esa-sceva/satcom-chunk-collection huggingface dataset:
# Incentive Price-Based Demand Response in Active Distribution Grids
## 5 Simulation Studies
### Case 3: Simulation of BESS Control with the Proposed IDR Method  
In case 2, the BESS are controlled to maximize the self-power consumption, which does not take into consideration of the grid voltages. As a result, the end nodes of the LV grid has over-voltages during noon and a slight under-voltage event in the evening. In this case study, the proposed IDR method which uses the distributed optimization technique as per (15) is used to compute the incentive prices of the BESS. Similar to case 2, the value of \(N_{p}\) is set to 4.0. At each time-step, the incentive price for the operation of each BESS is calculated using the procedure provided in Algorithm 1.  
The simulation results are shown in Figure 7. The first plot shows the incentive prices for the BESS at the end nodes. From the results of case 1 shown in Figure 5, it can be seen that over-voltages at end nodes occur after 10:00. Due to this fact, the calculated incentive prices for BESS are zero until that time in this case study. It is to be noted that a positive incentive price indicates reduction in electricity price for the customer, thus encouraging the customer to charge the BESS. On the other hand, a negative incentive price denotes increase in electricity price to the customer encouraging them to reduce the net electricity consumption, which could be achieved by discharging the BESS, if possible. Due to the calculated positive incentive prices during peak power production, the voltages are kept within the limits. The under-voltage event, which would have occurred during evening peak load period, is also avoided by generation of negative incentive price signals to the BESS during that period.  
The computation time for the execution of case 2 at each time-step is 0.3 s, while for case 3 it is about 0.6 to 3.26 s on a desktop computer representative of current standards. The execution time is higher in case 3 because of the iterations involved in the computation of incentive price signals. It is assumed in the simulation case studies that the uncertainties in the system parameters, measurements etc., are not high to cause a major difference in the results obtained. Also, the proposed algorithm is executed once in 10 min interval taking into account the updated measurements from the LV grid, hence any deviations in the demand response control caused by uncertainties will be corrected in the next time-step. The results of three simulation case studies provided in Table 1 lead to the following conclusions.  
* The proposed method may be more advantageous to the DSOs than the time-of-use tariffs in which the electricity prices are normally predetermined and fixed. However, in the proposed IDR method, the incentive prices are dynamically calculated based on the grid operating conditions. Hence using the proposed method, the DSO can minimize their operating costs and increase the hosting capacity of the grid.
* From the customers' point of view, the proposed method is able to make use of the incentives from the DSO to optimally use their DERs and reduce their electricity bills.
## 2 System Model and Problem Formulation
### Problem Formulation  
Based on (6) and (16), both MIMO radar sensing performance \(L_{\text{Rad}}(\mathbf{X},\gamma)\) and WPT performance \(L_{\text{WPT}}(\mathbf{X},h)\) depend on the transmit waveform \(\mathbf{X}\). Hence, a fundamental trade-off exists between radar sensing and WPT performance in UAV-enabled integrated RadWPT systems, because the optimal \(\mathbf{X}\) that minimizes \(L_{\text{Rad}}(\mathbf{X},\gamma)\) may not minimize \(L_{\text{WPT}}(\mathbf{X},h)\), and vice versa. In addition, \(L_{\text{Rad}}(\mathbf{X},\gamma)\) and \(L_{\text{WPT}}(\mathbf{X},h)\) are functions of the scaling factor (\(\gamma\)) and the UAV altitude (\(h\)), respectively; therefore, both \(\gamma\) and \(h\) have an impact on \(\mathbf{X}\). Therefore, \(\mathbf{X}\), \(\gamma\), and \(h\) should be optimized by taking into account the fundamental trade-off between radar sensing and WPT performance.  
To achieve a flexible trade-off between the two, we devised a weighted optimization to jointly design the transmit waveform (\(\mathbf{X}\)), the scaling factor (\(\gamma\)), and the UAV altitude (\(h\)) for UAV-enabled integrated RadWPT systems. As a result, our design problem is formulated as follows:  
\[\mathbf{P}_{1}:\min_{\mathbf{X},\gamma,h} \rho L_{\text{Rad}}(\mathbf{X},\gamma)+(1-\rho)L_{\text{WPT}}( \mathbf{X},h)\] s.t. \[[\mathbf{X}]_{n,\tau}=\frac{p_{T}}{N},\ \forall n\in\mathcal{N}, \tag{17}\] \[\mathbf{X}\succeq\mathbf{0},\] (18) \[H_{\text{min}}\leq h\leq H_{\text{max}}. \tag{19}\]  
where \(0\leq\rho\leq 1\) is a given weighting factor that determines the performance priority between radar sensing and WPT. In problem \(\mathbf{P}_{1}\), constraints (17) result from the per-antenna power constraint (2), constraint (1) is the positive semidefinite constraint from (17), and constraint (19) indicates the range of UAV altitude. Note that \(\rho=0\) and \(\rho=1\) correspond to the scenarios where only WPT or only MIMO radar sensing, respectively, is considered.
## 1 Introduction  
It is estimated that the world population will reach 10 billion by 2050 [1]. This situation highlights the problems of agricultural needs and demands. Of course, the solutions to these problems depend on efficient and fast production. Applications such as robotics, computer science, artificial intelligence, the Internet of Things (IoT), etc., can provide smart, efficient and fast products for smart farming. Smart farming aims to produce useful results for understanding the soil and to use information and communication technology (ICT) services to collect and process information provided by multiple sources. The amount of water, vegetation, pesticides, humidity, etc., which change according to time and place, require the continuous monitoring of products in terms of irrigation and spraying. Smart farming makes crop management easier and more efficient by using technological equipment in proportion to the specific needs of crops. In this way, this approach aims to use agricultural chemicals more appropriately, to save energy and products, to prevent agricultural pollution, to use smart technology solutions and to create environmentally conscious production. All of these contribute significantly to crop productivity. In this context, precision agriculture can combine multiple analysis processes and technological tools related to all stages of production, from planting to harvest [2].  
Finding solutions to agricultural demands depends on rapid production. For this reason, the number of robotic and computerized solutions used in smart agriculture has increased significantly in recent years due to their contributions [3; 4; 5]. As the size of farms grows, the size of their equipment, as well as tasks such as irrigation, spraying, planting, pruning, etc., significantly increases the farmer's workload. These tasks are highly suitable for autonomous robots, as they often require a large number of repetitions over a long period of time and a large area [6]. In addition, the application of industrial developments and technology to agriculture makes production sustainable by enabling more careful farming [7]. Therefore, to date, robotic agricultural solutions have been proposed in various agricultural solutions, such as fruit harvesting [8], monitoring [9], loading and unloading of agricultural material [10], irrigation [11], fertilization [12], weed detection [13], automatic grafting [14] and sowing [15]. Most of these studies have been carried out on agricultural fields with suitable ground and structured environments using wheeled robots.
## 4 Design of TLQoS
### HELLO Packet Interval  
The HELLO packet interval should be carefully chosen to reflect the actual network status in a dynamic wireless environment and maintain the network connectivity. In addition, the interval should not be too short to cause unnecessary resource consumption of the resource-constrained WBAN. Similar to the mechanism as discussed in [20], TLQoS exploits two HELLO packet interval, namely minimum HELLO interval denoted as \(HI^{min}\) and maximum HELLO interval denoted as \(HI^{max}\). Instead of sending the updates for every changes in the parameter, \(HI^{min}\) is used for periodic updates reflecting the time variant parameters such as delay, reliability and temperature. On the other hand,is chosen as higher value that ensures the network connectivity reflecting a time-invariant parameter such as hop-count. We define three threshold for HELLO packet update considering three time-variant parameters, namely delay update threshold, \(D_{up}^{th}\); reliability update threshold, \(R_{up}^{th}\); and temperature update threshold, \(T_{up}^{th}\). TLQoS triggers a HELLO packet if any of the following events occur:  
* _Event-1_ The recent change in delay, reliability and temperature exceeds \(D_{up}^{th}\) or \(R_{up}^{th}\) or \(T_{up}^{th}\) since the last update message sent at \(HI^{min}\) and current value of \(HI^{min}\) expires
* _Event-2_ The hop-count of a node changes due to changes in topology
* _Event-3_ The current value of \(HI^{max}\) expires since the last HELLO packet sent at \(HI^{max}\)  
Event-1 ensures that if no significant changes occur in delay, reliability or temperature since the last HELLO packet interval sent at \(HI^{min}\), the HELLO packet will not be transmitted. Event-2 ensures the requirement of taking immediate action due to the time-invariant parameter hop-count changes. Event-3 is used to maintain the network connectivity no matter whether any parameter changes or not.
# Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
## 1 Introduction
The computational overhead of the proposed DL model was compared and evaluated, and it was verified that the method proposed in this paper can achieve the same or a lower bit error rate with lower resource overhead compared with baseline model. We used BER as a performance indicator to measure the modulation and demodulation capabilities of the model under different \(E_{b}/N_{0}\) conditions. Currently, there is no unified mathematical relationship that can describe the changes in BER and \(E_{b}/N_{0}\) in multi-order modulation and demodulation, but the mathematical relationship between MPSK and MQAM can be used as a reference. For MPSK, the BER is approximately \(\frac{1}{log_{2}(M)}Q(\sqrt{2\frac{E_{b}}{N_{0}}}sin(\frac{\pi}{M}))\), and for MQAM, the BER is approximately \(\frac{4}{log_{2}(M)}(1-\frac{1}{\sqrt{M}})Q(\sqrt{\frac{E_{b}}{N_{0}}}\frac{ log_{2}(M)}{M-1})\), where \(Q(x)\) is the Gaussian \(Q\) function, defined as \(\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-\frac{x^{2}}{\sigma}}dt\). The rest of this paper is organized as follows: first, the introduction of related literature is given in Section 2; then, the description of the system model is given in Section 3; Section 4 details the structure of the proposed VQ-LSTM-AE; Section 5 gives the simulation results and discussion and analyzes the computational complexity; finally, a summary and future research areas are given.
## 6 Experiment and Result Analysis
### Limitations and Future Research Work
#### 6.10.1 Limitation 1: Occlusion Sensitivity in Real-World Scenarios  
The model's performance significantly degrades when processing heavily occluded faces (such as those covered by medical masks, sunglasses, or scarves). This stems from the fundamental dependence of geometric landmark calibration and texture analysis on visible facial regions. When key facial areas are occluded, both feature extraction pathways are affected by establishing temporal consistency and detecting texture anomalies. This limitation reveals the inherent challenges of the current appearance-based detection paradigm in real-world occluded environments.
## 3 Challenges in Terahertz-Based FSO Communication
### Atmospheric Dispersion and Its Impact on THz-FSO Communication
Despite these challenges, THz-FSO systems remain promising for use in ultra-high-speed, secure, and low-latency communication networks, provided that the dispersion effects are effectively mitigated. Future research should focus on the co-design of low-loss waveguides, optical dispersion compensation filters, and AI-driven adaptive beam alignment, ensuring that THz-FSO technology can meet the demands of next-generation networks, including 6G, inter-satellite links, and high-speed terrestrial communication [72].
# Research on Data Mining of Permission-Induced Risk for Android IoT Devices
## 1 Introduction
Currently, the Android permission privileges are not able to help the users to make correct decisions about risk of app or security [14; 17; 18; 19; 20; 21]. Figure 1: IoT devices Connected with Android devices and apps. In this paper, we propose a methodology with improved accuracy for malware detection by extracting features based on Android application permissions. The behavioural analysis of permissions helps to detect malware and benign apps. Specifically, we adopted three data mining techniques in our proposed methodology (1) Permission Ranking; (2) similarity-based Permission feature selection; and (3) association rule for permission mining. The Permission Ranking analysis is used to rank the permissions based on their risk. The similarity-based Permission is used to collect the subsets of permissions (individual permissions and group of permissions), which cause a security breach in malware apps. Moreover, the association rule for permission mining discovers meaningful relationships between the permissions. Additionally, the Random Forest classifier is mostly used the algorithm in the detection of malware. Therefore, we improve the accuracy of the random forest algorithm for permission induced malware detection. The improved random forest iteratively removes the unnecessary features. By restricting the upper limit of random forest regarding the number of generated trees based on essential and unnecessary features. So, the improved random forest algorithm contains a reduced but most essential set of features. Furthermore, the experimental results validate the proposed approach for malware detection approach, which can effectively detect malware with more accuracy as compared with the previously reported techniques. Our contribution includes three major folds:  
1. We develop a permission-based feature selection approach using (1) Permission Ranking, (2) similarity-based permission feature selection, for the identification of an essential subset of permissions. 2. We also evaluate the effectiveness of the mined association rules for the permission-based which improves the accuracy of prediction. 3. Finally, we enhance the performance of random forest algorithm by iteratively remove the unnecessary features and setting the upper limit on the number of trees in the random forest to improve the accuracy and recall rate, which leads to a secure data exchange between IoT devices and Android devices. After the brief introduction of the paper, the following sections are arranged as follows. Section 2 contains the previous reported research work on Android malware detection with two major solutions. Section 3 gives a brief overview to the proposed methodology based on extracted features from application permissions. Conducted experiments and results are discussed in Section 4. The last section concludes the contribution of paper and results.
# Stochastic Network Calculus-Aided Delay Analysis of Wireless-Power Line Mixed Networks
## 1 Introduction
Recently, stochastic network calculus (SNC) has been proposed to provide a statistical delay envelop for communication systems [15]. SNC in the signal-to-noise-ratio (SNR) domain has been further proposed to capture the service capability of wireless channels, wherein the logarithm operator in the Shannon capacity makes it difficult to express the statistics of the service process in a closed form [16]. In addition, SNC in the SNR domain can also be utilized to analyze the performance for other scenarios, e.g., sensing, controlling and computing, due to the well-known Chernoff's inequality. Last but not least, the convolution operator in the SNC approach makes it possible to deal with the delay analysis problem for tandem queues, which makes it convenient for us to evaluate the performance for the wireless-PLC mixed network in this work. To our knowledge, this is the first endeavor to portray the delay performance of mixed wireless-PLC by virtue of the SNC approach. In this paper, we model the wireless-PLC mixed network as a two-stage tandem queue, over which the data is first relayed via a wireless link and then transmitted through a PLC system. The statistical characteristics of the service incremental process of both channels are analyzed. Furthermore, the delay upper bound for our wireless-PLC mixed system is evaluated via SNC in the SNR domain under any traffic model.
###### Abstract  
The mechanical properties, structure, and morphology of milled and 3D-printed Ti-6 Al-4V alloys produced by selective laser melting were investigated in this study. The mechanical properties were investigated by means of nanoindentation, a tensile test, and a three-point bending test. An Atomic Force Microscope (AFM) was used to study the surface topography and roughness of both titanium alloys. The surface structure and phase analyses were studied by a scanning electron microscope (SEM) and through powder X-ray diffraction (XRD). The results from the nanoindentation experiments showed that the 3D-printed sample has higher indentation hardness and modulus than the milled one. The AFM observation of the surface topography of the samples showed that the milled sample has a higher roughness than the 3D-printed one. The tensile test results showed that the 3D-printed sample by means of Selective Laser Melting (SLM) technology has about 26% higher tensile strength and smaller elongation than the milled one. The three-point bending test revealed that the 3D-printed Ti-6 Al-4V sample has higher flexural strength than the milled one. It was found that the 3D-printed sample has a smaller crystal size than the milled one, which, according to the Hall-Petch relationship, leads to its higher indentation hardness.  
titanium alloys; milling; selective laser melting; mechanical properties; surface structure +
Footnote †: journal: Applied Sciences
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 2 Related Works  
In this section, we describe various research efforts regarding MQTT. There have been research approaches to use MQTT as an application, to improve functionalities of MQTT, to compare MQTT with other protocols, to combine synchronous communication with MQTT, and to focus on end-to-end services of MQTT in terms of security or QoS.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 4 A Novel Battery SoC Estimation Using a Data-Driven PHM Approach
### Multivariate Multi-Step Bidirectional LSTM  
A solution that addresses the vanishing gradients issue for time-dependent and sequential data series is a method called long short-term memory (LSTM) [33]. Recurrent neural networks, particularly LSTM, are highly effective for processing sequential information, setting them apart from traditional neural networks [34]. This model replaces the traditional neuron of the perceptron with a memory block [35]. LSTM can learn how to bridge minimal time lags of more than 1000 discrete timesteps.  
The LSTM structure calculation process is provided in [36]. Traditional RNNs, such as unidirectional LSTM (also known as forward-pass), are effective for handling sequential data. However, they are only trained in the forward direction. On the other hand, bidirectional learning involves training on both the forward and reverse paths, utilizing two separate hidden layers [37]. Bidi-LSTM leverages this information by independently computing both the forward and reverse paths [14]. The resulting output, which captures the flow of information, is also utilized for learning. As a result, features are extracted more effectively, leading to higher accuracy compared to traditional LSTM models.  
As stated above, a RNN computes only the forward hidden sequence \(\overleftarrow{h}\). By implementing the backward hidden sequence, \(\overleftarrow{h}\), the output sequence \(y\) is obtained by iterating layers from \(k=\{1\ldots n\}\) in the forward direction and \(k=\{n\ldots 1\}\) in the reverse direction [38,39]. The formulation of the bidirectional LSTM backward direction is given in (33) and the output function is finally achieved in (34):  
\[\overleftarrow{h}_{k}=\mathcal{H}(W_{x}\overleftarrow{h}\cdot x_{k}+W_{ \overleftarrow{h}}\overleftarrow{h}\cdot\overleftarrow{h}_{k-1}+b_{k}) \tag{33}\]  
\[\hat{y}_{k}=\sigma_{y}(W_{\overleftarrow{h}}y\cdot\overrightarrow{h}_{k}+W_{ \overleftarrow{h}}y\cdot\overleftarrow{h}_{k}+b_{k}) \tag{34}\]
# Design of a New Non-Coherent Cross-QAM-Based \(M\)-ary DCSK Communication System
## 3 Performance Analysis
### XQAM-MDCSK (\(M\geq 32\))
The conditional BER of \(i_{3}\) can be calculated as  
\[P_{b_{-}b_{-}}(\gamma_{s}) =\frac{4}{M}\Bigg{[}\Bigg{[}\Bigg{(}\sum_{t=1}^{2^{m-1}}q(2t-1) \Bigg{)}\Bigg{(}2^{-1}-\sum_{t=1}^{2^{m}}q(2t-1)\Bigg{)}+\Bigg{(}\sum_{t=1}^{2 ^{m-2}}\Big{(}q(2t-1)-q\Big{(}2t-1+2^{m+1}\Big{)}\Big{)}\Bigg{)}\] \[\times\Bigg{(}2^{m-2}+\sum_{t=1}^{2^{m-1}}q\Big{(}2t-1+2^{m-1} \Big{)}\Bigg{)}+2^{m-1}\sum_{t=1}^{2^{m}}q(2t-1)+\Bigg{(}2^{m-1}-\sum_{t=1}^{2 ^{m}}q(2t-1)\Bigg{)}\] \[\times\Bigg{(}\sum_{t=1}^{2^{m}}(-1)^{\left\lfloor\frac{t-1}{2^ {m-1}}\right\rfloor}q(2t-1)\Bigg{)}\Bigg{]}+\Bigg{[}\frac{1}{4}\sum_{t=1}^{2^{ m}}\sum_{t=1}^{2^{m-1}}q\Big{(}2t-1,\frac{-2t+2v+2^{m-1}}{\sqrt{2}},-\frac{1}{ \sqrt{2}}\Big{)}\] \[P_{b\_{,}i_{2}}(\gamma_{s}) = \frac{4}{M}\left[2\sum_{t=1}^{2^{m-2}}\left(q(2t-1)-q\left(2t-1+2 ^{m+1}\right)\right)\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right)\right. \tag{7}\]0 \[P_{b\_{,}i_{2}}(\gamma_{s}) = \frac{4}{M}\left[2\sum_{t=1}^{2^{m-2}}\left(q(2t-1)-q\left(2t-1+2 ^{m+1}\right)\right)\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right)\right. \tag{7}\]1  
Due to the symmetry, \(P_{b_{-}q_{3}}(\gamma_{s})\) is equal to \(P_{b_{-}j_{4}}(\gamma_{s})\), which is given by  
\[P_{b\_{,}i_{2}}(\gamma_{s}) = \frac{4}{M}\left[2\sum_{t=1}^{2^{m-2}}\left(q(2t-1)-q\left(2t-1+2 ^{m+1}\right)\right)\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right)\right. \tag{7}\]2 \[P_{b\_{,}i_{2}}(\gamma_{s}) = \frac{4}{M}\left[2\sum_{t=1}^{2^{m-2}}\left(q(2t-1)-q\left(2t-1+2 ^{m+1}\right)\right)\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right)\right. \tag{7}\]3 \[P_{b\_{,}i_{2}}(\gamma_{s}) = \frac{4}{M}\left[2\sum_{t=1}^{2^{m-2}}\left(q(2t-1)-q\left(2t-1+2 ^{m+1}\right)\right)\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right)\right. \tag{7}\]4 \[P_{b\_{,}i_{2}}(\gamma_{s}) = \frac{4}{M}\left[2\sum_{t=1}^{2^{m-2}}\left(q(2t-1)-q\left(2t-1+2 ^{m+1}\right)\right)\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right)\right. \tag{7}\]5 \[P_{b\_{,}i_{2}}(\gamma_{s}) = \frac{4}{M}\left[2\sum_{t=1}^{2^{m-2}}\left(q(2t-1)-q\left(2t-1+2 ^{m+1}\right)\right)\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right)\right. \tag{7}\]6 \[P_{b\_{,}i_{2}}(\gamma_{s}) = \frac{4}{M}\left[2\sum_{t=1}^{2^{m-2}}\left(q(2t-1)-q\left(2t-1+2 ^{m+1}\right)\right)\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right)\right. \tag{7}\]7 \[P_{b\_{,}i_{2}}(\gamma_{s}) = \frac{4}{M}\left[2\sum_{t=1}^{2^{m-2}}\left(q(2t-1)-q\left(2t-1+2 ^{m+1}\right)\right)\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right)\right. \tag{7}\]8 \[P_{b\_{,}i_{2}}(\gamma_{s}) = \frac{4}{M}\left[2\sum_{t=1}^{2^{m-2}}\left(q(2t-1)-q\left(2t-1+2 ^{m+1}\right)\right)\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right)\right. \tag{7}\]9 \[+ \left[\frac{M}{8}-\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right) ^{2}\right]\right].\]0  
Hence, based on the above derivations, \(P_{b}(\gamma_{\rm s})\) can be expressed as  
\[+ \left[\frac{M}{8}-\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right) ^{2}\right]\right].\]1  
where \(M=8,32,128,\ldots\) and \(m=\frac{(\log_{2}M-1)}{2}\).