7 concludes the paper.
## 1 Introduction  
Tooth extraction triggers a set of biological events that lead to unpreventable alveolar ridge resorption [1]. Resorption may occur to such an extent that implant placement becomes unfeasible unless bone augmentation procedures are undertaken [1]. Grafting post-extractive sockets, that is, applying ridge preservation procedures, has been shown to reduce or even minimize alveolar bone loss, in comparison to natural socket healing [2; 3; 4; 5]. The outcomes of these procedures are affected by the morphology of extraction sockets, the type of wound closure, the type of grafting materials, the use of barrier membranes, and the use of growth factors [4; 6; 7]. Autogenous bone is seldomly used as a ridge preservation bone graft, because of morbidity associated with its collection [8]. Xenografts seem a viable alternative [5; 7; 9] based on the morphological and composition similarities that can be observed between the mineral bone portions of humans and other mammals. Xenografts are produced by processing heterologous bone to make it non-antigenic [10; 11]. Among xenografts, recent studies have proposed an enzyme-deantigenic equine bone (EDEB) as an alternative to anorganic bovine bone (ABB). While the production of ABB involves high temperatures (>300\({}^{\circ}\)) that remove the organic bone component, EDEB is made by processing equine bone with antigen-degrading enzymes at a significantly lower temperature (less than 60 \({}^{\circ}\)C), thus allowing for the preservation of type I bone collagen unaltered within the graft [12]. Its clinical use is documented both in oral and maxillofacial surgeries [12; 13; 14; 15; 16; 17; 18; 19; 20; 21] as well as in orthopedic interventions [22; 23]. Recently, EDEB has been further developed by creating a freeze-dried mixture of EDEB granules (0.5-1 mm in diameter) combined with a polyethylene glycol/hydroxyl-propyl methyl cellulose-based hydrogel (PEG/HPMC), namely Exur(r), and added with type I collagen from the equine tendon (EDEBEX). This gel contains also a subsidiary amount of vitamin C (ascorbic acid) acting as a visco-modulator [24]. EDEBEX, thanks to this new carrier, is plastic and easy to handle when rehydrated. Histomorphometric clinical investigations showed that the amount of newly-formed bone observed after grafting EDEB is higher compared to ABB, both when performing sinus augmentation procedures [21] and post-extractive socket grafting [12]. In these investigations, EDEB was used without the carrier; at present, therefore, no clinical histomorphometric data on EDEBEX exist. This paper aims to preliminarily investigate the histomorphometric and short-term clinical outcome of EDEBEX when used to graft post-extractive sockets for the purpose of ridge preservation.
## 3 Thermodynamic Analysis
### Combined ORC-Dual Evaporator VCR Cycle Equations  
Thermal and exergy efficiency calculations for the combined ORC-dual evaporator VCR cycle can be observed from below mentioned equations.  
Coefficient of performance of refrigeration cycle:  
\[\text{COP}_{\text{VCR}}=\frac{\dot{\text{Q}}_{\text{evap,1}}+\dot{\text{Q}}_{ \text{evap,2}}}{\dot{\text{W}}_{\text{comp}}} \tag{11}\]  
Thermal efficiency of ORC is the same as Equation (4). Also, coefficient of performance of the combined system can be calculated from Equation (5). Carnot and exergy efficiency of formulas for ORC can be seen from Equations (6) and (7), respectively.  
Exergy efficiency of refrigeration cycle for dual evaporator system is given in Equation (12). This value can be calculated by dividing the sum of exergies transferred with heat in the evaporators to compressor power.  
\[\eta_{\text{lex,VCR}}=\frac{(\dot{\text{E}}_{\text{Qevap,1}}+\dot{\text{E}}_{ \text{Qevap,2}})}{\dot{\text{W}}_{\text{comp}}} \tag{12}\]  
Finally, exergy efficiency of combined system can be calculated from Equation (10).  
Energy and exergy equations related to components in refrigeration cycle of combined system are given in Table 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Energy and Exergy Equations** \\ \hline \multirow{6}{*}{COMPRESSOR} & \(\dot{\text{W}}_{\text{comp}}=\dot{\text{m}}_{\text{VCR}}\  
Energy and exergy equations related to components in the organic Rankine cycle of combined system are given in Table 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Energy and Energy Equations** \\ \hline \multirow{4}{*}{EXPANSION VALVE 3} & \(\mathrm{h_{6}}\)= \(\mathrm{h_{7}}\) \\  & \(\mathrm{\dot{E}_{dest,v,3}}\) = \(\mathrm{\dot{E}_{6}-\mathrm{\dot{E}_{7}}}\) \\  & \(\mathrm{EDR_{ev,3}}\) = \(\mathrm{\dot{E}_{dest,v,3}}\)/\(\mathrm{\dot{E}_{dest,total}}\) \\  & \(\mathrm{\dot{v}_{v,3}}=0\) \\ \hline \multirow{4}{*}{EXPANSION VALVE 1} & \(\mathrm{h_{8}}\)= \(\mathrm{h_{9}}\) \\  & \(\mathrm{\dot{E}_{dest,v,1}}\) = \(\mathrm{\dot{E}_{6}-\mathrm{\dot{E}_{9}}}\) \\  & \(\mathrm{EDR_{ev,1}}\) = \(\mathrm{\dot{E}_{dest,v,1}}\)/\(\mathrm{\dot{E}_{dest,total}}\) \\  & \(\mathrm{\dot{v}_{v,1}}=0\) \\ \hline \multirow{4}{*}{EVAPORATOR 1} & \(\mathrm{\dot{Q}_{evap,1}}\)= \(\mathrm{m_{VCR,1}}\)(\(\mathrm{h_{10}}\)-\(\mathrm{h_{9}}\)) \\  & \(\mathrm{m_{9}}=\mathrm{m_{10}}=\mathrm{m_{VCR,1}}\) \\  & \(\mathrm{\dot{E}_{dest,vap,1}}\) = \(\mathrm{\dot
# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
## 2 Materials and Methods
### Deep Neural Networks  
Artificial neuron networks (ANNs) are a computational model based on the structure and functions of biological neural networks [24]. DNNs are ANNs with multiple layers between the input and output layers, and deep learning is a machine learning technique that constructs artificial neural networks [25; 26; 27]. As the number of hidden layers increases, the characteristics of DNNs become more detailed and complex, and derive high-level function from input information. Based on this network model, the work of extracting features and determining the form of unstructured data is called deep learning.  
DNNs consist of three types of neuron layers: the input layer, the hidden layers, and the output layer [28]. The input layer receives input data, and the output layer returns output data. The hidden layers perform mathematical computations on the inputs. Depending on the weight and transfer function of the input values, an activation function is applied to the next layer, and activation is implemented in networks until the neurons reach the output layer [29]. The difference between the value of output layer and the expected target value are compared using a cost function. Reducing it by changing the weight determines the performance of the network model.
## 5 Problem Formulation  
### Primary Problem Formulation  
The objective of our work is to minimize the energy consumption cost for DL job processing while guaranteeing that DJR is met within the predetermined deadline. To do this, we define two types of decision variables: the job allocation vectors \(\mathcal{X}=(x_{1},x_{2},\cdots,x_{\mathcal{I}})\) and the frequency setting vectors \(\text{F}=(f_{1},f_{2},\cdots,f_{\mathcal{I}})\), where \(x_{i}=(x_{i}(1),\cdots,x_{i}(\mathcal{K}))\), \(\forall x_{i}(k)\in\{0,1\}\), and \(f_{i}=(f_{i}(1),\cdots,f_{i}(\mathcal{K}))\). If a certain \(i\)-th DL job is processed on the worker during time slot \(k\), then \(x_{i}(k)=1\), and the associated frequency value \(f_{i}(k)\) may be set within a feasible range. Let \(r(k)\) denote the amount of renewable energy generation at time slot \(k\). Then, based on Equations (7) and (8), we formulate the energy cost control problem as follows:  
**Problem 1** (Energy Cost Control Problem).: \[\min_{\mathcal{X},F} \sum_{\forall k\in\mathcal{K}}\max(\sum_{\forall i\in\mathcal{I}}E_ {i}(k)-r(k),0)\cdot c(k),\] (9) s.t. \[f_{i}^{-}\leq f_{i}(k)\leq f_{i}^{+},\ \forall i\in\mathcal{I},\ \forall k \in\mathcal{K},\] (10) \[a_{i}\leq k\cdot x_{i}(k)\leq d_{i},\ \forall i\in\mathcal{I},\ \forall k \in\mathcal{K},\] (11) \[\sum_{\forall k\in\mathcal{K}}x_{i}(k)\cdot\theta_{i}(k)\geq R_{ i},\ \forall i\in\mathcal{I},\] (12) \[x_{i}(k)\in\{0,1\},\ \forall i\in\mathcal{I},\ \forall k \in\mathcal{K}.\] (13)  
Constraints (10) ensure that the frequency setting values for each worker fall within the feasible range, bounded by the maximum value \(f_{i}^{+}\) and the minimum one \(f_{i}^{-}\). Constraints (11) ensure that the completion time of the \(i\)-th DL job does not exceed the predetermined deadline \(d_{i}\). Constraints (12) guarantee that the sum of DJCS across the assigned time slots satisfies the DJR. To comply with these constraints, it is necessary to find a proper slot allocation plan and frequency scaling solution. Lastly, constraints (13) enforce the integrity of the decision variables. Obviously, \(d_{i}-a_{i}\) represents the number of required available time slots for processing the \(i\)-th DL job. Let \(L_{i}\) denote the lower bound for the number of time slots required for the \(i\)-th DL job to meet the DJR \(R_{i}\). By setting \(f_{i}(k)=f_{i}^{+}\), and based on constraints (6), we calculate \(L_{i}\) as follows:  
\[L_{i}=\min\sum_{\forall k\in\mathcal{K}}x_{i}(k)\ \ \text{s.t.}\ \ (\ref{eq:d_i}). \tag{14}\]  
If we cannot find a value for \(L_{i}\) such that \(L_{i}\leq d_{i}-a_{i}\), it is impossible to meet the deadline of the \(i\)-th DL job within any of the potential solutions. Thus, our proposed RA-FS manager will attempt to drop that DL job request. Note that Problem (1) is classified as a non-convex mixed-integer nonlinear problem (non-convex MINLP) due to the product terms of the decision variables \(x_{i}(k)\) and \(f_{i}(k)\) included in both the objective function (9) and the constraints (12). To solve this issue, we present a reformulation for the original problem (1) in the next subsection.
# A Novel Approach to Wearable Image Recognition Systems to Aid Visually Impaired People
## 4 Evaluation
### Data Set Preparation  
In 2015, Baidu AI's image recognition algorithm won the first place with an accuracy of 99.7% in the Labeled Faces in the Wild (LFW) test. However, under the hardware conditions of this device, it is still necessary to retest its accuracy. In addition, it is difficult for visually impaired people to always take high-quality pictures, so this device cannot blindly pursue high accuracy, and it is necessary to select a reasonable threshold as a criterion for judging whether the recognition is successful.  
Two currently published image recognition datasets LFW (Labeled Faces in the Wild) [26] and PASCAL VOC (Visual Object Classes) [27] were used. LFW is a database for face recognition research that contains 13,000 face images. Each face has been labeled with the name of the person pictured. 1680 of the people pictured have two or more distinct photos in the data set. In this test, LFW will be used for the test of face recognition. And advantage algorithms [28; 29; 30; 31; 32] can be employed in data processing in future work.  
Our goal is not just to test accuracy at the algorithm level, but also to consider the hardware limitations of wearable devices and the needs of real-life users. According to the real life of visually impaired people, they often meet friends and relatives, so the number is not very large. Therefore, we define the number of faces they often see as 100. We have prepared three training sets and three test sets. Each training set contains 100 face images randomly selected from the LFW. Each test set contains 10 strange faces and 90 faces in the training set (but under other postures and expressions, not the original picture). Figure 7 shows some sample face of the test set.  
\begin{table}
\begin{tabular}{c c c} \hline
**Thread \(M_{i}\)** & **Condition** & **Return Result** \\ \hline \(M_{1}\) & Human beings in the picture (\(F(\alpha,M_{1})>0\)) & Number of the people \\ \hline \multirow{4}{*}{\(M_{2}\)} & \multirow{2}{*}{Image quality is unqualified (\(F(\alpha,M_{2})<0\))} & Left/right face is occluded \\ \cline{3-3}  & & Blurring pictures \\ \cline{3-3}  & & The light is too strong \\ \cline{3-3}  & Registered in the face database (\(F(\alpha,M_{2})>0\)) & name \\ \cline{3-3}  & Not added in the face database (\(F(\alpha,M_{2})<0\)) & appearance information (age, expression, glasses) \\ \hline \(M_{3}\) & Object (\(F(\alpha,M_{3})>0\)) & Object name \\ \hline \(M_{4}\) & Book or other text (\(F(\alpha,M_{4})>0\)) & sound of the text \\ \hline \end{tabular}
\end{table}
Table 3: Information returned by different threads in different situations.  
PASCAL VOC2007 is mainly used for the test of object recognition. It contains a total of 9963 images, all of which are shot in real scenes, including people, birds, bicycles, buses, cars, bottles, chairs, dining tables, plants and other 20 common objects in daily life. We have prepared two test sets, each of which will randomly select 300 images from VOC2007. Figure 8 shows some sample picture of the test set.
## 3 Results and Discussion  
### Statistical Analysis of the Quadratic Model  
A CCD with three variables and five levels was applied to evaluate the effects of key process variables, including extraction temperature (50-90 \({}^{\circ}\)C), extraction time (8-24 h), and C-PC to solvent ratio (1:20-1:100), on PCB extraction. The actual PCB yields obtained from the 20 experimental groups are listed in Table 2. The results from these 20 experimental sets were used to generate a regression equation (Equation (2)).  
\[\text{Expected PCB yield (\%)}= \tag{2}\] \[24.20+3.16\text{A}+1.12\text{B}+7.20\text{C}+1.70\text{AB}-0.646 2\text{AC}-0.8912\text{BC}-3.65\text{A}^{2}-1.32\text{B}^{2}-2.41\text{C}^{2}\]  
Based on the experimental results, a second-order polynomial regression equation and its mathematical expression in coded terms were automatically generated using Design Expert software. The PCB yields obtained from the 20 experimental groups ranged from 3.8 to 29.89%, with the lowest and highest yields recorded in runs 1 and 20, respectively. The fit summary analysis of the PCB extraction suggested that the quadratic model was the best fit for the system, with a \(p\) value of \(<\)0.0001 and a high coefficient of determination (R\({}^{2}\) = 0.9821). Additionally, the non-significant \(p\) value in the lack-of-fit analysis further validated the suitability of the model for predicting PCB yield. According to the model equation, PCB yield is influenced not only by linear and quadratic terms, but also by interactive effects among the factors. The extraction temperature (A, A\({}^{2}\)), extraction time (B,  
Figure 1: Schematic of the experimental procedure, illustrating the production of PCB from _A. maxima_.  
B\({}^{2}\)), C-PC to solvent ratio (C, C\({}^{2}\)), and interaction term AB were found to have significant effects on the PCB yield (\(p\) < 0.05). Among the parameters, the linear term C made the largest contribution to the PCB yield, with the highest coefficient value of 7.20, followed by A\({}^{2}\) (3.65), A (3.16), and C\({}^{2}\) (2.41).