esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Converter Structure and Operation Principle  
### Topology Description  
As depicted in Figure 1, the main circuit topology of MMC-SCES closely resembles that of traditional MMC. It comprises three parallel-connected phase units. Each phase unit consists of two arms, formed by a series connection of \(N\) ESMs and the arm inductance \(L_{0}\). In this configuration, \(U_{\text{dc}}\) represents the DC bus voltage, \(i_{\text{pj}}\) and \(i_{\text{nj}}\) are the currents of the upper and lower arms of phase \(j\), respectively (where \(j\) = a, b, c), while \(u_{\text{vj}}\) and \(i_{\text{vj}}\) denote the phase \(j\) equivalent output voltage and current of the AC side.  
The ESM consists of a half-bridge submodule section and an energy storage section. The half-bridge submodule includes two switches, T\({}_{1}\) and T\({}_{2}\), and a capacitor \(C_{\text{sm}}\), which operates similarly to a conventional half-bridge submodule in MMC. When switch T\({}_{1}\) is turned on and T\({}_{2}\) is off, the cell is inserted. Conversely, when T\({}_{1}\) is off and T\({}_{2}\) is on, the cell is bypassed. The energy storage section incorporates a bidirectional DC/DC converter, formed by switches T\({}_{3}\) and T\({}_{4}\), together with inductor \(L_{\text{sc}}\), which interfaces the supercapacitor \(C_{\text{sc}}\) with the main circuit. The bidirectional DC/DC converter enables a significant reduction in the voltage rating requirements for the energy storage system. The operating mode of the supercapacitor is governed by the switching devices of the energy storage section. When switch T\({}_{3}\) is turned on and T\({}_{4}\) is off, the converter operates in Buck mode, charging the supercapacitor. Conversely, when T\({}_{3}\) is turned off and T\({}_{4}\) is on, it operates in Boost mode, discharging the supercapacitor.  
Complementary firing pulses are applied to switches T\({}_{3}\) and T\({}_{4}\). Assuming that the on-time of the switch T\({}_{3}\) is \(T_{\text{on}}\) and the off-time is \(T_{\text{off}}\), then \(T_{\text{on}}+T_{\text{off}}=T\).  
Figure 1: Topology of the MMC-SCES.  
When switch T\({}_{3}\) is turned on and T\({}_{4}\) is off, according to [PERSON]'s voltage law (KVL), it can be stated as:  
\[U_{\rm L}=U_{\rm sm}-U_{\rm sc}, \tag{1}\]  
where \(U_{\rm L}\) is the voltage of the inductor in the energy storage, \(U_{\rm sc}\) is the supercapacitor voltage, and \(U_{\rm sm}\) is the submodule capacitor voltage.  
When switch T\({}_{3}\) is turned off and T\({}_{4}\) is on, according to KVL, it can be stated as:  
\[U_{\rm L}=U_{\rm sc}. \tag{2}\]  
At steady state, the following equation can be derived from inductor volt-second balance:  
\[(U_{\rm sm}-U_{\rm sc})T_{\rm on}=U_{\rm sc}T_{\rm off}. \tag{3}\]  
Consequently, the duty cycle of switch T\({}_{3}\) can be derived as:  
\[d=\frac{T_{\rm on}}{T}=\frac{U_{\rm sc}}{U_{\rm sm}}. \tag{4}\]  
The supercapacitor voltage exhibits direct proportionality to the submodule capacitor voltage, with the scaling factor governed by the duty cycle \(d\) of switch T\({}_{3}\).
## 3 System Model
### Threat Model and Assumptions
#### 4.1.1 Address Generation
```
Input:\(D_{min}\) ; \(i_{uk}\) Output: Routable \(NodeID\) in P2P overlay network ; a pair of \((i_{pub}\), \(i_{priv})\) for the \(NodeID\)
1 Suppose that the elliptic curve \(y^{2}=x^{3}+ax^{2}+bx+c\) and its generator point \(G\) have been shared
2 while\(Diff_{n}<D_{min}\)do
3\(rn\gets RNG()\);
4\(K\gets rn*G\);
5\(i_{pub}\gets rn\);
6\(i_{priv}\gets K\);
7\(NodeID\gets Sha1\) (\(\mathit{Blake2b\_512}\left(i_{pub}\left|\,i_{uk}\right.\right)\));
8\(Diff_{n}\leftarrow BinLen\left(\mathit{PreZero}\left(\mathit{NodeID}\right)\,\right)\);
9
10 end while return\(NodeID\) ; \((i_{pub}\), \(i_{priv})\)
```  
**Algorithm 1**Routable \(NodeID\) generation algorithm in P2P overlay network  
The \(NodeID\) is generated according to the algorithm above, then compared with the known conditions \(D_{min}\) until it is satisfied with the routable \(NodeID\). The \(NodeID\) and the matching public and private keys are output for communication authentication, encryption and decryption.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 2 System Model
### uRLLC Traffic
#### 2.5.3. Wireless Backhaul Network  
The analysis for backhaul connection behind the core gateway (GW) is neglected since it is common for all BSs and is assumed to be ideal. Thus, the received signal-to-interference-plus-noise ratio (SINR) to the \(b^{th}\) SBS from MBS on WBH is displayed as,  
\[\text{SINR}_{1b}=\frac{P_{1b}\ \eta_{1b}}{\sum_{h\in\text{B}\setminus\{b\}}P_{1h }\ \eta_{1h}+\varrho^{2}}, \tag{11}\]  
where \(\eta_{1b}\) represents the channel gain between MBS and SBS. For the WBH network, the transmission succeeds if the received SINR is \(\text{SINR}_{1b}\leq SINR^{TH}\), \(SINR^{TH}\) is a threshold SINR. Otherwise, the transmission has failed, necessitating retransmission. Clearly, the transmission success probability in a single transmission attempt is contingent upon the link length, bandwidth, and time slot length. The wireless backhaul transmission is time-slotted, and one packet is transmitted in each time slot. One hop is assumed for sub-6 GHz backhaul since the distance between the SBS and the MBS is often not great [51; 52]. Meanwhile, the radio access network connects users with (macro cell or small cell) BSs through wireless links, which usually have only one hop. Considering the Shannon capacity formula, where the number of bits \(br_{bl}\) that can be transmitted from BS \(b\) to uRLLC UE \(l\) in a single successful transmission through backhaul time slot \(\tau^{WBH}\) and \(\ W^{WBH}\) backhaul system bandwidth,  
\[br_{bl}=\tau^{WBH}\ W^{WBH}\text{log}_{2}(1+SINR^{TH}), \tag{12}\]  
Thus, \(T_{bl}^{WBH}\) the mean transmission packet delay for uRLLC UE \(l\) over the sub-6 GHz WBH link, is based on \(SINR_{1b}\) in (11), can be expressed as follows,  
\[T_{bl}^{WBH}=\tau^{WBH}\ \mathcal{q}_{bl}\frac{1}{\rho\tau^{WBH}}=\tau^{WBH} \ \mathcal{q}_{bl}\ \frac{2}{\beta\ \sin(2\pi\beta)\ (SINR^{TH})^{2\beta}}, \tag{13}\]  
where \(\mathcal{q}_{bl}\) is the required number of WBH time slots of a uRLLC UE for delivering its packet and equals \(\left\lceil\frac{\mathcal{Z}_{l}(t)}{br_{bl}}\right\rceil\); \(\rho\tau^{WBH}\) is the transmission success probability in a single transmission attempt to calculate the average number of transfers required to deliver a packet successfully as \(\frac{1}{\rho\tau^{WBH}}\), and \(\beta=\frac{2}{\varrho},\varrho>2\)_is_ pathloss exponent of WBH link.
## 2 Materials and Methods
### Study Sites and Measurements
#### 2.1.3 Eddy Covariance Flux and Environment Measurements  
Eddy covariance systems are used to observe the water vapor flux exchange between the canopy and the atmosphere in each plantation. Eddy covariance systems consist of a three-dimensional sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT, USA) and an open-path infrared gas analyzer (LI-7500, LI-COR, Lincoln, NE, USA), operating at a sampling frequency of 10 Hz, with data recorded by a CR5000 data logger. In addition, each site is equipped with an automatic meteorological observation system for continuous monitoring of photosynthetically active radiation (PAR), air temperature (Ta), relative humidity (RH), wind speed (Ws), soil moisture (SM), and precipitation [36]. VPD refers to the difference between the saturation vapor pressure and the actual vapor pressure in the air at a certain temperature [37]. The formula for calculating VPD is  
\[\mathrm{VPD}=(1-\mathrm{RH}/100)\times 0.611\times\mathrm{exp}\left(\frac{17.27 \times\mathrm{T_{a}}}{237.3+\mathrm{T_{a}}}\right) \tag{1}\]  
VPD represents the vapor pressure deficit (kPa), Ta represents air temperature (\({}^{\circ}\)C), and RH denotes relative humidity (%).
## 2 Experimental Section
### Evaluation of the Photocatalytic Activity and Assessment of the Optimal Load  
Unless differently specified, the activity of the Ti- and/or Zn-based photocatalysts toward the removal of MB was evaluated under UV irradiation. A 350-nm (3.54 eV) lamp with a power density of 10 mW cm\({}^{-2}\) acted as a radiation source. The as-supplied MB reagent grade was used as a dye. The desired amount of photocatalyst was dispersed in 3 mL of 15 \(\mu\)M MB solution. Before being exposed to the radiation, the solution was magnetically stirred in the dark for 1 h to achieve adsorption/desorption equilibrium between dye solution and photocatalyst. Then, it was centrifuged (1000 rpm, 2 min) to segregate the photocatalyst particles before performing the zero-time measurement. Subsequently, the suspension was exposed to the radiation, at given time intervals. At the end of each interval, the solution was centrifuged again, and its optical absorbance was measured to evaluate the MB concentration. A Perkin-Elmer Lambda 2 UV-Vis spectrophotometer was used for this purpose. Measurements were conducted also on 3 mL of 15 \(\mu\)M MB without the addition of any catalyst.  
In order to investigate the effect of the photocatalyst concentration on the degradation efficiency, different amounts of the oxide NFs (0.3, 1.0, 2.0 and 3.0 mg, corresponding to loads of 0.10, 0.33, 0.66, and 1.00 mg mL\({}^{-1}\)) were dispersed in the MB solution. The reusability of the best performing photocatalyst was further evaluated, under optimal load conditions, without any separation or washing procedure with water.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Code** & **TiBO/mmol** & **ZnAc\({}_{2}\)/mmol** & **Resulting Phase(s)** \\ \hline TZ10 & 2.60 & 0.00 & _a_-TiO\({}_{2}\) \\ TZ21 & 1.73 & 0.87 & ZnTiO\({}_{3}\) (_a_-TiO\({}_{2}\) + _r_-TiO\({}_{2}\)) \\ TZ11 & 1.30 & 1.30 & disordered Zn\({}_{2}\)TiO\({}_{4}\) (_r_-TiO\({}_{2}\)) \\ TZ12 & 0.87 & 1.73 & Zn\({}_{2}\)TiO\({}_{4}\) (_r_-TiO\({}_{2}\)) \\ TZ01 & 0.00 & 2.60 & ZnO \\ \hline \hline \end{tabular}
\end{table}
Table 1: Codes of the samples and stoichiometric amounts of titanium butoxide (TiBO) and zinc acetate (ZnAc\({}_{2}\)) utilized for their preparation. The phase(s) of the oxide formed is (are) also reported (with secondary phase in brackets).
## 2 Background  
### Capacitated Vehicle Routing Problem with Time Windows  
The Vehicle Routing Problem (VRP) is a classic combinatorial optimization problem. It involves finding an optimal route for a set of vehicles to fulfill the demands of a set of geographically distributed customers. Classical objective functions for VRP consider minimizing the total transportation costs, distance, or time.  
CVRPTW includes two additional constraints: vehicles possess a maximum carrying capacity (C), and clients may only be served during predefined time windows (TW). Figure 1 shows a representative scheme of an example problem instance of five clients. Nodes represent the depot and the five clients. Edges represent connections between nodes with their respective travel times. In this case, we consider two vehicles. If we consider that the journey starts at time \(t=0\), the first vehicle visits client 1 at time \(t_{1}=2\) and client 2 at time \(t_{2}=7\). This vehicle is back to the depot at \(t=10\). The second vehicle visits client 3 at \(t_{3}=2\), client 5 at \(  
The problem is especially interesting because of the difficulty in dealing with time-window constraints. As far as we know, the state of the art algorithm for this problem is the Hybrid-Genetic Search (HGS).  
The HGS algorithm [4] as implemented in [5] was first proposed as an algorithmic framework to address three VRP variants: multi-depot VRP, periodic VRP, and multi-depot periodic VRP with capacitated vehicles and constrained route duration. In ref. [6], this framework was extended to incorporate a large class of time-constrained VRPs including VRP with time windows, multi-depot VRP with time windows, and vehicle-site dependencies VRP with time windows. As we chose HGS to perform our experiments, further details of the algorithm will be presented in Section 4.1.
## 6 Discussion
### Standardization  
Standardization remains a foundation for reliable information exchange between BIM and BEM. The BIM2 IndiLight project demonstrated that defining and validating over 400 standardized parameters can provide a robust basis for property mapping. Standardized parameters are particularly valuable during the definition of EIRs, ERs, and IDMs, offering a consistent framework for information mapping. They also ensure uniform communication among stakeholders, reducing errors when transferring data between diverse software platforms. Of course, this is only the case if both application sides follow the standards. However, many BEM applications lack support for alphanumeric information import.  
Nonetheless, standardized parameters alone are not enough. The rapid evolution of design requirements and the nearly infinite variety of possible parameters make manual updating both time-consuming and error-prone. Furthermore, cross-border projects face conflicting national standards, underscoring the need for a more dynamic, adaptable approach. Such an approach should make parameter libraries more flexible and include default value libraries for practical implementation.
## 2 Results and Discussion  
### Detection of 2-Nonenal by Gas Chromatography-Mass Spectrometry  
Full scan mass spectrum was measured at an \(m/z\) range from 30 to 150 amu in order to determine the selected monitoring ion (SIM) for 2-nonenal (Mw: 140 daltons). Figure 1a shows a typical total ion chromatogram of 2-nonenal fragments obtained by 1 uL direct injection analysis of standard 2-nonenal (1 ug/mL solution). 2-Nonenal was eluted as a single and symmetrical peak within 10 min using a DB\(-\)1 column. As shown in Figure 1b, no molecular ions peak was detected, but several fragment ion peaks produced by cleavage of the 2-nonenal structure were observed on the spectra. Among these fragment ions, \(m/z=55\) [CH=CHCHO], \(m/z=83\) [CH\({}_{2}\)CH\({}_{2}\)CH=CHCHO], and \(m/z=111\) [CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH=CHCHO]) were selected for SIM mode detection.  
Figure 1: Chromatogram (**a**) and mass spectrum (**b**) obtained from standard 2-nonenal. The dotted lines in the structure represent the fragments of 2-nonenal.
# A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation
## 3 Materials and Methods  
### Dataset Description  
We used the dataset from Korea AI Challenge, the 'Crop Disease Diagnosis based on Agricultural Environmental Changes'. The data collection for the challenge was conducted by LG AI Research, and the challenge was hosted by Dacon Inc. (Seoul, Republic of Korea). The data can be downloaded from the Dacon website [26]. The dataset containing labels consists of 4675 samples. The provided samples consist of image data and environmental data from the same crop. The infected leaves, vegetables, or fruits with diseases are placed in the center of the image. The environmental data include the information of temperature, humidity, and dew point recorded at 10 min intervals for at least 48 h before capturing the crop disease images. The environmental data are provided in the form of comma-separated value files. One of the examples of the given crop image and environmental data is shown in Figure 1.  
Figure 1: An example of dataset: image (**left**) and environmental data (**right**) for “Chili–Anthracnose–Intermediate Stage”.  
The objective of the AI challenge is to predict the crop type, disease type, and severity of the disease using the given crop image and environmental data. In this study, we intend to define the term \"Crop Disease Diagnosis\" in the same way as in the AI Challenge, meaning the simultaneous classification of crop type, disease type, and disease severity. This approach allows us to use the class labels from the dataset utilized in the AI Challenge. The dataset provides class labels for 6 types of crops (including tomatoes, strawberries, and bell peppers), 9 types of diseases (such as normal, powdery mildew, and various nutrient deficiencies), and different levels of infection severity (early, intermediate, and terminal). Table 1 shows 25 classes of the dataset provided.
## 3 MPPT Techniques
### Intelligent MPPT Techniques  
Since conventional MPPT procedures are ineffective and prone to being caught between LMPPs, successfully tracking the GMPP in bad weather is a significant issue. The use of intelligent techniques is one viable way to measure GMPP under non-uniform sun  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **MPPT** \\ **Technique** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **PV Array** \\ **Dependency** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Control** \\ **Variable** \\ \end{tabular} } & \multicolumn{2}{c}{**Circuitry**} & \multirow{2}{*}{\begin{tabular}{c} **Parameter** \\ **Tuning** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Tracking** \\ **Accuracy** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Efficiency** \\ **Complexity** \\ \end{tabular} } \\ \cline{3-3} \cline{5-10}  & & & **V** & & **A** & & **D** \\ \hline
**P\&O** & No & ✓ & ✓ & ✓ & No & Moderate & High & Simple \\ \hline
**INC** & No & ✓ & ✓ & & ✓ & No & High & High & Complex \\ \hline
**HC** & No & ✓ & ✓ & ✓ & ✓ & No & Moderate & High & Simple \\ \hline
**CS** & Yes & ✓ & ✓ & ✓ & ✓ & Yes & Medium & Medium & Complex \\ \hline
**ESC** & No & ✓ & ✓ & ✓ & ✓ & No & High & High & Medium \\ \hline
**RCC** & No & ✓ & ✓ & ✓ & & Yes & Moderate & High & Complex \\ \hline
**SMC** & No & ✓ & ✓ & ✓ & No & Medium & High & Complex \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comparison of various online MPPT techniques.  
irradiation and partial shading. The intelligent MPPT techniques include both artificial evolutionary algorithms. The MPPT techniques that belong to this category include, but are not limited to, the FLC, ANN, PSO, GWO, GO, and Jaya algorithm (JAYA), etc. To address the aforementioned issue, intelligent MPPT methods are used under non-uniform and shadowed solar conditions.
# Whitening CNN-Based Rotor System Fault Diagnosis Model Features
## 1 Introduction
In essence, this standardisation method centres and scales the layer activations based on the mini-batch statistics [26]. Centring and scaling each layer activation per mini-batch effectively stabilises the gradient distribution for each corresponding weight [27]. Stabilised gradient distribution increases the consistency of the gradient descent. However, BN can be considered an incomplete normalisation method, since its design includes a compromise that separates it from whitening [26]. In addition to centering and scaling, whitening includes decorrelation, which has been argued to simplify the optimisation of the model weights by making the adjacent weights independent [28]. Decorrelation has been excluded from BN due to the heavy toll it introduces to the computation [26]. Fortunately, recent studies have shown how to implement full whitening of the activations without excessive computational cost. These studies have also shown that whitening improves the learning results over BN [29; 30; 31]. Despite the promising results regarding feature whitening techniques, CNN-based fault diagnosis models still mostly rely on BN. This study shows how whitening the layer activations improves the current state-of-the-art 1D-CNN-based algorithms for machine fault diagnosis. The novel experiments employ three previously published fault diagnosis models. These commonly known models are [PERSON]'s model [32], WDCNN [15] and SRD-CNN [18]. The experiments reveal the fault diagnosis accuracies of these baseline models in parallel with corresponding models employing a whitening normalisation technique instead of BN. The whitening technique is adapted from an earlier study showing that decorrelating layer inputs channel-wise and pixel-wise consistently improved 2D-CNN-based model performance and training convergence on image data [31]. This \"network deconvolution\" (ND) operation, developed in [31], is further adjusted and integrated with the three 1D-CNN-based fault diagnosis models for time series data in this study. The ex periments of this work complement the previously reported results with ND performance in 1D-CNN-based models. The three experiments in this study employ two vibration datasets acquired from different rotor systems. The first dataset is the thoroughly studied bearing fault dataset produced by Case Western Reserve University [33]. The second dataset consists of vibration data from three azimuth thrusters before and after bearing and gear related faults were noticed. Each experiment shows that models with whitened features achieve high performance. More specifically, the models with whitened features achieve mostly better or significantly better diagnosis accuracies compared to the baseline models in the three experiments.
# Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
## 3 Materials and Methods
### Implementation Details  
All neural networks in this work were deployed in Keras [29], a deep learning programming platform built upon TensorFlow [30]. Our code supports Nifti image format. A workstation with an Intel Core(tm) i7-9700K CPU (3.60 GHZ, 64 GB RAM) and two Nvidia GeForce Titan 2080 GPUs was used. The overall process of operating MC-Net is shown in Algorithm 1.  
All models were trained in 200 epochs with a batch size of 8 on the training subset of Dataset 1. The ADAM optimizer [31] was selected to adapt the weights of each component to minimize the loss function during training, as suggested by [19]. We chose a learning rate of 0.0001 and a periodic decay of 0.96 after every epoch [2]. An early-stopping technique [32] was used during all the DL models' training to avoid overfitting. We chose the epoch number of 200 heuristically, which ensures that the model can be sufficiently trained when we apply the early-stopping technique. We monitored validation loss during model training. If the validation loss did not show improvement after 10 epochs, we stopped the training process and saved the model with the best validation loss. We chose a batch size of 8 based on two considerations. First, a smaller batch size provided better model convergency in one study [33], and the model with a batch size of 8 performed better than a model with a batch size of 16. Second, batch sizes smaller than 8 will take substantially longer to train.  
Experiments were performed using two slightly different network structures (Figure 1). The first experiment involved a conventional UNet structure (\"U\") and was used to obtain the main results presented here, including those related to visual evaluation by clinicians. The other experiment involved an additional connection from input to output (\"U + O\") to preserve more information from the input images [2] and was used for comparison with the \"U\" experiment. For comparison with the final MC-Net with the two-stage multi-loss function, single-stage models trained with the L1-loss [2] and with L1 + TV loss were also employed (\"L1\" and \"L1 + TV\" models).  
Finally, since the mapping provided by DL algorithms is intrinsically non-linear, we performed an additional test to assess the preservation of information by feeding motion-free images into the various models and calculating the difference between the predicted results and the motion-free source images.