esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
###### Abstract  
Information-centric networking (ICN) has gained significant attention due to its in-network caching and named-based routing capabilities. Caching plays a crucial role in managing the increasing network traffic and improving the content delivery efficiency. However, caching faces challenges as routers have limited cache space while the network hosts tens of thousands of items. This paper focuses on enhancing the cache performance by maximizing the cache hit ratio in the context of software-defined networking-ICN (SDN-ICN). We propose a statistical model that generates users' content preferences, incorporating key elements observed in real-world scenarios. Furthermore, we introduce a graph neural network-double deep Q-network (GNN-DDQN) agent to make caching decisions for each node based on the user request history. Simulation results demonstrate that our caching strategy achieves a cache hit ratio 34.42% higher than the state-of-the-art policy. We also establish the robustness of our approach, consistently outperforming various benchmark strategies.  
information-centric networking; software-defined networking; graph neural network; deep reinforcement learning; intelligent caching +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Results
### Modeling Results Comparison between Selected Variables and all Variables  
Table 6 shows the modeling performance of all variables and selected variables under the machine learning methods. In the training phase, the result of the RF model with all variables (e.g., \(R^{2}\).training = 0.92, RMSE = 34.83 m\({}^{3}\) ha\({}^{-1}\)) is basically the same as that of the RF model with the selected variables (e.g., \(R^{2}\).training = 0.91, RMSE = 35.13 m\({}^{3}\) ha\({}^{-1}\)). And the results of the RF model are better than those of the SVR model with all variables (e.g., \(R^{2}\).training = 0.61, RMSE = 60.58 m\({}^{3}\) ha\({}^{-1}\)) and selected variables (e.g., \(R^{2}\).training = 0.54, RMSE = 65.60 m\({}^{3}\) ha\({}^{-1}\)). In the test phase, the result of the RF model with selected variables (e.g., \(R^{2}\).training = 0.58, RMSE = 65.03 m\({}^{3}\) ha\({}^{-1}\)) is basically consistent with the result of the RF model with all variables (e.g., \(R^{2}\).training = 0.58, RMSE = 66.04 m\({}^{3}\) ha\({}^{-1}\)). Similarly, the results of the RF model in the test phase are better than those of the SVR models with all variables (e.g., \(R^{2}\).training = 0.51, RMSE = 67.86 m\({}^{3}\) ha\({}^{-1}\)) and selected variables (e.g., \(R^{2}\).training = 0.54, RMSE = 66.00 m\({}^{3}\) ha\({}^{-1}\)). In the SVR model, the performance of all variables in the training phase is slightly better than that of the selected variables; and in the test phase, the performance of the selected variables is slightly better than that of the all variables.
## 3 Diagnostic Reasoning for the EPS
### Brief Description of ANFIS  
The Adaptive Neuro-Fuzzy Inference System (ANFIS) is the combination of artificial neural networks (ANN) with Takagi-Sugeno fuzzy reasoning [30]. A general fuzzy inference system (FIS) operates by the following steps:  
1. Input parameters are fuzzified by defining membership functions,
2. Logical operators like AND, OR, and NOT are applied to combine the membership functions of the input parameters to get a set of 'if-then' rules along with their weights,  
Figure 6: Pump motor characteristics for reduced voltage (F10).  
The consequent parts of the rules are implemented depending upon their firing strengths (weights), and  
4. The outputs are calculated, aggregated, and de-fuzzified to provide the final output.  
A generalized example to demonstrate the above-mentioned steps is shown in Figure 7. Consider a first-order Sugeno FIS for given two-parameter inputs A and B presented in the following sets of equations [30], as seen in Figure 7a.  
\[\begin{split}\text{Rule 1: If A is X1 and B is Y1,}\\ \text{then z1 = p1\times A+q1\times B+r1}\\ \text{Rule 2: If A is X2 and B is Y2,}\\ \text{then z2 = p2\times A+q2\times B+r2}\\ \end{split} \tag{3}\]  
where p1, p2, q1, q2, r1, and r2 are linear parameters, and X1, X2, Y1, and Y2 are non-linear parameters. The inputs of A and B are fuzzified by assigning membership functions, say \(\mu\) and \(\
u\). These membership functions represent the degree to which A and B satisfy pertaining to their fuzzy labels X1, Y1, X2, and Y2. Logical operator AND is applied to establish the relationship between the A and B using their membership functions. For example, consider the weights that could be calculated by the following equations:  
\[\begin{split}\text{w1}&=\text{AND ($\mu$X$ 1 (A), $\
u$Y$ 1 (B))}\\ \text{w2}&=\text{AND ($\mu$X$ 2 (A), $\
u$Y$ 2 (B))}\\ \end{split} \tag{5}\]  
Once the weights of the fired rules, w1 and w2, are computed, they are combined with the outputs z1 and z2. The total output 'z' is given by calculating the weighted average of the outputs, as given in Equation (7) [30]. While A and B are fuzzy sets in the antecedent, 'z' is a crisp function in the consequent side.  
\[\text{Output }z=(\text{z1 }\times\text{w1 }+\text{z2 }\times\text{w2})/(\text{w1 }+\text{w2}) \tag{7}\]  
Figure 7: (a) First order Takagi–Sugeno fuzzy reasoning (b) Adaptive Neuro-Fuzzy Inference System (ANFIS) structure (adapted from [30]).  
The ANN is used to tune the membership functions in the Takagi-Sugeno FIS, in order to optimize the relationship between the input parameters and their respective fuzzy labels, resulting in the algorithm, ANFIS (as seen in Figure 7b). The neural network in ANFIS estimates errors and adjusts the membership function parameters to reduce the errors. The parameters are estimated either by using backpropagation or by a hybrid method that combines least squares estimation with backpropagation.
# Blockchain and Cloud to Overcome the Problems of Buyer and Seller Watermarking Protocols
## 7 Implementation and Performance  
In the platform presented in Section 6, the entities of the protocol are implemented as C++ programs according to the experiences documented in [5,8,17]. The programs run on Linux operating system and communicate via OpenSSL standard socket library. They use the NTL and GNU Multi Precision Arithmetic libraries to implement the encryption/decryption and watermark insertion procedures. The cloud computing platform runs the release \"Pike\" of the OpenStack operating system, and executes the necessary processes interacting through the functions belonging to the API of OpenStack. The blockchain is implemented in Ethereum, whereas the smart contract is coded in Solidity.  
The watermark insertion is carried out according to the spread-spectrum technique described in [1] and represented by the formula:  
\[\tilde{x}=x+\alpha(2b-1)s\]  
Figure 2: Prototype platform implementing the proposed watermarking protocol.  
where \(x\) is a host signal feature obtained by calculating the cosine discrete transform, \(\tilde{x}\) is the corresponding watermarked feature, \(b\in\{0,1\}\) is the bit to embed, \(s\) is the component of a spreading sequence, and \(\alpha\) is a scaling factor able to control the watermark strength. Such a formula can be directly translated into the encrypted domain due to the homomorphism of the adopted cryptosystem, thus obtaining:  
\[E[\tilde{x}]=E[x]\cdot E[b]^{2\kappa s}\cdot E[\alpha s]^{-1}\]  
Moreover, in the first prototype implementation, the commutative behaviour of the cryptosystem \(\mathbb{E}\) is obtained by applying a variant of the well-known ElGamal algorithm [36; 37; 38; 39; 49; 50], which is well-described in [17]. However, such a cryptosystem has been adopted only to prove the correctness and feasibility of the proposed protocol, since it is not IND-CPA secure. In fact, the cryptosystem can be replaced by the lifted-ElGamal proxy re-encryption scheme widely documented in [51; 52; 53; 54; 55]. It is proved IND-CPA secure and is also characterized by the following relevant feature: both the first-level ciphertext and the second-level ciphertext satisfy the property of additive homomorphism [51].
## 3 Experimental Test Bench  
In the current study, a JetCAT P80 micro turbojet engine [26], which is part of the Aerospace Engineering Faculty's equipment, was employed. The engine (see Figure 1) is operated by a command-and-control panel provided by GUNT Germany that is responsible for engine startup and throttling. It is also instrumented with several transducers for thrust, combustion chamber pressure and temperature, outlet compressor temperature, fuel mass flow, air mass flow and shaft rotational speed. These parameters are displayed instantaneously at run time on dedicated displays installed on the command box, but they are also recorded with 1 Hz acquisition frequency via dedicated PC software. The data post-processing allows for precise characterization of the working regimes, either steady or transient.  
Figure 1: JetCAT P80 micro turbojet engine: (**a**) inlet view, (**b**) outlet view and (**c**) control panel with integrated display for instantaneous measured engine’s parameters.  
Acoustic measurements are performed using a \(\%\) 40 AQ microphone (from G.R.A.S. in Holte, Denmark) placed on a tripod 0.4 m from the engine, as shown in Figure 2. This high-precision condenser microphone is designed according to IEC 61094-4 [46] standards and is optimized for accurate sound measurements in random, diffuse and reverberant environments. It offers exceptional durability and reliability, capable of measuring sound pressure levels from 3.15 Hz to 16 kHz, with a maximum of 148 dB. Each 40 AQ microphone is individually calibrated at the factory and comes with a calibration chart detailing its unique open-circuit sensitivity and pressure frequency response. The 40 AQ is specifically engineered to provide precise sound-level measurements in diffuse sound fields, which can occur when multiple sound sources or reflective surfaces are present.  
The optical diagnosis of the exhaust jet plume is performed by a Z-type Schlieren setup. The system comprises two f/6 parabolic mirrors with 60\({}^{\prime\prime}\) focal length, an adjustable slit diaphragm (knife) for light cutoff, a point light source and a Phantom VEO710 high-speed camera. For sufficient time resolution, the camera is operated at 48 k fps at 320 \(\times\) 320 resolution with 1 us exposure time. To limit the optical distortions, the off-axis beam angles are set below 20\({}^{\circ}\)[26; 35]. The experimental test bench with the Schlieren system aligned at the exhaust of the micro turbojet engine is presented in Figure 3.  
Figure 3: Schlieren system: (**a**) PC live imagine sample and (**b**) overview.  
Figure 2: Acoustic measurement sketch: (**a**) engine view, (**b**) front view and (**c**) top view [26].
## 3 Materials and Methods
### Model Architecture and Training
```
1 Input\(\rightarrow\) Kvasir-Capsule or Kvasir v2 dataset \(K\)
2 Output\(\leftarrow\) Fine-tuned four DL models for GI disease classification
3 BEGIN
4 STEP 1: Pre-Processing of K
5 FOR EACH X IN KDO
6 Re-scale the dimension of X
7 Resize X to 224 \(\times\) 224 pixels
8 Normalize pixel values from the range [0, 255] to [0, 1]
9 END FOR
10 STEP 2: Data Splitting
11 SPLIT K INTO
12 Training set\(\rightarrow\) 70% \(of\)\(K\)
13 Testing set\(\rightarrow\) 15% \(of\)\(K\)
14 Validation set\(\rightarrow\) 15% \(of\)\(K\)
15 IF set's size \(<\) 200
16 Under-sampling to 200
17 ELSE
18 IF set's size \(>\) 200
19 Augmentation to 200
20 END IF
21 END IF
22 STEP 3: Model Pre-Training
23 FOR EACH D IN [EfficientNet-B0, ResNet101v2, InceptionV3, and Inception ResNetV2] DO
24 Load and pre-train D on the ImageNet dataset
25 END FOR
26 STEP 4: Model Fine-Tuning
27 FOR EACH pre-trained D DO
28 Fine-tune pre-trained D on the training set
29 Utilize the validation set for early stopping, based on the best fine-tuned model performance. 30 END FOR
31 STEP 5: Applying ILRC
32 Apply the ILRC through the training by determining the error of the training set. 33 IF the error of the training set is too high,
34 GOTO line 26. 35 ELSE
36 Calculate the error of the test set. 37 IF the error of the set is too high,
38 GOTO line 26. 39 ELSE
40 GOTO line 42. 41 END IF
42 Use the validation set to apply early stopping by assessing the model's best performance. 43 STEP 6: Model Assessment
44 FOR EACH fine-tuned D DO
45 Asses [PERSON]'s performance on the test set using measured metrics. 46 END FOR
47 END
```  
**Algorithm 1**The proposed ILRC algorithm for tuning the four DL models.
## 2 Methods  
Addressing the prevalent challenge in traditional machine vision where defects on textured materials are often arduously difficult to capture and photograph, and where distinguishing anomalous defects from the inherent texture of the material itself poses significant difficulties, we have initiated the optimization from the image acquisition stage. This paper proposes an enhanced scratch defect detection for textured materials based on optimized photometric stereo vision and FFT-Gabor filtering. It is described as a cooperative approach between image capturing hardware-based photometric stereo vision and image enhancement algorithm-based fast Fourier transform.  
The enhanced scratch detection method for textured materials, as illustrated in Figure 2, comprises a defect capturing process based on optimized photometric stereo vision and an image defect information enhancement process using the fast Fourier transform.  
Firstly, in order to overcome the possibility of defects being obscured under traditional single-light-source conditions and to balance image acquisition quality with computation time, we designed and optimized a novel hemispherical photometric stereo vision image acquisition device with light source selectivity. This enabled the acquisition of image information under multiple light sources. On this foundation, we introduced a photometric stereo vision algorithm with 8 light sources. The optimized photometric stereo vision image acquisition device captures material images under multi-angle light sources, which are then processed through the photometric stereo vision algorithm to obtain Albedo and curvature gradient images. This approach mitigates the effects of high reflections or shadows that occur under single-angle light sources.  
Figure 2: Enhanced scratch detection for textured materials based on optimized photometric stereo vision and FFT-Gabor filtering.  
Subsequently, to address the difficulty in distinguishing between the material's own texture and defects, we introduced an FFT-Gabor filtering-based image enhancement algorithm. This involves designing a Gabor filter, transforming the image into the frequency domain through the fast Fourier transform, convolving the frequency domain representation with the textured material surface image, and returning to the spatial domain to obtain a background image that emphasizes the material's own texture. Subtracting this background image from the original image achieves the effect of weakening the texture and highlighting the defects. Finally, this paper introduces the threshold segmentation method (OTSU) [22; 23] to realize the detection and localization of textured material defects.
# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
## 2 The Bradano River Case Study and the Available Dataset
of the main highways in the area, and extensive damage to agriculture, to dwellings, and to the Greek archeological site of Tavelo Palatine. In the days that followed, the phenomenon entered a recession stage and flood effects developed toward the coast, affecting smaller areas progressively farther away from the river.
## 5 The Relationship between Energy-Efficiency and Interference-Optimality
### Heterogeneous Node Deployment Mode  
**Theorem 8**.: _In the heterogeneous networks, when the network is energy-efficient, the probability that the network is also interference-optimal is approximately 1._  
In Section 4.2, when the transmission ranges are different in the network, the feasible regions of interference-optimality and energy-efficiency are shown in Figure 18. Considering Equation (40), the boundaries of energy-efficiency and interference-optimality are shown in Figure 19.  
Figure 19: The boundary of energy-efficiency and interference-optimality in heterogeneous networks.  
Figure 18: The feasible region of energy-efficiency and interference-optimality in heterogeneous networks.  
Similar to Figure 17, in Figure 19, when \(\gamma=4\), the feasible region of energy-efficiency is the largest and approximately equal to the feasible region of interference-optimality. The evaluation error can be found in Figure 20. In Figure 20, when \(\gamma=4\), the evaluation error between the feasible region of energy-efficiency and interference-optimality is only \(6.5\times 10^{-4}\), which is small and can be ignored. Therefore, in heterogeneous networks, the probability that the network is energy-efficient and interference-optimal at the same time is approximately equal to 1. Consequently, we can conclude that in heterogeneous WSNs, when the network is energy-efficient, the network is interference-optimal, not vice versa.  
According to the conclusions of Sections 5.1 and 5.2, on one hand, in heterogeneous WSNs, when the network is energy-efficient, the network is also interference-optimal with probability 1; on the other hand, since the transmission ranges are always heterogeneous in practice, we can observe that the energy-efficient network is the interference-optimal network.
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 3 Technological Background
### IEEE 802.15.5 Low Rate
#### 3.2.1 Energy-Saving Mechanisms  
In other WSNs solutions such as Zigbee, nodes' radios are forced to remain on listening for any incoming transmissions. This involves a considerable power consumption and, consequently, a reduction of the lifetime of nodes. To cope with this problem, the low-rate part of the IEEE 802.15.5 standard has two energy-saving modes, called Asynchronous Energy Saving (ASES) and Synchronous Energy Saving (SES). These modes define the duty cycles of nodes, that is, the time during which their transceivers are active or inactive. ASES and SES are intended, respectively, for asynchronous and synchronous communications.  
The mechanisms proposed by the IEEE 802.15.5 standard, the ASES and the SES mechanisms, have some similarities regarding the time structure in which they schedule active and inactive periods of the radio. Figure 3 shows this duty-cycle schedule for every node. The _wakeup interval_ (WI) refers to the length of one duty cycle. This cycle is divided into an _active duration_ (AD), where the radio transceiver is always on, followed by an _inactive duration_ (ID), where the radio, MCU and sensors enter low power modes (if available) to save energy.  
The length of a _wakeup interval_ is defined by the _wakeup order_ (WO) parameter according to the following expression:  
\[\begin{array}{c}WI=\begin{array}{c}meshBaseActiveDuration\ \times 2^{\text{WO}}\\ 0\leqslant WO\leqslant 14\end{array} \tag{1}\]  
Figure 3: Time structure of IEEE 802.15.5 energy-saving mechanisms.  
Figure 2: Topology examples. (**a**) IEEE 802.15.4 cluster tree; (**b**) IEEE 802.15.5 topology.  
Similarly, the _active order_ (AO) parameter controls the _active duration_:  
\[AD=meshBaseActiveDuration\times 2^{AO},\ 0\leq AO\leq WO\leq 14 \tag{2}\]  
where _meshBaseActiveDuration_ is a constant defined by the IEEE 802.15.5 standard with a 5 ms default value.
# Improved First-Order Motion Model of Image Animation with Enhanced Dense Motion and Repair Ability
## 3 Experiments and Results
### Measurement Metrics  
Generally, animation image quality assessment includes reconstruction quality and animation quality. In terms of reconstruction quality, given that image animation is a relatively new research problem, there are not many effective ways to evaluate this currently. For quantitative metrics, video reconstruction accuracy was used as a proxy for image animation quality. We applied the same metrics in our experiments.  
We defined \(\mathcal{L}_{1}\) error as the mean absolute difference between reconstructed and ground-truth video pixel values. As shown in Equation (14), \(n\) represents the total number of video frames, while \(H\) and \(W\) represent the height and width of the image, respectively. \(I_{hw}\) represents the pixel value at \((h,w)\) position in the real video frame, and \(\hat{I}_{hw}\) represents the pixel value at \((h,w)\) position in the reconstructed frame.  
\[\mathcal{L}_{1}=\left(\sum_{i=1}^{n}\sum_{h=0}^{H}\sum_{w=0}^{W}\left|I_{hw}- \hat{I}_{hw}\right|\right)/n \tag{14}\]  
Average keypoint distance (AKD) and missing keypoint rate (MKR) were used to evaluate the difference between poses of reconstructed and ground truth videos. Landmarks were extracted from both videos using public, body [21] (for TaiChiHD) and face [22] (for VoxCeleb) detectors. AKD is the average distance between corresponding landmarks, while MKR is the proportion of landmarks existing in the ground-truth video but missing in the reconstructed video.  
Average Euclidean distance (AED) was used to evaluate how well identity is preserved in reconstructed videos. Public reidentification networks for bodies [23] (for TaiChiHD) and for faces [24] (for VoxCeleb) extracted identity from reconstructed and ground-truth frame pairs. Then, the mean \(\mathcal{L}_{2}\) norm of their difference across all pairs was computed.  
Animation quality is usually evaluated by subjective video quality assessment. We will present some examples of image animation generation results for visual comparison.
## 2 Theoretical Background
### Overview of DRS
On that note, we produce the taxonomy of data source for building RS which are broadly classified as static and dynamic data categories, as shown in Figure 1. Basically, the most prevalent practice in building any RS is to collect several data either explicitly generated or implicitly that will form a rich source to learn the utility of recommendations for target users [27]. These data, which comprises user, item, and transaction records were rigorously analyzed and grouped into static and dynamic categories. Our motive is to differentiate between the static profiles and the most prevalent types of dynamic profiles, which make different judgments regarding user preferences [12]. * **Static data:** Static data are those attributes or features that can be used to learn the utility of recommendations for the user which do not change or take a longer period before they change [28]. Examples of this include, movie descriptions in a movie domain based on their genre attribute  
Figure 1: Taxonomy of data source for building recommendation. such as a thriller, drama, action, and comedy among others, or the actors, director, production date, etc. More information about users, such as the user's identity, age, gender, and profession could also be exploited as static data to make a personalized recommendation [14]. * **Dynamic attributes:** Dynamic attributes are those attributes that tend to change in the shortest possible time. These include user preference, social relationships, popularity of products, seasonal changes, among others [20]. An RS that assume a static data may end up generating recommendation that does not meet the user's current need [22]. Therefore, these dynamic attributes constitute different types of concept drift in RS that need to be precisely modeled to enhance the recommendation accuracy. In the next subsection, we discussed some of the possible concept drifts that we extracted from various studies in the literature for this review as follows.