esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 2 SON Operation Rationale, Use Cases, Standardization, Architectures and Dimensioning
### Essential SON Use-Cases  
According to 3 GPP and NGMN, SON use cases are classified into three basic categories with each category including subcategories of SON applications. The three basic ones and key pillars are:  
* Self-optimization.
* Self-configuration.
* Self-healing.  
Self-optimization includes activities such as coverage and capacity optimization, interference handling, Mobility Load Balancing and cell neighbor relation setting; self-healing relates mainly to maintenance issues such as cell outage detection; and self-configuration enables auto-connectivity and automated initial parameter configuration [23; 27; 31].  
Especially for the cases of self-optimization and self-configuration, it should be noted that the specific procedures introduced in 3 GPP TS 36.300 version 9.4.0 Release 9, as a capability of Evolved Universal Terrestrial Radio Access Network (E-UTRAN), were further developed up to their applicability in 5G [58; 64].
## 3 Non-Overlapping LZSS
By storing the differences \(\mathsf{LPnF}[j]-\mathsf{LPnF}[j-1]+1\geq 0\) for \(j\in[2\ldots n]\) in a unary bit sequence, we can linearly decode \(\mathsf{LPnF}\) from this unary bit sequence because we know that \(\mathsf{LPnF}[1]=0\). Since \(\mathsf{LPnF}[n]+n\leq n\) by the above lemma (in particular \(\mathsf{LPnF}[i]\leq\mathsf{LPF}[i]\)), the sequence has at most \(2n\) bits. Obviously, this sequence can be written sequentially from right to left in constant time per \(\mathsf{LPnF}\) value in reverse order \(\mathsf{LPnF}[n],\ldots,\mathsf{LPnF}[1]\) (the algorithm of [PERSON] and [PERSON] [25] computes \(\mathsf{LPnF}\) in this order). It is therefore possible to compute \(\mathsf{LPnF}\) within \(\mathcal{O}(n)\) bits on top of \(P\) and a compressed indexing data structures such as the FM-index [28] of the text: For that purpose, [PERSON] and [PERSON] [29] proposed an algorithm computing \(\mathsf{LPF}\) and \(P\) with the FM-index in \(\mathcal{O}(n\lg^{3}n)\) time, which was improved by [PERSON] and [PERSON] [30] to \(\mathcal{O}(n\lg^{2}n)\) time. However, the need of \(P\), using \(n\lg n\) bits when stored in a plain array, makes an approach that transforms \(\mathsf{LPF}\) to \(\mathsf{LPnF}\) after computing \(\mathsf{LPF}\) and \(P\) rather unattractive. In what follows, we present a different way that directly computes the non-overlapping LZSS factorization or \(\mathsf{LPnF}\) with near-linear or linear running time, without the need of \(P\).
## 2 Literature Review
### Lstm  
Traditional neural networks exhibit certain difficulties in handling continuous data because they operate only on input data. A recurrent neural network (RNN) is a sequence model of an ANN, where a cyclic connection exists between the weight units [38]. This cyclic structure is an algorithm that can perform calculations by combining the previous information with the current information. RNN is a multi-perceptron model, with a three-level structure comprising input, hidden, and output layers. However, RNNs exhibit limitations in learning the long-term dependencies of time series data. For instance, the gradient value decreases slightly with every occasion of passing through the neural network with the increase in length. This phenomenon causes problems of vanishing or exploding gradient, wherein the gradient becomes zero and disappears before backpropagation or the gradient becomes excessively large, respectively. Therefore, advanced RNN technology was developed to compensate for these limitations.  
LSTM [39] is a widely adopted RNN for training time series data by adjusting the information flow in the sequence data to capture complex temporal correlations. This method was proposed to solve the gradient problem of RNNs. Figure 1 depicts the structure of LSTM, where \(\sigma\) denotes the sigmoid activation function and \(tanh\) indicates the hyperbolic tangent activation function. LSTM comprises an input gate \(\mathbf{i_{t}}\), forget gate \(\mathbf{f_{t}}\), updating cell state, and output gate \(\mathbf{O_{t}}\). The input gate memorizes the current information, whereas the forget gate erases the memory. The cell state \(\mathbf{g_{t}}\), referred to as the long-term state, computes long-term memory. The hidden unit \(\mathbf{h_{t}}\) computes short-term memory. The equations for the LSTM structure can be expressed as follows:  
\[\mathbf{i_{t}}=\sigma(\mathbf{W_{x}}\mathbf{x_{t}}+\mathbf{W_{hi}}\mathbf{h_{t-1}}+\mathbf{b_{i}}), \tag{2}\]  
\[\mathbf{g_{t}}=\  
denote the weights for \(\mathbf{h_{t-1}}\) at each step. The LSTM overcomes the limitations of long-term memory using the cell state in the traditional RNN method.
## 4 Results and Discussion
### Potential for Other Applications  
To explore the application potential of our method on objects other than arc magnets, we attempted to use our model for the detection of image-based insulator defects in a high-voltage tower. The data used in this attempt were sourced from the Chinese Power Line Insulator Dataset (CPLID) [42], which provided 600 defect-free insulator images captured by unmanned aerial vehicles (UAVs) and 248 synthetic defective insulator images. All images in this dataset were derived from a synthesis of the ground truth and defective insulators due to the limited number of real defective insulator images. The purpose of this identification was to determine whether the insulator in each image has defects. The same data augmentation was still followed due to the small and unbalanced sample data. The number of images of both defect-free and defective insulators was expanded to 1200, which were also respectively divided into the training set, the validating set, and the testing set according to a ratio of 8:1:1. The model employed was still derived from a fine-tuned network based on the same pre-trained YOLOv5s and compressed with the help of pruning and distillation. An example of the detection of defective insulators is shown in Figure 10. We found that the defective insulators in the figure could be detected accurately. We chose the original YOLOv5s trained directly with the augmented CPLID data to compare with our model to investigate their performance in detecting defective insulators. As exhibited in Table 3, compared to the original YOLOv5s, our model reduced TNNP, FLOPS, MS, and AITC by 93.559%, 88.380%, 92.985%, and 5.634%, respectively. This indicates that the complexity and computing power dependence of the model were significantly smaller, implying a faster detection speed and a more convenient deployment. More importantly, mAP increased by 0.03% and _A\({}_{D}\)_ was boosted by 1.667% and improved to 100%, enabling the most accurate detection of defective insulators. It follows that there is considerable potential for broader applications given the advantages of our method in compressing models and improving accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **TNNP (M)** & **FLOPS (G)** & **MS (MB)** & **AITC (mS)** & **mAP (\%)** & \(\mathbf{A_{D}}\) **(\%)** \\ \hline YOLOv5s & 7.064 & 6.919 & 27.170 & 10.046 & 99.960 & 98.333 \\ Our model & 0.455 & 0.804 & 1.906 & 9.480 & 99.990 & 100.000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the proposed model and original YOLOv5s in the image-based insulator defect detection.  
Figure 9: Performance of different models for identifying surface defects.
# A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry
## 7 Experimental Results
### Comparison with the State-of-the-Art  
A comparison of the proposed inductive link system with other published works is shown in Table 2. The comparison is specific to the designs that use the inductive link for the simultaneous power and uplink data transmissions, where the power link could highly limit the performance of the uplink.  
Compared with the conventional LSK method [5; 33], the new LSK methods [9] and the PPSK method [10], our work achieves reliable uplink data transmission under a much lower coupling coefficient and larger coil separation. At the same time, our uplink coexists better with the WPT. When the wireless power of our work is continuously transferred to the implant, the uplink data are reliably transmitted to the external coil with negligible impacts on WPT (only 5.1% loss of PTE and 5% ripple on \(V_{\text{load}}\)).  
Compared with the multiple coil pair method [12], our work also achieves the simultaneous power and uplink data transmissions under a lower coupling coefficient and larger coil separation. Furthermore, our work only uses one implant coil for both harvesting wireless power and transmitting uplink data, which will be feasible for some specific applications that cannot implant multiple coils.  
The work in [11] is our previous dual-carrier uplink system, but using the conventional two-coil inductive link. It can transfer uplink data and wireless power reliably simultaneously through the same coil pair under weak coupling. However, the uplink in the previous work suffers a severe power carrier interference (SIR = \(-\)75.79 dB), which highly challenges the circuit design of the receiver and highly limits the performance of the uplink. Compared with [11], the proposed magnetically-balanced inductive link achieves a 65.72-dB improvement of the SIR. This is a significant improvement for the weak uplink signal receiving, which remarkably alleviates the challenge of receiving the weak uplink  
Figure 16: The magnetically-balanced inductive link is simulated with the human head model in ANSYS HFSS electromagnetic simulation suite.  
data signal under strong power carrier interference. In addition, the uplink data transmission causes only 5.1% loss of the PTE in the proposed inductive link, which is much less than that in the previous works. Therefore, compared with the conventional two-coil inductive link, the magnetically-balanced inductive link achieves significant improvement of SIR and less loss of PTE.
# Analysis of Cost Functions for Reinforcement Learning of Reaching Tasks in Humanoid Robots
## 4 Analysis of Influence of Different Cost Function Terms on the Learnt Motions
### Experimental Setting  
In our experiments, we focused on the learning of reaching movements on a humanoid robot TALOS [42], initially obtained from a human demonstration, as explained in Section 2.1. The initial demonstration represents a reaching motion for the right end-effector, i.e., the robot's right wrist, as shown in Figure 2. The final reaching position was within the robot's upper body reachable workspace, as shown in Figure 4. Therefore, out of TALOS' 32 degrees of freedom, the learning process was conducted on 18, i.e., those associated with the upper body joints. The lower body joints were fixed, maintaining their initial values. For the representation of joint trajectories, we employed the DMP methodology as outlined in [38]. Each joint trajectory is encoded with a DMP, which contains 20 parameters (weights) to shape the demonstrated trajectory. These parameters are initially computed from our stabilized user demonstration. To fully specify a DMP, we need an additional parameter specifying the final desired joint configuration. As a result, the parameter vector for the reinforcement learning procedure introduced in Section 3 comprises 360 values of weights (for 18 degrees of freedom), alongside additional 18 values specifying the final joint configuration (goal) on the trajectory. This brings the total number of parameters for the RL algorithm to determine to 378.  
Figure 4: Visual representation of the experimental setup. The robot needs to learn a reaching motion to the desired point in space represented with the green sphere.  
Figure 3: System architecture of the learning process used in this study.  
The motion refinement process was conducted in the Gazebo simulation environment of TALOS. The initial stable motion was optimized by the PI\({}^{2}\)-CMA method using a cost function (14). All learning sessions were performed with 150 updates, using 30 reaching motion rollouts per update. For the exploration strategy, we chose higher initial exploration values for the robot's arm joints and lower values for the head and torso joints. However, the PI\({}^{2}\)-CMA algorithm should be able to automatically determine the optimal covariance matrix within the learning process. By experimenting with different combinations of weights in cost function (14), we analyzed the effect of different cost terms on the final (optimal) reaching trajectory.
## 2 Background
### Mobile Networks Digitization and the 5G  
Different network types can be used to connect IoT devices. Wireless networks such as traditional mesh networks, ad-hoc networks, satellite networks, sensors networks, Bluetooth, and so on ensure Internet connectivity. In personal area networks, IEEE 802.15.x provides a large number of application specifications [15]. Another communication standard implemented for the support of wireless communication is the extended Wi-Fi networks, using 900 MHz bands instead of the legacy 2.4 GHz Wi-Fi band and supporting the concept of IoT [16]. However, coverage and scalability are two parameters that limit the adoption of the mentioned technologies in IoT services and machine-type communications. Reliability, data security, and interference pose another threat due to the use of unlicensed frequency bands. Cellular mobile networks have already been supporting connectivity between different applications, going beyond the stream of just mobile phones, tablets, and computers to accommodate other applications such as connected buildings, smart meters, and vending machines. Mobile networks should guarantee security and reliability because they are strongly regulated. Current machine-type and IoT applications use LTE cellular technology because of its high-speed characteristics and reliability. As projected by many studies, the estimated number of connected devices by 2020 could be around 50 billion [17]. Other disciplines outside the telecommunications area are going to be major contributors to network traffic and load; for example, in the domain of healthcare, there is going to be a large number of devices connected for health and medical conditions in the smart city domain. This means the current traffic could grow exponentially; future demand in terms of network connectivity requirements is going to surpass what LTE systems can currently achieve effectively. Hence, a need for the MNO business model is to digitize their networks to support the future trends of IoT and MTC applications. More data capacity, higher throughputs, massive machine-type communication (mMTC), and ultra-low latency applications are the main drivers of the 5G network system. The enhanced mobile broadband (eMBB), mMTC, and critical machine-type communication (uMTC) are the main group of use cases that will form the building blocks of 5G networks [18].
# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 5 Performance Evaluations
### Performance Evaluation Criteria  
Four quantitative criteria are selected to evaluate the proposed approach's performance. These evaluation criteria are explained as follows:  
1. Network Lifetime [31] is the amount of time that has passed since the network started running until the first node in the network stops working because its battery is depleted.
2. The packet delivery rate (PDR) [31] is the ratio of the number of successful messages sent by the source nodes that the sink node received.
3. The average end-to-end delay [31] is the average amount of time it takes for a data packet to travel from the source node to the sink.
4. EIF, or the Energy Imbalance Factor [31], is the average difference in energy between the nodes in the whole network.  
\[EIF=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(RE_{i}-RE_{avg}\right)^{2}}\]  
where \(n\) is the number of nodes, \(RE_{i}\) is the node's \(i\) residual energy, and \(RE_{avg}\) is all nodes' average residual energy.
# Fabrication and Characterization of Modified Graphene Oxide/PAN Hybrid Nanofiber Membrane
## 2 Materials and Methods  
### Materials  
Graphite flake (Bay Carbon Inc., Baycity, MI, USA), sodium nitrate (NaNO\({}_{3}\), \(\geq\) 99%, Sigma-Aldrich Korea, Seoul, Korea), KMnO\({}_{4}\) (99%, Sigma-Aldrich), sulfuric acid (98%, Duksan, Seoul, Korea), and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\), 35%, Samchen Co., Ltd., Seoul, Korea) were used for the synthesis of GO. Materials used to manufacture the nanofibers were polyacrylonitrile (PAN, Mw 150,000, Sigma-Aldrich) and N,N-dimethyl formamide (DMF, 99.5%, Duksan). PolydiallyIdimethylammonium chloride (PDDA) was used as a surfactant to modify the surface of GO. All chemicals were used without further purification.
###### Abstract  
Vehicular Ad-Hoc Networks (VANETs) are the backbone of the intelligent transportation system, which consists of high-speed vehicles with huge dynamic mobility. The communication takes place with a vehicle-to-vehicle, vehicle to infrastructure, with traffic signals. The major flaw of this kind of network is that due to high mobility in VANETs, the communication overhead is so high that it directly affects the efficiency of the network. Security is also holding a vital role in VANETs. The attackers can easily capture vehicle details of this type. To overcome this drawback, security should also need to get improved. This paper introduces Elliptical Curve Cryptography with Generic Algorithm based Privacy-Aware Secure Routing (ECC-GA-PASR), which is the combination of two methods such as optimal RSU distribution and elliptical curve cryptography (ECC) based authentication. RSU distribution is optimized by using the generic algorithm (GA) as well as to improve the authentication in trusted authority (TA) ECC algorithm is used. By using these two concepts, the proposed method reduced the communication overhead and increased the security of the network. The simulation is conducted throughput NS2 and SUMO. The performance analysis is performed concerning vehicle count, varying speed, and malicious activities. The parameters that are concentrated for this performance analysis are energy efficiency, packet delivery ratio, overhead, and packet loss. The performance of the proposed method is calculated and compared with earlier works such as S-ADDV, ES-ADDV, and ECC-ACO-AODV. Compared with the earlier works, the proposed ECC-GA-PASR produced 15% better efficiency, 12% better packet delivery ratio, 50% lower overhead, and 30% lower packet loss.  
P 2022 6 121. [[https://doi.org/10.3390/design6060121](https://doi.org/10.3390/design6060121)]([https://doi.org/10.3390/design6060121](https://doi.org/10.3390/design6060121))  
1  
Citation:[PERSON]; [PERSON]; [PERSON]; [PERSON].  
Privacy-Aware Secure Routing through Elliptical Curve Cryptography with Optimal RSU Distribution in VANETs. Designs  
2022
# Federated Learning-Based Service Caching in Multi-Access Edge Computing System
###### Abstract  
Multi-access edge computing (MEC) brings computations closer to mobile users, thereby decreasing service latency and providing location-aware services. Nevertheless, given the constrained resources of the MEC server, it is crucial to provide a limited number of services that properly fulfill the demands of users. Several static service caching approaches have been proposed. However, the effectiveness of these strategies is constrained by the dynamic nature of the system states and user demand patterns. To mitigate this problem, several investigations have been conducted on dynamic service caching techniques that can be categorized as centralized and distributed. However, centralized approaches typically require gathering comprehensive data from the entire system. This increases the burden on resources and raises concerns regarding data security and privacy. By contrast, distributed strategies require the formulation of complicated optimization problems without leveraging the inherent characteristics of the data. This paper proposes a distributed service caching strategy based on federated learning (SCFL) that works efficiently in a distributed system with user mobility. An autoencoder model is utilized to extract features regarding the service request distribution of individual MEC servers. The global model is then generated using federated learning, which is utilized to make service-caching decisions. Extensive experiments are conducted to demonstrate that the performance of the proposed method is superior to that of other methods.  
multi-access edge computing; service caching; federated learning; autoencoder; popularity prediction
## 4 Convolutional and Recurrent Neural Networks for Pixel-Based Crops Classification
### Training  
Learning the overall mapping function F requires the estimation of all network parameters \(\Theta\) of the three different model parts. This is simply achieved through minimizing the loss between each pixel class prediction \(F(X^{(i)})\) and the corresponding ground truth \(y^{(i)}\) with a supervised learning strategy. So, given a data set with \(n\) pixel samples \(\{X_{i}\}\) and the respective true classes set \(\{y_{i}\}\), we use categorical cross-entropy as the loss function:  
\[J(\Theta)=-1/n\sum_{i=1}^{n}\sum_{k=1}^{K}y_{k}^{(i)}\log(\hat{p}_{k}^{(i)}), \tag{11}\]  
where \(y_{k}^{(i)}\) cancels all classes loss except for the true one. Equation (11) is minimized using AMSGrad optimizer [71], an adaptive learning rate method that modifies the basic ADAM optimizer [72] algorithm. The overall algorithm update rule without the debiasing step is:  
\[m_{t} =\beta_{1}m_{t-1}+(1-\beta_{1})g_{t}, \tag{12}\] \[v_{t} =\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2},\] (13) \[\vartheta_{t} =\max(\vartheta_{t-1},v_{t}),\] (14) \[\theta_{t+1} =\theta_{t}-\frac{\eta}{\sqrt{\vartheta_{t}+\epsilon}}m_{t}. \tag{15}\]  
Equations (12) and (13) are the exponential decay of the gradient and gradient squared, respectively. Instead, with the Equation (14), keeping a higher \(v_{t}\) term results in a much lower learning rate, \(\eta\), fixing the exponential moving average and preventing to converge to a sub-optimal  
Figure 7: Pixel R-CNN convolutional layers. Firstly, output from TimeDistributedDense layer \(Y^{(i)}_{(timeD)}\) is reshaped in a 3-dimensional tensor \(\mathbf{Y}^{(i)}_{(reshape)}\) and then it feeds a stack of two convolutional layers that progressively reduce the first two dimensions, gradually extracting higher-level representations.  
point of the cost function. Moreover, we use a technique known as cosine aneling in order to cyclically vary the learning rate value between certain boundary values [73]. This value can be obtained with a preliminary training procedure, linearly increasing the learning rate while observing the value of the loss function. Finally, we employ, as only regularization methodology, \"Dropout\" [74] in the time representation stage, inserted between the LSTM and Time-Distributed layer. This simple tweak allows training a more robust and resilient to noise temporal patterns extraction stage. Indeed, forcing CNN to work without relying on certain temporal activations can greatly improve the abstraction of the generated representations distributing the knowledge across all available units.