esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Simplified Expressions for Large-Diameter EAHX Thermal Design  
Sensible heat transfer in the EAHX is modeled with the differential equations for diffusion in the soil, for advection in the air inside the pipe, and initial and boundary conditions [27]. Important boundary conditions are the outdoor air and the undisturbed soil temperatures.  
Outdoor air temperatures \(T_{\text{a0}}(t)\), with \(t\) denoting time, are often available from national meteorological services; undisturbed soil temperature \(T_{\text{sso}}(z,t)\) at EAHX depth \(z\) can be derived from soil temperatures \(T_{\text{s0}}(t)\) measured at a reference depth \(z_{0}\), as described in [PERSON] and Katz [30].  
For design purposes and depths with small thermal amplitude, [PERSON] and Katz's equation for \(T_{\text{so}}(z,t)\) is simplified to  
\[\hat{T}_{\text{so}}(z)=\overline{T}_{\text{so}}+\theta_{\text{so}}e^{-K(z-z_{0} )}\, \tag{2}\]  
with \(\hat{T}_{\text{so}}(z)\) and \(\overline{T}_{\text{so}}\) being the annual maximum and annual average values of undisturbed soil temperatures at depths \(z\) and \(z_{0}\), respectively, and \(\theta_{\text{so}}\) the soil temperature amplitude at depth \(z_{0}\).  
Although modeling of the heat transfer in EAHXs using commercial computational models is found in the scientific literature [27], since our design is concerned with the system's operation under specific and demanding (sizing) conditions, solutions derived from simplified heat transfer expressions remain relevant [21]. Examples of the successful use of simplified expressions validated against monitoring data are described in [PERSON] et al. [8], [PERSON] et al. [31], and [PERSON] et al. [32].  
In line with the above argument, the advice provided in standard EN 15241:2007 CEN [22] for EAHX design considers the following expression for EAHX outflow air temperature:  
\[T_{a}(x)=T_{\text{so}}+(T_{\text{a0}}-T_{\text{so}})\exp\left(-\frac{2\pi r_{0 }U}{\dot{m}_{a}c_{a}}x\right)\, \tag{3}\]  
where \(U\) is a joint convection-conduction heat transfer coefficient defined as  
\[U=\frac{h_{a}h_{s}}{h_{a}+h_{s}}\ ; \tag{4}\]  
with  
\[h_{a}=\left[4.13+0.23\left(\frac{T_{a}}{100}\right)-0.0077\left(\frac{T_{a}}{1 00}\right)^{2}\right]\frac{v_{\text{STP}}^{0.75}}{(2r_{0})^{0.25}}\] (5a) and \[h_{s}=\frac{2\pi\lambda_{s}}{\ln\left(\frac{R}{r_{0}}\right)} \tag{5b}\]  
denoting, respectively, the convective heat transfer coefficient between the air and the inner surface of the EAHX (from [PERSON]'s expression in [33]) and the conduction heat transfer coefficient in the cylindrical soil domain between the inner and outer radii, \(r_{0}\) and \(R\), respectively, the latter being the radial position where the thermal influence of the EAHX in the soil becomes negligible [34].
## 3 Relevant Research in Wind Farm Design and Optimization
### Electromagnetic Interference (EMI)
#### 3.2.6 Economic Modeling of Wind Farms
Equation (76) can be further decomposed to describe the NPV contribution of each one of the three main components of the electrical infrastructure:  
\[\text{NPV}\big{(}y(x_{\text{F}})\big{)}=\text{NPV}\left(C_{\text{MVAC}}\big{(} y(x_{\text{F}})\big{)}+C_{\text{SB}}\big{(}y(x_{\text{F}})\big{)}+C_{\text{ HVAC}}\big{(}y(x_{\text{F}})\big{)}\right) \tag{77}\]  
where \(C_{\text{MVAC}}\)[S], \(C_{\text{SB}}\)[S], \(C_{\text{HVAC}}\)[S] are the costs of the internal distribution network, cost for substations and the cost of the transmission lines, respectively. The present value of the total cost of the internal distribution network (\(C_{\text{MVAC}}\)[S]) is quantified as the summation of the effective value of the power to be transported by each section of the power line conductor \(P_{\text{MVAC}\;\text{RMS}j}\)[W], the length of each power line \(L_{\text{MVAC}j}\)[m], its respective cost per unit length \(C_{\text{F}\;\text{MVAC}j}\)[S/m], and the unit variable costs per unit length and power \(C_{\text{V}\;\text{MVAC}j}\)[S/mW]:  
\[C_{\text{MVAC}}\big{(}y(x_{\text{F}})\big{)}=\text{NPV}\bigg{(}\sum_{j=1}^{ \text{MVAC Lines}}\left(C_{\text{F}\;\text{MVAC}j}+C_{\text{V}\;\text{MVAC}j }\;P_{\text{MVAC}\;\text{RMS}j}\big{(}y(x_{\text{F}})\big{)}\right)\;L_{ \text{HVAC}j}\big{(}y(x_{\text{F}})\big{)}\bigg{)} \tag{78}\]  
In an equivalent way, the present value of the total cost of the HVAC transmission lines (\(C_{\text{HVAC}}\)) can calculated as follows:  
\[C_{\text{HVAC}}\big{(}y(x_{\text{F}})\big{)}=\text{NPV}\bigg{(}\sum_{j=1}^{ \text{HVAC Lines}}\left(C_{\text{F}\;\text{HVAC}j}+C_{\text{V}\;\text{HVAC}j }\;P_{\text{HVAC}\;\text{RMS}j}\big{(}y(x_{\text{F}})\big{)}\right)\;L_{ \text{HVAC}j}\big{(}y(x_{\text{F}})\big{)}\bigg{)} \tag{79}\]  
The total present value of the cost of the substations (\(C_{\text{SB}}\)) can be calculated as a summation of the annual effective value of the power to be carried by each substation \(P_{\text{SB}\;\text{RSM}j}\)[W], their respective unit fixed costs \(C_{\text{F}\;\text{SB}j}\)[S], and the unit variable costs per unit power \(C_{\text{V}\;\text{SB}j}\)[S/W], as shown in Equation (80):  
\[C_{\text{SB}}\big{(}y(x_{\text{F}})\big{)}=\text{NPV}\bigg{(}\sum_{j=1}^{ \text{Subs}}C_{\text{F}\;\text{SB}j}+C_{\text{V}\;\text{SB}j}\;P_{\text{SR} \;\text{RMS}j}\big{(}y(x_{\text{F}})\big{)}\bigg{)} \tag{80}\]  
Equations (78)-(80) assume that the cost of the electrical infrastructure increase linearly as a function of the power transmitted. This however, might not always be true as shown in Equations (67)-(71). The presented NPV model accounts for most fixed and variable costs. However, the inclusion of additional recurrent costs (e.g., landowner royalty fees, WT structural degradation, taxes, _etc._) might be necessary to complement the model.
# Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies
## 2 Preliminaries and Problem Formulation
### Graph Theory  
In this work, we focus on the overall behavior of dynamic systems relying on interactive communication networks. Each agent can be treated as a node and the path of information interaction between agents can be treated as an edge. Then the whole multi-agent system network can be described by a graph with nodes and edges. There is a communication link between two tracking nodes \((i,j)\), if node \(j\) is within the communication range of node \(i\). A weighted graph \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathcal{A})\) containing N nodes is composed of a node set \(\mathcal{V}=\{1,2,...,N\}\), an edge set \(\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}\) and a weighted adjacency matrix \(\mathcal{A}=[a_{ij}]\in\mathbb{R}^{N\times N}\), where \(i\) and \(j\) denote tracking agents. Nodes within the communication radius of node \(i\) are called neighbors of node \(i\), which is denoted as \(\mathcal{N}_{i}=\{j\mid j\in N,(j,i)\in\mathcal{E}\}\). When node \(j\) is out of the communication range of node \(i\) (\(j\
otin\mathcal{N}_{i}\)), the connection weight between the two tracking nodes is 0, which is denoted by \(a_{ij}=a_{ji}=0\), otherwise \(a_{ij}=a_{ji}>0\). In this work, we consider each node without self-loop i.e., \(a_{ii}=0\). Let \(h_{i}\) denote of connection weight between tracking agent \(i\) and the target agent, \(h_{i}>0\) if the former can communicate with the latter, otherwise \(h_{i}=0\). Denote the Laplacian matrix of communication topology with target agent as \(\mathcal{L}+\mathcal{H}\), where  
\[\mathcal{L}=\mathcal{D}-\mathcal{A}\]  
\[\mathcal{D}=diag\{\sum_{j\in\mathcal{V}_{1}}a_{1j},\sum_{j\in\mathcal{V}_{2}} a_{2j},...,\sum_{j\in\mathcal{V}_{N}}a_{Nj}\}\]  
\[\mathcal{H}=diag\{h_{1},h_{2},...,h_{N}\}.\]
## 1 Introduction
This adaptation leverages the preamble-based CAZAC-TD SNR estimator to work effectively with STBC-decoded signals, enhancing signal reliability and diversity in MIMO-OFDM systems. * The development of the CAZAC-FD SNR estimator: A new CAZAC-FD SNR estimation algorithm based on M2 criteria, similar to the Milan-FD SNR estimator, is developed. The frequency domain version of the CAZAC-TD SNR estimator leverages the modified CAZAC preamble structure for synchronization, resulting in no throughput penalty. * Comparative performance evaluation: The comparative evaluation of the newly developed CAZAC-FD SNR estimator against the existing Milan-FD SNR estimator, with both estimators derived using similar approaches, ensures a meaningful performance comparison and establishes a benchmark for future SNR estimation methods. * The performance of preamble-based CAZAC-TD and CAZAC-FD MIMO SNR estimators is evaluated in non-adaptive \(2\times 2\) and \(4\times 4\) STBC-assisted MIMO-OFDM systems, with the normalized Cramer-Rao bound (NCRB) used as a benchmark for the best achievable performance. By comparing the normalized mean square error (NMSE) of the estimators with the NCRB, valuable insights are gained into how closely the performance of the estimators approaches the theoretical optimum. The modified CAZAC preamble structure, utilized in both time and frequency domain SNR estimators, proves effective in estimating SNR, thereby highlighting the dual-domain functionality of the modified structure. The paper is organized as follows. Section 2 highlights the related research; Section 3 explains the overall system. Section 4 discusses the development of the preamble-based CAZAC-TD and CAZAC-FD SNR estimators, as well as the SNR threshold for modulation level selection in an adaptive modulation scheme. Section 5 presents the performance of the contrived SNR estimators and the performance of the adaptive STBC-assisted MIMO-OFDM system. Finally, Section 6 presents the conclusion.
# A Lightweight Hash-Based Blockchain Architecture for Industrial IoT
## 2 Related Work
### Considerations for Blockchain Based IIoT  
IIoT comprises many heterogeneous devices with limited resources. If we apply the existing blockchain technology, then it can affect the availability of the network. Therefore, when we consider the blockchain solution for IIoT, we have to consider this restriction. To improve the availability of blockchain-based IIoT, we have to consider below:  
1. Computational resources:The consensus algorithms employed in the mining process of the blockchain (PoW) require significant computational resources that are far beyond the capabilities of most IoT devices. Resource requirements [29] depend on the particular type of consensus protocol in the blockchain network. Typically, solutions tend to delegate these tasks to gateways, or to any other unconstrained devices capable of providing this functionality. Although there are initiatives to incorporate blockchain full nodes into IoT devices [30]; mining is still a key challenge for IoT due to its limitations. To solve this challenge, we have to consider the computational burden of consensus algorithms. That is, we have to consider whether the devices on the network can adequately serve their functionality during the mining process. Therefore, we have to apply or design a lightweight consensus algorithm, especially in the industrial field.
2. Latency: There is a significant latency associated with ensuring that transaction is confirmed by nodes which participating in the blockchain network. For example, in Bitcoin, it can take up to 30 min for a transaction to be confirmed. Most IoT applications have latency requirements, e.g., a service provider requesting data from a smart home sensor should not have to wait for several min. However, the latency requirements are stricter in the industrial field because IIoT can be used with huge manufacturing control systems or national infrastructures. For ensuring the latency, we have to use fast cryptographic functions and consensus algorithms for fast operation of the blockchain network.
3. Scalability: In a typical blockchain implementation, the target block for updating is broadcast and verified by all nodes. It leads to significant scalability issues since the broadcast traffic and processing overheads would increase quadratically with the number of nodes in the network. Especially in the case of the industrial field, the devices are placed in a wide location. The associated overheads are intractable as most IoT devices have limited bandwidth connections (e.g., Low-Power Wide-Area Networks such as LoRa network which has a bandwidth under 1 GHz.) and processing capabilities. Therefore, the blockchain network has designed to broadcast efficiently.
## 3 Results
### Quantitative SIFT Analysis and Statistical Validation

Table 6: Total number of SIFT keypoint matches between original and enhanced images for each method on the Test-C60 and OceanDark datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Proposed vs.**} & \multicolumn{4}{c}{**Test-C60**} & \multicolumn{4}{c}{**OceanDark**} \\ \cline{2-7}  & **R+** & **R\(-\)** & **\(p\)** & **R+** & **R\(-\)** & **\(p\)** \\ \hline WCD & 1953 & 0 & \(7.54443\times 10^{-12}\) & 10,572 & 5538 & 0.000286733 \\ UTV & 1827.5 & 125.5 & \(2.41749\times 10^{-9}\) & 4078 & 11,853 & \(1.63027\times 10^{-8}\) \\ UT & 1951 & 2 & \(8.3325\times 10^{-12}\) & 16,836 & 0 & \(8.71027\times 10^{-32}\) \\ USUIR & 1723 & 47 & \(2.50036\times 10^{-10}\) & 14,094.5 & 1836.5 & \(5.2497\times 10^{-19}\) \\ ULAP & 1790 & 40 & \(1.17628\times 10^{-10}\) & 10,123.5 & 4927.5 & \(8.15131\times 10^{-5}\) \\ UDCP & 1943 & 10 & \(1.22579\times 10^{-11}\) & 9856 & 5720 & 0.002242207 \\ TOPAL & 1950.5 & 2.5 & \(8.54366\times 10^{-12}\) & 16,653 & 0 & \(1.26866\times 10^{-31}\) \\ RGBHS & 1582.5 & 187.5 & \(1.39138\times 10^{-7}\) & 12,171 & 3229 & \(2.6401\times 10^{-11}\) \\ Input & 1953 & 0 & \(7.5411\times 10^{-12}\) & 14,056 & 1169 & \(3.459\times 10^{-22}\) \\
[11] & 1099.5 & 853.5 & \(0.388244815\) & 9243.5 & 6156.5 & \(0.021385042\) \\ IBLA & 1668.5 & 222.5 & \(2.05359\times 10^{-7}\) & 11,018.5 & 4381.5 & \(7.5607\times 10^{-7}\) \\ HP & 1651.5 & 178.5 & \(5.86926\times 10^{-8}\) & 14,028.5 & 1902.5 & \(1.25845\times 10^{-18}\) \\ HFM & 1586 & 184 & \(1.20452\times 10^{-7}\) & 10,432.5 & 5320.5 & \(0.000179125\) \\ FUnIEGAN & 1951.5 & 1.5 & \(8.12526\times 10^{-12}\) & 16,836 & 0 & \(8.72993\times 10^{-32}\) \\ CBF & 1645.5 & 65.5 & \(9.5145\times 10^{-10}\) & 14,208.5 & 1901.5 & \(7.57233\times 10^{-19}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: SIFT-based Wilcoxon significance test results for Test-C60 and OceanDark datasets. was obtained in terms of SIFT metric in the comparison with the [11] method, the fact that this method has lower AG and higher IL-NQE scores compared to the proposed method indicates that some of the obtained matches may be due to noise. In addition, the SIFT match images of the two leading methods, [PERSON] et al. 's and HFM, and our proposed method are shown in Figure 4. In the figure, it is clearly observed that the proposed method preserves the detail integrity qualitatively and superiorly.
## 3 Calculation of the Output Realization Probability of REG Stations
Combining Equations (6) and (7), the output realization probability \(Pr_{i,t}(P_{t,t})\) can be obtained, and the image of the output realization probability of the REG station \(i\) to achieve power at moment \(t\) is shown in Figure 4.  
According to the output realization probability of each REG station, the section margin is reasonably allocated to each REG station, which can ensure that each REG station has the highest probability of meeting the output power command and improve the reliability of the section power transmission.
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 4 Neutralization Systems
### Electronic Neutralizers
#### 4.1.2 GNSS Jamming  
GNSS (Global Navigation Satellite System) jamming is not a different technique from RF jamming, but simply refers to the jamming of GNSS signals. It is treated separately because of its relevance considering that GNSS signals are typically those most subject to neutralization. Signals received from satellites are characterized by low power value and, thus, are vulnerable to interfering signals; therefore, the technique under consideration can be effective. In fact, as reported in the study in [82], jamming the GPS receiver of a commercial drone can result in drifting and control difficulties, as well as preventing the return to home (RTH) procedure from working properly.  
To implement GNSS jamming, the same radio frequency techniques illustrated in the preceding paragraph can be used. These techniques have been analyzed and evaluated in relation to GPS signals in [83], together with successive pulses jamming, which involves transmitting a sequence of pulses over time with a small duty cycle to the central frequency of interest and can be seen as a particular implementation of noise jamming. Best results are obtained with smart jamming and sweep jamming--the first technique is the most effective when compared to its purpose (making the GPS signal to the receiver unusable), while the strength of the second one is the simplicity of implementation, although it should be noted that the obtained efficiency depends on the speed used to sweep the frequency band.  
GNSS jamming can be ineffective when malicious drones are equipped with IMU sensors. In this case, if the drone is equipped with a remote control link, RF jamming can also be useful. Similarly, GNSS jamming is highly important with m-drones not equipped with a remote control (i.e., RF jamming is not applicable), which follow a pre-programmed route with the aid of GNSS. Consequently, intrinsic weaknesses shown by RF and GNSS jamming can be compensated for by their simultaneous deployment in order to improve neutralization effectiveness.  
Finally, considering the strategic importance of GNSS services, it should be noted that much research has been done in order to prevent and/or mitigate GNSS jamming; some methods, including those ones based on antenna arrays, are illustrated in the study reported in [5]. Therefore, a CUS shall pay attention to the effects obtained by means of such a neutralization.
## 3 Experimental Results
### Transfer Learning
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline  & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} & \\ \hline
**SN** & **Data Description** & **Patient Data** & **Data Description** & **Patient Data** & **Accuracy** \\ \hline
1 & Healthy & 71 & Healthy & 10 & \(0.86\pm 0.02\) \\ \hline
2 & Unhealthy & 45 & Unhealthy & 10 & \(0.75\pm 0.01\) \\ \hline
3 & Healthy & 81 & Unhealthy & 55 & \(0.74\pm 0.01\) \\ \hline
4 & Healthy + Unhealthy & 81 + 45 & Unhealthy & 10 & \(0.77\pm 0.01\) \\ \hline
5 & Fine-tuning on Unhealthy from Healthy & 45 & Unhealthy & 10 & \(0.78\pm 0.01\) \\ \hline
6 & Augmentation (unhealthy & 81 & Unhealthy & 10 & \(0.75\pm 0.01\) \\ \hline
7 & Healthy + Augmentation (unhealthy & 81 + 81 & Unhealthy & 10 & \(0.73\pm 0.01\) \\ \hline
8 & Fine-Tuning on Unhealthy from healthy + Signal void & 45 & Unhealthy & 10 & \(0.76\pm 0.00\) \\ \hline
9 & Healthy + Signal void & 81 & Unhealthy & 10 & \(0.73\pm 0.01\) \\ \hline
10 & Signal void + Unhealthy & 45 & Unhealthy & 10 & \(0.74\pm 0.02\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: fMRI network classification for healthy and unhealthy data (patient counted as whole represent seven fMRI network images in each case). among all tested transfer learning strategies. The three transfer learning strategies were repeated in the presence of augmented data (Table 3 rows 6 to 10). Augmented data were produced by data augmentation techniques (Section 2.5) from healthy data to simulate unhealthy data. The recorded performances in these experiments remained in the same range as other transfer learning approaches.
## 1 Introduction
### Background  
Conventional techniques for diagnosing ductal carcinoma involve a biopsy as well as imaging examinations such as digital mammography, digital breast tomosynthesis, ultrasound, and magnetic resonance imaging (MRI) [8; 9; 10]. However, these imaging methods can be difficult to interpret when the malignancy is tiny or hardly identifiable [11; 12; 13]. There is a growing need for quick and accurate diagnoses due to a larger patient pool, which has increased the burden for radiologists and highlighted the importance of machine learning (ML) models [14; 15; 16].  
ML models trained on medical imaging data have demonstrated promising results for the diagnosis of ductal carcinoma [11], hence aiding radiologists in effective disease detection and classification. These models can be used to automate the examination of imaging data, leading to more precise diagnoses with less time spent [12]. However, the effectiveness of such models depends crucially on the access to labeled databases of medical images [17; 18].
## 2 Related Works
### DRL Methods for UAV Mobile Crowdsensing  
To date, several studies have investigated the application of DRL algorithms in the UAV Mobile Base Station (MBS) scenario, which is a sub-topic of MCS. In the UAV MBS scenario, a swarm of UAV serve as mobile base stations to provide long-term communication services for ground users. [PERSON] et al. [15] proposed a DRL model based on Deep Deterministic Policy Gradient (DDPG [34]) to provide the long-term communications coverage in the MBS scenario. Further, [PERSON] et al. [16] implemented DDPG in a fully distributed manner.  
Different from policy gradient methods, [PERSON] et al. [35] applied Graph Convolutional Reinforcement Learning (DGN [36]) in MBS. They modeled the UAV swarm as a graph, and used Graph Attention Network (GAT [26]) as a convolution kernel to extract adjacent information between neighboring UAVs. To further explore the potential of graph networks, [PERSON] et al. [37] designed a FANET based on GAT, named GAT-FANET, allowing two adjacent UAV agents within the communication range to communicate and exchange information at low costs. This work also applied Gated Recurrent Unit (GRU) as a memory unit to record and process long-term temporal information from the graph network.  
On the basis of MBS, [PERSON] et al. [38; 39] took practical factors such as obstacles and charging stations into consideration in the UAV MCS scenario. Based on the actor-critic network of DDPG, their DRL models used CNN to extract observed spatial information, and deployed a distributed experience replay buffer to store previous training information. [PERSON] et al. [40], [PERSON] et al. [41] and [PERSON] et al. [38] utilized the concept of the Long Short-term Memory (LSTM [42]) network to store sequential temporal information of previous interaction episodes. As a specific application of MCS, [PERSON] et al. [41] designed an approach for mobile crowdsensing, where mobile agents are required to retrieve data and refresh the sensors distributed in the city, with limited storage capacities of the sensors. [PERSON] et al. [43] proposed a more practical and challenging 3D MCS scene for disaster response simulation, where the UAVs' action space had been expanded to three dimensions.  
Compared with the UAV MBS and MCS works mentioned above, this paper proposes a more complicated and promising SAG-MCS scenario, which incorporates global and local observations from space and air, respectively, and encourages UAVs to interact with charging stations as ground nodes. While [38; 39; 40; 41] proposed multi-UAV MCS scenarios and used policy-based DRL methods as solutions which utilized LSTM to store temporal information of MCS systems, our approach selects the value-based method based on DQN and uses GRU as the memory unit, which performs similarly to LSTM but is more computationally efficient [44]. Furthermore, when most MADRL studies about MCS solved the problem with deterministic policies, our method learns a stochastic policy following [PERSON] et al. [37] to improve robustness.