esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results  
In this section, we describe the proposed case studies, the experiments conducted, and their results. A schema presenting all the experimentation processes is shown in Figure 5. First, actual data are collected from the simulated scenarios to train the surrogate models. Second, once the six surrogate models have been trained, they are tested using the testing dataset to select the best performing predictor for the next stage. Third, now the optimisation of the swarm parameters is conducted using the DFA\({}^{3}\), the selected surrogate model, and the ARGoS simulations to evaluate the individuals of the HEA. Finally, the optimal parameters are used to test our formation algorithm on 30 unseen scenarios per case study to address its robustness. The source code of the DFA\({}^{3}\), the problem instances, surrogate models, and datasets are available at [[https://gitlab.uni.lu/adars/dfa3](https://gitlab.uni.lu/adars/dfa3)]([https://gitlab.uni.lu/adars/dfa3](https://gitlab.uni.lu/adars/dfa3)) (accessed on 9 May 2023).  
Figure 4: Block diagram of the hybrid evolutionary algorithm (HEA).  
Figure 5: Schema of the experiments proposed. First, data are collected from ARGoS simulations to train the surrogate models and test them. Second, the best surrogate model is used by the proposed hybrid EA to optimise the UAV swarm parameters. Finally, the optimal parameterisation is tested on a set of unseen scenarios to address the system robustness.
# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 5 Experiment and Analysis
### Comparison of AMC Performance
This is because the QPSK and OQPSK signal patterns are too similar, and they are very easy to confuse when the noise reduction effect is limited with the DnCNN method. According to the Figure 9(b), DRdA-CA denoising makes the recognition accuracy of each modulation signal increase significantly. Although 16 QAM and 64 QAM still cannot achieve accurate classification when the SNR is larger than 2 dB, their recognition accuracy exhibits a continuous rising trend with an increase in the SNR. It demonstrates  
Figure 9: Modulation classification accuracy under different deep learning networks. that the strong denoising ability of the proposed DRdA-CA is beneficial for AMC. The classification accuracy of 16 QAM and 64 QAM can be further improved by adopting better modulation classification model after denoising, and this paper focuses on the design of the denoising network.
# Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection
## 1 Introduction  
Anomaly detection (AD) aims to determine instances that diverge from the \"normal\" data in the general sense [1; 2; 3; 4]. Meanwhile, anomaly segmentation (AS) looks into specific anomalous instances and precisely delineates the abnormal regions, such as identifying the locations of abnormal pixels. The combined field of anomaly detection and segmentation (AD&S) plays a critical role in various applications, including industrial inspection, security surveillance, and medical image analysis [3; 5; 6].
# Design of True Time Delay Millimeter Wave Beamformers for 5G Multibeam Phased Arrays
## 3 Numerical Results
### Rotman Lens Results
#### 3.2.2 Rotman Lens with Absorbers  
Aiming at reducing the unwanted reflections of incident waves in the side walls and enhancing the total efficiency of the lens, the dummy ports are replaced by two absorbing layers covered with metallic shields on each side wall, as shown in Figure 11. The dimensions of this structure are defined as \(22.334\times 18.163\times 0.3\) mm\({}^{3}\). The height of the absorbers is equal to the height of the substrate, and their material characteristics are chosen with an \(\epsilon_{r}=6.15\) and a \(tand\delta=0.7\). These material values have been chosen theoretically, by adjusting the value between the initial material and the material is then doped with the conductive molecules as described above. By engineering the substrate, we can efficiently absorb the incident waves and thus attenuate the standing waves of the parallel plate region.  
Figure 23: Ordinary Rotman lens with 4 dummy ports reflection coefficients.  
Figure 21: Ordinary Rotman lens with 4 dummy ports simulation results: (**a,c,e**) amplitudes \(|S_{ij}|\) for the \(i=1\)–\(3\) outputs when the input ports \(j=1,2,3\) are excited; and (**b,d,f**) group delay for the \(i=1\)–\(3\) outputs when input ports \(j=1,2,3\) are excited.  
Figure 22: Calculated array factor radiation patterns at 27 GHz and 30 GHz when the outputs of the proposed Rotman lens (Figure 10) are used to excite the phased array.  
Besides, the ultimate scope of this design is to prove that there is an easier to design and implement approach to absorb the incident waves in the side walls than the dummy ports.  
Figure 24a-f reveals the improved Rotman lens response in the whole frequency range of 27-33 GHz. This improvement is also justified from the array factor distribution in Figure 25, which is valid in the entire frequency range.  
Figure 24: Proposed Rotman lens with absorbers in the side walls simulation results: (**a,c,e**) amplitudes for the \(i=1\)–3 outputs when the input ports \(j=1,2,3\) are excited; and (**b,d,f**) group delay for the \(i=1\)–3 outputs when input ports \(j=1,2,3\) are excited.  
Figure 25: Calculated array factor radiation pattern when the outputs of the proposed Rotman lens (Figure 11) are used to excite the phased array.
# Routing Protocols for Delay Tolerant Networks:
## 3 Performance Evaluation  
We now proceed by conducting an extensive comparative study of 10 recently-presented, characteristic DTN routing papers (see Section 2.2). The performance evaluation is based on simulated experiments based on a reference benchmarking scenario. We use (1) synthetic mobility traces (based on three well-established models) and (2) real-world traces to evaluate the performance of the protocols in environments that represent both (i) indoor and (ii) outdoor cases. The combination of synthetic/real-world mobility traces, indoor/outdoor cases and the operating parameters of the high-level user application (for generating traffic) constitute a thorough benchmarking scenario. The benchmarking scenario and routing protocols are implemented in The Opportunistic Network Environment simulator (ONE) [20]. To the best of our knowledge, this is the first attempt to quantitatively compare the performance of such a large selection of DTN protocols.  
We here note that the ONE simulator already includes implementations for Direct-Delivery, First-Contact, Epidemic, Spray-and-Wait, PRoPHET and MaxProp. We implement the other four protocols, Fuzzy-Spray, FAD, SCAR and RAPID, by extending the ONE simulator. In order to validate the truthfulness of the protocol implementations, we conduct a set of small-scale experiments based on well-defined mobility and message generation patterns. In the sequel, we reproduced the evaluation scenario included in the original publications and validated that our protocol implementations provide similar results to the ones described by the original authors. In order to facilitate this process, we used real-time visualizations of the network execution for simulations of small-sized networks in combination with debugging messages, describing in detail the simulated execution of each protocol. We are confident that through this process, we fine-tuned the operation of all protocols, and our implementations achieve high truthfulness compared to the original protocols.
## 4 Applications in Optimization
### Differential Evolution (DE)
#### 4.2.5 Optimal Tuning of Speed Control for a Brushless DC Motor  
As a last example involving the DE algorithm, we consider the work of Rodriguez-Molina et al. [80], who assessed the efficiency of the controller performance in a Brushless Direct current (BLDC) motor in an uncertain environment. The performance of the BLDC depends highly on the adaptability of the controller gains. The authors proposed a chaotic adaptive tuning strategy for controller gains (CATSCG) for speed regulation of BLDC motors. The CATSCG includes two sequential dynamic optimization stages based on identification and predictive processes, with a novel chaotic online differential evolution (CODE) used to provide the controller gain at each predefined time interval.  
The CATSCG consisted of a two-stage tuning strategy; the dynamics associated with the future behavior of the BLDC motor are estimated in the first stage using the experience gained from the past optimization process. These dynamics are used in the second stage to find the controller gains that directly impact the closed-loop system responses through the novel Chaotic Online Differential Evolution (CODE) approach. Furthermore, CODE includes the Lozi chaotic initial populations in the differential evolution algorithm to enhance the diversity of candidate solutions in the dynamic environment, which further improves the quality of the controller gains under the effects of uncertainties.  
The proposed chaotic online differential evolution (CODE) is an optimizer based on the DE/rand/1/bin variant of differential evolution (see Section 4.2.1). It incorporatesa chaotic initialization and an elitist adaptation mechanism to improve the exploitative capacity and speed convergence of the original algorithm.  
In the case of CODE, the random numbers utilized in the population initialization are generated by a Lozi chaotic map with \(a=1.7\) and \(b=0.5\). This is intended to increase the initial diversity of solutions at the beginning of each CODE run. Concerning the initial condition of the Lozi discrete dynamics adopted in the adaptive tuning proposal, it is established randomly at the very first instant of the control strategy execution (i.e., when \(t=0\)). After that, the Lozi map dynamics evolve one step forward each time a new chaotic random number is required (i.e., when it is required to generate a new random variable while generating each individual from the initial population of CODE).  
Rodriguez-Molina et al.highlighted that among the tested optimizers (online differential evolution (ODE), online genetic algorithm (OGA), and online particle swarm optimization (OPSO)) in the adaptive controller tuning, the statistical results evidence that the use of CODE suitably balances the search capacities of exploration and exploitation of the algorithm such that the proposed CATSCG maintains the minimum possible motor velocity error under the effects of disturbances, uncertainties, noise, and reference velocity changes. They attributed the outstanding performance of CATSCG to the use of the chaotic initialization based on the Lozi map.
## 3 Double-Beam System Traversed by a Moving Force
### Mathematical Formulation
#### 3.1.3 Pinned-Fixed Boundary Conditions  
Substituting Equations (40) and (22) into Equation (53) and setting C to be one result in \[M_{nn}^{(i)}=\frac{1}{2}\rho_{2}A_{2}l\bigg{[}1+s_{\rm m}\Big{(}A_{n}^{(i)}\Big{)}^ {2}\bigg{]} \tag{77}\]  
where \(A_{n}^{(i)}\) has the same form as Equation (55).  
Substituting Equation (22) into Equation (52) and setting \(C\) to be one result in  
\[\ddot{q}_{n}^{(i)}+\Big{(}\omega_{n}^{(i)}\Big{)}^{2}q_{n}^{(i)}=-\frac{fA_{n} ^{(i)}}{M_{nn}^{(i)}}[\sin(r_{1}^{(n)}vt)-\frac{\sin r_{1}^{(n)}l}{\sinh r_{1}^ {(n)}l}\sinh(r_{1}^{(n)}vt)] \tag{78}\]  
The solution of Equation (78) is  
\[q_{n}^{(i)}(t)=p_{n}^{(i)}\Bigg{[}\frac{\sin\big{(}r_{1}^{(n)}vt\big{)}}{1- \big{(}s_{n}^{(i)}\big{)}^{2}}-\frac{\sin r_{1}^{(n)}l}{\sinh r_{1}^{(n)}l} \frac{\sinh\big{(}r_{1}^{(n)}vt\big{)}}{1+\big{(}s_{n}^{(i)}\big{)}^{2}}+( \frac{\sin r_{1}^{(n)}l}{\sinh r_{1}^{(n)}l}\frac{s_{n}^{(i)}}{1+\big{(}s_{n} ^{(i)}\big{)}^{2}}-\frac{s_{1}^{(i)}}{1-\big{(}s_{n}^{(i)}\big{)}^{2}})\sin \Big{(}\omega_{n}^{(i)}t\Big{)}\Bigg{]} \tag{79}\]  
where \(p_{n}^{(i)}\) and \(s_{n}^{(i)}\) have the same form as Equation (59).  
The displacements of the double beam systems can be worked by Equations (60) and (61).  
For a pinned-fixed beam, \(x_{\rm max}=0.45l\) and \(w_{s}\) is  
\[w_{s}=-\frac{fL^{3}}{48\sqrt{5}E_{2}I_{2}} \tag{80}\]
# Unsupervised Method Based on Adversarial Domain Adaptation for Bearing Fault Diagnosis
## 4 Experiment Result Analysis
### Preprocessing Data  
In the experiment, preprocessing is critical for preparing the input data for DANN and MCD. The preprocessing stage involves retrieving source and target instances, loading and separating data and labels, applying the downsampling technique, reshaping the data to match the models' input shape, and storing the preprocessed data. This prepares the data for training the models and aims to improve their performance. The details are shown below. Figure 7 represents the detailed process of data preprocessing. The first stage is to retrieve source and target instances from the CWRU and XJTU data sets. After that, loading the data is a step responsible for reading and separating data and labels from instances obtained from these two data sets. Then the downsampling technique is applied to the source and target data to reduce the dimensionality of the data. Data after reduced dimension and label arrays are utilized as inputs to reshape the length of 1-dimensional signal data to 1000 to match the expected input shape of the two models. Finally, the reshaped data and labels are stored in arrays for feeding into two models. In the model training, source and target data with labels will be fed into two models to update the weights to achieve convergence. A specific window length of 1000 is considered for 1-dimensional signal data. A window is a subset of consecutive data from the signal used for analysis. The input data is divided into overlapping or non-overlapping segments by selecting a window length to allow the network to capture local patterns and relationships within the signal. However, it does not address the processing of several sensor signals from different sensors. Multiple sensor signals can be handled as multiple channels in the input data. This means multiple dimensions represent each sensor signal instead of having a single dimension for the input signal data. Each dimension corresponds to a specific channel to allow the network to process and learn from different sensor data simultaneously. When dealing with 1-dimensional signal data, a technique involves applying 2-dimensional kernels along the time axis of the signal. This is achieved by treating the kernel as a sliding window that moves along the signal. A convolution operation occurs at each step, where the values within the window are combined with the corresponding kernel values. This process captures meaningful features and patterns in the signal, yielding a new output value at each step. Using 2-dimensional kernels enables the examination of local trends, edges, and other interconnected structures that may span neighboring time points within the 1-dimensional signal data.
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 4 Performance Evaluation and Discussion
### Effect of Environmental Temperature on Communication Performance  
We evaluated the BER when the environmental temperature changes. The oversampling was set as \(s_{L}=15\). For the temperature estimation, the maximum likelihood estimation was adapted as expressed in (17). Figure 11(a),b show the BER performances with respect to the temperature change, with and without the temperature information, respectively. As can be seen from in Figure 11(a), whereas the BER performances for the threshold-based method (Method A) do not change in different temperature conditions, the BER performances for the MLSE method (Method B) change according to the ambient temperature. This means that the MLSE method can establish optimal detection even when the environmental temperature changes. Furthermore, from Figure 11(b), Methods C and D cannot establish reliable communication because parameter mismatch occurs. On the other hand, Methods E and F can improve the BER performances based on the temperature estimation, which means the temperature is properly estimated. Based on the results in Figure 11, the signal-to-noise power ratio (SNR) that realizes a BER \(\leq 10^{-3}\) is summarized in Table 4. As compared with Method A and B, the MLSE method (Method B) improves the SNR by 3.5 dB and 6.8 dB in the cases when the temperature is 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C, respectively. Furthermore, the performance degradation for the MLSE method is only 0.5 dB when the environmental temperature is necessary to be estimated, comparing with the results between Methods B and F at a temperature of 35 \({}^{\circ}\)C.
## 4 Proof of Concept (Poc) of Implementation and Integration  
PoC hardware includes backhaul and access devices. An IP (Internet Protocol Address)-based wireless backhaul link is established using a reflective array antenna with an integrated WiGig transceiver. One HetNet access network is used in this paper. CP/UP segmentation is introduced into HetNet. A multi-RAT CP application implemented in the LTE eNB manages UP traffic offload between LTE and WiGig.  
The proposed PoC hardware for millimeter-wave integrated HetNet uses one LTE eNB as a macrocell and uses WiGig AP as a junior unit. The LTE baseband I/Q signal (In-phase/Quadrature  
Figure 5: Constellation diagram of backhaul link with one lens array antenna (LAA) at the TX (transport)and RX (receive) ends.  
signal) can be transmitted at a 50% compressed data rate. WiGig AP is connected to an LTE eNB through Ethernet. UE has an LTE modem and implements a WiGig device. The WiGig device supports MCS with the highest index #9, as shown in Table 1.  
The protocol stack of PoC is shown in Figure 6. An LTE-WiGig Integrated CP (multi-RAT CP) application is implemented on the top of the CP protocol stack. The multi-RAT CP monitors real-time traffic load and radio link status. The link status measured on UE is reported to eNB through CP signaling of LTE. The multi-RAT CP on eNB manages UP data processing based on the monitoring information. The UP data path between LTE and WiGig is switched by Open vSwitch (OVS). During WiGig usage, the multi-RAT CP controls WiGig activation and authentication. The PDCP SDU is sent over a WiGig or LTE link. Since the interface between LTE-eNB and WiGig-AP is IP-based, the various Layer 2 media can be used for backhaul, including 1-Gigabit, 2.5-Gigabit, 5-Gigabit, or 10-Gigabit Ethernet.  
To prove the concept of CP/UP segmentation, performance of LTE-WiGig handover is evaluated herein. The WiGig station (STA (Single-threaded apartment)) device attached to a linear drive moves  
Figure 6: LTE-WiGig integrated HetNet proof-of-concept (PoC) protocol stack.  
horizontally at a constant speed of 80 mm/s. The vertical distance between the AP and STA antennas is set to 200 mm so that the STA enters (or leaves) WiGig coverage. The UP data path handover is based on the availability of the WiGig link and reported RSSI (Received Signal Strength Indication). If the WiGig link is available and the RSSI exceeds the threshold \(RSSI_{th}\), the UP data is then offloaded to the WiGig link. Otherwise, the UP data will be carried by the LTE link.  
Figure 7 shows the transition of instantaneous UP throughput during an LTE-WiGig handover. Downlink UP data is transmitted from eNB using Iperf in TCP (Notes:Iperf is a network performance testing tool and TCP is a network transmission protocol.). The handover decision threshold RSSIth is set to \(-60\) dBm, while throughput is measured in the MAC layer. It can be observed from the figure that the data path achieves smooth handover based on the UE location and achieves a throughput of more than 1 Gbps when the UP path is switched to WiGig.
# Impact of Cytochrome P450 Enzymes on the Phase I Metabolism of Drugs
## 2 Examples of Drugs Metabolized by CYPs
### Voriconazole  
Voriconazole (Figure 2) is a broad-spectrum second-generation antifungal triazole frequently used for the prophylaxis and treatment of invasive fungal infections [30]. It is widely metabolized via the CYP isoenzyme CYP2C19 and, to a lesser extent, CYP2C9 and CYP3A4, to hydroxylated metabolites. Voriconazole \(N\)-oxide is the most abundant circulating metabolite [31], followed by 4-hydroxy voriconazole and dihydroxy-voriconazole [32], which, in turn, then undergo phase II reactions. Voriconazole \(N\)-oxide forms during the fluoropyrimidine \(N\)-oxidation reactions via CYP2C19 and CYP3A4, while 4-hydroxy voriconazole is formed through the methyl hydroxylation via CYP3A4 [33]. Currently, voriconazole is also recommended as a first-line therapy for COVID-19-associated pulmonary aspergillosis, which has recently aroused concerns due to increased mortality [34]. High intra- and interindividual variability in voriconazole's pharmacokinetics has been demonstrated. This compound has a relatively narrow therapeutic range; nevertheless, its metabolism has not yet been completely elucidated [35]. As a matter of fact, voriconazole overdoses are frequently observed, leading to a high risk of adverse effects. Inflammation may be a possible risk factor of voriconazole overdose in hematological patients, as well [36]. It has also to be considered that grape-fruit juice likely increases the oral bioavailability of this drug in children, as it selectively inhibits the intestinal CYP3A4; thus, an efficient quantification of voriconazole and its \(N\)-oxide derivative is strongly desired, and an ultra-performance liquid chromatographic method with tandem mass spectrometry detection has recently been developed [37]. Furthermore, much evidence shows cutaneous side effects related to voriconazole, such as photosensitivity and skin cancer [38]. In fact, it has been demonstrated that voriconazole enhances oxidative stress in human keratinocytes and facilitates UV-induced DNA damage through catalase inhibition [39]. Therefore, long-term voriconazole therapy can enhance the risk of cutaneous squamous cell carcinoma (eSCC), mainly in patients receiving immunosuppressive therapy [40]. Some authors suggest that acidic keratosis related to the use of voriconazole may precede the occurrence of cutaneous SCC and voriconazole-related SCC may lead to a worse prognosis in immunosuppressive conditions [41].