esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 3 RF Energy Harvesting Application in Medical Devices
### RF Energy Harvesting for Implantable Medical Devices
#### 3.2.1 Near Field
[177] used an inductive coupling method for the WPT system to recharge an implantable ECG monitoring device continuously for 23.6 h. The Tx and Rx coils were fabricated to charge the battery wirelessly, as depicted in Figure 8a. The mainboard, a battery, and the Rx coil were positioned together similarly to the ECG monitoring system, with the coil spacing set at 4 mm, taking into account the animal testing situations as illustrated in Figure 8b. The proposed ECG monitoring device was implanted into a rat to measure and transmit the ECG, as shown in Figure 8c,d. The WPT system achieved a charging voltage of 4.2 V with a PTE of 10% at an input power of 1.8 W. However, the WPT system suffers from electromagnetic interference. The biopotential is small and, thus, it is exposed to noise, such as 60 Hz, from the power line. As a result, the ECG was not measured accurately while utilizing the WPT. Magnetic resonant coupling is another type of near-field WPT technology that is often used to power IMDs. This method offers high PTE and has been used to power ventricular assist devices; the method used implantable wire coils (length = 9.5 cm, diameter = 22 mm) for energy delivery [168; 171]. However, the frequency employed in this technology is quite  
Figure 8: WPT system for implantable ECG monitoring device: (**a**) fabricated Tx and Rx coils; (**b**) mainboard, battery, and Rx coil positioned similar to the ECG device; (**c**) implantation of the ECG device in the rat; (**d**) WPT process in anesthetized rats [177]. Figure 7: Prototype and measurement setup of implantable WPT system: (**a**) fabricated Tx patch antenna; (**b**) fabricated RX implantable loop antenna; (**c**) metasurface design of 4 \(\times\) 4 unit cell array; (**d**) testing RX element inside skin-mimicking gel; (**e**) testing RX element inside minced pork; (**f**) testing RX element inside pork slab [175]. low, hence limiting the channel capacity and the potential data rate that is required for numerous applications, such as retinal prostheses and recording of multichannel neural networks [178; 179]. On the other hand, the study by [180] experimented with a wireless powering method based on near-field capacitive coupling (NCC) for efficient energy transmission to IMDs. The authors identified that the sub-GHz frequency range is the appropriate operating frequency of the NCC technique for subcutaneous power transfer by modeling the power link. The implementation of a conformal and flexible power receiver, as well as compliance with the IEEE C95.1 standard for safe absorption, are desirable features of this method. The NCC link was designed and tested on a non-human primate (NHP) cadaver, and the results show its ability to safely supply power (up to 0.1 W) to IMDs at a maximum efficiency of >50%.
## 4 Results and Discussion
### Results of Turbulence Models
#### 4.6.1 SST k-Omega  
The error ratios of force coefficients are presented in Figure 15 and the corresponding mean error ratios under different ranges of AOA are illustrated in Table 17. The models 16 m+blunt, 20 m+NACA4, 20 m+NACA4, 20 m+NACA4, 20 m+NACA4, 20 m+400 formula, and 20 m+400 formula provided the best performances, which were (0.21%, 0.00%), (0.78%, 1.38%), (2.80%, 1.22%), (4.98%, 1.11%), (5.75%, 1.54%), and (7.18%, 1.43%) respectively. At the range of (0\({}^{\circ}\), 2\({}^{\circ}\)), although the \(C_{l}\) of 12 m+NACA4 is the minimum, the \(C_{d}\) is relatively big. Considering the \(C_{d}\) and \(C_{l}\) comprehensively, 20 m+NACA4 is preferred. Similarly, at the range of (0\({}^{\circ}\), 4\({}^{\circ}\)), the result of 20 m+NACA4 is the best.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Simulation Configurations**} & \multicolumn{6}{c}{**Range of AOA(\({}^{\circ}\))**} \\ \cline{2-7}  & **(0\({}^{\circ}\), 0\({}^{\circ}\))** & **(0\({}^{\circ}\), 2\({}^{\circ}\))** & **(0\({}^{\circ}\), 4\({}^{\circ}\))** & **(0\({}^{\circ}\), 6\({}^{\circ}\))** & **(0\({}^{\circ}\), 8\({}^{\circ}\))** & **(0\({}^{\circ}\), 10\({
# Probabilistic Ship Detection and Classification Using Deep Learning
## 5 Experimental Results
### Performance  
#### 5.2.1 Results of the Single Image Detection  
The same hyper parameters used in the original Faster R-CNN [10] were applied to train our Faster R-CNN for ship detection. The hyper parameters used in our experiments were as follows: learning rate: 0.001, momentum: 0.9, and weight decay, 0.0005. The ZF net pre-trained on ImageNet was used as a base CNN to extract features and fine-tune the network using our ship dataset. The maximum iteration was set to 10,000. We use the Caffe [25] framework to train the Faster R-CNN on Ubuntu 16.04 LTS and NVIDIA GeForce GTX 980 on GPU. Table 3 shows the results of the ship detection using the Faster R-CNN fine-tuned by the above training set. The results of the ship detection using the Faster R-CNN by the test sample images are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Training Set** & **Test Set** & **Subtotal** \\ \hline Aircraft carrier & 750 & 250 & 1000 \\ Destroyer & 750 & 250 & 1000 \\ Submarine & 750 & 250 & 1000 \\ Container ship & 750 & 250 & 1000 \\ Bulk carrier & 750 & 250 & 1000 \\ Cruise ship & 750 & 250 & 1000 \\ Tugboat & 750 & 250 & 1000 \\ Total & 5250 & 1750 & 7000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of images in the ship dataset.
## 3 Experimental Validation
### Data Plane Transmission Performance Evaluation
The central wavelengths of the two lasers at ToR1 and ToR2 matched the wavelength routing map of the AWGR, as shown in Table 3. The SWS of ToR1 was controlled to select the wavelength laser at 1560.7 nm to send the optical packets to ToR3, otherwise the wavelength laser at 1559.09 nm was selected to send the optical packets to ToR4. At ToR2, the SWS selected the wavelength laser at 1562.26 nm or at 1560.64 nm to send the optical packets to ToR3 or ToR4, respectively. Figure 9c,d shows the switched optical packets from ToR1 and ToR2 to ToR3 or ToR4, respectively, according to the control signals provided to the SWS of ToR1 and ToR2. The optical packets were amplified by an EDFA to compensate the MZM loss and launched via a triplet lens collimator (Thorlabs TC18 FC-1550) into the free-space link and then collected by another collimator at the AWGR side. Next, an optical splitter divided the optical power of the optical packets between the input port 1 and port 2 of the AWGR for switching to the destination ToR3 (output port 3) or ToR4 (output port 4). Figure 8: (**a**). Experimental setup for 50-gigabyte-per-second end-to-end optical packet-switch transmission; (**b**) the actual setup. Figure 9: (**a**) The electrical signal generated by the pattern generator; (**b**) the control signal from the FPGA; (**c**) the switched optical packetized signals from ToR1 to ToR3 and ToR2 to ToR3; (**d**) the switched optical packetized signals from ToR1 to ToR4 and ToR2 to ToR4. A fiber delay line and an inline attenuator were used to ensure that the output signal from the AWGR was synchronized and with the same power intensity. The outputs of the AWGR were then transmitted to ToR3 or ToR4 via another pair of triplet lens collimators. A BER tester was used to evaluate the quality of the received 50-gigabyte-per-second optical packets. Figure 10 shows the BER curves of the optical packets evaluated at the receiver side of ToR3 and ToR4. The back-to-back BER of the optical packet is reported for reference. The results indicate that the fast switching has very limited impact on the signal quality, with only 0.6 dB power penalty. Therefore, these experimental results prove that it is feasible to produce a high data-rate (50 Gb/s) fast-optical-packet switch using our proposed fast SWS and AWGR OW-DCN switching system.
# Simulating Intraday Electricity Consumption with ForGAN
## 2 GANs and ForGAN: Idea, Structure and Algorithms
### Optimization Algorithm for Solving the Min-Max Problem  
Before detailing the optimization approach, two special characteristics of GANs that differ from conventional networks should be noted (see also [17]). First, the cost function of a traditional neural network is explicitly determined in terms of its own trainable parameters \(\theta\). However, in a GAN, the cost functions of both networks depend on two groups of trainable parameters, i.e., \(\theta_{D}\) and \(\theta_{G}\). Further, in a traditional neural network, one can tune all parameters during the training process. On the other hand, in GANs, each network can tune only its own weights and biases. This also needs a special sequential ordering of the training process.  
Given the explicit form of the min-max problem (1), one can also define the separate objective functions of the discriminator and the generator as  
\[J_{D}(\theta_{D};\theta_{G}) = \mathbb{E}_{x\sim P_{data}}[\log D(x)]+\mathbb{E}_{z\sim p_{z}}[ \log(1-D(G(z)))] \tag{2}\] \[J_{G}(\theta_{G}) = \mathbb{E}_{z\sim p_{z}}[\log(1-D(G(z)))] \tag{3}\]  
For solving the optimization problems in the min-max problem (1), a simultaneous gradient descent method is used. To update the parameters \(\theta_{D}\), \(\theta_{G}\), \(G\) and \(D\) are trained by backpropagating the loss through their respective models, which delivers the desired gradients. The following updating rules are obtained at step \(t+1\) with \(t\in\mathbb{N}\) for the parameter, respecting the order as  
\[\theta_{D}^{(t+1)}=\theta_{D}^{(t)}+\lambda_{t}^{D}\bigtriangledown_{\theta_{ D}}V(D^{(t)},G^{(t)})\quad\text{and}\quad\theta_{G}^{(t+1)}=\theta_{G}^{(t)}- \lambda_{t}^{G}\bigtriangledown_{\theta_{G}}V(D^{(t+1)},G^{(t)}), \tag{4}\]  
where \(\lambda_{t}^{D},\lambda_{t}^{D}\) are the learning rates for discriminator and generator updates at step \(t\), respectively. Note the minus sign in the update relation for \(\theta_{G}\). This occurs as we minimize  
Figure 2: Basic architecture of a GAN.  
\(J_{G}(\theta_{G})\). The choices of the learning rates are a highly researched subject in the calibration of neural networks in general. A detailed discussion is omitted here; however, the reader is referred to [18] for a detailed examination and recommendations of the choice of learning rates in GANs.  
**Remark 1**.: _Modifications of the gradient update [PERSON] et al. (see [12]) suggest to use \(k\in\mathbb{N}\) updating steps for \(\theta_{D}\), followed by one updating step for \(\theta_{G}\) to avoid overfitting of the discriminator. A further recommendation in [12] is to replace the objective function of the generator by_  
\[J_{G}(\theta_{G})=\mathbb{E}_{z\sim p_{z}}[\log(D(G(z)))] \tag{5}\]  
_and maximizing it to avoid the gradient of the objective function to vanish too fast. This is used in the implementation presented here, which then results in the minus in the second relation of (4) to be replaced by a plus sign._
## 3 Assemblability Analysis Based on Coordination Space Model
Some divided zones are valid for more KACs than others, e.g., the two zones marked with 3 are better than those marked with 1 or 2. The marked number is named the coordination zone index, which is the valid KACs' number in the coordination zone. If a pose in the zone marked with 3 is selected, only one KAC needs to be compensated. In this way, an assemblability optimization method is put forward by selecting a coordination zone with larger volume and KAC number. The larger volume means a better geometric consistency, and the larger KAC number means fewer KACs need to be compensated.
## 5 Medical Information System Architecture
### Medical Console  
The console is dedicated to the medical team responsible for patient flow and medical interactions and consists of multiple objects. Patients are patients recorded by the medical institution. The information found at the level of this object includes the following (as seen in Figure 9):  
* Medical record number.
* Patient's name.
* Personal Identification Number.
* Medical insurance status.
* Type of medical insurance.
* Date of death.
* Address.
* Gender.
* Date of birth.
* Disability status.
* Height.
* Blood type.
* RH factor.  
Dependent on this object are the following client-related pieces of information:  
* Patient encounters--Hospitalizations of the patient.
* Patient results--Results of medical analyses and procedures.
* Patient care plans--Care plans for patients.
* Patient allergies--Patient allergies.
* Patient diagnosis--Patient diagnoses.
* Patient medical tests--Medical tests for the patient.
* Patient immunizations--Patient vaccinations.
* Patient procedures--Patient procedures.
* Patient medication list--Medications the patient is on.
* Patient teams--Patient's medical team.
* Contact persons--Patient's contact persons.  
These patient-related pieces of information are grouped in Related Lists (forming a link between the patient and the specific nomenclature). A related list is a component that displays a list of related records based on a specific object. At the patient record level, various pieces of information about them can be found. At the level of this object, multiple forms are generated, such as GDPR (General Data Protection Regulation), to process personal data in the system. For medical procedures and tests, a patient's informed consent form is generated, as well as the patient's medical record containing information generated during their hospitalization in the medical institution.
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 4 An Objective Model for Coded Spatial Audio QoE Prediction
### AMBIQUAL Results
#### 4.6.2 Localization Accuracy  
As described in Section 4.4, localization accuracy is computed as a weighted product of similarity between Ambisonics reference and test directional channels as per eqn. (2). Objective model predictions of localization accuracy are presented in Figure 12c. Here, the aggregated mean values of the localization accuracy scores are shown for the 4 encoding schemes. The 95% confidence intervals are also shown.  
AMBIQUAL localization accuracy scores are compared with subjective MUSHRA scores in Figure 12e (per condition) and Figure 12g (per sample content). Results show good correlation between objective AMBIQUAL and subjective MUSHRA quality scores ([PERSON]'s corr = 0.922, [PERSON]'s corr = 0.919). Again, as with the quality test, the condition and not the sample content is the dominating factor influencing localization accuracy.  
Figure 11: Listening quality-subjective vs. AMBIQUAL results for single point audio sources: (**a**) aggregated subjective results and (**b**) AMBIQUAL quality predictions by encoding scheme (mean values with 95% confidence intervals shown) and scatter of subjective scores vs. AMBIQUAL results by encoding scheme (**c**) and by sample type (**d**).
# Buckling of Rectangular Composite Pipes under Torsion
## 2 Numerical Calculation  
### Energy Method  
To use a rectangular thin-walled pipe as a wing spar, a buckling analysis using the energy method was conducted on the cantilevered rectangular pipe under torque \(T\) shown in Figure 2. The rectangular pipe consisted of two vertical plates and two horizontal plates. The vertical and horizontal plates were assumed to be simply supported. The out-plane displacements \(v\) and \(w\) are described as follows:  
\[w=\sum_{m=1}^{\infty}\sum_{n=1}^{\infty}B_{mn}\sin\frac{m\pi x}{l}\sin\frac{n \pi y}{b} \tag{1}\]  
\[v=\sum_{m=1}^{\infty}\sum_{n=1}^{\infty}H_{mn}\sin\frac{m\pi x}{l}\sin\frac{n \pi z}{h} \tag{2}\]  
Note that the study discusses about local wall buckling of rectangular pipe, not global buckling of long column. Thus, warping is not included. As in reference [18], the plates' edge angles were assumed to be constant, as shown in Figure 3.  
Figure 1: Flight image of the Mars exploration airplane. In this case, the out-plane displacements \(v\) and \(w\) are described using \(\Phi_{mn}\) as follows:  
\[w=\sum_{m=1}^{\infty}\sum_{n=1}^{\infty}\sqrt{\frac{b}{h}}\Phi_{mn}\sin\frac{m \pi x}{l}\sin\frac{n\pi y}{b} \tag{3}\]  
\[v=\sum_{m=1}^{\infty}\sum_{n=1}^{\infty}\sqrt{\frac{h}{b}}\Phi_{mn}\sin\frac{m \pi x}{l}\sin\frac{n\pi z}{h} \tag{4}\]  
where \(n=1\), \(2\),... ; \(m=1\), \(2\),... ; and  
\[\frac{B_{mn}}{b}=\frac{H_{mn}}{h}=\frac{\Phi_{mn}}{\sqrt{bh}} \tag{5}\]  
\(\Phi_{mn}\) is a common coefficient to determine amplitudes of \(w\) and \(v\) and Equation (5) is the condition of constant the plates' edge angles. This assumption will be validated later. The total potential energy \(\Pi\) can be expressed in terms of the strain energy \(U\) and work of the external force \(W\) as  
\[\Pi=U-W \tag{6}\]  
The contributions of a single horizontal plate (in \(x\)-\(y\) plane) to \(U\) and \(W\) are written as  
\[U_{p}=\int_{V}\frac{1}{2}(\sigma_{x}\varepsilon_{x}+\sigma_{y}\varepsilon_{y }+\tau_{xy}\gamma_{xy})dV \tag{7}\]  
\[w=\sum_{m=1}^{\infty}\sum_{n=1}^{\infty}B_{mn}\sin\frac{m\pi x}{l}\sin\frac{n \pi y}{b} \tag{1}\]0  
\[w=\sum_{m=1}^{\infty}\sum_{n=1}^{\infty}B_{mn}\sin\frac{m\pi x}{l}\sin\frac{n \pi y}{b} \tag{1}\]1 Assumption of constant plate edge angles. \[w=\sum_{m=1}^{\infty}\sum_{n=1}^{\infty}B_{mn}\sin\frac{m\pi x}{l}\sin\frac{n \pi y}{b} \tag{1}\]2 A cantilevered rectangular pipe under torsion. For vertical plates, \(y\) replaced \(z\) in Equations (7) and (8) and related equations appearing hereafter. For horizontal plates (in \(x\)-\(y\) plane), by considering the bending strain due to the out-plane displacement \(w\), the strains can be described as follows:  
\[w=\sum_{m=1}^{\infty}\sum_{n=1}^{\infty}B_{mn}\sin\frac{m\pi x}{l}\sin\frac{n \pi y}{b} \tag{1}\]3  
Considering Hooke's Law for anisotropic layers  
\[\left[ \begin{array}{c}\sigma_{x}\\ \sigma_{y}\\ \tau_{xy}\end{array} \right]=\left[ \begin{array}{ccc}E_{xx}&E_{xy}&E_{xs}\\ E_{xy}&E_{yy}&E_{ys}\\ E_{xs}&E_{ys}&E_{ss}\end{array} \right]\left[ \begin{array}{c}\varepsilon_{x} \\ \varepsilon_{y}\\ \gamma_{xy}\end{array}
### Active Modeling and DC-Link Voltage Control Using Two-Stage ESO-Cascaded Topology Structure  
This section analyzes the DC bus voltage based on the topological architecture of the two-stage photovoltaic grid-connection system shown in Figure 4. The primary purpose of the front-end control is to achieve maximum power point tracking (MPPT) and voltage amplitude transformation. The post-control has three main tasks:  
1. Ensuring that the DC bus voltage operates within a safe range.
2. Controlling the transfer of active and reactive power between the photovoltaic power generation system and the power grid.
3. Achieving synchronous grid connection, among other functions.  
As shown in the figure, without considering the losses of the inverter and AC (Alternating Current) side filter, the terminal power \(P_{\text{dc}}\) on the DC side equals the power \(P_{\text{g}}\) on the grid side. Therefore, the power balance relationship on the DC bus is:  
\[P_{s}-P_{g}=\frac{1}{2}C\frac{du_{dc}^{2}}{dt} \tag{12}\]  
In the formula, \(P_{\text{s}}\) is DC power flowing through the front stage; \(u_{\text{dc}}\) means DC bus voltage; C represents DC side capacitance. According to the instantaneous power theory, the active power injected by the inverter into the power grid in the \(dq\) synchronous reference frame is:  
\[P_{g}=\frac{3}{2}\big{(}e_{d}i_{d}+e_{q}i_{q}\big{)} \tag{13}\]  
Figure 4: Applied Topological Architecture of the Two-stage PV Grid System.  
In Equation(13), \(e_{\text{d}}\) and \(e_{\text{q}}\) depict the \(dq\) axis component of the grid voltage; \(i_{\text{d}}\) and \(i_{\text{q}}\) represent the \(dq\) axis component of the grid current. If selecting the d-axis to align with the grid voltage vector, and \(e_{\text{d}}\) equals the amplitude of the grid phase voltage, \(e_{\text{q}}=0\). Therefore, we can rewrite Equation (12) as:  
\[P_{\text{s}}-\frac{3}{2}e_{d}i_{d}=\frac{1}{2}C\frac{du_{dc}^{2}}{dt} \tag{14}\]  
Based on Figure 4, construct a dynamic model of the DC bus:  
\[i_{dc}=C\frac{du_{dc}}{dt}=i_{\text{s}}-i_{\text{g}} \tag{15}\]  
In expression (15), \(i_{dc}\) represents the current flowing through the DC bus capacitor; \(i_{\text{s}}\) means the output current of the boost converter; \(i_{\text{g}}\) is the current input from the DC bus to the inverter. Assuming that the DC bus has been well controlled, \(P_{\text{s}}=P_{\text{dc}}\), the relationship between \(i_{\text{s}}\) and \(i_{d}^{\ast}\) can be derived:  
\[\left\{P_{\text{s}}=u_{dc}i_{\text{s}}\approx u_{dc}^{\ast}i_{\text{s}}\ P_{dc}=\frac{3}{2}e_{d}i_{d}\approx\frac{3}{2}e_{d}i_{d}^ {\ast}\ i_{\text{s}}=\frac{3e_{d}}{2u_{dc}^{\ast}}i_{d}^{\ast}\right\} \tag{16}\]  
Therefore, we can rewrite the dynamic model (Equation (15)):  
\[\frac{du_{dc}}{dt}=\frac{3e_{d}}{2 Cu_{dc}^{\ast}}i_{d}^{\ast}-\frac{i_{\text{g} }}{C} \tag{17}\]  
When designing a current inside loop control system, its bandwidth is usually much larger than the outside loop bandwidth to avoid dynamic interaction with the outer loop.
# Blockchain Applications in the Military Domain:
###### Abstract  
_Background_: Blockchain technology can transform military operations, increasing security and transparency and gaining efficiency. It addresses many problems related to data security, privacy, communication, and supply chain management. The most researched aspects are its integration with emerging technologies, such as artificial intelligence, the IoT, application in uncrewed aerial vehicles, and secure communications. _Methods_: A systematic review of 43 peer-reviewed articles was performed to discover the applications of blockchain in defense. Key areas analyzed include the role of blockchain in securing communications, fostering transparency, promoting real-time data sharing, and using smart contracts for maintenance management. Challenges were assessed, including scalability, interoperability, and integration with the legacy system, alongside possible solutions, such as sharding and optimized consensus mechanisms. _Results_: In the case of blockchain, great potential benefits were shown in enhancing military operations, including secure communication, immutable record keeping, and real-time integration of data with the IoT and AI. Smart contracts optimized resource allocation and reduced maintenance procedures. However, challenges remain, such as scalability, interoperability, and high energy requirements. Proposed solutions, like sharding and hybrid architecture, show promise to address these issues. _Conclusions_: Blockchain is set to revolutionize the efficiency and security of the military. Its potential is enormous, but it must overcome scalability, interoperability, and integration issues. Further research and strategic adoption will thus allow blockchain to become one of the cornerstones of future military operations.  
blockchain technology; military operations; data security; secure communication; supply chain management; smart contracts; cybersecurity; scalability; interoperability; operational efficiency