esa-sceva/satcom-chunk-collection huggingface dataset:
# A Computer-Aided Detection System for the Detection of Lung Nodules Based on 3D-ResNet
## 3 Method  
This paper proposes a two-stage CAD system framework. The implementation process of the proposed CAD system is shown in Figure 1. The two stages are: 1) candidate detection; and 2) false positive reduction. The network structure of the first stage is described in Figure 1a. It detects and extracts the candidate nodules from the preprocessed CT images. For this task, we designed a modified U-NET [15]. As shown in the bottom-left of Figure 1, the CT sections of X-Y, X-Z, and Y-Z are fed into the modified U-NET to detect candidate nodules. Finally, the candidate nodules in different dimensions of the sections are merged into 3D candidate nodules, which is the input of the next stage. The task of the second stage is to reduce false positives from the candidates generated by the first stage. A ResNet structure is employed at this stage, as shown in Figure 1b. As shown in the picture at the bottom-middle of Figure 1, we extracted five kinds of 3D samples with different sizes (40\({}^{3}\) means 40 \(\times\) 40 \(\times\) 40 mm\({}^{3}\), 44\({}^{3}\) means 44 \(\times\) 44 \(\times\) 44 mm\({}^{3}\), 48\({}^{3}\) means 48 \(\times\) 48 \(\times\) 48 mm\({}^{3}\), 52\({}^{3}\) means 52 \(\times\) 52 \(\times\) 52 mm\({}^{3}\), and 56\({}^{3}\) means 56 \(\times\) 56 \(\times\) 56 mm\({}^{3}\)) from each candidate and adjusted the size to 48 \(\times\) 48 \(\times\) 48 mm\({}^{3}\) uniformly. The setting of the sizes of 3D scans was determined by experimental trials and the experience of the researchers. Next, we fed them into a 34-level 3D residual network to reduce false positives and get the positive probability nodules. When training the models, we randomly divided the 888 scans into 5 parts: 3 for training, 1 for cross-validation, and 1 for testing. The details of the implementation of the two stages are introduced in the following subsections.
# Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions
## 2 Orthogonal Time Frequency Space Communication  
The main promise [9] behind OTFS is, under the hypothesis of the sufficient delay-Doppler resolution, to transform the time-varying multipath channel impulse response into a 2D function in the delay-Doppler domain, with each symbol experiencing the same channel gain.
# Performance Analysis of Hybrid Optical-Acoustic AUV Swarms for Marine Monitoring
## 4 Venus Device
### Optical Modem  
The performance of optical channel has been analyzed according to the model proposed in [15; 17]. The optical communication system is based on the use of LED sources. The transmission section is composed of three triplets of diode arrays with different wavelengths (red, solar and blue spectrum); currently, only the blue LEDs are used for data transmission. The receiving section is comprised of three high sensitivity PIN diodes.  
The modem, assembled in the ENEA laboratories, is inserted in a prismatic plexiglass structure completely filled with oil to avoid stressing the structure with the water pressure due to the immersion depth. Each side of the prism brings one set of array and one receiving sensor to cover all the directions both in transmission and in detection.  
Using analytical expressions, we evaluated the maximum achievable distance by an optical channel and how the turbidity of the water affects the performance. We started from the general expression for the SNR [17]:  
\[SNR=\left[\frac{P_{t}}{(tan\theta)^{2}}\frac{e^{-cr}}{4r^{2}}\frac{D^{2}cos \phi}{NEP}\right]^{2} \tag{4}\]  
where \(P_{t}\) is the transmitted power, \(\theta\) is the half angle transmitter beam width, \(c\) is the beam attenuation coefficient, \(r\) is the distance between transmitter and receiver, \(\phi\) is the angle between the optical axis and the line-of-sight between the transmitter and the receiver, NEP is the Noise Equivalent Power and \(D\) is the receiver aperture diameter.  
Then, for analytical tractability, we assumed the following conditions:  
* Considering that the emission cone of LED is about \(60^{\circ}\) and that the triplets of the LEDs are arranged on the three sides of a triangle with different pointing directions, we assumed an omnidirectional coverage for the optical communication, so to have \((tan\theta)^{2}=1\).
* We assumed that both transmitter and the receiver are positioned on the same plane, so that, on average, \((cos\phi)^{2}=1/2\). Then, we calculated the NEP [15] as: \[NEP=\sqrt{P_{bg-sn}^{2}+P_{sig-sn}^{2}+P_{dark-sn}^{2}+P_{amp-n}^{2}}\] (5) where the contributions are reported in Table 3.  
Figure 4: Maximum distance for a given \(SNR\) in case of acoustic communication.  
The term that contributes most to the NEP is the noise power accountable to the shot-noise of the received signal. Figure 5 shows the maximum achievable distance for a given SNR for different values of the diffuse attenuation coefficient \(K\)[17].  
Considering an On-Off-Keying (OOK) modulation format and a \(P_{b}\) equal to \(10^{-9}\), we need at least 12 dB for an effective communication. As shown in Figure 5, this corresponds to a maximum achievable distance varying from 5.9 m in the case of water with high turbidity \(K=0.6\) m\({}^{-1}\) to 58.7 m in the case of clear water \(K=0.037\) m\({}^{-1}\). The coefficient K depends on the wavelength used. In our case, we considered a wavelength in the range 470-525 nm.
## 1 Introduction
In the Netherlands, the already launched Grid Operators Platform for Congestion Solutions (GOPACS) implements cooperation between the Dutch TSO and several DSOs to solve congestion in the electricity grid [33]. The differences between such platforms are related to both the TSO and DSOs participation and the need for new market creation. This paper proposes an analytical quantification of how much the use of flexibility by the TSO can influence the DSO activities and the expected costs. The final goal is quantifying the flexibility that the TSO can procure from the distribution system without a harmful impact on the distribution network operation. Recently, the authors developed a methodology that, starting from public data on the energy consumption of a region or wider area, is capable of obtaining reasonable load and generation profiles at the TSO/DSO interfaces and also building a realistic representation of the grid below such an interface [34]. The obtained models are the synthetic networks of the real distribution networks. In this paper, the synthetic networks built with the developed procedure are used to calculate possible outbound conditions (e.g., excessive voltage variations, critical power flows, etc. ), and, finally, to propose the optimal operation of networks to fix any issue that can arise when flexibility products are offered to the TSO by the DERs. The paper investigates the expected interactions between the use of flexibility for power system balancing and security and the operation of distribution systems. The paper's main contribution is the methodology for modelling the role of distribution systems in providing flexible services. The paper aims at answering the following questions:  
* To what extent can the TSO exploit flexibility without causing issues at the distribution level? * What are the main issues caused by flexibility? * Are there operational actions that enable flexibility at the distribution level? * What are the expected costs to enable flexibility? TSOs can use the methodology to know in advance the expected level of flexibility services that the distribution system can offer and the relevant prices. DSOs can use the method to predict the exploitation of flexibility for operational and planning analysis. Market players can assess the expected impact of new flexibility providers. Finally, aggregators can simulate operative conditions to define better prices and quantities of services that their portfolio of customers could offer. The structure of the paper is as follows: Section 2 deals with flexibility services; Section 3 describes the main regulatory framework implemented in most important countries; Section 4 is devoted to the proposed approach; Section 5 describes the Case Study adopted. Finally, Section 6 is about the results and discussion.
## 2 Results and Discussion
### Evaluation of Catalytic Performance
#### 2.2.3 Recyclability Investigation  
Catalyst reusability was a critical parameter for evaluating industrial applicability. The stability of P-BVIMCl was systematically examined through hot filtration tests and cycling experiments. As shown in Figure 7a,b, the reaction system exhibited an almost negligible GLC yield increase after catalyst removal at 2.0 h through hot filtration, indicating little leaching of active components. After five consecutive reaction cycles, the catalyst demonstrated marginally decreased activity, confirming effective retention of active species throughout catalytic operations. FT-IR analysis of recycled P-BVIMCl (Figure 7c) revealed that recycled P-BVIMCl preserved spectral features compared to the fresh catalyst. SEM characterization (Figure 7d) further confirmed the maintained spherical particle morphology in both fresh and recycled catalysts. These comprehensive evaluations demonstrated the excellent stability of the P-BVIMCl catalyst and operational robustness under repeated cycling conditions.
## 3 Experiment Setting
### Performance Evaluation Metrics  
The performance evaluation of disaggregation algorithms has been one of the main challenges of NILM. It is important to produce a complete evaluation. However, much of the literature focuses on the accuracy of on/off detection [9; 25; 29], and only few studies considered the retrieving appliances' consumption information. We adopted a set of metrics introduced in the literature [32]. The set includes the following metrics:  
F-score, evaluating the capability of NILM algorithms on operation state detection of appliance \(i\), with ranges from 0 (0% accuracy) to 1 (100% accuracy):  
\[F_{Si}=\frac{2\times PC_{i}\times RC_{i}}{PC_{i}+RC_{i}}, \tag{2}\]  
where \(PC_{i}\) and \(RC_{i}\) are the recall and precision for appliance \(i\), which are described as follows:  
\[\left\{\begin{array}{l}RC_{i}=\frac{TP_{i}}{TP_{i}+FN_{i}}\\ PC_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}}\end{array}\right., \tag{3}\]  
where \(TP_{i}\) is the number of examples correctly labeled as ON state (true positive), \(FP_{i}\) means OFF state examples incorrectly labeled as ON state (false positive), and \(FN_{i}\) represents the ON state ones labeled as OFF (false negative).  
The PCE (power contribution error) estimates the power consumption contribution of each appliance according to the following formula:  
\[\text{PCE}_{\text{i}}=\frac{\left|\sum_{\text{t=1}}^{\text{H}}y_{\text{t}}^{ \text{i}}-\sum_{\text{t=1}}^{\text{H}}\hat{y}_{\text{t}}^{\text{i}}\right|}{ \sum_{\text{t=1}}^{\text{H}}P_{\text{t}}}, \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Appliance** & **Status** & **Average Power/W** \\ \hline \multirow{3}{*}{Clothes dryer} & Off & 0 \\  & On-state 1 & 245 \\  & On-state 2 & 4586 \\ \hline \multirow{3}{*}{Heat pump} & Off & 0 \\  & On-state 1 & 37 \\  & On-state 2 & 1767 \\ \hline \multirow{3}{*}{Dish washer} & Off & 0 \\  & On-state 1 & 139 \\ \cline{1-1}  & On-state 2 & 757 \\ \hline \multirow{2}{*}{Fridge} & Off & 0 \\ \cline{1-1}  & On-state1 & 130 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average powers of testing appliances in AMPds.  
where \(y^{i}\) and \(\dot{y}^{i}\) are the ground truth and estimated power consumption for appliance \(i\). The more accurate the algorithm, the closer to 0 the PCE value is. The PCE provides more information feedback to end-user and demand management utilities.  
The R2 score evaluates the accuracy of trace retrieve. For each appliance i, it is defined as:  
\[\mathrm{R2_{i}}=1-\frac{\left(\sum_{t=1}^{H}y_{t}^{i}-\sum_{t=1}^{H}\dot{y}_{t} ^{i}\right)^{2}}{\sum_{t=1}^{H}\left(y_{t}^{i}-\sum_{t=1}^{H}y_{t}^{i}/H\right) ^{2}}. \tag{5}\]  
In the comparison test, the root mean square error (RMSE) is used to measure estimation error. For each appliance i, it is defined as:  
\[\mathrm{RMSE_{i}}=\sqrt{\frac{\sum_{t=1}^{H}\left(y_{t}^{i}-\dot{y}_{t}^{i} \right)^{2}}{H}} \tag{6}\]
# Priority-Driven Resource Allocation with Reuse for Platooning in 5G Vehicular Network
## 1 Introduction
The proposed algorithm addresses the challenge of resource allocation between platoon and general vehicles within the vehicular network. It aims to ensure reliable communication for platoon vehicles, even under resource constraints, while minimizing resource allocation failures and communication delays for general vehicles. Unlike existing resource allocation methods, which fail to fully resolve resource shortages as vehicle density increases, the algorithm presented in this study addresses these issues through the efficient reuse of resources. The structure of this paper is organized as follows: Section 2 describes the related works of resource allocation algorithms in the V2X network. Section 3 provides an overview of the fundamental concepts related to NR-V2X communication, platoon communication, and sidelink resource allocation methods in NR-V2X. This section covers the technological advancements of NR-V2X and the latest 3 GPP standards for platoon communication. Section 4 focuses on system modeling and the proposed resource allocation algorithm, which form the core of this study. It presents the resource allocation process modeling, the mathematical formulations underlying the algorithm, and a detailed explanation of the operational principles of the proposed method. Section 5 offers a comparative performance analysis of the proposed algorithm against existing algorithms, assessing its resource efficiency and communication delay across various scenarios. The results are presented and analyzed visually through graphs. Finally, Section 6 concludes with a summary of the findings in the paper, discusses the contributions of the paper, and suggests potential directions for future research.
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 4 Simulation
### Traffic Congestion Comparison
This is because as the number of multicast group members increases, the multicast tree becomes larger, allowing DR to perceive the  
Figure 5: Link load congestion comparison. load status of more multicast tree nodes. This reduces the degree of flow congestion of RSPT, that is, the TrafficGap decreases. Overall, the maximum TrafficGap is around 17%, and in topologies with less than 200,000 nodes, the TrafficGap is below 5%. Therefore, the degree of flow congestion of RSPT and WAVE is quite similar, especially when the topology node number is less than 200,000.
## 5 Discussions  
To further investigate the feasibility and applicability of the energy density failure criterion, experimental results of seven types of rocks were used to verify the energy density failure criterion and compared with experimental values, such as the Mohr-Coulomb criterion (M-C) and Drucker-Prager criterion(D-P) calculation values. The results are presented in Table 3 and Figure 10.  
Figure 10 shows that the seven different rock strengths calculated by the D-P criterion are all lower than the experimental values, and their accuracy is the lowest compared to the M-C criterion and E-D criterion. Although the accuracy of the M-C criterion is higher than that of the D-P criterion, the relationship between the rock strength calculated by this criterion and the confining pressure is linear. The seven different rock test results showed that the relationship between the rock compressive strength and confining pressure is nonlinear. Therefore, when the confining pressure was high, the difference between the rock strength calculated using the M-C criterion and the test value was significant. The relationship between the compressive strength of the seven types of rocks calculated by the E-D criterion and the confining pressure was nonlinear. The calculated results were consistent with the test results, and the accuracy was higher than that of the M-C and D-P criteria. Therefore, the E-D criterion established in this study, based on the elastic strain energy, can be used to calculate the compressive strength of different types of rocks under different confining pressure conditions, and its feasibility and accuracy were further verified. Figure 9: Comparison between calculated results and test results. To further analyze the error between the calculation results of the M-C, D-P, and E-D criteria and the test results, Equation (13) was used to calculate the average relative error and root mean square error between the theoretical and test values [25]. The results are shown in Figure 11.  
\[\left\{ \begin{array}{l}MRE=\frac{1}{N}\sum\limits_{i=1}^{N}\frac{\left|\sigma _{1i}^{test}-\sigma_{1i}^{calc}\right|}{\sigma_{1i}^{test}}\times 100\%\\ RM=\sqrt{\frac{\sum\limits_{i=1}^{N}\left(\sigma_{1i}^{test}-\sigma_{1i}^{calc }\right)^{2}}{N}}\end{array} \right. \tag{13}\]  
where \(MRE\) is the average relative error, \(RM\) is root mean square error, \(N\) is the number of triaxial test groups, \(\sigma_{1i}^{test}\) is the test value, and \(\sigma_{1i}^{calc}\) is the calculated value.
# Basis Functions for a Transient Analysis of Linear Commensurate Fractional-Order Systems
## 3 Mathematical Prerequisites
### Mittag-Leffler Functions  
The diagonal part of Table 1 summarizes the defining relations of the complex Mittag-Leffler functions of complex argument \(z\): the classical MLF \(E_{\alpha}\) (\(z\)) with one parameter \(\alpha\); the function \(E_{\alpha,\beta}\) (\(z\)) with two parameters \(\alpha\), \(\beta\); the \(m\)-th derivative of the function \(E_{\alpha,\beta}\) (\(z\)) with respect to the argument \(z\); and the function \(E_{\alpha,\beta}^{\chi}(z)\) with three parameters \(\alpha\), \(\beta\), \(\chi\) [5]. For completeness, the classical function \(\exp(z)\) was added to the table and its known connections to MLFs are indicated. In the legend to Table 1, it is recalled that the corresponding parameters \(\alpha\), \(\beta\), \(\chi\) may be complex with positive real parts [6]. In fractional calculus, the parameters in question often appear as positive integers. It is obvious that the classical exponential function and the one-parameter and two-parameter MLFs are special cases of the three-parameter MLF, and that there is an unambiguous mapping between the integer derivatives of the two-parameter MLF and the three-parameter MLF. For \(\chi=1\), the three-parameter MLF and its derivatives can be expressed in terms of the two-parameter MLF and its derivatives. A further analysis shows that the condition \(\chi=1\) suffices for the construction of all variants of the formulas of the time responses of commensurate FOSs. While searching for basis functions, one can then focus on computationally simpler two-parameter MLFs. Selected Relations between Two-Parameter Functions \(E_{\alpha,\beta}\), \(E_{\alpha,\alpha}\) and their Derivatives  
Based on the analogy between the mathematical description of the response of classical and fractional systems, it is shown in Sections 4 and 5 that it may be convenient to choose two-parameter MLFs of type \(E_{\alpha,\alpha}\) (\(z\)) and their derivatives as basis functions for a semi-symbolic analysis. At the same time, however, it turns out that the Laplace transforms of some waveforms lead to MLFs and their derivatives with a different distribution of parameters, namely to \(E_{\alpha,\beta}\) functions. Therefore, it may be useful to be able to transform these functions and their derivatives to \(E_{\alpha,\alpha}\) (\(z\)) functions and their derivatives. However, the two-parameter MLF for the parameter \(\beta=0\), i.e., \(E_{\alpha,0}\) (\(z\)), contradicts the classical domain of MLF: \(\beta\in\) C, Re(\(\beta\)) \(>0\)[5]. The problem is that the gamma function, appearing in the defining relation of \(E_{\alpha,\beta}\) (\(z\)), exhibits an infinite limit at \(0\). However, since the corresponding gamma function appears in the denominator of the defining relation of
# Sixty-Nine-Element Voice Coil Deformable Mirror for Visible Light Communication
## 4 Discussion  
### Aberration of the Thin Mirror Due to Thermal Effect  
To investigate the effect of temperature rise due to the current in VCAs on the thin mirror, the model of VCA is analyzed using Maxwell and steady-state thermal modules in Ansys software. The procedure of thermal analysis is shown in Figure 6.  
In compact DMs, active cooling elements, such as air cooling and water cooling, are generally not used. The thermal convection of all parts in VCDM is through the air. In the ANSYS Steady-State Thermal module, the air convection heat transfer coefficient is set to 5 W/(m\({}^{2}\)K) [22]. According to the mechanical model of the VCDM, as shown in Figure 2, the internal heat generation of the voice coil is introduced by the ohmic loss calculated by the Maxwell module. After that, the thin mirror temperature obtained by the Steady-State Thermal module is transferred to the Static Structural module to calculate the thermal deformation of the thin mirror.  
The mirror material is CP1 Polyimide from NeXolve [23]. O-rings, springs, and struts are made of 316 Stainless Steel. The adhesive between the struts and the mirror is FR-4 Epoxy. The material of the permanent magnet is NdFe35, and the material of the coil is copper. To accelerate heat dissipation, the shell of the DM, spring fixing plate, and baseplate are all made of aluminum alloy. The parameters of the adopted materials are listed in Table 3.  
Figure 5: Electromagnetic force and efficiency as a function of current. The red circle is before optimization, and the black triangle is after optimization. (**a**) Force. (**b**) efficiency.  
Figure 6: The thermal analysis process of the VCDM.  
Figure 7a shows the temperature of the VCDM's thin mirror as a function of currents. The maximum temperature of the mirror surface increases along with the current. When the current is 0.1 A corresponding to a current density of 10 A/mm\({}^{2}\), the temperature rise of the VCDM is about 1 Celsius degree. Figure 7b shows the thermal deformation of the thin mirror due to temperature-rising non-uniform distribution. It indicates that the peak-to-valley (PV) of the thin mirror deformation increases along with the current. When the current density is 6 A/mm\({}^{2}\) corresponding to 0.06 A, the PV is 12.36 nm, which is about 0.022\(\lambda\) for \(\lambda\) = 550 nm. The root mean square (RMS) value of the mirror deformation is around 0, and it gradually deviates from 0 as the current increases. Therefore, the maximum control current of a single VCA should not be larger than 0.06 A for good thermal stability. At this time, the temperature and the deformation of the thin mirror are shown in inserted figures of Figure 7a,b.